Empirical Mode Decomposition (EMD) Transform for Spatial Analysis
نویسندگان
چکیده
‘Broadly speaking, spatial analysis can be defined as the formal quantitative study of phenomena that manifest themselves in space. This implies an attention to location, area, distance and interaction’ (Anselin et al., 1993). Spatial analysis has been a popular topic in geospatial sciences. Spatial analysis was viewed as being deeply rooted in spatial statistics, and quantitative geographers have devised statistical methods for analysing geographical phenomena. However, researchers have recently been calling for the concept to be broadened. Marble (2000) emphasized that some of modelling functions should also be considered as spatial analyses of the concept. As an example, he mentioned transportation optimization models. Anselin (2000) also suggested that spatial analysis needs to go beyond dealing with Euclidean space and physical geographical locations to include location in ‘social’ and ‘perceptual’ space. The development of spatial analysis is summarized Table 1.
منابع مشابه
Nonlinear and Non-stationary Vibration Analysis for Mechanical Fault Detection by Using EMD-FFT Method
The Hilbert-Huang transform (HHT) is a powerful method for nonlinear and non-stationary vibrations analysis. This approach consists of two basic parts of empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA). To achieve the reliable results, Bedrosian and Nuttall theorems should be satisfied. Otherwise, the phase and amplitude functions are mixed together and consequently, the ...
متن کاملA review on EEG based brain computer interface systems feature extraction methods
The brain – computer interface (BCI) provides a communicational channel between human and machine. Most of these systems are based on brain activities. Brain Computer-Interfacing is a methodology that provides a way for communication with the outside environment using the brain thoughts. The success of this methodology depends on the selection of methods to process the brain signals in each pha...
متن کاملA review on EEG based brain computer interface systems feature extraction methods
The brain – computer interface (BCI) provides a communicational channel between human and machine. Most of these systems are based on brain activities. Brain Computer-Interfacing is a methodology that provides a way for communication with the outside environment using the brain thoughts. The success of this methodology depends on the selection of methods to process the brain signals in each pha...
متن کاملA Time-Frequency approach for EEG signal segmentation
The record of human brain neural activities, namely electroencephalogram (EEG), is generally known as a non-stationary and nonlinear signal. In many applications, it is useful to divide the EEGs into segments within which the signals can be considered stationary. Combination of empirical mode decomposition (EMD) and Hilbert transform, called Hilbert-Huang transform (HHT), is a new and powerful ...
متن کاملEmpirical Mode Decomposition based Adaptive Filtering for Orthogonal Frequency Division Multiplexing Channel Estimation
This paper presents an empirical mode decomposition (EMD) based adaptive filter (AF) for channel estimation in OFDM system. In this method, length of channel impulse response (CIR) is first approximated using Akaike information criterion (AIC). Then, CIR is estimated using adaptive filter with EMD decomposed IMF of the received OFDM symbol. The correlation and kurtosis measures are used to sel...
متن کاملAdaptive Orthogonal Signal Decomposition Based on Empirical Mode Decomposition and Empirical Wavelet Transform Ligi
Empirical mode decomposition (EMD) and Empirical wavelet transform (EWT) are recently developed adaptive signal processing tools. These techniques decompose a signal accordingly to its contained information. The main issue with EMD is its lack of theory and in case of EWT a prior knowledge of the signal is required. IMF’s obtained as a result of applying EMD are quasi-orthogonal .This paper sug...
متن کامل