<p><strong>Abstract.</strong> A majority of studies involving remote sensing LULC classification conducted classification accuracy assessment without consideration of the training data uncertainty. In this study we present new concepts of LULC classification accuracies, namely the training-sample-based global accuracy and the classifier global accuracy, and a general expression of different measures of classification accuracy in terms of the sample dataset for classifier training and the sample dataset for evaluation of classification results. Through stochastic simulation of a two-feature and two-class case, we demonstrate that the training-sample confusion matrix should replace the commonly adopted reference-sample confusion matrix for evaluation of LULC classification results. We then propose a bootstrap-simulation approach for establishing 95% confidence intervals of classifier global accuracies.</p>

This paper presents a via-configurable logic block and a design methodology for realizing fine-grained dual-supply-voltage structured ASIC. Experiments with a 90nm process technology show that, given various timing budgets, our approach can achieve up to 44% energy reduction with 1.6% area overhead on level converters. Compared with GECVS, our approach converts up to 39% more high-supply voltage gates into low-supply voltage gates.

No abstract

No abstract