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Advances of Signal Processing for Signal, Image and Video Technology
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Advances of Signal Processing for Signal, Image and Video Technology

A special issue of Signals (ISSN 2624-6120).

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 4808

Special Issue Editor

Prof. Dr. Chi-Yi Tsai

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, TamKang University, 151 Yingzhuan Road, Tamsui District, New Taipei City 251, Taiwan
Interests: computer vision; computational complexity; convolutional neural nets; learning (artificial intelligence); mobile robots; object detection; robot vision; computer architecture; image classification; image colour analysis; image processing; video streaming
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Signal processing has an important role in the fields of image and video technology. In recent years, deep learning has become popular, and it provides new opportunities to develop novel signal processing algorithms based on data-driven approaches. Consequently, there is an increasingly urgent need for new signal processing approaches that can be used to solve a variety of image and video-processing applications. This Special Issue aims to invite the submission of original studies in the field of advanced signal processing. All high-quality works and reviews of machine learning and deep learning to promote advances in signal, image and video processing are welcome in, but not limited to, the following areas:

  • Advances in signal, image and video processing;
  • Data-driven-based signal processing;
  • Model-based signal processing;
  • Machine learning for signal, image and video processing;
  • Deep learning for signal, image and video processing;
  • Meta-learning for signal, image and video processing;
  • Transformer network for signal, image and video processing;
  • Generative adversarial network for signal, image and video processing;
  • Generic object tracking;
  • Multi-object tracking;
  • Nature language processing.

Prof. Dr. Chi-Yi Tsai
Guest Editor

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. Signals is an international peer-reviewed open access quarterly 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 1000 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

  • deep learning
  • data-driven signal processing
  • model-based signal processing
  • signal processing
  • image processing
  • image segmentation
  • image restoration
  • image recognition
  • video processing
  • visual tracking

Published Papers (2 papers)

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Research

22 pages, 23449 KiB  
Article
Classification and Discrimination of Birds and Small Drones Using Radar Micro-Doppler Spectrogram Images
by Ram M. Narayanan, Bryan Tsang and Ramesh Bharadwaj
Signals 2023, 4(2), 337-358; https://doi.org/10.3390/signals4020018 - 18 May 2023
Cited by 2 | Viewed by 2128
Abstract
This paper investigates the use of micro-Doppler spectrogram signatures of flying targets, such as drones and birds, to aid in their remote classification. Using a custom-designed 10-GHz continuous wave (CW) radar system, measurements from different scenarios on a variety of targets were recorded [...] Read more.
This paper investigates the use of micro-Doppler spectrogram signatures of flying targets, such as drones and birds, to aid in their remote classification. Using a custom-designed 10-GHz continuous wave (CW) radar system, measurements from different scenarios on a variety of targets were recorded to create datasets for image classification. Time/velocity spectrograms generated for micro-Doppler analysis of multiple drones and birds were used for target identification and movement classification using TensorFlow. Using support vector machines (SVMs), the results showed an accuracy of about 90% for drone size classification, about 96% for drone vs. bird classification, and about 85% for individual drone and bird distinction between five classes. Different characteristics of target detection were explored, including the landscape and behavior of the target. Full article
(This article belongs to the Special Issue Advances of Signal Processing for Signal, Image and Video Technology)
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22 pages, 1511 KiB  
Article
Verilog Design, Synthesis, and Netlisting of IoT-Based Arithmetic Logic and Compression Unit for 32 nm HVT Cells
by Raj Mouli Jujjavarapu and Alwin Poulose
Signals 2022, 3(3), 620-641; https://doi.org/10.3390/signals3030038 - 13 Sep 2022
Cited by 1 | Viewed by 2137
Abstract
Micro-processor designs have become a revolutionary technology almost in every industry. They brought the reality of automation and also electronic gadgets. While trying to improvise these hardware modules to handle heavy computational loads, they have substantially reached a limit in size, power efficiency, [...] Read more.
Micro-processor designs have become a revolutionary technology almost in every industry. They brought the reality of automation and also electronic gadgets. While trying to improvise these hardware modules to handle heavy computational loads, they have substantially reached a limit in size, power efficiency, and similar avenues. Due to these constraints, many manufacturers and corporate entities are trying many ways to optimize these mini beasts. One such approach is to design microprocessors based on the specified operating system. This approach came to the limelight when many companies launched their microprocessors. In this paper, we will look into one method of using an arithmetic logic unit (ALU) module for internet of things (IoT)-enabled devices. A specific set of operations is added to the classical ALU to help fast computational processes in IoT-specific programs. We integrated a compression module and a fast multiplier based on the Vedic algorithm in the 16-bit ALU module. The designed ALU module is also synthesized under a 32-nm HVT cell library from the Synopsys database to generate an overview of the areal efficiency, logic levels, and layout of the designed module; it also gives us a netlist from this database. The synthesis provides a complete overview of how the module will be manufactured if sent to a foundry. Full article
(This article belongs to the Special Issue Advances of Signal Processing for Signal, Image and Video Technology)
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