1,197 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

    Get PDF

    Graduate Catalog of Studies, 2023-2024

    Get PDF

    Undergraduate Catalog of Studies, 2023-2024

    Get PDF

    Applications of Machine Learning to the Monopole & Exotics Detector at the Large Hadron Collider

    Get PDF
    MoEDAL is the Monopole and Exotics Detector at the Large Hadron Collider. The Moedal Experiment uses Passive Nuclear Track Detector foils (NTDs) to look for magnetic monopoles, and other heavily ionising exotic particles at the Large Hadron Collider (LHC). Heavy particle radiation backgrounds at the Large Hadron Collider make image analysis of these NTD foils non-trivial compared to NTD image analysis under lower background conditions such as medical ion beam calibration or nuclear dosimetry. This thesis looks at multichannel and multidimensional Convolutional Neural Network (CNN) and Fully Convolutional Neural Network (FCN) based image recognition for identifying anomalous heavily ionising particle (HIP) etch pits within calibration NTD foils that have been exposed to both a calibration signal (heavy ion beam), and real LHC background exposure, serving as detector research and development for future MoEDAL NTD analyses. Image data was collected with Directed-Bright/Dark-Field illumination, parametrised at multiple off-axis illumination angles. Angular control of the light intensity distri- bution was achieved via a paired Fresnel lens and LED array. Information about the 3D structure of the etch pits is contained in these parametrised images which may as- sist in their identification and classification beyond what is possible in a simple 2D image. Convolutional Neural Network etch pit classifiers were trained using Xe, and Pb ion data with differing levels of LHC background exposure. An ensemble approach of combining classifiers trained on different objects, and data-channels is shown to improve classification performance. Transfer learning was used to generate Fully Convolutional Neural Networks for identifying HIP etch-pit candidates from wide area foil scan images. The performance of the FCN algorithm is evaluated using a novel MoEDAL R&D foil stack, in order to obtain blinded estimates of the signal acceptance and false prediction rate of an ML based NTD analysis. Additionally a method for pixel to pixel alignment of NTD foil scans is demonstrated that can be used for the training of U-Net FCN architectures

    Graduate Catalog of Studies, 2023-2024

    Get PDF

    Undergraduate Catalog of Studies, 2022-2023

    Get PDF

    20th SC@RUG 2023 proceedings 2022-2023

    Get PDF

    Comparing AI Algorithms for Optimizing Elliptic Curve Cryptography Parameters in Third-Party E-Commerce Integrations: A Pre-Quantum Era Analysis

    Full text link
    This paper presents a comparative analysis between the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), two vital artificial intelligence algorithms, focusing on optimizing Elliptic Curve Cryptography (ECC) parameters. These encompass the elliptic curve coefficients, prime number, generator point, group order, and cofactor. The study provides insights into which of the bio-inspired algorithms yields better optimization results for ECC configurations, examining performances under the same fitness function. This function incorporates methods to ensure robust ECC parameters, including assessing for singular or anomalous curves and applying Pollard's rho attack and Hasse's theorem for optimization precision. The optimized parameters generated by GA and PSO are tested in a simulated e-commerce environment, contrasting with well-known curves like secp256k1 during the transmission of order messages using Elliptic Curve-Diffie Hellman (ECDH) and Hash-based Message Authentication Code (HMAC). Focusing on traditional computing in the pre-quantum era, this research highlights the efficacy of GA and PSO in ECC optimization, with implications for enhancing cybersecurity in third-party e-commerce integrations. We recommend the immediate consideration of these findings before quantum computing's widespread adoption.Comment: 14 page

    20th SC@RUG 2023 proceedings 2022-2023

    Get PDF

    BDS GNSS for Earth Observation

    Get PDF
    For millennia, human communities have wondered about the possibility of observing phenomena in their surroundings, and in particular those affecting the Earth on which they live. More generally, it can be conceptually defined as Earth observation (EO) and is the collection of information about the biological, chemical and physical systems of planet Earth. It can be undertaken through sensors in direct contact with the ground or airborne platforms (such as weather balloons and stations) or remote-sensing technologies. However, the definition of EO has only become significant in the last 50 years, since it has been possible to send artificial satellites out of Earth’s orbit. Referring strictly to civil applications, satellites of this type were initially designed to provide satellite images; later, their purpose expanded to include the study of information on land characteristics, growing vegetation, crops, and environmental pollution. The data collected are used for several purposes, including the identification of natural resources and the production of accurate cartography. Satellite observations can cover the land, the atmosphere, and the oceans. Remote-sensing satellites may be equipped with passive instrumentation such as infrared or cameras for imaging the visible or active instrumentation such as radar. Generally, such satellites are non-geostationary satellites, i.e., they move at a certain speed along orbits inclined with respect to the Earth’s equatorial plane, often in polar orbit, at low or medium altitude, Low Earth Orbit (LEO) and Medium Earth Orbit (MEO), thus covering the entire Earth’s surface in a certain scan time (properly called ’temporal resolution’), i.e., in a certain number of orbits around the Earth. The first remote-sensing satellites were the American NASA/USGS Landsat Program; subsequently, the European: ENVISAT (ENVironmental SATellite), ERS (European Remote-Sensing satellite), RapidEye, the French SPOT (Satellite Pour l’Observation de laTerre), and the Canadian RADARSAT satellites were launched. The IKONOS, QuickBird, and GeoEye-1 satellites were dedicated to cartography. The WorldView-1 and WorldView-2 satellites and the COSMO-SkyMed system are more recent. The latest generation are the low payloads called Small Satellites, e.g., the Chinese BuFeng-1 and Fengyun-3 series. Also, Global Navigation Satellite Systems (GNSSs) have captured the attention of researchers worldwide for a multitude of Earth monitoring and exploration applications. On the other hand, over the past 40 years, GNSSs have become an essential part of many human activities. As is widely noted, there are currently four fully operational GNSSs; two of these were developed for military purposes (American NAVstar GPS and Russian GLONASS), whilst two others were developed for civil purposes such as the Chinese BeiDou satellite navigation system (BDS) and the European Galileo. In addition, many other regional GNSSs, such as the South Korean Regional Positioning System (KPS), the Japanese quasi-zenital satellite system (QZSS), and the Indian Regional Navigation Satellite System (IRNSS/NavIC), will become available in the next few years, which will have enormous potential for scientific applications and geomatics professionals. In addition to their traditional role of providing global positioning, navigation, and timing (PNT) information, GNSS navigation signals are now being used in new and innovative ways. Across the globe, new fields of scientific study are opening up to examine how signals can provide information about the characteristics of the atmosphere and even the surfaces from which they are reflected before being collected by a receiver. EO researchers monitor global environmental systems using in situ and remote monitoring tools. Their findings provide tools to support decision makers in various areas of interest, from security to the natural environment. GNSS signals are considered an important new source of information because they are a free, real-time, and globally available resource for the EO community
    • …
    corecore