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- Java1485-spectrum analysis using
Baldwin illustrates and explains forward and inverse Fourier transforms using both DFT and FFT algorithms. He also illustrates and explains the implementation of frequency filtering by modifying the complex spectrum in the frequency domain and transforming the modified complex spectrum back into the time domain.
Revised: Sat Oct 17 17:00:21 CDT 2015
This page is included in the following book:
Digital Signal Processing - DSP
Table of contents
Preface
A previous module titled
Fun with Java, How and Why Spectral Analysis Works explained some of the
fundamentals regarding spectral analysis.
The module titled
Spectrum Analysis using Java, Sampling Frequency, Folding Frequency, and the FFT
Algorithm presented and explained several Java programs for doing spectral
analysis, including both DFT programs and FFT programs. That module illustratedthe fundamental aspects of spectral analysis that center around the sampling
frequency and the Nyquist folding frequency.
The module titled
Spectrum Analysis using Java, Frequency Resolution versus Data Length used
similar Java programs to explain spectral frequency resolution.
The module titled
Spectrum Analysis using Java, Complex Spectrum and Phase Angle explained
issues involving the complex spectrum, the phase angle, and shifts in the timedomain.
This module will illustrate and explain
forward and
inverse Fourier transforms using both DFT and FFT algorithms. I will also illustrate andexplain the implementation of frequency filtering by modifying the complex
spectrum in the frequency domain and then transforming the modified complexspectra back into the time domain.
Viewing tip
I recommend that you open another copy of this module in a separate
browser window and use the following links to easily find and view the Figuresand Listings while you are reading about them.
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Figure 1. Forward and inverse transform of a time series using DFT algorithm.
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Figure 2. Forward and inverse transform of a time series using FFT algorithm.
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Figure 3. The signature of the complexToComplex method.
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Figure 4. Filtering in the frequency domain.
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Figure 5. Filtering in the frequency domain.
Questions & Answers
number of sport play by 50 student construct discrete data
width of the frangebany leaves on how to write a introduction
Solve the mean of variance
Step 1: Find the mean. To find the mean, add up all the scores, then divide them by the number of scores. ...
Step 2: Find each score's deviation from the mean. ...
Step 3: Square each deviation from the mean. ...
Step 4: Find the sum of squares. ...
Step 5: Divide the sum of squares by n – 1 or N.
kenneth
Is mistake done to something
Vutshila
What is the life teble
anas
statistics is the analyzing of data
how do you calculate mean
diveving the sum if all values
Shaynaynay
let A1,A2 and A3 events be independent,show that (A1)^c, (A2)^c and (A3)^c are independent?
data collected all over the world
Shaynaynay
construct a less than and more than table
The sample of 16 students is taken. The average age in the sample was 22 years with astandard deviation of 6 years. Construct a 95% confidence interval for the age of the population.
Bhartdarshan' is an internet-based travel agency wherein customer can see videos of the cities they plant to visit. The number of hits daily is a normally distributed random variable with a mean of 10,000 and a standard deviation of 2,400
a. what is the probability of getting more than 12,000 hits?
b. what is the probability of getting fewer than 9,000 hits?
Bhartdarshan'is an internet-based travel agency wherein customer can see videos of the cities they plan to visit. The number of hits daily is a normally distributed random variable with a mean of 10,000 and a standard deviation of 2,400.
a. What is the probability of getting more than 12,000 hits
Akshay
Sorry i want to learn more about this question
Bright
a= 0.20233
b=0.3384
Sufiyan
How do I interpret level of significance?
It depends on your business problem or in Machine Learning you could use ROC- AUC cruve to decide the threshold value
Shivam
how skewness and kurtosis are used in statistics
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Source:
OpenStax, Digital signal processing - dsp. OpenStax CNX. Jan 06, 2016 Download for free at https://legacy.cnx.org/content/col11642/1.38
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