Dynamic binary translation using run-time feedbacks

Dynamic binary translation is the process of translating, modifying and rewriting executable (binary) code from one machine to another at run-time. This process of low-level re-engineering consists of a reverse engineering phase followed by a forward engineering phase. UQDBT, the University of Queensland Dynamic Binary Translator, is a machine-adaptable translator. Adaptability is provided through the specification of properties of machines and their instruction sets, allowing the support of different pairs of source and target machines. Most binary translators are closely bound to a pair of machines, making analyses and code hard to reuse. Like most virtual machines, UQDBT performs generic optimizations that apply to a variety of machines. Frequently executed code is translated to native code by the use of edge weight instrumentation, which makes UQDBT converge more quickly than systems based on instruction speculation. In this paper, we describe the architecture and run-time feedback optimizations performed by the UQDBT system, and provide results obtained in the x86 and SPARC® platforms.

Microbial diversity within the water column of a larval rearing system for the ornate rock lobster (Panulirus ornatus)

The ornate tropical rock lobster, Panulirus ornatus has substantial potential as an aquaculture species though disease outbreaks during the animal's extended larval lifecycle are major constraints for success. In order to effectively address such disease-related issues, an improved understanding of the composition and dynamics of the microbial communities in the larval rearing tanks is required. This study used flow cytometry and molecular microbial techniques (clone libraries and denaturing gradient gel electrophoresis (DGGE)) to quantify and characterise the microbial community of the water column in the early stages (developmental stage I-II) of a P. ornatus larval rearing system. DGGE analysis of a 5000 L larval rearing trial demonstrated a dynamic microbial community with distinct changes in the community structure after initial stocking (day I to day 2) and from day 4 to day 5, after which the structure was relatively stable. Flow cytometry analysis of water samples taken over the duration of the trial demonstrated a major increase in bacterial load leading up to and peaking on the first day of the initial larval moult (day 7), before markedly decreasing prior to when > 50% of larvae moulted (day 9). A clone library of a day 10 water sample taken following a mass larval mortality event reflected high microbial diversity confirmed by statistical analysis indices. Sequences retrieved from both clone library and DGGE analyses were dominated by gamma- and alpha-Proteobacteria affiliated organisms with additional sequences affiliated with beta- and epsilon-Proteobacteria, Bacteroidetes, Cytophagales and Chlamydiales groups. Vibrio affiliated species were commonly retrieved in the clone library, though absent from DGGE analysis.

Stochastic dynamic programming for election timing: A game theory approach

In this paper, we consider dynamic programming for the election timing in the majoritarian parliamentary system such as in Australia, where the government has a constitutional right to call an early election. This right can give the government an advantage to remain in power for as long as possible by calling an election, when its popularity is high. On the other hand, the opposition's natural objective is to gain power, and it will apply controls termed as "boosts" to reduce the chance of the government being re-elected by introducing policy and economic responses. In this paper, we explore equilibrium solutions to the government, and the opposition strategies in a political game using stochastic dynamic programming. Results are given in terms of the expected remaining life in power, call and boost probabilities at each time at any level of popularity.

Stochastic models for mainland-island metapopulations in static and dynamic landscapes

This paper has three primary aims: to establish an effective means for modelling mainland-island metapopulations inhabiting a dynamic landscape: to investigate the effect of immigration and dynamic changes in habitat on metapopulation patch occupancy dynamics; and to illustrate the implications of our results for decision-making and population management. We first extend the mainland-island metapopulation model of Alonso and McKane [Bull. Math. Biol. 64:913-958,2002] to incorporate a dynamic landscape. It is shown, for both the static and the dynamic landscape models, that a suitably scaled version of the process converges to a unique deterministic model as the size of the system becomes large. We also establish that. under quite general conditions, the density of occupied patches, and the densities of suitable and occupied patches, for the respective models, have approximate normal distributions. Our results not only provide us with estimates for the means and variances that are valid at all stages in the evolution of the population, but also provide a tool for fitting the models to real metapopulations. We discuss the effect of immigration and habitat dynamics on metapopulations, showing that mainland-like patches heavily influence metapopulation persistence, and we argue for adopting measures to increase connectivity between this large patch and the other island-like patches. We illustrate our results with specific reference to examples of populations of butterfly and the grasshopper Bryodema tuberculata.

Temporal variation in habitat use by nekton in a subtropical estuarine system

Utilisation by fish of different estuarine habitats is known to vary at many different temporal scales, however no study to date has examined how utilisation varies at all the relevant times scales simultaneously. Here, we compare the utilisation by fish of sandy, intertidal foreshore habitats in a subtropical estuary at four temporal scales: between major spawning periods (spring/ summer and winter), among months within spawning periods, between the full and new moon each month, and between night and day within those lunar phases. Comparisons of assemblage composition, abundance of individuals and of fish in seven different,ecological guilds' were used to identify the temporal scales at which fish varied their use of unvegetated sandy habitats in the lower Noosa Estuary, Queensland, Australia. Fish assemblages were sampled with a seine net at three different regions. The most numerically dominant species caught were southern herring (Herklotsichthys castelnaui: Clupeidae), sand whiting (Sillago ciliata: Sillaginidae), weeping toadfish (Torquigener pleurogramma: Tetraodomidae), and silver biddy (Gerres subfasciatus: Gerreidae). Considerable variation at a range of temporal scales from short term (day versus night) to longer term (spawning periods) was detected for all but one of the variables examined. The clearest patterns were observed for diurnal effects, where generally abundance was greater at night than during the day. There were also strong lunar effects, although there were no consistent patterns between full moon and new moon periods. Significant differences among months within spawning periods were more common than differences between the actual spawning periods. The results clearly indicate that utilisation of sandy, unvegetated estuarine habitats is very dynamic and highly variable in space and time. (c) 2006 Elsevier B.V. All rights reserved.

Modeling and exploiting behavior patterns in dynamic environments

This paper presents a new approach to improving the effectiveness of autonomous systems that deal with dynamic environments. The basis of the approach is to find repeating patterns of behavior in the dynamic elements of the system, and then to use predictions of the repeating elements to better plan goal directed behavior. It is a layered approach involving classifying, modeling, predicting and exploiting. Classifying involves using observations to place the moving elements into previously defined classes. Modeling involves recording features of the behavior on a coarse grained grid. Exploitation is achieved by integrating predictions from the model into the behavior selection module to improve the utility of the robot's actions. This is in contrast to typical approaches that use the model to select between different strategies or plays. Three methods of adaptation to the dynamic features of the environment are explored. The effectiveness of each method is determined using statistical tests over a number of repeated experiments. The work is presented in the context of predicting opponent behavior in the highly dynamic and multi-agent robot soccer domain (RoboCup)

Multi-robot control in highly dynamic, competitive environments

The control and coordination of multiple mobile robots is a challenging task; particularly in environments with multiple, rapidly moving obstacles and agents. This paper describes a robust approach to multi-robot control, where robustness is gained from competency at every layer of robot control. The layers are: (i) a central coordination system (MAPS), (ii) an action system (AES), (iii) a navigation module, and (iv) a low level dynamic motion control system. The multi-robot coordination system assigns each robot a role and a sub-goal. Each robot’s action execution system then assumes the assigned role and attempts to achieve the specified sub-goal. The robot’s navigation system directs the robot to specific goal locations while ensuring that the robot avoids any obstacles. The motion system maps the heading and speed information from the navigation system to force-constrained motion. This multi-robot system has been extensively tested and applied in the robot soccer domain using both centralized and distributed coordination.

A low-cost, easy-to-use, real-time power system simulator

We present the design rationale and basic workings of a low-cost, easy-to-use power system simulator developed to support investigations into human interface design for a hydropower plant. The power system simulator is based on three important components: models of power system components, a data repository, and human interface elements. Dynamic Data Exchange (DDE) allows simulator components to communicate with each other within the simulator. To construct the modules of the simulator we have combined the advantages of commercial software such as Matlab/Simulink, ActiveX Control, Visual Basic and Excel and integrated them in the simulator. An important advantage of our approach is that further components of the simulator now can be developed independently. An initial assessment of the simulator indicates it is fit for intended purpose.

Towards a standards-based autonomic context management system

Pervasive computing applications must be sufficiently autonomous to adapt their behaviour to changes in computing resources and user requirements. This capability is known as context-awareness. In some cases, context-aware applications must be implemented as autonomic systems which are capable of dynamically discovering and replacing context sources (sensors) at run-time. Unlike other types of application autonomy, this kind of dynamic reconfiguration has not been sufficiently investigated yet by the research community. However, application-level context models are becoming common, in order to ease programming of context-aware applications and support evolution by decoupling applications from context sources. We can leverage these context models to develop general (i.e., application-independent) solutions for dynamic, run-time discovery of context sources (i.e., context management). This paper presents a model and architecture for a reconfigurable context management system that supports interoperability by building on emerging standards for sensor description and classification.

A real options based method for power system planning

A deregulated electricity market is characterized with uncertainties, with both long and short terms. As one of the major long term planning issues, the transmission expansion planning (TEP) is aiming at implementing reliable and secure network support to the market participants. The TEP covers two major issues: technical assessment and financial evaluations. Traditionally, the net present value (NPV) method is the most accepted for financial evaluations, it is simple to conduct and easy to understand. Nevertheless, TEP in a deregulated market needs a more dynamic approach to incorporate a project's management flexibility, or the managerial ability to adapt in response to unpredictable market developments. The real options approach (ROA) is introduced here, which has clear advantage on counting the future course of actions that investors may take, with understandable results in monetary terms. In the case study, a Nordic test system has been testified and several scenarios are given for network expansion planning. Both the technical assessment and financial evaluation have been conducted in the case study.

Optimal design of a regenerative dynamic dynamometer using genetic algorithms

This paper presents an approach for optimal design of a fully regenerative dynamic dynamometer using genetic algorithms. The proposed dynamometer system includes an energy storage mechanism to adaptively absorb the energy variations following the dynamometer transients. This allows the minimum power electronics requirement at the mains power supply grid to compensate for the losses. The overall dynamometer system is a dynamic complex system and design of the system is a multi-objective problem, which requires advanced optimisation techniques such as genetic algorithms. The case study of designing and simulation of the dynamometer system indicates that the genetic algorithm based approach is able to locate a best available solution in view of system performance and computational costs.