Explores statistical signal processing tools for wireless communications, including spectral estimation and signal detection, classification, and adaptive filtering.
Introduces statistical signal processing tools for wireless communications, emphasizing practical applications and hands-on experience with Python or Matlab.
Discusses the fundamentals of probability and stochastic processes, focusing on random variables, their properties, and applications in statistical signal processing.
Covers statistical signal processing tools for wireless communications, focusing on signals like train of pulses, harmonic signals, and smooth spectrum signals.
Introduces mathematical tools for communication systems and data science, focusing on stochastic processes and preparing students for advanced courses.
Explores neurobiological signal processing, covering spike modeling, signal classification, and data characterization using principal component analysis.