Breast Microwave Radar(BMR) is a novel imaging modality that is capable of producing high contrast images and can detect tumors of at least 4mm. To properly visualize the responses from the breast structures, BMR data sets must be reconstructed. In this paper, a real time BMR image formation technique is proposed. This approach is based on the use of a Single Instruction Multiple Thread(SIMT) interpolation method. By using this programming model, the proposed approach can be implemented on General Purpose Graphic Processing Unit (GPGPU) platform to speed up the reconstruction process. The proposed method yielded promising results when applied to simulated data sets obtained using anatomically accurate numeric phantoms. In average, the proposed approach yielded speed increases of one order of magnitude compared to its CPU counterpart, and two orders of magnitude with respect to current BMR reconstruction techniques.

In recent years, the use of radar technology has been proposed in a wide range of subsurface imaging applications. Traditionally, linear scan trajectories are used to acquire data in most subsurface radar applications. However, novel applications, such as breast microwave imaging and wood inspection, require the use of nonlinear scan trajectories in order to adjust to the geometry of the scanned area. This paper proposes a novel reconstruction algorithm for subsurface radar data acquired along cylindrical scan trajectories. The spectrum of the collected data is processed in order to locate the spatial origin of the target reflections and remove the spreading of the target reflections which results from the different signal travel times along the scan trajectory. The proposed algorithm was successfully tested using experimental data collected from phantoms that mimic high contrast subsurface radar scenarios, yielding promising results. Practical considerations such as spatial resolution and sampling constraints are discussed and illustrated as well.

Canada is one of the major exporters of wheat in the world. The quality of these exports is well known and factors such as lack of insect infestation are very important. The use of thermal images for subsequent analysis of temperatures profiles for grain classification and insect detection is a method under investigation. This paper presents an approach for automatic image segmentation of the wheat kernels based on the combined use of wavelet analysis and pulse coupled neural networks. It is shown that using wavelets as a preprocessing technique yields a consistent accurate segmentation in terms of the iteration number in which the network yields reliable edges of the wheat kernels. Subsequent analysis of these segmentations can determine internal qualities such as infestations.