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6th International Conference on Recent Trends on Electronics, Information, Communication and Technology, RTEICT 2021

作者： Rahuja, Nitin Valluru, Sudarshan K. Center for Control of Dynamical Systems and Computation Delhi Technological University Dept. of Electrical Engineering Delhi110042 India

ISBN: (纸本)9781665435598

With the development of computational resources in the past decade, the adoption of deep learning methodologies has taken a steep rise. Particularly, the use of various Convolutional Neural Network (CNN) based architectures for various computer vision and image classification problems in healthcare research can be witnessed. In this scope, performance evaluation and comparison of three different CNN architectures for multi-class classification of Electrocardiogram (ECG) signals is presented in this paper. Three distinct classes of ECG signal i.e. Normal Sinus Rhythm (NSR), Arrhythmia (ARR), and Congestive Heart failure (CHF) are utilized in this work, which is representative of an individual's heart conditions. The classification methodology adopted makes use of Continuous Wavelet Transform (CWT) which provides a 2-dimensional time-frequency representation of available samples of ECG signals. ECG dataset was collected from different PhysioBank databases, which provides openly-accessible medical research data. Images with time-frequency representation of ECG signals were applied as input to three CNN architectures namely, AlexNet, GoogLeNet, and SqueezeNet. Using transfer learning approach and modification in certain output layers of three architectures, ECG classification was performed and the performance of three CNN architectures was studied. The results revealed classification accuracy of 97.80%, 97.78% and 97.22% by AlexNet, GoogLeNet and SqueezeNet respectively. AlexNet outperformed GoogLeNet and SqueezeNet models in terms of accuracy as well as training time. © 2021 IEEE.

关键词： Electrocardiograms

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A Deep Neural Network Approach to Automatic Multi-Class Classification of Electrocardiogram Signals

A Deep Neural Network Approach to Automatic Multi-Class Clas...

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2021 International Conference on Intelligent Technologies, CONIT 2021

作者： Rahuja, Nitin Valluru, Sudarshan K. Delhi Technological University Center for Control of Dynamical Systems and Computation Dept. of Electrical Engineering Delhi110042 India

ISBN: (纸本)9781728185835

Classification of electrocardiogram (ECG) signals is essential for accurate clinical diagnosis of coronary illness. Deep Neural Network (DNN) has emerged as a promising tool for feature identification in ECG signals with advancements in signal processing techniques and artificial intelligence. Particularly, Convolutional Neural Network (CNN) has enabled intricate feature identification and classification of images. In this scope, an efficient classification methodology utilizing wavelet transform and AlexNet CNN is presented in this paper to classify the ECG signals into three different categories, namely: Arrythmia (ARR), Congestive Heart failure (CHF) and Normal Sinus Rhythm (NSR). Physionet research dataset is utilized in this work. Firstly, all ECG signals are converted to a corresponding scalogram using Continuous Wavelet Transform (CWT). Then, these scalograms are applied as input to AlexNet, which is a pretrained CNN. Finally, transfer learning and fine-tuning are done, and the performance of modified AlexNet is evaluated. The study revealed a good accuracy of 97.3%. © 2021 IEEE.

关键词： Electrocardiograms

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Non-euclidean monotone operator theory and applications

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 14872-14904页

作者： Alexander Davydov Saber Jafarpour Anton V. Proskurnikov Francesco Bullo Center for Control Dynamical Systems and Computation University of California Santa Barbara Santa Barbara CA Department of Electrical Computer and Energy Engineering University of Colorado Boulder Boulder CO Department of Electronics and Telecommunications Politecnico di Torino Turin Italy

While monotone operator theory is often studied on Hilbert spaces, many interesting problems in machine learning and optimization arise naturally in finite-dimensional vector spaces endowed with non-Euclidean norms, such as diagonally-weighted ℓ1 or ℓ1 norms. This paper provides a natural generalization of monotone operator theory to finitedimensional non-Euclidean spaces. The key tools are weak pairings and logarithmic norms. We show that the resolvent and reected resolvent operators of non-Euclidean monotone mappings exhibit similar properties to their counterparts in Hilbert spaces. Furthermore, classical iterative methods and splitting methods for finding zeros of monotone operators are shown to converge in the non-Euclidean case. We apply our theory to equilibrium computation and Lipschitz constant estimation of recurrent neural networks, obtaining novel iterations and tighter upper bounds via forward-backward splitting.

关键词： non-Euclidean norms monotone operator theory fixed point equations nonexpansive maps

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Comparative Analysis of GOA, WCA and PSO Tuned Fractional Order-PID controller for Cart-Inverted Pendulum

Comparative Analysis of GOA, WCA and PSO Tuned Fractional Or...

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Power and Renewable Energy Conference (IPRECON), IEEE International

作者： Neelam Verma Sudarshan K. Valluru Department of Electrical Engineering Center for Control of Dynamical Systems and Computation Delhi Technological University Delhi India

control of cart based inverted pendulum system is a classical problem of control system and is widely used to verify the effectiveness of control algorithms. In this paper, fractional order PID (FOPID) controller has been used to control the cart position and pendulum angle. Three optimization algorithms namely, particle swarm optimization (PSO), water cycle algorithm (WCA) and grasshopper optimization algorithm (GOA) has been used to tune the parameters of the FOPID controller for underline problem. Results shows that, FOPID controller was able to control both cart position and pendulum angle effectively. Further, it has also been shown that GOA tuned FOPID controller performs better as compared to WCA and PSO tuned FOPID controller. The entire system was simulated using MATLAB2018a.

关键词： Renewable energy sources Software packages control systems Particle swarm optimization Optimization Matlab

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On the Equivalence of Multi-Agent 2D Coverage control and Leader-Follower Consensus Network

On the Equivalence of Multi-Agent 2D Coverage Control and Le...

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American control Conference (ACC)

作者： Xiaotian Xu Alexander Davydov Yancy Diaz-Mercado Department of Mechanical Engineering University of Maryland College Park MD USA Department of Mechanical Engineering and the Center for Control Dynamical Systems and Computation University of California Santa Barbara CA USA

Coverage control algorithms seek to spatially distribute agents in a domain of coverage, e.g., to minimize proximity to all points. Leader-follower consensus network algorithms use local coordination rules to influence the behavior of a multi-agent system (MAS) as a whole through explicit control of a subset of agents (called leaders) and neighbor interactions. In this paper, the equivalence of these two classes of distributed algorithms for swarm robotics, that were once considered inherently different, is established. We present a swarm robotics application, where the real agents (i.e., the robots) in the domain of coverage are followers; and virtual agents (i.e., the leaders) are introduced based on the domain of coverage. The dynamics of followers are shown to be in the form of a weighted, state-dependent consensus protocol and the dynamics of the leaders (dependent on the evolution of the domain) are provided. Formulating a standard coverage algorithm (i.e., Lloyd’s algorithm) over 2D polygonal domains as a leader-follower consensus protocol makes the structure of the ensemble-level dynamics for the MAS explicit with respect to neighbor interaction. The resultant weighted graph Laplacian may contribute to the future investigation on the performance guarantees of a MAS tracking a time-varying domain. The equivalence of the two classes of algorithms is validated in simulation.

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Firing Rate Models as Associative Memory: Excitatory-Inhibitory Balance for Robust Retrieval

arXiv

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arXiv 2024年

作者： Betteti, Simone Baggio, Giacomo Bullo, Francesco Zampieri, Sandro Department of Information Engineering Università degli Studi di Padova Padova35131 Italy Center for Control Dynamical Systems and Computation University of California at Santa Barbara Santa BarbaraCA93106 IT United States

Firing rate models are dynamical systems widely used in applied and theoretical neuroscience to describe local cortical dynamics in neuronal populations. By providing a macroscopic perspective of neuronal activity, these models are essential for investigating oscillatory phenomena, chaotic behavior, and associative memory processes. Despite their widespread use, the application of firing rate models to associative memory networks has received limited mathematical exploration, and most existing studies are focused on specific models. Conversely, well-established associative memory designs, such as Hopfield networks, lack key biologically-relevant features intrinsic to firing rate models, including positivity and interpretable synaptic matrices that reflect excitatory and inhibitory interactions. To address this gap, we propose a general framework that ensures the emergence of re-scaled memory patterns as stable equilibria in the firing rate dynamics. Furthermore, we analyze the conditions under which the memories are locally and globally asymptotically stable, providing insights into constructing biologically-plausible and robust systems for associative memory *** Codes 37N25 (Primary) 34D45, 34D23 (Secondary) © 2024, CC BY.

关键词： Associative storage

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INPUT-DRIVEN DYNAMICS FOR ROBUST MEMORY RETRIEVAL IN HOPFIELD NETWORKS

arXiv

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arXiv 2024年

The Hopfield model provides a mathematically idealized yet insightful framework for understanding the mechanisms of memory storage and retrieval in the human brain. This model has inspired four decades of extensive research on learning and retrieval dynamics, capacity estimates, and sequential transitions among memories. Notably, the role and impact of external inputs has been largely underexplored, from their effects on neural dynamics to how they facilitate effective memory retrieval. To bridge this gap, we propose a novel dynamical system framework in which the external input directly influences the neural synapses and shapes the energy landscape of the Hopfield model. This plasticity-based mechanism provides a clear energetic interpretation of the memory retrieval process and proves effective at correctly classifying highly mixed inputs. Furthermore, we integrate this model within the framework of modern Hopfield architectures, using this connection to elucidate how current and past information are combined during the retrieval process. Finally, we embed both the classic and the new model in an environment disrupted by noise and compare their robustness during memory *** Codes 37N25 (Primary) 37C75, 34D45 (Secondary) © 2024, CC BY.

关键词： Associative storage

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On Weakly Contracting Dynamics for Convex Optimization

arXiv

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arXiv 2024年

作者： Centorrino, Veronica Davydov, Alexander Gokhale, Anand Russo, Giovanni Bullo, Francesco Scuola Superiore Meridionale Italy The Center for Control Dynamical Systems and Computation UC Santa Barbara Santa BarbaraCA93106 United States The Department of Information and Electric Engineering and Applied Mathematics University of Salerno Italy

We analyze the convergence behavior of globally weakly and locally strongly contracting dynamics. Such dynamics naturally arise in the context of convex optimization problems with a unique minimizer. We show that convergence to the equilibrium is linear-exponential, in the sense that the distance between each solution and the equilibrium is upper bounded by a function that first decreases linearly and then exponentially. As we show, the linear-exponential dependency arises naturally in certain dynamics with saturations. Additionally, we provide a sufficient condition for local input-to-state stability. Finally, we illustrate our results on, and propose a conjecture for, continuous-time dynamical systems solving linear programs. Copyright © 2024, The Authors. All rights reserved.

关键词： Dynamics

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Synchronization of Switched Complex Dynamical Networks with Impulses: State-Dependent Switching Approach

SSRN

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SSRN 2023年

作者： Yang, Dan Li, Xiaodi Song, Shiji School of Mathematics and Statistics Shandong Normal University Jinan250014 China Center for Control and Engineering Computation Shandong Normal University Jinan250014 China Shandong Normal University China

This paper studies the globally exponential synchronization (GES) of a class of complex dynamical networks (CDNs) with switching topology involving impulse action. A novel state-dependent switching (SDS) approach to tune the switching topology of CDNs is designed based on the information of the node dynamics with impulses. Different from the traditional SDS results which are based on continuous state space, some sufficient conditions for network synchronization based on SDS are proposed in the framework of impulse action. A relationship between impulse action, the network structure, and switching topology is established, which is crucial to design the SDS law. Two different impulse actions on the network dynamics are fully considered in this paper, namely, stabilizing continuous dynamics with desynchronizing impulses and destabilizing continuous dynamics with synchronizing impulses. Finally, two numerical simulations are given to demonstrate the effectiveness of the proposed control method. © 2023, The Authors. All rights reserved.

关键词： Synchronization

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Moire-pattern-modulated electronic structures in Sb_(2)Te_(3)/graphene heterostructure

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Nano Research 2022年第2期15卷 1115-1119页

作者： Yin Yin Guanyong Wang Chen Liu Haili Huang Jiayi Chen Jiaying Liu Dandan Guan Shiyong Wang Yaoyii Li Canhua Liu Hao Zheng Jinfeng Jia Key Laboratory of Artificial Structures and Quantum Control(Ministry of Education) Shenyang National Laboratory for Materials ScienceSchool of Physics and AstronomyShanghai Jiao Tong UniversityShanghai 200240China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Tsung-Dao Lee Institute Shanghai 200240China CAS Center for Excellence in Topological Quantum Computation University of Chinese Academy of SciencesBeijing 100190China

Moire superlattice has recently been found in topological insulators,which can lead to periodic modulation on the electronic *** this work,we report the low-temperature scanning tunneling microscopy study of Sb_(2)Te_(3) films grown on graphitized *** find that substrate temperature can strongly influence the rotation angles between Sb_(2)Te_(3) film and graphene *** kinds of moire patterns are observed at the first quintuple layer Sb_(2)Te_(3) film under different substrate *** shows complicated patterns with a rotation angle of nearly 0°relative to the substrate,another just exhibits simple 1 ×1 structure with a rotation angle of 30°.Other rotation angle like 8.2°is observed at higher substrate temperature as well,which is relatively *** of the d//dV curves from Sb_(2)Te_(3) films with different moire patterns indicates that the superstructure can offer degrees of freedom in tailoring electronic *** work may stimulate the further study on the moire modulation to the electronic properties of topological insulators.

关键词： moire pattern topological insulator electronic structure scanning tunneling microscopy

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