Biomedical Engineering - Theses
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ItemA predictive neural model of visual information processing( 2023-08)Recent understanding of how the brain processes sensory input has moved away from understanding sensory processing as just being the passive processing of input to a framework that employs a more active role of higher-level expectations in sensory processing. One such theory is predictive coding in which the brain generates a prediction of the sensory input that it will receive and compares this prediction with the actual sensory input. Because the transmission of visual information from the eyes to the brain takes time, for the brain to accurately respond to the real-time location of a moving object, the prediction mechanism has to take into account the change in object’s location during the period of transmission latency. Understanding such temporal prediction mechanism will extend our understanding of the way by which the brain actively interacts with the environment we live in. This research project investigated the predictive signal transmission pathways of the mammalian visual system and focused on the early stages of the visual pathway, including the retina, the Lateral Geniculate Nucleus (LGN) and the primary visual cortex (V1). These structures play critical roles in visual signal gathering and integration. Mathematical and computational models were constructed based on predictive coding strategies and spike-based neural coding principles, where neurons with specific firing timings are arranged into hierarchical areas, and upper areas predict the neuronal behaviours of lower areas that receive sensory stimuli. The first goal of the project is to investigate the encoding of visual information in precise neuron spike timings and neuronal interactions, because the temporal prediction mechanism involves small time scales and detailed object motions. We intend to show that results obtained via spike-based neural principles, which involves cumulative computations in small time scales, do not contradict with the results from classical rate-based neural networks that operate based on longer time scales, and results from physiological recordings. The second goal is to investigate the mechanism by which temporal prediction can be achieved using the spike-based neural network, given moving input stimuli. Through the project, we validated that a predictive coding network can be built based on spike-based neural principles, and it has the potential to encode moving stimuli with less error compared with rate-based approaches. Based on the model developed, the next step is to study the specific mechanism by which the alignment between the real and the perceived locations of a moving object can be achieved, i.e., a mechanism that compensates for the signal transmission delay from the eyes to the brain. Outcomes of the research are expected to advance our understanding about human visual system and provide new insights into the development of neural implants, prostheses and machine learning algorithms. The principles investigated are hypothesised to apply throughout the cerebral cortex. Consequently, the results are anticipated to have application to the processing of other modes of sensory stimulus, such as auditory and olfactory inputs, applications can also be expanded to the research areas of memory, motor control, cognition and decision making.
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ItemAcoustic beamforming analysis for wearable blind aid applications( 2018)The World Health Organisation estimates 36 million are blind worldwide; in addition, 217 million have severe or moderate visual impairment. Over the past decades, there has been substantial research in alleviating blindness and visual impairment. However, the blind community has yet to widely accept a single electronic travel aid (ETA) solution; the low cost white cane still remains the most popular device for orientation and mobility. One major limitation of current ETAs is their poor cost-benefit ratio. However, semiconductor advances may have reached a point where previous limitations are now surmountable as miniaturisation, flexible, low-cost and low-power circuits have been key enablers of wearable technology. Sonar has consistently been the preferred modality for single-sensor ETAs. The thesis aims to study performance characteristics of various beamforming aspects in their relation to developing a wearable-sonar system for blind aid applications. The scope of analysis covers 1) Classical Beamfomers 2) Beamforming Augmentation (Geometry, Shading, Adaptive Algorithms) 3) Spherical (3D) Beamsteering and Conformal Arrays