INVESTIGATING THE USE OF MAZE-CBM FOR HIGH SCHOOL STUDENTS

Recent legislation and initiatives set forth high academic expectations for all high school graduates in the area of reading (National Governors Association Center for Best Practices, 2010; Every Student Succeeds Act, 2015). To determine which students need additional support to meet these reading standards, teachers can conduct universal screening using formative assessments. Maze Curriculum-Based Measurement (Maze-CBM) is a commonly used screening and progress monitoring assessment that the National Center on Intensive Intervention (2013) and the Center on Instruction (Torgesen & Miller, 2009) recommend. Despite the recommendation to use Maze-CBM, little research has been conducted on the reliability and validity of Maze-CBM for measuring reading ability for students at the secondary level (Mitchell & Wexler, 2016). In the papers included in this dissertation, I present an initial investigation into the use of Maze-CBM for secondary students. In the first paper, I investigated prior studies of Maze-CBM for students in Grades 6 through 12. Next, in the second paper, I investigated the alternate-form reliability and validity for screening students in Grades 9 and 10 using signal detection theory methods. In the third paper, I examined the effect of genre on Maze-CBM scores with a sample of students in Grades 9 and 10 using multilevel modeling. When writing these three papers, I discovered several important findings related to Maze-CBM. First, there are few studies that have investigated the technical adequacy of Maze-CBM for screening and progress monitoring students in Grades 6 through 12. Additionally, only two studies (McMaster, Wayman, & Cao, 2006; Pierce, McMaster, & Deno, 2010) examined the technical adequacy of Maze-CBM for high school students. A second finding is that the reliability of Maze-CBM is often below acceptable levels for making screening decisions or progress monitoring decisions (.80 and above and .90 and above, respectively; Salvia, Ysseldyke, & Bolt, 2007) for secondary students. A third finding is that Maze-CBM scores show promise of being a valid screening tool for reading ability of secondary students. Finally, I found that the genre of the text used in the Maze-CBM assessment does impact scores on Maze-CBM for students in Grades 9 and 10.

Radio Frequency Superconducting Quantum Interference Device Meta-atoms and Metamaterials: Experiment, Theory, and Analysis

Metamamterials are 1D, 2D or 3D arrays of articial atoms. The articial atoms, called "meta-atoms", can be any component with tailorable electromagnetic properties, such as resonators, LC circuits, nano particles, and so on. By designing the properties of individual meta-atoms and the interaction created by putting them in a lattice, one can create a metamaterial with intriguing properties not found in nature. My Ph. D. work examines the meta-atoms based on radio frequency superconducting quantum interference devices (rf-SQUIDs); their tunability with dc magnetic field, rf magnetic field, and temperature are studied. The rf-SQUIDs are superconducting split ring resonators in which the usual capacitance is supplemented with a Josephson junction, which introduces strong nonlinearity in the rf properties. At relatively low rf magnetic field, a magnetic field tunability of the resonant frequency of up to 80 THz/Gauss by dc magnetic field is observed, and a total frequency tunability of 100% is achieved. The macroscopic quantum superconducting metamaterial also shows manipulative self-induced broadband transparency due to a qualitatively novel nonlinear mechanism that is different from conventional electromagnetically induced transparency (EIT) or its classical analogs. A near complete disappearance of resonant absorption under a range of applied rf flux is observed experimentally and explained theoretically. The transparency comes from the intrinsic bi-stability and can be tuned on/ off easily by altering rf and dc magnetic fields, temperature and history. Hysteretic in situ 100% tunability of transparency paves the way for auto-cloaking metamaterials, intensity dependent filters, and fast-tunable power limiters. An rf-SQUID metamaterial is shown to have qualitatively the same behavior as a single rf-SQUID with regards to dc flux, rf flux and temperature tuning. The two-tone response of self-resonant rf-SQUID meta-atoms and metamaterials is then studied here via intermodulation (IM) measurement over a broad range of tone frequencies and tone powers. A sharp onset followed by a surprising strongly suppressed IM region near the resonance is observed. This behavior can be understood employing methods in nonlinear dynamics; the sharp onset, and the gap of IM, are due to sudden state jumps during a beat of the two-tone sum input signal. The theory predicts that the IM can be manipulated with tone power, center frequency, frequency difference between the two tones, and temperature. This quantitative understanding potentially allows for the design of rf-SQUID metamaterials with either very low or very high IM response.

Ionospheric Turbulence Near the Upper Hybrid Layer: Theory and Experiment

The thesis presents experimental results, simulations, and theory on turbulence excited in magnetized plasmas near the ionosphere’s upper hybrid layer. The results include: The first experimental observations of super small striations (SSS) excited by the High-Frequency Auroral Research Project (HAARP) The first detection of high-frequency (HF) waves from the HAARP transmitter over a distance of 16x10^3 km The first simulations indicating that upper hybrid (UH) turbulence excites electron Bernstein waves associated with all nearby gyroharmonics Simulation results that indicate that the resulting bulk electron heating near the upper hybrid (UH) resonance is caused primarily by electron Bernstein waves parametrically excited near the first gyroharmonic. On the experimental side we present two sets of experiments performed at the HAARP heating facility in Alaska. In the first set of experiments, we present the first detection of super-small (cm scale) striations (SSS) at the HAARP facility. We detected density structures smaller than 30 cm for the first time through a combination of satellite and ground based measurements. In the second set of experiments, we present the results of a novel diagnostic implemented by the Ukrainian Antarctic Station (UAS) in Verdansky. The technique allowed the detection of the HAARP signal at a distance of nearly 16 Mm, and established that the HAARP signal was injected into the ionospheric waveguide by direct scattering off of dekameter-scale density structures induced by the heater. On the theoretical side, we present results of Vlasov simulations near the upper hybrid layer. These results are consistent with the bulk heating required by previous work on the theory of the formation of descending artificial ionospheric layers (DIALs), and with the new observations of DIALs at HAARP’s upgraded effective radiated power (ERP). The simulations that frequency sweeps, and demonstrate that the heating changes from a bulk heating between gyroharmonics, to a tail acceleration as the pump frequency is swept through the fourth gyroharmonic. These simulations are in good agreement with experiments. We also incorporate test particle simulations that isolate the effects of specific wave modes on heating, and we find important contributions from both electron Bernstein waves and upper hybrid waves, the former of which have not yet been detected by experiments, and have not been previously explored as a driver of heating. In presenting these results, we analyzed data from HAARP diagnostics and assisted in planning the second round of experiments. We integrated the data into a picture of experiments that demonstrated the detection of SSS, hysteresis effects in simulated electromagnetic emission (SEE) features, and the direct scattering of the HF pump into the ionospheric waveguide. We performed simulations and analyzed simulation data to build the understanding of collisionless heating near the upper hybrid layer, and we used these simulations to show that bulk electron heating at the upper hybrid layer is possible, which is required by current theories of DAIL formation. We wrote a test particle simulation to isolate the effects of electron Bernstein waves and upper hybrid layers on collisionless heating, and integrated this code to work with both the output of Vlasov simulations and the input for simulations of DAIL formation.

Anomaly Detection in Noisy Images

Finding rare events in multidimensional data is an important detection problem that has applications in many fields, such as risk estimation in insurance industry, finance, flood prediction, medical diagnosis, quality assurance, security, or safety in transportation. The occurrence of such anomalies is so infrequent that there is usually not enough training data to learn an accurate statistical model of the anomaly class. In some cases, such events may have never been observed, so the only information that is available is a set of normal samples and an assumed pairwise similarity function. Such metric may only be known up to a certain number of unspecified parameters, which would either need to be learned from training data, or fixed by a domain expert. Sometimes, the anomalous condition may be formulated algebraically, such as a measure exceeding a predefined threshold, but nuisance variables may complicate the estimation of such a measure. Change detection methods used in time series analysis are not easily extendable to the multidimensional case, where discontinuities are not localized to a single point. On the other hand, in higher dimensions, data exhibits more complex interdependencies, and there is redundancy that could be exploited to adaptively model the normal data. In the first part of this dissertation, we review the theoretical framework for anomaly detection in images and previous anomaly detection work done in the context of crack detection and detection of anomalous components in railway tracks. In the second part, we propose new anomaly detection algorithms. The fact that curvilinear discontinuities in images are sparse with respect to the frame of shearlets, allows us to pose this anomaly detection problem as basis pursuit optimization. Therefore, we pose the problem of detecting curvilinear anomalies in noisy textured images as a blind source separation problem under sparsity constraints, and propose an iterative shrinkage algorithm to solve it. Taking advantage of the parallel nature of this algorithm, we describe how this method can be accelerated using graphical processing units (GPU). Then, we propose a new method for finding defective components on railway tracks using cameras mounted on a train. We describe how to extract features and use a combination of classifiers to solve this problem. Then, we scale anomaly detection to bigger datasets with complex interdependencies. We show that the anomaly detection problem naturally fits in the multitask learning framework. The first task consists of learning a compact representation of the good samples, while the second task consists of learning the anomaly detector. Using deep convolutional neural networks, we show that it is possible to train a deep model with a limited number of anomalous examples. In sequential detection problems, the presence of time-variant nuisance parameters affect the detection performance. In the last part of this dissertation, we present a method for adaptively estimating the threshold of sequential detectors using Extreme Value Theory on a Bayesian framework. Finally, conclusions on the results obtained are provided, followed by a discussion of possible future work.

Precoder Detection for Cooperative Decode-and-Forward Relaying in OFDMA Systems

We consider an LTE network where a secondary user acts as a relay, transmitting data to the primary user using a decode-and-forward mechanism, transparent to the base-station (eNodeB). Clearly, the relay can decode symbols more reliably if the employed precoder matrix indicators (PMIs) are known. However, for closed loop spatial multiplexing (CLSM) transmit mode, this information is not always embedded in the downlink signal, leading to a need for effective methods to determine the PMI. In this thesis, we consider 2x2 MIMO and 4x4 MIMO downlink channels corresponding to CLSM and formulate two techniques to estimate the PMI at the relay using a hypothesis testing framework. We evaluate their performance via simulations for various ITU channel models over a range of SNR and for different channel quality indicators (CQIs). We compare them to the case when the true PMI is known at the relay and show that the performance of the proposed schemes are within 2 dB at 10% block error rate (BLER) in almost all scenarios. Furthermore, the techniques add minimal computational overhead over existent receiver structure. Finally, we also identify scenarios when using the proposed precoder detection algorithms in conjunction with the cooperative decode-and-forward relaying mechanism benefits the PUE and improves the BLER performance for the PUE. Therefore, we conclude from this that the proposed algorithms as well as the cooperative relaying mechanism at the CMR can be gainfully employed in a variety of real-life scenarios in LTE networks.

MICROFLUIDIC ASSAY PERFORMANCE ENHANCEMENT USING POROUS VOLUMETRIC DETECTION ELEMENTS FOR IMPEDEMETRIC AND OPTICAL SENSING

Microfluidic technologies have great potential to help create automated, cost-effective, portable devices for rapid point of care (POC) diagnostics in diverse patient settings. Unfortunately commercialization is currently constrained by the materials, reagents, and instrumentation required and detection element performance. While most microfluidic studies utilize planar detection elements, this dissertation demonstrates the utility of porous volumetric detection elements to improve detection sensitivity and reduce assay times. Impedemetric immunoassays were performed utilizing silver enhanced gold nanoparticle immunoconjugates (AuIgGs) and porous polymer monolith or silica bead bed detection elements within a thermoplastic microchannel. For a direct assay with 10 µm spaced electrodes the detection limit was 0.13 fM AuIgG with a 3 log dynamic range. The same assay was performed with electrode spacing of 15, 40, and 100 µm with no significant difference between configurations. For a sandwich assay the detection limit was10 ng/mL with a 4 log dynamic range. While most impedemetric assays rely on expensive high resolution electrodes to enhance planar senor performance, this study demonstrates the employment of porous volumetric detection elements to achieve similar performance using lower resolution electrodes and shorter incubation times. Optical immunoassays were performed using porous volumetric capture elements perfused with refractive index matching solutions to limit light scattering and enhance signal. First, fluorescence signal enhancement was demonstrated with a porous polymer monolith within a silica capillary. Next, transmission enhancement of a direct assay was demonstrated by infusing aqueous sucrose solutions through silica bead beds with captured silver enhanced AuIgGs yielding a detection limit of 0.1 ng/mL and a 5 log dynamic range. Finally, ex situ functionalized porous silica monolith segments were integrated into thermoplastic channels for a reflectance based sandwich assay yielding a detection limit of 1 ng/mL and a 5 log dynamic range. The simple techniques for optical signal enhancement and ex situ element integration enable development of sensitive, multiplexed microfluidic sensors. Collectively the demonstrated experiments validate the use of porous volumetric detection elements to enhance impedemetric and optical microfluidic assays. The techniques rely on commercial reagents, materials compatible with manufacturing, and measurement instrumentation adaptable to POC diagnostics.