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Neural Signals and Signal Processing
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Related lectures (32)
Neural Signals and Signal Processing
Explores neural signal modeling, nervous system structure, and fiber classification for understanding neural phenomena.
Neuromorphic Electronic Skins: Advancements and Challenges
Explores the advancements and challenges in neuromorphic electronic skins, aiming to enable intuitive use of replacement limbs and autonomous robots.
Neural Signals and Signal Processing
Explores neural signals, brain imaging techniques, and brain organization, emphasizing the importance of understanding brain imaging methods and measuring brain signals noninvasively.
Convolutional Neural Networks
Covers convolutional neural networks, filter operations, and their applications in signal processing and image analysis.
Estimation and Linear Prediction - Part 2
Explores power spectral density, Wiener-Khintchine theorem, ergodicity, and correlation estimation in random signals for signal processing.
Discrete Fourier Transform: Unlimited Duration Signals
Explores the Discrete Fourier Transform applied to signals of unlimited duration using various windows for improved accuracy.
Signals & Systems II: Second Order Statistics
Explores second order statistics in signal processing, stationarity in random signals, and the distinction between ergodic and non-ergodic processes.
Adaptive Signal Processing: Filtering & Neural Networks
Explores adaptive signal processing, gradient descent, and the LMS algorithm for efficient filtering and neural network training.
Ground Penetrating Radar: Data Analysis
Explores the automated picking of reinforcement bars within Ground Penetrating Radar data using machine learning and signal processing techniques.
Principal Components: Properties & Applications
Explores principal components, covariance, correlation, choice, and applications in data analysis.
Signal Representation
Discusses signal representation, focusing on mathematical expressions and inequalities in signal processing.
Understanding Statistics & Experimental Design
Explores t-tests, confidence intervals, ANOVA, and hypothesis testing in statistics, emphasizing the importance of avoiding false discoveries and understanding the logic behind statistical tests.
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