Developing TRACKER - Portable Monitoring System using Kalman Filtering to Track Rotational Movement of Bridges

The combined effects of flooding and scour are the primary causes of bridge failure over flowing water. Improvements in structural health monitoring and inertial sensors have led to the development of advanced monitoring systems that can provide bridge owners with detailed information on the performance of the structure and allow informed decisions to be made about time-critical safety issues following a storm event. However, such systems remain prohibitively expensive for the majority of smaller structures which make up the wider transport network. This thesis details the development of a robust, portable data acquisition logger (TRACK ER), which can be used to target vulnerable infrastructure during a storm event to increase the resilience of the wider transport network. TRACKER uses condition monitoring, recording quasi-static and dynamic deformations, to track the performance of a bridge under the combined effects of storm loading. A benefit of this method is that it requires no direct input force or prior knowledge of the bridge model. Traditionally, tiltmeters or accelerometers are used to measure rotation for structural health monitoring purposes but such sensors can struggle to isolate rotation from translational acceleration if the structure is linearly accelerating. Gyroscopes offer improved rotational measurement capabilities but gyroscope measurements are known to drift over time as a result of the iterative process of converting rate gyroscope data. This thesis will explore gyroscopes as a complementary sensor to accelerometers and introduce a Kalman filter that combines both inertial sensors measurement data to obtain optimised rotation data. To improve the performance of the Kalman filter, the filter is adapted to automatically update the process and noise measurement values. TRACKER, a robust, portable data acquisition logger, was developed and equipped with inertial sensors to provide a stand-alone system that can be rapidly deployed to target vulnerable infrastructure. Verification of the new logger was performed under controlled laboratory conditions to prove the validity of the new logger. The rotational data showed good agreement with rotational measurements obtained from an industry gold-standard vision-based measurement system. TRACKER was deployed on a variety of in-service bridges using different loading scenarios to demonstrate the ability of the new logging system, including loading from ambient weather conditions. TRACKER successfully tracked the performance of the structures, proving the ability of the logger to track the quasi-static and dynamic deformations of a structure during loading from traffic and environmental conditions

Efficient information distribution in the Internet of Medical Things (IoMT)

Towards the world of Internet of Things, people utilize knowledge from sensor streams in various kinds of smart applications including, but not limited to smart medical information systems. The number of sensed devices is rapidly increasing along with the amount of sensing data. Consequently, the bottleneck problem at the local gateway has become a huge concern given the critical loss and delay intolerant nature of medical data. Orthogonally to the existing solutions, we propose sensor data prioritization mechanism to enhance the information quality while utilizing resources using Value of Information (VoI) at the application level. Our approach adopts signal processing techniques and information theory related concepts to assess the VoI. We introduce basic yet convenient ways to enhance the efficiency of medical information systems, not only when considering the resource consumption, but also when performing updates, by selecting appropriate delay for wearable sensors to send data at optimal VoI. Our analysis shows some interesting results about the correlation and dependency of different sensor signals, that we use for the value assesment. This preliminary analysis could be an initiative for further investigation of VoI in medical data transmission using more advanced methods.Towards the world of Internet of Things, people utilize knowledge from sensor streams in various kinds of smart applications including, but not limited to smart medical information systems. The number of sensed devices is rapidly increasing along with the amount of sensing data. Consequently, the bottleneck problem at the local gateway has become a huge concern given the critical loss and delay intolerant nature of medical data. Orthogonally to the existing solutions, we propose sensor data prioritization mechanism to enhance the information quality while utilizing resources using Value of Information (VoI) at the application level. Our approach adopts signal processing techniques and information theory related concepts to assess the VoI. We introduce basic yet convenient ways to enhance the efficiency of medical information systems, not only when considering the resource consumption, but also when performing updates, by selecting appropriate delay for wearable sensors to send data at optimal VoI. Our analysis shows some interesting results about the correlation and dependency of different sensor signals, that we use for the value assesment. This preliminary analysis could be an initiative for further investigation of VoI in medical data transmission using more advanced methods

Kernel-based fault diagnosis of inertial sensors using analytical redundancy

Kernel methods are able to exploit high-dimensional spaces for representational advantage, while only operating implicitly in such spaces, thus incurring none of the computational cost of doing so. They appear to have the potential to advance the state of the art in control and signal processing applications and are increasingly seeing adoption across these domains. Applications of kernel methods to fault detection and isolation (FDI) have been reported, but few in aerospace research, though they offer a promising way to perform or enhance fault detection. It is mostly in process monitoring, in the chemical processing industry for example, that these techniques have found broader application. This research work explores the use of kernel-based solutions in model-based fault diagnosis for aerospace systems. Specifically, it investigates the application of these techniques to the detection and isolation of IMU/INS sensor faults – a canonical open problem in the aerospace field. Kernel PCA, a kernelised non-linear extension of the well-known principal component analysis (PCA) algorithm, is implemented to tackle IMU fault monitoring. An isolation scheme is extrapolated based on the strong duality known to exist between probably the most widely practiced method of FDI in the aerospace domain – the parity space technique – and linear principal component analysis. The algorithm, termed partial kernel PCA, benefits from the isolation properties of the parity space method as well as the non-linear approximation ability of kernel PCA. Further, a number of unscented non-linear filters for FDI are implemented, equipped with data-driven transition models based on Gaussian processes - a non-parametric Bayesian kernel method. A distributed estimation architecture is proposed, which besides fault diagnosis can contemporaneously perform sensor fusion. It also allows for decoupling faulty sensors from the navigation solution

On Improving the Accuracy and Reliability of GPS/INS-Based Direct Sensor Georeferencing

Due to the complementary error characteristics of the Global Positioning System (GPS) and Inertial Navigation System (INS), their integration has become a core positioning component, providing high-accuracy direct sensor georeferencing for multi-sensor mobile mapping systems. Despite significant progress over the last decade, there is still a room for improvements of the georeferencing performance using specialized algorithmic approaches. The techniques considered in this dissertation include: (1) improved single-epoch GPS positioning method supporting network mode, as compared to the traditional real-time kinematic techniques using on-the-fly ambiguity resolution in a single-baseline mode; (2) customized random error modeling of inertial sensors; (3) wavelet-based signal denoising, specially for low-accuracy high-noise Micro-Electro-Mechanical Systems (MEMS) inertial sensors; (4) nonlinear filters, namely the Unscented Kalman Filter (UKF) and the Particle Filter (PF), proposed as alternatives to the commonly used traditional Extended Kalman Filter (EKF). The network-based single-epoch positioning technique offers a better way to calibrate the inertial sensor, and then to achieve a fast, reliable and accurate navigation solution. Such an implementation provides a centimeter-level positioning accuracy independently on the baseline length. The advanced sensor error identification using the Allan Variance and Power Spectral Density (PSD) methods, combined with a wavelet-based signal de-noising technique, assures reliable and better description of the error characteristics, customized for each inertial sensor. These, in turn, lead to a more reliable and consistent position and orientation accuracy, even for the low-cost inertial sensors. With the aid of the wavelet de-noising technique and the customized error model, around 30 percent positioning accuracy improvement can be found, as compared to the solution using raw inertial measurements with the default manufacturer’s error models. The alternative filters, UKF and PF, provide more advanced data fusion techniques and allow the tolerance of larger initial alignment errors. They handle the unknown nonlinear dynamics better, in comparison to EKF, resulting in a more reliable and accurate integrated system. For the high-end inertial sensors, they provide only a slightly better performance in terms of the tolerance to the losses of GPS lock and orientation convergence speed, whereas the performance improvements are more pronounced for the low-cost inertial sensors

Wearable Smart Rings for Multi-Finger Gesture Recognition Using Supervised Learning

This thesis presents a wearable, smart ring with an integrated Bluetooth low-energy (BLE) module. The system uses an accelerometer and a gyroscope to collect fingers motion data. A prototype was manufactured, and its performance was tested. To detect complex finger movements, two rings are worn on the point and thumb fingers while performing the gestures. Nine pre-defined finger movements were introduced to verify the feasibility of the proposed method. Data pre-processing techniques, including normalization, statistical feature extraction, random forest recursive feature elimination (RF-RFE), and k-nearest neighbors sequential forward floating selection (KNN-SFFS), were applied to select well-distinguished feature vectors to enhance gesture recognition accuracy. Three supervised machine learning algorithms were used for gesture classification purposes, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes (NB). We demonstrated that when utilizing the KNN-SFFS recommended features as the machine learning input, our proposed finger gesture recognition approach not only significantly decreases the dimension of the feature vector, results in faster response time and prevents overfitted model, but also provides approximately similar machine learning prediction accuracy compared to when all elements of feature vectors were used. By using the KNN as the primary classifier, the system can accurately recognize six one-finger and three two-finger gestures with 97.1% and 97.0% accuracy, respectively

Sensors Utilisation and Data Collection of Underground Mining

This study reviews IMU significance and performance for underground mine drone localisation. This research has designed a Kalman filter which extracts reliable information from raw data. Kalman filter for INS combines different measurements considering estimated errors to produce a trajectory including time, position and attitude. To evaluate the feasibility of the proposed method, a prototype has been designed and evaluated. Experimental results indicate that the designed Kalman filter estimates the internal states of a system

New Approach of Indoor and Outdoor Localization Systems

Accurate determination of the mobile position constitutes the basis of many new applications. This book provides a detailed account of wireless systems for positioning, signal processing, radio localization techniques (Time Difference Of Arrival), performances evaluation, and localization applications. The first section is dedicated to Satellite systems for positioning like GPS, GNSS. The second section addresses the localization applications using the wireless sensor networks. Some techniques are introduced for localization systems, especially for indoor positioning, such as Ultra Wide Band (UWB), WIFI. The last section is dedicated to Coupled GPS and other sensors. Some results of simulations, implementation and tests are given to help readers grasp the presented techniques. This is an ideal book for students, PhD students, academics and engineers in the field of Communication, localization & Signal Processing, especially in indoor and outdoor localization domains

Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: A systematic review.

Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Monitoring (PARM) have been considered as a key paradigm for smart healthcare. Traditional methods for PARM focus on controlled environments with the aim of increasing the types of identifiable activity subjects complete and improving recognition accuracy and system robustness by means of novel body-worn sensors or advanced learning algorithms. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to open and connected uncontrolled environments by connecting heterogeneous cost-effective wearable devices and mobile apps. Little is currently known about whether traditional PARM technologies can tackle the new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand the use of IoT technologies in PARM studies, this paper will give a systematic review, critically examining PARM studies from a typical IoT layer-based perspective. It will firstly summarize the state-of-the-art in traditional PARM methodologies as used in the healthcare domain, including sensory, feature extraction and recognition techniques. The paper goes on to identify some new research trends and challenges of PARM studies in the IoT environments, and discusses some key enabling techniques for tackling them. Finally, this paper consider some of the successful case studies in the area and look at the possible future industrial applications of PARM in smart healthcare

Measuring and modelling towline responses using GPS aided inertial navigation

The offshore towage of large floating structures has been the broad subject of research since the 1960’s. The selection of a tug to engage in a tow is based on rules laid down by class and marine warranty surveyors derived from years of experience but a rigorous assessment of these rules based on a comprehensive real world datasets has not been possible. This is principally due to the nature of these tows, usually employing tugs chartered at short notice from the spot market, the long towline lengths when under tow and the high value of the tow itself. Given the commercial implications in being able to better match a suitable tug to any given tow, this research lays down the requirements of an ideal dataset, i.e. one that has a record of towline tensions, complete 6DOF of both the tug and tow all recorded to a universal timeline, along with the seastate experienced by the tow at any given point. It then reviews the historical restrictions in gathering this data and that the key issue has been gathering the motions of the unpowered tow and recording the towline tensions.A methodology is then developed which requires no interference with the towline and draws upon Kalman filters for optimal state estimation of the tug and tow’s position and attitude in 3D space driving a lumped mass simulation of the towline coded in MatLab. The stiffness properties of key elements of the towline are assessed by FEA and observations made on areas where normal industry practice’s may be lacking. Observations on advances in sensor technology as well as other areas for development are then made that provide fertile areas for further research. Finally the full code base for a MatLab, lumped mass simulator is presented in an appendix for future use.The offshore towage of large floating structures has been the broad subject of research since the 1960’s. The selection of a tug to engage in a tow is based on rules laid down by class and marine warranty surveyors derived from years of experience but a rigorous assessment of these rules based on a comprehensive real world datasets has not been possible. This is principally due to the nature of these tows, usually employing tugs chartered at short notice from the spot market, the long towline lengths when under tow and the high value of the tow itself. Given the commercial implications in being able to better match a suitable tug to any given tow, this research lays down the requirements of an ideal dataset, i.e. one that has a record of towline tensions, complete 6DOF of both the tug and tow all recorded to a universal timeline, along with the seastate experienced by the tow at any given point. It then reviews the historical restrictions in gathering this data and that the key issue has been gathering the motions of the unpowered tow and recording the towline tensions.A methodology is then developed which requires no interference with the towline and draws upon Kalman filters for optimal state estimation of the tug and tow’s position and attitude in 3D space driving a lumped mass simulation of the towline coded in MatLab. The stiffness properties of key elements of the towline are assessed by FEA and observations made on areas where normal industry practice’s may be lacking. Observations on advances in sensor technology as well as other areas for development are then made that provide fertile areas for further research. Finally the full code base for a MatLab, lumped mass simulator is presented in an appendix for future use