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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： K. E. Purushothaman N. Ragavendran S. P. Ramesh V. Ganesh Karthikeyan G. Uma Maheswari R. Saravanakumar Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering School of Computing Science & Engineering Galgotias University Greater Noida Uttar Pradesh India School of Computing (SoC) SASTRA Deemed to be University Thanjavur Tamil Nadu India RMK College of Engineering and Technology Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

This research explores the potential of blockchain and edge AI to revolutionize smart city planning and management. By incorporating these emerging technologies, cities can address current urban challenges, including traffic congestion, energy management, and data security. This research study highlights the benefits of blockchain, such as simplified processes, reduced costs, enhanced security, and transparency. Additionally, this study reviews the applications of edge AI in smart transportation, focusing on vehicle detection, counting, and identification algorithms. The potential of blockchain technology in smart energy trading, vehicle transfer, and encrypted communication is also discussed. Overall, this research study provides valuable insights for researchers to explore new application areas for blockchain and edge AI in the context of smart cities.

关键词： Smart cities Vehicle detection Scalability Urban planning Edge AI Blockchains Smart transportation Energy management Sustainable development Traffic congestion

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A Real-Time Approach to Recognition of Kazakh Sign Language

A Real-Time Approach to Recognition of Kazakh Sign Language

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2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Zholshiyeva, Lazzat Zhukabayeva, Tamara Turaev, Sherzod Berdiyeva, Meruyert Kuanysbaevna, Sengirbaeva Raykhan High School Of Information Technology And Engineering Astana International University Nur-Sultan Kazakhstan College Of Information Technology United Arab Emirates University Department Of Computer Science & Software Engineering Al Ain United Arab Emirates South Kazakhstan Medical Academy Department Of Medical Biophysics And It Shymkent Kazakhstan Special Boarding School №1 For Children With Hearing Impairment Shymkent Kazakhstan

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The interaction between man and computer is expanded with computer technology development. Hand gesture recognition is an essential technology for human-computer interaction, and it allows the recording and interpretation of hand gestures without some devices' use. Since the hand is a man's primary working tool, its position and orientation in space are crucial for many potential applications, such as interaction with virtual reality devices. Hand position estimation is a substantial process of hand gesture recognition. However, there are many difficulties in 3D hand pose estimating from a single RGB image. Media Pipe is a high-quality finger and hand-tracking framework. It uses in machine learning to detect 3D landmarks. The proposed paper provides a real-time approach to recognizing Kazakh gestures performance using a conventional camera and Media Pipe. © 2022 IEEE.

关键词： Virtual reality

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A Distributed Covert Channel of the Packet Ordering Enhancement Model Based on Data Compression

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computers, Materials & Continua 2020年第9期64卷 2013-2030页

作者： Lejun Zhang Xiaoyan Hu Zhijie Zhang Weizheng Wang Tianwen Huang Donghai Guan Chunhui Zhao Seokhoon Kim College of Information Engineering Yangzhou UniversityYangzhou225127China Division of Computer Science University of AizuAizu-Wakamatsu9658580Japan College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing211106China College of Information and Communication Engineering Harbin Engineering UniversityHarbin150001China Department of Computer Software Engineering Soonchunhyang UniversityAsanKorea

Covert channel of the packet ordering is a hot research *** technology is not enough to protect the security of both sides of *** channel needs to hide the transmission data and protect content of *** traditional methods are usually to use proxy technology such as tor anonymous tracking technology to achieve hiding from the ***,because the establishment of proxy communication needs to consume traffic,the communication capacity will be reduced,and in recent years,the tor technology often has vulnerabilities that led to the leakage of secret *** this paper,the covert channel model of the packet ordering is applied into the distributed system,and a distributed covert channel of the packet ordering enhancement model based on data compression(DCCPOEDC)is *** data compression algorithms are used to reduce the amount of data and transmission *** distributed system and data compression algorithms can weaken the hidden statistical probability of ***,they can enhance the unknowability of the data and weaken the time distribution characteristics of the data *** paper selected a compression algorithm suitable for DCCPOEDC and analyzed DCCPOEDC from anonymity,transmission efficiency,and transmission *** to the analysis results,it can be seen that DCCPOEDC optimizes the covert channel of the packet ordering,which saves the transmission time and improves the concealment compared with the original covert channel.

关键词： Covert channels information hiding data compression distributed system

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STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction

arXiv

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arXiv 2022年

作者： Ji, Jiahao Wang, Jingyuan Jiang, Zhe Jiang, Jiawei Zhang, Hu State Key Laboratory of Software Development Environment School of Computer Science & Engineering Beihang University Beijing China Peng Cheng Laboratory Shenzhen China Department of Computer & Information Science & Engineering The University of Florida United States MOE Engineering Research Center of Advanced Computer Application Technology School of Computer Science & Engineering Beihang University Beijing China

High-performance traffic flow prediction model designing, a core technology of Intelligent Transportation System, is a long-standing but still challenging task for industrial and academic communities. The lack of integration between physical principles and data-driven models is an important reason for limiting the development of this field. In the literature, physics-based methods can usually provide a clear interpretation of the dynamic process of traffic flow systems but are with limited accuracy, while data-driven methods, especially deep learning with black-box structures, can achieve improved performance but can not be fully trusted due to lack of a reasonable physical basis. To bridge the gap between purely data-driven and physics-driven approaches, we propose a physics-guided deep learning model named Spatio- Temporal Differential Equation Network (STDEN), which casts the physical mechanism of traffic flow dynamics into a deep neural network framework. Specifically, we assume the traffic flow on road networks is driven by a latent potential energy field (like water flows are driven by the gravity field), and model the spatio-temporal dynamic process of the potential energy field as a differential equation network. STDEN absorbs both the performance advantage of data-driven models and the interpretability of physics-based models, so is named a physics-guided prediction model. Experiments on three real-world traffic datasets in Beijing show that our model outperforms state-of-the-art baselines by a significant margin. A case study further verifies that STDEN can capture the mechanism of urban traffic and generate accurate predictions with physical meaning. The proposed framework of differential equation network modeling may also cast light on other similar applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Differential equations

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A Deep Learning Method to Translate Phonocardiogram (PCG) Signals to Electrocardiogram (ECG) Waveforms

A Deep Learning Method to Translate Phonocardiogram (PCG) Si...

引用

IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Wei Chen Zhi Li Guanglie Zhang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Mechanical Engineering City University of Hong Kong Hong Kong SAR China City University of Hong Kong Shenzhen Research Institute Shenzhen China

In clinical practice, an electrocardiogram (ECG), which analyzes the electrical activity of the human heart, is a common cardiovascular diagnostic test. The ECG data can be stored and diagnosed effectively. However, precise positioning of the ECG electrodes is necessary for a reliable ECG tracing. This has an impact on how ECG monitors are used to check on patients at home. On the other hand, the Phonocardiogram (PCG) is a standard method for quantifying the properties of heart sounds in order to monitor and assess cardiac activity. With the advancement of wearable sensors, it is now simpler to record the heart's sound. We proposed a deep learning approach to translating PCG signals to ECG waveforms. This technique can be utilized to monitor cardiovascular disease in consumer wearable device development. The proposed method is evaluated on a benchmark dataset of normal and anormal cardiac data. Experimental results show that our proposed approach is reasonable and effective. The generated ECG outputs can achieve a high accuracy, and the root mean squared error (RMSE) is 0.053.

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Gradient Compression and Correlation Driven Federated Learning for Wireless Traffic Prediction

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IEEE Transactions on Cognitive Communications and Networking 2024年

作者： Zhang, Chuanting Zhang, Haixia Dang, Shuping Shihada, Basem Alouini, Mohamed-Slim Shandong University School of Software Jinan250101 China Shandong University School of Control Science and Engineering Jinan250061 China Shandong University Shandong Key Laboratory of Wireless Communication Technologies Jinan250061 China University of Bristol Department of Electrical and Electronic Engineering BristolBS8 1UB United Kingdom Computer Electrical and Mathematical Science and Engineering Division Thuwal23955-6900 Saudi Arabia

Wireless traffic prediction plays an indispensable role in cellular networks to achieve proactive adaptation for communication systems. Along this line, Federated Learning (FL)-based wireless traffic prediction at the edge attracts enormous attention because of the exemption from raw data transmission and enhanced privacy protection. However FL-based wireless traffic prediction methods still rely on heavy data transmissions between local clients and the server for local model updates. Besides, how to model the spatial dependencies of local clients under the framework of FL remains uncertain. To tackle this, we propose an innovative FL algorithm that employs gradient compression and correlation-driven techniques, effectively minimizing data transmission load while preserving prediction accuracy. Our approach begins with the introduction of gradient sparsification in wireless traffic prediction, allowing for significant data compression during model training. We then implement error feedback and gradient tracking methods to mitigate any performance degradation resulting from this compression. Moreover, we develop three tailored model aggregation strategies anchored in gradient correlation, enabling the capture of spatial dependencies across diverse clients. Experiments have been done with two real-world datasets and the results demonstrate that by capturing the spatio-temporal characteristics and correlation among local clients, the proposed algorithm outperforms the state-of-the-art algorithms and can increase the communication efficiency by up to two orders of magnitude without losing prediction accuracy. © 2015 IEEE.

关键词： Prediction models

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AutoLog: A Log Sequence Synthesis Framework for Anomaly Detection

AutoLog: A Log Sequence Synthesis Framework for Anomaly Dete...

引用

IEEE International Conference on Automated software engineering (ASE)

作者： Yintong Huo Yichen Li Yuxin Su Pinjia He Zifan Xie Michael R. Lyu Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China School of Software Engineering Sun Yat-sen University Zhuhai China School of Data Science The Chinese University of Hong Kong Shenzhen (CUHK Shenzhen) China Huazhong University of Science and Technology Wuhan China

The rapid progress of modern computing systems has led to a growing interest in informative run-time logs. Various log-based anomaly detection techniques have been proposed to ensure software reliability. However, their implementation in the industry has been limited due to the lack of high-quality public log resources as training datasets. While some log datasets are available for anomaly detection, they suffer from limitations in (1) comprehensiveness of log events; (2) scalability over diverse systems; and (3) flexibility of log utility. To address these limitations, we propose AUTOLOG, the first automated log generation methodology for anomaly detection. AUTOLOG uses program analysis to generate runtime log sequences without actually running the system. AUTOLOG starts with probing comprehensive logging statements associated with the call graphs of an application. Then, it constructs execution graphs for each method after pruning the call graphs to find log-related execution paths in a scalable manner. Finally, AUTOLOG propagates the anomaly label to each acquired execution path based on human knowledge. It generates flexible log sequences by walking along the log execution paths with controllable parameters. Experiments on 50 popular Java projects show that AUTOLOG acquires significantly more (9x-58x) log events than existing log datasets from the same system, and generates log messages much faster (15x) with a single machine than existing passive data collection approaches. AUTOLOG also provides hyper-parameters to adjust the data size, anomaly rate, and component indicator for simulating different real-world scenarios. We further demonstrate AUTOLOG's practicality by showing that AUTOLOG enables log-based anomaly detectors to achieve better performance (1.93%) compared to existing log datasets. We hope AUTOLOG can facilitate the benchmarking and adoption of automated log analysis techniques.

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Virtual 3D Game-on simulation: An immersive learning framework for assisted driving

Virtual 3D Game-on simulation: An immersive learning framewo...

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2022 International Conference for Advancement in Technology, ICONAT 2022

作者： Johnson, Janet Mary Joy, Geethu Rose Sindhu, Y Joy, Emmanuel Digital Marketing Associate 10xDS- Exponential Digital Solution Trivandrum India Developer - Software Engineer UST Global Digital Transformation Solutions Bangalore India Karunya Institute of Technology and Sciences Department of Electronics and Media Coimbatore India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665425773

This paper demonstrates the design and architecture of a virtual driving simulation game. Virtual reality is an alternative world filled with computer-generated images that respond to human movements. Thus, early learners can drive without facing the real-world consequences. The outcome of this research is that it helps early driving learners to overcome the fear of driving. Early learners can easily get over panic and unwanted anxiety. The theoretical framework of this driving game simulation has been designed by using Unity 3D game engine and C# scripting. Gaming controls are given to the player either through the keyboard or through a joystick. The game is visually seen on the monitor screen and headsets are used for achieving realism. Maya and Blender software was used for modelling the framework of the game. We have achieved the output controls of the game by interfacing custom-designed electronic components such as the steering and the accelerator. The goal of this game is to create a virtual learning environment in which early learners can feel at ease while playing. The outcome of this game is to achieve self-confidence among the early learners that help them take away the fear built within them. Even though more women are entering the workforce, there is still a vulnerable segment of the population that is fearful of driving. This game may assist them in overcoming their anxiety and fear. © 2022 IEEE.

关键词： Virtual reality

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A biometric and physically unclonable function–Based authentication protocol for payload exchanges in internet of drones

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2024年 7卷

作者： Nyangaresi, Vincent Omollo Al-Joboury, Istabraq M. Al-sharhanee, Kareem Ali Najim, Ali Hamzah Abbas, Ali Hashim Hariz, Hussein Muhi Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and Technology Bondo 40601 Kenya Department of Applied Electronics Saveetha School of Engineering SIMATS Tamilnadu Chennai India Department of Medical Instruments Technical Engineering Al-Bayan University Technical College of Engineering Iraq Department of Communication Technical Engineering College of Engineering Techniques Al-Farahidi University Iraq Department of Computer Technical Engineering Imam Al-Kadhum College (IKC) Iraq College of Information Technology Imam Ja'afar Al-Sadiq University Al-Muthanna 66002 Iraq Department of Computer Techniques Engineering Mazaya University College Dhi-Qar Annasiriyah 64001 Iraq

Internet of Drones (IoD) has been deployed in numerous military and civilian domains to offer services such as target surveillance, traffic monitoring, disaster handling and environmental monitoring. However, message exchanges among the drones and ground station servers are via insecure wireless channels. In addition, drones may be deployed in hostile and unattended locations. This renders the IoD susceptible to numerous privacy and security threats such as drone capture and cloning. Therefore, many security solutions have been developed based on techniques such as blockchain and public key infrastructure. However, majority of these schemes are still susceptible to many attacks while some of them are inefficient for the resource-limited IoD devices. In this paper, a Physically Unclonable Function (PUF) challenge-response and biometric based robust authentication protocol is presented. Its formal security is carried out using the Real or Random (RoR) model, which demonstrates the robustness of the negotiated session key. In addition, its semantic analysis shows that it can withstand typical IoD attacks such as impersonation, replay, de-synchronization and spoofing. In terms of performance, it is shown to incur lower computation, energy and communication costs. © 2024 The Author(s)

关键词： Attacks Authentication Drones Privacy Security UAVs

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Di-Net: Decomposed Implicit Garment Transfer Network for Digital Clothed 3d Human

SSRN

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SSRN 2024年

作者： Zhong, Xiaojing Su, Yukun Wu, Zhonghua Lin, Guosheng Wu, Qingyao School of Software Engineering South China University of Technology China School of Computer Science and Engineering Nanyang Technological University Singapore Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Peng Cheng Laboratory China Tencent Wechat Department China SenseTime Research

3D virtual try-on tasks aim to generate realistic try-on results for full- body garments, allowing them to be observed from arbitrary perspectives. Recent methods often represent the 3D human form with a fixed topol- ogy, limiting garment representation and leading to higher computational demands. Furthermore, the challenge of flexible transfer over various body parts remains. To address these problems, we introduce DI-Net(Decomposed Implicit Garment Transfer Network), where garment transfer is decomposed into 2D image layout transfer and 3D texture layout transfer. We propose a complementary warping module that leverages dense correspondence learn- ing and sparse flow learning to align the body appearance and garments with the target pose for image layout transfer. Subsequently, the transferred garments are lifted to the 3D human mesh by constructing a pixel-aligned implicit function and predicting the texture based on geometric priors. By applying different semantic masks for garment transfer, we efficiently control the texture of the generated mesh. Experimental results show the effective- ness and superiority of our method in the 3D virtual try-on task. © 2024, The Authors. All rights reserved.

关键词： Textures

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