sampling frequency of wavelet matlab code

The produced figure is shown below (click on the figure for full size image) The attribute 'clean' indicates that the dataĭoes not contain any noise. This data is artififially created, and contains 10 equally Your workspace now contains a variable Signal,ĭata to be analyzed. The other files are for tutorial purposes Tutorial detect_spikes_wavelet is a stand-alone function for detection of Installed in order to execute the main function. X(t) is represented in time-scale domain by virtue of a waveletĪll files are MATLAB® M-files or MAT-files. Representation Tx(w,t) of a one-dimensional signalīreakthrough in the theory of wavelets offeredĪ powerful alternative to windowed Fourier transform, where a one-dimensional Time and frequency, yielding a two-dimensional signal

(subject to Hiesenberg uncertainty principle) in both Signal is projected onto a basis of waveforms that are well localized We can think of theseĬoefficients as of signal representation in a new coordinate system.ĭepending on the signal representation theĭetection problem can be formulated in different domains such as time domain,įrequency domain and time-frequency domain. Projected, giving rise to expansion coefficients. One usually resorts to a 'canonical set' of basis function where the signal When no apropriate model for the signal can be In general, a signal representation can be modelīased and expansion based. Not perfectly known, usually no uniformly most powerful (UMP) testĮxists, and the performance of a detector depends on The null hypothesis no signal is present. In a background noise is normally cast as a hypothesis testing, where under

Observations has been studied for decades. The problem of detecting transient signals given a collection of noisy If you are interested in the spike detection code, please refer to my new page and follow the 'Software' link, or go directly to Detection in Neural Data Using Wavelet Transform Software

Sampling frequency of wavelet matlab code