Recent developments in smartphone technology have increased user demand for indoors applications. The Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) are the two advanced systems for navigation technology. However, it is still difficult for GNSS to provide an accurate and practical navigation solution, especially in environments with little or no signal availability. These failings should be easy to overcome; because of their portability and multiple embedded hardware sensors, smartphones seem well positioned to make pedestrian navigation easy and convenient in any environment. Pedestrian Dead Reckoning (PDR) is one of the most commonly used technologies used for pedestrian navigation, but it is not always accurate because its positioning errors tend to accumulate over time. Therefore, this research introduces a new tool to overcome this failing; a Bluetooth Low-Energy (BLE) beacon can maintain and improve the accuracy of PDR. Moreover, a BLE beacon can be initialized from any user position in an indoor environment. The random and unpredictable positions of pedestrians inevitably result in the degradation of navigation guidance systems' data. To rectify this problem, we have implemented activity recognition technology to notify the proposed system so as to provide a more accurate heading estimate. This study proposes a Personal Navigation System (PNS) based on this technology; it can estimate navigation solutions in real time and combines the advantages of PDR and Bluetooth positioning technology. A series of experiments were conducted to evaluate the accuracy of the system and the efficacy of our proposed algorithms. Preliminary results show the average relative precision of PDR to be about 2.5%, when using a mobile hand-held device. The error of initial position from 2-D beacon positioning is less than two meters. The proposed system works well without post-processing, and the multi-sensor activity recognition system can determine the placement of the device when it is being carried or used by someone with close to 100% accuracy. Inventions 2016, 1, 25 2 of 19propose a smartphone indoor positioning engine that only uses the built-in sensor and computational resources [2]. Though the accuracy of such MEMS-grade sensors leaves much to be desired, thanks to constant technological innovation, no one doubts that smaller size and better performance could be expected in the near future.The Pedestrian Dead Reckoning (PDR) has been extensively studied as an effective approach for estimating two-dimensional position for pedestrian navigation. It uses inertial sensors to detect human movement patterns, noting each step and estimating heading direction. However, unavoidable errors crept in due to the algorithm itself as well as the hardware sensors. MEMS sensors are often not very accurate, humans frequently alter speed, posture, and direction, and there is no fixed relationship between the sensors in the device and the human body. In addition, possible errors in step-count caused by an i...

The research of diagnosis and treatment system of child mental health disorders is based on artificial neural network and expert system. It combines the diagnosis standard of ICD 10, DSM IV with 40 years clinical experiences and knowledge of senior child psychiatrists. It also combines computer science with child psychiatry, child psychology, psychological estimate, psychological therapy and so on. The learning samples come from the epidemiological data in more than a dozen nationwide hospitals. The correct rate of system diagnosis is 99%. The system can diagnose 61 kinds of child mental health disorders and give a treatment method suggestion.

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