A postdoctoral researcher who is experienced in Computer-based Diagnostic Instrumentation Development for bio-medical, biotechnological and aerospace applications.
Publications (4)
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Understanding the caveats of deploying a Spiking Neural Networks (SNNs) in an embedded system is important, due to their potential to achieve high efficiency in applications using event-based data. This work investigates the effects of the quantisation of SNNs from the perspective of deploying a model onto FPGAs. Three SNN models were trained using Quantisation-aware training (QAT). In addition, three different types of quantisation were applied on all three models. Further, these models are trained while they are represented through various custom bit-depths using Brevitas. Then, the performance metric curves such as accuracy, training loss, and test loss resulted from QAT were viewed as performance distribution, to show that the significant accuracy drop found in these curves manifests itself as a bi-modal distribution This work then investigates whether the decrease in accuracy is consistent across different models.
Identifying target volumes is a key component in image-guided adaptive radiotherapy. Furthermore, propagating contours from treatment planning images to images acquired during treatment is a difficult image registration problem due to organ motion. Among many deformable image registration techniques, block matching has been studied extensively in prostate and bladder cancer. Here, we propose a two-pass, three-dimensional disparity regularisation that accounts for anatomical constraints by distance and neighbouring motion vectors' orientation. This approach improves the Dice similarity score of the delineation/contour propagation and results in a non- iterative and non-pyramidal method with reduced computational time.
A two-stage algorithm that detects and locates damages in thin walled structures using Lamb wave signals is proposed. Isotropic plate and shell structures with adhesively bonded piezoelectric transducers in circular and rectangular array patterns are considered. Lamb waves are generated and sensed by these transducers in pitch-catch mode, before and after making damages in the structure-under-test for baseline subtraction. In the damage identification process, first the correlation coefficient is determined using current and baseline signals. Further, the Trilateration method is adopted to locate the damage using parameters like Time-of-Flight and Group velocity from the damage-scattered Lamb wave signals.
Sensor data validation is a key module in any fault diagnosis system. A pattern recognition based algorithm is therefore proposed to validate the reliability of an arbitrarily distributed sensor network and its data. Single-frequency Lamb wave experiments are conducted on aircraft qualified aluminium plates, which have perfectly bonded and debonded Lead Zirconate Titanate patches along with structural damage in the form of holes. Sensor debondings are examined using sensor network data. Simple features like standard deviation, autocorrelation are extracted from Lamb wave signals and employed in the pattern recognition system that distinctly identifies the sensor debonding albeit the structural damage is present.
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NON-SPIE: An Image Based Microtiter Plate Reader System for 96-wellFormat Fluorescence Assays
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