As part of an environmental observation and forecasting system, sensors deployed in the Columbia RIver Estuary (CORIE) gather information on physical dynamics and changes in estuary habi- tat. Of these, salinity sensors are particularly susceptible to bio- fouling, which gradually degrades sensor response and corrupts crit- ical data. Automatic fault detectors have the capability to identify bio-fouling early and minimize data loss. Complicating the devel- opment of discriminatory classi(cid:12)ers is the scarcity of bio-fouling onset examples and the variability of the bio-fouling signature. To solve these problems, we take a novelty detection approach that incorporates a parameterized bio-fouling model. These detectors identify the occurrence of bio-fouling, and its onset time as reliably as human experts. Real-time detectors installed during the sum- mer of 2001 produced no false alarms, yet detected all episodes of sensor degradation before the (cid:12)eld sta(cid:11) scheduled these sensors for cleaning. From this initial deployment through February 2003, our bio-fouling detectors have essentially doubled the amount of useful data coming from the CORIE sensors.

We propose a new transform coding algorithm that integrates all optimization steps into a coherent and consistent framework. Each iteration of the algorithm is designed to minimize coding distortion as a function of both the transform and quantizer designs. Our algorithm is a constrained version of the Linde-Buzo-Gray (LBG) algorithm for vector quantizer design. The reproduction vectors are constrained to lie at the vertices of a rectangular grid. A significant result of our approach is a new transform basis specifically designed to minimize mean-squared quantization distortion for both fixed-rate and entropy-constrained coding. For Gaussian distributed data, this transform reduces to the Karhunen-Loeve transform (KLT). However, in general the coding-optimal transform (COT) differs from the KLT enough to provide up to 1 dB improvement in compressed signal-to-noise ratio (SNR) on images. We describe a practical algorithm that finds the COT for a given signal. In addition, we present image compression results demonstrating the SNR improvement achieved with our algorithm relative to KLT based transform coding.

We establish a principled framework for adaptive transform cod(cid:173) ing. Transform coders are often constructed by concatenating an ad hoc choice of transform with suboptimal bit allocation and quan(cid:173) tizer design. Instead, we start from a probabilistic latent variable model in the form of a mixture of constrained Gaussian mixtures. From this model we derive a transform coding algorithm, which is a constrained version of the generalized Lloyd algorithm for vector quantizer design. A byproduct of our derivation is the introduc(cid:173) tion of a new transform basis, which unlike other transforms (PCA, DCT, etc.) is explicitly optimized for coding. Image compression experiments show adaptive transform coders designed with our al(cid:173) gorithm improve compressed image signal-to-noise ratio up to 3 dB compared to global transform coding and 0.5 to 2 dB compared to other adaptive transform coders.

The paper addresses the problem of resource allocation in local linear models for nonlinear principal component analysis (PCA). In the local PCA model, the data space is partitioned into regions and PCA is performed in each region. Our primary result indicates that the advantage of these models over conventional PCA has been significantly underestimated in previous work. We apply local PCA models to the problems of image dimension reduction and transform coding. Our results show that by allocating representation or coding resources to the different image regions, instead of using a fixed arbitrary dimension everywhere, substantial increases in dimension reduced or compressed image qualify can be achieved.