Minimum error contrast enhancement

Contrast enhancement is an image processing technique where the objective is to preprocess the image so that relevant information can be either seen or further processed more reliably. These techniques are typically applied when the image itself or the device used for image reproduction provides poor visibility and distinguishability of different regions of interest inthe image. In most studies, the emphasis is on the visualization of image data,but this human observer biased goal often results to images which are not optimal for automated processing. The main contribution of this study is to express the contrast enhancement as a mapping from N-channel image data to 1-channel gray-level image, and to devise a projection method which results to an image with minimal error to the correct contrast image. The projection, the minimum-error contrast image, possess the optimal contrast between the regions of interest in the image. The method is based on estimation of the probability density distributions of the region values, and it employs Bayesian inference to establish the minimum error projection.

Calculation of Activity Coefficients of Electrolytes of Other Charge Types than 1-1 by Equations Containing No Adjustable Parameters in Aqueous Solutions at 25 0C

Normally either the Güntelberg or Davies equation is used to predict activity coefficients of electrolytes in dilute solutions when no betterequation is available. The validity of these equations and, additionally, of the parameter-free equation used in the Bates-Guggenheim convention for activity coefficients were tested with experimentally determined activity coefficients of LaCl3, CaCl2, SrCl2 and BaCl2 in aqueous solutions at 298.15 K. The experimentalactivity coefficients of these electrolytes can be usually reproduced within experimental error by means of a two-parameter equation of the Hückel type. The best Hückel equations were also determined for all electrolytes considered. The data used in the calculations of this study cover almost all reliable galvanic cell results available in the literature for the electrolytes considered. The results of the calculations reveal that the parameter-free activity coefficient equations can only be used for very dilute electrolyte solutions in thermodynamic studies

Novel Methods for Error Modeling and Parameter Identification of a Redundant Serial-Parallel Hybrid Robot

To obtain the desirable accuracy of a robot, there are two techniques available. The first option would be to make the robot match the nominal mathematic model. In other words, the manufacturing and assembling tolerances of every part would be extremely tight so that all of the various parameters would match the “design” or “nominal” values as closely as possible. This method can satisfy most of the accuracy requirements， but the cost would increase dramatically as the accuracy requirement increases. Alternatively, a more cost-effective solution is to build a manipulator with relaxed manufacturing and assembling tolerances. By modifying the mathematical model in the controller, the actual errors of the robot can be compensated. This is the essence of robot calibration. Simply put, robot calibration is the process of defining an appropriate error model and then identifying the various parameter errors that make the error model match the robot as closely as possible. This work focuses on kinematic calibration of a 10 degree-of-freedom (DOF) redundant serial-parallel hybrid robot. The robot consists of a 4-DOF serial mechanism and a 6-DOF hexapod parallel manipulator. The redundant 4-DOF serial structure is used to enlarge workspace and the 6-DOF hexapod manipulator is used to provide high load capabilities and stiffness for the whole structure. The main objective of the study is to develop a suitable calibration method to improve the accuracy of the redundant serial-parallel hybrid robot. To this end, a Denavit–Hartenberg (DH) hybrid error model and a Product-of-Exponential (POE) error model are developed for error modeling of the proposed robot. Furthermore, two kinds of global optimization methods, i.e. the differential-evolution (DE) algorithm and the Markov Chain Monte Carlo (MCMC) algorithm, are employed to identify the parameter errors of the derived error model. A measurement method based on a 3-2-1 wire-based pose estimation system is proposed and implemented in a Solidworks environment to simulate the real experimental validations. Numerical simulations and Solidworks prototype-model validations are carried out on the hybrid robot to verify the effectiveness, accuracy and robustness of the calibration algorithms.

How Lean Six Sigma and Agile principles optimize ITIL-based processes

Imagine the potential implications of an organization whose business and IT processes are well aligned and are capable of reactively and proactively responding to the external and internal changes. The Philips IT Infrastructure and Operations department (I&O) is undergoing a series of transformation activities to help Philips business keeping up with the changes. I&O would serve a critical function in any business sectors; given that the I&O’s strategy switched from “design, build and run” to “specify, acquire and performance manage”, that function is amplified. In 2013, I&O’s biggest transforming programme I&O Futures engaged multiple interdisciplinary departments and programs on decommissioning legacy processes and restructuring new processes with respect to the Information Technology Internet Library (ITIL), helping I&O to achieve a common infrastructure and operating platform (CI&OP). The author joined I&O Futures in the early 2014 and contributed to the CI&OP release 1, during which a designed model Bing Box and its evaluations were conducted through the lens of six sigma’s structured define-measure-analyze-improve-control (DMAIC) improvement approach. This Bing Box model was intended to firstly combine business and IT principles, namely Lean IT, Agile, ITIL best practices, and Aspect-oriented programming (AOP) into a framework. Secondly, the author implemented the modularized optimization cycles according to the defined framework into Philips’ ITIL-based processes and, subsequently, to enhance business process performance as well as to increase efficiency of the optimization cycles. The unique of this thesis is that the Bing Box model not only provided comprehensive optimization approaches and principles for business process performance, but also integrated and standardized optimization modules for the optimization process itself. The research followed a design research guideline that seek to extend the boundaries of human and organizational capabilities by creating new and innovative artifacts. The Chapter 2 firstly reviewed the current research on Lean Six Sigma, Agile, AOP and ITIL, aiming at identifying the broad conceptual bases for this study. In Chapter 3, we included the process of constructing the Bing Box model. The Chapter 4 described the adoption of Bing Box model: two-implementation case validated by stakeholders through observations and interviews. Chapter 5 contained the concluding remarks, the limitation of this research work and the future research areas. Chapter 6 provided the references used in this thesis.