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The importance of road characteristics has been highlighted, as road characteristics are fundamental structures established to support many transportation-relevant services. However, there is still huge room for improvement in terms of types and performance of road characteristics detection. With the advantage of geographically tiled maps with high update rates, remarkable accessibility, and increasing availability, this paper proposes a novel simple deep-learning-based approach, namely joint convolutional neural networks (CNNs) adopting adaptive squares with combination rules to detect road characteristics from roadmap tiles. The proposed joint CNNs are responsible for the foreground and background image classification and various types of road characteristics classification from previous foreground images, raising detection accuracy. The adaptive squares with combination rules help efficiently focus road characteristics, augmenting the ability to detect them and provide optimal detection results. Five types of road characteristics—crossroads, T-junctions, Y-junctions, corners, and curves—are exploited, and experimental results demonstrate successful outcomes with outstanding performance in reality. The information of exploited road characteristics with location and type is, thus, converted from human-readable to machine-readable, the results will benefit many applications like feature point reminders, road condition reports, or alert detection for users, drivers, and even autonomous vehicles. We believe this approach will also enable a new path for object detection and geospatial information extraction from valuable map tiles.

This paper presents an RFID based psychiatric critical to psychiatric patient treatment. The work of tracking patient tracking system in a psychiatric patient care center. In psychiatric patients must include recording patients' location. this system, RFID Field Generators and Readers are installed RFID devices which are useful, small and cheap are of high in the environment, near the entrance and exit points for prominent to be used in tracking psychiatric patients. Due to tracking the patients. Every patient is required to wear the tag. current RFID techniques for tracking dynamic moving When a tag receives the call from a Field generator, it responds objects, several reasons which could usually cause the to a Reader with the ID of the Field generator issuing the interference and miss detection are (1) the interferences of calling. Based on the Field Generator IDs a tag responds, the signals from two Field Generators of overlapped cover location of the patient associated with the tag can be approximatio helyetimated. Byssociatedowing o t h the pagcatben ranges and (2) the interference of signals from close tags. In appoxmaelyesimted B s dong nt olyth paiet' our paper, we would propose an algorithm to solve it. moving patterns can be recorded but also the event of the o patient leaving the main gate of the care center be detected.In recent years, several researches have been conducted from hardware and software perspectives on the Besides the localization issue, in this paper we also employed a study ofro hrelabi andteardersigntbase on ree Graphic Coloring approach for solving the interference caused from Field Generator located one near another. Experimental space architecture [2] [3] is a major approach from hardware results show that this algorithm has significantly improved the perspective. Experiments have shown that the performance reliability of the tracking system. of a passive RFID system, due to its limited operating range, can be degraded significantly due to morphology distortion.

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