New mobile communication system design for Rayleigh environments based on compressed sensing‐source coding

Haneche, Houria; Ouahabi, Abdeldjalil; Boudraa, Bachir

doi:10.1049/iet-com.2018.5348

Cited by 27 publications

(17 citation statements)

References 30 publications

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“…Compressed sensing (CS) has already attracted great interest in various fields. Examples include medical imaging [1,2], communication systems [3][4][5][6], remote sensing [7], reconstruction algorithm design [8], image storage in databases [9], etc. Compressed sensing provides an alternative approach to Shannon vision to reduce the number of samples, and/or reduce transmission/storage costs.…”

Section: Introductionmentioning

confidence: 99%

Image Denoising Using a Compressive Sensing Approach Based on Regularization Constraints

Mahdaoui¹,

Ouahabi²,

Moulay³

2022

Preprint

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In remote sensing applications, one of the key points is the acquisition, real-time pre-processing and storage of information. Due to the large amount of information present in the form of images or videos, compression of this data is necessary. Compressed sensing (CS) is an efficient technique to meet this challenge. It consists in acquiring a signal, assuming that it can have a sparse representation, using a minimal number of non-adaptive linear measurements. After this CS process, a reconstruction of the original signal must be performed at the receiver. Reconstruction techniques are often unable to preserve the texture of the image and tend to smooth out its details. To overcome this problem, we propose in this work, a CS reconstruction method that combines the total variation regularization and the non-local self-similarity constraint. The optimization of this method is performed by the augmented Lagrangian which avoids the difficult problem of non-linearity and non-differentiability of the regularization terms. The proposed algorithm, called denoising compressed sensing by regularizations terms (DCSR), will not only perform image reconstruction but also denoising. To evaluate the performance of the proposed algorithm, we compare its performance with state-of-the-art methods, such as Nesterov's algorithm, group-based sparse representation and wavelet-based methods, in terms of denoising, and preservation of edges, texture and image details, as well as from the point of view of computational complexity. Our approach allows to gain up to 25% in terms of denoising efficiency, and visual quality using two metrics: PSNR and SSIM.

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Section: Introductionmentioning

confidence: 99%

Image Denoising Using a Compressive Sensing Approach Based on Regularization Constraints

Mahdaoui¹,

Ouahabi²,

Moulay³

2022

Preprint

Self Cite

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“…Engineers, practitioners and researchers are confronted daily with increasingly difficult technological problems at multiple scales of analysis, in terms of classification, segmentation, detection of contours or parameters of interest, noise reduction or elimination, compression for transmission or storage, synthesis or reconstruction, etc. This concerns many fields such as astrophysics [31], finance [32], fluid mechanics [33], thermodynamics [34], medicine and biology [35][36][37], multimedia [38], telecommunications [39,40], signal and image processing, and of course, the monitoring of cracks and detection of fractures in materials [41][42][43][44][45][46]. MRA based on wavelets can thus become an essential tool for solving the difficulties encountered in the above-mentioned fields.…”

Section: Wavelet-based Multiresolution Analysismentioning

confidence: 99%

Wavelet-based multiresolution analysis coupled with deep learning to efficiently monitor cracks in concrete

Arbaoui

Ouahabi

Jacques

et al. 2021

Frattura Ed Integrità Strutturale

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This paper proposes an efficient methodology to monitor the formation of cracks in concrete after non-destructive ultrasonic testing of a structure. The objective is to be able to automatically detect the initiation of cracks early enough, i.e. well before they are visible on the concrete surface, in order to implement adequate maintenance actions on civil engineering structures. The key element of this original approach is the wavelet-based multiresolution analysis of the ultrasonic signal received from a sample or a specimen of the studied material subjected to several types of solicitation. This analysis is finally coupled to an automatic identification scheme of the types of cracks based on artificial neural networks (ANNs), and in particular deep learning by convolutional neural networks (CNNs); a technology today at the cutting edge of machine learning, in particular for all applications of pattern recognition. Wavelet-based multiresolution analysis does not add any value in detecting fractures in concrete visible by optical inspection. However, the results of its implementation coupled with different CNN architectures show cracks in concrete can be identified at an early stage with a very high accuracy, i.e. around 99.8%, and a loss function of less than 0.1, regardless of the implemented learning architecture.

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“…Researchers, engineers and practitioners in various technical fields such as multimedia [25], telecommunications [26][27][28], medicine and biology [7,29,30], crack tracking and fracture detection [8,9,31,32,33], fluid mechanics [34], thermodynamics [35], astrophysics [36], finance [37,38], etc., are daily confronted with increasingly challenging technological problems at multiple scales of analysis, in terms of classification, segmentation, detection (of contours or parameters of interest), noise reduction or even elimination [39], compression for transmission or storage, synthesis or reconstruction, etc.…”

Section: Multiresolution Analysis Based On Waveletsmentioning

confidence: 99%

Concrete Cracks Monitoring using Deep Learning-based Multiresolution Analysis

Arbaoui¹,

Ouahabi²,

Jacques³

et al. 2021

Preprint

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In this paper, we propose a new methodology for crack monitoring in concrete structures. This approach is based on a n this paper, we propose a new methodology for monitoring cracks in concrete structures. This approach is based on a multi-resolution analysis of a sample or a specimen of the studied material subjected to several types of solicitation. The image obtained by ultrasonic investigation and processing by a dedicated wavelet will be analyzed according to several scales in order to detect internal cracks and crack initiation. The ultimate goal of this work is to propose an automatic crack type identification scheme based on convolutional neural networks (CNN). In this context, crack propagation can be monitored without access to the concrete surface and the goal is to detect cracks before they are visible on the concrete surface. The key idea allowing such a performance is the combination of two major data analysis tools which are wavelets and Deep Learning. This original procedure allows to reach a high accuracy close to 0.90. In this work, we have also implemented another approach for automatic detection of external cracks by deep learning from publicly available datasets.

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New mobile communication system design for Rayleigh environments based on compressed sensing‐source coding

Cited by 27 publications

References 30 publications

Image Denoising Using a Compressive Sensing Approach Based on Regularization Constraints

Image Denoising Using a Compressive Sensing Approach Based on Regularization Constraints

Wavelet-based multiresolution analysis coupled with deep learning to efficiently monitor cracks in concrete

Concrete Cracks Monitoring using Deep Learning-based Multiresolution Analysis

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