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This work proposes a SoC/FPGA based design and implementation of an architecture for embedded applications, presenting a hardware converted algorithm for an OPF classifier.
There are different ways to convert algorithms from floating-point to fixed-point format.
This work proposes two evolutionary approaches to accelerate the process of converting algorithms from floating to fixed-point format.
In this section, we convert algorithms (1.5) and (1.6) by releasing projection P C and construct two algorithms for finding the minimum norm element x ∗ of Γ : = EPA ∩ Fix ( S ).
Converting algorithms from generic Health-related Quality of Life (HRQOL) measures to preference-based measures is an increasingly common solution when health utility values are unavailable for cost-utility analysis.
This step converts the algorithm by replacing each arithmetic and assignment operation with a quantized computation as shown in code fragment 3.6.
Thanks to these communication functions, the designer can easily convert sequential algorithm into a parallel C code.
We design a general framework which allows to convert approximation algorithms for standard node coloring into algorithms for max coloring.
This high fidelity synthesis converts the CWAS algorithm in a very useful tool for source separation.
Firstly, the proposed algorithm converts fuzzy multiobjective optimization problem to a sequence of a crisp nonlinear programming problems.
The SCA algorithm converts a non-convex optimization problem into a convex one by an iterative convex approximation technique.
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