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portfolio
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publications
Performance evaluation of augmented reality on fog computing platform
Published in International Journal of Computer Science and Trends and Technology, 2020
Despite the massive development in cloud computing technologies to be in line with the evolution of the Internet of Things (IoT), cloud computing remains somewhat weak in handling applications that require real-time processing. Moreover, since most of the IoT applications are classified as real-time applications (such as augmented reality applications), it is not possible to rely entirely on cloud computing for these applications. According to Gartner, the number of IoT devices that are to connect to the Internet by 2020 will be around 5.8 billion. With this increase in the number of devices that may be located in a small geographical area (as the case of smart cities), the amount of data transferred to the cloud will be massive and require tremendous processing capabilities by the cloud to satisfy it. This is only costly, but most of the current clouds fail to match the requirements of such applications and devices, hence the need for a technology to eliminate this gap between cloud computing and the Internet of things. Cisco introduced Fog Computing technology, an extension of cloud computing that places the cloud nearer to the things that generate data where it provides real-time processing and storage. This research offers a practical study to implement an augmented reality application used to enhance learning about plants in a smart city applied in Barcelona. The authors studied Fog computing implementation for 8 different application placements. Results show that the HAFA and iFogStor-G application placement policies have achieved the best service time and the lowest use of cloud processors.
Recommended citation: Kinan ABBAS, and Ahmad Saker Ahmad. (2020). "Performance evaluation of augmented reality on fog computing platform." International Journal of Computer Science and Trends and Technology, 8(3), pp. 57-63.
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Méthode de démélange et dématriçage conjoints fondée sur la complétion de rang un pour les images multispectrales « snapshot »
Published in XXVIIIème Colloque Francophone de Traitement du Signal et des Images (GRETSI 2022), Nancy, France, 2022
This paper presents a joint unmixing and demosaicing method based on rank-one completion for snapshot multispectral images, introducing new approaches in signal and image processing.
Recommended citation: Kinan Abbas, Matthieu Puigt, Gilles Delmaire, Gilles Roussel. (2022). "Méthode de démélange et dématriçage conjoints fondée sur la complétion de rang un pour les images multispectrales « snapshot »." In XXVIIIème Colloque Francophone de Traitement du Signal et des Images (GRETSI 2022), Sep 2022, Nancy, France. ⟨hal-03684733⟩.
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Filtering-based Endmember Estimation from Snapshot Spectral Images
Published in 2nd Workshop on Low-Rank Models and Applications (LRMA 2022), 2022
This paper introduces a filtering-based method for endmember estimation in snapshot spectral imaging, highlighting advancements in image processing techniques.
Recommended citation: Kinan Abbas, Matthieu Puigt, Gilles Delmaire, Gilles Roussel. (2022). "Filtering-based endmember estimation from snapshot spectral images." In 2nd Workshop on Low-Rank Models and Applications (LRMA 2022).
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Filtering-Based Endmember Identification Method For Snapshot Spectral Images
Published in 2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2022
This paper introduces a method for filtering-based endmember identification in snapshot spectral imaging, focusing on advancements in signal processing and image restoration techniques.
Recommended citation: Kinan Abbas, Matthieu Puigt, Gilles Delmaire, Gilles Roussel. (2022). "Filtering-Based Endmember Identification Method For Snapshot Spectral Images." In 2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp. 1-5. IEEE. DOI: 10.1109/WHISPERS56178.2022.9955128.
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Joint Unmixing And Demosaicing Methods For Snapshot Spectral Images
Published in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023
This article presents a matrix-completion framework for locally-rank-one-based joint unmixing and demosaicing methods, focusing on advancements in snapshot spectral imaging.
Recommended citation: Kinan Abbas, Matthieu Puigt, Gilles Delmaire, Gilles Roussel. (2023). "Locally-Rank-One-Based Joint Unmixing and Demosaicing Methods for Snapshot Spectral Images. Part I: A Matrix-Completion Framework." In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 123-126. IEEE. DOI: 10.1109/ICASSP49357.2023.10096740
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Méthodes parcimonieuses de déconvolution et démélange pour les images multispectrales ‘snapshot’
Published in XXIXème Colloque Francophone de Traitement du Signal et des Images (GRETSI 2023), 2023
This paper discusses sparse methods for deconvolution and unmixing in snapshot multispectral images, focusing on innovative approaches to enhance image processing and analysis.
Recommended citation: Kinan Abbas, Matthieu Puigt, Gilles Delmaire, Gilles Roussel. (2023). "Méthodes parcimonieuses de déconvolution et démélange pour les images multispectrales snapshot In XXIXème Colloque Francophone de Traitement du Signal et des Images (GRETSI 2023), pp. 749-752.
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Locally-Rank-One-Based Joint Unmixing and Demosaicing Methods for Snapshot Spectral Images. Part I: a Matrix-Completion Framework
Published in IEEE Transactions on Computational Imaging, 2024
This article presents a matrix-completion framework for locally-rank-one-based joint unmixing and demosaicing methods, focusing on advancements in snapshot spectral imaging.
Recommended citation: Kinan Abbas, Matthieu Puigt, Gilles Delmaire, Gilles Roussel. (2024). "Locally-Rank-One-Based Joint Unmixing and Demosaicing Methods for Snapshot Spectral Images. Part I: a Matrix-Completion Framework." IEEE Transactions on Computational Imaging, vol. 10, pp. 848-862, 2024.
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Locally-Rank-One-Based Joint Unmixing and Demosaicing Methods for Snapshot Spectral Images. Part II: A Filtering-Based Framework
Published in IEEE Transactions on Computational Imaging, 2024
This article introduces a filtering-based framework for locally-rank-one-based joint unmixing and demosaicing methods, offering new insights into the processing of snapshot spectral images.
Recommended citation: Kinan Abbas, Matthieu Puigt, Gilles Delmaire, Gilles Roussel. (2024). "Locally-Rank-One-Based Joint Unmixing and Demosaicing Methods for Snapshot Spectral Images. Part II: A Filtering-Based Framework." IEEE Transactions on Computational Imaging, vol. 10, pp. 806-817, 2024.
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Fabry-Perot Spectral Deconvolution with Entropy-weighted Penalization
Published in IEEE Sensors Letters, 2024
This article explores Fabry-Perot spectral deconvolution techniques with entropy-weighted penalization, addressing advancements in sensor signal processing and spectral correction for CMOS sensors.
Recommended citation: Kinan Abbas, Pierre Chatelain, Matthieu Puigt, Gilles Delmaire, Gilles Roussel. (2024). "Fabry-Perot Spectral Deconvolution with Entropy-weighted Penalization." IEEE Sensors Letters, vol. 8, no. 9, pp. 1-4, Sept. 2024, Art no. 7004404.
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Modèles de diffusion pour la synthèse de textures anisotropes
Published in XXXe Colloque Francophone de Traitement du Signal et des Images (GRETSI 2025), Aug 2025, Strasbourg, France., 2025
This article explores the use of diffusion models for synthesizing anisotropic textures, leveraging a U-Net-based architecture trained on anisotropic texture datasets to reproduce direction-dependent statistical properties.
Recommended citation: Kinan Abbas, Leo Davy, Patrice Abry, Stéphane G. Roux. Modèles de diffusion pour la synthèse de textures anisotropes. XXXe Colloque Francophone de Traitement du Signal et des Images (GRETSI 2025), Aug 2025, Strasbourg, France
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Méthode comprimée et distribuée de factorisation pondérée en matrices non-négatives pour les matrices de grande dimension
Published in XXXe Colloque Francophone de Traitement du Signal et des Images XXXe Colloque Francophone de Traitement du Signal et des Images (GRETSI 2025), Aug 2025, Strasbourg, France., 2025
This paper proposes a compressed (by random projections) and distributed Weighted Non-Negative Matrix Factorization approach for large data matrices, avoiding memory-intensive computations by updating factor matrices in a distributed manner.
Recommended citation: Matthieu Puigt, Asmae El Hyani, Kinan Abbas, Gilles Roussel, and Guillaume Caron. (2025). "Méthode comprimée et distribuée de factorisation pondérée en matrices non-négatives pour les matrices de grande dimension." In Proceedings of XXXe Colloque Francophone de Traitement du Signal et des Images (GRETSI 2025), Aug 2025, Strasbourg, France
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Diffusion Models for Multifractal Texture Synthesis
Published in Accepted in the 33rd European Signal Processing Conference (EUSIPCO 2025), 2025
This article explores the use of diffusion models for synthesizing multifractal textures, leveraging a UNet-based architecture and various noise schedulers to reproduce complex, scale-invariant texture dynamics.
Recommended citation: Kinan Abbas, Patrice Abry, and Stephane Roux. (2025). "Diffusion Models for Multifractal Texture Synthesis." In Proceedings of the European Signal Processing Conference (EUSIPCO).
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Introduction to Algorithms and Programming
Course, IUT GIM, ULCO university, 2024
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