Review of works combining GNSS and insar in Europe

The Global Navigation Satellite System (GNSS) and Synthetic Aperture Radar Interferometry (InSAR) can be combined to achieve different goals, owing to their main principles. Both enable the collection of information about ground deformation due to the differences of two consequent acquisitions. Their variable applications, even if strictly related to ground deformation and water vapor determination, have encouraged the scientific community to combine GNSS and InSAR data and their derivable products. In this work, more than 190 scientific contributions were collected spanning the whole European continent. The spatial and temporal distribution of such studies, as well as the distinction in different fields of application, were analyzed. Research in Italy, as the most represented nation, with 47 scientific contributions, has been dedicated to the spatial and temporal distribution of its studied phenomena. The state-of-the-art of the various applications of these two combined techniques can improve the knowledge of the scientific community and help in the further development of new approaches or additional applications in different fields. The demonstrated usefulness and versability of the combination of GNSS and InSAR remote sensing techniques for different purposes, as well as the availability of free data, EUREF and GMS (Ground Motion Service), and the possibility of overcoming some limitations of these techniques through their combination suggest an increasingly widespread approach

Recent Advances in Fully Dynamic Graph Algorithms

In recent years, significant advances have been made in the design and analysis of fully dynamic algorithms. However, these theoretical results have received very little attention from the practical perspective. Few of the algorithms are implemented and tested on real datasets, and their practical potential is far from understood. Here, we present a quick reference guide to recent engineering and theory results in the area of fully dynamic graph algorithms

Electronic Locator of Vertical Interval Successions (ELVIS): The first large data-driven research project on musical style

The ELVIS project had three locally based teams (in Canada, at McGill, in Scotland, at Aberdeen, and in New England, USA, divided between MIT, the lead, and Yale), each of which focused on a different aspect of the overall research program: using computers to understand musical style. The central unifying concept of the ELVIS project was to study counterpoint: the way combinations of voices in polyphonic music (e.g. the soprano and bass voices in a hymn, or the viola and cello in a string quartet, as well as combinations of more than two voices) interact: i.e. what are the permissible vertical intervals (notes from two voices sounding at the same time) for a particular period, genre, or style. These vertical intervals, connected by melodic motions in individual voices, constitute Vertical Interval Successions. In more modern terms, this could be described as harmonic progressions of chords, but what made ELVIS particularly flexible was its ability to bridge the gap to earlier, contrapuntally-conceived music by using the diad (a two-note combination) rather than the triad (a combination of three notes in particular arrangements) as a basis (since triads and beyond may be expressed as sums of diads)

Experimental demonstration of ship target detection in GNSS-based passive radar combining target motion compensation and track-before-detect strategies

This work discusses methods and experimental results on passive radar detection of moving ships using navigation satellites as transmitters of opportunity. The reported study highlights as the adoption of proper strategies combining target motion compensation and track-before-detect methods to achieve long time integration can be fruitfully exploited in GNSS-based passive radar for the detection of maritime targets. The proposed detection strategy reduces the sensitivity of long-time integration methods to the adopted motion models and can save the computational complexity, making it appealing for real-time implementations. Experimental results obtained in three different scenarios (port operations, navigation in open area, and river shipping) comprising maritime targets belonging to different classes show as this combined approach can be employed with success in several operative scenarios of practical interest for this technology

GNSS-free outdoor localization techniques for resource-constrained IoT architectures : a literature review

Large-scale deployments of the Internet of Things (IoT) are adopted for performance improvement and cost reduction in several application domains. The four main IoT application domains covered throughout this article are smart cities, smart transportation, smart healthcare, and smart manufacturing. To increase IoT applicability, data generated by the IoT devices need to be time-stamped and spatially contextualized. LPWANs have become an attractive solution for outdoor localization and received significant attention from the research community due to low-power, low-cost, and long-range communication. In addition, its signals can be used for communication and localization simultaneously. There are different proposed localization methods to obtain the IoT relative location. Each category of these proposed methods has pros and cons that make them useful for specific IoT systems. Nevertheless, there are some limitations in proposed localization methods that need to be eliminated to meet the IoT ecosystem needs completely. This has motivated this work and provided the following contributions: (1) definition of the main requirements and limitations of outdoor localization techniques for the IoT ecosystem, (2) description of the most relevant GNSS-free outdoor localization methods with a focus on LPWAN technologies, (3) survey the most relevant methods used within the IoT ecosystem for improving GNSS-free localization accuracy, and (4) discussion covering the open challenges and future directions within the field. Some of the important open issues that have different requirements in different IoT systems include energy consumption, security and privacy, accuracy, and scalability. This paper provides an overview of research works that have been published between 2018 to July 2021 and made available through the Google Scholar database.5311-8814-F0ED | Sara Maria da Cruz Maia de Oliveira PaivaN/

Faster and better nested dissection orders for Customizable Contraction Hierarchies

Graph partitioning has many applications. We consider the acceleration of shortest path queries in road networks using Customizable Contraction Hierarchies (CCH). It is based on computing a nested dissection order by recursively dividing the road network into parts. Recently, with FlowCutter and Inertial Flow, two flow-based graph bipartitioning algorithms have been proposed for road networks. While FlowCutter achieves high-quality results and thus fast query times, it is rather slow. Inertial Flow is particularly fast due to the use of geographical information while still achieving decent query times. We combine the techniques of both algorithms to achieve more than six times faster preprocessing times than FlowCutter and even faster queries on the Europe road network. We show that, using 16 cores of a shared-memory machine, this preprocessing needs four minutes

Towards Mobility Data Science (Vision Paper)

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from the metadata. PDF has not been change