Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from http://reference.wolfram.com/language/ref/FrequencySamplingFilterKernel.html below:

FrequencySamplingFilterKernel—Wolfram Language Documentation

We deliver solutions for the AI era—combining symbolic computation, data-driven insights and deep technology expertise.

• Data & Computational Intelligence
• Model-Based Design
• Algorithm Development
• Wolfram|Alpha for Business
• Blockchain Technology
• Education Technology
• Quantum Computation

WolframConsulting.com

• See Also
  • LeastSquaresFilterKernel
  • EquirippleFilterKernel
  • ListConvolve
  • InverseFourierSequenceTransform
  • ListFourierSequenceTransform
• Related Guides
  • Signal Filtering & Filter Design

FrequencySamplingFilterKernel[{a₁,…,a_k}]

creates a finite impulse response (FIR) filter kernel using a frequency sampling method from amplitude values a_i.

A symmetric odd-length FIR lowpass kernel:

A type 1 FIR kernel with even symmetry and odd length:

A type 2 FIR kernel with even symmetry and even length:

A type 3 FIR kernel with odd symmetry and odd length:

A type 4 FIR kernel with odd symmetry and even length:

A symmetric odd-length FIR highpass kernel:

A symmetric even-length FIR bandpass kernel:

A full-band differentiator FIR kernel:

By default, first frequency is sampled at 0:

With "Shifted"->True, first frequency is offset from 0 by :

Smooth data by convolving it with a lowpass filter kernel:

Apply a derivative filter to rows of an image:

Apply a window to an FIR filter to reduce ripple in its magnitude response:

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4