Lattice structure on some fuzzy algebraic systems

In this paper, we study the lattice structure of some fuzzy algebraic systems such as (G-)fuzzy groups, some fuzzy ordered algebras and fuzzy hyperstructures. We prove that under suitable conditions, these structures form a distributive or modular lattice. This research partially is supported by the “ Fuzzy Systems and its Applications Center of Excelence, Shahid Bahonar University of Kerman, Iran”.     

Applications of the 4×4 determinant method and the POLYGON ENGINE

In the first half of the paper, various types of processing pertaining to a polygon, using the 4×4 determinant theories are explained along with a new containment test algorithm of a point in a polygon. In the latter half of the paper, a general-purpose geometric processor, the POLYGON ENGINE, is presented which can deal with various types of interference problems, such as Boolean operations in solid modelling, hidden line and surface eliminations, ray tracing and so on. It is, a successor of the TRIANGLE PROCESSOR and is also based upon the 4×4 determinant theories [4–6]. While the TRIANGLE PROCESSOR processes a triangulated polygon on a triangle-by-triangle basis, the POLYGON ENGINE can treat a polygon without triangulation. The latter is expected to be more functional, more efficient and easier to use.     

Ergodicity of filtering process by vanishing discount approach

A new approach to study ergodicity of filtering processes is presented. It is based on the vanishing discount approach to discounted functional of filtering process. We show that limit superior of the Cesaro averages of the functionals is the same for all initial conditions from which the uniqueness of invariant measures of filtering processes follows. The approach is based on certain assumption for which we provide a sufficient condition using concavity arguments. In addition we show the existence of solutions to the Poisson equation corresponding to filtering process with concave functional. The assumptions are then extended to the controlled case and using similar concave arguments we obtain the existence of solutions to the Bellman equation corresponding to partially observed average cost per unit time problem.     

基于动态时间规整的手势加速度信号识别

为了提高基于加速度传感器的动态手势识别算法的性能,本文采用了动态时间规整(DTW)识别算法。通过该算法计算测试模板和参考模板的相似度,从而得出识别结果。为了验证该方法,建立了一套手势加速度无线采集系统,并采集了41个志愿者的手势信息。实验结果表明,该方法手势平均识别率在97%以上。与HMM识别算法相比,DTW识别算法在识别的准确率上比HMM识别算法更具优势。     

实现多无线接入点网络连接模型的研究

本文提出一种负载均衡模型,此模型不仅考虑AP的信号强度,而且要考虑现有AP已接入无线工作站的数量还有连接质量因素。除此之外,还讨论了实现此模型的无线协议的扩充。     

Automatic annotation of tennis games: An integration of audio,vision, and learning

Fully automatic annotation of tennis game using broadcast video is a task with a great potential but with enormous challenges. In this paper we describe our approach to this task, which integrates computer vision, machine listening, and machine learning. At the low level processing, we improve upon our previously proposed state-of-the-art tennis ball tracking algorithm and employ audio signal processing techniques to detect key events and construct features for classifying the events. At high level analysis, we model event classification as a sequence labelling problem, and investigate four machine learning techniques using simulated event sequences. Finally, we evaluate our proposed approach on three real world tennis games, and discuss the interplay between audio, vision and learning. To the best of our knowledge, our system is the only one that can annotate tennis game at such a detailed level.     

聚众事件的建模与实现

在视频监控领域聚众等异常事件检测有着广泛的应用前景,然而相关研究在国内发展还比较缓慢。在这里给出了基于隐马尔科夫模型的聚众事件的检测方法,其简单过程如下:首先在高斯混合模型检测出目标的基础上,针对聚众事件视频序列的特性,完成了关于帧图像二元组的特征提取;然后,在合理选择初始模型的前提下使用Baum-Welch算法训练聚众事件的隐马尔科夫模型;最后通过实拍的视频序列验证其有效性。     

Adaptive signal processing of asset price dynamics with predictability analysis

In this paper we illustrate the optimal filtering of log returns of commodity prices in which both the mean and volatility are modulated by a hidden Markov chain with finite state space. The optimal estimate of the Markov chain and the parameters of the price model are given in terms of discrete-time recursive filters. We provide an application on a set of high frequency gold price data for the period 1973-2006 and analyse the h-step ahead price predictions against the Diebold-Kilian metric. Within the modelling framework where the mean and volatility are switching regimes, our findings suggest that a two-state hidden Markov model is sufficient to describe the dynamics of the data and the gold price is predictable up to a certain extent in the short term but almost impossible to predict in the long term. The proposed model is also benchmarked with ARCH and GARCH models with respect to price predictability and forecasting errors.     

Type-2 fuzzy Gaussian mixture models

This paper presents a new extension of Gaussian mixture models (GMMs) based on type-2 fuzzy sets (T2 FSs) referred to as T2 FGMMs. The estimated parameters of the GMM may not accurately reflect the underlying distributions of the observations because of insufficient and noisy data in real-world problems. By three-dimensional membership functions of T2 FSs, T2 FGMMs use footprint of uncertainty (FOU) as well as interval secondary membership functions to handle GMMs uncertain mean vector or uncertain covariance matrix, and thus GMMs parameters vary anywhere in an interval with uniform possibilities. As a result, the likelihood of the T2 FGMM becomes an interval rather than a precise real number to account for GMMs uncertainty. These interval likelihoods are then processed by the generalized linear model (GLM) for classification decision-making. In this paper we focus on the role of the FOU in pattern classification. Multi-category classification on different data sets from UCI repository shows that T2 FGMMs are consistently as good as or better than GMMs in case of insufficient training data, and are also insensitive to different areas of the FOU. Based on T2 FGMMs, we extend hidden Markov models (HMMs) to type-2 fuzzy HMMs (T2 FHMMs). Phoneme classification in the babble noise shows that T2 FHMMs outperform classical HMMs in terms of the robustness and classification rate. We also find that the larger area of the FOU in T2 FHMMs with uncertain mean vectors performs better in classification when the signal-to-noise ratio is lower.