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MIMO Receivers
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Related lectures (32)
QR Factorization: Least Squares System Resolution
MOOC: Linear Algebra (Part 3)
Covers the QR factorization method applied to solving a system of linear equations in the least squares sense.
Singular Value Decomposition
Explores Singular Value Decomposition, low-rank approximation, fundamental subspaces, and matrix norms.
Latent Factor Analysis: Movie Genre Classification
Explores latent factor analysis for movie genre classification based on male versus female leads.
Chaos and Lyapunov Exponents: Analyzing Predictability
Covers Lyapunov exponents, chaos measurement, and perturbation analysis in dynamical systems.
Diagonalization Techniques: Jacobi Method
Covers the Jacobi method, Givens rotation, QR decomposition, and diagonalization techniques in computational physics.
Linear Algebra: Orthogonal Projection and QR Factorization
Explores Gram-Schmidt process, orthogonal projection, QR factorization, and least squares solutions for linear systems.
Diagonalization Techniques: Jacobi Method
Explores the Jacobi method and diagonalization techniques, including similarity transformation, power methods, and QR decomposition.
QR Factorization: Orthogonal Bases
MOOC: Linear Algebra (Part 3)
Covers the QR factorization of a matrix A into Q and R.
LU Decomposition Algorithm
MOOC: Linear Algebra (Part 1)
Covers the LU decomposition algorithm, transforming a matrix into L and U.
Singular Value Decomposition: Fundamentals
Covers the fundamentals of Singular Value Decomposition, including properties, applications, and error measurement.
Matrix Decomposition: QR Factorization
Introduces QR factorization for matrix decomposition, emphasizing its importance in various applications and the implications of a well-chosen model.
Singular Value Decomposition: Example
MOOC: Linear Algebra (Part 3)
Explains the step-by-step process of finding the singular value decomposition of a matrix.
SVD: Singular Value Decomposition
Covers the concept of Singular Value Decomposition (SVD) for compressing information in matrices and images.
Matrix Decompositions: LU, Cholesky, QR, Eigendecomposition
Explores matrix decompositions, algorithms, computational complexity, and predator-prey interactions in numerical linear algebra.
Construction of an Iterative Method
Covers the construction of an iterative method for linear systems by decomposing a matrix A into P, T, and P_A.
LU Decomposition: Linear Systems Applications
MOOC: Linear Algebra (Part 1)
Covers the LU decomposition method applied to linear systems, presenting the system in two steps.
MIMO Detection
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Covers MIMO receivers, linear detectors, interference cancellation, and performance analysis in advanced wireless communications.
Cholesky Factorization: Theory and Algorithm
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Explores the Cholesky factorization method for symmetric positive definite matrices.
Construction of an Iterative Method
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Covers the construction of an iterative method for linear systems, emphasizing matrix decomposition and convexity.
Linear Regression: Least Squares Method
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Explains the method of least squares in linear regression to find the best-fitting line to a set of data points.
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