Related papers: Classifying Complex Faraday Spectra with Convoluti…

Fast radio bursts (FRBs) are millisecond-duration, bright ($\sim$Jy) extragalactic bursts, whose production mechanism is still unclear. Recently, two repeating FRBs were found to have a physically associated persistent radio source of…

In the event of a nuclear accident, or the detonation of a radiological dispersal device, quickly locating the source of the accident or blast is important for emergency response and environmental decontamination. At a specified time after…

Neural Networks are prone to having lesser accuracy in the classification of images with noise perturbation. Convolutional Neural Networks, CNNs are known for their unparalleled accuracy in the classification of benign images. But our study…

Fast radio bursts (FRBs) are millisecond-duration signals that are highly dispersed at distant galaxies. However, the physical origin of FRBs is still unknown. Coherent curvature emission by bunches, e.g., powered by starquakes, has already…

Radio source detection through conventional algorithms has been unreliable when trying to solve for large number of sources in the presence of low SINR and less number of snapshots. We address this by reformulating source detection as a…

Deep neural networks (DNNs) designed for computer vision and natural language processing tasks cannot be directly applied to the radio frequency (RF) datasets. To address this challenge, we propose to convert the raw RF data to data types…

In this work, we investigate the value of employing deep learning for the task of wireless signal modulation recognition. Recently in [1], a framework has been introduced by generating a dataset using GNU radio that mimics the imperfections…

Convolutional neural networks (CNNs) have achieved state-of-the-art results on many visual recognition tasks. However, current CNN models still exhibit a poor ability to be invariant to spatial transformations of images. Intuitively, with…

The spin-noise spectroscopy (SNS) method implies high efficiency of conversion of the spin-system magnetization to the Faraday rotation angle. Generally, this efficiency cannot be estimated using the characteristics of the regular…

We present a study of the rotation measure (RM) obtained from a sample of extra-galactic radio sources either embedded in or seen in projection through a sample of seven southern galaxy clusters (declination less than 30 degrees). We…

Lung nodule classification is a class imbalanced problem because nodules are found with much lower frequency than non-nodules. In the class imbalanced problem, conventional classifiers tend to be overwhelmed by the majority class and ignore…

Convolutional Neural Networks (CNNs) specialize in feature extraction rather than function mapping. In doing so they form complex internal hierarchical feature representations, the complexity of which gradually increases with a…

We present a 400-800 MHz polarimetric analysis of 128 non-repeating fast radio bursts (FRBs) from the first CHIME/FRB baseband catalog, increasing the total number of FRB sources with polarization properties by a factor of ~3. 89 FRBs have…

The NRAO VLA Sky Survey (NVSS) Rotation Measure (RM) catalogue is invaluable for the study of cosmic magnetism. However, the RM values reported in it can be affected by n$\pi$-ambiguity, resulting in deviations of the reported RM from the…

Contamination from galaxy fragments, identified as sources, is a major issue in large photometric galaxy catalogs. In this paper, we prove that this problem can be easily addressed with computer vision techniques. We use image cutouts to…

Neural networks have complex structures, and thus it is hard to understand their inner workings and ensure correctness. To understand and debug convolutional neural networks (CNNs) we propose techniques for testing the channels of CNNs. We…

In this paper, the advancements in structured light beams recognition using speckle-based convolutional neural networks (CNNs) have been presented. Speckle fields, generated by the interference of multiple wavefronts diffracted and…

Machine learning techniques have been increasingly used in astronomical applications and have proven to successfully classify objects in image data with high accuracy. The current work uses archival data from the Faint Images of the Radio…

We aim to unveil their 3-dimensional geometry of Abell 2255 through WSRT observations at 18, 21, 25, 85, and 200 cm. The polarization images of the cluster were processed through rotation measure (RM) synthesis, producing three final RM…

This paper investigates the numerical uncertainty of Convolutional Neural Networks (CNNs) inference for structural brain MRI analysis. It applies Random Rounding -- a stochastic arithmetic technique -- to CNN models employed in non-linear…