3439449
Classification of functional data: A segmentation approach
We suggest a classification and feature extraction method on functional data where the predictor variables are curves. The method, called functional segment discriminant analysis (FSDA), combines the classical linear discriminant analysis and support vector machine. FSDA is particularly useful for irregular functional data, characterized by spatial heterogeneity and local patterns like spikes. FSDA not only reduces the computation and storage burden by using a fraction of the spectrum, but also identifies important predictors and extracts features. FSDA is highly flexible, easy to incorporate information from other data sources and/or prior knowledge from the investigators. We apply FSDA to two public domain data sets and discuss the understanding developed from the study.
2524422
Versatile prediction and fast estimation of Architectural Vulnerability Factor from processor performance metrics
The shrinking processor feature size, lower threshold voltage and increasing clock frequency make modern processors highly vulnerable to transient faults. Architectural Vulnerability Factor (AVF) reflects the possibility that a transient fault eventually causes a visible error in the program output, and it indicates a system's susceptibility to transient faults. Therefore, the awareness of the AVF especially at early design stage is greatly helpful to achieve a trade-off between system performance and reliability. However, tracking the AVF during program execution is extremely costly, which makes accurate AVF prediction extraordinarily attractive to computer architects. In this paper, we propose to use Boosted Regression Trees, a nonparametric tree-based predictive modeling scheme, to identify the correlation across workloads, execution phases and processor configurations between a key processor structure's AVF and various performance metrics. The proposed method not only makes an accurate prediction but quantitatively illustrates individual performance variable's importance to the AVF. Moreover, to reduce the prediction complexity, we also utilize a technique named Patient Rule Induction Method to extract some simple selecting rules on important metrics. Applying these rules during run time can fast identify execution intervals with a relatively high AVF.
16012768
Continuous Optimization based-on Boosting Gaussian Mixture Model
A new estimation of distribution algorithm (EDA) based on Gaussian mixture model (GMM) is proposed, in which boosting, an efficient ensemble learning method, is adopted to estimate GMM. By boosting simple GMM with two components, it has the ability of learning the model structure and parameters automatically without any requirement for prior knowledge. Moreover, since boosting can be viewed as a gradient search for a good fit of some objective in function space, the new EDA is time efficient. A set of experiments is implemented to evaluate the efficiency and performance of the new algorithm. The results show that, with a relatively smaller population and less number of generations, the new algorithm can perform as well as compared EDAs in optimizing multimodal functions
37867009
Spatial Clustering with Obstacles Constraints Using Ant Colony and Particle Swarm Optimization
Spatial clustering is an important research topic in Spatial Data Mining (SDM). This paper proposes an Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) method for solving Spatial Clustering with Obstacles Constraints (SCOC). In the process of doing so, we first use improved ACO to obtain the shortest obstructed distance, which is an effective method for arbitrary shape obstacles, and then we develop a novel PKSCOC based on PSO and K-Medoids to cluster spatial data with obstacles. The PKSCOC algorithm can not only give attention to higher local constringency speed and stronger global optimum search, but also get down to the obstacles constraints and practicalities of spatial clustering. The experimental results demonstrate the effectiveness and efficiency of the proposed method, which performs better than Improved K-Medoids SCOC (IKSCOC) in terms of quantization error and has higher constringency speed than Genetic K-Medoids SCOC (GKSCOC).
7837008
A Forward-Secrecy WTLS Handshake Protocol Based on XTR
Because of the lack of the forward secrecy and the indefensibility from intermediator attacks and DOS attacks in WTLS, the security of current WTLS handshake protocol is far from satisfactory. In this paper we propose a new WTLS handshake protocol to solve these problems. Forward secrecy attribute has been added in the scheme, and a practicable WTLS handshake protocol has been designed on XTR system. Experiments have been taken on MSM6280 platform and the result turns out that the above security problems were efficiently solved and achieve a good performance on popular embedded platform. The conclusion can be drawn that the security and practicability of WTLS protocol has been greatly improved.
10205775
Kernel-based Global Folley-Sammon Discriminate Analysis
Kernel trick is a useful method dealing with the nonlinearity of patterns ' distribution, and widely used for non-linear pattern classification problem. Recently, many kernel-based Fisher discriminate methods were proposed, the features extracted by those methods are statistically uncorrelated, usually ineffectively when used in so called "small sample problem "(SSS), which exists in most FR tasks. To overcome this shortcoming, in this paper we present Kernel-based Global Folley-Sammon Discriminate Analysis (KGFS), it can be realized in three steps: firstly by combing kernel function, map data onto an implicit high-dimensional feature space, secondly construct orthogonal feature space there, finally calculate iterative global orthogonal discriminate vectors. Experiments have been done on ORL dataset. In terms of classification error rate performance, proposed kernel based method KGFS has a better performance than other three commonly used kernel-based methods, such as GDA, MGDAandKDDA.
709845
Supporting Live Video on Ad Hoc Wireless Networks: Unbalanced Multiple Description Coding, Single Path Transport and Recursive Error Concealment
Robust transmission of live video over ad hoc wireless networks presents new challenges: high bandwidth requirements are coupled with delay constraints; nodes are constrained in processing power and storage capacity; ad hoc wireless networks suffer from bursty packet losses that drastically degrade the viewing experience. Accordingly, we propose a simple, but practical unbalanced multiple description (UMD) codec that uses single path transport only. At the UMD decoder, the lost Low- Resolution (LR) frames can be receded from the corresponding ones of the correctly received High- Resolution (HR) stream. This makes LR more robust to packet losses and ensures continuous video playback. The simulation results show that the proposed UMD codec has higher decoded quality, smaller quality fluctuation and lower probability of pause than other error resilience techniques, especially when channel burstiness becomes large. We also propose a novel sequence-based error concealment (EC) algorithm for our UMD decoder. It recursively uses multi-frame recovery principle to frame-by-frame reduce error drift in the HR description with respect to the LR description. In fact, the algorithm can be applied to most UMD schemes. It has the advantage of working on integer-, half- or quarter- pixel precision and looking backward without buffering future frames. Experimental results show that error decreases the fastest if the UMD decoder uses our EC algorithm. It can provide satisfactory performance on both objective and subjective evaluation.