Artificial Neural Network Based Automatic Emergency Landing Site Selection for UAVs and Highly Automated Aircraft

In this report an artificial neural network (ANN) based automated emergency landing site selection system for unmanned aerial vehicle (UAV) and general aviation (GA) is described. The system aims increase safety of UAV operation by emulating pilot decision making in emergency landing scenarios using an ANN to select a safe landing site from available candidates. The strength of an ANN to model complex input relationships makes it a perfect system to handle the multicriteria decision making (MCDM) process of emergency landing site selection. The ANN operates by identifying the more favorable of two landing sites when provided with an input vector derived from both landing site's parameters, the aircraft's current state and wind measurements. The system consists of a feed forward ANN, a pre-processor class which produces ANN input vectors and a class in charge of creating a ranking of landing site candidates using the ANN. The system was successfully implemented in C++ using the FANN C++ library and ROS. Results obtained from ANN training and simulations using randomly generated landing sites by a site detection simulator data verify the feasibility of an ANN based automated emergency landing site selection system.

Medium-Coupled Bus-Based INS-GPS Sensor Fusion for Accurate and Reliable Positioning

A scheme for integration of stand-alone INS and GPS sensors is presented, with data interchange over an external bus. This ensures modularity and sensor interchangeability. Use of a medium-coupled scheme reduces data flow and computation, facilitating use in surface vehicles. Results show that the hybrid navigation system is capable of delivering high positioning accuracy.

The experience of forging new emergency nursing curriculum development, through a course advisory network

Queensland University of Technology (QUT), School of Nursing (SoN), has offered a postgraduate Graduate Certificate in Emergency Nursing since 2003, for registered nurses practising in an emergency clinical area, who fulfil key entry criteria. Feedback from industry partners and students evidenced support for flexible and extended study pathways in emergency nursing. Therefore, in the context of a growing demand for emergency health services and the need for specialist qualified staff, it was timely to review and redevelop our emergency specialist nursing courses. The QUT postgraduate emergency nursing study area is supported by a course advisory group, whose aim is to provide input and focus development of current and future course planning. All members of the course advisory were invited to form an expert panel to review current emergency course documents. A half day “brainstorm session”, planning and development workshop was held to review the emergency courses to implement changes from 2009. Results from the expert panel planning day include: proposal for a new emergency specialty unit; incorporation of the College of Emergency Nurses (CENA) Standards for Emergency Nursing Specialist in clinical assessment; modification of the present core emergency unit; enhancing the focus of the two other units that emergency students undertake; and opening the emergency study area to the Graduate Diploma in Nursing (Emergency Nursing) and Master of Nursing (Emergency Nursing). The conclusion of the brainstorm session resulted in a clearer conceptualisation, of the study pathway for students. Overall, the expert panel group of enthusiastic emergency educators and clinicians provided viable options for extending the career progression opportunities for emergency nurses. In concluding, the opportunity for collaboration across university and clinical settings has resulted in the design of a course with exciting potential and strong clinical relevance.

A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data

Background: A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN) from transcript profiling data. Results: The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting) problem and solved finally by formulating a Linear Program (LP). A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known regulatory associations. In each S. cerevisiae LP-SLGN, the number of nodes with a particular degree follows an approximate power law suggesting that its degree distributions is similar to that observed in real-world networks. Inspection of these LP-SLGNs suggests biological hypotheses amenable to experimental verification. Conclusion: A statistically robust and computationally efficient LP-based method for estimating the topology of a large sparse undirected graph from high-dimensional data yields representations of genetic networks that are biologically plausible and useful abstractions of the structures of real genetic networks. Analysis of the statistical and topological properties of learned LP-SLGNs may have practical value; for example, genes with high random walk betweenness, a measure of the centrality of a node in a graph, are good candidates for intervention studies and hence integrated computational – experimental investigations designed to infer more realistic and sophisticated probabilistic directed graphical model representations of genetic networks. The LP-based solutions of the sparse linear regression problem described here may provide a method for learning the structure of transcription factor networks from transcript profiling and transcription factor binding motif data.

Latency, Power and Performance Trade-offs in Network-on-Chips by Link Microarchitecture Exploration

This paper presents a power, latency and throughput trade-off study on NoCs by varying microarchitectural (e.g. pipelining) and circuit level (e.g. frequency and voltage) parameters. We change pipelining depth, operating frequency and supply voltage for 3 example NoCs - 16 node 2D Torus, Tree network and Reduced 2D Torus. We use an in-house NoC exploration framework capable of topology generation and comparison using parameterized models of Routers and links developed in SystemC. The framework utilizes interconnect power and delay models from a low-level modelling tool called Intacte[1]1. We find that increased pipelining can actually reduce latency. We also find that there exists an optimal degree of pipelining which is the most energy efficient in terms of minimizing energy-delay product.

On-Chip Clock Network Skew Measurement using Sub-Sampling

We present a technique for an all-digital on-chip delay measurement system to measure the skews in a clock distribution network. It uses the principle of sub-sampling. Measurements from a prototype fabricated in a 65 nm industrial process, indicate the ability to measure delays with a resolution of 0.5ps and a DNL of 1.2 ps.

Oxygen transport in a three-dimensional microvascular network incorporated with early tumour growth and preexisting vessel cooption: Numerical simulation study

We propose a dynamic mathematical model of tissue oxygen transport by a preexisting three-dimensional microvascular network which provides nutrients for an in situ cancer at the very early stage of primary microtumour growth. The expanding tumour consumes oxygen during its invasion to the surrounding tissues and cooption of host vessels. The preexisting vessel cooption, remodelling and collapse are modelled by the changes of haemodynamic conditions due to the growing tumour. A detailed computational model of oxygen transport in tumour tissue is developed by considering (a) the time-varying oxygen advection diffusion equation within the microvessel segments, (b) the oxygen flux across the vessel walls, and (c) the oxygen diffusion and consumption with in the tumour and surrounding healthy tissue. The results show the oxygen concentration distribution at different time points of early tumour growth. In addition, the influence of preexisting vessel density on the oxygen transport has been discussed. The proposed model not only provides a quantitative approach for investigating the interactions between tumour growth and oxygen delivery, but also is extendable to model other molecules or chemotherapeutic drug transport in the future study.

Comparative analysis of traffic state estimation ─ Cumulative counts-based and trajectory-based methods

The Macroscopic Fundamental Diagram (MFD) relates space-mean density and flow. Since the MFD represents the area-wide network traffic performance, studies on perimeter control strategies and network-wide traffic state estimation utilising the MFD concept have been reported. Most previous works have utilised data from fixed sensors, such as inductive loops, to estimate the MFD, which can cause biased estimation in urban networks due to queue spillovers at intersections. To overcome the limitation, recent literature reports the use of trajectory data obtained from probe vehicles. However, these studies have been conducted using simulated datasets; limited works have discussed the limitations of real datasets and their impact on the variable estimation. This study compares two methods for estimating traffic state variables of signalised arterial sections: a method based on cumulative vehicle counts (CUPRITE), and one based on vehicles’ trajectory from taxi Global Positioning System (GPS) log. The comparisons reveal some characteristics of taxi trajectory data available in Brisbane, Australia. The current trajectory data have limitations in quantity (i.e., the penetration rate), due to which the traffic state variables tend to be underestimated. Nevertheless, the trajectory-based method successfully captures the features of traffic states, which suggests that the trajectories from taxis can be a good estimator for the network-wide traffic states.

Intra and Inter-Molecular Communications Through Protein Structure Network

Communication within and across proteins is crucial for the biological functioning of proteins. Experiments such as mutational studies on proteins provide important information on the amino acids, which are crucial for their function. However, the protein structures are complex and it is unlikely that the entire responsibility of the function rests on only a few amino acids. A large fraction of the protein is expected to participate in its function at some level or other. Thus, it is relevant to consider the protein structures as a completely connected network and then deduce the properties, which are related to the global network features. In this direction, our laboratory has been engaged in representing the protein structure as a network of non-covalent connections and we have investigated a variety of problems in structural biology, such as the identification of functional and folding clusters, determinants of quaternary association and characterization of the network properties of protein structures. We have also addressed a few important issues related to protein dynamics, such as the process of oligomerization in multimers, mechanism on protein folding, and ligand induced communications (allosteric effect). In this review we highlight some of the investigations which we have carried out in the recent past. A review on protein structure graphs was presented earlier, in which the focus was on the graphs and graph spectral properties and their implementation in the study of protein structure graphs/networks (PSN). In this article, we briefly summarize the relevant parts of the methodology and the focus is on the advancement brought out in the understanding of protein structure-function relationships through structure networks. The investigations of structural/biological problems are divided into two parts, in which the first part deals with the analysis of PSNs based on static structures obtained from x-ray crystallography. The second part highlights the changes in the network, associated with biological functions, which are deduced from the network analysis on the structures obtained from molecular dynamics simulations.

Language and Letters of the Bluestocking Network : Sociolinguistic Issues in Eighteenth Century Epistolary English

This study deals with language change and variation in the correspondence of the eighteenth-century Bluestocking circle, a social network which provided learned men and women with an informal environment for the pursuit of scholarly entertainment. Elizabeth Montagu (1718 1800), a notable social hostess and a Shakespearean scholar, was one of their key figures. The study presents the reconstruction of Elizabeth Montagu s social networks from her youth to her later years with a special focus on the Bluestocking circle, and linguistic research on private correspondence between Montagu and her Bluestocking friends and family members between the years 1738 1778. The epistolary language use is investigated using the methods and frameworks of corpus linguistics, historical sociolinguistics, and social network analysis. The approach is diachronic and concerns real-time language change. The research is based on a selection of manuscript letters which I have edited and compiled into an electronic corpus (Bluestocking Corpus). I have also devised a network strength scale in order to quantify the strength of network ties and to compare the results of the linguistic research with the network analysis. The studies range from the reconstruction and analysis of Elizabeth Montagu s most prominent social networks to the analysis of changing morphosyntactic features and spelling variation in Montagu s and her network members correspondence. The linguistic studies look at the use of the progressive construction, preposition stranding and pied piping, and spelling variation in terms of preterite and past participle endings in the regular paradigm (-ed, - d, -d, - t, -t) and full / contracted spellings of auxiliary verbs. The results are analysed in terms of social network membership, sociolinguistic variables of the correspondents, and, when relevant, aspects of eighteenth-century linguistic prescriptivism. The studies showed a slight diachronic increase in the use of the progressive, a significant decrease of the stigmatised preposition stranding and increase of pied piping, and relatively informal but socially controlled epistolary spelling. Certain significant changes in Elizabeth Montagu s language use over the years could be attributed to her increasingly prominent social standing and the changes in her social networks, and the strength of ties correlated strongly with the use of the progressive in the Bluestocking Corpus. Gender, social rank, and register in terms of kinship/friendship had a significant influence in language use, and an effect of prescriptivism could also be detected. Elizabeth Montagu s network ties resulted in language variation in terms of network membership, her own position in a given network, and the social factors that controlled eighteenth-century interaction. When all the network ties are strong, linguistic variation seems to be essentially linked to the social variables of the informants.

Integrating voice and data on SALAN: an experimental local area network

This paper is concerned with the integration of voice and data on an experimental local area network used by the School of Automation, of the Indian Institute of Science. SALAN (School of Automation Local Area Network) consists of a number of microprocessor-based communication nodes linked to a shared coaxial cable transmission medium. The communication nodes handle the various low-level functions associated with computer communication, and interface user data equipment to the network. SALAN at present provides a file transfer facility between an Intel Series III microcomputer development system and a Texas Instruments Model 990/4 microcomputer system. Further, a packet voice communication system has also been implemented on SALAN. The various aspects of the design and implementation of the above two utilities are discussed.

Optimized group replacement scheduling for water pipeline network

Replacement of deteriorated water pipes is a capital-intensive activity for utility companies. Replacement planning aims to minimize total costs while maintaining a satisfactory level of service and is usually conducted for individual pipes. Scheduling replacement in groups is seen to be a better method and has the potential to provide benefits such as the reduction of maintenance costs and service interruptions. However, developing group replacement schedules is a complex task and often beyond the ability of a human expert, especially when multiple or conflicting objectives need to be catered for, such as minimization of total costs and service interruptions. This paper describes the development of a novel replacement decision optimization model for group scheduling (RDOM-GS), which enables multiple group-scheduling criteria by integrating new cost functions, a service interruption model, and optimization algorithms into a unified procedure. An industry case study demonstrates that RDOM-GS can improve replacement planning significantly and reduce costs and service interruptions.