25,279 research outputs found

    High-level Information Fusion for Constrained SMC Methods and Applications

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    Information Fusion is a field that studies processes utilizing data from various input sources, and techniques exploiting this data to produce estimates and knowledge about objects and situations. On the other hand, human computation is a new and evolving research area that uses human intelligence to solve computational problems that are beyond the scope of existing artificial intelligence algorithms. In previous systems, humans' role was mostly restricted for analysing a finished fusion product; however, in the current systems the role of humans is an integral element in a distributed framework, where many tasks can be accomplished by either humans or machines. Moreover, some information can be provided only by humans not machines, because the observational capabilities and opportunities for traditional electronic (hard) sensors are limited. A source-reliability-adaptive distributed non-linear estimation method applicable to a number of distributed state estimation problems is proposed. The proposed method requires only local data exchange among neighbouring sensor nodes. It therefore provides enhanced reliability, scalability, and ease of deployment. In particular, by taking into account the estimation reliability of each sensor node at any point in time, it yields a more robust distributed estimation. To perform the Multi-Model Particle Filtering (MMPF) in an adaptive distributed manner, a Gaussian approximation of the particle cloud obtained at each sensor node, along with a weighted Consensus Propagation (CP)-based distributed data aggregation scheme, are deployed to dynamically re-weight the particle clouds. The filtering is a soft-data-constrained variant of multi-model particle filter, and is capable of processing both soft human-generated data and conventional hard sensory data. If permanent noise occurs in the estimation provided by a sensor node, due to either a faulty sensing device or misleading soft data, the contribution of that node in the weighted consensus process is immediately reduced in order to alleviate its effect on the estimation provided by the neighbouring nodes and the entire network. The robustness of the proposed source-reliability-adaptive distributed estimation method is demonstrated through simulation results for agile target tracking scenarios. Agility here refers to cases in which the observed dynamics of targets deviate from the given probabilistic characterization. Furthermore, the same concept is applied to model soft data constrained multiple-model Probability Hypothesis Density (PHD) filter that can track agile multiple targets with non-linear dynamics, which is a challenging problem. In this case, a Sequential Monte Carlo-Probability Hypothesis Density (SMC-PHD) filter deploys a Random Set (RS) theoretic formulation, along with Sequential Monte Carlo approximation, a variant of Bayes filtering. In general, the performance of Bayesian filtering-based methods can be enhanced by using extra information incorporated as specific constraints into the filtering process. Following the same principle, the new approach uses a constrained variant of the SMC-PHD filter, in which a fuzzy logic approach is used to transform the inherently vague human-generated data into a set of constraints. These constraints are then enforced on the filtering process by applying them as coefficients to the particles' weights. Because the human generated Soft Data (SD), reports on target-agility level, the proposed constrained-filtering approach is capable of dealing with multiple agile target tracking scenarios

    Soft X-ray emission from the inner disk of M33

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    We present a study, based on archival XMM-Newton observations, of the extended X-ray emission associated with the inner disk of M33. After the exclusion of point sources with L_X > 2 x 10^{35} erg/s (0.3-6 keV), we investigate the morphology and spectrum of the residual X-ray emission. This residual emission has a soft X-ray spectrum which can be fitted with a two-temperature thermal model, with kT = 0.2 keV and 0.6 keV. The soft X-ray surface brightness distribution shows a strong correlation with FUV emission, indicative of a close connection between recent star-formation activity and the production of soft X-rays. Within 3.5 kpc of the nucleus of M33, the soft X-ray and FUV surface brightness distributions exhibit similar radial profiles. This implies that the ratio of the soft X-ray luminosity (0.3-2.0 keV) to the star formation rate (SFR) per unit disk area remains fairly constant within this inner disk region. We derive a value for this ratio of 1-1.5 x 10^{39} (erg/s)/(M_sun/yr), consistent with previous studies. In the same region, the ratio of soft X-ray luminosity to stellar mass (derived from K-band photometry) is 4 x 10^{28} erg/s/M_sun, a factor of 5-10 higher than is typical of dwarf elliptical galaxies, suggesting that 10-20% of the unresolved emission seen in M33 may originate in its old stellar population. The remainder of the soft X-ray emission is equally split between two spatial components, one which closely traces the spiral arms of the galaxy and the other more smoothly distributed across the inner disk of M33. The former must represent a highly clumped low-filling factor component linked to sites of recent or ongoing star formation, whereas the distribution of the latter gives few clues as to its exact origin.Comment: 13 pages, 4 figures, 5 tables. Accepted for publication in MNRA

    An XMM-Newton observation of the massive, relaxed galaxy cluster ClJ1226.9+3332 at z=0.89

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    A detailed X-ray analysis of an XMM-Newton observation of the high-redshift (z=0.89) galaxy cluster ClJ1226.9+3332 is presented. The X-ray temperature is found to be 11.5{+1.1}{-0.9}keV, the highest X-ray temperature of any cluster at z>0.6. In contrast to MS1054-0321, the only other very hot cluster currently known at z>0.8, ClJ1226.9+3332 features a relaxed X-ray morphology, and its high overall gas temperature is not caused by one or several hot spots. The system thus constitutes a unique example of a high redshift, high temperature, relaxed cluster, for which the usual hydrostatic equilibrium assumption, and the X-ray mass is most reliable. A temperature profile is constructed (for the first time at this redshift) and is consistent with the cluster being isothermal out to 45% of the virial radius. Within the virial radius (corresponding to a measured overdensity of a factor of 200), a total mass of (1.4+/-0.5)*10^15 M_solar is derived, with a gas mass fraction of 12+/-5%. The bolometric X-ray luminosity is (5.3+/-0.2)*10^45 erg/s. The probabilities of finding a cluster of this mass within the volume of the discovery X-ray survey are 8*10^{-5} for Omega_M=1 and 0.64 for Omega_M=0.3, making Omega_M=1 highly unlikely. The entropy profile suggests that entropy evolution is being observed. The metal abundance (of Z=0.33{+0.14}{-0.10} Z_solar), gas mass fraction, and gas distribution are consistent with those of local clusters; thus the bulk of the metals were in place by z=0.89.Comment: 13 pages, 8 figures. Accepted for publication in MNRA

    Gamma-ray observations of Cygnus X-1 above 100 MeV in the hard and soft states

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    We present the results of multi-year gamma-ray observations by the AGILE satellite of the black hole binary system Cygnus X-1. In a previous investigation we focused on gamma-ray observations of Cygnus X-1 in the hard state during the period mid-2007/2009. Here we present the results of the gamma-ray monitoring of Cygnus X-1 during the period 2010/mid-2012 carried out for which includes a remarkably prolonged `soft state' phase (June 2010 -- May 2011). Previous 1--10 MeV observations of Cyg X-1 in this state hinted at a possible existence of a non-thermal particle component with substantial modifications of the Comptonized emission from the inner accretion disk. Our AGILE data, averaged over the mid-2010/mid-2011 soft state of Cygnus X-1, provide a significant upper limit for gamma-ray emission above 100 MeV of F_soft < 20 x 10^{-8} ph/cm^2/s, excluding the existence of prominent non-thermal emission above 100 MeV during the soft state of Cygnus X-1. We discuss theoretical implications of our findings in the context of high-energy emission models of black hole accretion. We also discuss possible gamma-ray flares detected by AGILE. In addition to a previously reported episode observed by AGILE in October 2009 during the hard state, we report a weak but important candidate for enhanced emission which occurred at the end of June 2010 (2010-06-30 10:00 - 2010-07-02 10:00 UT) exactly in coincidence with a hard-to-soft state transition and before an anomalous radio flare. An appendix summarizes all previous high-energy observations and possible detections of Cygnus X-1 above 1 MeV.Comment: 16 pages, 12 figures, 1 table, accepted for publication in Ap

    A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

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    In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes
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