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Martin Rupp

Martin Rupp is a cryptographer, mathematician and cyber-scientist. He has been developing and implementing cybersecurity solutions for banks and security relevant organizations for 20 years. Currently he is researching attack scenarios and the role of AI in ATC cyber-security.

Cybersecurity in Aviation and Air Traffic Control: Regulatory Bodies, Existing Solutions and ATSEP Qualification Requirements

This article looks at the actual cybersecurity ecosystem in aviation and air traffic control: are there norms, documents proposed by the aviation regulation bodies?  What are the existing solutions? Who are the current cybersecurity vendors that propose a solution for aviation, and especially for airports and air traffic control.

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What is machine learning: the ID3 Classifier

The ID3 - or ID3 (Iterative Dichotomiser 3) - is a supervised classifier based on decision tree learning methodology. The ID3 classifier generates a decision tree from a set of data (the training data) which can be used to classify new data.

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Tutorial - What is Machine Learning (ML) ?

This article will give an overview of the basic and fundamental notions of ML. It is part of a series developed for practitioners. The goal is to be rapidly able to apply and make use of ML.

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Tutorial - Using the NextGen 8 GHz Pulse Module for Artificial Intelligence to Understand Finger-Counting-Based Gestures

In the following we introduce a SkyRadar NextGen 8 GHz Pulse application for artificial intelligence. In this application students learn how neural networks / artificial intelligence, and especially Convolution Neural networks can be applied to perform the automatic detection of a human gesture by using RADAR technologies.

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Tutorial: Overview of Results in the Domain of Classifications of RADAR Objects Using Hidden Markov Models, Neural Networks and Other M.L. Techniques

In this article, we shall detail the main results and implementation regarding the processing and classification of RADAR objects using Machine Learning (M.L) techniques such as Hidden Markov Models, Neural Networks or others.

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Tutorial: Support Vector Machines for Automatic RADAR Recognition - A Theoretical Foundation

In the present tutorial, we shall explain what are the Support Vector Machines (SVMs) and how the kernel-based SVM classifiers are working.

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Tutorial: Understanding Machine Learning and Deep Learning

In what follows we will present examples of Deep Learning networks and detail their various designs. It is a detailed tutorial, written for students and engineers who want to acquire a profound understanding of the subject.

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The Powers and Limits of Bayesian Classifiers (Tutorial)

In this tutorial, we will have a detailed look at one of the most powerful classes of machine learning and Artificial Intelligence algorithms that exists: the Bayesian Classifiers.

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Artificial Intelligence: Hidden Markov Model Classifiers and RADAR Objects Classification by Machine Learning 2/2

In the first part of this series, we introduced the general concepts needed for understanding the Hidden Markov Models Classifiers. Namely: Bayesian Logic, The concept of Bayesian Classifiers and Bayesian networks. All these notions are now grouped to form a new type of classifier which can accurately model and classify time-series data such as the RADAR data.

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