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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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The concept of effective value, the root mean square (RMS) value, is crucial in understanding electrical circuits and power delivery. This idea emerges from the necessity to measure the effectiveness of a voltage or current source in supplying power to a resistive load.
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Time scaling of signals is a crucial concept in signal processing that affects the Fourier series representation without altering its coefficients. The process modifies the fundamental frequency, thereby changing how the series represents the signal over time. This principle is essential in various applications, including audio and image processing, where signal manipulation is frequent. Understanding function symmetries is fundamental to simplifying the Fourier series.
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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
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The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
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Related Experiment Video

Updated: Jul 17, 2025

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

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A distribution-based selective optimization method for eliminating periodic defects in harmonic signals.

Qing-Yuan Xin1, Yong-Chen Pei1, Huiqi Lu2

  • 1School of Mechanical and Aerospace Engineering, Jilin University, Nanling Campus, Changchun 130025, China.

Mechanical Systems and Signal Processing
|September 1, 2023
PubMed
Summary
This summary is machine-generated.

A new selective optimisation method (SOM) effectively distinguishes and removes noise and defect components from measurement signals. This signal processing technique enhances accuracy in defect detection across various fields.

Keywords:
Defect eliminationError distribution statisticsHarmonic signalPeriodic defectSelective optimization fitting

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Area of Science:

  • Signal Processing
  • Metrology
  • Data Analysis

Background:

  • Measurement signals are often corrupted by environmental noise and periodic defects.
  • Existing methods struggle to accurately differentiate defect components from valid signals.
  • This limitation hinders precise defect detection and signal analysis.

Purpose of the Study:

  • To propose a novel distribution-based selective optimisation method (SOM) for mitigating noise and defect effects.
  • To develop a technique capable of simultaneously removing periodic defect components and performing signal-fitting regression.
  • To validate the effectiveness, accuracy, and feasibility of the SOM in both theoretical and real-world scenarios.

Main Methods:

  • The selective optimisation method (SOM) is introduced as a signal classifier based on error distribution.
  • It operates as a binary- or multiple-class classifier to identify and isolate defect components.
  • The method integrates signal-fitting regression alongside defect component elimination.

Main Results:

  • Theoretical simulations demonstrate the SOM's capability to separate defect components from measurement signals.
  • The method achieves satisfactory signal-fitting regression, producing accurate curve fits.
  • Criteria for selecting operation variables under various parameter conditions are detailed.

Conclusions:

  • The proposed SOM effectively mitigates noise and periodic defects in measurement signals.
  • It offers accurate separation of defect components and robust signal fitting.
  • The SOM shows broad applicability in signal processing and defect detection for mechanical, electronic, and instrumentation fields.