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32nd European Signal Processing Conference, EUSIPCO 2024

作者： Chappon, E. Pustelnik, N. Tachella, J. Denneulin, L. Ferrari, A. Langlois, M. Laboratoire de Physique ENSL CNRS UMR 5672 LyonF-69342 France Laboratoire de Recherche de l’EPITA EPITA Le Kremlin-Bicêtre94270 France Université Côte d’Azur Observatoire de la Côte d’Azur CNRS Lab. J.-L. Lagrange France Univ Lyon Univ. Lyon 1 ENS de Lyon CNRS Centre de Recherche Astrophysique de Lyon UMR5574 Saint-Genis-Laval69230 France

ISBN: (纸本)9789464593617

Studying circumstellar environments is crucial for understanding exoplanets and stellar systems. Instruments like SPHERE can extract information about these environments by leveraging advanced image reconstruction methods, possibly based on deep learning. This work focuses on unfolded proximal neural networks based on Condat-Vũ iterations and proposes a new nonlinear formulation. To evaluate and compare the performance of the proposed reconstruction strategies, two datasets dedicated to circumstellar environments analysis in the context of high-contrast imagery have been created offering different level of complexity in the evaluation of the performance. © 2024 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： deep learning High constrast image recovery nonlinear algorithms proximal algorithms unfolded algorithms

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Phase Space Power Analysis for PC-based Rootkit Detection 22

Phase Space Power Analysis for PC-based Rootkit Detection

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Annual ACM Southeast Conference ACMSE

作者： McDonald, J. Todd Clark, Rebecca C. Hively, Lee M. Russ, Samuel H. Univ S Alabama Mobile AL 36688 USA Oak Ridge Natl Lab Oak Ridge TN USA

ISBN: (纸本)9781450386975

Within the current threat landscape of cyberattacks, adversaries use a wide array of techniques to gain a persistent and covert presence on systems. Rootkits are a popular form of attack vector because they are a specialized form of malware that use stealth and administrative privilege to operate without detection. They can also use sophisticated active and passive measures to actually cripple normal defensive software. Zero-day rootkits are particularly dangerous in advanced persistent threat (APT) scenarios because typical signature-based methods of malware scanning are ineffective at detecting them. In this paper, we consider a more robust technique of dynamic malware detection that allows use of side-channel system properties as process-indicative data, allowing observation outside of a rootkit's ability to influence. In particular, we extend prior work that has used CPU power measurements as a means to detect rootkit execution. In this work we use a Data Acquisition (DAQ) system to collect data from an embedded analog power sensor to record low-frequency digital readings from three channels of a PC power supply. Of particular focus, we use a data-driven nonlinear phase space algorithm (NLPSA) to analyze power readings and perform supervised learning to discern infected versus non-infected states. Our initial case study using Windows 10 and Ubuntu rootkits shows that NLPSA can achieve perfect accuracy in distinguishing rootkit execution and normal system operation, confirming other similar studies where side-channel data are in view.

关键词： Malware Detection Dynamic Analysis nonlinear algorithms Phase Space Rootkits

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Friction State Classification Based on Vehicle Inertial Measurements 9th

Friction State Classification Based on Vehicle Inertial Meas...

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9th IFAC International Symposium on Advances in Automotive Control (AAC)

作者： Selmanaj, Donald Corno, Matteo Savaresi, Sergio M. Polytech Univ Tirana Dept Automat Sheshi Nene Tereza 4 Tirana Albania Politecn Milan Dipartimento Elettron Informaz & Bioingn Piazza L da Vinci 32 I-20133 Milan Italy

Tire-road friction is the most important characteristic defining the planar dynamics of wheeled vehicles. It has consequences on the drivability, stability and tuning of the active vehicle dynamics control systems. This paper proposes two online friction estimation methods designed for the adaptation of vehicle dynamics control algorithms. The problem is framed as a classification problem where inertial measurements are used to discriminate between high and low friction regimes. The first method merges a recursive least-squares (RLS) algorithm with a heuristic bistable logic to classify the friction condition and promptly react to its changes. The second method runs a classification algorithm on the slip-acceleration characteristic. Both methods simultaneously account for the longitudinal and lateral dynamics and are tested on experimental data. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Friction Vehicles dynamics Classification Recursive algorithms nonlinear algorithms

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Friction State Classification Based on Vehicle Inertial Measurements

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IFAC-PapersOnLine 2019年第5期52卷 72-77页

作者： Donald Selmanaj Matteo Corno Sergio M. Savaresi Department of Automation Polytechnic University of Tirana Sheshi “Nënë Tereza” Nr.4 Tirana Albania Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Piazza L. da Vinci 32 20133 Milano Italy

关键词： Friction Vehicles dynamics Classification Recursive algorithms nonlinear algorithms

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An embedded 3(2) pair of nonlinear methods for solving first order initial-value ordinary differential systems

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NUMERICAL algorithms 2017年第3期75卷 509-529页

作者： Ramos, Higinio Singh, Gurjinder Kanwar, V. Bhatia, Saurabh Univ Salamanca Sci Comp Grp Plaza Merced E-37008 Salamanca Spain Escuela Politecn Super Zamora Campus Viriato Zamora 49022 Spain Panjab Univ Univ Inst Engn & Technol Chandigarh 160014 India

In this paper, we present two families of second-order and third-order explicit methods for numerical integration of initial-value problems of ordinary differential equations. Firstly, a family of second-order methods with two free parameters is derived by considering a suitable rational approximation to the theoretical solution of the problem at some grid points. Imposing that the principal term of the local truncation error of this family vanishes, we obtain an expression for one of the parameters in terms of the other. With this approach, a new one-parameter family of third-order methods is obtained. By selecting any 3(2) pair of second and third order methods, they can be implemented as an embedded type method, thus leading to a variable step-size formulation. We have considered one 3(2) pair of second and third order methods and made a comparison of numerical results with several ode solvers which are currently used in practice. The comparison of numerical results shows that the embedded 3(2) pair outperforms the methods considered for comparison.

关键词： nonlinear algorithms Ordinary differential equations Initial-value problems Rational approximations A-stability

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Kalman filtering with state constraints: a survey of linear and nonlinear algorithms

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IET CONTROL THEORY AND APPLICATIONS 2010年第8期4卷 1303-1318页

作者： Simon, D. Cleveland State Univ Dept Elect & Comp Engn Cleveland OH 44115 USA

The Kalman filter is the minimum-variance state estimator for linear dynamic systems with Gaussian noise. Even if the noise is non-Gaussian, the Kalman filter is the best linear estimator. For nonlinear systems it is not possible, in general, to derive the optimal state estimator in closed form, but various modifications of the Kalman filter can be used to estimate the state. These modifications include the extended Kalman filter, the unscented Kalman filter, and the particle filter. Although the Kalman filter and its modifications are powerful tools for state estimation, we might have information about a system that the Kalman filter does not incorporate. For example, we may know that the states satisfy equality or inequality constraints. In this case we can modify the Kalman filter to exploit this additional information and get better filtering performance than the Kalman filter provides. This paper provides an overview of various ways to incorporate state constraints in the Kalman filter and its nonlinear modifications. If both the system and state constraints are linear, then all of these different approaches result in the same state estimate, which is the optimal constrained linear state estimate. If either the system or constraints are nonlinear, then constrained filtering is, in general, not optimal, and different approaches give different results.

关键词： Signal processing theory extended Kalman filter optimal constrained linear state estimate constraint theory nonlinear algorithms Other topics in statistics linear dynamic systems state estimation minimum variance state estimator Gaussian noise state constraints Kalman filters Filtering methods in signal processing optimal state estimator unscented Kalman filter

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Comparison and Analysis of nonlinear algorithms for Compressed Sensing in MRI

Comparison and Analysis of Nonlinear Algorithms for Compress...

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32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10)

作者： Yu, Yeyang Hong, Mingjian Liu, Feng Wang, Hua Crozier, Stuart Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld Australia Chongqing Univ Sch Software Engn Chongqing 630044 Peoples R China

ISBN: (纸本)9781424441242

Compressed sensing (CS) theory has been recently applied in Magnetic Resonance Imaging (MRI) to accelerate the overall imaging process. In the CS implementation, various algorithms have been used to solve the nonlinear equation system for better image quality and reconstruction speed. However, there are no explicit criteria for an optimal CS algorithm selection in the practical MRI application. A systematic and comparative study of those commonly used algorithms is therefore essential for the implementation of CS in MRI. In this work, three typical algorithms, namely, the Gradient Projection For Sparse Reconstruction (GPSR) algorithm, Interior-point algorithm (l(1)_ls), and the Stagewise Orthogonal Matching Pursuit (StOMP) algorithm are compared and investigated in three different imaging scenarios, brain, angiogram and phantom imaging. The algorithms' performances are characterized in terms of image quality and reconstruction speed. The theoretical results show that the performance of the CS algorithms is case sensitive;overall, the StOMP algorithm offers the best solution in imaging quality, while the GPSR algorithm is the most efficient one among the three methods. In the next step, the algorithm performances and characteristics will be experimentally explored. It is hoped that this research will further support the applications of CS in MRI.

关键词： GPSR algorithm StOMP algorithm biomedical MRI biomedical MRI compressed sensing implementation data compression gradient projection for sparse reconstruction image quality image reconstruction image reconstruction speed interior-point algorithm iterative methods magnetic resonance imaging medical image processing nonlinear algorithms nonlinear equation system nonlinear equations stagewise orthogonal matching pursuit

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Comments on "Distributed nonlinear control algorithms for network consensus" [Automatica 44 (2008) 2375-2381]

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AUTOMATICA 2010年第9期46卷 1568-1568页

作者： Liu, Xiwei Fudan Univ Key Lab Nonlinear Sci Chinese Minist Educ Sch Math Shanghai 200433 Peoples R China

关键词： Consensus nonlinear algorithms

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Kernel-Based Learning for Statistical Signal Processing in Cognitive Radio Networks

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IEEE SIGNAL PROCESSING MAGAZINE 2013年第4期30卷 126-136页

作者： Ding, Guoru Wu, Qihui Yao, Yu-Dong Wang, Jinlong Chen, Yingying PLA Univ Sci & Technol Coll Commun Engn Nanjing Jiangsu Peoples R China Southeast Univ Nanjing Jiangsu Peoples R China PLA Univ Sci & Technol Nanjing Jiangsu Peoples R China Stevens Inst Technol Hoboken NJ 07030 USA Carleton Univ Ottawa ON K1S 5B6 Canada Spar Aerosp Ltd Montreal PQ Canada Qualcomm Inc San Diego CA USA IEEE Nanjing Sect Nanjing Jiangsu Peoples R China Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ USA Alcatel Lucent Holmdel NJ USA

Kernel-based learning (KBL) methods have recently become prevalent in many engineering applications, notably in signal processing and communications. The increased interest is mainly driven by the practical need of being able to develop efficient nonlinear algorithms, which can obtain significant performance improvements over their linear counterparts at the price of generally higher computational complexity. In this article, an overview of applying various KBL methods to statistical signal processing-related open issues in cognitive radio networks (CRNs) is presented. It is demonstrated that KBL methods provide a powerful set of tools for CRNs and enable rigorous formulation and effective solutions to both long-standing and emerging design problems.

关键词： Machine learning Learning systems Kernel Cognitive radio Signal processing algorithms nonlinear algorithms

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A Parallel Python library for nonlinear systems

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JOURNAL OF SUPERCOMPUTING 2011年第3期58卷 438-448页

作者： Migallon, Hector Migallon, Violeta Penades, Jose Univ Miguel Hernandez Dept Fis & Arquitectura Comp Alicante 03202 Spain Univ Alicante Dept Ciencia Comp & Inteligencia Artificial Alicante 03071 Spain

In this paper, we present PyPANCG, a Python library-interface that implements both the conjugate gradient method and the preconditioned conjugate gradient method for solving nonlinear systems. We describe the use of the library and its advantages in order to get fast development. The aim of this library is to develop high performance scientific codes for high-end computers hiding many of the underlying low-level programming complexities from users with the use of a high-level Python interface. The library has been designed for adapting to different stages of the design process, depending on whether the purpose is computational performance or fast development. Experimental results report the performance of our approach on different parallel computers.

关键词： Parallel libraries nonlinear algorithms Python high-level interfaces

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