摘要：# Convolution of two 1-D signals# Convolution operationdef convolution_1d(signal, kernel):signal_length = len(signal)kernel_length
1-D Signals
# Convolution of two 1-D signals# Convolution operationdef convolution_1d(signal, kernel):signal_length = len(signal)kernel_length = len(kernel)output_length = signal_length + kernel_length - 1output = [0] * output_lengthfor i in range(output_length):for j in range(kernel_length):if(i>=j and i-j>=0 and i-j[0.5, 2.0, 4.0, 6.0, 8.0, 7.0, 2.5]Sinusoidal signals
import numpy as npimport matplotlib.pyplot as pltdef convolution_1d(signal, kernel):signal_length = len(signal)kernel_length = len(kernel)output_length = signal_length + kernel_length - 1output = np.zeros(output_length)for i in range(output_length):for j in range(kernel_length):if(i>=j and i-j>=0 and i-jWhite Noise
import numpy as npimport matplotlib.pyplot as pltdef convolution_1d(signal, kernel):signal_length = len(signal)kernel_length = len(kernel)output_length = signal_length + kernel_length - 1output = np.zeros(output_length)for i in range(output_length):for j in range(kernel_length):if(i>=j and i-j>=0 and i-jImpulse signals
import numpy as npimport matplotlib.pyplot as pltdef convolution_1d(signal, kernel):signal_length = len(signal)kernel_length = len(kernel)output_length = signal_length + kernel_length - 1output = np.zeros(output_length)for i in range(output_length):for j in range(kernel_length):if(i>=j and i-j>=0 and i-jStep Signals
import numpy as npimport matplotlib.pyplot as pltdef convolution_1d(signal, kernel):signal_length = len(signal)kernel_length = len(kernel)output_length = signal_length + kernel_length - 1output = np.zeros(output_length)for i in range(output_length):for j in range(kernel_length):if(i>=j and i-j>=0 and i-jPractical Signals
import numpy as npimport matplotlib.pyplot as plt# Function for 1D convolutiondef convolution_1d(signal, kernel):signal_length = len(signal)kernel_length = len(kernel)output_length = signal_length + kernel_length - 1output = np.zeros(output_length)for i in range(output_length):for j in range(kernel_length):if(i>=j and i-j>=0 and i-j知乎学术咨询：
https://www.zhihu.com/consult/people/792359672131756032?isMe=1担任《Mechanical System and Signal Processing》《中国电机工程学报》等期刊审稿专家，擅长领域：信号滤波/降噪，机器学习/深度学习，时间序列预分析/预测，设备故障诊断/缺陷检测/异常检测。
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基于改进高斯-拉普拉斯滤波器的微震信号平滑降噪方法（MATLAB）
完整数据可通过知乎学术咨询获得
非平稳信号的凸一维全变差降噪方法（MATLAB）
算法可迁移至金融时间序列，地震/微震信号，机械振动信号，声发射信号，电压/电流信号，语音信号，声信号，生理信号（ECG,EEG,EMG）等信号。
完整数据可通过知乎学术咨询获得
基于广义交叉验证阈值的微震信号降噪方法（MATLAB）
算法可迁移至金融时间序列，地震/微震信号，机械振动信号，声发射信号，电压/电流信号，语音信号，声信号，生理信号（ECG,EEG,EMG）等信号。
完整数据可通过知乎学术咨询获得
来源：常识指导员
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