Applications of machine learning

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dc.contributor.authorYuen, Brosnan
dc.contributor.supervisorLu, Tao
dc.date.accessioned2020-09-02T04:06:17Z
dc.date.available2020-09-02T04:06:17Z
dc.date.copyright2020en_US
dc.date.issued2020-09-01
dc.degree.departmentDepartment of Electrical and Computer Engineeringen_US
dc.degree.levelMaster of Applied Science M.A.Sc.en_US
dc.description.abstractIn this thesis, many machine learning algorithms were applied to electrocardiogram (ECG), spectral analysis, and Field Programmable Gate Arrays (FPGAs). In ECG, QRS complexes are useful for measuring the heart rate and for the segmentation of ECG signals. QRS complexes were detected using WaveletCNN Autoencoder filters and ConvLSTM detectors. The WaveletCNN Autoencoders filters the ECG signals using the wavelet filters, while the ConvLSTM detects the spatial temporal patterns of the QRS complexes. For the spectral analysis topic, the detection of chemical compounds using spectral analysis is useful for identifying unknown substances. However, spectral analysis algorithms require vast amounts of data. To solve this problem, B-spline neural networks were developed for the generation of infrared and ultraviolet/visible spectras. This allowed for the generation of large training datasets from a few experimental measurements. Graphical Processing Units (GPUs) are good for training and testing neural networks. However, using multiple GPUs together is hard because PCIe bus is not suited for scattering operations and reduce operations. FPGAs are more flexible as they can be arranged in a mesh or toroid or hypercube configuration on the PCB. These configurations provide higher data throughput and results in faster computations. A general neural network framework was written in VHDL for Xilinx FPGAs. It allows for any neural network to be trained or tested on FPGAs.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/12089
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectmachine learningen_US
dc.subjectECGen_US
dc.subjectelectrocardiogramen_US
dc.subjectQRSen_US
dc.subjectQRS complexen_US
dc.subjectFPGAen_US
dc.subjectspectrumen_US
dc.subjectGANen_US
dc.subjectwaveleten_US
dc.subjectCNNen_US
dc.subjectconvlstmen_US
dc.subjectwaveletcnnen_US
dc.subjectknnen_US
dc.subjectVHDLen_US
dc.titleApplications of machine learningen_US
dc.typeThesisen_US

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