Hidden Markov models (HMM) are successfully applied in various elds of time series analysis. Colored noise, e.g. due to ltering, violates basic assumptions of the model. While it is well-known how to consider auto-regressive (AR) ltering, there is no algorithm to take into account moving-average (MA) ltering in parameter estimation exactly. We present an approximate likelihood estimator for MA-...

Auto white-balance plays a very important role in computer vision, and also is a prerequisite of color processing algorithms. For keeping the color constancy in the real-time outdoor environment, a simple and flexible auto white balance algorithm based on the color histogram overlap of the image is presented in this paper. After looking at a numerous images under different illuminance, an essen...

The Generalized Lotka Voltera (GLV) formalism has been introduced in order to explain the power law distributions in the individual wealth (wi(t)) (Pareto law) and financial markets returns (fluctuations) (r) as a result of the auto-catalytic (multiplicative random) character of the individual capital dynamics. As long as the multiplicative random factor (λ) is extracted from the same probabili...

OpenTuner can help users achieve better or more portable performance in their speci c domain through program autotuning. A key challenge for users seeking good autotuning performance in OpenTuner is selecting a search approach appropriate for problem. However, not only are current in-situ learning search approaches not robust enough to handle all search spaces, but there are also too many possi...

A detector for the case of a radar target with known Doppler and unknown complex amplitude in complex Gaussian noise with unknown parameters has been derived. The detector assumes that the noise is an Auto-Regressive (AR) process with Gaussian autocorrelation function which is a suitable model for ground clutter in most scenarios involving airborne radars. The detector estimates the unknown...

OpenTuner can help users achieve better or more portable performance in their speci c domain through program autotuning. A key challenge for users seeking good autotuning performance in OpenTuner is selecting a search approach appropriate for problem. However, not only are current in-situ learning search approaches not robust enough to handle all search spaces, but there are also too many possi...

The process of empirical autotuning results in the generation of many code variants which are tested, found to be suboptimal, and discarded. By retaining annotated performance profiles of each variant tested over the course of many autotuning runs of the same code across different hardware environments and different input datasets, we can apply machine learning algorithms to generate classifier...