Color-based road detection in urban traffic scenes
Road detection is a key issue for autonomous driving in urban traffic. In this paper, after a brief overview of existing methods, we present a road-area detection algorithm based on color images. This algorithm is composed of two modules: boundaries are first estimated based on the intensity image and road areas are subsequently detected based on the full color image. In the first module, an edge image of the scene is analyzed to obtain the candidates for the left and right road borders and to delimit the area that will subsequently be used to compute the mean and variance of the Gaussian distribution, assumed to be obeyed by the color components of road surfaces. The second module effectively extracts the road area and reinforces boundaries that most appropriately fit the road-extraction result. The combination of these modules can overcome basic problems due to inaccuracies in edge detection based on the intensity image alone and due to the computational complexity of segmentation algorithms based on color images. Experimental results on real road scenes have substantiated the effectiveness of the proposed method.

title: An XML-Based Implementation of Multimodal Affective Annotation
abstract: In simple cases, affective computing is a computational device recognizing and acting upon the emotions of its user or having (or simulating having) emotions of its own in complex cases. Multimodal technology is currently one of the hottest focuses in affective computing research. However, the lack of a large-scale multimodal database limits the research to some respective and scattered fields, such as affective recognition by video or by audio. This paper describes the development and implementation of an XML-based multimodal affective annotation system which is called MAAS (Multimodal Affective Annotation System). MAAS contains a hierarchical affective annotation model based on the 3-dimensional affect space derived from Mehrabian’s PAD temperament scale. The final annotation file is formed in XML format in order to interchange the resources with other research groups conveniently.

title: Real-time pose estimation for outdoor mobile robots using range data
abstract: Pose estimation is a key issue in mobile robots and important to moving object tracking. In this paper, we present a tangent based hybrid pose estimation algorithm with real-time performance using range data from laser radar, which consists of Iterative Tangent weighted Closest Point (ITCP) and Hough transform based Tangent Angle Histogram (HTAH) algorithms to overcome problems with past methods, such as local minimum, aperture-like, and high computation problems. The algorithm has been tested on both synthetic and real range data in outdoor environments. Experimental results demonstrate its high accuracy, low computation, wide applicability and high robustness to aperture-like problems, occlusion and noise.

title: Deception Detection Via Blob Motion Pattern Analysis
abstract: Deception dectection is one of the most difficult problems in affect recognition and expression research area. Recently, non-verbal methods of detecting deception have appeared to be promising.  presented a proof-of-concept study based on the blob analysis of some suspects' interviews and mock experiments video clips. In this paper, we present our recent research work in the direction of developing an automated deception detection system. We propose a blob motion pattern analysis approach to solve this problem.

title: Direct solution of the parametric Stochastic Distribution Control problem
abstract: The Stochastic Distribution Control (SDC) problem is a generalised form of the minimum variance control problem where non-Gaussian noise distributions are encountered. The problem has been previously solved using two alternative approaches. When it is assumed that the output Probability Distribution Function (PDF) is measurable, then a parameterized controller is obtained. If on the other hand this assumption is removed (which corresponds to most practical cases), then the controller found is no longer parameterisable (i.e. it is a control action sequence). Both these approaches have thus far been solved using local Newtonian methods. In this paper a third alternative is presented which combines the desirable features of the previous two methods by finding a parameterized controller, without having to assume that the output PDF is directly measurable at the same time. In addition, global direct search algorithms are used to avoid convergence to local solutions. The approach is demonstrated on a SISO nonlinear system corrupted by non-Gaussian input noise.

title: Robust fault diagnosis for non-Gaussian stochastic systems based on the rational square-root approximation model
abstract: The task of robust fault detection and diagnosis of stochastic distribution control (SDC) systems with uncertainties is to use the measured input and the system output PDFs to still obtain possible faults information of the system. Using the rational square-root B-spline model to represent the dynamics between the output PDF and the input, in this paper, a robust nonlinear adaptive observer-based fault diagnosis algorithm is presented to diagnose the fault in the dynamic part of such systems with model uncertainties. When certain conditions are satisfied, the weight vector of the rational square-root B-spline model proves to be bounded. Convergency analysis is performed for the error dynamic system raised from robust fault detection and fault diagnosis phase. Computer simulations are given to demonstrate the effectiveness of the proposed algorithm.

title: Control Flow Optimization Via Dynamic Reconvergence Prediction
abstract: This paper presents a novel microarchitecture technique for accurately predicting control flow reconvergence dynamically. A reconvergence point is the earliest dynamic instruction in the program where we can expect program paths to reconverge regardless of the outcome or target of the current branch. Thus, even if the immediate control flow after a branch is uncertain, execution following the reconvergence point is certain. This paper proposes a novel hardware re-convergence predictor which is both implementable and accurate, with a 4KB predictor achieving more than 95% accuracy for SPEC INT, and larger implementations achieving greater than 99% accuracy. The information provided from reconvergence prediction can increase the effectiveness of a range of previously proposed performance optimizations, including speculative multithreading, control independence, and squash reuse. This paper also demonstrates a new technique that takes advantage of the dynamic reconvergence prediction information in order to predict a wrong path excursion ahead of branch resolution. On average, 34% of wrong path fetches are eliminated.

title: Minimum Entropy Control Algorithm for General Dynamic Stochastic Systems
abstract: In order to measure the uncertainty of the stochastic systems subjected to arbitrary noise disturbance instead of Gaussian white noise, the minimum entropy control of tracking errors for dynamic stochastic systems is presented in this paper. Different from conventional hypothesis, it is assumed that the system output and noise obey multi-to-one mapping, which is more general in the practical application. A controller design was described based on minimizing system output error entropy and a recursive optimization algorithm was set up for dynamic, non-Gaussian and nonlinear system. This approach only used the formula of the probability density function of the tracking error to calculate the controller and it did not need to know the style of the system model and the probability density function of noise, which often is difficult to measure in fact. An illustrative example is utilized to demonstrate the efficiency of the minimum entropy control algorithm and the approving simulation results have been gained

title: Maximal Total Length Of k Disjoint Cycles In Bipartite Graphs
abstract: Let k, s and n be three integers with s≥k≥2, n≥2k+1. Let G=(V1,V2;E) be a bipartite graph with |V1|=|V2|=n. If the minimum degree of G is at least s+1, then G contains k vertex-disjoint cycles covering at least min(2n,4s) vertices of G.