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Joakim Aalstad Alslie, Aril Bernhard Ovesen, Tor-Arne Schmidt Nordmo, Håvard Dagenborg Johansen, Pål Halvorsen, Michael Alexander Riegler and Dag Johansen
Video monitoring and surveillance of commercial fisheries in world oceans has been proposed by the governing bodies of several nations as a response to crimes such as overfishing. Traditional video monitoring systems may not be suitable due to limitation...
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Jun Na, Handuo Zhang, Jiaxin Lian and Bin Zhang
To fully unleash the potential of edge devices, it is popular to cut a neural network into multiple pieces and distribute them among available edge devices to perform inference cooperatively. Up to now, the problem of partitioning a deep neural network (...
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Sepehr Tabrizchi, Shaahin Angizi and Arman Roohi
Convolutional Neural Networks (CNNs), due to their recent successes, have gained lots of attention in various vision-based applications. They have proven to produce incredible results, especially on big data, that require high processing demands. However...
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Pekka Pääkkönen, Daniel Pakkala, Jussi Kiljander and Roope Sarala
The current approaches for energy consumption optimisation in buildings are mainly reactive or focus on scheduling of daily/weekly operation modes in heating. Machine Learning (ML)-based advanced control methods have been demonstrated to improve energy e...
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Liliana I. Carvalho and Rute C. Sofia
Mobile sensing has been gaining ground due to the increasing capabilities of mobile and personal devices that are carried around by citizens, giving access to a large variety of data and services based on the way humans interact. Mobile sensing brings se...
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Mário P. Véstias
The convolutional neural network (CNN) is one of the most used deep learning models for image detection and classification, due to its high accuracy when compared to other machine learning algorithms. CNNs achieve better results at the cost of higher com...
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Wenbin Li, Hakim Hacid, Ebtesam Almazrouei and Merouane Debbah
The union of Edge Computing (EC) and Artificial Intelligence (AI) has brought forward the Edge AI concept to provide intelligent solutions close to the end-user environment, for privacy preservation, low latency to real-time performance, and resource opt...
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Se-Yeong Oh, Junho Jeong, Sang-Woo Kim, Young-Uk Seo and Joosang Youn
Along with the recent development of artificial intelligence technology, convergence services that apply technology are undergoing active development in various industrial fields. In particular, artificial intelligence-based object recognition technologi...
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Khadijeh Alibabaei, Eduardo Assunção, Pedro D. Gaspar, Vasco N. G. J. Soares and João M. L. P. Caldeira
The concept of the Internet of Things (IoT) in agriculture is associated with the use of high-tech devices such as robots and sensors that are interconnected to assess or monitor conditions on a particular plot of land and then deploy the various factors...
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Ruicheng Gao, Zhancai Dong, Yuqi Wang, Zhuowen Cui, Muyang Ye, Bowen Dong, Yuchun Lu, Xuaner Wang, Yihong Song and Shuo Yan
In this study, a deep-learning-based intelligent detection model was designed and implemented to rapidly detect cotton pests and diseases. The model integrates cutting-edge Transformer technology and knowledge graphs, effectively enhancing pest and disea...
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Roberto G. Pacheco, Kaylani Bochie, Mateus S. Gilbert, Rodrigo S. Couto and Miguel Elias M. Campista
In computer vision applications, mobile devices can transfer the inference of Convolutional Neural Networks (CNNs) to the cloud due to their computational restrictions. Nevertheless, besides introducing more network load concerning the cloud, this approa...
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Feng Zhou, Shijing Hu, Xin Du, Xiaoli Wan and Jie Wu
In the current field of disease risk prediction research, there are many methods of using servers for centralized computing to train and infer prediction models. However, this centralized computing method increases storage space, the load on network band...
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Mário P. Véstias, Rui Policarpo Duarte, José T. de Sousa and Horácio C. Neto
Deep learning is now present in a wide range of services and applications, replacing and complementing other machine learning algorithms. Performing training and inference of deep neural networks using the cloud computing model is not viable for applicat...
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Claudia I. Gonzalez, Patricia Melin and Oscar Castillo
This paper presents a new general type-2 fuzzy logic method for edge detection applied to color format images. The proposed algorithm combines the methodology based on the image gradients and general type-2 fuzzy logic theory to provide a powerful edge d...
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Georgios Venitourakis, Christoforos Vasilakis, Alexandros Tsagkaropoulos, Tzouma Amrou, Georgios Konstantoulakis, Panagiotis Golemis and Dionysios Reisis
Aiming at effectively improving photovoltaic (PV) park operation and the stability of the electricity grid, the current paper addresses the design and development of a novel system achieving the short-term irradiance forecasting for the PV park area, whi...
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Zhuo Li, Hengyi Li and Lin Meng
Currently, with the rapid development of deep learning, deep neural networks (DNNs) have been widely applied in various computer vision tasks. However, in the pursuit of performance, advanced DNN models have become more complex, which has led to a large ...
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John S. Venker, Luke Vincent and Jeff Dix
A Spiking Neural Network (SNN) is realized within a 65 nm CMOS process to demonstrate the feasibility of its constituent cells. Analog hardware neural networks have shown improved energy efficiency in edge computing for real-time-inference applications, ...
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Noel Daniel Gundi, Pramesh Pandey, Sanghamitra Roy and Koushik Chakraborty
Increasing processing requirements in the Artificial Intelligence (AI) realm has led to the emergence of domain-specific architectures for Deep Neural Network (DNN) applications. Tensor Processing Unit (TPU), a DNN accelerator by Google, has emerged as a...
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Zichao Shen, Neil Howard and Jose Nunez-Yanez
This paper investigates the energy savings that near-subthreshold processors can obtain in edge AI applications and proposes strategies to improve them while maintaining the accuracy of the application. The selected processors deploy adaptive voltage sca...
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Jennifer Hasler
Large-scale field-programmable analog arrays (FPAA) have the potential to handle machine inference and learning applications with significantly low energy requirements, potentially alleviating the high cost of these processes today, even in cloud-based s...
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