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Related Concept Videos

Passive Filters01:27

Passive Filters

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 frequency...
Bandpass Sampling01:17

Bandpass Sampling

In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2. The spectrum...
Active Filters01:25

Active Filters

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:
Parallel Resonance01:23

Parallel Resonance

The parallel RLC circuit is an arrangement where the resistor (R), inductor (L), and capacitor (C) are all connected to the same nodes and, as a result, share the same voltage across them. The parallel RLC circuit is analyzed in terms of admittance (Y), which reflects the ease with which current can flow. The admittance is given by:
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

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.
For a discrete-time periodic signal x[n]...
Upsampling01:22

Upsampling

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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Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters
15:25

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

Published on: February 4, 2018

Nonuniform directional filter banks with arbitrary frequency partitioning.

Lili Liang1, Guangming Shi, Xuemei Xie

  • 1Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an China. llliang@mail.xidian.edu.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|June 10, 2010
PubMed
Summary

We introduce a novel 2-D nonsubsampled nonuniform directional filter bank (NUDFB) for advanced image directional representation. This new method offers arbitrary frequency partitioning for superior directional information extraction.

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Area of Science:

  • Image processing
  • Signal processing
  • Applied mathematics

Background:

  • Directional filter banks (DFBs) are crucial for directional image representation.
  • Existing directional transforms lack flexibility in frequency partitioning and directional extraction.

Purpose of the Study:

  • To propose a novel 2-D nonsubsampled nonuniform directional filter bank (NUDFB).
  • To develop a design method for NUDFB enabling arbitrary frequency partitioning and enhanced directional information extraction.

Main Methods:

  • The design utilizes the pseudopolar Fourier transform and its geometric properties.
  • A 1-D nonsubsampled nonuniform filter bank is employed to generate nonuniform wedge-shaped subbands.
  • The method simplifies 2-D filter design by relying solely on 1-D operations, avoiding complexities of traditional 2-D fan filters.

Main Results:

  • The proposed NUDFB successfully generates nonuniform wedge-shaped subbands.
  • The design method simplifies the process by avoiding complex 2-D filter design challenges.
  • Demonstrated effectiveness in image directional decomposition.

Conclusions:

  • The NUDFB provides a flexible and effective approach for directional image representation.
  • The pseudopolar transform-based design method simplifies the creation of complex directional filter banks.
  • NUDFB offers advantages over existing directional transforms for extracting directional information based on image content.