Pulse-coupled neural network performance for real-time identification of vegetation during forced landing

Safety concerns in the operation of autonomous aerial systems require safe-landing protocols be followed during situations where the mission should be aborted due to mechanical or other failure. This article presents a pulse-coupled neural network (PCNN) to assist in the vegetation classification in a vision-based landing site detection system for an unmanned aircraft. We propose a heterogeneous computing architecture and an OpenCL implementation of a PCNN feature generator. Its performance is compared across OpenCL kernels designed for CPU, GPU, and FPGA platforms. This comparison examines the compute times required for network convergence under a variety of images to determine the plausibility for real-time feature detection.

Active awareness : supporting the intentional disclosure of awareness information in collaborative systems

This thesis opens up the design space for awareness research in CSCW and HCI. By challenging the prevalent understanding of roles in awareness processes and exploring different mechanisms for actively engaging users in the awareness process, this thesis provides a better understanding of the complexity of these processes and suggests practical solutions for designing and implementing systems that support active awareness. Mutual awareness, a prominent research topic in the fields of Computer-Supported Cooperative Work (CSCW) and Human-Computer Interaction (HCI) refers to a fundamental aspect of a person’s work: their ability to gain a better understanding of a situation by perceiving and interpreting their co-workers actions. Technologically-mediated awareness, used to support co-workers across distributed settings, distinguishes between the roles of the actor, whose actions are often limited to being the target of an automated data gathering processes, and the receiver, who wants to be made aware of the actors’ actions. This receiver-centric view of awareness, focusing on helping receivers to deal with complex sets of awareness information, stands in stark contrast to our understanding of awareness as social process involving complex interactions between both actors and receivers. It fails to take into account an actors’ intimate understanding of their own activities and the contribution that this subjective understanding could make in providing richer awareness information. In this thesis I challenge the prevalent receiver-centric notion of awareness, and explore the conceptual foundations, design, implementation and evaluation of an alternative active awareness approach by making the following five contributions. Firstly, I identify the limitations of existing awareness research and solicit further evidence to support the notion of active awareness. I analyse ethnographic workplace studies that demonstrate how actors engage in an intricate interplay involving the monitoring of their co-workers progress and displaying aspects of their activities that may be of relevance to others. The examination of a large body of awareness research reveals that while disclosing information is a common practice in face-to-face collaborative settings it has been neglected in implementations of technically mediated awareness. Based on these considerations, I introduce the notion of intentional disclosure to describe the action of users actively and deliberately contributing awareness information. I consider challenges and potential solutions for the design of active awareness. I compare a range of systems, each allowing users to share information about their activities at various levels of detail. I discuss one of the main challenges to active awareness: that disclosing information about activities requires some degree of effort. I discuss various representations of effort in collaborative work. These considerations reveal that there is a trade-off between the richness of awareness information and the effort required to provide this information. I propose a framework for active awareness, aimed to help designers to understand the scope and limitations of different types of intentional disclosure. I draw on the identified richness/effort trade-off to develop two types of intentional disclosure, both of which aim to facilitate the disclosure of information while reducing the effort required to do so. For both of these approaches, direct and indirect disclosure, I delineate how they differ from related approaches and define a set of design criteria that is intended to guide their implementation. I demonstrate how the framework of active awareness can be practically applied by building two proof-of-concept prototypes that implement direct and indirect disclosure respectively. AnyBiff, implementing direct disclosure, allows users to create, share and use shared representations of activities in order to express their current actions and intentions. SphereX, implementing indirect disclosure, represents shared areas of interests or working context, and links sets of activities to these representations. Lastly, I present the results of the qualitative evaluation of the two prototypes and analyse the results with regard to the extent to which they implemented their respective disclosure mechanisms and supported active awareness. Both systems were deployed and tested in real world environments. The results for AnyBiff showed that users developed a wide range of activity representations, some unanticipated, and actively used the system to disclose information. The results further highlighted a number of design considerations relating to the relationship between awareness and communication, and the role of ambiguity. The evaluation of SphereX validated the feasibility of the indirect disclosure approach. However, the study highlighted the challenges of implementing cross-application awareness support and translating the concept to users. The study resulted in design recommendations aimed to improve the implementation of future systems.

A method for risk analysis across governance systems : a great barrier reef case study

Healthy governance systems are key to delivering sound environmental management outcomes from global to local scales. There are, however, surprisingly few risk assessment methods that can pinpoint those domains and sub-domains within governance systems that are most likely to influence good environmental outcomes at any particular scale, or those if absent or dysfunctional, most likely to prevent effective environmental management. This paper proposes a new risk assessment method for analysing governance systems. This method is then tested through its preliminary application to a significant real-world context: governance as it relates to the health of Australia's Great Barrier Reef (GBR). The GBR exists at a supra-regional scale along most of the north eastern coast of Australia. Brodie et al (2012 Mar. Pollut. Bull. 65 81-100) have recently reviewed the state and trend of the health of the GBR, finding that overall trends remain of significant concern. At the same time, official international concern over the governance of the reef has recently been signalled globally by the International Union for the Conservation of Nature (IUCN). These environmental and political contexts make the GBR an ideal candidate for use in testing and reviewing the application of improved tools for governance risk assessment. © 2013 IOP Publishing Ltd.

Correlation between extent of osteolytic damage and metastatic burden of human breast cancer metastasis in nude mice : real-time PCR quantitation

Orthotopic or intracardiac injection of human breast cancer cell lines into immunocompromised mice allows study of the molecular basis of breast cancer metastasis. We have established a quantitative real-time PCR approach to analyze metastatic spread of human breast cancer cells inoculated into nude mice via these routes. We employed MDA-MB-231 human breast cancer cells genetically tagged with a bacterial β-galactosidase (Lac-Z) retroviral vector, enabling their detection by TaqMan® real-time PCR. PCR detection was linear, specific, more sensitive than conventional PCR, and could be used to directly quantitate metastatic burden in bone and soft organs. Attesting to the sensitivity and specificity of the PCR detection strategy, as few as several hundred metastatic MDA-MB-231 cells were detectable in 100 μm segments of paraffin-embedded lung tissue, and only in samples adjacent to sections that scored positive by histological detection. Moreover, the measured real-time PCR metastatic burden in the bone environment (mouse hind-limbs, n = 48) displayed a high correlation to the degree of osteolytic damage observed by high resolution X-ray analysis (r2 = 0.972). Such a direct linear relationship to tumor burden and bone damage substantiates the so-called 'vicious cycle' hypothesis in which metastatic tumor cells promote the release of factors from the bone which continue to stimulate the tumor cells. The technique provides a useful tool for molecular and cellular analysis of human breast cancer metastasis to bone and soft organs, can easily be extended to other cell/marker/organ systems, and should also find application in preclinical assessment of anti-metastatic modalities.

Tensor-based item recommendation using probabilistic ranking in social tagging systems

A common problem with the use of tensor modeling in generating quality recommendations for large datasets is scalability. In this paper, we propose the Tensor-based Recommendation using Probabilistic Ranking method that generates the reconstructed tensor using block-striped parallel matrix multiplication and then probabilistically calculates the preferences of user to rank the recommended items. Empirical analysis on two real-world datasets shows that the proposed method is scalable for large tensor datasets and is able to outperform the benchmarking methods in terms of accuracy.

Enabling safe autonomous driving in real-world city traffic using multiple criteria decision making

This paper addresses the topic of real-time decision making for autonomous city vehicles, i.e., the autonomous vehicles' ability to make appropriate driving decisions in city road traffic situations. The paper explains the overall controls system architecture, the decision making task decomposition, and focuses on how Multiple Criteria Decision Making (MCDM) is used in the process of selecting the most appropriate driving maneuver from the set of feasible ones. Experimental tests show that MCDM is suitable for this new application area.

Power buffer with model predictive control for stability of vehicular power systems with constant power loads

Electrification of vehicular systems has gained increased momentum in recent years with particular attention to constant power loads (CPLs). Since a CPL potentially threatens system stability, stability analysis of hybrid electric vehicle with CPLs becomes necessary. A new power buffer configuration with battery is introduced to mitigate the effect of instability caused by CPLs. Model predictive control (MPC) is applied to regulate the power buffer to decouple source and load dynamics. Moreover, MPC provides an optimal tradeoff between modification of load impedance, variation of dc-link voltage and battery current ripples. This is particularly important during transients or starting of system faults, since battery response is not very fast. Optimal tradeoff becomes even more significant when considering low-cost power buffer without battery. This paper analyzes system models for both voltage swell and voltage dip faults. Furthermore, a dual mode MPC algorithm is implemented in real time offering improved stability. A comprehensive set of experimental results is included to verify the efficacy of the proposed power buffer.

Methodology for developing real-time motorway traffic risk identification models using individual-vehicle data

Most of existing motorway traffic safety studies using disaggregate traffic flow data aim at developing models for identifying real-time traffic risks by comparing pre-crash and non-crash conditions. One of serious shortcomings in those studies is that non-crash conditions are arbitrarily selected and hence, not representative, i.e. selected non-crash data might not be the right data comparable with pre-crash data; the non-crash/pre-crash ratio is arbitrarily decided and neglects the abundance of non-crash over pre-crash conditions; etc. Here, we present a methodology for developing a real-time MotorwaY Traffic Risk Identification Model (MyTRIM) using individual vehicle data, meteorological data, and crash data. Non-crash data are clustered into groups called traffic regimes. Thereafter, pre-crash data are classified into regimes to match with relevant non-crash data. Among totally eight traffic regimes obtained, four highly risky regimes were identified; three regime-based Risk Identification Models (RIM) with sufficient pre-crash data were developed. MyTRIM memorizes the latest risk evolution identified by RIM to predict near future risks. Traffic practitioners can decide MyTRIM’s memory size based on the trade-off between detection and false alarm rates. Decreasing the memory size from 5 to 1 precipitates the increase of detection rate from 65.0% to 100.0% and of false alarm rate from 0.21% to 3.68%. Moreover, critical factors in differentiating pre-crash and non-crash conditions are recognized and usable for developing preventive measures. MyTRIM can be used by practitioners in real-time as an independent tool to make online decision or integrated with existing traffic management systems.

Improving safety for driverless city vehicles : real-time communication and decision making

This paper elaborates on the Cybercars-2 Wireless Communication Framework for driverless city vehicles, which is used for Vehicle-to-Vehicle and Vehicle-to-Infrastructure communication. The developed framework improves the safety and efficiency of driverless city vehicles. Furthermore, this paper also elaborates on the vehicle control software architecture. On-road tests of both the communication framework and its application for real-time decision making show that the communication framework is reliable and useful for improving the safe operation of driverless city vehicles.

Towards increased road safety : real-time decision making for driverless city vehicles

This work elaborates on the topic of decision making for driverless city vehicles, particularly focusing on the aspects on how to develop a reliable approach which meets the requirements of safe city traffic. Decision making in this context refers to the problem of identifying the most appropriate driving maneuver to be performed in a given traffic situation. The overall decision making problem is decomposed into two consecutive stages. The first stage is safety-crucial, representing the decision regarding the set of feasible driving maneuvers. The second stage represents the decision regarding the most appropriate driving maneuver from the set of feasible ones. The developed decision making approach has been implemented in C++ and initially tested in a 3D simulation environment and, thereafter, in real-world experiments. The real-world experiments also included the integration of wireless communication between vehicles.

Исследования и испытания алгоритмов автоматической пилотажно-навигационной системы малогабаритного беспилотного летательного аппарата = Research and testing of automatic navigation management system’s algorithms for small unmanned aerial vehicle

В статье представлено развитие принципа построения автоматической пилотажно-навигационной системы (АПНС) для беспилотного летательного аппарата (БЛА). Принцип заключается в синтезе комплексных систем управления БПЛА не только на основе использования алгоритмов БИНС, но и алгоритмов, объединяющих в себе решение задач формирования и отработки сформированной траектории резервированной системой управления и навигации. Приведены результаты аналитического исследования и данные летных экспериментов разработанных алгоритмов АПНС БЛА, обеспечивающих дополнительное резервирование алгоритмов навигации и наделяющих БЛА новым функциональной способностью по выходу в заданную точку пространства с заданной скоростью в заданный момент времени с учетом атмосферных ветровых возмущений. Предложена и испытана методика идентификации параметров воздушной атмосферы: направления и скорости W ветра. Данные летных испытаний полученного решения задачи терминальной навигации демонстрируют устойчивую работу синтезированных алгоритмов управления в различных метеоусловиях. The article presents a progress in principle of development of automatic navigation management system (ANMS) for small unmanned aerial vehicle (UAV). The principle defines a development of integrated control systems for UAV based on tight coupling of strap down inertial navigation system algorithms and algorithms of redundant flight management system to form and control flight trajectory. The results of the research and flight testing of the developed ANMS UAV algorithms are presented. The system demonstrates advanced functional redundancy of UAV guidance. The system enables new UAV capability to perform autonomous multidimensional navigation along waypoints with controlled speed and time of arrival taking into account wind. The paper describes the technique for real-time identification of atmosphere parameters such as wind direction and wind speed. The flight test results demonstrate robustness of the algorithms in diverse meteorological conditions.

Multiple criteria-based real-time decision making by autonomous city vehicles

This paper addresses the topic of real-time decision making for autonomous city vehicles, i.e. the autonomous vehicles’ ability to make appropriate driving decisions in city road traffic situations. After decomposing the problem into two consecutive decision making stages, and giving a short overview about previous work, the paper explains how Multiple Criteria Decision Making (MCDM) can be used in the process of selecting the most appropriate driving maneuver.

Exploring the usefulness of structural–functional approaches to analyse governance of planning systems

Existing planning theories tend to be limited in their analytical scope and often fail to account for the impact of many interactions between the multitudes of stakeholders involved in strategic planning processes. Although many theorists rejected structural–functional approaches from the 1970s, this article argues that many of structural–functional concepts remain relevant and useful to planning practitioners. In fact, structural–functional approaches are highly useful and practical when used as a foundation for systemic analysis of real-world, multi-layered, complex planning systems to support evidence-based governance reform. Such approaches provide a logical and systematic approach to the analysis of the wider governance of strategic planning systems that is grounded in systems theory and complementary to existing theories of complexity and planning. While we do not propose its use as a grand theory of planning, this article discusses how structural–functional concepts and approaches might be applied to underpin a practical analysis of the complex decision-making arrangements that drive planning practice, and to provide the evidence needed to target reform of poorly performing arrangements.

Managing risk in process-aware information systems

This research contributes a fully-operational approach for managing business process risk in near real-time. The approach consists of a language for defining risks on top of process models, a technique to detect such risks as they eventuate during the execution of business processes, a recommender system for making risk-informed decisions, and a technique to automatically mitigate the detected risks when they are no longer tolerable. Through the incorporation of risk management elements in all stages of the lifecycle of business processes, this work contributes to the effective integration of the fields of Business Process Management and Risk Management.