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2009 ieee circuits and systems international conference on testing and diagnosis, ICTD'09

ISBN: (纸本)9781424425877

The proceedings contain 172 papers. The topics discussed include: study of the test flow optimization method in radar fault isolation;network cable fault location based on the wide pulse time domain reflection;research and application about general-purpose electronic system test platform;research and implementation of glitch capture technology;research on clock synchronization of wireless distributed measurement system;design and implementation of FC-AE-1553 point transmission hardware platform;a survey of neuromorphic engineering-biological nervous system realized on silicon;the research and application of the electric capacitor detecting sea ice thickness and its detecting system;a platform based on CORBA for open networked measurement system;and design for low power testing of computation modules with contiguous subspace in VLSI.

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2009 ieee circuits and systems international conference on testing and diagnosis, ICTD 09: Preface

2009 IEEE Circuits and Systems International Conference on T...

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2009 ieee circuits and systems international conference on testing and diagnosis, ICTD'09 2009年

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Chair's Introduction to 2009 ieee circuits and systems international conference on testing and diagnosis

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Journal of Electronic Science and Technology of China 2009年第4期7卷 289-289页

作者： Rueywen Liu General Chair of ICTD‘09 University of Notre Dame Notre Dame

Based on the recommendation of ICTD＇09 TPC members, this Special Issue of the Journal of Electronic Science ＆ Technology of China （JESTC） contained 22 high quality papers selected from the Proceedings of 2009 ieee circuits and systems international conference on testing and diagnosis （ICTD ＇09） which is fully sponsored by the ieee circuits and systems Society （CASS）, and is technically co-sponsored by the University of Electronic Science and Technology of China （UESTC）, the Chinese Institute of Electronics （CIE）, the China Instrument ＆ Control Society （CIS）, and organized by UESTC.

关键词： ieee this Chair’s Introduction to 2009 ieee circuits and systems international conference on testing and diagnosis

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Chairs Introduction to 2009 ieee circuits and systems international conference on testing and diagnosis

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Journal of Electronic Science and Technology 2009年第4期000卷

作者： Rueywen Liu University of Notre Dame Notre Dame Indiana 46556

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A Fault diagnosis Strategy for Analog circuits Based on Composite Neural Networks 3

A Fault Diagnosis Strategy for Analog Circuits Based on Comp...

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3rd international conference on Integrated circuits and Communication systems, ICICACS 2025

作者： Gui, Tengyue Xu, Haobin Xu, Weimin Chen, Lifeng China Tobacco Zhejiang Industrial Co. Ltd Zhejiang Hangzhou China

ISBN: (纸本)9798331508456

Analog circuit fault diagnosis is a technology that leads and has basic significance in electronic engineering and is vital in ensuring the maintainability and safety of electronic systems. This paper presents a fault diagnosis based on composite neural networks. The method used allows for the benefits of the feedforward neural network and the recurrent neural networks to be absorbed, while learning some static and dynamic characteristics of the behavior of the circuit. A precondition for success in this method is the introduction of an adjustable weighting schema used in weighting the importance of the components in the network. This enhances the accuracy and reliability of the diagnosis process. The experimental results show that the composite neural network diagnostic method has achieved remarkable performance for different types of faults, thus proving its superiority as a fault diagnosis method compared with the standard neural network model. In addition, this composite neural network possesses wide compatibility, which is well demonstrated in this paper, indicating that it can be applied in fault diagnosis for different circuit topologies and working environments. © 2025 ieee.

关键词： Analog circuits

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Comparative Study of Intelligent Algorithms in Fault diagnosis of Aviation Mechanical systems 1

Comparative Study of Intelligent Algorithms in Fault Diagnos...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Shen, Jingjing Comac Shanghai Aircraft Design and Research Institute Shanghai China

ISBN: (纸本)9798331529246

The key components such as engines and turbines in aviation machinery systems operate under extreme conditions such as high temperature, high pressure, and strong vibration, which can easily lead to complex faults and pose significant challenges to accurate and timely fault diagnosis. In response to this issue, this article compares and studies the application of various intelligent algorithms in the fault diagnosis of aviation mechanical systems. Firstly, typical intelligent algorithms such as Convolutional Neural Networks (CNN) are selected to extract features and preprocess the collected fault data;Subsequently, corresponding fault diagnosis models are constructed, and each model is trained and parameter optimized;Finally, use the model for diagnostic testing. The CNN algorithm achieved a diagnostic accuracy of 0.935000 samples after 100 iterations, with a training time of 80 seconds and an inference time of 22 milliseconds. In the above data conclusions, the CNN algorithm has shown advantages in the fault diagnosis of aviation mechanical systems, providing important references for improving the efficiency and reliability of fault diagnosis. © 2025 ieee.

关键词： Convolutional neural networks

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Foundational Techniques and Principles for VLSI testing using Machine Learning 6

Foundational Techniques and Principles for VLSI Testing usin...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Nalla, Sindhu Nagarajan, G. Dept of Electronic Engineering Satyabhama Institute of Science and Technology Chennai India Dept. of CSE Satyabhama institute of Science and Technology Chennai India

ISBN: (纸本)9798331522667

With the advent of Very-Large-Scale Integration (VLSI), testing has turned out to be much more troublesome as their size develops. Effective as these traditional VLSI testing methods are in simplification, implementing when it comes to modern circuits teeming strewn with millions of components is suitable and this is where they often falter. Machine learning (ML), as a recent sibling in the family of curtain technologies has opened doors to innovative solutions that may improve VLSI testing, ranging from faster detection and lower fake p/f ratio causing masking costs. A systematic survey of existing methods, techniques and frameworks that utilize ML for VLSI testing is presented in this paper. The survey has encompassed a wide set of ML paradigms, many under the umbrella of supervised/unsupervised/reinforcement learning to study their relevance in different VLSI testing phases (e.g., for fault diagnosis/test pattern generation/design-for-testability considerations). Lastly, the paper discusses how ML frameworks have to be integrated into established VLSI testing workflows and what possible opportunities/trade-offs with current incorporation practices. Performance Metrics: It also aims to evaluate key performance metrics, such as the accuracy/efficiency of a method or its scalability that may be thoroughly analyzed for an ML-based VLSI testing practice. The survey also points out vital gaps in the existing research and suggests possible ways to fill in these gaps by focusing on maybe developing more scalable, explainable & universally applicable ML models for VLSI testing. In addition, by organizing the vast body of work in this area and providing insights into how different approaches have been employed towards achieving specific testing objectives, it is envisaged that researchers and practitioners working on VLSI test research using machine learning will find a useful resource for advancing their contributions. © 2025 ieee.

关键词： Design-for-Testability Fault diagnosis Machine Learning Test Pattern Generation VLSI testing

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Advancing IC Fault Localization and diagnosis Through Machine Learning

Advancing IC Fault Localization and Diagnosis Through Machin...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Rajasekar, P. Kumar, Alok Prema Latha, V. Jayapal, N. Santhoshi, Boddu Soundarya Karthik, K. Sivaramkumar, Mathiyalagan School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India Guru Gobind Singh Educational Society Department of Electrical and Electronics Engineering Technical Campus Jharkhand Bokaro827013 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Trichy621215 India S.R.K.R Engineering College Department of Mechanical Engineering Andhra Pradesh Bhimavaram534204 India Velammal College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Madurai625009 India Saveetha University Department of Biosciences Chennai602105 India

ISBN: (纸本)9798331509828

Fault localization and diagnosis of Integrated circuits (ICs) are essential for maintaining dependability in contemporary electronic systems. This research presents a sophisticated system utilizing Data Augmentation, Wrapper-based feature selection, and deep learning to improve the precision of defect identification and localization. Data Augmentation methods, such as synthetic fault data generation via SMOTE, rectify dataset imbalances, facilitating the model's generalization across various fault scenarios. Wrapper-based feature selection utilizes Recursive Feature Elimination (RFE) to discern the most significant characteristics, including timing delays, voltage levels, and signal patterns, so diminishing dimensionality while preserving essential information. The framework is centred around a deep learning model that integrates Convolutional Neural Networks (CNNs) for spatial fault detection and Long Short-Term Memory (LSTM) networks for temporal analysis. Experimental findings indicate a notable enhancement in fault localization precision, less false positives, and improved model interpretability. This method offers a scalable and resilient alternative for integrated circuit defect diagnosis, tackling the increasing complexity of contemporary electronic systems. © 2025 ieee.

关键词： Convolutional neural networks

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Diabetic Retinopathy diagnosis with Deep Learning for Early Detection and Staging 3

Diabetic Retinopathy Diagnosis with Deep Learning for Early ...

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3rd international conference on Integrated circuits and Communication systems, ICICACS 2025

作者： Santosh Kumar, S. Lakshmi, S Anbu Bhuwaneshwari, M. Prakasha, G. Raksha, P.R. Rastogi, Shailesh Bengaluru India Sri Venkateshwara College of Engineering Department of E&ce Bengaluru India

ISBN: (纸本)9798331508456

Diabetic retinopathy (DR) remains a leading cause of blindness among individuals with diabetes, emphasizing the need for early detection and precise staging to ensure effective management and treatment. Traditionally, diagnosing DR has relied on manual analysis of retinal images a process that is not only time-intensive but also vulnerable to human error. This study presents a novel approach leveraging deep learning techniques for the automated detection and staging of diabetic retinopathy through retinal fundus imaging. By employing a Convolutional Neural Network (CNN), the model classifies images across the spectrum of DR stages, from no DR to the proliferative stage. Training was conducted using a substantial dataset of retinal fundus images, enhanced by data augmentation to bolster the model's resilience. The results indicate that the proposed CNN significantly outperforms traditional image processing methods, achieving notable gains in diagnostic accuracy, sensitivity, and specificity. Moreover, the model's ability to reliably classify DR severity demonstrates its potential for clinical use, facilitating early diagnosis and personalized treatment planning. These findings underscore the promise of deep learning as a transformative tool in diabetic retinopathy care, offering healthcare professionals a dependable and efficient aid for decision-making and vision preservation. © 2025 ieee.

关键词： Convolutional neural networks

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Power Line Noise Acquisition and Analysis System for the 0-50MHz Frequency Band

Power Line Noise Acquisition and Analysis System for the 0-5...

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2025 international conference on Electrical Automation and Artificial Intelligence, ICEAAI 2025

作者： Zhu, Enguo Li, Ran Ren, Yi Li, Xuan China Electric Power Research Institute Metrology Dept. Beijing China

ISBN: (纸本)9798331506797

In this paper, a design scheme of power line noise acquisition and analysis equipment is proposed, which aims to effectively monitor and analyze noise data of power line noise, the hardware of the system mainly includes filter circuits, analog front-ends circuits, ADC sampling circuits, DAC circuits, digital processing circuits, etc., for the purpose of sampling high-frequency level data of power line noise. The system software processes this level data through steps such as decomposition and FFT (Fast Fourier Transform) conversion to analyze the power line noise signals. Then, by employing algorithm models, it quickly categorizes, displays, and plays back the noise frequency, intensity, and type of the interference. Thus, it provides a stable noise source for the design of power line communication systems' noise resistance performance and power consumption. © 2025 ieee.

关键词： Communication testing Fast Fourier Transform Nosie collection Power Line Communication

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