15288744
Cost/performance tradeoff of n-select square root implementations
Hardware square-root units require large numbers of gates even for iterative implementations. In this paper we present four low-cost high-performance fully-pipelined n-select implementations (nS-Root) based on a non-restoring-remainder square root algorithm. The nS-Root uses a parallel array of carry-save adders (CSAs). For a square root bit calculation, a CSA is used once. This means that the calculations can be fully pipelined. It also uses the n-way root-select technique to speedup the square root calculation. The cost/performance evaluation shows that n=2 or n=2.5 is a suitable solution for designing a high-speed fully pipelined square root unit while keeping the low-cost.
11857431
Implementation of single precision floating point square root on FPGAs
The square root operation is hard to implement on FPGAs because of the complexity of the algorithms. In this paper, we present a non-restoring square root algorithm and two very simple single precision floating point square root implementations based on the algorithm on FPGAs. One is low-cost iterative implementation that uses a traditional adder/subtracter. The operation latency is 25 clock cycles and the issue rate is 24 clock cycles. The other is high-throughput pipelined implementation that uses multiple adder/subtracters. The operation latency is 15 clock cycles and the issue rate is one clock cycle. It means that the pipelined implementation is capable of accepting a square root instruction on every clock cycle.
5262698
An integrated algorithm of incremental and robust PCA
Principal component analysis (PCA) is a well-established technique in image processing and pattern recognition. Incremental PCA and robust PCA are two interesting problems with numerous potential applications. However, these two issues have only been separately addressed in the previous studies. In this paper, we present a novel algorithm for incremental and robust PCA by seamlessly integrating the two issues together. The proposed algorithm has the advantages of both incremental PCA and robust PCA. Moreover, unlike most M-estimation based robust algorithms, it is computational efficient. Experimental results on dynamic background modelling are provided to show the performance of the algorithm with a comparison to the conventional batch-mode and nonrobust algorithms.
21618923
Meaning and precision of adaptive fuzzy systems with Gaussian-type membership functions
Fuzzy systems are unique in that they possess both linguistic input/output descriptions (the fuzzy if then rules) and numerical input/output descriptions (the input/output functions they implement). Adaptive fuzzy systems, also known as neural fuzzy systems, learn the functions they implement through training with numerical data. Precision--evaluated by the mean square error--has been the most important performance index. The linguistic meanings of the fuzzy if-then rules appear to be insignificant for adaptive fuzzy systems. We show that the linguistic meanings of system parameters can be used to set better initial values, thus reduce the mean square error. On the other hand, for adaptive fuzzy systems with Gaussian-type membership functions, there exist a trade-off between how meaningful the fuzzy if-then rules are and how precise the implemented function is. The fuzzy if-then rules may fail to provide a coarse description of the input/output function after training even when the mean square error is small. We propose a "mean square distance" that measures the meaning of the fuzzy system in terms of the discrepancy between the fuzzy if-then rules and the implemented numerical function. Performance of adaptive fuzzy systems are evaluated using both the mean square error (precision) and the mean square distance (meaning) in time series prediction experiments with Mackey Glass and Box Jenkins data. The effects of weight adaptation methods and varying Gaussian widths are studied. Adaptive fuzzy systems with Gaussian-type membership functions that perform well in precision (have small mean square error) may not have meaningful fuzzy if-then rules anymore.
18109263
Automatically Acquiring Training Sets for Web Information Gathering
The traditional techniques rely on human effort to acquire training sets, which is expensive and inefficient. In this paper we present an alternative method to automatically acquire training sets without heavy investment of user efforts. The proposed method tends to fill a gap for effectiveness of using Web data in Web mining, and contributes to Web information gathering. The evaluation shows that the method is adequate to yield an promising achievement
28387556
Perceiving Environments for Intelligent Agents
One of the important characteristics for intelligent agents is to be able to assess their environments in order to decide on the correct action to take. It is always difficult to do so because many factors including uncertain information, knowledge and bounded time will affect intelligent agent to perceive their environments. In this paper, we propose a procedure descriptive framework to perceive the environments for intelligent agents. The process of belief updating in this framework remains to be constantly changing, until the point of decision making is reached. During the dynamic change of beliefs, the intelligent agents will incorporate their knowledge about other agents, and the possible uncertain information they received from their local sensors and other agents.
1293275
Getting work done on the web: supporting transactional queries
Many searches on the web have a transactional intent. We argue that pages satisfying transactional needs can be distinguished from the more common pages that have some information and links, but cannot be used to execute a transaction. Based on this hypothesis, we provide a recipe for constructing a transaction annotator. By constructing an annotator with one corpus and then demonstrating its classification performance on another,we establish its robustness. Finally, we show experimentally that a search procedure that exploits such pre-annotation greatly outperforms traditional search for retrieving transactional pages.