Discusses signal representation, focusing on mathematical expressions and inequalities in signal processing.
Explores the physical sense of potentials, equilibrium between subsystems, and work and free energy in systems.
Covers the Fourier transform, its properties, applications in signal processing, and differential equations, emphasizing the concept of derivatives becoming multiplications in the frequency domain.
Covers the properties of signal/data representations using Fourier transformations and matrices.
Explores nuclear magnetic resonance, MRI principles, pulse sequences, image reconstruction, safety considerations, and volume normalization in brain imaging.
Explains signal sampling, the need for discrete sampling, errors, quantization, and choosing the right sampling period.
Explores signals, instruments, and systems, covering ADC, Fourier Transform, sampling, signal reconstruction, aliasing, and anti-alias filters.
