Digital Processing for EELS Data Xiang Yang WATLABS, Univeristy of Waterloo.
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Transcript of Digital Processing for EELS Data Xiang Yang WATLABS, Univeristy of Waterloo.
Digital Processing for EELS DataDigital Processing for EELS Data
Xiang YangXiang Yang
WATLABS, Univeristy of WaterlooWATLABS, Univeristy of Waterloo
Signals and NoiseSignals and Noise --1 --1
Signal: any Signal: any usefuluseful
informationinformation
Noise: any Noise: any unwatedunwated
informationinformation
Signals and NoiseSignals and Noise --2 --2
SSignalignal:: what you are measuring that is the result of what you are measuring that is the result of
the presence of your analytethe presence of your analyte
NoiseNoise: : extraneous information that can interfere with extraneous information that can interfere with
or alter the signal. or alter the signal.
Types of NoiseTypes of Noise --1 --1
Random Noise: sign & magnitude --unpredictableRandom Noise: sign & magnitude --unpredictable
Non-RandomNon-Random Noise Noise: :
sign & magnitude – correlated with some eventsign & magnitude – correlated with some event
Types of NoiseTypes of Noise --2 --2
Fundamental Noise: Fundamental Noise: ------- Due to the nature of light and matter------- Due to the nature of light and matter ------- Cannot be totally eliminated------- Cannot be totally eliminated
Non-FundamentalNon-Fundamental Noise Noise: : ------- Mostly due to instrumentation------- Mostly due to instrumentation ------- can be eliminated (theoretically)------- can be eliminated (theoretically)
sX
deviation standardmean
NoiseSignal
Signal to Noise Ratio
(SNR)
Noise SourcesNoise Sources
Signal Source
Detector
Analog Treatments
Analog to DigitalConversion
Non-monochromate light source
Detector’s Dark Current, electromagnetic interference, etc.
Circuit noise, baseline, electromagnetic interference, etc.
Quantization effects
SNR EnhancementSNR Enhancement HardwareHardware
Dwell Time v.s. SNRDwell Time v.s. SNR
Communication Communication between Computer & between Computer & MachineMachine
Ensemble AveragingEnsemble Averaging Collect multiple signals over Collect multiple signals over
the same time or wavelength the same time or wavelength ((x-axisx-axis) domain) domain
Calculate the mean signal at Calculate the mean signal at each point in the domaineach point in the domain
Re-plot the averaged signalRe-plot the averaged signal
Since noise is random (some Since noise is random (some +/ some -), this helps reduce +/ some -), this helps reduce the overall noise by the overall noise by cancellation!cancellation!
Boxcar AveragingBoxcar Averaging
– Take an average of Take an average of 2 or more signals in 2 or more signals in some domainsome domain
– Plot these points as Plot these points as the average signal in the average signal in the same domainthe same domain
– Can be done with Can be done with just one set of datajust one set of data
– You lose some detail You lose some detail in the overall signalin the overall signal
Digital FilteringDigital Filtering
Weighted Digital FilteringWeighted Digital Filtering
Fast Fourier Transform Digital FilteringFast Fourier Transform Digital Filtering
Weighted FilteringWeighted Filtering
Fast Fourier Transformation FilteringFast Fourier Transformation Filtering
Main Point: Noise is of a higher
frequency than the information
FFT FilteringFFT Filtering
Noisy Data(Time Domain)
Tranformed Data(Frequency Domain)
FT
Modified Data
(Freq. Domain)
Low Pass Filter
Filtered Signal
FT
FTFT
Filtering
FFT ---- Real SampleFFT ---- Real Sample
First Fourier Tranform
Cut off Frequency (0.003 Hz)
Thank You !