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8th IEEE International Conference for Convergence in technology, I2CT 2023

作者： Sampath Kumar, Pattem. Vijayasree, J. Saikumar, K. Ayad Alkhafaji, Mohammed Obaid, Ahmed J. Ali Zearah, Sajad Malla Reddy Institute of Engineering & Technology Hyderabad India Department of Electronic and Communication India Koneru Lakshmaiah Education Foundation Hyderabad Dept of ECE R.V.S Nagar Moinabad-Chilkur Rd near AP Police Academy Telangana Aziznagar India National University of Science and Technology College of Engineering Technology Department of Medical Device Industry Engineering Dhi Qar Iraq University of Kufa Faculty of Computer Science and Mathematics Iraq Al-ayen University Scientific Research Center Thi-Qar Iraq

ISBN: (纸本)9798350334012

Food is one of the essential factors in human growth and development. The recent results of the internet of things and robots have already shown the beginnings of a new food monitorization. Automation in the restaurants and food sector did not originate technically, so humanoid-related IoT platforms are necessary. In this research, an advanced monitoring algorithm for the automation of food & health system performs using the (IoT). The suggested system develop using food quality sensing devices. Each detector was equipped with the above qualities and connected using breadboards and relay boards. There is tremendous fear and motivation in the thought that there may not be enough food to go around as the human population expands exponentially. To construct a monitoring system that can identify the presence of people, gases, and temperature differences, we employed website creation in combination with the (IoT). The designed application has attained a sensitivity of 97.53%, an accuracy of 98.83%, a recall of 97.98%, and a detection rate of 98.91%. The implemented technology is beneficial to researchers and society. The proposed IoT-based automation of food & health monitoring system compete with present technology and outperforms the methodology. © 2023 IEEE.

关键词： Internet of things

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Reconstructing Satellites in 3D from Amateur Telescope Images

arXiv

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arXiv 2024年

作者： Chang, Zhiming Liu, Boyang Xia, Yifei Bai, Weimin Guo, Youming Shi, Boxin Sun, He College of Future Technology National Biomedical Imaging Center Peking University China College of Engineering Peking University Beijing China Earthrise Edu&Tech China National Key Laboratory for Multimedia Information Processing National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China Institute of Optics and Electronics Chinese Academy of Sciences Chengdu China

This paper proposes a framework for the 3D reconstruction of satellites in low-Earth orbit, utilizing videos captured by small amateur telescopes. The video data obtained from these telescopes differ significantly from data for standard 3D reconstruction tasks, characterized by intense motion blur, atmospheric turbulence, pervasive background light pollution, extended focal length and constrained observational perspectives. To address these challenges, our approach begins with a comprehensive preprocessing workflow that encompasses deep learning-based image restoration, feature point extraction and camera pose initialization. We apply a customized Structure from Motion (SfM) approach, followed by an improved 3D Gaussian splatting algorithm, to achieve high-fidelity 3D model reconstruction. Our technique supports simultaneous 3D Gaussian training and pose estimation, enabling the robust generation of intricate 3D point clouds from sparse, noisy data. The procedure is further bolstered by a post-editing phase designed to eliminate noise points inconsistent with our prior knowledge of a satellite’s geometric constraints. We validate our approach on synthetic datasets and actual observations of China’s Space Station and International Space Station, showcasing its significant advantages over existing methods in reconstructing 3D space objects from ground-based observations. © 2024, CC BY.

关键词： Atmospheric turbulence

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Synergy Analysis of Industrial, research and University Training centers Collaboration: A Case Study in Thailand's Eastern Economic Corridor

Synergy Analysis of Industrial, Research and University Trai...

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Portland International Conference on Management of engineering and technology (PICMET)

作者： Udom Lewlomphaisarl Ukrit Pungjitwisut Wassana Pullpol Kumpee Suksomboon Arnuphap Dowrueng Industrial IoT and Automation Research Group (IIARG) Sustainable Manufacturing Center (SMC) Eastern Economic Corridor of Innovation (EECi) Rayong Thailand EEC Automation Park Faculty of Engineering Burapha University Chonburi Thailand Intelligent SCADA Technology Research Team (IST) Advanced Control and Electronics Research Group (ACERG) National Electronics and Computer Technology Center (NECTEC) Pathum Thani Thailand National Electronics and Computer Technology Center (NECTEC) National Science and Technology Development Agency (NSTDA) Pathum Thani Thailand

The Royal Thai Government has outlined a 20-year strategy to achieve high-income status by 2036. “Thailand 4.0” is guided by the concept of “Industry 4.0”, which aims to transform the country into an innovative, value-driven industry, with a focus on 12 areas such as Automation & Robotics, Aviation & Logistics, Biofuel & Biochemicals, and Digital. The development of Eastern Economic Corridor (EEC) lies at the heart of Thailand 4.0 scheme. The EEC is an area-based development initiative aimed at revitalizing the prestigious East Coast, where numerous business developers have experienced a rewarding investment journey and exceptional success over the past 30 *** paper introduces a synergy analysis of the 3-phase collaboration approach between the EEC Automation Park located on the campus of Burapha University as the main university in the EEC and the Sustainable Manufacturing center (SMC) at Automation Robotics and Intelligent System (ARIPOLIS) in Eastern Economic Corridor of Innovation (EECi) with strong partners from industry e.g. Mitsubishi Electric Factory Automation (Thailand) Limited. This 3-phase approach:}{1. Build an island demonstrator, 2. Link production demonstration lines and 3. Transfer to 100 factories in the EEC helps managers realize possible difficulties and systematically prepare solutions in advance.

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A new design of a digital filter for an efficient field programmable gate array using quantum dot technology

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Materials Science and engineering: B 2024年 300卷

作者： Taghavirashidizadeh, Ali Ahmadpour, Seyed-Sajad Ahmed, Suhaib Jafari Navimipour, Nima Ramkrishna Kassa, Sankit Yalcin, Senay Tehran Iran Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Chitkara University Institute of Engineering and Technology Punjab India Department of Electronics and Communication Engineering Model Institute of Engineering and Technology Jammu India Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan Pune India Department of Energy System Engineering School of Engineering and Natural Sciences Bahçeşehir University Istanbul Turkey

Digital filtering algorithms are most frequently used to implement generic-based Field-programmable gate arrays (FPGAs) chips, which are used for higher sampling rates. In the filtering structure, delay and occupied areas play a vital role. Since the existing structures suffered from shortcomings such as high delay and high occupied area, implementing a high-performance digital filter circuit with high speed and low occupied area based on unique technology can significantly improve the performance of whole FPGA structures. One of the best technologies to implement this vital structure to solve these shortcomings is quantum-dot cellular automata (QCA) technology. This paper presents several new efficient full adders for digital filter applications based on quantum technology, including a multiplier, AND gate, and accumulator. The QCADesigner 2.0.3 tool is used to create and validate the suggested designs. According to the results, all designed circuits have simple structures with few quantum cells, low area, and low latency. © 2023

关键词： Nanocrystals

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Optimizing Antenna Design for 6G Applications Using Machine Learning Techniques

Optimizing Antenna Design for 6G Applications Using Machine ...

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

作者： Praveen Kumar Sharma Vivek Prakash Yadav Aashiwal Nirmal Mukesh Sourabh Jawale Ankit Kumar Research Center for Electrical and Information Technology Seoul National University of Science and Technology Seoul Republic of Korea Department of Electronics and Communication Engineering Independent Researcher: FEAL: Flexible Electronics and Antenna Lab Pilani India Department of Electronics and Communication Engineering Independent Researcher: FEAL: Flexible Electronics and Antenna Lab Mumbai India Department of Computer Science and Engineering Medicaps University Indore India Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Republic of Korea

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

The emergence of 6G networks requires innovative antenna designs that operate at extremely high frequencies. The proposed paper explores the application of advanced machine learning techniques to optimize the design of antennas for 6G applications at 15 GHz. Traditional design methods are often time-consuming and depend on expert knowledge, whereas machine learning can automate and expedite this process. We developed a dataset incorporating critical antenna design parameters such as geometric dimensions and applied algorithms to predict key performance metrics like return loss, gain, and bandwidth. Our results demonstrate that neural networks, in particular, offer superior predictive accuracy, significantly reducing design iteration times. This integration of machine learning into antenna design not only improves performance but also facilitates the development of adaptive antenna systems, meeting the stringent demands of 6G networks. This research highlights the potential of machine learning to revolutionize antenna design, enabling rapid and efficient development of next-generation communication systems.

关键词： 6G mobile communication Technological innovation Machine learning algorithms Communication systems Design methodology Neural networks Machine learning Prediction algorithms Antennas Next generation networking

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Does Your Neural Code Completion Model Use My Code? A Membership Inference Approach

arXiv

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arXiv 2024年

作者： Wan, Yao Wan, Guanghua Zhang, Shijie Zhang, Hongyu Zhou, Pan Jin, Hai Sun, Lichao National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Huazhong University of Science and Technology China University of Leigh United States

Recent years have witnessed significant progress in developing deep learning-based models for automated code completion. Examples of such models include CodeGPT and StarCoder. These models are typically trained from a large amount of source code collected from open-source communities such as GitHub. Although using source code in GitHub has been a common practice for training deep-learning-based models for code completion, it may induce some legal and ethical issues such as copyright infringement. In this paper, we investigate the legal and ethical issues of current neural code completion models by answering the following question: Is my code used to train your neural code completion model? To this end, we tailor a membership inference approach (termed CodeMI) that was originally crafted for classification tasks to a more challenging task of code completion. In particular, since the target code completion models perform as opaque black boxes, preventing access to their training data and parameters, we opt to train multiple shadow models to mimic their behavior. The acquired posteriors from these shadow models are subsequently employed to train a membership classifier. Subsequently, the membership classifier can be effectively employed to deduce the membership status of a given code sample based on the output of a target code completion model. We comprehensively evaluate the effectiveness of this adapted approach across a diverse array of neural code completion models, (i.e., LSTM-based, CodeGPT, CodeGen, and StarCoder). Experimental results reveal that the LSTM-based and CodeGPT models suffer the membership leakage issue, which can be easily detected by our proposed membership inference approach with an accuracy of 0.842, and 0.730, respectively. Interestingly, our experiments also show that the data membership of current large language models of code, e.g., CodeGen and StarCoder, is difficult to detect, leaving amper space for further improvement. Finally, we also t

关键词： Deep learning

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Spiking Neural Network-Based Control of an Unmanned Aerial System Implemented on a Customized Neural Flight Simulation Environment

Spiking Neural Network-Based Control of an Unmanned Aerial S...

引用

American Control Conference (ACC)

作者： Omar A. Garcia A. Diego Chavez Arana Eduardo S. Espinoza Ignacio Rubio Scola Luis Rodolfo Garcia Carrillo Andrew T. Sornborger Center for Research and Advanced Studies of the National Polytechnic Institute in Mexico City Mexico Klipsch School of Electrical and Computer Engineering New Mexico State University Las Cruces NM USA Conahcyt Center for Research and Advanced Studies of the National Polytechnic Institute in Mexico City Mexico Department of Industrial Products Engineering of the National Institute of Industrial Technology CONICET and National University of Rosario Argentina Los Alamos National Laboratory Los Alamos NM USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

A prototyping environment for the development of Spiking Neural Networks (SNN) is integrated with a physics-based flight simulator with the objective of stabilizing a quad rotorcraft Unmanned Aerial System (UAS) via neuromorphic controllers. Making use of the Neural engineering Framework (NEF), SNN-based Proportional+Derivative (PD) controllers are designed for the translational and rotational dynamics of the UAS. An online Model in the Loop (MIL) evaluation scenario was implemented, showing that the proposed neuromorphic controllers are capable of stabilizing the quad rotorcraft UAS in both regulation and trajectory tracking tasks.

关键词： Trajectory tracking Neuromorphics Heuristic algorithms Aerospace simulation Spiking neural networks Stability analysis Regulation

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Novel design of fractional cholesterol dynamics and drug concentrations model with analysis on machine predictive networks

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computers in Biology and Medicine 2025年 184卷 109423-109423页

作者： Raja, Muhammad Junaid Ali Asif Hassan, Shahzaib Ahmed Chang, Chuan-Yu Raza, Hassan Mubeen, Rikza Masood, Zaheer Raja, Muhammad Asif Zahoor Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Douliu64002 Taiwan Federal Medical and Dental College Shaheed Zulfiqar Ali Bhutto Medical University Islamabad44000 Pakistan Foundation University Medical College Foundation University Islamabad Pakistan Department of Electrical Engineering Capital University of Science and Technology Islamabad Pakistan Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliu64002 Taiwan

Within the intricate fabric of human physiology, cholesterol, a lipid present in cell membranes exerts a discernible effect on the concentration of the drug in human body that influence the aspects of drug pharmacokinetics. The objective of this work is to design a case study based fractional order cholesterol drug interaction model that encapsulates the nuanced dynamics inherent in the multifaceted human physiology with identification of essential variables including drug concentration Ksb and cholesterol level γ. The strength of nonlinear autoregressive with exogenous inputs (NARX) neural networks are exploited to predict the temporal dynamics that reveal the hidden intricacies and subtle patterns within the fractional model. Grünwald–Letnikov (GL) based fractional solver is used to generate the synthetic data, serving as a robust foundation for training, testing and validation of the NARX neural networks for different use cases of cholesterol drug interaction control strategies. A thorough comparative analysis based on exhaustive simulation unveiled a marginal distinction between the results obtained from NARX and the outcomes of fractal technique showing remarkably low MSE in the range of 10−12. The strength of the designed methodology is further verified by using other performance metrics such as MSE, regression index, autocorrelation and cross correlation. The integration of genetic and genomic information tailor the model to address the unique characteristics of individual patient facilitating advancement in precision medicines. © 2024 Elsevier Ltd

关键词： Cholesterol Pharmacokinetics Drug interaction Fractional models Neural networks Artificial intelligence

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Multi-Objective Discrete Extremal Optimization of Variable-Length Blocks-Based CNN by Joint NAS and HPO for Intrusion Detection in IIoT

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Lu, Kang-Di Huang, Jia-Cheng Zeng, Guo-Qiang Chen, Min-Rong Geng, Guang-Gang Weng, Jian Donghua University College of Information Science and Technology Shanghai201620 China Jinan University College of Cyber Security The National Joint Engineering Research Center of Network Security Detection and Protection Technology Guangzhou510632 China South China Normal University School of Computer Science Guangzhou510631 China

Industrial Internet of Things (IIoT) is an important part of industrial infrastructure but facing serious and evolving security threats in recent years. Deep learning has been widely considered as a promising solution for enhancing the security of IIoT. However, these existing deep learning models utilized in the intrusion detection of IIoT are manually developed that not only greatly rely on the experience of the designers but also is lack of utility due to the high model complexity. By taking into account the trade-off between the model performance and model complexity, this paper makes the first attempt to propose a multi-objective joint optimization method of neural architecture search (NAS) and hyper-parameter optimization (HPO) based on multi-objective discrete extremal optimization (MODEO) to automatically design a lightweight convolutional neural network (CNN) for the intrusion detection task of IIoT, abbreviated as MODEO-CNN. A novel hybrid variable-length encoding strategy is developed by combing binary and integer encoding to characterize both the neural architectures including the number of blocks, the blocks-based network topology and the corresponding architecture parameters in CNN block, and some important hyper-parameters including batch size, learning rate, weight optimizer and regularization. The individual-based discrete multi-objective evolutionary process of MODEO is designed to obtain the Pareto-optimal CNN models. Three widely-used IIoT intrusion detection datasets, including the Gas Pipeline, BoT-IoT, and Power System Attack datasets, have been used to illustrate the superiority of the proposed MODEO-CNN over the state-of-the-art hand-craft models and two single-objective fixed-length blocks-based NAS models in terms of accuracy, precision, recall, F1-Score, and model's million floating point operations. © 2004-2012 IEEE.

关键词： Encoding (symbols)

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SharDAG: Scaling DAG-Based Blockchains Via Adaptive Sharding

SharDAG: Scaling DAG-Based Blockchains Via Adaptive Sharding

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International Conference on Data engineering

作者： Feng Cheng Jiang Xiao Cunyang Liu Shijie Zhang Yifan Zhou Bo Li Baochun Li Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Hong Kong University of Science and Technology Hong Kong University of Toronto Canada

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Directed Acyclic Graph (DAG)-based blockchain (a.k.a distributed ledger) has become prevalent for supporting highly concurrent applications. Its inherent parallel data structure accelerates block generation significantly, shifting the bottleneck from performance to storage scalability. An intuitive solution is to apply state sharding that divides the entire ledger (i.e., transactions and states) into multiple shards. While each node only stores proportional transactions, it suffers from the challenges of storing and ensuring the processing consistency of cross-shard transactions. In this paper, we propose SharDAG, a new mechanism that leverages adaptive sharding for DAG-based blockchains to achieve high performance and strong consistency. The key idea of SharDAG is to exploit unique characteristics - silent assets - and design a lightweight processing mechanism based on avatar account caching. Furthermore, we design a Byzantine resilient cross-shard verification mechanism with a theoretically optimal number of participating nodes, which guarantees the consistency and security of avatar account aggregation. Our comprehensive evaluations on real-world workloads demonstrate that SharDAG presents up to 3.8 x throughput improvement compared to the state-of-the-art and reduces the storage overhead of cross-shard transactions.

关键词： Sharding Directed acyclic graph Distributed ledger Avatars Scalability Receivers Throughput

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