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Convolutional neural network for Printed circuit Board Verification

Convolutional Neural Network for Printed circuit Board Verif...

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distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International conference on

作者： Namgiri Snehith Swarna Bodempudi K. Rohith Sai M Satya Vardhani Syed Shameem S.D.V. Sai Anudeep Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram India

One of the most important steps in the manufacturing of electronic goods is the creation of printed circuit boards (PCB). An electronic device's PCBs are the first stage in production, so even a small error there could cause serious flaws in the finished product. Finding and identifying flaws in PCBs is crucial given the size and demand of the PCB industry. In this paper, we propose a reference comparison method for PCB defect localization and classification that employs a template and a test picture and integrates machine learning and image processing techniques. We introduce a solution pipeline that works in three stages: Subtraction of Images where the test images are subtracted to generate a mask that highlights the regions of defect on the test image, Contour search to find the contour of the defects from the generated test image, and Model Inference where the contours found above are extracted from the images and fed into our machine learning model which then classifies those defects in the test image. We train the machine learning model, in our case, a Convolutional neural network (CNN) that forms the artificially intelligent (AI) part of our solution which can identify the type of defect present given the image of the defect as input. Our experimental findings demonstrate that, compared to other traditional methods utilized for this work, our model diagnoses the afflicted parts with 97% accuracy.

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Mercury: A Simple Transport Layer Scheduler to Accelerate distributed DNN Training 41

Mercury: A Simple Transport Layer Scheduler to Accelerate Di...

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41st IEEE conference on Computer Communications (IEEE INFOCOM)

作者： Duan, Qingyang Wang, Zeqin Xu, Yuedong Liu, Shaoteng Wu, Jun Fudan Univ Sch Informat Sci & Technol Shanghai Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China Fudan Univ Sch Comp Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9781665458221

Communication scheduling is crucial to improve the efficiency of training large deep learning models with data parallelism, in which the transmission order of layerwise deep neural network (DNN) tensors is determined for a better computation-communication overlap. Prior approaches adopt tensor partitioning to enhance the priority scheduling with finer granularity. However, a startup time slot inserted before each tensor partition will neutralize this scheduling gain. Tuning the optimal partition size is difficult and the application-layer solutions cannot eliminate the partitioning overhead. In this paper, we propose Mercury, a simple transport layer scheduler that does not partition the tensors, but moves the priority scheduling to the transport layer at the packet granularity. The packets with the highest priority in the Mercury buffer will be transmitted first. Mercury achieves the near-optimal overlapping between communication and computation. It leverages immediate aggregation at the transport layer to enable the coincident gradient push and parameter pull. We implement Mercury in MXNet and conduct comprehensive experiments on five DNN models in an 8-node cluster with 10Gbps Ethernet. Experimental results show that Mercury can achieve about 1:18 similar to 2:18x speedup over vanilla MXNet, and 1:08 similar to 2:04x speedup over the state-of-the-art tensor partitioning solution.

关键词： Training Deep learning Tensors Processor scheduling Computational modeling Bandwidth Parallel processing

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Multi-scale Frequency Attention Fusion network for Optical and SAR Image Registration

Multi-scale Frequency Attention Fusion Network for Optical a...

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International conference on Signal processing Proceedings (ICSP)

作者： Xin Hu Yan Wu Zhikang Li Zhifei Yang Ming Li School of Electronic Engineering Xidian University Xi’an China National Key Laboratory of Radar Signal Processing Xidian University Xi’an China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Image registration is a prerequisite for remote sensing image fusion and classification, and registration accuracy affects the performance of these tasks. However, there are significant nonlinear radiation differences between optical and synthetic aperture radar (SAR) images and speckle noise in SAR, which limits the ability of pure convolutional neural networks (CNNs) to obtain modality-shared features. To solve these problems, this paper proposes a Multi-scale Frequency Attention Fusion network for Optical and SAR image Registration (MFAF-OS-RegNet), which includes a Multi-scale Frequency Channel Attention Fusion (MFCAF) module and a Momentum Contrastive Learning (MCL) strategy. Firstly, we extract repeatable keypoints distributed on the edges and develop a novel MFCAF module to obtain modality-shared features. In the MFCAF module, multi-scale feature fusion is used to improve the positioning accuracy of matching points, and frequency channel attention is used to mine more favorable features. Secondly, we design a MCL strategy to reduce the interference of speckle noise in network training. The momentum teacher corrects handcrafted labels by learning the essential characteristics of the data for robust matching. Finally, we filter outliers and estimate transformation parameters based on the initial matching correspondence output by MFAF-OS-RegNet. Experimental results on a publicly available OS-dataset show that the proposed method outperforms other state-of-the-art methods.

关键词： Optical filters Integrated optics Optical fiber networks Optical imaging Radar polarimetry Adaptive optics Optical sensors Optical signal processing Nonlinear optics Synthetic aperture radar

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Towards Communication-Efficient distributed Background Subtraction 14th

Towards Communication-Efficient Distributed Background Subtr...

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14th Asian conference on Intelligent Information and Database Systems (ACIIDS)

作者： Hung Ngoc Phan Synh Viet-Uyen Ha Phuong Hoai Ha UiT Arctic Univ Norway Tromso Norway Ho Chi Minh Int Univ Vietnam Natl Univ Ho Chi Minh City Vietnam

ISBN: (纸本)9789811982330;9789811982347

Road traffic monitoring is one of the essential components in data analysis for urban air pollution prevention. In road traffic monitoring, background subtraction is a critical approach where moving objects are extracted via facilitating motion information of interest to static surroundings, known as backgrounds. To work with various contextual dynamics of nature scenes, supervised models of background subtraction aim to solve a gradient-based optimization problem on multi-modal sequences of videos by training a convolutional neural network. As video datasets are scaling up, distributing the model learning on multiple processing elements is a pivotal technique to leverage the computational power among various devices. However, one of major challenges in distributed machine learning is communication overhead. This paper introduces a new communication-efficient distributed framework for background subtraction (CEDFrame), alleviating the communication overhead in distributed training with video data. The new framework utilizes event-triggered communication on a ring topology among workers and the Partitioned Globally Address Space (PGAS) paradigm for asynchronous computation. Through the new framework, we investigate how training a background subtraction tolerates the tradeoffs between communication avoidance and accuracy in model learning. The experimental results on NVIDIA DGX-2 using the CDnet-2014 dataset show that the new framework can reduce the communication overhead by at least 94.71% while having a negligible decrement in testing accuracy (at most 2.68%).

关键词： Traffic monitoring distributed machine learning Asynchronous computation Parallel computing Background subtraction Change detection

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Machine Learning: The Driving Force Behind Intelligent Systems and Predictive Analytics

Machine Learning: The Driving Force Behind Intelligent Syste...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International conference on

作者： Deepali Virmani Md. Atheeq Sultan Ghori Nishant Tyagi R. P. Ambilwade Priyanka Rajesh Patil M. K. Sharma Vivekananda Institute of Professional Studies-Technical Campus School of Engineering and Technology Department of Computer Science and Engineering Telangana University Nizamabad Telangana State Management G.L. Bajaj Institute of Management and Research Greater Noida Computer Science National Defence Academy Pune Department of Electronics and Telecommunication Engineering Prof Ram Meghe College of Engineering and Management Badnera Mathematics Chaudhary Charan Singh University Meerut Uttar Pradesh India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

This technical analysis explores the profound impact of machine learning on intelligent systems and predictive analytics. It examines algorithmic fundamentals and explores models such as linear regression, support vector machines and neural networks, which are the pillars of supervised learning. The research explores practical applications in natural language processing, where advanced neural network architectures such as Transformers redefine, language understanding and computer vision using convolutional neural networks for image recognition and object recognition. In addition, the analysis covers the basic steps of data processing, including data cleaning, transformation and refinement, as well as feature design and model optimization. In addition, the research addresses the challenges of machine learning, emphasizing the importance of reducing bias and ethical considerations. The study concludes with a look into the future of machine learning, envisioning advances in quantum computing to solve complex problems, combinatorial learning for distributed data problems, and synthetic data generation as a solution to data protection problems. This comprehensive study provides an overview of the current state of machine learning, challenges and promising developments in intelligent system innovation and predictive analytics.

关键词： Technological innovation Ethics Computer vision Quantum computing Computational modeling neural networks Force

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Simulation of Effectiveness Evaluation Model in Complex Dataset Prediction Based on neural network Algorithm

Simulation of Effectiveness Evaluation Model in Complex Data...

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Internet of Things, Robotics and distributed Computing (ICIRDC), International conference on

作者： Yujin Chen Guangdong Baiyun University Guangzhou China

With the advent of the big data era, various industries have generated massive datasets. Large scale time series datasets, as important and complex data objects, bring opportunities and challenges to various industries. By analyzing time series data, valuable information such as periodicity, basic trends, and association rules can be mined, and future trends can also be predicted. Time series prediction is a typical time series analysis task, which is of great significance for assisting decision-making, resource allocation, and taking stop loss measures in advance. Different application scenarios have different processing requirements for time series data, and these problems cannot be effectively solved using traditional time series methods. This article proposes an effectiveness evaluation model for time series prediction based on deep neural networks (DNN). Firstly, the original data is decomposed, and the decomposed components are input into the model as input vectors for fitting prediction. Finally, the fitting prediction results of each component are added together to obtain the final prediction result. The simulation results show that the model proposed in this article has higher accuracy and stability compared to traditional prediction models, and meets the prediction requirements of the model.

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Equipping Federated Graph neural networks with Structure-aware Group Fairness

Equipping Federated Graph Neural Networks with Structure-awa...

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IEEE International conference on Data Mining (ICDM)

作者： Nan Cui Xiuling Wang Wendy Hui Wang Violet Chen Yue Ning Stevens Institute of Technology 1 Castle Point Terrace Hoboken New Jersey USA

Graph neural networks (GNNs) are used for graph data processing across various domains. Centralized training of GNNs often faces challenges due to privacy and regulatory issues, making federated learning (FL) a preferred solution in a distributed paradigm. However, GNNs may inherit biases from training data, causing these biases to propagate to the global model in distributed scenarios. To address this issue, we introduce $\mathrm{F}^distributed$GNN, a Fair Federated Graph neural network, to enhance group fairness. Recognizing that bias originates from both data and algorithms, $\mathrm{F}^distributed$GNN aims to mitigate both types of bias under federated settings. We offer theoretical insights into the relationship between data bias and statistical fairness metrics in GNNs. Building on our theoretical analysis, $\mathrm{F}^distributed$GNN features a fairness-aware local model update scheme and a fairness-weighted global model update scheme, considering both data bias and local model fairness during aggregation. Empirical evaluations show $\mathrm{F}^distributed$GNN outperforms SOTA baselines in fairness and accuracy.

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A Semantic Segmentation Algorithm for distributed Energy Data Storage Optimization based on neural networks 7

A Semantic Segmentation Algorithm for Distributed Energy Dat...

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7th IEEE International conference on Smart Cloud, SmartCloud 2022

作者： Mao, Dong Li, Zhongxu Chen, Zuge Rao, Hanyu Zhang, Jiuding Liu, Zehan New Technology Research Center State Grid Zhejiang Electric Power Corporation Information & Telecommunication Branch Hangzhou China State Grid Zhejiang Electric Power Company Internet Department Hangzhou China Hangzhou Innovation Institute of Beihang University The Research Center of Big Data and Computational Intelligence Hangzhou China

ISBN: (数字)9781665451796

ISBN: (纸本)9781665451796

There are many kinds of energy data, how to realize unified storage, processing and sharing of energy data is a big problem. As the national energy data center, State Grid aims to build a database that can store distributed heterogeneous asynchronous energy data. The storage of image files in the big energy database will take up a lot of space in the system, but not all parts of the image are needed. Therefore, it is very necessary to accurately segment the effective area of the image to store it so as to achieve the purpose of data compression. This paper proposes the Attention U-Net framework, which combines the traditional semantic segmentation network U-Net with the Attention module to focus on the region of interest in the image, emphasize foreground information, and suppress background information. The results show that compared with U-Net, the accuracy is improved by 1.77% and after the segmentation is completed, each image saves an average of 2MB of storage space. © 2022 IEEE.

关键词： Semantic Segmentation

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P³: distributed Deep Graph Learning at Scale 15

<i>P</i><SUP>3</SUP>: Distributed Deep Graph Learning at Sca...

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USENIX Annual Technical conference / 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI)

作者： Gandhi, Swapnil Iyer, Anand Padmanabha Microsoft Res Redmond WA 98052 USA

ISBN: (纸本)9781939133229

Graph neural networks (GNNs) have gained significant attention in the recent past, and become one of the fastest growing subareas in deep learning. While several new GNN architectures have been proposed, the scale of real-world graphs-in many cases billions of nodes and edges-poses challenges during model training. In this paper, we present P-3, a system that focuses on scaling GNN model training to large real-world graphs in a distributed setting. We observe that scalability challenges in training GNNs are fundamentally different from that in training classical deep neural networks and distributed graph processing;and that commonly used techniques, such as intelligent partitioning of the graph do not yield desired results. Based on this observation, P-3 proposes a new approach for distributed GNN training. Our approach effectively eliminates high communication and partitioning overheads, and couples it with a new pipelined push-pull parallelism based execution strategy for fast model training. P-3 exposes a simple API that captures many different classes of GNN architectures for generality. When further combined with a simple caching strategy, our evaluation shows that P-3 is able to outperform existing state-of-the-art distributed GNN frameworks by up to 7x.

关键词： Graph neural networks

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FPGA Accelerated Convolutional neural network for Detection of Cardiac Arrhythmia

FPGA Accelerated Convolutional Neural Network for Detection ...

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International conference on VLSI Systems, Architecture, Technology and Applications (VLSI-SATA)

作者： Soumyashree Mangaraj Pawan Oraon Samit Ari Ayas Kanta Swain Kamalakanta Mahapatra Department of Electronics and Communication Engineering National Institute of Technology Rourkela India

ISBN: (数字)9798350362268

ISBN: (纸本)9798350362275

Electrocardiogram signals (ECG) are vital features to identify a healthy body; diagnosing cardiovascular diseases (CVDs) automatically with resource constraint devices at the edge has caught a significant attention in the rapid growth of smart health-care system. The rapidly developing smart world of IoT demands for low latency, less complex, and energy efficient architectures, and their deployment in real time scenarios. Field Programmable Gate Arrays (FPGAs) is a trading distributed embedded platform proving parallel processing, and flexibility in re-design with its re-configurable advancements. This work demonstrates a spilt 2D convolutional neural network (CNN) performing ECG beat classification on Xilinx's ZCU 104 FPGA board. The convolution has been carried out depth-wise, and performing a channel-wise addition of the half-split feature maps, so as to minimize the resource utilization by reducing the trainable parameters. A customized FPGA IP has been generated using Vitis High Level Synthesis (HLS) tool, and is implemented on ZCU 104 FPGA for the proposed less complex light-weight model. The design has achieved an accuracy of 98.64% in software, and consumed a total power of 4.177W with a latency of 2.19E8ns in classifying arrhythmia reference to Association for the Advancement of Medical Instrumentation-AAMI EC57 standard.

关键词： Convolution Arrhythmia Systems architecture Electrocardiography Very large scale integration Software Convolutional neural networks

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