Demand of energy increases in the global and exponential exhaustion is favored of resources by fossil fuel for electricity production with the new systems development. Compared with all other remainder energies, the specialist sun energy is the most bountiful energy and it's typically easy to be changed into electrical energy. The main thing of using solar panel is to produce electrical energy from sun's energy but the optimum energy can be generated by tracking solar panel due to the sun movement from east to west. The problem can be solved by proposed systems where the sun tracking by solar panel that based on high intensity of sun ray. This paper concentrates on tracking the sun by using servo motor coupled with solar panel. So that, the largest quantity of sun light at the incident panel along the day at any time is better than that for method of fixed panel array which is less efficient. The microcontroller Arduino (mode UNO) was programmed by using C++ language while the track of sun light processing was implemented by using light depending resistor (LDR), Chip IC H-bridge and microcontroller Arduino (UNO) circuits have been designed by using Proteus software. By circuit design and sun tracking control process, the cost reduction has been improved and high amount of energy was saved when implemented this system.

The technology of fusion image is dominance strongly over domain research for recent years, the techniques of fusion have various applications in real time used and proposed such as purpose of military and remote sensing etc., the fusion image is very efficient in processing of digital image. Single image produced from two images or more information of relevant combining process results from multi sensor fusion image. FPGA is the best implementation types of most technology enabling wide spread.This device works with modern versions for different critical characteristics same huge number of elements logic in order to permit complex algorithm implemented. In this paper,filters are designed and implemented in FPGA utilized for disease specified detection from images CT/MRI scanned where the samples are taken for human's brain with various medical images and the processing of fusion employed by using technique Stationary Wavelet Transform and Principal Component Analysis (SWT-PCA). Accuracy image output increases when implemented this technique and that was done by sampling down eliminating where effects blurring and artifacts doesn't influenced. The algorithm of SWT-PCA parameters quality measurements like NCC, MSE, PSNR, coefficients and Eigen values.The advantages significant of this system that provide real time, time rapid to market and portability beside the change parametric continuing in the DWT transform. The designed and simulation of module proposed system has been done by using MATLAB simulink and blocks generator system, Xilinx synthesized with synthesis tool (XST) and implemented in XilinxSpartan 6-SP605 device.

<p><span>Code words traditional can be decoding when applied in artificial neural network. Nevertheless, explored rarely for encoding of artificial neural network so that it proposed encoder for artificial neural network forward with major structure built by Self Organizing Feature Map (SOFM). According to number of bits codeword and bits source mentioned the dimension of forward neural network at first then sets weight of distribution proposal choosing after that algorithm appropriate using for sets weight initializing and finally sets code word uniqueness check so that matching with existing. The spiking neural network (SNN) using as decoder of neural network for processing of decoding where depending on numbers of bits codeword and bits source dimension the spiking neural network structure built at first then generated sets codeword by network neural forward using for train spiking neural network after that when whole error reached minimum the process training stop and at last sets code word decode accepted. In tests simulation appear that feasible decoding and encoding neural network while performance better for structure network neural forward a proper condition is achieved with γ node output degree. The methods of mathematical traditional can not using for decoding generated Sets codeword by encoder network of neural so it is prospect good for communication security. </span></p>

This paper represents a new features selection method to improve an existed feature type. Topographical (TGH) features provide large set of features by assigning each image pixel to the related feature depending on image gradient and Hessian matrix. Such type of features was handled by a proposed features selection method. A face recognition feature selector (FRFS) method is presented to inspect TGH features. FRFS depends in its main concept on linear discriminant analysis (LDA) technique, which is used in evaluating features efficiency. FRFS studies feature behavior over a dataset of images to determine the level of its performance. At the end, each feature is assigned to its related level of performance with different levels of performance over the whole image. Depending on a chosen threshold, the highest set of features is selected to be classified by SVM classifier

Forward neural network (FNN) execution relying on the algorithm of training and architecture selection. Different parameters using for nip out the architecture of FNN such as the connections number among strata, neurons hidden number in each strata hidden and hidden strata number. Feature architectural combinations exponential could be uncontrollable manually so specific architecture can be design automatically by using special algorithm which build system with ability generalization better. Determination of architecture FNN can be done by using the algorithm of optimization numerous. In this paper methodology new proposes achievement where FNN neurons respective with hidden layers estimation work where in this work collect algorithm training self organizing feature map (SOFM) with advantages to explain how the best architectural selected automatically by SOFM from criteria error testing based on architecture populated. Different size of dataset benchmark of 4 classifications tested for approach proposed.