Bergmann T., Balzer M., Hopp T., Van De Kamp T., Kopmann A., Jerome N.T., Zapf M.

in VISIGRAPP 2017 – Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 3 (2017) 330-334.

Abstract

Copyright © 2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved. The computer gaming industry is traditionally the moving power and spirit in the development of computer visualization hardware and software. This year, affordable and high quality virtual reality headsets became available and the science community is eager to get benefit from it. This paper describes first experiences in adapting the new hardware for three different visualization use cases. In all three examples existing visualization pipelines were extended by virtual reality technology. We describe our approach, based on the HTC Vive VR headset, the open source software Blender and the Unreal Engine 4 game engine. The use cases are from three different fields: large-scale particle physics research, X-ray-imaging for entomology research and medical imaging with ultrasound computer tomography. Finally we discuss benefits and limits of the current virtual reality technology and present an outlook to future developments.

Jerome N.T., Chilingaryan S., Shkarin A., Kopmann A., Zapf M., Lizin A., Bergmann T.

in VISIGRAPP 2017 – Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 3 (2017) 152-163.

Abstract

Copyright © 2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.With data sets growing beyond terabytes or even petabytes in scientific experiments, there is a trend of keeping data at storage facilities and providing remote cloud-based services for analysis. However, accessing these data sets remotely is cumbersome due to additional network latency and incomplete metadata description. To ease data browsing on remote data archives, our WAVE framework applies an intelligent cache management to provide scientists with a visual feedback on the large data set interactively. In this paper, we present methods to reduce the data set size while preserving visual quality. Our framework supports volume rendering and surface rendering for data inspection and analysis. Furthermore, we enable a zoom-on-demand approach, where a selected volumetric region is reloaded with higher details. Finally, we evaluated the WAVE framework using a data set from the entomology science research.

Lautner S., Lenz C., Hammel J., Moosmann J., Kuhn M., Caselle M., Vogelgesang M., Kopmann A., Beckmann F.

in Proceedings of SPIE – The International Society for Optical Engineering, 10391 (2017), 1039118. DOI:10.1117/12.2287221

Abstract

© 2017 SPIE. Water transport from roots to shoots is a vital necessity in trees in order to sustain their photosynthetic activity and, hence, their physiological activity. The vascular tissue in charge is the woody body of root, stem and branches. In gymnosperm trees, like spruce trees (Picea abies (L.) Karst.), vascular tissue consists of tracheids: elongated, protoplast- free cells with a rigid cell wall that allow for axial water transport via their lumina. In order to analyze the over-all water transport capacity within one growth ring, time-consuming light microscopy analysis of the woody sample still is the conventional approach for calculating tracheid lumen area. In our investigations at the Imaging Beamline (IBL) operated by the Helmholtz-Zentrum Geesthacht (HZG) at PETRA III storage ring of the Deutsches Elektronen-Synchrotron DESY, Hamburg, we applied SRμCT on small wood samples of spruce trees in order to visualize and analyze size and formation of xylem elements and their respective lumina. The selected high-resolution phase-contrast technique makes full use of the novel 20 MPixel CMOS area detector developed within the cooperation of HZG and the Karlsruhe data by light microscopy analysis and, hence, prove, that μCT is a most appropriate method to gain valid information on xylem cell structure and tree water transport capacity.

Losel P., Heuveline V.

in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10129 LNCS (2017) 121-128. DOI:10.1007/978-3-319-52280-7_12

Abstract

© Springer International Publishing AG 2017. Segmenting the blood pool and myocardium from a 3D cardiovascular magnetic resonance (CMR) image allows to create a patient-specific heart model for surgical planning in children with complex congenital heart disease (CHD). Implementation of semi-automatic or automatic segmentation algorithms is challenging because of a high anatomical variability of the heart defects, low contrast, and intensity variations in the images. Therefore, manual segmentation is the gold standard but it is labor-intensive. In this paper we report the set-up and results of a highly scalable semi-automatic diffusion algorithm for image segmentation. The method extrapolates the information from a small number of expert manually labeled reference slices to the remaining volume. While results of most semi-automatic algorithms strongly depend on well-chosen but usually unknown parameters this approach is parameter-free. Validation is performed on twenty 3D CMR images.

M. Heethoff, V. Heuveline, H. Hartenstein, W. Mexner, T. van de Kamp, A. Kopmann

Final report, BMBF Programme: “Erforschung kondensierter Materie”, 2016.

Executive summary

Die Synchrotron-Röntgentomographie ist eine einzigartige Abbildungsmethode zur Untersuchung innerer Strukturen – insbesondere in undurchsichtigen Proben. In den letzten Jahren konnte die räumliche und zeitliche Auflösung der Methode stark verbessert werden. Die Auswertung der Datensätze ist allerdings bedingt durch ihre Größe und die Komplexität der abgebildeten Strukturen herausfordernd. Der Verbund für Funktionsmorphologie und Systematik hat sich mit dem Projekt ASTOR das Ziel gesetzt, den Zugang zur Röntgentomographie durch eine integrierte Analyseumgebung für biologische Nutzer zu erleichtern.
Durch den interdisziplinären Zusammenschluss von Biologen, Informatikern, Mathematikern und Ingenieuren war es möglich, die gesamte Datenverarbeitungskette zu betrachten. Es sind weitgehend automatisierte Datenverarbeitungs- und -transfermethoden entstanden. Die tomographischen Aufnahmen werden online rekonstruiert und in die ASTOR Analyseumgebung transferiert. Die Daten stehen anschließend über virtuelle Rechner den Nutzern sowohl bei ANKA als auch außerhalb zur Verfügung. Ein Autorisierungsschema für den Zugriff wurde erarbeitet. Die Analyseinfrastruktur besteht aus einem temporären Datenspeicher, dem Virtualisierungsserver, sowie der Anbindung an Beamlines und Langzeitarchiv. Die Analyseumgebung bietet neben kostenintensiven kommerziellen Programmen neu entwickelte Werkzeuge an. Hervorzuheben sind hier die ASTOR- Segmentierungsfunktionen, die den bislang sehr zeit- und arbeitsintensiven Arbeitsschritt um ein Vielfaches beschleunigen. Die automatische Segmentierung lässt sich transparent über in nur wenigen Schichten markierte Bereiche steuern und erzielt ein bislang unerreichtes automatisches Segmentierungsergebnis.
Die Analyseumgebung hat sich als sehr effizient für die Datenauswertung und Methodenentwicklung erwiesen. Neben den Antragstellern wird das System inzwischen von weiteren Nutzern erfolgreich eingesetzt. Im Verlauf des Projektes wurde in mehreren Strahlzeiten ein umfangreicher Satz an Beispielaufnahmen über einen breiten Bereich von Organismen aufgenommen. Ausgewählte Proben wurden als Vorlage für die Methodenentwicklung segmentiert und klassifiziert. Im Verlauf des Projektes konnte die Zahl der Aufnahmen innerhalb einer Messwoche auf zunächst 400 und zum Schluss sogar auf bis zu 1000 drastisch erhöht werden.
Mit ASTOR ist es gelungen, eine durchgehende Analyseumgebung aufzubauen, und damit den nächsten Schritt im Ausbau solcher Experimentiereinrichtungen aufzuzeigen. Für die gewählte Anwendung, die Funktionsmorphologie, ist es erstmals möglich, auch quantitative Reihenuntersuchungen an kleinen Organismen durchzuführen. Die Auswertesystematik ist nicht auf diese Anwendung beschränkt, sondern vielmehr ein generelles Beispiel für datenintensive Experimente. Das ebenfalls von der BMBF-Verbundforschung geförderte Projekt NOVA setzt die begonnenen Aktivitäten in diesem Sinne fort und beabsichtigt durch synergistische Zusammenarbeit einen offenen Datenkatalog für eine gesamte Community zu erstellen.

Steinmann J.L., Blomley E., Brosi M., Brundermann E., Caselle M., Hesler J.L., Hiller N., Kehrer B., Mathis Y.-L., Nasse M.J., Raasch J., Schedler M., Schonfeldt P., Schuh M., Schwarz M., Siegel M., Smale N., Weber M., Muller A.-S.

in Physical Review Letters, 117 (2016), 174802. DOI:10.1103/PhysRevLett.117.174802

Abstract

© 2016 American Physical Society. Using arbitrary periodic pulse patterns we show the enhancement of specific frequencies in a frequency comb. The envelope of a regular frequency comb originates from equally spaced, identical pulses and mimics the single pulse spectrum. We investigated spectra originating from the periodic emission of pulse trains with gaps and individual pulse heights, which are commonly observed, for example, at high-repetition-rate free electron lasers, high power lasers, and synchrotrons. The ANKA synchrotron light source was filled with defined patterns of short electron bunches generating coherent synchrotron radiation in the terahertz range. We resolved the intensities of the frequency comb around 0.258 THz using the heterodyne mixing spectroscopy with a resolution of down to 1 Hz and provide a comprehensive theoretical description. Adjusting the electron’s revolution frequency, a gapless spectrum can be recorded, improving the resolution by up to 7 and 5 orders of magnitude compared to FTIR and recent heterodyne measurements, respectively. The results imply avenues to optimize and increase the signal-to-noise ratio of specific frequencies in the emitted synchrotron radiation spectrum to enable novel ultrahigh resolution spectroscopy and metrology applications from the terahertz to the x-ray region.

Mohr, Hannes

Master Thesis, Faculty for Physics, Karlsruhe Institute of Technology, 2016.

Abstract

In this work we present an evaluation of GPUs as a possible L1 Track Trigger for the High Luminosity LHC, effective after Long Shutdown 3 around 2025.

The novelty lies in presenting an implementation based on calculations done entirely in software, in contrast to currently discussed solutions relying on specialized hardware, such as FPGAs and ASICs.
Our solution relies on using GPUs for the calculation instead, offering floating point calculations as well as flexibility and adaptability. Normally the involved data transfer latencies make GPUs unfeasible for use in low latency environments. To this end we use a data transfer scheme based on RDMA technology. This mitigates the normally involved overheads.
We based our efforts on previous work by the collaboration of the KIT and the English track trigger group [An FPGA-based track finder for the L1 trigger of the CMS experiment at the high luminosity LHC] whose algorithm was implemented on FPGAs.
In addition to the Hough transformation used regularly, we present our own version of the algorithm based on a hexagonal layout of the binned parameter space. With comparable computational latency and workload, the approach produces significantly less fake track candidates than the traditionally used method. This comes at a cost of efficiency of around 1 percent.

This work focuses on the track finding part of the proposed L1 Track Trigger and only looks at the result of a least squares fit to make an estimate of the performance of said seeding step. We furthermore present our results in terms of overall latency of this novel approach.

While not yet competitive, our implementation has surpassed initial expectations and are on the same order of magnitude as the FPGA approach in terms of latencies. Some caveats apply at the moment. Ultimately, more recent technology, not yet available to us in the current discussion will have to be tested and benchmarked to come to a more complete assessment of the feasibility of GPUs as a means of track triggering
at the High-Luminosity-LHC’s CMS experiment.

 

First assessor: Prof. Dr. Marc Weber
Second assessor: Prof. Dr. Ulrich Husemann

Supervised by Dipl.-Inform. Timo Dritschler

Bergmann T., Balzer M., Bormann D., Chilingaryan S.A., Eitel K., Kleifges M., Kopmann A., Kozlov V., Menshikov A., Siebenborn B., Tcherniakhovski D., Vogelgesang M., Weber M.

in 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2015 (2016), 7581841. DOI:10.1109/NSSMIC.2015.7581841

Abstract

© 2015 IEEE. The EDELWEISS experiment, located in the underground laboratory LSM (France), is one of the leading experiments using cryogenic germanium (Ge) detectors for a direct search for dark matter. For the EDELWEISS-III phase, a new scalable data acquisition (DAQ) system was designed and built, based on the ‘IPE4 DAQ system’, which has already been used for several experiments in astroparticle physics.

Harbaum T., Seboui M., Balzer M., Becker J., Weber M.

in Proceedings – 24th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2016 (2016) 184-191, 7544775. DOI:10.1109/FCCM.2016.52

Abstract

© 2016 IEEE. Modern high-energy physics experiments such as the Compact Muon Solenoid experiment at CERN produce an extraordinary amount of data every 25ns. To handle a data rate of more than 50Tbit/s a multi-level trigger system is required, which reduces the data rate. Due to the increased luminosity after the Phase-II-Upgrade of the LHC, the CMS tracking system has to be redesigned. The current trigger system is unable to handle the resulting amount of data after this upgrade. Because of the latency of a few microseconds the Level 1 Track Trigger has to be implemented in hardware. State-of-the-art pattern recognition filter the incoming data by template matching on ASICs with a content addressable memory architecture. An implementation on an FPGA, which replaces the content addressable memory of the ASIC, has not been possible so far. This paper presents a new approach to a content addressable memory architecture, which allows an implementation of an FPGA based design. By combining filtering and track finding on an FPGA design, there are many possibilities of adjusting the two algorithms to each other. There is more flexibility enabled by the FPGA architecture in contrast to the ASIC. The presented design minimizes the stored data by logic to optimally utilize the available resources of an FPGA. Furthermore, the developed design meets the strong timing constraints and possesses the required properties of the content addressable memory.

Amstutz C. et al.

in 2016 IEEE-NPSS Real Time Conference, RT 2016 (2016), 7543102. DOI:10.1109/RTC.2016.7543102

Abstract

© 2016 IEEE.A new tracking system is under development for operation in the CMS experiment at the High Luminosity LHC. It includes an outer tracker which will construct stubs, built by correlating clusters in two closely spaced sensor layers for the rejection of hits from low transverse momentum tracks, and transmit them off-detector at 40 MHz. If tracker data is to contribute to keeping the Level-1 trigger rate at around 750 kHz under increased luminosity, a crucial component of the upgrade will be the ability to identify tracks with transverse momentum above 3 GeV/c by building tracks out of stubs. A concept for an FPGA-based track finder using a fully time-multiplexed architecture is presented, where track candidates are identified using a projective binning algorithm based on the Hough Transform. A hardware system based on the MP7 MicroTCA processing card has been assembled, demonstrating a realistic slice of the track finder in order to help gauge the performance and requirements for a full system. This paper outlines the system architecture and algorithms employed, highlighting some of the first results from the hardware demonstrator and discusses the prospects and performance of the completed track finder.