Durability of Wireless Charging Systems Embedded Into Concrete Pavements for Electric Vehicles

Point clouds are widely used in various applications such as 3D modeling, geospatial analysis, robotics, and more. One of the key advantages of 3D point cloud data is that, unlike other data formats like texture, it is independent of viewing angle, surface type, and parameterization. Since each point in the point cloud is independent of the other, it makes it the most suitable source of data for tasks like object recognition, scene segmentation, and reconstruction. Point clouds are complex and verbose due to the numerous attributes they contain, many of which may not be always necessary for rendering, making retrieving and parsing a heavy task. As Sensors are becoming more precise and popular, effectively streaming, processing, and rendering the data is also becoming more challenging. In a hierarchical continuous LOD system, the previously fetched and rendered data for a region may become unavailable when revisiting it. To address this, we use a non-persistence cache using hash-map which stores the parsed point attributes, which still has some limitations, such as the dataset needing to be refetched and reprocessed if the tab or browser is closed and reopened which can be addressed by persistence caching. On the web, popularly persistence caching involves storing data in server memory, or an intermediate caching server like Redis. This is not suitable for point cloud data where we have to store parsed and processed large point data making point cloud visualization rely only on non-persistence caching. The thesis aims to contribute toward better performance and suitability of point cloud rendering on the web reducing the number of read requests to the remote file to access data.We achieve this with the application of client-side-based LRU Cache and Private File Open Space as a combination of both persistence and non-persistence caching of data. We use a cloud-optimized data format, which is better suited for web and streaming hierarchical data structures. Our focus is to improve rendering performance using WebGPU by reducing access time and minimizing the amount of data loaded in GPU. Preliminary results indicate that our approach significantly improves rendering performance and reduce network request when compared to traditional caching methods using WebGPU

Enabling Artificial Intelligence Analytics on The Edge

This thesis introduces a novel distributed model for handling in real-time, edge-based video analytics. The novelty of the model relies on decoupling and distributing the services into several decomposed functions, creating virtual function chains (V F C model). The model considers both computational and communication constraints. Theoretical, simulation and experimental results have shown that the V F C model can enable the support of heavy-load services to an edge environment while improving the footprint of the service compared to state-of-the art frameworks. In detail, results on the V F C model have shown that it can reduce the total edge cost, compared with a monolithic and a simple frame distribution models. For experimenting on a real-case scenario, a testbed edge environment has been developed, where the aforementioned models, as well as a general distribution framework (Apache Spark ©), have been deployed. A cloud service has also been considered. Experiments have shown that V F C can outperform all alternative approaches, by reducing operational cost and improving the QoS. Finally, a migration model, a caching model and a QoS monitoring service based on Long-Term-Short-Term models are introduced

FLSH -- Friendly Library for the Simulation of Humans

Computer models of humans are ubiquitous throughout computer animation and computer vision. However, these models rarely represent the dynamics of human motion, as this requires adding a complex layer that solves body motion in response to external interactions and according to the laws of physics. FLSH is a library that facilitates this task for researchers and developers who are not interested in the nuisances of physics simulation, but want to easily integrate dynamic humans in their applications. FLSH provides easy access to three flavors of body physics, with different features and computational complexity: skeletal dynamics, full soft-tissue dynamics, and reduced-order modeling of soft-tissue dynamics. In all three cases, the simulation models are built on top of the pseudo-standard SMPL parametric body model.Comment: Project website: https://gitlab.com/PabloRamonPrieto/fls

FlashBack: Immersive Virtual Reality on Weak Mobile Devices via Rendering Memoization

Virtual Reality Head-mounted Displays (HMDs) are attracting users with the promise of full sensory immersion in virtual environments. Creating the illusion of immersion for a near-eye display results in very heavy rendering workloads: low latency, high framerate, and high visual quality are all needed. Tethered VR setups in which the HMD is bound to a powerful gaming desktop limit mobility and exploration, and are difficult to deploy widely. Products such as Google Cardboard and Samsung Gear VR purport to offer any user a mobile VR experience, but their GPUs are power-constrained and therefore fail to produce acceptable frame rate and latency for even scenes of modest visual quality. We present FlashBack, an unorthodox design point for HMD VR that eschews all real-time scene rendering. Instead, FlashBack aggressively precomputes and caches all possible images that a VR user might encounter. FlashBack memoizes costly rendering effort in an offline step to build a cache full of panoramic images. During runtime, FlashBack constructs and maintains a hierarchical storage cache index to quickly lookup images that the user should be seeing. On a cache miss, FlashBack uses fast approximations of the correct image while concurrently fetching better cache entries for future requests. Moreover, FlashBack not only works for static scenes, but also for dynamic scenes with moving and animated objects. We evaluate a prototype implementation of FlashBack and report up to an 8x improvement in framerate, 97x reduction in energy consumption per frame, and 15x latency reduction compared to a locally-rendered mobile VR setup. In some cases, FlashBack even delivers better framerates and responsiveness than a tethered HMD configuration on graphically complex scenes

Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems

Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of the vehicle networks, it is rather challenging to make timely and accurate decisions of vehicle behaviors. Moreover, in the presence of mobile wireless communications, the privacy and security of vehicle information are at constant risk. In this context, a new paradigm is urgently needed for various applications in dynamic vehicle environments. As a distributed machine learning technology, federated learning (FL) has received extensive attention due to its outstanding privacy protection properties and easy scalability. We conduct a comprehensive survey of the latest developments in FL for ITS. Specifically, we initially research the prevalent challenges in ITS and elucidate the motivations for applying FL from various perspectives. Subsequently, we review existing deployments of FL in ITS across various scenarios, and discuss specific potential issues in object recognition, traffic management, and service providing scenarios. Furthermore, we conduct a further analysis of the new challenges introduced by FL deployment and the inherent limitations that FL alone cannot fully address, including uneven data distribution, limited storage and computing power, and potential privacy and security concerns. We then examine the existing collaborative technologies that can help mitigate these challenges. Lastly, we discuss the open challenges that remain to be addressed in applying FL in ITS and propose several future research directions

Age of Processing-Based Data Offloading for Autonomous Vehicles in Multi-RATs Open RAN

Today, vehicles use smart sensors to collect data from the road environment. This data is often processed onboard of the vehicles, using expensive hardware. Such onboard processing increases the vehicle's cost, quickly drains its battery, and exhausts its computing resources. Therefore, offloading tasks onto the cloud is required. Still, data offloading is challenging due to low latency requirements for safe and reliable vehicle driving decisions. Moreover, age of processing was not considered in prior research dealing with low-latency offloading for autonomous vehicles. This paper proposes an age of processing-based offloading approach for autonomous vehicles using unsupervised machine learning, Multi-Radio Access Technologies (multi-RATs), and Edge Computing in Open Radio Access Network (O-RAN). We design a collaboration space of edge clouds to process data in proximity to autonomous vehicles. To reduce the variation in offloading delay, we propose a new communication planning approach that enables the vehicle to optimally preselect the available RATs such as Wi-Fi, LTE, or 5G to offload tasks to edge clouds when its local resources are insufficient. We formulate an optimization problem for age-based offloading that minimizes elapsed time from generating tasks and receiving computation output. To handle this non-convex problem, we develop a surrogate problem. Then, we use the Lagrangian method to transform the surrogate problem to unconstrained optimization problem and apply the dual decomposition method. The simulation results show that our approach significantly minimizes the age of processing in data offloading with 90.34 % improvement over similar method

Specifications of view services for GMES Core_003 VHR2 coverage

For the so-called DataWareHouse concept (DWH) within the GMES Initial Operations period 2011-2014, data access management is funded through a Delegation Agreement between the EC and ESA. The Core_003 VHR2 dataset is one of the satellite coverages that are defined as CORE datasets within the DWH with fixed specifications which will be of-fered to a broad range of users and activities. JRC was asked by DG Enterprise to provide technical specifications for the implementation of a view service for the Core_003 datasets as part of the Administrative Arrangement n. 5 between DG Enterprise and JRC. This report provides an overview about different view service types with their specific characteristics and use cases. Since compliance with INSPIRE implementing rules is a goal to be achieved by GMES services, the spe-cific requirements of INSPIRE for view services have been taken into account. The Core_003 datasets have been ana-lysed with regard to their parameters that are important for the inclusion in view services. Based on the results of the analyses, recommendations are given for the implementation of the view services as well as for the data processing and configuration of the Core_003 datasets.JRC.H.6-Digital Earth and Reference Dat

From Capture to Display: A Survey on Volumetric Video

Volumetric video, which offers immersive viewing experiences, is gaining increasing prominence. With its six degrees of freedom, it provides viewers with greater immersion and interactivity compared to traditional videos. Despite their potential, volumetric video services poses significant challenges. This survey conducts a comprehensive review of the existing literature on volumetric video. We firstly provide a general framework of volumetric video services, followed by a discussion on prerequisites for volumetric video, encompassing representations, open datasets, and quality assessment metrics. Then we delve into the current methodologies for each stage of the volumetric video service pipeline, detailing capturing, compression, transmission, rendering, and display techniques. Lastly, we explore various applications enabled by this pioneering technology and we present an array of research challenges and opportunities in the domain of volumetric video services. This survey aspires to provide a holistic understanding of this burgeoning field and shed light on potential future research trajectories, aiming to bring the vision of volumetric video to fruition.Comment: Submitte