Department of Electrical and Systems Engineering

The design of MR-compatible robotic systems is a challenging task given the magnetic nature of the scanning environment but also the limitations imposed by the geometric characteristics of the imaging modality. The latter issue is often referred to as geometric MR-compatibility and was treated through image-based analyses as part of the design of a new interventional robotic device. Examinations on geometric MR-compatibility focused on ways to quantify the available space inside a cylindrical scanner, considerations regarding the effective field-of-view of an MR scanner, representations of the attainable anatomical region as defined for needle targeting applications, and computer simulations using threedimensional digital models representing the patient. Geometric considerations are relevant both to the design of an MRcompatible robotic device but also its operation, as for example when using patient-specific data for intervention planning purposes. A preoperative planning procedure developed for the new robotic device will also be described.

Purpose The aim of this work was to develop and test a general methodology for the planning and performance of robot-assisted, MR-guided interventions. This methodology also includes the employment of software tools with appropriately tailored routines to effectively exploit the capabilities of MRI and address the relevant spatial limitations. Methods The described methodology consists of: (1) patient-customized feasibility study that focuses on the geometric limitations imposed by the gantry, the robotic hardware, and interventional tools, as well as the patient;

In this paper we define and analyze notions of detectability in stochastic discrete event systems (SDES). More specifically, we introduce the notions of A-detectability and AA-detectability which focus on characterizing our ability to estimate the true current state of a given SDES with increasing certainty as we observe more output symbols. We discuss observer-based techniques that can be used to verify A-detectability, and provide associated necessary and sufficient conditions for this property to hold. We also present and discuss the notion of AA-detectability, which encompasses a broader class of SDES than A-detectability. Our approach is differentiated from the existing notion of stochastic detectability because it takes into account the probability of problematic observation sequences (that do not allow us to perfectly know the system state), whereas stochastic detectability considers each observation sequence that can be generated by the underlying system even if this observation sequence can only be generated with very small probability. The notions of A-and AA-detectability that are introduced are inspired by combining existing notions for detectability in deterministic discrete event systems and diagnosability in SDES.

Given a sequence of observations, classification among two known hidden Markov models (HMMs) can be accomplished with a classifier that minimizes the probability of error (i.e., the probability of misclassification) by enforcing the maximum a posteriori probability (MAP) rule. For this MAP classifier, the a priori probability of error (before any observations are made) can be obtained, as a function of the length of the sequence of observations, by summing up the probability of error over all possible observation sequences of the given length, which is a computationally expensive task. In this paper, we obtain an upper bound on the probability of error of the MAP classifier. Our results are based on a suboptimal decision rule that ignores the order with which observations occur and relies solely on the empirical frequencies with which different symbols appear. We describe necessary and sufficient conditions under which this bound on the probability of error decreases exponentially with the length of the observation sequence. Apart from the usefulness of the suboptimal rule in bounding the probability of misclassification, its numerous advantages (such as low computational complexity, reduced storage requirements, and potential applicability to distributed or decentralized decision schemes) could prove a useful alternative to the MAP rule for HMM classification in many applications.

In many emerging security applications, a system designer frequently needs to ensure that a certain property of a given system (that may reveal important details about the system's operation) be kept secret (opaque) to outside observers (eavesdroppers). Motivated by such applications, several researchers have formalized, analyzed, and described methods to verify notions of opacity in discrete event systems of interest. This paper introduces and analyzes a notion of opacity in systems that can be modeled as probabilistic finite automata or hidden Markov models. We consider a setting where a user needs to choose a specific hidden Markov model (HMM) out of m possible (different) HMMs, but would like to "hide" the true system from eavesdroppers, by not allowing them to have an arbitrary level of certainty as to which system has been chosen. We describe necessary and sufficient conditions (that can be checked with polynomial complexity), under which the intruder cannot distinguish the true HMM, namely, the intruder cannot achieve a level of confidence

In control systems, sensor deception is a class of attacks where an attacker manipulates sensor readings to cause damage to the system. Our work investigates quantitative measurements to detect this class of attacks in the context of stochastic supervisory control. We introduce the notion ofsafe systems, which is a first step to generalize qualitative intrusion detection conditions to quantitative intrusion detection conditions. We provide sufficient and necessary conditions to verify if a system is-safe. Moreover, we provide an algorithm that verifies these conditions, which implies that the problem is decidable.

In this paper, we explore communication protocols between two or more agents in an initially partially known environment. We assume two types of agents (A and B), where an agent of Type A constitutes an information source (e.g., a mobile sensor) with its own local objective expressed in temporal logic, and an agent of Type B constitutes an agent that accomplishes its own mission (e.g., search and rescue mission) also expressed in temporal logic. An agent of Type B requests information from an agent of Type A to update its knowledge about the environment. In this work, we develop an algorithm that is able to verify if a communication protocol exists, for any possible initial plan executed by an agent of Type B.

A discrete event system possesses the property of detectability if it allows an observer to perfectly estimate the current state of the system after a finite number of observed symbols, i.e., detectability captures the ability of an observer to eventually perfectly estimate the system state. In this paper we analyze detectability in stochastic discrete event systems (SDES) that can be modeled as probabilistic finite automata. More specifically, we define the notion of A-detectability, which characterizes our ability to estimate the current state of a given SDES with increasing certainty as we observe more output symbols. The notion of A-detectability is differentiated from previous notions for detectability in SDES because it takes into account the probability of problematic observation sequences (that do not allow us to perfectly deduce the system state), whereas previous notions for detectability in SDES considered each observation sequence that can be generated by the underlying ...