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Algorithms
Special Issues
Digital Signal Processing Algorithms and Applications
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Digital Signal Processing Algorithms and Applications

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 6413

Special Issue Editor

Dr. Barry Cardiff

E-Mail Website
Guest Editor
School of Electrical, Electronic & Communications Engineering, University College Dublin, Dublin, Ireland
Interests: algorithms; DSP; communication theory; modulation; reception techniques; wireless transceiver; digitally assisted analog circuits, high-speed ADC; digital calibration; signal processing for bio-medical applications and machine learning at the edge

Special Issue Information

Dear Colleagues,

We invite you to submit your latest cutting-edge research in the area of sophisticated new and emerging digital signal processing (DSP) algorithms in the field of digitally assisted analog circuits. The emphasis will be on the DSP component, not the analog circuits per se, although we expect that any submitted work will be supported by laboratory measurements and hardware validation.

We solicit high-quality papers which address both the theoretical and practical issues of these algorithms. Submissions are welcome in the conventional wide range of application fields such as power amplifiers (PAs) and ADC linearization but also for more recent algorithms and application spaces. The use of machine learning to replace or enhance the model-based calibration approaches is also an area of particular interest.

Dr. Barry Cardiff
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. Algorithms is an international peer-reviewed open access monthly 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 1600 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

  • algorithms
  • DSP
  • digitally assisted analog circuits
  • high-speed ADC
  • digital calibration
  • wireless transceiver
  • signal processing for bio-medical applications and machine learning at the edge

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

Published Papers (3 papers)

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Research

14 pages, 1828 KiB  
Article
TransPCGC: Point Cloud Geometry Compression Based on Transformers
by Shiyu Lu, Huamin Yang and Cheng Han
Algorithms 2023, 16(10), 484; https://doi.org/10.3390/a16100484 - 19 Oct 2023
Viewed by 2419
Abstract
Due to the often substantial size of the real-world point cloud data, efficient transmission and storage have become critical concerns. Point cloud compression plays a decisive role in addressing these challenges. Recognizing the importance of capturing global information within point cloud data for [...] Read more.
Due to the often substantial size of the real-world point cloud data, efficient transmission and storage have become critical concerns. Point cloud compression plays a decisive role in addressing these challenges. Recognizing the importance of capturing global information within point cloud data for effective compression, many existing point cloud compression methods overlook this crucial aspect. To tackle this oversight, we propose an innovative end-to-end point cloud compression method designed to extract both global and local information. Our method includes a novel Transformer module to extract rich features from the point cloud. Utilization of a pooling operation that requires no learnable parameters as a token mixer for computing long-distance dependencies ensures global feature extraction while significantly reducing both computations and parameters. Furthermore, we employ convolutional layers for feature extraction. These layers not only preserve the spatial structure of the point cloud, but also offer the advantage of parameter independence from the input point cloud size, resulting in a substantial reduction in parameters. Our experimental results demonstrate the effectiveness of the proposed TransPCGC network. It achieves average Bjontegaard Delta Rate (BD-Rate) gains of 85.79% and 80.24% compared to Geometry-based Point Cloud Compression (G-PCC). Additionally, in comparison to the Learned-PCGC network, our approach attains an average BD-Rate gain of 18.26% and 13.83%. Moreover, it is accompanied by a 16% reduction in encoding and decoding time, along with a 50% reduction in model size. Full article
(This article belongs to the Special Issue Digital Signal Processing Algorithms and Applications)
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14 pages, 559 KiB  
Article
Constant-Beamwidth LCMV Beamformer with Rectangular Arrays
by Vitor Probst Curtarelli and Israel Cohen
Algorithms 2023, 16(8), 385; https://doi.org/10.3390/a16080385 - 10 Aug 2023
Cited by 2 | Viewed by 1603
Abstract
This paper presents a novel approach utilizing uniform rectangular arrays to design a constant-beamwidth (CB) linearly constrained minimum variance (LCMV) beamformer, which also improves white noise gain and directivity. By employing a generalization of the convolutional Kronecker product beamforming technique, we decompose a [...] Read more.
This paper presents a novel approach utilizing uniform rectangular arrays to design a constant-beamwidth (CB) linearly constrained minimum variance (LCMV) beamformer, which also improves white noise gain and directivity. By employing a generalization of the convolutional Kronecker product beamforming technique, we decompose a physical array into virtual subarrays, each tailored to achieve a specific desired feature, and we subsequently synthesize the original array’s beamformer. Through simulations, we demonstrate that the proposed approach successfully achieves the desired beamforming characteristics while maintaining favorable levels of white noise gain and directivity. A comparative analysis against existing methods from the literature reveals that the proposed method performs better than the existing methods. Full article
(This article belongs to the Special Issue Digital Signal Processing Algorithms and Applications)
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17 pages, 1330 KiB  
Article
Hardware Suitability of Complex Natural Resonances Extraction Algorithms in Backscattered Radar Signals
by Andres Gallego and Francisco Roman
Algorithms 2023, 16(8), 370; https://doi.org/10.3390/a16080370 - 31 Jul 2023
Viewed by 1430
Abstract
Complex natural resonances (CNRs) extraction methods such as matrix pencil method (MPM), Cauchy, vector-fitting Cauchy method (VCM), or Prony’s method decompose a signal in terms of frequency components and damping factors based on Baum’s singularity expansion method (SEM) either in the time or [...] Read more.
Complex natural resonances (CNRs) extraction methods such as matrix pencil method (MPM), Cauchy, vector-fitting Cauchy method (VCM), or Prony’s method decompose a signal in terms of frequency components and damping factors based on Baum’s singularity expansion method (SEM) either in the time or frequency domain. The validation of these CNRs is accomplished through a reconstruction of the signal based on these complex poles and residues and a comparison with the input signal. Here, we perform quantitative performance metrics in order to have an evaluation of each method’s hardware suitability factor before selecting a hardware platform using benchmark signals, simulations of backscattering scenarios, and experiments. Full article
(This article belongs to the Special Issue Digital Signal Processing Algorithms and Applications)
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