2,413 research outputs found
Michelle Hartmann Puryear : Distinguished Achievement Award - Public Service
Get PDFMichelle Hartmann Puryear ’89 won the Distinguished Achievement Award - Public Service in 2008, her bio is archived here from the SNC website
Magnetohydrodynamic turbulence in a Hartmann duct flow at finite magnetic Reynolds number
No full textThe dynamics of turbulent flow at finite magnetic Reynolds numbers can be very complex due to the coupled nature of the evolution equations for the flow and magnetic fields. In this regime, the Hartmann flow in a straight rectangular duct with streamwise periodicity is studied with the help of direct numerical simulations (DNS) and the effect of magnetic Reynolds number on turbulent statistics is quantified by comparing the results with the numerical results obtained using the quasistatic approximation
Voices of Women
No full textThe media can be accessed here: http://streaming.osu.edu/KnowledgeBank/BuckeyeHistoryLive/Voices_of_Women.MP3This episode of Buckeye History Live combines excerpts from five oral histories with Ohio State University women from the Voices of Women project, a collaboration between The Women's Place and the University Archives. Deb Ballam, emeritus professor of business and former Director of The Women’s Place, talks about her involvement in activism as an undergraduate at Miami and then her advocacy for women’s and LGBT rights as a law student at OSU.
Rudine Sims Bishop, emeritus professor of education, discusses her identities as both a woman and an African-American and the impact those identities have had on the trajectory of her life and career. Shirley Dunlap Bowser, 1956 OSU grad and former member of the Board of Trustees, tells about her time as an undergraduate. Gay Hadley, former Assistant Vice President for Human Resources, talks about her experiences as a non-traditional student and a University administrator. Susan Hartmann, emeritus professor of history and women’s studies and former Director of the Center for Women’s Studies, discusses being a female professor at OSU.Audio: Welcome and Introductions (00:00:00-00:01:02); Interviewee - Dr. Deborah Ballam, Emeritus Professor of Business, The Ohio State University, Former Director of The Women's Place (00:01:03-00:20:13); Interviewee - Dr. Rudine Sims Bishop, Emeritus Professor of Education, The Ohio State University (00:20:14-00:27:32); Interviewee - Shirley Dunlap Bowser, Former Member of The Ohio State University Board of Trustees (00:27:33-00:44:44); Interviewee - Gay Hadley, Former Assistant Vice President for Human Resources, The Ohio State University (00:44:45-00:57:29); Interviewee - Susan Hartmann, Emeritus Professor of History and Women's Studies, The Ohio State University, Former Director of the Center for Women's Studies (00:57:30-01:14:49); Conclusion (01:14:50-01:15:12
Methodenentwicklung für qualitative Metabolitanalysen und deren beispielhafte Anwendung an ausgewählten Zelllinien
Get PDFHartmann M. Methodenentwicklung für qualitative Metabolitanalysen und deren beispielhafte Anwendung an ausgewählten Zelllinien. Bielefeld: Universitätsbibliothek; 2013
Shack-Hartmann reflective micro profilometer
No full textWe present a quantitative phase imaging microscope based on a Shack-Hartmann sensor, that directly reconstructs the optical path difference (OPD) in reflective mode. Comparing with the holographic or interferometric methods, the SH technique needs no reference beam in the setup, which simplifies the system. With a preregistered reference, the OPD image can be reconstructed from a single shot. Also, the method has a rather relaxed requirement on the illumination coherence, thus a cheap light source such as a LED is feasible in the setup. In our previous research, we have successfully verified that a conventional transmissive microscope can be transformed into an optical path difference microscope by using a Shack-Hartmann wavefront sensor under incoherent illumination. The key condition is that the numerical aperture of illumination should be smaller than the numerical aperture of imaging lens. This approach is also applicable to characterization of reflective and slightly scattering surfaces.Team Raf Van de Pla
Deep Learning Wavefront Sensing: Via Raw Shack-Hartmann Images
No full textThe Delft Center for Systems and Control (DCSC) 'Smart Optics' aim to achieve higher resolution imaging through Adaptive Optics (AO). Adaptive optics is a modern technique for detecting and correcting real-time wavefront aberrations and is widely used in biomedical imaging and astronomical imaging. Wavefront sensing lies at the core of Adaptive Optics and is known to pose some challenges. Measurement of the wavefront cannot be done directly and has to be estimated through an intensity distribution on a detector. One approach to wavefront sensing is by using a Shack-Hartmann (SH) sensor. A Shack-Hartmann sensor (a pupil-plane sensor) subdivides the wavefront into N spatial areas using sub-apertures. The individual slopes across all sub-apertures are integrated to reconstruct the wavefront. The major advantage of using a Shack-Hartmann sensor is its fast operation speed, caused by the linear relationship between local slopes and original wavefront. This enables real-time wavefront reconstruction. The Shack-Hartmann sensor however, has some limitations. Its ability to reconstruct higher-order aberrations is restricted by the amount of lenses within the micro-lens array. Furthermore, a centroiding algorithm is used to compute the local slopes. Going from spots to centroids decreases the amount of informative pixels and greatly limits its wavefront reconstruction potential. Moreover, these centroiding algorithms often add a measure of uncertainty since spots can have irregular shapes or cross-over/overlap. In this Master Thesis a novel approach to phase reconstruction from the raw SH measurement is proposed. Here, we show that Deep Learning techniques in combination with a micro-lens array can surpass traditional SH phase reconstruction methods and alleviate their current limitations. The proposed method uses the entire Shack-Hartmann Pattern (HP) as input to a neural network, supplying the network with more information than existing Deep Learning SHWR methods, which still rely on centroids. Using this approach, we can combine the accuracy of sensor-less techniques with the speed of a Shack-Hartmann sensor. Three different neural network architectures are considered in this thesis. Two of these neural networks (Alex-Net and Xception) are adapted to output a series of Zernike coefficients. Using these estimated Zernike coefficients, a wavefront can be reconstructed. The remaining neural network, U-Net, performs a direct pixel-wise estimation of the phase-map. The input Shack-Hartmann patterns are created using different micro-lens array (MLA) geometries, consisting of 25-, 256- or 900 lenses. The networks are evaluated on their ability to reconstruct a combination of 32- or 100- Zernike coefficients.Mechanical Engineering | Systems and Contro
Estimation of the total error of modal wavefront reconstruction with Zernike polynomials and Hartmann-Shack test
No full textThe paper discusses the influence of the Hartmann-(Shack) wavefront sensor geometry on the total error of modal wavefront reconstruction. A mathematical model is proposed which describes modal wavefront reconstruction based on Hartmann or Hartmann-Shack sensor in terms of linear operators. The modal covers the most general case and is not limited by the orthogonality of decomposition basis or by the method chosen for decomposition. The total reconstruction error is calculated for any given statistics of the wavefront to be measured. Based on this estimate, total reconstruction error is calculated for regular and randomised Hartmann masks. The calculations demonstrate that use of random masks with non-regular Fourier spectra for Zernike wavefront reconstruction for atmospheric turbulence allows to double the number of decomposition modes with the same total error.Electronic InstrumentationElectrical Engineering, Mathematics and Computer Scienc
How do extrinsic factors influence the decision of young adults to become an entrepreneur?
No full text‘How do extrinsic factors influence the decision of young adults to become an entrepreneur?’ Michelle Hartmann and Aiko Thumm, 2018: Applied Double Degree Bachelor, Linnaeus University Växjö, Sweden and ICN Business School Nancy, France. Even though governments all over the world are putting a spotlight on entrepreneurs and entrepreneurship as a whole, the motivations of why to become a venture creator is rarely touched upon. In general, there are two forms of possible influences on entrepreneurial intentions, namely intrinsic and extrinsic drivers. In order to further describe the phenomenon of extrinsic factors influencing entrepreneurial intentions, this study aims to describe the interplay of three extrinsic factors for venture creation. These three factors are entrepreneurship enhancing education, role models as well as influence of opportunity and necessity. A descriptive, qualitative study has been chosen for that purpose. During semi-structured interviews, the narrative story of the six participants is told. The findings revolve around the narratives of the respondents’ propositions towards the three aforementioned extrinsic factors. This paper shows that the present educational system only partially conveys necessary knowledge and entrepreneurial skills. Furthermore, this study suggests, that there is more than only a positively influencing role model, videlicet, a negative example representing things the young adult does not want to become. In addition, the study depicts the predicament of a clear differentiation between necessity and opportunity entrepreneurship. Lastly this paper concludes, that more than one factor are motivational drivers for young entrepreneurs and therefore opens a wide research area for future fellow entrepreneurship researchers
Correction to:Characterization, modeling, and remediation of karst in a changing environment (Environmental Earth Sciences, (2018), 77, 12, (476), 10.1007/s12665-018-7660-7)
Get PDFIn the original publication, the author name Andrew Hartmann was published incorrectly. The correct name should be Andreas Hartmann.</p
Terahertz wavefronts measured using the Hartmann sensor principle
Get PDFWe demonstrate for the first time that the Hartmann wavefront sensor (HWS) principle can be applied for characterizing the wavefronts of terahertz (THz) electromagnetic radiation. The THz Hartmann wavefront sensor consists of a metallic plate with an array of holes and a twodimensional scanable pyro-electric detector. The THz radiation with different wavefronts was generated by a far-infrared gas laser operated at 2.5 THz in combination with a number of objects that result in known wavefronts. To measure the wavefront, a beam passing through an array of holes generates intensity spots, for which the positions of the individual spot centroids are measured and compared with reference positions. The reconstructed wavefronts are in good agreement with the model expectations.QN/Quantum NanoscienceApplied Science
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