Quasi-simultaneous observations of radio and X-ray variability in three radio-quiet Seyfert galaxies

Radio variability in some radio-quiet (RQ) active galactic nuclei suggests emission from regions close to the central engine, possibly the outer accretion disc corona. If the origins of the radio and the X-ray emission are physically related, their emission may be temporarily correlated, possibly with some time delays. We present the results of quasi-simultaneous radio and X-ray monitoring of three RQ Seyfert galaxies, Mrk 110, Mrk 766, and NGC 4593, carried out with the Very Large Array at 8.5 GHz over a period of about 300 days, and with the Rossi X-ray Timing Explorer at 2-10 keV over a period of about 2000 days. The radio core variability is likely detected in the highest resolution (A configuration) observations of Mrk 110 and NGC 4593, with a fractional variability amplitude of 6.3% and 9.5%, respectively. A cross-correlation analysis suggests an apparently strong (Pearson r = -0.89) and highly significant correlation (p = 1 x 10^(-6)) in Mrk 110, with the radio lagging the X-ray by 56 days. However, a further analysis of the r values distribution for physically unrelated long time delays, reveals that this correlation is not significant. This occurs since the Pearson correlation assumes white noise, while both the X-ray and the radio light curves follow red noise, which dramatically increases the chance, by a factor of ~ 10^3, to get extremely high r values in uncorrelated data sets. A significantly longer radio monitoring with a higher sampling rate, preferably with a high-resolution fixed radio array, is required in order to reliably detect a delay.Comment: Accepted for publication in MNRA

Autonomous computational intelligence-based behaviour recognition in security and surveillance

This paper presents a novel approach to sensing both suspicious, and task-specific behaviours through the use of advanced computational intelligence techniques. Locating suspicious activity in surveillance camera networks is an intensive task due to the volume of information and large numbers of camera sources to monitor. This results in countless hours of video data being streamed to disk without being screened by a human operator. To address this need, there are emerging video analytics solutions that have introduced new metrics such as people counting and route monitoring, alongside more traditional alerts such as motion detection. There are however few solutions that are sufficiently robust to reduce the need for human operators in these environments, and new approaches are needed to address the uncertainty in identifying and classifying human behaviours, autonomously, from a video stream. In this work we present an approach to address the autonomous identification of human behaviours derived from human pose analysis. Behavioural recognition is a significant challenge due to the complex subtleties that often make up an action; the large overlap in cues results in high levels of classification uncertainty. False alarms are significant impairments to autonomous detection and alerting systems, and over reporting can lead to systems being muted, disabled, or decommissioned. We present results on a Computational-Intelligence based Behaviour Recognition (CIBR) that utilises artificial intelligence to learn, optimise, and classify human activity. We achieve this through extraction of skeleton recognition of human forms within an image. A type-2 Fuzzy logic classifier then converts the human skeletal forms into a set of base atomic poses (standing, walking, etc.), after which a Markov-chain model is used to order a pose sequence. Through this method we are able to identify, with good accuracy, several classes of human behaviour that correlate with known suspicious, or anomalous, behaviours

Models of emission line profiles and spectral energy distributions to characterize the multi-frequency properties of active galactic nuclei

The spectra of Active Galactic Nuclei (AGNs) are often characterized by a wealth of emission lines with different profiles and intensity ratios that led to a complicated classification. Their electro-magnetic radiation spans more than 10 orders of magnitude in frequency. In spite of the differences between various classes, the origin of their activity is attributed to a combination of emitting components, surrounding an accreting Super Massive Black Hole, in the so called Unified Model. Currently, the execution of sky surveys, with instruments operating at various frequencies, provides the possibility to detect and to investigate the properties of AGNs on very large statistical samples. Thanks to the spectroscopic surveys that allow investigation of many objects, we have the opportunity to place new constraints on the nature and evolution of AGNs. In this contribution we present the results obtained by working on multi-frequency data and we discuss their relations with the available optical spectra. We compare our findings with the AGN Unified Model predictions, and we present a revised technique to select AGNs of different types from other line emitting objects. We discuss the multi-frequency properties in terms of the innermost structures of the sources.Comment: 11 pages, 4 figures. Proceedings of the XI Serbian Conference on Spectral Line Shapes in Astrophysics. Accepted for publication on Atom

Doppler Factor Estimation for Fermi Blazars

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