[PDF][PDF] Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles.
C Kyrkou, T Theocharides - CVPR workshops, 2019 - openaccess.thecvf.com
Abstract UnmannedAerial Vehicles (UAVs), equipped with camera sensors can facilitate
enhanced situational awareness for many emergency response and disaster management …
enhanced situational awareness for many emergency response and disaster management …
Data reduction based on machine learning algorithms for fog computing in IoT smart agriculture
Highlights•It is a challenge to manage a massive amount of data generated by sensors in
IoT.•Combining machine learning with data compression results in a larger data …
IoT.•Combining machine learning with data compression results in a larger data …
Soft errors in DNN accelerators: A comprehensive review
Deep learning tasks cover a broad range of domains and an even more extensive range of
applications, from entertainment to extremely safety-critical fields. Thus, Deep Neural …
applications, from entertainment to extremely safety-critical fields. Thus, Deep Neural …
Edge intelligence: Challenges and opportunities of near-sensor machine learning applications
The number of connected IoT devices is expected to reach over 20 billion by 2020. These
range from basic sensor nodes that log and report the data for cloud processing, to the ones …
range from basic sensor nodes that log and report the data for cloud processing, to the ones …
A roadmap toward the resilient internet of things for cyber-physical systems
The Internet of Things (IoT) is a ubiquitous system connecting many different devices-the
things-which can be accessed from the distance. The cyber-physical systems (CPSs) …
things-which can be accessed from the distance. The cyber-physical systems (CPSs) …
Testability and dependability of AI hardware: Survey, trends, challenges, and perspectives
F Su, C Liu, HG Stratigopoulos - IEEE Design & Test, 2023 - ieeexplore.ieee.org
Hardware realization of artificial intelligence (AI) requires new design styles and even
underlying technologies than those used in traditional digital processors or logic circuits …
underlying technologies than those used in traditional digital processors or logic circuits …
[HTML][HTML] Machine learning in resource-scarce embedded systems, FPGAs, and end-devices: A survey
The number of devices connected to the Internet is increasing, exchanging large amounts of
data, and turning the Internet into the 21st-century silk road for data. This road has taken …
data, and turning the Internet into the 21st-century silk road for data. This road has taken …
Deep learning for edge computing: Current trends, cross-layer optimizations, and open research challenges
In the Machine Learning era, Deep Neural Networks (DNNs) have taken the spotlight, due to
their unmatchable performance in several applications, such as image processing, computer …
their unmatchable performance in several applications, such as image processing, computer …
[HTML][HTML] HED-FL: A hierarchical, energy efficient, and dynamic approach for edge Federated Learning
The increasing data produced by IoT devices and the need to harness intelligence in our
environments impose the shift of computing and intelligence at the edge, leading to a novel …
environments impose the shift of computing and intelligence at the edge, leading to a novel …
Towards energy-efficient and secure edge AI: A cross-layer framework ICCAD special session paper
The security and privacy concerns along with the amount of data that is required to be
processed on regular basis has pushed processing to the edge of the computing systems …
processed on regular basis has pushed processing to the edge of the computing systems …