Essays about: "Train signals"
Showing result 21 - 25 of 40 essays containing the words Train signals.
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21. Development of new criteria for train detection and evaluation in critical conditions
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Railway signaling is of paramount importance to ensure traffic management andsafety on the rail network. The main lines are divided into sections called ‘blocks’,which are governed by a fixed signal installation. To prevent trains from colliding,each block allows one train at once. READ MORE
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22. Audio representation for environmental sound classification using convolutional neural networks
University essay from Lunds universitet/Matematik LTHAbstract : A convolutional neural network (CNN) training framework is described and implemented. The framework is used to train and evaluate an audio classification system, focused on evaluating differences in audio representation. The dataset used is ESC-50, containing 50 different classes of audio. READ MORE
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23. Signal Extraction from Scans of Electrocardiograms
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : In this thesis, we propose a Deep Learning method for fully automated digitization of ECG (Electrocardiogram) sheets. We perform the digitization of ECG sheets in three steps: layout detection, column-wise signal segmentation, and finally signal retrieval - each of them performed by a Convolutional Neural Network. READ MORE
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24. 5G Positioning using Machine Learning
University essay from Linköpings universitet/ReglerteknikAbstract : Positioning is recognized as an important feature of fifth generation (\abbrFiveG) cellular networks due to the massive number of commercial use cases that would benefit from access to position information. Radio based positioning has always been a challenging task in urban canyons where buildings block and reflect the radio signal, causing multipath propagation and non-line-of-sight (NLOS) signal conditions. READ MORE
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25. Developing a spiking neural model of Long Short-Term Memory architectures
University essay from Lunds universitet/Fysiska institutionen; Lunds universitet/FörbränningsfysikAbstract : Current advances in Deep Learning have shown significant improvements in common Machine Learning applications such as image, speech and text recognition. Specifically, in order to process time series, deep Neural Networks (NNs) with Long Short-Term Memory (LSTM) units are widely used in sequence recognition problems to store recent information and to use it for future predictions. READ MORE