One of the main goals of computer graphics is the fast synthesis of photorealistic image of virtual 3D scenes. The work presented in this thesis addresses this goal of speed and realism. In real-time realistic rendering, we encounter certain problems that are difficult to solve in the traditional 3-dimensional geometric space. We show that using an image-space approach can provide effective solutions to these problems. Unlike geometric space algorithms that operate on 3D primitives such as points, edges, and polygons, image-space algorithms operate on 2D snapshot images of the 3D geometric data. Operating in image-space effectively decouples the geometric complexity of the 3D data from the run-time of the rendering algorithm. Other important advantages of image-space algorithms include ease of implementation on modern graphics hardware, and fast computation of approximate solutions to certain lighting calculations. We have applied the image-space approach and developed algorithms for three prominent problems in real-time realistic rendering, namely, representing and lighting large 3D scenes in the context of grass rendering, rendering caustics, which is a complex indirect illumination effect, and subsurface scattering for rendering of translucent objects.

This study evaluated the potential of using Passive Radiofrequency Identification [RFID] Technology as basis for a Real Time Location Tracking System [RTLTS] to assist an individual with a visual impairment participating in physical activities, such as basketball. While RTLTSs exist based upon other technologies, Passive RFID Technology had yet to be examined for its potential in such an application. In this study, a system was designed and modified, for Passive RFID Technology, in order to enhance the performance of the RTLTS. More specifically, the first iteration of the software code employed the use of multiple RFID antennas to cooperatively designate unique zones, which allowed for the identification of a user’s general position. A series of tests were then conducted to assess the system’s accuracy. In addition, it was determined whether or not factors, such as the addition of multiple tags identifying an object, change in the attenuation level and height level of the antenna, or the position of the tag, had a significant effect on the accuracy of the system. This study showed that the system’s greatest accuracy was obtained when six distinct Passive RFID tags were used to identify the object and when the antenna had a direct line of sight to the tags. Results also showed that these read accuracy rates were indeed comparable to other technologies. Therefore, there exists optimism in using Passive RFID technology as a basis for a RTLTS to assist an individual with a visual impairment.

This thesis discusses the development of an advanced real-time embedded systems course offered at California Polytechnic State University, San Luis Obispo, which aims to prepare students to design modern complex real-time embedded systems. It describes the goals of the real-time embedded systems curriculum, which includes an introductory and advanced course. Finally, this paper discusses the challenges of creating a successful advanced real-time embedded systems course and proposes changes to the current advanced real-time embedded systems course in response to those challenges.

Measurement of the dynamics of flexible structures is difficult because the motion is often complex and the structures are not well suited to the attachment of sensors. As a result, non-contact optical systems are used. However, optical systems produce large amounts of data which make their use in real-time measurement difficult. Conventional computers are not well suited to the processing requirements associated with data from optical systems. In this thesis, algorithms and architectures to reduce the data bandwidth of an optical measurement system are investigated. Simulations of the ability of the algorithms to find a target on a linear-array charge-coupled device (CCD) camera are performed. The running maximum algorithm provides the best accuracy and speed and therefore is recommended. A real-time architecture to implement the running maximum algorithm is developed. The architecture allows the optical system to operate at 9700 frames/second. Experimental results from a prototype system show very good accuracy for both static and dynamic measurements. / Master of Science

Yes / Model checking has been extensively used to verify various systems. However, this usually has been done by experts who have a good understanding of model checking and who are familiar with the syntax of both modelling and property specification languages. Unfortunately, this is not an easy task for nonexperts to learn description languages for modelling and formal logics/languages for property specification. In particular, property specification is very daunting and error-prone for nonexperts. In this paper, we present a methodology to facilitate probabilistic model checking for nonexperts. The methodology helps nonexpert users model their systems and express their requirements without any knowledge of the modelling and property specification languages.

Brain computer interfaces (BCIs) are devices capable of relaying information directly from the brain to a digital device. BCIs have been proposed for a diverse range of clinical and commercial applications; for example, to allow paralyzed subjects to communicate, or to improve machine human interactions. At their core, BCIs need to predict the current state of the brain from variables measuring functional physiology. Functional near infrared spectroscopy (fNIRS) is a non-invasive optical technology able to measure hemodynamic changes in the brain. Along with electroencephalography (EEG), fNIRS is the only technique that allows non-invasive and portable sensing of brain signals. Portability and wearability are very desirable characteristics for BCIs, as they allow them to be used in contexts beyond the laboratory, extending their usability for clinical and commercial applications, as well as for ecologically valid research. Unfortunately, due to limited access to the brain, non-invasive BCIs tend to suffer from low accuracy in their estimation of the brain state. It has been suggested that feedback could increase BCI accuracy as the brain normally relies on sensory feedback to adjust its strategies. Despite this, presenting relevant and accurate feedback in a timely manner can be challenging when processing fNIRS signals, as they tend to be contaminated by physiological and motion artifacts. In this dissertation, I present the hardware and software solutions we proposed and developed to deal with these challenges. First, I will talk about ninjaNIRS, the wearable open source fNIRS device we developed in our laboratory, which could help fNIRS neuroscience and BCIs to become more accessible. Next, I will present an adaptive filter strategy to recover the neural responses from fNIRS signals in real-time, which could be used for feedback and classification in a BCI paradigm. We showed that our wearable fNIRS device can operate autonomously for up to three hours and can be easily carried in a backpack, while offering noise equivalent power comparable to commercial devices. Our adaptive multimodal Kalman filter strategy provided a six-fold increase in contrast to noise ratio of the brain signals compared to standard filtering while being able to process at least 24 channels at 400 samples per second using a standard computer. This filtering strategy, along with visual feedback during a left vs right motion imagery task, showed a relative increase of accuracy of 37.5% compared to not using feedback. With this, we show that it is possible to present relevant feedback for fNIRS BCI in real-time. The findings on this dissertation might help improve the design of future fNIRS BCIs, and thus increase the usability and reliability of this technology.

<p>Packet transmission scheduling plays a key role in Quality of Service (QoS) support for real-time traffic and efficient radio resource utilization in a wireless mesh network (WMN). It is a highly complicated problem due to the fact that any scheduling decision at one mesh access point (AP) may affect the scheduling decisions in the entire network. The strict delay requirement of real-time applications makes the scheduling problem even more challenging.</p> <p> In this thesis, the packet transmission scheduling problem for real-time constant-bit-rate (CBR) traffic in a WMN is first formulated as a standard integer linear programming problem, which takes into consideration both the multihop packet transmission delay and timeline coordinations of the mesh APs. The objective is to efficiently utilize the radio resources, subject to available bandwidth of the mesh APs, co-channel interference, and packet transmission latency requirement.</p> <p>Two heuristic schemes, namely AP-based scheduling (ABS) and connection-based scheduling (CBS) schemes, are then proposed to support real-time CBR traffic. ABS makes scheduling decisions on a per-AP basis. Scheduling decisions at APs with a higher traffic load are determined before those at APs with a lower traffic load. ABS achieves close-to-optimum capacity but may go through multiple iterations before reaching a feasible solution. CBS makes scheduling decisions on a connection-by-connection basis. It gives a higher priority to connections with more hops. In CBS, connections with a lower priority can only use resources remaining from serving all higher priority connections. CBS requires much lower complexity than ABS while achieving capacity performance slightly lower than ABS.</p> <p>We extend the proposed ABS and CBS scheduling schemes for supporting real-time variable bit rate (VBR) traffic in a WMN. By combining the concept of effective bandwidth and the proposed scheduling schemes, both delay and packet loss performance of the VBR traffic can be effectively satisfied. The scheduling schemes are further extended for supporting real-time traffic in a WMN with multi-radio APs.</p> <p>All the scheduling decisions are done at the time when new connection requests arrive and the results are used to make admission control decisions. In this sense, the work in this thesis is for both packet transmission scheduling and admission control for real-time traffic in WMNs.</p> / Thesis / Doctor of Philosophy (PhD)

<p> Many safety-critical software applications are hard real-time systems. They have stringent timing requirements that have to be met. We present descriptions of timing behaviors that include precise definitions as well as analysis of how functional timing requirements (FTRs) interact with performance timing requirements (PTRs), and how these concepts can be used by software designers. The definitions explicitly show how to specify timing requirements with tolerances on time durations. </p> <p> This thesis shows the importance of specifying both FTRs and PTRs, by revealing the fact that their interaction directly determines the final implementability of real-time systems. By studying this interaction under three environmental assumptions, we find that the implementability results of the timing properties are different in each environment, but they are closely related. The results allow us to predict the system's implementability without developing or verifying the actual implementation. This also shows that we can sometimes significantly reduce the sampling frequency on the target platform, and still implement the timing requirement correctly. </p> <p> We present a component-based approach to formalizing common timing requirements and provide a pre-verified implementation of one of these requirements. The verification is performed using the theorem proving tool PVS. This allows domain experts to specify the tolerance in each individual timing requirement precisely. The pre-verified implementation of a timing requirement is demonstrated by applying the method in two examples. These examples show that both the design and verification effort are reduced significantly using a pre-verified template. </p> <p> A primary focus of this thesis is on how to include tolerances on timing durations in the specification, implementation and verification of timing behaviors in hard real-time applications. </p> / Thesis / Doctor of Philosophy (PhD)

UAV (Unmanned Airborne Vehicle) is a source of entertainment and a pleasurable experience, attracting many young people to pursue it as a hobby. With the potential increase in the number of UAVs, the risk of using them for malicious purposes also increases. In addition, birds and UAVs have very similar maneuvers during flights. These UAVs can also carry a significant payload, which can have unintended consequences. Therefore, detecting UAVs near red-zone areas is an important problem. In addition, small UAVs can record video from large distances without being spotted by the naked eye. An appropriate network of sensors may be needed to foresee the arrival of such entities from a safe distance before they pose any danger to the surrounding areas. Despite the growing interest in UAV detection, limited research has been conducted in this area due to a lack of available data for model training. This thesis proposes a novel approach to address this challenge by leveraging experimental data collected in real-time using high-sensitivity sensors instead of relying solely on simulations. This approach allows for improved model accuracy and a better representation of the complex and dynamic environments in which UAVs operate, which are difficult to simulate accurately. The thesis further explores the application of machine learning and sensor fusion algorithms to detect UAVs and distinguish them from other objects, such as birds, in real-time. Specifically, the thesis utilizes YOLOv3 with deep sort and sensor fusion algorithms to achieve accurate UAV detection. In this study, we employed YOLOv3, a deep learning model known for its high efficiency and complexity, to facilitate real-time drone versus bird detection. To further enhance the reliability of the system, we incorporated sensor fusion, leading to a more stable and accurate real-time system, and mitigating the incidence of false detections. Our study indicates that the YOLOv3 model outperformed the state-of-the-art models in terms of both speed and robustness, achieving a high level of confidence with a score above 95%. Moreover, the YOLOv3 model demonstrated a promising capability in real-time drone versus bird detection, which suggests its potential for practical applications

The transit industry is changing rapidly due to technology, which in turn changes business models, ridership, travel patterns, and the transit workforce. As transit agencies introduce new technology systems, research is needed on how these systems impact demand for paratransit and on-demand mobility services. This research addresses this topic by studying the impact of technology on demand-responsive transportation and urban mobility. Over the past two decades, this sector has been transformed by cloud computing, machine learning, artificial intelligence, ridesharing, and mobility-on-demand. This dissertation explores the adoption of new technology by transit agencies and service providers, focusing on implementing app-based dynamic technologies for dispatching and scheduling demand-responsive transportation modes such as microtransit services, on-demand transit, and paratransit. Although studies on technological changes in other sectors have been conducted, public transit agencies need a more systematic approach to adopting new technology. Current literature on technology adoption in public transit focuses on the benefits and outcomes of technology adoption, with limited discussions of the challenges faced in adopting and implementing technologies. Comprehensive research on the emerging and evolving transit technological landscape is essential to bridge this gap. This research examines how transit agencies react to internal and external technological changes as their operational, tactical, and strategic operating conditions evolve. The aim is to enhance the current comprehension of the topic by providing a comprehensive overview of the technology adoption methodology and to offer practical planning and policy recommendations where possible. A mixed-methods approach was applied to explore the research questions. Transit practitioners and managers in the Washington DC region were surveyed, and the analysis techniques employed included cross-tabulation and descriptive statistics. This dissertation focuses on gaining insight into adopting real-time dynamic dispatching and scheduling, on-demand transit, and microtransit technologies, including the opinions of transit practitioners and policymakers involved in facilitating technology adoption. Specifically, the study aims to: 1) understand the impact of adopting emerging paratransit technologies; 2) investigate on-demand transit system performance outcomes under ridership, on-time performance, and operating costs, using a survey and expert interviews; and 3) investigate the use of a multicriteria decision-making approach to evaluate accessibility considerations in microtransit adoption planning and design strategies. The results suggest that current technology adoption approaches in transit can significantly enhance decision-making and transit outcomes while addressing the equity and accessibility needs of the community and maintaining coverage and route frequency. The Socio-Technical-Systems (STS) approach was applied to help understand the adoption of new technology in demand response transit. This approach provides insights into technology, accessibility, decision-making, functionality, and interchangeability, enhancing our understanding of social complexity. Additionally, this research introduces a multi-level decision-making framework to measure service performance and provides insights into the impact of transportation technology on planning, policy, and decision-making processes. / Doctor of Philosophy / This research examines how transportation technology advancements affect mobility in the United States. It focuses on how transit agencies adapt to technological changes inside and outside the organization as their operating conditions evolve at operational, tactical, and strategic levels. This study aims to provide a comprehensive understanding of this subject by offering a thorough overview of the technology adoption process and practical planning and policy recommendations where appropriate. The study delves into how real-time information coupled with new business models create more accessible transit options and informed decisions. The research investigates on-demand transit, microtransit, and real-time dynamic dispatching and scheduling, which pose challenges regarding demand and costs. These technologies aim to maximize operational capacity, route frequencies, and reduce vehicle travel time and mileage while considering the uncertainties of funding and travel behaviors that arise with technology adoption. The study examines three key technologies: 1) real-time dynamic dispatching and scheduling in paratransit; 2) performance outcomes of on-demand transit services in the Washington DC region; and 3) a multi-attribute decision-making approach in evaluating microtransit accessibility. The research reviews the technology adoption methods employed by transit agencies. It discusses the potential technology deployment of future projects in three domains: real-time dynamic dispatching and scheduling, on-demand transit, and microtransit accessibility.