HIGH CAPACITY AND OPTIMIZED IMAGE STEGANOGRAPHY TECHNIQUE BASED ON ANT COLONY OPTIMIZATION ALGORITHM

The tremendous development of digital technology, it is mandatory to address the security while transmitting information over network in a way that observer couldn’t depict it. Measures to be taken to provide the security by establishing hidden communication using steganography principle which is help to camouflage the secret information in some carrier file such as text, image, audio and video. In this era of hidden data communication, image becoming an effective tool on account of their frequency, capability and accuracy. Image steganography uses an image as a carrier medium to hide the secret data. The main motive of this article is that the uses the combination of frequency domain and optimization method inorder to increasing in robustness. In this article, Integer Wavelet transform is performed into the host image and coefficients have been transformed. ACO optimization algorithm is used to find the optimal coefficients where to hide the data. Furthermore, sample images and information having been demonstrated which proved the increased robustness as well as high level of data embedding capacity

Sistema de control cinemático guiado y colaborativo por percepción de las trayectorias de las extremidades superiores

This project studies the morphology of the human arm in order to build a robot capable of imitating the movements of the same, learning them and repeating them under a kinematic control routine, for this it was necessary to learn trajectories, which are obtained using artificial vision making use of the Kinect; from which the spatial coordinates of each joint are extracted, and subsequently processed by means of a mathematical model to obtain joint positions, calculate the kinematic model of the robot, and develop a routine for kinematic control that establishes the relationship between the speeds of the joints . The system allows the user to start learning their movements, and then simulate that learning on the virtual robot. As well as activating the physical robot to perform the learned movements. When comparing the results, it was determined that the standard deviation of the trajectories with and without control does not change to a greater extent; but the points that are within the deviation in the control part are more proportional, this because the stability of the trajectories improves when applying kinematic control.Este proyecto estudia la morfología del brazo humano con la finalidad de construir un robot capaz de imitar los movimientos del mismo, aprenderlos y repetirlos bajo una rutina de control cinemático, para esto fue necesario realizar un aprendizaje de trayectorias, las cuales se obtienen mediante visión artificial haciendo uso del Kinect; del cual se extraen las coordenadas espaciales de cada articulación, y posteriormente se procesan mediante un modelo matemático para obtener las posiciones articulares, calcular el modelo cinemático del robot, y desarrollar una rutina para el control cinemático que establece la relación entre las velocidades de las articulaciones. El sistema le permite al usuario poder iniciar un aprendizaje de sus movimientos, y posteriormente simular dicho aprendizaje en el robot virtual. Así como también activar el robot físico para que realice los movimientos aprendidos. Al comparar los resultados se determinó que la desviación estándar de las trayectorias con y sin control, no cambia en mayor medida; pero los puntos que se encuentran dentro de la desviación en la parte de control son más proporcionales, esto debido a que la estabilidad de las trayectorias mejora al aplicarle el control cinemático