SAR Image Simulation of Complex Target Including Multiple Scattering

We present a GPU-based computation for simulating the synthetic aperture radar (SAR) image of the complex target. To be more realistic, we included the multiple scattering field and antenna pattern tracking in producing the SAR echo signal for both Stripmap and Spotlight modes. Of the signal chains, the computation of the backscattering field is the most computationally intensive. To resolve the issue, we implement a computation parallelization for SAR echo signal generation. By profiling, the overall processing was identified to find which is the heavy loading stage. To further accommodate the hardware structure, we made extensive modifications in the CUDA kernel function. As a result, the computation efficiency is much improved, with over 224 times the speed up. The computation complexity by comparing the CPU and GPU computations was provided. We validated the proposed simulation algorithm using canonical targets, including a perfectly electric conductor (PEC), dielectric spheres, and rotated/unrotated dihedral corner reflectors. Additionally, the targets can be a multi-layered dielectric coating or a layered medium. The latter case aimed to evaluate the polarimetric response quantitively. Then, we simulated a complex target with various poses relative to the SAR imaging geometry. We show that the simulated images have high fidelity in geometric and radiometric specifications. The decomposition of images from individual scattering bounce offers valuable exploitation of the scattering mechanisms responsible for imaging certain target features.

A Study and Analysis of Improved Binary Pattern Technique in Dynamic Images

A dynamic image has a distinct quantity of object movement from one to another. It can be any object such as a car, person, an object moving from one point X to another point Y. Image consists of a sense of movement. Applications of object tracking are biometrics tracking, AR uses, video surveillance, passage monitoring, vehicle navigation, etc. Challenges in tracking multifaceted objects are fast movement, geometric conversion, blurring, messy background, artifacts, etc. To resolve this problem by merge all small features with nearby texture features. Texture feature describes the plane space and configuration of an area. A mixture of color and texture feature improves the object details and to increase the strength of the object's illustration. In Existing methods such as binary pattern method all object features are removed, so it is difficult to predict the exact pixel movement. The proposed method of improved binary pattern is also tracking the small changes in the pixel difference in one frame to other. Compared with the existing algorithms, IBP method measures the spatial arrangement of local image texture which reduces the overall processing cost and improves the strength of objective image. To track the similarities and difference of the object in each and every frame efficiently and effectively Improved Local Binary Pattern tracking algorithm was proposed. This proposed technique is an effective way to analysis complicated real time situations compared with other methods.

Frontoparietal action-oriented codes support novel instruction implementation

A key aspect of human cognitive flexibility concerns the ability to convert complex symbolic instructions into novel behaviors. Previous research proposes that this transformation is supported by two neurocognitive states: an initial declarative maintenance of task knowledge, and an implementation state necessary for optimal task execution. Furthermore, current models predict a crucial role of frontal and parietal brain regions in this process. However, whether declarative and procedural signals independently contribute to implementation remains unknown. We report the results of an fMRI experiment in which participants executed novel instructed stimulus-response associations. We then used a pattern-tracking procedure to quantify the contribution of format-unique signals during instruction implementation. This revealed independent procedural and declarative representations of novel S-Rs in frontoparietal areas, prior to execution. Critically, the degree of procedural activation predicted subsequent behavioral performance. Altogether, our results suggest an important contribution of frontoparietal regions to the neural architecture that regulates cognitive flexibility.