Noise: Signal Processing

Related lectures (30)

Covers the concepts of analog, discrete, and digital signals, sampling times, frequencies, and pulses.

Explores ADCs fundamentals for IoT smart sensors, covering quantization, non-idealities, and topologies.

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

Covers the basics of signals and systems in communication, including modulation, medical imaging, Fourier analysis, and biological systems.

Explores parametric spectrum estimation methods, including line and smooth spectra, and delves into heart rate variability analysis.

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

Explores IQ demodulation using FPGA and MATLAB for noisy quantum signals.

Explores neural signal processing for brain-computer interfaces, including decoding techniques like Kalman filters and spike sorting.

Focuses on implementing a square function generator using Speedgoat FPGA technology and real-time signal processing techniques.

Explores neural signal processing methods, fMRI designs, GLM analysis, and quality control.

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