Constant modulus based blind adaptive multiuser detection.

Thesis (Ph.D.)-University of KwaZulu-Natal, Durban, 2004.Signal processing techniques such as multi user detection (MUD) have the capability of greatly enhancing the performance and capacity of future generation wireless communications systems. Blind adaptive MUD's have many favourable qualities and their application to OS-COMA systems has attracted a lot of attention. The constant modulus algorithm is widely deployed in blind channel equalizations applications. The central premise of this thesis is that the constant modulus cost function is very suitable for the purposes of blind adaptive MUD for future generation wireless communications systems. To prove this point, the adaptive performance of blind (and non-blind) adaptive MUD's is derived analytically for all the schemes that can be made to fit the same generic structure as the constant modulus scheme. For the first time, both the relative and absolute performance levels of the different adaptive algorithms are computed, which gives insights into the performance levels of the different blind adaptive MUD schemes, and demonstrates the merit of the constant modulus based schemes. The adaptive performance of the blind adaptive MUD's is quantified using the excess mean square error (EMSE) as a metric, and is derived for the steady-state, tracking, and transient stages of the adaptive algorithms. If constant modulus based MUD's are suitable for future generation wireless communications systems, then they should also be capable of suppressing multi-rate DS-COMA interference and also demonstrate the ability to suppress narrow band interference (NBI) that arises in overlay systems. Multi-rate DS-COMA provides the capability of transmitting at various bit rates and quality of service levels over the same air interface. Limited spectrum availability may lead to the implementation of overlay systems whereby wide-band COMA signal are collocated with existing narrow band services. Both overlay systems and multi-rate DS-COMA are important features of future generation wireless communications systems. The interference patterns generated by both multi-rate OS-COMA and digital NBI are cyclostationary (or periodically time varying) and traditional MUD techniques do not take this into account and are thus suboptimal. Cyclic MUD's, although suboptimal, do however take the cyclostationarity of the interference into account, but to date there have been no cyclic MUD's based on the constant modulus cost function proposed. This thesis thus derives novel, blind adaptive, cyclic MUD's based on the constant modulus cost function, for direct implementation on the FREquency SHift (FRESH) filter architecture. The FRESH architecture provides a modular and thus flexible implementation (in terms of computational complexity) of a periodically time varying filter. The operation of the blind adaptive MUD on these reduced complexity architectures is also explored.· The robustness of the new cyclic MUD is proven via a rigorous mathematical proof. An alternate architecture to the FRESH filter is the filter bank. Using the previously derived analytical framework for the adaptive performance of MUD's, the relative performance of the adaptive algorithms on the FRESH and filter bank architectures is examined. Prior to this thesis, no conclusions could be drawn as to which architecture would yield superior performance. The performance analysis of the adaptive algorithms is also extended in this thesis in order to consider the effects of timing jitrer at the receiver, signature waveform mismatch, and other pertinent issues that arise in realistic implementation scenarios. Thus, through a careful analytical approach, which is verified by computer simulation results, the suitability of constant modulus based MUD's is established in this thesis

Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives

Part 2 of this monograph builds on the introduction to tensor networks and their operations presented in Part 1. It focuses on tensor network models for super-compressed higher-order representation of data/parameters and related cost functions, while providing an outline of their applications in machine learning and data analytics. A particular emphasis is on the tensor train (TT) and Hierarchical Tucker (HT) decompositions, and their physically meaningful interpretations which reflect the scalability of the tensor network approach. Through a graphical approach, we also elucidate how, by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volumes of data/parameters, thereby alleviating or even eliminating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification (support tensor machines, canonical correlation analysis, higher order partial least squares), generalized eigenvalue decomposition, Riemannian optimization, and in the optimization of deep neural networks. Part 1 and Part 2 of this work can be used either as stand-alone separate texts, or indeed as a conjoint comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions.Comment: 232 page

Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives

Part 2 of this monograph builds on the introduction to tensor networks and their operations presented in Part 1. It focuses on tensor network models for super-compressed higher-order representation of data/parameters and related cost functions, while providing an outline of their applications in machine learning and data analytics. A particular emphasis is on the tensor train (TT) and Hierarchical Tucker (HT) decompositions, and their physically meaningful interpretations which reflect the scalability of the tensor network approach. Through a graphical approach, we also elucidate how, by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volumes of data/parameters, thereby alleviating or even eliminating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification (support tensor machines, canonical correlation analysis, higher order partial least squares), generalized eigenvalue decomposition, Riemannian optimization, and in the optimization of deep neural networks. Part 1 and Part 2 of this work can be used either as stand-alone separate texts, or indeed as a conjoint comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions.Comment: 232 page

Position location in wireless MIMO communication systems

Motivation and objectives -- Contributions -- Organization of the thesis -- Wireless communication channels -- Overview of wireless position location systems -- Fundamentals of array signal processing -- Mimo and space-time processing -- Bidirectional mimo channel model -- The system model -- The bidirectional beamforming MIMO channel -- Joint estimation of multipath parameters for Mimo systenms -- The proposed maximum likelihood multipath parameter estimation algorithms -- The proposed subspace-based multipath parameter estimation algorithm -- The cramer-rao lower bound -- Position location of mobile terminal in mimo systems -- The proposed hybrid TDOA/AOA/AOD location method for Mimo systems -- Analysis of the proposed location method for MIMO systems

Measurements, Models, Systems and Design

531 s.

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

A Summary of NASA Rotary Wing Research: Circa 20082018

The general public may not know that the first A in NASA stands for Aeronautics. If they do know, they will very likely be surprised that in addition to airplanes, the A includes research in helicopters, tiltrotors, and other vehicles adorned with rotors. There is, arguably, no subsonic air vehicle more difficult to accurately analyze than a vehicle with lift-producing rotors. No wonder that NASA has conducted rotary wing research since the days of the NACA and has partnered, since 1965, with the U.S. Army in order to overcome some of the most challenging obstacles to understanding the behavior of these vehicles. Since 2006, NASA rotary wing research has been performed under several different project names [Gorton et al., 2015]: Subsonic Rotary Wing (SRW) (20062012), Rotary Wing (RW) (20122014), and Revolutionary Vertical Lift Technology (RVLT) (2014present). In 2009, the SRW Project published a report that assessed the status of NASA rotorcraft research; in particular, the predictive capability of NASA rotorcraft tools was addressed for a number of technical disciplines. A brief history of NASA rotorcraft research through 2009 was also provided [Yamauchi and Young, 2009]. Gorton et al. [2015] describes the system studies during 20092011 that informed the SRW/RW/RVLT project investment prioritization and organization. The authors also provided the status of research in the RW Project in engines, drive systems, aeromechanics, and impact dynamics as related to structural dynamics of vertical lift vehicles. Since 2009, the focus of research has shifted from large civil VTOL transports, to environmentally clean aircraft, to electrified VTOL aircraft for the urban air mobility (UAM) market. The changing focus of rotorcraft research has been a reflection of the evolving strategic direction of the NASA Aeronautics Research Mission Directorate (ARMD). By 2014, the project had been renamed the Revolutionary Vertical Lift Technology Project. In response to the 2014 NASA Strategic Plan, ARMD developed six Strategic Thrusts. Strategic Thrust 3B was defined as the Ultra-Efficient Commercial VehiclesVertical Lift Aircraft. Hochstetler et al. [2017] uses Thrust 3B as an example for developing metrics usable by ARMD to measure the effectiveness of each of the Strategic Thrusts. The authors provide near-, mid-, and long-term outcomes for Thrust 3B with corresponding benefits and capabilities. The importance of VTOL research, especially with the rapidly expanding UAM market, eventually resulted in a new Strategic Thrust (to begin in 2020): Thrust 4Safe, Quiet, and Affordable Vertical Lift Air Vehicles. The underlying rotary wing analysis tools used by NASA are still applicable to traditional rotorcraft and have been expanded in capability to accommodate the growing number of VTOL configurations designed for UAM. The top-level goal of the RVLT Project remains unchanged since 2006: Develop and validate tools, technologies and concepts to overcome key barriers for vertical lift vehicles. In 2019, NASA rotary wing/VTOL research has never been more important for supporting new aircraft and advancements in technology. 2 A decade is a reasonable interval to pause and take stock of progress and accomplishments. In 10 years, digital technology has propelled progress in computational efficiency by orders of magnitude and expanded capabilities in measurement techniques. The purpose of this report is to provide a compilation of the NASA rotary wing research from ~2008 to ~2018. Brief summaries of publications from NASA, NASA-funded, and NASA-supported research are provided in 12 chapters: Acoustics, Aeromechanics, Computational Fluid Dynamics (External Flow), Experimental Methods, Flight Dynamics and Control, Drive Systems, Engines, Crashworthiness, Icing, Structures and Materials, Conceptual Design and System Analysis, and Mars Helicopter. We hope this report serves as a useful reference for future NASA vertical lift researchers