Covers Gaussian random variables, affine transformations, and linear systems driven by Gaussian noise in multivariable control.
Explores isometries in Euclidean spaces, including translations, rotations, and linear symmetries, with a focus on matrices.
Covers the concept of affine transformations in functions, including shifting and compressing functions.
Explores different types of noise in devices and circuits, including interference noise, inherent noise, and random signals.
Covers image processing techniques including noise addition, filtering, and image enhancement using various filters and tools.
Explores electrical metrology, covering random variables, noise sources, and their impact on electronic devices.
Covers the concept of sparse regression and the use of Gaussian additive noise in the context of MAP estimator and regularization.
Explores Gaussian vectors, moment generating functions, independence, density functions, affine transformations, and quadratic forms.
Explores different types of noise in electrical systems and their impact on electronic devices.
Explores electronic, thermomechanical, and amplifier noise, calibration of amplitude, frequency tracking, and system limits.
Covers the Spike Response Model (SRM) in computational neuroscience and its relation to the adaptive leaky integrate-and-fire model.
Explores physics-informed imaging systems, including lensless imaging, deep learning for imaging challenges, and the development of noise models for low-light videos.
