Raja Syamsul Azmir Raja Abdullah

X-Band Trisection Substrate-Integrated Waveguide Quasi-Elliptic Filter

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

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.

The concept of a forward scattering micro-sensors radar network for situational awareness

The concept of a novel forward scattering micro-radar wireless network for ground targets detection and recognition is presented. The system topology and structure are described first, followed by a summary of the systempsilas capabilities and applications. Signal processing strategies used for target detection, parameter estimation and automatic target recognition are briefly explained and supported with experimental results.

A CIRCULAR SYNTHETIC APERTURE RADAR FOR ON-THE-GROUND OBJECT DETECTION

Detecting an on-the-ground object is a subject of interest for use in some applications. Foreign Object Detection (FOD), which is an important issue in aviation safety, is a possible application. In this way, radar imaging, has several inherent advantages over other on-the-ground object detection techniques. This paper will introduce a ground-based Circular Synthetic Aperture Radar, which detects and localizes various objects, based on their re∞ection properties of microwaves. Here, wideband Linear Frequency Modulated (LFM) chirp pulses are employed for the transmission and reception of re∞ection pulses, both to and from the object under test. Once the pulses are received by the radar, a processing algorithm (proposed later in this paper) is executed to conflrm detection. In order to verify the validity of the model, a prototype was developed and a series of fleld experiments was carried out. The results show that the proposed system has the ability to detect and localize on-the-ground objects with dimensions as small as 2cm high and 1cm diameter, located several metres away. Furthermore, the resolution of the system was analysed and results indicate that the system is capable of distinguishing multiple objects in close proximity to each other, which therefore, makes it suitable for FOD applications by some small modiflcations.

Forward scattering micro sensor for vehicle classification

In this paper we present results in studies into the use of forward scattering radar and SISAR (shadow inverse synthetic aperture radar) micro-sensors for vehicle classification. A number of experiments that represent non-ideal scenarios are introduced and the results show that the proposed system does provide a viable classification system. We also studied the performance of the system when a network of sensors is used. Two scenarios of network deployment were tested and in both cases classification performance improved when compared to a single sensor system. In this paper, we used a single carried frequency to obtain both database and for classification. Further work is being currently carried out to study the effect of using different carrier frequencies on system performance.

3d experimental detection and discrimination of malignant and benign breast tumor using nn-based uwb imaging system

This paper presents both simulation and experimental study to detect and locate breast tumors along with their classification as malignant and/or benign in three dimensional (3D) breast model. The contrast between the dielectric properties of these two tumor types is the main key. These dielectric properties are mainly controlled by the water and blood content of tumors. For simulation, electromagnetic simulator software is used. The experiment is conducted using commercial Ultrawide-Band (UWB) transceivers, Neural Network (NN) based Pattern Recognition (PR) software for imaging and homogenous breast phantom. The 3D homogeneous breast phantom and tumors are fabricated using pure petroleum jelly and a mixture of wheat flour and water respectively. The simulation and experimental setups are performed by transmitting the UWB signals from one side of the breast model and receiving from opposite side diagonally. Using discrete cosine transform (DCT) of received signals, we have trained and tested the developed experimental Neural Network model. In 3D breast model, the achieved detection accuracy of tumor existence is around 100%, while the locating accuracy in terms of (x, y, z) position of a tumor within the breast reached approximately 89.2% and 86.6% in simulation and experimental works respectively. For classification, the permittivity and conductivity detection accuracy are 98.0% and 99.1% in simulation, and 98.6% and 99.5% in experimental works respectively. Tumor detection and type specification 3D may lead to successful clinical implementation followed by saving of precious human lives in the near future.

Miniaturized Ultra-Wideband Antenna Using Microstrip Negative Index Metamaterial

Abstract A three left-handed metamaterial unit cell antenna is presented in this article for ultra-wideband applications. Each left-handed metamaterial unit cell is a combination of a modified octagonal split-ring resonator, an octagonal spiral resonator, a capacitance-loaded strip, and a wire in order to achieve a design that simultaneously exhibits both negative electrical permittivity and negative magnetic permeability, which promises an extraordinary index of negative refraction to enhance the radiated power of the antenna, consequently improving the antenna system. The antenna design was etched on an FR4 epoxy substrate (Farnell Electronic Components Limited, Selangor D.E., Malaysia) with an evident compact size of 25 × 25 × 1.6 mm 3 . Return loss measurements demonstrated that this antenna achieves 94% bandwidth for a voltage standing wave ratio less than 2 over the frequency band of 5.2–13.9 GHz, with a maximum gain and directivity of 3.85 dBi and 5.45 dB, respectively, at 10.5 GHz. These measurement results show good agreement with those of the simulations as well as good omni-directional characteristics within its operating frequency band. The proposed metamaterial antenna is compact and highly directive and has a tunable operational frequency especially for ultra-wideband applications.

Segmenting CT images of bronchogenic carcinoma with bone metastases using PET intensity markers approach

Segmenting CT images of bronchogenic carcinoma with bone metastases using PET intensity markers approach Background. The evolution of medical imaging plays a vital role in the management of patients with cancer. In oncology, the impact of PET/CT imaging has been contributing widely to the patient treatment by its large advantages over anatomical imaging from screening to staging. PET images provide the functional activity inside the body while CT images demonstrate the anatomical information. Hence, the existence of cancer cells can be recognized in PET image but since the structural location and position cannot be defined on PET images, we need to retrieve the information from CT images. Methods. In this study, we highlight the localization of bronchogenic carcinoma by using high activity points on PET image as references to extract regions of interest on CT image. Once PET and CT images have been registered using cross correlation, coordinates of the candidate points from PET are fed into seeded region growing algorithm to define the boundary of lesion on CT. The region growing process continues until a significant change in bilinear pixel values is reached. Results. The method has been tested over eleven images of a patient having bronchogenic carcinoma with bone metastases. The results show that the mean standard error for over segmented pixels is 33% while for the under segmented pixels is 3.4%. Conclusions. Although very simple in implementation, region growing can result in good precision ROIs. The region growing method highly depends on where the growing process starts. Here, by using the data acquired from other modality, we tried to guide the segmentation process to achieve better segmentation results.

Analysis of forward scattering radar for ground target detection

This paper describes aspects of ground target detection using a forward scattering micro radar (FSR). It begins with a brief description of the system, followed by a more detailed analytical study of FSR in terms of power budget analysis and potential cross-range resolution. From this analysis, theoretical models are constructed and compared to the experimental data

COMPARATIVE STUDY ON WAVELET FILTER AND THRESHOLDING SELECTION FOR GPS/INS DATA FUSION

Navigation and guidance of an autonomous vehicle require determination of the position and velocity of the vehicle. Therefore, fusing the Inertial Navigation System (INS) and Global Positioning System (GPS) is important. Various methods have been applied to smooth and predict the INS and GPS errors. Recently, wavelet de-noising methodologies have been applied to improve the accuracy and reliability of the GPS/INS system. In this work, analysis of real data to identify the optimal wavelet filter for each GPS and INS component for high quality error estimation is presented. A comprehensive comparison of various wavelet thresholding selections with different level of decomposition is conducted to study the effect on GPS/INS error estimation while maintaining the original features of the signal. Results show that while some wavelet filters and thresholding selection algorithms perform better than others on each of the GPS and INS components, no specific parameter selection perform uniformly better than others.