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Results on the spectral behavior of random matrices as the dimension increases are applied to the problem of detecting the number of sources impinging on an array of sensors. A common strategy to solve this problem is to estimate the…

Broad band power density spectra offer a window to understanding turbulent behavior in the emission mechanism and, at the highest frequencies, in the putative inner engines powering long GRBs. We describe a chirp search method which steps…

We present the preliminary results from two new surveys of blazars that have direct implications on the GLAST detection of extragalactic sources from two different perspectives: microwave selection and a combined deep X-ray/radio selection.…

Inspiraling binaries of compact objects are primary targets for current and future gravitational-wave observatories. Waveforms computed in General Relativity are used to search for these sources, and will probably be used to extract source…

The site and mechanism of gamma-ray burst (GRB) prompt emission is still unknown. Although internal shocks have been widely discussed as the emission site of GRBs, evidence supporting other emission sites have been also suggested recently,…

Generalized likelihood ratio (GLR) test statistics are often used in the detection of spatial clustering in case-control and case-population datasets to check for a significantly large proportion of cases within some scanning window. The…

Relativistic jets generated in gamma-ray bursts (GRBs) produce luminous transient events, yet the fundamentals of jet composition and radiation mechanisms remain unclear. One means of identifying a magnetically-dominated outflow would be…

The prompt emission from gamma-ray bursts (GRBs) still requires a physical explanation. Studies of time-resolved GRB spectra, observed in the keV-MeV range, show that a hybrid model consisting of two components, a photospheric and a…

The complex structure of the light curves of Swift GRBs has made the identification of breaks, and the interpretation of the blast wave caused by the burst, more difficult than in the pre-Swift era. We aim to identify breaks, which are…

This paper introduces a unified framework for the detection of a source with a sensor array in the context where the noise variance and the channel between the source and the sensors are unknown at the receiver. The Generalized Maximum…

LoRa is a popular low-power wide-area network (LPWAN) technology that uses spread-spectrum to achieve long-range connectivity and resilience to noise and interference. For energy efficiency reasons, LoRa adopts a pure ALOHA access scheme,…

This paper addresses the problem of detecting a moving target embedded in Gaussian noise with an unknown covariance matrix for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. To end it, assume that obtaining a set…

Detecting fast radio bursts (FRBs) with frequency-dependent intensity remains a challenge, as existing search algorithms do not account for the spectral shape, potentially leading to non-detections. We propose a novel detection statistic,…

Global Navigation Satellite Systems (GNSS) are integrated into many devices. However, civilian GNSS signals are usually not cryptographically protected. This makes attacks that forge signals relatively easy. Considering modern devices often…

We propose an extension to the so-called PD detector. The PD detector jointly monitors received power and correlation profile distortion to detect the presence of GNSS carry-off-type spoofing, jamming, or multipath. We show that…

The GMRT Radio Halo Survey, carried out at 610 MHz to investigate the statistical properties of cluster radio halos in a complete cluster sample selected in the redshift interval z=0.2-0.4, has significantly improved our understanding of…

It is difficult to choose detection thresholds for tests of non-stationarity that assume {\em a priori} a noise model if the data is statistically uncharacterized to begin with. This is a potentially serious problem when an automated…

As radar systems will be equipped with thousands of antenna elements and wide bandwidth, the associated costs and power consumption become exceedingly high, and a potential solution is to adopt low-resolution quantization technology, which…

Traditional SLAM algorithms are typically based on artificial features, which lack high-level information. By introducing semantic information, SLAM can own higher stability and robustness rather than purely hand-crafted features. However,…

We present a novel machine learning based approach for detecting galaxy-scale gravitational lenses from interferometric data, specifically those taken with the International LOFAR Telescope (ILT), which is observing the northern radio sky…