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Eng. Proc., 2020, ECSA-7 2020

7th International Electronic Conference on Sensors and Applications

Online | 15–30 November 2020

Volume Editors: Stefano Mariani, Thomas B. Messervey, Alberto Vallan, Stefan Bosse and Francisco Falcone

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Cover Story (view full-size image): This Special Issue of Engineering Proceedings is a collection of the papers presented at the 7th International Electronic Conference on Sensors and Applications (ECSA-7), held online from the 15 to [...] Read more.
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Editorial
Preface: Proceedings of the 7th International Electronic Conference on Sensors and Applications
Eng. Proc. 2020, 2(1), 97; https://doi.org/10.3390/engproc2020002097 - 19 Feb 2021
Viewed by 495
Abstract
This issue of Engineering Proceedings gathers the papers presented at the 7th International Electronic Conference on Sensors and Applications (ECSA-7), held online on 15–30 November 2020 through the sciforum.net platform developed by MDPI.  Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)

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Proceeding Paper
Analysis of Preload Effect in the Axisymmetric Damped Steel Wire Using Ultrasonic Guided Wave Monitoring
Eng. Proc. 2020, 2(1), 1; https://doi.org/10.3390/ecsa-7-08162 - 14 Nov 2020
Viewed by 314
Abstract
Guided ultrasonic wave propagation characteristics in the axisymmetric prestressed viscoelastic waveguide, using the semi-analytical finite element (SAFE) method, are studied broadly for acoustic emission monitoring. For the numerical investigation, a single high strength steel wire is considered. The SAFE method for an axisymmetric [...] Read more.
Guided ultrasonic wave propagation characteristics in the axisymmetric prestressed viscoelastic waveguide, using the semi-analytical finite element (SAFE) method, are studied broadly for acoustic emission monitoring. For the numerical investigation, a single high strength steel wire is considered. The SAFE method for an axisymmetric cross-section in cylindrical-coordinates is utilized to analyze the two main influencing factors of steel wire in a practical scenario, namely, material damping and initial tension. For pre-stress effect, the calculation shows that the initial tensile stress can increase and decrease the energy velocity and attenuation factor of most modal waves above the cut-off frequency, which is linear. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Multimodal Stimulation System to Control Fibroblast Proliferation Using Optical and Ultrasonic Stimulation
Eng. Proc. 2020, 2(1), 2; https://doi.org/10.3390/ecsa-7-08163 - 14 Nov 2020
Viewed by 302
Abstract
An optical stimulation shows various effects for skin regeneration and wound treatment by using different wavelength. Similarly, ultrasound stimulation can improve skin wrinkles and contours by inducing the contraction and synthesis of collagen to reduce local fat accumulation. In this study, using commercially [...] Read more.
An optical stimulation shows various effects for skin regeneration and wound treatment by using different wavelength. Similarly, ultrasound stimulation can improve skin wrinkles and contours by inducing the contraction and synthesis of collagen to reduce local fat accumulation. In this study, using commercially available light-emitting diodes (LEDs) for skin regeneration masks (415 nm, 630 nm, 850 nm), a single wavelength and multiple wavelengths were applied to fibroblast cells in various ways to control the proliferation effect of skin cells. In addition, ultrasonic stimulation was applied simultaneously to quantitatively evaluate the proliferation effect of fibroblasts. As a result, it was confirmed that there was an effect on fibroblast cell proliferation when the LED light stimulation of a specific wavelength was applied, and also the proliferation activity of skin cells increased even in the multimodal stimulation by applying a combination of LEDs and ultrasound. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Inkjet-Printed Flexible Temperature Sensor Based on Silver Nanoparticles Ink
Eng. Proc. 2020, 2(1), 3; https://doi.org/10.3390/ecsa-7-08216 - 14 Nov 2020
Cited by 1 | Viewed by 419
Abstract
In this research, a flexible inkjet-printed temperature sensor with in-house silver nanoparticles ink is presented and compared with the sensor printed with commercial silver nanoparticles ink. These sensors have an average width of 0.5 ± 0.04 mm in the latter and 0.5 ± [...] Read more.
In this research, a flexible inkjet-printed temperature sensor with in-house silver nanoparticles ink is presented and compared with the sensor printed with commercial silver nanoparticles ink. These sensors have an average width of 0.5 ± 0.04 mm in the latter and 0.5 ± 0.03 mm in the former. These serpentine-structure sensors were printed on polyethylene terephthalate (PET) substrate by using a Fujifilm Dimatix 2850 printer. The corresponding results indicating resistance have been recorded in the range of 30–100 °C to evaluate the sensor performance. The result of the studies showed that there was a linear relationship between the resistance and temperature for both ink types. The printed sensors developed using the in-house ink presented higher sensitivity, 0.1086 Ω/°C, compared to the commercial ink, which was 0.0543 Ω/°C. Therefore, the flexible inkjet-printed temperature sensor with the in-house silver nanoparticles ink is recommended for the large-scale productions and implementations. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Extended Abstract
Intelligent Multi-Electrode Array for Real-Time Treatment Monitoring of Antipsychotic Clozapine
Eng. Proc. 2020, 2(1), 4; https://doi.org/10.3390/ecsa-7-08211 - 14 Nov 2020
Viewed by 220
Abstract
Schizophrenia is a challenging mental health disorder [1]. While various antipsychotics have [...] Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Pipeline Bonded Joints Assembly and Operation Health Monitoring with Embedded FBG Sensors
Eng. Proc. 2020, 2(1), 5; https://doi.org/10.3390/ecsa-7-08208 - 14 Nov 2020
Cited by 1 | Viewed by 229
Abstract
Offshore oil and gas platforms present a harsh environment for their installed infrastructure, with pipelines that are subjected to both a corrosive atmosphere and transport of aggressive chemicals being the most critical. These conditions have prompted the industry to substitute metallic pipelines for [...] Read more.
Offshore oil and gas platforms present a harsh environment for their installed infrastructure, with pipelines that are subjected to both a corrosive atmosphere and transport of aggressive chemicals being the most critical. These conditions have prompted the industry to substitute metallic pipelines for composite counterparts, often made from fiber-reinforced plastics assembled with bonded joints. Various technologies have emerged in recent years to assess the health of these composite pipelines. In particular, robust speckle metrology techniques such as shearography, although not capable of long-term monitoring, have produced very satisfactory results. However, these inspection techniques require specialized equipment and trained personnel to be flown to offshore platforms, which can incur in non-trivial inspection costs. In this paper, we propose and demonstrate a robust and cost-effective approach to monitor pipeline bonded joints during assembly and operation using fiber Bragg grating (FBG) sensors embedded into the joints’ adhesive layer. This approach allows for informed decisions on when to perform targeted in-depth inspections (e.g., with shearography) based on both real-time and long-term feedback of the FBG sensors data, resulting in lower monitoring costs, a severe increase in monitoring uptime (up to full uptime), and increased operational security. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Low-Cost Cloud-Enabled Wireless Monitoring System for Linear Fresnel Solar Plants
Eng. Proc. 2020, 2(1), 6; https://doi.org/10.3390/ecsa-7-08173 - 14 Nov 2020
Viewed by 311
Abstract
This paper presents the design of a cost-effective online wireless monitoring system for two linear Fresnel reflector (LFR) solar plants located in two different countries. The first LFR plant is installed in the SEKEM medical center near Belbis city, Egypt, while the second [...] Read more.
This paper presents the design of a cost-effective online wireless monitoring system for two linear Fresnel reflector (LFR) solar plants located in two different countries. The first LFR plant is installed in the SEKEM medical center near Belbis city, Egypt, while the second is installed in the campus of the University of Palermo, Italy. The proposed system is a standalone system that reduces the interaction of labor as it offers online wireless monitoring for important parameters of the LFR such as solar irradiance, ambient temperature, outlet and inlet collector temperature and heat transfer fluid flow. For that purpose, a wireless sensor network (WSN) based on Arduino Mega boards coupled with XBee modules are used. The ZigBee XBee modules operate at 2.4 GHz, which have the advantages of being low cost and relatively low power consumption. The wireless nodes are supplied by solar paneled power banks, and send the data to a cloud in order to monitor both LFR plants remotely. The proposed system has been implemented and tested successfully before the future deployment on the LFR plants. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Student Sensor Lab at Home: Safe Repurposing of Your Gadgets
Eng. Proc. 2020, 2(1), 7; https://doi.org/10.3390/ecsa-7-08268 - 14 Nov 2020
Viewed by 295
Abstract
The COVID-19 pandemic imposed various restrictions on the accessibility of conventional teaching laboratories. Enabling learning and experimenting at home became necessary to support the practical element of students’ learning. Unfortunately, it is not viable to provide or share a fully featured sensor lab [...] Read more.
The COVID-19 pandemic imposed various restrictions on the accessibility of conventional teaching laboratories. Enabling learning and experimenting at home became necessary to support the practical element of students’ learning. Unfortunately, it is not viable to provide or share a fully featured sensor lab to every student because of the prohibitive costs involved. Therefore, repurposing electronic devices that are common to students can bring about the sought-after practical learning experience without the hefty price tag. In distinction to the conventional lab instruments, however, consumer-grade devices are not designed for use with external sensors and/or electronic circuitry. They are not professionally maintained, do not undergo periodic safety tests, and are not calibrated. Nevertheless, nearly all modern computers, laptops, tablets or smartphones are equipped with high-quality audio inputs and outputs that can generate and record signals in the audible frequency range (20 Hz–20 kHz). Despite cutting off the direct currents completely, this range might be sufficient for working with a variety of sensors. In this presentation we look at the possibilities of making sure that such repurposing by design prevents any potential harm to the learner and to her or his personal equipment. These features seem essential for unsupervised lone experimenting and avoiding damage to expensive devices. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Highly Sensitive Hydrogen Sensor Based on Palladium-Coated Tapered Optical Fiber at Room Temperature
Eng. Proc. 2020, 2(1), 8; https://doi.org/10.3390/ecsa-7-08186 - 14 Nov 2020
Viewed by 283
Abstract
This paper describes the application of a palladium (Pd)-coated tapered optical fiber in order to develop a hydrogen (H2) sensor. A transducing channel was fabricated with multimode optical fiber (MMF) with cladding and core diameters of 125 µm and 62.5 µm, [...] Read more.
This paper describes the application of a palladium (Pd)-coated tapered optical fiber in order to develop a hydrogen (H2) sensor. A transducing channel was fabricated with multimode optical fiber (MMF) with cladding and core diameters of 125 µm and 62.5 µm, respectively, in order to enhance the evanescent field of light propagation through the fiber. The multimode optical fiber was tapered from a cladding diameter of 125 µm to a waist diameter of 20 µm, waist-length of 10 mm, and down taper and up of 5 mm, and coated with Pd using the drop-casting technique. In order to establish the palladium’s properties, various characterization techniques were applied, such as Field Emission Scanning Electron Microscopy (FESEM), Energy Dispersive X-ray (EDX), and X-ray Diffraction (XRD). The developed palladium sensor functioned reproducibly at a gas concentration of 0.125% to 1.00% H2 at room temperature in the synthetic air. In this case, the response and recovery times were 50 and 200 s, respectively. Furthermore, this study demonstrated that the production of a dependable, effective, and reproducible H2 sensor by applying a basic, cost-effective method is possible. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
A Synthetic Wide-Bandwidth Radar System Using Software Defined Radios
Eng. Proc. 2020, 2(1), 9; https://doi.org/10.3390/ecsa-7-08174 - 14 Nov 2020
Cited by 1 | Viewed by 319
Abstract
In this paper, we present a synthetic wide-bandwidth radar system using software-defined radios (SDR), and demonstrate the proposed approach using a Universal Software Radio Peripheral (USRP) device. Normally, USRP devices have tens of MHz bandwidth, and cannot generate large bandwidth sweeps to achieve [...] Read more.
In this paper, we present a synthetic wide-bandwidth radar system using software-defined radios (SDR), and demonstrate the proposed approach using a Universal Software Radio Peripheral (USRP) device. Normally, USRP devices have tens of MHz bandwidth, and cannot generate large bandwidth sweeps to achieve cm-level range resolution. By using a synthetic wide-bandwidth approach, we can generate frequency sweeps up to 5 GHz bandwidth and obtain high-resolution range profiles. We will first summarize the mathematical details of the proposed approach, then present a pure Python-based solution using the UHD library, a GNU radio and Octave-based implementation, and finally present experimental results for two different test cases. The developed code is available in a public GitHub repository. Compared to frequency-modulated continuous-wave (FMCW) radars with a voltage-controlled oscillator, the sweep time or the experimental duration are longer, but very large bandwidth sweeps can be realized easily by using low-cost USRP devices, and sweeps are more accurate. All of our experimental results indicate the effectiveness of the proposed low-cost software-defined radar system. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Design of an Embedded Broadband Thermoelectric Power Sensor in the InP DHBT Process
Eng. Proc. 2020, 2(1), 10; https://doi.org/10.3390/ecsa-7-08206 - 14 Nov 2020
Viewed by 296
Abstract
The thermopile-based thermoelectric sensor has emerged as an important approach for microwave power measurement. It employs the Seebeck effect, which converts the microwave power into the heat and generates the thermovoltage. However, the output thermovoltage generally exhibits a frequency-dependent feature, which affects measurement [...] Read more.
The thermopile-based thermoelectric sensor has emerged as an important approach for microwave power measurement. It employs the Seebeck effect, which converts the microwave power into the heat and generates the thermovoltage. However, the output thermovoltage generally exhibits a frequency-dependent feature, which affects measurement accuracy. Besides, the low sensitivity of the current existed planar thermopile-based sensor constrains its further application. This is mainly caused by the heat loss of the substrate in the conversion process of microwave power-heat-electricity. In this paper, a novel embedded power sensor based on the indium phosphide (InP) double heterojunction bipolar transistor (DHBT) process is presented. The thermopile is embedded in the benzocyclobutene (BCB) to prevent the heat loss, and the embedded structure also enables this sensor to eliminate the need for microelectromechanical system (MEMS) technology. The electromagnetic simulation by ANSYS high frequency structure simulator (HFSS) and thermal simulation by ANSYS Steady-State Thermal are combined to evaluate the sensor performance. The result shows that the output voltage increases with the input power linearly, and the proposed sensor is almost independent of the microwave frequency. A sensitivity of l.07 mV/mW has been achieved up to 200 GHz, with the port return loss lower than −15.8 dB. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Evaluation of SAR in Human Body Models Exposed to EMF at 865 MHz Emitted from UHF RFID Fixed Readers Working in the Internet of Things (IoT) System
Eng. Proc. 2020, 2(1), 11; https://doi.org/10.3390/ecsa-7-08240 - 14 Nov 2020
Viewed by 235
Abstract
The aim of this ongoing study was to evaluate the specific energy absorption rate (SAR) values in the body of a person present near-fixed readers of ultra-high frequency (UHF) radio frequency identification (RFID) passive tags incorporated in real-time locating systems (RTLS), operating at [...] Read more.
The aim of this ongoing study was to evaluate the specific energy absorption rate (SAR) values in the body of a person present near-fixed readers of ultra-high frequency (UHF) radio frequency identification (RFID) passive tags incorporated in real-time locating systems (RTLS), operating at a frequency range of 865–868 MHz, considering various exposure scenarios. The modelled electromagnetic field (EMF) source was a rectangular microstrip antenna designed at resonance frequency in free space at 865 MHz. The SAR values in the body exposed to EMF 5 cm away from the UHF RFID readers need consideration with respect to general public exposure limits, when the radiated power exceeds 8 W. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Coping with Motion Artifacts by Analog Front-End ECG Microchips under Variable Digital Resolution and Gain
Eng. Proc. 2020, 2(1), 12; https://doi.org/10.3390/ecsa-7-08247 - 14 Nov 2020
Viewed by 387
Abstract
The development of portable ECG technology has found growing markets, from wearable ECG sensors to ambulatory ECG recorders, encountering challenges of moderately complex to tightly regulated devices. This study investigated how a typical 0.5–40 Hz bandwidth ECG is affected by motion artifact when [...] Read more.
The development of portable ECG technology has found growing markets, from wearable ECG sensors to ambulatory ECG recorders, encountering challenges of moderately complex to tightly regulated devices. This study investigated how a typical 0.5–40 Hz bandwidth ECG is affected by motion artifact when using analog front-end (AFE) integrated circuits such as the AD823X family. It is known that the typical amplitude resolution of current mobile health ECG devices is 10–12 bits, and sometimes 16-bits, which is enough for monitoring but might be insufficient to identify the small potential amplitudes useful in diagnoses. The interest now is on the interplay of how a digital resolution choice and variable gain can cope with motion artifacts inherent in mobile health devices. With our methodology for a rapid prototyping of an ECG device, and using the AFE AD8232 and Bluetooth communication, a specific cardiac monitor ECG configuration was evaluated under two microcontroller systems of different resolution: a generic Arduino Nano board which featured a 10-bit analog-to-digital converter (ADC) and the 24-bit ADC of Silicon Labs C8051F350 board. The ECG cardiac monitor setup, recommended by Analog Devices, featuring two gain values under these two different microcontroller systems, was explored as to its ability to solve motion artifact problems. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Inclusive Human Intention Prediction with Wearable Sensors: Machine Learning Techniques for the Reaching Task Use Case
Eng. Proc. 2020, 2(1), 13; https://doi.org/10.3390/ecsa-7-08234 - 14 Nov 2020
Cited by 1 | Viewed by 293
Abstract
Human intentions prediction is gaining importance with the increase in human–robot interaction challenges in several contexts, such as industrial and clinical. This paper compares Linear Discriminant Analysis (LDA) and Random Forest (RF) performance in predicting the intention of moving towards a target during [...] Read more.
Human intentions prediction is gaining importance with the increase in human–robot interaction challenges in several contexts, such as industrial and clinical. This paper compares Linear Discriminant Analysis (LDA) and Random Forest (RF) performance in predicting the intention of moving towards a target during reaching movements on ten subjects wearing four electromagnetic sensors. LDA and RF prediction accuracy is compared to observation-sample dimension and noise presence, training and prediction time. Both algorithms achieved good accuracy, which improves as the sample dimension increases, although LDA presents better results for the current dataset. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
IoT-Based COVID-19 SOP Compliance and Monitoring System for Businesses and Public Offices
Eng. Proc. 2020, 2(1), 14; https://doi.org/10.3390/ecsa-7-08267 - 14 Nov 2020
Cited by 2 | Viewed by 1078
Abstract
We propose a low-cost internet of things (IoT)-enabled COVID-19 standard operating procedure (SOP) compliance system that counts the number of people entering and leaving a vicinity, ensures physical distancing, monitors body temperature and warns attendees and managers of violations. The system comprises of [...] Read more.
We propose a low-cost internet of things (IoT)-enabled COVID-19 standard operating procedure (SOP) compliance system that counts the number of people entering and leaving a vicinity, ensures physical distancing, monitors body temperature and warns attendees and managers of violations. The system comprises of multiple sensor nodes communicating with a centralized server. The data stored on the server can be used for compliance auditing, real-time monitoring, and planning purposes. The system does not record the personal information of attendees nor provide contact tracing information. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Development of a Compact Optical Measurement System to Quantify the Optical Properties of Fluorescently Labeled Cervical Cancer Cells
Eng. Proc. 2020, 2(1), 15; https://doi.org/10.3390/ecsa-7-08256 - 14 Nov 2020
Viewed by 209
Abstract
The flow cytometer is an instrument that can measure the characteristics of cells such as the number of cells, the degree of internal composition of the cells, the size of the cells, and the cell cycle etc. This equipment has been used to [...] Read more.
The flow cytometer is an instrument that can measure the characteristics of cells such as the number of cells, the degree of internal composition of the cells, the size of the cells, and the cell cycle etc. This equipment has been used to study leukemia, DNA and RNA analysis, protein expression, cell death, and immune response. However, a flow cytometer is expensive equipment and requires an operator with expertise for use and maintenance. When only simple data are needed, such as measuring the number of cells or quantitative analysis of cell growth and inhibition, the use of a flow cytometer is not suitable in terms of cost and requires unnecessary measurement time consumption. In this study, a compact optical measurement system using commercially available light-emitting diodes (LED), photodiode, and Arduino Mega ADK was developed, and the body structure was printed and utilized by a 3D printer. Cervical cancer cells, known as one of the major cancers of women, were fluorescently treated with fluorescent dyes such as Calcein-AM and DiD, and performance of the system was verified. The side scattering measured using various filters with different transmission wavelengths of light showed high linearity in proportion to the number of cells. By measuring the side scattering of the untreated cervical cancer cells, fluorescence scattering could be confirmed from the difference in the side scattering intensity according to the fluorescence treatment. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
High Frequency Performance of Piezoelectric Diaphragms for Impedance-Based SHM Applications
Eng. Proc. 2020, 2(1), 16; https://doi.org/10.3390/ecsa-7-08185 - 14 Nov 2020
Viewed by 255
Abstract
Piezoelectric transducers are used in a wide variety of applications, including damage detection in structural health monitoring (SHM) applications. Among the various methods for detecting structural damage, the electromechanical impedance (EMI) method is one of the most investigated in recent years. In this [...] Read more.
Piezoelectric transducers are used in a wide variety of applications, including damage detection in structural health monitoring (SHM) applications. Among the various methods for detecting structural damage, the electromechanical impedance (EMI) method is one of the most investigated in recent years. In this method, the transducer is typically excited with low frequency signals up to 500 kHz. However, recent studies have indicated the use of higher frequencies, usually above 1 MHz, for the detection of some types of damage and the monitoring of some structures’ characteristics that are not possible at low frequencies. Therefore, this study investigates the performance of low-cost piezoelectric diaphragms excited with high frequency signals for SHM applications based on the EMI method. Piezoelectric diaphragms have recently been reported in the literature as alternative transducers for the EMI method and, therefore, investigating the performance of these transducers at high frequencies is a relevant subject. Experimental tests were carried out with piezoelectric diaphragms attached to two aluminum bars, obtaining the impedance signatures from diaphragms excited with low and high frequency signals. The analysis was performed using the real part of the impedance signatures and two basic damage indices, one based on the Euclidean norm and the other on the correlation coefficient. The experimental results indicate that piezoelectric diaphragms are usable for the detection of structural damage at high frequencies, although the sensitivity decreases. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
An Unsupervised Learning Approach for Early Damage Detection by Time Series Analysis and Deep Neural Network to Deal with Output-Only (Big) Data
Eng. Proc. 2020, 2(1), 17; https://doi.org/10.3390/ecsa-7-08281 - 14 Nov 2020
Cited by 2 | Viewed by 413
Abstract
Dealing with complex engineering problems characterized by Big Data, particularly in structural engineering, has recently received considerable attention due to its high societal importance. Data-driven structural health monitoring (SHM) methods aim at assessing the structural state and detecting any adverse change caused by [...] Read more.
Dealing with complex engineering problems characterized by Big Data, particularly in structural engineering, has recently received considerable attention due to its high societal importance. Data-driven structural health monitoring (SHM) methods aim at assessing the structural state and detecting any adverse change caused by damage, so as to guarantee structural safety and serviceability. These methods rely on statistical pattern recognition, which provides opportunities to implement a long-term SHM strategy by processing measured vibration data. However, the successful implementation of the data-driven SHM strategies when Big Data are to be processed is still a challenging issue, since the procedures of feature extraction and/or feature classification may end up being time-consuming and complex. To enhance the current damage detection procedures, in this work we propose an unsupervised learning method based on time series analysis, deep learning and the Mahalanobis distance metric for feature extraction, dimensionality reduction and classification. The main novelty of this strategy is the simultaneous dealing with the significant issue of Big Data analytics for damage detection, and distinguishing damage states from the undamaged one in an unsupervised learning manner. Large-scale datasets relevant to a cable-stayed bridge have been handled to validate the effectiveness of the proposed data-driven approach. Results have shown that the approach is highly successful in detecting early damage, even when Big Data are to be processed. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Urban Microclimate Monitoring and Modeling through an Open-Source Distributed Network of Wireless Low-Cost Sensors and Numerical Simulations
Eng. Proc. 2020, 2(1), 18; https://doi.org/10.3390/ecsa-7-08270 - 14 Nov 2020
Viewed by 366
Abstract
The use of wireless sensor networks (WSN) to address and improve the environmental quality of the built environment is gaining more and more prominence in modern cities. In this scope, our work aims to assess the spatial variability of local climate in relation [...] Read more.
The use of wireless sensor networks (WSN) to address and improve the environmental quality of the built environment is gaining more and more prominence in modern cities. In this scope, our work aims to assess the spatial variability of local climate in relation to the urban morphology and the distribution of materials and vegetation. Furthermore, on-site measured data have been exploited to run and benchmark numerical models for the simulation and visualization of multiple climate parameters, such as outdoor thermal comfort. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Moisture-Responsive Polymer Films on Flexible Substrates for Optical Sensing of Humidity
Eng. Proc. 2020, 2(1), 19; https://doi.org/10.3390/ecsa-7-08182 - 14 Nov 2020
Cited by 1 | Viewed by 278
Abstract
In this paper, the possibility to design flexible humidity sensors by spin-coating of
moisture-sensitive polymer on three types of substrates—poly(ethylene terephthalate) (PET),
polylactide (PLA) and composite polysiloxane is investigated. The optical properties, surface
morphology and roughness of the substrates covered with polymer are [...] Read more.
In this paper, the possibility to design flexible humidity sensors by spin-coating of
moisture-sensitive polymer on three types of substrates—poly(ethylene terephthalate) (PET),
polylactide (PLA) and composite polysiloxane is investigated. The optical properties, surface
morphology and roughness of the substrates covered with polymer are studied by transmittance
measurements and surface profiling, respectively. Thin polymer films of amphiphilic copolymer
obtained by partial acetalization of poly(vinyl alcohol) are used as humidity sensitive media. The
sensing properties are probed through transmittance measurements at different levels of relative
humidity (RH). The influence of substrate type is studied by comparing the hysteresis of flexible
sensors with those that are deposited on glass substrates. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Simulation of FBG Temperature Sensor Array for Oil Identification via Random Forest Classification
Eng. Proc. 2020, 2(1), 20; https://doi.org/10.3390/ecsa-7-08177 - 14 Nov 2020
Cited by 1 | Viewed by 272
Abstract
Water–oil separation is important in the oil industry, as the incorrect classification of oil can lead to losses in the production and have an environmental impact. This paper proposes the use of fiber Bragg grating (FBG) temperature sensor array to identify the oil [...] Read more.
Water–oil separation is important in the oil industry, as the incorrect classification of oil can lead to losses in the production and have an environmental impact. This paper proposes the use of fiber Bragg grating (FBG) temperature sensor array to identify the oil in water–emulsion–oil systems, using only the temperature responses for oil classification results in operational and economic benefits. To demonstrate the possibility of using the FBG temperature sensor to classify oil level, the temperature distribution of an oil storage tank, with 2 m height and 0.8 m in diameter, is simulated using thermal distribution models. Then, the temperature effect in a 2 m long FBG array with a different number and distribution of FBGs is simulated using the transfer matrix method. In each case, we extract the wavelength shift (Δλ), total width at half the maximum (FWHM) and the location of the FBG in the fiber. For the oil classification, we dichotomized the fluids into oil and non-oil (water and emulsion). Due to the low separability of the classes, the random forest algorithm was chosen for classification, starting with 200 FBG equidistant sensors and decreasing to 6, with different distributions along the fiber. As expected, the highest accuracy occurs with the 200 FBGs array (96%). However, it was possible to classify the oil with an accuracy of 94.89% with only 8 FBGs, using tests for two proportions (with a significance of 5%); the accuracy of 8 FBGs is the same as of 50 FBGs. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
A Data Cleaning Approach for a Structural Health Monitoring System in a 75 MW Electric Arc Ferronickel Furnace
Eng. Proc. 2020, 2(1), 21; https://doi.org/10.3390/ecsa-7-08245 - 14 Nov 2020
Cited by 1 | Viewed by 401
Abstract
Within a model of scientific and technical cooperation between the smelting company Cerro Matoso S.A. (CMSA) and the Universidad Nacional de Colombia (UNAL), a project was developed in order to take advantage of the data that were obtained from a sensor network in [...] Read more.
Within a model of scientific and technical cooperation between the smelting company Cerro Matoso S.A. (CMSA) and the Universidad Nacional de Colombia (UNAL), a project was developed in order to take advantage of the data that were obtained from a sensor network in a ferronickel electric arc furnace at CMSA to improve the structural health monitoring process. Through this sensor network, online data are obtained on the temperature measurement along the refractory lining of the electric furnace, as well as heat fluxes and chemical characterization of the minerals on each stage of the process. These data are stored in a local database, which stores several years of historical data with valuable information for control and analysis purposes. These data reflect the behavior of the industrial process and can be used in the development of machine learning models to predict some of the electric arc furnace operation parameters, and thus improve the decision-making process. Currently, most of the data are analyzed by the experts of the structural control department, but, due to the large amount of data, the development of analytical tools is necessary to support their work. This paper proposes a data cleaning approach for improving data quality by creating a set of rules and filters based on both expert judgment and best practices in data quality. A statistical analysis was also carried out in order to detect variables with anomalies and outliers, which do not reflect real operation parameters and belong to anomalous data that should not be considered for modelling. With the proposed process, the quality of the data was improved and abnormal data were eliminated in order to consolidate a clean data set for later use in the development of machine learning models. This work contributes on understanding data cleansing rules that must be considered in order to reflect the real behavior of the electric furnace operation for further analysis and modeling tasks. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
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Proceeding Paper
Adaptation and Selection Techniques Based on Deep Learning for Human Activity Recognition Using Inertial Sensors
Eng. Proc. 2020, 2(1), 22; https://doi.org/10.3390/ecsa-7-08159 - 14 Nov 2020
Viewed by 251
Abstract
Deep learning techniques have been widely applied to Human Activity Recognition (HAR), but a specific challenge appears when HAR systems are trained and tested with different subjects. This paper describes and evaluates several techniques based on deep learning algorithms for adapting and selecting [...] Read more.
Deep learning techniques have been widely applied to Human Activity Recognition (HAR), but a specific challenge appears when HAR systems are trained and tested with different subjects. This paper describes and evaluates several techniques based on deep learning algorithms for adapting and selecting the training data used to generate a HAR system using accelerometer recordings. This paper proposes two alternatives: autoencoders and Generative Adversarial Networks (GANs). Both alternatives are based on deep neural networks including convolutional layers for feature extraction and fully-connected layers for classification. Fast Fourier Transform (FFT) is used as a transformation of acceleration data to provide an appropriate input data to the deep neural network. This study has used acceleration recordings from hand, chest and ankle sensors included in the Physical Activity Monitoring Data Set (PAMAP2) dataset. This is a public dataset including recordings from nine subjects while performing 12 activities such as walking, running, sitting, ascending stairs, or ironing. The evaluation has been performed using a Leave-One-Subject-Out (LOSO) cross-validation: all recordings from a subject are used as testing subset and recordings from the rest of the subjects are used as training subset. The obtained results suggest that strategies using autoencoders to adapt training data to testing data improve some users’ performance. Moreover, training data selection algorithms with autoencoders provide significant improvements. The GAN approach, using the generator or discriminator module, also provides improvement in selection experiments. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Deep Learning for the Prediction of Temperature Time Series in the Lining of an Electric Arc Furnace for Structural Health Monitoring at Cerro Matoso (CMSA)
Eng. Proc. 2020, 2(1), 23; https://doi.org/10.3390/ecsa-7-08246 - 14 Nov 2020
Cited by 1 | Viewed by 468
Abstract
Cerro Matoso SA (CMSA) is located in Montelibano, Colombia. It is one of the biggest producers of ferronickel in the world. The structural health monitoring process performed in the electric arc furnaces at CMSA is of great importance in the maintenance and control [...] Read more.
Cerro Matoso SA (CMSA) is located in Montelibano, Colombia. It is one of the biggest producers of ferronickel in the world. The structural health monitoring process performed in the electric arc furnaces at CMSA is of great importance in the maintenance and control of ferronickel production. The control of thermal and dimensional conditions of the electric furnace aims to detect and prevent failures that may affect its physical integrity. A network of thermocouples distributed radially and at different heights from the furnace wall, are responsible for monitoring the temperatures in the electric furnace lining. In order to optimize the operation of the electric furnace, it is important to predict the temperature at some points. However, this can be difficult due the number of variables which it depends on. To predict the temperature behavior in the electric furnace lining, a deep learning model for time series prediction has been developed. Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and other combinations were tested. GRU characterized by its multivariate and multi output type had the lowest square error. A study of the best input variables for the model that influence the temperature behavior is also carried out. Some of the input variables are the power, current, impedance, calcine chemistry, temperature history, among others. The methodology to tune the parameters of the GRU deep learning model is described. Results show an excellent behavior for predicting the temperatures 6 h into the future with root mean square errors of 3%. This model will be integrated to a software that obtains data for a time window from the Distributed Control System (DCS) to feed the model. In addition, this software will have a graphical user interface used by the operators furnace in the control room. Results of this work will improve the process of structural control and health monitoring at CMSA. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Investigation of Particle Steering for Different Cylindrical Permanent Magnets in Magnetic Drug Targeting
Eng. Proc. 2020, 2(1), 24; https://doi.org/10.3390/ecsa-7-08269 - 14 Nov 2020
Cited by 1 | Viewed by 309
Abstract
Magnetic Drug Targeting is a promising cancer treatment that offers the possibility of increasing therapeutic efficiency while reducing the patient’s side-effects. Thereby, the cancer-drug is bounded to magnetic nanoparticles, which are injected into a vessel and guided through the cardiovascular system into the [...] Read more.
Magnetic Drug Targeting is a promising cancer treatment that offers the possibility of increasing therapeutic efficiency while reducing the patient’s side-effects. Thereby, the cancer-drug is bounded to magnetic nanoparticles, which are injected into a vessel and guided through the cardiovascular system into the tumor by an external magnetic field. However, a successful navigation depends on several multiphysical parameters including the properties of the nanoparticles, the flow characteristics of blood, and the gradient of the applied magnetic field. To investigate their impact, the propagation of particle packets within a 45 bifurcation vessel was modeled in COMSOL Multiphysics®. Therefore, magnets with varying radius-to-length ratios and magnetizations (radial and axial) were placed right before the bifurcation. Furthermore, different fluid velocities in addition to the influence of the gravitational force were evaluated. Overall, a strong dependency of the particle steering on the fluid velocity and the magnet’s radius-to-length ratio was observed. Moreover, a radial magnetization has a greater impact on the particle propagation, while the gravitation can be neglected for higher velocities. However, when a single permanent magnet is used, the results depict that it is a fine line between deflecting or trapping a particle at the vessel wall. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Real Application For A Data Acquisition System From Sensors Based On Embedded Systems
Eng. Proc. 2020, 2(1), 25; https://doi.org/10.3390/ecsa-7-08275 - 14 Nov 2020
Viewed by 245
Abstract
Data acquisition systems are one of the main components for sensors and remote monitoring strategies required in a real process. Normally, data acquisition is performed through commercial solutions that are adaptable to a specific solution, and expansion capabilities are associated with the products [...] Read more.
Data acquisition systems are one of the main components for sensors and remote monitoring strategies required in a real process. Normally, data acquisition is performed through commercial solutions that are adaptable to a specific solution, and expansion capabilities are associated with the products (HW/SW) of the same company, which results in limited possibilities of expansion. As a contribution to solving this problem, a hardware development project with embedded systems and focused on the Internet of Things was designed. A data acquisition system was proposed and validated through a real application using the prototype built, monitoring variables in a photovoltaic system such as voltage and current to analyze the behavior of the solar panels. Testing and evaluation of the prototype were carried out in several experiments, where the most common failures of a photovoltaic plant were emulated, finding that the recorded data provide the necessary information to identify the moments in which the system being monitored presents problems. In this way, it was found that the developed system can be used as a remote monitoring system since the information that the device takes through the current and voltage sensors can be sent to a server through an Internet connection for data processing, graph generation, or statistical analysis according to the requirements. These features allow a friendly presentation of the data to an end-user. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Smartphone-Based Optical Fiber Sensor for the Assessment of a Fed-Batch Bioreactor
Eng. Proc. 2020, 2(1), 26; https://doi.org/10.3390/ecsa-7-08157 - 02 Dec 2020
Viewed by 511
Abstract
Industry is currently in a period of great expansion, the so-called “Industry 4.0”. This period relies on the development of new sensor technologies for the generation of systems capable of collecting, distributing, and delivering information. Particularly in chemical and biochemical industries, the development [...] Read more.
Industry is currently in a period of great expansion, the so-called “Industry 4.0”. This period relies on the development of new sensor technologies for the generation of systems capable of collecting, distributing, and delivering information. Particularly in chemical and biochemical industries, the development of portable monitoring devices can improve many process parameters, such as safety and productivity. In this work, the design of a smartphone-based optical fiber sensing platform for the online assessment of fed-batch fermentation systems is reported. The setup is comprised of a smartphone equipped with a 3D-printed case that couples optical fibers to the phone, and of an application for collecting images from the camera and then analyzing the pixel intensity. Finally, the obtained intensities are correlated to the broth refraction index, which is function of the sucrose concentration. We calculated the sensitivity of this sensor as 85.83 RIU−1 (refractive index units), and then compared its performance to results obtained with a handheld refractometer and with Monod model predictions. It showed to be a reliable, portable, and low-cost instrument for the online monitoring of bioreactors that can be easily reproducible on-site by simply printing it. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Detection of Velocity Based on Change in the Apparent Size
Eng. Proc. 2020, 2(1), 27; https://doi.org/10.3390/ecsa-7-08158 - 02 Dec 2020
Viewed by 439
Abstract
This article discusses a concept for developing a vision-based sensing system for measuring the velocity of an object which is based on the concept of apparent size. Objects at a finite distance from the eye look smaller than their real dimension. Movement of [...] Read more.
This article discusses a concept for developing a vision-based sensing system for measuring the velocity of an object which is based on the concept of apparent size. Objects at a finite distance from the eye look smaller than their real dimension. Movement of the object causes to change in its apparent size. In this work a mathematical relation is obtained which relates infinitesimal change in apparent size to the infinitesimal change in spatial coordinates of the object in the form of an ordinary differential equation. A mechanical device is fabricated for measuring the apparent size. Then by knowing the change in apparent size due to motion, change in displacement is calculated. Experiments were conducted to measure the average velocity of regular shaped object based on the change in its apparent size due to its motion. The average magnitude of error between average velocity calculated from the change in apparent size through the equation and from the actual displacement is about 2% and it is varying in between 0% and 5%. Results show the possibility to develop a vision sensor system to measure the velocity of objects by using high-speed cameras when the real size of the object is known and also it may be possible to develop vision velocity sensors for mobile robot applications. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Sensor-Based Gas Leakage Detector System
Eng. Proc. 2020, 2(1), 28; https://doi.org/10.3390/ecsa-7-08278 - 14 Nov 2020
Cited by 1 | Viewed by 742
Abstract
Liquefied Petroleum Gas (LPG) is a main source of fuel, especially in urban areas because it is clean compared to firewood and charcoal. Gas leakage is a major problem in the industrial sector, residential premises, etc. Nowadays, home security has become a major [...] Read more.
Liquefied Petroleum Gas (LPG) is a main source of fuel, especially in urban areas because it is clean compared to firewood and charcoal. Gas leakage is a major problem in the industrial sector, residential premises, etc. Nowadays, home security has become a major issue because of increasing gas leakage. Gas leakage is a source of great anxiety with ateliers, residential areas and vehicles like Compressed Natural Gas (CNG), buses, and cars which are run on gaspower. One of the preventive methods to stop accidents associated with the gas leakage is to install a gas leakage detection kit at vulnerable places. The aim of this paper is to propose and discuss a design of a gas leakage detection system that can automatically detect, alert and control gas leakage. This proposed system also includes an alerting system for the users. The system is based on a sensor that easily detects a gas leakage. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Application of Multilayer Perceptron Method on Heat Flow Meter Results for Reducing the Measurement Time
Eng. Proc. 2020, 2(1), 29; https://doi.org/10.3390/ecsa-7-08272 - 14 Nov 2020
Cited by 1 | Viewed by 236
Abstract
To reduce the impact on climate change, many countries have developed strategies for the building sector with a goal to reduce the energy demands and carbon emission of buildings. As most buildings that exist today will very likely exist in foreseeable future, many [...] Read more.
To reduce the impact on climate change, many countries have developed strategies for the building sector with a goal to reduce the energy demands and carbon emission of buildings. As most buildings that exist today will very likely exist in foreseeable future, many buildings will need to undergo major renovations. One of the most important parameters in determining the transmission heat losses through the building envelope is the U-value, i.e., thermal transmittance, and it is simply the rate of heat transfer per unit temperature. Since the U-value is one of the most important parameters regarding building energy performance, envelope elements that do not perform well in terms of transmission heat losses must undergo a renovation processes. The in-situ U-value of building elements is usually determined by the Heat Flux Method (HFM). One of the issues of current application of the HFM is the measurement duration. This paper explores the possibilities of reducing the measurement time by predicting the heat flux rate using a multilayer perceptron (MLP), a class of artificial neural network. The MLP uses two input layers that are the interior and exterior air temperatures, and the output layer that is the predicted heat flux rate. The predicted value is trained by comparing the predicted heat flux rates with the measured values, and by rearranging the neural network parameters (weights and biases) in corresponding neurons by minimizing the mean squared error defined for trained values (measured versus predicted heat flux rates). The use of MLP shows promising results for predicting the heat flux rates for the analyzed cases (4 days HFM results) when the training is performed on 2/3 or 1/2 of the overall measurement time. The application of the MLP could be in reducing the in-situ measurement time when determining heat losses through building elements in shorter time periods. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Wafer-Level Packaged CMOS-SOI-MEMS Thermal Sensor at Wide Pressure Range for IoT Applications
Eng. Proc. 2020, 2(1), 30; https://doi.org/10.3390/ecsa-7-08191 - 14 Nov 2020
Viewed by 304
Abstract
Wafer-level processed and wafer-level packaged low-cost microelectromechanical system (MEMS) thermal sensors are required for a wide range of Internet of Things (IoT) and wearables applications. Recently, a new generation of uncooled thermal sensors based on CMOS-SOI-MEMS technology has emerged, with higher performance compared [...] Read more.
Wafer-level processed and wafer-level packaged low-cost microelectromechanical system (MEMS) thermal sensors are required for a wide range of Internet of Things (IoT) and wearables applications. Recently, a new generation of uncooled thermal sensors based on CMOS-SOI-MEMS technology has emerged, with higher performance compared to commercial thermal sensors (bolometers, thermopiles, and pyroelectric sensors). The technology is implemented in commercial CMOS FABs and is, therefore, based on mature technology and implemented at a low cost. When packaged in a high vacuum, the sensors are dubbed TMOS and are applied for uncooled IR radiation. At atmospheric pressure, the sensors may function as gas sensors, dubbed GMOS. This paper focuses on the study of the thermal performance of wafer-level processed and packaged TMOS and GMOS sensors, where the pressure varies between high vacuum (0.01 Pa) and atmospheric pressure. The present study is based on analytical thermal modeling for gaining physical insight, 3D simulation is performed by ANSYS software, and finally, the measurements of the packaged devices are compared with the modeling and simulations. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Performance Optimization of a Differential Method for Localization of Capsule Endoscopes
Eng. Proc. 2020, 2(1), 31; https://doi.org/10.3390/ecsa-7-08271 - 14 Nov 2020
Cited by 3 | Viewed by 383
Abstract
Although capsule endoscopy is already used for diagnosis of the gastrointestinal tract, a method to precisely localize the capsules, important for accurate diagnosis, is lacking. Static magnetic localization is a promising solution for that purpose. In this paper, the simulation of a differential [...] Read more.
Although capsule endoscopy is already used for diagnosis of the gastrointestinal tract, a method to precisely localize the capsules, important for accurate diagnosis, is lacking. Static magnetic localization is a promising solution for that purpose. In this paper, the simulation of a differential static magnetic localization system with dynamic geomagnetic compensation was optimized. First, a convergence-test for the position and orientation errors as a function of the dimension of the computational domain was conducted. Subsequently, the diameter-to-length ratio of a permanent magnet was varied and the corresponding position and orientation errors, as well as the mean magnetic flux density measured at the sensor positions, were compared. The results revealed that for a computational domain radius of 800 mm, the position and orientation errors converged to less than 0.1 mm and 0.1°, respectively. The position and orientation errors were also of that order, even with the smallest permanent magnet employed in the study. Furthermore, the mean magnetic flux density measured at the sensors of the proposed magnetic localization system would be detectable using state-of-the-art magnetometers. It is concluded that the differential localization method is also feasible for small permanent magnets, which is especially important considering the limited space within endoscopy capsules. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Stator Winding Fault Phase Identification Using Piezoelectric Sensors in Three-Phase Induction Motors
Eng. Proc. 2020, 2(1), 32; https://doi.org/10.3390/ecsa-7-08183 - 14 Nov 2020
Cited by 2 | Viewed by 299
Abstract
Three-phase induction motors (TIMs) play a key role in industrial production lines. Due to their robustness and versatility, TIMs are commonly used to drive different devices like fans, conveyors, sieves, and compressors. However, these devices are often exposed to mechanical and electrical faults. [...] Read more.
Three-phase induction motors (TIMs) play a key role in industrial production lines. Due to their robustness and versatility, TIMs are commonly used to drive different devices like fans, conveyors, sieves, and compressors. However, these devices are often exposed to mechanical and electrical faults. Among them, failures in stator winding insulation lead to severe damage to the TIMs and can cause operational interruptions. Therefore, several approaches have been developed to monitor electrical faults in induction motors. The acoustic emission (AE) stands out as an efficient non-invasive technique (NIT) for TIM diagnosis. In this work, the AE analysis was applied to detect winding insulation faults and identify which electrical phase was affected. To achieve this proposal, a TIM was subjected to insulation faults in each of the three electrical phases, and the acoustic signals were acquired by four piezoelectric sensors attached to the motor. These signals were processed using a new technique, which calculates the energy of a specific range of the signal spectrum and assigns the energy values of each piezoelectric sensor to a coordinate axis (x, y). By ploting the values for each fault condition, this technique allows the detection of insulation faults and correctly identifies the affected phase by clustering the resulting values. Finally, the proposed methodology presented satisfactory results in winding insulation diagnosis. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
An Application of Wavelet Analysis to Assess Partial Discharge Evolution by Acoustic Emission Sensor
Eng. Proc. 2020, 2(1), 33; https://doi.org/10.3390/ecsa-7-08244 - 14 Nov 2020
Cited by 1 | Viewed by 282
Abstract
Under normal operation, insulation systems of high voltage electrical devices, like power transformers, are constantly subjected to multiple types of stresses (electrical, thermal, mechanical, environmental, etc.), which can lead to a degradation of the machine insulation. One of the main indicators of the [...] Read more.
Under normal operation, insulation systems of high voltage electrical devices, like power transformers, are constantly subjected to multiple types of stresses (electrical, thermal, mechanical, environmental, etc.), which can lead to a degradation of the machine insulation. One of the main indicators of the dielectric degradation process is the presence of partial discharges (PD). Although it starts due to operational stresses, PD can cause a progressive insulation deterioration, since it is characterized by localized current pulses that emit heat, UV radiation, acoustic, and electromagnetic waves. In this sense, acoustic emission (AE) transducers are wildly applied in PD detection. The goal is to reduce maintenance costs by predictive actions and avoid total failures. Becasue of the progressive deterioration, the assessment of the PD evolution is crucial for improving the maintenance planning and ensure the operation of the transformer. Based on this issue, this article presents a new wavelet -based analysis in order to characterize the PD evolution. Three levels of failures were carried out in a transformer and the acoustic signals captured by a lead zirconate titanate piezoelectric transducer were processed by discrete wavelet transform. The experimental results revealed that the energy of the approximation levels increasing with the failure evolution. More specifically, levels 4, 6, 7, and 10 presented a linear fit to characterize the phenomena, enhancing the applicability of the proposed approach to transformer monitoring. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
A Time Series Autoencoder for Load Identification via Dimensionality Reduction of Sensor Recordings
Eng. Proc. 2020, 2(1), 34; https://doi.org/10.3390/ecsa-7-08255 - 02 Dec 2020
Viewed by 489
Abstract
Current progress in sensor technology is setting the ground to push toward satisfactory solutions to challenging engineering problems, like e.g., system identification and Structural Health Monitoring (SHM). In civil engineering, SHM is often based on the analysis of vibrational recordings, represented by time [...] Read more.
Current progress in sensor technology is setting the ground to push toward satisfactory solutions to challenging engineering problems, like e.g., system identification and Structural Health Monitoring (SHM). In civil engineering, SHM is often based on the analysis of vibrational recordings, represented by time histories of displacements and/or accelerations, collected through pervasive sensor networks and shaped as Multivariate Time Series (MTS). Despite the great advances in soft computing techniques such as neural networks, inverse problems featuring regression tasks on raw vibrational measurements are still challenging. Developing dimensionality reduction tools, able to infer complex correlations within and across the recorded time series, is then of paramount importance. In this work, we designed an AutoEncoder (AE) capable of condensing MTS-shaped data in a reduced format featuring a few latent variables only. The obtained reduced data representation enhances the solution of inverse problems, like e.g., the identification of the parameters governing the dynamic load applied to a structural system. Numerical examples, aimed at the identification of the loading conditions on a shear-type building, are reported to assess the effectiveness of the proposed procedure. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Human Periodontal Ligament Characterization by Means of Vibrational Spectroscopy and Electron Microscopy
Eng. Proc. 2020, 2(1), 35; https://doi.org/10.3390/ecsa-7-08176 - 14 Nov 2020
Cited by 1 | Viewed by 213
Abstract
Human periodontal ligament (PDL) is a membrane-like connective tissue interposed between the tooth root and the alveolar bone, the main component of which is represented by collagen fibers. During the early stage of application of orthodontic forces, different changes occur in PDL. For [...] Read more.
Human periodontal ligament (PDL) is a membrane-like connective tissue interposed between the tooth root and the alveolar bone, the main component of which is represented by collagen fibers. During the early stage of application of orthodontic forces, different changes occur in PDL. For this reason, its characterization with conventional and non-conventional techniques can be extremely interesting. We investigated samples of PDL of orthodontic patients, aged between 13 and 21 years, using different experimental techniques. Morphological characterization of PDL samples was carried out by using a scanning electron microscope. Fourier-Transform Infrared (μ-FT-IR) and Raman (μ-RS) microspectroscopies were used for biochemical characterization of PDL samples. A biochemical characterization of PDL tissues with clear evidence of contributions from collagen, lipid and other protein was obtained. The analysis of Amide I and Amide III components was also performed, giving an indication of the protein secondary structure. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Reducing Food Waste with a Tiny CMOS-MEMS Gas Sensor, Dubbed GMOS
Eng. Proc. 2020, 2(1), 36; https://doi.org/10.3390/ecsa-7-08190 - 14 Nov 2020
Viewed by 262
Abstract
We present a tiny combustion-type gas sensor (named GMOS) fabricated using standard CMOS-SOI-MEMS technology. It is a low-cost thermal sensor with an embedded heater, catalytic layer and suspended transistor as a sensing element. The sensor principle relies on the combustion reaction of the [...] Read more.
We present a tiny combustion-type gas sensor (named GMOS) fabricated using standard CMOS-SOI-MEMS technology. It is a low-cost thermal sensor with an embedded heater, catalytic layer and suspended transistor as a sensing element. The sensor principle relies on the combustion reaction of the gas that takes place on the catalytic layer. The exothermic combustion leads to a sensor temperature increase, which modifies the transistor current-voltage characteristics. The GMOS is useful for detecting different gases, such as ethanol, acetone and especially ethylene, as well as their mixtures. The sensor demonstrates an excellent sensitivity to ethylene of 40 mV/ppm and selective ethylene detection using nanoparticle catalytic layers of Pt, as well as TiO2. Along with its low energy consumption, GMOS is a promising technology for low-cost ethylene detection systems at different stages in the food supply chain, and it may help reduce global fruit and vegetable loss and waste. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Effect of the Different Crystallinity of Ionic Liquid Based Solid Polymer Electrolyte on the Performance of Amperometric Gas Sensor
Eng. Proc. 2020, 2(1), 37; https://doi.org/10.3390/ecsa-7-08166 - 14 Nov 2020
Viewed by 211
Abstract
Solid polymer electrolytes (SPE) based on ionic liquid, poly-(vinylidene fluoride) and solvent N-methyl-pyrrolidone represent an effective component in electrochemical sensors. The advantage lies in their composition, which offers an opportunity to prepare SPE layers with a different porosity and microstructure. The study shows [...] Read more.
Solid polymer electrolytes (SPE) based on ionic liquid, poly-(vinylidene fluoride) and solvent N-methyl-pyrrolidone represent an effective component in electrochemical sensors. The advantage lies in their composition, which offers an opportunity to prepare SPE layers with a different porosity and microstructure. The study shows how the SPEs of different crystallinities affect the performance of an amperometric gas sensor from the point of view of current response (sensitivity), limit of detection and current fluctuations. The morphology of SPE has an impact not only on its conductivity but also on sensor sensitivity due to the morphology of the interface SPE/working electrode (WE). Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
A Sub-6 GHz Vital Signs Sensor Using Software Defined Radios
Eng. Proc. 2020, 2(1), 38; https://doi.org/10.3390/ecsa-7-08197 - 14 Nov 2020
Viewed by 230
Abstract
Recently, there has been high demand for contactless devices for monitoring vital signs, therefore developing a low-cost contactless breathing sensor would have a great benefit for many patients and healthcare workers. In this paper, we propose a contactless sub-6 GHz breathing sensor with [...] Read more.
Recently, there has been high demand for contactless devices for monitoring vital signs, therefore developing a low-cost contactless breathing sensor would have a great benefit for many patients and healthcare workers. In this paper, we propose a contactless sub-6 GHz breathing sensor with an implementation using a low-cost universal software radio peripheral (USRP) B205-mini device. A detailed performance analysis of the proposed system with different sensor algorithms is presented. The proposed system estimates the channel phase shift and detects the presence of low frequency oscillations in the estimated phase shift. Compared to 24 or 77 GHz FMCW-radar-based systems using distance measurements, the proposed system is simpler, can be built using more economical RF components, and requires lower sampling frequencies. Another key advantage of the proposed system is that even a very narrow unused frequency band is enough for the operation of the sensor. When operated at frequencies shared by other devices, the proposed system can turn off the transmitter at randomly selected intervals to detect the presence of other transmission activities, and can then switch to a different operating frequency. We provide both Python- and Octave/MATLAB-based implementations, which are available in a public GitHub repository. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Modelling the Influence of the 2.4 GHz Electromagnetic Field on the User of a Wearable Internet of Things (IoT) Device for Monitoring Hazards in the Work Environment
Eng. Proc. 2020, 2(1), 39; https://doi.org/10.3390/ecsa-7-08238 - 14 Nov 2020
Cited by 1 | Viewed by 286
Abstract
The aim was to test the hypothesis that there is an insignificant influence on humans from the absorption of an 2.4 GHz electromagnetic field (EMF) emitted by wearable Internet of Things (IoT) devices (using Meandered Inverted-F Antenna (MIFA) for Wi-Fi and Bluetooth technologies) [...] Read more.
The aim was to test the hypothesis that there is an insignificant influence on humans from the absorption of an 2.4 GHz electromagnetic field (EMF) emitted by wearable Internet of Things (IoT) devices (using Meandered Inverted-F Antenna (MIFA) for Wi-Fi and Bluetooth technologies) for monitoring hazards in the work environment. To quantify problem, the specific energy absorption rate (SAR) was calculated in a multi-layer ellipsoidal model of the IoT device user’s head exposed to EMF from MIFA attached to a headband or to a helmet. SAR values may be significant when a modelled IoT wearable device is attached to a headband, but not to a helmet. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Nonlinear Filter for a System with Randomly Delayed Measurements and Inputs
Eng. Proc. 2020, 2(1), 40; https://doi.org/10.3390/ecsa-7-08236 - 14 Nov 2020
Viewed by 270
Abstract
This paper deals with a remote state estimation problem for a nonlinear system. In a typical networked control system (NCS) scenario, the estimator and controller are remotely located, and they are connected with the plant through a common communication network. Traditional Bayesian filters [...] Read more.
This paper deals with a remote state estimation problem for a nonlinear system. In a typical networked control system (NCS) scenario, the estimator and controller are remotely located, and they are connected with the plant through a common communication network. Traditional Bayesian filters assume that the measurements are always available. However, this may not be the case in reality. As the sensor measurements are transmitted to the remotely located estimator through an unreliable communication channel, delay may arise during data transfer. Similarly, the control signal is also applied remotely, and it reaches to the plant through a similar unreliable communication channel, and due to which here also delay may occur. In this paper, the authors develop a generalized framework of nonlinear filtering where the states can be estimated in the presence of arbitrary random delay in (i) transmission of measurement from sensor to the estimator and (ii) transmission of input from the remotely located controller to the system. The filtering algorithm in such a scenario is realized with deterministic sample points. The performance of the proposed method is tested experimentally on one simulation problem. With the help of the simulation result, it is shown that the developed method performs better than traditional non-delayed nonlinear filters in the presence of arbitrary delay in measurement and input. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Terrestrial and Satellite-Based Positioning and Navigation Systems—A Review with a Regional and Global Perspective
Eng. Proc. 2020, 2(1), 41; https://doi.org/10.3390/ecsa-7-08262 - 14 Nov 2020
Viewed by 478
Abstract
Satellite-based navigation techniques have revolutionized modern-day surveying with unprecedented accuracies along with the traditional and terrestrial-based navigation techniques. However, the satellite-based techniques gain popularity due to their ease and availability. The position and attitude sensors mounted on satellites, aerial, and ground-based platforms as [...] Read more.
Satellite-based navigation techniques have revolutionized modern-day surveying with unprecedented accuracies along with the traditional and terrestrial-based navigation techniques. However, the satellite-based techniques gain popularity due to their ease and availability. The position and attitude sensors mounted on satellites, aerial, and ground-based platforms as well as different types of equipment play a vital role in remote sensing providing navigation and data. The presented review in this paper describes the terrestrial (LORAN-C, Omega, Alpha, Chayka) and satellite-based systems with their major features and peculiar applications. The regional and global navigation satellite systems (GNSS) can provide the position of a static object or a moving object i.e., in Kinematic mode. The GNSS systems include the NAVigation Satellite Timing And Ranging Global Positioning System (NAVSTAR GPS), of the United States of America (USA); the Globalnaya navigatsionnaya sputnikovaya sistema (GLObal NAvigation Satellite System, GLONASS), of Russia; BEIDOU, of China; and GALILEO, of the European Union (EU). Among the initial satellite-based regional navigation systems included are the TRANSIT of the US and TSYKLON of what was then the USSR which became operational in the 1960s. Regional systems developed in the last decade include the Quasi-Zenith Satellite System (QZSS) and the Indian Regional Navigation Satellite System (IRNSS). Currently, these global and regional satellite-based systems provide their services with accuracies of the order of 10–20 m using the trilateration method of surveying for civil use. The terrestrial and satellite-based augmented systems (SBAS) were further developed along with different surveying techniques to improve the accuracies up to centimeters or millimeter levels for precise applications. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Promoting Autonomy in Care: Combining Sensor Technology and Social Robotics for Health Monitoring
Eng. Proc. 2020, 2(1), 42; https://doi.org/10.3390/ecsa-7-08239 - 14 Nov 2020
Cited by 1 | Viewed by 430
Abstract
As the world’s population grows significantly older, there are not enough caregivers in many countries for all the elderly people in need of care. To promote their autonomy while also supporting their caregivers, we propose a health monitoring system comprised of a social [...] Read more.
As the world’s population grows significantly older, there are not enough caregivers in many countries for all the elderly people in need of care. To promote their autonomy while also supporting their caregivers, we propose a health monitoring system comprised of a social robot, and various wearable and non-wearable sensors. Through the use of patient-reported outcome measures (PROMs), captured in conversation with the social robot, the subjective health status of the user is determined. This is supplemented by objective information gathered from wearable and non-wearable sensors used to measure numerous biosignals. By combining the subjective data obtained from interaction with the user and the objective data from the sensor network, a health report for both users and caregivers is generated. The data are visualized for the user and caregiver in a customizable and easily accessible health monitoring dashboard, which also warns the user and their caregivers when the data deviate from the expected values or ranges. The goal is to use this information to improve the quality of care, as changes in the user’s health status can be determined more quickly by themselves and their caregivers. The proposed system establishes a good base for further testing and optimization together with the user, to ensure a useful and appropriate combination of sensors and technological devices that the user is comfortable with. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
A NIR-Spectroscopy-Based Approach for Detection of Fluids in Rectangular Glass Micro-Capillaries
Eng. Proc. 2020, 2(1), 43; https://doi.org/10.3390/ecsa-7-08250 - 14 Nov 2020
Viewed by 274
Abstract
In this work, we present a micro-opto-fluidic platform to distinguish water and alcohol samples flowing in rectangular glass micro-capillaries laid onto a bulk Aluminum mirror illuminated by the broadband radiation emitted by a Tungsten lamp. The fluid detection is based on the spectral [...] Read more.
In this work, we present a micro-opto-fluidic platform to distinguish water and alcohol samples flowing in rectangular glass micro-capillaries laid onto a bulk Aluminum mirror illuminated by the broadband radiation emitted by a Tungsten lamp. The fluid detection is based on the spectral analysis of the light reflected by the micro-structure in the near-infrared region from 1.0 μm to 1.7 μm. A theoretical model was implemented to study light propagation in the channel, taking into account absorption effects, and the results of the simulation are in good agreement with the experimental spectra obtained by testing water, ethanol, isopropanol and ethylene glycol. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
The Influence of Plasticizers on Determination of Cationic Surfactants in Pharmaceutical Disinfectants by Direct Potentiometric Surfactant Sensor
Eng. Proc. 2020, 2(1), 44; https://doi.org/10.3390/ecsa-7-08248 - 14 Nov 2020
Viewed by 289
Abstract
Ion selective liquid membrane type membranes are usually made of a PVC, ionophore and a plasticizer. Plasticizers soften the PVC but due to their lipophilicity they influence the ionophore, the ion exchange across the membrane, membrane resistance and consequently the analytical signal. The [...] Read more.
Ion selective liquid membrane type membranes are usually made of a PVC, ionophore and a plasticizer. Plasticizers soften the PVC but due to their lipophilicity they influence the ionophore, the ion exchange across the membrane, membrane resistance and consequently the analytical signal. The aim of the research was to investigate the influence of four different plasticizers, in formulation with the same ionophore, on the analytical properties of the sensor membrane towards two often used cationic surfactants, then select the best membrane formulation and test it on real samples of six pharmaceutical disinfectants containing cationic surfactants. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Wearable Wireless Biosensors for Spatiotemporal Grip Force Profiling in Real Time
Eng. Proc. 2020, 2(1), 45; https://doi.org/10.3390/ecsa-7-08252 - 14 Nov 2020
Viewed by 216
Abstract
The temporal evolution of individual grip force profiles of a novice using a robotic system for minimally invasive endoscopic surgery is analyzed on the basis of thousands of individual sensor data recorded in real time through a wearable wireless sensor glove system. The [...] Read more.
The temporal evolution of individual grip force profiles of a novice using a robotic system for minimally invasive endoscopic surgery is analyzed on the basis of thousands of individual sensor data recorded in real time through a wearable wireless sensor glove system. The spatio-temporal grip force profiles from specific sensor locations in the dominant hand performing a four-step pick-and-drop simulator task reveal skill-relevant differences in force deployment by the small finger (fine grip force control) and the middle finger (gross grip force contribution) by comparison with the profiles of a highly proficient expert. Cross-disciplinary insights from systems neuroscience, cognitive behavioral science, and robotics, with implications for biologically inspired AI for human–robot interactions, highlight the functional significance of spatio-temporal grip force profiling. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Characterization of Hand Gestures by a Smartphone-Based Optical Fiber Force Myography Sensor
Eng. Proc. 2020, 2(1), 46; https://doi.org/10.3390/ecsa-7-08178 - 14 Nov 2020
Viewed by 217
Abstract
In this paper, a smartphone-integrated, optical fiber sensor based on the force myography technique (FMG), which characterizes the stimuli of the forearm muscles in terms of mechanical pressures, was proposed for the identification of hand gestures. The device’s flashlight excites a pair of [...] Read more.
In this paper, a smartphone-integrated, optical fiber sensor based on the force myography technique (FMG), which characterizes the stimuli of the forearm muscles in terms of mechanical pressures, was proposed for the identification of hand gestures. The device’s flashlight excites a pair of polymer optical fibers and the output signals are detected by the camera. The light intensity is modulated through wearable, force-driven microbending transducers placed in the forearm and the acquired optical signals are processed by an algorithm based on decision trees and residual error. The sensor provided a hit rate of 87% regarding four postures, yielding reliable performance with a simple, portable, and low-cost setup embedded on a smartphone. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Transfer Printing of Conductive Thin Films on PDMS with Soluble Substrates for Flexible Biosensors
Eng. Proc. 2020, 2(1), 47; https://doi.org/10.3390/ecsa-7-08181 - 14 Nov 2020
Viewed by 236
Abstract
The resolution of commercially available electrocorticography (ECoG) electrodes is limited due to the large electrode spacing and, therefore, allows only a limited identification of the active nerve cell area. This paper describes a novel manufacturing process for neural implants with higher spatial resolution [...] Read more.
The resolution of commercially available electrocorticography (ECoG) electrodes is limited due to the large electrode spacing and, therefore, allows only a limited identification of the active nerve cell area. This paper describes a novel manufacturing process for neural implants with higher spatial resolution combining micro technological processes and Polydimethylsiloxane (PDMS) as the flexible, biocompatible material. The conductive electrode structure is deposited on a water-soluble transfer substrate by Physical Vapor Deposition (PVD) processes. Subsequently, the structure is contacted. Finally, the transfer to PDMS and dissolution of the transfer substrate takes place. In this way, high-resolution conductive structures can be produced on the PDMS. Transferred gold structures exhibit higher adhesion and conductivity than transferred platinum structures. The adhesion was improved by applying a silica surface modification to the conductive layer prior to transferring. Furthermore, the conductive layer is flexible, conductive up to an elongation of 10%, and resistant to sodium chloride solution, mimicking brain fluids. Using the introduced production process, an ECoG electrode was manufactured and characterized for its functionality in an electrochemical impedance measurement. Furthermore, the electrodes are flexible enough to adapt to different shapes. The transfer process can also be carried out in a three-dimensional mold to produce electrodes tailored to the individual patient. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Development of a Twenty-Two-Point Multichannel Temperature Data Logger Specially Customized and Coupled to a 160Wpeak Hybrid Photovoltaic/Thermal (PV/T) Flat Plate Solar Air Heater
Eng. Proc. 2020, 2(1), 48; https://doi.org/10.3390/ecsa-7-08276 - 14 Nov 2020
Cited by 1 | Viewed by 596
Abstract
A low cost multichannel temperature data logger was designed and fabricated in this study. The design was done using Max6675 temperature sensors and linear monolithic (LMs) temperature sensors. This data logger is an electronic device that records data over time based on microcontroller. [...] Read more.
A low cost multichannel temperature data logger was designed and fabricated in this study. The design was done using Max6675 temperature sensors and linear monolithic (LMs) temperature sensors. This data logger is an electronic device that records data over time based on microcontroller. The utilization of data logger in this work is to accomplish the task of monitoring the temperature measurement of the 160Wpeak hybrid photovoltaic/thermal (PV/T) flat plate solar air heater. This data logger is just customized for this equipment—the hybrid photovoltaic/thermal solar air heater. The developed prototype was powered both internally and externally. It equally has a retrievable memory card module. The time series of the sensor was set at one minute interval. The trend of the temperature flow pattern measured from the hybrid photovoltaic/thermal (PV/T) flat plate solar air heater was in consonance with the solar radiation flow pattern. This indicates that the peaks of the temperature plots fall at the peaks of the plots of solar radiation. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Intelligent Plant Disease Identification System Using Machine Learning
Eng. Proc. 2020, 2(1), 49; https://doi.org/10.3390/ecsa-7-08160 - 14 Nov 2020
Viewed by 360
Abstract
Agriculture is the backbone of every country in the world. In India, most of the rural population still depends on agriculture. The agricultural sector provides major employment in rural areas. Furthermore, it contributes a significant amount to India’s gross domestic product (GDP). Therefore, [...] Read more.
Agriculture is the backbone of every country in the world. In India, most of the rural population still depends on agriculture. The agricultural sector provides major employment in rural areas. Furthermore, it contributes a significant amount to India’s gross domestic product (GDP). Therefore, protecting and enhancing the agricultural sector helps in the development of India’s economy. In this work, a real-time decision support system integrated with a camera sensor module was designed and developed for identification of plant disease. Furthermore, the performance of three machine learning algorithms, such as Extreme Learning Machine (ELM) and Support Vector Machine (SVM) with linear and polynomial kernels was analyzed. Results demonstrate that the performance of the extreme learning machine is better when compared to the adopted support vector machine classifier. It is also observed that the sensitivity of the support vector machine with a polynomial kernel is better when compared to the other classifiers. This work appears to be of high social relevance, because the developed real-time hardware is capable of detecting different plant diseases. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Early Fire Detection System in Tanzania Markets
Eng. Proc. 2020, 2(1), 50; https://doi.org/10.3390/ecsa-7-08215 - 14 Nov 2020
Viewed by 396
Abstract
Application of wireless sensor networks (WSN) and Internet of Things (IoT) used to provide real-time monitoring of fire outbreak in markets. The system integrates three subsystems namely; sensing subsystem which uses multiple sensors for detecting fire outbreaks. Data processing subsystem which collects data [...] Read more.
Application of wireless sensor networks (WSN) and Internet of Things (IoT) used to provide real-time monitoring of fire outbreak in markets. The system integrates three subsystems namely; sensing subsystem which uses multiple sensors for detecting fire outbreaks. Data processing subsystem which collects data from the sensing subsystem through Xbee, analyses, and uploads data to the cloud. If values exceed the sensor threshold, an alarm is triggered and notification is sent to stakeholders via mobile application subsystem. The integration between sensing, data processing, and mobile application subsystems pave a new way for the mitigation of fire outbreaks at its early stage. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Carbon Screen-Printed Electrode Coated with Poly (Toluidine blue) as an Electrochemical Sensor for the Detection of Tyramine
Eng. Proc. 2020, 2(1), 51; https://doi.org/10.3390/ecsa-7-08171 - 14 Nov 2020
Viewed by 414
Abstract
In the present work the surface modification of a carbon screen-printed electrode by electrochemical polymerization of toluidine blue (TB) for determination of tyramine is described. The electrochemical polymerization of the electrode with TB was done by cyclic voltammetry at a scan rate of [...] Read more.
In the present work the surface modification of a carbon screen-printed electrode by electrochemical polymerization of toluidine blue (TB) for determination of tyramine is described. The electrochemical polymerization of the electrode with TB was done by cyclic voltammetry at a scan rate of 50 mV/s and a potential sweep between ‒0.7 V and 1.0 V in the presence of 0.5 mM TB in an electrolyte solution. At each cycle, the polymer film started to deposit on the carbon screen-printed electrode which was repeated 20 times. For parameter optimization the electrochemical behavior of the modified electrode was analyzed by amperometric methods such as cyclic voltammetry (CV) and differential pulse voltammetry (DPV). A phosphate buffer solution (PBS) was used as an electrolyte for all the amperometric experiments. The electrochemically modified poly-TB-coated carbon-screen-printed electrode showed an oxidation peak potential of tyramine at 0.67 V. The unmodified carbon-screen-printed electrode showed the tyramine oxidation peak potential at 0.9 V. Based on the voltammetric results, it was found that the poly-TB-modified carbon-screen-printed electrode showed higher sensitivity (1.78 µA nM−1 cm−2) than a bare carbon-screen-printed electrode toward tyramine detection. Tyramine in 0.1 M PBS (pH 7.4) was analyzed by cyclic voltammetry from the potential of ‒0.7 to 1.0 V at a scan rate of 50 mV/s. The poly-TB-modified carbon-screen-printed electrode exhibited a linear response between catalytic peak current and tyramine concentration from 0.02 µM to 270.5 µM with a lower detection limit of 0.007 µM (S/N = 3). Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Analysis, Design and Practical Validation of an Augmented Reality Teaching System Based on Microsoft HoloLens 2 and Edge Computing
Eng. Proc. 2020, 2(1), 52; https://doi.org/10.3390/ecsa-7-08210 - 14 Nov 2020
Cited by 1 | Viewed by 572
Abstract
In recent years, the education sector has incorporated the use of new technologies and computing devices into classrooms, which allowed for implementing new ways for enhancing teaching and learning. One of these new technologies is augmented reality (AR), which enables creating experiences that [...] Read more.
In recent years, the education sector has incorporated the use of new technologies and computing devices into classrooms, which allowed for implementing new ways for enhancing teaching and learning. One of these new technologies is augmented reality (AR), which enables creating experiences that mix reality and virtual elements in an attractive and visual way, thus helping teachers to foster student interest in learning certain subjects and abstract concepts in novel visual ways. This paper proposes to harness the potential of the latest AR devices in order to enable giving AR-enabled lectures and hands-on labs. Specifically, it proposes an architecture for providing low-latency AR education services in a classroom or a laboratory. Such a low latency is achieved thanks to the use of edge computing devices, which offload the cloud from the traditional tasks that are required by dynamic AR applications (e.g., near real-time data processing, communications among AR devices). Depending on the specific AR application and the number of users, the wireless link (usually WiFi) could be overloaded if the network has not been properly designed, and the overall performance of the application can be compromised, leading to high latency and even to wireless communication failure. In order to tackle this issue, radio channel measurements and simulation results have been performed by means of an in-house developed 3D ray-launching tool, which is able to model and simulate the behaviour of an AR-enabled classroom/laboratory in terms of radio propagation and quality of service. To corroborate the obtained theoretical results, a Microsoft HoloLens 2 teaching application was devised and tested, thus demonstrating the feasibility of the proposed approach. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Acoustic Description of Bird Broiler Vocalisations in a Real-Life Intensive Farm and Its Impact on Animal Welfare: A Comparative Analysis of Recordings
Eng. Proc. 2020, 2(1), 53; https://doi.org/10.3390/ecsa-7-08164 - 14 Nov 2020
Viewed by 251
Abstract
The poultry meat industry is one of the most efficient biological systems to transform cereal protein into high quality protein for human consumption at a low cost. However, to supply the increasing demand of white meat, intensive production is required whiche generates stress [...] Read more.
The poultry meat industry is one of the most efficient biological systems to transform cereal protein into high quality protein for human consumption at a low cost. However, to supply the increasing demand of white meat, intensive production is required whiche generates stress for the animals, which can be a major source of welfare problems. In this study, a comparative acoustic analysis of two entire production cycles of an intensive broiler Ross 308 poultry farm in the Mediterranean area has been performed. The following step to consolidate the analysis is to stablise a clear comparison among the performance of the indicators (Leq, Leq variation, Peak Frequency (PF) and PF variation) in the conditions of two different recording campaigns corresponding to summer and winter entire production cycles. The acoustic maps of PF, Leq and the related variations should be validated in an inter-campaign comparison, which may also arise the possibility of changes due to the season of the year. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Low-Cost Environmental and Motion Sensor Data for Complex Activity Recognition: Proof of Concept
Eng. Proc. 2020, 2(1), 54; https://doi.org/10.3390/ecsa-7-08194 - 14 Nov 2020
Cited by 1 | Viewed by 250
Abstract
The merge of new sensing technologies with machine learning methods can be used as a tool to recognize complex activities. A wearable particulate matter (PM) sensor, in combination with a motion tracker, was provided to 97 individuals for 7 days in two seasons. [...] Read more.
The merge of new sensing technologies with machine learning methods can be used as a tool to recognize complex activities. A wearable particulate matter (PM) sensor, in combination with a motion tracker, was provided to 97 individuals for 7 days in two seasons. These data sets were used in three different models, constructed by the classification of activity. Using algorithms IBk, J48 and RandomForest for hourly (minute) values, an accuracy of 31.0 (23.1)%, 28.6 (22.0)% and 35.7 (23.0)%, respectively, was achieved. Most misclassified instances concern vaguely defined activities. Low accuracy can also be explained with the differences in time scales. The accuracy could be improved by more clearly defining the activities and collecting per-minute data. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
A Comparison between Piezoelectric Sensors Applied to Multiple Partial Discharge Detection by Advanced Signal Processing Analysis
Eng. Proc. 2020, 2(1), 55; https://doi.org/10.3390/ecsa-7-08243 - 14 Nov 2020
Cited by 2 | Viewed by 274
Abstract
The development of sensors applied to failure detection systems for power transformers is a critical concern since this device stands out as a strategic component of the electric power system. Among the most common issues is the presence of partial discharges (PDs) in [...] Read more.
The development of sensors applied to failure detection systems for power transformers is a critical concern since this device stands out as a strategic component of the electric power system. Among the most common issues is the presence of partial discharges (PDs) in the insulation system of the transformer, which can lead the device to total failure. Aiming to prevent unexpected damages, several PD monitoring approaches have been developed. One of the most promising is the Acoustic Emission (AE) technique, which captures the acoustic signals generated by PDs using piezoelectric sensors. Although many studies have proved the effectiveness of AE, most signal processing approaches are strictly related to the frequency analysis of PD signals, which can hide important information such as the repetition rate of the failure. This article presents a comparison between two types of piezoelectric transducers: the microfiber composite (MFC) and the lead zirconate titanate (PZT). To ensure the detection of multiple PDs, time–frequency analysis was carried out by short-time Fourier transform (STFT). Intending to compare the sensibility of the transducers, the AE signals were windowed, and the root mean square (RMS) value was extracted for each part of the signal. The results indicate that spectrogram and RMS analysis have great potential to detect multiple PD activity. Although MFC was two times more sensitive to PD detection than the PZT sensor, PZT presents a higher frequency response band (0–100 kHz) than MFC (80 kHz). Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Comprehensive Optimization of the Tripolar Concentric Ring Electrode with Respect to the Accuracy of Laplacian Estimation Based on the Finite Dimensions Model of the Electrode
Eng. Proc. 2020, 2(1), 56; https://doi.org/10.3390/ecsa-7-08167 - 14 Nov 2020
Viewed by 201
Abstract
Optimization performed in this study is based on the finite dimensions model of the concentric ring electrode as opposed to the negligible dimensions model widely used in the past. This makes the optimization problem comprehensive since all of the electrode parameters, including, for [...] Read more.
Optimization performed in this study is based on the finite dimensions model of the concentric ring electrode as opposed to the negligible dimensions model widely used in the past. This makes the optimization problem comprehensive since all of the electrode parameters, including, for the first time, the radius of the central disc and individual widths of concentric rings, are optimized simultaneously. The optimization criterion used is maximizing the accuracy of the surface Laplacian estimation since the ability to estimate the Laplacian at each electrode constitutes the primary biomedical significance of concentric ring electrodes. Even though the obtained results and derived principles defining optimal electrode configurations are illustrated on tripolar (two concentric rings) electrodes, they were also confirmed for quadripolar (three rings) and pentapolar (four rings) electrodes and are likely to continue to hold for any higher number of concentric rings. For tripolar concentric ring electrodes, the optimal configuration was compared to previously proposed, linearly increasing inter-ring distances and constant inter-ring distances in configurations of the same size and based on the same finite dimensions model of the electrode. The obtained results suggest that previously proposed configurations correspond to almost two-fold and more than three-fold increases in Laplacian estimation error, respectively, compared to the optimal configuration proposed in this study. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Technical and Economic Viability Analysis of Optical Fiber Sensors for Monitoring Industrial Bioreactors
Eng. Proc. 2020, 2(1), 57; https://doi.org/10.3390/ecsa-7-08161 - 14 Nov 2020
Cited by 1 | Viewed by 227
Abstract
Bioreactors are employed in several industries, such as pharmaceutics, energy, biomedic and food. To ensure the proper operation of these bioreactors, Enzyme-Linked Immunosorbent Assay (ELISA) and High-Performance Liquid Chromatography (HPLC) systems are commonly used. Although ELISA and HPLC provide very precise results, they [...] Read more.
Bioreactors are employed in several industries, such as pharmaceutics, energy, biomedic and food. To ensure the proper operation of these bioreactors, Enzyme-Linked Immunosorbent Assay (ELISA) and High-Performance Liquid Chromatography (HPLC) systems are commonly used. Although ELISA and HPLC provide very precise results, they are incapable of real-time monitoring and present high operational costs. Given this context, in this work, we discuss the technical and economic viability of implementing fiber optics-based monitoring systems in lieu of traditional ELISA and HPLC systems. We selected fed-batch ethanol fermentative systems for our analysis, as the fed-batch mode is not only prevalent in different fermentative industries, but ethanol production represents a major sector of the Brazilian economy, with annual production in excess of 35 billion liters. Then, a simple fiber sensing system for measuring the refractive index of the fermentation broth, capable of real-time monitoring the fermentation process, is proposed and the advantages of the real-time process control are discussed. Afterward, we present the long-term economic gains of implementing such a system. We estimate that, by using readily commercially available components, the typical Brazilian ethanol plants will see a return for their investment in a time as short as 50 days, with a 5-year Internal Rate of Return (IRR) of 742%/year by setting up a fiber-optic monitoring system over HPLC. With the provided list of components, these numbers can be easily adjusted for industries worldwide, providing incredibly attractive economic prospects. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Impact of the Sensor Temperature on Low Acetone Concentration Detection Using AlGaN/GaN HEMTs
Eng. Proc. 2020, 2(1), 58; https://doi.org/10.3390/ecsa-7-08193 - 14 Nov 2020
Viewed by 275
Abstract
In this work, we report on AlGaN/GaN HEMT sensors for acetone concentration below 100ppm and in a broad range of the sensor temperature varying from RT to 300 C. At RT, in the presence of acetone, a smooth and monotonic [...] Read more.
In this work, we report on AlGaN/GaN HEMT sensors for acetone concentration below 100ppm and in a broad range of the sensor temperature varying from RT to 300 C. At RT, in the presence of acetone, a smooth and monotonic decrease of the current is observed with a rather large response of 15A/ppm and with a large response time (several minutes) and memory effect. At a high temperature (300 C), a current decrease is first observed just after the acetone injection, then followed by an increase, which saturates and stabilizes at a constant value. In order to clarify this unexpected behaviour, a detailed study of the sensor response versus the temperature and acetone injection flow is carried out. The outcome of this investigation is that a competition between the current variations induced by both the sensor and gas flow temperature difference from one side and the acetone dipolar moment from the other side can explain this transient. Our study highlights that AlGaN/GaN HEMT-based sensors allow for very sensitive acetone detection at both room and high temperatures. Nevertheless, care must be taken during the characterization and operation of such sensors especially at high operating temperatures. On the other hand, the high temperature operation helps to improve the sensor response and suppress the memory effect. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
A Self-Learning and Adaptive Control Scheme for Phantom Prosthesis Control Using Combined Neuromuscular and Brain-Wave Bio-Signals
Eng. Proc. 2020, 2(1), 59; https://doi.org/10.3390/ecsa-7-08169 - 14 Nov 2020
Cited by 1 | Viewed by 279
Abstract
The control scheme in a myoelectric prosthesis includes a pattern recognition section whose task is to decode an input signal, produce a respective actuation signal and drive the motors in the prosthesis limb towards the completion of the user’s intended gesture motion. The [...] Read more.
The control scheme in a myoelectric prosthesis includes a pattern recognition section whose task is to decode an input signal, produce a respective actuation signal and drive the motors in the prosthesis limb towards the completion of the user’s intended gesture motion. The pattern recognition architecture works with a classifier which is typically trained and calibrated offline with a supervised learning framework. This method involves the training of classifiers which form part of the pattern recognition scheme, but also induces additional and often undesired lead time in the prosthesis design phase. In this study, a three-phase identification framework is formulated to design a control architecture capable of self-learning patterns from bio-signal inputs from electromyography (neuromuscular) and electroencephalography (brain wave) biosensors, for a transhumeral amputee case study. The results show that the designed self-learning framework can help reduce lead time in prosthesis control interface customisation, and can also be extended as an adaptive control scheme to minimise the performance degradation of the prosthesis controller. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Implementation of a WSN-Based IIoT Monitoring System within the Workshop of a Solar Protection Curtains Company
Eng. Proc. 2020, 2(1), 60; https://doi.org/10.3390/ecsa-7-08202 - 14 Nov 2020
Viewed by 249
Abstract
Nowadays, the implementation of automated manufacturing processes within a wide variety of industrial environments is not understood without the Industry 4.0 concept and the context-aware possibilities given by the Industrial Internet of Things (IIoT). In this sense, Wireless Sensor Networks (WSN) play a [...] Read more.
Nowadays, the implementation of automated manufacturing processes within a wide variety of industrial environments is not understood without the Industry 4.0 concept and the context-aware possibilities given by the Industrial Internet of Things (IIoT). In this sense, Wireless Sensor Networks (WSN) play a key role due to their inherent mobility, ease of deployment and maintenance, scalability and low power consumption, among others. This work proposes the deployment and optimization of a WSN in the facilities of the Galeo Enrollables Company, located in Navarre (Spain), in order to optimize the manufacturing processes of technical curtains. For that purpose, radio propagation measurements as well as 3D Ray Launching simulations have been performed in order to characterize the wireless channel of this harsh industrial environment. Then, low cost sensors and actuators have been selected to prepare different wireless motes in order to deploy them on different machines/workstations present within the scenario. The information gathered by the motes is then transmitted to a central node, which conditions the data for input into the Enterprise Resource Planning (ERP) system. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Toward Near Real-Time Kinematics Differential Correction: In View of Geometrically Augmented Sensor Data for Mobile Microclimate Monitoring
Eng. Proc. 2020, 2(1), 61; https://doi.org/10.3390/ecsa-7-08274 - 14 Nov 2020
Viewed by 303
Abstract
In the scenario of massive urbanization and global climate change, the acquisition of microclimatic data in urban areas plays a key role in responsive adaptation and mitigation strategies. The enrichment of kinematic sensor data with precise, high-frequency and robust positioning directly relates to [...] Read more.
In the scenario of massive urbanization and global climate change, the acquisition of microclimatic data in urban areas plays a key role in responsive adaptation and mitigation strategies. The enrichment of kinematic sensor data with precise, high-frequency and robust positioning directly relates to the possibility of creating added-value services devoted to improving the life-quality of urban communities. This work presents a low-cost cloud-connected mobile monitoring platform for multiple environmental parameters and their spatial variation in the urban context. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Evaluation of Feature Selection Techniques in a Multifrequency Large Amplitude Pulse Voltammetric Electronic Tongue
Eng. Proc. 2020, 2(1), 62; https://doi.org/10.3390/ecsa-7-08242 - 14 Nov 2020
Viewed by 292
Abstract
An electronic tongue is a device composed of a sensor array that takes advantage of the cross sensitivity property of several sensors to perform classification and quantification in liquid substances. In practice, electronic tongues generate a large amount of information that needs to [...] Read more.
An electronic tongue is a device composed of a sensor array that takes advantage of the cross sensitivity property of several sensors to perform classification and quantification in liquid substances. In practice, electronic tongues generate a large amount of information that needs to be correctly analyzed, to define which interactions and features are more relevant to distinguish one substance from another. This work focuses on implementing and validating feature selection methodologies in the liquid classification process of a multifrequency large amplitude pulse voltammetric (MLAPV) electronic tongue. Multi-layer perceptron neural network (MLP NN) and support vector machine (SVM) were used as supervised machine learning classifiers. Different feature selection techniques were used, such as Variance filter, ANOVA F-value, Recursive Feature Elimination and model-based selection. Both 5-fold Cross validation and GridSearchCV were used in order to evaluate the performance of the feature selection methodology by testing various configurations and determining the best one. The methodology was validated in an imbalanced MLAPV electronic tongue dataset of 13 different liquid substances, reaching a 93.85% of classification accuracy. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Compact Planar Inverted F Antenna (PIFA) for Smart Wireless Body Sensors Networks
Eng. Proc. 2020, 2(1), 63; https://doi.org/10.3390/ecsa-7-08253 - 14 Nov 2020
Cited by 2 | Viewed by 288
Abstract
In this paper a dual band, a dual band Planar Inverted F antenna (PIFA) is designed for wireless communication intended to be used in wireless body sensor networks. The designed PIFA operates at two different frequency bands, 2.45 GHz Industrial, Scientific and Medical [...] Read more.
In this paper a dual band, a dual band Planar Inverted F antenna (PIFA) is designed for wireless communication intended to be used in wireless body sensor networks. The designed PIFA operates at two different frequency bands, 2.45 GHz Industrial, Scientific and Medical band (ISM) and 5.2 GHz (HiperLAN band). In body-centric wireless networks, antennas need to be integrated with wireless wearable sensors. An antenna is an essential part of wearable body sensor networks. For on-body communications, antennas need to be less sensitive to human body effects. For body-centric communications, wearable devices need to communicate with the devices located over the surface, and there is a need of communication from on-body devices to off-body units. Based on this need, a dual band planar inverted F antenna is designed that works at two different frequency bands, i.e., 2.45 GHz and 5.2 GHz. The 2.45 GHz is proposed for establishing communication among the wireless sensor devices attached to the human body, while 5.2 GHz is proposed for the communications for from on-body to off-body devices. The proposed antenna is very compact, and due to having ground plane at the backside it is less sensitive to the effects of the human body tissues. Computer Simulation Technology (CST) microwave studio™ was used for antenna design and simulation purposes. Performance parameters such as return loss, bandwidth, radiation pattern and efficiency of this antenna are shown and investigated. These performance parameters of the proposed antenna have been investigated at free space and close proximity to the human body. Simulation results and analysis show that the performance parameters produce very good results for both frequency bands. Due to its compact size, low sensitivity to human body tissues, and dual band functionality, it will be a good candidate for wireless wearable body sensor networks. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Multispectral Sensing and Data Integration for the Study of Heritage Architecture
Eng. Proc. 2020, 2(1), 64; https://doi.org/10.3390/ecsa-7-08198 - 14 Nov 2020
Viewed by 316
Abstract
The recording and processing of terrestrial multispectral information can have significant value for built heritage studies. The efficient adoption of active and passive sensing techniques operating at multiple wavelengths and the integrated analyses of the produced data is essential for enhanced observation of [...] Read more.
The recording and processing of terrestrial multispectral information can have significant value for built heritage studies. The efficient adoption of active and passive sensing techniques operating at multiple wavelengths and the integrated analyses of the produced data is essential for enhanced observation of historical architecture, especially for the implementation of rapid non-destructive surveys, which can provide an overall assessment of the state-of-preservation of a historical structure to indicate areas of interest for more detailed diagnostics. Based on this rationale, the presented work aims at providing methods for prompt recording, fusion, and integrated visual analysis of two-dimensional multispectral results to study architectural heritage. Spectral images—captured with a modified digital camera—thermograms, photogrammetrically produced orthophoto-maps, and spatial raster data produced from point clouds are integrated and analyzed. The results are evaluated within the scope of studying building materials, deterioration patterns, and hidden defects, towards the employment of advanced geomatics approaches to monitor built heritage effectively. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Breath Sounds as a Biomarker for Screening Infectious Lung Diseases
Eng. Proc. 2020, 2(1), 65; https://doi.org/10.3390/ecsa-7-08200 - 14 Nov 2020
Viewed by 319
Abstract
Periodic monitoring of breath sounds is essential for early screening of obstructive upper respiratory tract infections, such as inflammation of the airway typically caused by viruses. As an immediate first step, there is a need to detect abnormalities in breath sounds. The adult [...] Read more.
Periodic monitoring of breath sounds is essential for early screening of obstructive upper respiratory tract infections, such as inflammation of the airway typically caused by viruses. As an immediate first step, there is a need to detect abnormalities in breath sounds. The adult average male lung capacity is approximately 6 liters and the manifestation of pulmonary diseases, unfortunately, remains undetected until their advanced stages when the disease manifests into severe conditions. Additionally, such rapidly progressing conditions, which arise due to viral infections that need to be detected via adventitious breath sounds to take immediate therapeutic action, demand frequent monitoring. These tests are usually conducted by a trained physician by means of a stethoscope, which requires an in-person visit to the hospital. During a pandemic situation such as COVID-19, it is difficult to provide periodic screening of large volumes of people with the existing medical infrastructure. Fortunately, smartphones are ubiquitous, and even developing countries with skewed doctor-to-patient ratios typically have a smartphone in every household. With this technology accessibility in mind, we present a smartphone-based solution that monitors breath sounds from the user via the in-built microphone of their smartphone and our Artificial Intelligence (AI) -based anomaly detection engine. The presented automated classifier for abnormal breathing sounds is able to detect abnormalities in the early stages of respiratory dysfunctions with respect to their individual normal baseline vesicular breath sounds, with an accuracy of 95%, and it can flag them, and thus enhances the possibility of early detection. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Electrospray Printing of Graphene Layers for Chemiresistive Gas Sensors
Eng. Proc. 2020, 2(1), 66; https://doi.org/10.3390/ecsa-7-08203 - 14 Nov 2020
Viewed by 517
Abstract
In this work, we investigate the electrospray technique for the preparation of graphene layers for use in chemiresistive gas sensors. A dispersion of reduced graphene oxide (rGO) in isopropyl alcohol (0.1 mg/mL) is electrosprayed and the rGO flakes are deposited onto a polymeric [...] Read more.
In this work, we investigate the electrospray technique for the preparation of graphene layers for use in chemiresistive gas sensors. A dispersion of reduced graphene oxide (rGO) in isopropyl alcohol (0.1 mg/mL) is electrosprayed and the rGO flakes are deposited onto a polymeric substrate with printed interdigitated electrodes. The surface area of the substrate covered with rGO is mainly determined by the distance between the needle and the substrate, while the rGO deposition pattern strongly depends on the flowrate and the applied voltage. Homogeneous layers of rGO are obtained in a stable cone-jet regime, and the room temperature detection behavior of the sensors towards NO2, O3 and CO is assessed. The sensors were not capable of detecting CO (up to 5 ppm), but they detected 0.2 ppm NO2 and 0.05 ppm O3. The results are encouraging regarding the use of electrospray for the production of low-cost and low-power gas sensors based on graphene for air quality applications. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
An IoT and Blockchain Based System for Monitoring and Tracking Real-Time Occupancy for COVID-19 Public Safety
Eng. Proc. 2020, 2(1), 67; https://doi.org/10.3390/ecsa-7-08207 - 14 Nov 2020
Cited by 1 | Viewed by 512
Abstract
The COVID-19 pandemic has brought several limitations regarding physical distancing in order to reduce the interactions among large groups that could have prolonged close contact. For health reasons, such physical distancing requirements should be guaranteed in private and public spaces. In Spain, occupancy [...] Read more.
The COVID-19 pandemic has brought several limitations regarding physical distancing in order to reduce the interactions among large groups that could have prolonged close contact. For health reasons, such physical distancing requirements should be guaranteed in private and public spaces. In Spain, occupancy is restricted by law but, in practice, certain spaces may become overcrowded, existing law infringements in places that rely on occupancy estimations that are not accurate enough. For instance, although the number of passengers who enter a public transportation service is known, it is difficult to determine the actual occupancy of such a vehicle, since it is commonly unknown when and where passengers descend. Despite a number of counting systems existing, they are either prone to counting errors in overcrowded scenarios or require the active involvement of the people to be counted (e.g., going through a lathe or tapping a card when entering or exiting a monitored area) or of a person who manages the entering/exit process. This paper presents a novel IoT occupancy system that allows estimating in real time the people occupancy level of public spaces such as buildings, classrooms, businesses or moving transportation vehicles. The proposed system is based on autonomous wireless devices that, after powering them on, do not need active actions from the passengers/users and require a minimum amount of infrastructure. The system does not collect any personal information to ensure user privacy and includes a decentralized traceability subsystem based on blockchain, which guarantees the availability, security and immutability of the collected information. Such data will be shared among smart city stakeholders to ensure public safety and then deliver transparent decision-making based on data-driven analysis and planning. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Defining Data-Driven Analytical Methods on Improving Energy-Efficiency in Apartment Buildings
Eng. Proc. 2020, 2(1), 68; https://doi.org/10.3390/ecsa-7-08209 - 14 Nov 2020
Viewed by 335
Abstract
Energy efficiency is one of the key characteristics of smart cities and data-driven analytical methods, especially including Internet of Things (IoT) sensors, and meaningful indicators are provided to support initiatives but also changing behavior at the citizen level. The analysis is often undertaken [...] Read more.
Energy efficiency is one of the key characteristics of smart cities and data-driven analytical methods, especially including Internet of Things (IoT) sensors, and meaningful indicators are provided to support initiatives but also changing behavior at the citizen level. The analysis is often undertaken in closed systems that contain sensors, data acquisition, analysis and visualization. To improve the effectiveness of energy-efficiency initiatives in climate programs, harmonization of analytical methods and quality assurance of the data are required. This paper provides an overview of these themes based on the findings from two European Union (EU)-funded projects, European Regional Development Fund (ERDF) 6Aika Climate Friendly Housing Companies and Horizon 2020 mySMARTLife. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Comparative Measurement of the PPG Signal on Different Human Body Positions by Sensors Working in Reflection and Transmission Modes
Eng. Proc. 2020, 2(1), 69; https://doi.org/10.3390/ecsa-7-08204 - 14 Nov 2020
Cited by 1 | Viewed by 366
Abstract
The paper deals with the photoplethysmographic (PPG) optical sensor usage for non-invasive acquisition of vital information about the cardiovascular system from different parts of the human skin surface. Finger-ring and ear-clip realizations of the transmission-type PPG sensor were tested first. For the next [...] Read more.
The paper deals with the photoplethysmographic (PPG) optical sensor usage for non-invasive acquisition of vital information about the cardiovascular system from different parts of the human skin surface. Finger-ring and ear-clip realizations of the transmission-type PPG sensor were tested first. For the next PPG signal recording, the reflection PPG sensor was placed on fingers and on a wrist. PPG signal properties were described by energetic and temporal parameters and their statistical parameters together with determined instantaneous heart rate values. Our final aim was to find conditions, limitations, and recommendations for development of a wearable PPG sensor working in a magnetic field environment. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Morphological Study of Insect Mechanoreceptors to Develop Artificial Bio-Inspired Mechanosensors
Eng. Proc. 2020, 2(1), 70; https://doi.org/10.3390/ecsa-7-08199 - 14 Nov 2020
Viewed by 270
Abstract
Mechanoreceptors of the insect play a vital role for the insect to sense and monitor the environmental parameters, like flow, tactile pressure, etc. This paper presents the studies made on the morphology of the mechanoreceptor of the insect Blattella asahinai (scientific name [...] Read more.
Mechanoreceptors of the insect play a vital role for the insect to sense and monitor the environmental parameters, like flow, tactile pressure, etc. This paper presents the studies made on the morphology of the mechanoreceptor of the insect Blattella asahinai (scientific name of cockroach) that is a hair-like structure known as trichoid sensilla, by scanning electron microscope and confocal laser microscope. The scanned images show the details of sensilla components in which the hair is embedded in the sockets, which are connected with the cuticle and joint membrane, where the dendrite touches at the base of the hair passing through the cuticle layers. The images also show that the tubular bodies and microtubules are tightly compacted inside the dendrite. This paper presents the details of how the sensilla work when an external stimulus act on them. The hair deflects with the disturbance of the cuticle and joint membrane, and this deformed hair leans on the dendrite, which is attached at the base of the hair that in turn presses the tubular bodies and microtubules, which develop negative ions passing down through the dendrite to the neuron, which provides information as an electric signal to the brain of the insect so that it responds for necessary action. Based on the morphological studies, sensing mechanism, material properties of the components, and design principles will be evolved for the development of an artificial bio-inspired sensor. A solid works model of the sensilla is also presented. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Detection of Hotspots and Performance Deteriotations in PV Modules under Partial Shading Conditions Using Infrared Thermography
Eng. Proc. 2020, 2(1), 71; https://doi.org/10.3390/ecsa-7-08201 - 14 Nov 2020
Cited by 1 | Viewed by 269
Abstract
Operating photovoltaic (PV) modules are frequently shaded by nearby structures, vegetation, droppings, etc., and this reduces the effective incident solar radiation received by the modules. Shading also reduces the power output of PV modules and, under certain conditions, causes the formation of hotspots. [...] Read more.
Operating photovoltaic (PV) modules are frequently shaded by nearby structures, vegetation, droppings, etc., and this reduces the effective incident solar radiation received by the modules. Shading also reduces the power output of PV modules and, under certain conditions, causes the formation of hotspots. In this study, a wide variety of partial shading scenarios were investigated to evaluate their effects on the output current, voltage and efficiencies, and hotspot formation in mono-crystalline and poly-crystalline PV modules operating under the ambient conditions experienced in Nsukka, Nigeria. Sixteen shading cases were considered, including 20%, 40%, 60% and 80% of the modules’ surface areas shaded parallel to the long sides, parallel to the short sides, diagonally and randomly. Test ambient conditions, module outputs and surface thermal patterns were simultaneously monitored using a digital solarimeter, multimeter and infrared thermal imager, respectively. The outputs of the modules decreased to almost zero when as little as 40% of the module surfaces were shaded, with the reductions in performance being more severe in the mono-crystalline modules than in the poly-crystalline modules. The infrared thermography revealed the thermal patterns under the different shading conditions and showed that the random shading of the modules was the most likely to result in hotspots. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Computationally Efficient Magnetic Position System Calibration
Eng. Proc. 2020, 2(1), 72; https://doi.org/10.3390/ecsa-7-08219 - 14 Nov 2020
Viewed by 263
Abstract
Properties such as high resolution, contactless (and thus wear-free) measurement, low power consumption, robustness against temperature and contamination as well as low cost make magnetic position and orientation systems appealing for a large number of industrial applications. Nevertheless, one major practical challenge is [...] Read more.
Properties such as high resolution, contactless (and thus wear-free) measurement, low power consumption, robustness against temperature and contamination as well as low cost make magnetic position and orientation systems appealing for a large number of industrial applications. Nevertheless, one major practical challenge is their sensitivity to fabrication tolerances. In this work, we propose a novel method for magnetic position system calibration based on the analytical computation of the magnetic field and on the application of an evolutionary optimization algorithm. This scheme enables the calibration of more than 10 degrees of freedom within a few seconds on standard quad-core ×86 processors, and is demonstrated by calibrating a highly cost-efficient 3D-printed 3-axis magnetic joystick. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Node Distribution Optimization in Positioning Sensor Networks through Memetic Algorithms in Urban Scenarios
Eng. Proc. 2020, 2(1), 73; https://doi.org/10.3390/ecsa-7-08220 - 14 Nov 2020
Cited by 1 | Viewed by 316
Abstract
Local Positioning Systems rely on ad-hoc node deployments which fit the environment characteristics in order to reduce system uncertainties. The obtainment of competitive results through these systems requires the solution of the Node Location Problem. This problem has been assigned as NP-Hard; therefore, [...] Read more.
Local Positioning Systems rely on ad-hoc node deployments which fit the environment characteristics in order to reduce system uncertainties. The obtainment of competitive results through these systems requires the solution of the Node Location Problem. This problem has been assigned as NP-Hard; therefore, a heuristic solution is recommended for addressing this complex problem. Genetic Algorithms (GA) have shown an excellent trade-off between diversification and intensification in the literature. However, in Non-Line-of-Sight environments in which there is not continuity in the fitness function evaluation among contiguous solutions, challenges arise for the GA. Consequently, in this paper, we introduce a Memetic Algorithm (MA) with a Local Search strategy for exploring the most different individuals of the population in search of improving the NLP results in urban scenarios for the first time. Results show that the MA proposed outperforms the GA optimization and attains an improvement of 6.51% in accuracy in the scenario proposed. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Enhancement of Power Quality Using Voltage and Hall Effect Current Sensors Applied on Controlled Single-Phase Active Power Filter
Eng. Proc. 2020, 2(1), 74; https://doi.org/10.3390/ecsa-7-08212 - 14 Nov 2020
Viewed by 206
Abstract
Switched-mode power supply and semiconductor devices have been gaining more and more applicability in the industrial scenario. With the massive use of these devices, several problems related to power quality and equipment degradation have been arising, caused mainly by the harmonic content present [...] Read more.
Switched-mode power supply and semiconductor devices have been gaining more and more applicability in the industrial scenario. With the massive use of these devices, several problems related to power quality and equipment degradation have been arising, caused mainly by the harmonic content present in current and voltage. Power quality problems have direct implications for business productivity, causing high economic losses. Therefore, it is mandatory to develop solutions that mitigate these problems. Active power filters (APF) are power electronic equipment capable of compensating power quality problems. Moreover, it presents the ability to dynamically adjust their modes of operation in response to changes in load or the power systems. Active power filters present the economical advantage of using the same current and voltage sensors already existing in a power system. Aiming to develop an APF for single-phase applications, this work proposes a shunt controlled active power filter using hall effect current and voltage sensor, aiming to compensate harmonic currents provided by nonlinear loads. It was proposed two proportional-integral compensators, where the external loop is responsible for link-CC control and the inner loop is responsible for current compensation. The results show the applicability of the proposed APF, since all harmonic content was filtered. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Low-Energy and Modular Wearable Device for Wireless Measurement of Physiological Signals
Eng. Proc. 2020, 2(1), 75; https://doi.org/10.3390/ecsa-7-08213 - 14 Nov 2020
Viewed by 259
Abstract
The most common way for accessing healthcare and monitoring physiological signals is based on commercial devices. Most of them are, in general, expensive, highly invasive, and require sophisticated infrastructure for operating. Nowadays, wearable devices (WD) offer an attractive technology for circumventing the limitations [...] Read more.
The most common way for accessing healthcare and monitoring physiological signals is based on commercial devices. Most of them are, in general, expensive, highly invasive, and require sophisticated infrastructure for operating. Nowadays, wearable devices (WD) offer an attractive technology for circumventing the limitations of classic medical devices. The design of WD, however, remains a challenging task to reach high-performance, reliability, and to be ergonomic. In this work, we develop, to the best of our knowledge, a novel WD with two main highlights. (i) Our device is based on a low-power 32-bit microcontroller, embedding a Bluetooth Low Energy (BLE) module for wireless data streaming with a mobile application for signal monitoring and recording, alongside a warning notification system. (ii) The proposed WD has a modular and flexible design, such that the user can increase the number of sensors by sharing the acquisition and processing system, thus reducing the hardware requirements and exhibiting a minimally invasive arrangement. For all the WD stages, we show their design methodology, the tests for characterizing their performance, and the results obtained from a case of study. For the latter, we consider two sensor prototypes for measuring the corporal temperature with a passive sensor, as well as the breath and heart rates via photoplethysmography signals. Results show that our WD is a cost-effective alternative and a promising tool for healthcare monitoring, as it operates in agreement with physiological levels with high-reliability. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Design and Simulation of a Point-of-Care Microfluidic Device for Acoustic Blood Cell Separation
Eng. Proc. 2020, 2(1), 76; https://doi.org/10.3390/ecsa-7-08221 - 14 Nov 2020
Viewed by 412
Abstract
Hematology tests, considered as an initial step in the patient diagnostic process, require laboratory equipment and technicians which is a time- and labor-consuming procedure. Such facilities may be available in a few central laboratories in under-resourced countries. The growing need for low cost [...] Read more.
Hematology tests, considered as an initial step in the patient diagnostic process, require laboratory equipment and technicians which is a time- and labor-consuming procedure. Such facilities may be available in a few central laboratories in under-resourced countries. The growing need for low cost and rapid diagnostic tests contributes to point-of-care (POC) medical diagnostic devices providing convenient and rapid test tools particularly in areas with limited medical resources. In the present study, a comprehensive numerical simulation of a POC blood cell separation device (POC-BCS) has been modeled using a finite element method. Tag-less separation of blood cells, i.e., platelets, red blood cells, and white blood cells, was carried out using standing surface acoustic waves (SSAWs) generated by interdigital transducers (IDTs) located at lateral sides of the microfluidic channel. Blood sample intake along with sheath flow was introduced via two symmetrical tilted angle inlets and a middle inlet, respectively. Superposition of acoustic radiation force applied by SSAWs accompanied by drag force caused by medium flow drove the blood cells toward different path lines correlated to their size. White blood cells were sorted out in the middle outlet and red blood cells and platelets were sorted out through the separate locations of the side outlets. Each cell was then guided to their respected visualization chamber for further image processing analysis. The results of the presented numerical study would be very promising in designing and optimizing the POC blood testing device. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Sons al balcó: Soundscape Map of the Confinement in Catalonia
Eng. Proc. 2020, 2(1), 77; https://doi.org/10.3390/ecsa-7-08180 - 14 Nov 2020
Cited by 2 | Viewed by 331
Abstract
In this project, we aim to study the effect that the lockdown due to the COVID-19 pandemic has caused on the perception of noise in Catalonia. In Sons al Balcó, the research activities cohabit with the dynamic collaboration with citizens and other [...] Read more.
In this project, we aim to study the effect that the lockdown due to the COVID-19 pandemic has caused on the perception of noise in Catalonia. In Sons al Balcó, the research activities cohabit with the dynamic collaboration with citizens and other stakeholders to create social and environmental impact, to widen awareness and design tools to improve citizenship development and empowerment. The initial scientific hypothesis is that the annoyance coming from outdoor noise, minimized by the lockdown effect, could be associated with better perception of the soundscape by people. Sons al Balcó allows validating this hypothesis in two different ways. On the one hand, by means of subjective questionnaires conducted to people living in pre-defined diverse acoustic areas (urban, suburban and rural environments), and on the other hand, by the use of objective measurements of the noise levels, and the study of the soundscape in these areas, using short pieces of video collected by citizens. For this purpose, we designed an on line test to be conducted by any citizen aiming to contribute to this wide study for all the territory of Catalonia, both from rural areas and from cities. A communication campaign was conducted to reach a significant participation. During the lockdown, more than 350 questionnaires and videos were collected, and a first map of the soundscape of the confinement in Catalonia was depicted. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Screening for Atrial Fibrillation: Improving Efficiency of Manual Review of Handheld Electrocardiograms
Eng. Proc. 2020, 2(1), 78; https://doi.org/10.3390/ecsa-7-08195 - 14 Nov 2020
Viewed by 338
Abstract
Atrial fibrillation (AF) is a common irregular heart rhythm associated with a five-fold increase in stroke risk. It is often not recognised as it can occur intermittently and without symptoms. A promising approach to detect AF is to use a handheld electrocardiogram (ECG) [...] Read more.
Atrial fibrillation (AF) is a common irregular heart rhythm associated with a five-fold increase in stroke risk. It is often not recognised as it can occur intermittently and without symptoms. A promising approach to detect AF is to use a handheld electrocardiogram (ECG) sensor for screening. However, the ECG recordings must be manually reviewed, which is time-consuming and costly. Our aims were to: (i) evaluate the manual review workload; and (ii) evaluate strategies to reduce the workload. In total, 2141 older adults were asked to record their ECG four times per day for 1–4 weeks in the SAFER (Screening for Atrial Fibrillation with ECG to Reduce stroke) Feasibility Study, producing 162,515 recordings. Patients with AF were identified by: (i) an algorithm classifying recordings based on signal quality (high or low) and heart rhythm; (ii) a nurse reviewing recordings to correct algorithm misclassifications; and (iii) two cardiologists independently reviewing recordings from patients with any evidence of rhythm abnormality. It was estimated that 30,165 reviews were required (20,155 by the nurse, and 5005 by each cardiologist). The total number of reviews could be reduced to 24,561 if low-quality recordings were excluded from review; 18,573 by only reviewing ECGs falling under certain pathological classifications; and 18,144 by only reviewing ECGs displaying an irregularly irregular rhythm for the entire recording. The number of AF patients identified would not fall considerably: from 54 to 54, 54 and 53, respectively. In conclusion, simple approaches may help feasibly reduce the manual workload by 38.4% whilst still identifying the same number of patients with undiagnosed, clinically relevant AF. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
A Tunable CMOS Image Sensor with High Fill-Factor for High Dynamic Range Applications
Eng. Proc. 2020, 2(1), 79; https://doi.org/10.3390/ecsa-7-08235 - 14 Nov 2020
Cited by 1 | Viewed by 253
Abstract
Several CMOS imager sensors were proposed to obtain high dynamic range imager (>100 dB). However, as drawback these imagers implement a large number of transistors per pixel resulting in a low fill factor, high power consumption and high complexity CMOS image sensors. In [...] Read more.
Several CMOS imager sensors were proposed to obtain high dynamic range imager (>100 dB). However, as drawback these imagers implement a large number of transistors per pixel resulting in a low fill factor, high power consumption and high complexity CMOS image sensors. In this work, a new operation mode for 3 T CMOS image sensors is presented for high dynamic range (HDR) applications. The operation mode consists of biasing the conventional reset transistor as active load to photodiode generating a reference current. The output voltage achieves a steady state when the photocurrent becomes equal to the reference current, similar to the inverter operation in the transition region. At a specific bias voltage, the output swings from o to Vdd in a small light intensity range; however, high dynamic range is achieve using multiple readout at different bias voltage. For high dynamic range operation different values of bias voltage can be applied from each one, and the signal can be captured to compose a high dynamic range image. Compared to other high dynamic range architectures this proposed CMOS image pixel show as advantage high fill-factor (3 T) and lower complexity. Moreover, as the CMOS pixel does not operate in integration mode, de readout can be performed at higher speed. A prototype was fabricated at 3.3 V 0.35 µm CMOS technology. Experimental results are shown by applying five different control voltage from 0.65 to 1.2 V is possible to obtain a dynamic range of about 100 dB. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Acquiring Wearable Photoplethysmography Data in Daily Life: The PPG Diary Pilot Study
Eng. Proc. 2020, 2(1), 80; https://doi.org/10.3390/ecsa-7-08233 - 14 Nov 2020
Viewed by 339
Abstract
The photoplethysmogram (PPG) signal is widely measured by smart watches and fitness bands for heart rate monitoring. New applications of the PPG are also emerging, such as to detect irregular heart rhythms, track infectious diseases, and monitor blood pressure. Consequently, datasets of PPG [...] Read more.
The photoplethysmogram (PPG) signal is widely measured by smart watches and fitness bands for heart rate monitoring. New applications of the PPG are also emerging, such as to detect irregular heart rhythms, track infectious diseases, and monitor blood pressure. Consequently, datasets of PPG signals acquired in daily life are valuable for algorithm development. The aim of this pilot study was to assess the feasibility of acquiring PPG data in daily life. A single subject was asked to wear a wrist-worn PPG sensor six days a week for four weeks, and to keep a diary of daily activities. The sensor was worn for 75.0% of the time, signals were acquired for 60.6% of the time, and signal quality was high for 30.5% of the time. This small pilot study demonstrated the feasibility of acquiring PPG data during daily living. Key lessons were learnt for future studies: (i) devices which are waterproof and require charging less frequently may provide signals for a greater proportion of the time; (ii) data should either be stored on the device or streamed via a reliable connection to a second device for storage; (iii) it may be beneficial to acquire signals during the night or during periods of low activity to achieve high signal quality; and (iv) there are several promising areas for PPG algorithm development including the design of pulse wave analysis techniques to track changes in cardiovascular properties in daily life. The dataset and code are publicly available at DOI: 10.5281/zenodo.3268500. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Deterministic Propagation Approach for Millimeter-Wave Outdoor Smart Parking Solution Deployment
Eng. Proc. 2020, 2(1), 81; https://doi.org/10.3390/ecsa-7-08231 - 14 Nov 2020
Viewed by 251
Abstract
Impact factor as an indicator of efficiency or sustainability is entirely correlated with the continuous development of the smart city concept technology application. Intelligent transportation systems (ITSs) and particularly autonomous vehicles are expected to play an important role in this challenging environment. Fast [...] Read more.
Impact factor as an indicator of efficiency or sustainability is entirely correlated with the continuous development of the smart city concept technology application. Intelligent transportation systems (ITSs) and particularly autonomous vehicles are expected to play an important role in this challenging environment. Fast and secure connections will be pivotal in order to achieve this new vehicular communications’ application era. The use of millimeter-wave (mmWave) frequency range is the most promising approach to allow these real-time, high-demand applications that require higher bandwidth with the minimum possible latency. However, an in-depth mmWave-channel characterization of the environment is required for a proper mmWave-based solution deployment. In this work, a complete radio wave propagation channel characterization for a mmWave smart parking solution deployment in a complex outdoor environment was assessed at a 28 GHz frequency band. The considered scenario is a parking lot placed in an open free university campus area surrounded by inhomogeneous vegetation. The vehicle and vegetation density within the scenario, in terms of inherent transceivers density and communication impairments, leads to overall system operation challenges, given by multiple communication links operation at line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. By means of an in-house developed 3D ray launching (3D-RL) algorithm, the impact of variable vegetation density is addressed, providing precise modelling estimations of large-scale multipath propagation effects in terms of received power levels and path loss. The obtained results along with the proposed simulation methodology can aid in an adequate characterization of an mmWave communication channel for new vehicular communications networks, applications, and deployments, considering the outdoor conditions as well as the impact of different vegetation densities, for current as well as for future wireless technologies. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
A Sensor Data-Based Approach for the Definition of Condition Taxonomies for a Hydraulic Pump
Eng. Proc. 2020, 2(1), 82; https://doi.org/10.3390/ecsa-7-08223 - 14 Nov 2020
Viewed by 256
Abstract
Condition monitoring (CM) is an important application in industry for detecting machine failures in an incipient stage. Based on sensor data, computational intelligence methods provide efficient solutions for the analysis of high dimensional process data with the ability to detect and predict complex [...] Read more.
Condition monitoring (CM) is an important application in industry for detecting machine failures in an incipient stage. Based on sensor data, computational intelligence methods provide efficient solutions for the analysis of high dimensional process data with the ability to detect and predict complex condition states. IOT gateways are affordable devices with the ability to implement data ingestion and data analytics tasks on an edge device providing the possibility to implement condition monitoring in real-time on the device. In this work, we present an experimental bench for the sensorization of a hydraulic installation based on IoT gateways in order to detect several blocking states in a hydraulic pump and to avoid the cavitation problem. The experiments of 15 different blocking conditions yield a novel dataset with process sensor information for the described problem. The dataset is analyzed from a data quality point of view to find a meaningful categorization of fault conditions, which are feasible concerning implementation in a condition monitoring system. We use an exploratory data analysis approach, which is based on principal component analysis, provides data visualization of the different blocking conditions of the experiment, and allows us to decide on a proper fault categorization by detecting clearly separated data groups. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Multivariate Spectra Analysis: PLSR vs. PCA + MLR
Eng. Proc. 2020, 2(1), 83; https://doi.org/10.3390/ecsa-7-08226 - 14 Nov 2020
Viewed by 281
Abstract
For mixtures of compounds with very similar spectral features, common for larger organic molecules, multivariate analysis (MVA) methods can be applied to determine the concentration of the individual components. We analyzed photoacoustic spectra of mixtures of different volatile organic compounds with and without [...] Read more.
For mixtures of compounds with very similar spectral features, common for larger organic molecules, multivariate analysis (MVA) methods can be applied to determine the concentration of the individual components. We analyzed photoacoustic spectra of mixtures of different volatile organic compounds with and without different feature selection and feature projection methods. These include: Multiple Linear Regression (MLR), Principal Component Analysis (PCA), Partial Least Squares Regression (PLSR) and Random Forest Algorithm (RFA). Even though PLSR provided the best prediction accuracy, the other techniques also exhibited some advantages. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Automatic Detection of Arrhythmias Using a YOLO-Based Network with Long-Duration ECG Signals
Eng. Proc. 2020, 2(1), 84; https://doi.org/10.3390/ecsa-7-08229 - 14 Nov 2020
Viewed by 439
Abstract
Early detection of arrhythmias is very important. Recently, wearable devices are being used to monitor the patient’s heartbeat to detect an arrhythmia. However, there are not satisfactory algorithms for real-time monitoring of arrhythmias in a wearable device. In this work, a novel fast [...] Read more.
Early detection of arrhythmias is very important. Recently, wearable devices are being used to monitor the patient’s heartbeat to detect an arrhythmia. However, there are not satisfactory algorithms for real-time monitoring of arrhythmias in a wearable device. In this work, a novel fast and simple arrhythmia detection algorithm based on YOLO is proposed. The algorithm can detect each heartbeat on long-duration electrocardiogram (ECG) signals without R-peak detection and can classify an arrhythmia simultaneously. The model replaces the 2D Convolutional Neural networks (CNN) with a 1D CNN and the bounding box with a bounding window to utilize raw ECG signals. Results demonstrate that the proposed algorithm has high performance in speed and mean average precisionin detecting an arrhythmia. Furthermore, the bounding window can predict different window lengths on different types of arrhythmia. Therefore, the model can choose an optimal heartbeat window length for arrhythmia classification. Since the proposed model is a compact 1D CNN model based on YOLO, it can be used in a wearable device and embedded system. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
A Workflow for Affective Computing and Stress Recognition from Biosignals
Eng. Proc. 2020, 2(1), 85; https://doi.org/10.3390/ecsa-7-08227 - 14 Nov 2020
Cited by 1 | Viewed by 304
Abstract
Affective computing and stress recognition from biosignals have a high potential in various medical applications such as early intervention, stress management and risk prevention, as well as monitoring individuals’ mental health. This paper presents an automated processing workflow for the psychophysiological recognition of [...] Read more.
Affective computing and stress recognition from biosignals have a high potential in various medical applications such as early intervention, stress management and risk prevention, as well as monitoring individuals’ mental health. This paper presents an automated processing workflow for the psychophysiological recognition of emotion and stress states. Our proposed workflow allows the processing of biosignals in their raw state as obtained from wearable sensors. It consists of five stages: (1) Biosignal Preprocessing—raw data conversion and physiological data triggering, relevant information selection, artifact and noise filtering; (2) Feature Extraction—using different mathematical groups including amplitude, frequency, linearity, stationarity, entropy and variability, as well as cardiovascular-specific characteristics; (3) Feature Selection—dimension reduction and computation optimization using Forward Selection, Backward Elimination and Brute Force methods; (4) Affect Classification—machine learning using Support Vector Machine, Random Forest and k-Nearest Neighbor algorithms; (5) Model Validation—performance matrix computation using k-Cross, Leave-One-Subject-Out and Split Validations. All workflow stages are integrated into embedded functions and operators, allowing an automated execution of the recognition process. The next steps include further development of the algorithms and the integration of the developed tools into an easy-to-use system, thereby satisfying the needs of medical and psychological staff. Our automated workflow was evaluated using our uulmMAC database, previously developed for affective computing and machine learning applications in human–computer interaction. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
A New Readout Method for a High Sensitivity Capacitance Sensor Based on Weakly Coupled Resonators
Eng. Proc. 2020, 2(1), 86; https://doi.org/10.3390/ecsa-7-08230 - 14 Nov 2020
Viewed by 503
Abstract
This paper proposes a new readout method for a sensor to detect minute variations in the capacitance. A sensor is based on the weakly coupled electrical resonators that use an amplitude ratio (AR) as an output signal. A new readout scheme with a [...] Read more.
This paper proposes a new readout method for a sensor to detect minute variations in the capacitance. A sensor is based on the weakly coupled electrical resonators that use an amplitude ratio (AR) as an output signal. A new readout scheme with a relatively higher output sensitivity is proposed to measure the relative changes in the input capacitor. A mathematical model is derived to express the readout output as a function of change in the capacitance. To validate the theoretical model, a system is modelled and designed using an industry-standard electronic circuit design environment. SPICE simulation results are presented for a wide range of design parameters, such as varying coupling factors between the two electrical resonators. Sensitivity comparison between the existing and the proposed AR readout is presented to show the effectiveness of the method of detection proposed in this work. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Suppression of an Effect of Terrain Unevenness on Accuracy of Height Measurement in UAV with Integrated Ultrasound Altimeter during Landing
Eng. Proc. 2020, 2(1), 87; https://doi.org/10.3390/ecsa-7-08263 - 14 Nov 2020
Viewed by 249
Abstract
The goal of this paper is to examine filtration possibilities of ultrasonically measured height of unmanned aerial vehicle (UAV) for the suppression of terrain unevenness. The article presents two basic methods of the filtration; namely, moving average method and Kalman filter, and it [...] Read more.
The goal of this paper is to examine filtration possibilities of ultrasonically measured height of unmanned aerial vehicle (UAV) for the suppression of terrain unevenness. The article presents two basic methods of the filtration; namely, moving average method and Kalman filter, and it carries out performance comparisons of the two methods with simulated data. The comparison implies that the performance of the two methods depends on character of the observed terrain and also on the accuracy of the initial ultrasound measurements before filtration. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Experimental Study on Cabin Carbon Dioxide Concentration in Light Passenger Vehicles
Eng. Proc. 2020, 2(1), 88; https://doi.org/10.3390/ecsa-7-08266 - 14 Nov 2020
Viewed by 370
Abstract
This paper discusses the initial experimental results of monitoring carbon dioxide (CO2) and total volatile organic compounds (TVOC) inside automobiles with different cabin sizes and with different numbers of occupants. The initial study shows that the CO2 and TVOC concentrations [...] Read more.
This paper discusses the initial experimental results of monitoring carbon dioxide (CO2) and total volatile organic compounds (TVOC) inside automobiles with different cabin sizes and with different numbers of occupants. The initial study shows that the CO2 and TVOC concentrations are inversely proportional to cabin volume and proportional to passenger numbers and time when the metabolic activities were maintained at the same level. This study was aimed at short distance travel on normal roads, and further studies are to be carried out for long distance running on highways to make sound decisions on automatic air inflow control to maintain the in-cabin air within permissible levels of CO2. The study shows that a CO2 concentration of 1500 ppm is reached by all three light passenger vehicle types used within 20 minutes with a single person and reached a CO2 level of nearly 3000 ppm within the same time with two passengers in the cabin. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Wireless Sensor Network Based Epileptic Seizure Detector
Eng. Proc. 2020, 2(1), 89; https://doi.org/10.3390/ecsa-7-08277 - 14 Nov 2020
Viewed by 265
Abstract
Epilepsy is a focal sensory (neurological) disease in which cerebrum movement becomes abnormal, sparking off seizures or a period of surprising conduct, sensations, and occasionally the loss of consciousness. The diagnosis of epileptic seizures is mainly done by methods of electroencephalogram (EEG) observation. [...] Read more.
Epilepsy is a focal sensory (neurological) disease in which cerebrum movement becomes abnormal, sparking off seizures or a period of surprising conduct, sensations, and occasionally the loss of consciousness. The diagnosis of epileptic seizures is mainly done by methods of electroencephalogram (EEG) observation. Even though this technique is precise, it is not easy for the patient because the EEG anodes must be appended to the scalp, which is uncomfortable for the patient. Seizure indications can fluctuate broadly. A few people with epilepsy just gaze vacantly for a couple of moments during a seizure, while others jerk their arms or legs repeatedly. This makes constant monitoring at home significantly more troublesome. Epilepsy patients are often unable to move freely for fear of convulsions. After analyzing all the issues related to epilepsy, we proposed a plan to make a cheap and usable device as a solution so that an epileptic patient can move like a normal human being. With this device, if an epileptic patient falls ill anywhere, their information will be sent to the nearest hospital and their emergency contact numbers in the form of Short Message Service (SMS). Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
IoT-Based Framework for Smart Waste Monitoring and Control System: A Case Study for Smart Cities
Eng. Proc. 2020, 2(1), 90; https://doi.org/10.3390/ecsa-7-08224 - 14 Nov 2020
Cited by 1 | Viewed by 389
Abstract
Environmental sanitation is very essential for healthy living. In our daily livelihood, garbage bins are usually kept without proper monitoring until they are filled to the point of overflowing onto the surroundings and spilling out, resulting in environmental pollution, which has serious health-related [...] Read more.
Environmental sanitation is very essential for healthy living. In our daily livelihood, garbage bins are usually kept without proper monitoring until they are filled to the point of overflowing onto the surroundings and spilling out, resulting in environmental pollution, which has serious health-related issues to human beings and the environment. For smart cities, garbage bins need to be monitored and controlled to ensure a healthy and clean environment. In the present technological advancement, real-time monitoring and control of waste disposal is a challenging area that needs urgent attention by the research community. The traditional approach of monitoring waste in garbage bins placed in strategic locations is a very tedious and inefficient way that consumes time, human effort, and cost, and this is also not in agreement with smart city requirements. This research paper presents the design and implementation of an internet of things (IoT) based Arduino microcontroller working with the ultrasonic sensors that detects the level of waste in the garbage bin placed in garbage locations and constantly at regular intervals display the status information as “filled”, “half-filled”, or “empty” on an LCD screen, as well as send the content level information at those intervals to a central web-server system that displays the garbage bin levels graphically. This is achieved using a microcontroller, a Wi-Fi module, and ultrasonic sensors. The programming of the Arduino Uno microcontroller was done with an Arduino IDE and embedded C programming language. The communication with the web server was done using the hypertext preprocessor PHP scripting programming language. The prototype was designed and simulated using Proteus 8.0 professional simulation software. This process helps to automate garbage bin monitoring and control. Experimental results demonstrate a promising solution to waste management and control. A number of testing runs were performed to evaluate the device workability in real situations. The measured distances from the garbage bins were transmitted to a website; this web page performs analytic and visualization and displays a bar chart showing the levels of the garbage waste, time, and location in real time for viewing. The proposed prototype is an innovative system that will help to keep the smart cities clean and tidy using ultrasonic sensors. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Real-Time Concrete Crack Detection and Instance Segmentation using Deep Transfer Learning
Eng. Proc. 2020, 2(1), 91; https://doi.org/10.3390/ecsa-7-08260 - 14 Nov 2020
Cited by 1 | Viewed by 623
Abstract
Cracks on concrete infrastructure are one of the early indications of structural degradation which needs to be identified early as possible to carry out early preventive measures to avoid further damage. In this paper, we propose to use YOLACT: a real-time instance segmentation [...] Read more.
Cracks on concrete infrastructure are one of the early indications of structural degradation which needs to be identified early as possible to carry out early preventive measures to avoid further damage. In this paper, we propose to use YOLACT: a real-time instance segmentation algorithm for automatic concrete crack detection. This deep learning algorithm is used with transfer learning to train the YOLACT network to identify and localize cracks with their corresponding masks which can be used to identify each crack instance. The transfer learning techniques allowed us to train the network on a relatively small dataset of 500 crack images. To train the YOLACT network, we created a dataset with ground-truth masks from images collected from publicly available datasets. We evaluated the trained YOLACT model for concrete crack detection with ResNet-50 and ResNet-101 backbone architectures for both precision and speed of detection. The trained model achieved high mAP results with real-time frame rates when tested on concrete crack images on a single GPU. The YOLACT algorithm was able to correctly segment multiple cracks with individual instance level masks with high localization accuracy. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
A Proximal Algorithm for Fork-Choice in Distributed Ledger Technology for Context-Based Clustering on Edge Computing
Eng. Proc. 2020, 2(1), 92; https://doi.org/10.3390/ecsa-7-08261 - 15 Nov 2020
Viewed by 362
Abstract
The major challenges of operating data-intensive of Distributed Ledger Technology (DLT) are (1) to reach consensus on the main chain as a set of validators cast public votes to decide on which blocks to finalize and (2) scalability on how to increase the [...] Read more.
The major challenges of operating data-intensive of Distributed Ledger Technology (DLT) are (1) to reach consensus on the main chain as a set of validators cast public votes to decide on which blocks to finalize and (2) scalability on how to increase the number of chains which will be running in parallel. In this paper, we introduce a new proximal algorithm that scales DLT in a large-scale Internet of Things (IoT) devices network. We discuss how the algorithm benefits the integrating DLT in IoT by using edge computing technology, taking the scalability and heterogeneous capability of IoT devices into consideration. IoT devices are clustered dynamically into groups based on proximity context information. A cluster head is used to bridge the IoT devices with the DLT network where a smart contract is deployed. In this way, the security of the IoT is improved and the scalability and latency are solved. We elaborate on our mechanism and discuss issues that should be considered and implemented when using the proposed algorithm, we even show how it behaves with varying parameters like latency or when clustering. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Practical Aspects of Acoustic Leaky-Wave Antennas Applied to Underwater Direction Finding
Eng. Proc. 2020, 2(1), 93; https://doi.org/10.3390/ecsa-7-08218 - 14 Nov 2020
Viewed by 276
Abstract
Acoustic leaky-wave antennas (ALWAs) have demonstrated the capacity to steer directive sound waves in frequency-dependent directions, due to the inherent dispersive radiation characteristic of leaky modes. Compared to more conventional uniform linear array (ULA) acoustic traducers for electronic beam steering (which rely on [...] Read more.
Acoustic leaky-wave antennas (ALWAs) have demonstrated the capacity to steer directive sound waves in frequency-dependent directions, due to the inherent dispersive radiation characteristic of leaky modes. Compared to more conventional uniform linear array (ULA) acoustic traducers for electronic beam steering (which rely on multiple sensors), the ALWA allows for single microphone operation. Thus, ALWAs offer a direct mechanism to scan a directive acoustic beam in the angular space by simply sweeping the operating frequency of the acoustic signal, which envisions cost-efficient single-transducer direction finders for SONAR applications. In this paper, we study for the first time some important features of an ALWA for acoustic underwater Direction-of-Arrival (DoA) estimation applications. First, we report for the first time on the necessity to shape the radiated ALWA beams in both far- and near-field zones to improve the DoA estimation performance, following similar techniques recently applied for low-cost frequency-scanned direction-finding radars based on LWAs. Furthermore, the capacity to reduce the Side Lobe Level (SLL) has been analyzed in order to improve performance, demonstrating aperture tapering techniques to the ALWA for the first time. These acoustic behaviour aspects have a considerable interest in real applications of ALWA in innovative SONAR systems for underwater scenarios. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
A Combined Model-Order Reduction and Deep Learning Approach for Structural Health Monitoring under Varying Operational and Environmental Conditions
Eng. Proc. 2020, 2(1), 94; https://doi.org/10.3390/ecsa-7-08258 - 30 Dec 2020
Viewed by 715
Abstract
Nowadays, the aging, deterioration, and failure of civil structures are challenges of paramount importance, increasingly motivating the search of advanced Structural Health Monitoring (SHM) tools. In this work, we propose a SHM strategy for online structural damage detection and localization, combining Deep Learning [...] Read more.
Nowadays, the aging, deterioration, and failure of civil structures are challenges of paramount importance, increasingly motivating the search of advanced Structural Health Monitoring (SHM) tools. In this work, we propose a SHM strategy for online structural damage detection and localization, combining Deep Learning (DL) and Model-Order Reduction (MOR). The developed data-based procedure is driven by the analysis of vibration and temperature recordings, shaped as multivariate time series and collected on the fly through pervasive sensor networks. Damage detection and localization are treated as a supervised classification task considering a finite number of predefined damage scenarios. During a preliminary offline phase, for each damage scenario, a collection of synthetic structural responses and temperature distributions, is numerically generated through a physics-based model. Several loading and thermal conditions are considered, thanks to a suitable parametrization of the problem, which controls the dependency of the model on operational and environmental conditions. Because of the huge amount of model evaluations, MOR techniques are employed in order to relieve the computational burden that is associated to the dataset construction. Finally, a deep neural network, featuring a stack of convolutional layers, is trained by assimilating both vibrational and thermal data. During the online phase, the trained DL network processes new incoming recordings in order to classify the actual state of the structure, thus providing information regarding the presence and localization of the damage, if any. Numerical performances of the proposed approach are assessed on the monitoring of a two-storey frame under low intensity seismic excitation. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
A Stochastic Model to Describe the Scattering in the Response of Polysilicon MEMS
Eng. Proc. 2020, 2(1), 95; https://doi.org/10.3390/engproc2020002095 - 05 Feb 2021
Viewed by 979
Abstract
The current miniaturization trend in the market of inertial microsystems is leading to movable device parts with sizes comparable to the characteristic length-scale of the polycrystalline silicon film morphology. The relevant output of micro electro-mechanical systems (MEMS) is thus more and more affected [...] Read more.
The current miniaturization trend in the market of inertial microsystems is leading to movable device parts with sizes comparable to the characteristic length-scale of the polycrystalline silicon film morphology. The relevant output of micro electro-mechanical systems (MEMS) is thus more and more affected by a scattering, induced by features resulting from the micro-fabrication process. We recently proposed an on-chip testing device, specifically designed to enhance the aforementioned scattering in compliance with fabrication constraints. We proved that the experimentally measured scattering cannot be described by allowing only for the morphology-affected mechanical properties of the silicon films, and etch defects must be properly accounted for too. In this work, we discuss a fully stochastic framework allowing for the local fluctuations of the stiffness and of the etch-affected geometry of the silicon film. The provided semi-analytical solution is shown to catch efficiently the measured scattering in the C-V plots collected through the test structure. This approach opens up the possibility to learn on-line specific features of the devices, and to reduce the time required for their calibration. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
New Autonomous Intelligent Sensor Design Approach for Multiple Parameter Inference
Eng. Proc. 2020, 2(1), 96; https://doi.org/10.3390/engproc2020002096 - 07 Feb 2021
Cited by 1 | Viewed by 987
Abstract
The determination of multiple parameters via luminescence sensing is of great interest for many applications in different fields, like biosensing and biological imaging, medicine, and diagnostics. The typical approach consists in measuring multiple quantities and in applying complex and frequently just approximated mathematical [...] Read more.
The determination of multiple parameters via luminescence sensing is of great interest for many applications in different fields, like biosensing and biological imaging, medicine, and diagnostics. The typical approach consists in measuring multiple quantities and in applying complex and frequently just approximated mathematical models to characterize the sensor response. The use of machine learning to extract information from measurements in sensors have been tried in several forms before. But one of the problems with the approaches so far, is the difficulty in getting a training dataset that is representative of the measurements done by the sensor. Additionally, extracting multiple parameters from a single measurement has been so far an impossible problem to solve efficiently in luminescence. In this work a new approach is described for building an autonomous intelligent sensor, which is able to produce the training dataset self-sufficiently, use it for training a neural network, and then use the trained model to do inference on measurements done on the same hardware. For the first time the use of machine learning additionally allows to extract two parameters from one single measurement using multitask learning neural network architectures. This is demonstrated here by a dual oxygen concentration and temperature sensor. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
SHM and Efficient Strategies for Reduced-Order Modeling
Eng. Proc. 2020, 2(1), 98; https://doi.org/10.3390/engproc2020002098 - 02 Mar 2021
Viewed by 445
Abstract
Within model-based approaches to structural health monitoring (SHM), numerical simulations must be tailored to continuously adapt to the degradation processes and to the possibly changing environment. This model update stage of the analysis brings two competing requirements: the accuracy of the model, with [...] Read more.
Within model-based approaches to structural health monitoring (SHM), numerical simulations must be tailored to continuously adapt to the degradation processes and to the possibly changing environment. This model update stage of the analysis brings two competing requirements: the accuracy of the model, with a more detailed description of the phenomena required where damage is supposed to take place; the efficiency of the model, to reduce the overall computational burden and allow for real-time (or close to real-time) computing. Without resorting to AI-based strategies, approaches solely based on proper orthogonal decomposition (POD) and domain decomposition (DD) techniques proved rather efficient in handling the aforementioned trade-off between the diverging requirements of accuracy and efficiency. In this work, we discuss a further improvement over our recently proposed methodology that consists of: a DD of the entire structure into sub-regions, which can be designed to decouple regions more prone to get damaged from regions that are instead less affected by the degradation processes; a POD-based selective model order reduction for all the domains, with adjustable and heterogeneous accuracy requirements. The approach is assessed through an illustrative example related to beam dynamics, with results provided in terms of both accuracy and computational efficiency, or speedup with respect to the full-order model. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Temperature Fiber-Optic Sensor with ZnO ALD Coating
Eng. Proc. 2020, 2(1), 99; https://doi.org/10.3390/engproc2020002099 - 04 Mar 2021
Viewed by 579
Abstract
This study presents a microsphere-based fiber-optic sensor with a ZnO Atomic Layer Deposition (ALD) coating thickness of 100 nm for temperature measurements. Metrological properties of the sensor were investigated over the temperature range of 100 °C to 300 °C, with a 10 °C [...] Read more.
This study presents a microsphere-based fiber-optic sensor with a ZnO Atomic Layer Deposition (ALD) coating thickness of 100 nm for temperature measurements. Metrological properties of the sensor were investigated over the temperature range of 100 °C to 300 °C, with a 10 °C step. An interferometric signal is used to control whether the microstructure is whole. Spectrum shift of a reflected signal is used to ascertain changes in the measured parameter. With changing temperature, the peak position of a reflected signal also changes. The R2 coefficient of the presented sensor indicates a good linear fit of over 0.99 to the obtained data. The sensitivity of the sensor investigated in this study equals 0.019 nm/°C. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
Proceeding Paper
Immunochromatographic Tests for Mycotoxins Detection with the Use of Ultrasmall Magnetite Nanoparticles
Eng. Proc. 2020, 2(1), 100; https://doi.org/10.3390/engproc2020002100 - 02 Apr 2021
Viewed by 462
Abstract
The use of small (with a diameter of 7–12 nm) superparamagnetic Fe3O4 nanoparticles as carriers for antibodies in lateral flow immunoassay is considered. Increased total surface area for such suspension provides a concentration of analytes with an increase in their [...] Read more.
The use of small (with a diameter of 7–12 nm) superparamagnetic Fe3O4 nanoparticles as carriers for antibodies in lateral flow immunoassay is considered. Increased total surface area for such suspension provides a concentration of analytes with an increase in their concentration up to 50 times. When the concentrated complexes were redissolved, aggregates with diameters of 100–500 nm were obtained, serving as colored markers in the assay. Further, magnetite–antibodies complexes are more tolerant to methanol (up to 30%) than native antibodies, thus providing minimal dilution of tested extracts. Lateral flow tests for mycotoxins zearalenone, T2-toxin, and aflatoxin B1 were developed and demonstrated applicability to control food products and raw materials. Full article
(This article belongs to the Proceedings of 7th International Electronic Conference on Sensors and Applications)
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