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Related Concept Videos

Active Filters01:25

Active Filters

1.2K
Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
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Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
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Passive Filters01:27

Passive Filters

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Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff...
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Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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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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Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
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Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
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Related Experiment Video

Updated: Dec 24, 2025

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters
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An Area-Efficient and Highly Linear Reconfigurable Continuous-Time Filter for Biomedical Sensor Applications.

Jinyong Zhang1,2, Shing-Chow Chan2, Hui Li3

  • 1College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China.

Sensors (Basel, Switzerland)
|April 11, 2020
PubMed
Summary

This study introduces a compact, reconfigurable continuous-time filter for biopotential signal conditioning. The novel design offers wide frequency tuning, low power consumption, and excellent linearity, making it ideal for analog front-ends.

Keywords:
biomedical sensorscontinuous-timecurrent-steering (CS)low frequencylow-pass filter (LPF)notch filter (NF)reconfigurable filter

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Area of Science:

  • Electronics Engineering
  • Biomedical Engineering
  • Signal Processing

Background:

  • Biopotential signal acquisition requires analog front-ends with filters capable of precise frequency tuning and high linearity.
  • Existing filters often struggle with large silicon area, high power consumption, or limited tunability, especially for ultra-low frequencies.

Purpose of the Study:

  • To propose a compact, high-linearity, reconfigurable continuous-time filter for biopotential conditioning.
  • To achieve wide frequency-tuning capability and low power consumption in a monolithic design.

Main Methods:

  • Utilized an active filter topology with a novel operational-transconductance-amplifier (OTA)-based current-steering (CS) integrator.
  • Designed a reconfigurable structure capable of operating as a low-pass filter (LPF) or notch filter (NF).
  • Fabricated a prototype circuit using a 0.18 μm complementary-metal-oxide-semiconductor (CMOS) process.

Main Results:

  • Achieved a compact filter size (0.068 mm²) with low power consumption (25 μW at 1.8 V).
  • Demonstrated linear frequency tuning from 0.05 Hz to 300 Hz for the LPF with total harmonic distortion (THD) below -76 dB.
  • Reported low input-referred noise (5.5 μVrms for LPF, 6.4 μVrms for NF).

Conclusions:

  • The proposed filter offers superior linearity and a smaller on-chip capacitor compared to conventional designs.
  • Its ultra-low cutoff frequency and linear tuning capability make it a highly suitable analog front-end for biopotential acquisition systems.