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Diffstat (limited to 'admin/meeting-notes/2025-10-13.md')
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diff --git a/admin/meeting-notes/2025-10-13.md b/admin/meeting-notes/2025-10-13.md index 85ed266..93f22d2 100644 --- a/admin/meeting-notes/2025-10-13.md +++ b/admin/meeting-notes/2025-10-13.md @@ -24,9 +24,9 @@ Module 4: Data Analysis and Processing - 4.1. Introduction to Data Analysis (describe the Panda Toolbox) -> One Lecture - 4.2. We should focus on effective ways of looking at data (philosophy of making that make sense) -> One Lecture -- 4.2. I/O file handling (different types of data files: ASCII, CSV, TXT, JPEG,...) -> Here they will learn how to read/handle: datasets including images. -> One Lecture -- 4.3. Statistical Analysis (mean/averaging, standard deviation, variance...) -> **_Reminder: Ciprian to send application from Spectroscopy and CFD -> One Lecture_** -- 4.4. Data Cleaning (linear regression, least square fitting, extrapolation, moving average) -> One Lecture -- 4.5. Data Filtering & Signal Processing (filters: Gaussian Filter, Sobel edge detection, Kalman, Butterworth, FFT) -> We need to give a lecture of what is/how it works Digital filter design (lowpass, highpass, bandpass, filter response curves, how does one select a given filter). One lecture all theory so that in the second lecture they learn how to select different filters. -> Two lectures -- 4.6 Image processing: apply some of the filtering to make images sharper/crispier, teach them how to make videos/gifs out of images. -> One Lecture. Possible application: **_from CFD density data obtain Synthetic Schlieren images (Ciprian will send you this!)_** +- 4.3. I/Ofile handling (different types of data files: ASCII, CSV, TXT, JPEG,...) -> Here they will learn how to read/handle: datasets including images. -> One Lecture +- 4.4. Statistical Anasis (mean/averaging, standard deviation, variance...) -> **_Reminder: Ciprian to send application from Spectroscopy and CFD -> One Lecture_** +- 4.5. Data Cleaning (linear regression, least square fitting, extrapolation, moving average) -> One Lecture +- 4.6. Data Filtering & Signal Processing (filters: Gaussian Filter, Sobel edge detection, Kalman, Butterworth, FFT) -> We need to give a lecture of what is/how it works Digital filter design (lowpass, highpass, bandpass, filter response curves, how does one select a given filter). One lecture all theory so that in the second lecture they learn how to select different filters. -> Two lectures +- 4.7 Image processing: apply some of the filtering to make images sharper/crispier, teach them how to make videos/gifs out of images. -> One Lecture. Possible application: **_from CFD density data obtain Synthetic Schlieren images (Ciprian will send you this!)_**
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