Signals & Systems I: Introduction to Communication Systems

Related lectures (30)

Page 3 of 3

Covers Fourier transform, sampling, reconstruction, Nyquist frequency, and ideal signal reconstruction.

Covers signal modulation, sampling, and their applications in communication systems.

Explores discrete signals, Fourier transform, modulation, convolution, DFT properties, and signal periodicity.

Discusses radar technologies, their applications in robotics, and challenges in mmWave imaging.

Covers the concepts of quantization, coding, and sampling in signal processing.

Covers the principles of digital communication, focusing on the Nyquist Sampling Theorem and signal space dimension.

Explores Fourier analysis, including reconstruction formula, Fourier series, and spectrum interpretation.

Explains signal sampling, the need for discrete sampling, errors, quantization, and choosing the right sampling period.

Explores ideal sampling, Fourier transformation, spectral repetition, and analog signal reconstruction.

Covers complex baseband and passband signals, modulation, demodulation, and signal recovery.