Browsing by Subject "Mathematical optimization -- Computer programs."
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Item Image compression and recovery using compressive sampling and particle swarm optimization.(2009-08-25T16:32:33Z) Van Ruitenbeek, Benjamin D.; Sturgill, David Brian.; Computer Science.; Baylor University. Dept. of Computer Science.We present a novel method for sparse signal recovery using Particle Swarm Optimization and demonstrate an application in image compression. Images are compressed with compressive sampling, and then reconstructed with particle swarm techniques. Several enhancements to the basic particle swarm algorithm are shown to improve signal recovery accuracy. We also present techniques specifically for reconstructing sparse image data and evaluate their performance.Item The influence of load on kinematics of computer-simulated sagittal-plane lifting.(2006-05-11T17:03:31Z) Newman, Patrick S.; Garner, Brian Alan, 1966-; Mechanical Engineering.; Baylor University. Dept. of Mechanical Engineering.Researchers have shown that lifting kinematics change predictably with increased load. To test whether these kinematics patterns are intrinsic or voluntary, a computer model was developed to simulate lifting in the sagittal plane. The eight-degree-of-freedom model included the ankle, knee, hip, shoulder, elbow, neck, and two back joints. Strength limits were assigned to model joints according to position-dependent average male data obtained from the literature. Using both forward and inverse dynamics approaches, the model was programmed to lift various loads while tracking lift kinematics measured from a human subject. Simulation results suggest that, contrary to common hypotheses, observed lifting patterns are not dictated by physical law (intrinsic) but are chosen for efficiency and stability (voluntary). In this study, a method for isolating kinematic dependencies is introduced. It is anticipated that the results will help in the understanding of motion perception, lifting technique, and low-back pain.