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Sensors, Volume 19, Issue 16 (August-2 2019)

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Open AccessArticle
Development of an Eye Tracking-Based Human-Computer Interface for Real-Time Applications
Sensors 2019, 19(16), 3630; https://doi.org/10.3390/s19163630 (registering DOI)
Received: 30 July 2019 / Revised: 15 August 2019 / Accepted: 16 August 2019 / Published: 20 August 2019
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Abstract
In this paper, the development of an eye-tracking-based human–computer interface for real-time applications is presented. To identify the most appropriate pupil detection algorithm for the proposed interface, we analyzed the performance of eight algorithms, six of which we developed based on the most [...] Read more.
In this paper, the development of an eye-tracking-based human–computer interface for real-time applications is presented. To identify the most appropriate pupil detection algorithm for the proposed interface, we analyzed the performance of eight algorithms, six of which we developed based on the most representative pupil center detection techniques. The accuracy of each algorithm was evaluated for different eye images from four representative databases and for video eye images using a new testing protocol for a scene image. For all video recordings, we determined the detection rate within a circular target 50-pixel area placed in different positions in the scene image, cursor controllability and stability on the user screen, and running time. The experimental results for a set of 30 subjects show a detection rate over 84% at 50 pixels for all proposed algorithms, and the best result (91.39%) was obtained with the circular Hough transform approach. Finally, this algorithm was implemented in the proposed interface to develop an eye typing application based on a virtual keyboard. The mean typing speed of the subjects who tested the system was higher than 20 characters per minute. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle
Adaptive Neuro-Fuzzy Fusion of Multi-Sensor Data for Monitoring a Pilot’s Workload Condition
Sensors 2019, 19(16), 3629; https://doi.org/10.3390/s19163629 (registering DOI)
Received: 19 July 2019 / Revised: 13 August 2019 / Accepted: 16 August 2019 / Published: 20 August 2019
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Abstract
To realize an early warning of unbalanced workload in the aircraft cockpit, it is required to monitor the pilot’s real-time workload condition. For the purpose of building the mapping relationship from physiological and flight data to workload, a multi-source data fusion model is [...] Read more.
To realize an early warning of unbalanced workload in the aircraft cockpit, it is required to monitor the pilot’s real-time workload condition. For the purpose of building the mapping relationship from physiological and flight data to workload, a multi-source data fusion model is proposed based on a fuzzy neural network, mainly structured using a principal components extraction layer, fuzzification layer, fuzzy rules matching layer, and normalization layer. Aiming at the high coupling characteristic variables contributing to workload, principal component analysis reconstructs the feature data by reducing its dimension. Considering the uncertainty for a single variable to reflect overall workload, a fuzzy membership function and fuzzy control rules are defined to abstract the inference process. An error feedforward algorithm based on gradient descent is utilized for parameter learning. Convergence speed and accuracy can be adjusted by controlling the gradient descent rate and error tolerance threshold. Combined with takeoff and initial climbing tasks of a Boeing 737–800 aircraft, crucial performance indicators—including pitch angle, heading, and airspeed—as well as physiological indicators—including electrocardiogram (ECG), respiration, and eye movements—were featured. The mapping relationship between multi-source data and the comprehensive workload level synthesized using the NASA task load index was established. Experimental results revealed that the predicted workload corresponding to different flight phases and difficulty levels showed clear distinctions, thereby proving the validity of data fusion. Full article
(This article belongs to the collection Multi-Sensor Information Fusion)
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Open AccessArticle
Smoke Obscuration Measurements in Reduced-Scale Fire Modelling Based on Froude Number Similarity
Sensors 2019, 19(16), 3628; https://doi.org/10.3390/s19163628 (registering DOI)
Received: 2 July 2019 / Revised: 7 August 2019 / Accepted: 19 August 2019 / Published: 20 August 2019
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Abstract
A common method for investigating various fire- and smoke-related phenoma is a reduced-scale fire modelling that uses the conservation concept of Froude number as its primary similarity criterion. Smoke obscuration measurements were not commonly used in this approach. In this paper, we propose [...] Read more.
A common method for investigating various fire- and smoke-related phenoma is a reduced-scale fire modelling that uses the conservation concept of Froude number as its primary similarity criterion. Smoke obscuration measurements were not commonly used in this approach. In this paper, we propose a new type of optical densitometer that allows for smoke obscuration density measurements on a reduced-scale. This device uses a set of mirrors to increase the optical path length, so that the device may follow the geometrical scale of the model, but that still measures smoke obscuration as if it were in full scale. The principle of operation is based on the Bougher-Lambert-Beer law, with modifications related to the Froude number-based scaling principles, to streamline the measurements. The proposed low-budget (< $1000) device was built, calibrated with a set of the reference optical filters, and used in a series of full- (1:1) and reduced-scale (1:4) experiments with n-Heptane fires in a small compartment. The main limitation of this study is the assumption that there is similar soot production in full- and reduced-scale fires, which may not be true for many Froude-number scaling applications. Therefore, it must be investigated in a case-by-case basis. In our case, the results are promising. The measured obscuration in the reduced-scale had a 10% error versus averaged measurements in full-scale measurements. Moreover, there were well represented transient changes of the smoke layer optical density during the combustion and after the smoke layer settled. Full article
(This article belongs to the Section Optical Sensors)
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Open AccessFeature PaperArticle
Target Doppler Rate Estimation Based on the Complex Phase of STFT in Passive Forward Scattering Radar
Sensors 2019, 19(16), 3627; https://doi.org/10.3390/s19163627 (registering DOI)
Received: 19 July 2019 / Revised: 7 August 2019 / Accepted: 17 August 2019 / Published: 20 August 2019
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Abstract
This article presents a novel approach to the estimation of motion parameters of objects in passive forward scattering radars (PFSR). In such systems, most frequency modulated signals which are used have parameters that depend on the geometry of a radar scene and an [...] Read more.
This article presents a novel approach to the estimation of motion parameters of objects in passive forward scattering radars (PFSR). In such systems, most frequency modulated signals which are used have parameters that depend on the geometry of a radar scene and an object’s motion. Worth noting is that in bistatic (or multistatic) radars forward scattering geometry is present thus in this case only Doppler measurements are available while the range measurement is unambiguous. In this article the modulation factor, also called the Doppler rate, was determined based on the chirp rate (equivalent Doppler rate) estimation concept in the time-frequency (TF) domain. This approach utilizes the idea of the complex phase of the short-time Fourier transform (STFT) and its modification known from the literature. Mathematical dependencies were implemented and verified and the simulation results were described. The accuracy of the considered estimators were also verified using the Cramer-Rao lower bound (CRLB) to which simulated data for the considered estimators was compared. The proposed method was validated using a real-life signal collected from a radar operating in PFSR geometry. The Doppler rate provided by a car crossing the baseline between the receiver and the GSM transmitter was estimated. Finally, the concept of using CR estimation, which in the case of PFSR can be understood as Doppler rate, was confirmed on the basis of both simulated and real-life data. Full article
(This article belongs to the Special Issue Recent Advancements in Radar Imaging and Sensing Technology)
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Open AccessArticle
Implementation of Radiating Elements for Radiofrequency Front-Ends by Screen-Printing Techniques for Internet of Things Applications
Sensors 2019, 19(16), 3626; https://doi.org/10.3390/s19163626 (registering DOI)
Received: 28 July 2019 / Revised: 15 August 2019 / Accepted: 16 August 2019 / Published: 20 August 2019
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Abstract
The advent of the Internet of Things (IoT) has led to embedding wireless transceivers into a wide range of devices, in order to implement context-aware scenarios, in which a massive amount of transceivers is foreseen. In this framework, cost-effective electronic and Radio Frequency [...] Read more.
The advent of the Internet of Things (IoT) has led to embedding wireless transceivers into a wide range of devices, in order to implement context-aware scenarios, in which a massive amount of transceivers is foreseen. In this framework, cost-effective electronic and Radio Frequency (RF) front-end integration is desirable, in order to enable straightforward inclusion of communication capabilities within objects and devices in general. In this work, flexible antenna prototypes, based on screen-printing techniques, with conductive inks on flexible low-cost plastic substrates is proposed. Different parameters such as substrate/ink characteristics are considered, as well as variations in fabrication process or substrate angular deflection in device performance. Simulation and measurement results are presented, as well as system validation results in a real test environment in wireless sensor network communications. The results show the feasibility of using screen-printing antenna elements on flexible low-cost substrates, which can be embedded in a wide array of IoT scenarios. Full article
(This article belongs to the Special Issue Sensor Systems for Internet of Things)
Open AccessFeature PaperArticle
Analyzing Spinal Shape Changes During Posture Training Using a Wearable Device
Sensors 2019, 19(16), 3625; https://doi.org/10.3390/s19163625 (registering DOI)
Received: 31 July 2019 / Revised: 8 August 2019 / Accepted: 16 August 2019 / Published: 20 August 2019
Viewed by 139 | PDF Full-text (3974 KB) | XML Full-text | Supplementary Files
Abstract
Lower back pain is one of the most prevalent diseases in Western societies. A large percentage of European and American populations suffer from back pain at some point in their lives. One successful approach to address lower back pain is postural training, which [...] Read more.
Lower back pain is one of the most prevalent diseases in Western societies. A large percentage of European and American populations suffer from back pain at some point in their lives. One successful approach to address lower back pain is postural training, which can be supported by wearable devices, providing real-time feedback about the user’s posture. In this work, we analyze the changes in posture induced by postural training. To this end, we compare snapshots before and after training, as measured by the Gokhale SpineTracker™. Considering pairs of before and after snapshots in different positions (standing, sitting, and bending), we introduce a feature space, that allows for unsupervised clustering. We show that resulting clusters represent certain groups of postural changes, which are meaningful to professional posture trainers. Full article
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
Open AccessArticle
Application and Optimization of Wavelet Transform Filter for North-Seeking Gyroscope Sensor Exposed to Vibration
Sensors 2019, 19(16), 3624; https://doi.org/10.3390/s19163624 (registering DOI)
Received: 6 June 2019 / Revised: 3 August 2019 / Accepted: 14 August 2019 / Published: 20 August 2019
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Abstract
Conventional wavelet transform (WT) filters have less effect on de-noising and correction of a north-seeking gyroscope sensor exposed to vibration, since the optimal wavelet decomposed level for de-noising is difficult to determine. To solve this problem, this paper proposes an optimized WT filter [...] Read more.
Conventional wavelet transform (WT) filters have less effect on de-noising and correction of a north-seeking gyroscope sensor exposed to vibration, since the optimal wavelet decomposed level for de-noising is difficult to determine. To solve this problem, this paper proposes an optimized WT filter which is suited to the magnetic levitation gyroscope (GAT). The proposed method was tested on an equivalent mock-up network of the tunnels associated with the Hong Kong‒Zhuhai‒Macau Bridge. The gyro-observed signals exposed to vibration were collected in our experiment, and the empirical values of the optimal wavelet decomposed levels (from 6 to 10) for observed signals were constrained and validated by the high-precision Global Navigation Satellite System (GNSS) network. The result shows that the lateral breakthrough error of the tunnel was reduced from 12.1 to 3.8 mm with a ratio of 68.7%, which suggests that the method is able to correct the abnormal signal of a north-seeking gyroscope sensor exposed to vibration. Full article
(This article belongs to the Special Issue Gyroscopes and Accelerometers)
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Open AccessArticle
Simultaneous Calibration of Odometry and Head-Eye Parameters for Mobile Robots with a Pan-Tilt Camera
Sensors 2019, 19(16), 3623; https://doi.org/10.3390/s19163623 (registering DOI)
Received: 13 June 2019 / Revised: 6 August 2019 / Accepted: 10 August 2019 / Published: 20 August 2019
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Abstract
In the field of robot navigation, the odometric parameters, such as wheel radii and wheelbase length, and the relative pose of the optical sensing camera with respect to the robot are very important criteria for accurate operation. Hence, these parameters are necessary to [...] Read more.
In the field of robot navigation, the odometric parameters, such as wheel radii and wheelbase length, and the relative pose of the optical sensing camera with respect to the robot are very important criteria for accurate operation. Hence, these parameters are necessary to be estimated for more precise operation. However, the odometric and head-eye parameters are typically estimated separately, which is an inconvenience and requires longer calibration time. Even though several researchers have proposed simultaneous calibration methods that obtain both odometric and head-eye parameters simultaneously to reduce the calibration time, they are only applicable to a mobile robot with a fixed camera mounted, not for mobile robots equipped with a pan-tilt motorized camera systems, which is a very common configuration and widely used for wide view. Previous approaches could not provide the z-axis translation parameter between head-eye coordinate systems on mobile robots equipped with a pan-tilt camera. In this paper, we present a full simultaneous mobile robot calibration of head–eye and odometric parameters, which is appropriate for a mobile robot equipped with a camera mounted on the pan-tilt motorized device. After a set of visual features obtained from a chessboard or natural scene and the odometry measurements are synchronized and received, both odometric and head-eye parameters are iteratively adjusted until convergence prior to using a nonlinear optimization method for more accuracy. Full article
(This article belongs to the Section Physical Sensors)
Open AccessArticle
Pressure-Pair-Based Floor Localization System Using Barometric Sensors on Smartphones
Sensors 2019, 19(16), 3622; https://doi.org/10.3390/s19163622 (registering DOI)
Received: 10 July 2019 / Revised: 13 August 2019 / Accepted: 18 August 2019 / Published: 20 August 2019
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Abstract
As smartphone technology advances and its market penetration increases, indoor positioning for smartphone users is becoming an increasingly important issue. Floor localization is especially critical to indoor positioning techniques. Numerous research efforts have been proposed for improving the floor localization accuracy using information [...] Read more.
As smartphone technology advances and its market penetration increases, indoor positioning for smartphone users is becoming an increasingly important issue. Floor localization is especially critical to indoor positioning techniques. Numerous research efforts have been proposed for improving the floor localization accuracy using information from barometers, accelerometers, Bluetooth Low Energy (BLE), and Wi-Fi signals. Despite these existing efforts, no approach has been able to determine what floor smartphone users are on with near 100% accuracy. To address this problem, we present a novel pressure-pair based method called FloorPair, which offers near 100% accurate floor localization. The rationale of FloorPair is to construct a relative pressure map using highly accurate relative pressure values from smartphones with two novel features: first, we marginalized the uncertainty from sensor drifts and unreliable absolute pressure values of barometers by paring the pressure values of two floors, and second, we maintained high accuracy over time by applying an iterative optimization method, making our method sustainable. We evaluated the validity of the FloorPair approach by conducting extensive field experiments in various types of buildings to show that FloorPair is an accurate and sustainable floor localization method. Full article
(This article belongs to the Section State-of-the-Art Sensors Technologies)
Open AccessArticle
Nonintrusive Appliance Load Monitoring: An Overview, Laboratory Test Results and Research Directions
Sensors 2019, 19(16), 3621; https://doi.org/10.3390/s19163621 (registering DOI)
Received: 12 July 2019 / Revised: 6 August 2019 / Accepted: 17 August 2019 / Published: 20 August 2019
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Abstract
Nonintrusive appliance load monitoring (NIALM) allows disaggregation of total electricity consumption into particular appliances in domestic or industrial environments. NIALM systems operation is based on processing of electrical signals acquired at one point of a monitored area. The main objective of this paper [...] Read more.
Nonintrusive appliance load monitoring (NIALM) allows disaggregation of total electricity consumption into particular appliances in domestic or industrial environments. NIALM systems operation is based on processing of electrical signals acquired at one point of a monitored area. The main objective of this paper was to present the state-of-the-art in NIALM technologies for the smart home. This paper focuses on sensors and measurement methods. Different intelligent algorithms for processing signals have been presented. Identification accuracy for an actual set of appliances has been compared. This article depicts the architecture of a unique NIALM laboratory, presented in detail. Results of developed NIALM methods exploiting different measurement data are discussed and compared to known methods. New directions of NIALM research are proposed. Full article
(This article belongs to the Special Issue Sensor Technology for Smart Homes)
Open AccessArticle
Improving the Performance of Pseudo-Random Single-Photon Counting Ranging Lidar
Sensors 2019, 19(16), 3620; https://doi.org/10.3390/s19163620 (registering DOI)
Received: 14 July 2019 / Revised: 6 August 2019 / Accepted: 17 August 2019 / Published: 20 August 2019
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Abstract
A new encoding method is proposed to improve the performance of pseudo-random single-photon counting ranging (PSPCR) Lidar. The encoding principle and methodology are presented. In addition, the influence of detector’s dead time on the detection probability is analyzed with theoretical derivation and Monte [...] Read more.
A new encoding method is proposed to improve the performance of pseudo-random single-photon counting ranging (PSPCR) Lidar. The encoding principle and methodology are presented. In addition, the influence of detector’s dead time on the detection probability is analyzed with theoretical derivation and Monte Carlo simulation. Meanwhile, we propose using macro code as the analysis unit to quantitatively analyze the detection probability and single-photon detection efficiency of the traditional PSPCR Lidar and the modulated PSPCR Lidar. The Monte Carlo simulation and experiment prove that the proposed method exhibited better ranging performance than the traditional PSPCR Lidar system. Full article
Open AccessArticle
Prototyping a System for Truck Differential Lock Control
Sensors 2019, 19(16), 3619; https://doi.org/10.3390/s19163619 (registering DOI)
Received: 17 July 2019 / Revised: 13 August 2019 / Accepted: 16 August 2019 / Published: 20 August 2019
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Abstract
The article deals with the development of a mechatronic system for locking vehicle differentials. An important benefit of this system is that it prevents the jamming of the vehicle in difficult adhesion conditions. The system recognizes such a situation much sooner than the [...] Read more.
The article deals with the development of a mechatronic system for locking vehicle differentials. An important benefit of this system is that it prevents the jamming of the vehicle in difficult adhesion conditions. The system recognizes such a situation much sooner than the driver and is able to respond immediately, ensuring smooth driving in off-road or snowy conditions. This article describes the control algorithm of this mechatronic system, which is designed for firefighting, military, or civilian vehicles with a drivetrain configuration of up to 10 × 10, and also explains the input signal processing and the control of actuators. The main part of this article concerns prototype testing on a vehicle. The results are an evaluation of one of the many experiments and monitor the proper function of the developed mechatronic system. Full article
(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transport)
Open AccessArticle
Analysis of Sensitivity, Linearity, Hysteresis, Responsiveness, and Fatigue of Textile Knit Stretch Sensors
Sensors 2019, 19(16), 3618; https://doi.org/10.3390/s19163618 (registering DOI)
Received: 11 July 2019 / Revised: 1 August 2019 / Accepted: 13 August 2019 / Published: 20 August 2019
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Abstract
Wearable technology is widely used for collecting information about the human body and its movement by placing sensors on the body. This paper presents research into electronic textile strain sensors designed specifically for wearable applications which need to be lightweight, robust, and comfortable. [...] Read more.
Wearable technology is widely used for collecting information about the human body and its movement by placing sensors on the body. This paper presents research into electronic textile strain sensors designed specifically for wearable applications which need to be lightweight, robust, and comfortable. In this paper, sixteen stretch sensors, each with different conductive stretch fabrics, are evaluated: EeonTex (Eeonyx Corporation), knitted silver-plated yarn, and knitted spun stainless steel yarn. The sensors’ performance is tested using a tensile tester while monitoring their resistance with a microcontroller. Each sensor was analyzed for its sensitivity, linearity, hysteresis, responsiveness, and fatigue through a series of dynamic and static tests. The findings show that for wearable applications a subset of the silver-plated yarn sensors had better ranked performance in terms of sensitivity, linearity, and steady state. EeonTex was found to be the most responsive, and the stainless steel yarn performed the worst, which may be due to the characteristics of the knit samples under test. Full article
(This article belongs to the Section Physical Sensors)
Open AccessReview
Photoacoustic Imaging with Capacitive Micromachined Ultrasound Transducers: Principles and Developments
Sensors 2019, 19(16), 3617; https://doi.org/10.3390/s19163617 (registering DOI)
Received: 11 July 2019 / Revised: 15 August 2019 / Accepted: 18 August 2019 / Published: 20 August 2019
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Abstract
Photoacoustic imaging (PAI) is an emerging imaging technique that bridges the gap between pure optical and acoustic techniques to provide images with optical contrast at the acoustic penetration depth. The two key components that have allowed PAI to attain high-resolution images at deeper [...] Read more.
Photoacoustic imaging (PAI) is an emerging imaging technique that bridges the gap between pure optical and acoustic techniques to provide images with optical contrast at the acoustic penetration depth. The two key components that have allowed PAI to attain high-resolution images at deeper penetration depths are the photoacoustic signal generator, which is typically implemented as a pulsed laser and the detector to receive the generated acoustic signals. Many types of acoustic sensors have been explored as a detector for the PAI including Fabry–Perot interferometers (FPIs), micro ring resonators (MRRs), piezoelectric transducers, and capacitive micromachined ultrasound transducers (CMUTs). The fabrication technique of CMUTs has given it an edge over the other detectors. First, CMUTs can be easily fabricated into given shapes and sizes to fit the design specifications. Moreover, they can be made into an array to increase the imaging speed and reduce motion artifacts. With a fabrication technique that is similar to complementary metal-oxide-semiconductor (CMOS), CMUTs can be integrated with electronics to reduce the parasitic capacitance and improve the signal to noise ratio. The numerous benefits of CMUTs have enticed researchers to develop it for various PAI purposes such as photoacoustic computed tomography (PACT) and photoacoustic endoscopy applications. For PACT applications, the main areas of research are in designing two-dimensional array, transparent, and multi-frequency CMUTs. Moving from the table top approach to endoscopes, some of the different configurations that are being investigated are phased and ring arrays. In this paper, an overview of the development of CMUTs for PAI is presented. Full article
(This article belongs to the Section Physical Sensors)
Open AccessArticle
Micrometer Backstepping Control System for Linear Motion Single Axis Robot Machine Drive
Sensors 2019, 19(16), 3616; https://doi.org/10.3390/s19163616 (registering DOI)
Received: 20 July 2019 / Revised: 13 August 2019 / Accepted: 16 August 2019 / Published: 20 August 2019
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Abstract
In order to cut down influence on the uncertainty disturbances of a linear motion single axis robot machine, such as the external load force, the cogging force, the column friction force, the Stribeck force, and the parameters variations, the micrometer backstepping control system, [...] Read more.
In order to cut down influence on the uncertainty disturbances of a linear motion single axis robot machine, such as the external load force, the cogging force, the column friction force, the Stribeck force, and the parameters variations, the micrometer backstepping control system, using an amended recurrent Gottlieb polynomials neural network and altered ant colony optimization (AACO) with the compensated controller, is put forward for a linear motion single axis robot machine drive system mounted on the linear-optical ruler with 1 um resolution. To achieve high-precision control performance, an adaptive law of the amended recurrent Gottlieb polynomials neural network based on the Lyapunov function is proposed to estimate the lumped uncertainty. Besides this, a novel error-estimated law of the compensated controller is also proposed to compensate for the estimated error between the lumped uncertainty and the amended recurrent Gottlieb polynomials neural network with the adaptive law. Meanwhile, the AACO is used to regulate two variable learning rates in the weights of the amended recurrent Gottlieb polynomials neural network to speed up the convergent speed. The main contributions of this paper are: (1) The digital signal processor (DSP)-based current-regulation pulse width modulation (PWM) control scheme being successfully applied to control the linear motion single axis robot machine drive system; (2) the micrometer backstepping control system using an amended recurrent Gottlieb polynomials neural network with the compensated controller being successfully derived according to the Lyapunov function to diminish the lumped uncertainty effect; (3) achieving high-precision control performance, where an adaptive law of the amended recurrent Gottlieb polynomials neural network based on the Lyapunov function is successfully applied to estimate the lumped uncertainty; (4) a novel error-estimated law of the compensated controller being successfully used to compensate for the estimated error; and (5) the AACO being successfully used to regulate two variable learning rates in the weights of the amended recurrent Gottlieb polynomials neural network to speed up the convergent speed. Finally, the effectiveness of the proposed control scheme is also verified by the experimental results. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
Open AccessArticle
A Real-Time, Non-Contact Method for In-Line Inspection of Oil and Gas Pipelines Using Optical Sensor Array
Sensors 2019, 19(16), 3615; https://doi.org/10.3390/s19163615 (registering DOI)
Received: 25 June 2019 / Revised: 31 July 2019 / Accepted: 16 August 2019 / Published: 20 August 2019
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Abstract
Corrosion is considered as one of the most predominant causes of pipeline failures in the oil and gas industry and normally cannot be easily detected at the inner surface of pipelines without service disruption. The real-time inspection of oil and gas pipelines is [...] Read more.
Corrosion is considered as one of the most predominant causes of pipeline failures in the oil and gas industry and normally cannot be easily detected at the inner surface of pipelines without service disruption. The real-time inspection of oil and gas pipelines is extremely vital to mitigate accidents and maintenance cost as well as to improve the oil and gas transport efficiency. In this paper, a new, non-contact optical sensor array method for real-time inspection and non-destructive evaluation (NDE) of pipelines is presented. The proposed optical method consists of light emitting diodes (LEDs) and light dependent resistors (LDRs) to send light and receive reflected light from the inner surface of pipelines. The uniqueness of the proposed method lies in its accurate detection as well as its localization of corrosion defects, based on the utilization of optical sensor array in the pipeline, and also the flexibility with which this system can be adopted for pipelines with different services, sizes, and materials, as well as the method’s economic viability. Experimental studies are conducted considering corrosion defects with different features and dimensions to confirm the robustness and accuracy of the method. The obtained data are processed with discrete wavelet transform (DWT) for noise cancelation and feature extraction. The estimated sizes of the corrosion defects for different physical parameters, such as inspection speed and lift-off distance, are investigated and, finally, some preliminary tests are conducted based on the implementation of the proposed method on an in-line developed smart pipeline inspection gauge (PIG) for in-line inspection (ILI) application, with resulting success. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
Railway Wheel Flat Detection System Based on a Parallelogram Mechanism
Sensors 2019, 19(16), 3614; https://doi.org/10.3390/s19163614 (registering DOI)
Received: 12 July 2019 / Revised: 7 August 2019 / Accepted: 17 August 2019 / Published: 20 August 2019
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Abstract
Wheel flats are a key fault in railway systems, which can bring great harm to vehicle operation safety. At present, most wheel flat detection methods use qualitative detection and do not meet practical demands. In this paper, we used a railway wheel flat [...] Read more.
Wheel flats are a key fault in railway systems, which can bring great harm to vehicle operation safety. At present, most wheel flat detection methods use qualitative detection and do not meet practical demands. In this paper, we used a railway wheel flat measurement method based on a parallelogram mechanism to detect wheel flats dynamically and quantitatively. Based on our experiments, we found that system performance was influenced by the train speed. When the train speed was higher than a certain threshold, the wheel impact force would cause vibration of the measuring mechanism and affect the detection accuracy. Since the measuring system was installed at the on-site entrance of the train garage, to meet the speed requirement, a three-dimensional simulation model was established, which was based on the rigid-flexible coupled multibody dynamics theory. The speed threshold of the measuring mechanism increased by the reasonable selection of the damping coefficients of the hydraulic damper, the measuring positions, and the downward displacements of the measuring ruler. Finally, we applied the selected model parameters to the parallelogram mechanism, where field measurements showed that the experimental results were consistent with the simulation results. Full article
(This article belongs to the Section Physical Sensors)
Open AccessArticle
Fiber Ring Laser Directional Torsion Sensor with Ultra-Wide Linear Response
Sensors 2019, 19(16), 3613; https://doi.org/10.3390/s19163613 (registering DOI)
Received: 22 July 2019 / Revised: 9 August 2019 / Accepted: 16 August 2019 / Published: 20 August 2019
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Abstract
In this paper, a comprehensive passive torsion measurement is performed firstly in a 40-cm-long polarization maintaining fiber-based Sagnac interferometer (PMF-SI), and the non-linear torsion response is found and investigated. Then, a fiber laser torsion sensor (FLTS) with a dual-ring-cavity structure is proposed and [...] Read more.
In this paper, a comprehensive passive torsion measurement is performed firstly in a 40-cm-long polarization maintaining fiber-based Sagnac interferometer (PMF-SI), and the non-linear torsion response is found and investigated. Then, a fiber laser torsion sensor (FLTS) with a dual-ring-cavity structure is proposed and experimentally demonstrated, in which the PMF-SI is utilized as the optical filter as well as the sensing unit. In particular, the highly sensitive linear range is adjusted through fine phase modulation, and owing to the flat-top feature of fringes, an ~83.6% sensitivity difference is effectively compressed by the generated lasing. The experimental results show that, without any pre-twisting, the ultra-wide linear response from –175 to 175 rad/m is gained, and the torsion sensitivities are 2.46 and 1.55 nm/rad with high linearity (>0.99) in the clockwise and anti-clockwise directions, respectively. Additionally, a high extinction ratio (>42 dB) and small line-width (~0.14 nm) are obtained in the proposed FLTS, and the corresponding detection limit reaches 0.015 rad/m. Full article
(This article belongs to the Section Optical Sensors)
Open AccessArticle
An Edge-Fog Secure Self-Authenticable Data Transfer Protocol
Sensors 2019, 19(16), 3612; https://doi.org/10.3390/s19163612 (registering DOI)
Received: 1 July 2019 / Revised: 14 August 2019 / Accepted: 16 August 2019 / Published: 19 August 2019
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Abstract
Development of the Internet of Things (IoT) opens many new challenges. As IoT devices are getting smaller and smaller, the problems of so-called “constrained devices” arise. The traditional Internet protocols are not very well suited for constrained devices comprising localized network nodes with [...] Read more.
Development of the Internet of Things (IoT) opens many new challenges. As IoT devices are getting smaller and smaller, the problems of so-called “constrained devices” arise. The traditional Internet protocols are not very well suited for constrained devices comprising localized network nodes with tens of devices primarily communicating with each other (e.g., various sensors in Body Area Network communicating with each other). These devices have very limited memory, processing, and power resources, so traditional security protocols and architectures also do not fit well. To address these challenges the Fog computing paradigm is used in which all constrained devices, or Edge nodes, primarily communicate only with less-constrained Fog node device, which collects all data, processes it and communicates with the outside world. We present a new lightweight secure self-authenticable transfer protocol (SSATP) for communications between Edge nodes and Fog nodes. The primary target of the proposed protocol is to use it as a secure transport for CoAP (Constrained Application Protocol) in place of UDP (User Datagram Protocol) and DTLS (Datagram Transport Layer Security), which are traditional choices in this scenario. SSATP uses modified header fields of standard UDP packets to transfer additional protocol handling and data flow management information as well as user data authentication information. The optional redundant data may be used to provide increased resistance to data losses when protocol is used in unreliable networks. The results of experiments presented in this paper show that SSATP is a better choice than UDP with DTLS in the cases, where the CoAP block transfer mode is used and/or in lossy networks. Full article
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Open AccessArticle
Seasonal Time Series Forecasting by F1-Fuzzy Transform
Sensors 2019, 19(16), 3611; https://doi.org/10.3390/s19163611
Received: 24 July 2019 / Revised: 15 August 2019 / Accepted: 17 August 2019 / Published: 19 August 2019
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Abstract
We present a new seasonal forecasting method based on F1-transform (fuzzy transform of order 1) applied on weather datasets. The objective of this research is to improve the performances of the fuzzy transform-based prediction method applied to seasonal time series. The [...] Read more.
We present a new seasonal forecasting method based on F1-transform (fuzzy transform of order 1) applied on weather datasets. The objective of this research is to improve the performances of the fuzzy transform-based prediction method applied to seasonal time series. The time series’ trend is obtained via polynomial fitting: then, the dataset is partitioned in S seasonal subsets and the direct F1-transform components for each seasonal subset are calculated as well. The inverse F1-transforms are used to predict the value of the weather parameter in the future. We test our method on heat index datasets obtained from daily weather data measured from weather stations of the Campania Region (Italy) during the months of July and August from 2003 to 2017. We compare the results obtained with the statistics Autoregressive Integrated Moving Average (ARIMA), Automatic Design of Artificial Neural Networks (ADANN), and the seasonal F-transform methods, showing that the best results are just given by our approach. Full article
(This article belongs to the Special Issue Intelligent Systems in Sensor Networks and Internet of Things)
Open AccessArticle
Radio Frequency Fingerprint-Based Intelligent Mobile Edge Computing for Internet of Things Authentication
Sensors 2019, 19(16), 3610; https://doi.org/10.3390/s19163610
Received: 10 June 2019 / Revised: 5 August 2019 / Accepted: 16 August 2019 / Published: 19 August 2019
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Abstract
In this paper, a light-weight radio frequency fingerprinting identification (RFFID) scheme that combines with a two-layer model is proposed to realize authentications for a large number of resource-constrained terminals under the mobile edge computing (MEC) scenario without relying on encryption-based methods. In the [...] Read more.
In this paper, a light-weight radio frequency fingerprinting identification (RFFID) scheme that combines with a two-layer model is proposed to realize authentications for a large number of resource-constrained terminals under the mobile edge computing (MEC) scenario without relying on encryption-based methods. In the first layer, signal collection, extraction of RF fingerprint features, dynamic feature database storage, and access authentication decision are carried out by the MEC devices. In the second layer, learning features, generating decision models, and implementing machine learning algorithms for recognition are performed by the remote cloud. By this means, the authentication rate can be improved by taking advantage of the machine-learning training methods and computing resource support of the cloud. Extensive simulations are performed under the IoT application scenario. The results show that the novel method can achieve higher recognition rate than that of traditional RFFID method by using wavelet feature effectively, which demonstrates the efficiency of our proposed method. Full article
(This article belongs to the Special Issue Green, Energy-Efficient and Sustainable Networks)
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Open AccessArticle
Evaluation of Sentinel-3A OLCI Products Derived Using the Case-2 Regional CoastColour Processor over the Baltic Sea
Sensors 2019, 19(16), 3609; https://doi.org/10.3390/s19163609
Received: 9 July 2019 / Revised: 11 August 2019 / Accepted: 13 August 2019 / Published: 19 August 2019
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Abstract
In this study, the Level-2 products of the Ocean and Land Colour Instrument (OLCI) data on Sentinel-3A are derived using the Case-2 Regional CoastColour (C2RCC) processor for the SentiNel Application Platform (SNAP) whilst adjusting the specific scatter of Total Suspended Matter (TSM) for [...] Read more.
In this study, the Level-2 products of the Ocean and Land Colour Instrument (OLCI) data on Sentinel-3A are derived using the Case-2 Regional CoastColour (C2RCC) processor for the SentiNel Application Platform (SNAP) whilst adjusting the specific scatter of Total Suspended Matter (TSM) for the Baltic Sea in order to improve TSM retrieval. The remote sensing product “kd_z90max” (i.e., the depth of the water column from which 90% of the water-leaving irradiance are derived) from C2RCC-SNAP showed a good correlation with in situ Secchi depth (SD). Additionally, a regional in-water algorithm was applied to derive SD from the attenuation coefficient Kd(489) using a local algorithm. Furthermore, a regional in-water relationship between particle scatter and bench turbidity was applied to generate turbidity from the remote sensing product “iop_bpart” (i.e., the scattering coefficient of marine particles at 443 nm). The spectral shape of the remote sensing reflectance (Rrs) data extracted from match-up stations was evaluated against reflectance data measured in situ by a tethered Attenuation Coefficient Sensor (TACCS) radiometer. The L2 products were evaluated against in situ data from several dedicated validation campaigns (2016–2018) in the NW Baltic proper. All derived L2 in-water products were statistically compared to in situ data and the results were also compared to results for MERIS validation from the literature and the current S3 Level-2 Water (L2W) standard processor from EUMETSAT. The Chl-a product showed a substantial improvement (MNB 21%, RMSE 88%, APD 96%, n = 27) compared to concentrations derived from the Medium Resolution Imaging Spectrometer (MERIS), with a strong underestimation of higher values. TSM performed within an error comparable to MERIS data with a mean normalized bias (MNB) 25%, root-mean square error (RMSE) 73%, average absolute percentage difference (APD) 63% n = 23). Coloured Dissolved Organic Matter (CDOM) absorption retrieval has also improved substantially when using the product “iop_adg” (i.e., the sum of organic detritus and Gelbstoff absorption at 443 nm) as a proxy (MNB 8%, RMSE 56%, APD 54%, n = 18). The local SD (MNB 6%, RMSE 62%, APD 60%, n = 35) and turbidity (MNB 3%, RMSE 35%, APD 34%, n = 29) algorithms showed very good agreement with in situ data. We recommend the use of the SNAP C2RCC with regionally adjusted TSM-specific scatter for water product retrieval as well as the regional turbidity algorithm for Baltic Sea monitoring. Besides documenting the evaluation of the C2RCC processor, this paper may also act as a handbook on the validation of Ocean Colour data. Full article
(This article belongs to the Special Issue Remote Sensing of Ocean Colour: Theory and Applications)
Open AccessArticle
Modeling and Control of a Six Degrees of Freedom Maglev Vibration Isolation System
Sensors 2019, 19(16), 3608; https://doi.org/10.3390/s19163608
Received: 6 July 2019 / Revised: 13 August 2019 / Accepted: 16 August 2019 / Published: 19 August 2019
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Abstract
The environment in space provides favorable conditions for space missions. However, low frequency vibration poses a great challenge to high sensitivity equipment, resulting in performance degradation of sensitive systems. Due to the ever-increasing requirements to protect sensitive payloads, there is a pressing need [...] Read more.
The environment in space provides favorable conditions for space missions. However, low frequency vibration poses a great challenge to high sensitivity equipment, resulting in performance degradation of sensitive systems. Due to the ever-increasing requirements to protect sensitive payloads, there is a pressing need for micro-vibration suppression. This paper deals with the modeling and control of a maglev vibration isolation system. A high-precision nonlinear dynamic model with six degrees of freedom was derived, which contains the mathematical model of Lorentz actuators and umbilical cables. Regarding the system performance, a double closed-loop control strategy was proposed, and a sliding mode control algorithm was adopted to improve the vibration isolation performance. A simulation program of the system was developed in a MATLAB environment. A vibration isolation performance in the frequency range of 0.01–100 Hz and a tracking performance below 0.01 Hz were obtained. In order to verify the nonlinear dynamic model and the isolation performance, a principle prototype of the maglev isolation system equipped with accelerometers and position sensors was developed for the experiments. By comparing the simulation results and the experiment results, the nonlinear dynamic model of the maglev vibration isolation system was verified and the control strategy of the system was proved to be highly effective. Full article
(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transport)
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Open AccessArticle
Joint Banknote Recognition and Counterfeit Detection Using Explainable Artificial Intelligence
Sensors 2019, 19(16), 3607; https://doi.org/10.3390/s19163607
Received: 10 July 2019 / Revised: 10 August 2019 / Accepted: 14 August 2019 / Published: 19 August 2019
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Abstract
We investigated machine learning-based joint banknote recognition and counterfeit detection method. Unlike existing methods, since the proposed method simultaneously recognize banknote type and detect counterfeit detection, it is significantly faster than existing serial banknote recognition and counterfeit detection methods. Furthermore, we propose an [...] Read more.
We investigated machine learning-based joint banknote recognition and counterfeit detection method. Unlike existing methods, since the proposed method simultaneously recognize banknote type and detect counterfeit detection, it is significantly faster than existing serial banknote recognition and counterfeit detection methods. Furthermore, we propose an explainable artificial intelligence method for visualizing regions that contributed to the recognition and detection. Using the visualization, it is possible to understand the behavior of the trained machine learning system. In experiments using the United State Dollar and the European Union Euro banknotes, the proposed method shows significant improvement in computation time from conventional serial method. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle
The Heading Weight Function: A Novel LiDAR-Based Local Planner for Nonholonomic Mobile Robots
Sensors 2019, 19(16), 3606; https://doi.org/10.3390/s19163606
Received: 7 June 2019 / Revised: 12 August 2019 / Accepted: 16 August 2019 / Published: 19 August 2019
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Abstract
In this paper, we present a novel method for obstacle avoidance designed for a nonholonomic mobile robot. The method relies on light detection and ranging (LiDAR) readings, which are mapped into a polar coordinate system. Obstacles are taken into consideration when they are [...] Read more.
In this paper, we present a novel method for obstacle avoidance designed for a nonholonomic mobile robot. The method relies on light detection and ranging (LiDAR) readings, which are mapped into a polar coordinate system. Obstacles are taken into consideration when they are within a predefined radius from the robot. A central part of the approach is a new Heading Weight Function (HWF), in which the beams within the aperture angle of the LiDAR are virtually weighted in order to generate the best trajectory candidate for the robot. The HWF is designed to find a solution also in the case of a local-minima situation. The function is coupled with the robot’s controller in order to provide both linear and angular velocities. We tested the method both by simulations in a digital environment with a range of different static obstacles, and in a real, experimental environment including static and dynamic obstacles. The results showed that when utilizing the novel HWF, the robot was able to navigate safely toward the target while avoiding all obstacles included in the tests. Our findings thus show that it is possible for a robot to navigate safely in a populated environment using this method, and that sufficient efficiency in navigation may be obtained without basing the method on a global planner. This is particularly promising for navigation challenges occurring in unknown environments where models of the world cannot be obtained. Full article
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
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Open AccessArticle
Physical and Tactical Demands of the Goalkeeper in Football in Different Small-Sided Games
Sensors 2019, 19(16), 3605; https://doi.org/10.3390/s19163605
Received: 20 June 2019 / Revised: 9 August 2019 / Accepted: 14 August 2019 / Published: 19 August 2019
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Abstract
Background: Several studies have examined the differences between the different small-sided game (SSG) formats. However, only one study has analysed how the different variables that define SSGs can modify the goalkeeper’s behavior. The aim of the present study was to analyze how the [...] Read more.
Background: Several studies have examined the differences between the different small-sided game (SSG) formats. However, only one study has analysed how the different variables that define SSGs can modify the goalkeeper’s behavior. The aim of the present study was to analyze how the modification of the pitch size in SSGs affects the physical demands of the goalkeepers. Methods: Three professional male football goalkeepers participated in this study. Three different SSG were analysed (62 m × 44 m for a large pitch; 50 m × 35 m for a medium pitch and 32 m × 23 m for a small pitch). Positional data of each goalkeeper was gathered using an 18.18 Hz global positioning system. The data gathered was used to compute players’ spatial exploration index, standard ellipse area, prediction ellipse area The distance covered, distance covered in different intensities and accelerations/decelerations were used to assess the players’ physical performance. Results and Conclusions: There were differences between small and large SSGs in relation to the distances covered at different intensities and pitch exploration. Intensities were lower when the pitch size was larger. Besides that, the pitch exploration variables increased along with the increment of the pitch size. Full article
(This article belongs to the Special Issue Advanced Sensors Technology in Education)
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Open AccessArticle
Real-Time Photometric Calibrated Monocular Direct Visual SLAM
Sensors 2019, 19(16), 3604; https://doi.org/10.3390/s19163604
Received: 28 June 2019 / Revised: 12 August 2019 / Accepted: 16 August 2019 / Published: 19 August 2019
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Abstract
To solve the illumination sensitivity problems of mobile ground equipment, an enhanced visual SLAM algorithm based on the sparse direct method was proposed in this paper. Firstly, the vignette and response functions of the input sequences were optimized based on the photometric formation [...] Read more.
To solve the illumination sensitivity problems of mobile ground equipment, an enhanced visual SLAM algorithm based on the sparse direct method was proposed in this paper. Firstly, the vignette and response functions of the input sequences were optimized based on the photometric formation of the camera. Secondly, the Shi–Tomasi corners of the input sequence were tracked, and optimization equations were established using the pixel tracking of sparse direct visual odometry (VO). Thirdly, the Levenberg–Marquardt (L–M) method was applied to solve the joint optimization equation, and the photometric calibration parameters in the VO were updated to realize the real-time dynamic compensation of the exposure of the input sequences, which reduced the effects of the light variations on SLAM’s (simultaneous localization and mapping) accuracy and robustness. Finally, a Shi–Tomasi corner filtered strategy was designed to reduce the computational complexity of the proposed algorithm, and the loop closure detection was realized based on the oriented FAST and rotated BRIEF (ORB) features. The proposed algorithm was tested using TUM, KITTI, EuRoC, and an actual environment, and the experimental results show that the positioning and mapping performance of the proposed algorithm is promising. Full article
(This article belongs to the Special Issue Intelligent Vehicles)
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Open AccessArticle
Energy Efficient Range-Free Localization Algorithm for Wireless Sensor Networks
Sensors 2019, 19(16), 3603; https://doi.org/10.3390/s19163603
Received: 18 July 2019 / Revised: 6 August 2019 / Accepted: 14 August 2019 / Published: 19 August 2019
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Abstract
In this paper, an energy-efficient localization algorithm is proposed for precise localization in wireless sensor networks (WSNs) and the process is accomplished in three steps. Firstly, the beacon nodes discover their one-hop neighbor nodes with additional tone requests and reply packets over the [...] Read more.
In this paper, an energy-efficient localization algorithm is proposed for precise localization in wireless sensor networks (WSNs) and the process is accomplished in three steps. Firstly, the beacon nodes discover their one-hop neighbor nodes with additional tone requests and reply packets over the media access control (MAC) layer to avoid collision of packets. Secondly, the discovered one-hop unknown nodes are divided into two sets, i.e. unknown nodes with direct communication, and with indirect communication for energy efficiency. In direct communication, source beacon nodes forward the information directly to the unknown nodes, but a common beacon node is selected for communication which reduces overall energy consumption during transmission in indirect communication. Finally, a correction factor is also introduced, and localized unknown nodes are upgraded into helper nodes for reducing the localization error. To analyze the efficiency and effectiveness of the proposed algorithm, various simulations are conducted and compared with the existing algorithms. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle
Bin-Picking for Planar Objects Based on a Deep Learning Network: A Case Study of USB Packs
Sensors 2019, 19(16), 3602; https://doi.org/10.3390/s19163602
Received: 2 July 2019 / Revised: 16 August 2019 / Accepted: 16 August 2019 / Published: 19 August 2019
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Abstract
Random bin-picking is a prominent, useful, and challenging industrial robotics application. However, many industrial and real-world objects are planar and have oriented surface points that are not sufficiently compact and discriminative for those methods using geometry information, especially depth discontinuities. This study solves [...] Read more.
Random bin-picking is a prominent, useful, and challenging industrial robotics application. However, many industrial and real-world objects are planar and have oriented surface points that are not sufficiently compact and discriminative for those methods using geometry information, especially depth discontinuities. This study solves the above-mentioned problems by proposing a novel and robust solution for random bin-picking for planar objects in a cluttered environment. Different from other research that has mainly focused on 3D information, this study first applies an instance segmentation-based deep learning approach using 2D image data for classifying and localizing the target object while generating a mask for each instance. The presented approach, moreover, serves as a pioneering method to extract 3D point cloud data based on 2D pixel values for building the appropriate coordinate system on the planar object plane. The experimental results showed that the proposed method reached an accuracy rate of 100% for classifying two-sided objects in the unseen dataset, and 3D appropriate pose prediction was highly effective, with average translation and rotation errors less than 0.23 cm and 2.26°, respectively. Finally, the system success rate for picking up objects was over 99% at an average processing time of 0.9 s per step, fast enough for continuous robotic operation without interruption. This showed a promising higher successful pickup rate compared to previous approaches to random bin-picking problems. Successful implementation of the proposed approach for USB packs provides a solid basis for other planar objects in a cluttered environment. With remarkable precision and efficiency, this study shows significant commercialization potential. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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Open AccessArticle
Active Learning on Dynamic Clustering for Drift Compensation in an Electronic Nose System
Sensors 2019, 19(16), 3601; https://doi.org/10.3390/s19163601
Received: 12 July 2019 / Revised: 11 August 2019 / Accepted: 16 August 2019 / Published: 19 August 2019
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Abstract
Drift correction is an important concern in Electronic noses (E-nose) for maintaining stable performance during continuous work. A large number of reports have been presented for dealing with E-nose drift through machine-learning approaches in the laboratory. In this study, we aim to counter [...] Read more.
Drift correction is an important concern in Electronic noses (E-nose) for maintaining stable performance during continuous work. A large number of reports have been presented for dealing with E-nose drift through machine-learning approaches in the laboratory. In this study, we aim to counter the drift effect in more challenging situations in which the category information (labels) of the drifted samples is difficult or expensive to obtain. Thus, only a few of the drifted samples can be used for label querying. To solve this problem, we propose an innovative methodology based on Active Learning (AL) that selectively provides sample labels for drift correction. Moreover, we utilize a dynamic clustering process to balance the sample category for label querying. In the experimental section, we set up two E-nose drift scenarios—a long-term and a short-term scenario—to evaluate the performance of the proposed methodology. The results indicate that the proposed methodology is superior to the other state-of-art methods presented. Furthermore, the increasing tendencies of parameter sensitivity and accuracy are analyzed. In addition, the Label Efficiency Index (LEI) is adopted to measure the efficiency and labelling cost of the AL methods. The LEI values indicate that our proposed methodology exhibited better performance than the other presented AL methods in the online drift correction of E-noses. Full article
(This article belongs to the Special Issue Electronic Noses)
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