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Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Sreelakshmi Ganti Ravi Kumar Vankayalapati P. Krishnamoorthy Pallavi S Thakare Ullal Akshatha Nayak P Vignesh Department of Electronics and Communication Engineering Geethanjali College of Engineering and Technology Medchal India Equinix Texas USA Department of Computer Science and Engineering Sasi Institute of Technology & Engineering West Godavari India Department of Information Technology Dr D. Y. Patil Institute of Technology Pimpri India Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Mathematics KCG College of Technology Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

A network of computing devices and sensors, the Internet of Things (IoT) rapidly exchanges data to assess circumstances and propose new services. intelligent home technologies rely on IoT. Contemporary smart home systems offer automatic lighting control, smoke detectors, temperature sensors, and smart lock installations. But these technologies raise serious privacy and security concerns. Security flaws let hackers access sensitive data, sendoff false alarms, and take control monitoring systems. Smart home technology is vulnerable to many threats because consumers are wary of it. Recommended steps are preprocessing, sensing, and model training. Preparing raw, unstructured data for analysis. DHT11 humidity and temperature sensors and HC-SR501 PIR motion detectors collect data. It trained the model using Multiscale DGCN data. Better than GCN and BiLSTM, Multiscale DGCN performs. It uses Internet of Things-based smart home security data well, with an average accuracy rate of 93.87%. Identifying outliers in sensor data solves major smart home security challenges. Increasing smart home system accuracy reduces security concerns and boosts user trust, which may drive adoption.

关键词： Temperature sensors Accuracy Bidirectional long short term memory Smart homes DH-HEMTs Safety Security Internet of Things Integrated circuit modeling intelligent sensors

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intelligent Rail Sentinel a perspective of Indian Railway

Intelligent Rail Sentinel a perspective of Indian Railway

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ieee Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Arun D. Limgaokar Sharvari S. Hinge Yash S. Vinchu Vivek S. Rane Prashant B. Patel Anup Vibhute Department of Instrumentation Engineering Dr. D.Y. Patil Institute of Technology Pimpri Pune Maharashtra India Pune India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The paper presents a PLC and SCADA-based advanced safety system design for railway signaling and interlocking. While the Indian Railway systems are infrequently beset by issues, a solitary mishap over an extended timeframe might profoundly adversely affect various facets of life and property. These issues can be resolved through the implementation of interlocking and railway signaling systems. In railways, railway personnel continue to supervise the signaling and interlocking systems, which remain partially manual. The implementation of PLC and SCADA can effectively address these challenges. The primary objective of this paper is to examine dispatcher telecommunications with crews, telecommunications with signals governing switchgears, signals monitoring track temperature, and the acquisition and storage of real-time video through CCTV for telecommunications in emergencies and automatic railway gates. The proposed solution depends on PLCs that can execute signaling and interlocking functions automatically. Moreover, these processes can be altered, monitored, and corrected through the use of a SCADA system, hence minimizing human participation and alleviating safety issues.

关键词： Temperature sensors Temperature measurement Automation SCADA systems Switches Logic gates Rail transportation Telecommunications Safety System analysis and design

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Technology-based sports performance enhancement: A conference review

Intelligent Sports and Health

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intelligent Sports and Health 2025年第2期1卷 98-102页

作者： Hongyou Liu School of Physical Education & Sports Science South China Normal University Guangzhou China

This review synthesizes the keynote presentations delivered at the 2024 international conference on Sports Technology and Performance Analysis (ICSTPA 2024), highlighting advances in the interdisciplinary development of science and technology applied to health promotion, sports performance and physical education. Real-time physiological monitoring via AI-powered wearable technologies (e.g., wearable ECG shirts and cuffless blood pressure systems) enables personalized health and metabolic management. Measurement techniques for athletic performance (i.e., vertical jumps) have evolved from traditional jump mats to advanced automated systems based on deep neural networks, significantly improving accuracy and applicability in diverse environments. Additionally, machine learning-driven analyses of extensive match datasets in sports such as squash and soccer have identified hidden tactical patterns, thus providing quantitative foundations for tactical and training optimizations. Moreover, interactive educational innovations have emerged from the integration of wearable devices, mobile applications, and observational coaching tools. A novel psychological-fatigue threshold (PFT) concept has also been introduced, which incorporates psychological factors into the evaluation of decision-making and tactical performance of soccer players under high-intensity conditions. The ICSTPA 2024 conference demonstrated that technology-driven innovations are profoundly reshaping sports performance enhancement, health management, and motor skill development. Future research directions highlighted include expanding the application of AI-driven tools for real-time feedback, further refinement of psychological assessment frameworks, and enhanced integration of technology within practical sports training environments.

关键词： Sports performance Motor skill acquisition Multidisciplinary interaction data-driven analysis

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data processing, analysis and Machine Learning of Electrical Atomic Force Microscopy (AFM) in data Cube mode: conduction of a nanomaterial for Lithium-ion battery electrodes

Data processing, analysis and Machine Learning of Electrical...

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international Image Processing, Applications and systems conference (IPAS)

作者： Rosine Coq Germanicus Peter De Wolf Petr Klapetek Unicaen Ensicaen CNRS CRISMAT UMR6508 Normandie Univ Caen France Bruker Nano Surfaces Santa Barbara CA USA Czech Metrology Institute Brno Czech Republic

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

With the increasing integration of functional systems, nanoscale characterization has become crucial not only for material investigation but also for advancing the understanding of local behavior and optimizing performances. This paper details data processing techniques and machine learning methodologies for the analysis and prediction of advanced electrical Atomic Force Microscopy datasets. The datasets are acquired using a hyperspectral data Cube approach, which combines both mapping and spectroscopy acquisitions. In the context of decarbonization and electrification, current challenges are related to energy storage and transport. A cathode material used in Lithium-ion battery electrodes is investigated for its electrical transport properties. To comprehensively characterize the electrical properties, the conductive mode of Atomic Force Microscopy is employed using the data Cube method, which enables current-voltage spectroscopy at each pixel of the acquisition. The analysis of experimental datasets reveals localized electrical behavior of the material’s components as a function of applied voltage. Furthermore, machine learning algorithms are employed to predict current maps and cluster distinct regions based on their electrical characteristics.

关键词： Lithium-ion batteries Atomic measurements Atomic force microscopy Spectroscopy Microscopy Force Conductivity Nanoscale devices Cathodes Hyperspectral imaging

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State of Charge Estimation of Lithium-ion Batteries Using Long Short Term Memory Network in Electric Vehicle Applications

State of Charge Estimation of Lithium-ion Batteries Using Lo...

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ieee SSD international Multi-conference on systems, Signals and Devices

作者： Marwa Gaich Sabeur Jemmali Bilal Manai LATIS- Laboratory of Advanced Technology and Intelligent Systems National School of Engineers of Sousse University of Sousse Sousse Tunisie Department of Electrical Engineering CEGEP de l'Outaouais Gatineau Québec Canada

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Precise State-of-Charge (SoC) estimation is crucial for accurate electric vehicle range prediction and efficient battery charge management. This paper explores a computationally efficient machine learning approach to address SoC-estimation-related issues. A Long Short-Term Memory (LSTM) network is utilized for predicting the SoC of lithiumion batteries, leveraging real-time data on temperature, voltage and current. The proposed LSTM model demonstrates remarkable performance, achieving a mean absolute error of 1.02%, as well as a 1.35% root mean squared error at 25°C, while ensuring low computational overhead, making it suitable for practical implementation in Battery Management systems (BMSs). This work lays a solid foundation for future advancements in BMS design, offering significant improvements in SoC prediction accuracy, reliability and efficiency. The findings suggest that LSTM networks can effectively model the complex dynamics of battery behavior, even when having varied operational conditions.

关键词： Accuracy Computational modeling Estimation Battery management systems Machine learning Voltage Electric vehicles State of charge Vehicle dynamics Long short term memory

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Relationship Sharing-based Trustworthy Verifiable Multi-Party Verification in Decentralized Identity

Relationship Sharing-based Trustworthy Verifiable Multi-Part...

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ieee international conference on Big data Security on Cloud (BigdataSecurity), High Performance and Smart computing (HPSC) and intelligent data and Security (IDS)

作者： Yuqing Zhang Keke Gai Jing Yu Kai Ding School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China School of Information Engineering Minzu University of China Beijing China

ISBN: (数字)9798331595104

ISBN: (纸本)9798331595111

Decentralized Identity (DID) management is pivotal for data security and privacy-preserving in the digital era, yet existing systems face critical challenges, including single point of failure, privacy leakage, and high computational overhead. To address these limitations, this paper proposes a multi-party verifiable trust validation mechanism based on Verifiable Relation Sharing (VRS). The mechanism skillfully integrates Multi-Verifier Zero-Knowledge proofs (MVZK) with Verifiable Secret Sharing (VSS) to securely share secret attribute vectors among multiple verifiers. This enables the generation of Zero-Knowledge Proof (ZKP) of relationships without revealing the actual data. This mechanism ensures that each verifier obtains partial information, thereby effectively defending against single-point attacks, and reducing computational and communication costs through distributed verification. Experimental results confirm the practicality within DID ecosystems, offering a scalable and privacy-preserving solution for multi-party verification and a foundational framework for secure DID verification in blockchain.

关键词： Privacy data privacy Protocols Costs Scalability Ecosystems Memory management Vectors Blockchains Faces

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An intelligent and Dynamic Replication Scheme for Reliable Cloud Environment

An Intelligent and Dynamic Replication Scheme for Reliable C...

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Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Damodhar M Ch. D V Subba Rao Department of Computer Science and Engineering Sri Venkateswara University College of Engineering Tirupati India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Recently, cloud computing research, especially Virtual Machine based replication schemes and their applications, were becoming prevalent. A cloud data center (DC) comprises of hosts with an enormous of virtual machines (VMs) where determining replica quantity and replication order is a tedious task to minimize cost and bandwidth. Majority of the existing dynamic replication techniques rely on budget constraints and server failure rate to guarantee customer requirements. But, there is a strong need to determine replication order of servers to retain the performance and system stability. In this paper, we suggest two different algorithms for better selection with proper and intelligent placement of replicas in the cloud region. The proposed algorithms towards dynamic replication are request based replica selection (RRS) and ant colony optimization (ACO). The foremost proposed algorithm was request based replica selection used for obtaining the best selected replicas depends upon the VM performance ranking and failure probability based on the task requirement. Conversely, the subsequent offered algorithm is ACO that is helpful to find the best location to place the designated replica depends on the least-cost distance metric, and the replicas availability in order to encompass and regulate with the entire network load and the link bottlenecks. The performance of proposed techniques using CloudSim was evaluated against various pro-active fault tolerance strategies. The simulation results gives that dynamic replica selection provides efficient task replication when compared with the existing pro-active algorithms. Additionally, ACO realizes high availability, low cost, and less bandwidth utilization.

关键词： Cloud computing Ant colony optimization Costs Heuristic algorithms Simulation Bandwidth Throughput Virtual machines Stability analysis Servers

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A Novel Rate of Penetration Fusion Modeling Method based on Improved Dung Beetle Optimization Algorithm and Support Vector Regression

A Novel Rate of Penetration Fusion Modeling Method based on ...

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ieee international conference on Industrial Technology (ICIT)

作者： Yang Zhou Zihan Zhang Shipeng Chen Min Wu Xin Chen School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Future Technology China University of Geosciences Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

The rate of penetration (ROP) is a critical indi-cator for evaluating drilling efficiency. Developing an accurate ROP model is essential for optimizing drilling performance and addressing process control challenges. However, ROP modeling in deep geological drilling is complicated by nonlinearity, diverse working conditions, and high-dimensional variations. To overcome these challenges, a fusion modeling approach for ROP is proposed. First, the fuzzy C-means clustering method is applied to classify drilling data into different working con-ditions. Based on this classification, support vector regression is employed to develop ROP sub-models, effectively addressing nonlinearity. To further enhance model accuracy, an improved dung beetle optimization algorithm (IDBO) is designed to deter-mine optimal model parameters and integrate the sub-models, thereby resolving issues related to multiple working conditions and high-dimensional variations. The IDBO incorporates four key enhancements, average weight, chaos disturbance, modified local search, and re-updating of the best solution, to strength-en its global search capability. Comparative results using the ieee CEC2017 benchmark test functions demonstrate that the proposed algorithm outperforms others in 12 test functions, highlighting its strong global optimization ability. Additionally, results from real-world drilling data validate the effectiveness of the proposed modeling approach in practical applications.

关键词： Drilling Support vector machines Employee welfare Chaos Geology Clustering methods Clustering algorithms Benchmark testing data models Optimization

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An intelligent SCADA System for Power Distribution Network Cable Fault Detection with Real-Time Monitoring and Autonomous Maintenance

An Intelligent SCADA System for Power Distribution Network C...

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international conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： S M Nahidul Islam Shameem Ahmad Md. Mahbub-E-Elahi Prodhan Md. Shoaib Khan Chowdhury Sakib Bin Mizan Abir Ahmed Effat Jahan Md. Rifat Hazari Chowdhury Akram Hossain Department of Electrical and Electronic Engineering Faculty of Engineering American International University-Bangladesh Dhaka Bangladesh Department of Computer Engineering Faculty of Engineering American International University-Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

Supervisory Control and data acquisition (SCADA) systems are significant for boosting industrial operations, but established systems often struggle with fault management and real-time monitoring. To address these issues, this paper proposes an advanced SCADA system that integrates IoT technologies, multi-agent systems, and predictive maintenance for seamless data collection, analysis, and decision-making. Machine learning enhances fault detection and cybersecurity, while a modular hardware design allows scalability and flexibility to various industrial environments. The system is highly sensitive to detecting faults, including open circuit faults, line-to-ground faults, line-to-line faults, double line to ground faults and three-phase faults. It improves operational safety and efficiency by accurately locating faults and notifying professionals in real time. Extensive simulations and hardware tests were conducted to validate its performance in different fault scenarios. The results show that the proposed SCADA system has successfully detected different types of faults, offering a unified approach for fault management, maintenance, and real-time monitoring.

关键词： Power cables Fault detection Scalability SCADA systems Electrical fault detection Real-time systems Hardware Maintenance Circuit faults Predictive maintenance

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On-Device Neural Architecture Search

On-Device Neural Architecture Search

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ieee international conference on Consumer Electronics (ICCE)

作者： Andrea Mattia Garavagno Edoardo Ragusa Paolo Gastaldo Antonio Frisoli Claudio Loconsole Department of Electrical Electronic Telecommunication Engineering and Naval Architecture DITEN University of Genoa Genoa Italy Department of Excellence in Robotics & AI Scuola Superiore Sant'Anna Pisa Italy Institute of Mechanical Intelligence Scuola Superiore Sant'Anna Pisa Italy Faculty of Technological and Innovation Sciences Universitas Mercatorum Rome Italy

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

This paper proposes a new approach to near-sensor computing, in which a lightweight Neural Architecture Search (NAS) is performed directly on the deployment device to find the best tiny neural architecture for analyzing the real-time data acquired through sensors. This new adaptation capability can be particularly useful in the case of human-machine interfaces for which the neural network analyzing the biometrical data can be re-designed each time the user changes, after a guided data collection procedure, fighting the typical data variations between individuals on a new level. To implement the proposed approach a new NAS has been designed and then validated on the Italian Sign Language dataset (ISL), a collection of surface electromyography (sEMG) signals of the signs of the Italian alphabet, using several embedded systems. Moreover, further validation on the Case Western Reserve University dataset (CWRU), a benchmark for intelligent fault diagnosis, is presented to suggest another possible application of the proposed approach. When run on a Raspberry Pi 4, the proposed NAS performs beyond the state of the art proposing a tiny neural architecture having 0.63 times less RAM occupancy and 5.96 percentage points of more accuracy in the case of the ISL dataset; and 0.44 times less RAM occupancy and 0.2 percentage points of more accuracy in the case of the CWRU dataset.

关键词： Performance evaluation Fault diagnosis Biometrics Sign language Costs Human-machine systems Random access memory Computer architecture Real-time systems Neural architecture search

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