Software tools and efficient algorithms for the feature detection, feature tracking, event localization, and visualization of large sets of atmospheric data

Data sets describing the state of the earth's atmosphere are of great importance in the atmospheric sciences. Over the last decades, the quality and sheer amount of the available data increased significantly, resulting in a rising demand for new tools capable of handling and analysing these large, multidimensional sets of atmospheric data. The interdisciplinary work presented in this thesis covers the development and the application of practical software tools and efficient algorithms from the field of computer science, aiming at the goal of enabling atmospheric scientists to analyse and to gain new insights from these large data sets. For this purpose, our tools combine novel techniques with well-established methods from different areas such as scientific visualization and data segmentation. In this thesis, three practical tools are presented. Two of these tools are software systems (Insight and IWAL) for different types of processing and interactive visualization of data, the third tool is an efficient algorithm for data segmentation implemented as part of Insight.Insight is a toolkit for the interactive, three-dimensional visualization and processing of large sets of atmospheric data, originally developed as a testing environment for the novel segmentation algorithm. It provides a dynamic system for combining at runtime data from different sources, a variety of different data processing algorithms, and several visualization techniques. Its modular architecture and flexible scripting support led to additional applications of the software, from which two examples are presented: the usage of Insight as a WMS (web map service) server, and the automatic production of a sequence of images for the visualization of cyclone simulations. The core application of Insight is the provision of the novel segmentation algorithm for the efficient detection and tracking of 3D features in large sets of atmospheric data, as well as for the precise localization of the occurring genesis, lysis, merging and splitting events. Data segmentation usually leads to a significant reduction of the size of the considered data. This enables a practical visualization of the data, statistical analyses of the features and their events, and the manual or automatic detection of interesting situations for subsequent detailed investigation. The concepts of the novel algorithm, its technical realization, and several extensions for avoiding under- and over-segmentation are discussed. As example applications, this thesis covers the setup and the results of the segmentation of upper-tropospheric jet streams and cyclones as full 3D objects. Finally, IWAL is presented, which is a web application for providing an easy interactive access to meteorological data visualizations, primarily aimed at students. As a web application, the needs to retrieve all input data sets and to install and handle complex visualization tools on a local machine are avoided. The main challenge in the provision of customizable visualizations to large numbers of simultaneous users was to find an acceptable trade-off between the available visualization options and the performance of the application. Besides the implementational details, benchmarks and the results of a user survey are presented.

Chemische und physikalische Charakterisierung von Aerosolpartikeln aus besonderen anthropogenen Emissionsquellen : ein integrativer Ansatz mittels online (HR-ToF-AMS)- und komplementärer offline (ATR-FTIR-Spektroskopie)-Analytik

Die Gesundheitseffekte von Aerosolpartikeln werden stark von ihren chemischen und physikalischen Eigenschaften und somit den jeweiligen Bildungsprozessen und Quellencharakteristika beeinflusst. Während die Hauptquellen der anthropogenen Partikelemissionen gut untersucht sind, stellen die spezifischen Emissionsmuster zahlreicher kleiner Aerosolquellen, welche lokal und temporär zu einer signifikanten Verschlechterung der Luftqualität beitragen können, ein Forschungsdesiderat dar.rnIn der vorliegenden Arbeit werden in kombinierten Labor- und Feldmessungen durch ein integratives Analysekonzept mittels online (HR-ToF-AMS ) und filterbasierter offline (ATR-FTIR-Spektroskopie ) Messverfahren die weitgehend unbekannten physikalischen und chemischen Eigenschaften der Emissionen besonderer anthropogener Aerosolquellen untersucht. Neben einem Fußballstadion als komplexe Mischung verschiedener Aerosolquellen wie Frittieren und Grillen, Zigarettenrauchen und Pyrotechnik werden die Emissionen durch Feuerwerkskörper, landwirtschaftliche Intensivtierhaltung (Legehennen), Tief- und Straßenbauarbeiten sowie abwasserbürtige Aerosolpartikel in die Studie mit eingebunden. Die primären Partikelemissionen der untersuchten Quellen sind vorrangig durch kleine Partikelgrößen (dp < 1 µm) und somit eine hohe Lungengängigkeit gekennzeichnet. Dagegen zeigen die Aerosolpartikel im Stall der landwirtschaftlichen Intensivtierhaltung sowie die Emissionen durch die Tiefbauarbeiten einen hohen Masseanteil von Partikeln dp > 1 µm. Der Fokus der Untersuchung liegt auf der chemischen Charakterisierung der organischen Partikelbestandteile, welche für viele Quellen die NR-PM1-Emissionen dominieren. Dabei zeigen sich wichtige quellenspezifische Unterschiede in der Zusammensetzung der organischen Aerosolfraktion. Die beim Abbrand von pyrotechnischen Gegenständen freigesetzten sowie die abwasserbürtigen Aerosolpartikel enthalten dagegen hohe relative Gehalte anorganischer Substanzen. Auch können in einigen spezifischen Emissionen Metallverbindungen in den AMS-Massenspektren nachgewiesen werden. Über die Charakterisierung der Emissionsmuster und -dynamiken hinaus werden für einige verschiedenfarbige Rauchpatronen sowie die Emissionen im Stall der Intensivtierhaltung Emissionsfaktoren bestimmt, die zur quantitativen Bilanzierung herangezogen werden können. In einem weiteren Schritt werden anhand der empirischen Daten die analytischen Limitierungen der Aerosolmassenspektrometrie wie die Interferenz organischer Fragmentionen durch (Hydrogen-)Carbonate und mögliche Auswertestrategien zur Überwindung dieser Grenzen vorgestellt und diskutiert.rnEine umfangreiche Methodenentwicklung zur Verbesserung der analytischen Aussagekraft von organischen AMS-Massenspektren zeigt, dass für bestimmte Partikeltypen einzelne Fragmentionen in den AMS-Massenspektren signifikant mit ausgewählten funktionellen Molekülgruppen der FTIR-Absorptionsspektren korrelieren. Bedingt durch ihre fehlende Spezifität ist eine allgemeingültige Interpretation von AMS-Fragmentionen als Marker für verschiedene funktionelle Gruppen nicht zulässig und häufig nur durch die Ergebnisse der komplementären FTIR-Spektroskopie möglich. Des Weiteren wurde die Verdampfung und Ionisation ausgewählter Metallverbindungen im AMS analysiert. Die Arbeit verdeutlicht, dass eine qualitative und quantitative Auswertung dieser Substanzen nicht ohne Weiteres möglich ist. Die Gründe hierfür liegen in einer fehlenden Reproduzierbarkeit des Verdampfungs- und Ionisationsprozesses aufgrund von Matrixeffekten sowie der in Abhängigkeit vorangegangener Analysen (Verdampferhistorie) in der Ionisationskammer und auf dem Verdampfer statt-findenden chemischen Reaktionen.rnDie Erkenntnisse der Arbeit erlauben eine Priorisierung der untersuchten anthropogenen Quellen nach bestimmten Messparametern und stellen für deren Partikelemissionen den Ausgangpunkt einer Risikobewertung von atmosphärischen Folgeprozessen sowie potentiell negativen Auswirkungen auf die menschliche Gesundheit dar. rn

Dynamic distance analysis : new geometric data structures and algorithms for the real-time calculation of tolerance violating regions in the digital mockup process

In vielen Industriezweigen, zum Beispiel in der Automobilindustrie, werden Digitale Versuchsmodelle (Digital MockUps) eingesetzt, um die Konstruktion und die Funktion eines Produkts am virtuellen Prototypen zu überprüfen. Ein Anwendungsfall ist dabei die Überprüfung von Sicherheitsabständen einzelner Bauteile, die sogenannte Abstandsanalyse. Ingenieure ermitteln dabei für bestimmte Bauteile, ob diese in ihrer Ruhelage sowie während einer Bewegung einen vorgegeben Sicherheitsabstand zu den umgebenden Bauteilen einhalten. Unterschreiten Bauteile den Sicherheitsabstand, so muss deren Form oder Lage verändert werden. Dazu ist es wichtig, die Bereiche der Bauteile, welche den Sicherhabstand verletzen, genau zu kennen. rnrnIn dieser Arbeit präsentieren wir eine Lösung zur Echtzeitberechnung aller den Sicherheitsabstand unterschreitenden Bereiche zwischen zwei geometrischen Objekten. Die Objekte sind dabei jeweils als Menge von Primitiven (z.B. Dreiecken) gegeben. Für jeden Zeitpunkt, in dem eine Transformation auf eines der Objekte angewendet wird, berechnen wir die Menge aller den Sicherheitsabstand unterschreitenden Primitive und bezeichnen diese als die Menge aller toleranzverletzenden Primitive. Wir präsentieren in dieser Arbeit eine ganzheitliche Lösung, welche sich in die folgenden drei großen Themengebiete unterteilen lässt.rnrnIm ersten Teil dieser Arbeit untersuchen wir Algorithmen, die für zwei Dreiecke überprüfen, ob diese toleranzverletzend sind. Hierfür präsentieren wir verschiedene Ansätze für Dreiecks-Dreiecks Toleranztests und zeigen, dass spezielle Toleranztests deutlich performanter sind als bisher verwendete Abstandsberechnungen. Im Fokus unserer Arbeit steht dabei die Entwicklung eines neuartigen Toleranztests, welcher im Dualraum arbeitet. In all unseren Benchmarks zur Berechnung aller toleranzverletzenden Primitive beweist sich unser Ansatz im dualen Raum immer als der Performanteste.rnrnDer zweite Teil dieser Arbeit befasst sich mit Datenstrukturen und Algorithmen zur Echtzeitberechnung aller toleranzverletzenden Primitive zwischen zwei geometrischen Objekten. Wir entwickeln eine kombinierte Datenstruktur, die sich aus einer flachen hierarchischen Datenstruktur und mehreren Uniform Grids zusammensetzt. Um effiziente Laufzeiten zu gewährleisten ist es vor allem wichtig, den geforderten Sicherheitsabstand sinnvoll im Design der Datenstrukturen und der Anfragealgorithmen zu beachten. Wir präsentieren hierzu Lösungen, die die Menge der zu testenden Paare von Primitiven schnell bestimmen. Darüber hinaus entwickeln wir Strategien, wie Primitive als toleranzverletzend erkannt werden können, ohne einen aufwändigen Primitiv-Primitiv Toleranztest zu berechnen. In unseren Benchmarks zeigen wir, dass wir mit unseren Lösungen in der Lage sind, in Echtzeit alle toleranzverletzenden Primitive zwischen zwei komplexen geometrischen Objekten, bestehend aus jeweils vielen hunderttausend Primitiven, zu berechnen. rnrnIm dritten Teil präsentieren wir eine neuartige, speicheroptimierte Datenstruktur zur Verwaltung der Zellinhalte der zuvor verwendeten Uniform Grids. Wir bezeichnen diese Datenstruktur als Shrubs. Bisherige Ansätze zur Speicheroptimierung von Uniform Grids beziehen sich vor allem auf Hashing Methoden. Diese reduzieren aber nicht den Speicherverbrauch der Zellinhalte. In unserem Anwendungsfall haben benachbarte Zellen oft ähnliche Inhalte. Unser Ansatz ist in der Lage, den Speicherbedarf der Zellinhalte eines Uniform Grids, basierend auf den redundanten Zellinhalten, verlustlos auf ein fünftel der bisherigen Größe zu komprimieren und zur Laufzeit zu dekomprimieren.rnrnAbschießend zeigen wir, wie unsere Lösung zur Berechnung aller toleranzverletzenden Primitive Anwendung in der Praxis finden kann. Neben der reinen Abstandsanalyse zeigen wir Anwendungen für verschiedene Problemstellungen der Pfadplanung.

Smart Mobility: integrazione di crowdsourcing e opendata per percorsi multimodali e personalizzati.

Lo scopo che questa tesi ha è di inserirsi nell’ambito della Smart Mobility, in particolare nell'ambito dell’accessibilità urbana. Obiettivo primario è di offrire un software capace di adattarsi alle capacità dell’utente nel muoversi nell’ambiente urbano, in particolare riguardo alle barriere architettoniche che lo ostacolano. Quello che il programma deve fare è offrire percorsi per l’utente, personalizzati rispetto alle sue richieste. Affinché sia possibile tutto ciò, sono necessarie delle fondamenta, nella fattispecie una fonte di dati geografici e uno strumento che permetta di ricercare percorsi da essi e di essere modificato per includere le modifiche volute. La fonte di dati geografici scelta è stata OpenStreetMap, un progetto di crowdsourcing che punta a creare una mappa globale completamente accessibile fino al suo livello più basso e utilizzabile da chiunque, purché sia rispettata la sua licenza. Da questa scelta, derivano i software utilizzabili per calcolare i percorsi: la tesi ne esplorerà in particolare due GraphHopper e OpenTripPlanner, entrambi progetti open source. L’ultimo, ma non meno importante, scopo della tesi è effettivamente implementare un algoritmo di routing capace di considerare le preferenze degli utenti. Queste preferenze, infatti, non devono solo permettere di escludere percorsi con una barriera/architettonica cui l’utente non può accedere, ma anche di favorire percorsi con le facility che l’utente preferisce e di sfavorire quelli con facility che l’utente non preferisce.

Progettazione ed implementazione di un recommendation system di articoli scientifici basato su Apache Mahout

Il focus di questo elaborato è sui sistemi di recommendations e le relative caratteristiche. L'utilizzo di questi meccanism è sempre più forte e presente nel mondo del web, con un parallelo sviluppo di soluzioni sempre più accurate ed efficienti. Tra tutti gli approcci esistenti, si è deciso di prendere in esame quello affrontato in Apache Mahout. Questa libreria open source implementa il collaborative-filtering, basando il processo di recommendation sulle preferenze espresse dagli utenti riguardo ifferenti oggetti. Grazie ad Apache Mahout e ai principi base delle varie tipologie di recommendationè stato possibile realizzare un applicativo web che permette di produrre delle recommendations nell'ambito delle pubblicazioni scientifiche, selezionando quegli articoli che hanno un maggiore similarità con quelli pubblicati dall'utente corrente. La realizzazione di questo progetto ha portato alla definizione di un sistema ibrido. Infatti l'approccio alla recommendation di Apache Mahout non è completamente adattabile a questa situazione, per questo motivo le sue componenti sono state estese e modellate per il caso di studio. Siè cercato quindi di combinare il collaborative filtering e il content-based in un unico approccio. Di Apache Mahout si è mantenuto l'algoritmo attraverso il quale esaminare i dati del data set, tralasciando completamente l'aspetto legato alle preferenze degli utenti, poichè essi non esprimono delle valutazioni sugli articoli. Del content-based si è utilizzata l'idea del confronto tra i titoli delle pubblicazioni. La valutazione di questo applicativo ha portato alla luce diversi limiti, ma anche possibili sviluppi futuri che potrebbero migliorare la qualità delle recommendations, ma soprattuto le prestazioni. Grazie per esempio ad Apache Hadoop sarebbe possibile una computazione distribuita che permetterebbe di elaborare migliaia di dati con dei risultati più che discreti.

Sviluppo di un modello di costo per Spark Sql

La quantità di dati che vengono generati e immagazzinati sta aumentando sempre più grazie alle nuove tecnologie e al numero di utenti sempre maggiore. Questi dati, elaborati correttamente, permettono quindi di ottenere delle informazioni di valore strategico che aiutano nell’effettuare decisioni aziendali a qualsiasi livello, dalla produzione fino al marketing. Sono nati soprattutto negli ultimi anni numerosi framework proprietari e open source che permettono l'elaborazione di questi dati sfruttando un cluster. In particolare tra i più utilizzati e attivi in questo momento a livello open source troviamo Hadoop e Spark. Obiettivo di questa tesi è realizzare un modello di Spark per realizzare una funzione di costo che sia non solo implementabile all’interno dell’ottimizzatore di Spark SQL, ma anche per poter effettuare delle simulazioni di esecuzione di query su tale sistema. Si è quindi studiato nel dettaglio con ducumentazione e test il comportamento del sistema per realizzare un modello. I dati ottenuti sono infine stati confrontati con dati sperimentali ottenuti tramite l'utilizzo di un cluster. Con la presenza di tale modello non solo risulta possibile comprendere in maniera più approfondita il reale comportamento di Spark ma permette anche di programmare applicazioni più efficienti e progettare con maggiore precisione sistemi per la gestione dei dataset che sfruttino tali framework.

ABCtoolbox: a versatile toolkit for approximate Bayesian computations

Background The estimation of demographic parameters from genetic data often requires the computation of likelihoods. However, the likelihood function is computationally intractable for many realistic evolutionary models, and the use of Bayesian inference has therefore been limited to very simple models. The situation changed recently with the advent of Approximate Bayesian Computation (ABC) algorithms allowing one to obtain parameter posterior distributions based on simulations not requiring likelihood computations. Results Here we present ABCtoolbox, a series of open source programs to perform Approximate Bayesian Computations (ABC). It implements various ABC algorithms including rejection sampling, MCMC without likelihood, a Particle-based sampler and ABC-GLM. ABCtoolbox is bundled with, but not limited to, a program that allows parameter inference in a population genetics context and the simultaneous use of different types of markers with different ploidy levels. In addition, ABCtoolbox can also interact with most simulation and summary statistics computation programs. The usability of the ABCtoolbox is demonstrated by inferring the evolutionary history of two evolutionary lineages of Microtus arvalis. Using nuclear microsatellites and mitochondrial sequence data in the same estimation procedure enabled us to infer sex-specific population sizes and migration rates and to find that males show smaller population sizes but much higher levels of migration than females. Conclusion ABCtoolbox allows a user to perform all the necessary steps of a full ABC analysis, from parameter sampling from prior distributions, data simulations, computation of summary statistics, estimation of posterior distributions, model choice, validation of the estimation procedure, and visualization of the results.

Postreperfusion cardiac arrest and resuscitation during orthotopic liver transplantation: dynamic visualization and analysis of physiologic recordings

We recently reported on the Multi Wave Animator (MWA), a novel open-source tool with capability of recreating continuous physiologic signals from archived numerical data and presenting them as they appeared on the patient monitor. In this report, we demonstrate for the first time the power of this technology in a real clinical case, an intraoperative cardiopulmonary arrest following reperfusion of a liver transplant graft. Using the MWA, we animated hemodynamic and ventilator data acquired before, during, and after cardiac arrest and resuscitation. This report is accompanied by an online video that shows the most critical phases of the cardiac arrest and resuscitation and provides a basis for analysis and discussion. This video is extracted from a 33-min, uninterrupted video of cardiac arrest and resuscitation, which is available online. The unique strength of MWA, its capability to accurately present discrete and continuous data in a format familiar to clinicians, allowed us this rare glimpse into events leading to an intraoperative cardiac arrest. Because of the ability to recreate and replay clinical events, this tool should be of great interest to medical educators, researchers, and clinicians involved in quality assurance and patient safety.

Package Patterns for Visual Architecture Recovery

Recovering the architecture is the first step towards reengineering a software system. Many reverse engineering tools use top-down exploration as a way of providing a visual and interactive process for architecture recovery. During the exploration process, the user navigates through various views on the system by choosing from several exploration operations. Although some sequences of these operations lead to views which, from the architectural point of view, are mode relevant than others, current tools do not provide a way of predicting which exploration paths are worth taking and which are not. In this article we propose a set of package patterns which are used for augmenting the exploration process with in formation about the worthiness of the various exploration paths. The patterns are defined based on the internal package structure and on the relationships between the package and the other packages in the system. To validate our approach, we verify the relevance of the proposed patterns for real-world systems by analyzing their frequency of occurrence in six open-source software projects.

Using Context Information to Re-architect a System

Successful software systems cope with complexity by organizing classes into packages. However, a particular organization may be neither straightforward nor obvious for a given developer. As a consequence, classes can be misplaced, leading to duplicated code and ripple effects with minor changes effecting multiple packages. We claim that contextual information is the key to rearchitecture a system. Exploiting contextual information, we propose a technique to detect misplaced classes by analyzing how client packages access the classes of a given provider package. We define locality as a measure of the degree to which classes reused by common clients appear in the same package. We then use locality to guide a simulated annealing algorithm to obtain optimal placements of classes in packages. The result is the identification of classes that are candidates for relocation. We apply the technique to three applications and validate the usefulness of our approach via developer interviews.

QUANTIFYING UNCERTAINTY IN GENOTYPE CALLS

Genome-wide association studies (GWAS) are used to discover genes underlying complex, heritable disorders for which less powerful study designs have failed in the past. The number of GWAS has skyrocketed recently with findings reported in top journals and the mainstream media. Mircorarrays are the genotype calling technology of choice in GWAS as they permit exploration of more than a million single nucleotide polymorphisms (SNPs)simultaneously. The starting point for the statistical analyses used by GWAS, to determine association between loci and disease, are genotype calls (AA, AB, or BB). However, the raw data, microarray probe intensities, are heavily processed before arriving at these calls. Various sophisticated statistical procedures have been proposed for transforming raw data into genotype calls. We find that variability in microarray output quality across different SNPs, different arrays, and different sample batches has substantial inuence on the accuracy of genotype calls made by existing algorithms. Failure to account for these sources of variability, GWAS run the risk of adversely affecting the quality of reported findings. In this paper we present solutions based on a multi-level mixed model. Software implementation of the method described in this paper is available as free and open source code in the crlmm R/BioConductor.

An integral conservative gridding--algorithm using Hermitian curve interpolation

The problem of re-sampling spatially distributed data organized into regular or irregular grids to finer or coarser resolution is a common task in data processing. This procedure is known as 'gridding' or 're-binning'. Depending on the quantity the data represents, the gridding-algorithm has to meet different requirements. For example, histogrammed physical quantities such as mass or energy have to be re-binned in order to conserve the overall integral. Moreover, if the quantity is positive definite, negative sampling values should be avoided. The gridding process requires a re-distribution of the original data set to a user-requested grid according to a distribution function. The distribution function can be determined on the basis of the given data by interpolation methods. In general, accurate interpolation with respect to multiple boundary conditions of heavily fluctuating data requires polynomial interpolation functions of second or even higher order. However, this may result in unrealistic deviations (overshoots or undershoots) of the interpolation function from the data. Accordingly, the re-sampled data may overestimate or underestimate the given data by a significant amount. The gridding-algorithm presented in this work was developed in order to overcome these problems. Instead of a straightforward interpolation of the given data using high-order polynomials, a parametrized Hermitian interpolation curve was used to approximate the integrated data set. A single parameter is determined by which the user can control the behavior of the interpolation function, i.e. the amount of overshoot and undershoot. Furthermore, it is shown how the algorithm can be extended to multidimensional grids. The algorithm was compared to commonly used gridding-algorithms using linear and cubic interpolation functions. It is shown that such interpolation functions may overestimate or underestimate the source data by about 10-20%, while the new algorithm can be tuned to significantly reduce these interpolation errors. The accuracy of the new algorithm was tested on a series of x-ray CT-images (head and neck, lung, pelvis). The new algorithm significantly improves the accuracy of the sampled images in terms of the mean square error and a quality index introduced by Wang and Bovik (2002 IEEE Signal Process. Lett. 9 81-4).

Simulation of four stroke engine cycle for a 4 - valve pentroof engine in KIVA 3VR2

The main purpose of this project is to understand the process of engine simulation using the open source CFD code called KIVA. This report mainly discusses the simulation of the 4-valve Pentroof engine through KIVA 3VR2. KIVA is an open source FORTRAN code which is used to solve the fluid flow field in the engines with the transient 2D and 3D chemically reactive flow with spray. It also focuses on the complete procedure to simulate an engine cycle starting from pre- processing until the final results. This report will serve a handbook for the using the KIVA code.

Evaluation of surface defect detection in reinforced concrete bridge decks using terrestrial LiDAR

Routine bridge inspections require labor intensive and highly subjective visual interpretation to determine bridge deck surface condition. Light Detection and Ranging (LiDAR) a relatively new class of survey instrument has become a popular and increasingly used technology for providing as-built and inventory data in civil applications. While an increasing number of private and governmental agencies possess terrestrial and mobile LiDAR systems, an understanding of the technology’s capabilities and potential applications continues to evolve. LiDAR is a line-of-sight instrument and as such, care must be taken when establishing scan locations and resolution to allow the capture of data at an adequate resolution for defining features that contribute to the analysis of bridge deck surface condition. Information such as the location, area, and volume of spalling on deck surfaces, undersides, and support columns can be derived from properly collected LiDAR point clouds. The LiDAR point clouds contain information that can provide quantitative surface condition information, resulting in more accurate structural health monitoring. LiDAR scans were collected at three study bridges, each of which displayed a varying degree of degradation. A variety of commercially available analysis tools and an independently developed algorithm written in ArcGIS Python (ArcPy) were used to locate and quantify surface defects such as location, volume, and area of spalls. The results were visual and numerically displayed in a user-friendly web-based decision support tool integrating prior bridge condition metrics for comparison. LiDAR data processing procedures along with strengths and limitations of point clouds for defining features useful for assessing bridge deck condition are discussed. Point cloud density and incidence angle are two attributes that must be managed carefully to ensure data collected are of high quality and useful for bridge condition evaluation. When collected properly to ensure effective evaluation of bridge surface condition, LiDAR data can be analyzed to provide a useful data set from which to derive bridge deck condition information.

Review of free-space optical communications with diverging beam

The report reviews the technology of Free-space Optical Communication (FSO) and simulation methods for testing the performance of diverged beam in the technology. In addition to the introduction, the theory of turbulence and its effect over laser is also reviewed. In the simulation revision chapter, on-off keying (OOK) and diverged beam is assumed in the transmitter, and in the receiver, avalanche photodiode (APD) is utilized to convert the photon stream into electron stream. Phase screens are adopted to simulate the effect of turbulence over the phase of the optical beam. Apart from this, the method of data processing is introduced and retrospected. In the summary chapter, there is a general explanation of different beam divergence and their performance.