Shape matching and clustering

Generalising knowledge and matching patterns is a basic human trait in re-using past experiences. We often cluster (group) knowledge of similar attributes as a process of learning and or aid to manage the complexity and re-use of experiential knowledge [1, 2]. In conceptual design, an ill-defined shape may be recognised as more than one type. Resulting in shapes possibly being classified differently when different criteria are applied. This paper outlines the work being carried out to develop a new technique for shape clustering. It highlights the current methods for analysing shapes found in computer aided sketching systems, before a method is proposed that addresses shape clustering and pattern matching. Clustering for vague geometric models and multiple viewpoint support are explored

Intelligent computational sketching support for conceptual design

Sketches, with their flexibility and suggestiveness, are in many ways ideal for expressing emerging design concepts. This can be seen from the fact that the process of representing early designs by free-hand drawings was used as far back as in the early 15th century [1]. On the other hand, CAD systems have become widely accepted as an essential design tool in recent years, not least because they provide a base on which design analysis can be carried out. Efficient transfer of sketches into a CAD representation, therefore, is a powerful addition to the designers' armoury.It has been pointed out by many that a pen-on-paper system is the best tool for sketching. One of the crucial requirements of a computer aided sketching system is its ability to recognise and interpret the elements of sketches. 'Sketch recognition', as it has come to be known, has been widely studied by people working in such fields: as artificial intelligence to human-computer interaction and robotic vision. Despite the continuing efforts to solve the problem of appropriate conceptual design modelling, it is difficult to achieve completely accurate recognition of sketches because usually sketches implicate vague information, and the idiosyncratic expression and understanding differ from each designer

Shape matching and clustering in design

Generalising knowledge and matching patterns is a basic human trait in re-using past experiences. We often cluster (group) knowledge of similar attributes as a process of learning and or aid to manage the complexity and re-use of experiential knowledge [1, 2]. In conceptual design, an ill-defined shape may be recognised as more than one type. Resulting in shapes possibly being classified differently when different criteria are applied. This paper outlines the work being carried out to develop a new technique for shape clustering. It highlights the current methods for analysing shapes found in computer aided sketching systems, before a method is proposed that addresses shape clustering and pattern matching. Clustering for vague geometric models and multiple viewpoint support are explored

Three-dimensional cloud volume reconstruction from the Multi-angle Imaging SpectroRadiometer

Clouds continue to contribute the largest uncertainty to estimates and interpretations of the Earth's energy budget, and their representation in climate models has been recognized for decades as a dominant source of uncertainty in climate change projections. It has been suggested that understanding the 3-D structure of cloud would lead to better understanding of the Earth's radiative and latent fluxes. Indeed, knowing cloud 3-D geometry could lead to: 1) improving our understanding of cloud microphysical properties and processes, and 2) improving our knowledge of the radiative effects of cloud on the Earth's energy budget. The Multi-angle Imaging SpectroRadiometer (MISR) is on board the Terra satellite, in its 17th year of operation as of 2017. MISR provides nine views of the same scene that allow scientists to visualize the 3-D structure of observed clouds to a certain extent. Taking advantage of such multi-angle characteristic, this project aims to reconstruct cloud volumes from MISR data. The reconstruction domain is defined such that it takes into account the curvature of the Earth’s ellipsoidal surface. The input satellite images used are the Radiometric Camera-by-camera Cloud Masks at 1.1 km resolution developed by the MISR science team, and custom cloud masks at 275 m resolution developed from MISR RGB images in this project. Due to the time difference between each camera view angle, wind correction is performed on the input cloud masks. For the reconstruction method, “ray casting” algorithms that fully account for the instrument's geometric properties are developed. The reconstruction results are presented for three hand-picked MISR cloud scenes. Strengths and limitations of the reconstruction method are explored, and the outlook for the use of the reconstruction results are discussed

Improved Surgical Technique for Heterotopic Aortic Transplantation in Mice

Transplant arteriosclerosis is the main limitation for long-term survival of solid organ transplant recipients. Animal models would provide invaluable tools to investigate the cellular and molecular mechanisms underlying the pathogenesis of transplant arteriosclerosis, as well as for studies with novel drugs and other reagents for the prevention of the disease. We have therefore developed a modified technique for aortic transplantation in mice. The central suture ligation of the recipient abdominal aorta allowed a simpler end-to-side anastomosis of a segment of the donor thoracic aorta into the infrarenal portion of the recipient abdominal aorta. Using this technique, the overall survival rate was 94%. We also observed typical aspects of chronic rejection of the aortic allografts not observed with isografts. Our new technique is relatively easy to perform and has a low incidence of thrombosis, thus being useful for studying various aspects of transplant arteriosclerosis

High levels of soluble herpes virus entry mediator in sera of patients with allergic and autoimmune diseases

Herpes virus entry mediator (HVEM) is a newly discovered member of the tumor necrosis factor receptor (TNFR) superfamily that has a role in herpes simplex virus entry, in T cell activation and in tumor immunity. We generated mAb against HVEM and detected soluble HVEM (SHVEM) in the sera of patients with various autoimmune diseases. HVEM was constitutively expressed on CD4+ and CD8+ T cells, CD19+ B cells, CD14+ monocytes, neutrophils and dendritic cells. In three-way MLR, mAb 122 and 139 were agonists and mAb 108 had blocking activity. An ELISA was developed to detect sHVEM in patient sera. sHVEM levels were elevated in sera of patients with allergic asthma, atopic dermatitis and rheumatoid arthritis. The mAbs discussed here may be useful for studies of the role of HVEM in immune responses. Detection of soluble HVEM might have diagnostic and prognostic value in certain immunological disorders

Three-Dimensional Cloud Volume Reconstruction from the Multi-angle Imaging SpectroRadiometer

Characterizing 3-D structure of clouds is needed for a more complete understanding of the Earth’s radiative and latent heat fluxes. Here we develop and explore a ray casting algorithm applied to data from the Multi-angle Imaging SpectroRadiometer (MISR) onboard the Terra satellite, in order to reconstruct 3-D cloud volumes of observed clouds. The ray casting algorithm is first applied to geometrically simple synthetic clouds to show that, under the assumption of perfect, clear-conservative cloud masks, the reconstruction method yields overestimation in the volume whose magnitude depends on the cloud geometry and the resolution of the reconstruction grid relative to the image pixel resolution. The method is then applied to two hand-picked MISR scenes, fully accounting for MISR’s viewing geometry for reconstructions over the Earth’s ellipsoidal surface. The MISR Radiometric Camera-by-camera Cloud Mask (RCCM) at 1.1-km resolution and the custom cloud mask at 275-m resolution independently derived from MISR’s red, green, and blue channels are used as input cloud masks. A wind correction method, termed cloud spreading, is applied to the cloud masks to offset potential cloud movements over short time intervals between the camera views of a scene. The MISR cloud-top height product is used as a constraint to reduce the overestimation at the cloud top. The results for the two selected scenes show that the wind correction using the cloud spreading method increases the reconstructed volume up to 4.7 times greater than without the wind correction, and that the reconstructed volume generated from the RCCM is up to 3.5 times greater than that from the higher-resolution custom cloud mask. Recommendations for improving the presented cloud volume reconstructions, as well as possible future passive remote sensing satellite missions, are discussed