Lectures related to Sampling Theorem

Covers embedded systems, power management, communication methods, and field instruments.

Explores signal reconstruction basics, including interpolation techniques and formulas using triangular and sinc functions.

Fourier Transform: Concepts and Applications

Covers the Fourier transform, its properties, applications in signal processing, and differential equations, emphasizing the concept of derivatives becoming multiplications in the frequency domain.

The Sampling Theorem

Covers the sampling theorem, impulse train sampling, bandlimited signals, and the Nyquist rate.

Neural Signals and Signal Processing

Covers neural signals, brain enthusiasm, neuroimaging, and statistical analysis in neuroimaging studies.

Sampling and Reconstruction

Covers sampling, Fourier Transform, and reconstruction using low-pass filters in signal processing.

Smart Sensors for the IoT: ADCs Fundamentals

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

Signal Processing: Sampling and Reconstruction

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