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dc.contributorThiran, Jean-Philippe
dc.contributorRuiz Hidalgo, Javier
dc.contributor.authorSainz Lorenzo, Yeray
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2019-05-16T07:03:31Z
dc.date.available2019-05-16T07:03:31Z
dc.date.issued2019-02-19
dc.identifier.urihttp://hdl.handle.net/2117/133019
dc.descriptionUltrasound (US) is a widely used medical imaging modality mostly because of its non-invasive and real-time characteristics. Recent advances in US imaging (e.g. ultrafast imaging, 3D imaging, elastography, functional imaging etc.) gave rise to a crucial challenge: dealing with the huge amount of data that has to be transferred and processed in real-time. To address this problem, the LTS5 is focusing on two main aspects: 1) Maximizing the image quality for a given amount of data using advanced image reconstruction methods 2) Minimizing the data-rate to reach a given image q
dc.description.abstractUS devices generate a set of signals that are carried from a transducer probe to a computer for further processing in order to obtain images. Those signals are transmitted between both ends through a set of cables, making up a high capacity data transmission channel. In order to achieve a portable US device, it will be required to transfer the data through a much lower capacity channel. To reduce the data - rate, deep/convolutional neural networks are used for this purpose in this master thesis, showing that it is possible to reduce remarkably the data rates generated by those devices while keeping a high quality in the final reconstructed ultrasound images.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.rightsS'autoritza la difusió de l'obra mitjançant la llicència Creative Commons o similar 'Reconeixement-NoComercial- SenseObraDerivada'
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació
dc.subject.lcshNeural networks (Computer science)
dc.subject.lcshData compression (Telecommunication)
dc.subject.lcshMachine learning
dc.subject.otherDeep learning
dc.subject.otherUltrasound
dc.subject.otherDeep Neural Networks
dc.titleDeep learning for ultrasound data-rate reduction
dc.typeMaster thesis
dc.subject.lemacXarxes neuronals (Informàtica)
dc.subject.lemacDades -- Compressió (Telecomunicació)
dc.subject.lemacAprenentatge automàtic
dc.identifier.slugETSETB-230.137914
dc.rights.accessOpen Access
dc.date.updated2019-03-29T06:51:05Z
dc.audience.educationlevelMàster
dc.audience.mediatorEscola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona


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