Label-free Medical Image Quality Evaluation by Semantics-aware Contrastive Learning in IoMT

ACKNOWLEDGMENT For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.Peer reviewedPostprin

Domain-adapted driving scene understanding with uncertainty-aware and diversified generative adversarial networks

Funding Information: This work was supported by Fisheries Innovation & Sustainability (FIS) and the U.K. Department for Environment, Food & Rural Affairs (DEFRA) under grant number FIS039 and FIS045A.Peer reviewedPublisher PD

Part-Whole Relational Few-Shot 3D Point Cloud Semantic Segmentation

The author wishes to extend sincere appreciation to Professor Lin Shi for the generous provision of equipment support, which significantly aided in the successful completion of this research. Furthermore, the author expresses gratitude to Associate Professor Ning Li and Teacher Wei Guan for their invaluable academic guidance and unwavering support. Their expertise and advice played a crucial role in shaping the direction and quality of this research.Peer reviewe

Fine-grained RNN with Transfer Learning for Energy Consumption Estimation on EVs

This work is supported by the Engineering and Physical Sciences Research Council, under PETRAS SRF grant MAGIC (EP/S035362/1) and the University of Glasgow Impact Acceleration Account.Peer reviewedPostprin

A Hint-Based Random Access Protocol for mMTC in 5G Mobile Network

With the increasing popularity of machine-type communication (MTC) devices, several new challenges are encountered by the legacy long term evolution (LTE) system. One critical issue is that a massive number of MTC devices trying to conduct random access procedures may cause significant collisions and long delays. In this work, we present a new random access mechanism by splitting the contention-based preambles in LTE into two logically disjoint parts, one for the user equipment (UE) being paged and the other for the UEs not being paged. Since the IDs of paged UEs are known by the base station, a novel hash-based random access, which we call hint, is possible. The main idea is to pre-allocate preambles to paged UEs in a contention-free manner and confines non-paged UEs to contend in a separate region. We further build a mathematical model to find the optimal ratio of pre-allocated preambles. Extensive simulations are conducted to validate our results

Compound Scaling Encoder-Decoder (CoSED) Network for Diabetic Retinopathy Related Bio-marker Detection

ACKNOWLEDGMENT For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission. This work was supported by Cancer Research UK (CRUK) under Grant EDDPJT-May23/100001Peer reviewedPostprin

Improving Synthetic to Realistic Semantic Segmentation with Parallel Generative Ensembles for Autonomous Urban Driving

Semantic segmentation is paramount for autonomous vehicles to have a deeper understanding of the surrounding traffic environment and enhance safety. Deep neural networks (DNN) have achieved remarkable performances in semantic segmentation. However, training such a DNN requires a large amount of labelled data at pixel level. In practice, it is a labour-intensive task to manually annotate dense pixel-level labels. To tackle the problem associated with a small amount of labelled data, Deep Domain Adaptation (DDA) methods have recently been developed to examine the use of synthetic driving scenes so as to significantly reduce the manual annotation cost. Despite remarkable advances, these methods unfortunately suffer from the generalisability problem that fails to provide a holistic representation of the mapping from the source image domain to the target image domain. In this paper, we therefore develop a novel ensembled DDA to train models with different up-sampling strategies, discrepancy and segmentation loss functions. The models are, therefore, complementary with each other to achieve better generalisation in the target image domain. Such a design does not only improve the adapted semantic segmentation performance, but also strengthen the model reliability and robustness. Extensive experimental results demonstrate the superiorities of our approach over several state-of-the-art methods