Phase noise and spur reduction in an array of direct digital synthesizers

Many applications, including communications, test and measurement, and radar, require the generation of signals with a high degree of spectral purity. One method for producing tunable, low-noise source signals is to combine the outputs of multiple direct digital synthesizers (DDSs) arranged in a parallel configuration. In such an approach, if all noise is uncorrelated across channels, the noise will decrease relative to the combined signal power, resulting in a reduction of sideband noise and an increase in SNR. However, in any real array, the broadband noise and spurious components will be correlated to some degree, limiting the gains achieved by parallelization. This thesis examines the potential performance benefits that may arise from using an array of DDSs, with a focus on several types of common DDS errors, including phase noise, phase truncation spurs, quantization noise spurs, and quantizer nonlinearity spurs. Measurements to determine the level of correlation among DDS channels were made on a custom 14-channel DDS testbed. The investigation of the phase noise of a DDS array indicates that the contribution to the phase noise from the DACs can be decreased to a desired level by using a large enough number of channels. In such a system, the phase noise qualities of the source clock and the system cost and complexity will be the main limitations on the phase noise of the DDS array. The study of phase truncation spurs suggests that, at least in our system, the phase truncation spurs are uncorrelated, contrary to the theoretical prediction. We believe this decorrelation is due to the existence of an unidentified mechanism in our DDS array that is unaccounted for in our current operational DDS model. This mechanism, likely due to some timing element in the FPGA, causes some randomness in the relative phases of the truncation spurs from channel to channel each time the DDS array is powered up. This randomness decorrelates the phase truncation spurs, opening the potential for SFDR gain from using a DDS array. The analysis of the correlation of quantization noise spurs in an array of DDSs shows that the total quantization noise power of each DDS channel is uncorrelated for nearly all values of DAC output bits. This suggests that a near N gain in SQNR is possible for an N-channel array of DDSs. This gain will be most apparent for low-bit DACs in which quantization noise is notably higher than the thermal noise contribution. Lastly, the measurements of the correlation of quantizer nonlinearity spurs demonstrate that the second and third harmonics are highly correlated across channels for all frequencies tested. This means that there is no benefit to using an array of DDSs for the problems of in-band quantizer nonlinearities. As a result, alternate methods of harmonic spur management must be employed.

A comparative approach to web evaluation and website evaluation methods.

There is still a lack of an engineering approach for building Web systems, and the field of measuring the Web is not yet mature. In particular, there is an uncertainty in the selection of evaluation methods, and there are risks of standardizing inadequate evaluation practices. It is important to know whether we are evaluating the Web or specific website(s). We need a new categorization system, a different focus on evaluation methods, and an in-depth analysis that reveals the strengths and weaknesses of each method. As a contribution to the field of Web evaluation, this study proposes a novel approach to view and select evaluation methods based on the purpose and platforms of the evaluation. It has been shown that the choice of the appropriate evaluation method(s) depends greatly on the purpose of the evaluation.

Understanding and validating accelerometry as a measure of physical activity in children with intellectual disabilities

Background Physical activity in children with intellectual disabilities is a neglected area of study, which is most apparent in relation to physical activity measurement research. Although objective measures, specifically accelerometers, are widely used in research involving children with intellectual disabilities, existing research is based on measurement methods and data interpretation techniques generalised from typically developing children. However, due to physiological and biomechanical differences between these populations, questions have been raised in the existing literature on the validity of generalising data interpretation techniques from typically developing children to children with intellectual disabilities. Therefore, there is a need to conduct population-specific measurement research for children with intellectual disabilities and develop valid methods to interpret accelerometer data, which will increase our understanding of physical activity in this population. Methods Study 1: A systematic review was initially conducted to increase the knowledge base on how accelerometers were used within existing physical activity research involving children with intellectual disabilities and to identify important areas for future research. A systematic search strategy was used to identify relevant articles which used accelerometry-based monitors to quantify activity levels in ambulatory children with intellectual disabilities. Based on best practice guidelines, a novel form was developed to extract data based on 17 research components of accelerometer use. Accelerometer use in relation to best practice guidelines was calculated using percentage scores on a study-by-study and component-by-component basis. Study 2: To investigate the effect of data interpretation methods on the estimation of physical activity intensity in children with intellectual disabilities, a secondary data analysis was conducted. Nine existing sets of child-specific ActiGraph intensity cut points were applied to accelerometer data collected from 10 children with intellectual disabilities during an activity session. Four one-way repeated measures ANOVAs were used to examine differences in estimated time spent in sedentary, moderate, vigorous, and moderate to vigorous intensity activity. Post-hoc pairwise comparisons with Bonferroni adjustments were additionally used to identify where significant differences occurred. Study 3: The feasibility on a laboratory-based calibration protocol developed for typically developing children was investigated in children with intellectual disabilities. Specifically, the feasibility of activities, measurements, and recruitment was investigated. Five children with intellectual disabilities and five typically developing children participated in 14 treadmill-based and free-living activities. In addition, resting energy expenditure was measured and a treadmill-based graded exercise test was used to assess cardiorespiratory fitness. Breath-by-breath respiratory gas exchange and accelerometry were continually measured during all activities. Feasibility was assessed using observations, activity completion rates, and respiratory data. Study 4: Thirty-six children with intellectual disabilities participated in a semi-structured school-based physical activity session to calibrate accelerometry for the estimation of physical activity intensity. Participants wore a hip-mounted ActiGraph wGT3X+ accelerometer, with direct observation (SOFIT) used as the criterion measure. Receiver operating characteristic curve analyses were conducted to determine the optimal accelerometer cut points for sedentary, moderate, and vigorous intensity physical activity. Study 5: To cross-validate the calibrated cut points and compare classification accuracy with existing cut points developed in typically developing children, a sub-sample of 14 children with intellectual disabilities who participated in the school-based sessions, as described in Study 4, were included in this study. To examine the validity, classification agreement was investigated between the criterion measure of SOFIT and each set of cut points using sensitivity, specificity, total agreement, and Cohen’s kappa scores. Results Study 1: Ten full text articles were included in this review. The percentage of review criteria met ranged from 12%−47%. Various methods of accelerometer use were reported, with most use decisions not based on population-specific research. A lack of measurement research, specifically the calibration/validation of accelerometers for children with intellectual disabilities, is limiting the ability of researchers to make appropriate and valid accelerometer use decisions. Study 2: The choice of cut points had significant and clinically meaningful effects on the estimation of physical activity intensity and sedentary behaviour. For the 71-minute session, estimations for time spent in each intensity between cut points ranged from: sedentary = 9.50 (± 4.97) to 31.90 (± 6.77) minutes; moderate = 8.10 (± 4.07) to 40.40 (± 5.74) minutes; vigorous = 0.00 (± .00) to 17.40 (± 6.54) minutes; and moderate to vigorous = 8.80 (± 4.64) to 46.50 (± 6.02) minutes. Study 3: All typically developing participants and one participant with intellectual disabilities completed the protocol. No participant met the maximal criteria for the graded exercise test or attained a steady state during the resting measurements. Limitations were identified with the usability of respiratory gas exchange equipment and the validity of measurements. The school-based recruitment strategy was not effective, with a participation rate of 6%. Therefore, a laboratory-based calibration protocol was not feasible for children with intellectual disabilities. Study 4: The optimal vertical axis cut points (cpm) were ≤ 507 (sedentary), 1008−2300 (moderate), and ≥ 2301 (vigorous). Sensitivity scores ranged from 81−88%, specificity 81−85%, and AUC .87−.94. The optimal vector magnitude cut points (cpm) were ≤ 1863 (sedentary), ≥ 2610 (moderate) and ≥ 4215 (vigorous). Sensitivity scores ranged from 80−86%, specificity 77−82%, and AUC .86−.92. Therefore, the vertical axis cut points provide a higher level of accuracy in comparison to the vector magnitude cut points. Study 5: Substantial to excellent classification agreement was found for the calibrated cut points. The calibrated sedentary cut point (ĸ =.66) provided comparable classification agreement with existing cut points (ĸ =.55−.67). However, the existing moderate and vigorous cut points demonstrated low sensitivity (0.33−33.33% and 1.33−53.00%, respectively) and disproportionately high specificity (75.44−.98.12% and 94.61−100.00%, respectively), indicating that cut points developed in typically developing children are too high to accurately classify physical activity intensity in children with intellectual disabilities. Conclusions The studies reported in this thesis are the first to calibrate and validate accelerometry for the estimation of physical activity intensity in children with intellectual disabilities. In comparison with typically developing children, children with intellectual disabilities require lower cut points for the classification of moderate and vigorous intensity activity. Therefore, generalising existing cut points to children with intellectual disabilities will underestimate physical activity and introduce systematic measurement error, which could be a contributing factor to the low levels of physical activity reported for children with intellectual disabilities in previous research.

Methods matter: computational modelling in public health policy and planning

This work is aimed at understanding and unifying information on epidemiological modelling methods and how those methods relate to public policy addressing human health, specifically in the context of infectious disease prevention, pandemic planning, and health behaviour change. This thesis employs multiple qualitative and quantitative methods, and presents as a manuscript of several individual, data-driven projects that are combined in a narrative arc. The first chapter introduces the scope and complexity of this interdisciplinary undertaking, describing several topical intersections of importance. The second chapter begins the presentation of original data, and describes in detail two exercises in computational epidemiological modelling pertinent to pandemic influenza planning and policy, and progresses in the next chapter to present additional original data on how the confidence of the public in modelling methodology may have an effect on their planned health behaviour change as recommended in public health policy. The thesis narrative continues in the final data-driven chapter to describe how health policymakers use modelling methods and scientific evidence to inform and construct health policies for the prevention of infectious diseases, and concludes with a narrative chapter that evaluates the breadth of this data and recommends strategies for the optimal use of modelling methodologies when informing public health policy in applied public health scenarios.

An investigation of imputation methods for discrete databases and multi-variate time series

When it comes to information sets in real life, often pieces of the whole set may not be available. This problem can find its origin in various reasons, describing therefore different patterns. In the literature, this problem is known as Missing Data. This issue can be fixed in various ways, from not taking into consideration incomplete observations, to guessing what those values originally were, or just ignoring the fact that some values are missing. The methods used to estimate missing data are called Imputation Methods. The work presented in this thesis has two main goals. The first one is to determine whether any kind of interactions exists between Missing Data, Imputation Methods and Supervised Classification algorithms, when they are applied together. For this first problem we consider a scenario in which the databases used are discrete, understanding discrete as that it is assumed that there is no relation between observations. These datasets underwent processes involving different combina- tions of the three components mentioned. The outcome showed that the missing data pattern strongly influences the outcome produced by a classifier. Also, in some of the cases, the complex imputation techniques investigated in the thesis were able to obtain better results than simple ones. The second goal of this work is to propose a new imputation strategy, but this time we constrain the specifications of the previous problem to a special kind of datasets, the multivariate Time Series. We designed new imputation techniques for this particular domain, and combined them with some of the contrasted strategies tested in the pre- vious chapter of this thesis. The time series also were subjected to processes involving missing data and imputation to finally propose an overall better imputation method. In the final chapter of this work, a real-world example is presented, describing a wa- ter quality prediction problem. The databases that characterized this problem had their own original latent values, which provides a real-world benchmark to test the algorithms developed in this thesis.

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.

Methods for Modeling and Analyzing Concurrent Software

Concurrent software executes multiple threads or processes to achieve high performance. However, concurrency results in a huge number of different system behaviors that are difficult to test and verify. The aim of this dissertation is to develop new methods and tools for modeling and analyzing concurrent software systems at design and code levels. This dissertation consists of several related results. First, a formal model of Mondex, an electronic purse system, is built using Petri nets from user requirements, which is formally verified using model checking. Second, Petri nets models are automatically mined from the event traces generated from scientific workflows. Third, partial order models are automatically extracted from some instrumented concurrent program execution, and potential atomicity violation bugs are automatically verified based on the partial order models using model checking. Our formal specification and verification of Mondex have contributed to the world wide effort in developing a verified software repository. Our method to mine Petri net models automatically from provenance offers a new approach to build scientific workflows. Our dynamic prediction tool, named McPatom, can predict several known bugs in real world systems including one that evades several other existing tools. McPatom is efficient and scalable as it takes advantage of the nature of atomicity violations and considers only a pair of threads and accesses to a single shared variable at one time. However, predictive tools need to consider the tradeoffs between precision and coverage. Based on McPatom, this dissertation presents two methods for improving the coverage and precision of atomicity violation predictions: 1) a post-prediction analysis method to increase coverage while ensuring precision; 2) a follow-up replaying method to further increase coverage. Both methods are implemented in a completely automatic tool.

IMPROVED SAMPLE INTRODUCTION SYSTEMS FOR INDUCTIVELY COUPLED PLASMA OPTICAL EMISSION SPECTROMETRY AND THEIR APPLICATION TO THE ANALYSIS OF SIMPLE AND COMPLEX MATRICES

The objective of this thesis is to explore new and improved methods for greater sample introduction efficiency and enhanced analytical performance with inductively coupled plasma optical emission spectrometry (ICP-OES). Three projects are discussed in which the capabilities and applications of ICP-OES are expanded: 1. In the first project, a conventional ultrasonic nebuliser was modified to replace the heater/condenser with an infrared heated pre-evaporation tube. In continuation from previous works with pre-evaporation, the current work investigated the effects of heating with infrared block and rope heaters on two different ICP-OES instruments. Comparisons were made between several methods and setups in which temperatures were varied. By monitoring changes to sensitivity, detection limit, precision, and robustness, and analyzing two certified reference materials, a method with improved sample introduction efficiency and comparable analytical performance to a previous method was established. 2. The second project involved improvements to a previous work in which a multimode sample introduction system (MSIS) was modified by inserting a pre-evaporation tube between the MSIS and torch. The new work focused on applying an infrared heated ceramic rope for pre-evaporation. This research was conducted in all three MSIS modes (nebulisation mode, hydride generation mode, and dual mode) and on two different ICP-OES instruments, and comparisons were made between conventional setups in terms of sensitivity, detection limit, precision, and robustness. By tracking both hydride-forming and non-hydride forming elements, the effects of heating in combination with hydride generation were probed. Finally, optimal methods were validated by analysis of two certified reference materials. 3. A final project was completed in collaboration with ZincNyx Energy Solutions. This project sought to develop a method for the overall analysis of a 12 M KOH zincate fuel, which is used in green energy backup systems. By employing various techniques including flow injection analysis and standard additions, a final procedure was formulated for the verification of K concentration, as well as the measurement of additives (Al, Fe, Mg, In, Si), corrosion products (such C from CO₃²¯), and Zn particles both in and filtered from solution. Furthermore, the effects of exposing the potassium zincate electrolyte fuel to air were assessed.

New Rigorous Decomposition Methods for Mixed-integer Linear and Nonlinear Programming

Process systems design, operation and synthesis problems under uncertainty can readily be formulated as two-stage stochastic mixed-integer linear and nonlinear (nonconvex) programming (MILP and MINLP) problems. These problems, with a scenario based formulation, lead to large-scale MILPs/MINLPs that are well structured. The first part of the thesis proposes a new finitely convergent cross decomposition method (CD), where Benders decomposition (BD) and Dantzig-Wolfe decomposition (DWD) are combined in a unified framework to improve the solution of scenario based two-stage stochastic MILPs. This method alternates between DWD iterations and BD iterations, where DWD restricted master problems and BD primal problems yield a sequence of upper bounds, and BD relaxed master problems yield a sequence of lower bounds. A variant of CD, which includes multiple columns per iteration of DW restricted master problem and multiple cuts per iteration of BD relaxed master problem, called multicolumn-multicut CD is then developed to improve solution time. Finally, an extended cross decomposition method (ECD) for solving two-stage stochastic programs with risk constraints is proposed. In this approach, a CD approach at the first level and DWD at a second level is used to solve the original problem to optimality. ECD has a computational advantage over a bilevel decomposition strategy or solving the monolith problem using an MILP solver. The second part of the thesis develops a joint decomposition approach combining Lagrangian decomposition (LD) and generalized Benders decomposition (GBD), to efficiently solve stochastic mixed-integer nonlinear nonconvex programming problems to global optimality, without the need for explicit branch and bound search. In this approach, LD subproblems and GBD subproblems are systematically solved in a single framework. The relaxed master problem obtained from the reformulation of the original problem, is solved only when necessary. A convexification of the relaxed master problem and a domain reduction procedure are integrated into the decomposition framework to improve solution efficiency. Using case studies taken from renewable resource and fossil-fuel based application in process systems engineering, it can be seen that these novel decomposition approaches have significant benefit over classical decomposition methods and state-of-the-art MILP/MINLP global optimization solvers.

Handgrip strength cut-off for cardiometabolic risk index among Colombian children and adolescents : the FUPRECOL study

Abstract Objective: Evidence shows an association between muscular strength (MS) and health among youth, however low muscular strength cut-points for the detection of high metabolic risk in Latin-American populations are scarce. The aim of this study was two-fold: to explore potential age- and sex-specific thresholds of MS, for optimal cardiometabolic risk categorization among Colombian children and adolescents; and to investigate if cardiometabolic risk differed by MS group by applying the receiver operating characteristic curve (ROC) cut point. Methods: This is a secondary analysis of a cross-sectional study (the FUPRECOL study), published elsewhere. The FUPRECOL study assessments were conducted during the 2014– 2015 school year. MS was estimated by a handle dynamometer on 1,950 children and adolescents from Colombia, using the MS relative to weight (handgrip strength/body mass). A metabolic risk score was computed from the following components: waist circumference, triglycerides, HDL-c, glucose, systolic and diastolic blood pressure. ROC analysis showed a significant discriminatory accuracy of MS in identifying the low/high metabolic risk in children and adolescents and both gender. Results: In children, handgrip strength/body mass level for a low metabolic risk were 0.359 and 0.376 in girls and boys, respectively. In adolescents, these points were 0.440 and 0.447 in girls and boys, respectively. Conclusion: In conclusion, the results suggest a hypothetical MS level relative to weight for having a low metabolic risk, which could be used to identify youths at risk.

The science of assessment : identifying and predicting environmental impacts

The historical challenge of environmental impact assessment (EIA) has been to predict project-based impacts accurately. Both EIA legislation and the practice of EIA have evolved over the last three decades in Canada, and the development of the discipline and science of environmental assessment has improved how we apply environmental assessment to complex projects. The practice of environmental assessment integrates the social and natural sciences and relies on an eclectic knowledge base from a wide range of sources. EIA methods and tools provide a means to structure and integrate knowledge in order to evaluate and predict environmental impacts.----- This Chapter will provide a brief overview of how impacts are identified and predicted. How do we determine what aspect of the natural and social environment will be affected when a mine is excavated? How does the practitioner determine the range of potential impacts, assess whether they are significant, and predict the consequences? There are no standard answers to these questions, but there are established methods to provide a foundation for scoping and predicting the potential impacts of a project.----- Of course, the community and publics play an important role in this process, and this will be discussed in subsequent chapters. In the first part of this chapter, we will deal with impact identification, which involves appplying scoping to critical issues and determining impact significance, baseline ecosystem evaluation techniques, and how to communicate environmental impacts. In the second part of the chapter, we discuss the prediction of impacts in relation to the complexity of the environment, ecological risk assessment, and modelling.

Measuring the use of information and communication technologies (ICTs) in the classroom

In 2003, the “ICT Curriculum Integration Performance Measurement Instrument” was developed froman extensive review ofthe contemporary international and Australian research pertaining to the definition and measurement of ICT curriculum integration in classrooms (Proctor, Watson, & Finger, 2003). The 45-item instrument that resulted was based on theories and methodologies identified by the literature review. This paper describes psychometric results from a large-scale evaluation of the instrument subsequently conducted, as recommended by Proctor, Watson, and Finger (2003). The resultant 20-item, two-factor instrument, now called “Learning with ICTs: Measuring ICT Use in the Curriculum,” is both statistically and theoretically robust. This paper should be read in association with the original paper published in Computers in the Schools(Proctor, Watson, & Finger, 2003) that described in detail the theoretical framework underpinning the development of the instrument.

Generative and evolutionary models for design

The paper describes three design models that make use of generative and evolutionary systems. The models describe overall design methods and processes. Each model defines a set of tasks to be performed by the design team, and in each case one of the tasks requires a generative or evolutionary design system. The architectures of these systems are also broadly described.

Review of vibration-based damage detection and condition assessment of bridge structures using structural health monitoring

As a part of vital infrastructure and transportation networks, bridge structures must function safely at all times. However, due to heavier and faster moving vehicular loads and function adjustment, such as Busway accommodation, many bridges are now operating at an overload beyond their design capacity. Additionally, the huge renovation and replacement costs always make the infrastructure owners difficult to undertake. Structural health monitoring (SHM) is set to assess condition and foresee probable failures of designated bridge(s), so as to monitor the structural health of the bridges. The SHM systems proposed recently are incorporated with Vibration-Based Damage Detection (VBDD) techniques, Statistical Methods and Signal processing techniques and have been regarded as efficient and economical ways to solve the problem. The recent development in damage detection and condition assessment techniques based on VBDD and statistical methods are reviewed. The VBDD methods based on changes in natural frequencies, curvature/strain modes, modal strain energy (MSE) dynamic flexibility, artificial neural networks (ANN) before and after damage and other signal processing methods like Wavelet techniques and empirical mode decomposition (EMD) / Hilbert spectrum methods are discussed here.

A cluster-randomised intervention trial against Schistosoma japonicum in the Peoples' Republic of China: bovine and human transmission

Background Zoonotic schistosomiasis japonica is a major public health problem in China. Bovines, particularly water buffaloes, are thought to play a major role in the transmission of schistosomiasis to humans in China. Preliminary results (1998–2003) of a praziquantel (PZQ)-based pilot intervention study we undertook provided proof of principle that water buffaloes are major reservoir hosts for S. japonicum in the Poyang Lake region, Jiangxi Province. Methods and Findings Here we present the results of a cluster-randomised intervention trial (2004–2007) undertaken in Hunan and Jiangxi Provinces, with increased power and more general applicability to the lake and marshlands regions of southern China. The trial involved four matched pairs of villages with one village within each pair randomly selected as a control (human PZQ treatment only), leaving the other as the intervention (human and bovine PZQ treatment). A sentinel cohort of people to be monitored for new infections for the duration of the study was selected from each village. Results showed that combined human and bovine chemotherapy with PZQ had a greater effect on human incidence than human PZQ treatment alone. Conclusions The results from this study, supported by previous experimental evidence, confirms that bovines are the major reservoir host of human schistosomiasis in the lake and marshland regions of southern China, and reinforce the rationale for the development and deployment of a transmission blocking anti-S. japonicum vaccine targeting bovines.