An Improved Traffic Matrix Decomposition Method with Frequency-Domain Regularization

We propose a novel network traffic matrix decomposition method named Stable Principal Component Pursuit with Frequency-Domain Regularization (SPCP-FDR), which improves the Stable Principal Component Pursuit (SPCP) method by using a frequency-domain noise regularization function. An experiment demonstrates the feasibility of this new decomposition method.Comment: Accepted to IEICE Transactions on Information and System

Structural Analysis of Network Traffic Matrix via Relaxed Principal Component Pursuit

The network traffic matrix is widely used in network operation and management. It is therefore of crucial importance to analyze the components and the structure of the network traffic matrix, for which several mathematical approaches such as Principal Component Analysis (PCA) were proposed. In this paper, we first argue that PCA performs poorly for analyzing traffic matrix that is polluted by large volume anomalies, and then propose a new decomposition model for the network traffic matrix. According to this model, we carry out the structural analysis by decomposing the network traffic matrix into three sub-matrices, namely, the deterministic traffic, the anomaly traffic and the noise traffic matrix, which is similar to the Robust Principal Component Analysis (RPCA) problem previously studied in [13]. Based on the Relaxed Principal Component Pursuit (Relaxed PCP) method and the Accelerated Proximal Gradient (APG) algorithm, we present an iterative approach for decomposing a traffic matrix, and demonstrate its efficiency and flexibility by experimental results. Finally, we further discuss several features of the deterministic and noise traffic. Our study develops a novel method for the problem of structural analysis of the traffic matrix, which is robust against pollution of large volume anomalies.Comment: Accepted to Elsevier Computer Network

Combining Vision Verification with a High Level Robot Programming Language

This thesis describes work on using vision verification within an object level language for describing robot assembly (RAPT). The motivation for this thesis is provided by two problems. The first is how to enhance a high level robot programming language so that it can encompass vision commands to locate workpieces of an assembly. The second is how to find a way of making full use of sensory information to update the robot system's knowledge about the environment. The work described in this thesis consists of three parts: (1) adding vision commands into the RAPT input language so that the user can specify vision verification tasks; (2) implementing a symbolic geometrical reasoning system so that vision data can be reasoned about symbolically at compile time in order to speed up run time operations; (3) providing a framework which enables the RAPT system to make full use of the sensory information. The vision commands allow partial information about positions to be combined with sensory information in a general way, and the symbolic reasoning system allows much of the reasoning work about vision information to be done before the actual information is obtained. The framework combines a verification vision facility with an object level language in an intelligent way so that all ramifications of the effects of sensory data are taken account of. The heart of the framework is the modifying factor array. The position of each object is expressed as the product of two parts: the planned position and the difference between this and "he actual one. This difference, referred to as the modifying factor of an object, is stored in the modifying factor array. The planned position is described by the user in the usual way in a RAPT program and its value is inferred by the RAPT reasoning system. Modifying factors of objects whose positions are directly verified are defined at compile time as symbolic expressions containing variables whose value will become known at run time. The modifying factors of other objects (not directly verified) may be dependent upon positions of objects which are verified. At compile time the framework reasons about the influence of the sensory information on the objects which are not verified directly by the vision system, and establishes connections among modifying factors of objects in each situation. This framework makes the representation of the influence of vision information on the robot's knowledge of the environment compact and simple. All the programming has been done. It has been tested with simulated data and works successfully

Solar Radio Bursts with Spectral Fine Structures in Preflares

A good observation of preflare activities is important for us to understand the origin and triggering mechanism of solar flares, and to predict the occurrence of solar flares. This work presents the characteristics of microwave spectral fine structures as preflare activities of four solar flares observed by Ond\v{r}ejov radio spectrograph in the frequency range of 0.8--2.0 GHz. We found that these microwave bursts which occurred 1--4 minutes before the onset of flares have spectral fine structures with relatively weak intensities and very short timescales. They include microwave quasi-periodic pulsations (QPP) with very short period of 0.1-0.3 s and dot bursts with millisecond timescales and narrow frequency bandwidths. Accompanying these microwave bursts, there are filament motions, plasma ejection or loop brightening on the EUV imaging observations and non-thermal hard X-ray emission enhancements observed by RHESSI. These facts may reveal certain independent non-thermal energy releasing processes and particle acceleration before the onset of solar flares. They may be conducive to understand the nature of solar flares and predict their occurrence