The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present in the signal.

One of the key properties of the DFT is its linearity. This property implies that the DFT of a sum of sequences equals the sum of their individual DFTs. Another important property is time-shifting. When a sequence is shifted in the time domain, its DFT undergoes a corresponding phase shift.

Frequency-shifting in the time domain results in shifting the indices of the DFT. If a sequence is multiplied by a complex exponential, its DFT is shifted accordingly in the frequency domain. Time reversal, which inverts the sequence in the time domain, affects the symmetry of the DFT. If a sequence is reversed, the DFT components are reordered and conjugated.

The conjugation property states that if a sequence is conjugated, the DFT components are also conjugated and reordered. The convolution theorem is particularly powerful, as it simplifies the process of convolution in the time domain to simple multiplication in the frequency domain.

Due to its periodic nature, the DFT is extensively used in signal processing applications to transition between time and frequency domains. This periodicity arises from the inherent sampling process in the DFT, making it a versatile tool for analyzing and manipulating signals. The ability to simplify complex operations and provide clear insights into the frequency components of a signal underscores the DFT's importance in various signal-processing tasks.