@conference {
	title = {Nonlinear Filtering of Diffusion Processes: A Generic Example},
	author = {Blankenship,  Gilmer L., Baras, John S.},
	year = {1979},
	month = {12/1979},
	abstract = {An analytical procedure is described for~ treating a generic nonlinear filtering problem.},
	address = {Ft. Lauderdale, Florida},
	booktitle = {18th IEEE Conference on Decision and Control}
}
@conference {
	title = {Linear Stochastic Systems with Memoryless Nonlinearities},
	author = {Baras, John S., Goldberg, Allen Jay.},
	year = {1979},
	month = {12/1979},
	abstract = {Our motivation to study this class of systems comes from a basic need to develop analytical methods to analyze and evaluate radar systems " performance in a realistic environment and in particular with distributed targets. We need, therefore, simple probabilistic~models for the various "noise " processes that influence ~radar behavior in such an environment (e.g. amplitude Scintillation, angle noise~, sea clutter and~sea multipath effects ) . In our ~previous work we have succeeded in developing such models that match very well experimental evidence concerning the statistics of such noise processes . The models developed consist of linear stochastic systems coupled with smooth memoryless nonlinearities. In this paper we report our current efforts to study such systems by analyzing the functional expansion that expresses the input-output behavior . In particular , we establish conditions for the statistical validity of a finite functional expansion and techniques for obtaining directlly the required kernels from experimental data . We also discuss applications to some related detection problems . Finally , we indicate how these~results can be extended to more general classes of systems than the ones we have considered to date.},
	address = {Ft. Lauderdale, Florida},
	booktitle = {18th IEEE Conference on Decision and Control},
	pages = {p. 1038}
}
@conference {
	title = {Algorithms for the Computer Control of Urban Traffic},
	author = {Baras, John S., Levine, William S.},
	year = {1979},
	month = {08/1979},
	abstract = {Recent developments in computer technology have made substantial computational~capability, in the form of mini- and micro-computers, available at modest prices.~This, coupled with the available traffic sensors, controllable traffic signals and~communications, makes the computer control of traffic feasible in many urban areas~today. Many such systems are either operating or are being planned.~

Software for today{\textquoteright}s hardware environment can be oriented toward several, or~many, small machines each of which controls a small local area. Alternatively,the software can do more pre-processing of data in a small machine located near~the detector thereby reducing communication costs.~

This paper describes a set of filtering, prediction and control algorithms~that are primarily intended for traffic responsive control of networks. A way~to partition the network algorithms among a collection of microprocessors is~proposed. Because of the way in which the control computations can be~partitioned, the algorithms will also work well for isolated intersections.},
	address = {University of California, Berkeley},
	booktitle = {International Symposium on Traffic Control Systems}
}
@conference {
	title = {On the Conditional Density Equation in Nonlinear Filtering Problems},
	author = {Blankenship,  Gilmer L., Baras, John S.},
	year = {1979},
	month = {06/1979},
	abstract = {We consider the unnormalized conditional density equation arising from a very simple, albeit nontrivial, nonlinear filtering problem. After establishing existence and uniqueness of solutions for this stochastic partial differential equation, we derive an expression for the solution as a "doubly~stochastic{\textquoteright}{\textquoteright} Feynnnann - Kac path integral. The results are extensions of previous results by Pardoux, Chow and others, in that certain operators appearing in this stochastic partial differential equation are unbounded. We then consider the problem of computing the requisite Wiener integral in exact or approximate form. In a different direction an {\textquoteright}{\textquoteright}assumed density{\textquoteright}{\textquoteright} approximation method is applied to this specific stochastic p. d. e. Certain relations with quantum filtering problems are discussed and the possibility of a direct {\textquoteright}distributed{\textquoteright} implementation is investigated. Finally we show how by appropriate nonlinear transformations, these ideas and techniques can be extended to an interesting class of nonlinear filtering problems with multiplicative noise.},
	address = {Grignano, Italy},
	booktitle = {1979 IEEE International Symposium on Information Theory},
	pages = { p. 97}
}
@conference {
	title = {Quantum Filtering with Counting Measurements},
	author = {Baras, John S.},
	year = {1979},
	month = {06/1979},
	abstract = {Continuous time quantum filtering problems are formulated based on ideas from quantum stochastic processes. This formulation extends previous work in~that it allows modelling of the interaction between the measurement process and~the state of the quantum field. However, the abstract quantum measurements~are restricted to those that can be implemented by counting measurements. The~type and properties of the resulting point process are discussed. Following ideas~of Davies on Quantum stochastic processes the optimal filtering problem, including~optimal measurement selection (from the above type) is formulated. Methods of~solutions are discussed and examples illustrate the results. The operator~differential equation satisfied by the density operator is analyzed and relations~with stochastic partial differential equations of a specific type are illustrated.},
	address = {Grignano, Italy},
	booktitle = {1979 IEEE International Symposium on Information Theory},
	pages = {pp.1- 8}
}
@conference {
	title = {On the Point Process Disorder Problem and Applications to Urban Traffic Estimation},
	author = {Dorsey, Arthur J., Baras, John S., Levine, William S.},
	year = {1979},
	month = {03/1979},
	abstract = {We consider here the point process disorder problem in its generality and we develop the maximum likelihood and MAP estimates of the disorder time T. Applications of the results to the platoon determination problem in urban traffic show that both estimators outperform previous solutions to the problem.},
	address = {Baltimore, MD},
	booktitle = {1979 Conference on Information Sciences and Systems},
	pages = { pp. 37-42},
	organization = {Johns Hopkins University}
}
@conference {
	title = {Block Diagonal Dominance and Design of Decentralized Compensators},
	author = {Bennett, William H., Baras, John S.},
	year = {1979},
	month = {03/1979},
	abstract = {An extension of Rosenbrock{\textquoteright}s design method for linear multivariable systems is presented, based on the concept of block diagonal dominance for rational transfer function matrices. The technique allows independent~design of compensators for low-interacting subsystems. The flexibility of the method with respect to partitioning and measures of gain leads to improved estimates for overall system stability under decentralized compensator~designs. Various new directions and extensions suggested by our methods are discussed and examples illustrate the theory.},
	address = {Baltimore, MD},
	booktitle = {1979 Conference on Information Sciences and Systems},
	pages = {pp. 343-351},
	organization = {Johns Hopkins University}
}
@conference {
	title = {Traffic Estimation Based On Point Process Models},
	author = {Baras, John S., Levine, William S., Dorsey, Arthur J., Lin, Tahsin L.},
	year = {1979},
	month = {02/1979},
	abstract = {Two families of models for the relation between detector data and the flow of traffic on urban streets are developed. The first describes the relation between the queues at traffic signal and data from nearby detectors. The second model~headways statistics for urban traffic thereby relating detector data to the passage of platoons of vehicles. Recent results in the theory of point processes then give the non-linear minimum error variance filters/predictors corresponding to these models. It is shown that these optimal~estimators are computationally feasible in a microprocessor. The algorithm also performs very well when tested using data derived from UTCS-I traffic simulation.},
	address = {Asilomar conference grounds, Pacific Grove,  CA},
	booktitle = {Engineering Foundation Conference on Issues in Control of Urban Traffic Systems},
	pages = {pp. 266-285}
}
