Structural topology optimisation based on the Boundary Element and Level Set methods

The research work presented in this thesis is related to the development of structural optimisation algorithms based on the boundary element and level set methods for two and three-dimensional linear elastic problems. In the initial implementation, a stress based evolutionary structural optimisation (ESO) approach has been used to add and remove material simultaneously for the solution of two-dimensional optimisation problems. The level set method (LSM) is used to provide an implicit description of the structural geometry, which is also capable of automatically handling topological changes, i.e. holes merging with each other or with the boundary. The classical level set based optimisation methods are dependent on initial designs with pre-existing holes. However, the proposed method automatically introduces internal cavities utilising a stress based hole insertion criteria, and thereby eliminates the use of initial designs with pre-existing holes. A detailed study has also been carried out to investigate the relationship between a stress and topological derivative based hole insertion criteria within a boundary element method (BEM) and LSM framework. The evolving structural geometry (i.e. the zero level set contours) is represented by non-uniform rational b-splines (NURBS), providing a smooth geometry throughout the optimisation process and completely eliminating jagged edges. The BEM and LSM are further combined with a shape sensitivity approach for the solution of minimum compliance problems in two-dimensions. The proposed sensitivity based method is capable of automatically inserting holes during the optimisation process using a topological derivative approach. In order to investigate the associated advantages and disadvantages of the evolutionary and sensitivity based optimisation methods a comparative study has also been carried out. There are two advantages associated with the use of LSM in three-dimensional topology optimisation. Firstly, the LSM may readily be applied to three-dimensional space, and it is shown how this can be linked to a 3D BEM solver. Secondly, the holes appear automatically through the intersection of two surfaces moving towards each other. Therefore, the use of LSM eliminates the need for an additional hole insertion mechanism as both shape and topology optimisation can be performed at the same time. A complete algorithm is proposed and tested for BEM and LSM based topology optimisation in three-dimensions. Optimal geometries compare well against those in the literature for a range of benchmark examples

Pose independent target recognition system using pulsed Ladar imagery

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 95-97).Although a number of object recognition techniques have been developed to process LADAR scanned terrain scenes, these techniques have had limited success in target discrimination in part due to low-resolution data and limits in available computation power. We present a pose-independent Automatic Target Detection and Recognition System that uses data from an airborne 3D imaging Ladar sensor. The Automatic Target Recognition system uses geometric shape and size signatures from target models to detect and recognize targets under heavy canopy and camouflage cover in extended terrain scenes. A method for data integration was developed to register multiple scene views to obtain a more complete 3D surface signature of a target. Automatic target detection was performed using the general approach of"3D cueing," which determines and ranks regions of interest within a large-scale scene based on the likelihood that they contain the respective target. Each region of interest is then passed to an ATR algorithm to accurately identify the target from among a library of target models. Automatic target recognition was performed using spin-image surface matching, a pose-independent algorithm that determines correspondences between a scene and a target of interest. Given a region of interest within a large-scale scene, the ATR algorithm either identifies the target from among a library of 10 target models or reports a "none of the above" outcome. The system performance was demonstrated on five measured scenes with targets both out in the open and under heavy canopy cover, where the target occupied between 1 to 10% of the scene by volume. The ATR section of the system was successfully demonstrated for twelve measured data scenes with targets both out in the open andunder heavy canopy and camouflage cover. Correct target identification was also demonstrated for targets with multiple movable parts that are in arbitrary orientations. The system achieved a high recognition rate (over 99%) along with a low false alarm rate (less than 0.01%) The contributions of this thesis research are: 1) I implemented a novel technique for reconstructing multiple-view 3D Ladar scenes. 2) I demonstrated that spin-image-based detection and recognition is feasible for terrain data collected in the field with a sensor that may be used in a tactical situation and 3) I demonstrated recognition of articulated objects, with multiple movable parts. Immediate benefits of the presented work will be to the area of Automatic Target Recognition of military ground vehicles, where the vehicles of interest may include articulated components with variable position relative to the body, and come in many possible configurations. Other application areas include human detection and recognition for Homeland Security, and registration of large or extended terrain scenes.by Alexandru N. Vasile.M.Eng

ONLINE HIERARCHICAL MODELS FOR SURFACE RECONSTRUCTION

Applications based on three-dimensional object models are today very common, and can be found in many fields as design, archeology, medicine, and entertainment. A digital 3D model can be obtained by means of physical object measurements performed by using a 3D scanner. In this approach, an important step of the 3D model building process consists of creating the object's surface representation from a cloud of noisy points sampled on the object itself. This process can be viewed as the estimation of a function from a finite subset of its points. Both in statistics and machine learning this is known as a regression problem. Machine learning views the function estimation as a learning problem to be addressed by using computational intelligence techniques: the points represent a set of examples and the surface to be reconstructed represents the law that has generated them. On the other hand, in many applications the cloud of sampled points may become available only progressively during system operation. The conventional approaches to regression are therefore not suited to deal efficiently with this operating condition. The aim of the thesis is to introduce innovative approaches to the regression problem suited for achieving high reconstruction accuracy, while limiting the computational complexity, and appropriate for online operation. Two classical computational intelligence paradigms have been considered as basic tools to address the regression problem: namely the Radial Basis Functions and the Support Vector Machines. The original and innovative aspect introduced by this thesis is the extension of these tools toward a multi-scale incremental structure, based on hierarchical schemes and suited for online operation. This allows for obtaining modular, scalable, accurate and efficient modeling procedures with training algorithms appropriate for dealing with online learning. Radial Basis Function Networks have a fast configuration procedure that, operating locally, does not require iterative algorithms. On the other side, the computational complexity of the configuration procedure of Support Vector Machines is independent from the number of input variables. These two approaches have been considered in order to analyze advantages and limits of each of them due to the differences in their intrinsic nature