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http://dx.doi.org/10.22937/IJCSNS.2022.22.3.38

Using Hierarchical Performance Modeling to Determine Bottleneck in Pattern Recognition in a Radar System  

Alsheikhy, Ahmed (Electrical Engineering Department, College of Engineering, Northern Border University)
Almutiry, Muhannad (Electrical Engineering Department, College of Engineering, Northern Border University)
Publication Information
International Journal of Computer Science & Network Security / v.22, no.3, 2022 , pp. 292-302 More about this Journal
Abstract
The radar tomographic imaging is based on the Radar Cross-Section "RCS" of the materials of a shape under examination and investigation. The RCS varies as the conductivity and permittivity of a target, where the target has a different material profile than other background objects in a scene. In this research paper, we use Hierarchical Performance Modeling "HPM" and a framework developed earlier to determine/spot bottleneck(s) for pattern recognition of materials using a combination of the Single Layer Perceptron (SLP) technique and tomographic images in radar systems. HPM provides mathematical equations which create Objective Functions "OFs" to find an average performance metric such as throughput or response time. Herein, response time is used as the performance metric and during the estimation of it, bottlenecks are found with the help of OFs. The obtained results indicate that processing images consumes around 90% of the execution time.
Keywords
hierarchical performance modeling; bottlenecks; tomographic images; radar systems; single layer perceptron;
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