ПОРІВНЯЛЬНИЙ АНАЛІЗ МЕТОДІВ ПОШУКУ НАЙБЛИЖЧИХ ТОЧОК НА ЗОБРАЖЕННЯХ ОБ’ЄКТІВ ТЕХНІЧНИХ СИСТЕМ

The subject of the study is the process of mapping the construction of point clouds of technical systems using the algorithm of the nearest points. The goal is to minimize the alignment criterion by converting a set of cloud points Y into a set of cloud points X in a software product that uses an iterative closest point (ICP) algorithm. Objectives: to analyze the properties of input images that contain point clouds; to review the algorithms for identifying and comparing key points; implement a cloud comparison algorithm using the ISR algorithm; consider an example of the algorithm for estimating the approximate values of the elements of mutual orientation; implement software that allows you to compare files that contain point clouds and draw conclusions about the possibility of comparing them. The methods used are: search for points using the algorithm of iterative nearest points, the algorithm for estimating the approximate values of the elements of mutual orientation, the method of algorithm theory for the analysis of file structures STL (standard template library - format template library) format. The following results were obtained. The choice of the ICP algorithm for the task of reconstruction of the object of technical systems is substantiated; the main features of the ISR algorithm are considered; the algorithm of comparison of key points, and also optimization that allows reducing criterion of combination at the reconstruction of three-dimensional objects of technical systems results. Conclusions. The study found that the iterative near-point algorithm is more detailed and accurate when modeling objects. At the same time, this method requires very accurate values and when calculating the degree of proximity, the complexity of calculation by this algorithm increases many times. Whereas the algorithm for estimating the approximate values of the elements of mutual orientation does not require information about the approximate orientation of the point clouds, which simplifies the work and reduces the simulation time. It was found that not all files are comparable. Therefore, the software is implemented, which gives an opinion on the possibility of comparing points in the proposed two files, which contain clouds of points in the structure of the STL format.

Analysis of the Current Situation of Piano Education in Colleges and Universities in the Information Age and Research on Countermeasures

In view of the low application ability of piano improvisational accompaniment of music majors, this paper proposes a method of big data combined with MIDI keyboard and Kinect depth sensor to achieve the purpose of recognizing chord progression and judging fingering when students perform, and realizes the auxiliary teaching system. Firstly, the information of color and depth images is obtained, and the state transition diagram of chord transposition and chord gesture template library are constructed as the system initialization conditions. Secondly, using the traditional skin color modeling and background difference method as well as the current depth data, the gesture recognition is realized by template matching. Finally, the correctness of chord progression is judged, and comprehensive fingering application is used to score and evaluate. The experimental results show that the system has high robustness and can be effectively applied to piano teaching.

Optimizing MultiStack Parallel (MSP) Sorting Algorithm

Mobile smartphones/laptops are becoming much more powerful in terms of core count and memory capacity. Demanding games and parallel applications/algorithms can hopefully take advantages of the hardware. Our parallel MSPSort algorithm is one of those examples. However, MSPSort can be optimized and fine tuned even further to achieve its highest capabilities. To evaluate the effectiveness of MSPSort, two Linux systems are quad core ARM Cortex-A72 and 24-core AMD ThreadRipper R9-2920. It has been demonstrated that MSPSort is comparable to the well-known parallel standard template library sorting functions, i.e. Balanced QuickSort and Multiway MergeSort in various aspects such as run time and memory requirements.

The TRACE-Seq method tracks recombination alleles and identifies clonal reconstitution dynamics of gene targeted human hematopoietic stem cells

Targeted DNA correction of disease-causing mutations in hematopoietic stem and progenitor cells (HSPCs) may enable the treatment of genetic diseases of the blood and immune system. It is now possible to correct mutations at high frequencies in HSPCs by combining CRISPR/Cas9 with homologous DNA donors. Because of the precision of gene correction, these approaches preclude clonal tracking of gene-targeted HSPCs. Here, we describe Tracking Recombination Alleles in Clonal Engraftment using sequencing (TRACE-Seq), a methodology that utilizes barcoded AAV6 donor template libraries, carrying in-frame silent mutations or semi-randomized nucleotides outside the coding region, to track the in vivo lineage contribution of gene-targeted HSPC clones. By targeting the HBB gene with an AAV6 donor template library consisting of ~20,000 possible unique exon 1 in-frame silent mutations, we track the hematopoietic reconstitution of HBB targeted myeloid-skewed, lymphoid-skewed, and balanced multi-lineage repopulating human HSPC clones in mice. We anticipate this methodology could potentially be used for HSPC clonal tracking of Cas9 RNP and AAV6-mediated gene targeting outcomes in translational and basic research settings.