A fundamental problem in employing deep learning algorithms in the medical field is the lack of labeled data and severe class imbalance. In this work, we present novel ways to enlarge small scale datasets. We introduce an autoencoder framework comprised of an encoder and a StyleGAN generator to embed images into the latent space of StyleGAN. The autoencoder learns the disentangled latent representation of the data allowing for encoding real images to the latent space and manipulating the latent vector in a meaningful manner. We suggest ways to use the encoder along with the unique architecture of the StyleGAN generator to control the synthesized images and thus, create class-specific images that can be used to train and improve existing deep learning algorithms.
Quasi-distributed sensing, e.g. Quasi-Distributed Acoustic Sensing (Q-DAS), with optical fibers is commonly used for various applications. Its excellent performance is well known, however, it necessitates high sampling rates and expensive hardware for acquisition and processing. In this paper, we introduce a technique, based on Compressed Sensing (CS) theory, to locate discrete reflectors' along a sensing fiber with a smaller number of samples than required according to Nyquist criterion. The technique is based on the fact that the fiber profile consists of a limited number of discrete reflectors and significantly weaker reflections of Rayleigh back-scatterers, and thus can be approximated as a sparse signal. The task of reconstructing a sparse signal from a sub-Nyquist sampled signal using Orthogonal Matching Pursuit (OMP) is presented and tested experimentally.
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