9389346
Efficient encoding of quasi-cyclic low-density parity-check codes
Efficient Encoding of Quasi-Cyclic Low-Density Parity-Check Codes Quasi-cyclic (QC) low-density parity-check (LDPC) codes form an important subclass of LDPC codes. These codes have encoding advantage over other types of LDPC codes. This paper addresses the issue of efficient encoding of QC-LDPC codes. Two methods are presented to find the generator matrices of QC-LDPC codes in systematic-circulant form from their parity-check matrices given in circulant form. Based on the systematic-circulation form of the generator matrix of a QC-LDPC code, various types of encoding circuits using simple shift registers are devised. It is shown that the encoding complexity of a QC-LDPC code is linearly proportional to the number of parity bits of the code for serial encoding, and to the length of the code for high-speed parallel encoding.
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
22828362
Construction of low-density parity-check codes by superposition
This paper presents a superposition method for constructing low-density parity-check (LDPC) codes. Several classes of structured LDPC codes are constructed. Codes in these classes perform well with iterative decoding, and their Tanner graphs have girth at least six.
17433940
Construction of Quasi-Cyclic LDPC Codes for AWGN and Binary Erasure Channels: A Finite Field Approach
In the late 1950s and early 1960s, finite fields were successfully used to construct linear block codes, especially cyclic codes, with large minimum distances for hard-decision algebraic decoding, such as Bose-Chaudhuri-Hocquenghem (BCH) and Reed-Solomon (RS) codes. This paper shows that finite fields can also be successfully used to construct algebraic low-density parity-check (LDPC) codes for iterative soft-decision decoding. Methods of construction are presented. LDPC codes constructed by these methods are quasi-cyclic (QC) and they perform very well over the additive white Gaussian noise (AWGN), binary random, and burst erasure channels with iterative decoding in terms of bit-error probability, block-error probability, error-floor, and rate of decoding convergence, collectively. Particularly, they have low error floors. Since the codes are QC, they can be encoded using simple shift registers with linear complexity.
40545913
Algebraic Constructions of Quasi-cyclic LDPC Codes - Part I: For AWGN and Binary Random Erasure Channels
This paper is the first part of a sequence of two papers that present algebraic constructions of quasi-cyclic LDPC codes for AWGN, binary random and burst erasure channels. In this paper, a class of quasi-cyclic LDPC codes for both AWGN and binary random erasure channels is constructed based on finite fields and special vector representations of finite field elements.
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.
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.
183594
Efficient Type Checking for a Subclass of Regular Expression Types
Type checking is an important problem in statically typed XML processing languages. Most type systems are implemented based on tree automata for the tree structure of XML documents and schemas. It is known that the complexity of inclusion problem for tree automata is in EXPTIME. In fact, most definitions in XML Schema are actually DTDs. The inclusion problem of DTDs can be reduced to inclusion of one-unambiguous regular expressions in DTDs. We have developed a method for type checking that works for regular expression types which are DTDs. The method is based onone-unambiguous regular expressions together with using the information of labels in DTDs, which can work in top-down or bottom-up manner. Although our method supports DTDs only now, it is general and can support more general schemas by integrating with the conventional method of type checking. The experiments show that our method is more efficient in practice.
13418028
Discovering matching dependencies
Matching dependencies (MDs) are recently proposed for various data quality applications such as detecting the violation of integrity constraints and duplicate object identification. In this paper, we study the problem of discovering matching dependencies for a given database instance. First, we formally define the measures, support and confidence, for evaluating the utility of MDs in the given database instance. Then, we study the discovery of MDs with certain utility requirements of support and confidence. Exact algorithms are developed, together with pruning strategies to improve the time performance. Finally, our experimental evaluation demonstrates the efficiency of the proposed methods.
15705241
Max Regional Aggregate over Sensor Networks
Nowadays, wireless sensor networks are widely used in many environmental monitoring applications. However, due to the limitation of the current hardware technology, sensors are often battery powered and it is very difficult to change batteries. Therefore, for applications over wireless sensor networks, it is a critical issue to save the energy of sensors. Many attempts have been made to answer various types of queries energy-efficiently, such as max, top-k, and skylines. However, all of them return the readings of individual sensors that satisfy the query constraint. In practice, query results based on individual sensor readings are unreliable because sensor readings are often noisy. Thus, in this paper, we present a new type of query, max aggregate query over a region (i.e. max regional aggregate), which aims to find a fixed-size region whose regional aggregate is the maximum among all the possible regions with the same size. Compared to traditional max queries, max regional aggregate is more reliable in detecting events. Designing an energy-efficient approach to answer max regional aggregate is non-trivial because a huge number of regions need to be investigated. Thus, in this paper, we propose a novel two-level sampling approach, with region and sensor sampling, to collect the sensor readings intelligently and compute the approximate max regional aggregate based on the collected results. Specifically, region sampling is used to select regions and sensor sampling isused to choose the sensors within a selected region. Our extensive simulation results demonstrate that the proposed two-level sampling approach can answer the max regional aggregate energy-efficiently with a desirable accuracy.
7211665
Dynamic data dependence tracking and its application to branch prediction
To continue to improve processor performance, microarchitects seek to increase the effective instruction level parallelism (ILP) that can be exploited in applications. A fundamental limit to improving ILP is data dependences among instructions. If data dependence information is available at run-time, there are many uses to improve ILP. Prior published examples include decoupled branch execution architectures and critical instruction detection. In this paper, we describe an efficient hardware mechanism to dynamically track the data dependence chains of the instructions in the pipeline. This information is available on a cycle-by-cycle basis to the microengine for optimizing its performance. We then use this design in a new value-based branch prediction design using available register value information (ARVI). From the use of data dependence information, the ARVI branch predictor has better prediction accuracy over a comparably sized hybrid branch predictor With ARVI used as the second-level branch predictor the improved prediction accuracy results in a 12.6% performance improvement on average across the SPEC95 integer benchmark suite.