https://www.um.edu.mt/library/oar/handle/123456789/8339 Mon, 20 Apr 2026 04:38:59 GMT 2026-04-20T04:38:59Z https://www.um.edu.mt/library/oar/handle/123456789/144591 Title: Multitemporal and multispectral data fusion for super-resolution of Sentinel-2 images Authors: Tarasiewicz, Tomasz; Nalepa, Jakub; Farrugia, Reuben A.; Valentino, Gianluca; Chen, Mang; Briffa, Johann A.; Kawulok, Michal Abstract: Multispectral Sentinel-2 (S-2) images are a valuable source of Earth observation data; however, spatial resolution of their spectral bands limited to 10-, 20-, and 60-m ground sampling distance (GSD) remains insufficient in many cases. This problem can be addressed with super-resolution (SR), aimed at reconstructing a high-resolution (HR) image from a low-resolution (LR) observation. For S-2, spectral information fusion allows for enhancing the 20- and 60-m bands to the 10-m resolution. Also, there were attempts to combine multitemporal stacks of individual S-2 bands; however, these two approaches have not been combined so far. In this article, we introduce DeepSent—a new deep network for super-resolving multitemporal series of multispectral S-2 images. It is underpinned with information fusion performed simultaneously in the spectral and temporal dimensions to generate an enlarged multispectral image (MSI). In our extensive experimental study, we demonstrate that our solution outperforms other state-of-the-art techniques that realize either multitemporal or multispectral data fusion. Furthermore, we show that the advantage of DeepSent results from how these two fusion types are combined in a single architecture, which is superior to performing such fusion in a sequential manner. Importantly, we have applied our method to super-resolve real-world S-2 images, enhancing the spatial resolution of all the spectral bands to 3.3-m nominal GSD, and we compare the outcome with very HR WorldView-2 images. We have made our implementation publicly available, and we expect it will increase the possibilities of exploiting super-resolved S-2 images in real-life applications. Sun, 01 Jan 2023 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/144591 2023-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/140933 Title: Model-driven federated learning for channel estimation in millimeter-wave massive MIMO systems Authors: Yi, Qin; Yang, Ping; Liu, Zilong; Huang, Yiqian; Zammit, Saviour Abstract: This paper investigates the model-driven federated learning (FL) for channel estimation in multi-user millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Firstly, we formulate it as a sparse signal recovery problem by exploiting the beamspace domain sparsity of the mmWave channels. Then, we propose an FL-based learned approximate message passing (LAMP) channel estimation scheme, namely FL-LAMP, where the LAMP network is trained by an FL framework. Specifically, the base station (BS) and users jointly train the LAMP network, where the users update the local LAMP network parameters by local datasets consisting of measurement signals and beamspace channels, and the BS calculates the global LAMP network parameters by aggregating the local network parameters from all the users. The beamspace channel can thus be obtained in real time from the measurement signal based on the parameters of the trained LAMP network. Simulation results demonstrate that the proposed FL-LAMP scheme can achieve better channel estimation accuracy than the existing orthogonal matching pursuit (OMP) and approximate message passing (AMP) schemes, and provides satisfactory prediction capability for multipath channels. Mon, 01 Apr 2024 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/140933 2024-04-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/139002 Title: Three women, three generations : an in-depth case study of language retention and shift in one family from the Maltese Australian community in Melbourne Authors: Muscat, Adrian Abstract: This paper analysis one family pertaining to the Maltese Australian community in Melbourne and investigates the retention of the Maltese language The Maltese Australian community is a small community that is getting smaller since migration from Malta to Australia has largely stopped Thus the Maltese language is spoken mostly by the first generation of immigrants who left the island after the Second World War seeking a better future The second generation born in Australia usually understands the language but lacks the opportunity or the will to speak the language except with members of the family The third generation raised in a multicultural country normally has very little fluency in the Maltese language The investigation is grounded in interview data gathered among a family of three generations of Maltese origin in Melbourne The findings of this research show that the aging population of the Maltese community and the dominance of the English language do not favour the retention of the Maltese language in the future With the end of the first generation of post-World War Two migrants and the emergence of the fourth and fifth generations probably there will be an absolute shift to English the de-facto national language of Australia Mon, 01 Jan 2024 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/139002 2024-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/135636 Title: CA-FedRC : codebook adaptation via federated reservoir computing in 5G NR Authors: Ye, Ziqiang; Liao, Sikai; Gao, Yulan; Fang, Shu; Xiao, Yue; Xiao, Ming; Zammit, Saviour Abstract: With the burgeon deployment of the fifth-generation new radio (5 G NR) networks, the codebook plays a crucial role in enabling the base station (BS) to acquire the channel state information (CSI). Different 5 G NR codebooks incur varying overheads and exhibit performance disparities under diverse channel conditions, necessitating codebook adaptation based on channel conditions to reduce feedback overhead while enhancing performance. However, existing methods of 5 G NR codebooks adaptation require significant overhead for model training and feedback or fall short in performance. To address these limitations, this letter introduces a federated reservoir computing framework designed for efficient codebook adaptation in computationally and feedback resource-constrained mobile devices. This framework utilizes a novel series of indicators as input training data, striking an effective balance between performance and feedback overhead. Compared to conventional models, the proposed codebook adaptation via federated reservoir computing (CA-FedRC), achieves rapid convergence and significant loss reduction in both speed and accuracy. Extensive simulations under various channel conditions demonstrate that our algorithm not only reduces resource consumption of users but also accurately identifies channel types, thereby optimizing the trade-off between spectrum efficiency, computational complexity, and feedback overhead. Wed, 01 Jan 2025 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/135636 2025-01-01T00:00:00Z