Browsing by Author "Goss, Kelly Christine"

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    Detection algorithms for an alternative mode of high-density optical data storage
    (2006) Goss, Kelly Christine; Potter, Michael E.
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    Detection systems and algorithms for multiplexed quantum dots
    (2012) Goss, Kelly Christine; Potter, Michael E.; Messier, Geoffrey G.
    Quantum Dots (QDs) are semiconductor nanocrystals that absorb light and re-emit at a wavelength dependent on its size and shape. A group of quantum dots can be designed to have a unique spectral emission by varying the size of the quantum dots (wavelength) and number of quantum dots (optical power) [l]. This technology is refered to as Multiplexed Quantum Dots (MxQD) and when it was first proposed, MxQD tags were created with 6 optical power levels and one QD colour or 3 QD colours and 2 optical power levels. It was hypothesized that a realistic limit to the number of tags would be a system of 6 optical power levels and 6 QD colours resulting in 46655 unique tags. In recent work, the fabrication and detection of 9 unique tags [2] was demonstrated which is still far from the predicted capability of the technology. The limitations affecting the large number of unique tags are both the fabrication methods and the data detection algorithms used to read the spectral emissions. This thesis makes contributions toward improving the data detection algorithms for MxQD tags. To accomplish this, a communications system model is developed that includes the inteference between QD colours, Inter-Symbol Interference (ISI), and additive noise. The model is developed for the two optical detectors, namely a Charge-Coupled Device (CCD) spectrometer and photodiode detectors. The model also includes an analytical expression for the Signal-to-Noise Ratio (S R) of the detectors. For the CCD spectrometer, this model is verified with an experimental prototype. With the models in place, communications systems tools are applied that overĀ­come both ISI and noise. This is an improvement over previous work in the field that only considered algorithms to overcome the ISI or noise separately. Specifically, this thesis outlines the proposal of a matched filter to improve SNR, a Minimum Mean Square Error (MMSE) equalizer that mitigates ISI in the presence of noise and a Maximum Likelihood Sequence (MLS) detection algorithm to provide the best detection performance at the cost of higher computational complexity. Simulations demonstrate that the improved data detection algorithms could read 46,655 MxQD tags with over 97% accuracy compared to 11 % or 60% with traditional data detection algorithms for the CCD spectrometer and photo diode detector respectively