Browsing by Subject "deep learning"
Now showing items 1-9 of 9
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Automatic Discovery and Geotagging of Objects from Street View Imagery
(2018)Many applications, such as autonomous navigation, urban planning, and asset monitoring, rely on the availability of accurate information about objects and their geolocations. In this paper, we propose the automatic detection ... -
Deep Learning-based embedded Intrusion Detection Systems for CAN bus in Automotive Networks
(IEEE, 2022)Rising complexity of in-vehicle electronics is enabling new capabilities like autonomous driving and active safety. However, rising automation also increases risk of security threats which is compounded by lack of in-built ... -
Design of AI-based lane changing modules in connected and autonomous vehicles: a survey
(2022)Lane changing is one of the complex driving tasks as it requires the vehicle to be aware of its highlydynamic surrounding environment, make decisions, and enact them in a timely manner. By exploiting both sensors and ... -
Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN
(2023)Vehicles today comprise intelligent systems like connected autonomous driving and advanced driving assistance systems (ADAS) to enhance the driving experience, which is enabled through increased connectivity to ... -
Exploring Lightweight Federated Learning for Distributed Load Forecasting
(2023)Federated Learning (FL) is a distributed learning scheme that enables deep learning to be applied to sensitive data streams and applications in a privacy-preserving manner. This paper focuses on the use of FL for analyzing ... -
FPGA-based Deep-Learning Accelerators for Energy Efficient Motor Imagery EEG classification
(2022)In recent years, Deep Learning has emerged as a powerful framework for analysing and decoding bio-signals like Electroencephalography (EEG) with applications in brain computer interfaces (BCI) and motor control. Deep ... -
A Lightweight Multi-Attack CAN Intrusion Detection System on Hybrid FPGAs
(2022)Rising connectivity in vehicles is enabling new capabilities like connected autonomous driving and advanced driver assistance systems (ADAS) for improving the safety and reliability of next-generation vehicles. This ... -
Multi-spatial-temporal remote sensing and machine learning for mapping management impacts on peatlands in Ireland
(Trinity College Dublin. School of Natural Sciences. Discipline of Geography, 2024)Peatlands, constituting over half of terrestrial wetland ecosystems across the globe, hold critical ecological significance and are large stores of Carbon (C). In Ireland, the wetland landscape is dominated by rare oceanic ... -
Real-time zero-day Intrusion Detection System for Automotive Controller Area Network on FPGAs
(2023)Increasing automation in vehicles enabled by in- creased connectivity to the outside world has exposed vulnerabilities in previously siloed automotive networks like controller area networks (CAN). Attributes of CAN such ...