AI 中文总结
本文开发了带嵌入式神经网络的实时自混合干涉传感器，通过STM32U575ZI微控制器运行残差神经网络实现目标位移实时重建，验证了其鲁棒性与低能耗特性，为相关传感器发展奠定基础。
AI 中文摘要
自混合干涉是一种测量方法，其原理是激光束在目标上反射后重新注入到发射激光本身，通过监测激光两端的电压可获取目标位置信息，但分析该信号存在难度。过往研究中，神经网络已被成功用于处理这类数据。本文提出了一种基于自混合干涉的集成传感器更新原型，该传感器带有嵌入式神经网络，由半导体激光器（兼具光发射与探测功能）及用于数据处理的嵌入式平台组成，平台包含ADC（模数转换器）与STM32U575ZI微控制器，微控制器运行残差神经网络，负责从输入ADC的干涉信号中重建目标位移。我们评估了神经网络对不受欢迎的信号幅度变化的鲁棒性、微控制器运行网络所需的不同网络权重量化选择的影响，以及系统的能耗，最后展示了完全在嵌入式平台上实时运行的目标位移重建演示。研究结果为基于自混合干涉与嵌入式神经网络的鲁棒、低功耗、通用型传感器的发展铺平了道路。
英文摘要
Self-mixing interferometry is a measurement ap- proach in which a laser beam is re-injected into the emitting laser itself after reflection on a target. Information about the position of the target can be obtained from monitoring the voltage across the laser. However, analyzing this signal is difficult. In previous works, neural networks have been used with great success to process this data. In this article, we present an updated prototype of an integrated sensor based on self-mixing interferometry with embedded neural networks. It consists of a semiconductor laser (acting both as light emitter and detector) equipped with an embedded platform for data processing. The platform includes an ADC (Analog-to-Digital Converter) and an STM32U575ZI microcontroller. The microcontroller runs a residual neural network in charge of reconstructing the displacement of a target from the interferometric signal entering the ADC. We assess the robustness of the neural network to unwanted signal amplitude variations, the impact of different network weights quantization choices required to run the network on the microcontroller, and the energy consumption of the system. Finally, we provide a demonstration of target displacement reconstruction fully running on the embedded platform in real-time. Our results pave the way towards robust, low power and versatile sensors based on self-mixing interferometry and embedded neural networks.
Journal refP. -E. Novac, L. Rodriguez and S. Barland, "Real-time smart self-mixing interferometry sensor with embedded neural network," in IEEE Transactions on Instrumentation and Measurement, 2026
