In this work, we present POUTINE, a novel estimator of the auto-correlation function (or more generally, the cross-correlation function) of ergodic stationary signals, an important task in a variety of applications. This estimator sparsely and non-adaptively samples the process via Bernoulli selection, generalizing the classical estimator in a natural way, and offering significant sampling reductions while sacrificing a modest degree of accuracy. Both the mean and variance of our estimator are explicitly analyzed, and in particular, we show that POUTINE gives an unbiased estimate of the classical estimator, which in turn gives an unbiased estimate of the underlying second-order statistics of interest. Furthermore, we show that POUTINE is a consistent estimator with variance approaching zero asymptotically. We demonstrate favorable performance of this approach for a simple stochastic process.

Cognitive radio (CR) has been suggested as a method for promoting more efficient spectrum usage through the principles of spectrum sensing and dynamic spectrum management. The IEEE 802.22 working group in particular is developing a wireless regional-area network (WRAN) standard that will employ unlicensed CR devices operating on a noninterfering basis in the commercial broadcast television (TV) bands. Before such TV-band CR networks can be deployed, the maximum levels of first adjacent and taboo channel interference that a digital television (DTV) receiver can tolerate must be determined. In this context, we will first provide an overview of the IEEE 802.22 WRAN system. Afterwards, we will report on an experimental study that was undertaken to characterize IEEE 802.22 WRAN interference limits into Advanced Television Systems Committee (ATSC) based DTV receivers. Lastly, we will discuss the implications of these WRAN interference limits in terms of the maximum allowable radiated power and out-of-band emission limits that are imposed on WRAN end-user devices.

In several recent movie blockbusters, including Enemy of the State and Patriot Games, scientists magnify digital satellite imagery to determine the identities of people captured on surveillance video. These individuals appear rather blocky at the coarsest resolution scales due to undersampling by the image sensor array, but they become remarkably clear after zooming in on a particular region-of-interest. Obviously, real-world video enhancement algorithms are simply not capable of calculating the perfect results produced in Hollywood. However, additional detail can be extracted from an image sequence by integrating several adjacent digital video frames to compute a high-resolution video still. Extensive prior research has been conducted on the Bayesian super-resolution enhancement of visible light image sequences. For night vision applications, super-resolution enhancement must be applied to image sequences with frames acquired in the infrared region of the electromagnetic spectrum. Most of these applications are DoD-related (military), and they include the enhancement of infrared reconnaissance and surveillance imagery, as well as the enhancement of infrared imagery for automated target tracking and real time multisensor fusion. Several computational simulations have been performed, which visually depict how well the proposed technique enhances short night vision image sequences consisting of two-to-five frames This technique is capable of increasing resolution by factors up to 16/spl times/, although video stills appear somewhat blocky at higher magnification factors.