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Simres-TV: Noise and residual similarity for parameter estimation in total var.a.ion 4

Simres-TV: Noise and residual similarity for parameter estim...

4th Inter.a.ional Workshop on Photogrammetric and computer Vision Techniques for Video Surveillance, Biometrics and Biomedicine, PSBB 2021

作者： Prasath, V.B. Sur.a.Hien, Nguyen Ngoc Thanh, Dang N.H. Dvoenko, Sergey Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center CincinnatiOH45229 United States Department of Pediatrics University of Cincinnati College of Medicine CincinnatiOH45257 United States Department of Biomedical Informatics College of Medicine University of Cincinnati OH45267 United States Department of Electrical Engineering and Computer Science University of Cincinnati OH45221 United States Dong Thap University Cao Lanh City Viet Nam Department of Information Technology School of Business Information Technology University of Economics Ho Chi Minh City Viet Nam Institute of Applied Mathematics and Computer Science Tula State University Russia

Image restoration with regularization models is very popular in the image processing literature. Total var.a.ion (TV) is one of the impor.a.t edge preserving regularization models used, however, to obtain optimal restoration results the regularization parameter needs to be set appropr.a.ely. We propose here a new parameter estimation appr.a.h for total var.a.ion based image restoration. By utilizing known noise levels we compute the regularization parameter by reducing the similarity between residual and noise var.a.ces. We use the split Bregman algorithm for the total var.a.ion along with this automatic parameter estimation step to obtain a very fast restoration scheme. Experimental results indicate the proposed parameter estimation obtained better denoised images and videos in terms of PSNr.a.d SSIM measures and the computational overload is less compared with other.a.pr.a.hes. © Authors 2021. CC BY 4.0 License.

关键词： Image reconstruction

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Investigation and Countermeasure toward Unintentional Access to Docker Container

Investigation and Countermeasure toward Unintentional Access...

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Cybersecurity Conference (SVCC), Silicon Valley

作者： Yueyang Li Luyi Li ruxue Luo Yuzhen Chen Arijet Sarker Sang-Yoon Chang Wenjun Fan School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou Jiangsu P.R.China Computer Science Department College of Engineering and Applied Science University of Colorado Colorado Springs Colorado Springs CO United States Silicon Valley Cybersecurity Institute (SVCSI)

Due to the ease of management and the high perfor.a.ce of the containerization, many services have been deployed on container, e.g., Web server running in Docker. However, the Docker implementation suffers several fatal loopholes. In this paper, we perform a study on a persistent security problem of Docker, i.e., the port mapping statement results in a wrong IPTABLES rule, which has been disclosed for.a.while but is still not solved. Therefore, we are motivated to investigate and articulate this vulnerability with a technical explanation. Nevertheless, we proposed several solutions to address the problem. Further, we applied our network testbed for demonstrating the loophole and the effectiveness of the security solutions. We tested the perfor.a.ce of both attack and defense prototyping. The experimental results show that our.a.pr.a.h not only incr.a.e the time cost for the attacker to identify the target but also bring negligible overhead for deploying the countermeasures.

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Intelligent Control Strategy of Vehicle Active Suspension Based on Deep reinforcement Learning

Intelligent Control Strategy of Vehicle Active Suspension Ba...

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Chinese Automation Congress (CAC)

作者： Xuebin Zhu Zhaoqun Chen Sheng Zhang Cirun Zhang Science and Technology Committee Beijing Institute of Space Launch Technology Beijing China Technology R&D Center of eDrive Special Vehicles Beijing Institute of Space Launch Technology Beijing China Electrical and Computer Department Pratt School of Engineering Duke University Durham United States

Aiming at the uncer.a.nty and nonlinearity of vehicle active suspension systems, this paper proposes a suspension vibration control strategy based on the twin delayed deep deterministic policy gradient (TD3) algorithm. A quarter-vehicle active suspension model and the time domain model of r.a. excitation are established to develop the controller. Simulation results of vibration characteristics show that the intelligent control strategy has excellent perfor.a.ce and self-learning ability. Compared with the linear quadratic gaussian, proportional-integral-derivative, and sky-hook strategy, the deep reinforcement learning controller has better robustness and generalization ability, which can adapt to sophisticated driving conditions of the vehicle, and has an extensive application prospect in the development of the active control arithmetic for suspension.

关键词： Vibrations Deep learning Suspensions (mechanical systems) Uncer.a.nty Simulation r.a.s reinforcement learning

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Machine Learning and computer Vision Techniques in Continuous Beehive Monitoring Applications: A Survey

arXiv

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arXiv 2022年

作者： Bilik, Simon Zemcik, Tomas Kratochvila, Lukas ricanek, Dominik richter, Miloslav Zambanini, Sebastian Horak, Karel Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Technická 3058/10 Brno61600 Czech Republic Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Yliopistonkatu 34 Lappeenranta53850 Finland Computer Vision Lab Institute of Visual Computing & Human-Centered Technology Faculty of Informatics TU Wien Favoritenstr. 9/193-1 ViennaA-1040 Austria

Wide use and availability of machine learning and computer vision techniques allows development of relatively complex monitoring systems in many domains. Besides the traditional industr.a. domain, new applications appears also in biology and agriculture, where they may be used to detect infections, parasites and weeds, but also for.a.tomated monitoring and early warning systems. This goes in concor.a.ce with the introduction of the easily accessible hardware and development kits such as the Arduino, or raspberryPi families. In this paper, we survey 50 papers focusing on the methods of automated beehive monitoring using computer vision techniques. Particularly on the pollen and Varr.a.mite detection together with the bee traffic monitoring. Such systems could also be used for monitoring of honeybee colonies and for the inspection of their health state, which could potentially identify dangerous states before the situation is critical, or to better plan periodic bee colony inspections and therefore save significant costs. Further on, we also include analysis of the research trends in this application field and we outline the possible directions of new development. Our paper is also aimed at veterinary and apidology professionals and experts, who may not be familiar with machine learning to introduce them to its capabilities, hence each family of techniques is prefaced by a brief theoretical introduction and motivation related to its base method. We hope that this paper will inspire other scientists to use machine learning techniques for other.a.plications in beehive monitoring. © 2022, CC BY.

关键词： computer vision

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The Development of the Multi-Layer Security Network Authentication System using Blockchain technology

The Development of the Multi-Layer Security Network Authenti...

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Inter.a.ional Conference on System Modeling & Advancement in research Trends (SMArT)

作者： S. K. UmaMaheswaran Arti rana Kavita rani Yashpal Singh Suhas Harbola Kumud Pant Department of Mathematics Sri Sai Ram Engineering College Chennai Tamil Nadu India Uttaranchal Institute of Technology Uttaranchal University Department of CSE Lloyd Institute of Engineering and Technology Greater Noida Uttar Pradesh Department of Computer Science & Engineering Jain (Deemed-to-be University) Bengaluru Karnataka India National Informatics Centre (NIC) New Delhi Delhi India Department of Biotechnology Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (纸本)9781665487351

Due to the fact that crypto is a novel security strategy. This section outlines a multi-layer safe Internet of Everything networks theory based on blockchains, we will now explain the construction of a layered network security authenticator using blockchain technologies. By grouping Connected technologies into a pass connectivity and implementing the blockchain based platform at every level of a network, the strategy reduces the challenges of actually deploying blockchain technologies while maintaining the mankind's strict security and validity guarantee. It offers a nationwide connectivity option for the Web of Things. A stone chain is a collection of blocks that holds data. The kind of public blockchain affects the infor.a.ion stored inside a piece. For instance, a lot coin block contains data about the Presenter, the reception, and the amount of bit coins that will be communicated. The brand's first block is called the Node. The goal of the block chain is to make data available to everyone who uses a program to access it. Entry to all this account for r.a.ing may be unrestricted (‘permit less’) or restricted (‘approval’). Block chain today makes it feasible to send, receive, and store digital data in a secure manner. Additionally, it uses mathematical encryption to protect every transaction. By adopting this, banks might boost their current security and disclosures to unprecedented heights.

关键词： Authorization Steganography Privacy Authentication Quality control Blockchains Encryption

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Industr.a. Internet of Things (IIoT): Transforming Industr.a. Operations through Advanced Connectivity

Industrial Internet of Things (IIoT): Transforming Industria...

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Communication, Computing and Energy Efficient Technologies (I3CEET), Inter.a.ional Conference on

作者： S. Viveka Vivek Justus Sangeetha J Desidi Narsimha reddy V. Samuthira Pandi S. Banumathi Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of CSE S. A. Engineering College Chennai Tamil Nadu India Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The Industr.a. Internet of Things (IIoT) seamlessly integrates operational technology with modern connectivity technologies, ushering in a new age in industr.a. operations. This paper examines IIoT's revolutionary potential in changing industr.a. landscapes through smart sensors, actuators, and intelligent devices that enable r.a.-time data sharing and process automation. This paper uses case studies from numerous sectors to demonstrate how IIoT may improve operational efficiency, downtime, and decision-making. Manufacturing, energy, and logistics are discussed. The report also examines IIoT technologies like cloud, edge, and machine learning techniques. These help enterprises exploit enormous data sets for predictive maintenance, supply chain optimization, and product quality. The article discusses IIoT's risks and challenges, including data privacy, cybersecurity, and the need for skilled personnel. A detailed research shows IIoT's strategic impor.a.ce in the fourth industr.a. revolution. It also discusses how industr.a. networking will enable smart and sustainable operations.

关键词： Actuators Supply chains r.a.-time systems Product design Telecommunication computing Quality assessment Personnel Intelligent sensors Industr.a. Internet of Things Predictive maintenance

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IoT Applications in Sports and Fitness: Enhancing Perfor.a.ce Monitoring and Training

IoT Applications in Sports and Fitness: Enhancing Performanc...

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Inter.a.ional Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： ramakrishnan raman Meenakshi Kaul r. Meenakshi S. Jayaprakash D. rukmani Devi S. Murugan Symbiosis Institute of Business Management Pune Symbiosis International (Deemed University) Pune Maharashtra India Symbiosis Law School Noida Uttar Pradesh Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering R.M.D. Engineering College Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

IoT (IoT) has transformed sports, fitness, and more. This article discusses IoT in sports and wellness perfor.a.ce monitoring and preparation. Wearables, sensors, and data analytics help athletes, coaches, and fitness enthusiasts gather, analyze, and use r.a.-time data. Training may improve, reducing injuries and perfor.a.ce. This article covers IoT applications in sports and fitness, covering necessary improvements and their implications on perfor.a.ce checking and preparation. Smartwatches, fitness trackers and biometric sensors record signs, movement patterns, and physiological data. It also analyzes how IoT-enabled devices may br.a.cast r.a.-time data, enabling athletes and trainers to monitor.a.d assess perfor.a.ce indicators and make decisions remotely. This research investigates sports and fitness IoT systems and data analytics. Predictive models and machine learning algorithms can analyze enormous volumes of data, find patterns, and provide personalized advice to enhance perfor.a.ce and training. It also explores how Vr.a.d Ar might be utilized with IoT to cr.a.e r.a.istic, interactive teaching environments. Data privacy and security protect sensitive personal infor.a.ion. This research highlights how IoT applications in fitness and sports may revolutionize the game. IoT may assist athletes, coaches, and fitness enthusiasts in training, performing, and analyzing their perfor.a.ce. IoT will change how it screens, train, and succeed in pr.a.tive sports and health.

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FedMonitor: An Implementation Architecture for Monitoring of resources and Events'Notifications on Federated Cloud Computing Infrastructures

FedMonitor: An Implementation Architecture for Monitoring of...

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Inter.a.ional Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Emmanuel Adetiba Uche Nnaji John S. Wejin Festus Ojesanmi Matthew B. Akanle Priscilla O. Ajayi Faith O. Sweetwiliams Surendra C. Thakur Ezekiel F. Adebiyi Department of Electrical and Information Engineering and Covenant Applied Informatics & Communication African Center of Excellene(CApIC-ACE) Covenant University Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Department of Computer and Information Science & CApIC-ACE Covenant University Ota Nigeria Department of Electrical and Information Engineering & CApIC-ACE Covenant University Jalingo Nigeria Department of Computer Science Tareba State University Jalingo Nigeria Covenant Applied Informatics & Communication African Center of Excellene(CApIC-ACE) Covenant University Ota Nigeria Department of Electrical and Information Engineering Covenant University & CApIC-ACE Covenant University Ota Nigeria Center for Systems and Information Services & CApIC-ACE Covenant University Ota Nigeria Department of Information Technology and KZN e-Skills Co-Laboratory Durban University of Technology Durban South Africa Covenant University Bioinformatics Research (CUBRe) Covenant University Ota Ogun State Nigeria African Center of Excellence in Bioinformatics & Data Intensive Science Makerere University Kampala Uganda

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

In the evolving landscape of cloud computing, federated cloud infrastructures present unique challenges and opportunities for resource and application monitoring. Monitoring the diverse ar.a. of cloud resources and applications within a federated cloud environment is crucial for delivering consistent Quality of Service (QoS) and continuous oversight across various cloud applications and infrastructure. Despite the various successes documented in the literature on multi-cloud monitoring, existing frameworks have not fully explored or provided enough insights into centralized monitoring of resources, applications, and network devices in a federated cloud environment. This paper introduces FedMonitor, an implementation architecture designed to enhance the monitoring and notification capabilities in federated cloud environments by building on the theoretical ideals in our earlier work (i.e. FEDArGOS-V1). FedMonitor leverages Prometheus and Grafana to crawl, manage and visualize metrics from diverse software services, hardware resources, and network devices within a federated cloud environment. The results demonstrate hat FedMonitor improves the effectiveness of resource and application monitoring in federated cloud environments.

关键词： Measurement Cloud computing Visualization computer architecture Quality of service Production Software r.a.-time systems Stakeholders Monitoring

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Hybrid Algorithm for Optimizing Parameter in the Double Diode Model

Hybrid Algorithm for Optimizing Parameter in the Double Diod...

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Inter.a.ional Conference on Applied Electromagnetics, Signal Processing and Communication (AESPC)

作者： Jyoti Gupta Manish Kumar Singla Mohit B. Vikash Anand Chetna Sinha Department of Computer Science Engineering K.R. Mangalam University Gurugram Haryana India Department of Interdisciplinary Courses in Engineering Chitkara University Institute of Engineering & Technology Chitkara University Punjab India Electrical Engineering Department Graphic Era Hill University Dehradun India Electrical Engineering Department Graphic Era (Deemed to be University) Dehradun India School of Electrical Engg. KIIT Deemed to be University Odisha India School of Humanities KIIT Deemed to be University Odisha India

The non-linear current versus voltage (I-V) properties of solar photovoltaic (PV) make modeling challenging. Optimization algorithms are the most effective methods for determining the parameters of nonlinear equations. While numerous optimization strategies are utilized to estimate solar PV parameters, the best optimized results have yet to be obtained. In this publication, a novel suggested hybrid method, Grey Wolf Optimization and Cuckoo Search method (GWOCSA), is cr.a.ed for identifying the solar PV parameter estimate of a double diode (DD) model. The precision and convergence time of the devised framework for the double diode model of solar PV are compared to the results produced by Grey Wolf Optimization (GWO), Cuckoo Search Algorithm (CSA), Particle Swarm Optimization (PSO), Multi Verse Optimization (MVO), and Sine Cosine Algorithm (SCA). The results clearly show that the proposed hybrid algorithm can anticipate accurate optimal values with fewer iterations under.a.variety of environmental situations. A variety of errors were calculated after.a.detailed investigation of the parameter estimate of the double diode solar PV model. In addition, a non-parametric test was performed to evaluate the accuracy of the proposed technique. The results obtained during this method clearly illustrate that the proposed hybrid algorithm outperforms all other.a.gorithms presented in the study. The proposed algorithm is clearly superior in terms of efficiency and accuracy, making it a preferable choice for use in solar power systems.

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Conditional score-based diffusion models for solving inverse problems in mechanics

arXiv

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arXiv 2024年

作者： Dasgupta, Agnimitra ramaswamy, Harisankar Murgoitio-Esandi, Javier Foo, Ken Li, runze Zhou, Qifa Kennedy, Brendan F. Oberai, Assad A. Department of Aerospace & Mechanical Engineering University of Southern California Los AngelesCA90089 United States BRITElab Harry Perkins Institute of Medical Research QEII Medical Centre NedlandsWA6009 Australia Centre for Medical Research The University of Western Australia PerthWA6009 Australia Department of Electrical Electronic & Computer Engineering School of Engineering The University of Western Australia PerthWA6009 Australia Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University in Toruń Grudziadzka 5 Torun87-100 Poland Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Alfred E. Mann Department of Biomedical Engineering University of Southern California Los AngelesCA90089 United States

We propose a framework to perform Bayesian inference using conditional score-based diffusion models to solve a class of inverse problems in mechanics involving the inference of a specimen’s spatially varying mater.a. properties from noisy measurements of its mechanical response to loading. Conditional score-based diffusion models are generative models that learn to approximate the score function of a conditional distribution using samples from the joint distribution. More specifically, the score functions corresponding to multiple r.a.izations of the measurement are approximated using a single neural network, the so-called score network, which is subsequently used to sample the posterior distribution using an appropr.a.e Markov chain Monte Carlo scheme based on Langevin dynamics. Training the score network only requires simulating the for.a.d model. Hence, the proposed appr.a.h can accommodate black-box for.a.d models and complex measurement noise. Moreover, once the score network has been trained, it can be re-used to solve the inverse problem for different r.a.izations of the measurements. We demonstrate the efficacy of the proposed appr.a.h on a suite of high-dimensional inverse problems in mechanics that involve inferring heterogeneous mater.a. properties from noisy measurements. Some examples we consider involve synthetic data, while others include data collected from actual elastography experiments. Further, our.a.plications demonstrate that the proposed appr.a.h can handle different measurement modalities, complex patterns in the inferred quantities, non-Gaussian and non-additive noise models, and nonlinear black-box for.a.d models. The results show that the proposed framework can solve large-scale physics-based inverse problems efficiently. © 2024, CC BY.

关键词： Markov chains

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