Lectures related to Discrete time and continuous time

Covers determinantal point processes, sine-process, and their extrapolation in different spaces.

Explores protocol-induced delays, time-varying sampling, wireless control networks, and system stability.

Explores the shift from continuous to discrete control systems, focusing on the challenges and benefits of digital implementation.

Covers the fundamentals of discrete-time signals, including analysis, synthesis, synchronization, cooling laws, and signal operations.

Explores the basics of upsampling in signal processing, including continuous time, digital domain, and frequency domain techniques.

Explores birth and death chains, hitting probabilities, and expected game durations in Markov chains.

Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.

Covers the importance of sampling in signal processing, including the sampling theorem and signal reconstruction.

Covers the definitions and state probabilities of discrete-time Markov chains.

Covers the fundamentals of detection and estimation theory, focusing on mean-squared error and hypothesis testing.

Explores the transition from analog to digital control systems using microprocessors.

Covers the dynamics of discrete systems and the concept of orbits in Riemann varieties.

Covers the fundamental properties of systems in discrete and continuous time domains.

Explores complex random vectors, stationarity, ergodicity, and the analysis of signals.

Explores the stability of networked control systems under packet loss and stochastic dropouts, emphasizing mean-square stability and robustness.

Explores Z-transform, system properties, and analysis of discrete systems, focusing on stability and transfer functions.

Explores statistical properties, signal representation, discrete signals, and binary signals.

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

Explores the generation of stochastic processes, including Gaussian processes, Markov processes, Poisson processes, and circulant embedding.

Explores discrete signals, linear systems, categorization examples, and convolution properties in signal processing.