Advanced sensors technology survey

This project assesses the state-of-the-art in advanced or 'smart' sensors technology for NASA Life Sciences research applications with an emphasis on those sensors with potential applications on the space station freedom (SSF). The objectives are: (1) to conduct literature reviews on relevant advanced sensor technology; (2) to interview various scientists and engineers in industry, academia, and government who are knowledgeable on this topic; (3) to provide viewpoints and opinions regarding the potential applications of this technology on the SSF; and (4) to provide summary charts of relevant technologies and centers where these technologies are being developed

Impact detection techniques using fibre-optic sensors for aerospace & defence

Impact detection techniques are developed for application in the aerospace and defence industries. Optical fibre sensors hold great promise for structural health monitoring systems and methods of interrogating fibre Bragg gratings (FBG) are investigated given the need for dynamic strain capture and multiplexed sensors. An arrayed waveguide grating based interrogator is developed. The relationships between key performance indicators, such as strain range and linearity of response, and parameters such as the FBG length and spectral width are determined. It was found that the inclusion of a semiconductor optical amplifier could increase the signal-to-noise ratio by ~300% as the system moves to its least sensitive. An alternative interrogator is investigated utilising two wave mixing in erbium-doped fibre in order to create an adaptive system insensitive to quasistatic strain and temperature drifts. Dynamic strain sensing was demonstrated at 200 Hz which remained functional while undergoing a temperature shift of 8.5 °C. In addition, software techniques are investigated for locating impact events on a curved composite structure using both time-of-flight triangulation and neural networks. A feature characteristic of composite damage creation is identified in dynamic signals captured during impact. An algorithm is developed which successfully distinguishes between signals characteristic of a non-damaging impact with those from a damaging impact with a classification accuracy of 93 – 96%. Finally, a demonstrator system is produced to exhibit some of the techniques developed in this thesis

Distributed Fiber Ultrasonic Sensor and Pattern Recognition Analytics

Ultrasound interrogation and structural health monitoring technologies have found a wide array of applications in the health care, aerospace, automobile, and energy sectors. To achieve high spatial resolution, large array electrical transducers have been used in these applications to harness sufficient data for both monitoring and diagnoses. Electronic-based sensors have been the standard technology for ultrasonic detection, which are often expensive and cumbersome for use in large scale deployments. Fiber optical sensors have advantageous characteristics of smaller cross-sectional area, humidity-resistance, immunity to electromagnetic interference, as well as compatibility with telemetry and telecommunications applications, which make them attractive alternatives for use as ultrasonic sensors. A unique trait of fiber sensors is its ability to perform distributed acoustic measurements to achieve high spatial resolution detection using a single fiber. Using ultrafast laser direct-writing techniques, nano-reflectors can be induced inside fiber cores to drastically improve the signal-to-noise ratio of distributed fiber sensors. This dissertation explores the applications of laser-fabricated nano-reflectors in optical fiber cores for both multi-point intrinsic Fabry–Perot (FP) interferometer sensors and a distributed phase-sensitive optical time-domain reflectometry (φ-OTDR) to be used in ultrasound detection. Multi-point intrinsic FP interferometer was based on swept-frequency interferometry with optoelectronic phase-locked loop that interrogated cascaded FP cavities to obtain ultrasound patterns. The ultrasound was demodulated through reassigned short time Fourier transform incorporating with maximum-energy ridges tracking. With tens of centimeters cavity length, this approach achieved 20kHz ultrasound detection that was finesse-insensitive, noise-free, high-sensitivity and multiplex-scalability. The use of φ-OTDR with enhanced Rayleigh backscattering compensated the deficiencies of low inherent signal-to-noise ratio (SNR). The dynamic strain between two adjacent nano-reflectors was extracted by using 3×3 coupler demodulation within Michelson interferometer. With an improvement of over 35 dB SNR, this was adequate for the recognition of the subtle differences in signals, such as footstep of human locomotion and abnormal acoustic echoes from pipeline corrosion. With the help of artificial intelligence in pattern recognition, high accuracy of events’ identification can be achieved in perimeter security and structural health monitoring, with further potential that can be harnessed using unsurprised learning

Enabling Technology in Optical Fiber Communications: From Device, System to Networking

This book explores the enabling technology in optical fiber communications. It focuses on the state-of-the-art advances from fundamental theories, devices, and subsystems to networking applications as well as future perspectives of optical fiber communications. The topics cover include integrated photonics, fiber optics, fiber and free-space optical communications, and optical networking

Force-Torque Sensing in Robotics

Being able to perform dynamic motions repeatably and reliably is an active research topic. The present thesis aims to contribute to this by improving the accuracy of force-torque sensing in robots. It focuses primarily on six axis force-torque sensors, although other sources of force-torque sensing are explored. Force sensing technologies, calibration procedures of these sensors and the use of force-torque sensing in robotics are described with the aim to familiarize the reader with the problem to solve. The problem is tackled in two ways: improving the accuracy of six axis force-torque sensors and exploring the use of tactile sensor arrays as force-torque sensors. The contributions of this thesis are : the development of the Model Based In situ calibration method for improving measurements of sensors already mounted on robots and the improvement in performance of the robot as a consequence; the design of a calibration device to improve the reliability and speed of calibration; and the improvement of force sensing information of a capacitive tactile array and its use on a robot as force-torque information source. The developed algorithms were tested on the humanoid robotic platform iCub

A meta-learning algorithm for respiratory flow prediction from FBG-based wearables in unrestrained conditions

The continuous monitoring of an individual's breathing can be an instrument for the assessment and enhancement of human wellness. Specific respiratory features are unique markers of the deterioration of a health condition, the onset of a disease, fatigue and stressful circumstances. The early and reliable prediction of high-risk situations can result in the implementation of appropriate intervention strategies that might be lifesaving. Hence, smart wearables for the monitoring of continuous breathing have recently been attracting the interest of many researchers and companies. However, most of the existing approaches do not provide comprehensive respiratory information. For this reason, a meta-learning algorithm based on LSTM neural networks for inferring the respiratory flow from a wearable system embedding FBG sensors and inertial units is herein proposed. Different conventional machine learning approaches were implemented as well to ultimately compare the results. The meta-learning algorithm turned out to be the most accurate in predicting respiratory flow when new subjects are considered. Furthermore, the LSTM model memory capability has been proven to be advantageous for capturing relevant aspects of the breathing pattern. The algorithms were tested under different conditions, both static and dynamic, and with more unobtrusive device configurations. The meta-learning results demonstrated that a short one-time calibration may provide subject-specific models which predict the respiratory flow with high accuracy, even when the number of sensors is reduced. Flow RMS errors on the test set ranged from 22.03 L/min, when the minimum number of sensors was considered, to 9.97 L/min for the complete setting (target flow range: 69.231 ± 21.477 L/min). The correlation coefficient r between the target and the predicted flow changed accordingly, being higher (r = 0.9) for the most comprehensive and heterogeneous wearable device configuration. Similar results were achieved even with simpler settings which included the thoracic sensors (r ranging from 0.84 to 0.88; test flow RMSE = 10.99 L/min, when exclusively using the thoracic FBGs). The further estimation of respiratory parameters, i.e., rate and volume, with low errors across different breathing behaviors and postures proved the potential of such approach. These findings lay the foundation for the implementation of reliable custom solutions and more sophisticated artificial intelligence-based algorithms for daily life health-related applications

Publications of the Jet Propulsion Laboratory 1989

This bibliography describes and indexes by primary author the externally distributed technical reporting, released during 1989, that resulted from scientific and engineering work performed, or managed, by JPL. Three classes of publications are included: JPL publications in which the information is complete for a specific accomplishment; articles from the quarterly Telecommunications and Data Acquisition (TDA) Progress Report; and articles published in the open literature

Unsupervised anomaly detection applied to F-OTDR

Distributed acoustic sensors (DASs) based on direct-detection Φ-OTDR use the light–matter interaction between light pulses and optical fiber to detect mechanical events in the fiber environment. The signals received in Φ-OTDR come from the coherent interference of the portion of the fiber illuminated by the light pulse. Its high sensitivity to minute phase changes in the fiber results in a severe reduction in the signal to noise ratio in the intensity trace that demands processing techniques be able to isolate events. For this purpose, this paper proposes a method based on Unsupervised Anomaly Detection techniques which make use of concepts from the field of deep learning and allow the removal of much of the noise from the Φ-OTDR signals. The fact that this method is unsupervised means that no human-labeled data are needed for training and only event-free data are used for this purpose. Moreover, this method has been implemented and its performance has been tested with real data showing promising results

Singlemode-Multimode-Singlemode Optical Fibre Structures for Optical Sensing

This thesis describes theoretical and experimental investigations on all-fibre multimode interference (MMI) devices using a singlemode-multimode-singlemode (SMS) fibre structure for use as a new type of edge filter for a ratiometric wavelength measurement system and as novel stand alone sensors. The use of two edge filters, so called X-type edge filters based on SMS fibre structures in a ratiometric wavelength measurement system is proposed and demonstrated. The use of X-type edge filters can improve the resolution and accuracy of wavelength measurement compared to the use of one edge filter in a conventional ratiometric system. Several aspects of the SMS edge filters have been investigated, including the effect of misalignment the SMS fibre cores due to fabrication tolerances, polarization dependence, and temperature dependence. These aspects can impair the performance of a ratiometric wavelength measurement system. Several approaches have been proposed and demonstrated to achieve high resolution and accuracy of wavelength measurement. Misalignment effects due to the splicing process on the spectral characteristics and PDL of SMS fibre structure-based edge filters are investigated numerically and experimentally. A limit for the tolerable misalignment of the cores of an SMS fibre structure-based edge filter is proposed, beyond which the edge filter’s spectral performance degrades unacceptably. It is found that a low PDL for an SMS fibre structure-based edge filter can be achieved with small lateral core offsets. Furthermore, the rotational core offsets position is proposed to minimize the PDL. Analysis of the temperature dependence of SMS X-type edge filters is presented. The temperature variation in the system can be determined and compensated by using an expanded ratiometric scheme with an additional reference arm. New sensing applications of multimode interference in an SMS fibre structure are proposed and demonstrated as a temperature sensor, a voltage sensor based on the strain effect, and a strain sensor with very low temperature dependence. All the sensors utilize a simple intensity-based interrogation system using a ratiometric power measurement system. It is found that the temperature and strain characteristics of SMS fibre structures are linear in nature and can be used for temperature and strain sensors. Based on the strain effect in an SMS fibre structure, a voltage sensor is also proposed. The SMS fibre structure is attached to a piezoelectric (PZT) stack transducer. The displacement of the PZT due to the voltage induces a strain on the SMS fibre structure and in turn results in a change in the ratio response. Finally, a strain sensor with very low temperature induced strain measurement error is investigated. For this purpose two SMS fibre structures were proposed and demonstrated in a ratiometric power measurement scheme, one SMS structure acts as the strain sensor and the other SMS structure acts as the temperature monitor. The use of this configuration can effectively minimize the temperature induced strain measurement error

Silicon photonic Bragg-based devices : hardware and software

L'avènement de la photonique intégrée a attiré beaucoup de recherche et d'attention industrielle au cours des deux dernières décennies, plusieurs croyant qu'il s'agit d'une révolution équivalente à la microélectronique. Tout en tirant parti des procédés de fabrication de masse hérités de la microélectronique, la photonique sur silicium est compacte, éconergitique et permet l'intégration complète de dispositifs et de circuits photoniques à l'échelle nanométrique pour des applications cruciales dans les télécommunications, la détection et le calcul optique. À l'instar des débuts de la microélectronique, les efforts de recherche actuels en photonique sur silicium sont principalement consacrés à la proposition, à la conception et la caractérisation de composants standardisés en vue d'une éventuelle intégration de masse dans des circuits photoniques. Les principaux défis associés à ce développement comprennent la complexité de la théorie électromagnétique dans le fonctionnement des dispositifs, les variations et les non-uniformités du procédé de fabrication limitant les performances, et les ressources informatiques considérables nécessaires pour modéliser avec précision des circuits photoniques complexes. Dans ce mémoire, ces trois limitations sont abordées sous forme de contributions de recherche originales. Basées sur des dispositifs photoniques sur silicium et l'apprentissage machine, les contributions de ce mémoire concernent toutes les réseaux de Bragg intégrés, dont le principe de fonctionnement de base est la réflexion optique sélective en fréquence. Premièrement, un nouveau filtre optique double-bande basé sur les réseaux de Bragg multimodes est introduit pour des applications dans les télécommunications. Deuxièmement, une nouvelle architecture de filtre accordable basée sur un coupleur contra-directionnel à étage unique avec un dispositif de micro-chauffage segmenté permettant des profils de température arbitraires démontre une accordabilité de la bande passante record et des capacités de compensation des erreurs de fabrication lorsqu'opérée par un algorithme de contrôle. Troisièmement, un modèle d'apprentissage machine basé sur un réseau de neurones artificiels est introduit et démontré pour la conception de coupleurs contra-directionnels et le diagnostic de fabrication, ouvrant la voie à la production de masse de systèmes photoniques intégrés basée sur les données.The advent of integrated photonics has attracted a lot of research and industrial attention in the last two decades, as it is believed to be a hardware revolution similar to microelectronics. While leveraging microelectronics-inherited mass-production-grade fabrication processes for full scalability, the silicon photonic paradigm is compact, energy efficient and allows the full integration of nano-scale optical devices and circuits for crutial applications in telecommunications, sensing, and optical computing. Similar to early-day microelectronics, current research efforts in silicon photonics are put toward the proposal, design and characterization of standardized components in sights of eventual black-box building block circuit design. The main challenges associated with this development include the complexity of electromagnetic theory in device operation, the performance-limiting fabrication process variations and non-uniformities, and the considerable computing resources required to accurately model complex photonic circuitry. In this work, these three bottlenecks are addressed in the form of original research contributions. Based on silicon photonic devices and machine learning, the contributions of this thesis pertain to integrated Bragg gratings, whose basic operating principle is frequency-selective optical transmission. First, a novel dual-band optical filter based on multimode Bragg gratings is introduced for applications in telecommunications. Second, a novel tunable filter architecture based on a single-stage contra-directional coupler with a segmented micro-heating device allowing arbitrary temperature profiles demonstrates record-breaking bandwidth tunability and on-chip fabrication error compensation capabilities when operated by a control algorithm. Third, an artificial neural network-based machine learning model is introduced and demonstrated for large-parameter-space contra-directional coupler inverse design and fabrication diagnostics, paving the way for the data-driven mass production of integrated photonic systems