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electrical programmable multilevel nonvolatile photonic random-access memory

Light(Science & Applications) 2023年第11期12卷 2325-2334页

作者： Jiawei Meng Yaliang Gui Behrouz Movahhed Nouri Xiaoxuan Ma Yifei Zhang Cosmin-Constantin Popescu Myungkoo Kang Mario Miscuglio Nicola Peserico Kathleen Richardson Juejun Hu Hamed Dalir Volker J.Sorger Department of Electrical and Computer Engineering George Washington UniversityWashington DC 20052USA Florida Semiconductor Institute University of FloridaGainesvilleFL 32603USA Department of Electrical and Computer Engineering University of FloridaGainesvilleFL 32603USA Department of Materials Science&Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA CREOL The College of Optics&PhotonicsUniversity of Central FloridaOrlandoFL 32816USA

Photonic Random-Access Memories(P-RAM)are an essential component for the on-chip non-von Neumann photonic computing by eliminating optoelectronic conversion losses in data *** Phase-Change Materials(PCMs)have been showed multilevel memory capability,but demonstrations still yield relatively high optical loss and require cumbersome WRITE-ERASE approaches increasing power consumption and system package *** we demonstrate a multistate electrically programmed low-loss nonvolatile photonic memory based on a broadband transparent phase-change material(Ge2Sb2Se5,GSSe)with ultralow absorption in the amorphous state.A zero-staticpower and electrically programmed multi-bit P-RAM is demonstrated on a silicon-on-insulator platform,featuring efficient amplitude modulation up to 0.2 dB/μm and an ultralow insertion loss of total 0.12 dB for a 4-bit memory showing a 100×improved signal to loss ratio compared to other phase-change-materials based photonic *** further optimize the positioning of dual microheaters validating performance *** we demonstrate a half-a-million cyclability test showcasing the robust approach of this material and ***-loss photonic retention-of-state adds a key feature for photonic functional and programmable circuits impacting many applications including neural networks,LiDAR,and sensors for example.

关键词： retention random eliminating

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Towards an Internet-like Power Grid

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Journal of Modern Power Systems and Clean Energy 2022年第1期10卷 1-11页

作者： Iasonas Kouveliotis-Lysikatos Nikos Hatziargyriou Yanli Liu Felix Wu Department of Electrical Engineering and Computer Science KTH-Royal Institute of TechnologyTeknikringen 3310040StockholmSweden School of Electrical and Computer Engineering National Technical University of Athens15773 AthensGreece School of Electrical and Information Engineering Tianjin UniversityTianjin300072China Department of Electrical and Electronic Engineering The University of Hong KongHong KongChina Department of Electrical Engineering and Computer Sciences University of CaliforniaBerkeleyCA 94720USA

The great challenges faced by modern power systems require a fresh look at the conventional operation paradigm. The significant challenges faced by modern power systems require an innovative method for the conventional operation paradigm. We claim that the decarbonization of the power grid and extensive electrification of numerous sectors of human activity can only be fostered by a self-adaptable and smart power grid that manifests similar qualities to those of the Internet. The Internet is constructed on a layered architecture that facilitates technology innovations and its intelligence is distributed throughout a hierarchy of networks. In this paper, the fundamental differences between the network data flows and power flows are examined, and the basic requirements for an innovative operation paradigm are highlighted. The current power grid is operated in a highly inflexible, centralized manner to meet increased security goals. A new highly flexible, distributed architecture can be realized by distributing the operation responsibility in smaller areas or even in grid components that can make autonomous decisions. The characteristics of such a power grid are presented, and the key features and advances for the on-going transition to a sustainable power system are identified. Finally, a case study on distributed voltage control is presented and discussed.

关键词： Smart grid Internet distributed intelligence decentralized architecture

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Joint Active and Passive Beamforming for NLOS Target Detection in RIS-Assisted Mono-Static Radar 58

Joint Active and Passive Beamforming for NLOS Target Detecti...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Zeng, Cengcang Liang, Yifan Li, Hongbin Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States

ISBN: (纸本)9798350354058

Reconfigurable intelligent surfaces (RIS) have gained widespread application in the field of communications and are gradually extending their utility to the radar domain. This paper focuses on a mono-static RIS-assisted radar system. The problem is to jointly design the active beamformer for the radar and passive beamformer for the RIS. We begin by introducing a general signal model. In order to enhance target illumination and echo reception, we maximize the received signal power through the optimization of both the radar beamformer and the phase-shift matrix for the RIS. We provide a closed-form solution for the resulting optimization problem. The proposed RIS-assisted radar is employed for non-line-of sight (NLOS) target detection, where the radar is obstructed from the target. A statistical analysis of the detection performance is presented. Simulation results underscore the significant impact of RIS size and radar-RIS distance on detection performance. Moreover, we demonstrate that RIS can offer a practical solution for NLOS target detection. © 2024 IEEE.

关键词： Beamforming

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Human intelligence-based metaverse for co-learning of students and smart machines

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7695-7718页

作者： Lee, Chang-Shing Wang, Mei-Hui Reformat, Marek Huang, Sheng-Hui Department of Computer Science and Information Engineering National University of Tainan Tainan Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Information Technology Institute University of Social Sciences Warsaw Poland

This paper proposes a Human Intelligence (HI)-based Computational Intelligence (CI) and Artificial Intelligence (AI) Fuzzy Markup Language (CI&AI-FML) Metaverse as an educational environment for co-learning of students and machines. The HI-based CI&AI-FML Metaverse is based on the spirit of the Heart Sutra that equips the environment with teaching principles and cognitive intelligence of ancient words of wisdom. There are four stages of the Metaverse: preparation and collection of learning data, data preprocessing, data analysis, and data evaluation. During the data preparation stage, the domain experts construct a learning dictionary with fuzzy concept sets describing different terms and concepts related to the course domains. Then, the students and teachers use the developed CI&AI-FML learning tools to interact with machines and learn together. Once the teachers prepare relevant material, students provide their inputs/texts representing their levels of understanding of the learned concepts. A Natural Language Processing (NLP) tool, Chinese Knowledge Information Processing (CKIP), is used to process data/text generated by students. A focus is put on speech tagging, word sense disambiguation, and named entity recognition. Following that, the quantitative and qualitative data analysis is performed. Finally, the students’ learning progress, measured using progress metrics, is evaluated and analyzed. The experimental results reveal that the proposed HI-based CI&AI-FML Metaverse can foster students' motivation to learn and improve their performance. It has been shown in the case of young students studying Software engineering and learning English. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Students

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SPARSE SPIKING NEURAL NETWORK: EXPLOITING HETEROGENEITY IN TIMESCALES FOR PRUNING RECURRENT SNN 12

SPARSE SPIKING NEURAL NETWORK: EXPLOITING HETEROGENEITY IN T...

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12th International Conference on Learning Representations, ICLR 2024

作者： Chakraborty, Biswadeep Kang, Beomseok Kumar, Harshit Mukhopadhyay, Saibal Department of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States

Recurrent Spiking Neural Networks (RSNNs) have emerged as a computationally efficient and brain-inspired learning model. The design of sparse RSNNs with fewer neurons and synapses helps reduce the computational complexity of RSNNs. Traditionally, sparse SNNs are obtained by first training a dense and complex SNN for a target task, and, then, pruning neurons with low activity (activity-based pruning) while maintaining task performance. In contrast, this paper presents a task-agnostic methodology for designing sparse RSNNs by pruning a large randomly initialized model. We introduce a novel Lyapunov Noise Pruning (LNP) algorithm that uses graph sparsification methods and utilizes Lyapunov exponents to design a stable sparse RSNN from a randomly initialized RSNN. We show that the LNP can leverage diversity in neuronal timescales to design a sparse Heterogeneous RSNN (HRSNN). Further, we show that the same sparse HRSNN model can be trained for different tasks, such as image classification and temporal prediction. We experimentally show that, in spite of being task-agnostic, LNP increases computational efficiency (fewer neurons and synapses) and prediction performance of RSNNs compared to traditional activity-based pruning of trained dense models. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Neurons

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Relationship of IQ with Causal Connections Between Brain Salient Network Regions 31

Relationship of IQ with Causal Connections Between Brain Sal...

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31st National and 9th International Iranian Conference on Biomedical engineering, ICBME 2024

作者： Ghoorchian, Mina Soltanian-Zadeh, Hamid Pedram, Mir Mohsen Sadeghi, Mohsen Mir Mohammad Institute for Cognitive Science Studies Department of Cognitive Science Tehran Iran University of Tehran School of Electrical and Computer Engineering Tehran Iran Kharazmi University Department of Electrical and Computer Engineering Faculty of Engineering Tehran Iran

ISBN: (纸本)9798331529710

IQ is one of the indicators that has always been of interest to psychiatrists, doctors and cognitive science researchers. Since this index plays a key role in people's lives and also in the occurrence of brain abnormalities, many studies have been conducted on it. In this paper, we used the resting state magnetic resonance imaging data available in the HCP database to classify people into three groups with low, medium, and high IQ and applied machine learning methods on the matrix of causal connections between related network regions. We applied brain highlights. We calculated the matrix of causal relationships in this network by the spDCM algorithm and then obtained the differences of the three groups using the ANOVA statistical test and 6 edges out of 169 edges with p-value © 2024 IEEE.

关键词： Support vector regression

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Magnetic Signature-Based Model Using Machine Learning for electrical and Mechanical Faults Classification of Wind Turbine Drive Trains

Magnetic Signature-Based Model Using Machine Learning for El...

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2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024

作者： Swain, Akhyurna Abdellatif, Elmouatamid Pong, Philip W.T. New Jersey Institute of Technology Department of Electrical and Computer Engineering NJ07102 United States

ISBN: (纸本)9798350313604

Signal processing and fault indicators analysis are essential for efficient fault detection, classification, and diagnosis of wind turbines. Accordingly, existing works proposed the installation of multiple intrusive sensors (e.g., current, voltage, and accelerometer) for data collection in order to detect and classify the faults in wind turbine drive trains (WTDT). However, these sensors are scattered on the drive train and have a limited local reach on its components making it technically difficult to install. Therefore, signals from these sensors are not able to detect multi parameter phenomena such as coupling of the mechanical and electrical components of the drive train which contains essential fault information. This work proposes the use of magnetic signatures as fault condition indicators of the complete drive train due to the ability of contactless measurement of this signal without opening the main components of the drive train. This is achieved by performing non-destructive magnetic modeling and analysis of the entire drive train. The air gap magnetic flux density of the wind generator is demonstrated as a good fault condition indicator for different common faults occurring on the gearbox, bearings, and the generator. The proposed model is validated using a supervised machine learning classification algorithm in a way to distinguish between electrical and mechanical faults. © 2024 IEEE.

关键词： Wind turbines

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Deletion-Anticipative Data Selection with a Limited Budget 41

Deletion-Anticipative Data Selection with a Limited Budget

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41st International Conference on Machine Learning, ICML 2024

作者： Sim, Rachael Hwee Ling Fan, Jue Tian, Xiao Jaillet, Patrick Low, Bryan Kian Hsiang Department of Computer Science National University of Singapore Singapore Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States

Learners with a limited budget can use supervised data subset selection and active learning techniques to select a smaller training set and reduce the cost of acquiring data and training machine learning (ML) models. However, the resulting high model performance, measured by a data utility function, may not be preserved when some data owners, enabled by the GDPR's right to erasure, request their data to be deleted from the ML model. This raises an important question for learners who are temporarily unable or unwilling to acquire data again: During the initial data acquisition of a training set of size k, can we proactively maximize the data utility after future unknown deletions? We propose that the learner anticipates/estimates the probability that (i) each data owner in the feasible set will independently delete its data or (ii) a number of deletions occur out of k, and justify our proposal with concrete real-world use cases. Then, instead of directly maximizing the data utility function, the learner can maximize the expected or risk-averse post-deletion utility based on the anticipated probabilities. We further propose how to construct these deletion-anticipative data selection (DADS) maximization objectives to preserve monotone submodularity and near-optimality of greedy solutions, how to optimize the objectives and empirically evaluate DADS' performance on real-world datasets. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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Effective Posture Classification Using Statistically Significant Data From Flexible Pressure Sensors

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2024年第5期3卷 173-180页

作者： Yoon, Jungeun Moon, Aekyeung Son, Seung Woo Electronics and Telecommunication Research Institute Daejeon34129 Korea Republic of University of Massachusetts Lowell Electrical and Computer Engineering Department LowellMA01854 United States

Advancements in flexible and printable sensor technologies to overcome the limitations of conventional rigid counterparts offer an excellent opportunity to design various healthcare applications for humans, and their potential flexibility can be used in real-time health monitoring and personalized physical conditions with minimal or no inconvenience. However, managing a large volume of obtained sensor datasets and ensuring accurate predictions can take time and effort. While statistical analysis and the Pearson correlation coefficient can reduce data volume, whether this would lead to losing important information and affect downstream application performance is still being determined. In this article, we use posture classification as an exemplar of timely services in digital healthcare, especially for bedsores or decubitus ulcers. Our sensors, placed under hospital beds, have a thickness of just 0.4 mm and collect pressure data from 28 sensors (7 \times 4 ) at an 8-Hz cycle, categorizing postures into four types from five patients. We then collected sensor data to explore the possibility of using a small number of pressure sensors for patient posture classification. Next, we apply a statistical analysis to the datasets obtained to select the featured sensor data cells and evaluate the performance of posture classification models on various groups of sensors. Our evaluation involves the analysis of reduced datasets through statistical methods and the Pearson correlation coefficient. The classification performance using datasets comprising five featured and 28 sensors are 0.93 and 0.99, respectively. These results suggest comparable performance and the viability of useful classifiers for both the cases. Consequently, comparable posture classification performance can be achieved using only 17.9% of the entire dataset. © 2022 IEEE.

关键词： Classification (of information)

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Memory Enabled Segmentation of Terrain for Traversability based Reactive Navigation

Memory Enabled Segmentation of Terrain for Traversability ba...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Saucedo, Mario A.V. Patel, Akash Kanellakis, Christoforos Nikolakopoulos, George Luleå University of Technology Robotics & Ai Team Department of Computer Electrical and Space Engineering LuleåSE-97187 Sweden

ISBN: (纸本)9798350325706

This article presents a novel 2D traversability image estimation for local reactive navigation, that attributes the fusion of a novel Convolutional Neural Network (CNN) for coarse semantic segmentation on terrain roughness, with surface geometric normals. The proposed segmentation model consists of a U-Net based Encoder-Decoder architecture with a MobileNet V3 Large backbone for real-time performance. At the bottom layer, the bottleneck block commonly found in a U-Net has been enhanced with an Atrous Spatial Pyramid Pooling (ASPP) block. In addition, a SEResNet based decoder instead of the classical stacked convolution blocks of U-Net has been implemented, while a concatenation layer has been added at the output. Moreover, the development of a novel memory module to dynamically update the semantic segmentation image based on certainty heat maps is also shown. The efficacy of the proposed scheme has been evaluated in real-life environments such as indoors, outdoors and subterranean (SubT) environments on a Pioneer 3AT mobile robot. © 2023 IEEE.

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