Many emerging applications call for wideband analog-to-digital converters and some require medium-to-high resolution. Incorporating such ADCs allows for shifting as much of the signal processing tasks as possible to the digital domain, where more flexible and programmable circuits are available. However, realizing such ADCs with the existing single stage architectures is very challenging. Therefore, parallel ADC architectures such as time-interleaved structures are used. Unfortunately, such architectures require high-speed high-precision sample-and-hold (S/H) stages that are challenging to implement. In this thesis, a parallel ADC architecture, namely, the frequency-translating hybrid ADC (FTH-ADC) is proposed to increase the conversion speed of the ADCs, which is also suitable for applications requiring medium-to-high resolution ADCs. This architecture addresses the sampling problem by sampling on narrowband baseband subchannels, i.e., sampling is accomplished after splitting the wideband input signals into narrower subbands and frequency-translating them into baseband where identical narrowband baseband S/Hs can be used. Therefore, lower-speed, lower-precision S/Hs are required and single-chip CMOS implementation of the entire ADC is possible. A proof of concept board-level implementation of the FTH-ADC is used to analyze the effects of major analog non-idealities and errors. Error measurement and compensation methods are presented. Using four 8-bit, 100 MHz subband ADCs, four 25 MHz Butterworth filters, two 64-tap FIR reconstruction filters, and four 10-tap FIR compensation filters, a total system with an effective sample rate of 200 MHz is implemented with an effective number of bits of at least 7 bits over the entire 100 MHz input bandwidth. In addition, one path of an 8-GHz, 4-bit, FTH-ADC system, including a highly-linear mixer and a 5th-order, 1 GHz, Butterworth Gm-C filter, is implemented in a 90 nm CMOS technology. Followed by a 4-bit, 4-GHz subband ADC, the blocks consume a total power of 52 mW from a 1.2 V supply, and occupy an area of 0.05 mm2. The mixer-filter has a THD ≤ 5% (26 dB) over its full 1 GHz bandwidth and provides a signal with a voltage swing of 350 mVpp for the subsequent ADC stage.

In modern mobile wireless communication, Base Stations (BS) are the most important equipment to build up the mobile network. One of the key elements in BS is the RF filter, which plays a key role to secure the coverage and reliability of the BS. Especially, at Transmitter (Tx) side, the filter must have a high capability to handle the power sent from Power Amplifier (PA) to antenna in any circumstances to ensure the coverage demand. Otherwise, the breakdown will be encountered, setting the power flow in the BS system in an abnormal manner that, finally can lead to the shut down of BS or destroy the system permanently. In this project, three methods using two simulation tools to predict the power handling capability of the RF/microwave resonator which is the elementary component in the BS’s filter are proposed. Power handling tests of selected configurations corresponding to the simulations are implemented as well. In the next stage, the results from the prediction and measurement are compared. Finally, the conclusions of correlation between the prediction and measurement of microwave resonator power handling will be derived.

A methodology was developed to dissect diesel particulate filters to study the time effect of loading for two different fuels: ULSD and a biodiesel blend. Filters loaded with soot from a diesel engine for exposure times of 1, 2, 5 and 10 hours were fractured and samples of filter substrates were analyzed using Raman spectroscopy and scanning electron microscopy. Observations revealed the sharp rise in pressure drop to be attributable to the clogging of the pores in the channel wall, leading to the formation of a pore-bridge. Cross sectional imaging of wall sections revealed this pore-bridge to be shallow, with significant particulate depositions limited to the first quarter of the depth of the filter walls. Images revealed increasingly dense deposits and the formation of coarse particles and soot cakes. Raman spectroscopy revealed no significant graphitization of the soot cake. The dissection methodology exhibits significant potential for future studies on DPFs.

A methodology was developed to dissect diesel particulate filters to study the time effect of loading for two different fuels: ULSD and a biodiesel blend. Filters loaded with soot from a diesel engine for exposure times of 1, 2, 5 and 10 hours were fractured and samples of filter substrates were analyzed using Raman spectroscopy and scanning electron microscopy. Observations revealed the sharp rise in pressure drop to be attributable to the clogging of the pores in the channel wall, leading to the formation of a pore-bridge. Cross sectional imaging of wall sections revealed this pore-bridge to be shallow, with significant particulate depositions limited to the first quarter of the depth of the filter walls. Images revealed increasingly dense deposits and the formation of coarse particles and soot cakes. Raman spectroscopy revealed no significant graphitization of the soot cake. The dissection methodology exhibits significant potential for future studies on DPFs.

Inertial sensors such as Gyroscope and Accelerometer show systematic as well as random errors in the measurement. Furthermore, double integration method shows accumulation of error in position estimation due to inherent accelerometer bias drift. The primary objective of this research was to evaluate ADXL 335 acceleration sensor for better position estimation using acceleration bias drift error model. In addition, measurement data was recorded with four point rotation test for investigation of error characteristics. The fitted model was validated by using nonlinear regression analysis. The secondary objective was to examine the effect of bias drift and scale factor errors by introducing error model in Kalman Filter smoothing algorithm. The study showed that the accelerometer may be used for short distance mobile robot position estimation. This research would also help to establish a generalized test procedure for evaluation of accelerometer in terms of sensitivity, accuracy and data reliability.

The increase in energy demand throughout the world has forced the oil industry to develop and expand on current technologies to optimize well productivity. Distributed temperature sensing has become a current and fairly inexpensive way to monitor performance in hydraulic fractured wells in real time by the aid of fiber optic. However, no applications have yet been attempted to describe or estimate the fracture parameters using distributed temperature sensing as the observation parameter. The Ensemble Kalman Filter, a recursive filter, has proved to be an effective tool in the application of inverse problems to determine parameters of non-linear models. Even though large amounts of data are acquired as the information used to apply an estimation, the Ensemble Kalman Filter effectively minimizes the time of operation by only using “snapshots” of the ensembles collected by various simulations where the estimation is updated continuously to be calibrated by comparing it to a reference model. A reservoir model using ECLIPSE is constructed that measures temperature throughout the wellbore. This model is a hybrid representation of what distributed temperature sensing measures in real-time throughout the wellbore. Reservoir and fracture parameters are selected in this model with similar properties and values to an unconventional well. However, certain parameters such as fracture width are manipulated to significantly diminish the computation time. A sensitivity study is performed for all the reservoir and fracture parameters in order to understand which parameters require more or less data to allow the Ensemble Kalman Filter to arrive to an acceptable estimation. Two fracture parameters are selected based on their low sensitivity and importance in fracture design to perform the Ensemble Kalman Filter on various simulations. Fracture permeability has very low sensitivity. However, when applying the estimation the Ensemble Kalman Filter arrives to an acceptable estimation. Similarly fracture halflength, with medium sensitivity, arrives to an acceptable estimation around the same number of integration steps. The true effectiveness of the Ensemble Kalman Filter is presented when both parameters are estimated jointly and arrive to an acceptable estimation without being computationally expensive. The effectiveness of the Ensemble Kalman Filter is directly connected to the quantity of data acquired. The more data available to run simulations, the better and faster the filter performs.

Rigid-body registration is an important research area with major applications in computer-assisted and image-guided surgery. In these surgeries, often the relationship between the preoperative and intraoperative images taken from a patient must be established. This relationship is computed through a registration process, which finds a set of transformation parameters that maps some point fiducials measured on a patient anatomy to a preoperative model. Due to point measurement error caused by medical measurement instruments, the estimated registration parameters are imperfect and this reduces the accuracy of the performed registrations. Medical measurement instruments often perturb the collected points from the patient anatomy by heterogeneous noise. If the noise characteristics are known, they can be incorporated in the registration algorithm in order to more reliably and accurately estimate the registration parameters and their variances. Current techniques employed in rigid-body registration are primarily based on the well-known Iterative Closest Points (ICP) algorithm. Such techniques are susceptible to the existence of noise in the data sets, and are also very sensitive to the initial alignment errors. Also, the literature offers no analytical solution on how to estimate the accuracy of the performed registrations in the presence of heterogenous noise. In an effort to alleviate these problems, we propose and validate various novel registration techniques based on the Unscented Kalman Filter (UKF) algorithm. This filter is generally employed for analyzing nonlinear systems corrupted by additive heterogenous Gaussian noise. First, we propose a new registration algorithm to fit two data sets in the presence of arbitrary Gaussian noise, when the corresponding points between the two data sets are assumed to be known. Next, we extend this algorithm to perform surface-based registration, where point correspondences are not available, but the data sets are roughly aligned. A solution to multi-body point and surface-based registration problem is then proposed based on the UKF algorithm. The outputs of the proposed UKF registration algorithms are then utilized to estimate the accuracy of the performed registration. For the first time, novel derivations are presented that can estimate the distribution of registration error at a target in the presence of an arbitrary Gaussian noise. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2008-09-28 07:25:38.229

Polymerization processes require adequate monitoring to ensure that the final product meets specification. Various on-line measuring techniques have been developed and implemented to track polymer properties in reactors. For most processes, however, on-line measurement cannot be implemented. In other situations, certain polymer properties or states might not be measurable and hence have to be estimated. This work deals with improving an on-line optimization technique and demonstrating its eff ectiveness by sensitivity analysis. In addition, state estimation is used as a tool to reconstruct states that are unavailable for measurement in a styrene and butyl methacrylate batch-fed solution free-radical copolymerization process subject to on-line optimization. A hybrid extended Kalman filter is used to observe the nonlinear dynamic system which is subject to real-time dynamic optimization. With very limited measurement information, the states of the system were reconstructed. Additional unobservable quantities were reconstructed using the process model and estimated states. / Thesis (Master, Chemical Engineering) -- Queen's University, 2009-09-28 16:02:55.974

No description available.

No description available.