Kernel-phases are observable quantities used in high resolution astronomical imaging used for superresolution image creation. It can be seen as a generalization of closure phases for redundant arrays. For this reason, when the wavefront quality requirement are met, it is an alternative to aperture masking interferometry that can be executed without a mask while retaining phase error rejection properties. The observables are computed through linear algebra from the Fourier transform of direct images. They can then be used for statistical testing, model fitting, or image reconstruction. (Wikipedia).
Proof that the Kernel of a Linear Transformation is a Subspace
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Proof that the Kernel of a Linear Transformation is a Subspace
From playlist Proofs
Determine the Kernel of a Linear Transformation Given a Matrix (R3, x to 0)
This video explains how to determine the kernel of a linear transformation.
From playlist Kernel and Image of Linear Transformation
Introduction to the Kernel and Image of a Linear Transformation
This video introduced the topics of kernel and image of a linear transformation.
From playlist Kernel and Image of Linear Transformation
Kernel Recipes 2018 - CPU Idle Loop Rework - Rafael J. Wysocki
The CPU idle loop is the piece of code executed by logical CPUs if they have no tasks to run. If the CPU supports idle states allowing it to draw less power while not executing any instructions, the idle loop invokes a CPU idle governor to select the most suitable idle state for the CPU an
From playlist Kernel Recipes 2018
Kernel Recipes 2022 - Checking your work: validating the kernel by building and testing in CI
The Linux kernel is one of the most complex pieces of software ever written. Being in ring 0, bugs in the kernel are a big problem, so having confidence in the correctness and robustness of the kernel is incredibly important. This is difficult enough for a single version and configuration
From playlist Kernel Recipes 2022
Find the Kernel of a Matrix Transformation (Give Direction Vector)
This video explains how to determine direction vector a line that represents for the kernel of a matrix transformation
From playlist Kernel and Image of Linear Transformation
Concept Check: Describe the Kernel of a Linear Transformation (Projection onto y=x)
This video explains how to describe the kernel of a linear transformation that is a projection onto the line y = x.
From playlist Kernel and Image of Linear Transformation
Select Which Vectors are in the Kernel of a Matrix (2 by 3)
This video explains how to determine which vectors for a list are in the kernel of a matrix.
From playlist Kernel and Image of Linear Transformation
This video lesson is part of a complete course on neuroscience time series analyses. The full course includes - over 47 hours of video instruction - lots and lots of MATLAB exercises and problem sets - access to a dedicated Q&A forum. You can find out more here: https://www.udemy.
From playlist NEW ANTS #3) Time-frequency analysis
Algorithms for motion of networks by weighted mean curvature – Selim Esedoğlu – ICM2018
Mathematics in Science and Technology Invited Lecture 17.13 Algorithms for motion of networks by weighted mean curvature Selim Esedoğlu Abstract: I will report on recent developments in a class of algorithms, known as threshold dynamics, for computing the motion of interfaces by mean cur
From playlist Mathematics in Science and Technology
Shot-noise random fields: some geometric ...images - Agnès Desolneux
Agnès Desolneux École normale supérieure de Cachan; Member, School of Mathematics November 10, 2014 Shot-noise random fields can model a lot of different phenomena that can be described as the additive contributions of randomly distributed points. In the first part of the talk, I will giv
From playlist Mathematics
Depthwise Separable Convolution - A FASTER CONVOLUTION!
In this video, I talk about depthwise Separable Convolution - A faster method of convolution with less computation power & parameters. We mathematically prove how it is faster, and discuss applications where it is used in modern research. If you liked that video, hit that like button. If
From playlist Deep Learning Research Papers
DEFCON 19: Owned Over Amateur Radio: Remote Kernel Exploitation in 2011
Speaker: Dan Rosenberg Originally considered to be the stuff of myth, remote kernel exploits allow attackers to bypass all operating system protection mechanisms and gain instant root access to remote systems. While reviewing prior work in remote kernel exploitation, this talk will go ove
From playlist DEFCON 19
Cyber Security Interview Questions Part - 3 | Operating System Interview Questions | Simplilearn
This video on cybersecurity interview questions part 3 will focus on operating systems and applications questions. Here, you will be acquainted with various questions related to operating systems, sniffing tools, Linux, and many more. 🔥Enroll for Free Cyber Security Course & Get Your Compl
From playlist Cyber Security Playlist [2023 Updated]🔥
Peter Mörters: Metastability of the contact process on evolving scale-free networks
We study the contact process in the regime of small infection rates on scale-free networks evolving by stationary dynamics. A parameter allows us to interpolate between slow (static) and fast (mean-field) network dynamics. For two paradigmatic classes of networks we investigate transitions
From playlist Probability and Statistics
Lecture 1 | Random polytopes | Zakhar Kabluchko | EIMI
Online school "Randomness online" November 4 – 8, 2020 https://indico.eimi.ru/event/40/
From playlist Talks of Mathematics Münster's reseachers
Determine a Basis for the Kernel of a Matrix Transformation (3 by 4)
This video explains how to determine a basis for the kernel of a matrix transformation.
From playlist Kernel and Image of Linear Transformation
Lecture 14: Optimistic Concurrency Control
Lecture 14: Optimistic Concurrency Control MIT 6.824: Distributed Systems (Spring 2020) https://pdos.csail.mit.edu/6.824/
From playlist MIT 6.824 Distributed Systems (Spring 2020)