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30th International Conference on Systems, Signals and Image Processing, IWSSIP 2023

作者： Kalendar, Marija Kokolanski, Zivko Gerazov, Branislav Nadzinski, Gorjan Poposka, Marija Zlatkova, Aleksandra Donchevski, Filip Markovska, Marija Sapeha, Anastasiia Karpov, Kirill Efnusheva, Danijela Gleich, Dusan Sarjas, Andrej Pongrac, Blaz Siemens, Eduard Ss Cyril and Methodius University Faculty of Electrical Engineering and Information Technologies Skopje Macedonia Anhalt University of Applied Sciences Department of Electrical Mechanical and Industrial Engineering Köthen Germany University of Maribor Faculty of Electrical Engineering and Computer Science Maribor Slovenia

ISBN: (纸本)9798350337297

Starting from the period of the Covid-19 pandemic, when the presence of students in the university laboratories was very limited, the research work on remote laboratories significantly increased. The most affected aspect of students' study experience at that time were the laboratory experiments intended to be conducted on-premises, in the university laboratories. The UbiLAB framework was envisioned as a digital space for implementing and conducting laboratory work and experiments that are rarely completely transferred into the digital world. Here we have identified two most difficult challenges: achieving a nearly realistic substitute for learning experiences in the actual physical laboratories, and a nearly realistic substitute for the social element of collaborative learning and 'making friends' in the process. The innovative cloud-based UbiLAB framework addresses both challenges, digitizing various and diverse actual laboratory experiments through a simple and widely available platform, while enhancing the reality of students' experience in the process. © 2023 IEEE.

关键词： Students

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Signal Processing Techniques for Classifying Heartbeat Patterns in ECG Signals for Cardiac Disease Detection Using Graph Convolutional Networks 4

Signal Processing Techniques for Classifying Heartbeat Patte...

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

作者： Alzubaidi, Laith H. Priyadharshini, C. Vasanthakumar, G.U. Samala, Srinivas Ananthi, S. College of Technical Engineering The Islamic University of Al Diwaniyah Department of Computers Techniques Engineering Al Diwaniyah Iraq Bharathiyar Institute of Engineering for Women Department of Electronics and Communication Engineering Salem India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Sr University Department of Electronics and Communication Engineering Warangal India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350352931

In recent days, the information about electrical activity is obtained in the form of Electro-Cardo-Diograms (ECG) which is become a typical way of monitoring heart rhythm and function of patients. In existing, the Convolutional Neural Networks (CNN) is utilized for the effective detection of Cardiac Disease (CD). However, the CNN model may not extract the spatial features from the signals effectively. To overcome this issue, Graph Convolutional Network (GCN) is proposed to extract the spatial features from signal effectively. Initially, the data is taken from Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) dataset. Then, min-max normalization is utilized to preprocess the data and the high frequency noise is removed with Butterworth Frequency Filter (BFF). After that, the preprocessed data is further processed into classification with proposed GCN model. In the classification, the convolutional layers are utilized to extract the spatial features by using graph-based structure. Finally, the fully connected layer with softmax activation function is utilized to classify the CD into normal or abnormal labels. From the results, the proposed GCN model established better results than existing CNN including precision (99.75%), recall (98.60%), F1-score (98.20%) and Area Under Curve (99.70%) respectively. © 2024 IEEE.

关键词： Butterworth filters

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Receding-Horizon Control of Constrained Switched Systems with Neural Networks as Parametric Function Approximators

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SN computer science 2023年第1期4卷 1-16页

作者： Markolf, Lukas Stursberg, Olaf Control and System Theory Department of Electrical Engineering and Computer Science University of Kassel Wilhelmshöher Allee 71-73 Kassel 34121 Germany

This work studies receding-horizon control of discrete-time switched linear systems subject to polytopic constraints for the continuous states and inputs. The objective is to approximate the optimal receding-horizon control strategy for cases in which the online computation is intractable due to the necessity of solving mixed-integer quadratic programs in each discrete time instant. The proposed approach builds upon an approximated optimal finite-horizon control law in closed-loop form with guaranteed constraint satisfaction. The paper derives the properties of recursive feasibility and asymptotic stability for the proposed approach. A numerical example is provided for illustration and evaluation of the approach. © 2022, The Author(s).

关键词： Model approximation Neural networks Online control Optimization Reinforcement learning Stability

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Reusing Deep Learning Models: Challenges and Directions in Software engineering

Reusing Deep Learning Models: Challenges and Directions in S...

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2023 IEEE John Vincent Atanasoff Symposium on Modern Computing, JVA 2023

作者： Davis, James C. Jajal, Purvish Jiang, Wenxin Schorlemmer, Taylor R. Synovic, Nicholas Thiruvathukal, George K. Purdue University Department of Electrical & Computer Engineering West LafayetteIN United States Loyola University Chicago Department of Computer Science ChicagoIL United States

ISBN: (纸本)9798350328899

Deep neural networks (DNNs) achieve state-of-The-Art performance in many areas, including computer vision, system configuration, and question-Answering. However, DNNs are expensive to develop, both in intellectual effort (e.g., devising new architectures) and computational costs (e.g., training). Re-using DNNs is a promising direction to amortize costs within a company and across the computing industry. As with any new technology, however, there are many challenges in re-using DNNs. These challenges include both missing technical capabilities and missing engineering practices. This vision paper describes challenges in current approaches to DNN re-use. We summarize studies of re-use failures across the spectrum of re-use techniques, including conceptual (e.g., re-using based on a research paper), adaptation (e.g., re-using by building on an existing implementation), and deployment (e.g., direct re-use on a new device). We outline possible advances that would improve each kind of re-use. © 2023 IEEE.

关键词： Deep neural networks

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Privacy preserving transparent supply chain management through Hyperledger Fabric

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Blockchain(Research and Applications) 2022年第2期3卷 117-137页

作者： Deebthik Ravi Sashank Ramachandran Raahul Vignesh Vinod Ramesh Falmari M.Brindha Department of Electrical and Electronics Engineering National Institute of TechnologyTiruchirappalli620015Tamil NaduIndia Department of Computer Science and Engineering National Institute of TechnologyTiruchirappalli620015Tamil NaduIndia

The revolution of blockchain technology started in the form of a cryptocurrency called *** recent years,this decentralized technology has attained worldwide adoption and growth in multiple sectors,including egovernance,e-commerce,and asset *** disrupted these sectors,the choice of blockchain technology to solve real-world problems concerning supply chain seems to be an innovative *** this paper,an attempt is made to analyze blockchain's ability to improve the standards of supply chain *** paper includes a methodology to implement the suggested idea using a permissioned blockchain platform—Hyperledger *** the paper's scope,the existing supply chain problems,such as data integrity,provenance transparency,privacy,and security,are given emphasis more specifically in the context of the coffee supply chain industry,while also concurrently attempting to generalize the solution to manage other supply chain activities ***,the final objective of this research is to identify whether permissioned blockchain platforms could help stakeholders in the supply chain industry engage in a less-corruptible alternative to traditional web technology and whether they could enable a more positively nuanced blockchain system that draws the best balance between traditional web technology and a public blockchain,including privacy protection and security.

关键词： Permissioned blockchain Supply chain Hyperledger Fabric Provenance Privacy protection Security Modularity

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Ultrastrong Light-Matter and Matter-Matter Coupling

Ultrastrong Light-Matter and Matter-Matter Coupling

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO/PR 2022

作者： Kono, Junichiro Department of Electrical and Computer Engineering Department of Physics and Astronomy Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States

This talk will describe our recent studies of light-matter and matter-matter coupling phenomena in condensed matter in the ultrastrong coupling regime. © IEEE 2022.

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Semi-automated Hierarchical Clustering Model for 4D-STEM Datasets

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 709-710页

作者： Shi, Chuqiao Mao, Nannan Yang, Yao Kong, Jing Han, Yimo Department of Materials Science and NanoEngineering Rice University Houston TX United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA United States Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA United States Department of Chemistry University of California Berkeley CA United States

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Utilizing Convolutional Neural Networks for Real-Time Object Tracking

Utilizing Convolutional Neural Networks for Real-Time Object...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Yadav, Dhananjay Kumar Sharma, Neeraj Prakash, Febin Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Electrical Engineering Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Utilizing Convolutional Neural Networks (CNNs) for actual-time item monitoring is a modern-day famous approach for visual tracking obligations. CNNs can construct vital functions from the input frames and learn to recognize and localize objects with excessive accuracy in the evaluation of preceding methods. Despite accomplishing the desired accuracy, the latest processes have adopted adaptive function extraction and reuse strategies to reduce the present-day fee. Several processes take proposals from the latest improvements in detection and segmentation fields to further improve tracking accuracy, with proposals to combine cease-to-quit tracking fashions with place-inspiration-based total object detection strategies or adaptive characteristic state-of-the-art frameworks. In addition, the latest studies have proposed novel architectures and first-class-tuning techniques optimized for tracking, in addition to addressing the difficulty of modern-day version wafting by introducing reminiscence modules and line latest techniques. All in all, advances in CNN-based tracking techniques have substantially progressed the precision and robustness of ultra-modern actual-time item tracking. © 2024 IEEE.

关键词： Convolutional improvements modern-day monitoring optimized

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DEEP LEARNING-BASED CHANNEL ESTIMATION FOR RIS-ASSISTED UAV-ENABLED WIRELESS COMMUNICATIONS SYSTEM

DEEP LEARNING-BASED CHANNEL ESTIMATION FOR RIS-ASSISTED UAV-...

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2023 International Conference on Innovation, Communication and engineering, ICICE 2023

作者： Tesfaw, Belayneh A. Lin, Hsin-Piao Tarekegn, Getaneh B. Juang, Rong-Terng Jeng, Shiann-Shiun Department of International Graduate Program in Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Institute of Aerospace and System Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Electrical and Computer Engineering National Yang-Ming Chiao Tung University Hsinchu30010 Taiwan Department of Electrical Engineering National Dong Hwa University Hualien Taiwan

ISBN: (纸本)9781839539954

Wireless communication via unmanned aerial vehicles (UAVs) has drawn a great deal of attention due to its flexibility in establishing line-of-sight (LoS) communications. However, in complex urban and dynamic environments, the movement of UAVs can be blocked by trees and high-rise buildings that obstruct directional paths. This problem can be effectively addressed by employing reconfigurable intelligent surfaces (RIS). To achieve this goal, accurate channel estimation in RIS-assisted UAV-enabled wireless communications is crucial. This paper proposes an accurate channel estimation model using long short-term memory (LSTM) for a multi-user RIS-assisted UAV-enabled wireless communications system. Simulation results showed that LSTM can effectively improve the channel estimation performance of RIS-assisted UAV-enabled wireless communications. © The Institution of engineering & Technology 2023.

关键词： Channel estimation

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Cyber Security Threat Detection in Internet of Things Using Optimized Deep Learning Technique 3

Cyber Security Threat Detection in Internet of Things Using ...

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3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Mariappan, Umasree Balakrishnan, D. Rajendran, S. Alagusundar, Nanditha Sheriff, A. Ashiq Aadith, K. Sri Vidya College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Virudhunagar626001 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Krishnankoil626126 India Kalasalingam Academy of Research and Education Department of Electrical and Electronics Engineering Tami lNadu Krishnankoil626126 India

ISBN: (纸本)9798350302288

Cybersecurity threat detection in the Internet of Things (IoT) refers to the process of identifying and addressing potential security risks and attacks within IoT systems and devices. It involves monitoring IoT networks and devices for abnormal behavior, detecting intrusions or unauthorized access attempts, ensuring the integrity of data, managing vulnerabilities, analyzing behavioral patterns, and staying informed about emerging threats. This ongoing process aims to maintain the security and integrity of IoT infrastructure by proactively identifying and mitigating potential security threats. Recent studies have demonstrated that deep learning algorithms are highly efficient and offer numerous advantages compared to earlier methods when it comes to conducting security assessments of IoT devices. Dual Channel Convolution Neural Network with Spider Monkey Optimization (DCCNN-SMO), a deep learning technique which visualized colored images for finding harmful intrusions in IoT network. Here, DCCNN used two channels of CNN and combined in fully connected layer for detecting intrusions in IoT network. The hyper parameters of DCCNN were fine tune by SMO which enhance the performance of DCCNN. In this paper, the convergence speed and effectively fine tuning the hyper parameters of DCCNN an Adapted SMO (ASMO) is introduced for better detection of intrusions in IoT network. The position update equation of the basic SMO algorithm is modified by incorporating a dual seek scheme. This approach, employed in the local leader (LL) phase, global leader (GL) phase, and LL decision phase, facilitates the acquisition of fresh outcomes, representing best hyper parameters of DCCNN for better detection of intrusion in IoT network. The performance of the proposed DCCNN-ASMO is demonstrated to be superior in terms of accuracy, precision, and recall, as evidenced by the experimental results. © 2023 IEEE.

关键词： Internet of things

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