In this paper, a new technique for human identification task based on heart sound signals has been proposed. It utilizes a feature level fusion technique based on canonical correlation analysis. For this purpose a robust pre-processing scheme based on the wavelet analysis of the heart sounds is introduced. Then, three feature vectors are extracted depending on the cepstral coefficients of different frequency scale representation of the heart sound namely; the mel, bark, and linear scales. Among the investigated feature extraction methods, experimental results show that the mel-scale is the best with 94.4% correct identification rate. Using a hybrid technique combining MFCC and DWT, a new feature vector is extracted improving the system's performance up to 95.12%. Finally, canonical correlation analysis is applied for feature fusion. This improves the performance of the proposed system up to 99.5%. The experimental results show significant improvements in the performance of the proposed system over methods adopting single feature extraction.

Due to the great advances in biomedical digital signal processing, new biometric traits have showed noticeable improvements in authentication systems. Recently, the ElectroCardioGram (ECG) and the PhonoCardioGraph (PCG) have been proposed as novel biometrics. This paper aims to review the previous studies related to the usage of the ECG and PCG signals in human recognition. In addition, we discuss briefly the most important techniques and methodologies used by researchers in the preprocessing, feature extraction and classification of the ECG and PCG signals. At the end, we introduce some future considerations that can be applied in this topic such as: the fusion between different techniques previously used, use both ECG and PCG signals in a multimodal biometric authentication system and building a prototype system for real-time authentication.

In robotic navigation, path planning is aimed at getting the optimum collision-free path between a starting and target locations. The optimality criterion depends on the surrounding environment and the running conditions. In this paper, we propose a general, robust, and fast path planning framework for robotic navigation using level set methods. A level set speed function is proposed such that the minimum cost path between the starting and target locations in the environment, is the optimum planned path. The speed function is controlled by one parameter, which takes one of three possible values to generate either the safest, the shortest, or the hybrid planned path. The hybrid path is much safer than the shortest path, but less shorter than the safest one. The main idea of the proposed technique is to propagate a monotonic wave front with a particular speed function from a starting location until the target is reached and then extracts the optimum planned path between them by solving an ordinary differential equation (ODE) using an efficient numerical scheme. The framework supports both local and global planning for both 2D and 3D environments. The robustness of the proposed framework is demonstrated by correctly extracting planned paths of complex maps.

While the performance of Robust Principal Component Analysis (RPCA), in terms of the recovered low-rank matrices, is quite satisfactory to many applications, the time efficiency is not, especially for scalable data. We propose to solve this problem using a novel fast incremental RPCA (FRPCA) approach. The low rank matrices of the incrementally-observed data are estimated using a convex optimization model that exploits information obtained from the preestimated low-rank matrices of the original observations. The evaluation results supports the potential of FRPCA for fast, yet accurate, recovery of the low-rank matrices. The proposed FRPCA boosts the efficiency of the traditional RPCA by multiple hundreds of times, while scarifying less than 1% of accuracy.

We propose a method to eliminate the imbalance vibrations in magnetic bearing systems using discrete-time gain-scheduled Q-parametrization controllers. Imbalance in rotating machines generates variable frequencies sinusoidal disturbance forces that cause the vibrations. Since the frequency of vibrations equals the rotational speed, the free parameter Q of the Q-parametrization controllers is scheduled as a function of the rotational speed to achieve rejection of the imbalance sinusoidal disturbance forces at all operating speeds. First, we present a mathematical model for the magnetic bearing in state space from which includes the effect of imbalance. Next, we explain the discrete-time Q-parametrization controller design for the magnetic bearing to achieve robust stability mid rejection of the variable frequencies sinusoidal disturbance forces. Finally, several simulation results are presented. The results showed that elimination of the imbalance vibrations are achieved at all operating speeds, and moreover robust stability is also achieved

We propose a low-cost low-rank-based framework for the operation of wireless surveillance systems. The proposed framework has two modes of operations: an initialization offline mode, in which low-rank terms of few initial frames are recovered using RPCA. Then these recovered low-rank terms are transmitted over the wireless network to the receiver. In the real-time mode of operation, sparse terms of the captured frames are calculated using FRPCA, then transmitted to the receiver. Transmission of only the sparse terms greatly saves the used bandwidth and hence the cost of the transmission process.