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International Conference on computer Communications and Networks (ICCCN)

作者： Yue Xu Jingwen Zhang Hao Zhou Xiaoyan Wang Zhi Liu Yusheng Ji LINKE Lab School of Computer Science and Technology Univ. of Science and Technology of China China College of Engineering Ibaraki University Japan Department of Mathematical and Systems Engineering Shizuoka University Japan Information Systems Architecture Research Division National Institute of Informatics Japan

ISBN: (数字)9781728166070

ISBN: (纸本)9781728166087

By integrating both energy harvesting and backscatter communication technologies, so-called `battery-free tag' emerges as a promising solution to the energy related issues in IoT. However, such tags present new challenges due to the unstable energy supply and excitation signals, which are critical to realize successful backscatter communications. In this paper, we present a novel system, denoted as ecUWB, which enables robust communication for battery-free tags where unstable WiFi signals act as the unified source for both energy supply and excitation signals. At tag side, we propose a charging-transmission division scheme to achieve better signal utilization, and introduce a re-transmission mechanism for possible excitation signal interruption. At receiver side, we implement a simply method which is based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to predict the uncontrollable signals, and propose an energy-centric tag scheduling method according to the tag energy estimation results. Extensive experiments are carried out on customized tags and NI USRP platform. The results show that ecUWB outperforms the existing ones in terms of performance and efficiency under the unstable WiFi signals.

关键词： Wireless fidelity Backscatter Energy harvesting Receivers systems architecture Power supplies Modulation

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MammoFL: Mammographic Breast Density Estimation using Federated Learning

arXiv

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arXiv 2022年

作者： Muthukrishnan, Ramya Heyler, Angelina Katti, Keshava Pati, Sarthak Mankowski, Walter Alahari, Aprupa Sanborn, Michael Conant, Emily F. Scott, Christopher Winham, Stacey Vachon, Celine Chaudhari, Pratik Kontos, Despina Bakas, Spyridon Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States Massachusetts Institute of Technology CambridgeMA United States Research and Development GSK CambridgeMA United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine Indiana IN United States Research Center for Federated Learning in Medicine Indiana University School of Medicine Indiana IN United States University of Pennsylvania PhiladelphiaPA United States Department of Pathology & Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Health Sciences Research Mayo Clinic RochesterMN United States

In this study, we automate quantitative mammographic breast density estimation with neural networks and show that this tool is a strong use case for federated learning on multi-institutional datasets. Our dataset included bilateral CC-view and MLO-view mammographic images from two separate institutions. Two U-Nets were separately trained on algorithm-generated labels to perform segmentation of the breast and dense tissue from these images and subsequently calculate breast percent density (PD). The networks were trained with federated learning and compared to three non-federated baselines, one trained on each single-institution dataset and one trained on the aggregated multi-institution dataset. We demonstrate that training on multi-institution datasets is critical to algorithm generalizability. We further show that federated learning on multi-institutional datasets improves model generalization to unseen data at nearly the same level as centralized training on multi-institutional datasets, indicating that federated learning can be applied to our method to improve algorithm generalizability while maintaining patient privacy. Copyright © 2022, The Authors. All rights reserved.

关键词： Image segmentation

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Electro-Thermal Co-Design of a Cooling System-Integrated High-Frequency Transformer

Electro-Thermal Co-Design of a Cooling System-Integrated Hig...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Yongwan Park Sevket Yuruker Shiladri Chakraborty Alireza Khaligh Raphael Mandel Patrick McCluskey Michael Ohadi Lauren Boteler Miguel Hinojosa Department of Electrical and Computer Engineering The Institute for Systems Research University of Maryland College Park MD USA Department of Mechanical Engineering University of Maryland College Park MD USA U.S. Army Research Laboratory Adelphi MD USA

ISBN: (数字)9781728146294

ISBN: (纸本)9781728146300

This paper presents electro-thermal design optimization of a high-frequency planar transformer with an integrated thermal management system involving a liquid-cooled chamber for combined core and winding cooling. The designed transformer is for a 10 kW, 600V to 800 V, 1 MHz dual-active-bridge converter, working as the dc-dc stage of high power-density on-board electric vehicle chargers. Effect of design variables like number of turns of the transformer and cooler height on the transformer's electrical parameters such as leakage inductance, ac resistance and parasitic capacitance is estimated. The dependence of converter efficiency on these parameters is estimated using simulations and analyses and potential trade-offs of the design are investigated. Similarly, thermal modeling is used to characterize the thermal performance of different designs, evaluated on the basis of the required coolant temperature to maintain the transformer winding and core at a specified temperature. Based on a combination of the preceding analyses, optimal designs are identified which simultaneously ensure good electrical and thermal performance.

关键词： Windings Transformer cores Capacitance Inductance Cooling Thermal management Mathematical model

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Binding the Acoustic Features of an Auditory Source through Temporal Coherence

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Cerebral cortex communications 2021年第4期2卷 tgab060页

作者： Mohsen Rezaeizadeh Shihab Shamma Department of Electrical and Computer Engineering and Institute for Systems Research University of Maryland College Park 20742 USA.

Numerous studies have suggested that the perception of a target sound stream (or source) can only be segregated from a complex acoustic background mixture if the acoustic features underlying its perceptual attributes (e.g., pitch, location, and timbre) induce temporally modulated responses that are mutually correlated (or coherent), and that are uncorrelated (incoherent) from those of other sources in the mixture. This "temporal coherence" hypothesis asserts that attentive listening to one acoustic feature of a target enhances brain responses to that feature but would also concomitantly (1) induce mutually excitatory influences with other coherently responding neurons, thus enhancing (or binding) them all as they respond to the attended source; by contrast, (2) suppressive interactions are hypothesized to build up among neurons driven by temporally incoherent sound features, thus relatively reducing their activity. In this study, we report on EEG measurements in human subjects engaged in various sound segregation tasks that demonstrate rapid binding among the temporally coherent features of the attended source regardless of their identity (pure tone components, tone complexes, or noise), harmonic relationship, or frequency separation, thus confirming the key role temporal coherence plays in the analysis and organization of auditory scenes.

关键词： Attention Cocktail Party EEG Source Segregation Temporal Coherence

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Automated Chinese Language Proficiency Scoring by utilizing Siamese Convolutional Neural Network and fusion based approach

Automated Chinese Language Proficiency Scoring by utilizing ...

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International Conference on engineering and Emerging Technologies (ICEET)

作者： Anne Kwong Junaid Hussain Muzamal Patrick Yu Zhang Guimin Lin Dpt. Research and Development Digital Galaxy Technologies Limited Honkkong Guangdong Sheng China Al-Khawarizmi Institute of Computer Science-KICS University of Engineering and Technology Lahore Lahore Pakistan

ISBN: (数字)9781728145815

ISBN: (纸本)9781728145822

The previous approaches have failed to effectually score the language proficiency of a non-native speakers especially in case of non- English languages which are complex and a slight change of pronunciation can alter the nature of the word. In this study, we proposed an automated language scoring system to test the proficiency of Chinese language. We have employed a novel fusion approach of a 38-feature based model and a Siamese convolutional neural network (Siamese CNN) which can accuracy identify the difference between the native speech and the test taker's speech. The results show that out model have achieved comparable performance to the state of the art and solved the pronunciation problems as well. Furthermore, we have provided a fusion based approach and provided extensive amount of experiments which shows that our method is state of the art and can be utilized in real time Chinese language proficiency scoring.

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Towards Spectrum-Efficient 6G: Revolutionizing Beamforming with Vision and Deep Learning

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IEEE Network 2025年

作者： Mirza, Ahmad Faisal Hassan, Tayyib Ul Ullah, Syed Asad Basheer, Shakila Chauhdary, Sajjad Hussain Dev, Kapal Hassan, Syed Ali Islamabad44000 Pakistan Quetta87300 Pakistan Princess Nourah bint Abdulrahman University College of Computer and Information Science Department of Information Systems P.O. BOX 84428 Riyadh11671 Saudi Arabia Munster Technological University Department of Computer Science Bishopstown CorkT12 P928 Ireland University of Johannesburg Department of institute of intelligent systems Auckland Park 2006 South Africa Chitkara University Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Punjab Rajpura140401 India

As the world transitions to 6G networks, wireless communication is set for transformative advancements, enabling applications such as autonomous transportation, immersive augmented reality, and smart cities. These networks demand ultra-reliable, low-latency, and high-bandwidth communication, coupled with unprecedented spectrum efficiency and adaptability. This magazine explores how vision-assisted beamforming (VB) leverages visual data from base station (BS) cameras combined with advanced machine learning (ML) techniques to enhance signal quality, connectivity, and adaptability in dynamic environments. By enabling intelligent, real-time beam alignment, this approach ensures stronger, more reliable connections while mitigating inefficiencies in exhaustive beam training. Adapting seamlessly to rapidly changing scenarios, VB provides scalable and efficient communication systems, paving the way for smarter networks. Using real-world datasets, deep learning (DL) models demonstrate how visual context significantly enhances beamforming accuracy and efficiency. A case study on vehicleto- everything (V2X) communication showcases the paradigm’s ability to address critical 6G challenges by achieving precise beamforming without the need for channel state information (CSI). Unlike traditional methods reliant on sequential frames, this approach achieves accurate predictions using a single frame, reducing complexity and latency while delivering robust, high quality connectivity in real time. © 1986-2012 IEEE.

关键词： Beamforming

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Cooperative Emergent Swarming Through Deep Reinforcement Learning

Cooperative Emergent Swarming Through Deep Reinforcement Lea...

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International Conference on Control and Automation (ICCA)

作者： Tony X. Lin Daniel M. Lofaro Donald A. Sofge School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Distributed Autonomous Systems Group U.S. Naval Research Laboratory Washington DC USA

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

This paper studies the problem of designing a decentralized controller that is able to induce a desired emergent behavior in robot swarms. We consider a robot swarm composing of agents that have identical dynamics and sensors and are unable to communicate among each other. Our proposed approach leverages deep reinforcement learning methods to search for an appropriate control policy that achieves our desired group behavior. We show that our method is able to capture targets in a predator-prey case-study. Simulation and experimental results using an autonomous blimp research platform are also provided.

关键词： Reinforcement learning Decentralized control Neural networks Predator prey systems Vehicle dynamics Training Testing

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Charge and Energy Transfer Dynamics of Hybridized Exciton-Polaritons in 2D Halide Perovskites

arXiv

引用

arXiv 2023年

作者： Anantharaman, Surendra B. Lynch, Jason Stevens, Christopher E. Munley, Christopher Li, Chentao Hou, Jin Zhang, Hao Torma, Andrew Darlington, Thomas Coen, Francis Li, Kevin Majumdar, Arka Schuck, P. James Mohite, Aditya Harutyunyan, Hayk Hendrickson, Joshua R. Jariwala, Deep Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States KBR Inc. BeavercreekOH45431 United States Air Force Research Laboratory Sensors Directorate Wright-Patterson Air Force Base OH45433 United States Department of Physics University of Washington SeattleWA98195 United States Department of Physics Emory University AtlantaGA30322 United States Department of Chemical and Biomolecular Engineering Rice University HoustonTX77005 United States Department of Materials Science and Nanoengineering Rice University HoustonTX77005 United States Applied Physics Program Smalley‐Curl Institute Rice University HoustonTX77005 United States Department of Mechanical Engineering Columbia University New YorkNY10027 United States Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States

Excitons, bound electron-hole pairs, in Two-Dimensional Hybrid Organic Inorganic Perovskites (2D HOIPs) are capable of forming hybrid light-matter states known as exciton-polaritons (E-Ps) when the excitonic medium is confined in an optical cavity. In the case of 2D HOIPs, they can self-hybridize into E-Ps at specific thicknesses of the HOIP crystals that form a resonant optical cavity with the excitons. However, the fundamental properties of these self-hybridized E-Ps in 2D HOIPs, including their role in ultrafast energy and/or charge transfer at interfaces, remain unclear. Here, we demonstrate that > 0.5 µm thick 2D HOIP crystals on Au substrates are capable of supporting multiple-orders of self-hybridized E-P modes. These E-Ps have high Q factors (> 100) and modulate the optical dispersion for the crystal to enhance sub-gap absorption and emission. Through varying excitation energy and ultrafast measurements, we also confirm energy transfer from higher energy upper E-Ps to lower energy, lower E-Ps. Finally, we also demonstrate that E-Ps are capable of charge transport and transfer at interfaces. Our findings provide new insights into charge and energy transfer in E-Ps opening new opportunities towards their manipulation for polaritonic devices. © 2023, CC BY.

关键词： Charge transfer

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A Hybrid Approach for Weather-based Power Interruption Forecasting using Multilayer Perceptrons and Parametric Models 12

A Hybrid Approach for Weather-based Power Interruption Forec...

引用

12th IEEE PES Asia-Pacific Power and Energy engineering Conference, APPEEC 2020

作者： Wei, Longfei Yang, Xiaolei Liu, Guangyi Dai, Renchang Sundararajan, Aditya Sarwat, Arif I. Global Energy Interconnection Research Institute North America San JoseCA United States State Grid Sichuan Electric Power Company Chengdu Sichuan China Oak Ridge National Laboratory Power and Energy Systems Group Oak RidgeTN United States Florida International University Department of Electrical Computer Engineering MiamiFL United States

ISBN: (纸本)9781728157481

This paper investigates the impact of various weather conditions on the reliability performance of power distribution networks. Especially, a hybrid approach based on multilayer perceptrons (MLPs) and parametric models is proposed to forecast the daily numbers of sustained and momentary power interruptions in the distribution management area using chronological weather data. First, the parametric regression models are implemented to analyze the relationship between power interruptions and different weather characteristics including temperature, wind speed, rain precipitation, air pressure, and lightning. The selected weather characteristics and corresponding parametric models are then integrated as inputs to formulate a MLP neural network model to forecast the daily numbers of power interruptions. In addition, a modified extreme learning machine (ELM) based hierarchical learning algorithm is introduced for training the formulated forecasting model. Finally, the real power interruption data collected from a Florida electric utility is used to verify the applicability and effectiveness of the proposed hybrid approach. © 2020 IEEE.

关键词： Electric utilities

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Proximity perception in human-centered robotics: A survey on sensing systems and applications

arXiv

引用

arXiv 2021年

作者： Navarro, Stefan Escaida Mühlbacher-Karrer, Stephan Alagi, Hosam Zangl, Hubert Koyama, Keisuke Hein, Björn Duriez, Christian Smith, Joshua R. Inria Lille-Nord Europe France JOANNEUM RESEARCH ROBOTICS Institute for Robotics and Mechatronics Klagenfurt Austria Karlsruhe Germany Karlsruhe Institute of Technology IAR - IPR Karlsruhe Germany University of Applied Sciences Karlsruhe Germany Alpen-Adria-Universität Klagenfurt Institute of Smart System Technologies Sensors and Actuators Klagenfurt Austria Osaka University Department of Systems Innovation Graduate School of Engineering Science Osaka Japan University of Washington Allen School of Computer Science and Engineering Department of Electrical Engineering Seattle United States

—Proximity perception is a technology that has the potential to play an essential role in the future of robotics. It can fulfill the promise of safe, robust, and autonomous systems in industry and everyday life, alongside humans, as well as in remote locations in space and underwater. In this survey paper, we cover the developments of this field from the early days up to the present, with a focus on human-centered robotics. Here, proximity sensors are typically deployed in two scenarios: first, on the exterior of manipulator arms to support safety and interaction functionality, and second, on the inside of grippers or hands to support grasping and exploration. Starting from this observation, we propose a categorization for the approaches found in the literature. To provide a basis for understanding these approaches, we devote effort to present the technologies and different measuring principles that were developed over the years, also providing a summary in form of a table. Then, we show the diversity of applications that have been presented in the literature. Finally, we give an overview of the most important trends that will shape the future of this domain. © 2021, CC BY.

关键词： Surveys

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