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.

We consider a multiple access relay network where multiple sources send independent data to a single destination through multiple relays, which may inject falsified data into the network. To detect the malicious relays and discard (erase) data from them, tracing bits are embedded in the information data at each source node. In addition, parity bits are added to correct the errors caused by fading and noise. When the total amount of redundancy, tracing bits plus parity bits, is fixed, an increase in parity bits to increase the reliability requires a decrease in tracing bits, which leads to a less accurate detection of malicious behavior of relays, and vice versa. We investigate the tradeoff between the tracing bits and the parity bits in minimizing the probability of decoding error and maximizing the throughput in multisource, multirelay networks under falsified data injection attacks. The energy and throughput gains provided by the optimal allocation of redundancy and the tradeoff between reliability and security are analyzed.

SIFT has been proven to be the most robust local invariant feature descriptor. SIFT is designed mainly for gray images. However, color provides valuable information in object description and matching tasks. Many objects can be misclassified if their color contents are ignored. This paper addresses this problem and proposes a novel colored local invariant feature descriptor. Instead of using the gray space to represent the input image, the proposed approach builds the SIFT descriptors in a color invariant space. The built Colored SIFT (CSIFT) is more robust than the conventional SIFT with respect to color and photometrical variations. The evaluation results support the potential of the proposed approach.

Cooperative relaying is gaining a significant attention in that, intermediate relay nodes assist the source nodes to enhance the overall network efficiency. In network coded cooperative communications, the relay node linearly combines the data received from the sources and forward the linear combination to the destination. In this paper, we present the maximum likelihood decoding scheme for a network composed of two sources, single relay, and single destination. We derive a closed form expression for upper bound on the word error probability. The simulation results show that the analytical upper bound is very tight especially at higher values of the SNR. The results also show that there exists an error floor in the error probability. Therefore, the closed form expression of the upper bound is used to study the reasons of this error floor and how to mitigate it.

We analyze the probability of decoding error in a multi-source, multi-relay wireless network, in which the adversary may inject falsified data through captured relay nodes. To detect malicious relay nodes and discard (erase) the data from those relay nodes, tracing bits are embedded in the information data at each source node. Parity bits are also added to the information data to correct the errors caused by the the channel impairments such as fading and noise. We analyze the tradeoff between the tracing bits and the parity bits in minimizing the probability of decoding error in multi-source, multi-relay networks under falsified data injection attacks.