196,174 research outputs found
ET sensitivity to the anisotropic Stochastic Gravitational Wave Background
No full textWe study the sensitivity of a pair of Einstein Telescopes (ET) (hypothetically located at the two sites currently under consideration for ET) to the anisotropies of the Stochastic Gravitational Wave Background (SGWB). We focus on the l =0,2,4 multipoles of an expansion of the SGWB in spherical harmonics, since the sensitivity to other multipoles is suppressed due to the fact that this pair of detector operates in a regime for which the product between the observed frequency and the distance between the two sites is much smaller than one. In this regime, the interferometer overlap functions for the anisotropic signal acquire very simple analytic expressions. These expressions can also be applied to any other pairs of interferometers (each one of arbitrary opening angle between its two arms) operating in this regime. Once the measurements at the vertices of the two sites are optimally combined, the sensitivity to the multipoles of the SGWB depends only on the latitude of the two sites, on the difference of their longitude, but not on the orientation of their arms
Multi-layer occupancy grid mapping for autonomous vehicles navigation
No full textPerception of the surrounding is a crucial task in most of the autonomous driving scenarios. For this reason most vehicles are equipped with a broad range of sensors like lidar, radar, cameras and ultrasound to sense the space around the car. On the other end, planning algorithms need a simple and usable representation of the obstacle around. One of the biggest drawbacks of such a wide range of sensors is the need to resolve conflicting information and identify false positives. What we propose in this paper is an effective framework for sensor fusion and occupancy grid creation capable of retrieving a uniform representation of the ambient around the vehicle and able to handle conflictual information from different sensors
Probing the galactic and extragalactic gravitational wave backgrounds with space-based interferometers
No full textWe employ the formalism developed in [1] and [2] to study the prospect of detecting an anisotropic Stochastic Gravitational Wave Background (SGWB) with the Laser Interferometer Space Antenna (LISA) alone, and combined with the proposed space-based interferometer Taiji. Previous analyses have been performed in the frequency domain only. Here, we study the detectability of the individual coefficients of the expansion of the SGWB in spherical harmonics, by taking into account the specific motion of the satellites. This requires the use of time-dependent response functions, which we include in our analysis to obtain an optimal estimate of the anisotropic signal. We focus on two applications. Firstly, the reconstruction of the anisotropic galactic signal without assuming any prior knowledge of its spatial distribution. We find that both LISA and LISA with Taiji cannot put tight constraints on the harmonic coefficients for realistic models of the galactic SGWB. We then focus on the discrimination between a galactic signal of known morphology but unknown overall amplitude and an isotropic extragalactic SGWB component of astrophysical origin. In this case, we find that the two surveys can confirm, at a confidence level ≳ 3σ, the existence of both the galactic and extragalactic background if both have amplitudes as predicted in standard models. We also find that, in the LISA-only case, the analysis in the frequency domain (under the assumption of a time average of data taken homogeneously across the year) provides a nearly identical determination of the two amplitudes as compared to the optimal analysis
Vehicle state estimation based on Kalman filters
No full textVehicle state estimation represents a prerequisite for ADAS (Advanced Driver-Assistant Systems) and, more in general, for autonomous driving. In particular, algorithms designed for path or trajectory planning require the continuous knowledge of some data such as the lateral velocity and heading angle of the vehicle, together with its lateral position with respect to the road boundaries. Vehicle state estimation can be assessed by means of extended and unscented Kalman filters (EKF and UKF, respectively), that have been well treated in the literature. Referring to an experimental case study, the presented work deals with the design and the real time implementation of two different adaptive Kalman filters for vehicle sideslip and positioning estimation. Accuracy have been assessed by means of an automotive optical sensor
Flow injection determination of Pb and Cd traces with graphite furnace atomic absorption spectrometry
No full textThe preconcentration and recovery of lead and cadmium traces at ng l(-1) level were evaluated in standard solutions and natural aqueous samples using a FIAS (Flow Injection Atomic Spectrometry) apparatus. The method is based on retention of the complex formed between Pb or Cd and 1,2-dihydroxy-3,5-benzendisulphonic acid (Tiron) on a macroporous anion-exchange resin. The recovery of the analytes was obtained by elution with 0.1 M HCl and their determination was performed by Graphite Furnace Atomic Absorption Spectrometry (GFAAS). The detection limits were 9 and 7 ng l(-1) for Pb and Cd respectively. The effects of sample solution pH and composition and of interfering agents as well as reagent purity are discussed. The technique was applied to the analysis of natural waters
Event-Based Object Detection and Tracking - A Traffic Monitoring Use Case -
No full textTraffic monitoring is an important task in many scenarios, in urban roads to identify dangerous behavior and on-highway to check for vehicles moving in the wrong direction. This task is usually performed using conventional cameras but these sensors suffer from fast illumination changes, particularly at night, and extreme weather conditions. This paper proposes a solution for object detection and tracking using event-based cameras. This new technology presents many advantages to address traditional cameras limitations; the most evident are the high dynamic range and temporal resolution. However, due to the different nature of the provided data, solutions need to be implemented to process them in an efficient way. In this work, we propose two solutions for object detection, one based on standard geometrical approaches and one using a deep learning framework. We also release a novel dataset for this task, and present a complete application for road monitoring using event cameras (Dataset available at: https://airlab.deib.polimi.it/datasets-and-tools/)
Clothoidal Mapping of Road Line Markings for Autonomous Driving High-Definition Maps
No full textLane-level HD maps are crucial for trajectory planning and control in current autonomous vehicles. For this reason, appropriate line models should be adopted to define them. Whereas mapping algorithms often rely on inaccurate representations, clothoid curves possess peculiar smoothness properties that make them desirable representations of road lines in control algorithms. We propose a multi-stage pipeline for the generation of lane-level HD maps from monocular vision relying on clothoidal spline models. We obtain measurements of the line positions using a line detection algorithm, and we exploit a graph-based optimization framework to reach an optimal fitting. An iterative greedy procedure reduces the model complexity removing unnecessary clothoids. We validate our system on a real-world dataset, which we make publicly available for further research at https://airlab.deib.polimi.it/datasets-and-tools/
Complexation of Hexaaquoiron(III) with Highly Charged Ligands: Kinetics of Conversion of Outer-Into Inner-Sphere Complexes
No full textReal time monitoring of pig activity: practical difficulties in pigs' behaviour labelling
No full textVideo recording of two pens was made throughout 24 hours a day using a CCD camera placed 5 m above the floor. For the study 33 fattening pigs housed on a fully slatted floor were utilized. The software, called Eyenamic (Leroy et al., 2006b; Bloemen et al., 1997), measured activity levels of the pigs in practical conditions. Every second the algorithm logged the camera image and the activity index of the animals and stored these data for further analysis. Finally the data image files were visually checked and labelled in order to score animals' behaviour and perform a behavioural model to predict pigs aggressiveness as an early warning system to avoid animal losses
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