Applied Numerical Analysis. By C.F. Gerald. Pp. xii, 340. £3·60. 1970. (Addison-Wesley.)

Chapter 8 establishes the relationship between the input and output power spectral densities of a linear system. Limitations on results are carefully detailed and the case of oscillator noise is considered. The theory is extended to multiple input-multiple output systems.

Linear System Theory

Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.

Medical Image Fusion: A survey of the state of the art

Medical image fusion: A survey of the state of the art

The Self-Organizing Map (SOM) algorithm has attracted a great deal of interest among researches and practitioners in a wide variety of fields. The SOM has been analyzed extensively, a number of variants have been developed and, perhaps most notably, it has been applied extensively within fields ranging from engineering sciences to medicine, biology, and economics. We have collected a comprehensive list of 5384 scientific papers that use the algorithms, have benefited from them, or contain analyses of them. The list is intended to serve as a source for literature surveys. The present addendum contains 2092 new articles, mainly from the years 1998-2002. We have provided a keyword index to help finding articles of interest, and additionally a modern automatically constructed variant of a thematic index: a WEBSOM interface to the whole article collection of years 1981-2000. The SOM of SOMs is available at http://websom.hut.fi/websom/somref/search.cgi for browsing and searching the collection.

Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum

Reverse engineering ordinarily uses laser scanners since they can sample 3D data quickly and accurately relative to other systems. These laser scanner systems, however, yield an enormous amount of irregular and scattered digitized point data that requires intensive reconstruction processing. Reconstruction of freeform objects consists of two main stages: parameterization and surface fitting. Selection of an appropriate parameterization is essential for topology reconstruction as well as surface fitness. Current parameterization methods have topological problems that lead to undesired surface fitting results, such as noisy self-intersecting surfaces. Such problems are particularly common with concave shapes whose parametric grid is self-intersecting, resulting in a fitted surface that considerably twists and changes its original shape. In such cases, other parameterization approaches should be used in order to guarantee non-self-intersecting behavior. The parameterization method described in this paper is based on two stages: 2D initial parameterization; and 3D adaptive parameterization. Two methods were developed for the first stage: partial differential equation (PDE) parameterization and neural network self organizing maps (SOM) parameterization. The Gradient Descent Algorithm (GDA) and Random Surface Error Correction (RSEC), both of which are iterative surface fitting methods, were developed and implemented.

Parameterization and reconstruction from 3D scattered points based on neural network and PDE techniques

Precision inspection of free-form surface is difficult with current industry practices that rely on accurate fixtures. Alternatively, the measurements can be aligned to the part model using a geometry-based registra- tion method, such as the iterative closest point (ICP) method, to achieve a fast and automatic inspection process. This paper discusses various techniques that accelerate the registration process and improve the efficiency of the ICP method. First, the data structures of approximated nearest nodes and topological neighbor facets are combined to speed up the closest point calculation. The closest point calculation is further improved with the cached facets across iteration steps. The registration efficiency can also be enhanced by incorporating signal-to-noise ratio into the transformation of correspondence sets to reduce or remove the noise of outliers. Last, an acceleration method based on linear or quadratic extrapolation is fine-tuned to provide the fast yet robust iteration process. These techniques have been implemented on a four-axis blade inspection machine where no accurate fixture is required. The tests of measurement simulations and inspection case studies indicated that the presented registration method is accurate and efficient.

/pdf/efficient-registration-for-precision-inspection-of-free-form-7qjujd6yof.pdf

Efficient registration for precision inspection of free-form surfaces

Reverse engineering is an important process in CAD systems today. Yet several open problems lead to a bottleneck in the reverse engineering process. First, because the topology of the object to be reconstructed is unknown, point connectivity relations are undefined. Second, the fitted surface must satisfy global and local shape preservation criteria that are undefined explicitly. In reverse engineering, object reconstruction is based both on parameterization and on fitting. Nevertheless, the above problems are influenced mainly by parameterization. In order to overcome the above problems, the paper proposes a neural network, Self Organizing Map (SOM) method, for creating a 3D parametric grid. The main advantage of the proposed SOM method is that it detects both the orientation of the grid and the position of the sub-boundaries. The neural network grid converges to the sampled shape through an adaptive learning process. The SOM method is applied directly on 3D sampled data and avoids the projection anomalies common to other methods. The paper also presents boundary correction and growing grid extensions to the SOM method. In the surface fitting stage, an RSEC (Random Surface Error Correction) fitting method based on the SOM method was developed and implemented.

Adaptive reconstruction of freeform objects with 3D SOM neural network grids

The subject methods and apparatus allow the buyer of an automobile to configure and design a personalized interior space prior to purchasing the automobile. The buyer selects modular interior components from a library of choices provided by the manufacturer, and designs his/her custom automobile by using an interactive design domain system that allows realistic visualization of the developing interior with the incorporation of the buyer's anthropometric information. The method and apparatus guarantee conformance to applicable safety, geometric and functional constraints through the application of a rules-database. The produced automobile will fit exactly the customer's taste, physical dimensions, and personal requirements. The apparatus includes a computer that hosts a decision-support and feedback system, a database of safety, geometric and functional rules, and a database of interior components. The apparatus also includes a device for realistic visualization of the designed vehicle interior as well as suitable devices to measure the body dimensions that are needed for the design. A business transaction is contemplated in which the buyer custom-designs the vehicle interior using modular components after the sale transaction but before the manufacturing step.

Method and apparatus for re-configurable vehicle interior design and business transaction

This paper describes an advanced closed loop quality control methodology for reconfigurable manufacturing systems. The methodology enables rapid root-cause diagnostics for faster ramp-up of reconfigurable systems through integration of the Reconfigurable Inspection Machine (RIM) and the Stream of Variations (SoV) methodology. The RIM enables reconfigurable, rapid, and accurate inspection using non-contact sensors while the SoV methodology is used to quickly analyze the measurements and identify the root-cause of the errors produced during machining. The feasibility of the industrial concept was experimentally validated. A machining error was introduced during machining of an engine head. Measurement information collected by the RIM was processed and used to locate the root-cause of the error using the SoV methodology.

Jacob Barhak

Papers

Parameterization and reconstruction from 3D scattered points based on neural network and PDE techniques

Efficient registration for precision inspection of free-form surfaces

Adaptive reconstruction of freeform objects with 3D SOM neural network grids

Method and apparatus for re-configurable vehicle interior design and business transaction

Integration of reconfigurable inspection with stream of variations methodology