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Explores electronic, thermomechanical, and amplifier noise, calibration of amplitude, frequency tracking, and system limits.
Explores different types of noise in devices and circuits, including interference noise, inherent noise, and random signals.
Introduces the fundamentals of noise in electronics, covering origins, signal types, power characteristics, and noise sources.
Explores diffusion models, focusing on generating samples from a distribution and the importance of denoising in the process.
Explores physics-informed imaging systems, including lensless imaging, deep learning for imaging challenges, and the development of noise models for low-light videos.
Explores OFDM channel estimation for wireless receivers, emphasizing the importance of accurate estimation in wireless communication systems.
Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
Covers Gaussian Mixture Models, Denoising, Data Classification, and Spike Sorting using Principal Component Analysis.
Explores the factors influencing the annoyance level of noise, such as loudness, spectrum, and temporal features.
Explores noise in microelectronics, covering statistical characteristics, noise spectrum impact on circuits, and noise reduction techniques.
