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arXiv 2022年

作者： Han, Zhichao Wu, Yuwei Li, Tong Zhang, Lu Pei, Liuao Xu, Long Li, Chengyang Ma, Changjia Xu, Chao Shen, Shaojie Gao, Fei State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Huzhou Institute of Zhejiang University Huzhou313000 China Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

As a core part of autonomous driving systems, motion planning has received extensive attention from academia and industry. However, real-time trajectory planning capable of spatial-temporal joint optimization is challenged by nonholonomic dynamics, particularly in the presence of unstructured environments and dynamic obstacles. To bridge the gap, we propose a real-time trajectory optimization method that can generate a high-quality whole-body trajectory under arbitrary environmental constraints. By leveraging the differential flatness property of car-like robots, we simplify the trajectory representation and analytically formulate the planning problem while maintaining the feasibility of the nonholonomic dynamics. Moreover, we achieve efficient obstacle avoidance with a safe driving corridor for unmodelled obstacles and signed distance approximations for dynamic moving objects. We present comprehensive benchmarks with State-of-the-Art methods, demonstrating the significance of the proposed method in terms of efficiency and trajectory quality. Real-world experiments verify the practicality of our algorithm. We will release our codes for the research community.1 Copyright © 2022, The Authors. All rights reserved.

关键词： Autonomous vehicles

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All-Optical, Tunable Third Harmonic Generation in the Ultraviolet Range using Reconfigurable Chalcogenide Metasurfaces

All-Optical, Tunable Third Harmonic Generation in the Ultrav...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Jiannan Gao Maria Antonietta Vincenti Jesse Frantz Anthony Clabeau Xingdu Qiao Liang Feng Michael Scalora Natalia M. Litchinitser Department of Electrical and Computer Engineering Duke University Durham NC USA Department of Information Engineering University of Brescia Brescia Italy US Naval Research Laboratory Washington DC USA University Research Foundation Greenbelt MD USA Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA USA Department of Materials Science and Engineering University of Pennsylvania Philadelphia PA USA Aviation and Missile Center US Army CCDC Redstone Arsenal AL USA

We theoretically predict and experimentally demonstrate ultra-high-speed, tunable third harmonic generation (THG) in the opaque spectral range of Arsenic trisulfide (As 2 S 3 ) glass metasurfaces.

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

We theoretically predict and experimentally demonstrate ultra-high-speed, tunable third harmonic generation (THG) in the opaque spectral range of Arsenic trisulfide (As 2 S 3 ) glass metasurfaces.

关键词： Chalcogenides Laser theory Arsenic Glass Lasers and electrooptics Metasurfaces Frequency conversion

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A single magnetic tunnel junction for the implementation of a spiking neuron

A single magnetic tunnel junction for the implementation of ...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Davi R. Rodrigues Rayan Moukhader Adrien Pontlevy Abbass Hamadeh Zhongming Zeng Mario Carpentieri Giovanni Finocchio Department of Electrical and Information Engineering Politecnico di Bari Bari Italy Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences University of Messina Messina Italy Fachbereich Physik and Landesforschungszentrum OPTIMAS Technische Universität Kaiserslautern Kaiserslautern Germany Key Laboratory of Multifunctional Nanomaterials and Smart Systems Suzhou Institute of Nano-Tech and Nano-Bionics CAS Suzhou People’s Republic of China

We propose a magnetic tunnel junction device to perform bio-realistic neuron firing for spiking neural networks. Spiking neural networks is the third generation of artificial neural networks and promises significantly reduced energy consumption, especially for sparse time-dependent data, by mimicking the biological brain. Magnetic tunnel junctions allow for drop-in replacement in current SNN CMOS-based hardware implementations to increase scalability and efficiency. The magnetization dynamics in the magnetic tunnel junction fully emulates the features of the Huxley-Hodgkin model of the neuron. Hence, the device allows for the implementation of biologically plausible algorithms. The device also emulates the simpler and commonly used Leaky-Integrate and Fire model.

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A Deep Learning Method for Predicting the COVID-19 ICU Patient Outcome Fusing X-Rays, Respiratory Sounds, And ICU Parameters

SSRN

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SSRN 2023年

作者： Wu, Yunan Rocha, Bruno Machado Kaimakamis, Evangelos Cheimariotis, Grigorios-Aris Petmezas, Georgios Chatzis, Evangelos Kilintzis, Vassilis Stefanopoulos, Leandros Pessoa, Diogo Marques, Alda Carvalho, Paulo Paiva, Rui Pedro Kotoulas, Serafeim Bitzani, Militsa Katsaggelos, Aggelos K. Maglaveras, Nicos The Department of Electrical and Computer Engineering Northwestern University EvanstonIL United States The Department of Informatics Engineering Centre for Informatics and Systems of the University of Coimbra The University of Coimbra Coimbra Portugal 1st Intensive Care Unit "G. Papanikolaou" General Hospital of Thessaloniki Greece 2nd Department of Obstetrics and Gynaecology Laboratory of Computing Medical Informatics and Biomedical - Imaging Technologies Medical School Aristotle University of Thessaloniki Greece University of Aveiro Aveiro Portugal

Assessing the health status of critically ill patients with COVID-19 and predicting their outcome are highly challenging problems and one of the reasons for poor management of ICU resources worldwide. A better pathophysiological understanding of patients’ state evolution in the ICU can enhance effective medical interventions. Therefore, there is a need to monitor and analyze the pulmonary function of a ICU patient with COVID-19 and its impact on cardiovascular and other systems. To achieve this, chest X-rays (CXRs), respiratory sounds and all the routinely monitored parameters, scores and metrics in the COVID-19 ICU were recorded from 171 ICU patients with COVID-19 from June 2020 until December 2021. Features were extracted from respiratory sounds, deep learning analysis was conducted on CXRs, and logistic regression analysis was performed on routine ICU clinical variables. Deep learning pipelines were established to classify patients’ outcomes (survival or death) at two time points (ICU mortality or 90-day mortality) using three input configurations: (a) CXRs, (b) a fusion of CXRs and respiratory sounds features, or (c) a fusion of CXRs, respiratory sounds features, and principal features of the ICU clinical measurements. The performance of the latter approach was promising, achieving, for ICU mortality, an accuracy of 0.761 and an AUC of 0.759, and for 90-day mortality, an accuracy of 0.743 and an AUC of 0.752, while the performance of approaches (a) and (b) was worse. Therefore, using multi-source data and longitudinal COVID-19 ICU data offers a better prediction of the outcome in the ICU, thereby optimizing medical decisions and interventions. Furthermore, we show that adding the adventitious respiratory sounds features significantly increased AUC and accuracy for mortality prediction of ICU patients with COVID-19. © 2023, The Authors. All rights reserved.

关键词： COVID-19

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Towards Robust and Scalable Dispatch Modeling of Long-Duration Energy Storage

arXiv

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arXiv 2024年

作者： Guerra, Omar J. Dalvi, Sourabh Thatte, Amogh A. Cowiestoll, Brady Jorgenson, Jennie Hodge, Bri-Mathias National Renewable Energy Laboratory 15013 Denver West Parkway GoldenCO80401 United States Colorado School of Mines Advanced Energy Systems Graduate Program 1500 Illinois Street GoldenCO80401 United States Renewable and Sustainable Energy Institute University of Colorado BoulderCO80309 United States Department of Electrical Computer & Energy Engineering University of Colorado BoulderCO80309 United States

Energy storage technologies, including short-duration, long-duration, and seasonal storage, are seen as technologies that can facilitate the integration of larger shares of variable renewable energy, such as wind and solar photovoltaics, in power systems. However, despite recent advances in the techno-economic modeling of energy storage (particularly for short-duration applications), the operation and economics of long-duration energy storage are still incomplete in power systems modeling platforms. For instance, existing modeling approaches for long-duration storage are often based either on an oversimplified representation of power system operations or limited representation of storage technologies, e.g., evaluation of only a single application. This manuscript presents an overview of the challenges of modeling long-duration energy storage technologies, as well as a discussion regarding the capabilities and limitations of existing approaches. We used two test power systems with high shares of both solar photovoltaics- and wind (70% - 90% annual variable renewable energy shares) to assess long-duration energy storage dispatch approaches. Our results estimate that better dispatch modeling of long-duration energy storage could increase the associated operational value by 4% - 14% and increase the standard capacity credit by 14% - 34%. Thus, a better long-duration energy storage dispatch could represent significant cost saving opportunities for electric utilities and system operators. In addition, existing long-duration dispatch modeling approaches were tested in terms of both improved system value (e.g., based on production cost and standard capacity credit) and scalability (e.g., based on central processing unit time and peak memory usage). Both copper plate and nodal representations of the power system were considered. Although the end volume target dispatch approach, i.e., based on mid-term scheduling, showed promising performance in terms of both improved system

关键词： Scalability

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Performance Analysis of OMA/NOMA-aided Satellite Communication Networks: A Stochastic Geometry Approach

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IEEE Transactions on Communications 2024年

作者： Li, Kecheng Wang, Jun Hou, Tianwei Li, Anna Yue, Xinwei Liu, Yuanwei Chen, Wei School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom KU Leuven Leuven3001 Belgium School of Computing and Communications Lancaster University LancasterLA1 4WA United Kingdom Key Laboratory of Information and Communication Systems Ministry of Information Industry China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

The increasing quality of service requirements and demand for satellite services necessitate higher data rates, spectral efficiency, and stability in satellite communication networks. Therefore, this paper investigates the non-orthogonal multiple access (NOMA) assisted satellite communication networks, where multiple users are uniformly distributed over a spherical hat according to the homogeneous Poisson point process (HPPP). Based on the characteristics of HPPP, we analyze the distance distributions of users. To evaluate the performance of the proposed networks, we first derive the closed-form expressions and approximated expressions of the outage probability (OP) for paired NOMA users. To obtain more insights into the proposed networks, the ergodic rate and diversity orders for paired NOMA users are also derived. Spectral efficiency is derived for NOMA and orthogonal multiple access (OMA) assisted satellite communication networks. Our analytical results demonstrate that the diversity order of the proposed networks is all one. Numerical results confirm that: 1) compared to OMA, the proposed NOMA-assisted satellite network demonstrates superior outage performance and spectral efficiency, especially in the higher power regimes;2) fading factors have negligible effects on OP and spectral efficiency;and 3) under certain target rates for both near and far users, the outage performance of far users in NOMA-assisted satellite communication networks is superior to that of near users. © 2024 Institute of electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Satellite communication systems

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Non-linear State-space Model Identification from Video Data using Deep Encoders

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IFAC-PapersOnLine 2021年第7期54卷 697-701页

作者： Gerben I. Beintema Roland Toth Maarten Schoukens Department of Electrical Engineering Eindhoven University of Technology 5600 MB Eindhoven The Netherlands Systems and Control Laboratory Institute for Computer Science and Control Kende u. 13-17 H-1111 Budapest Hungary

Identifying systems with high-dimensional inputs and outputs, such as systems measured by video streams, is a challenging problem with numerous applications in robotics, autonomous vehicles and medical imaging. In this paper, we propose a novel non-linear state-space identification method starting from high-dimensional input and output data. Multiple computational and conceptual advances are combined to handle the high-dimensional nature of the data. An encoder function, represented by a neural network, is introduced to learn a reconstructability map to estimate the model states from past inputs and outputs. This encoder function is jointly learned with the dynamics. Furthermore, multiple computational improvements, such as an improved reformulation of multiple shooting and batch optimization, are proposed to keep the computational time under control when dealing with high-dimensional and large datasets. We apply the proposed method to a video stream of a simulated environment of a controllable ball in a unit box. The study shows low simulation error with excellent long term prediction capability of the model obtained using the proposed method.

关键词： Non-linear State-Space Modelling Deep Learning Pixels Multiple Shooting

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WSTac: Interactive Surface Perception based on Whisker-Inspired and Self-Illuminated Vision-Based Tactile Sensor

arXiv

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arXiv 2023年

作者： Lei, Kai Chong Sou, Kit Wa Chan, Wang Sing Yan, Jiayi Ping, Siqi Peng, Dengfeng Ding, Wenbo Zhang, Xiao-Ping Tsinghua-Berkeley Shenzhen Institute Shenzhen International Graduate School Tsinghua University China Shenzhen International Graduate School Tsinghua University China Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic Engineering Shenzhen University China Tsinghua-Berkeley Shenzhen Institute Shenzhen International Graduate School Tsinghua University RISC-V International Open Source Laboratory China Department of Electrical Computer and Biomedical Engineering Ryerson University Canada

Modern Visual-Based Tactile Sensors (VBTSs) use cost-effective cameras to track elastomer deformation, but struggle with ambient light interference. Solutions typically involve using internal LEDs and blocking external light, thus adding complexity. Creating a VBTS resistant to ambient light with just a camera and an elastomer remains a challenge. In this work, we introduce WStac, a self-illuminating VBTS comprising a mechanoluminescence (ML) whisker elastomer, camera, and 3D printed parts. The ML whisker elastomer, inspired by the touch sensitivity of vibrissae, offers both light isolation and high ML intensity under stress, thereby removing the necessity for additional LED modules. With the incorporation of machine learning, the sensor effectively utilizes the dynamic contact variations of 25 whiskers to successfully perform tasks like speed regression, directional identification, and texture classification. Videos are available at: https://***/view/wstac/. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost effectiveness

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Nonlocal Time-Resolved Terahertz Spectroscopy in the Near Field

Nonlocal Time-Resolved Terahertz Spectroscopy in the Near Fi...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Angela Pizzuto Enrique Castro-Camus William Wilson Wonsik Choi Xiuling Li Daniel M. Mittleman Department of Physics Brown University Providence RI USA Department of Physics University of Marburg Marburg Germany Loma del Bosque 115 Lomas del Campestre Centro de Investigaciones en Optica A. C. Leon Guanajuato Mexico Center for Nanoscale Systems Harvard University Cambridge MA Department of Electrical and Computer Engineering Holonyak Micro and Nanotechnology Laboratory University of Illinois at Urbana-Champaign Champaign IL USA Department of Electrical and Computer Engineering University of Texas Austin TX USA School of Engineering Brown University Providence RI USA

We demonstrate the first "nonlocal" pump-probe measurement using a scattering scanning near-field microscopy technique by laterally displacing the pump and probe spots. We observe nonlocal effects associated... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Spectroscopy Microscopy Scattering Measurement by laser beam Displacement measurement Probes Pump lasers

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Robust sub-wavelength grating coupler for on-chip silicon nitride waveguides

Robust sub-wavelength grating coupler for on-chip silicon ni...

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Frontiers in Optics, FIO 2019 - Part of Frontiers in Optics + Laser Science APS/DLS

作者： Begović, Amir Anderson, Stephen Chen, Alexander Rena Huang, Z. Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute United States US Army Research Laboratory Sensors and Electronic Devices Directorate United States Department of Physics Rensselaer Polytechnic Institute United States

A sub-wavelength grating (SWG) coupler is designed for coupling from a lensed fiber to an on-chip SiN waveguide. The designed SWG is compared with an inverse taper and analyzed for fabrication inaccuracies and misalig... 详细信息

ISBN: (纸本)9781943580675

关键词： Silicon nitride

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