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Annual Conference of Industrial Electronics Society

作者： Kae Doki Akihiro Torii Suguru Mototani Yuki Funabora Shinji Doki Kohjiro Hashimoto Dept. of Electrical Engineering Aichi Institute of Technology Aichi Japan Dept. of Electrical Engineering and Computer Science Nagoya University Aichi Japan Dept. of Control and Information Systems Engineering National Institute of Technology Kumamoto Japan

The authors have researched on a method of human action modeling to realize systems such as to support human human operations or watch persons to prevent various kinds of accidents. In order to recognize or support various kinds of human actions, a certain human action model is necessary in these systems. Therefore, we have proposed a modeling method of human actions, which is extracted statistically from enormous data acquired from various kinds of sensors by long-term observation of human actions and situations around persons. However, human action elements composing a model should have been extracted heuristically by a designer. In this paper, an extraction method of human action elements is proposed in order to extract frequent human action elements automatically from acquired data. In the proposed method, a series of acquired time series data is divided into short partial ones, which are modeled by Hidden Markov Models(HMM). Then, the transition network of generated HMMs is constructed based on the likelihood between the original data and each HMM. In the obtained network, a transition sequence with the only one edge is regarded as a frequent human action element. Extraction results with artificial and actual human action data are shown in this paper in order to verify the usefulness of the proposed method.

关键词： Hidden Markov models Time series analysis Data models Data mining Robot sensing systems

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Optimal distributed control for platooning via sparse coprime factorizations

Optimal distributed control for platooning via sparse coprim...

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American Control Conference (ACC)

作者： Şerban Sabău Cristian Oară Sean Warnick Ali Jadbabaie Electrical and Computer Engineering Dept. Stevens Institute of Technology Hoboken NJ Automatic Control Dept. University “Politehnica” Bucharest Faculty of the Computer Science Department Brigham Young University Electrical and Systems Engineering Dept. University of Pennsylvania

We introduce a novel distributed control architecture for disturbances attenuation in heterogeneous platoons of linear and time-invariant autonomous agents that inherits the performance achievable by the leader-follower control policies. We provide a Youla-like parameterization for the class of all distributed controllers such that the sparsity constraints are imposed on the controllers' left coprime factors, outlining a new concept of structural constraints in distributed control. The proposed scheme lends itself to optimal controller design via norm based costs, it guarantees string stability and completely eliminates the accordion effect from the behavior of the platoon. The distributed controller is proven to attain global optimality for heterogeneous platoons and ℋ 2 costs and can easily reconfigure such as to remain optimal during formation merging/exiting maneuvers. The scheme is amenable to a GPS time base synchronization mechanism, allowing for the exact compensation of the time delays induced by the wireless communications.

关键词： Vehicles Standards Decentralized control Stability analysis Linear systems Transfer functions Electronic mail

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Reconfigurable band-notched UWB antenna for cognitive radio applications 16

Reconfigurable band-notched UWB antenna for cognitive radio ...

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16th Mediterranean Microwave Symposium, MMS 2016

作者： Ibrahim, Ahmed A. Shubair, Raed M. Electronics and Communications Dept. Minia University Elminia Egypt Institute of Micro- and Sensor Systems University of Magdeburg Germany Electrical and Computer Engineering Department Khalifa University United Arab Emirates Research Laboratory of Electronics Massachetts Institute of Technology United States

ISBN: (纸本)9781509025862

A compact reconfigurable band-notched UWB antenna is presented in this paper. The antenna has a partial ground plane with a rectangular slot to achieve the desired bandwidth for UWB from 3.1GHz to 12GHz. The antenna has a single band-notch frequency which is achieved using a switched U-shaped slot, inserted in the radiating patch. This slot is used to decrease interference with WIMAX applications from 3.3GHz to 3.9GHz. The switching reconfiguration of the proposed antenna is achieved through two ideal switches. The antenna inhibits two modes of operation, which result from controlling the switch on/off. The proposed antenna has VSWR © 2016 IEEE.

关键词： Cognitive radio

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A compact spectacles shaped patch antenna for UWB applications

A compact spectacles shaped patch antenna for UWB applicatio...

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International Conference on electrical and computer engineering (ICECE)

作者： M. Tarikul Islam M. Samsuzzaman M. Z. Mahmud M.T. Islam Dept. of Computer and Communication Engineering Patuakhali Science and Technology University Patuakhai Bangladesh Dept. of Electrical Electronic and Systems Engineering Universiti Kebangsaan Malaysia Malaysia

ISBN: (纸本)9781509029648

In this paper, a new compact spectacles shaped patch UWB antenna using a microstrip fed line is proposed. The new design is consist of a spectacles shaped patch along with tapered slot ground *** proposed antenna has a compact electrical dimension of 0.24 λ × 0.21 λ × 0.016 λ and fed with a 50-ohm microstrip transmission line. computer Simulation technology (CST) based on finite-difference time-domain method and High Frequency Structural Simulator (HFSS) based on finite element method is implemented in this research. In simulation, a wider frequency bandwidth of 8.5 GHz (3.0 - 11.5 GHz) with excellent impedance matching which covers entire UWB frequency, stable radiation pattern and constant gain is achieved. The proposed antenna is low profile, inexpensive and has excellent characteristics of UWB.

关键词： Ultra wideband antennas Microstrip antennas Antenna radiation patterns Microstrip Slot antennas Bandwidth

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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A passivity based control signal guaranteeing joint limit avoidance in redundant robots

A passivity based control signal guaranteeing joint limit av...

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Mediterranean Conference on Control and Automation (MED)

作者： Dimitrios Papageorgiou Abdelrahem Atawnih Zoe Doulgeri Center for Research and Technology Hellas (CERTH) Greece Dept. of Electrical and Computer Engineering Aristotle University of Thessaloniki Greece

ISBN: (纸本)9781467383479

In this work we propose a torque control signal that guarantees joint limit avoidance of a redundant arm. Its design is based on the prescribed performance control methodology that enables guarantees on the satisfaction of inequality constraints regarding the system output. It is proved that the proposed signal preserves the passivity of closed loop robot dynamics with respect to joint velocities thus allowing its use with any passive control law designed to attract the robot towards a task goal. Experimental results with a KUKA LWR4+ for a task involving the tip's position motion along a linear path confirm theoretical findings and demonstrate the proposed signal's performance in two scenarios of a feasible and an unfeasible path.

关键词： Redundancy Manipulators Torque Robot kinematics Dynamics Kinematics

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Dataflow to Hardware Synthesis Framework on FPGAs

Dataflow to Hardware Synthesis Framework on FPGAs

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International Symposium on computer Architecture and High Performance Computing Workshops (SBAC-PADW)

作者： Youngsoo Kim Shrikant Jadhav Clay S. Gloster Dept. of Electrical Engineering San Jose State University San Jose CA Dept. of Electrical and Computer Engineering North Carolina A&T University Greensboro NC Dept. of Computer Systems Technology North Carolina A&T University Greensboro NC

ISBN: (纸本)9781509048458

We present a dataflow based performance estimation and synthesis framework that will help hardware designers quantify the algorithm performance and synthesize their HW designs onto Field Programmable Gate Arrays (FPGAs). Typically, Digital Signal Processing (DSP) systems are designed by making gradual architectural choices in HW refinement steps. These decisions are based on performance quantification by high level DSP algorithm developers and HW implementation engineers. The main obstacle to this refinement is the provision of reasonably correct performance estimations to guide HW designers in Design Space Exploration (DSE) at an early stage. HW designers face challenges when they need to quantify the performance of their designs, especially when resources are limited. We use dataflow models by describing their hardware detail only as necessary. Dataflow based performance estimation achieves the efficient generation of qualitative and quantitative parameters for the assessment of HW candidates. Reconfigurable logic can be used to off-load the primary computational kernel onto a custom computing machine in order to reduce execution time by an order of magnitude as compared to kernel execution on a general purpose processor. Specifically, FPGAs can be used to accelerate these kernels using hardware-based custom logic implementations. In this paper, we demonstrate a framework for algorithm acceleration from the dataflow to synthesized HDL design. Experimental results show a linear speedup by adding reasonably small processing elements in FPGA as opposed to using a software implementation running on a typical general purpose processor.

关键词： Hardware Algorithm design and analysis Digital signal processing Estimation Field programmable gate arrays Software Discrete wavelet transforms

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Measurement of repeat effects in Chicago's criminal social network

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Applied Computing and Informatics 2016年第2期12卷 154-160页

作者： Kump, Paul Alonso, David Haro Yang, Yongyi Candella, Joseph Lewin, Jonathan Wernick, Miles N. Medical Imaging Research Center and Dept. of Electrical and Computer Engineering Illinois Institute of Technology 3300 S. Federal St. Chicago 60616 IL United States Chicago Police Department 3510 S. Michigan Ave. Chicago 60653 IL United States

The “near-repeat” effect is a well-known criminological phenomenon in which the occurrence of a crime incident gives rise to a temporary elevation of crime risk within close physical proximity to an initial incident. Adopting a social network perspective, we instead define a near repeat in terms of geodesic distance within a criminal social network, rather than spatial distance. Specifically, we report a statistical analysis of repeat effects in arrest data for Chicago during the years 2003–2012. We divide the arrest data into two sets (violent crimes and other crimes) and, for each set, we compare the distributions of time intervals between repeat incidents to theoretical distributions in which repeat incidents occur only by chance. We first consider the case of the same arrestee participating in repeat incidents (“exact repeats”) and then extend the analysis to evaluate repeat risks of those arrestees near one another in the social network. We observe repeat effects that diminish as a function of geodesic distance and time interval, and we estimate typical time scales for repeat crimes in Chicago. © 2016 The Authors

关键词： Crime analysis Predictive policing Social network analysis Violent crime

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Evaluation of epoxy coated resistors in high voltage DC surge environments

Evaluation of epoxy coated resistors in high voltage DC surg...

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2016 IEEE International Power Modulator and High Voltage Conference, IPMHVC 2016

作者： Zirnheld, Jennifer Casey, Peter Francis, Akeem Thompson, Kyle Burke, Kevin Singletary, Michael Olabisi, Shola Glodzik, William Energy Systems Integration Dept. of Electrical Engineering University at Buffalo 230 Davis Hall BuffaloNY14260 United States Electrical Computer and Telecommunications Engineering Technology Rochester Institute of Technology 78 Lomb Memorial Drive RochesterNY14623 United States HVR Advanced Power Components Inc. 2090 Old Union Rd. CheektowagaNY14227 United States

ISBN: (纸本)9781509023547

The performance data of high voltage resistors in specific pulsed power applications is limited. A series of tests were performed on epoxy coated high voltage ceramic composite resistors. With the implementation of these resistors in unique applications that result in their exposure to high energy surge environments such as industrial motor drives and electric vehicles, there is a need for a better understanding of performance metrics under these conditions. Thermodynamic data and resistance parameters were experimentally determined with fully and partially coated epoxy resistors. This paper will explore the added benefits of the epoxy, beyond the mechanical strength and aesthetics. The potential gains include a stability of resistance and thermal surface radiation. Data collected indicates that complete epoxy coated resistors show increased performance, maintaining the pretested resistance 54% more accurately and decreasing the radiation temperature exposure to other components by 10.45°C. © 2016 IEEE.

关键词： Resistors

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Application of pattern spectra and convolutional neural networks to the analysis of simulated Cherenkov Telescope Array data 37

Application of pattern spectra and convolutional neural netw...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Aschersleben, J. Peletier, R.F. Vecchi, M. Wilkinson, M.H.F. Abdalla, H. Abe, H. Abe, S. Abusleme, A. Acero, F. Acharyya, A. Acín Portella, V. Ackley, K. Adam, R. Adams, C. Adhikari, S.S. Aguado-Ruesga, I. Agudo, I. Aguilera, R. Aguirre-Santaella, A. Aharonian, F. Alberdi, A. Alfaro, R. Alfaro, J. Alispach, C. Aloisio, R. Alves Batista, R. Amans, J.-P. Amati, L. Amato, E. Ambrogi, L. Ambrosi, G. Ambrosio, M. Ammendola, R. Anderson, J. Anduze, M. Angüner, E.O. Antonelli, L.A. Antonuccio, V. Antoranz, P. Anutarawiramkul, R. Aragunde Gutierrez, J. Aramo, C. Araudo, A. Araya, M. Arbet-Engels, A. Arcaro, C. Arendt, V. Armand, C. Armstrong, T. Arqueros, F. Arrabito, L. Arsioli, B. Artero, M. Asano, K. Ascasíbar, Y. Ashley, M. Attinà, P. Aubert, P. Singh, C.B. Baack, D. Babic, A. Backes, M. Baena, V. Bajtlik, S. Baktash, A. Balazs, C. Balbo, M. Ballester, O. Ballet, J. Balmaverde, B. Bamba, A. Bandiera, R. Baquero Larriva, A. Barai, P. Barbier, C. Barbosa Martins, V. Barcelo, M. Barkov, M. Barnard, M. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Bastieri, D. Batista, P.I. Batkovic, I. Bauer, C. Bautista-González, R. Baxter, J. Becciani, U. Becerra González, J. Becherini, Y. Beck, G. Becker Tjus, J. Bednarek, W. Belfiore, A. Bellizzi, L. Belmont, R. Benbow, W. Berge, D. Bernardini, E. Bernardos, M.I. Bernlöhr, K. Berti, A. Berton, M. Bertucci, B. Beshley, V. Bhatt, N. Bhattacharyya, S. Bhattacharyya, W. Bhattacharyya, S. Bi, B. Bicknell, G. Biederbeck, N. Bigongiari, C. Biland, A. Bird, R. Bissaldi, E. Biteau, J. Bitossi, M. Blanch, O. Blank, M. Blazek, J. Bobin, J. Boccato, C. Bocchino, F. Boehm, C. Bohacova, M. Boisson, C. Boix, J. Bolle, J.-P. Bolmont, J. Bonanno, G. Bonavolontà, C. Bonneau Arbeletche, L. Bonnoli, G. Bordas, P. Borkowski, J. Bórquez, S. Bose, R. Bose, D. Bosnjak, Z. Bottacini, E. Böttcher, M. Botticella, M.T. Boutonnet, C. Bouyjou, F. Bozhilov, V. Bozzo, E. Brahimi, L. Braiding, C. Brau-Nogué, S. Kapteyn Astronomical Institute University of Groningen PO Box 800 Groningen9700 AV Netherlands Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen PO Box 407 GroningenNL-9700 AK Netherlands Centre for Space Research North-West University Potchefstroom2520 South Africa Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwa-no-ha Chiba Kashiwa277-8582 Japan Pontificia Universidad Católica de Chile Av. Libertador Bernardo O'Higgins 340 Santiago Chile AIM CEA CNRS Université Paris-Saclay Université Paris Diderot Sorbonne Paris Cité CEA Paris-Saclay IRFU DAp Bat 709 Orme des Merisiers Gif-sur-Yvette91191 France Centre for Advanced Instrumentation Dept. of Physics Durham University South Road DurhamDH1 3LE United Kingdom Port d'Informació Científica Edifici D Carrer de l'Albareda Bellaterrra Cerdanyola del Vallès08193 Spain School of Physics and Astronomy Monash University MelbourneVIC3800 Australia Laboratoire Leprince-Ringuet École Polytechnique UMR 7638 CNRS/IN2P3 Institut Polytechnique de Paris Palaiseau91128 France Department of Physics Columbia University 538 West 120th Street New YorkNY10027 United States University of Oslo Department of Physics Sem Saelandsvei 24 - PO Box 1048 Blindern OsloN-0316 Norway EMFTEL Department IPARCOS Universidad Complutense de Madrid Madrid28040 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Campus UAB Carrer de Can Magrans s/n Cerdanyola del Vallés08193 Spain Instituto de Física Teórica UAM/CSIC Departamento de Física Teórica Universidad Autónoma de Madrid c/Nicolás Cabrera 13-15 Campus de Cantoblanco UAM Madrid28049 Spain Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Universidad Nacional Autónoma de México Delegación Coyoacán Ciudad de México04510 Mexico University of Geneva Département de Physique Nucléaire et Corpusculaire 24 rue d

The Cherenkov Telescope Array (CTA) will be the next generation gamma-ray observatory and will be the major global instrument for very-high-energy astronomy over the next decade, offering 5 − 10 × better flux sensitivity than current generation gamma-ray telescopes. Each telescope will provide a snapshot of gamma-ray induced particle showers by capturing the induced Cherenkov emission at ground level. The simulation of such events provides images that can be used as training data for convolutional neural networks (CNNs) to determine the energy of the initial gamma rays. Compared to other state-of-the-art algorithms, analyses based on CNNs promise to further enhance the performance to be achieved by CTA. Pattern spectra are commonly used tools for image classification and provide the distributions of the shapes and sizes of various objects comprising an image. The use of relatively shallow CNNs on pattern spectra would automatically select relevant combinations of features within an image, taking advantage of the 2D nature of pattern spectra. In this work, we generate pattern spectra from simulated gamma-ray events instead of using the raw images themselves in order to train our CNN for energy reconstruction. This is different from other relevant learning and feature selection methods that have been tried in the past. Thereby, we aim to obtain a significantly faster and less computationally intensive algorithm, with minimal loss of performance. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Gamma rays

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