This paper proposes a novel method of killing birds and applying vaccines for suppressing the spread of avian influenza via a nonlinear switching control approach. The switching strategy is based on the population of the susceptible birds and the population of the susceptible humans. There are four switching cases. For the first three switching cases, the elimination control force and the quarantine control force are equal to either zero or one. For the last switching case, they are equal to one minus a scalar divided by the population of the susceptible birds and one minus another scalar divided by the population of the susceptible humans, respectively. The system state vectors of the avian influenza model are guaranteed to reach the desirable equilibrium point. Also, the positivity requirements on the system states as well as the constraints on both the lower bounds and the upper bounds of both the elimination control force and the quarantine control force are guaranteed to be satisfied. Computer numerical simulation results show that the proposed control strategy is very effective and efficient.

With the rapidly development of smart grid, communication network will play more and more fundamental role in many smart grid applications and services. The interaction between power system and communication network will appear almost everywhere in the new services of smart grid, the investigation of mixture system combined power system and communication network reveals the mutual influence of each other, and will give accuracy and quantitative data for the planning of the future smart grid. This paper presents a novel cosimulation platform combined power system and communication network to meet the decision-making need in smart grid environment. The platform connects power system simulator and communication system simulator together via a middleware with interfaces, a synchronization method is proposed for the correct time and sequence of data exchange, a time step adjustment algorithm is proposed as well to balance the requirement of accuracy and efficiency.

In this paper, frequency estimation of a two-dimensional (2D) cisoid in the presence of additive white Gaussian noise is addressed. By utilizing the rank-one property of the 2D noise-free data matrix, the frequencies are estimated in a separable manner from the principal left and right singular vectors according to an iterative weighted least squares procedure. We have also derived the mean and variance expressions for the frequency estimates, which show that they are approximately unbiased and their accuracy achieves Cramér-Rao lower bound (CRLB) at sufficiently high signal-to-noise ratio conditions. Computer simulation results are included to corroborate the theoretical development as well as to contrast the performance of the proposed algorithm with the weighted phase averager and iterative quadratic maximum likelihood method as well as CRLB.

The problem of frequency estimation for noisy sinusoidal signals from multiple segments or channels, which are referred to as gapped data, is addressed. Based on linear prediction and weighted least squares techniques, an iterative relaxation-based frequency estimator is devised and analyzed. The proposed algorithm is also extended to harmonically related frequencies. Computer simulations are conducted to compare the estimation performance of the developed approach with an existing multichannel frequency estimator and Crame¿r-Rao lower bound.

It is well known that visual cues of lip movement contain important speech relevant information. This paper presents an automatic lipreading system for small vocabulary speech recognition tasks. Using the lip segmentation and modeling techniques we developed earlier, we obtain a visual feature vector composed of outer and inner mouth features from the lip image sequence for recognition. A spline representation is employed to transform the discrete-time sampled features from the video frames into the continuous domain. The spline coefficients in the same word class are constrained to have similar expression and are estimated from the training data by the EM algorithm. For the multiple-speaker/speaker-independent recognition task, an adaptive multimodel approach is proposed to handle the variations caused by various talking styles. After building the appropriate word models from the spline coefficients, a maximum likelihood classification approach is taken for the recognition. Lip image sequences of English digits from 0 to 9 have been collected for the recognition test. Two widely used classification methods, HMM and RDA, have been adopted for comparison and the results demonstrate that the proposed algorithm deliver the best performance among these methods.

Authentication system solely based on visual lip information is of advantage since the uttering characteristics/manner is unique to individual. This paper presents the results of studying for the most appropriate features extracted from the lip motion for person authentication application. The geometric-based, shape-based and inner lip features are derived from a 14-point active shape model (ASM) lip model. The dynamic features reflecting the differential changes of the feature parameters are also considered in the experiment. A database consists of 40 speakers and each of their utterance last for 3 seconds. Various combinations of features obtained by analyzing this database are fed into a hidden Markov model (HMM) classifier for processing. It is observed that the best result has been obtained when both shape-based features and inner lip features are used.

Lip image analysis has attracted much interest in recent years because some important speech information is contained in the shape and movement of the lip. To extract such information from the images, accurate and robust lip region segmentation is of vital importance. However, most of the current lip segmentation methods fail to provide accurate results if the person has a beard. We propose a "one object, multiple background" clustering method to solve the problem. Since the non-lip region becomes inhomogeneous in the presence of a beard, multiple background clusters can produce better fitting to a rather complex background region than a single cluster. Spatial information in terms of the physical distance towards the lip center is incorporated to enhance the differentiation between the lip and background region. Experimental results demonstrate that our algorithm provides accurate lip segmentation results for images with beards.

An authentication system solely based on visual lip information is of advantage since the uttering characteristics/manner is unique to the individual and difficult to imitate. This paper presents a study of using lip shape-based and intensity features in person authentication. These features are derived from a 14-point active shape model (ASM) lip model with the use of principal component analysis (PCA). The differential change of the feature parameters reflecting the uttering characteristics are also considered in the study. A database containing the visual utterance of 40 speakers has been generated and each of the utterances is of duration 3 seconds. The visual features are then extracted from this database and a hidden Markov model (HMM) classifier is used to perform the analysis. It is observed that the best authentication result is obtained when the first 8 modes of the intensity profile is used together with the lip shaped-based parameters.

Recently, lip image analysis has received much attention because its visual information is shown to provide improvement for speech recognition and speaker authentication. Lip image segmentation plays an important role in lip image analysis. In this paper, a new fuzzy clustering method for lip image segmentation is presented. This clustering method takes both the color information and the spatial distance into account while most of the current clustering methods only deal with the former. In this method, a new dissimilarity measure, which integrates the color dissimilarity and the spatial distance in terms of an elliptic shape function, is introduced. Because of the presence of the elliptic shape function, the new measure is able to differentiate the pixels having similar color information but located in different regions. A new iterative algorithm for the determination of the membership and centroid for each class is derived, which is shown to provide good differentiation between the lip region and the nonlip region. Experimental results show that the new algorithm yields better membership distribution and lip shape than the standard fuzzy c-means algorithm and four other methods investigated in the paper.

A new lip contour extraction algorithm that combines the merits of the point-based lip model and the parametric lip model is presented. A 16-point lip model is used to describe the lip contour. With the aid of the FCMS (fuzzy clustering method incorporating shape function), a robust probability map is generated and a region-based cost function can be established. An iterative optimization procedure has been developed to fit the lip model to the probability map. In each iteration, the adjustment of the 16 lip points is governed by three pieces of quadratic curves which constrain the points to form a physical lip shape. Experimental results show that the proposed approach provides satisfactory results for 5,000 lip images of over 20 individuals. A real-time lip contour extraction system has also been implemented.

In this paper, we describe the application of a novel spatial fuzzy clustering algorithm to the lip segmentation problem. The proposed spatial fuzzy clustering algorithm is able to take into account both the distributions of data in feature space and the spatial interactions between neighboring pixels during clustering. By appropriate pre- and postprocessing utilizing the color and shape properties of the lip region, successful segmentation of most lip images is possible. Comparative study with some existing lip segmentation algorithms such as the hue filtering algorithm and the fuzzy entropy histogram thresholding algorithm has demonstrated the superior performance of our method.

In lip segmentation, most of the clustering methods are based on color information. In this paper, a new fuzzy clustering method that combines the color information and the shape information in the measure is proposed. An elliptic shape function is introduced in the measure that modifies the measure according to the distance of the pixel from the lip center with which the differentiation between the lip and non-lip regions is enhanced. Compared with the standard fuzzy c-means algorithm, experimental results show that the new algorithm has better membership distribution and shape. This method is applicable to other images with a well-predicted shape.

This paper presents a new optimization procedure for extracting the point-based lip contour using the active shape model (ASM). A 14-point ASM lip model is used to describe the lip contour. With the aid of fuzzy clustering analysis, a probability map of the color lip image is obtained and a region-based cost function is established. The new optimization procedure operates on the spatial domain (actual contour points) and all the points are pulled towards their desirable locations in each iteration. Hence, the lip contour evolution becomes better controlled and consequently fast convergence is achieved. The new procedure can also achieve real-time performance on lip contour extraction and tracking from lip image sequence.

The use of visual information from lip movements can improve the accuracy and robustness of a speech recognition system. Accurate extraction of visual features associated with the lips is thus very important. In this paper, we present a deformable model-based technique for lip contour extraction from color lip images. The geometric lip model we propose is able to fit different lip shapes. Our method starts by generating a probability map of the lip image using spatial fuzzy clustering. Then the optimum set of model parameters that partitions a lip probability map into lip region and non-lip region is found, such that the joint probability of the two regions is maximised. Experimental results indicate that accurate and robust lip contour extraction is possible using this approach.

A clustering-based lip segmentation algorithm is described here. The fuzzy clustering algorithm presented here is able to take into account the local smoothness property of image data. The objective functional of our algorithm utilizes a new distance metric that takes into account the influence of the neighboring pixels on the centre pixel in a 3 by 3 window. Computational steps involved in the segmentation of color lip image and segmentation result for a set of color lip images are given.

This paper presents a frequency weighted generalized total least squares linear prediction for estimating closely spaced sinusoids. In this method, the received data is first processed by a pole-zero prefilter and then a generalized total least squares linear prediction is applied to the prefiltered signal. A procedure of optimizing the generalized solution is introduced. By computer simulations, it is shown that the solution can outperform the existing well known total least squares solutions especially in low signal-to-noise ratio.

This paper presents a general total least squares (GTLS) solution for linear prediction to estimate closely spaced sinusoids. It is found that the TLS prediction error is not a good criterion to provide a robust solution. In this paper, a frequency weighted prediction error approach is introduced. Experimental results show that the GTLS solution based on the frequency weighted prediction error can give a robust performance even in very low SNR.

The authors describe the application of the singular value decomposition Prony (SVDP) algorithm to the analysis of the time-variant structure of a microwave line-of-sight multipath channel. The algorithm deduces the multipath ray delays and associated amplitudes from measurements made in the frequency domain. The capability of this algorithm to resolve closely spaced rays is demonstrated by analyzing both computer simulated data and field measurements. The authors present experimental results of the time-variant structure of the transmittance H(jw,t) and include the evolution in time of the amplitude, group delay, and the constituting rays thus giving direct insight into the underlying multipath mechanism of line-of-sight microwave links.>

This study is concerned with using modern spectral analysis techniques to derive estimates of multipath ray parameters (delays and amplitudes) from measurements of selective fading over a wide band at microwave frequencies. The applicability of the general category of autoregressive methods is addressed, and the specific method adopted, the Prony algorithm, is described. An illustrative example in which this is applied to a typical field measurement is followed by an investigation into the capabilities and limitations of the algorithm. A statistical analysis of the method has been carried out using a database of laboratory measurements. The Prony algorithm's ability to resolve delays to better than half the Fourier limit is shown, and its tendency to yield biased estimates is studied.>