During this field survey, we measured and recorded a few quality parameters of wireless communication in a substation switchyard. A microprocessor-based measurement system was used for data collection and analysis. We investigated long-term noise variation in this specific environment. Based on our measurement and post-processing analysis we conclude that the so-called Classic/Bayesian assumpti...

This paper deals with the application of the Multi Objective Generalized Predictive Control (MOGPC) to level control in a laboratory process. The major characteristic of the considered plant is that the manual draining vane can take many positions causing changes in plant dynamics and strong disturbances in the process. The controller is based on a set of Controlled Auto Regressive Integrated M...

Consider observing a collection of discrete events within a network that reflect how network nodes influence one another. Such data are common in spike trains recorded from biological neural networks, interactions within a social network, and a variety of other settings. Data of this form may be modeled as self-exciting point processes, in which the likelihood of future events depends on the pa...

Parametric identification approaches in the Linear Parameter-Varying (LPV) setting require optimal prior selection of a set of functional dependencies, used in the parametrization of the model coefficients, to provide accurate model estimates of the underlying system. Consequently, data-driven estimation of these functional dependencies has a paramount importance, especially when very limited a...

Predicting stock price is a trend yet very challenging task. It because the prices depend upon several internal and external factors. Stock prediction can be useful for financial sectors government help in informed decision-making. This paper analyzes market of K-Electric Karachi. found that K-electric on refinery sector. The data two sectors. Also, compares based moving average, auto-regressiv...

The Least Mean Square algorithm has been used for estimation of auto regressive processes before [1]. Here in, a modified LMS algorithm is presented that can be used for estimation of processes with high correlation. Board level experiments are carried out to test the effectiveness of the algorithm in active noise cancellation. Results are summarized in the end.

A new spectral envelope estimation procedure is proposed to recover details beyond band limitation imposed by the Shannon’s sampling theory when interpreting periodic excitation of voiced sounds as the sampling operation in the frequency domain. The proposed procedure is a hybrid of STRAIGHT, a F0-adaptive spectral envelope estimation and the auto regressive model parameter estimation. Wavelet ...

Gold mineralisation is the result of physico-chemical and thermal processes of the earth’s interior. We may view a geological process of gold mineralization as a stochastic process Z(x): xD, where D may be considered as a mineral deposit. In the case of gold mineralization, samples drawn at regular intervals may be considered as following a discrete stochastic process. The point of interest is...

A non-linear Auto-Regressive Exogenous-input model (NARXM) river flow forecasting output-updating procedure is presented. This updating procedure is based on the structure of a multi-layer neural network. The NARXM-neural network updating procedure is tested using the daily discharge forecasts of the soil moisture accounting and routing (SMAR) conceptual model operating on five catchments havin...