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Eng. Proc., 2022, ECSA-9

The 9th International Electronic Conference on Sensors and Applications

Online | 1–15 November 2022

Volume Editors: Francisco Falcone, Stefano Mariani and Jean-marc Laheurte

Number of Papers: 88
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Cover Story (view full-size image): The 9th International Electronic Conference on Sensors and Applications is sponsored by MDPI and the scientific journal Sensors (ISSN 1424-8220, IF 3.847). It focused on eight thematic areas [...] Read more.
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Research

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Abstract
Rapid Detection of Rice Adulteration Using a Low-Cost Electronic Nose and Machine Learning Modelling
Eng. Proc. 2022, 27(1), 1; https://doi.org/10.3390/ecsa-9-13291 - 01 Nov 2022
Viewed by 305
Abstract
Food fraud is one of the primary issues that may threaten consumers’ trust and confidence in the food industry. Detecting food fraud, such as rice adulteration, is challenging since the adulterant looks identical to authentic rice. Moreover, the detection procedure is commonly time-consuming [...] Read more.
Food fraud is one of the primary issues that may threaten consumers’ trust and confidence in the food industry. Detecting food fraud, such as rice adulteration, is challenging since the adulterant looks identical to authentic rice. Moreover, the detection procedure is commonly time-consuming and requires high-cost instruments in order to analyse samples in the laboratory. Therefore, this study aimed to develop a rapid method to detect rice adulteration using a low-cost and portable electronic nose (e-nose) coupled with machine learning (ML). Six types of adulterated rice samples were prepared by mixing the authentic rice (i.e., premium grade rice, organic rice, aromatic rice) with the respective adulterants (i.e., regular grade rice, rice from a different origin, non-organic rice, and non-aromatic rice) from 0% to 100% with a 10% increment by weight. Artificial neural networks (ANN) were used to develop prediction models to estimate adulteration levels using the e-nose sensor readings acquired from the rice samples as inputs. The ML models showed that the e-nose sensors successfully predicted the six types of adulterated rice samples at various adulteration levels from 0% to 100% with high accuracy (Model 1, correlation coefficient, R = 0.95; Model 2 = 0.92; Model 3 = 0.96; Model 4 = 0.96; Model 5 = 0.98; and Model 6 = 0.94). The proposed method effectively detects various combinations of adulterated rice at different mixing ratios using rapid, contactless, portable, and low-cost digital sensing devices combined with machine learning. This may help the rice industry to fight rice fraud effectively and ensure high product compliance with food quality and safety standards. Full article
Abstract
Photophysical Properties of Some Naphthalimide Derivatives
Eng. Proc. 2022, 27(1), 4; https://doi.org/10.3390/ecsa-9-13356 - 01 Nov 2022
Viewed by 171
Abstract
Naphthalimide derivatives possess many interesting properties such as strong emission, high quantum efficiency, good photostability, thermal stability, etc. The electronic absorption and fluorescence spectra of naphthalimides are sensitive to the polarity of surrounding environment, and these derivatives can be excellent candidates for fluorescent [...] Read more.
Naphthalimide derivatives possess many interesting properties such as strong emission, high quantum efficiency, good photostability, thermal stability, etc. The electronic absorption and fluorescence spectra of naphthalimides are sensitive to the polarity of surrounding environment, and these derivatives can be excellent candidates for fluorescent sensors for water detection in solution because the emission is strongly depended on the solvent polarity and it is quenched even at low water levels. In order to find out more information about the excited state dynamics of the naphthalimide derivatives, time-resolved fluorescence experiments were conducted in solvents of different polarities, and lifetimes from 0.5 ns to 9 ns were obtained. The transient absorption map in dioxane, dimethylformamide and methanol in the presence or absence of water revealed ground state bleaching bands (GSB) in the range of 230–290 nm, whereas an absorption band in excited state (ESA) occurred at shorter wavelengths from 210 to 295 nm. At longer wavelength, negative bands appeared, which can be assigned to the stimulated emissions (SE). In addition, the quantum yields with absolute values from 0.01 to 0.87 were found depending on the solvent nature. Full article
Abstract
Photophysical Studies of Poly(3,4-Ethylenedioxythiophene/Cucurbit[7]uril) Polypseudorotaxane and Polyrotaxane by Transient Absorption and Time-Resolved Fluorescence Spectroscopy
Eng. Proc. 2022, 27(1), 5; https://doi.org/10.3390/ecsa-9-13375 - 01 Nov 2022
Viewed by 143
Abstract
The UV-Vis absorption, fluorescence, and phosphorescence spectra of poly(3,4-ethylenedioxythiophene/cucurbit[7]uril), polypseudorotaxane (1), and polyrotaxane (2) in water and acetonitrile solutions were investigated. To achieve a deeper insight into the optical properties, the transient absorptions, lifetimes, and quantum yields have been [...] Read more.
The UV-Vis absorption, fluorescence, and phosphorescence spectra of poly(3,4-ethylenedioxythiophene/cucurbit[7]uril), polypseudorotaxane (1), and polyrotaxane (2) in water and acetonitrile solutions were investigated. To achieve a deeper insight into the optical properties, the transient absorptions, lifetimes, and quantum yields have been carried out on compounds 1 and 2. The transient absorption demonstrated an excited-state processes and involvement of high energy electronic states (Sn > 1). The transient absorption map in acetonitrile revealed at 210, 240, 300, and 315 nm a ground states bleaching bands (GSB), whereas at shorter wavelengths an absorption in excited states (ESA) and more than one excited state (Sn > 1). At 382 and 420 nm wavelength two negative bands appeared which were assigned to the stimulated emissions (SE). At longer wavelengths, i.e., 605, 625, and 710 nm, other stimulated emissions appeared that are probably a result of the triplet manifold, confirming their phosphorescence properties. Additionally, the quantum yield with absolute values in the range 5–25%, and phosphorescence lifetime with values in the range 1–9 μs were evaluated. Full article
Abstract
Highly Selective Electrochemical Profiling of Heroin in Street Samples
Eng. Proc. 2022, 27(1), 15; https://doi.org/10.3390/ecsa-9-13222 - 01 Nov 2022
Viewed by 139
Abstract
The trafficking and consumption of drugs of abuse are global concerns that threaten social structures and jeopardizes the security of nations [...] Full article
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Abstract
Instrumented Treadmill with an Accelerometry System: A Valid and Reliable Tool for Running Analysis
Eng. Proc. 2022, 27(1), 88; https://doi.org/10.3390/ecsa-9-13202 - 01 Nov 2022
Viewed by 70
Abstract
Concurrent biofeedback has been demonstrated to be an effective strategy for reducing running-related injuries (RRI) [1–3]. The majority of these RRI are overuse injuries related to impact accelerations [4,5]. However, information regarding impact accelerations is not accessible to the entire population since it [...] Read more.
Concurrent biofeedback has been demonstrated to be an effective strategy for reducing running-related injuries (RRI) [1–3]. The majority of these RRI are overuse injuries related to impact accelerations [4,5]. However, information regarding impact accelerations is not accessible to the entire population since it requires an accelerometry system. The objective of this study was to investigate the validity and reliability of a new accelerometry system placed directly into the treadmill (AccTrea) and compare it to the traditional system placed directly on the athlete’s body (AccAthl). Thirty recreational athletes with no history of lower body injuries performed two running tests on different days. They ran for 5 min at 10 km/h and at a 0% slope and acceleration impacts and spatiotemporal parameters were collected in two sets of 10 s during the last minute taken in each measurement session. The first session intended to assess the validity of an AccTrea versus an AccAthl, and the second session intended to test its reliability. The results showed that AccTrea is a valid and reliable tool for measuring spatiotemporal parameters such as step length (validity intraclass correlation coefficient (ICC) = 0.94; reliability ICC = 0.92), step time (validity ICC = 0.95; reliability ICC = 0.96), and step frequency (validity ICC = 0.95; reliability ICC = 0.96) during running. The peak acceleration impact variables manifested a high reliability for both left (reliability ICC = 0.88) and right legs (reliability ICC = 0.85), and the peak impact asymmetry demonstrated a modest validity (ICC = 0.55). The valid and reliable results make the AccTrea system an appropriate tool with which to inform athletes about their running mechanics, bringing the laboratory data closer to the running community.  Full article

Other

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Proceeding Paper
Voltammetric Sensors Based on the Electropolymerized Phenolic Acids or Triphenylmethane Dyes for the Antioxidant Analysis
Eng. Proc. 2022, 27(1), 2; https://doi.org/10.3390/ecsa-9-13178 - 01 Nov 2022
Viewed by 176
Abstract
Sensors with electrochemically formed polymeric films as a sensitive layer are of high interest in electroanalysis. Voltammetric sensors based on the glassy carbon electrodes (GCEs) covered with carbon nanomaterials and electropolymerized phenolic acids (gallic and ellagic) or triphenylmethane dyes (thymolphthalein and aluminon) were [...] Read more.
Sensors with electrochemically formed polymeric films as a sensitive layer are of high interest in electroanalysis. Voltammetric sensors based on the glassy carbon electrodes (GCEs) covered with carbon nanomaterials and electropolymerized phenolic acids (gallic and ellagic) or triphenylmethane dyes (thymolphthalein and aluminon) were developed. Conditions of potentiodynamic electropolymerization were optimized. The electrodes were characterized with scanning electron microscopy, cyclic voltammetry, chronoamperometry, and electrochemical impedance spectroscopy (EIS). In the differential pulse mode, sensors provided a sensitive and selective response to different classes of the antioxidants (capsaicinoids, flavanones, and flavonols). The practical applicability of the sensors was demonstrated on food and plant samples. Full article
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Proceeding Paper
MOX Resistive Microsensors for Low Concentration Methane Detection
Eng. Proc. 2022, 27(1), 3; https://doi.org/10.3390/ecsa-9-13175 - 01 Nov 2022
Viewed by 182
Abstract
A series of MOX resistive sensors with CuO and CoO-sensitive films were prepared using an eco-friendly technique (sol-gel). The sensor transducers are based on a custom-made alumina wafer with gold (Au) or platinum (Pt) interdigital electrodes (IDE) printed onto the alumina surface. The [...] Read more.
A series of MOX resistive sensors with CuO and CoO-sensitive films were prepared using an eco-friendly technique (sol-gel). The sensor transducers are based on a custom-made alumina wafer with gold (Au) or platinum (Pt) interdigital electrodes (IDE) printed onto the alumina surface. The sensors’ responses (sensor electrical resistance variations, measured at the IDE’s contact pads) were recorded under lab conditions (dried target and carrier gas from gas cylinders) in a constant gas flow and with a 1.5 Volts direct current (DC) being applied to the IDE as sensor operating voltage. Full article
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Proceeding Paper
Anion Dual Mode Fluoro-Chromogenic Chemosensor Based on a BODIPY Core
Eng. Proc. 2022, 27(1), 6; https://doi.org/10.3390/ecsa-9-13191 - 01 Nov 2022
Viewed by 159
Abstract
Herein, we report the synthesis and chromo-fluorogenic behavior of a BODIPY derivative. The BODIPY core was functionalized with a phenyl group at the meso-position and a formyl group at position 2 introduced through the Vilsmeier Haack reaction. The compound showed an absorption [...] Read more.
Herein, we report the synthesis and chromo-fluorogenic behavior of a BODIPY derivative. The BODIPY core was functionalized with a phenyl group at the meso-position and a formyl group at position 2 introduced through the Vilsmeier Haack reaction. The compound showed an absorption band at 492 nm and an emission band at 508 nm, with a ΦF = 0.84. The evaluation of the chemosensing ability of the BODIPY was investigated in the presence of several anions with environmental and biomedical relevance, and a simultaneous colorimetric and fluorimetric response was observed for cyanide and fluoride anions. Full article
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Proceeding Paper
A BODIPY Derivative for Selective Fluorescent Chemosensing of Iron (III)
Eng. Proc. 2022, 27(1), 7; https://doi.org/10.3390/ecsa-9-13190 - 01 Nov 2022
Viewed by 142
Abstract
A BODIPY derivative functionalized with a phenyl group at the meso-position was synthesized and characterized through 1H NMR and UV–Vis absorption/emission spectroscopies. The compound showed an absorption band at 497 nm and a fluorescence band at 513 nm, with a Φ [...] Read more.
A BODIPY derivative functionalized with a phenyl group at the meso-position was synthesized and characterized through 1H NMR and UV–Vis absorption/emission spectroscopies. The compound showed an absorption band at 497 nm and a fluorescence band at 513 nm, with a ΦF = 0.68 in acetonitrile. The evaluation of the chemosensing ability of the BODIPY was investigated in the presence of several ions with environmental and biomedical relevance. A highly selective fluorimetric response was observed for Fe3+ through fluorescence quenching upon successive additions of this cation. Full article
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Proceeding Paper
A Novel Imidazole Derivative: Synthesis, Characterization and Chemosensory Ability for Ions
Eng. Proc. 2022, 27(1), 8; https://doi.org/10.3390/ecsa-9-13184 - 01 Nov 2022
Viewed by 165
Abstract
Imidazoles have been explored over the years as optical chemosensors for their ability to coordinate with analytes, through specific binding sites, especially for ions, provided by the nitrogen heteroatom. Consequently, a novel 2,4,5-triheteroarylimidazole was synthetized bearing indolyl and furyl moieties. The compound was [...] Read more.
Imidazoles have been explored over the years as optical chemosensors for their ability to coordinate with analytes, through specific binding sites, especially for ions, provided by the nitrogen heteroatom. Consequently, a novel 2,4,5-triheteroarylimidazole was synthetized bearing indolyl and furyl moieties. The compound was characterized by the usual spectroscopic techniques, and the preliminary chemosensory ability was carried out in acetonitrile and acetonitrile/water (25:75) in the presence of ions with biological, medicinal and environmental relevance. In an aqueous medium, the new compound showed a slight enhancement of fluorescence in the presence of HSO4. As for cations, an enhancement of the fluorescence was observed upon interaction with Fe2+, Sn2+, Fe3+ and Al3+. On the other hand, a quenching of fluorescence was observed in the presence of Cu2+. Full article
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Proceeding Paper
Success and Failure in Antibody Recognition by Surface-Type Sensors: Essential Prerequisites
Eng. Proc. 2022, 27(1), 9; https://doi.org/10.3390/ecsa-9-13221 - 01 Nov 2022
Viewed by 348
Abstract
In order to determinate small molecular compounds (so-called haptens) in biological media, especially when the concentrations of compounds are at trace concentrations, it is necessary to produce antibodies with high affinity and high selectivity. Since the hapten is not able to stimulate the [...] Read more.
In order to determinate small molecular compounds (so-called haptens) in biological media, especially when the concentrations of compounds are at trace concentrations, it is necessary to produce antibodies with high affinity and high selectivity. Since the hapten is not able to stimulate the animals to produce specific antibodies directly, it should be bound to protein carrier. The manner of chemical binding of the hapten to a protein determines the character of the antibody specificity to small molecules which are analyzed. One of the highly sensitive methods in the small molecule determination at low concentration is competitive SPR-based immunoassay. To use the method effectively, a definite sensor surface sensitive to the specific antibody is needed to achieve the lowest value in the limit of detection (LOD) in immunoassay. The most evident class of small molecules to be determined in biological media at the lowest concentration is the steroid hormones, particularly estrogens. We have developed the sensor surface by binding the target molecules (estradiol, E2) directly to the gold surface through the specific linker to provide the closest distance to the surface along with biocompatibility to achieve maximal response in antibody–antigen interaction. As an antibody we have used a commercial monoclonal antibody raised to the 6-position in E2 with BSA (E26*BSA_CC). We failed to observe the specific binding of the antibody to the sensor surface. Then, we suggested that the main factor hindering this interaction is the wrong choice regarding the hapten–carrier conjugated with carrier protein. In order to confirm the assumption, we took the serum obtained from animal immunization by the antigen where BSA- is attached to the 3- or 17-position in E2 (E23*BSA_CC or E217*BSA_CC) instead of 6-position. Only the polyclonal antibody obtained with E23*BSA_CC resulted as expected in its successful binding to the sensitive sensor surface identification of low molecular weight analytes. Full article
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Proceeding Paper
On-Chip Tests for the Characterization of the Mechanical Strength of Polysilicon
Eng. Proc. 2022, 27(1), 10; https://doi.org/10.3390/ecsa-9-13363 - 01 Nov 2022
Viewed by 181
Abstract
Microelectromechanical systems (MEMS) are nowadays widespread in the sensor market, with several different applications. New production techniques and ever smaller device geometries require a continuous investigation of potential failure mechanisms in such devices. This work presents an experimental on-chip setup to assess the [...] Read more.
Microelectromechanical systems (MEMS) are nowadays widespread in the sensor market, with several different applications. New production techniques and ever smaller device geometries require a continuous investigation of potential failure mechanisms in such devices. This work presents an experimental on-chip setup to assess the geometry- and material-dependent strength of stoppers adopted to limit the deformation of movable parts, using an electrostatically actuated device. A series of comb-finger and parallel plate capacitors are used to provide a rather large stroke to a shuttle, connected to the anchors through flexible springs. Upon application of a varying voltage, failure of stoppers of variable size is observed and confirmed by post-mortem ΔCV curves. The results of the experimental campaign are collected to infer the stochastic property of the strength of polycrystalline, columnar silicon films. Full article
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Proceeding Paper
An IoT Braille Display towards Assisting Visually Impaired Students in Mexico
Eng. Proc. 2022, 27(1), 11; https://doi.org/10.3390/ecsa-9-13194 - 01 Nov 2022
Viewed by 181
Abstract
According to the World Health Organization, 2.2 billion people globally have some vision impairment. Blind and vision impairment children can undergo poor motor, language, and cognitive evolution, bringing lower levels of educational success. Our proposal aims to design and develop a one-character refreshable [...] Read more.
According to the World Health Organization, 2.2 billion people globally have some vision impairment. Blind and vision impairment children can undergo poor motor, language, and cognitive evolution, bringing lower levels of educational success. Our proposal aims to design and develop a one-character refreshable braille display that is affordable and easy to use through the Internet of Things (IoT) technology. Reading is essential to acquire knowledge by allowing an affordable form of reading based on braille, a handy tool for teaching and training blind and visually impaired people can be reached. Full article
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Proceeding Paper
A Sulfo-Cyanine Dye as a Colorimetric Chemosensor for Metal Cation Recognition
Eng. Proc. 2022, 27(1), 12; https://doi.org/10.3390/ecsa-9-13219 - 01 Nov 2022
Viewed by 182
Abstract
Metal cations play important roles in several industrial and biochemical processes. However, high levels of some cations are toxic and consequently cause serious health and environmental problems. Because of these features, the search for organic molecules capable of coordinating these analytes is an [...] Read more.
Metal cations play important roles in several industrial and biochemical processes. However, high levels of some cations are toxic and consequently cause serious health and environmental problems. Because of these features, the search for organic molecules capable of coordinating these analytes is an increasingly studied topic in the scientific community, especially those with optical responses (optical chemosensors). Following the research group’s interest in heterocyclic optical chemosensors for various ions, a sulfo-cyanine, which absorbs and emits in the NIR region, was studied as a chemosensor for the recognition of metal cations with biological and environmental relevance. Chemosensing studies showed that this sulfo-cyanine displayed a highly sensitive colorimetric response, from blue to colorless, for Cu2+ and Fe3+ in acetonitrile solution. Full article
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Proceeding Paper
Quinoline-Based Hydrazone Derivative as a Biocide Chemosensor: Synthesis and Sensing Studies
Eng. Proc. 2022, 27(1), 13; https://doi.org/10.3390/ecsa-9-13199 - 01 Nov 2022
Viewed by 154
Abstract
Tributyltin (TBT) is an organic biocide used on antifouling paints to avoid biofouling on boats and submersed structures. It is toxic to a variety of aquatic organisms and was banned by the Rotterdam Convention in 1998. TBT sensing is an important issue as [...] Read more.
Tributyltin (TBT) is an organic biocide used on antifouling paints to avoid biofouling on boats and submersed structures. It is toxic to a variety of aquatic organisms and was banned by the Rotterdam Convention in 1998. TBT sensing is an important issue as the biocide is still affecting aquatic environments as some countries did not sign the convention and are still using it. Currently, TBT monitoring methods are based on sampling and laboratory analysis, which is expensive, time-consuming, and require expert users. Therefore, a new simple and fast TBT sensing method would be of high interest. In this work, a new quinoline-based hydrazone derivative was synthesized by a condensation reaction in 67% yield. The new compound was characterized by the usual spectroscopic and spectrometric techniques. The preliminary chemosensory study of the hydrazone derivative in the presence of TBT in acetonitrile solution resulted in a color change from colorless to red together with the appearance of fluorescence. This interaction was confirmed by spectrophotometric and spectrofluorimetric titrations, which revealed that 17 equivalents of TBT led to the maximum optical signal in terms of fluorescence intensity and absorbance. Full article
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Proceeding Paper
An Optimized Methodology to Achieve Irreversible Bonding between PDMS and Polyimides for Biomedical Sensors
Eng. Proc. 2022, 27(1), 14; https://doi.org/10.3390/ecsa-9-13223 - 01 Nov 2022
Viewed by 145
Abstract
Polyimide (PI) and polydimethylsiloxane (PDMS) are widely used materials in biomedical sensor development. The hydrophobic property of PDMS makes it difficult to bind with other materials, such as PI, which is commonly used in sensor applications. This paper employs the chemical functionalization of [...] Read more.
Polyimide (PI) and polydimethylsiloxane (PDMS) are widely used materials in biomedical sensor development. The hydrophobic property of PDMS makes it difficult to bind with other materials, such as PI, which is commonly used in sensor applications. This paper employs the chemical functionalization of the PDMS and PI surfaces via epoxy-thiol click chemistry to achieve irreversible bonding. The bonding strength between the PDMS and PI is tested using a peel-off test method where adhesive and cohesive failures are observed. To demonstrate the importance of strong bonding, a wireless pressure sensor is developed. The sensor is tested for cyclic pressures over 1 million cycles with no evidence of bonding failures. This irreversible bonding can improve sensor integrity, reliability, and stability, especially for biomedical applications. Full article
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Proceeding Paper
Indoor Position Estimation Using Ultrasonic Beacon Sensors and Extended Kalman Filter
Eng. Proc. 2022, 27(1), 16; https://doi.org/10.3390/ecsa-9-13353 - 01 Nov 2022
Viewed by 147
Abstract
With the invention of GPS and related technologies, outdoor positional systems have become very accurate. However, there is still a need for efficient, reliable, and less expensive technology for indoor navigation. There are lots of techniques used for indoor navigation, such as acoustic, [...] Read more.
With the invention of GPS and related technologies, outdoor positional systems have become very accurate. However, there is still a need for efficient, reliable, and less expensive technology for indoor navigation. There are lots of techniques used for indoor navigation, such as acoustic, Wi-Fi-based, proximity-based, infrared systems and SLAM algorithms. In this study, accurate position estimation was attempted by combining the acceleration and gyroscope data and the raw distance data with the help of the Extended Kalman Filter (EKF). Initially, a position estimation was obtained using the Recursive Least Square (RLS) method with a trilateration algorithm. This solution was used as a starting point for RLS. Here, the first solution point is updated as the initial solution for each distance, and the result calculated by the RLS method is updated as the next solution. This approach enables the distance measurement and position estimation to be executed simultaneously, avoids the unnecessary waiting time, and speeds up the positioning estimation. After that, this position estimation is fused with the acceleration and gyroscope data. In order to test the designed algorithm, synthetic data were used. As a result of these tests, it has been observed that this EKF structure created for indoor navigation gives accurate results. Full article
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Proceeding Paper
A Deep-Learning-Based Approach for Saliency Determination on Point Clouds
Eng. Proc. 2022, 27(1), 17; https://doi.org/10.3390/ecsa-9-13271 - 01 Nov 2022
Viewed by 197
Abstract
Laser scanners recording a huge number of data points from different surfaces are widely used to capture the exact geometry of 3D objects. These large amounts of data require intelligent solutions to be examined and processed efficiently. Deep-learning-based approaches have found their way [...] Read more.
Laser scanners recording a huge number of data points from different surfaces are widely used to capture the exact geometry of 3D objects. These large amounts of data require intelligent solutions to be examined and processed efficiently. Deep-learning-based approaches have found their way into many data analytics applications for processing such large datasets, categorizing them, or even determining the most informative portion of the data. This research focused on 3D deep-learning techniques directly applied to point clouds to determine the most important features of a 3D shape. More specifically, this research adopted PointNet as a backbone architecture for feature extraction from 3D point clouds and computed a gradient-based class activation mapping (Grad-CAM) on each object to create a 3D importance/saliency map. Experiments confirmed the success of the proposed approach in the determination of important features of 3D objects as compared with the ground truth. Full article
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Proceeding Paper
Optimization of Graded Arrays of Resonators for Energy Harvesting in Sensors as a Markov Decision Process Solved via Reinforcement Learning
Eng. Proc. 2022, 27(1), 18; https://doi.org/10.3390/ecsa-9-13216 - 01 Nov 2022
Viewed by 179
Abstract
The design optimization of the grading of a resonator array for energy harvesting in sensors is described. Attention is paid to set the resonator heights, possibly removing resonators whenever convenient. Instead of employing time-consuming heuristic approaches that require verifying the physical understanding of [...] Read more.
The design optimization of the grading of a resonator array for energy harvesting in sensors is described. Attention is paid to set the resonator heights, possibly removing resonators whenever convenient. Instead of employing time-consuming heuristic approaches that require verifying the physical understanding of the problem and tuning the design ruling parameters, the optimization task is treated as a Markov decision process, in which states describe specific system configurations, and actions represent the modifications to the current design. The physics-based understanding of the problem is exploited to constrain the set of possible modifications to the mechanical system. Finite elements simulations are exploited to evaluate the action effects and to inform the reinforcement learning agent. The proximal policy optimization algorithm is employed to solve the Markov decision problem. The procedure is demonstrated to be able to automatically produce configurations, enhancing the mechanical system performance. The proposed framework is generalizable to a large class of problems involving the design optimization of sensors. Full article
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Proceeding Paper
A Pilot on the Endocrine Effects of Hormonal Replacement Therapy on Menopausal T1 Diabetics Using Wearable Sensors
Eng. Proc. 2022, 27(1), 19; https://doi.org/10.3390/ecsa-9-13215 - 01 Nov 2022
Viewed by 294
Abstract
Menopause is an under-reported and under-researched life stage for women living with type 1 diabetes (T1D) despite its lasting approximately 20 years. Menopause is associated with metabolic dysfunction leading to weight gain, impaired insulin sensitivity, hypertension, and hypercholesterolemia, each of which diminishes longevity [...] Read more.
Menopause is an under-reported and under-researched life stage for women living with type 1 diabetes (T1D) despite its lasting approximately 20 years. Menopause is associated with metabolic dysfunction leading to weight gain, impaired insulin sensitivity, hypertension, and hypercholesterolemia, each of which diminishes longevity for women living with diabetes. Its symptoms, affecting cognition, sleep patterns, mood, cardiac and vascular health, and physical health, are known to impact glucose variability dynamics. Associated vasomotor symptoms of hot-flushes/night-sweats, mood swings, anxiety, depression, and sexual dysfunction make it difficult to differentiate between symptoms of menopause and hypoglycemia. While it is recognized that transitioning through menopause increases the potential for developing diabetes, there are no recommendations on managing glucose variability or insulin resistance for women with pre-existing diabetes. Increasingly, women using wearable glucose sensor technologies, insulin pumps, and artificial pancreas systems have self-identified increased glucose variability in the data sets provided by wearable digital health technologies. These datasets, collected in real-time from women outside of traditional research settings, would have been unimaginable 20 years ago. Women highlighting the knowledge deficit are driven to learn and share more about menopause. Their quest for clinician support about the impact of hormone replacement therapies (HRT) on glycemic variability, and the associated risk of developing additional comorbidities, further illustrates the importance of this subject. This work uses datasets from wearable sensors, contributed by women with T1D to inform an understanding of their collective perimenopause and menopause journeys. Glucose readings across a period of weeks, leading up to and immediately following the initiation of prescribed medications, have been analyzed to investigate the physiological effects of HRT on the endocrine system of this sample of menopausal T1D women. Self-management and peer support is bridging the research void, which is why there should be more research into this topic. Full article
Proceeding Paper
On the Use of Deep Learning Decompositions and Physiological Measurements for the Prediction of Preterm Pregnancies in a Cohort of Patients in Active Labor
Eng. Proc. 2022, 27(1), 20; https://doi.org/10.3390/ecsa-9-13192 - 01 Nov 2022
Viewed by 156
Abstract
Preterm pregnancies are one of the leading causes of morbidity and mortality amongst children under the age of five. This is a global issue and has been identified as an area requiring active research. The emphasis now is to identify and develop methods [...] Read more.
Preterm pregnancies are one of the leading causes of morbidity and mortality amongst children under the age of five. This is a global issue and has been identified as an area requiring active research. The emphasis now is to identify and develop methods of predicting the likelihood of preterm birth. This paper uses physiological data from a group of patients in active labor. The dataset contains information about fetal heart rate (FHR) and maternal heart rate (MHR) for all patients and electrohysterogram (EHG) recordings for the measurement of uterine contractions. For the physiological data analysis and associated signal processing, we utilize deep wavelet scattering (DWS). This is an unsupervised decomposition and feature extraction method combining characteristics from deep learning convolutions, as well as the classical wavelet transform, to observe and investigate the extent to which active preterm labor can be accurately identified from an acquired physiological signal, the results of which were compared with the metaheuristic linear series decomposition learner (LSDL). Additional machine learning algorithms are tested on the acquired physiological data to allow for the identification of optimal model architecture for this specific physiological data. Full article
Proceeding Paper
Irregular Temperature Variation Effects on Damage Detection Based on Impedance Measurement from Piezoelectric Transducers
Eng. Proc. 2022, 27(1), 21; https://doi.org/10.3390/ecsa-9-13188 - 01 Nov 2022
Viewed by 155
Abstract
Piezoelectric transducers have been extensively investigated for the development of non-destructive techniques in structural health monitoring systems. Among the various techniques that have been proposed, the electromechanical impedance technique stands out for its simplicity of installation, where a piezoelectric transducer operates simultaneously as [...] Read more.
Piezoelectric transducers have been extensively investigated for the development of non-destructive techniques in structural health monitoring systems. Among the various techniques that have been proposed, the electromechanical impedance technique stands out for its simplicity of installation, where a piezoelectric transducer operates simultaneously as a sensor and an actuator, establishing a relationship between the electrical impedance of the transducer and the integrity of the structure. Although many studies have reported the feasibility of this technique, some practical challenges have hampered its effective application in real structures, where one of the most critical problems has been the temperature variation. In order to mitigate the temperature effects, damage indices and compensation methods have been proposed in recent years and satisfactory results have been obtained. However, these compensation methods are typically tested in laboratories using small structures with uniform temperature variation. On the other hand, large structures in real applications may be subject to irregular temperature variation. Therefore, this study aims to investigate the effects of irregular temperature variation on the impedance signatures of piezoelectric transducers and, consequently, on the feasibility of detecting structural damage. Experimental tests were performed on an aluminum plate with multiple piezoelectric transducers installed under different temperature conditions, and the impedance signatures were qualitatively and quantitatively analyzed using damage indices. The results indicate that the irregular temperature variation can make some damage indices and compensation techniques unfeasible in real applications with large structures. Full article
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Proceeding Paper
Measurement of Sugar Concentration by Multimodal Fiber Optics Sensor
Eng. Proc. 2022, 27(1), 22; https://doi.org/10.3390/ecsa-9-13273 - 01 Nov 2022
Viewed by 159
Abstract
In this work, we report on the fabrication and testing of a fiber optics sensor based on multimodal interference (MMI) that is capable of measuring sugar concentration in aqueous solutions. The sensor has a simple structure, built by splicing a segment of multimode [...] Read more.
In this work, we report on the fabrication and testing of a fiber optics sensor based on multimodal interference (MMI) that is capable of measuring sugar concentration in aqueous solutions. The sensor has a simple structure, built by splicing a segment of multimode fiber (MMF) between two single-mode fibers (SMS architecture). The sensor’s operating mechanism is based on the spectral shift due to changes in the effective refractive index (RI) of the media surrounding the MMF. The optical sensor was tested with fructose and sucrose, which were diluted in distilled water in a concentration range from 0% to 18.5%. The proposed device exhibits a linear response with a sensitivity of around 0.17524 nm/% and 0.16321 nm/% for sucrose and fructose, respectively. In addition, this optical sensor shows the advantage of simple construction, low cost, and linear response, it does not require additional processes or coatings on the optical fiber. Full article
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Proceeding Paper
Data Acquisition and Processing Algorithm for Total and Static Pressure Measurement System
Eng. Proc. 2022, 27(1), 23; https://doi.org/10.3390/ecsa-9-13332 - 01 Nov 2022
Viewed by 136
Abstract
In aviation measurement of static and total pressure is widely used to determine the flight conditions. Results of pressure measurements are used to monitor flight attitude, equivalent speed, Mach number, vertical velocity etc. The algorithm for data acquisition and processing for developed pressure [...] Read more.
In aviation measurement of static and total pressure is widely used to determine the flight conditions. Results of pressure measurements are used to monitor flight attitude, equivalent speed, Mach number, vertical velocity etc. The algorithm for data acquisition and processing for developed pressure measurement system is presented in this paper. Full article
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Proceeding Paper
Safety Measures for Hydrogen Generation Based on Sensor Signal Algorithms
Eng. Proc. 2022, 27(1), 24; https://doi.org/10.3390/ecsa-9-13284 - 01 Nov 2022
Viewed by 179
Abstract
In the last decade, the use of electrolyzers in various sectors has facilitated the generation of hydrogen for multiple applications, such as an alternative fuel source for vehicles, generation of green hydrogen through renewable energies, or energy storage through metal hydride tanks, among [...] Read more.
In the last decade, the use of electrolyzers in various sectors has facilitated the generation of hydrogen for multiple applications, such as an alternative fuel source for vehicles, generation of green hydrogen through renewable energies, or energy storage through metal hydride tanks, among others. Regardless of their application, electrolyzers are characterised by complex operation and dependence on various operating parameters, which means that their implementation in a real system is not immediate. This paper presents sensor-based algorithms aimed at ensuring safe and stable operation of a Proton Exchange Membrane Electrolyzer (PEMEL) framed within a smart microgrid powered by renewable energy. Algorithms developed to consider factors such as operating temperature and pressure, availability of feed water or the presence of water in the phase separator are presented. The goal of these algorithms is to maintain the operation of the PEMEL within nominal ranges in order to avoid degradation and/or malfunction of the materials and equipment involved in the system. The algorithms are programmed in a programmable logic controller that is responsible for managing the complete operating cycle of the PEMEL. The sensors and actuators are described, together with their relevance in the operation of the PEMEL. Finally, experimental results of their implementation and real-time operation are provided. Full article
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Proceeding Paper
Fabrication of Nanoporous Platinum Films with Dealloying Method for Hydrogen Sensor Application
Eng. Proc. 2022, 27(1), 25; https://doi.org/10.3390/ecsa-9-13317 - 01 Nov 2022
Viewed by 185
Abstract
In this research, a nanoporous platinum film is synthesized using the dealloying method. Platinum–copper (Pt–Cu) alloy films with a thickness of approximately 50 nm are prepared on glass using the magnetron-co-sputtering technique. In order to obtain nanoporous Pt, the Pt–Cu alloy films are [...] Read more.
In this research, a nanoporous platinum film is synthesized using the dealloying method. Platinum–copper (Pt–Cu) alloy films with a thickness of approximately 50 nm are prepared on glass using the magnetron-co-sputtering technique. In order to obtain nanoporous Pt, the Pt–Cu alloy films are dealloyed in 1 M nitric acid solution for different intervals. It is observed that when dealloyed in the nitric acid solution for 5 h, Cu was completely removed from the alloy, and nanoporous Pt with a regular pore structure was obtained. Then, the fabricated nanoporous Pt film is tested for hydrogen detection in the concentration range of 10 ppm and–5% hydrogen at various temperatures. The results demonstrated that the sensitivity of nanoporous Pt is about 6.5 for exposure to 1% hydrogen, and the sensing mechanism of nanoporous Pt could be explained by the surface scattering phenomenon. Full article
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Proceeding Paper
Applications of the Internet of Things (IoT) in Real-Time Monitoring of Contaminants in the Air, Water, and Soil
Eng. Proc. 2022, 27(1), 26; https://doi.org/10.3390/ecsa-9-13335 - 01 Nov 2022
Viewed by 179
Abstract
Sensor networks using the Internet of Things (IoT) are gaining momentum for real-time monitoring of the environment. Increased use of natural resources due to a rise in agriculture production, manufacturing, and civil infrastructure poses a challenge to sustainable growth and development of the [...] Read more.
Sensor networks using the Internet of Things (IoT) are gaining momentum for real-time monitoring of the environment. Increased use of natural resources due to a rise in agriculture production, manufacturing, and civil infrastructure poses a challenge to sustainable growth and development of the global economy. For sustainable use of natural resources (including air, soil, and water), data-driven modeling is needed to understand and simulate contaminant transport and proliferation. Different logging devices are specifically designed to integrate with environmental sensors that send real-time data to the cloud using IoT systems for monitoring. The IoT systems use an LTE network or Wi-Fi to transmit air, water, and soil quality data to the cloud networks. This seamless integration between the logging devices and IoT sensors creates an autonomous monitoring system that can observe environmental parameters in real-time. Various federal organizations and industries have implemented the IoT-based sensor network to monitor real-time air quality parameters (particulate matter, gaseous pollutants), water quality parameters (turbidity, pH, temperature, and specific conductance), and soil parameters (moisture content, soil nutrients). Although several organizations have used IoT systems to monitor environmental parameters, a proper framework to make the monitoring systems reliable and cost-efficient was not explored. The main objective of this study is to present a framework that combines a sensing layer, a network layer, and a visualization layer, allowing modelers and other stakeholders to observe a progressive trend in environmental data while being cost-efficient. This efficient real-time monitoring framework with IoT systems helps in developing robust statistical and mathematical models. The sustainable development of smart cities while maintaining public health requires reliable environmental monitoring data that can be possible by the proposed IoT framework. Full article
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Proceeding Paper
Modelling and FEM Simulation of Love Wave SAW-Based Dichloromethane Gas Sensor
Eng. Proc. 2022, 27(1), 27; https://doi.org/10.3390/ecsa-9-13267 - 01 Nov 2022
Viewed by 217
Abstract
In this paper, surface acoustic wave (SAW) technology based on love waves was designed in three dimensions for finite element modelling (FEM) and analysis in order to detect volatile organic compounds (VOC). A thin layer of polyisobutylene (PIB), which acted as the sensing [...] Read more.
In this paper, surface acoustic wave (SAW) technology based on love waves was designed in three dimensions for finite element modelling (FEM) and analysis in order to detect volatile organic compounds (VOC). A thin layer of polyisobutylene (PIB), which acted as the sensing layer, was placed on top of the guiding layer of SiO2 and interdigitated electrodes (IDE), which were modelled on a piezoelectric substrate. The substrate selected was 64° YZ-cut Lithium niobate (LiNbO3) for love wave generation, and the lightweight electrodes were made of Aluminium (Al). Analytical simulations were conducted using COMSOL Multiphysics 6.0 software. Full article
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Proceeding Paper
A Microfluidic Device Based on Standing Surface Acoustic Waves for Sorting and Trapping Microparticles
Eng. Proc. 2022, 27(1), 28; https://doi.org/10.3390/ecsa-9-13362 - 01 Nov 2022
Viewed by 206
Abstract
Microfluidic devices can provide innovative means to handle and control the transport of (bio)particles within a fluid flow. The advantage of microscale devices is that different components can be integrated in a single chip at low cost, with a negligible power consumption, compared [...] Read more.
Microfluidic devices can provide innovative means to handle and control the transport of (bio)particles within a fluid flow. The advantage of microscale devices is that different components can be integrated in a single chip at low cost, with a negligible power consumption, compared to alternative solutions. In this work, a numerical investigation is developed on the use of standing surface acoustic waves (SAWs) generated within a microfluidic channel in order to manipulate microparticles. Far-field waves are generated via inter-digital transducers (IDTs), travel on the surface of a piezoelectric substrate and finally interfere in the channel, giving rise to a standing wave solution in terms of acoustic pressure. Results are reported for different geometries of the channel, to define the sensitivity of the acoustic pressure field to the relevant geometric features of the channel. This investigation shows how the acoustic radiation and drag forces interact with each other to move and focus the particles, possibly leading to a separation of heterogeneous ones, and generally provide a way to manipulate them at a small scale. Full article
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Proceeding Paper
A Wireless Resonant LC Sensor for Glucose Detection
Eng. Proc. 2022, 27(1), 29; https://doi.org/10.3390/ecsa-9-13365 - 01 Nov 2022
Viewed by 107
Abstract
This paper proposes a wireless resonant inductive and capacitive (LC) sensor for glucose sensing. A sensor composed of a capacitor with interdigital electrodes and an inductor for glucose sensing is presented. Resonance frequency and impedance were measured as the sensing parameters. A glucose [...] Read more.
This paper proposes a wireless resonant inductive and capacitive (LC) sensor for glucose sensing. A sensor composed of a capacitor with interdigital electrodes and an inductor for glucose sensing is presented. Resonance frequency and impedance were measured as the sensing parameters. A glucose beverage concentration from 0% to 44% is used, resulting in a resonance frequency change from 1.9217 MHz to 1.8681 MHz, and the impedance of the sensor changes from 170.33 Ω to 110.68 Ω. The relationship of both resonance frequency and impedance to glucose beverage concentration is well presented by a decreasing exponential function. Using an exponential regression, the resonance frequency shows an average regression error of 1.38%. Likewise, the impedance shows an average error of 3.47%. The linear range of the sensor is also analyzed in a glucose concentration range between 0% and 4%. The sensor exhibited a sensitivity of 424.6 kHz and 721.6416 Ω, respectively, with a linear regression r2 of 0.9853 and 0.9553, respectively. Full article
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Proceeding Paper
Multiclass Classification of Brain Tumors with Various Deep Learning Models
Eng. Proc. 2022, 27(1), 30; https://doi.org/10.3390/ecsa-9-13367 - 01 Nov 2022
Viewed by 187
Abstract
Brain cancer is one of the most dangerous cancer types in the world, and thousands of people are suffering from malignant brain tumors. Depending on the level of cancer, early diagnosis can be a lifesaver. However, thousands of scans must be studied in [...] Read more.
Brain cancer is one of the most dangerous cancer types in the world, and thousands of people are suffering from malignant brain tumors. Depending on the level of cancer, early diagnosis can be a lifesaver. However, thousands of scans must be studied in order to classify tumor types with high accuracy. Deep learning models can handle that amount of data, and they can present results with high accuracy. It is already known that deep learning models can give different results depending on the dataset. In this paper, the effectiveness of some of the deep learning models on two different publicly available MRI (Magnetic Resonance Imaging) brain tumor datasets is examined. The reason for choosing this topic is that we are trying to find the best solution to classify tumors in the datasets. Different deep learning models are used separately on preprocessed datasets with the Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing variable to extract features from images and classify them. Datasets are shuffled randomly for 80% training, 10% validation, and 10% testing. For fine-tuning, models are modified so that the output channel of the classifier is equal to the number of classes in the datasets. The results show that pre-trained and fine-tuned ResNet, RegNet, and Vision Transformer (ViT) deep learning models can achieve accuracies higher than 90% and that they can be used as classifiers when diagnosis is required. Full article
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Proceeding Paper
Numerical Study of a PVDF-Based Strain Sensor for Damage Detection of an Asphalt Concrete Pavement Subject to Dynamic Loads
Eng. Proc. 2022, 27(1), 31; https://doi.org/10.3390/ecsa-9-13318 - 01 Nov 2022
Viewed by 137
Abstract
This paper studies the performance of Polyvinylidene fluoride (PVDF)–based strain sensor subject to dynamic loads with different load-moving velocities and the strain sensor’s performance for bottom-up crack detection of an asphalt pavement subject to dynamic loads. The core of the strain sensor is [...] Read more.
This paper studies the performance of Polyvinylidene fluoride (PVDF)–based strain sensor subject to dynamic loads with different load-moving velocities and the strain sensor’s performance for bottom-up crack detection of an asphalt pavement subject to dynamic loads. The core of the strain sensor is a metalized PVDF sensing film packaged with three protection layers. The encapsulated strain sensor adopts an H-shape to optimize the overall performance. Two numerical models are built in this paper and validate that the voltage output of the PVDF-based strain sensor can well capture the peak lateral strain with the propagation of the bottom-up cracks or the variation of a load moving velocity. Additionally, the sensor has better performance when it is in its lateral alignment position. Full article
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Proceeding Paper
Study of Absorbance and Fluorescence Properties of Laccase and Catechol Solutions in the UV Range
Eng. Proc. 2022, 27(1), 32; https://doi.org/10.3390/ecsa-9-13333 - 01 Nov 2022
Viewed by 129
Abstract
Laccase is an enzyme belonging to the oxidoreductase class and has copper atoms in the catalytic centre. The catalytic property of this enzyme consists of the enzymatic oxidation of the phenolic compounds, in the corresponding quinones, with the concomitant reduction of molecular oxygen [...] Read more.
Laccase is an enzyme belonging to the oxidoreductase class and has copper atoms in the catalytic centre. The catalytic property of this enzyme consists of the enzymatic oxidation of the phenolic compounds, in the corresponding quinones, with the concomitant reduction of molecular oxygen to water. There is growing interest in developing innovative sensing methods for detecting phenolic substrates, such as catechol. Preliminary absorption and fluorescence measurements were carried out in the UV-visible range to evaluate the possibility of using the variations produced in the spectra of laccase and/or catechol to monitor the presence of this substrate. The absorption and fluorescence emission increase upon UV excitation was detected. By monitoring the time course of the fluorescence signal, an evident increase in the signal detected in the UV range is observed until a saturation level is reached. The observed variations in the spectra, in the presence of the catechol, are discussed also in terms of the interactions between the enzyme and the phenolic compound. The results are very promising for the design of new optical detection methods for polyphenol pollutants. Full article
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Proceeding Paper
Fault Detection on Sensors of the Quadrotor System Using Bayesian Network and Two-Stage Kalman Filter
Eng. Proc. 2022, 27(1), 33; https://doi.org/10.3390/ecsa-9-13352 - 01 Nov 2022
Viewed by 149
Abstract
In recent years, model-based fault techniques have become popular due to their capability to reduce calculation cost. Bayesian Network and two-stage Kalman filter-based methods have recently become quite popular due to their robustness. In this paper, a model-based fault diagnosis method is presented [...] Read more.
In recent years, model-based fault techniques have become popular due to their capability to reduce calculation cost. Bayesian Network and two-stage Kalman filter-based methods have recently become quite popular due to their robustness. In this paper, a model-based fault diagnosis method is presented that uses a Bayesian network and two-stage Kalman filter (TSKF) together to robustly determine the sensor faults in an Unmanned Aerial Vehicle (UAV) system. By using these two approaches together, the robustness of the fault detection in the sensor improved. For demonstrating the behavior of the proposed method, numerical simulations were performed in MATLAB/SimulinkTM environment. The results show that the proposed method is capable of detecting faults more robustly. Full article
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Proceeding Paper
Distributed Remote-Controlled Sensor Network for Monitoring Complex Gas Environment Based on Intelligent Gas Analyzers
Eng. Proc. 2022, 27(1), 34; https://doi.org/10.3390/ecsa-9-13208 - 01 Nov 2022
Viewed by 133
Abstract
The increasing complexity of modern industrial production and potential threats of chemical or biological pollution due to accidents, transport incidents or terrorist attacks require the continuous monitoring of changes occurring in complex gas mixtures to eliminate effects such changes in a timely fashion. [...] Read more.
The increasing complexity of modern industrial production and potential threats of chemical or biological pollution due to accidents, transport incidents or terrorist attacks require the continuous monitoring of changes occurring in complex gas mixtures to eliminate effects such changes in a timely fashion. To make adequate decisions ina short timeframe, it is necessary to have complete information about a wide range of potential xenobiotics within the monitoring area. In this study, such an important scientific and practical problem is solved by the formation of a network of e-nose intelligent analyzers based on a cross-reactive array of chemical sensors using piezoelectric elements of a quartz crystal microbalance (QCM) as physical transducers connected by means of telecommunication protocols. A distributed remote-controlled sensor network provides operational control of the perimeter of a protected and/or dangerous zone by monitoring the gaseous environment, and can be used for the control of technological processes, a security alarm forhigh-risk objects andthe generation of information about danger in order to prevent personnel from entering a workingarea with a potentially hazardous atmosphere. Full article
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Proceeding Paper
Three-Dimensional Modelling and Visualization of Stone Inscriptions Using Close-Range Photogrammetry—A Case Study of Hero Stone
Eng. Proc. 2022, 27(1), 35; https://doi.org/10.3390/ecsa-9-13343 - 01 Nov 2022
Viewed by 202
Abstract
Stone inscriptions and archaeological structures are an asset to humankind which contain the history of the past. Estampage is the traditional method used to obtain the replica of the inscriptions which is primarily used to decrypt texts and for documentation purposes. Presently, close-range [...] Read more.
Stone inscriptions and archaeological structures are an asset to humankind which contain the history of the past. Estampage is the traditional method used to obtain the replica of the inscriptions which is primarily used to decrypt texts and for documentation purposes. Presently, close-range photogrammetry is a useful remote sensing technique to digitize these inscriptions for study as well as preservation. The current study focuses on the creation of a 3D model of a hero stone using digital camera technology. These photographs were acquired using a Sony Alpha7 III camera with a 35 mm full-frame CMOS sensor. Two hundred and sixty-one images/frames were acquired from different heights above ground and with various positions and angles around the stone inscription to cover it all around. The data acquired were processed in a series of steps which included image matching, dense point cloud generation, mesh reconstruction, and texturing of the model. As the sensor is non-metric, two markers acquired from the field were added to the scene to scale it accurately. The dimensions of the hero stone are computed as 2.3 × 1.3 ft and the resulting model had a reprojection error of less than 0.011 pixels. The processed model has 10,915,514 facets (TIN) and 8000 × 8000 × 4 textures providing a realistic appearance. The recent developments in computer vision using the structure from motion (SfM) approach enables the reconstruction of the hero stone accurately with realistic textures and details useful for preservation work. Full article
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Proceeding Paper
Development of Starfish-Shaped Two-Ring Microelectromechanical Systems (MEMS) Vibratory Ring Gyroscope with C-Shaped Springs for Higher Sensitivity
Eng. Proc. 2022, 27(1), 36; https://doi.org/10.3390/ecsa-9-13342 - 01 Nov 2022
Viewed by 172
Abstract
Microelectromechanical Systems (MEMS) vibratory gyroscopes are one of the integral inertial sensors of the inertial measurement unit (IMU). The usage of MEMS vibratory gyroscopes as inertial sensors has risen enormously in many applications, from household to automotive, smartphones to space applications, smart gadgets [...] Read more.
Microelectromechanical Systems (MEMS) vibratory gyroscopes are one of the integral inertial sensors of the inertial measurement unit (IMU). The usage of MEMS vibratory gyroscopes as inertial sensors has risen enormously in many applications, from household to automotive, smartphones to space applications, smart gadgets to military applications, and so on. This paper presents the mathematical modelling and initial development of the starfish structure with C-shaped springs for a MEMS vibratory ring gyroscope (VRG). The symmetric design methodology of VRGs corroborates higher sensitivity, mode matching, good thermal stability, better resolution, and shock resistance in extreme conditions. The proposed VRG has been designed and investigated using ANSYSTM software. This novel design incorporates a two-ring structure, with inner and outer rings, and with 16 C-shaped springs. The outer ring’s radius is 1000 μm and the whole VRG structure is supported by the outer eight small square pillars. The gyroscope structure’s wine-glass mode driving and sensing resonant frequencies were recorded at 51.50 kHz and 52.16 kHz. The mode mismatch between driving and sensing resonant frequency was measured at 0.66 kHz, which is relatively low compared to the other structures of vibratory gyroscopes. The proposed design provides high shock absorption with higher sensitivity for space applications for the control and manoeuvring of mini satellites for space applications. Full article
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Proceeding Paper
Using GPS Tracking Collars and Sensors to Monitor the Grazing Activity of Browsing Goats in Forest Rangeland
Eng. Proc. 2022, 27(1), 37; https://doi.org/10.3390/ecsa-9-13331 - 01 Nov 2022
Cited by 1 | Viewed by 211
Abstract
The recent advancements in sensor technologies to monitor and record behavioral activities of livestock provide an accurate scope to extend the database and understand the animal behavior under actual grazing conditions. The aim of this work was to determine the seasonal variation in [...] Read more.
The recent advancements in sensor technologies to monitor and record behavioral activities of livestock provide an accurate scope to extend the database and understand the animal behavior under actual grazing conditions. The aim of this work was to determine the seasonal variation in grazing activities of goats using the Global Positioning System (GPS) and leg sensor technologies. The study was conducted in the Southern Mediterranean forest pasture of Northern Morocco. Eight dairy alpine goats have been fitted with GPS tracking collars and tri-axial accelerometers over a 3-day period of each grazing season (spring, summer, and fall). Most of the behavioral activity of goats was dedicated to grazing (36 to 59%), followed by resting (22 to 30%) and walking without grazing (10 to 24%). During summer and fall, goats traveled longer distances compared to the spring. The combination of the two studied sensors provided useful data information to understand the behavioral activity of goat grazing in forest pasture. Full article
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Proceeding Paper
Uncertainty Quantification at the Microscale: A Data-Driven Multi-Scale Approach
Eng. Proc. 2022, 27(1), 38; https://doi.org/10.3390/ecsa-9-13351 - 01 Nov 2022
Viewed by 125
Abstract
Data-driven formulations are currently developed to deal with the complexity of the multi-physics governing the response of microelectromechanical systems (MEMS) to external stimuli and can be extremely helpful. Such devices are in fact characterized by a hierarchy of length and timescales, which are [...] Read more.
Data-driven formulations are currently developed to deal with the complexity of the multi-physics governing the response of microelectromechanical systems (MEMS) to external stimuli and can be extremely helpful. Such devices are in fact characterized by a hierarchy of length and timescales, which are difficult to fully account for in a purely model-based approach. In this work, we specifically refer to a (single-axis) Lorentz force micro-magnetometer designed for navigation purposes. Due to an alternating current flowing in a slender mechanical part (beam) and featuring an ad hoc set frequency, the microsystem is driven into resonance so that its sensitivity to the magnetic field is improved. A reduced-order physical model was formerly developed for the aforementioned movable part of the device; this model was then used to feed and speed up a multi-physics and multi-objective topology optimization procedure, aiming to design a robust and performing magnetometer. The stochastic effects, which are responsible for the scattering in the experimental data at the microscale, were not accounted for in such a model-based approach. A recently proposed formulation is here discussed and further extended to allow for such stochastic effects. The proposed multi-scale deep learning approach features: at the material scale, a convolutional neural network adopted to learn the scattering in the mechanical properties of polysilicon, induced by its morphology; and, at the device scale, two feedforward neural networks, one adopted to upscale the mechanical properties, while the other learns a microstructure-informed mapping between the geometric imperfections induced by the microfabrication process and the effective response of the movable part of the magnetometer. The data-driven models are linked through the physical model to provide a kind of hybrid solution to the problem. Results relevant to different neural network architectures are here discussed, along with a proposal to frame the approach as a multi-fidelity, uncertainty quantification procedure. Full article
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Proceeding Paper
An FTIR Spectroscopy Investigation on Different Methods of Lipid Extraction from HepG2 Cells
Eng. Proc. 2022, 27(1), 39; https://doi.org/10.3390/ecsa-9-13263 - 01 Nov 2022
Viewed by 165
Abstract
Fourier transform infrared (FTIR) spectroscopy is a non-invasive technique that is largely used for studying lipidomics. Lipids are a primary class of biological molecules that play numerous vital roles in various processes. In the present work, we adopted FTIR spectroscopy for monitoring the [...] Read more.
Fourier transform infrared (FTIR) spectroscopy is a non-invasive technique that is largely used for studying lipidomics. Lipids are a primary class of biological molecules that play numerous vital roles in various processes. In the present work, we adopted FTIR spectroscopy for monitoring the lipid extraction efficiency of different methods used for extracting lipids from hepatocarcinoma cells. The spectra acquired from samples obtained with the selected methods showed the contributions of different functional groups. A qualitative comparison indicated that all the spectra exhibited similar lipid species profiles. The peak intensity attributed to the CH2 asymmetric stretching mode has been used for a quantitative comparison of the efficiency of the different extraction methods. Full article
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Proceeding Paper
Advanced Quartz Microbalance Sensors for Gas-Phase Applications: Effect of Adsorbate on Shear Bond Stiffness between Physical Transducer and Superlattice of Latex Nanoparticles
Eng. Proc. 2022, 27(1), 40; https://doi.org/10.3390/ecsa-9-13204 - 01 Nov 2022
Viewed by 171
Abstract
New sensitive architectures built on soft surface architectures or nano-sized blocks also require a rethinking of the principles of the operation of traditional physical recording methods. Here, we report an experimental study of complex loadings for classical quartz crystal microbalance (QCM) that appear [...] Read more.
New sensitive architectures built on soft surface architectures or nano-sized blocks also require a rethinking of the principles of the operation of traditional physical recording methods. Here, we report an experimental study of complex loadings for classical quartz crystal microbalance (QCM) that appear on the surface with flexible spatial organization and variable coupling by which the interface architecture is connected to the transducer. Sensitive layers are superlattices formed on 100 nm LB1 latex nanoparticles self-assembled during the contact line deposition in evaporating sessile droplets with or without nonionic surfactant TWEEN® 20. It was shown that QCM resonance frequency change is not primarily determined by the adsorbate mass alone (as for LB1&TWEEN® 20 mixture), but rather by the link by which interfacial architecture is bound to the transducer (as for LB1 superlattice). A model has been proposed and substantiated in which the manifestation of anti-Sauerbrey behavior is associated with changes under the action of water vapor in the characteristics of the contact area of intra-film 3D mountainous deposits with the transducer surface. The possibility of a gaseous analyte not only to change the loading of QCM but also the features of the mechanical behavior of the mass associated with the surface opens the way to the creation of a new class of highly selective sensors of especially dangerous or critically important analytes. Due to the selective effect of the analyte on the processes of interfacial friction in the contact layer between the sensitive architecture and the sensor substrate, a contrast pattern of response to the target analyte can be formed. This is due not so much to the large magnitude of the response itself, but to the fact that the change in the analytical signal is opposite to the “usual” Sauerbray-like shift of the resonant frequency. Full article
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Proceeding Paper
3D Porous Plasmonic Nanoarchitectures for SERS-Based Chemical Sensing
Eng. Proc. 2022, 27(1), 41; https://doi.org/10.3390/ecsa-9-13200 - 01 Nov 2022
Viewed by 162
Abstract
Bio- and chemical SERS-sensing using plasmonic nanostructures can be dramatically improved by creating hot spots—i.e., sub-10 nm gaps between nanoparticles—which confine large electromagnetic fields on nanometric volume. Here we report a 3D porous wedge-shaped gold nanostructure that contains high-density Raman-active nanogaps produced by [...] Read more.
Bio- and chemical SERS-sensing using plasmonic nanostructures can be dramatically improved by creating hot spots—i.e., sub-10 nm gaps between nanoparticles—which confine large electromagnetic fields on nanometric volume. Here we report a 3D porous wedge-shaped gold nanostructure that contains high-density Raman-active nanogaps produced by pulsed laser deposition. The resulting structures consist of arrays of densely packed gold nanoparticles and nanopores that exhibit a number of functionalities, including size selectivity, spectral tunability and strong electromagnetic field amplification. The possibility of effective enhancement of the Raman intensity of Rhodamine 6G molecules upon resonant excitation that is outside the region of surface plasmon resonance excitation in 3D Au nanostructures is demonstrated. Full article
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Proceeding Paper
Method for Damage Detection of CFRP Plates Using Lamb Waves and Digital Signal Processing Techniques
Eng. Proc. 2022, 27(1), 42; https://doi.org/10.3390/ecsa-9-13357 - 01 Nov 2022
Viewed by 138
Abstract
The identification and severity of structural damages in carbon fiber reinforced polymer (CFRP), especially in the early stage, is critical in structural health monitoring (SHM) of composite materials. Among several approaches used to accomplish this goal, ultrasound inspection using Lamb waves has been [...] Read more.
The identification and severity of structural damages in carbon fiber reinforced polymer (CFRP), especially in the early stage, is critical in structural health monitoring (SHM) of composite materials. Among several approaches used to accomplish this goal, ultrasound inspection using Lamb waves has been taking place within non-destructive testing (NDT) methods. Likewise, the use of digital signal processing techniques for structural damage diagnosis has become popular due to the fact that it provides relevant information through feature extraction. In this context, this paper presents an alternative strategy based on the use of root mean square deviation (RMSD) and correlation coefficient deviation metric (CCDM) representative indices to extract the most sensitive information related to damage in CFRP plates through ultrasonic NDT signals in specific frequency ranges. In the experimental analysis, CFRP coupons were subjected to two types of damages: cracking and delamination. The signals, generated by piezoelectric transducers attached to the host structure using the pitch-catch method of Lamb waves, were subject to signal processing parameters based on the proposed approach. The results reveal that the proposed method was able to characterize the different types of damage in CFRP, as well as their severity in specific frequency bands. The results indicate the feasibility of the proposed method to detect and characterize damage in composite materials in a simple way, which is attractive for industrial applications. Full article
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Proceeding Paper
Attention Mechanism-Driven Sensor Placement Strategy for Structural Health Monitoring
Eng. Proc. 2022, 27(1), 43; https://doi.org/10.3390/ecsa-9-13354 - 01 Nov 2022
Viewed by 171
Abstract
Automated vibration-based structural health monitoring (SHM) strategies have been recently proven to be promising in the presence of aging and material deterioration threatening the safety of civil structures. Within such a framework, ensuring high-quality and informative data is a critical aspect that is [...] Read more.
Automated vibration-based structural health monitoring (SHM) strategies have been recently proven to be promising in the presence of aging and material deterioration threatening the safety of civil structures. Within such a framework, ensuring high-quality and informative data is a critical aspect that is highly dependent on the deployment of the sensors in the network and on their capability to provide damage-sensitive features to be exploited. This paper presents a novel data-driven approach to the optimal sensor placement devised to identify sensor locations that maximize the information effectiveness for SHM purposes. The optimization of the sensor network is addressed by means of a deep neural network (DNN) equipped with an attention mechanism, a state-of-the-art technique in natural language processing (NLP) that is useful in focusing on a limited number of important components in the information stream. The trained attention mechanism eventually allows for quantifying the relevance of each sensor in terms of the so-called attention scores, thereby enabling to identify the most useful input channels to solve the relevant downstream SHM task. With reference to the damage localization task, framed here as a classification problem handling a set of predefined damage scenarios, the DNN is trained to locate damage on labeled data that had been simulated to emulate the effects of damage under different operational conditions. The capabilities of the proposed method are demonstrated by referring to an eight-story shear building, characterized by damage states possibly located at any story and of unknown severity. Full article
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Proceeding Paper
Digital Compasses for Orientation-Tilt Monitoring in Offshore Deep-Sea Infrastructures: The KM3NeT Case
Eng. Proc. 2022, 27(1), 44; https://doi.org/10.3390/ecsa-9-13213 - 01 Nov 2022
Viewed by 181
Abstract
The KM3NeT Collaboration is currently constructing two neutrino detectors in the depths of the Mediterranean Sea. An excellent angular resolution will be necessary for an accurate reconstruction of neutrino direction, much as a precise knowledge of the position and orientation of the detector [...] Read more.
The KM3NeT Collaboration is currently constructing two neutrino detectors in the depths of the Mediterranean Sea. An excellent angular resolution will be necessary for an accurate reconstruction of neutrino direction, much as a precise knowledge of the position and orientation of the detector components will be mandatory in order to achieve the required angular resolution. For High-Energy Neutrino Astrophysics program, an angular resolution < 0.05 deg is expected for the sparser detector if synchronization ~1 ns, positioning < 20 cm, and orientation < 3 deg are guaranteed for the Detection Units. The KM3NeT orientation-tilt system, known as “Digital Compasses”, is an Attitude and Heading Reference System (AHRS) board coupled to the inner Central Logic Boards of the detection modules. The AHRS integrates a 3D-magnetometer containing an Anisotropic Magnetoresistive Sensor to estimate the Earth’s magnetic field with a 3D-accelerometer equipped with a Micro-Electro Mechanical System that estimates the acceleration field intensity. The performance of the Digital Compasses, together with the reconstruction of orientation-tilt magnitudes and calibration, will be presented and discussed in this contribution. Full article
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Proceeding Paper
Identification of Magnetic and Gravitational Field Patterns for Localization in Space
Eng. Proc. 2022, 27(1), 45; https://doi.org/10.3390/ecsa-9-13327 - 01 Nov 2022
Viewed by 157
Abstract
Establishing control over a mission to explore space is still one of the most difficult tasks. In order to achieve such mission control, we need communications into space through the transmission and reception of radio signals. To improve communication conditions, we propose a [...] Read more.
Establishing control over a mission to explore space is still one of the most difficult tasks. In order to achieve such mission control, we need communications into space through the transmission and reception of radio signals. To improve communication conditions, we propose a tracking system to locate space gadgets and transmit signals at minimum distances to reduce free space attenuation. We propose the case of a satellite sent off to the Moon or Mars, namely points where tracking devices can no longer reach them. In this paper, we discuss the methods and strategies to carry out this idea. The fingerprint of magnetic and gravitational fields can give us information to differentiate the quantity of electromagnetic waves that are received at a point in space in three dimensions. Each planet has specific characteristics, including a field around the planet, whether magnetic, electrical or otherwise, that protects its surface. The use of a spectrometer of masses allows us to identify the neighboring magnetic field as well as the compositions of celestial bodies and is a clear solution for the observation and monitoring of a planet. Additionally, the use of an oscillator is proposed to enhance the spectrometer. In conjunction with the use of a magnetometer, we can obtain an accurate measurement of the field of a celestial body, magnetic or not, and its composition. In addition, with the integration of an accelerometer, the altitude will be transformed into speed data, and to analyze its variation, we turn these data into gravitational force and define if the satellite is closer to the atmosphere of the celestial body. Attached to the sensing stage, a network of SatComs will be used to amplify the received signal and reach the ground station. Two SatComs per orbit will be positioned at specific Lagrange points of the celestial body. Full article
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Proceeding Paper
Numerical Study of a Microfluidic-Based Strain Sensor: Proof of Concept
Eng. Proc. 2022, 27(1), 46; https://doi.org/10.3390/ecsa-9-13323 - 01 Nov 2022
Viewed by 111
Abstract
This paper conducts a numerical study to prove the concept of a low-cost microfluidic-based strain sensor and investigates the key design parameters that affect the sensor sensitivity by using both theoretical and finite element models. The strain sensor is composed of an electrolyte-enabled [...] Read more.
This paper conducts a numerical study to prove the concept of a low-cost microfluidic-based strain sensor and investigates the key design parameters that affect the sensor sensitivity by using both theoretical and finite element models. The strain sensor is composed of an electrolyte-enabled microchannel integrated with a pair of interconnects and silicone-based packaging. The results show that the strain sensor has the highest sensitivity at the following chosen design parameters: the width and length of the primary microchannel are set at 0.2 mm and 20 mm, width ratio equals 2, and number of grid lines is 10. Full article
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Proceeding Paper
Prediction of Emotional Measures via Electrodermal Activity (EDA) and Electrocardiogram (ECG)
Eng. Proc. 2022, 27(1), 47; https://doi.org/10.3390/ecsa-9-13358 - 01 Nov 2022
Viewed by 185
Abstract
Affect recognition is a signal and pattern recognition problem that plays a major role in affective computing. The affective state of a person reflects their emotional state, which could be measured through the arousal and valence dimensions, as per the circumplex model. We [...] Read more.
Affect recognition is a signal and pattern recognition problem that plays a major role in affective computing. The affective state of a person reflects their emotional state, which could be measured through the arousal and valence dimensions, as per the circumplex model. We attempt to predict the arousal and valence values by exploiting the Remote Collaborative and Affective Interactions (RECOLA) data set RECOLA is a publicly available data set of spontaneous and natural interactions that represent various human emotional and social behaviours, recorded as audio, video, electrodermal activity (EDA) and electrocardiogram (ECG) biomedical signals. In this work, we focus on the biomedical signal recordings contained in RECOLA. The signals are processed, accompanied with pre-extracted features, and accordingly labelled with their corresponding arousal or valence annotations. EDA and ECG features are fused at feature-level. Ensemble regressors are then trained and tested to predict arousal and valence values. The best performance is achieved by optimizable ensemble regression, with a testing root mean squared error (RMSE) of 0.0154 for arousal and 0.0139 for valence predictions. Our solution has achieved good prediction performance for the arousal and valence measures, using EDA and ECG features. Future work will integrate visual data into the solution. Full article
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Proceeding Paper
Screening of Essential Oil Antioxidant Capacity Using Electrode Modified with Carboxylated Multi-Walled Carbon Nanotubes
Eng. Proc. 2022, 27(1), 48; https://doi.org/10.3390/ecsa-9-13181 - 01 Nov 2022
Viewed by 166
Abstract
Essential oils are of interest in analytical chemistry due to their bioactive constituents and wide application area. The voltammetric behavior of essential oils (from 15 types of plant material) at an electrode modified with carboxylated multi-walled carbon nanotubes was studied for the first [...] Read more.
Essential oils are of interest in analytical chemistry due to their bioactive constituents and wide application area. The voltammetric behavior of essential oils (from 15 types of plant material) at an electrode modified with carboxylated multi-walled carbon nanotubes was studied for the first time. Oxidation peaks at 0.0–0.75 and 0.75–1.5 V were obtained on differential pulse voltammograms in a neutral medium, caused by the electrooxidation of phenolic constituents and terpenoids. A two-step chronoamperometric method was developed for the evaluation of the antioxidant capacity of the essential oils. Screening of 37 samples of essential oils was performed. The data agree with the standard antioxidant parameters. Full article
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Proceeding Paper
Refractive Index Sensing in a Disposable Micro-Channel Provided with Integrated Reflectors Based on Laser Beam Shift
Eng. Proc. 2022, 27(1), 49; https://doi.org/10.3390/ecsa-9-13195 - 01 Nov 2022
Viewed by 166
Abstract
In this work, we present a compact micro-opto-fluidic sensing platform for the measurement of volumetric refractive index (RI) variations of ultra-low volumes of fluids with respect to a reference liquid. In the instrumental configuration, we employed a disposable plastic micro-channel, which was customized [...] Read more.
In this work, we present a compact micro-opto-fluidic sensing platform for the measurement of volumetric refractive index (RI) variations of ultra-low volumes of fluids with respect to a reference liquid. In the instrumental configuration, we employed a disposable plastic micro-channel, which was customized with integrated back and front aluminum reflectors, deposited by sputtering. The presence of the double metallization is exploited to create a zigzag guiding path for the radiation provided by a semiconductor laser diode, so that light crosses the fluid under test multiple times before reaching a 1-D Position Sensitive Detector (PSD). According to Snell law, when fluids with different RI indices fill the channel, the radiation is deflected at different angles and the output beam shifts along the channel surface. RI variations are monitored by measuring the position of the output light spot on the surface of the PSD. To validate the results, a theoretical model based on ray optics was developed to study the propagation of the radiation travelling through the fluidic channel. Experimental results showed a beam displacement per RI unit up to 3234 μm/RIU, in agreement with the prediction of the analytical model. The proposed sensing method is label-free, contactless, non-invasive, and biologically safe. Moreover, the micro-opto-fluidic sensing platform could be exploited in a wide range of applications, ranging from biology to medicine to the agri-food industry. Full article
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Proceeding Paper
Integrating Internet-of-Things (IoT) into a Cultural Game Authoring Tool: An Innovative Approach in Maker Education
Eng. Proc. 2022, 27(1), 50; https://doi.org/10.3390/ecsa-9-13371 - 01 Nov 2022
Viewed by 133
Abstract
Recently, integrating Internet-of-Things (IoT) for an interactive learning has become a topic of interest. For cultural inclusion, young individuals need to be more aware of the culturally diverse community and embrace it. However, this is not possible without proper cultural education and awareness. [...] Read more.
Recently, integrating Internet-of-Things (IoT) for an interactive learning has become a topic of interest. For cultural inclusion, young individuals need to be more aware of the culturally diverse community and embrace it. However, this is not possible without proper cultural education and awareness. With maker-based education, individuals can participate in active learning where they share their cultural heritage story. Hence, this research focuses on analyzing whether integrating IoT into a cultural game authoring tool will be an interesting and innovative approach in maker-based education for culture. Full article
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Proceeding Paper
Experiment with Cuffless Estimation of Arterial Blood Pressure from the Signal Sensed by the Optical PPG Sensor
Eng. Proc. 2022, 27(1), 51; https://doi.org/10.3390/ecsa-9-13220 - 01 Nov 2022
Viewed by 177
Abstract
The paper describes the development, testing, and verification of practical usability of the indirect cuffless method for estimation of arterial blood pressure (ABP) values from the photo-plethysmography (PPG) signal sensed by the optical PPG sensor. The proposed procedure uses time domain features (systolic/diastolic [...] Read more.
The paper describes the development, testing, and verification of practical usability of the indirect cuffless method for estimation of arterial blood pressure (ABP) values from the photo-plethysmography (PPG) signal sensed by the optical PPG sensor. The proposed procedure uses time domain features (systolic/diastolic pulse time ratios and partial areas around the pulses) extracted from the second derivative of the PPG signal. The linear regression method is next used to calculate the relation between the determined PPG wave features and the blood pressure values measured in parallel using a blood pressure monitor. ABP values are finally estimated by the inverse conversion characteristic calculated from these linear relations. Summary estimation errors obtained from first-step experiments achieve acceptable values of about 8/3% for systolic/diastolic ABPs. However, further improvements are necessary before usage of the proposed procedure. Full article
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Proceeding Paper
Light-Tunable Hybrid Organic–Inorganic Nanostructured Layers for Virtual Sensor Arrays
Eng. Proc. 2022, 27(1), 52; https://doi.org/10.3390/ecsa-9-13347 - 01 Nov 2022
Viewed by 126
Abstract
Electronic nose (EN) is the most advanced technology for the classification and identification of complex gaseous mixtures. The idea is to use a small set of low-selective sensors instead of a huge number of specific ones. The response of such an array is [...] Read more.
Electronic nose (EN) is the most advanced technology for the classification and identification of complex gaseous mixtures. The idea is to use a small set of low-selective sensors instead of a huge number of specific ones. The response of such an array is a chemical image (CI) which is a mathematical (or graphic) representation of an analyte. The ability of an EN system to distinguish different mixtures is defined by its ability to produce a unique CI. The latter is defined and limited, first of all, by the sensor’s adsorption properties. We propose the approach to increase the versatility of low-selective sensor arrays by using virtual sensors. By “virtual sensor”, in this case we mean a sensor able to change its adsorption properties in conditions of illumination. Within this scenario, we were able to successfully distinguish between homologous alcohols (methyl, ethyl, and isopropyl) using organic–inorganic nanostructured sensitive layers based on ZnO nanoparticles and phthalocyanines (Pc). The performance of quartz crystal microbalance sensors with hybrid nanostructured organic–inorganic layers is increased in comparison with single-Pc ones. Using illumination allowed us to obtain additional responses which could be considered as virtual sensors. Both factors played the decisive role in the successful discrimination of the alcohols. Full article
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Proceeding Paper
Rolling Element Bearing Faults Detection and Classification Technique Using Vibration Signals
Eng. Proc. 2022, 27(1), 53; https://doi.org/10.3390/ecsa-9-13339 - 01 Nov 2022
Viewed by 218
Abstract
Early and accurate detection of bearing faults is essential for the safe and reliable working of industrial machinery units. The main problem of the traditional fault diagnosis method is manually extracting the features which require the experimenter’s experience and expert knowledge. Therefore, the [...] Read more.
Early and accurate detection of bearing faults is essential for the safe and reliable working of industrial machinery units. The main problem of the traditional fault diagnosis method is manually extracting the features which require the experimenter’s experience and expert knowledge. Therefore, the shallow diagnostic model’s classification rate does not produce good results. To address this issue, this research proposes a technique to detect and classify bearing faults based on an effective convolutional neural network (CNN) model, which is capable of performing complex vibration signals and removing the impact of expert expertise on the feature extraction process. A time-moving segmentation window is used to segment the vibration raw signal and the segmented signals are decomposed up to two levels using DWT. After that, decomposed signals are converted into grayscale images to train and test the proposed CNN model. To verify the performance of the model, CWRU bearing dataset and MFPT dataset are used. The proposed CNN model achieves the highest accuracy in terms of performance both under different load conditions as well as under noisy situations with varying SNR values. The experimental findings show that the proposed system is effective and extremely dependable in detecting bearing faults. Full article
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Proceeding Paper
Recreating Lunar Environments by Fusion of Multimodal Data Using Machine Learning Models
Eng. Proc. 2022, 27(1), 54; https://doi.org/10.3390/ecsa-9-13326 - 01 Nov 2022
Viewed by 169
Abstract
The latest satellite infrastructure for data processing, transmission and reception can certainly be improved by upgrading tools used to deal with very large amounts of data from every different sensor incorporated within the space missions. In order to develop a better technique to [...] Read more.
The latest satellite infrastructure for data processing, transmission and reception can certainly be improved by upgrading tools used to deal with very large amounts of data from every different sensor incorporated within the space missions. In order to develop a better technique to process data, in this paper we will take an insight into multimodal data fusion using machine learning algorithms. This paper discusses how machine learning models are used to recreate environments from heterogeneous, multi-modal data sets. In particular, for those models based on neural networks, the most important difficulty is the vast number of training objects of the connected neural network based on Convolutional Neural Networks (CNN) to avoid overfitting and underfitting of the models. Full article
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Proceeding Paper
Predictive IoT Temperature Sensor
Eng. Proc. 2022, 27(1), 55; https://doi.org/10.3390/ecsa-9-13337 - 01 Nov 2022
Viewed by 189
Abstract
Temperature sensors are widely employed in control systems that maintain a required temperature in a vessel or container irrespective of the temperature changes in the outer environment. However, the limited power of the heater/cooler (the plant of the control system) might lead to [...] Read more.
Temperature sensors are widely employed in control systems that maintain a required temperature in a vessel or container irrespective of the temperature changes in the outer environment. However, the limited power of the heater/cooler (the plant of the control system) might lead to uncomfortable or even unacceptable deviations from the required temperature. This behavior can be mitigated if the control system can have access not only to the present temperature in the vessel but also to the forecasted environmental temperature. This situation occurs, among other situations, in industrial vessels that require elevated temperatures during their operation but shut down out of hours. To start heating these to the required temperature at the beginning of a working shift wastes processing time until the required temperature is reached. It is more productive to turn on heating in advance in order to get the vessel ready on time. In order to achieve fully autonomous automatic operation, the sensor should have some intelligence and access to the temperature forecast, which can be provided over the internet. Both these requirements can be met by employing a WiFi-enabled microcontroller. We present the development of a predictive IoT temperature sensor based on the ESP32 microcontroller, which uses the internet service to obtain the time and weather forecast and upload temperature logs to a cloud server for convenient remote access and storage. Full article
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Proceeding Paper
Continuous Rapid Accurate Measurement of the Output Frequency of Ultrasonic Oscillating Temperature Sensors
Eng. Proc. 2022, 27(1), 56; https://doi.org/10.3390/ecsa-9-13340 - 01 Nov 2022
Viewed by 188
Abstract
Ultrasonic oscillating temperature sensors (UOTSes) allow sensing aggregate temperatures across, for example, a complete room, and react to the temperature changes within milliseconds. However, their output frequency is to be measured with relatively high accuracy (standard crystal oscillators might be insufficient) and resolution [...] Read more.
Ultrasonic oscillating temperature sensors (UOTSes) allow sensing aggregate temperatures across, for example, a complete room, and react to the temperature changes within milliseconds. However, their output frequency is to be measured with relatively high accuracy (standard crystal oscillators might be insufficient) and resolution (down to 0.01%). For this reason, wide adoption of these sensors requires development of a robust, inexpensive and convenient way of measuring their output frequency. We tested various microcontrollers and ways of measuring frequency using built in timers. Utilising the direct memory access (DMA) mode for STM32 microcontrollers allowed recording measurements for every half period of the incoming frequency. Despite every individual measurement being inaccurate on its own, their moving average allows achieving arbitrary accuracy (at the expense of measurement latency) along with providing frequencies after every half period of the UOTS output pulses. This capability not only exceeds the needs of, say, room temperature measurement, but also provides an opportunity to study short-term variations in aggregate temperatures that can be useful for studying non-stationary heat distributions and flows. Full article
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Proceeding Paper
Channel Estimation in the Interplanetary Internet Using Deep Learning and Federated Learning
Eng. Proc. 2022, 27(1), 57; https://doi.org/10.3390/ecsa-9-13325 - 01 Nov 2022
Viewed by 174
Abstract
Intelligent signal processing holds great importance for the future of resilient, adaptable communications networks. The unique qualities of deep space require an interplanetary Internet to be highly autonomous, efficient, and adaptable to varying Quality of Service (QoS). Deep learning has shown great promise [...] Read more.
Intelligent signal processing holds great importance for the future of resilient, adaptable communications networks. The unique qualities of deep space require an interplanetary Internet to be highly autonomous, efficient, and adaptable to varying Quality of Service (QoS). Deep learning has shown great promise in the field of signal processing for being computationally efficient, capable of handling errors from nonlinear effects (e.g., hardware impairments), and handling low signal-to-noise ratios. A recent survey by Pham et al. notes that none of the papers studied the improvements in classification in the high-order modulation regime. Additionally, these papers did not explore performance of their models in resource limited environments. A hierarchical interplanetary Internet that imposes a variety of constraints on its nodes offers a unique opportunity to explore realistic tradeoffs in model performance. This paper seeks to leverage the processing, storage, and data transmission capabilities of each level of the interplanetary Internet through federated learning. This will reduce data redundancy between nodes and minimize overhead transmission costs on the network. The goals of this project are the following: (i) detail possible insights into future channel estimation techniques applied to noisy, nonlinear models; (ii) explore application of deep learning models for high-order modulation schemes; (iii) quantify the resource-demand reduction resulting from the use of a deep neural network for intelligent signal processing; and (iv) analyze the adaptability of an interdependent system of deep neural networks in the context of a centralized/decentralized federated learning network. Full article
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Proceeding Paper
Finite Element Simulation for Predicting the Magnetic Flux Density for Electromagnetic Vibration Energy Harvester
Eng. Proc. 2022, 27(1), 58; https://doi.org/10.3390/ecsa-9-13341 - 01 Nov 2022
Viewed by 162
Abstract
The current revolution in the field of electromagnetic vibration energy harvester requires that both wireless sensor nodes and relevant power sources be cost- and size-optimized while ensuring that, during design/fabrication of the sensor’s power sources, the power deliverable to the sensors be maximum. [...] Read more.
The current revolution in the field of electromagnetic vibration energy harvester requires that both wireless sensor nodes and relevant power sources be cost- and size-optimized while ensuring that, during design/fabrication of the sensor’s power sources, the power deliverable to the sensors be maximum. Flux density dependency on the nature of the magnetic coupling material of VEH magnet-coil transducer is well reported while reports on size-optimized but improved performance in the VEH is available. This paper presents on the realization of an approach to ensure an accurate prediction of size-optimized but maximum power output on the electromagnetic transducer of a VEH. The adopted approach justifiably verifies the geometrically determined flux density on a Finite Element Magnetic Method Software (FEMM) on the permanent magnet (NdFeB N52) as a basis for optimization. An empirical formula—which predicts size-optimized flux density and could be used to predict the performance of a miniature energy harvester for wireless sensor nodes application—was formulated. For the geometry presented in this work, where lc and Nc2  are the effective length and turns on the reference coil, the magnetic flux density, coupling coefficients, coil width and transducer thickness were predicted to optimize at 0.4373 T, 0.3978μ3lcNc2 Tmm, 4.00 mm and 18.40 mm, respectively, with all corresponding to instances when the flux density per unit volume on the coil was approximately 0.4373/μ3v¯c2Tmm3. The above optimized values were measured on magnet-coil geometry with the smallest overall thickness. However, in comparison to other models, the coil thickness in the optimized geometry was not the least. Full article
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Proceeding Paper
Visualisation and Analysis of Digital and Analog Temperature Sensors in PV Generator through IoT Software
Eng. Proc. 2022, 27(1), 59; https://doi.org/10.3390/ecsa-9-13283 - 01 Nov 2022
Viewed by 166
Abstract
Temperature is a critical factor for the performance and operation of photovoltaic (PV) generators, whose efficiency and electrical generation decreases as the temperature rises. For this reason, it is essential to sensor PV modules in order to continuously measure and track their operating [...] Read more.
Temperature is a critical factor for the performance and operation of photovoltaic (PV) generators, whose efficiency and electrical generation decreases as the temperature rises. For this reason, it is essential to sensor PV modules in order to continuously measure and track their operating temperature. This paper presents a network of digital temperature sensors (DS18B20) and a set of analogue sensors (PT-100) for a 1.1 kW polycrystalline PV array. The physical layout of the sensors on the modules is different depending on the nature of sensor for comparison purposes. These sensors are described in terms of their implementation and configuration, as well as the advantages and disadvantages of each installation. In addition, a model that estimates cell temperature from ambient temperature and incident solar irradiance is incorporated. Regarding data acquisition, an industrial controller and a remote input/output unit gather analogue measurements, whereas a low-cost Arduino board retrieves data from the digital sensors network. Both sensor signals are stored in a database, so experimental measurements and estimated data are visualised simultaneously using a web-based IoT software (Grafana) in real time. Finally, results under real operating conditions are reported and the data are analysed, proving the suitability between each sensor type and the model. Full article
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Proceeding Paper
A Multi-Fidelity Deep Neural Network Approach to Structural Health Monitoring
Eng. Proc. 2022, 27(1), 60; https://doi.org/10.3390/ecsa-9-13344 - 01 Nov 2022
Viewed by 166
Abstract
The structural health monitoring (SHM) of civil structures and infrastructures is becoming a crucial issue in our smart and hyper-connected age. Due to structural aging and to unexpected loading conditions, partially linked to extreme events caused by the climate change, reliable and real-time [...] Read more.
The structural health monitoring (SHM) of civil structures and infrastructures is becoming a crucial issue in our smart and hyper-connected age. Due to structural aging and to unexpected loading conditions, partially linked to extreme events caused by the climate change, reliable and real-time SHM schemes are currently facing a burst in development and applications. In this work, we propose a procedure that relies upon a surrogate modeling scheme based on a multi-fidelity (MF) deep neural network (DNN), which has been conceived to sense and identify a structural damage under operational (and possibly environmental) variability. By exploiting the sensor recordings from a densely deployed network within a fully stochastic framework, the MF-DNN model is adopted to feed a Markov chain Monte Carlo (MCMC) sampling procedure and update the probability distribution of the structural state, conditioned on noisy observations. As information regarding the health of real structures is usually rather limited, the datasets to train the MF-DNN are generated with physical (e.g., finite element) models: high-fidelity (HF) and low-fidelity (LF) models are adopted to simulate the structural response under the mentioned varying conditions, respectively, in the presence or absence of a structural damage. As far as the architecture of the DNN is concerned, the MF approach is obtained by merging a fully connected LF-DNN and a long short-term memory HF-DNN. The LF-DNN mimics the output of the sensor network in the undamaged condition, while the HF-DNN is exploited to improve the LF model and appropriately catch the structural response in the presence of a pre-defined set of damaged patterns. Thanks to the adaptive enrichment of the LF signals carried out by the MF-DNN, the proposed model updating strategy is reported capable of locating (and possibly quantifying) a damage event. Full article
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Proceeding Paper
Understanding the Behavior of Gas Sensors Using Explainable AI
Eng. Proc. 2022, 27(1), 61; https://doi.org/10.3390/ecsa-9-13350 - 01 Nov 2022
Viewed by 290
Abstract
Exposure to pollutants like ozone and nitrogen dioxide gas can cause serious health issues and harm the environment. Therefore, the interest in air quality and its impact on health and well-being has been steadily increasing over the years, making low-cost gas sensing devices [...] Read more.
Exposure to pollutants like ozone and nitrogen dioxide gas can cause serious health issues and harm the environment. Therefore, the interest in air quality and its impact on health and well-being has been steadily increasing over the years, making low-cost gas sensing devices combined with artificial intelligence (AI) increasingly popular due to their flexibility and small form factor. While AI provides state-of-the-art performance, it makes the system less transparent and more difficult to trust its decisions. With the aid of three different approaches, this paper seeks to understand and explain the predictions made by complex models for gas sensors. The use of such techniques can increase our confidence in the AI systems embedded in our products in terms of fairness, or impartiality, and robustness, or reliability. This also improves our understanding of sensor behavior and provides a more robust explanation for algorithmic choices. Full article
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Proceeding Paper
Composites of Functionalized Multi-Walled Carbon Nanotube and Sodium Alginate for Tactile Sensing Applications
Eng. Proc. 2022, 27(1), 62; https://doi.org/10.3390/ecsa-9-13349 - 01 Nov 2022
Viewed by 187
Abstract
Flexible–tactile sensors are predicted to soon be extensively used in wearable devices. Various materials in flexible-sensor fabrication offer sensing properties with multiple capabilities. There is a crucial research opportunity in the field of flexible–tactile sensors for these materials, including nanocomposites. While the nanocomposites’ [...] Read more.
Flexible–tactile sensors are predicted to soon be extensively used in wearable devices. Various materials in flexible-sensor fabrication offer sensing properties with multiple capabilities. There is a crucial research opportunity in the field of flexible–tactile sensors for these materials, including nanocomposites. While the nanocomposites’ electrical properties mainly depend on nanofillers, the mechanical properties are determined by their polymer components. Carbon nanotubes (CNTs) are one of the most promising materials among nanofillers due to their high electrical conductivity, thermal stability, and durability. However, CNTs should be processed to increase the binding capacity of the polymer structure. In this study, the nanocomposite used for sensor manufacturing consisted of acid-functionalized CNTs and sodium alginate as the nanofiller and the polymer material, respectively. The sensor material was cross-linked using calcium chloride and glycerin was involved in the sensor fabrication to test its effect on sensing and flexibility. It is critical to note that sodium alginate and glycerin are biocompatible and biodegradable substances. In the scope of this study, the impedance changes of the fabricated tactile sensors were examined in the 100 Hz–10 MHz frequency range and equivalent circuits of the sensors were created. Additionally, impedance changes were obtained when alternating forces were applied to the sensors. The results showed that the frequency responses of the sensors differed from each other in different frequency ranges. In addition, each sensor had different sensing mechanisms in specific frequency ranges and the sensor made with glycerin had higher flexibility but less sensitivity. Full article
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Proceeding Paper
The Interplanetary Internet for Observation and Monitoring of the Solar System
Eng. Proc. 2022, 27(1), 63; https://doi.org/10.3390/ecsa-9-13328 - 01 Nov 2022
Viewed by 192
Abstract
The solar system is still uncommunicated and unknown for humankind. To acquire more knowledge about the solar system, we send satellites and rovers to explore those planets; however, it is costly and takes a lot of effort. Soil retains information about the environment [...] Read more.
The solar system is still uncommunicated and unknown for humankind. To acquire more knowledge about the solar system, we send satellites and rovers to explore those planets; however, it is costly and takes a lot of effort. Soil retains information about the environment of celestial bodies, and we can process that information to make decisions about future infrastructure settlements that could provide advantages for the interplanetary Internet. The interplanetary Internet communications must be scalable, interoperable, secure, and easy for data transmission. However, before thinking about carrying out soil analysis through surface exploration, we can see that the first step is to analyze it using sensing satellites studying the structure of their data collection orbits through intelligent vision. In this paper we propose the use of cameras mounted on sensing satellites for the soil analysis during orbit (high-resolution, infrared, spectral, optical) for general scanning of surface elements with AI postprocessing, and mass spectrometer for spectroscopy. This equipment will analyze the chemical composition of the surfaces, the magnetic field lines, and the material radiation, detect rocks and gas elements, and identify the surface characteristics, among others. In this paper, we discuss how to develop the architecture of an interplanetary Internet physical platform with space-to-ground observations and measurements. A satellite orbiting a celestial body will become a sensor node with physical layers designed with relays and a modular setup, as well as a data transport method and location estimation sensing system, as a basis for the interplanetary Internet system. The design of the interplanetary Internet must consider the information from the analysis and observation of celestial bodies’ variables and parameters, as a fundamental flow of information that must be transported through the network to be further analyzed and used. Full article
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Proceeding Paper
Air Temperature Measurement Using CMOS-SOI-MEMS Sensor Dubbed Digital TMOS
Eng. Proc. 2022, 27(1), 64; https://doi.org/10.3390/ecsa-9-13224 - 01 Nov 2022
Viewed by 212
Abstract
Air temperature is an important meteorological parameter and is used for numerous purposes. Air temperature is usually observed using a radiation shield with ventilation, to obtain proper measurements by providing shade from direct solar radiation and increasing the heat exchange between the sensor [...] Read more.
Air temperature is an important meteorological parameter and is used for numerous purposes. Air temperature is usually observed using a radiation shield with ventilation, to obtain proper measurements by providing shade from direct solar radiation and increasing the heat exchange between the sensor and the atmosphere. In rural areas, such auxiliary equipment is not available and it is still a challenge to obtain the air temperature accurately without aspiration. In this study, we describe a novel, qualified, complementary metal-oxide-semiconductor-Microelectromechanical systems (CMOS-MEMS) low-cost sensor, dubbed Digital Thermal-MOS (TMOS), for remote temperature sensing of air temperature. The novel key ideas of this study are (i) the use of the Digital TMOS, (ii) a narrow optical bandpass filter (4.26 um +/− 90 nm) corresponding to the CO2 carbon dioxide absorption band; (iii) simultaneously measuring the weather parameters. Full article
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Proceeding Paper
Morphometric Analysis of Suswa River Basin Using Geospatial Techniques
Eng. Proc. 2022, 27(1), 65; https://doi.org/10.3390/ecsa-9-13225 - 01 Nov 2022
Viewed by 179
Abstract
Analysing the morphological features of the drainage basin helps to understand its hydrological characteristics and the association of water, with soil, topography, and vegetation of the catchment. Morphometric analysis reveals the linear, areal, and relief aspects of a drainage basin. In this study, [...] Read more.
Analysing the morphological features of the drainage basin helps to understand its hydrological characteristics and the association of water, with soil, topography, and vegetation of the catchment. Morphometric analysis reveals the linear, areal, and relief aspects of a drainage basin. In this study, morphometric analysis has been performed using geospatial techniques to evaluate the hydrological characteristics of the Suswa River basin. The SRTM (Shutter Radar Topography Mission) DEM at 30 m resolution has been used to delineate the basin and drainage network in the Arc GIS Software with the help of Spatial Analysis Tools. From this research, we have derived that the basin is having sub-dendritic to dendritic drainage pattern, and the average drainage density of the basin is 2.84 km/km2. The elongation ratio of the Suswa River basin is 0.46 which implies that the basin is elongated in shape with moderate relief. This study concludes that morphometric analysis based on GIS & remote sensing techniques is a competent tool for hydrological studies. The present study would be beneficial to various managers and decision-makers for the organization working on watershed management and sustainable natural resources management. Full article
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Proceeding Paper
A Bluetooth 5 Opportunistic Edge Computing System for Vehicular Scenarios
Eng. Proc. 2022, 27(1), 66; https://doi.org/10.3390/ecsa-9-13214 - 01 Nov 2022
Viewed by 208
Abstract
The limitations of many IoT devices in terms of storage, computing power and energy consumption require them to be connected to other devices when performing computationally intensive tasks, as happens with IoT systems based on edge computing architectures. However, the lack of wireless [...] Read more.
The limitations of many IoT devices in terms of storage, computing power and energy consumption require them to be connected to other devices when performing computationally intensive tasks, as happens with IoT systems based on edge computing architectures. However, the lack of wireless connectivity in the places where IoT nodes are deployed or through which they move is still a problem. One of the solutions to mitigate this problem involves using opportunistic networks, which provide connectivity and processing resources efficiently while reducing the communications traffic with remote clouds. Thus, opportunistic networks are helpful in situations when wireless communication coverage is not available, as occurs in certain rural areas, during natural disasters, in wars or when other factors cause network disruptions, as well as in other IoT scenarios in which the cloud becomes saturated (for example, due to an excessive amount of concurrent communications or when denial-of-service (DoS) attacks occur). This article presents the design and initial validation of a novel opportunistic edge computing (OEC) system based on Bluetooth 5 and the use of low-cost single-board computers (SBCs). After describing the proposed OEC system, experimental results are presented for a real opportunistic vehicular IoT scenario. Specifically, the latency and packet loss are measured thanks to the use of an experimental testbed made of two separate IoT networks (each conformed by an IoT node and an OEC gateway): one located in a remote office and another one inside a moving vehicle, which was driven at different vehicular speeds. The obtained results show average latencies ranging from 716 to 955 ms with packet losses between 7% and 27%. As a result, the developed system is useful for providing opportunistic services to moving IoT nodes with relatively low latency requirements. Full article
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Proceeding Paper
Cobalt Detection Using Fluorescent Dye Layers
Eng. Proc. 2022, 27(1), 67; https://doi.org/10.3390/ecsa-9-13217 - 01 Nov 2022
Viewed by 169
Abstract
In this paper, we report the preliminary results regarding the use of fluorescent dye calcein (C30H26N2O13) as a sensor for the detection of cobalt levels in aqueous solutions. The sensor cell based on calcein is [...] Read more.
In this paper, we report the preliminary results regarding the use of fluorescent dye calcein (C30H26N2O13) as a sensor for the detection of cobalt levels in aqueous solutions. The sensor cell based on calcein is built by fixed-in layers by means of thermoplastic polyurethane (TPU) and adjusted to pH = 7. The layer shows a fluorescence emission in the range of λ = 545 nm to 570 nm when it is excited by optical fields at a wavelength centered at 465 nm. By the contact of different cobalt concentrations with the calcein layer structure, quenching of the fluorescence intensity is observed. The results indicate that the sensor exhibits a linear response of the fluorescence quenching related to the cobalt concentration level in the range of 10−5 to 10−3 mol/L. Additionally, the proposed sensor has a simple experimental set-up, low cost, and does not require additional complex instrumentation. Full article
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Proceeding Paper
Satellite Requirements for Observation of Close Proximity Celestial Bodies
Eng. Proc. 2022, 27(1), 68; https://doi.org/10.3390/ecsa-9-13329 - 01 Nov 2022
Viewed by 202
Abstract
Celestial bodies of our solar system remain as a major unexplored and unexploited reserve of natural resources available to humans. Furthermore, those constitute a valuable source of information about the origins and evolution of the solar system and an alternative to establish human [...] Read more.
Celestial bodies of our solar system remain as a major unexplored and unexploited reserve of natural resources available to humans. Furthermore, those constitute a valuable source of information about the origins and evolution of the solar system and an alternative to establish human settlements in the future. Observation and understanding of the land conditions of those celestial bodies is vital to learn more about those celestial bodies, to generate accurate maps of them, to look for natural resources of interest, and to evaluate the feasibility and help in the preparation of future land missions. A satellite constellation constitutes an important infrastructure element to observe those celestial bodies and to transmit the retrieved information back to Earth. Nonetheless, the operation of sensing satellites in other planets needs understanding of the requirements to perform such observations. In this paper, we discuss those sensing requirements from the point of view of orbits and payload requirements for one of our closest neighbors of the solar system (Moon, Mars). To analyze the orbit of the sensing satellite, we discuss the required altitude to facilitate ground observation, the orbit’s conditions (such as radiation levels and orbital perturbations, among others), suitable orbit configurations, required number of satellites, and ways to estimate the required time to perform full observation of the celestial body. To evaluate suitable payloads, we discuss available information in the literature (such as known atmospheric and land conditions) to determine the best observation frequencies and determine the best kind of payload (such as sensors, a camera, or a lower frequency observation payload) to study that celestial body. Finally, we discuss some important considerations such as the requirements of satellite communication link to transmit the retrieved information back to Earth. Full article
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Proceeding Paper
Arduino-Based Sensing Platform for Rapid, Low-Cost, and High-Sensitivity Detection and Quantification of Analytes in Fluidic Samples
Eng. Proc. 2022, 27(1), 69; https://doi.org/10.3390/ecsa-9-13277 - 01 Nov 2022
Viewed by 244
Abstract
Lateral flow assays (LFAs; aka. rapid tests) are inexpensive paper-based devices for rapid and specific detection of analyte of interest (e.g., COVID virus) in fluidic samples. Areas of application of LFAs cover a broad spectrum, spanning from agriculture to food/water safety to point-of-care [...] Read more.
Lateral flow assays (LFAs; aka. rapid tests) are inexpensive paper-based devices for rapid and specific detection of analyte of interest (e.g., COVID virus) in fluidic samples. Areas of application of LFAs cover a broad spectrum, spanning from agriculture to food/water safety to point-of-care medical testing and, most recently, to detection of COVID-19 infection. While these low-cost and rapid tests are specific to the target analyte, their sensitivity and limit of detection are far inferior to their laboratory-based counterparts. In addition, rapid tests normally cannot quantify the concentration of target analyte and only provide qualitative/binary detection. We have developed a low-cost, end-user sensing platform that significantly improves the sensitivity of rapid tests. The developed platform is based on Arduino and utilizes low-cost far infrared, single-element detectors to offer sensitive and semi-quantitative results from commercially available rapid tests. The sensing paradigm integrated to the low-cost device is based on radiometric detection of photothermal responses of rapid tests in the frequency domain when exposed to modulated laser excitation. As a proof of principle, we studied commercially available rapid tests for detection of THC (the principal psychoactive constituent of cannabis) in oral fluid with different concentrations of control positive solutions and, subsequently, interpret them with the developed sensor. Results suggest that the developed end-user sensor is not only able to improve the detection limit of the rapid test by approximately an order of magnitude from 25 ng/mL to 5 ng/mL, but also offers the ability to obtain semi-quantitative insight into concentration of THC in the fluidic samples. Full article
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Proceeding Paper
FPGA Implementation of ECG Signal Processing for Use in a Neonatal Heart Rate Monitoring System
Eng. Proc. 2022, 27(1), 70; https://doi.org/10.3390/ecsa-9-13258 - 01 Nov 2022
Viewed by 187
Abstract
A field-programmable gate array (FPGA) based system for digital filtering in a neonatal heart rate monitoring system is presented. The system employs electric potential sensors (EPS) and contains a single hardware filter stage for antialiasing. The remaining digital signal processing required to provide [...] Read more.
A field-programmable gate array (FPGA) based system for digital filtering in a neonatal heart rate monitoring system is presented. The system employs electric potential sensors (EPS) and contains a single hardware filter stage for antialiasing. The remaining digital signal processing required to provide a clinical standard electrocardiogram (ECG) is performed on the FPGA (myRIO 1900, National Instruments Corporation of Austin, Austin, TX, USA). This is compared with a previous microprocessor version (Raspberry Pi 3, BCM2837 processor, Raspberry Pi Ltd, Cambridge, UK) containing a dual hardware/software filtering scheme, with the aim of simplifying the analog front end and allowing for reconfigurable filtering in the digital domain. A custom neonate phantom was employed to emulate real world conditions and ambient noise. The developed FPGA system was shown to have a signal quality comparable with the microprocessor implementation, with an average signal-to-noise ratio loss of 2%. A 12 dB increase in the attenuation of the predominant 50 Hz noise was shown, indicating filter effectiveness gains. The phantom was used to broadcast data from the preterm infant cardio-respiratory signals database (PICSDB) and the FPGA filtering scheme was shown to remove the majority of the ambient 50 Hz noise with an average reduction of 30 dB, and provided a clean ECG signal. These results demonstrate that FPGA-filtered EPS ECGs have comparable signal quality to the combined HW/SW filtering implementation, with a reduction in complexity and power consumption. Full article
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Proceeding Paper
Validation of the Polar H10 Accelerometer in a Sports-Based Environment
Eng. Proc. 2022, 27(1), 71; https://doi.org/10.3390/ecsa-9-13346 - 01 Nov 2022
Viewed by 186
Abstract
The Polar H10 is a low-cost wearable with a heart rate monitor and tri-axial accelerometer with potential for many applications. While the device’s heart rate monitor has been widely studied, there is no research validating the accelerometer specifically. The purpose of this study [...] Read more.
The Polar H10 is a low-cost wearable with a heart rate monitor and tri-axial accelerometer with potential for many applications. While the device’s heart rate monitor has been widely studied, there is no research validating the accelerometer specifically. The purpose of this study was to conduct a validation of the Polar H10 accelerometer to establish static and dynamic validity during a sports-based task. Static validity was determined by computing the relative error when using a level guide to hold each axis of the Polar H10 against gravity. Fifteen healthy adults (8F/7M) participated in sports-based tasks while wearing the Polar H10 (Polar Electro, Kempele, Finland) and a comparison device, the MetaMotionR inertial measurement unit (MbientLab Inc., San Francisco, CA, USA). Dynamic validity was characterized using Pearson’s correlation coefficient and root mean square error (RMSE). Additionally, common features in human activity recognition (mean magnitude, root mean square, power, and signal magnitude area) were computed in 2 s windows and compared via RMSE and Wilcoxon rank sum tests. When held against gravity, the Polar H10 had relative errors ranging from 2.620% to 4.288%, suggesting high static validity. During sports-based tasks, the accelerometers had correlations between 0.888 and 0.954, indicating sufficient concurrent validity for all axes, as well as acceleration magnitude. The differences in acceleration features were minimal (RMSE for mean, root mean square, power, and signal magnitude were 0.003 G, 0.004 G, 0.112 G, and 0.017 G, respectively), but all reached significance (p < 0.001). These results provide evidence for the use of the Polar H10 accelerometer to measure movement during sport-like activities. Full article
Proceeding Paper
VNetOS: Virtualised Distributed and Parallel Sensor Network Operating Environment for the IoT and SHM
Eng. Proc. 2022, 27(1), 72; https://doi.org/10.3390/ecsa-9-13212 - 01 Nov 2022
Viewed by 164
Abstract
Dealing with distributed and parallel computing in strong heterogeneous environments, e.g., distributed sensor networks, is still a challenge at the algorithmic, communication, and application levels. Heterogeneity is related to different computer and network (communication) architectures. Virtualization can hide and unify heterogeneity. In addition [...] Read more.
Dealing with distributed and parallel computing in strong heterogeneous environments, e.g., distributed sensor networks, is still a challenge at the algorithmic, communication, and application levels. Heterogeneity is related to different computer and network (communication) architectures. Virtualization can hide and unify heterogeneity. In addition to interprocess communication and synchronization, the unified access and monitoring of computing nodes (devices, computers, processors) is required to handle distributed and parallel systems in a comfortable and easy-to-access manner. Especially in education, the access to and control of a large set of computing nodes are difficult, which lowers the learning curve significantly. In this work, a unified distributed and parallel framework and Web tools are introduced using virtual machines (VM) and Web browsers to control them. The framework enables the control, monitoring, and study of distributed-parallel systems, especially addressing sensor networks and IoT networks. Nodes can be arranged in a graphical drawing world or be script-based. Virtual network nodes are assigned to VM instances that can be created inside the browser using Web worker processes or can be attached to externally running VM instances via a Web control API. New VM instances or processes can be started and controlled instantly. The graphical UI provides access to the internal and external nodes, programming editors, and monitor shells. The VMs can be generic, but in this work, there is a focus on JavaScript and Lua. The framework provides augmented virtuality, i.e., a coupling of physical and virtual worlds. Full article
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Proceeding Paper
LoRaWAN Coverage Analysis in the Transportation Sector: A Real-World Approach
Eng. Proc. 2022, 27(1), 73; https://doi.org/10.3390/ecsa-9-13321 - 01 Nov 2022
Viewed by 209
Abstract
In this paper, the LoRaWAN network coverage provided by many gateways across Germany is evaluated mainly concerning the transportation sector (German rail routes). For this purpose, related works are first analyzed. Based on this, the objective of the measurement is concretized, and a [...] Read more.
In this paper, the LoRaWAN network coverage provided by many gateways across Germany is evaluated mainly concerning the transportation sector (German rail routes). For this purpose, related works are first analyzed. Based on this, the objective of the measurement is concretized, and a microcontroller measurement module is prototypically developed and built. Along a previously defined route, the measurement module continuously attempts to send GNSS data via LoRaWAN. The received data will be stored and evaluated, including metadata such as RSSI. The evaluation is not only concerned with network coverage, but also with some considerations of possible use cases in the transportation sector. Full article
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Proceeding Paper
A Capacitive Biosensor for the Early Detection of Pancreatic Cancer Using Carbohydrate Antigen 19-9
Eng. Proc. 2022, 27(1), 74; https://doi.org/10.3390/ecsa-9-13322 - 01 Nov 2022
Viewed by 157
Abstract
Pancreatic cancer has one of the highest cancer mortality rates, as it is often detected in late stages, when unresectable tumours are present. Researchers have identified a biomarker associated with the early detection of pancreatic cancer, called Carbohydrate Antigen 19-9 (CA19-9)—researchers have recommended [...] Read more.
Pancreatic cancer has one of the highest cancer mortality rates, as it is often detected in late stages, when unresectable tumours are present. Researchers have identified a biomarker associated with the early detection of pancreatic cancer, called Carbohydrate Antigen 19-9 (CA19-9)—researchers have recommended it for pancreatic cancer screening, and for the monitoring of the efficacy of pancreatic cancer treatments. The development of a biosensor for the detection of CA19-9 is discussed in this paper. The biosensor uses capacitive spectroscopy on gold interdigitated electrodes. This electrochemical transducer mechanism was selected as appropriate due to its increased popularity in point-of-care applications. Mouse monoclonal anti-CA19-9 antibodies were covalently bound to the gold surface using cysteamine hydrochloride and glutaraldehyde, and immobilization was verified with a Zeiss AxioObserver fluorescence microscope. Next, the antigen was prepared in different concentrations, and added to the prepared electrodes. Impedance spectroscopy was carried out using the PalmSens4 Electrochemical Interface, where five different concentrations of CA19-9 were detected in this process. The concentrations ranged from 10 U/mL to 300 U/mL, which includes the threshold concentration of CA19-9 for the detection of pancreatic cancer, of 37 U/mL. This biosensor is, therefore, suited to detect the CA19-9 concentrations needed for pancreatic cancer screening. Full article
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Proceeding Paper
Conscious Walk Methodology Design for Acoustic, Air Quality and Biodiversity Evaluation in Urban Environments
Eng. Proc. 2022, 27(1), 75; https://doi.org/10.3390/ecsa-9-13336 - 01 Nov 2022
Viewed by 256
Abstract
Environmental noise and air pollution, as well as poor green infrastructure quality, are major concerns for the European population due to their impacts on citizens’ health, especially for those citizens living in urban environments, which materializes in a rising number of complaints to [...] Read more.
Environmental noise and air pollution, as well as poor green infrastructure quality, are major concerns for the European population due to their impacts on citizens’ health, especially for those citizens living in urban environments, which materializes in a rising number of complaints to public administration. This issue is further stressed for urban areas located close to aggressive sources of such pollutants, such as airports, railways, highways, or leisure areas. To attend to this situation from the viewpoint of citizens’ everyday lives, this paper proposes a hybrid methodology in the form of a collective campaign in which citizens, especially those from environments that have a stronger impact, cooperate with scientists to collect high quality acoustic, chemical, and biodiversity data. The campaign consists of a conscious walk that considers acoustic measurements conducted by both experts and citizens, coupled with air quality measurements and biodiversity descriptions. The final goal of the method is to obtain subjective and objective data on the soundscape, air quality, and biodiversity in order to evaluate a pre-designed route in an urban location, namely, in the surroundings of Parc de la Ciutadella, Barcelona, Spain. Full article
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Proceeding Paper
Unsupervised and Computationally Lightweight Spectrum Sensing in IoT Devices
Eng. Proc. 2022, 27(1), 76; https://doi.org/10.3390/ecsa-9-13159 - 01 Nov 2022
Viewed by 129
Abstract
Principal component analysis (PCA) is a widespread technique in data analysis. Recently, the L1-norm has been proposed as an alternative criterion to classical L2-norm in PCA due to its greater robustness to outliers. The present work shows that, with a whitening step, L1-PCA [...] Read more.
Principal component analysis (PCA) is a widespread technique in data analysis. Recently, the L1-norm has been proposed as an alternative criterion to classical L2-norm in PCA due to its greater robustness to outliers. The present work shows that, with a whitening step, L1-PCA can perform spectrum sensing and modulation recognition in IoT applications. Numerical experiments confirm this finding. Full article
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Proceeding Paper
Polarimetric Distortion Analysis of L- and S-Band Airborne SAR (LS-ASAR): A Precursor Study of the Spaceborne Dual-Frequency L- and S-Band NASA ISRO Synthetic Aperture Radar (NISAR) Mission
Eng. Proc. 2022, 27(1), 77; https://doi.org/10.3390/ecsa-9-13186 - 01 Nov 2022
Viewed by 165
Abstract
The polarimetric calibration (PolCal) is an essential process to ensure the minimization of distortions from airborne and spaceborne SAR data for scattering-based characterization of the targeted objects. The present study investigates the polarimetric distortions in the L-and S-band airborne dual-frequency SAR data. The [...] Read more.
The polarimetric calibration (PolCal) is an essential process to ensure the minimization of distortions from airborne and spaceborne SAR data for scattering-based characterization of the targeted objects. The present study investigates the polarimetric distortions in the L-and S-band airborne dual-frequency SAR data. The L- and S-band airborne SAR (LS-ASAR) is a precursor mission of the spaceborne dual-frequency L- and S-band NASA ISRO Synthetic Aperture Radar (NISAR). The present work utilizes the LS-ASAR data acquired over the Rosamond Corner Reflector Array (RCRA). The polarimetric signature analysis of co-pol and cross-pol channels shows that perfect behavior is shown by the co-pol signature but the distortions could be easily identified in the cross-pol signatures. Full article
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Proceeding Paper
Chlorophyll Estimation from Multivariate Regression Analysis and Deep Learning Using Remote Sensing Data
Eng. Proc. 2022, 27(1), 78; https://doi.org/10.3390/ecsa-9-13319 - 01 Nov 2022
Viewed by 125
Abstract
The Orinoco river is in Venezuela and flows into the Caribbean sea. The chlorophyll concentration in the ocean delta changes due to the dust deposition from the Orinoco river which affects the primary productivity. The wet and dry deposition measurements were obtained from [...] Read more.
The Orinoco river is in Venezuela and flows into the Caribbean sea. The chlorophyll concentration in the ocean delta changes due to the dust deposition from the Orinoco river which affects the primary productivity. The wet and dry deposition measurements were obtained from Modern-Era Retrospective analysis for Research and Applications (MERRA) a NASA climate reanalysis of meteorology, atmospheric chemistry, land, ocean, and aerosols data on a broad range of weather and climate timescales and places. Researchers were not sure how wet and dry deposition from the Orinoco river has affected the chlorophyll concentration in the ocean. Aerosol optical depth (AOD), dry and wet deposition data were obtained from MERRA. Altimetry data of the Orinoco river and chlorophyll concentration data were also obtained from the Giovanni database from 2016 to March 2022. Linear regression analysis of altimetry and chlorophyll concentration showed that the latter did not depend on the water levels. Univariate models for each of the parameters of AOD, wet, and dry deposition were done. Bivariate models were done, adding one additional variable at a time, and finally a multivariate model was built for the prediction of chlorophyll concentration. From the analysis, it was seen that the multivariate models have a higher correlation between chlorophyll and the independent variables. Of all the variables, wet deposition is a better predictor of chlorophyll concentration. A deep learning neural network architecture is developed for performing the forecasting of chlorophyll concentration from past values. Full article
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Proceeding Paper
Assessment of FABDEM on the Different Types of Topographic Regions in India Using Differential GPS Data
Eng. Proc. 2022, 27(1), 79; https://doi.org/10.3390/ecsa-9-13368 - 01 Nov 2022
Viewed by 199
Abstract
The Forest and Buildings removed Copernicus DEM (FABDEM) represents a global DEM generated through the elimination of height biases arising due to buildings and trees in the Copernicus global 30 m (GLO-30) digital elevation model (DEM). Copernicus GLO-30 DEM is a digital surface [...] Read more.
The Forest and Buildings removed Copernicus DEM (FABDEM) represents a global DEM generated through the elimination of height biases arising due to buildings and trees in the Copernicus global 30 m (GLO-30) digital elevation model (DEM). Copernicus GLO-30 DEM is a digital surface model (DSM) generated from edited DEM called WorldDEM, which in itself is a product generated from SAR Interferometry (InSAR)-based TanDEM-X DEM. It has the potential to be used as a digital terrain model (DTM) for many applications, such as in engineering, environmental, and hydrological studies. The current experiment evaluates the accuracy of FABDEM using ground control points (GCPs) collected through a Differential GPS (DGPS) survey at the three experimental sites in India, namely, the Dehradun site in Uttarakhand, Jaipur site in Rajasthan, and Kendrapara site in Odisha. The selected three experimental sites represent varied topographic conditions in the Indian region. The FABDEM heights are converted into WGS84 heights using geoidal undulations (N) as per the Earth Gravitational Model-EGM2008, which is the vertical datum for FABDEM. Statistical measures such as MAE, RMSE, and LE90 are used to assess the accuracies of FABDEM. The RMSE computed for FABDEM in the sites at Dehradun, Jaipur, and Kendrapara are 5.96 m, 2.77 m, and 4.29 m, respectively. The study thus reveals that the FABDEM has relatively high accuracy in the experimental sites at Jaipur and Dehradun, considering their topography. However, the accuracy was found to be relatively low in the alluvial plains of the Kendrapara site. Full article
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Proceeding Paper
Effect of Strain on Properties of Metal Doped VO2 Based Thermal Sensors on Muscovite Substrate
Eng. Proc. 2022, 27(1), 80; https://doi.org/10.3390/ecsa-9-13320 - 01 Nov 2022
Viewed by 181
Abstract
In this work, VO2 based thermal sensing thin film synthesized on flexible muscovite substrates by direct oxidation of deposited vanadium metal, were investigated for the impact of doping and strain on their electrical properties. We investigated both undoped and Ti doped VO [...] Read more.
In this work, VO2 based thermal sensing thin film synthesized on flexible muscovite substrates by direct oxidation of deposited vanadium metal, were investigated for the impact of doping and strain on their electrical properties. We investigated both undoped and Ti doped VO2 on muscovite substrate and compared with those on Quartz substrate. Both doped and undoped VO2 were found to undergo phase transition due to effect of heat as well as mechanical strain on muscovite substrate. On the other hand, the Ti doped VO2, on both quartz and muscovite substrate showed significant reduction in the transition temperature compared to the undoped VO2 thin films on these two substrates. When subjected to mechanical strain, the VO2 thin film on muscovite substrates resulted in a decrease or an increase in resistance depending on whether the applied strain was tensile or compressive, respectively. The resistance change was also steeper around the transition temperature compared to room temperature, exhibiting high gauge factor. This metal doped VO2 on flexible muscovite substrate has the significantly low transition temperature which causes the VO2 film to undergo phase transition at a near-room temperature and enables it to be used as a temperature sensor with enhanced sensitivity. Full article
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Proceeding Paper
CMOS-MEMS Gas Sensor Dubbed GMOS for SelectiveAnalysis of Gases with Tiny Edge Machine Learning
Eng. Proc. 2022, 27(1), 81; https://doi.org/10.3390/ecsa-9-13316 - 01 Nov 2022
Viewed by 175
Abstract
Embedded machine learning, TinyML, is a relatively new and fast-growing field of ML, enabling on-device sensor data analytics at low power requirements. This paper presents possible improvements to GMOS, a gas sensor, using TinyML technology. GMOS is a low-cost catalytic gas sensor, fabricated [...] Read more.
Embedded machine learning, TinyML, is a relatively new and fast-growing field of ML, enabling on-device sensor data analytics at low power requirements. This paper presents possible improvements to GMOS, a gas sensor, using TinyML technology. GMOS is a low-cost catalytic gas sensor, fabricated with the standard CMOS-SOI process, based on a suspended thermal transistor MOS (TMOS). Exothermic combustion reactions lead to temperature increases, which modify the suspended transistor’s (used as the sensing element) current-voltage characteristics. We were able to use GMOS measurements for gas classification (both for gas types, as well as concentration), resulting in high-proficiency gas detection at a low cost. Our preliminary results show great successes in the detection of ethanol and acetone gases. Moreover, we believe the method could be generalized to more gas types, concentrations, and gas mixes in future research. Full article
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Proceeding Paper
Robust Underwater Image Classification Using Image Segmentation, CNN, and Dynamic ROI Approximation
Eng. Proc. 2022, 27(1), 82; https://doi.org/10.3390/ecsa-9-13218 - 01 Nov 2022
Viewed by 126
Abstract
Finding classified rectangular regions of interest (ROIs) in underwater images is still a challenge, and more so if the images pose low quality with respect to illumination conditions, sharpness, and noise. These ROIs can help humans find relevant regions in the image quickly [...] Read more.
Finding classified rectangular regions of interest (ROIs) in underwater images is still a challenge, and more so if the images pose low quality with respect to illumination conditions, sharpness, and noise. These ROIs can help humans find relevant regions in the image quickly or they can be used as input for automated structural health monitoring (SHM). This task itself should be conducted automatically, e.g., used for underwater inspection. Underwater inspections of technical structures, e.g., piles of a sea mill energy harvester, typically aim to find material changes in the construction, e.g., rust or pockmark coverage, to make decisions about repair and to assess the operational safety. We propose and evaluate a hybrid approach with segmented classification using small-scaled CNN classifiers (with fewer than 20,000 hyperparameters and 3M unity vector operations) and a reconstruction of labelled ROIs by using an iterative mean and expandable bounding box algorithm. The iterative bounding box algorithm combined with bounding box overlap checking suppressed wrong spurious segment classifications and represented the best and most accurate matching ROI for a specific classification label, e.g., surfaces with pockmark coverage. The overall classification accuracy (true-positive classification) with respect to a single segment is about 70%, but with respect to the iteratively expanded ROI bounding boxes, it is about 90%. Full article
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Proceeding Paper
Time-Domain Analysis of Acoustic Emission Signals during the First Layer Manufacturing in FFF Process
Eng. Proc. 2022, 27(1), 83; https://doi.org/10.3390/ecsa-9-13285 - 01 Nov 2022
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Abstract
Additive manufacturing (AM) has been playing a crucial role in the fourth industrial revolution. Sensor-based monitoring technologies are essential for detecting defects and providing feedback for process control. Acoustic emission (AE) sensors have been used for a long time in a wide range [...] Read more.
Additive manufacturing (AM) has been playing a crucial role in the fourth industrial revolution. Sensor-based monitoring technologies are essential for detecting defects and providing feedback for process control. Acoustic emission (AE) sensors have been used for a long time in a wide range of processes and fields, but they are still a challenge in AM processes. This work presents a study on the AE signals in the time-domain—raw and root mean square (RMS) values—regarding their behavior during the manufacture of a single-layer part in the fused filament fabrication process for two infill patterns. The tests were conducted on a cartesian 3D printer using polylactic acid material. The AE sensor was attached to the printer table through a magnetic coupling, and the signal was collected by an oscilloscope at 1 MHz sampling frequency. It was found that the raw AE signals behaved quite differently not just for the two infill patterns, but within the same pattern. The raw and RMS AE signals contained many spikes along the whole process, but the higher ones were those generally occurring at the end and/or start of a fabrication line. The RMS values, however, were useful for finding the start and end times of each fabricated line for both patterns. The mean RMS values showed nearly constant but distinct averages for the extruder-only, table-only and extruder–table movements. Full article
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Proceeding Paper
Assessment and Monitoring of Optically Active Water Quality Parameters on Wetland Ecosystems Based on Remote Sensing Approach: A Case Study on Harike and Keshopur Wetland over Punjab Region, India
Eng. Proc. 2022, 27(1), 84; https://doi.org/10.3390/ecsa-9-13361 - 01 Nov 2022
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Abstract
Wetlands play a vital role in sustainable ecological development. They hold balanced environment conditions, filter the surface and sub-surface water, and moderate the local weather condition. The current study is mainly focused on assessment and monitoring of optically active water quality parameters on [...] Read more.
Wetlands play a vital role in sustainable ecological development. They hold balanced environment conditions, filter the surface and sub-surface water, and moderate the local weather condition. The current study is mainly focused on assessment and monitoring of optically active water quality parameters on wetland ecosystems over the Harike and Keshopur wetlands in Punjab region, India. Sentinel-2 multispectral imager (MSI) product have been analyzed in two phases: Pre-monsoon and Post-monsoon during period from 2018 to 2021 to extract spatial and temporal variations of water quality parameters. A normalized difference water index (NDWI) has been utilized to extract the water and non-water pixels, and the semi-analytical inversion model is used to retrieve the optically water quality parameters. The images of derived chlorophyll concentrations and total suspended matter have been found ranging from 0 to 36 mg/m3 and 0 to 156 mg/m3. This study revealed that the semi-analytical model is very helpful to identify the small scale changes in optically active constituents through using multispectral imagery. Water quality parameters monitoring is an important indicator to measure the productivity and eutrophication of the river water system. This research will help in understanding the water cycle and water conditions, and is paramount to researchers, scientists, and policy makers for sustainable management. The current study also concluded that the significant reduction of highly biodiversity wetland area is required to conserve. Full article
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Proceeding Paper
Land Use and Land Coverage Analysis with Google Earth Engine and Change Detection in the Sonipat District of the Haryana State in India
Eng. Proc. 2022, 27(1), 85; https://doi.org/10.3390/ecsa-9-13366 - 01 Nov 2022
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Abstract
The natural environment is of the utmost significance not only for a particular location but also for the entire world. This is because the natural environment provides essential environmental services to the human population. However, the environment is being negatively impacted by human [...] Read more.
The natural environment is of the utmost significance not only for a particular location but also for the entire world. This is because the natural environment provides essential environmental services to the human population. However, the environment is being negatively impacted by human activity as well as population growth. The most significant impact is felt in the national capital region. Using the Google Earth Engine (GEE) cloud platform and the QGIS desktop, the purpose of this research was to analyze the changes in land use and land cover (LULC) transformations that have taken place in the Sonipat district of India over the past ten years (2011–2021). Change detection (CD) of an LULC map is a method that examines shifts in LULC throughout time. Landsat 7 and the Sentinel 2 satellite image collections were utilized in this study. The study area was divided into four LULC categories using the most likely classified approach to quantify the changes over the aforementioned period. The results indicated that between 2011 and 2021, cropland in the study area decreased by about 11%. Built-up and urban areas increased by 3%. With the help of this study, decision-makers will be able to make choices that are appropriate in the given situation. The findings emphasize the value of satellite monitoring in reducing the rate of environmental degradation in the Sonipat district. Full article
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Proceeding Paper
Combination Process of a Pneumatic Artificial Muscle and a Fiber Optical Sensor System
Eng. Proc. 2022, 27(1), 86; https://doi.org/10.3390/ecsa-9-13290 - 01 Nov 2022
Viewed by 137
Abstract
A McKibben artificial muscle is a typical soft actuator, and it features flexibility, lightweight, and low cost. It consists of a rubber tube and a sleeve which is woven with spiral fibers, and contracts axially by applying pneumatic pressure to the rubber tube. [...] Read more.
A McKibben artificial muscle is a typical soft actuator, and it features flexibility, lightweight, and low cost. It consists of a rubber tube and a sleeve which is woven with spiral fibers, and contracts axially by applying pneumatic pressure to the rubber tube. We have developed the combination structure of the McKibben artificial muscle and the optical fiber which works as a contractile displacement sensor. The optical fiber can be braided into the sleeve which is the necessary component of the artificial muscle, which means that the optical fiber works as not only the sensor element but also the actuator element. In the previous sensor system, the light-receiving part (photo IC diode) and the light-emitting part (LED) were located at the base and tip sides of the artificial muscle, respectively. This configuration has a limitation in applications and the possibility of electrical line troubles. In this report, the LED and the photo IC diode are arranged at the base end of the artificial muscle by improving the fabrication process. Through the process, the optical fiber from the base can be returned to the base again via the tip, and the LED and photo IC diode can be located at the base side of the artificial muscle. Experimentally, the relation between the sensor output and contractile displacement of the artificial muscle was confirmed. Full article
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Proceeding Paper
Weed Detection in Grassland and Field Areas Employing RGB Imagery with a Deep Learning Algorithm Using Rumex obtusifolius Plants as a Case Study
Eng. Proc. 2022, 27(1), 87; https://doi.org/10.3390/ecsa-9-13950 - 03 Jan 2023
Viewed by 209
Abstract
The bluntleaf dock/ broad-leaved dock (Rumex obtusifolius) is a fast growing, highly competitive and resistant weed. It is endemic to Austria and generally a very common weed in Europe. Rumex obtusifolius prefers nutrient-rich, moist soils. As a light germinator, it spreads [...] Read more.
The bluntleaf dock/ broad-leaved dock (Rumex obtusifolius) is a fast growing, highly competitive and resistant weed. It is endemic to Austria and generally a very common weed in Europe. Rumex obtusifolius prefers nutrient-rich, moist soils. As a light germinator, it spreads easily in patchy plant stands. Its taproot can penetrate compacted, waterlogged and oxygen-poor soil layers to a depth of 2.60 m. It is considered a pest in agriculture, both in field and pasture, because of its rapid growth, ability to vegetatively propagate from leftover roots and its extensive taproot system. The most important management strategy is to prevent dock plants from establishing. If plants are already present in the field, the population must be assessed. If there are up to two dock plants per square meter, single-stock measures such as pricking out or tilling and reseeding are used. If there are more than two plants per square meter, uprooting will help. Furthermore, it will become necessary to adjust the crop rotation. The application of pesticides is possible; however, mechanical removal is preferred. The goal of this study is to develop a CNN (convolutional neural network) that is specially trained to identify dock plants and to capture location and position in the field/pasture. RGB photographs (n = 2500) were collected using an unmanned aerial vehicle and handheld cameras from March to August 2021. The obtained dataset contained photographs showcasing dock plants in all sizes and forms to include different phenotypes and age difference. The network was also trained to differentiate between whole plants and plant parts such as leaves. Full article
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