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John S. Thompson: Technology Challenges in the Development of the First Single-Chip DSP

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IEEE Solid-State Circuits Magazine 2017年第2期9卷 21-24页

作者： Victor B. Lawrence Bryan Ackland Stevens Institute of Technology Center for Intelligent Networked Systems New Jersey Department of Electrical and Computer Engineering Stevens Institute of Technology

As we reflect on John Thompson's career, we realize how significant his achievements have been and recognize the impact he has had on our industry. His achievements span several decades.

关键词： Digital signal processing Laboratories Patents Speech processing Program processors computer architecture

Spectral Analysis of Backbone Networks Against Targeted Attacks

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Spectral Analysis of Backbone Networks Against Targeted Atta...

DRCN 2017 - Design of Reliable Communication Networks;13th International Conference

作者： Tristan A. Shatto Egemen K. Cetinkaya Department of Electrical and Computer Engineering Missouri University of Science and Technology Intelligent Systems Center Missouri University of Science and Technology

ISBN: (纸本)9781509008940

Network science has been a central focus to correctly model and study resilience characteristics of communication networks. There have been many metrics used to represent connectivity of graphs; however, they do not suffice to compare networks with different numbers of nodes and links. The normalized Laplacian spectra enables network scientists to analyze network structures beyond what traditional graph metrics lacks. In this paper, we study the normalized Laplacian spectra of five backbone networks against targeted attacks. The physical and logical level of four commercial and one research backbone provider networks is studied. The intelligent attacks are modeled based on important graph centrality metrics of betweenness, closeness, and degree. Our results indicate that spectra of eigenvalues converge to zero after attacks. Moreover, we also identify that while in some scenarios different centrality-based attack strategies yield identical eigenvalue distribution, in other scenarios different attacks yield different eigenvalue distributions.

关键词： Multicast communication Cognitive radio Routing protocols Delays Switches Channel allocation

Unveiling stimulation secrets of electrical excitation of neural tissue using a circuit probability theory

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arXiv 2018年

作者： Wang, Hao Wang, Jiahui Thow, Xin Yuan Lee, Sanghoon Peh, Wendy Yen Xian Ng, Kian Ann He, Tianyiyi Thakor, Nitish V. Lee, Chengkuo Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore National University of Singapore Singapore117456 Singapore Center For Intelligent Sensor and MEMS National University of Singapore Singapore117581 NUS Graduate School for Integrative Sciences and Engineering National University of Singapore Singapore117456 Singapore Hybrid Integrated Flexible Electronic Systems National University of Singapore Singapore117583

electrical excitation of neural tissue has wide applications, but how electrical stimulation interacts with neural tissue remains to be elucidated. Here, we propose a new theory, named the Circuit-Probability theory, to reveal how this physical interaction really happen. We show that many empirical models, including strength-duration relationship and linear-nonlinear-Poisson model, can be theoretically explained, derived, and amended using our theory. Furthermore, this theory can explain the complex nonlinear and resonant phenomena and fit in vivo experiment data. In this letter, we validated an entirely new framework to study electrical stimulation on neural tissue, which is to simulate voltage waveforms using a proper circuit first, and then calculate the excitation probability stochastically. Copyright © 2018, The Authors. All rights reserved.

关键词： Probability

An improved deep belief network model for road safety analyses

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arXiv 2018年

作者： Pan, Guangyuan Fu, Liping Thakali, Lalita Muresan, Matthew Yu, Ming Department of Civil & Environmental Engineering University of Waterloo WaterlooONN2L 3G1 Canada Intelligent Transportation Systems Research Center Wuhan University of Technology Mailbox 125 No. 1040 Heping Road Wuhan Hubei430063 China Department of Electrical & Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

—Crash prediction is a critical component of road safety analyses. A widely adopted approach to crash prediction is application of regression based techniques. The underlying calibration process is often time-consuming, requiring significant domain knowledge and expertise and cannot be easily automated. This paper introduces a new machine learning (ML) based approach as an alternative to the traditional techniques. The proposed ML model is called regularized deep belief network, which is a deep neural network with two training steps: it is first trained using an unsupervised learning algorithm and then fine-tuned by initializing a Bayesian neural network with the trained weights from the first step. The resulting model is expected to have improved prediction power and reduced need for the time-consuming human intervention. In this paper, we attempt to demonstrate the potential of this new model for crash prediction through two case studies including a collision data set from 800 km stretch of Highway 401 and other highways in Ontario, Canada. Our intention is to show the performance of this ML approach in comparison to various traditional models including negative binomial (NB) model, kernel regression (KR), and Bayesian neural network (Bayesian NN). We also attempt to address other related issues such as effect of training data size and training parameters. Copyright © 2018, The Authors. All rights reserved.

关键词： Accident prevention

Degrees of Freedom of the Full-Duplex Asymmetric MIMO Three-Way Channel with Unicast and Broadcast Messages

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Degrees of Freedom of the Full-Duplex Asymmetric MIMO Three-...

作者： Elmahdy, Adel M. El-Keyi, Amr Mohasseb, Yahya Elbatt, Tamer Nafie, Mohammed Seddik, Karim G. Khattab, Tamer Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Wireless Intelligent Networks Center Nile University Giza Egypt Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada Department of Communications Military Technical College Cairo11331 Egypt Department of EECE Faculty of Engineering Cairo University Giza12613 Egypt Electronics and Communications Engineering Department American University in Cairo New Cairo11835 Egypt Electrical Engineering Department Qatar University Doha2713 Qatar

In this paper, we characterize the total degrees of freedom (DoFs) of the full-duplex asymmetric multiple-input multiple- output (MIMO) three-way channel. Each node has a separate-antenna full-duplex MIMO transceiver with a different number of antennas, where each antenna can be configured for either signal transmission or reception. We study this system under two message configurations;the first configuration is when each node has two unicast messages to be delivered to the two other nodes, while the second configuration is when each node has two unicast messages as well as one broadcast message to be delivered to the two other nodes. For each configuration, we first derive upper bounds on the total DoF of the system. Cut-set bounds in conjunction with genie-aided bounds are derived to characterize the achievable total DoF. Afterward, we analytically derive the optimal number of transmit and receive antennas at each node to maximize the total DoF of the system, subject to the total number of antennas at each node. Finally, the achievable schemes for each configuration are constructed. The proposed schemes are mainly based on zero-forcing and null-space transmit beamforming. We show that the derived outer and inner bounds on the total DoF are tight for each message configuration. © 1972-2012 IEEE.

关键词： Degrees of freedom (mechanics)

Machine discovery of partial differential equations from spatiotemporal data

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arXiv 2019年

作者： Yuan, Ye Li, Junlin Li, Liang Jiang, Frank Tang, Xiuchuan Zhang, Fumin Liu, Sheng Goncalves, Jorge Voss, Henning U. Li, Xiuting Kurths, Jürgen Ding, Han School of Artificial Intelligence and Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta30332 United States School of Power and Mechanical Engineering Wuhan University Wuhan430074 China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Luxembourg Centre for Systems Biomedicine University of Luxembourg BelvauxL-4367 Luxembourg Citigroup Biomedical Imaging Center Weill Cornell Medical College New York10021 United States Research Domain IV - Transdisciplinary Concepts & Methods Potsdam Institute for Climate Impact Research Potsdam14473 Germany School of Mechanical Science and Engineering State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal System Discovery (S3d), decides which physical terms are necessary and which can be removed (because they are physically negligible in the sense that they do not affect the dynamics too much) from a pool of candidate functions. The method is built on the recent development of Sparse Bayesian Learning;which enforces the sparsity in the to-be-identified PDEs, and therefore can balance the model complexity and fitting error with theoretical guarantees. Without leveraging prior knowledge or assumptions in the discovery process, we use an automated approach to discover ten types of PDEs, including the famous Navier-Stokes and sine-Gordon equations, from simulation data alone. Moreover, we demonstrate our data-driven discovery process with the Complex Ginzburg-Landau Equation (CGLE) using data measured from a traveling-wave convection experiment. Our machine discovery approach presents solutions that has the potential to inspire, support and assist physicists for the establishment of physical laws from measured spatiotemporal data, especially in notorious fields that are often too complex to allow a straightforward establishment of physical law, such as biophysics, fluid dynamics, neuroscience or nonlinear optics. Copyright © 2019, The Authors. All rights reserved.

关键词： Nonlinear optics

Deep EndoVO: A recurrent convolutional neural network (RCNN) based visual odometry approach for endoscopic capsule robots

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arXiv 2017年

作者： Turan, Mehmet Almalioglu, Yasin Araujo, Helder Konukoglu, Ender Sitti, Metin Physical Intelligence Department Max Planck Institute for Intelligent Systems Germany Computer Engineering Department Bogazici Univesity Turkey Institute for Systems and Robotics Universidade de Coimbra Portugal Computer Vision Laboratory Department of Information Technology and Electrical Engineering ETH Zurich Switzerland

Ingestible wireless capsule endoscopy is an emerging minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologies. Medical device companies and many research groups have recently made substantial progresses in converting passive capsule endoscopes to active capsule robots, enabling more accurate, precise, and intuitive detection of the location and size of the diseased areas. Since a reliable real time pose estimation functionality is crucial for actively controlled endoscopic capsule robots, in this study, we propose a monocular visual odometry (VO) method for endoscopic capsule robot operations. Our method lies on the application of the deep Recurrent Convolutional Neural Networks (RCNNs) for the visual odometry task, where Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are used for the feature extraction and inference of dynamics across the frames, respectively. Detailed analyses and evaluations made on a real pig stomach dataset proves that our system achieves high translational and rotational accuracies for different types of endoscopic capsule robot trajectories. Copyright © 2017, The Authors. All rights reserved.

关键词： Vision

Autofluorescence imaging for improved visualization of joint structures during arthroscopic surgery

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Journal of Experimental Orthopaedics 2017年第1期4卷 1-9页

作者： Nguyen, Duy Tan van Horssen, Pepijn Derriks, Hans van de Giessen, Martijn van Leeuwen, Ton Present Address: Department of Family Medicine University of Gent Ghent Belgium Department of Orthopaedic Surgery University of Amsterdam Amsterdam Netherlands Department of Physics and Medical Technology VU University Medical Center Amsterdam Netherlands Biomedical Engineering and Physics University of Amsterdam Amsterdam Netherlands Present Address: St. Maartenskliniek Nijmegen Netherlands Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Intelligent Systems Faculty of Electrical Engineering Applied Mathematics and Computer Science Delft University of Technology Delft Netherlands

Background: The purpose of our study is to develop the arthroscopic autofluorescence imaging (AFI) system to improve the visualization during arthroscopic surgery by real-time enhancing the contrast between joint structures with autofluorescence imaging. Its validity was evaluated around the arthroscopic anterior cruciate ligament (ACL) reconstruction, specifically improving the contrast between the femoral insertion site and its background. The feasibility of the AFI system was validated with bovine and human knees. The spectral responses of the femoral insertion site and its surrounding bone and cartilage were measured with a fluorospectrometer. A prototype of the AFI system was developed based on the spectral responses (SR) and test images of the insertion site. The accuracy was validated by evaluating the overlap between manually segmented insertion sites on the white light color images and on the corresponding spectral unmixed autofluorescence images. The final prototype of the AFI system was tested during arthroscopy in cadaveric knees. Results: The results showed that the joint structures have different SRs. Spectral unmixing enabled separation of the SRs and improved the contrast between the joint structures. The agreement between visible light and autofluorescence ligament insertions had a mean Dice coefficient of 0.84 and the mean Dice coefficient of the interobserver variability for visible light imaging was 0.85. Conclusions: We have shown that the femoral insertion site can be accurately visualized with autofluorescence imaging combined with spectral unmixing. The AFI system demonstrates the feasibility of real-time and subject-specific visualization of the femoral insertion site which can facilitate anatomic ACL reconstruction. In addition, the AFI system can facilitate arthroscopic procedures in other joints and can also be used as a diagnostic tool. © 2017, The Author(s).

关键词： Anterior cruciate ligament Arthroscopic autofluorescence imaging system Arthroscopy Fluorescence Fluorescence arthroscope

EndoSensorFusion: Particle filtering-based multi-sensory data fusion with switching state-space model for endoscopic capsule robots

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arXiv 2017年

作者： Turan, Mehmet Almalioglu, Yasin Gilbert, Hunter Araujo, Helder Cemgil, Taylan Sitti, Metin Physical Intelligence Department Max Planck Institute for Intelligent Systems Department of Information Technology and Electrical Engineering Eth Zurich Computer Engineering Department Bogazici Univesity Mechanical Engineering Department Louisiana State University Baton RougeLA70803 United States Institute for Systems and Robotics University of Coimbra Computer Engineering Department Bogazici Univesity Physical Intelligence Department Max Planck Institute for Intelligent Systems

A reliable, real time multi-sensor fusion functionality is crucial for localization of actively controlled capsule endoscopy robots, which are an emerging, minimally invasive diagnostic and therapeutic technology for the gastrointestinal (GI) tract. In this study, we propose a novel multi-sensor fusion approach based on a particle filter that incorporates an online estimation of sensor reliability and a non-linear kinematic model learned by a recurrent neural network. Our method sequentially estimates the true robot pose from noisy pose observations delivered by multiple sensors. We experimentally test the method using 5 degree-of-freedom (5-DoF) absolute pose measurement by a magnetic localization system and a 6-DoF relative pose measurement by visual odometry. In addition, the proposed method is capable of detecting and handling sensor failures by ignoring corrupted data, providing the robustness expected of a medical device. Detailed analyses and evaluations are presented using ex-vivo experiments on a porcine stomach model prove that our system achieves high translational and rotational accuracies for different types of endoscopic capsule robot trajectories. Copyright © 2017, The Authors. All rights reserved.

关键词： Monte Carlo methods