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Road condition analysis is an important research topic in many fields (such as intelligent transportation, road safety, road design analysis, and traffic analysis) and depends on road geometry parameters such as longitudinal profile and cross-slope. In this study, the extraction of road geometry parameters by unmanned aerial vehicle (UAV) with LiDAR and by a mobile photogrammetric system (MPS) designed by our research group was investigated. The purpose of this study was to obtain geometric parameters (such as road longitudinal profile and cross-slope) by using digital terrain model (DTM) surfaces derived from point cloud data acquired using UAV-LiDAR and MPS. For this purpose, a framework was developed for the extraction and comparison of longitudinal and cross-sectional profiles. First, the ground filtering approach was used to extract ground points and DTM surfaces generated from an appropriate interpolation algorithm by using ground points. Cross-sectional/longitudinal profiles of the road sections were extracted and compared with reference data. A comparison of the longitudinal profiles obtained from DTMs derived from the MPS and from UAV-LiDAR revealed root mean square error values of 1.8 cm and 2.3 cm, respectively. The average deviation of cross-slopes for both surfaces was 0.19% and 0.18%, respectively. These results show that road geometric parameters can be obtained from DTM surfaces with high accuracy. It can be concluded from the results of this study that MPS can be a favorable alternative for studies on road geometry parameters extraction.

Ahstract-An analog Cellular Neural Network (CNN) architecture employin g quantum dots to realize various real time ima g e processin g applications such as ed g e and line detections and motion estimation is proposed. In order to obtain pro g rammability to switch between applications, memristive connections between nei g hborin g cells, and for si g nal amplification and lockin g resonant tunnelin g diodes (RTDs) are utilized. Simulations are carried out on a 2D array of the proposed cell structure to demonstrate ed g e detection and line detection tasks. This work also provides analytical models and simulation results to prove above mentioned real time ima g e processin g functionalities.

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