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Signal Processing and Machine Learning for Smart Sensing Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (20 October 2022) | Viewed by 49393

Special Issue Editors


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Guest Editor
Department of Electrical Engineering, National Ilan University, Yilan 26047, Taiwan
Interests: adaptive signal processing; machine learning; IoT; noise cancellation
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Chongqing Key Lab of Mobile Communications Technology, School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Interests: wireless localization and sensing; signal processing and detection; machine learning and information fusion
Special Issues, Collections and Topics in MDPI journals
School of Informatics, Xiamen University, Xiamen 361005, China
Interests: GNSS; multi-Source positioning; radio navigation
Special Issues, Collections and Topics in MDPI journals
AME-GEOLOC, University Gustave Eiffel, F-44344 Bouguenais, France
Interests: multisensory fusion for indoor/outdoor pedestrian positioning; GNSS positioning in urban environments; integrity monitoring
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
ALGORITMI Research Centre, Universidade do Minho, 4800-058 Guimarães, Portugal
Interests: neural networks; pattern recognition; machine learning; image processing; outdoor robotics; artificial intelligence; indoor localization and positioning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on advanced signal processing and machine learning technologies for smart sensing applications. Successful examples include radio navigation, indoor/outdoor positioning, mm-wave sensing, speech denoising, noise cancellation, etc. One of the objectives of this Special Issue is to present smart sensing applications that leverage state-of-the-art signal processing and machine learning technologies. The other main purpose is to promote interdisciplinary collaborations between researchers in the fields of signal processing and machine learning technologies for smart sensing applications.

The emerging trends for smart sensing include: (1) the integration of sensors with low-power embedded signal processing into one system, (2) the integration of multiple sensors in the same system to extract more useful data, and (3) the use of compressive sensing techniques to extract the useful information from original sensor output. To achieve these goals, sophisticated signal processing and machine learning technologies are required.

Prof. Dr. Ying-Ren Chien
Prof. Dr. Mu Zhou
Dr. Ao Peng
Dr. Ni Zhu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (18 papers)

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Editorial

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7 pages, 3835 KiB  
Editorial
Signal Processing and Machine Learning for Smart Sensing Applications
by Ying-Ren Chien, Mu Zhou, Ao Peng, Ni Zhu and Joaquín Torres-Sospedra
Sensors 2023, 23(3), 1445; https://doi.org/10.3390/s23031445 - 28 Jan 2023
Cited by 1 | Viewed by 2397
Abstract
The Special Issue “Signal Processing and Machine Learning for Smart Sensing Applications” focused on the publication of advanced signal processing methods by means of state-of-the-art machine learning technologies for smart sensing applications [...] Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning for Smart Sensing Applications)

Research

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15 pages, 2117 KiB  
Article
MT-GCNN: Multi-Task Learning with Gated Convolution for Multiple Transmitters Localization in Urban Scenarios
by Wenyu Wang, Lei Zhu, Zhen Huang, Baozhu Li, Lu Yu and Kaixin Cheng
Sensors 2022, 22(22), 8674; https://doi.org/10.3390/s22228674 - 10 Nov 2022
Cited by 3 | Viewed by 1546
Abstract
With the advance of the Internet of things (IoT), localization is essential in varied services. In urban scenarios, multiple transmitters localization is faced with challenges such as nonline-of-sight (NLOS) propagation and limited deployment of sensors. To this end, this paper proposes the MT-GCNN [...] Read more.
With the advance of the Internet of things (IoT), localization is essential in varied services. In urban scenarios, multiple transmitters localization is faced with challenges such as nonline-of-sight (NLOS) propagation and limited deployment of sensors. To this end, this paper proposes the MT-GCNN (Multi-Task Gated Convolutional Neural Network), a novel multiple transmitters localization scheme based on deep multi-task learning, to learn the NLOS propagation features and achieve the localization. The multi-task learning network decomposes the problem into a coarse localization task and a fine correction task. In particular, the MT-GCNN uses an improved gated convolution module to extract features from sparse sensing data more effectively. In the training stage, a joint loss function is proposed to optimize the two branches of tasks. In the testing stage, the well-trained MT-GCNN model predicts the classified grids and corresponding biases jointly to improve the overall performance of localization. In the urban scenarios challenged by NLOS propagation and sparse deployment of sensors, numerical simulations demonstrate that the proposed MT-GCNN framework has more accurate and robust performance than other algorithms. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning for Smart Sensing Applications)
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12 pages, 1879 KiB  
Article
Adaptive Null Widening Beamforming Algorithm in Spatially Correlated Color Noise
by Shijing Xiao, Bin Li and Qing Wang
Sensors 2022, 22(16), 6182; https://doi.org/10.3390/s22166182 - 18 Aug 2022
Cited by 1 | Viewed by 1359
Abstract
Under the background of spatially correlated color noise, the incidence angle of a jamming signal in a high-speed moving platform rapidly changes, which leads to the degradation of the anti-interference performance and the waveform distortion of the adaptive beamformer. In this paper, a [...] Read more.
Under the background of spatially correlated color noise, the incidence angle of a jamming signal in a high-speed moving platform rapidly changes, which leads to the degradation of the anti-interference performance and the waveform distortion of the adaptive beamformer. In this paper, a projection-constrained null broadening beamforming algorithm based on the Toeplitz matrix structure is proposed. The algorithm first extracts the subspace of the covariance matrix of the steering vector of the pre-determined extended angle interval and constructs the constraint matrix and the projection transformation matrix. The received signal covariance matrix with a Toeplitz structure is then constructed using the correlation number between each array element and the pre-set reference array element. Finally, the constructed covariance matrix is transformed through projection, and the weight of each array element is constrained by the constraint matrix. The theoretical optimal solution of adaptive wide null beamforming in spatially correlated color noise is obtained. The simulation results show that, compared with the existing robust adaptive beamforming algorithms, the proposed algorithm can efficiently improve the distortion of adaptive anti-jamming beams, and can achieve null broadening in the jamming direction under the condition of spatially correlated color noise, which improves the output signal to the interference-plus-noise ratio (SINR) of the adaptive beamformer. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning for Smart Sensing Applications)
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14 pages, 3315 KiB  
Article
Development of Joint Activity Angle Measurement and Cloud Data Storage System
by Chiu-Ching Tuan, Yi-Chao Wu, Wen-Ling Yeh, Chun-Chieh Wang, Chi-Heng Lu, Shao-Wei Wang, Jack Yang, Tsair-Fwu Lee and Hsuan-Kai Kao
Sensors 2022, 22(13), 4684; https://doi.org/10.3390/s22134684 - 21 Jun 2022
Cited by 2 | Viewed by 1857
Abstract
In this study, we developed a range of motion sensing system (ROMSS) to simulate the function of the elbow joint, with errors less than 0.76 degrees and 0.87 degrees in static and dynamic verification by the swinging and angle recognition modules, respectively. In [...] Read more.
In this study, we developed a range of motion sensing system (ROMSS) to simulate the function of the elbow joint, with errors less than 0.76 degrees and 0.87 degrees in static and dynamic verification by the swinging and angle recognition modules, respectively. In the simulation process, the ɣ correlation coefficient of the Pearson difference between the ROMSS and the universal goniometer was 0.90, the standard deviations of the general goniometer measurements were between ±2 degrees and ±2.6 degrees, and the standard deviations between the ROMSS measurements were between ±0.5 degrees and ±1.6 degrees. With the ROMSS, a cloud database was also established; the data measured by the sensor could be uploaded to the cloud database in real-time to provide timely patient information for healthcare professionals. We also developed a mobile app for smartphones to enable patients and healthcare providers to easily trace the data in real-time. Historical data sets with joint activity angles could be retrieved to observe the progress or effectiveness of disease recovery so the quality of care could be properly assessed and maintained. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning for Smart Sensing Applications)
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16 pages, 4861 KiB  
Article
Fall Detection with the Spatial-Temporal Correlation Encoded by a Sequence-to-Sequence Denoised GAN
by Wei-Wen Hsu, Jing-Ming Guo, Chien-Yu Chen and Yao-Chung Chang
Sensors 2022, 22(11), 4194; https://doi.org/10.3390/s22114194 - 31 May 2022
Cited by 2 | Viewed by 1965
Abstract
Falling is a major cause of personal injury and accidental death worldwide, in particular for the elderly. For aged care, a falling alarm system is highly demanded so that medical aid can be obtained immediately when the fall accidents happen. Previous studies on [...] Read more.
Falling is a major cause of personal injury and accidental death worldwide, in particular for the elderly. For aged care, a falling alarm system is highly demanded so that medical aid can be obtained immediately when the fall accidents happen. Previous studies on fall detection lacked practical considerations to deal with real-world situations, including the camera’s mounting angle, lighting differences between day and night, and the privacy protection for users. In our experiments, IR-depth images and thermal images were used as the input source for fall detection; as a result, detailed facial information is not captured by the system for privacy reasons, and it is invariant to the lighting conditions. Due to the different occurrence rates between fall accidents and other normal activities, supervised learning approaches may suffer from the problem of data imbalance in the training phase. Accordingly, in this study, anomaly detection is performed using unsupervised learning approaches so that the models were trained only with the normal cases while the fall accident was defined as an anomaly event. The proposed system takes sequential frames as the inputs to predict future frames based on a GAN structure, and it provides (1) multi-subject detection, (2) real-time fall detection triggered by motion, (3) a solution to the situation that subjects were occluded after falling, and (4) a denoising scheme for depth images. The experimental results show that the proposed system achieves the state-of-the-art performance and copes with the real-world cases successfully. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning for Smart Sensing Applications)
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17 pages, 9736 KiB  
Article
Integration of Deep Learning Network and Robot Arm System for Rim Defect Inspection Application
by Wei-Lung Mao, Yu-Ying Chiu, Bing-Hong Lin, Chun-Chi Wang, Yi-Ting Wu, Cheng-Yu You and Ying-Ren Chien
Sensors 2022, 22(10), 3927; https://doi.org/10.3390/s22103927 - 22 May 2022
Cited by 8 | Viewed by 2698
Abstract
Automated inspection has proven to be the most effective approach to maintaining quality in industrial-scale manufacturing. This study employed the eye-in-hand architecture in conjunction with deep learning and convolutional neural networks to automate the detection of defects in forged aluminum rims for electric [...] Read more.
Automated inspection has proven to be the most effective approach to maintaining quality in industrial-scale manufacturing. This study employed the eye-in-hand architecture in conjunction with deep learning and convolutional neural networks to automate the detection of defects in forged aluminum rims for electric vehicles. RobotStudio software was used to simulate the environment and path trajectory for a camera installed on an ABB robot arm to capture 3D images of the rims. Four types of surface defects were examined: (1) dirt spots, (2) paint stains, (3) scratches, and (4) dents. Generative adversarial network (GAN) and deep convolutional generative adversarial networks (DCGAN) were used to generate additional images to expand the depth of the training dataset. We also developed a graphical user interface and software system to mark patterns associated with defects in the images. The defect detection algorithm based on YOLO algorithms made it possible to obtain results more quickly and with higher mean average precision (mAP) than that of existing methods. Experiment results demonstrated the accuracy and efficiency of the proposed system. Our developed system has been shown to be a helpful rim defective detection system for industrial applications. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning for Smart Sensing Applications)
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19 pages, 53375 KiB  
Article
Multi-Signal Detection Framework: A Deep Learning Based Carrier Frequency and Bandwidth Estimation
by Meiyan Lin, Xiaoxu Zhang, Ye Tian and Yonghui Huang
Sensors 2022, 22(10), 3909; https://doi.org/10.3390/s22103909 - 21 May 2022
Cited by 8 | Viewed by 3062
Abstract
Multi-signal detection is of great significance in civil and military fields, such as cognitive radio (CR), spectrum monitoring, and signal reconnaissance, which refers to jointly detecting the presence of multiple signals in the observed frequency band, as well as estimating their carrier frequencies [...] Read more.
Multi-signal detection is of great significance in civil and military fields, such as cognitive radio (CR), spectrum monitoring, and signal reconnaissance, which refers to jointly detecting the presence of multiple signals in the observed frequency band, as well as estimating their carrier frequencies and bandwidths. In this work, a deep learning-based framework named SigdetNet is proposed, which takes the power spectrum as the network’s input to localize the spectral locations of the signals. In the proposed framework, Welch’s periodogram is applied to reduce the variance in the power spectral density (PSD), followed by logarithmic transformation for signal enhancement. In particular, an encoder-decoder network with the embedding pyramid pooling module is constructed, aiming to extract multi-scale features relevant to signal detection. The influence of the frequency resolution, network architecture, and loss function on the detection performance is investigated. Extensive simulations are carried out to demonstrate that the proposed multi-signal detection method can achieve better performance than the other benchmark schemes. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning for Smart Sensing Applications)
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15 pages, 2172 KiB  
Article
2D-DOA Estimation in Switching UCA Using Deep Learning-Based Covariance Matrix Completion
by Ruru Mei, Ye Tian, Yonghui Huang and Zhugang Wang
Sensors 2022, 22(10), 3754; https://doi.org/10.3390/s22103754 - 14 May 2022
Cited by 6 | Viewed by 2283
Abstract
In this paper, we study the two-dimensional direction of arrival (2D-DOA) estimation problem in a switching uniform circular array (SUCA), which means performing 2D-DOA estimation with a reduction in the number of radio frequency (RF) chains. We propose a covariance matrix completion algorithm [...] Read more.
In this paper, we study the two-dimensional direction of arrival (2D-DOA) estimation problem in a switching uniform circular array (SUCA), which means performing 2D-DOA estimation with a reduction in the number of radio frequency (RF) chains. We propose a covariance matrix completion algorithm for 2D-DOA estimation in a SUCA. The proposed algorithm estimates the complete covariance matrix of a fully sampled UCA (FUCA) from the sample covariance matrix of the SUCA through a neural network. Afterwards, the MUSIC algorithm is performed for 2D-DOA estimation with the completed covariance matrix. We conduct Monte Carlo simulations to evaluate the performance of the proposed algorithm in various scenarios; the performance of 2D-DOA estimation in the SUCA gradually approaches that in the FUCA as the SNR or the number of snapshots increases, which means that the advantages of a FUCA can be preserved with fewer RF chains. In addition, the proposed algorithm is able to implement underdetermined 2D-DOA estimation. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning for Smart Sensing Applications)
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36 pages, 7291 KiB  
Article
Entropy-Based Concentration and Instantaneous Frequency of TFDs from Cohen’s, Affine, and Reassigned Classes
by David Bačnar, Nicoletta Saulig, Irena Petrijevčanin Vuksanović and Jonatan Lerga
Sensors 2022, 22(10), 3727; https://doi.org/10.3390/s22103727 - 13 May 2022
Cited by 3 | Viewed by 2432
Abstract
This paper explores three groups of time–frequency distributions: the Cohen’s, affine, and reassigned classes of time–frequency representations (TFRs). This study provides detailed insight into the theory behind the selected TFRs belonging to these classes. Extensive numerical simulations were performed with examples that illustrate [...] Read more.
This paper explores three groups of time–frequency distributions: the Cohen’s, affine, and reassigned classes of time–frequency representations (TFRs). This study provides detailed insight into the theory behind the selected TFRs belonging to these classes. Extensive numerical simulations were performed with examples that illustrate the behavior of the analyzed TFR classes in the joint time–frequency domain. The methods were applied both on synthetic and real-life non-stationary signals. The obtained results were assessed with respect to time–frequency concentration (measured by the Rényi entropy), instantaneous frequency (IF) estimation accuracy, cross-term presence in the TFRs, and the computational cost of the TFRs. This study gives valuable insight into the advantages and limitations of the analyzed TFRs and assists in selecting the proper distribution when analyzing given non-stationary signals in the time–frequency domain. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning for Smart Sensing Applications)
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16 pages, 7411 KiB  
Article
Gridless Underdetermined Direction of Arrival Estimation in Sparse Circular Array Using Inverse Beamspace Transformation
by Ye Tian, Yonghui Huang, Xiaoxu Zhang and Xiaogang Tang
Sensors 2022, 22(8), 2864; https://doi.org/10.3390/s22082864 - 8 Apr 2022
Cited by 1 | Viewed by 1679
Abstract
Underdetermined DOA estimation, which means estimating more sources than sensors, is a challenging problem in the array signal processing community. This paper proposes a novel algorithm that extends the underdetermined DOA estimation in a Sparse Circular Array (SCA). We formulate this problem as [...] Read more.
Underdetermined DOA estimation, which means estimating more sources than sensors, is a challenging problem in the array signal processing community. This paper proposes a novel algorithm that extends the underdetermined DOA estimation in a Sparse Circular Array (SCA). We formulate this problem as a matrix completion problem. Meanwhile, we propose an inverse beamspace transformation combined with the Gridless SPICE (GLS) algorithm to complete the covariance matrix sampled by SCA. The DOAs are then obtained by solving a polynomial equation with using the Root-MUSIC algorithm. The proposed algorithm is named GSCA. Monte-Carlo simulations are performed to evaluate the GSCA algorithm, the spatial spectrum plots and RMSE curves demonstrated that the GSCA algorithm can give reasonable results of underdetermined DOA estimation in SCA. Meanwhile, the performance of the algorithm under various configurations of SCA is also evaluated. Numerical results indicated that the GSCA algorithm can provide access to solve the DOA estimation problem in Uniform Circular Array (UCA) when random sensor failures occur. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning for Smart Sensing Applications)
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20 pages, 8783 KiB  
Article
Effectiveness of Artificial Neural Networks for Solving Inverse Problems in Magnetic Field-Based Localization
by Ai-ichiro Sasaki
Sensors 2022, 22(6), 2240; https://doi.org/10.3390/s22062240 - 14 Mar 2022
Cited by 4 | Viewed by 2373
Abstract
Recently, indoor localization has become an active area of research. Although there are various approaches to indoor localization, methods that utilize artificially generated magnetic fields from a target device are considered to be the best in terms of localization accuracy under non-line-of-sight conditions. [...] Read more.
Recently, indoor localization has become an active area of research. Although there are various approaches to indoor localization, methods that utilize artificially generated magnetic fields from a target device are considered to be the best in terms of localization accuracy under non-line-of-sight conditions. In magnetic field-based localization, the target position must be calculated based on the magnetic field information detected by multiple sensors. The calculation process is equivalent to solving a nonlinear inverse problem. Recently, a machine-learning approach has been proposed to solve the inverse problem. Reportedly, adopting the k-nearest neighbor algorithm (k-NN) enabled the machine-learning approach to achieve fairly good performance in terms of both localization accuracy and computational speed. Moreover, it has been suggested that the localization accuracy can be further improved by adopting artificial neural networks (ANNs) instead of k-NN. However, the effectiveness of ANNs has not yet been demonstrated. In this study, we thoroughly investigated the effectiveness of ANNs for solving the inverse problem of magnetic field-based localization in comparison with k-NN. We demonstrate that despite taking longer to train, ANNs are superior to k-NN in terms of localization accuracy. The k-NN is still valid for predicting fairly accurate target positions within limited training times. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning for Smart Sensing Applications)
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17 pages, 1351 KiB  
Article
Jamming Strategy Optimization through Dual Q-Learning Model against Adaptive Radar
by Hongdi Liu, Hongtao Zhang, Yuan He and Yong Sun
Sensors 2022, 22(1), 145; https://doi.org/10.3390/s22010145 - 26 Dec 2021
Cited by 16 | Viewed by 3806
Abstract
Modern adaptive radars can switch work modes to perform various missions and simultaneously use pulse parameter agility in each mode to improve survivability, which leads to a multiplicative increase in the decision-making complexity and declining performance of the existing jamming methods. In this [...] Read more.
Modern adaptive radars can switch work modes to perform various missions and simultaneously use pulse parameter agility in each mode to improve survivability, which leads to a multiplicative increase in the decision-making complexity and declining performance of the existing jamming methods. In this paper, a two-level jamming decision-making framework is developed, based on which a dual Q-learning (DQL) model is proposed to optimize the jamming strategy and a dynamic method for jamming effectiveness evaluation is designed to update the model. Specifically, the jamming procedure is modeled as a finite Markov decision process. On this basis, the high-dimensional jamming action space is disassembled into two low-dimensional subspaces containing jamming mode and pulse parameters respectively, then two specialized Q-learning models with interaction are built to obtain the optimal solution. Moreover, the jamming effectiveness is evaluated through indicator vector distance measuring to acquire the feedback for the DQL model, where indicators are dynamically weighted to adapt to the environment. The experiments demonstrate the advantage of the proposed method in learning radar joint strategy of mode switching and parameter agility, shown as improving the average jamming-to-signal radio (JSR) by 4.05% while reducing the convergence time by 34.94% compared with the normal Q-learning method. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning for Smart Sensing Applications)
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17 pages, 1395 KiB  
Article
Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning
by Ying-Ren Chien, Cheng-Hsuan Wu and Hen-Wai Tsao
Sensors 2021, 21(18), 6049; https://doi.org/10.3390/s21186049 - 9 Sep 2021
Cited by 22 | Viewed by 2982
Abstract
Poor-quality sleep substantially diminishes the overall quality of life. It has been shown that sleep arousal serves as a good indicator for scoring sleep quality. However, patients are conventionally asked to perform overnight polysomnography tests to collect their physiological data, which are used [...] Read more.
Poor-quality sleep substantially diminishes the overall quality of life. It has been shown that sleep arousal serves as a good indicator for scoring sleep quality. However, patients are conventionally asked to perform overnight polysomnography tests to collect their physiological data, which are used for the manual judging of sleep arousals. Even worse, not only is this process time-consuming and cumbersome, the judgment of sleep-arousal events is subjective and differs widely from expert to expert. Therefore, this work focuses on designing an automatic sleep-arousal detector that necessitates only a single-lead electroencephalogram signal. Based on the stacking ensemble learning framework, the automatic sleep-arousal detector adopts a meta-classifier that stacks four sub-models: one-dimensional convolutional neural networks, recurrent neural networks, merged convolutional and recurrent networks, and random forest classifiers. This meta-classifier exploits both advantages from deep learning networks and conventional machine learning algorithms to enhance its performance. The embedded information for discriminating the sleep-arousals is extracted from waveform sequences, spectrum characteristics, and expert-defined statistics in single-lead EEG signals. Its effectiveness is evaluated using an open-accessed database, which comprises polysomnograms of 994 individuals, provided by PhysioNet. The improvement of the stacking ensemble learning over a single sub-model was up to 9.29%, 7.79%, 11.03%, 8.61% and 9.04%, respectively, in terms of specificity, sensitivity, precision, accuracy, and area under the receiver operating characteristic curve. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning for Smart Sensing Applications)
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13 pages, 6252 KiB  
Article
Exploration of Driver Posture Monitoring Using Pressure Sensors with Lower Resolution
by Mingming Zhao, Georges Beurier, Hongyan Wang and Xuguang Wang
Sensors 2021, 21(10), 3346; https://doi.org/10.3390/s21103346 - 12 May 2021
Cited by 10 | Viewed by 4213
Abstract
Pressure sensors are good candidates for measuring driver postural information, which is indicative for identifying driver’s intention and seating posture. However, monitoring systems based on pressure sensors must overcome the price barriers in order to be practically feasible. This study, therefore, was dedicated [...] Read more.
Pressure sensors are good candidates for measuring driver postural information, which is indicative for identifying driver’s intention and seating posture. However, monitoring systems based on pressure sensors must overcome the price barriers in order to be practically feasible. This study, therefore, was dedicated to explore the possibility of using pressure sensors with lower resolution for driver posture monitoring. We proposed pressure features including center of pressure, contact area proportion, and pressure ratios to recognize five typical trunk postures, two typical left foot postures, and three typical right foot postures. The features from lower-resolution mapping were compared with those from high-resolution Xsensor pressure mats on the backrest and seat pan. We applied five different supervised machine-learning techniques to recognize the postures of each body part and used leave-one-out cross-validation to evaluate their performance. A uniform sampling method was used to reduce number of pressure sensors, and five new layouts were tested by using the best classifier. Results showed that the random forest classifier outperformed the other classifiers with an average classification accuracy of 86% using the original pressure mats and 85% when only 8% of the pressure sensors were available. This study demonstrates the feasibility of using fewer pressure sensors for driver posture monitoring and suggests research directions for better sensor designs. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning for Smart Sensing Applications)
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17 pages, 25544 KiB  
Article
Intelligent Recognition of Chirp Radar Deceptive Jamming Based on Multi-Pulse Information Fusion
by Xuegang Lan, Tao Wan, Kaili Jiang, Ying Xiong and Bin Tang
Sensors 2021, 21(8), 2693; https://doi.org/10.3390/s21082693 - 11 Apr 2021
Cited by 6 | Viewed by 2672
Abstract
The perception of jamming types is very important for protecting our radar in complex electromagnetic environments. Radar active deceptive jamming based on digital radio frequency memory (DRFM) has a high coherence with the target echo, which confuses the information of the target echo [...] Read more.
The perception of jamming types is very important for protecting our radar in complex electromagnetic environments. Radar active deceptive jamming based on digital radio frequency memory (DRFM) has a high coherence with the target echo, which confuses the information of the target echo and achieves the effect of hiding the real target. Traditional deceptive jamming recognition methods need to extract complex features and artificially set classification thresholds, which is inefficient. The existing neural network-based jamming identification methods still follow the pattern of signal modulation-type identification, so there are fewer types of jamming that can be identified, and the identification accuracy is low in the case of low jamming-to-noise ratios (JNR). This paper studies the input of jamming recognition networks and proposes an improved intelligent identification method for chirp radar deceptive jamming. This method fuses three short-time Fourier transform time–frequency graphs disturbed by three consecutive pulse periods into a new graph as the input of the convolutional neural network (CNN). Using a CNN to classify the time–frequency image has realized the recognition of a variety of common deceptive jamming techniques. Similarly, by changing the network input, the original signal is used to replace the echo signal, which improves the accuracy of the jamming recognition in the case of a low JNR. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning for Smart Sensing Applications)
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21 pages, 15057 KiB  
Article
Low-Rank Matrix Recovery from Noise via an MDL Framework-Based Atomic Norm
by Anyong Qin, Lina Xian, Yongliang Yang, Taiping Zhang and Yuan Yan Tang
Sensors 2020, 20(21), 6111; https://doi.org/10.3390/s20216111 - 27 Oct 2020
Cited by 3 | Viewed by 2075
Abstract
The recovery of the underlying low-rank structure of clean data corrupted with sparse noise/outliers is attracting increasing interest. However, in many low-level vision problems, the exact target rank of the underlying structure and the particular locations and values of the sparse outliers are [...] Read more.
The recovery of the underlying low-rank structure of clean data corrupted with sparse noise/outliers is attracting increasing interest. However, in many low-level vision problems, the exact target rank of the underlying structure and the particular locations and values of the sparse outliers are not known. Thus, the conventional methods cannot separate the low-rank and sparse components completely, especially in the case of gross outliers or deficient observations. Therefore, in this study, we employ the minimum description length (MDL) principle and atomic norm for low-rank matrix recovery to overcome these limitations. First, we employ the atomic norm to find all the candidate atoms of low-rank and sparse terms, and then we minimize the description length of the model in order to select the appropriate atoms of low-rank and the sparse matrices, respectively. Our experimental analyses show that the proposed approach can obtain a higher success rate than the state-of-the-art methods, even when the number of observations is limited or the corruption ratio is high. Experimental results utilizing synthetic data and real sensing applications (high dynamic range imaging, background modeling, removing noise and shadows) demonstrate the effectiveness, robustness and efficiency of the proposed method. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning for Smart Sensing Applications)
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17 pages, 4542 KiB  
Article
A Real-Time Dual-Microphone Speech Enhancement Algorithm Assisted by Bone Conduction Sensor
by Yi Zhou, Yufan Chen, Yongbao Ma and Hongqing Liu
Sensors 2020, 20(18), 5050; https://doi.org/10.3390/s20185050 - 5 Sep 2020
Cited by 14 | Viewed by 5006
Abstract
The quality and intelligibility of the speech are usually impaired by the interference of background noise when using internet voice calls. To solve this problem in the context of wearable smart devices, this paper introduces a dual-microphone, bone-conduction (BC) sensor assisted beamformer and [...] Read more.
The quality and intelligibility of the speech are usually impaired by the interference of background noise when using internet voice calls. To solve this problem in the context of wearable smart devices, this paper introduces a dual-microphone, bone-conduction (BC) sensor assisted beamformer and a simple recurrent unit (SRU)-based neural network postfilter for real-time speech enhancement. Assisted by the BC sensor, which is insensitive to the environmental noise compared to the regular air-conduction (AC) microphone, the accurate voice activity detection (VAD) can be obtained from the BC signal and incorporated into the adaptive noise canceller (ANC) and adaptive block matrix (ABM). The SRU-based postfilter consists of a recurrent neural network with a small number of parameters, which improves the computational efficiency. The sub-band signal processing is designed to compress the input features of the neural network, and the scale-invariant signal-to-distortion ratio (SI-SDR) is developed as the loss function to minimize the distortion of the desired speech signal. Experimental results demonstrate that the proposed real-time speech enhancement system provides significant speech sound quality and intelligibility improvements for all noise types and levels when compared with the AC-only beamformer with a postfiltering algorithm. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning for Smart Sensing Applications)
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10 pages, 3609 KiB  
Case Report
Sensing of Microvascular Vasomotion Using Consumer Camera
by Itaru Kaneko, Yutaka Yoshida, Emi Yuda and Junichiro Hayano
Sensors 2021, 21(18), 6256; https://doi.org/10.3390/s21186256 - 18 Sep 2021
Cited by 1 | Viewed by 2045
Abstract
In this paper, we will introduce a method for observing microvascular waves (MVW) by extracting different images from the available images in the video taken with consumer cameras. Microvascular vasomotion is a dynamic phenomenon that can fluctuate over time for a variety of [...] Read more.
In this paper, we will introduce a method for observing microvascular waves (MVW) by extracting different images from the available images in the video taken with consumer cameras. Microvascular vasomotion is a dynamic phenomenon that can fluctuate over time for a variety of reasons and its sensing is used for variety of purposes. The special device, a side stream dark field camera (SDF camera) was developed in 2015 for the medical purpose to observe blood flow from above the epidermis. However, without using SDF cameras, smart signal processing can be combined with a consumer camera to analyze the global motion of microvascular vasomotion. MVW is a propagation pattern of microvascular vasomotions which reflects biological properties of vascular network. In addition, even without SDF cameras, MVW can be analyzed as a spatial and temporal pattern of microvascular vasomotion using a combination of advanced signal processing with consumer cameras. In this paper, we will demonstrate that such vascular movements and MVW can be observed using a consumer cameras. We also show a classification using it. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning for Smart Sensing Applications)
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