<p> Vowel harmony has been of great interest in phonological research. It has been widely accepted that vowel harmony is a phonetically natural phenomenon, which means that it is a common pattern because it provides advantages to the speaker in articulation and to the listener in perception. </p><p> Exceptional patterns proved to be a challenge to the phonetically grounded analysis as they, by their nature, introduce phonetically disadvantageous sequences to the surface form, that consist of harmonically different vowels. Such forms are found, for example in the Finnish stem <i>tuoli</i> 'chair' or in the Hungarian suffixed form <i><b>hi:d-hoz</b></i> 'to the bridge', both word forms containing a mix of front and back vowels. There has recently been evidence shown that there might be a phonetic level explanation for some exceptional patterns, as the possibility that some vowels participating in irregular stems (like the vowel [i] in the Hungarian stem <i> hi:d</i> 'bridge' above) differ in some small phonetic detail from vowels in regular stems. The main question has not been raised, though: does this phonetic detail matter for speakers? Would they use these minor differences when they have to categorize a new word as regular or irregular?</p><p> A different recent trend in explaining morphophonological exceptionality by looking at the phonotactic regularities characteristic of classes of stems based on their morphological behavior. Studies have shown that speakers are aware of these regularities, and use them as cues when they have to decide what class a novel stem belongs to. These sublexical phonotactic regularities have already been shown to be present in some exceptional patterns vowel harmony, but many questions remain open: how is learning the static generalization linked to learning the allomorph selection facet of vowel harmony? How much does the effect of consonants on vowel harmony matter, when compared to the effect of vowel-to-vowel correspondences?</p><p> This dissertation aims to test these two ideas --- that speakers use phonetic cues and/or that they use sublexical phonotactic regularities in categorizing stems as regular or irregular --- and attempt to answer the more detailed questions, like the effect of consonantal patterns on exceptional patterns or the link between allomorph selection and static phonotactic generalizations as well. The phonetic hypothesis is tested on the Hungarian antiharmonicity pattern (stems with front vowels consistently selecting back suffixes, like in the example <i>hi:d-hoz</i> 'to the bridge' above), and the results indicate that while there may be some small phonetic differences between vowels in regular and irregular stems, speakers do not use these, or even enhanced differences when they have to categorize stems.</p><p> The sublexical hypothesis is tested and confirmed by looking at the disharmonicity pattern in Finnish. In Finnish, stems that contain both back and certain front vowels are frequent and perfectly grammatical, like in the example <i> tuoli</i> 'chair' above, while the mixing of back and some other front vowels is very rare and mostly confined to loanwords like <i>amat&oslash;&oslash;ri </i> 'amateur'. It will be seen that speakers do use sublexical phonotactic regularities to decide on the acceptability of novel stems, but certain patterns that are phonetically or phonologically more natural (vowel-to-vowel correspondences) seem to matter much more than other effects (like consonantal effects).</p><p> Finally, a computational account will be given on how exceptionality might be learned by speakers by using maximum entropy grammars available in the literature to simulate the acquisition of the Finnish disharmonicity pattern. It will be shown that in order to clearly model the overall behavior on the exact pattern, the learner has to have access not only to the lexicon, but also to the allomorph selection patterns in the language.</p>

Real world perceptual environments are characterized by objects that often co-occur, occlude one another, and display time-variant behavior. In addition there may be variations in the signal-to-noise ratio. Successful object recognition depends on the extraction of adequate disambiguating features, which are neither easily identifiable nor stationary in such environments. In an effort to improve recognition accuracy and do so efficiently, Adaptive Perceptual Systems have emerged that re-configure their signal processing in response to variations in the signal to ensure extraction of adequate features. Key to adaptive signal processing is determining when and in what manner to modify signal processing. Symbolic object models play a pivotal role in this process by serving to interpret data, predict signal behavior and account for interference from objects simultaneously present. Unfortunately, symbolic object models are typically hand-crafted, a tedious error-prone task that constitutes a knowledge acquisition bottleneck, which limits object database size and impedes deployment for new and changing environments. This thesis explores the integration of feature extraction with model construction, viewing the two processes as driving one another until the goal of producing unambiguous symbolic object models is satisfied. The paradigm has been fielded to acquire acoustic-event models for a sound understanding system.

<p> The objective of this dissertation is the design, development, and experimental evaluation of novel algorithms and reconfigurable radio architectures for spectrally efficient cognitive networking in terrestrial, airborne, and underwater environments. Next-generation wireless communication architectures and networking protocols that maximize spectrum utilization efficiency in congested/contested or low-spectral availability (extreme) communication environments can enable a rich body of applications with unprecedented societal impact. In recent years, underwater wireless networks have attracted significant attention for military and commercial applications including oceanographic data collection, disaster prevention, tactical surveillance, offshore exploration, and pollution monitoring. Unmanned aerial systems that are autonomously networked and fully mobile can assist humans in extreme or difficult-to-reach environments and provide cost-effective wireless connectivity for devices without infrastructure coverage. </p><p> Cognitive radio (CR) has emerged as a promising technology to maximize spectral efficiency in dynamically changing communication environments by adaptively reconfiguring radio communication parameters. At the same time, the fast developing technology of software-defined radio (SDR) platforms has enabled hardware realization of cognitive radio algorithms for opportunistic spectrum access. However, existing algorithmic designs and protocols for shared spectrum access do not effectively capture the interdependencies between radio parameters at the physical (PHY), medium-access control (MAC), and network (NET) layers of the network protocol stack. In addition, existing off-the-shelf radio platforms and SDR programmable architectures are far from fulfilling runtime adaptation and reconfiguration across PHY, MAC, and NET layers. Spectrum allocation in cognitive networks with multi-hop communication requirements depends on the location, network traffic load, and interference profile at each network node. As a result, the development and implementation of algorithms and cross-layer reconfigurable radio platforms that can jointly treat space, time, and frequency as a unified resource to be dynamically optimized according to inter- and intra-network interference constraints is of fundamental importance. </p><p> In the next chapters, we present novel algorithmic and software/hardware implementation developments toward the deployment of spectrally efficient terrestrial, airborne, and underwater wireless networks. In Chapter 1 we review the state-of-art in commercially available SDR platforms, describe their software and hardware capabilities, and classify them based on their ability to enable rapid prototyping and advance experimental research in wireless networks. Chapter 2 discusses system design and implementation details toward real-time evaluation of a software-radio platform for all-spectrum cognitive channelization in the presence of narrowband or wideband primary stations. All-spectrum channelization is achieved by designing maximum signal-to-interference-plus-noise ratio (SINR) waveforms that span the whole continuum of the device-accessible spectrum, while satisfying peak power and interference temperature (IT) constraints for the secondary and primary users, respectively. In Chapter 3, we introduce the concept of all-spectrum channelization based on max-SINR optimized sparse-binary waveforms, we propose optimal and suboptimal waveform design algorithms, and evaluate their SINR and bit-error-rate (BER) performance in an SDR testbed. Chapter 4 considers the problem of channel estimation with minimal pilot signaling in multi-cell multi-user multi-input multi-output (MIMO) systems with very large antenna arrays at the base station, and proposes a least-squares (LS)-type algorithm that iteratively extracts channel and data estimates from a short record of data measurements. Our algorithmic developments toward spectrally-efficient cognitive networking through joint optimization of channel access code-waveforms and routes in a multi-hop network are described in Chapter 5. Algorithmic designs are software optimized on heterogeneous multi-core general-purpose processor (GPP)-based SDR architectures by leveraging a novel software-radio framework that offers self-optimization and real-time adaptation capabilities at the PHY, MAC, and NET layers of the network protocol stack. Our system design approach is experimentally validated under realistic conditions in a large-scale hybrid ground-air testbed deployment. Chapter 6 reviews the state-of-art in software and hardware platforms for underwater wireless networking and proposes a software-defined acoustic modem prototype that enables (i) cognitive reconfiguration of PHY/MAC parameters, and (ii) cross-technology communication adaptation. The proposed modem design is evaluated in terms of effective communication data rate in both water tank and lake testbed setups. In Chapter 7, we present a novel receiver configuration for code-waveform-based multiple-access underwater communications. The proposed receiver is fully reconfigurable and executes (i) all-spectrum cognitive channelization, and (ii) combined synchronization, channel estimation, and demodulation. Experimental evaluation in terms of SINR and BER show that all-spectrum channelization is a powerful proposition for underwater communications. At the same time, the proposed receiver design can significantly enhance bandwidth utilization. Finally, in Chapter 8, we focus on challenging practical issues that arise in underwater acoustic sensor network setups where co-located multi-antenna sensor deployment is not feasible due to power, computation, and hardware limitations, and design, implement, and evaluate an underwater receiver structure that accounts for multiple carrier frequency and timing offsets in virtual (distributed) MIMO underwater systems.</p><p>

Signal understanding systems have the difficult task of interpreting environmental signals: decomposing them and explaining their components in terms of an arbitrary number of instances of perceptual object categories whose properties can interact with one another. This dissertation addresses the problem of designing blackboard-based perceptual systems for interpreting signals from complex environments. A "complex environment" is one that can (1) produce signal-to-noise ratios that vary unpredictably over time, and (2) can contain perceptual objects that mutually interfere with each others' signal signature, or have arbitrary time-dependent behaviors. The traditional design paradigm for perceptual systems assumes that some particular set of fixed front-end signal processing algorithms (SPAs) can provide adequate evidence for reliable interpretations regardless of the range of possible scenarios in the environment. In complex environments, with their dynamic character, however, a commitment to parameter values inappropriate to the current scenario can render a perceptual system unable to interpret entire classes of environmental events correctly. To address these problems, this research advocates a new view of signal interpretation as the product of two interacting search processes. The first search process involves the dynamic, context-dependent selection of signal features and interpretation hypotheses, and the second involves the dynamic, context-dependent selection of appropriate SPAs for extracting evidence to support the features. For structuring bidirectional interaction between the search processes, this dissertation presents the Integrated Processing and Understanding of Signals (IPUS) architecture as a formal and domain-independent blackboard-based approach. The architecture is instantiated by a domain's formal signal processing theory, and has four components for organizing and applying signal processing theory: discrepancy detection, discrepancy diagnosis, differential diagnosis, and signal reprocessing. IPUS uses an iterative process of "discrepancy detection, diagnosis, reprocessing" for converging to the appropriate SPAs and interpretations. Convergence is driven by the goal of eliminating or reducing various categories of interpretation uncertainty. This dissertation discusses the IPUS architecture's features, the basic problem of auditory scene analysis (the application domain used in testing IPUS), and evaluates performance results in experiment suites that test the utility of the reprocessing loop and the ability of the architecture to apply special-purpose SPAs effectively. Although the specific research reported herein focuses on acoustic signal understanding, the general IPUS framework appears applicable to the design of perceptual systems for a wide variety of sensory modalities.