6983597
A Comparative Study of Parallel Prefix Adders in FPGA Implementation of EAC
Several regular parallel trees have been proposed over the years to optimize logic depth, area, fan-out and in- terconnect count for logic circuits. In this paper, we propose a comparative study of different parallel prefix trees used in the design of a new end-around carry (EAC) adder targeting FPGA technology. This new adder is based on the fast 128-bit binary floating-point EAC adder which has been implemented in the IBM POWER6 microprocessor's fused multiply-add unit. The parallel prefix tree implemented on the IBM's EAC adder is a Kogge-Stone tree which has been chosen for its high performance and its low power consumption. Our comparative study highlights the main performance differences among fourteen different architecture configurations when targeting an FPGA EAC adder design. We focus on the area requirements and the critical path delay of these designs. Our experimental results show that there is one architecture configuration with the lower area requirement and the higher performance.
26669443
Bottom-up discovery of frequent rooted unordered subtrees
In the past decade, XML has emerged as the standard language for information exchanging over the Internet. Due to its tree-structure paradigm, XML is superior for its capability of storing, querying, and manipulating complex data. Therefore, discovering frequent tree patterns over tree-structured data has become an interesting topic for XML data management. In this paper, we propose a tree mining algorithm, named BUXMiner, for finding a special class of frequent trees, called rooted unordered trees, from a tree-structured database. BUXMiner employs an efficient bottom-up approach to enumerate all candidate trees over a compact global tree guide and computes the frequent trees based on the tree guide. In addition to BUXMiner, we also propose a mining approach called BUMXMiner to discover the maximal frequent rooted unordered trees. We compare BUXMiner with previous tree-structure mining algorithms, namely XQPMinerTID and FastXMiner, which were also proposed to discover rooted unordered trees. The experimental results show that our algorithm outperforms XQPMinerTID and FastXMiner in terms of efficiency. The performance results from real-world applications also indicate the usefulness of our proposed tree mining algorithms in a variety of web applications, such as analysis of web page access patterns and mining frequent XML query patterns for caching.
8051159
A Web-based Framework For On-line Collaborative Learning
This paper presents a framework for design of online courses and collaborative learning based on Web technologies. It provides the following features: construction of structured learning materials in a distributed environment; personalization of course contents in the run-time that adapts to the progress of individual students; it can supply relevant Web resources to the students by analyzing the learning history dynamically; students in a group could exchange their learning experiences and discover relevant knowledge via the communication channels provided by the framework. The proposed techniques could be deployed to a variety of online computer-assisted modules to improve the effectiveness of Web-based e-learning
8340253
UGE4B: An Universal Grid Environment for Bioinformatics Research
Recent years, grid technology has been used widely in the fields of scientific research. In this paper, we present a universal grid environment for bioinformatics research (UGE4B). Although there have been several similar projects successfully constructed in the world, we need to meet some unique requirements from bioinformatics research in China. UGE4B consists of 6 components, including portal, task management, data management, information server, security and computing node. As a national-wide bioinformatics research platform, UGE4B is built through interconnecting multiple sub grids provided by each bioinformatics institutes. Besides provides a universal portal to end users for bioinformatics computing and data accessing, UGE4B also supported some special bioinformatics applications
5747087
Efficient Processing of Warping Time Series Join of Motion Capture Data
Discovering non-trivial matching subsequences from two time series is very useful in synthesizing novel time series. This can be applied to applications such as motion synthesis where smooth and natural motion sequences are often required to be generated from existing motion sequences. We first address this problem by defining it as a problem of l-e-join over two time series. Given two time series, the goal of l-e-join is to find those non-trivial matching subsequences by detecting maximal l-connections from the e-matching matrix of the two time series. Given a querying motion sequence, the l-e-join can be applied to retrieve all connectable motion sequences from a database of motion sequences. To support efficient l-e-join of time series, we propose a two-step filter-and-refine algorithm, called Warping Time Series Join (WTSJ) algorithm. The filtering step serves to prune those sparse regions of the e-matching matrix where there are no maximal l-connections without incurring costly computation. The refinement step serves to detect closed l-connections within regions that cannot be pruned by the filtering step. To speed up the computation of e-matching matrix, we propose a block-based time series summarization method, based on which the block-wise e-matching matrix is first computed. Lots of pairwise distance computation of elements can then be avoided by applying the filtering algorithm on the block-wise e-matching matrix.Extensive experiments on l-e-join of motion capture sequences are conducted. The results confirm the efficiency and effectiveness of our proposed algorithm in processing l-e-join of motion capture time series.
116588251
On-line evolutionary exponential family mixture
This paper deals with evolutionary clustering, which refers to the problem of clustering data with distribution drifting along time. Starting from a density estimation view to clustering problems, we propose two general on-line frameworks. In the first framework, i.e., historical data dependent (HDD), current model distribution is designed to approximate both current and historical data distributions. In the second framework, i.e., historical model dependent (HMD), current model distribution is designed to approximate both current data distribution and historical model distribution. Both frameworks are based on the general exponential family mixture (EFM) model. As a result, all conventional clustering algorithms based on EFMs can be extended to evolutionary setting under the two frameworks. Empirical results validate the two frameworks.
3708864
A New Systolic Architecture for Modular Division
A new systolic architecture for modular division is proposed in this paper. The architecture is based on a new hardware algorithm for modular division that is extended from the plus-minus algorithm for the greatest common divisor computation. Both the area complexity and the time complexity of the new architecture are linear with respect to the operand bit length. Compared to the architecture using a redundant number representation proposed by Kaihara and Takagi, the new architecture allows a doubled throughput due primarily to the greatly reduced critical path delay, while the area is only about 20 percent larger. Moreover, it is shown that, with a small addition to the control logic, the architecture can also be used to perform Montgomery modular multiplication, thereby efficiently realizing a unified modular multiplier/divider
2171580
Dependent Types with Subtyping and Late-Bound Overloading
We present a calculus with dependent types, subtyping, and late-bound overloading. Besides its theoretical interest this work is motivated by several practical needs that range form the definition of logic encodings to proof specialization and reuse and to object-oriented extension of the SML module system. The theoretical study of this calculus is not straightforward. While confluence is relatively easy to prove, subject reduction is much harder. We were not able to add overloading to any existing system with dependent types and subtyping, and prove subject reduction. This is why we also define here as by-product a new subtyping system for dependent types that improves previous systems and enjoys several properties (notably the transitivity elimination property). The calculus with overloading is then obtained as a conservative extension of this new system. Another difficult point is strong normalization, which is a necessary condition to the decidability of subtyping and typing relations. The calculus with overloading is not strongly normalizing. However, we show that a reasonably useful fragment of the calculus enjoys this property and that its strong normalization implies the decidability of its subtyping and typing relations. The article is divided into two parts: the first three scetions provide a general overview of the systems and its motivations and can be read separately; the remaining sections develop the formal study.
39222179
A New ART Neural Networks for Remote Sensing Image Classification
A new ART2A-C algorithm based on fuzzy operators to cluster the remote sensing images and aerials is proposed in this paper. By combining two ART ANNs with higher performance, the traditional ART2A-C is developed with the fuzzy operators introduced in matching rule. Then the proposed method is applied to the classification and the new network is implemented as well as two other existed ARTs respectively. Experimental results show that the new method outperforms the traditional ones.
110367358
Unified synthesis techniques for high performance fpga designs
As the device geometry keeps shrinking in today's deep sub-micron technologies, interconnect delay dominates the critical path. In the current FPGA design flow, there are two major drawbacks: the separation of technology-independent optimization and technology mapping in the traditional logic synthesis flow, and the separation of logic synthesis and physical implementation in the traditional implementation flow. Such drawbacks result in sub-optimal performance and a long timing closure cycle. In this thesis we propose the utilization of unified synthesis techniques in the context of FPGAs to address these issues and improve the overall design performance. In Chapter 3 we bridge the gap between technology-independent optimization and technology mapping by combining logic decomposition and mapping. In Chapter 4, we introduce a novel algorithm named SCPlace [17] to perform simultaneous clustering during placement. In Chapter 5 we introduce an algorithm named SPD to perform logic duplication during placement. In Chapter 6 we build the first complete academic CAD flow, which can be easily plugged in to a state-of-the-art commercial tool, Altera's Quartus 4.0 software. Our unified synthesis and placement tool, SPCD, combines both the clustering and duplication techniques. Our experimental results show up to over 30% improvement in performance compared with the current state-of-the-art academic flow.
37994970
Image coding quality assessment using fuzzy integrals with a three-component image model
Based on importance measures and fuzzy integrals, a new assessment method for image coding quality is presented in this paper. The proposed assessment is based on two subevaluations. In the first subevaluation, errors on edges, textures, and flat regions are computed individually. The errors are then assessed using an assessment function. A global evaluation with Sugeno fuzzy integral is then obtained based on the importance measure of edge, texture, and flat region. In the second subevaluation, an importance measure is first established depending on the types of regions where errors occur, a subtle evaluation is then obtained using Sugeno fuzzy integral on all pixels of the image. A final evaluation is obtained based on the two subevaluations. Experimental results show that this new image quality assessment closely approximates human subjective tests such as mean opinion score with a high correlation coefficient of 0.963, which is a significant improvement over peak signal-to-noise ratio, picture quality scale, and weighted mean square error, three other image coding quality assessment methods, which have the correlation coefficients of 0.821, 0.875, and 0.716, respectively.