Terahertz Technology Solutions (Canada)

This introductory, yet in-depth, book explains the physical principles of electronic imaging and sensing and provides the reader with the information necessary to understand the design, operation, and practical applications of contemporary electronic imaging and sensing systems. The text has strong practical focus and contains examples of biomedical applications of optical electronic imaging and sensing. Each chapter draws upon the authors’ extensive research, teaching, and industrial experience and provides a useful resource for undergraduate and graduate students, as well as a convenient reference for scientists and engineers working in the field of electronic imaging and sensing.

We use semi-analytic models of structure formation to interpret gravitational lensing measurements of substructure in galaxy cluster cores (R<=250kpc/h) at z=0.2. The dynamic range of the lensing-based substructure fraction measurements is well matched to the theoretical predictions, both spanning f_sub~0.05-0.65. The structure formation model predicts that f_sub is correlated with cluster assembly history. We use simple fitting formulae to parameterize the predicted correlations: Delta_90 = tau_90 + alpha_90 * log(f_sub) and Delta_50 = tau_50 + alpha_50 * log(f_sub), where Delta_90 and Delta_50 are the predicted lookback times from z=0.2 to when each theoretical cluster had acquired 90% and 50% respectively of the mass it had at z=0.2. The best-fit parameter values are: alpha_90 = (-1.34+/-0.79)Gyr, tau_90 = (0.31+/-0.56)Gyr and alpha_50 = (-2.77+/-1.66)Gyr, tau_50 = (0.99+/-1.18)Gyr. Therefore (i) observed clusters with f_sub<~0.1 (e.g. A383, A1835) are interpreted, on average, to have formed at z>~0.8 and to have suffered <=10% mass growth since z~0.4, (ii) observed clusters with f_sub>~0.4 (e.g. A68, A773) are interpreted as, on average, forming since z~0.4 and suffering >10% mass growth in the ~500Myr preceding z=0.2, i.e. since z=0.25. In summary, observational measurements of f_sub can be combined with structure formation models to estimate the age and assembly history of observed clusters. The ability to ``age-date'' approximately clusters in this way has numerous applications to the large clusters samples that are becoming available.

Most image processing and visualization applications allow users to configure computation parameters and manipulate the resulting visualizations. SCIRun, VolView, MeVisLab, and the Medical Interaction Toolkit (MITK) are four image processing and visualization frameworks that were built for these purposes. All frameworks are freely available and all allow the use of the ITK C++ library. In this paper, the benefits and limitations of each visualization framework are presented to aid both application developers and users in the decision of which framework may be best to use for their application. The analysis is based on more than 50 evaluation criteria, functionalities, and example applications. We report implementation times for various steps in the creation of a reference application in each of the compared frameworks. The data-flow programming frameworks, SCIRun and MeVisLab, were determined to be best for developing application prototypes, while VolView was advantageous for nonautomatic end-user applications based on existing ITK functionalities, and MITK was preferable for automated end-user applications that might include new ITK classes specifically designed for the application.