Spectral Decomposition Approach for Transient Analysis of Multi-Server Discrete-Time Queues
Discrete time queueing systems are extensively used in modeling the ATM environment at the cell level. This paper examines the transient behavior of a discrete time FCFS multiple server queue. For the arrival process we assume a general discrete time Markovian batch arrival process, where the batch size distribution of the arrivals in successive slots is governed by a finite state discrete time Markov chain. We show that once the spectral decomposition of the “probability generating matrix” of the arrival process is obtained, the complete solution in the transform domain may be given. Using the complex analysis technique and Cauchy's integral formula, we present an efficient numerical method for the numerical calculation of a few performance measures of interest, namely transient probability of queue being empty, and the mean of the queue length distribution. We also generalize our numerical method in the situations where the superposition of a number of independent arrival sources are fed to the queue. It is shown that the numerical complexity of obtaining the transient solution in this case can be substantially reduced by using the approach based on the Kronecker product. The finite buffer case is also analyzed.

title: The transient solution of time-dependent M/M/1 queues
abstract: The transient behavior of time-dependent M/M/1 queues is studied. The boundary probability function pi /sub 0/(t), which is the probability that the queue is empty at time t, is shown with analyticity arguments to satisfy a Volterra-type integral equation. The boundary integral equation is derived, and a numerical algorithm is used to solve the integral equation and to find the expected queue size from pi /sub 0/(t). The approach can be applied to many other types of time-dependent queues. Examples are given.

title: Performance study of Markov modulated fluid flow models with priority traffic
abstract: The steady-state performance of generic high-speed queuing system with priority traffic is studied, using the Markov modulated fluid models. the focus is on systems with traffic partitioned into two priorities according to a splitting factor alpha , so that low priority traffic can be discarded when congestion occurs. The results are used to further study issues of congestion control with priority traffic. In particular, the expected values, waiting times, variances of the buffer contents, and tail distributions are evaluated. It is found that the waiting time is more sensitive to changes of alpha than the buffer contents. This shows that for time-constrained traffic, the priority assignment is important for properly balancing the tradeoff between packet loss and delays. This model can be considered as a direct extension of the fluid-flow model to include priorities.<<ETX>>

title: The transient performance analysis of voice/data integrated networks
abstract: Quasi-birth-death (QBD) processes are used as performance models for voice/data integration schemes. It is assumed that voice and data packets of different bandwidths require different numbers of servers for service. Voice packets are lost when there are no available servers; data packets are queued in a buffer when there are not enough servers. A new approach is proposed to find the transient solutions of the QBD processes. The method is applied to a QBD model of a voice/data integration scheme to find time-dependent performance measures such as the expected number of data or voice packets in the system and the voice packet blocking probability.<<ETX>>

title: A Novel Dimensionality Reduction Technique Based on Independent Component Analysis for Modeling Microarray Gene Expression Data
abstract: DNA microarray experiments generating thousands of gene 
expression measurements, are being used to gather information from tissue and cell samples regarding gene expression differences that will be useful in diagnosing disease. But one challenge of microarray studies is the fact that the number n of samples collected is relatively small compared to the number p of genes per sample which are usually in thousands. In statistical terms this very large number of predictors compared to a small number of samples or observations makes the classification problem difficult. This is known as the ”curse of dimensionality problem”. An efficient way to solve this problem is by using dimensionality reduction techniques. Principle Component Analysis(PCA) is a leading method for dimensionality reduction of gene expression data which is optimal in the sense of least square error. In this paper we propose a new dimensionality reduction technique for specific bioinformatics applications based on Independent component Analysis(ICA). Being able to exploit higher order statistics to identify a linear model result, this ICA based dimensionality reduction technique 
outperforms PCA from both statistical and biological 
significance aspects. We present experiments on NCI 60 
dataset to show this result.

title: Sequential Proximity-Based Clustering for Telecommunication Network Alarm Correlation
abstract: Alarm correlation for fault management in large telecommunication networks demands scalable and reliable algorithms. In this paper, we propose a clustering based alarm correlation approach using sequential proximity between alarms. We define two novel distance metrics appropriate for measuring similarity between alarm sequences obtained from interval-based division: 1) the two-digit binary metric that values the occurrences of two alarms in neighboring intervals to tolerate the false separation of alarms due to interval-based alarm sequence division, and 2) the sequential ordering-based distance metric that considers the time of arrival for different alarms within the same interval. We validate both metrics by applying them with hierarchical clustering using real-world cellular network alarm data. The efficacy of the proposed sequential proximity based alarm clustering is demonstrated through a comparative study with existing similarity metrics.

title: Adaptive Noise Reduction for Engineering Drawings Based on Primitives and Noise Assessment
abstract: In this paper, a novel adaptive noise reduction method for engineering drawings is proposed based on the assessment of both primitives and noise. Unlike the current approaches, our method takes into account the special features of engineering drawings and assesses the characteristics of primitives and noise such that adaptive procedures and parameters are applied for noise reduction. For this purpose, we first analyze and categorize various types of noise in engineering drawings. The algorithms for average linewidth assessment, noise distribution assessment and noise level assessment are then proposed. These three assessments are combined to describe the features of the noise of each individual engineering drawing. Finally, median filters and morphological filters, which can adjust their template size and structural element adaptively according to different noise level and type, are used for adaptive noise reduction. Preliminary experimental results show that our approach is effective for noise reduction of engineering drawings.

title: A Novel Approach Using PCA and SVM for Face Detection
abstract: Nowadays, face detection and recognition have gained importance in security and information access. In this paper, an efficient method of face detection based on principal components analysis (PCA) and support vector machine (SVM) is proposed. It firsly filter the face potential area using statistical feature which is generated by analyzing local histogram distribution. And then, SVM classifier is used to detect face feature in the test image, SVM has great performance in classification task. PCA is used to reduce dimension of sample data. After PCA transform, the feature vectors, which are used for training SVM classifier, are generated. Our tests in this paper are based on CMU face database. The experimental results demonstrate that the proposed method is encouraging with a successful detection rate.

title: Discover Gene Specific Local Co-regulations Using Progressive Genetic Algorithm
abstract: The problem of gene specific co-regulation discovery is that, for a particular gene of interest, identify its closely coregulated genes and the associated subsets of experimental conditions in which such co-regulations occur. The coregulations are local in the sense that they occur in some subsets of full experimental conditions. In this paper, we propose an innovative method for finding gene specific coregulations using genetic algorithm (GA). Two novel ad hoc GAs, the single-stage and two-stage progressive GA, are proposed. They are called progressive because the initial population for the GA in a window position inherits the top-ranked individuals obtained in the preceding window position, enabling them to achieve better accuracy than the nonprogressive algorithm. Experimental results with real-life gene expression data demonstrate the efficiency and effectiveness of our technique in discovering gene specific coregulations