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IEEE Annual Conference on Decision and Control

作者： Giuseppe Notarstefano Department of Engineering Universita del Salento via Monteroni 73100 Lecce Italy

ISBN: (纸本)9781479978878

A new challenge for learning algorithms in cyberphysical network systems is the distributed solution of big-data classification problems, i.e., problems in which both the number of training samples and their dimension is high. Motivated by several problem set-ups in Machine Learning, in this paper we consider a special class of quadratic optimization problems involving a "large" number of input data, whose dimension is "big". To solve these quadratic optimization problems over peer-to-peer networks, we propose an asynchronous, distributed algorithm that scales with both the number and the dimension of the input data (training samples in the classification problem). The proposed distributed optimization algorithm relies on the notion of "core-set" which is used in geometric optimization to approximate the value function associated to a given set of points with a smaller subset of points. By computing local core-sets on a smaller version of the global problem and exchanging them with neighbors, the nodes reach consensus on a set of active constraints representing an approximate solution for the global quadratic program.

关键词： dimensions Set of points incoming data data classification active constraint quadratic programming distributed algorithms Quadratic Peer-to-peer network Distribute learning algorithms optimization algorithms

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Synthesis of Sparse Planar Array using Modified Bayesian optimization Algorithm

Synthesis of Sparse Planar Array using Modified Bayesian Opt...

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Electromagnetic Metrology Symposium

作者： B. V. Ha R. E. Zich M. Mussetta P. Pirinoli Dipartimento di Energia Politecnico di Milano Dipartimento di Elettronica Politecnico di Torino

ISBN: (纸本)9781467357067

Controlling the position of elements in sparse planar array would be the hardest work in the synthesis procedure since the array has to satisfy multiple design constraints e.g. number of elements, elements spacing and arrays aperture. In this paper, a simple method is implemented to effectively control 2-D sparse array. The approach implements two sets of data to separately manage the x- and y-coordinate of each element in the array. The array is then synthesized as an optimization problem using the recently improved version of Bayesian optimization Algorithm. As a proof of concept, the results of a 108 element sparse planar array are here presented and discussed.

关键词： Planar antennas Elements y-coordinate optimization algorithms arrays synthesis design constraints Dataset Bayesian inference

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Coding Sensor Outputs for Injection Attacks Detection

Coding Sensor Outputs for Injection Attacks Detection

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IEEE Annual Conference on Decision and Control

作者： Fei Miao Quanyan Zhu Miroslav Pajic George J. Pappas Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA USA 19014 Department of Electrical and Computer Engineering New York University Brooklyn NY USA 11201

ISBN: (纸本)9781467360890

This paper considers a method of coding the sensor outputs in order to detect stealthy false data injection attacks. An intelligent attacker can design a sequence of data injection to sensors that pass the state estimator and statistical fault detector, based on knowledge of the system parameters. To stay undetected, the injected data should increase the state estimation errors while keep the estimation residues in a small range. We employ a coding matrix to the original sensor outputs to increase the estimation residues, such that the alarm will be triggered by the detector even under intelligent data injection attacks. This is a low cost method compared with encryption over sensor communication networks. We prove the conditions the coding matrix should satisfy under the assumption that the attacker does not know the coding matrix yet. An iterative optimization algorithm is developed to compute a feasible coding matrix, and, we show that in general, multiple feasible coding matrices exist.

关键词： State estimation Injections Sensors system parameter cryptography Alarm Output optimization algorithms Satisfaction

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ROBUST COMMON CAROTID ARTERY LUMEN DETECTION IN B-MODE ULTRASOUND IMAGES USING LOCAL PHASE SYMMETRY

ROBUST COMMON CAROTID ARTERY LUMEN DETECTION IN B-MODE ULTRA...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Jose Rouco Aurelio Campilho INEB - Instituto de Engenharia Biomedica Porto Portugal Faculdade de Engenharia Universidade do Porto Rua Roberto Frias 4200-465 Porto Portugal

ISBN: (纸本)9781479903573

This paper presents a new method for automatic common carotid artery detection in B-mode ultrasonography. The proposed method is based on the location of phase symmetry patterns at apropriate scale of analysis. The local phase information is derived from the monogenic signal and isotropic lognormal band-pass filters, and the resulting common carotid artery is located using a dynamic programming optimization algorithm. The experiments show that the proposed method is more robust to noise than previous approaches, although additional research is required for robust common carotid artery detection on the more complicated cases.

关键词： Ultrasonography Common Carotid Arteries Carotid Arteries Ultrasonic Waves images optimization algorithms Symmetry phase symmetry

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Sparse waveform design for all-spectrum channelization

Sparse waveform design for all-spectrum channelization

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： George Sklivanitis Panos P. Markopoulos Stella N. Batalama Dimitris A. Pados Dept. of Electrical Engineering State University of New York at Buffalo 14260 United States of America Dept. of Electrical and Microelectronic Engineering Rochester Institute of Technology NY 14623 United States of America

ISBN: (纸本)9781509041183

We introduce maximum-SINR sparse-binary waveforms that modulate data information symbols from any finite alphabet and span the whole continuum of the available/device-accessible spectrum. We offer an optimal algorithm that designs the proposed waveforms by maximizing the signal-to-interference-plus-noise ratio (SINR) at the output of the maximum-SINR linear receiver. In addition, we offer a suboptimal algorithm for the same problem with significantly reduced computational complexity. The post-filtering SINR improvements attained by the proposed waveforms in a single-input single-output (SISO) communication system with colored interference are presented analytically. Simulation studies compare the proposed waveforms with their conventional non-sparse counterparts and demonstrate their superior SINR performance.

关键词： All-spectrum binary code sequences maximum SINR sparsity waveform design waveforms SINR Traffic channelization optimization algorithms LINEAR RECEIVERS

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(Nearly) Optimal algorithms for Private Online Learning in Full-information and Bandit Settings 13

(Nearly) Optimal Algorithms for Private Online Learning in F...

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Annual Conference on Neural Information Processing Systems

作者： Adam Smith Abhradeep Thakurta Pennsylvania State University Stanford University and Microsoft Research Silicon Valley Campus

ISBN: (纸本)9781632660244

We give differentially private algorithms for a large class of online learning algorithms, in both the full information and bandit settings. Our algorithms aim to minimize a convex loss function which is a sum of smaller convex loss terms, one for each data point. To design our algorithms, we modify the popular mirror descent approach, or rather a variant called follow the approximate leader. The technique leads to the first nonprivate algorithms for private online learning in the bandit setting. In the full information setting, our algorithms improve over the regret bounds of previous work (due to Dwork, Naor, Pitassi and Rothblum (2010) and Jain, Kothari and Thakurta (2012)). In many cases, our algorithms (in both settings) match the dependence on the input length, T, of the optimal nonprivate regret bounds up to logarithmic factors in T. Our algorithms require logarithmic space and update time.

关键词： e-learning algorithms Regret refresh time optimization algorithms LOSS FUNCTION solidification

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High-Throughput Beamforming Receiver for Millimeter Wave Mobile Communication

High-Throughput Beamforming Receiver for Millimeter Wave Mob...

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IEEE Global Communications Conference

作者： Bei Yin Shadi Abu-Surra Gary Xu Thomas Henige Eran Pisek Zhouyue Pi Joseph R. Cavallaro ECE Department Rice University Houston TX USA Dallas Technology Laboratory Samsung Electronics Dallas TX USA

ISBN: (纸本)9781479913510

In this paper, we present a novel FPGA-based high-throughput beamforming MIMO receiver for millimeter wave mobile communication. With vast spectrum and small antenna element size, millimeter wave communication becomes very attractive and promising to support next generation mobile communication (5G). However, the high data rate requirement challenges both algorithm and architecture. In order to support the high data rate and to reduce the overhead of selecting the best beam pair, we propose a novel beamforming synchronization scheme more suitable for mobile communication. By further optimizing the algorithm and the architecture, we present a complete mobile receiver based on FPGA, which includes RF frontend, ADC, beamforming control, synchronization, channel estimator, soft MAP detector, and channel decoder. The design operates at 28 GHz carrier frequency with 500 MHz bandwidth. The throughput can reach 1.52 Gbps. We also performed the indoor and outdoor over-the-air transmission field tests. This work provides a platform for future millimeter wave mobile communication research.

关键词： millimeter wave mobile communication beamforming FPGA Beamforming millimeter wave Field programmable gate arrays mobile communication Channel Decoder Building construction ARCHITECTURE Throughput optimization algorithms

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An Hybrid Senor Localization Algorithm for Wireless Sensor Networks

An Hybrid Senor Localization Algorithm for Wireless Sensor N...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Lichuan Liu Geetha Mavidi Department of Electrical Engineering Northern Illinois University

ISBN: (纸本)9781479906505

Localization, the determination of geographical location of sensors is a fundamental problem in wireless sensor networks. In this paper we consider a static wireless network in which the reference nodes are static-non moving. We propose a positioning method known as hybrid optimizing algorithm (HOA) which combines steepest decent method and Taylor series expansion method. The steepest decent method converges quickly at initial iterations and has less computational complexity. The performance of Taylor series method depends on initial estimation. We conduct simulation experiments to evaluate the performance of these methods. Results show that HOA achieves better performance on position accuracy as it takes the advantages of both the methods.

关键词： general nodes (unknown location) hybrid optimizing method Localization reference nodes (known location) steepest decent method wireless sensor networks Wireless sensor networks reference node Matrilin-3 Wireless networks Taylor series steepest descent method accuracy of position steeping Geographical Locations optimization algorithms

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Model discovery on the fly using continuous data assimilation

arXiv

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arXiv 2024年

作者： Newey, Joshua Whitehead, Jared P. Carlson, Elizabeth

We review an algorithm developed for parameter estimation within the Continuous Data Assimilation (CDA) approach. We present an alternative derivation for the algorithm presented in a paper by Carlson, Hudson, and Larios (CHL) [21]. This derivation relies on the same assumptions as the previous derivation but frames the problem as a finite dimensional root-finding problem. Within the approach we develop, the algorithm developed in [21] is simply a realization of Newton’s method. We then consider implementing other derivative based optimization algorithms;we show that the Levenberg Maqrquardt algorithm has similar performance to the CHL algorithm in the single parameter estimation case and generalizes much better to fitting multiple parameters. We then implement these methods in three example systems: the Lorenz’63 model, the two-layer Lorenz’96 model, and the Kuramoto-Sivashinsky equation. © 2024, CC BY.

关键词： optimization algorithms

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A∗ for Graphs of Convex Sets

arXiv

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arXiv 2024年

作者： Sundar, Kaarthik Rathinam, Sivakumar Los Alamos Research Laboratory NM88220 United States Texas A&M University College StationTX77843 United States

We present a novel algorithm that fuses the existing convex-programming based approach with heuristic information to find optimality guarantees and near-optimal paths for the Shortest Path Problem in the Graph of Convex Sets (SPPGCS). Our method, inspired by A∗, initiates a best-first-like procedure from a designated subset of vertices and iteratively expands it until further growth is neither possible nor beneficial. Traditionally, obtaining solutions with bounds for an optimization problem involves solving a relaxation, modifying the relaxed solution to a feasible one, and then comparing the two solutions to establish bounds. However, for SPP-GCS, we demonstrate that reversing this process can be more advantageous, especially with Euclidean travel costs. In other words, we initially employ A∗ to find a feasible solution for SPP-GCS, then solve a convex relaxation restricted to the vertices explored by A∗ to obtain a relaxed solution, and finally, compare the solutions to derive bounds. We present numerical results to highlight the advantages of our algorithm over the existing approach in terms of the sizes of the convex programs solved and computation time. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

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