The State of AI-Empowered Backscatter Communications: A Comprehensive Survey

E-print/Working paper (2023)

The Internet of Things (IoT) is undergoing significant advancements, driven by the emergence of Backscatter Communication (BC) and Artificial Intelligence (AI). BC is an energy-saving and cost-effective ... [more ▼]

The Internet of Things (IoT) is undergoing significant advancements, driven by the emergence of Backscatter Communication (BC) and Artificial Intelligence (AI). BC is an energy-saving and cost-effective communication method where passive backscatter devices communicate by modulating ambient Radio-Frequency (RF) carriers. AI has the potential to transform our way of communicating and interacting and represents a powerful tool for enabling the next generation of IoT devices and networks. By integrating AI with BC, we can create new opportunities for energy-efficient and low-cost communication and open the door to a range of innovative applications that were previously not possible. This paper brings these two technologies together to investigate the current state of AI-powered BC. We begin with an introduction to BC and an overview of the AI algorithms employed in BC. Then, we delve into the recent advances in AI-based BC, covering key areas such as backscatter signal detection, channel estimation, and jammer control to ensure security, mitigate interference, and improve throughput and latency. We also explore the exciting frontiers of AI in BC using B5G/6G technologies, including backscatter-assisted relay and cognitive communication networks, backscatter-assisted MEC networks, and BC with RIS, UAV, and vehicular networks. Finally, we highlight the challenges and present new research opportunities in AI-powered BC. This survey provides a comprehensive overview of the potential of AI-powered BC and its insightful impact on the future of IoT. [less ▲]

Introduction to the special issue: the persistent challenges to European Banking Union

in Journal of European Integration (2023), 45(1), 1-14

The papers of this special issue investigate the persistent challenges to European Banking Union and explore the tensions between broader financial stability objectives and national political and socio ... [more ▼]

The papers of this special issue investigate the persistent challenges to European Banking Union and explore the tensions between broader financial stability objectives and national political and socio-economic pressures through a diversity of lenses. In this introduction, we examine two main issues that need to be addressed in order to strengthen Banking Union: the incomplete institutional design of Banking Union and the difficulties encountered in applying the different elements of Banking Union to loosen sovereign-bank ties. These elements include the so-called ‘single’ rulebook, supervision, resolution, and financial mechanisms to support and resolve banks. [less ▲]

Spaceborne GNSS reflectometry for land remote sensing studies

Doctoral thesis (2023)

Understanding, quantifying and monitoring soil moisture is important for many applications, e.g., agriculture, weather forecasting, occurrence of heatwaves, droughts and floods, and human health. At a ... [more ▼]

Understanding, quantifying and monitoring soil moisture is important for many applications, e.g., agriculture, weather forecasting, occurrence of heatwaves, droughts and floods, and human health. At a large scale, satellite microwave remote sensing has been used to retrieve soil moisture information. Surface water has also been detected and monitored through remote sensing orbital platforms equipped with passive microwave, radar, and optical sensors. The use of reflected L-band Global Navigation Satellite System (GNSS) signals represents an emerging remote sensing concept to retrieve geophysical parameters. In GNSS Reflectometry (GNSS-R) these signals are repurposed to infer properties of the surface from which they reflect as they are sensitive to variations in biogeophysical parameters. NASA's Cyclone GNSS (CYGNSS) is the first mission fully dedicated to spaceborne GNSS-R. The eight-satellite constellation measures Global Positioning System (GPS) reflected L1 (1575.42 MHz) signals. Spire Global, Inc. has also started developing their GNSS-R mission, with four satellites currently in orbit. In this thesis we propose and validate a method to retrieve large-scale near-surface soil moisture and a method to map and monitor inundations using spaceborne GNSS-R. Our soil moisture model is based on the assumption that variations in surface reflectivity are linearly related to variations in soil moisture and uses a new method to normalize the observations with respect to the angle of incidence. The normalization method accounts for the spatially varying effects of coherent and incoherent scattering. We found a median unbiased root-mean-square error (ubRMSE) of 0.042 cm3 cm-3 when comparing our method to two years of Soil Moisture Active Passive (SMAP) data and a median ubRMSE of 0.059 cm3 cm-3 compared to the observations of 207 in-situ stations. Our results also showed an improved temporal resolution compared to sensors traditionally used for this purpose. Assessing Spire and CYGNSS data over a region in south east Australia, we observed a similar behavior in terms of surface reflectivity and sensitivity to soil moisture. As Spire satellites collect data from multiple GNSS constellations, we found that it is important to differentiate the observations when calibrating a soil moisture model. The inundation mapping method that we propose is based on a track-wise approach. When classifying the reflections track by track the influence of the angle of incidence and the GNSS transmitted power are minimized or eliminated. With CYGNSS data we produced more than four years of monthly surface water maps over the Amazon River basin and the Pantanal wetlands complex with a spatial resolution of 4.5 km. With GNSS-R we could overcome some of the limitations of optical and microwave remote sensing methods for inundation mapping. We used a set of metrics commonly used to evaluate classification performance to assess our product and discussed the differences and similarities with other products. [less ▲]

Neue Erkenntnisse zu den Lesegewohnheiten luxemburgischer Schülerinnen und Schüler

Poster (2023, February 23)

Investigation of Shared Genetic Risk Factors Between Parkinson's Disease and Cancers

in Movement Disorders (2023), n/a(n/a),

Abstract Background Epidemiological studies that examined the association between Parkinson's disease (PD) and cancers led to inconsistent results, but they face a number of methodological difficulties ... [more ▼]

Abstract Background Epidemiological studies that examined the association between Parkinson's disease (PD) and cancers led to inconsistent results, but they face a number of methodological difficulties. Objective We used results from genome-wide association studies (GWASs) to study the genetic correlation between PD and different cancers to identify common genetic risk factors. Methods We used individual data for participants of European ancestry from the Courage-PD (Comprehensive Unbiased Risk Factor Assessment for Genetics and Environment in Parkinson's Disease; PD, N = 16,519) and EPITHYR (differentiated thyroid cancer, N = 3527) consortia and summary statistics of GWASs from iPDGC (International Parkinson Disease Genomics Consortium; PD, N = 482,730), Melanoma Meta-Analysis Consortium (MMAC), Breast Cancer Association Consortium (breast cancer), the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (prostate cancer), International Lung Cancer Consortium (lung cancer), and Ovarian Cancer Association Consortium (ovarian cancer) (N comprised between 36,017 and 228,951 for cancer GWASs). We estimated the genetic correlation between PD and cancers using linkage disequilibrium score regression. We studied the association between PD and polymorphisms associated with cancers, and vice versa, using cross-phenotypes polygenic risk score (PRS) analyses. Results We confirmed a previously reported positive genetic correlation of PD with melanoma (Gcorr = 0.16 [0.04; 0.28]) and reported an additional significant positive correlation of PD with prostate cancer (Gcorr = 0.11 [0.03; 0.19]). There was a significant inverse association between the PRS for ovarian cancer and PD (odds ratio [OR] = 0.89 [0.84; 0.94]). Conversely, the PRS of PD was positively associated with breast cancer (OR = 1.08 [1.06; 1.10]) and inversely associated with ovarian cancer (OR = 0.95 [0.91; 0.99]). The association between PD and ovarian cancer was mostly driven by rs183211 located in an intron of the NSF gene (17q21.31). Conclusions We show evidence in favor of a contribution of pleiotropic genes to the association between PD and specific cancers. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA. [less ▲]

Les Italiens dans le Luxembourg des migrations (3e partie)

Article for general public (2023)

Reconstruct the biomass particles fields in the particle-fluid problem using continuum methods by applying the physics-informed neural network

in Results in Engineering (2023), 17

The motion of particles in the moving grate combustion chamber is used as the case study. These problems are categorized as particle-fluid problems. They are typically solved using Lagrangian-Eulerian ... [more ▼]

The motion of particles in the moving grate combustion chamber is used as the case study. These problems are categorized as particle-fluid problems. They are typically solved using Lagrangian-Eulerian methods, one of which is the coupling between the discrete element method (DEM, which is applied to the particles phase) and the computational fluid dynamics method (CFD, which is applied to the fluid phase). The current study's objective is to avoid coupling and instead, focusing on using the CFD method only. There are dense piles of particles moving on the grates in the biomass combustion chamber. We assumed the dense particles' behaviors similar to the fluid, and then, applied the fluid governing equations to the particles phase. The virtual fields of the velocities, pressure and density are specified for the particles' phase. Afterward, the physics-informed neural network (PINN) is used to reconstruct particles' fields and additionally to investigate the capability of the predicted fields to satisfy the fluid governing equations. This model has the benefit of reconstructing the particles' fields without the need for boundaries and initial conditions. The precision of the model is assessed by comparing the test data set with the exact data obtained from the eXtended discrete element method (XDEM is an in-house software). It is demonstrated that the trained neural network delivered high accuracy and is capable of predicting all outputs with an error value of less than 2 percent. Additionally, to choose the optimum architecture for the neural network, the effect of the number of hidden layers and neurons is studied. [less ▲]

The Europeanization of Parliamentary Administrations

in Christiansen, Thomas; Griglio, Elena; Lupo, Nicola (Eds.) The Routledge Hanbook of Parliamentary Administrations (2023)

How to deal with rule of law-concerns in surrender procedures?

in Luchtman, Michiel; Ligeti, Katalin; Tosza, Stanislaw (Eds.) Of swords and shields: due process and crime control in times of globalization - Liber amicorum prof. dr. J.A.E. Vervaele (2023)

Smart charging of EVs: Would you share your data for money?

Scientific Conference (2022, December 11)

Many governments worldwide aim to eventually replace most combustion engines on the roads with electric vehicles (EVs). But this change causes an additional load on the electrical grid, especially if many ... [more ▼]

Many governments worldwide aim to eventually replace most combustion engines on the roads with electric vehicles (EVs). But this change causes an additional load on the electrical grid, especially if many EVs are charged simultaneously at peak times. Smart charging is a solution to better distribute the load throughout the day or night, while considering consumer preferences. For home charging, the idea is for EV users to always plug in their EVs when they are at home, and for the energy supplier to then decide when to charge which EV. By using (sensitive) consumer data, such as charging history, location of the smartphone and calendar information, the energy supplier can plan and optimize the charging of the EVs even better. In a survey, we seek to understand which of these data consumers are willing to share for smart charging, and what factors, such as privacy concerns and data sharing habits, influence this decision. Furthermore, in an experiment within the survey, we investigate whether consumers are more willing to share data if they receive monetary incentives. Our research design is based on the theoretical framework of Barth and de Jong (2017). 20 participants took part in the pretest, after which we adjusted the survey. We then shared the survey through various channels. [less ▲]