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IEEE International Symposium on Biomedical Imaging

作者： Haifeng Wang Dong Liang Kevin F. King Leslie Ying Department of Electrical Engineering and Computer Science University of Wisconsin Milwaukee Milwaukee WI USA Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China Global Applied Science Laboratory GE Healthcare Systems Waukesha WI USA

Compressed sensing (CS) has been successfully applied to accelerate conventional magnetic resonance imaging (MRI) with Fourier encoding. However in Fourier encoding, the low spatial frequency always has to be fully sampled such that the high frequency is insufficiently sampled at high accelerations, leading to serious loss of resolution. Non-Fourier encoding has been studied in the context of compressed sensing for 2-D imaging, but with limited improvement. In this paper, we propose a novel 3-D acquisition method using hybrid encoding. The method exploits random encoding with a circulant structure along the “phase encoding” direction, while keeping conventional Fourier encoding along the readout and slice encoding directions. A 2-D random undersampling pattern is used for accelerating the scans. Both simulation and experimental results demonstrate that the proposed method preserves much better resolution than the conventional 3-D Fourier encoding when the same acceleration factors are used.

关键词： Encoding Compressed sensing Magnetic resonance imaging Image reconstruction Acceleration Image coding

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An almost linear biped

An almost linear biped

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IEEE Conference on Decision and Control

作者： M.W. Spong R. Lozano R. Mahony Coordinated Science Laboratory University of Illinois Urbana-Champaign Urbana USA Heudiasyc UMR CNRS 6599 Universite de Technologie de Compiégne Compiegne France Department of Electrical and Computer Systems Engineering Monash University Clayton VIC Australia

ISBN: (纸本)0780366387

We discuss the nonlinear control of a novel biped robot with three-degrees-of-freedom and two control inputs, whose dynamics are linear apart from the gravitational torque and the impact equations. We show that the equations of motion are locally feedback linearizable by nonlinear change of coordinates and nonlinear feedback in a region that includes all walking gaits of interest. We combine the feedback linearization control with control of the resulting linear system and the nonlinear impact equations to generate a stable walking gait.

关键词： Leg Feedback Torque control Legged locomotion Torso Nonlinear control systems Control systems Robot kinematics Nonlinear equations Ear

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278 nm deep ultraviolet LEDs with 11% external quantum efficiency

278 nm deep ultraviolet LEDs with 11% external quantum effic...

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Device Research Conference

作者： Max Shatalov Wenhong Sun Alex Lunev Xuhong Hu Alex Dobrinsky Yuri Bilenko Jinwei Yang Michael Shur Remis Gaska Craig Moe Gregory Garrett Michael Wraback Sensor Electronic Technology Inc. Columbia SC USA Department of Electrical Computer and Systems Engineering and Center of Integrated Electronics Rensselaer Polytechnic Institute Troy NY USA U.S. Army Research Laboratory Adelphi MD USA

III-Nitride based deep ultraviolet (DUV) light emitting diodes (LEDs) offer smaller size, wider choice of peak emission wavelengths, lower power consumption and reduced cost compared to mercury vapor lamps and other UV light sources. Increasing efficiency of DUV LEDs accelerates their applications in bio-agent detection, analytical instrumentation, phototherapy, disinfection, biotechnology and sensing. We report on 278 nm DUV LEDs with external quantum efficiency exceeding 10 % achieved by improvements of material quality and light extraction.

关键词： Light emitting diodes Instruments

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Decision support information integration platform for context-driven interdiction operations in counter-smuggling missions

Decision support information integration platform for contex...

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IEEE/SICE International Symposium on System Integration

作者： David Sidoti Diego F. M. Ayala Sravanth Sankavaram Xu Han Manisha Mishra Woosun An David Kellmeyer James Hansen Krishna R. Pattipati Electrical and Computer Engineering Department University of Connecticut Storrs CT SIMNET Co. Ltd Seoul South Korea Space & Naval Warfare Systems Center (SPAWAR) San Diego CA US Naval Research Laboratory Washington DC US

Context-driven decision making is at the top of the Navy's agenda of important concepts to be embedded in future proactive decision support systems for command decision making. There are manifold challenges associated with relaying contextual data in a timely manner to the decision maker. In the counter-smuggling domain, for example, although high value information is accessible, it is dispersed across databases and the decision making team. There are numerous research challenges in integrating this information in an efficient manner to effectively present viable courses of action to a decision making team. In this paper, we propose a decision support tool for counter-smuggling missions modeled as a stochastic control problem of dynamically managing assets to maximize the probability of detecting and interdicting maritime illicit trafficking operations. We additionally propose a method to explain the algorithm behavior to the human decision maker and provide them with interactive controls to develop “what-if” solutions or to constrain solutions to a desired path.

关键词： Resource management Uncertainty Decision making Decision support systems Planning Google Earth

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Thermomechanical reliability investigation of large temperature excursions in power electronics packages

Thermomechanical reliability investigation of large temperat...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Puqi Ning Khai Ngo Fred Wang Power Electronics & Electric Power Systems Research Center Oak Ridge National Laboratory Knoxville TN USA Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN USA Center for Power Electronics Systems Virginia Polytechnic Institute and State University Blacksburg VA USA

This study confirms that high stresses between the metallization layer and ceramic lead to significant failures in DBC substrate. The driving forces behind several different failure modes are discussed. Further understanding of these failure mechanisms enables the modules to be engineered for longtime operation and helps to enhance the reliability of system-level operation.

关键词： Substrates Stress Copper Simulation Reliability Aluminum oxide Finite element methods

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A Context-Enhanced Full-Resolution Floor Plan Segmentation Network for Topological Semantic Mapping

A Context-Enhanced Full-Resolution Floor Plan Segmentation N...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhengcai Cao Yiyang Sun Zhe Ma MengChu Zhou State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China College of Information Science and Technology Beijing University of Chemical Technology Beijing China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Topological semantic maps provide a practical solution to enhance indoor navigation for the Partially Sighted or Visually Impaired (PSVI). Segmenting indoor floor plans and extracting boundaries are key to constructing these maps. The existing methods exhibit low accuracy in segmentation. To achieve desired high segmentation accuracy, we introduce a Context-Enhanced Full-Resolution Network (CEFRN) for floor plan segmentation. It is designed to harness the shallow detailed features and inter-category contextual dependencies inherent in floor plans. CEFRN integrates modified residual blocks to capture the low-stage full-resolution features while maintaining its compactness. A position attention module is employed to refine the deep-stage contextual information. We also propose a two-dimensional deep supervision method to merge features from both stages, which significantly boosts the feature representation ability of CEFRN. Finally, a practical topological semantic mapping method for PSVI indoor navigation is introduced. Experimental results demonstrate that CEFRN’s segmentation accuracy well exceeds the state-of-the-art methods’. It can be used to well support accurate topological semantic mapping.

关键词： Accuracy Hospitals Indoor navigation Semantics Feature extraction Mobile robots Floors Intelligent robots Autonomous vehicles Drones

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Coherency-Constrained Spectral Clustering for Proactive Load Shedding: A Tri-Level Model

Coherency-Constrained Spectral Clustering for Proactive Load...

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IEEE General Meeting Power& Energy Society

作者： Mario D. Baquedano-Aguilar Arturo Bretas Sean Meyn Department of Electrical & Computer Engineering University of Florida Gainesville FL USA Distributed Systems Group Pacific Northwest National Laboratory Richland WA USA G2Elab Grenoble INP CNRS Université Grenoble Alpes Grenoble France

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

In low-inertia power grids, traditional load-shedding methods can be ineffective, causing instability and unnecessary grid islanding. This work introduces a distributed tri-level optimization model for proactive load shedding to prevent grid islanding considering generator coherency identification. Preventing islanding is crucial because it reduces the need for additional load shedding and transmission line tripping, thereby maintaining frequency stability. This is especially important in systems heavily dependent on non-dispatchable resources like wind and solar. The model employs generator coherency identification through spectral clustering using synchronizing coefficients. The results are then used as constraints in a dynamic graph based on power flow measurements to identify coherent areas for load shedding under highly stressed conditions. This approach offers practical applications and holds promise for real-world use.

关键词： Wind Islanding Power transmission lines Load shedding Power system stability Transmission line measurements Generators Stability analysis Load modeling Stress measurement

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Convolutional Neural Networks for Epileptic Seizure Prediction

arXiv

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arXiv 2018年

作者： Eberlein, Matthias Hildebrand, Raphael Tetzlaff, Ronald Hoffmann, Nico Kuhlmann, Levin Brinkmann, Benjamin Müller, Jens Technische Universität Dresden Faculty of Electrical and Computer Engineering Institute of Circuits and Systems Dresden01062 Germany Technische Universität Dresden Faculty of Computer Science Computer Graphics and Visualisation Dresden01062 Germany Faculty of Information Technology Monash University VIC Clayton3800 Australia Department of Medicine St Vincent's Hospital Melbourne VIC Fitzroy3065 Australia Mayo Systems Electrophysiology Laboratory Departments of Neurology and Biomedical Engineering Mayo Clinic RochesterMN55905 United States

Epilepsy is the most common neurological disorder and an accurate forecast of seizures would help to overcome the patient's uncertainty and helplessness. In this contribution, we present and discuss a novel methodology for the classification of intracranial electroencephalography (iEEG) for seizure prediction. Contrary to previous approaches, we categorically refrain from an extraction of hand-crafted features and use a convolutional neural network (CNN) topology instead for both the determination of suitable signal characteristics and the binary classification of preictal and interictal segments. Three different models have been evaluated on public datasets with long-term recordings from four dogs and three patients. Overall, our findings demonstrate the general applicability. In this work we discuss the strengths and limitations of our methodology. Copyright © 2018, The Authors. All rights reserved.

关键词： Electroencephalography

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A Leader-Follower Based parallel Accelerated Particle Swarm Optimization Algorithm for Smart Grid Resource Allocation

A Leader-Follower Based Parallel Accelerated Particle Swarm ...

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Annual North-American Power Symposium

作者： Sheroze Liaquat Robert Fourney Timothy M. Hansen Tanveer Hussain Berk Celik Department of Electrical Engineering and Computer Science South Dakota State University Brookings USA Power and Energy Systems Idaho National Laboratory Idaho Falls ID USA AVENUES Université de Technologie de Compiègne Compiègne France

Smart grid resource allocation (SGRA) is a large-scale demand response optimization problem to lower the electricity cost of customers by intelligently shifting their schedulable devices via an aggregator. The SGRA problem aims to maximize the aggregator profit while still considering the inconvenience and the availability constraints of each customer's assets. A heuristic approach provides a reliable method to find near-optimal solutions to non-convex, complex, and multi-dimensional problems by reducing the computational time and not relying on gradients. To further improve the computational time of the heuristics, high-performance parallel computing concepts can be used to divide the formulated problem into smaller sub-problems. This research proposes a leader-follower based parallel Accelerated Particle Swarm Optimization (APSO) to find near-optimal solutions of SGRA using the OpenMP framework in MATLAB. The leader-follower topology allows the parallel execution of each particle, resulting in lower execution time. The proposed parallel APSO was both significantly faster (2 - 7×) than existing SGRA literature methods, and achieves similar aggregator profit, customer savings, and peak demand reduction.

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Convergence of a Distributed Least Squares

arXiv

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arXiv 2019年

作者： Xie, Siyu Zhang, Yaqi Guo, Lei The Department of Electrical and Computer Engineering Wayne State University DetroitMI48202 United States Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Science University of Chinese Academy of Sciences Beijing100049 China

In this paper, we consider a least-squares (LS)-based distributed algorithm build on a sensor network to estimate an unknown parameter vector of a dynamical system, where each sensor in the network has partial information only but is allowed to communicate with its neighbors. Our main task is to generalize the well-known theoretical results on the traditional LS to the current distributed case by establishing both the upper bound of the accumulated regrets of the adaptive predictor and the convergence of the distributed LS estimator, with the following key features compared with the existing literature on distributed estimation: Firstly, our theory does not need the previously imposed independence, stationarity or Gaussian property on the system signals, and hence is applicable to stochastic systems with feedback. Secondly, the cooperative excitation condition introduced and used in this paper for the convergence of the distributed LS estimate is the weakest possible one, which shows that even if any individual sensor cannot estimate the unknown parameter by the traditional LS, the whole network can still fulfill the estimation task by the distributed *** Codes 93E24, 93E35, 93E10 Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

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