Design and Realization of Fully-digital Microwave and Mm-wave Multi-beam Arrays with FPGA/RF-SOC Signal Processing

There has been a constant increase in data-traffic and device-connections in mobile wireless communications, which led the fifth generation (5G) implementations to exploit mm-wave bands at 24/28 GHz. The next-generation wireless access point (6G and beyond) will need to adopt large-scale transceiver arrays with a combination of multi-input-multi-output (MIMO) theory and fully digital multi-beam beamforming. The resulting high gain array factors will overcome the high path losses at mm-wave bands, and the simultaneous multi-beams will exploit the multi-directional channels due to multi-path effects and improve the signal-to-noise ratio. Such access points will be based on electronic systems which heavily depend on the integration of RF electronics with digital signal processing performed in Field programmable gate arrays (FPGA)/ RF-system-on-chip (SoC). This dissertation is directed towards the investigation and realization of fully-digital phased arrays that can produce wideband simultaneous multi-beams with FPGA or RF-SoC digital back-ends. The first proposed approach is a spatial bandpass (SBP) IIR filter-based beamformer, and is based on the concepts of space-time network resonance. A 2.4 GHz, 16-element array receiver, has been built for real-time experimental verification of this approach. The second and third approaches are respectively based on Discrete Fourier Transform (DFT) theory, and a lens plus focal planar array theory. Lens based approach is essentially an analog model of DFT. These two approaches are verified for a 28 GHz 800 MHz mm-wave implementation with RF-SoC as the digital back-end. It has been shown that for all proposed multibeam beamformer implementations, the measured beams are well aligned with those of the simulated. The proposed approaches differ in terms of their architectures, hardware complexity and costs, which will be discussed as this dissertation opens up. This dissertation also presents an application of multi-beam approaches for RF directional sensing applications to explore white spaces within the spatio-temporal spectral regions. A real-time directional sensing system is proposed to capture the white spaces within the 2.4 GHz Wi-Fi band. Further, this dissertation investigates the effect of electro-magnetic (EM) mutual coupling in antenna arrays on the real-time performance of fully-digital transceivers. Different algorithms are proposed to uncouple the mutual coupling in digital domain. The first one is based on finding the MC transfer function from the measured S-parameters of the antenna array and employing it in a Frost FIR filter in the beamforming backend. The second proposed method uses fast algorithms to realize the inverse of mutual coupling matrix via tridiagonal Toeplitz matrices having sparse factors. A 5.8 GHz 32-element array and 1-7 GHz 7-element tightly coupled dipole array (TCDA) have been employed to demonstrate the proof-of-concept of these algorithms

Signal-to-noise ratio estimation in digital computer simulation of lowpass and bandpass systems with applications to analog and digital communications, volume 3

Techniques are developed to estimate power gain, delay, signal-to-noise ratio, and mean square error in digital computer simulations of lowpass and bandpass systems. The techniques are applied to analog and digital communications. The signal-to-noise ratio estimates are shown to be maximum likelihood estimates in additive white Gaussian noise. The methods are seen to be especially useful for digital communication systems where the mapping from the signal-to-noise ratio to the error probability can be obtained. Simulation results show the techniques developed to be accurate and quite versatile in evaluating the performance of many systems through digital computer simulation

Adaptive Cognitive Interaction Systems

Adaptive kognitive Interaktionssysteme beobachten und modellieren den Zustand ihres Benutzers und passen das Systemverhalten entsprechend an. Ein solches System besteht aus drei Komponenten: Dem empirischen kognitiven Modell, dem komputationalen kognitiven Modell und dem adaptiven Interaktionsmanager. Die vorliegende Arbeit enthält zahlreiche Beiträge zur Entwicklung dieser Komponenten sowie zu deren Kombination. Die Ergebnisse werden in zahlreichen Benutzerstudien validiert

Signal Processing Methods for the Analysis of the Electrocardiogram

Das Elektrokardiogramm (EKG) zeichnet die elektrische Aktivität des Herzens auf der Brust- oberfläche auf. Dieses Signal kann einfach und kostengünstig aufgenommen werden und wird daher in einer Vielzahl von mobilen und stationären Anwendungen genutzt. Es ist über die letzten 100 Jahre zum Goldstandard bei der Diagnose vieler kardiologischer Krankheiten geworden. Herzerkrankungen bleiben ein relevantes Thema in unserer Gesellschaft, da sie zu 30 % aller Todesfälle weltweit führen. Allein die koronare Herzkrankheit ist die häufigste Todesursache überhaupt. Weiterhin sind 2 bis 3 % der Europäer von Herzrhythmusstörungen wie Vorhofflimmern und Vorhofflattern betroffen. Die damit verbundenen geschätzten Kosten in der Europäischen Union belaufen sich auf 26 Milliarden Euro pro Jahr. In allen diesen Fällen ist die Aufzeichnung des EKGs der erste unumgängliche Schritt für eine verlässliche Diagnose und erfolgreiche Therapie. Im Rahmen dieser Dissertation wurden eine Reihe von Algorithmen zur Signalver- arbeitung des EKG entwickelt, die automatisch die rhythmischen und morphologischen Eigenschaften aus dem EKG extrahieren und dadurch den diagnostischen Prozess und die Entscheidungsfindung des Arztes unterstützen. In einem ersten Projekt wurde das Phänomen der postextrasystolischen T-Wellen-Änderung (PEST) untersucht. Die aus der PEST ex- trahierten Biomarker haben wir als Prädiktoren für Herzversagen postuliert. Ein zweites Projekt handelte vom Entwurf eines akkuraten Algorithmus zur Detektion und Annotation der P-Welle im EKG. Als Referenz während der Entwicklung wurden intrakardial gemessene Signale verwendet. Eine dritte Untersuchg hatte das Ziel, das physiologische Phänomen der respiratorischen Sinusarrhythmie (RSA) besser zu verstehen. In diesem Projekt wurde ein Algorithmus zur Trennung der Herzratenvariabilität (HRV) in ihre atmungsabhängige und ihre atmungsunabhn ̈gige Komponente untersucht. Letzterer Anteil der HRV könnte neue Erkenntnisse über die Regulationsmechanismen des kardiovaskulären Systems liefern. In der vierten und letzten Studie wurde der Einfluss mentaler Belastung auf das EKG während der Autofahrt untersucht. Eine Vielzahl von Deskriptoren wurden gefunden, die eine gefährliche mentale Beanspruchung detektieren und somit den Fahrer vor einem möglichen Unfall schützen können. Wir schließen aus diesen Untersuchungen, dass gut entwickelte Methoden der Signalver- arbeitung des EKG das Potential haben, die Belastung der Patienten, die an Herzerkrankungen leiden, und die Anzahl der Verkehrsunfälle zu reduzieren

Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids

This reprint presents advances in operation and maintenance in solar plants, wind farms and microgrids. This compendium of scientific articles will help clarify the current advances in this subject, so it is expected that it will please the reader

Research and Development of a General Purpose Instrument DAQ-Monitoring Platform applied to the CLOUD/CERN experiment

The current scientific environment has experimentalists and system administrators allocating large amounts of time for data access, parsing and gathering as well as instrument management. This is a growing challenge since there is an increasing number of large collaborations with significant amount of instrument resources, remote instrumentation sites and continuously improved and upgraded scientific instruments. DAQBroker is a new software designed to monitor networks of scientific instruments while also providing simple data access methods for any user. Data can be stored in one or several local or remote databases running on any of the most popular relational databases (MySQL, PostgreSQL, Oracle). It also provides the necessary tools for creating and editing the metadata associated with different instruments, perform data manipulation and generate events based on instrument measurements, regardless of the user’s know-how of individual instruments. Time series stored in a DAQBroker database also benefit from several statistical methods for time series classification, comparison and event detection as well as multivariate time series analysis methods to determine the most statistically relevant time series, rank the most influential time series and also determine the periods of most activity during specific experimental periods. This thesis presents the architecture behind the framework, assesses the performance under controlled conditions and presents a use-case under the CLOUD experiment at CERN, Switzerland. The univariate and multivariate time series statistical methods applied to this framework are also studied.O processo de investigação científica moderno requer que tanto experimentalistas como administradores de sistemas dediquem uma parte significativa do seu tempo a criar estratégias para aceder, armazenar e manipular instrumentos científicos e os dados que estes produzem. Este é um desafio crescente considerando o aumento de colaborações que necessitam de vários instrumentos, investigação em áreas remotas e instrumentos científicos com constantes alterações. O DAQBroker é uma nova plataforma desenhada para a monitorização de instrumentos científicos e ao mesmo tempo fornece métodos simples para qualquer utilizador aceder aos seus dados. Os dados podem ser guardados em uma ou várias bases de dados locais ou remotas utilizando os gestores de bases de dados mais comuns (MySQL, PostgreSQL, Oracle). Esta plataforma também fornece as ferramentas necessárias para criar e editar versões virtuais de instrumentos científicos e manipular os dados recolhidos dos instrumentos, independentemente do grau de conhecimento que o utilizador tenha com o(s) instrumento(s) utilizado(s). Séries temporais guardadas numa base de dados DAQBroker beneficiam de um conjunto de métodos estatísticos para a classificação, comparação e detecção de eventos, determinação das séries com maior influência e os sub-períodos experimentais com maior actividade. Esta tese apresenta a arquitectura da plataforma, os resultados de diversos testes de esforço efectuados em ambientes controlados e um caso real da sua utilização na experiência CLOUD, no CERN, Suíça. São estudados também os métodos de análise de séries temporais, tanto singulares como multivariadas aplicados na plataforma

Air Force Institute of Technology Contributions to Air Force Research and Development, Calendar Year 1986

This report highlights AFIT\u27s contributions to Air Force research and development activities in 1986

Evaluation of glottal characteristics for speaker identification.

Based on the assumption that the physical characteristics of people's vocal apparatus cause their voices to have distinctive characteristics, this thesis reports on investigations into the use of the long-term average glottal response for speaker identification. The long-term average glottal response is a new feature that is obtained by overlaying successive vocal tract responses within an utterance. The way in which the long-term average glottal response varies with accent and gender is examined using a population of 352 American English speakers from eight different accent regions. Descriptors are defined that characterize the shape of the long-term average glottal response. Factor analysis of the descriptors of the long-term average glottal responses shows that the most important factor contains significant contributions from descriptors comprised of the coefficients of cubics fitted to the long-term average glottal response. Discriminant analysis demonstrates that the long-term average glottal response is potentially useful for classifying speakers according to their gender, but is not useful for distinguishing American accents. The identification accuracy of the long-term average glottal response is compared with that obtained from vocal tract features. Identification experiments are performed using a speaker database containing utterances from twenty speakers of the digits zero to nine. Vocal tract features, which consist of cepstral coefficients, partial correlation coefficients and linear prediction coefficients, are shown to be more accurate than the long-term average glottal response. Despite analysis of the training data indicating that the long-term average glottal response was uncorrelated with the vocal tract features, various feature combinations gave insignificant improvements in identification accuracy. The effect of noise and distortion on speaker identification is examined for each of the features. It is found that the identification performance of the long-term average glottal response is insensitive to noise compared with cepstral coefficients, partial correlation coefficients and the long-term average spectrum, but that it is highly sensitive to variations in the phase response of the speech transmission channel. Before reporting on the identification experiments, the thesis introduces speech production, speech models and background to the various features used in the experiments. Investigations into the long-term average glottal response demonstrate that it approximates the glottal pulse convolved with the long-term average impulse response, and this relationship is verified using synthetic speech. Furthermore, the spectrum of the long-term average glottal response extracted from pre-emphasized speech is shown to be similar to the long-term average spectrum of pre-emphasized speech, but computationally much simpler