Best Selling Books by Peter Mueller

Spread spectrum signals require several kinds of processing. First, the effects of tracking error, Doppler shifts, etc. must be compensated out or ignored in some clever way to give the ''raw'' signal. Second, the raw signal at high bandwidth must be decoded to give the low bandwidth information stream. Often this requires comparison of the raw signal with a properly-synchronized pseudo random reference signal. Third, the low bandwidth signal may or may not be burst error correction coded and hence require appropriate decoding. In this study we found that all three steps in spread spectrum decoding could be approached optically. Specific, stand-alone developments from this program were two in number. First we developed a ''generalized matched filter'' which can be used just as a matched filter but which out performs the matched filter in several important ways. Second, we developed a way of obtaining a high spectral resolution tunable acoustooptic filter using existing components. Both developments should find wide application outside spread spectrum decoding.

A research problem that has received increased attention in recent years is extending Bayesian nonparametric methods to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. First, analyzing how the performance of such extensions differs, and second, understanding which features are worthwhile in order to produce better results. This article proposes answers to those questions focusing on predictive inference and continuous covariates. Specifically, we show that 1) nonparametric models using different strategies for modeling continuous covariates can show noteworthy differences when they are being used for prediction, even though they produce otherwise similar posterior inference results, and 2) when the predictive density is a mixture, it is convenient to make the weights depend on the covariates in order to produce sensible estimators. Such claims are supported by comparing the Linear DDP (an extension of the Sethuraman representation) and the Conditional DP (which augments the nonparametric distribution to include the covariates). Unlike the Conditional DP, the weights in the predictive mixture density of the Linear DDP are not covariate-dependent. This results in poor estimators of the predictive density. Specifically, in a simulation example, the Linear DDP wrongly introduces an additional mode into the predictive density, while in an application to a pharmacokinetic study, it produces unrealistic concentration-time curves.

An imaging protocol with a multi-slice CT scanner that allows comprehensive assessment of patients with haematuria is described. This protocol allows evaluation of the kidneys, ureters and bladder in a single examination using CT. This approach should streamline the diagnostic work-up of patients with haematuria.