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Lecture
Signal Processing: Basics and Applications
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Related lectures (32)
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Signal Processing: Basics and Spectral Analysis
Covers the basics of signal processing, linear estimation, and digital filters.
Signals, Instruments, and Systems
Explores signals, instruments, and systems, covering ADC, Fourier Transform, sampling, signal reconstruction, aliasing, and anti-alias filters.
Sampling and Reconstruction
Covers sampling, Fourier Transform, and reconstruction using low-pass filters in signal processing.
Fourier Transform and Sampling
Covers the Fourier transform of sampled signals, reconstruction, and harmonic response.
Sampling: DT-time processing of CT signals
Covers the importance of sampling in signal processing, including the sampling theorem and signal reconstruction.
Signal Processing: Sampling and Reconstruction
Covers the concepts of quantization, coding, and sampling in signal processing.
Fourier Transform: Basics and Applications
Covers the basics of the Fourier transform and its applications in signal processing.
Signal Processing: Sampling and Reconstruction
Covers Fourier transform, sampling, reconstruction, Nyquist frequency, and ideal signal reconstruction.
Signal processing and vector spaces
Emphasizes the significance of vector spaces in signal processing, offering a unified framework for various signal types and system design.
Frequency Estimation (Theory)
Covers the theory of numerical methods for frequency estimation on deterministic signals, including Fourier series and transform, Discrete Fourier transform, and the Sampling theorem.
