Real-Time Recognition System for Traffic Signs

The aim of this thesis project is to develop the Traffic Sign Recognition algorithm for real time. Inreal time environment, vehicles move at high speed on roads. For the vehicle intelligent system itbecomes essential to detect, process and recognize the traffic sign which is coming in front ofvehicle with high relative velocity, at the right time, so that the driver would be able to pro-actsimultaneously on instructions given in the Traffic Sign. The system assists drivers about trafficsigns they did not recognize before passing them. With the Traffic Sign Recognition system, thevehicle becomes aware of the traffic environment and reacts according to the situation.The objective of the project is to develop a system which can recognize the traffic signs in real time.The three target parameters are the system’s response time in real-time video streaming, the trafficsign recognition speed in still images and the recognition accuracy. The system consists of threeprocesses; the traffic sign detection, the traffic sign recognition and the traffic sign tracking. Thedetection process uses physical properties of traffic signs based on a priori knowledge to detect roadsigns. It generates the road sign image as the input to the recognition process. The recognitionprocess is implemented using the Pattern Matching algorithm. The system was first tested onstationary images where it showed on average 97% accuracy with the average processing time of0.15 seconds for traffic sign recognition. This procedure was then applied to the real time videostreaming. Finally the tracking of traffic signs was developed using Blob tracking which showed theaverage recognition accuracy to 95% in real time and improved the system’s average response timeto 0.04 seconds. This project has been implemented in C-language using the Open Computer VisionLibrary.

Self-reported conflict management competence among nursing students on the point of graduating and registered nurses with professional experience

Objective: It has been shown that specific competence is necessary for preventing and managing conflicts in healthcare settings. The aim of this descriptive and correlation study was to investigate and compare the self-reported conflict management competence (CMC) of nursing students who were on the point of graduating (NSPGs), and the CMC of registered nurses (RNs) with professional experience. Methods: The data collection, which consisted of soliciting answers to items measuring CMC in the Nurse Professional Competence (NPC) Scale, was performed as a purposive selection of 11 higher education institutions (HEIs) in Sweden. Three CMC items from the NPC Scale were answered by a total of 569 nursing students who were on the point of graduating and 227 RN registered nurses with professional experience. Results: No significant differences between NSPGs and RNs were found, and both groups showed a similar score pattern, with the lowest score for the item: “How do you perceive your ability to develop the group and strengthen competence in conflict management and problem-solving, based on knowledge of group dynamics?”. RNs with long professional experience (>24 months) rated their overall CMC as significantly better than RNs with short (<24 months) professional experience did (p = .05). NSPGs who had experience of international studies during their nursing education reported higher CMC, compared with those who did not have this experience (p = .03). RNs who reported a high degree of utilisation of CMC during the previous month scored higher regarding self-reported overall CMC (p < .0001). Conclusions: Experience of international studies during nursing education, or long professional experience, resulted in higher self-reported CMC. Hence, the CMC items in the NPC Scale can be suitable for identifying self-reported conflict management competence among NSPGs and RNs