Non-stationary power signal processing for pattern recognition using HS-transform

A new approach to time-frequency transform and pattern recognition of non-stationary power signals is presented in this paper. In the proposed work visual localization, detection and classification of non-stationary power signals are achieved using hyperbolic S-transform known as HS-transform and automatic pattern recognition is carried out using GA based Fuzzy C-means algorithm. Time-frequency analysis and feature extraction from the non-stationary power signals are done by HS-transform. Various non-stationary power signal waveforms are processed through HS-transform with hyperbolic window to generate time-frequency contours for extracting relevant features for pattern classification. The extracted features are clustered using Fuzzy C-means algorithm and finally the algorithm is optimized using genetic algorithm to refine the cluster centers. The average classification accuracy of the disturbances is 93.25% and 95.75% using Fuzzy C-means and genetic based Fuzzy C-means algorithm, respectively.     

2D Vector field approximation using linear neighborhoods

We present a vector field approximation for two-dimensional vector fields that preserves their topology and significantly reduces the memory footprint. This approximation is based on a segmentation. The flow within each segmentation region is approximated by an affine linear function. The implementation is driven by four aims: (1) the approximation preserves the original topology; (2) the maximal approximation error is below a user-defined threshold in all regions; (3) the number of regions is as small as possible; and (4) each point has the minimal approximation error. The generation of an optimal solution is computationally infeasible. We discuss this problem and provide a greedy strategy to efficiently compute a sensible segmentation that considers the four aims. Finally, we use the region-wise affine linear approximation to compute a simplified grid for the vector field.     

Sorting helps for voronoi diagrams

It is well known that, using standard models of computation, Ω(n logn) time is required to build a Voronoi diagram forn point sites. This follows from the fact that a Voronoi diagram algorithm can be used to sort. However, if the sites are sorted before we start, can the Voronoi diagram be built any faster? We show that for certain interesting, although nonstandard, types of Voronoi diagrams, sorting helps. These nonstandard types of Voronoi diagrams use a convex distance function instead of the standard Euclidean distance. A convex distance function exists for any convex shape, but the distance functions based on polygons (especially triangles) lead to particularly efficient Voronoi diagram algorithms. Specifically, a Voronoi diagram using a convex distance function based on a triangle can be built inO (n log logn) time after initially sorting then sites twice. Convex distance functions based on other polygons require more initial sorting.     

利用Voronoi图构建多级TIN

由于地形地貌的复杂性，地形高程点的数据量往往很大。由离散高程点生成的TIN同样也具有相当大的数据量，这严重制约了TIN的分析、三维可视化等处理速度。为此，文中根据LOD的设计思想，采用逐级综合，生成多级TIN。随后，将多级TIN进行合并，统一存储，消除了冗余数据。采用局部更新、分级调用的策略使得处理速度显著提高，且受数据量的影响很小，可以较好地应用在地理信息系统中。     

Segmenting dot patterns by voronoi diagram concavity

This correspondence defines a signed distance, called ``internal concavity,' on paths of the Voronoi diagram of a dot pattern. An algorithm using internal concavity to segment dot patterns is described. The segmentation algorithm produces subsets of the Dirichlet tessellation (Delaunay triangulation) of the dot pattern.     

A multilevel approach to pattern processing

This paper presents a methodology for describing multilevel pattern processing systems. It is suggested that any pattern processor can be adequately described in terms of multiple hierarchies of two types of fundamental mechanism: (1) a process which performs the pattern recognition functions of analysis and synthesis and (2) a process which performs the syntactic functions of parsing and generation. A computer implementation of these principles is outlined which enables a range of systems to be configured. Examples of speech and non-speech pattern processing are presented.     

Twig pattern查询处理研究综述和分析 *

介绍了 twig pattern查询处理和索引技术的研究现状 ,对一些典型的 twig pattern查询处理方法进行了分析和评价 ,指出其中存在的优点和不足 ,展望了未来 twig pattern查询处理研究的关键问题和研究方向。     

Approximate voronoi cell computation on spatial data streams

Several studies have exploited the properties of Voronoi diagrams to improve the efficiency of variations of the nearest neighbor search on stored datasets. However, the significance of Voronoi diagrams and their basic building blocks, Voronoi cells, has been neglected when the geometry data is incrementally becoming available as a data stream. In this paper, we study the problem of Voronoi cell computation for fixed 2-d site points when the locations of the neighboring sites arrive as a spatial data stream. We show that the non-streaming solution to the problem does not meet the memory requirements of many realistic scenarios over a sliding window. Hence, we propose AVC-SW, an approximate streaming algorithm that computes (1 + ε)-approximations to the actual exact Voronoi cell in O(κ) where κ is its sample size. With the sliding window model and random arrival of points, we show both analytically and experimentally that for given window size w and parameter k, AVC-SW reduces the expected memory requirements of the classic algorithm from O(w) to regardless of the distribution of the points in the 2-d space. This is a significant improvement for most of the real-world scenarios where w ≫ k.     

Stochastic syntax-directed translations in syntactic pattern processing

Stochastic context-free grammars are used in syntactic pattern processing to model sources of noise, ambiguity in pattern classes, and other random phenomena. Stochastic syntax-directed translation schemata take these concepts one step further by describing both the syntactic structure and the probabilities of grammar-based mappings between context-free languages.     

多连通域Voronoi图的算法及数据存储

通过对两种主要单连通域Voronoi图算法的剖析,改进初始化算法和数据结构,得到便于工程应用的单连通域Voronoi算法,并将波阵面传播的思想扩展应用到求多连通域的Voronoi图,形成新的多连通域问题算法,从而解决了工程中特别是分层制造技术中Voronoi图应用的一般性问题.     

网络处理模式匹配算法研究*

主要从多个角度研究了经典的15种单模式和7种多模式匹配算法,并以可编程网络处理器为测试平台对其中的5种单模式和4种多模式匹配算法分别在匹配时间、占用存储空间以及预处理时间方面进行了性能测试。根据测试得出了各自测试中的最优算法。     

Recent trends in image processing and pattern recognition

Multimedia Tools and Applications -     

Efficient processing of partially specified twig pattern queries

As huge volumes of data are organized or exported in tree-structured form, it is quite necessary to extract useful information from these data collections using effective and efficient query processing methods. A natural way of retrieving desired information from XML documents is using twig pattern (TP), which is, actually, the core component of existing XML query languages. Twig pattern possesses the inherent feature that query nodes on the same path have concrete precedence relationships. It is this featu...     

UV-diagram: a voronoi diagram for uncertain spatial databases

The Voronoi diagram is an important technique for answering nearest-neighbor queries for spatial databases. We study how the Voronoi diagram can be used for uncertain spatial data, which are inherent in scientific and business applications. Specifically, we propose the Uncertain-Voronoi diagram (or UV-diagram), which divides the data space into disjoint “UV-partitions”. Each UV-partition $P$ is associated with a set $S$ of objects, such that any point $q$ located in $P$ has the set $S$ as its nearest neighbor with nonzero probabilities. The UV-diagram enables queries that return objects with nonzero chances of being the nearest neighbor (NN) of a given point $q$ . It supports “continuous nearest-neighbor search”, which refreshes the set of NN objects of $q$ , as the position of $q$ changes. It also allows the analysis of nearest-neighbor information, for example, to find out the number of objects that are the nearest neighbors of any point in a given area. A UV-diagram requires exponential construction and storage costs. To tackle these problems, we devise an alternative representation of a UV-diagram, by using a set of UV-cells. A UV-cell of an object $o$ is the extent $e$ for which $o$ can be the nearest neighbor of any point $q \in e$ . We study how to speed up the derivation of UV-cells by considering its nearby objects. We also use the UV-cells to design the UV-index, which supports different queries, and can be constructed in polynomial time. We have performed extensive experiments on both real and synthetic data to validate the efficiency of our approaches.     

Distributed and scalable sequential pattern mining through stream processing

Scalability is a primary issue in existing sequential pattern mining algorithms for dealing with a large amount of data. Previous work, namely sequential pattern mining on the cloud (SPAMC), has already addressed the scalability problem. It supports the MapReduce cloud computing architecture for mining frequent sequential patterns on large datasets. However, this existing algorithm does not address the iterative mining problem, which is the problem that reloading data incur additional costs. Furthermore, it did not study the load balancing problem. To remedy these problems, we devised a powerful sequential pattern mining algorithm, the sequential pattern mining in the cloud-uniform distributed lexical sequence tree algorithm (SPAMC-UDLT), exploiting MapReduce and streaming processes. SPAMC-UDLT dramatically improves overall performance without launching multiple MapReduce rounds and provides perfect load balancing across machines in the cloud. The results show that SPAMC-UDLT can significantly reduce execution time, achieves extremely high scalability, and provides much better load balancing than existing algorithms in the cloud.     

Novel method and system for pattern recognition and processing using data encoded as Fourier series in Fourier space

A method and system for pattern recognition and processing is reported that has a data structure and theoretical basis that are unique. This novel approach anticipates the signal processing action of an ensemble of neurons as a unit and intends to simulate aspects of brain that give rise to capabilities such as intelligence, pattern recognition, and reasoning that have not been reproduced with past approaches such as neural networks that are based individual simulated “neuronal units.” Information representative of physical characteristics or representations of physical characteristics is transformed into a Fourier series in Fourier space within an input context of the physical characteristics that is encoded in time as delays corresponding to modulation of the Fourier series at corresponding frequencies. Associations are formed between Fourier series by filtering the Fourier series and by using a spectral similarity between the filtered Fourier series to determine the association based on Poissonian probability. The associated Fourier series are added to form strings of Fourier series. Each string is ordered by filtering it with multiple selected filters to form multiple time order formatted subset Fourier series, and by establishing the order through associations with one or more initially ordered strings to form an ordered string. Associations are formed between the ordered strings to form complex ordered strings that relate similar items of interest. The components of the system based on the algorithm are active based on probability using weighting factors based on activation rates.