Constructing a high-quality dense map in real-time is essential for robotics, AR/VR, and digital twins applications. As Neural Radiance Field (NeRF) greatly improves the mapping performance, in this paper, we propose a NeRF-based mapping method that enables higher-quality reconstruction and real-time capability even on edge computers. Specifically, we propose a novel hierarchical hybrid representation that leverages implicit multiresolution hash encoding aided by explicit octree SDF priors, describing the scene at different levels of detail. This representation allows for fast scene geometry initialization and makes scene geometry easier to learn. Besides, we present a coverage-maximizing keyframe selection strategy to address the forgetting issue and enhance mapping quality, particularly in marginal areas. To the best of our knowledge, our method is the first to achieve high-quality NeRF-based mapping on edge computers of handheld devices and quadrotors in real-time. Experiments demonstrate that our method outperforms existing NeRF-based mapping methods in geometry accuracy, texture realism, and time consumption. The code will be released at: https://github.com/SYSU-STAR/H2-Mapping

Recent studies show edge computing-based road anomaly detection systems which may also conduct data collection simultaneously. However, the edge computers will have small data storage but we need to store the collected audio samples for a long time in order to update existing models or develop a novel method. Therefore, we should consider an approach for efficient storage management methods while preserving high-fidelity audio. A hardware-perspective approach, such as using a low-resolution microphone, is an intuitive way to reduce file size but is not recommended because it fundamentally cuts off high-frequency components. On the other hand, a computational file compression approach that encodes collected high-resolution audio into a compact code should be recommended because it also provides a corresponding decoding method. Motivated by this, we propose a way of simple yet effective pre-trained autoencoder-based data compression method. The pre-trained autoencoder is trained for the purpose of audio super-resolution so it can be utilized to encode or decode any arbitrary sampling rate. Moreover, it will reduce the communication cost for data transmission from the edge to the central server. Via the comparative experiments, we confirm that the zero-shot audio compression and decompression highly preserve anomaly detection performance while enhancing storage and transmission efficiency.

The number of connected devices collecting data is continually expanding. This requires more storage and computational capacity and more Artificial Intelligence (AI) to be brought at the Edge: Eurotech combines rugged embedded and Edge computers, computational power and IoT components to enable Edge AI. By bringing these high-performance computing capacity to the Edge, Eurotech enables Artificial Intelligence (AI) applications directly on field devices. They are able to process data autonomously and perform Machine Learning (ML) in the field and apply Deep learning (DL) models and algorithms for advanced autonomous applications, such as Autonomous Driving. The virtually unlimited capacity of the Cloud can be integrated with sophisticated and high-performance Edge Computers in the field, enabling the "Intelligent Edge".

Data generated this way are incredibly sparse, i.e., only a Multi-relational knowledge graphs (KGs) [1] are rich data tiny fraction of possible triples are observed or even valid, structures used to model a variety of systems like industrial as well as streaming in nature such that triples can appear projects [2] and mathematical proofs [3]. It is therefore not multiple times and underlie stochastic variations. Using graph surprising that the interest in machine learning algorithms embedding, we reformulate the anomaly detection task as capable of dealing with graph-structured data has increased a link prediction task: events in the automation system are lately [4]. This broad applicability of graphs becomes apparent equivalent to new edges appearing in its graph representation when summarizing them as lists of triple statements that can be evaluated using the learned embeddings. However, (node, edge, node), e.g., (M.Hamill, plays, L.Skywalker) and we found that standard graph embedding algorithms perform (L.Skywalker, appearsIn, StarWars) - with individual entries poorly on such industrial graphs, mainly because they expect being called subject, predicate and object.

Compared with previous types of networks, 5G networks are both more in need of automation and more amenable to automation. Automation tools are still evolving and machine learning is not yet common in carrier-grade networking, but rapid change is expected. Emerging standards from 3GPP, ETSI, ITU and the open source software community anticipate increased use of automation, artificial intelligence (AI) and machine learning (ML). And key suppliers' activities add credibility to the vision and promise of artificially intelligent network operations. "Growing complexity and the need to solve repetitive tasks in 5G and future radio systems necessitate new automation solutions that take advantage of state-of-the-art artificial intelligence and machine learning techniques that boost system efficiency," wrote Ericsson's chief technology officer (CTO), Erik Ekudden, recently.

The number of connected devices collecting data is continually expanding. This requires more storage and computational capacity and more Artificial Intelligence (AI) to be brought at the Edge: Eurotech combines rugged embedded and Edge computers, computational power and IoT platrofms to enable Edge AI. By bringing these high-performance computing capacity to the Edge, Eurotech enables Artificial Intelligence (AI) applications directly on field devices. They are able to process data autonomously and perform Machine Learning (ML) in the field and apply Deep learning (DL) models and algorithms for advanced autonomous applications, such as Autonomous Driving. The virtually unlimited capacity of the Cloud can be integrated with sophisticated and high-performance Edge Computers in the field, enabling the "Intelligent Edge".