Decomposition Algorithms for Stochastic Programming on a Computational Grid

We describe algorithms for two-stage stochastic linear programming with recourse and their implementation on a grid computing platform. In particular, we examine serial and asynchronous versions of the L-shaped method and a trust-region method. The parallel platform of choice is the dynamic, heterogeneous, opportunistic platform provided by the Condor system. The algorithms are of master-worker type (with the workers being used to solve second-stage problems, and the MW runtime support library (which supports master-worker computations) is key to the implementation. Computational results are presented on large sample average approximations of problems from the literature.Comment: 44 page

On Robotic Work-Space Sensing and Control

Industrial robots are fast and accurate when working with known objects at precise locations in well-structured manufacturing environments, as done in the classical automation setting. In one sense, limited use of sensors leaves robots blind and numb, unaware of what is happening in their surroundings. Whereas equipping a system with sensors has the potential to add new functionality and increase the set of uncertainties a robot can handle, it is not as simple as that. Often it is difficult to interpret the measurements and use them to draw necessary conclusions about the state of the work space. For effective sensor-based control, it is necessary to both understand the sensor data and to know how to act on it, giving the robot perception-action capabilities. This thesis presents research on how sensors and estimation techniques can be used in robot control. The suggested methods are theoretically analyzed and evaluated with a large focus on experimental verification in real-time settings. One application class treated is the ability to react fast and accurately to events detected by vision, which is demonstrated by the realization of a ball-catching robot. A new approach is proposed for performing high-speed color-based image analysis that is robust to varying illumination conditions and motion blur. Furthermore, a method for object tracking is presented along with a novel way of Kalman-filter initialization that can handle initial-state estimates with infinite variance. A second application class treated is robotic assembly using force control. A study of two assembly scenarios is presented, investigating the possibility of using force-controlled assembly in industrial robotics. Two new approaches for robotic contact-force estimation without any force sensor are presented and validated in assembly operations. The treated topics represent some of the challenges in sensor-based robot control, and it is demonstrated how they can be used to extend the functionality of industrial robots

Vision Based Tracker for Dart Catching Robot

The objective of this thesis has been to develop the foundation for a robot system that catches darts. A dart board is to be mounted on a robot. When a dart is thrown at the board, it is detected by cameras, and an algorithm predicts where the dart will hit the board. The goal is then to move the board in such a way that the dart hits at the desired coordinate, typically the bull's eye. This report describes the different components developed to realize this system, including image acquisition, camera calibration, image analysis and modeling and estimation of the dart trajectory

Diffuse large B-cell lymphoma - tumour characteristics on RNA and protein level associated with prognosis

Diffuse large B-cell lymphoma (DLBCL) is the most frequent lymphoma subtype. In Sweden 450 new cases are diagnosed annually. With modern anthracycline-containing chemotherapy DLBCL is potentially curable, with an estimated overall cure rate of approximately 50% for patients with advanced stage disease. Through molecular profiling of DLBCL the ?cell-of-origin concept? has been established: patients with tumours expressing genes characteristic of germinal center B-cells, ?GC-profile? has a significantly better survival than patients with tumors expressing genes normally induced during in vitro activation of peripheral blood B-cells, ?ABC-profile?. The first study (n=125) aimed to identify a protein pattern that could be used for discriminating germinal center derived (GC) and activated B-cell like (ABC)/non-GC DLBCL, using immunohistochemistry (IHC). BCL6, CD10 and CD40 were chosen as markers of a GC-phenotype, CD23 as a marker of pre/early GC-origin and CD138 as a marker of post-GC origin (i.e non-GC). No prognostically different subgroups, corresponding to GC or ABC (non-GC) could be identified. A new finding was the positive prognostic impact of CD23 and CD40 expression. In the second study (n=125) the prognostic effect of CD40, but not CD23, was confirmed. The effect of CD40 effect could not be explained by association with the GC-phenotype or by an enhanced autologous tumour response, as detected by tumour infiltrating helper and cytotoxic T-lymphocytes. The prognostic effect of a GC versus non-GC phenotype according to Hans et al (Blood 2004) was confirmed. The third study (n=122) identified the tissue microarray technique to be unreliable for immunohistochemical detection in GC vs. non-GC phenotypes, mostly due to difficulties interpreting BCL6 status. In the fourth study tumours from patients with cured (n=24) versus primary chemotherapy-refractory DLBCL (n=13), were investigated with respect to gene expression profiles, using spotted 55K oligonucleotide arrays produced in Lund. The genes that most differed between chemotherapy sensitive and refractory tumours mainly coded for proteins expressed by cells in the tumour microenvironment, and not by the tumour cells themselves. Confirmative IHC showed that the frequency of tumour infiltrating lymphocytes, macrophages and reactive cells expressing proteolytic and pro-inflammatory proteins were higher in the chemo-sensitive cohort, indicating that the microenvironment has an impact on the response to chemotherapy in DLBCL

Robotic Work-Space Sensing and Control

Industrial robots are traditionally programmed using only the internal joint position sensors, in a sense leaving the robot blind and numb. Using external sensors, such as cameras and force sensors, allows the robot to detect the existence and position of objects in an unstructured environment, and to handle contact situations not possible using only position control. This thesis presents work on how external sensors can be used in robot control. A vision-based robotic ball-catcher was implemented, showing how high-speed computer vision can be used for robot control with hard time constraints. Special attention is payed to tracking of a flying ball with an arbitrary number of cameras, how to initialize the tracker when no information about the initial state is available, and how to dynamically update the robot trajectory when the end point of the trajectory is modified due to new measurements. In another application example, force control was used to perform robotic assembly. It is shown how force sensing can be used to handle uncertain position