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Activation maps of convolutional neural networks as a tool for brain degeneration tracking in early diagnosis of dementia in Parkinson's disease based on magnetic resonance imaging

Research Square

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Research Square 2021年

作者： Suwalska, Aleksandra Siuda, Joanna Kocot, Szymon Zmuda, Weronika Rudzinska-Bar, Monika Polanska, Joanna Department of Data Science and Engineering Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Neurology School of Medicine Medical University of Silesia Katowice Poland Department of Neurology Faculty of Medicine and Health Sciences Andrzej Frycz Modrzewski Krakow University Krakow Poland

Identification of Parkinson’s Disease (PD) patients at risk for development of dementia is crucial for early intervention. However, diagnosing dementia in PD patients requires advanced, time-consuming and expensive imaging techniques (such as DaT-SCAN-SPECT). The study aims to prove the usefulness of convolutional neural networks (CNN) for the identification of brain areas related to the progress of cognitive impairment by using standard Magnetic Resonance Imaging (MRI) sequences. T1&T2 sequences of 18 patients were used in the pilot study. Activation maps were generated, and the brain regions most involved in the classification process were identified, showing areas potentially significant in the diagnosis of cognitive impairment severity. The cerebellum was proven significant for distinguishing the above-mentioned classes in relative cerebellum volume (ANOVA p-value=0.0038 with large effect size η2=0.5254) and folding (p-value=0.0031, η2=0.5357), which is consistent with reports by other authors. Our analysis demonstrates that convolutional neural networks combined with a proper image preprocessing pipeline could be used for feature extraction in MRI sequences and can successfully support the identification of disease-specific abnormalities of the brain image. © 2021, CC BY.

关键词： Magnetic resonance imaging

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Distributed formation control of manipulators' end-effector with internal model-based disturbance rejection

arXiv

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arXiv 2021年

作者： Wu, Haiwen Jayawardhana, Bayu de Marina, Hector Garcia Xu, Dabo Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands Department of Computer Architecture and Automatic Control Faculty of Physics Universidad Complutense de Madrid Madrid28040 Spain The School of Automation Nanjing University of Science and Technology Nanjing210094 China

This paper addresses the problem of end-effector formation control for manipulators that are subjected to external disturbances: input disturbance torques and disturbance forces at each end-effector. The disturbances are assumed to be non-vanishing and are superposition of finite number of sinusoidal and step signals. The formation control objective is achieved by assigning virtual springs between end-effectors, by adding damping terms at joints, and by incorporating internal model-based dynamic compensators to counteract the effect of the disturbances;all of which presents a clear physical interpretation of the proposed approach. Simulation results are presented to illustrate the effectiveness of the proposed approach. © 2021, CC BY.

关键词： End effectors

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Refinement of Massey Inequality : (To Nicolae Ţǎpuş on his 70th birthday)

Refinement of Massey Inequality : (To Nicolae Ţǎpuş on hi...

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2019 IEEE International Symposium on Information Theory, ISIT 2019

作者： Popescu, Pantelimon George Omar Choudary, Marios Computer Science and Engineering Department Faculty of Automatic Control and Computers University Politehnica of Bucharest Splaiul Independenţei 313 Bucharest060042 Romania

ISBN: (纸本)9781538692912

关键词： Information theory

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Machine Learning for the control and Monitoring of Electric Machine Drives: Advances and Trends

arXiv

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arXiv 2021年

作者： Zhang, Shen Wallscheid, Oliver Porrmann, Mario School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Joby Aviation San CarlosCA94070 United States Department of Automatic Control Paderborn University Paderborn33098 Germany Institute of Computer Science Osnabrüeck University Wachsbleiche 27 Osnabrück49090 Germany

This review paper systematically summarizes the existing literature on utilizing machine learning (ML) techniques for the control and monitoring of electric machine drives. It is anticipated that with the rapid progress in learning algorithms and specialized embedded hardware platforms, machine learning-based data-driven approaches will become standard tools for the automated high-performance control and monitoring of electric drives. Additionally, this paper also provides some outlook toward promoting its widespread application in the industry with a focus on deploying ML algorithms onto embedded system-on-chip (SoC) field-programmable gate array (FPGA) devices. © 2021, CC BY.

关键词： Embedded systems

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On the Promise of the Stochastic Generalized Gauss-Newton Method for Training DNNs

arXiv

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arXiv 2020年

作者： Gargiani, Matilde Diehl, Moritz Zanelli, Andrea Hutter, Frank Automatic Control Laboratory ETH Zurich Department of Microsystems Engineering Department of Mathematics University of Freiburg Department of Microsystems Engineering University of Freiburg Department of Computer Science University of Freiburg Bosch Center for Artificial Intelligence

Following early work on Hessian-free methods for deep learning, we study a stochastic generalized Gauss-Newton method (SGN) for training deep neural networks. SGN is a second-order optimization method, with efficient iterations, that we demonstrate to often require substantially fewer iterations than standard SGD to converge. As the name suggests, SGN uses a Gauss-Newton approximation for the Hessian matrix, and, in order to efficiently compute an approximate search direction, relies on the conjugate gradient method combined with forward and reverse automatic differentiation. Despite the success of SGD and its first-order variants in deep learning applications, and despite Hessian-free methods based on the Gauss-Newton Hessian approximation having been already theoretically proposed as practical methods for training neural networks, we believe that SGN has a lot of undiscovered and yet not fully displayed potential in big mini-batch scenarios. For this setting, we demonstrate that SGN does not only substantially improve over SGD in terms of the number of iterations, but also in terms of runtime. This is made possible by an efficient, easy-to-use and flexible implementation of SGN we propose in the Theano deep learning platform, which, unlike Tensorflow and Pytorch, supports forward automatic differentiation. This enables researchers to further study and improve this promising optimization technique and hopefully reconsider stochastic second-order methods as competitive optimization techniques for training DNNs;we also hope that the promise of SGN may lead to forward automatic differentiation being added to Tensorflow or Pytorch. Our results also show that in big mini-batch scenarios SGN is more robust than SGD with respect to its hyperparameters (we never had to tune its step-size for our benchmarks!), which eases the expensive process of hyperparameter tuning that is instead crucial for the performance of first-order methods. Copyright © 2020, The Authors. All righ

关键词： Stochastic systems

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Decision support system based on pattern detection in iterative dermoscopic screening

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computer Methods and Programs in Biomedicine 2025年 269卷 108841页

作者： Salwowska, Natalia Kempski, Aleksander Zielonka, Piotr Pietkiewicz, Paweł Borys, Damian Department of Dermatology School of Medicine Medical University of Silesia Katowice Poland Department of Systems Biology and Engineering Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Dermatology Private Practice Polish Dermatoscopy Group Poznań Poland Biotechnology Centre Silesian University of Technology Gliwice Poland

Background and Objective: Videodermoscopy is the gold standard in melanoma diagnosis. A new research trend aims to support specialists’ work by automating this examination method, but most research focuses on developing detectors to replace dermatologists. Our study aims to present a novel idea which focuses on supporting the examination process instead of completely automating it. Instead of detecting and classifying all naevi each time, our approach focuses on full screening at the first visit and shortening the examination at subsequent visits. During the following examinations, images of all naevi are taken by supporting staff and are compared with previously taken images using feature-matching or co-registration-based algorithms. An unmatched naevus needs to be checked by the specialist, meaning that a new naevus appeared or an old one had changed. Methods: The similarity between images (ranging from 0% to 100%) was obtained with characteristic points searching (ORB method) and co-registration-based methods. No dataset presenting changes of naevi over time exists, so augmenting the images from PH2 and HAM10000 datasets was required to simulate the time flow. Results: The decision bound was set, so the true positive rate of each method was equal to 99%, giving the specificity of ORB equal to 97% (PH2) and 89% (HAM10000). Similar tests were performed using a co-registration algorithm giving specificity equal to 23% (PH2) and 19% (HAM10000). Conclusions: We believe that employing our approach will allow supporting staff to perform screening without experts’ help, decreasing examination time and costs and increasing the accessibility of dermoscopic examinations. © 2025 The Authors

关键词： Dermoscopy Machine vision Melanoma Pattern recognition Template matching

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Asymptotic Learning Requirements for Stealth Attacks on Linearized State Estimation

arXiv

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arXiv 2021年

作者： Sun, Ke Esnaola, Iñaki Tulino, Antonia M. Poor, H. Vincent The Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States The DIETI Università degli Studi di Napoli Federico II Naples80138 Italy The ECE Department New York University Tandon School of Engineering BrooklynNY10003 United States

Information-theoretic stealth attacks are data injection attacks that minimize the amount of information acquired by the operator about the state variables, while simultaneously limiting the Kullback-Leibler divergence between the distribution of the measurements under attack and the distribution under normal operation with the aim of controling the probability of attack detection. For Gaussian distributed state variables, attack construction requires knowledge of the second order statistics of the state variables, which is estimated from a finite number of past realizations using a sample covariance matrix. Within this framework, the attack performance is studied for the attack construction with the sample covariance matrix. This results in an analysis of the amount of data required to learn the covariance matrix of the state variables used on the attack construction. The ergodic attack performance is characterized using asymptotic random matrix theory tools, and the variance of the attack performance is bounded. The ergodic performance and the variance bounds are assessed with simulations on IEEE test systems. © 2021, CC BY-NC-ND.

关键词： Covariance matrix

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A stochastic perturbed augmented Lagrangian method for smooth convex constrained minimization

arXiv

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arXiv 2025年

作者： Singh, Nitesh Kumar Necoara, Ion School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun248007 India Automatic Control and Systems Engineering Department University Politehnica Bucharest Bucharest060042 Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy Bucharest050711 Romania

This paper considers smooth convex optimization problems with many functional constraints. To solve this general class of problems we propose a new stochastic perturbed augmented Lagrangian method, called SGDPA, where a perturbation is introduced in the augmented Lagrangian function by multiplying the dual variables with a subunitary parameter. Essentially, we linearize the objective and one randomly chosen functional constraint within the perturbed augmented Lagrangian at the current iterate and add a quadratic regularization that leads to a stochastic gradient descent update for the primal variables, followed by a perturbed random coordinate ascent step to update the dual variables. We provide a convergence analysis in both optimality and feasibility criteria for the iterates of SGDPA algorithm using basic assumptions on the problem. In particular, when the dual updates are assumed to be bounded, we prove sublinear rates of convergence for the iterates of algorithm SGDPA of order O(k−1/2) when the objective is convex and of order O(k−1) when the objective is strongly convex, where k is the iteration counter. Under some additional assumptions, we prove that the dual iterates are bounded and in this case we obtain convergence rates of order O(k−1/4) and O(k−1/2) when the objective is convex and strongly convex, respectively. Preliminary numerical experiments on problems with many quadratic constraints demonstrate the viability and performance of our method when compared to some existing state-of-the-art optimization methods and software. © 2025, CC BY.

关键词： Lagrange multipliers

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Compositional Synthesis of Symbolic Models for Infinite Networks ⁎

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IFAC-PapersOnLine 2020年第2期53卷 1868-1873页

作者： Abdalla Swikir Navid Noroozi Majid Zamani Department of Electrical and Computer Engineering Technical University of Munich Germany Laboratory for Systems Theory and Automatic Control Otto-von-Guericke University Magdeburg Germany Computer Science Department University of Colorado BoulderUSA. Institute of Informatics Ludwig Maximilian University Munich Germany

In this paper, we provide a compositional method for the construction of symbolic models (a.k.a. finite abstractions) for infinite networks of discrete-time control systems. The concrete infinite network and its symbolic model are related by a so-called alternating simulation function which allows one to quantify the mismatch between the output behavior of the infinite interconnection of concrete subsystems and that of their symbolic models. We show that such an alternating simulation function can be obtained compositionally by assuming some small-gain type conditions and composing so-called local alternating simulation functions constructed for subsystems. Assuming certain stability property of concrete subsystems, we also provide a technique to synthesize their symbolic models together with their corresponding local alternating simulation functions. Finally, we apply our results to a traffic network divided into infinitely many cells.

关键词： Compositionality interconnected systems small-gain condition infinite networks symbolic models

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Evolving the very act of reading with socio-collaborative dimensions - Ar-annotations over physical books

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer Science 2020年第2期82卷 3-14页

作者： Stănică, Iulia-Cristina Moldoveanu, Alin Alabbasi, Hesham Adnan Moldoveanu, Florica Grădinaru, Alexandru Dascălu, Maria Morar, Anca Asavei, Victor Rădoi, Mireille Faculty of Automatic Control and Computer Science University POLITEHNICA of Bucharest Romania Department of Engineering in Foreign Language University POLITEHNICA of Bucharest Romania Computer Science Department College of Education Mustansiriyah University Iraq Central University Library of Bucharest "Carol I" Romania

Revitalizing reading and modernizing books have become important issues today, when technology is everywhere around us. New means are needed to make reading and physical books attractive to all people, regardless of age, hobbies or occupation. Our current paper presents an innovative project called Lib2Life which creates a mobile application that can improve books by adding annotations and social capabilities to them. © 2020, Politechnica University of Bucharest. All rights reserved.

关键词： Annotation Digital reading Digitization

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