In this paper the authors propose a simplified digital hardware implementation of a neuron with nonlinear function using piecewise linear approximation technique. The sigmoidal function is selected as an activation function in this design .The proposed digital neuron could be used as a basic building block to make a general architecture of a neural network. The paper has incorporated the description of the main building block needed and problems faced during construction of neuron architecture. It also includes the preparation of inputs in a specified format required for piecewise linear approximation technique. Design methodology of activation function is discussed in this paper, which shows also the simulation results as well as FPGA implementation of the design.Index Terms-Artificial neural network, sigmoid function, Piecewise linear approximation, digital hardware implementation.
Abstract-Image edge detection is a process where true edges of an image are identified. In past, gradient based methods in which first or second order pixel difference is used to find discontinuities and if magnitude value of gradient is higher than certain threshold then that pixel under observation is identified as edge pixel. These methods are full of error, because in addition to true edges they also find false edges and infect false edges are more in comparison to true edges. To solve such problem, swarm intelligence based ant colony optimization based edge detection method is detailed where numbers of falsely detected edges are very small. The performance of the ant colony optimization (ACO) is done in terms of Peak Signal to Noise Ratio, Performance Ratio and Efficiency.
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