Transfer Functions and Control Algorithms

Related lectures (32)

Page 3 of 4

Explores analog and digital signal processing, A/D and D/A conversion, resolution, settling time, and digital interfaces in electronic circuits.

Covers Analog-Digital and Digital-Analog conversion, including sampling, quantization, Flash converter, weighted current generation, and R2R network.

Explores digitization in neural interfaces, covering MOS models, CMOS amplifiers, noise factors, DC offset, and ADC basics.

Covers sampling, cross-validation, quantifying performance, optimal model determination, overfitting detection, and classification sensitivity.

Covers the theory and applications of graph coloring, focusing on disassortative stochastic block models and planted coloring.

Covers the consequences of undersampling signals and the stroboscopic effect.

Covers the process of making pizza, sampling, averages, dispersion, residuals, and normal distribution.

Explores efficient stochastic numerical methods for modeling and learning, covering topics like the Analytical Engine and kinase inhibitors.

Covers Markov chains and their applications in algorithms, focusing on Markov Chain Monte Carlo sampling and the Metropolis-Hastings algorithm.

Explores the basics of analog-to-digital and digital-to-analog conversion processes, including sampling and coding.