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Jean-Paul Cipria

Written By: Jean-Paul Cipria - Juil• 15•17
Jean-Paul Cipria - Ingénieur Senior

Jean-Paul Cipria

cipria[ at ]nanotechinnov.com

Sciences for Engineers : 25 Years Experiences

TECHNOLOGIES INNOVATIONS and SCIENCES INTEGRATIONS for GROUPS

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Created :2017-07-15 07:39:11. – Modified : 2017-07-25 13:30:00.


Quaternions de l'axe k - Rotations de vues 3d

How to Rotate a Satellite without Gimbal Locking ? Axis k Quaternions – 3D Views Rotations – ©J.P. Cipria


Khi2 - Erreur à 5% sur l'Exponentielle - ©J.P. Cipria

Khi² Measurements/Theory Matching – Example with à 5% Exponential Error – ©J.P. Cipria

Scientific Publications

Localization with Ultra Wide Band Electromagnetic Short Pulse Signal

  • [CIPRIA-2012] : Cipria, Jean-Paul – « MATLAB Accuracy Localization Simulation for Electromagnetics Waves in a AWGN Propagation Channel with Ultra Wide Band Radio Frequencies Signals. 1800 Matlab codes lines. » – Université de Valenciennes – 2012

Abstract : This publication details how we can estimate the localization range detection errors for a Ultra Wide Band signal. A white gaussian noise is added to the channel path. The first part includes the Cramer-Rao theories and concepts aspects and the relationships with this physics subject. The second part details the used algorithms for the Matlab statistical simulations. The third part conclues on the localizations standards deviations obtained by simulation and the matching with the points, or physics measurements values with the theorical Cramer- Rao Lower Bound.

Master Thesis Results

Localization Accuracy (Standard Deviation) versus AWGN Noise.

Localization Accuracy – Standard Deviation versus AWGN Noise – ©J.P. Cipria – 2016.

Sale of Master Thesis

Master Thesis Extracts :

Émission et Réception LTE en UWB - Ultra Large Bande. Simulation Algorithmique MATLAB. 1800 lignes de Code.

Ultra Wide Band Short with Pulse Ssignal – LTE Emission and Reception Localization Accuracy – Matlab Algorithms Simulation. 1800 codes Lines – ©J.P. Cipria – 2016.

Statistical Entropy and Bayesian Inferences in Neurosciences

  • [Cipria-2016] : CIPRIA, Jean-Paul – « From Maximal Entropy Method to Bayesian Inference in Neurosciences – Matlab Simulation (Extracts) » – 2016.

Abstract : This Matlab study shows how to link two physics concepts : Information Entropy and Bayesian Inference. The entropy is used by physicists to view the « most probable » best informations brain pictures therefore Bayesian Inference is a statistic method to generalyze a data set to the « most probable » concept by the brain. The first part shows how to use maximal entropy method to find missing informations on the choosen transformation display. A second part displays some MEM pictures. The last part discuses about a statistical methods issued by stationary principle law on the neurosciences.

Maximum Entropy Method - How to detect the most probable mathematics transformation to view in medecin, nuclear displays ? (Gamma and Khi2 Densities)

How to detect the most probable mathematics transformation to view in medecine, nuclear displays ? Maximum Entropy Method – Gamma and Khi2 Densities ©J.P. Cipria – 2016.

Keys : Maximum Entropy Method, MEM, Bayesian Inference.

Contacts

Mail

  • jean-paul.cipria[ at ]nanotechinnov.com

Research Gate Publications

Apec Personal Page

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Jean-Paul Cipria
26/05/2017

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