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This study deals with the problem of optimizing transmit power in high density heterogeneous networks. In the communication network, effective methods of allocating transmit power, in order to reduce the total transmit power, but still ensure the quality of service of the user equipment, is a big challenge. number of power consumption optimization problems in core station links, with the goal of maximizing network energy efficiency while ensuring user experience. To solve this non-convex optimization problem, the authors first propose some iterative algorithms to find the convergence point such as the "descent" method, the "Lagrange" method. Then, the authors evaluate the convergence point of each method as well as consider the complexity of each algorithm when put into application. Finally, the simulation results will show the convergence value and compare the performance with the technologies being used today to confirm the effectiveness of the proposed algorithm., 6 pages, 5 figures

Gesture recognition is one of the key aspects of robot communication systems. There are many image recognition techniques that are being developed to use in many different intelligent systems. In the paper, we perform the image processing techniques that include artificial intelligence technologies and deep learning in gesture recognition to apply for smart-home systems. We propose the gesture recognition model including the histogram of oriented gradient (HOG) and support vector machine (SVM) detection algorithms combining the kernel correlation filter (KCF) algorithm for tracking objects and a multi-layer convolution neural network (CNN) for classifications. The results show that the proposal algorithm is applicable for real environments with accuracy up to 99% per 6 seconds.