We investigate high-rate quantization for various detection and reconstruction loss criteria. A new distortion measure is introduced which accounts for global loss in best attainable binary hypothesis testing performance. The distortion criterion is related to the area under the receiver-operating-characteristic (ROC) curve. Specifically, motivated by Sanov's theorem, we define a performance curve as the trajectory of the pair of optimal asymptotic Type I and Type II error rates of the most powerful Neyman-Pearson test of the hypotheses. The distortion measure is then defined as the difference between the area-under-the-curve (AUC) of the optimal pre-encoded hypothesis test and the AUC of the optimal post-encoded hypothesis test. As compared to many previously introduced distortion measures for decision making, this distortion measure has the advantage of being independent of any detection thresholds or priors on the hypotheses, which are generally difficult to specify in the code design process. A high-resolution Zador-Gersho type of analysis is applied to characterize the point density and the inertial profile associated with the optimal high-rate vector quantizer. The analysis applies to a restricted class of high-rate quantizers that have bounded cells with vanishing volumes. The optimal point density is used to specify a Lloyd-type algorithm which allocates its finest resolution to regions where the gradient of the pre-encoded likelihood ratio has greatest magnitude.

This paper describes the use of a hierarchical design representation standard, CHDStd, as part of the architecture of the Chip Hierarchical Design System (CHDS). Details are given on CHDStd-based hierarchy mechanisms and processes required to support Forward Timing-Driven Hierarchical Design capabilities needed for chip design using 0.25u-0.18u technologies and beyond. These capabilities solve some of the key challenges identified by the semiconductor industry's Design Productivity Crisis. This paper identifies the role of hierarchy for handling difficult chip design information issues and for large complex chip design.

The usefulness of statistical clustering algorithms developed for automatic segmentation of lesions and organs in magnetic resonance imaging (MRI) intensity data sets suffers from spatial nonstationarities introduced into the data sets by the acquisition instrumentation. The major intensity inhomogeneity in MRI is caused by variations in the B1-field of the radio frequency (RF) coil. A three-step method was developed to model and then reduce the effect. Using a least squares formulation, the inhomogeneity is modeled as a maximum variation order two polynomial. In the log domain the polynomial model is subtracted from the actual patient data set resulting in a compensated data set. The compensated data set is exponentiated and rescaled. Statistical comparisons indicate volumes of significant corruption undergo a large reduction in the inhomogeneity, whereas volumes of minimal corruption are not significantly changed. Acting as a preprocessor, the proposed technique can enhance the role of statistical segmentation algorithms in body MRI data sets.