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International Conference on Telecommunications (ConTEL)

作者： Denis Gračanin R. Benjamin Knapp Thomas L. Martin Sarah Parker Dept. of Computer Science Virginia Tech Blacksburg VA USA Inst. for Creativity Arts and Technology Virginia Tech Blacksburg VA USA Dept. of Electrical and Computer Engineering Virginia Tech Blacksburg VA USA Virginia Tech Carilion Research Inst. Virginia Tech Blacksburg VA USA

US and Europe have been experiencing an unprecedented increase in the number of older adults. Increased life expectancy and increasingly aging population present challenges in terms of costs associated with elderly healthcare and wellbeing, as well as decreasing availability of health personnel. Similar trends are occurring in the rest of the world. There is a strong push to promote remote telehealth monitoring centers or virtual care centers (VCCs) as a way to address health care needs, especially for the aging population. technology advances, such as Internet of Things and Smart Built Environments, support proliferation of smart and secure telehealth care that can be implemented at all levels. Remote patient monitoring, combined with personalized interventions and coaching, serves as the foundation for VCCs. However, there are trade-offs with regard to privacy and changes in the living environment and the overall scalability. We need to explore how technology can be utilized to provide health services while protecting and preserving privacy of all stakeholders. A distributed VCC (DVCC), created from individual VCCs and crossing regional and national borders, can provide effective, continuous service with ability to adjust to unforeseen events and emergencies. We discuss a meta-model-based approach to protect patients' data while improving the efficiency of the care center personnel, which will allow remote care centers to more easily scale as need to provide round-the-clock monitoring.

关键词： Privacy Personnel Stress Data privacy Monitoring Medical services Biomedical monitoring

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-dept. literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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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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Iterative programming of noisy memory cells

arXiv

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arXiv 2019年

作者： Horovitz, Michal Yaakobi, Eitan Gad, Eyal En Bruck, Jehoshua Dept. of Computer Science Tel-Hai College Galilee Research Institute - Migal Upper Galilee Israel Dept. of Computer Scienc Technion-Israel Inst. of Tech. Haifa32000 Israel Micron Technology MilipitasCA95035 United States Dept. of Electrical Engineering California Inst. of Tech. PasadenaCA91125 United States

In this paper, we study a model, which was first presented by Bunte and Lapidoth, that mimics the programming operation of memory cells. Under this paradigm we assume that cells are programmed sequentially and individually. The programming process is modeled as transmission over a channel, while it is possible to read the cell state in order to determine its programming success, and in case of programming failure, to reprogram the cell again. Reprogramming a cell can reduce the bit error rate, however this comes with the price of increasing the overall programming time and thereby affecting the writing speed of the memory. An iterative programming scheme is an algorithm which specifies the number of attempts to program each cell. Given the programming channel and constraints on the average and maximum number of attempts to program a cell, we study programming schemes which maximize the number of bits that can be reliably stored in the memory. We extend the results by Bunte and Lapidoth and study this problem when the programming channel is either the BSC, BEC, or Z channel. For the BSC and the BEC our analysis is also extended for the case where the error probabilities on consecutive writes are not necessarily the same. Lastly, we also study a related model which is motivated by the synthesis process of DNA molecules. Copyright © 2019, The Authors. All rights reserved.

关键词： Cells

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Dealing with qualitative and quantitative features in legal domains

arXiv

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arXiv 2019年

作者： Budán, Maximiliano C.D. Cobo, María Laura Martínez, Diego I. Rotolo, Antonino Inst. for Computer Science and Eng. UNS-CONICET Dept. of Computer Science and Engineering Universidad Nacional del Sur Argentina Dept. of Mathematics Universidad Nacional de Santiago del Estero Argentina Dept. of Mathematics University of Bologna Italy Dept. of Computer Science and Eng. Universidad Nacional del Sur Argentina

In this work we enrich a formalism for argumentation by including a formal characterization of features related to the knowledge, in order to capture proper reasoning in legal domains. We add meta-data information to the arguments in the form of labels representing quantitative and qualitative data about them. These labels are propagated through an argumentative graph according to the relations of support, conflict, and aggregation between arguments. Copyright © 2019, The Authors. All rights reserved.

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Leveraging randomized compiling for the QITE algorithm

arXiv

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arXiv 2021年

作者： Ville, Jean-Loup Morvan, Alexis Hashim, Akel Naik, Ravi K. Lu, Marie Mitchell, Bradley Kreikebaum, John-Mark O’Brien, Kevin P. Wallman, Joel J. Hincks, Ian Emerson, Joseph Smith, Ethan Younis, Ed Iancu, Costin Santiago, David I. Siddiqi, Irfan Quantum Nanoelectronics Laboratory Dept. of Physics University of California at Berkeley BerkeleyCA94720 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Inst. for Quantum Computing Dept. of Applied Mathematics University of Waterloo WaterlooONN2L 3G1 Canada Quantum Benchmark Inc. KitchenerONN2H 5G5 Canada

The success of the current generation of Noisy Intermediate-Scale Quantum (NISQ) hardware shows that quantum hardware may be able to tackle complex problems even without error correction. One outstanding issue is that of coherent errors arising from the increased complexity of these devices. These errors can accumulate through a circuit, making their impact on algorithms hard to predict and mitigate. Iterative algorithms like Quantum Imaginary Time Evolution are susceptible to these errors. This article presents the combination of both noise tailoring using Randomized Compiling and error mitigation with a purification. We also show that Cycle Benchmarking gives an estimate of the reliability of the purification. We apply this method to the Quantum Imaginary Time Evolution of a Transverse Field Ising Model and report an energy estimation and a ground state infidelity both below 1%. Our methodology is general and can be used for other algorithms and platforms. We show how combining noise tailoring and error mitigation will push forward the performance of NISQ devices. © 2021, CC BY.

关键词： Benchmarking

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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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Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models

arXiv

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arXiv 2021年

作者： Shoeibi, Afshin Sadeghi, Delaram Moridian, Parisa Ghassemi, Navid Heras, Jonathan Alizadehsani, Roohallah Khadem, Ali Kong, Yinan Nahavandi, Saeid Zhang, Yu-Dong Gorriz, Juan M. The Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran The Dept. of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran The Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran The Department of Mathematics and Computer Science University of la Rioja La Rioja Spain Deakin University VIC3217 Australia The School of Engineering Macquarie University Sydney2109 Australia The Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States The Department of Informatics University of Leicester Leicester United Kingdom The Department of Signal Theory Networking and Communications Universidad de Granada Spain The Department of Psychiatry. University of Cambridge United Kingdom

Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the brain, the function of some brain regions is out of balance, leading to the lack of coordination between thoughts, actions, and emotions. This study provides various intelligent deep learning (DL)-based methods for automated SZ diagnosis via electroencephalography (EEG) signals. The obtained results are compared with those of conventional intelligent methods. To implement the proposed methods, the dataset of the inst.tute of Psychiatry and Neurology in Warsaw, Poland, has been used. First, EEG signals were divided into 25 s time frames and then were normalized by z-score or norm L2. In the classification step, two different approaches were considered for SZ diagnosis via EEG signals. In this step, the classification of EEG signals was first carried out by conventional machine learning methods, e.g., support vector machine, k-nearest neighbors, decision tree, naïve Bayes, random forest, extremely randomized trees, and bagging. Various proposed DL models, namely, long short-term memories (LSTMs), one-dimensional convolutional networks (1D-CNNs), and 1DCNN-LSTMs, were used in the following. In this step, the DL models were implemented and compared with different activation functions. Among the proposed DL models, the CNN-LSTM architecture has had the best performance. In this architecture, the ReLU activation function with the z-score and L2-combined normalization was used. The proposed CNN-LSTM model has achieved an accuracy percentage of 99.25. © 2021, CC BY-NC-ND.

关键词： Diseases

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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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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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