Muhammad Naveed Tabassum

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.

Compressive sensing based high-resolution passive bistatic radar

Passive bistatic radar (PBR) systems using different communication signals can only offer low-resolution target detection due to their inherent low bandwidth. In this paper, compressive sensing (CS) is applied to multichannel FM and GSM PBR to achieve improved range-Doppler resolutions and avoid some limitations of classical multiband PBR processing. In CS context, block-structured time-domain dictionary which is formed from multichannel signals suffers from coherence when fine range resolution is employed. To overcome such a pitfall, this work first transforms the dictionary to spectral domain where only the most important spectral components are retained. Principle component analysis followed by a whitening method are then applied to this spectrally transformed data in order to reduce the dimensionality of problem, thereby reducing the dictionary size and most importantly fulfilling the required condition of dictionary incoherence for better CS-based recovery. Two different block-structured dictionary formations are tested. The performance of the recovery of spatially close targets, in both FM and GSM PBR setups, are reported.

CS based processing for high resolution GSM passive bistatic radar

Passive bistatic radar (PBR) systems use existing RF broad - 1 cast and communication signals in the environment for surveillance and tracking applications. GSM mobile com - 1 munication signal based PBR systems are suitable for short-range surveillance systems, but the low-bandwidth of the signal results in low range resolutions when classical cross-correlation based processing is used for target detection. An alternative and more robust approach based on compressive sensing (CS) is proposed here to achieve high range resolution 1 by performing fine gridding for the target scene. To avoid the 1 increased coherence and computational load associated with the fine gridding, preprocessing steps are introduced in this paper, which involve choosing a suitable CS basis by application of spectral and subspace transformations. By so doing, resolution improvement is achieved when a single channel GSM signal and CS are employed for target detection.

Sequential adaptive elastic net approach for single-snapshot source localization

This paper proposes efficient algorithms for accurate recovery of direction-of-arrivals (DoAs) of sources from single-snapshot measurements using compressed beamforming (CBF). In CBF, the conventional sensor array signal model is cast as an underdetermined complex-valued linear regression model and sparse signal recovery methods are used for solving the DoA finding problem. A complex-valued pathwise weighted elastic net (c-PW-WEN) algorithm is developed that finds solutions at the knots of penalty parameter values over a path (or grid) of elastic net (EN) tuning parameter values. c-PW-WEN also computes least absolute shrinkage and selection operator (LASSO) or weighted LASSO in its path. A sequential adaptive EN (SAEN) method is then proposed that is based on c-PW-WEN algorithm with adaptive weights that depend on previous solution. Extensive simulation studies illustrate that SAEN improves the probability of exact recovery of true support compared to conventional sparse signal recovery approaches such as LASSO, EN, or orthogonal matching pursuit in several challenging multiple target scenarios. The effectiveness of SAEN is more pronounced in the presence of high mutual coherence.

Pathwise least angle regression and a significance test for the elastic net

Least angle regression (LARS) by Efron et al. (2004) is a novel method for constructing the piece-wise linear path of Lasso solutions. For several years, it remained also as the de facto method for computing the Lasso solution before more sophisticated optimization algorithms preceded it. LARS method has recently again increased its popularity due to its ability to find the values of the penalty parameters, called knots, at which a new parameter enters the active set of non-zero coefficients. Significance test for the Lasso by Lockhart et al. (2014), for example, requires solving the knots via the LARS algorithm. Elastic net (EN), on the other hand, is a highly popular extension of Lasso that uses a linear combination of Lasso and ridge regression penalties. In this paper, we propose a new novel algorithm, called pathwise (PW-)LARS-EN, that is able to compute the EN knots over a grid of EN tuning parameter α values. The developed PW-LARS-EN algorithm decreases the EN tuning parameter and exploits the previously found knot values and the original LARS algorithm. A covariance test statistic for the Lasso is then generalized to the EN for testing the significance of the predictors. Our simulation studies validate the fact that the test statistic has an asymptotic Exp(1) distribution.

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.

Compressive sensing based passive bistatic radar processing using time-domain complex data

Passive bistatic radar (PBR) systems exploit communication signals available in the environment for surveillance and tracking applications. Currently, GSM mobile communication signals are almost everywhere specially in the populated areas and road networks. PBR systems based on GSM signals are quite suitable for short-range surveillance systems. But the low-bandwidth of the signal results in low range resolution when classical cross-correlation based processing is used for target detection. An improved PBR processing method is described here which uses a single GSM channel and compressive sensing (CS) measurements. The CS is employed to process time-domain complex samples of received signal for the purpose of improving both range and Doppler resolutions. Different target scenarios are considered to illustrate the effectiveness of proposed CS-based processing scheme.