Let's take an example.
Suppose that the intermediate frequency is 10,7 MHz (FM).
The local oscillator works on 110,7 MHz.
First case: You receive a signal of 100 MHz, the mixer will generate a frequency of 110,7 + 100 = 210,7 MHz, which will be rejected by the band-pass filter. The difference of the two frequencies is 110,7 - 100 = 10,7 MHz (desired one).
Second case: You receive a signal of 121,4 MHz. The sum of that frequency and the local oscillator is 232,1 MHz, which will be rejected. The difference is 121,4 - 110,7 = 10,7 MHz. So the image frequency in that case is going to be 121,4 MHz.
f (image) = 2 * f (local oscillator) + fc ................. if f ( l.o ) > fc f (image) = 2* f (local oscillator) - fc
You can calculate a wave's frequency by dividing the speed of the wave by its wavelength. The formula is: frequency = speed of wave / wavelength.
c=frequency x wavelength
To calculate the total number of pixels in an image, multiply the width of the image in pixels by the height of the image in pixels. This will give you the total pixel count of the image.
Assuming that the receiver uses a high-side local oscillator and an IF of 455 KHz, the image frequency is 910 KHz above. When tuned to 1600 KHz, the image frequency would be 2,510 KHz.
The pixel size formula used to calculate the dimensions of an image is: Image width (in pixels) x Image height (in pixels) Total number of pixels in the image.
To calculate the pixel size of an image, you need to divide the width or height of the image in pixels by the physical size of the image in inches. This will give you the pixel size per inch.
In image processing, frequency refers to the rate at which pixel values change in an image. High-frequency components correspond to rapid changes in intensity, often associated with edges and fine details, while low-frequency components represent smoother areas and gradual intensity changes. Frequency analysis, such as through the Fourier Transform, allows for the separation and manipulation of these components, enabling techniques like filtering and image enhancement. Understanding frequency is crucial for various applications, including compression, noise reduction, and feature extraction.
To calculate cumulative frequency, you first need to have a frequency distribution table. Start by adding up the frequencies of the first category. Then, for each subsequent category, add the frequency to the cumulative frequency of the previous category. The final cumulative frequency will be the total number of observations in the data set.
speed=frequency x wavelenth xD
period
If=lo-rf