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

Resilient transmission of H.264/AVC video sequences using probabilistic neural networks

H.264/AVC is expected to become an essential component in the delivery of wireless multimedia content. While achieving high compression ratios, this codec is extremely vulnerable to transmission errors. These errors generally result in spatio-temporal propagation of distorted macroblocks (MBs) which significantly degrade the perceptual quality of the reconstructed video sequences. This paper presents a scheme for resilient transmission of H.264/AVC streams in noisy environments. The proposed algorithm exploits the redundant information which is inherent in the neighboring MBs and applies a Probabilistic Neural Network (PNN) classifier to detect visually impaired MBs. This algorithm achieves Peak Signal-to-Noise Ratio (PSNR) gains of up to 14.29 dB when compared to the standard decoder. Moreover, this significant gain in quality is achieved with minimal overheads and no additional bandwidth requirement, thus making it suitable for conversational and multicast/ broadcast services where feedback-based transport protocols cannot be applied.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

Flux position estimation using current derivatives for the sensorless control of AC machines

This paper considers the general principles of saliency tracking in AC machines. Special attention is given to pulse type injection for the extraction of a rotor or flux position signal. The saliency is tracked by measuring the current derivative resulting from voltage ‘test’ vector application. Results show tracking of ‘saturation’ saliency on a standard induction motor to be used for sensorless control of ac machines in the low and zero speed region.peer-reviewe

Fast multi-view video plus depth coding with hierarchical bi-prediction

This research work is partially funded by STEPS-Malta and partially by the EU–ESF 1.25.The Multi-view Video Coding (MVC) standard was developed for efficient encoding of multi-view videos. Part of it requires the calculation of both disparity and motion estimations using a bi-prediction structure. These estimations involve an exhaustive search for the optimal compensation vectors from multiple forward and backward reference frames which, while being very efficient in terms of compression, results in high computational costs. This paper proposes a solution that utilizes the multi-view geometry along with the available depth data, to calculate more accurate predictors for both motion and disparity estimations, and for both directions of the prediction structure. Simulation results demonstrate that this technique is reliable enough to allow a substantial reduction in the search areas in all the reference frames. This in turn results in a significant speed-up gain of 3.2 times with a negligible influence on the coding efficiency, while encoding both the color and the depth MVVs.peer-reviewe

Line tracking algorithm for scribbled drawings

This paper describes a line tracking algorithm that may be used to extract lines from paper based scribbles. The proposed algorithm improves the performance of existing sparse-pixel line tracking techniques that are used in vectorization by introducing perceptual saliency and Kalman filtering concepts to the line tracking. Furthermore, an adaptive sampling size is used such that it is possible to adjust the size of the tracking step to reflect the stroke curvature.peer-reviewe

Automatization techniques for processing biomedical signals using machine learning methods

The Signal Processing Group (Department of Signal Theory and Communications, University Carlos III, Madrid, Spain) offers the expertise of its members in the automatic processing of biomedical signals. The main advantages in this technology are the decreased cost, the time saved and the increased reliability of the results. Technical cooperation for the research and development with internal and external funding is sought

A Tutorial on Clique Problems in Communications and Signal Processing

Since its first use by Euler on the problem of the seven bridges of K\"onigsberg, graph theory has shown excellent abilities in solving and unveiling the properties of multiple discrete optimization problems. The study of the structure of some integer programs reveals equivalence with graph theory problems making a large body of the literature readily available for solving and characterizing the complexity of these problems. This tutorial presents a framework for utilizing a particular graph theory problem, known as the clique problem, for solving communications and signal processing problems. In particular, the paper aims to illustrate the structural properties of integer programs that can be formulated as clique problems through multiple examples in communications and signal processing. To that end, the first part of the tutorial provides various optimal and heuristic solutions for the maximum clique, maximum weight clique, and $k$-clique problems. The tutorial, further, illustrates the use of the clique formulation through numerous contemporary examples in communications and signal processing, mainly in maximum access for non-orthogonal multiple access networks, throughput maximization using index and instantly decodable network coding, collision-free radio frequency identification networks, and resource allocation in cloud-radio access networks. Finally, the tutorial sheds light on the recent advances of such applications, and provides technical insights on ways of dealing with mixed discrete-continuous optimization problems