Advances in Design and Signal Processing of Sensors

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (10 January 2024) | Viewed by 2594

Special Issue Editors

College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China
Interests: optical imaging; point cloud processing; signal processing
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: micro (optical) electromechanical system design and dynamic testing; intelligent detection and control; multi-physical field polarization parameter imaging and detection

Special Issue Information

Dear Colleagues,

In recent years, with the continuous development of cutting-edge technologies such as artificial intelligence, the Internet of Things and 5G, sensors have become an indispensable presence in industrial development. The rapid introduction of robots, unmanned aerial vehicles and autonomous vehicles, as well as the in-depth construction of smart cities, have also created broad development opportunities for sensor researches.

However, the sensor field still faces many unresolved problems in the design and signal processing for applications in complex environments. Typical challenges include the miniaturization, intellectualization and integration of sensors, the amplification and denoising of weak signals, fault diagnosis, and the heterogeneous data fusion.

This Special Issue is dedicated to new advances in the design and signal processing of sensors. We invite prospective authors to submit innovative and high-quality papers with original perspectives. The Special Issue is open to both original research articles and review articles covering all relevant progress in the fields, including, but not limited to:

  1. Sensors’ principles and design including MEMS IMU, lidar, gas sensors, magnetic fluid accelerometers.
  2. Miniaturization and integration of sensors.
  3. Signal denoising, feature extraction and fault diagnosis based on sensor fusion.
  4. Calibration method, signal collection and testing of sensors.
  5. Sensors’ applications, AI applications in machine/deep learning, reinforcement learning.

Dr. Xiaobin Xu
Dr. Yun Cao
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. Applied Sciences 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 2400 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

  • sensors
  • signal processing
  • feature extraction
  • fault diagnosis
  • AI applications

Published Papers (2 papers)

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Research

14 pages, 3858 KiB  
Article
Minimum Variance Distortionless Response—Hanbury Brown and Twiss Sound Source Localization
Appl. Sci. 2023, 13(10), 6013; https://doi.org/10.3390/app13106013 - 13 May 2023
Cited by 1 | Viewed by 834
Abstract
Sound source target localization is an extremely useful technique that is currently utilized in many fields. The Hanbury Brown and Twiss (HBT) interference target localization method based on sound fields is not accurate enough for localization at low signal-to-noise ratios (below 0 dB). [...] Read more.
Sound source target localization is an extremely useful technique that is currently utilized in many fields. The Hanbury Brown and Twiss (HBT) interference target localization method based on sound fields is not accurate enough for localization at low signal-to-noise ratios (below 0 dB). To address this problem, this paper introduces Minimum Variance Distortionless Response (MVDR) beamforming and proposes a new MVDR-HBT algorithm. Specifically, for narrowband signals, the inverse of the correlation matrix of the sound signal is calculated, and a guiding vector is constructed to compute the MVDR direction weights. These direction weights are then used to weight the correlation function of the HBT algorithm. Subsequently, the MVDR-HBT algorithm is extended from narrowband signals to broadband signals. As a result, the directivity of the HBT algorithm is optimized for wide- and narrowband signals, resulting in improved localization accuracy. Finally, the target localization accuracy of the MVDR-HBT algorithm is analyzed through simulation and localization experiments. The results show that the MVDR-HBT algorithm can accurately determine the direction of a sound source, with localization errors at different positions that are smaller than those produced by HBT. The localization performance of MVDR-HBT is considerably better than that of HBT, further verifying the simulation results. This study provides a new idea for target localization within an acoustic propagation medium (air). Full article
(This article belongs to the Special Issue Advances in Design and Signal Processing of Sensors)
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19 pages, 8649 KiB  
Article
Penetration Overload Prediction Method Based on a Deep Neural Network with Multiple Inputs
Appl. Sci. 2023, 13(4), 2351; https://doi.org/10.3390/app13042351 - 11 Feb 2023
Viewed by 976
Abstract
In the process of high-speed penetration, penetrating ammunition is prone to problems such as penetration overload signal vibration and mixings and projectile attitude deflection. It is easy to misjudge if a fuze relies only on the overload data from the ground or the [...] Read more.
In the process of high-speed penetration, penetrating ammunition is prone to problems such as penetration overload signal vibration and mixings and projectile attitude deflection. It is easy to misjudge if a fuze relies only on the overload data from the ground or the utilized program, and the actual penetration overload measured under actual launch conditions cannot be taken as the dynamic judgement basis. Therefore, a real-time penetration overload prediction method based on a deep neural network is proposed, which can predict overload values according to the projectile parameter settings, the real-time collection of overload information, and the calculation speed and assist the fuze in judging the target layer and projectile attitude. In this paper, we adopt a deep learning model with multiple time series inputs and modify the input coding mode so that the model can output a 48 μs overload curve within 20 μs, meeting the real-time signal processing requirements of the high-speed missile penetration process. The mean squared error between the predicted curve and the actual curve is 0.221 for the prediction of multilayer penetrating targets and 0.452 for the prediction of thick penetrating targets. A penetration overload prediction function can be realized. Full article
(This article belongs to the Special Issue Advances in Design and Signal Processing of Sensors)
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