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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Chinnasamy, P. Subashini, B. Nijanthan, Nijanthan Madasamy Raja, G. Swetha, Devulapally Sreenivasulu Reddy, L. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India School of Computing Srmist Department of Data Science and Business Systems Kattankulathur India Paavai Engineering College Tamilnadu Namakkal India Paavai Engineering College Tamilnadu Namakkal India Vaagdevi Engineering College Dept. of Computer Science and Engineering Warangal Dist. Telangana Bolli Kunta506005 India School of Advanced Sciences Kalasalingam Academy of Research and Education Department of Mathematics Tamilnadu Krishnankoil India

ISBN: (纸本)9798331509675

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms. © 2025 IEEE.

关键词： Smart contract

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Read Voltage Dependency of Random Telegraph Noise in the Intermediate State of TaOx-based ReRAM

Read Voltage Dependency of Random Telegraph Noise in the Int...

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Annual International Symposium on Reliability Physics

作者： Kenshin Yamauchi Naoko Misawa Hisashi Shima Yasuhisa Naitoh Hiroyuki Akinaga Chihiro Matsui Ken Takeuchi Dept. of Electrical Engineering and Information Systems The University of Tokyo Tokyo Japan Device Technology Research Institute National Institute of Advanced Industrial Science and Technology Ibaraki Japan

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

This work investigates random telegraph noise (RTN) behavior in the intermediate resistance states of 40nm TaOx-based analog ReRAM. Uniquely in the intermediate resistance states, both the frequency and amplitude of RTN fluctuations tend to increase with the read voltage. Considering the trade-off between increasing them and enhancing margins among adjacent resistance states as read voltage rises, a 0.2V readout minimizes RTN-induced misdetection of resistance states, achieving a significantly low error rate of 0.19%.

关键词： Resistance Fluctuations Voltage measurement Error analysis Neuromorphic engineering Noise Electrical resistance measurement Physics Guidelines

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Digital Twin-Driven Bearing-Fault Detection in Induction Motor and Drives using Graph Sampling and Aggregation Network

Digital Twin-Driven Bearing-Fault Detection in Induction Mot...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haraprasad Badajena Suryanarayan Majhi Bivash Chakraborty Mamata Jenamani Aurobinda Routray Ronit Dutta Advanced Technology Development Centre Indian Institute of Technology Kharagpur India Industrial and Systems Engineering Indian Institute of Technology Kharagpur India Dept. of Electrical Engineering Indian Institute of Technology Kharagpur India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Bearing fault diagnosis is crucial for ensuring the reliability and safety of industrial systems, particularly in preventing operational failures and maintaining product quality. Traditional signal processing methods and deep learning algorithms, while useful, often overlook the complex structural relationships within sensor data, limiting their diagnostic effectiveness. To address this, we present a novel Digital Twin-Driven Fault Diagnosis Framework that integrates graph-based learning techniques with advanced signal analysis. Our approach employs XGBoost and GraphSAGE embeddings to capture both spatial and temporal correlations within the current signals. The raw sensor data is then processed using a sliding window technique and time-frequency domain features are extracted then transformed into a graph structure that represents the intricate relationship in the signal. GraphSAGE is then applied to these graph structures, generating embeddings that enhance fault detection accuracy. Additionally, XGBoost is utilized for classification, improving the overall robustness of the system. The proposed method, deployed on edge devices, delivers real-time diagnostics, providing a scalable and efficient solution for industrial applications. Experimental results using real-world datasets demonstrate that our method significantly outperforms state-of-the-art algorithms, improving detection accuracy and Area under the curve (AUC) scores up to 97% and 99%, respectively.

关键词： Fault diagnosis Time-frequency analysis Accuracy Correlation Fault detection Image edge detection Signal processing algorithms Feature extraction Real-time systems Speech processing

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On the Connection Between Magnetic-Field Odometry Aided Inertial Navigation and Magnetic-Field SLAM

arXiv

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arXiv 2025年

作者： Skog, Isaac Kok, Manon Hendeby, Gustaf Huang, Chuan Edridge, Thomas School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Delft Center for Systems and Control Delft University of Technology Delft Netherlands Dept. of Electrical Engineering Linköping University Linköping Sweden

Magnetic-field simultaneous localization and mapping (SLAM) using consumer-grade inertial and magnetometer sensors offers a scalable, cost-effective solution for indoor localization. However, the rapid error accumulation in the inertial navigation process limits the feasible exploratory phases of these systems. Advances in magnetometer array processing have demonstrated that odometry information, i.e., displacement and rotation information, can be extracted from local magnetic field variations and used to create magnetic-field odometry-aided inertial navigation systems. The error growth rate of these systems is significantly lower than that of standalone inertial navigation systems. This study seeks an answer to whether a magnetic-field SLAM system fed with measurements from a magnetometer array can indirectly extract odometry information — without requiring algorithmic modifications — and thus sustain longer exploratory phases. The theoretical analysis and simulation results show that such a system can extract odometry information and indirectly create a magnetic field odometry-aided inertial navigation system during the exploration phases. However, practical challenges related to map resolution and computational complexity remain significant. © 2025, CC BY.

关键词： Inertial navigation systems

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Integrating Cluster Analysis and Artificial Neural Networks to Predict Warpage in Fan-out Panel-Level Packaging

Integrating Cluster Analysis and Artificial Neural Networks ...

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Thermal, Mechanical and Multiphysics Simulation and Experiments in Micro-Electronics and Micro-systems EuroSimE

作者： M. C. Huang H.C. Cheng K. N. Chiang Dept. of Power Mechanical Engineering National Tsing Hua University Advanced Microsystem Packaging and Nano-Mechanics Research Lab Hsinchu Taiwan R.O.C Dept. of Aerospace and Systems Engineering Feng Chia University Multi-Scale Computation Mechanics and Measurement Lab Taichung Taiwan R.O.C

ISBN: (数字)9798350393002

ISBN: (纸本)9798350393019

Fan-Out Panel-Level Packaging (FO-PLP) offers high efficiency in area utilization, which helps reduce production costs. This study establishes a $320 ~\text{mm} \times$ 320 mm three dimension FO-PLP model using the Finite Element Method (FEM), combined with process modeling technology to simulate the actual manufacturing process closely, ultimately obtaining the warpage values after the debonding process. To reduce the high computational cost required for simulation, this study employs FEM to create FO-PLP models with various feature sizes, which are used as training and testing data for machine learning. Machine learning is then utilized to predict the overall warpage behavior of the FO-PLP after processing. In order to reduce the time cost of training the model and optimize its prediction accuracy, this study adopts Cluster Analysis to simplify the data points in the training dataset, selecting representative points as new training data. Subsequently, an Artificial Neural Network (ANN) is used to predict the warpage results of the package, ensuring the quality of the representative points selected by the clustering method. Additionally, these representative points effectively reduce the complexity of the model structure while maintaining good accuracy, and they help decrease the training time cost. Compared to regularization, representative points reflect similar effects in terms of model performance.

关键词： Training Accuracy Costs Artificial neural networks Machine learning Packaging Data models Finite element analysis Computational efficiency Testing

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Incorporating the Com-B Model for Behavior Change into an Agent-Based Model of Smoking Behaviors: An Object-Oriented Design 24

Incorporating the Com-B Model for Behavior Change into an Ag...

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Proceedings of the Winter Simulation Conference

作者： David Tian Hazel Y. Squires Charlotte Buckley Duncan Gillespie Harry Tattan-Birch Lion Shahab Robert West Alan Brennan Jamie Brown Robin C. Purshouse Dept. of Automatic Control and Systems Engineering University of Sheffield UK Sheffield Centre for Health and Related Research School of Medicine and Population Health University of Sheffield UK Institute of Epidemiology and Health University College London UK

ISBN: (纸本)9798331534202

Modeling trajectories in cigarette smoking prevalence, initiation and quitting for populations and subgroups of populations is important for policy planning and evaluation. This paper proposes an agent-based model (ABM) design for simulating the smoking behaviors of a population using the Capability, Opportunity, Motivation - Behavior (COM-B) model. Capability, Opportunity and Motivation are modeled as latent composite attributes which are composed of observable factors associated with smoking behaviors. Three forms of the COM-B model are proposed to explain the transitions between smoking behaviors: initiating regular smoking uptake, making a quit attempt and quitting successfully. The ABM design follows object-oriented principles and extends an existing generic software architecture for mechanism-based modeling. The potential of the model to assess the impact of smoking policies is illustrated and discussed.

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Towards Industry 5.0 within Micro-Injection moulding Production and Industrialisation: a proof of concept

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Procedia Computer Science 2025年 253卷 3007-3014页

作者： Benedetta Stampone Valerio Pulcini Giuseppina Vitale Antonio Grieco University of Salento Dept. of Innovation Engineering Campus Ecotekne Via per Monteroni 73100 Lecce Italy Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA)- National Research Council of Italy (CNR) via P. Lembo 38F 70124 Bari Italy

Industry 4.0 has improved the manufacturing industry with a main focus on process automation to increase mass productivity and performance. Industry 5.0 plans to complement the former paradigm, leveraging a human-centric vision where operators and machines assist and support each other. In this research, the authors develop a Digital Twin (DT) model to provide useful information to the stakeholders involved in a micro-injection (µIM) process to optimize the activities and facilitate their work. The final goal is to implement the three pillars of Industry 5.0 in a real case study set within the micro-injection moulding; to do so, the developed solution takes account of the stakeholders’ needs and how to facilitate their interaction with the machine, addressing human-centricity. In addition, it deals with the processes’ resilience, allowing the possibility to adapt to new, different inputs. Finally, it addresses sustainability, helping the already low-waste µIM process to have even less waste, optimizing the processing conditions.

关键词： Industry 5.0 Micro-injection moulding Process Modeling Digital Twin Human-centricity

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Video Deblurring by Sharpness Prior Detection and Edge Information

arXiv

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arXiv 2025年

作者： Tian, Yang Brau, Fabio Rossolini, Giulio Buttazzo, Giorgio Meng, Hao The College of Intelligent Systems Science and Engineering Harbin Engineering University Nantong 145 Harbin150001 China DIEE University of Cagliari Via Universit 40 Cagliari09124 Italy Dept. of Excellence in Robotics and AI Sant’Anna School of Advanced Study Via Moruzzi 1 Pisa56124 Italy

Video deblurring is essential task for autonomous driving, facial recognition, and security surveillance. Traditional methods directly estimate motion blur kernels, often introducing artifacts and leading to poor results. Recent approaches utilize the detection of sharp frames within video sequences to enhance deblurring. However, existing datasets rely on fixed number of sharp frames, which may be too restrictive for some applications and may introduce a bias during model training. To address these limitations and enhance domain adaptability, this work first introduces GoPro Random Sharp (GoProRS), a new dataset where the the frequency of sharp frames within the sequence is customizable, allowing more diverse training and testing scenarios. Furthermore, it presents a novel video deblurring model, called SPEINet, that integrates sharp frame features into blurry frame reconstruction through an attention-based encoder-decoder architecture, a lightweight yet robust sharp frame detection and an edge extraction phase. Extensive experimental results demonstrate that SPEINet outperforms state-of-the-art methods across multiple datasets, achieving an average of +3.2% PSNR improvement over recent techniques. Given such promising results, we believe that both the proposed model and dataset pave the way for future advancements in video deblurring based on the detection of sharp frames. © 2025, CC BY-SA.

关键词： Video recording

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD)

Blockchain Integration for Robust Intellectual Property Prot...

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Electronics and Renewable systems (ICEARS), International Conference on

作者： P. Chinnasamy Subashini. B Nijanthan Nijanthan Madasamy Raja. G Devulapally Swetha Sreenivasulu Reddy L Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Data Science and Business Systems School of Computing SRMIST India Computer Science and Engineering Paavai Engineering College Namakkal Tamilnadu India Information Technology Paavai Engineering College Namakkal Tamilnadu India Dept. of Computer Science and Engineering Vaagdevi Engineering College Telangana India Department of Mathematics School of Advanced Sciences Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

关键词： Patents Technological innovation Costs Smart contracts Intellectual property Product development Blockchains Security Protection Resilience

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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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