Improving motion vector prediction using linear regression

The motion vectors take a large portion of the H.264/AVC encoded bitstream. This video coding standard employs predictive coding to minimize the amount of motion vector information to be transmitted. However, the motion vectors still accounts for around 40% of the transmitted bitstream, which suggests further research in this area. This paper presents an algorithm which employs a feature selection process to select the neighboring motion vectors which are most suitable to predict the motion vectors mv being encoded. The selected motion vectors are then used to approximate mv using Linear Regression. Simulation results have indicated a reduction in Mean Squared Error (MSE) of around 22% which results in reducing the residual error of the predictive coded motion vectors. This suggests that higher compression efficiencies can be achieved using the proposed Linear Regression based motion vector predictor.peer-reviewe

Driver fatigue monitoring system using support vector machines

Driver fatigue is one of the leading causes of traffic accidents. This paper presents a real-time non-intrusive fatigue monitoring system which exploits the driver's facial expression to detect and alert fatigued drivers. The presented approach adopts the Viola-Jones classifier to detect the driver's facial features. The correlation coefficient template matching method is then applied to derive the state of each feature on a frame by frame basis. A Support Vector Machine (SVM) is finally integrated within the system to classify the facial appearance as either fatigued or otherwise. Using this simple and cheap implementation, the overall system achieved an accuracy of 95.2%, outperforming other developed systems employing expensive hardware to reach the same objective.peer-reviewe

A game theoretical approach for coded cooperation in cognitive radio networks

In this paper, the authors focus on a game theoretical approach for cooperation in cognitive radio (CR) networks. In particular, an integrated design framework which relies on the effective SNR methodology is proposed. The authors derive a distributed power allocation policy aimed at maximizing the reliability of a cooperative BIC OFDM link wherein pragmatic modulation and coding schemes are considered. More in detail, the cognitive devices adapt their power to enable efficient cooperation and coexistence between cognitive nodes and primary networks. First of all, the gain due to the cooperation protocol is analytically derived, resorting to a simple first order recursive equation that depends on the current channel conditions. Then, after an accurate formalization of the optimization problem, a distributed iterative solution based on a novel algorithm, named Successive Set Reduction, is proposed. In particular, the authors show that : i) the proposed power allocation policy takes into account the cooperative gain through a simple scalar value, named cooperative effective SNR; ii) it is effective in improving the packet error rate performance with respect to other conventional power allocation strategies, thus allowing a better coverage for the secondary network; iii) the convergence of the distributed algorithm has exponential speed and requires only local signaling between secondary users. © 2012 IEEE