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In Fixed Channel Allocation or Fixed Channel Assignment (FCA) each cell is given a predetermined set of frequency channels. FCA requires manual frequency planning, which is an arduous task in time-division multiple access (TDMA) and frequency-division multiple access (FDMA) based systems since such systems are highly sensitive to co-channel interference from nearby cells that are reusing the ...
Directional component analysis (DCA) [1] [2] [3] is a statistical method used in climate science for identifying representative patterns of variability in space-time data-sets such as historical climate observations, [1] weather prediction ensembles [2] or climate ensembles.
In digital signal processing, a discrete Fourier series (DFS) is a Fourier series whose sinusoidal components are functions of discrete time instead of continuous time. A specific example is the inverse discrete Fourier transform (inverse DFT).
Detrended correspondence analysis (DCA) is a multivariate statistical technique widely used by ecologists to find the main factors or gradients in large, species-rich but usually sparse data matrices that typify ecological community data.
From uniformly spaced samples it produces a function of frequency that is a periodic summation of the continuous Fourier transform of the original continuous function. In simpler terms, when you take the DTFT of regularly-spaced samples of a continuous signal, you get repeating (and possibly overlapping) copies of the signal's frequency ...
Dependent component analysis (DCA) is a blind signal separation (BSS) method and an extension of Independent component analysis (ICA). ICA is the separating of mixed signals to individual signals without knowing anything about source signals.
Dynamic Frequency Selection (DFS) is a channel allocation scheme specified for wireless LANs, commonly known as Wi-Fi. It is designed to prevent electromagnetic interference by avoiding co-channel operation with systems that predated Wi-Fi, such as military radar , satellite communication , and weather radar , and also to provide on aggregate a ...
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing.. The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified.