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The purpose of this thesis was to explore deep learning methods of segmentation, classification and tracking of objects in LiDAR data. To do this a complete pipeline was developed, consisting of background filtering, clustering, tracking, labeling and visualization. The objects that were focused on were pedestrians, cyclists, cars and animals, in different environments. Background segmentation and

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The emergence of the Faster-Than-Nyquist signaling (FTN) technology has im- proved the tight spectrum resources by compressing the transmission interval between adjacent pulses. In a natural environment, data transmission via a wire- less medium and through multi-path, at a high data rate, the leakage of these paths among symbols can cause severe inter-symbol interference (ISI). In order to compen