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Abdeldjalil Ouahabi is Full Professor at the University of Tours in France. He is currently leading a research team at the Department of Computer Science at the University of Bouira in Algeria. His research interests include Image and Signal Processing, Biomedical Engineering and Machine Learning. Prof. Ouahabi is the author of over 170 published papers in these areas and he is a member of the editorial board of several Web of Science journals. He has also served as General Chairman of various international conferences.
Automatic pain recognition from facial expressions is a challenging problem that has attracted a significant attention from the research community. This article provides a comprehensive analysis on the topic by comparing some popular and Off-the-Shell CNN (Convolutional Neural Network) architectures, including MobileNet, GoogleNet, ResNeXt-50, ResNet18, and DenseNet-161. We use these networks in two distinct modes: stand alone mode or feature extractor mode. In stand alone mode, the models (i.e., the networks) are used for directly estimating the pain. In feature extractor mode, the “values” of the middle layers are extracted and used as inputs to classifiers, such as SVR (Support Vector Regression) and RFR (Random Forest Regression). We perform extensive experiments on the benchmarking and publicly available database called UNBC-McMaster Shoulder Pain. The obtained results are interesting as they give valuable insights into the usefulness of the hidden CNN layers for automatic pain estimation.
Safaa El Morabit; Atika Rivenq; Mohammed-En-Nadhir Zighem; Abdenour Hadid; Abdeldjalil Ouahabi; Abdelmalik Taleb-Ahmed. Automatic Pain Estimation from Facial Expressions: A Comparative Analysis Using Off-the-Shelf CNN Architectures. Electronics 2021, 10, 1926 .
AMA StyleSafaa El Morabit, Atika Rivenq, Mohammed-En-Nadhir Zighem, Abdenour Hadid, Abdeldjalil Ouahabi, Abdelmalik Taleb-Ahmed. Automatic Pain Estimation from Facial Expressions: A Comparative Analysis Using Off-the-Shelf CNN Architectures. Electronics. 2021; 10 (16):1926.
Chicago/Turabian StyleSafaa El Morabit; Atika Rivenq; Mohammed-En-Nadhir Zighem; Abdenour Hadid; Abdeldjalil Ouahabi; Abdelmalik Taleb-Ahmed. 2021. "Automatic Pain Estimation from Facial Expressions: A Comparative Analysis Using Off-the-Shelf CNN Architectures." Electronics 10, no. 16: 1926.
Cooperative machine learning has many applications, such as data annotation, where an initial model trained with partially labeled data is used to predict labels for unseen data continuously. Predicted labels with a low confidence value are manually revised to allow the model to be retrained with the predicted and revised data. In this paper, we propose an alternative to this approach: an initial training process called Deep Unsupervised Active Learning. Using the proposed training scheme, a classification model can incrementally acquire new knowledge during the testing phase without manual guidance or correction of decision making. The training process consists of two stages: the first stage of supervised training using a classification model, and an unsupervised active learning stage during the test phase. The labels predicted during the test phase, with high confidence, are continuously used to extend the knowledge base of the model. To optimize the proposed method, the model must have a high initial recognition rate. To this end, we exploited the Visual Geometric Group (VGG16) pre-trained model applied to three datasets: Mathematical Image Analysis (AMI), University of Science and Technology Beijing (USTB2), and Annotated Web Ears (AWE). This approach achieved impressive performance that shows a significant improvement in the recognition rate of the USTB2 dataset by coloring its images using a Generative Adversarial Network (GAN). The obtained performances are interesting compared to the current methods: the recognition rates are 100.00%, 98.33%, and 51.25% for the USTB2, AMI, and AWE datasets, respectively.
Yacine Khaldi; Amir Benzaoui; Abdeldjalil Ouahabi; Sebastien Jacques; Abdelmalik Taleb-Ahmed. Ear Recognition Based on Deep Unsupervised Active Learning. IEEE Sensors Journal 2021, PP, 1 -1.
AMA StyleYacine Khaldi, Amir Benzaoui, Abdeldjalil Ouahabi, Sebastien Jacques, Abdelmalik Taleb-Ahmed. Ear Recognition Based on Deep Unsupervised Active Learning. IEEE Sensors Journal. 2021; PP (99):1-1.
Chicago/Turabian StyleYacine Khaldi; Amir Benzaoui; Abdeldjalil Ouahabi; Sebastien Jacques; Abdelmalik Taleb-Ahmed. 2021. "Ear Recognition Based on Deep Unsupervised Active Learning." IEEE Sensors Journal PP, no. 99: 1-1.
In this paper, we propose a new methodology for crack detection and monitoring in concrete structures. This approach is based on a multiresolution analysis of a sample or a specimen of concrete material subjected to several types of solicitation. The image obtained by ultrasonic investigation and processed by a customized wavelet is analyzed at various scales in order to detect internal cracks and crack initiation. The ultimate objective 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. This is achieved through the combination of two major data analysis tools which are wavelets and deep learning. This original procedure is shown to yield a high accuracy close to 90%. In order to evaluate the performance of the proposed CNN architectures, we also used an open access database, SDNET2018, for the automatic detection of external cracks.
Ahcene Arbaoui; Abdeldjalil Ouahabi; Sébastien Jacques; Madina Hamiane. Concrete Cracks Detection and Monitoring Using Deep Learning-Based Multiresolution Analysis. Electronics 2021, 10, 1772 .
AMA StyleAhcene Arbaoui, Abdeldjalil Ouahabi, Sébastien Jacques, Madina Hamiane. Concrete Cracks Detection and Monitoring Using Deep Learning-Based Multiresolution Analysis. Electronics. 2021; 10 (15):1772.
Chicago/Turabian StyleAhcene Arbaoui; Abdeldjalil Ouahabi; Sébastien Jacques; Madina Hamiane. 2021. "Concrete Cracks Detection and Monitoring Using Deep Learning-Based Multiresolution Analysis." Electronics 10, no. 15: 1772.
Biomass is an attractive energy source that can be used for production of heat, power, and transport fuels and when produced and used on a sustainable basis, can make a large contribution to reducing greenhouse gas emissions. Anaerobic digestion (AD) is a suitable technology for reducing organic matter and generating bioenergy in the form of biogas. This study investigated the factors allowing the optimization of the process of biogas production from the digestion of wheat straw (WS). The statistical analysis of the experiments carried out showed that ultrasonic processing plays a fundamental role with the sonication density and solids concentration leading to improved characteristics of WS, reducing particle size, and increasing concentration of soluble chemical oxygen demand. The higher the sonicating power used, the more the waste particles are disrupted. The optimality obtained under mesophilic conditions for WS pretreated with 4% w/w (weight by weight) H2O2 at temperature 36 °C under 10 min of ultrasonication at 24 kHz with a power of 200 W improves the methane yield by 64%.
Yasmine Ouahabi; Kenza Bensadok; Abdeldjalil Ouahabi. Optimization of the Biomethane Production Process by Anaerobic Digestion of Wheat Straw Using Chemical Pretreatments Coupled with Ultrasonic Disintegration. Sustainability 2021, 13, 7202 .
AMA StyleYasmine Ouahabi, Kenza Bensadok, Abdeldjalil Ouahabi. Optimization of the Biomethane Production Process by Anaerobic Digestion of Wheat Straw Using Chemical Pretreatments Coupled with Ultrasonic Disintegration. Sustainability. 2021; 13 (13):7202.
Chicago/Turabian StyleYasmine Ouahabi; Kenza Bensadok; Abdeldjalil Ouahabi. 2021. "Optimization of the Biomethane Production Process by Anaerobic Digestion of Wheat Straw Using Chemical Pretreatments Coupled with Ultrasonic Disintegration." Sustainability 13, no. 13: 7202.
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.
Ahcene Arbaoui; Abdeldjalil Ouahabi; Sébastien Jacques; Madina Hamiane. Concrete Cracks Monitoring using Deep Learning-based Multiresolution Analysis. 2021, 1 .
AMA StyleAhcene Arbaoui, Abdeldjalil Ouahabi, Sébastien Jacques, Madina Hamiane. Concrete Cracks Monitoring using Deep Learning-based Multiresolution Analysis. . 2021; ():1.
Chicago/Turabian StyleAhcene Arbaoui; Abdeldjalil Ouahabi; Sébastien Jacques; Madina Hamiane. 2021. "Concrete Cracks Monitoring using Deep Learning-based Multiresolution Analysis." , no. : 1.
A new source coding is proposed for secure and robust speech communications. The method is based on the combination of compressed sensing and split-multistage vector quantization. The proposed codec is integrated in an end-to-end communication system, and its performance is investigated in real mobile communication conditions. Channel compensation techniques are considered to mitigate the Rayleigh channel effects usually observed in mobile communications. Using the proposed speech coding scheme instead of current standards (e.g., AMR-WB) within the communication system results in a new end-to-end mobile communication design. The proposed design increases the transmission speed, robustness, and security without additional costs. For a bit rate of 8.85 kbit/s and in 10 dB Rayleigh environment, the recovered speech has a good perceptual evaluation of speech quality score close to 3.14 and a fair coherence speech intelligibility index value of around 0.47. Comparison with recent CS-based speech coding methods shows the merit of the proposed coder.
Houria Haneche; Abdeldjalil Ouahabi; Bachir Boudraa. Compressed Sensing-Speech Coding Scheme for Mobile Communications. Circuits, Systems, and Signal Processing 2021, 40, 5106 -5126.
AMA StyleHouria Haneche, Abdeldjalil Ouahabi, Bachir Boudraa. Compressed Sensing-Speech Coding Scheme for Mobile Communications. Circuits, Systems, and Signal Processing. 2021; 40 (10):5106-5126.
Chicago/Turabian StyleHouria Haneche; Abdeldjalil Ouahabi; Bachir Boudraa. 2021. "Compressed Sensing-Speech Coding Scheme for Mobile Communications." Circuits, Systems, and Signal Processing 40, no. 10: 5106-5126.
Single-Sample Face Recognition (SSFR) is a computer vision challenge. In this scenario, there is only one example from each individual on which to train the system, making it difficult to identify persons in unconstrained environments, mainly when dealing with changes in facial expression, posture, lighting, and occlusion. This paper discusses the relevance of an original method for SSFR, called Multi-Block Color-Binarized Statistical Image Features (MB-C-BSIF), which exploits several kinds of features, namely, local, regional, global, and textured-color characteristics. First, the MB-C-BSIF method decomposes a facial image into three channels (e.g., red, green, and blue), then it divides each channel into equal non-overlapping blocks to select the local facial characteristics that are consequently employed in the classification phase. Finally, the identity is determined by calculating the similarities among the characteristic vectors adopting a distance measurement of the K-nearest neighbors (K-NN) classifier. Extensive experiments on several subsets of the unconstrained Alex and Robert (AR) and Labeled Faces in the Wild (LFW) databases show that the MB-C-BSIF achieves superior and competitive results in unconstrained situations when compared to current state-of-the-art methods, especially when dealing with changes in facial expression, lighting, and occlusion. The average classification accuracies are 96.17% and 99% for the AR database with two specific protocols (i.e., Protocols I and II, respectively), and 38.01% for the challenging LFW database. These performances are clearly superior to those obtained by state-of-the-art methods. Furthermore, the proposed method uses algorithms based only on simple and elementary image processing operations that do not imply higher computational costs as in holistic, sparse or deep learning methods, making it ideal for real-time identification.
Insaf Adjabi; Abdeldjalil Ouahabi; Amir Benzaoui; Sébastien Jacques. Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition. Sensors 2021, 21, 728 .
AMA StyleInsaf Adjabi, Abdeldjalil Ouahabi, Amir Benzaoui, Sébastien Jacques. Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition. Sensors. 2021; 21 (3):728.
Chicago/Turabian StyleInsaf Adjabi; Abdeldjalil Ouahabi; Amir Benzaoui; Sébastien Jacques. 2021. "Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition." Sensors 21, no. 3: 728.
A real-time architecture of medical image semantic segmentation called Fully Convolution dense Dilated Network, is proposed to improve the segmentation efficiency while ensuring high accuracy. Considering low resolution and contrast, interferences of shadows, as well as differences in nodules’ position and size, accurate ultrasound images’ segmentation cannot be obtained easily. Therefore, a novel layer that integrates the advantages of dense connectivity, dilated convolutions and factorized filters, is proposed in an attempt to remain efficient while retaining remarkable accuracy. Dense connectivity combines low-level fine segmentation with high-level coarse segmentation to extract more features from ultrasound images. Dilated convolution can expand the receptive field of the filter, and the problem of differences in nodules’ size and position can be solved with different sizes of filters. This study also introduces factorized filters into the network to further optimize the efficiency of the model. In addition, aiming at the class imbalance problem in medical image semantic segmentation, a loss function optimization method is proposed which further improves the accuracy of the network. A thorough set of experiments based on thyroid dataset show that the proposed model achieves state-of-the-art performance in terms of robustness and efficiency.
Abdeldjalil Ouahabi; Abdelmalik Taleb-Ahmed. Deep learning for real-time semantic segmentation: Application in ultrasound imaging. Pattern Recognition Letters 2021, 144, 27 -34.
AMA StyleAbdeldjalil Ouahabi, Abdelmalik Taleb-Ahmed. Deep learning for real-time semantic segmentation: Application in ultrasound imaging. Pattern Recognition Letters. 2021; 144 ():27-34.
Chicago/Turabian StyleAbdeldjalil Ouahabi; Abdelmalik Taleb-Ahmed. 2021. "Deep learning for real-time semantic segmentation: Application in ultrasound imaging." Pattern Recognition Letters 144, no. : 27-34.
Single sample face recognition (SSFR) is a computer vision challenge. In this scenario, there is only one example from each individual on which to train the system, making it difficult to identify persons in unconstrained environments, particularly when dealing with changes in facial expression, posture, lighting, and occlusion. This paper suggests a different method based on a variant of the Binarized Statistical Image Features (BSIF) descriptor called Multi-Block Color-Binarized Statistical Image Features (MB-C-BSIF) to resolve the SSFR Problem. First, the MB-C-BSIF method decomposes a facial image into three channels (e.g., red, green, and blue), then it divides each channel into equal non-overlapping blocks to select the local facial characteristics that are consequently employed in the classification phase. Finally, the identity is determined by calculating the similarities among the characteristic vectors adopting a distance measurement of the k-nearest neighbors (K-NN) classifier. Extensive experiments on several subsets of the unconstrained Alex & Robert (AR) and Labeled Faces in the Wild (LFW) databases show that the MB-C-BSIF achieves superior results in unconstrained situations when compared to current state-of-the-art methods, especially when dealing with changes in facial expression, lighting, and occlusion. Furthermore, the suggested method employs algorithms with lower computational cost, making it ideal for real-time applications.
Insaf Adjabi; Amir Benzaoui; Abdeldjalil Ouahabi; Sebastien Jacques. Multi-Block Color-Binarized Statistical Images for Single Sample Face Recognition. 2020, 1 .
AMA StyleInsaf Adjabi, Amir Benzaoui, Abdeldjalil Ouahabi, Sebastien Jacques. Multi-Block Color-Binarized Statistical Images for Single Sample Face Recognition. . 2020; ():1.
Chicago/Turabian StyleInsaf Adjabi; Amir Benzaoui; Abdeldjalil Ouahabi; Sebastien Jacques. 2020. "Multi-Block Color-Binarized Statistical Images for Single Sample Face Recognition." , no. : 1.
This paper suggests a new technique for trabecular bone characterization using fractal analysis of X‐Ray and MRI texture images for osteoporosis diagnosis. Osteoporosis is a chronic disease characterized by a decrease in bone density that can lead to fracture and disability. In essence, the proposed fractal model makes use of the differential box‐counting method (DBCM) to estimate the fractal dimension (FD) after an appropriate image preprocessing stage that ensures a robust estimation process. In this study, we showed that within the frequency domain generated through discrete cosine transform (DCT), only a quarter of DCT coefficients are enough to characterize osteoporotic tissues. The algorithmic complexity of the developed approach is of the order of where N stands for the size of the image, which, in turn, likely yields important gain in terms of medication cost. We report a successful separation of healthy and pathological cases in term of both P − value (using statistical Wilcoxon rank sum test) and margin difference. A comparative statistical analysis has been performed using a publicly available database that contains a set of MRI and X‐Ray texture images of both healthy and osteoporotic bone tissues. The statistical results demonstrated the feasibility and accepted performance level of our fractal model‐based diagnosis to discriminate healthy and unhealthy trabecular bone tissues. The developed approach has been implemented on a medical device prototype.
Soraya Zehani; Abdeldjalil Ouahabi; Mourad Oussalah; Malika Mimi; Abdelmalik Taleb‐Ahmed. Bone microarchitecture characterization based on fractal analysis in spatial frequency domain imaging. International Journal of Imaging Systems and Technology 2020, 31, 141 -159.
AMA StyleSoraya Zehani, Abdeldjalil Ouahabi, Mourad Oussalah, Malika Mimi, Abdelmalik Taleb‐Ahmed. Bone microarchitecture characterization based on fractal analysis in spatial frequency domain imaging. International Journal of Imaging Systems and Technology. 2020; 31 (1):141-159.
Chicago/Turabian StyleSoraya Zehani; Abdeldjalil Ouahabi; Mourad Oussalah; Malika Mimi; Abdelmalik Taleb‐Ahmed. 2020. "Bone microarchitecture characterization based on fractal analysis in spatial frequency domain imaging." International Journal of Imaging Systems and Technology 31, no. 1: 141-159.
Face recognition is one of the most active research fields of computer vision and pattern recognition, with many practical and commercial applications including identification, access control, forensics, and human-computer interactions. However, identifying a face in a crowd raises serious questions about individual freedoms and poses ethical issues. Significant methods, algorithms, approaches, and databases have been proposed over recent years to study constrained and unconstrained face recognition. 2D approaches reached some degree of maturity and reported very high rates of recognition. This performance is achieved in controlled environments where the acquisition parameters are controlled, such as lighting, angle of view, and distance between the camera–subject. However, if the ambient conditions (e.g., lighting) or the facial appearance (e.g., pose or facial expression) change, this performance will degrade dramatically. 3D approaches were proposed as an alternative solution to the problems mentioned above. The advantage of 3D data lies in its invariance to pose and lighting conditions, which has enhanced recognition systems efficiency. 3D data, however, is somewhat sensitive to changes in facial expressions. This review presents the history of face recognition technology, the current state-of-the-art methodologies, and future directions. We specifically concentrate on the most recent databases, 2D and 3D face recognition methods. Besides, we pay particular attention to deep learning approach as it presents the actuality in this field. Open issues are examined and potential directions for research in facial recognition are proposed in order to provide the reader with a point of reference for topics that deserve consideration.
Insaf Adjabi; Abdeldjalil Ouahabi; Amir Benzaoui; Abdelmalik Taleb-Ahmed. Past, Present, and Future of Face Recognition: A Review. Electronics 2020, 9, 1188 .
AMA StyleInsaf Adjabi, Abdeldjalil Ouahabi, Amir Benzaoui, Abdelmalik Taleb-Ahmed. Past, Present, and Future of Face Recognition: A Review. Electronics. 2020; 9 (8):1188.
Chicago/Turabian StyleInsaf Adjabi; Abdeldjalil Ouahabi; Amir Benzaoui; Abdelmalik Taleb-Ahmed. 2020. "Past, Present, and Future of Face Recognition: A Review." Electronics 9, no. 8: 1188.
Face recognition is one of the most active research fields of computer vision and pattern recognition, with many practical and commercial applications including identification, access control, forensics, and human-computer interactions. However, identifying a face in a crowd raises serious questions about individual freedoms and poses ethical issues. Significant methods, algorithms, approaches, and databases have been proposed over recent years to study constrained and unconstrained face recognition. 2D approaches reached some degree of maturity and reported very high rates of recognition. This performance is achieved in controlled environments where the acquisition parameters are controlled, such as lighting, angle of view, and distance between the camera-subject. However, if the ambient conditions (e.g., lighting) or the facial appearance (e.g., pose or facial expression) change, this performance will degrade dramatically. 3D approaches were proposed as an alternative solution to the problems mentioned above. The advantage of 3D data lies in its invariance to pose and lighting conditions, which has enhanced recognition systems efficiency. 3D data, however, is somewhat sensitive to changes in facial expressions. This review presents the history of face recognition technology, the current state-of-the-art methodologies, and future directions. We specifically concentrate on the most recent databases, 2D and 3D face recognition methods. Besides, we pay particular attention to deep learning approach as it presents the actuality in this field. Open issues are examined and potential directions for research in facial recognition are proposed in order to provide the reader with a point of reference for topics that deserve consideration.
Insaf Adjabi; Abdeldjalil Ouahabi; Amir Benzaoui; Abdelmalik Taleb-Ahmed. Past, Present, and Future of Face Recognition: A Review. 2020, 1 .
AMA StyleInsaf Adjabi, Abdeldjalil Ouahabi, Amir Benzaoui, Abdelmalik Taleb-Ahmed. Past, Present, and Future of Face Recognition: A Review. . 2020; ():1.
Chicago/Turabian StyleInsaf Adjabi; Abdeldjalil Ouahabi; Amir Benzaoui; Abdelmalik Taleb-Ahmed. 2020. "Past, Present, and Future of Face Recognition: A Review." , no. : 1.
A reliable environment perception is a crucial task for autonomous driving, especially in dense traffic areas. Recent improvements and breakthroughs in scene understanding for intelligent transportation systems are mainly based on deep learning and the fusion of different modalities. In this context, we introduce OLIMP: A heterOgeneous Multimodal Dataset for Advanced EnvIronMent Perception. This is the first public, multimodal and synchronized dataset that includes UWB radar data, acoustic data, narrow-band radar data and images. OLIMP comprises 407 scenes and 47,354 synchronized frames, presenting four categories: pedestrian, cyclist, car and tram. The dataset includes various challenges related to dense urban traffic such as cluttered environment and different weather conditions. To demonstrate the usefulness of the introduced dataset, we propose a fusion framework that combines the four modalities for multi object detection. The obtained results are promising and spur for future research.
Amira Mimouna; Ihsen Alouani; Anouar Ben Khalifa; Yassin El Hillali; Abdelmalik Taleb-Ahmed; Atika Menhaj; Abdeldjalil Ouahabi; Najoua Essoukri Ben Amara. OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception. Electronics 2020, 9, 560 .
AMA StyleAmira Mimouna, Ihsen Alouani, Anouar Ben Khalifa, Yassin El Hillali, Abdelmalik Taleb-Ahmed, Atika Menhaj, Abdeldjalil Ouahabi, Najoua Essoukri Ben Amara. OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception. Electronics. 2020; 9 (4):560.
Chicago/Turabian StyleAmira Mimouna; Ihsen Alouani; Anouar Ben Khalifa; Yassin El Hillali; Abdelmalik Taleb-Ahmed; Atika Menhaj; Abdeldjalil Ouahabi; Najoua Essoukri Ben Amara. 2020. "OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception." Electronics 9, no. 4: 560.
We propose a novel speech enhancement approach based on compressed sensing. The method performs noise subtraction in the measurement domain in addition to sparse recovery. Dictionary learning, using K-singular value decomposition algorithm, is performed to create an overcomplete dictionary. The noise in the measurement domain is estimated during pauses. Voice activity detection (VAD) is used to classify speech/silence frames. Based on the VAD output, a mask function is created, and applied to the noisy speech spectrogram. Furthermore, from each active-speech observation vector, the estimated noise observation vector is subtracted. The enhanced speech spectra are obtained by sparse recovery using orthogonal matching pursuit. Our method is tested for various types of noise, including babble, market, police siren, piano, factory, and white noises. Comparison with recent state-of-the-art methods is performed in terms of segmental signal to noise ratio, perceptual evaluation of speech quality, and short-time objective intelligibility. The results reveal the advantages of the proposed method.
Houria Haneche; Bachir Boudraa; Abdeldjalil Ouahabi. A new way to enhance speech signal based on compressed sensing. Measurement 2019, 151, 107117 .
AMA StyleHouria Haneche, Bachir Boudraa, Abdeldjalil Ouahabi. A new way to enhance speech signal based on compressed sensing. Measurement. 2019; 151 ():107117.
Chicago/Turabian StyleHouria Haneche; Bachir Boudraa; Abdeldjalil Ouahabi. 2019. "A new way to enhance speech signal based on compressed sensing." Measurement 151, no. : 107117.
A new end-to-end communication system is proposed to increase transmission speed, robustness, and security in order to meet the requirements of mobile systems that know an exponentially increasing data amount over time. The design relies on the use of compressed sensing-source coding instead of the supported speech coding standards in actual mobile communication systems. The proposed compressed sensing-source coding method allows reducing the speech coding complexity by using simple quantisation and binary encoding, saving communication system resources, and encrypting communications without additional costs. The performance of the resulting communication system is evaluated for speech communication via 10 dB Rayleigh environment in terms of perceptual evaluation of speech quality (PESQ) scores and coherence speech intelligibility index (CSII) when convolutional coding, orthogonal frequency division multiplexing, and diversity schemes are used. Results report that for a bit rate of 12.8 kbit/s the proposed scheme achieves fair speech intelligibility justified by a CSII value of 0.5, and offers good output speech quality measure, providing a PESQ of 3.33 for the same bit rate.
Houria Haneche; Abdeldjalil Ouahabi; Bachir Boudraa. New mobile communication system design for Rayleigh environments based on compressed sensing‐source coding. IET Communications 2019, 13, 2375 -2385.
AMA StyleHouria Haneche, Abdeldjalil Ouahabi, Bachir Boudraa. New mobile communication system design for Rayleigh environments based on compressed sensing‐source coding. IET Communications. 2019; 13 (15):2375-2385.
Chicago/Turabian StyleHouria Haneche; Abdeldjalil Ouahabi; Bachir Boudraa. 2019. "New mobile communication system design for Rayleigh environments based on compressed sensing‐source coding." IET Communications 13, no. 15: 2375-2385.
The operations of digitization, transmission and storage of medical data, particularly images, require increasingly effective encoding methods not only in terms of compression ratio and flow of information but also in terms of visual quality. At first, there was DCT (discrete cosine transform) then DWT (discrete wavelet transform) and their associated standards in terms of coding and image compression. The 2nd-generation wavelets seeks to be positioned and confronted by the image and video coding methods currently used. It is in this context that we suggest a method combining bandelets and the SPIHT (set partitioning in hierarchical trees) algorithm. There are two main reasons for our approach: the first lies in the nature of the bandelet transform to take advantage of capturing the geometrical complexity of the image structure. The second reason is the suitability of encoding the bandelet coefficients by the SPIHT encoder. Quality measurements indicate that in some cases (for low bit rates) the performance of the proposed coding competes with the well-established ones (H.264 or MPEG4 AVC and H.265 or MPEG4 HEVC) and opens up new application prospects in the field of medical imaging.
Merzak Ferroukhi; Abdeldjalil Ouahabi; Mokhtar Attari; Yassine Habchi; Abdelmalik Taleb-Ahmed. Medical Video Coding Based on 2nd-Generation Wavelets: Performance Evaluation. Electronics 2019, 8, 88 .
AMA StyleMerzak Ferroukhi, Abdeldjalil Ouahabi, Mokhtar Attari, Yassine Habchi, Abdelmalik Taleb-Ahmed. Medical Video Coding Based on 2nd-Generation Wavelets: Performance Evaluation. Electronics. 2019; 8 (1):88.
Chicago/Turabian StyleMerzak Ferroukhi; Abdeldjalil Ouahabi; Mokhtar Attari; Yassine Habchi; Abdelmalik Taleb-Ahmed. 2019. "Medical Video Coding Based on 2nd-Generation Wavelets: Performance Evaluation." Electronics 8, no. 1: 88.
The operations of digitization, transmission and storage of medical data, particularly images require increasingly effective encoding methods not only in terms of compression ratio and flow of information but also in terms of visual quality. At first, there was DCT (discrete cosine transform) then DWT (discrete wavelet transform) and their associated standards in terms of coding and image compression. After that, the 2nd generation wavelets seeks to be positioned and confronted to the image and video coding methods currently used. It is in this context that we suggested a method combining bandelets and SPIHT (set partitioning in hierarchical trees) algorithm. There are two main reasons for our approach: the first lies in the nature of the bandelet transform to take advantage by capturing the geometrical complexity of the image structure. The second reason stems in the suitability of encoding the bandelet coefficients by the SPIHT encoder. Quality measurements shows that in some cases (for low bit rates) the performances of the proposed coding compete with the well-established ones and opens up new application prospects in the field of medical imaging.
Merzak Ferroukhi; Abdeldjalil Ouahabi; Mokhtar Attari; Yacine Habchi; Mohamed Beladgham; Abdelmalik Taleb-Amed. Medical Video Coding Based on 2nd Generation Wavelets: Performance Evaluation. 2018, 1 .
AMA StyleMerzak Ferroukhi, Abdeldjalil Ouahabi, Mokhtar Attari, Yacine Habchi, Mohamed Beladgham, Abdelmalik Taleb-Amed. Medical Video Coding Based on 2nd Generation Wavelets: Performance Evaluation. . 2018; ():1.
Chicago/Turabian StyleMerzak Ferroukhi; Abdeldjalil Ouahabi; Mokhtar Attari; Yacine Habchi; Mohamed Beladgham; Abdelmalik Taleb-Amed. 2018. "Medical Video Coding Based on 2nd Generation Wavelets: Performance Evaluation." , no. : 1.
Wafaa Rmili; Abdeldjalil Ouahabi; Roger Serra; René Leroy. An automatic system based on vibratory analysis for cutting tool wear monitoring. Measurement 2016, 77, 117 -123.
AMA StyleWafaa Rmili, Abdeldjalil Ouahabi, Roger Serra, René Leroy. An automatic system based on vibratory analysis for cutting tool wear monitoring. Measurement. 2016; 77 ():117-123.
Chicago/Turabian StyleWafaa Rmili; Abdeldjalil Ouahabi; Roger Serra; René Leroy. 2016. "An automatic system based on vibratory analysis for cutting tool wear monitoring." Measurement 77, no. : 117-123.
Said Aissou; Sébastien Jacques; Zahra Mokrani; Djamila Rekioua; Toufik Rekioua; Abdeldjalil Ouahabi. Relevance of the P & O MPPT Technique in an Original PV-Powered Water Pumping Application. Journal of Energy and Power Engineering 2015, 9, 1 .
AMA StyleSaid Aissou, Sébastien Jacques, Zahra Mokrani, Djamila Rekioua, Toufik Rekioua, Abdeldjalil Ouahabi. Relevance of the P & O MPPT Technique in an Original PV-Powered Water Pumping Application. Journal of Energy and Power Engineering. 2015; 9 (11):1.
Chicago/Turabian StyleSaid Aissou; Sébastien Jacques; Zahra Mokrani; Djamila Rekioua; Toufik Rekioua; Abdeldjalil Ouahabi. 2015. "Relevance of the P & O MPPT Technique in an Original PV-Powered Water Pumping Application." Journal of Energy and Power Engineering 9, no. 11: 1.
Image denoising is a very important step in cryo-transmission electron microscopy (cryo-TEM) and the energy filtering TEM images before the 3D tomography reconstruction, as it addresses the problem of high noise in these images, that leads to a loss of the contained information. High noise levels contribute in particular to difficulties in the alignment required for 3D tomography reconstruction. This paper investigates the denoising of TEM images that are acquired with a very low exposure time, with the primary objectives of enhancing the quality of these low-exposure time TEM images and improving the alignment process. We propose denoising structures to combine multiple noisy copies of the TEM images. The structures are based on Bayesian estimation in the transform domains instead of the spatial domain to build a novel feature preserving image denoising structures; namely: wavelet domain, the contourlet transform domain and the contourlet transform with sharp frequency localization. Numerical image denoising experiments demonstrate the performance of the Bayesian approach in the contourlet transform domain in terms of improving the signal to noise ratio (SNR) and recovering fine details that may be hidden in the data. The SNR and the visual quality of the denoised images are considerably enhanced using these denoising structures that combine multiple noisy copies. The proposed methods also enable a reduction in the exposure time.
Soumia Sid Ahmed; Zoubeida Messali; Abdeldjalil Ouahabi; Sylvain Trepout; Cedric Messaoudi; Sergio Marco. Nonparametric Denoising Methods Based on Contourlet Transform with Sharp Frequency Localization: Application to Low Exposure Time Electron Microscopy Images. Entropy 2015, 17, 3461 -3478.
AMA StyleSoumia Sid Ahmed, Zoubeida Messali, Abdeldjalil Ouahabi, Sylvain Trepout, Cedric Messaoudi, Sergio Marco. Nonparametric Denoising Methods Based on Contourlet Transform with Sharp Frequency Localization: Application to Low Exposure Time Electron Microscopy Images. Entropy. 2015; 17 (5):3461-3478.
Chicago/Turabian StyleSoumia Sid Ahmed; Zoubeida Messali; Abdeldjalil Ouahabi; Sylvain Trepout; Cedric Messaoudi; Sergio Marco. 2015. "Nonparametric Denoising Methods Based on Contourlet Transform with Sharp Frequency Localization: Application to Low Exposure Time Electron Microscopy Images." Entropy 17, no. 5: 3461-3478.