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24th IEEE International Workshop on Signal Processing Advances in Wireless communications, SPAWC 2023

作者： Toka, Mesut Kaushik, Aryan Armada, Ana Garcia Shin, Wonjae Ajou University Department of Electrical and Computer Engineering Korea Republic of University of Sussex School of Engineering and Informatics Brigthon BN1 9RH United Kingdom Universidad Carlos Iii de Madrid Department of Signal Theory and Communications Leganés28911 Spain

ISBN: (纸本)9781665496261

In this paper, we analyze the outage behavior of a reconfigurable intelligent surface (RIS)-aided low earth orbit (LEO) satellite network based on orthogonal frequency division multiplexing (OFDM). In the satellite network, a RIS node with multiple reflecting elements is deployed between the satellite and ground user since there are strong blockages in the line-of-sight link. Due to the very high velocity of the LEO satellite, the satellite-RIS link suffers from a severe Doppler shift. We propose deploying the RIS node between the satellite and ground user to overcome the performance degradation caused by the Doppler shift in the satellite link. To evaluate the system performance, we obtain an approximated closed-form outage probability (OP) expression corresponding to any sub-carrier. Also, an asymptotic expression for the obtained OP is provided to present further insights. The theoretical results validated by the Monte Carlo simulations demonstrate that the effects of the Doppler shift can be significantly decreased as the number of RIS elements and sub-carrier spacing are increased. © 2023 IEEE.

关键词： Orthogonal frequency division multiplexing

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Secure Communication with a Multiple-Antennas Untrusted Relay in Presence of Randomly Distributed Adversary Jammers

SSRN

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SSRN 2024年

作者： Saedi, Hamed Mohammadi, Abbas Kuhestani, Ali The Electrical Engineering Department Amirkabir University of Technology Tehran*** Iran The Communications and Electronics Department Faculty of Electrical and Computer Engineering Qom University of Technology Qom*** Iran

In this article, we examine the achievable secrecy rate of a cooperative network including one source, one destination and one untrusted amplify-and-forward relay, where randomly located adversary jammers try to damage the wireless communications. In the studied system model, the relay has multiple-antennas, while the other nodes have a single-antenna. Additionally, the adversary jammers are placed following a Poisson Point Process (PPP) who propagate jamming with the goal of damaging the untrusted relay’s channel. The untrusted relay receives by maximal-ratio combining (MRC) strategy to mitigate the impact of jammers and retransmits the signal to the destination by exploiting maximum ratio transmission (MRT). To forbid the powerful untrusted relay from capturing the message sent by the source, the destination injects a pre-known artificial noise towards the relay, which is known as destination-assisted cooperative jamming. We represent a new closed-form solution for the ergodic secrecy rate (ESR) of the considered system model with Rayleigh fading channels. In the next step, we design an optimization problem for maximizing the achievable secrecy rate. The result is a new closed-form formula for the optimal power allocation (OPA) between source and destination. Several numerical results are provided to gain engineering insights from the impact of network/system parameters on the ESR. Our results illustrate that by increasing the number of relay antennas or the density of jammers, the ESR is reduced. © 2024, The Authors. All rights reserved.

关键词： Antennas

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NodalNet: A Node Based Programming Interactive Educational Tool in Developing Neural Network for Beginners 14

NodalNet: A Node Based Programming Interactive Educational T...

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14th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2022

作者： Versoza, Edwil Tecson, Gabriel James Bran Viaje, Jhun Andrada, Theodore Andrew Flores, Lanze Deluta, Christian Melendres, Erving Andres, Ben Salvador, Geoffrey Bandala, Argel Concepcion, Ronnie Polytechnic University of the Philippines Department of Electronics and Communications Engineering Manila Philippines De la Salle University Department of Electronics and Computer Engineering Manila Philippines De la Salle University Department of Manufacturing Engineering and Management Manila Philippines

ISBN: (纸本)9781665464932

Machine Learning, a subset of Artificial Intelligence, is now being integrated into image and speech classification, object and action recognition, medical image analysis, security and surveillance, and autonomous driving. It is usually executed by creating a neural network which is defined as the process of modeling a computer system to mimic the function of the human brain. However, learning to develop a neural network is a challenge for students, especially those who have little background in programming. This study focuses on developing a node-based programming tool called NodalNet, a beginner-friendly and interactive educational tool for creating a neural network designed for image classification applications. Java programming language, Java swing library, and Eclipse integrated development environment (IDE) are used to build the NodalNet. It can be used for offline deployment using laptops, and computers, and for online deployment using a technique called the application programming interface (API) method. NodalNet has built-in components that can be connected through nodes, and it has a data labeling feature that helps the users in classifying the raw data inputs used before the training process. The NodalNet can develop and train simple neural networks, some image recognition samples are FlowerNET, DogCatNET, and NumberNET which have significant results in their accuracy. A survey conducted shows that 77.78% of the respondents answered that it is easy to train using the NodalNet. This tool offers a positive impact as an interactive educational tool for developing a neural network. © 2022 IEEE.

关键词： Java programming language

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Application of Image Recognition Algorithms in the Detection of Philippine Lime Diseases

Application of Image Recognition Algorithms in the Detection...

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IEEE International Conference on Industry 4.0, Artificial Intelligence, and communications Technology (IAICT)

作者： Mikho J Pelingon Valenzuela Franco Carlos Maria Leonora C Guico Jan Kevin A Galicia Department of Electronics Computer and Communications Engineering Ateneo de Manila University Quezon City Philippines

Calamansi has been declared as one of the most important fruit growing crops in the Philippines. However, due to certain bacteria, it is susceptible to certain diseases affecting its harvest rate. This paper aims to effectively monitor the state of the calamansi at its healthy state and at its diseased state. Specifically, it classifies diseases such as Citrus Canker, Citrus Scab, and Citrus Browning by utilizing existing image processing techniques for disease detection of different fruits and determining which algorithm is most apt for this application in terms of precision, accuracy and recall. Techniques such as K-Means Clustering, utilization of an Artificial Neural Network (ANN), feature extraction through GLCM along with the usage of a minimum distance classifier, a Support Vector Machine (SVM) classifier and other techniques and/or their combinations were explored and measured. The researchers performed two kinds of tests: 1×1 comparison and merged comparison. For the 1×1 comparison, making use of GrabCut, color feature extraction, and SVM produced the best overall results, with an overall average of 98% for precision, 95% for accuracy, 91% for recall, and 94% for F-score. Adaptive Gaussian Filtering along with texture feature extraction and SVM was the most accurate for detecting calamansi fruits with citrus canker and citrus scab. Overall, the two methods acquired the same average accuracy of 61%

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Optimization for IoT Jobs in Multi-Cloud Computing Environments

Optimization for IoT Jobs in Multi-Cloud Computing Environme...

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Signal Processing and Advance Research in Computing (SPARC), International Conference on

作者： Anshul Atre Kamlesh Kumar Singh Brijesh Kumar Chaurasia Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Lucknow UP INDIA Department of Electronics Communications and Engineering Amity School of Engineering & Technology Amity University Lucknow UP INDIA Department of Computer Science & Engineering Pranveer Singh Institute of Technology Kanpur India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Effective network communication is essential in the current digital era, and cloud computing (CC) and the Internet of Things (IoT) are significant aspects of daily life. Accelerating and lowering the latency of data transfers to the cloud for processing, analysis, and storage is the aim of cloud computing. IoT integration and job scheduling are the two most challenging issues in the multi-cloud computing environment because of the diverse and numerous IoT devices. The significant objectives of cloud computing-based IoT optimization are to reduce the total time needed to complete all input operations, determine which resources are required for the Message Queuing Telemetry Transport (MQTT), and other crucial protocols used in this work to display delay and evaluate the performance of different jobs at different IoT nodes and bit counts. Results and analysis show the viability of cloudSim is successfully achieved with the use of delayed quality of communication of jobs for different nodes.

关键词： Cloud computing Protocols Processor scheduling Quality of service Signal processing Internet of Things Telemetry Security Resource management Optimization

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Developing a Smart Drip Infusion System to Elevate Precision in Healthcare

Developing a Smart Drip Infusion System to Elevate Precision...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Sefemo Molefinyane Abid Yahya V. Hamsadhwani R. Rohini Ravi Samikannu K.Maruliya Begam Department of Electrical and Communications Systems Engineering Botswana International University of Science and Technology Palapye Botswana Department of Electrical and Electronics Engineering Periyar Maniammai Institute of Science and Technology Thanjavur India Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Tiruchengode India Department of Computer Science and Communications Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technology Science Tiruchengode India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Intravenous (IV) therapy is a critical component of modern healthcare, yet traditional IV systems are prone to human errors, manual adjustments, and limited monitoring capabilities, which can jeopardize patient safety and hinder healthcare efficiency. This paper presents the development of the Smartdrip IV Infusion System, a sophisticated and automated solution that integrates advanced sensors, control algorithms, and intuitive user interfaces to address these limitations. The Smartdrip system minimizes human intervention, provides real-time monitoring, and automates fluid administration with enhanced precision and safety features. The system demonstrates high reliability through simulation and prototype testing in detecting anomalies, issuing alerts, and ensuring accurate infusion rates, thereby improving patient outcomes and reducing healthcare provider workload. The results validate the Smartdrip system as a transformative approach to optimizing IV therapy in diverse clinical settings.

关键词： Fluids Medical treatment Prototypes User interfaces Sensor phenomena and characterization Ubiquitous computing Sensor systems Real-time systems Safety Testing

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Security Improvement for Deep Learning-Based Semantic Communication Systems

Security Improvement for Deep Learning-Based Semantic Commun...

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International Conference on Advanced Technologies for communications, ATC

作者： Hung Ngo Luu Tan Van-Dinh Nguyen Thien Huynh-The Toan-Van Nguyen Phuong Luu Vo School of Computer Science and Engineering International University-VNU HCMC Vietnam Vietnam National University Ho Chi Minh City Vietnam College of Engineering and Computer Science & Center for Environmental Intelligence VinUniversity Hanoi Vietnam Department of Computer and Communications Engineering HCMC University of Technology and Education

ISBN: (数字)9798350353983

ISBN: (纸本)9798350353990

Semantic communication is becoming increasingly popular for wireless image transmission due to its superior communication efficiency. However, current deep learning-based semantic systems designed for semantic communication, though efficient, remain vulnerable to eavesdropping and often overlook security measures at the physical layer. To address this issue, this paper presents a deep learning-based system with joint source-channel coding (JSCC) and cyclical consistent generative adversarial network that enhances the security of semantic communication systems. We also design a convolutional neural network for the encoder and decoder which is trained to extract and transmit semantic features while minimizing the risk of privacy leakage. The artificial noise at the physical layer is employed at the source to degrade the eavesdropping ability of the eavesdropper. We show through experiments that under the artificial noise strategy, the legitimate user achieves a higher structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) than the eavesdropper. Moreover, the semantic system with JSCC offers better SSIM and PSNR than the separated source and channel coding models while preserving the confidentiality of semantic information during wireless transmission. This enhanced security framework opens new opportunities for secure and reliable communication of semantic information in diverse applications.

关键词： Wireless communication PSNR Noise Semantic communication Feature extraction Physical layer Decoding Security Communication system security Eavesdropping

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A Generative Denoising Approach for Near-Field XL-MIMO Channel Estimation

A Generative Denoising Approach for Near-Field XL-MIMO Chann...

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Jin, Zhenzhou You, Li Kwan Ng, Derrick Wing Xia, Xiang-Gen Gao, Xiqi Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China University of New South Wales School of Electrical Engineering and Telecommunications Sydney2052 Australia University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States

ISBN: (纸本)9798350351255

In this paper, we investigate the near-field (NF) channel estimation (CE) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. Considering the pronounced NF effects in XL-MIMO communications, we first establish a joint angle-distance (AD) domain-based spherical-wavefront physical channel model that captures the inherent sparsity of XL-MIMO channels in the NF region. Leveraging the sparsity of the channel, the CE is approached as a task of reconstructing sparse signals. Anchored in this framework, we first propose a compressed sensing algorithm to acquire a preliminary channel estimation. Harnessing the powerful latent representation capability of generative artificial intelligence (GenAI), we further propose a GenAI-based approach to refine the estimated channel by employing advanced image denoising techniques. Specifically, we perceive the estimated channel as a noisy color image. Then, we derive the evidence lower bound (ELBO) of the design objective utilizing variational inference and reparameterization techniques, and propose a generative diffusion probabilistic model (GDM) dedicated to denoising. Experimental results indicate that the proposed GDM is capable of offering substantial performance gain in CE compared to existing benchmark approaches in NF XL-MIMO systems. © 2024 IEEE.

关键词： Compressed sensing

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Performance Analysis of CPU Utilization in Cloud Environments using Machine Learning Models

Performance Analysis of CPU Utilization in Cloud Environment...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Archana Naik Kavitha Sooda Karunakara Rai B Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering B. M. S. College of Engineering Bangalore India Department of Electronics and Communications Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Cloud environments offer extensive services, making them a preferred choice for businesses and startups to host and maintain databases. Efficient resource management, including automatic scaling, is crucial for sustaining virtual machine environments. This paper addresses the challenge of forecasting CPU workloads to optimize resource allocation. Terraform was utilized to create and launch instances in the cloud environment, specifically using Amazon Web Services (AWS). A CPU-intensive application was executed on the server, and CPU utilization data was collected at regular intervals to serve as the dataset for the experiment. Three models—Gated Recurrent Unit, Long Short-Term Memory Autoencoder, and Seasonal Auto-Regressive Integrated Moving Average—were compared for short-term workload prediction. The results demonstrate that SARIMA outperforms the LSTM autoencoder by 2.91%, GRU by 1.78%, and LSTM autoencoder by 1.13% based on MAPE, MSE, MAE, and RMSE metrics.

关键词： Measurement Cloud computing Web services Autoencoders Predictive models Virtual machines Performance analysis Resource management Servers Long short term memory

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Dual-Level Data Compression and Reduction in IoT Networks: Enhancing Efficiency through Sensor Node and Gateway Optimization

Dual-Level Data Compression and Reduction in IoT Networks: E...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Pushan Kumar Dutta Pronaya Bhattacharya Sushil Kumar Singh El-Sayed M. El-kenawy Richa Pandey School of Engineering and Technology Amity University Kolkata Computer Engineering Department Marwadi University Rajkot Gujarat India Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Computer Science and Engineering Graphic Era Hill University Haldwani Uttarakhand

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

The proliferation of Internet of Things (IoT) devices has led to unprecedented challenges in data management and transmission efficiency. This paper introduces a novel dual-level approach to data compression and reduction in IoT networks, addressing these challenges by optimizing data handling at both sensor node and gateway levels. Unlike conventional single-level techniques, our method provides a more comprehensive solution to data optimization in IoT ecosystems. At the sensor node level, we implement Run Length Encoding (RLE) compression, while at the gateway level, we integrate progressive data clustering and reduction strategies. This dual-level approach demonstrates significant improvements in data reduction compared to single-point optimization methods. Our comparative analysis reveals the synergistic effects of combining node-level compression with gateway-level data reduction, resulting in enhanced overall system performance. We evaluate the scalability and adaptability of our approach across diverse IoT applications, considering energy efficiency implications for resource-constrained devices. Additionally, we discuss alternative data reduction techniques such as attribute reduction, canonical polyadic decomposition, and edge computing-based filtering, contextualizing our method within the broader landscape of IoT data optimization. The paper also examines the impact of our approach on network bandwidth utilization and explores future directions for multi-level data optimization in complex IoT architectures. Our findings suggest that the proposed dual-level method offers a promising solution for managing the growing data challenges in IoT networks, paving the way for more efficient and scalable IoT systems.

关键词： Temperature measurement Wireless communication Wireless sensor networks Data compression Bandwidth Logic gates Encoding Energy efficiency Internet of Things Artificial intelligence

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