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Electronics
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Recent Advances in Signal Processing and Applications
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Recent Advances in Signal Processing and Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".

Deadline for manuscript submissions: closed (15 September 2024) | Viewed by 10153

Special Issue Editors

Prof. Dr. Mário P. Véstias

E-Mail Website
Guest Editor
INESC INOV, Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Lisboa, Portugal
Interests: computer architecture; digital system design; reconfigurable computing; embedded computing; deep learning; deep neural networks; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Rui Policarpo Duarte

E-Mail Website
Guest Editor
Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa, 1000-029 Lisbon, Portugal
Interests: FPGAs; approximate computing; digital design optimization; DSP; fault-tolerant systems; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

From analog to digital, signal processing is present in almost all domains where it is used to convert and transform data. Signal processing algorithms and techniques allow us to analyze and transform all types of data, such as audio, image, and video. The amount and diversity of data created and consumed is increasing quite quickly. Therefore, new algorithms are being proposed to deal with new data.

Signal processing is mostly digital and executed in software using a generic or graphics processor. With the utilization of more complex and computationally demanding algorithms, more powerful multi-core processors are being used. On the other hand, more computationally efficient solutions are being obtained by using dedicated hardware designed with application-specific integrated circuits or field-programmable gate arrays.

Signal processing is applied in many different applications: image processing, audio processing, speech recognition and synthesis, RADAR and LIDAR processing, model analysis, medical data analysis, weather forecasting, image classification, object detection, image segmentation, etc.

This Special Issue aims to collect studies on recent advances in signal processing and its applications. Potential topics include, but are not limited to:

  • Signal processing algorithms for image, data, and audio;
  • Algorithms for LIDAR processing;
  • Machine learning algorithms for signal processing;
  • Signal processing with deep learning;
  • New architectures for signal processing;
  • Signal processing architectures in FPGA and ASIC;
  • Algorithms for speech synthesis and translation;
  • Signal processing on the edge;
  • Signal processing on devices for the Internet of Things;
  • New applications from novel signal processing algorithms;
  • Other related topics.

Prof. Dr. Mário P. Véstias
Dr. Rui Policarpo Duarte
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. Electronics 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

  • signal processing algorithms
  • signal processing devices
  • smart signal processing
  • signal processing applications

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

20 pages, 12278 KiB  
Article
A Study on Ways to Expand the Speed Operation Range of Dental Laboratory Handpiece Motors Using Low-Cost Hall Sensors
by Gwangmin Park and Jong Suk Lim
Electronics 2024, 13(21), 4259; https://doi.org/10.3390/electronics13214259 - 30 Oct 2024
Viewed by 948
Abstract
In the dental prosthetics field, BLDC micro-motors are primarily used for implant procedures. As dental materials become increasingly harder, there is a growing demand for higher performance motors to precisely process these materials. However, difficulties in motor development arise due to price competitiveness. [...] Read more.
In the dental prosthetics field, BLDC micro-motors are primarily used for implant procedures. As dental materials become increasingly harder, there is a growing demand for higher performance motors to precisely process these materials. However, difficulties in motor development arise due to price competitiveness. Dental motors require capabilities such as low-speed, high-torque for drilling operations and high-speed rotation for precise machining of high-strength dental materials. Additionally, there is a requirement to address thermal issues to prevent user burns due to motor-generated heat. Typically, motors requiring precision control utilize high-resolution position sensors like encoders or resolvers to acquire and control the rotor’s position. However, the high cost of these sensors and constraints such as increased device size pose challenges in maintaining price competitiveness. In this paper, we propose a control algorithm that satisfies the requirements for low-speed, high-torque and high-speed operation requirements for precision machining without hardware modifications using a BLDC micro-motor equipped with an inexpensive Hall sensor commonly used in the dental field. Furthermore, the algorithm is designed to ensure stable operation even in the event of Hall sensor failure or malfunction, and its effectiveness is validated through experiments. Full article
(This article belongs to the Special Issue Recent Advances in Signal Processing and Applications)
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20 pages, 23226 KiB  
Article
Signal Processing to Characterize and Evaluate Nonlinear Acoustic Signals Applied to Underwater Communications
by María Campo-Valera, Dídac Diego-Tortosa, Ignacio Rodríguez-Rodríguez, Jorge Useche-Ramírez and Rafael Asorey-Cacheda
Electronics 2024, 13(21), 4192; https://doi.org/10.3390/electronics13214192 - 25 Oct 2024
Cited by 2 | Viewed by 1292
Abstract
Nonlinear acoustic signals, specifically the parametric effect, offer significant advantages over linear signals because the low frequencies generated in the medium due to the intermodulation of the emitted frequencies are highly directional and can propagate over long distances. Due to these characteristics, a [...] Read more.
Nonlinear acoustic signals, specifically the parametric effect, offer significant advantages over linear signals because the low frequencies generated in the medium due to the intermodulation of the emitted frequencies are highly directional and can propagate over long distances. Due to these characteristics, a detailed analysis of these signals is necessary to accurately estimate the Time of Arrival (ToA) and amplitude parameters. This is crucial for various communication applications, such as sonar and underwater location systems. The research addresses a notable gap in the literature regarding comparative methods for analyzing nonlinear acoustic signals, particularly focusing on ToA estimation and amplitude parameterization. Two types of nonlinear modulations are examined: parametric Frequency-Shift Keying (FSK) and parametric sine-sweep modulation, which correspond to narrowband and broadband signals, respectively. The first study evaluates three ToA estimation methods—threshold, power variation (Pvar), and cross-correlation methods for the modulations in question. Following ToA estimation, the amplitude of the received signals is analyzed using acoustic signal processing techniques such as time-domain, frequency-domain, and cross-correlation methods. The practical application is validated through controlled laboratory experiments, which confirm the robustness and effectiveness of the existing methods proposed under study for nonlinear (parametric) acoustic signals. Full article
(This article belongs to the Special Issue Recent Advances in Signal Processing and Applications)
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37 pages, 1450 KiB  
Article
FPGA-Based Design of a Ready-to-Use and Configurable Soft IP Core for Frame Blocking Time-Sampled Digital Speech Signals
by Nettimi Satya Sai Srinivas, Nagarajan Sugan, Lakshmi Sutha Kumar, Malaya Kumar Nath and Aniruddha Kanhe
Electronics 2024, 13(21), 4180; https://doi.org/10.3390/electronics13214180 - 24 Oct 2024
Viewed by 1499
Abstract
‘Frame blocking’ or ‘Framing’ is a technique that divides a time-sampled speech or audio signal into consecutive and equi-sized short-time frames, either overlapped or non-overlapped, for analysis. The framing hardware architectures (FHA) in the literature support framing speech or audio samples of specific [...] Read more.
‘Frame blocking’ or ‘Framing’ is a technique that divides a time-sampled speech or audio signal into consecutive and equi-sized short-time frames, either overlapped or non-overlapped, for analysis. The framing hardware architectures (FHA) in the literature support framing speech or audio samples of specific word size with specific frame size and frame overlap size. However, speech and audio applications often require framing signal samples of varied word sizes with varied frame sizes and frame overlap sizes. Therefore, the existing FHAs must be redesigned appropriately to keep up with the variability in word size, frame size and frame overlap size, as demanded across multiple applications. Redesigning the existing FHAs for each specific application is laborious, prompting the need for a configurable intellectual property (IP) core. The existing FHAs are inappropriate for creating configurable IP cores as they lack adaptability to accommodate variability in frame size and frame overlap size. Therefore, to address these issues, a novel FHA, adaptable to accommodate the desired variability, is proposed. Furthermore, the proposed FHA is transformed into a field-programmable gate array-based soft, ready-to-use and configurable frame blocking IP core using the Xilinx® Vivado™ tool. The resulting IP core is versatile, offering configurability for framing in numerous applications incorporating real-time digital speech and audio systems. This research article discusses the proposed FHA and frame blocking IP core in detail. Full article
(This article belongs to the Special Issue Recent Advances in Signal Processing and Applications)
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30 pages, 18530 KiB  
Article
Dimensionality Reduction for the Real-Time Light-Field View Synthesis of Kernel-Based Models
by Martijn Courteaux, Hannes Mareen, Bert Ramlot, Peter Lambert and Glenn Van Wallendael
Electronics 2024, 13(20), 4062; https://doi.org/10.3390/electronics13204062 - 15 Oct 2024
Cited by 1 | Viewed by 1166
Abstract
Several frameworks have been proposed for delivering interactive, panoramic, camera-captured, six-degrees-of-freedom video content. However, it remains unclear which framework will meet all requirements the best. In this work, we focus on a Steered Mixture of Experts (SMoE) for 4D planar light fields, which [...] Read more.
Several frameworks have been proposed for delivering interactive, panoramic, camera-captured, six-degrees-of-freedom video content. However, it remains unclear which framework will meet all requirements the best. In this work, we focus on a Steered Mixture of Experts (SMoE) for 4D planar light fields, which is a kernel-based representation. For SMoE to be viable in interactive light-field experiences, real-time view synthesis is crucial yet unsolved. This paper presents two key contributions: a mathematical derivation of a view-specific, intrinsically 2D model from the original 4D light field model and a GPU graphics pipeline that synthesizes these viewpoints in real time. Configuring the proposed GPU implementation for high accuracy, a frequency of 180 to 290 Hz at a resolution of 2048×2048 pixels on an NVIDIA RTX 2080Ti is achieved. Compared to NVIDIA’s instant-ngp Neural Radiance Fields (NeRFs) with the default configuration, our light field rendering technique is 42 to 597 times faster. Additionally, allowing near-imperceptible artifacts in the reconstruction process can further increase speed by 40%. A first-order Taylor approximation causes imperfect views with peak signal-to-noise ratio (PSNR) scores between 45 dB and 63 dB compared to the reference implementation. In conclusion, we present an efficient algorithm for synthesizing 2D views at arbitrary viewpoints from 4D planar light-field SMoE models, enabling real-time, interactive, and high-quality light-field rendering within the SMoE framework. Full article
(This article belongs to the Special Issue Recent Advances in Signal Processing and Applications)
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14 pages, 3673 KiB  
Article
Bearing Fault Vibration Signal Denoising Based on Adaptive Denoising Autoencoder
by Haifei Lu, Kedong Zhou and Lei He
Electronics 2024, 13(12), 2403; https://doi.org/10.3390/electronics13122403 - 19 Jun 2024
Cited by 3 | Viewed by 1592
Abstract
Vibration signal analysis is regarded as a fundamental approach in diagnosing faults in rolling bearings, and recent advancements have shown notable progress in this domain. However, the presence of substantial background noise often results in the masking of these fault signals, posing a [...] Read more.
Vibration signal analysis is regarded as a fundamental approach in diagnosing faults in rolling bearings, and recent advancements have shown notable progress in this domain. However, the presence of substantial background noise often results in the masking of these fault signals, posing a significant challenge for researchers. In response, an adaptive denoising autoencoder (ADAE) approach is proposed in this paper. The data representations are learned by the encoder through convolutional layers, while the data reconstruction is performed by the decoder using deconvolutional layers. Both the encoder and decoder incorporate adaptive shrinkage units to simulate denoising functions, effectively removing interfering information while preserving sensitive fault features. Additionally, dropout regularization is applied to sparsify the network and prevent overfitting, thereby enhancing the overall expressive power of the model. To further enhance ADAE’s noise resistance, shortcut connections are added. Evaluation using publicly available datasets under scenarios with known and unknown noise demonstrates that ADAE effectively enhances the signal-to-noise ratio in strongly noisy backgrounds, facilitating accurate diagnosis of faults in rolling bearings. Full article
(This article belongs to the Special Issue Recent Advances in Signal Processing and Applications)
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20 pages, 26514 KiB  
Article
Improved Underwater Single-Vector Acoustic DOA Estimation via Vector Convolution Preprocessing
by Haitao Dong, Jian Suo, Zhigang Zhu and Siyuan Li
Electronics 2024, 13(9), 1796; https://doi.org/10.3390/electronics13091796 - 6 May 2024
Cited by 5 | Viewed by 1452
Abstract
Remote passive sonar detection with underwater acoustic vector sensor (UAVS) has attracted increasing attention due to its merit in measuring the full sound field information. However, the accurate estimation of the direction-of-arrival (DOA) remains a challenging problem, especially under low signal-to-noise ratio (SNR) [...] Read more.
Remote passive sonar detection with underwater acoustic vector sensor (UAVS) has attracted increasing attention due to its merit in measuring the full sound field information. However, the accurate estimation of the direction-of-arrival (DOA) remains a challenging problem, especially under low signal-to-noise ratio (SNR) conditions. In this paper, a novel convolution (COV)-based single-vector acoustic preprocessing method is proposed on the basis of the single-vector acoustic preprocessing model. In view of the theoretical analysis of the classical single-vector acoustic DOA estimation method, the principle of preprocessing can be described as “to achieve an improved denoising performance in the constraint of equivalent amplitude gain and phase response.” This can be naturally guaranteed by our proposed COV method. In addition, the upper bound with matched filtering (MF) preprocessing is provided in the consideration of the optimal linear signal processing for weak signal detection under Gaussian noise. Numerical analyses demonstrate the effectiveness of our proposed preprocessing method with both vector array signal processing-based and intensity-based methods. Experimental verification conducted in South China Sea further verifies the effectiveness of our approach for practical applications. This work can lay a solid foundation in improving underwater remote vector acoustic DOA estimation under low SNR, and can provide important guidance for future research work. Full article
(This article belongs to the Special Issue Recent Advances in Signal Processing and Applications)
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