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KinGloves: Rapid Prototyping of Usability Enhanced Sensing Gloves for Parkinson's Disease

KinGloves: Rapid Prototyping of Usability Enhanced Sensing G...

Challenges, Opportunities, Innovations and Applications in Electronic Textiles (E-Textiles), International Conference on the

作者： Vignesh Ravichandran Shubham Chomal Rachel Arruda Md Abdullah Al Rumon Anne-Marie Dupre Kofoworola Fapohunda Puneet Tandon Jim Owens Gretchen Macht Carolyn Kosiba-Quiterio Dhaval Solanki Kunal Mankodiya Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston USA Indian Institute of Information Technology Design and Manufacturing Department of Mechanical Industrial and Systems Engineering University of Rhode Island Kingston USA Department of Textiles Fashion Merchandising and Design University of Rhode Island Kingston USA Department of Physical Therapy University of Rhode Island Kingston USA Nautilus Defense Pawtucket USA Rock Steady Boxing East Greenwich USA

ISBN: (数字)9783000804298

ISBN: (纸本)9798331516871

Parkinson's Disease (PD) is a progressive neurological movement disorder causing motor (movement) impairments, leading to difficulty in performing activities of daily living such as eating, writing, cutting vegetables, buttoning a shirt, tying shoelaces, brushing teeth, etc. Although sensing clothes has gained popularity in monitoring disease progression in home settings, they pose usability challenges arising from movement/cognitive symptoms that hamper their adoption and adherence. In this study, we employ a user-centered design process with an aim to improve the wearability of gloves through a multi-phase study involving 23 people with PD (PwPD). The goal was to design gloves with suitable wrist closure and finger insertion mechanisms to enable independent upper-body movement assessments. The first phase conducted with 4 PwPD helped identify preferred and undesired glove-wearing mechanics through 12 daily living tasks. Next, a rapid prototyping process for textiles, using a laser cutter, produced three design concepts for evaluation by 9 PwPD. The best-performing prototype design was chosen for capacitive sensing thread integration and design revision. The capacitive sensing threads play a crucial role in accurately measuring finger flexion and extension, providing high-resolution data on hand movements that are critical for assessing finger movements associated with Parkinson's Disease symptoms. After incorporating participant feedback, the improved design had an average reduction of 23% in time to wear and 250% in time to take off when tested with 18 PwPD. In particular, features such as a view window for fingers and use of zippers had been reported by participants to improve glove usability.

关键词： Wrist Technological innovation Parkinson's disease User centered design Writing Rapid prototyping Sensors Usability Yarn Textiles

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Gene Signature Selection for Cancer Prediction Using an Integrated Approach of Genetic Algorithm and Support Vector Machine

Gene Signature Selection for Cancer Prediction Using an Inte...

引用

Congress on Evolutionary Computation

作者： K.Y. Chan H.L. Zhu C.C. Lau S.H. Ling Department of Industrial and Systems Engineering Hong Kong Polytechnic University Hong Kong China Research Institute of Innovative Products and Technologies Hong Kong Polytechnic University Hong Kong China Texas Childrens Cancer Center Department of Pediatrics Baylor University College of Medicine Houston TX USA School of Electrical Electronic and Computer University of Western Australia Australia

Classification of tumor types based on genomic information is essential for improving future cancer diagnosis and drug development. Since DNA microarray studies produce a large amount of data, effective analytical methods have to be developed to sort out whether specific cancer samples have distinctive features of gene expression over normal samples or other types of cancer samples. In this paper, an integrated approach of support vector machine (SVM) and genetic algorithm (GA) is proposed for this purpose. The proposed approach can simultaneously optimize the feature subset and the classifier through a common solution coding mechanism. As an illustration, the proposed approach is applied in searching the combinational gene signatures for predicting histologic response to chemotherapy of osteosarcoma patients, which is the most common malignant bone tumor in children. Cross-validation results show that the proposed approach outperforms other existing methods in terms of classification accuracy. Further validation using an independent dataset shows misclassification of only one of fourteen patient samples suggesting that the selected gene signatures can reflect the chemoresistance in osteosarcoma.

关键词： Support vector machines Classification algorithms Optimization Tumors Cancer Genetic algorithms Surgery

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Assessing alternatives of including social robots in urban transport using fuzzy trigonometric operators based decision-making model

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Technological Forecasting and Social Change 2023年第1期194卷

作者： Deveci, Muhammet Pamucar, Dragan Gokasar, Ilgin Zaidan, Bilal Bahaa Martinez, Luis Pedrycz, Witold Department of Industrial Engineering Turkish Naval Academy National Defence University Istanbul Tuzla34940 Turkey Royal School of Mines Imperial College London LondonSW7 2AZ United Kingdom Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade11000 Serbia Department of Civil Engineering Bogazici University Istanbul Bebek34342 Turkey International Graduate School of Artificial Intelligence College of Management National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliu City64002 Taiwan Department of Computer Sciences University of Jaén Jaén23071 Spain Department of Electrical and Computer Engineering Faculty of Engineering University of Alberta 9211 116 Street NW EdmontonABT6G 1H9 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey College of Engineering Yuan Ze University Taiwan

Current trends point to a not-too-distant future with qualitatively advanced interactions between humans and social robots. It is critical to consider the possibility of forming meaningful social relationships with robots when defining the future of human-robot interactions, as well as studying how these interactions will evolve to the point where humans are unable to distinguish between humans and robots in urban transportation. In this study, the advantages of using social robots in urban transportation are prioritized by using a multi-criteria decision-making tool, which consists of two consecutive stages, namely: i) a novel fuzzy sine trigonometry based on the logarithmic method of additive weights (fuzzy ST-LMAW) that is proposed to calculate the criteria weights;ii) a nonlinear fuzzy Aczel-Alsina function based the weighted aggregate sum product assessment (fuzzy ALWAS-WASPAS) that is developed to select and rank the alternatives. The proposed model enables flexible nonlinear processing of complex and uncertain information encountered in real applications. A case study is developed to rank three alternatives with twelve sub-criteria grouped into four aspects using the proposed method. The results show that the most advantageous alternative is to replace people with social robots as safety drivers in level four autonomous vehicles due to their possible impact on transportation. © 2023 The Authors

关键词： Human robot interaction

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Laser written optical Physical Unclonable Functions: a route to commercialization.

Laser written optical Physical Unclonable Functions: a route...

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International Conference on Transparent Optical Networks

作者： Dimitris Alexandropoulos Christos Tselios Simone Mazzucato Panagiotis Rizomiliotis Konstantinos Moustakas Thomas Kamalakis Research Centre ATHENA Industrial Systems Institute Patras Greece Department of Materials Science University of Patras Patras Greece SISMA S.p.A. via dell’Industria 1 - 36013 Piovene Rocchette VI Italy Department of Informatics and Telematics Harokopio University of Athens Athens Greece Department of Electrical and Computer Engineering University of Patras Patras Greece

ISBN: (数字)9798350377323

ISBN: (纸本)9798350377330

This paper presents a fabrication approach towards commercialization of optical Physical Unclonable Functions (PUFs) for anti-counterfeiting. We address the limitations of existing complex and expensive implementations by demonstrating strong, intrinsic optical PUFs using computer-Generated Holograms (CGHs) fabricated with industrial-friendly lasers. This novel method offers exceptional security with very low probability of cloning through a low-cost, production-line friendly set-up and a robust signature encoding scheme that utilizes nonlinear dimensionality reduction techniques, to differentiate legitimate variations from counterfeiting attempts. This research paves the way for widespread adoption of PUF technology for real-world applications.

关键词： Integrated optics Laser theory Optical device fabrication Supply chains Cloning Physical unclonable function Adaptive optics

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Added value of intraoperative data for predicting postoperative complications: Development and validation of a mysurgeryrisk extension

arXiv

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arXiv 2019年

作者： Datta, Shounak Loftus, Tyler J. Ruppert, Matthew M. Giordano, Chris Adhikari, Lasith Peng, Ying-Chih Ren, Yuanfang Shickel, Benjamin Feng, Zheng Lipori, Gloria Upchurch, Gilbert R. Li, Xiaolin Rashidi, Parisa Ozrazgat-Baslanti, Tezcan Bihorac, Azra Department of Medicine University of Florida GainesvilleFL United States Department of Surgery University of Florida GainesvilleFL United States Department of Anesthesiology University of Florida GainesvilleFL United States Department of Industrial and Systems Engineering University of Florida GainesvilleFL United States Department of Biomedical Engineering University of Florida GainesvilleFL United States Department of Electrical and Computer Engineering University of Florida GainesvilleFL United States University of Florida GainesvilleFL United States University of Florida Health GainesvilleFL United States

Objective: To test the hypothesis that accuracy, discrimination, and precision in predicting postoperative complications improve when using both preoperative and intraoperative data input features versus preoperative data alone. Background: Models that predict postoperative complications often ignore important intraoperative events and physiological changes. Incorporation of intraoperative physiological data may improve model performance. Methods: This retrospective cohort analysis included 43,943 adults undergoing 52,529 inpatient surgeries at a single institution during a five-year period. Random forest machine learning models in the validated MySurgeryRisk platform made patient-level predictions for three postoperative complications and mortality during hospital admission using electronic health record data and patient neighborhood characteristics. For each outcome, one model trained with preoperative data alone and one model trained with both preoperative and intraoperative data. Models were compared by accuracy, discrimination (expressed as AUROC: area under the receiver operating characteristic curve), precision (expressed as AUPRC: area under the precision-recall curve), and reclassification indices (NRI). Results: Machine learning models incorporating both preoperative and intraoperative data had greater accuracy, discrimination, and precision than models using preoperative data alone for predicting all three postoperative complications (intensive care unit length of stay >48 hours, mechanical ventilation >48 hours, and neurological complications including delirium) and in-hospital mortality (accuracy: 88% vs. 77%, AUROC: 0.93 vs. 0.87, AUPRC: 0.21 vs. 0.15). Overall reclassification improvement was 2.9-10.0% for complications and 11.2% for in-hospital mortality. Conclusions: Incorporating both preoperative and intraoperative data significantly increased accuracy, discrimination, and precision for machine learning models predicting postoperative complication

关键词： Forecasting

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A tensor-based framework for studying eigenvector multicentrality in multilayer networks

arXiv

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arXiv 2017年

作者： Wu, Mincheng He, Shibo Zhang, Yongtao Chen, Jiming Sun, Youxian Liu, Yang-Yu Zhang, Junshan Vincent Poor, H. State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou Zhejiang310027 China Channing Division of Network Medicine Brigham and Women's Hospital Harvard Medical School BostonMA02115 United States Center for Cancer Systems Biology Dana-Farber Cancer Institute BostonMA02115 United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Department of Electrical Engineering Princeton University PrincetonNJ08544 United States

Centrality is widely recognized as one of the most critical measures to provide insight in the structure and function of complex networks. While various centrality measures have been proposed for single-layer networks, a general framework for studying centrality in multilayer networks (i.e., multicentrality) is still lacking. In this study, a tensor-based framework is introduced to study eigenvector multicentrality, which enables the quantification of the impact of interlayer influence on multicentrality, providing a systematic way to describe how multicentrality propagates across different layers. This framework can leverage prior knowledge about the interplay among layers to better characterize multicentrality for varying scenarios. Two interesting cases are presented to illustrate how to model multilayer influence by choosing appropriate functions of interlayer influence and design algorithms to calculate eigenvector multicentrality. This framework is applied to analyze several empirical multilayer networks, and the results corroborate that it can quantify the influence among layers and multicentrality of nodes effectively. Copyright © 2017, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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Dynamic Janus metasurfaces in the visible spectral region

arXiv

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arXiv 2021年

作者： Yu, Ping Li, Jianxiong Zhang, Shuang Jin, Zhongwei Schütz, Gisela Qiu, Cheng-Wei Hirscher, Michael Liu, Na Max Planck Institute for Intelligent Systems Heisenbergstrasse 3 Stuttgart70569 Germany School of Physics & Astronomy University of Birmingham BirminghamB15 2TT United Kingdom Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore117583 Singapore Suzhou Industrial Park Suzhou215123 China Kirchhoff Institute for Physics University of Heidelberg Im Neuenheimer Feld 227 Heidelberg69120 Germany

Janus monolayers have long been captivated as a popular notion for breaking in-plane and out-of-plane structural symmetry. Originated from chemistry and materials science, the concept of Janus functions have been recently extended to ultrathin metasurfaces by arranging meta-atoms asymmetrically with respect to the propagation or polarization direction of the incident light. However, such metasurfaces are intrinsically static and the information they carry can be straightforwardly decrypted by scanning the incident light directions and polarization states, once the devices are fabricated. In this Letter, we present a dynamic Janus metasurface scheme in the visible spectral region. In each super unit cell, three plasmonic pixels are categorized into two sets. One set contains a magnesium nanorod and a gold nanorod that are orthogonally oriented with respect to each other, working as counter pixels. The other set only contains a magnesium nanorod. The effective pixels on the Janus metasurface can be reversibly regulated by hydrogenation/dehydrogenation of the magnesium nanorods. Such dynamic controllability at visible frequencies allows for flat optical elements with novel functionalities including beam steering, bifocal lensing, holographic encryption, and dual optical function switching. © 2021, CC BY.

关键词： Magnesium

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Extremal mechanisms for local differential privacy

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Peter Kairouz Sewoong Oh Pramod Viswanath Google MPI for Intelligent Systems Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL Department of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign Urbana IL

Local differential privacy has recently surfaced as a strong measure of privacy in contexts where personal information remains private even from data analysts. Working in a setting where both the data providers and data analysts want to maximize the utility of statistical analyses performed on the released data, we study the fundamental trade-off between local differential privacy and utility. This trade-off is formulated as a constrained optimization problem: maximize utility subject to local differential privacy constraints. We introduce a combinatorial family of extremal privatization mechanisms, which we call staircase mechanisms, and show that it contains the optimal privatization mechanisms for a broad class of information theoretic utilities such as mutual information and f-divergences. We further prove that for any utility function and any privacy level, solving the privacy-utility maximization problem is equivalent to solving a finite-dimensional linear program, the outcome of which is the optimal staircase mechanism. However, solving this linear program can be computationally expensive since it has a number of variables that is exponential in the size of the alphabet the data lives in. To account for this, we show that two simple privatization mechanisms, the binary and randomized response mechanisms, are universally optimal in the low and high privacy regimes, and well approximate the intermediate regime.

关键词： estimation f-divergences hypothesis testing information theoretic utilities local differential privacy mutual information privacy-preserving machine learning algorithms statistical inference

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Modeling Photovoltaic Optimized Charging of Electric Vehicles

Modeling Photovoltaic Optimized Charging of Electric Vehicle...

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IEEE PES Innovative Smart Grid Technologies Europe

作者： Lena-Marie Ritte Stefan Mischinger Kai Strunz Johannes Eckstein ILF Consulting Engineers Power Systems Industrial Plants Prof. Dr. K. Strunz are with SENSE Laboratory Department of Electrical Engineering and Computer Science EON Encrgic AG

ISBN: (纸本)9781467325950

When charged with renewable energy, electric vehicles can reduce the CO_2 emissions of the transportation sector. As a result, the German government has set the target of one million electric vehicles by 2020. At the same time, it is predicted that renewable energies will contribute 35% to the national energy consumption by 2020. Because photovoltaic systems can be connected to the same grid node as electric vehicles, they seem to be the first choice for charging electric vehicles with renewable energies. In this paper, the synergies between photovoltaic systems and electric vehicles will be analyzed from a technical as well as from an economic point of view. Specifically, a model has been developed in order to determine the amount of solar energy used by the electric vehicle, the grid load/feed-in and the cost savings for owners of an electric vehicle and a photovoltaic system.

关键词： Electric vehicles Photovoltaic systems Charging algorithms Integration of renewable energies

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On faster convergence of cyclic block coordinate descent-type methods for strongly convex minimization

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2017年第1期18卷

作者： Xingguo Li Tuo Zhao Raman Arora Han Liu Mingyi Hong Department of Electrical and Computer Engineering University of Minnesota Twin Cities Minneapolis MN School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta GA Department of Computer Science Johns Hopkins University Baltimore MD Department of Electrical Engineering and Computer Science Northwestern University Evanston IL

The cyclic block coordinate descent-type (CBCD-type) methods, which perform iterative updates for a few coordinates (a block) simultaneously throughout the procedure, have shown remarkable computational performance for solving strongly convex minimization problems. Typical applications include many popular statistical machine learning methods such as elastic-net regression, ridge penalized logistic regression, and sparse additive regression. Existing optimization literature has shown that for strongly convex minimization, the CBCD-type methods attain iteration complexity of O(p log(1/ε)), where ε is a pre-specified accuracy of the objective value, and p is the number of blocks. However, such iteration complexity explicitly depends on p, and therefore is at least p times worse than the complexity O(log(1/ε)) of gradient descent (GD) methods. To bridge this theoretical gap, we propose an improved convergence analysis for the CBCD-type methods. In particular, we first show that for a family of quadratic minimization problems, the iteration complexity O(log2(p) · log(1/ε)) of the CBCD-type methods matches that of the GD methods in term of dependency on p, up to a log2 p factor. Thus our complexity bounds are sharper than the existing bounds by at least a factor of p/log2(p). We also provide a lower bound to confirm that our improved complexity bounds are tight (up to a log2(p) factor), under the assumption that the largest and smallest eigenvalues of the Hessian matrix do not scale with p. Finally, we generalize our analysis to other strongly convex minimization problems beyond quadratic ones.

关键词： cyclic block coordinate descent gradient descent improved iteration complexity quadratic minimization strongly convex minimization

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