Full dimension (FD) multiple-input multiple-output (MIMO) technology has recently attracted substantial research attention in the 3rd Generation Partnership Project (3GPP) as a promising technique for the next-generation of wireless communication networks. FD-MIMO scenarios utilize a planar two-dimensional (2D) active antenna system (AAS) that not only allows a large number of antenna elements to be placed within feasible base station (BS) form factors, but also provides the ability of elevation beamforming. This dissertation presents the elevation beamforming analysis for cellular networks utilizing FD massive MIMO antenna arrays. In particular, two architectures are proposed for the AAS - the uniform linear array (ULA) and the uniform circular array (UCA) of antenna ports, where each port is mapped to a group of vertically arranged antenna elements with a corresponding downtilt weight vector. To support FD-MIMO techniques, this dissertation presents two different 3D ray-tracing channel modeling approaches, the ITU based ‘antenna port approach’ and the 3GPP technical report (TR) 36.873 based ‘antenna element approach’. The spatial correlation functions (SCF)s for both FD-MIMO arrays are characterized based on the antenna port approach. The resulting expressions depend on the underlying angular distributions and antenna patterns through the Fourier series coefficients of the power spectra and are therefore valid for any 3D propagation environment. Simulation results investigate the performance patterns of the two arrays as a function of several channel and array parameters. The SCF for the ULA of antenna ports is then characterized in terms of the downtilt weight vectors, based on the more recent antenna element approach. The derived SCFs are used to form the Rayleigh correlated 3D channel model. All these aspects are put together to provide a mathematical framework for the design of elevation beamforming schemes in single-cell and multi-cell scenarios. Finally, this dissertation proposes to use the double scattering channel to model limited scattering in realistic propagation environments and derives deterministic equivalents of the signal-to-interference-plus-noise ratio (SINR) and ergodic rate with regularized zeroforcing (RZF) precoding. The performance of a massive MIMO system is shown to be limited by the number of scatterers. To this end, this dissertation points out future research directions

The topic of multiple input multiple output (MIMO) radar recently gained considerable interest because it can transmit partially correlated or fully independent waveforms. The inherited waveform diversity helps MIMO radars identify more targets and adds flexibility to the beampattern design. The realized advantages come at the expense of enhanced processing requirements and increased system complexity. In this regards, a closed-form method is derived to generate practical finite-alphabet waveforms with specific correlation properties to match the desired beampattern. Next, the performance of adaptive estimation techniques is examined. Indeed, target localization or reflection coefficient estimation usually involves optimizing a given cost-function over a grid of points. The estimation performance is directly affected by the grid resolution. In this work, the cost function of Capon and amplitude and phase estimation (APES) adaptive beamformers are reformulated. The new cost functions can be evaluated using the two-dimensional fast-Fourier-transform (2D-FFT) which reduces the estimation runtime. Generalized expressions of the Cram´er-Rao lower bound are computed to assess the performance of our estimators. Afterward, a novel estimation algorithm based on the monopulse technique is proposed. In comparison with adaptive methods, monopulse requires less number of received pulses. Hence, it is widely used for fast target localization and tracking purposes. This work suggests an approach that localizes two point targets present in the hemisphere using one set of four antennas. To separate targets sharing the same elevation or azimuth angles, a second set of antennas is required. Two solutions are suggested to combine the outputs from the antenna sets and improve the overall detection performance. The last part of the dissertation focuses on the application and implementation side of radars rather than the theoretical aspects. It describes the realized hardware and software design of a compact portable 24 GHz frequency-modulated-continuous-wave (FMCW) radar. The prototype can assist the visually impaired during their outdoor journeys and prevents collisions with their surrounding environment. Moreover, the device performs diverse tasks such as range-direction mapping, velocity estimation, presence detection, and vital sign monitoring. The experimental result section demonstrates the device’s capabilities in different use-cases.

Stethoscopes are the most commonly used medical devices for diagnosing heart conditions because they are inexpensive, noninvasive, and light enough to be carried around by a clinician. Auscultation with a stethoscope requires considerable skill and experience, but the introduction of digital stethoscopes allows for the automation of this task. Auscultation waveform segmentation, which is the process of determining the boundaries of heart sound and murmur segments, is the primary challenge in automating the diagnosis of various heart conditions. The purpose of this thesis is to improve the accuracy and efficiency of established techniques for detecting, segmenting, and classifying heart sounds and murmurs in digitized phonocardiogram audio files. Two separate segmentation techniques based on the discrete wavelet transform (DWT) and the simplicity transform are integrated into a MATLAB software system that is capable of automatically detecting and classifying sound segments. The performance of the two segmentation methods for recognizing normal heart sounds and several different heart murmurs is compared by quantifying the results with clinical and technical metrics. The two clinical metrics are the false negative detection rate (FNDR) and the false positive detection rate (FPDR), which count heart cycles rather than sound segments. The wavelet and simplicity methods have a 4% and 9% respective FNDR, so it is unlikely that either method would not detect a heart condition. However, the 22% and 0% respective FPDR signifies that the wavelet method is likely to detect false heart conditions, while the simplicity method is not. The two technical metrics are the true murmur detection rate (TMDR) and the false murmur detection rate (FMDR), which count sound segments rather than heart cycles. Both methods are equally likely to detect true murmurs given their 83% TMDR. However, the 13% and 0% respective FMDR implies that the wavelet method is susceptible to detecting false murmurs, while the simplicity method is not. Simplicity-based segmentation, therefore, demonstrates superior performance to wavelet-based segmentation, as both are equally likely to detect true murmurs, but only the simplicity method has no chance of detecting false murmurs.

The presence of acoustic noise in audio recordings is an ongoing issue that plagues many applications. This ambient background noise is difficult to reduce due to its unpredictable nature. Many single channel noise reduction techniques exist but are limited in that they may distort the desired speech signal due to overlapping spectral content of the speech and noise. It is therefore of interest to investigate the use of multichannel noise reduction algorithms to further attenuate noise while attempting to preserve the speech signal of interest. Specifically, this thesis looks to investigate the use of microphone arrays in conjunction with multichannel noise reduction algorithms to aid aiding in speaker identification. Recording a speaker in the presence of acoustic background noise ultimately limits the performance and confidence of speaker identification algorithms. In situations where it is impossible to control the noise environment where the speech sample is taken, noise reduction algorithms must be developed and applied to clean the speech signal in order to give speaker identification software a chance at a positive identification. Due to the limitations of single channel techniques, it is of interest to see if spatial information provided by microphone arrays can be exploited to aid in speaker identification. This thesis provides an exploration of several time domain multichannel noise reduction techniques including delay sum beamforming, multi-channel Wiener filtering, and Spatial-Temporal Prediction filtering. Each algorithm is prototyped and filter performance is evaluated using various simulations and experiments. A three-dimensional noise model is developed to simulate and compare the performance of the above methods and experimental results of three data collections are presented and analyzed. The algorithms are compared and recommendations are given for the use of each technique. Finally, ideas for future work are discussed to improve performance and implementation of these multichannel algorithms. Possible applications for this technology include audio surveillance, identity verification, video chatting, conference calling and sound source localization.

Systémy rozpoznávání řeči v dnešní době dosahují poměrně vysoké úspěšnosti. V případě řeči, která je snímána vzdáleným mikrofonem a je tak narušena množstvím šumu a dozvukem (reverberací), je ale přesnost rozpoznávání značně zhoršena. Tento problém je možné zmírnit využitím mikrofonních polí. Tato práce se zabývá technikami, které umožňují kombinovat signály z více mikrofonů tak, aby byla zlepšena kvalita výsledného signálu a tedy i přesnost rozpoznávání. Práce nejprve shrnuje teorii rozpoznávání řeči a uvádí nejpoužívanější algoritmy pro zpracování mikrofonních polí. Následně jsou demonstrovány a analyzovány výsledky použití dvou metod pro beamforming a metody dereverberace vícekanálových signálů. Na závěr je vyzkoušen alternativní způsob beamformingu za použití neuronových sítí.

abstract: Ultrasound B-mode imaging is an increasingly significant medical imaging modality for clinical applications. Compared to other imaging modalities like computed tomography (CT) or magnetic resonance imaging (MRI), ultrasound imaging has the advantage of being safe, inexpensive, and portable. While two dimensional (2-D) ultrasound imaging is very popular, three dimensional (3-D) ultrasound imaging provides distinct advantages over its 2-D counterpart by providing volumetric imaging, which leads to more accurate analysis of tumor and cysts. However, the amount of received data at the front-end of 3-D system is extremely large, making it impractical for power-constrained portable systems. In this thesis, algorithm and hardware design techniques to support a hand-held 3-D ultrasound imaging system are proposed. Synthetic aperture sequential beamforming (SASB) is chosen since its computations can be split into two stages, where the output generated of Stage 1 is significantly smaller in size compared to the input. This characteristic enables Stage 1 to be done in the front end while Stage 2 can be sent out to be processed elsewhere. The contributions of this thesis are as follows. First, 2-D SASB is extended to 3-D. Techniques to increase the volume rate of 3-D SASB through a new multi-line firing scheme and use of linear chirp as the excitation waveform, are presented. A new sparse array design that not only reduces the number of active transducers but also avoids the imaging degradation caused by grating lobes, is proposed. A combination of these techniques increases the volume rate of 3-D SASB by 4\texttimes{} without introducing extra computations at the front end. Next, algorithmic techniques to further reduce the Stage 1 computations in the front end are presented. These include reducing the number of distinct apodization coefficients and operating with narrow-bit-width fixed-point data. A 3-D die stacked architecture is designed for the front end. This highly parallel architecture enables the signals received by 961 active transducers to be digitalized, routed by a network-on-chip, and processed in parallel. The processed data are accumulated through a bus-based structure. This architecture is synthesized using TSMC 28 nm technology node and the estimated power consumption of the front end is less than 2 W. Finally, the Stage 2 computations are mapped onto a reconfigurable multi-core architecture, TRANSFORMER, which supports different types of on-chip memory banks and run-time reconfigurable connections between general processing elements and memory banks. The matched filtering step and the beamforming step in Stage 2 are mapped onto TRANSFORMER with different memory configurations. Gem5 simulations show that the private cache mode generates shorter execution time and higher computation efficiency compared to other cache modes. The overall execution time for Stage 2 is 14.73 ms. The average power consumption and the average Giga-operations-per-second/Watt in 14 nm technology node are 0.14 W and 103.84, respectively. / Dissertation/Thesis / Doctoral Dissertation Engineering 2019

Millimeter wave (mmWave) spectrum above 30 GHz offers us an opportunity to pursue high-data-rate transmission using a channel bandwidth up to several gigahertz. To provide reliable link quality in mmWave frequency bands, hybrid analog-digital beamforming plays a crucial role in overcoming severe path loss and, meanwhile, satisfies the demand for low-power-consumption radio frequency (RF) devices. Implementing hybrid beamforming based on available channel state information (CSI) is a common solution in the hybrid beamforming literature. However, many reference methods underestimate the computational complexity of channel estimation for large antenna arrays or subsequent steps, such as the singular value decomposition of a channel matrix. To this end, we present a low-complexity scheme that exploits implicit channel knowledge to facilitate the design of hybrid beamforming for frequency-selective fading channels. We start from the study of channel estimation using the orthogonal matching pursuit (OMP) algorithm and realize that the problems of channel estimation and analog beam selection are equivalent if the candidates for analog beamforming vectors in the codebooks are mutually orthogonal. This implies that when orthogonal codebooks are employed, the observations used in channel estimation for large antenna arrays can be used to implement hybrid beamforming directly. The above-mentioned observations can be regarded as \textbf{implicit CSI}; they are coupling coefficients of all possible pairs of analog beamforming vectors on both sides of the channel. The idea of using implicit CSI to implement hybrid beamforming is further extended to the cases of non-orthogonal codebooks. Instead of calculating the mutual information between the transmitter and receiver, we focus on small-size coupling matrices between beam patterns selected by using appropriate key parameters as performance indicators. Therefore, the proposed hybrid beamforming method becomes much simpler: it amounts to collecting different sets of large-power coupling coefficients to construct multiple alternatives to an effective channel matrix. Then, the set yielding the largest Frobenius norm (or the largest absolute value of the determinant) of the effective channel provides the solution to the hybrid beamforming problem. The proposed hybrid beamforming approach clearly shows that the performance of hybrid beamforming is dominated by the quality of the coupling coefficients. Considering a fixed-length training sequence, we exploit mmWave channels' sparsity shown in the delay and angular domains to refine the quality of the coupling coefficients as well as to improve the hybrid beamforming performance.

Wideband beamforming have been widely used in modern radar systems. One of the powerful wideband beamforming techniques that is capable of achieving a high selectivity over a wide bandwidth is the nested array (NA) beamformer. Such a beamformer consists of nested antenna arrays, 2-D spatio-temporal filters, and multirate filterbanks. Speed of operation is bounded by the speed of the hardware implementation. This dissertation presents the use of a systematic methodology for design space exploration of the NA beamformer basic building blocks. The efficient systolic array design in terms of the highest possible clock speed of each block was selected for hardware implementation. The proposed systolic array designs and the conventional designs were implemented in FPGA hardware to verify their functionality and compare their erformance. The implementations results confirm that the proposed systolic array implementations are faster and requires less hardware resources than the published designs. The overall beamformer FPGA implementation is constructed based on the analysis of efficient systolic arrays designs of the beamformer building blocks. The implemented overall structure is then validated to ensure its proper operation. Further, the implementation performance is evaluated in terms of accuracy and error analysis in comparison to the MATLAB simulations. The new methodology is based on the systematic methodology to close the gap between the modern wideband radar I/O rates and the silicon operating speed. This new metodology is applied to the interpolator block as an example. The proposed methodology is simulated and tested using MATLAB object oriented programming (OOP) to ensure the proper operation. / Graduate / 2020-11-17

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Radio frequency interference (RFI) mitigation enables radio astronomical observation in frequency bands that are shared with many modern satellite and ground based devices by filtering out the interference in corrupted bands. The present work documents the development of a beamformer (spatial filter) equipped with RFI mitigation capabilities. The beamformer is intended for systems with antenna arrays designed for large bandwidths. Because array data post processing on large bandwidths would require massive memory space beyond feasible limits, there is a need for a RFI mitigation system capable of doing processing on the data as it arrives in real-time; storing only a data reduced result into long term memory. The real-time system is designed to be implemented on both the FLAG phased array feed (PAF) on the Green Bank telescope in West Virginia, as well as future radio astronomy projects. It will also serve as the anti-jamming component in communications applications developed for the United States office of naval research (ONR). Implemented on a graphical processing unit (GPU), this beamformer demonstrates a working single step filter using nVidia's CUDA technology, technology with high-speed parallelism that makes real-time RFI mitigation possible.