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ACS/IEEE International Conference on computer Systems and Applications

作者： Majdi Rawashdeh Awny Alnusair Muder Almiani Lina Sawalha Dept. of Business Information Technology Princess Sumaya University for Technology Amman Jordan Dept. of Informatics & Computer Science Indiana University Kokomo Kokomo IN USA Computer Information Systems Dept. Al-Hussein Bin Talal University Ma'an Jordan Dept. of Electrical and Computer Engineering Western Michigan University Kalamazoo MI USA

ISBN: (数字)9781728150529

ISBN: (纸本)9781728150536

Understanding and reusing complex APIs is a difficult and time consuming process, especially in legacy code and open-source software libraries and frameworks. While some primary API features are documented properly, most others lack enough documentation and design knowledge that can help developers find, understand, and reuse the needed software components and other library features. As such, the source-code itself is often the only reliable means for programmers to rely on when performing their reuse practices. In order to leverage program understanding and software reuse, this paper describes an ontology-based system named JMentor that we have developed for this purpose. Firstly, JMentor is capable of recovering the lost design decisions by reverse engineering design patterns. Secondly, JMentor provides a mechanism for locating and retrieving reusable software components. Thirdly, JMentor provides an approach for automatically recommending source-code snippets that show an example of how the user can properly reuse the retrieved components. JMentor mechanisms are based on ontology and semantic reasoning techniques. Its evaluation case studies show evidence that such techniques can improve software understanding and reuse practices.

关键词： Ontologies Software Semantics Object oriented modeling Software libraries Task analysis

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An implementation of data exchange in environmental monitoring using authenticated attribute-based encryption with revocation

An implementation of data exchange in environmental monitori...

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2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2018

作者： Munsyi Sudarsono, Amang Harun Al Rasvid, M. Udin Dept. of Informatics and Computer Engineering Magister Program of Engineering Technology Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Universitas Muhammadiyah Banjarmasin Banjarmasin Indonesia

ISBN: (纸本)9781538680797

Internet of things era grown very rapidly in Industrial Revolution 4.0, there are many researchers use the Wireless Sensor Network (WSN) technology to obtain the data for environmental monitoring. The data obtained from WSN will be sent to the Data Center, where users can view and collect all of data from the Data Center using end devices such as personal computer, laptop, and mobile phone. The Data Center would be very dangerous, because everyone can intercept, track and even modify the data. Security requirement to ensure the confidentiality all of stored data in the data center and give the authenticity in data has not changed during the collection process. Ciphertext Policy Attribute-Based Encryption (CP-ABE) can become a solution to secure the confidentiality for all of data. Only users with appropriate rule of policy can get the original data. To guarantee there is no changes during the collection process of the data then require the time stamp digital signature for securing the data integrity. To protect the confidentiality and data integrity, we propose a security mechanism using CP-ABE with user revocation and Time Stamp Digital Signature using Elliptic Curve Cryptography (ECC) 384 bits. Our system can do the revocation for the users who did the illegal access. Our system is not only securing the data but also providing the guarantee that is no changes during the collection process of the data from the Data Center. © 2018 IEEE.

关键词： Wireless sensor networks

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A Novel Circuit Topology and Its Design for Class-E~2 DC-DC Converter

A Novel Circuit Topology and Its Design for Class-E~2 DC-DC ...

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IEEE Energy Conversion Congress and Exposition

作者： Yusuke Ogi Fumiya Ebihara Xiuqin Wei Hiroo Sekiya Dept. Electrical Electronic and Computer Engineering Chiba Institute of Technology Chiba Japan Dept. Electrical and Electronic Engineering Faculty of Engineering Chiba Institute of Technology Chiba Japan Dept. Applied and Cognitive Informatics Chiba University Chiba Japan

This paper presents a novel class-E 2 dc-dc converter and its design procedure. The proposed converter can maintain high-frequency and high-efficiency operations while realizing low switch-voltage stresses. Additionally, by using the presented design procedure, the proposed converter satisfying the zero-voltage-switching and zero-derivative-switching (ZVS/ZDS) conditions can be implemented without applying any tuning processes. A design example of the proposed class-E 2 converter is shown along with its LTspice simulation and laboratory experiment. The results from the LTspice simulator and laboratory experiment show quantitative agreement with the analytical predictions, which indicates the validity of the proposed topology and design procedure.

关键词： Switches Inverters Switching circuits Resonant frequency DC-DC power converters Inductors Integrated circuits

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Learning Information Visualization Algorithms using Tulu

Learning Information Visualization Algorithms using Tulu

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Latin American Conference on Learning Objects and technology (LACLO)

作者： Armando Arce-Orozco Antonio Gonzalez-Torres Erick Mata-Montero School of Computing Costa Rica Institute of Technology Cartago Costa Rica School of Informatics UNA Costa Rica Dept. of Computer Engineering Costa Rica Institute of Technology Cartago Costa Rica

Information visualization libraries are toolkits that frequently require users to have a deep understanding of their internal structure and design. Their features sometimes include a wide range of visualization methods from simple low-level drawings all the way to high-level sophisticated visualizations. This complexity is an obstacle for researchers, students, and developers who need to design and implement appealing visual representations that can be incorporated into their products efficiently and easily. This paper presents Tulu, a library that runs on top of Diököl, a programming environment created with Lua and OpenVG. Tulu is aimed to serve as an alternative for creating visualization systems. This library offers several advantages over traditional libraries including its extensibility, portability, and size. With respect to extensibility, it allows the adaptation and extension of the logic of the visualization techniques implemented in its catalog. Concerning portability, it runs on several operating systems. Finally, in regard to size, Diököl's space requirement is less than 1 MB, which makes it fitting for small single-board computers, such as the Raspberry Pi device, aimed at teaching basic computer science concepts.

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Automated Assessment of Pain Intensity Based on EEG Signal Analysis

Automated Assessment of Pain Intensity Based on EEG Signal A...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Panagiotis A. Bonotis Dimosthenis C. Tsouros Panagiotis N. Smyrlis Alexandros T. Tzallas Nikolaos Giannakeas Evripidis Glavas Markos G. Tsipouras Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Informatics and Telecommunication University of Ioannina Arta Greece UNIVEYE IKE Science and Technology Park of Epirus Ioannina Greece Dept. of Electrical and Computer Engineering Univ. of Western Macedonia Kozani Greece

Objective characterization of pain intensity is necessary under certain clinical conditions. The portable electroencephalogram (EEG) is a cost-effective assessment tool and lately, new methods using efficient analysis of related dynamic changes in brain activity in the EEG recordings proved that these can reflect the dynamic changes of pain intensity. In this paper, a novel method for automated assessment of pain intensity using EEG data is presented. EEG recordings from twenty-two (22) healthy volunteers are recorded with the Emotiv EPOC+ using the Cold Pressor Test (CPT) protocol. The relative power of each brain band's energy for each channel is extracted and the stochastic forest algorithm is employed for discrimination across five classes, depicting the pain intensity. Obtained results in terms of classification accuracy reached high levels (72.7%), which renders the proposed method suitable for automated pain detection and quantification of its intensity.

关键词： Protocols Pain Forestry Tools Electroencephalography Mobile handsets Signal analysis

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Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review

arXiv

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arXiv 2020年

作者： Khodatars, Marjane Shoeibi, Afshin Sadeghi, Delaram Ghassemi, Navid Jafari, Mahboobeh Moridian, Parisa Khadem, Ali Alizadehsani, Roohallah Zare, Assef Kong, Yinan Khosravi, Abbas Nahavandi, Saeid Hussain, Sadiq Acharya, U. Rajendra Berk, Michael The Dept. of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran The Faculty of Electrical Engineering FPGA Lab K. N. Toosi University of Technology Tehran Iran The Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran The Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran The Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Deakin University VIC3217 Australia Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran The School of Engineering Macquarie University Sydney2109 Australia The Dibrugarh University Assam786004 India The Dept. of Electronics and Computer Engineering Ngee Ann Polytechnic 599489 Singapore The Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan The Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore The Deakin University IMPACT - the Institute for Mental and Physical Health and Clinical Translation School of Medicine Barwon Health Geelong Australia The Orygen The National Centre of Excellence in Youth Mental Health Centre for Youth Mental Health Florey Institute for Neuroscience and Mental Health The Department of Psychiatry The University of Melbourne Melbourne Australia

Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) techniques can aid physicians to apply automatic diagnosis and rehabilitation procedures. AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques. Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally. DL methods for diagnosis of ASD have been focused on neuroimaging-based approaches. Neuroimaging techniques are non-invasive disease markers potentially useful for ASD diagnosis. Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain. Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging. In this paper, studies conducted with the aid of DL networks to distinguish ASD are investigated. Rehabilitation tools provided for supporting ASD patients utilizing DL networks are also assessed. Finally, we will present important challenges in the automated detection and rehabilitation of ASD and propose some future works. © 2020, CC BY-NC-ND.

关键词： Neuroimaging

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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Design of 6.78 MHz SiC MOSFET Class-E Inverter with a Class-Φ High-Speed Driver

Design of 6.78 MHz SiC MOSFET Class-E Inverter with a Class-...

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IEEE Energy Conversion Congress and Exposition

作者： Haruma Yogi Xiuqin Wei Hiroo Sekiya Takashi Hikihara Dept. Electrical Electronic and Computer Engineering Chiba Institute of Technology Chiba Japan Dept. Electrical and Electronic Engineering Chiba Institute of Technology Chiba Japan Dept. Applied and Cognitive Informatics Chiba University Chiba Japan Dept. Electcical Engineering Kyoto University Kyoto Japan

This paper presents a design challenge of a SiC MOSFET class-E inverter with a class- high-speed driver. The driver is based on a class- inverter which is a novel class-E inverter with a harmonic resonant filter, enabling the driver to achieve not only the class-E zero-voltage-switching/zero-derivative-switching (ZVS/ZDS) conditions but also a low switch-voltage stress. In this paper, the class-E inverter and its driver are regarded as a whole system to be designed. Additionally, the whole system is designed by using the numerical design procedure. A design example is also given along with its laboratory experiment under 156 W output power and 6.78 MHz operating frequency. All the switch-voltage waveforms obtained from laboratory experiment satisfy the class-E ZVS/ZVDS conditions. It is also seen from the experimental results that the numerical calculations agreed with the experimental ones quantitatively, which shows the validity of the design procedure presented in this paper.

关键词： Class-E inverter class-E ZVS/ZVDS conditions SiC MOSFET driver high-frequency and high-efficiency

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Dynamic Multicast Traffic engineering with Efficient Rerouting for Software-Defined Networks

Dynamic Multicast Traffic Engineering with Efficient Rerouti...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Jian-Jhih Kuo Sheng-Hao Chiang Shan-Hsiang Shen De-Nian Yang Wen-Tsuen Chen Dept. of Computer Science & Information Engineering National Chung Cheng University Chiayi Taiwan Dept. of Computer Science & Information Engineering National Taiwan University of Science & Technology Taipei Taiwan Academia Sinica Inst. of Information Science Taipei Taiwan Dept. of Computer Science National Tsing Hua University Hsinchu Taiwan

Traffic engineering (TE) and efficient network updating have been considered as separate problems in previous SDN research. Traffic engineering mostly focuses on static traffic and does not consider the rerouting overheads to support dynamic traffic. Efficient network updating assumes the new routing is provided by TE and focuses on minimizing only the rerouting overheads, and therefore, the improved new routing with bandwidth consumption similar to the new routing from TE but much lower rerouting overheads has not been explored. In this paper, we explore Multi-tree Low-overhead Multicast Rerouting (MLMR) to jointly solve both problems for SDN multicast. We prove that MLMR is NP-hard and design a new approximation algorithm, named Multicast Rerouting and Update Scheduling Algorithm (MRUSA). Equipped with the notions of deterioration indicator, motivator, and inhibitor, MRUSA provides incremental tree updating and multi-tree update scheduling to address the trade-off between the bandwidth consumption and rerouting overheads. Frequent rerouting due to tiny changes of multicast users can be effectively avoided, because rerouting time for each group can be correctly identified. Simulations and implementation on real SDNs with YouTube traffic manifest that the total cost can be reduced by at least 35% compared with SPT and ST, and the computation time is small for massive SDN.

关键词： Routing Bandwidth Unicast Approximation algorithms Multicast algorithms computer science Control systems

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A Method for Solving Many-Objective Optimization Problems in Magnetics

A Method for Solving Many-Objective Optimization Problems in...

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International Symposium on Electromagnetic Fields in Mechatronics, Electrical and Electronic engineering (ISEF) Book of Abstracts

作者： Paolo Di Barba Maria Evelina Mognaschi Slawomir Wiak Dept of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Inst of Mechatronics and Information Systems Lodz University of Technology Lodz Poland

Many-objective problems, characterized by a high-dimensional objective space, are a source of challenges for standard methods of multi-objective optimization. An evolutionary algorithm based on the concept of least-co... 详细信息

ISBN: (数字)9781728115603

ISBN: (纸本)9781728115610

关键词： Optimization Permanent magnet motors Shape Linear programming Traction motors Brushless DC motors Torque

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