No description available.

No description available.

There have been several accidents involving autonomous vehicles on public roadways under scenarios that are normally avoidable by competent human drivers. This thesis contains a review of these accidents and their causes as a result of inadequate hazard mitigation. As a solution to this problem, a novel design pattern is proposed. This design pattern was developed from a hazard analysis using Systems Theoretic Process Analysis ( STPA ) methodologies that analyzed the circumstances common to several of these accidents. To demonstrate the effectiveness of the novel design pattern, an example system is constructed and tested in simulation against several accident scenarios similar to the ones studied. The results are then explained to demonstrate the effectiveness of the proposed design pattern. / Thesis / Master of Applied Science (MASc)

Yes / The advent of Internet of Things (IoT) has escalated the information sharing among various smart devices by many folds, irrespective of their geographical locations. Recently, applications like e-healthcare monitoring has attracted wide attention from the research community, where both the security and the effectiveness of the system are greatly imperative. However, to the best of our knowledge none of the existing literature can accomplish both these objectives (e.g., existing systems are not secure against physical attacks). This paper addresses the shortcomings in existing IoT-based healthcare system. We propose an enhanced system by introducing a Physical Unclonable Function (PUF)-based authentication scheme and a data driven fault-tolerant decision-making scheme for designing an IoT-based modern healthcare system. Analyses show that our proposed scheme is more secure and efficient than existing systems. Hence, it will be useful in designing an advanced IoT-based healthcare system. / Supported in part by Singapore Ministry of Education Academic Research Fund Tier 1 (R-263-000- D63-114). / Research Development Fund Publication Prize Award winner, July 2020.

The field of robotics has advanced to the point where robots are being developed for use in fire environments to perform firefighting tasks. These environments contain varying levels of fire and smoke, both of which obstruct robotic perception sensors. In order to effectively use robots in fire environments, the issue of perception in the presence of smoke and fire needs to be addressed. The goal of this research was to address the problem of perception, specifically rangefinding, in fire smoke environments. A series of tests were performed in fire smoke filled environments to evaluate the performance of different commercial rangefinders and cameras as well as a long-wavelength infrared (LWIR) stereo vision system developed in this research. The smoke was varied from dense, low temperature smoke to light, high temperature smoke for evaluation in a range of conditions. Through small-scale experiments on eleven different sensors, radar and LWIR cameras outperformed other perception sensors within both smoke environments. A LWIR stereo vision system was developed for rangefinding and compared to radar, LIDAR, and visual stereo vision in large-scale testing, demonstrating the ability of LWIR stereo vision to rangefind in dense smoke when LIDAR and visual stereo vision fail. LWIR stereo vision was further developed for improved rangefinding in fire environments. Intensity misalignment between cameras and stereo image filtering were addressed quantitatively. Tests were performed with approximately isothermal scenes and thermally diverse scenes to select subsystem methods. In addition, the effects of image filtering on feature distortion were assessed. Rangefinding improvements were quantified with comparisons to ground truth data. Improved perception in varying levels of clear and smoke conditions was developed through sensor fusion of LWIR stereo vision and a spinning LIDAR. The data were fused in a multi-resolution 3D voxel domain using evidential theory to model occupied and free space states. A heuristic method was presented to separate significantly attenuated LIDAR returns from low-attenuation returns. Sensor models were developed for both return types and LWIR stereo vision. The fusion system was tested in a range of conditions to demonstrate its ability for improved performance over individual sensor use in fire environments. / Ph. D.

This thesis presents a novel self-powered infrastructural traffic monitoring approach that estimates traffic information by combining three detection techniques. The traffic information can be obtained from the presented approach includes vehicle counts, speed estimation and vehicle classification based on size. Two categories of sensors are used including IR Lidar and IR camera. With the two sensors, three detection techniques are used: Time of Flight (ToF) based, vision based and Laser spot flow based. Each technique outputs observations about vehicle location at different time step. By fusing the three observations in the framework of Kalman filter, vehicle location is estimated, based on which other concerned traffic information including vehicle counts, speed and class is obtained. In this process, high reliability is achieved by combing the strength of each techniques. To achieve self-powering, a dynamic power management strategy is developed to reduce system total energy cost and optimize power supply in traffic monitoring based on traffic pattern recognition. The power manager attempts to adjust the power supply by reconfiguring system setup according to its estimation about current traffic condition. A system prototype has been built and multiple field experiments and simulations were conducted to demonstrate traffic monitoring accuracy and power reduction efficacy. / Master of Science

This dissertation proposes a new framework to Non-Field-of-view (NFOV) sound source localization and tracking in indoor environments. The approach takes advantage of sound signal information to localize target position through auditory sensors combination with other sensors within grid-based recursive estimation structure for tracking using nonlinear and non-Gaussian observations. Three approaches to NFOV target localization are investigated. These techniques estimate target positions within the Recursive Bayesian estimation (RBE) framework. The first proposed technique uses a numerical fingerprinting solution based on acoustic cues of a fixed microphone array in a complex indoor environment. The Interaural level differences (ILDs) of microphone pair from a given environment are constructed as an a priori database, and used for calculating the observation likelihood during estimation. The approach was validated in a parametrically controlled testing environment, and followed by real environment validations. The second approach takes advantage of acoustic sensors in combination with an optical sensor to assist target estimation in NFOV conditions. This hybrid of the two sensors constructs observation likelihood through sensor fusion. The third proposed model-based technique localizes the target by taking advantage of wave propagation physics: the properties of sound diffraction and reflection. This approach allows target localization without an a priori knowledge database which is required for the first two proposed techniques. To demonstrate the localization performance of the proposed approach, a series of parameterized numerical and experimental studies were conducted. The validity of the formulation and applicability to the actual environment were confirmed. / Ph. D.

With an increase in age comes an increase in the risk of frailty and mobility decline, which can lead to dangerous falls and can even be a cause of mortality. Despite these serious consequences, healthcare systems remain reactive, highlighting the need for technologies to predict functional mobility decline. In this thesis, we present an end-to-end autonomous functional mobility assessment system that seeks to bridge the gap between robotics research and clinical rehabilitation practices. Unlike many fully integrated black-box models, our approach emphasizes the need for a system that is both reliable as well as transparent to facilitate its endorsement and adoption by healthcare professionals and patients. Our proposed system is characterized by the sensor fusion of multimodal data using an optimization framework known as factor graphs. This method, widely used in robotics, enables us to obtain visually interpretable 3-D estimations of the human body in recorded footage. These representations are then used to implement autonomous versions of standardized assessments employed by physical therapists for measuring lower-limb mobility, using a combination of custom neural networks and explainable models. To improve the accuracy of the estimations, we investigate the application of the Koopman operator framework to learn linear representations of human dynamics: We leverage these outputs as prior information to enhance the temporal consistency across entire movement sequences. Furthermore, inspired by the inherent stability of natural human movement, we propose ways to impose stability constraints in the dynamics during the training of linear Koopman models. In this light, we propose a sufficient condition for the stability of discrete-time linear systems that can be represented as a set of convex constraints. Additionally, we demonstrate how it can be seamlessly integrated into larger-scale gradient descent optimization methods. Lastly, we report the performance of our human pose detection and autonomous mobility assessment systems by evaluating them on outcome mobility datasets collected from controlled laboratory settings and unconstrained real-life home environments. While we acknowledge that further research is still needed, the study results indicate that the system can demonstrate promising performance in assessing mobility in home environments. These findings underscore the significant potential of this and similar technologies to revolutionize physical therapy practices.

This thesis investigates the integration of transfer learning with ResNet-101 and compares its performance with VGG-19 for 3D object detection in autonomous vehicles. ResNet-101 is a deep Convolutional Neural Network with 101 layers and VGG-19 is a one with 19 layers. The research emphasizes the fusion of camera and lidar outputs to enhance the accuracy of 3D bounding box estimation, which is critical in occluded environments. Selecting an appropriate backbone for feature extraction is pivotal for achieving high detection accuracy. To address this challenge, we propose a method leveraging transfer learning with ResNet- 101, pretrained on large-scale image datasets, to improve feature extraction capabilities. The averaging technique is used on output of these sensors to get the final bounding box. The experimental results demonstrate that the ResNet-101 based model outperforms the VGG-19 based model in terms of accuracy and robustness. This study provides valuable insights into the effectiveness of transfer learning and multi-sensor fusion in advancing the innovation in 3D object detection for autonomous driving. / Master of Science / In the realm of computer vision, the quest for more accurate and robust 3D object detection pipelines remains an ongoing pursuit. This thesis investigates advanced techniques to im- prove 3D object detection by comparing two popular deep learning models, ResNet-101 and VGG-19. The study focuses on enhancing detection accuracy by combining the outputs from two distinct methods: one that uses a monocular camera to estimate 3D bounding boxes and another that employs lidar's bird's-eye view (BEV) data, converting it to image-based 3D bounding boxes. This fusion of outputs is critical in environments where objects may be partially obscured. By leveraging transfer learning, a method where models that are pre-trained on bigger datasets are finetuned for certain application, the research shows that ResNet-101 significantly outperforms VGG-19 in terms of accuracy and robustness. The approach involves averaging the outputs from both methods to refine the final 3D bound- ing box estimation. This work highlights the effectiveness of combining different detection methodologies and using advanced machine learning techniques to advance 3D object detec- tion technology.

This dissertation aims to provide critical methodological advancements for sensor fusion and physics-informed machine learning in metal additive manufacturing (MAM) to assist practitioners in detecting quality control structural anomalies. In MAM, there is an urgent need to improve knowledge of the internal layer fusion process and geometric variation occurring during the directed energy deposition processes. A core challenge lies in the cyclic heating process, which results in various structural abnormalities and deficiencies, reducing the reproducibility of manufactured components. Structural abnormalities include microstructural heterogeneities, porosity, deformation and distortion, and residual stresses. Data-driven monitoring in MAM is needed to capture process variability, but challenges arise due to the inability to capture the thermal history distribution process and structural changes below the surface due to limitations in in-situ data collection capabilities, physical domain knowledge integration, and multi-data and multi-physical data fusion. The research gaps in developing system-based generalizable artificial intelligence (AI) and machine learning (ML) to detect abnormalities are threefold. (1) Limited fusion of various types of sensor data without handcrafted selection of features. (2) There is a lack of physical domain knowledge integration for various systems, geometries, and materials. (3) It is essential to develop sensor and system integration platforms to enable a holistic view to make quality control predictions in the additive manufacturing process. In this dissertation, three studies utilize various data types and ML methodologies for predicting in-process anomalies. First, a complementary sensor fusion methodology joins thermal and ultrasonic image data capturing layer fusion and structural knowledge for layer-wise porosity segmentation. Secondly, a physics-informed data-driven methodology for joining thermal infrared image data with Goldak heat flux improves thermal history simulation and deformation detection. Lastly, a physics-informed machine learning methodology constrained by thermal physical functions utilizes in-process multi-modal monitoring data from a digital twin environment to predict distortion in the weld bead. This dissertation provides current practitioners with data-driven and physics-based interpolation methods, multi-modal sensor fusion, and anomaly detection insights trained and validated with three case studies.