A Polygon Detection Algorithm for Robot Visual Servoing
The problem of detecting polygon structures in images arises in many applications, from building extraction to cryo-electron microscopy. In this paper, A polygon detection algorithm (PDA) based on line detection is proposed. First of all, we introduce a parallel thinning algorithm to eliminate useless feature points. Then the feature points are organized as disjoint feature contours. For each independent contour, we detect line segment candidates based on RANSAC algorithm. With these candidates, a polygon (triangle, rectangle or other polygons) is composed. Compared to the state-of-the-art shape detection algorithms, PDA is much faster and it can detect any polygons such as triangles, rectangles, etc. We introduce PDA to a 6DOFs robot visual servoing platform and the experimental results show that significant improvements in time efficiency and performance robustness of the proposed algorithm have been achieved.

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: PAD Model Based Facial Expression Analysis
abstract: The validity of PAD (Pleasure-Arousal-Dominance) theory in vision area and the feasibilityT on PAD based models for facial expression analysis are discussed in this paper. Three new models based on PAD theory are proposed and their feasibility is verified by experiments on Cohn-Kanade dataset and PAD dataset which is collected from well-designed psychological experiments. After combining Gabor feature and SVM (Support Vector Machine), the result can be further improved. Compared with the basic expression models, our experiments show that the predominance of PAD based model is that it can represent almost any states of expression. Finally, our preliminary experiments show that distinguishing different grades of the same expression is promising by our models.

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: Color-based road detection in urban traffic scenes
abstract: 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: Adaptive Tracking Control for the Output PDFs Based on Dynamic Neural Networks
abstract: In this paper, a novel adaptive tracking control strategy is established for general non-Gaussian stochastic systems based on two-step neural network models. The objective is to control the conditional PDF of the system output to follow a given target function by using dynamic neural network models. B-spline neural networks are used to model the dynamic output probability density functions (PDFs), then the concerned problem is transferred into the tracking of given weights corresponding to the desired PDF. The dynamic neural networks with undetermined parameters are employed to identify the nonlinear relationships between the control input and the weights. To achieve control objective, an adaptive state feedback controller is given to estimate the unknown parameters and control the nonlinear dynamics.

title: Sensor fault estimator and its application for linear multi-variable dynamic systems with delayed state and input
abstract: For systems with multiple time delays, a novel state and sensor noise observer is designed to estimate system states and sensor noises at the same time. In this design, descriptor system approach and linear matrix inequality technique are adopted. Unbounded sensor faults will make the system fail unavoidably, it is indispensable to derive a reliable control scheme against sensor failures. Using the estimated state and sensor fault, a fault-tolerant observer-based controller is proposed, which makes the system work well not matter whether sensor faults occur or not.

title: Analysis of DC characteristics of GaAs double heterojunction bipolar transistors (DHBTs) with a pseudomorphic GaAsSb base
abstract: In this paper, DC characteristics in GaAs DHBTs with pseudomorphic GaAsSb base layer are investigated using an analytical approach. Devices using different GaAs 1− x Sb x base thickness with different Sb compositions were simulated to gain an in-depth understanding on the effect of base layer design on the device DC performance. Our calculated results show that, in the GaAsSb base with high Sb composition, the strain plays a non-negligible role in determining the overall device performance. The reduction of B–E turn-on voltage using a large Sb composition of GaAs 1− x Sb x could be partially compensated by strain in the base. Furthermore, for the devices using high Sb composition, a thin base layer has to be used to ensure elastic strain. In order to maintain reasonable base conductivity that is critical for device microwave performance, high base doping is required. However, our simulation reveals a drastic reduction of DC gain with the increase in base doping, while a high emitter doping may be helpful for the compensation of the degradation of β. A proper design of the device structure by taking the aforementioned issues into account is important for improving device DC and microwave performance.

title: Zero Assignment for Robust  $H_{2}/H_{\infty}$ Fault Detection Filter Design
abstract: In practical engineering, it is inevitable that a system is perturbed by noise signals. Unfortunately, H infin /H infin filtering may fail to detect some faults when the noise distribution matrix are the same as the fault distribution matrix. In this paper, it is shown that the dynamic feedback gain of a dynamic filter introduces additional zeros to the filter, and both the filter poles and the additional zeros can be assigned arbitrarily. In order to attenuate band-limited noises, the zero assignment technique is used, and an optimal dynamic fault detection filtering approach is proposed by locating the zeros to the noise frequencies and optimizing the poles. Compared to other dynamic filter design approaches, the zero assignment technique gives a better tradeoff between more design freedom and computation costs. As shown in the simulation, a better noise attenuation and fault detection performance have been obtained. The zero assignment in multivariable fault detection filter design would be the main contribution of this paper.

title: Delay-dependent fault detection and diagnosis using B-spline neural networks and nonlinear filters for time-delay stochastic systems
abstract: A new fault detection and diagnosis (FDD) scheme is studied in this paper for the continuous-time stochastic dynamic systems with time delays, where the available information for the FDD is the input and the measured output probability density functions (PDFs) of the system. The square-root B-spline neural networks is used to formulate the output PDFs with the dynamic weightings. As a result, the concerned FDD problem can be transformed into a robust FDD problem subjected to a continuous time uncertain nonlinear system with time delays. Delay-dependent criteria to detect and diagnose the system fault are provided by using linear matrix inequality (LMI) techniques. It is shown that this new criterion can provide higher sensitivity performance than the existing result. Simulations are given to demonstrate the efficiency of the proposed approach.