https://www.um.edu.mt/library/oar/handle/123456789/5626 Fri, 03 Jul 2026 21:09:19 GMT 2026-07-03T21:09:19Z https://www.um.edu.mt/library/oar/handle/123456789/147489 Title: A LiFi-based innovative 6G solution for hospitals using green wavelength, directly modulated laser Authors: Sharma, Ajay; Xuereb, Peter A.; Garg, Lalit Abstract: This paper proposes an innovative light-fidelity (Li-Fi) system for high-speed communication in hospital environments that operates at a green wavelength of 500 nm with Directly Modulated Laser (DML). The proposed system shows an excellent performance and achieves a Q factor of 18.84, a bit error rate (BER) of 1.6e-79, and a signal-to noise ratio (SNR) of 74.94 dB, which is significantly better than the previous research. It also has a range of up to 25 m line-of-sight (LOS) and can transfer data at speeds in excess of 1 Gbps, making it significantly faster than previous work conducted with much lower LOS ranges while being robust against interference. New applications of DML combined with optical splitters contribute to providing signal stability and system scalability, overcoming problems such as low range. This design ensures safe, reliable, and non-intrusive communication, ideal for applications that require high data reliability, such as real-time imaging and telemedicine in hospitals. This new Li-Fi system is found to be compatible with modern hospital power requirements, and it also provides a solid foundation for future 6G communication networks. Thu, 01 Jan 2026 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/147489 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/147402 Title: Enhancing hospital security and patient monitoring through WhoFi-inspired LiFi channel sensing with privacy preservation Authors: Sharma, Ajay; Garg, Lalit; Xuereb, Peter Albert Abstract: Ensuring both secure connectivity and patient safety has become a growing concern in modern hospitals. Although LiFi (Light Fidelity) provides high-speed and interference-free communication, the possibility of using it as a sensing platform has not been investigated to the full extent. This paper presents a LiFi-based system that integrates WHOFi for hospital security and patient monitoring. With simulations based on MATLAB, we simulate the change in LiFi channels due to human presence, movement, and falls and extract statistical and spectral characteristics of the machine learning classifier. The system has a high accuracy of around 94% in activity recognition (empty, movement, fall) and the Equal Error Rate (EER) of 5% in staff authentication. Such a solution is privacy-sensitive, non-invasive and inherently limited to the room boundaries, unlike camera-based or wearable systems, which increase the level of security and patient monitoring in healthcare settings. The findings point to the two-fold nature of LiFi as a communication and sensing technology, which opens the potential for smart hospital infrastructures. This numerical evaluation study will be expanded to hardware testbeds and deep learning models to be applicable in the real world in the future. The proposed system enhances hospital data security and patient tracking efficiency using optical wireless communication. Sun, 01 Feb 2026 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/147402 2026-02-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/146322 Title: Special issue “Towards a higher education of the future : transformational roles of edge intelligence” Authors: Doshi, Ruchi; Hu, Yu-Chen; Garg, Lalit; Fagbola, Temitayo Abstract: Higher Education of the Future (HEF) is anticipated to be a scalable educational framework that is driven by new digital learning architectures and platforms, as well as collaborative learning systems, that are able to completely guarantee self-paced, customizable, personalized and flexible teaching/learning experiences. The HEF concept strongly points toward a “learning from everywhere” model. The need for HEF is motivated, among other things, by the fact that most state-of-the-art higher education system models currently being used for driving and transitioning higher education are structurally, socially, and technologically incapacitated to meet the key requirements towards delivering a foreseeable smart, real-time intelligence driven HEF. [excerpt] Mon, 01 Jan 2024 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/146322 2024-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/146069 Title: Feedback loops and bias in machine learning algorithms for predictive policing Abstract: Predictive policing describes several emerging practices of implementing artificial intelligence and machine learning in police work, specifically in attempting to predict future crimes through algorithmic crime forecasting. These emerging practices have introduced many new opportunities for improved police work, but critics of predictive policing have raised both ethical and practical concerns. These concerns include the risk of feedback loops and bias. This thesis aims to contribute to this ongoing debate by examining how algorithmic crime forecasting tools produce bias and feedback loops and by exploring if it is possible to create algorithmic crime forecasting tools with reduced tendencies towards bias and feedback loops. Specifically, the focus is on the seminal and widely adopted PredPol system, which is based on an earthquake prediction system known as Epidemic Type Aftershock Sequence (ETAS). The methodology used in this studywas to replicate studies detailing the PredPol system, as well as studies criticising it. Based on previous findings by critics, a synthetic population and urn modelling was used to demonstrate the negative tendencies of the system. Based on this, an original framework was developed for evaluating modifications made to the algorithm by measuring the effectiveness in reducing feedback loop tendencies and improving fairness. This is done through metrics like the Predictive Accuracy Index (PAI), variations in the mean conditional intensity rates, λ, and the total fairness score, which evaluates the consistency of law enforcement attention across different demographic groups. To reduce the algorithm’s tendencies towards bias and feedback loops, a modified algorithm using rejection sampling and a fairness penalty was developed. While the proposed algorithmic adjustments lead to increased fairness and reduced feedback loop generation in predictive policing, they also introduce some trade-offs in predictive performance, particularly noted in the PAI values. However, the enhancements significantly mitigate biased policing practices and reduce the perpetuation of historical inequities, aligning more closely with ethical standards. Description: M.Sc. ICT(Melit.) Wed, 01 Jan 2025 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/146069 2025-01-01T00:00:00Z