How to resolve the algorithm Fast Fourier transform step by step in the Java programming language
How to resolve the algorithm Fast Fourier transform step by step in the Java programming language
Table of Contents
Problem Statement
Calculate the FFT (Fast Fourier Transform) of an input sequence. The most general case allows for complex numbers at the input and results in a sequence of equal length, again of complex numbers. If you need to restrict yourself to real numbers, the output should be the magnitude (i.e.: sqrt(re2 + im2)) of the complex result. The classic version is the recursive Cooley–Tukey FFT. Wikipedia has pseudo-code for that. Further optimizations are possible but not required.
Let's start with the solution:
Step by Step solution about How to resolve the algorithm Fast Fourier transform step by step in the Java programming language
Fast Fourier Transform (FFT) is a mathematical algorithm used to transform a signal from the time domain to the frequency domain. This transform is commonly used in digital signal processing, image processing, and other fields.
In this Java implementation, the FFT algorithm is implemented for an array of complex numbers.
Complex Number Class:
The Complex
class represents complex numbers with real and imaginary parts. It provides methods for addition, subtraction, and multiplication of complex numbers.
Utility Methods:
This utility method performs bit reversal on a given integer n
using the specified number of bits
. Bit reversal is used to reorder the elements of the input array for the FFT algorithm.
FFT Algorithm:
The fft
method performs the FFT algorithm on the input array buffer
.
Main Method:
In the main
method, sample input data is provided in the form of an array of doubles. These values are converted to a complex number array and passed to the fft
method. The resulting transformed complex numbers are then printed.
Algorithm Overview:
The FFT algorithm consists of the following steps:
- Bit Reversal: The elements of the input array are rearranged in bit-reversed order.
- Butterfly Operations: The algorithm iterates through the array, performing "butterfly" operations. In each butterfly operation, pairs of complex numbers are combined using trigonometric functions to calculate their frequency components.
- Recursive Structure: The algorithm is applied recursively to smaller and smaller subarrays until the entire array is processed.
Output:
The output of the FFT algorithm is an array of complex numbers. Each complex number represents a frequency component of the original signal. The magnitude of the complex number indicates the strength of that frequency component, and the angle represents its phase.
Source code in the java programming language
import static java.lang.Math.*;
public class FastFourierTransform {
public static int bitReverse(int n, int bits) {
int reversedN = n;
int count = bits - 1;
n >>= 1;
while (n > 0) {
reversedN = (reversedN << 1) | (n & 1);
count--;
n >>= 1;
}
return ((reversedN << count) & ((1 << bits) - 1));
}
static void fft(Complex[] buffer) {
int bits = (int) (log(buffer.length) / log(2));
for (int j = 1; j < buffer.length / 2; j++) {
int swapPos = bitReverse(j, bits);
Complex temp = buffer[j];
buffer[j] = buffer[swapPos];
buffer[swapPos] = temp;
}
for (int N = 2; N <= buffer.length; N <<= 1) {
for (int i = 0; i < buffer.length; i += N) {
for (int k = 0; k < N / 2; k++) {
int evenIndex = i + k;
int oddIndex = i + k + (N / 2);
Complex even = buffer[evenIndex];
Complex odd = buffer[oddIndex];
double term = (-2 * PI * k) / (double) N;
Complex exp = (new Complex(cos(term), sin(term)).mult(odd));
buffer[evenIndex] = even.add(exp);
buffer[oddIndex] = even.sub(exp);
}
}
}
}
public static void main(String[] args) {
double[] input = {1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0};
Complex[] cinput = new Complex[input.length];
for (int i = 0; i < input.length; i++)
cinput[i] = new Complex(input[i], 0.0);
fft(cinput);
System.out.println("Results:");
for (Complex c : cinput) {
System.out.println(c);
}
}
}
class Complex {
public final double re;
public final double im;
public Complex() {
this(0, 0);
}
public Complex(double r, double i) {
re = r;
im = i;
}
public Complex add(Complex b) {
return new Complex(this.re + b.re, this.im + b.im);
}
public Complex sub(Complex b) {
return new Complex(this.re - b.re, this.im - b.im);
}
public Complex mult(Complex b) {
return new Complex(this.re * b.re - this.im * b.im,
this.re * b.im + this.im * b.re);
}
@Override
public String toString() {
return String.format("(%f,%f)", re, im);
}
}
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