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European Conference on Antennas and Propagation

作者： Jian Yang Per-Simon Kildal Miroslav Pantaleev Dept. of Signals and Systems Chalmers University of Technology SE-41296 Gothenburg Sweden Onsala Space Observatory Chalmers University of Technology S-41296 Gothenburg Sweden

ISBN: (纸本)9781467321877

An overview of different versions of the Eleven feed and recent development is presented in the paper. Eleven feeds have a nearly constant beamwidth and a fix phase center, with a performance of an aperture efficiency higher than 60%, -10 dB reflection coefficient, -0.2dB radiation efficiency, and -15dB cross-polar level, over a decade bandwidth. The Eleven feed can be very compact after applying newly developed optimization algorithms, and some new results of the feed in dual-reflector antennas are reported here.

关键词： decade-band feed reflector antenna radio tele-scope VLBI SKA Reflector antennas Very Long Baseline Interferometry FEEDS Fodder Dual reflector antenna aperture efficiency Reflection coefficient optimization algorithms

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Spectral Reconstruction from Dispersive Blur: A Novel Light Efficient Spectral Imager

Spectral Reconstruction from Dispersive Blur: A Novel Light ...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Yuanyuan Zhao Xuemei Hu Hui Guo Zhan Ma Tao Yue Xun Cao Nanjing University

ISBN: (纸本)9781728132945

Developing high light efficiency imaging techniques to retrieve high dimensional optical signal is a long-term goal in computational photography. Multispectral imaging, which captures images of different wavelengths and boosting the abilities for revealing scene properties, has developed rapidly in the last few decades. From scanning method to snapshot imaging, the limit of light collection efficiency is kept being pushed which enables wider applications especially under the light-starved scenes. In this work, we propose a novel multispectral imaging technique, that could capture the multispectral images with a high light efficiency. Through investigating the dispersive blur caused by spectral dispersers and introducing the difference of blur (DoB) constraints, we propose a basic theory for capturing multispectral information from a single dispersive-blurred image and an additional spectrum of an arbitrary point in the scene. Based on the theory, we design a prototype system and develop an optimization algorithm to realize snapshot multispectral imaging. The effectiveness of the proposed method is verified on both the synthetic data and real captured images.

关键词： collection efficiency images multispectral imagery Snapshots dispersion optimization algorithms scanning method Light synthetic data imaging instrument

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Gauss-Newton Observers for Mode and Continuous State Estimation of Nonlinear Switched Systems

Gauss-Newton Observers for Mode and Continuous State Estimat...

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Proceedings of the 46th IEEE Conference on Decision and Control, Pages 4135-4592

作者： Rudy Kajdan Didier Aubry Frederic Kratz ENSI de Bourges Laboratoire de Vision et Robotique Bourges France

ISBN: (纸本)9781424414970;1424414970

The purpose of this paper is to design a moving horizon observer for mode and continuous state estimation of nonlinear switched systems. Using the past output measurements and the model equations, mode and continuous state estimation is expressed as an optimization problem which is solved using the Gauss-Newton algorithm. The performances of this method are illustrated on a simulated two-tank system.

关键词： switched systems nonlinear systems estimation optimization algorithms

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Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint 16

Adaptive Maximization of Pointwise Submodular Functions With...

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Annual Conference on Neural Information Processing Systems

作者： Nguyen Viet Cuong Huan Xu Department of Engineering University of Cambridge Stewart School of Industrial & Systems Engineering Georgia Institute of Technology

ISBN: (纸本)9781510838819

We study the worst-case adaptive optimization problem with budget constraint that is useful for modeling various practical applications in artificial intelligence and machine learning. We investigate the near-optimality of greedy algorithms for this problem with both modular and non-modular cost functions. In both cases, we prove that two simple greedy algorithms are not near-optimal but the best between them is near-optimal if the utility function satisfies pointwise submodularity and pointwise cost-sensitive submodularity respectively. This implies a combined algorithm that is near-optimal with respect to the optimal algorithm that uses half of the budget. We discuss applications of our theoretical results and also report experiments comparing the greedy algorithms on the active learning problem.

关键词： greedy algorithm Budgets Artificial Intelligence optimization algorithms Modularity

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Robust PCA via Nonconvex Rank Approximation

Robust PCA via Nonconvex Rank Approximation

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IEEE International Conference on Data Mining

作者： Zhao Kang Chong Peng Qiang Cheng Department of Computer Science Southern Illinois University

ISBN: (纸本)9781467395052

Numerous applications in data mining and machine learning require recovering a matrix of minimal rank. Robust principal component analysis (RPCA) is a general framework for handling this kind of problems. Nuclear norm based convex surrogate of the rank function in RPCA is widely investigated. Under certain assumptions, it can recover the underlying true low rank matrix with high probability. However, those assumptions may not hold in real-world applications. Since the nuclear norm approximates the rank by adding all singular values together, which is essentially a l_1-norm of the singular values, the resulting approximation error is not trivial and thus the resulting matrix estimator can be significantly biased. To seek a closer approximation and to alleviate the above-mentioned limitations of the nuclear norm, we propose a nonconvex rank approximation. This approximation to the matrix rank is tighter than the nuclear norm. To solve the associated nonconvex minimization problem, we develop an efficient augmented Lagrange multiplier based optimization algorithm. Experimental results demonstrate that our method outperforms current state-of-the-art algorithms in both accuracy and efficiency.

关键词： Rank of data data mining approximation principal components analysis Approximation error Atomic nuclei matrices optimization algorithms

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CT SLICE RETRIEVAL BY SHAPE ELLIPSES DESCRIPTORS FOR SKULL REPAIRING

CT SLICE RETRIEVAL BY SHAPE ELLIPSES DESCRIPTORS FOR SKULL R...

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IEEE International Conference on Image Processing

作者： Marcelo Rudek Osiris Canciglieri Andreas Jahnen Gerson Linck Bichinho Industrial and Systems Engineering Graduate Program - Pontifical Catholic University of Parana PUCPR Brazil. Ressource Centre for HealthCare Technologies (SANTEC) Public Research Centre Henri Tudor Luxembourg. Technology in Healthcare Postgraduate Program - Pontifical Catholic University of Parana PUCPR Brazil.

ISBN: (纸本)9781479923427

This paper addresses the problem of automated prosthesis modelling and manufacturing, whose machining parameters are based on images extracted from different medical databases. The specific case of 3D surface restoration of a defective skull was used as study case. A method based on adjusted ellipses on skull bone curvature performs the symbolic representation of searching parameters. The superellipse concept permits to define geometric parameters to fit an ellipse on each Computed Tomography slice. Those ellipse descriptors can be used as a template for the retrieval of similar images from databases whose parameters match the sampled image. The similarity is measured according to the best fitness values through an optimization algorithm. The slices found by similarity are retrieved from all databases in order to build the 3D model. Experiments show that the proposed method is a promising technique for content based image retrieval.

关键词： Prosthesis Modelling Image Retrieval Ellipse 3D Medical Imaging Ellipse image retrieval Medical imaging content-based image retrieval Skull retrieval Database X-Ray Computed Tomography Three Dimensions Descriptors images geometric parameter optimization algorithms

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Solving Traveling Salesman Problem with Hybrid Estimation of Distribution Algorithm

Solving Traveling Salesman Problem with Hybrid Estimation of...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Libo Song Chang Liu Jun Zhu Haibo Shi Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781538604915

A hybrid distribution estimation algorithm for traveling salesman problem is proposed. Firstly, based on the distributed estimation algorithm, a new effective probability model is proposed to solve the traveling salesman problem. Secondly, in order to speed up the optimization of the algorithm to prevent the algorithm falling into the local optimal, the extreme optimization algorithm is combined to form a hybrid distribution estimation algorithm to improve the effectiveness of the algorithm. Then through the public TSPLIB data set, it is proved that the hybrid distribution estimation algorithm is effective, and the algorithm can solve this kind of problem well. Finally, a new idea is proposed to verify the validity of the traveling salesman problem, and the algorithm is tested by the proposed algorithm. The experimental results show that the proposed hybrid distribution estimation algorithm has a good performance in solving the traveling salesman problem.

关键词： Urban areas Estimation Traveling salesman problems optimization Sociology Statistics Encoding travelling salesman problems Sociology algorithms urban area Allocation Algorithm coding optimization algorithms HYBRID Hybrid electric vehicles

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Last Iterate Convergence of Incremental Methods and Applications in Continual Learning

arXiv

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arXiv 2024年

作者： Cai, Xufeng Diakonikolas, Jelena Department of Computer Sciences University of Wisconsin-Madison United States

Incremental gradient and incremental proximal methods are a fundamental class of optimization algorithms used for solving finite sum problems, broadly studied in the literature. Yet, without strong convexity, their convergence guarantees have primarily been established for the ergodic (average) iterate. Motivated by applications in continual learning, we obtain the first convergence guarantees for the last iterate of both incremental gradient and incremental proximal methods, in general convex smooth (for both) and convex Lipschitz (for the proximal variants) settings. Our oracle complexity bounds for the last iterate nearly match (i.e., match up to a square-root-log or a log factor) the best known oracle complexity bounds for the average iterate, for both classes of methods. We further obtain generalizations of our results to weighted averaging of the iterates with increasing weights and for randomly permuted ordering of updates. We study incremental proximal methods as a model of continual learning with generalization and argue that large amount of regularization is crucial to preventing catastrophic forgetting. Our results generalize last iterate guarantees for incremental methods compared to state of the art, as such results were previously known only for overparameterized linear models, which correspond to convex quadratic problems with infinitely many solutions. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

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Distributed Minimum Cost Multicasting with Lossless Source Coding and Network Coding

Distributed Minimum Cost Multicasting with Lossless Source C...

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Proceedings of the 46th IEEE Conference on Decision and Control, Pages 5511-5970

作者： Tao Cui Tracey Ho Lijun Chen Division of Engineering and Applied Science California State University Sacramento USA

ISBN: (纸本)9781424414970;1424414970

In this paper, we consider minimum cost lossless source coding for multiple multicast sessions. Each session comprises a set of correlated sources whose information is demanded by a set of sink nodes. We propose a distributed end-to-end algorithm which operates over given multicast trees, and a back-pressure algorithm which optimizes routing and coding over the whole network. Unlike other existing algorithms, the source rates need not be centrally coordinated;the sinks control transmission rates across the sources. With random network coding, the proposed approach yields completely distributed and optimal algorithms for intra-session network coding. We prove the convergence of our proposed algorithms. Some practical considerations are also discussed. Experimental results are provided to complement our theoretical analysis.

关键词： lossless source coding Multicast Network coding optimization algorithms Random Network coding TRANSMISSION RATE multicast communication solidification Distribute algorithms Network routing Theoretical analysis Sinks source rate

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COMPLEXITY AND NUMERICAL EXPERIMENTS OF A NEW ADAPTIVE GENERIC PROXIMAL BUNDLE METHOD

arXiv

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arXiv 2024年

作者： Guigues, Vincent Monteiro, Renato D.C. Tran, Benoit School of Applied Mathematics FGV Praia de Botafogo Rio de Janeiro Brazil Georgia Institute of Technology AtlantaGA30332-0205 United States

This paper develops an adaptive generic proximal bundle method, shows its complexity, and presents numerical experiments comparing this method with two bundle methods on a set of optimization problems. Copyright ©... 详细信息

关键词： optimization algorithms

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