An adaptive spherical view representation for navigation in changing environments

Real-world environments such as houses and offices change over time, meaning that a mobile robot’s map will become out of date. In previous work we introduced a method to update the reference views in a topological map so that a mobile robot could continue to localize itself in a changing environment using omni-directional vision. In this work we extend this longterm updating mechanism to incorporate a spherical metric representation of the observed visual features for each node in the topological map. Using multi-view geometry we are then able to estimate the heading of the robot, in order to enable navigation between the nodes of the map, and to simultaneously adapt the spherical view representation in response to environmental changes. The results demonstrate the persistent performance of the proposed system in a long-term experiment.

Long-term experiments with an adaptive spherical view representation for navigation in changing environments

Real-world environments such as houses and offices change over time, meaning that a mobile robot’s map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metrictopological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store model of human memory, incorporates a spherical metric representation of the observed visual features for each node in the map, which enables the robot to estimate its heading and navigate using multi-view geometry, as well as representing the local 3D geometry of the environment. A series of experiments demonstrate the persistence performance of the proposed system in real changing environments, including analysis of the long-term stability.

Priority threat management of invasive plant species in the Lake Eyre Basin

This project is led by scientists in conservation decision appraisal and brings together a group of experts working across the Lake Eyre Basin (LEB). The LEB covers a sixth of Australia, with an array of globally significant natural values that are threatened by invasive plants, among other things. Managers at various levels are investing in attempts to control, contain and eradicate these invasive plant species, under severe time and resources limitations. To date there has been no basin-wide assessment of which weed management strategies and locations provide the best investments for maximising outcomes for biodiversity per unit cost. Further, there has been no assessment of the extent of ecosystem intactness that may be lost without effective invasive plant species management strategies. Given that there are insufficient resources to manage all invasive plant species everywhere, this information has the potential to improve current investment decisions. Here, we provide a prioritisation of invasive plant management strategies in the LEB. Prioritisation was based on cost-effectiveness for biodiversity benefits. We identify the key invasive plant species to target to protect ecosystem intactness across the bioregions of the LEB, the level of investment required and the likely reduction in invasive species dominance gained per dollar spent on each strategy. Our focus is on strategies that are technically and socially feasible and reduce the likelihood that high impact invasive plant species will dominate native ecosystems, and therefore change their form and function. The outputs of this work are designed to help guide decision-making and further planning and investment in weed management for the Basin. Experts in weed management, policy-making, community engagement, biodiversity and natural values of the Basin, attended a workshop and agreed upon 12 strategies to manage invasive plants. The strategies focused primarily on 10 weeds which were considered to have a high potential for broad, significant impacts on natural ecosystems in the next 50 years and for which feasible management strategies could be defined. Each strategy consisted of one or more supporting actions, many of which were spatially linked to IBRA (Interim Biogeographical Regionalisation of Australia) bioregions. The first strategy was an over-arching recommendation for improved mapping, information sharing, education and extension efforts in order to facilitate the more specific weed management strategies. The 10 more specific weed management strategies targeted the control and/or eradication of the following high-impact exotic plants: mesquite, parkinsonia, rubber vine, bellyache bush, cacti, mother of millions, chinee apple, athel pine and prickly acacia, as well as a separate strategy for eradicating all invasive plants from one key threatened ecological community, the GAB (Great Artesian Basin dependant) mound springs. Experts estimated the expected biodiversity benefit of each strategy as the reduction in area that an invasive plant species is likely to dominate in over a 50-year period, where dominance was defined as more than 30% coverage at a site. Costs were estimated in present day terms over 50 years largely during follow up discussions post workshop. Cost-effectiveness was then calculated for each strategy in each bioregion by dividing the average expected benefit by the average annual costs. Overall, the total cost of managing 12 invasive plant strategies over the next 50 years was estimated at $1.7 billion. It was estimated that implementation of these strategies would result in a reduction of invasive plant dominance by 17 million ha (a potential 32% reduction), roughly 14% of the LEB. If only targeting Weeds of National Significance (WONS), the total cost was estimated to be $113 million over the next 50 years. Over the next 50 years, $2.3 million was estimated to eradicate all invasive plant species from the Great Artesian Basin Mound Springs threatened ecological community. Prevention and awareness programs were another key strategy targeted across the Basin and estimated at $17.5 million in total over 50 years. The cost of controlling, eradicating and containing buffel grass were the most expensive, over $1.5 billion over 50 years; this strategy was estimated to result in a reduction in buffel grass dominance of a million ha in areas where this species is identified as an environmental problem. Buffel grass has been deliberately planted across the Basin for pasture production and is by far the most widely distributed exotic species. Its management is contentious, having economic value to many graziers while posing serious threats to biodiversity and sites of high cultural and conservation interest. The strategy for containing and locally eradicating buffel grass was a challenge to cost based on expert knowledge, possibly because of the dual nature of this species as a valued pastoral grass and environmental weed. Based on our conversations with experts, it appears that control and eradication programs for this species, in conservation areas, are growing rapidly and that information on the most cost-effective strategies for this species will continue to develop over time. The top five most cost-effective strategies for the entire LEB were for the management of: 1) parkinsonia, 2) chinee apple, 3) mesquite, 4) rubber vine and 5) bellyache bush. Chinee apple and mother of millions are not WONS and have comparatively small populations within the semi-arid bioregions of Queensland. Experts felt that there was an opportunity to eradicate these species before they had the chance to develop into high-impact species within the LEB. Prickly acacia was estimated to have one of the highest benefits, but the costs of this strategy were high, therefore it was ranked 7th overall. The buffel grass strategy was ranked the lowest (10th) in terms of cost effectiveness. The top five most cost-effective strategies within and across the bioregions were the management of: 1) parkinsonia in the Channel Country, 2) parkinsonia in the Desert Uplands, 3) mesquite in the Mitchell Grass Downs, 4) parkinsonia in the Mitchell Grass Downs, and 5) mother of millions in the Desert Uplands. Although actions for several invasive plant species like parkinsonia and prickly acacia were concentrated in the Queensland part of the LEB, the actions involved investing in containment zones to prevent the spread of these species into other states. In the NT and SA bioregions of the LEB, the management of athel pine, parkinsonia and cacti were the main strategies. While outside the scientific research goals of study, this work highlighted a number of important incidental findings that led us to make the following recommendations for future research and implementation of weed management in the Basin: • Ongoing stakeholder engagement, extension and participation is required to ensure this prioritisation effort has a positive impact in affecting on-ground decision making and planning. • Short term funding for weed management was identified as a major reason for failure of current efforts, hence future funding needs to be secure and ongoing. • Improved mapping and information sharing is essential to implement effective weed management. • Due to uncertainties in the outcomes and impacts of management options, strategies should be implemented as part of an adaptive management program. The information provided in this report can be used to guide investment for controlling high-impact invasive plant species for the benefits of biodiversity conservation. We do not present a final prioritisation of invasive plant strategies for the LEB, and we have not addressed the cultural, socio-economic or spatial components necessary for an implementation plan. Cost-effectiveness depends on the objectives used; in our case we used the intactness of ecosystems as a surrogate for expected biodiversity benefits, measured by the extent that each invasive plant species is likely to dominate in a bioregion. When other relevant factors for implementation are considered the priorities may change and some actions may not be appropriate in some locations. We present the costs, ecological benefits and cost-effectiveness of preventing, containing, reducing and eradicating the dominance of high impact invasive plants through realistic management actions over the next 50 years. In doing so, we are able to estimate the size of the weed management problem in the LEB and provide expert-based estimates of the likely outcomes and benefits of implementing weed management strategies. The priorities resulting from this work provide a prospectus for guiding further investment in management and in improving information availability.

A frequency adaptive single-phase Phase-Locked Loop with harmonic rejection

An improved Phase-Locked Loop (PLL) for extracting phase and frequency of the fundamental component of a highly distorted grid voltage is presented. The structure of the single-phase PLL is based on the Synchronous Reference Frame (SRF) PLL and uses an All Pass Filter (APF) to generate the quadrature component from the single phase input voltage. In order to filter the harmonic content, a Moving Average Filter (MAF) is used, and performance is improved by designing a lead compensator and also a feed-forward compensator. The simulation results are compared to show the improved performance with feed-forward. In addition, the frequency dependency of MAF is dealt with by a proposed method for adaption to the frequency. This method changes the window size based on the frequency on a sample-by-sample basis. By using this method, the speed of resizing can be reduced in order to decrease the output ripples caused by window size variations.

Can social resilience inform SA/SIA for adaptive planning for climate change in vulnerable regions?

Social resilience concepts are gaining momentum in environmental planning through an emerging understanding of the socio-ecological nature of biophysical systems. There is a disconnect, however, between these concepts and the sociological and psychological literature related to social resilience. Further still, both schools of thought are not well connected to the concepts of social assessment (SA) and social impact assessment (SIA) that are the more standard tools supporting planning and decision-making. This raises questions as to how emerging social resilience concepts can translate into improved SA/SIA practices to inform regional-scale adaptation. Through a review of the literature, this paper suggests that more cross-disciplinary integration is needed if social resilience concepts are to have a genuine impact in helping vulnerable regions tackle climate change.

Trait resilience fosters adaptive coping when control opportunities are high : implications for the motivating potential of active work

Purpose: We examine the interaction between trait resilience and control in predicting coping and performance. Drawing on a person–environment fit perspective, we hypothesized resilient individuals would cope and perform better in demanding work situations when control was high. In contrast, those low in resilience would cope and perform better when control was low. Recognizing the relationship between trait resilience and performance also could be indirect, adaptive coping was examined as a mediating mechanism through which high control enables resilient individuals to demonstrate better performance. Methodology: In Study 1 (N = 78) and Study 2 (N = 94), participants completed a demanding inbox task in which trait resilience was measured and high and low control was manipulated. Study 3 involved surveying 368 employees on their trait resilience, control, and demand at work (at Time 1), and coping and performance 1 month later at Time 2. Findings: For more resilient individuals, high control facilitated problem-focused coping (Study 1, 2, and 3), which was indirectly associated with higher subjective performance (Study 1), mastery (Study 2), adaptive, and proficient performance (Study 3). For more resilient individuals, high control also facilitated positive reappraisal (Study 2 and 3), which was indirectly associated with higher adaptive and proficient performance (Study 3). Implications: Individuals higher in resilience benefit from high control because it enables adaptive coping. Originality/value: This research makes two contributions: (1) an experimental investigation into the interaction of trait resilience and control, and (2) investigation of coping as the mechanism explaining better performance.

Adaptive estimation of HMM transition probabilities

This paper presents new schemes for recursive estimation of the state transition probabilities for hidden Markov models (HMM's) via extended least squares (ELS) and recursive state prediction error (RSPE) methods. Local convergence analysis for the proposed RSPE algorithm is shown using the ordinary differential equation (ODE) approach developed for the more familiar recursive output prediction error (RPE) methods. The presented scheme converges and is relatively well conditioned compared with the ...

On adaptive HMM state estimation

In this paper new online adaptive hidden Markov model (HMM) state estimation schemes are developed, based on extended least squares (ELS) concepts and recursive prediction error (RPE) methods. The best of the new schemes exploit the idempotent nature of Markov chains and work with a least squares prediction error index, using a posterior estimates, more suited to Markov models then traditionally used in identification of linear systems.

Adaptive estimation of hidden semi-Markov chains with parameterised transition probabilities and exponential decaying states

This paper develops maximum likelihood (ML) estimation schemes for finite-state semi-Markov chains in white Gaussian noise. We assume that the semi-Markov chain is characterised by transition probabilities of known parametric from with unknown parameters. We reformulate this hidden semi-Markov model (HSM) problem in the scalar case as a two-vector homogeneous hidden Markov model (HMM) problem in which the state consist of the signal augmented by the time to last transition. With this reformulation we apply the expectation Maximumisation (EM ) algorithm to obtain ML estimates of the transition probabilities parameters, Markov state levels and noise variance. To demonstrate our proposed schemes, motivated by neuro-biological applications, we use a damped sinusoidal parameterised function for the transition probabilities.

Adaptive finite element analysis of elliptic problems based on bubble-type local mesh generation

A new mesh adaptivity algorithm that combines a posteriori error estimation with bubble-type local mesh generation (BLMG) strategy for elliptic differential equations is proposed. The size function used in the BLMG is defined on each vertex during the adaptive process based on the obtained error estimator. In order to avoid the excessive coarsening and refining in each iterative step, two factor thresholds are introduced in the size function. The advantages of the BLMG-based adaptive finite element method, compared with other known methods, are given as follows: the refining and coarsening are obtained fluently in the same framework; the local a posteriori error estimation is easy to implement through the adjacency list of the BLMG method; at all levels of refinement, the updated triangles remain very well shaped, even if the mesh size at any particular refinement level varies by several orders of magnitude. Several numerical examples with singularities for the elliptic problems, where the explicit error estimators are used, verify the efficiency of the algorithm. The analysis for the parameters introduced in the size function shows that the algorithm has good flexibility.

Scale adaptive tracking using mean shift and efficient feature matching

The mean shift tracker has achieved great success in visual object tracking due to its efficiency being nonparametric. However, it is still difficult for the tracker to handle scale changes of the object. In this paper, we associate a scale adaptive approach with the mean shift tracker. Firstly, the target in the current frame is located by the mean shift tracker. Then, a feature point matching procedure is employed to get the matched pairs of the feature point between target regions in the current frame and the previous frame. We employ FAST-9 corner detector and HOG descriptor for the feature matching. Finally, with the acquired matched pairs of the feature point, the affine transformation between target regions in the two frames is solved to obtain the current scale of the target. Experimental results show that the proposed tracker gives satisfying results when the scale of the target changes, with a good performance of efficiency.