Autonomous vehicles (AV) are rapidly becoming integrated into everyday life, with several
countries anticipating their inclusion in public transport networks in the coming years. Safety …

Edge devices are seeing tremendous growth in sensing and computational capabilities.
Running state-of-the-art deep neural network (NN) based data processing on multi-core …

Multiscale computational modelling is challenging due to the high computational cost of
direct numerical simulation by finite elements. To address this issue, concurrent multiscale …

Attention-based methods for deep neural networks constitute a technique that has attracted
increased interest in recent years. Attention mechanisms can focus on important parts of a …

In safety-critical applications such as automated driving, perception errors may create an
imminent risk to vulnerable road users (VRU). To mitigate the occurrence of unexpected and …

With the continuous progress of modern industry, rotating machinery is gradually developing
toward complexity and intelligence. The fault diagnosis technology of rotating machinery is …

The safety in driving is improved and driving workload is minimized, the provided
information is understandably and the cognitive load on the driver is low. For the …

The success of CNNs for various vision tasks has been accompanied by a significant
increase in required FLOPs and parameter quantities, which has impeded the deployment of …

In this paper, a large‐scale dataset called the Iran Autonomous Driving Dataset (IADD) is
presented, aiming to improve the generalization capability of the deep networks outside of …

With the significant advancements of Large Language Models (LLMs) in the field of Natural
Language Processing (NLP), the development of image-text multimodal models has …