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Special Issue "Advances in Digital Signal and Image Processing, Techniques, and Computations with Multidisciplinary Applications"

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".

Deadline for manuscript submissions: 20 December 2023 | Viewed by 3839

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Special Issue Editor

Dr. Honarvar Shakibaei Asli

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Centre for Life-Cycle Engineering and Management, School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK
Interests: signals and systems; digital filter design; digital image processing; medical image processing; pattern recognition

Special Issue Information

Dear Colleagues,

Image processing is a rapidly evolving technique applied in several research fields. Processing digital signals, a major objective in many scientific domains, can be achieved through image processing approaches. This path includes the analysis, classification, and manipulation of signals using operations such as filtering, compression, feature extraction, enhancement, and spectral analysis.

This Special Issue aims to highlight innovative ideas and algorithms for treating different types of discrete signals using image processing algorithms.

We welcome original and novel contributions, including research papers and extensive reviews, addressing the impact and relevance of electronic signal processing using image processing applications.

We welcome submissions detailing new theories and evolutionary methods for digital signal processing using image processing approaches. A non-exhaustive list of topics is as follows:

Digital signal processing using machine learning;
Deep learning for digital signal processing;
Image restoration and noise reduction;
Image classification, segmentation, and clustering;
Object detection and tracking;
Medical imaging for EEG/ECG signal processing;
Feature selection, extraction, and learning;
Digital signal detection and recognition using image processing techniques;
Motion analysis of digital signals.

Dr. Honarvar Shakibaei Asli
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. Electronics 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 2200 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 processing
image processing
machine learning
deep learning

Published Papers (5 papers)

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Research

Open AccessArticle

Seismic Image Identification and Detection Based on Tchebichef Moment Invariant

Andong Lu

and

Barmak Honarvar Shakibaei Asli

Electronics 2023, 12(17), 3692; https://doi.org/10.3390/electronics12173692 - 31 Aug 2023

Viewed by 408

Abstract

The research focuses on the analysis of seismic data, specifically targeting the detection, edge segmentation, and classification of seismic images. These processes are fundamental in image processing and are crucial in understanding the stratigraphic structure and identifying oil and natural gas resources. However, there is a lack of sufficient resources in the field of seismic image detection, and interpreting 2D seismic image slices based on 3D seismic data sets can be challenging. In this research, image segmentation involves image preprocessing and the use of a U-net network. Preprocessing techniques, such as Gaussian filter and anisotropic diffusion, are employed to reduce blur and noise in seismic images. The U-net network, based on the Canny descriptor is used for segmentation. For image classification, the ResNet-50 and Inception-v3 models are applied to classify different types of seismic images. In image detection, Tchebichef invariants are computed using the Tchebichef polynomials’ recurrence relation. These invariants are then used in an optimized multi-class SVM network for detecting and classifying various types of seismic images. The promising results of the SVM model based on Tchebichef invariants suggest its potential to replace Hu moment invariants (HMIs) and Zernike moment invariants (ZMIs) for seismic image detection. This approach offers a more efficient and dependable solution for seismic image analysis in the future. Full article

(This article belongs to the Special Issue Advances in Digital Signal and Image Processing, Techniques, and Computations with Multidisciplinary Applications)

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Open AccessArticle

Design and Implementation of an Orbitrap Mass Spectrometer Data Acquisition System for Atmospheric Molecule Identification

Wei Wang

and

Yongping Li

Electronics 2023, 12(11), 2387; https://doi.org/10.3390/electronics12112387 - 25 May 2023

Viewed by 719

Abstract

Orbitrap mass spectrometers have gained widespread popularity in ground-based environmental component analysis. However, their application in atmospheric exploration for space missions remains limited. Existing data acquisition solutions for Orbitrap instruments primarily rely on commercial systems and computer-based spectrum analysis. In this study, we developed a self-designed data acquisition solution specifically tailored for atmospheric molecule detection. The implementation involved directly integrating a spectrum analysis algorithm onto a field programmable gate array (FPGA), enabling miniaturization, real-time performance, and meeting the desired requirements. The system comprises signal conditioning circuits, analog-to-digital conversion (ADC) circuits, programmable logic circuits, and related software. These components facilitate real-time spectrum analysis and signal processing on hardware, enabling high-speed acquisition and analysis of signals generated by the Orbitrap. Experimental results demonstrate that the system can sample front-end analog signals at a rate of 25 MHz and differentiate signal spectra with an error margin of less than 7 kHz. This establishes the viability of the designed data acquisition system for atmospheric mass spectrometry analysis. Full article

(This article belongs to the Special Issue Advances in Digital Signal and Image Processing, Techniques, and Computations with Multidisciplinary Applications)

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Open AccessArticle

Four-Term Recurrence for Fast Krawtchouk Moments Using Clenshaw Algorithm

Barmak Honarvar Shakibaei Asli

and

Maryam Horri Rezaei

Electronics 2023, 12(8), 1834; https://doi.org/10.3390/electronics12081834 - 12 Apr 2023

Cited by 1 | Viewed by 806

Abstract

Krawtchouk polynomials (KPs) are discrete orthogonal polynomials associated with the Gauss hypergeometric functions. These polynomials and their generated moments in 1D or 2D formats play an important role in information and coding theories, signal and image processing tools, image watermarking, and pattern recognition. In this paper, we introduce a new four-term recurrence relation to compute KPs compared to their ordinary recursions (three-term) and analyse the proposed algorithm speed. Moreover, we use Clenshaw’s technique to accelerate the computation procedure of the Krawtchouk moments (KMs) using a fast digital filter structure to generate a lattice network for KPs calculation. The proposed method confirms the stability of KPs computation for higher orders and their signal reconstruction capabilities as well. The results show that the KMs calculation using the proposed combined method based on a four-term recursion and Clenshaw’s technique is reliable and fast compared to the existing recursions and fast KMs algorithms. Full article

(This article belongs to the Special Issue Advances in Digital Signal and Image Processing, Techniques, and Computations with Multidisciplinary Applications)

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Open AccessArticle

A Semi-Fragile, Inner-Outer Block-Based Watermarking Method Using Scrambling and Frequency Domain Algorithms

and

Electronics 2023, 12(4), 1065; https://doi.org/10.3390/electronics12041065 - 20 Feb 2023

Viewed by 712

Abstract

Image watermarking is most often used to prove that an image belongs to someone and to make sure that the image is the same as was originally produced. The type of watermarking used for the detection of originality and tampering is known as authentication-type watermarking. In this paper, a blind semi-fragile authentication watermarking method is introduced. Although the main concern in this paper is authenticating the image, watermarking for proving ownership is additionally implemented. The method considers the image as two main parts: an inner part and an outer part. The inner and outer parts are divided into non-overlapping blocks. The block size of the inner and outer part are different. The outer blocks have a greater area than the inner blocks so that their watermark-holding capacity is greater, providing enough robustness for semi-fragility. The method is semi-fragile and the watermarked image is authenticated despite the JPEG being compressed to 75% quality. The embedded watermark also survives innocent types of image operations, such as intensity adjustment, histogram equalization and gamma correction. Semi-fragile and selectively fragile authentication is valuable and in high demand specifically because it survives these innocent image operations while detecting ill-intentioned tampering. In this work, we embed a binary watermark into the inner and outer parts of images using a scrambling algorithm, discrete wavelet transform (DWT) and discrete cosine transform (DCT) in the blocks. The proposed methodology has high image quality after watermarking, with a PSNR value of 40.577, and high quality is also achieved after JPEG compression. The embedding process provides acceptable image quality after tamper attacks, including JPEG compression, Gaussian noise, average filtering, and scaling attacks with PSNR values greater than 29. Experimental results obtained show that the proposed semi-fragile watermarking algorithm is more robust, secure and resistant than other algorithms in the literature. Full article

(This article belongs to the Special Issue Advances in Digital Signal and Image Processing, Techniques, and Computations with Multidisciplinary Applications)

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Open AccessArticle

Increasing the Speed of Multiscale Signal Analysis in the Frequency Domain

Viliam Ďuriš

Sergey G. Chumarov

and

Vladimir I. Semenov

Electronics 2023, 12(3), 745; https://doi.org/10.3390/electronics12030745 - 02 Feb 2023

Cited by 1 | Viewed by 685

Abstract

In the Mallat algorithm, calculations are performed in the time domain. To speed up the signal conversion at each level, the wavelet coefficients are sequentially halved. This paper presents an algorithm for increasing the speed of multiscale signal analysis using fast Fourier transform. In this algorithm, calculations are performed in the frequency domain, which is why the authors call this algorithm multiscale analysis in the frequency domain. For each level of decomposition, the wavelet coefficients are determined from the signal and can be calculated in parallel, which reduces the conversion time. In addition, the zoom factor can be less than two. The Mallat algorithm uses non-symmetric wavelets, and to increase the accuracy of the reconstruction, large-order wavelets are obtained, which increases the transformation time. On the contrary, in our algorithm, depending on the sample length, the wavelets are symmetric and the time of the inverse wavelet transform can be faster by 6–7 orders of magnitude compared to the direct numerical calculation of the convolution. At the same time, the quality of analysis and the accuracy of signal reconstruction increase because the wavelet transform is strictly orthogonal. Full article

(This article belongs to the Special Issue Advances in Digital Signal and Image Processing, Techniques, and Computations with Multidisciplinary Applications)

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