A powerful parametric spectral estimation technique, 2D-ARMA (Auto Regressive Moving Average) modeling, has been applied to contrast transfer function (CTF) detection in electron microscopy. Parametric techniques such as AR (auto regressive) and ARMA models allow a more exact determination of the CTF than traditional methods based only on the Fourier Transform (FT). Previous works revealed that...

Recursive subspace model identification (RSMI) has been developed for a decade. Most of RSMIs are only applied for open loop data. In this paper, we propose a new recursive subspace model identification which can be applied under open loop and closed loop data. The key technique of this derivation of the proposed algorithm is to bring the Vector Auto Regressive with eXogenous input (VARX) model...

In this study, it was investigated to what extent linear auto regressive models with external input (ARX) and auto regressive moving average models with external input (ARMAX) could be used to describe the inside air temperature of an unheated, naturally ventilated greenhouse under Western European conditions. Outside air temperature and relative humidity, global solar radiation, and cloudiness...

The current COVID-19 pandemic and the preventive measures taken to contain spread of disease have drastically changed patterns our behavior. movement restrictions significant influences on behavior environment energy profiles. In 2020, reliability power system became critical under lockdown conditions chaining in electrical consumption will a long-term effect Unlike previous studies that covere...

This paper presents an approach for automatic classification of pulsed Terahertz (THz), or T-ray, signals highlighting their potential in biomedical, pharmaceutical and security applications. T-ray classification systems supply a wealth of information about test samples and make possible the discrimination of heterogeneous layers within an object. In this paper, a novel technique involving the ...

Artificial neural networks and fuzzy systems, have gradually established themselves as a popular tool in approximating complicated nonlinear systems and time series forecasting. This paper investigates the hypothesis that the nonlinear mathematical models of multilayer perceptron and radial basis function neural networks and the Takagi–Sugeno (TS) fuzzy system are able to provide a more accurat...

Abstract The introduction of advanced metering infrastructure (AMI) smart meters has given rise to fine-grained electricity usage data at different levels time granularity. AMI collects high-frequency daily energy consumption that enables utility companies and aggregators perform a rich set grid operations such as demand response, monitoring, load forecasting many more. However, the privacy con...

Forecasting is defined as the process of estimating change in uncertain situations. One most vital aspects many applications temperature forecasting. Using Daily Delhi Climate Dataset, we utilize time series forecasting techniques to examine predictability temperature. In this paper, a hybrid model based on combination Wavelet Decomposition (WD) and Seasonal Auto-Regressive Integrated Moving Av...

BACKGROUND Outbreaks of hand-foot-mouth disease (HFMD) have been reported for many times in Asia during the last decades. This emerging disease has drawn worldwide attention and vigilance. Nowadays, the prevention and control of HFMD has become an imperative issue in China. Early detection and response will be helpful before it happening, using modern information technology during the epidemic....

India is basically an agricultural country and the success or failure of the harvest and water scarcity in any year is always considered with the greatest concern. The average annual or seasonal rainfall at a place does not give sufficient information regarding its capacity to support crop production. Rainfall distribution pattern is the most important. The rainfall forecasting is scientificall...