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Linearity assessment between lower limb joint angles and angular accelerations at standard maximum vertical jumps with long-short and no countermovement

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Gait & Posture 2023年 106卷 S174-S175页

作者： Rodrigues, Carlos Correia, Miguel Abrantes, João Benedetti, Marco Nadal, Jurandir Faculty of Engineering- University of Porto Biomedical Engineering Program Porto Portugal INESC TEC - Institute for Systems and Computer Engineering- Technology and Science C-BER - Centre for Biomedical Engineering Research Porto Portugal Faculty of Engineering- University of Porto Department of Electrical and Computer Engineering Porto Portugal CICANT - Centre for Research in Applied Communication- Culture and New Technologies MovLab - Laboratory of Technologies for Interactions and Interfaces Lisbon Portugal Federal University of Pernambuco Department of Electronic and Systems Recife Brazil Federal University of Rio de Janeiro Biomedical Engineering Program Rio de Janeiro Brazil

IntroductionStandard maximum vertical jump (MVJ) has been applied to assess lower limb long countermovement (CM) on countermovement jump (CMJ) and short CM on drop jump (DJ) for comparison to squat jump (SJ) without CM [1,2]. Research questionNevertheless, the ability for lower limb joint accelerations depending on lower limb joint angles at long, short and no CM remains an open issue [3,4], in particular linearity assessment of these relations at long, short and no CM in association with MVJ whole-body impulsion. MethodsFor this reason, we applied piecewise linear detection of segmented subphases on lower limb joint angles ( θ)-angular acceleration ( α) diagrams for the hip (H), knee (K) and ankle (A) during impulse phases on CMJ, DJ and SJ. Selected sample corresponds to best MVJ trials based on higher flight-time of 3 CMJ, DJ and SJ repetitions for six healthy untrained subjects with ages (21.5±1.4) years, (76.7±9.3)kg mass and (1.79±0.06)m height. Adhesive reflective markers were applied at lower limb and torso, with sagittal hip, knee, and ankle θ, α obtained by inverse kinematics. H,K,A ( θ, α) diagrams were plotted and impulsion subphases segmented at selected instants of ∂ a/∂ q phase reversal with linear fit by the least-squares method, Fig. 1. Fig. 1. Hip, knee, ankle joint ( θ, α) diagrams at CMJ, DJ, SJ with segmented subphases time span(Δt), |∂α/∂θ| slope magnitudes and R² linear fit regression. ResultsSJ presented larger number of ( θ, α) subphases than DJ, both larger than CMJ with higher |∂ α/∂ θ| variation at DJ and SJ than CMJ, pointing to higher complexity of neuromuscular control and contributing to explain reduced SJ, DJ performance without CM (0.33±0.05)m and with short CM (0.27±0.03)m in relation to CMJ with long CM (0.36±0.04)m for untrained selected subjects, Fig. 1. Thus, despite the larger total time span Δt at CMJ (0.76±0.10)s in relation to SJ (0.36±0.08)s and DJ (0.23±0.05)s, CMJ presented lower number of subphases with longer time span

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High-density, nonvolatile SRAM using monolithic 3D integration of InGaZnO thin-film transistors and Hf 0.5 Zr 0.5 O 2 -based ferroelectric capacitors

Device

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Device 2025年

作者： Qihan Liu Yu Li Liwei Liu Hao Jiang Haozhe Zhu Zexing Chen Yang Yang Mengwei Si Can Li Peng Lin Yingfen Wei Qi Liu State Key Laboratory of Integrated Chips and Systems Frontier Institute of Chip and System Fudan University Shanghai China Institute of Microelectronics of the Chinese Academy of Sciences Beijing China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong China College of Computer Science and Technology Zhejiang University Hangzhou China

Monolithic three-dimensional (M3D) integration of back-end-of-line (BEOL)-compatible and high-performance transistors and memory devices offer a promising strategy to overcome the limitations of conventional silicon-based computing techniques. Specifically, static random-access memory (SRAM), while valued for speed and endurance, faces the challenges of a large footprint and static power consumption. Here, we present a four-transistor-two-capacitor (4T2C) nonvolatile SRAM (nvSRAM) utilizing vertically stacked indium gallium zinc oxide (IGZO) thin-film transistors (TFTs) and Hf 0.5 Zr 0.5 O 2 (HZO)-based ferroelectric capacitors (FeCaps) under a BEOL-compatible thermal budget. The HZO FeCaps and IGZO TFTs are optimized toward their M3D integration with high performance and inter-tier uniformity. Our 3-tier M3D-integrated 4T2C nvSRAM achieves a 2-transistor (2T) footprint and data retention at 85°C for 10 5 s after power off. Moreover, design space and optimization strategies of the 4T2C nvSRAM are explored for higher operation speed, lower energy consumption, and more robust data stability.

关键词： monolithic 3D integration back-end-of-line nonvolatile SRAM ferroelectric capacitor IGZO transistor emerging memory DTI-2: Explore

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Ruddlesden-Popper chalcogenides push the limit of mechanical stiffness and glass-like thermal conductivity in single crystals

arXiv

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arXiv 2023年

作者： Hoque, Md Shafkat Bin Hoglund, Eric R. Zhao, Boyang Bao, De-Liang Zhou, Hao Thakur, Sandip Osei-Agyemang, Eric Hattar, Khalid Scott, Ethan A. Surendran, Mythili Tomko, John A. Gaskins, John T. Aryana, Kiumars Makarem, Sara Alwen, Adie Hodge, Andrea Balasubramanian, Ganesh Giri, Ashutosh Feng, Tianli Hachtel, Jordan A. Ravichandran, Jayakanth Pantelides, Sokrates T. Hopkins, Patrick E. Department of Mechanical and Aerospace Engineering University of Virginia CharlottesvilleVA22904 United States Department of Materials Science and Engineering University of Virginia CharlottesvilleVA22904 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37830 United States Mork Family Department of Chemical Engineering and Materials Science University of Southern California Los AngelesCA90089 United States Department of Physics and Astronomy Vanderbilt University NashvilleTN37235 United States Department of Mechanical Engineering University of Utah Salt Lake CityUT84112 United States Department of Mechanical Industrial and Systems Engineering University of Rhode Island KingstonRI02881 United States Department of Materials Design and Innovation University at Buffalo The State University of New York BuffaloNY14260 United States Department of Mechanical Engineering and Mechanics Lehigh University 19 Memorial Drive West BethlehemPA18015 United States Nuclear Engineering University of Tennessee KnoxvilleTN37996 United States Sandia National Laboratories AlbuquerqueNM87185 United States Laser thermal analysis CharlottesvilleVA22902 United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37235 United States Department of Physics University of Virginia CharlottesvilleVA22904 United States

Insulating materials featuring ultralow thermal conductivity for diverse applications also require robust mechanical properties. Conventional thinking, however, which correlates strong bonding with high atomic-vibration-mediated heat conduction, led to diverse weakly bonded materials that feature ultralow thermal conductivity and low elastic moduli. One must, therefore, search for strongly-bonded single crystals in which heat transport is impeded by other means. Here, we report intrinsic, glass-like, ultralow thermal conductivity and ultrahigh elastic-modulus/thermal-conductivity ratio in single-crystalline Ruddlesden-Popper Ban+1ZrnS3n+1, n = 2,3, which are derivatives of BaZrS3. Their key features are strong anharmonicity and intra-unit-cell rock-salt blocks. The latter produce strongly bonded intrinsic superlattices, impeding heat conduction by broadband reduction of phonon velocities and mean free paths and concomitant strong phonon localization. The present study initiates a paradigm of "mechanically stiff phonon glasses". © 2023, CC BY.

关键词： Single crystals

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An electrical model characterization of an electronic nose chemical sensor using a programmable system-on-a-chip based AFE

An electrical model characterization of an electronic nose c...

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International Young Engineers Forum (YEF-ECE)

作者： Joã o J. M. Santos Susana I. C. J. Palma Carina Esteves Hugo Gamboa Joã o Pedro Oliveira Ana C. A. Roque Department of Electrical and Computer Engineering NOVA School of Science and Technology NOVA University of Lisbon Caparica Portugal Associate Laboratory i4HB &#x2013 Institute for Health and Bioeconomy NOVA School of Science and Technology NOVA University of Lisbon Caparica Portugal UCIBIO &#x2013 Applied Molecular Biosciences Unit Department of Chemistry NOVA School of Science and Technology NOVA University of Lisbon Caparica Portugal LIBPhys - Laboratory for Instrumentation Biomedical Engineering and Radiation Physics NOVA School of Science and Technology NOVA University of Lisbon Caparica Portugal Center for Technology and Systems (CTS) &#x2013 UNINOVA NOVA School of Science and Technology NOVA University of Lisbon Caparica Portugal

ISBN: (数字)9781665467315

ISBN: (纸本)9781665467322

The diagnosis of many diseases involves invasive detection methods, which are both painful and stressing for patients. In the last decades, the ever-growing development in electronic nose (E-Nose) technology made them great candidates for non-invasive disease detection methods. Such devices mimic the human olfactory system through a set of sensors which produce signals that can be associated with diseases. Recently, a class of low-cost and innovative ionogel sensors, developed by our group demonstrated their full applicability in E-Nose systems, opening a new and promising approach to the field. However, the operation of such sensor needs a background calibration phase which relies on the correct characterization and parameterization of the corresponding electrical sensor *** paper proposes a model characterization methodology based on a set of frequency responses acquisitions of the sensor, under several humidity conditions. To obtain a flexible acquisition tool capable of acquiring accurate results, an analog front-end (AFE) circuit to interface with the interdigitated electrode (IDE) sensors is presented. Such AFE circuit is fully implemented using a programmable system-on-a-chip (PSoC), helping to reduce system size and cost. Lastly, a comparison between the electrical model and data acquired with the proposed system is presented.

关键词： Electrodes Chemical sensors Humidity Sensor phenomena and characterization Electronic noses Sensor systems Sensors

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Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network

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PLoS Computational Biology 2021年第9期17卷 e1009345-e1009345页

作者： Zhao, Zhengqiao Woloszynek, Stephen Agbavor, Felix Mell, Joshua Chang Sokhansanj, Bahrad A. Rosen, Gail L. Ecological and Evolutionary Signal-Processing and Informatics Laboratory Department of Electrical and Computer Engineering College of Engineering Drexel University PhiladelphiaPA United States Beth Israel Deaconess Medical Center BostonMA United States Harvard Medical School BostonMA United States School of Biomedical Engineering Science and Health Systems Drexel University PhiladelphiaPA United States College of Medicine Drexel University PhiladelphiaPA United States

Recurrent neural networks with memory and attention mechanisms are widely used in natural language processing because they can capture short and long term sequential information for diverse tasks. We propose an integrated deep learning model for microbial DNA sequence data, which exploits convolutional neural networks, recurrent neural networks, and attention mechanisms to predict taxonomic classifications and sample-associated attributes, such as the relationship between the microbiome and host phenotype, on the read/ sequence level. In this paper, we develop this novel deep learning approach and evaluate its application to amplicon sequences. We apply our approach to short DNA reads and full sequences of 16S ribosomal RNA (rRNA) marker genes, which identify the heterogeneity of a microbial community sample. We demonstrate that our implementation of a novel attention- based deep network architecture, Read2Pheno, achieves read-level phenotypic prediction. Training Read2Pheno models will encode sequences (reads) into dense, meaningful representations: learned embedded vectors output from the intermediate layer of the network model, which can provide biological insight when visualized. The attention layer of Read2Pheno models can also automatically identify nucleotide regions in reads/sequences which are particularly informative for classification. As such, this novel approach can avoid pre/post-processing and manual interpretation required with conventional approaches to microbiome sequence classification. We further show, as proof-of-concept, that aggregating read-level information can robustly predict microbial community properties, host phenotype, and taxonomic classification, with performance at least comparable to conventional approaches. An implementation of the attention-based deep learning network is available at https://***/EESI/sequence_attention (a python package) and https://***/ EESI/seq2att (a command line tool). Copyright: © 2021 Zhao et

关键词： Classification (of information)

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SYNAPSE: An international roadmap to large brain imaging

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Physics Reports 2023年第1期999卷 1-60页

作者： Stampfl, Anton P.J. Liu, Zhongdong Hu, Jun Sawada, Kei Takano, H. Kohmura, Yoshiki Ishikawa, Tetsuya Lim, Jae-Hong Je, Jung-Ho Low, Chian-Ming Teo, Alvin Tok, Eng Soon Tan, Tin Wee Ban, Kenneth Libedinsky, Camilo Tan, Francis Chee Kuan Chen, Kuan-Peng Yang, An-Cheng Chuang, Chao-Chun Chen, Nan-Yow Shih, Chi-Tin Lee, Ting-Kuo Yang, De-Nian Lai, Hsu-Chao Shuai, Hong-Han Cheng, Chang-Chieh Ching, Yu-Tai Li, Chia-Wei Charng, Ching-Che Lo, Chung-Chuan Chiang, Ann-Shyn Recur, Benoit Petibois, Cyril Cheng, Chia-Liang Chen, Hsiang-Hsin Yang, Shun-Min Hwu, Yeukuang Rojviriya, Catleya Rugmai, Supagorn Rujirawat, Saroj Margaritondo, Giorgio Australian Nuclear Science and Technology Organization Lucas Heights 2234 NSW Australia Henan university of Technology Zhengzhou China Shanghai Institute of Applied Physics Chinese Academy of Science Shanghai China RIKEN SPring-8 Center Hyogo 679-5148 Japan Pohang Accelerator Laboratory Pohang University of Science and Technology Pohang Gyeongbuk 37673 South Korea Department of Materials Science and Engineering Pohang University of Science and Technology Gyeongbuk Pohang 37673 South Korea Department of Pharmacology Department of Anaesthesia and Healthy Longevity Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore School of Chemical and Life Sciences Nanyang Polytechnic Singapore eMaGIC-Lab Department of Physics National University of Singapore Singapore National Supercomputing Center Singapore Department of Psychology The N1 Institute for Health National University of Singapore Singapore Institute of Molecular and Cell Biology A*STAR Singapore Department of Anaesthesia National University Hospital National University Health System Singapore National Center for High-Performance Computing Hsinchu Taiwan Department of Physics Tunghai University Taichung Taiwan Department of Physics National Tsing Hua University Hsinchu Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Information Technology Service Center National Yang Ming Chiao Tung University Hsinchu Taiwan National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Life Science National Tsing Hua University Hsinchu Taiwan Institute of Systems Neuroscience and Brain Research Center National Tsing Hua University Hsinchu Taiwan Institute of Physics Academia Sinica Taipei Taiwan Department of Physics National Dong Hwa University

Since 2020, synchrotron radiation facilities in several Asia-Pacific countries have been collaborating in a major project called “SYNAPSE” (Synchrotrons for Neuroscience: an Asia-Pacific Scientific Enterprise). They use x-ray imaging to attack in a coordinated fashion one of the major issues in modern science: the structure of animal and human brains, including neurons and connections. The objective is to develop Google-like maps also including detailed structural and functional information for selected regions of interest. The sheer mass of data needed for the objective poses huge problems for the acquisition, processing, storage and use of images. In order to complete the task within a reasonable time, the key element of the SYNAPSE strategy is the parallel and coordinated work of several facilities on the same specimens. This article reviews different aspects of the enterprise, including the foundations of synchrotron radiation, coherence and of its role in advanced imaging, electron accelerators, x-ray optics and detectors. This will provide the foundation for an extensive presentation of the different components of SYNAPSE, with an overview of results already obtained within the consortium. © 2022

关键词： Brain mapping Phase contrast Radiology Supercomputing Synchrotron X-rays

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Attack Identification and Correction for PMU GPS Spoofing in Unbalanced Distribution systems

arXiv

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arXiv 2020年

作者： Zhang, Ying Wang, Jianhui Liu, Jianzhe Electrical and Computer Engineering Department Southern Methodist University DallasTX75205 United States Argonne National Laboratory Energy Systems Division LemontIL60439 United States

—Due to the vulnerability of civilian global positioning system (GPS) signals, the accuracy of phasor measurement units (PMUs) can be greatly compromised by GPS spoofing attacks (GSAs), which introduce phase shifts into true phase angle measurements. Focusing on simultaneous GSAs for multiple PMU locations, this paper proposes a novel identification and correction algorithm in distribution systems. A sensitivity analysis of state estimation residuals on a single GSA phase angle is firstly implemented. An identification algorithm using a probing technique is proposed to determine the locations of spoofed PMUs and the ranges of GSA phase shifts. Based on the identification results, these GSA phase shifts are determined via an estimation algorithm that minimizes the mismatch between measurements and system states. Further, with the attacked PMU data corrected, the system states are recovered. Simulations in unbalanced IEEE 34-bus and 123-bus distribution systems demonstrates the efficiency and accuracy of the proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Phasor measurement units

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Learning-based attacks in cyber-physical systems: Exploration, detection, and control cost trade-offs

arXiv

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arXiv 2020年

作者： Rangi, Anshuka Khojasteh, Mohammad Javad Franceschetti, Massimo Department of Electrical and Computer Engineering University of California San DiegoCA92093 United States Laboratory for Information and Decision Systems Massachusetts Institute of Technology MA02139 United States

We study the problem of learning-based attacks in linear systems, where the communication channel between the controller and the plant can be hijacked by a malicious attacker. We assume the attacker learns the dynamics of the system from observations, then overrides the controller’s actuation signal, while mimicking legitimate operation by providing fictitious feedback about the sensor readings to the controller. On the other hand, the controller is on a lookout to detect the presence of the attacker and tries to enhance the detection performance by carefully crafting its control signals. We study the trade-offs between the information acquired by the attacker from observations, the detection capabilities of the controller, and the control cost. Specifically, we provide tight upper and lower bounds on the expected Ε-deception time, namely the time required by the controller to make a decision regarding the presence of an attacker with confidence at least (1-Εlog(1/Ε)). We then show a probabilistic lower bound on the time that must be spent by the attacker learning the system, in order for the controller to have a given expected Ε-deception time. We show that this bound is also order optimal, in the sense that if the attacker satisfies it, then there exists a learning algorithm with the given order expected deception time. Finally, we show a lower bound on the expected energy expenditure required to guarantee detection with confidence at least 1-Εlog(1/Ε). Copyright © 2020, The Authors. All rights reserved.

关键词： Cyber Physical System

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Double-Free-Layer Magnetic Tunnel Junctions for Probabilistic Bits

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Physical Review Applied 2021年第4期15卷 044049-044049页

作者： Kerem Y. Camsari Mustafa Mert Torunbalci William A. Borders Hideo Ohno Shunsuke Fukami Department of Electrical and Computer Engineering University of California Santa Barbara California 93106 USA Birck Nanotechnology Center Purdue University West Lafayette Indiana 47907 USA Laboratory for Nanoelectronics and Spintronics Research Institute of Electrical Communication Tohoku University Japan Center for Spintronics Research Network Tohoku University Japan Center for Science and Innovation in Spintronics Tohoku University Japan WPI Advanced Institute for Materials Research Tohoku University Japan Center for Innovative Integrated Electronic Systems Tohoku University Japan

Naturally random devices that exploit ambient thermal noise have recently attracted attention as hardware primitives for accelerating probabilistic computing applications. One such approach is to use a low barrier nanomagnet as the free layer of a magnetic tunnel junction (MTJ), the magnetic fluctuations of which are converted to resistance fluctuations in the presence of a stable fixed layer. Here, we propose and theoretically analyze a MTJ with no fixed layers but two free layers that are circularly shaped disk magnets. We use an experimentally benchmarked model that accounts for finite-temperature magnetization dynamics, bias-dependent charge, and spin-polarized currents as well as the dipolar coupling between the free layers. We obtain analytical results for statistical averages of fluctuations that are in good agreement with the numerical model. We find that the free layers with low diameters fluctuate to randomize the resistance of the MTJ in an approximately bias-independent manner. We show how such MTJs can be used to build a binary stochastic neuron (or a p-bit) in hardware. Unlike earlier stochastic MTJs that need to operate at a specific bias point to produce random fluctuations, the proposed design can be random for a wide range of bias values, independent of spin-transfer-torque pinning. Moreover, in the absence of a carefully optimized stabled fixed layer, the symmetric double-free-layer stack can be manufactured using present-day magnetoresistive random-access memory (MRAM) technology by minimal changes to the fabrication process. Such devices can be used as hardware accelerators in energy-efficient computing schemes that require a large throughput of tunably random bits.

关键词： Spintronics Superparamagnetism Magnetic tunnel junctions

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Robust Federated Learning: The Case of Affine Distribution Shifts

arXiv

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arXiv 2020年

作者： Reisizadeh, Amirhossein Farnia, Farzan Pedarsani, Ramtin Jadbabaie, Ali Department of Electrical and Computer Engineering UC Santa Barbara Santa BarbaraCA United States Laboratory for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA United States

Federated learning is a distributed paradigm for training models using samples distributed across multiple users in a network, while keeping the samples on users’ devices with the aim of efficiency and protecting users privacy. In such settings, the training data is often statistically heterogeneous and manifests various distribution shifts across users, which degrades the performance of the learnt model. The primary goal of this paper is to develop a robust federated learning algorithm that achieves satisfactory performance against distribution shifts in users’ samples. To achieve this goal, we first consider a structured affine distribution shift in users’ data that captures the device-dependent data heterogeneity in federated settings. This perturbation model is applicable to various federated learning problems such as image classification where the images undergo device-dependent imperfections, e.g. different intensity, contrast, and brightness. To address affine distribution shifts across users, we propose a Federated Learning framework Robust to Affine distribution shifts (FLRA) that is robust against affine distribution shifts to the distribution of observed samples. To solve the FLRA’s distributed minimax optimization problem, we propose a fast and efficient optimization method and provide convergence and performance guarantees via a gradient Descent Ascent (GDA) method. We further prove generalization error bounds for the learnt classifier to show proper generalization from empirical distribution of samples to the true underlying distribution. We perform several numerical experiments to empirically support FLRA. We show that an affine distribution shift indeed suffices to significantly decrease the performance of the learnt classifier in a new test user, and our proposed algorithm achieves a significant gain in comparison to standard federated learning and adversarial training methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Gradient methods

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