difference between TC533AX and TC533BX selec temperature controllers
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The continuous wavelet transform (CWT) is obtained by convolving a signal with an infinite number of functions, generated by translating (t) and scaling (a) a certain mother wavelet function:
ya,t(s)=(x∗fa,t)(s)ya,t(s)=(x∗fa,t)(s)
The resulting transform is two dimensional (a,t) where the parameters are varied continuously. The inverse transform requires an infinite number of coefficients. The advantage of this transform is you get smoothly varying local frequency and scales and detection of features may be easier.
However, most signals are given in a discrete setting (e.g. images) and you don’t necessarily need smoothly varying parameters to get all interesting signal features or to reconstruct the signal from its wavelet coefficients. A discrete wavelet transform (DWT) is sufficient for these signals.
ya,t(s)=(x∗fa,t)(s)ya,t(s)=(x∗fa,t)(s)
The resulting transform is two dimensional (a,t) where the parameters are varied continuously. The inverse transform requires an infinite number of coefficients. The advantage of this transform is you get smoothly varying local frequency and scales and detection of features may be easier.
However, most signals are given in a discrete setting (e.g. images) and you don’t necessarily need smoothly varying parameters to get all interesting signal features or to reconstruct the signal from its wavelet coefficients. A discrete wavelet transform (DWT) is sufficient for these signals.
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