We have implemented super-resolution techniques such as machine learning methods and solving a least squares problem with a prior to perform demosaicing. These techniques include k-nearest neighbors (KNN), linear regression, and alternating direction method of multipliers with a total variation prior (ADMM TV). For all methods, parameters were optimized to minimize the mean-squared-error (MSE) ...

Utilizing historical data from the Quandl API, this experiment investigates use of Convolutional Neural Networks (CNNs) in stock price prediction. The information for Microsoft Corporation (MSFT) spans dates January 1, 2013, and May 18, 2018. high, low, open, close prices are scaled, input sequences length 6 created order to capture temporal dependencies. Dense layers, 1D convolutional max pool...

We use natural gradient (NG) learning neural networks (NNs) for modeling and identifying nonlinear systems with memory. The nonlinear system is comprised of a discrete-time linear filter H followed by a zero-memory nonlinearity g(·). The NN model is composed of a linear adaptive filter Q followed by a two-layer memoryless nonlinear NN. A Kalman filter-based technique and a search-and-converge m...

We consider the problem of sequentially choosing between a set of unbiased Monte Carlo estimators to minimize the mean-squared-error (MSE) of a final combined estimate. By reducing this task to a stochastic multi-armed bandit problem, we show that well developed allocation strategies can be used to achieve an MSE that approaches that of the best estimator chosen in retrospect. We then extend th...

We consider model-based prediction of a finite population total when a monotone transformation of the survey variable makes it appropriate to assume additive, homoscedastic errors. As the transformation to achieve this does not necessarily simultaneously produce an easily parameterized mean function, we assume only that the mean is a smooth function of the auxiliary variable and estimate it non...

The finite sample properties of the Fourier estimator of integrated volatility under market microstructure noise are studied. Analytic expressions for the bias and the mean squared error of the contaminated estimator are derived. These formulae can be practically used to design optimal MSE-based estimators, which are very robust and efficient in the presence of noise. Moreover an empirical anal...

In this paper linear parallel interference cancellation (PIC) schemes are described and analysed using matrix algebra. It is shown that the linear PIC, whether conventional or weighted, can be seen as a linear matrix filter applied directly to the chip-matched filtered received signal vector. An expression for the exact bit error rate is obtained and conditions on the eigenvalues of the code co...

Agencies and policy makers are interested in constructing reliable estimates for areas with small sample sizes, where areas often refer to geographic areas and demographic groups. The estimation for such areas is known as small area estimation. Procedures based on models have been used to construct estimates for the small area means, by exploiting auxiliary information. Mixed models are suitabl...