Raja Syamsul Azmir Raja Abdullah

Bio: Raja Syamsul Azmir Raja Abdullah is an academic researcher from Universiti Putra Malaysia. The author has contributed to research in topics: Radar & Bistatic radar. The author has an hindex of 19, co-authored 127 publications receiving 1221 citations. Previous affiliations of Raja Syamsul Azmir Raja Abdullah include University of Birmingham & Universiti Teknologi MARA.

Automatic ground target classification using forward scattering radar

Abstract: Experimental study is undertaken of the feasibility of forward scattering radar (FSR) and its application to automatic ground target classification. The radar itself, fundamental theoretical analysis, target recognition algorithm and the target's classification subsystem are introduced. For target recognition, the effect of shadow inverse synthetic aperture radar is used. The radar experimental set-up and experimentation results are discussed. For classification, a system is proposed, which extracts features from the radar measurements by using Fourier transform and principle component analysis and uses a nearest neighbour classifier. Speed estimation in FSR is also introduced. By analysing 850 experimentally obtained car signatures, the performance of the system is evaluated and the effectiveness of the system is confirmed. The limitations of the work and its future are also discussed.

Artificial neural network approach in radar target classification

Abstract: Problem statement: This study unveils the potential and utilization of Neural Network (NN) in radar applications for target classification. The radar system under test is a special of it kinds and known as Forward Scattering Radar (FSR). In this study the target is a ground vehicle which is represented by typical public road transport. The features from raw radar signal were extracted manually prior to classification process using Neural Network (NN). Features given to the proposed network model are identified through radar theoretical analysis. Multi-Layer Perceptron (MLP) back-propagation neural network trained with three 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. Approach: Two types of classifications were analyzed. The first one is to classify the exact type of vehicle, four vehicle types were selected. The second objective is to grouped vehicle into their categories. The proposed NN architecture is compared to the K Nearest Neighbor classifier and the performance is evaluated. Results: Based on the results, the proposed NN provides a higher percentage of successful classification than the KNN classifier. Conclusion/Recommendation: The result presented here show that NN can be effectively employed in radar classification applications.

X-band trisection substrate-integrated waveguide quasi-elliptic filter

Abstract: A narrowband trisection substrate-integrated waveguide elliptic filter with coplanar waveguide (CPW) input and output ports is proposed and demonstrated for X-band applications. The filter is formed by incorporating metallized vias in a substrate (RT/Duroid) to create cross-coupled waveguide resonators. The result is an attenuation pole of finite frequency on the high side of the passband, therefore exhibiting asymmetric frequency response. The fabricated trisection filter with a centre frequency of 10.05 GHz exhibits an insertion loss of 3.16 dB for 3% bandwidth and a return loss of -20 dB. The rejection is larger than 15 dB at 10.37 GHz.

Experimental Breast Tumor Detection Using Nn-Based UWB Imaging

Abstract: This paper presents a system with experimental comple-ment to a simulation work for early breast tumor detection. The ex-periments are conducted using commercial Ultrawide-Band (UWB) transceivers, Neural Network (NN) based Pattern Recognition (PR) software for imaging and proposed breast phantoms for homogenous and heterogeneous tissues. The proposed breast phantoms (homoge-neous and heterogeneous) and tumor are constructed using available low cost materials and their mixtures with minimal e®ort. A speci¯c glass is used as skin. All the materials and their mixtures are con-sidered according to the ratio of the dielectric properties of the breast tissues. Experiments to detect tumor are performed in regular noisy room environment. The UWB signals are transmitted from one side of the breast phantom (for both cases) and received from opposite side diagonally repeatedly. Using discrete cosine transform (DCT) of these received signals, a Neural Network (NN) module is developed, trained and tested. The tumor existence, size and location detection rates for both cases are highly satisfactory, which are approximately: (i) 100%,95.8% and 94.3% for homogeneous and (ii) 100%, 93.4% and 93.1% for heterogeneous cases respectively. This gives assurance of early de- tection and the practical usefulness of the developed system in near future.

A Review of Navigation Systems (Integration and Algorithms)

Abstract: Significant developments and technical trends in the area of navigation systems are reviewed. In particular, the integration of the Global Positioning System (GPS) and Inertial Navigation System (INS) has been an important development in modern navigation. The review concentrates also on the analysis, investigation, assessment and performance evaluation of existing integrated navigation systems of accuracy, performance, low cost and all the issues that aid in optimizing their operating efficiency. The integration of GPS and INS has been successfully used in practice during the past decades. However, much of the work has focused on the use of a high accuracy Inertial Measurement Unit (IMU), which is an inertial sensors block without navigation solution output, and hence, this research area is also reviewed in this paper.

'The Micro-Doppler Effect in Radar' by V.C. Chen

Abstract: This is a book review of 'The Micro-Doppler Effect in Radar' by V. C. Chen. Artech House, 16 Sussex Street, London, SW1V 4RW, UK. 2011. 290pp + diskette. Illustrated. £90. ISBN 978-1-60807-057-2.

Massive MIMO for Maximal Spectral Efficiency: How Many Users and Pilots Should Be Allocated?

Abstract: Massive MIMO is a promising technique for increasing the spectral efficiency (SE) of cellular networks, by deploying antenna arrays with hundreds or thousands of active elements at the base stations and performing coherent transceiver processing. A common rule-of-thumb is that these systems should have an order of magnitude more antennas $M$ than scheduled users $K$ because the users’ channels are likely to be near-orthogonal when $M/K > 10$ . However, it has not been proved that this rule-of-thumb actually maximizes the SE. In this paper, we analyze how the optimal number of scheduled users $K^\star$ depends on $M$ and other system parameters. To this end, new SE expressions are derived to enable efficient system-level analysis with power control, arbitrary pilot reuse, and random user locations. The value of $K^\star$ in the large- $M$ regime is derived in closed form, while simulations are used to show what happens at finite $M$ , in different interference scenarios, with different pilot reuse factors, and for different processing schemes. Up to half the coherence block should be dedicated to pilots and the optimal $M/K$ is less than 10 in many cases of practical relevance. Interestingly, $K^\star$ depends strongly on the processing scheme and hence it is unfair to compare different schemes using the same $K$ .

A Survey of Small-Scale Unmanned Aerial Vehicles: Recent Advances and Future Development Trends

Abstract: This paper provides a brief overview on the recent advances of small-scale unmanned aerial vehicles (UAVs) from the perspective of platforms, key elements, and scientific research. The survey starts with an introduction of the recent advances of small-scale UAV platforms, based on the information summarized from 132 models available worldwide. Next, the evolvement of the key elements, including onboard processing units, navigation sensors, mission-oriented sensors, communication modules, and ground control station, is presented and analyzed. Third, achievements of small-scale UAV research, particularly on platform design and construction, dynamics modeling, and flight control, are introduced. Finally, the future of small-scale UAVs' research, civil applications, and military applications are forecasted.

Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies

Abstract: This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems