An improvement in efficiency of direct digital frequency synthesis (DDFS) systems is demanded for low power frequency synthesis in wireless communications. Concurrently a reduction in cost is important for disposable, low resolution frequency synthesis in biomedical instrumentation systems. To meet both these needs a new ROM-less architecture is presented here that uses less than half the circuit area of previous state of the art systems and improves the efficiency by operating at up to a tenth of the power consumption. The main contribution presented in this thesis is a novel, efficient method of interpolation for DDFS that uses the nonlinear response of the CMOS differential switch already present in the high speed current steering DAC. The nonlinear response provides a smooth transition between the conventional, quantised DAC output. This interpolation may be performed with the conventionally discarded phase bits leading to highly compact and efficient DDFS architectures for application in instrumentation and communications systems. DDFS systems typically consist of a large overflowing accumulator to generate the phase, a ROM lookup table to convert the phase to amplitude and a DAC to perform the digital to analogue conversion. Approximations are often used to reduce the size of the ROM, however the most efficient DDFS systems remove the ROM completely and calculate the phase to amplitude conversion directly or store the conversion in a non-linear DAC. State of the art, high speed CMOS DACs consisting of thermometer decoded arrays of current steering cells are often used to reduce non-ideal effects that cause unwanted transients leading to a degradation in spectral purity (SFDR). A novel ROM-less technique is introduced here that uses the non-linear response of a current cell consisting of an ideal current source and differential current switch to interpolate between the output levels of a non-linear DAC. Using this technique two architectures are developed. A compact architecture using only four or six current cells suitable for instrumentation applications and a thermometer decoded architecture using 64 current cells for communications applications that require better spectral purity. The compact architecture is 100% efficient as all the bias current is used to form the output. The only additional component is a small linear phase DAC. One compact system with a nonlinear DAC of four current cells achieved an SFDR of -40dBc up to output frequencies of 1MHz for dielectrophoresis consumed only 5μW/MHz and a second compact system with a six cell nonlinear DAC for electrical impedance spectroscopy, achieved an SFDR of -48dBc for output frequencies up to 1MHz and consumed only 8μW/MHz. As an extension to improve the SFDR a segmented system with 64 current cells was developed. The larger number of current cells required the use of a modified thermometer decoder that had the added benefit of improving the spectral purity by linearising the response of each cell. The total active area was 0.6mm², less than half of state of the art ROM-less DDFS systems that include a DAC. Although measurement results of the 64 cell system were disappointing, simulations suggest that these problems may be solved in a future chip that should be able to achieve -70dBc SFDR at 100MHz. Despite the loss in performance from simulation to measurement, the measured 64 cell system still meets the spectral purity requirements of UMTS and Bluetooth, -60dBc SFDR.

We forecast US GDP sampled quarterly over horizons ranging from one quarter to three years. Using AR-MIDAS models we study three lag polynomials: the Almon lag, the exponential Almon lag and the beta lag, and nine macroeconomic variables, sampled weekly or monthly. Our benchmark model is an AR(1) and we compare forecast errors using RMSE. In all instances the AR-MIDAS achieves lower forecast errors compared to the benchmark model. The predictor sampled weekly generally performs better compared to other predictors, which are sampled monthly.

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This thesis proposes and evaluates the application of a low-frequency disturbance injection, as an active islanding detection method, in a microgrid with multiple electronically-interfaced Distributed Generation (DG) units. Each DG unit is interfaced to the microgrid through a two-level Voltage-Sourced Converter (VSC). The low-frequency disturbance signal for islanding detection is injected through the q-axis control of each VSC unit. The low-frequency signal is at 1 Hz with an amplitude of up to 2.5 % of the rated VA of the VSC unit and augments the reference signal of the q-axis control. The effectiveness of the low-frequency injection for islanding detection is examined under two distinct VSC control scenarios. In the first scenario, each VSC only injects pre-determined real- and reactive-power components in the system and does not participate in frequency/voltage control. In the second scenario, the VSC controls are also equipped with frequency/real-power and voltage/reactive-power droop characteristics and thus share power and participate in frequency and voltage control of the microgrid, specifically in the islanded mode. The investigations reported in this thesis show that the proposed islanding detection method can effectively detect an islanding event under both VSC control strategies, subject to the conditions that UL and/or IEEE anti-islanding standards impose. The studies show that an islanding event can be detected within 536 ms subsequent to the instant of islanding. As part of this thesis, an eigen analysis software tool has been developed that can systematically investigate the impact of low-frequency disturbance injection on the small-signal stability and dynamic performance of the microgrid, prior and subsequent to an islanding event. This thesis concludes that the low-frequency disturbance injection-based method can be successfully applied to a multi-DG system, since (i) islanding detection is achieved within applicable standards requirements by all DG units in the system, and (ii) the low-frequency disturbance injection signal has no noticeable impact on the dynamics nor the small-signal stability of the system if its magnitude is kept below a pre specified limit.

This thesis proposes and evaluates the application of a low-frequency disturbance injection, as an active islanding detection method, in a microgrid with multiple electronically-interfaced Distributed Generation (DG) units. Each DG unit is interfaced to the microgrid through a two-level Voltage-Sourced Converter (VSC). The low-frequency disturbance signal for islanding detection is injected through the q-axis control of each VSC unit. The low-frequency signal is at 1 Hz with an amplitude of up to 2.5 % of the rated VA of the VSC unit and augments the reference signal of the q-axis control. The effectiveness of the low-frequency injection for islanding detection is examined under two distinct VSC control scenarios. In the first scenario, each VSC only injects pre-determined real- and reactive-power components in the system and does not participate in frequency/voltage control. In the second scenario, the VSC controls are also equipped with frequency/real-power and voltage/reactive-power droop characteristics and thus share power and participate in frequency and voltage control of the microgrid, specifically in the islanded mode. The investigations reported in this thesis show that the proposed islanding detection method can effectively detect an islanding event under both VSC control strategies, subject to the conditions that UL and/or IEEE anti-islanding standards impose. The studies show that an islanding event can be detected within 536 ms subsequent to the instant of islanding. As part of this thesis, an eigen analysis software tool has been developed that can systematically investigate the impact of low-frequency disturbance injection on the small-signal stability and dynamic performance of the microgrid, prior and subsequent to an islanding event. This thesis concludes that the low-frequency disturbance injection-based method can be successfully applied to a multi-DG system, since (i) islanding detection is achieved within applicable standards requirements by all DG units in the system, and (ii) the low-frequency disturbance injection signal has no noticeable impact on the dynamics nor the small-signal stability of the system if its magnitude is kept below a pre specified limit.

Radio Frequency IDentification (RFID) is a wireless communications technology which allows companies to secure their assets and increase the portability of information. This research was motivated by the increased commercial use of RFID technology. Existing security protocols with high levels of security have high computation requirements, and less intensive protocols can allow a tag to be tracked. The techniques proposed in this thesis result in the increase of ciphertexts available without a significant increase in processing power or storage requirements. The addition of random inputs to the generation of ciphertexts will increase the number of possible results without requiring a more advanced encryption algorithm or an increased number of stored encryption keys. Four methods of altering the plaintext/ciphertext pair (random block, set pattern, random pattern, and indexed placement) are analyzed to determine the effectiveness of each method. The number of ciphertexts generated, generation time, and generation errors were recorded to determine which of the four proposed methods would be the most beneficial in a RFID system. The comparison of these method characteristics determined that the set pattern placement method provided the best solution. The thesis also discusses how RFID transmissions appear to attackers and explains how the random inputs reduce effectiveness of current system attacks. In addition to improving the anonymity of RFID tag transmissions, the concept of authenticating random inputs is also introduced in this thesis. These methods help prevent an adversary from easily associating a tag with its transmissions, thus increasing the security of the RFID system.

Over the last fifty years financial markets have seen an enormous expansion and development both in size and variety. An industry that was once small and secluded has transformed into an essential part of today’s economy. Such changes should in part be attributed to substantial advances in computer technology. The latest allowed for a transition from face-to-face trading on organized exchanges to a distributed system of electronic markets with new mechanisms serving the purposes of efficiency, transparency and liquidity. In majority of cases this new trading system is driven by a double auction market mechanism, in which market participants submit buy and sell orders, aiming to strike a balance between certainty of execution and attractiveness of trade price. Generally, information about outstanding buy and sell orders is made available to market participants in the form of a limit order book. It has been suggested by multiple prior research that limit order books contain information that could be used to derive market sentiment and predict future price movement. In the current study we have presented ideas behind double auction market mechanism and have attempted to model run and reversal market regimes using a simple and intuitive Hierarchical Hidden Markov Model. We have proposed a statistical measure of the limit order book imbalance and have used it to build observation (feature) vector for our model. We have built Limit Order Book analyzer – the software tool that has become essential for data cleaning and validation, as well as extraction of feature vector components from the data. We have used the model on high frequency tick-by-tick trade and limit order book data from the Toronto Stock Exchange. We have performed the analysis of computational results; for this purpose we have used a sample of annualized returns of stocks which comprised the TSX60 index at the time of data collection; we have performed the comparative analysis of our results with a simple daily buy & hold trading strategy as well as results of the trade price and volume model presented in the prior research.

Dynamic properties of soils (shear stiffness and damping ratio) are critical for the design of structures subjected to vibrations. The dynamic properties of a benchmark standardized laboratory sand (Ottawa silica sand) were evaluated with two different resonant column devices, utilising software with different analytical approaches for the evaluation of soil properties. The dynamic properties (shear modulus and damping ratio) are evaluated as a function of the shear strain level. The results are compared to evaluate the effect of the type of equipment and the form of the data analysis on the measured dynamic properties of the samples. The results are discussed in light of the applicability of the procedures in practice, the ease of the testing methods, and the errors they introduced into analysis and design. In general, the shear wave velocities obtained from the two different devices are in good agreement. However, the damping ratios they give show considerable differences as strains increase. Dynamic properties are typically measured by curve fitting of the transfer function between the excitation and the response using the resonant column device. However, the force function generated by sinusoidal sweep or random noise excitations induce different shear strain levels at different frequencies. Consequently, the shape of the measured transfer function is distorted and differs from the theoretical transfer function for an equivalent single-degree-of-freedom system. The difference between the measured and theoretical transfer functions as well as the bias in the computed dynamic properties becomes more pronounced with the increase in shear strain. This study presents a new methodology for the evaluation of dynamic properties from an equivalent constant-strain transfer function. The soil specimen is excited simultaneously using a sinusoidal excitation (carrier signal) at the required strain level and a small amplitude, narrow band random noise. The strain level induced by the fixed sine is shown to control the resonant frequency of the specimen; whereas the random noise introduces the required frequency bandwidth to determine the transfer function and hence the dynamic properties at a constant strain level. The new methodology also shows a good potential for the evaluation of frequency effects on the dynamic properties of soils in resonant column testing.

Unsatisfactory performance of a control system may have different root causes, of which diagnosis and control have been subjects of interest. Numerous approaches have been used to identify the source of the oscillatory behavior of control systems. This work will focus on the nonlinearities introduced by process equipment, more specifically, static friction (stiction) in control valves. Using shape-based stiction detection methods and surrogate testing for time series, a new detection method is proposed for systems containing one or more sticky valves. Performance of this method is validated by both simulation and industrial data. The existence of stiction in a control valve may lead to oscillations in all loops of the process. In this work, frequency analysis of multi-loop processes oscillating due to stiction will be presented. Derivation of a general mathematical representation of the condition, under which oscillations occur in a multi-loop system because of stiction, is the contribution of the proposed analysis. The proposed condition for occurrence of oscillations provides a compensation framework for this problem. In this scheme, given dynamics of the system and severity of stiction, the appropriate tuning for the controller will be found which reduces or removes oscillations from the system. An alternative compensation algorithm will also be proposed, which aims removal of oscillations from systems for which the previously proposed approach cannot permanently remove undesirable oscillations. Achieving a non-oscillatory output without making the valve stem to move more aggressively, is the main characteristic of this algorithm. / Process Control

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