Intelligent Fiber Sensors via Digital Signal Processing and Machine Learning

基于光纤的传感器是敏感且坚固的设备，其对恶劣的外部环境波动（例如，温度、电磁干扰、辐射等）具有弹性。并且很好地用作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，其典型部署涉及庞大的光谱分析仪（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。信息将被编码在光信号的振幅和相位中，以增加其对周围环境变化的敏感性，同时消除收发器处对额外硬件的需求。将开发估计和校准策略以补偿光学器件制造缺陷和变化。由于与典型的无线（MHz）/有线（GHz）信道的带宽要求相比，环境条件变化相对缓慢（kHz），因此最初用于数字通信的相应成熟的半导体集成电路（高速电子器件）以极低的成本提供了显著的计算能力。多余的计算带宽可用于通过通信和成像中常见的成熟数字信号处理（DSP）技术来补偿设备伪影。在信息信道和发送信号都随时间变化的通信信道中，已经开发了复杂的技术来以高保真度估计发送的消息。在感测的情况下，通过限制发射机中的探测信号，接收机可以通过监视探测信号来容易地估计或感测介质中的波动。最后，光学感测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被监测的某些环境参数相关联的特定签名。由于有了图形处理器等高端和负担得起的计算设备，培训时间大大缩短。感兴趣的特征可以很容易地以高度的确定性进行分类，无论是跌倒在传感器启用的地毯地板上的老年人，还是地下采矿槽的地震稳定性。拟议的研究致力于将商用电子数字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增加，学员将获得实践经验，以集成各种最先进的硬件和软件组件，以解决从医疗保健到矿产部门的应用。

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.