Saritha Unnikrishnan

Zahid Ali, Saritha Unnikrishnan, Eoghan Furey et al.

This paper addresses the challenge of efficient rendezvous in multihop cognitive radio networks, where existing channel-hopping algorithms designed for single-hop scenarios incur increased delay and coordination inefficiencies in multinode topologies. To overcome these limitations, we propose a Multihop Dual Modular Clock Algorithm (M-DMCA), which systematically extends modular clock-based rendezvous to multihop environments while preserving efficient channel coordination. The proposed scheme enables dual-channel selection per timeslot and incorporates a lightweight three-way handshake mechanism to improve coordination among intermediate nodes. Simulation results under worst-case conditions, including high primary user activity, asymmetric channel availability, and dense network settings, demonstrate that M-DMCA significantly reduces rendezvous time compared to existing approaches, achieving up to 24% improvement. These results demonstrate the suitability of M-DMCA for timely node discovery in dynamic emergency response scenarios.

Zahid Ali, Saritha Unnikrishnan, Eoghan Furey et al.

In post-disaster environments, damaged communication infrastructure severely limits coordination among emergency response teams. Cognitive radio networks (CRNs) enable rapidly deployable communication by allowing nodes to opportunistically access available spectrum. However, existing multihop rendezvous protocols typically rely on N-1 termination conditions, which can lead to premature termination, resulting in incomplete neighbour discovery and invalid network topology formation. This work identifies this limitation as a previously overlooked issue in multihop rendezvous protocols. This paper proposes a Multihop Reliable Dual-Modular Clock Algorithm (MR-DMCA) that eliminates premature termination and ensures reliable network formation. The proposed protocol introduces a coordinate-assisted neighbour validation mechanism and an autonomous termination strategy that guarantees complete neighbour and topology discovery before protocol termination. Although implemented within MR-DMCA, the proposed validation and termination approach is applicable to a wider class of multihop rendezvous protocols. Extensive simulations demonstrate that, in a worst-case scalable scenario with 20 nodes and 20 channels under high primary radio activity (m=2), MR-DMCA achieves 100% accurate neighbour and topology discovery while reducing rendezvous time by up to 76%, 37%, and 17% compared with baseline protocols. The results highlight that addressing premature termination is critical for reliable multihop rendezvous in cognitive radiobased emergency communication networks.

Talha Meraj, Tom Flannery, Charlie Cummins et al.

Accurate tumour localization and diagnosis is a critical component of clinical care for brain cancers. Magnetic Resonance Imaging (MRI) is the most commonly used imaging modality due to its superior soft-tissue contrast. However, standard MRI often exhibits limited contrast and imaging artifacts, which necessitates the use of contrast agents to enhance lesion visibility. The administration of chemical contrast agents is not always feasible and may be contraindicated in patients with renal impairment or other health conditions. As a result, developing accurate and non-invasive contrast enhanced MRI (CEMRI) synthesis methods has clinical importance. In recent years, numerous approaches for CEMRI synthesis have been proposed, predominantly relying on generative artificial intelligence models. While these methods demonstrate promising performance, their dependence on implicit feature learning often limits their ability to preserve anatomical boundaries and tumour-specific fine structures. To address these challenges, we propose an anatomically aware frequency-and-structure-guided vision transformer (AA-ViT), for CEMRI synthesis using pre-contrast MRI modalities (T1, T2, and FLAIR). Experiments on the BraTS 2021 dataset demonstrate that the proposed method preserves anatomical and lesion boundaries, achieving higher PSNR and SSIM than state-of-the-art approaches. Clinical evaluation by three neuroradiologists and a neurosurgeon on 19 randomly selected cases across diverse gliomas yielded a mean score of 3.94/5, providing preliminary clinical validation rarely seen in prior studies. Synthetic post-contrast scans from our model could lower scanning costs, shorten imaging time, and avoid the potential risks of using gadolinium-based contrast agents.