/library/oar/handle/123456789/8337 Sun, 02 Nov 2025 14:17:16 GMT 2025-11-02T14:17:16Z /library/oar/handle/123456789/140266 Title: Real-time multi-camera tracking and od-matrix estimation of vehicles Abstract: With computer vision, it is possible to capture data which is of great use to urban planners and infrastructure engineers. Informed decisions can then be taken to evolve existing and new infrastructure in a more robust and greener way. Data can be captured with the use of a single-camera tracker, which detects and tracks vehicles and pedestrians in the camera view. However, in more complex scenarios, such as a roundabout or intersection, the use of a single camera is not sufficient. For this study, a single-camera tracker, developed by Greenroads Ltd, is readily available [...] Description: M.Sc. ICT(Melit.) Mon, 01 Jan 2024 00:00:00 GMT /library/oar/handle/123456789/140266 2024-01-01T00:00:00Z /library/oar/handle/123456789/140265 Title: Detecting anomalies from roadside video streams Abstract: The interconnected nature of road networks implies that anomalies on narrow residential roads can ripple through the entire traffic system, particularly in high‐ traffic areas as common for the Maltese Islands. Detecting anomalies in such en‐ vironments using roadside cameras is challenging due to the multitude of normal and anomalous events, changes in illumination, obstructions, complex anomalies, and difficult viewing angles. This thesis investigates anomaly detection methods tailored to the realistic road and data limitations typical of Maltese urban roads. Classical anomaly detection, which identifies anomalies from structured data, and deep learning‐based techniques, which detect anomalies directly from video input, were evaluated. The literature review revealed limited evaluations on realistic datasets for both methods. The classical method was developed to filter out ID switch artifacts and identify specific anomalies using a combination of filtering, DBSCAN clustering, masking, and rule‐based techniques. For the deep learning method, an AE model with the STAE [1] architecture was chosen for its ability to capture temporal rep‐ resentation. Both methods were evaluated on video datasets collected in Malta and a relabeled Street Scene [2] dataset. The classical method demonstrated high reliability in detecting anomalies in structured data, achieving an 82% true positive rate and a 3% false positive rate for a local dataset. However, the data acquisition method did not accurately record all anomalies, reducing the true positive rate for actual video anomalies. The deep learning method showed strong performance across all datasets, achiev‐ ing an 83% AUC and a 25% EER for a dataset recorded in the same location. Per‐ formance was slightly reduced for locations with heavy shadows, as shown on a second local dataset. Segmenting frames into tiles and augmenting datasets improved performance in shadow‐affected conditions, as did masking irrelevant regions. An event‐level comparison showed both methods performed similarly in detecting non‐typical vehicle paths. The classical method excelled at identifying non‐typical object locations and was more robust against changes in scene dynam‐ ics, is more modular, and easier to debug. The deep learning method was better at detecting non‐typical slow‐moving and non‐typical vehicles and was more resilient to variations in the data acquisition method within the Intelligent Traffic System (ITS). However, neither method effectively detected unforeseen anomalies. Over‐ all, this thesis provides valuable insights and guidance for choosing the most ap‐ propriate anomaly detection methods tailored to different types of anomalies in complex urban road environments. Description: M.Sc. ICT(Melit.) Mon, 01 Jan 2024 00:00:00 GMT /library/oar/handle/123456789/140265 2024-01-01T00:00:00Z /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 /library/oar/handle/123456789/139002 2024-01-01T00:00:00Z /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 /library/oar/handle/123456789/135636 2025-01-01T00:00:00Z