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Simultaneous Information and Energy Transmission: A Finite Block-Length Analysis 19

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Simultaneous Information and Energy Transmission: A Finite B...

19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018

作者： Perlaza, Samir M. Tajer, Ali Vincentpoor, H. de Lyon CITI Laboratory A Joint Laboratory between the Institut National de Recherche en Informatique et en Automatique 6 Av. des Arts Villeurbanne69621 France Rensselaer Polytechnic Institute Department of Electrical Computer and Systems Engineering TroyNY12180 United States Department of Electrical Engineering Princeton University PrincetonNJ08544 United States

ISBN: (纸本)9781538635124

In this paper, a non-asymptotic analysis of the fundamental limits of simultaneous information and energy transmission (SIET) is presented. The notion of the information-energy capacity region, i.e., the largest set of simultaneously achievable information and energy rates, is revisited in a context in which transmissions occur within a finite number of channel uses and strictly positive decoding error probability (DEP) and energy shortage probability (ESP) are tolerated. The focus is on the case of one transmitter, one information receiver and one energy harvester communicating through binary symmetric memoryless channels. In this case, some outer bounds on the information transmission rate and the energy transmission rate are presented. More specifically, given a finite block-length, a DEP, and an ESP, four scenarios arise depending on whether an average or maximal probability constraint is imposed on the DEP and the ESP. For each scenario, the limits on the information rate and energy rate beyond which a transmission is no longer possible are presented (impossibility results). These results reveal the competitive interaction between the information transmission and energy transmission tasks identifying a certain regime in which increasing the information rate necessarily implies decreasing the energy rate and vice versa. © 2018 IEEE.

关键词： Probability

Error correction maximization for deep image hashing

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arXiv 2018年

作者： Xu, Xiang Wang, Xiaofang Kitani, Kris M. Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh United States Robotics Institute Carnegie Mellon University Pittsburgh United States

We propose to use the concept of the Hamming bound to derive the optimal criteria for learning hash codes with a deep network. In particular, when the number of binary hash codes (typically the number of image categories) and code length are known, it is possible to derive an upper bound on the minimum Hamming distance between the hash codes. This upper bound can then be used to define the loss function for learning hash codes. By encouraging the margin (minimum Hamming distance) between the hash codes of different image categories to match the upper bound, we are able to learn theoretically optimal hash codes. Our experiments show that our method significantly outperforms competing deep learning-based approaches and obtains top performance on benchmark datasets. Copyright © 2018, The Authors. All rights reserved.

关键词： Hash functions

An HMM-based averaging approach for creating mean motion data from a full-body Motion Capture system to support the development of a biomechanical model: Sensor-assisted functionalization of furniture in the hospital and care sector

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Current Directions in Biomedical engineering 2018年第1期4卷 1-5页

作者： Kitzig, Andreas Demmer, Julia Bolten, Tobias Naroska, Edwin Stockmanns, Gudrun Viga, Reinhard Grabmaier, Anton Niederrhein University of Applied Sciences Faculty of Electrical Engineering and Computer Science Ambient Intelligence Laboratory Reinarzstr. 49 Krefeld 47805 Germany University of Duisburg Essen Department of Electronic Components and Circuits Bismarkstr. 81 Duisburg 47057 Germany Fraunhofer Institute for Microelectronic Circuits and Systems (IMS) Finkenstraße 61 Duisburg 47057 Germany

Motion capture systems or MoCap systems are used for game development and in the field of sports for the assessment and digitalization of human movement. Furthermore, MoCap systems are also used in the medical and therapeutic field for the analysis of human movement patterns. As examples gait analysis or examination of the musculoskeletal system and its function should be mentioned. Most application relate to a specific person and their movement or to the comparison of movements of different people. Within the scope of this paper an averaged motion sequence is supposed to be generated from MoCap data in order to be able to use it in the field of biomechanical modeling and simulation. For the averaging of individual movement sequences of different persons a Hidden Markov Model (HMM) based approach is presented. © 2018 author name(s).

关键词： Hidden Markov models

The unusual M-dwarf Warm Jupiter TOI-1899∼b: Refinement of orbital and planetary parameters

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arXiv 2023年

作者： Lin, Andrea S.J. Libby-Roberts, Jessica E. Alvarado-Montes, Jaime A. Cañas, Caleb I. Kanodia, Shubham Han, Te Hebb, Leslie Jensen, Eric L.N. Mahadevan, Suvrath Powers, Luke C. Swaby, Tera N. Wisniewski, John Beard, Corey Bender, Chad F. Blake, Cullen H. Cochran, William D. Diddams, Scott A. Frazier, Robert C. Fredrick, Connor Gully-Santiago, Michael Halverson, Samuel Logsdon, Sarah E. McElwain, Michael W. Morley, Caroline Ninan, Joe P. Rajagopal, Jayadev Ramsey, Lawrence W. Robertson, Paul Roy, Arpita Schwab, Christian Stefánsson, Guðmundur Stevens, Daniel J. Terrien, Ryan C. Wright, Jason T. Department of Astronomy & Astrophysics 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States Center for Exoplanets and Habitable Worlds 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States School of Mathematical and Physical Sciences Macquarie University Balaclava Road North RydeNSW2109 Australia The Macquarie University Astrophysics and Space Technologies Research Centre Macquarie University Balaclava Road North RydeNSW2109 Australia NASA Goddard Space Flight Center 8800 Greenbelt Road GreenbeltMD20771 United States Earth and Planets Laboratory Carnegie Institution for Science 5241 Broad Branch Road NW WashingtonDC20015 United States Department of Physics & Astronomy The University of California Irvine IrvineCA92697 United States Physics Department Hobart and William Smith Colleges 300 Pulteney Street GenevaNY14456 United States Department of Astronomy Cornell University 245 East Ave IthacaNY14850 United States Department of Physics & Astronomy Swarthmore College SwarthmorePA19081 United States ETH Zurich Institute for Particle Physics & Astrophysics Zurich Switzerland Department of Physics & Astronomy University of Wyoming 1000 E. University Dept 3905 LaramieWY82071 United States NASA Headquarters 300 Hidden Figures Way SW WashingtonDC20546 United States Steward Observatory University of Arizona 933 N Cherry Ave TucsonAZ85721 United States Department of Physics and Astronomy University of Pennsylvania 209 South 33rd Street PhiladelphiaPA19104 United States McDonald Observatory Center for Planetary Systems Habitability The University of Texas AustinTX78712 United States Electrical Computer & Energy Engineering 425 UCB University of Colorado BoulderCO80309 United States Department of Physics 390 UCB University of Colorado BoulderCO80309 United States National Institute of Standards & Technology 325 Broadway BoulderCO80305 United

TOI-1899 b is a rare exoplanet, a temperate Warm Jupiter orbiting an M-dwarf, first discovered by Cañas et al. (2020) from a TESS single-transit event. Using new radial velocities (RVs) from the precision RV spectrographs HPF and NEID, along with additional TESS photometry and ground-based transit follow-up, we are able to derive a much more precise orbital period of (formula presented) d, along with a radius of Rp = 0.99±0.03 RJ. We have also improved the constraints on planet mass, Mp = 0.67±0.04 MJ, and eccentricity, which is consistent with a circular orbit at (formula presented). TOI-1899 b occupies a unique region of parameter space as the coolest known (Teq ≈ 380 K) Jovian-sized transiting planet around an M-dwarf;we show that it has great potential to provide clues regarding the formation and migration mechanisms of these rare gas giants through transmission spectroscopy with JWST as well as studies of tidal evolution. © 2023, CC BY.

关键词： Orbits

Action anticipation for collaborative environments: The impact of contextual information and uncertainty-based prediction

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arXiv 2019年

作者： Canuto, Clebeson Moreno, Plinio Samatelo, Jorge Vassallo, Raquel Santos-Victor, José CT-II Department of Electrical Engineering Federal University Espírito Santo Av. Fernando Ferrari 514 Goiabeiras VitóriaES29075-910 Brazil North Tower Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Av. Rovisco Pais 1 Lisbon1049-001 Portugal

For effectively interacting with humans in collaborative environments, machines need to be able to predict (i.e. anticipate) future events, in order to execute actions in a timely manner. However, the observation of the human limbs movements may not be sufficient to anticipate their actions in an unambiguous manner. In this work we consider two additional sources of information (i.e. context) over time, gaze movements and object information, and study how these additional contextual cues improve the action anticipation performance. We address action anticipation as a classification task, where the model takes the available information as the input, and predicts the most likely action. We propose to use the uncertainty about each prediction as an online decision-making criterion for action anticipation. Uncertainty is modeled as a stochastic process applied to a time-based neural network architecture, which improves the conventional class-likelihood (i.e. deterministic) criterion. The main contributions of this paper are three-fold: (i) we propose a deep architecture that outperforms previous results in the action anticipation task, when using the Acticipate collaborative dataset;(ii) we show that contextual information is important do disambiguate the interpretation of similar actions;(iii) we propose the minimization of uncertainty as a more effective criterion for action anticipation, when compared with the maximization of class probability. Our results on the Acticipate dataset showed the importance of contextual information and the uncertainty criterion for action anticipation. We achieve an average accuracy of 98.75% in the anticipation task using only an average of 25% of observations. In addition, considering that a good anticipation model should also perform well in the action recognition task, we achieve an average accuracy of 100% in action recognition on the Acticipate dataset, when the entire observation set is used. Copyright © 2019, The Authors. All ri

关键词： Forecasting

IROS 2019 lifelong robotic vision challenge lifelong object recognition report

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arXiv 2020年

作者： She, Qi Feng, Fan Liu, Qi Chan, Rosa H.M. Hao, Xinyue Lan, Chuanlin Yang, Qihan Lomonaco, Vincenzo Parisi, German I. Ma, Liang Wu, Jianwen Zhong, Qiaoyong Xie, Di Pu, Shiliang Graffieti, Gabriele Pellegrini, Lorenzo Lomonaco, Vincenzo Maltoni, Davide Wang, Zhengwei Brophy, Eoin Ward, Tomás E. Zhou, Liguang Yang, Lin Goel, Vidit Sheet, Debdoot Kumar, Somesh Song, Soonyong Bae, Heechul Han, Hyonyoung Son, Youngsung Piyasena, Duvindu Kanagarajah, Sathursan Lam, Siew-Kei Wu, Meiqing Department of Electrical Engineering City University of Hong Kong Hong Kong Department of Electronic Engineering Tsinghua University Beijing University of Posts and Telecommunications Beijing China School of Electronic Information Wuhan University Wuhan China ContinualAI Research University of Bologna Italy ContinualAI Research University of Hamburg Germany Intel Labs and ContinualAI Research Hikvision Research Institute Hangzhou China University of Bologna Bologna Italy V-SENSE School of Computer Science and Statistics Trinity College Dublin Dublin Ireland Inisht Centre for Data Analytics School of Computing Dublin City University Dublin Ireland Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Peking University Beijing China Shandong University Qingdao China Beijing Film Academy Beijing China Indian Institute of Technology Kharagpur India Korea Republic of Nanyang Technological University Singapore Singapore

This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top 8 finalists (out of over 150 teams). The competition dataset (L)ifel(O)ng (R)obotic V(IS)ion (OpenLORIS) - Object Recognition (OpenLORIS-object) is designed for driving lifelong/continual learning research and application in robotic vision domain, with everyday objects in home, office, campus, and mall scenarios. The dataset explicitly quantifies the variants of illumination, object occlusion, object size, camera-object distance/angles, and clutter information. Rules are designed to quantify the learning capability of the robotic vision system when faced with the objects appearing in the dynamic environments in the contest. Individual reports, dataset information, rules, and released source code can be found at the project homepage. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

Contrastive functional connectivity defines neurophysiology-informed symptom dimensions in major depression

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Cell reports. Medicine

Cell reports. Medicine 2025年 102151页

作者： Hao Zhu Xiaoyu Tong Nancy B Carlisle Hua Xie Corey J Keller Desmond J Oathes Feng Liu Charles B Nemeroff Gregory A Fonzo Yu Zhang Department of Bioengineering Lehigh University Bethlehem PA USA. Department of Psychology Lehigh University Bethlehem PA USA. Center for Neuroscience Research Children's National Hospital Washington DC USA. Wu Tsai Neuroscience Institute Stanford University Stanford CA USA Department of Psychiatry and Behavioral Sciences Stanford University School of Medicine Stanford CA USA Veterans Affairs Palo Alto Healthcare System and the Sierra Pacific Mental Illness Research Education and Clinical Center (MIRECC) Palo Alto CA USA. Center for Brain Imaging and Stimulation Department of Psychiatry University of Pennsylvania Perelman School of Medicine Philadelphia PA USA Center for Neuromodulation in Depression and Stress Department of Psychiatry University of Pennsylvania Perelman School of Medicine Philadelphia PA USA Penn Brain Science Translation Innovation and Modulation Center University of Pennsylvania Perelman School of Medicine Philadelphia PA USA Departments of Neurology Neurosurgery Bioengineering and Neuroscience University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Department of Systems and Enterprises Stevens Institute of Technology Hoboken NJ USA. Center for Psychedelic Research and Therapy Department of Psychiatry and Behavioral Sciences Dell Medical School The University of Texas at Austin Austin TX USA. Department of Bioengineering Lehigh University Bethlehem PA USA Department of Electrical and Computer Engineering Lehigh University Bethlehem PA USA. Electronic address: yuzi20@lehigh.edu.

Major depressive disorder (MDD) is highly heterogeneous, posing challenges for effective treatment due to complex interactions between clinical symptoms and neurobiological features. To address this, we apply contrastive principal-component analysis to fMRI-based resting-state functional connectivity, isolating disorder-specific variations by contrasting data from 233 MDD patients and 285 healthy controls. Subsequently, we use sparse canonical correlation analysis to identify two significant dimensions linking distinct brain circuits with clinical profiles. One dimension relates to an internalizing-externalizing symptom spectrum involving visual and limbic networks and is associated with cognitive task reaction times. The other dimension, linked to personality traits protective against depression (e.g., extraversion), is driven by dorsal attention network connections and correlates with cognitive control and psychomotor performance. This approach illuminates stable symptom dimensions and their neurophysiological underpinnings, aiding in precision phenotyping for MDD and supporting the development of targeted, individualized therapeutic strategies for mental health care.

关键词： brain-symptom dimension contrastive machine learning disease heterogeneity fMRI functional connectivity major depressive disorder

Corrigendum to "PSFHS challenge report: pubic symphysis and fetal head segmentation from intrapartum ultrasound images" [Medical Image Analysis 99 (2025),103353]

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Medical image analysis 2025年 104卷 103651页

作者： Jieyun Bai Zihao Zhou Zhanhong Ou Gregor Koehler Raphael Stock Klaus Maier-Hein Marawan Elbatel Robert Martí Xiaomeng Li Yaoyang Qiu Panjie Gou Gongping Chen Lei Zhao Jianxun Zhang Yu Dai Fangyijie Wang Guénolé Silvestre Kathleen Curran Hongkun Sun Jing Xu Pengzhou Cai Lu Jiang Libin Lan Dong Ni Mei Zhong Gaowen Chen Víctor M Campello Yaosheng Lu Karim Lekadir Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization Jinan University Guangzhou China Auckland Bioengineering Institute The University of Auckland Auckland New Zealand. Electronic address: jbai996@aucklanduni.ac.nz. Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization Jinan University Guangzhou China. Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Computer Science and Engineering The Chinese University of Hong Kong China. Computer Vision and Robotics Group University of Girona Girona Spain. Canon Medical Systems (China) Co. LTD Beijing China. College of Artificial Intelligence Nankai University Tianjin China. College of Computer Science and Electronic Engineering Hunan University Changsha China. School of Medicine University College Dublin Dublin Ireland. School of Computer Science University College Dublin Dublin Ireland. School of Statistics & Mathematics Zhejiang Gongshang University Hangzhou China. School of Computer Science & Engineering Chongqing University of Technology Chongqing China. National-Regional Key Technology Engineering Laboratory for Medical Ultrasound & Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging & School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China. NanFang Hospital of Southern Medical University Guangzhou China. Zhujiang Hospital of Southern Medical University Guangzhou China. Departament de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain. Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization Jinan University Guangzhou China. Electronic address: tluys@***. Departament de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Institució Catalana de Recerca i Estudis Avançats (ICREA) Barcelona Spain.

A hybrid agent-based secondary control for microgrids with increased fault-tolerance needs

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A hybrid agent-based secondary control for microgrids with i...

Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion, MEDPOWER 2018

作者： Bintoudi, Angelina D. Bintoudi, Angelina D. Zyglakis, Lampros Apostolos C. Tsolakis Ioannidis, Dimosthenis Al-Agtash, S. Al-Agtash, S. Martinez-Ramos, Jose L. Onen, Ahmet Azzopardi, Brian Hadjidemetriou, Lenos Martensen, Nis Mounir, Khiat Borg, Nicholas Fragale, Nunziatina Demoulias, Charis Tzovaras, Dimitrios Information Technologies Institute Center for Research and Technology Hellas Thessaloniki Greece Department of Computer Engineering German Jordanian University Amman Jordan Department of Computer Engineering Santa Clara University CA United States Department of Electrical Engineering Universidad de Sevilla Seville Spain Department of Electrical and Electronics Engineering Abdullah Gul University Kayseri Turkey MCAST Triq Kordin Malta KIOS Research Center of Excellence University of Cyprus Nicosia Cyprus Energynautics GmbH Darmstadt Germany Département de Génie Électrique Laboratoire SCAMRE Ecole Nationale Polytechnique d'Oran Algeria Electronic Systems Design Ltd Malta GeoSYS Ltd San Gwann Malta Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (纸本)9781839531330

This paper proposes a hybrid secondary control architecture for microgrids with AC-coupled droop-controlled units, based on both centralised and distributed control principles. The proposed secondary control is based on a multi-agent system (MAS), complemented by a microgrid centralised controller (MGCC). The system is able to adjust the droop curves dynamically in order to achieve voltage/frequency restoration as well as active/reactive power optimal allocation, based on the actual status of the controllable units, in particular, the state-of-charge of batteries and maximum power point of photovoltaics. The distributed nature of the agents is also fully exploited because the proposed framework retains operability even under fault on secondary MGCC. To evaluate the proposed framework, a scenario-based performance analysis has been tested over a simulated AC islanded microgrid, where communication from the MGCC is suddenly interrupted and the MAS is required to reconfigure in order to maintain the same control objectives. MATLAB/Simulink simulations have been realised using detailed physical form models for a small-scale microgrid, while the implementation of the MGCC and MAS is accomplished through Java Agent Development (JADE) framework. © 2018 Institution of engineering and Technology. All rights reserved.

关键词： Multi agent systems