Search results for "Cnn"
showing 6 items of 36 documents
One and Two Dimensional Convolutional Neural Networks for Seizure Detection Using EEG Signals
2021
Deep learning for the automated detection of epileptic seizures has received much attention during recent years. In this work, one dimensional convolutional neural network (1D-CNN) and two dimensional convolutional neural network (2D-CNN) are simultaneously used on electroencephalogram (EEG) data for seizure detection. Firstly, using sliding windows without overlap on raw EEG to obtain the definite one-dimension time EEG segments (1D-T), and continuous wavelet transform (CWT) for 1D-T signals to obtain the two-dimension time-frequency representations (2D-TF). Then, 1D-CNN and 2D-CNN model architectures are used on 1D-T and 2D-TF signals for automatic classification, respectively. Finally, t…
Generative adversarial networks with bio-inspired primary visual cortex for Industry 4.0
2022
Biologicalization (biological transformation) is an emerging trend in Industry 4.0 affecting digitization of manufacturing and related processes. It brings up the next generation of manufacturing technology and systems that extensively use biological and bio-inspired principles, materials, functions, structures and resources. This research is a contribution to the further convergence of computer and human vision for more robust and accurate automated object recognition and image generation. We present VOneGANs, a novel class of generative adversarial networks (GANs) with the qualitatively updated discriminative component. The new model incorporates a biologically constrained digital primary…
One Dimensional Convolutional Neural Networks for Seizure Onset Detection Using Long-term Scalp and Intracranial EEG
2021
Epileptic seizure detection using scalp electroencephalogram (sEEG) and intracranial electroencephalogram (iEEG) has attracted widespread attention in recent two decades. The accurate and rapid detection of seizures not only reflects the efficiency of the algorithm, but also greatly reduces the burden of manual detection during long-term electroencephalogram (EEG) recording. In this work, a stacked one-dimensional convolutional neural network (1D-CNN) model combined with a random selection and data augmentation (RS-DA) strategy is proposed for seizure onset detection. Firstly, we segmented the long-term EEG signals using 2-sec sliding windows. Then, the 2-sec interictal and ictal segments w…
Video analīze, riteņbraukšanas sacensību laika kontroles automatizācijai
2016
Pēdējo gadu laikā ir novērots ievērojams progress datorredzes jomā, aparatūras un mašīnmācīšanās rīku uzlabojumi ļauj apmācīt pat ļoti dziļus neironu tīklus, kuri spēj atpazīt ievērojami vairāk pazīmes nekā neironu tīkli ar nelielu slāņu skaitu. Attēlu klasifikācijas un objektu atrašanas uzdevumos iespaidīgus rezultātus uzrāda konvolucionālie neironu tīkli. Dažādu sporta sacensību nozīmīga sastāvdaļa ir laika kontrole, šajā darbā tiks sīkāk apskatītas metodes, ar kurām ir iespējams automatizēt riteņbraukšanas sacensību laika kontroles sistēmu, kura ir balstīta uz foto finišu. Sportistu rezultāts tiek noteikts izmantojot kadrus no video materiāla ar lielu kadru skaitu sekundē, tāpēc šī darba…
Seizure Prediction Using EEG Channel Selection Method
2022
Seizure prediction using intracranial electroencephalogram (iEEG) is still challenging because of complicated signals in spatial and time domains. Feature selection in the spatial domain (i.e., channel selection) has been largely ignored in this field. Hence, in this paper, a novel approach of iEEG channel selection strategy combined with one-dimensional convolutional neural networks (1D-CNN) was presented for seizure prediction. First, 15-sec and 30-sec iEEG segments with an increasing number of channels (from one channel to all channels) were sequentially fed into 1D-CNN models for training and testing. Then, the channel case with the best classification rate was selected for each partici…
Attēlu un to atbilstošo segmentācijas masku ģenerēšana ar neironu tīkliem
2021
GAN tīkli kalpo kā pierādījums, ka dators spēj sintezēt attēlus, balstoties uz doto treniņa datu kopu un tos var izmantot tādiem uzdevumiem, kā, piemēram, attēlu restaurēšanai, izšķirtspējas palielināšanai, stila pārnesei u.c. Šie tīkli varētu noderēt, lai ģenerētu unikālus attēlu resursus tālākai apstrādei un izmantošanai, bet bieži vien lietotājiem ir pašiem jāizgriež nepieciešamais objekts. To varētu arī automatizēt. Šī bakalaura darba nolūks ir izpētīt ar GAN tīkliem saistītos jēdzienus, praktiski izmēģināt GAN tīkla trenēšanu, analizēt tā rezultātus un kā arī izpētīt, kā konvolutīvs neironu tīkls segmentēs ģenerētus attēlus.