Cognitive radio is a very popular research area in the communication community as cost and bandwidth can be saved by sensing the available licensed spectrum for unlicensed users. This paves the way for the application of cognitive radio in control systems over wireless communication links. A typical control system comprises a sensor interconnected with an actuator, a plant and controller. In this paper, it is assumed that the sensor and estimator communicate through a cognitive radio link. The state estimator needs to adjust to this new communication media as it is affected by interruptions from primary users, resulting in packet losses. The cognitive radio link is modeled as multiple semi-Markov processes each of which can capture the channel dynamics. Two different cases are considered. The first case assumes that acknowledgement of packet arrivals is available at the estimator while the second case does not. For the first case, sufficient conditions are derived for the stability of the peak covariance process which guarantees the stability of the estimator covariance. For the second case, an estimator is designed. Numerical examples are given to show the performance of the proposed estimators. Several applications of the proposed work are also discussed.

Smart grid, equipped with modern communication infrastructures, is subject to possible cyber attacks. Particularly, false report attacks which replace the sensor reports with fraud ones may cause the instability of the whole power grid or even result in a large area blackout. In this paper, a trustiness system is introduced to the controller, who computes the trustiness of different sensors by comparing its prediction, obtained from Kalman filtering, on the system state with the reports from sensor. The trustiness mechanism is discussed and analyzed for the Linear Quadratic Regulation (LQR) controller. Numerical simulations show that the trustiness system can effectively combat the cyber attacks to smart grid.

In this letter, filtering over wireless communication channels subject to packet losses is considered. The packet losses are assumed to follow a Bernoulli distribution. The latter is interpreted as a special case of a Markov process for which hybrid filtering theory is shown to provide an exact solution. Unlike existing works where only the linear optimal state estimate is derived as a linear function of measurements, this paper shows that the optimal state estimate is in fact a nonlinear function of measurements. In addition, the optimal estimator is derived explicitly. Illustrative examples compare the performance of the linear optimal estimator and the optimal estimator, and show that the latter offer superior performance, in particular for unstable systems.

A novel preferential image segmentation method is proposed that performs image segmentation and object recognition using mathematical morphologies. The method preferentially segments objects that have intensities and boundaries similar to those of objects in a database of prior images. A tree of shapes is utilized to represent the content distributions in images, and curve matching is applied to compare the boundaries. The algorithm is invariant to contrast change and similarity transformations of translation, rotation and scale. A performance evaluation of the proposed method using a large image dataset is provided. Experimental results show that the proposed approach is promising for applications such as object segmentation and video tracking with cluttered backgrounds.

In this paper, achievable rates for compound Gaussian multiple-input–multiple-output (MIMO) channels are derived. Two types of channels, modeled in the frequency domain, are considered when: 1) the channel frequency response matrix$H$belongs to a subset of$H^{\infty}$normed linear space, and 2) the power spectral density (PSD) matrix of the Gaussian noise belongs to a subset of$L_1$space. The achievable rates of these two compound channels are related to the maximin of the mutual information rate. The minimum is with respect to the set of all possible$H$matrices or all possible PSD matrices of the noise. The maximum is with respect to all possible PSD matrices of the transmitted signal with bounded power. For the compound channel modeled by the set of$H$matrices, it is shown, under certain conditions, that the code for the worst case channel can be used for the whole class of channels. For the same model, the water-filling argument implies that the larger the set of matrices$H$, the smaller the bandwidth of the transmitted signal will be. For the second compound channel, the explicit relation between the maximizing PSD matrix of the transmitted signal and the minimizing PSD matrix of the noise is found. Two PSD matrices are related through a Riccati equation, which is always present in Kalman filtering and liner-quadratic Gaussian control problems.

Mobile-to-mobile networks are characterized by node mobility that makes the propagation environment time varying and subject to fading. As a consequence, the statistical characteristics of the received signal vary continuously, giving rise to a Doppler power spectral density (DPSD) which varies from one observation instant to the next. The current models do not capture and track the time varying characteristics. This paper is concerned with dynamical modeling of time varying mobile-to-mobile channels, parameter estimation and identification from received signal measurements. The evolution of the propagation environment is described by stochastic differential equations, whose parameters can be determined by approximating the band-limited DPSD using the Gauss-Newton method. However, since the DPSD is not available online, we propose to use a filter-based expectation maximization algorithm and Kalman filter to estimate the channel parameters and states, respectively. The scheme results in a finite dimensional filter which only uses the first and second order statistics. The algorithm is recursive allowing the inphase and quadrature components and parameters to be estimated online from received signal measurements. The algorithms are tested using experimental data collected from moving sensor nodes in indoor and outdoor environments demonstrating the method's viability.

Wireless communication networks are characterized by nodes and scatters mobility which make the propagation environment time-varying and subject to fading. These variations are captured by random time-varying impulse responses. The latter are fairly general finite energy functions of both time and space that cannot be specified by a finite number of parameters. In this letter, we show that the impulse responses can be approximated in a mean square sense as close as desired by impulse responses that can be realized by stochastic differential equations (SDEs). The behaviors of the SDEs are characterized by small finite dimensional parameter sets.

Curve evolution implementations of the Mumford-Shah functional are of broad interest in image segmentation. These implementations, however, have initialization problems. A mathematical analysis of the initialization problem for the Chan-Vese implementation is provided in this paper. The initialization problem is a result of the non-convexity of the MumfordShah functional and the top-down hierarchy of the model's use of global region information in the image. Based on the analysis, efficient implementation methods are proposed for the Chan-Vese models. The proposed methods do not have to solve PDEs and thus work fast. The advantages of level set methods, such as automatic handling of topological changes, are preserved. These methods work well for images without strong noise. Initialization problems, however, still exist. A bottom-up image segmentation method is proposed that alleviates the initialization problem, based on region competition and the Mumford Shah functional. This algorithm extends the method in Jean Michel Morel, et al., (1995) and is able to automatically and efficiently segment objects in complicated images. Using a bottom-up hierarchy, the method avoids the initialization problem in the Chan-Vese model and works for images with multiple junctions and color images. It is then extended to textured images using Gabor filters and fractal methods. Experimental results show that the proposed method works well and is robust to the effects of noise

Curve evolution implementations of the Mumford-Shah functional are of broad interest in image segmentation. These implementations, however, have initialization problems. A mathematical analysis of the initialization problem for the bi-modal Chan-Vese model is provided in this paper. The initialization problem is a result of the non-convexity of the Mumford-Shah functional and the top-down hierarchy of the model's use of global region information in the image. An efficient image segmentation method is proposed that alleviates the initialization problem, based on region growing, region competition and the Mumford Shah functional. This algorithm is able to automatically and efficiently segment objects in complicated images. Using a bottom-up hierarchy, the method avoids the initialization problem in the Chan-Vese model and works for images with multiple junctions and color images. It can be extended to textured images. Experimental results show that the proposed method is robust to the effects of noise

Efficient implementation methods are proposed for Chan-Vese models. The proposed methods do not require solutions of PDEs and are therefore fast. The advantages of level set methods, such as automatic handling of topological changes, are preserved. These methods utilize region information to guide the evolution of initial curves. Gaussian smoothing is applied to regularize the evolving curves. These algorithms are able to automatically and efficiently segment objects in complicated images. Experimental results show that the proposed methods work efficiently for images without strong noise. However, they still have initialization problems, as do the Chan-Vese models

Due to node mobility and environmental changes in mobile ad-hoc networks, the ad-hoc channel is time varying and subject to fading. As a consequence of these variations, the statistical characteristics of the received signal vary continuously, giving rise to a Doppler power spectral density (DPSD) which varies from one observation instant to the next. As a result, the traditional models can no longer capture and track complex time variations in the propagation environment. These time variations compel us to introduce more advanced dynamical models in order to capture higher order dynamics of the ad-hoc channel. A stochastic ad-hoc short term fading channel model, in which the evolution of the dynamical channel is described by a stochastic state space representation, is derived. The parameters of the stochastic state space model are determined by approximating the band limited DPSD. Inphase and quadrature components of the ad-hoc channel are derived. Numerical results show that link performance for ad-hoc case is worse than cellular case, but the performance gap shrinks with increased mobility

This paper presents a new method for tracking a mobile based on Aulin's wave scattering model This model takes into account non line of sight and multipath propagation environments, which are usually encountered in wireless fading channels. According to Aulin's model, the received instantaneous electric field at the base station is a nonlinear function of the mobile location and velocity. A method based on particle filtering (sequential Monte Carlo methods) that copes with nonlinearities in order to estimate the mobile location and velocity is proposed. In contrast to standard target tracking literature we do not rely on linearized motion models, measurement relations, and Gaussian assumptions. Numerical results are presented to evaluate the accuracy of the proposed method. They demonstrate significant accuracy improvement over known algorithms

In this paper, a new geometric snake model is proposed, based upon techniques of curve evolution and the utilization of gradient information and region information simultaneously. This model successfully solves the boundary leakage problem. With the help of a hierarchical approach, it can handle complicated cases, such as triple junctions. Furthermore, it supports vector-valued images and can be easily extended to handle color and textured images. Experimental results demonstrate the model's power in image segmentation.

In this paper, a new model is proposed for image segmentation that integrates the curve evolution and anisotropic diffusion methods. The curve evolution method, utilizing both gradient and region information, segments an image into multiple regions. During the evolution of the curve, anisotropic diffusion is adaptively applied to the image to remove noise while preserving boundary information. Coupled partial differential equations (PDE’s) are used to implement the method. Experimental results show that the proposed model is successful for complex images with high noise.

The power control of wireless networks is formulated using a stochastic optimal control framework, in which the evolution of the channel is described by stochastic differential equations (SDEs). The latter capture the spatio-temporal variations of the communication link, as well as the randomness. This class of models is more realistic than the static models usually encountered in the literature. Under this scenario, average and probabilistic Quality of Service (QoS) measures are introduced to evaluate the performance of any control strategy by using Chernoff bounds. Moreover, the Chernoff bound is computed explicitly, while the solution of the stochastic optimal power control is obtained through pathwise optimization. The pathwise optimization can be solved using linear programming if predictable control strategies are introduced. Finally, if predictable control strategies do not hold, it is shown that the proposed power control problem reduces to particular convex optimizations.