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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Darwich, Mahmoud Khalil, Kasem Bayoumi, Magdy University of Mount Union Department of Mathematics and Computer Science AllianceOH44601 United States University of Mississippi University Department of Electrical and Computer Engineering MS38677 United States University of Louisiana at Lafayette Department of Electrical and Computer Engineering LA70504 United States

ISBN: (纸本)9798331529529

With the increasing demand for high-quality video streaming services, there is a need to enhance the Quality of Experience (QoE) for users, especially in environments with variable network conditions. This research paper proposes a novel approach leveraging AI-driven edge computing to implement adaptive multi-path video streaming. The proposed method uses machine learning algorithms to dynamically select the optimal streaming paths and adjust video quality in real-time based on network conditions and user preferences. By utilizing edge computing resources, this approach aims to reduce latency, improve bandwidth utilization, and provide a more reliable and high-quality video streaming experience. © 2024 IEEE.

关键词： Video streaming

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End-to-End Secure Video Streaming Using Homomorphic Encryption Techniques 2

End-to-End Secure Video Streaming Using Homomorphic Encrypti...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Darwich, Mahmoud Khalil, Kasem Bayoumi, Magdy University of Mount Union Department of Mathematics and Computer Science AllianceOH44601 United States University of Mississippi University Department of Electrical and Computer Engineering MS38677 United States University of Louisiana Department of Electrical and Computer Engineering LafayetteLA70504 United States

ISBN: (纸本)9798331529529

In the rapidly evolving landscape of digital media, video streaming has become a ubiquitous method of content delivery. However, the increasing sophistication of cyber threats poses significant risks to the security and privacy of streamed content. Traditional encryption methods, while effective in many scenarios, often fall short in providing comprehensive protection without compromising performance and user experience. This paper introduces a novel approach to achieving end-to-end secure video streaming by leveraging homomorphic encryption techniques. Homomorphic encryption allows for computations to be performed on encrypted data without needing to decrypt it first, thereby maintaining data privacy and security throughout the streaming process. Our proposed method integrates homomorphic encryption into the video streaming pipeline, ensuring that video data remains secure from the point of origin to the end user. We discuss the theoretical foundations of homomorphic encryption, detail the implementation of our approach, and evaluate its performance against conventional encryption methods. Experimental results demonstrate that our technique provides robust security with minimal impact on streaming quality and latency, making it a viable solution for secure video streaming in various applications. © 2024 IEEE.

关键词： Video streaming

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Mutual Retinex: Combining Transformer and CNN for Image Enhancement

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IEEE Transactions on Emerging Topics in Computational Intelligence 2024年第3期8卷 2240-2252页

作者： Jiang, Kui Wang, Qiong An, Zhaoyi Wang, Zheng Zhang, Cong Lin, Chia-Wen Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan430024 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu30013 Taiwan

Images captured in low-light or underwater environments are often accompanied by significant degradation, which can negatively impact the quality and performance of downstream tasks. While convolutional neural networks (CNNs) and Transformer architectures have made significant progress in computer vision tasks, there are few efforts to harmonize them into a more concise framework for enhancing such images. To this end, this study proposes to aggregate the individual capability of self-attention (SA) and CNNs for accurate perturbation removal while preserving background contents. Based on this, we carry forward a Retinex-based framework, dubbed as Mutual Retinex, where a two-branch structure is designed to characterize the specific knowledge of reflectance and illumination components while removing the perturbation. To maximize its potential, Mutual Retinex is equipped with a new mutual learning mechanism, involving an elaborately designed mutual representation module (MRM). In MRM, the complementary information between reflectance and illumination components are encoded and used to refine each other. Through the complementary learning via the mutual representation, the enhanced results generated by our model exhibit superior color consistency and naturalness. Extensive experiments have shown the significant superiority of our mutual learning based method over thirteen competitors on the low-light task and ten methods on the underwater image enhancement task. In particular, our proposed Mutual Retinex respectively surpasses the state-of-the-art method MIRNet-v2 by 0.90 dB and 2.46 dB in PSNR on the LOL 1000 and FIVEK datasets, while with only 19.8% model parameters. © 2017 IEEE.

关键词： Image enhancement

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Sustainable Solutions for Emerging Issues in Wireless Technology

Sustainable Solutions for Emerging Issues in Wireless Techno...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Al-Dmour, Nidal A. Alnawayseh, Saif. E. A. Ihsan, Anaum Irfan, Muhammad Kousar, Shagufta Ali, Dilawer Abbas, Tahir College of Engineering Department of Computer Engineering Mutah University Jordan College of Engineering Department of Electrical Engineering Mutah University Al-Karak Jordan Lahore Pakistan Multan Pakistan Applied Science Research Center Applied Science Private University Amman Jordan The TIMES Institute Department of Computer Science Multan60000 Pakistan

ISBN: (纸本)9798350369106

The rapid evolution of wireless technology has transformed communication, offering unprecedented accessibility and convenience. However, significant challenges like spectrum scarcity, security vulnerabilities, and environmental concerns must be addressed for sustainable growth. This paper provides an overview of these issues, including the impact of regulatory barriers and increased data demand on network infrastructure. It also suggests measures to support the development of effective wireless communication policies, ensuring sustainable progress as technologies advance towards 5G, MIMO, and IoT. The goal is to identify improvements for more robust wireless communication systems. © 2024 IEEE.

关键词： 5G Accessibility Communication IOT Network Infrastructure Security Wireless technology

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Numerical Predictions of Laminar Forced Convection Heat Transfer with and without Buoyancy Effects from an Isothermal Horizontal Flat Plate to Supercritical Nitrogen

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Frontiers in Heat and Mass Transfer 2024年第3期22卷 889-917页

作者： K.S.Rajendra Prasad Sathya Sai T.R.Seetharam Adithya Garimella Department of Mechanical and Industrial Engineering Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipal576104India Sustainable Energy Technology Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft2600 AANetherlands Department of Mechanical Engineering PES UniversityBengaluru560085India

Numerical predictions are made for Laminar Forced convection heat transfer with and without buoyancy effects for Supercritical Nitrogen flowing over an isothermal horizontal flat plate with a heated surface facing *** are performed by varying the value ofΔT from5 to 30 K and P_(∞)/P_(cr)ratio from1.1 to *** of all the thermophysical properties of supercritical Nitrogen is *** wall temperatures are chosen in such a way that two values of Tw are less than T∗(T*is the temperature at which the fluid has a maximum value of Cp for the given pressure),one value equal to T∗and two values greater than T∗.Three different values of U∞are used to obtain Re∞range of 3.6×10_(4)to 4.74×10^(5)for forced convection without buoyancy effects and Gr_(∞)/Re^(2)_(∞)range of 0.011 to 3.107 for the case where buoyancy effects are *** different forms of correlations are proposed based on numerical predictions and are compared with actual numerical *** has been found that in all six forms of correlations,the maximum deviations are found to occur in those cases where the pseudocritical temperature TT∗lies between the wall temperature and bulk fluid temperature.

关键词： Supercritical nitrogen laminar flow numerical methods forced convection heat transfer isothermal horizontal surface

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Transformer Models for Enhanced Calcifications Detection in Mammography 27th

Transformer Models for Enhanced Calcifications Detection in...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Cantone, Marco Marrocco, Claudio Tortorella, Francesco Bria, Alessandro Department of Electrical and Information Engineering University of Cassino and Southern Latium Via Gaetano Di Biasio 43 FR Cassino03043 Italy Department of Information and Electrical Engineering and Applied Mathematics University of Salerno SA Fisciano84084 Italy

ISBN: (纸本)9783031782008

In recent years, the image analysis landscape is witnessing a paradigm shift with the emergence of the vision transformer as a better alternative to Convolutional Neural Networks (CNNs). Transformers process sequences globally with self-attention capturing long-range features, while CNNs extract features locally through convolutional operations. We propose the adoption of Swin Transformer as backbone for calcification cluster detection in mammography, assessing its efficacy through a comprehensive experimental study comparing transformer-based and CNN-based models. Our experiments conducted on the large-scale mammography image database OMI-DB demonstrate a notable superiority of the Swin Transformer architecture. The best-performing Swin backbone obtained a sensitivity of 80.67% at 0.1 false positive per image, with a +3.34% improvement over the best convolutional backbone. Our findings underscore the efficacy of transformer-based architectures for detecting clusters of calcifications in mammography, offering improved diagnostic accuracy in this field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distribution transformers

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Detecting Persian Signatures in Realistic Images using the YOLO Algorithm 20

Detecting Persian Signatures in Realistic Images using the Y...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Jafarian, Shiva Shabaninia, Elham Nezamabadi-Pour, Hossein Shahid Bahonar University of Kerman Department of Electrical Engineering Kerman Iran Graduate University of Advanced Technology Department of Applied Mathematics Kerman Iran

ISBN: (纸本)9798350383942

While crucial for document forensics and security, detecting Persian signatures in real-world scenarios poses a considerable challenge due to the distinctive features of Persian signatures - characterized by complex combinations of shapes and letters instead of conventional names - along with issues like noise, cluttered backgrounds, and more. Furthermore, the scarcity of annotated datasets complicates signature detection in natural settings. This paper addresses these challenges by introducing a new dataset for Persian documents, merging publicly available signatures with diverse backgrounds to create semi-realistic images reflecting real-world conditions. Additionally, the proposed method leverages the YOLOv5 architecture for signature detection. The experimental findings illustrate that, under various lighting conditions, background complexities, and signature distortions, this approach effectively and accurately detects Persian signatures. © 2024 IEEE.

关键词： Network security

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A novel deep dual self-attention and Bi-LSTM fusion framework for Parkinson’s disease prediction using freezing of gait: a biometric application

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Multimedia Tools and Applications 2024年第33期83卷 80179-80200页

作者： Habib, Zeeshan Mughal, Muhammad Ali Khan, Muhammad Attique Hamza, Ameer Alturki, Nazik Jamel, Leila Department of Electrical Engineering HITEC University Taxila Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia

Parkinson’s disease (PD) disorder is caused by the imbalance of inhibitory dopamine and excitatory acetylcholine neurotransmitters, which causes hindrance in locomotion. Freezing of gait (FOG), tremors, and bradykinesia are the most debilitating and disconcerting symptoms of Parkinson’s Disease (PD). FOG episodes occur due to neurological control disorder and motorized impairments, which sternly hinder voluntary movement and forward locomotion. The computational intelligence techniques can effectively predict PD from the sensor data. To detect the FOG episodes often experienced by PD patients, this paper utilizes a 5G spectrum that operates at 4.8 GHz. The main idea of the proposed system is to extract the wireless channel characteristics information (containing amplitude variances) that can be integrated into a 5G communication system. This system collects data through wireless devices such as dipole antennas, network interface cards, and RF signal generators. Five human activities were performed: fast walking, slow walking, sitting on a chair, FOG episodes, and voluntary stopping. We proposed a novel deep dual self-attention and Bi-LSTM fusion architecture for PD prediction to train the collected dataset. Two architectures have been proposed, namely Deep Dual Attention Neural Network (D2ANN) and BiLSTM Neural Network (BNN), for the classification of PD using Chanel State Information (CSI) data. After that, the most prominent information is extracted using self-attention activation. A newly informed weighted fusion approach is proposed, and tree growth optimization is employed for the best feature selection. Finally, the selected features are classified using a multilayer full SoftMax neural network. The experimental process was conducted on FOG wireless sensor network data and obtained the highest accuracy of 98.66%. Compared to the results with state-of-the-art techniques, the proposed framework shows improved accuracy, recall, and precision rates. © The Author(s

关键词： Network architecture

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Enhancing Farsi Text Recognition via Iteratively Using a Language Model 20

Enhancing Farsi Text Recognition via Iteratively Using a Lan...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Afkari-Fahandari, Ali Asadi-Zeydabadi, Fatemeh Shabaninia, Elham Nezamabadi-Pour, Hossein Shahid Bahonar University of Kerman Department of Electrical Engineering Kerman Iran Graduate University of Advanced Technology Department of Applied Mathematics Kerman Iran

ISBN: (纸本)9798350383942

In the present era, there is a growing demand for a reliable Farsi text recognition system, that addresses the needs of both industrial and individual users. The process of text recognition is typically accompanied by a subsequent post-processing step, which aims to tailor the text to specific language constraints. However, the iterative use of post-correction offers valuable insights for enhancing the precision of the recognized text. Meanwhile, building upon the recent successes of Transformer-based approaches in natural language processing and computer vision, we propose a Farsi text recognition model. This model seamlessly integrates a decoupled convolutional Transformer-based vision model for initial predictions and a Transformer-based language model for refining the obtained predictions. Evaluation of well-known Farsi OCR datasets, including Iranshahr, Sadri, and Shotor, demonstrates the model's superior performance, surpassing existing literature with recognition accuracies of 97.59%, 99.25%, and 99.97% on Iranshahr, Sadri, and Shotor datasets, respectively. © 2024 IEEE.

关键词： Optical character recognition

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Integrating local binary patterns and convolutional neural networks with a balanced gray encoded pooling mechanism

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Multimedia Tools and Applications 2025年 1-27页

作者： Lai, Cheng-Ling Tseng, Ling-Shen Chang, Chih-Hung Chen, Yi-Han Wu, Chih-Hung Department of Electrical Engineering National University of Kaohsiung 700 Kaohsiung University Rd Nanzih District Kaohsiung81148 Taiwan Department of Applied Mathematics National University of Kaohsiung 700 Kaohsiung University RdNanzih District Kaohsiung81148 Taiwan

Local binary pattern (LBP) is a commonly adopted method for texture description in image analysis applications. However, when used in deep convolutional neural networks (CNNs), standard LBP often fails to retain crucial feature uniformity and diversity during dimensionality reduction, which limits its effectiveness in complex image classification tasks. To address these issues, a novel LBP encoding method utilizing balanced Gray code (BGC) is introduced. In this method, similar LBP features are re-ordered as neighbors to maximally preserve feature integrity and diversity through the pooling process. This approach improves the compatibility of LBP with CNN architectures, enhancing both accuracy and robustness. An efficient algorithm is designed that integrates BGC transformation, LBP counting, and sum pooling for dimensionality reduction without sacrificing key feature characteristics. Several LBP-embedded network architectures based on ResNet have been developed and tested, demonstrating significant performance improvements in the proposed method. The experimental results reveal that our proposed method outperforms typical CNN models with a 15% accuracy improvement for classifying the STL-10 benchmark images. Additionally, our proposed method demonstrates greater robustness to illumination variations, highlighting the potential of this method for enhanced feature representation in deep learning. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Dimensionality reduction

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