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Since the introduction of optical fibers in 1960's in communication systems, researchers have encountered many challenges to improve the signal quality at the receiver as well as transmitting the signal as distant as possible. The former was achieved by employing coherent receivers, which let us use M-array modulation formats, such as QPSK, or QAM, and polarization of the signal. The later is accomplished by the advent of optical amplifiers. Optical amplifiers enable us to compensate for the loss occurred within the fiber optic line, without the need for optical-electrical signal conversion. These amplifiers add noise to the line which interacts with the nonlinearity in the fiber line. This interaction causes phase change in the propagating signal called nonlinear phase noise, which degrades the system performance. In this study we will derive an analytical expression for the linear and nonlinear phase noise variance in dispersion unmanaged fiber optic systems, using a first-order perturbation theory. We use numerical examples to depict the proposed system performance in terms of nonlinear phase noise variance. We will conclude that the nonlinear phase variance in a dispersion unmanaged system is much lower than the corresponding noise variance in a dispersion managed system. We will use this concept and will introduce more dispersion in the line by adding fiber brag gratings (FBGs) throughout the fiber link. Through numerical simulations, we will illustrate the improvement we get by adding FBG in each span. We will show that employing FBG improves the system performance for systems working at symbol rates 5 GBaud, which we get the best improvement to less than 20 GBaud, and beyond 20 GBaud there will be no improvement. Nowadays, telecommunication systems based on fiber optics are working at symbol rates around 28 GBaud. We will introduce new models to reduce the nonlinear phase, by splitting digital back propagation (DBP) between transmitter and receiver, and using optical phase conjugation (OPC) in the line. We will prove that the new proposed models lower the phase noise variance significantly, for single pulses. We will also illustrate numerical examples to validate the betterment they provide in terms of Q-factor. / Thesis / Master of Applied Science (MASc)

A growing industry demand for smart pressure sensors that can be quickly calibrated to compensate for sensor drift, nonlinearity effects, and hysteresis without the need for expensive equipment has led to the development of a self-calibrating pressure sensor. Pressure sensor inaccuracies are often resolved with sensor calibration, which typically requires the use of laboratory equipment that can produce a known, standard pressure to actuate the sensor. The developed MEMS-based, self-calibrating pressure sensor is a piezoresistive-type sensor with a sensing element made from a silicon on insulator (SOI) wafer using deep reactive-ion etching to create a hollow reference cavity. Using a micro-heater to heat the small, air-filled reference cavity of the sensing element, a standard pressure is generated to actuate the sensor's pressure-sensitive membrane, creating a self-calibration effect. Previous work focused on modeling and improving the thermal performance of the sensor identified potential solutions to extend the sensor's calibration and operating range without increasing the micro-heater's power consumption. This report focuses on using a water liquid-to-vapor phase change inside the sensor's reference cavity to increase the sensor's effective range and response time without increasing power demands. A combination of Ansys Fluent CFD modeling and benchtop experiments were used to guide the development of the two-phase, self-calibrating pressure sensor. A two-phase benchtop testing rig was built to demonstrate the anticipated effects of a liquid-to-vapor phase change in a closed domain and to provide experimental data to anchor CFD models. Due to the complexity of modeling a phase-change within a closed domain with Ansys Fluent R21.1, the CFD modeling was performed in two stages. First, the two-phase benchtop rig was modeled, and validated using benchtop test data to verify the Volume of Fluid multiphase model setup in Ansys Fluent. Then, a 2D Ansys Fluent model of the self-calibrating pressure sensor's reference cavity using the validated multiphase model was made, demonstrating the potential temperature, pressure, and density gradients inside the reference cavity at steady state. Using the guidance from the benchtop testing and CFD modeling, a prototype two-phase, self-calibrating pressure sensor was fabricated with a water volume fraction of at least 0.1 in the reference cavity. Testing the prototype two-phase sensor showed that the addition of a water liquid-to-vapor phase change inside the sensor's reference cavity can nearly triple the sensor's effective range of operation and self-calibration without increasing the power consumption of the cavity micro-heater. / Master of Science / Highly sensitive pressure sensors are essential to many modern engineering applications. For a pressure sensor to be accurate and functional, it must be properly calibrated with a known, standard pressure range that overlaps with the sensor's intended operating range. Mechanical wear, material aging, and thermal effects all reduce a pressure sensor's accuracy over time, requiring recalibration which often involves expensive equipment and long downtimes. To eliminate the need for additional equipment and the removal of the pressure sensor from its use-site for calibration, the authors have developed a pressure sensor capable of self-calibration. The self-calibrating sensor uses a MEMS sensing element with an integrated micro-actuator in the form of a small heating element to create the standard pressure range necessary for calibration. Previous work focused on modeling the thermal performance of the sensor identified potential solutions to extend the sensor's calibration and operating range without increasing the micro-heater's power consumption. This report focuses on using a water liquid-to-vapor phase change inside the sensor's reference cavity to increase the sensor's effective range and response time without increasing power demands. To help guide the development of the two-phase, self-calibrating sensor, a benchtop testing rig and CFD model were used to examine the effects of heating a liquid inside of a closed domain. A 2D CFD model of the sensor's reference cavity was also used to provide insight into the expected temperature and pressure gradients inside the sensing element after heating with the micro-actuator. Using the guidance from the CFD models, a prototype two-phase, self-calibrating pressure sensor was fabricated. Testing the prototype two-phase sensor showed that the addition of a water liquid-to-vapor phase change inside the sensor's reference cavity can nearly triple the sensor's effective range of operation and self-calibration without increasing the power consumption of the cavity micro-heater.

Profile monitoring is a well-known approach used in statistical process control where the quality of the product or process is characterized by a profile or a relationship between a response variable and one or more explanatory variables. Profile monitoring is conducted over two phases, labeled as Phase I and Phase II. In Phase I profile monitoring, regression methods are used to model each profile and to detect the possible presence of out-of-control profiles in the historical data set (HDS). The out-of-control profiles can be detected by using the statis-tic. However, previous methods of calculating the statistic are based on using all the data in the HDS including the data from the out-of-control process. Consequently, the ability of using this method can be distorted if the HDS contains data from the out-of-control process. This work provides a new profile monitoring methodology for Phase I analysis. The proposed method, referred to as the cluster-based profile monitoring method, incorporates a cluster analysis phase before calculating the statistic. Before introducing our proposed cluster-based method in profile monitoring, this cluster-based method is demonstrated to work efficiently in robust regression, referred to as cluster-based bounded influence regression or CBI. It will be demonstrated that the CBI method provides a robust, efficient and high breakdown regression parameter estimator. The CBI method first represents the data space via a special set of points, referred to as anchor points. Then a collection of single-point-added ordinary least squares regression estimators forms the basis of a metric used in defining the similarity between any two observations. Cluster analysis then yields a main cluster containing at least half the observations, with the remaining observations comprising one or more minor clusters. An initial regression estimator arises from the main cluster, with a group-additive DFFITS argument used to carefully activate the minor clusters through a bounded influence regression frame work. CBI achieves a 50% breakdown point, is regression equivariant, scale and affine equivariant and distributionally is asymptotically normal. Case studies and Monte Carlo results demonstrate the performance advantage of CBI over other popular robust regression procedures regarding coefficient stabil-ity, scale estimation and standard errors. The cluster-based method in Phase I profile monitoring first replaces the data from each sampled unit with an estimated profile, using some appropriate regression method. The estimated parameters for the parametric profiles are obtained from parametric models while the estimated parameters for the nonparametric profiles are obtained from the p-spline model. The cluster phase clusters the profiles based on their estimated parameters and this yields an initial main cluster which contains at least half the profiles. The initial estimated parameters for the population average (PA) profile are obtained by fitting a mixed model (parametric or nonparametric) to those profiles in the main cluster. Profiles that are not contained in the initial main cluster are iteratively added to the main cluster provided their statistics are "small" and the mixed model (parametric or nonparametric) is used to update the estimated parameters for the PA profile. Those profiles contained in the final main cluster are considered as resulting from the in-control process while those not included are considered as resulting from an out-of-control process. This cluster-based method has been applied to monitor both parametric and nonparametric profiles. A simulated example, a Monte Carlo study and an application to a real data set demonstrates the detail of the algorithm and the performance advantage of this proposed method over a non-cluster-based method is demonstrated with respect to more accurate estimates of the PA parameters and improved classification performance criteria. When the profiles can be represented by vectors, the profile monitoring process is equivalent to the detection of multivariate outliers. For this reason, we also compared our proposed method to a popular method used to identify outliers when dealing with a multivariate response. Our study demonstrated that when the out-of-control process corresponds to a sustained shift, the cluster-based method using the successive difference estimator is clearly the superior method, among those methods we considered, based on all performance criteria. In addition, the influence of accurate Phase I estimates on the performance of Phase II control charts is presented to show the further advantage of the proposed method. A simple example and Monte Carlo results show that more accurate estimates from Phase I would provide more efficient Phase II control charts. / Ph. D.

Circadian rhythms are periodic physiological cycles that recur about every 24 hours, by means of which organisms integrate their physiology and behavior to the daily cycle of light and temperature imposed by the rotation of the earth. Circadian derives from the Latin word circa "about" and dies "day". Circadian rhythms have three noteworthy properties. They are endogenous, that is, they persist in the absence of external cues (in an environment of constant light intensity, temperature, etc.). Secondly, they are temperature compensated, that is, the nearly 24 hour period of the endogenous oscillator is remarkably independent of ambient temperature. Finally, they are phase shifted by light. The circadian rhythm can be either advanced or delayed by applying a pulse of light in constant darkness. Consequently, the circadian rhythm will synchronize to a periodic light-dark cycle, provided the period of the driving stimulus is not too far from the period of the endogenous rhythm. A window on the molecular mechanism of 24-hour rhythms was opened by the identification of circadian rhythm mutants and their cognate genes in Drosophila, Neurospora, and now in other organisms. Since Konopka and Benzer first discovered the period mutant in Drosophila in 1971 (Konopka and Benzer, 1971), there have been remarkable developments. Currently, the consensus opinion of molecular geneticists is that the 24-hour period arises from a negative feedback loop controlling the transcription of clock genes. However, a better understanding of this mechanism requires an approach that integrates both mathematical and molecular biology. From the recent discoveries in molecular biology and through a mathematical approach, we propose that the mechanism of circadian rhythm is based upon the combination of both negative and positive feedback. / Master of Science

Amoebae of strain CL of Physarum polycephalum undergo apogamic development to form multinucleate plasmodia. During the amoebal-plasmodial transition, large uninucleate cells become irreversibly committed to plasmodium development. The transformation of an amoeba to a plasmodium involves a change in the tubulin isotypes expressed and a radical restructuring of cellular microtubules. During the transition the amoebal cytoplasmic microtubules, centrioles and cytoplasmic MTOC must disappear and the plasmodial-specific tubulin isotypes and intranuclear microtubule organising centre (MTOC) must be acquired. In developing cultures, amoebae lose the ability to flagellate before they become committed. Enriched suspensions of committed cells can be obtained by inducing asynchronous differentiating cultures to flagellate and passing the cells through a glass bead column. The resulting committed cells can be cultured, with some synchrony, to form plasmodia on bacterial lawns or in axenic liquid medium but cannot be cultured on axenic agar medium. During mating, cells lose the ability to flagellate early in plasmodium development. Committed cells from mating mixtures can be enriched in a similar way to committed cells of CL and have similar growth characteristics. Uninucleate committed cells of CL have the same DNA content as amoebae and plasmodia but have 6-10 times the amount of RNA. Apogamic committed cells express tubulin isotypes characteristic of amoebae, but after culture in axenic liquid medium, the cells express plasmodial-specific tubulin isotypes. Results suggest that plasmodial-specific tubulin isotypes are switched on in quadrinucleate cells. The amoebal cytoskeleton persists in binucleate and quadrinucleate cells but has disappeared in larger multinucleate cells. Mitosis in uninucleate committed cells is intranuclear (plasmodial-type). The amoebal MTOCs are eliminated during the first few mitotic cycles after commitment and do not become the plasmodial intranuclear MTOCs. Centriole loss apparently occurs before MTOC loss.

One of the central questions of materials science is which crystallographic structure a certain alloy or compound will adopt as a function of elemental composition, pressure and temperature. This question can be traced back all the way from the Bronze Age via the first steel makers of the Middle Ages and the metallurgists of the 19th century to the present day. Experiences drawn from centuries of alloy making have given rise to well-established rules of thumb for alloy development and detailed phase diagrams for equilibrium conditions. However, a rigorous theory for single-phase alloys out of equilibrium is less well established. This study employs state-of-the-art electronic structure calculations based on density functional theory to tackle this problem. This method employs a reformulation of quantum mechanics to solve the many-body Schrodinger equation that describes the system. In our case, the system is a titanium alloy, where titanium is substitutionally alloyed with elements such as aluminium, chromium, vanadium and molybdenum. We find that chromium and vanadium stabilise the β phase, while scandium destabilises it. The strength of this effect is directly proportional to the additional d-electrons present in the alloying element. The effect appears to be additive, and the positional effects of the alloying atoms appear to be small. Using the results from the calculations we can construct new phase diagrams and equations of state for these alloys. This gives us a theoretical confirmation for established rules of thumb and provides us with new insights when constructing new alloys.

Three new reconstruction methods for quantitative phase imaging, including two interrelated two-dimensional methods, called multifilter phase imaging with partially coherent light and phase optical transfer function recovery, which lead to a third three-dimensional method, called tomographic deconvolution phase microscopy, were developed in response to a growing need among biomedical end users for solutions which can be integrated on standard microscope platforms. The performance of these new methods were evaluated using modelling and simulation as well as experimentation with known test cases. In addition to the development of new methods, existing methods for quantitative phase imaging were applied to characterize the effects of manufacturing, cleaving, and fusion splicing in large-mode-area erbium- and ytterbium-doped optical fibers.

ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada / Current IRIG standards provide guidelines for system phase noise and several manufactures provide receivers and transmitters that perform within this standard. However, legacy receivers and transmitters that do not meet the current IRIG standards are sometimes still used during a mission. This paper will address how phase noise outside of the current IRIG standard affects the performance of an FM data link when using a trellis demodulator, as well as the performance of an SOQPSK data link in a high phase noise environment. Bit error rate performance and test results at several different rates with various phase noise masks are presented in this paper.

The goal of this thesis project was to design and build a three-phase inverter controlled by the TMS320F2812 DSP by Texas Instruments. The TMS320F2812 is controlled in order to make inverters generate output waveforms which mimic the main reference signal coming from a computer. The project included building three different inverters on two platforms including auxiliary circuits and designing five pulse width modulation (PWM) switching algorithms for the inverters. The motivation was that a newly designed inverter was required as an intermediary device between a computer and a laboratory-scaled model of a wind turbine. This type of wind turbine is used to educate students and engineers and to extract experimental wind power data. However, since commercial inverters don’t follow the main reference signal which is sent from the computer in order to operate the laboratory-scaled wind turbine, a controllable and variable inverter needed to be designed to receive that signal. The results are as follows. The voltage source inverter (VSI) and the current-controlled voltage source inverter (CC-VSI) were built on the VSI platform, and the current source inverter (CSI) was built on the CSI platform. Furthermore, the TMS320F2812’s analog digital converter (ADC) driver circuit and the output LC filter were also designed as auxiliary circuits. Five PWM switching programs were written; three switching algorithms for the VSI, and one algorithm each for the CC-VSI and the CSI. The output waveforms from the combination of hardware and software mentioned above were captured, and they follow the main reference signal very well. Although each of the inverters performed well, the VSI in combination with the Space Vector PWM switching algorithm produced the cleanest output voltage waveforms with the least amount of noise. The inverters built in this thesis project can be applied to the laboratory-scaled wind turbine, the maximum power tracking in solar panels, and equipment for analyzing digital signal processing. However, before using the inverters in those applications, much work remains to be done to solve the problems related to the signal distortion caused by the dead band time, harmonic signals caused by the fixed switching frequency, and the reliability issues caused by mounting on the bread board. In conclusion, although this thesis does not illustrate the entire process of or explain every requirement for building the three inverters, enough information about the topology of the inverters, the hardware design, and the PWM switching algorithms is provided in this thesis to enable one to remake all three of the three-phase inverters. / text