Effects of Luminosity Functions Induced by Relativistic Beaming on Statistics of Cosmological Gamma-Ray Bursts

We study the effects of the beaming-induced luminosity function on statistics of observed GRBs, assuming the cosmological scenario. We select and divide the BATSE 4B data into 588 long bursts (T$_{90}>2.5$ sec) and 149 short bursts (T$_{90}<2.5$ sec), and compare the statistics calculated in each subgroup. The of the long bursts is $ 0.2901\pm 0.0113$, and that of the short bursts is $0.4178\pm 0.0239$, which is a Euclidean value. For luminosity function models, we consider a cylindrical-beam and a conic-beam. We take into account the spatial distribution of GRB sources as well. A broad luminosity function is naturally produced when one introduces beaming of GRBs. We calculate the maximum detectable redshift of GRBs, $z_{\rm max}$. The estimated $z_{\rm max}$ for the cylindrical-beam case is as high as $\sim 14$ for the long bursts and $\sim 3$ for the short bursts. The large $z_{\rm max}$ value for the short bursts is rather surprising in that the for this subgroup is close to the so-called Euclidean value, 0.5. We calculate the fraction of bursts whose redshifts are larger than a certain redshift $z'$, i.e. $f_{\rm > z'}$. When we take $z'=3.42$ and apply the luminosity function derived for the cylindrical-beam, the expected $f_{\rm > z'}$ is $\sim 75$ for long bursts. When we increase the opening angle of the conic beam to $\Delta \theta =3^\circ.0$, $f_{\rm > z'}$ decreases to $\sim 20$ at ${\rm z'=3.42}$. We conclude that the beaming-induced luminosity functions are compatible with the redshift distribution of observed GRBs and that the apparent Euclidean value of $$ may not be due to the Euclidean space distribution but to the luminosity distribution.Comment: Accepted for publication in the Astronomical Journal (vol. 548, Feb. 20 2001

Robotic Searching for Stationary, Unknown and Transient Radio Sources

Searching for objects in physical space is one of the most important tasks for humans. Mobile sensor networks can be great tools for the task. Transient targets refer to a class of objects which are not identifiable unless momentary sensing and signaling conditions are satisfied. The transient property is often introduced by target attributes, privacy concerns, environment constraints, and sensing limitations. Transient target localization problems are challenging because the transient property is often coupled with factors such as sensing range limits, various coverage functions, constrained mobility, signal correspondence, limited number of searchers, and a vast searching region. To tackle these challenge tasks, we gradually increase complexity of the transient target localization problem such as Single Robot Single Target (SRST), Multiple Robots Single Target (MRST), Single Robot Multiple Targets (SRMT) and Multiple Robots Multiple Targets (MRMT). We propose the expected searching time (EST) as a primary metric to assess the searching ability of a single robot and the spatiotemporal probability occupancy grid (SPOG) method that captures transient characteristics of multiple targets and tracks the spatiotemporal posterior probability distribution of the target transmissions. Besides, we introduce a team of multiple robots and develop a sensor fusion model using the signal strength ratio from the paired robots in centralized and decentralized manners. We have implemented and validated the algorithms under a hardware-driven simulation and physical experiments

MARINE TOURISM RESOURCE DEVELOPMENT IN KOREA

Resource /Energy Economics and Policy,