@conference {
	title = {Sensor Scheduling Problems},
	author = {Baras, John S., Bensoussan, Alain.},
	year = {1988},
	month = {12/1988},
	abstract = {We consider the nonlinear filtering problem of a vector difussion process, when several noisy vector observations with possibly different dimension of their range space are available. At each time any number of these observations~(or sensors) can be utilized in the signal processing performed by the nonlinear filter. The problem considered is the optimal selection of a schedule of these sensors from the available set, so as to optimally estimate a function of the state at the final time. Optimality is measured by a combined performance measure that allocates penalties for~errors in estimation, for switching between sensor schedules and for running a sensor. The solution is obtained in the form of a system of quasi-variational inequalities in the space of solutions of certain Zakai equations.},
	address = {Austin, Texas},
	booktitle = {27th IEEE Conference on Decision and Control},
	pages = {pp. 2342-2345}
}
@conference {
	title = {Nonlinear Filtering Asymptotics, Large Deviations and Observers for Nonlinear Systems},
	author = {Baras, John S., James, Matthew R.},
	year = {1988},
	month = {11/1988},
	abstract = {We present a methodology for the construction of the dynamic observers for nonlinear systems. The basic methodology used is to construct these observers as limits of nonlinear filters when certain noise parameters tend to zero. We, therefore, make contact with some interesting results on large derivations~of filtered diffusion processes. Some examples illustrate the results. ~},
	address = {Osaka, Japan},
	booktitle = {20th ISCIE Symposium on Stochastic Systems Theory and its Applications},
	pages = {pp. 1-12}
}
@conference {
	title = {Symbolic and Numeric Real-Time Signal Processing},
	author = {Baras, John S.},
	year = {1988},
	month = {08/1988},
	abstract = {We consider real-time sequential detection and estimation problems for non-gaussian signal and noise models. We develop optimal algorithms and several architectures for realtime implementation based on numerical algorithms, including asynchronous implementations of multigrid algorithms. These implementations are of high complexity, costly and cannot easily accommodate model variability. We then propose and analyze a different class of algorithms, which are symbolic, of the neural network type. The preliminary results presented here demonstrate that these algorithms have remarkably lower complexity and cost, work well under model variability and their performance is nearly optimal. We also discuss how these type of algorithms are incorporated in the DELPHI system for integrated design of signal processing systems.},
	address = {San Diego, California},
	booktitle = {SPIE{\textquoteright}s 32nd Annual International Technical Symposium on Optical and Optoelectronic Applied Science and Engineering, in Real Time Signal Processing XI},
	pages = {pp. 112-121}
}
@conference {
	title = {An Observer Design for Nonlinear Control Systems},
	author = {James, Matthew R., Baras, John S.},
	year = {1988},
	month = {06/1988},
	abstract = {We present an observer design for systems with controlled nonlinear dynamics and nonlinear observation. The design is a development of earlier work, which was motivated by nonlinear filtering asymptotics. The basic design requires that the initial conditions belong to a bounded region determined by the data and design parameters. However, for a certain class of systems, no such a priori knowledge is required. To illustrate the utility of our design, several examples are given.},
	address = {Antibes, France},
	booktitle = {The 8th International Conference on Analysis and Optimization of Systems},
	pages = {pp. 170-180},
	organization = {INRIA}
}
@conference {
	title = {The Conditional Adjoint Process},
	author = {Baras, John S., Elliott, Robert J., Kohlmann, Michael.},
	year = {1988},
	month = {06/1988},
	abstract = {The adjoint process and minimum principle for a partially observed diffusion can be obtained by differentiating the statement that a control u{\textquoteright} is optimal. Using stochastic flows the variation in the cost resulting from a change in an optimal control can be computed explicitly. The technical difficulty is to justify the differentiation.},
	address = {Antibes, France},
	booktitle = {The 8th International Conference on Analysis and Optimization of Systems},
	pages = {pp. 654-662},
	organization = {INRIA}
}
