14235163
Robust controllability of interval fractional order linear time invariant systems
We consider uncertain fractional-order linear time invariant (FO-LTI) systems with interval coefficients. Our focus is on the robust controllability issue for interval FO-LTI systems in state-space form. We revisit the controllability problem for the case when there is no interval uncertainty. It turns out that the controllability check for FO-LTI systems amounts to checking the controllability of conventional integer order state space. Based on this fact, we further show that, for interval FO-LTI systems, the key is to check the linear dependency of a set of interval vectors. Illustrative examples are presented.
14714015
Robust stability check of fractional order linear time invariant systems with interval uncertainties
For uncertain fractional-order linear time invariant (FO-LTI) systems with interval coefficients described in state space form, the robust stability check problem is solved for the first time in this paper. Both the checking procedure and the Matlab code are presented with two illustrative examples. The conservatism is shown to be small.
712
Rational Radial Distortion Models with Analytical Undistortion Formulae
The common approach to radial distortion is by the means of polynomial approximation, which introduces distortion-specific parameters into the camera model and requires estimation of these distortion parameters. The task of estimating radial distortion is to find a radial distortion model that allows easy undistortion as well as satisfactory accuracy. This paper presents a new class of rational radial distortion models with easy analytical undistortion formulae. Experimental results are presented to show that with this class of rational radial distortion models, satisfactory and comparable accuracy is achieved.
5638961
Blind Detection and Compensation of Camera Lens Geometric Distortions
Author(s): Ma, Lili; Chen, YangQuan; Moore, Kevin L | Abstract: This paper presents a blind detection and compensation technique for camera lens geometric distortions. The lens distortion introduces higher-order correlations in the frequency domain and in turn it can be detected using higher-order spectral analysis tools without assuming any specific calibration target. The existing blind lens distortion removal method only considered a single-coefficient radial distortion model. In this paper, two coefficients are considered to model approximately the geometric distortion. All the models considered have analytical closed-form inverse formulae.
17642380
Evolutionary Modeling of Ordinary Differential Equations for Dynamic Systems
This paper presents a new idea of modeling one-dimensional dynamic systems by higher-order ordinary differential equation (HODE) models in stead of by the ARMA models used in the traditional time series analysis. Accordingly, based on the idea of hybrid evolutionary modeling, we modify the HEMA algorithm in (Cao et al., 1998a; Cao et al., 1998b) to approach the modeling problem of HODEs for dynamic systems. Two applications of time series are used to demonstrate its effectiveness and advantages.
18271710
Verification Framework for Dynamic Collaborative Services in Service-Oriented Architecture
This paper introduces a dynamic verification framework for collaborative services in service-oriented architecture (SOA). Collaboration plays a critical role in SOA, an effective verification framework for collaboration will greatly reduce the effort for rapid and adaptive service composition and evaluation of applications based on collaborative services. The verification framework provides a service-oriented infrastructure for completeness and consistency analyses, model-checking, simulation, and policy enforcement of process specification based on collaborative services in SOA. The paper presents the concepts, architecture, profiling techniques and illustrative examples that demonstrate the concepts and the techniques
6316461
Partitioned optimization algorithms for multiple sequence alignment
Multiple sequence alignment is an important and difficult problem in molecular biology and bioinformatics. In this paper, we propose a partitioning approach that significantly improves the solution time and quality by utilizing the locality structure of the problem. The algorithm solves the multiple sequence alignment in three stages. First, an automated and suboptimal partitioning strategy is used to divide the set of sequences into several subsections. Then a multiple sequence alignment algorithm based on ant colony optimization is used to align the sequences of each subsection. Finally, the alignment of original sequences can be obtained by assembling the result of each subsection. The ant colony algorithm is highly optimized in order to avoid local optimal traps and converge to global optimal efficiently. Experimental results show that the algorithm can significantly reduce the running time and improve the solution quality on large-scale multiple sequence alignment benchmarks.
1550167
Verification of arithmetic circuits using binary moment diagrams
Abstract.Binary moment diagrams (BMDs) provide a canonical representation for linear functions similar to the way binary decision diagrams (BDDs) represent Boolean functions. Within the class of linear functions, we can embed arbitrary functions from Boolean variables to real, rational, or integer values. BMDs can thus model the functionality of data path circuits operating over word-level data. Many important functions, including integer multiplication, that cannot be represented efficiently at the bit level with BDDs, have simple representations at the word level with BMDs. Furthermore, BMDs can represent Boolean functions as a special case. We propose a hierarchical approach to verifying arithmetic circuits, where component modules are first shown to implement their word-level specifications. The overall circuit functionality is then verified by composing the component functions and comparing the result to the word-level circuit specification. Multipliers with word sizes of up to 256 bits have been verified by this technique.
2396817
Automatic Pattern-Taxonomy Extraction for Web Mining
In this paper, we propose a model for discovering frequent sequential patterns, phrases, which can be used as profile descriptors of documents. It is indubitable that we can obtain numerous phrases using data mining algorithms. However, it is difficult to use these phrases effectively for answering what users want. Therefore, we present a pattern taxonomy extraction model which performs the task of extracting descriptive frequent sequential patterns by pruning the meaningless ones. The model then is extended and tested by applying it to the information filtering system. The results of the experiment show that pattern-based methods outperform the keyword-based methods. The results also indicate that removal of meaningless patterns not only reduces the cost of computation but also improves the effectiveness of the system.