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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: closed (20 February 2025) | Viewed by 5935

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

Published Papers (7 papers)

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16 pages, 4179 KiB  
Article
A Multi-Feature Automatic Evaluation of the Aesthetics of 3D Printed Surfaces
by Jarosław Fastowicz, Mateusz Tecław and Krzysztof Okarma
Appl. Sci. 2025, 15(9), 4852; https://doi.org/10.3390/app15094852 - 27 Apr 2025
Viewed by 193
Abstract
Additive manufacturing is one of the continuously developing areas of technology that still requires reliable monitoring and quality assessment of obtained products. Considering the relatively long time necessary for manufacturing larger products, one of the most desired solutions is video quality monitoring of [...] Read more.
Additive manufacturing is one of the continuously developing areas of technology that still requires reliable monitoring and quality assessment of obtained products. Considering the relatively long time necessary for manufacturing larger products, one of the most desired solutions is video quality monitoring of the manufactured object’s surface. This makes it possible to stop the printing process if the quality is unacceptable. It helps to save the filament, energy, and time, preventing the production of items with poor aesthetic quality. In the paper, several approaches to image-based surface quality assessment are discussed and combined towards a high correlation with the subjective perception of typical quality degradations of the 3D printed surfaces, exceeding 0.9. Although one of the most significant limitations of using full-reference image quality-assessment metrics might be the lack of reference images, it can be overcome by using mutual similarity calculated for image regions. For the created dataset containing 107 samples with subjective aesthetic quality scores, it is shown that the combination of even two metrics using their weighted sum and product significantly outperforms any elementary metric or feature when considering correlations with subjective quality scores. Full article
(This article belongs to the Special Issue Advanced Digital Signal Processing and Its Applications)
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17 pages, 894 KiB  
Article
All-Integer Quantization for Low-Complexity Min-Sum Successive Cancellation Polar Decoder
by Wittawad Pimsri, Patinya Muangkammuen, Puripong Suthisopapan and Virasit Imtawil
Appl. Sci. 2025, 15(6), 3241; https://doi.org/10.3390/app15063241 - 16 Mar 2025
Viewed by 412
Abstract
It is widely acknowledged in communication theory that polar codes have been proven to achieve channel capacity across a range of communication channels. However, their exceptional performance is usually evaluated through simulations or analyses conducted under the assumption of infinite precision, i.e., floating-point [...] Read more.
It is widely acknowledged in communication theory that polar codes have been proven to achieve channel capacity across a range of communication channels. However, their exceptional performance is usually evaluated through simulations or analyses conducted under the assumption of infinite precision, i.e., floating-point arithmetic, which represents an ideal numerical computation. To address this implementation challenge, this work proposes a min-sum successive cancellation (MS-SC) polar decoder employing all-integer quantization to improve practicality in real-world scenarios. To balance the trade-off between practicality and decoding performance, we investigate whether 5-bit all-integer quantization is the optimal choice for the MS-SC polar decoder. Moreover, the simulation results over fading channels show that the proposed decoder achieves a performance almost equivalent to the high-precision successive cancellation (SC) decoder. The integer-based calculation for the MS-SC polar decoder reduces computational complexity by 75% compared to the conventional SC decoding algorithm with infinite-precision computation. Full article
(This article belongs to the Special Issue Advanced Digital Signal Processing and Its Applications)
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27 pages, 3984 KiB  
Article
Enhanced Framework for Lossless Image Compression Using Image Segmentation and a Novel Dynamic Bit-Level Encoding Algorithm
by Erdal Erdal and Alperen Önal
Appl. Sci. 2025, 15(6), 2964; https://doi.org/10.3390/app15062964 - 10 Mar 2025
Viewed by 705
Abstract
This study proposes a dynamic bit-level encoding algorithm (DEA) and introduces the S+DEA compression framework, which enhances compression efficiency by integrating the DEA with image segmentation as a preprocessing step. The novel approaches were validated on four different datasets, demonstrating strong performance and [...] Read more.
This study proposes a dynamic bit-level encoding algorithm (DEA) and introduces the S+DEA compression framework, which enhances compression efficiency by integrating the DEA with image segmentation as a preprocessing step. The novel approaches were validated on four different datasets, demonstrating strong performance and broad applicability. A dedicated data structure was developed to facilitate lossless storage and precise reconstruction of compressed data, ensuring data integrity throughout the process. The evaluation results showed that the DEA outperformed all benchmark encoding algorithms, achieving an improvement percentage (IP) value of 45.12, indicating its effectiveness as a highly efficient encoding method. Moreover, the S+DEA compression algorithm demonstrated significant improvements in compression efficiency. It consistently outperformed BPG, JPEG-LS, and JPEG2000 across three datasets. While it performed slightly worse than JPEG-LS in medical images, it remained competitive overall. A dataset-specific analysis revealed that in medical images, the S+DEA performed close to the DEA, suggesting that segmentation alone does not enhance compression in this domain. This emphasizes the importance of exploring alternative preprocessing techniques to enhance the DEA’s performance in medical imaging applications. The experimental results demonstrate that the DEA and S+DEA offer competitive encoding and compression capabilities, making them promising alternatives to existing frameworks. Full article
(This article belongs to the Special Issue Advanced Digital Signal Processing and Its Applications)
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12 pages, 2147 KiB  
Article
Adaptive Neural-Network-Based Lossless Image Coder with Preprocessed Input Data
by Grzegorz Ulacha and Ryszard Stasinski
Appl. Sci. 2025, 15(5), 2603; https://doi.org/10.3390/app15052603 - 28 Feb 2025
Viewed by 389
Abstract
It is shown in this paper that the appropriate preprocessing of input data may result in an important reduction of Artificial Neural Network (ANN) training time and simplification of its structure, while improving its performance. The ANN is working as a data predictor [...] Read more.
It is shown in this paper that the appropriate preprocessing of input data may result in an important reduction of Artificial Neural Network (ANN) training time and simplification of its structure, while improving its performance. The ANN is working as a data predictor in a lossless image coder. Its adaptation is done for each coded pixel separately; no initial training using learning image sets is necessary. This means that there is no extra off-line time needed for initial ANN training, and there are no problems with network overfitting. There are two concepts covered in this paper: Replacement of image pixels by their differences diminishes data variability and increases ANN convergence (Concept 1); Preceding ANN by advanced predictors reduces ANN complexity (Concept 2). The obtained codecs are much faster than one without modifications, while their data compaction properties are clearly better. It outperforms the JPEG-LS codec by approximately 10%. Full article
(This article belongs to the Special Issue Advanced Digital Signal Processing and Its Applications)
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28 pages, 8081 KiB  
Article
PortraitEmotion3D: A Novel Dataset and 3D Emotion Estimation Method for Artistic Portraiture Analysis
by Shao Liu, Sos Agaian and Artyom Grigoryan
Appl. Sci. 2024, 14(23), 11235; https://doi.org/10.3390/app142311235 - 2 Dec 2024
Cited by 1 | Viewed by 1389
Abstract
Facial Expression Recognition (FER) has been widely explored in realistic settings; however, its application to artistic portraiture presents unique challenges due to the stylistic interpretations of artists and the complex interplay of emotions conveyed by both the artist and the subject. This study [...] Read more.
Facial Expression Recognition (FER) has been widely explored in realistic settings; however, its application to artistic portraiture presents unique challenges due to the stylistic interpretations of artists and the complex interplay of emotions conveyed by both the artist and the subject. This study addresses these challenges through three key contributions. First, we introduce the PortraitEmotion3D (PE3D) dataset, designed explicitly for FER tasks in artistic portraits. This dataset provides a robust foundation for advancing emotion recognition in visual art. Second, we propose an innovative 3D emotion estimation method that leverages three-dimensional labeling to capture the nuanced emotional spectrum depicted in artistic works. This approach surpasses traditional two-dimensional methods by enabling a more comprehensive understanding of the subtle and layered emotions often in artistic representations. Third, we enhance the feature learning phase by integrating a self-attention module, significantly improving facial feature representation and emotion recognition accuracy in artistic portraits. This advancement addresses this domain’s stylistic variations and complexity, setting a new benchmark for FER in artistic works. Evaluation of the PE3D dataset demonstrates our method’s high accuracy and robustness compared to existing state-of-the-art FER techniques. The integration of our module yields an average accuracy improvement of over 1% in recent FER systems. Additionally, combining our method with ESR-9 achieves a comparable accuracy of 88.3% on the FER+ dataset, demonstrating its generalizability to other FER benchmarks. This research deepens our understanding of emotional expression in art and facilitates potential applications in diverse fields, including human–computer interaction, security, healthcare diagnostics, and the entertainment industry. Full article
(This article belongs to the Special Issue Advanced Digital Signal Processing and Its Applications)
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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
Cited by 2 | Viewed by 905
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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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 1114
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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