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Biped robot has become more general purpose in our live as general behavioral patterns. It can walk very similar to human walking pattern. Therefore, many robots have been researched and developed in recent years. Through this paper a planner biped robot is modeled for specific task. This paper tries to explore the potential of using Simulated Annealing (SA) methodologies in the Inverse Kinematic Problem (IKP),utility and effectiveness of this method for the solveIKP of biped robot is presented. It presents a new objective function to find the optimal posture of biped robot by employing some constrain in the objective function to meet best posture. A comparison between the proposed method and the classical method using Genetic Algorithm (GA) are made through the Matlab 2009a software to show the efficiency of the new method. Experimental results demonstrate that better performance can be achieved with this method.

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This work simulate an artificial human arm. It aims to allow this arm to implement the actual movement of the human arm in virtual reality by solving the inverse kinematic of human arm using a hybrid algorithm. Inverse kinematics problem is not linear; this means that the analytical solutions are available only in limited cases. This paper presents a strategy based on analytical solutions along with non-linear optimization algorithm solutions to solve the Inverse Kinematic Problem (IKP). The analytical solution is used to reduce the size of the problem of variables from 7 angles to one variable. Nonlinear optimization is used to find the approximate solution that makes computation time is very small. Virtual reality in the MATLAB environment provides communications and controls that have been developed on the basis of Virtual Reality Modeling Language (VRML).

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Face recognition is important in human identification. The biological recognition technique acts as a good method and broad applications in security areas. This work presents a method to improve the face recognition accuracy using a combination of Principal Component Analysis (PCA), and Wavelet Transform. Wavelet Transform is used to decompose the input image with different levels and rearrangement of subband of wavelet in a way that extract a good information from the image; PCA is used as data redundancy and take the better representation of input data. We apply the proposed method on standard face recognition dataset, the ORL data and dataset from our environment to make the proposed method be practical. The comparison for different levels of wavelet show that the third level has better recognition accuracy with respect to other levels .Finally the performance of the proposed method is compared with other methods and gives better recognition accuracy.

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The goal of face detection is to locate all regions that contain aface. This paper has a simple face detection procedure, first to segmentskin region from an image, and second, to decide these regions containhuman face or not. Our procedure is based on hybrid skin colorsegmentation using three color spaces RGB, YCbCr and HIS and humanface features using entropy.For the purpose of extracting feature, rather than looking at thewhole image of the face, and put the entropy based on the selection ofskin region, which selects high informative segments of the facial image,compared with entropy of ORL image using the Euclidean distance .Alsothe golden ratio and the size of skin region decide where this region isface or no through the fuzzy system. Fuzzy logic got great acceptance ofthe various fields therefore it used in this paper to cover the difference inthe parameter of face. The method provides a suitable method forextracting information. The proposed method has been tested on variousreal images and its performance is found to be quite satisfactory withdetection accuracy 94.74 %.

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This work presents a simulation methodology applied to a human arm. It isaimed to allow the human-assisting manipulators to perform complex movementbased on electromyography (EMG) signal for patient person in Virtual Reality(VR). This work achieves better classification with multiple parameters based KNearestNeighbor for different movements of a prosthetic arm. A K- NearestNeighbor (K-NN) rule is one of the simplest and the most important methods inpattern recognition. The method implements in the 3D space and uses theMATLAB Ver.2009a approach. This methodology can be used with differentrobots to test the behavior of system and the different motion

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