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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. [ABSTRACT FROM AUTHOR]

New Psychoactive Substances (NPS) impose a new challenge on the legal and health care system, yet, there is little information available about how new substances spread based on hospitalization of intoxicated patients. The aims of this study were: (i) to investigate the frequency of NPS among suspected drug intoxicated patients, (ii) to study the connection between blood concentration and clinical symptoms, (iii) to determine their half-life with a time-series blood sampling protocol. During the observation period, 116 suspected drug intoxicated patients were sampled. The samples were analyzed for alcohol, 20 classical illicit and licit drugs, and for 78 NPS. Clinical symptoms were registered on-site (by the Emergency Medical Services) and (also) at hospital admittance. NPS were detected in 51 patients of which cathinones were found in 4, the synthetic cannabinoids (SCs) 5 F-MDMB-PINACA and 5 F-MDMB-PICA in 23-23, and CUMYL-CH-MEGACLONE in 2 cases. Poison severity scores (PSS) showed mild to moderate intoxications overall. Connection between blood concentration and severity of clinical symptoms were inconclusive. The calculated half-life of 5 F-MDMB-PINACA and 5 F-MDMB-PICA was 2.50 and 2.68 h, respectively. The ratio of SCs among the selected intoxicated patients was higher than expected from seizure data which could be the consequence of targeted patient selection. The clinical symptoms and the severity of intoxication cannot be characterized simply by NPS blood levels. The short half-life of SCs can explain the relatively rapid consolidation of intoxication symptoms. In the Budapest region, the majority of hospitalized NPS intoxications was caused by the synthetic cannabinoids 5F-MDMB-PINACA and 5F-MDMB-PICA in 2018-19. No correlation between blood concentration and symptoms severity could be established. The clinical symptoms of synthetic cannabinoid users improved quickly and no ICU treatment was necessary. The half-life of 5F-MDMB-PINACA and 5F-MDMB-PICA was proved to be 2.50 hours and 2.68 hours, respectively. [ABSTRACT FROM AUTHOR]

A self-driving car is a vehicle that is able to perceive its surroundings and navigate in it without human action. Radar sensors, lasers, computer vision and GPS technologies help it to drive individually (Figure 1). They interpret the sensed information to calculate routes and navigate between obstacles and traffic elements.- Sufficiently accurate navigation and information about the current position of the vehicle are indispensable for transport. These expectations are fulfilled in the case of a human driver: the knowledge on traffic rules and signs make possible to navigate through even difficult situations. Self-driving systems substitute humans by monitoring and evaluating the surrounding environment and its objects without the background information of the driver. This analysing process is vulnerable. Sudden or unexpected situations may occur but high precision navigation and background GPS databases can complement sensor-detected data. The assistance of global navigation has been used in cars for decades. Drivers can easily plan their routes and reach their destination by using car GPS units. However, these devices do not provide accurate positioning: there may be a difference of several metres from the real location. Self-driving cars also use navigation to complement sensor data. Although there are already autonomous system tests on motorways and countryside roads, in densely built-in areas this technology faces complications due to accuracy problems. The dilution of precision (DOP) values can be extremely high in larger settlements because high buildings may hide southern sky (where satellite signs are sensed from on our latitude). We can achieve centimetre-level accuracy (if the conditions are ideal) with geodesic RTK (real-time kinematic) GPS systems. This high-precision position data is derived from satellite-based positioning systems. Measurements of the phase of the signal's carrier wave are real-time corrected by a single reference or an interpolated virtual station. In this research we use RTK GPS technology in order to work out a spatial database. These measurements can also be less precise in dense cities, but there is time during fieldwork to try to eliminate inaccuracy. We have chosen a sample area in the inner city of Budapest, Hungary where we located all traffic signs, pedestrian crossings and other important elements. As self-driving cars need precise position data of these terrain objects, we have tried to work with a maximum error of a few decimetres. We have examined online map providers if they have feasible data structure and some base data. The implemented structure is similar to OpenStreetMap DB, in which there are already some traffic lights in important crossings. With this preliminary test database, we would like to filter out dangerous situations. If the camera of the car does not see a traffic sign because of a tree or a truck, information about it will be available from the database. If a pedestrian crossing is hardly visible and the sensor does not recognize it, the background GIS data will warn the car that there may be inattentive people on the road. A test application has also been developed (Figure 2.), in which our Postgres/Postgis database records have been inserted. In the next phase of the project we try to test our database in the traffic. We plan to drive through the sample area and observe the GPS accuracy in the recognition of the located signs. This research aims to achieve higher safety in the field of autonomous driving. By having a refreshable cartographic GIS database in the memory of a self-driving car, there is a smaller chance of risking human life. However, the maintenance demands a high amount of work. Because of this we should concentrate only on the most important signs. Even the cars can be able to supervise the content of the database if there is a large number of them on the road. The frequent production and analysis of point clouds is also an option to get nearer to safe automatized traffic. [ABSTRACT FROM AUTHOR]

Self-driving cars are able to sense their surroundings and navigate in it with the help of radar, lasers, computer vision and GNSS. The perceived information is used to plan routes and navigate between the various elements of transport.GPS devices make navigation easier for vehicle drivers. Although they help to reach our destination, today's system works with an error of several metres. Due to this difference GNSS data can only complement the work of sensors in self-driving cars. The testing phase of autonomous systems faces some complications in dense cities. The extremely high DOP (dilution of precision) values usually refer to building-blocked signals.However, accurate information about the current position of the vehicle are essential for cars in traffic. Human drivers complement the deficiency of GPS with the knowledge on traffic rules and signs. The sensor-based evaluation of the surroundings is a vulnerable process in autonomous traffic, but high precision GNSS and background GIS databases can make it safer.In our project we have used a real-time kinematic (RTK) GPS system that is able to reach centimetre-level accuracy. The sample area is a part of the downtown of Budapest, Hungary. Here all the relevant traffic signs, lights and pedestrian crossings were located. A preliminary database has been set up from the measured points. As self-driving cars need precise position data of these terrain objects, we have tried to work with a maximum error of a few decimetres.We have inserted our data into Postgres/Postgis spatial database system. With this we plan to solve sudden and dangerous situations. If the camera of the car does not see a traffic sign because of another object, information about it will be available from the database. If a pedestrian crossing is hardly visible and the sensor does not recognize it, the background GIS data will warn the car that there may be inattentive people around. An application has also been developed that we will test: it will be able to observe GPS accuracy in the recognition of the located signs during driving through the sample area.The main aim of this research is to achieve safer autonomous driving circumstances. A refreshable cartographic GIS database in the car memory is an additional device that ensures smaller human life risk. The future maintenance of this data system is also in our hands: human work can be replaced by point cloud analysis and later by the updating work of self-driving cars.EFOP-3.6.3-VEKOP-16-2017-00001: Talent Management in Autonomous Vehicle Control Technologies – The Project is supported by the Hungarian Government and co-financed by the European Social Fund. [ABSTRACT FROM AUTHOR]