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MRS Advances

作者： Costa, L. Al-Hashimi, M. Heeney, M. Terekhov, A. Rajput, D. Hofmeister, W. Verma, A. Department of Materials Science and Engineering University of Tennessee - Knoxville TullahomaTN37388 United States Center for Laser Applications University of Tennessee Space Institute TullahomaTN37388 United States Department of Chemistry Texas AandM University at Qatar P.O. Box 23874 Doha Qatar Department of Chemistry and Centre for Plastic Electronics Imperial College London LondonSW7 2AZ United Kingdom Department of Electrical Engineering and Computer Science Texas AandM University - Kingsville KingsvilleTX78363 United States

We report on the preparation of conjugated polymer nanofibers via template-synthesis. A femtosecond laser machined fused silica template, with a micro-patterned array of high-aspect ratio surface nanopores, was first spin-coated with a polyvinyl alcohol (PVA) solution to form a water-soluble release layer. The PVA-lined template was then coated with a conjugated poly(3-hexylselenophene) (P3HS) - solvent solution. The solvent was allowed to dissipate, and the resulting {template / PVA liner / conjugated P3HS film} stack was then submerged in water to dissolve the PVA and release the conjugated polymer film from the template. The resulting conjugated polymer film exhibits an array of micro-patterned polymer nanofibers that constitute a negative replica of the PVA-lined template. The nanofibers come arranged in a vertical architecture that spans over the surface of the supporting film. © 2017 Materials Research Society.

关键词： Conjugated polymers

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A correlation based SVM-recursive multiple feature elimination classifier for breast cancer disease using microarray 5

A correlation based SVM-recursive multiple feature eliminati...

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5th International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016

作者： Kavitha, K.R. Rajendran, G. Syamili Varsha, J. Department of Computer Science and Applications Amrita School of Engineering Amritapuri Amrita Viswa Vidyapeetham Amrita University India

ISBN: (纸本)9781509020287

Support Vector Machine (SVM) [2], is most widely popular learning algorithm used for classification of large dataset. Our project aims to generate a classifier for breast cancer genes microarray by using modified-SVM-RFE algorithm. This breast cancer microarray contains a large number of genes and its expression, so it necessary to reduce the number of genes before applying for classification. So the most efficient algorithm that can be applied for classification of microarray is SVM-RFE [3][8], which is an embedded method, which performs backward single gene elimination as well as classification of the dataset. A new modified algorithm is proposed with less computation over SVM-RFE. SVM-RFE generates the rank of the features and eliminates one lowest rank irrelevant feature, in each iteration. Since our microarray contains 47,294 genes its very computational overhead to reduce the dimension. So the modified algorithm which removes more than one irrelevant genes in single iteration of SVM-RFE algorithm. And also this algorithm only removes irrelevant gene, it does not remove the correlated genes. So before applying SVM-RFE, our research focuses on finding out the correlated genes and extracting a new gene from the two, and then apply SVM-RFE on the new set of genes. So our proposed method is Correlation based Support Vector Machine Recursive Multiple Feature Elimination (CSVM-RMFE) algorithm which first extracts a new genes from two correlated genes called virtual gene and then apply SVM-RMFE to generate a classifier. This SVM-RMFE algorithm eliminate multiple feature so that the classification time can be reduced and its accuracy can be increased. © 2016 IEEE.

关键词： Data mining

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Source Separation of Heartbeat Sounds for Effective E-Auscultation

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Journal of The Institution of Engineers (India): Series B 2016年第1期97卷 69-75页

作者： Geethu, R.S. Krishnakumar, M. Pramod, K.V. George, Sudhish N. Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri KollamKerala India Department of Computer Applications Cochin University of Science and Technology CochinKerala India Department of Electronics and Communication Engineering National Institute of Technology Calicut KozhikodeKerala India

This paper proposes a cost effective solution for improving the effectiveness of e-auscultation. Auscultation is the most difficult skill for a doctor, since it can be acquired only through experience. The heart sound mixtures are captured by placing the four numbers of sensors at appropriate auscultation area in the body. These sound mixtures are separated to its relevant components by a statistical method independent component analysis. The separated heartbeat sounds can be further processed or can be stored for future reference. This idea can be used for making a low cost, easy to use portable instrument which will be beneficial to people living in remote areas and are unable to take the advantage of advanced diagnosis methods. © 2015, The Institution of Engineers (India).

关键词： Blind source separation

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Author Correction: National identity predicts public health support during a global pandemic

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Nature communications 2022年第1期13卷 1949页

作者： Jay J Van Bavel Aleksandra Cichocka Valerio Capraro Hallgeir Sjåstad John B Nezlek Tomislav Pavlović Mark Alfano Michele J Gelfand Flavio Azevedo Michèle D Birtel Aleksandra Cislak Patricia L Lockwood Robert Malcolm Ross Koen Abts Elena Agadullina John Jamir Benzon Aruta Sahba Nomvula Besharati Alexander Bor Becky L Choma Charles David Crabtree William A Cunningham Koustav De Waqas Ejaz Christian T Elbaek Andrej Findor Daniel Flichtentrei Renata Franc Biljana Gjoneska June Gruber Estrella Gualda Yusaku Horiuchi Toan Luu Duc Huynh Agustin Ibanez Mostak Ahamed Imran Jacob Israelashvili Katarzyna Jasko Jaroslaw Kantorowicz Elena Kantorowicz-Reznichenko André Krouwel Michael Laakasuo Claus Lamm Caroline Leygue Ming-Jen Lin Mohammad Sabbir Mansoor Antoine Marie Lewend Mayiwar Honorata Mazepus Cillian McHugh John Paul Minda Panagiotis Mitkidis Andreas Olsson Tobias Otterbring Dominic J Packer Anat Perry Michael Bang Petersen Arathy Puthillam Julián C Riaño-Moreno Tobias Rothmund Hernando Santamaría-García Petra C Schmid Drozdstoy Stoyanov Shruti Tewari Bojan Todosijević Manos Tsakiris Hans H Tung Radu G Umbreș Edmunds Vanags Madalina Vlasceanu Andrew Vonasch Meltem Yucel Yucheng Zhang Mohcine Abad Eli Adler Narin Akrawi Hamza Alaoui Mdarhri Hanane Amara David M Amodio Benedict G Antazo Matthew Apps F Ceren Ay Mouhamadou Hady Ba Sergio Barbosa Brock Bastian Anton Berg Maria P Bernal-Zárate Michael Bernstein Michał Białek Ennio Bilancini Natalia Bogatyreva Leonardo Boncinelli Jonathan E Booth Sylvie Borau Ondrej Buchel C Daryl Cameron Chrissie F Carvalho Tatiana Celadin Chiara Cerami Hom Nath Chalise Xiaojun Cheng Luca Cian Kate Cockcroft Jane Conway Mateo Andres Córdoba-Delgado Chiara Crespi Marie Crouzevialle Jo Cutler Marzena Cypryańska Justyna Dabrowska Michael A Daniels Victoria H Davis Pamala N Dayley Sylvain Delouvee Ognjan Denkovski Guillaume Dezecache Nathan A Dhaliwal Alelie B Diato Roberto Di Paolo Marianna Drosinou Uwe Dulleck Jānis Ekmanis Arhan S Ertan Tom W Etienne Hapsa Hossain Farhana Fahima Farkhari Har Department of Psychology and Neural Science New York University New York NY USA. jay.vanbavel@nyu.edu. School of Psychology University of Kent Canterbury England. Department of Economics Middlesex University London London England. Department of Strategy and Management Norwegian School of Economics Bergen Norway. SWPS University of Social Sciences and Humanities Poznań Poland. Department of Psychological Sciences College of William and Mary Williamsburg VA USA. Institute of Social Sciences Ivo Pilar Zagreb Croatia. Department of Philosophy Macquarie University Sydney NSW Australia. Stanford Graduate School of Business Stanford University Stanford CA USA. Institute of Communication Science Friedrich-Schiller University Jena Jena Germany. flavio.azevedo@uni-jena.de. School of Human Sciences Institute for Lifecourse Development University of Greenwich London England. Department of Experimental Psychology University of Oxford Oxford England. Center for Human Brain Health School of Psychology University of Birmingham Birmingham England. Department of Psychology Macquarie University Sydney NSW Australia. KU Leuven Leuven Belgium. National Research University Higher School of Economics (HSE) Moscow Russia. De La Salle University Manila Philippines. Department of Psychology University of the Witwatersrand Johannesburg South Africa. Department of Political Science Aarhus University Aarhus Denmark. X University Toronto ON Canada. Department of Government Dartmouth College Hanover NH USA. Department of Psychology University of Toronto Toronto ON Canada. Gatton College of Business and Economics University of Kentucky Lexington KY USA. Department of Mass Communication National University of Science and Technology (NUST) Islamabad Pakistan. Department of Management Aarhus University Aarhus Denmark. Faculty of Social and Economic Sciences Comenius University Bratislava Slovakia. IntraMed Buenos Aires Argentina. Macedonian Academy of Sciences and

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Diagnosis of liver disease using correlation distance metric based K-nearest neighbor approach

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International Symposium on Intelligent Systems Technologies and applications, ISTA 2016

作者： Singh, Aman Pandey, Babita Department of Computer Science and Engineering Lovely Professional University JalandharPunjab144411 India Department of Computer Applications Lovely Professional University JalandharPunjab144411 India

ISBN: (纸本)9783319479514

Mining meaningful information from huge medical datasets is a key aspect of automated disease diagnosis. In recent years, liver disease has emerged as one of the commonly occurring disease worldwide. In this study, a correlation distance metric and nearest rule based k-nearest neighbor approach is presented as an effective prediction model for liver disease. Intelligent classification algorithms employed on liver patient dataset are linear discriminant analysis (LDA), diagonal linear discriminant analysis (DLDA), quadratic discriminant analysis (QDA), diagonal quadratic discriminant analysis (DQDA), least squares support vector machine (LSSVM) and k-nearest neighbor (KNN) based approaches. K-fold cross validation method is used to validate the performance of mentioned classifiers. It is observed that KNN based approaches are superior to all classifiers in terms of attained accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive (NPV) value rates. Furthermore, KNN with correlation distance metric and nearest rule based machine learning approach emerged as the best predictive model with highest diagnostic accuracy. Especially, the proposed model attained remarkable sensitivity by reducing the false negative rates. © Springer International Publishing AG 2016.

关键词： Data mining

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Stabilization of Cascaded Two-Port Networked Systems Against Nonlinear Perturbations

arXiv

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arXiv 2017年

作者： Zhao, Di Khong, Sei Zhen Qiu, Li Department of Electronic & Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Institute for Mathematics and its Applications University of Minnesota MinneapolisMN55455 United States

A networked control system (NCS) consisting of cascaded two-port communication channels between the plant and controller is modeled and analyzed. Towards this end, the robust stability of a standard closed-loop system in the presence of conelike perturbations on the system graphs is investigated. The underlying geometric insights are then exploited to analyze the two-port NCS. It is shown that the robust stability of the two-port NCS can be guaranteed when the nonlinear uncertainties in the transmission matrices are sufficiently small in norm. The stability condition, given in the form of "arcsin" of the uncertainty bounds, is both necessary and sufficient. Copyright © 2017, The Authors. All rights reserved.

关键词： Networked control systems

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Scheduling algorithm considering response time for mixed tasks on multiprocessor systems 16

Scheduling algorithm considering response time for mixed tas...

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31st Annual ACM Symposium on Applied Computing, SAC 2016

作者： Kuo, Chin-Fu Huang, Yu-Hao Lu, Yung-Feng Chou, Shih-Chun Department of Computer Science and Information Engineering National University of Kaohsiung Kaohsiung Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan Innovative DigiTech-Enabled Applications and Services Institute Institute for Information Industry Taipei Taiwan

ISBN: (纸本)9781450337397

The purpose of this paper is to study the scheduling problem of mixed task sets on multiprocessor systems. We propose a scheduling framework to improve the average response time of aperiodic jobs in mixed tasks. Our proposed scheduling framework consists of two phases: In the first phase, we considers how to assign each periodic task to a proper processor by different task allocation heuristics. Then, on a processor the unused capacity left by periodic tasks is set as the size of the Total Bandwidth (TB) Server. In the second phase, at run time when an aperiodic job arrives in the system, it will be assigned to a proper TB Server with the minimal virtual deadline and scheduled with periodic jobs on the same processor under Earliest Deadline First (EDF). A series of experiments were conducted to evaluate the proposed framework. © 2016 ACM.

关键词： Scheduling algorithms

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Fuzzy logic for decision-enablement: A novel context-awareness framework for smarter environments

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International Journal of High Performance Computing and Networking 2017年第1-2期10卷 64-77页

作者： Rajaraajeswari, S. Selvarani, R. Raj, Pethuru Mohanavadivu, P. Department of Computer Applications Raja Rajeswari College of Engineering Bangalore560074 India Computer Science and Engineering Department Alliance University Bangalore562106 India Infrastructure Architect IBM Global Cloud Center of Excellence IBM India Bangalore560045 India TCL Canada 1441 rue Carrié-Derick MontréalQCH3C 4S9 Canada

With the emergence of pioneering technologies and tools in the information and communication technology (ICT) space, our everyday environments (personal, social, professional, etc.) are deeply ICT-enabled to be smarter in their operations and offerings. Smarter healthcare is one shining example. Provisioning real-time and precision-centric healthcare facilities for people especially those who are in disabled, debilitated, and diseased conditions is being made possible with the impressive advancements (the growing array of smart sensors, digitised entities, wearables, robots, controllers, and other disposable and diminutive actuators). The growing variety and volume of connected devices enable remote diagnostics, real-time monitoring, measuring, and management by transmitting various body health parameters for ensuring perfect decision-making and medication. In this paper, we have described an adaptive framework leveraging the pivotal power of fuzzy logic, which is famous for rule-based decision-making, for accelerating the systematic realisation of smarter environments especially for the healthcare segment. Copyright © 2017 Inderscience Enterprises Ltd.

关键词： Fuzzy logic

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FP-DNN: An Automated Framework for Mapping Deep Neural Networks onto FPGAs with RTL-HLS Hybrid Templates

FP-DNN: An Automated Framework for Mapping Deep Neural Netwo...

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Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM)

作者： Yijin Guan Hao Liang Ningyi Xu Wenqiang Wang Shaoshuai Shi Xi Chen Guangyu Sun Wei Zhang Jason Cong Center for Energy-Efficient Computing and Applications Peking University Beijing China PKU/UCLA Joint Research Institute Microsoft Research Asia Beijing China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology China PKU/UCLA Joint Research Institute in Science and Engineering UCLA PKU/UCLA Joint Research Institute Peking University Computer Science Department University of California Los Angeles USA

DNNs (Deep Neural Networks) have demonstrated great success in numerous applications such as image classification, speech recognition, video analysis, etc. However, DNNs are much more computation-intensive and memory-intensive than previous shallow models. Thus, it is challenging to deploy DNNs in both large-scale data centers and real-time embedded systems. Considering performance, flexibility, and energy efficiency, FPGA-based accelerator for DNNs is a promising solution. Unfortunately, conventional accelerator design flows make it difficult for FPGA developers to keep up with the fast pace of innovations in DNNs. To overcome this problem, we propose FP-DNN (Field Programmable DNN), an end-to-end framework that takes TensorFlow-described DNNs as input, and automatically generates the hardware implementations on FPGA boards with RTL-HLS hybrid templates. FP-DNN performs model inference of DNNs with our high-performance computation engine and carefully-designed communication optimization strategies. We implement CNNs, LSTM-RNNs, and Residual Nets with FPDNN, and experimental results show the great performance and flexibility provided by our proposed FP-DNN framework.

关键词： Computational modeling Field programmable gate arrays Kernel Hardware Analytical models Data models Generators

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Implementation of ternary logic in QCA using SPICE macro-modeling

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Journal of engineering Technology 2016年第2期5卷 143-155页

作者： Bhattacharjee, Pritam Dey, Arijit Das, Kunal Chakraborty, Swarnendu Kumar Goswami, Rajat Subhra Department of Electronics and Computer Engineering National Institute of Technology Arunachal Pradesh Yupia Dist.- PapumpareArunachal Pradesh791112 India Department of Computer Science and Engineering Narula Institute of Technology 81 Nilgunj Road Kolkata700109 India Department of Computer Applications B.P. Poddar Institute of Management and Technology 137 VIP Road Kolkata700052 India

In this paper, we introduce a new trend in Quantum dot Cellular Automata - Ternary QCA (tQCA) and have justified its presence using SPICE (Simulation Program with Integrated Circuit Emphasis). The macro-modeling which we have shown is concise and compact. Our proposed model for metal-island tQCA has helped to estimate its logic performance. This work sets a mark-up in the domain of cellular automata and it has shown that it can operate in the milli-volt (∼60mV) regime. © 2016, American Society for engineering Education. All rights reserved.

关键词： SPICE

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