[Introduction]  [Signal arithmetic]  [Signals and noise]   [Smoothing]   [Differentiation]  [Peak Sharpening]  [Harmonic analysis]   [Fourier convolution]  [Fourier deconvolution]  [Fourier filter]   [Peak area measurement]  [Linear Least Squares]  [Multicomponent Spectroscopy]  [Iterative Curve Fitting]  [Hyperlinear quantitative absorption spectrophotometry] [Appendix and Case Studies]  [Peak Finding and Measurement]  [iPeak]   [iSignal]  [Peak Fitters]   [iFilter]  [iPower]  [List of downloadable software]  [Interactive tools]

The Animated Zoo

 of Matlab/Octave Signal Processing Tools and Demos

 


Signal averaging (aka ensemble averaging) to reduce random noise


The first derivative is the slope of the tangent at each point (script).

Detecting and measuring peaks on a curved, unstable baseline with the findpeaksb.m function.
Slideshow of applications of the interactive peak detector, iPeak


Jumping from peak to peak in iPeak (space and tab keys)


Demonstration of differentiation on the frequency spectrum of a Gaussian


Manually setting custom first-guess peak position in the interactive peak fitter, ipf.m

Interactive smoothing and differentiation with iSignal.



Interactive smoothing using iSignal in frequency spectrum mode.



Iterative curve fitting a very noisy signal using peakfit.m

Demonstration of various functions of iSignal.



Following the evolution of an exponential pulse using peakfit.m


The waveform and the power spectrum of a rectangular pulsed sine wave of variable duration.



Finding and fitting variable numbers of peaks. Peak detection followed by iterative curve fitting using findpeaksfit.m

Selecting and fitting an exponentially-broadened Gaussian using the interactive peak fitter, ipf.m


The waveform and the power spectrum of a Gaussian-modulated sine wave of variable duration.

Detecting and measuring peaks with the autofindpeaks.m function.

Applying multiple curve fits to a signal that is changing with time.


Using iFilter to isolate different harmonics of a complex waveform.



Using the interactive peak fitter to zoom in to a group of overlapping peaks, set the baseline correction mode, and perform a fit.

Effect of changing width on the frequency spectrum of a Gaussian


Hyperlinear Quantitative Absorption Spectrophotometry

Manual baseline subtraction in the interactive peak fitter, ipf.m.


Fourier deconvolution of a Gaussian-broadened rectangular pulse, demonstrating the effect of changing the deconvolution width.

The iPower demonstrator shows the frequency spectra
of various types of signals
.


Fourier deconvolution of a Gaussian-broadened narrow peak, with the aim of recovering the original peak width.


Effect of smoothing on peak height, noise, and signal/noise ratio


Looking for the peaks in a complex experimental signal using iPeak.m

Testing the effect of random noise on the precision
of measured peak parameters
measured by iterative curve fitting.


Real-time measurement of temperature as a function of time using a Raspberry Pi 3.


 Measuring peak height and area by different methods in a variety of signal types, using measurepeaks.m.  The individual peaks are displayed separately in the right-hand panel, with the peaks, valleys, and tangent lines shown in different colors.

Background information on these and other signal processing methods is available in:

A Pragmatic Introduction to Signal Processing

An illustrated essay available in Web, OpenOffice, Word , and PDF  format




Verbatim Unsolicited Comments from Users

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Copyright (c) 2014, 2017 Thomas C. O'Haver
 
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sub-license, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.


THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


First edition created in 2006. Last updated March, 2017. Created with SeaMonkey. This page is part of "A Pragmatic Introduction to Signal Processing", a retirement project and international community service, created and maintained by Prof. Tom O'Haver , Department of Chemistry and Biochemistry, The University of Maryland at College Park. Comments, suggestions and questions should be directed to Prof. O'Haver at toh@umd.edu. Number of unique visits to this website since May 17, 2008: