19,141 research outputs found
Systematic search for gamma-ray periodicity in active galactic nuclei detected by the Fermi Large Area Telescope
We use nine years of gamma-ray data provided by the Fermi Large Area
Telescope (LAT) to systematically study the light curves of more than two
thousand active galactic nuclei (AGN) included in recent Fermi-LAT catalogs.
Ten different techniques are used, which are organized in an automatic
periodicity-search pipeline, in order to search for evidence of periodic
emission in gamma rays. Understanding the processes behind this puzzling
phenomenon will provide a better view about the astrophysical nature of these
extragalactic sources. However, the observation of temporal patterns in
gamma-ray light curves of AGN is still challenging. Despite the fact that there
have been efforts on characterizing the temporal emission of some individual
sources, a systematic search for periodicities by means of a full likelihood
analysis applied to large samples of sources was missing. Our analysis finds 11
AGN, of which 9 are identified for the first time, showing periodicity at more
than 4sigma in at least four algorithms. These findings will help in solving
questions related to the astrophysical origin of this periodic behavior.Comment: 16 pages, 5 figures, 4 tables. Accepted by Ap
Spectrum Sensing of DVB-T2 Signals in Multipath Channels for Cognitive Radio Networks
© 2018 VDE VERLAG GMBHIn this paper, spectrum sensing of digital video broadcasting-second generation terrestrial (DVB-T2) signals in different fading environments with energy detection (ED) is considered. ED is known to achieve an increased performance among low computational complexity detectors, but it is susceptible to noise uncertainty. By taking into consideration the edge pilot and scattered pilot periodicity in DVB-T2 signals, a low computational complex noise power estimator is proposed. It is shown analytically that the choice of detector depends on the environment, the detector requirements, the available prior knowledge and with the noise power estimator. Simulation confirm that with the noise power estimator, ED significantly outperforms the pilot correlation-based detectors. Simulation also show that the proposed scheme enables ED to obtain increased detection performance in fading channels
TRUFAS, a wavelet based algorithm for the rapid detection of planetary transits
Aims: We describe a fast, robust and automatic detection algorithm, TRUFAS,
and apply it to data that are being expected from the CoRoT mission. Methods:
The procedure proposed for the detection of planetary transits in light curves
works in two steps: 1) a continuous wavelet transformation of the detrended
light curve with posterior selection of the optimum scale for transit
detection, and 2) a period search in that selected wavelet transformation. The
detrending of the light curves are based on Fourier filtering or a discrete
wavelet transformation. TRUFAS requires the presence of at least 3 transit
events in the data. Results: The proposed algorithm is shown to identify
reliably and quickly the transits that had been included in a standard set of
999 light curves that simulate CoRoT data. Variations in the pre-processing of
the light curves and in the selection of the scale of the wavelet transform
have only little effect on TRUFAS' results. Conclusions: TRUFAS is a robust and
quick transit detection algorithm, especially well suited for the analysis of
very large volumes of data from space or ground-based experiments, with long
enough durations for the target-planets to produce multiple transit events.Comment: 9 pages, 10 figures, accepted by A&
VLSI implementation of an energy-aware wake-up detector for an acoustic surveillance sensor network
We present a low-power VLSI wake-up detector for a sensor network that uses acoustic signals to localize ground-base vehicles. The detection criterion is the degree of low-frequency periodicity in the acoustic signal, and the periodicity is computed from the "bumpiness" of the autocorrelation of a one-bit version of the signal. We then describe a CMOS ASIC that implements the periodicity estimation algorithm. The ASIC is functional and its core consumes 835 nanowatts. It was integrated into an acoustic enclosure and deployed in field tests with synthesized sounds and ground-based vehicles.Fil: Goldberg, David H.. Johns Hopkins University; Estados UnidosFil: Andreou, Andreas. Johns Hopkins University; Estados UnidosFil: Julian, Pedro Marcelo. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; Argentina. Universidad Nacional del Sur. Departamento de IngenierĂa ElĂ©ctrica y de Computadoras; ArgentinaFil: Pouliquen, Philippe O.. Johns Hopkins University; Estados UnidosFil: Riddle, Laurence. Signal Systems Corporation; Estados UnidosFil: Rosasco, Rich. Signal Systems Corporation; Estados Unido
Spectrum Sensing of DVB-T2 Signals using a Low Computational Noise Power Estimation
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted ncomponent of this work in other works.Cognitive radio is a promising technology that answers the spectrum scarcity problem arising from the proliferation of wireless networks and mobile services. In this paper, spectrum sensing of digital video broadcasting-second generation terrestrial (DVB-T2) signals in AWGN, WRAN and COST207 multipath fading environment are considered. ED is known to achieve an increased performance among low computational complexity detectors, but it is susceptible to noise uncertainty. Taking into consideration the edge pilot and scattered pilot periodicity in DVB-T2 signals, a low computational noise power estimator is proposed. Analytical forms for the detector are derived. Simulation results show that with the noise power estimator, ED significantly outperforms the pilot correlation-based detectors. Simulation also show that the proposed scheme enables ED to obtain increased detection performance in multi-path fading environments. Moreover, based on this algorithm a practical sensing scheme for cognitive radio networks is proposed.Peer reviewedFinal Accepted Versio
BaNa: a noise resilient fundamental frequency detection algorithm for speech and music
Fundamental frequency (F0) is one of the essential features in many acoustic related applications. Although numerous F0 detection algorithms have been developed, the detection accuracy in noisy environments still needs improvement. We present a hybrid noise resilient F0 detection algorithm named BaNa that combines the approaches of harmonic ratios and Cepstrum analysis. A Viterbi algorithm with a cost function is used to identify the F0 value among several F0 candidates. Speech and music databases with eight different types of additive noise are used to evaluate the performance of the BaNa algorithm and several classic and state-of-the-art F0 detection algorithms. Results show that for almost all types of noise and signal-to-noise ratio (SNR) values investigated, BaNa achieves the lowest Gross Pitch Error (GPE) rate among all the algorithms. Moreover, for the 0 dB SNR scenarios, the BaNa algorithm is shown to achieve 20% to 35% GPE rate for speech and 12% to 39% GPE rate for music. We also describe implementation issues that must be addressed to run the BaNa algorithm as a real-time application on a smartphone platform.Peer ReviewedPostprint (author's final draft
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