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---
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title: Homework 1, student's Problem
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tags:
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- work-about
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---
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# 0780
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---
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title: A log for test
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tags:
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- log
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date: 2024-02-28
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---
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A log for test
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---
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title: Problem
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tags:
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- research-about
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---
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* UWB signal ejection
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* S parameter -> Frequency spectrum
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* Know why VNA don't have time domain (circuit view)
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{}
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---
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title: Intermediate Frequency Bandwidth
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tags:
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- equipment
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- VNA
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- research-about
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---
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---
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title: Dota2 Learning Road
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tags:
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- data2
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- game
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---
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# Map 7.33
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* [A, Nathan, et al. “Dota 2 New Map April 2023 - Dota 2 Guide.” _IGN_, https://www.ign.com/wikis/dota-2/Dota_2_New_Map_April_2023. Accessed 18 Sept. 2023.](https://www.ign.com/wikis/dota-2/Dota_2_New_Map_April_2023)
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---
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title: Equipment Research MOC
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tags:
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- MOC
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---
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---
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title: UWB signal characterization experiment by VNA demo
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tags:
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- experiment
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---
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# Experiment Graph Overview
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In this experiment, we use VNA Port 1 to eject signal and port2 to receive the signal reflecting by the reflection medium, such as burned tissue.
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And we can use VNA to get scattering parameter to do analysis in frequency spectrum, including amplitude information and phase information.
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# Experiment Explanations
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## What is VNA
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A Vector Network Analyzer (VNA) is a sophisticated electronic instrument used in the field of radio frequency (RF) and microwave engineering. Its primary function is to measure and characterize the electrical behavior of high-frequency components, such as antennas, cables, and passive RF devices like filters and amplifiers. VNAs are essential tools in the design, testing, and maintenance of RF and microwave systems.
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## Ejecting Signal
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The VNA generates an "ejecting signal," also known as the "incident signal" or "test signal", which is usually be a continuous wave (CW) or narrowband signal. In our VNA equipment, the signal is CW signal, which is at discrete frequencies sweeping in the specific frequency range.
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Here I want to name the signal, which is at discrete frequencies sweeping in the specific frequency range, **frequency sweeping signal**
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**So we can not acquire UWB signal directly in VNA.**
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Here are two possible solutions:
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1. Modem our sweep signal to UWB signal.
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* I have already find the way to modem chirp signal to UWB signal, here:
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[Chirp BOK BPSK.pdf](https://pinktalk.online/research_career/attachments/CN101267424A.pdf)
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* Not sure if we can modem our frequency sweeping signal to UWB signal.
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2. Direct using our frequency sweeping signal.
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* Though we don't directly eject UWB signal, our ejecting signal also contains discrete frequencies, which are composition of UWB signal. In this way, we can analysis different frequencies component in UWB separately.
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* Speculatively, we guess the high frequency part's phase information can provide the range detection function. The low frequency part's amplitude information will have a relation with the reflecting medium.
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In this experiment demo, we use solution 2.
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## Data we get
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In this experiment, we can only get scattering parameter, S11 and S12.
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Using this parameter, considering incident as a constant we can get frequency spectrum information, though the signal is not UWB signal
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# Experiment Step
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## 1. Set up Experiment equipment
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* Prepare the experimental apparatus
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* VNA, Keysight E5063A 100kHz - 6.5GHz
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* UWB antennas
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* N, male - SMA male
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* VNA calibration
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* Load the UWB antennas to VNA port1 and port2
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## 2. Set reference data
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* In antenna's near field and far filed, such as 20cm and 40cm, we need to set a specified medium and get S11 and S12 trace data.
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* This two experiments will be our reference. Later we can get other S11 and S12 to compare with this data get range and material.
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## 3. Data collection
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Collect S11 and S12 trace in different reflection mediums and distances between antennas and mediums.
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## 4. Data analysis
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We want to build relationship between our data with distance and reflection mediums to show that UWB can have the ability to detect burning tissue level.
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---
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title: Log 2023.07.06 - 路过人间，谁有意见
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tags:
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- log
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- music
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---
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---
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title: Log 2023.09.11 - Get some interesting blog here
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tags:
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- log
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- front-end
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---
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* [_Building a Frontend Framework; Reactivity and Composability With Zero Dependencies_. https://18alan.space/posts/how-hard-is-it-to-build-a-frontend-framework.html. Accessed 11 Sept. 2023.](https://18alan.space/posts/how-hard-is-it-to-build-a-frontend-framework.html)
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---
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title: Log 2023.09.18 - A Normal Learning Day
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tags:
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- log
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---
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* learn 红色高棉 by wiki
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* try to use [TXYZ](https://txyz.ai/) to read paper
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---
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title: Spectrum Analyzer
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tags:
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- equipment
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- research-about
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---
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# What is spectrum analyzer?
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Measure input signal power spectrum
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# Spectrum Analyzer for UWB
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## Papers
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### Measurements of UWB through-the-wall propagation using spectrum analyzer and the Hilbert transform
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[*pdf* - Measurements of UWB through-the-wall propagation using spectrum analyzer and the Hilbert transform](https://pinktalk.online/equipment_research/attachments/mop.23107.pdf)
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# Reference
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* [_Understanding Basic Spectrum Analyzer Operation_. _www.youtube.com_, https://www.youtube.com/watch?v=P5gxNGckjLc. Accessed 13 Sept. 2023.](https://pinktalk.online/%E6%96%87%E5%AD%A6/%E5%8F%A5%E5%AD%90/Feeling/)
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* https://www.bilibili.com/video/BV1kG4y1q72V/?spm_id_from=333.337.search-card.all.click&vd_source=c47136abc78922800b17d6ce79d6e19f
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---
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title: Curve Similarity
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tags:
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- signal-processing
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- signal
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- algorithm
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date: 2024-03-18
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---
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# Method
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* [DTW(Dynamic Time Warping)](computer_sci/deep_learning_and_machine_learning/Trick/DTW.md)
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* [Manhattan Distance](signal_processing/curve_similarity/manhattan_distance.md)
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---
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title: Manhattan Distance
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tags:
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- algorithm
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- distance
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date: 2024-03-18
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---
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在曼哈顿的街道布局中，所有街道都是相互垂直的，因此从一个点到达另一个点，需要沿着水平方向和垂直方向分别移动一定的距离。曼哈顿距离就是这两个距离的总和。
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$$
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d(A, B) = |x_A - x_B| + |y_A - y_B|
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$$
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曼哈顿距离具有以下特点：
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- 曼哈顿距离是非负的。
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- 曼哈顿距离满足三角不等式。
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- 曼哈顿距离是欧几里得距离的下界。
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曼哈顿距离在许多领域都有应用，
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- 图像处理：曼哈顿距离可以用于**计算图像的边缘强度**。
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- 机器学习：曼哈顿距离可以用于计算两个数据点的距离，并用于分类和回归任务。
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- 自然语言处理：曼哈顿距离可以用于**计算两个文本的相似度**。
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# Demo
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## Manhattan Distance for Image Edge Strength
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```python
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import numpy as np
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import cv2
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def manhattan_distance(image):
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"""
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计算图像边缘强度
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Args:
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image: 形状为 (h, w, 3) 的数组，代表图像
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Returns:
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形状为 (h, w) 的数组，代表边缘强度
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"""
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# 将图像转换为灰度图像，后面的系数是人眼对红绿蓝敏感度的权重
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gray_image = np.dot(image[...,:3], [0.2989, 0.5870, 0.1140])
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# 对图像进行高斯滤波
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filtered_image = cv2.GaussianBlur(gray_image, (5, 5), 0)
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# 计算每个像素点与其相邻像素点的曼哈顿距离
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edge_image = np.zeros_like(filtered_image)
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for i in range(1, filtered_image.shape[0] - 1):
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for j in range(1, filtered_image.shape[1] - 1):
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edge_image[i, j] = np.sum(np.abs(filtered_image[i-1:i+2, j-1:j+2] - filtered_image[i, j]))
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return edge_image
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# 读取图像
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image = cv2.imread('image.png')
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# 计算图像边缘强度
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edge_image = manhattan_distance(image)
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# 显示边缘图像
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cv2.imshow('Edge Image', edge_image)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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```
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@ -28,6 +28,10 @@ date: 2024-01-12
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* [Hilbert Transform - Envelope](signal_processing/envelope/hilbert_transform.md)
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## Curve similarity
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## Filter
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* [Chebyshev Filter](signal_processing/filter/chebyshev_filter.md)
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