The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
B S Manjunath - One of the best experts on this subject based on the ideXlab platform.
-
rotation invariant texture classification using a complete space frequency model
IEEE Transactions on Image Processing, 1999Co-Authors: G M Haley, B S ManjunathAbstract:A method of rotation-invariant texture classification based on a complete space-frequency model is introduced. A polar, analytic form of a two-dimensional (2-D) Gabor wavelet is developed, and a multiresolution family of these wavelets is used to compute information-conserving Microfeatures. From these Microfeatures a micromodel, which characterizes spatially localized amplitude, frequency, and directional behavior of the texture, is formed. The essential characteristics of a texture sample, its macrofeatures, are derived from the estimated selected parameters of the micromodel. Classification of texture samples is based on the macromodel derived from a rotation invariant subset of macrofeatures. In experiments, comparatively high correct classification rates were obtained using large sample sets.
-
rotation invariant texture classification using modified gabor filters
International Conference on Image Processing, 1995Co-Authors: G M Haley, B S ManjunathAbstract:A method of rotation invariant texture classification based on a joint space-frequency model is introduced. Multiresolution filters, based on a truly analytic form of a polar 2-D Gabor (1946) wavelet, are used to compute spatial frequency-specific but spatially localized Microfeatures. These Microfeatures constitute an approximate basis set for the representation of the texture sample. The essential characteristics of a texture sample, its macrofeatures, are derived from the statistics of its Microfeatures. A texture is modeled as a multivariate Gaussian distribution of macrofeatures. Classification is based on a rotation invariant subset of macrofeatures.
G M Haley - One of the best experts on this subject based on the ideXlab platform.
-
rotation invariant texture classification using a complete space frequency model
IEEE Transactions on Image Processing, 1999Co-Authors: G M Haley, B S ManjunathAbstract:A method of rotation-invariant texture classification based on a complete space-frequency model is introduced. A polar, analytic form of a two-dimensional (2-D) Gabor wavelet is developed, and a multiresolution family of these wavelets is used to compute information-conserving Microfeatures. From these Microfeatures a micromodel, which characterizes spatially localized amplitude, frequency, and directional behavior of the texture, is formed. The essential characteristics of a texture sample, its macrofeatures, are derived from the estimated selected parameters of the micromodel. Classification of texture samples is based on the macromodel derived from a rotation invariant subset of macrofeatures. In experiments, comparatively high correct classification rates were obtained using large sample sets.
-
rotation invariant texture classification using modified gabor filters
International Conference on Image Processing, 1995Co-Authors: G M Haley, B S ManjunathAbstract:A method of rotation invariant texture classification based on a joint space-frequency model is introduced. Multiresolution filters, based on a truly analytic form of a polar 2-D Gabor (1946) wavelet, are used to compute spatial frequency-specific but spatially localized Microfeatures. These Microfeatures constitute an approximate basis set for the representation of the texture sample. The essential characteristics of a texture sample, its macrofeatures, are derived from the statistics of its Microfeatures. A texture is modeled as a multivariate Gaussian distribution of macrofeatures. Classification is based on a rotation invariant subset of macrofeatures.
Jonathan P Rothstein - One of the best experts on this subject based on the ideXlab platform.
-
an analysis of superhydrophobic turbulent drag reduction mechanisms using direct numerical simulation
Physics of Fluids, 2010Co-Authors: Michael B Martell, Jonathan P Rothstein, Blair J PerotAbstract:These surfaces have been shown to provide drag reduction in laminar and turbulent flows. In this work, direct numerical simulation is used to investigate the drag reducing performance of superhydrophobic surfaces in turbulent channel flow. Slip velocities, wall shear stresses, and Reynolds stresses are determined for a variety of superhydrophobic surface microfeature geometry configurations at friction Reynolds numbers of Re180, Re395, and Re590. This work provides evidence that superhydrophobic surfaces are capable of reducing drag in turbulent flow situations by manipulating the laminar sublayer. For the largest microfeature spacing, an average slip velocity over 80% of the bulk velocity is obtained, and the wall shear stress reduction is found to be greater than 50%. The simulation results suggest that the mean velocity profile near the superhydrophobic wall continues to scale with the wall shear stress and the log layer is still present, but both are offset by a slip velocity that is primarily dependent on the microfeature spacing. © 2010 American Institute of Physics. doi:10.1063/1.3432514
-
drag reduction in turbulent flows over superhydrophobic surfaces
Physics of Fluids, 2009Co-Authors: Robert Daniello, Nicholas E Waterhouse, Jonathan P RothsteinAbstract:In this paper, we demonstrate that periodic, micropatterned superhydrophobic surfaces, previously noted for their ability to provide laminar flow drag reduction, are capable of reducing drag in the turbulent flow regime. Superhydrophobic surfaces contain micro- or nanoscale hydrophobic features which can support a shear-free air-water interface between peaks in the surface topology. Particle image velocimetry and pressure drop measurements were used to observe significant slip velocities, shear stress, and pressure drop reductions corresponding to drag reductions approaching 50%. At a given Reynolds number, drag reduction is found to increase with increasing feature size and spacing, as in laminar flows. No observable drag reduction was noted in the laminar regime, consistent with previous experimental results for the channel geometry considered. The onset of drag reduction occurs at a critical Reynolds number where the viscous sublayer thickness approaches the scale of the superhydrophobic Microfeatures ...
-
dns of turbulent channel flow past ultrahydrophobic surfaces with periodic Microfeatures
Bulletin of the American Physical Society, 2007Co-Authors: Michael B Martell, Blair Perot, Jonathan P RothsteinAbstract:The interaction between solid surfaces and liquids is of fundamental importance in engineering flows where solid surfaces are the primary means for controlling or manipulating fluids. We will show that by treating a solid surface to make it ultrahydrophobic it is possible to significantly enhance how the surface interacts with a flowing liquid. In particular, in our recent work, we have shown that ultrahydrophobic surfaces can be utilized to reduce drag [1, 2] and enhance mixing [3] in low to moderate Reynolds number internal flows. In this presentation we will demonstrate through a series of numerical simulations that ultrahydrophobic surfaces can also be used to delay the transition to turbulence and dramatically reduce drag in both external and internal turbulent flows. Ultrahydrophobic surfaces are engineered by taking materials with micron or nanoscale surfaces roughness and chemically treating them to make them hydrophobic. Because of the hydrophobicity of these microscale and nanoscale protrusions, the water does not move into the pores on the surface, rather it remains in contact with only the peaks of the surface topology resulting in a shear-free air-water interface. The underlying physical mechanism for drag reduction is a slip along the shear-free air-water interface supported between the peaks of micro or nanoscale protrusions present on the ultrahydrophobic surface. We will present the results from direct numerical simulations of the flow over a series of model ultrahydrophobic surfaces chosen to directly match the surfaces used in our concurrent experiments. In the simulations, the top of the microposts or microridges are assumed to be no-slip and the air-water interface between them is assumed to be shearfree and flat. Both of these assumptions are reasonable, however, under certain conditions, the air-water interface can deflect and the recirculation of air within the gaps between the microposts or microridges can produce some drag along the air-water interface. The numerical algorithm used to simulate the has been under development for almost a decade [4, 5] and over the last year it has been modified to apply directly to turbulent flows past ultrahydrophobic surfaces. The code uses non-uniform grid spacing
Michael B Martell - One of the best experts on this subject based on the ideXlab platform.
-
an analysis of superhydrophobic turbulent drag reduction mechanisms using direct numerical simulation
Physics of Fluids, 2010Co-Authors: Michael B Martell, Jonathan P Rothstein, Blair J PerotAbstract:These surfaces have been shown to provide drag reduction in laminar and turbulent flows. In this work, direct numerical simulation is used to investigate the drag reducing performance of superhydrophobic surfaces in turbulent channel flow. Slip velocities, wall shear stresses, and Reynolds stresses are determined for a variety of superhydrophobic surface microfeature geometry configurations at friction Reynolds numbers of Re180, Re395, and Re590. This work provides evidence that superhydrophobic surfaces are capable of reducing drag in turbulent flow situations by manipulating the laminar sublayer. For the largest microfeature spacing, an average slip velocity over 80% of the bulk velocity is obtained, and the wall shear stress reduction is found to be greater than 50%. The simulation results suggest that the mean velocity profile near the superhydrophobic wall continues to scale with the wall shear stress and the log layer is still present, but both are offset by a slip velocity that is primarily dependent on the microfeature spacing. © 2010 American Institute of Physics. doi:10.1063/1.3432514
-
dns of turbulent channel flow past ultrahydrophobic surfaces with periodic Microfeatures
Bulletin of the American Physical Society, 2007Co-Authors: Michael B Martell, Blair Perot, Jonathan P RothsteinAbstract:The interaction between solid surfaces and liquids is of fundamental importance in engineering flows where solid surfaces are the primary means for controlling or manipulating fluids. We will show that by treating a solid surface to make it ultrahydrophobic it is possible to significantly enhance how the surface interacts with a flowing liquid. In particular, in our recent work, we have shown that ultrahydrophobic surfaces can be utilized to reduce drag [1, 2] and enhance mixing [3] in low to moderate Reynolds number internal flows. In this presentation we will demonstrate through a series of numerical simulations that ultrahydrophobic surfaces can also be used to delay the transition to turbulence and dramatically reduce drag in both external and internal turbulent flows. Ultrahydrophobic surfaces are engineered by taking materials with micron or nanoscale surfaces roughness and chemically treating them to make them hydrophobic. Because of the hydrophobicity of these microscale and nanoscale protrusions, the water does not move into the pores on the surface, rather it remains in contact with only the peaks of the surface topology resulting in a shear-free air-water interface. The underlying physical mechanism for drag reduction is a slip along the shear-free air-water interface supported between the peaks of micro or nanoscale protrusions present on the ultrahydrophobic surface. We will present the results from direct numerical simulations of the flow over a series of model ultrahydrophobic surfaces chosen to directly match the surfaces used in our concurrent experiments. In the simulations, the top of the microposts or microridges are assumed to be no-slip and the air-water interface between them is assumed to be shearfree and flat. Both of these assumptions are reasonable, however, under certain conditions, the air-water interface can deflect and the recirculation of air within the gaps between the microposts or microridges can produce some drag along the air-water interface. The numerical algorithm used to simulate the has been under development for almost a decade [4, 5] and over the last year it has been modified to apply directly to turbulent flows past ultrahydrophobic surfaces. The code uses non-uniform grid spacing
Allan C Guymon - One of the best experts on this subject based on the ideXlab platform.
-
photopolymerized Microfeatures guide adult spiral ganglion and dorsal root ganglion neurite growth
Otology & Neurotology, 2018Co-Authors: Alison E Seline, Allan C Guymon, Braden Leigh, Mark Ramirez, Marlan R HansenAbstract:HYPOTHESIS Microtopographical patterns generated by photopolymerization of methacrylate polymer systems will direct growth of neurites from adult neurons, including spiral ganglion neurons (SGNs). BACKGROUND Cochlear implants (CIs) provide hearing perception to patients with severe to profound hearing loss. However, their ability to encode complex auditory stimuli is limited due, in part, to poor spatial resolution caused by spread of the electrical currents in the inner ear. Directing the regrowth of SGN peripheral processes towards stimulating electrodes could help reduce current spread and improve spatial resolution provided by the CI. Previous work has demonstrated that micro- and nano-scale patterned surfaces precisely guide the growth of neurites from a variety of neonatal neurons including SGNs. Here, we sought to determine the extent to which adult neurons likewise respond to these topographical surface features. METHODS Photopolymerization was used to fabricate methacrylate polymer substrates with micropatterned surfaces of varying amplitudes and periodicities. Dissociated adult dorsal root ganglion neurons (DRGNs) and SGNs were cultured on these surfaces and the alignment of the neurite processes to the micropatterns was determined. RESULTS Neurites from both adult DRGNs and SGNs significantly aligned to the patterned surfaces similar to their neonatal counterparts. Further DRGN and SGN neurite alignment increased as the amplitude of the Microfeatures increased. Decreased pattern periodicity also improved neurite alignment. CONCLUSION Microscale surface topographic features direct the growth of adult SGN neurites. Topographical features could prove useful for guiding growth of SGN peripheral axons towards a CI electrode array.
-
photopolymerized Microfeatures for directed spiral ganglion neurite and schwann cell growth
Biomaterials, 2013Co-Authors: Bradley W Tuft, Joseph C Clarke, Scott P White, Bradley A Guymon, Krystian X Perez, Marlan R Hansen, Allan C GuymonAbstract:Abstract Cochlear implants (CIs) provide auditory perception to individuals with severe hearing impairment. However, their ability to encode complex auditory stimuli is limited due, in part, to poor spatial resolution caused by electrical current spread in the inner ear. Directing nerve cell processes towards target electrodes may reduce the problematic current spread and improve stimulatory specificity. In this work, photopolymerization was used to fabricate micro- and nano-patterned methacrylate polymers to probe the extent of spiral ganglion neuron (SGN) neurite and Schwann cell (SGSC) contact guidance based on variations in substrate topographical cues. Micropatterned substrates are formed in a rapid, single-step reaction by selectively blocking light with photomasks which have parallel line-space gratings with periodicities of 10–100 μm. Channel amplitudes of 250 nm–10 μm are generated by modulating UV exposure time, light intensity, and photoinitiator concentration. Gradual transitions are observed between ridges and grooves using scanning electron and atomic force microscopy. The transitions stand in contrast to vertical features generated via etching lithographic techniques. Alignment of neural elements increases significantly with increasing feature amplitude and constant periodicity, as well as with decreasing periodicity and constant amplitude. SGN neurite alignment strongly correlates ( r = 0.93) with maximum feature slope. Multiple neuronal and glial types orient to the patterns with varying degrees of alignment. This work presents a method to fabricate gradually-sloping micropatterns for cellular contact guidance studies and demonstrates spatial control of inner ear neural elements in response to micro- and nano-scale surface topography.