Lectures related to The spectrum of interpolated signals

Covers practical sampling, interpolation challenges, spectral representation, and Fourier Transform properties.

Explores the relationships between various transforms and signal embedding techniques.

Covers the Fast Fourier Transform (FFT) algorithm, interpolation, filters, image processing, and experimental techniques in TEM and STM.

Time Series: Spectral Analysis and Linear Filtering

Explores spectral analysis, aliasing, and linear filtering in time series data.

Spectral Representation: Downsampling

Covers the spectral representation of downsampling and rational sampling rate change.

Oversampled D-A: Interpolation and Distortion

Explores oversampled D-A conversion, interpolation methods, and in-band distortion compensation.

Reconstruction Theorem: Sampling Theorem Elements

Explores the reconstruction theorem and the sampling conditions for accurate signal reconstruction based on the sampling frequency and signal bandwidth.

Sampling Theorem and Control Systems

Explores the Sampling Theorem, digital control, signal reconstruction, and anti-aliasing filters.

Spectral Analysis: Integrated Spectrum and Autocovariance

Explores spectral analysis, integrated spectrum, autocovariance, estimation, and convergence in time series models.

Estimation & Spectral Analysis in Time Series

Covers the estimation of time series models and spectral analysis in depth.

Karplus-Strong revisited and the DTFT

Explores the Karplus-Strong algorithm with decay and the DTFT of signals.

Signals, Frequencies and Bandwidth MOOC: Information, Computation, Communication: Introduction to computational thinking

Introduces signals, frequencies, and bandwidth, including pure sinusoids, Fourier theory, and spectral representation.

Signal Sampling: Interpolation

Explores signal sampling theory, interpolation techniques, and the importance of the sampling theorem in signal processing.

Signal Reconstruction: Basics MOOC: Information, Computation, Communication: Introduction to computational thinking

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

Discrete Fourier Transform: Frequency Periodicity and Reconstruction

Explores frequency periodicity in the discrete Fourier transform for signal reconstruction.

Wave mechanics: Harmonic oscillations

Covers harmonic oscillations, energy eigenstates, and the stationary Schrödinger equation in wave mechanics.

Neurophysiological Data Analysis

Explores neurophysiological data analysis, covering AP identification, firing rates, subthreshold activity, FFT spectral analysis, and event-triggered analysis using MATLAB.

Sampling and Reconstruction: Gaussian Signal Quality Analysis

Explores Fourier analysis, sampling, and signal reconstruction quality assessment through practical experiments.

Signals, Instruments, and Systems Log in to Mediaspace to watch this video

Explores signals, instruments, and systems, covering ADC, Fourier Transform, sampling, signal reconstruction, aliasing, and anti-alias filters.

The Sampling Theorem Log in to Mediaspace to watch this video

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