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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=742054
• 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坚固的设备，可以抵御恶劣的外部环境波动（例如温度，电磁干扰，辐射等），并且可以作为基础设施中的分布式传感介质。通常用于制造过程的高端校准和控制，它们的典型部署涉及笨重的频谱分析仪（电气或光学）和昂贵的可调谐激光源。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，这些技术已经彻底改变了有线和无线技术中的数字通信。信息将以光信号的幅度和相位进行编码，以增加其对不断变化的环境的灵敏度，同时消除对收发器额外硬件的需求。估计和校准策略将开发补偿光学器件制造缺陷和变化。与典型的无线(MHz)/有线（GHz）信道的带宽要求相比，环境条件变化相对缓慢（kHz），因此原本用于数字通信的相应成熟的半导体集成电路（高速电子）以极低的成本提供了惊人的计算能力。多余的计算带宽可以通过成熟的数字信号处理（DSP）技术用于补偿通信和成像中常见的器件伪影。在通信信道中，信息信道和发射信号都随时间变化，因此已经开发出复杂的技术来高保真地估计发送的消息。在传感的情况下，通过限制发射器中的探测信号，接收器可以很容易地通过监测探测信号来估计或感知介质中的波动。最后，光学传感信号的数字化意味着图像识别和机器学习中的常用技术可以应用于提取与被监测的某些环境参数相关的特定签名。由于图形处理单元等高端和负担得起的计算设备的可用性，培训时间大大缩短。感兴趣的特征可以很容易地以高度确定性进行分类，无论是在传感器启用的地毯地板上摔倒的老年人，还是地下采矿斜槽的地震稳定性。提出的研究努力将商业上可用的电子数字DSP和机器学习应用于光纤传感的前沿。随着传感器在我们的日常生活中不断普及，学员将获得实践经验，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产行业的应用。