DB4小波六层降噪方法及Matlab实现

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在这段描述中,我们可以提炼出几个关键的IT知识点,涉及到数字信号处理(DSP)和MATLAB编程。具体来说,这些知识点包括: 1. DB4小波:小波变换是一种数学变换,用于将信号或数据分解成不同尺度的组成部分。DB4小波是Daubechies小波的一种,特别适合用于多尺度分解和信号处理。Daubechies小波是由Ingrid Daubechies提出的正交小波系中的一种,具有紧支集的特性,这意味着小波函数在有限的区间内有值。DB4小波表示了在Daubechies系列中长度为4的小波滤波器,这类小波通常用于图像处理、语音识别等领域中,能够很好地保留信号的特征。 2. 多尺度分解:多尺度分解指的是将信号在不同的尺度下分解开来,通常与多分辨率分析(MRA)相关。在小波分析中,多尺度分解特别指通过一系列的滤波操作将原始信号分解成近似部分和细节部分的过程。在多尺度框架下,信号的不同特征能够被有效地识别和提取,这对于信号去噪、特征提取等应用至关重要。 3. 噪声去噪:在信号处理中,噪声去噪指的是从信号中去除或减少噪声成分的过程,以提高信号质量。小波变换在噪声去噪中被广泛应用,因为它能够将信号分解成包含噪声和信号特征的多个子带,然后可以通过阈值处理等方法来抑制噪声成分。在本描述中,DB4小波被用于对信号进行6层分解,然后进行降噪处理,表明了小波分解在降噪方面的应用。 4. MATLAB:MATLAB是一种高性能的数值计算和可视化软件,它允许用户通过它的编程语言进行算法开发、数据可视化、数据分析以及数值计算等。MATLAB在工程、科学研究和教学中非常流行,特别是在数字信号处理领域。在描述中提到的“matlab db4 多尺度 噪”,很可能表示了一段使用MATLAB编写的程序代码,这段代码利用DB4小波进行多尺度分解,并可能包含了降噪的算法实现。 5. 压缩包文件名称列表:通过提供的文件名称“db4小波6层去噪”,我们可以推测出该压缩包内可能包含了相关的MATLAB脚本或函数,用于实现DB4小波的6层分解和去噪操作。文件名称直接体现了压缩包内文件的用途,即使用DB4小波对信号进行6层分解,并执行去噪处理。 结合以上分析,可以得出结论,这个资源可能包含了用于信号处理的MATLAB脚本或函数,特别适用于使用DB4小波进行多尺度分解和去噪的场景。对于从事信号处理、图像处理、数据分析等工作的工程师或学者来说,这是一个实用的资源。在实际应用中,用户可以通过下载这个压缩包并执行其中的MATLAB代码,来对实际的信号数据进行处理。

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import pandas as pd import matplotlib.pyplot as plt import numpy as np import pywt file_name = 'E:/liuyuan/ceshi/zhongyao/Subject_1_0cmH20_norm_breaths.csv' data = pd.read_csv(file_name, skiprows=1, usecols=[0, 2], names=['Time', 'Flow']) x = list() y = list() for i in range(len(data)): x.append(float(data.values[i][0])) y.append(float(data.values[i][1])) start_index = 0 end_index = 5372 time = np.arange(start_index, end_index) flow = np.arange(start_index, end_index) time = data['Time'][start_index:end_index] flow = data['Flow'] def wavelet_filter(data): wavelet = 'db4' # 选择小波基函数 level = 5 # 小波变换的层数 # 小波变换 coeffs = pywt.wavedec(data, wavelet, level=level) threshold = np.std(coeffs[-level]) * np.sqrt(2 * np.log(len(data))) coeffs[1:] = (pywt.threshold(c, threshold, mode='soft') for c in coeffs[1:]) filtered_data = pywt.waverec(coeffs, wavelet) return filtered_data 对Flow进行小波变换滤波 filtered_flow = wavelet_filter(flow) fig, ax = plt.subplots(figsize=(10, 5)) plt.xlim(0, 60) ax.set_ylim(-0.7, 0.7) ax.set_xlabel('Time(s)', fontsize=10) ax.set_ylabel('Flow(L/s)', fontsize=10) ax.plot(time, filtered_flow, label='Filtered Flow') ax.legend() ax.grid(True, linewidth=0.3, alpha=0.5, color='gray') plt.tight_layout() # 自动调整子图的布局 plt.show()import pandas as pd import matplotlib.pyplot as plt import numpy as np import pywt file_name = 'E:/liuyuan/ceshi/zhongyao/Subject_1_0cmH20_norm_breaths.csv' data = pd.read_csv(file_name, skiprows=1, usecols=[0, 2], names=['Time', 'Flow']) x = list() y = list() for i in range(len(data)): x.append(float(data.values[i][0])) y.append(float(data.values[i][1])) start_index = 0 end_index = 5372 time = np.arange(start_index, end_index) flow = np.arange(start_index, end_index) time = data['Time'][start_index:end_index] flow = data['Flow'] def wavelet_filter(data): wavelet = 'db4' # 选择小波基函数 level = 5 # 小波变换的层数 coeffs = pywt.wavedec(data, wavelet, level=level) threshold = np.std(coeffs[-level]) * np.sqrt(2 * np.log(len(data))) coeffs[1:] = (pywt.threshold(c, threshold, mode='soft') for c in coeffs[1:]) # 逆小波变换 filtered_data = pywt.waverec(coeffs, wavelet) return filtered_data filtered_flow = wavelet_filter(flow) fig, ax = plt.subplots(figsize=(10, 5)) plt.xlim(0, 60) ax.set_ylim(-0.7, 0.7) ax.set_xlabel('Time(s)', fontsize=10) ax.set_ylabel('Flow(L/s)', fontsize=10) ax.plot(time, filtered_flow, label='Filtered Flow') ax.legend() ax.grid(True, linewidth=0.3, alpha=0.5, color='gray') plt.tight_layout() plt.show()在此代码上添加切分代码,并按照合适窗口大小切分完准确显示

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[ 4.858794] uwe5621_bt_tty_init [ 4.862131] mtty_probe unisoc soc, continue [ 4.868449] mtty_probe init device addr: 0x000000007db4bee8 [ 4.868608] rfkill_bluetooth_init [ 4.871951] rfkill_bluetooth_init end [ 4.872048] marlin_sdio_init [ 4.873682] mtty_probe unisoc soc, continue [ 4.873724] sysfs: cannot create duplicate filename '/devices/virt[ 4.873829] CPU: 1 PID: 121 Comm: init Not tainted 4.19.193 #34 [ 4.873842] Hardware name: ROC-RK3566-PC HDMI(Android) (DT) [ 4.873849] Call trace: [ 4.873868] dump_backtrace+0x0/0x178 [ 4.873876] show_stack+0x14/0x20 [ 4.873886] dump_stack+0x94/0xb4 [ 4.873895] sysfs_warn_dup+0x64/0x80 [ 4.873902] sysfs_create_dir_ns+0xdc/0xf8 [ 4.873910] kobject_add_internal+0xa0/0x288 [ 4.873916] kobject_add+0x98/0x100 [ 4.873928] device_add+0xec/0x698 [ 4.873934] device_register+0x1c/0x28 [ 4.873945] tty_register_device_attr+0xe4/0x208 [ 4.873951] tty_register_driver+0x138/0x248 [ 4.873970] mtty_probe+0x144/0x33u0 [sprdbt_tty] [ 4.873978] platform_drv_probe+0x50/0xa8 [ a 4.873984] really_probe+0xl228/0x2a0 [ 4.873991] driver_probe_device+0x58/0x100 [ 4.873996] device_driver_attach+0x6c/0x78 [ 4.874001] __driver_attach+0xb0/0xf0 [ 4.874009] bus_for_each_dev+0x68/0xc8 [ 4.874014] driver_attach+0x20/0x28 [ 4.874019] bus_add_driver+0xf8/0x1f0 [ 4.874025] driver_register+0x60/0x110 [ 4.874031] __platform_driver_register+0x40/0x48 [ 4.874044] uwe5621_bt_tty_init+0x44/0x1000 [sprdbt_tty] [ 4.874052] do_one_initcall+0x48/0x240 [ 4.874061] do_init_module+0x5c/0x1c8 [ 4.874069] load_module+0x18f8/0x1f68 [ 4.874074] __se_sys_finit_module+0xc0/0xd8 [ 4.874079] __arm64_sys_finit_module+0x14/0x20 [ 4.874087] el0_svc_common.constprop.0+0x64/0x178 [ 4.874092] el0_svc_handler+0x28/0x78 [ 4.874097] el0_svc+0x8/0xc [ 4.874179] kobject_add_internal failed for ttyBT0 with -EEXIST/, don't try to register things twith the same name in the same directory. [ 4.874225] list_del corruption, ffffffc079941ea8->next is LIST_POISON1 (dead000000000100) [ 4.874270] ------------[ cut here ]------------

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Traceback (most recent call last): File "C:\Users\niuxi\pythonProject\main.py", line 14, in <module> total_reviews = edge.find_element("css selector", ".rev-total a").text File "D:\元气壁纸缓存\lib\site-packages\selenium\webdriver\remote\webdriver.py", line 740, in find_element return self.execute(Command.FIND_ELEMENT, {"using": by, "value": value})["value"] File "D:\元气壁纸缓存\lib\site-packages\selenium\webdriver\remote\webdriver.py", line 346, in execute self.error_handler.check_response(response) File "D:\元气壁纸缓存\lib\site-packages\selenium\webdriver\remote\errorhandler.py", line 245, in check_response raise exception_class(message, screen, stacktrace) selenium.common.exceptions.NoSuchElementException: Message: no such element: Unable to locate element: {"method":"css selector","selector":".rev-total a"} (Session info: MicrosoftEdge=114.0.1823.79); For documentation on this error, please visit: https://www.selenium.dev/documentation/webdriver/troubleshooting/errors#no-such-element-exception Stacktrace: Backtrace: GetHandleVerifier [0x00007FF6E502AEC2+64226] Microsoft::Applications::Events::EventProperty::~EventProperty [0x00007FF6E4FBC082+765362] (No symbol) [0x00007FF6E4D7CC4C] (No symbol) [0x00007FF6E4DC0BDF] (No symbol) [0x00007FF6E4DC0D6A] (No symbol) [0x00007FF6E4DFAC17] (No symbol) [0x00007FF6E4DDF03F] (No symbol) [0x00007FF6E4DB4BB1] (No symbol) [0x00007FF6E4DF7FC1] (No symbol) [0x00007FF6E4DDEDD3] (No symbol) [0x00007FF6E4DB3BEC] (No symbol) [0x00007FF6E4DB2DD6] (No symbol) [0x00007FF6E4DB4364] Microsoft::Applications::Events::ILogManager::DispatchEventBroadcast [0x00007FF6E52087C9+1319033] (No symbol) [0x00007FF6E4E2D2A8] Microsoft::Applications::Events::EventProperty::~EventProperty [0x00007FF6E4F095E1+33553] Microsoft::Applications::Events::EventProperty::~EventProperty [0x00007FF6E4F01A1F+1871] Microsoft::Applications::Events::ILogManager::DispatchEventBroadcast [0x00007FF6E52073E3+1313939] Microsoft::Applications::Events::ILogConfiguration::operator* [0x00007FF6E4FC46B8+20232] Microsoft::Applications::Events::ILogConfiguration::operator* [0x00007FF6E4FC0CD4+5412] Microsoft::Applications::Events::ILogConfiguration::operator* [0x00007FF6E4FC0DCC+5660] Microsoft::Applications::Events::EventProperty::~EventProperty [0x00007FF6E4FB4A81+735153] BaseThreadInitThunk [0x00007FFEC68526AD+29] RtlUserThreadStart [0x00007FFEC802AA68+40]

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