Application of the MST clustering to the high energy gamma-ray sky. III - New detections of gamma-ray emission from blazars

We present the results of a photon cluster search in the gamma-ray sky observed by the Fermi Large Area Telescope, using the new Pass 8 dataset, at energies higher than 10 GeV. By means of the Minimum Spanning Tree (MST) algorithm, we found 25 clusters associated with catalogued blazars not previously known as gamma-ray emitters. The properties of these sources are discussed.Comment: 10 pages, 3 figures. Accepted for publication in Astrophysics & Space Scienc

Application of the MST clustering to the high energy gamma-ray sky. I - New possible detection of high-energy gamma-ray emission associated with BL Lac objects

In this paper we show an application of the Minimum Spanning Tree (MST) clustering method to the high-energy gamma-ray sky observed at energies higher than 10 GeV in 6.3 years by the Fermi-Large Area Telescope. We report the detection of 19 new high-energy gamma-ray clusters with good selection parameters whose centroid coordinates were found matching the positions of known BL Lac objects in the 5th Edition of the Roma-BZCAT catalogue. A brief summary of the properties of these sources is presented.Comment: 11 pages, 7 figures. Accepted for publication in Astrophysics & Space Scienc

A new flaring high energy gamma-ray source

We report the detection of a new gamma-ray source in the Fermi-LAT sky using a source detection tool based on the Minimal Spanning Tree algorithm. The source, not reported in previous LAT catalogues but very recently observed in the X-rays and optical bands, is characterized by an increasing gamma-ray activity in 2012 June-September, reaching a weekly peak flux of (3.3+-0.6)*10^-7 photons cm^-2 s^-1. A search for a possible counterpart provides indication that it can be associated with the radio source NVSS J141828+354250 whose optical SDSS colours are typical of a blazar.Comment: 4 pages, 3 figures. Accepted for publication in Astronomy & Astrophysic

Detecting ADS-B Spoofing Attacks using Deep Neural Networks

The Automatic Dependent Surveillance-Broadcast (ADS-B) system is a key component of the Next Generation Air Transportation System (NextGen) that manages the increasingly congested airspace. It provides accurate aircraft localization and efficient air traffic management and also improves the safety of billions of current and future passengers. While the benefits of ADS-B are well known, the lack of basic security measures like encryption and authentication introduces various exploitable security vulnerabilities. One practical threat is the ADS-B spoofing attack that targets the ADS-B ground station, in which the ground-based or aircraft-based attacker manipulates the International Civil Aviation Organization (ICAO) address (a unique identifier for each aircraft) in the ADS-B messages to fake the appearance of non-existent aircraft or masquerade as a trusted aircraft. As a result, this attack can confuse the pilots or the air traffic control personnel and cause dangerous maneuvers. In this paper, we introduce SODA - a two-stage Deep Neural Network (DNN)-based spoofing detector for ADS-B that consists of a message classifier and an aircraft classifier. It allows a ground station to examine each incoming message based on the PHY-layer features (e.g., IQ samples and phases) and flag suspicious messages. Our experimental results show that SODA detects ground-based spoofing attacks with a probability of 99.34%, while having a very small false alarm rate (i.e., 0.43%). It outperforms other machine learning techniques such as XGBoost, Logistic Regression, and Support Vector Machine. It further identifies individual aircraft with an average F-score of 96.68% and an accuracy of 96.66%, with a significant improvement over the state-of-the-art detector.Comment: Accepted to IEEE CNS 201

Minimum Spanning Tree cluster analysis of the LMC region above 10 GeV: detection of the SNRs N 49B and N 63A

We present the results of a cluster search in the gamma-ray sky images of the Large Magellanic Cloud region by means of the Minimum Spanning Tree algorithm at energies higher than 10 GeV, using 9 years of Fermi-LAT data. Several significant clusters were found, the majority of which associated with previously known gamma-ray sources. New significant clusters associated with the supernova remnants N 49B and N 63A are also found, and confirmed with a Maximum Likelihood analysis of the Fermi-LAT data.Comment: 11 pages, 3 figures. Accepted for publication in Astrophysics and Space Scienc

Adote um vira lata : Sistema de gerenciamento de adoção de animais

Orientador: Prof. Dr. Alexander Robert KutzkeMonografia (graduação) - Universidade Federal do Paraná, Setor de Educação Profissional e Tecnológica, Curso de Graduação em Análise e Desenvolvimento de SistemasInclui referências: p. 78-80Resumo : A presença de animais de estimação nos lares brasileiros está em constante crescimento. O Brasil possui a quarta maior população de animais de estimação no mundo. No entanto, é preocupante o número de animais abandonados registrados no país, que atualmente chega a 30 milhões. Para combater essa negligência, a cultura da adoção tem sido promovida como uma medida efetiva. Com o objetivo de incentivar essa prática, foi desenvolvido o sistema Adote Um Vira Lata, que centraliza a divulgação e a adoção de animais abandonados ou em situação de risco por meio da interação social. Esse projeto combina o crescente interesse pela interação virtual na sociedade com os benefícios de abordar o problema do abandono, muitas vezes subestimado. A concepção desta aplicação baseou-se nos princípios da UML, resultando na criação de diagramas de Classe e Casos de Uso. No contexto das operações de back-end, optou-se pelo desenvolvimento de uma API utilizando Node.js, em conjunto com um banco de dados MongoDB hospedado na nuvem, fornecido pelo serviço Heroku. Quanto à implementação das interfaces e às requisições da API, escolheu-se o framework React.js devido à sua característica de componentização. Por fim, os dados quantitativos e qualitativos ilustram uma transformação positiva na adoção de animais, destacando como o sistema Adote Um Vira-Lata promove o bem-estar dos animais e mobiliza a comunidade em prol da proteção anima