首页|Studies from University of Cagliari Yield New Data on Machine Learning [A Generative Adversarial Network (GAN) Fingerprint Approach Over LTE]
Studies from University of Cagliari Yield New Data on Machine Learning [A Generative Adversarial Network (GAN) Fingerprint Approach Over LTE]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news originating from Cagliari, Italy, by Ne wsRx correspondents, research stated, “Recent advancements in communication tech nologies have significantly enhanced localization techniques, improving both acc uracy and operating modes.” The news journalists obtained a quote from the research from University of Cagli ari: “Initially, localization methods relied on global navigation satellite syst ems, offering high accuracy but proving inefficient in Non-Line-of-Sight scenari os. Furthermore, the absence of a passive mode, where the user can be localized without explicitly requesting it, renders these methods unsuitable for applicati ons like passive tracking systems. Fingerprinting methods, a pattern matching te chniques based on signal power estimation from target devices and distance estim ation from reference points, can be seen as a valid and promising alternative. H owever, these methods face limitations due to extensive measurement campaigns ne eded to establish accurate sampling systems within specific areas and the substa ntial amount of data required for machine learning algorithms to achieve optimal performance. This study introduces a novel fingerprinting method capable of pas sive operation, involving all smartphones within a designated area, suitable for both indoor and outdoor scenarios. The proposed solution leverages Generative A dversarial Networks (GANs) to augment fingerprinting datasets, enhancing machine learning models’ capabilities.”
University of CagliariCagliariItalyEuropeCyborgsEmerging TechnologiesMachine Learning
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