Closed-loop subspace-based identification algorithm using third-order cumulants

The problem of closed-loop system identification for coloured noise system without any knowledge of feedback controller is considered. We develop a solution to this problem in the framework of subspace identification based on high-order cumulants. The key of the developed algorithm is using the properties that the third-order cumulants are insensitive to any coloured Gaussian noises. By post-multiplying a suitable instrumental variable to the noise terms, the cross third-order cumulants are constructed that become zero when the noises are Gaussian distributed, and meanwhile the column rank of extended observability matrix is maintained. Thus, the standard subspace identification algorithms can be extended to closed-loop system corrupted by arbitrary coloured noises. A numerical simulation is presented to demonstrate the proposed algorithm.     

一种基于高阶累积量的因果结构学习算法

从观测数据中学习因果结构具有重要的应用价值。目前,一类学习因果结构的方法是基于函数因果模型假设,通过检验噪声与原因变量的独立性来学习因果结构。然而,该类方法涉及高计算复杂度的独立性检验过程,影响结构学习算法的实用性和鲁棒性。为此,提出了一种在线性非高斯模型下,利用高阶累积量作为独立性评估的因果结构学习算法。该算法主要分为两个步骤,第一个步骤是利用基于条件独立性约束的方法学习到因果结构的马尔可夫等价类,第二个步骤是定义了一种基于高阶累积量的得分,该得分可以判别两个随机变量的独立性,从而可以从马尔可夫等价类中搜索到最佳独立性得分的因果结构作为算法的输出。该算法的优势在于：a)相比基于核方法的独立性检验,该方法有较低的计算复杂度;b)基于得分搜索的方法,可以得到一个最匹配数据生成过程的模型,提高学习方法的鲁棒性。实验结果表明,基于高阶累积量的因果结构学习方法在合成数据中F₁得分提高了5%,并在真实数据中学习到更多的因果方向。     

A SGeMS code for pattern simulation of continuous and categorical variables: FILTERSIM

The new multiple-point geostatistical algorithm (FILTERSIM), which can handle both categorical and continuous variable training images, is implemented in the SGeMS software. The spatial patterns depicted by the training image are first summarized into a few filter scores; then classified into pattern groups in the filter score space. The sequential simulation approach proceeds by associating each conditioning data event to a closest pattern group using some distance function. A training pattern is then sampled from that group and pasted back onto the simulation grid. Local multiple-point statistics carried by patterns are captured from the training image, and reproduced in the simulation realizations. Hence complex multiple-scale geological structures can be re-constructed in the simulation grid, conditional to a variety of sub-surface data such as well data and seismic survey.     

基于高阶累积量和DNN模型的井下信号识别方法

针对矿井复杂异构的无线环境,提出一种基于高阶累积量和DNN模型的井下信号识别方法,实现了井下BPSK,QPSK,8PSK,2FSK,4FSK,8FSK,32QAM,64QAM,OFDM等数字信号的自动调制识别。分析得到9种数字信号的高阶累积量理论值,并通过傅里叶变换提高信号辨识度;分析井下小尺度衰落信道对高阶累积量的影响,推导出经过井下衰落信道后信号的高阶累积量计算表达式,根据高阶累积量理论值构造特征参数并训练DNN模型,实现信号识别。仿真分析结果表明,该方法在矿井Nakagami-m衰落信道下有出色的调制识别性能,信噪比为-5 dB时平均正确识别率为89.2%以上,信噪比为5 dB以上时平均正确识别率为100%。该方法为在特殊复杂环境下的信号识别检测提供了新思路。     

基于小波和高阶累积量的数字调制识别算法

利用基于高阶累积量的数字调制识别算法对数字调制信号进行分类识别时,六阶及六阶以上累积量的计算过于复杂,且多进制频移键控(MFSK)与8PSK信号各阶累积量的值均相等,直接计算无法识别。针对此问题,提出了一种基于小波和高阶累积量相结合的分类算法,先对MFSK与8PSK信号进行小波变换,再利用四阶累积量进行识别。实验证明,利用该算法所提取的特征参数能有效抑制高斯白噪声,除了识别2ASK/BPSK,4ASK,2FSK,4FSK,QPSK,8PSK信号外,还可识别16QAM,并且计算量小,易于实现。当信噪比大于等于3dB时,总体识别率达到96%。与已有算法相比,仿真结果证明了该算法的优越性。     

基于双通道变分自编码器的高光谱图像分类

针对现有高光谱图像变分自编码器（variational autoencoder,VAE）分类算法存在空间和光谱特征利用效率低的问题,提出一种基于双通道变分自编码器的高光谱图像深度学习分类算法。通过构建一维条件变分自编码器（conditional variational autoencoder,CVAE）特征提取框架和二维循环通道条件变分自编码（channel-recurrent conditional variational autoencoders,CRCVAE）特征提取框架分别提取高光谱图像的光谱特征和空间特征,将光谱特征向量和空间特征向量叠加形成空谱联合特征向量,将联合特征送入Softmax分类器中进行分类。在Indian pines和Pavia University两种高光谱数据集上进行了分析验证,实验结果显示,与其他算法相比,提出的算法在总分类精度、平均分类精度和Kappa系数等评价指标上至少提高了3.40、2.75和3.57个百分点,结果显示提出的算法得到了最高的分类精度和更好的可视化效果。     

基于多分辨率生成对抗网络的空间数据不确定性重建方法

在空间数据不确定性重建领域,多点统计法（MPS）得到了广泛的应用,但由于计算成本较高,其适用性受到了影响。通过使用金字塔结构的全卷积生成对抗网络（GAN）模型学习不同分辨率的训练图像,提出了一种基于多分辨率GAN模型的空间数据重建方法。该方法从高分辨率训练图像中捕获细节特征,从低分辨率训练图像中捕获大范围特征,因此该方法重建的图像包含训练图像的全局和局部结构信息,同时保持一定的随机性。把所提算法与MPS中的代表性算法以及应用于空间数据重建的GAN方法进行对比的结果表明,所提方法10次重建的总时间降低了约1 h,其平均孔隙度与训练图像孔隙度的差值降低至0.000 2,并且其变差函数曲线和多点连接性函数（MPC）曲线更接近于训练图像,可见所提算法重建质量更好。     

Three-dimensional reconstruction of human limbs from tomographic views

The recently developed medical imaging technique of tomography can be used with amazing success to learn about the interior of a human body. Each tomograph, however, is limited to only one section of the specific human limb. A series of tomographs taken at irregular intervals and various angles would provide three-dimensional information of the interior of the limb. Currently available 3-D surface display algorithms have limitations, particularly when applied to clinically important image data requiring fast and flexible interactive analysis. In addition to the problem of computation time is the cost of specialized hardware. A new algorithm has been designed for use with three-dimensional medical images which attempts to overcome these limitations. A computer graphics system is described which reconstructs three-dimensional images from tomographic sectional data. This 3-D surface algorithm can be exploited in planning reconstructive bone surgery. An example which illustrates the versatility and speed of the new algorithm is presented.     

MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets

Information related to land cover is immensely important to global change science. In the past decade, data sources and methodologies for creating global land cover maps from remote sensing have evolved rapidly. Here we describe the datasets and algorithms used to create the Collection 5 MODIS Global Land Cover Type product, which is substantially changed relative to Collection 4. In addition to using updated input data, the algorithm and ancillary datasets used to produce the product have been refined. Most importantly, the Collection 5 product is generated at 500-m spatial resolution, providing a four-fold increase in spatial resolution relative to the previous version. In addition, many components of the classification algorithm have been changed. The training site database has been revised, land surface temperature is now included as an input feature, and ancillary datasets used in post-processing of ensemble decision tree results have been updated. Further, methods used to correct classifier results for bias imposed by training data properties have been refined, techniques used to fuse ancillary data based on spatially varying prior probabilities have been revised, and a variety of methods have been developed to address limitations of the algorithm for the urban, wetland, and deciduous needleleaf classes. Finally, techniques used to stabilize classification results across years have been developed and implemented to reduce year-to-year variation in land cover labels not associated with land cover change. Results from a cross-validation analysis indicate that the overall accuracy of the product is about 75% correctly classified, but that the range in class-specific accuracies is large. Comparison of Collection 5 maps with Collection 4 results show substantial differences arising from increased spatial resolution and changes in the input data and classification algorithm.     

Applying temporal constraints to the dynamic stereo problem

An algorithm is presented for the problem of the stereopsis of time-varuing images (the dynamic stereo problem). Dynamic stereopsis is the integration of two problems; static stereopsis and temporal correspondence. Rather than finding the intersection of these problems to be more difficult, it was found that by solving the two problem simultaneously, and thus incorporating the spatio-temporal context within which a scene exists, some of the hard subproblems belonging to stereopsis and temporal correspondence could be avoided. The algorithm relies on a general smoothness assumption to assign both disparity and temporal matches. A simple model of the motion of three-dimensional features is used to guide the matching process and to identify conditional matches which violate a general smoothness assumption. A spatial proximity rule is used to further restrict possible matches. The algorithm has been tested on both synthetic and real input sequences. Input sequences were chosen from three-dimensional moving light displays and from “real” grey-level digitized images.     

一种新的快速计算Legendre矩的方法

正交矩在模式识别，图像分析等领域有成功的应用，但由于正交矩的复杂性，有关正交矩的快速算法研究尚未得到很好的解决，该文提出一种 新的快速计算Ｌegendre矩的方法，该方法把基于像素点的二维Ｌegendre矩转换为线段的形式来计算，在计算出所有线段的积分后，使用扩展的Ｈatamian滤波方法来计算一维的Ｌegendre矩。结果显示新的算法有效地降低了计算的复杂度，并且，该方法能用于处理任意形状的物体。     

A ground truth based vanishing point detection algorithm

A Bayesian probability-based vanishing point detection algorithm is presented which introduces the use of multiple features and training with ground truth data to determine vanishing point locations. The vanishing points of 352 images were manually identified to create ground truth data. Each intersection is assigned a probability of being coincident with a ground truth vanishing point, based upon conditional probabilities of a number of features. The results of this algorithm are demonstrated to be superior to the results of a similar algorithm where each intersection is considered to be of equal importance. The advantage of this algorithm is that multiple features derived from ground truth training are used to determine vanishing point location.     

Vision-based mobile robots on highways

Intelligent vehicles are mobile robots on highways. They are expected to improve the safety, efficiency and environmental impacts of the current highway traffic systems. Vision systems will play an important role as sensors for the intelligent vehicles. This paper first compares the vision sensors with other sensing methods from an application point of view and then describes two vision systems, one which we have developed and another which we are developing. Two important features are required for the vision systems applied to intelligent vehicles: three-dimensional (3D) measurement capability and real-time operation. We chose a trinocular stereo vision scheme among a number of 3D vision processing methods because it is suitable for real-time operations with dedicated processor architectures. The trinocular stereo algorithm requires a large number of operations, but all the operations are relatively straightforward and, therefore, they are suitable for custom architecture implementation. The system takes three images simultaneously by using three TV cameras installed on a single horizontal line at the front grill of the test car. Vertical edges are extracted from these images and the spatial offsets (or disparities) among the images are calculated for measuring the distances to the objects. The first version was developed and installed in a car for highway testing. Two custom digital processors were developed: one for edge detection and the other for stereo matching. The test results were encouraging and the architectures based on ASIC (Application Specific Integrated Circuits) are 800 and 550 times more efficient, respectively, compared with conventional microprocessors for edge detection and stereo matching. The second version is currently being developed in order to further reduce the silicon area size. It uses hybrid analog/digital circuit technology while the first version uses only digital circuits. We are developing a hybrid analog/digital array processor chip which includes a large number of processing elements. Each processing element includes a digital memory unit, a data flow control switch unit and an analog arithmetic/logic unit. The analog arithmetic/logic unit reduces the silicon area size significantly compared with the digital one. The data flows among multiple processing elements in the array chip in a form of analog voltage. The data flow is controlled by the data flow switches. The digital memory unit controls the set-up of the data flow control switch and arithmetic/logic units.     

An effective approach for land-cover classification from airborne lidar fused with co-registered data

Airborne lidar provides accurate height information of objects on the earth and has been recognized as a reliable and accurate surveying tool in many applications. In particular, lidar data offer vital and significant features for urban land-cover classification, which is an important task in urban land-use studies. In this article, we present an effective approach in which lidar data fused with its co-registered images (i.e. aerial colour images containing red, green and blue (RGB) bands and near-infrared (NIR) images) and other derived features are used effectively for accurate urban land-cover classification. The proposed approach begins with an initial classification performed by the Dempster–Shafer theory of evidence with a specifically designed basic probability assignment function. It outputs two results, i.e. the initial classification and pseudo-training samples, which are selected automatically according to the combined probability masses. Second, a support vector machine (SVM)-based probability estimator is adopted to compute the class conditional probability (CCP) for each pixel from the pseudo-training samples. Finally, a Markov random field (MRF) model is established to combine spatial contextual information into the classification. In this stage, the initial classification result and the CCP are exploited. An efficient belief propagation (EBP) algorithm is developed to search for the global minimum-energy solution for the maximum a posteriori (MAP)-MRF framework in which three techniques are developed to speed up the standard belief propagation (BP) algorithm. Lidar and its co-registered data acquired by Toposys Falcon II are used in performance tests. The experimental results prove that fusing the height data and optical images is particularly suited for urban land-cover classification. There is no training sample needed in the proposed approach, and the computational cost is relatively low. An average classification accuracy of 93.63% is achieved.     

Blind parametric identification of non-Gaussian FIR systems using higher order cumulants

Two approaches are introduced for the identification of linear time-invariant systems when only output data are available. The input sequences are independent and must be non-Gaussian. To estimate the parameters of the system, we use only the fourth-order cumulants of the output, which may be contaminated by an additive, zero mean, Gaussian noise of unknown variance. To measure the performance of the proposed algorithms against existing methods, we compared them with the Zhang's algorithm. Simulations verify an apparent performance of the second algorithm, compared with the first and Zhang's algorithms, in a low signal-to-noise ratio and for small data. The simulation results show that the first algorithm has the same performance compared with Zhang's one. But the second algorithm achieves better performance compared with the first and Zhang is algorithm. For validation purposes, the second algorithm is used to search for a model able to describe and simulate the data set representing the wind speed.     

Algorithm of gaussian sum filter based on high-order UKF for dynamic state estimation

In this work we consider the state estimation problem in nonlinear/non-Gaussian systems. A new version of Gaussian sum estimation algorithm is developed here based on high-order unscented Kalman filter (HUKF). A sigma point selection method, high-order unscented transformation (HUT) technique is proposed for the HUKF, which can approximate the Gaussian distributions more accurately. We present the systematic formulation of Gaussian filters and develop efficient and accurate numerical integration of the optimal filter. We then go on to extend the use of the HUKF to discrete-time, nonlinear systems with additive, possibly non-Gaussian noise. The resulting filtering algorithm, called the Gaussian sum high-order unscented Kalman filter (GS-HUKF) approximates the predicted and posterior densities as a finite number of weighted sums of Gaussian densities. It is corroborated in the theoretical analysis and the simulation that the proposed Gaussian sum HUKF has integrated advantages with respect to computational accuracy and time complexity for nonlinear non-Gaussian filtering problems.     

WCNS高精度并行软件的大规模计算研究

本文通过求解任意坐标系下的定常雷诺平均N-S方程和SST两方程湍流模型,采用五阶精度的加权紧致非线性格式(WCNS-E-5),实现流场的高精度数值模拟;基于分布式存储系统,采用MPI并行编程环境、非堵塞通信机制和遗传算法负载平衡,实现高精度模拟软件的并行化。在国防科学技术大学高性能计算应用研究中心的"天河"系统上完成软件移植、测试,通过对DLR-F6翼身组合体的模拟,说明软件并行策略和开发的正确性。最后,实现某民机全机的高精度并行模拟,网格规模达到1亿,为下一步WCNS高精度并行软件的大规模工程实际应用打下了坚实基础。     

数字卫星广播系统中的调制识别研究

通信信号的调制类型识别在非协作通信中具有重要研究意义。针对卫星通信中调制方式不断向高阶发展的情况,提出了一种对高阶数字调相信号的调制方式自动识别算法。该算法利用四阶以及更高阶的信号累积量和高阶信号的相位分布特征来提取出信号的特征参数,对QPSK, 8PSK , 16APSK和32 APSK 四种调制方式进行了有效区分。文中给出了接收信号的处理流程图,并对算法进行了仿真。理论和仿真结果都表明,这种算法对信号的相位偏差具有不变性,同时对加性高斯噪声也不敏感,具有一定的稳健性,在特定的数据长度和较低信噪比条件下,可得到很高的识别率(>95%)。     

Frame-rate spatial referencing based on invariant indexing and alignment with application to online retinal image registration

This paper describes an algorithm to continually and accurately estimate the absolute location of a diagnostic or surgical tool (such as a laser) pointed at the human retina, from a series of image frames. We treat the problem as a registration problem using diagnostic images to build a spatial map of the retina and then registering each online image against this map. Since the image location where the laser strikes the retina is easily found, this registration determines the position of the laser in the global coordinate system defined by the spatial map. For each online image, the algorithm computes similarity invariants, locally valid despite the curved nature of the retina, from constellations of vascular landmarks. These are detected using a high-speed algorithm that iteratively traces the blood vessel structure. Invariant indexing establishes initial correspondences between landmarks from the online image and landmarks stored in the spatial map. Robust alignment and verification steps extend the similarity transformation computed from these initial correspondences to a global, high-order transformation. In initial experimentation, the method has achieved 100 percent success on 1024 /spl times/ 1024 retina images. With a version of the tracing algorithm optimized for speed on 512 /spl times/ 512 images, the computation time is only 51 milliseconds per image on a 900MHz PentiumIII processor and a 97 percent success rate is achieved. The median registration error in either case is about 1 pixel.     

基于高阶累积量和三角矩的联合多参数特征OFDM信号盲识别

提出一种瑞利衰落信道条件下的多载波调制盲识别算法,用以区分多载波调制信号(如OFDM)和数字单载波调制信号(如:MASK、MFSK、MPSK、MQAM)。该算法对传统算法进行了改进,提出了利用信号的高阶累积量构造的组合识别参数以及信号的三角矩特征参数来分类单载波信号与OFDM多载波信号。该算法不需要预先知道信号的载波频率和波特率,只需从中频信号直接进行识别处理。仿真结果表明,该算法具有抗多径能力强、识别率高的优点,在SNR高于0 dB时识别率可达100%。