22 papers · ranked by Valyu relevance
Chinmaya Kumar Dehury, Boris Sedlak, Alaa Saleh, Ilir Murturi + 3 more
We are moving from an information age to the age of intelligence. A decade, or possibly less than that, data will not be the gold anymore rather the derived intelligence out of the data and the information we posses from the edge of the network. Existing Edge Intelligence research focuses mainly on two directions…
Zhi Zhou, Xu Chen, En Li, Liekang Zeng + 2 more
—With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet-of-Things (IoT)…
Sayed Khushal Shah, Zeenat Tariq, Jeehwan Lee, Yugyung Lee + 4 more
Edge intelligence (EI) has received a lot of interest because it can reduce latency, increase efficiency, and preserve privacy. More significantly, as the Internet of Things (IoT) has proliferated, billions of portable and embedded devices have been interconnected, producing zillions of gigabytes on edge networks.…
Silvana Trindade, Luiz F. Bittencourt, Nelson L. S. da Fonseca
Federated learning has been explored as a promising solution for training at the edge, where end devices collaborate to train models without sharing data with other entities. Since the execution of these learning models occurs at the edge, where resources are limited, new solutions must be developed. In this paper, we…
Sukhpal Singh Gill, Muhammed Golec, Jianmin Hu, Minxian Xu + 11 more
'Junhui Du' 'Huaming Wu' 'Guneet Kaur Walia' 'Subramaniam Subramanian Murugesan' 'B. Ali' 'Mohit Kumar' 'Kejiang Ye' 'Prabal Verma' 'Surendra Kumar' 'Félix Cuadrado' 'Steve Uhlig'] Abstract—Edge Artificial Intelligence (AI) incorporates a network of interconnected systems and devices that receive, cache, process, and…
Nu Wen, Ying Zhou, Yang Wang, Ye Zheng + 6 more
'Yankun Wang' 'Minmin Li' 'Giovanni Pau' 'Yongxing Li'] In the intelligent transportation field, object recognition, detection, and location applications face significant real-time challenges. To address these issues, we propose an automatic sensor-based data loading and unloading optimization strategy for algorithm…
Chuanyi Guo, Zheng He, Mutian Niu, Kai Liu
Edge deployment represents the most decentralized and responsive pathway for AI in PLF, where intelligence is moved from central servers directly onto the data-capturing devices themselves ([vfaf050-F4]). In this model, AI algorithms run on resource-constrained hardware, such as embedded systems (e.g., NVIDIA Jetson…
Wenquan Jin, Rongxu Xu, Sunhwan Lim, Dong-Hwan Park + 2 more
'Dohyeun Kim'] Computation offloading enables intensive computational tasks in edge computing to be separated into multiple computing resources of the server to overcome hardware limitations. Deep learning derives the inference approach based on the learning approach with a volume of data using a sufficient computing…
Sabuzima Nayak, Ripon Patgiri, Lilapati Waikhom, Arif Ahmed
Edge technology aims to bring Cloud resources (specifically, the compute, storage, and network) to the closed proximity of the Edge devices, i.e., smart devices where the data are produced and consumed. Embedding computing and application in Edge devices lead to emerging of two new concepts in Edge technology, namely…
Asier Garcia-Perez, Raúl Miñón, Ana I. Torre-Bastida, Ekaitz Zulueta-Guerrero + 1 more
'Ekaitz Zulueta-Guerrero' 'Francesco Longo'] In recent years, more and more devices are connected to the network, generating an overwhelming amount of data. This term that is booming today is known as the Internet of Things. In order to deal with these data close to the source, the term Edge Computing arises. The main…
Yassine Himeur, Aya Nabil Sayed, Abdullah Alsalemi, Fayçal Bensaali + 1 more
'Abbes Amira'] The digital landscape of the Internet of Energy (IoE) is on the brink of a revolutionary transformation with the integration of edge Artificial Intelligence (AI). This comprehensive review elucidates the promise and potential that edge AI holds for reshaping the IoE ecosystem. Commencing with a…
Gongfan Chen, Abdullah Alsharef, Edward Jaselskis, Hai Dong
Image classification is increasingly being utilized on construction sites to automate project monitoring, driven by advancements in reality-capture technologies and artificial intelligence (AI). Deploying real-time applications remains a challenge due to the limited computing resources available on-site, particularly…
Gabriele Lohmann, Eric Lacosse, Thomas Ethofer, Vinod J. Kumar + 2 more
In recent years, the prediction of individual behaviour from the fMRI-based functional connectome has become a major focus of research. The motivation behind this research is to find generalizable neuromarkers of cognitive functions. However, insufficient prediction accuracies and long scan time requirements are still…
Anton Pashkov, Ivan Dakhtin, Inna Feklicheva, Julia Shmotina + 1 more
Intelligence is increasingly recognized as a critical factor in successful behavioral and emotional regulation. Neuroimaging techniques coupled with machine learning algorithms have proven to be valuable tools for uncovering the neural foundations of individual cognitive abilities. Nevertheless, current…
Authors not listed
Graphene nanoribbons (GNRs) offer promising platforms for single-molecule sensing due to their quasi-one-dimensional channels and discrete electronic states, providing superior sensitivity toward molecular perturbations. While prior studies emphasize smoother edges as essential for optimal performance, the potential…
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Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Dorothea Metzen, Christina Stammen, Christoph Fraenz, Caroline Schlüter + 4 more
Previous research investigating relations between general intelligence and graph-theoretical properties of the brain’s intrinsic functional network has yielded contradictory results. A promising approach to tackle such mixed findings is multi-center analysis. For this study, we analyzed data from four independent data…
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Effective visualization of complex synthesis routes is critical for computeraided synthesis planning (CASP), yet current solutions are limited in scope, integration flexibility, and chemical intuition. We introduce RouteWise, a versatile, containerized web application designed to address these unmet needs. Its modular…
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Identifying synthesis routes from knowledge graphs poses challenges beyond retrosynthesis, including path–finding artifacts and data issues. We introduce “SynGPS”, a novel algorithm that overcomes these limitations by identifying viable routes even with common artifacts. SynGPS can resolve nonsensical cycles…
Aslan S Dizaji, Mohammad-Reza Khodaei, Hamid Soltanian-Zadeh
Natural intelligence is one of the vastly explored research areas in cognitive science. Its evolution and manifestation through behavioral patterns in animal kingdom have been extensively investigated. Since early days of cognitive sciences, there have been considerable efforts to simulate intelligent behaviors through…
Kirsten Hilger, Matthew J. Euler
Intelligence captures the cognitive ability level of an individual person and electroencephalography (EEG) has been used for decades to identify neurocognitive processes related to intelligence. The mismatch negativity (MMN) is a component of the event-related brain potential (ERP) that is elicited when, within a…
Timofey Adamovich, Victoria Ismatullina, Nadezhda Chipeeva, Ilya Zakharov + 2 more
Network neuroscience investigates the brain’s connectome, revealing that cognitive functions are underpinned by dynamic neural networks. This study investigates how distinct cognitive abilities—working memory and inhibition—are supported by unique brain network configurations, which are constructed by estimating…