@article{KhodaverdiWeberStreunetal.2006, author = {Khodaverdi, M. and Weber, S. and Streun, M. and Parl, C. and Ziemons, Karl}, title = {High resolution imaging with ClearPET™ Neuro - first animal images}, series = {2005 IEEE Nuclear Science Symposium Conference Record, Vol. 3}, journal = {2005 IEEE Nuclear Science Symposium Conference Record, Vol. 3}, isbn = {1082-3654}, pages = {1641 -- 1644}, year = {2006}, abstract = {The ClearPET™ Neuro is the first full ring scanner within the Crystal Clear Collaboration (CCC). It consists of 80 detector modules allocated to 20 cassettes. LSO and LuYAP:Ce crystals in phoswich configuration in combination with position sensitive photomultiplier tubes are used to achieve high sensitivity and realize the acquisition of the depth of interaction (DOI) information. The complete system has been tested concerning the mechanical and electronical stability and interplay. Moreover, suitable corrections have been implemented into the reconstruction procedure to ensure high image quality. We present first results which show the successful operation of the ClearPET™ Neuro for artefact free and high resolution small animal imaging. Based on these results during the past few months the ClearPET™ Neuro System has been modified in order to optimize the performance.}, language = {en} } @article{KhodaverdiPaulySchroderetal.2002, author = {Khodaverdi, M. and Pauly, F. and Schroder, G. and Ziemons, Karl and Sievering, R. and Halling, H.}, title = {Preliminary studies of a micro-CT for a combined small animal PET/CT scanner}, series = {2001 IEEE Nuclear Science Symposium Conference Record, Vol. 3}, journal = {2001 IEEE Nuclear Science Symposium Conference Record, Vol. 3}, issn = {1082-3654}, pages = {1605 -- 1606}, year = {2002}, abstract = {We are developing an X-ray computed tomography (CT) system which will be combined with a high resolution animal PET system. This permits acquisition of both molecular and anatomical images in a single machine. In particular the CT will also be utilized for the quantification of the animal PET data by providing accurate data for attenuation correction. A first prototype has been built using a commercially available plane silicon diode detector. A cone-beam reconstruction provides the images using the Feldkamp algorithm. First measurements with this system have been performed on a mouse. It could be shown that the CT setup fulfils all demands for a high quality image of the skeleton of the mouse. It is also suited for soft tissue measurements. To improve contrast and resolution and to acquire the X-ray energy further development of the system, especially the use of semiconductor detectors and iterative reconstruction algorithms are planned.}, language = {en} } @article{KhodaverdiChaziioannouWeberetal.2004, author = {Khodaverdi, M. and Chaziioannou, A. F. and Weber, S. and Ziemons, Karl and Halling, H. and Pietrzyk, U.}, title = {Investigation of different microCT scanner configurations by GEANT4 simulations}, series = {2003 IEEE Nuclear Science Symposium Conference Record, Vol. 4}, journal = {2003 IEEE Nuclear Science Symposium Conference Record, Vol. 4}, issn = {1082-3654}, pages = {2989 -- 2993}, year = {2004}, abstract = {This study has been performed to design the combination of the new ClearPET TM (ClearPET is a trademark of the Crystal Clear Collaboration), a small animal Positron Emission Tomography (PET) system, with a microComputed Tomography (microCT) scanner. The properties of different microCT systems have been determined by simulations based on GEANT4. We demonstrate the influence of the detector material and the X-ray spectrum on the obtained contrast. Four different detector materials (selenium, cadmium zinc telluride, cesium iodide and gadolinium oxysulfide) and two X-ray spectra (a molybdenum and a tungsten source) have been considered. The spectra have also been modified by aluminum filters of varying thickness. The contrast between different tissue types (water, air, brain, bone and fat) has been simulated by using a suitable phantom. The results indicate the possibility to improve the image contrast in microCT by an optimized combination of the X-ray source and detector material.}, language = {en} } @article{KhodaverdiChatziioannouWeberetal.2005, author = {Khodaverdi, M. and Chatziioannou, A. F. and Weber, S. and Ziemons, Karl and Halling, H. and Pietrzyk, U.}, title = {Investigation of different MicroCT scanner configurations by GEANT4 simulations}, series = {IEEE Transactions on Nuclear Science}, volume = {52}, journal = {IEEE Transactions on Nuclear Science}, number = {1}, isbn = {0018-9499}, pages = {188 -- 192}, year = {2005}, abstract = {This study has been performed to design the combination of the new ClearPET (ClearPET is a trademark of the Crystal Clear Collaboration), a small animal positron emission tomography (PET) system, with a micro-computed tomography (microCT) scanner. The properties of different microCT systems have been determined by simulations based on GEANT4. We will demonstrate the influence of the detector material and the X-ray spectrum on the obtained contrast. Four different detector materials (selenium, cadmium zinc telluride, cesium iodide and gadolinium oxysulfide) and two X-ray spectra (a molybdenum and a tungsten source) have been considered. The spectra have also been modified by aluminum filters of varying thickness. The contrast between different tissue types (water, air, brain, bone and fat) has been simulated by using a suitable phantom. The results indicate the possibility to improve the image contrast in microCT by an optimized combination of the X-ray source and detector material.}, language = {en} } @article{KhedimSchwarzerFaberetal.2004, author = {Khedim, Ahmed and Schwarzer, Klemens and Faber, Christian and M{\"u}ller, Christoph}, title = {Production d{\´e}centralis{\´e}e de l'eau potable {\`a} l'{\´e}nergie solaire}, series = {Desalination. 168 (2004), H. 1-3}, journal = {Desalination. 168 (2004), H. 1-3}, isbn = {0011-9164}, pages = {13 -- 20}, year = {2004}, language = {en} } @article{KhayyamJamaliBabHadiasharetal.2020, author = {Khayyam, Hamid and Jamali, Ali and Bab-Hadiashar, Alireza and Esch, Thomas and Ramakrishna, Seeram and Jalili, Mahdi and Naebe, Minoo}, title = {A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modeling with Application in Industry 4.0}, series = {IEEE Access}, volume = {8}, journal = {IEEE Access}, number = {Art. 9108222}, publisher = {IEEE}, address = {New York, NY}, issn = {2169-3536}, doi = {10.1109/ACCESS.2020.2999898}, pages = {111381 -- 111393}, year = {2020}, abstract = {To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements.}, language = {en} } @article{KhayyamJamaliBabHadiasharetal.2020, author = {Khayyam, Hamid and Jamali, Ali and Bab-Hadiashar, Alireza and Esch, Thomas and Ramakrishna, Seeram and Jalil, Mahdi and Naebe, Minoo}, title = {A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modelling with Application in Industry 4.0}, series = {IEEE Access}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York, NY}, isbn = {2169-3536}, doi = {10.1109/ACCESS.2020.2999898}, pages = {1 -- 12}, year = {2020}, abstract = {To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements.}, language = {en} } @article{KhaydukovaZadorozhnayaKirsanovetal.2014, author = {Khaydukova, M. M. and Zadorozhnaya, O. A. and Kirsanov, D. O. and Iken, Heiko and Rolka, David and Sch{\"o}ning, Michael Josef and Babain, V. A. and Vlasov, Yu. G. and Legin, A. V.}, title = {Multivariate processing of atomic-force microscopy images for detection of the response of plasticized polymeric membranes}, series = {Russian journal of applied chemistry}, volume = {87}, journal = {Russian journal of applied chemistry}, number = {3}, publisher = {Springer}, address = {Dordrecht}, issn = {1608-3296 (E-Journal); 1070-4272 (Print)}, doi = {10.1134/S1070427214030112}, pages = {307 -- 314}, year = {2014}, abstract = {The possibility of using the atomic-force microscopy as a method for detection of the analytical signal from plasticized polymeric sensor membranes was analyzed. The surfaces of cadmium-selective membranes based on two polymeric matrices were examined. The digital images were processed with multivariate image analysis techniques. A correlation was found between the surface profile of an ion-selective membrane and the concentration of the ion in solution.}, language = {en} } @article{KezerashviliDachwald2021, author = {Kezerashvili, Roman Ya and Dachwald, Bernd}, title = {Preface: Solar sailing: Concepts, technology, and missions II}, series = {Advances in Space Research}, volume = {67}, journal = {Advances in Space Research}, number = {9}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0273-1177}, doi = {10.1016/j.asr.2021.01.037}, pages = {2559 -- 2560}, year = {2021}, language = {en} } @article{KeusgenJuengerKrestetal.2003, author = {Keusgen, Michael and J{\"u}nger, Martina and Krest, Ingo and Sch{\"o}ning, Michael Josef}, title = {Biosensoric detection of the cysteine sulphoxide alliin}, series = {Sensors and Actuators B. 95 (2003), H. 1-3}, journal = {Sensors and Actuators B. 95 (2003), H. 1-3}, isbn = {0925-4005}, pages = {297 -- 302}, year = {2003}, language = {en} }