This paper describes the implementation of a scanning signal detector and automatic modulation classification system. The classification technique is a completely blind method, with no prior knowledge of the signal's center frequency, bandwidth, or symbol rate. An energy detector forms the initial approximations of the signal parameters. The energy detector used in the wideband sweep is reused to obtain fine estimates of the center frequency and bandwidth of the signal. The subsequent steps reduce the effect of frequency offset and sample timing error, resulting in a constellation of the modulation of interest. The cumulant of the constellation is compared to a set of known ideal cumulant values, forming the classification estimate. The algorithm uses two platforms that together provide high speed parallel processing and flexible run-time operation. High-rate spectral scanning using an energy detector is run in parallel with a variable down sampling path; both are highly pipelined structures, which allows for high data throughput. A pair of processing cores is used to record spectral usage and signal characteristics as well as perform the actual classification. The resulting classification system can accurately identify modulations below 5 dB of signal-to-noise ratio (SNR) for some cases of the phase shift keying family of modulations but requires a much higher SNR to accurately classify higher-order modulations. These estimates tend toward classifying all signals as binary phase shift keying because of limits of the noise power estimation part of the cumulant normalization process. Other effects due to frequency offset and synchronization timing are discussed. / Master of Science

Electrochemical CO2 reduction reaction (CO2R) is a promising route to help reduce greenhouse gas emissions and reach carbon dioxide net zero emissions to combat global warming. Currently, in order to investigate catalytically produced products from CO2R offline methods such as gas chromatography (GC) and nuclear magnetic resonance (NMR) are used. These offline methods have a time resolution on the minutes to hours scale which leads to uncertainty of evaluating how products are produced from CO2R, such as knowing if a product is produced from electrochemical means or chemical conversion, and if a product is being produced in a linear rate of production or a different rate. This is where the ability to have real-time analysis of the products generated from CO2R is desirable, as it can more definitively answer many of these questions. Yet few analytical techniques have been developed in detail so far to achieve real-time analysis. Herein, we show the use of selected-ion flow-tube mass spectrometry (SIFT-MS) that quantitatively measures in realtime an array of 10 C1, C2, and C3 products from CO2R such as ethanol, ethylene or methane. The custom-developed SIFT-MS selected ion mode scan measures the concentration of gas and liquid-phase products of CO2R at the same time and is compatible with any electrolyzer cell. We demonstrate that the SIFT-MS technique can reliably and accurately determine product concentration in real-time through the evaluation of Cu foil and its comparison to traditional techniques. Considering the narrow range of developed and deployed techniques for real-time quantitative product analysis for CO2R, this study on SIFT-MS is a critical tool for future research in accelerating and optimizing catalyst design for electrochemical CO2R applications. / Thesis / Master of Applied Science (MASc) / The electrochemical reduction of carbon dioxide can be used within electrolyzer devices to help mitigate greenhouse gas emissions to combat global warming. The process is when carbon dioxide is extracted from sources such as industrial plants and undergoes electrochemical reduction to be converted into 16 or more products that can be then sold within the market for profit. The common analysis methods currently used to analyze how much of each product is produced from an electrolyzer device does not reveal all the information needed to best design electrolyzer devices. This has led way to new analysis methods that are being explored that can find all the information needed for product analysis that leads to optimal electrolyzer design. This work investigated uses a special type of mass spectrometry that will allow for the full information to be found on the products from electrochemical carbon dioxide reduction leading to enhanced electrolyzer designs.

The structure of an RS232 monitor intended for use in online system maintenance of real-time, industrial process control systems is presented. The monitor is written in PLM86 for use in an Intel 8086 based system in which system software is organized into a set of foreground interrupts and an infinite background loop. The monitor, which resides in the background loop, utilizes a programming technique referred to as psuedo-concurrent programming to eliminate the lockout problem associated with the background loop structure. The psuedo-concurrent technique is explained. Maintenance procedures are described and possible implementations utilizing the monitor are suggested. / Master of Science

This thesis aims to address the real-time planning and nonlinear control of quadrupedal locomotion such that the resulting gaits are robust to various kinds of disturbances. Specifically, this work addresses two scenarios. Namely, a quasi-static formulation in which an inertial appendage (i.e., a tail) is used to assist the quadruped in negating external push disturbances, and an agile formulation which is derived in a manner such that an appendage could easily be added in future work to examine the affect of tails on agile and high-speed motions. Initially, this work presents a unified method in which bio-inspired articulated serpentine robotic tails may be integrated with walking robots, specifically quadrupeds, in order to produce stable and highly robust locomotion. The design and analysis of a holonomically constrained 2 degree of freedom (DOF) tail is shown and its accompanying nonlinear dynamic model is presented. The model created is used to develop a hierarchical control scheme which consists of a high-level path planner and a full-order nonlinear low-level controller. The high-level controller is based on model predictive control (MPC) and acts on a linear inverted pendulum (LIP) model which has been extended to include the forces produced by the tail by augmenting the LIP model with linearized tail dynamics. The MPC is used to generate center of mass (COM) and tail trajectories and is subject to the net ground reaction forces of the system, tail shape, and torque saturation of the tail in order to ensure overall feasibility of locomotion. At the lower level, a full-order nonlinear controller is implemented to track the generated trajectories using quadratic program (QP) based input-output (I-O) feedback linearization which acts on virtual constraints. The analytical results of the proposed approach are verified numerically through simulations using a full-order nonlinear model for the quadrupedal robot, Vision60, augmented with a tail, totaling at 20 DOF. The simulations include a variety of disturbances to show the robustness of the presented hierarchical control scheme. The aforementioned control scheme is then extended in the latter portion of this thesis to achieve more dynamic, agile, and robust locomotion. In particular, we examine the use of a single rigid body model as the template model for the real-time high-level MPC, which is linearized using variational based linearization (VBL) and is solved at 200 Hz as opposed to an event-based manner. The previously defined virtual constraints controller is also extended so as to include a control Lyapunov function (CLF) which contributes to both numerical stability of the QP and aids in stability of the output dynamics. This new hierarchical scheme is validated on the A1 robot, with a total of 18 DOF, through extensive simulations to display agility and robustness to ground height variations and external disturbances. The low-level controller is then further validated through a series of experiments displaying the ability for this algorithm to be readily transferred to hardware platforms. / Master of Science / This thesis aims to address the real-time planning and nonlinear control of four legged walking robots such that the resulting gaits are robust to various kinds of disturbances. Initially, this work presents a method in which a robotic tail can be integrated with legged robots to produce very stable walking patterns. A model is subsequently created to develop a multi-layer control scheme which consists of a high-level path planner, based on a reduced-order model and model predictive control techniques, that determines the trajectory for the quadruped and tail, followed by a low-level controller that considers the full-order dynamics of the robot and tail for robust tracking of the planned trajectory. The reduced-order model considered here enforces quasi-static motions which are slow but generally stable. This formulation is validated numerically through extensive full-order simulations of the Vision60 robot. This work then proceeds to develop an agile formulation using a similar multi-layer structure, but uses a reduced-order model which is more amenable to dynamic walking patterns. The low-level controller is also augmented slightly to provide additional robustness and theoretical guarantees. The latter control algorithm is extensively numerically validated in simulation using the A1 robot to show the large increase in robustness compared to the quasi-static formulation. Finally, this work presents experimental validation of the low-level controller formulated in the latter half of this work.

The growth of the automobile Industry in the past 50 years is radical. The development of chassis control systems have grown drastically due to the demand for safer, faster and more comfortable vehicles. For example, the invention of Anti-lock Braking System (ABS) has resulted in saving more than a million lives since its adaptation while also allowing the vehicles to commute faster. As we move into the autonomous vehicles era, demand for additional information about tire-road interaction to improve the performance of the onboard chassis control systems, is high. This is due to the fact that the interaction between the tire and the road surface determines the stability boundary limits of the vehicles. In this research, a real-time system to classify the road surface into five major categories was developed. The five surfaces include Dry Asphalt, Wet Asphalt, Snow, and Ice and dry Concrete. tri-axial accelerometers were placed on the inner liner of the tires. An advanced signal processing technique was utilized along with a machine learning model to classify the road surfaces. The instrumented Volkswagen Jetta with intelligent tires was retrofitted with new instrumentation for collecting data and evaluating the performance of the developed real-time system. A comprehensive study on road surface classification was performed in order to determine the features of the classification algorithm. Performance of the real-time system is discussed in details and compared with offline results. / Master of Science / The automobile industry has been improving road transportation safety over the past 50 years. While we enter the autonomous vehicles era, the safety of the vehicle is of primary concern. In order to get the autonomous vehicles to production, we will have to improve the on board vehicle control systems to adapt to all surfaces. Gaining more accurate information about the tire and road interaction will help in improving the control systems. Tires have always been considered a passive element of the vehicle. However, more recently, the idea of “tire as a sensor” has surfaced and has become one of the major research thrusts in tire as well as vehicle companies. The intelligent tire research at the Center for Tire Research (CenTiRe) begun in 2010 and has been going strong. In this work, we have developed a classification algorithm to classify the road surfaces in real-time based on acceleration measured inside the tire. The information regarding the road surface would be highly beneficial for the developing new control strategies, automate service vehicles and aid surface prediction in autonomous vehicles.

This thesis investigates and develops a direct method for transient stability analysis using the energy approach [1] and the Phasor Measurement Units (PMUs). The originality of this new method results from a combination of a prediction of the post-fault trajectory based on the PMUs and the Transient Energy Function of a multimachine system. Thanks to the PMUs, the weakness of the direct methods, which is the over-simplification of the generator model, is overcome. This new method consists of fitting a curve to the data of the post-fault path provided by PMUs and identifying the controlling unstable equilibrium point (c.u.e.p.). Two second-order linear models have been estimated and evaluated from a prediction viewpoint. These are a polynomial function and an auto-regressive model. These parameters have been estimated by means of the least-squares estimator. They have been compared to the model proposed by Y. Ohura et al. [6], which has been upgraded into an iterative algorithm. The post-fault trajectory is predicted until the exit point located on the Potential Energy Boundary Surface (p.e.b.s.) is reached. In order to detect with efficiency this exit point and to find the c.u.e.p., it is proposed a combination of the so called "Ball-Drop" method [22] and an improved version of the Shadowing method. These combined procedures give accurate results when they are compared to the step-by-step method, which directly integrates the differential equations using a fourth-order Runga-Kutta method. The simulations have been carried out on a 3-machine system and on the 10-machine New-England power system. / Master of Science

As the number of processing cores contained in modern processors continues to increase, cache hierarchies are becoming more complex. This added complexity has the effect of increasing the potential cost of any cache misses on such architectures. When cache misses become more costly, minimizing them becomes even more important, particularly in terms of scalability concerns. In this thesis, we consider the problem of cache-aware real-time scheduling on multiprocessor systems. One avenue for improving real-time performance on multi-core platforms is task partitioning. Partitioning schemes statically assign tasks to cores, eliminating task migrations and reducing system overheads. Unfortunately, no current partitioning schemes explicitly consider cache effects when partitioning tasks. We develop the LWFG (Largest Working set size First, Grouping) cache-aware partitioning algorithm, which seeks to schedule tasks which share memory with one another in such a way as to minimize the total number of cache misses. LWFG minimizes cache misses by partitioning tasks that share memory onto the same core and by distributing the system's sum working set size as evenly as possible across the available cores. We evaluate the LWFG partitioning algorithm against several other commonly-used partitioning heuristics on a modern 48-core platform running ChronOS Linux. Our evaluation shows that in some cases, the LWFG partitioning algorithm increases execution efficiency by as much as 15% (measured by instructions per cycle) and decreases mean maximum tardiness by up to 60%. / Master of Science

The I-81 Corridor in Virginia traverses the western part of the state, connecting Bristol in the south to Winchester in the north. A study carried out at the Virginia Tech Center for Transportation Research identified traffic safety, work zone safety and traffic control, trucking issues, and intercity traveler information needs as important issues that deserve attention on the I-81 Corridor in Virginia. Analysis of work zone accident statistics showed a need for real-time systems to enhance work zone safety. Real-time advanced warning and traffic control systems provide a means of dynamic information dissemination and advanced warning, thereby enhancing work zone safety and facilitating traffic control. The focus of this research was on the development of functional and system requirements for a real-time advanced warning and traffic control system for work zones. This task was based on the examination of work zone accidents and their causes. The functional requirements include advanced warning, surveillance, advisory, and control functions. Each of these functions consists of several sub-functions. The needs with respect to each of these functions have also been identified. System requirements such as real-time operation, credibility, portability, ease of installation, and adaptability were also identified. Evaluation criteria and potential Measures Of Effectiveness (MOEs) for the evaluation of the system were also identified. Additionally, issues related to the evaluation of the system, such as time duration for evaluation and data collection techniques were identified and examined. / Master of Science

Real-time channels are established between a source and a destination to guarantee in-time delivery of real-time messages in multi-hop networks. In this thesis, we propose two schemes to establish real-time channels for soft real-time applications whose timeliness properties are characterized using Jensen's Time Utility Functions (TUFs) that are non-increasing. The two algorithms are (1) Localized Decision for Utility accrual Channel Establishment (LocDUCE) and (2) Global Decision for Utility accrual Channel Establishment (GloDUCE). Since finding a feasible path optimizing multiple constraints is an NP-Complete problem, these schemes heuristically attempt to maximize the system-wide accrued utility. The channel establishment algorithms assume the existence of a utility-aware packet scheduling algorithm at the interfaces. The route selection is based on delay estimation performed at the source, destination, and all routers in the path, from source to destination. We simulate the algorithms, measure and compare their performance with open shortest path first (OSPF). Our simulation experiments show that for most of the cases considered LocDUCE and GloDUCE perform better than OSPF. We also implement the schemes in a proof-of-concept style routing module and measure the performance of the schemes and compare them to OSPF. Our experiments on the implementation follow the same trend as the simulation study and show that LocDUCE and GloDUCE have a distinct advantage over OSPF and accrue higher system-wide utility. These schemes also react better to variation in the loading of the links. Among the two proposed approaches, we observe that GloDUCE performs better than LocDUCE under conditions of increased downstream link loads. / Master of Science

This thesis investigates a new architecture for modeling and simulating complex distributed real-time systems. Modeling adequately a large distributed real time system may involve, due to its complexity, several different theoretical vehicles such as queuing theory, finite state machines, and others. Currently there are no software tools, which would offer combining such heterogeneous features into a single comprehensive simulation environment. This study involves integrating 3 tools, SES/workbench, an offline simulator using queuing theory as its modeling discipline, ObjecTime as a real-time simulator based on finite state machines as its modeling discipline, and VxWorks real-time kernel used for free modeling in the VMEbus environment. We developed an architecture, which connects all 3 simulators into an integrated system, in which parameters and simulation results can be freely exchanged between tools. In addition, the system is enhanced by a web-based interface, which can be used to provide input and obtain output of the entire system and help in distributing the simulation over the Internet. The new architecture was extensively tested and applied to a large-scale distributed embedded simulation in a military environment.