5759823
Construction of Regular and Irregular LDPC Codes: Geometry Decomposition and Masking
Two algebraic methods for systematic construction of structured regular and irregular low-density parity-check (LDPC) codes with girth of at least six and good minimum distances are presented. These two methods are based on geometry decomposition and a masking technique. Numerical results show that the codes constructed by these methods perform close to the Shannon limit and as well as random-like LDPC codes. Furthermore, they have low error floors and their iterative decoding converges very fast. The masking technique greatly simplifies the random-like construction of irregular LDPC codes designed on the basis of the degree distributions of their code graphs
45108712
Near-Shannon-limit quasi-cyclic low-density parity-check codes
This letter presents two classes of quasi-cyclic low-density parity-check codes that perform close to the Shannon limit.
14817991
A General Cost Model for Dimensionality Reduction in High Dimensional Spaces
Similarity search usually encounters a serious problem in the high dimensional space, known as the "curse of dimensionality". In order to speed up the retrieval efficiency, previous approaches usually reduce the dimensionality of the entire data set to a fixed lower value before building indexes (referred to as global dimensionality reduction (GDR)). More recent works focus on locally reducing the dimensionality of data to different values (called the local dimensionality reduction (LDR)). However, so far little work has formally evaluated the effectiveness and efficiency of both GDR and LDR for range queries. Motivated by this, in this paper, we propose a general cost model for both GDR and LDR, in light of which we introduce a novel LDR method, PRANS. It can achieve high retrieval efficiency with the guarantee of optimality given by the formal model. Finally, a B+-tree index is constructed over the reduced partitions for fast similarity search. Extensive experiments validate the correctness of our cost model on both real and synthetic data sets, and demonstrate the efficiency and effectiveness of the proposed PRANS method.
7600762
Automatic Scoring of Children's Read-Aloud Text Passages and Word Lists
Assessment of reading proficiency is typically done by asking subjects to read a text passage silently and then answer questions related to the text. An alternate approach, measuring reading-aloud proficiency, has been shown to correlate well with the aforementioned common method and is used as a paradigm in this paper. 
 
We describe a system that is able to automatically score two types of children's read speech samples (text passages and word lists), using automatic speech recognition and the target criterion "correctly read words per minute". Its performance is dependent on the data type (passages vs. word lists) as well as on the relative difficulty of passages or words for individual readers. Pearson correlations with human assigned scores are around 0.86 for passages and around 0.80 for word lists.
37631893
Stereo Vision Based Floor Plane Extraction and Camera Pose Estimation
In this paper, we present a unified approach for floor plane extraction and camera pose estimation. A histogram based method is performed on the reconstructed 3-D points obtained by an onboard video-rate stereo vision system to extract candidate points of the floor plane and determine the pose of vision system simultaneously. The obstacle area is easily localized given the floor plane region. In order to improve reliability and accuracy of camera pose estimation results, the Least Median of Squares (LMedS) based fitting method is applied to estimate the floor plane parameters with the extracted candidate points. The precise pose of the onboard stereo vision system is directly acquired related to the floor plane parameters. Experimental results in real indoor environments are discussed and show the good performance.
2641288
A novel spectral coding in a large graph database
Retrieving related graphs containing a query graph from a large graph database is a key issue in many graph-based applications, such as drug discovery and structural pattern recognition. Because sub-graph isomorphism is a NP-complete problem [4], we have to employ a filter-and-verification framework to speed up the search efficiency, that is, using an effective and efficient pruning strategy to filter out the false positives (graphs that are not possible in the results) as many as possible first, then validating the remaining candidates by subgraph isomorphism checking. In this paper, we propose a novel filtering method, a spectral encoding method, i.e. GCoding. Specifically, we assign a signature to each vertex based on its local structures. Then, we generate a spectral graph code by combining all vertex signatures in a graph. Based on spectral graph codes, we derive a necessary condition for sub-graph isomorphism. Then we propose two pruning rules for sub-graph search problem, and prove that they satisfy the no-false-negative requirement (no dismissal in answers). Since graph codes are in numerical space, we take this advantage and conduct efficient filtering over graph codes. Extensive experiments show that GCoding outperforms existing counterpart methods.
11023965
Enhancing Edit Distance on Real Sequences Filters using Histogram Distance on Fixed Reference Ordering
Distance functions are the main tools to measure similarity of two sequences and to search the closest sequences to given query sequence. Several well known distance functions, however, have asymptotical time complexity of O(mn) which cannot be fully afforded by systems that deal with large volumes of data. These distance functions, including edit distance on real sequences (EDR) (L. Chen et al., 2005), have pruning methods to reduce execution time by dismissing false candidates as early as possible. In this paper, we propose the histogram distance on fixed reference (HDFR) ordering, with various reference histogram construction methods, to improve the filtering power of the pruning methods in EDR. Experiments show that a decrease in EDR execution time is observed after HDFR is applied. While we base our experiments on EDR, HDFR can also be applied to other distance functions with appropriate pruning methods