University of Manitoba

Young-Jin Cha

Rahmat Ali

Alireza Mostafavi

A computer-implemented method for analyzing a thermographic image to detect an article of interest (AOI) comprises processing the image using a machine learning algorithm configured to detect the AOI and comprising a convolutional neural network (CNN); and displaying the image with location of the AOI being indicated if determined to be present. The CNN features a series pair of convolution modules configured to receive the image and form a reduced size feature map; an in-depth module thereafter and configured to learn correlations and contextual features of the image; and a superficial module after a first of the series convolution module pair and configured to extract features relevant to the AOI. Also, a computer-implemented method for generating synthetic training data based on authentic training data comprises a first neural network configured to generate the synthetic data and a second neural network configured to compare it to the authentic data to determine closeness.

Computer-implemented methods for analyzing thermographic image having article of interest using machine learning and for training machine learning algorithm therefor

A method for generating anti-noise comprises receiving a sound signal representative of ambient sound including noise from a noise source, anti-noise from an anti-noise generator, and propagation noise from environment; processing the sound signal using a deep learning algorithm configured to generate an anti-noise signal to form anti-noise; and outputting the anti-noise signal to the anti-noise generator. The deep learning algorithm features an iterative encoder module forming plural feature maps; an attention module generating plural attention maps respectively based on the feature maps; a recurrent neural network (RNN), with long short-term memory layers receiving the feature map of the final iteration of the encoder module, predicting a future portion of the sound signal and modelling temporal features of the feature map of the final encoder module iteration; and an iterative decoder module mapping the output of the RNN to the anti-noise signal having common dimensions as the sound signal.

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Young-Jin Cha