Vector Indexing Research Articles

Vector Programming Using Structural Recursion

Vector programming is an important topic in many Introduction to Computer Science courses. Despite the importance of vectors, learning vector programming is a source for frustration to many students given that they feel left adrift when it comes to resolving vector indexing errors. Even though the size of a vector is a natural number, there have been no efforts to define a useful recursive data definition to help beginners design vector processing functions. This article defines the concept of a vector interval and describes how to exploit its recursive structure to design vector processing functions. The described methodology provides a context beginners can use to reason about proper vector indexing instead of leaving them adrift with this responsibility. A key feature of properly using the described methodology is that if students process the correct vector interval then vector indexing errors can not arise. The classroom deployment of this approach is described in detail. Students, to date, have found vector intervals helpful in avoiding out-of-bounds indexing errors when all the vector elements of the interval are processed.

An identification framework for print-scan books in a large database

In this paper, we propose an identification framework to determine copyright infringement in the form of illegally distributed print-scan books in a large database. The framework contains following main stages: image pre-processing, feature vector extraction, clustering, and indexing, and hierarchical search. The image pre-processing stage provides methods for alleviating the distortions induced by a scanner or digital camera. From the pre-processed image, we propose to generate feature vectors that are robust against distortion. To enhance the clustering performance in a large database, we use a clustering method based on the parallel-distributed computing of Hadoop MapReduce. In addition, to store the clustered feature vectors efficiently and minimize the searching time, we investigate an inverted index for feature vectors. Finally, we implement a two-step hierarchical search to achieve fast and accurate on-line identification. In a simulation, the proposed identification framework shows accurate and robust in the presence of print-scan distortions. The processing time analysis in a parallel computing environment gives extensibility of the proposed framework to massive data. In the matching performance analysis, we empirically and theoretically find that in terms of query time, the optimal number of clusters scales with O(N) for N print-scan books.

Key Technology in a Lightweight WPF-Based Electronic Map Engine

This paper introduces key technology in design and development of a lightweight WPF-Based electronic map engine, systematically analyzes characteristics of WPF technology, designs the structure of electronic map engine components, elaborates on data organization and management, vector and raster spatial data indexing method, presents a electronic map engine specific for nautical map display style.

DSP Implementation of Lattice Vector Indexing for Image Compression

DSP Implementation of Lattice Vector Indexing for Image Compression

DSP Implementation of Lattice Vector Indexing for Image Compression

Image-related interactions are forming an increasingly large part of modern communications, bringing the need for efficient and effective compression. Image compression is important for effective storage and transmission of images. Image processing applications are increasingly being done on embedded systems because image processing involves high computation and data requirements. This paper work describes the implementation of indexing of vectors quantized using lattice structure on TMS320C6713 Digital Signal Processor board. In this work for quantization of vectors D 4 lattice structure is selected. The proposed indexing technique is based on direct assignment of indices to the vectors. Generation of the codebook and the indexing of leader are not required in this method . The code is written in C in Code Composer Studio (CCS) in order to program the DSP processor. With the help of profile statistic available in CCS, statistical analysis is also carried out.

A Dynamic Resource Management Scheme on P2P-Based Grid Systems

Efficient multi-dimensional data indexing mechanism is one of the fundamental requirement for Grid computing systems. Peer-to-peer organization of Grid resource discovery approach would have several desirable features, including high scalability, high reliability, self-organization and self-healing. Most of the research in this area only considers static resource.In this paper, we describe a tree vector indexing scheme for Grid systems, in order to support range queries and at the same time preserve the important attributes of grid systems.

Dual Reporter Comparative Indexing of rAAV Pseudotyped Vectors in Chimpanzee Airway

Selecting the most efficient recombinant adeno-associated virus (rAAV) serotype for airway gene therapy has been difficult due to cross-specific differences in tropism and immune response between humans and animal models. Chimpanzees--the closest surviving genetic relative of humans--provide a valuable opportunity to select the most effective serotypes for clinical trials in humans. However, designing informative experiments using this protected species is challenging due to limited availability and experimental regulations. We have developed a method using Renilla luciferase (RL) and firefly luciferase (FL) reporters to directly index the relative transduction and immune response of two promising rAAV serotypes following lung coinfection. Analysis of differential luciferase activity in chimpanzee airway brushings demonstrated a 20-fold higher efficiency for rAAV1 over rAAV5 at 90 days, a finding that was similar in polarized human airway epithelia. T-cell responses to AAV5 capsid were stronger than AAV1 capsid. This dual vector indexing approach may be useful in selecting lead vector serotypes for clinical gene therapy and suggests rAAV1 is preferred for cystic fibrosis.

Design and implementation of FAQ automatic return system based on similarity computation

FAQ (frequently asked question) is widely used on the Internet, but most FAQ's asking and answering are not automatic. This paper introduces the design and implementation of a FAQ automatic return system based on semantic similarity computation, including computation model choosing, FAQ characters analyzing, FAQ data formal expressing, feature vector indexing, and weight computing and so on. According to FAQ features of sentence length short, two mapping, strong domain characteristics etc. Vector Space Model with special semantic process was selected in system, and corresponding algorithm of similarity computation was proposed too. Experiment shows that the system has a good performance for high frequent and common questions.

Indexing algorithms for Z/sub n/, A/sub n/, D/sub n/, and D/sub n//sup ++/ lattice vector quantizers

This paper describes vector indexing algorithms valid for a large class of lattices (Z/sub n/, A/sub n/, D/sub n/, and D/sub n//sup ++/ including as special cases the Gosset (E/sub 8/) and Barnes-Wall (/spl Lambda//sub 16//sup n/) lattices), widely used in audio-visual signal compression. The indexing mechanism relies on reverse lexicographic ordering of the vectors, in classes of equivalence defined as sets of vectors obtained by signed permutations of the components of initial vectors called leaders. The approach makes it possible to trade the size of the lookup tables-or codebooks-for arithmetic operations. The reduction of codebook sizes leads in turn to reduced encoder and decoder complexities. Pursuing the goal of best tradeoff between storage requirements and arithmetic complexity, two algorithms, based on the proposed indexing mechanisms, allowing on-the-fly generation of adaptive portions of codebooks are then described. These algorithms make it possible to overcome the problems of lattice truncating usually encountered in lattice vector quantization (LVQ). Combined with product codes, these indexing techniques lead to increased compression performances.

Low-complexity indexing method for Z/sup n/ and D/sub n/ lattice quantizers

Code vector indexing is a key problem in lattice quantization applications. In order to solve this problem, we propose a method based on the idea of a coding table encompassing a set of points as small as possible. Our method works for both spherical and pyramidal code books. It provides a good tradeoff between computational complexity and storage requirements.

Indexing of incommensurate satellite reflections in X-ray powder diffraction pattern of (Bi, Pb)2Sr2Ca2Cu3Ox (2223) phase

X-ray powder diffraction data and refined unit cell parameters for the (Bi, Pb)2Sr2Ca2Cu3Ox (2223) superconducting phase, indexed using incommensurate modulation principles, are reported. Comparison of the results obtained with one- and two-component modulation wave vectors showed that the two-component modulation wave vector is superior for indexing the satellite reflections. A two-component wave vector indexing result seems likely when only the powder diffraction data are considered.