State complexity of basic operations on suffix-free regular languages

We investigate the state complexity of basic operations for suffix-free regular languages. The state complexity of an operation for regular languages is the number of states that are necessary and sufficient in the worst-case for the minimal deterministic finite-state automaton that accepts the language obtained from the operation. We establish the precise state complexity of catenation, Kleene star, reversal and the Boolean operations for suffix-free regular languages.     

User utility discovery for priority-based network resource pricing

While equilibrium analysis has been commonly used for network pricing under the assumption that user utility functions are precisely known, many researchers have criticized the validity of the assumption. In this paper, we propose a solution for bridging the gap between the existing theoretical work on optimal pricing and the unavailability of precise user utility information in real networks. In the proposed method, the service provider obtains increasingly more accurate estimates of user utility functions by iteratively changing the prices of service levels and observing the users’ service-level choices under various prices. Our study’s contribution is twofold. First, we have developed a general principle for estimating user utility functions. Especially, we present the utility estimation for dynamic user population. Second, we have developed a method for setting prices that can optimize the extraction of information about user utility functions. The extensive simulation results demonstrate the effectiveness of our method.     

Sliding mode control for polytopic differential inclusion systems

The stabilization problem of polytopic differential inclusion (PDI) systems is investigated by using sliding mode control. Sliding surface is designed and sufficient conditions for asymptotic stability of sliding mode dynamics are derived. A novel feedback law is established to make the state of system reach the sliding surface in a finite time. Finally, an example is given to illustrate the validity of the proposed design.     

一种基于ECB模式的NSL协议改进方案

安全协议形式化方法大都在一个很高的抽象层次建立协议模型。但是协议的许多安全问题是在很低的抽象层次产生。本文分析了ECB工作模式下的NSL协议的安全问题,并通过破坏同态性的方法给出一种改进方案。通过扩展规则的BAN逻辑对NSL协议建模和分析,验证结果表明协议中存在着安全漏洞,新的改进方案可以避免这种攻击。     

A note on on-line broadcast scheduling with deadlines

In this paper, we study an on-line broadcast scheduling problem with deadlines, in which the requests asking for the same page can be satisfied simultaneously by broadcasting this page, and every request is associated with a release time, deadline and a required page with a unit size. The objective is to maximize the number of requests satisfied by the schedule. In this paper, we focus on an important special case where all the requests have their spans (the difference between release time and deadline) less than 2. We give an optimal online algorithm, i.e., its competitive ratio matches the lower bound of the problem.     

APS-FeW: Improving TCP throughput over multihop adhoc networks

TCP is a reliable transport protocol tuned to perform well over traditional wired networks. Although it performs well for wired networks, TCP’s implicit assumption that any packet loss is due to congestion is not valid any longer in mobile ad hoc networks. It is observed that TCP induces the over-action of routing protocol and reduces the performance of the connection. Fraction window increment (FeW) scheme for TCP improves the connection performance by limiting TCP’s aggressiveness. But to some extent, this limitation is too strict in that it eliminates the possibility to deliver more bytes under the same congestion window. To solve this problem, we propose an adaptive packet size (APS) scheme to work on top of FeW for TCP. The proposed scheme utilizes the advantages of both legacy TCP and FeW to achieve high performance over multihop 802.11 networks. Extensive simulation results demonstrate that APS over FeW outperforms FeW alone by 10–25% according to different scenarios, e.g., chain-topology, grid-topology, and random-topology with mobility.     

Visual Tracking by Continuous Density Propagation in Sequential Bayesian Filtering Framework

Particle filtering is frequently used for visual tracking problems since it provides a general framework for estimating and propagating probability density functions for nonlinear and non-Gaussian dynamic systems. However, this algorithm is based on a Monte Carlo approach and the cost of sampling and measurement is a problematic issue, especially for high-dimensional problems. We describe an alternative to the classical particle filter in which the underlying density function has an analytic representation for better approximation and effective propagation. The techniques of density interpolation and density approximation are introduced to represent the likelihood and the posterior densities with Gaussian mixtures, where all relevant parameters are automatically determined. The proposed analytic approach is shown to perform more efficiently in sampling in high-dimensional space. We apply the algorithm to real-time tracking problems and demonstrate its performance on real video sequences as well as synthetic examples.     

Constructive approximation to real function by wavelet neural networks

We present a type of single-hidden layer feed-forward wavelet neural networks. First, we give a new and quantitative proof of the fact that a single-hidden layer wavelet neural network with n + 1 hidden neurons can interpolate n + 1 distinct samples with zero error. Then, without training, we constructed a wavelet neural network X _a (x, A), which can approximately interpolate, with arbitrary precision, any set of distinct data in one or several dimensions. The given wavelet neural network can uniformly approximate any continuous function of one variable.     

Femtosecond laser-induced silicon surface morphology in water confinement

This article investigates the use of femtosecond laser induced surface morphology on silicon wafer surface in water confinement. Unlike irradiation of silicon surfaces in the air, there are no laser induced periodic structures, but irregular roughness is formed when the silicon wafer is ablated under water. The unique discovery of a smoothly processed silicon surface in water confinement under certain laser parameter combinations may help improve laser direct micromachining surface quality in industrial applications.     

Imbalanced text classification: A term weighting approach

The natural distribution of textual data used in text classification is often imbalanced. Categories with fewer examples are under-represented and their classifiers often perform far below satisfactory. We tackle this problem using a simple probability based term weighting scheme to better distinguish documents in minor categories. This new scheme directly utilizes two critical information ratios, i.e. relevance indicators. Such relevance indicators are nicely supported by probability estimates which embody the category membership. Our experimental study using both Support Vector Machines and Naïve Bayes classifiers and extensive comparison with other classic weighting schemes over two benchmarking data sets, including Reuters-21578, shows significant improvement for minor categories, while the performance for major categories are not jeopardized. Our approach has suggested a simple and effective solution to boost the performance of text classification over skewed data sets.