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Applied Sciences
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Advanced Digital Signal Processing and Its Applications
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Advanced Digital Signal Processing and Its Applications

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: 20 February 2025 | Viewed by 1189

Special Issue Editors

Prof. Dr. Aleksandr Cariow

E-Mail Website
Guest Editor
Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Szczecin Żołnierska 52, 71-210 Szczecin, Poland
Interests: digital signal; image and video processing; algorithms; numbers; computations; parallel processing; embedded systems; parallel computing; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Marek Parfieniuk

E-Mail Website
Guest Editor
Department of Artificial Intelligence and Multimedia, Faculty of Computer Science, University of Bialystok, 20 B Świerkowa Street, 15-328 Białystok, Poland
Interests: design, implementation, and applications of digital filter banks and transforms; subband and transform-based signal processing: speech, audio, and image coding and enhancement; applications of algebra, especially matrix factorizations and hypercomplex numbers (quaternions) to digital signal processing; multimedia communications, standards, and systems; advanced programming
Dr. Adam Nowosielski

E-Mail Website
Guest Editor
Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Szczecin Żołnierska 52, 71-210 Szczecin, Poland
Interests: image processing; pattern recognition; computer vision; biometrics

Special Issue Information

Dear Colleagues,

The continuous progress of science and technology requires the development of information processing systems to seek new progressive methods and design solutions for performing calculations. Primary information is derived from various sources in the form of signals. By default, this means that signals can be one-dimensional, two-dimensional (images) and multi-dimensional. In our proposed Special Issue, we plan to address a wide range of issues related to broadly understood digital signal processing and its application in modern electronic systems and telecommunication networks. This Special Issue will highlight the latest technological developments in digital signal processing, as well as deep learning. We invite researchers and investigators to submit original research or review articles to this Special Issue.

The scope of this Special Issue will be broadly interpreted to include, but not be limited to, the following topics:

  • Signal registration, representation and analysis;
  • Automatic extraction of data from signals;
  • Algorithms for real-time digital signal processing (DSP);
  • Digital signal processing for communication and networking;
  • Image, video and multidimensional signal processing;
  • Internet-of-things signals;
  • Machine learning for digital signal processing;
  • Digital signal processing for machine learning;
  • Medical signal processing;
  • Multimedia signal processing;
  • 3D signal analysis and applications;
  • Applied digital signal processing systems;
  • DSP-based object detection and recognition;
  • Satellite image processing;
  • Image compression;
  • Radar and sonar signal processing;
  • Complex and hypercomplex digital signal processing;
  • VLSI signal processing;
  • Low-power circuits and systems for DSP applications;
  • Audio signal processing and voice recognition;
  • Sensor array and multichannel signal processing;
  • Signal processing methods for efficient implementation;
  • OFDM and multicarrier signal processing;
  • Digital signal processing for MIMO communications;
  • Signal processing at the brain–computer interfaces;
  • Neural network applications for digital signal processing;
  • Signal processing for autonomous systems;
  • Battery-powered DSP systems.

Prof. Dr. Aleksandr Cariow
Dr. Marek Parfieniuk
Dr. Adam Nowosielski
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

  • digital signal, image and video processing: theory and applications
  • image encoding, compression and analysis
  • fast algorithms
  • VLSI-based (ASIC, ASSP, ASIP, FPGA, SoC, NoC) DSP systems
  • DSP circuits and units: design and application
  • convolution neural networks

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Published Papers (2 papers)

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20 pages, 1893 KiB  
Article
Fast Type-II Hartley Transform Algorithms for Short-Length Input Sequences
by Marina Polyakova and Aleksandr Cariow
Appl. Sci. 2024, 14(22), 10719; https://doi.org/10.3390/app142210719 - 19 Nov 2024
Viewed by 377
Abstract
This paper presents the type-II fast discrete Hartley transform (DHT-II) algorithms for input data sequences of lengths from 2 to 8. The starting point for developing the eight algorithms is the representation of DHT-II as a matrix–vector product. The underlying matrices usually have [...] Read more.
This paper presents the type-II fast discrete Hartley transform (DHT-II) algorithms for input data sequences of lengths from 2 to 8. The starting point for developing the eight algorithms is the representation of DHT-II as a matrix–vector product. The underlying matrices usually have a good block structure. These matrices must then be successfully factorized to obtain a computational procedure that reduces the number of operations in computing the matrix–vector product. In some cases, it is necessary to pre-decompose the original matrices into submatrices and rearrange the rows and/or columns of the resulting matrices to find the factorizations that would substantially save the arithmetic operations. As a result of applying the pointed transformations, we synthesized the final algorithms with reduced computational complexity. The correctness of the obtained algorithmic solutions was theoretically justified using the rigorous mathematical background of each of them. Then, the complex algorithms were further tested using the MATLAB R2023b software to confirm their performance. Finally, an evaluation of the computational complexity for each obtained solution was compared with the computational complexity of the direct calculation of the matrix–vector product and existing fast DHT-II algorithms. The obtained factorizations of the DHT-II transformation matrices on average reduce the number of additions by 5% and the number of multiplications by 73% compared with the direct calculation of the matrix–vector product. Full article
(This article belongs to the Special Issue Advanced Digital Signal Processing and Its Applications)
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Other

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65 pages, 2635 KiB  
Tutorial
Understanding the Flows of Signals and Gradients: A Tutorial on Algorithms Needed to Implement a Deep Neural Network from Scratch
by Przemysław Klęsk
Appl. Sci. 2024, 14(21), 9972; https://doi.org/10.3390/app14219972 - 31 Oct 2024
Viewed by 420
Abstract
Theano, TensorFlow, Keras, Torch, PyTorch, and other software frameworks have remarkably stimulated the popularity of deep learning (DL). Apart from all the good they achieve, the danger of such frameworks is that they unintentionally spur a black-box attitude. Some practitioners play around with [...] Read more.
Theano, TensorFlow, Keras, Torch, PyTorch, and other software frameworks have remarkably stimulated the popularity of deep learning (DL). Apart from all the good they achieve, the danger of such frameworks is that they unintentionally spur a black-box attitude. Some practitioners play around with building blocks offered by frameworks and rely on them, having a superficial understanding of the internal mechanics. This paper constitutes a concise tutorial that elucidates the flows of signals and gradients in deep neural networks, enabling readers to successfully implement a deep network from scratch. By “from scratch”, we mean with access to a programming language and numerical libraries but without any components that hide DL computations underneath. To achieve this goal, the following five topics need to be well understood: (1) automatic differentiation, (2) the initialization of weights, (3) learning algorithms, (4) regularization, and (5) the organization of computations. We cover all of these topics in the paper. From a tutorial perspective, the key contributions include the following: (a) proposition of R and S operators for tensors—rashape and stack, respectively—that facilitate algebraic notation of computations involved in convolutional, pooling, and flattening layers; (b) a Python project named hmdl (“home-made deep learning”); and (c) consistent notation across all mathematical contexts involved. The hmdl project serves as a practical example of implementation and a reference. It was built using NumPy and Numba modules with JIT and CUDA amenities applied. In the experimental section, we compare hmdl implementation to Keras (backed with TensorFlow). Finally, we point out the consistency of the two in terms of convergence and accuracy, and we observe the superiority of the latter in terms of efficiency. Full article
(This article belongs to the Special Issue Advanced Digital Signal Processing and Its Applications)
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