No description available.

A new broadband beamformer composed of nested arrays (NAs), multi-dimensional (MD) filters, and multirate techniques is proposed for both linear and planar arrays. It is shown that this combination results in frequency-invariant response. For a given number of sensors, the advantage of using NAs is that the effective aperture for low temporal frequencies is larger than in the case of using uniform arrays. This leads to high spatial selectivity for low frequencies. For a given aperture size, the proposed beamformer can be implemented with significantly fewer sensors and less computation than uniform arrays with a slight deterioration in performance. Taking advantage of the Noble identity and polyphase structures, the proposed method can be efficiently implemented. Simulation results demonstrate the good performance of the proposed beamformer in terms of frequency-invariant response and computational requirements. The broadband beamformer requires a filter bank with a non-compatible set of sampling rates which is challenging to be designed. To address this issue, a filter bank design approach is presented. The approach is based on formulating the design problem as an optimization problem with a performance index which consists of a term depending on perfect reconstruction (PR) and a term depending on the magnitude specifications of the analysis filters. The design objectives are to achieve almost perfect reconstruction (PR) and have the analysis filters satisfying some prescribed frequency specifications. Several design examples are considered to show the satisfactory performance of the proposed method. A new blind multi-stage space-time equalizer (STE) is proposed which can separate narrowband sources from a mixed signal. Neither the direction of arrival (DOA) nor a training sequence is assumed to be available for the receiver. The beamformer and equalizer are jointly updated to combat both co-channel interference (CCI) and inter-symbol interference (ISI) effectively. Using subarray beamformers, the DOA, possibly time-varying, of the captured signal is estimated and tracked. The estimated DOA is used by the beamformer to provide strong CCI cancellation. In order to alleviate inter-stage error propagation significantly, a mean-square-error sorting algorithm is used which assigns detected sources to different stages according to the reconstruction error at different stages. Further, to speed up the convergence, a simple-yet-efficient DOA estimation algorithm is proposed which can provide good initial DOAs for the multi-stage STE. Simulation results illustrate the good performance of the proposed STE and show that it can effectively deal with changing DOAs and time variant channels. / Graduate / 0544 / imanmoaz@uvic.ca

While current approaches to digital image processing within the context of machine learning and deep learning are motivated by biological processes within the human brain, they are, however, also limited due to the current state of the art of input and output devices as well as the algorithms that are concerned with the processing of their data. In order to generate digital images from real-world scenes, the utilized digital images' underlying lattice formats are predominantly based on rectangular or square structures. Yet, the human visual perception system suggests an alternative approach that manifests itself within the sensory cells of the human eye in the form of hexagonal arrangements. As previous research demonstrates that hexagonal arrangements can provide different benefits to image processing systems in general, this contribution is concerned with the synthesis of both worlds in the form of the biologically inspired hexagonal deep learning for hexagonal image processing. This contribution is therefore concerned with the design, the implementation, and the evaluation of hexagonal solutions to currently developed approaches in the form of hexagonal deep neural networks. For this purpose, the respectively realized hexagonal functionality had to be built from the ground up as hexagonal counterparts to otherwise conventional square lattice format based image processing and deep learning based systems. Furthermore, hexagonal equivalents for artificial neural network based operations, layers, as well as models and architectures had to be realized. This also encompasses the related evaluation metrics for hexagonal lattice format based representations of digital images and their conventional counterparts in comparison. Therefore, the developed hexagonal image processing and deep learning framework Hexnet functions as a first general application-oriented open science framework for hexagonal image processing within the context of machine learning. To enable the evaluation of hexagonal approaches, a set of different application areas and use cases within conventional and hexagonal image processing – astronomical, medical, and industrial image processing – are provided that allow an assessment of hexagonal deep neural networks in terms of their classification capabilities as well as their general performance. The obtained and presented results demonstrate the possible benefits of hexagonal deep neural networks and their hexagonal representations for image processing systems. It is shown that hexagonal deep neural networks can result in increased classification capabilities given different basic geometric shapes and contours, which in turn partially translate into their real-world applications. This is indicated by a relative improvement in F1-score for the proposed hexagonal and square models, ranging from 1.00 (industrial image processing) to 1.03 (geometric primitives) with single classes even reaching a relative improvement of over 1.05. However, possible disadvantages are also given by the increased complexity of hexagonal algorithms. This is evident by the present potential in regard to runtime optimizations that have yet to be realized for certain hexagonal operations in comparison to their currently deployed square equivalents.:1 Introduction and Motivation 2 Fundamentals and Methods 3 Implementation 4 Test Results, Evaluation, and Discussion 5 Conclusion and Outlook