One of the most remarkable phenomena in loop quantum cosmology is that, at least for homogeneous cosmological models, the big bang is replaced with a big bounce that connects our Universe with a previous branch without passing through a cosmological singularity. The goal of this work is to study the existence of singularities in loop quantum cosmology, including inhomogeneities, and check whether the behavior obtained in the purely homogeneous setting continues to be valid. With this aim, we focus our attention on the three-torus Gowdy cosmologies with linearly polarized gravitational waves and use effective dynamics to carry out the analysis. For this model, we prove that all the potential cosmological singularities are avoided, generalizing the results about resolution of singularities to this scenario with inhomogeneities. We also demonstrate that, if a bounce in the (Bianchi background) volume occurs, the inhomogeneities increase the value of this volume at the bounce with respect to its counterpart in the homogeneous case.
The development of ambient intelligence (AmI) applications usually implies dealing with complex sensor access and context reasoning tasks, which may significantly slow down the application development cycle when vertically assumed. To face this issue, we present CASanDRA, a middleware which provides easily consumable context information about a given user and his environment, retrieving and fusing data from personal mobile devices and external sensors. The framework is built following a layered service oriented approach. The output data from every CASanDRA's layer are fully accessible through semantic interfaces; this allows AmI applications to retrieve raw context features, aggregated context data and complex ' images of context', depending on their information needs. Moreover, different query modes -subscription, event-based, continuous and on-demand- are available. The current 'mobile-assisted' version of CASanDRA is composed by a CASanDRA Server, developed on an applications container and hosting the system intelligence, and CASanDRA Lite, a mobile client bundling a set of sensor level acquisition services. How an AmI application may be effortlessly built on CASanDRA is described in the paper through the design of an ' Ambient Home Care Monitor'.
In the last few years, many health monitoring systems have been designed to fullfil the needs of a large range of scenarios. Although many of those systems provide good ad hoc solutions, most of them lack of mechanisms that allow them to be easily reused. This paper is focused on describing an open platform, the micro Web of Things Open Platform (µWoTOP), which has been conceived to improve the connectivity and reusability of context data to deliver different kinds of health, wellness and ambient home care services. µWoTOP is based on a resource-oriented architecture which may be embedded in mobile and resource constrained devices enabling access to biometric, ambient or activity sensors and actuator resources through uniform interfaces defined according to a RESTful fashion. Additionally, µWoTOP manages two communication modes which allow delivering user context information according to different methods, depending on the requirements of the consumer application. It also generates alert messages based on standards related to health care and risk management, such as the Common Alerting Protocol, in order to make its outputs compatible with existing systems.
We present the first cluster catalog extracted from combined space-based (Planck) and ground-based (South Pole Telescope; SPT-SZ) millimeter data. We developed and applied a matched multi-filter (MMF) capable of dealing with the different transfer functions and resolutions of the two datasets. We verified that it produces results consistent with publications from Planck and SPT collaborations when applied on the datasets individually. We also verified that Planck and SPT-SZ cluster fluxes are consistent with each other. When applied blindly to the combined dataset, the MMF generated a catalog of 419 detections ($S/N>5$), of which 323 are already part of the SPT-SZ or PSZ2 catalogs; 54 are new SZ detections, which have been identified in other catalogs or surveys; and 42 are new unidentified candidates. The MMF takes advantage of the complementarity of the two datasets, Planck being particularly useful for detecting clusters at a low redshift ($z<0.3$), while SPT is efficient at finding higher redshift ($z>0.3$) sources. This work represents a proof of concept that blind cluster extraction can be performed on combined, inhomogeneous millimeter datasets acquired from space and ground. This result is of prime importance for planned ground-based cosmic microwave background (CMB) experiments (e.g., Simons Observatory, CMB-S4) and envisaged CMB space missions (e.g., PICO, Backlight) that will detect hundreds of thousands of clusters in the low mass regime ($M_{500} \leqslant 10^{14} M_\odot$), for which the various sources of intra-cluster emission (gas, dust, synchrotron) will be of the same order of magnitude and hence require broad ground and space frequency coverage with a comparable spatial resolution for adequate separation.
In this paper a new localization method based on parametric channel models is presented. In order to reduce localization errors when working with real data, we also propose to calculate the median value of several received signal strength measurements prior to the application of the localization method. The performance of the method is shown through both numerical simulations and some tests with real data from a wireless sensor network.
The combination of X-ray and SZ observations can potentially improve the cluster detection efficiency when compared to using only one of these probes, since both probe the same medium: the hot ionized gas of the intra-cluster medium. We present a method based on matched multifrequency filters (MMF) for detecting galaxy clusters from SZ and X-ray surveys. This method builds on a previously proposed joint X-ray-SZ extraction method (Tarr\'io et al. 2016) and allows to blindly detect clusters, that is finding new clusters without knowing their position, size or redshift, by searching on SZ and X-ray maps simultaneously. The proposed method is tested using data from the ROSAT all-sky survey and from the Planck survey. The evaluation is done by comparison with existing cluster catalogues in the area of the sky covered by the deep SPT survey. Thanks to the addition of the X-ray information, the joint detection method is able to achieve simultaneously better purity, better detection efficiency and better position accuracy than its predecessor Planck MMF, which is based on SZ maps only. For a purity of 85%, the X-ray-SZ method detects 141 confirmed clusters in the SPT region, whereas to detect the same number of confirmed clusters with Planck MMF, we would need to decrease its purity to 70%. We provide a catalogue of 225 sources selected by the proposed method in the SPT footprint, with masses ranging between 0.7 and 14.5 $\cdot 10^{14}$ Msun and redshifts between 0.01 and 1.2.
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