Mubashir Alam

Efficient techniques to enhance nearfield imaging of human head for anomaly detection

This paper proposes efficient algorithms to enhance the nearfield electromagnetic imaging of human head. Forward problem is modeled using SAM head phantom with brain tumor anomalies, surrounded by a circular applicator antenna array. Scattered signals are compressively sensed (CS) at a limited number of sensing positions, and the sensed signals are preprocessed efficiently using a proposed novel technique to maximize information extraction. A dictionary is formed and then implemented in CS based inverse problem analysis. Reconstructed images are enhanced using new post-processing techniques to improve the spatial resolution. Image quality is analyzed using the quality metric in terms of peak signal-to-noise ratio (PSNR). The quality of the reconstructed images and the corresponding PSNR values reveals the validity of the imaging techniques.

Compressed sensing based nearfield electromagnetic imaging

This paper proposes a novel method of nearfield electromagnetic imaging using compressed sensing technique. Orthogonal matching pursuit (OMP) reconstruction algorithm is implemented for reconstruction of the target space. A dictionary is tested considering head imaging of single and multiple brain tumor targets. The received scattered time-domain signals are captured using spatial compressed sensing and later interpolated for full target space. These signals are also processed for temporal compressed sensing using background subtraction. Simulation of the forward problem it is conducted using CST Microwave Studio using frequency range of 300-3000 megahertz. The quality of reconstructed images reveals the potential of the proposed method.

Enhanced noninvasive imaging system for dispersive highly coherent space

A new noninvasive nearfield electromagnetic imaging (EMI) system for highly coherent and compressively sensed (CS) data at only few sensing positions is presented in this paper. Principal component analysis (PCA) in combination with spatial CS and background subtraction is implemented for the enhanced imaging of highly dispersive and coherent target space. The proposed imaging system is applied by forming an incoherent dictionary, which is later tested and validated for head imaging of single and multiple brain tumor targets using CS based sparse recovery. The head imaging model containing the tumor with an applicator antenna array around it is designed using CST Microwave Studio. Consequently, enhanced imaging results reveal the potential of the developed imaging system.

Innovative nearfield electromagnetic imaging system

An innovative reconstruction system using compressed sensing for nearfield electromagnetic imaging is presented in this paper. The proposed imaging system is tested and validated by creating a dictionary for head imaging of single and multiple brain tumor targets. The scattered time-domain signals are collected at few sensor positions, considering a limited number of possible spatial locations of tumor targets, and using spatial compressed sensing. TPhe sensed signals are further preprocessed for spectral sparsity in frequency domain, resulting in further reduction in the number of samples. Simulation of the forward problem is presented, considering a head model, using CST Microwave Studio tool. Image reconstruction is performed considering various levels of signal to noise ratio. The quality of the reconstructed images of the target space reveals the potential of the developed imaging system.

FPGA IMPLEMENTATION OF SPACE-TIME ADAPTIVE PROCESSING (STAP) ALGORITHM FOR TARGET DETECTION IN PASSIVE RADARS

Space-Time Adaptive Processing (STAP) algorithm has recently been used in Passive Bi-static Radars (PBR) because it removes the clutter and non-cooperative transmitter efiectively making the target detection easy in harsh environments like air-ground. Real- time implementation of STAP is a very challenging task as it is computationally-intensive, time-critical and resource-hungry process. This paper focuses on the Field-Programmable Gate Array (FPGA) implementation of STAP algorithm for passive radar using FM radio as transmitter of opportunity. The signals of interest were collected using an eight-channel software-deflned radar with a uniform circular array (UCA). The STAP processing was simulated using MATLAB and hardware implementation was carried out on a Xilinx Virtex-6 FPGA. The system is tested using experimental radar data. Timing and Power analysis of hardware implementation justifles that FPGA provides a fast and reliable platform for STAP real-time radar processing.

Nearfield Imaging for Noninvasive Monitoring of Hyperthermia Treatment

Monitoring of thermal distribution in hyperthermia treatment depends on invasive intraluminal or interstitial probes. This research aims at developing a proficient platform that addresses some challenges of hyperthermia therapy. A model of forward problem is developed, incorporating dispersive wideband models of tissue properties. A tool is also developed to generate a dictionary that relates scattered signals to material features. Solution of the inverse problem is conducted based on compressed sensing techniques. With the dependence of tissue electrical properties on temperature, thermal maps are generated. Practical aspects of the nonlinearity associated with wideband power amplifiers are incorporated in the model. Analysis of the reconstructed images reveals the validity of the proposed techniques. In particular, encouraging results are obtained of thermal mapping, denoting the potential of using nearfield imaging as a noninvasive thermometry tool, in monitoring hyperthermia treatment.

Improving target signature in a software defined, multi-band, multi-channel passive radar

The use of passive radar for detection of targets of different type has gained considerable popularity in past few years. This radar uses the so-called the ”signals of the opportunities” which are already in the environment. These opportunistic signals can be of different types, both in terms of frequency and modulation type. Therefore, instead of designing a separate hardware receiving system, a better approach will be design a single software defined receiver having capability to capture any signal of interest by just changing antenna system. Also to obtain various signals of interests, a multi-channel receiver system is desired so that advance array processing algorithms like digital beamforming can be utilized. Therefore , a state of the art software defined, multi-band, and a multi-channel passive radar system has been designed and being tested. One important step in the processing of signals for final target detection is the cancelation of direct path signal coming from main transmitter. Usually this is done by a combination of digital beamforming and adaptive filters based interference cancelation algorithms. An improved digital beamformer is designed with complete control of side-lobes to suppress the direct path signal. Also improved interference cancelation is done by using a cascade (two-stage) of adaptive filters. By using the combination of these techniques it will be shown that a considerable weak target can be detected.

On modeling and hardware implementation of Space-Time Adaptive Processing (STAP) for target detection in passive BI-static radar

This paper presents the modeling and partial FPGA implementation of Space Time Adaptive Processing (STAP) algorithm for Passive Bi-static Radar (PBR) using commercial FM transmitter as illuminator of opportunity. Two simulated targets were inserted in the real-time recorded FM eight channel data signal. The simulated targets were detected after effective zero Doppler frequency clutter removal with the help of optimum matched filtering. Hardware implementation of STAP processor including phase correction, Doppler frequency correction and Kronecker product is provided and explained. The system was designed and developed using Xilinx FPGA Virtex-6 platform.