In signal processing, Discrete-Time Fourier Transforms (DTFTs) play a critical role in analyzing discrete-time signals in the frequency domain. Various properties of the DTFTs such as linearity, time-shifting, frequency-shifting, time reversal, conjugation, and time scaling help understand and manipulate these signals for different applications.

The linearity property of DTFTs is fundamental. If two discrete-time signals are multiplied by constants a and b respectively, and then combined to form a resultant signal, the DTFT of this resultant signal is the weighted sum of the DTFTs of the individual signals.

The time-shifting property of DTFTs indicates that delaying a signal by n₀ units in time domain introduces a phase shift of e^−jωn0 in its DTFT.

The frequency-shifting property occurs when a discrete-time signal x[n] is multiplied by a complex exponential e^jω0n. This multiplication shifts the frequency components of the signal by ω₀.

Time reversal shows another fascinating property. If a signal x[n] is reversed in time, i.e., x[−n], its frequency domain representation is reflected around the origin.

The conjugation property reveals that taking the complex conjugate of a signal x[n], denoted as x∗[n], results in the DTFT X∗(e^−jω), which reflects and conjugates the frequency components.

Lastly, the time scaling property demonstrates that if a discrete-time signal is scaled by a factor k, the signal retains values only at intervals that are multiples of k. The DTFT of the scaled signal x[kn] compresses the frequency components by k. Therefore, the DTFT of x[kn] is X(e^jωk), showing the compression of frequency components by the factor k.

Understanding these properties allows for efficient signal processing, aiding in various applications such as filtering, modulation, and signal analysis.