9,366 research outputs found
Cirurgia de hartmann; estudo de 24 casos.
No full textTrabalho de Conclusão de Curso - Universidade Federal de Santa Catarina, Centro de Ciências da Saúde, Departamento de Clínica Cirúrgica, Curso de Medicina, Florianópolis, 199
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
Recommended from our members
Book Review: Accretion Processes in Star Formation (2nd Edition), Lee Hartmann
No full textBook Review: Accretion Processes in Star Formation (2nd Edition), Lee Hartmann. Cambridge University Press (2009).The Meteoritics & Planetary Science archives are made available by the Meteoritical Society and the University of Arizona Libraries. Contact [email protected] for further information.Migrated from OJS platform February 202
Noise-insensitive centroiding algorithm for a Shack-Hartmann sensor
No full textA Shack-Hartmann wavefront sensor is the most popular device for wavefront sensing in the field of adaptive optics. The sensing errors of a Shack-Hartmann sensor are caused by various factors, including the noise of a detector, the magnitude and the profile of the spot irradiance distribution, etc. This paper investigates the noise effect in the two major centroiding algorithms, i.e., the center-of-mass method and the correlation method. For this work, wavefront sensing using a Shack-Hartmann sensor is simulated computationally. In this simulation, the input wavefront and the corresponding spot images are generated. Sequentially, the centroids of spots are found, wavefront is reconstructed. From the results of the simulation, an optimal threshold value is proposed for the center-of-mass algorithm. The proposed algorithm is verified through a wavefront-sensing experiment. In the experiment, a wavefront with defocus is generated and measured
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
An FPGA Architecture for Extracting Real-Time Zernike Coefficients from Measured Phase Gradients
No full textZernike modes are commonly used in adaptive optics systems to represent optical wavefronts. However, real-time calculation of Zernike modes is time consuming due to two factors: the large factorial components in the radial polynomials used to define them and the large inverse matrix calculation needed for the linear fit. This paper presents an efficient parallel method for calculating Zernike coefficients from phase gradients produced by a Shack-Hartman sensor and its real-time implementation using an FPGA by pre-calculation and storage of subsections of the large inverse matrix. The architecture exploits symmetries within the Zernike modes to achieve a significant reduction in memory requirements and a speed-up of 2.9 when compared to published results utilising a 2D-FFT method for a grid size of 8×8. Analysis of processor element internal word length requirements show that 24-bit precision in precalculated values of the Zernike mode partial derivatives ensures less than 0.5% error per Zernike coefficient and an overall error of <1%. The design has been synthesized on a Xilinx Spartan-6 XC6SLX45 FPGA. The resource utilisation on this device is <3% of slice registers, <15% of slice LUTs, and approximately 48% of available DSP blocks independent of the Shack-Hartmann grid size. Block RAM usage is <16% for Shack-Hartmann grid sizes up to 32×32
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
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)
No full textIn 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
No full textWe 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
- …
