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Special Issue "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: 30 June 2021.

Special Issue Editors

Prof. Dr. Ying-Ren Chien
E-Mail Website
Guest Editor
Department of Electrical Engineering, National Ilan University, Taiwan
Interests: adaptive signal processing; IoT; machine learning; interference cancellation
Prof. Dr. Mu Zhou
E-Mail Website
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
Dr. Ao Peng
E-Mail Website
Guest Editor
School of Informatics, Xiamen University, Xiamen, China
Interests: GNSS; multi-Source positioning; radio navigation
Dr. Ni Zhu
E-Mail Website
Guest Editor
GEOLOC laboratory, University Gustave Eiffel, Marne la Vallée, France
Interests: multisensory fusion for indoor/outdoor pedestrian positioning; GNSS positioning in urban environments; integrity monitoring
Dr. Joaquín Torres-Sospedra
E-Mail Website
Guest Editor

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 papers will be 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 2200 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 (4 papers)

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Research

Open AccessArticle
Exploration of Driver Posture Monitoring Using Pressure Sensors with Lower Resolution
Sensors 2021, 21(10), 3346; https://doi.org/10.3390/s21103346 - 12 May 2021
Viewed by 258
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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Open AccessArticle
Intelligent Recognition of Chirp Radar Deceptive Jamming Based on Multi-Pulse Information Fusion
Sensors 2021, 21(8), 2693; https://doi.org/10.3390/s21082693 - 11 Apr 2021
Viewed by 297
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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Open AccessArticle
Low-Rank Matrix Recovery from Noise via an MDL Framework-Based Atomic Norm
Sensors 2020, 20(21), 6111; https://doi.org/10.3390/s20216111 - 27 Oct 2020
Viewed by 510
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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Open AccessArticle
A Real-Time Dual-Microphone Speech Enhancement Algorithm Assisted by Bone Conduction Sensor
Sensors 2020, 20(18), 5050; https://doi.org/10.3390/s20185050 - 05 Sep 2020
Viewed by 898
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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