Waterjet guided laser processing is technology using laminar waterjet as optical waveguide to process materials. The stability of capillary waterjet as the optical waveguide is investigated with the effects of cavitation. Combined with the fluid interface tracking method and the cavitation model, a numerical simulation model of water jet is established. The inlet pressure ranges from 0Mpa to 5Mpa. The hydraulic flip and cavitation effect lead to a constricted water jet which is produced by sharp edges orifices. The theory of constricted laminar water jet with long breakup length theoretically can be used as optical waveguide is based on total reflection.

Micro-electro-mechanical system (MEMS) scanning micromirrors are playing an increasingly important role in active structured light systems. However, the initial phase error of the structured light generated by a scanning micromirror seriously affects the accuracy of the corresponding system. This paper reports an optoelectronic integrated sensor with high irradiance responsivity and high linearity that can be used to correct the phase error of the micromirror. The optoelectronic integrated sensor consists of a large-area photodetector (PD) and a receiving circuit, including a post amplifier, an operational amplifier, a bandgap reference, and a reference current circuit. The optoelectronic sensor chip is fabricated in a 180 nm CMOS process. Experimental results show that with a 5 V power supply, the optoelectronic sensor has an irradiance responsivity of 100 mV/(μW/cm²) and a −3 dB bandwidth of 2 kHz. The minimal detectable light power is about 19.4 nW, which satisfies the requirements of many active structured light systems. Through testing, the application of the chip effectively reduces the phase error of the micromirror to 2.5%.

A wireless traffic sensor network refers to the development of low-power and low-cost traffic sensors on both sides of the vehicle and road. The ability of self-organizing nodes is used to automatically create a network for real-time and accurate monitoring, identification, and monitoring of vehicles, to obtain complete road traffic information of the entire road network for traffic decision-making, and to facilitate the flow of people. This paper aims to study an intelligent traffic detection system based on a wireless sensor network, which collects real-time data information through sensor nodes and uses the wireless network to transmit the data information to the platform for monitoring so that the driver can understand the road in real time from a long distance Information and effective road safety guarantees. This paper firstly summarizes the wireless sensor network and data image aggregation technology, then experimentally explores the data collection planning based on the spatial self-association model, and secondly designs the wireless traffic sensor network design. Through comparative analysis of traffic flow, the experimental results show that the accuracy of using wireless traffic sensors is as high as 97%. It shows that the wireless traffic sensor improves the transmission efficiency and the ability of the network to pass.

Scanning micromirrors represent a crucial component in micro-opto-electro-mechanical systems (MOEMS), with a broad range of applications across diverse fields. However, in practical applications, several factors inherent to the fabrication process and the surrounding usage environment exert a considerable influence on the accuracy of measurements obtained with the micromirror. Therefore, it is essential to calibrate the scanning micromirror and its measurement system. This paper presents a novel scanning micromirror calibration method based on the prediction of a particle swarm optimization-least squares support vector machine (PSO-LSSVM). The objective is to establish a correspondence between the actual deflection angle of the micromirror and the output of the measurement system employing a regression algorithm, thereby enabling the prediction of the tilt angle of the micromirror. The decision factor ($R^2$) for this model at the x-axis reaches a value of 0.9947.