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content/.trash/Untitled 2.md
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content/.trash/Untitled 2.md
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@ -8,4 +8,4 @@ date: 2023-12-03
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# Web Framework
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* [Flask](computer_sci/code_frame_learn/flask/MOC.md)
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* [Flask](computer_sci/code_frame_learn/web/flask/MOC.md)
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---
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title: Flask - MOC
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date: 2023-12-03
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---
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* [MSGI Introduction](computer_sci/code_frame_learn/flask/MSGI.md)
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content/computer_sci/code_frame_learn/web/flask/MOC.md
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content/computer_sci/code_frame_learn/web/flask/MOC.md
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---
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title: Flask - MOC
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date: 2023-12-03
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---
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* [MSGI Introduction](computer_sci/code_frame_learn/web/flask/MSGI.md)
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content/game/dota2/analyze_platform.md
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content/game/dota2/analyze_platform.md
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---
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title: Dota2 Analyze Platform
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tags:
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- game
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- dota2
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date: 2024-05-13
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---
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# Platform
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* [Stratz](https://stratz.com/)
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# Some articles about this platform
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* [IMP: Decoding Your Performance](https://medium.com/stratz/imp-decoding-your-performance-c251dcb42b93#id_token=eyJhbGciOiJSUzI1NiIsImtpZCI6ImEzYjc2MmY4NzFjZGIzYmFlMDA0NGM2NDk2MjJmYzEzOTZlZGEzZTMiLCJ0eXAiOiJKV1QifQ.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.IQAOjw_MfhYeGjurgTbsTBni3nhMM40TV_3837VJa2NdfMiz2aiXMpgaOAmgz-kxoJjRvR-Gcu4x72nBbOV7vfLXeN-L8YDMNWjYn7vDNIEeBpD6yuws5bUFNOg7PVTzPAfhIF9Zqmx8_SjOkppkB67tAbQXR04n0wQYNyvwFHo-BZ8xdZDQEMSz0qB_F27Fjmz83C7o57wGMdg-gTwpLVJYA37Z_kdav4prwpufS8Z5k5hj85TgVeq_lXMVrJXHCKiNmoprpeslyAn-MN6xu9Zr-rMqMhOQwXUx7QOhTJZ8XAhH8oImc-_u5nzD_hZZVkl8JDZSb5wYobyg462HWg)
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content/signal/hardware/ADC/jitter.md
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content/signal/hardware/ADC/jitter.md
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---
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title: Jitter
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tags:
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- advanced
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- hardware
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- basic
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date: 2024-05-15
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---
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# Introduction
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Clock edges have timing uncertainty, typically called jitter, which degrades the noise performance of the ADC.
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# Reference
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* https://www.youtube.com/watch?v=z_23tV1Ek0E
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* chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.ti.com/content/dam/videos/external-videos/en-us/2/3816841626001/5529003238001.mp4/subassets/TIPL-4704-Jitter-vs-SNR.pdf
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content/signal/hardware/oscilloscope/DCA.md
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content/signal/hardware/oscilloscope/DCA.md
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---
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title: Digital Sampling Oscilloscope or Digital Communication Analyzer
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tags:
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- advanced
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- hardware
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- devices
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date: 2024-05-14
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---
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# Overview for DCA
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这是一个很好的例子,用记录自行车车轮旋转来理解Real-time示波器和采样示波器的区别。
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我们想来记录自行车车轮的旋转,以每旋转一度为单位捕捉车轮的全部旋转过程。
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方法一是使用帧率非超高的录像机,其速度足以在单次旋转的时间内捕捉 360 幅图像。使用这种方法,您可以在看到车轮转动一次后捕捉到所有独特的角度位置。
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方法二是使用一台相机,它一次只能拍一张照片,不过这台相机可以进行编程,使其在发出信号后的特定时间内拍摄图像。因此又能力去编程每转360°后去拍摄一张照片,这样就可以每转一圈拍摄 1°、2°、3°... 的照片。再拍摄360张照片后,可以将它们按顺序排列在一起,制作出整个旋转过程的动画。
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# Key Differences for DCA
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## Viewing Waveforms
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*A real-time oscilloscope uses an internal sample clock and stores the data sampled before and after the trigger signal.*
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对于Real-Time示波器,有一个独立于采集信号的内部采样时钟。其采样速度比采集信号更快,根据采样定理可以有效地捕捉到input signal的各种细节,同时可以决定何时开始采集。同时,因为触发可以基于任意标准,如voltage level或者其他什么模式,可以为调试带来更大的灵活性。同时还允许collect pre-trigger samples,为我们了解事件前后的信号行为。
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# Reference
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* https://www.keysight.com/blogs/en/tech/bench/2022/05/09/real-time-vs-sampling-oscilloscopes-what-are-the-differences
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* https://www.youtube.com/watch?v=j2vch6wAddc
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content/signal/hardware/oscilloscope/triggering.md
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content/signal/hardware/oscilloscope/triggering.md
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---
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title: Tigger in DSO
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tags:
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- advanced
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- hardware
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- signal
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date: 2024-05-14
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---
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# Simple to Say
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Triggering的目的是为了synchronize input signal和time base signal,通过triggering可以让repetitive waveforms再显示屏上找到固定的位置而不会随机出现。
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# Reference
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* https://www.youtube.com/watch?v=j2vch6wAddc
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* https://www.youtube.com/watch?v=F_UKMbf9uJw
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@ -179,4 +179,5 @@ def EMD(signal, max_imf = 10, tolerance = 0.01):
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* [Huang, Norden E., et al. “The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis.” _Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences_, vol. 454, no. 1971, Mar. 1998, pp. 903–95. _DOI.org (Crossref)_, https://doi.org/10.1098/rspa.1998.0193.](https://royalsocietypublishing.org/doi/abs/10.1098/rspa.1998.0193#purchaseArea)
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* https://www.youtube.com/watch?v=K-LhNvr-CSk
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* https://towardsdatascience.com/decomposing-signal-using-empirical-mode-decomposition-algorithm-explanation-for-dummy-93a93304c541⭐
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* https://en.wikipedia.org/wiki/Hilbert_spectrum
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* https://en.wikipedia.org/wiki/Hilbert_spectrum
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* https://www.youtube.com/watch?v=BLUL8i0KzKg
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