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5th IEEE Middle East and Africa Conference on Biomedical Engineering, MECBME 2020

作者： El Halabi, Nashaat Abi Zeid Daou, Roy Achkar, Roger Hayek, Ali Boercsoek, Josef American University of Science and Technology Department of Computer and Communications Engineering Faculty of Engineering Beirut Lebanon Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon American University of Science and Technology Department of Computer and Communications Engineering Faculty of Engineering Beirut Lebanon Computer Science University of Kassel Department of Computer Architecture and System Programming Faculty of Electrical Engineering Germany Computer Science University of Kassel Department of Computer Architecture and System Programming Faculty of Electrical Engineering Germany

ISBN: (纸本)9781728123585

Epilepsy is a medical problem that tackles lots of patients. It limits the life activity of such patients due to the seizures that occur anytime and anywhere. Thus, creating a monitoring system that could make their life easier and allow them to perform daily life activities as safe as possible has been studied. This paper is a continuity for a project where a system monitors epileptic patients using a set of wearable sensors connected to several processing units, the main sensors are Electroencephalogram (EEG) for the electrical activity of the brain, electrocardiogram (ECG) for the electrical activity of the heart, and accelerometers to monitor the posture of the patient, features extracted from EEG and ECG signals are used for prediction/detection of seizures. Thus, the objective of this paper is to present a comparative study in order to choose the appropriate classification method for seizure prediction and detection. Five classification methods were studied: Decision Trees, Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Ensemble Learning. For each of the classification methods, the accuracy, the specificity, the sensitivity, the precision, the false detection rate and the Fmeasure were studied and compared. The results for seizure prediction system show that the highest accuracy, specificity and F-Measure, and lowest false detection rate were achieved with Bagged Trees Ensemble Learning, the highest sensitivity and precision were achieved with Quadratic Support Vector Machine. However, for seizure detection system, the highest accuracy and F-Measure were achieved with Linear Support Vector, the highest sensitivity and specificity were achieved with Fine Gaussian Support Vector Machine, and the highest precision and lowest false detection rate were achieved with Subspace Discriminant Ensemble Learning. © 2020 IEEE.

关键词： Support vector machines

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Deep Transfer Learning for 3d Point Cloud Understanding: A Comprehensive Survey

SSRN

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SSRN 2023年

作者： Sohail, Shahab Saquib Himeur, Yassine Amira, Abbes Fadli, Fodil Mansoor, Wathiq Atalla, Shadi Copiaco, Abigail Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi110062 India College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Department of Computer Science University of Sharjah United Arab Emirates Institute of Artificial Intelligence De Montfort University Leicester United Kingdom Department of Architecture & Urban Planning Qatar University Doha2713 Qatar

3D point cloud (3DPC) has significantly evolved and benefited from the advance of deep learning. However, the latter faces various issues, including the lack of data or annotated data, the existence of a significant gap between training data and test data, and the requirement for high computational resources. To that end, deep transfer learning (DTL) has been widely investigated as it allows knowledge transfer from synthetic to real data and mitigates data annotation constraints. Additionally, it helps reduce the discrepancy between the source and target domains using domain adaptation. Despite that, a detailed survey on its contributions to 3DPC is still missing. To the best of the authors' knowledge, this paper presents the first study shedding light on this aspect. Typically, DTL's background is presented along with the datasets and evaluation metrics. Following, a well-defined taxonomy of existing frameworks is presented. Lastly, open challenges are identified before deriving future directions. © 2023, The Authors. All rights reserved.

关键词： Knowledge management

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Efficient Optical Control of Magnon Dynamics in van der Waals Ferromagnets

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Ultrafast Science 2024年第6期4卷 53-60页

作者： Yu Gong Zhonghua Yang Alem Teklu Ti Xie Noah Kern Andrew F.May Michael McGuire Christian Brennan Er-Jia Guo Narayanan Kuthirummal John Cetin Qian Zhang Ming Hu Cheng Gong Department of Physics and Astronomy College of CharlestonCharlestonSC 29424USA Department of Mechanical Engineering University of South CarolinaColumbiaSC 29208USA College of Architecture and Civil Engineering Shenyang University of TechnologyShenyang 110870China Department of Electrical and Computer Engineering and Quantum Technology Center University of MarylandCollege ParkMD 20742USA Materials Science and Technology Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Beijing National Laboratory for Condensed Matter Physics and Institute of Physics Chinese Academy of SciencesBeijing 100190China

Optical control of magnons in two-dimensional(2D)materials promises new functionalities for spintronics and magnonics in atomically thin ***,we report control of magnon dynamics,using laser polarization,in a ferromagnetic van der Waals(vdW)material,*** magnon amplitude,frequency,and lifetime are controlled and monitored by time-resolved pump-probe *** show substantial(over 25%)and continuous modulation of magnon dynamics as a function of incident laser *** results suggest that the modification of the effective demagnetization field and magnetic anisotropy by the pump laser pulses with different polarizations is due to anisotropic optical *** implies that pump laser pulses modify the local spin environment,which enables the launch of magnons with tunable *** first-principles calculations confirm the anisotropic optical absorption of different crystal *** findings suggest a new route for the development of opto-spintronic or opto-magnonic devices.

关键词： magnon dynamics atomically thin devicesherewe control magnon dynamicsusing van der waals ferromagnets optical control modulation magnon dynamics ferromagnetic van der waals vdw materialfe co gete optical control magnons

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Distributed Learning by Local Training ADMM

Distributed Learning by Local Training ADMM

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IEEE Conference on Decision and Control

作者： Xiaoxing Ren Nicola Bastianello Karl H. Johansson Thomas Parisini Department of Electrical and Electronic Engineering Imperial College London London United Kingdom 2School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Trieste Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we focus on distributed learning over peer-to-peer networks. In particular, we address the challenge of expensive communications (which arise when e.g. training neural networks), by proposing a novel local training algorithm, LTADMM. We extend the distributed ADMM enabling the agents to perform multiple local gradient steps per communication round (local training). We present a preliminary convergence analysis of the algorithm under a graph regularity assumption, and show how the use of local training does not compromise the accuracy of the learned model. We compare the algorithm with the state of the art for a classification task, and in different set-ups. The results are very promising showing a great performance of LT-ADMM, and paving the way for future important theoretical developments.

关键词： Training computer aided instruction Analytical models Accuracy Distance learning Neural networks Classification algorithms Topology Peer-to-peer computing Convergence

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A Methodology for Selective Protection of Matrix Multiplications: A Diagnostic Coverage and Performance Trade-off for CNNs Executed on GPUs

A Methodology for Selective Protection of Matrix Multiplicat...

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International Conference on System Reliability and Science (ICSRS)

作者： Javier Fernández Irune Agirre Jon Perez-Cerrolaza Jaume Abella Francisco J. Cazorla Ikerlan Technological Research Center Basque Research and Technology Alliance (BRTA) Spain Computer Architecture-Operating Systems Department Barcelona Supercomputing Center Spain

The ability of CNNs to efficiently and accurately perform complex functions, such as object detection, has fostered their adoption in safety-related autonomous systems. These algorithms require high computational performance platforms that exploit high levels of parallelism. The detection, control and mitigation of random errors in these underlying high computational platforms become a must according to functional safety standards. In this paper, we propose protecting, with a catalog of diagnostic techniques, the most computationally expensive operation of the CNNs, the matrix multiplication. However, this protection entails a performance penalty, and the complete CNN protection may be unaffordable for those systems operating with strict real-time constraints. This paper proposes a three-stage methodology to selectively protect CNN layers to achieve the required diagnostic coverage and performance trade-off: i) sensitivity analysis to misclassification per CNN layers using a statistical fault injection campaign, ii) layer-by- layer performance impact and diagnostic coverage analysis, and iii) selective layer protection. Furthermore, we propose a strategy to effectively compute the achievable diagnostic coverage of large matrices implemented on GPUs. Finally, we apply the proposed methodology and strategy in Tiny YOLO-v3, an object detector based on CNNs.

关键词： Sensitivity analysis Autonomous systems Object detection Detectors Parallel processing Real-time systems Safety

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Ensemble of CNN classifiers using Choquet Fuzzy Integral Technique for PCB Defect Classification

Ensemble of CNN classifiers using Choquet Fuzzy Integral Tec...

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IEEE International Conference on computer Communication and the Internet (ICCCI)

作者： Boonchana Purahong Woranidtha Krungseanmuang Kasi Tenghongsakul Tuanjai Archevapanich Parkpoom Khunthawiwone Nitjaree Satayarak Attasit Lasakul Department of Computer Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Robotics and Computational Intelligent Systems School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Electrical Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Electronics and Communication Engineering Faculty of Engineering and Architecture Rajamagala University of Technology Suvarnabhumi Nonthaburi Thailand

ISBN: (数字)9798350351767

ISBN: (纸本)9798350351774

This paper presents a novel method for detecting defects in printed circuit boards (PCBs) using an ensemble of classifiers based on the Choquet fuzzy integral. Our approach employs convolutional neural network (CNN) models, specifically ResNet152, VGG19, and InceptionV3 as base classifiers to identify six types of PCB defects: spurs, mouse bites, short circuits, open circuits, spurious copper, and pinholes. Given the critical role of PCBs in ensuring electronic equipment reliability, effective defect detection methods like ours are essential. We employ pre-trained CNN models for feature extraction and classification of PCB defects. Following this, we combine the prediction scores using the Choquet fuzzy integral to derive more accurate final labels, exceeding the accuracy of standalone models. Our approach is tested on PCB images obtained from public repositories, captured using a linear scan CCD. The evaluation results demonstrate average precision, recall, F-score, and accuracy of 93.0%, 95.2%, 95.1%, and 95.1%, respectively.

关键词： Measurement Accuracy Electronic equipment Printed circuits Predictive models Feature extraction Internet

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A New Belief-Based Incomplete Pattern Unsupervised Classification Method : Extended Abstract

A New Belief-Based Incomplete Pattern Unsupervised Classific...

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International Conference on Data Engineering

作者： Zuowei Zhang Zhe Liu Zongfang Ma Yiru Zhang Hao Wang Institut de Recherche en Informatique et Systèmes Aléatoires University of Rennes 1 France School of Information and Control Engineering Xi’an University of Architecture and Technology China Department of Computer Science Norwegian University of Science and Technology Norway

Imputing the incomplete patterns in clustering tasks is a common but risky procedure, because the estimated values may affect the real distribution of the data and deteriorate the results. To address this problem, a new belief-based incomplete pattern unsupervised classification method (BPC) with uncertainty and imprecision reasoning is proposed in this paper. First, the complete patterns are grouped into a few clusters to obtain the corresponding reliable centers, and thereby are divided into reliable patterns and unreliable ones by an optimization method. Second, a basic classifier trained by reliable patterns is employed to classify unreliable patterns and incomplete patterns edited by the neighbors. Finally, some imprecise patterns are carefully reassigned again by a new distance-based rule depending on the obtained reliable centers and belief functions theory. The simulation results show that the BPC has the potential to deal with real datasets.

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A Spatio-Temporal Switchable Data Prefetcher for Convolutional Neural Networks

A Spatio-Temporal Switchable Data Prefetcher for Convolution...

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International SoC Design Conference, ISOCC

作者： Jihoon Jang Hyun Kim Hyokeun Lee Department of Electrical and Information Engineering Research Center for Electrical and Information Technology Seoul National University of Science and Technology Seoul Korea Secure and Advanced Computer Architecture (SACA) Research Group North Carolina State University (NCSU) Raleigh NC USA

In this paper, we propose a spatio-temporal switchable data prefetcher that can adapt to the locality characteristics of CNN models. The proposed prefetcher records the recent delta history by leveraging two tables. The first table predicts spatial address patterns by comparing the delta score with the last delta, while the second table predicts temporal address patterns by recording and reordering the delta sequence from the delta history. Consequently, the proposed prefetcher is capable of appropriately switching between these two prediction methodologies based on spatial and temporal localities. The experimental results on CNN inference workloads show that we achieved high average accuracy of 83.8% and coverage of 81.6%, and hence the proposed prefetcher improves system performance by 33.8% over a baseline with no data prefetcher and 21% over the best-performing prior spatio-temporal prefetcher.

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Human-centered GeoAI foundation models: where GeoAI meets human dynamics

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Urban Informatics 2025年第1期4卷 1-15页

作者： Ye, Xinyue Du, Jiaxin Li, Xinyu Shaw, Shih-Lung Fu, Yanjie Dong, Xishuang Zhang, Zhe Wu, Ling Department of Landscape Architecture and Urban Planning & Center for Geospatial Sciences Applications and Technology Texas A&M University College Station USA Department of Computer Science and Engineering Texas A&M University College Station USA College of Computing Grand Valley State University Allendale USA Department of Geography & Sustainability University of Tennessee Knoxville USA School of Computing and AI Arizona State University Tempe USA Department of Electrical & Computer Engineering Prairie View A&M University Prairie View USA Department of Geography Texas A&M University College Station USA Department of Justice Studies Prairie View A&M University Prairie View USA

This study examines the role of human dynamics within Geospatial Artificial Intelligence (GeoAI), highlighting its potential to reshape the geospatial research field. GeoAI, emerging from the confluence of geospatial technologies and artificial intelligence, is revolutionizing our comprehension of human-environmental interactions. This revolution is powered by large-scale models trained on extensive geospatial datasets, employing deep learning to analyze complex geospatial phenomena. Our findings highlight the synergy between human intelligence and AI. Particularly, the humans-as-sensors approach enhances the accuracy of geospatial data analysis by leveraging human-centric AI, while the evolving GeoAI landscape underscores the significance of human–robot interaction and the customization of GeoAI services to meet individual needs. The concept of mixed-experts GeoAI, integrating human expertise with AI, plays a crucial role in conducting sophisticated data analyses, ensuring that human insights remain at the forefront of this field. This paper also tackles ethical issues such as privacy and bias, which are pivotal for the ethical application of GeoAI. By exploring these human-centric considerations, we discuss how the collaborations between humans and AI transform the future of work at the human-technology frontier and redefine the role of AI in geospatial contexts.

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Scintillation event imaging with a single photon avalanche diode camera

Communications Engineering

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Communications Engineering 2024年第1期3卷 135页

作者： Bocchieri, Alex Charbon, Edoardo Velten, Andreas Department of Computer Sciences University of Wisconsin - Madison Madison WI United States Advanced Quantum Architecture Laboratory (AQUA) Swiss Federal Institute of Technology Lausanne (EPFL) Neuchâtel Switzerland Department of Biostatistics and Medical Informatics University of Wisconsin - Madison Madison WI United States Department of Electrical and Computer Engineering University of Wisconsin - Madison Madison WI United States

Position and time measurements of scintillation events encode information about the radiation source. Single photon avalanche diode (SPAD) arrays offer multiple-megapixel spatial resolution and tens of picoseconds temporal resolution for detecting single photons. Current lensless designs for measuring scintillation events use sensors that are lower in spatial resolution. Camera-based designs use sensors that are lower in temporal resolution or readout rate and cannot image individual interactions. Here we propose to image scintillation events in a thick, monolithic scintillator using a high-resolution SPAD camera. We demonstrate that a commercial SPAD camera is able to gather sufficient signal to image individual scintillation events and observe 3D shifts in their spatial distribution. Simulations show that a SPAD camera can localize individual scintillation events in 3D. We report direct imaging of gamma-ray interactions in a scintillator with a SPAD camera. The proposed design may allow to measure complex signatures of individual particles interacting in the scintillator. © The Author(s) 2024.

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