/library/oar/handle/123456789/22539 2025-11-05T10:50:30Z /library/oar/handle/123456789/29523 Title: Registration of thermographic video for dynamic temperature analysis in humans Abstract: The use of infrared thermography in medical applications has increased in popularity in recent years. It facilitates the detection and examination of skin thermal signatures, under both normal and abnormal conditions. Thermography has been employed in numerous biomedical fields, including breast cancer detection, cutaneous temperature monitoring during exercise and the analysis of normative temperature patterns. Thermal imaging may be dynamic or static in nature. Using static thermography, the steady state conditions and spatial distributions of the thermal patterns within a target are analysed at a particular instant, usually following an acclimatisation period. In contrast, via dynamic thermography, both spatial and temporal variations are considered, making the acquired data more informative. However, issues including involuntary target movement and the dynamic temperature changes undergone by the target need to be considered. Video registration was opted for in this work. Four steps constitute the registration process. The Speeded-Up Robust Features (SURF) detector was utilised in the feature detection stage. Matching features between images were then found based on the sum of squared differences (SSD) error, following which an affine geometric transformation was computed to adequately map the images in consideration. Bilinear interpolation was then utilised to calculate pixel values in non-integer coordinates. Two video registration methods were proposed in this work to address the primary issues associated with dynamic thermography. Data was gathered from nine participants for the testing of these methods. Following implementation, their performance was assessed both qualitatively and quantitatively, and a two-sample ttest was applied to verify that the difference between the mean errors per method was statistically significant. Dynamic temperature analysis was also carried out on the extracted temperature data in both the time and frequency domains, where cyclic patterns having different frequencies and magnitudes were observed across all participants. Such behaviour has not been documented in literature thus far, which implies that the biological significance of these patterns is yet to be determined. Description: B.ENG.(HONS) 2017-01-01T00:00:00Z /library/oar/handle/123456789/27526 Title: Registration of thermographic video for dynamic temperature analysis in humans Abstract: The use of infrared thermography in medical applications has increased in popularity in recent years. It facilitates the detection and examination of skin thermal signatures, under both normal and abnormal conditions. Thermography has been employed in numerous biomedical fields, including breast cancer detection, cutaneous temperature monitoring during exercise and the analysis of normative temperature patterns. Thermal imaging may be dynamic or static in nature. Using static thermography, the steady state conditions and spatial distributions of the thermal patterns within a target are analysed at a particular instant, usually following an acclimatisation period. In contrast, via dynamic thermography, both spatial and temporal variations are considered, making the acquired data more informative. However, issues including involuntary target movement and the dynamic temperature changes undergone by the target need to be considered. Video registration was opted for in this work. Four steps constitute the registration process. The Speeded-Up Robust Features (SURF) detector was utilised in the feature detection stage. Matching features between images were then found based on the sum of squared differences (SSD) error, following which an affine geometric transformation was computed to adequately map the images in consideration. Bilinear interpolation was then utilised to calculate pixel values in non-integer coordinates. Two video registration methods were proposed in this work to address the primary issues associated with dynamic thermography. Data was gathered from nine participants for the testing of these methods. Following implementation, their performance was assessed both qualitatively and quantitatively, and a two-sample ttest was applied to verify that the difference between the mean errors per method was statistically significant. Dynamic temperature analysis was also carried out on the extracted temperature data in both the time and frequency domains, where cyclic patterns having different frequencies and magnitudes were observed across all participants. Such behaviour has not been documented in literature thus far, which implies that the biological significance of these patterns is yet to be determined. Description: B.ENG.(HONS) 2017-01-01T00:00:00Z /library/oar/handle/123456789/25446 Title: Modelling of stage 2 sleep EEG data Abstract: Humans spend approximately a third of their lives sleeping. Undoubtedly, sleep is essential to human health and sleep research continues to reveal more about the characteristics and structures of sleep. During a night’s sleep, brain activity cycles through a number of stages, each with its own characteristics that can be clearly extracted from an electroencephalogram (EEG), which records the brain electrical signals from the human scalp. EEG recordings for stage two sleep contain two hallmark events known as sleep spindles and K-complexes. Spindles have a strong clinical significance because they tend to change with age and atypical spindling is associated with a range of disorders and diseases. In particular, spindles hold promise as a biomarker of dementia. Sleep spindles are generally extracted manually by human experts from voluminous sleep EEG recordings. Since this process is time consuming and prone to human bias, many studies have recently tried to implement automatic spindle detectors which label spindle activity in an EEG recording. This dissertation investigates the operation of two different spindle detectors and compares their performance when scoring spindles in two sleep EEG databases, one of which is open access. One of the detectors is a root-mean-square (RMS) amplitude detector which is commonly used for discussion and comparison in the literature. It identifies spindles based on the temporal characteristics of the EEG signal. The second detector is an autoregressive switching multiple model (AR-SMM) detector which consists of a number of mathematical models representing different modes of the EEG signal: background EEG and spindle activity. These models are trained on pre-scored data and are then used to score spindles in new, incoming EEG data. This work has shown that overall the RMS detector exhibited better performance over the two EEG datasets tested and was found to be less sensitive to the amount of data used to extract the necessary detector parameters. The lower AR-SMM detector performance may have been due to the quality of the data used for training and thus future work can investigate how this can be improved using data representative of fundamental spindle characteristics and not marred by noise, disturbances or artefacts. Description: B.ENG.(HONS) 2017-01-01T00:00:00Z /library/oar/handle/123456789/25443 Title: An IoT solution for traffic light control Abstract: Traffic congestion is the plague of our time. The relentless increase in the number of vehicles on the road in a small country such as Malta inevitably results in clogged roads; particularly in urban areas. Developments in sensor technology, advanced hardware and the advent of the Internet led to the Internet of Things (IoT) and consequently the possibility of IoT-based intelligent transportation systems. The aim of this project is to implement a real-time IoT solution which adjusts the traffic light timings controlling an urban signalised junction. This solution aims to minimise the queue length in the junction. In this project, the Rue D’Argens and Sliema road junction is considered. However, the methodologies used in this project can be applied to any signalised junction. The aim of the project is achieved by first developing a micro model of the junction in question on the chosen traffic simulator package. A macro model is also developed and validated by comparing its behaviour with that of the micro model. To transfer the sensor data from the simulator to the cloud, a communications link is established between the traffic simulator and the cloud platform. Finally, after analysing the available optimisation algorithms, the chosen algorithm is implemented on the cloud platform and optimal traffic light timings are obtained. With everything in place, realtime simulations of commonplace traffic scenarios can take place within the complete system. Results will show that with the system developed, the real-time optimisation algorithm is able to find optimal traffic light timings leading to significant reductions in the total queue length at the junction. Description: B.ENG.(HONS) 2017-01-01T00:00:00Z