Related papers: Radio Sources Segmentation and Classification with…

Context: Observations at the radio continuum band below the gigahertz band are key when the nature and properties of nonthermal sources are investigated because their radio radiation is strongest at these frequencies. The low radio…

Remnant radio galaxies represent the dying phase of radio-loud active galactic nuclei (AGN). Large samples of remnant radio galaxies are important for quantifying the radio galaxy life cycle. The remnants of radio-loud AGN can be identified…

We proposed a machine learning approach to identify and distinguish dusty stellar sources employing supervised and unsupervised methods and categorizing point sources, mainly evolved stars, using photometric and spectroscopic data collected…

Hepatocellular carcinoma (HCC) is a common type of liver cancer whose early-stage diagnosis is a common challenge, mainly due to the manual assessment of hematoxylin and eosin-stained whole slide images, which is a time-consuming process…

In order to determine the relationship between the faint X-ray and faint radio source populations, and hence to help understand the X-ray and radio emission mechanisms in those faint source populations, we have made a deep 1.4GHz Very Large…

In this work we propose a novel approach to perform segmentation by leveraging the abstraction capabilities of convolutional neural networks (CNNs). Our method is based on Hough voting, a strategy that allows for fully automatic…

This paper proposes a multi-spectral random forest classifier with suitable feature selection and masking for tree cover estimation in urban areas. The key feature of the proposed classifier is filtering out the built-up region using…

A small sample of 7 southern Compact Steep Spectrum (CSS) radio sources has been selected as part of the study of a larger flux-limited complete sample of radio sources. High resolution images, using the VLBI network in the southern…

Context: The Deep Extragalactic VLBI-Optical Survey (DEVOS) aims at constructing a large sample of compact radio sources up to two orders of magnitude fainter than those studied in other Very Long Baseline Interferometry (VLBI) surveys.…

High Efficiency Video Coding (HEVC) significantly reduces bit-rates over the proceeding H.264 standard but at the expense of extremely high encoding complexity. In HEVC, the quad-tree partition of coding unit (CU) consumes a large…

We consider the problem of determining the host galaxies of radio sources by cross-identification. This has traditionally been done manually, which will be intractable for wide-area radio surveys like the Evolutionary Map of the Universe…

Object categories inherently form a hierarchy with different levels of concept abstraction, especially for fine-grained categories. For example, birds (Aves) can be categorized according to a four-level hierarchy of order, family, genus,…

Thanks to the application of deep learning technology in point cloud processing of the remote sensing field, point cloud segmentation has become a research hotspot in recent years, which can be applied to real-world 3D, smart cities, and…

Curvilinear structure segmentation is important in medical imaging, quantifying structures such as vessels, airways, neurons, or organ boundaries in 2D slices. Segmentation via pixel-wise classification often fails to capture the small and…

We apply classical machine vision and machine deep learning methods to prototype signal classifiers for the search for extraterrestrial intelligence. Our novel approach uses two-dimensional spectrograms of measured and simulated radio…

Acquiring channel knowledge is required by many applications. For instance, handover in cellular networks is mainly decided based on the knowledge of pathloss. In contrast to traditional statistical distance-determined models that might…

We present a novel application of Vector quantised variational autoencoders (VQ-VAEs) to deep 1.28 GHz radio continuum images taken from the MeerKAT Galaxy Cluster Legacy Survey (MGCLS).VQ-VAEs are deep learning models widely used in modern…

We present a Deep-Learning (DL) pipeline developed for the detection and characterization of astronomical sources within simulated Atacama Large Millimeter/submillimeter Array (ALMA) data cubes. The pipeline is composed of six DL models: a…

To phased microphone array for sound source localization, algorithm with both high computational efficiency and high precision is a persistent pursuit. In this paper convolutional neural network (CNN) a kind of deep learning is…

This paper presents a basic property of region dividing of ReLU (rectified linear unit) deep learning when new layers are successively added, by which two new perspectives of interpreting deep learning are given. The first is related to…