The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning. We use AlexNet architecture with convolutional neural networks for this purpose. Four test images are selected from the ImageNet database for the classification purpose. We cropped the images for various portion areas and conducted experiments. The results show the effectiveness of deep learning based image classification using AlexNet.

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Economic load dispatch is the process of allocating the required load demand between the available generators in power system while satisfying all units and system equality and inequality constraints. Economic Load Dispatch solutions are found by solving the conventional methods such as lambda iteration, Gradient search method, Linear Programming and Dynamic Programming while at the same minimizing fuel costs, but convergence is too slow, so in order to get fast convergence and accurate results we are using artificial neural network. Artificial neural network is well-known in the area of power systems. It is a very powerful solution algorithm because of its rapid convergence near the solution. This property is especially useful for power system applications because an initial guess near the solution is easily attained. In this paper a three generator system is considered and by using lambda iteration method Economic Load Dispatch is determined and 150 patterns for different loads will be derived from same method to train neural network. As it is too slow method, we proposed a soft computing based approach i.e. Back Propagation Neural Network (BPNN) for determining the optimal flow. This method provides fast and accurate results when compared with the conventional method.

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Acquisition is a most important process and a challenge task for identifying visible satellites, coarse values of carrier frequency, and code phase of the satellite signals in designing software defined Global positioning system (GPS) receiver. This paper presents a new, simple, efficient and faster GPS acquisition via sub-sampled fast Fourier transform (ssFFT). The proposed algorithm exploits the recently developed sparse FFT (or sparse IFFT) that computes in sub-linear time. Further it uses the property of fourier transforms (FT): Aliasing a signal in the time domain corresponds to sub-sampling it in the frequency domain, and vice versa. The ssFFT is an FFT algorithm that computes sub-sampled version of the data by an integer factor 'd', and hence, the computational complexity is proportionately reduced by a factor of 'd log d' compared to conventional FFT-based algorithms for any length of the input GPS signal. The simulation results show that the proposed ssFFT based GPS acquisition computation is 8.5571 times faster than the conventional FFT-based acquisition computation time. The implementation of this method in an FPGA provides very fast processing of incoming GPS samples that satisfies real-time positioning requirements.

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