Vehicle Classification Using Neural Network in Forward Scattering Radar

This thesis unveils the potential and utilization of Neural Network (NN) in radar applications for target classification. The radar system under test is a special of its kind and known as Forward Scattering Radar (FSR). FSR is a special type of bistatic radar which the transmitted energy is scattered by a target and the target is so close to the transmitter-receiver baseline. Recent works had shown that FSR can be effectively used for classification, but the result can be further improved by using advance classification method. To proof this, result from FSR experiment were used. The target used for this experiment is a ground vehicle which is represented by typical public road transport. New features from raw radar signal were determined and extracted manually prior to classification process using Neural Network (NN). Two types of features in the time and frequency domain signature were examined, namely time required for counting zero crossings, first main lobe width, second main lobe- width and the number of lobes. Multilayer perceptron (MLP) back-propagation neural network trained with back propagation algorithm was implemented and analyzed. In NN classifier, the unknown target is sent to the network trained by the known targets to attain the accurate output. Two tasks of classifications are analyzed. The first task is to recognize the exact type of vehicle, four vehicle types were selected: Vauxhall Astra, Renault Traffic, Vauxhall Combo and Honda Civic. The second task is to group vehicle into their categories: small, medium and large. The proposed NN provides high percentage of successful classification which is 90% and 98% of overall data was correctly classified in vehicle recognition and vehicle categorisation respectively. The result presented show that NN can be effectively employed in FSR system as a classification method

Multiple description coding (MDC) for video transmission in cognitive radio network systems

Over recent years, Cognitive Radio (CR) network has been extensively investigated to improve spectrum utilization and satisfy the demand of bandwidth for multimedia services such as video transmission. The size of video stream requires a large volume of network resource and becoming a challenging problem to maintain or improve the quality performance of video transmission. Multiple Description Coding (MDC) is one of the promising methods used to improve the error resilient in video transmission. Each description generated from MDC provides a low but acceptable video quality and could be enhanced into a higher quality if both are received at the receiver. The objective of this paper is to investigate the performance of video transmission in CR system using joint design of MDC method with H.264/AVC coding technique. The video performance was evaluated in three different channels; error free, random erroneous and CR channel. The simulation results show that the proposed MDC design improved the video quality performance by 3.33 % compared to the conventional Single Description Coding (SDC)

Alaska Hot Scoop Enterprise / Intan Nurbaizura Zainuddin ... [et al.]

Name of the company: Name of our company is Alaska Hot Scoop Enterprise. • Nature of business: Our business is based food manufacturing and distributing the products. • Industry profile: Our business is owned actively by partnership, incorporated as an Alaska Hot Scoop Enterprise. As a small startup company, we recognize the limitation of attempting to manufacture our products in a small premise. So that, our companies can more concentrate on making the products, as well as give good services to produce our products based on our customer demand. • Location of the business: Our business is locates at Giant Hypermarket at Senawang. We choose this location because it is situated in the center area of people passage from every place in Senawang. • Date of the business commencement: Our business will begin to operate on 1st January 2006

Investigation of the chemical, strength, adhesion and morphological properties of fly ash based geopolymer-modified bitumen

The current study seeks to investigate the chemical, strength, adhesion and morphological properties of fly-ash based geopolymer-modified bitumen (GMB). The geopolymer was prepared by mixing class F fly ash with alkaline solution (sodium silicate and sodium hydroxide), and was then mixed with an 80/100 penetration grade bitumen at different concentrations of 3, 5, 7 and 9% (by weight of bitumen) to produce the GMB. The chemical and strength properties of the binders were determined using the Fourier Transform Infrared Spectroscopy (FTIR) and Impact tests, respectively. The Surface Free Energy (SFE) and Atomic Force Microscope (AFM) tests were conducted to establish the adhesion and morphology of the binders. The modification of base bitumen with geopolymer resulting the improvement in structural chain mobility properties and improved storage stability compared to those of the control sample. The result of FTIR analysis showed that the incorporation of geopolymer into base bitumen did not cause any change in the functional groups, where the peaks of aromatic Cdouble bondC stretching is at around 1600 cm−1 (stretch), 1475 cm−1 (stretch) and 900–600 cm−1 (out-of-plane bend). The peak at around 1390 cm−1 clearly shows the stretch of the C-N amine group. The result of SFE test shows that GMB has better resistance to moisture damage. Finally, the AFM analysis revealed morphological changes in all GMB samples. In general, the addition of 5% geopolymer can be considered as is the optimum concentration for bitumen modification

Author Correction: A Randomised Controlled Trial on the Effectiveness and Adherence of Modified Alternate-day Calorie Restriction in Improving Activity of Non-Alcoholic Fatty Liver Disease

An amendment to this paper has been published and can be accessed via a link at the top of the paper