No description available.

No description available.

No description available.

Detection and choice research have largely focused on the effects of relative reinforcer frequencies or magnitudes. The effects of punishment have received much less attention. This thesis investigated the effects of punishment on human signal-detection performance using a number of different procedures. These included punisher frequency and magnitude variations, different types of punishers (point loss & time-outs), variations in stimulus disparity, and different detection tasks (judgments of stimulus arrays containing either more blue or red objects, or judgments of statements that were either true or false). It examined whether punishers have similar, but opposite, effects to reinforcers on detection performance, and whether the effects of punishment were successfully captured by existing models of punishment and choice. Experiment 1 varied the relative frequency or magnitude of time-out punishers for errors using the blue/red task. Participants were systematically biased away from the response alternative associated with the higher rate or magnitude of time-out punishers in two of three procedures. Experiment 2 varied the relative frequency of point-loss punishers using the blue/red task and the true/false task. Participants were systematically biased away from the alternative associated with the higher rate of point-loss punishers for the true/false task. Experiment 3 examined the effects of punishment on response bias from a psychophysical perspective. Previous detection research which varied stimulus discriminability while holding reinforcers ratios constant and unequal (Johnstone & Alsop, 2000; McCarthy & Davison, 1984) found that a criterion location measure (e.g., c, Green & Swets, 1966) was a better descriptor of isobias functions compared to a likelihood ratio measure (e.g., log β[G], Green & Swets, 1966). Experiment 3 varied stimulus discriminability while holding punisher ratios constant and unequal. Like previous research, isobias functions were consistent with a criterion location measure. Experiments 4, 5, 6, and 7 examined contemporary models of choice and punishment. Experiments 4, 5, and 6 varied the relative reinforcer ratio in detection tasks, both with and without the inclusion of an equal rate of punishment. Experiment 7 held the reinforcer ratio constant and unequal, and varied the durations of time-out punishers. Increases in preference (for the richer alternative) from reinforcer-only conditions to reinforcer + punisher conditions would support a subtractive model of punishment, while decreases in preference would support an additive model of punishment. Experiment 4 was a between-groups study using time-out punishers. It supported the predictions of an additive model. Experiment 5 used three different procedures in a preliminary within-subjects design, evaluating which procedure was best suited for a larger within-subjects experiment (Experiment 6). In Experiment 6, participants sat four reinforcer-only and four reinforcer + punisher conditions where reinforcers were point-gains and punishers were point-losses. The results from Experiment 6 were mixed - some participants showed increased preference while others showed little change or a slight decrease. This appeared related to the order in which participants received the reinforcer-only and reinforcer + punisher conditions. Experiment 7 also found no consistent change in preference with increases in time-out durations. Instead, there was a slow increase in bias on the richer alternative across the eight sessions. Overall, punishers had similar, but opposite, effects to reinforcers in detection procedures (Experiments 1, 2, & 3). These effects were successfully captured by Davison and Tustin�s (1978) model of detection. The later experiments did not provide support for a subtractive model punishment model of choice, which had provided the best descriptor in corresponding concurrent-schedule research. Instead, Experiment 4 supported an additive model, and Experiments 5, 6, and 7 provided no evidence for either model - limitations and implications of these studies are discussed. However, the present thesis illustrates that the signal detection procedure is promising for studying the combined effects of reinforcement and punishment, and may offer a worthwhile complement to standard concurrent-schedule choice procedures.

Adaptive signal processing has evolved in the last thirty years to the point where its use in sensors such as radar and sonar and in communications is indispensable. High frequency (HF) skywave radars benefit in particular from spatial and spatio-temporal adaptive filters, detectors and estimators due to their operation in an environment which is crowded with natural and man-made interferences, as well as significant temporal and spatial distortions due to ionospheric propagation. While adaptive processing is important for other types of sensors, including airborne radars, HF radar systems are particularly well-suited to its application, given the modern digital receiver-per-element arrays and radar facilities able to host large computational resources. This allows use of algorithms viewed as merely theoretical benchmarks for other systems. / However, despite the tremendous advances in radar adaptive signal processing theory since its foundation in the 1960s, a number of important issues have still not been addressed fully. In particular, the problem of limitations in available training data for adaptive estimation has, if anything, become more acute in recent years. In the case of HF radar, the hundreds of degrees of freedom presented by the typical HF array prevent the application of conventional techniques, not because of computational cost, but due to insufficient training sample support. Furthermore, new architectures for next generation systems including two-dimensional transmit and receive antenna arrays with MIMO technology to support non-causal adaptivity on transmit will further increase the demand for training data, making an already significant problem even more important in the future. / The following broad problems are found to be the most important at this stage: Without a prior knowledge of particular radar scenarios, how can the suitability of its adaptively reconstructed model for an associated radar inference be verified; what are the ultimate capabilities of adaptive techniques in the pre-asymptotic domain, beyond which the adaptive detection/ estimation problem cannot provide a consistent solution, and how can that limit be assessed in the absence of defined exact finite-sample statistical properties or by resorting to standard large-sample asymptotics; given a limited training data volume, what is this mix of credible a priori assumptions (parametric models) regarding this radar scenario, on one hand, and its adaptive estimation on the other? / Clearly each of these major questions is too complex to be comprehensively addressed in a single study. But this thesis (and the associated publications), by providing further understandings in each of these areas, introduces important results to the field of adaptive processing in the presence of low training sample support. / Thesis (PhDTelecommunications)--University of South Australia, 2009

This thesis verifies the Capability Exchange Signalling (CES) protocol, a control protocol for multimedia communications developed by the International Telecommunication Union. Using the CES protocol as a vehicle, the thesis also proposes and investigates the idea of exploiting structural regularities of state spaces to obtain symbolic representations of state spaces and their associated deterministic Finite State Automata (FSA) that represent service (or protocol) languages. This is an important step towards parametric verification of the CES protocol, because the symbolic representations can be analysed for arbitrary values of parameters. It also provides valuable experience for parametric verification of other protocols having integer parameters (such as channel capacities). / Thesis (PhDComputerSystemsEng)--University of South Australia, 2006.

No description available.

vii, 200 leaves : ill. ; 29 cm / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, Dept. of Psychology, 1975

Adaptive signal processing has evolved in the last thirty years to the point where its use in sensors such as radar and sonar and in communications is indispensable. High frequency (HF) skywave radars benefit in particular from spatial and spatio-temporal adaptive filters, detectors and estimators due to their operation in an environment which is crowded with natural and man-made interferences, as well as significant temporal and spatial distortions due to ionospheric propagation. While adaptive processing is important for other types of sensors, including airborne radars, HF radar systems are particularly well-suited to its application, given the modern digital receiver-per-element arrays and radar facilities able to host large computational resources. This allows use of algorithms viewed as merely theoretical benchmarks for other systems. / However, despite the tremendous advances in radar adaptive signal processing theory since its foundation in the 1960s, a number of important issues have still not been addressed fully. In particular, the problem of limitations in available training data for adaptive estimation has, if anything, become more acute in recent years. In the case of HF radar, the hundreds of degrees of freedom presented by the typical HF array prevent the application of conventional techniques, not because of computational cost, but due to insufficient training sample support. Furthermore, new architectures for next generation systems including two-dimensional transmit and receive antenna arrays with MIMO technology to support non-causal adaptivity on transmit will further increase the demand for training data, making an already significant problem even more important in the future. / The following broad problems are found to be the most important at this stage: Without a prior knowledge of particular radar scenarios, how can the suitability of its adaptively reconstructed model for an associated radar inference be verified; what are the ultimate capabilities of adaptive techniques in the pre-asymptotic domain, beyond which the adaptive detection/ estimation problem cannot provide a consistent solution, and how can that limit be assessed in the absence of defined exact finite-sample statistical properties or by resorting to standard large-sample asymptotics; given a limited training data volume, what is this mix of credible a priori assumptions (parametric models) regarding this radar scenario, on one hand, and its adaptive estimation on the other? / Clearly each of these major questions is too complex to be comprehensively addressed in a single study. But this thesis (and the associated publications), by providing further understandings in each of these areas, introduces important results to the field of adaptive processing in the presence of low training sample support. / Thesis (PhDTelecommunications)--University of South Australia, 2009

The fungus Stagonospora nodorum is the causal agent of leaf and glume blotch disease on wheat and is an emerging model for the study of the interaction between plants and necrotrophic fungal pathogens. Signal transduction plays a critical role during infection by allowing the pathogen to sense and appropriately respond to environmental changes. The role of signal transduction in the pathogenicity of S. nodorum was analysed by the targeted inactivation of genes encoding a Gá subunit (Gna1) and a mitogen-activated protein kinase (Mak2). Strains carrying the inactivated genes were impaired in virulence and demonstrated a host of phenotypic impairments such as abolished sporulation. Therefore, it was hypothesised that Gna1 and Mak2 regulate downstream effector molecules that are critical for pathogenic development. A 2D gel-based proteomic approach was used to compare the extracellular and intracellular proteomes of the wild-type fungus and signalling mutants for differences in protein abundance. Tandem mass spectrometry (LC-MS/MS) analysis and patternmatching against the S. nodorum genome sequence led to the identification of 26 genes from 34 differentially abundant protein spots. These genes possess probable roles in protein cycling, plant cell wall degradation, stress response, nucleotide metabolism, proteolysis, quinate and secondary metabolism. A putative short-chain dehydrogenase gene (Sch1) was identified and its expression was shown to be reduced in both signalling mutants. The transcript level of Sch1 increased during the latter period of infection coinciding with pycnidiation. Sch1 was inactivated by targeted gene deletion. Mutants were able to effectively colonise the host but asexual sporulation was dramatically reduced and pycnidial ontogeny was severely disrupted. Furthermore, the sch1 mutants showed alterations in the metabolome. GC-MS analysis identified a metabolite which accumulated in the sch1 mutants. Computational and database analyses indicated that the compound possesses a cyclic carbon backbone. Based on these findings, Sch1 may be a suitable target for fungicides that inhibit asexual sporulation and the accumulated compound may be used to design novel antifungal compounds. 2D SDS-PAGE analysis identified increased abundance of another putative short-chain dehydrogenase (Sch2) and a nitroreductase in the sch1-deleted background. It was also shown that Sch2 was regulated by Gna1.