@conference {
	title = {Level-triggered Control of a Scalar Linear System},
	author = {Rabi, Maben., Baras, John S.},
	year = {2007},
	month = {06/2007},
	abstract = {In networked control systems, because of limits on communication rates, control waveforms are frequently piece-wise constant with limits on rates at which they can switch levels. It is, therefore, natural to expect event triggered control switchings to perform more efficiently than time-triggered ones. This article verifies the validity of this supposition. A method for designing good level-triggered control schemes is obtained by reducing the continuous time problem to one in discrete time. Then, by numerical procedures, the performance of the level-triggered schemes is computed for comparison with that of the periodically switched control scheme.},
	address = {Athens, Greece},
	booktitle = {Proceedings of the 15th IEEE Mediterranean Conference on Control and Automation},
	pages = {pp. 1-6}
}
@conference {
	title = {Multiple Sampling for Estimation on a Finite Horizon},
	author = {Rabi, Maben., Moustakides, George V., Baras, John S.},
	year = {2006},
	month = {12/2006},
	abstract = {We discuss some multiple sampling problems that arise in finite horizon real-time estimation when there is an upper limit on the number of allowable samples. Measuring estimation quality by the aggregate squared error, we compare the performances of the best deterministic, level-triggered and the optimal sampling schemes. We restrict the signal to be either a Wiener or an Ornstein-Uhlenbeck process. For the Wiener process, we provide closed form expressions and series expansions, whereas for the Ornstein Uhlenbeck process, procedures for numerical computation. Our results indicate that the best level-triggered sampling is almost optimal when the signal is stable.},
	address = {San Diego, CA},
	booktitle = {45th IEEE Conference on Decision and Control},
	pages = {pp. 1351-1357}
}
@conference {
	title = {Efficient Sampling for Keeping Track of an Ornstein-Uhlenbeck Process},
	author = {Rabi, Maben., Moustakides, George V., Baras, John S.},
	year = {2006},
	month = {06/2006},
	abstract = {We consider estimation and tracking problems in sensor networks with constraints in the hierarchy of inference making, on the sharing of data and inter-sensor communications. We identify as a typical representative for such problems tracking of a process when the number and type of measurements are constrained. As the simplest representative of such problems, which still encompasses all the key issues involved, we analyze efficient sampling schemes for tracking an Ornstein- Uhlenbeck process. We consider sampling based on time, based on amplitude (event-triggered sampling) and optimal sampling (optimal stopping). We obtain the best sampling rule in each case as the solution to a constrained optimization problem. We compare the performances of the various sampling schemes and show that the event-triggered sampling performs close to optimal. Implications and extensions are discussed.},
	address = {Anconia, Italy},
	booktitle = {14th Mediterranean Conference on Control and Automation},
	pages = {pp. 1-6}
}
@conference {
	title = {Maximum Entropy Models, Dynamic Games, and Robust Output Feedback Control for Automata},
	author = {Baras, John S., Rabi, Maben.},
	year = {2005},
	month = {12/2005},
	abstract = {In this paper, we develop a framework for designing controllers for automata which are robust with respect to uncertainties. A deterministic model for uncertainties is introduced, leading to a dynamic game formulation of the robust control problem. This problem is solved using an appropriate information state. We derive a Hidden Markov Model as the maximum entropy stochastic model for the automaton. A risk-sensitive stochastic control problem is formulated and solved for this Hidden Markov Model. The two problems are related using small noise limits.},
	address = {Seville, Spain},
	booktitle = {44th IEEE Conference on Decision and Control and the European Control Conference (CDC-ECC {\textquoteright}05)},
	pages = {pp.1043-1049}
}
@conference {
	title = {Sampling of Diffusion Processes for Real-Time Estimation},
	author = {Rabi, Maben., Baras, John S.},
	year = {2004},
	month = {12/2004},
	abstract = {This paper addresses the causal sampling of observations of a diffusion process that results in a good quality continuous estimator based upon these samples. The optimal sampling scheme with a fixed number of samples is found by solving an optimal (multiple) stopping problem. This is solved explicitly in a special case. A class of threshold approximations is also described.},
	address = {Atlantis, Paradise Island, Bahamas},
	booktitle = {43rd IEEE Conference on Decision and Control},
	pages = {pp. 4163-4168}
}
@conference {
	title = {Intrusion Detection with Support Vector Machines and Generative Models},
	author = {Baras, John S., Rabi, Maben.},
	year = {2002},
	month = {09/2002},
	abstract = {This paper address the task of detecting intrusions in the form of malicious attach on programs running OD a host computer system by inspecting the trace of system calls made by these programs. We use {\textquoteright}attack-tree{\textquoteright} type generative models for such intrusions to select features that are used by a Support Vector Machine Classifier. Our approach combines the ability of an HMM generative model to handle variable-length strings, i.e. the traces, and the non-asymptotic nature of Support Vector Machines that permits them to work well with small training sets.},
	address = {Sao Paulo, Brazil},
	booktitle = {5th International Conference, ISC 2002, Springer lecture notes in Computer Science (LNCS)},
	pages = {pp 32-47}
}
@article {
	title = {Adaptive Sampling For Linear State Estimation},
	author = {Rabi, Maben., Moustakides, George V., Baras, John S.},
	year = {2012},
	month = {03/2012},
	abstract = {When a sensor has continuous measurements but sends occasional messages over a data network to a supervisor which estimates the state, the available packet rate fixes the achievable quality of state estimation. When such rate limits turn stringent, the sensor{\textquoteright}s messaging policy should be designed anew. What are good causal messaging policies? What should message packets contain? What is the lowest possible distortion in a causal estimate at the supervisor? Is Delta sampling better than periodic sampling? We answer these questions for a Markov state process under an idealized model of the network and the assumption of perfect state measurements at the sensor. If the state is a scalar or a vector of low dimension, then we can ignore sample quantization. If in addition, we can ignore jitter in the transmission delays over the network, then our search for efficient messaging policies simplifies. First, each message packet should contain the value of the state at that time. Thus a bound on the number of data packets becomes a bound on the number of state samples. Second, the remaining choice in messaging is entirely about the times when samples are taken. For a scalar, linear diffusion process, we study the problem of choosing the causal sampling times that will give the lowest aggregate squared error distortion. We stick to finite horizons and impose a hard upper bound N on the number of allowed samples. We cast the design as a problem of choosing an optimal sequence of stopping times. We reduce this to a nested sequence of problems, each asking for a single optimal stopping time. Under an unproven but natural assumption about the least-square estimate at the supervisor, each of these single stopping problems are of standard form. The optimal stopping times are random times when the estimation error exceeds designed envelopes. For the case where the state is a Brownian motion, we give analytically: the shape of the optimal sampling envelopes, the shape of the envelopes under optimal Delta sampling, and their performances. Surprisingly, we find that Delta sampling performs badly. Hence, when the rate constraint is a hard limit on the number of samples over a finite horizon, we should not use Delta sampling.},
	pages = {pp. 672-702},
	volume = {Volume 50},
	number = {No. 2},
	journal = {SIAM Journal on Control and Optimization}
}
@techreport {
	title = {Efficient Sampling for Keeping Track of an Ornstein-Uhlenbeck Process},
	author = {Rabi, Maben., Moustakides, George V.},
	year = {2006},
	month = {01/2006},
	abstract = {We consider estimation and tracking problems in sensor networks with constraints in the hierarchy of inference making, on the sharing of data and inter-sensor communications. We identify as a typical representative for such problems tracking of a process when the number and type of measurements in constrained. As the simplest representative of such problems, which still encompasses all the key issues involved we analyze efficient sampling schemes for tracking an Ornstein-Uhlenbeck process. We considered sampling based on time, based on amplitude (event-triggered sampling) and optimal sampling (optimal stopping). We obtained the solution in each case as a constrained optimization problem. We compare the performance of the various sampling schemes and show that the event-triggered sampling performs close to optimal. Implications and extensions are discussed.},
	number = {Number: ISR TR 2006-3}
}
@techreport {
	title = {Multiple Sampling for Estimation on a Finite Horizon},
	author = {Rabi, Maben., Moustakides, George V.},
	year = {2006},
	month = {01/2006},
	abstract = {We discuss some multiple sampling problems that arise in real-time estimation problems with limits on the number of samples. The quality of estimation is measured by an aggregate squared error over a finite horizon. We compare the performances of the best detereministic, level-triggered and the optimal sampling schemes. We restrict the signal to be either a Wiener process or an Ornstein-Uhlenbeck process. For the Wiener process, we provide closed form expressions and series expansions. For the Ornstein Uhlenbeck process, we provide procedures for numerical computation. Our results show that level-triggered sampling is almost optimal when the signal is stable.},
	number = {Number: ISR TR 2006-7}
}
