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Smart Sensing Systems: Algorithms and Applications—Selected Papers from Signal Processing Symposium 2021 (SPSympo-2021)

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

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 26775

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Prof. Dr. Piotr Samczynski

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Guest Editor

Politechnika Warszawska, Warsaw University of Technology, 00-661 Warszawa, Poland
Interests: SAR/ISAR; passive radars; passive SAR/ISAR; noise radars; radar signal processing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Pawel Strumillo

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Guest Editor

Institute of Electronics, Lodz University of Technology, 90-924 Lodz, Poland
Interests: biomedical signals and images; sensory substitution systems; human–computer interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, developments in smart sensing systems incorporating technology from different fields have intensified. This Special Issue is devoted to intelligent sensing systems that include Information and Communications Technologies (ICT) for signal and image acquisition, transmission, processing, and analysis for a broad range of applications, including remote sensing systems (radars, sonars, imaging, sensor networks), IoT, telemedicine, medical diagnosis, treatment and rehabilitation, robotics, human–system interaction, environment monitoring, and space technologies, which are the main topics of the Signal Processing Symposium (SPSympo) to be held on 21–23 September 2021 in Łódź, Poland. Participants of the conference are invited to submit the extended version of their paper published in the conference proceedings. However, the Special Issue is not limited to conference participants only. Submissions from all researchers working in the field shall also be welcome.

The Special Issue aims to highlight smart sensing systems. Topics include but are not limited to:

Audio signal processing and voice recognition;
Algorithms for real-time processing;
Cognitive functions;
Compression techniques;
Fuzzy logic;
Human factor in signal processing;
Image processing and recognition;
Localization and tracking;
Man–machine interface;
Radar and sonar imaging;
Radar signal processing;
RF and radar technology;
Security applications;
Geoscience and remote sensing;
Signal processing components;
Signal and image processing for medical applications;
Space technology;
Tele-informatics and communication;
Tomography and medical image reconstruction;
Waveform design techniques.

Prof. Dr. Piotr Samczynski
Prof. Dr. Pawel Strumillo
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.

Keywords

signal processing
sensors
smart sensors
radar sensors
medical sensors
remote sensing

Published Papers (11 papers)

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Research

15 pages, 4560 KiB

Open AccessArticle

Analysis of New Orthogonal Transforms for Digital Watermarking

by Piotr Bogacki and Andrzej Dziech

Sensors 2022, 22(7), 2628; https://0-doi-org.brum.beds.ac.uk/10.3390/s22072628 - 29 Mar 2022

Cited by 2 | Viewed by 1501

Abstract

The paper focuses on the application of new orthogonal transforms in digital watermarking. Novel types of transforms and their characteristics are presented. Potential methods for watermark embedding and recovery are also proposed. They assume embedding hidden information in the transform domains using the luminance channel of the original image. Image spectra are obtained by dividing the original image into smaller blocks that then are further processed by performing the forward transform operation. A watermark is embedded by modifying the spectral coefficients with relatively low values. Since there are various types of transforms, the latter process is realized in an adaptive manner. The proposed solutions were evaluated by measuring the level of visual distortion with respect to the total size of the inserted data. Additionally, the bit error rate (BER) in the recovery phase is also analyzed. The elaborated methods seem to be useful for applications in digital signal and image processing where high imperceptibility and low BER are of great importance. New orthogonal transforms were proved to be useful in watermarking tasks, and in some cases, they can even outperform the classic DCT approach. Full article

(This article belongs to the Special Issue Smart Sensing Systems: Algorithms and Applications—Selected Papers from Signal Processing Symposium 2021 (SPSympo-2021))

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32 pages, 7223 KiB

Open AccessArticle

Sensing System for Plegic or Paretic Hands Self-Training Motivation

by Igor Zubrycki, Ewa Prączko-Pawlak and Ilona Dominik

Sensors 2022, 22(6), 2414; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062414 - 21 Mar 2022

Viewed by 2323

Abstract

Patients after stroke with paretic or plegic hands require frequent exercises to promote neuroplasticity and to improve hand joint mobilization. Available devices for hand exercising are intended for persons with some level of hand control or provide continuous passive motion with limited patient involvement. Patients can benefit from self-exercising where they use the other hand to exercise the plegic or paretic one. However, post-stroke neuropsychological complications, apathy, and cognitive impairments such as forgetfulness make regular self-exercising difficult. This paper describes Przypominajka v2—a system intended to support self-exercising, remind about it, and motivate patients. We propose a glove-based device with an on-device machine-learning-based exercise scoring, a tablet-based interface, and a web-based application for therapists. The feasibility of on-device inference and the accuracy of correct exercise classification was evaluated on four healthy participants. Whole system use was described in a case study with a patient with a paretic hand. The anomaly classification has an accuracy of 91.3% and

f_{1}

value of 91.6% but achieves poorer results for new users (78% and 81%). The case study showed that patients had a positive reaction to exercising with Przypominajka, but there were issues relating to sensor glove: ease of putting on and clarity of instructions. The paper presents a new way in which sensor systems can support the rehabilitation of after-stroke patients with an on-device machine-learning-based classification that can accurately score and contribute to patient motivation. Full article

(This article belongs to the Special Issue Smart Sensing Systems: Algorithms and Applications—Selected Papers from Signal Processing Symposium 2021 (SPSympo-2021))

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19 pages, 5522 KiB

Open AccessArticle

Wireless Capsule Endoscope Localization with Phase Detection Algorithm and Adaptive Body Model

by Paweł Oleksy and Łukasz Januszkiewicz

Sensors 2022, 22(6), 2200; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062200 - 11 Mar 2022

Cited by 3 | Viewed by 2618

Abstract

Wireless capsule endoscopes take and send photos of the human digestive tract, which are used for medical diagnosis. The capsule’s location enables exact identification of the regions with lesions. This can be carried out by analyzing the parameters of the electromagnetic wave received from the capsule. Because the human body is a complex heterogeneous environment that impacts the propagation of wireless signals, determining the distance between the transmitter and the receiver based on the received power level is challenging. An enhanced approach of identifying the location of endoscope capsules using a wireless signal phase detection algorithm is presented in this paper. For each capsule position, this technique uses adaptive estimation of human body model permittivity. This approach was tested using computer simulations in Remcom XFdtd software using a numerical, heterogeneous human body model, as well as measurements with physical phantom. The type of transmitting antenna employed in the capsule also has a significant impact on the suggested localization method’s accuracy. As a result, the helical antenna, which is smaller than the dipole, was chosen as the signal’s source. For both the numerical and physical phantom studies, the proposed technique with adaptive body model enhances localization accuracy by roughly 30%. Full article

(This article belongs to the Special Issue Smart Sensing Systems: Algorithms and Applications—Selected Papers from Signal Processing Symposium 2021 (SPSympo-2021))

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22 pages, 1966 KiB

Open AccessArticle

Comparison of Empirical Mode Decomposition and Singular Spectrum Analysis for Quick and Robust Detection of Aerodynamic Instabilities in Centrifugal Compressors

by Mateusz Stajuda, David García Cava and Grzegorz Liśkiewicz

Sensors 2022, 22(5), 2063; https://0-doi-org.brum.beds.ac.uk/10.3390/s22052063 - 07 Mar 2022

Cited by 4 | Viewed by 2197

Abstract

Aerodynamic instabilities in centrifugal compressors are dangerous phenomena affecting machine efficiency and in severe cases leading to failure of the compressing system. Quick and robust instability detection during compressor operation is a challenge of utmost importance from an economical and safety point of view. Rapid indication of instabilities can be obtained using a pressure signal from the compressor. Detection of aerodynamic instabilities using pressure signal results in specific challenges, as the signal is often highly contaminated with noise, which can influence the performance of detection methods. The aim of this study is to investigate and compare the performance of two non-linear signal processing methods—Empirical Mode Decomposition (EMD) and Singular Spectrum Analysis (SSA)—for aerodynamic instability detection. Two instabilities of different character, local—inlet recirculation and global—surge, are considered. The comparison focuses on the robustness, sensitivity and pace of detection—crucial parameters for a successful detection method. It is shown that both EMD and SSA perform similarly for the analysed machine, despite different underlying principles of the methods. Both EMD and SSA have great potential for instabilities detection, but tuning of their parameters is important for robust detection. Full article

(This article belongs to the Special Issue Smart Sensing Systems: Algorithms and Applications—Selected Papers from Signal Processing Symposium 2021 (SPSympo-2021))

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24 pages, 15785 KiB

Open AccessArticle

Hand Measurement System Based on Haptic and Vision Devices towards Post-Stroke Patients

by Katarzyna Koter, Martyna Samowicz, Justyna Redlicka and Igor Zubrycki

Sensors 2022, 22(5), 2060; https://0-doi-org.brum.beds.ac.uk/10.3390/s22052060 - 07 Mar 2022

Cited by 3 | Viewed by 2347

Abstract

Diagnostics of a hand requires measurements of kinematics and joint limits. The standard tools for this purpose are manual devices such as goniometers which allow measuring only one joint simultaneously, making the diagnostics time-consuming. The paper presents a system for automatic measurement and computer presentation of essential parameters of a hand. Constructed software uses an integrated vision system, a haptic device for measurement, and has a web-based user interface. The system provides a simplified way to obtain hand parameters, such as hand size, wrist, and finger range of motions, using the homogeneous-matrix-based notation. The haptic device allows for active measurement of the wrist’s range of motion and additional force measurement. A study was conducted to determine the accuracy and repeatability of measurements compared to the gold standard. The system functionality was confirmed on five healthy participants, with results showing comparable results to manual measurements regarding fingers’ lengths. The study showed that the finger’s basic kinematic structure could be measured by a vision system with a mean difference to caliper measurement of 4.5 mm and repeatability with the Standard Deviations up to 0.7 mm. Joint angle limits measurement achieved poorer results with a mean difference to goniometer of 23.6º. Force measurements taken by the haptic device showed the repeatability with a Standard Deviation of 0.7 N. The presented system allows for a unified measurement and a collection of important parameters of a human hand with therapist interface visualization and control with potential use for post-stroke patients’ precise rehabilitation. Full article

(This article belongs to the Special Issue Smart Sensing Systems: Algorithms and Applications—Selected Papers from Signal Processing Symposium 2021 (SPSympo-2021))

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28 pages, 8441 KiB

Open AccessArticle

Performance Analysis of Public Safety Cognitive Radio MANET for Diversified Traffic

by Piotr Gajewski, Jerzy Łopatka and Piotr Łubkowski

Sensors 2022, 22(5), 1927; https://0-doi-org.brum.beds.ac.uk/10.3390/s22051927 - 01 Mar 2022

Cited by 3 | Viewed by 1908

Abstract

This paper presents properties of a mobile ad hoc network (MANET) with dynamic spectrum management (DSM) and is devoted to the concept and implementation of the new traffic engine that is used in a High-Fidelity simulator of MANET with cognitive nodes for special applications. The communication traffic generated by each node is defined according to its role in the hierarchical structure of the operational scenario, determining its priorities, permission to use particular real time and non-real time services. The service usage is a source based model, defined in the user’s profile containing its statistical properties, describing periodicity, duration and randomness of traffic generation. The overall traffic generated by the node is a combination of traffics related to specific services. Their statistical parameters are based on real exercises results. The model was defined in the Matlab environment and next verified using the MAENA simulator for complex, operational scenarios. The achieved results show that use of both central and distributed DSM provides a better performance of the MANET network with complex traffic. Full article

(This article belongs to the Special Issue Smart Sensing Systems: Algorithms and Applications—Selected Papers from Signal Processing Symposium 2021 (SPSympo-2021))

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23 pages, 4369 KiB

Open AccessArticle

Segmentation of Glottal Images from High-Speed Videoendoscopy Optimized by Synchronous Acoustic Recordings

by Bartosz Kopczynski, Ewa Niebudek-Bogusz, Wioletta Pietruszewska and Pawel Strumillo

Sensors 2022, 22(5), 1751; https://0-doi-org.brum.beds.ac.uk/10.3390/s22051751 - 23 Feb 2022

Cited by 4 | Viewed by 1911

Abstract

Laryngeal high-speed videoendoscopy (LHSV) is an imaging technique offering novel visualization quality of the vibratory activity of the vocal folds. However, in most image analysis methods, the interaction of the medical personnel and access to ground truth annotations are required to achieve accurate detection of vocal folds edges. In our fully automatic method, we combine video and acoustic data that are synchronously recorded during the laryngeal endoscopy. We show that the image segmentation algorithm of the glottal area can be optimized by matching the Fourier spectra of the pre-processed video and the spectra of the acoustic recording during the phonation of sustained vowel /i:/. We verify our method on a set of LHSV recordings taken from subjects with normophonic voice and patients with voice disorders due to glottal insufficiency. We show that the computed geometric indices of the glottal area make it possible to discriminate between normal and pathologic voices. The median of the Open Quotient and Minimal Relative Glottal Area values for healthy subjects were 0.69 and 0.06, respectively, while for dysphonic subjects were 1 and 0.35, respectively. We also validate these results using independent phoniatrician experts. Full article

(This article belongs to the Special Issue Smart Sensing Systems: Algorithms and Applications—Selected Papers from Signal Processing Symposium 2021 (SPSympo-2021))

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18 pages, 7314 KiB

Open AccessArticle

Improved 2D Ground Target Tracking in GPS-Based Passive Radar Scenarios

by Pedro Gomez-del-Hoyo, Nerea del-Rey-Maestre, María-Pilar Jarabo-Amores, David Mata-Moya and María-de-Cortés Benito-Ortiz

Sensors 2022, 22(5), 1724; https://0-doi-org.brum.beds.ac.uk/10.3390/s22051724 - 23 Feb 2022

Cited by 12 | Viewed by 2301

Abstract

Global Positioning System (GPS) satellites offer promising opportunity for Passive Radar systems due to their global coverage and the availability of multiple satellites throughout the world. However, their low power at ground level limits system coverage. In this paper, a GPS based Passive Radar which exploits a single illumination source, and uses digital array processing for ground targets localization is presented. To face signal power problems, a processing scheme combining reconstructed reference signals, adaptive filtering techniques and spatial filtering is implemented. Conventional beamforming techniques are used to increase the level of the target echo before the detection stage, and high resolution DoA estimation techniques are applied to estimate targets azimuth. Ground target localization in local Cartesian space is performed taking into account the system geometry, range and azimuth information. Both synthetic and real radar data are used to analyse system operation. During the measurement campaign, a cooperative vehicle was used for validation purposes. Results confirm that ground targets detection and localization are feasible using a single GPS transmitter. Full article

(This article belongs to the Special Issue Smart Sensing Systems: Algorithms and Applications—Selected Papers from Signal Processing Symposium 2021 (SPSympo-2021))

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19 pages, 2433 KiB

Open AccessArticle

Multipath Propagation of Acoustic Signal in a Swimming Pool—Source Localization Problem

by Jacek Misiurewicz, Konrad Bruliński, Wiesław Klembowski, Krzysztof Stefan Kulpa and Jan Pietrusiewicz

Sensors 2022, 22(3), 1162; https://0-doi-org.brum.beds.ac.uk/10.3390/s22031162 - 03 Feb 2022

Cited by 5 | Viewed by 1864

Abstract

This paper explores the problem of severe multipath propagation of underwater acoustic signals in a swimming pool. The problem appeared in a study that examined a system used to signal emergency situations (i.e., pre-drowning symptoms detected by a wearable device on a pool user’s wrist) and locate the signal source. A swimming pool acoustic environment is characterized by the presence of large flat reflecting planes surrounding a small volume of water. The reflections are numerous and much stronger than in typical hydroacoustic applications. In this paper, we attempted to create a model of the swimming pool response, one that is suitable for simulation experiments with detection and localization of emergency signals. Then, we explore the possible remedies for the localization system, applied on the transmit side (waveform design) and on the receive side (receiver placement and signal processing). Finally, we present an algorithm for object localization, considering the possible reflections with a multi-hypothesis approach. Full article

(This article belongs to the Special Issue Smart Sensing Systems: Algorithms and Applications—Selected Papers from Signal Processing Symposium 2021 (SPSympo-2021))

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18 pages, 1790 KiB

Open AccessArticle

Single-Channel Blind Source Separation of Spatial Aliasing Signal Based on Stacked-LSTM

by Mengchen Zhao, Xiujuan Yao, Jing Wang, Yi Yan, Xiang Gao and Yanan Fan

Sensors 2021, 21(14), 4844; https://0-doi-org.brum.beds.ac.uk/10.3390/s21144844 - 16 Jul 2021

Cited by 14 | Viewed by 3169

Abstract

Aiming at the problem of insufficient separation accuracy of aliased signals in space Internet satellite-ground communication scenarios, a stacked long short-term memory network (Stacked-LSTM) separation method based on deep learning is proposed. First, the coding feature representation of the mixed signal is extracted. Then, the long sequence input is divided into smaller blocks through the Stacked-LSTM network with the attention mechanism of the SE module, and the deep feature mask of the source signal is trained to obtain the Hadamard product of the mask of each source and the coding feature of the mixed signal, which is the encoding feature representation of the source signal. Finally, characteristics of the source signal is decoded by 1-D convolution to to obtain the original waveform. The negative scale-invariant source-to-noise ratio (SISNR) is used as the loss function of network training, that is, the evaluation index of single-channel blind source separation performance. The results show that in the single-channel separation of spatially aliased signals, the Stacked-LSTM method improves SISNR by 10.09∼38.17 dB compared with the two classic separation algorithms of ICA and NMF and the three deep learning separation methods of TasNet, Conv-TasNet and Wave-U-Net. The Stacked-LSTM method has better separation accuracy and noise robustness. Full article

(This article belongs to the Special Issue Smart Sensing Systems: Algorithms and Applications—Selected Papers from Signal Processing Symposium 2021 (SPSympo-2021))

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26 pages, 10485 KiB

Open AccessArticle

Multi-Sensor Perception Strategy to Enhance Autonomy of Robotic Operation for Uncertain Peg-in-Hole Task

by Li Qin, Hongyu Wang, Yazhou Yuan and Shufan Qin

Sensors 2021, 21(11), 3818; https://0-doi-org.brum.beds.ac.uk/10.3390/s21113818 - 31 May 2021

Cited by 1 | Viewed by 2362

Abstract

The peg-in-hole task with object feature uncertain is a typical case of robotic operation in the real-world unstructured environment. It is nontrivial to realize object perception and operational decisions autonomously, under the usual visual occlusion and real-time constraints of such tasks. In this paper, a Bayesian networks-based strategy is presented in order to seamlessly combine multiple heterogeneous senses data like humans. In the proposed strategy, an interactive exploration method implemented by hybrid Monte Carlo sampling algorithms and particle filtering is designed to identify the features’ estimated starting value, and the memory adjustment method and the inertial thinking method are introduced to correct the target position and shape features of the object respectively. Based on the Dempster–Shafer evidence theory (D-S theory), a fusion decision strategy is designed using probabilistic models of forces and positions, which guided the robot motion after each acquisition of the estimated features of the object. It also enables the robot to judge whether the desired operation target is achieved or the feature estimate needs to be updated. Meanwhile, the pliability model is introduced into repeatedly perform exploration, planning and execution steps to reduce interaction forces, the number of exploration. The effectiveness of the strategy is validated in simulations and in a physical robot task. Full article

(This article belongs to the Special Issue Smart Sensing Systems: Algorithms and Applications—Selected Papers from Signal Processing Symposium 2021 (SPSympo-2021))

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