Search Results - parallel estimation ((((window algorithm) OR (learning algorithm))) OR (bees algorithm))
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Resource Minimization in a Real-time Depth-map Processing System on FPGA
Published 2011“…Depth-map algorithm allows camera system to estimate depth. It is a computational intensive algorithm, but can be implemented with high speed on hardware due to the parallelism property. …”
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2
New CFAR algorithm and circuit development for radar receiver
Published 2020“…To detect targets in such environments, it needs a robust CFAR algorithm that excises the target spikes and clutter edges from the CFAR window to give the best possible estimation of the noise background. …”
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Thesis -
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MINIMIZATION OF RESOURCE UTILIZATION FOR A REAL-TIME DEPTH-MAP COMPUTATIONAL MODULE ON FPGA
Published 2011“…Depth-map algorithm allows camera system to estimate depth in many applications. …”
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4
Super resolution imaging using modified lanr based on separable filtering
Published 2019“…The underlying idea is to process and reconstruct information in low and high frequency sub-bands based on separable property of neighbourhood filtering to achieve fast parallel and vectorized operation, while enhancing algorithmic performance by reducing computational burden resulting from computing the weighted function of every pixel for each pixel in an image. …”
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5
Cryptanalysis on the modulus N=p2q and design of rabin-like cryptosystem without decryption failure
Published 2015“…Moreover, for the purpose of empirical evidences, some parameters are chosen in the course of the process to validate the efficiency in terms of algorithmic running time and memory consumptions. We then conduct a comparative analysis toward estimating the running time during the encryption and decryption process. …”
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6
Short-term Gini coefficient estimation using nonlinear autoregressive multilayer perceptron model
Published 2024“…System Identification (SI), a methodology utilized in domains like engineering and mathematical modeling to construct or refine dynamic system models from captured data, relies significantly on the Nonlinear Auto-Regressive (NAR) model due to its reliability and capability of integrating nonlinear functions, complemented by contemporary machine learning strategies and computational algorithms to approximate complex system dynamics to address these limitations. …”
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