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Jean-Paul Cipria

Written By: Jean-Paul Cipria - Déc• 26•17

. Who is Jean-Paul CIPRIA ?
A Telecom Engineer ?
A Sciences Professor ?
A Surveyor ?

Cipria-Topography-Jalon-Alignement-2

 

SCIENCE INTEGRATION and TECHNOLOGY EVANGELIST
cipria[ at ]nanotechinnov.com

My job is to integrate knowledges, understanding, methods in enterprise not essentially distributed to employees but dispatched to managers and directors.

I use to say : « ENGINEERS ARE SOLVING PROBLEMS YOU DIDN’T KNOW YOU HAD IN WAYS YOU DON’T UNDERSTAND. »

cipria[ at ]nanotechinnov.com

MATLAB/Simulink/Flowstate Conception
Control Law Modelization Specialist
Armament Algorithms Expert

Surveyor Topographer – Terrestrial Localization Expert

Created :2017-12-26 07:39:11. – Modified : 2018-06-20 18:32:54.

2018 in Action

Algorithmes Expert in Armament and Defence

Dynamic Modelling and Expert Control Law on Armament Systems.
Confidential Defence.

Optical Collimation : Longitudinal Axis Lens Shifting.

Spherical Trajectory - ©Jean-Paul Cipria 2017

Matlab Simulation

Simulink Integrator

Simulink Integration and Embedded Code Generation.

Senior Engineer in Medium Range Missile MMP

Matlab/Simulink Model-Based Design for DO-178 Using Tool Qualification Kits for On-Board Systems.
Confidential Defence.

Missile-MM40-Exocet-MBDA

Missile-MM40-Exocet-MBDA

Surveyor and Topographer on Navigation Devices

Parc Chenonceaux Meaux - Polygonales - Points Rayonnés

Polygonales – Points Rayonnés – Relative Position References

2017 in Action

PhD in Physics

  1. « How did Doctorant DON’T Understand Physics ? » – 06/2017.
    .
  2. « Do Scientific References and Measurements have Clear Relationships ? » – 2016.
    .
  3. « Do Paradygmes-Concepts-Models-Simulations-Measurements and Understanding have Logical Trajectories ? » – 2015
    .

Physics PhD Annexes

Matlab Simulations and Algorithmics – Thematics Context – 01/2017.

  1. « Space Context ».
    .
  2. « Physics Context ».
    .
  3. « Algorithmics Context ».
    .

Surveyor and Topographer

  1. « Measurements on Earth. Why is it so Complex AND Difficult ? » – 09/2017.
    1. Leveling and Local Altimetry : How is the height of earth objects ?
    2. Covadis and Autocad Maps and Plans : How do we represent plane projections ?
    3. WGS84 and RFG89 : Global Geodetic Reference Frames.
      1. How do we locate earth on space ?
      2. How do we theorise an earth form for localisation in two dimensions ?
      3. How do we measure altitude in the third dimension and theorise another form on earth ?
      4. How are time and duration on earth ?
        .
  2. Altitudes on Expert Measurements by reference to geoid.
  3. Angles, gisements and Positioning on Earth by reference to Conic Projection and by Ellipsoid.

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

.
Jean-Paul Cipria
30/03/2018

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