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IEEE Southeastcon

作者： Mahmoud Darwich Kasem Khalil Magdy Bayoumi Department of Mathematics and Computer Science University of Mount Union Alliance OH Department of Electrical and Computer Engineering University of Mississippi University MS Department of Electrical and Computer Engineering University of Louisiana at Lafayette LA

In the rapidly evolving domain of video streaming, the integration of deep learning techniques for transmission optimization represents a significant leap forward. This research delves into the development and implementation of a novel deep learning-based framework designed to enhance video streaming quality while minimizing bandwidth consumption and latency issues. Utilizing advanced neural network architectures, the proposed solution intelligently adapts to varying network conditions, ensuring optimal video quality and user experience. Experimental results demonstrate a marked improvement in streaming efficiency, highlighting the potential of AI-driven techniques in revolutionizing video streaming technologies. This study not only presents a viable solution to prevalent streaming challenges but also opens new avenues for future research in smart video transmission.

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

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

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

ISBN: (数字)9798331529529

ISBN: (纸本)9798331529536

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.

关键词： Data privacy Privacy Pipelines Streaming media User experience Robustness Quality assessment Homomorphic encryption Protection Video recording

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Online searching trend on Covid-19 using Google trend: infodemiological study in Malaysia

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International Journal of Information Technology (Singapore) 2022年第2期14卷 675-680页

作者： Izhar, Tengku Adil Tengku Torabi, Torab Faculty of Information Management Universiti Teknologi MARA (UiTM) UiTM Selangor Shah Alam Malaysia Department of Computer Science and Information Technology La Trobe University Melbourne Australia

Since January 2020, the emergence of Covid-19 has sparked a worldwide search for information about Covid-19. People frequently use the internet to search the information on the virus. However, the pandemic have triggered the information-seeking trends among public. As a result, large amount of information could lead to infodemic. It will create public concerned such as panic and paranoid because this information spread rapidly. The aim of this study is to analyze information about Covid-19 that has been searched in Malaysia. We investigated online search behavior related to the Covid-19 outbreak among public by using Google Trends to understand public searching behavior on Covid-19. The findings from this study can be used as a tool to monitor public searching activities on Covid-19, which could predict future action regarding the outbreak. © 2021, Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Covid-19 Google trend Infodemic Infodemiological Searching behavior

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Utilizing Swarm Intelligence for Optimized Video Data Distribution in Edge Networks

Utilizing Swarm Intelligence for Optimized Video Data Distri...

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Computing and Machine Intelligence (ICMI), International Conference on

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

The proliferation of high-definition video streaming services poses significant challenges in data distribution, particularly in edge networks where bandwidth and resources are often limited. This paper explores the application of swarm intelligence algorithms to optimize video data distribution in such networks. We propose a novel framework that employs swarm-based techniques, specifically focusing on Particle Swarm Opti-mization (PSO) and Ant Colony Optimization (ACO), to enhance streaming. By simulating real-world edge network conditions, we implemented and evaluated our framework. Our approach dy-namically adjusts the data flow based on network conditions and user demand, ensuring optimal resource utilization and improved streaming quality. Initial results demonstrate a significant en-hancement in data throughput and resource allocation efficiency. Specifically, our framework improved video data distribution efficiency by $35\%$ compared to traditional methods. Furthermore, the use of ACO algorithms resulted in a $40\%$ reduction in latency and a $35\%$ increase in overall user satisfaction, as measured by standard Quality of Experience $(\text{QoE})$ metrics. The PSO algorithm showed a notable improvement in balancing load among edge nodes, leading to a $25\%$ increase in network resource optimization.

关键词： Measurement Ant colony optimization Bandwidth Streaming media Throughput Resource management Quality of experience

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Adaptive Multi-Path Video Streaming Using AI-Driven Edge Computing for Enhanced Quality of Experience (QoE)

Adaptive Multi-Path Video Streaming Using AI-Driven Edge Com...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

ISBN: (数字)9798331529529

ISBN: (纸本)9798331529536

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.

关键词： Machine learning algorithms Packet loss Bandwidth Streaming media Real-time systems Quality assessment Quality of experience Reliability Video recording Edge computing

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High Accuracy WiFi Sensing for Vital Sign Detection with Multi - Task Contrastive Learning

High Accuracy WiFi Sensing for Vital Sign Detection with Mul...

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IEEE Conference on Vehicular Technology (VTC)

作者： Yilun Wang Peng Cheng Shenghong Li Branka Vucetic Yonghui Li School of Electrical and Information Engineering The University of Sydney Australia Department of Computer Science and Information Technology La Trobe University Australia Data 61 CSIRO Australia

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

WiFi sensing has emerged as a promising technique in the healthcare industry, enabling contact-free monitoring of vital signs by detecting changes in WiFi signals resulting from physiological activities. State-of-the-art WiFi sensing uses channel state information (CSI) to analyze signal characteristics, capturing subtle changes due to heartbeats and breathing. However, existing methods face challenges in concurrently measuring respiration and heart rates, and they exhibit high sensitivity to environmental factors and individual differences, limiting the detection accuracy of a trained model in real-world environments. In this paper, we propose a novel multi-task contrastive learning framework for concurrent detection of respiration and heart rates. We introduce multi-task learning with hard-shared layers to exploit the physiological link between breathing and heartbeat. Additionally, we leverage contrastive learning to improve our model's ability to differentiate and prioritize CSI changes related to respiratory and cardiac activi-ties. The experimental results demonstrate the proposed model's ability to accurately measure respiratory and heart rates in challenging scenarios, including long-distance and non-line-of-sight conditions, even when utilizing omnidirectional antennas.

关键词： Training Vehicular and wireless technologies Accuracy Source separation Heart beat Contrastive learning Multitasking

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On the Shielding Properties of Woven Microfiber Materials for Potential EMC Applications of IoT Devices

On the Shielding Properties of Woven Microfiber Materials fo...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Rafiq, Muhammad N. Mitra, Dipankar Iftikhar, Adnan Kirmani, Abdul M. Braaten, Benjamin D. University of Wisconsin-La Crosse Department of Computer Science La CrosseWI United States Comstas University Department of Electrical and Computer Engineering Islamabad Campus Islamabad Pakistan Islamabad Pakistan North Dakota State University Department of Electrical and Computer Engineering FargoND United States

ISBN: (纸本)9781665480093

Internet of Things (IoT) has emerged as a consequential platform for next-generation wireless communication. As IoT requires millions of wireless devices to be connected and exchange data frequently among them in an open large-scale communication, it is imperative to ensure electromagnetic compatibility (EMC) of those wireless devices. In this paper, the shielding properties of woven carbon microfiber materials (CMM) placed over the aperture of a metallic enclosure is evaluated for frequency ranges from 1 GHz - 4 GHz for potential EM shielding applications of wireless IoT devices. Initially, shielding effectiveness of CMM samples were measured and simulated followed by the parametric simulation to predict the conductivity of the samples. The results were then compared to the measured ones. A good agreement between the simulated, measured, and analytical results demonstrated that CMM though having less conductivity than traditional metallic shields, shows comparable shielding effectiveness, which makes it a possible candidate for EM shielding due to its light weight, corrosion resistance, and flexibility. © 2022 IEEE.

关键词： Internet of things

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Optimizing QoE in IoT-Based Video Streaming through Deep Learning Algorithms

Optimizing QoE in IoT-Based Video Streaming through Deep Lea...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

The demand for video streaming services over Internet of Things (IoT) networks has surged, yet maintaining a high Quality of Experience (QoE) remains challenging due to network heterogeneity and resource constraints. This paper presents an innovative deep learning-based approach to optimize QoE in IoT-based video streaming. Leveraging convolutional neural networks (CNN) and long short-term memory (LSTM) networks, the proposed framework dynamically adapts video streaming parameters in real-time, such as bit rate, frame rate, and resolution, based on the existing network conditions and device capabilities. Through extensive simulations and real-world deployments, our approach exhibited a 24.5% reduction in re-buffering ratio, a 12.5% increase in average bitrate, and a 15% improvement in video quality measured by SSIM, compared to conventional methods. Furthermore, it reduced network bandwidth consumption by up to 150% without compromising on video quality. These results underscore the effectiveness of applying deep learning algorithms to optimize video streaming in complex IoT environments.

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Partial NOMA Based Online Task Offloading for Multi-layer Mobile Computing Networks

Partial NOMA Based Online Task Offloading for Multi-Layer Mo...

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IEEE International Conference on Communications (ICC)

作者： Guangchen Wang Peng Cheng Zhuo Chen Branka Vucetic Yonghui Li School of Electrical and Information Engineering The University of Sydney Australia Department of Computer Science and Information Technology La Trobe University Australia Data 61 CSIRO Australia

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Mobile edge computing (MEC) enables mobile devices (MDs) to offload their computational tasks to the network edge, significantly reducing transmission delay and energy consumption. In this paper, we develop a novel partial NOMA (PNOMA) based task offloading scheme in a multi-layer mobile computing network (MD-MEC-Cloud). PNOMA combines the high throughput of NOMA with the low interference of OMA for efficient, low-latency transmission. Furthermore, the PNOMA-based multi-layer collaborations enable rapid task processing across various computing requirements. We formulate a non-convex mixed-integer optimization problem aimed at minimizing the average delay across all MDs. To address this challenging problem, we propose an algorithm called reincarnating proximal policy optimization (RPPO), which uses online inference solutions to significantly reduce complexity. In addition, we incorporate accumulated apriori information into RPPO for fast retraining and design both a reward function and an evaluation phase to ensure the communication/computation constraints are met with a high probability. Simulation results demonstrate that the proposed task offloading scheme outperforms existing methods.

关键词： NOMA Multi-access edge computing Simulation Throughput Inference algorithms Delays Complexity theory

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Dictionary-Based PLS Approach to Pharmacokinetic Mapping in DCE-MRI Using Tofts Model 8th

Dictionary-Based PLS Approach to Pharmacokinetic Mapping in ...

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8th International Conference on ICT for Sustainable Development, ICT4SD 2023

作者： Krishnappa, Krutthika Hirebasur Gowda, Nithin Vajuvalli Narayana Department of Computer Science Southern University and A&M College Baton RougeLA United States Dayananda Sagar College of Engineering Karnataka Bengaluru India

ISBN: (纸本)9789819949311

Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is an approach that employs MRI to monitor changes in blood flow and tissue perfusion following the injection of a contrast agent. DCE-MRI may be used to calculate PharmacoKinetic (PK) maps and disclose specifics about the manner in which the body distributes and eliminates medications. These maps can be employed to improve drug dosage and treatment planning, as well as to comprehend the pharmacokinetics of medicines in various tissues. To improve the speed and accuracy of PK map estimation, a dictionary-based Partial Least Squares (PLS) approach based on the Tofts model has been proposed. The Tofts model is a widely used PK model that describes the transport and metabolism of a contrast agent in tissue. The dictionary-based PLS approach utilizes a pre-constructed dictionary of Tofts model parameters, which can be used to speed up the PK map estimation process. Instead of fitting the Tofts model to the DCE-MRI data from scratch, the algorithm uses the pre-constructed dictionary to identify the most likely set of parameters that best fit the data. This can significantly reduce the computational time required for PK map estimation, making it more practical for clinical use. These techniques were assessed using simulation model. The results show the PLS approach was able to estimate PK maps accurately for simulated. The low Root Mean Square Error (RMSE) suggests that error will fall within the permissible range of tolerance. The computational time for the PLS approach was found to be significantly less than TM-TD and TM-FD approaches. As a result, the proposed TM-PLS approach, which is based on Tofts model, exhibits promising performance in terms of computational time, and errors that are moderately higher than the traditional curve fitting model for the determination of PK maps in DCE-MRI, which is a crucial parameter for the diagnosis and treatment planning. © The Author(s), under exclusive license to Spr

关键词： Pharmacokinetics

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