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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Stephen R. Schnelle Jason N. Laska Chinmay Hegde Marco F. Duarte Mark A. Davenport Richard G. Baraniuk Department of Electrical and Computer Engineering Rice University Houston TX 77005 Program in Applied and Computational Mathematics Princeton University Princeton NJ 08544

ISBN: (纸本)9781424442959

This paper develops a new class of algorithms for signal recovery in the distributed compressive sensing (DCS) framework. DCS exploits both intra-signal and inter-signal correlations through the concept of joint sparsity to further reduce the number of measurements required for recovery. DCS is well-suited for sensor network applications due to its universality, computational asymmetry, tolerance to quantization and noise, and robustness to measurement loss. In this paper we propose recovery algorithms for the sparse common and innovation joint sparsity model. Our approach leads to a class of efficient algorithms, the Texas Hold 'Em algorithms, which are scalable both in terms of communication bandwidth and computational complexity.

关键词： Signal reconstruction multisensor systems data compression Multisensor systems Signal reconstruction Data compression New classes algorithms Texas Signal restoration Quantization Scalability

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Fitting Dense and Sparse Reduced Data 21st

Fitting Dense and Sparse Reduced Data

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21st International Multi-Conference on Advanced computer Systems, ACS 2018

作者： Kozera, Ryszard Wiliński, Artur Faculty of Applied Informatics and Mathematics Warsaw University of Life Sciences - SGGW ul. Nowoursynowska 159 Warsaw02-776 Poland Department of Computer Science and Software Engineering The University of Western Australia 35 Stirling Highway Crawley PerthWA6009 Australia

ISBN: (纸本)9783030033132

This paper addresses the topic of fitting reduced data represented by the sequence of interpolation points (Formula Presented) in arbitrary Euclidean space Em. The parametric curve γ together with its knots (Formula Presented) (for which (Formula Presented)) are both assumed to be unknown. We look at some recipes to estimate T in the context of dense versus sparse M for various choices of interpolation schemes (Formula Presented). For M dense, the convergence rate to approximate γ with (Formula Presented) is considered as a possible criterion to force a proper choice of new knots (Formula Presented). The latter incorporates the so-called exponential parameterization "retrieving" the missing knots T from the geometrical spread of M. We examine the convergence rate in approximating γ by commonly used interpolants (Formula Presented) based here on M and exponential parameterization. In contrast, for M sparse, a possible optional strategy is to select (Formula Presented) which optimizes a certain cost function depending on the family of admissible knots (Formula Presented). This paper focuses on minimizing "an average acceleration" within the family of natural splines (Formula Presented) fitting M with (Formula Presented) admitted freely in the ascending order. Illustrative examples and some applications listed supplement theoretical component of this work. © 2019, Springer Nature Switzerland AG.

关键词： Interpolation

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Guest Editorial to the Special Issue Public Transport Optimization: From Theory to Practice

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Operations Research Forum 2024年第3期5卷 73页

作者： Cacchiani, Valentina Müller-Hannemann, Matthias Rojas-Marcos, Federico Perea Department of Electrical Electronic and Information Engineering “Guglielmo Marconi” University of Bologna Viale Risorgimento 2 Bologna 40136 Italy Institute for Computer Science Martin Luther University Halle-Wittenberg Von-Seckendorff-Platz 1 Halle D 06120 Germany Department of Applied Mathematics II and Institute of Mathematics (IMUS) University of Seville Avda. Reina Mercedes s/n Seville 41012 Spain

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GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images

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Mathematical Biosciences and Engineering 2024年第8期21卷 6847-6869页

作者： Aggarwal, Sonam Gupta, Isha Kumar, Ashok Kautish, Sandeep Almazyad, Abdulaziz S. Mohamed, Ali Wagdy Werner, Frank Shokouhifar, Mohammad Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Model Institute of Engineering and Technology J&K Jammu India Chandigarh University Punjab Mohali140413 India Department of Computer Engineering College of Computer and Information Sciences King Saud University P.O. Box 51178 Riyadh11543 Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza12613 Egypt Applied Science Research Center Applied Science Private University Amman11931 Jordan Faculty of Mathematics Otto-von-Guericke University Magdeburg39016 Germany Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam

Convolutional Neural Networks (CNNs) have received substantial attention as a highly effective tool for analyzing medical images, notably in interpreting endoscopic images, due to their capacity to provide results equivalent to or exceeding those of medical specialists. This capability is particularly crucial in the realm of gastrointestinal disorders, where even experienced gastroenterologists find the automatic diagnosis of such conditions using endoscopic pictures to be a challenging endeavor. Currently, gastrointestinal findings in medical diagnosis are primarily determined by manual inspection by competent gastrointestinal endoscopists. This evaluation procedure is labor-intensive, time-consuming, and frequently results in high variability between laboratories. To address these challenges, we introduced a specialized CNN-based architecture called GastroFuse-Net, designed to recognize human gastrointestinal diseases from endoscopic images. GastroFuse-Net was developed by combining features extracted from two different CNN models with different numbers of layers, integrating shallow and deep representations to capture diverse aspects of the abnormalities. The Kvasir dataset was used to thoroughly test the proposed deep learning model. This dataset contained images that were classified according to structures (cecum, z-line, pylorus), diseases (ulcerative colitis, esophagitis, polyps), or surgical operations (dyed resection margins, dyed lifted polyps). The proposed model was evaluated using various measures, including specificity, recall, precision, F1-score, Mathew’s Correlation Coefficient (MCC), and accuracy. The proposed model GastroFuse-Net exhibited exceptional performance, achieving a precision of 0.985, recall of 0.985, specificity of 0.984, F1-score of 0.997, MCC of 0.982, and an accuracy of 98.5%. ©2024 the Author(s), licensee AIMS Press.

关键词： Endoscopy

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Machine learning based non-Newtonian fluid model with molecular fidelity

arXiv

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arXiv 2020年

作者： Lei, Huan Wu, Lei Weinan, E. Department of Computational Mathematics Science & Engineering Department of Statistics & Probability Michigan State University MI48824 United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University NJ08544 United States

We introduce a machine-learning-based framework for constructing continuum non-Newtonian fluid dynamics model directly from a micro-scale description. Polymer solution is used as an example to demonstrate the essential ideas. To faithfully retain molecular fidelity, we establish a micro-macro correspondence via a set of encoders for the micro-scale polymer configurations and their macro-scale counterparts, a set of nonlinear conformation tensors. The dynamics of these conformation tensors can be derived from the micro-scale model and the relevant terms can be parametrized using machine learning. The final model, named the deep non-Newtonian model (DeePN2), takes the form of conventional non-Newtonian fluid dynamics models, with a new form of the objective tensor derivative. Numerical results demonstrate the accuracy of DeePN2. Copyright © 2020, The Authors. All rights reserved.

关键词： Viscous flow

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Distributed Robustness Analysis of Interconnected Uncertain Systems Using Chordal Decomposition

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IFAC Proceedings Volumes 2014年第3期47卷 2594-2599页

作者： Sina Khoshfetrat Pakazad Anders Hansson Martin S. Andersen Anders Rantzer Division of Automatic Control Department of Electrical Engineering Linköping University Sweden Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Department of Automatic Control Lund University Sweden

Large-scale interconnected uncertain systems commonly have large state and uncertainty dimensions. Aside from the heavy computational cost of performing robust stability analysis in a centralized manner, privacy requirements in the network can also introduce further issues. In this paper, we utilize IQC analysis for analyzing large-scale interconnected uncertain systems and we evade these issues by describing a decomposition scheme that is based on the interconnection structure of the system. This scheme is based on the so-called chordal decomposition and does not add any conservativeness to the analysis approach. The decomposed problem can be solved using distributed computational algorithms without the need for a centralized computational unit. We further discuss the merits of the proposed analysis approach using a numerical experiment.

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Improved Maximum Angle Estimate for Longest-Edge Bisection

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International Journal of Computational Geometry and Applications 2021年第4期31卷 183-192页

作者： Korotov, Sergey Lund, Lars Fredrik Vatne, Jon Eivind Division of Mathematics and Physics Ukk Mälardalen University Box 883 Västerås 721 23 Sweden Department of Computer Science Electrical Engineering and Mathematical Sciences Faculty of Engineering and Science Western Norway University of Applied Sciences Postbox 7030 Bergen 5020 Norway Department of Economics Norwegian Business School (BI) Kong Christian Frederiks plass 5 Bergen 5006 Norway

In this note we construct an upper estimate on the maximum angles of triangles generated by the longest-edge bisection algorithms applied to a triangle. This upper bound considerably improves upon one inherited from the well-known minimum angle estimation, thus providing tighter two-sided estimation of the angles generated by bisections. This estimate also guarantees the validity of the maximum angle condition, widely used in finite element analysis and computer graphics. © 2021 World Scientific Publishing Company.

关键词： Finite element method Longest-edge bisection Maximum angle condition Mesh refinement

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Online conflict-free coloring for intervals

Online conflict-free coloring for intervals

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Sixteenth Annual ACM-SIAM Symposium on Discrete Algorithms

作者： Fiat, Amos Levy, Meital Matoušek, Jiří Pach, Elchanan Mossel János Sharir, Micha Smorodinsky, Shakhar Wagner, Uli Welzl, Emo School of Computer Science Tel Aviv University Tel Aviv Israel Charles University Prague Czech Republic Department of Statistics University of California at Berkeley Berkeley CA United States Courant Institute of Mathematical Sciences New York University New York NY United States Institute for Theoretical Computer Science ETH Zürich Switzerland Department of Applied Mathematics Charles University Prague Czech Republic

We consider an online version of the conflict-free coloring of a set of points on the line, where each newly inserted point must be assigned a color upon insertion, and at all times the coloring has to be conflict-free, in the sense that in every interval I there is a color that appears exactly once in I. We present several deterministic and randomized algorithms for achieving this goal, and analyze their performance, that is, the maximum number of colors that they need to use, as a function of the number n of inserted points. We first show that a natural and simple (deterministic) approach may perform rather poorly, requiring Ω(√n) colors in the worst case. We then modify this approach, to obtain an efficient deterministic algorithm that uses a maximum of Θ(log2 n) colors. Next, we present two randomized solutions. The first algorithm requires an expected number of at most O(log 2 n) colors, and produces a coloring which is valid with high probability, and the second one, which is a variant of our efficient deterministic algorithm, requires an expected number of at most O(log n log log n) colors but always produces a valid coloring. We also analyze the performance of the simplest proposed algorithm when the points are inserted in a random order, and present an incomplete analysis that indicates that, with high probability, it uses only O(log n) colors. Finally, we show that in the extension of this problem to two dimensions, where the relevant ranges are disks, n colors may be required in the worst case. The average-case behavior for disks, and cases involving other planar ranges, are still open.

关键词： Online systems

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KRONECKER PRODUCT MATRICES FOR COMPRESSIVE SENSING

KRONECKER PRODUCT MATRICES FOR COMPRESSIVE SENSING

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Marco F. Duarte Richard G. Baraniuk Program in Applied and Computational Mathematics Princeton University Princeton NJ 08544 Department of Electrical and Computer Engineering Rice University Houston TX 77005

ISBN: (纸本)9781424442959

Compressive sensing (CS) is an emerging approach for acquisition of signals having a sparse or compressible representation in some basis. While CS literature has mostly focused on problems involving 1-D and 2-D signals, many important applications involve signals that are multidimensional. We propose the use of Kronecker product matrices in CS for two purposes. First, we can use such matrices as sparsifying bases that jointly model the different types of structure present in the signal. Second, the measurement matrices used in distributed measurement settings can be easily expressed as Kronecker products. This new formulation enables the derivation of analytical bounds for sparse approximation and CS recovery of multidimensional signals.

关键词： Data compression multidimensional signal processing signal reconstruction

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Multidimensional arrays with maximal linear complexity

Multidimensional arrays with maximal linear complexity

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International Workshop on Signal Design and Its Applications in Communications (IWSDA)

作者： Domingo Gomez-Perez Ana I. Gomez Jaime Gutierrez Oscar Moreno Department of Mathematics Universidad de Cantabria Spain Department of Applied Mathematics and Computer Science Universidad de Cantabria Spain Gauss Research Foundation San Juan Puerto Rico

Multidimensional arrays have proven to be useful in watermarking, therefore interest in this subject has increased in the previous years along with the number of publications. For one dimensional arrays (sequences), linear complexity is regarded as standard measure of complexity. Although linear complexity of sequences has been widely studied, only recently, we have extended it to the study of multidimensional arrays. In this paper, we show that the concept of multidimensional linear complexity is a powerful tool, by examining the results for selected constructs. We have obtained the linear complexity of logartihmic Moreno-Tirkel arrays and we show that they show high multidimensional linear complexity. Finally, we explicitly provide the minimal generators for quadratic Moreno-Tirkel arrays. The results show that these techniques are effective in finding the multidimensional linear complexity of the constructions, representing only a small fraction of the applicability of multidimensional linear complexity. The study of multidimensional arrays provides new ways to understand sequences and set the basis for forthcoming proof of the three years old conjectures related with CDMA sequences.

关键词： Complexity theory Watermarking Generators Cryptography computer science Multiaccess communication

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