Generality, representation, and control have been the central issues in machine recognition. Model-based recognition is the search for consistent matches of the model and image features. We present a comparative framework for the evaluation of different approaches, particularly those of ACRONYM, RAF, and Ikeuchi et al. The strengths and weaknesses of these approaches are discussed and compared and the remedies are suggested. Various tradeoffs made in the implementations are analyzed with respect to the systems' intended task-domains. The requirements for a versatile recognition system are motivated. Several directions for future research are pointed out.

Building robust recognition systems requires a careful understanding of the effects of error in sensed features. Error in these image features results in a region of uncertainty in the possible image location of each additional model feature. We present an accurate, analytic approximation for this uncertainty region when model poses are based on matching three image and model points, for both Gaussian and bounded error in the detection of image points, and for both scaled-orthographic and perspective projection models. This result applies to objects that are fully three- dimensional, where past results considered only two-dimensional objects. Further, we introduce a linear programming algorithm to compute the uncertainty region when poses are based on any number of initial matches. Finally, we use these results to extend, from two-dimensional to three- dimensional objects, robust implementations of alignmentt interpretation- tree search, and ransformation clustering.

In this thesis, a mixture-model cluster analysis technique under different covariance structures of the component densities is developed and presented, to capture the compactness, orientation, shape, and the volume of component clusters in one expert system to handle Gaussian high dimensional heterogeneous data sets to achieve flexibility in currently practiced cluster analysis techniques. Two approaches to parameter estimation are considered and compared; one using the Expectation-Maximization (EM) algorithm and another following a Bayesian framework using the Gibbs sampler. We develop and score several forms of the ICOMP criterion of Bozdogan (1994, 2004) as our fitness function; to choose the number of component clusters, to choose the correct component covariance matrix structure among nine candidate covariance structures, and to select the optimal parameters and the best fitting mixture-model. We demonstrate our approach on simulated datasets and a real large data set, focusing on early detection of breast cancer. We show that our approach improves the probability of classification error over the existing methods.

Fault isolation is the process of reasoning required to find the cause of a system failure. In a model-based approach, the available information is a model of the system and some observations. Using knowledge of how the system generally behaves, as given in the system model, together with partial observations of the events of the current situation the task is to deduce the failure causing event(s). In our setting, the observable events manifest themselves in a message log. We study post mortem fault isolation for moderately concurrent discrete event systems where the temporal order of logged messages contains little information. To carry out fault isolation one has to study the correlation between observed events and fault events of the system. In general, such study calls for exploration of the state space of the system, which is exponential in the number of system components. Since we are studying a restricted class of all possible systems we may apply aggressive specialized abstraction policies in order to allow fault isolation without ever considering the often intractably large state space of the system. In this thesis we describe a mathematical framework as well as a prototype implementation and an experimental evaluation of such abstraction techniques. The method is efficient enough to allow for not only post mortem fault isolation but also design time diagnosability analysis of the system, which can be seen as a non-trivial way of analyzing all possible observations of the system versus the corresponding fault isolation outcome. This work has been supported by VINNOVA’s Competence Center ISIS.

A problem when testing timeliness of event-triggered real-time systems is that response times depend on the execution order of concurrent tasks. Conventional testing methods ignore task interleaving and timing and thus do not help determine which execution orders need to be exercised to gain confidence in temporal correctness. This thesis presents and evaluates a framework for testing of timeliness that is based on mutation testing theory. The framework includes two complementary approaches for mutation-based test case generation, testing criteria for timeliness, and tools for automating the test case generation process. A scheme for automated test case execution is also defined. The testing framework assumes that a structured notation is used to model the real-time applications and their execution environment. This real-time system model is subsequently mutated by operators that mimic potential errors that may lead to timeliness failures. Each mutated model is automatically analyzed to generate test cases that target execution orders that are likely to lead to timeliness failures. The validation of the theory and methods in the proposed testing framework is done iteratively through case-studies, experiments and proof-of-concept implementations. This research indicates that an adapted form of mutation-based testing can be used for effective and automated testing of timeliness and, thus, for increasing the confidence level in real-time systems that are designed according to the event-triggered paradigm.

This dissertation concerns testability of event-triggered real-time systems. Real-time systems are known to be hard to test because they are required to function correct both with respect to what the system does and when it does it. An event-triggered real-time system is directly controlled by the events that occur in the environment, as opposed to a time-triggered system, which behavior with respect to when the system does something is constrained, and therefore more predictable. The focus in this dissertation is the behavior in the time domain and it is shown how testability is affected by some factors when the system is tested for timeliness. This dissertation presents a survey of research that focuses on software testability and testability of real-time systems. The survey motivates both the view of testability taken in this dissertation and the metric that is chosen to measure testability in an experiment. We define a method to generate sets of traces from a model by using a meta algorithm on top of a model checker. Defining such a method is a necessary step to perform the experiment. However, the trace sets generated by this method can also be used by test strategies that are based on orderings, for example execution orders. An experimental study is presented in detail. The experiment investigates how testability of an event-triggered real-time system is affected by some constraining properties of the execution environment. The experiment investigates the effect on testability from three different constraints regarding preemptions, observations and process instances. All of these constraints were claimed in previous work to be significant factors for the level of testability. Our results support the claim for the first two of the constraints while the third constraint shows no impact on the level of testability. Finally, this dissertation discusses the effect on the event-triggered semantics when the constraints are applied on the execution environment. The result from this discussion is that the first two constraints do not change the semantics while the third one does. This result indicates that a constraint on the number of process instances might be less useful for some event-triggered real-time systems.

AbstractThe development of embedded computer control systems(ECS) requires a synergetic integration of heterogeneoustechnologies and multiple engineering disciplines. Withincreasing amount of functionalities and expectations for highproduct qualities, short time-to-market, and low cost, thesuccess of complexity control and built-in flexibility turn outto be one of the major competitive edges for many ECS products.For this reason, modeling and modularity assessment constitutetwo critical subjects of ECS engineering.In the development ofECS, model-based design is currently being exploited in most ofthe sub-systems engineering activities. However, the lack ofsupport for formalization and systematization associated withthe overall systems modeling leads to problems incomprehension, cross-domain communication, and integration oftechnologies and engineering activities. In particular, designchanges and exploitation of "components" are often risky due tothe inability to characterize components' properties and theirsystem-wide contexts. Furthermore, the lack of engineeringtheories for modularity assessment in the context of ECS makesit difficult to identify parameters of concern and to performearly system optimization. This thesis aims to provide a more complete basis for theengineering of ECS in the areas of systems modeling andmodularization. It provides solution domain models for embeddedcomputer control systems and the software subsystems. Thesemeta-models describe the key system aspects, design levels,components, component properties and relationships with ECSspecific semantics. By constituting the common basis forabstracting and relating different concerns, these models willalso help to provide better support for obtaining holisticsystem views and for incorporating useful technologies fromother engineering and research communities such as to improvethe process and to perform system optimization. Further, amodeling framework is derived, aiming to provide a perspectiveon the modeling aspect of ECS development and to codifyimportant modeling concepts and patterns. In order to extendthe scope of engineering analysis to cover flexibility relatedattributes and multi-attribute tradeoffs, this thesis alsoprovides a metrics system for quantifying componentdependencies that are inherent in the functional solutions.Such dependencies are considered as the key factors affectingcomplexity control, concurrent engineering, and flexibility.The metrics system targets early system-level design and takesinto account several domain specific features such asreplication and timing accuracy. Keywords:Domain-Specific Architectures, Model-basedSystem Design, Software Modularization and Components, QualityMetrics. / QC 20100524

Traditional diagnosis has been performed with hardware redundancy and limit checking. The development of more powerful computers have made a new kind of diagnosis possible. Todays computing power allows models of the system to be run in real time and thus making model-based diagnosis possible. The objective with this thesis is to investigate the potential of model-based diagnosis, especially when combined with active diagnosis. The diagnosis system has been applied on a model of the JAS39 Gripen fuel pressurization system. With the sensors available today no satisfying diagnosis system can be built, however, by adding a couple of sensors and using active model-based diagnosis all faults can be detected and isolated into a group of at most three components. Since the diagnosis system in this thesis only had a model of the real system to be tested at, this thesis is not directly applicable on the real system. What can be used is the diagnosis approach and the residuals and decision structure developed here.

Hydrogen fuel cell technology has emerged as an efficient and clean alternative to internal combustion engines for powering vehicles, and hydrogen powertrains will aid in addressing key environmental issues such as urban air quality and global warming. This work demonstrates the effectiveness of a „hardware-in-loop‟ (HIL) simulation system for validating the safety and effectiveness of control algorithms for a hydrogen fuel cell hybrid passenger vehicle. A significant amount of the work completed in conjunction with the thesis topic was the design and construction of the fuel cell vehicle for competition. Producing a „rolling test bench‟ that generates data to be used to create HIL simulation models required nearly two years of work before an acceptable level of reliability was reached to produce usable data. Some detail will be given in this thesis regarding the infrastructure modifications required to safely build a hydrogen fuel cell vehicle, as well as the design challenges faced in the integration of a fuel cell power module, two electric drive motors, a nickel metal hydride battery, and required power electronics into a small sport utility vehicle originally designed for an internal combustion powertrain. The virtual control validation performed involved designing dynamic models of the systems of interest and performing real-time simulation to ensure that the appropriate controller response is observed. For this thesis, emphasis was placed on several key vehicle control topics. Communication robustness was evaluated to ensure that the complicated vehicle communication network could effectively handle traffic from the six powertrain sub-controllers. Safety algorithms were tested for appropriate response to fault conditions. Control systems were developed and tuned offline reducing the amount of time required for in-vehicle development and testing. Software-in-the-loop simulation was used to check initial code integrity and to validate the hardware-in-the-loop vehicle models. The methodology presented in this work was found to be sufficient for a thorough safety and rationality evaluation of control strategies for hybrid fuel cell vehicles.

Vehicle design and control is an attractive area of research in that it embodies a convergence of societal need, technical limitation, and emerging capability. Environmental, political, and monetary concerns are driving the automotive industry towards sustainable transportation, manifested as increasing powertrain electrification in a gradual transition to fossil-free energy vectors. From an electrochemical degradation and control systems perspective, this transition introduces significant technical uncertainty. Initial indications are that the initial battery designs will have twice the required capacity due to degradation concerns. As the battery is a major contributor to the cost of these vehicles the over-sizing represents a significant threat to the ability of OEMs to produce cost-competitive vehicles. This potential barrier is further amplified when the combustion engine is removed and battery-electric or fuel-cell hybrid vehicles are considered. This thesis researches the application of model-based design for optimal design of fuel cell hybrid powertrains considering power source degradation. The intent is to develop and evaluate tools that can determine the optimal sizing and control of the powertrain; reducing the amount of over-sizing by numerically optimization rather than a sub-optimal heuristic design. A baseline hybrid fuel cell vehicle model is developed and validated to a hybrid fuel cell SUV designed and built at the University of Waterloo. Lithium-ion battery degradation models are developed and validated to data captured off a hybrid powertrain test stand built as part of this research. A fuel cell degradation model is developed and integrated into the vehicle model. Lifetime performance is modeled for four hybrid control strategies, demonstrating a significant impact of the hybrid control strategy on powertrain degradation. A plug-in variation of the architecture is developed. The capacity degradation of the battery is found to be more significant than the power degradation. Blended and All-electric charge-depleting hybrid control strategies are integrated and lifetime performance is simulated. The blended charge-depleting control strategy demonstrated significantly less degradation than the all-electric strategy. An oversized battery is integrated into the vehicle model and the benefit of oversizing on reducing the battery degradation rate is demonstrated.