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It has been widely recognized that the dynamic range information of an application can be exploited to reduce the datapath bitwidth of either processors or ASICs, and therefore the overall circuit area, delay and power consumption. While recent proposals of analytical dynamic range estimation methods have shown significant advantages over the traditional profiling-based method in terms of runtime, we argue that the rather simplistic treatment of input correlation may lead to significant error. We instead introduce a new analytical method based on a mathematical tool called Karhunen-Loéve Expansion (KLE), which enables the orthogonal decomposition of random processes. We show that when applied to linear systems, this method can not only lead to much more accurate result than previously possible, thanks to its capability to capture and propagate both spatial and temporal correlation, but also richer information than the value bounds previously produced, which enables the exploration of interesting trade-off between circuit performance and signal-to-noise ratio.

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This paper describes a methodology developed for calibrating optical detector for light engineering, especially for devices used at low level light, including auroral imager, star sensor, astronomical camera and similar optical instruments. In order to know the physical meaning of optical sensor output, calibration is the first and most important process in a complete analysis of observed data. It is found that optical sensors, like CCDs, are not perfectly linear systems as they were assumed. After bias frame subtraction, the number of ADU counts is not exactly proportional to the number of incident photons. A key component of this paper is non-linearity correction. One of current applications using this method is auroral imager which is used for measuring aurora, high-altitude clouds, and other atmospheric optical objects light intensity, which is the first step to complete an optical object tomography simulation.