Control Engineering

شرکت برای عموم دانشجویان آزاد می‌باشد.

یادتون نره غذای شنبه رو بزنید😉

#کنترل_مدرن

سلام، دوستانی که در کلاس حل تمرین کنترل مدرن حضور دارند دقت نمایند که إن شاء الله شنبه ساعت ۱۲ کلاس خواهیم داشت. لطفا این افراد به بقیه دوستان نیز اطلاع‌رسانی نمایند. همچنین کانال اطلاع‌رسانی حل تمرین کنترل مدرن به آدرس زیر در تلگرام قابل دسترس است: https://t.me/MdrnCtrlSys

سلام دوستان اگه برای نوشتن CV، انگیزه نامه یا توصیه نامه و…. کمک خواستید یکی از دوستان عزیز هستن که میتونن کمکتون کنن اگه خواستین با ایدی زیر در ارتباط باشین @sopcvrec

‍ Median filter:

This filter replaces each sample of the signal with the median of a fixed number of neighboring samples. The result is a signal that removes spikes or impulses, while preserving the edges and transitions. Here is an example of applying a median filter to a noisy square wave: @ControlEngineering98

import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import medfilt

# Generate a noisy square wave
x = np.linspace(0, 10, 1000)
y = np.sign(np.sin(2 * np.pi * x)) + 0.2 * np.random.randn(1000)

# Apply a median filter with a window size of 11
window = 11
y_smooth = medfilt(y, window)

# Plot the original and smoothed signals
plt.plot(x, y, label='Original')
plt.plot(x, y_smooth, label='Median')
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.show()

@ControlEngineering98

‍ Savitzky-Golay filter:

This filter fits a polynomial of a given degree to a sliding window of the signal using a least-squares method. The result is a smoother signal that preserves the shape and features better than the moving average filter. Here is an example of applying a Savitzky-Golay filter to the same noisy sine wave: @ControlEngineering98

import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import savgol_filter

# Generate a noisy sine wave
x = np.linspace(0, 10, 1000)
y = np.sin(2 * np.pi * x) + 0.2 * np.random.randn(1000)

# Apply a Savitzky-Golay filter with a window size of 51 and a polynomial degree of 3
window = 51
degree = 3
y_smooth = savgol_filter(y, window, degree)

# Plot the original and smoothed signals
plt.plot(x, y, label='Original')
plt.plot(x, y_smooth, label='Savitzky-Golay')
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.show()

@ControlEngineering98

‍ Moving average filter:

This filter smooths out the signal by taking the average of a fixed number of neighboring samples. The result is a less noisy signal, but also a delayed and less sharp one. Here is an example of applying a moving average filter to a noisy sine wave: @ControlEngineering98

import numpy as np
import matplotlib.pyplot as plt

# Generate a noisy sine wave
x = np.linspace(0, 10, 1000)
y = np.sin(2 * np.pi * x) + 0.2 * np.random.randn(1000)

# Apply a moving average filter with a window size of 11
window = 11
kernel = np.ones(window) / window
y_smooth = np.convolve(y, kernel, mode='same')

# Plot the original and smoothed signals
plt.plot(x, y, label='Original')
plt.plot(x, y_smooth, label='Moving average')
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.show()

@ControlEngineering98

Signal smoothing filters, are techniques that can be used to reduce noise and enhance the underlying signal in a dataset. There are different types of signal smoothing filters, such as

moving average

, Savitzky-Golay, median, and Hampel

filters. Each filter has its own advantages and disadvantages, depending on the characteristics of the signal and the noise. Here is a brief overview of each filter:

Signal smoothing filters, are techniques that can be used to reduce noise and enhance the underlying signal in a dataset. There are different types of signal smoothing filters, such as

moving average

, Savitzky-Golay, median, and Hampel

filters. Each filter has its own advantages and disadvantages, depending on the characteristics of the signal and the noise. Here is a brief overview of each filter:

عجب

مایکروسافت ورود دستیار هوش مصنوعی Copilot به ویندوز 10 را تایید کرد https://www.bleepingcomputer.com/news/microsoft/microsoft-confirms-copilot-ai-assistant-coming-to-windows-10/ توی این لینک مایکروسافت توضیح داده که چطور میتونید ویندوز کوپایلت رو به ویندوز ده اضافه کنید

دوستانی که ویندوز 11 دارن حتما بروزرسانی جدید رو دریافت و از کوپایلت استفاده کنن خیییلی قابلیتهای خوبی بطور مثال اگه ویندوز به مشکل بخوره بهش دسترسی میدید خودش میره مشکل رو پیدا و برطرف میکنه هر تغییری هر کاری که در ویندوز بخایید انجام بدید فقط تایپ کنید فوق العاده ‌س