(b) Simulate the model (1) to produce T = 100 measurements y1:T . Based on these measurements compute the optimal (in the sense that it minimizes the mean square error) estimate of xt | y1:t for t = 1, . . . , T . Implement a bootstrap particle filter and compare to the optimal estimates. You can for example perform this comparison by plotting the root mean square estimate (RMSE) ε(N) as a func...

OBJECTIVE To evaluate the accuracy of body mass index (BMI) as a predictor of body fat in elderly women. RESEARCH METHODS AND PROCEDURES A total of 1423 women aged 67 +/- 5 (mean +/- SD, range: 60 to 88) years were consecutively enrolled into the study. Fat mass (FM) was measured using DXA. RESULTS BMI explained 72.9% of FM variance (p < 0.0001), with a root mean square error of estimate (R...

The benefits of Free Space Optics (FSO) motivate to use it for future high data rate demanding communication applications. However, widespread growth of the technology has been hampered by reduced availability due to weather influences on the link. The fog has been analyzed as the most detrimental for FSO communication. There are some models that predict fog attenuation in terms of visibility. ...

Accurately estimating software effort is probably the biggest challenge facing software developers. Estimates done at the proposal stage has high degree of inaccuracy, where requirements for the scope are not defined to the lowest details, but as the project progresses and requirements are elaborated, accuracy and confidence on estimate increases. It is important to choose the right software ef...

Pulp properties are key factors to assessing the performance of a refining process, evaluating operational conditions, and optimizing the process of stock preparation. This paper presents a data-driven approach to estimate the beating degree and wet weight of pulp after refining using case-based reasoning (CBR). Historical data generated in a refining process at a paper mill was used to evaluat...

‎This paper has two aims. The first is forecasting inflation in Iran using Macroeconomic variables data in Iran (Inflation rate, liquidity, GDP, prices of imported goods and exchange rates) , and the second is comparing the performance of forecasting vector auto regression (VAR), Bayesian Vector-Autoregressive (BVAR), GARCH, time series and neural network models by which Iran's inflation is for...

This paper presents the corpus-driven approach in building the computational model of fundamental frequency, or F0, for Lithuanian language. The model was obtained by training the HMM-based speech synthesis system HTS on six hours of speech coming from multiple speakers. Several gender specific models, using different parameters and different contextual factors, were investigated. The models we...

––In this Paper, Optimization of error is considered using power adaptation algorithm while transmission is carried out The Maximum Power Adaptation algorithm(MAPAA) is based on updating the amount of power transmitted for each bit according to its importance in the image quality as measured by the mean-square error. This Maximum Power Adaptation Algorithm is used to find the optimum power dist...

The Artificial Neural Network (ANN) fitting tool is used for the prediction of solar radiation. Solar radiation data from 12 Indian stations with different climatic conditions are used for training and testing the ANN. The Levenberg-Marquard (LM) algorithm is used in this analysis. The results of ANN model are compared with measured data on the basis of root mean square error (RMSE) and mean bi...