Energy-Efficient selective activation in Femtocell Networks

Provisioning the capacity of wireless networks is difficult when peak load is significantly higher than average load, for example, in public spaces like airports or train stations. Service providers can use femtocells and small cells to increase local capacity, but deploying enough femtocells to serve peak loads requires a large number of femtocells that will remain idle most of the time, which wastes a significant amount of power. To reduce the energy consumption of over-provisioned femtocell networks, we formulate a femtocell selective activation problem, which we formalize as an integer nonlinear optimization problem. Then we introduce GREENFEMTO, a distributed femtocell selective activation algorithm that deactivates idle femtocells to save power and activates them on-the-fly as the number of users increases. We prove that GREENFEMTO converges to a locally Pareto optimal solution and demonstrate its performance using extensive simulations of an LTE wireless system. Overall, we find that GREENFEMTO requires up to 55% fewer femtocells to serve a given user load, relative to an existing femtocell power-saving procedure, and comes within 15% of a globally optimal solution

Push & Pull: autonomous deployment of mobile sensors for a complete coverage

Mobile sensor networks are important for several strategic applications devoted to monitoring critical areas. In such hostile scenarios, sensors cannot be deployed manually and are either sent from a safe location or dropped from an aircraft. Mobile devices permit a dynamic deployment reconfiguration that improves the coverage in terms of completeness and uniformity. In this paper we propose a distributed algorithm for the autonomous deployment of mobile sensors called Push&Pull. According to our proposal, movement decisions are made by each sensor on the basis of locally available information and do not require any prior knowledge of the operating conditions or any manual tuning of key parameters. We formally prove that, when a sufficient number of sensors are available, our approach guarantees a complete and uniform coverage. Furthermore, we demonstrate that the algorithm execution always terminates preventing movement oscillations. Numerous simulations show that our algorithm reaches a complete coverage within reasonable time with moderate energy consumption, even when the target area has irregular shapes. Performance comparisons between Push&Pull and one of the most acknowledged algorithms show how the former one can efficiently reach a more uniform and complete coverage under a wide range of working scenarios.Comment: Technical Report. This paper has been published on Wireless Networks, Springer. Animations and the complete code of the proposed algorithm are available for download at the address: http://www.dsi.uniroma1.it/~novella/mobile_sensors

LIDAR DERIVED SALT MARSH TOPOGRAPHY AND BIOMASS: DEFINING ACCURACY AND SPATIAL PATTERNS OF UNCERTAINTY

As valuable and vulnerable blue carbon ecosystems, salt marshes require adaptable and robust monitoring methods that span a range of spatiotemporal scales. The application of unmanned aerial vehicle (UAV) based remote sensing is a key tool in achieving this goal. Due to the particular characteristics of tidal wetlands, however, there are challenges in obtaining research and management relevant data with the requisite level of accuracy. In this study, the spatial patterns in uncertainty stemming from scan angle, binning method, vegetation structure and platform surface morphology are examined in the context of UAV light detection and ranging (LiDAR) derived digital elevation models (DEM). The results demonstrate that overlapping the UAV flight paths sufficiently to avoid sole reliance on LIDAR data with scan angles exceeding 15 degrees is advisable. Furthermore, the spatial arrangement of halophyte species and marsh morphology has a clear influence on DEM accuracy. The largest errors were associated with sudden structural transitions at the marsh channel boundaries. The DEMmean was found to be the most accurate for bare ground, while the DEMmin was the most accurate for channels and the middle to high marsh vegetation (MAEs = −0.01m). For the low to middle vegetation, all the trialled DEMs returned a similar magnitude of mean error (MAE = ± 0.03m). The accuracy difference between the two vegetation associations examined appears to be connected to variations in coverage, height and biomass. Overall, these findings reinforce the link between salt marsh biogeomorphic complexity and the spatial distribution and magnitude of LiDAR DEM erro

Spherical Collapse in Chameleon Models

We study the gravitational collapse of an overdensity of nonrelativistic matter under the action of gravity and a chameleon scalar field. We show that the spherical collapse model is modified by the presence of a chameleon field. In particular, we find that even though the chameleon effects can be potentially large at small scales, for a large enough initial size of the inhomogeneity the collapsing region possesses a thin shell that shields the modification of gravity induced by the chameleon field, recovering the standard gravity results. We analyse the behaviour of a collapsing shell in a cosmological setting in the presence of a thin shell and find that, in contrast to the usual case, the critical density for collapse depends on the initial comoving size of the inhomogeneity.Comment: matches printed versio

An Army of Me: Sockpuppets in Online Discussion Communities

In online discussion communities, users can interact and share information and opinions on a wide variety of topics. However, some users may create multiple identities, or sockpuppets, and engage in undesired behavior by deceiving others or manipulating discussions. In this work, we study sockpuppetry across nine discussion communities, and show that sockpuppets differ from ordinary users in terms of their posting behavior, linguistic traits, as well as social network structure. Sockpuppets tend to start fewer discussions, write shorter posts, use more personal pronouns such as "I", and have more clustered ego-networks. Further, pairs of sockpuppets controlled by the same individual are more likely to interact on the same discussion at the same time than pairs of ordinary users. Our analysis suggests a taxonomy of deceptive behavior in discussion communities. Pairs of sockpuppets can vary in their deceptiveness, i.e., whether they pretend to be different users, or their supportiveness, i.e., if they support arguments of other sockpuppets controlled by the same user. We apply these findings to a series of prediction tasks, notably, to identify whether a pair of accounts belongs to the same underlying user or not. Altogether, this work presents a data-driven view of deception in online discussion communities and paves the way towards the automatic detection of sockpuppets.Comment: 26th International World Wide Web conference 2017 (WWW 2017

Diachronic analysis of farmers' strategies within a protected area of central Italy

The farm can be considered as the decision unit in the agricultural land management, therefore it is the most suitable scale to analyse the farmers’ strategies of production. In this paper we describe the results of a comparison between two enquiries carried out in 1992-93 and 2009-10 on more than 30 farms, corresponding to about 1500 ha of utilised agricultural area (UAA), within the boundaries of the San Rossore, Migliarino, Massaciuccoli Regional Park (central-western Italy). We calculate a set of agri-environmental indicators both at territory and farm scale in order to point out the changes occurred over almost twenty years in the farms’ structure, management and production features. The results showed that the major differences were related to fertilisers management (clearly decreasing), to the reduction of some crop types (industrial crops) and to the strong decrease of the gross marketable production. Furthermore, apparently stable indicators, such as the utilised agricultural area and the farm tractors’ power, were actually the result of the compensation of contrasting trends. Farmers’ behaviours were substantially homogeneous within the same typology of farm, highlighting common evolution strategies. The desirability of the occurred changes was also evaluated, underlining the improvement of environmental sustainability of the current cropping systems and a greater social acceptability of agricultural activities, while the evaluation from the farmers’ point of view was less satisfactory

Machine learning techniques for MRI feature-based detection of frontotemporal lobar degeneration

Making a diagnosis of neurodegenerative diseases at an early stage is one of the most significant challenges of modern neuroscience. Although this family of diseases remains without a cure, the effectiveness of their medical treatment largely relies on the timing of their detection. For certain groups of diseases, such as Fronto-Temporal Dementia (FTD), trained professionals can effectively reach a correct diagnosis through the visual analysis of Magnetic Resonance Imaging, in its functional (fMRI) or raw (MRI) version. However, this operation is time-consuming and may be subject to personal interpretation. In this paper, we explore the performance of a group of machine learning algorithms to formulate a correct FTD diagnosis, in order to provide medical professionals with a supporting tool. The dataset consists of MRI data acquired on 30 subjects, and the experiments are carried out by investigating different fMRI techniques based on a Multi-Voxel Pattern Analysis (MVPA) approach. The results obtained show high accuracy in identifying FTD in elderly patients when Support Vector Machine and Random Forest techniques are used, with outcomes varying based on the fMRI methods

Lapex: A Phoswich balloon experiment for hard X-ray astronomy

Satellite and balloon observations have shown that several classes of celestial objects are hard ( 15 keV) energy band with a sensitivity of approx 10 mCrab has been performed with the UCSD/MIT instrument (A4) on board the HEAO 1 satellite. About 70 X-ray sources were detected, including galactic and extragalactic objects. Hard X-ray emission has been detected in the Galaxy from X-ray pulsars. Extragalactic sources of hard X-ray emission include clusters of galaxies, QSOs, BL Lac objects, Seyfert galaxies. The essential characteristics of the Large Area Phoswich Experiment (LAPEX) for crowded sky field observations are described. It has: (1) a broad energy band of operation (20-300 keV); (2) a 3 sigma sensitivity of about 1 mCrab in 10,000 s of live observing time; and (3) imaging capabilities with an angular resolution of about 20'

Agricultural land management: Capturing synergies among climate change adaptation, greenhouse gas mitigation and agricultural productivity

Report 3b of the project “Adaptation of Smallholder Agriculture to Climate Change in Kenya