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Much progress has recently been made in global optimization, with particular attention devoted to robust nature-inspired stochastic methods for difficult, high-dimensional problems. This paper presents a computational study of an adaptation of one such method, particle swarm optimization (PSO), which is analyzed for parallelization on readily-available heterogeneous parallel computational hardware:...
Electrocardiogram (ECG) signals obtained during driving simulations provide an indication of physiological response during complex driving tasks. The present study contributes to the literature on this response through the analysis of features in time-frequency representations, specifically, continuous wavelet transforms (CWTs). The main frequency band in the CWT of ECG signals, which corresponds...
Accelerated acquisition of long-term and longitudinal signals has motivated new approaches for processing and analysis. Topological data analysis (TDA) has recently been proposed for analyzing these large and complex data. TDA provides significant insight through coarse, global structures, while maintaining the local structure of measurements. TDA visualizations enhance its interpretive power. However,...
Wavelet entropy is an analysis tool applicable to biomedical signals, including EEG and ECG. The current paper proposes an application of methods from visual analytics to time-frequency energy distribution in ECG signals, with the goal of studying heart rate variability from the main ECG band corresponding to heart rate. Such an approach can complement standard time- and frequency-domain techniques...
Global optimization of dynamic cost functions is important in many engineering applications. For these tasks, global optima change over time, or are greatly affected by dynamic noise. Nature-based stochastic methods, including genetic algorithms, particle swarm optimization (PSO), and differential evolution, have been particularly effective in dynamic optimization. However, these methods are generally...
Particle swarm optimization (PSO) is a robust and popular stochastic population-based global optimization method that simulates social behavior among independent agents (particles). PSO is increasingly used to solve difficult high-dimensional and dynamic problems, where the global optima change over time. To better address the challenges inherent in these problems, interactive visualization is employed...
Particle swarm optimization (PSO) is a robust and popular stochastic population-based global optimization method that simulates social behavior among independent agents (particles). PSO is increasingly used to solve difficult high-dimensional and dynamic problems, where the global optima change over time. To better address the challenges inherent in these problems, interactive visualization is employed...
A visual traffic simulation framework has been developed for analyzing gas consumption and traffic flow, and for optimizing traffic lights. The city of North Bay, Canada, is targeted in this study, but the basic framework can be used and the same procedure followed to analyze traffic flow and gas usage in other cities with similar layouts. The simulation is in the form of an agent-based serious game...
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