Object recognition using shape-from-shading

This paper investigates whether surface topography information extracted from intensity images using a recently reported shape-from-shading (SFS) algorithm can be used for the purposes of 3D object recognition. We consider how curvature and shape-index information delivered by this algorithm can be used to recognize objects based on their surface topography. We explore two contrasting object recognition strategies. The first of these is based on a low-level attribute summary and uses histograms of curvature and orientation measurements. The second approach is based on the structural arrangement of constant shape-index maximal patches and their associated region attributes. We show that region curvedness and a string ordering of the regions according to size provides recognition accuracy of about 96 percent. By polling various recognition schemes. including a graph matching method. we show that a recognition rate of 98-99 percent is achievable

New constraints on data-closeness and needle map consistency for shape-from-shading

This paper makes two contributions to the problem of needle-map recovery using shape-from-shading. First, we provide a geometric update procedure which allows the image irradiance equation to be satisfied as a hard constraint. This not only improves the data closeness of the recovered needle-map, but also removes the necessity for extensive parameter tuning. Second, we exploit the improved ease of control of the new shape-from-shading process to investigate various types of needle-map consistency constraint. The first set of constraints are based on needle-map smoothness. The second avenue of investigation is to use curvature information to impose topographic constraints. Third, we explore ways in which the needle-map is recovered so as to be consistent with the image gradient field. In each case we explore a variety of robust error measures and consistency weighting schemes that can be used to impose the desired constraints on the recovered needle-map. We provide an experimental assessment of the new shape-from-shading framework on both real world images and synthetic images with known ground truth surface normals. The main conclusion drawn from our analysis is that the data-closeness constraint improves the efficiency of shape-from-shading and that both the topographic and gradient consistency constraints improve the fidelity of the recovered needle-map

Learning Backtrackless Aligned-Spatial Graph Convolutional Networks for Graph Classification.

In this paper, we develop a novel Backtrackless Aligned-Spatial Graph Convolutional Network (BASGCN) model to learn effective features for graph classification. Our idea is to transform arbitrary-sized graphs into fixed-sized backtrackless aligned grid structures and define a new spatial graph convolution operation associated with the grid structures. We show that the proposed BASGCN model not only reduces the problems of information loss and imprecise information representation arising in existing spatially-based Graph Convolutional Network (GCN) models, but also bridges the theoretical gap between traditional Convolutional Neural Network (CNN) models and spatially-based GCN models. Furthermore, the proposed BASGCN model can both adaptively discriminate the importance between specified vertices during the convolution process and reduce the notorious tottering problem of existing spatially-based GCNs related to the Weisfeiler-Lehman algorithm, explaining the effectiveness of the proposed model. Experiments on standard graph datasets demonstrate the effectiveness of the proposed model

Development of a complex intervention to support exercise self-management for people with Parkinson's.

Purpose: The value of exercise for people with Parkinson's (PwP) is undisputed, and is associated with improved health outcomes and potential to slow down the rate of symptom decline. However, the optimum means to support long-term participation in exercise remains undetermined. Current exercise provision within physiotherapy is commonly time limited. Moreover, research has shown that when current services cease, adherence declines and the effects on outcomes diminish. Therefore, a sustainable means of maintaining activity, beyond the end of normal physiotherapy care, is required, which necessitates a different approach to support People PwP to be more active. Aims: To develop an evidence based intervention to equip PwP to self-manage their exercise participation. Methods: A multi-step mixed methods approach was adopted. A systematic review of the literature exploring barriers and motivators to exercise and a comprehensive review of the exercise literature for PwP was conducted. Consultation with a convenience sample drawn from UK wide specialist physiotherapists and the Parkinson's community was conducted to explore barriers and facilitators to exercise delivery and participation. The findings were used to inform the development of a multi-component intervention aimed at promoting exercise self-management. PwP were involved in refining the intervention. Results: The literature review identified that simply prescribing exercise in isolation is ineffective to promote long-term changes in exercise behaviour. Amalgamating findings from the systematic and comprehensive literature reviews with consultation finding identified key ingredients to support long-term exercise self-management were identified. These included: individualised exercise programmes, contextualised education, and the provision of strategies to support PwP to develop an exercise habit. While the benefits of exercise were widely acknowledged, a need was identified for services to develop exercise self-confidence, to empower PwP with the knowledge and skills they need to embed exercise within their everyday routine. Access to professionals with specialist Parkinson's training was highly valued, either on a 1:1 basis or within a group. 1:1 interventions were thought to develop confidence, whereas group-based exercise provided opportunity for shared learning and development of a social network. Transport and costs were reported as key barriers; accessibility and sustainability were key to long-term participation. This process informed the development of the PDConnect programme. PDConnect is an evidence-informed exercise intervention underpinned by empowerment theory, with the aim of providing PwP with a toolkit of behaviour change techniques to promote participation in exercise and exercise self-management. The PDConnect combines specialist physiotherapy, group-based exercise and self-management support, with education and behaviour change strategies threaded throughout. The programme consists of three components: (i) six sessions of 1-1-specialist physiotherapy delivered at home; (ii) 12 sessions of group-based exercise, delivered once a week for 12 weeks; (iii) 12 weeks of self-management, with a support session each month. Conclusion(s): Aligning with the Medical Research Council guidelines for developing complex interventions, the feasibility and acceptability of the PDConnect programme is currently being tested and evaluated by those delivering and receiving the intervention. Impact: Promoting exercise self-management is beneficial to the NHS to reduce health service utilisation and prevent secondary complications related to sedentary behaviour. Funding acknowledgements: This development of the PDConnect intervention was not funded. Current work to explore the feasibility and acceptability of the PDConnect intervention is jointly funded by Parkinson's UK and the Chief Scientist Office, Scotland

Waste Water Brine Purification through Electrodialysis Ion Exchange

Reutilizing resources onboard the International Space Station (ISS) and for future deep space missions are critical for mission longevity and sustainability. Waste water brine produced from water recovery systems contain chemical species that could be processed into a potential fertilizer for future plant systems.Fertilizer production can be achieved through a process called electrodialysis ion exchange. Waste water containing inorganic salt components are fed through a series of ion exchange membranes to produce fertilizer (a phosphate rich stream), electrolysis-grade water, and other useful commodities.A test bed was constructed to conduct controlled experiments and an experimental design procedure developed to determine the feasibility of the process. Conductivity and pH probes were utilized to determine the ion concentration in each of the product streams, along with ion chromatography (IC) to define the exact concentration of each ion in every stream throughout the experiment. This is crucial in order to convey the effectiveness of ion removal from the incoming waste water stream.The waste water and electrolyte streams were prepared in the lab prior to experimentation. Additionally, the ion exchange membrane configurations were developed and Opto 22 data analysis software incorporated to conduct measurements in real time.Ions successfully diffused across their respective membranes into the concentrate, acid, and base streams. This resulted in pure water, a phosphate rich stream, and a separate anion/hydrogen and cation/hydroxide stream

Soliton response to transient trap variations

The response of bright and dark solitons to rapid variations in an expulsive longitudinal trap is investigated. We concentrate on the effect of transient changes in the trap frequency in the form of temporal delta kicks and the hyperbolic cotangent functions. Exact expressions are obtained for the soliton profiles. This is accomplished using the fact that a suitable linear Schrodinger stationary state solution in time can be effectively combined with the solutions of non-linear Schrodinger equation, for obtaining solutions of the Gross-Pitaevskii equation with time dependent scattering length in a harmonic trap. Interestingly, there is rapid pulse amplification in certain scenarios

Boson-Fermion coherence in a spherically symmetric harmonic trap

We consider the photoassociation of a low-density gas of quantum-degenerate trapped fermionic atoms into bosonic molecules in a spherically symmetric harmonic potential. For a dilute system and the photoassociation coupling energy small compared to the level separation of the trap, only those fermions in the single shell with Fermi energy are coupled to the bosonic molecular field. Introducing a collective pseudo-spin operator formalism we show that this system can then be mapped onto the Tavis-Cummings Hamiltonian of quantum optics, with an additional pairing interaction. By exact diagonalization of the Hamiltonian, we examine the ground state and low excitations of the Bose-Fermi system, and study the dynamics of the coherent coupling between atoms and molecules. In a semiclassical description of the system, the pairing interaction between fermions is shown to result in a self-trapping transition in the photoassociation, with a sudden suppression of the coherent oscillations between atoms and molecules. We also show that the full quantum dynamics of the system is dominated by quantum fluctuations in the vicinity of the self-trapping solution.Comment: 16 pages, 14 figure

Subspace Structure Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing

Hyperspectral unmixing is a crucial task for hyperspectral images (HSI) processing, which estimates the proportions of constituent materials of a mixed pixel. Usually, the mixed pixels can be approximated using a linear mixing model. Since each material only occurs in a few pixels in real HSI, sparse nonnegative matrix factorization (NMF) and its extensions are widely used as solutions. Some recent works assume that materials are distributed in certain structures, which can be added as constraints to sparse NMF model. However, they only consider the spatial distribution within a local neighborhood and define the distribution structure manually, while ignoring the real distribution of materials that is diverse in different images. In this paper, we propose a new unmixing method that learns a subspace structure from the original image and incorporate it into the sparse NMF framework to promote unmixing performance. Based on the self-representation property of data points lying in the same subspace, the learned subspace structure can indicate the global similar graph of pixels that represents the real distribution of materials. Then the similar graph is used as a robust global spatial prior which is expected to be maintained in the decomposed abundance matrix. The experiments conducted on both simulated and real-world HSI datasets demonstrate the superior performance of our proposed method