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Digital Processing for EELS Data

Digital Processing for EELS Data. Xiang Yang WATLABS, Univeristy of Waterloo. Signals and Noise --1. Noise: any unwated information. Signal: any useful information. Signals and Noise --2. S ignal :

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Digital Processing for EELS Data

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  1. Digital Processing for EELS Data Xiang Yang WATLABS, Univeristy of Waterloo

  2. Signals and Noise --1 Noise: any unwated information Signal: any useful information

  3. Signals and Noise --2 • Signal: what you are measuring that is the result of the presence of your analyte • Noise: extraneous information that can interfere with or alter the signal.

  4. Types of Noise --1 • Random Noise: sign & magnitude --unpredictable • Non-Random Noise: sign & magnitude – correlated with some event

  5. Types of Noise --2 • Fundamental Noise: ------- Due to the nature of light and matter ------- Cannot be totally eliminated • Non-Fundamental Noise: ------- Mostly due to instrumentation ------- can be eliminated (theoretically)

  6. Signal to Noise Ratio (SNR)

  7. Signal Source Detector Analog Treatments Analog to Digital Conversion Noise Sources Non-monochromate light source Detector’s Dark Current, electromagnetic interference, etc. Circuit noise, baseline, electromagnetic interference, etc. Quantization effects

  8. SNR Enhancement • Hardware

  9. Dwell Time v.s. SNR • Communication between Computer & Machine

  10. Ensemble Averaging • Collect multiple signals over the same time or wavelength (x-axis) domain • Calculate the mean signal at each point in the domain • Re-plot the averaged signal • Since noise is random (some +/ some -), this helps reduce the overall noise by cancellation!

  11. Boxcar Averaging • Take an average of 2 or more signals in some domain • Plot these points as the average signal in the same domain • Can be done with just one set of data • You lose some detail in the overall signal

  12. Digital Filtering • Weighted Digital Filtering • Fast Fourier Transform Digital Filtering

  13. Weighted Filtering

  14. Fast Fourier Transformation Filtering Main Point:Noise is of a higher frequency than the information

  15. FFT Filtering Filtered Signal Noisy Data (Time Domain) Modified Data (Freq. Domain) FT FT Low Pass Filter Tranformed Data (Frequency Domain)

  16. FT FT Filtering

  17. FFT ---- Real Sample

  18. First Fourier Tranform Cut off Frequency (0.003 Hz)

  19. Thank You !

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