Krzysztof Dmitruk

This paper investigates the effectiveness of compensated summation techniques in improving the numerical accuracy of algorithms involving the summation of long sequences of floating‐point numbers, such as boundary value problems for ordinary differential equations. Three parallel programming frameworks—OpenACC, CUDA, and SYCL—are evaluated in terms of their performance, numerical accuracy, and cross‐platform applicability. Experiments are conducted on both GPU and CPU architectures. Two data layouts, namely column‐wise and row‐wise formats, are introduced to improve memory access efficiency through better coalescing. Three summation methods are considered: ordinary summation, Kahan compensated summation, and Neumaier compensated summation. Parallel implementations are developed using both single and double precision arithmetic. The results are compared with their sequential counterparts and selected library functions. The experimental results demonstrate that parallel implementations with compensated summation techniques significantly improve numerical accuracy while maintaining a negligible impact on the execution time of BVP parallel solvers. Furthermore, the proposed approach provides results comparable to library functions, while outperforming the evaluated reference implementations for the largest considered problem sizes.

Geometric inaccuracies in the design of X-ray baggage scanners can lead to significant image artifacts, such as banding and discontinuities, which compromise security screening effectiveness. Although comprehensive commercial solutions are available, constructing a custom X-ray scanner requires the precise alignment of detector arrays. This is a complex and time-consuming process when performed manually. The core of the proposed method is a computational model that calculates the optimal position and orientation for each detector card based on user-defined scanner dimensions and hardware parameters. To validate the geometry created with this method, its performance was compared against flat and arc-shaped geometries. The results demonstrate that the proposed method successfully generates geometries that produce continuous and artifact-free images. The study concludes that the developed software tool provides a robust and practical solution, significantly simplifying the complex task of scanner construction and accelerating the development of reliable, custom X-ray inspection systems.

An X-ray scanning and image processing have a vast range of applications in the security. An image of a content of some package being passed for example to an airplane or to the court house may help to figure out if there are any dangerous objects inside that package and to avoid possible threatening situation. As the raw X-ray images are not always easy to analyze and interpret, some image processing methods like an object detection, a frequency resolution increase or a pseudocolouring are being used. In this paper, we propose a pseudocoloring improvement over material based approach. By addition of the edge detection methods we fill and sharpen colour layers over the image, making it easier to interpret. We demonstrate the effectiveness of the methods using real data, acquired from a professional dual energy X-ray scanner.