A confirmation of the long-standing muon g − 2 discrepancy requires both experimental and theoretical progress. On the theory side, the hadronic corrections are under close scrutiny, as they induce ...the leading uncertainty of the Standard Model prediction. Recently, the MUonE experiment has been proposed at CERN to provide a new determination of the leading hadronic contribution to the muon g − 2 via the measurement of the differential cross section of muon-electron scattering. The precision expected at this experiment raises the question whether possible new physics (NP) could affect its measurements. We address this issue studying possible NP signals in muon-electron collisions due to heavy or light mediators, depending on whether their mass is higher or lower than Oð1 GeVÞ. We analyze the former in a modelindependent way via an effective field theory approach, whereas for the latter we focus on scenarios with light scalar and vector bosons. Using existing experimental bounds, we show that possible NP effects in muon-electron collisions are expected to lie below MUonE's sensitivity. This result confirms and reinforces the physics case of the MUonE proposal.
Contributions of a spin-0 axionlike particle (ALP) to lepton dipole moments, g−2 and EDMs are examined. Barr-Zee and light-by-light loop effects from a light pseudoscalar ALP are found to be capable ...of resolving the longstanding muon g−2 discrepancy at the expense of relatively large ALP−γγ couplings. The compatibility of such large couplings with direct experimental constraints and perturbative unitarity bounds is discussed. Future tests of such a scenario are described. For CP-violating ALP couplings, the electron EDM is found to probe much smaller, theoretically more easily accommodated ALP interactions. Future planned improvement in electron EDM searches is advocated as a way to not only significantly constrain ALP parameters, but also potentially unveil a new source of CP violation which could have far-reaching ramifications.
We propose a scheme where the three relevant physics scales related to the supersymmetry, electroweak, and baryon minus lepton (B−L) breakings are linked together and occur at the TeV scale. The ...phenomenological implications in the Higgs and leptonic sectors are discussed.
This paper presents the current state of development of a free Matlab tool for photogrammetric reconstruction developed at the University of Padova, Italy. The goal of this software is mostly ...educational, i.e. allowing students to have a close look to the specific steps which lead to the computation of a dense point cloud. As most of recently developed photogrammetric softwares, it is based on a Structure from Motion approach. Despite being mainly motivated by educational purposes, certain implementation details are clearly inspired by recent research works, e.g. limiting the computational burden of the feature matching by determining a suboptimal set of features to be considered, using information provided by external sensors to ease the matching process.
UAV-BASED RIVER PLASTIC DETECTION WITH A MULTISPECTRAL CAMERA Cortesi, I.; Masiero, A.; Tucci, G. ...
International archives of the photogrammetry, remote sensing and spatial information sciences.,
05/2022, Letnik:
XLIII-B3-2022
Journal Article, Conference Proceeding
Recenzirano
Odprti dostop
Plastic is the third world’s most produced material by industry (after concrete and steel), but people recycle only 9% of plastic that they have used. The other parts are either burned or accumulated ...in landfills and in the environment, the latter being the cause of many serious consequences, in particular when considering a long-term scenario. A significant part the plastic waste is dispersed in the aquatic environment, having a dramatic impact on the aquatic flora and fauna. This motivated several works aiming at the development of methodologies and automatic or semi-automatic tools for the plastic pollution detection, in order to enable and facilitate its recovery. This paper deals with the problem of plastic waste automatic detection in the fluvial and aquatic environment. The goal is that of exploiting the well-recognized potential of machine learning tools in object detection applications. A machine learning tool, based on random forest classifiers, has been developed to properly detect plastic objects in multi-spectral imagery collected by an unmanned aerial vehicle (UAV). In the developed approach, the outcome is determined by the combination of two random forest classifiers and of an area-based selection criterion. The approach is tested on 154 images collected by a multi-spectral proximity sensor, namely the MAIA-S2 camera, in a fluvial environment, on the Arno river (Italy), where an artificial controlled scenario was created by introducing plastic samples anchored to the ground. The obtained results are quite satisfactory in terms of object detection accuracy and recall (both higher than 98%), while presenting a remarkably lower performance in terms of precision and quality. The overall performance appears also to be dependent on the UAV flight altitude, being worse at higher altitudes, as expected.
The development of remote sensing techniques dramatically improved the human knowledge of natural phenomena and the real time monitoring and interpretation of the events happening in the environment. ...The recently developed terrestrial, aerial and satellite remote sensors caused the availability of huge amount of data. The large size of such data is leading the research community to the search for efficient methods for real time information extraction, and, more in general, understanding the collected data. Nowadays, this is typically done by means of artificial intelligence-based methods, and, more specifically, usually by means of machine learning tools. Focusing on semantic segmentation, which is clearly related to a proper interpretation of the acquired remote sensing data, supervised machine learning is often used: it is based on the availability of a set of ground truth labeled data, which are used in order to properly train a machine learning classifier. Despite the latter, after a proper training phase, usually allows to obtain quite effective segmentation results, the ground truth labeled data production is usually a very laborious and time consuming task, performed by a human operator. Motivated by the latter consideration, this work aims at introducing a graphical interface developed in order to support semi-automatic semantic segmentation of images acquired by a UAS. Certain of the potentialities of the proposed graphical are shown in the specific case of plastic litter detection in multi-spectral images.
The Smart City concept is taking momentum recently as big metropolises as well as mid-size cities are intensifying their efforts to improve the life of people living in dense urban environment. Local ...governments are eager to have up-to-date information of every aspect of city life, including environmental data, such as air and water quality parameters; mobility data, such as traffic flow, including vehicles, transit passengers; crowd control, such as public events, mobility in hospitals; life quality data, such as social status, education level, health records; etc. Monitoring all these very different data streams in space and time is a formidable challenge. While on the data acquisition side, tremendous progress has been achieved, as sensors have been deployed in increasingly large numbers on both mobile and static platforms, there is a lack of creating accurate geotags, as the quality of georeferencing varies over a large scale. It is important to note that the data acquisition is becoming largely customer-based, as smart devices are efficient sensor systems and with advancing communication technologies, crowdsourcing is quickly becoming the dominant data source on mobile platforms. In this paper, we investigate the potential to exploit the ranging capabilities of imaging and communication sensors and use the strength of the spatial network formed by the sensors to improve the georeferencing of a group of platforms operating in a close environment, such as UAS swarm or a platoon of autonomous vehicles. Transportation in cities and in general mobility are of great interest to Smart Cities, they represent one of the most significant components of the activities, so having an optimized transportation system is essential to reduce carbon footprint, decrease commute time, and just improve the quality of life. To assess the performance of collaborative navigation based accurate georeferencing, data was acquired at a simulated intersection area at The Ohio State University, where multiple vehicles, pedestrians and cyclists were moving around. In addition, drones were flying above the area. Here we report about our initial results.
Plastic pollution has a severe impact on the ecosystem, altering its natural equilibrium and causing serious health issues to both flora and fauna. Several actions have already been undertaken in ...order to reduce the plastic litter dispersion in the environment, both in terms of changing the human behavior, reducing the use of plastics and avoiding their dispersion, and of implementing methods for detecting and collecting the already dispersed ones. This paper focuses on the latter, and, in particular, on plastic litter detection on the fluvial environment. To this aim, an Unmanned Aerial Vehicle, provided with a multi-spectral and a thermal camera, have been used, in order to: (i) allow affordable periodic monitoring of relatively long river reaches, (ii) detect even quite small macro-plastics, based on their spectral signature. More specifically, since the cameras deployed in our data collection campaigns are not synchronized, this work aims at presenting the developed strategy for the co-registration of the acquired imagery, which results to be quite challenging given the few amount of visual features recognizable on the images acquired flying at a limited altitude over a river. The proposed methodology, which is based on the correlation maximization between multi-spectral and thermal images, provided reasonable results on the considered case study. The obtained values of normalized intersection over union of plastic areas are over 80%.
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