A technique for resolving complex repetitive waveforms into sine or cosine waves and a DC component is known as: (A) Fourier Analysis (B) Harmonic Analysis (C) Frequency Analysis (D) Armstrong's Principle

The technique for resolving complex repetitive waveforms into sine or cosine waves and a DC component is known as (A) Fourier Analysis. Fourier analysis is a mathematical method that decomposes any function or signal into a set of basic functions, which are sine and cosine functions, each with a different frequency – collectively known as harmonics. In addition to these harmonics, if the original function has a non-zero average, there will also be a direct current (DC) component which represents the average value of the function over time.

Extra: Fourier analysis is based on the principle that almost any function, even very complex waveforms, can be broken down into a series of sines and cosines. These basic functions are called Fourier series when dealing with periodic signals, or Fourier transform when the signal is not periodic or the analysis is performed over a continuous range of frequencies.

This analysis is particularly useful in electrical engineering for signal processing, where engineers often need to deal with complex waves. By breaking a waveform down into its frequency components, they can analyze and understand the behaviour of different parts of an electrical system, such as filters and circuits.

In essence, it provides a way to move back and forth between the time domain, where we see signals as they change over time, and the frequency domain, where we see signals as a collection of frequencies. Understanding the frequency content of a signal is critical for various applications such as audio signal processing, telecommunications, and even in fields outside of electrical engineering like optics and quantum physics.