Intelligent Fiber Sensors via Digital Signal Processing and Machine Learning

通过数字信号处理和机器学习的智能光纤传感器

• 批准号: RGPIN-2021-02559
• 负责人: Yam, Scott
• 金额: $ 2.04万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752317
• 关键词: Intelligent; Fiber; Sensors; via; Digital

Optical fiber based sensors are sensitive and robust devices that are resilient to harsh external environment fluctuations (e.g. temperature, electromagnetic interference, radiation etc.) and serve well as a distributed sensing medium in infrastructure. Often used for high end calibration and control of manufacturing processes, their typical deployment involves bulky spectrum analyzers (electrical or optical), and costly tunable laser sources. The proposed research will address these challenges by applying detection schemes and signal processing techniques that have revolutionized digital communications in both wireline and wireless technologies. Information will be encoded in both the amplitude and phase of the optical signal to increase its sensitivity to the changing ambient environment, while removing the need for additional hardware at the transceivers. Estimation and calibration strategies will be developed to compensate for optical device fabrication defects and variations. As the environmental conditions vary relatively slowly (kHz) compared to the bandwidth requirement of a typical wireless (MHz)/ wireline (GHz) channel, the correspondingly matured semiconductor integrated circuit (high-speed electronic) originally intended for digital communications provides dramatic computation power at an extremely low cost. The excess computing bandwidth can be used to compensate for device artefacts via mature digital signal processing (DSP) technique common in communication and imaging. In a communication channel, where both the information channel and transmitted signal are varying in time, sophisticated techniques have been developed to estimate the sent messages with high fidelity. In the case of sensing, by restricting the probing signal in the transmitter, the receiver can readily estimate or sense the fluctuations in the medium by monitoring the probe signal. Finally, the digitization of the optical sensing signal means that common techniques in image recognition and machine learning can be applied to extract a particular signature associated with certain environmental parameters being monitored. The training duration has been significantly reduced due to the availability of high-end and affordable computation devices such as graphical processing units. Features of interest can be readily classified with high degree of certainty, whether it is a senior citizen falling on a sensor-enabled carpeted floor, or the seismic stability of an underground mining chute. The proposed research strives to apply commercially available electronics digital DSP and machine learning to the cutting edge of optical fiber sensing. As sensors continue to proliferate in our daily life, trainees will be provided hands-on experiences to integrate various state-of-the-art hardware and software components to address applications from the healthcare to the mineral sectors.

基于光纤的传感器是敏感且健壮的设备，可抵御刺激性的外部环境波动（例如温度，电磁干扰，辐射等），并充当基础设施中的分布式传感介质。通常用于高端校准和对制造过程的控制，它们的典型部署涉及笨重的频谱分析仪（电气或光学）以及昂贵的可调激光源。拟议的研究将通过应用检测方案和信号处理技术来解决这些挑战，这些技术彻底改变了有线和无线技术的数字通信。信息将在光学信号的幅度和阶段中编码，以提高其对不断变化的环境环境的敏感性，同时消除收发器中对其他硬件的需求。将制定估计和校准策略来补偿光学设备的缺陷和变化。与典型的无线（MHz）/有线（GHz）通道的带宽需求相比，环境条件的变化相对较慢（KHz），相应成熟的半导体集成电路（高速电子）最初用于数字通信的旨在以极低的成本提供巨大的计算能力。多余的计算带宽可用于通过成熟的数字信号处理（DSP）技术在通信和成像中常见的技术来补偿设备伪像。在通信渠道中，信息通道和传输信号随时间变化而变化，已经开发出复杂的技术来估算以高忠诚度估算发送的消息。在传感的情况下，通过限制发射机中的探测信号，接收器可以通过监视探针信号来容易地估算或感知介质中的波动。最后，光学传感信号的数字化意味着可以应用图像识别和机器学习中的常见技术来提取与要监视的某些环境参数相关的特定签名。由于高端和负担得起的计算设备（例如图形处理单元）的可用性，训练持续时间已大大减少。无论是落在传感器的地毯上，还是地下采矿斜槽的地震稳定性，都可以轻松地确定感兴趣的特征。拟议的研究旨在将市售的电子数字DSP和机器学习应用于光纤感测的最前沿。随着传感器在我们的日常生活中继续扩散，学员将获得动手体验，以整合各种最先进的硬件和软件组件，以解决从医疗保健到矿物领域的应用程序。