Skip to search formSkip to main contentSkip to account menu

Adjoint filter

In signal processing, the adjoint filter mask of a filter mask is reversed in time and the elements are complex conjugated. Its name is derived from… 
Wikipedia (opens in a new tab)

Related topics

Related topics

6 relations
AutocorrelationHilbert spacePolyphase matrixRefinable function

Broader (1)

Digital signal processing

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2019
2019
Adjoint invariants of Frobenius kernels of GL(m) and elements of the center of Dist(GL(m))
Abstract The first goal of this article is to explicitly describe how to translate adjoint invariants of the Frobenius kernel Gr… 
2017
2017
Concours adjoint administratif d'État : externe, interne, catégorie C (4e éd.) Bernadette Lavaud,... Dominique Herbaut,... Pierre-Brice Lebrun,...
2016
2016
Muon g-2 in GMSB with Adjoint Messengers
We explored the sparticle mass spectrum in light of the muon g-2 anomaly and the little hierarchy problem in a class of gauge… 
2013
2013
Error Covariance Sensitivity and Forecast Impact Estimation with Adjoint 4D-Var
2011
2011
Adjoint torelons, and the persistence of color electric flux tubes in the deconfined phase
It is argued that the adjoint torelon loop, i.e. a Polyakov loop in the adjoint representation running in a spatial, rather than… 
2009
2009
Cool Head, Warm Heart: Governance and the Mission of Microfinance in the Case of MC2 Micro-Banks, Cameroon
2009
2009
Next Generation Waveform Based Three-Dimensional Models and Metrics to Improve Nuclear Explosion Monitoring in the Middle East
Abstract : Improving current Middle East wave speed models with full waveforms required confidence in sources and recordings… 
2001
2001
Etat actuel de l'éducation de base dans le monde : constats et mesures à prendre
Du 26 au 28 avril 2000, s'est tenu a Dakar au Senegal le dernier «Forum mondial sur l'education». Pierre Luisoni, alors adjoint… 
1994
1994
Adjoint nullator-norator networks
1989
1989
Adjoint Operator Algorithms for Faster Learning in Dynamical Neural Networks
A methodology for faster supervised learning in dynamical nonlinear neural networks is presented. It exploits the concept of… 
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)