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Infrastructure Enabled autonomy

https://share.summari.com/infrastructure-enabled-autonomymd-github?utm_source=Chrome

The Infrastructure Enabled Autonomy system (IEA) uses less hardware, bandwidth, energy, and money to maintain a controlled environment for a vehicle to operate when in highly congested environments

The solution lies in the removal of sensing and high computational tasks from the vehicles, allowing static ground stations with multi sensorsensing packs (MSSPs) to sense the surrounding environment and direct the vehicles from start to goal.

This approach allows for a distribution or computational power, and offloaded expensive hardware from each individual vehicle to a static set of MSSPs on a connected network that provides the vehicles in the environments with the wireless commands to achieve safe transportation.

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Intelligence Architecture for Autonomous Vehicles

https://share.summari.com/intelligence-architecture-for-autonomous-vehiclesmd-github?utm_source=Chrome

• Several studies have illustrated the potential for dramatic societal, environmental, and economic benefits from significant penetration of autonomous driving.
• However, current approaches to autonomous driving require the automotive manufacturers to shoulder the primary responsibility and liability associated with replacing human perception and decision making with automation.
• We propose a new approach that will re-balance the responsibility and liabilities associated with autonomous driving between traditional automotive manufacturers, infrastructure players, and third-party players
• Infrastructure becomes a critical enabler for autonomy
• The auto industry has evolved over decades, with safety as a primary driving factor in the design and development. While the physical components of the modern automobile have become quite safe and reliable, the

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Supportive 5G Infrastructure

cost-efficient global broadband coverage is already becoming a key pillar. Indeed, we are still far from providing universal and affordable broadband connectivity, despite this being a key part of the Sustainable Development Goals (Target 9.c). Currently, both Mobile Network Operators and governments still lack independent analysis of the strategies that can help achieve this target with the cellular technologies available (4G and 5G). Therefore, this paper undertakes quantitative assessment demonstrating how current 5G policies affect universal broadband, as well as drawing conclusions over how decisions made now affect future evolution to 6G. Using a method based on an open-source techno-economic codebase, combining remote sensing with least-cost network algorithms, performance analytics are provided for different 4G and 5G universal broadband strategies. As an example, the assessment approach is applied to India, the world’s second-largest mobile market and a country with ve

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Real-Time Global Illumination in Web-Browsers

Abstract

Global illumination is important for a rendered image to appear realistic. Accurate global illumination can be achieved but most algorithms are very computationally expensive and only suitable for o✏ine rendering where long rendering times can be allowed, and not for real-time rendering where many images every second is needed. We have looked at the limited case of global illumination for certain real-time applications in web-browsers. We present a comparative study of three academic papers that describe algorithms that might be suitable and successfully implemented and adapted them to our context. The results vary in terms of real-time performance, precomputation time and visual fidelity. One algorithm produced results that are not very accurate compared to a reference image but might be visually convincing enough while having the advantage of very high performance and no precomputation. The other two algorithms both produce results of higher fid

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REVIEW ARTICLE

FAIR Geovisualizations: Definitions, Challenges, and the Road

Ahead

Auriol Degbelo Institute for Geoinformatics, University of M ̈unster auriol.degbelo@uni-muenster.de

ARTICLE HISTORY Compiled November 16, 2021

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Edge-Native Intelligence for 6G Communications

Edge-Native Intelligence for 6G Communications.md · GitHub

https://share.summari.com/edge-native-intelligence-for-6g-communicationsmd-github?utm_source=Chrome

• The unprecedented surge of data volume in wireless networks empowered with artificial intelligence (AI) opens up new horizons for providing ubiquitous data-driven intelligent services
• Traditional cloud-centric machine learning (ML)-based services are implemented by collecting datasets and training models centrally

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AI-Native Network Slicing for 6G Networks

AI-Native Network Slicing for 6G Networks.md · GitHub

https://share.summari.com/vuvitaec4869690164d7a82ead1c84ec3cca0ad?utm_source=Chrome

AI-native network slicing architecture for 6G networks

• Space networks, e.g., low earth orbit (LEO) satellites, air networks, unmanned aerial vehicles (UAVs), and ground networks are integrated into a space-air-ground integrated network (SAGIN) to provide global coverage and on-demand services

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Edge Computing in IoT: A 6G Perspective

Edge Computing in IoT.md · GitHub

https://share.summari.com/vuvitae6eb0af811e2555cea9cba30c892bf788?utm_source=Chrome

• Multiaccess Edge Computing (MEC) is considered as a promising solution to provide cloud-computing capabilities within the radio access network (RAN) closer to the end users. However, very little has been said about the key factors of MEC deployment to meet the diverse demands of future applications.
• To support the two major transformations in network infrastructures and network services, edge computing has gained significant attention

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-Edge Intelligence in Softwarized 6G

Edge Intelligence in Softwarized 6G.md · GitHub

https://share.summari.com/edge-intelligence-in-softwarized-6gmd-github?utm_source=Chrome

• The 6G vision is envisaged to enable agile network expansion and rapid deployment of new on-demand microservices (e.g., visibility services for data traffic management, mobile edge computing services) closer to the network's edge IoT devices.
• providing one of the critical features of network visibility services, i.e., data flow prediction in the network, is challenging at the edge devices within a dynamic cloud-native environment as the traffic flow characteristics are random and sporadic
• A novel edge-native framework to provide an intelligent prognosis technique called deep learning is proposed which accurately predicts the statistical characteristics of data traffic and verifies the trained model against the ground truth observations

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Semantic Mapping for Autonomous Navigation and Exploration

Abstract

The last two decades have seen enormous progress in the sensors and algorithms for 3D perception, giving robots the means to build accurate spatial maps and localize themselves in them in real time. The geometric information in these maps is invaluable for navigation while avoiding obstacles, but insufficient, by itself, for robots to robustly perform tasks according to human goals and preferences. Semantic mapping is a promising framework to provide robots with a richer representation of their environment by augmenting spatial maps with semantic labels–in other words, a map of what is where. However, for semantic maps to fulfill their potential to improve robotic capabilities, we need systems capable of building and continuously updating these maps from noisy and ambiguous sensor streams with acceptable levels of accuracy and latency. In this thesis, we make several contributions to address these challenges and demonstrate their bene