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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Swamy, B. Venkata Barmola, Pareshwar Prasad Thangavel, Senthil Kaliappan, S. Patel, Hardik Abhyankar, Girish B V Raju Institute of Technology Department of Chemistry Medak India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Paypal Inc CA United States Lovely Professional University Division of Research and Development Phagwara India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Electronics & Communication Engineering Post Limda India Faculty of Law Pune India

ISBN: (纸本)9798350352931

The increasing complexity and interconnectedness of Internet of Things (IoT) software systems necessitate the development of intelligent solutions for predictive maintenance and security. Conventional techniques often fail to provide real-time insights and proactive responses due to the diverse and dynamic nature of IoT environments. To address these challenges, cognitive technologies offer promising avenues for enhancing the operational efficiency and security of IoT networks. This paper introduces Cognitive Twins, an AI-driven framework designed to optimize predictive maintenance and strengthen security in IoT software systems. Cognitive Twins leverage advanced machine learning models and real-time data streams to create dynamic digital replicas of IoT devices and software components. The framework employs a combination of deep learning-based anomaly detection, reinforcement learning for proactive maintenance scheduling, and natural language processing (NLP) for automated security log analysis. By continuously learning from device interactions and evolving threat patterns, Cognitive Twins predict potential failures and detect security threats before they occur, enabling real-time decision-making and automated responses. Cognitive Twins were evaluated on a large-scale IoT network, consisting of 500 nodes across multiple application domains. The framework achieved a predictive maintenance accuracy of 96.8%, reducing downtime by 35% compared to traditional models. In security applications, Cognitive Twins identified cyber threats with a detection rate of 98.3%, lowering the false positive rate to 1.5%. © 2024 IEEE.

关键词： Reinforcement learning

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Explainable Multiple Receptive Attention Network for Expert Cardiologist Compatible Incomplete Kawasaki Disease Diagnosis on Echocardiography 1

Explainable Multiple Receptive Attention Network for Expert ...

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1st IEEE International Conference on Artificial Intelligence for Medicine, Health and Care, AIMHC 2024

作者： Lee, Kyungsu Lee, Haeyun Lee, Moon Hwan Yang, Jaeseung Kim, Sewoong Eun, Youngsoon Eun, Lucy Youngmin Hwang, Jae Youn Department of Electrical Engineering and Computer Science Daegu Gyeongbuk Institute of Science and Technology Daegu Korea Republic of Production Engineering Research Team Samsung SDI Yongin Korea Republic of Division of Pediatric Cardiology Yonsei University College of Medicine Seoul Korea Republic of

ISBN: (纸本)9798350371987

Identifying incomplete Kawasaki disease (KD) has been challenging due to its atypical clinical manifestations. Differentiating it from other febrile illnesses, including COVID-19, is crucial for pediatric patients. Early detection of coronary artery abnormalities through echocardiographic examination is vital for accurate diagnosis and favorable outcomes. With the increased prevalence of KD among pediatric populations, there is a need for continued research and innovative diagnostic tools to improve early detection and management. To address this, we introduce a Multiple Receptive Attention Network (MRANet) incorporating a multi-receptive attention layer, designed to enhance the discrimination of incomplete KD from echocardiographic images, achieving better sensitivity and specificity. A total of 147 echocardiographic imaging datasets were utilized for training MRANet and other state-of-the-art deep learning models. The performance of MRANet was compared with compatible deep learning networks for evaluation. The results demonstrate that MRANet outperforms other advanced deep learning methodologies. MRANet’s performance is comparable to that of an experienced pediatric cardiologist in detecting coronary artery abnormalities for accurate KD diagnosis. This study highlights the potential of MRANet as a valuable tool for aiding early detection and management of complex conditions in medical imaging and computer vision. Further research and validation are warranted to establish MRANet as a reliable tool in pediatric cardiology practice. ©2024 IEEE.

关键词： Echocardiography

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An Implementation of Coding Rule Checking Function for C Programming Study 4

An Implementation of Coding Rule Checking Function for C Pro...

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4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022

作者： Kyaw, Htoo Htoo Sandi Kaneko, Keiichi Funabiki, Nobuo Ueda, Kiyoshi Tokyo University of Agriculture and Technology Division of Advanced Information Technology and Computer Science Institute of Engineering Tokyo Japan Okayama University Faculty of Engineering Department of Electrical and Communication Engineering Okayama Japan Nihon University College of Engineering Department of Computer Science Fukushima Japan

ISBN: (纸本)9781665419048

Presently, C programming is essential for information technology (IT) students to study programming languages, algorithms, and computer architecture. It has been taught as a basic programming language in many universities. Due to the limited lecture time, coding rules to make readable codes are rarely educated, although they are essential in improving programming abilities of students. In this paper, we implement a coding rule checking function for C programming study of novice students. It helps a teacher to check the correctness of the names of variables and functions and the coding styles in the source codes submitted by the students and to give feedback to them. As preliminary evaluation, we verify the correctness of the implemented function through testing 109 source codes from one student. © 2022 IEEE.

关键词： Students

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Earthquake Early Warning System Utilizing an CNN-LSTM-TL Based Method for Detection and Parameters Classification 1

Earthquake Early Warning System Utilizing an CNN-LSTM-TL Bas...

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1st IEEE International Conference on Electronics, Computing, Communication and Control technology, ICECCC 2024

作者： Kalpanadevi, D. Siva, M. Chavan, Chetan Shashikant Kaliappan, S. Jothilakshmi, S. Venkata Ramana, K. School of Science Gitam University Department of Computer Science Karnataka Bengaluru India Easwari Engineering College Department of Civil Engineering Chennai India Dr. D. Y. Patil Institute of Technology Department of Civil Engineering Pimpri Pune India Lovely Professional University Department of Division of Research and Development Punjab Phagwara India RMK College of Engineering and Technology Department of Chemistry Kavaraipettai India QIS College of Engineering and Technology Department of CSE Ongole India

ISBN: (纸本)9798350371802

The obvious first line of protection against powerful earthquake motion is to reinforce houses and other structures. The goal of real-time earthquake catastrophe prevention, in contrast to real-time seismology, is to mitigate damage while an earthquake is still underway. A disaster preventive measure that can be put into action in real-time in the event of an earthquake requires an early warning system (EEW). In order to avoid disasters, should not rely solely on EEW. The order of preprocessing, feature selection, and training the model must be meticulously followed. The preparation phase includes data encoding and normalization. Feature selection incorporates principal component analysis and linear discriminant analysis. It utilized CNN-LSTM-TL for the model's training. The results demonstrate a remarkable 96.49% accuracy. © 2024 IEEE.

关键词： Earthquakes

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Applying Deep-Learning-Based computer Vision to Wireless Communications: Methodologies, Opportunities, and Challenges

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2021年 2卷 132-143页

作者： Tian, Yu Pan, Gaofeng Alouini, Mohamed-Slim Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

Deep learning (DL) has seen great success in the computer vision (CV) field, and related techniques have been used in security, healthcare, remote sensing, and many other areas. As a parallel development, visual data has become universal in daily life, easily generated by ubiquitous low-cost cameras. Therefore, exploring DL-based CV may yield useful information about objects, such as their number, locations, distribution, motion, etc. Intuitively, DL-based CV can also facilitate and improve the designs of wireless communications, especially in dynamic network scenarios. However, so far, such work is rare in the literature. The primary purpose of this article, then, is to introduce ideas about applying DL-based CV in wireless communications to bring some novel degrees of freedom to both theoretical research and engineering applications. To illustrate how DL-based CV can be applied in wireless communications, an example of using a DL-based CV with a millimeter-wave (mmWave) system is given to realize optimal mmWave multiple-input and multiple-output (MIMO) beamforming in mobile scenarios. In this example, we propose a framework to predict future beam indices from previously observed beam indices and images of street views using ResNet, 3-dimensional ResNext, and a long short-term memory network. The experimental results show that our frameworks achieve much higher accuracy than the baseline method, and that visual data can significantly improve the performance of the MIMO beamforming system. Finally, we discuss the opportunities and challenges of applying DL-based CV in wireless communications. © 2020 IEEE.

关键词： computer vision

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Visible Light Communications via Intelligent Reflecting Surfaces: Metasurfaces vs Mirror Arrays

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2021年 2卷 1-20页

作者： Abdelhady, Amr M. Salem, Ahmed K. Sultan Amin, Osama Shihada, Basem Alouini, Mohamed-Slim Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

We propose two types of intelligent reflecting systems based on programmable metasurfaces and mirrors to focus the incident optical power towards a visible light communication receiver. We derive the required phase gradients for the metasurface array reflector and the required orientations of each mirror in the mirrors array reflector to achieve power focusing. Based on which, we derive the irradiance expressions for the two systems in the detector plane to characterize their performance in terms of aiming and focusing capabilities. We show analytically that the number of reflecting elements along with the relative source - reflector dimensions determine the system power focusing capability. Moreover, we quantify analytically the received power gain compared with reflector-free systems. In addition, we introduce a new simple metric to assess the relative reflectors' performance for a given source, detector, reflector layout. Finally, we verify the analytical findings regarding absolute and relative reflectors' performance via numerical simulations. © 2020 IEEE.

关键词： Focusing

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Modeling and Analysis of Dynamic Charging for EVs: A Stochastic Geometry Approach

IEEE Open Journal of Vehicular Technology

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IEEE Open Journal of Vehicular technology 2021年 2卷 17-44页

作者： Nguyen, Duc Minh Kishk, Mustafa A. Alouini, Mohamed-Slim Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

With the increasing demand for greener and more energy efficient transportation solutions, electric vehicles (EVs) have emerged to be the future of transportation across the globe. However, currently, one of the biggest bottlenecks of EVs is the battery. Small batteries limit the EVs driving range, while big batteries are expensive and not environmentally friendly. One potential solution to this challenge is the deployment of charging roads, i.e., dynamic wireless charging systems installed under the roads that enable EVs to be charged while driving. In this paper, we use tools from stochastic geometry to establish a framework that enables evaluating the performance of charging roads deployment in metropolitan cities. We first present the course of actions that a driver should take when driving from a random source to a random destination in order to maximize dynamic charging during the trip. Next, we analyze the distribution of the distance to the nearest charging road. This distribution is vital for studying multiple performance metrics such as the trip efficiency, which we define as the fraction of the total trip spent on charging roads. Next, we derive the probability that a given trip passes through at least one charging road. The derived probability distributions can be used to assist urban planners and policy makers in designing the deployment plans of dynamic wireless charging systems. In addition, they can also be used by drivers and automobile manufacturers in choosing the best driving routes given the road conditions and level of energy of EV battery. © 2020 IEEE.

关键词： Electric vehicles

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Advancing ECT Imaging: Residual UNet Deep Neural Network Approach 1st

Advancing ECT Imaging: Residual UNet Deep Neural Network App...

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1st International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Deabes, Wael Khalifa, Fahmi Abdel-Hakim, Alaa E. Bouazza, Kheir Eddine Computational Engineering Mathematical Sciences Department Texas A&M University SA San AntonioTX78224 United States Electrical and Computer Engineering Department Morgan State University BaltimoreMD21251 United States Computer Science Department in Jamoum Umm Al-Qura University Makkah Saudi Arabia Computer and Information Science Division Higher Colleges of Technology Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031823763

Recently, there has been significant progress in Electrical Capacitance Tomography (ECT) image reconstruction, yet there remains room for enhancing both speed and quality in these reconstructions. Deep Learning (DL) offers a promising avenue due to its capacity to handle intricate nonlinear functions. This study introduces DL-based ECT image reconstruction model based on Residual UNet and termed as ECT_ResUNet. A sizable dataset containing permittivity distribution and corresponding capacitance measurements was created to train and evaluate the proposed model. The ECT_ResUNet processes modulated capacitance vectors, representing them as 66×66 images. The model’s effectiveness and practicality were assessed using unseen samples, demonstrating precise image reconstruction with an average Image Error (IE) of 0.0918 and Correlation Coefficient (CC) of 0.9527. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

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Efficient Lane Detection based on Feature Aggregation for Advanced Driver Assistance Systems

Efficient Lane Detection based on Feature Aggregation for Ad...

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2023 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2023

作者： Jhong, Sin-Ye Ko, Ching-Hsiang Su, Yi-Feng Hua, Kai-Lung Chen, Yung-Yao National Cheng Kung University Dept. of Engineering Science Tainan Taiwan National Taiwan University of Science and Technology Dept. of Electronic and Computer Engineering Taipei Taiwan Automotive Research and Testing Center Research and Development Division Changhua Taiwan National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350302714

As we enter the era of smart cars and electric vehicles, the significance of autonomous driving systems, especially crucial components like lane detection technology, increases dramatically. However, advanced solutions often face daunting challenges, especially under adverse environmental and lighting conditions, yielding varying detection results. This variability prompts automakers to enforce numerous restrictions on auxiliary functions, often deterring potential customers from upgrading relevant services. This study addresses these limitations and introduces a sophisticated deep learning-based lane detection model to supersede existing technologies. The model fuses an efficient feature extraction network with a Recurrent Feature-Shift Aggregator (RESA) and a Bilateral Up-Sampling Decoder (BUSD). These elements work harmoniously, enabling enhanced global information extraction and fortifying detection capability under challenging conditions. The methodology of this study is segregated into three parts: Efficient Backbone and Neck for feature extraction, Global Feature Enhancement Decoder for maximizing feature map details, and the actual implementation of a Lane Assist System. These modules collectively help us calculate high-accuracy lane coordinates, thereby overcoming the limitations of previous lane detection methods. Following the successful creation and validation of the model, the study extends the implementation to the Hexagon vehicle control platform, where the Lane Departure and Lane Keeping assistance systems are deployed. The proposed technology is validated through rigorous experiments and analyses, utilizing the highly recognized TuSimple dataset, custom-collected Taiwanese scene data, and actual road tests. Results illustrate high and consistent detection performance under diverse environmental conditions, validating the technology's effectiveness and feasibility. © 2023 IEEE.

关键词： Advanced driver assistance systems

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VAE-KRnet and its Applications to Variational Bayes

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Communications in Computational Physics 2022年第4期31卷 1049-1082页

作者： Xiaoliang Wan Shuangqing Wei Department of Mathematics Center for Computation and TechnologyLouisiana State UniversityBaton Rouge 70803USA Division of Electrical&Computer Engineering Louisiana State UniversityBaton Rouge 70803USA

In this work,we have proposed a generative model,called VAE-KRnet,for density estimation or approximation,which combines the canonical variational autoencoder(VAE)with our recently developed flow-based generativemodel,called *** is used as a dimension reduction technique to capture the latent space,and KRnet is used to model the distribution of the latent *** a linear model between the data and the latent variable,we show that VAE-KRnet can be more effective and robust than the canonical ***-KRnet can be used as a density model to approximate either data distribution or an arbitrary probability density function(PDF)known up to a ***-KRnet is flexible in terms of *** the number of dimensions is relatively small,KRnet can effectively approximate the distribution in terms of the original random *** high-dimensional cases,we may use VAE-KRnet to incorporate dimension *** important application of VAE-KRnet is the variational Bayes for the approximation of the posterior *** variational Bayes approaches are usually based on the minimization of the Kullback-Leibler(KL)divergence between the model and the *** highdimensional distributions,it is very challenging to construct an accurate densitymodel due to the curse of dimensionality,where extra assumptions are often introduced for *** instance,the classical mean-field approach assumes mutual independence between dimensions,which often yields an underestimated variance due to *** alleviate this issue,we include into the loss the maximization of the mutual information between the latent random variable and the original random variable,which helps keep more information from the region of low density such that the estimation of variance is *** experiments have been presented to demonstrate the effectiveness of our model.

关键词： Deep learning variational Bayes uncertainty quantification Bayesian inverse problems generative modeling

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