Abstract: A method and system for augmenting a training dataset for a generative adversarial network (GAN). The training dataset includes labelled data samples and unlabelled data samples. The method includes: receiving generated samples generated using a first neural network of the GAN and the unlabelled samples of training dataset; determining a decision value for a sample from a decision function, wherein the sample is a generated sample of the generated samples or an unlabelled sample of the unlabelled samples of the training dataset; comparing the decision value to a threshold; in response to determining that the decision value exceeds the threshold: predicting a label for a sample; assigning the label to the sample; and augmenting the training dataset to include the sample with the assigned label as a labelled sample.

Abstract: Method and system for performing semi-supervised regression with a generative adversarial network (GAN) that includes a generator comprising a first neural network and a discriminator comprising a second neural network, comprising: outputting, from the first neural network, generated samples derived from a random noise vector; inputting, to the second neural network, the generated samples, a plurality of labelled training samples, and a plurality of unlabelled training samples; and outputting, from the second neural network, a predicted continuous label for each of a plurality of the generated samples and unlabelled samples.

Abstract: A method and system for augmenting a training dataset for a generative adversarial network (GAN). The training dataset includes labelled data samples and unlabelled data samples. The method includes: receiving generated samples generated using a first neural network of the GAN and the unlabelled samples of training dataset; determining a decision value for a sample from a decision function, wherein the sample is a generated sample of the generated samples or an unlabelled sample of the unlabelled samples of the training dataset; comparing the decision value to a threshold; in response to determining that the decision value exceeds the threshold: predicting a label for a sample; assigning the label to the sample; and augmenting the training dataset to include the sample with the assigned label as a labelled sample.

Abstract: Method and system for performing semi-supervised regression with a generative adversarial network (GAN) that includes a generator comprising a first neural network and a discriminator comprising a second neural network, comprising: outputting, from the first neural network, generated samples derived from a random noise vector; inputting, to the second neural network, the generated samples, a plurality of labelled training samples, and a plurality of unlabelled training samples; and outputting, from the second neural network, a predicted continuous label for each of a plurality of the generated samples and unlabelled samples.

Abstract: Wireless communication system for underground pipeline inspection. The system includes a plurality of sensor nodes moved by robots within the pipeline and each sensor node includes a radio transceiver. A plurality of spaced apart, above ground relay nodes are deployed along the pipeline, each relay node including a radio transceiver for communication with the sensor nodes. A remote monitoring center is provided in communication with the relay nodes, whereby a leak detected by a sensor node is communicated to the remote monitoring center. Each sensor node may further include a microcontroller, an accelerometer and a timer.

Abstract: Wireless communication system for underground pipeline inspection. The system includes a plurality of sensor nodes moved by robots within the pipeline and each sensor node includes a radio transceiver. A plurality of spaced apart, above ground relay nodes are deployed along the pipeline, each relay node including a radio transceiver for communication with the sensor nodes. A remote monitoring center is provided in communication with the relay nodes, whereby a leak detected by a sensor node is communicated to the remote monitoring center. Each sensor node may further include a microcontroller, an accelerometer and a timer.