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Corrigendum: Moving Next-Generation Phase-Field Models to BMS Applications -A Case Study that Confirms Professor Uzi Landau's Foresight [J. Electrochem. Soc., 171, 063507 (2024)]

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Journal of The Electrochemical Society 2024年第8期171卷 089002-089002页

作者： Tushar K. Telmasre Anthony César Concepción Suryanarayana Kolluri Lubhani Mishra Raghav S. Thiagarajan Aditya Naveen Matam Akshay Subramaniam Taylor R. Garrick Venkat R. Subramanian Materials Science and Engineering Program Texas Materials Institute The University of Texas at Austin Austin TX 78712 United States of America Walker Department of Mechanical Engineering The University of Texas at Austin Austin TX 78712 United States of America Battery Materials and Systems Research General Motors Global Research and Development Center Warren MI 48090 United States of America Chandra Department of Electrical and Computer Engineering The University of Texas at Austin Austin TX 78712 United States of America Energy Storage and Propulsion R&D and Manufacturing Engineering General Motors Technical Center Warren MI 48092 United States of America Affiliate Faculty Oden Institute for Computational Science and Engineering The University of Texas at Austin Austin TX 78712 United States of America

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A 3.47 e− Read Noise, 81 dB Dynamic Range Backside-Illuminated Multispectral Imager for Near-Infrared Fluorescence Image-Guided Surgery

A 3.47 e− Read Noise, 81 dB Dynamic Range Backside-Illumina...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Steven Blair Amit Deliwala Eric Chen Sailesh Subashbabu Anthony Li Mebin George Missael Garcia Nan Cui Zhongmin Zhu Stefan Andonovski Borislav Kondov Sinisa Stojanoski Magdalena Bogdanovska Todorovska Gordana Petrusevska Goran Kondov Viktor Gruev Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL USA Department of Electrical and Systems Engineering Washington University in St. Louis St. Louis MO USA Medical Faculty Saints Cyril and Methodius University in Skopje Skopje Republic of North Macedonia Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign Urbana IL USA

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

Near-infrared fluorescence image-guided surgery relies on an interdisciplinary community to develop near-infrared fluorescent markers and near-infrared sensitive cameras capable of mapping relevant structures during surgical procedures. As biochemists pursue a new generation of near-infrared fluorophores aimed at surgical oncology and other applications, optoelectronic engineers developing near-infrared imagers have been slow to adopt architectural improvements that will enhance outcomes in existing operations and to document optoelectronic characteristics that are needed to predict endpoints for new procedures. Here we present a single-chip snapshot multispectral imaging system that integrates arrays of bandpass optical filters and six-transistor backside-illuminated pixels to provide RGB-NIR images with low read noise (3.47 e − ) and high dynamic range (81 dB). This imaging system is being used in clinical studies for sentinel lymph node mapping during breast cancer surgery.

关键词： Optical filters Surgery Image sensors Dynamic range Band-pass filters Gain

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Editor-in-Chief

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IEEE Access 2024年 12卷 i-xi页

作者： Derek Abbott Sonia Aissa Anuradha Annaswamy Nirwan Ansari Jun Cai Kun-Shan Chen Yixin Chen J.-C. Chiao Ayman El-Baz Shaikh Fattah Hoay Gooi Maya Gokhale Chih-Lin Ⅰ Giuseppe Iannaccone Yumi Iwashita Bruce Jacob Abbas Jamalipour Bahram Javidi Weihua Jiang Okyay Kaynak Hulya Kirkici Germano Lambert-Torres Shengtao Li Michal Mrozowski Michele Nappi Dalma Novak Hugo Proença Yi Qian Zhihua Qu Susanto Rahardja Mehrdad Saif Ali Serpengüzel Peter H. Siegel Ashitey Trebi-Ollennu Daniela Tuninetti Ge Wang Keith A. Wear Andreas Weisshaar C. Patrick Yue Enrico Zio School of Electrical and Electronic Engineering University of Adelaide Adelaide SA Australia Institut National de la Recherche Scientifique (INRS) Québec City Canada Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Concordia University Montreal Canada Guilin University of Technology Guilin China Washington University St. Louis MO USA Southern Methodist University Dallas TX USA University of Louisville/Bioengineering Department Louisville KY USA Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Nanyang Technological University Jurong West Singapore Lawrence Livermore National Laboratory Livermore CA USA China Mobile Research Institute Beijing China University of Pisa Pisa Italy NASA-JPL California Institute of Technology Pasadena CA USA Florida Polytechnic University Lakeland FL USA The University of Sydney Sydney NSW Australia University of Connecticut Storrs CT USA Nagaoka University of Technology Nagaoka Niigata Japan Department of Electrical and Electronic Engineering Boğaziçi University Istanbul Turkey University of South Alabama Mobile AL USA Gnarus Institute Itajuba Brazil Xi’an Jiaotong University Xi’an China Gdansk University of Technology Gdansk Poland University of Salerno Fisciano Italy Octane Wireless Hanover MD USA University of Beira Interior Covilhã Portugal Department of Electrical and Computer Engineering University of NebraskaLincoln Lincoln NE USA University of Central Florida Orlando Florida USA Singapore Institute of Technology 10 Dover Dr Singapore Infocomm Technology Cluster 10 Dover Dr Singapore University of Windsor Windsor Canada Department of Physics Koç University Istanbul Turkey California Institute of Technology (CalTech) Pasadena California USA NASA-JPL Mobility and Robotics Systems S

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A reconfigurable scan network based ic identification for embedded devices

A reconfigurable scan network based ic identification for em...

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2018 Design, Automation and Test in Europe Conference and Exhibition, DATE 2018

作者： Aramoon, Omid Chen, Xi Qu, Gang Department of Electrical and Computer Engineering and Institute for Systems Research University of Maryland College Park United States

ISBN: (纸本)9783981926316

Most of the Internet of Things (IoT) and embedded devices are resource constrained, making it impractical to secure them with the traditional computationally expensive crypto-based solutions. However, security and privacy are crucial in many IoT applications such as health monitoring. In this paper, we consider one of the most fundamental security problems: how to identify and authenticate an embedded device. We consider the fact that embedded devices are designed by reusing IP cores with reconfigurable scan network (RSN) as the standard testing facility and propose to generate unique integrated circuit (IC) identifications (IDs) based on different configurations for the RSN. These circuit IDs not only solve the IC and device identification and authentication problems, they can also be considered as a lightweight security primitive in other applications such as IC metering and IP fingerprinting. We demonstrate through the ITC'02 benchmarks that the proposed approach can easily create from 107 to 10186 unique IDs without any overhead. Finally, our method complies with the IEEE standards and thus has high practical value. © 2018 EDAA.

关键词： Authentication

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Author Correction: Super-resolution multicolor fluorescence microscopy enabled by an apochromatic super-oscillatory lens with extended depth-of-focus

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Nature communications 2024年第1期15卷 1510页

作者： Wenli Li Pei He Dangyuan Lei Yulong Fan Yangtao Du Bo Gao Zhiqin Chu Longqiu Li Kaipeng Liu Chengxu An Weizheng Yuan Yiting Yu Ningbo Institute of Northwestern Polytechnical University College of Mechanical Engineering Northwestern Polytechnical University Xi'an 710072 China. Key Laboratory of Micro/Nano Systems for Aerospace (Ministry of Education) Northwestern Polytechnical University Xi'an 710072 China. Shaanxi Province Key Laboratory of Micro and Nano Electro-Mechanical Systems Northwestern Polytechnical University Xi'an 710072 China. Department of Materials Science and Engineering City University of Hong Kong Hong Kong 999077 China. dangylei@cityu.edu.hk. Department of Materials Science and Engineering City University of Hong Kong Hong Kong 999077 China. The Institute of AI and Robotics Fudan University Shanghai 200433 China. Key Laboratory of Spectral Imaging Technology of Chinese Academy of Sciences Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi'an 710119 China. Department of Electrical and Electronic Engineering Joint Appointment with School of Biomedical Sciences The University of Hong Kong Hong Kong 999077 China. State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin Heilongjiang 150001 China. Ningbo Institute of Northwestern Polytechnical University College of Mechanical Engineering Northwestern Polytechnical University Xi'an 710072 China. yyt@***. Key Laboratory of Micro/Nano Systems for Aerospace (Ministry of Education) Northwestern Polytechnical University Xi'an 710072 China. yyt@***. Shaanxi Province Key Laboratory of Micro and Nano Electro-Mechanical Systems Northwestern Polytechnical University Xi'an 710072 China. yyt@***.

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Decoding Categorical Speech Perception from Evoked Brain Responses

Decoding Categorical Speech Perception from Evoked Brain Res...

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IEEE Region 10 Symposium

作者： Md Sultan Mahmud Faruk Ahmed Mohammed Yeasin Gavin M. Bidelman Department of Electrical and Computer Engineering University of Memphis TN USA School of Communication Sciences & Disorders University of Memphis Memphis TN USA Institute for Intelligent Systems University of Memphis Memphis TN USA Department of Anatomy and Neurobiology University of Tennessee Health Sciences Center Memphis TN USA

ISBN: (数字)9781728173665

ISBN: (纸本)9781728173672

Categorical perception (CP) is important to understand how fast and accurately speech sounds are invariantly perceived by humans despite widespread variability in their physical acoustic properties. In this work, we built a framework to understand the temporal (when in time) characteristics of speech evoked responses that differentiate prototypical vowels (true phonetic categories) from ambiguous ones (lacking a clear phonetic identity). We recorded event-related potentials (ERPs) from in young, healthy adults as they rapidly classified speech sounds along an acoustic-phonetic continuum. Source derived response features were submitted to support vector machine (SVM) classifiers. Whole-brain data provided the best decoding of prototypical from ambiguous speech with an accuracy of 95.16% [area under the curve (AUC) 95.14%; F1-score 95.00%] at 120 ms. Separate analyses using left hemisphere (LH) and right hemisphere (RH) data showed that LH activity was a better predictor of speech categorization as compared to RH (89.03 % vs. 86.45% accuracy). Notably, CP was decoded less robustly and later in time using hemisphere-specific data compared to whole-brain data. Our results are consistent with notions that the early ERPs (e.g., P2) are sensitive to CP and the general dominance of LH in auditory-linguistic processing.

关键词： Support vector machines Decoding Electroencephalography Brain modeling Speech processing Kernel Task analysis

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Doublet Thermal Metadevice

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Physical Review Applied 2019年第4期11卷 044021-044021页

作者： Jiaxin Li Ying Li Tianlong Li Wuyi Wang Longqiu Li Cheng-Wei Qiu State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin 150001 China Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore Department of Analytical Physical and Colloidal Chemistry Institute of Pharmacy Sechenov University Moscow 119991 Russia

Thermal metamaterials have served as a practical way to manipulate heat flow thanks to their judiciously designed structures, which achieve various functionalities. However, the connection and interplay between various possible structural configurations and their functions’ switchability have long been neglected and this is usually perceived as “one configuration, one function.” Here, we propose a doublet thermal metadevice in which we utilize the local configuration of two unequal conductivity phases to manipulate global temperature distribution. Based on effective medium theory, we apply the phase interchange identity to show that the functionalities of transparency, cloaking, concentrating, and rotating can be unified. The doublet thermal metadevice is capable of realizing tunable multifunctions by simply adjusting the angular displacement of specific sublayers of the assembly. The functional diversity of the device can be further extended in two ways. One way is to use a secondary transformation and the other way is to alter the shape parameter of the unit phase. This work provides a different framework to understand and design thermal metamaterials, which might be extended to other disciplines such as optics, electromagnetics, and acoustics.

关键词： Heat transfer Thermal conductivity Energy applications Metamaterials

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STARdom: An architecture for trusted and secure human-centered manufacturing systems

arXiv

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arXiv 2021年

作者： Rožanec, Jože M. Zajec, Patrik Kenda, Klemen Mladenić, Dunja Cauli, Nino Soldatos, John Papamartzivanos, Dimitrios Alonso, Rubén Veliou, Entso Sofianidis, Georgios Menesidou, Sofia Anna Theodoropoulos, Spyros Kyriazis, Dimosthenis Fortuna, Blaž Novalija, Inna Giannetsos, Thanassis Recupero, Diego Reforgiato Jožef Stefan International Postgraduate School Jamova 39 Ljubljana1000 Slovenia Jožef Stefan Institute Jamova 39 Ljubljana1000 Slovenia Qlector d.o.o. Rovšnikova 7 Ljubljana1000 Slovenia Ubitech Ltd Digital Security & Trusted Computing Group Athens Greece Department of Informatics and Computer Engineering University of West Attica Agiou Spyridonos Street Egaleo Athens12243 Greece R2M Solution Srl Pavia Italy Department of Computer Science University of Cagliari Cagliari Italy Department of Digital Systems University of Piraeus Piraeus Greece Department of Electrical and Computer Engineering National Technical University of Athens Athens Greece INTRASOFT International 19.5 KM Markopoulou Ave. PeaniaGR 19002 Greece

There is a lack of a single architecture specification that addresses the needs of trusted and secure Artificial Intelligence systems with humans in the loop, such as human-centered manufacturing systems at the core of the evolution towards Industry 5.0. To realize this, we propose an architecture that integrates forecasts, Explainable Artificial Intelligence, supports collecting users' feedback and uses Active Learning and Simulated Reality to enhance forecasts and provide decision-making recommendations. The architecture security is addressed as a general concern. We align the proposed architecture with the Big Data Value Association Reference Architecture Model. We tailor it for the domain of demand forecasting and validate it on a real-world case study. Copyright © 2021, The Authors. All rights reserved.

关键词： Industry 4.0

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Augmentation of the reconstruction performance of Fuzzy C-Means with an optimized fuzzification factor vector

arXiv

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arXiv 2020年

作者： Xu, Kaijie Pedrycz, Witold Li, Zhiwu School of Electro-Mechanical Engineering Xidian University Xi’an710071 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Department of remote sensing science and technology School of Electronic Engineering Xidian University Xi’an710071 China Systems Research Institute Polish Academy of Sciences Warsaw Poland Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Institute of Systems Engineering Macau University of Science and Technology 999078 China

Information granules have been considered to be the fundamental constructs of Granular Computing (GrC). As a useful unsupervised learning technique, Fuzzy C-Means (FCM) is one of the most frequently used methods to construct information granules. The FCM-based granulation-degranulation mechanism plays a pivotal role in GrC. In this paper, to enhance the quality of the degranulation (reconstruction) process, we augment the FCM-based degranulation mechanism by introducing a vector of fuzzification factors (fuzzification factor vector) and setting up an adjustment mechanism to modify the prototypes and the partition matrix. The design is regarded as an optimization problem, which is guided by a reconstruction criterion. In the proposed scheme, the initial partition matrix and prototypes are generated by the FCM. Then a fuzzification factor vector is introduced to form an appropriate fuzzification factor for each cluster to build up an adjustment scheme of modifying the prototypes and the partition matrix. With the supervised learning mode of the granulation-degranulation process, we construct a composite objective function of the fuzzification factor vector, the prototypes and the partition matrix. Subsequently, the particle swarm optimization (PSO) is employed to optimize the fuzzification factor vector to refine the prototypes and develop the optimal partition matrix. Finally, the reconstruction performance of the FCM algorithm is enhanced. We offer a thorough analysis of the developed scheme. In particular, we show that the classical FCM algorithm forms a special case of the proposed scheme. Experiments completed for both synthetic and publicly available datasets show that the proposed approach outperforms the generic data reconstruction approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Granulation

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Publisher Correction: The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

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Genome biology 2025年第1期26卷 31页

作者： Jacob Matthew Schreiber Carles A Boix Jin Wook Lee Hongyang Li Yuanfang Guan Chun-Chieh Chang Jen-Chien Chang Alex Hawkins-Hooker Bernhard Schölkopf Gabriele Schweikert Mateo Rojas Carulla Arif Canakoglu Francesco Guzzo Luca Nanni Marco Masseroli Mark James Carman Pietro Pinoli Chenyang Hong Kevin Y Yip Jefrey P Spence Sanjit Singh Batra Yun S Song Shaun Mahony Zheng Zhang Wuwei Tan Yang Shen Yuanfei Sun Minyi Shi Jessika Adrian Richard S Sandstrom Nina P Farrell Jessica M Halow Kristen Lee Lixia Jiang Xinqiong Yang Charles B Epstein J Seth Strattan Bradley E Bernstein Michael P Snyder Manolis Kellis William S Noble Anshul Bharat Kundaje Department of Genetics Stanford University Stanford CA USA. jmschreiber91@***. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA. Department of Genetics Stanford University Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. Department of Research and Development DeepSeq.AI San Francisco CA USA. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Department of Empirical Inference Max Planck Institute for Intelligent Systems Stuttgart Germany. School of Life Sciences University of Dundee Dundee UK. Department of Electronics Information and Bioengineering Politecnico Di Milano Milan Italy. Department of Computational Biology University of Lausanne Lausanne Switzerland. Department of Computer Science and Engineering The Chinese University of Hong Kong Sha Tin Hong Kong. Sanford Burnham Prebys Medical Discovery Institute San Diego CA USA. Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA. Department of Statistics University of California Berkeley Berkeley CA USA. Department of Biochemistry & Molecular Biology Center for Eukaryotic Gene Regulation Pennsylvania State University University Park PA USA. Department of Statistics Pennsylvania State University University Park PA USA. Department of Electrical and Computer Engineering Texas A&M University College Station TX USA. Altius Institute Seattle WA USA. Epigenomics Program The Broad Institute of MIT and Harvard Cambridge MA USA. Department of Genome Sciences University of Washington Seattle WA USA. Department of Computer Science Stanford University Stanford CA USA.

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