Lecture

Oversampled D-A: Interpolation and Distortion

Related lectures (28)

Smart Sensors for the IoT: ADCs Fundamentals

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

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

Sampling and Spectrum of Signals

Explores sampling signals and their spectrum, highlighting the significance of choosing the right sampling frequency for accurate signal representation.

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.

Electrical Metrology: Noise Reduction Techniques

Explores noise reduction techniques in electrical metrology, covering properties of charge, current, voltage, noise sources, and filtering methods.

Signal Sampling: Interpolation

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

Practical Sampling and Interpolation

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

Quantum and nanocomputing

Explores digital design principles, ADC architectures, FPGA-based ADCs, figures of merit, and lower bounds in digital design.

Reconstruction: Undersampling and Filtering

Explores the impact of undersampling on signals and the solution of pre-filtering to avoid the stroboscopic effect.

Fitting the Transmitter Spectrum

Explains how to fit the transmitter spectrum within the channel's bandwidth constraints.

Oversampled A-D Conversion

Covers oversampled analog-to-digital conversion and its impact on signal-to-noise ratio.

FIR-based sampling rate conversion

Covers rational sampling rate change, interpolation, Lagrange approximation, and FIR-based conversion.

LTI Systems: Analysis and Properties

Covers the analysis and properties of Linear Time-Invariant (LTI) systems.

Quantum Computing Fundamentals

Covers quantum computing fundamentals, ADC architectures, oversampling advantages, noise shaping, glitch phenomena, and digital integrated circuit noise.

A/D and D/A Conversion Fundamentals

Covers the fundamentals of A/D and D/A conversion techniques and their implementation.

Error Analysis and Interpolation

Explores error analysis and limitations in interpolation on evenly distributed nodes.

Signal Reconstruction: Sampling Theorem and Interpolation Formula

Explores signal reconstruction through the sampling theorem and interpolation techniques, focusing on the sinc function's role in accurate signal interpolation.

Signals, Instruments, and Systems

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

Lagrange Interpolation

Introduces Lagrange interpolation for approximating data points with polynomials, discussing challenges and techniques for accurate interpolation.

Interpolation by Intervals: Lagrange Interpolation

Covers Lagrange interpolation using intervals to find accurate polynomial approximations.