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Asilomar Conference on Signals, systems, and computers

作者： Yanning Shen Xiao Fu Georgios B. Giannakis Nicholas D. Sidiropoulos Dept. of Elec. & Comput. Engr. and Digital Technology Center University of Minnesota USA School of Electrical Engineering and Computer Science Oregon State University USA Dept. of Electrical and Computer Engineering University of Virginia USA

Discovering the connectivity patterns of directed networks is a crucial step towards understanding complex systems such as human brains and financial markets. Network inference approaches aim at estimating the hidden topology given nodal observations. Existing approaches relying on structural equation models (SEMs) require full knowledge of exogenous inputs, which may be unrealistic in certain applications. Recent tensor-based alternatives advocate reformulation of SEMs as a three-way tensor decomposition task that only requires second-order statistics of exogenous inputs for identifying the hidden topology. However, the tensor-based methods are computationally expensive, and is hard to incorporate prior information of the network structure (e.g., sparsity and local smoothness), but prior information is often important for enhancing performance. The present work puts forth a joint diagonalizaition (JD)-based formulation for directed network inference. JD can be viewed as a variant of tensor decomposition, but features more efficient algorithms and can readily incorporate prior information of network topology. New topology identification guarantees that do not rely on knowledge of exogenous inputs are established. Judiciously designed simulations are presented to showcase the effectiveness of the proposed approach.

关键词： Structural equation models CANDE-COMP/PARAFAC (CP) decomposition Joint diagonalization Network topology inference

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Robust network topology inference

Robust network topology inference

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Santiago Segarra Antonio G. Marques Gonzalo Mateos Alejandro Ribeiro Institute for Data Systems and Society Massachusetts Institute of Technology Cambridge USA Dept. of Signal Theory and Communications King Juan Carlos University Madrid Spain Dept. of Electrical and Computer Engineering University of Rochester NY USA Dept. of Electrical and Systems Engineering University of Pennsylvania Philadelphia USA

ISBN: (纸本)9781509041183

We address the problem of identifying a graph structure from the observation of signals defined on its nodes. Fundamentally, the unknown graph encodes direct relationships between signal elements, which we aim to recover from observable indirect relationships generated by a diffusion process on the graph. We put forth a novel network topology inference approach whereby we: i) identify the eigenvectors of a matrix representation of the graph from realizations of the diffused signal;and ii) rely on these (possibly imperfect) spectral templates to estimate the eigenvalues by imposing desirable properties on the graph to be recovered. Robust algorithms with quantifiable performance are developed for the pragmatic settings where the eigenvectors are estimated with errors, or, when the eigenbasis is only partially known. Numerical tests showcase the effectiveness of the proposed algorithm in recovering amino-acid networks.

关键词： Network topology inference graph signal processing network deconvolution Eigenvector Network topology Line graph Matrix representation Graphics processing Diffusion processes signals

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Experimental demonstration of high-rate measurement-device-independent quantum key distribution over asymmetric channels

arXiv

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arXiv 2018年

作者： Liu, Hui Wang, Wenyuan Wei, Kejin Fang, Xiao-Tian Li, Li Liu, Nai-Le Liang, Hao Zhang, Si-Jie Zhang, Weijun Li, Hao You, Lixing Wang, Zhen Lo, Hoi-Kwong Chen, Teng-Yun Xu, Feihu Pan, Jian-Wei Shanghai Branch Hefei National Laboratory for Physical Sciences at Microscale Department of Modern Physics University of Science and Technology of China Shanghai201315 China CAS Center for Excellence Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai201315 China Dept. of Electrical & Computer Engineering Dept. of Physics University of Toronto TorontoONM5S 3G4 Canada State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China

Measurement-device-independent quantum key distribution (MDI-QKD) can eliminate all detector side channels and it is practical with current technology. Previous implementations of MDI-QKD all use two symmetric channels with similar losses. However, the secret key rate is severely limited when different channels have different losses. Here we report the results of the first high-rate MDI-QKD experiment over asymmetric channels. By using the recent 7-intensity optimization approach, we demonstrate >10x higher key rate than previous best-known protocols for MDI-QKD in the situation of large channel asymmetry, and extend the secure transmission distance by more than 20-50 km in standard telecom fiber. The results have moved MDI-QKD towards widespread applications in practical network settings, where the channel losses are asymmetric and user nodes could be dynamically added or deleted. Copyright © 2018, The Authors. All rights reserved.

关键词： Quantum cryptography

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Blind Parallel Interrogation of Ultrasonic Neural Dust Motes Based on Canonical Polyadic Decomposition: a Simulation Study

Blind Parallel Interrogation of Ultrasonic Neural Dust Motes...

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European Signal Processing Conference

作者： Alexander Bertrand Dongjin Seo Jose M. Carmena Michel M. Maharbiz Elad Alon Jan M. Rabaey KU Leuven - Dept. Electrical Engineering Stadius Center for Dynamical Systems Signal Processing and Data Analytics Dept. of Electrical Engineering and Computer Sciences University of California Berkeley Helen Wills Neuroscience Institute University of California Berkeley

Neural dust (ND) is a wireless ultrasonic backscatter system for communicating with implanted sensor devices, referred to as ND motes (NDMs). Due to its scalability, ND could allow to chronically record electro-physiological signals in the brain cortex at a micro-scale pitch. The free-floating NDMs are read out by an array of ultrasonic (US) transducers through passive backscattering, by sequentially steering a US beam to the target NDM. In order to perform such beam steering, the NDM positions or the channels between the NDMs and the US transducers have to be estimated, which is a non-trivial task. Furthermore, such a sequential beam steering approach is too slow to sample a dense ND grid with a sufficiently high sampling rate. In this paper, we propose a new ND interrogation scheme which is fast enough to completely sample the entire ND grid, and which does not need any information on the NDM positions or the per-NDM channel characteristics. For each sample time, the US transducers transmit only a few grid-wide US beams to the entire ND grid, in which case the reflected beams will consist of mixtures of multiple NDM signals. We arrange the demodulated backscattered signals in a 3-way tensor, and then use a canonical polyadic decomposition (CPD) to blindly estimate the neural signals from each underlying NDM. Based on a validated simulation model, we demonstrate that this new CPD-based interrogation scheme allows to reconstruct the neural signals from the entire ND grid with a sufficiently high accuracy, even at relatively low SNR regimes.

关键词： Tensile stress Transducers Backscatter Brain modeling Signal processing Acoustics Data models

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Smart City Service Acceleration on FPGAs

Smart City Service Acceleration on FPGAs

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IEEE International Conference on Big Data Computing Service and Applications (BigDataService)

作者： Youngsoo Kim Marcus Garcia Alan Chen Nathan Wong Shrikant Jadhav Clay S. Gloster Dept. of Electrical Engineering San Jose State University San Jose CA USA Dept. of Computer Systems Technology North Carolina A & T State University Greensboro NC

ISBN: (纸本)9781509063192

Today's cyber-physical systems (CPS) for the emerging Smart cities includes hardware and software with intelligent sensing and controls. In Smart cities, the use of high definition images, videos, and context information has become a requirement for urban street data collection and processing. Field Programmable Gate Array enabled data centers and processing shows the great potential for its high computation needs. In this paper, we developed embedded controls, and FPGA design accelerating many Smart city server side programs. Those CPS futuristic applications includes illegal dumping detection, pedestrian detection and car plate recognition and performed an Urban Information systems project for the Smart City of San Jose.

关键词： Smart cities Field programmable gate arrays Videos Acceleration Hardware Cameras

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Improving the Accuracy of Arctan for Face Detection

Improving the Accuracy of Arctan for Face Detection

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Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM)

作者： Youngsoo Kim Hossein Shahdoost Shrikant Jadhav Clay S. Gloster Dept. of Electrical Engineering San Jose State University San Jose CA Dept. of Computer Systems Technology North Carolina Agricultural & Technical State University Greensboro NC

Significant barriers to real time face detection have been the complexity of computation kernels, minimal cost and superior accuracy requirements for both software and hardware implementation based on traditional high performance computing. It is desirable to develop variable precision face detection block for high dynamic range applications including night vision and infrared face detection applications. This paper developed an Arctan function for face detection which supports input ranges upto 360 degrees for Histogram of Oriented Graph. Our implementation takes advantage of mathematical identities for the pedestrian HOG computation. We compare our HOG block design to fixed point implementations and found that using floating point HOG is not be computationally expensive and can accelerate face detection process.

关键词： Face detection Hardware Dynamic range Field programmable gate arrays electrical engineering computers Real-time systems

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Toward opportunistic services for the industrial Internet of Things

Toward opportunistic services for the industrial Internet of...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Giancarlo Fortino Claudio Savaglio Mengchu Zhou Dept. of Informatics Modelling Electronics and Systems (DIMES) University of Calabria Rende CS Italy Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

Services are expected to represent the real drivers for all Internet of Things (IoT) application scenarios, including Industrial IoT (IIoT). Indeed, rather than focusing only on products, enterprises are now investing on product-service hybrids to improve operational efficiency, boost productivity and, most importantly, build new markets, hence diversifying their revenues. In this direction, IoT service modeling is a crucial activity requiring a multi-facet in-dept. examination. In this paper, we first discuss the importance of IoT services by eliciting related benefits and challenges. Then, we tackle service modeling from both a developer-oriented (comprising highlevel descriptive metamodels and ontologies) and an enterprise-oriented (comprising operational models subject to formal verification and execution, such as extensions of the conventional Business Process Models, Petri Nets and WorkFlows) perspective, providing a conceptual mapping between these two complementary approaches. The proposed new paradigm of “Opportunistic IoT Service” extends the available models by explicitly considering essential features for service provision, i.e., dynamicity, context-awareness, co-location and transience.

关键词： Business Computational modeling Context modeling Analytical models Biological system modeling Ontologies Sensors

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Identification-based linear control of a twin rotor MIMO system via dynamical neural networks 14

Identification-based linear control of a twin rotor MIMO sys...

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14th International Conference on electrical engineering, Computing Science and Automatic Control, CCE 2017

作者： Armenta, Carlos Bernal, Miguel Hernandez, Francisco Villafuerte, Raul Dept. of Electrical and Electronics Engineering Sonora Institute of Technology 5 de Febrero 818 Sur Cd. ObregonC.P. 85000 Mexico Research Center on Information Technology and Systems Hidalgo State University Carr. Pachuca-Tulancingo Km. 4.5 Mineral de la ReformaCP 42184 Mexico

ISBN: (纸本)9781538634059

This paper presents a real-time implementation of an identification-based linear control law on a twin rotor MIMO system. Identification is performed via dynamical neural networks which-under mild conditions-guarantee exponential convergence to zero of the identification error. Once the plant model is obtained and linearized, a stabilizing linear state feedback control law is synthesized via linear matrix inequalities. Based on the same linearization, trajectory tracking is performed by a PID controller. Simulation as well as real-time results are discussed. © 2017 IEEE.

关键词： Linear matrix inequalities

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Design and analysis of a novel brushlesshigh temperature superconducting synchronous machine

Design and analysis of a novel brushlesshigh temperature sup...

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2017 IEEE International Electric Machines and Drives Conference, IEMDC 2017

作者： Mishra, Subhendu Lipo, T.A. Pamidi, S.V. Dept. of Electrical and Computer Engineering FAMU-FSU College of Engineering TallahasseeFL United States Center for Advanced Power Systems Florida State University TallahasseeFL United States

ISBN: (纸本)9781509042814

This paper discusses a high-efficiency synchronous generator using high-Temperature superconductor (HTS) excitation. A novel design is proposed in which no excitation exists on the rotor. Instead, the stator is excited using DC HTS cables. The design is analyzed with finite element method and is further compared with a permanent magnet (PM) machine with similar machine dimensions. It was shown that the HTS machine provides a greater torque compared to the PM machine with higher efficiency. © 2017 IEEE.

关键词： Finite element method

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Crowdsourcing predictors of residential electric energy usage

arXiv

引用

arXiv 2017年

作者： Wagy, Mark D. Bongard, Josh C. Bagrow, James P. Hines, Paul D.H. Dept. of Psychological and Brain Sciences Dartmouth College HanoverNH United States Dept. of Computer Science Complex Systems Center University of Vermont BurlingtonVT United States Dept. of Mathematics and Statistics Complex Systems Center University of Vermont BurlingtonVT United States Dept. of Electrical and Biomedical Engineering Complex Systems Center University of Vermont BurlingtonVT United States

Crowdsourcing has been successfully applied in many domains including astronomy, cryptography and biology. In order to test its potential for useful application in a Smart Grid context, this paper investigates the extent to which a crowd can contribute predictive hypotheses to a model of residential electric energy consumption. In this experiment, the crowd generated hypotheses about factors that make one home different from another in terms of monthly energy usage. To implement this concept, we deployed a web-based system within which 627 residential electricity customers posed 632 questions that they thought predictive of energy usage. While this occurred, the same group provided 110,573 answers to these questions as they accumulated. Thus users both suggested the hypotheses that drive a predictive model and provided the data upon which the model is built. We used the resulting question and answer data to build a predictive model of monthly electric energy consumption, using random forest regression. Because of the sparse nature of the answer data, careful statistical work was needed to ensure that these models are valid. The results indicate that the crowd can generate useful hypotheses, despite the sparse nature of the dataset. Copyright © 2017, The Authors. All rights reserved.

关键词： Crowdsourcing

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