Automated droplet measurement (ADM): an enhanced video processing software for rapid droplet measurements

This paper identifies and addresses the bottlenecks that hamper the currently available software to perform in situ measurement on droplet-based microfluidic. The new and more universal object-based background extraction operation and automated binary threshold value selection make the processing step of our video processing software (ADM) fully automated. The ADM software, which is based on OpenCV image processing library, is made to perform measurements with high processing speed using efficient code. As the processing speed is higher than the data transfer speed from the video camera to permanent storage of computer, we integrate the camera software development kit (SDK) with ADM. The integration allows simultaneous operations of the video transfer/streaming and the video processing. As a result, the total time for droplet measurement using the new process flow with the integrated program is shortened significantly. ADM is also validated by comparing with both manual analysis and DMV software. ADM will be publicly released as a free tool. The software can also be used on a video file or files without the integration with the camera SDK.Singapore. Ministry of Education (Tier 2 Grant 2011-T2-1-0-36

Efficient scheduling methods under limited resources

研究成果の概要 (和文) : 機械や作業者などのリソースが複数のタスクを掛け持ちし，かつ各タスクの実行時間の不確実性が高い状況下で，納期短縮と遅延防止を両立させるスケジューリング方法について検討した．クリティカル・チェーン・プロジェクト・マネジメントと呼ばれる手法をベースに，メイクスパンを最小化する組合せ最適化問題を混合整数線形計画問題に変換し，タスク数が大きくない場合に厳密な最適解が求められる枠組みを構築した．またタスク数が多い場合にはメタヒューリスティクスによって近似解を求める枠組みを構築した．さらにメイクスパン算出までの枠組みをmax-plus代数系の線形方程式で表現し，簡潔な表現形で解を求める方法も考案した．研究成果の概要 (英文) : We have focused on scheduling methods to both shorten the makespan and avoid delay. The targeted situation was that a single resource such as machine or worker is engaged in multiple tasks, and the duration of each task is highly uncertain. Based on a method called CCPM (Critical Chain Project Management), we reduced the combinatorial optimization problem of minimizing the makespan to a mixed-integer-linear-programming problem. The constructed framework can compute the exact optimal solution for a smaller number of tasks. By contrast, for a greater number of tasks, the developed metaheuristics can obtain an approximate solution within a realistic time. Furthermore, we represented the framework of calculating the makespan with a set of max-plus-linear algebraic equations, for which the solution can be obtained with simple operations

Visual Anxiolytics: developing theory and design guidelines for abstract affective visualizations aimed at alleviating episodes of anxiety

Visual Anxiolytics is a novel term proposed to describe affective visualizations of which affective quality is predetermined and designed to alleviate anxiety and anxious pathology. This thesis presents ground theory and visual guidelines to inform the design of screen-based interfaces to give users aspects of a restorative and anxiolytic environment at a time when attention restoration is least likely and anxiety highly probable; during sedentary screen-time. Visual Anxiolytics are introduced as an affective layer of the interface capable of communicating affect through aesthetic, abstract, ambient emotion visualizations existing in the periphery of the screen and users’ vision. Their theory is brought into the field of Visual Communication Design from a number of disciplines; primarily Affective Computing, Human-Computer Interaction, Psychology, and Neuroscience. Visual Anxiolytics attempt to alleviate anxiety through restoration of attentional cognitive resources by rendering the digital environment restorative and by elicitation of positive emotions through affect communication. Design guidelines analyse and describe properties of anxiolytic affective visual attributes color, shape, motion, and visual depth, as well as compositional characteristics of Visual Anxiolytics. Potential implications for future research in emotion visualization and affect communication are discussed

Manufacturing Metrology

Metrology is the science of measurement, which can be divided into three overlapping activities: (1) the definition of units of measurement, (2) the realization of units of measurement, and (3) the traceability of measurement units. Manufacturing metrology originally implicates the measurement of components and inputs for a manufacturing process to assure they are within specification requirements. It can also be extended to indicate the performance measurement of manufacturing equipment. This Special Issue covers papers revealing novel measurement methodologies and instrumentations for manufacturing metrology from the conventional industry to the frontier of the advanced hi-tech industry. Twenty-five papers are included in this Special Issue. These published papers can be categorized into four main groups, as follows: Length measurement: covering new designs, from micro/nanogap measurement with laser triangulation sensors and laser interferometers to very-long-distance, newly developed mode-locked femtosecond lasers. Surface profile and form measurements: covering technologies with new confocal sensors and imagine sensors: in situ and on-machine measurements. Angle measurements: these include a new 2D precision level design, a review of angle measurement with mode-locked femtosecond lasers, and multi-axis machine tool squareness measurement. Other laboratory systems: these include a water cooling temperature control system and a computer-aided inspection framework for CMM performance evaluation

Morphometric Analysis to Characterize the Differentiation of Mesenchymal Stem Cells into Smooth Muscle Cells in Response to Biochemical and Mechanical Stimulation

The morphology and biochemical phenotype of cells are closely linked. This relationship is important in progenitor cell bioengineering, which generates functional, tissue-specific cells from uncommitted precursors. Advances in biofabrication have demonstrated that cell shape can regulate cell behavior and alter phenotype-specific functions. Establishing accessible and rigorous techniques for quantifying cell shape will therefore facilitate assessment of cellular responses to environmental stimuli, and will enable more comprehensive understanding of developmental, pathological, and regenerative processes. For progenitor cells being induced into specific lineages, this ability becomes a pertinent means for validating their degree of differentiation and may lead to novel strategies for controlling cell phenotype. In our approach, we used the differentiation of adult human mesenchymal stem cells (MSCs) into smooth muscle cells (SMCs) as a model system to investigate the relationship between cell shape and phenotype. These cell types are responsive to mechanical and biochemical stimuli and the shape of SMCs is a recognized marker of differentiated state, providing a system in which morphological and biochemical phenotype are both understood and inducible. By applying exogenous stimuli, we changed cell shape and examined the corresponding cellular phenotype. In the first Aim, we applied stretch to MSCs on 2D collagen sheets to promote differentiation. Using mathematical shape factors, we quantified the morphological changes in response to defined stretch parameters. In the second Aim, we investigated the use of input energy as a means of controlling cell shape and corresponding differentiation. We examined how combinations of stretch parameters that produce equal energy input impacted morphology, and postulated that cell shape is a function of energy input. In the third Aim, we translated our method of quantifying shape factors into 3D culture, and validated the method by investigating the differentiation of MSCs into SMCs by mechanical and growth factor stimulation. We used the shape factors to quantify morphological differences and compared these changes to biochemical markers. Our results demonstrate that mechanical stretch influences multiple aspects of MSC phenotype, including cell morphology. Shape factors described these changes objectively and quantitatively, and enabled the identification of relationships between SMC shape and differentiated state. Similar morphological responses could be induced using different combinations of stretch parameters that resulted in equal energy input. Cell shape followed a linear relationship with energy input despite the variance introduced by using MSCs from different patients. Only one SMC gene marker directly exhibited this relationship; however, partial least squares regression analysis revealed that other genes were also associated with shape factors. Translation of the shape quantification method into 3D systems revealed that while the additional dimensionality hindered comparison of morphology between 2D and 3D samples, these shape factors were still applicable within 3D systems. Differences in cell morphology caused by growth factors and mechanical stretch in 3D constructs were elucidated by shape analysis, and these phenotypic changes were corroborated through biochemical assays. Taken together, these results validate the use of cell shape as means of characterizing phenotype and the process of progenitor cell differentiation. The automated method we developed generates a robust set of morphological parameters that provide a way to characterize the differentiation of MSCs into SMCs. This work has implications in our understanding of the relationship between cell morphology and phenotype, and may lead to new ways to control and improve differentiation efficiency in a variety of cell and tissue systems.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145833/1/brandanw_1.pd

Road Condition Mapping by Integration of Laser Scanning, RGB Imaging and Spectrometry

Roads are important infrastructure and are primary means of transportation. Control and maintenance of roads are substantial as the pavement surface deforms and deteriorates due to heavy load and influences of weather. Acquiring detailed information about the pavement condition is a prerequisite for proper planning of road pavement maintenance and rehabilitation. Many companies detect and localize the road pavement distresses manually, either by on-site inspection or by digitizing laser data and imagery captured by mobile mapping. The automation of road condition mapping using laser data and colour images is a challenge. Beyond that, the mapping of material properties of the road pavement surface with spectrometers has not yet been investigated. This study aims at automatic mapping of road surface condition including distress and material properties by integrating laser scanning, RGB imaging and spectrometry. All recorded data are geo-referenced by means of GNSS/ INS. Methods are developed for pavement distress detection that cope with a variety of different weather and asphalt conditions. Further objective is to analyse and map the material properties of the pavement surface using spectrometry data. No standard test data sets are available for benchmarking developments on road condition mapping. Therefore, all data have been recorded with a mobile mapping van which is set up for the purpose of this research. The concept for detecting and localizing the four main pavement distresses, i.e. ruts, potholes, cracks and patches is the following: ruts and potholes are detected using laser scanning data, cracks and patches using RGB images. For each of these pavement distresses, two or more methods are developed, implemented, compared to each other and evaluated to identify the most successful method. With respect to the material characteristics, spectrometer data of road sections are classified to indicate pavement quality. As a spectrometer registers almost a reflectivity curve in VIS, NIR and SWIR wavelength, indication of aging can be derived. After detection and localization of the pavement distresses and pavement quality classes, the road condition map is generated by overlaying all distresses and quality classes. As a preparatory step for rut and pothole detection, the road surface is extracted from mobile laser scanning data based on a height jump criterion. For the investigation on rut detection, all scanlines are processed. With an approach based on iterative 1D polynomial fitting, ruts are successfully detected. For streets with the width of 6 m to 10 m, a 6th order polynomial is found to be most suitable. By 1D cross-correlation, the centre of the rut is localized. An alternative method using local curvature shows a high sensitivity to the shape and width of a rut and is less successful. For pothole detection, the approach based on polynomial fitting generalized to two dimensions. As an alternative, a procedure using geodesic morphological reconstruction is investigated. Bivariate polynomial fitting encounters problems with overshoot at the boundary of the regions. The detection is very successful using geodesic morphology. For the detection of pavement cracks, three methods using rotation invariant kernels are investigated. Line Filter, High-pass Filter and Modified Local Binary Pattern kernels are implemented. A conceptual aspect of the procedure is to achieve a high degree of completeness. The most successful variant is the Line Filter for which the highest degree of completeness of 81.2 % is achieved. Two texture measures, the gradient magnitude and the local standard deviation are employed to detect pavement patches. As patches may differ with respect to homogeneity and may not always have a dark border with the intact pavement surface, the method using the local standard deviation is more suitable for detecting the patches. Linear discriminant analysis is utilized for asphalt pavement quality analysis and classification. Road pavement sections of ca. 4 m length are classified into two classes, namely: “Good” and “Bad” with the overall accuracy of 77.6 %. The experimental investigations show that the developed methods for automatic distress detection are very successful. By 1D polynomial fitting on laser scanlines, ruts are detected. In addition to ruts also pavement depressions like shoving can be revealed. The extraction of potholes is less demanding. As potholes appear relatively rare in the road networks of a city, the road segments which are affected by potholes are selected interactively. While crack detection by Line Filter works very well, the patch detection is more challenging as patches sometimes look very similar to the intact surface. The spectral classification of pavement sections contributes to road condition mapping as it gives hints on aging of the road pavement.Straßen bilden die primären Transportwege für Personen und Güter und sind damit ein wichtiger Bestandteil der Infrastruktur. Der Aufwand für Instandhaltung und Wartung der Straßen ist erheblich, da sich die Fahrbahnoberfläche verformt und durch starke Belastung und Wettereinflüsse verschlechtert. Die Erfassung detaillierter Informationen über den Fahrbahnzustand ist Voraussetzung für eine sachgemäße Planung der Fahrbahnsanierung und -rehabilitation. Viele Unternehmen detektieren und lokalisieren die Fahrbahnschäden manuell entweder durch Vor-Ort-Inspektion oder durch Digitalisierung von Laserdaten und Bildern aus mobiler Datenerfassung. Eine Automatisierung der Straßenkartierung mit Laserdaten und Farbbildern steht noch in den Anfängen. Zudem werden bisher noch nicht die Alterungszustände der Asphaltdecke mit Hilfe der Spektrometrie bewertet. Diese Studie zielt auf den automatischen Prozess der Straßenzustandskartierung einschließlich der Straßenschäden und der Materialeigenschaften durch Integration von Laserscanning, RGB-Bilderfassung und Spektrometrie ab. Alle aufgezeichneten Daten werden mit GNSS / INS georeferenziert. Es werden Methoden für die Erkennung von Straßenschäden entwickelt, die sich an unterschiedliche Datenquellen bei unterschiedlichem Wetter- und Asphaltzustand anpassen können. Ein weiteres Ziel ist es, die Materialeigenschaften der Fahrbahnoberfläche mittels Spektrometrie-Daten zu analysieren und abzubilden. Derzeit gibt es keine standardisierten Testdatensätze für die Evaluierung von Verfahren zur Straßenzustandsbeschreibung. Deswegen wurden alle Daten, die in dieser Studie Verwendung finden, mit einem eigens für diesen Forschungszweck konfigurierten Messfahrzeug aufgezeichnet. Das Konzept für die Detektion und Lokalisierung der wichtigsten vier Arten von Straßenschäden, nämlich Spurrillen, Schlaglöcher, Risse und Flickstellen ist das folgende: Spurrillen und Schlaglöcher werden aus Laserdaten extrahiert, Risse und Flickstellen aus RGB- Bildern. Für jede dieser Straßenschäden werden mindestens zwei Methoden entwickelt, implementiert, miteinander verglichen und evaluiert um festzustellen, welche Methode die erfolgreichste ist. Im Hinblick auf die Materialeigenschaften werden Spektrometriedaten der Straßenabschnitte klassifiziert, um die Qualität des Straßenbelages zu bewerten. Da ein Spektrometer nahezu eine kontinuierliche Reflektivitätskurve im VIS-, NIR- und SWIR-Wellenlängenbereich aufzeichnet, können Merkmale der Asphaltalterung abgeleitet werden. Nach der Detektion und Lokalisierung der Straßenschäden und der Qualitätsklasse des Straßenbelages wird der übergreifende Straßenzustand mit Hilfe von Durchschlagsregeln als Kombination aller Zustandswerte und Qualitätsklassen ermittelt. In einem vorbereitenden Schritt für die Spurrillen- und Schlaglocherkennung wird die Straßenoberfläche aus mobilen Laserscanning-Daten basierend auf einem Höhensprung-Kriterium extrahiert. Für die Untersuchung zur Spurrillen-Erkennung werden alle Scanlinien verarbeitet. Mit einem Ansatz, der auf iterativer 1D-Polynomanpassung basiert, werden Spurrillen erfolgreich erkannt. Für eine Straßenbreite von 8-10m erweist sich ein Polynom sechsten Grades als am besten geeignet. Durch 1D-Kreuzkorrelation wird die Mitte der Spurrille erkannt. Eine alternative Methode, die die lokale Krümmung des Querprofils benutzt, erweist sich als empfindlich gegenüber Form und Breite einer Spurrille und ist weniger erfolgreich. Zur Schlaglocherkennung wird der Ansatz, der auf Polynomanpassung basiert, auf zwei Dimensionen verallgemeinert. Als Alternative wird eine Methode untersucht, die auf der Geodätischen Morphologischen Rekonstruktion beruht. Bivariate Polynomanpassung führt zu Überschwingen an den Rändern der Regionen. Die Detektion mit Hilfe der Geodätischen Morphologischen Rekonstruktion ist dagegen sehr erfolgreich. Zur Risserkennung werden drei Methoden untersucht, die rotationsinvariante Kerne verwenden. Linienfilter, Hochpassfilter und Lokale Binäre Muster werden implementiert. Ein Ziel des Konzeptes zur Risserkennung ist es, eine hohe Vollständigkeit zu erreichen. Die erfolgreichste Variante ist das Linienfilter, für das mit 81,2 % der höchste Grad an Vollständigkeit erzielt werden konnte. Zwei Texturmaße, nämlich der Betrag des Grauwert-Gradienten und die lokale Standardabweichung werden verwendet, um Flickstellen zu entdecken. Da Flickstellen hinsichtlich der Homogenität variieren können und nicht immer eine dunkle Grenze mit dem intakten Straßenbelag aufweisen, ist diejenige Methode, welche die lokale Standardabweichung benutzt, besser zur Erkennung von Flickstellen geeignet. Lineare Diskriminanzanalyse wird zur Analyse der Asphaltqualität und zur Klassifikation benutzt. Straßenabschnitte von ca. 4m Länge werden zwei Klassen („Gut“ und „Schlecht“) mit einer gesamten Accuracy von 77,6 % zugeordnet. Die experimentellen Untersuchungen zeigen, dass die entwickelten Methoden für die automatische Entdeckung von Straßenschäden sehr erfolgreich sind. Durch 1D Polynomanpassung an Laser-Scanlinien werden Spurrillen entdeckt. Zusätzlich zu Spurrillen werden auch Unebenheiten des Straßenbelages wie Aufschiebungen detektiert. Die Extraktion von Schlaglöchern ist weniger anspruchsvoll. Da Schlaglöcher relativ selten in den Straßennetzen von Städten auftreten, werden die Straßenabschnitte mit Schlaglöchern interaktiv ausgewählt. Während die Rissdetektion mit Linienfiltern sehr gut funktioniert, ist die Erkennung von Flickstellen eine größere Herausforderung, da Flickstellen manchmal der intakten Straßenoberfläche sehr ähnlich sehen. Die spektrale Klassifizierung der Straßenabschnitte trägt zur Straßenzustandsbewertung bei, indem sie Hinweise auf den Alterungszustand des Straßenbelages liefert

Apollo Lightcraft Project

This second year of the NASA/USRA-sponsored Advanced Aeronautical Design effort focused on systems integration and analysis of the Apollo Lightcraft. This beam-powered, single-stage-to-orbit vehicle is envisioned as the shuttlecraft of the 21st century. The five person vehicle was inspired largely by the Apollo Command Module, then reconfigured to include a new front seat with dual cockpit controls for the pilot and co-pilot, while still retaining the 3-abreast crew accommodations in the rear seat. The gross liftoff mass is 5550 kg, of which 500 kg is the payload and 300 kg is the LH2 propellant. The round trip cost to orbit is projected to be three orders of magnitude lower than the current space shuttle orbiter. The advanced laser-driven 5-speed combined-cycle engine has shiftpoints at Mach 1, 5, 11 and 25+. The Apollo Lightcraft can climb into low Earth orbit in three minutes, or fly to any spot on the globe in less than 45 minutes. Detailed investigations of the Apollo Lightcraft Project this second year further evolved the propulsion system design, while focusing on the following areas: (1) man/machine interface; (2) flight control systems; (3) power beaming system architecture; (4) re-entry aerodynamics; (5) shroud structural dynamics; and (6) optimal trajectory analysis. The principal new findings are documented. Advanced design efforts for the next academic year (1988/1989) will center on a one meter+ diameter spacecraft: the Lightcraft Technology Demonstrator (LTD). Detailed engineering design and analyses, as well as critical proof-of-concept experiments, will be carried out on this small, near-term machine. As presently conceived, the LTD could be constructed using state of the art components derived from existing liquid chemical rocket engine technology, advanced composite materials, and high power laser optics

Enhanced algorithms for lesion detection and recognition in ultrasound breast images

Mammography is the gold standard for breast cancer detection. However, it has very high false positive rates and is based on ionizing radiation. This has led to interest in using multi-modal approaches. One modality is diagnostic ultrasound, which is based on non-ionizing radiation and picks up many of the cancers that are generally missed by mammography. However, the presence of speckle noise in ultrasound images has a negative effect on image interpretation. Noise reduction, inconsistencies in capture and segmentation of lesions still remain challenging open research problems in ultrasound images. The target of the proposed research is to enhance the state-of-art computer vision algorithms used in ultrasound imaging and to investigate the role of computer processed images in human diagnostic performance. [Continues.