Performance and Accuracy Comparisons of Classification Methods and Perspective Solutions for UAV-Based Near-Real-Time "Out of the Lab" Data Processing.

Today, integration into automated systems has become a priority in the development of remote sensing sensors carried on drones. For this purpose, the primary task is to achieve real-time data processing. Increasing sensor resolution, fast data capture and the simultaneous use of multiple sensors is one direction of development. However, this poses challenges on the data processing side due to the increasing amount of data. Our study intends to investigate how the running time and accuracy of commonly used image classification algorithms evolve using Altum Micasense multispectral and thermal acquisition data with GSD = 2 cm spatial resolution. The running times were examined for two PC configurations, with a 4 GB and 8 GB DRAM capacity, respectively, as these parameters are closer to the memory of NRT microcomputers and laptops, which can be applied "out of the lab". During the accuracy assessment, we compared the accuracy %, the Kappa index value and the area ratio of correct pixels. According to our results, in the case of plant cover, the Spectral Angles Mapper (SAM) method achieved the best accuracy among the validated classification solutions. In contrast, the Minimum Distance (MD) method achieved the best accuracy on water surface. In terms of temporality, the best results were obtained with the individually constructed decision tree classification. Thus, it is worth developing these two directions into real-time data processing solutions.

[Innovation in medicine: opportunities of 3D modeling and printing for perioperative care of cardio and thoracic surgical patients. Experiences in Hungary]. / Innovációs lehetoségek a medicinában: 3D tervezési és 3D nyomtatási lehetoségek a felnott szív- és mellkassebészeti betegellátásban. Magyarországi tapasztalatok.

Use of 3D planning and 3D printing is expanding in healthcare. One of the common applications is the creation of anatomical models for the surgical procedure from DICOM files. These patient-specific models are used for multiple purposes, including visualization of complex anatomical situations, simulation of surgical procedures, patient education and facilitating communication between the different disciplines during clinical case discussions. Cardiac and thoracic surgical applications of this technology development include the use of patient-specific 3D models for exploration of ventricle and aorta function and surgical procedural planning in oncology. The 3D virtual and printed models provide a new visualization perspective for the surgeons and more efficient communication between the different clinical disciplines. The 3D project was started at the Semmelweis University with the cooperation of the Thoracic Surgery Department of the National Institute of Oncology in 2018. The authors want to share their experiences in 3D designed medical tools. Orv Hetil. 2019; 160(50): 1967-1975.