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

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

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Prof. Dr. Piotr Samczynski
E-Mail Website1 Website2
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
E-Mail Website
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 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.

Keywords

  • signal processing
  • sensors
  • smart sensors
  • radar sensors
  • medical sensors
  • remote sensing

Published Papers (2 papers)

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Research

Article
Single-Channel Blind Source Separation of Spatial Aliasing Signal Based on Stacked-LSTM
Sensors 2021, 21(14), 4844; https://doi.org/10.3390/s21144844 - 16 Jul 2021
Viewed by 759
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. [...] Read more.
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
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Article
Multi-Sensor Perception Strategy to Enhance Autonomy of Robotic Operation for Uncertain Peg-in-Hole Task
Sensors 2021, 21(11), 3818; https://doi.org/10.3390/s21113818 - 31 May 2021
Viewed by 825
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 [...] Read more.
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
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