Person tracking and reidentification: Introducing Panoramic Appearance Map (PAM) for feature representation

This paper develops a concept of Panoramic Appearance Map (PAM) for performing person reidentification in a multi-camera setup. Each person is tracked in multiple cameras and the position on the floor plan is determined using triangulation. Using the geometry of the cameras and the person location, a panoramic map centered at the person’s location is created with the horizontal axis representing the azimuth angle and vertical axis representing the height. Each pixel in the map image gets color information from the cameras which can observe it. The maps between different tracks are compared using a distance measure based on weighted SSD in order to select the best match. Temporalintegration by registering multiple maps over the tracking period improves the matching performance. Experimental results of matching persons between two camera sets show the effectiveness of the approach. This work has been sponsored by the Technical Support Working Group (TSWG) of US Department of Defence (DoD).     

Nonbinary Quantum Goppa Codes Exceeding the Quantum Gilbert-Varshamov Bound

An explicit construction for nonbinary quantum Goppa codes exceeding the quantum Gilbert-Varshamov bound is given. First, we introduce a weighted symplectic inner product and show a method how to transform weighted codes into quantum codes with respect to the standard symplectic inner product. Then an algorithm to construct a quantum code out of any hyperelliptic curve is presented and implemented in Magma. Finally, we apply a generalization of this algorithm to a tower of function fields by Stichtenoth and show that these codes lie above the quantum Gilbert-Varshamov bound.      

Sorting-based localization and stable computation of zeros of a polynomial. II

The stability of a sorting-based scheme for identifying polynomial zeros under coefficient perturbation is discussed. A method is proposed for simultaneous reconstruction (with a logarithmic estimate of time complexity) of the coefficients of an arbitrary polynomial from the values of its zeros. The method of identification of polynomial zeros is based on the operator of localization of extremal elements of a numerical sequence after its preliminary sorting. The method is extended to pattern recognition. Part I of this article is published in No. 1 (2007). __________ Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 161–174, March–April 2007.     

Authentication of Individuals using Hand Geometry Biometrics: A Neural Network Approach

Biometric based systems for individual authentication are increasingly becoming indispensable for protecting life and property. They provide ways for uniquely and reliably authenticating people, and are difficult to counterfeit. Biometric based authenticity systems are currently used in governmental, commercial and public sectors. However, these systems can be expensive to put in place and often impose physical constraint to the users. This paper introduces an inexpensive, powerful and easy to use hand geometry based biometric person authentication system using neural networks. The proposed approach followed to construct this system consists of an acquisition device, a pre-processing stage, and a neural network based classifier. One of the novelties of this work comprises on the introduction of hand geometry’s related, position independent, feature extraction and identification which can be useful in problems related to image processing and pattern recognition. Another novelty of this research comprises on the use of error correction codes to enhance the level of performance of the neural network model. A dataset made of scanned images of the right hand of fifty different people was created for this study. Identification rates and Detection Cost Function (DCF) values obtained with the system were evaluated. Several strategies for coding the outputs of the neural networks were studied. Experimental results show that, when using Error Correction Output Codes (ECOC), up to 100% identification rates and 0% DCF can be obtained. For comparison purposes, results are also given for the Support Vector Machine method.     

用创新计算动力学研究神经网络的组合创新

创新计算动力学研究创新的基本规律,不仅为创新的机械化实现奠定基础,也为人类的创新活动提供规则化支持。文中利用创新计算动力学的基本定律研究神经网络技术的组合创新活动,归纳了组合创新的重要前提,同时证明了神经网络的组合创新过程是符合创新计算的基本定律之联想与组合定律。最后,提出了相似组合、对立组合和信息域关联组合三种具体的组合创新模式。由于创新活动的广泛性和创新计算动力学的一般性,它们对于神经网络的未来发展以及其它理论或技术的创新都具有一定的启发意义。     

生物发酵过程的微分几何变结构控制

本文首先建立了生物发酵过程的仿射非线性数学模型,然后采用微分几何精确线性化理论,把非线性系统转化成了一个线性系统,最后在此基础上应用非线性变结构控制理论指数趋近律法进行设计,实现了一种较新的对发酵过程的控制。仿真结果表明,该控制系统具有良好的动静态性能。     

一种面向移动计算终端的几何压缩方法

鉴于移动计算终端具有显示屏幕小、对模型精度要求较低和处理能力相对较弱、处理高细节度的模型十分吃力,以及受带宽所限,需要对模型进行渐进传输等特点。而已有的几何压缩和简化方法又对移动计算终端的这些特点针对性不够,为此,围绕在移动计算终端上进行3维图形的显示,根据移动计算终端的特点,提出了一个实现过程简单、简化效率较高的几何压缩方法,同时提出了一个基于顶点相邻三角形单位法向量变化的边折叠几何压缩算法,并通过计算顶点相邻三角形单位法向量差值来识别出模型的特征点,提出了一个特征保留的策略。实践表明,完整的基于单位法向量变化和特征保留的边折叠几何压缩算法可以在保持模型外观的同时,有效地降低模型的规模。最后给出了该压缩方法在移动计算终端上的应用情况。通过用该压缩算法对模型进行预处理,实现了在移动计算终端上进行3维图形显示的这一目的。随着移动计算技术的发展,移动3维图形的应用将会越来越多,因此该压缩方法在移动3维图形计算方面具有较好的应用前景。     

Testing equality of covariance matrices when data are incomplete

In the statistics literature, a number of procedures have been proposed for testing equality of several groups’ covariance matrices when data are complete, but this problem has not been considered for incomplete data in a general setting. This paper proposes statistical tests for equality of covariance matrices when data are missing. A Wald test (denoted by T₁), a likelihood ratio test (LRT) (denoted by R), based on the assumption of normal populations are developed. It is well-known that for the complete data case the classic LRT and the Wald test constructed under the normality assumption perform poorly in instances when data are not from multivariate normal distributions. As expected, this is also the case for the incomplete data case and therefore has led us to construct a robust Wald test (denoted by T₂) that performs well for both normal and non-normal data. A re-scaled LRT (denoted by R^*) is also proposed. A simulation study is carried out to assess the performance of T₁, T₂, R, and R^* in terms of closeness of their observed significance level to the nominal significance level as well as the power of these tests. It is found that T₂ performs very well for both normal and non-normal data in both small and large samples. In addition to its usual applications, we have discussed the application of the proposed tests in testing whether a set of data are missing completely at random (MCAR).     

Robust optimal decisions with imprecise forecasts

A robust minimax approach for optimal investment decisions with imprecise return forecasts and risk estimations in financial portfolio management is considered. Single-period and multi-period mean-variance optimization models are extended to worst-case design with multiple rival risk estimations and return forecasts. In multi-period stochastic formulation of classical mean-variance portfolio optimization problem, an investor makes an investment decision based on expectations and/or scenarios up to some intermediate times prior to the horizon and, consequently, rebalances or restructures the portfolio. Multi-period portfolio optimization entails the construction of a scenario tree representing a discretized estimate of uncertainties and associated probabilities in future stages. It is well known that return forecasts and risk estimations are inherently inaccurate and there are different rival estimates, or scenario trees. Robust optimization models are presented and imprecise nature of moment forecasts to reduce the risk of making a decision based on the wrong scenario is addressed. The worst-case performance is guaranteed in view of all rival risk and return scenarios and will only improve when any scenario other than the worst-case is realized. The ex-ante performance of minimax models is tested using historical data and backtesting results are presented.     

Data-based supervisory control of uncertain systems with application to automatic drug delivery for anesthesia

Control of nonlinear noisy systems affected by large uncertainties is approached via the introduction of a supervisor which, whenever needed, switches on, in feedback to the plant, a controller selected from a finite set of precomputed controllers. A Lyapunov-based falsification criterion allows one to ensure robust stability in the presence of uncertain constant parameters and exogenous bounded disturbances without a priori information on the system. Applicability of the method is illustrated through simulations on an automatic drug delivery system for anesthesia.