@conference {
	title = {Frequency Domain Design of Linear Distributed Systems},
	author = {Baras, John S.},
	year = {1980},
	month = {12/1980},
	abstract = {Using techniques from the theory of operators and the Hardy spaces of analytic functions we extend to certain classes of linear distributed systems a well known design technique for lumped parameter systems: dynamic-pole-relocation.},
	address = {Albuquerque, New Mexico},
	booktitle = {19th IEEE Conference On Decision and Control},
	pages = {pp. 728-732}
}
@conference {
	title = {Group Invariance Methods in NonLinear Filtering Of Diffusion Processes},
	author = {Baras, John S.},
	year = {1980},
	month = {12/1980},
	address = {Albuquerque, New Mexico},
	booktitle = {19th IEEE Conference On Decision and Control},
	pages = {pp. 72-79}
}
@conference {
	title = {Group Invariance Methods in Nonlinear Filtering of Diffusion Processes},
	author = {Baras, John S.},
	year = {1980},
	month = {06/1980},
	abstract = {Given two {\textquotedblleft}nonlinear filtering problems{\textquotedblright} described by the processes

dxi(t)=fi(xi(t))dt+gi(xi(t))dwi(t)dxi(t)=fi(xi(t))dt+gi(xi(t))dwi(t)

dyi(t)=hi(xi(t))dt+dvi(t),i=1,2,dyi(t)=hi(xi(t))dt+dvi(t),i=1,2,

we define a notion of strong equivalence relating the solutions to the corresponding Mortensen-Zakai equations

dui(t,x)=Liui(t,x)dt+Li1ui(t,x)dyit,i=1,2,dui(t,x)=Luii(t,x)dt+L1iui(t,x)dyti,i=1,2,

which allows solution of one problem to be obtained easily from solutions of the other. We give a geometric picture of this equivalence as a group of local transformations acting on manifolds of solutions. We then show that by knowing the full invariance group of the time invariant equations

dui(t,x)=Liui(t,x)dt,i=1,2,

we can analyze strong equivalence for the filtering problems. In particular if the two time invariant parabolic operators are in the same orbit of the invariance group we can show strong equivalence for the filtering problems. As a result filtering problems are separated into equivalent classes which correspond to orbits of invariance groups of parabolic operators. As specific example we treat V. Bene{\v s}{\textquoteright}s case establishing from this point of view the necessity of the Riccati equation.},
	address = {Savoie, France},
	booktitle = {Stochastic Systems: The Mathematics of Filtering and Identification and Application},
	pages = {pp. 565-572}
}
@conference {
	title = {Block Diagonal Dominance and Design of Decentralized Compensators},
	author = {Bennett, William H., Baras, John S.},
	year = {1980},
	month = {06/1980},
	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 measured of gain leads to improved estimates for overall~system stability under decentralized compensator design.~ Various new directions~and extensions suggested by our methods are discussed and examples illustrate~the theory.},
	address = {Toulouse, France},
	booktitle = {IFAC Symposium on Large Scale Systems: Theory and Applications},
	pages = {pp. 93-102}
}
@conference {
	title = {Some Results on Computer Control of Urban Traffic},
	author = {Baras, John S., Levine, William S.},
	year = {1980},
	month = {06/1980},
	abstract = {The problem of controlling vehicle traffic on an urban street network is a particular example of a large scale systems problem,~In a reasonably typical~network there will be one thousand or more traffic signals, there may be several hundred sensors (vehicle detectors) and the network is typically distributed over~several hundred square miles. It is now quite common to control these systems primarily from a central computer. The traffic control problem is modelled by a kind of "store and forward" network in which vehicles are {\textquoteright}stored{\textquoteright} in queues at the signals and "forwarded{\textquoteright} in platoons. The data from detectors is modelled as a point process that is statistically related~to the queues and platoons.~It would be very tempting to design optimal feedback control algorithms for these~systems. However, the size of the problems makes this impossible. Instead sub-optimal controls are developed. These sub-optimal controls use the minimum error variance estimator of the queues at each intersection as determined from the above model to improve the control provided by several intuitively derived sub-optimal~controls. The sub-optimal controls have been tested using a good simulation of urban traffic. The sub-optimal controls give substantially better performance than benchmark open loop controls. Conditions under which the network can be decomposed~than~into subnetworks without significant degradation in performance are also given.~},
	address = {Toulouse, France},
	booktitle = {IFAC Symposium on Large Scale Systems: Theory and Applications},
	pages = {pp. 449-458}
}
