A non-binary analog-to-digital converter (ADC) based on β-expansion, called β-encoder, is so far reported to achieve robustness against large process variation and wide environment change. Quantization error of β-encoder is not uniformly distributed, which makes the mean squared error (MSE) evaluation difficult. An analysis method for giving the upper bound of MSE of the quantization error is p...

An alternative to extrinsic information transfer (EXIT) charts called mean squared error (MSE) charts that use a measure related to the MSE instead of mutual information is proposed. Using the relationship between mutual information and minimum mean squared error (MMSE), a relationship between the rate of any code and the area under a plot of MSE versus signal to noise ratio (SNR) is obtained, ...

An alternative to extrinsic information transfer (EXIT) charts called mean squared error (MSE) charts that use a measure related to the MSE instead of mutual information is proposed. Using the relationship between mutual information and minimum mean squared error (MMSE), a relationship between the rate of any code and the area under a plot of MSE versus signal to noise ratio (SNR) is obtained, ...

In this paper we consider a regression model and a general family of shrinkage estimators of regression coefficients. We derive the formula for the mean squared error (MSE) of the estimators for each individual regression coefficient. It is shown analytically that the usual shrinkage estimators are dominated by their positivepart variants in terms of MSE.

This paper focuses on the methodology used in applying the Time Series Data Mining techniques to financial time series data for calculating currency exchange rates of US dollars to Indian Rupees. Four Models namely Multiple Regression in Excel, Multiple Linear Regression of Dedicated Time Series Analysis in Weka, Vector Autoregressive Model in R and Neural Network Model using NeuralWorks Predic...

Wind energy is an essential source of renewable that has gained popularity in recent years. Accurately forecasting wind production crucial for efficient management and distribution. This paper proposes a machine learning-based approach using Support Vector Regression (SVR) Random Forest (RFR) to forecast production. The proposed methodology involves data collection, preprocessing, feature selec...

We study biased control variates (BCVs), whose purpose is to improve the efficiency of stochastic simulation experiments. BCVs replace the control-simulation mean with an approximation; the resulting control-variate estimator is biased. This bias may not be a significant issue for finite sample sizes, however, because our estimator minimizes the more general mean-squared-error (mse), i.e., the ...

In this paper, we present performance of pilot based channel estimation techniques such as Least Squares (LS) and Linear Minimum Mean Square Error (LMMSE) for different interpolation methods and modulation schemes. The performance is evaluated using the mean squared error (MSE) as the performance metric of interest for downlink LTE system. Simulation results show that for low SNR environment, a...