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Advance in Digital Signal Processing and Its Implementation

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 (30 April 2023) | Viewed by 15340

Special Issue Editor


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Guest Editor
Department of Applied Electronics and Intelligent Systems, Technical University Gheorghe Asachi, 700506 Iasi, Romania
Interests: VLSI algorithms and architecture; fast transforms; digital signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is safe to say that another industrial revolution is currently taking place, which can be called the digital revolution. Digital signal processing (DSP) plays a key role in this context, and optimization of the implementation of DSP algorithms and architectures is an essential part of research and design for many modern applications in this field. There are many applications in this area with an increasingly number of performance requirements due to data processing and transmission of huge data volumes at high speeds, with resource constraints specific to portable devices. Optimizing such computationally intensive applications is a challenging issue that requires a clever design of the algorithm or architecture.

Taking into consideration these aspects, this Special Issue on “Advances in Digital Signal Processing and Its Implementation” in the journal Applied Sciences aims to gather the most up-to-date industrial and academic research achievements within the most important developments on DSP and its implementations. Additionally, it aims to underline advances in this area, as well as to provide insights into the theoretical and practical problems related to recent discoveries in this field from different points of view.

Topics of interest for this Special Issue include but are not limited to:

  • New DSP algorithms and applications;
  • New adaptive/learning algorithms;
  • Signal processing methods for multimedia applications;
  • Signal processing methods for IoT applications;
  • Signal processing methods for medicine;
  • Tensor based signal processing;
  • Sparsity-aware DSP algorithms;
  • VLSI signal processing;
  • Signal processing methods for an efficient implementation;
  • Optimization of the VLSI implementation of multimedia blocks;
  • Low-power circuits and systems for DSP applications.

Prof. Dr. Doru Florin Chiper
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. 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.

Published Papers (8 papers)

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Research

18 pages, 7038 KiB  
Article
A Novel ASIC Implementation of Two-Dimensional Image Compression Using Improved B.G. Lee Algorithm
by Tanya Mendez, Vishnumurthy Kedlaya K, Dayananda Nayak, H. S. Mruthyunjaya and Subramanya G. Nayak
Appl. Sci. 2023, 13(16), 9094; https://doi.org/10.3390/app13169094 - 9 Aug 2023
Viewed by 907
Abstract
A 2D Discrete Cosine Transform and Inverse Discrete Cosine Transform using the B.G. Lee algorithm, incorporating a signed error-tolerant adder for additions, and a signed low-power fixed-point multiplier to perform multiplications are proposed and designed in this research. A novel Application Specific Integrated [...] Read more.
A 2D Discrete Cosine Transform and Inverse Discrete Cosine Transform using the B.G. Lee algorithm, incorporating a signed error-tolerant adder for additions, and a signed low-power fixed-point multiplier to perform multiplications are proposed and designed in this research. A novel Application Specific Integrated Circuit hardware implementation is used for the 2D DCT/IDCT computation of each 8 × 8 image block by optimizing the input data using the concepts of pipelining. An enhanced speed in processing and optimized arithmetic computations was observed due to the eight-stage pipeline architecture. The 2D DCT/IDCT of each 8 × 8 image segment can be quickly processed in 34 clock cycles with a substantially reduced level of circuit complexity. The B.G. Lee algorithm has been implemented using signed error-tolerant adders, signed fixed-point multipliers, and shifters, reducing computational complexity, power, and area. The Cadence Genus tool synthesized the proposed architecture with gpdk-90 nm and gpdk-45 nm technology libraries. The proposed method showed a significant reduction of 31.01%, 12.17%, and 21.11% in power, area, and PDP in comparison to the existing image compression architectures. An improved PSNR of the reconstructed image was also achieved compared to existing designs. Full article
(This article belongs to the Special Issue Advance in Digital Signal Processing and Its Implementation)
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17 pages, 2998 KiB  
Article
An Efficient Algorithm and Architecture for the VLSI Implementation of Integer DCT That Allows an Efficient Incorporation of the Hardware Security with a Low Overhead
by Doru Florin Chiper and Arcadie Cracan
Appl. Sci. 2023, 13(12), 6927; https://doi.org/10.3390/app13126927 - 8 Jun 2023
Cited by 1 | Viewed by 904
Abstract
In this paper, we propose a new hardware algorithm for an integer based discrete cosine transform (IntDCT) that was designed to allow an efficient VLSI implementation of the discrete cosine transform using the systolic array architectural paradigm. The proposed algorithm demonstrates multiple benefits [...] Read more.
In this paper, we propose a new hardware algorithm for an integer based discrete cosine transform (IntDCT) that was designed to allow an efficient VLSI implementation of the discrete cosine transform using the systolic array architectural paradigm. The proposed algorithm demonstrates multiple benefits specific to integer transforms with efficient hardware implementation and sufficient precision in approximating irrational transform coefficients for practical applications. The proposed integer DCT algorithm can be efficiently restructured into five regular and modular computational structures of lengths of four and one of length two called pseudo-cycle convolutions which translate into efficient VLSI implementations using systolic arrays. Moreover, besides an efficient VLSI implementation with high-speed performances due to the parallel decomposition of the proposed integer DCT algorithm, the proposed VLSI architecture uses a tag control mechanism that facilitates the integration of an obfuscation technique that significantly improves the hardware security with low overheads, maintaining all the implementation performances. Full article
(This article belongs to the Special Issue Advance in Digital Signal Processing and Its Implementation)
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14 pages, 1209 KiB  
Communication
A Low-Complexity Channel Estimation Based on a Least-Squares Algorithm in OFDM Systems
by Yung-An Kao and Kun-Feng Wu
Appl. Sci. 2022, 12(9), 4258; https://doi.org/10.3390/app12094258 - 22 Apr 2022
Cited by 2 | Viewed by 1363
Abstract
As the channel frequency responses (CFRs) at virtual pilot subcarriers are assumed to be zero, the estimated CFRs will have a leakage effect for discrete Fourier transform (DFT)-based channel estimation in OFDM systems. The CFRs at odd pilot subcarriers and even pilot subcarriers [...] Read more.
As the channel frequency responses (CFRs) at virtual pilot subcarriers are assumed to be zero, the estimated CFRs will have a leakage effect for discrete Fourier transform (DFT)-based channel estimation in OFDM systems. The CFRs at odd pilot subcarriers and even pilot subcarriers are related if the number of maximum channel delay points is smaller than or equal to half the number of pilots (including virtual pilots). According to this correlation, we propose a low-complexity least-squares (LS) method to estimate the CFRs at virtual even and odd pilot subcarriers, respectively. This will solve the problem of the leakage effect in DFT-based channel estimation. The proposed method does not need to know the statistical properties of the channel or insert extra pilots as with some estimation methods. Furthermore, although this method has less computation than the LS method, both have almost the same channel estimation efficiency in simulation. The channel estimation efficiency of our proposed method is still similar to that of the LS method, even if the number of maximum channel delay points is greater than half the number of pilots. Therefore, the proposed low-complexity method is very suitable for equalizer hardware implementation. Full article
(This article belongs to the Special Issue Advance in Digital Signal Processing and Its Implementation)
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20 pages, 60601 KiB  
Article
Broadband Spectral Analysis Algorithm with High-Frequency Resolution for Elimination of Overlap Interference between Adjacent Channels
by Xianhai Wang, Teng Wang, Chuan Yin, Jun Han, Qiao Meng and Chen Wang
Appl. Sci. 2021, 11(21), 10262; https://doi.org/10.3390/app112110262 - 1 Nov 2021
Cited by 2 | Viewed by 1907
Abstract
Spectral lines can be analysed to determine the physical properties of molecular clouds and the astrochemical processes in the formation area of massive stars. To improve the observation technology of radio astronomy, this paper proposes and compares two spectral analysis algorithms (improved weighted [...] Read more.
Spectral lines can be analysed to determine the physical properties of molecular clouds and the astrochemical processes in the formation area of massive stars. To improve the observation technology of radio astronomy, this paper proposes and compares two spectral analysis algorithms (improved weighted overlap-add (IWOLA) + FFT and IWOLA + weighted overlap-add (WOLA)). The proposed algorithms can obtain an ultra-high-frequency resolution for real-valued wideband signals, eliminate the signal overlapping interference between adjacent channels, substantially decrease the required hardware resources, especially multipliers, adders, and memory resources, and reduce the system design complexity. The IWOLA + FFT algorithm consists of an improved weighted overlap-add (IWOLA) filter bank, fast Fourier transform (FFT), a specific decimation for the output data from the IWOLA filter bank, and a selection for part of the output data from the FFT. The IWOLA + WOLA algorithm consists of the same modules as the IWOLA + FFT algorithm, with the second-stage FFT replaced by the modules of the weighted overlap-add (WOLA) filter bank and the accumulation for each sub-band. Based on an analysis of the underlying principles and characteristics of both algorithms, the IWOLA + FFT algorithm demonstrated a spectrum with a high frequency resolution and a comparable performance to an ultra-large-scale FFT, based on two smaller FFTs and a flexible architecture instead of a ultra-large-scale FFT. The IWOLA + WOLA algorithm contains the same function as the IWOLA + FFT algorithm and demonstrates a higher performance. The proposed algorithms eliminated the interference between the adjacent channels within the entire Nyquist frequency bandwidth. The simulation results verify the accuracy and spectral analysis performances of the proposed algorithms. Full article
(This article belongs to the Special Issue Advance in Digital Signal Processing and Its Implementation)
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16 pages, 2275 KiB  
Article
Speckle Noise Detection and Removal for Laser Speech Measurement Systems
by Yahui Wang, Wenxi Zhang, Zhou Wu, Xinxin Kong and Hongxin Zhang
Appl. Sci. 2021, 11(21), 9870; https://doi.org/10.3390/app11219870 - 22 Oct 2021
Cited by 7 | Viewed by 2003
Abstract
Laser speech measurement is a new sound capture technology based on Laser Doppler Vibrometry (LDV). It avoids the need for contact, is easily concealed and is ideal for remote speech acquisition, which has led to its wide-scale adoption for military and security applications. [...] Read more.
Laser speech measurement is a new sound capture technology based on Laser Doppler Vibrometry (LDV). It avoids the need for contact, is easily concealed and is ideal for remote speech acquisition, which has led to its wide-scale adoption for military and security applications. However, lasers are easily affected by complex detection environments. Thus, speckle noise often appears in the measured speech, seriously affecting its quality and intelligibility. This paper examines all of the characteristics of impulsive noise in laser measured speech and proposes a novel automatic impulsive noise detection and removal method. This method first foregrounds noise using decorrelation based on a linear prediction (LP) model that improves the noise-to-signal ratio (NSR) of the measured signal. This makes it possible to detect the position of noise through a combination of the average short-time energy and kurtosis. The method not only precisely locates small clicks (with a duration of just a few samples), but also finds the location of longer bursts and scratches (with a duration of up to a hundred samples). The located samples can then be replaced by more appropriate samples whose coding is based on the LP model. This strategy avoids unnecessary processing and obviates the need to compromise the quality of the relatively large fraction of samples that are unaffected by speckle noise. Experimental results show that the proposed automatic speckle noise detection and removal method outperforms other related methods across a wide range of degraded audio signals. Full article
(This article belongs to the Special Issue Advance in Digital Signal Processing and Its Implementation)
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16 pages, 571 KiB  
Article
Low-Complexity Recursive Least-Squares Adaptive Algorithm Based on Tensorial Forms
by Ionuț-Dorinel Fîciu, Cristian-Lucian Stanciu, Cristian Anghel and Camelia Elisei-Iliescu
Appl. Sci. 2021, 11(18), 8656; https://doi.org/10.3390/app11188656 - 17 Sep 2021
Cited by 5 | Viewed by 2344
Abstract
Modern solutions for system identification problems employ multilinear forms, which are based on multiple-order tensor decomposition (of rank one). Recently, such a solution was introduced based on the recursive least-squares (RLS) algorithm. Despite their potential for adaptive systems, the classical RLS methods require [...] Read more.
Modern solutions for system identification problems employ multilinear forms, which are based on multiple-order tensor decomposition (of rank one). Recently, such a solution was introduced based on the recursive least-squares (RLS) algorithm. Despite their potential for adaptive systems, the classical RLS methods require a prohibitive amount of arithmetic resources and are sometimes prone to numerical stability issues. This paper proposes a new algorithm for multiple-input/single-output (MISO) system identification based on the combination between the exponentially weighted RLS algorithm and the dichotomous descent iterations in order to implement a low-complexity stable solution with performance similar to the classical RLS methods. Full article
(This article belongs to the Special Issue Advance in Digital Signal Processing and Its Implementation)
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20 pages, 451 KiB  
Article
An Insightful Overview of the Wiener Filter for System Identification
by Laura-Maria Dogariu, Jacob Benesty, Constantin Paleologu and Silviu Ciochină
Appl. Sci. 2021, 11(17), 7774; https://doi.org/10.3390/app11177774 - 24 Aug 2021
Cited by 6 | Viewed by 3009
Abstract
Efficiently solving a system identification problem represents an important step in numerous important applications. In this framework, some of the most popular solutions rely on the Wiener filter, which is widely used in practice. Moreover, it also represents a benchmark for other related [...] Read more.
Efficiently solving a system identification problem represents an important step in numerous important applications. In this framework, some of the most popular solutions rely on the Wiener filter, which is widely used in practice. Moreover, it also represents a benchmark for other related optimization problems. In this paper, new insights into the regularization of the Wiener filter are provided, which is a must in real-world scenarios. A proper regularization technique is of great importance, especially in challenging conditions, e.g., when operating in noisy environments and/or when only a low quantity of data is available for the estimation of the statistics. Different regularization methods are investigated in this paper, including several new solutions that fit very well for the identification of sparse and low-rank systems. Experimental results support the theoretical developments and indicate the efficiency of the proposed techniques. Full article
(This article belongs to the Special Issue Advance in Digital Signal Processing and Its Implementation)
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17 pages, 1545 KiB  
Article
An Efficient Multistage Approach for Blind Source Separation of Noisy Convolutive Speech Mixture
by Junaid Bahadar Khan, Tariqullah Jan, Ruhul Amin Khalil, Nasir Saeed and Muhannad Almutiry
Appl. Sci. 2021, 11(13), 5968; https://doi.org/10.3390/app11135968 - 27 Jun 2021
Cited by 1 | Viewed by 1624
Abstract
This paper proposes a novel efficient multistage algorithm to extract source speech signals from a noisy convolutive mixture. The proposed approach comprises two stages named Blind Source Separation (BSS) and de-noising. A hybrid source prior model separates the source signals from the noisy [...] Read more.
This paper proposes a novel efficient multistage algorithm to extract source speech signals from a noisy convolutive mixture. The proposed approach comprises two stages named Blind Source Separation (BSS) and de-noising. A hybrid source prior model separates the source signals from the noisy reverberant mixture in the BSS stage. Moreover, we model the low- and high-energy components by generalized multivariate Gaussian and super-Gaussian models, respectively. We use Minimum Mean Square Error (MMSE) to reduce noise in the noisy convolutive mixture signal in the de-noising stage. Furthermore, the two proposed models investigate the performance gain. In the first model, the speech signal is separated from the observed noisy convolutive mixture in the BSS stage, followed by suppression of noise in the estimated source signals in the de-noising module. In the second approach, the noise is reduced using the MMSE filtering technique in the received noisy convolutive mixture at the de-noising stage, followed by separation of source signals from the de-noised reverberant mixture at the BSS stage. We evaluate the performance of the proposed scheme in terms of signal-to-distortion ratio (SDR) with respect to other well-known multistage BSS methods. The results show the superior performance of the proposed algorithm over the other state-of-the-art methods. Full article
(This article belongs to the Special Issue Advance in Digital Signal Processing and Its Implementation)
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