Video object tracking using adaptive Kalman filter

In this paper, a new video moving object tracking method is proposed. In initialization, a moving object selected by the user is segmented and the dominant color is extracted from the segmented target. In tracking step, a motion model is constructed to set the system model of adaptive Kalman filter firstly. Then, the dominant color of the moving object in HSI color space will be used as feature to detect the moving object in the consecutive video frames. The detected result is fed back as the measurement of adaptive Kalman filter and the estimate parameters of adaptive Kalman filter are adjusted by occlusion ratio adaptively. The proposed method has the robust ability to track the moving object in the consecutive frames under some kinds of real-world complex situations such as the moving object disappearing totally or partially due to occlusion by other ones, fast moving object, changing lighting, changing the direction and orientation of the moving object, and changing the velocity of moving object suddenly. The proposed method is an efficient video object tracking algorithm.     

An adaptive local grid refinement and peak/valley capture algorithm to solve nonlinear transport problems with moving sharp-fronts

Highly nonlinear advection–dispersion-reaction equations govern numerous transport phenomena. Robust, accurate, and efficient algorithms to solve these equations hold the key to the success of applying numerical models to field problems. This paper presents the development and verification of a computational algorithm to approximate the highly nonlinear transport equations of reactive chemical transport and multiphase flow. The algorithm was developed based on the Lagrangian-Eulerian decoupling method with an adaptive ZOOMing and Peak/valley Capture (LEZOOMPC) scheme. It consists of both backward and forward node tracking, rough element determination, peak/valley capturing, and adaptive local grid refinement. A second-order tracking was implemented to accurately and efficiently track all fictitious particles. Shanks’ method was introduced to deal with slowly converging case. The accuracy and efficiency of the algorithm were verified with the Burger equation for a variety of cases. The robustness of the algorithm to achieve convergent solutions was demonstrated by highly nonlinear reactive contaminant transport and multiphase flow problems.     

Adaptive 1-D and 2-D Filtering with Internally Passive Structures

Internally passive filters such as wave digital filters (WDF) and orthogonal filters, which have desirable properties, are developed as filter structures for adaptive filters. The equations for updating the multiplier coefficients as well as for computing the gradients are derived for these filters. The stability monitoring during the adaptation process for these filters is simple to implement. Simulation results are included to demonstrate the capability of these filters in system identification and noise cancellation. Moreover, motivated by applications of multidimensional adaptive filters in video compression and image enhancement, the proposed adaptive wave digital filters have been extended to two dimensions and applied as adaptive fan filters. Computer simulations are also conducted to show their potential applications to two-dimensional system identification and image enhancement.     

MQAM 功放器件非线性失真均衡器

本文给出了一种具有记忆能力的自适应非线性失真预均衡器,用于补偿MQAM高功率放大器在非线性区域工作时引入的具有记忆的3阶非线性失真,采用Volterra级数模型分析并补偿这种非线性失真,系统模拟证明这种结构简单的预均衡器对系统的非线性失真具有较好的校正作用。     

Adaptive robust fuzzy control for a class of uncertain chaotic systems

In this paper, the output feedback control of uncertain chaotic systems is addressed via an adaptive robust fuzzy approach. Fuzzy logic systems are employed to approximate uncertain nonlinear functions in the chaotic systems. Because only partial information of the system’s states is needed to be known, an observer is given to estimate the unmeasured states. Compared with the existing results in the observer design, the prior knowledge on dynamic uncertainties is relaxed and a class of more general chaotic systems is considered as well as robustness to the approximation error is improved. It can be proven that the closed-loop system is stable in the sense that all the variables are bounded. Simulation example for the unified chaotic systems is given to verify the effectiveness of the proposed method. This work was supported in part by the National Natural Science Foundation of China (60874056) and the Foundation of Educational Department of Liaoning Province (2008312).     

自适应回音抵消技术分析与探讨

对回音抵消器中的关键技术--自适应滤波技术及双方同时说话/回音路径跳变检测与控制技术进行了介绍、分析与比较,探讨了现有技术的优缺点,并提出了该技术领域进一步的研究方向.     

移动通信系统中的智能天线技术

论述了现有系统面临着提高系统容量和改善通信质量的双重压力,而智能天线技术引入空分多址的概念,能够有效地提高系统容量。分析了智能天线技术的原理;给出了两种时分多址系统中的智能天线系统结构和比较;介绍了智能天线技术中的自适应波束合成算法。     

ABEEM方法预测响应函数

在密度泛函理论和原子-键电负性均衡模型基础上,定义了与化学键有关的响应函数以及化学键区域的Fukui函数,建立了一套快速确定分子中各区域(包括原子区域和化学键区域)响应函数的新方法.对大量分子的响应函数的计算结果表明,该方法得到的响应函数可以较好地预测分子中各点的反应活性,并更加快捷省时,展示了原子-键电负性均衡模型的广阔应用前景.     

Model selection for regression on a fixed design

We deal with the problem of estimating some unknown regression function involved in a regression framework with deterministic design points. For this end, we consider some collection of finite dimensional linear spaces (models) and the least-squares estimator built on a data driven selected model among this collection. This data driven choice is performed via the minimization of some penalized model selection criterion that generalizes on Mallows' C _p . We provide non asymptotic risk bounds for the so-defined estimator from which we deduce adaptivity properties. Our results hold under mild moment conditions on the errors. The statement and the use of a new moment inequality for empirical processes is at the heart of the techniques involved in our approach. Received: 2 July 1997 / Revised version: 20 September 1999 / Published online: 6 July 2000     

An adaptive multiscale inverse scattering approach to photothermal depth profilometry

Photothermal depth profilometry is formulated as a nonlinear inverse scattering problem. Starting with the one-dimensional heat diffusion equation, we derive a mathematical model relating arbitrary variation in the depth-dependent thermal conductivity to observed thermal wavefields at the surface of a material sample. The form of the model is particularly convenient for incorporation into a nonlinear optimization framework for is particularly convenient for incorporation into a nonlinear optimization framework for recovering the conductivity based on thermal wave data obtained at multiple frequencies. We develop an adaptive, multiscale algorithm for solving this highly ill-posed inverse problem. The algorithm is designed to produce an accurate, low-order representation of the thermal conductivity by automatically controlling the level of detail in the reconstruction. This control is designed to reflect both (1) the nature of the underlying physics, which says that scale should decrease with depth, and (2) the particular structure of the conductivity profile, which may require a sparse collection of fine-scale components to adequately represent significant features such as a layering structure. The approach is demonstrated in a variety of synthetic examples representative of nondestructive evaluation problems seen in the steel industry.The work of authors E. L. Miller and I. Yavuz was supported by a CAREER Award from the National Science Foundation MIP-9623721, an ODDR&E MURI under Air Force Office of Scientific Research contract F49620-96-1-0028, and the Army Research Office Demining MURI under grant DAAG55-97-1-0013. The work of authors L. Nicolaides and A. Mandelis was supported by a research contract from Material and Manufacturing Ontario (MMO).