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IS and T International Symposium on Electronic Imaging: Machine Learning for Scientific Imaging, MLSI 2022

作者： Guo, Zhen Song, Jung Ki Barbastathis, George Glinsky, Michael E. Vaughan, Courtenay T. Larson, Kurt W. Alpert, Bradley K. Levine, Zachary H. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Centre Singapore138002 Singapore Sandia National Laboratory AlbuquerqueNM87123 United States Applied and Computational Mathematics Division National Institute of Standards and Technology Boulder CO80305 United States Quantum Measurement Division National Institute of Standards and Technology GaithersburgMD20899 United States

Limited-angle X-ray tomography reconstruction is an ill-conditioned inverse problem in general. Especially when the projection angles are limited and the measurements are taken in a photon-limited condition, reconstructions from classical algorithms such as filtered backprojection may lose fidelity and acquire artifacts due to the missing-cone problem. To obtain satisfactory reconstruction results, prior assumptions, such as total variation minimization and nonlocal image similarity, are usually incorporated within the reconstruction algorithm. In this work, we introduce deep neural networks to determine and apply a prior distribution in the reconstruction process. Our neural networks learn the prior directly from synthetic training samples. The neural nets thus obtain a prior distribution that is specific to the class of objects we are interested in reconstructing. In particular, we used deep generative models with 3D convolutional layers and 3D attention layers which are trained on 3D synthetic integrated circuit (IC) data from a model dubbed CircuitFaker. We demonstrate that, when the projection angles and photon budgets are limited, the priors from our deep generative models can dramatically improve the IC reconstruction quality on synthetic data compared with maximum likelihood estimation. Training the deep generative models with synthetic IC data from CircuitFaker illustrates the capabilities of the learned prior from machine learning. We expect that if the process were reproduced with experimental data, the advantage of the machine learning would persist. The advantages of machine learning in limited angle X-ray tomography may further enable applications in low-photon nanoscale imaging. © 2022, Society for Imaging Science and Technology

关键词： Photons

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Object Detection in Autonomous Driving Systems Using YOLOv5 and Kalman Filtering 3

Object Detection in Autonomous Driving Systems Using YOLOv5 ...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Mohamed, Aezeden Nii, Rainier Binga, Kipas Lakhanpal, Sorabh Nagaveni, P. Kumar, T. Rajesh School of Mechanical Engineering Png University of Technology Lae Papua New Guinea School of Mathematics and Computer Science Ng University of Technology Lae Papua New Guinea Angau Memorial General Hospital Lae Papua New Guinea Lovely Professional University Punjab Phagwara India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331542375

This paper describes an enhanced approach to perform object detection and tracking for the use of autonomous driving systems by using YOLOv5 model for the detection process, and Kalman filtering for tracking process. The objective is to overcome the difficult tasks of identifying and determining the position of several objects in the context of rapidly changing and diverse conditions that define urban roads, highways, parking lots and in adverse meteorological conditions. For object detection, the relatively accurate and fast YOLOv5 is applied, and for object tracking, we select Kalman filtering based on its stability in cases of occlusions or other changes between frames. The system is tested on several datasets including the urban, highway, parking lot, and adverse weather conditions and measures such as precision rate, recall rate, F1 rate and MOTA, and computational time are used to measure the performance of the algorithm. The findings show that the presented approach gains a high detection rate, consistent object tracking, and sufficiently fast processing, which can be effective for self-driven vehicles. Nevertheless, it has a slightly poor performance in low visibility complicated traffic condition, which is common in real-world application. [This work demonstrates how YOLOv5 and Kalman filtering can be used in autonomous driving and notes that such an approach provides a stable method for object detection and tracking in various conditions for autonomous driving]. In future related improvements, the performance of the developed system will be optimized for operation at even more severe conditions. © 2025 IEEE.

关键词： Kalman filters

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Performance Analysis of Distributed Filtering under Mismatched Noise Covariances

arXiv

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arXiv 2024年

作者： Lyu, Xiaoxu Wen, Guanghui Shi, Ling Duan, Peihu Duan, Zhisheng The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The Laboratory of Security Operation and Control for Intelligent Autonomous Systems Department of Systems Science School of Mathematics Southeast University Nanjing211189 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China

This paper systematically investigates the performance of consensus-based distributed filtering under mismatched noise covariances. First, we introduce three performance evaluation indices for such filtering problems, namely the standard performance evaluation index, the nominal performance evaluation index, and the estimation error covariance. We derive difference expressions among these indices and establish one-step relations among them under various mismatched noise covariance scenarios. We particularly reveal the effect of the consensus fusion on these relations. Furthermore, the recursive relations are introduced by extending the results of the one-step relations. Subsequently, we demonstrate the convergence of these indices under the collective observability condition, and show this convergence condition of the nominal performance evaluation index can guarantee the convergence of the estimation error covariance. Additionally, we prove that the estimation error covariance of the consensus-based distributed filter under mismatched noise covariances can be bounded by the Frobenius norms of the noise covariance deviations and the trace of the nominal performance evaluation index. Finally, the effectiveness of the theoretical results is verified by numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Wiener filtering

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Locating highly connected clusters in large networks with HyperLogLog counters

arXiv

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arXiv 2021年

作者： Weedage, Lotte Litvak, Nelly Stegehuis, Clara Department of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands

In this paper we introduce a new method to locate highly connected clusters in a network. Our proposed approach adapts the HYPERBALL algorithm [8] to localize regions with a high density of small subgraph patterns in large graphs in a memory-efficient manner. We use this method to evaluate three measures of subgraph connectivity: conductance, the number of triangles, and transitivity. We demonstrate that our algorithm, applied to these measures, helps to identify clustered regions in graphs, and provides good seed sets for community detection algorithms such as PageRank-Nibble. We analytically obtain the performance guarantees of our new algorithms, and demonstrate their effectiveness in a series of numerical experiments on synthetic and real-world networks. hyperloglog, probabilistic counting, network clustering © 2021, CC BY.

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Using Sparse Autoencoders to Perform Blind Source Separation of High-Density Myoelectric Signal

Using Sparse Autoencoders to Perform Blind Source Separation...

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IEEE Workshop on Machine Learning for Signal Processing

作者： Marcelo Ramos Romano Leonardo Tomazeli Duarte Leonardo Abdala Elias Neural Engineering Research Laboratory Center for Biomedical Engineering University of Campinas Campinas SP Brazil School of Applied Sciences University of Campinas Limeira SP Brazil Department of Electronics and Biomedical Engineering School of Electrical and Computer Engineering University of Campinas Campinas SP Brazil

A recent approach to address the blind source separation (BSS) problem relies on autoencoders with a discriminator as a generative adversarial network (GAN). However, this model is difficult to train due to the lack of metrics that ensure the convergence of a GAN. In this study, we try to provide an alternative solution by replacing the discriminator with appropriate regularization functions based on prior information on the sources. We tested this solution in the high-density electromyogram (HD-EMG) separation problem for a scenario with synthetic but realistic data and compared it with a classic algorithm from the literature. An interesting feature of the proposal is that it can be easily adapted for other source separation problems. Moreover, although the performance is slightly worse than the state-of the-art HD-EMG algorithm, which considers very detailed information on the problem, our proposal provides good results with a mean rate of agreement of 91.88% between the true and estimated spikes.

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JARVIS-Leaderboard:a large scale benchmark of materials design methods

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npj Computational Materials 2024年第1期10卷 2280-2296页

作者： Kamal Choudhary Daniel Wines Kangming Li Kevin F.Garrity Vishu Gupta Aldo H.Romero Jaron T.Krogel Kayahan Saritas Addis Fuhr Panchapakesan Ganesh Paul R.C.Kent Keqiang Yan Yuchao Lin Shuiwang Ji Ben Blaiszik Patrick Reiser Pascal Friederich Ankit Agrawal Pratyush Tiwary Eric Beyerle Peter Minch Trevor David Rhone Ichiro Takeuchi Robert B.Wexler Arun Mannodi-Kanakkithodi Elif Ertekin Avanish Mishra Nithin Mathew Mitchell Wood Andrew Dale Rohskopf Jason Hattrick-Simpers Shih-Han Wang Luke E.K.Achenie Hongliang Xin Maureen Williams Adam J.Biacchi Francesca Tavazza Material Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD20899USA Department of Materials Science and Engineering University of Toronto27 King’s College CirTorontoONCanada Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL 60208USA Lewis-Sigler Institute for Integrative Genomics Princeton UniversityPrincetonNJ 08544USA Ludwig Institute for Cancer Research Princeton UniversityPrincetonNJ 08544USA Department of Physics and Astronomy West Virginia UniversityMorgantownWV 26506USA Materials Science and Technology Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Center for Nanophase Materials Science Oak Ridge National LaboratoryOak RidgeTN 37831USA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Computer Science and Engineering Texas A&M UniversityCollege StationTX 77843USA Globus University of ChicagoChicagoIL 60637USA Data Science and Learning Division Argonne National LabLemontIL 60439USA Institute of Nanotechnology Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Institute of Theoretical Informatics Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Department of Chemistry and Biochemistry and Institute for Physical Science and Technology University of MarylandCollege ParkMD 20742USA Department of Physics Applied Physics and AstronomyRensselaer Polytechnic InstituteTroyNY12180USA Department of Materials Science and Engineering University of MarylandCollege ParkMD20742USA Department of Chemistry and Institute of Materials Science and Engineering Washington University in St.LouisSt.LouisMO63130USA School of Materials Engineering Purdue UniversityWest LafayetteIN47907USA Department of Mechanical Science and Engineering University of Illinois Urbana-ChampaignUrbanaIllinois 61801USA Materials Research Laboratory University of Illinois Urbana-ChampaignUrbanaIL 61801USA Theore

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many *** science,in particular,encompasses a variety of experimental and theoretical approaches that require careful *** efforts have been developed previously to mitigate these ***,a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still *** work introduces JARVIS-Leaderboard,an open-source and community-driven platform that facilitates benchmarking and enhances *** platform allows users to set up benchmarks with customtasks and enables contributions in the form of dataset,code,and meta-data *** cover the following materials design categories:Artificial Intelligence(AI),Electronic Structure(ES).

关键词： rigorous perfect enable

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Protein engineering in the deep learning era

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mLife 2024年第4期3卷 477-491页

作者： Bingxin Zhou Yang Tan Yutong Hu Lirong Zheng Bozitao Zhong Liang Hong Institute of Natural Sciences Shanghai Jiao Tong UniversityShanghaiChina Shanghai National Center for Applied Mathematics(SJTU center) Shanghai Jiao Tong UniversityShanghaiChina School of Information Science and Engineering East China University of Science and TechnologyShanghaiChina Shanghai Artificial Intelligence Laboratory ShanghaiChina School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghaiChina Department of Cell and Developmental Biology University of Michigan Medical SchoolAnn ArborMichiganUSA School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghaiChina Zhangjiang Institute for Advanced Study Shanghai Jiao Tong UniversityShanghaiChina

Advances in deep learning have significantly aided protein engineering in addressing challenges in industrial production,healthcare,and environmental *** review frames frequently researched problems in protein understanding and engineering from the perspective of deep *** provides a thorough discussion of representation methods for protein sequences and structures,along with general encoding pipelines that support both pre‐training and supervised learning *** summarize state‐of‐the‐art protein language models,geometric deep learning techniques,and the combination of distinct approaches to learning from multi‐modal biological ***,we outline common downstream tasks and relevant benchmark datasets for training and evaluating deep learning models,focusing on satisfying the particular needs of protein engineering applications,such as identifying mutation sites and predicting properties for candidates'virtual *** review offers biologists the latest tools for assisting their engineering projects while providing a clear and comprehensive guide for computer scientists to develop more powerful solutions by standardizing problem formulation and consolidating data *** research can foresee a deeper integration of the communities of biology and computer science,unleashing the full potential of deep learning in protein engineering and driving new scientific breakthroughs.

关键词： artificial intelligence geometric deep learning protein engineering protein language model synthetic biology

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Battery Monitoring System with IoT Integration

Battery Monitoring System with IoT Integration

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Advancements in electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： Sanjith Krishna S Premchand V H Shri Ram B Sakthi Satchidhanandan S Anitha N Hariharan S.V B.E. Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India B.E. Computer Science Engineering Kumaraguru College of Technology Coimbatore India B.E. Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India Department of Mathematics Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Battery Monitoring Systems (BMS) have played a pivotal role in our trajectory towards greener and sustainable living, especially in our commutation methods. The rise of electric cars and two-wheelers has put renewed focus on high density energy storage and their safety. Existing BMS’s often provide the bare minimum in terms of protection and regulation of energy, often falling short of certain safety standards or lack thereof. Within this research, we task ourselves to identify and provide an alternative, viable, cost-effective method to ensure safe monitoring and especially safe debugging of faulty battery packs. We employ off-the-shelf components and integrate them into existing BMS to provide an easier way for debugging via Internet of Things (IoT) to allow local technicians access to the same information as the Original Equipment Manufacturer (OEM) themselves. Therefore, our aim is to provide a platform for people to troubleshoot their BMS’ on electric vehicles and perform predictive maintenance via remote monitoring, providing the same flexibility of a regular combustion car.

关键词： Temperature measurement Buildings Debugging Programming Electric vehicles Batteries State of charge Internet of Things Remote monitoring Predictive maintenance

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computer-aided system for extending the performance of diabetes analysis and prediction 7

Computer-aided system for extending the performance of diabe...

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7th International Conference on Software engineering and computer Systems and 4th International Conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021

作者： Murad, Saydul Akbar Azmi, Zafril Rizal M Hakami, Zaid Hafiz Prottasha, Nusrat Jahan Kowsher, Md Universiti Malaysia Pahang Faculty of Computing Malaysia Jazan University Faculty of Computer Science and Information Technology Jazan Saudi Arabia Daffodils International University Department of Computer Science and Engineering Bangladesh Noakhali Science and Technology University Department of Applied Mathematics Bangladesh

ISBN: (纸本)9781665414074

Every year, diabetes causes health difficulties for hundreds of millions of individuals throughout the world. Patients' medical records may be utilized to quantify symptoms, physical characteristics, and clinical laboratory test data, which may then be utilized to undertake biostatistics analysis to uncover patterns or characteristics that are now undetected. In this work, we have used six machine learning algorithms to give the prediction of diabetes patients and the reason for diabetes are illustrated in percentage using pie charts. The machine learning algorithms used to predict the risks of Type 2 diabetes. User can self-assess their diabetes risk once the model has been trained. Based on the experimental results in AdaBoost Classifier's, the accuracy achieved is almost 98 percent. © 2021 IEEE.

关键词： Forecasting

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An AI Architecture for Power MOS Rds-on-Driven Lifetime Estimation

An AI Architecture for Power MOS Rds-on-Driven Lifetime Esti...

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Smart Systems Integration (SSI)

作者： Carmelo Pino Francesco Rundo Concetto Spampinato Sebastiano Battiato ADG Power and Discretes R&D Division STMicroelectronics Catania Italy Department of Electrical Electronics and Computer Engineering PerCeiVe Lab Group University of Catania Catania Italy Department of Mathematics and Computer Science IP-LAB Group University of Catania Catania Italy

The components lifetime estimation is one of the main challenge in all engineering process specially in power electronics. The problem involves several domains of interest and is particularly felt, because, the estimation of the residual life with an acceptable degree of confidence, allows to carry out preventive measures in order to avoid the consequences of the malfunction and failure of the device at the end of its life. Considering a Power MOS, the distinctive element for understanding the end of life of this component can be obtained by observing the $Rds_{on}$ curve during its life. The $Rds_{on}$ curve works in an operating range during the correct life cycle, this curve exceeds a certain threshold when the device begins to have the first signs of degradation. The proposed method allows to evaluate the variation of the $Rds_{on}$ by making predictions for understand when the device may stop working well. The method is based on a deep learning model trained with a dataset containing the cycle of life of several Power MOS. The pipeline is based on Artificial Intelligence techniques with regularization of the Jacobian of the gradient, designed to compensate for the inevitable measurement and conversion noises present in the system based on ST MCU. The experiments concern predictions of the $Rds_{on}$ at 10, 15 and 20 steps and allow to obtain a good degree of accuracy for predicting the $\mathbf{RDS}_{-}\mathbf{on}$ at least 20 samples before. This allows to guarantee a preventive intervention when the prediction exceed the threshold considered for the correct operating range. The proposed pipeline allows to robustly characterize the threshold voltage of the device by means of a simple acquisition of voltages and currents at power-on, being able to monitor the evolution of this voltage over time and each time the device is used. The obtained results and the cross-validation performances are very promising and allow us to state that the proposed method

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