As system complexity continues to increase, the integration of software and hardware subsystems within system-on-a-chip (SOC) presents significant challenges in post-silicon validation, testing, and in-situ debugging across hardware and software layers. The deep integration of software and hardware components within SOCs often prevents the use of traditional analysis methods to observe and monitor the internal state of these components. This situation is further exacerbated for in-situ debugging and testing in which physical access to traditional debug and trace interfaces is unavailable, infeasible, or cost prohibitive. In this dissertation, we present a system-level observation framework (SOF) that provides minimally intrusive methods for dynamically monitoring and analyzing deeply integrated hardware and software components within embedded systems. The SOF monitors hardware and software events by inserting additional logic within hardware cores and by listening to processor trace ports. The SOF provides visibility for monitoring complex execution behavior of software applications without affecting the system execution. The SOF utilizes a dedicated event-streaming interface that allows efficient observation and analysis of rapidly occurring events at runtime. The event-streaming interface supports three alternatives: (1) an in-order priority-based event stream controller, (2) a round-robin priority-based event stream controller, and (3) a priority-level based event stream controller. The in-order priority-based event stream controller, which uses efficient pipelined hardware architecture, ensures that events are reported in-order based on the time of the event occurrence. While the in-order priority-based event stream controller provides high throughput for reporting events, significant area requirement can be incurred. The round-robin priority-based event stream controller is an area-efficient event stream ordering technique with acceptable tradeoffs in event stream throughput. To further reduce area requirement, the SOF supports a priority-level based event stream controller that provides an in-ordering method with smaller area requirements than the round-robin priority-based event stream controller. Comprehensive experimental results using a complete prototype system implementation are presented to quantify the tradeoffs in area, throughput, and latency for the various event streaming interfaces considering several execution scenarios.

Real time monitoring of the shipboard power system is a complex task to address. Unlike the terrestrial power system, the shipboard power system is a comparatively smaller system but with more complexity in terms of its system operation. This requires the power system to be continuously monitored to detect any type of fluctuations or disturbances. Planning metering systems in the power system of a ship is a challenging task not only due to the dimensionality of the problem, but also due to the need for reducing redundancy while improving network observability and efficient data collection for a reliable state estimation process. This research is geared towards the use of a Genetic Algorithm for intelligent placement of meters in a shipboard system for real time power system monitoring taking into account different system topologies and critical parameters to be measured from the system. The algorithm predicts the type and location of meters for identification and collection of measurements from the system. The algorithm has been tested with several system topologies.

abstract: Recently, automation, shared use, and electrification are proposed and viewed as the “three revolutions” in the future transportation sector to significantly relieve traffic congestion, reduce pollutant emissions, and increase transportation system sustainability. Motivated by the three revolutions, this research targets on the passenger-focused scheduled transportation systems, where (1) the public transit systems provide high-quality ridesharing schedules/services and (2) the upcoming optimal activity planning systems offer the best vehicle routing and assignment for household daily scheduled activities. The high quality of system observability is the fundamental guarantee for accurately predicting and controlling the system. The rich information from the emerging heterogeneous data sources is making it possible. This research proposes a modeling framework to systemically account for the multi-source sensor information in urban transit systems to quantify the estimated state uncertainty. A system of linear equations and inequalities is proposed to generate the information space. Also, the observation errors are further considered by a least square model. Then, a number of projection functions are introduced to match the relation between the unique information space and different system states, and its corresponding state estimate uncertainties are further quantified by calculating its maximum state range. In addition to optimizing daily operations, the continuing advances in information technology provide precious individual travel behavior data and trip information for operational planning in transit systems. This research also proposes a new alternative modeling framework to systemically account for boundedly rational decision rules of travelers in a dynamic transit service network with tight capacity constraints. An agent-based single-level integer linear formulation is proposed and can be effectively by the Lagrangian decomposition. The recently emerging trend of self-driving vehicles and information sharing technologies starts creating a revolutionary paradigm shift for traveler mobility applications. By considering a deterministic traveler decision making framework, this research addresses the challenges of how to optimally schedule household members’ daily scheduled activities under the complex household-level activity constraints by proposing a set of integer linear programming models. Meanwhile, in the microscopic car-following level, the trajectory optimization of autonomous vehicles is also studied by proposing a binary integer programming model. / Dissertation/Thesis / Doctoral Dissertation Civil, Environmental and Sustainable Engineering 2018