Multidimensional signal processing | Signal processing
In signal processing, multidimensional signal processing covers all signal processing done using multidimensional signals and systems. While multidimensional signal processing is a subset of signal processing, it is unique in the sense that it deals specifically with data that can only be adequately detailed using more than one dimension. In m-D digital signal processing, useful data is sampled in more than one dimension. Examples of this are image processing and multi-sensor radar detection. Both of these examples use multiple sensors to sample signals and form images based on the manipulation of these multiple signals.Processing in multi-dimension (m-D) requires more complex algorithms, compared to the 1-D case, to handle calculations such as the fast Fourier transform due to more degrees of freedom. In some cases, m-D signals and systems can be simplified into single dimension signal processing methods, if the considered systems are separable. Typically, multidimensional signal processing is directly associated with digital signal processing because its complexity warrants the use of computer modelling and computation. A multidimensional signal is similar to a single dimensional signal as far as manipulations that can be performed, such as sampling, Fourier analysis, and filtering. The actual computations of these manipulations grow with the number of dimensions. (Wikipedia).
Strategies for multirate signals
This is part of an online course on beginner/intermediate applied signal processing, which presents theory and implementation in MATLAB and Python. The course is designed for people interested in applying signal processing methods to applications in time series analysis. More info here: h
From playlist Signal processing in MATLAB and Python
Characterization of Random, Multivariate Signals
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Multivariable (vector) probability density function representations, including the multivariate Gaussian density. The covariance matrix and in
From playlist Random Signal Characterization
Determining Signal Similarities
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Find a signal of interest within another signal, and align signals by determining the delay between them using Signal Processing Toolbox™. For more on Signal Processing To
From playlist Signal Processing and Communications
Digital Signal Processing 9: Multirate Digital Signal Processi - Prof Ambikairajah
Digital Signal Processing Multirate Digital Signal Processing Electronic Whiteboard-Based Lecture - Lecture notes available from: http://eemedia.ee.unsw.edu.au/contents/elec3104/LectureNotes/
From playlist ELEC3104 Digital Signal Processing by Prof. E. Ambikairajah
Multivariable Calculus: Cross Product
In this video we explore how to compute the cross product of two vectors using determinants.
From playlist Multivariable Calculus
Local linearity for a multivariable function
A visual representation of local linearity for a function with a 2d input and a 2d output, in preparation for learning about the Jacobian matrix.
From playlist Multivariable calculus
Philipp Grohs: Wavelets, shearlets and geometric frames - Part 2
Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b
From playlist Analysis and its Applications
(PP 6.1) Multivariate Gaussian - definition
Introduction to the multivariate Gaussian (or multivariate Normal) distribution.
From playlist Probability Theory
Multidimensional spectroscopy with quantum light and in optical cavities by Shaul Mukamel
Open Quantum Systems DATE: 17 July 2017 to 04 August 2017 VENUE: Ramanujan Lecture Hall, ICTS Bangalore There have been major recent breakthroughs, both experimental and theoretical, in the field of Open Quantum Systems. The aim of this program is to bring together leaders in the Open Q
From playlist Open Quantum Systems
From playlist CS50 Sections 2012
Caroline Chaux : L'échantillonnage
Recording during the thematic meeting : "Hommage à Claude Shannon" the November 2, 2016 at the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Math
From playlist Hommage/Tribute - Claude Shannon - Nov 2016
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Determine the period of a signal by measuring the distance between the peaks, and find peaks in a noisy signal using Signal Processing Toolbox™. For more on Signal Process
From playlist Signal Processing and Communications
Nikos Sidiropoulos: "Supervised Learning and Canonical Decomposition of Multivariate Functions"
Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021 Workshop III: Mathematical Foundations and Algorithms for Tensor Computations "Supervised Learning and Canonical Decomposition of Multivariate Functions (Joint work with Nikos Kargas)" Nikos Sidiropoulos - Uni
From playlist Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021
Deep Learning with Tensorflow - The Long Short Term Memory Model
Enroll in the course for free at: https://bigdatauniversity.com/courses/deep-learning-tensorflow/ Deep Learning with TensorFlow Introduction The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance,
From playlist Deep Learning with Tensorflow
Positional embeddings in transformers EXPLAINED | Demystifying positional encodings.
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From playlist The Transformer explained by Ms. Coffee Bean
11_2_1 The Geomtery of a Multivariable Function
Understanding the real-life 3D meaning of a multivariable function.
From playlist Advanced Calculus / Multivariable Calculus
Sparse matrices in sparse analysis - Anna Gilbert
Members' Seminar Topic: Sparse matrices in sparse analysis Speaker: Anna Gilbert Affiliation: University of Michigan; Member, School of Mathematics Date: October 28, 2019 For more video please visit http://video.ias.edu
From playlist Mathematics
Why multichannel beamforming is useful for wireless communication
Wireless communication systems like 5G and WiFi usually have to serve many users simultaneously and they have to deal with multiple paths between two radios when operating in a scattering rich environment. This video covers how multichannel beamforming and spatial diversity is used to over
From playlist Understanding Phased Array Systems and Beamforming