A Design and Implementation of Network Traffic Monitoring System for PC-room Management

Abstract. This study proposes a network traffic monitoring system that will support the operation, management, expansion and design of a network system for its users through an analysis and diagnosis of the network-related equipments and lines in the PC-room. The proposed monitoring system is lightweight for its uses under the wireless environment and applies a web-based technology using JAVA to overcome the limits to managerial space, inconveniences and platform dependency and to raise its applicability to and usability on the real network based on performances, fault analyses and their calculation algorithm. The traffic monitoring system implemented in this study will allow users to effectively fulfill the network management including network diagnoses, fault detection, network configuration and design through the web, as well as to help users with their managements by presenting how to apply a simple network

A Noise-Aware Coding Scheme for Texture Classification

Texture-based analysis of images is a very common and much discussed issue in the fields of computer vision and image processing. Several methods have already been proposed to codify texture micro-patterns (texlets) in images. Most of these methods perform well when a given image is noise-free, but real world images contain different types of signal-independent as well as signal-dependent noises originated from different sources, even from the camera sensor itself. Hence, it is necessary to differentiate false textures appearing due to the noises, and thus, to achieve a reliable representation of texlets. In this proposal, we define an adaptive noise band (ANB) to approximate the amount of noise contamination around a pixel up to a certain extent. Based on this ANB, we generate reliable codes named noise tolerant ternary pattern (NTTP) to represent the texlets in an image. Extensive experiments on several datasets from renowned texture databases, such as the Outex and the Brodatz database, show that NTTP performs much better than the state-of-the-art methods