KEYWORDS: Cameras, Human-machine interfaces, Education and training, Image processing, Safety, Detection and tracking algorithms, Target detection, Signal detection, Internet of things, Deep learning
With the development of the Internet of Things, timely alarm and real-time monitoring of the Internet of Things have become a reality and become more and more important. Traditional remote monitoring technology of the Internet of Things has been unable to meet the rapid demand for the development of modern Internet of Things industry. In order to meet the needs of the new era, real-time, secure, intelligent and extensible monitoring terminals have become a research hotspot in the modern Internet of Things industry. This paper proposes a human fall alarm system, including the establishment of arm hardware platform, the implantation of Linux system, the development of drivers and applications, the training of deep learning model, the deployment and optimization of application programs. The human falling alarm system has high practical value in the practical application, which can realize the real-time alarm and monitoring of human falling, and effectively improve the safety and convenience of home, office and other places. At the same time, the system also has a strong scalability, according to the actual demand for flexible customization and expansion.
Aiming at the problems of blindness, slow convergence and easy to fall into local optimal solution in the early stage of traditional ant colony algorithm search, an improved ant colony algorithm was proposed. The algorithm first introduces the gravitational potential field function, constructs new heuristic information, and improves the convergence speed. Secondly, learn from the wolf pack update rules, improve the pheromone update function, and give full play to the feedback function of pheromone. Finally, the path is optimized locally to remove redundant nodes. The comparison experiment with the traditional ant colony algorithm proves that the improved algorithm shortens the running time, the convergence speed and the global search ability are better than the traditional ant colony algorithm, and it is not easy to fall into the local optimal solution, which verifies the effectiveness of the algorithm.
Aiming at the difficult problem of high precision frequency stabilization of semiconductor laser diode, the laser frequency control is realized through the design of the semiconductor drive system. Above all, the relationship between the emission frequency and the temperature of LD is derived theoretically. Then the temperature corresponding to the stable frequency is obtained. According to the desired temperature stability of LD, temperature control system is designed, which is composed of a temperature setting circuit, temperature gathering circuit, the temperature display circuit, analog PID control circuit and a semiconductor refrigerator control circuit module. By sampling technology, voltage of platinum resistance is acquired, and the converted temperature is display on liquid crystal display. PID analog control circuit controls speed stability and precision of temperature control. The constant current source circuit is designed to provide the reference voltage by a voltage stabilizing chip, which is buffered by an operational amplifier. It is connected with the MOSFET to drive the semiconductor laser to provide stable current for the semiconductor laser. PCB circuit board was finished and the experimental was justified. The experimental results show that: the design of the temperature control system could achieve the goal of temperature monitoring. Meanwhile, temperature can be stabilized at 40°C ± 0.1°C. The output voltage of the constant current source is 2 V. The current is 35 mA.
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