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A deformable model-based visual system for mobile robot topologic navigation

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A deformable model-based visual system for mobile robot topo...

Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453)

作者： M. Mata J.M. Armingol A. de la Escalera F.J. Rodriguez Computer Architecture and Automation Dpt. Universidad Europea de Madrid Madrid Spain Systems Engineering and Automation Dpt. Universidad Carlos III de Madrid Madrid Spain

Deformable models have been intensively studied in image analysis through the last decade, and are used for detection and recognition of flexible or rigid templates under diverse viewing conditions. Genetic algorithm (GA) based deformable models are used for generic visual landmark detection and interpretation. The developed system allows topologic localization and navigation using natural and artificial landmarks, exploiting deformable models' ability for handling landmark perspective variations. The resulting perception module has been integrated successfully in a complex navigation system. Various experimental results in real environments are presented on this paper, showing the effectiveness and capacity of the landmark detection and reading system.

关键词： Deformable models Visual system Mobile robots Navigation Robotics and automation Image recognition Object detection Object recognition Uncertainty Image motion analysis

SFIDE: A simulation infrastructure for data centers 49

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SFIDE: A simulation infrastructure for data centers

49th Summer computer Simulation Conference, SCSC 2017, Part of the 2017 Summer Simulation Multi-Conference, SummerSim 2017

作者： Penas, Ignacio Zapater, Marina Risco-Martín, José L. Ayala, José L. Green Integrated Systems Laboratory Technical University of Madrid Av. Complutense 30 Madrid28040 Spain Dpt. of Computer Architecture and Automation Complutense University of Madrid C/Prof. José García Santesmases 9 Madrid28040 Spain

ISBN: (纸本)9781510838284

Data centers are huge power consumers and have very high operational costs. Both industry and academia have proposed strategies at multiple levels (server, room layout, cooling, workload allocation, etc.) to increase the efficiency of these facilities. Testing the impact of variables so different in nature such as layout or workload allocation can only be managed by simulators. Current simulation infrastructures are either focused on data room thermal dynamics, or target only a specific stage of data center operations, such as workload allocation. Moreover, they are geared for specific use-cases such as HPC or cloud computing. In this paper we present a data center modeling and simulation framework, for both HPC and cloud applications, to assess data center performance, thermal behavior, energy efficiency and operational cost. Our goal is to show the possibilities of the current data center modeling and simulation framework. Furthermore, as we provide a fully configurable, flexible and scalable infrastructure any kind of policy, data center size or workload amount could easily be implemented over the simulator. We also provide the data sets used to validate our models and policies, obtained from real servers and data centers, so as to enable researchers to test their strategies in a realistic setup. © 2017 Society for Modeling & Simulation International (SCS).

关键词： Smart power grids

Predictive and diagnosis models of stroke from hemodynamic signal monitoring

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arXiv 2023年

作者： García-Terriza, Luis Risco-Martín, José L. Roselló, Gemma Reig Ayala, José L. Dpt. of Computer Architecture and Automation Complutense University of Madrid Spain Stroke Care Unit Hospital Universitario de La Princesa Spain

This work presents a novel and promising approach to the clinical management of acute stroke. Using machine learning techniques, our research has succeeded in developing accurate diagnosis and prediction real-time models from hemodynamic data. These models are able to diagnose stroke subtype with 30 minutes of monitoring, to predict the exitus during the first 3 hours of monitoring, and to predict the stroke recurrence in just 15 minutes of monitoring. Patients with difficult access to a CT scan, and all patients that arrive at the stroke unit of a specialized hospital will benefit from these positive results. The results obtained from the real-time developed models are the following: stroke diagnosis around 98% precision (97.8% Sensitivity, 99.5% Specificity), exitus prediction with 99.8% precision (99.8% Sens., 99.9% Spec.) and 98% precision predicting stroke recurrence (98% Sens., 99% Spec.). © 2023, CC BY-SA.

关键词： Forecasting

Multi-faceted modeling in the analysis and optimization of iot complex systems

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Multi-faceted modeling in the analysis and optimization of i...

2020 Summer computer Simulation Conference, SCSC 2020, Held at the 2020 Summer Simulation Multi-Conference, SummerSim 2020

作者： Cárdenas, Román Arroba, Patricia Moya, José M. Risco-Martín, José L. CCS-Center for Computational Simulation Universidad Politécnica de Madrid ETSI Telecomunicación Avenida Complutense 30 Madrid28040 Spain Dpt. of Computer Architecture and Automation CCS-Center for Computational Simulation Universidad Complutense de Madrid C/ Prof. José García Santesmases 9 Madrid28040 Spain

ISBN: (纸本)9781713819462

Modeling, Simulation, and Optimization tools are used in the development process of complex systems for improving their overall performance. However, enforcing full coverage in the validation process of these models may add unbearable simulation time overheads. In this research, we propose the implementation of multi-faceted sets of models with different degrees of complexity for optimizing IoT complex systems. With this approach, elements of the system are optimized using simpler and faster models that reduce simulation times, enabling the exploration of more scenarios in less time. Optimized components can be then validated extensively using more complex models, ensuring full coverage, and reducing potential failures. ©2020 Society for Modeling & Simulation International (SCS)

关键词： System of systems

Decision Support Framework for Automating the Optimization of Edge Computing Federations

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Decision Support Framework for Automating the Optimization o...

Annual Modeling and Simulation Conference (ANNSIM)

作者： Antonio F. Rodríguez-Liria Román Cárdenas Patricia Arroba José M. Moya José L. Risco-Martín Gabriel Wainer Laboratorio de Sistemas Integrados (LSI) CCS–Center for Computational Simulation Universidad Politécnica de Madrid Madrid Spain Dpt. of Computer Architecture and Automation Universidad Complutense de Madrid Madrid Spain Dpt. of Systems and Computer Engineering Carleton University Ottawa ON Canada

Modern IoT applications present demanding Quality of Service (QoS) requirements that can no longer rely on a centralized solution to offload compute-intensive tasks. Edge Computing tackles these limitations by extending the Cloud to the edge of the network. However, the distributed and complex nature of Edge infrastructures introduces new technical challenges that must be addressed to develop a successful solution. Modeling, Simulation, and Optimization (M&S&O) tools can aid in the automatic design of modern Edge deployments. Here we present a decision support module that allows practitioners to automatically improve aspects of their Edge infrastructure by applying different optimization algorithms, such as simulated annealing, tabu search, and stochastic hill climbing. Furthermore, the structure of this module allows us to extend the available optimization methods if needed. We illustrate how this tool can help us to improve energy consumption policies of Edge Computing facilities and reduce their operational costs.

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Multi-objective Optimization of Energy Consumption and Execution Time in a Single Level Cache Memory for Embedded Systems

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arXiv 2023年

作者： Álvarez, Josefa Díaz Risco-Martín, José L. Manuel Colmenar, J. Centro Universitario de Mérida Universidad de Extremadura Mérida06800 Spain Dpt. of Computer Architecture and Automation Complutense University of Madrid C/Prof. José García Santesmases 9 Madrid28040 Spain Dpt. of Computer Science Rey Juan Carlos University Móstoles28933 Spain

Current embedded systems are specifically designed to run multimedia applications. These applications have a big impact on both performance and energy consumption. Both metrics can be optimized selecting the best cache configuration for a target set of applications. Multi-objective optimization may help to minimize both conflicting metrics in an independent manner. In this work, we propose an optimization method that based on Multi-Objective Evolutionary Algorithms, is able to find the best cache configuration for a given set of applications. To evaluate the goodness of candidate solutions, the execution of the optimization algorithm is combined with a static profiling methodology using several well-known simulation tools. Results show that our optimization framework is able to obtain an optimized cache for Mediabench applications. Compared to a baseline cache memory, our design method reaches an average improvement of 64.43% and 91.69% in execution time and energy consumption, respectively. © 2023, CC BY-SA.

关键词： Evolutionary algorithms

Adiabatic training for Variational Quantum Algorithms

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arXiv 2024年

作者： Acosta, Ernesto Gutiérrez, Carlos Cano Botella, Guillermo Campos, Roberto Dpt. Computer Science and AI University of Granada Granada18071 Spain Computer Architecture and Automation Department Complutense University of Madrid Madrid28040 Spain Theoretical Physics Department Complutense University of Madrid Madrid28040 Spain

This paper presents a new hybrid Quantum Machine Learning (QML) model composed of three elements: a classical computer in charge of the data preparation and interpretation;a Gate-based Quantum computer running the Variational Quantum Algorithm (VQA) representing the Quantum Neural Network (QNN);and an adiabatic Quantum computer where the optimization function is executed to find the best parameters for the VQA. As of the moment of this writing, the majority of QNNs are being trained using gradient-based classical optimizers having to deal with the barren-plateau effect [5]. Some gradient-free classical approaches such as Evolutionary Algorithms have also been proposed to overcome this effect [4]. However, to the knowledge of the authors, adiabatic quantum models have not been used to train VQAs. The paper compares the results of gradient-based classical algorithms against adiabatic optimizers showing the feasibility of integration for gate-based and adiabatic quantum computing models, opening the door to modern hybrid QML approaches for High Performance Computing. © 2024, CC BY-NC-SA.

关键词： Quantum computers

A New Family of XDEVS Simulators for Enhanced Performance

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A New Family of XDEVS Simulators for Enhanced Performance

Annual Modeling and Simulation Conference (ANNSIM)

作者： Román Cárdenas Patricia Arroba José L. Risco-Martín Laboratorio de Sistemas Integrados (LSI) CCS–Center for Computational Simulation Universidad Politécnica de Madrid ETSI Telecomunicación Madrid Spain Dpt. of Computer Architecture and Automation Universidad Complutense de Madrid Madrid Spain

Modeling and Simulation (M&S) techniques are extensively used for analyzing complex systems. Among different approaches, the Discrete EVent System specification (DEVS) mathematical formalism presents a robust methodology for defining complex models. The xDEVS framework is a cross-platform tool that provides a uniform Application Programming Interface (API) to simulate DEVS-based models using different programming languages like C++, Java, or Python. However, executing DEVS models on embedded devices equipped with small processors usually requires lower-level programming languages. This paper introduces xDEVS/Rust and xDEVS/C, two new xDEVS interfaces written in Rust and C, respectively. These new implementations bring xDEVS closer to supporting the execution of DEVS models in embedded systems. Also, they feature remarkably high performance, making them a valuable option for simulating complex systems in traditional platforms. We illustrate how competitive these new implementations are by comparing them with some of the fastest state-of-the-art simulators.

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Mercury: a Modeling, Simulation, and Optimization Framework for Data Stream-Oriented IoT Applications

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arXiv 2023年

作者： Cárdenas, Román Arroba, Patricia Blanco, Roberto Malagón, Pedro Risco-Martín, José L. Moya, José M. Laboratorio de Sistemas Integrados Universidad Politécnica de Madrid 28040 Spain Dpt. of Computer Architecture and Automation Universidad Complutense de Madrid Madrid28040 Spain Center for Computational Simulation Universidad Politécnica de Madrid 28660 Spain

The Internet of Things is transforming our society by monitoring users and infrastructures' behavior to enable new services that will improve life quality and resource management. These applications require a vast amount of localized information to be processed in real-time so, the deployment of new fog computing infrastructures that bring computing closer to the data sources is a major concern. In this context, we present Mercury, a Modeling, Simulation, and Optimization (M&S&O) framework to analyze the dimensioning and the dynamic operation of real-time fog computing scenarios. Our research proposes a location-aware solution that supports data stream analytics applications including FaaS-based computation offloading. Mercury implements a detailed structural and behavioral simulation model, providing fine-grained simulation outputs, and is described using the Discrete Event System Specification (DEVS) mathematical formalism, helping to validate the model's implementation. Finally, we present a case study using real traces from a driver assistance scenario, offering a detailed comparison with other state-of-the-art simulators. © 2023, CC BY-SA.

关键词： Fog computing