基于改进型高斯混合模型的嵌入式实时运动检测系统

混合高斯模型由于其计算量大,算法结构复杂,难以在嵌入式系统中实现运动物体的实时检测,为解决此问题,文中提出了一种基于改进型混合高斯模型的实时运动检测方案,对混合高斯模型进行简化和结构调整,同时进行了C语言层面和CPU层级的优化,使其更合适于嵌入式平台,并详细分析了DM6446平台的软硬件设计,介绍了该算法在DM6446平台上的实现过程;实验结果表明:该系统能够有效克服外界环境变化带来的干扰,能够实时检测,可以实现多目标跟踪。     

高斯混合模型盲信号分离方法的CUDA实现

对一组线性瞬时混合信号,采用高斯混合模型拟合各个独立源的概率密度分布进行分离,其复杂度随信号源数量、高斯混合模型阶数的增加急剧上升。提出用统一计算设备架构(compute unified device architecture,CUDA)对该分离方法进行设计,实现该方法的并行加速处理。实验结果表明,此加速方案可以有效降低该盲分离方法的时间复杂度。     

基于CPU+FPGA的实时临空信道模拟器设计

信道建模是研究临近空间通信技术、评估系统传输特性的基础和重要方法。临近空间飞行器从低速向高速发展,信道变化频次随飞行器速度增大而愈加频繁。为实现对临空通信过程中信道衰落的实时仿真,研制基于中央处理器(Central Processing Unit,CPU)和现场可编辑门阵列(Field-Programmable Gate Array,FPGA)架构、参数1 ms更新一次的临空信道模拟器。该模拟器内置气象性损耗模型和无线信道衰落模型,CPU端通过设计状态机控制模块、优化参数计算逻辑、优化操作系统来压缩信道参数的计算时间。FPGA部分设计关键信息同步方案、基于状态机的双保障更新机制保证参数的可靠稳定传输、并行加速处理实现信息交互和增强信号处理速度。实测结果表明,上述方案有效提升了临空信道模拟器的参数计算速度和信号处理速度,可实现信道参数随收发机运动1 ms更新一次。     

高维混合入侵检测数据的单类分类算法研究

针对传统的方法未能有效地处理具有高维、混合以及不平衡的特性的入侵检测数据,结合单类分类概念和均值计算方法,提出一种单类分类K近邻(KNN)均值算法。其使用主成分分析混合(PCA mix)方法对高维混合数据进行降维预处理,并运用Bootstrap方法计算决策边界,提高准确性。通过使用三个不同的公开数据集进行实验,并与单类分类支持向量机(OCSVM)算法作对比,其分辨精度提高至94%以上,充分验证了提出算法的优越性。     

基于混合PSO的高斯混合模型地形分类

提出了一种基于改进的混合粒子群优化(particle swarm optimization,PSO)算法的高斯混合模型地形分类方法。高斯混合模型的求解通常是使用期望最大化算法(expectation maximization,EM),然而EM算法易陷入局部最优,收敛速度不稳定且对初值敏感。因此引入混合PSO算法,并对其进行了一系列改进。实验结果表明:改进后的算法较其它优化算法提高了全局搜索能力和收敛速度,利用该算法求解高斯混合模型可以提高参数估计的精度,并且在户外场景图像的地形分类实验中所提出的地形分类方法也表现优良。     

一个用于数据并行语言计算划分的时序优化模型

一个程序中数据并行语句的计算划分(CP)对该程序的运行性能有决定性的作用.尽管人们对这一问题已经进行了广泛的研究,但这些研究的重点都集中在如何提高被选择计算划分的空间局部性上.针对并行循环结构的计算划分问题,提出了一个时序优化模型.在该模型中,一个计算划分被表示成一个有向图,在把并行语句中的操作映射到各个处理器的同时,给出了被分配到不同处理器上的操作之间的相关性.对于一条数据并行语句,时序优化模型对它的每个计算划分选择方案分别采用多种有效的优化策略进行优化;并综合考虑各个计算划分选择方案的负载平衡性、处理器间的操作依赖性、数据访问的空间局部性和时间局部性四个方面的因素,估算每个方案的执行效率;最后从这些方案中选择一个执行效率最优的方案作为该语句的计算划分.作者已在HPF编译器p-HPF采用时序优化模型实现了对FORALL结构的支持.实验结果表明,该模型具有非常好的通用性,对不同领域多种数据并行问题均取得了理想的加速比.同时,只需略微改动,该模型也可用于其他类型数据并行语句的计算划分.     

自适应多模快速背景差算法

在多高斯模型的基础上,从场景中模型分布不均匀性出发,提出了一种新的快速背景差算法。该算法针对混合高斯模型中固定模型数量不足的问题,建立了模型产生和退出的机制,使模型数量能够自动适应场景特点,实现了高斯模型的实时自适应分布,即提高了准确性又有效地减少了模型的总量;同时,针对混合高斯模型中计算量大的问题,对模型参数的计算进行了优化,将耗时的浮点运算转化为整型运算,减少了计算量;算法中引入了生存时间和模型重现频率的概念,通过对模型重现频率的限制有效抑制高频噪声。与混合高斯模型的实验结果对比说明,该快速算法保持了原算法的优点,执行速度提高1倍以上,检测结果准确,算法内存消耗小,前景轮廓清晰,抑制高频噪声的能力强,整体效果优于混合高斯模型的背景差算法。     

引力搜索优化ELM的企业财务危机预警方法

为提高企业财务危机的预测准确率,提出一种基于引力搜索算法优化核极限学习机(KELM)的并行模型PHGSA-KELM。模型考虑了特征选择机制和参数优化两者对KELM模型起着同等重要的作用,提出改进的引力搜索算法(HGSA)同步实现特征选择机制和KELM参数优化,同时设计的线性加权多目标函数综合考虑了分类精度和特征子集数量,改善了算法的分类性能,并且基于多核平台的多线程并行方式进一步提高了算法的计算效率。通过真实数据集的实验结果表明,提出的模型不仅获得了较少的特征子集个数,找出了与企业财务危机紧密相关的特征,得到了很高的分类准确率,并且计算效率也得到极大提高,是一种有效的企业财务危机预警模型。     

一种基于高斯混合模型的不均衡分类方法

为应对不均衡分类问题,提高分类准确率,提出了一种基于高斯混合模型的混合采样集成方法GMHSE(Gaussian-Mixture-model-based Hybrid Sampling Ensemble method),首先通过高斯混合模型将数据划分成多个类簇,然后在每个类簇上混合采样获得多个数据子集,最后基于Baggi...     

面向高维混合不平衡信贷数据的单类分类方法

为实现对高维混合、不平衡信贷数据中的不良贷款者的准确预测,从降维预处理和分类算法两方面进行优化,提出一种基于混合数据主成分分析（Principal Component Analysis of Mixed Data,PCAmix）预处理的单类[K]近邻[（K]-Nearest Neighbor,[KNN）]计算均值算法。针对传统的主成分分析（Principal Component Analysis,PCA）不能直接处理定性变量的问题,使用PCAmix降维预处理数据,为规避不平衡数据在二分类模型中性能较差的缺点,采用单类分类和[K]近邻算法邻居计算的思想,仅采用多数类训练模型。利用Bootstrap方法找到最佳的决策边界,使得正负样本最大限度地分离,最终准确预测客户的违约风险。采用UCI数据库中的German和Default个人信用评分数据集进行验证,实验结果表明该算法在处理高维混合、不平衡的信贷数据上具有较好的分类效果。     

Annealed Kullback–Leibler divergence minimization for generalized TSP,spot identification and gene sorting

This work explores learning LCGM (lattice-connected Gaussian mixture) models by annealed Kullback–Leibler (KL) divergence minimization for a hybrid of topological and statistical pattern analysis. The KL divergence measures the general criteria of learning an LCGM model that is composed of a lattice of multivariate Gaussian units. A planar lattice emulates topological order of cortex-like neighboring relations and built-in parameters of connected Gaussian units represent statistical features of unsupervised data. Learning an LCGM model involves collateral optimization tasks of resolving mixture combinatorics and extracting geometric features from high-dimensional patterns. Under assumption that mixture combinatorics encoded by Potts variables obey the Boltzmann distribution, approximating their joint probability by the product of individual probabilities is qualified by the KL divergence whose minimization under physical-like deterministic annealing faithfully optimizes involved mixture combinatorics and geometric features. Numerical simulations show the proposed annealed KL divergence minimization is effective and reliable for solving generalized TSP, spot identification, self-organization and visualization and sorting of yeast gene expressions.     

多种群并行协作的粒子群算法

针对高维复杂优化问题在求解时容易产生维数灾难导致算法极易陷入局部最优的问题,提出一种能够综合考虑高维复杂优化问题的特性,动态调整进化策略的多种群并行协作的粒子群算法。该算法在分析高维复杂问题求解过程中的粒子特点的基础上,建立融合环形拓扑、全连接形拓扑和冯诺依曼拓扑结构的粒子群算法的多种群并行协作的网络模型。该模型结合3种拓扑结构的粒子群算法在解决高维复杂优化问题时的优点,设计一种基于多群落粒子广播-反馈的动态进化策略及其进化算法,实现高维复杂优化环境中拓扑的动态适应,使算法在求解高维单峰函数和多峰函数时均具有较强的搜索能力。仿真结果表明,该算法在求解高维复杂优化问题的寻优精度和收敛速度方面均有良好的性能。     

A real-time CFAR thresholding method for target detection in hyperspectral images

In order to support immediate decision-making in critical circumstances such as military reconnaissance and disaster rescue, real-time onboard implementation of target detection is greatly desired. In this paper, a real-time thresholding method (RT-THRES) is proposed to obtain the constant false alarm rate (CFAR) thresholds for target detection in real-time circumstances. RT-THRES utilizes Gaussian mixture model (GMM) to track and fit the distribution of the target detector’s outputs. GMM is an extension to Gaussian probability density function, which could approximate any distribution smoothly. In this method, GMM is utilized to model the detector’s output, and then the detection threshold is calculated to achieve a CFAR detection. The conventional GMM’s parameter estimation by Expectation-Maximization (EM) requires all data samples in the dataset to be involved during the procedure and the the parameters would be re-estimated when new data samples available. Thus, GMM is difficult to be applied in real-time processing when newly observed data samples coming progressively. To improve GMM’s application availability in time-critical circumstance, an optimization strategy is proposed by introducing the Incremental GMM (IGMM) which allows GMM’s parameter to be estimated online incrementally. Experiments on real hyperspectral image and synthetic dataset suggest that RT-THRES can track and model the detection outputs’ distribution accurately which ensures the accuracy of the calculation of CFAR thresholds. Moreover, by applying the optimization strategy the computational consumption of RT-THRES maintains relatively low.

     

一种基于奔腾SIMD指令的快速背景提取方法

论文提出一种基于Intel奔腾SIMD指令的快速背景提取方法。在一种改进的混合高斯背景模型中,Jeffrey值的计算和背景模型的更新等存在着很高的内在SIMD并行性,通过将数据按照SSE数据类型组织,实现了混合高斯背景模型的SIMD算法。实验结果表明:嵌入奔腾SIMD指令的方法比传统计算提高75%左右的性能,加速了背景提取的速度,达到了实时处理的要求,具有较大的实际应用价值。     

Particle Swarm Optimization for Sorted Adapted Gaussian Mixture Models

Recently, we introduced the sorted Gaussian mixture models (SGMMs) algorithm providing the means to tradeoff performance for operational speed and thus permitting the speed-up of GMM-based classification schemes. The performance of the SGMM algorithm depends on the proper choice of the sorting function, and the proper adjustment of its parameters. In the present work, we employ particle swarm optimization (PSO) and an appropriate fitness function to find the most advantageous parameters of the sorting function. We evaluate the practical significance of our approach on the text-independent speaker verification task utilizing the NIST 2002 speaker recognition evaluation (SRE) database while following the NIST SRE experimental protocol. The experimental results demonstrate a superior performance of the SGMM algorithm using PSO when compared to the original SGMM. For comprehensiveness we also compared these results with those from a baseline Gaussian mixture model-universal background model (GMM-UBM) system. The experimental results suggest that the performance loss due to speed-up is partially mitigated using PSO-derived weights in a sorted GMM-based scheme.     

Parallel implementation of background subtraction algorithms for real-time video processing on a supercomputer platform

Results of evaluation of the background subtraction algorithms implemented on a supercomputer platform in a parallel manner are presented in the article. The aim of the work is to chose an algorithm, a number of threads and a task scheduling method, that together provide satisfactory accuracy and efficiency of a real-time processing of high-resolution camera images, maintaining the cost of resources usage at a reasonable level. Two selected algorithms: the Gaussian mixture models and the Codebook, are presented and their computational complexity is discussed. Various approaches to the parallel implementation, including assigning the image pixels to threads, the task scheduling methods and the thread management systems, are presented. The experiments were performed on a supercomputer cluster, using a single machine with 12 physical cores. The accuracy and performance of the implemented algorithms were evaluated for varying image resolutions and numbers of concurrent processing threads. On a basis of the evaluation results, an optimal configuration for the parallel implementation of the system for real-time video content analysis on a supercomputer platform was proposed.     

基于并行结构的DOA聚类算法及FPGA实现

脉冲到达角（DOA）是脉冲信号分选中可利用的重要参数。目前,利用DOA进行的脉冲分选都是基于传统的串行聚类算法,实时性能差。本文针对阈值分割的聚类方式,设计了一种基于并行流水结构的实时聚类算法,使单个DOA的聚类可在单周期内完成,并通过对聚类数目分裂过多的情况进行控制,保证了算法的稳定性和有效性。文章还介绍了算法在FPGA上的实现方法,以及应用在XilinxV2P芯片上的实时性能,并对其聚类性能进行了比较分析。     

补偿模型误差的采油过程多目标优化

通过对采油过程的分析,本文建立了以最大化区块产油量和最小化单位产油量综合能耗为目标的优化模型.针对单位产油量综合能耗模型的输出与实际值存在较大误差,利用高斯混合模型(GMM)对单位产油量综合能耗混合模型误差特性进行描述,实现对模型的误差补偿,并将误差补偿后的单位产油量综合能耗引入到已建的优化模型中,使得优化结果更接近实际最优值.在此基础上,采用带精英策略的快速非支配排序遗传算法(NSGA-Ⅱ)用于所建的多目标优化模型求解.最后,以某采油作业区一区块生产过程为例进行仿真验证,结果表明了所建模型和优化算法的有效性.     

Highly accurate real-time image steganalysis based on GPU

With the development of steganography, it is required to build high-dimensional feature spaces to detect those sophisticated steganographic schemes. However, the huge time cost prevents the practical deployment of high-dimensional features for steganalysis. SRM and DCTR are important steganalysis feature sets in spatial domain and JPEG domain, respectively. It is necessary to accelerate the extraction of DCTR and SRM to make them more usable in practice, especially for some real-time applications. In this paper, both DCTR and SRM are implemented on the GPU device to exploit the parallel power of the GPU and some optimization methods are presented. For implementation of DCTR, we first utilize the separability and symmetry of two-dimensional discrete cosine transform in decompression and convolution. Then, in order to make phase-aware histograms favorable for parallel GPU processing, we convert them into ordinary 256-dimensional histograms. For SRM, in computing residuals, we specify the computation sequence and spilt the inseparable two-dimensional kernel into several row vectors. When computing the four-dimensional co-occurrences, we convert them into one-dimensional histograms which are more suitable for parallel computing. The experimental results show that the proposed methods can greatly accelerate the extraction of DCTR and SRM, especially for images of large size. Our methods can be applied to the real-time steganalysis system.     

Multimode process monitoring with PCA mixture model

For multimode processes, Gaussian mixture model (GMM) has been applied to estimate the probability density function of the process data under normal-operational condition in last few years. However, learning GMM with the expectation maximization (EM) algorithm from process data can be difficult or even infeasible for high-dimensional and collinear process variables. To address this issue, a novel multimode process monitoring approach based on PCA mixture model is proposed. First, the PCA technique is directly applied to the covariance matrix of each Gaussian component to reduce the dimension of process variables and to obtain nonsingular covariance matrices. Then the Bayesian Ying-Yang incremental EM algorithm is adopted to automatically optimize the number of mixture components. With the obtained PCA mixture model, a novel process monitoring scheme is derived for fault detection of multimode processes. Three case studies are provided to evaluate the monitoring performance of the proposed method.