Four-window technique for measuring optical-phase-space-time-frequency tomography

A new approach, the four-window technique, was developed to measure optical phase-space-time-frequency tomography (OPSTFT). The four-window technique is based on balanced heterodyne detection with two local oscillator (LO) fields. This technique can provide independent control of position, momentum, time and frequency resolution. The OPSTFT is a Wigner distribution function of two independent Fourier transform pairs, phase-space and time-frequency. The OPSTFT can be applied for early disease detection.

Quantum dot / optical protein bio-nano hybrid system biosensing

The integration of novel nanomaterials with highly-functional biological molecules has advanced multiple fields including electronics, sensing, imaging, and energy harvesting. This work focuses on the creation of a new type of bio-nano hybrid substrate for military biosensing applications. Specifically it is shown that the nano-scale interactions of the optical protein bacteriorhodopsin and colloidal semiconductor quantum dots can be utilized as a generic sensing substrate. This work spans from the basic creation of the protein to its application in a novel biosensing system. The functionality of this sensor design originates from the unique interactions between the quantum dot and bacteriorhodopsin molecule when in nanoscale proximity. A direct energy transfer relationship has been established between coreshell quantum dots and the optical protein bacteriorhodopsin that substantially enhances the protein’s native photovoltaic capabilities. This energy transfer phenomena is largely distance dependent, in the sub-10nm realm, and is characterized experimentally at multiple separation distances. Experimental results on the energy transfer efficiency in this hybrid system correlate closely to theoretical predictions. Deposition of the hybrid system with nano-scale control has allowed for the utilization of this energy transfer phenomena as a modulation point for a functional biosensor prototype. This work reveals that quantum dots have the ability to activate the bacteriorhodopsin photocycle through both photonic and non-photonic energy transfer mechanisms. By altering the energy transferred to the bacteriorhodopsin molecule from the quantum dot, the electrical output of the protein can be modulated. A biosensing prototype was created in which the energy transfer relationship is altered upon target binding, demonstrating the applicability of a quantum dot/bacteriorhodopsin hybrid system for sensor applications. The electrical nature of this sensing substrate will allow for its efficient integration into a nanoelectronics array form, potentially leading to a small-low power sensing platform for remote toxin detection applications.

MAGNETO-PHOTONIC CRYSTALS FOR OPTICAL SENSING APPLICATIONS

Among the optical structures investigated for optical sensing purpose, a significant amount of research has been conducted on photonic crystal based sensors. A particular advantage of photonic crystal based sensors is that they show superior sensitivity for ultra-small volume sensing. In this study we investigate polarization changes in response to the changes in the cover index of magneto-optic active photonic band gap structures. One-dimensional photonic-band gap structures fabricated on iron garnet materials yield large polarization rotations at the band gap edges. The enhanced polarization effects serve as an excellent tool for chemical sensing showing high degree of sensitivity for photonic crystal cover refractive index changes. The one dimensional waveguide photonic crystals are fabricated on single-layer bismuth-substituted rare earth iron garnet films ((Bi, Y, Lu)3(Fe, Ga)5O12 ) grown by liquid phase epitaxy on gadolinium gallium garnet substrates. Band gaps have been observed where Bragg scattering conditions links forward-going fundamental waveguide modes to backscattered high-order waveguide modes. Large near-band-edge polarization rotations which increase progressively with backscattered-mode order have been experimentally demonstrated for multiple samples with different composition, film thickness and fabrication parameters. Experimental findings are supported by theoretical analysis of Bloch modes polarization states showing that large near stop-band edge rotations are induced by the magneto-photonic crystal. Theoretical and experimental analysis conducted on polarization rotation sensitivity to waveguide photonic crystal cover refractive index changes shows a monotonic enhancement of the rotation with cover index. The sensor is further developed for selective chemical sensing by employing Polypyrrole as the photonic crystal cover layer. Polypyrrole is one of the extensively studied conducting polymers for selective analyte detection. Successful detection of aqueous ammonia and methanol has been achieved with Polypyrrole deposited magneto-photonic crystals.

Comparison of Computer-Based and Optical Face Recognition Paradigms

The main objectives of this thesis are to validate an improved principal components analysis (IPCA) algorithm on images; designing and simulating a digital model for image compression, face recognition and image detection by using a principal components analysis (PCA) algorithm and the IPCA algorithm; designing and simulating an optical model for face recognition and object detection by using the joint transform correlator (JTC); establishing detection and recognition thresholds for each model; comparing between the performance of the PCA algorithm and the performance of the IPCA algorithm in compression, recognition and, detection; and comparing between the performance of the digital model and the performance of the optical model in recognition and detection. The MATLAB © software was used for simulating the models. PCA is a technique used for identifying patterns in data and representing the data in order to highlight any similarities or differences. The identification of patterns in data of high dimensions (more than three dimensions) is too difficult because the graphical representation of data is impossible. Therefore, PCA is a powerful method for analyzing data. IPCA is another statistical tool for identifying patterns in data. It uses information theory for improving PCA. The joint transform correlator (JTC) is an optical correlator used for synthesizing a frequency plane filter for coherent optical systems. The IPCA algorithm, in general, behaves better than the PCA algorithm in the most of the applications. It is better than the PCA algorithm in image compression because it obtains higher compression, more accurate reconstruction, and faster processing speed with acceptable errors; in addition, it is better than the PCA algorithm in real-time image detection due to the fact that it achieves the smallest error rate as well as remarkable speed. On the other hand, the PCA algorithm performs better than the IPCA algorithm in face recognition because it offers an acceptable error rate, easy calculation, and a reasonable speed. Finally, in detection and recognition, the performance of the digital model is better than the performance of the optical model.