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D.J. Warne, R. Hayward, N. Kelson, J. Banks, L. Mejias
Safety concerns in the operation of autonomous aerial systems require safe-landing protocols be followed during situations where the mission should be aborted due to mechanical or other failure. This article presents a pulse-coupled neural network (PCNN) to assist in the vegetation classification in a vision-based landing site detection system for an unmanned aircraft. We propose […]
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Ryan J. Meuth, Donald C. Wunsch II
Modern graphics processing units (GPU) are used for much more than simply 3D graphics applications. From machine vision to finite element analysis, CPU’s are being used in diverse applications, collectively called general purpose graphics processor utilization. This paper explores the capabilities and limitations of modern GPU’s and surveys the neural computation technologies that have been […]
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Jayram Moorkanikara Nageswaran, Micah Richert, Nikil Dutt, Jeffrey L Krichmar
Biological neural systems are well known for their robust and power-efficient operation in highly noisy environments. Biological circuits are made up of low-precision, unreliable and massively parallel neural elements with highly reconfigurable and plastic connections. Two of the most interesting properties of the neural systems are its self-organizing capabilities and its template architecture. Recent research […]
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Yi Xiao, Chi Leung, Tze-Yui Ho, Ping-Man Lam
Vector quantization (VQ) is an effective technique applicable in a wide range of areas, such as image compression and pattern recognition. The most time-consuming procedure of VQ is codebook training, and two of the frequently used training algorithms are LBG and self-organizing map (SOM). Nowadays, desktop computers are usually equipped with programmable graphics processing units […]

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