Top Research Papers on Embedded Systems

An account for determining when an AI system can be said to embody certain values is proposed, which understands embodied values as the result of design activities intended to embed those values in such systems.

A lightweight object detection model, which is developed based on Mobilenet-v2, which achieves up to 75.9% mAP in the VOC dataset and can be applied in embedded systems with limited computational resources.

Deep learning inference on embedded devices is a burgeoning field with myriad applications because tiny embedded devices are omnipresent. But we must overcome major challenges before we can benefit from this opportunity. Embedded processors are severely resource constrained. Their nearest mobile counterparts exhibit at least a 100 -- 1,000x difference in compute capability, memory availability, and power consumption. As a result, the machine-learning (ML) models and associated ML inference framework must not only execute efficiently but also operate in a few kilobytes of memory. Also, the embe...

An overview of TSN in automotive applications is provided and the recent technological developments relevant to the adoption of T SNP are discussed, which points at the open challenges and future research directions.

This study shows the feasibility of deployment of the customized model on cheap and power-efficient embedded hardware without compromising mean average detection accuracy (83.64%) and achieved frame rate up to 30 fps even for the difficult scenarios such as overlapping apples, complex background, less exposure of apple due to leaves and branches.

This article comprehensively reviews the emerging concept of Internet of Intelligent Things (IoIT), adopting an integrated perspective centred on the areas of embedded systems, edge computing, and machine learning. With rapid developments in these areas, new solutions are emerging to address previously unsolved problems, demanding novel research and development paradigms. In this sense, this article aims to fulfil some important research gaps, laying down the foundations for cutting-edge research works following an ever-increasing trend based on embedded devices powered by compressed artificia...

This work focuses on the induced point dipole formulation of polarizable QM/MM approaches and shows how efficient and linear scaling implementations have allowed their application to the modeling of complex biosystems, including Born-Oppenheimer dynamics, enhanced sampling techniques and nonadiabatic descriptions.

Renewable energy is projected to play an important role in reducing greenhouse gas emissions and in realising the climate change goals. Large scale development of variable renewable energy, which is regarded as non-dispatchable, requires additional power system quality services such as voltage regulation, frequency regulation and inertial response. Energy storage provides an important means to supply these services but there are many uncertainties in terms of technology, market readiness, economics, and regulatory requirements. The aim of this study is to undertake a global state-of-the-art re...

Some of the major problems existing today in AI-powered embedded systems are presented, highlighting possible solutions and research directions to support them, increasing their security, safety, and time predictability.

In this survey, existing contextual embedding models, cross-lingual polyglot pre-training, the application of contextual embeddings in downstream tasks, model compression, and model analyses are reviewed.

Overall, EHR-based predictive models offer promising results for improving clinical outcomes, although several gaps in the literature remain, and most study designs were observational.

This paper proposes a random walk and word embedding based ontology embedding method named OWL2Vec*, which encodes the semantics of an OWL ontology by taking into account its graph structure, lexical information and logical constructors.

A survey of dynamic network embedding inspects the data model, representation learning technique, evaluation and application of current related works and derives common patterns from them and builds a taxonomy that refines the category hierarchy by typical learning models.

This work develops three classes of attacks to systematically study information that might be leaked by embeddings, and extensively evaluates the attacks on various state-of-the-art embedding models in the text domain.

This work presents a simple yet effective unsupervised network embedding method for attributed multiplex network called DMGI, inspired by Deep Graph Infomax (DGI) that maximizes the mutual information between local patches of a graph, and the global representation of the entire graph.

This paper proposes Automated Concatenation of Embeddings (ACE) to automate the process of finding better concatenations of embeddings for structured prediction tasks, based on a formulation inspired by recent progress on neural architecture search.

The unified embedding framework developed to model semantic embeddings for personalized search, and the system to serve embedding-based retrieval in a typical search system based on an inverted index are introduced.

The core of DualE lies a specific design of dual-quaternion-based multiplication, which universally models relations as the compositions of a series of translation and rotation operations.

This work proposes a graph neural network-based representation learning framework for heterogeneous hypergraphs, an extension of conventional graphs, which can well characterize multiple non-pairwise relations and shows that relationships beyond pairwise are also advantageous in the spammer detection.

Language Embedded Radiance Fields (LERFs) are proposed, a method for grounding language embeddings from off-the-shelf models like CLIP into NeRF, which enable these types of open-ended language queries in 3D.