Applications and Challenges of Digital Signal Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 November 2023) | Viewed by 865

Special Issue Editors

Department of Information Management, National Taipei University of Business, Taipei 100025, Taiwan
Interests: artificial intelligence; deep learning; natural language processing
Science and Technology Policy Research and Information Center, National Applied Research Laboratories, 14F., No. 106, Sec. 2, Heping E. Rd., Da'an Dist., Taipei 10636, Taiwan
Interests: sensor analysis; optics patent analysis; silicon photonics; solar cell; technology and innovation management; semiconductor industry analysis
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Special Issue Information

Dear Colleagues,

Digital Signal Processing (DSP) is an essential field of study in modern engineering and technology. DSP deals with the manipulation and analysis of signals, which are represented digitally as sequences of numbers. DSP finds its application in a wide range of fields such as communications, medical imaging, audio processing, radar systems, and many more. The goal of this Special Issue is to invite interested parties to publish original manuscripts and present state-of-the-art research regarding DSP, including novel algorithms and applications, large-scale computational science, artificial intelligence, machine learning, deep learning, and so on.

Topics of interest include, but are not limited to:

  • Signal processing for communication systems
  • Speech and audio signal processing
  • Image and video signal processing
  • Biomedical signal processing
  • Adaptive signal processing
  • Machine learning for signal processing
  • Signal processing for sensor networks:

Dr. Chun Chieh Lin
Dr. Chin-Yuan Fan
Guest Editors

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Keywords

  • machine learning
  • deep learning
  • digital signal processing
  • adaptive filtering
  • speech and audio signal processing
  • image and video processing
  • biomedical signal processing
  • noise reduction
  • signal distortion

Published Papers (1 paper)

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Research

16 pages, 1148 KiB  
Article
Performance of Iterative Coded CDMA Receivers with APP Feedback: A Use of a Weighted Delay Filter
by Ali Altalbe and Muhammad Tahir
Appl. Sci. 2023, 13(16), 9175; https://doi.org/10.3390/app13169175 - 11 Aug 2023
Cited by 1 | Viewed by 527
Abstract
The prohibitive computational complexity of optimal coded multiuser detection necessitates using suboptimal detectors in practical implementations. The filter is very computationally simple and is also demonstrated to provide faster convergence and superior bit error rate (BER) performance. Further investigation of the weighted delay [...] Read more.
The prohibitive computational complexity of optimal coded multiuser detection necessitates using suboptimal detectors in practical implementations. The filter is very computationally simple and is also demonstrated to provide faster convergence and superior bit error rate (BER) performance. Further investigation of the weighted delay filter concept produces a second filter—derived via the joint likelihood function. It is analytically demonstrated that extrinsic feedback systems will not benefit from weighted delay filtering. A system model is provided that introduces the notion of feedback ‘residue’, which is shown to be the key difference between a-posterior probability (APP) and extrinsic systems when determining the parallel interference cancellation (PIC) output statistics. It is analytically shown that the weighted delay filter derived via a maximum signal-to-noise ratio (SNR) approach is identical to a weighted delay filter derived via the joint likelihood function. It is analytically shown that when extrinsic feedback is used in a coded-code division multiple access (C-CDMA) system, no benefit will be realised by weighted delay filtering, as soft outputs from previous cycles are a merely scaled, noisy version of the most recent data. The notion of a ‘feedback residue’ for systems with APP feedback is introduced, and it is empirically shown that this residue term is a key consideration when determining the PIC output statistics. Using the ‘residual feedback’ model, it is shown that when APP feedback is utilised, data from previous cycles is not simply “a scaled, noisy version” of the current data. For this reason, benefits may be realised by APP feedback use. The simulation results shows that the residue may be trivial at small loads, the residue builds to the substantial value of nearly 0.4 at a reasonably modest load of K/N=15/10, and continues to grow as the load increases. Full article
(This article belongs to the Special Issue Applications and Challenges of Digital Signal Processing)
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