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The Large Area Telescope (LAT) on board the \emph{Fermi} Gamma-ray Space Telescope has been continuously providing good quality survey data of the entire sky in the high energy range from 30 MeV to 500 GeV and above since August 2008. A…

We introduce a discriminative regression approach to supervised classification in this paper. It estimates a representation model while accounting for discriminativeness between classes, thereby enabling accurate derivation of categorical…

A novel topological-data-analytical (TDA) method is proposed to distinguish, from noise, small holes surrounded by high-density regions of a probability density function. The proposed method is robust against additive noise and outliers.…

The rapid increase in serendipitous X-ray source detections requires the development of novel approaches to efficiently explore the nature of X-ray sources. If even a fraction of these sources could be reliably classified, it would enable…

Significant challenges exist in efficient data analysis of most advanced experimental and observational techniques because the collected signals often include unwanted contributions--such as background and signal distortions--that can…

Radio frequency interference (RFI) is a significant challenge faced by today's radio astronomers. While most past efforts were devoted to cleaning the RFI from the data, we develop a novel method for categorizing and cataloguing RFI for…

Results of the application of pattern recognition techniques to the problem of identifying Giant Radio Sources (GRS) from the data in the NVSS catalog are presented and issues affecting the process are explored. Decision-tree pattern…

Observations of present and future X-ray telescopes include a large number of serendipidious sources of unknown types. They are a rich source of knowledge about X-ray dominated astronomical objects, their distribution, and their evolution.…

A large fraction of the detections to be made by the Gamma-ray Large Area Space Telescope (GLAST) will initially be unidentified. We argue that traditional methodological approaches to identify individuals and/or populations of $\gamma$-ray…

This paper proposes an improved linear discriminant analysis called spectrally-corrected and regularized LDA (SRLDA). This method integrates the design ideas of the sample spectrally-corrected covariance matrix and the regularized…

With the development and widespread use of wireless devices in recent years (mobile phones, Internet of Things, Wi-Fi), the electromagnetic spectrum has become extremely crowded. In order to counter security threats posed by rogue or…

In this paper, we have proposed a novel algorithm for identifying the modulation scheme of an unknown incoming signal in order to mitigate the interference with primary user in Cognitive Radio systems, which is facilitated by using…

The Very Large Array (VLA) has been used in C-configuration to map an area \~0.3 square degrees at 1.4 GHz with 5sigma sensitivities of 0.305, 0.325, 0.380 and 0.450 mJy/beam over four equal subareas. Radio properties are presented for 62…

Cognitive Radios (CRs) build upon Software Defined Radios (SDRs) to allow for autonomous reconfiguration of communication architectures. In recent years, CRs have been identified as an enabler for Dynamic Spectrum Access (DSA) applications…

At GeV energies, the sky is dominated by the interstellar emission from the Galaxy. With limited statistics and spatial resolution, accurately separating point sources is therefore challenging. Here we present the first application of deep…

Linear discriminant analysis (LDA), a traditional classification tool, suffers from limitations such as sensitivity to noise and computational challenges when dealing with non-invertible within-class scatter matrices. Traditional stepwise…

I describe an observational program aimed at understanding the radio emission from distant, rapidly evolving galaxy populations. These observations were carried out at 1.4 and 8.5 GHz with the VLA centered on the Hubble Deep Field. Further…

A new family of radiation sensors is introduced which do not require any optics. The sensors consist of arrays of elementary sub-sensors with natural cosine-law or similar angular sensitivity supplemented with a signal processing unit that…

In this paper, we study the partial multi-label (PML) image classification problem, where each image is annotated with a candidate label set consists of multiple relevant labels and other noisy labels. Existing PML methods typically design…

The study of machine learning (ML) techniques for the autonomous classification of astrophysical sources is of great interest, and we explore its applications in the context of a multifrequency data-frame. We test the use of supervised ML…