diff --git a/content/.trash/research_plan_MayToJuly.md b/content/.trash/research_plan_MayToJuly.md
new file mode 100644
index 000000000..a151abc52
--- /dev/null
+++ b/content/.trash/research_plan_MayToJuly.md
@@ -0,0 +1,7 @@
+---
+title: 科研计划 5月 - 7月
+tags:
+  - plan
+date: 2024-04-29
+---
+
diff --git a/content/atlas.md b/content/atlas.md
index 3271dafa2..354a3a6fc 100644
--- a/content/atlas.md
+++ b/content/atlas.md
@@ -20,7 +20,7 @@ date: 2024-02-28
 
 * [Physics](physics/physics_MOC.md)
 
-* [Signal Processing](signal_processing/signal_processing_MOC.md)
+* [Signal Processing](signal/signal_processing/signal_processing_MOC.md)
 
 * [Data Science](data_sci/data_sci_MOC.md)
 
diff --git a/content/computer_sci/multiThread_and_multiProcess/attachments/Pasted image 20240426171635.png b/content/computer_sci/multiThread_and_multiProcess/attachments/Pasted image 20240426171635.png
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new file mode 100644
index 000000000..ee8fa7b77
--- /dev/null
+++ b/content/computer_sci/multiThread_and_multiProcess/basic.md
@@ -0,0 +1,26 @@
+---
+title: What's Multi-Thread and Multi-Process Coding
+tags:
+  - multi-code
+  - advanced
+  - code-design
+date: 2024-04-26
+---
+
+
+# Introduction
+
+
+## Thread vs. Process
+
+### Overview
+
+![](computer_sci/multiThread_and_multiProcess/attachments/Pasted%20image%2020240426171635.png)
+
+### Process
+
+
+# Reference
+
+* https://ycc.idv.tw/multithread-multiprocess-gil.html
+* [https://www.youtube.com/watch?v=4rLW7zg21gI⭐⭐⭐](https://www.youtube.com/watch?v=4rLW7zg21gI)
\ No newline at end of file
diff --git a/content/plan/research_plan/科研计划Main Thread.xmind b/content/plan/research_plan/科研计划Main Thread.xmind
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diff --git a/content/research_career/UWB_about/UWB_signal_generate.md b/content/research_career/UWB_about/UWB_signal_generate.md
index 66227e84c..7eac73b4c 100644
--- a/content/research_career/UWB_about/UWB_signal_generate.md
+++ b/content/research_career/UWB_about/UWB_signal_generate.md
@@ -56,7 +56,7 @@ Diodes:
 
 In this article, [Circularly Polarized Ultra-Wideband Radar System for Vital Signs Monitoring](https://ieeexplore.ieee.org/document/6491501), it uses AD9959 DDS to control UWB pulse repetition frequency (PRF). This DDS has the capability to generate sinusoids up to 250MHz at 0.1-Hz frequency tuning resolution. The DDS has four channels, one for transmitting pulse, one for storing reference pulse from receiver.
 
-The outputs from the DDS, the sinusoids will be amplified by [op-amps](signal_processing/device_and_components/op_amp.md)(Texas Instruments Incorporated OPA699, in this article). After amplifying, the signal will be fed to [step recovery diode](signal_processing/device_and_components/SRD.md)(SRD). 
+The outputs from the DDS, the sinusoids will be amplified by [op-amps](signal/signal_processing/device_and_components/op_amp.md)(Texas Instruments Incorporated OPA699, in this article). After amplifying, the signal will be fed to [step recovery diode](signal/signal_processing/device_and_components/SRD.md)(SRD). 
 
 The **cascaded shunt mode SRD** with **decreasing lifetime method** of pulse generation produces high amplitude pulses of 3 $V_{p-p}$ at low PRFs (megahertz range), thus the pulse generator can directly drive the antenna subsystem saving the need for expensive broadband power amplifiers
 
diff --git a/content/research_career/UWB_about/report/UWB_signal_generate.md b/content/research_career/UWB_about/report/UWB_signal_generate.md
index d4f627a43..ba531c00a 100644
--- a/content/research_career/UWB_about/report/UWB_signal_generate.md
+++ b/content/research_career/UWB_about/report/UWB_signal_generate.md
@@ -56,7 +56,7 @@ Diodes:
 
 In this article, [Circularly Polarized Ultra-Wideband Radar System for Vital Signs Monitoring](https://ieeexplore.ieee.org/document/6491501), it uses AD9959 DDS to control UWB pulse repetition frequency (PRF). This DDS has the capability to generate sinusoids up to 250MHz at 0.1-Hz frequency tuning resolution. The DDS has four channels, one for transmitting pulse, one for storing reference pulse from receiver.
 
-The outputs from the DDS, the sinusoids will be amplified by [op-amps](signal_processing/device_and_components/op_amp.md)(Texas Instruments Incorporated OPA699, in this article). After amplifying, the signal will be fed to [step recovery diode](signal_processing/device_and_components/SRD.md)(SRD). 
+The outputs from the DDS, the sinusoids will be amplified by [op-amps](signal/signal_processing/device_and_components/op_amp.md)(Texas Instruments Incorporated OPA699, in this article). After amplifying, the signal will be fed to [step recovery diode](signal/signal_processing/device_and_components/SRD.md)(SRD). 
 
 The **cascaded shunt mode SRD** with **decreasing lifetime method** of pulse generation produces high amplitude pulses of 3 $V_{p-p}$ at low PRFs (megahertz range), thus the pulse generator can directly drive the antenna subsystem saving the need for expensive broadband power amplifiers
 
diff --git a/content/research_career/UWB_about/report/基于高频信号大趋势包络提取后的信号关键点提取.md b/content/research_career/UWB_about/report/基于高频信号大趋势包络提取后的信号关键点提取.md
index 2da8d3544..0a4eb44c4 100644
--- a/content/research_career/UWB_about/report/基于高频信号大趋势包络提取后的信号关键点提取.md
+++ b/content/research_career/UWB_about/report/基于高频信号大趋势包络提取后的信号关键点提取.md
@@ -20,7 +20,7 @@ $$
 H(\mu)(t) = \frac{1}{\pi} \text{p.v.} \int_{\infty}^{\infty} \frac{\mu(t)}{t-\tau}d\tau
 $$
 
-![](signal_processing/algorithm/envelope/attachments/Pasted%20image%2020240102150350.png)
+![](signal/signal_processing/algorithm/envelope/attachments/Pasted%20image%2020240102150350.png)
 
 ```MATLAB
 analytical = hilbert(signal)
diff --git a/content/signal/hardware/frequency_mixer.md b/content/signal/hardware/frequency_mixer.md
new file mode 100644
index 000000000..fd6d53f39
--- /dev/null
+++ b/content/signal/hardware/frequency_mixer.md
@@ -0,0 +1,9 @@
+---
+title: Frequency Mixer
+tags:
+  - hardware
+  - basic
+  - ciruit
+  - ciruit-componets
+date: 2024-04-28
+---
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diff --git a/content/signal_processing/algorithm/EMD/basic.md b/content/signal/signal_processing/algorithm/EMD/basic.md
similarity index 92%
rename from content/signal_processing/algorithm/EMD/basic.md
rename to content/signal/signal_processing/algorithm/EMD/basic.md
index 34e93d667..b7918d950 100644
--- a/content/signal_processing/algorithm/EMD/basic.md
+++ b/content/signal/signal_processing/algorithm/EMD/basic.md
@@ -20,11 +20,11 @@ EMD is similar to Fourier Transform (FT). FT assumes our signal is periodic and
 
 ## Overview
 
-![](signal_processing/algorithm/EMD/attachments/Pasted%20image%2020240417160805.png)
+![](signal/signal_processing/algorithm/EMD/attachments/Pasted%20image%2020240417160805.png)
 
 ## Flow Chart
 
-![](signal_processing/algorithm/EMD/attachments/Pasted%20image%2020240417160534.png)
+![](signal/signal_processing/algorithm/EMD/attachments/Pasted%20image%2020240417160534.png)
 
 ## Step by Step
 
@@ -47,7 +47,7 @@ Input  $x(t)$,
 		* Residuum signal is just a constant, monotonic, or just have 1 extremum
 
 
-![](signal_processing/algorithm/EMD/attachments/Pasted%20image%2020240417160436.png)
+![](signal/signal_processing/algorithm/EMD/attachments/Pasted%20image%2020240417160436.png)
 
 
 ## Hilbert Spectral Analysis (HSA)
@@ -56,11 +56,11 @@ Input  $x(t)$,
 
 To see:
 
-[Instantaneous Frequency⭐](signal_processing/basic_knowledge/instantaneous_frequency.md)
+[Instantaneous Frequency⭐](signal/signal_processing/basic_knowledge/instantaneous_frequency.md)
 
 ### HSA after EMD
 
-![](signal_processing/algorithm/EMD/attachments/2d8bbe7b82ba09ec5220d81af8a5c22.jpg)
+![](signal/signal_processing/algorithm/EMD/attachments/2d8bbe7b82ba09ec5220d81af8a5c22.jpg)
 
 得到这些IMF之后，我们的信号$x(t)$可以表达为，
 
diff --git a/content/signal_processing/algorithm/EMD/eemd.md b/content/signal/signal_processing/algorithm/EMD/eemd.md
similarity index 87%
rename from content/signal_processing/algorithm/EMD/eemd.md
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index 5e3e799c5..e9bc0fec2 100644
--- a/content/signal_processing/algorithm/EMD/eemd.md
+++ b/content/signal/signal_processing/algorithm/EMD/eemd.md
@@ -11,25 +11,25 @@ date: 2024-04-23
 
 # Intro
 
-集合经验模态分解（Ensemble Empirical Mode Decomposition, EEMD）是一种改进的[EMD](signal_processing/algorithm/EMD/basic.md)方法，它通过引入白噪声来解决EMD中的**模态混叠问题**。
+集合经验模态分解（Ensemble Empirical Mode Decomposition, EEMD）是一种改进的[EMD](signal/signal_processing/algorithm/EMD/basic.md)方法，它通过引入白噪声来解决EMD中的**模态混叠问题**。
 
 
 模态混叠是指在分解过程中，不同时间尺度的信号成分错误地混合在一起，导致分解结果不准确。
 
-![](signal_processing/algorithm/EMD/attachments/Pasted%20image%2020240423162631.png)
+![](signal/signal_processing/algorithm/EMD/attachments/Pasted%20image%2020240423162631.png)
 
 上述figure就是一个很好的例子，连续低频正弦信号上叠加了间歇性高频震动的调制信号，因为间歇性高频震动的调制信号干扰了Maximum点的选择，使得局部极值在很短的时间间隔发生多次跳变，进而使得我们的IMF并不准确，不同时间尺度的信号成分错误地混合在一起。
 
 以下我们也通过我们写的EMD做了示范：
 
 
-![](signal_processing/algorithm/EMD/attachments/Figure_1.png)
+![](signal/signal_processing/algorithm/EMD/attachments/Figure_1.png)
 
 
-![](signal_processing/algorithm/EMD/attachments/Figure_3.png)
+![](signal/signal_processing/algorithm/EMD/attachments/Figure_3.png)
 
 
-![](signal_processing/algorithm/EMD/attachments/Figure_2.png)
+![](signal/signal_processing/algorithm/EMD/attachments/Figure_2.png)
 
 很明显，在IMF1，发生了混叠
 
@@ -190,11 +190,11 @@ def EEMD(signal, max_imf = 10, tolerance = 0.01, iterations = 10):
 
 通过EEMD，结果如下：
 
-![](signal_processing/algorithm/EMD/attachments/Figure_1%201.png)
+![](signal/signal_processing/algorithm/EMD/attachments/Figure_1%201.png)
 
-![](signal_processing/algorithm/EMD/attachments/Figure_2%201.png)
+![](signal/signal_processing/algorithm/EMD/attachments/Figure_2%201.png)
 
-![](signal_processing/algorithm/EMD/attachments/Figure_3%201.png)
+![](signal/signal_processing/algorithm/EMD/attachments/Figure_3%201.png)
 
 EEMD的前几个IMF将高频噪声和白噪声过滤，在IMF7显示了信号原有的模态
 # Reference
diff --git a/content/signal/signal_processing/algorithm/PSD_estimation/PSD_estimation.md b/content/signal/signal_processing/algorithm/PSD_estimation/PSD_estimation.md
new file mode 100644
index 000000000..c87548346
--- /dev/null
+++ b/content/signal/signal_processing/algorithm/PSD_estimation/PSD_estimation.md
@@ -0,0 +1,8 @@
+---
+title: Power spectral density estimation
+tags:
+  - signal-processing
+  - statistics
+date: 2023-11-30
+---
+[Power spectral density estimation](signal/signal_processing/basic_knowledge/concept/Spectral_density.md)(PSDE, or SDE)，功率谱估计是随机信号处理的重要研究内容之一
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diff --git a/content/signal_processing/algorithm/advanced_statistic/autocorrelation/autocorrelation.md b/content/signal/signal_processing/algorithm/advanced_statistic/autocorrelation/autocorrelation.md
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index f1ebe56e6..fe0cca155 100644
--- a/content/signal_processing/algorithm/advanced_statistic/autocorrelation/autocorrelation.md
+++ b/content/signal/signal_processing/algorithm/advanced_statistic/autocorrelation/autocorrelation.md
@@ -31,9 +31,9 @@ cov(X, Y) & = \frac{\sum(x-\overline{x})(y-\overline{y})}{n-1} \\
 \end{split}
 \end{equation}
 $$
-![](signal_processing/algorithm/advanced_statistic/autocorrelation/attachments/Pasted%20image%2020240415171344.png)
+![](signal/signal_processing/algorithm/advanced_statistic/autocorrelation/attachments/Pasted%20image%2020240415171344.png)
 
-![](signal_processing/algorithm/advanced_statistic/autocorrelation/attachments/Pasted%20image%2020240415171351.png)
+![](signal/signal_processing/algorithm/advanced_statistic/autocorrelation/attachments/Pasted%20image%2020240415171351.png)
 
 
 Covariance is hard to **interpret** because it is sensitive to the **scale**
@@ -43,7 +43,7 @@ To solve the scale effect, here's the correlation:
 
 ## Correlation
 
-![](signal_processing/algorithm/advanced_statistic/autocorrelation/attachments/Pasted%20image%2020240415171510.png)
+![](signal/signal_processing/algorithm/advanced_statistic/autocorrelation/attachments/Pasted%20image%2020240415171510.png)
 
 
 We can quantify the strength of the relationship with correlation (**Pearson’s correlation**)
@@ -61,13 +61,13 @@ $corr(X, Y)$ is between -1 to 1
 >  NOTE: When we’re talking about correlation, we’re only talking about using **straight line**
 
 
-![](signal_processing/algorithm/advanced_statistic/autocorrelation/attachments/Pasted%20image%2020240415171736.png)
+![](signal/signal_processing/algorithm/advanced_statistic/autocorrelation/attachments/Pasted%20image%2020240415171736.png)
 
 For correlation, we usually use **p-value** to **quantify the confidence** of the straight line relationship. **The more samll p-value, the more confident we say they are straight line relationship**; Like the figure:
 
-![](signal_processing/algorithm/advanced_statistic/autocorrelation/attachments/Pasted%20image%2020240415171834.png)
+![](signal/signal_processing/algorithm/advanced_statistic/autocorrelation/attachments/Pasted%20image%2020240415171834.png)
 
-![](signal_processing/algorithm/advanced_statistic/autocorrelation/attachments/Pasted%20image%2020240415171855.png)
+![](signal/signal_processing/algorithm/advanced_statistic/autocorrelation/attachments/Pasted%20image%2020240415171855.png)
 
 
 About P-value, you have better know what's [significance test](math/Statistics/significance_test/whats_the_significance_test.md)
@@ -221,7 +221,7 @@ $$
 # Application
 
 
-* [Period Detection by Autocorrelation](signal_processing/algorithm/advanced_statistic/autocorrelation/period_detection.md)
+* [Period Detection by Autocorrelation](signal/signal_processing/algorithm/advanced_statistic/autocorrelation/period_detection.md)
 # Reference
 
 * https://pinkr1ver.notion.site/Autocorrelation-Analysis-Power-Spectral-Density-330755770347472989062c6b31f18a21?pvs=4
diff --git a/content/signal_processing/algorithm/advanced_statistic/autocorrelation/period_detection.md b/content/signal/signal_processing/algorithm/advanced_statistic/autocorrelation/period_detection.md
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diff --git a/content/signal_processing/algorithm/curve_similarity/curve_similarity.md b/content/signal/signal_processing/algorithm/curve_similarity/curve_similarity.md
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index c7160ec51..98c011dee 100644
--- a/content/signal_processing/algorithm/curve_similarity/curve_similarity.md
+++ b/content/signal/signal_processing/algorithm/curve_similarity/curve_similarity.md
@@ -10,4 +10,4 @@ date: 2024-03-18
 # Method
 
 * [DTW(Dynamic Time Warping)](computer_sci/deep_learning_and_machine_learning/Trick/DTW.md)
-* [Manhattan Distance](signal_processing/algorithm/curve_similarity/manhattan_distance.md)
\ No newline at end of file
+* [Manhattan Distance](signal/signal_processing/algorithm/curve_similarity/manhattan_distance.md)
\ No newline at end of file
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index d0bfeaa58..e2e015bca 100644
--- a/content/signal_processing/algorithm/envelope/hilbert_transform.md
+++ b/content/signal/signal_processing/algorithm/envelope/hilbert_transform.md
@@ -9,7 +9,7 @@ date: 2024-01-12
 
 # Introduction
 
-![](signal_processing/algorithm/envelope/attachments/Pasted%20image%2020240103160713.png)
+![](signal/signal_processing/algorithm/envelope/attachments/Pasted%20image%2020240103160713.png)
 
 # Envelope Explanation
 ## Envelope and Fine Structure
@@ -36,7 +36,7 @@ date: 2024-01-12
 
 早期关于包络和瞬时相位的研究都是基于笛卡尔坐标系x-y
 
-![](signal_processing/algorithm/envelope/attachments/Pasted%20image%2020240102155308.png)
+![](signal/signal_processing/algorithm/envelope/attachments/Pasted%20image%2020240102155308.png)
 
 有关系：
 $$
@@ -73,7 +73,7 @@ $$
 H(\mu)(t) = \frac{1}{\pi} \text{p.v.} \int_{\infty}^{\infty} \frac{\mu(t)}{t-\tau}d\tau
 $$
 
-![](signal_processing/algorithm/envelope/attachments/Pasted%20image%2020240102150350.png)
+![](signal/signal_processing/algorithm/envelope/attachments/Pasted%20image%2020240102150350.png)
 
 
 The Hilbert transform is given by the [Cauchy principal value](math/real_analysis/cauchy_principal_value.md) of the convolution with the function $1/(\pi t)$.
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--- a/content/signal_processing/algorithm/filter/chebyshev_filter.md
+++ b/content/signal/signal_processing/algorithm/filter/chebyshev_filter.md
@@ -54,7 +54,7 @@ T_{n+1}(x) & = 2xT_n(x)-T_{n-1}(x)
 \end{split}
 \end{equation}
 $$
-![](signal_processing/algorithm/filter/attachments/Pasted%20image%2020240108161455.png)
+![](signal/signal_processing/algorithm/filter/attachments/Pasted%20image%2020240108161455.png)
 #### 第二类切比雪夫多项式
 
 $$
@@ -67,7 +67,7 @@ U_{n+1}(x) & = 2xU_n(x) - U_{n-1}(x)
 \end{equation}
 $$
 
-![](signal_processing/algorithm/filter/attachments/Pasted%20image%2020240108161800.png)
+![](signal/signal_processing/algorithm/filter/attachments/Pasted%20image%2020240108161800.png)
 
 ### 正交性
 
diff --git a/content/signal_processing/algorithm/impulse_generating/gaussian_impulse.md b/content/signal/signal_processing/algorithm/impulse_generating/gaussian_impulse.md
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diff --git a/content/signal_processing/basic_knowledge/FT/fourier_transform.md b/content/signal/signal_processing/basic_knowledge/FT/fourier_transform.md
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--- a/content/signal_processing/basic_knowledge/FT/fourier_transform.md
+++ b/content/signal/signal_processing/basic_knowledge/FT/fourier_transform.md
@@ -7,13 +7,13 @@ date: 2023-11-30
 ---
 # Almost Fourier Transform
 
-![](signal_processing/basic_knowledge/attachments/Pasted%20image%2020230919152200.png)
+![](signal/signal_processing/basic_knowledge/attachments/Pasted%20image%2020230919152200.png)
 
 It is important to see there are 2 different frequencies here:
 1. The frequency of the original signal
 2. The frequency with which the **little rotating vector winds around the circle**
 
-![](signal_processing/basic_knowledge/attachments/Pasted%20image%2020230919152234.png)
+![](signal/signal_processing/basic_knowledge/attachments/Pasted%20image%2020230919152234.png)
 
 Different patterns appear as we wind up this graph, but it is clear that the x-coordinate for the center of mass is important when the winding frequency is 3; The same number as the original signal
 
@@ -28,7 +28,7 @@ $$
 因为在Fourier transform中，convention way是顺时针旋转，所以使用$e^{-2\pi ift}$，那如何衡量center of mass呢，如下图：
 
 
-![](signal_processing/basic_knowledge/attachments/Pasted%20image%2020230919152357.png)
+![](signal/signal_processing/basic_knowledge/attachments/Pasted%20image%2020230919152357.png)
 
 
 $$
@@ -43,7 +43,7 @@ $$
 
 这个就是Almost Fourier Transform, 但是实际情况上，Fourier transform倾向于得到scaled center mass，越长的time，旋转越多圈，其Fourier transform也会成倍放大
 
-![](signal_processing/basic_knowledge/attachments/Pasted%20image%2020230919152720.png)
+![](signal/signal_processing/basic_knowledge/attachments/Pasted%20image%2020230919152720.png)
 
 
 # Fourier Transform (FT)
@@ -118,7 +118,7 @@ $$
 
 ## 复数形式推导
 
-![](signal_processing/basic_knowledge/attachments/Pasted%20image%2020230919153109.png)
+![](signal/signal_processing/basic_knowledge/attachments/Pasted%20image%2020230919153109.png)
 
 
 ## 三角函数推导
@@ -184,7 +184,7 @@ $$
 
 **For $X[k]$, it means a $\cos$ wine like this:**
 
-![](signal_processing/basic_knowledge/attachments/Pasted%20image%2020230919153401.png)
+![](signal/signal_processing/basic_knowledge/attachments/Pasted%20image%2020230919153401.png)
 
 # Fast Fourier transform(FFT)
 
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diff --git a/content/signal_processing/basic_knowledge/concept/smith_graph.md b/content/signal/signal_processing/basic_knowledge/concept/smith_graph.md
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diff --git a/content/signal_processing/basic_knowledge/concept/stationary.md b/content/signal/signal_processing/basic_knowledge/concept/stationary.md
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index ab11fef51..c8b145dda 100644
--- a/content/signal_processing/basic_knowledge/concept/stationary.md
+++ b/content/signal/signal_processing/basic_knowledge/concept/stationary.md
@@ -33,7 +33,7 @@ $$
 平稳信号具有Ergodicity，即各态历经，即多样本**集合平均**和单一样本**时间平均**相同
 
 
-![](signal_processing/basic_knowledge/concept/attachments/Pasted%20image%2020240417144416.png)
+![](signal/signal_processing/basic_knowledge/concept/attachments/Pasted%20image%2020240417144416.png)
 
 $$
 \mu_x=E\{x(n)\}=\lim_{M\rightarrow\infty}\frac{1}{2M+1}\sum_{n=-M}^Mx(n)
diff --git a/content/signal_processing/basic_knowledge/concept/what_is_dB.md b/content/signal/signal_processing/basic_knowledge/concept/what_is_dB.md
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index 5a0064726..215a8d894 100644
--- a/content/signal_processing/basic_knowledge/random_signal_basic.md
+++ b/content/signal/signal_processing/basic_knowledge/random_signal_basic.md
@@ -70,7 +70,7 @@ $$
 
 对于拥有Ergodicity的信号，可以用时间平均代替集合平均，即
 
-![](signal_processing/basic_knowledge/attachments/Screenshot_from_2022-10-18_10-53-17.png)
+![](signal/signal_processing/basic_knowledge/attachments/Screenshot_from_2022-10-18_10-53-17.png)
   
 
 $$
diff --git a/content/signal_processing/basic_knowledge/stability_of_discrete_system.md b/content/signal/signal_processing/basic_knowledge/stability_of_discrete_system.md
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index a97987677..27440bb4d 100644
--- a/content/signal_processing/basic_knowledge/stability_of_discrete_system.md
+++ b/content/signal/signal_processing/basic_knowledge/stability_of_discrete_system.md
@@ -47,7 +47,7 @@ $$
 
 最后一个等式来自无穷几何级数，而等式仅在 $|0.5z^{−1}| < 1$ 时成立，可以以 z 为变量写成 $|z| > 0.5$。因此，收敛域为 $|z| > 0.5$。在这种情况下，收敛域为复平面“挖掉”原点为中心的半径为 0.5 的圆盘。
 
-![](signal_processing/basic_knowledge/attachments/Pasted%20image%2020240115112204.png)
+![](signal/signal_processing/basic_knowledge/attachments/Pasted%20image%2020240115112204.png)
 
 
 
diff --git a/content/signal_processing/device_and_components/SRD.md b/content/signal/signal_processing/device_and_components/SRD.md
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index 9e5c123de..a4f178b8a 100644
--- a/content/signal_processing/device_and_components/SRD.md
+++ b/content/signal/signal_processing/device_and_components/SRD.md
@@ -28,4 +28,4 @@ date: 2023-11-02
 
 总之，阶跃恢复二极管（SRD）是一种特殊的二极管，它在高频、脉冲和微波应用中具有广泛的应用，因为它可以产生非常快速的电流和电压变化，适用于各种电子电路中的特殊应用。
 
-![](signal_processing/device_and_components/attachments/Pasted%20image%2020231102154725.png)
+![](signal/signal_processing/device_and_components/attachments/Pasted%20image%2020231102154725.png)
diff --git a/content/signal_processing/device_and_components/VNA_learn.md b/content/signal/signal_processing/device_and_components/VNA_learn.md
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rename from content/signal_processing/device_and_components/VNA_learn.md
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index 0737e64ef..c9017186a 100644
--- a/content/signal_processing/device_and_components/VNA_learn.md
+++ b/content/signal/signal_processing/device_and_components/VNA_learn.md
@@ -18,7 +18,7 @@ About what is VNA: [VNA Research](research_career/UWB_about/report/VNA_research.
 
 1. Reference Calibration
 
-	基准校准是通过标准的开路、短路和负载器（Load）标准件来进行校准；因为这些标准件已经知道它们的[S参数](signal_processing/basic_knowledge/concept/scattering_parameters.md)响应，因此可以用来校准
+	基准校准是通过标准的开路、短路和负载器（Load）标准件来进行校准；因为这些标准件已经知道它们的[S参数](signal/signal_processing/basic_knowledge/concept/scattering_parameters.md)响应，因此可以用来校准
 
 	在LiteVNA产品中，
 	* 中间没有内针的为开路校准件  
@@ -49,24 +49,24 @@ About what is VNA: [VNA Research](research_career/UWB_about/report/VNA_research.
 
 ### Verify Calibration
 
-可以使用[Smith Graph](signal_processing/basic_knowledge/concept/smith_graph.md)来验证我们的Calibration
+可以使用[Smith Graph](signal/signal_processing/basic_knowledge/concept/smith_graph.md)来验证我们的Calibration
 
 开路状态下，Smith Graph的标记点应该在电阻线的最右端，表明阻抗无限大，且表现出纯电阻性
 
-![](signal_processing/device_and_components/attachments/Pasted%20image%2020231007162754.png)
+![](signal/signal_processing/device_and_components/attachments/Pasted%20image%2020231007162754.png)
 
 PORT1链接短路校准件，查看史密斯图标记点应该在史密斯图上电阻线的最左端(阻抗为0，并且表现纯电阻性)。
 
-![](signal_processing/device_and_components/attachments/Pasted%20image%2020231007162817.png)
+![](signal/signal_processing/device_and_components/attachments/Pasted%20image%2020231007162817.png)
 
 PORT1链接50欧姆校准件，查看史密斯图标记点应该在史密斯图上电阻线的中心(阻抗为50欧姆，并且表现纯电阻性)。
 
-![](signal_processing/device_and_components/attachments/Pasted%20image%2020231007162826.png)
+![](signal/signal_processing/device_and_components/attachments/Pasted%20image%2020231007162826.png)
 
 
-链接一根可以确认阻抗与谐振都正常的天线（可以把一根天线定位对照组并妥善保管），可以通过拨轮移动标记点至[驻波比](signal_processing/basic_knowledge/concept/SWR.md)最低点，并同步观察该频率在史密斯图上的点是否在正中心（或者无限接近中心）。同时可以看屏幕最上面的参数，如图显示，我的这条对照天线最好的驻波比为1.021，此时对应的频率2.455GHz，史密斯图中阻抗为50.72Ω+j748mΩ
+链接一根可以确认阻抗与谐振都正常的天线（可以把一根天线定位对照组并妥善保管），可以通过拨轮移动标记点至[驻波比](signal/signal_processing/basic_knowledge/concept/SWR.md)最低点，并同步观察该频率在史密斯图上的点是否在正中心（或者无限接近中心）。同时可以看屏幕最上面的参数，如图显示，我的这条对照天线最好的驻波比为1.021，此时对应的频率2.455GHz，史密斯图中阻抗为50.72Ω+j748mΩ
 
-![](signal_processing/device_and_components/attachments/Pasted%20image%2020231007162914.png)
+![](signal/signal_processing/device_and_components/attachments/Pasted%20image%2020231007162914.png)
 
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index 4a9a6310f..c8dc116d1 100644
--- a/content/signal_processing/device_and_components/cable/cable.md
+++ b/content/signal/signal_processing/device_and_components/cable/cable.md
@@ -8,7 +8,7 @@ date: 2023-12-05
 ---
 # Structure
 
-![](signal_processing/device_and_components/attachments/Pasted%20image%2020231204110242.png)
+![](signal/signal_processing/device_and_components/attachments/Pasted%20image%2020231204110242.png)
 
 * Conductor is located at the center of the cable
 * Other layers is to protect
@@ -79,7 +79,7 @@ date: 2023-12-05
 
 Chinese translation: 实心导体和绞合导体
 
-![](signal_processing/device_and_components/attachments/Pasted%20image%2020231204112611.png)
+![](signal/signal_processing/device_and_components/attachments/Pasted%20image%2020231204112611.png)
 <center><strong>Solid Conductor is in left, Stranded Conductor is in right</strong></center>
 
 
@@ -100,7 +100,7 @@ Chinese translation: 实心导体和绞合导体
 > [!hint] 
 >  根据前哥说的趋肤效应([Skin effect](https://zh.wikipedia.org/wiki/%E9%9B%86%E8%86%9A%E6%95%88%E6%87%89))，高频信号的电子喜欢在金属表面移动，因此实心导体可能已经被淘汰了。
 >  
->  [skin effect note](signal_processing/device_and_components/cable/skin_effect.md)
+>  [skin effect note](signal/signal_processing/device_and_components/cable/skin_effect.md)
 
 ### Stranded Constructions
 
@@ -111,9 +111,9 @@ Chinese translation: 实心导体和绞合导体
 
 #### Bunched Stranded Conductor
 
-![](signal_processing/device_and_components/attachments/Pasted%20image%2020231204114304.png)
+![](signal/signal_processing/device_and_components/attachments/Pasted%20image%2020231204114304.png)
 
-![](signal_processing/device_and_components/attachments/Pasted%20image%2020231204114312.png)
+![](signal/signal_processing/device_and_components/attachments/Pasted%20image%2020231204114312.png)
 
 Bunched strands are simply gathered together without any specific arrangement.
 
@@ -121,7 +121,7 @@ Bunched strands are simply gathered together without any specific arrangement.
 
 #### Concentric Lay Stranded Conductor
 
-![](signal_processing/device_and_components/attachments/Pasted%20image%2020231204130617.png)
+![](signal/signal_processing/device_and_components/attachments/Pasted%20image%2020231204130617.png)
 
 Concentric stranding (同心绞合)
 
@@ -146,17 +146,17 @@ In uni-lay stranding, every layer is twisted in the same direction.
 
 In a rope lay construction, the stranded conductors or strands are arranged in a spiral fashion to form a rope-like structure. This is a departure from the traditional uni-lay or multi-lay construction.
 
-![](signal_processing/device_and_components/attachments/Pasted%20image%2020231204153959.png)
+![](signal/signal_processing/device_and_components/attachments/Pasted%20image%2020231204153959.png)
 
-![](signal_processing/device_and_components/attachments/Pasted%20image%2020231204154015.png)
+![](signal/signal_processing/device_and_components/attachments/Pasted%20image%2020231204154015.png)
 
 
 
 # Cable Structure
 
-![](signal_processing/device_and_components/attachments/Pasted%20image%2020231204160536.png)
+![](signal/signal_processing/device_and_components/attachments/Pasted%20image%2020231204160536.png)
 
-![](signal_processing/device_and_components/attachments/Pasted%20image%2020231204165012.png)
+![](signal/signal_processing/device_and_components/attachments/Pasted%20image%2020231204165012.png)
 
 1. **Standard Conductor（标准导体）：**
     
@@ -238,11 +238,11 @@ In a rope lay construction, the stranded conductors or strands are arranged in a
 
 # Letters on Cable
 
-![](signal_processing/device_and_components/attachments/Pasted%20image%2020231204160640.png)
+![](signal/signal_processing/device_and_components/attachments/Pasted%20image%2020231204160640.png)
 
 ## Size
 
-* AWG - [American Wire Gauge](signal_processing/device_and_components/cable/AWG.md)
+* AWG - [American Wire Gauge](signal/signal_processing/device_and_components/cable/AWG.md)
 * $mm^2$ - Square millimeters
 * MCM - Thousand Circular Mils
 * KCMil - Thousand Circular Mils
@@ -272,7 +272,7 @@ In a rope lay construction, the stranded conductors or strands are arranged in a
 
 ## Quality Control Certified
 
-* [UL, TUV, ISO ... ...](signal_processing/device_and_components/quality_control_certified/qcc.md)
+* [UL, TUV, ISO ... ...](signal/signal_processing/device_and_components/quality_control_certified/qcc.md)
 
 # Cable Properties - Especially for RF circuit
 
@@ -286,7 +286,7 @@ RF cables are quite different to audio cables. As in audio cables we can run cab
 * Frequency you're currently trying to transmit
 * The length of the cable
 
-RF circuits need to consider impedance matching, and the most likely to fluctuate in impedance is the cable. So the antenna cable we used for our radio systems is usually **[coax cable](signal_processing/device_and_components/cable/coax_cable.md) with a nice BNC connector**.
+RF circuits need to consider impedance matching, and the most likely to fluctuate in impedance is the cable. So the antenna cable we used for our radio systems is usually **[coax cable](signal/signal_processing/device_and_components/cable/coax_cable.md) with a nice BNC connector**.
 
 
 
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--- a/content/signal_processing/device_and_components/cable/coax_cable.md
+++ b/content/signal/signal_processing/device_and_components/cable/coax_cable.md
@@ -8,10 +8,10 @@ date: 2023-12-05
 ---
 
 
-![](signal_processing/device_and_components/cable/attachments/Pasted%20image%2020231205144443.png)
+![](signal/signal_processing/device_and_components/cable/attachments/Pasted%20image%2020231205144443.png)
 
 
-A coaxial cable as a transmission line consisting of an inner conducting wire of radius A and an outer conducting sheath of radius B. The space between the two conductors is filled with a dielectric. The fields are entirely contained internally, so coaxial cables are completely protected from outside interference. However, they are difficult to fabricate, [unbalanced](signal_processing/device_and_components/cable/coax_cable_imbalance.md) and lossy over long distances, so their use is constrained to close range applications.
+A coaxial cable as a transmission line consisting of an inner conducting wire of radius A and an outer conducting sheath of radius B. The space between the two conductors is filled with a dielectric. The fields are entirely contained internally, so coaxial cables are completely protected from outside interference. However, they are difficult to fabricate, [unbalanced](signal/signal_processing/device_and_components/cable/coax_cable_imbalance.md) and lossy over long distances, so their use is constrained to close range applications.
 
 
 
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diff --git a/content/signal_processing/device_and_components/oscilloscope/oscilloscope.md b/content/signal/signal_processing/device_and_components/oscilloscope/oscilloscope.md
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index f66f0d5d2..2c51e6182 100644
--- a/content/signal_processing/device_and_components/oscilloscope/oscilloscope.md
+++ b/content/signal/signal_processing/device_and_components/oscilloscope/oscilloscope.md
@@ -18,7 +18,7 @@ An oscilloscope is a testing and measurement instrument that can display the dyn
 
 In analog oscilloscopes, the CRT is the main component. Like the figure:
 
-![](signal_processing/device_and_components/oscilloscope/attachments/Pasted%20image%2020240412115407.png)
+![](signal/signal_processing/device_and_components/oscilloscope/attachments/Pasted%20image%2020240412115407.png)
 
 The vertical system is responsible for sending the main image to the vertical deflection plate of the CRT. Vertical systems amplify or attenuate input signals. The horizontal system is responsible for the movement of the electron beam from left to right. Trigger the system determines when to draw the waveform on the CRT.
 
@@ -26,7 +26,7 @@ The Z-axis circuit below is in charge of brightness control of the electron beam
 
 Let's see the vertical subsystem of the oscilloscope. The basic form of an analog oscillsocpe consists of an attenuator, preamplifier, delay line, and main amplifier. Like the figure:
 
-![](signal_processing/device_and_components/oscilloscope/attachments/Pasted%20image%2020240412115836.png)
+![](signal/signal_processing/device_and_components/oscilloscope/attachments/Pasted%20image%2020240412115836.png)
 
 * **Attenuator**: attenuating the input signal and allows for AC or DC coupling.
 	* 直接输入的信号可能超过示波器屏幕显示范围，因此通过衰减器降低信号幅度；衰减器通过**Partial Attenuation(部分衰竭)** 来保持信号的细节和波形特征
@@ -42,12 +42,12 @@ Let's see the vertical subsystem of the oscilloscope. The basic form of an analo
 * **Horizontal subsystem** as figure:
 	* The horizontal signal system provides a **deflection voltage** to the horizontal plate to move the electron beam horizontally. For this purpose, the scanning generator circuit generates sawtooth (or ramp) signals to control the scanning rate of the beam. The sawtooth or ramp signal rises linearly and can measure the time between two events. The scanning generator is calibrated in a timely manner, hence it is also known as a **time base**.
 
-![](signal_processing/device_and_components/oscilloscope/attachments/Pasted%20image%2020240412153613.png)
+![](signal/signal_processing/device_and_components/oscilloscope/attachments/Pasted%20image%2020240412153613.png)
 
 * **Trigger system** as figure:
 	* The trigger system determines the time when the oscilloscope draws the waveform on the screen.
 
-![](signal_processing/device_and_components/oscilloscope/attachments/Pasted%20image%2020240412154021.png)
+![](signal/signal_processing/device_and_components/oscilloscope/attachments/Pasted%20image%2020240412154021.png)
 
 显像管屏幕内部涂有磷，因此当电子与之碰撞时，屏幕就会发光。水平系统负责将光束从左向右移动。当光束到达屏幕的最右侧时，它会迅速返回左侧，重新开始这个过程。这个过程称为扫描（或跟踪或扫描）。
 
@@ -58,7 +58,7 @@ Let's see the vertical subsystem of the oscilloscope. The basic form of an analo
 
 ## Principle
 
-![](signal_processing/device_and_components/oscilloscope/attachments/Pasted%20image%2020240412154704.png)
+![](signal/signal_processing/device_and_components/oscilloscope/attachments/Pasted%20image%2020240412154704.png)
 
 
 
diff --git a/content/signal_processing/device_and_components/quality_control_certified/qcc.md b/content/signal/signal_processing/device_and_components/quality_control_certified/qcc.md
similarity index 100%
rename from content/signal_processing/device_and_components/quality_control_certified/qcc.md
rename to content/signal/signal_processing/device_and_components/quality_control_certified/qcc.md
diff --git a/content/signal_processing/radio_communication/Near_far_field.md b/content/signal/signal_processing/radio_communication/Near_far_field.md
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rename from content/signal_processing/radio_communication/Near_far_field.md
rename to content/signal/signal_processing/radio_communication/Near_far_field.md
diff --git a/content/signal/signal_processing/signal_processing_MOC.md b/content/signal/signal_processing/signal_processing_MOC.md
new file mode 100644
index 000000000..633838595
--- /dev/null
+++ b/content/signal/signal_processing/signal_processing_MOC.md
@@ -0,0 +1,64 @@
+---
+title: Signal Processing - MOC
+tags:
+  - MOC
+  - signal-processing
+date: 2024-03-18
+---
+# Basic
+
+## Classic Signal Processing
+
+* [Fourier Transform](signal/signal_processing/basic_knowledge/FT/fourier_transform.md)
+* [Fourier transform pairs and properties derivation](signal/signal_processing/basic_knowledge/FT/fourier_transform_pairs_derivation.md)
+* [FBW - Fractional Band Width](signal/signal_processing/basic_knowledge/concept/FBW.md)
+* [What's Linear in Signal Mean?](signal/signal_processing/basic_knowledge/concept/linear.md)
+
+## Modern Signal Processing
+
+* [Random Signal Basic](signal/signal_processing/basic_knowledge/random_signal_basic.md)
+* [Power spectral density estimation](signal/signal_processing/algorithm/PSD_estimation/PSD_estimation.md)
+* [What's Stationary mean in Signal?](signal/signal_processing/basic_knowledge/concept/stationary.md)
+* [Instantaneous Frequency](signal/signal_processing/basic_knowledge/instantaneous_frequency.md)
+# Devices and Components
+
+* [✨Learn VNA in practical way](signal/signal_processing/device_and_components/VNA_learn.md)
+* [Cable](signal/signal_processing/device_and_components/cable/cable.md)
+* [oscilloscope](signal/signal_processing/device_and_components/oscilloscope/oscilloscope.md)
+
+# Signal Algorithm about
+
+## Envelope
+
+* [Hilbert Transform - Envelope](signal/signal_processing/algorithm/envelope/hilbert_transform.md)
+
+## Curve similarity
+
+* [Curve Similarity - MOC](signal/signal_processing/algorithm/curve_similarity/curve_similarity.md)
+
+## Filter
+
+* [Chebyshev Filter](signal/signal_processing/algorithm/filter/chebyshev_filter.md)
+
+## Generating Impulse
+
+* [Gaussian Impulse Generating](signal/signal_processing/algorithm/impulse_generating/gaussian_impulse.md)
+
+## Autocorrelation
+
+* [Autocorrelation in Signal Processing](signal/signal_processing/algorithm/advanced_statistic/autocorrelation/autocorrelation.md)
+* [Period Detection by Autocorrelation](signal/signal_processing/algorithm/advanced_statistic/autocorrelation/period_detection.md)
+
+
+## Empirical Mode Decomposition
+
+* [EMD Basic](signal/signal_processing/algorithm/EMD/basic.md)
+* [EEMD](signal/signal_processing/algorithm/EMD/eemd.md)
+
+# Software
+
+## CST MWS
+
+* [CST MWS Basic](signal/signal_processing/software/simulation/CST/basic.md)
+
+
diff --git a/content/signal_processing/software/simulation/CST/basic.md b/content/signal/signal_processing/software/simulation/CST/basic.md
similarity index 100%
rename from content/signal_processing/software/simulation/CST/basic.md
rename to content/signal/signal_processing/software/simulation/CST/basic.md
diff --git a/content/signal_processing/algorithm/PSD_estimation/PSD_estimation.md b/content/signal_processing/algorithm/PSD_estimation/PSD_estimation.md
deleted file mode 100644
index 4372e66cd..000000000
--- a/content/signal_processing/algorithm/PSD_estimation/PSD_estimation.md
+++ /dev/null
@@ -1,8 +0,0 @@
----
-title: Power spectral density estimation
-tags:
-  - signal-processing
-  - statistics
-date: 2023-11-30
----
-[Power spectral density estimation](signal_processing/basic_knowledge/concept/Spectral_density.md)(PSDE, or SDE)，功率谱估计是随机信号处理的重要研究内容之一
\ No newline at end of file
diff --git a/content/signal_processing/signal_processing_MOC.md b/content/signal_processing/signal_processing_MOC.md
deleted file mode 100644
index 00bfac9b2..000000000
--- a/content/signal_processing/signal_processing_MOC.md
+++ /dev/null
@@ -1,64 +0,0 @@
----
-title: Signal Processing - MOC
-tags:
-  - MOC
-  - signal-processing
-date: 2024-03-18
----
-# Basic
-
-## Classic Signal Processing
-
-* [Fourier Transform](signal_processing/basic_knowledge/FT/fourier_transform.md)
-* [Fourier transform pairs and properties derivation](signal_processing/basic_knowledge/FT/fourier_transform_pairs_derivation.md)
-* [FBW - Fractional Band Width](signal_processing/basic_knowledge/concept/FBW.md)
-* [What's Linear in Signal Mean?](signal_processing/basic_knowledge/concept/linear.md)
-
-## Modern Signal Processing
-
-* [Random Signal Basic](signal_processing/basic_knowledge/random_signal_basic.md)
-* [Power spectral density estimation](signal_processing/algorithm/PSD_estimation/PSD_estimation.md)
-* [What's Stationary mean in Signal?](signal_processing/basic_knowledge/concept/stationary.md)
-* [Instantaneous Frequency](signal_processing/basic_knowledge/instantaneous_frequency.md)
-# Devices and Components
-
-* [✨Learn VNA in practical way](signal_processing/device_and_components/VNA_learn.md)
-* [Cable](signal_processing/device_and_components/cable/cable.md)
-* [oscilloscope](signal_processing/device_and_components/oscilloscope/oscilloscope.md)
-
-# Signal Algorithm about
-
-## Envelope
-
-* [Hilbert Transform - Envelope](signal_processing/algorithm/envelope/hilbert_transform.md)
-
-## Curve similarity
-
-* [Curve Similarity - MOC](signal_processing/algorithm/curve_similarity/curve_similarity.md)
-
-## Filter
-
-* [Chebyshev Filter](signal_processing/algorithm/filter/chebyshev_filter.md)
-
-## Generating Impulse
-
-* [Gaussian Impulse Generating](signal_processing/algorithm/impulse_generating/gaussian_impulse.md)
-
-## Autocorrelation
-
-* [Autocorrelation in Signal Processing](signal_processing/algorithm/advanced_statistic/autocorrelation/autocorrelation.md)
-* [Period Detection by Autocorrelation](signal_processing/algorithm/advanced_statistic/autocorrelation/period_detection.md)
-
-
-## Empirical Mode Decomposition
-
-* [EMD Basic](signal_processing/algorithm/EMD/basic.md)
-* [EEMD](signal_processing/algorithm/EMD/eemd.md)
-
-# Software
-
-## CST MWS
-
-* [CST MWS Basic](signal_processing/software/simulation/CST/basic.md)
-
-
diff --git a/content/synthetic_aperture_radar_imaging/SAR_Explained.md b/content/synthetic_aperture_radar_imaging/SAR_Explained.md
index 75da20fb9..f9d4fb1d9 100644
--- a/content/synthetic_aperture_radar_imaging/SAR_Explained.md
+++ b/content/synthetic_aperture_radar_imaging/SAR_Explained.md
@@ -36,7 +36,7 @@ The transmitter generates successive short bursts or pulses of microwave at regu
 
 * Radar can measure *amplitude* and *phase*
 * Radar can only measure part of echoes.
-* The strength of the reflected echo is the backscattering coefficient ([sigma nought](synthetic_aperture_radar_imaging/radiometric_calibration.md)）and is expressed in [decibels(dB)](signal_processing/basic_knowledge/concept/what_is_dB.md)
+* The strength of the reflected echo is the backscattering coefficient ([sigma nought](synthetic_aperture_radar_imaging/radiometric_calibration.md)）and is expressed in [decibels(dB)](signal/signal_processing/basic_knowledge/concept/what_is_dB.md)
 
 ## Radar Resolution