27831253
Sparse Matrix Representations in a Functional Language
This paper investigates several sparse matrix representation schemes and associated algorithms in Haskell for solving linear systems of equations arising from solving realistic computational fluid dynamics problems using a finite element algorithm. This work complements that of Wainwright and Sexton (1992) in that a Choleski direct solver (with an emphasis on its forward/backward substitution steps) is examined. Experimental evidence comparing time and space efficiency of these matrix representation schemes is reported, together with associated forward/backward substitution implementations. Our results are in general agreement with Wainwright and Sexton's.
35595997
Parallel simulation of a multi-dimensional computational fluid dynamics problem
The simulation of computational fluid dynamics problems in two and more dimensions involves computations of multiple degrees of freedom, such as the components of velocity, which are an obvious source of parallelism. Further to our previous investigation in this area we report on our current experiences in extracting such parallelism on a distributed network platform. A finite element method for simulating incompressible viscous flows is parallelised using a domain decomposition approach. Various issues that arise in this implementation are discussed in the light of our experimental results.
32875582
Invariant object recognition by shape space analysis
This paper describes a new approach to invariant object recognition. In this approach, an object is represented by a set of key points called landmarks. All possible translation, scaling, and rotation of the object are placed into an equivalent class and associated to a single point in a complex projective space called the shape space. Object recognition is then achieved by distance calculations in this shape space. This approach is invariant to object translation, scaling, and rotation, and is computationally simple. Our experimental results also indicate that it is insensitive to noise and moderate occlusions.
59762733
The application of artificial neural networks in editing noisy seismic data
206756450
Nonlinear Dimensionality Reduction by Locally Linear Inlaying
High-dimensional data is involved in many fields of information processing. However, sometimes, the intrinsic structures of these data can be described by a few degrees of freedom. To discover these degrees of freedom or the low-dimensional nonlinear manifold underlying a high-dimensional space, many manifold learning algorithms have been proposed. Here we describe a novel algorithm, locally linear inlaying (LLI), which combines simple geometric intuitions and rigorously established optimality to compute the global embedding of a nonlinear manifold. Using a divide-and-conquer strategy, LLI gains some advantages in itself. First, its time complexity is linear in the number of data points, and hence LLI can be implemented efficiently. Second, LLI overcomes problems caused by the nonuniform sample distribution. Third, unlike existing algorithms such as isometric feature mapping (Isomap), local tangent space alignment (LTSA), and locally linear coordination (LLC), LLI is robust to noise. In addition, to evaluate the embedding results quantitatively, two criteria based on information theory and Kolmogorov complexity theory, respectively, are proposed. Furthermore, we demonstrated the efficiency and effectiveness of our proposal by synthetic and real-world data sets.
15202321
FREEDOM-7: A High Fidelity Seven Axis Haptic Device with Application to Surgical Training
A seven axis haptic device, called the Freedom-7, is described in relation to its application to surgical training. The generality of its concept makes it also relevant to most other haptic applications. The design ratio- nale is driven by a long list of requirements since such a device is meant to interact with the human hand: uniform response, balanced inertial proper- ties, static balancing, low inertia, high frequency response, high resolution, low friction, arbitrary reorientation, and low visual intrusion. Some basic performance gures are also reported.
41465544
Towards automated progress assessment of workpackage components in construction projects using computer vision
Traditional methods of collecting data on the progress of construction projects often involve human judgement, high costs, and are too infrequent to provide managers with timely and accurate control data. This study proposes a system that aims to address some of these shortcomings through automation. Our first contribution is a framework for the automatic generation of work packages. We describe the conceptual model and develop a prototype implementation, based on the results of a survey designed to capture the current practice of formulating work packages as performed in the industry. Our second contribution is a system that employs computer vision (CV) techniques to report on the progress of these work packages automatically. This is achieved by analysing sequences of digital images acquired regularly by on-site cameras. The result of this analysis, when compared with the expected state of construction, provides progress feedback on the work packages, allowing a project to be monitored more effectively.