WiMAX: Network Entry Phase Optimization for Bandwidth Improvement Solution

Worldwide Interoperability for Microwave access (WiMAX) service providers is concerned about having enough Internet-backbone to support a potentially large number of users. Network entry process directly influences the initial delay that users are experiencing. Therefore, efficiently combine DCD interval, UCD interval and initial ranging interval in network entry process is predicted to influence the network performance, as presented in this paper

DIFS modifications to support QoS in IEEE 802.11g DCF ad-hoc networks

This paper describes and investigates the QoS provisioning technique used in IEEE 802.11g ad-hoc structure. This research then propose better scheme to support QoS by modifying the DCF Interframe Space (DIFS) to use new values to bias towards the high priority traffic flow and distinguish it from the low priority traffic.Simulations are done using NS-2 and the findings presented. Results showed that better throughput can be achieved to provide better traffic flows on high priority traffi

An Enhancement Of The Spectral Statistical Test For Randomness

Random Numbers Play Essential Roles In Cryptography, Modeling And Simulation Applications. NIST Statistical Test Suite For Randomness Is The Most Comprehensive Set Of Random Tests. It Has Been Popular And Used As A Benchmark Test For Randomness. One Of The Random Tests Is Spectral Test. There Has Been Serious Problem In Spectral Test As Pointed Out By Few Researchers. In This Paper, Further Theoretical Improvement Shall Be Proposed On The Spectral Test Based On Computational Observation Being Made On Random Noise. A Recommendation On The Spectral Test Setting For Short Cryptographic Keys Shall Also Be Made

A Psychovisual Model based on Discrete Orthonormal Transform

Discrete Orthonormal Transform has been a basis for digital image processing. The lesser coefficients of a Discrete Orthonormal Transform to reconstruct an image is the more compact support the Discrete Orthonormal Transform provides to an image. Tchebychev Moment Transform has been shown to provide a more compact support to an image than the popular Discrete Cosine Transform. This paper will investigate the contribution of each coefficient of the Discrete Orthonormal Transform to the image reconstruction. The error threshold in image reconstruction will be the primitive of Psychovisual Model to an image. An experimental result shall show that the Psychovisual Model will provide a statistically efficient error threshold for image reconstruction

Remote sensing image classification using soft computing approach

Mangrove forest is an important costal ecosystem in the tropical and sub-tropical coastal regions. It is among the most productivity, ecologically, environmentally and biologically diverse ecosystem in the world. With the improvement of remote sensing technology such as remote sensing images, it provides the alternative for better way of mangrove mapping because covered wider area of ground survey. Image classification is the important part of remote sensing, image analysis and pattern recognition. It is defined as the extraction of differentiated classes; land use and land cover categories from raw remote sensing digital satellite data. One pixel in the satellite image possibly covers more than one object on the ground, within-class variability, or other complex surface cover patterns that cannot be properly described by one class. A pixel in remote sensing images might represent a mixture of class covers, within-class variability, or other complex surface cover patterns. However, this pixel cannot be correctly described by one class. These may be caused by ground characteristics of the classes and the image spatial resolution This project was about the unsupervised classification for satellite image by using fuzzy logic technique. In this project, the method of unsupervised classification was implemented as compared to supervised classification. Nowadays, many situations on this earth were captured by the satellite. Therefore, it was important to be able to classify out the things or objects that had been captured by the satellite. In this project, Fuzzy Inference System (FIS) of Fuzzy Logic Toolbox in matlab was selected to do for unsupervised classification. The types of FIS technique selected to do for the classification include Fuzzy Mamdani and Fuzzy Sugeno. These two methods are used to compare which one can provide a better output. Key Researchers: Dr Mohd Faizal Abdollah Othman bin Mohd Prof Dr. Hj. Shahrin bin Sahib@Sahibuddin Prof Dr. Nanna Suryana Email: [email protected] Tel. No: 06-3316662 Vote No: PJP/2009/FTMK(8D)S55

Thresholding and Fuzzy Rule-Based Classification Approaches in Handling Mangrove Forest Mixed Pixel Problems Associated with in QuickBird Remote Sensing Image Analysis

Mangrove forest is an important costal ecosystem in the tropical and sub-tropical coastal regions. It is among the most productivity, ecologically, environmentally and biologically diverse ecosystem in the world. With the improvement of remote sensing technology such as remote sensing images, it provides the alternative for better way of mangrove mapping because covered wider area of ground survey. Image classification is the important part of remote sensing, image analysis and pattern recognition. It is defined as the extraction of differentiated classes; land use and land cover categories from raw remote sensing digital satellite data. One pixel in the satellite image possibly covers more than one object on the ground, within-class variability, or other complex surface cover patterns that cannot be properly described by one class. A pixel in remote sensing images might represent a mixture of class covers, within-class variability, or other complex surface cover patterns. However, this pixel cannot be correctly described by one class. These may be caused by ground characteristics of the classes and the image spatial resolution. Therefore, the aim of this research is to obtain the optimal threshold value for each class of landuse/landcover using a combination of thresholding and fuzzy rule-based classification techniques. The proposed techniques consist of three main steps; selecting training site, identifying threshold value and producing classification map. In order to produce the final mangrove classification map, the accuracy assessment is conducted through ground truth data, spectroradiometer and expert judgment. The assessment discovered the relationship between the image and condition on the ground, and the spectral signature of surface material in identifying the geographical object. Keywords Mangrove, Remote Sensing Satellite Image, Threshold, Fuzzy Rule-Based Classificatio

Threshold verification using statistical approach for fast attack detection

Network has grows to a mammoth size and becoming more complex, thus exposing the services it offers towards multiple types of intrusion vulnerabilities.One method to overcome intrusion is by introducing Intrusion Detection System (IDS) for detecting the threat before it can damage the network resources.IDS have the ability to analyze network traffic and recognize incoming and on-going network attack.In detecting intrusion attack, Information gathering on such activity can be classified into fast attack and slow attack.Yet, majority of the current intrusion detection systems do not have the ability to differentiate between these two types of attacks. Early detection of fast attack is very useful in a real time environment; in which it can help the targeted network from further intrusion that could let the intruder to gain access to the vulnerable machine.To address this challenge, this paper introduces a fast attack detection framework that set a threshold value to differentiate between the normal network traffic and abnormal network traffic on the victim perspective. The threshold value is abstract with the help of suitable set of feature used to detect the anomaly in the network. By introducing the threshold value, anomaly based detection can build a complete profile to detect any intrusion threat as well as at the same time reducing it false alarm alert

Understanding Network Congestion Effects On Performance: Article Review

Networking communications have become popular worldwide in human daily services. Network Congestion (NC) happens whenever because nodes and links are overloaded. Such situations affect the network expected performance and its services quality. Congestion /NC occurs as a results of its subnets’ links overload, which gradually (overtime) affects the network performance with an increase of transmission delay, a slowdown of throughput as generally perceived by network’s users. NC is considerable as the basis problem in network performance quality acceptance; and most of its existing problem solutions are expected still playing a great role in the future networks model, which will be running mostly too many multimedia applications. However, various researches over past years have initiated the study on the causes leading to congestion and, different lessons can be learnt from NC situations analysis to understand its relationship with the future network’s performance. This paper presents an analytical review of NC occurrence causes and the fundamentals of the existing control solutions/frameworks as available and studied from some former and recent networks publications. A particular attention has been paid throughout this study to found out how NC may still affect the future networks performance (i.e. QoS in the world of multimedia networks). And the coverage/content of this paper is expected to serve as a quick access to the knowledge essentials for researchers on related subject as stated in this paper topic