@conference {
	title = {Time-Recursive Computation and Real-Time Parallel Architectures, with Application on the Modulated Lapped Transform},
	author = {Frantzeskakis, Emmanuel., Baras, John S., Liu, Ray K J.},
	year = {1993},
	month = {07/1993},
	abstract = {The time-recursive computation has been proved particularly useful for the real-time evaluation of one and two-dimensional block transforms. Unlike the FFT based ones, time-recursive architectures require only local communication. Also, they are modular and regular, thus they are very appropriate for VLSI implementation and they allow high degree of parallelism.

In this paper, we establish an architectural framework for parallel time-recursive computation. We consider a class of linear operators that consists of the discrete time, time invariant, compactly supported, but otherwise arbitrary kernel functions. We specify the properties of the linear operators that can be implemented efficiently in a time-recursive way. Based on these properties, we develop a routine that produces a time-recursive architectural implementation for a given operator. This routine is instructive for the design of a CAD tool that will facilitate the architecture derivation.

Using this background, we design an architecture for the Modulated Lapped Transform (commonly called Modified Discrete Cosine Transform), which has linear cost in operator counts.},
	address = {San Diego, California},
	booktitle = {The 1993 SPIE International Symposium on Optical Science and Engineering},
	pages = {pp. 100-111}
}
@conference {
	title = {Time Series Modeling by Perceptrons: A Likelihood Approach},
	author = {Sonmez, Kemal M., Baras, John S.},
	year = {1993},
	month = {07/1993},
	abstract = {We consider neural network learning problems in which the objective is to learn the relationship between the inputs and the probability distribution of a proposition. We regard successive truth values of the proposition as a dependent binary time series whose instantaneous probability of truth is a function of the past behavior of the joint process between the analog inputs and binary output truth values. In this context, we identify the gradient descent learning algorithm using the Kullback-Leibler relative entropy cost function on a perceptron with a Maximum~Partial Likelihood (MPL) estimator of the perceptron model for the probability of a binary event in terms of its covariates. The implications of this result are: (i) The neural network models obtained by relative entropy learning are shown to have the nice large sample (i.e. training set size) characteristics of MPL estimates: consistency and asymptotic normality. (ii) An important and widely used statistical inference technique, logistic regression, can efficiently be implemented on analog perceptrons for time series modeling and prediction.},
	address = {Portland, Oregon},
	booktitle = {The World Congress on Neural Networks},
	pages = {pp. 601-604}
}
