This thesis combines Keysight’s SystemVue software with a Vector Signal Analyzer (VSA) and Vector Signal Generator (VSG) to test receiver equalization schemes over physical channels. The testing setup, “Equalization Verification,” is intended to be able to evaluate any equalization scheme over any physical channel, and a decision-directed feed-forward LMS equalizer is used as an example. The decision-directed feed-forward LMS equalizer is shown to decrease the BER from 10-2 to 10-3 (average of all trials) over a CAT7 and CAT6A cable, both simulated and physical, for 1GHz and 2GHz carrier, and 80MHz data rate. A wireless channel, 2.4GHz Dipole Antenna, is also tested to show that the addition of the equalization scheme decreases BER from 10-5 to less than 10-5. Then the simulation and equalization parameters (LMS step size, PRBS, etc.) are changed to further verify the equalization scheme. The simulated channel BER results do not always match the physical channel BER results, but the equalization scheme does decrease BER for both wired and wireless channels. Then transistor-based equalization model is created using both HDL SystemVue components and blocks easily implemented by transistors. The model is then verified using HDL, Spice, and SystemVue simulation. Overall this thesis accomplishes its goal of creating a testing setup, Equalization Verification, to show that adding a given simulated equalization scheme in SystemVue can improve the quality of the link, by decreasing BER by at least an order of magnitude, over a specific physical channel.

The 13,000 school districts in the United States are important institutional sites where consequential educational policy decisions – from school zoning to local funding initiatives– are contested and implemented. Despite their importance as institutions, there is very little quantitative research on school district leadership and governance. We do not currently know if the identity of school district leaders has any effect on the academic performance of school districts, or if school district leaders from different backgrounds tend to advance different educational policies for their districts. In this dissertation, I leverage new longitudinal datasets on superintendent and school board tenures, matched to data on district-level achievement and policy variation, to examine how school district leadership may matter for students’ educational experiences. This dissertation consists of three empirical papers that examine different aspects of school district leadership and governance. Chapter 1 uses a novel dataset of superintendent employment histories in 26 states, matched to student achievement data, to estimate variation in superintendent effectiveness. I use data on superintendents who move between multiple school districts as an estimation strategy to separate superintendent effects from other district-level factors that affect student achievement. To estimate superintendent effectiveness, I adapt value-added modeling strategies from the principal and teacher effects literature, and use simulation analyses to further justify my modeling approaches. Across model specifications, I find that the standard deviation of superintendent effects ranges from 0.03-0.10 SD on student achievement. These are moderate effect sizes, and indicate the importance of studying school district leadership as a factor that can have important implications for student-level outcomes. Chapter 2 examines the superintendent appointment process using mixed methods data from California and Florida over the past decade from 2009-2019. Given that superintendents serve important political functions within school districts, I examine whether school board members tend to appoint superintendents who share their own partisan political affiliations. During this period, I do not find any evidence that school boards prefer to hire co-partisan superintendents. Instead, school boards prioritize superintendent candidates who show strong social-emotional intelligence, and who demonstrate the capacity to develop relationships with a broad range of community stakeholders. I conclude the chapter by examining how these results may be shifting in light of an emergent conservative movement to polarize school board politics. Chapter 3 examines whether the partisan political characteristics of school districts affects the likelihood that school districts hold tax and bond elections, or vote to approve these local educational funding increases, conditional on elections being held. It also uses data from California and Florida over the past decade from 2009-2019. With appropriate statistical controls, I find no evidence that school board or voter partisanship affects the probability of districts holding or passing tax or bond measures. Taken together, the three papers make important contributions to our understanding of how school districts function as organizations, and how the attributes of school district leaders shape students’ educational environments.

Landeszuweisungen stellen eine zentrale Einnahmenquelle der kommunalen Ebene dar und sind für deren Aufgabenerfüllung unverzichtbar. Einen erheblichen Teil dieser Mittel erhalten die Kommunen dabei über den kommunalen Finanzausgleich. Dieser trägt in hohem Maße zu einer Verstetigung der kommunalen Einnahmenbasis bei, indem der Großteil der Finanzmittel pauschal an die Kommunen weitergereicht wird, und unterstützt darüber hinaus einen (anteiligen) Abbau der Finanzkraftunterschiede zwischen den Kommunen. Eine weitere Ergänzung der kommunalen Einnahmenseite bilden Fördermittelprogramme der Länder. Diesen liegt im Normalfall ein bestimmter Förderzweck zugrunde, über den die Landesebene indirekt Einfluss auf die kommunale Ausgabenpolitik ausüben kann (sog. Politik der Goldenen Zügel). Anders als im in hohem Maße regelgebundenen kommunalen Finanzausgleichssystem sind diese Mittel in vielen Fällen nur auf Antrag verfügbar und bedürfen darüber hinaus üblicherweise eines Eigenfinanzierungsanteils durch die Kommunen. Hieraus erwachsen Risiken der Fehlallokation kommunaler Ausgaben und einer Benachteiligung von Kommunen, die Eigenanteile nicht aufbringen können. Hinzu kommt eine nicht unerhebliche Förderbürokratie, sowohl beim Fördermittelgeber als auch bei den Fördermittelempfängern. Die vorliegende Studie ist aus einer Zuarbeit für die Kommission zur Vereinfachung und Verbesserung von Förderverfahren im Freistaat Sachsen entstanden und befasst sich mit der Frage nach der Übertragbarkeit einzelner Fachförderprogramme in den kommunalen Finanzausgleich (KFA). Nach einer Abgrenzung der unterschiedlichen Zuweisungsformen innerhalb und außerhalb des KFA wird zu diesem Zweck im Anschluss ein finanzwissenschaftlicher Kriterienkatalog aufgestellt. Anhand sowohl allokativer als auch fiskalischer Kriterien aus diesem Katalog werden dann die 24 größten Fachförderprogramme dahingehend untersucht, inwieweit sie sich für eine Übertragung in den kommunalen Finanzausgleich (konkret für Sachsen: ins SächsFAG) eignen. Der erarbeitete Kriterienkatalog ist dabei kein abschließendes Entscheidungsschema, sondern vielmehr eine Orientierungshilfe, die bei der oftmals komplexen Frage über die sachgerechte Art der Zuweisung helfen kann. Im Ergebnis bieten sich fünf der 24 Programme für eine Übertragung in den KFA an, darunter das vom Finanzvolumen her größte Programm zu Förderung von Kindertagesstätten. Darüber hinaus ist für die Richtlinien zur Feuerwehrförderung, zur Investitionsförderung im Schulbereich sowie zur Förderung der schulischen Ganztagsangebote eine Überführung in den KFA sinnvoll und empfehlenswert. Bei insgesamt elf Programmen ist eine Überführung aufgrund verschiedener KO-Kriterien ausgeschlossen. Bei den übrigen acht Programmen ist die Empfehlung nicht eindeutig, sodass eine Überführung in den KFA nur mit Einschränkungen in Betracht kommt. Hierzu gehören unter anderem die Förderrichtlinien für die Sportförderung oder die Schulsozialarbeit.

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<p> This thesis addresses the design of fractionally-spaced equalizers for a digital communication system which is susceptible to Adjacent Channel Interference (ACI). ACI can render an otherwise well designed system prone to excess bit errors. Algorithms for a trained adaptive FIR linear fractionally-spaced equalizer (FSE) with explicit sidelobe control are developed in order to provide robustness to ACI. The explicit sidelobe control is achieved by imposing a quadratic inequality constraint on the frequency response of the equalizer at a discrete set of frequency points in the sidelobe region.</p> <p> Algorithms are developed for both block adaptive and symbol-by-symbol adaptive modes. These algorithms use interior point optimization techniques to find the optimal equalizer coefficients. In the block adaptive mode, the problem is reformulated as a Second Order Cone Program (SOCP). In the symbol-by-symbol adaptive mode, the philosophy of the barrier approach to interior point methods is adopted. The concept of a central path and the Method of Analytic Centers (MAC) are used to develop two practically implementable algorithms, namely IPM2 and SBM, for performing symbol-by-symbol adaptive, fractionally-spaced equalization, with multiple quadratic inequality constraints.</p> <p> The performance of the proposed algorithms is compared to that of the Wiener filter, and the standard RLS algorithm with explicit diagonal loading. In the computer simulations, the proposed algorithms perform better in the sense that they provide the desired robustness when the communication model is prone to intermittent interferers in the sidelobe region of the frequency response of the FSE. Although the proposed algorithms have a moderately higher computational cost, their insensitivity to the deleterious effects of ACI make them an attractive choice in certain applications.</p> / Thesis / Master of Applied Science (MASc)

Bridging machine learning technologies to multiple-input-multiple-output (MIMO) communications systems is a primary driving force for next-generation wireless systems. This dissertation introduces a variety of neural network structures for symbol detection/equalization tasks in MIMO systems configured with two different waveforms, orthogonal frequency-division multiplexing (OFDM) and orthogonal time frequency and space (OTFS). The former one is the major air interface in current cellular systems. The latter one is developed to handle high mobility. For the sake of real-time processing, the introduced neural network structures are incorporated with inductive biases of wireless communications signals and operate in an online training manner. The utilized inductive priors include the shifting invariant property of quadrature amplitude modulation, the time-frequency relation inherent in OFDM signals, the multi-mode feature of massive antennas, and the delay-Doppler representation of doubly selective channel. In addition, the neural network structures are rooted in reservoir computing - an efficient neural network computational framework with decent generalization performance for limited training datasets. Therefore, the resulting neural network structures can learn beyond observation and offer decent transmission reliability in the low signal-to-noise ratio (SNR) regime. This dissertation includes comprehensive simulation results to justify the effectiveness of the introduced NN architectures compared with conventional model-based approaches and alternative neural network structures. / Doctor of Philosophy / An important topic for next-generation wireless systems is the integration of machine learning technologies with conventional communications systems. This dissertation introduces several neural network architectures to solve the transmission problems in wireless communications systems. The discussion focuses on the following major modern communications technologies: multiple-input-multiple-output (MIMO), orthogonal frequency-division multiplexing (OFDM), and orthogonal time frequency space (OTFS). In today's cellular networks, MIMO and OFDM are the major air-interface. OTFS is a novel technique that has been designed to work in a high-mobility setting. The implemented neural network structures are integrated with inductive biases of wireless communications signals and operate in an online training mode with limited training datasets. The neural network architectures, in particular, are based on reservoir computing, which is an efficient neural network computational system. A learning algorithm's inductive bias (also known as learning bias) is a collection of assumptions that the learner makes to infer outputs from unknown inputs. The dissertation introduces four different inductive priors from four different perspectives of MIMO communications systems. As a result, the neural network architectures can learn beyond observation and provide good generalization output in scenarios having model mismatch issues. The dissertation provides extensive simulation results to support the efficacy of the implemented NN architectures compared to alternative neural network models and traditional model-based approaches.

This study approached the concept of equity in public elementary and secondary school finance as a complex, multi-variable phenomena. The purpose of this study was to develop a procedure for determining whether or not an equitable distribution of current expenditure per pupil in a state had been achieved based upon the interrelationships of multiple fiscal, non-fiscal school and community variables, and pupil output measures. Six orthogonal factors, derived from principal components analysis and varimax rotation of 24 school and community variables from 131 Virginia school districts during the 1987-88 school year, became the independent variables in multiple regression analysis with school finance data and student output data as the dependent variables. Community Type, Fiscal Capacity, and Economic Composition of a District Population accounted for 61% of the variability in current expenditure per pupil and 63% of the variability in the percentage of students planning to attend a four year college. Over 70% of the variability in Achievement Test Scores was accounted for by Black Family Structure, Fiscal Capacity, Economic Composition of a District Population, and Community Type characteristics. / Ph. D.