Software is finding its way into an increasing range of devices (phones, medical equipment, cars...). A challenge is to design verification methods to ensure correctness of software. We focus on model checking, an approach in which an abstract model of the implementation and a specification of requirements are provided. The task is to answer automatically whether the system conforms with its specification.We concentrate on (i) timed systems, and (ii) parameterized systems. Timed systems can be modeled and verified using the classical model of timed automata. Correctness is translated to language inclusion between two timed automata representing the implementation and the specification. We consider variants of timed automata, and show that the problem is at best highly complex, at worst undecidable. A parameterized system contains a variable number of components. The problem is to verify correctness regardless of the number of components. Regular model checking is a prominent method which uses finite-state automata. We present a semi-symbolic minimization algorithm combining the partition refinement algorithm by Paige and Tarjan with decision diagrams. Finally, we consider systems which are both timed and parameterized: Timed Petri Nets (TPNs), and Timed Networks (TNs). We present a method for checking safety properties of TPNs based on forward reachability analysis with acceleration. We show that verifying safety properties of TNs is undecidable when each process has at least two clocks, and explore decidable variations of this problem.

This thesis deals with the design of a robust and safe control algorithm to aim at an artificial pancreas. More precisely we will be interested in controlling the stabilizing part of a classical cure. To meet this objective, the design of a robust nonlinear model predictive controller based on the solution of a saddle point optimization problem is considered. Also, to test the controller performances in a realistic case, numerical simulations on a FDA validated testing platform are envisaged.In a first part, we present an extension of the usual nonlinear model predictive controller designed to robustly control, in a sampled-data framework, systems described by nonlinear ordinary differential equations. This controller, which computes the best control input by considering the solution of a constrained saddle point optimization problem, is called saddle point model predictive controller (SPMPC). Using this controller, it is proved that the closed-loop is Ultimately Bounded and, with some assumptions on the problem structure, Input-to State practically Stable. Then, we are interested in numerically solving the corresponding control problem. To do so, we propose an algorithm inspired from the augmented Lagrangian technique and which makes use of adjoint model.In a second part, we consider the application of this controller to the problem of artificial blood glucose control. After a modeling phase, two models are retained. A simple one will be used to design the controller and a complex one will be used to simulate realistic virtual patients. This latter is needed to validate our control approach. In order to compute a good control input, the SPMPC controller needs the full state value. However, the sensors can only provide the value of blood glucose. That is why the design of an adequate observer is envisaged. Then, numerical simulations are performed. The results show the interest of the approach. For all virtual patients, no hypoglycemia event occurs and the time spent in hyperglycemia is too short to induce damageable consequences. Finally, the interest of extending the SPMPC approach to consider the control of time delay systems in a sampled-data framework is numerically explored.

In the information age of today, vast amounts of sensitive and confidential data is exchanged over an array of different mediums. Accompanied with this phenomenon is a comparable increase in the number and types of attacks to acquire this information. Information security and data consistency have hence, become quintessentially important. Log file analysis has proven to be a good defense mechanism as logs provide an accessible record of network activities in the form of server generated messages. However, manual analysis is tedious and prohibitively time consuming. Traditional log analysis techniques, based on pattern matching and data mining approaches, are ad hoc and cannot readily adapt to different kinds of log files. The goal of this research is to explore the use of grammar inference for log file analysis in order to build a more adaptive, flexible and generic method for message categorization, anomaly detection and reporting. The grammar inference process employs robust parsing, islands grammars and source transformation techniques. We test the system by using three different kinds of log file training sets as input and infer a grammar and generate message categories for each set. We detect anomalous messages in new log files using the inferred grammar as a catalog of valid traces and present a reporting program to extract the instances of specified message categories from the log files. / Thesis (Master, Computing) -- Queen's University, 2008-05-22 14:12:30.199

For opportunistic spectrum access and spectrum sharing in cognitive radio networks, one key problem is how to develop wireless access schemes for secondary users so that harmful interference to primary users can be avoided and quality-of-service (QoS) of secondary users can be guaranteed. In this research, dynamic wireless access protocols for secondary users are designed and optimized for both infrastructure-based and ad-hoc wireless networks. Under the infrastructure-based model, the secondary users are connected through a controller (i.e., an access point). In particular, the problem of wireless access for eHealth applications is considered. In a single service cell, an innovative wireless access scheme, called electromagnetic interference (EMI)-aware prioritized wireless access, is proposed to address the issues of EMI to the medical devices and QoS differentiation for different eHealth applications. Afterwards, the resource management problem for multiple service cells, specifically, in multiple spatial reuse time-division multiple access (STDMA) networks is addressed. The problem is formulated as a dual objective optimization problem that maximizes the spectrum utilization of secondary users and minimizes their power consumption subject to the EMI constraints for active and passive medical devices and minimum throughput guarantee for secondary users. Joint scheduling and power control algorithms based on greedy approaches are proposed to solve the problem with much less computational complexity. In an ad-hoc wireless network, the robust transmission scheduling and power control problem for collision-free spectrum sharing between secondary and primary users in STDMA wireless networks is investigated. Traditionally, the problem only considers the average link gains; therefore, QoS violation can occur due to improper power allocation with respect to instantaneous channel gain realization. To overcome this problem, a robust power control problem is formulated. A column generation based algorithm is proposed to solve the problem by considering only the potential subset of variables when solving the problem. To increase the scalability, a novel distributed two-stage algorithm based on the distributed column generation method is then proposed to obtain the near-optimal solution of the robust transmission schedules for vertical spectrum sharing in an ad-hoc wireless network.

The present paper develops a procedure based on multivariate copulas for simulating multivariate non-normal data that satisfies a pre-specified covariance matrix. The covariance matrix used, can comply with a specific moment structure form (e.g., a factor analysis or a general SEM model). So the method is particularly useful for Monte Carlo evaluation of SEM models in the context of non-normal data. The new procedure for non-normal data simulation is theoretically described and also implemented on the widely used R environment. The quality of the method is assessed by performing Monte Carlo simulations. Within this context a one-sample test on the observed VC-matrix is involved. This test is robust against normality violations. This test is defined through a particular SEM setting. Finally, an example for Monte Carlo evaluation of SEM modeling of non-normal data using this method is presented. (author's abstract) / Series: Research Report Series / Department of Statistics and Mathematics

For opportunistic spectrum access and spectrum sharing in cognitive radio networks, one key problem is how to develop wireless access schemes for secondary users so that harmful interference to primary users can be avoided and quality-of-service (QoS) of secondary users can be guaranteed. In this research, dynamic wireless access protocols for secondary users are designed and optimized for both infrastructure-based and ad-hoc wireless networks. Under the infrastructure-based model, the secondary users are connected through a controller (i.e., an access point). In particular, the problem of wireless access for eHealth applications is considered. In a single service cell, an innovative wireless access scheme, called electromagnetic interference (EMI)-aware prioritized wireless access, is proposed to address the issues of EMI to the medical devices and QoS differentiation for different eHealth applications. Afterwards, the resource management problem for multiple service cells, specifically, in multiple spatial reuse time-division multiple access (STDMA) networks is addressed. The problem is formulated as a dual objective optimization problem that maximizes the spectrum utilization of secondary users and minimizes their power consumption subject to the EMI constraints for active and passive medical devices and minimum throughput guarantee for secondary users. Joint scheduling and power control algorithms based on greedy approaches are proposed to solve the problem with much less computational complexity. In an ad-hoc wireless network, the robust transmission scheduling and power control problem for collision-free spectrum sharing between secondary and primary users in STDMA wireless networks is investigated. Traditionally, the problem only considers the average link gains; therefore, QoS violation can occur due to improper power allocation with respect to instantaneous channel gain realization. To overcome this problem, a robust power control problem is formulated. A column generation based algorithm is proposed to solve the problem by considering only the potential subset of variables when solving the problem. To increase the scalability, a novel distributed two-stage algorithm based on the distributed column generation method is then proposed to obtain the near-optimal solution of the robust transmission schedules for vertical spectrum sharing in an ad-hoc wireless network.

In this thesis, robust set-valued estimation is studied and its application to in-flight alignment of strapdown inertial navigation systems (SINS) with large heading uncertainty is performed. It is known that the performance of the Kalman filter is vulnerable to modeling errors. One of the estimation methods, which are robust against modeling errors, is robust set-valued estimation. In this approach, the filter calculates the set of all possible states, which are consistent with uncertainty inputs satisfying an integral quadratic constraint (IQC) for given measured system outputs. In this thesis, robust set-valued filter with deterministic input is derived. In-flight alignment of SINS with Kalman filtering using external measurements is a widely used technique to eliminate the initial errors. However, if the initial errors are large then the performance of standard Kalman filtering technique is degraded due to modeling error caused by linearization process. To solve this problem, a novel linear norm-bounded uncertain error model is proposed where the remaining second orders terms due to linearization process are considered as norm-bounded uncertainty regarding only the heading error is large. Using the uncertain error model, the robust set-valued filter is applied to in-flight alignment problem. The comparison of the Kalman filter and the robust filter is done on a simulated trajectory and a real-time data. The simulation results show that the modeling errors can be compensated to some extent in Kalman filter by increasing the process noise covariance matrix. However, for very large initial heading errors, the proposed method outperforms the Kalman filter.

ABSTRACT ROBUST DATA HIDING SCHEME WITH TURBO CODES &Uuml / NAL, BariS M.S., Department of Electrical And Electronics Engineering Supervisor: Prof. Dr. R&uuml / yal ERG&Uuml / L December 2005, 127 pages This study describes the design and implementation of a robust data hiding algorithm which is provided by turbo codes. As the digital technology grows up, it is getting easy to copy and distribute multimedia products without getting legal permission. This has forced researchers to study in digital watermarking areas. Along with watermarking researches, data hiding studies have gained interest in the last decade. Different watermark and data hiding algorithms have been proposed considering different requirements and properties such as robustness, fidelity, invisibility and data hiding capacity. In this thesis, robustness of watermarking systems and fidelity requirement in watermark models are considered and use of turbo codes is proposed with data embedding systems to improve system performance in terms of robustness. Fundamental watermarking algorithms in DCT domain are analyzed and simulated. Their performances in terms of robustness are presented. Data hiding algorithm which is based on projection and perturbation in transform domain is implemented in visual C. Then turbo codes are applied to this algorithm to improve system robustness. Improvement provided by turbo codes is demonstrated and compared with other discussed watermarking systems.

Flutter is one of the most important phenomena in which aerodynamic surfaces become unstable in certain flight conditions. Since the 1930&amp / #8217 / s many studies were conducted in the areas of flutter prediction in design stage, research of design methods for flutter prevention, derivation and confirmation of flutter flight envelopes via tests, and in similar subjects for aircraft wings. With the use of controllers in 1960&amp / #8217 / s, studies on the active flutter suppression began. First the classical controllers were used. Then, with the improvement of the controller synthesis methods, optimal controllers and later robust controllers started to be used. However, there are not many studies in the literature about fully movable control surfaces, commonly referred to as fins. Fins are used as missile control surfaces, and they can also be used as a horizontal stabilizer or as a canard in aircraft. In the scope of this thesis, controllers satisfying the performance and flutter suppression requirements of a fin are synthesized and compared. For this purpose, H2, Hinf, and mu controllers are used. A new flutter suppression method is proposed and used. In order to assess the performance of this method, results obtained are compared with the results of another flutter suppression method given in the literature. or the purpose of implementation of the controllers developed, aeroelastic model equations are derived by using the typical section wing model with thin airfoil assumption. The controller synthesis method is tested for aeroelastic models that are veloped for various flow regimes / namely, steady incompressible subsonic, unsteady incompressible subsonic, nsteady compressible subsonic, and unsteady compressible supersonic.

In this thesis a class of robust non-linear controllers for a gantry crane system are discussed. The gantry crane has three degrees of freedom, all of which are interrelated. These are the horizontal traverse of the cart, the vertical motion of the goods (i.e. rope length) and the swing angle made by the goods during the movement of the cart. The objective is to control all three degrees of freedom. This means achieving setpoint control for the cart and the rope length and cancellation of the swing oscillations. A mathematical model of the gantry crane system is developed using Lagrangian dynamics. In this thesis it is shown that a model of the gantry crane system can be represented as two sub models which are coupled by a term which includes the rope length as a parameter. The first system will consist of the cart and swing dynamics and the other system is the hoist dynamics. The mathematical model of these two systems will be derived independent of the other system. The model that is comprised of the two sub models is verified as an accurate model of a gantry crane system and it will be used to simulate the performance of the controllers using Matlab. For completeness a fully coupled mathematical model of the gantry crane system is also developed. A detailed design of a gain scheduled sliding mode controller is presented. This will guarantee the controller's robustness in the presence of uncertainties and bounded matched disturbances. This controller is developed to achieve cart setpoint and swing control while achieving rope length setpoint control. A non gain scheduled sliding mode controller is also developed to determine if the more complex gain scheduled sliding mode controller gives any significant improvement in performance. In the implementation of both sliding mode controllers, all system states must be available. In the real-time gantry crane system used in this thesis, the cart velocity and the swing angle velocity are not directly available from the system. They will be estimated using an alpha-beta state estimator. To overcome this limitation and provide a more practical solution an optimal output feedback model following controller is designed. It is demonstrated that by expressing the system and the model for which the system is to follow in a non-minimal state space representation, LQR techniques can be used to design the controller. This produces a dynamic controller that has a proper transfer function, and negates the need for the availability of all system states. This thesis presents an alternative method of solving the LQR problem by using a generic eigenvalue solution to solve the Riccati equation and thus determine the optimal feedback gains. In this thesis it is shown that by using a combination of sliding mode and H??? control techniques, a non-linear controller is achieved which is robust in the presence of a wide variety of uncertainties and disturbances. A supervisory controller is also described in this thesis. The supervisory control is made up of a feedforward and a feedback component. It is shown that the feedforward component is the crane operator's action, and the feedback component is a sliding mode controller which compensates as the system's output deviates from the desired trajectory because of the operator's inappropriate actions or external disturbances such as wind gusts and noise. All controllers are simulated using Matlab and implemented in real-time on a scale model of the gantry crane system using the program RTShell. The real-time results are compared against simulated results to determine the controller's performance in a real-time environment.