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Sample records for fabricating convoluted shaped

  1. A Novel Method of Fabricating Convoluted Shaped Electrode Arrays for Neural and Retinal Prostheses

    PubMed Central

    Bhandari, R.; Negi, S.; Rieth, L.; Normann, R. A.; Solzbacher, F.

    2008-01-01

    A novel fabrication technique has been developed for creating high density (6.25 electrodes/mm2), out of plane, high aspect ratio silicon-based convoluted microelectrode arrays for neural and retinal prostheses. The convoluted shape of the surface defined by the tips of the electrodes could compliment the curved surfaces of peripheral nerves and the cortex, and in the case of retina, its spherical geometry. The geometry of these electrode arrays has the potential to facilitate implantation in the nerve fascicles and to physically stabilize it against displacement after insertion. This report presents a unique combination of variable depth dicing and wet isotropic etching for the fabrication of a variety of convoluted neural array geometries. Also, a method of deinsulating the electrode tips using photoresist as a mask and the limitations of this technique on uniformity are discussed. PMID:19122774

  2. Adapting line integral convolution for fabricating artistic virtual environment

    NASA Astrophysics Data System (ADS)

    Lee, Jiunn-Shyan; Wang, Chung-Ming

    2003-04-01

    Vector field occurs not only extensively in scientific applications but also in treasured art such as sculptures and paintings. Artist depicts our natural environment stressing valued directional feature besides color and shape information. Line integral convolution (LIC), developed for imaging vector field in scientific visualization, has potential of producing directional image. In this paper we present several techniques of exploring LIC techniques to generate impressionistic images forming artistic virtual environment. We take advantage of directional information given by a photograph, and incorporate many investigations to the work including non-photorealistic shading technique and statistical detail control. In particular, the non-photorealistic shading technique blends cool and warm colors into the photograph to imitate artists painting convention. Besides, we adopt statistical technique controlling integral length according to image variance to preserve details. Furthermore, we also propose method for generating a series of mip-maps, which revealing constant strokes under multi-resolution viewing and achieving frame coherence in an interactive walkthrough system. The experimental results show merits of emulating satisfyingly and computing efficiently, as a consequence, relying on the proposed technique successfully fabricates a wide category of non-photorealistic rendering (NPR) application such as interactive virtual environment with artistic perception.

  3. Patterned fabric defect detection via convolutional matching pursuit dual-dictionary

    NASA Astrophysics Data System (ADS)

    Jing, Junfeng; Fan, Xiaoting; Li, Pengfei

    2016-05-01

    Automatic patterned fabric defect detection is a promising technique for textile manufacturing due to its low cost and high efficiency. The applicability of most existing algorithms, however, is limited by their intensive computation. To overcome or alleviate the problem, this paper presents a convolutional matching pursuit (CMP) dual-dictionary algorithm for patterned fabric defect detection. A preprocessing with mean sampling is performed to eliminate the influence of background texture of fabric defects. Subsequently, a set of defect-free image blocks are selected as a sample set by sliding window. Dual-dictionary and sparse coefficiencies of the defect-free sample set are obtained via CMP and the K-singular value decomposition (K-SVD) based on a Gabor filter. Then we employ the defect-free and defective fabric image's projections onto the dual-dictionary as features for defect detection. Finally, the test results are determined by comparing the distance between the features to be measured. Experimental results reveal that the proposed algorithm is effective for patterned fabric defect detection and an acceptable average detection rate reaches by 94.2%.

  4. Illustrating Surface Shape in Volume Data via Principal Direction-Driven 3D Line Integral Convolution

    NASA Technical Reports Server (NTRS)

    Interrante, Victoria

    1997-01-01

    The three-dimensional shape and relative depth of a smoothly curving layered transparent surface may be communicated particularly effectively when the surface is artistically enhanced with sparsely distributed opaque detail. This paper describes how the set of principal directions and principal curvatures specified by local geometric operators can be understood to define a natural 'flow' over the surface of an object, and can be used to guide the placement of the lines of a stroke texture that seeks to represent 3D shape information in a perceptually intuitive way. The driving application for this work is the visualization of layered isovalue surfaces in volume data, where the particular identity of an individual surface is not generally known a priori and observers will typically wish to view a variety of different level surfaces from the same distribution, superimposed over underlying opaque structures. By advecting an evenly distributed set of tiny opaque particles, and the empty space between them, via 3D line integral convolution through the vector field defined by the principal directions and principal curvatures of the level surfaces passing through each gridpoint of a 3D volume, it is possible to generate a single scan-converted solid stroke texture that may intuitively represent the essential shape information of any level surface in the volume. To generate longer strokes over more highly curved areas, where the directional information is both most stable and most relevant, and to simultaneously downplay the visual impact of directional information in the flatter regions, one may dynamically redefine the length of the filter kernel according to the magnitude of the maximum principal curvature of the level surface at the point around which it is applied.

  5. Shape Engineered Nanoparticle Fabrication for Biomedical Applications

    NASA Astrophysics Data System (ADS)

    Nasrullah, Azeem

    Semiconductor fabrication research has developed technologies that allow for the deposition and patterning of thin films, and can be applied to many different industries, including the field of medicine. One such application is the fabrication of nanoparticles. There is a wide variety of nanoparticle-based medical diagnostics and therapies, including drug delivery and cancer imaging. Most of the nanoparticles being studied are chemically synthesized and spherical in shape, and studies have shown that other shapes can be more useful in certain applications, especially those that involve in vivo analysis and treatment. Fabrication of particles using a tool set developed from the semiconductor industry can allow for a detailed study of size and shape dependence on nanoparticle uptake in the bloodstream. Particle fabrication is achieved using thin film deposition, ion beam proximity lithography, wet etching, and lift-off, all similar to techniques commonly found in the semiconductor industry. The particles are formed using patterns developed with proximity lithography, and this represents the largest effort in this work. An ion beam, generated by a saddle-field ion source, is used to irradiate a polymeric resist with a thin membrane stencil mask placed in close proximity to the resist coated substrate in order to define the pattern. A saddle-field ion source was constructed and characterized for proximity lithography, with a beam diameter of 4.8 mm for a +/-5% tolerance in current density, a source size range of 0.3--0.9 mm, an average brightness value of 15 nAcm2˙sr , and average exposure times of ≈30 s. Stencil masks were fabricated from silicon nitride membranes in order to generate the pattern for the nanoparticles, and the particles were fabricated using a bi-layer resist and a sacrificial copper layer for release into solution.

  6. Variations in size, shape and asymmetries of the third frontal convolution in hominids: paleoneurological implications for hominin evolution and the origin of language.

    PubMed

    Balzeau, Antoine; Gilissen, Emmanuel; Holloway, Ralph L; Prima, Sylvain; Grimaud-Hervé, Dominique

    2014-11-01

    The study of brain structural asymmetries as anatomical substrates of functional asymmetries in extant humans, great apes, and fossil hominins is of major importance in understanding the structural basis of modern human cognition. We propose methods to quantify the variation in size, shape and bilateral asymmetries of the third frontal convolution (or posterior inferior frontal gyrus) among recent modern humans, bonobos and chimpanzees, and fossil hominins using actual and virtual endocasts. These methodological improvements are necessary to extend previous qualitative studies of these features. We demonstrate both an absolute and relative bilateral increase in the size of the third frontal convolution in width and length between Pan species, as well as in hominins. We also observed a global bilateral increase in the size of the third frontal convolution across all species during hominin evolution, but also non-allometric intra-group variations independent of brain size within the fossil samples. Finally, our results show that the commonly accepted leftward asymmetry of Broca's cap is biased by qualitative observation of individual specimens. The trend during hominin evolution seems to be a reduction in size on the left compared with the right side, and also a clearer definition of the area. The third frontal convolution considered as a whole projects more laterally and antero-posteriorly in the right hemisphere. As a result, the left 'Broca's cap' looks more globular and better defined. Our results also suggest that the pattern of brain asymmetries is similar between Pan paniscus and hominins, leaving the gradient of the degree of asymmetry as the only relevant structural parameter. As the anatomical substrate related to brain asymmetry has been present since the appearance of the hominin lineage, it is not possible to prove a direct relationship between the extent of variations in the size, shape, and asymmetries of the third frontal convolution and the origin of

  7. Fabrication of trough-shaped solar collectors

    DOEpatents

    Schertz, William W.

    1978-01-01

    There is provided a radiant energy concentration and collection device formed of a one-piece thin-walled plastic substrate including a plurality of nonimaging troughs with certain metallized surfaces of the substrate serving as reflective side walls for each trough. The one-piece plastic substrate is provided with a seating surface at the bottom of each trough which conforms to the shape of an energy receiver to be seated therein.

  8. Optofluidic fabrication for 3D-shaped particles.

    PubMed

    Paulsen, Kevin S; Di Carlo, Dino; Chung, Aram J

    2015-01-01

    Complex three-dimensional (3D)-shaped particles could play unique roles in biotechnology, structural mechanics and self-assembly. Current methods of fabricating 3D-shaped particles such as 3D printing, injection moulding or photolithography are limited because of low-resolution, low-throughput or complicated/expensive procedures. Here, we present a novel method called optofluidic fabrication for the generation of complex 3D-shaped polymer particles based on two coupled processes: inertial flow shaping and ultraviolet (UV) light polymerization. Pillars within fluidic platforms are used to deterministically deform photosensitive precursor fluid streams. The channels are then illuminated with patterned UV light to polymerize the photosensitive fluid, creating particles with multi-scale 3D geometries. The fundamental advantages of optofluidic fabrication include high-resolution, multi-scalability, dynamic tunability, simple operation and great potential for bulk fabrication with full automation. Through different combinations of pillar configurations, flow rates and UV light patterns, an infinite set of 3D-shaped particles is available, and a variety are demonstrated. PMID:25904062

  9. Optofluidic fabrication for 3D-shaped particles

    NASA Astrophysics Data System (ADS)

    Paulsen, Kevin S.; di Carlo, Dino; Chung, Aram J.

    2015-04-01

    Complex three-dimensional (3D)-shaped particles could play unique roles in biotechnology, structural mechanics and self-assembly. Current methods of fabricating 3D-shaped particles such as 3D printing, injection moulding or photolithography are limited because of low-resolution, low-throughput or complicated/expensive procedures. Here, we present a novel method called optofluidic fabrication for the generation of complex 3D-shaped polymer particles based on two coupled processes: inertial flow shaping and ultraviolet (UV) light polymerization. Pillars within fluidic platforms are used to deterministically deform photosensitive precursor fluid streams. The channels are then illuminated with patterned UV light to polymerize the photosensitive fluid, creating particles with multi-scale 3D geometries. The fundamental advantages of optofluidic fabrication include high-resolution, multi-scalability, dynamic tunability, simple operation and great potential for bulk fabrication with full automation. Through different combinations of pillar configurations, flow rates and UV light patterns, an infinite set of 3D-shaped particles is available, and a variety are demonstrated.

  10. Net shape fabrication of Alpha Silicon Carbide turbine components

    NASA Technical Reports Server (NTRS)

    Storm, R. S.

    1982-01-01

    Development of Alpha Silicon Carbide components by net shape fabrication techniques has continued in conjunction with several turbine engine programs. Progress in injection molding of simple parts has been extended to much larger components. Turbine rotors fabricated by a one piece molding have been successfully spin tested above design speeds. Static components weighing up to 4.5 kg and 33 cc in diameter have also been produced using this technique. Use of sintering fixtures significantly improves dimensional control. A new Si-SiC composite material has also been developed with average strengths up to 1000 MPa (150 ksi) at 1200 C.

  11. Laser engineered net shaping for direct fabrication of metal components

    SciTech Connect

    Dimos, D.; Schlienger, M.E.

    1997-09-01

    Sandia National Laboratories is developing a new technology to fabricate three-dimensional metallic components directly from CAD solid models. This process, called Laser Engineered Net Shaping (LENS{trademark}), exhibits enormous potential to revolutionize the way in which metal parts, such as complex prototypes, tooling, and small lot production parts, are produced. To perform the process, metal powder is injected into a molten pool created by a focused, high powered laser beam. Simultaneously, the substrate on which the deposition is occurring is scanned under the beam/powder interaction zone to fabricate the desired cross-sectional geometry. Consecutive layers are sequentially deposited, thereby producing a three-dimensional metal component.

  12. Fabrication of Custom-Shaped Grafts for Cartilage Regeneration

    PubMed Central

    Koo, Seungbum; Hargreaves, Brian A.; Gold, Garry E.; Dragoo, Jason L.

    2011-01-01

    Transplantation of engineered cartilage grafts is a promising method to treat diseased articular cartilage. The interfacial areas between the graft and the native tissues play an important role in the successful integration of the graft to adjacent native tissues. The purposes of the study were to create a custom shaped graft through 3D tissue shape reconstruction and rapid-prototype molding methods using MRI data, and to test the accuracy of the custom shaped graft against the original anatomical defect. An iatrogenic defect on the distal femur was identified with a 1.5 Tesla MRI and its shape was reconstructed into a three-dimensional (3D) computer model by processing the 3D MRI data. First, the accuracy of the MRI-derived 3D model was tested against a laser-scan based 3D model of the defect. A custom-shaped polyurethane graft was fabricated from the laser-scan based 3D model by creating custom molds through computer aided design and rapid-prototyping methods. The polyurethane tissue was laser-scanned again to calculate the accuracy of this process compared to the original defect. The volumes of the defect models from MRI and laser-scan were 537 mm3 and 405 mm3, respectively, implying that the MRI model was 33% larger than the laser-scan model. The average (±SD) distance deviation of the exterior surface of the MRI model from the laser-scan model was 0.4±0.4 mm. The custom-shaped tissue created from the molds was qualitatively very similar to the original shape of the defect. The volume of the custom-shaped cartilage tissue was 463 mm3 which was 15% larger than the laser-scan model. The average (±SD) distance deviation between the two models was 0.04±0.19 mm. Custom-shaped engineered grafts can be fabricated from standard sequence 3-D MRI data with the use of CAD and rapid-prototyping technology, which may help solve the interfacial problem between native cartilage and graft, if the grafts are custom made for the specific defect. The major source of error in

  13. Fabrication of a helical coil shape memory alloy actuator

    SciTech Connect

    O`Donnell, R.E.

    1992-02-01

    A fabrication process was developed to form, heat treat, and join NiTi shape memory alloy helical coils for use as mechanical actuators. Tooling and procedures were developed to wind both extension and compression-type coils on a manual lathe. Heat treating fixtures and techniques were used to set the ``memory`` of the NiTi alloy to the desired configuration. A swaging process was devised to fasten shape memory alloy extension coils to end fittings for use in actuator testing and for potential attachment to mechanical devices. The strength of this mechanical joint was evaluated.

  14. Fabrication of a helical coil shape memory alloy actuator

    SciTech Connect

    O'Donnell, R.E.

    1992-02-01

    A fabrication process was developed to form, heat treat, and join NiTi shape memory alloy helical coils for use as mechanical actuators. Tooling and procedures were developed to wind both extension and compression-type coils on a manual lathe. Heat treating fixtures and techniques were used to set the memory'' of the NiTi alloy to the desired configuration. A swaging process was devised to fasten shape memory alloy extension coils to end fittings for use in actuator testing and for potential attachment to mechanical devices. The strength of this mechanical joint was evaluated.

  15. EDITORIAL: Designer fabrication: nanotemplates get in shape Designer fabrication: nanotemplates get in shape

    NASA Astrophysics Data System (ADS)

    Demming, Anna

    2013-02-01

    People working in device design rarely see something that works without thinking how it could be made to work better. The work on anodic aluminum oxide materials in this issue provides a case in point [1]. Over the past century researchers have observed, manipulated and exploited the porous structures that result when anodizing aluminum in for example oxalic, sulfuric, and phosphoric acid solutions [1, 2]. The self-organized pore arrays have demonstrated the potential to facilitate high through-put, low-cost fabrication of nanocomposites as well as other nanostructures. The straight self-aligned nanochannels in porous anodic aluminum oxide (AAO) have long been accepted as an inherent property of these films and for many applications they are an attractive attribute. However, researchers in Taiwan have considered a novel manifestation of AAO materials which may enhance their natural attributes by generating arrays that bend [3]. Their work is an example of how even well studied systems continue to harbour surprises and scope for creative innovation. As the authors point out, 'This novel fan-out platform facilitates probing and handling many signals from different areas on a sample's surface and is therefore promising for applications in detection and manipulation at the nanoscale level'. It has long been recognized that the inter-pore distance, pore diameter and pore depth in AAO can be controlled by changing the anodization conditions. These accommodating features have motivated researchers to seek a better understanding of how to optimize fabrication conditions. A collaboration of researchers in Sweden, Chile and Uruguay studied the structural and optical properties of silver nanowires electrodeposited in commercially available nanoporous alumina templates, with a nominal pore diameter of 20 nm [4]. Their results revealed a decrease in the uniformity of pore filling with increasing deposition overpotential and suggested that overpotentials were preferred for the

  16. EDITORIAL: Designer fabrication: nanotemplates get in shape Designer fabrication: nanotemplates get in shape

    NASA Astrophysics Data System (ADS)

    Demming, Anna

    2013-02-01

    People working in device design rarely see something that works without thinking how it could be made to work better. The work on anodic aluminum oxide materials in this issue provides a case in point [1]. Over the past century researchers have observed, manipulated and exploited the porous structures that result when anodizing aluminum in for example oxalic, sulfuric, and phosphoric acid solutions [1, 2]. The self-organized pore arrays have demonstrated the potential to facilitate high through-put, low-cost fabrication of nanocomposites as well as other nanostructures. The straight self-aligned nanochannels in porous anodic aluminum oxide (AAO) have long been accepted as an inherent property of these films and for many applications they are an attractive attribute. However, researchers in Taiwan have considered a novel manifestation of AAO materials which may enhance their natural attributes by generating arrays that bend [3]. Their work is an example of how even well studied systems continue to harbour surprises and scope for creative innovation. As the authors point out, 'This novel fan-out platform facilitates probing and handling many signals from different areas on a sample's surface and is therefore promising for applications in detection and manipulation at the nanoscale level'. It has long been recognized that the inter-pore distance, pore diameter and pore depth in AAO can be controlled by changing the anodization conditions. These accommodating features have motivated researchers to seek a better understanding of how to optimize fabrication conditions. A collaboration of researchers in Sweden, Chile and Uruguay studied the structural and optical properties of silver nanowires electrodeposited in commercially available nanoporous alumina templates, with a nominal pore diameter of 20 nm [4]. Their results revealed a decrease in the uniformity of pore filling with increasing deposition overpotential and suggested that overpotentials were preferred for the

  17. Thermocapillary Technique for Shaping and Fabricating Optical Ribbon Waveguides

    NASA Astrophysics Data System (ADS)

    Fiedler, Kevin; Troian, Sandra

    The demand for ever increasing bandwidth and higher speed communication has ushered the next generation optoelectronic integrated circuits which directly incorporate polymer optical waveguide devices. Polymer melts are very versatile materials which have been successfully cast into planar single- and multimode waveguides using techniques such as embossing, photolithography and direct laser writing. In this talk, we describe a novel thermocapillary patterning method for fabricating waveguides in which the free surface of an ultrathin molten polymer film is exposed to a spatially inhomogeneous temperature field via thermal conduction from a nearby cooled mask pattern held in close proximity. The ensuring surface temperature distribution is purposely designed to pool liquid selectively into ribbon shapes suitable for optical waveguiding, but with rounded and not rectangular cross sectional areas due to capillary forces. The solidified waveguide patterns which result from this non-contact one step procedure exhibit ultrasmooth interfaces suitable for demanding optoelectronic applications. To complement these studies, we have also conducted finite element simulations for quantifying the influence of non-rectangular cross-sectional shapes on mode propagation and losses. Kf gratefully acknowledges support from a NASA Space Technology Research Fellowship.

  18. Distal Convoluted Tubule

    PubMed Central

    Ellison, David H.

    2014-01-01

    The distal convoluted tubule is the nephron segment that lies immediately downstream of the macula densa. Although short in length, the distal convoluted tubule plays a critical role in sodium, potassium, and divalent cation homeostasis. Recent genetic and physiologic studies have greatly expanded our understanding of how the distal convoluted tubule regulates these processes at the molecular level. This article provides an update on the distal convoluted tubule, highlighting concepts and pathophysiology relevant to clinical practice. PMID:24855283

  19. Fabrication of ceramic components using mold shape deposition manufacturing

    NASA Astrophysics Data System (ADS)

    Cooper, Alexander G.

    Mold Shape Deposition Manufacturing (Mold SDM) is a new process for the fabrication of geometrically complex, structural ceramic components. This thesis describes the development of the Mold SDM process, including process steps, materials selection, planning strategies and automation. Initial characterization results are presented and these are used to compare the process to competing manufacturing processes. A range of current and potential applications for ceramic, as well as metal and polymer parts are discussed. The benefits and limitations of ceramic materials for structural applications are discussed to motivate the need for a manufacturing process capable of rapidly producing high quality, geometrically complex, structural ceramic components. The Mold SDM process was developed to address this need. Mold SDM is based on Shape Deposition Manufacturing (SDM) and uses SDM techniques to build fugitive wax molds which can then be used to build ceramic parts by gelcasting. SDM is an additive-subtractive layered manufacturing process which allows it to build geometrically complex parts. The subtraction step differentiates Mold SDM from other layered manufacturing processes and allows accurate, high quality surfaces to be produced. The performance of the process was increased by identifying the key material properties and then selecting improved materials combinations. Candidate materials were evaluated in terms of machinability, shrinkage, heat resistance and chemical compatibility. A number of preferred materials combinations were developed and used to produce ceramic, metal and polymer parts. A number of new process planning strategies and build techniques were developed. The manufacturability analysis determines whether a part is manufacturable and the orientation selection guidelines help in the selection of optimum build directions. New decomposition techniques take advantage of process capabilities to improve part quality and build rate. Initial process

  20. Fabrication of Adhesive Lenses Using Free Surface Shaping

    NASA Astrophysics Data System (ADS)

    Hoheisel, D.; Kelb, C.; Wall, M.; Roth, B.; Rissing, L.

    2013-09-01

    Two approaches for fabricating polymer lenses are presented in this paper. Both are based on filling circular holes with UV curing adhesives. Initially, the viscous adhesive material creates a liquid and spherical free surface due to its own surface tension. This shape is then preserved by curing with UV-hardening light. For the first approach, the holes are generated in a 4 inch Si-wafer by deep reactive ion etching (DRIE) and for the second, a polydimethylsiloxane (PDMS) mould is manufactured. Three types of UV-curing adhesives are investigated (NOA 61, NOA 88 and NEA 121 by Norland Products). Preliminary to the determination of the lens curvature, a contact angle goniometer is used for taking side view images of the lenses. The radius of curvature is then extracted via image processing with the software MATLAB®. Furthermore, the surface roughness of the PDMS mould and the generated lenses is measured with a white light interferometer to characterize the casting process. The resolution power of the generated lenses is evaluated by measurement of their point spread functions (psf) and modulation transfer functions (mtf), respectively.

  1. Fabrication of silicon-based shape memory alloy micro-actuators

    NASA Technical Reports Server (NTRS)

    Johnson, A. David; Busch, John D.; Ray, Curtis A.; Sloan, Charles L.

    1992-01-01

    Thin film shape memory alloy has been integrated with silicon in a new actuation mechanism for microelectromechanical systems. This paper compares nickel-titanium film with other actuators, describes recent results of chemical milling processes developed to fabricate shape memory alloy microactuators in silicon, and describes simple actuation mechanisms which have been fabricated and tested.

  2. Method for fabricating uranium alloy articles without shape memory effects

    DOEpatents

    Banker, John G.

    1985-01-01

    Uranium-rich niobium and niobium-zirconium alloys possess a characteristic known as shape memory effect wherein shaped articles of these alloys recover their original shape when heated. The present invention circumvents this memory behavior by forming the alloys into the desired configuration at elevated temperatures with "cold" matched dies and maintaining the shaped articles between the dies until the articles cool to ambient temperature.

  3. Method for fabricating uranium alloy articles without shape memory effects

    DOEpatents

    Banker, J.G.

    1980-05-21

    Uranium-rich niobium and niobium-zirconium alloys possess a characteristic known as shape memory effect wherein shaped articles of these alloys recover their original shape when heated. The present invention circumvents this memory behavior by forming the alloys into the desired configuration at elevated temperatures with cold matched dies and maintaining the shaped articles between the dies until the articles cool to ambient temperature.

  4. Adjustable reed for weaving net-shaped tailored fabrics

    NASA Technical Reports Server (NTRS)

    Farley, Gary L. (Inventor)

    1995-01-01

    An apparatus and method for forming woven fabrics through the use of an adjustable reed. The adjustable reed has multiple groups of reed wires that guide the warp yarns. The groups of reed wires move on reed rails parallel to the warp direction. In addition, rail expanders permit the space between the reed wires to be modified and telescoping rods attached to the rail sliders can be turned to permit the reed wires to be skewed to alter the fill yarn angle. These adjustments to the reed permit simultaneous variation of fill yarn angles and fabric widths and allow these variations to be made during fabrication, without the need to halt production.

  5. Fabrication of porous NiTi shape memory alloy structures using laser engineered net shaping.

    PubMed

    Krishna, B Vamsi; Bose, Susmita; Bandyopadhyay, Amit

    2009-05-01

    Porous NiTi alloy samples were fabricated with 12-36% porosity from equiatomic NiTi alloy powder using laser engineered net shaping (LENS). The effects of processing parameters on density and properties of laser-processed NiTi alloy samples were investigated. It was found that the density increased rapidly with increasing the specific energy input up to 50 J/mm(3). Further increase in the energy input had small effect on density. High cooling rates associated with LENS processing resulted in higher amount of cubic B2 phase, and increased the reverse transformation temperatures of porous NiTi samples due to thermally induced stresses and defects. Transformation temperatures were found to be independent of pore volume, though higher pore volume in the samples decreased the maximum recoverable strain from 6% to 4%. Porous NiTi alloy samples with 12-36% porosity exhibited low Young's modulus between 2 and 18 GPa as well as high compressive strength and recoverable strain. Because of high open pore volume between 36% and 62% of total volume fraction porosity, these porous NiTi alloy samples can potentially accelerate the healing process and improve biological fixation when implanted in vivo. Thus porous NiTi is a promising biomaterial for hard tissue replacements. PMID:18937263

  6. Net-Shape Tailored Fabrics For Complex Composite Structures

    NASA Technical Reports Server (NTRS)

    Farley, Gary L.

    1995-01-01

    Proposed novel looms used to make fabric preforms for complex structural elements, both stiffening elements and skin, from continuous fiber-reinforced composite material. Components of looms include custom reed and differential fabric takeup system. Structural parts made best explained by reference to curved "I" cross-section frame. Technology not limited to these fiber orientations or geometry; fiber angles, frame radius of curvature, frame height, and flange width changed along length of structure. Weaving technology equally applicable to structural skins, such as wing of fuselage skins.

  7. Method for fabrication of cylindrical microlenses of selected shape

    DOEpatents

    Snyder, James J.; Baer, Thomas M.

    1992-01-01

    The present invention provides a diffraction limited, high numerical aperture (fast) cylindrical microlens. The method for making the microlens is adaptable to produce a cylindrical lens that has almost any shape on its optical surfaces. The cylindrical lens may have a shape, such as elliptical or hyperbolic, designed to transform some particular given input light distribution into some desired output light distribution. In the method, the desired shape is first formed in a glass preform. Then, the preform is heated to the minimum drawing temperature and a fiber is drawn from it. The cross-sectional shape of the fiber bears a direct relation to the shape of the preform from which it was drawn. During the drawing process, the surfaces become optically smooth due to fire polishing. The present invention has many applications, such as integrated optics, optical detectors and laser diodes. The lens, when connected to a laser diode bar, can provide a high intensity source of laser radiation for pumping a high average power solid state laser. In integrated optics, a lens can be used to couple light into and out of apertures such as waveguides. The lens can also be used to collect light, and focus it on a detector.

  8. Method for fabrication of cylindrical microlenses of selected shape

    DOEpatents

    Snyder, J.J.; Baer, T.M.

    1992-01-14

    The present invention provides a diffraction limited, high numerical aperture (fast) cylindrical microlens. The method for making the microlens is adaptable to produce a cylindrical lens that has almost any shape on its optical surfaces. The cylindrical lens may have a shape, such as elliptical or hyperbolic, designed to transform some particular given input light distribution into some desired output light distribution. In the method, the desired shape is first formed in a glass preform. Then, the preform is heated to the minimum drawing temperature and a fiber is drawn from it. The cross-sectional shape of the fiber bears a direct relation to the shape of the preform from which it was drawn. During the drawing process, the surfaces become optically smooth due to fire polishing. The present invention has many applications, such as integrated optics, optical detectors and laser diodes. The lens, when connected to a laser diode bar, can provide a high intensity source of laser radiation for pumping a high average power solid state laser. In integrated optics, a lens can be used to couple light into and out of apertures such as waveguides. The lens can also be used to collect light, and focus it on a detector. 11 figs.

  9. Progress in net shape fabrication of alpha sic turbine components

    NASA Technical Reports Server (NTRS)

    Sweeting, T. B.; Frechette, F. J.; Macbeth, J. W.

    1984-01-01

    An update of the status of ceramic component development of the AGT Programs is presented. Activity on AGTO Program focussed on the following: successful transition from the prototype to engine configuration rotor, investigation of alternate rotor molding techniques, and completion of scroll assemblies. Progress on the Garrett AGT Program was highlighted by the introduction of plastic molding and extrusion to parts which were previously fabricated by slip casting and isopressing respectively.

  10. Manipulating cell shape by placing cells into micro-fabricated chambers.

    PubMed

    Chang, Fred; Atilgan, Erdinc; Burgess, David; Minc, Nicolas

    2014-01-01

    Cell shape is an important cellular parameter that influences the spatial organization and function of cells. However, it has often been challenging to study the effects of cell shape because of difficulties in experimentally controlling cell shape in a defined way. We describe here a method of physically manipulating sea urchin cells into specified shapes by inserting them into micro-fabricated chambers of different shapes. This method allows for generation of large systematic and quantitative data sets and may be adaptable for different cell types and contexts. PMID:24633802

  11. Fabrication of bamboo-shaped GaN nanorods

    NASA Astrophysics Data System (ADS)

    Li, H.; Li, J. Y.; He, M.; Chen, X. L.; Zhang, Z.

    Bamboo-shaped GaN nanorods were formed through a simple sublimation method. They were characterized by means of X-ray powder diffraction (XRD), transmission electron microscopy (TEM), high-resolution transmission electron microscopy (HRTEM) and selected-area electron diffraction (SAED). The TEM image showed that the nanorods were bamboo-like. XRD, HRTEM and SAED patterns indicated that the nanorods were single-crystal wurtzite GaN.

  12. Fabrication of Pillar Shaped Electrode Arrays for Artificial Retinal Implants

    PubMed Central

    Kim, Eui Tae; Seo, Jong-Mo; Woo, Se Joon; Zhou, Jing Ai; Chung, Hum; Kim, Sung June

    2008-01-01

    Polyimide has been widely applied to neural prosthetic devices, such as the retinal implants, due to its well-known biocompatibility and ability to be micropatterned. However, planar films of polyimide that are typically employed show a limited ability in reducing the distance between electrodes and targeting cell layers, which limits site resolution for effective multi-channel stimulation. In this paper, we report a newly designed device with a pillar structure that more effectively interfaces with the target. Electrode arrays were successfully fabricated and safely implanted inside the rabbit eye in suprachoroidal space. Optical Coherence Tomography (OCT) showed well-preserved pillar structures of the electrode without damage. Bipolar stimulation was applied through paired sites (6:1) and the neural responses were successfully recorded from several regions in the visual cortex. Electrically evoked cortical potential by the pillar electrode array stimulation were compared to visual evoked potential under full-field light stimulation.

  13. Near net shape processing for solar thermal propulsion hardware using directed light fabrication

    SciTech Connect

    Milewski, J.O.; Fonseca, J.C.; Lewis, G.K.

    1998-12-01

    Directed light fabrication (DLF) is a direct metal deposition process that fuses gas delivered powder, in the focal zone of a high powered laser beam to form fully fused near net shaped components. The near net shape processing of rhenium, tungsten, iridium and other high temperature materials may offer significant cost savings compared with conventional processing. This paper describes a 3D parametric solid model, integrated with a manufacturing model, and creating a control field which runs on the DLF machine directly depositing a fully dense, solid metal, near net shaped, nozzle component. Examples of DLF deposited rhenium, iridium and tantalum, from previous work, show a continuously solidified microstructure in rod and tube shapes. Entrapped porosity indicates the required direction for continued process development. These combined results demonstrate the potential for a new method to fabricate complex near net shaped components using materials of interest to the space and aerospace industries.

  14. New gelling systems to fabricate complex-shaped transparent ceramics

    NASA Astrophysics Data System (ADS)

    Yang, Yan; Wu, Yiquan

    2013-06-01

    The aim of this work was to prepare transparent ceramics with large size and complex-shapes by a new water-soluble gelling agent poly(isobutylene-alt-maleic anhydride). Alumina was used as an example of the application of the new gelling system. A stable suspension with 38vol% was prepared by ball milling. Trapped bubbles were removed before casting to obtain homogenous green bodies. The microstructure and particle distribution of alumina raw material were tested. The thermal behavior of the alumina green body was investigated, which exhibited low weight loss when compared with other gelling processes. The influence of solid loading and gelling agent addition were studied on the basis of rheological behavior of the suspension. The microstructures of alumina powders, green bodies before and after de-bindering process, were compared to understand the gelling condition between alumina particles and gelling agent.

  15. Method of forming variable cross-sectional shaped three-dimensional fabrics

    NASA Technical Reports Server (NTRS)

    Mohamed, Mansour H. (Inventor); Zhang, Zhong-Huai (Inventor)

    1992-01-01

    Method of weaving a variable cross-sectional shaped three-dimensional fabric which utilizes different weft yarn insertion from at least one side of the warp layers for selectively inserting weft yarns into different portions of the fabric cross-sectional profile defined by the warp yarn layers during the weaving process. If inserted from both sides of the warp yarn layers, the weft yarns may be inserted simultaneously or alternately from each side of the warp yarn layers. The vertical yarn is then inserted into the fabric by reciprocation of a plurality of harnesses which separate the vertical yarn into a plurality of vertical yarn systems as required by the shape of the three-dimensional fabric being formed.

  16. Scalable, Shape-specific, Top-down Fabrication Methods for the Synthesis of Engineered Colloidal Particles

    PubMed Central

    Merkel, Timothy J.; Herlihy, Kevin P.; Nunes, Janine; Orgel, Ryan M.; Rolland, Jason P.; DeSimone, Joseph M.

    2010-01-01

    The search for a method to fabricate non-spherical colloidal particles from a variety of materials is of growing interest. As the commercialization of nanotechnology continues to expand, the ability to translate particle fabrication methods from a laboratory to an industrial scale is of increasing significance. In this article, we examine several of the most readily scalable top-down methods for the fabrication of such shape specific particles and compare their capabilities with respect to particle composition, size, shape and complexity as well as the scalability of the method. We offer an extensive examination of Particle Replication In Non-wetting Templates (PRINT®) with regards to the versatility and scalability of this technique. We also detail the specific methods used in PRINT particle fabrication, including harvesting, purification and surface modification techniques, with examination of both past and current methods. PMID:20000620

  17. Asymmetric quantum convolutional codes

    NASA Astrophysics Data System (ADS)

    La Guardia, Giuliano G.

    2016-01-01

    In this paper, we construct the first families of asymmetric quantum convolutional codes (AQCCs). These new AQCCs are constructed by means of the CSS-type construction applied to suitable families of classical convolutional codes, which are also constructed here. The new codes have non-catastrophic generator matrices, and they have great asymmetry. Since our constructions are performed algebraically, i.e. we develop general algebraic methods and properties to perform the constructions, it is possible to derive several families of such codes and not only codes with specific parameters. Additionally, several different types of such codes are obtained.

  18. Shape-controllable, bottom-up fabrication of microlens using oblique angle deposition.

    PubMed

    Choi, Hee Ju; Kang, Eun Kyu; Ju, Gun Wu; Song, Young Min; Lee, Yong Tak

    2016-07-15

    This Letter reports a novel method for the simple fabrication of microlens arrays with a controlled shape and diameter on glass substrates. Multilayer stacks of silicon dioxide deposited by oblique angle deposition with hole mask patterns enable microlens formation. Precise control of mask height and distance, as well as oblique angle steps between deposited layers, supports the controllability of microlens geometry. The fabricated microlens arrays with designed geometry exhibit uniform optical properties. PMID:27420527

  19. Understanding the microstructure and properties of components fabricated by laser engineered net shaping (LENS)

    SciTech Connect

    GRIFFITH,MICHELLE L.; ENSZ,MARK T.; PUSKAR,JOSEPH D.; ROBINO,CHARLES V.; BROOKS,JOHN A.; PHILLIBER,JOEL A.; SMUGERESKY,JOHN E.; HOFMEISTER,W.H.

    2000-05-18

    Laser Engineered Net Shaping (LENS) is a novel manufacturing process for fabricating metal parts directly from Computer Aided Design (CAD) solid models. The process is similar to rapid prototyping technologies in its approach to fabricate a solid component by layer additive methods. However, the LENS technology is unique in that fully dense metal components with material properties that are similar to that of wrought materials can be fabricated. The LENS process has the potential to dramatically reduce the time and cost required realizing functional metal parts. In addition, the process can fabricate complex internal features not possible using existing manufacturing processes. The real promise of the technology is the potential to manipulate the material fabrication and properties through precision deposition of the material, which includes thermal behavior control, layered or graded deposition of multi-materials, and process parameter selection. This paper describes the authors' research to understand solidification aspects, thermal behavior, and material properties for laser metal deposition technologies.

  20. Fabrication and Characterization of Carbon Nanofiber Reinforced Shape Memory Epoxy (CNFR-SME) Composites

    NASA Astrophysics Data System (ADS)

    Wang, Jiuyang

    Shape memory polymers have a wide range of applications due to their ability to mechanically change shapes upon external stimulus, while their achievable composite counterparts prove even more versatile. An overview of literature on shape memory materials, fillers and composites was provided to pave a foundation for the materials used in the current study and their inherent benefits. This study details carbon nanofiber and composite fabrication and contrasts their material properties. In the first section, the morphology and surface chemistry of electrospun-poly(acrylonitrile)-based carbon nanofiber webs were tailored through various fabrication methods and impregnated with a shape memory epoxy. The morphologies, chemical compositions, thermal stabilities and electrical resistivities of the carbon nanofibers and composites were then characterized. In the second section, an overview of thermal, mechanical and shape memory characterization techniques for shape memory polymers and their composites was provided. Thermal and mechanical properties in addition to the kinetic and dynamic shape memory performances of neat epoxy and carbon nanofiber/epoxy composites were characterized. The various carbon nanofiber web modifications proved to have notable influence on their respective composite performances. The results from these two sections lead to an enhanced understanding of these carbon nanofiber reinforced shape memory epoxy composites and provided insight for future studies to tune these composites at will.

  1. Fabrication and Testing of a Leading-Edge-Shaped Heat Pipe

    NASA Technical Reports Server (NTRS)

    Glass, David E.; Merrigan, Michael A.; Sena, J. Tom; Reid, Robert S.

    1998-01-01

    The development of a refractory-composite/heat-pipe-cooled leading edge has evolved from the design stage to the fabrication and testing of a full size, leading-edge-shaped heat pipe. The heat pipe had a 'D-shaped' cross section and was fabricated from arc cast Mo-4lRe. An artery was included in the wick. Several issues were resolved with the fabrication of the sharp leading edge radius heat pipe. The heat pipe was tested in a vacuum chamber at Los Alamos National Laboratory using induction heating and was started up from the frozen state several times. However, design temperatures and heat fluxes were not obtained due to premature failure of the heat pipe resulting from electrical discharge between the induction heating apparatus and the heat pipe. Though a testing anomaly caused premature failure of the heat pipe, successful startup and operation of the heat pipe was demonstrated.

  2. Scalable shape-controlled fabrication of curved microstructures using a femtosecond laser wet-etching process.

    PubMed

    Bian, Hao; Yang, Qing; Chen, Feng; Liu, Hewei; Du, Guangqing; Deng, Zefang; Si, Jinhai; Yun, Feng; Hou, Xun

    2013-07-01

    Materials with curvilinear surface microstructures are highly desirable for micro-optical and biomedical devices. However, realization of such devices efficiently remains technically challenging. This paper demonstrates a facile and flexible method to fabricate curvilinear microstructures with controllable shapes and dimensions. The method composes of femtosecond laser exposures and chemical etching process with the hydrofluoric acid solutions. By fixed-point and step-in laser irradiations followed by the chemical treatments, concave microstructures with different profiles such as spherical, conical, bell-like and parabola were fabricated on silica glasses. The convex structures were replicated on polymers by the casting replication process. In this work, we used this technique to fabricate high-quality microlens arrays and high-aspect-ratio microwells which can be used in 3D cell culture. This approach offers several advantages such as high-efficient, scalable shape-controllable and easy manipulations. PMID:23623098

  3. Rapid fabrication of cylindrical microlens array by shaped femtosecond laser direct writing

    NASA Astrophysics Data System (ADS)

    Luo, Zhi; Wang, Cong; Yin, Kai; Dong, Xinran; Chu, Dongkai; Duan, Ji'an

    2016-07-01

    In this study, a remarkable spatial shaping approach is proposed to transform Gaussian femtosecond laser into quasi-Bessel optical field with compressed central lobe and amplified side lobes of the spatial intensity profile. Based on this technique, inward bulge trench (IBT) structures are fabricated with high efficiency on the surface of PMMA by a single illumination step, whose cross-sectional profile is opposite to the results fabricated by Gaussian beam. And plano-convex cylindrical microlens array, which is consistent in size and shape throughout a large sample area, is formed through simply piecing together the IBT structures during fabricating process. Furthermore, numerical simulations of optical field in radial direction and on-axial direction are exploited to rationalize the dependence of the patterned microstructures on the spatial intensity distribution of femtosecond laser.

  4. Free form fabrication of metallic components using laser engineered net shaping (LENS{trademark})

    SciTech Connect

    Griffith, M.L.; Keicher, D.M.; Atwood, C.L.

    1996-09-01

    Solid free form fabrication is one of the fastest growing automated manufacturing technologies that has significantly impacted the length of time between initial concept and actual part fabrication. Starting with CAD renditions of new components, several techniques such as stereolithography and selective laser sintering are being used to fabricate highly accurate complex three-dimensional concept models using polymeric materials. Coupled with investment casting techniques, sacrificial polymeric objects are used to minimize costs and time to fabricate tooling used to make complex metal castings. This paper will describe recent developments in a new technology, known as LENS{sup {trademark}} (Laser Engineered Net Shaping), to fabricate metal components directly from CAD solid models and thus further reduce the lead times for metal part fabrication. In a manner analogous to stereolithography or selective sintering, the LENS{sup {trademark}} process builds metal parts line by line and layer by layer. Metal particles are injected into a laser beam, where they are melted and deposited onto a substrate as a miniature weld pool. The trace of the laser beam on the substrate is driven by the definition of CAD models until the desired net-shaped densified metal component is produced.

  5. Femtosecond laser microchannels fabrication based on electrons dynamics control using temporally or spatially shaped pulses

    NASA Astrophysics Data System (ADS)

    Yan, Xueliang; Hu, Jie; Li, Xiaowei; Xia, Bo; Liu, Pengjun; Lu, Yongfeng; Jiang, Lan

    2014-11-01

    With ultrashort pulse durations and ultrahigh power densities, femtosecond laser presents unique advantages of high precision and high quality fabrication of microchannels in transparent materials. In our study, by shaping femtosecond laser pulse energy distribution in temporal or spatial domains, localized transient electrons dynamics and the subsequent processes, such as phase changes, can be controlled, leading to the dramatic increases in the capability of femtosecond laser microchannels fabrication. The temporally shaped femtosecond laser pulse trains can significantly enhance the material removal rate in both water-assisted femtosecond laser drilling and femtosecond laser irradiation followed by chemical etching. Besides, high-aspect-ratio and small-diameter microchannels are drilled by spatially shaped femtosecond laser pulses.

  6. Fabrication method for cores of structural sandwich materials including star shaped core cells

    DOEpatents

    Christensen, Richard M.

    1997-01-01

    A method for fabricating structural sandwich materials having a core pattern which utilizes star and non-star shaped cells. The sheets of material are bonded together or a single folded sheet is used, and bonded or welded at specific locations, into a flat configuration, and are then mechanically pulled or expanded normal to the plane of the sheets which expand to form the cells. This method can be utilized to fabricate other geometric cell arrangements than the star/non-star shaped cells. Four sheets of material (either a pair of bonded sheets or a single folded sheet) are bonded so as to define an area therebetween, which forms the star shaped cell when expanded.

  7. Fabrication method for cores of structural sandwich materials including star shaped core cells

    DOEpatents

    Christensen, R.M.

    1997-07-15

    A method for fabricating structural sandwich materials having a core pattern which utilizes star and non-star shaped cells is disclosed. The sheets of material are bonded together or a single folded sheet is used, and bonded or welded at specific locations, into a flat configuration, and are then mechanically pulled or expanded normal to the plane of the sheets which expand to form the cells. This method can be utilized to fabricate other geometric cell arrangements than the star/non-star shaped cells. Four sheets of material (either a pair of bonded sheets or a single folded sheet) are bonded so as to define an area therebetween, which forms the star shaped cell when expanded. 3 figs.

  8. Laser Spray Fabrication for Net-Shape Rapid Product Realization LDRD

    SciTech Connect

    Atwood, C.L.; Ensz, M.T.; Greene, D.L.; Griffith, M.L.; Harwell, L.D.; Jeantette, F.P.; Keicher, D.M.; Oliver, M.S.; Reckaway, D.E.; Romero, J.A.; Schlienger, M.E.; Smugeresky, J.D.

    1999-04-01

    The primary purpose of this LDRD project was to characterize the laser deposition process and determine the feasibility of fabricating complex near-net shapes directly from a CAD solid model. Process characterization provided direction in developing a system to fabricate complex shapes directly from a CAD solid model. Our goal for this LDRD was to develop a system that is robust and provides a significant advancement to existing technologies (e.g., polymeric-based rapid prototyping, laser welding). Development of the process will allow design engineers to produce functional models of their designs directly from CAD files. The turnaround time for complex geometrical shaped parts will be hours instead of days and days instead of months. With reduced turnaround time, more time can be spent on the product-design phase to ensure that the best component design is achieved. Maturation of this technology will revolutionize the way the world produces structural components.

  9. Powder-Coated Towpreg: Avenues to Near Net Shape Fabrication of High Performance Composites

    NASA Technical Reports Server (NTRS)

    Johnston, N. J.; Cano, R. J.; Marchello, J. M.; Sandusky, D. A.

    1995-01-01

    Near net shape parts were fabricated from powder-coated preforms. Key issues including powder loss during weaving and tow/tow friction during braiding were addressed, respectively, by fusing the powder to the fiber prior to weaving and applying a water-based gel to the towpreg prior to braiding. A 4:1 debulking of a complex 3-D woven powder-coated preform was achieved in a single step utilizing expansion rubber molding. Also, a process was developed for using powder-coated towpreg to fabricate consolidated ribbon having good dimensional integrity and low voids. Such ribbon will be required for in situ fabrication of structural components via heated head advanced tow placement. To implement process control and ensure high quality ribbon, the ribbonizer heat transfer and pulling force were modeled from fundamental principles. Most of the new ribbons were fabricated from dry polyarylene ether and polymide powders.

  10. Understanding deep convolutional networks.

    PubMed

    Mallat, Stéphane

    2016-04-13

    Deep convolutional networks provide state-of-the-art classifications and regressions results over many high-dimensional problems. We review their architecture, which scatters data with a cascade of linear filter weights and nonlinearities. A mathematical framework is introduced to analyse their properties. Computations of invariants involve multiscale contractions with wavelets, the linearization of hierarchical symmetries and sparse separations. Applications are discussed. PMID:26953183

  11. Laser Engineered Net Shaping (LENS(TM)): A Tool for Direct Fabrication of Metal Parts

    SciTech Connect

    Atwood, C.; Ensz, M.; Greene, D.; Griffith, M.; Harwell, L.; Reckaway, D.; Romero, T.; Schlienger, E.; Smugeresky, J.

    1998-11-05

    For many years, Sandia National Laboratories has been involved in the development and application of rapid prototyping and dmect fabrication technologies to build prototype parts and patterns for investment casting. Sandia is currently developing a process called Laser Engineered Net Shaping (LENS~) to fabricate filly dense metal parts dwectly from computer-aided design (CAD) solid models. The process is similar to traditional laser-initiated rapid prototyping technologies such as stereolithography and selective laser sintering in that layer additive techniques are used to fabricate physical parts directly from CAD data. By using the coordinated delivery of metal particles into a focused laser beam apart is generated. The laser beam creates a molten pool of metal on a substrate into which powder is injected. Concurrently, the substrate on which the deposition is occurring is moved under the beam/powder interaction zone to fabricate the desired cross-sectiwal geometry. Consecutive layers are additively deposited, thereby producing a three-dmensional part. This process exhibits enormous potential to revolutionize the way in which metal parts, such as complex prototypes, tooling, and small-lot production parts, are produced. The result is a comple~ filly dense, near-net-shape part. Parts have been fabricated from 316 stainless steel, nickel-based alloys, H13 tool steel, and titanium. This talk will provide a general overview of the LENS~ process, discuss potential applications, and display as-processed examples of parts.

  12. Laser engineered net shaping (LENS) for the fabrication of metallic components

    SciTech Connect

    Griffith, M.L.; Keicher, D.L.; Romero, J.A.; Atwood, C.L.; Harwell, L.D.; Greene, D.L.; Smugeresky, J.E.

    1996-06-01

    Solid free form fabrication is a fast growing automated manufacturing technology that has reduced the time between initial concept and fabrication. Starting with CAD renditions of new components, techniques such as stereolithography and selective laser sintering are being used to fabricate highly accurate complex 3-D objects using polymers. Together with investment casting, sacrificial polymeric objects are used to minimize cost and time to fabricate tooling used to make complex metal casting. This paper describes recent developments in LENS{trademark} (Laser Engineered Net Shaping) to fabricate the metal components {ital directly} from CAD solid models and thus further reduce the lead time. Like stereolithography or selective sintering, LENS builds metal parts line by line and layer by layer. Metal particles are injected into a laser beam where they are melted and deposited onto a substrate as a miniature weld pool. The trace of the laser beam on the substrate is driven by the definition of CAD models until the desired net-shaped densified metal component is produced.

  13. Fabrication and Characteristics of Free Standing Shaped Pupil Masks for TPF-Coronagraph

    NASA Technical Reports Server (NTRS)

    Balasubramanian, Kunjithapatham; Echternach, Pierre M.; Dickie, Matthew R.; Muller, Richard E.; White, Victor E.; Hoppe, Daniel J.; Shaklan, Stuart B.; Belikov, Ruslan; Kasdin, N. Jeremy; Vanderbei, Robert J.; Ceperley, Daniel; Neureuther, Andrew R.

    2006-01-01

    Direct imaging and characterization of exo-solar terrestrial planets require coronagraphic instruments capable of suppressing star light to 10-10. Pupil shaping masks have been proposed and designed1 at Princeton University to accomplish such a goal. Based on Princeton designs, free standing (without a substrate) silicon masks have been fabricated with lithographic and deep etching techniques. In this paper, we discuss the fabrication of such masks and present their physical and optical characteristics in relevance to their performance over the visible to near IR bandwidth.

  14. Controllable fabrication of PS/Ag core-shell-shaped nanostructures

    PubMed Central

    2012-01-01

    In this paper, based on the previous steps, a facile in situ reduction method was developed to controllably prepare polystyrene/Ag (PS/Ag) core-shell-shaped nanostructures. The crucial procedure includes surface treatment of polystyrene core particles by cationic polyelectrolyte polyethyleneimine, in situ formation of Ag nanoparticles, and immobilization of the Ag nanoparticles onto the surface of the polystyrene colloids via functional group NH from the polyethyleneimine. The experimental parameters, such as the reaction temperature, the reaction time, and the silver precursors were optimized for improvement of dispersion and Ag coat coverage of the core-shell-shaped nanostructures. Ultimately, the optimum parameters were obtained through a series of experiments, and well-dispersed, uniformly coated PS/Ag core-shell-shaped nanostructures were successfully fabricated. The formation mechanism of the PS/Ag core-shell-shaped nanostructures was also explained. PMID:23092195

  15. Tunable Diffractive Optical Elements Based on Shape-Memory Polymers Fabricated via Hot Embossing.

    PubMed

    Schauer, Senta; Meier, Tobias; Reinhard, Maximilian; Röhrig, Michael; Schneider, Marc; Heilig, Markus; Kolew, Alexander; Worgull, Matthias; Hölscher, Hendrik

    2016-04-13

    We introduce actively tunable diffractive optical elements fabricated from shape-memory polymers (SMPs). By utilizing the shape-memory effect of the polymer, at least one crucial attribute of the diffractive optical element (DOE) is tunable and adjustable subsequent to the completed fabrication process. A thermoplastic, transparent, thermoresponsive polyurethane SMP was structured with diverse diffractive microstructures via hot embossing. The tunability was enabled by programming a second, temporary shape into the diffractive optical element by mechanical deformation, either by stretching or a second embossing cycle at low temperatures. Upon exposure to the stimulus heat, the structures change continuously and controllable in a predefined way. We establish the novel concept of shape-memory diffractive optical elements by illustrating their capabilities, with regard to tunability, by displaying the morphing diffractive pattern of a height tunable and a period tunable structure, respectively. A sample where an arbitrary structure is transformed to a second, disparate one is illustrated as well. To prove the applicability of our tunable shape-memory diffractive optical elements, we verified their long-term stability and demonstrated the precise adjustability with a detailed analysis of the recovery dynamics, in terms of temperature dependence and spatially resolved, time-dependent recovery. PMID:26998646

  16. Fabrication of cone-shaped boron doped diamond and gold nanoelectrodes for AFM-SECM.

    PubMed

    Avdic, A; Lugstein, A; Wu, M; Gollas, B; Pobelov, I; Wandlowski, T; Leonhardt, K; Denuault, G; Bertagnolli, E

    2011-04-01

    We demonstrate a reliable microfabrication process for a combined atomic force microscopy (AFM) and scanning electrochemical microscopy (SECM) measurement tool. Integrated cone-shaped sensors with boron doped diamond (BDD) or gold (Au) electrodes were fabricated from commercially available AFM probes. The sensor formation process is based on mature semiconductor processing techniques, including focused ion beam (FIB) machining, and highly selective reactive ion etching (RIE). The fabrication approach preserves the geometry of the original AFM tips resulting in well reproducible nanoscaled sensors. The feasibility and functionality of the fully featured tips are demonstrated by cyclic voltammetry, showing good agreement between the measured and calculated currents of the cone-shaped AFM-SECM electrodes. PMID:21368355

  17. Laundering durable antibacterial cotton fabrics grafted with pomegranate-shaped polymer wrapped in silver nanoparticle aggregations

    NASA Astrophysics Data System (ADS)

    Liu, Hanzhou; Lv, Ming; Deng, Bo; Li, Jingye; Yu, Ming; Huang, Qing; Fan, Chunhai

    2014-08-01

    To improve the laundering durability of the silver functionalized antibacterial cotton fabrics, a radiation-induced coincident reduction and graft polymerization is reported herein where a pomegranate-shaped silver nanoparticle aggregations up to 500 nm can be formed due to the coordination forces between amino group and silver and the wrapping procedure originated from the coincident growth of the silver nanoparticles and polymer graft chains. This pomegranate-shaped silver NPAs functionalized cotton fabric exhibits outstanding antibacterial activities and also excellent laundering durability, where it can inactivate higher than 90% of both E. coli and S. aureus even after 50 accelerated laundering cycles, which is equivalent to 250 commercial or domestic laundering cycles.

  18. Fabrication of cone-shaped boron doped diamond and gold nanoelectrodes for AFM-SECM

    NASA Astrophysics Data System (ADS)

    Avdic, A.; Lugstein, A.; Wu, M.; Gollas, B.; Pobelov, I.; Wandlowski, T.; Leonhardt, K.; Denuault, G.; Bertagnolli, E.

    2011-04-01

    We demonstrate a reliable microfabrication process for a combined atomic force microscopy (AFM) and scanning electrochemical microscopy (SECM) measurement tool. Integrated cone-shaped sensors with boron doped diamond (BDD) or gold (Au) electrodes were fabricated from commercially available AFM probes. The sensor formation process is based on mature semiconductor processing techniques, including focused ion beam (FIB) machining, and highly selective reactive ion etching (RIE). The fabrication approach preserves the geometry of the original AFM tips resulting in well reproducible nanoscaled sensors. The feasibility and functionality of the fully featured tips are demonstrated by cyclic voltammetry, showing good agreement between the measured and calculated currents of the cone-shaped AFM-SECM electrodes.

  19. Progress in net shape fabrication of alpha SiC turbine components

    NASA Technical Reports Server (NTRS)

    Storm, R. S.; Naum, R. G.

    1983-01-01

    The development status of component technology in an automotive gas turbine Ceramic Applications in Turbine Engines program is discussed, with attention to such materials and processes having a low cost, net shape fabrication potential as sintered alpha-SiC that has been fashioned by means of injection molding, slip casting, and isostatic pressing. The gas turbine elements produced include a gasifier turbine rotor, a turbine wheel, a connecting duct, a combustor baffle, and a transition duct.

  20. Development of a method for fabricating metallic matrix composite shapes by a continuous mechanical process

    NASA Technical Reports Server (NTRS)

    Divecha, A. P.

    1974-01-01

    Attempts made to develop processes capable of producing metal composites in structural shapes and sizes suitable for space applications are described. The processes must be continuous and promise to lower fabrication costs. Special attention was given to the aluminum boride (Al/b) composite system. Results show that despite adequate temperature control, the consolidation characteristics did not improve as expected. Inadequate binder removal was identified as the cause responsible. An Al/c (aluminum-graphite) composite was also examined.

  1. Vision-based in-line fabric defect detection using yarn-specific shape features

    NASA Astrophysics Data System (ADS)

    Schneider, Dorian; Aach, Til

    2012-01-01

    We develop a methodology for automatic in-line flaw detection in industrial woven fabrics. Where state of the art detection algorithms apply texture analysis methods to operate on low-resolved ({200 ppi) image data, we describe here a process flow to segment single yarns in high-resolved ({1000 ppi) textile images. Four yarn shape features are extracted, allowing a precise detection and measurement of defects. The degree of precision reached allows a classification of detected defects according to their nature, providing an innovation in the field of automatic fabric flaw detection. The design has been carried out to meet real time requirements and face adverse conditions caused by loom vibrations and dirt. The entire process flow is discussed followed by an evaluation using a database with real-life industrial fabric images. This work pertains to the construction of an on-loom defect detection system to be used in manufacturing practice.

  2. Fine-tuned grayscale optofluidic maskless lithography for three-dimensional freeform shape microstructure fabrication.

    PubMed

    Song, Suk-Heung; Kim, Kibeom; Choi, Sung-Eun; Han, Sangkwon; Lee, Ho-Suk; Kwon, Sunghoon; Park, Wook

    2014-09-01

    This article presents free-floating three-dimensional (3D) microstructure fabrication in a microfluidic channel using direct fine-tuned grayscale image lithography. The image is designed as a freeform shape and is composed of gray shades as light-absorbing features. Gray shade levels are modulated through multiple reflections of light in a digital micromirror device (DMD) to produce different height formations. Whereas conventional photolithography has several limitations in producing grayscale colors on photomask features, our method focuses on a maskless, single-shot process for fabrication of freeform 3D micro-scale shapes. The fine-tuned gray image is designed using an 8-bit grayscale color; thus, each pixel is capable of displaying 256 gray shades. The pattern of the UV light reflecting on the DMD is transferred to a photocurable resin flowing through a microfluidic channel. Here, we demonstrate diverse free-floating 3D microstructure fabrication using fine-tuned grayscale image lithography. Additionally, we produce polymeric microstructures with locally embedded gray encoding patterns, such as grayscale-encoded microtags. This functional microstructure can be applied to a biophysical detection system combined with 3D microstructures. This method would be suitable for fabricating 3D microstructures that have a specific morphology to be used for particular biological or medical applications. PMID:25166099

  3. Simple and Reliable Fabrication of Bioinspired Mushroom-Shaped Micropillars with Precisely Controlled Tip Geometries.

    PubMed

    Yi, Hoon; Kang, Minsu; Kwak, Moon Kyu; Jeong, Hoon Eui

    2016-08-31

    We present a simple yet scalable method with detailed process protocols for fabricating dry adhesives with mushroom-shaped micropillars of controlled tip geometries. The method involves using photo-lithography with a bilayer stack combining SU-8 and lift-off resist, and subsequent replica molding process. This approach utilizes widely used and commercially available materials and can thus be used to generate mushroom-shaped micropillars with precisely controlled tip diameters and thicknesses in a simple, reproducible, and cost-effective manner. The fabricated mushroom-shaped micropillar arrays exhibited highly different tendencies in adhesion strength and repeatability depending on tip geometries, such as tip diameter and thickness, thereby demonstrating the importance of precise tunability of tip geometry of micropillars. The fabricated dry adhesives with optimized tip geometries not only exhibited strong pull-off strength of up to ∼34.8 N cm(-2) on the Si surface but also showed high durability. By contrast, dry adhesives with nonoptimized tips displayed low pull-off strength of ∼3.6 N cm(-2) and poor durability. PMID:27548917

  4. Ring beam shaping optics fabricated with ultra-precision cutting for YAG laser processing

    NASA Astrophysics Data System (ADS)

    Kuwano, Ryoichi; Koga, Toshihiko; Tokunaga, Tsuyoshi; Wakayama, Toshitaka; Otani, Yukitoshi; Fujii, Nobuyuki

    2012-03-01

    In this study, a method for generating ring intensity distribution at a refraction-type lens with an aspheric element was proposed, and the beam shaping optical element was finished using only ultra-precision cutting. The shape of the optical element and its irradiance pattern were determined from numerical calculation based on its geometrical and physical optics. An ultra-precision lathe was employed to fabricate beam shaping optical elements, and acrylic resin was used as the material. The transmittance of an optical element (a rotationally symmetrical body) with an aspheric surface fabricated using a single-crystal diamond tool was over 98%, and its surface roughness was 9.6 nm Ra. The method enabled the formation of a circular melting zone on a piece of stainless steel with a thickness of 300 μm through pulse YAG laser ( λ 1:06 μm) processing such that the average radius was 610 μm and the width was 100-200 μm. Circular processing using a ring beam shaping optical element can be realized by single-pulse beam irradiation without beam scanning.

  5. Programmable and self-demolding microstructured molds fabricated from shape-memory polymers

    NASA Astrophysics Data System (ADS)

    Meier, Tobias; Bur, Julia; Reinhard, Maximilian; Schneider, Marc; Kolew, Alexander; Worgull, Matthias; Hölscher, Hendrik

    2015-06-01

    We introduce shape memory polymers as materials to augment molds with programmable switching between different micro and nanostructures as functional features of the mold and self-demolding properties. These polymer molds can be used for hot embossing (or nanoimprinting) and casting. Furthermore, they enable the replication of nano- and microstructures on curved surfaces as well as embedded structures like on the inside walls of a microfluidic channel. The shape memory polymer molds can be replicated from master molds fabricated by conventional techniques. We tested their durability for microfabrication processes and demonstrated the advantages of shape memory molds for hot embossing and casting by replicating microstructures with high aspect ratios and optical grade surface quality.

  6. Microfluidic fabrication of polymeric and biohybrid fibers with predesigned size and shape.

    PubMed

    Boyd, Darryl A; Adams, Andre A; Daniele, Michael A; Ligler, Frances S

    2014-01-01

    A "sheath" fluid passing through a microfluidic channel at low Reynolds number can be directed around another "core" stream and used to dictate the shape as well as the diameter of a core stream. Grooves in the top and bottom of a microfluidic channel were designed to direct the sheath fluid and shape the core fluid. By matching the viscosity and hydrophilicity of the sheath and core fluids, the interfacial effects are minimized and complex fluid shapes can be formed. Controlling the relative flow rates of the sheath and core fluids determines the cross-sectional area of the core fluid. Fibers have been produced with sizes ranging from 300 nm to ~1 mm, and fiber cross-sections can be round, flat, square, or complex as in the case with double anchor fibers. Polymerization of the core fluid downstream from the shaping region solidifies the fibers. Photoinitiated click chemistries are well suited for rapid polymerization of the core fluid by irradiation with ultraviolet light. Fibers with a wide variety of shapes have been produced from a list of polymers including liquid crystals, poly(methylmethacrylate), thiol-ene and thiol-yne resins, polyethylene glycol, and hydrogel derivatives. Minimal shear during the shaping process and mild polymerization conditions also makes the fabrication process well suited for encapsulation of cells and other biological components. PMID:24430733

  7. Microfluidic Fabrication of Polymeric and Biohybrid Fibers with Predesigned Size and Shape

    PubMed Central

    Boyd, Darryl A.; Adams, Andre A.; Daniele, Michael A.; Ligler, Frances S.

    2014-01-01

    A “sheath” fluid passing through a microfluidic channel at low Reynolds number can be directed around another “core” stream and used to dictate the shape as well as the diameter of a core stream. Grooves in the top and bottom of a microfluidic channel were designed to direct the sheath fluid and shape the core fluid. By matching the viscosity and hydrophilicity of the sheath and core fluids, the interfacial effects are minimized and complex fluid shapes can be formed. Controlling the relative flow rates of the sheath and core fluids determines the cross-sectional area of the core fluid. Fibers have been produced with sizes ranging from 300 nm to ~1 mm, and fiber cross-sections can be round, flat, square, or complex as in the case with double anchor fibers. Polymerization of the core fluid downstream from the shaping region solidifies the fibers. Photoinitiated click chemistries are well suited for rapid polymerization of the core fluid by irradiation with ultraviolet light. Fibers with a wide variety of shapes have been produced from a list of polymers including liquid crystals, poly(methylmethacrylate), thiol-ene and thiol-yne resins, polyethylene glycol, and hydrogel derivatives. Minimal shear during the shaping process and mild polymerization conditions also makes the fabrication process well suited for encapsulation of cells and other biological components. PMID:24430733

  8. Microfluidic fabrication of complex-shaped microfibers by liquid template-aided multiphase microflow.

    PubMed

    Choi, Chang-Hyung; Yi, Hyunmin; Hwang, Sora; Weitz, David A; Lee, Chang-Soo

    2011-04-21

    This study presents a simple microfluidic approach to the rapid fabrication of complex-shaped microfibers (e.g., single hollow, double hollow, and microbelt), with highly uniform structures, based on a combination of the spontaneous formation of polymeric jet streams and in situ photopolymerization. Two laminar flows of a photocurable fluid and a liquid template (nonpolymerizing fluid) spontaneously form jet streams in equilibrium states in microfluidic channels because of the minimization of the interfacial energy between the two fluids. The formation of the jet streams strongly depends on the spreading coefficients and the evolution time along the downstream of the microfluidic system. Thus, the simple control of the spreading coefficients can guide microfibers into various shapes. The sizes of the core and shell of the hollow fibers can also be readily manipulated by the flow rates of the polymerizing fluid and the liquid template phase. Asymmetric hollow fibers can also be produced in different evolutionary states in the microfluidic system. The microfluidic approach shown here represents a significant step toward the easy fabrication of microfibers with readily controllable structures and geometries. We anticipate that this novel fabrication approach and the prediction method based on spreading coefficients presented in this work can be applied to produce a wide variety of functional microfibrous materials. PMID:21390381

  9. Microstereolithography-Based Fabrication of Anatomically Shaped Beta-Tricalcium Phosphate Scaffolds for Bone Tissue Engineering

    PubMed Central

    Du, Dajiang; Asaoka, Teruo; Shinohara, Makoto; Kageyama, Tomonori; Ushida, Takashi; Furukawa, Katsuko Sakai

    2015-01-01

    Porous ceramic scaffolds with shapes matching the bone defects may result in more efficient grafting and healing than the ones with simple geometries. Using computer-assisted microstereolithography (MSTL), we have developed a novel gelcasting indirect MSTL technology and successfully fabricated two scaffolds according to CT images of rabbit femur. Negative resin molds with outer 3D dimensions conforming to the femur and an internal structure consisting of stacked meshes with uniform interconnecting struts, 0.5 mm in diameter, were fabricated by MSTL. The second mold type was designed for cortical bone formation. A ceramic slurry of beta-tricalcium phosphate (β-TCP) with room temperature vulcanization (RTV) silicone as binder was cast into the molds. After the RTV silicone was completely cured, the composite was sintered at 1500°C for 5 h. Both gross anatomical shape and the interpenetrating internal network were preserved after sintering. Even cortical structure could be introduced into the customized scaffolds, which resulted in enhanced strength. Biocompatibility was confirmed by vital staining of rabbit bone marrow mesenchymal stromal cells cultured on the customized scaffolds for 5 days. This fabrication method could be useful for constructing bone substitutes specifically designed according to local anatomical defects. PMID:26504839

  10. Microstereolithography-Based Fabrication of Anatomically Shaped Beta-Tricalcium Phosphate Scaffolds for Bone Tissue Engineering.

    PubMed

    Du, Dajiang; Asaoka, Teruo; Shinohara, Makoto; Kageyama, Tomonori; Ushida, Takashi; Furukawa, Katsuko Sakai

    2015-01-01

    Porous ceramic scaffolds with shapes matching the bone defects may result in more efficient grafting and healing than the ones with simple geometries. Using computer-assisted microstereolithography (MSTL), we have developed a novel gelcasting indirect MSTL technology and successfully fabricated two scaffolds according to CT images of rabbit femur. Negative resin molds with outer 3D dimensions conforming to the femur and an internal structure consisting of stacked meshes with uniform interconnecting struts, 0.5 mm in diameter, were fabricated by MSTL. The second mold type was designed for cortical bone formation. A ceramic slurry of beta-tricalcium phosphate (β-TCP) with room temperature vulcanization (RTV) silicone as binder was cast into the molds. After the RTV silicone was completely cured, the composite was sintered at 1500°C for 5 h. Both gross anatomical shape and the interpenetrating internal network were preserved after sintering. Even cortical structure could be introduced into the customized scaffolds, which resulted in enhanced strength. Biocompatibility was confirmed by vital staining of rabbit bone marrow mesenchymal stromal cells cultured on the customized scaffolds for 5 days. This fabrication method could be useful for constructing bone substitutes specifically designed according to local anatomical defects. PMID:26504839

  11. Net shape fabrication of calcium phosphate scaffolds with multiple material domains.

    PubMed

    Xie, Yangmin; Rustom, Laurence E; McDermott, Anna M; Boerckel, Joel D; Johnson, Amy J Wagoner; Alleyne, Andrew G; Hoelzle, David J

    2016-03-01

    Calcium phosphate (CaP) materials have been proven to be efficacious as bone scaffold materials, but are difficult to fabricate into complex architectures because of the high processing temperatures required. In contrast, polymeric materials are easily formed into scaffolds with near-net-shape forms of patient-specific defects and with domains of different materials; however, they have reduced load-bearing capacity compared to CaPs. To preserve the merits of CaP scaffolds and enable advanced scaffold manufacturing, this manuscript describes an additive manufacturing process that is coupled with a mold support for overhanging features; we demonstrate that this process enables the fabrication of CaP scaffolds that have both complex, near-net-shape contours and distinct domains with different microstructures. First, we use a set of canonical structures to study the manufacture of complex contours and distinct regions of different material domains within a mold. We then apply these capabilities to the fabrication of a scaffold that is designed for a 5 cm orbital socket defect. This scaffold has complex external contours, interconnected porosity on the order of 300 μm throughout, and two distinct domains of different material microstructures. PMID:26744897

  12. Convolutional coding techniques for data protection

    NASA Technical Reports Server (NTRS)

    Massey, J. L.

    1975-01-01

    Results of research on the use of convolutional codes in data communications are presented. Convolutional coding fundamentals are discussed along with modulation and coding interaction. Concatenated coding systems and data compression with convolutional codes are described.

  13. Fabrication

    NASA Technical Reports Server (NTRS)

    Angel, Roger; Helms, Richard; Bilbro, Jim; Brown, Norman; Eng, Sverre; Hinman, Steve; Hull-Allen, Greg; Jacobs, Stephen; Keim, Robert; Ulmer, Melville

    1992-01-01

    What aspects of optical fabrication technology need to be developed so as to facilitate existing planned missions, or enable new ones? Throughout the submillimeter to UV wavelengths, the common goal is to push technology to the limits to make the largest possible apertures that are diffraction limited. At any one wavelength, the accuracy of the surface must be better than lambda/30 (rms error). The wavelength range is huge, covering four orders of magnitude from 1 mm to 100 nm. At the longer wavelengths, diffraction limited surfaces can be shaped with relatively crude techniques. The challenge in their fabrication is to make as large as possible a reflector, given the weight and volume constraints of the launch vehicle. The limited cargo diameter of the shuttle has led in the past to emphasis on deployable or erectable concepts such as the Large Deployable Reflector (LDR), which was studied by NASA for a submillimeter astrophysics mission. Replication techniques that can be used to produce light, low-cost reflecting panels are of great interest for this class of mission. At shorter wavelengths, in the optical and ultraviolet, optical fabrication will tax to the limit the most refined polishing methods. Methods of mechanical and thermal stabilization of the substrate will be severely stressed. In the thermal infrared, the need for large aperture is tempered by the even stronger need to control the telescope's thermal emission by cooled or cryogenic operation. Thus, the SIRTF mirror at 1 meter is not large and does not require unusually high accuracy, but the fabrication process must produce a mirror that is the right shape at a temperature of 4 K. Future large cooled mirrors will present more severe problems, especially if they must also be accurate enough to work at optical wavelengths. At the very shortest wavelengths accessible to reflecting optics, in the x-ray domain, the very low count fluxes of high energy photons place a premium on the collecting area. It is

  14. Determinate-state convolutional codes

    NASA Technical Reports Server (NTRS)

    Collins, O.; Hizlan, M.

    1991-01-01

    A determinate state convolutional code is formed from a conventional convolutional code by pruning away some of the possible state transitions in the decoding trellis. The type of staged power transfer used in determinate state convolutional codes proves to be an extremely efficient way of enhancing the performance of a concatenated coding system. The decoder complexity is analyzed along with free distances of these new codes and extensive simulation results is provided of their performance at the low signal to noise ratios where a real communication system would operate. Concise, practical examples are provided.

  15. Free form fabrication using the laser engineered net shaping (LENS{trademark}) process

    SciTech Connect

    Keicher, D.M.; Romero, J.A.; Atwood, C.L.; Griffith, M.L.; Jeantette, F.P.; Harwell, L.D.; Greene, D.L.; Smugeresky, J.E.

    1996-12-31

    Sandia National Laboratories is developing a technology called Laser Engineered Net Shaping{trademark} (LENS{trademark}). This process allows complex 3-dimensional solid metallic objects to be directly fabricated for a CAD solid model. Experiments performed demonstrate that complex alloys such as Inconel{trademark} 625 and ANSI stainless steel alloy 316 can be used in the LENS{trademark} process to produce solid metallic-shapes. In fact, the fabricated structures exhibit grain growth across the deposition layer boundaries. Mechanical testing data of deposited 316 stainless steel material indicates that the deposited material strength and elongation are greater than that reported for annealed 316 stainless steel. Electron microprobe analysis of the deposited Inconel{trademark} 625 material shows no compositional degradation of the 625 alloy and that 100% dense structures can be obtained using this technique. High speed imaging used to acquire process data during experimentation shows that the powder particle size range can significantly affect the stability, and subsequently, the performance of the powder deposition process. Finally, dimensional studies suggest that dimensional accuracy to {+-} 0.002 inches (in the horizontal direction) can be maintained.

  16. Fabrication and In Vitro Deployment of a Laser-Activated Shape Memory Polymer Vascular Stent

    SciTech Connect

    Baer, G M; Small IV, W; Wilson, T S; Benett, W J; Matthews, D L; Hartman, J; Maitland, D J

    2007-04-25

    Vascular stents are small tubular scaffolds used in the treatment of arterial stenosis (narrowing of the vessel). Most vascular stents are metallic and are deployed either by balloon expansion or by self-expansion. A shape memory polymer (SMP) stent may enhance flexibility, compliance, and drug elution compared to its current metallic counterparts. The purpose of this study was to describe the fabrication of a laser-activated SMP stent and demonstrate photothermal expansion of the stent in an in vitro artery model. A novel SMP stent was fabricated from thermoplastic polyurethane. A solid SMP tube formed by dip coating a stainless steel pin was laser-etched to create the mesh pattern of the finished stent. The stent was crimped over a fiber-optic cylindrical light diffuser coupled to an infrared diode laser. Photothermal actuation of the stent was performed in a water-filled mock artery. At a physiological flow rate, the stent did not fully expand at the maximum laser power (8.6 W) due to convective cooling. However, under zero flow, simulating the technique of endovascular flow occlusion, complete laser actuation was achieved in the mock artery at a laser power of {approx}8 W. We have shown the design and fabrication of an SMP stent and a means of light delivery for photothermal actuation. Though further studies are required to optimize the device and assess thermal tissue damage, photothermal actuation of the SMP stent was demonstrated.

  17. Fabrication and characterization of cuprous oxide solar cell with net-shaped counter electrode

    NASA Astrophysics Data System (ADS)

    Basuki, Stefanus; Uranus, Henri P.; Pangaribuan, Julinda

    2015-01-01

    In this work, simple solar cells using cuprous oxide were fabricated and characterized. The solar cells in this experiment used cuprous oxide plate as detecting electrode and copper wires which were woven into a net-shape with a gap size of 2 x 2 cm as a counter electrode. Twenty samples of solar cells were fabricated with oxide layer which were thermally grown in temperature up to 550 oC. Samples with variations in oxidation time (15 minutes, 30 minutes, 40 minutes, and 45 minutes) and distance between electrodes (2 cm, 3 cm, and 4 cm) with an electrolyte solution of NaCl with molarity of 2.188 mol/l were produced. The samples were characterized by measuring their V-I curve. For this purpose, a simple, own-made solar simulator were fabricated and characterized. Using curve fitting technique, parameters such as FF (Fill Factor), efficiency, open circuit voltage, short circuit current, internal resistance, and performance degradation as a function of time of the cells were extracted. The result shows optimum efficiency of 4.573. 10-4%, while optimum oxidation time is 40 minutes and optimum distance between electrodes is 3 cm.

  18. Fabrication and in vitro deployment of a laser-activated shape memory polymer vascular stent

    PubMed Central

    Baer, Géraldine M; Small, Ward; Wilson, Thomas S; Benett, William J; Matthews, Dennis L; Hartman, Jonathan; Maitland, Duncan J

    2007-01-01

    Background Vascular stents are small tubular scaffolds used in the treatment of arterial stenosis (narrowing of the vessel). Most vascular stents are metallic and are deployed either by balloon expansion or by self-expansion. A shape memory polymer (SMP) stent may enhance flexibility, compliance, and drug elution compared to its current metallic counterparts. The purpose of this study was to describe the fabrication of a laser-activated SMP stent and demonstrate photothermal expansion of the stent in an in vitro artery model. Methods A novel SMP stent was fabricated from thermoplastic polyurethane. A solid SMP tube formed by dip coating a stainless steel pin was laser-etched to create the mesh pattern of the finished stent. The stent was crimped over a fiber-optic cylindrical light diffuser coupled to an infrared diode laser. Photothermal actuation of the stent was performed in a water-filled mock artery. Results At a physiological flow rate, the stent did not fully expand at the maximum laser power (8.6 W) due to convective cooling. However, under zero flow, simulating the technique of endovascular flow occlusion, complete laser actuation was achieved in the mock artery at a laser power of ~8 W. Conclusion We have shown the design and fabrication of an SMP stent and a means of light delivery for photothermal actuation. Though further studies are required to optimize the device and assess thermal tissue damage, photothermal actuation of the SMP stent was demonstrated. PMID:18042294

  19. Fabrication and characterization of graphitic carbon nanostructures with controllable size, shape, and position.

    PubMed

    Du, Rongbing; Ssenyange, Solomon; Aktary, Mirwais; McDermott, Mark T

    2009-05-01

    The incorporation of carbon materials in micro- and nanoscale devices is being widely investigated due to the promise of enhanced functionality. Challenges in the positioning and addressability of carbon nanotubes provide the motivation for the development of new processes to produce nanoscale carbon materials. Here, the fabrication of conducting, nanometer-sized carbon structures using a combination of electron beam lithography (EBL) and carbonisation is reported. EBL is used to directly write predefined nanometer-sized patterns in a thin layer of negative resist in controllable locations. Careful heat treatment results in carbon nanostructures with the size, shape, and location originally defined by EBL. The pyrolysis process results in significant shrinkage of the structures in the vertical direction and minimal loss in the horizontal direction. Characterization of the carbonized material indicates a structure consisting of both amorphous and graphitized carbon with low levels of oxygen. The resistivity of the material is similar to other disordered carbon materials and the resistivity is maintained from the bulk to the nanoscale. This is demonstrated by fabricating a nanoscale structure with predictable resistance. The ability to fabricate these conductive structures with known dimensions and in predefined locations can be exploited for a number of applications. Their use as nanoband electrodes is also demonstrated. PMID:19235195

  20. Facile moldless fabrication of disk-shaped and reed blood cell-like microparticles using photopolymerization of tripropylene glycol diacrylate

    NASA Astrophysics Data System (ADS)

    Choi, Jongchul; Won, June; Song, Simon

    2014-12-01

    A facile method for the moldless fabrication of 2- or 3-dimensional microparticles is proposed by using a photopolymerization technique. Using only a monomer solution of tripropylene glycol diacrylate, a film mask and standard UV lithography equipment, we were able to fabricate microparticles of various shapes, such as disks, dimpled disks similar in shape to red blood cells, and slender gourd shapes, unlike previous moldless fabrication techniques requiring expensive and/or sophisticated equipment. The simple method could produce more than one million particles in a single batch, indicating that it can be applied to the mass production of polymer microparticles. Analyses of scanning electron micrographs and optical micrographs of the microparticles indicated that their size distribution was highly monodisperse. Detailed fabrication processes and statistics on the microparticle sizes are given in this paper.

  1. A fabrication method of unique Nafion® shapes by painting for ionic polymer–metal composites

    NASA Astrophysics Data System (ADS)

    Trabia, Sarah; Hwang, Taeseon; Kim, Kwang J.

    2016-08-01

    Ionic polymer–metal composites (IPMC) are useful actuators because of their ability to be fabricated in different shapes and move in various ways. However, producing unique or intricate shapes can be difficult based upon the current fabrication techniques. Presented here is a fabrication method of producing the Nafion® membrane or thin film through a painting method. Using an airbrush, a Nafion water dispersion is sprayed onto an acrylonitrile butadiene styrene surface with a stencil of the desired shape. To verify that this method of fabrication produces a Nafion membrane similar to that which is commercially available, a sample that was made using the painting method and Nafion 117 purchased from DuPont™ were tested for various characteristics and compared. The results show promising similarities. The painted Nafion sample was chemically plated with platinum and compared with a traditional IPMC for its displacement and blocking force capabilities. The painted IPMC sample showed comparable results.

  2. Entanglement-assisted quantum convolutional coding

    SciTech Connect

    Wilde, Mark M.; Brun, Todd A.

    2010-04-15

    We show how to protect a stream of quantum information from decoherence induced by a noisy quantum communication channel. We exploit preshared entanglement and a convolutional coding structure to develop a theory of entanglement-assisted quantum convolutional coding. Our construction produces a Calderbank-Shor-Steane (CSS) entanglement-assisted quantum convolutional code from two arbitrary classical binary convolutional codes. The rate and error-correcting properties of the classical convolutional codes directly determine the corresponding properties of the resulting entanglement-assisted quantum convolutional code. We explain how to encode our CSS entanglement-assisted quantum convolutional codes starting from a stream of information qubits, ancilla qubits, and shared entangled bits.

  3. WFIRST-AFTA coronagraph shaped pupil masks: design, fabrication, and characterization

    NASA Astrophysics Data System (ADS)

    Balasubramanian, Kunjithapatham; White, Victor; Yee, Karl; Echternach, Pierre; Muller, Richard; Dickie, Matthew; Cady, Eric; Prada, Camilo Mejia; Ryan, Daniel; Poberezhskiy, Ilya; Kern, Brian; Zhou, Hanying; Krist, John; Nemati, Bijan; Eldorado Riggs, A. J.; Zimmerman, Neil T.; Kasdin, N. Jeremy

    2016-01-01

    NASA WFIRST-AFTA mission study includes a coronagraph instrument to find and characterize exoplanets. Various types of masks could be employed to suppress the host starlight to about 10-9 level contrast over a broad spectrum to enable the coronagraph mission objectives. Such masks for high-contrast internal coronagraphic imaging require various fabrication technologies to meet a wide range of specifications, including precise shapes, micron scale island features, ultralow reflectivity regions, uniformity, wave front quality, and achromaticity. We present the approaches employed at JPL to produce pupil plane and image plane coronagraph masks by combining electron beam, deep reactive ion etching, and black silicon technologies with illustrative examples of each, highlighting milestone accomplishments from the High Contrast Imaging Testbed at JPL and from the High Contrast Imaging Lab at Princeton University.

  4. Fabrication and Characterization of Cylindrical Light Diffusers Comprised of Shape Memory Polymer

    SciTech Connect

    Small IV, W; Buckley, P R; Wilson, T S; Loge, J M; Maitland, K D; Maitland, D J

    2007-01-29

    We have developed a technique for constructing light diffusing devices comprised of a flexible shape memory polymer (SMP) cylindrical diffuser attached to the tip of an optical fiber. Devices were fabricated by casting an SMP rod over the cleaved tip of an optical fiber and media blasting the SMP rod to create a light diffusing surface. The axial and polar emission profiles and circumferential (azimuthal) uniformity were characterized for various blasting pressures, nozzle-to-sample distances, and nozzle translation speeds. The diffusers were generally strongly forward-directed and consistently withstood over 8 W of incident infrared laser light without suffering damage when immersed in water. These devices are suitable for various endoluminal and interstitial biomedical applications.

  5. Fabrication of shape controlled Fe{sub 3}O{sub 4} nanostructure

    SciTech Connect

    Zheng, Y.Y.; Wang, X.B.; Shang, L.; Li, C.R.; Cui, C.; Dong, W.J.; Tang, W.H.; Chen, B.Y.

    2010-04-15

    Shape-controlled Fe{sub 3}O{sub 4} nanostructure has been successfully prepared using polyethylene glycol as template in a water system at room temperature. Different morphologies of Fe{sub 3}O{sub 4} nanostructures, including spherical, cubic, rod-like, and dendritic nanostructure, were obtained by carefully controlling the concentration of the Fe{sup 3+}, Fe{sup 2+}, and the molecular weight of the polyethylene glycol. Transmission Electron Microscope images, X-ray powder diffraction patterns and magnetic properties were used to characterize the final product. This easy procedure for Fe{sub 3}O{sub 4} nanostructure fabrication offers the possibility of a generalized approach to the production of single and complex nanocrystalline oxide with tunable morphology.

  6. Lipid Nanotube Tailored Fabrication of Uniquely Shaped Polydopamine Nanofibers as Photothermal Converters.

    PubMed

    Ding, Wuxiao; Chechetka, Svetlana A; Masuda, Mitsutoshi; Shimizu, Toshimi; Aoyagi, Masaru; Minamikawa, Hiroyuki; Miyako, Eijiro

    2016-03-18

    Helically coiled and linear polydopamine (PDA) nanofibers were selectively fabricated with two different types of lipid nanotubes (LNTs) that acted as templates. The obtained coiled PDA-LNT hybrid showed morphological advantages such as higher light absorbance and photothermal conversion effect compared to a linear counterpart. Laser irradiation of the coiled PDA-LNT hybrid induced a morphological change and subsequent release of the encapsulated guest molecule. In cellular experiments, the coiled PDA-LNT efficiently eliminated HeLa cells because of its strong affinity with the tumor cells. This work illustrates the first approach to construct characteristic morphologies of PDA nanofibers using LNTs as simple templates, and the coiled PDA-LNT hybrid exhibits attractive photothermal features derived from its unique coiled shape. PMID:26849459

  7. Fabrication of porous titanium scaffold with controlled porous structure and net-shape using magnesium as spacer.

    PubMed

    Kim, Sung Won; Jung, Hyun-Do; Kang, Min-Ho; Kim, Hyoun-Ee; Koh, Young-Hag; Estrin, Yuri

    2013-07-01

    This paper reports a new approach to fabricating biocompatible porous titanium with controlled pore structure and net-shape. The method is based on using sacrificial Mg particles as space holders to produce compacts that are mechanically stable and machinable. Using magnesium granules and Ti powder, Ti/Mg compacts with transverse rupture strength (~85 MPa) sufficient for machining were fabricated by warm compaction, and a complex-shape Ti scaffold was eventually produced by removal of Mg granules from the net-shape compact. The pores with the average size of 132-262 μm were well distributed and interconnected. Due to anisotropy and alignment of the pores the compressive strength varied with the direction of compression. In the case of pores aligned with the direction of compression, the compressive strength values (59-280 MPa) high enough for applications in load bearing implants were achieved. To verify the possibility of controlled net-shape, conventional machining process was performed on Ti/Mg compact. Compact with screw shape and porous Ti scaffold with hemispherical cup shape were fabricated by the results. Finally, it was demonstrated by cell tests using MC3T3-E1 cell line that the porous Ti scaffolds fabricated by this technique are biocompatible. PMID:23623100

  8. Fabrication and Characterization of Nitinol-Copper Shape Memory Alloy Bimorph Actuators

    NASA Astrophysics Data System (ADS)

    Wongweerayoot, E.; Srituravanich, W.; Pimpin, A.

    2015-02-01

    This study aims to examine the effect of annealing conditions on nitinol (NiTi) characteristics and applies this knowledge to fabricate a NiTi-copper shape memory alloy bimorph actuator. The effect of the annealing conditions was investigated at various temperatures, i.e., 500, 600, and 650 °C, for 30 min. With the characterizations using x-ray diffraction, energy dispersive spectroscopy, and differential scanning calorimetry techniques, the results showed that annealing temperatures at 600 and 650 °C were able to appropriately form the crystalline structure of NiTi. However, at these high annealing temperatures, the oxide on a surface was unavoidable. In the fabrication of actuator, the annealing at 650 °C for 30 min was chosen, and it was performed at two pre-stressing conditions, i.e., straight and curved molds. From static and dynamic response experiments, the results suggested that the annealing temperature significantly affected the deflection of the actuator. On the other hand, the effect of pre-stressing conditions was relatively small. Furthermore, the micro gripper consisting of two NiTi-copper bimorph actuators successfully demonstrated for the viability of small object manipulation as the gripper was able to grasp and hold a small plastic ball with its weight of around 0.5 mg.

  9. Facile 3D Metal Electrode Fabrication for Energy Applications via Inkjet Printing and Shape Memory Polymer

    NASA Astrophysics Data System (ADS)

    Roberts, R. C.; Wu, J.; Hau, N. Y.; Chang, Y. H.; Feng, S. P.; Li, D. C.

    2014-11-01

    This paper reports on a simple 3D metal electrode fabrication technique via inkjet printing onto a thermally contracting shape memory polymer (SMP) substrate. Inkjet printing allows for the direct patterning of structures from metal nanoparticle bearing liquid inks. After deposition, these inks require thermal curing steps to render a stable conductive film. By printing onto a SMP substrate, the metal nanoparticle ink can be cured and substrate shrunk simultaneously to create 3D metal microstructures, forming a large surface area topology well suited for energy applications. Polystyrene SMP shrinkage was characterized in a laboratory oven from 150-240°C, resulting in a size reduction of 1.97-2.58. Silver nanoparticle ink was patterned into electrodes, shrunk, and the topology characterized using scanning electron microscopy. Zinc-Silver Oxide microbatteries were fabricated to demonstrate the 3D electrodes compared to planar references. Characterization was performed using 10M potassium hydroxide electrolyte solution doped with zinc oxide (57g/L). After a 300s oxidation at 3Vdc, the 3D electrode battery demonstrated a 125% increased capacity over the reference cell. Reference cells degraded with longer oxidations, but the 3D electrodes were fully oxidized for 4 hours, and exhibited a capacity of 5.5mA-hr/cm2 with stable metal performance.

  10. Die and telescoping punch form convolutions in thin diaphragm

    NASA Technical Reports Server (NTRS)

    1965-01-01

    Die and punch set forms convolutions in thin dished metal diaphragm without stretching the metal too thin at sharp curvatures. The die corresponds to the metal shape to be formed, and the punch consists of elements that progressively slide against one another under the restraint of a compressed-air cushion to mate with the die.

  11. Near-Net Shape Fabrication Using Low-Cost Titanium Alloy Powders

    SciTech Connect

    Dr. David M. Bowden; Dr. William H. Peter

    2012-03-31

    The use of titanium in commercial aircraft production has risen steadily over the last half century. The aerospace industry currently accounts for 58% of the domestic titanium market. The Kroll process, which has been used for over 50 years to produce titanium metal from its mineral form, consumes large quantities of energy. And, methods used to convert the titanium sponge output of the Kroll process into useful mill products also require significant energy resources. These traditional approaches result in product forms that are very expensive, have long lead times of up to a year or more, and require costly operations to fabricate finished parts. Given the increasing role of titanium in commercial aircraft, new titanium technologies are needed to create a more sustainable manufacturing strategy that consumes less energy, requires less material, and significantly reduces material and fabrication costs. A number of emerging processes are under development which could lead to a breakthrough in extraction technology. Several of these processes produce titanium alloy powder as a product. The availability of low-cost titanium powders may in turn enable a more efficient approach to the manufacture of titanium components using powder metallurgical processing. The objective of this project was to define energy-efficient strategies for manufacturing large-scale titanium structures using these low-cost powders as the starting material. Strategies include approaches to powder consolidation to achieve fully dense mill products, and joining technologies such as friction and laser welding to combine those mill products into near net shape (NNS) preforms for machining. The near net shape approach reduces material and machining requirements providing for improved affordability of titanium structures. Energy and cost modeling was used to define those approaches that offer the largest energy savings together with the economic benefits needed to drive implementation. Technical

  12. An innovative method and experiment for fabricating bulgy shape nanochannel using AFM

    NASA Astrophysics Data System (ADS)

    Lin, Zone-Ching; Jheng, Hao-Yuan; Ding, Hao-Yang

    2015-08-01

    The paper proposes using atomic force microscopy (AFM) and the concept of specific down force energy (SDFE) to establish an innovative offset cycle cutting method for fabricating a bulgy shape nanochannel on a single-crystal silicon substrate. In the offset cycle cutting method, cutting is performed at a constant down force in all cutting passes. After the first cutting pass, the AFM probe is offset rightward for the second pass and subsequently offset leftward to the middle (i.e., between the positions of the first two cutting passes) for the third cutting pass. Applying a step-by-step method to modify the offset distance and approach the defined SDFE value, this study determined the depth of the middle cutting pass and smaller values of upward bulginess and downward indentation at the bottom of the nanochannel. The nanochannel width can be increased by increasing the number of offset cycle cutting passes. In addition, by applying the proposed method, this study involved a simulation and experiment concerning the cutting path plan of bulgy shape nanochannels. Furthermore, using a small down force along the burr path is proposed for reducing burr height. The results of the simulation and experiment were compared to verify the feasibility of the method.

  13. Glass-on-Glass Fabrication of Bottle-Shaped Tunable Microlasers and their Applications

    NASA Astrophysics Data System (ADS)

    Ward, Jonathan M.; Yang, Yong; Nic Chormaic, Síle

    2016-04-01

    We describe a novel method for making microbottle-shaped lasers by using a CO2 laser to melt Er:Yb glass onto silica microcapillaries or fibres. This is realised by the fact that the two glasses have different melting points. The CO2 laser power is controlled to flow the doped glass around the silica cylinder. In the case of a capillary, the resulting geometry is a hollow, microbottle-shaped resonator. This is a simple method for fabricating a number of glass whispering gallery mode (WGM) lasers with a wide range of sizes on a single, micron-scale structure. The Er:Yb doped glass outer layer is pumped at 980 nm via a tapered optical fibre and WGM lasing is recorded around 1535 nm. This structure facilitates a new way to thermo-optically tune the microlaser modes by passing gas through the capillary. The cooling effect of the gas flow shifts the WGMs towards shorter wavelengths and thermal tuning of the lasing modes over 70 GHz is achieved. Results are fitted using the theory of hot wire anemometry, allowing the flow rate to be calibrated with a flow sensitivity as high as 72 GHz/sccm. Strain tuning of the microlaser modes by up to 60 GHz is also demonstrated.

  14. Glass-on-Glass Fabrication of Bottle-Shaped Tunable Microlasers and their Applications.

    PubMed

    Ward, Jonathan M; Yang, Yong; Nic Chormaic, Síle

    2016-01-01

    We describe a novel method for making microbottle-shaped lasers by using a CO2 laser to melt Er:Yb glass onto silica microcapillaries or fibres. This is realised by the fact that the two glasses have different melting points. The CO2 laser power is controlled to flow the doped glass around the silica cylinder. In the case of a capillary, the resulting geometry is a hollow, microbottle-shaped resonator. This is a simple method for fabricating a number of glass whispering gallery mode (WGM) lasers with a wide range of sizes on a single, micron-scale structure. The Er:Yb doped glass outer layer is pumped at 980 nm via a tapered optical fibre and WGM lasing is recorded around 1535 nm. This structure facilitates a new way to thermo-optically tune the microlaser modes by passing gas through the capillary. The cooling effect of the gas flow shifts the WGMs towards shorter wavelengths and thermal tuning of the lasing modes over 70 GHz is achieved. Results are fitted using the theory of hot wire anemometry, allowing the flow rate to be calibrated with a flow sensitivity as high as 72 GHz/sccm. Strain tuning of the microlaser modes by up to 60 GHz is also demonstrated. PMID:27121151

  15. Glass-on-Glass Fabrication of Bottle-Shaped Tunable Microlasers and their Applications

    PubMed Central

    Ward, Jonathan M.; Yang, Yong; Nic Chormaic, Síle

    2016-01-01

    We describe a novel method for making microbottle-shaped lasers by using a CO2 laser to melt Er:Yb glass onto silica microcapillaries or fibres. This is realised by the fact that the two glasses have different melting points. The CO2 laser power is controlled to flow the doped glass around the silica cylinder. In the case of a capillary, the resulting geometry is a hollow, microbottle-shaped resonator. This is a simple method for fabricating a number of glass whispering gallery mode (WGM) lasers with a wide range of sizes on a single, micron-scale structure. The Er:Yb doped glass outer layer is pumped at 980 nm via a tapered optical fibre and WGM lasing is recorded around 1535 nm. This structure facilitates a new way to thermo-optically tune the microlaser modes by passing gas through the capillary. The cooling effect of the gas flow shifts the WGMs towards shorter wavelengths and thermal tuning of the lasing modes over 70 GHz is achieved. Results are fitted using the theory of hot wire anemometry, allowing the flow rate to be calibrated with a flow sensitivity as high as 72 GHz/sccm. Strain tuning of the microlaser modes by up to 60 GHz is also demonstrated. PMID:27121151

  16. Fabrication and lithium storage performance of sugar apple-shaped SiOx@C nanocomposite spheres

    NASA Astrophysics Data System (ADS)

    Li, Mingqi; Zeng, Ying; Ren, Yurong; Zeng, Chunmei; Gu, Jingwei; Feng, Xiaofang; He, Hongyan

    2015-08-01

    Nonstoichiometric SiOx is a kind of very attractive anode material for high-energy lithium-ion batteries because of a high specific capacity and facile synthesis. However, the poor electrical conductivity and unstable electrode structure of SiOx severely limit its electrochemical performance as anode in lithium-ion batteries. In this work, highly durable sugar apple-shaped SiOx@C nanocomposite spheres are fabricated to achieve significantly improved electrochemical performance. The composite is synthesized by homogenous one-pot synthesis, using ethyltriethoxysilanes (EtSi(OEt)3) and resorcinol/formaldehyde (RF) as starting materials. The morphology, composition and structure of the composite are investigated by scanning electron microscopy (SEM), transmission electron microscopy (TEM), elemental analysis (EA) and X-ray photoelectron spectroscopy (XPS). At a current density of 50 mA g-1, the sugar apple-shaped SiOx@C spheres exhibit a stable discharge capacity of about 630 mAh g-1 calculated on the total mass of both SiOx and C. At a current density of 100 mA g-1, a stable discharge capacity of about 550 mAh g-1 is obtained and the capacity has been kept up to 400 cycles. The excellent cycling performance is attributed to the homogeneous dispersion of SiOx in disordered carbon at the nanometer scale and the unique structure of the composite.

  17. Some easily analyzable convolutional codes

    NASA Technical Reports Server (NTRS)

    Mceliece, R.; Dolinar, S.; Pollara, F.; Vantilborg, H.

    1989-01-01

    Convolutional codes have played and will play a key role in the downlink telemetry systems on many NASA deep-space probes, including Voyager, Magellan, and Galileo. One of the chief difficulties associated with the use of convolutional codes, however, is the notorious difficulty of analyzing them. Given a convolutional code as specified, say, by its generator polynomials, it is no easy matter to say how well that code will perform on a given noisy channel. The usual first step in such an analysis is to computer the code's free distance; this can be done with an algorithm whose complexity is exponential in the code's constraint length. The second step is often to calculate the transfer function in one, two, or three variables, or at least a few terms in its power series expansion. This step is quite hard, and for many codes of relatively short constraint lengths, it can be intractable. However, a large class of convolutional codes were discovered for which the free distance can be computed by inspection, and for which there is a closed-form expression for the three-variable transfer function. Although for large constraint lengths, these codes have relatively low rates, they are nevertheless interesting and potentially useful. Furthermore, the ideas developed here to analyze these specialized codes may well extend to a much larger class.

  18. Fabrication and evaluation results of a micro elliptical collimator lens for a beam shape form of laser diode

    NASA Astrophysics Data System (ADS)

    Okada, K.; Oohira, F.; Hosogi, M.; Hashiguchi, G.; Mihara, Y.; Ogawa, K.

    2005-12-01

    This paper describes a new fabrication process of a micro elliptical collimator lens to form a beam shape for LD(Laser Diode), and the evaluation results of the optical characteristic for this lens. Beam shape of LD is an ellipse because divergent light angle is different between horizontal and vertical direction, which increases a coupling loss with an optical fiber. In this presentation, we propose the lens to form the divergent light of an elliptical beam shape to the collimated light of a circular beam shape. This lens makes it possible to reduce the coupling loss with the optical fiber. For this purpose, we designed one lens, which has different curvature radiuses between incident and output surfaces. In the incident surface, the divergent light is formed to the convergent light, and in the output surface, the convergent light is formed to the collimated light. We simulated the optical characteristic of this lens, and designed for various parameters. In order to fabricate this lens, we propose a new process using a chemically absorbed monomolecular layer, which has an excellent hydrophobic property. This layer is patterned and deposited by a photolithographic technique. Next, we drop a UV(Ultra Violet) cure material on the hydrophilic area, as the result, we can fabricate a micro elliptical lens shape. The curvature radius of this lens can be controlled by the amount of a dropped UV cure material and an elliptical pattern size in horizontal and vertical direction. The formed lens shapes are transferred by the electro-plating and then the micro dies are fabricated. And they are used for molding the plastic lens.

  19. Fabrication of AlGaN/GaN Ω-shaped nanowire fin-shaped FETs by a top-down approach

    NASA Astrophysics Data System (ADS)

    Im, Ki-Sik; Sindhuri, Vodapally; Jo, Young-Woo; Son, Dong-Hyeok; Lee, Jae-Hoon; Cristoloveanu, Sorin; Lee, Jung-Hee

    2015-06-01

    An AlGaN/GaN-based Ω-shaped nanowire fin-shaped FET (FinFET) with a fin width of 50 nm was fabricated using tetramethylammonium hydroxide (TMAH)-based lateral wet etching. An atomic layer deposited (ALD) HfO2 side-wall layer served as the etching mask. ALD Al2O3 and TiN layers were used as the gate dielectric and gate metal, respectively. The Ω-shaped gate structure fully depletes the active fin body and almost completely separates the depleted fin from the underlying thick GaN buffer layer, resulting in superior device performance. The top-down processing proposed in this work provides a viable pathway towards gate-all-around devices for III-nitride semiconductors.

  20. Convolutional virtual electric field for image segmentation using active contours.

    PubMed

    Wang, Yuanquan; Zhu, Ce; Zhang, Jiawan; Jian, Yuden

    2014-01-01

    Gradient vector flow (GVF) is an effective external force for active contours; however, it suffers from heavy computation load. The virtual electric field (VEF) model, which can be implemented in real time using fast Fourier transform (FFT), has been proposed later as a remedy for the GVF model. In this work, we present an extension of the VEF model, which is referred to as CONvolutional Virtual Electric Field, CONVEF for short. This proposed CONVEF model takes the VEF model as a convolution operation and employs a modified distance in the convolution kernel. The CONVEF model is also closely related to the vector field convolution (VFC) model. Compared with the GVF, VEF and VFC models, the CONVEF model possesses not only some desirable properties of these models, such as enlarged capture range, u-shape concavity convergence, subject contour convergence and initialization insensitivity, but also some other interesting properties such as G-shape concavity convergence, neighboring objects separation, and noise suppression and simultaneously weak edge preserving. Meanwhile, the CONVEF model can also be implemented in real-time by using FFT. Experimental results illustrate these advantages of the CONVEF model on both synthetic and natural images. PMID:25360586

  1. Fabrication of MEMS ZnO dome-shaped-diaphragm transducers for high-frequency ultrasonic imaging

    NASA Astrophysics Data System (ADS)

    Feng, Guo-Hua; Sharp, Charles C.; Zhou, Q. F.; Pang, Wei; Sok Kim, Eun; Shung, K. K.

    2005-03-01

    This paper presents the microfabrication technique for a dome-shaped-diaphragm transducer (DSDT) for 200 MHz cellular microstructure imaging. The DSDT uses piezoelectric ZnO film to generate acoustic waves, and is fabricated on a silicon substrate. The fabricated DSDTs have been tested with a pulse-echo method using a quartz target, and shown to produce an echo signal at 210 MHz with 20% bandwidth. The DSDT fabrication uses spherical balls to precisely shape wax molds, onto which parylene is deposited as a support layer for the DSDT. The wax molds are removed by toluene to release the parylene dome diaphragms. Piezoelectric ZnO film is sputter deposited on the parylene dome diaphragm. E-Solder silver epoxy is placed and cured on the back surface to function both as an acoustic backing and as structural support. Quarter wavelength thick parylene is deposited on the front side of the wafer for acoustic matching. The fabrication technique for the DSDTs is meant for low-cost mass production of the devices for high-frequency biomedical imaging. Moreover, the technique allows the precise control of the radius and curvature of the dome-shaped diaphragm through adjusting the size of the front-to-backside thru holes using different radii of the spherical balls.

  2. Cost-efficient and flexible fabrication of rectangular-shaped microlens arrays with controllable aspect ratio and spherical morphology

    NASA Astrophysics Data System (ADS)

    Hu, Yang; Yang, Qing; Chen, Feng; Bian, Hao; Deng, Zefang; Du, Guangqing; Si, Jinhai; Yun, Feng; Hou, Xun

    2014-02-01

    This paper presents a cost-efficient and flexible approach to the development of controllable-shape concave and convex microlens arrays by using femtosecond laser wet etch and replication techniques. Periodic concave rectangular-shaped microlens arrays with different length-width ratios were achieved by firstly introducing periodic microcraters on silica glass using an 800 nm femtosecond laser, and subsequently enlarging the craters into microlens with smooth curved surfaces in hydrofluoric (HF) acid solution. The concave microlens can serve as molds of replication to obtain convex microlenses on polymers. Over 10,000 rectangular-shaped concave and convex microlens with controllable aspect ratio, high fill factor and spherical morphology can be fabricated in 5 h. A simulation result and a projection experiment result verify the optical performance of these rectangular-shaped spherical microlens arrays (MLAs).

  3. Fabrication of petal-shaped masks for suppression of the on-axis Poisson spot in telescope systems.

    PubMed

    Shiri, Ron; Stein, Ryan; Murphy, Kaitlin; Hagopian, Kimberly; Salari, Shirin; Sankar, Shannon; Hagopian, John; Showalter, Matthew; Stevenson, Thomas; Quijada, Manuel; Threat, Felix; Friedlander, Jay; Dillon, Thomas; Livas, Jeffrey

    2016-04-01

    The presence of a bright (Poisson) spot in the geometrical shadow of circular/spherical shapes has been known for the past two centuries. A broad class of telescopes that involve simultaneous transmit and receive require suppression of the reflected light from the secondary mirror on the detector. For instance, the on-axis design of optical telescope for the evolved Laser Interferometric Space Antenna (eLISA), a re-scoped version of the baseline LISA mission concept, requires suppression of reflected laser light from the secondary mirror on the detector. In the past few years, the hypergaussian functions with petal-shaped realization have been shown to significantly suppress intensity along the optical axis. This work reports on fabrication of a series of petal-shaped masks using a variety of techniques such as 3D printing, photolithography, and wire Electro Discharge Machining. These masks are designed and fabricated to operate in the range of Fresnel numbers between 4 and 120. This paper discusses the challenges, successes, and failures of each fabrication technique and the optical performance of typical masks with suggestions for potential follow up work. PMID:27131659

  4. Fabrication of petal-shaped masks for suppression of the on-axis Poisson spot in telescope systems

    NASA Astrophysics Data System (ADS)

    Shiri, Ron; Stein, Ryan; Murphy, Kaitlin; Hagopian, Kimberly; Salari, Shirin; Sankar, Shannon; Hagopian, John; Showalter, Matthew; Stevenson, Thomas; Quijada, Manuel; Threat, Felix; Friedlander, Jay; Dillon, Thomas; Livas, Jeffrey

    2016-04-01

    The presence of a bright (Poisson) spot in the geometrical shadow of circular/spherical shapes has been known for the past two centuries. A broad class of telescopes that involve simultaneous transmit and receive require suppression of the reflected light from the secondary mirror on the detector. For instance, the on-axis design of optical telescope for the evolved Laser Interferometric Space Antenna (eLISA), a re-scoped version of the baseline LISA mission concept, requires suppression of reflected laser light from the secondary mirror on the detector. In the past few years, the hypergaussian functions with petal-shaped realization have been shown to significantly suppress intensity along the optical axis. This work reports on fabrication of a series of petal-shaped masks using a variety of techniques such as 3D printing, photolithography, and wire Electro Discharge Machining. These masks are designed and fabricated to operate in the range of Fresnel numbers between 4 and 120. This paper discusses the challenges, successes, and failures of each fabrication technique and the optical performance of typical masks with suggestions for potential follow up work.

  5. Approximating large convolutions in digital images.

    PubMed

    Mount, D M; Kanungo, T; Netanyahu, N S; Piatko, C; Silverman, R; Wu, A Y

    2001-01-01

    Computing discrete two-dimensional (2-D) convolutions is an important problem in image processing. In mathematical morphology, an important variant is that of computing binary convolutions, where the kernel of the convolution is a 0-1 valued function. This operation can be quite costly, especially when large kernels are involved. We present an algorithm for computing convolutions of this form, where the kernel of the binary convolution is derived from a convex polygon. Because the kernel is a geometric object, we allow the algorithm some flexibility in how it elects to digitize the convex kernel at each placement, as long as the digitization satisfies certain reasonable requirements. We say that such a convolution is valid. Given this flexibility we show that it is possible to compute binary convolutions more efficiently than would normally be possible for large kernels. Our main result is an algorithm which, given an m x n image and a k-sided convex polygonal kernel K, computes a valid convolution in O(kmn) time. Unlike standard algorithms for computing correlations and convolutions, the running time is independent of the area or perimeter of K, and our techniques do not rely on computing fast Fourier transforms. Our algorithm is based on a novel use of Bresenham's (1965) line-drawing algorithm and prefix-sums to update the convolution incrementally as the kernel is moved from one position to another across the image. PMID:18255522

  6. Fabrication of a membrane filter with controlled pore shape and its application to cell separation and strong single cell trapping

    NASA Astrophysics Data System (ADS)

    Choi, Dong-Hoon; Yoon, Gun-Wook; Park, Jeong Won; Ihm, Chunhwa; Lee, Dae-Sik; Yoon, Jun-Bo

    2015-10-01

    A porous membrane filter is one of the key components for sample preparation in lab-on-a-chip applications. However, most of the membranes reported to date have only been used for size-based separation since it is difficult to provide functionality to the membrane or improve the performance of the membrane. In this work, as a method to functionalize the membrane filter, controlling the shape of the membrane pores is suggested, and a convenient and mass-producible fabrication method is provided. With the proposed method, membrane filters with round, conical and funnel shape pores were successfully fabricated, and we demonstrated that the sidewall slope of the conical shape pores could be precisely controlled. To verify that the membrane filter can be functionalized by controlled pore shape, we investigated filtration and trapping performance of the membrane filter with conical shape pores. In a filtration test of 1000 cancer cells (MCF-7, a breast cancer cell line) spiked in phosphate buffered saline (PBS) solution, 77% of the total cancer cells were retained on the membrane, and each cell from among 99.3% of the retained cells was automatically isolated in a single conical pore during the filtration process. Thanks to its engineered pore shape, trapping ability of the membrane with conical pores is dramatically improved. Microparticles trapped in the conical pores maintain their locations without any losses even at a more than 30 times faster external flow rate com-pared with those mounted on conventional cylindrical pores. Also, 78% of the cells trapped in the conical pores withstand an external flow of over 300 μl min-1 whereas only 18% of the cells trapped in the cylindrical pores remain on the membrane after 120 μl min-1 of an external flow is applied.

  7. The Convolution Method in Neutrino Physics Searches

    SciTech Connect

    Tsakstara, V.; Kosmas, T. S.; Chasioti, V. C.; Divari, P. C.; Sinatkas, J.

    2007-12-26

    We concentrate on the convolution method used in nuclear and astro-nuclear physics studies and, in particular, in the investigation of the nuclear response of various neutrino detection targets to the energy-spectra of specific neutrino sources. Since the reaction cross sections of the neutrinos with nuclear detectors employed in experiments are extremely small, very fine and fast convolution techniques are required. Furthermore, sophisticated de-convolution methods are also needed whenever a comparison between calculated unfolded cross sections and existing convoluted results is necessary.

  8. Effect of Fabrication-Dependent Shape and Composition of Solid-State Nanopores on Single Nanoparticle Detection

    PubMed Central

    Liu, Shuo; Yuzvinsky, Thomas D.; Schmidt, Holger

    2013-01-01

    Solid-state nanopores can be fabricated in a variety of ways and form the basis for label-free sensing of single nanoparticles: as individual nanoparticles traverse the nanopore, they alter the ionic current across it in a characteristic way. Typically, nanopores are described by the diameter of their limiting aperture, and less attention has been paid to other, fabrication-dependent parameters. Here, we report a comprehensive analysis of the properties and sensing performance of three types of nanopore with identical 50nm aperture, but fabricated using three different techniques: direct ion beam milling, ion beam sculpting, and electron beam sculpting. The nanopores differ substantially in physical shape and chemical composition as identified by ion-beam assisted cross sectioning and energy dispersive x-ray spectroscopy. Concomitant differences in electrical sensing of single 30nm beads, such as variations in blockade depth, duration, and electric field dependence, are observed and modeled using hydrodynamic simulations. The excellent agreement between experiment and physical modeling shows that the physical properties (shape) and not the chemical surface composition determine the sensing performance of a solid-state nanopore in the absence of deliberate surface modification. Consequently, nanoparticle sensing performance can be accurately predicted once the full three dimensional structure of the nanopore is known. PMID:23697604

  9. Fabrication and Characterization of a Micromachined Swirl-Shaped Ionic Polymer Metal Composite Actuator with Electrodes Exhibiting Asymmetric Resistance

    PubMed Central

    Feng, Guo-Hua; Liu, Kim-Min

    2014-01-01

    This paper presents a swirl-shaped microfeatured ionic polymer-metal composite (IPMC) actuator. A novel micromachining process was developed to fabricate an array of IPMC actuators on a glass substrate and to ensure that no shortcircuits occur between the electrodes of the actuator. We demonstrated a microfluidic scheme in which surface tension was used to construct swirl-shaped planar IPMC devices of microfeature size and investigated the flow velocity of Nafion solutions, which formed the backbone polymer of the actuator, within the microchannel. The unique fabrication process yielded top and bottom electrodes that exhibited asymmetric surface resistance. A tool for measuring surface resistance was developed and used to characterize the resistances of the electrodes for the fabricated IPMC device. The actuator, which featured asymmetric electrode resistance, caused a nonzero-bias current when the device was driven using a zero-bias square wave, and we propose a circuit model to describe this phenomenon. Moreover, we discovered and characterized a bending and rotating motion when the IPMC actuator was driven using a square wave. We observed a strain rate of 14.6% and a displacement of 700 μm in the direction perpendicular to the electrode surfaces during 4.5-V actuation. PMID:24824370

  10. Convolution-deconvolution in DIGES

    SciTech Connect

    Philippacopoulos, A.J.; Simos, N.

    1995-05-01

    Convolution and deconvolution operations is by all means a very important aspect of SSI analysis since it influences the input to the seismic analysis. This paper documents some of the convolution/deconvolution procedures which have been implemented into the DIGES code. The 1-D propagation of shear and dilatational waves in typical layered configurations involving a stack of layers overlying a rock is treated by DIGES in a similar fashion to that of available codes, e.g. CARES, SHAKE. For certain configurations, however, there is no need to perform such analyses since the corresponding solutions can be obtained in analytic form. Typical cases involve deposits which can be modeled by a uniform halfspace or simple layered halfspaces. For such cases DIGES uses closed-form solutions. These solutions are given for one as well as two dimensional deconvolution. The type of waves considered include P, SV and SH waves. The non-vertical incidence is given special attention since deconvolution can be defined differently depending on the problem of interest. For all wave cases considered, corresponding transfer functions are presented in closed-form. Transient solutions are obtained in the frequency domain. Finally, a variety of forms are considered for representing the free field motion both in terms of deterministic as well as probabilistic representations. These include (a) acceleration time histories, (b) response spectra (c) Fourier spectra and (d) cross-spectral densities.

  11. The trellis complexity of convolutional codes

    NASA Technical Reports Server (NTRS)

    Mceliece, R. J.; Lin, W.

    1995-01-01

    It has long been known that convolutional codes have a natural, regular trellis structure that facilitates the implementation of Viterbi's algorithm. It has gradually become apparent that linear block codes also have a natural, though not in general a regular, 'minimal' trellis structure, which allows them to be decoded with a Viterbi-like algorithm. In both cases, the complexity of the Viterbi decoding algorithm can be accurately estimated by the number of trellis edges per encoded bit. It would, therefore, appear that we are in a good position to make a fair comparison of the Viterbi decoding complexity of block and convolutional codes. Unfortunately, however, this comparison is somewhat muddled by the fact that some convolutional codes, the punctured convolutional codes, are known to have trellis representations that are significantly less complex than the conventional trellis. In other words, the conventional trellis representation for a convolutional code may not be the minimal trellis representation. Thus, ironically, at present we seem to know more about the minimal trellis representation for block than for convolutional codes. In this article, we provide a remedy, by developing a theory of minimal trellises for convolutional codes. (A similar theory has recently been given by Sidorenko and Zyablov). This allows us to make a direct performance-complexity comparison for block and convolutional codes. A by-product of our work is an algorithm for choosing, from among all generator matrices for a given convolutional code, what we call a trellis-minimal generator matrix, from which the minimal trellis for the code can be directly constructed. Another by-product is that, in the new theory, punctured convolutional codes no longer appear as a special class, but simply as high-rate convolutional codes whose trellis complexity is unexpectedly small.

  12. Multiple beam interference lithography: A tool for rapid fabrication of plasmonic arrays of arbitrary shaped nanomotifs.

    PubMed

    Vala, M; Homola, J

    2016-07-11

    A novel method enabling rapid fabrication of 2D periodic arrays of plasmonic nanoparticles across large areas is presented. This method is based on the interference of multiple coherent beams originating from diffraction of large-diameter collimated beam on a transmission phase mask. Mutual orientation of the interfering beams is determined by parameters of the used phase mask. Herein, parameters of the phase mask (periods and modulation depth) are selected to yield an interference pattern with high contrast and narrow well-separated maxima. Finally, multiple beam interference lithography (MBIL)-based fabrication of periodic plasmonic arrays with selected nanomotifs including discs, disc dimers, rods and bowtie antennas is demonstrated. PMID:27410838

  13. On the growth and form of cortical convolutions

    NASA Astrophysics Data System (ADS)

    Tallinen, Tuomas; Chung, Jun Young; Rousseau, François; Girard, Nadine; Lefèvre, Julien; Mahadevan, L.

    2016-06-01

    The rapid growth of the human cortex during development is accompanied by the folding of the brain into a highly convoluted structure. Recent studies have focused on the genetic and cellular regulation of cortical growth, but understanding the formation of the gyral and sulcal convolutions also requires consideration of the geometry and physical shaping of the growing brain. To study this, we use magnetic resonance images to build a 3D-printed layered gel mimic of the developing smooth fetal brain; when immersed in a solvent, the outer layer swells relative to the core, mimicking cortical growth. This relative growth puts the outer layer into mechanical compression and leads to sulci and gyri similar to those in fetal brains. Starting with the same initial geometry, we also build numerical simulations of the brain modelled as a soft tissue with a growing cortex, and show that this also produces the characteristic patterns of convolutions over a realistic developmental course. All together, our results show that although many molecular determinants control the tangential expansion of the cortex, the size, shape, placement and orientation of the folds arise through iterations and variations of an elementary mechanical instability modulated by early fetal brain geometry.

  14. Fabrication and static characterization of carbon-fiber-reinforced polymers with embedded NiTi shape memory wire actuators

    NASA Astrophysics Data System (ADS)

    de Araújo, C. J.; Rodrigues, L. F. A.; Coutinho Neto, J. F.; Reis, R. P. B.

    2008-12-01

    In this work, unidirectional carbon-fiber-reinforced polymers (CFRP) with embedded NiTi shape memory alloy (SMA) wire actuators were manufactured using a universal testing machine equipped with a thermally controlled chamber. Beam specimens containing cold-worked, annealed and trained NiTi SMA wires distributed along their neutral plane were fabricated. Several tests in a three-point bending mode at different constant temperatures were performed. To verify thermal buckling effects, electrical activation of the specimens was realized in a cantilevered beam mode and the influence of the SMA wire actuators on the tip deflection of the composite is demonstrated.

  15. Runge-Kutta based generalized convolution quadrature

    NASA Astrophysics Data System (ADS)

    Lopez-Fernandez, Maria; Sauter, Stefan

    2016-06-01

    We present the Runge-Kutta generalized convolution quadrature (gCQ) with variable time steps for the numerical solution of convolution equations for time and space-time problems. We present the main properties of the method and a convergence result.

  16. Symbol synchronization in convolutionally coded systems

    NASA Technical Reports Server (NTRS)

    Baumert, L. D.; Mceliece, R. J.; Van Tilborg, H. C. A.

    1979-01-01

    Alternate symbol inversion is sometimes applied to the output of convolutional encoders to guarantee sufficient richness of symbol transition for the receiver symbol synchronizer. A bound is given for the length of the transition-free symbol stream in such systems, and those convolutional codes are characterized in which arbitrarily long transition free runs occur.

  17. Rolling-Convolute Joint For Pressurized Glove

    NASA Technical Reports Server (NTRS)

    Kosmo, Joseph J.; Bassick, John W.

    1994-01-01

    Rolling-convolute metacarpal/finger joint enhances mobility and flexibility of pressurized glove. Intended for use in space suit to increase dexterity and decrease wearer's fatigue. Also useful in diving suits and other pressurized protective garments. Two ring elements plus bladder constitute rolling-convolute joint balancing torques caused by internal pressurization of glove. Provides comfortable grasp of various pieces of equipment.

  18. The general theory of convolutional codes

    NASA Technical Reports Server (NTRS)

    Mceliece, R. J.; Stanley, R. P.

    1993-01-01

    This article presents a self-contained introduction to the algebraic theory of convolutional codes. This introduction is partly a tutorial, but at the same time contains a number of new results which will prove useful for designers of advanced telecommunication systems. Among the new concepts introduced here are the Hilbert series for a convolutional code and the class of compact codes.

  19. Fabrication and characterization of shape memory polymers at small-scales

    NASA Astrophysics Data System (ADS)

    Wornyo, Edem

    The objective of this research is to thoroughly investigate the shape memory effect in polymers, characterize, and optimize these polymers for applications in information storage systems. Previous research effort in this field concentrated on shape memory metals for biomedical applications such as stents. Minimal work has been done on shape memory polymers; and the available work on shape memory polymers has not characterized the behaviors of this category of polymers fully. Copolymer shape memory materials based on diethylene glycol dimethacrylate (DEGDMA) crosslinker, and tert butyl acrylate (tBA) monomer are designed. The design encompasses a careful control of the backbone chemistry of the materials. Characterization methods such as dynamic mechanical analysis (DMA), differential scanning calorimetry (DSC); and novel nanoscale techniques such as atomic force microscopy (AFM), and nanoindentation are applied to this system of materials. Designed experiments are conducted on the materials to optimize spin coating conditions for thin films. Furthermore, the recovery, a key for the use of these polymeric materials for information storage, is examined in detail with respect to temperature. In sum, the overarching objectives of the proposed research are to: (i) Design shape memory polymers based on polyethylene glycol dimethacrylate (PEGDMA) and diethylene glycol dimethacrylate (DEGDMA) crosslinkers, 2-hydroxyethyl methacrylate (HEMA) and tert-butyl acrylate monomer (tBA). (ii) Utilize dynamic mechanical analysis (DMA) to comprehend the thermomechanical properties of shape memory polymers based on DEGDMA and tBA. (iii) Utilize nanoindentation and atomic force microscopy (AFM) to understand the nanoscale behavior of these SMPs, and explore the strain storage and recovery of the polymers from a deformed state. (iv) Study spin coating conditions on thin film quality with designed experiments. (iv) Apply neural networks and genetic algorithms to optimize these systems.

  20. Direct Fabrication of Free-Standing MOF Superstructures with Desired Shapes by Micro-Confined Interfacial Synthesis.

    PubMed

    Kim, Jin-Oh; Min, Kyoung-Ik; Noh, Hyunwoo; Kim, Dong-Hwi; Park, Soo-Young; Kim, Dong-Pyo

    2016-06-13

    Recently, metal-organic frameworks (MOFs) with multifunctional pore chemistry have been intensively investigated for positioning the desired morphology at specific locations onto substrates for manufacturing devices. Herein, we develop a micro-confined interfacial synthesis (MIS) approach for fabrication of a variety of free-standing MOF superstructures with desired shapes. This approach for engineering MOFs provides three key features: 1) in situ synthesis of various free-standing MOF superstructures with controlled compositions, shape, and thickness using a mold membrane; 2) adding magnetic functionality into MOF superstructures by loading with Fe3 O4 nanoparticles; 3) transferring the synthesized MOF superstructural array on to flat or curved surface of various substrates. The MIS route with versatile potential opens the door for a number of new perspectives in various applications. PMID:27140805

  1. Achieving unequal error protection with convolutional codes

    NASA Technical Reports Server (NTRS)

    Mills, D. G.; Costello, D. J., Jr.; Palazzo, R., Jr.

    1994-01-01

    This paper examines the unequal error protection capabilities of convolutional codes. Both time-invariant and periodically time-varying convolutional encoders are examined. The effective free distance vector is defined and is shown to be useful in determining the unequal error protection (UEP) capabilities of convolutional codes. A modified transfer function is used to determine an upper bound on the bit error probabilities for individual input bit positions in a convolutional encoder. The bound is heavily dependent on the individual effective free distance of the input bit position. A bound relating two individual effective free distances is presented. The bound is a useful tool in determining the maximum possible disparity in individual effective free distances of encoders of specified rate and memory distribution. The unequal error protection capabilities of convolutional encoders of several rates and memory distributions are determined and discussed.

  2. Search for optimal distance spectrum convolutional codes

    NASA Technical Reports Server (NTRS)

    Connor, Matthew C.; Perez, Lance C.; Costello, Daniel J., Jr.

    1993-01-01

    In order to communicate reliably and to reduce the required transmitter power, NASA uses coded communication systems on most of their deep space satellites and probes (e.g. Pioneer, Voyager, Galileo, and the TDRSS network). These communication systems use binary convolutional codes. Better codes make the system more reliable and require less transmitter power. However, there are no good construction techniques for convolutional codes. Thus, to find good convolutional codes requires an exhaustive search over the ensemble of all possible codes. In this paper, an efficient convolutional code search algorithm was implemented on an IBM RS6000 Model 580. The combination of algorithm efficiency and computational power enabled us to find, for the first time, the optimal rate 1/2, memory 14, convolutional code.

  3. Prototype fabrication and preliminary in vitro testing of a shape memory endovascular thrombectomy device.

    PubMed

    Small, Ward; Wilson, Thomas S; Buckley, Patrick R; Benett, William J; Loge, Jeffrey M; Hartman, Jonathan; Maitland, Duncan J

    2007-09-01

    An electromechanical microactuator comprised of shape memory polymer (SMP) and shape memory nickel-titanium alloy (nitinol) was developed and used in an endovascular thrombectomy device prototype. The microactuator maintains a straight rod shape until an applied current induces electro-resistive (Joule) heating, causing the microactuator to transform into a corkscrew shape. The straight-to-corkscrew transformation geometry was chosen to permit endovascular delivery through (straight form) and retrieval of (corkscrew form) a stroke-causing thrombus (blood clot) in the brain. Thermal imaging of the microactuator during actuation in air indicated that the steady-state temperature rise caused by Joule heating varied quadratically with applied current and that actuation occurred near the glass transition temperature of the SMP (86 degrees C). To demonstrate clinical application, the device was used to retrieve a blood clot in a water-filled silicone neurovascular model. Numerical modeling of the heat transfer to the surrounding blood and associated thermal effects on the adjacent artery potentially encountered during clinical use suggested that any thermal damage would likely be confined to localized areas where the microactuator was touching the artery wall. This shape memory mechanical thrombectomy device is a promising tool for treating ischemic stroke without the need for infusion of clot-dissolving drugs. PMID:17867358

  4. Adaptive decoding of convolutional codes

    NASA Astrophysics Data System (ADS)

    Hueske, K.; Geldmacher, J.; Götze, J.

    2007-06-01

    Convolutional codes, which are frequently used as error correction codes in digital transmission systems, are generally decoded using the Viterbi Decoder. On the one hand the Viterbi Decoder is an optimum maximum likelihood decoder, i.e. the most probable transmitted code sequence is obtained. On the other hand the mathematical complexity of the algorithm only depends on the used code, not on the number of transmission errors. To reduce the complexity of the decoding process for good transmission conditions, an alternative syndrome based decoder is presented. The reduction of complexity is realized by two different approaches, the syndrome zero sequence deactivation and the path metric equalization. The two approaches enable an easy adaptation of the decoding complexity for different transmission conditions, which results in a trade-off between decoding complexity and error correction performance.

  5. Hydrothermal fabrication of octahedral-shaped Fe3O4 nanoparticles and their magnetorheological response

    NASA Astrophysics Data System (ADS)

    Jung, H. S.; Choi, H. J.

    2015-05-01

    Octahedral-shaped Fe3O4 nanoparticles were synthesized in the presence of 1,3-diaminopropane using a hydrothermal method and assessed as a potential magnetorheological (MR) material. Their morphology, crystal structure, and magnetic properties were examined by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, and vibrating sample magnetometry, respectively. The MR characteristics of the octahedral-shaped, Fe3O4 nanoparticle-based MR particles when dispersed in silicone oil with a 10 vol. % particle concentration were examined using a rotational rheometer under an external magnetic field. The resulting MR fluids exhibited a Bingham-like behavior with a distinctive yield stress from their flow curves.

  6. Single-step direct laser fabrication of complex shaped microoptical components

    NASA Astrophysics Data System (ADS)

    Žukauskas, Albertas; Tikuišis, Kristupas K.; Ščiuka, Mindaugas; Melninkaitis, Andrius; Gadonas, Roaldas; Reinhardt, Carsten; Malinauskas, Mangirdas

    2012-06-01

    We report on the fabrication of the minimized conventional microoptical components out of the hybrid organic- inorganic SZ2080 and SG4060 photoresins using laser direct writing technique. An ascending laser focus multiscan approach is introduced as a method for the structuring of 2D nanolines. The diameters and heights of the nanolines are comparable to the ones written with the electron beam lithography. Using our proposed laser direct writing approach one can write 3D microstructures with the 2D nanofeatures in a single step procedure. As demonstration of this technology, microlenses with 1D, 2D and circular transmission gratings were fabricated. Additionally, for the rst time, ISO certied laser-induced damage testing was applied to determine the optical breakdown threshold of the SZ2080 photoresin used for the laser direct writing.

  7. Microfluidic fabrication of shape-tunable alginate microgels: effect of size and impact velocity.

    PubMed

    Hu, Yuandu; Azadi, Glareh; Ardekani, Arezoo M

    2015-04-20

    We report on a capillary-based microfluidic platform for the fabrication of non-spherical sodium alginate microgels. The sodium alginate droplets were crosslinked off-chip in a mixture of barium acetate and glycerol solution. Novel morphologies such as tear drop, lamp-like, mushroom-like, double-dimpled and bowl-like microgels were fabricated by controlling the size, impact velocity (at the crosslinking solution/oil interface), and concentration of sodium alginate solution. We monitored the microscale deformation process in situ at the interface and proposeed a deformation mechanism resulting in unique morphologies. Additionally, we constructed microgel superstructures by assembling the non-spherical alginate microgels to spherical poly(N-isopropylacrylamide) (pNIPAAm) microgels via electrostatic interaction. PMID:25662685

  8. Near-net-shape fabrication of continuous Ag-Clad Bi-Based superconductors

    SciTech Connect

    Lanagan, M. T. et al.

    1998-04-01

    We have developed a near-net-shape process for Ag-clad Bi-2212 superconductors as an alternative to the powder-in-tube process. This new process offers the advantages of nearly continuous processing, minimization of processing steps, reasonable ability to control the Bi-2212/Ag ratio, and early development of favorable texture of the Bi-2212 grains. Superconducting properties are discussed.

  9. A Finger-Shaped Tactile Sensor for Fabric Surfaces Evaluation by 2-Dimensional Active Sliding Touch

    PubMed Central

    Hu, Haihua; Han, Yezhen; Song, Aiguo; Chen, Shanguang; Wang, Chunhui; Wang, Zheng

    2014-01-01

    Sliding tactile perception is a basic function for human beings to determine the mechanical properties of object surfaces and recognize materials. Imitating this process, this paper proposes a novel finger-shaped tactile sensor based on a thin piezoelectric polyvinylidene fluoride (PVDF) film for surface texture measurement. A parallelogram mechanism is designed to ensure that the sensor applies a constant contact force perpendicular to the object surface, and a 2-dimensional movable mechanical structure is utilized to generate the relative motion at a certain speed between the sensor and the object surface. By controlling the 2-dimensional motion of the finger-shaped sensor along the object surface, small height/depth variation of surface texture changes the output charge of PVDF film then surface texture can be measured. In this paper, the finger-shaped tactile sensor is used to evaluate and classify five different kinds of linen. Fast Fourier Transformation (FFT) is utilized to get original attribute data of surface in the frequency domain, and principal component analysis (PCA) is used to compress the attribute data and extract feature information. Finally, low dimensional features are classified by Support Vector Machine (SVM). The experimental results show that this finger-shaped tactile sensor is effective and high accurate for discriminating the five textures. PMID:24618775

  10. A finger-shaped tactile sensor for fabric surfaces evaluation by 2-dimensional active sliding touch.

    PubMed

    Hu, Haihua; Han, Yezhen; Song, Aiguo; Chen, Shanguang; Wang, Chunhui; Wang, Zheng

    2014-01-01

    Sliding tactile perception is a basic function for human beings to determine the mechanical properties of object surfaces and recognize materials. Imitating this process, this paper proposes a novel finger-shaped tactile sensor based on a thin piezoelectric polyvinylidene fluoride (PVDF) film for surface texture measurement. A parallelogram mechanism is designed to ensure that the sensor applies a constant contact force perpendicular to the object surface, and a 2-dimensional movable mechanical structure is utilized to generate the relative motion at a certain speed between the sensor and the object surface. By controlling the 2-dimensional motion of the finger-shaped sensor along the object surface, small height/depth variation of surface texture changes the output charge of PVDF film then surface texture can be measured. In this paper, the finger-shaped tactile sensor is used to evaluate and classify five different kinds of linen. Fast Fourier Transformation (FFT) is utilized to get original attribute data of surface in the frequency domain, and principal component analysis (PCA) is used to compress the attribute data and extract feature information. Finally, low dimensional features are classified by Support Vector Machine (SVM). The experimental results show that this finger-shaped tactile sensor is effective and high accurate for discriminating the five textures. PMID:24618775

  11. Properties of Porous TiNbZr Shape Memory Alloy Fabricated by Mechanical Alloying and Hot Isostatic Pressing

    NASA Astrophysics Data System (ADS)

    Ma, L. W.; Chung, C. Y.; Tong, Y. X.; Zheng, Y. F.

    2011-07-01

    In the past decades, systematic researches have been focused on studying Ti-Nb-based SMAs by adding ternary elements, such as Mo, Sn, Zr, etc. However, only arc melting or induction melting methods, with subsequent hot or cold rolling, were used to fabricate these Ni-free SMAs. There is no work related to powder metallurgy and porous structures. This study focuses on the fabrication and characterization of porous Ti-22Nb-6Zr (at.%) shape memory alloys produced using elemental powders by means of mechanical alloying and hot isostatic pressing. It is found that the porous Ti-22Nb-6Zr alloys prepared by the HIP process exhibit a homogenous pore distribution with spherical pores, while the pores have irregular shape in the specimen prepared by conventional sintering. X-ray diffraction analysis showed that the solid solution-treated Ti-22Nb-6Zr alloy consists of both β phase and α″ martensite phase. Morphologies of martensite were observed. Finally, the porous Ti-22Nb-6Zr SMAs produced by both MA and HIP exhibit good mechanical properties, such as superior superelasticity, with maximum recoverable strain of ~3% and high compressive strength.

  12. A study of shape-dependent partial volume correction in pet imaging using ellipsoidal phantoms fabricated via rapid prototyping

    NASA Astrophysics Data System (ADS)

    Mille, Matthew M.

    Positron emission tomography (PET) with 2-[18F]fluoro-2-deoxy-D-glucose (FDG) is being increasingly recognized as an important tool for quantitative assessment of tumor response because of its ability to capture functional information about the tumor's metabolism. However, despite many advances in PET technology, measurements of tumor radiopharmaceutical uptake in PET are still challenged by issues of accuracy and consistency, thereby compromising the use of PET as a surrogate endpoint in clinical trials. One limiting component of the overall uncertainty in PET is the relatively poor spatial resolution of the images which directly affects the accuracy of the tumor radioactivity measurements. These spatial resolution effects, colloquially known as the partial volume effect (PVE), are a function of the characteristics of the scanner as well as the tumor being imaged. Previous efforts have shown that the PVE depends strongly on the tumor volume and the background-to-tumor activity concentration ratio. The PVE is also suspected to be a function of tumor shape, although to date no systematic study of this effect has been performed. This dissertation seeks to help fill the gap in the current knowledge about the shape-dependence of the PVE by attempting to quantify, through both theoretical calculation and experimental measurement, the magnitude of the shape effect for ellipsoidal tumors. An experimental investigation of the tumor shape effect necessarily requires tumor phantoms of multiple shapes. Hence, a prerequisite for this research was the design and fabrication of hollow tumor phantoms which could be filled uniformly with radioactivity and imaged on a PET scanner. The phantom fabrication was achieved with the aid of stereolithography and included prolate ellipsoids of various axis ratios. The primary experimental method involved filling the tumor phantoms with solutions of 18F whose activity concentrations were known and traceable to primary radioactivity standards

  13. Design, simulation, fabrication, packaging, and characterization of a MEMS-based mirror array for femtosecond pulse-shaping in phase and amplitude.

    PubMed

    Weber, Stefan M; Bonacina, Luigi; Noell, Wilfried; Kiselev, Denis; Extermann, Jérôme; Jutzi, Fabio; Lani, Sébastien; Nenadl, Ondrej; Wolf, Jean-Pierre; de Rooij, Nico F

    2011-07-01

    We present an in-detail description of the design, simulation, fabrication, and packaging of a linear micromirror array specifically designed for temporal pulse shaping of ultrashort laser pulses. The innovative features of this device include a novel comb-drive actuator allowing both piston and tilt motion for phase- and amplitude-shaping, and an X-shaped laterally reinforced spring preventing lateral snap-in while providing high flexibility for both degrees of freedom. PMID:21806226

  14. Design, simulation, fabrication, packaging, and characterization of a MEMS-based mirror array for femtosecond pulse-shaping in phase and amplitude

    NASA Astrophysics Data System (ADS)

    Weber, Stefan M.; Bonacina, Luigi; Noell, Wilfried; Kiselev, Denis; Extermann, Jérôme; Jutzi, Fabio; Lani, Sébastien; Nenadl, Ondrej; Wolf, Jean-Pierre; de Rooij, Nico F.

    2011-07-01

    We present an in-detail description of the design, simulation, fabrication, and packaging of a linear micromirror array specifically designed for temporal pulse shaping of ultrashort laser pulses. The innovative features of this device include a novel comb-drive actuator allowing both piston and tilt motion for phase- and amplitude-shaping, and an X-shaped laterally reinforced spring preventing lateral snap-in while providing high flexibility for both degrees of freedom.

  15. Nanograined Net-Shaped Fabrication of Rhenium Components by EB-PVD

    SciTech Connect

    Singh, Jogender; Wolfe, Douglas E.

    2004-02-04

    Cost-effective net-shaped forming components have brought considerable interest into DoD, NASA and DoE. Electron beam physical vapor deposition (EB-PVD) offers flexibility in forming net-shaped components with tailored microstructure and chemistry. High purity rhenium (Re) components including rhenium-coated graphite balls, Re- plates and tubes have been successfully manufactured by EB-PVD. EB-PVD Re components exhibited sub-micron and nano-sized grains with high hardness and strength as compared to CVD. It is estimated that the cost of Re components manufactured by EB-PVD would be less than the current CVD and powder-HIP Technologies.

  16. Nanograined Net-Shaped Fabrication of Rhenium Components by EB-PVD

    NASA Astrophysics Data System (ADS)

    Singh, Jogender; Wolfe, Douglas E.

    2004-02-01

    Cost-effective net-shaped forming components have brought considerable interest into DoD, NASA and DoE. Electron beam physical vapor deposition (EB-PVD) offers flexibility in forming net-shaped components with tailored microstructure and chemistry. High purity rhenium (Re) components including rhenium-coated graphite balls, Re- plates and tubes have been successfully manufactured by EB-PVD. EB-PVD Re components exhibited sub-micron and nano-sized grains with high hardness and strength as compared to CVD. It is estimated that the cost of Re components manufactured by EB-PVD would be less than the current CVD and powder-HIP Technologies.

  17. Fabrication of a Bioactive, PCL-based "Self-fitting" Shape Memory Polymer Scaffold.

    PubMed

    Nail, Lindsay N; Zhang, Dawei; Reinhard, Jessica L; Grunlan, Melissa A

    2015-01-01

    Tissue engineering has been explored as an alternative strategy for the treatment of critical-sized cranio-maxillofacial (CMF) bone defects. Essential to the success of this approach is a scaffold that is able to conformally fit within an irregular defect while also having the requisite biodegradability, pore interconnectivity and bioactivity. By nature of their shape recovery and fixity properties, shape memory polymer (SMP) scaffolds could achieve defect "self-fitting." In this way, following exposure to warm saline (~60 ºC), the SMP scaffold would become malleable, permitting it to be hand-pressed into an irregular defect. Subsequent cooling (~37 ºC) would return the scaffold to its relatively rigid state within the defect. To meet these requirements, this protocol describes the preparation of SMP scaffolds prepared via the photochemical cure of biodegradable polycaprolactone diacrylate (PCL-DA) using a solvent-casting particulate-leaching (SCPL) method. A fused salt template is utilized to achieve pore interconnectivity. To realize bioactivity, a polydopamine coating is applied to the surface of the scaffold pore walls. Characterization of self-fitting and shape memory behaviors, pore interconnectivity and in vitro bioactivity are also described. PMID:26556112

  18. Electron Beam Freeform Fabrication (EBF3) for Cost Effective Near-Net Shape Manufacturing

    NASA Technical Reports Server (NTRS)

    Taminger, Karen M.; Hafley, Robert A.

    2006-01-01

    Manufacturing of structural metal parts directly from computer aided design (CAD) data has been investigated by numerous researchers over the past decade. Researchers at NASA Langley Research Center are developing a new solid freeform fabrication process, electron beam freeform fabrication (EBF3), as a rapid metal deposition process that works efficiently with a variety of weldable alloys. EBF3 deposits of 2219 aluminium and Ti-6Al-4V have exhibited a range of grain morphologies depending upon the deposition parameters. These materials have exhibited excellent tensile properties comparable to typical handbook data for wrought plate product after post-processing heat treatments. The EBF3 process is capable of bulk metal deposition at deposition rates in excess of 2500 cubic centimeters per hour (150 in3/hr) or finer detail at lower deposition rates, depending upon the desired application. This process offers the potential for rapidly adding structural details to simpler cast or forged structures rather than the conventional approach of machining large volumes of chips to produce a monolithic metallic structure. Selective addition of metal onto simpler blanks of material can have a significant effect on lead time reduction and lower material and machining costs.

  19. Electron Beam Freeform Fabrication for Cost Effective Near-Net Shape Manufacturing

    NASA Technical Reports Server (NTRS)

    Taminger, Karen M.; Hafley, Robert A.

    2006-01-01

    Manufacturing of structural metal parts directly from computer aided design (CAD) data has been investigated by numerous researchers over the past decade. Researchers at NASA Langley Research Center are developing a new solid freeform fabrication process, electron beam freeform fabrication (EBF3), as a rapid metal deposition process that works efficiently with a variety of weldable alloys. EBF3 deposits of 2219 aluminium and Ti-6Al-4V have exhibited a range of grain morphologies depending upon the deposition parameters. These materials have exhibited excellent tensile properties comparable to typical handbook data for wrought plate product after post-processing heat treatments. The EBF3 process is capable of bulk metal deposition at deposition rates in excess of 2500 cm3/hr (150 in3/hr) or finer detail at lower deposition rates, depending upon the desired application. This process offers the potential for rapidly adding structural details to simpler cast or forged structures rather than the conventional approach of machining large volumes of chips to produce a monolithic metallic structure. Selective addition of metal onto simpler blanks of material can have a significant effect on lead time reduction and lower material and machining costs.

  20. Magnetic fabric of saucer-shaped sills in the Karoo Large Igneous Province

    NASA Astrophysics Data System (ADS)

    Polteau, S.; Ferre, E. C.; Planke, S.; Neumann, E.; Chevallier, L.

    2007-12-01

    Magmatic sill intrusions commonly exhibit a saucer geometry in undeformed sedimentary basins and volcanic rifted margins. Current emplacement models are based on the analysis of the intrusion geometry and their spatial relationships with potential feeders, not on the knowledge of the magma flow geometry. The Karoo Basin of South Africa hosts hundreds of saucer-shaped sills. Amongst these, the Golden Valley Sill is well-exposed and displays the connections with adjacent and nested saucers. A combination of detailed fieldwork observations and the anisotropy of magnetic susceptibility measurements were used to identify strain markers that can be interpreted in terms of magma flow directions. A total of 113 localities (6 specimens/site), mostly including opposite sill margins, have been sampled for anisotropy of magnetic susceptibility (AMS) analyses. The magnetic properties were defined by measuring hysteresis cycles and K-T curves on 34 and 19 specimens, respectively. The majority of the localities display well-defined magnetic foliations that consistently dip outward from the centre of the Golden Valley Sill. This orientation of the magnetic foliation most likely represents inflation/deflation cycles of the intruding sill that interacts with non-static enclosing walls. In addition, four magma channels were identified and display an imbrication of the magnetic foliation that indicates an outward magma flow direction. In conclusion, the observed magma flow geometries derived from macroscopic flow indicators and the AMS data correlate well and are used to constrain an emplacement model for the Golden Valley Sill Complex. Finally, the emplacement model of sill complexes repeats the cycle -injection of magma - formation of a saucer-shaped sill - pressure build up - fracturation and pressure drop - channeling of magma - injection of (new batch of) magma - formation of a new saucer-shaped sill- until the magma supply stops.

  1. Top-Down Particle Fabrication: Control of Size and Shape for Diagnostic Imaging and Drug Delivery

    PubMed Central

    Canelas, Dorian A.; Herlihy, Kevin P.; DeSimone, Joseph M.

    2009-01-01

    This review discusses rational design of particles for use as therapeutic vectors and diagnostic imaging agent carriers. The emerging importance of both particle size and shape is considered, and the adaptation and modification of soft lithography methods to produce nanoparticles is highlighted. To this end, studies utilizing particles made via a process called Particle Replication In Non-wetting Templates (PRINT™) are discussed. In addition, insights gained into therapeutic cargo and imaging agent delivery from related types of polymer-based carriers are considered. PMID:20049805

  2. Fabrication of anatomically-shaped cartilage constructs using decellularized cartilage-derived matrix scaffolds.

    PubMed

    Rowland, Christopher R; Colucci, Lina A; Guilak, Farshid

    2016-06-01

    The native extracellular matrix of cartilage contains entrapped growth factors as well as tissue-specific epitopes for cell-matrix interactions, which make it a potentially attractive biomaterial for cartilage tissue engineering. A limitation to this approach is that the native cartilage extracellular matrix possesses a pore size of only a few nanometers, which inhibits cellular infiltration. Efforts to increase the pore size of cartilage-derived matrix (CDM) scaffolds dramatically attenuate their mechanical properties, which makes them susceptible to cell-mediated contraction. In previous studies, we have demonstrated that collagen crosslinking techniques are capable of preventing cell-mediated contraction in CDM disks. In the current study, we investigated the effects of CDM concentration and pore architecture on the ability of CDM scaffolds to resist cell-mediated contraction. Increasing CDM concentration significantly increased scaffold mechanical properties, which played an important role in preventing contraction, and only the highest CDM concentration (11% w/w) was able to retain the original scaffold dimensions. However, the increase in CDM concentration led to a concomitant decrease in porosity and pore size. Generating a temperature gradient during the freezing process resulted in unidirectional freezing, which aligned the formation of ice crystals during the freezing process and in turn produced aligned pores in CDM scaffolds. These aligned pores increased the pore size of CDM scaffolds at all CDM concentrations, and greatly facilitated infiltration by mesenchymal stem cells (MSCs). These methods were used to fabricate of anatomically-relevant CDM hemispheres. CDM hemispheres with aligned pores supported uniform MSC infiltration and matrix deposition. Furthermore, these CDM hemispheres retained their original architecture and did not contract, warp, curl, or splay throughout the entire 28-day culture period. These findings demonstrate that given the

  3. Bernoulli convolutions and 1D dynamics

    NASA Astrophysics Data System (ADS)

    Kempton, Tom; Persson, Tomas

    2015-10-01

    We describe a family {φλ} of dynamical systems on the unit interval which preserve Bernoulli convolutions. We show that if there are parameter ranges for which these systems are piecewise convex, then the corresponding Bernoulli convolution will be absolutely continuous with bounded density. We study the systems {φλ} and give some numerical evidence to suggest values of λ for which {φλ} may be piecewise convex.

  4. Net Shaped Component Fabrication of Refractory Metal Alloys using Vacuum Plasma Spraying

    NASA Technical Reports Server (NTRS)

    Sen, S.; ODell, S.; Gorti, S.; Litchford, R.

    2006-01-01

    The vacuum plasma spraying (VPS) technique was employed to produce dense and net shaped components of a new tungsten-rhenium (W-Re) refractory metal alloy. The fine grain size obtained using this technique enhanced the mechanical properties of the alloy at elevated temperatures. The alloy development also included incorporation of thermodynamically stable dispersion phases to pin down grain boundaries at elevated temperatures and thereby circumventing the inherent problem of recrystallization of refractory alloys at elevated temperatures. Requirements for such alloys as related to high temperature space propulsion components will be discussed. Grain size distribution as a function of cooling rate and dispersion phase loading will be presented. Mechanical testing and grain growth results as a function of temperature will also be discussed.

  5. Coset Codes Viewed as Terminated Convolutional Codes

    NASA Technical Reports Server (NTRS)

    Fossorier, Marc P. C.; Lin, Shu

    1996-01-01

    In this paper, coset codes are considered as terminated convolutional codes. Based on this approach, three new general results are presented. First, it is shown that the iterative squaring construction can equivalently be defined from a convolutional code whose trellis terminates. This convolutional code determines a simple encoder for the coset code considered, and the state and branch labelings of the associated trellis diagram become straightforward. Also, from the generator matrix of the code in its convolutional code form, much information about the trade-off between the state connectivity and complexity at each section, and the parallel structure of the trellis, is directly available. Based on this generator matrix, it is shown that the parallel branches in the trellis diagram of the convolutional code represent the same coset code C(sub 1), of smaller dimension and shorter length. Utilizing this fact, a two-stage optimum trellis decoding method is devised. The first stage decodes C(sub 1), while the second stage decodes the associated convolutional code, using the branch metrics delivered by stage 1. Finally, a bidirectional decoding of each received block starting at both ends is presented. If about the same number of computations is required, this approach remains very attractive from a practical point of view as it roughly doubles the decoding speed. This fact is particularly interesting whenever the second half of the trellis is the mirror image of the first half, since the same decoder can be implemented for both parts.

  6. YVO 4:Eu 3+ arrays with flower-like and rod-like shape fabricated by a hydrothermal method

    NASA Astrophysics Data System (ADS)

    Bao, Amurisana; Lai, Hua; Yang, Yuming; Xu, Weiwei; Tao, Chunyan; Zhang, Hua; Yang, Hua

    2008-09-01

    Large-scale well-aligned rod-like and flower-like YVO 4:Eu 3+ crystals were prepared on glass substrates by a hydrothermal method in a controllable way with additive ethylenediamine tetraacetic acid disodium salt [Na 2H 2L·2H 2O]. No extra surfactants or templates were used. In the synthesis process, well-aligned YVO 4:Eu 3+ microrods were fabricated on YVO 4:Eu 3+-seed-coated substrates. The YVO 4:Eu 3+ seed precursor was prepared by a sol-gel reaction. And well-defined flower-like YVO 4:Eu 3+ microstructures were fabricated on bare substrates. The products were characterized by X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), and photoluminescence (PL). TEM and SEM studies reveal that the flower-like superstructure is composed of dozens of radially oriented rhombus-shaped microrods and the crystalline microrods with rectangle cross-sections and well-defined crystallographic faces are grown directly onto the substrates. This convenient method may be applicable to prepare other orthovanadate phosphors with 3D morphologies.

  7. Design and fabrication of a bat-inspired flapping-flight platform using shape memory alloy muscles and joints

    NASA Astrophysics Data System (ADS)

    Furst, Stephen J.; Bunget, George; Seelecke, Stefan

    2013-01-01

    This work focuses on the development of a concept for a micro-air vehicle (MAV) based on a bio-inspired flapping motion that is generated from integrated smart materials. Since many smart materials have their own biomimetic characteristics and the potential to be highly efficient, lightweight, and streamlined, they are ideal candidates for use in structural or actuator components in MAVs. In this work, shape memory alloy (SMA) actuator wires are used as analogs for biological muscles, and super-elastic SMAs are implemented as flexible joints capable of large bending angles. While biological organisms have an intrinsic sensing array composed of nerves, the SMA wires also provide self-sensing by virtue of a phase-dependent resistance change. Study of the biology and flight characteristics of natural fliers concluded that the bat provides an ideal platform for SMA muscle wires because of its comparatively low wingbeat frequency and superb maneuverability. A first-generation prototype is built to further the understanding of fabricating Nature’s designs. The engineering design is then improved further in a second-generation prototype that combines 3D printing and new techniques for embedding SMA wires and shaping SMA joints for improved robustness, reproducibility, and lifetime. These prototypes are on display at the North Carolina Museum of Natural Science’s Nature Research Center, which has the goal of bridging the gaps between biology and engineering.

  8. Design and Fabrication of a Biodegradable, Covalently Crosslinked Shape-Memory Alginate Scaffold for Cell and Growth Factor Delivery

    PubMed Central

    Wang, Lin; Shansky, Janet; Borselli, Cristina; Mooney, David

    2012-01-01

    The successful use of transplanted cells and/or growth factors for tissue repair is limited by a significant cell loss and/or rapid growth factor diffusion soon after implantation. Highly porous alginate scaffolds formed with covalent crosslinking have been used to improve cell survival and growth factor release kinetics, but require open-wound surgical procedures for insertion and have not previously been designed to readily degrade in vivo. In this study, a biodegradable, partially crosslinked alginate scaffold with shape-memory properties was fabricated for minimally invasive surgical applications. A mixture of high and low molecular weight partially oxidized alginate modified with RGD peptides was covalently crosslinked using carbodiimide chemistry. The scaffold was compressible 11-fold and returned to its original shape when rehydrated. Scaffold degradation properties in vitro indicated ∼85% mass loss by 28 days. The greater than 90% porous scaffolds released the recombinant growth factor insulin-like growth factor-1 over several days in vitro and allowed skeletal muscle cell survival, proliferation, and migration from the scaffold over a 28-day period. The compressible scaffold thus has the potential to be delivered by a minimally invasive technique, and when rehydrated in vivo with cells and/or growth factors, could serve as a temporary delivery vehicle for tissue repair. PMID:22646518

  9. Do a bit more with convolution.

    PubMed

    Olsthoorn, Theo N

    2008-01-01

    Convolution is a form of superposition that efficiently deals with input varying arbitrarily in time or space. It works whenever superposition is applicable, that is, for linear systems. Even though convolution is well-known since the 19th century, this valuable method is still missing in most textbooks on ground water hydrology. This limits widespread application in this field. Perhaps most papers are too complex mathematically as they tend to focus on the derivation of analytical expressions rather than solving practical problems. However, convolution is straightforward with standard mathematical software or even a spreadsheet, as is demonstrated in the paper. The necessary system responses are not limited to analytic solutions; they may also be obtained by running an already existing ground water model for a single stress period until equilibrium is reached. With these responses, high-resolution time series of head or discharge may then be computed by convolution for arbitrary points and arbitrarily varying input, without further use of the model. There are probably thousands of applications in the field of ground water hydrology that may benefit from convolution. Therefore, its inclusion in ground water textbooks and courses is strongly needed. PMID:18181860

  10. Cost-Benefit Analysis for the Advanced Near Net Shape Technology (ANNST) Method for Fabricating Stiffened Cylinders

    NASA Technical Reports Server (NTRS)

    Stoner, Mary Cecilia; Hehir, Austin R.; Ivanco, Marie L.; Domack, Marcia S.

    2016-01-01

    This cost-benefit analysis assesses the benefits of the Advanced Near Net Shape Technology (ANNST) manufacturing process for fabricating integrally stiffened cylinders. These preliminary, rough order-of-magnitude results report a 46 to 58 percent reduction in production costs and a 7-percent reduction in weight over the conventional metallic manufacturing technique used in this study for comparison. Production cost savings of 35 to 58 percent were reported over the composite manufacturing technique used in this study for comparison; however, the ANNST concept was heavier. In this study, the predicted return on investment of equipment required for the ANNST method was ten cryogenic tank barrels when compared with conventional metallic manufacturing. The ANNST method was compared with the conventional multi-piece metallic construction and composite processes for fabricating integrally stiffened cylinders. A case study compared these three alternatives for manufacturing a cylinder of specified geometry, with particular focus placed on production costs and process complexity, with cost analyses performed by the analogy and parametric methods. Furthermore, a scalability study was conducted for three tank diameters to assess the highest potential payoff of the ANNST process for manufacture of large-diameter cryogenic tanks. The analytical hierarchy process (AHP) was subsequently used with a group of selected subject matter experts to assess the value of the various benefits achieved by the ANNST method for potential stakeholders. The AHP study results revealed that decreased final cylinder mass and quality assurance were the most valued benefits of cylinder manufacturing methods, therefore emphasizing the relevance of the benefits achieved with the ANNST process for future projects.

  11. Variation in mouthguard thickness according to heating conditions during fabrication Part 2: sheet shape and effect of thermal shrinkage.

    PubMed

    Takahashi, Mutsumi; Koide, Kaoru

    2016-06-01

    The aim of this study was to investigate the influence of the thermal shrinkage to thickness of the mouthguard with the heating method by the setting position of a sheet and the working model using an ethylene vinyl acetate sheet prepared by extrusion. Mouthguards were fabricated with EVA sheets (4.0 mm thick) using a vacuum-forming machine. Two forming conditions were compared: the square sheet was pinched by the clamping frame attached to the forming machine (S); and the round sheet was pinched at the top and bottom and stabilized by the circle tray (R). The sheet was aligned to make the sheet's extrusion direction vertical (V) or parallel (P) to the midline of the working model. The following two heating conditions were compared: (i) the sheet was molded when it sagged 15 mm below the level of the sheet frame measured at the top of the post in condition S (S-0), or that sagged 10 mm in condition R (R-0) in the usual position; (ii) the sheet frame was lowered by 50 mm from the ordinary height (S-50, R-50). Postmolding thickness was determined using a measuring device. Measurement points were the incisal and molar portion. Differences in the change of thickness of mouthguards molded under different heating conditions and extrusion directions for each sheet shape were analyzed by two-way analysis of variance (anova). The results of this study showed that by lowering the height of the sheet frame, the difference of the sheet temperature of each part was reduced. Among all sheets, condition V produced under S-50 and R-50 had the largest thickness independently of shape sheet. Furthermore, thickness reduction is effectively suppressed by aligning the direction of the extruded sheet to be vertical to the midline of the model. PMID:26337263

  12. Fabrication of cyclodextrins-procainamide supramolecular self-assembly: shape-shifting of nanosheet into microtubular structure.

    PubMed

    Siva, S; Kothai Nayaki, S; Rajendiran, N

    2015-05-20

    Encapsulation behavior of α- and β-cyclodextrins (α-CD, β-CD) with procainamide hydrochloride (PCA) has been investigated by absorption, fluorescence, time-resolved fluorescence, proton nuclear magnetic resonance spectroscopy, scanning electron microscope, Fourier transform-infrared spectroscopy, differential scanning calorimetry, and powder X-ray diffraction techniques. Spectral results revealed that PCA forms 1:2 drug-CD2 inclusion complexes with CDs. Novel supramolecular self-assemblies have been fabricated by inclusion complexation of PCA with α-CD/β-CD and characterized by transmission electron microscope and micro-Raman imaging. The obtained results from transmission electron microscope indicated that PCA/α-CD complex could form nano-sized particles. However, when the macrocyclic ring with six glucose units was switched into seven glucose units, the resultant PCA/β-CD complex could be self-assembled to micro-sized tubular structures. Shape-shifting of 2D nanosheet into 1D microtube by simple rolling mechanism was analyzed. Thermodynamic parameters of inclusion process were determined by Parameter Method 3 calculations. PMID:25817651

  13. Spring Characteristics of Circular Arc Shaped 3D Micro-cantilevers Fabricated Using III-V Semiconductor Strain-driven Bending Process

    NASA Astrophysics Data System (ADS)

    Sasaki, T. K.; Iwase, H.; Wang, J.; Akabori, M.; Yamada, S.

    2011-12-01

    We have investigated the characteristics of the circular arc shaped 3D micro-cantilever. The cantilever was fabricated using strain driven self-bending process based on the epitaxial growth. The arc shape can be used as the spring element in the various micro electro- mechanical- system (MEMS) applications. In this work, we focused on the force-deflection relationships, which would give the spring constant of the arc spring. In the case of the arc spring, spring constant depends on the dimensional parameters such as width, thickness and curvature radius. From the dependence of curvature radius on width, there seems no stress relaxation in width direction (transverse to deformation direction). Moreover, the measured spring constants of the fabricated cantilevers were also larger than the estimated ones from the curvature radius. The discrepancy indicates a possibility of stiffness enhancement of the circular arc shaped cantilevers.

  14. Sequential Syndrome Decoding of Convolutional Codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Truong, T. K.

    1984-01-01

    The algebraic structure of convolutional codes are reviewed and sequential syndrome decoding is applied to those codes. These concepts are then used to realize by example actual sequential decoding, using the stack algorithm. The Fano metric for use in sequential decoding is modified so that it can be utilized to sequentially find the minimum weight error sequence.

  15. Number-Theoretic Functions via Convolution Rings.

    ERIC Educational Resources Information Center

    Berberian, S. K.

    1992-01-01

    Demonstrates the number theory property that the number of divisors of an integer n times the number of positive integers k, less than or equal to and relatively prime to n, equals the sum of the divisors of n using theory developed about multiplicative functions, the units of a convolution ring, and the Mobius Function. (MDH)

  16. Precise two-dimensional D-bar reconstructions of human chest and phantom tank via sinc-convolution algorithm

    PubMed Central

    2012-01-01

    Background Electrical Impedance Tomography (EIT) is used as a fast clinical imaging technique for monitoring the health of the human organs such as lungs, heart, brain and breast. Each practical EIT reconstruction algorithm should be efficient enough in terms of convergence rate, and accuracy. The main objective of this study is to investigate the feasibility of precise empirical conductivity imaging using a sinc-convolution algorithm in D-bar framework. Methods At the first step, synthetic and experimental data were used to compute an intermediate object named scattering transform. Next, this object was used in a two-dimensional integral equation which was precisely and rapidly solved via sinc-convolution algorithm to find the square root of the conductivity for each pixel of image. For the purpose of comparison, multigrid and NOSER algorithms were implemented under a similar setting. Quality of reconstructions of synthetic models was tested against GREIT approved quality measures. To validate the simulation results, reconstructions of a phantom chest and a human lung were used. Results Evaluation of synthetic reconstructions shows that the quality of sinc-convolution reconstructions is considerably better than that of each of its competitors in terms of amplitude response, position error, ringing, resolution and shape-deformation. In addition, the results confirm near-exponential and linear convergence rates for sinc-convolution and multigrid, respectively. Moreover, the least degree of relative errors and the most degree of truth were found in sinc-convolution reconstructions from experimental phantom data. Reconstructions of clinical lung data show that the related physiological effect is well recovered by sinc-convolution algorithm. Conclusions Parametric evaluation demonstrates the efficiency of sinc-convolution to reconstruct accurate conductivity images from experimental data. Excellent results in phantom and clinical reconstructions using sinc-convolution

  17. About closedness by convolution of the Tsallis maximizers

    NASA Astrophysics Data System (ADS)

    Vignat, C.; Hero, A. O., III; Costa, J. A.

    2004-09-01

    In this paper, we study the stability under convolution of the maximizing distributions of the Tsallis entropy under energy constraint (called hereafter Tsallis distributions). These distributions are shown to obey three important properties: a stochastic representation property, an orthogonal invariance property and a duality property. As a consequence of these properties, the behavior of Tsallis distributions under convolution is characterized. At last, a special random convolution, called Kingman convolution, is shown to ensure the stability of Tsallis distributions.

  18. Multiple deep convolutional neural networks averaging for face alignment

    NASA Astrophysics Data System (ADS)

    Zhang, Shaohua; Yang, Hua; Yin, Zhouping

    2015-05-01

    Face alignment is critical for face recognition, and the deep learning-based method shows promise for solving such issues, given that competitive results are achieved on benchmarks with additional benefits, such as dispensing with handcrafted features and initial shape. However, most existing deep learning-based approaches are complicated and quite time-consuming during training. We propose a compact face alignment method for fast training without decreasing its accuracy. Rectified linear unit is employed, which allows all networks approximately five times faster convergence than a tanh neuron. An eight learnable layer deep convolutional neural network (DCNN) based on local response normalization and a padding convolutional layer (PCL) is designed to provide reliable initial values during prediction. A model combination scheme is presented to further reduce errors, while showing that only two network architectures and hyperparameter selection procedures are required in our approach. A three-level cascaded system is ultimately built based on the DCNNs and model combination mode. Extensive experiments validate the effectiveness of our method and demonstrate comparable accuracy with state-of-the-art methods on BioID, labeled face parts in the wild, and Helen datasets.

  19. Fabrication of a three-layer SU-8 mould with inverted T-shaped cavities based on a sacrificial photoresist layer technique.

    PubMed

    Liu, Junshan; Zhang, Dong; Sha, Baoyong; Yin, Penghe; Xu, Zheng; Liu, Chong; Wang, Liding; Xu, Feng; Wang, Lin

    2014-10-01

    A novel method for fabricating a three-layer SU-8 mould with inverted T-shaped cavities is presented. The first two SU-8 layers were spin coated and exposed separately, and simultaneously developed to fabricate the bottom and the horizontal part of the inverted T-shaped cavity. Then, a positive photoresist was filled into the cavity, and a wet lapping process was performed to remove the excess photoresist and make a temporary substrate. The third SU-8 layer was spin coated on the temporary substrate to make the vertical part of the inverted T-shaped cavity. The sacrificial photoresist layer can prevent the first two SU-8 layers from being secondly exposed, and make a temporary substrate for the third SU-8 layer at the same time. Moreover, the photoresist can be easily removed with the development of the third SU-8 layer. A polydimethylsiloxane (PDMS) microchip with arrays of T-shaped cantilevers for studying the mechanics of cells was fabricated by using the SU-8 mould. PMID:24850230

  20. Design and fabrication of 3D-printed anatomically shaped lumbar cage for intervertebral disc (IVD) degeneration treatment.

    PubMed

    Serra, T; Capelli, C; Toumpaniari, R; Orriss, I R; Leong, J J H; Dalgarno, K; Kalaskar, D M

    2016-01-01

    Spinal fusion is the gold standard surgical procedure for degenerative spinal conditions when conservative therapies have been unsuccessful in rehabilitation of patients. Novel strategies are required to improve biocompatibility and osseointegration of traditionally used materials for lumbar cages. Furthermore, new design and technologies are needed to bridge the gap due to the shortage of optimal implant sizes to fill the intervertebral disc defect. Within this context, additive manufacturing technology presents an excellent opportunity to fabricate ergonomic shape medical implants. The goal of this study is to design and manufacture a 3D-printed lumbar cage for lumbar interbody fusion. Optimisations of the proposed implant design and its printing parameters were achieved via in silico analysis. The final construct was characterised via scanning electron microscopy, contact angle, x-ray micro computed tomography (μCT), atomic force microscopy, and compressive test. Preliminary in vitro cell culture tests such as morphological assessment and metabolic activities were performed to access biocompatibility of 3D-printed constructs. Results of in silico analysis provided a useful platform to test preliminary cage design and to find an optimal value of filling density for 3D printing process. Surface characterisation confirmed a uniform coating of nHAp with nanoscale topography. Mechanical evaluation showed mechanical properties of final cage design similar to that of trabecular bone. Preliminary cell culture results showed promising results in terms of cell growth and activity confirming biocompatibility of constructs. Thus for the first time, design optimisation based on computational and experimental analysis combined with the 3D-printing technique for intervertebral fusion cage has been reported in a single study. 3D-printing is a promising technique for medical applications and this study paves the way for future development of customised implants in spinal

  1. Fabrication of transparent, tough, and conductive shape-memory polyurethane films by incorporating a small amount of high-quality graphene.

    PubMed

    Jung, Yong Chae; Kim, Jin Hee; Hayashi, Takuya; Kim, Yoong Ahm; Endo, Morinobu; Terrones, Mauricio; Dresselhaus, Mildred S

    2012-04-23

    We report a mechanically strong, electrically and thermally conductive, and optically transparent shape-memory polyurethane composite which was fabricated by introducing a small amount (0.1 wt%) of high-quality graphene as a filler. Geometrically large (≈4.6 μm(2)), but highly crystallized few-layer graphenes, verified by Raman spectroscopy and transmission electron microscopy, were prepared by the sonication of expandable graphite in an organic solvent. Oxygen- containing functional groups at the edge plane of graphene were crucial for an effective stress transfer from the graphene to polyurethane. Homogeneously dispersed few-layered graphene enabled polyurethane to have a high shape recovery force of 1.8 MPa cm(-3). Graphene, which is intrinsically stretchable up to 10%, will enable high-performance composites to be fabricated at relatively low cost and we thus envisage that such composites may replace carbon nanotubes for various applications in the near future. PMID:22328293

  2. Dip TIPS as a Facile and Versatile Method for Fabrication of Polymer Foams with Controlled Shape, Size and Pore Architecture for Bioengineering Applications

    PubMed Central

    Kasoju, Naresh; Kubies, Dana; Kumorek, Marta M.; Kříž, Jan; Fábryová, Eva; Machová, Lud'ka; Kovářová, Jana; Rypáček, František

    2014-01-01

    The porous polymer foams act as a template for neotissuegenesis in tissue engineering, and, as a reservoir for cell transplants such as pancreatic islets while simultaneously providing a functional interface with the host body. The fabrication of foams with the controlled shape, size and pore structure is of prime importance in various bioengineering applications. To this end, here we demonstrate a thermally induced phase separation (TIPS) based facile process for the fabrication of polymer foams with a controlled architecture. The setup comprises of a metallic template bar (T), a metallic conducting block (C) and a non-metallic reservoir tube (R), connected in sequence T-C-R. The process hereinafter termed as Dip TIPS, involves the dipping of the T-bar into a polymer solution, followed by filling of the R-tube with a freezing mixture to induce the phase separation of a polymer solution in the immediate vicinity of T-bar; Subsequent free-drying or freeze-extraction steps produced the polymer foams. An easy exchange of the T-bar of a spherical or rectangular shape allowed the fabrication of tubular, open- capsular and flat-sheet shaped foams. A mere change in the quenching time produced the foams with a thickness ranging from hundreds of microns to several millimeters. And, the pore size was conveniently controlled by varying either the polymer concentration or the quenching temperature. Subsequent in vivo studies in brown Norway rats for 4-weeks demonstrated the guided cell infiltration and homogenous cell distribution through the polymer matrix, without any fibrous capsule and necrotic core. In conclusion, the results show the “Dip TIPS” as a facile and adaptable process for the fabrication of anisotropic channeled porous polymer foams of various shapes and sizes for potential applications in tissue engineering, cell transplantation and other related fields. PMID:25275373

  3. A convolutional neural network neutrino event classifier

    DOE PAGESBeta

    Aurisano, A.; Radovic, A.; Rocco, D.; Himmel, A.; Messier, M. D.; Niner, E.; Pawloski, G.; Psihas, F.; Sousa, A.; Vahle, P.

    2016-09-01

    Here, convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology withoutmore » the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.« less

  4. Deep Learning with Hierarchical Convolutional Factor Analysis

    PubMed Central

    Chen, Bo; Polatkan, Gungor; Sapiro, Guillermo; Blei, David; Dunson, David; Carin, Lawrence

    2013-01-01

    Unsupervised multi-layered (“deep”) models are considered for general data, with a particular focus on imagery. The model is represented using a hierarchical convolutional factor-analysis construction, with sparse factor loadings and scores. The computation of layer-dependent model parameters is implemented within a Bayesian setting, employing a Gibbs sampler and variational Bayesian (VB) analysis, that explicitly exploit the convolutional nature of the expansion. In order to address large-scale and streaming data, an online version of VB is also developed. The number of basis functions or dictionary elements at each layer is inferred from the data, based on a beta-Bernoulli implementation of the Indian buffet process. Example results are presented for several image-processing applications, with comparisons to related models in the literature. PMID:23787342

  5. Fabrication and characterization of a foamed polylactic acid (PLA)/ thermoplastic polyurethane (TPU) shape memory polymer (SMP) blend for biomedical and clinical applications

    NASA Astrophysics Data System (ADS)

    Song, Janice J.; Srivastava, Ijya; Kowalski, Jennifer; Naguib, Hani E.

    2014-03-01

    Shape memory polymers (SMP) are a class of stimuli-responsive materials that are able to respond to external stimulus such as heat by altering their shape. Bio-compatible SMPs have a number of advantages over static materials and are being studied extensively for biomedical and clinical applications (such as tissue stents and scaffolds). A previous study has demonstrated that the bio-compatible polymer blend of polylactic acid (PLA)/ thermoplastic polyurethane (TPU) (50/50 and 70/30) exhibit good shape memory properties. In this study, the mechanical and thermo-mechanical (shape memory) properties of TPU/PLA SMP blends were characterized; the compositions studied were 80/20, 65/35, and 50/50 TPU/PLA. In addition, porous TPU/PLA SMP blends were fabricated with a gas-foaming technique; and the morphology of the porous structure of these SMPs foams were characterized with scanning electron microscopy (SEM). The TPU/PLA bio-compatible SMP blend was fabricated with melt-blending and compression molding. The glass transition temperature (Tg) of the SMP blends was determined with a differential scanning calorimeter (DSC). The mechanical properties studied were the stress-strain behavior, tensile strength, and elastic modulus; and the thermomechanical (or shape memory) properties studied were the shape fixity rate (Rf), shape recovery rate (Rr), response time, and the effect of recovery temperature on Rr. The porous 80/20 PLA/TPU SMP blend was found to have the highest tensile strength, toughness and percentage extension, as well as the lowest density and uniform pore structure in the micron and submicron scale. The porous 80/20 TPU/PLA SMP blend may be further developed for specific biomedical and clinical applications where a combination of tensile strength, toughness, and low density are required.

  6. Quantum convolutional codes derived from constacyclic codes

    NASA Astrophysics Data System (ADS)

    Yan, Tingsu; Huang, Xinmei; Tang, Yuansheng

    2014-12-01

    In this paper, three families of quantum convolutional codes are constructed. The first one and the second one can be regarded as a generalization of Theorems 3, 4, 7 and 8 [J. Chen, J. Li, F. Yang and Y. Huang, Int. J. Theor. Phys., doi:10.1007/s10773-014-2214-6 (2014)], in the sense that we drop the constraint q ≡ 1 (mod 4). Furthermore, the second one and the third one attain the quantum generalized Singleton bound.

  7. Satellite image classification using convolutional learning

    NASA Astrophysics Data System (ADS)

    Nguyen, Thao; Han, Jiho; Park, Dong-Chul

    2013-10-01

    A satellite image classification method using Convolutional Neural Network (CNN) architecture is proposed in this paper. As a special case of deep learning, CNN classifies classes of images without any feature extraction step while other existing classification methods utilize rather complex feature extraction processes. Experiments on a set of satellite image data and the preliminary results show that the proposed classification method can be a promising alternative over existing feature extraction-based schemes in terms of classification accuracy and classification speed.

  8. Spatio-spectral concentration of convolutions

    NASA Astrophysics Data System (ADS)

    Hanasoge, Shravan M.

    2016-05-01

    Differential equations may possess coefficients that vary on a spectrum of scales. Because coefficients are typically multiplicative in real space, they turn into convolution operators in spectral space, mixing all wavenumbers. However, in many applications, only the largest scales of the solution are of interest and so the question turns to whether it is possible to build effective coarse-scale models of the coefficients in such a manner that the large scales of the solution are left intact. Here we apply the method of numerical homogenisation to deterministic linear equations to generate sub-grid-scale models of coefficients at desired frequency cutoffs. We use the Fourier basis to project, filter and compute correctors for the coefficients. The method is tested in 1D and 2D scenarios and found to reproduce the coarse scales of the solution to varying degrees of accuracy depending on the cutoff. We relate this method to mode-elimination Renormalisation Group (RG) and discuss the connection between accuracy and the cutoff wavenumber. The tradeoff is governed by a form of the uncertainty principle for convolutions, which states that as the convolution operator is squeezed in the spectral domain, it broadens in real space. As a consequence, basis sparsity is a high virtue and the choice of the basis can be critical.

  9. Convolutional Neural Network Based dem Super Resolution

    NASA Astrophysics Data System (ADS)

    Chen, Zixuan; Wang, Xuewen; Xu, Zekai; Hou, Wenguang

    2016-06-01

    DEM super resolution is proposed in our previous publication to improve the resolution for a DEM on basis of some learning examples. Meanwhile, the nonlocal algorithm is introduced to deal with it and lots of experiments show that the strategy is feasible. In our publication, the learning examples are defined as the partial original DEM and their related high measurements due to this way can avoid the incompatibility between the data to be processed and the learning examples. To further extent the applications of this new strategy, the learning examples should be diverse and easy to obtain. Yet, it may cause the problem of incompatibility and unrobustness. To overcome it, we intend to investigate a convolutional neural network based method. The input of the convolutional neural network is a low resolution DEM and the output is expected to be its high resolution one. A three layers model will be adopted. The first layer is used to detect some features from the input, the second integrates the detected features to some compressed ones and the final step transforms the compressed features as a new DEM. According to this designed structure, some learning DEMs will be taken to train it. Specifically, the designed network will be optimized by minimizing the error of the output and its expected high resolution DEM. In practical applications, a testing DEM will be input to the convolutional neural network and a super resolution will be obtained. Many experiments show that the CNN based method can obtain better reconstructions than many classic interpolation methods.

  10. Blind Identification of Convolutional Encoder Parameters

    PubMed Central

    Su, Shaojing; Zhou, Jing; Huang, Zhiping; Liu, Chunwu; Zhang, Yimeng

    2014-01-01

    This paper gives a solution to the blind parameter identification of a convolutional encoder. The problem can be addressed in the context of the noncooperative communications or adaptive coding and modulations (ACM) for cognitive radio networks. We consider an intelligent communication receiver which can blindly recognize the coding parameters of the received data stream. The only knowledge is that the stream is encoded using binary convolutional codes, while the coding parameters are unknown. Some previous literatures have significant contributions for the recognition of convolutional encoder parameters in hard-decision situations. However, soft-decision systems are applied more and more as the improvement of signal processing techniques. In this paper we propose a method to utilize the soft information to improve the recognition performances in soft-decision communication systems. Besides, we propose a new recognition method based on correlation attack to meet low signal-to-noise ratio situations. Finally we give the simulation results to show the efficiency of the proposed methods. PMID:24982997

  11. Recent Advances in Near-Net-Shape Fabrication of Al-Li Alloy 2195 for Launch Vehicles

    NASA Technical Reports Server (NTRS)

    Wagner, John; Domack, Marcia; Hoffman, Eric

    2007-01-01

    Recent applications in launch vehicles use 2195 processed to Super Lightweight Tank specifications. Potential benefits exist by tailoring heat treatment and other processing parameters to the application. Assess the potential benefits and advocate application of Al-Li near-net-shape technologies for other launch vehicle structural components. Work with manufacturing and material producers to optimize Al-Li ingot shape and size for enhanced near-net-shape processing. Examine time dependent properties of 2195 critical for reusable applications.

  12. Differences in the thickness of mouthguards fabricated from ethylene vinyl acetate copolymer sheets with differently arranged v-shaped grooves: part 2 - effect of shape on the working model.

    PubMed

    Takahashi, Mutsumi; Koide, Kaoru; Mizuhashi, Fumi

    2014-12-01

    The aim of this study was to evaluate the change in thickness of a working model mouthguard sheet due to different shape. Mouthguards were fabricated with ethylene vinyl acetate (EVA) sheets (4.0 mm thick) using a vacuum-forming machine. Two shapes of the sheet were compared: normal sheet or v-shaped groove 10-40 mm from the anterior end. Additionally, two shapes of the working model were compared; the basal plane was vertical to the tooth axis of the maxillary central incisor (condition A), and the occlusal plane was parallel to the basal plane (condition B). Sheets were heated until they sagged 15 mm below the clamp. Postmolding thickness was determined for the incisal portion (incisal edge and labial surface) and molar portion (cusp and buccal surface). Differences in the change in thickness due to the shape of the sheets and model were analyzed using two-way anova followed by a Bonferroni's multiple comparison tests. The thickness of the mouthguard sheet with v-shaped grooves was more than that of the normal sheet at all measuring points under condition A and condition B (P < 0.01). The thickness of condition B was less than that of condition A, there the incisal portion in the normal sheet and the incisal edge in the sheet with v-shaped grooves (P < 0.01). The present results suggested that thickness after molding was secured by the use of the sheet with v-shaped grooves. In particular, the model with the undercut on the labial surface may be clinically useful. PMID:25039583

  13. The analysis of convolutional codes via the extended Smith algorithm

    NASA Technical Reports Server (NTRS)

    Mceliece, R. J.; Onyszchuk, I.

    1993-01-01

    Convolutional codes have been the central part of most error-control systems in deep-space communication for many years. Almost all such applications, however, have used the restricted class of (n,1), also known as 'rate 1/n,' convolutional codes. The more general class of (n,k) convolutional codes contains many potentially useful codes, but their algebraic theory is difficult and has proved to be a stumbling block in the evolution of convolutional coding systems. In this article, the situation is improved by describing a set of practical algorithms for computing certain basic things about a convolutional code (among them the degree, the Forney indices, a minimal generator matrix, and a parity-check matrix), which are usually needed before a system using the code can be built. The approach is based on the classic Forney theory for convolutional codes, together with the extended Smith algorithm for polynomial matrices, which is introduced in this article.

  14. Fabrication of ordered arrays of micro- and nanoscale features with control over their shape and size via templated solid-state dewetting

    PubMed Central

    Ye, Jongpil

    2015-01-01

    Templated solid-state dewetting of single-crystal films has been shown to be used to produce regular patterns of various shapes. However, the materials for which this patterning method is applicable, and the size range of the patterns produced are still limited. Here, it is shown that ordered arrays of micro- and nanoscale features can be produced with control over their shape and size via solid-state dewetting of patches patterned from single-crystal palladium and nickel films of different thicknesses and orientations. The shape and size characteristics of the patterns are found to be widely controllable with varying the shape, width, thickness, and orientation of the initial patches. The morphological evolution of the patches is also dependent on the film material, with different dewetting behaviors observed in palladium and nickel films. The mechanisms underlying the pattern formation are explained in terms of the influence on Rayleigh-like instability of the patch geometry and the surface energy anisotropy of the film material. This mechanistic understanding of pattern formation can be used to design patches for the precise fabrication of micro- and nanoscale structures with the desired shapes and feature sizes. PMID:25951816

  15. QCDNUM: Fast QCD evolution and convolution

    NASA Astrophysics Data System (ADS)

    Botje, M.

    2011-02-01

    The QCDNUM program numerically solves the evolution equations for parton densities and fragmentation functions in perturbative QCD. Un-polarised parton densities can be evolved up to next-to-next-to-leading order in powers of the strong coupling constant, while polarised densities or fragmentation functions can be evolved up to next-to-leading order. Other types of evolution can be accessed by feeding alternative sets of evolution kernels into the program. A versatile convolution engine provides tools to compute parton luminosities, cross-sections in hadron-hadron scattering, and deep inelastic structure functions in the zero-mass scheme or in generalised mass schemes. Input to these calculations are either the QCDNUM evolved densities, or those read in from an external parton density repository. Included in the software distribution are packages to calculate zero-mass structure functions in un-polarised deep inelastic scattering, and heavy flavour contributions to these structure functions in the fixed flavour number scheme. Program summaryProgram title: QCDNUM version: 17.00 Catalogue identifier: AEHV_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEHV_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU Public Licence No. of lines in distributed program, including test data, etc.: 45 736 No. of bytes in distributed program, including test data, etc.: 911 569 Distribution format: tar.gz Programming language: Fortran-77 Computer: All Operating system: All RAM: Typically 3 Mbytes Classification: 11.5 Nature of problem: Evolution of the strong coupling constant and parton densities, up to next-to-next-to-leading order in perturbative QCD. Computation of observable quantities by Mellin convolution of the evolved densities with partonic cross-sections. Solution method: Parametrisation of the parton densities as linear or quadratic splines on a discrete grid, and evolution of the spline

  16. Convolutional coding combined with continuous phase modulation

    NASA Technical Reports Server (NTRS)

    Pizzi, S. V.; Wilson, S. G.

    1985-01-01

    Background theory and specific coding designs for combined coding/modulation schemes utilizing convolutional codes and continuous-phase modulation (CPM) are presented. In this paper the case of r = 1/2 coding onto a 4-ary CPM is emphasized, with short-constraint length codes presented for continuous-phase FSK, double-raised-cosine, and triple-raised-cosine modulation. Coding buys several decibels of coding gain over the Gaussian channel, with an attendant increase of bandwidth. Performance comparisons in the power-bandwidth tradeoff with other approaches are made.

  17. Convolution neural networks for ship type recognition

    NASA Astrophysics Data System (ADS)

    Rainey, Katie; Reeder, John D.; Corelli, Alexander G.

    2016-05-01

    Algorithms to automatically recognize ship type from satellite imagery are desired for numerous maritime applications. This task is difficult, and example imagery accurately labeled with ship type is hard to obtain. Convolutional neural networks (CNNs) have shown promise in image recognition settings, but many of these applications rely on the availability of thousands of example images for training. This work attempts to under- stand for which types of ship recognition tasks CNNs might be well suited. We report the results of baseline experiments applying a CNN to several ship type classification tasks, and discuss many of the considerations that must be made in approaching this problem.

  18. Geometric multi-resolution analysis and data-driven convolutions

    NASA Astrophysics Data System (ADS)

    Strawn, Nate

    2015-09-01

    We introduce a procedure for learning discrete convolutional operators for generic datasets which recovers the standard block convolutional operators when applied to sets of natural images. They key observation is that the standard block convolutional operators on images are intuitive because humans naturally understand the grid structure of the self-evident functions over images spaces (pixels). This procedure first constructs a Geometric Multi-Resolution Analysis (GMRA) on the set of variables giving rise to a dataset, and then leverages the details of this data structure to identify subsets of variables upon which convolutional operators are supported, as well as a space of functions that can be shared coherently amongst these supports.

  19. Convolution Inequalities for the Boltzmann Collision Operator

    NASA Astrophysics Data System (ADS)

    Alonso, Ricardo J.; Carneiro, Emanuel; Gamba, Irene M.

    2010-09-01

    We study integrability properties of a general version of the Boltzmann collision operator for hard and soft potentials in n-dimensions. A reformulation of the collisional integrals allows us to write the weak form of the collision operator as a weighted convolution, where the weight is given by an operator invariant under rotations. Using a symmetrization technique in L p we prove a Young’s inequality for hard potentials, which is sharp for Maxwell molecules in the L 2 case. Further, we find a new Hardy-Littlewood-Sobolev type of inequality for Boltzmann collision integrals with soft potentials. The same method extends to radially symmetric, non-increasing potentials that lie in some {Ls_{weak}} or L s . The method we use resembles a Brascamp, Lieb and Luttinger approach for multilinear weighted convolution inequalities and follows a weak formulation setting. Consequently, it is closely connected to the classical analysis of Young and Hardy-Littlewood-Sobolev inequalities. In all cases, the inequality constants are explicitly given by formulas depending on integrability conditions of the angular cross section (in the spirit of Grad cut-off). As an additional application of the technique we also obtain estimates with exponential weights for hard potentials in both conservative and dissipative interactions.

  20. Convolutional fountain distribution over fading wireless channels

    NASA Astrophysics Data System (ADS)

    Usman, Mohammed

    2012-08-01

    Mobile broadband has opened the possibility of a rich variety of services to end users. Broadcast/multicast of multimedia data is one such service which can be used to deliver multimedia to multiple users economically. However, the radio channel poses serious challenges due to its time-varying properties, resulting in each user experiencing different channel characteristics, independent of other users. Conventional methods of achieving reliability in communication, such as automatic repeat request and forward error correction do not scale well in a broadcast/multicast scenario over radio channels. Fountain codes, being rateless and information additive, overcome these problems. Although the design of fountain codes makes it possible to generate an infinite sequence of encoded symbols, the erroneous nature of radio channels mandates the need for protecting the fountain-encoded symbols, so that the transmission is feasible. In this article, the performance of fountain codes in combination with convolutional codes, when used over radio channels, is presented. An investigation of various parameters, such as goodput, delay and buffer size requirements, pertaining to the performance of fountain codes in a multimedia broadcast/multicast environment is presented. Finally, a strategy for the use of 'convolutional fountain' over radio channels is also presented.

  1. Convolution formulations for non-negative intensity.

    PubMed

    Williams, Earl G

    2013-08-01

    Previously unknown spatial convolution formulas for a variant of the active normal intensity in planar coordinates have been derived that use measured pressure or normal velocity near-field holograms to construct a positive-only (outward) intensity distribution in the plane, quantifying the areas of the vibrating structure that produce radiation to the far-field. This is an extension of the outgoing-only (unipolar) intensity technique recently developed for arbitrary geometries by Steffen Marburg. The method is applied independently to pressure and velocity data measured in a plane close to the surface of a point-driven, unbaffled rectangular plate in the laboratory. It is demonstrated that the sound producing regions of the structure are clearly revealed using the derived formulas and that the spatial resolution is limited to a half-wavelength. A second set of formulas called the hybrid-intensity formulas are also derived which yield a bipolar intensity using a different spatial convolution operator, again using either the measured pressure or velocity. It is demonstrated from the experiment results that the velocity formula yields the classical active intensity and the pressure formula an interesting hybrid intensity that may be useful for source localization. Computations are fast and carried out in real space without Fourier transforms into wavenumber space. PMID:23927105

  2. Quasi-In vivo Heart Electrocardiogram Measurement of ST Period Using Convolution of Cell Network Extracellular Field Potential Propagation in Lined-Up Cardiomyocyte Cell-Network Circuit

    NASA Astrophysics Data System (ADS)

    Kaneko, Tomoyuki; Nomura, Fumimasa; Yasuda, Kenji

    2011-07-01

    A model for the quasi-in vivo heart electrocardiogram (ECG) measurement of the ST period has been developed. As the part of ECG data at the ST period is the convolution of the extracellular field potentials (FPs) of cardiomyocytes in a ventricle, we have fabricated a lined-up cardiomyocyte cell-network on a lined-up microelectrode array and a circular microelectrode in an agarose microchamber, and measured the convoluted FPs. When the ventricular tachyarrhythmias of beating occurred in the cardiomyocyte network, the convoluted FP profile showed similar arrhythmia ECG-like profiles, indicating the convoluted FPs of the in vitro cell network include both the depolarization data and the propagation manner of beating in the heart.

  3. Cell differentiation on disk- and string-shaped hydrogels fabricated from Ca(2+) -responsive self-assembling peptides.

    PubMed

    Fukunaga, Kazuto; Tsutsumi, Hiroshi; Mihara, Hisakazu

    2016-11-01

    We recently developed a self-assembling peptide, E1Y9, that self-assembles into nanofibers and forms a hydrogel in the presence of Ca(2+) . E1Y9 derivatives conjugated with functional peptide sequences derived from extracellular matrices (ECMs) reportedly self-assemble into peptide nanofibers that enhance cell adhesion and differentiation. In this study, E1Y9/E1Y9-IKVAV-mixed hydrogels were constructed to serve as artificial ECMs that promote cell differentiation. E1Y9 and E1Y9-IKVAV co-assembled into networked nanofibers, and hydrogels with disk and string shapes were formed in response to Ca(2+) treatment. The neuronal differentiation of PC12 cells was facilitated on hydrogels of both shapes that contained the IKVAV motifs. Moreover, long neurites extended along the long axis of the string-shaped gel, suggesting that the structure of hydrogels of this shape can affect cellular orientation. Thus, E1Y9 hydrogels can potentially be used as artificial ECMs with desirable bioactivities and shapes that could be useful in tissue engineering applications. © 2015 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 106: 476-483, 2016. PMID:26501895

  4. A dual-directional light-control film with a high-sag and high-asymmetrical-shape microlens array fabricated by a UV imprinting process

    NASA Astrophysics Data System (ADS)

    Lin, Ta-Wei; Chen, Chi-Feng; Yang, Jauh-Jung; Liao, Yunn-Shiuan

    2008-09-01

    A dual-directional light-control film with a high-sag and high-asymmetric-shape long gapless hexagonal microlens array fabricated by an ultra-violent (UV) imprinting process is presented. Such a lens array is designed by ray-tracing simulation and fabricated by a micro-replication process including gray-scale lithography, electroplating process and UV curing. The shape of the designed lens array is similar to that of a near half-cylindrical lens array with a periodical ripple. The measurement results of a prototype show that the incident lights using a collimated LED with the FWHM of dispersion angle, 12°, are diversified differently in short and long axes. The numerical and experimental results show that the FWHMs of the view angle for angular brightness in long and short axis directions through the long hexagonal lens are about 34.3° and 18.1° and 31° and 13°, respectively. Compared with the simulation result, the errors in long and short axes are about 5% and 16%, respectively. Obviously, the asymmetric gapless microlens array can realize the aim of the controlled asymmetric angular brightness. Such a light-control film can be used as a power saving screen compared with convention diffusing film for the application of a rear projection display.

  5. SU-E-T-08: A Convolution Model for Head Scatter Fluence in the Intensity Modulated Field

    SciTech Connect

    Chen, M; Mo, X; Chen, Y; Parnell, D; Key, S; Olivera, G; Galmarini, W; Lu, W

    2014-06-01

    Purpose: To efficiently calculate the head scatter fluence for an arbitrary intensity-modulated field with any source distribution using the source occlusion model. Method: The source occlusion model with focal and extra focal radiation (Jaffray et al, 1993) can be used to account for LINAC head scatter. In the model, the fluence map of any field shape at any point can be calculated via integration of the source distribution within the visible range, as confined by each segment, using the detector eye's view. A 2D integration would be required for each segment and each fluence plane point, which is time-consuming, as an intensity-modulated field contains typically tens to hundreds of segments. In this work, we prove that the superposition of the segmental integrations is equivalent to a simple convolution regardless of what the source distribution is. In fact, for each point, the detector eye's view of the field shape can be represented as a function with the origin defined at the point's pinhole reflection through the center of the collimator plane. We were thus able to reduce hundreds of source plane integration to one convolution. We calculated the fluence map for various 3D and IMRT beams and various extra-focal source distributions using both the segmental integration approach and the convolution approach and compared the computation time and fluence map results of both approaches. Results: The fluence maps calculated using the convolution approach were the same as those calculated using the segmental approach, except for rounding errors (<0.1%). While it took considerably longer time to calculate all segmental integrations, the fluence map calculation using the convolution approach took only ∼1/3 of the time for typical IMRT fields with ∼100 segments. Conclusions: The convolution approach for head scatter fluence calculation is fast and accurate and can be used to enhance the online process.

  6. Some partial-unit-memory convolutional codes

    NASA Technical Reports Server (NTRS)

    Abdel-Ghaffar, K.; Mceliece, R. J.; Solomon, G.

    1991-01-01

    The results of a study on a class of error correcting codes called partial unit memory (PUM) codes are presented. This class of codes, though not entirely new, has until now remained relatively unexplored. The possibility of using the well developed theory of block codes to construct a large family of promising PUM codes is shown. The performance of several specific PUM codes are compared with that of the Voyager standard (2, 1, 6) convolutional code. It was found that these codes can outperform the Voyager code with little or no increase in decoder complexity. This suggests that there may very well be PUM codes that can be used for deep space telemetry that offer both increased performance and decreased implementational complexity over current coding systems.

  7. Image statistics decoding for convolutional codes

    NASA Technical Reports Server (NTRS)

    Pitt, G. H., III; Swanson, L.; Yuen, J. H.

    1987-01-01

    It is a fact that adjacent pixels in a Voyager image are very similar in grey level. This fact can be used in conjunction with the Maximum-Likelihood Convolutional Decoder (MCD) to decrease the error rate when decoding a picture from Voyager. Implementing this idea would require no changes in the Voyager spacecraft and could be used as a backup to the current system without too much expenditure, so the feasibility of it and the possible gains for Voyager were investigated. Simulations have shown that the gain could be as much as 2 dB at certain error rates, and experiments with real data inspired new ideas on ways to get the most information possible out of the received symbol stream.

  8. Bacterial colony counting by Convolutional Neural Networks.

    PubMed

    Ferrari, Alessandro; Lombardi, Stefano; Signoroni, Alberto

    2015-08-01

    Counting bacterial colonies on microbiological culture plates is a time-consuming, error-prone, nevertheless fundamental task in microbiology. Computer vision based approaches can increase the efficiency and the reliability of the process, but accurate counting is challenging, due to the high degree of variability of agglomerated colonies. In this paper, we propose a solution which adopts Convolutional Neural Networks (CNN) for counting the number of colonies contained in confluent agglomerates, that scored an overall accuracy of the 92.8% on a large challenging dataset. The proposed CNN-based technique for estimating the cardinality of colony aggregates outperforms traditional image processing approaches, becoming a promising approach to many related applications. PMID:26738016

  9. Effect of the surface texturing shapes fabricated using dry etching on the extraction efficiency of vertical light-emitting diodes

    NASA Astrophysics Data System (ADS)

    Lee, H. C.; Park, J. B.; Bae, J. W.; Thuy, Pham Thi Thu; Yoo, M. C.; Yeom, G. Y.

    2008-08-01

    On the surfaces of GaN-based light-emitting diodes (LEDs) having an n-side-up vertical electrode structure formed by the laser lift-off, various shapes of photoresist-patterned surface textures were formed by inductively coupled plasma etching and their effect on the light emission efficiencies was investigated. By the formation of various shapes of surface textures, the light output efficiency was increased from 37% to 45% compared to that without surface textures. The increase of light output efficiency was related to the increase of sidewall scattering, the decrease of reflected loss, and the decrease of cavity wall effect that occurs for the vertical LEDs by the increase of sidewall surface area.

  10. Hydrothermal fabrication of octahedral-shaped Fe{sub 3}O{sub 4} nanoparticles and their magnetorheological response

    SciTech Connect

    Jung, H. S.; Choi, H. J.

    2015-05-07

    Octahedral-shaped Fe{sub 3}O{sub 4} nanoparticles were synthesized in the presence of 1,3-diaminopropane using a hydrothermal method and assessed as a potential magnetorheological (MR) material. Their morphology, crystal structure, and magnetic properties were examined by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, and vibrating sample magnetometry, respectively. The MR characteristics of the octahedral-shaped, Fe{sub 3}O{sub 4} nanoparticle-based MR particles when dispersed in silicone oil with a 10 vol. % particle concentration were examined using a rotational rheometer under an external magnetic field. The resulting MR fluids exhibited a Bingham-like behavior with a distinctive yield stress from their flow curves.

  11. Examination of anticipated chemical shift and shape distortion effect on materials commonly used in prosthetic socket fabrication when measured using MRI: a validation study.

    PubMed

    Safari, Mohammad Reza; Rowe, Philip; Buis, Arjan

    2013-01-01

    The quality of lower-limb prosthetic socket fit is influenced by shape and volume consistency during the residual limb shape-capturing process (i.e., casting). Casting can be quantified with magnetic resonance imaging (MRI) technology. However, chemical shift artifact and image distortion may influence the accuracy of MRI when common socket/casting materials are used. We used a purpose-designed rig to examine seven different materials commonly used in socket fabrication during exposure to MRI. The rig incorporated glass marker tubes filled with water doped with 1 g/L copper sulfate (CS) and 9 plastic sample vials (film containers) to hold the specific material specimens. The specimens were scanned 9 times in different configurations. The absolute mean difference of the glass marker tube length was 1.39 mm (2.98%) (minimum = 0.13 mm [0.30%], maximum = 5.47 mm [14.03%], standard deviation = 0.89 mm). The absolute shift for all materials was <1.7 mm. This was less than the measurement tolerance of +/-2.18 mm based on voxel (three-dimensional pixel) dimensions. The results show that MRI is an accurate and repeatable method for dimensional measurement when using matter containing water. Additionally, silicone and plaster of paris plus 1 g/L CS do not show a significant shape distortion nor do they interfere with the MRI image of the residual limb. PMID:23516081

  12. Multi-modal vertebrae recognition using Transformed Deep Convolution Network.

    PubMed

    Cai, Yunliang; Landis, Mark; Laidley, David T; Kornecki, Anat; Lum, Andrea; Li, Shuo

    2016-07-01

    Automatic vertebra recognition, including the identification of vertebra locations and naming in multiple image modalities, are highly demanded in spinal clinical diagnoses where large amount of imaging data from various of modalities are frequently and interchangeably used. However, the recognition is challenging due to the variations of MR/CT appearances or shape/pose of the vertebrae. In this paper, we propose a method for multi-modal vertebra recognition using a novel deep learning architecture called Transformed Deep Convolution Network (TDCN). This new architecture can unsupervisely fuse image features from different modalities and automatically rectify the pose of vertebra. The fusion of MR and CT image features improves the discriminativity of feature representation and enhances the invariance of the vertebra pattern, which allows us to automatically process images from different contrast, resolution, protocols, even with different sizes and orientations. The feature fusion and pose rectification are naturally incorporated in a multi-layer deep learning network. Experiment results show that our method outperforms existing detection methods and provides a fully automatic location+naming+pose recognition for routine clinical practice. PMID:27104497

  13. Fabrication of MEMS-based Micro-fluxgate Sensor with Runway-shaped Co-based Amorphous Alloy Core

    NASA Astrophysics Data System (ADS)

    Wu, Shaobin; Chen, Shi; Ouyang, Jun; Zuo, Chao; Yu, Lei; Yang, Xiaofei

    2011-01-01

    High-precision magnetic micro-sensor is an interdisciplinary subject of magnetic field measurement techniques and micro-electromechanical systems (MEMS) technology. A micro-fluxgate magnetic sensor based MEMS technology was designed and fabricated in this paper. This device is a micro-magnetic sensor with a symmetric construction, closed magnetic circuits and differential form. A 25μm thick Fluxgate core of runway model, made by Co-based amorphous alloy, was etched by laser and pasted on the substrate accurately. Excitation coil and sensing coil of 3D solenoid structure were prepared by RF magnetron sputtering and UV-lithography. The minimum line width of the coil is 50 μm. The experimental result shows that micro-fluxgate devices with the size of 5.7mm×7.1mm×60μm had a stable structure.

  14. Solution-Based Fabrication of Narrow-Disperse ABC Three-Segment and Θ-Shaped Nanoparticles.

    PubMed

    Zhang, Zhen; Zhou, Changming; Dong, Haiyan; Chen, Daoyong

    2016-05-17

    Nanoparticles sized tens of nm with not only a highly complex but also a highly regular nanostructure, although ubiquitous in nature, are very difficult to prepare artificially. Herein, we report efficient solution-based preparation of narrow-disperse ABC three-segment hierarchical nanoparticles (HNPs) with a size of tens of nm through a three-level hierarchical self-assembly of A-b-B-b-C triblock copolymers in solution. An ABC HNP is composed of three nanoparticles, A, B, and C that are linearly connected; in the ABC HNP, the B nanoparticle is sandwiched between the A and C nanoparticles. The method for the preparation is highly efficient, because all of the A-b-B-b-C chains in the solution are converted into the ABC HNPs. Furthermore, the ABC HNPs self-assembled into Θ-shaped HNPs tens nm in size. Both the ABC and Θ-shaped HNPs, are highly complex but highly regular, and are novel HNPs, and they should be very promising for addressing various theoretical and practical problems. PMID:27071692

  15. Convolution modeling of two-domain, nonlinear water-level responses in karst aquifers (Invited)

    NASA Astrophysics Data System (ADS)

    Long, A. J.

    2009-12-01

    Convolution modeling is a useful method for simulating the hydraulic response of water levels to sinking streamflow or precipitation infiltration at the macro scale. This approach is particularly useful in karst aquifers, where the complex geometry of the conduit and pore network is not well characterized but can be represented approximately by a parametric impulse-response function (IRF) with very few parameters. For many applications, one-dimensional convolution models can be equally effective as complex two- or three-dimensional models for analyzing water-level responses to recharge. Moreover, convolution models are well suited for identifying and characterizing the distinct domains of quick flow and slow flow (e.g., conduit flow and diffuse flow). Two superposed lognormal functions were used in the IRF to approximate the impulses of the two flow domains. Nonlinear response characteristics of the flow domains were assessed by observing temporal changes in the IRFs. Precipitation infiltration was simulated by filtering the daily rainfall record with a backward-in-time exponential function that weights each day’s rainfall with the rainfall of previous days and thus accounts for the effects of soil moisture on aquifer infiltration. The model was applied to the Edwards aquifer in Texas and the Madison aquifer in South Dakota. Simulations of both aquifers showed similar characteristics, including a separation on the order of years between the quick-flow and slow-flow IRF peaks and temporal changes in the IRF shapes when water levels increased and empty pore spaces became saturated.

  16. Flexible Fabrication of Shape-Controlled Collagen Building Blocks for Self-Assembly of 3D Microtissues.

    PubMed

    Zhang, Xu; Meng, Zhaoxu; Ma, Jingyun; Shi, Yang; Xu, Hui; Lykkemark, Simon; Qin, Jianhua

    2015-08-12

    Creating artificial tissue-like structures that possess the functionality, specificity, and architecture of native tissues remains a big challenge. A new and straightforward strategy for generating shape-controlled collagen building blocks with a well-defined architecture is presented, which can be used for self-assembly of complex 3D microtissues. Collagen blocks with tunable geometries are controllably produced and released via a membrane-templated microdevice. The formation of functional microtissues by embedding tissue-specific cells into collagen blocks with expression of specific proteins is described. The spontaneous self-assembly of cell-laden collagen blocks into organized tissue constructs with predetermined configurations is demonstrated, which are largely driven by the synergistic effects of cell-cell and cell-matrix interactions. This new strategy would open up new avenues for the study of tissue/organ morphogenesis, and tissue engineering applications. PMID:25920010

  17. Blind source separation of convolutive mixtures

    NASA Astrophysics Data System (ADS)

    Makino, Shoji

    2006-04-01

    This paper introduces the blind source separation (BSS) of convolutive mixtures of acoustic signals, especially speech. A statistical and computational technique, called independent component analysis (ICA), is examined. By achieving nonlinear decorrelation, nonstationary decorrelation, or time-delayed decorrelation, we can find source signals only from observed mixed signals. Particular attention is paid to the physical interpretation of BSS from the acoustical signal processing point of view. Frequency-domain BSS is shown to be equivalent to two sets of frequency domain adaptive microphone arrays, i.e., adaptive beamformers (ABFs). Although BSS can reduce reverberant sounds to some extent in the same way as ABF, it mainly removes the sounds from the jammer direction. This is why BSS has difficulties with long reverberation in the real world. If sources are not "independent," the dependence results in bias noise when obtaining the correct separation filter coefficients. Therefore, the performance of BSS is limited by that of ABF. Although BSS is upper bounded by ABF, BSS has a strong advantage over ABF. BSS can be regarded as an intelligent version of ABF in the sense that it can adapt without any information on the array manifold or the target direction, and sources can be simultaneously active in BSS.

  18. Accelerated unsteady flow line integral convolution.

    PubMed

    Liu, Zhanping; Moorhead, Robert J

    2005-01-01

    Unsteady flow line integral convolution (UFLIC) is a texture synthesis technique for visualizing unsteady flows with high temporal-spatial coherence. Unfortunately, UFLIC requires considerable time to generate each frame due to the huge amount of pathline integration that is computed for particle value scattering. This paper presents Accelerated UFLIC (AUFLIC) for near interactive (1 frame/second) visualization with 160,000 particles per frame. AUFLIC reuses pathlines in the value scattering process to reduce computationally expensive pathline integration. A flow-driven seeding strategy is employed to distribute seeds such that only a few of them need pathline integration while most seeds are placed along the pathlines advected at earlier times by other seeds upstream and, therefore, the known pathlines can be reused for fast value scattering. To maintain a dense scattering coverage to convey high temporal-spatial coherence while keeping the expense of pathline integration low, a dynamic seeding controller is designed to decide whether to advect, copy, or reuse a pathline. At a negligible memory cost, AUFLIC is 9 times faster than UFLIC with comparable image quality. PMID:15747635

  19. Metaheuristic Algorithms for Convolution Neural Network.

    PubMed

    Rere, L M Rasdi; Fanany, Mohamad Ivan; Arymurthy, Aniati Murni

    2016-01-01

    A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent). PMID:27375738

  20. Metaheuristic Algorithms for Convolution Neural Network

    PubMed Central

    Fanany, Mohamad Ivan; Arymurthy, Aniati Murni

    2016-01-01

    A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent). PMID:27375738

  1. Fabrication of cone-shaped CNF/SiC-coated Si-nanocone composite structures and their excellent field emission performance

    NASA Astrophysics Data System (ADS)

    Teng, I.-Ju; Hsu, Hui-Lin; Jian, Sheng-Rui; Kuo, Cheng-Tzu; Juang, Jenh-Yih

    2012-11-01

    Novel cone-shaped carbon nanofiber (CNF)/silicon carbide (SiC)-coated Si-nanocone (Si-NC) composite structures with excellent field emission (FE) performance have been fabricated by a simple microwave plasma chemical vapour deposition process. Transmission electron microscopy analyses reveal that the newly developed cone-shaped composite structures are composed of bamboo-like herringbone CNFs grown vertically on the tips of conical SiC layers with a flat-top Si cone embedded underneath. For this CNF/SiC-coated Si-NC composite array, a ultra-low threshold field of 0.32 V μm-1 (at 10 mA cm-2), a large emission current density of 668 mA cm-2 at 1.05 V μm-1, and a field enhancement factor as high as ~48 349 are obtained. In addition, the FE lifetime test performed at a large emission current density of 200 mA cm-2 under an applied field of 1 V μm-1 shows no discernible decay during a period of over 260 minutes. We deduce that this superior FE performance can be attributed to the specific bamboo-like herringbone CNFs with numerous open graphitic edges and a faceted top end, and the conical base SiC/Si structures with sufficient adhesion to the substrate surface. Such a novel structure with promising emission characteristics makes it a potential material for electron field emitters.

  2. Fabrication of cone-shaped CNF/SiC-coated Si-nanocone composite structures and their excellent field emission performance.

    PubMed

    Teng, I-Ju; Hsu, Hui-Lin; Jian, Sheng-Rui; Kuo, Cheng-Tzu; Juang, Jenh-Yih

    2012-12-01

    Novel cone-shaped carbon nanofiber (CNF)/silicon carbide (SiC)-coated Si-nanocone (Si-NC) composite structures with excellent field emission (FE) performance have been fabricated by a simple microwave plasma chemical vapour deposition process. Transmission electron microscopy analyses reveal that the newly developed cone-shaped composite structures are composed of bamboo-like herringbone CNFs grown vertically on the tips of conical SiC layers with a flat-top Si cone embedded underneath. For this CNF/SiC-coated Si-NC composite array, a ultra-low threshold field of 0.32 V μm(-1) (at 10 mA cm(-2)), a large emission current density of 668 mA cm(-2) at 1.05 V μm(-1), and a field enhancement factor as high as ~48,349 are obtained. In addition, the FE lifetime test performed at a large emission current density of 200 mA cm(-2) under an applied field of 1 V μm(-1) shows no discernible decay during a period of over 260 minutes. We deduce that this superior FE performance can be attributed to the specific bamboo-like herringbone CNFs with numerous open graphitic edges and a faceted top end, and the conical base SiC/Si structures with sufficient adhesion to the substrate surface. Such a novel structure with promising emission characteristics makes it a potential material for electron field emitters. PMID:23108379

  3. Noise-enhanced convolutional neural networks.

    PubMed

    Audhkhasi, Kartik; Osoba, Osonde; Kosko, Bart

    2016-06-01

    Injecting carefully chosen noise can speed convergence in the backpropagation training of a convolutional neural network (CNN). The Noisy CNN algorithm speeds training on average because the backpropagation algorithm is a special case of the generalized expectation-maximization (EM) algorithm and because such carefully chosen noise always speeds up the EM algorithm on average. The CNN framework gives a practical way to learn and recognize images because backpropagation scales with training data. It has only linear time complexity in the number of training samples. The Noisy CNN algorithm finds a special separating hyperplane in the network's noise space. The hyperplane arises from the likelihood-based positivity condition that noise-boosts the EM algorithm. The hyperplane cuts through a uniform-noise hypercube or Gaussian ball in the noise space depending on the type of noise used. Noise chosen from above the hyperplane speeds training on average. Noise chosen from below slows it on average. The algorithm can inject noise anywhere in the multilayered network. Adding noise to the output neurons reduced the average per-iteration training-set cross entropy by 39% on a standard MNIST image test set of handwritten digits. It also reduced the average per-iteration training-set classification error by 47%. Adding noise to the hidden layers can also reduce these performance measures. The noise benefit is most pronounced for smaller data sets because the largest EM hill-climbing gains tend to occur in the first few iterations. This noise effect can assist random sampling from large data sets because it allows a smaller random sample to give the same or better performance than a noiseless sample gives. PMID:26700535

  4. A Geometric Construction of Cyclic Cocycles on Twisted Convolution Algebras

    NASA Astrophysics Data System (ADS)

    Angel, Eitan

    2010-09-01

    In this thesis we give a construction of cyclic cocycles on convolution algebras twisted by gerbes over discrete translation groupoids. In his seminal book, Connes constructs a map from the equivariant cohomology of a manifold carrying the action of a discrete group into the periodic cyclic cohomology of the associated convolution algebra. Furthermore, for proper étale groupoids, J.-L. Tu and P. Xu provide a map between the periodic cyclic cohomology of a gerbe twisted convolution algebra and twisted cohomology groups. Our focus will be the convolution algebra with a product defined by a gerbe over a discrete translation groupoid. When the action is not proper, we cannot construct an invariant connection on the gerbe; therefore to study this algebra, we instead develop simplicial notions related to ideas of J. Dupont to construct a simplicial form representing the Dixmier-Douady class of the gerbe. Then by using a JLO formula we define a morphism from a simplicial complex twisted by this simplicial Dixmier-Douady form to the mixed bicomplex of certain matrix algebras. Finally, we define a morphism from this complex to the mixed bicomplex computing the periodic cyclic cohomology of the twisted convolution algebras.

  5. Two dimensional convolute integers for machine vision and image recognition

    NASA Technical Reports Server (NTRS)

    Edwards, Thomas R.

    1988-01-01

    Machine vision and image recognition require sophisticated image processing prior to the application of Artificial Intelligence. Two Dimensional Convolute Integer Technology is an innovative mathematical approach for addressing machine vision and image recognition. This new technology generates a family of digital operators for addressing optical images and related two dimensional data sets. The operators are regression generated, integer valued, zero phase shifting, convoluting, frequency sensitive, two dimensional low pass, high pass and band pass filters that are mathematically equivalent to surface fitted partial derivatives. These operators are applied non-recursively either as classical convolutions (replacement point values), interstitial point generators (bandwidth broadening or resolution enhancement), or as missing value calculators (compensation for dead array element values). These operators show frequency sensitive feature selection scale invariant properties. Such tasks as boundary/edge enhancement and noise or small size pixel disturbance removal can readily be accomplished. For feature selection tight band pass operators are essential. Results from test cases are given.

  6. Error-trellis syndrome decoding techniques for convolutional codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Truong, T. K.

    1985-01-01

    An error-trellis syndrome decoding technique for convolutional codes is developed. This algorithm is then applied to the entire class of systematic convolutional codes and to the high-rate, Wyner-Ash convolutional codes. A special example of the one-error-correcting Wyner-Ash code, a rate 3/4 code, is treated. The error-trellis syndrome decoding method applied to this example shows in detail how much more efficient syndrome decordig is than Viterbi decoding if applied to the same problem. For standard Viterbi decoding, 64 states are required, whereas in the example only 7 states are needed. Also, within the 7 states required for decoding, many fewer transitions are needed between the states.

  7. Error-trellis Syndrome Decoding Techniques for Convolutional Codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Truong, T. K.

    1984-01-01

    An error-trellis syndrome decoding technique for convolutional codes is developed. This algorithm is then applied to the entire class of systematic convolutional codes and to the high-rate, Wyner-Ash convolutional codes. A special example of the one-error-correcting Wyner-Ash code, a rate 3/4 code, is treated. The error-trellis syndrome decoding method applied to this example shows in detail how much more efficient syndrome decoding is than Viterbi decoding if applied to the same problem. For standard Viterbi decoding, 64 states are required, whereas in the example only 7 states are needed. Also, within the 7 states required for decoding, many fewer transitions are needed between the states.

  8. Robustly optimal rate one-half binary convolutional codes

    NASA Technical Reports Server (NTRS)

    Johannesson, R.

    1975-01-01

    Three optimality criteria for convolutional codes are considered in this correspondence: namely, free distance, minimum distance, and distance profile. Here we report the results of computer searches for rate one-half binary convolutional codes that are 'robustly optimal' in the sense of being optimal for one criterion and optimal or near-optimal for the other two criteria. Comparisons with previously known codes are made. The results of a computer simulation are reported to show the importance of the distance profile to computational performance with sequential decoding.

  9. Knowledge Based 3d Building Model Recognition Using Convolutional Neural Networks from LIDAR and Aerial Imageries

    NASA Astrophysics Data System (ADS)

    Alidoost, F.; Arefi, H.

    2016-06-01

    In recent years, with the development of the high resolution data acquisition technologies, many different approaches and algorithms have been presented to extract the accurate and timely updated 3D models of buildings as a key element of city structures for numerous applications in urban mapping. In this paper, a novel and model-based approach is proposed for automatic recognition of buildings' roof models such as flat, gable, hip, and pyramid hip roof models based on deep structures for hierarchical learning of features that are extracted from both LiDAR and aerial ortho-photos. The main steps of this approach include building segmentation, feature extraction and learning, and finally building roof labeling in a supervised pre-trained Convolutional Neural Network (CNN) framework to have an automatic recognition system for various types of buildings over an urban area. In this framework, the height information provides invariant geometric features for convolutional neural network to localize the boundary of each individual roofs. CNN is a kind of feed-forward neural network with the multilayer perceptron concept which consists of a number of convolutional and subsampling layers in an adaptable structure and it is widely used in pattern recognition and object detection application. Since the training dataset is a small library of labeled models for different shapes of roofs, the computation time of learning can be decreased significantly using the pre-trained models. The experimental results highlight the effectiveness of the deep learning approach to detect and extract the pattern of buildings' roofs automatically considering the complementary nature of height and RGB information.

  10. Space reactor shielding fabrication

    NASA Technical Reports Server (NTRS)

    Welch, F. H.

    1972-01-01

    The fabrication of space reactor neutron shielding by a melting and casting process utilizing lithium hydride is described. The first neutron shield fabricated is a large pancake shape 86 inches in diameter, containing about 1700 pounds of lithium hydride. This shield, fabricated by the unique melting and casting process, is the largest lithium hydride shield ever built.

  11. Maximum-likelihood estimation of circle parameters via convolution.

    PubMed

    Zelniker, Emanuel E; Clarkson, I Vaughan L

    2006-04-01

    The accurate fitting of a circle to noisy measurements of circumferential points is a much studied problem in the literature. In this paper, we present an interpretation of the maximum-likelihood estimator (MLE) and the Delogne-Kåsa estimator (DKE) for circle-center and radius estimation in terms of convolution on an image which is ideal in a certain sense. We use our convolution-based MLE approach to find good estimates for the parameters of a circle in digital images. In digital images, it is then possible to treat these estimates as preliminary estimates into various other numerical techniques which further refine them to achieve subpixel accuracy. We also investigate the relationship between the convolution of an ideal image with a "phase-coded kernel" (PCK) and the MLE. This is related to the "phase-coded annulus" which was introduced by Atherton and Kerbyson who proposed it as one of a number of new convolution kernels for estimating circle center and radius. We show that the PCK is an approximate MLE (AMLE). We compare our AMLE method to the MLE and the DKE as well as the Cramér-Rao Lower Bound in ideal images and in both real and synthetic digital images. PMID:16579374

  12. Voltage measurements at the vacuum post-hole convolute of the Z pulsed-power accelerator

    SciTech Connect

    Waisman, E. M.; McBride, R. D.; Cuneo, M. E.; Wenger, D. F.; Fowler, W. E.; Johnson, W. A.; Basilio, L. I.; Coats, R. S.; Jennings, C. A.; Sinars, D. B.; Vesey, R. A.; Jones, B.; Ampleford, D. J.; Lemke, R. W.; Martin, M. R.; Schrafel, P. C.; Lewis, S. A.; Moore, J. K.; Savage, M. E.; Stygar, W. A.

    2014-12-08

    Presented are voltage measurements taken near the load region on the Z pulsed-power accelerator using an inductive voltage monitor (IVM). Specifically, the IVM was connected to, and thus monitored the voltage at, the bottom level of the accelerator’s vacuum double post-hole convolute. Additional voltage and current measurements were taken at the accelerator’s vacuum-insulator stack (at a radius of 1.6 m) by using standard D-dot and B-dot probes, respectively. During postprocessing, the measurements taken at the stack were translated to the location of the IVM measurements by using a lossless propagation model of the Z accelerator’s magnetically insulated transmission lines (MITLs) and a lumped inductor model of the vacuum post-hole convolute. Across a wide variety of experiments conducted on the Z accelerator, the voltage histories obtained from the IVM and the lossless propagation technique agree well in overall shape and magnitude. However, large-amplitude, high-frequency oscillations are more pronounced in the IVM records. It is unclear whether these larger oscillations represent true voltage oscillations at the convolute or if they are due to noise pickup and/or transit-time effects and other resonant modes in the IVM. Results using a transit-time-correction technique and Fourier analysis support the latter. Regardless of which interpretation is correct, both true voltage oscillations and the excitement of resonant modes could be the result of transient electrical breakdowns in the post-hole convolute, though more information is required to determine definitively if such breakdowns occurred. Despite the larger oscillations in the IVM records, the general agreement found between the lossless propagation results and the results of the IVM shows that large voltages are transmitted efficiently through the MITLs on Z. These results are complementary to previous studies [R. D. McBride et al., Phys. Rev. ST Accel. Beams 13, 120401 (2010)] that

  13. Voltage measurements at the vacuum post-hole convolute of the Z pulsed-power accelerator

    NASA Astrophysics Data System (ADS)

    Waisman, E. M.; McBride, R. D.; Cuneo, M. E.; Wenger, D. F.; Fowler, W. E.; Johnson, W. A.; Basilio, L. I.; Coats, R. S.; Jennings, C. A.; Sinars, D. B.; Vesey, R. A.; Jones, B.; Ampleford, D. J.; Lemke, R. W.; Martin, M. R.; Schrafel, P. C.; Lewis, S. A.; Moore, J. K.; Savage, M. E.; Stygar, W. A.

    2014-12-01

    Presented are voltage measurements taken near the load region on the Z pulsed-power accelerator using an inductive voltage monitor (IVM). Specifically, the IVM was connected to, and thus monitored the voltage at, the bottom level of the accelerator's vacuum double post-hole convolute. Additional voltage and current measurements were taken at the accelerator's vacuum-insulator stack (at a radius of 1.6 m) by using standard D -dot and B -dot probes, respectively. During postprocessing, the measurements taken at the stack were translated to the location of the IVM measurements by using a lossless propagation model of the Z accelerator's magnetically insulated transmission lines (MITLs) and a lumped inductor model of the vacuum post-hole convolute. Across a wide variety of experiments conducted on the Z accelerator, the voltage histories obtained from the IVM and the lossless propagation technique agree well in overall shape and magnitude. However, large-amplitude, high-frequency oscillations are more pronounced in the IVM records. It is unclear whether these larger oscillations represent true voltage oscillations at the convolute or if they are due to noise pickup and/or transit-time effects and other resonant modes in the IVM. Results using a transit-time-correction technique and Fourier analysis support the latter. Regardless of which interpretation is correct, both true voltage oscillations and the excitement of resonant modes could be the result of transient electrical breakdowns in the post-hole convolute, though more information is required to determine definitively if such breakdowns occurred. Despite the larger oscillations in the IVM records, the general agreement found between the lossless propagation results and the results of the IVM shows that large voltages are transmitted efficiently through the MITLs on Z . These results are complementary to previous studies [R. D. McBride et al., Phys. Rev. ST Accel. Beams 13, 120401 (2010)] that showed efficient

  14. Voltage measurements at the vacuum post-hole convolute of the Z pulsed-power accelerator

    DOE PAGESBeta

    Waisman, E. M.; McBride, R. D.; Cuneo, M. E.; Wenger, D. F.; Fowler, W. E.; Johnson, W. A.; Basilio, L. I.; Coats, R. S.; Jennings, C. A.; Sinars, D. B.; et al

    2014-12-08

    Presented are voltage measurements taken near the load region on the Z pulsed-power accelerator using an inductive voltage monitor (IVM). Specifically, the IVM was connected to, and thus monitored the voltage at, the bottom level of the accelerator’s vacuum double post-hole convolute. Additional voltage and current measurements were taken at the accelerator’s vacuum-insulator stack (at a radius of 1.6 m) by using standard D-dot and B-dot probes, respectively. During postprocessing, the measurements taken at the stack were translated to the location of the IVM measurements by using a lossless propagation model of the Z accelerator’s magnetically insulated transmission lines (MITLs)more » and a lumped inductor model of the vacuum post-hole convolute. Across a wide variety of experiments conducted on the Z accelerator, the voltage histories obtained from the IVM and the lossless propagation technique agree well in overall shape and magnitude. However, large-amplitude, high-frequency oscillations are more pronounced in the IVM records. It is unclear whether these larger oscillations represent true voltage oscillations at the convolute or if they are due to noise pickup and/or transit-time effects and other resonant modes in the IVM. Results using a transit-time-correction technique and Fourier analysis support the latter. Regardless of which interpretation is correct, both true voltage oscillations and the excitement of resonant modes could be the result of transient electrical breakdowns in the post-hole convolute, though more information is required to determine definitively if such breakdowns occurred. Despite the larger oscillations in the IVM records, the general agreement found between the lossless propagation results and the results of the IVM shows that large voltages are transmitted efficiently through the MITLs on Z. These results are complementary to previous studies [R. D. McBride et al., Phys. Rev. ST Accel. Beams 13, 120401 (2010)] that showed

  15. ARKCoS: artifact-suppressed accelerated radial kernel convolution on the sphere

    NASA Astrophysics Data System (ADS)

    Elsner, F.; Wandelt, B. D.

    2011-08-01

    We describe a hybrid Fourier/direct space convolution algorithm for compact radial (azimuthally symmetric) kernels on the sphere. For high resolution maps covering a large fraction of the sky, our implementation takes advantage of the inexpensive massive parallelism afforded by consumer graphics processing units (GPUs). Its applications include modeling of instrumental beam shapes in terms of compact kernels, computation of fine-scale wavelet transformations, and optimal filtering for the detection of point sources. Our algorithm works for any pixelization where pixels are grouped into isolatitude rings. Even for kernels that are not bandwidth-limited, ringing features are completely absent on an ECP grid. We demonstrate that they can be highly suppressed on the popular HEALPix pixelization, for which we develop a freely available implementation of the algorithm. As an example application, we show that running on a high-end consumer graphics card our method speeds up beam convolution for simulations of a characteristic Planck high frequency instrument channel by two orders of magnitude compared to the commonly used HEALPix implementation on one CPU core, while typically maintaining a fractional RMS accuracy of about 1 part in 105.

  16. Projection of fMRI data onto the cortical surface using anatomically-informed convolution kernels.

    PubMed

    Operto, G; Bulot, R; Anton, J-L; Coulon, O

    2008-01-01

    As surface-based data analysis offer an attractive approach for intersubject matching and comparison, the projection of voxel-based 3D volumes onto the cortical surface is an essential problem. We present here a method that aims at producing representations of functional brain data on the cortical surface from functional MRI volumes. Such representations are for instance required for subsequent cortical-based functional analysis. We propose a projection technique based on the definition, around each node of the gray/white matter interface mesh, of convolution kernels whose shape and distribution rely on the geometry of the local anatomy. For one anatomy, a set of convolution kernels is computed that can be used to project any functional data registered with this anatomy. Therefore resulting in anatomically-informed projections of data onto the cortical surface, this kernel-based approach offers better sensitivity, specificity than other classical methods and robustness to misregistration errors. Influences of mesh and volumes spatial resolutions were also estimated for various projection techniques, using simulated functional maps. PMID:17931891

  17. Evaluation of convolutional neural networks for visual recognition.

    PubMed

    Nebauer, C

    1998-01-01

    Convolutional neural networks provide an efficient method to constrain the complexity of feedforward neural networks by weight sharing and restriction to local connections. This network topology has been applied in particular to image classification when sophisticated preprocessing is to be avoided and raw images are to be classified directly. In this paper two variations of convolutional networks--neocognitron and a modification of neocognitron--are compared with classifiers based on fully connected feedforward layers (i.e., multilayer perceptron, nearest neighbor classifier, auto-encoding network) with respect to their visual recognition performance. Beside the original neocognitron a modification of the neocognitron is proposed which combines neurons from perceptron with the localized network structure of neocognitron. Instead of training convolutional networks by time-consuming error backpropagation, in this work a modular procedure is applied whereby layers are trained sequentially from the input to the output layer in order to recognize features of increasing complexity. For a quantitative experimental comparison with standard classifiers two very different recognition tasks have been chosen: handwritten digit recognition and face recognition. In the first example on handwritten digit recognition the generalization of convolutional networks is compared to fully connected networks. In several experiments the influence of variations of position, size, and orientation of digits is determined and the relation between training sample size and validation error is observed. In the second example recognition of human faces is investigated under constrained and variable conditions with respect to face orientation and illumination and the limitations of convolutional networks are discussed. PMID:18252491

  18. FAST PIXEL SPACE CONVOLUTION FOR COSMIC MICROWAVE BACKGROUND SURVEYS WITH ASYMMETRIC BEAMS AND COMPLEX SCAN STRATEGIES: FEBeCoP

    SciTech Connect

    Mitra, S.; Rocha, G.; Gorski, K. M.; Lawrence, C. R.; Huffenberger, K. M.; Eriksen, H. K.; Ashdown, M. A. J. E-mail: graca@caltech.edu E-mail: Charles.R.Lawrence@jpl.nasa.gov E-mail: h.k.k.eriksen@astro.uio.no

    2011-03-15

    Precise measurement of the angular power spectrum of the cosmic microwave background (CMB) temperature and polarization anisotropy can tightly constrain many cosmological models and parameters. However, accurate measurements can only be realized in practice provided all major systematic effects have been taken into account. Beam asymmetry, coupled with the scan strategy, is a major source of systematic error in scanning CMB experiments such as Planck, the focus of our current interest. We envision Monte Carlo methods to rigorously study and account for the systematic effect of beams in CMB analysis. Toward that goal, we have developed a fast pixel space convolution method that can simulate sky maps observed by a scanning instrument, taking into account real beam shapes and scan strategy. The essence is to pre-compute the 'effective beams' using a computer code, 'Fast Effective Beam Convolution in Pixel space' (FEBeCoP), that we have developed for the Planck mission. The code computes effective beams given the focal plane beam characteristics of the Planck instrument and the full history of actual satellite pointing, and performs very fast convolution of sky signals using the effective beams. In this paper, we describe the algorithm and the computational scheme that has been implemented. We also outline a few applications of the effective beams in the precision analysis of Planck data, for characterizing the CMB anisotropy and for detecting and measuring properties of point sources.

  19. Segmenting delaminations in carbon fiber reinforced polymer composite CT using convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Sammons, Daniel; Winfree, William P.; Burke, Eric; Ji, Shuiwang

    2016-02-01

    Nondestructive evaluation (NDE) utilizes a variety of techniques to inspect various materials for defects without causing changes to the material. X-ray computed tomography (CT) produces large volumes of three dimensional image data. Using the task of identifying delaminations in carbon fiber reinforced polymer (CFRP) composite CT, this work shows that it is possible to automate the analysis of these large volumes of CT data using a machine learning model known as a convolutional neural network (CNN). Further, tests on simulated data sets show that with a robust set of experimental data, it may be possible to go beyond just identification and instead accurately characterize the size and shape of the delaminations with CNNs.

  20. a Convolutional Network for Semantic Facade Segmentation and Interpretation

    NASA Astrophysics Data System (ADS)

    Schmitz, Matthias; Mayer, Helmut

    2016-06-01

    In this paper we present an approach for semantic interpretation of facade images based on a Convolutional Network. Our network processes the input images in a fully convolutional way and generates pixel-wise predictions. We show that there is no need for large datasets to train the network when transfer learning is employed, i. e., a part of an already existing network is used and fine-tuned, and when the available data is augmented by using deformed patches of the images for training. The network is trained end-to-end with patches of the images and each patch is augmented independently. To undo the downsampling for the classification, we add deconvolutional layers to the network. Outputs of different layers of the network are combined to achieve more precise pixel-wise predictions. We demonstrate the potential of our network based on results for the eTRIMS (Korč and Förstner, 2009) dataset reduced to facades.

  1. UFLIC: A Line Integral Convolution Algorithm for Visualizing Unsteady Flows

    NASA Technical Reports Server (NTRS)

    Shen, Han-Wei; Kao, David L.; Chancellor, Marisa K. (Technical Monitor)

    1997-01-01

    This paper presents an algorithm, UFLIC (Unsteady Flow LIC), to visualize vector data in unsteady flow fields. Using the Line Integral Convolution (LIC) as the underlying method, a new convolution algorithm is proposed that can effectively trace the flow's global features over time. The new algorithm consists of a time-accurate value depositing scheme and a successive feed-forward method. The value depositing scheme accurately models the flow advection, and the successive feed-forward method maintains the coherence between animation frames. Our new algorithm can produce time-accurate, highly coherent flow animations to highlight global features in unsteady flow fields. CFD scientists, for the first time, are able to visualize unsteady surface flows using our algorithm.

  2. Deep learning for steganalysis via convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Qian, Yinlong; Dong, Jing; Wang, Wei; Tan, Tieniu

    2015-03-01

    Current work on steganalysis for digital images is focused on the construction of complex handcrafted features. This paper proposes a new paradigm for steganalysis to learn features automatically via deep learning models. We novelly propose a customized Convolutional Neural Network for steganalysis. The proposed model can capture the complex dependencies that are useful for steganalysis. Compared with existing schemes, this model can automatically learn feature representations with several convolutional layers. The feature extraction and classification steps are unified under a single architecture, which means the guidance of classification can be used during the feature extraction step. We demonstrate the effectiveness of the proposed model on three state-of-theart spatial domain steganographic algorithms - HUGO, WOW, and S-UNIWARD. Compared to the Spatial Rich Model (SRM), our model achieves comparable performance on BOSSbase and the realistic and large ImageNet database.

  3. Study on Expansion of Convolutional Compactors over Galois Field

    NASA Astrophysics Data System (ADS)

    Arai, Masayuki; Fukumoto, Satoshi; Iwasaki, Kazuhiko

    Convolutional compactors offer a promising technique of compacting test responses. In this study we expand the architecture of convolutional compactor onto a Galois field in order to improve compaction ratio as well as reduce X-masking probability, namely, the probability that an error is masked by unknown values. While each scan chain is independently connected by EOR gates in the conventional arrangement, the proposed scheme treats q signals as an element over GF(2q), and the connections are configured on the same field. We show the arrangement of the proposed compactors and the equivalent expression over GF(2). We then evaluate the effectiveness of the proposed expansion in terms of X-masking probability by simulations with uniform distribution of X-values, as well as reduction of hardware overheads. Furthermore, we evaluate a multi-weight arrangement of the proposed compactors for non-uniform X distributions.

  4. Two-dimensional convolute integers for analytical instrumentation

    NASA Technical Reports Server (NTRS)

    Edwards, T. R.

    1982-01-01

    As new analytical instruments and techniques emerge with increased dimensionality, a corresponding need is seen for data processing logic which can appropriately address the data. Two-dimensional measurements reveal enhanced unknown mixture analysis capability as a result of the greater spectral information content over two one-dimensional methods taken separately. It is noted that two-dimensional convolute integers are merely an extension of the work by Savitzky and Golay (1964). It is shown that these low-pass, high-pass and band-pass digital filters are truly two-dimensional and that they can be applied in a manner identical with their one-dimensional counterpart, that is, a weighted nearest-neighbor, moving average with zero phase shifting, convoluted integer (universal number) weighting coefficients.

  5. Image Super-Resolution Using Deep Convolutional Networks.

    PubMed

    Dong, Chao; Loy, Chen Change; He, Kaiming; Tang, Xiaoou

    2016-02-01

    We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality. PMID:26761735

  6. Face Detection Using GPU-Based Convolutional Neural Networks

    NASA Astrophysics Data System (ADS)

    Nasse, Fabian; Thurau, Christian; Fink, Gernot A.

    In this paper, we consider the problem of face detection under pose variations. Unlike other contributions, a focus of this work resides within efficient implementation utilizing the computational powers of modern graphics cards. The proposed system consists of a parallelized implementation of convolutional neural networks (CNNs) with a special emphasize on also parallelizing the detection process. Experimental validation in a smart conference room with 4 active ceiling-mounted cameras shows a dramatic speed-gain under real-life conditions.

  7. New syndrome decoder for (n, 1) convolutional codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Truong, T. K.

    1983-01-01

    The letter presents a new syndrome decoding algorithm for the (n, 1) convolutional codes (CC) that is different and simpler than the previous syndrome decoding algorithm of Schalkwijk and Vinck. The new technique uses the general solution of the polynomial linear Diophantine equation for the error polynomial vector E(D). A recursive, Viterbi-like, algorithm is developed to find the minimum weight error vector E(D). An example is given for the binary nonsystematic (2, 1) CC.

  8. Automatic localization of vertebrae based on convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Shen, Wei; Yang, Feng; Mu, Wei; Yang, Caiyun; Yang, Xin; Tian, Jie

    2015-03-01

    Localization of the vertebrae is of importance in many medical applications. For example, the vertebrae can serve as the landmarks in image registration. They can also provide a reference coordinate system to facilitate the localization of other organs in the chest. In this paper, we propose a new vertebrae localization method using convolutional neural networks (CNN). The main advantage of the proposed method is the removal of hand-crafted features. We construct two training sets to train two CNNs that share the same architecture. One is used to distinguish the vertebrae from other tissues in the chest, and the other is aimed at detecting the centers of the vertebrae. The architecture contains two convolutional layers, both of which are followed by a max-pooling layer. Then the output feature vector from the maxpooling layer is fed into a multilayer perceptron (MLP) classifier which has one hidden layer. Experiments were performed on ten chest CT images. We used leave-one-out strategy to train and test the proposed method. Quantitative comparison between the predict centers and ground truth shows that our convolutional neural networks can achieve promising localization accuracy without hand-crafted features.

  9. Fine-grained representation learning in convolutional autoencoders

    NASA Astrophysics Data System (ADS)

    Luo, Chang; Wang, Jie

    2016-03-01

    Convolutional autoencoders (CAEs) have been widely used as unsupervised feature extractors for high-resolution images. As a key component in CAEs, pooling is a biologically inspired operation to achieve scale and shift invariances, and the pooled representation directly affects the CAEs' performance. Fine-grained pooling, which uses small and dense pooling regions, encodes fine-grained visual cues and enhances local characteristics. However, it tends to be sensitive to spatial rearrangements. In most previous works, pooled features were obtained by empirically modulating parameters in CAEs. We see the CAE as a whole and propose a fine-grained representation learning law to extract better fine-grained features. This representation learning law suggests two directions for improvement. First, we probabilistically evaluate the discrimination-invariance tradeoff with fine-grained granularity in the pooled feature maps, and suggest the proper filter scale in the convolutional layer and appropriate whitening parameters in preprocessing step. Second, pooling approaches are combined with the sparsity degree in pooling regions, and we propose the preferable pooling approach. Experimental results on two independent benchmark datasets demonstrate that our representation learning law could guide CAEs to extract better fine-grained features and performs better in multiclass classification task. This paper also provides guidance for selecting appropriate parameters to obtain better fine-grained representation in other convolutional neural networks.

  10. A Discriminative Representation of Convolutional Features for Indoor Scene Recognition

    NASA Astrophysics Data System (ADS)

    Khan, Salman H.; Hayat, Munawar; Bennamoun, Mohammed; Togneri, Roberto; Sohel, Ferdous A.

    2016-07-01

    Indoor scene recognition is a multi-faceted and challenging problem due to the diverse intra-class variations and the confusing inter-class similarities. This paper presents a novel approach which exploits rich mid-level convolutional features to categorize indoor scenes. Traditionally used convolutional features preserve the global spatial structure, which is a desirable property for general object recognition. However, we argue that this structuredness is not much helpful when we have large variations in scene layouts, e.g., in indoor scenes. We propose to transform the structured convolutional activations to another highly discriminative feature space. The representation in the transformed space not only incorporates the discriminative aspects of the target dataset, but it also encodes the features in terms of the general object categories that are present in indoor scenes. To this end, we introduce a new large-scale dataset of 1300 object categories which are commonly present in indoor scenes. Our proposed approach achieves a significant performance boost over previous state of the art approaches on five major scene classification datasets.

  11. Fabricating superhydrophilic wool fabrics.

    PubMed

    Chen, Dong; Tan, Longfei; Liu, Huiyu; Hu, Junyan; Li, Yi; Tang, Fangqiong

    2010-04-01

    A simple method for fabricating environmentally stable superhydrophilic wool fabrics is reported here. An ultrathin silica layer coated on the wool altered both the surface roughness and the surface energy of the fiber and endowed the wool fabrics with excellent water absorption. The process of coating silica sols was dependent on an acid solution of low pH, which influenced the electrostatic interactions between nanoparticles and wool fibers. The morphology and composition of silica-sol-coated wool fabrics were characterized by a combination of SEM, TEM, EDX, FTIR, and XPS measurements. The possible mechanism and size effect of silica nanoparticles on the hydrophilic property of wool fabric were discussed. The washing fastness of the superhydrophilic wool fabrics in perchlorethylene and water was also evaluated. This study shows that wool fabrics modified by optical transparence, chemical stability, and nontoxic silica sols are promising in constructing smart textiles. PMID:19908843

  12. Testing airplane fabrics

    NASA Technical Reports Server (NTRS)

    Proll, A

    1924-01-01

    The following considerations determine the strength of airplane fabrics: 1. maximum air forces acting on the surfaces (including local stresses); 2. tensions produced in the fabrics, in the directions of both warp and filling; 3. factor of safety required. The question of the permissible depression of the fabric as affecting the aerodynamic requirements in regard to the maintenance of shape of the section, the tenacity and extensibility of the layer of dope, its strength and its permeability to water is almost as important.

  13. Learning Contextual Dependence With Convolutional Hierarchical Recurrent Neural Networks

    NASA Astrophysics Data System (ADS)

    Zuo, Zhen; Shuai, Bing; Wang, Gang; Liu, Xiao; Wang, Xingxing; Wang, Bing; Chen, Yushi

    2016-07-01

    Existing deep convolutional neural networks (CNNs) have shown their great success on image classification. CNNs mainly consist of convolutional and pooling layers, both of which are performed on local image areas without considering the dependencies among different image regions. However, such dependencies are very important for generating explicit image representation. In contrast, recurrent neural networks (RNNs) are well known for their ability of encoding contextual information among sequential data, and they only require a limited number of network parameters. General RNNs can hardly be directly applied on non-sequential data. Thus, we proposed the hierarchical RNNs (HRNNs). In HRNNs, each RNN layer focuses on modeling spatial dependencies among image regions from the same scale but different locations. While the cross RNN scale connections target on modeling scale dependencies among regions from the same location but different scales. Specifically, we propose two recurrent neural network models: 1) hierarchical simple recurrent network (HSRN), which is fast and has low computational cost; and 2) hierarchical long-short term memory recurrent network (HLSTM), which performs better than HSRN with the price of more computational cost. In this manuscript, we integrate CNNs with HRNNs, and develop end-to-end convolutional hierarchical recurrent neural networks (C-HRNNs). C-HRNNs not only make use of the representation power of CNNs, but also efficiently encodes spatial and scale dependencies among different image regions. On four of the most challenging object/scene image classification benchmarks, our C-HRNNs achieve state-of-the-art results on Places 205, SUN 397, MIT indoor, and competitive results on ILSVRC 2012.

  14. Convolutional neural networks for mammography mass lesion classification.

    PubMed

    Arevalo, John; Gonzalez, Fabio A; Ramos-Pollan, Raul; Oliveira, Jose L; Guevara Lopez, Miguel Angel

    2015-08-01

    Feature extraction is a fundamental step when mammography image analysis is addressed using learning based approaches. Traditionally, problem dependent handcrafted features are used to represent the content of images. An alternative approach successfully applied in other domains is the use of neural networks to automatically discover good features. This work presents an evaluation of convolutional neural networks to learn features for mammography mass lesions before feeding them to a classification stage. Experimental results showed that this approach is a suitable strategy outperforming the state-of-the-art representation from 79.9% to 86% in terms of area under the ROC curve. PMID:26736382

  15. Convolution seal for transition duct in turbine system

    SciTech Connect

    Flanagan, James Scott; LeBegue, Jeffrey Scott; McMahan, Kevin Weston; Dillard, Daniel Jackson; Pentecost, Ronnie Ray

    2015-03-10

    A turbine system is disclosed. In one embodiment, the turbine system includes a transition duct. The transition duct includes an inlet, an outlet, and a passage extending between the inlet and the outlet and defining a longitudinal axis, a radial axis, and a tangential axis. The outlet of the transition duct is offset from the inlet along the longitudinal axis and the tangential axis. The transition duct further includes an interface member for interfacing with a turbine section. The turbine system further includes a convolution seal contacting the interface member to provide a seal between the interface member and the turbine section.

  16. Convolution seal for transition duct in turbine system

    SciTech Connect

    Flanagan, James Scott; LeBegue, Jeffrey Scott; McMahan, Kevin Weston; Dillard, Daniel Jackson; Pentecost, Ronnie Ray

    2015-05-26

    A turbine system is disclosed. In one embodiment, the turbine system includes a transition duct. The transition duct includes an inlet, an outlet, and a passage extending between the inlet and the outlet and defining a longitudinal axis, a radial axis, and a tangential axis. The outlet of the transition duct is offset from the inlet along the longitudinal axis and the tangential axis. The transition duct further includes an interface feature for interfacing with an adjacent transition duct. The turbine system further includes a convolution seal contacting the interface feature to provide a seal between the interface feature and the adjacent transition duct.

  17. Is turbulent mixing a self-convolution process?

    PubMed

    Venaille, Antoine; Sommeria, Joel

    2008-06-13

    Experimental results for the evolution of the probability distribution function (PDF) of a scalar mixed by a turbulent flow in a channel are presented. The sequence of PDF from an initial skewed distribution to a sharp Gaussian is found to be nonuniversal. The route toward homogeneization depends on the ratio between the cross sections of the dye injector and the channel. In connection with this observation, advantages, shortcomings, and applicability of models for the PDF evolution based on a self-convolution mechanism are discussed. PMID:18643510

  18. A Fortran 90 code for magnetohydrodynamics. Part 1, Banded convolution

    SciTech Connect

    Walker, D.W.

    1992-03-01

    This report describes progress in developing a Fortran 90 version of the KITE code for studying plasma instabilities in Tokamaks. In particular, the evaluation of convolution terms appearing in the numerical solution is discussed, and timing results are presented for runs performed on an 8k processor Connection Machine (CM-2). Estimates of the performance on a full-size 64k CM-2 are given, and range between 100 and 200 Mflops. The advantages of having a Fortran 90 version of the KITE code are stressed, and the future use of such a code on the newly announced CM5 and Paragon computers, from Thinking Machines Corporation and Intel, is considered.

  19. Visualizing Vector Fields Using Line Integral Convolution and Dye Advection

    NASA Technical Reports Server (NTRS)

    Shen, Han-Wei; Johnson, Christopher R.; Ma, Kwan-Liu

    1996-01-01

    We present local and global techniques to visualize three-dimensional vector field data. Using the Line Integral Convolution (LIC) method to image the global vector field, our new algorithm allows the user to introduce colored 'dye' into the vector field to highlight local flow features. A fast algorithm is proposed that quickly recomputes the dyed LIC images. In addition, we introduce volume rendering methods that can map the LIC texture on any contour surface and/or translucent region defined by additional scalar quantities, and can follow the advection of colored dye throughout the volume.

  20. New Syndrome Decoding Techniques for the (n, K) Convolutional Codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Truong, T. K.

    1983-01-01

    This paper presents a new syndrome decoding algorithm for the (n,k) convolutional codes (CC) which differs completely from an earlier syndrome decoding algorithm of Schalkwijk and Vinck. The new algorithm is based on the general solution of the syndrome equation, a linear Diophantine equation for the error polynomial vector E(D). The set of Diophantine solutions is a coset of the CC. In this error coset a recursive, Viterbi-like algorithm is developed to find the minimum weight error vector (circumflex)E(D). An example, illustrating the new decoding algorithm, is given for the binary nonsystemmatic (3,1)CC.

  1. New syndrome decoding techniques for the (n, k) convolutional codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Truong, T. K.

    1984-01-01

    This paper presents a new syndrome decoding algorithm for the (n, k) convolutional codes (CC) which differs completely from an earlier syndrome decoding algorithm of Schalkwijk and Vinck. The new algorithm is based on the general solution of the syndrome equation, a linear Diophantine equation for the error polynomial vector E(D). The set of Diophantine solutions is a coset of the CC. In this error coset a recursive, Viterbi-like algorithm is developed to find the minimum weight error vector (circumflex)E(D). An example, illustrating the new decoding algorithm, is given for the binary nonsystemmatic (3, 1)CC. Previously announced in STAR as N83-34964

  2. Simplified Syndrome Decoding of (n, 1) Convolutional Codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Truong, T. K.

    1983-01-01

    A new syndrome decoding algorithm for the (n, 1) convolutional codes (CC) that is different and simpler than the previous syndrome decoding algorithm of Schalkwijk and Vinck is presented. The new algorithm uses the general solution of the polynomial linear Diophantine equation for the error polynomial vector E(D). This set of Diophantine solutions is a coset of the CC space. A recursive or Viterbi-like algorithm is developed to find the minimum weight error vector cirumflex E(D) in this error coset. An example illustrating the new decoding algorithm is given for the binary nonsymmetric (2,1)CC.

  3. Continuous speech recognition based on convolutional neural network

    NASA Astrophysics Data System (ADS)

    Zhang, Qing-qing; Liu, Yong; Pan, Jie-lin; Yan, Yong-hong

    2015-07-01

    Convolutional Neural Networks (CNNs), which showed success in achieving translation invariance for many image processing tasks, are investigated for continuous speech recognitions in the paper. Compared to Deep Neural Networks (DNNs), which have been proven to be successful in many speech recognition tasks nowadays, CNNs can reduce the NN model sizes significantly, and at the same time achieve even better recognition accuracies. Experiments on standard speech corpus TIMIT showed that CNNs outperformed DNNs in the term of the accuracy when CNNs had even smaller model size.

  4. A digital model for streamflow routing by convolution methods

    USGS Publications Warehouse

    Doyle, W.H., Jr.; Shearman, H.O.; Stiltner, G.J.; Krug, W.O.

    1984-01-01

    U.S. Geological Survey computer model, CONROUT, for routing streamflow by unit-response convolution flow-routing techniques from an upstream channel location to a downstream channel location has been developed and documented. Calibration and verification of the flow-routing model and subsequent use of the model for simulation is also documented. Three hypothetical examples and two field applications are presented to illustrate basic flow-routing concepts. Most of the discussion is limited to daily flow routing since, to date, all completed and current studies of this nature involve daily flow routing. However, the model is programmed to accept hourly flow-routing data. (USGS)

  5. Faster GPU-based convolutional gridding via thread coarsening

    NASA Astrophysics Data System (ADS)

    Merry, B.

    2016-07-01

    Convolutional gridding is a processor-intensive step in interferometric imaging. While it is possible to use graphics processing units (GPUs) to accelerate this operation, existing methods use only a fraction of the available flops. We apply thread coarsening to improve the efficiency of an existing algorithm, and observe performance gains of up to 3.2 × for single-polarization gridding and 1.9 × for quad-polarization gridding on a GeForce GTX 980, and smaller but still significant gains on a Radeon R9 290X.

  6. Simple, Inexpensive, and Rapid Approach to Fabricate Cross-Shaped Memristors Using an Inorganic-Nanowire-Digital-Alignment Technique and a One-Step Reduction Process.

    PubMed

    Xu, Wentao; Lee, Yeongjun; Min, Sung-Yong; Park, Cheolmin; Lee, Tae-Woo

    2016-01-20

    A rapid, scalable, and designable approach to produce a cross-shaped memristor array is demonstrated using an inorganic-nanowire digital-alignment technique and a one-step reduction process. Two-dimensional arrays of perpendicularly aligned, individually conductive Cu-nanowires with a nanometer-scale Cux O layer sandwiched at each cross point are produced. PMID:26585580

  7. Convolution and non convolution Perfectly Matched Layer techniques optimized at grazing incidence for high-order wave propagation modelling

    NASA Astrophysics Data System (ADS)

    Martin, Roland; Komatitsch, Dimitri; Bruthiaux, Emilien; Gedney, Stephen D.

    2010-05-01

    We present and discuss here two different unsplit formulations of the frequency shift PML based on convolution or non convolution integrations of auxiliary memory variables. Indeed, the Perfectly Matched Layer absorbing boundary condition has proven to be very efficient from a numerical point of view for the elastic wave equation to absorb both body waves with non-grazing incidence and surface waves. However, at grazing incidence the classical discrete Perfectly Matched Layer method suffers from large spurious reflections that make it less efficient for instance in the case of very thin mesh slices, in the case of sources located very close to the edge of the mesh, and/or in the case of receivers located at very large offset. In [1] we improve the Perfectly Matched Layer at grazing incidence for the seismic wave equation based on an unsplit convolution technique. This improved PML has a cost that is similar in terms of memory storage to that of the classical PML. We illustrate the efficiency of this improved Convolutional Perfectly Matched Layer based on numerical benchmarks using a staggered finite-difference method on a very thin mesh slice for an isotropic material and show that results are significantly improved compared with the classical Perfectly Matched Layer technique. We also show that, as the classical model, the technique is intrinsically unstable in the case of some anisotropic materials. In this case, retaining an idea of [2], this has been stabilized by adding correction terms adequately along any coordinate axis [3]. More specifically this has been applied to the spectral-element method based on a hybrid first/second order time integration scheme in which the Newmark time marching scheme allows us to match perfectly at the base of the absorbing layer a velocity-stress formulation in the PML and a second order displacement formulation in the inner computational domain.Our CPML unsplit formulation has the advantage to reduce the memory storage of CPML

  8. Convolutional neural network architectures for predicting DNA–protein binding

    PubMed Central

    Zeng, Haoyang; Edwards, Matthew D.; Liu, Ge; Gifford, David K.

    2016-01-01

    Motivation: Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA–protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications. Results: We present a systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets. We identify the best-performing architectures by varying CNN width, depth and pooling designs. We find that adding convolutional kernels to a network is important for motif-based tasks. We show the benefits of CNNs in learning rich higher-order sequence features, such as secondary motifs and local sequence context, by comparing network performance on multiple modeling tasks ranging in difficulty. We also demonstrate how careful construction of sequence benchmark datasets, using approaches that control potentially confounding effects like positional or motif strength bias, is critical in making fair comparisons between competing methods. We explore how to establish the sufficiency of training data for these learning tasks, and we have created a flexible cloud-based framework that permits the rapid exploration of alternative neural network architectures for problems in computational biology. Availability and Implementation: All the models analyzed are available at http://cnn.csail.mit.edu. Contact: gifford@mit.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307608

  9. Classification of Histology Sections via Multispectral Convolutional Sparse Coding*

    PubMed Central

    Zhou, Yin; Barner, Kenneth; Spellman, Paul

    2014-01-01

    Image-based classification of histology sections plays an important role in predicting clinical outcomes. However this task is very challenging due to the presence of large technical variations (e.g., fixation, staining) and biological heterogeneities (e.g., cell type, cell state). In the field of biomedical imaging, for the purposes of visualization and/or quantification, different stains are typically used for different targets of interest (e.g., cellular/subcellular events), which generates multi-spectrum data (images) through various types of microscopes and, as a result, provides the possibility of learning biological-component-specific features by exploiting multispectral information. We propose a multispectral feature learning model that automatically learns a set of convolution filter banks from separate spectra to efficiently discover the intrinsic tissue morphometric signatures, based on convolutional sparse coding (CSC). The learned feature representations are then aggregated through the spatial pyramid matching framework (SPM) and finally classified using a linear SVM. The proposed system has been evaluated using two large-scale tumor cohorts, collected from The Cancer Genome Atlas (TCGA). Experimental results show that the proposed model 1) outperforms systems utilizing sparse coding for unsupervised feature learning (e.g., PSD-SPM [5]); 2) is competitive with systems built upon features with biological prior knowledge (e.g., SMLSPM [4]). PMID:25554749

  10. Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks.

    PubMed

    Dosovitskiy, Alexey; Fischer, Philipp; Springenberg, Jost Tobias; Riedmiller, Martin; Brox, Thomas

    2016-09-01

    Deep convolutional networks have proven to be very successful in learning task specific features that allow for unprecedented performance on various computer vision tasks. Training of such networks follows mostly the supervised learning paradigm, where sufficiently many input-output pairs are required for training. Acquisition of large training sets is one of the key challenges, when approaching a new task. In this paper, we aim for generic feature learning and present an approach for training a convolutional network using only unlabeled data. To this end, we train the network to discriminate between a set of surrogate classes. Each surrogate class is formed by applying a variety of transformations to a randomly sampled 'seed' image patch. In contrast to supervised network training, the resulting feature representation is not class specific. It rather provides robustness to the transformations that have been applied during training. This generic feature representation allows for classification results that outperform the state of the art for unsupervised learning on several popular datasets (STL-10, CIFAR-10, Caltech-101, Caltech-256). While features learned with our approach cannot compete with class specific features from supervised training on a classification task, we show that they are advantageous on geometric matching problems, where they also outperform the SIFT descriptor. PMID:26540673

  11. Enhancing Neutron Beam Production with a Convoluted Moderator

    SciTech Connect

    Iverson, Erik B; Baxter, David V; Muhrer, Guenter; Ansell, Stuart; Gallmeier, Franz X; Dalgliesh, Robert; Lu, Wei; Kaiser, Helmut

    2014-10-01

    We describe a new concept for a neutron moderating assembly resulting in the more efficient production of slow neutron beams. The Convoluted Moderator, a heterogeneous stack of interleaved moderating material and nearly transparent single-crystal spacers, is a directionally-enhanced neutron beam source, improving beam effectiveness over an angular range comparable to the range accepted by neutron beam lines and guides. We have demonstrated gains of 50% in slow neutron intensity for a given fast neutron production rate while simultaneously reducing the wavelength-dependent emission time dispersion by 25%, both coming from a geometric effect in which the neutron beam lines view a large surface area of moderating material in a relatively small volume. Additionally, we have confirmed a Bragg-enhancement effect arising from coherent scattering within the single-crystal spacers. We have not observed hypothesized refractive effects leading to additional gains at long wavelength. In addition to confirmation of the validity of the Convoluted Moderator concept, our measurements provide a series of benchmark experiments suitable for developing simulation and analysis techniques for practical optimization and eventual implementation at slow neutron source facilities.

  12. Fluence-convolution broad-beam (FCBB) dose calculation.

    PubMed

    Lu, Weiguo; Chen, Mingli

    2010-12-01

    IMRT optimization requires a fast yet relatively accurate algorithm to calculate the iteration dose with small memory demand. In this paper, we present a dose calculation algorithm that approaches these goals. By decomposing the infinitesimal pencil beam (IPB) kernel into the central axis (CAX) component and lateral spread function (LSF) and taking the beam's eye view (BEV), we established a non-voxel and non-beamlet-based dose calculation formula. Both LSF and CAX are determined by a commissioning procedure using the collapsed-cone convolution/superposition (CCCS) method as the standard dose engine. The proposed dose calculation involves a 2D convolution of a fluence map with LSF followed by ray tracing based on the CAX lookup table with radiological distance and divergence correction, resulting in complexity of O(N(3)) both spatially and temporally. This simple algorithm is orders of magnitude faster than the CCCS method. Without pre-calculation of beamlets, its implementation is also orders of magnitude smaller than the conventional voxel-based beamlet-superposition (VBS) approach. We compared the presented algorithm with the CCCS method using simulated and clinical cases. The agreement was generally within 3% for a homogeneous phantom and 5% for heterogeneous and clinical cases. Combined with the 'adaptive full dose correction', the algorithm is well suitable for calculating the iteration dose during IMRT optimization. PMID:21081826

  13. A Mathematical Motivation for Complex-Valued Convolutional Networks.

    PubMed

    Tygert, Mark; Bruna, Joan; Chintala, Soumith; LeCun, Yann; Piantino, Serkan; Szlam, Arthur

    2016-05-01

    A complex-valued convolutional network (convnet) implements the repeated application of the following composition of three operations, recursively applying the composition to an input vector of nonnegative real numbers: (1) convolution with complex-valued vectors, followed by (2) taking the absolute value of every entry of the resulting vectors, followed by (3) local averaging. For processing real-valued random vectors, complex-valued convnets can be viewed as data-driven multiscale windowed power spectra, data-driven multiscale windowed absolute spectra, data-driven multiwavelet absolute values, or (in their most general configuration) data-driven nonlinear multiwavelet packets. Indeed, complex-valued convnets can calculate multiscale windowed spectra when the convnet filters are windowed complex-valued exponentials. Standard real-valued convnets, using rectified linear units (ReLUs), sigmoidal (e.g., logistic or tanh) nonlinearities, or max pooling, for example, do not obviously exhibit the same exact correspondence with data-driven wavelets (whereas for complex-valued convnets, the correspondence is much more than just a vague analogy). Courtesy of the exact correspondence, the remarkably rich and rigorous body of mathematical analysis for wavelets applies directly to (complex-valued) convnets. PMID:26890348

  14. Convolutional Neural Network Based Fault Detection for Rotating Machinery

    NASA Astrophysics Data System (ADS)

    Janssens, Olivier; Slavkovikj, Viktor; Vervisch, Bram; Stockman, Kurt; Loccufier, Mia; Verstockt, Steven; Van de Walle, Rik; Van Hoecke, Sofie

    2016-09-01

    Vibration analysis is a well-established technique for condition monitoring of rotating machines as the vibration patterns differ depending on the fault or machine condition. Currently, mainly manually-engineered features, such as the ball pass frequencies of the raceway, RMS, kurtosis an crest, are used for automatic fault detection. Unfortunately, engineering and interpreting such features requires a significant level of human expertise. To enable non-experts in vibration analysis to perform condition monitoring, the overhead of feature engineering for specific faults needs to be reduced as much as possible. Therefore, in this article we propose a feature learning model for condition monitoring based on convolutional neural networks. The goal of this approach is to autonomously learn useful features for bearing fault detection from the data itself. Several types of bearing faults such as outer-raceway faults and lubrication degradation are considered, but also healthy bearings and rotor imbalance are included. For each condition, several bearings are tested to ensure generalization of the fault-detection system. Furthermore, the feature-learning based approach is compared to a feature-engineering based approach using the same data to objectively quantify their performance. The results indicate that the feature-learning system, based on convolutional neural networks, significantly outperforms the classical feature-engineering based approach which uses manually engineered features and a random forest classifier. The former achieves an accuracy of 93.61 percent and the latter an accuracy of 87.25 percent.

  15. Deep Convolutional Neural Networks for large-scale speech tasks.

    PubMed

    Sainath, Tara N; Kingsbury, Brian; Saon, George; Soltau, Hagen; Mohamed, Abdel-rahman; Dahl, George; Ramabhadran, Bhuvana

    2015-04-01

    Convolutional Neural Networks (CNNs) are an alternative type of neural network that can be used to reduce spectral variations and model spectral correlations which exist in signals. Since speech signals exhibit both of these properties, we hypothesize that CNNs are a more effective model for speech compared to Deep Neural Networks (DNNs). In this paper, we explore applying CNNs to large vocabulary continuous speech recognition (LVCSR) tasks. First, we determine the appropriate architecture to make CNNs effective compared to DNNs for LVCSR tasks. Specifically, we focus on how many convolutional layers are needed, what is an appropriate number of hidden units, what is the best pooling strategy. Second, investigate how to incorporate speaker-adapted features, which cannot directly be modeled by CNNs as they do not obey locality in frequency, into the CNN framework. Third, given the importance of sequence training for speech tasks, we introduce a strategy to use ReLU+dropout during Hessian-free sequence training of CNNs. Experiments on 3 LVCSR tasks indicate that a CNN with the proposed speaker-adapted and ReLU+dropout ideas allow for a 12%-14% relative improvement in WER over a strong DNN system, achieving state-of-the art results in these 3 tasks. PMID:25439765

  16. Fast space-varying convolution using matrix source coding with applications to camera stray light reduction.

    PubMed

    Wei, Jianing; Bouman, Charles A; Allebach, Jan P

    2014-05-01

    Many imaging applications require the implementation of space-varying convolution for accurate restoration and reconstruction of images. Here, we use the term space-varying convolution to refer to linear operators whose impulse response has slow spatial variation. In addition, these space-varying convolution operators are often dense, so direct implementation of the convolution operator is typically computationally impractical. One such example is the problem of stray light reduction in digital cameras, which requires the implementation of a dense space-varying deconvolution operator. However, other inverse problems, such as iterative tomographic reconstruction, can also depend on the implementation of dense space-varying convolution. While space-invariant convolution can be efficiently implemented with the fast Fourier transform, this approach does not work for space-varying operators. So direct convolution is often the only option for implementing space-varying convolution. In this paper, we develop a general approach to the efficient implementation of space-varying convolution, and demonstrate its use in the application of stray light reduction. Our approach, which we call matrix source coding, is based on lossy source coding of the dense space-varying convolution matrix. Importantly, by coding the transformation matrix, we not only reduce the memory required to store it; we also dramatically reduce the computation required to implement matrix-vector products. Our algorithm is able to reduce computation by approximately factoring the dense space-varying convolution operator into a product of sparse transforms. Experimental results show that our method can dramatically reduce the computation required for stray light reduction while maintaining high accuracy. PMID:24710398

  17. Design, fabrication, and characterization of a planar, silicon-based, monolithically integrated micro laminar flow fuel cell with a bridge-shaped microchannel cross-section

    NASA Astrophysics Data System (ADS)

    López-Montesinos, P. O.; Yossakda, N.; Schmidt, A.; Brushett, F. R.; Pelton, W. E.; Kenis, P. J. A.

    2011-05-01

    We report the fabrication of a planar, silicon-based, monolithically integrated micro laminar flow fuel cell (μLFFC) using standard MEMS and IC-compatible fabrication technologies. The μLFFC operates with acid supported solutions of formic acid and potassium permanganate, as a fuel and oxidant respectively. The micro-fuel cell design features two in-plane anodic and cathodic microchannels connected via a bridge to confine the diffusive liquid-liquid interface away from the electrode areas and to minimize crossover. Palladium high-active-surface-area catalyst was selectively integrated into the anodic microchannel by electrodeposition, whereas no catalyst was required in the cathodic microchannel. A three-dimensional (3D) diffusion-convection model was developed to study the behavior of the diffusion zone and to extract appropriate cell-design parameters and operating conditions. Experimentally, we observed peak power densities as high as 26 mW cm-2 when operating single cells at a flow rate of 60 μL min-1 at room temperature. The miniature membraneless fuel cell design presented herein offers potential for on-chip power generation, which has long been prohibited by integration complexities associated with the membrane.

  18. Using convolutional decoding to improve time delay and phase estimation in digital communications

    DOEpatents

    Ormesher, Richard C.; Mason, John J.

    2010-01-26

    The time delay and/or phase of a communication signal received by a digital communication receiver can be estimated based on a convolutional decoding operation that the communication receiver performs on the received communication signal. If the original transmitted communication signal has been spread according to a spreading operation, a corresponding despreading operation can be integrated into the convolutional decoding operation.

  19. There is no MacWilliams identity for convolutional codes. [transmission gain comparison

    NASA Technical Reports Server (NTRS)

    Shearer, J. B.; Mceliece, R. J.

    1977-01-01

    An example is provided of two convolutional codes that have the same transmission gain but whose dual codes do not. This shows that no analog of the MacWilliams identity for block codes can exist relating the transmission gains of a convolutional code and its dual.

  20. The uniform continuity of characteristic function from convoluted exponential distribution with stabilizer constant

    NASA Astrophysics Data System (ADS)

    Devianto, Dodi

    2016-02-01

    It is constructed convolution of generated random variable from independent and identically exponential distribution with stabilizer constant. The characteristic function of this distribution is obtained by using Laplace-Stieltjes transform. The uniform continuity property of characteristic function from this convolution is obtained by using analytical methods as basic properties.

  1. The effect of whitening transformation on pooling operations in convolutional autoencoders

    NASA Astrophysics Data System (ADS)

    Li, Zuhe; Fan, Yangyu; Liu, Weihua

    2015-12-01

    Convolutional autoencoders (CAEs) are unsupervised feature extractors for high-resolution images. In the pre-processing step, whitening transformation has widely been adopted to remove redundancy by making adjacent pixels less correlated. Pooling is a biologically inspired operation to reduce the resolution of feature maps and achieve spatial invariance in convolutional neural networks. Conventionally, pooling methods are mainly determined empirically in most previous work. Therefore, our main purpose is to study the relationship between whitening processing and pooling operations in convolutional autoencoders for image classification. We propose an adaptive pooling approach based on the concepts of information entropy to test the effect of whitening on pooling in different conditions. Experimental results on benchmark datasets indicate that the performance of pooling strategies is associated with the distribution of feature activations, which can be affected by whitening processing. This provides guidance for the selection of pooling methods in convolutional autoencoders and other convolutional neural networks.

  2. Generalized Viterbi algorithms for error detection with convolutional codes

    NASA Astrophysics Data System (ADS)

    Seshadri, N.; Sundberg, C.-E. W.

    Presented are two generalized Viterbi algorithms (GVAs) for the decoding of convolutional codes. They are a parallel algorithm that simultaneously identifies the L best estimates of the transmitted sequence, and a serial algorithm that identifies the lth best estimate using the knowledge about the previously found l-1 estimates. These algorithms are applied to combined speech and channel coding systems, concatenated codes, trellis-coded modulation, partial response (continuous-phase modulation), and hybrid ARQ (automatic repeat request) schemes. As an example, for a concatenated code more than 2 dB is gained by the use of the GVA with L = 3 over the Viterbi algorithm for block error rates less than 10-2. The channel is a Rayleigh fading channel.

  3. Tomography by iterative convolution - Empirical study and application to interferometry

    NASA Technical Reports Server (NTRS)

    Vest, C. M.; Prikryl, I.

    1984-01-01

    An algorithm for computer tomography has been developed that is applicable to reconstruction from data having incomplete projections because an opaque object blocks some of the probing radiation as it passes through the object field. The algorithm is based on iteration between the object domain and the projection (Radon transform) domain. Reconstructions are computed during each iteration by the well-known convolution method. Although it is demonstrated that this algorithm does not converge, an empirically justified criterion for terminating the iteration when the most accurate estimate has been computed is presented. The algorithm has been studied by using it to reconstruct several different object fields with several different opaque regions. It also has been used to reconstruct aerodynamic density fields from interferometric data recorded in wind tunnel tests.

  4. Plane-wave decomposition by spherical-convolution microphone array

    NASA Astrophysics Data System (ADS)

    Rafaely, Boaz; Park, Munhum

    2001-05-01

    Reverberant sound fields are widely studied, as they have a significant influence on the acoustic performance of enclosures in a variety of applications. For example, the intelligibility of speech in lecture rooms, the quality of music in auditoria, the noise level in offices, and the production of 3D sound in living rooms are all affected by the enclosed sound field. These sound fields are typically studied through frequency response measurements or statistical measures such as reverberation time, which do not provide detailed spatial information. The aim of the work presented in this seminar is the detailed analysis of reverberant sound fields. A measurement and analysis system based on acoustic theory and signal processing, designed around a spherical microphone array, is presented. Detailed analysis is achieved by decomposition of the sound field into waves, using spherical Fourier transform and spherical convolution. The presentation will include theoretical review, simulation studies, and initial experimental results.

  5. Visualization of vasculature with convolution surfaces: method, validation and evaluation.

    PubMed

    Oeltze, Steffen; Preim, Bernhard

    2005-04-01

    We present a method for visualizing vasculature based on clinical computed tomography or magnetic resonance data. The vessel skeleton as well as the diameter information per voxel serve as input. Our method adheres to these data, while producing smooth transitions at branchings and closed, rounded ends by means of convolution surfaces. We examine the filter design with respect to irritating bulges, unwanted blending and the correct visualization of the vessel diameter. The method has been applied to a large variety of anatomic trees. We discuss the validation of the method by means of a comparison to other visualization methods. Surface distance measures are carried out to perform a quantitative validation. Furthermore, we present the evaluation of the method which has been accomplished on the basis of a survey by 11 radiologists and surgeons. PMID:15822811

  6. Finding the complete path and weight enumerators of convolutional codes

    NASA Technical Reports Server (NTRS)

    Onyszchuk, I.

    1990-01-01

    A method for obtaining the complete path enumerator T(D, L, I) of a convolutional code is described. A system of algebraic equations is solved, using a new algorithm for computing determinants, to obtain T(D, L, I) for the (7,1/2) NASA standard code. Generating functions, derived from T(D, L, I) are used to upper bound Viterbi decoder error rates. This technique is currently feasible for constraint length K less than 10 codes. A practical, fast algorithm is presented for computing the leading nonzero coefficients of the generating functions used to bound the performance of constraint length K less than 20 codes. Code profiles with about 50 nonzero coefficients are obtained with this algorithm for the experimental K = 15, rate 1/4, code in the Galileo mission and for the proposed K = 15, rate 1/6, 2-dB code.

  7. Drug-Drug Interaction Extraction via Convolutional Neural Networks

    PubMed Central

    Liu, Shengyu; Tang, Buzhou; Chen, Qingcai; Wang, Xiaolong

    2016-01-01

    Drug-drug interaction (DDI) extraction as a typical relation extraction task in natural language processing (NLP) has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM) with a large number of manually defined features. Recently, convolutional neural networks (CNN), a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extraction. Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction. The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%. PMID:26941831

  8. Highly parallel vector visualization using line integral convolution

    SciTech Connect

    Cabral, B.; Leedom, C.

    1995-12-01

    Line Integral Convolution (LIC) is an effective imaging operator for visualizing large vector fields. It works by blurring an input image along local vector field streamlines yielding an output image. LIC is highly parallelizable because it uses only local read-sharing of input data and no write-sharing of output data. Both coarse- and fine-grained implementations have been developed. The coarse-grained implementation uses a straightforward row-tiling of the vector field to parcel out work to multiple CPUs. The fine-grained implementation uses a series of image warps and sums to compute the LIC algorithm across the entire vector field at once. This is accomplished by novel use of high-performance graphics hardware texture mapping and accumulation buffers.

  9. Enhanced Line Integral Convolution with Flow Feature Detection

    NASA Technical Reports Server (NTRS)

    Lane, David; Okada, Arthur

    1996-01-01

    The Line Integral Convolution (LIC) method, which blurs white noise textures along a vector field, is an effective way to visualize overall flow patterns in a 2D domain. The method produces a flow texture image based on the input velocity field defined in the domain. Because of the nature of the algorithm, the texture image tends to be blurry. This sometimes makes it difficult to identify boundaries where flow separation and reattachments occur. We present techniques to enhance LIC texture images and use colored texture images to highlight flow separation and reattachment boundaries. Our techniques have been applied to several flow fields defined in 3D curvilinear multi-block grids and scientists have found the results to be very useful.

  10. A convolution model of rock bed thermal storage units

    NASA Astrophysics Data System (ADS)

    Sowell, E. F.; Curry, R. L.

    1980-01-01

    A method is presented whereby a packed-bed thermal storage unit is dynamically modeled for bi-directional flow and arbitrary input flow stream temperature variations. The method is based on the principle of calculating the output temperature as the sum of earlier input temperatures, each multiplied by a predetermined 'response factor', i.e., discrete convolution. A computer implementation of the scheme, in the form of a subroutine for a widely used solar simulation program (TRNSYS) is described and numerical results compared with other models. Also, a method for efficient computation of the required response factors is described; this solution is for a triangular input pulse, previously unreported, although the solution method is also applicable for other input functions. This solution requires a single integration of a known function which is easily carried out numerically to the required precision.

  11. Small convolution kernels for high-fidelity image restoration

    NASA Technical Reports Server (NTRS)

    Reichenbach, Stephen E.; Park, Stephen K.

    1991-01-01

    An algorithm is developed for computing the mean-square-optimal values for small, image-restoration kernels. The algorithm is based on a comprehensive, end-to-end imaging system model that accounts for the important components of the imaging process: the statistics of the scene, the point-spread function of the image-gathering device, sampling effects, noise, and display reconstruction. Subject to constraints on the spatial support of the kernel, the algorithm generates the kernel values that restore the image with maximum fidelity, that is, the kernel minimizes the expected mean-square restoration error. The algorithm is consistent with the derivation of the spatially unconstrained Wiener filter, but leads to a small, spatially constrained kernel that, unlike the unconstrained filter, can be efficiently implemented by convolution. Simulation experiments demonstrate that for a wide range of imaging systems these small kernels can restore images with fidelity comparable to images restored with the unconstrained Wiener filter.

  12. Deep convolutional neural networks for ATR from SAR imagery

    NASA Astrophysics Data System (ADS)

    Morgan, David A. E.

    2015-05-01

    Deep architectures for classification and representation learning have recently attracted significant attention within academia and industry, with many impressive results across a diverse collection of problem sets. In this work we consider the specific application of Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR) data from the MSTAR public release data set. The classification performance achieved using a Deep Convolutional Neural Network (CNN) on this data set was found to be competitive with existing methods considered to be state-of-the-art. Unlike most existing algorithms, this approach can learn discriminative feature sets directly from training data instead of requiring pre-specification or pre-selection by a human designer. We show how this property can be exploited to efficiently adapt an existing classifier to recognise a previously unseen target and discuss potential practical applications.

  13. Invariant Descriptor Learning Using a Siamese Convolutional Neural Network

    NASA Astrophysics Data System (ADS)

    Chen, L.; Rottensteiner, F.; Heipke, C.

    2016-06-01

    In this paper we describe learning of a descriptor based on the Siamese Convolutional Neural Network (CNN) architecture and evaluate our results on a standard patch comparison dataset. The descriptor learning architecture is composed of an input module, a Siamese CNN descriptor module and a cost computation module that is based on the L2 Norm. The cost function we use pulls the descriptors of matching patches close to each other in feature space while pushing the descriptors for non-matching pairs away from each other. Compared to related work, we optimize the training parameters by combining a moving average strategy for gradients and Nesterov's Accelerated Gradient. Experiments show that our learned descriptor reaches a good performance and achieves state-of-art results in terms of the false positive rate at a 95 % recall rate on standard benchmark datasets.

  14. Asymptotic expansions of Mellin convolution integrals: An oscillatory case

    NASA Astrophysics Data System (ADS)

    López, José L.; Pagola, Pedro

    2010-01-01

    In a recent paper [J.L. López, Asymptotic expansions of Mellin convolution integrals, SIAM Rev. 50 (2) (2008) 275-293], we have presented a new, very general and simple method for deriving asymptotic expansions of for small x. It contains Watson's Lemma and other classical methods, Mellin transform techniques, McClure and Wong's distributional approach and the method of analytic continuation used in this approach as particular cases. In this paper we generalize that idea to the case of oscillatory kernels, that is, to integrals of the form , with c[set membership, variant]R, and we give a method as simple as the one given in the above cited reference for the case c=0. We show that McClure and Wong's distributional approach for oscillatory kernels and the summability method for oscillatory integrals are particular cases of this method. Some examples are given as illustration.

  15. Convolutional Neural Networks for patient-specific ECG classification.

    PubMed

    Kiranyaz, Serkan; Ince, Turker; Hamila, Ridha; Gabbouj, Moncef

    2015-08-01

    We propose a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system using an adaptive implementation of 1D Convolutional Neural Networks (CNNs) that can fuse feature extraction and classification into a unified learner. In this way, a dedicated CNN will be trained for each patient by using relatively small common and patient-specific training data and thus it can also be used to classify long ECG records such as Holter registers in a fast and accurate manner. Alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. The experimental results demonstrate that the proposed system achieves a superior classification performance for the detection of ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB). PMID:26736826

  16. Drug-Drug Interaction Extraction via Convolutional Neural Networks.

    PubMed

    Liu, Shengyu; Tang, Buzhou; Chen, Qingcai; Wang, Xiaolong

    2016-01-01

    Drug-drug interaction (DDI) extraction as a typical relation extraction task in natural language processing (NLP) has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM) with a large number of manually defined features. Recently, convolutional neural networks (CNN), a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extraction. Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction. The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%. PMID:26941831

  17. Resampling of data between arbitrary grids using convolution interpolation.

    PubMed

    Rasche, V; Proksa, R; Sinkus, R; Börnert, P; Eggers, H

    1999-05-01

    For certain medical applications resampling of data is required. In magnetic resonance tomography (MRT) or computer tomography (CT), e.g., data may be sampled on nonrectilinear grids in the Fourier domain. For the image reconstruction a convolution-interpolation algorithm, often called gridding, can be applied for resampling of the data onto a rectilinear grid. Resampling of data from a rectilinear onto a nonrectilinear grid are needed, e.g., if projections of a given rectilinear data set are to be obtained. In this paper we introduce the application of the convolution interpolation for resampling of data from one arbitrary grid onto another. The basic algorithm can be split into two steps. First, the data are resampled from the arbitrary input grid onto a rectilinear grid and second, the rectilinear data is resampled onto the arbitrary output grid. Furthermore, we like to introduce a new technique to derive the sampling density function needed for the first step of our algorithm. For fast, sampling-pattern-independent determination of the sampling density function the Voronoi diagram of the sample distribution is calculated. The volume of the Voronoi cell around each sample is used as a measure for the sampling density. It is shown that the introduced resampling technique allows fast resampling of data between arbitrary grids. Furthermore, it is shown that the suggested approach to derive the sampling density function is suitable even for arbitrary sampling patterns. Examples are given in which the proposed technique has been applied for the reconstruction of data acquired along spiral, radial, and arbitrary trajectories and for the fast calculation of projections of a given rectilinearly sampled image. PMID:10416800

  18. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.

    PubMed

    He, Kaiming; Zhang, Xiangyu; Ren, Shaoqing; Sun, Jian

    2015-09-01

    Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224 × 224) input image. This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with another pooling strategy, "spatial pyramid pooling", to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based image classification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures despite their different designs. On the Pascal VOC 2007 and Caltech101 datasets, SPP-net achieves state-of-the-art classification results using a single full-image representation and no fine-tuning. The power of SPP-net is also significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method is 24-102 × faster than the R-CNN method, while achieving better or comparable accuracy on Pascal VOC 2007. In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our methods rank #2 in object detection and #3 in image classification among all 38 teams. This manuscript also introduces the improvement made for this competition. PMID:26353135

  19. Solvent directed fabrication of Bi{sub 2}WO{sub 6} nanostructures with different morphologies: Synthesis and their shape-dependent photocatalytic properties

    SciTech Connect

    Mi, Yuwei; Zeng, Suyuan; Li, Lei; Zhang, Qingfu; Wang, Suna; Liu, Caihua; Sun, Dezhi

    2012-09-15

    Graphical abstract: The morphologies of the Bi{sub 2}WO{sub 6} nanostructures can be easily tuned by altering the solvent composition during the reaction, which will yield flower-like, pancake-like and tubular nanostructures, respectively. Highlights: ► The morphologies of Bi{sub 2}WO{sub 6} can be controlled by tuning the solvent composition. ► The effects of solvent on the morphologies of Bi{sub 2}WO{sub 6} were carefully investigated. ► The growth mechanisms for the as-prepared samples were investigated. ► The morphologies of the samples greatly affect their photocatalytic activities. -- Abstract: In this work, Bi{sub 2}WO{sub 6} with complex morphologies, namely, flower-like, pancake-like, and tubular shapes have been controllably synthesized by a facile solvothermal process. The as-obtained samples are systematically investigated using X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM) and high resolution transmission electron microscopy (HRTEM). The effects of solvents on the morphologies of Bi{sub 2}WO{sub 6} nanostructures are systematically investigated. According to the time-dependent experiments, a two-step growth mode basing on Ostwald ripening process and self-assembly has been proposed for the formation of the flower-like and pancake-like Bi{sub 2}WO{sub 6} nanostructures. The photocatalytic properties of Bi{sub 2}WO{sub 6} nanostructures are strongly dependent on their shapes, sizes, and structures for the degradation of rhodamine B (RhB) under visible-light irradiation. The deduced reasons for the differences in the photocatalytic activities of these Bi{sub 2}WO{sub 6} nanostructures are further discussed.

  20. 10-7 contrast ratio at 4.5λ/D: New results obtained in laboratory experiments using nano-fabricated coronagraph and multi-Gaussian shaped pupil masks

    NASA Astrophysics Data System (ADS)

    Chakraborty, Abhijit; Thompson, Laird A.; Rogosky, Michael

    2005-04-01

    We present here new experimental results on high contrast imaging of 10-7 at 4.λ/D (λ=0.820 microns) by combining a circular focal plane mask (coronagraph) of 2.5λ/D diameter and a multi-Gaussian pupil plane mask. Both the masks were fabricated on very high surface quality (λ/30) BK7 optical substrates using nano-fabrication techniques of photolithography and metal lift-off. This process ensured that the shaped masks have a useable edge roughness better than λ/4 (rms error better than 0.2 microns), a specification that is necessary to realize the predicted theoretical limits of any mask design. Though a theoretical model predicts a contrast level of 10-12, the background noise of the observed images was speckle dominated which reduced the contrast level to 4x10-7 at 4.5λ/D. The optical setup was built on the University of Illinois Seeing Improvement System (UnISIS) optics table which is at the Coude focus of the 2.5-m telescope of the Mt. Wilson Observatory. We used a 0.820 micron laser source coupled with a 5 micron single-mode fiber to simulate an artificial star on the optical test bench of UnISIS.

  1. 10(-7) contrast ratio at 4.5lambda/D: New results obtained in laboratory experiments using nano-fabricated coronagraph and multi-Gaussian shaped pupil masks.

    PubMed

    Chakraborty, Abhijit; Thompson, Laird; Rogosky, Michael

    2005-04-01

    We present here new experimental results on high contrast imaging of 10-7 at 4.lambda/D (lambda=0.820 microns) by combining a circular focal plane mask (coronagraph) of 2.5lambda/D diameter and a multi-Gaussian pupil plane mask. Both the masks were fabricated on very high surface quality (lambda/30) BK7 optical substrates using nano-fabrication techniques of photolithography and metal lift-off. This process ensured that the shaped masks have a useable edge roughness better than lambda/4 (rms error better than 0.2 microns), a specification that is necessary to realize the predicted theoretical limits of any mask design. Though a theoretical model predicts a contrast level of 10-12, the background noise of the observed images was speckle dominated which reduced the contrast level to 4x10-7 at 4.5lambda/D. The optical setup was built on the University of Illinois Seeing Improvement System (UnISIS) optics table which is at the Coude focus of the 2.5-m telescope of the Mt. Wilson Observatory. We used a 0.820 micron laser source coupled with a 5 micron single-mode fiber to simulate an artificial star on the optical test bench of UnISIS. PMID:19495130

  2. Mitochondrial and Metabolic Dysfunction in Renal Convoluted Tubules of Obese Mice: Protective Role of Melatonin

    PubMed Central

    Giugno, Lorena; Lavazza, Antonio; Reiter, Russel J.; Rodella, Luigi Fabrizio; Rezzani, Rita

    2014-01-01

    Obesity is a common and complex health problem, which impacts crucial organs; it is also considered an independent risk factor for chronic kidney disease. Few studies have analyzed the consequence of obesity in the renal proximal convoluted tubules, which are the major tubules involved in reabsorptive processes. For optimal performance of the kidney, energy is primarily provided by mitochondria. Melatonin, an indoleamine and antioxidant, has been identified in mitochondria, and there is considerable evidence regarding its essential role in the prevention of oxidative mitochondrial damage. In this study we evaluated the mechanism(s) of mitochondrial alterations in an animal model of obesity (ob/ob mice) and describe the beneficial effects of melatonin treatment on mitochondrial morphology and dynamics as influenced by mitofusin-2 and the intrinsic apoptotic cascade. Melatonin dissolved in 1% ethanol was added to the drinking water from postnatal week 5–13; the calculated dose of melatonin intake was 100 mg/kg body weight/day. Compared to control mice, obesity-related morphological alterations were apparent in the proximal tubules which contained round mitochondria with irregular, short cristae and cells with elevated apoptotic index. Melatonin supplementation in obese mice changed mitochondria shape and cristae organization of proximal tubules, enhanced mitofusin-2 expression, which in turn modulated the progression of the mitochondria-driven intrinsic apoptotic pathway. These changes possibly aid in reducing renal failure. The melatonin-mediated changes indicate its potential protective use against renal morphological damage and dysfunction associated with obesity and metabolic disease. PMID:25347680

  3. Cygrid: A fast Cython-powered convolution-based gridding module for Python

    NASA Astrophysics Data System (ADS)

    Winkel, B.; Lenz, D.; Flöer, L.

    2016-06-01

    Context. Data gridding is a common task in astronomy and many other science disciplines. It refers to the resampling of irregularly sampled data to a regular grid. Aims: We present cygrid, a library module for the general purpose programming language Python. Cygrid can be used to resample data to any collection of target coordinates, although its typical application involves FITS maps or data cubes. The FITS world coordinate system standard is supported. Methods: The regridding algorithm is based on the convolution of the original samples with a kernel of arbitrary shape. We introduce a lookup table scheme that allows us to parallelize the gridding and combine it with the HEALPix tessellation of the sphere for fast neighbor searches. Results: We show that for n input data points, cygrids runtime scales between O(n) and O(nlog n) and analyze the performance gain that is achieved using multiple CPU cores. We also compare the gridding speed with other techniques, such as nearest-neighbor, and linear and cubic spline interpolation. Conclusions: Cygrid is a very fast and versatile gridding library that significantly outperforms other third-party Python modules, such as the linear and cubic spline interpolation provided by SciPy. http://https://github.com/bwinkel/cygrid

  4. Recognizing subsurface target responses in ground penetrating radar data using convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Sakaguchi, Rayn T.; Morton, Kenneth D.; Collins, Leslie M.; Torrione, Peter A.

    2015-05-01

    Improved performance in the discrimination of buried threats using Ground Penetrating Radar (GPR) data has recently been achieved using features developed for applications in computer vision. These features, designed to characterize local shape information in images, have been utilized to recognize patches that contain a target signature in two-dimensional slices of GPR data. While these adapted features perform very well in this GPR application, they were not designed to specifically differentiate between target responses and background GPR data. One option for developing a feature specifically designed for target differentiation is to manually design a feature extractor based on the physics of GPR image formation. However, as seen in the historical progression of computer vision features, this is not a trivial task. Instead, this research evaluates the use of convolutional neural networks (CNNs) applied to two-dimensional GPR data. The benefit of using a CNN is that features extracted from the data are a learned parameter of the system. This has allowed CNN implementations to achieve state of the art performance across a variety of data types, including visual images, without the need for expert designed features. However, the implementation of a CNN must be done carefully for each application as network parameters can cause performance to vary widely. This paper presents results from using CNNs for object detection in GPR data and discusses proper parameter settings and other considerations.

  5. Design and fabrication of a bending rotation fatigue test rig for in situ electrochemical analysis during fatigue testing of NiTi shape memory alloy wires.

    PubMed

    Neelakantan, Lakshman; Zglinski, Jenni Kristin; Frotscher, Matthias; Eggeler, Gunther

    2013-03-01

    The current investigation proposes a novel method for simultaneous assessment of the electrochemical and structural fatigue properties of nickel-titanium shape memory alloy (NiTi SMA) wires. The design and layout of an in situ electrochemical cell in a custom-made bending rotation fatigue (BRF) test rig is presented. This newly designed test rig allows performing a wide spectrum of experiments for studying the influence of fatigue on corrosion and vice versa. This can be achieved by performing ex situ and∕or in situ measurements. The versatility of the combined electrochemical∕mechanical test rig is demonstrated by studying the electrochemical behavior of NiTi SMA wires in 0.9% NaCl electrolyte under load. The ex situ measurements allow addressing various issues, for example, the influence of pre-fatigue on the localized corrosion resistance, or the influence of hydrogen on fatigue life. Ex situ experiments showed that a pre-fatigued wire is more susceptible to localized corrosion. The synergetic effect can be concluded from the polarization studies and specifically from an in situ study of the open circuit potential (OCP) transients, which sensitively react to the elementary repassivation events related to the local failure of the oxide layer. It can also be used as an indicator for identifying the onset of the fatigue failure. PMID:23556847

  6. Design and fabrication of a bending rotation fatigue test rig for in situ electrochemical analysis during fatigue testing of NiTi shape memory alloy wires

    NASA Astrophysics Data System (ADS)

    Neelakantan, Lakshman; Zglinski, Jenni Kristin; Frotscher, Matthias; Eggeler, Gunther

    2013-03-01

    The current investigation proposes a novel method for simultaneous assessment of the electrochemical and structural fatigue properties of nickel-titanium shape memory alloy (NiTi SMA) wires. The design and layout of an in situ electrochemical cell in a custom-made bending rotation fatigue (BRF) test rig is presented. This newly designed test rig allows performing a wide spectrum of experiments for studying the influence of fatigue on corrosion and vice versa. This can be achieved by performing ex situ and/or in situ measurements. The versatility of the combined electrochemical/mechanical test rig is demonstrated by studying the electrochemical behavior of NiTi SMA wires in 0.9% NaCl electrolyte under load. The ex situ measurements allow addressing various issues, for example, the influence of pre-fatigue on the localized corrosion resistance, or the influence of hydrogen on fatigue life. Ex situ experiments showed that a pre-fatigued wire is more susceptible to localized corrosion. The synergetic effect can be concluded from the polarization studies and specifically from an in situ study of the open circuit potential (OCP) transients, which sensitively react to the elementary repassivation events related to the local failure of the oxide layer. It can also be used as an indicator for identifying the onset of the fatigue failure.

  7. Shape-dependent electron transfer kinetics and catalytic activity of NiO nanoparticles immobilized onto DNA modified electrode: fabrication of highly sensitive enzymeless glucose sensor.

    PubMed

    Sharifi, Ensiyeh; Salimi, Abdollah; Shams, Esmaeil; Noorbakhsh, Abdollah; Amini, Mohammad K

    2014-06-15

    Herein we describe improved electron transfer properties and catalytic activity of nickel oxide nanoparticles (NiONPs) via the electrochemical deposition on DNA modified glassy carbon electrode (DNA/GCE) surface. NiONPs deposited on the bare and DNA-coated GCE showed different morphologies, electrochemical kinetics and catalytic activities. The atomic force microscopy (AFM) images revealed the formation of triangular NPs on the DNA/GCE that followed the shape produced by the DNA template, while the electrodeposition of NiONPs on the bare GCE surface led to the formation of spherical nanoparticles. Electrochemical impedance spectroscopy (EIS) measurements revealed lower charge-transfer resistance (Rct) of triangular NiONPs compared to spherical NPs. Furthermore, the electrocatalytic activity of triangular NiONPs compared to spherical NPs toward glucose oxidation in alkaline media was significantly improved. The amperometric oxidation of glucose at NiONP-DNA/GCE, yielded a very high sensitivity of 17.32 mA mM(-1)cm(-2) and an unprecedented detection limit of 17 nM. The enhanced electron transfer properties and electrocatalytic activity of NiONP-DNA/GCE can be attributed to the higher fraction of sharp corners and edges present in the triangular NiONPs compared to the spherical NPs. The developed sensor was successfully applied to the determination of glucose in serum samples. PMID:24525015

  8. Design and fabrication of a bending rotation fatigue test rig for in situ electrochemical analysis during fatigue testing of NiTi shape memory alloy wires

    SciTech Connect

    Neelakantan, Lakshman; Zglinski, Jenni Kristin; Eggeler, Gunther; Frotscher, Matthias

    2013-03-15

    The current investigation proposes a novel method for simultaneous assessment of the electrochemical and structural fatigue properties of nickel-titanium shape memory alloy (NiTi SMA) wires. The design and layout of an in situ electrochemical cell in a custom-made bending rotation fatigue (BRF) test rig is presented. This newly designed test rig allows performing a wide spectrum of experiments for studying the influence of fatigue on corrosion and vice versa. This can be achieved by performing ex situ and/or in situ measurements. The versatility of the combined electrochemical/mechanical test rig is demonstrated by studying the electrochemical behavior of NiTi SMA wires in 0.9% NaCl electrolyte under load. The ex situ measurements allow addressing various issues, for example, the influence of pre-fatigue on the localized corrosion resistance, or the influence of hydrogen on fatigue life. Ex situ experiments showed that a pre-fatigued wire is more susceptible to localized corrosion. The synergetic effect can be concluded from the polarization studies and specifically from an in situ study of the open circuit potential (OCP) transients, which sensitively react to the elementary repassivation events related to the local failure of the oxide layer. It can also be used as an indicator for identifying the onset of the fatigue failure.

  9. Optimal convolution SOR acceleration of waveform relaxation with application to semiconductor device simulation

    NASA Technical Reports Server (NTRS)

    Reichelt, Mark

    1993-01-01

    In this paper we describe a novel generalized SOR (successive overrelaxation) algorithm for accelerating the convergence of the dynamic iteration method known as waveform relaxation. A new convolution SOR algorithm is presented, along with a theorem for determining the optimal convolution SOR parameter. Both analytic and experimental results are given to demonstrate that the convergence of the convolution SOR algorithm is substantially faster than that of the more obvious frequency-independent waveform SOR algorithm. Finally, to demonstrate the general applicability of this new method, it is used to solve the differential-algebraic system generated by spatial discretization of the time-dependent semiconductor device equations.

  10. Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

    PubMed

    Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

    2016-01-01

    Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility. PMID:26752681

  11. Method for Veterbi decoding of large constraint length convolutional codes

    NASA Technical Reports Server (NTRS)

    Hsu, In-Shek (Inventor); Truong, Trieu-Kie (Inventor); Reed, Irving S. (Inventor); Jing, Sun (Inventor)

    1988-01-01

    A new method of Viterbi decoding of convolutional codes lends itself to a pipline VLSI architecture using a single sequential processor to compute the path metrics in the Viterbi trellis. An array method is used to store the path information for NK intervals where N is a number, and K is constraint length. The selected path at the end of each NK interval is then selected from the last entry in the array. A trace-back method is used for returning to the beginning of the selected path back, i.e., to the first time unit of the interval NK to read out the stored branch metrics of the selected path which correspond to the message bits. The decoding decision made in this way is no longer maximum likelihood, but can be almost as good, provided that constraint length K in not too small. The advantage is that for a long message, it is not necessary to provide a large memory to store the trellis derived information until the end of the message to select the path that is to be decoded; the selection is made at the end of every NK time unit, thus decoding a long message in successive blocks.

  12. Deep convolutional neural networks for classifying GPR B-scans

    NASA Astrophysics Data System (ADS)

    Besaw, Lance E.; Stimac, Philip J.

    2015-05-01

    Symmetric and asymmetric buried explosive hazards (BEHs) present real, persistent, deadly threats on the modern battlefield. Current approaches to mitigate these threats rely on highly trained operatives to reliably detect BEHs with reasonable false alarm rates using handheld Ground Penetrating Radar (GPR) and metal detectors. As computers become smaller, faster and more efficient, there exists greater potential for automated threat detection based on state-of-the-art machine learning approaches, reducing the burden on the field operatives. Recent advancements in machine learning, specifically deep learning artificial neural networks, have led to significantly improved performance in pattern recognition tasks, such as object classification in digital images. Deep convolutional neural networks (CNNs) are used in this work to extract meaningful signatures from 2-dimensional (2-D) GPR B-scans and classify threats. The CNNs skip the traditional "feature engineering" step often associated with machine learning, and instead learn the feature representations directly from the 2-D data. A multi-antennae, handheld GPR with centimeter-accurate positioning data was used to collect shallow subsurface data over prepared lanes containing a wide range of BEHs. Several heuristics were used to prevent over-training, including cross validation, network weight regularization, and "dropout." Our results show that CNNs can extract meaningful features and accurately classify complex signatures contained in GPR B-scans, complementing existing GPR feature extraction and classification techniques.

  13. Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields

    PubMed Central

    Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

    2016-01-01

    Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility. PMID:26752681

  14. Innervation of the renal proximal convoluted tubule of the rat

    SciTech Connect

    Barajas, L.; Powers, K. )

    1989-12-01

    Experimental data suggest the proximal tubule as a major site of neurogenic influence on tubular function. The functional and anatomical axial heterogeneity of the proximal tubule prompted this study of the distribution of innervation sites along the early, mid, and late proximal convoluted tubule (PCT) of the rat. Serial section autoradiograms, with tritiated norepinephrine serving as a marker for monoaminergic nerves, were used in this study. Freehand clay models and graphic reconstructions of proximal tubules permitted a rough estimation of the location of the innervation sites along the PCT. In the subcapsular nephrons, the early PCT (first third) was devoid of innervation sites with most of the innervation occurring in the mid (middle third) and in the late (last third) PCT. Innervation sites were found in the early PCT in nephrons located deeper in the cortex. In juxtamedullary nephrons, innervation sites could be observed on the PCT as it left the glomerulus. This gradient of PCT innervation can be explained by the different tubulovascular relationships of nephrons at different levels of the cortex. The absence of innervation sites in the early PCT of subcapsular nephrons suggests that any influence of the renal nerves on the early PCT might be due to an effect of neurotransmitter released from renal nerves reaching the early PCT via the interstitium and/or capillaries.

  15. Toward an optimal convolutional neural network for traffic sign recognition

    NASA Astrophysics Data System (ADS)

    Habibi Aghdam, Hamed; Jahani Heravi, Elnaz; Puig, Domenec

    2015-12-01

    Convolutional Neural Networks (CNN) beat the human performance on German Traffic Sign Benchmark competition. Both the winner and the runner-up teams trained CNNs to recognize 43 traffic signs. However, both networks are not computationally efficient since they have many free parameters and they use highly computational activation functions. In this paper, we propose a new architecture that reduces the number of the parameters 27% and 22% compared with the two networks. Furthermore, our network uses Leaky Rectified Linear Units (ReLU) as the activation function that only needs a few operations to produce the result. Specifically, compared with the hyperbolic tangent and rectified sigmoid activation functions utilized in the two networks, Leaky ReLU needs only one multiplication operation which makes it computationally much more efficient than the two other functions. Our experiments on the Gertman Traffic Sign Benchmark dataset shows 0:6% improvement on the best reported classification accuracy while it reduces the overall number of parameters 85% compared with the winner network in the competition.

  16. Remote Sensing Image Fusion with Convolutional Neural Network

    NASA Astrophysics Data System (ADS)

    Zhong, Jinying; Yang, Bin; Huang, Guoyu; Zhong, Fei; Chen, Zhongze

    2016-12-01

    Remote sensing image fusion (RSIF) is referenced as restoring the high-resolution multispectral image from its corresponding low-resolution multispectral (LMS) image aided by the panchromatic (PAN) image. Most RSIF methods assume that the missing spatial details of the LMS image can be obtained from the high resolution PAN image. However, the distortions would be produced due to the much difference between the structural component of LMS image and that of PAN image. Actually, the LMS image can fully utilize its spatial details to improve the resolution. In this paper, a novel two-stage RSIF algorithm is proposed, which makes full use of both spatial details and spectral information of the LMS image itself. In the first stage, the convolutional neural network based super-resolution is used to increase the spatial resolution of the LMS image. In the second stage, Gram-Schmidt transform is employed to fuse the enhanced MS and the PAN images for further improvement the resolution of MS image. Since the spatial resolution enhancement in the first stage, the spectral distortions in the fused image would be decreased in evidence. Moreover, the spatial details can be preserved to construct the fused images. The QuickBird satellite source images are used to test the performances of the proposed method. The experimental results demonstrate that the proposed method can achieve better spatial details and spectral information simultaneously compared with other well-known methods.

  17. Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields

    NASA Astrophysics Data System (ADS)

    Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

    2016-01-01

    Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

  18. A discrete convolution kernel for No-DC MRI

    NASA Astrophysics Data System (ADS)

    Zeng, Gengsheng L.; Li, Ya

    2015-08-01

    An analytical inversion formula for the exponential Radon transform with an imaginary attenuation coefficient was developed in 2007 (2007 Inverse Problems 23 1963-71). The inversion formula in that paper suggested that it is possible to obtain an exact MRI (magnetic resonance imaging) image without acquiring low-frequency data. However, this un-measured low-frequency region (ULFR) in the k-space (which is the two-dimensional Fourier transform space in MRI terminology) must be very small. This current paper derives a FBP (filtered backprojection) algorithm based on You’s formula by suggesting a practical discrete convolution kernel. A point spread function is derived for this FBP algorithm. It is demonstrated that the derived FBP algorithm can have a larger ULFR than that in the 2007 paper. The significance of this paper is that we present a closed-form reconstruction algorithm for a special case of under-sampled MRI data. Usually, under-sampled MRI data requires iterative (instead of analytical) algorithms with L1-norm or total variation norm to reconstruct the image.

  19. A quantum algorithm for Viterbi decoding of classical convolutional codes

    NASA Astrophysics Data System (ADS)

    Grice, Jon R.; Meyer, David A.

    2015-07-01

    We present a quantum Viterbi algorithm (QVA) with better than classical performance under certain conditions. In this paper, the proposed algorithm is applied to decoding classical convolutional codes, for instance, large constraint length and short decode frames . Other applications of the classical Viterbi algorithm where is large (e.g., speech processing) could experience significant speedup with the QVA. The QVA exploits the fact that the decoding trellis is similar to the butterfly diagram of the fast Fourier transform, with its corresponding fast quantum algorithm. The tensor-product structure of the butterfly diagram corresponds to a quantum superposition that we show can be efficiently prepared. The quantum speedup is possible because the performance of the QVA depends on the fanout (number of possible transitions from any given state in the hidden Markov model) which is in general much less than . The QVA constructs a superposition of states which correspond to all legal paths through the decoding lattice, with phase as a function of the probability of the path being taken given received data. A specialized amplitude amplification procedure is applied one or more times to recover a superposition where the most probable path has a high probability of being measured.

  20. Cell osmotic water permeability of isolated rabbit proximal convoluted tubules.

    PubMed

    Carpi-Medina, P; González, E; Whittembury, G

    1983-05-01

    Cell osmotic water permeability, Pcos, of the peritubular aspect of the proximal convoluted tubule (PCT) was measured from the time course of cell volume changes subsequent to the sudden imposition of an osmotic gradient, delta Cio, across the cell membrane of PCT that had been dissected and mounted in a chamber. The possibilities of artifact were minimized. The bath was vigorously stirred, the solutions could be 95% changed within 0.1 s, and small osmotic gradients (10-20 mosM) were used. Thus, the osmotically induced water flow was a linear function of delta Cio and the effect of the 70-microns-thick unstirred layers was negligible. In addition, data were extrapolated to delta Cio = 0. Pcos for PCT was 41.6 (+/- 3.5) X 10(-4) cm3 X s-1 X osM-1 per cm2 of peritubular basal area. The standing gradient osmotic theory for transcellular osmosis is incompatible with this value. Published values for Pcos of PST are 25.1 X 10(-4), and for the transepithelial permeability Peos values are 64 X 10(-4) for PCT and 94 X 10(-4) for PST, in the same units. These results indicate that there is room for paracellular water flow in both nephron segments and that the magnitude of the transcellular and paracellular water flows may vary from one segment of the proximal tubule to another. PMID:6846543

  1. Toward Content Based Image Retrieval with Deep Convolutional Neural Networks

    PubMed Central

    Sklan, Judah E.S.; Plassard, Andrew J.; Fabbri, Daniel; Landman, Bennett A.

    2015-01-01

    Content-based image retrieval (CBIR) offers the potential to identify similar case histories, understand rare disorders, and eventually, improve patient care. Recent advances in database capacity, algorithm efficiency, and deep Convolutional Neural Networks (dCNN), a machine learning technique, have enabled great CBIR success for general photographic images. Here, we investigate applying the leading ImageNet CBIR technique to clinically acquired medical images captured by the Vanderbilt Medical Center. Briefly, we (1) constructed a dCNN with four hidden layers, reducing dimensionality of an input scaled to 128×128 to an output encoded layer of 4×384, (2) trained the network using back-propagation 1 million random magnetic resonance (MR) and computed tomography (CT) images, (3) labeled an independent set of 2100 images, and (4) evaluated classifiers on the projection of the labeled images into manifold space. Quantitative results were disappointing (averaging a true positive rate of only 20%); however, the data suggest that improvements would be possible with more evenly distributed sampling across labels and potential re-grouping of label structures. This prelimainry effort at automated classification of medical images with ImageNet is promising, but shows that more work is needed beyond direct adaptation of existing techniques. PMID:25914507

  2. Forecasting natural aquifer discharge using a numerical model and convolution.

    PubMed

    Boggs, Kevin G; Johnson, Gary S; Van Kirk, Rob; Fairley, Jerry P

    2014-01-01

    If the nature of groundwater sources and sinks can be determined or predicted, the data can be used to forecast natural aquifer discharge. We present a procedure to forecast the relative contribution of individual aquifer sources and sinks to natural aquifer discharge. Using these individual aquifer recharge components, along with observed aquifer heads for each January, we generate a 1-year, monthly spring discharge forecast for the upcoming year with an existing numerical model and convolution. The results indicate that a forecast of natural aquifer discharge can be developed using only the dominant aquifer recharge sources combined with the effects of aquifer heads (initial conditions) at the time the forecast is generated. We also estimate how our forecast will perform in the future using a jackknife procedure, which indicates that the future performance of the forecast is good (Nash-Sutcliffe efficiency of 0.81). We develop a forecast and demonstrate important features of the procedure by presenting an application to the Eastern Snake Plain Aquifer in southern Idaho. PMID:23914881

  3. Turbo-decoding of a convolutionally encoded OCDMA system

    NASA Astrophysics Data System (ADS)

    Efinger, Daniel; Fritsch, Robert

    2005-02-01

    We present a novel multiple access scheme for Passive Optical Networks (PON) based on optical Code Division Multiple Access (OCDMA). Di erent from existing proposals for implementing OCDMA, we replaced the predominating orthogonal or weakly correlated signature codes (e.g. Walsh-Hadamard codes (WHC)) by convolutional codes. Thus CDMA user separation and forward error correction (FEC) are combined. The transmission of the coded bits over the multiple access fiber is carried through optical BPSK. This requires electrical field strength detection rather than direct detection (DD) at the receiver end. Since orthogonality gets lost, we have to employ a multiuser receiver to overcome the inherently strong correlation. Computational complexity of multiuser detection is the major challenge and we show how complexity can be reduced by applying the turbo principle known from soft-decoding of concatenated codes. The convergence behavior of the iterative multiuser receiver is investigated by means of extrinsic information transfer charts (EXIT-chart). Finally, we present simulation results of bit error ratio (BER) vs. signal-to-noise ratio (SNR) including a standard single mode fiber in order to demonstrate the superior performance of the proposed scheme compared to those using orthogonal spreading techniques.

  4. A deep convolutional neural network for recognizing foods

    NASA Astrophysics Data System (ADS)

    Jahani Heravi, Elnaz; Habibi Aghdam, Hamed; Puig, Domenec

    2015-12-01

    Controlling the food intake is an efficient way that each person can undertake to tackle the obesity problem in countries worldwide. This is achievable by developing a smartphone application that is able to recognize foods and compute their calories. State-of-art methods are chiefly based on hand-crafted feature extraction methods such as HOG and Gabor. Recent advances in large-scale object recognition datasets such as ImageNet have revealed that deep Convolutional Neural Networks (CNN) possess more representation power than the hand-crafted features. The main challenge with CNNs is to find the appropriate architecture for each problem. In this paper, we propose a deep CNN which consists of 769; 988 parameters. Our experiments show that the proposed CNN outperforms the state-of-art methods and improves the best result of traditional methods 17%. Moreover, using an ensemble of two CNNs that have been trained two different times, we are able to improve the classification performance 21:5%.

  5. Convoluted nozzle design for the RL10 derivative 2B engine

    NASA Technical Reports Server (NTRS)

    1985-01-01

    The convoluted nozzle is a conventional refractory metal nozzle extension that is formed with a portion of the nozzle convoluted to show the extendible nozzle within the length of the rocket engine. The convoluted nozzle (CN) was deployed by a system of four gas driven actuators. For spacecraft applications the optimum CN may be self-deployed by internal pressure retained, during deployment, by a jettisonable exit closure. The convoluted nozzle is included in a study of extendible nozzles for the RL10 Engine Derivative 2B for use in an early orbit transfer vehicle (OTV). Four extendible nozzle configurations for the RL10-2B engine were evaluated. Three configurations of the two position nozzle were studied including a hydrogen dump cooled metal nozzle and radiation cooled nozzles of refractory metal and carbon/carbon composite construction respectively.

  6. Directional Radiometry and Radiative Transfer: the Convoluted Path From Centuries-old Phenomenology to Physical Optics

    NASA Technical Reports Server (NTRS)

    Mishchenko, Michael I.

    2014-01-01

    This Essay traces the centuries-long history of the phenomenological disciplines of directional radiometry and radiative transfer in turbid media, discusses their fundamental weaknesses, and outlines the convoluted process of their conversion into legitimate branches of physical optics.

  7. Convolutions of Hilbert Modular Forms and Their Non-Archimedean Analogues

    NASA Astrophysics Data System (ADS)

    Panchishkin, A. A.

    1989-02-01

    The author constructs non-Archimedean analytic functions which interpolate special values of the convolution of two Hilbert cusp forms on a product of complex upper half-planes.Bibliography: 15 titles.

  8. A convolution model for computing the far-field directivity of a parametric loudspeaker array.

    PubMed

    Shi, Chuang; Kajikawa, Yoshinobu

    2015-02-01

    This paper describes a method to compute the far-field directivity of a parametric loudspeaker array (PLA), whereby the steerable parametric loudspeaker can be implemented when phased array techniques are applied. The convolution of the product directivity and the Westervelt's directivity is suggested, substituting for the past practice of using the product directivity only. Computed directivity of a PLA using the proposed convolution model achieves significant improvement in agreement to measured directivity at a negligible computational cost. PMID:25698012

  9. Minimal-memory realization of pearl-necklace encoders of general quantum convolutional codes

    SciTech Connect

    Houshmand, Monireh; Hosseini-Khayat, Saied

    2011-02-15

    Quantum convolutional codes, like their classical counterparts, promise to offer higher error correction performance than block codes of equivalent encoding complexity, and are expected to find important applications in reliable quantum communication where a continuous stream of qubits is transmitted. Grassl and Roetteler devised an algorithm to encode a quantum convolutional code with a ''pearl-necklace'' encoder. Despite their algorithm's theoretical significance as a neat way of representing quantum convolutional codes, it is not well suited to practical realization. In fact, there is no straightforward way to implement any given pearl-necklace structure. This paper closes the gap between theoretical representation and practical implementation. In our previous work, we presented an efficient algorithm to find a minimal-memory realization of a pearl-necklace encoder for Calderbank-Shor-Steane (CSS) convolutional codes. This work is an extension of our previous work and presents an algorithm for turning a pearl-necklace encoder for a general (non-CSS) quantum convolutional code into a realizable quantum convolutional encoder. We show that a minimal-memory realization depends on the commutativity relations between the gate strings in the pearl-necklace encoder. We find a realization by means of a weighted graph which details the noncommutative paths through the pearl necklace. The weight of the longest path in this graph is equal to the minimal amount of memory needed to implement the encoder. The algorithm has a polynomial-time complexity in the number of gate strings in the pearl-necklace encoder.

  10. Bayesian Vision for Shape Recovery

    NASA Technical Reports Server (NTRS)

    Jalobeanu, Andre

    2004-01-01

    We present a new Bayesian vision technique that aims at recovering a shape from two or more noisy observations taken under similar lighting conditions. The shape is parametrized by a piecewise linear height field, textured by a piecewise linear irradiance field, and we assume Gaussian Markovian priors for both shape vertices and irradiance variables. The observation process. also known as rendering, is modeled by a non-affine projection (e.g. perspective projection) followed by a convolution with a piecewise linear point spread function. and contamination by additive Gaussian noise. We assume that the observation parameters are calibrated beforehand. The major novelty of the proposed method consists of marginalizing out the irradiances considered as nuisance parameters, which is achieved by Laplace approximations. This reduces the inference to minimizing an energy that only depends on the shape vertices, and therefore allows an efficient Iterated Conditional Mode (ICM) optimization scheme to be implemented. A Gaussian approximation of the posterior shape density is computed, thus providing estimates both the geometry and its uncertainty. We illustrate the effectiveness of the new method by shape reconstruction results in a 2D case. A 3D version is currently under development and aims at recovering a surface from multiple images, reconstructing the topography by marginalizing out both albedo and shading.

  11. Text-Attentional Convolutional Neural Network for Scene Text Detection

    NASA Astrophysics Data System (ADS)

    He, Tong; Huang, Weilin; Qiao, Yu; Yao, Jian

    2016-06-01

    Recent deep learning models have demonstrated strong capabilities for classifying text and non-text components in natural images. They extract a high-level feature computed globally from a whole image component (patch), where the cluttered background information may dominate true text features in the deep representation. This leads to less discriminative power and poorer robustness. In this work, we present a new system for scene text detection by proposing a novel Text-Attentional Convolutional Neural Network (Text-CNN) that particularly focuses on extracting text-related regions and features from the image components. We develop a new learning mechanism to train the Text-CNN with multi-level and rich supervised information, including text region mask, character label, and binary text/nontext information. The rich supervision information enables the Text-CNN with a strong capability for discriminating ambiguous texts, and also increases its robustness against complicated background components. The training process is formulated as a multi-task learning problem, where low-level supervised information greatly facilitates main task of text/non-text classification. In addition, a powerful low-level detector called Contrast- Enhancement Maximally Stable Extremal Regions (CE-MSERs) is developed, which extends the widely-used MSERs by enhancing intensity contrast between text patterns and background. This allows it to detect highly challenging text patterns, resulting in a higher recall. Our approach achieved promising results on the ICDAR 2013 dataset, with a F-measure of 0.82, improving the state-of-the-art results substantially.

  12. A staggered-grid convolutional differentiator for elastic wave modelling

    NASA Astrophysics Data System (ADS)

    Sun, Weijia; Zhou, Binzhong; Fu, Li-Yun

    2015-11-01

    The computation of derivatives in governing partial differential equations is one of the most investigated subjects in the numerical simulation of physical wave propagation. An analytical staggered-grid convolutional differentiator (CD) for first-order velocity-stress elastic wave equations is derived in this paper by inverse Fourier transformation of the band-limited spectrum of a first derivative operator. A taper window function is used to truncate the infinite staggered-grid CD stencil. The truncated CD operator is almost as accurate as the analytical solution, and as efficient as the finite-difference (FD) method. The selection of window functions will influence the accuracy of the CD operator in wave simulation. We search for the optimal Gaussian windows for different order CDs by minimizing the spectral error of the derivative and comparing the windows with the normal Hanning window function for tapering the CD operators. It is found that the optimal Gaussian window appears to be similar to the Hanning window function for tapering the same CD operator. We investigate the accuracy of the windowed CD operator and the staggered-grid FD method with different orders. Compared to the conventional staggered-grid FD method, a short staggered-grid CD operator achieves an accuracy equivalent to that of a long FD operator, with lower computational costs. For example, an 8th order staggered-grid CD operator can achieve the same accuracy of a 16th order staggered-grid FD algorithm but with half of the computational resources and time required. Numerical examples from a homogeneous model and a crustal waveguide model are used to illustrate the superiority of the CD operators over the conventional staggered-grid FD operators for the simulation of wave propagations.

  13. Deep convolutional networks for pancreas segmentation in CT imaging

    NASA Astrophysics Data System (ADS)

    Roth, Holger R.; Farag, Amal; Lu, Le; Turkbey, Evrim B.; Summers, Ronald M.

    2015-03-01

    Automatic organ segmentation is an important prerequisite for many computer-aided diagnosis systems. The high anatomical variability of organs in the abdomen, such as the pancreas, prevents many segmentation methods from achieving high accuracies when compared to state-of-the-art segmentation of organs like the liver, heart or kidneys. Recently, the availability of large annotated training sets and the accessibility of affordable parallel computing resources via GPUs have made it feasible for "deep learning" methods such as convolutional networks (ConvNets) to succeed in image classification tasks. These methods have the advantage that used classification features are trained directly from the imaging data. We present a fully-automated bottom-up method for pancreas segmentation in computed tomography (CT) images of the abdomen. The method is based on hierarchical coarse-to-fine classification of local image regions (superpixels). Superpixels are extracted from the abdominal region using Simple Linear Iterative Clustering (SLIC). An initial probability response map is generated, using patch-level confidences and a two-level cascade of random forest classifiers, from which superpixel regions with probabilities larger 0.5 are retained. These retained superpixels serve as a highly sensitive initial input of the pancreas and its surroundings to a ConvNet that samples a bounding box around each superpixel at different scales (and random non-rigid deformations at training time) in order to assign a more distinct probability of each superpixel region being pancreas or not. We evaluate our method on CT images of 82 patients (60 for training, 2 for validation, and 20 for testing). Using ConvNets we achieve maximum Dice scores of an average 68% +/- 10% (range, 43-80%) in testing. This shows promise for accurate pancreas segmentation, using a deep learning approach and compares favorably to state-of-the-art methods.

  14. Text-Attentional Convolutional Neural Network for Scene Text Detection.

    PubMed

    He, Tong; Huang, Weilin; Qiao, Yu; Yao, Jian

    2016-06-01

    Recent deep learning models have demonstrated strong capabilities for classifying text and non-text components in natural images. They extract a high-level feature globally computed from a whole image component (patch), where the cluttered background information may dominate true text features in the deep representation. This leads to less discriminative power and poorer robustness. In this paper, we present a new system for scene text detection by proposing a novel text-attentional convolutional neural network (Text-CNN) that particularly focuses on extracting text-related regions and features from the image components. We develop a new learning mechanism to train the Text-CNN with multi-level and rich supervised information, including text region mask, character label, and binary text/non-text information. The rich supervision information enables the Text-CNN with a strong capability for discriminating ambiguous texts, and also increases its robustness against complicated background components. The training process is formulated as a multi-task learning problem, where low-level supervised information greatly facilitates the main task of text/non-text classification. In addition, a powerful low-level detector called contrast-enhancement maximally stable extremal regions (MSERs) is developed, which extends the widely used MSERs by enhancing intensity contrast between text patterns and background. This allows it to detect highly challenging text patterns, resulting in a higher recall. Our approach achieved promising results on the ICDAR 2013 data set, with an F-measure of 0.82, substantially improving the state-of-the-art results. PMID:27093723

  15. Output-sensitive 3D line integral convolution.

    PubMed

    Falk, Martin; Weiskopf, Daniel

    2008-01-01

    We propose an output-sensitive visualization method for 3D line integral convolution (LIC) whose rendering speed is largely independent of the data set size and mostly governed by the complexity of the output on the image plane. Our approach of view-dependent visualization tightly links the LIC generation with the volume rendering of the LIC result in order to avoid the computation of unnecessary LIC points: early-ray termination and empty-space leaping techniques are used to skip the computation of the LIC integral in a lazy-evaluation approach; both ray casting and texture slicing can be used as volume-rendering techniques. The input noise is modeled in object space to allow for temporal coherence under object and camera motion. Different noise models are discussed, covering dense representations based on filtered white noise all the way to sparse representations similar to oriented LIC. Aliasing artifacts are avoided by frequency control over the 3D noise and by employing a 3D variant of MIPmapping. A range of illumination models is applied to the LIC streamlines: different codimension-2 lighting models and a novel gradient-based illumination model that relies on precomputed gradients and does not require any direct calculation of gradients after the LIC integral is evaluated. We discuss the issue of proper sampling of the LIC and volume-rendering integrals by employing a frequency-space analysis of the noise model and the precomputed gradients. Finally, we demonstrate that our visualization approach lends itself to a fast graphics processing unit (GPU) implementation that supports both steady and unsteady flow. Therefore, this 3D LIC method allows users to interactively explore 3D flow by means of high-quality, view-dependent, and adaptive LIC volume visualization. Applications to flow visualization in combination with feature extraction and focus-and-context visualization are described, a comparison to previous methods is provided, and a detailed performance

  16. A convolution-superposition dose calculation engine for GPUs

    SciTech Connect

    Hissoiny, Sami; Ozell, Benoit; Despres, Philippe

    2010-03-15

    Purpose: Graphic processing units (GPUs) are increasingly used for scientific applications, where their parallel architecture and unprecedented computing power density can be exploited to accelerate calculations. In this paper, a new GPU implementation of a convolution/superposition (CS) algorithm is presented. Methods: This new GPU implementation has been designed from the ground-up to use the graphics card's strengths and to avoid its weaknesses. The CS GPU algorithm takes into account beam hardening, off-axis softening, kernel tilting, and relies heavily on raytracing through patient imaging data. Implementation details are reported as well as a multi-GPU solution. Results: An overall single-GPU acceleration factor of 908x was achieved when compared to a nonoptimized version of the CS algorithm implemented in PlanUNC in single threaded central processing unit (CPU) mode, resulting in approximatively 2.8 s per beam for a 3D dose computation on a 0.4 cm grid. A comparison to an established commercial system leads to an acceleration factor of approximately 29x or 0.58 versus 16.6 s per beam in single threaded mode. An acceleration factor of 46x has been obtained for the total energy released per mass (TERMA) calculation and a 943x acceleration factor for the CS calculation compared to PlanUNC. Dose distributions also have been obtained for a simple water-lung phantom to verify that the implementation gives accurate results. Conclusions: These results suggest that GPUs are an attractive solution for radiation therapy applications and that careful design, taking the GPU architecture into account, is critical in obtaining significant acceleration factors. These results potentially can have a significant impact on complex dose delivery techniques requiring intensive dose calculations such as intensity-modulated radiation therapy (IMRT) and arc therapy. They also are relevant for adaptive radiation therapy where dose results must be obtained rapidly.

  17. Dose calculation of megavoltage IMRT using convolution kernels extracted from GafChromic EBT film-measured pencil beam profiles

    NASA Astrophysics Data System (ADS)

    Naik, Mehul S.

    Intensity-modulated radiation therapy (IMRT) is a 3D conformal radiation therapy technique that utilizes either a multileaf intensity-modulating collimator (MIMiC used with the NOMOS Peacock system) or a multileaf collimator (MLC) on a conventional linear accelerator for beam intensity modulation to afford increased conformity in dose distributions. Due to the high-dose gradient regions that are effectively created, particular emphasis should be placed in the accurate determination of pencil beam kernels that are utilized by pencil beam convolution algorithms employed by a number of commercial IMRT treatment planning systems (TPS). These kernels are determined from relatively large field dose profiles that are typically collected using an ion chamber during commissioning of the TPS, while recent studies have demonstrated improvements in dose calculation accuracy when incorporating film data into the commissioning measurements. For this study, it has been proposed that the shape of high-resolution dose kernels can be extracted directly from single pencil beam (beamlet) profile measurements acquired using high-precision dosimetric film in order to accurately compute dose distributions, specifically for small fields and the penumbra regions of the larger fields. The effectiveness of GafChromic EBT film as an appropriate dosimeter to acquire the necessary measurements was evaluated and compared to the conventional silver-halide Kodak EDR2 film. Using the NOMOS Peacock system, similar dose kernels were extracted through deconvolution of the elementary pencil beam profiles using the two different types of films. Independent convolution-based calculations were performed using these kernels, resulting in better agreement with the measured relative dose profiles, as compared to those determined by CORVUS TPS' finite-size pencil beam (FSPB) algorithm. Preliminary evaluation of the proposed method in performing kernel extraction for an MLC-based IMRT system also showed

  18. Convolution effect on TCR log response curve and the correction method for it

    NASA Astrophysics Data System (ADS)

    Chen, Q.; Liu, L. J.; Gao, J.

    2016-09-01

    Through-casing resistivity (TCR) logging has been successfully used in production wells for the dynamic monitoring of oil pools and the distribution of the residual oil, but its vertical resolution has limited its efficiency in identification of thin beds. The vertical resolution is limited by the distortion phenomenon of vertical response of TCR logging. The distortion phenomenon was studied in this work. It was found that the vertical response curve of TCR logging is the convolution of the true formation resistivity and the convolution function of TCR logging tool. Due to the effect of convolution, the measurement error at thin beds can reach 30% or even bigger. Thus the information of thin bed might be covered up very likely. The convolution function of TCR logging tool was obtained in both continuous and discrete way in this work. Through modified Lyle-Kalman deconvolution method, the true formation resistivity can be optimally estimated, so this inverse algorithm can correct the error caused by the convolution effect. Thus it can improve the vertical resolution of TCR logging tool for identification of thin beds.

  19. Iterative sinc-convolution method for solving planar D-bar equation with application to EIT.

    PubMed

    Abbasi, Mahdi; Naghsh-Nilchi, Ahmad-Reza

    2012-08-01

    The numerical solution of D-bar integral equations is the key in inverse scattering solution of many complex problems in science and engineering including conductivity imaging. Recently, a couple of methodologies were considered for the numerical solution of D-bar integral equation, namely product integrals and multigrid. The first one involves high computational complexity and other one has low convergence rate disadvantages. In this paper, a new and efficient sinc-convolution algorithm is introduced to solve the two-dimensional D-bar integral equation to overcome both of these disadvantages and to resolve the singularity problem not tackled before effectively. The method of sinc-convolution is based on using collocation to replace multidimensional convolution-form integrals- including the two-dimensional D-bar integral equations - by a system of algebraic equations. Separation of variables in the proposed method allows elimination of the formulation of the huge full matrices and therefore reduces the computational complexity drastically. In addition, the sinc-convolution method converges exponentially with a convergence rate of O(e-cN). Simulation results on solving a test electrical impedance tomography problem confirm the efficiency of the proposed sinc-convolution-based algorithm. PMID:25099566

  20. A one-parameter family of transforms, linearizing convolution laws for probability distributions

    NASA Astrophysics Data System (ADS)

    Nica, Alexandru

    1995-03-01

    We study a family of transforms, depending on a parameter q∈[0,1], which interpolate (in an algebraic framework) between a relative (namely: - iz(log ℱ(·)) '(-iz)) of the logarithm of the Fourier transform for probability distributions, and its free analogue constructed by D. Voiculescu ([16, 17]). The classical case corresponds to q=1, and the free one to q=0. We describe these interpolated transforms: (a) in terms of partitions of finite sets, and their crossings; (b) in terms of weighted shifts; (c) by a matrix equation related to the method of Stieltjes for expanding continued J-fractions as power series. The main result of the paper is that all these descriptions, which extend basic approaches used for q=0 and/or q=1, remain equivalent for arbitrary q∈[0, 1]. We discuss a couple of basic properties of the convolution laws (for probability distributions) which are linearized by the considered family of transforms (these convolution laws interpolate between the usual convolution — at q=1, and the free convolution introduced by Voiculescu — at q=0). In particular, we note that description (c) mentioned in the preceding paragraph gives an insight of why the central limit law for the interpolated convolution has to do with the q-continuous Hermite orthogonal polynomials.

  1. Mechanics of a Knitted Fabric

    NASA Astrophysics Data System (ADS)

    Poincloux, Samuel; Lechenault, Frederic; Adda-Bedia, Mokhtar

    A simple knitted fabric can be seen as a topologically constrained slender rod following a periodic path. The non-linear properties of the fabric, such as large reversible deformation and characteristic shape under stress, arise from topological features known as stitches and are distinct from the constitutive yarn properties. Through experiments we studied a model stockinette fabric made of a single elastic thread, where the mechanical properties and local stitch displacements were measured. Then, we derived a model based on the yarn bending energy at the stitch level resulting in an evaluation of the displacement fields of the repetitive units which describe the fabric shape. The comparison between the predicted and the measured shape gives very good agreement and the right order of magnitude for the mechanical response is captured. This work aims at providing a fundamental framework for the understanding of knitted systems, paving the way to thread based smart materials. Contract ANR-14-CE07-0031-01 METAMAT.

  2. In Situ Fabrication Technologies

    NASA Technical Reports Server (NTRS)

    Rolin, Terry D.; Hammond, Monica

    2005-01-01

    A manufacturing system is described that is internal to controlled cabin environments which will produce functional parts to net shape with sufficient tolerance, strength and integrity to meet application specific needs such as CEV ECLS components, robotic arm or rover components, EVA suit items, unforeseen tools, conformal repair patches, and habitat fittings among others. Except for start-up and shut-down, fabrication will be automatic without crew intervention under nominal scenarios. Off-nominal scenarios may require crew and/or Earth control intervention. System will have the ability to fabricate using both provisioned feedstock materials and feedstock refined from in situ regolith.

  3. Gamma convolution models for self-diffusion coefficient distributions in PGSE NMR

    NASA Astrophysics Data System (ADS)

    Röding, Magnus; Williamson, Nathan H.; Nydén, Magnus

    2015-12-01

    We introduce a closed-form signal attenuation model for pulsed-field gradient spin echo (PGSE) NMR based on self-diffusion coefficient distributions that are convolutions of n gamma distributions, n ⩾ 1 . Gamma convolutions provide a general class of uni-modal distributions that includes the gamma distribution as a special case for n = 1 and the lognormal distribution among others as limit cases when n approaches infinity. We demonstrate the usefulness of the gamma convolution model by simulations and experimental data from samples of poly(vinyl alcohol) and polystyrene, showing that this model provides goodness of fit superior to both the gamma and lognormal distributions and comparable to the common inverse Laplace transform.

  4. Weighing classes and streams: toward better methods for two-stream convolutional networks

    NASA Astrophysics Data System (ADS)

    Kim, Hoseong; Uh, Youngjung; Ko, Seunghyeon; Byun, Hyeran

    2016-05-01

    The emergence of two-stream convolutional networks has boosted the performance of action recognition by concurrently extracting appearance and motion features from videos. However, most existing approaches simply combine the features by averaging the prediction scores from each recognition stream without realizing that some classes favor greater weight for appearance than motion. We propose a fusion method of two-stream convolutional networks for action recognition by introducing objective functions of weights with two assumptions: (1) the scores from streams do not weigh the same and (2) the weights vary across different classes. We evaluate our method by extensive experiments on UCF101, HMDB51, and Hollywood2 datasets in the context of action recognition. The results show that the proposed approach outperforms the standard two-stream convolutional networks by a large margin (5.7%, 4.8%, and 3.6%) on UCF101, HMDB51, and Hollywood2 datasets, respectively.

  5. Improving Ship Detection with Polarimetric SAR based on Convolution between Co-polarization Channels

    PubMed Central

    Li, Haiyan; He, Yijun; Wang, Wenguang

    2009-01-01

    The convolution between co-polarization amplitude only data is studied to improve ship detection performance. The different statistical behaviors of ships and surrounding ocean are characterized a by two-dimensional convolution function (2D-CF) between different polarization channels. The convolution value of the ocean decreases relative to initial data, while that of ships increases. Therefore the contrast of ships to ocean is increased. The opposite variation trend of ocean and ships can distinguish the high intensity ocean clutter from ships' signatures. The new criterion can generally avoid mistaken detection by a constant false alarm rate detector. Our new ship detector is compared with other polarimetric approaches, and the results confirm the robustness of the proposed method. PMID:22399964

  6. Punctured Parallel and Serial Concatenated Convolutional Codes for BPSK/QPSK Channels

    NASA Technical Reports Server (NTRS)

    Acikel, Omer Fatih

    1999-01-01

    As available bandwidth for communication applications becomes scarce, bandwidth-efficient modulation and coding schemes become ever important. Since their discovery in 1993, turbo codes (parallel concatenated convolutional codes) have been the center of the attention in the coding community because of their bit error rate performance near the Shannon limit. Serial concatenated convolutional codes have also been shown to be as powerful as turbo codes. In this dissertation, we introduce algorithms for designing bandwidth-efficient rate r = k/(k + 1),k = 2, 3,..., 16, parallel and rate 3/4, 7/8, and 15/16 serial concatenated convolutional codes via puncturing for BPSK/QPSK (Binary Phase Shift Keying/Quadrature Phase Shift Keying) channels. Both parallel and serial concatenated convolutional codes have initially, steep bit error rate versus signal-to-noise ratio slope (called the -"cliff region"). However, this steep slope changes to a moderate slope with increasing signal-to-noise ratio, where the slope is characterized by the weight spectrum of the code. The region after the cliff region is called the "error rate floor" which dominates the behavior of these codes in moderate to high signal-to-noise ratios. Our goal is to design high rate parallel and serial concatenated convolutional codes while minimizing the error rate floor effect. The design algorithm includes an interleaver enhancement procedure and finds the polynomial sets (only for parallel concatenated convolutional codes) and the puncturing schemes that achieve the lowest bit error rate performance around the floor for the code rates of interest.

  7. Patient-specific dosimetry based on quantitative SPECT imaging and 3D-DFT convolution

    SciTech Connect

    Akabani, G.; Hawkins, W.G.; Eckblade, M.B.; Leichner, P.K.

    1999-01-01

    The objective of this study was to validate the use of a 3-D discrete Fourier Transform (3D-DFT) convolution method to carry out the dosimetry for I-131 for soft tissues in radioimmunotherapy procedures. To validate this convolution method, mathematical and physical phantoms were used as a basis of comparison with Monte Carlo transport (MCT) calculations which were carried out using the EGS4 system code. The mathematical phantom consisted of a sphere containing uniform and nonuniform activity distributions. The physical phantom consisted of a cylinder containing uniform and nonuniform activity distributions. Quantitative SPECT reconstruction was carried out using the Circular Harmonic Transform (CHT) algorithm.

  8. Intraocular lens fabrication

    DOEpatents

    Salazar, M.A.; Foreman, L.R.

    1997-07-08

    This invention describes a method for fabricating an intraocular lens made from clear Teflon{trademark}, Mylar{trademark}, or other thermoplastic material having a thickness of about 0.025 millimeters. These plastic materials are thermoformable and biocompatable with the human eye. The two shaped lenses are bonded together with a variety of procedures which may include thermosetting and solvent based adhesives, laser and impulse welding, and ultrasonic bonding. The fill tube, which is used to inject a refractive filling material is formed with the lens so as not to damage the lens shape. A hypodermic tube may be included inside the fill tube. 13 figs.

  9. Intraocular lens fabrication

    DOEpatents

    Salazar, Mike A.; Foreman, Larry R.

    1997-01-01

    This invention describes a method for fabricating an intraocular lens made rom clear Teflon.TM., Mylar.TM., or other thermoplastic material having a thickness of about 0.025 millimeters. These plastic materials are thermoformable and biocompatable with the human eye. The two shaped lenses are bonded together with a variety of procedures which may include thermosetting and solvent based adhesives, laser and impulse welding, and ultrasonic bonding. The fill tube, which is used to inject a refractive filling material is formed with the lens so as not to damage the lens shape. A hypodermic tube may be included inside the fill tube.

  10. Midpoint Shapes.

    ERIC Educational Resources Information Center

    Welchman, Rosamond; Urso, Josephine

    2000-01-01

    Emphasizes the importance of children exploring hands-on and minds-on mathematics. Presents a midpoint shape activity for students to explore the midpoint shape of familiar quadrilaterals, such as squares and rectangles. (KHR)

  11. Fabric fastenings

    NASA Technical Reports Server (NTRS)

    Walen, E D; Fisher, R T

    1920-01-01

    The study of aeronautical fabrics has led to a consideration of the best methods of attaching and fastening together such materials. This report presents the results of an investigation upon the proper methods of attaching fabrics to airplane wings. The methods recommended in this report have been adopted by the military services.

  12. Low cost damage tolerant composite fabrication

    NASA Technical Reports Server (NTRS)

    Palmer, R. J.; Freeman, W. T.

    1988-01-01

    The resin transfer molding (RTM) process applied to composite aircraft parts offers the potential for using low cost resin systems with dry graphite fabrics that can be significantly less expensive than prepreg tape fabricated components. Stitched graphite fabric composites have demonstrated compression after impact failure performance that equals or exceeds that of thermoplastic or tough thermoset matrix composites. This paper reviews methods developed to fabricate complex shape composite parts using stitched graphite fabrics to increase damage tolerance with RTM processes to reduce fabrication cost.

  13. Surface parametrization and shape description

    NASA Astrophysics Data System (ADS)

    Brechbuehler, Christian; Gerig, Guido; Kuebler, Olaf

    1992-09-01

    Procedures for the parameterization and description of the surface of simply connected 3-D objects are presented. Critical issues for shape-based categorization and comparison of 3-D objects are addressed, which are generality with respect to object complexity, invariance to standard transformations, and descriptive power in terms of object geometry. Starting from segmented volume data, a relational data structure describing the adjacency of local surface elements is generated. The representation is used to parametrize the surface by defining a continuous, one-to-one mapping from the surface of the original object to the surface of a unit sphere. The mapping is constrained by two requirements, minimization of distortions and preservation of area. The former is formulated as the goal function of a nonlinear optimization problem and the latter as its constraints. Practicable starting values are obtained by an initial mapping based on a heat conduction model. In contract to earlier approaches, the novel parameterization method provides a mapping of arbitrarily shaped simply connected objects, i.e., it performs an unfolding of convoluted surface structures. This global parameterization allows the systematical scanning of the object surface by the variation of two parameters. As one possible approach to shape analysis, it enables us to expand the object surface into a series of spherical harmonic functions, extending the concept of elliptical Fourier descriptors for 2-D closed curves. The novel parameterization overcomes the traditional limitations of expressing an object surface in polar coordinates, which restricts such descriptions to star-shaped objects. The numerical coefficients in the Fourier series form an object-centered, surface-oriented descriptor of the object''s form. Rotating the coefficients in parameter space and object space puts the object into a standard position and yields a spherical harmonic descriptor which is invariant to translations, rotations

  14. Validation of the Pinnacle³ photon convolution-superposition algorithm applied to fast neutron beams.

    PubMed

    Kalet, Alan M; Sandison, George A; Phillips, Mark H; Parvathaneni, Upendra

    2013-01-01

    We evaluate a photon convolution-superposition algorithm used to model a fast neutron therapy beam in a commercial treatment planning system (TPS). The neutron beam modeled was the Clinical Neutron Therapy System (CNTS) fast neutron beam produced by 50 MeV protons on a Be target at our facility, and we implemented the Pinnacle3 dose calculation model for computing neutron doses. Measured neutron data were acquired by an IC30 ion chamber flowing 5 cc/min of tissue equivalent gas. Output factors and profile scans for open and wedged fields were measured according to the Pinnacle physics reference guide recommendations for photon beams in a Wellhofer water tank scanning system. Following the construction of a neutron beam model, computed doses were then generated using 100 monitor units (MUs) beams incident on a water-equivalent phantom for open and wedged square fields, as well as multileaf collimator (MLC)-shaped irregular fields. We compared Pinnacle dose profiles, central axis doses, and off-axis doses (in irregular fields) with 1) doses computed using the Prism treatment planning system, and 2) doses measured in a water phantom and having matching geometry to the computation setup. We found that the Pinnacle photon model may be used to model most of the important dosimetric features of the CNTS fast neutron beam. Pinnacle-calculated dose points among open and wedged square fields exhibit dose differences within 3.9 cGy of both Prism and measured doses along the central axis, and within 5 cGy difference of measurement in the penumbra region. Pinnacle dose point calculations using irregular treatment type fields showed a dose difference up to 9 cGy from measured dose points, although most points of comparison were below 5 cGy. Comparisons of dose points that were chosen from cases planned in both Pinnacle and Prism show an average dose difference less than 0.6%, except in certain fields which incorporate both wedges and heavy blocking of the central axis. All

  15. Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection

    NASA Astrophysics Data System (ADS)

    Wang, Haibo; Cruz-Roa, Angel; Basavanhally, Ajay; Gilmore, Hannah; Shih, Natalie; Feldman, Mike; Tomaszewski, John; Gonzalez, Fabio; Madabhushi, Anant

    2014-03-01

    Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is mitotic count, which involves quantifying the number of cells in the process of dividing (i.e. undergoing mitosis) at a specific point in time. Currently mitosis counting is done manually by a pathologist looking at multiple high power fields on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical or textural attributes of mitoses or features learned with convolutional neural networks (CNN). While handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely unsupervised feature generation methods, there is an appeal to attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. In this paper, we present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing performance by

  16. Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features.

    PubMed

    Wang, Haibo; Cruz-Roa, Angel; Basavanhally, Ajay; Gilmore, Hannah; Shih, Natalie; Feldman, Mike; Tomaszewski, John; Gonzalez, Fabio; Madabhushi, Anant

    2014-10-01

    Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is the mitotic count, which involves quantifying the number of cells in the process of dividing (i.e., undergoing mitosis) at a specific point in time. Currently, mitosis counting is done manually by a pathologist looking at multiple high power fields (HPFs) on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical, or textural attributes of mitoses or features learned with convolutional neural networks (CNN). Although handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely supervised feature generation methods, there is an appeal in attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. We present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color, and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing the performance

  17. Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features

    PubMed Central

    Wang, Haibo; Cruz-Roa, Angel; Basavanhally, Ajay; Gilmore, Hannah; Shih, Natalie; Feldman, Mike; Tomaszewski, John; Gonzalez, Fabio; Madabhushi, Anant

    2014-01-01

    Abstract. Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is the mitotic count, which involves quantifying the number of cells in the process of dividing (i.e., undergoing mitosis) at a specific point in time. Currently, mitosis counting is done manually by a pathologist looking at multiple high power fields (HPFs) on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical, or textural attributes of mitoses or features learned with convolutional neural networks (CNN). Although handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely supervised feature generation methods, there is an appeal in attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. We present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color, and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing the

  18. Digital Tomosynthesis System Geometry Analysis Using Convolution-Based Blur-and-Add (BAA) Model.

    PubMed

    Wu, Meng; Yoon, Sungwon; Solomon, Edward G; Star-Lack, Josh; Pelc, Norbert; Fahrig, Rebecca

    2016-01-01

    Digital tomosynthesis is a three-dimensional imaging technique with a lower radiation dose than computed tomography (CT). Due to the missing data in tomosynthesis systems, out-of-plane structures in the depth direction cannot be completely removed by the reconstruction algorithms. In this work, we analyzed the impulse responses of common tomosynthesis systems on a plane-to-plane basis and proposed a fast and accurate convolution-based blur-and-add (BAA) model to simulate the backprojected images. In addition, the analysis formalism describing the impulse response of out-of-plane structures can be generalized to both rotating and parallel gantries. We implemented a ray tracing forward projection and backprojection (ray-based model) algorithm and the convolution-based BAA model to simulate the shift-and-add (backproject) tomosynthesis reconstructions. The convolution-based BAA model with proper geometry distortion correction provides reasonably accurate estimates of the tomosynthesis reconstruction. A numerical comparison indicates that the simulated images using the two models differ by less than 6% in terms of the root-mean-squared error. This convolution-based BAA model can be used in efficient system geometry analysis, reconstruction algorithm design, out-of-plane artifacts suppression, and CT-tomosynthesis registration. PMID:26208308

  19. Artificial convolution neural network techniques and applications for lung nodule detection.

    PubMed

    Lo, S B; Lou, S A; Lin, J S; Freedman, M T; Chien, M V; Mun, S K

    1995-01-01

    We have developed a double-matching method and an artificial visual neural network technique for lung nodule detection. This neural network technique is generally applicable to the recognition of medical image pattern in gray scale imaging. The structure of the artificial neural net is a simplified network structure of human vision. The fundamental operation of the artificial neural network is local two-dimensional convolution rather than full connection with weighted multiplication. Weighting coefficients of the convolution kernels are formed by the neural network through backpropagated training. In addition, we modeled radiologists' reading procedures in order to instruct the artificial neural network to recognize the image patterns predefined and those of interest to experts in radiology. We have tested this method for lung nodule detection. The performance studies have shown the potential use of this technique in a clinical setting. This program first performed an initial nodule search with high sensitivity in detecting round objects using a sphere template double-matching technique. The artificial convolution neural network acted as a final classifier to determine whether the suspected image block contains a lung nodule. The total processing time for the automatic detection of lung nodules using both prescan and convolution neural network evaluation was about 15 seconds in a DEC Alpha workstation. PMID:18215875

  20. A generalized recursive convolution method for time-domain propagation in porous media.

    PubMed

    Dragna, Didier; Pineau, Pierre; Blanc-Benon, Philippe

    2015-08-01

    An efficient numerical method, referred to as the auxiliary differential equation (ADE) method, is proposed to compute convolutions between relaxation functions and acoustic variables arising in sound propagation equations in porous media. For this purpose, the relaxation functions are approximated in the frequency domain by rational functions. The time variation of the convolution is thus governed by first-order differential equations which can be straightforwardly solved. The accuracy of the method is first investigated and compared to that of recursive convolution methods. It is shown that, while recursive convolution methods are first or second-order accurate in time, the ADE method does not introduce any additional error. The ADE method is then applied for outdoor sound propagation using the equations proposed by Wilson et al. in the ground [(2007). Appl. Acoust. 68, 173-200]. A first one-dimensional case is performed showing that only five poles are necessary to accurately approximate the relaxation functions for typical applications. Finally, the ADE method is used to compute sound propagation in a three-dimensional geometry over an absorbing ground. Results obtained with Wilson's equations are compared to those obtained with Zwikker and Kosten's equations and with an impedance surface for different flow resistivities. PMID:26328719

  1. Profile of CT scan output dose in axial and helical modes using convolution

    NASA Astrophysics Data System (ADS)

    Anam, C.; Haryanto, F.; Widita, R.; Arif, I.; Dougherty, G.

    2016-03-01

    The profile of the CT scan output dose is crucial for establishing the patient dose profile. The purpose of this study is to investigate the profile of the CT scan output dose in both axial and helical modes using convolution. A single scan output dose profile (SSDP) in the center of a head phantom was measured using a solid-state detector. The multiple scan output dose profile (MSDP) in the axial mode was calculated using convolution between SSDP and delta function, whereas for the helical mode MSDP was calculated using convolution between SSDP and the rectangular function. MSDPs were calculated for a number of scans (5, 10, 15, 20 and 25). The multiple scan average dose (MSAD) for differing numbers of scans was compared to the value of CT dose index (CTDI). Finally, the edge values of MSDP for every scan number were compared to the corresponding MSAD values. MSDPs were successfully generated by using convolution between a SSDP and the appropriate function. We found that CTDI only accurately estimates MSAD when the number of scans was more than 10. We also found that the edge values of the profiles were 42% to 93% lower than that the corresponding MSADs.

  2. Convolutional neural network based sensor fusion for forward looking ground penetrating radar

    NASA Astrophysics Data System (ADS)

    Sakaguchi, Rayn; Crosskey, Miles; Chen, David; Walenz, Brett; Morton, Kenneth

    2016-05-01

    Forward looking ground penetrating radar (FLGPR) is an alternative buried threat sensing technology designed to offer additional standoff compared to downward looking GPR systems. Due to additional flexibility in antenna configurations, FLGPR systems can accommodate multiple sensor modalities on the same platform that can provide complimentary information. The different sensor modalities present challenges in both developing informative feature extraction methods, and fusing sensor information in order to obtain the best discrimination performance. This work uses convolutional neural networks in order to jointly learn features across two sensor modalities and fuse the information in order to distinguish between target and non-target regions. This joint optimization is possible by modifying the traditional image-based convolutional neural network configuration to extract data from multiple sources. The filters generated by this process create a learned feature extraction method that is optimized to provide the best discrimination performance when fused. This paper presents the results of applying convolutional neural networks and compares these results to the use of fusion performed with a linear classifier. This paper also compares performance between convolutional neural networks architectures to show the benefit of fusing the sensor information in different ways.

  3. The VLSI design of an error-trellis syndrome decoder for certain convolutional codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Jensen, J. M.; Hsu, I.-S.; Truong, T. K.

    1986-01-01

    A recursive algorithm using the error-trellis decoding technique is developed to decode convolutional codes (CCs). An example, illustrating the very large scale integration (VLSI) architecture of such a decode, is given for a dual-K CC. It is demonstrated that such a decoder can be realized readily on a single chip with metal-nitride-oxide-semiconductor technology.

  4. The VLSI design of error-trellis syndrome decoding for convolutional codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Jensen, J. M.; Truong, T. K.; Hsu, I. S.

    1985-01-01

    A recursive algorithm using the error-trellis decoding technique is developed to decode convolutional codes (CCs). An example, illustrating the very large scale integration (VLSI) architecture of such a decode, is given for a dual-K CC. It is demonstrated that such a decoder can be realized readily on a single chip with metal-nitride-oxide-semiconductor technology.

  5. Experimental Post-Hole Convolute Plasma Studies on a 1-MA Linear Transformer Driver (LTD)*

    NASA Astrophysics Data System (ADS)

    Gomez, M. R.; Gilgenbach, R. M.; French, D. M.; Zier, J. C.; Lau, Y. Y.; Cuneo, M. E.; Lopez, M. R.; Mazarakis, M. G.

    2009-11-01

    Post-hole convolutes are used to combine current from several parallel transmission lines, such that there is a low-inductance path to a single anode-cathode gap at the load. Experimental observations of the post-hole convolute are difficult to make on large systems, such as the Z-Machine at Sandia National Laboratories. A single post-hole convolute has been designed as the load for the 1 MA LTD at U. of Michigan. The geometry of the design allows diagnostic access to the post-hole region. The goal of these experiments is to monitor plasma formation in the convolute and to measure the current losses as a result of that plasma. Diagnostics under development for this experiment include B-dots for current measurement, optical spectroscopy for plasma composition, temperature and density measurements, and pinhole and laser diagnostics for imaging plasma dynamics. Experimental results will be compared to Particle-In-Cell simulations of this system using MAGIC PIC.* Research supported by Sandia National Labs subcontacts to UM. MRG sponsored by SSGF through NNSA and JZ sponsored by NPSC through DOE. Sandia is a multi-program laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the US DOE's NNSA under contract DE-AC04-94AL85000.

  6. Convolutional FEC design considerations for data transmission over PSK satellite channels

    NASA Astrophysics Data System (ADS)

    Garrison, G. J.; Wong, V. C.

    Simulation results are provided for rate R = 1/2 convolutional error correcting codes suited to data transmission over BPSK, gray coded QPSK, and OQPSK channels. The burst generation mechanism resulting from differential encoding/decoding is analyzed in terms of the impairment to code performance and offsetting internal/external interleaving techniques are described.

  7. Self-shaping of bioinspired chiral composites

    NASA Astrophysics Data System (ADS)

    Rong, Qing-Qing; Cui, Yu-Hong; Shimada, Takahiro; Wang, Jian-Shan; Kitamura, Takayuki

    2014-08-01

    Self-shaping materials such as shape memory polymers have recently drawn considerable attention owing to their high shape-changing ability in response to changes in ambient conditions, and thereby have promising applications in the biomedical, biosensing, soft robotics and aerospace fields. Their design is a crucial issue of both theoretical and technological interest. Motivated by the shape-changing ability of Towel Gourd tendril helices during swelling/deswelling, we present a strategy for realizing self-shaping function through the deformation of micro/nanohelices. To guide the design and fabrication of self-shaping materials, the shape equations of bent configurations, twisted belts, and helices of slender chiral composite are developed using the variation method. Furthermore, it is numerically shown that the shape changes of a chiral composite can be tuned by the deformation of micro/nanohelices and the fabricated fiber directions. This work paves a new way to create self-shaping composites.

  8. Protein similarity from knot theory: geometric convolution and line weavings.

    PubMed

    Erdmann, Michael A

    2005-01-01

    Shape similarity is one of the most elusive and intriguing questions of nature and mathematics. Proteins provide a rich domain in which to test theories of shape similarity. Proteins can match at different scales and in different arrangements. Sometimes the detection of common local structure is sufficient to infer global alignment of two proteins; at other times it provides false information. Proteins with very low sequence identity may share large substructures, or perhaps just a central core. There are even examples of proteins with nearly identical primary sequences in which alpha-helices have become beta-sheets. Shape similarity can be formulated (i) in terms of global metrics, such as RMSD or Hausdorff distance, (ii) in terms of subgraph isomorphisms, such as the detection of shared substructures with similar relative locations, or (iii) purely topologically, in terms of structure preserving transformations. Existing protein structure detection programs are built on the first two types of similarity. The third forms the foundations of knot theory. The thesis of this paper is this: Protein similarity detection leads naturally to algorithms operating at the metric, relational, and isotopic scales. The paper introduces a definition of similarity based on atomic motions that preserve local backbone topology without incurring significant distance errors. Such motions are motivated by the physical requirements for rearranging subsequences of a protein. Similarity detection then seeks rigid body motions able to overlay pairs of substructures, each related by a substructure-preserving motion, without necessarily requiring global structure preservation. This definition is general enough to span a wide range of questions: One can ask for full rearrangement of one protein into another while preserving global topology, as in drug design; or one can ask for rearrangements of sets of smaller substructures, preserving local but not global topology, as in protein evolution

  9. Quantifying the interplay effect in prostate IMRT delivery using a convolution-based method

    SciTech Connect

    Li, Haisen S.; Chetty, Indrin J.; Solberg, Timothy D.

    2008-05-15

    The authors present a segment-based convolution method to account for the interplay effect between intrafraction organ motion and the multileaf collimator position for each particular segment in intensity modulated radiation therapy (IMRT) delivered in a step-and-shoot manner. In this method, the static dose distribution attributed to each segment is convolved with the probability density function (PDF) of motion during delivery of the segment, whereas in the conventional convolution method (''average-based convolution''), the static dose distribution is convolved with the PDF averaged over an entire fraction, an entire treatment course, or even an entire patient population. In the case of IMRT delivered in a step-and-shoot manner, the average-based convolution method assumes that in each segment the target volume experiences the same motion pattern (PDF) as that of population. In the segment-based convolution method, the dose during each segment is calculated by convolving the static dose with the motion PDF specific to that segment, allowing both intrafraction motion and the interplay effect to be accounted for in the dose calculation. Intrafraction prostate motion data from a population of 35 patients tracked using the Calypso system (Calypso Medical Technologies, Inc., Seattle, WA) was used to generate motion PDFs. These were then convolved with dose distributions from clinical prostate IMRT plans. For a single segment with a small number of monitor units, the interplay effect introduced errors of up to 25.9% in the mean CTV dose compared against the planned dose evaluated by using the PDF of the entire fraction. In contrast, the interplay effect reduced the minimum CTV dose by 4.4%, and the CTV generalized equivalent uniform dose by 1.3%, in single fraction plans. For entire treatment courses delivered in either a hypofractionated (five fractions) or conventional (>30 fractions) regimen, the discrepancy in total dose due to interplay effect was negligible.

  10. Fabrication of Molded Magnetic Article

    NASA Technical Reports Server (NTRS)

    Bryant, Robert G. (Inventor); Namkung, Min (Inventor); Wincheski, Russell A. (Inventor); Fox, Robert L. (Inventor)

    2001-01-01

    A molded magnetic article and fabrication method are provided. Particles of ferromagnetic material embedded in a polymer binder are molded under heat and pressure into a geometric shape. Each particle is an oblate spheroid having a radius-to-thickness aspect ratio approximately in the range of 15-30. Each oblate spheroid has flattened poles that are substantially in perpendicular alignment to a direction of the molding pressure throughout the geometric shape.

  11. Schapiro Shapes

    ERIC Educational Resources Information Center

    O'Connell, Emily

    2009-01-01

    This article describes a lesson on Schapiro Shapes. Schapiro Shapes is based on the art of Miriam Schapiro, who created a number of works of figures in action. Using the basic concepts of this project, students learn to create their own figures and styles. (Contains 1 online resource.)

  12. Two-level pipelined systolic array for multi-dimensional convolution

    SciTech Connect

    Kung, H.T.; Ruane, L.M.; Yen, D.W.L.

    1982-11-01

    This paper describes a systolic array for the computation of n-dimensional (n-D) convolutions of any positive integer n. Systolic systems usually achieve high performance by allowing computations to be pipelined over a large array of processing elements. To achieve even higher performance, the systolic array of this paper utilizes a second level of pipelining by allowing the processing elements themselves to be pipelined to an arbitrary degree. Moreover, it is shown that as far as orders of magnitude are concerned, the total amount of memory required by the systolic array is no more than that needed by any convolution device that reads in each input data item only once. Thus if only schemes that use the minimum-possible I/O are considered, the systolic array is not only high performance, but also optimal in terms of the amount of required memory.

  13. Systolic array architecture for convolutional decoding algorithms: Viterbi algorithm and stack algorithm

    SciTech Connect

    Chang, C.Y.

    1986-01-01

    New results on efficient forms of decoding convolutional codes based on Viterbi and stack algorithms using systolic array architecture are presented. Some theoretical aspects of systolic arrays are also investigated. First, systolic array implementation of Viterbi algorithm is considered, and various properties of convolutional codes are derived. A technique called strongly connected trellis decoding is introduced to increase the efficient utilization of all the systolic array processors. The issues dealing with the composite branch metric generation, survivor updating, overall system architecture, throughput rate, and computations overhead ratio are also investigated. Second, the existing stack algorithm is modified and restated in a more concise version so that it can be efficiently implemented by a special type of systolic array called systolic priority queue. Three general schemes of systolic priority queue based on random access memory, shift register, and ripple register are proposed. Finally, a systematic approach is presented to design systolic arrays for certain general classes of recursively formulated algorithms.

  14. Automatic detection of cell divisions (mitosis) in live-imaging microscopy images using Convolutional Neural Networks.

    PubMed

    Shkolyar, Anat; Gefen, Amit; Benayahu, Dafna; Greenspan, Hayit

    2015-08-01

    We propose a semi-automated pipeline for the detection of possible cell divisions in live-imaging microscopy and the classification of these mitosis candidates using a Convolutional Neural Network (CNN). We use time-lapse images of NIH3T3 scratch assay cultures, extract patches around bright candidate regions that then undergo segmentation and binarization, followed by a classification of the binary patches into either containing or not containing cell division. The classification is performed by training a Convolutional Neural Network on a specially constructed database. We show strong results of AUC = 0.91 and F-score = 0.89, competitive with state-of-the-art methods in this field. PMID:26736369

  15. Eye and sheath folds in turbidite convolute lamination: Aberystwyth Grits Group, Wales

    NASA Astrophysics Data System (ADS)

    McClelland, H. L. O.; Woodcock, N. H.; Gladstone, C.

    2011-07-01

    Eye and sheath folds are described from the turbidites of the Aberystwyth Group, in the Silurian of west Wales. They have been studied at outcrop and on high resolution optical scans of cut surfaces. The folds are not tectonic in origin. They occur as part of the convolute-laminated interval of each sand-mud turbidite bed. The thickness of this interval is most commonly between 20 and 100 mm. Lamination patterns confirm previous interpretations that convolute lamination nucleated on ripples and grew during continued sedimentation of the bed. The folds amplified vertically and were sheared horizontally by continuing turbidity flow, but only to average values of about γ = 1. The strongly curvilinear fold hinges are due not to high shear strains, but to nucleation on sinuous or linguoid ripples. The Aberystwyth Group structures provide a warning that not all eye folds in sedimentary or metasedimentary rocks should be interpreted as sections through high shear strain sheath folds.

  16. Quantitative Analysis of Isotope Distributions In Proteomic Mass Spectrometry Using Least-Squares Fourier Transform Convolution

    PubMed Central

    Sperling, Edit; Bunner, Anne E.; Sykes, Michael T.; Williamson, James R.

    2008-01-01

    Quantitative proteomic mass spectrometry involves comparison of the amplitudes of peaks resulting from different isotope labeling patterns, including fractional atomic labeling and fractional residue labeling. We have developed a general and flexible analytical treatment of the complex isotope distributions that arise in these experiments, using Fourier transform convolution to calculate labeled isotope distributions and least-squares for quantitative comparison with experimental peaks. The degree of fractional atomic and fractional residue labeling can be determined from experimental peaks at the same time as the integrated intensity of all of the isotopomers in the isotope distribution. The approach is illustrated using data with fractional 15N-labeling and fractional 13C-isoleucine labeling. The least-squares Fourier transform convolution approach can be applied to many types of quantitive proteomic data, including data from stable isotope labeling by amino acids in cell culture and pulse labeling experiments. PMID:18522437

  17. Quantum Fields Obtained from Convoluted Generalized White Noise Never Have Positive Metric

    NASA Astrophysics Data System (ADS)

    Albeverio, Sergio; Gottschalk, Hanno

    2016-05-01

    It is proven that the relativistic quantum fields obtained from analytic continuation of convoluted generalized (Lévy type) noise fields have positive metric, if and only if the noise is Gaussian. This follows as an easy observation from a criterion by Baumann, based on the Dell'Antonio-Robinson-Greenberg theorem, for a relativistic quantum field in positive metric to be a free field.

  18. Time-convoluted hotspot temperature field on a metal skin due to sustained arc stroke heating

    NASA Astrophysics Data System (ADS)

    Lee, T. S.; Su, W. Y.

    A previously developed time-convoluted heat-conduction theory is applied to the case of a metal plate whose heat source is sustained over time. Integral formulas are formally derived, and their utilization in practical arc-heating work is examined. The results are compared with experimental ones from titanium and aluminum plates subjected to sustained heating due to step switch-on dc arc sources, and reasonable agreement is found.

  19. Directed light fabrication

    NASA Astrophysics Data System (ADS)

    Lewis, G. K.; Nemec, R.; Milewski, J.; Thoma, D. J.; Cremers, D.; Barbe, M.

    1994-09-01

    Directed Light Fabrication (DLF) is a rapid prototyping process being developed at Los Alamos National Laboratory to fabricate metal components. This is done by fusing gas delivered metal powder particles in the focal zone of a laser beam that is programmed to move along or across the part cross section. Fully dense metal is built up a layer at a time to form the desired part represented by a 3 dimensional solid model from CAD software. Machine 'tool paths' are created from the solid model that command the movement and processing parameters specific to the DLF process so that the part can be built one layer at a time. The result is a fully dense, near net shape metal part that solidifies under rapid solidification conditions.

  20. Directed light fabrication

    SciTech Connect

    Lewis, G.K.; Nemec, R.; Milewski, J.; Thoma, D.J.; Cremers, D.; Barbe, M.

    1994-09-01

    Directed Light Fabrication (DLF) is a rapid prototyping process being developed at Los Alamos National Laboratory to fabricate metal components. This is done by fusing gas delivered metal powder particles in the focal zone of a laser beam that is, programmed to move along or across the part cross section. Fully dense metal is built up a layer at a time to form the desired part represented by a 3 dimensional solid model from CAD software. Machine ``tool paths`` are created from the solid model that command the movement and processing parameters specific to the DLF process so that the part can be built one layer at a time. The result is a fully dense, near net shape metal part that solidifies under rapid solidification conditions.

  1. Hardware efficient implementation of DFT using an improved first-order moments based cyclic convolution structure

    NASA Astrophysics Data System (ADS)

    Xiong, Jun; Liu, J. G.; Cao, Li

    2015-12-01

    This paper presents hardware efficient designs for implementing the one-dimensional (1D) discrete Fourier transform (DFT). Once DFT is formulated as the cyclic convolution form, the improved first-order moments-based cyclic convolution structure can be used as the basic computing unit for the DFT computation, which only contains a control module, a barrel shifter and (N-1)/2 accumulation units. After decomposing and reordering the twiddle factors, all that remains to do is shifting the input data sequence and accumulating them under the control of the statistical results on the twiddle factors. The whole calculation process only contains shift operations and additions with no need for multipliers and large memory. Compared with the previous first-order moments-based structure for DFT, the proposed designs have the advantages of less hardware consumption, lower power consumption and the flexibility to achieve better performance in certain cases. A series of experiments have proven the high performance of the proposed designs in terms of the area time product and power consumption. Similar efficient designs can be obtained for other computations, such as DCT/IDCT, DST/IDST, digital filter and correlation by transforming them into the forms of the first-order moments based cyclic convolution.

  2. Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification.

    PubMed

    Pang, Shan; Yang, Xinyi

    2016-01-01

    In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods. PMID:27610128

  3. Flexible algorithm for real-time convolution supporting dynamic event-related fMRI

    NASA Astrophysics Data System (ADS)

    Eaton, Brent L.; Frank, Randall J.; Bolinger, Lizann; Grabowski, Thomas J.

    2002-04-01

    An efficient algorithm for generation of the task reference function has been developed that allows real-time statistical analysis of fMRI data, within the framework of the general linear model, for experiments with event-related stimulus designs. By leveraging time-stamped data collection in the Input/Output time-aWare Architecture (I/OWA), we detect the onset time of a stimulus as it is delivered to a subject. A dynamically updated list of detected stimulus event times is maintained in shared memory as a data stream and delivered as input to a real-time convolution algorithm. As each image is acquired from the MR scanner, the time-stamp of its acquisition is delivered via a second dynamically updated stream to the convolution algorithm, where a running convolution of the events with an estimated hemodynamic response function is computed at the image acquisition time and written to a third stream in memory. Output is interpreted as the activation reference function and treated as the covariate of interest in the I/OWA implementation of the general linear model. Statistical parametric maps are computed and displayed to the I/OWA user interface in less than the time between successive image acquisitions.

  4. Using hybrid GPU/CPU kernel splitting to accelerate spherical convolutions

    NASA Astrophysics Data System (ADS)

    Sutter, P. M.; Wandelt, B. D.; Elsner, F.

    2015-06-01

    We present a general method for accelerating by more than an order of magnitude the convolution of pixelated functions on the sphere with a radially-symmetric kernel. Our method splits the kernel into a compact real-space component and a compact spherical harmonic space component. These components can then be convolved in parallel using an inexpensive commodity GPU and a CPU. We provide models for the computational cost of both real-space and Fourier space convolutions and an estimate for the approximation error. Using these models we can determine the optimum split that minimizes the wall clock time for the convolution while satisfying the desired error bounds. We apply this technique to the problem of simulating a cosmic microwave background (CMB) anisotropy sky map at the resolution typical of the high resolution maps produced by the Planck mission. For the main Planck CMB science channels we achieve a speedup of over a factor of ten, assuming an acceptable fractional rms error of order 10-5 in the power spectrum of the output map.

  5. Vehicle detection based on visual saliency and deep sparse convolution hierarchical model

    NASA Astrophysics Data System (ADS)

    Cai, Yingfeng; Wang, Hai; Chen, Xiaobo; Gao, Li; Chen, Long

    2016-06-01

    Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification. These types of methods generally have high processing times and low vehicle detection performance. To address this issue, a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed. A visual saliency calculation is firstly used to generate a small vehicle candidate area. The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection. The experimental results demonstrate that the proposed method is with 94.81% correct rate and 0.78% false detection rate on the existing datasets and the real road pictures captured by our group, which outperforms the existing state-of-the-art algorithms. More importantly, high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle.

  6. Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification

    PubMed Central

    Yang, Xinyi

    2016-01-01

    In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods. PMID:27610128

  7. Convolutional neural network approach for buried target recognition in FL-LWIR imagery

    NASA Astrophysics Data System (ADS)

    Stone, K.; Keller, J. M.

    2014-05-01

    A convolutional neural network (CNN) approach to recognition of buried explosive hazards in forward-looking long-wave infrared (FL-LWIR) imagery is presented. The convolutional filters in the first layer of the network are learned in the frequency domain, making enforcement of zero-phase and zero-dc response characteristics much easier. The spatial domain representations of the filters are forced to have unit l2 norm, and penalty terms are added to the online gradient descent update to encourage orthonormality among the convolutional filters, as well smooth first and second order derivatives in the spatial domain. The impact of these modifications on the generalization performance of the CNN model is investigated. The CNN approach is compared to a second recognition algorithm utilizing shearlet and log-gabor decomposition of the image coupled with cell-structured feature extraction and support vector machine classification. Results are presented for multiple FL-LWIR data sets recently collected from US Army test sites. These data sets include vehicle position information allowing accurate transformation between image and world coordinates and realistic evaluation of detection and false alarm rates.

  8. Fabrication of metallic glass structures

    DOEpatents

    Cline, C.F.

    1983-10-20

    Amorphous metal powders or ribbons are fabricated into solid shapes of appreciable thickness by the application of compaction energy. The temperature regime wherein the amorphous metal deforms by viscous flow is measured. The metal powders or ribbons are compacted within the temperature regime.

  9. Fabrication of metallic glass structures

    DOEpatents

    Cline, Carl F.

    1986-01-01

    Amorphous metal powders or ribbons are fabricated into solid shapes of appreciable thickness by the application of compaction energy. The temperature regime wherein the amorphous metal deforms by viscous flow is measured. The metal powders or ribbons are compacted within the temperature range.

  10. Some rate 1/3 and 1/4 binary convolutional codes with an optimum distance profile

    NASA Technical Reports Server (NTRS)

    Johannesson, R.

    1977-01-01

    A tabulation of binary systematic convolutional codes with an optimum distance profile for rates 1/3 and 1/4 is given. A number of short rate 1/3 binary nonsystematic convolutional codes are listed. These latter codes are simultaneously optimal for the following distance measures: distance profile, minimum distance, and free distance; they appear attractive for use with Viterbi decoders. Comparisons with previously known codes are made.

  11. Fullerene embedded shape memory nanolens array.

    PubMed

    Jeon, Sohee; Jang, Jun Young; Youn, Jae Ryoun; Jeong, Jun-Ho; Brenner, Howard; Song, Young Seok

    2013-01-01

    Securing fragile nanostructures against external impact is indispensable for offering sufficiently long lifetime in service to nanoengineering products, especially when coming in contact with other substances. Indeed, this problem still remains a challenging task, which may be resolved with the help of smart materials such as shape memory and self-healing materials. Here, we demonstrate a shape memory nanostructure that can recover its shape by absorbing electromagnetic energy. Fullerenes were embedded into the fabricated nanolens array. Beside the energy absorption, such addition enables a remarkable enhancement in mechanical properties of shape memory polymer. The shape memory nanolens was numerically modeled to impart more in-depth understanding on the physics regarding shape recovery behavior of the fabricated nanolens. We anticipate that our strategy of combining the shape memory property with the microwave irradiation feature can provide a new pathway for nanostructured systems able to ensure a long-term durability. PMID:24253423

  12. Fullerene Embedded Shape Memory Nanolens Array

    PubMed Central

    Jeon, Sohee; Jang, Jun Young; Youn, Jae Ryoun; Jeong, Jun-ho; Brenner, Howard; Song, Young Seok

    2013-01-01

    Securing fragile nanostructures against external impact is indispensable for offering sufficiently long lifetime in service to nanoengineering products, especially when coming in contact with other substances. Indeed, this problem still remains a challenging task, which may be resolved with the help of smart materials such as shape memory and self-healing materials. Here, we demonstrate a shape memory nanostructure that can recover its shape by absorbing electromagnetic energy. Fullerenes were embedded into the fabricated nanolens array. Beside the energy absorption, such addition enables a remarkable enhancement in mechanical properties of shape memory polymer. The shape memory nanolens was numerically modeled to impart more in-depth understanding on the physics regarding shape recovery behavior of the fabricated nanolens. We anticipate that our strategy of combining the shape memory property with the microwave irradiation feature can provide a new pathway for nanostructured systems able to ensure a long-term durability. PMID:24253423

  13. Fullerene Embedded Shape Memory Nanolens Array

    NASA Astrophysics Data System (ADS)

    Jeon, Sohee; Jang, Jun Young; Youn, Jae Ryoun; Jeong, Jun-Ho; Brenner, Howard; Song, Young Seok

    2013-11-01

    Securing fragile nanostructures against external impact is indispensable for offering sufficiently long lifetime in service to nanoengineering products, especially when coming in contact with other substances. Indeed, this problem still remains a challenging task, which may be resolved with the help of smart materials such as shape memory and self-healing materials. Here, we demonstrate a shape memory nanostructure that can recover its shape by absorbing electromagnetic energy. Fullerenes were embedded into the fabricated nanolens array. Beside the energy absorption, such addition enables a remarkable enhancement in mechanical properties of shape memory polymer. The shape memory nanolens was numerically modeled to impart more in-depth understanding on the physics regarding shape recovery behavior of the fabricated nanolens. We anticipate that our strategy of combining the shape memory property with the microwave irradiation feature can provide a new pathway for nanostructured systems able to ensure a long-term durability.

  14. Convolution-based estimation of organ dose in tube current modulated CT

    NASA Astrophysics Data System (ADS)

    Tian, Xiaoyu; Segars, W. Paul; Dixon, Robert L.; Samei, Ehsan

    2016-05-01

    Estimating organ dose for clinical patients requires accurate modeling of the patient anatomy and the dose field of the CT exam. The modeling of patient anatomy can be achieved using a library of representative computational phantoms (Samei et al 2014 Pediatr. Radiol. 44 460–7). The modeling of the dose field can be challenging for CT exams performed with a tube current modulation (TCM) technique. The purpose of this work was to effectively model the dose field for TCM exams using a convolution-based method. A framework was further proposed for prospective and retrospective organ dose estimation in clinical practice. The study included 60 adult patients (age range: 18–70 years, weight range: 60–180 kg). Patient-specific computational phantoms were generated based on patient CT image datasets. A previously validated Monte Carlo simulation program was used to model a clinical CT scanner (SOMATOM Definition Flash, Siemens Healthcare, Forchheim, Germany). A practical strategy was developed to achieve real-time organ dose estimation for a given clinical patient. CTDIvol-normalized organ dose coefficients ({{h}\\text{Organ}} ) under constant tube current were estimated and modeled as a function of patient size. Each clinical patient in the library was optimally matched to another computational phantom to obtain a representation of organ location/distribution. The patient organ distribution was convolved with a dose distribution profile to generate {{≤ft(\\text{CTD}{{\\text{I}}\\text{vol}}\\right)}\\text{organ, \\text{convolution}}} values that quantified the regional dose field for each organ. The organ dose was estimated by multiplying {{≤ft(\\text{CTD}{{\\text{I}}\\text{vol}}\\right)}\\text{organ, \\text{convolution}}} with the organ dose coefficients ({{h}\\text{Organ}} ). To validate the accuracy of this dose estimation technique, the organ dose of the original clinical patient was estimated using Monte Carlo program with TCM profiles explicitly modeled

  15. Fabrication of tungsten wire reinforced nickel-base alloy composites

    NASA Technical Reports Server (NTRS)

    Brentnall, W. D.; Toth, I. J.

    1974-01-01

    Fabrication methods for tungsten fiber reinforced nickel-base superalloy composites were investigated. Three matrix alloys in pre-alloyed powder or rolled sheet form were evaluated in terms of fabricability into composite monotape and multi-ply forms. The utility of monotapes for fabricating more complex shapes was demonstrated. Preliminary 1093C (2000F) stress rupture tests indicated that efficient utilization of fiber strength was achieved in composites fabricated by diffusion bonding processes. The fabrication of thermal fatigue specimens is also described.

  16. Determining micro- and macro- geometry of fabric and fabric reinforced composites

    NASA Astrophysics Data System (ADS)

    Huang, Lejian

    Textile composites are made from textile fabric and resin. Depending on the weaving pattern, composite reinforcements can be characterized into two groups: uniform fabric and near-net shape fabric. Uniform fabric can be treated as an assembly of its smallest repeating pattern also called a unit cell; the latter is a single component with complex structure. Due to advantages of cost savings and inherent toughness, near-net shape fabric has gained great success in composite industries, for application such as turbine blades. Mechanical properties of textile composites are mainly determined by the geometry of the composite reinforcements. The study of a composite needs a computational tool to link fabric micro- and macro-geometry with the textile weaving process and composite manufacturing process. A textile fabric consists of a number of yarns or tows, and each yarn is a bundle of fibers. In this research, a fiber-level approach known as the digital element approach (DEA) is adopted to model the micro- and macro-geometry of fabric and fabric reinforced composites. This approach determines fabric geometry based on textile weaving mechanics. A solver with a dynamic explicit algorithm is employed in the DEA. In modeling a uniform fabric, the topology of the fabric unit cell is first established based on the weaving pattern, followed by yarn discretization. An explicit algorithm with a periodic boundary condition is then employed during the simulation. After its detailed geometry is obtained, the unit cell is then assembled to yield a fabric micro-geometry. Fabric micro-geometry can be expressed at both fiber- and yarn-levels. In modeling a near-net shape fabric component, all theories used in simulating the uniform fabric are kept except the periodic boundary condition. Since simulating the entire component at the fiber-level requires a large amount of time and memory, parallel program is used during the simulation. In modeling a net-shape composite, a dynamic molding

  17. Fabrication Technology

    SciTech Connect

    Blaedel, K.L.

    1993-03-01

    The mission of the Fabrication Technology thrust area is to have an adequate base of manufacturing technology, not necessarily resident at Lawrence Livermore National Laboratory (LLNL), to conduct the future business of LLNL. The specific goals continue to be to (1) develop an understanding of fundamental fabrication processes; (2) construct general purpose process models that will have wide applicability; (3) document findings and models in journals; (4) transfer technology to LLNL programs, industry, and colleagues; and (5) develop continuing relationships with the industrial and academic communities to advance the collective understanding of fabrication processes. The strategy to ensure success is changing. For technologies in which they are expert and which will continue to be of future importance to LLNL, they can often attract outside resources both to maintain their expertise by applying it to a specific problem and to help fund further development. A popular vehicle to fund such work is the Cooperative Research and Development Agreement with industry. For technologies needing development because of their future critical importance and in which they are not expert, they use internal funding sources. These latter are the topics of the thrust area. Three FY-92 funded projects are discussed in this section. Each project clearly moves the Fabrication Technology thrust area towards the goals outlined above. They have also continued their membership in the North Carolina State University Precision Engineering Center, a multidisciplinary research and graduate program established to provide the new technologies needed by high-technology institutions in the US. As members, they have access to and use of the results of their research projects, many of which parallel the precision engineering efforts at LLNL.

  18. Groundwater response to changing water-use practices in sloping aquifers using convolution of transient response functions

    NASA Astrophysics Data System (ADS)

    Steward, David R.; Yang, Xiaoying; Chacon, Sergio

    2009-02-01

    This study examines the impact of a sloping base on the movement of transients through groundwater systems. Dimensionless variables and regression of model results are employed to develop functions relating the transient change in saturated thickness to the distance upgradient and downgradient from recharge or withdrawal. Convolution of these transient response functions (made possible due to linearity of partial differential equations in the model) enables computation of changes in saturated thickness over recharge/withdrawal that varies over space and time. Establishing the criteria and form of these functions led to the discovery of fundamental underlying properties: upgradient and downgradient responses may be scaled to achieve a symmetrical relationship, expressions are developed to compute change in saturated thickness at the location of water use, and downgradient response at large times form a S-shaped curve that effectively adds a diffusive component to the average velocity of the kinematic wave approximation, where the hydraulic gradient is equal to the bed slope. Hydrogeologic data for three study regions in the High Plains Aquifer are summarized, and model results are presented for changes in saturated thickness. The transient response functions are used to reconstruct and interpret groundwater response to historical water-use practices and to predict future changes in saturated thickness for a series of hypothetical alternative water-use scenarios. Depressions in groundwater elevation are observed both upgradient and downgradient from areas of high water use; however, these depressions preferentially move downgradient over time and may continue to spread downgradient far into the future. This approach quantifies and provides understanding of the impacts of changes in natural and anthropogenic hydrologic forcings on aquifer systems.

  19. A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations

    PubMed Central

    Lu, Le; Seff, Ari; Cherry, Kevin M.; Hoffman, Joanne; Wang, Shijun; Liu, Jiamin; Turkbey, Evrim; Summers, Ronald M.

    2015-01-01

    Automated Lymph Node (LN) detection is an important clinical diagnostic task but very challenging due to the low contrast of surrounding structures in Computed Tomography (CT) and to their varying sizes, poses, shapes and sparsely distributed locations. State-of-the-art studies show the performance range of 52.9% sensitivity at 3.1 false-positives per volume (FP/vol.), or 60.9% at 6.1 FP/vol. for mediastinal LN, by one-shot boosting on 3D HAAR features. In this paper, we first operate a preliminary candidate generation stage, towards ~100% sensitivity at the cost of high FP levels (~40 per patient), to harvest volumes of interest (VOI). Our 2.5D approach consequently decomposes any 3D VOI by resampling 2D reformatted orthogonal views N times, via scale, random translations, and rotations with respect to the VOI centroid coordinates. These random views are then used to train a deep Convolutional Neural Network (CNN) classifier. In testing, the CNN is employed to assign LN probabilities for all N random views that can be simply averaged (as a set) to compute the final classification probability per VOI. We validate the approach on two datasets: 90 CT volumes with 388 mediastinal LNs and 86 patients with 595 abdominal LNs. We achieve sensitivities of 70%/83% at 3 FP/vol. and 84%/90% at 6 FP/vol. in mediastinum and abdomen respectively, which drastically improves over the previous state-of-the-art work. PMID:25333158

  20. Variable-shape solar-energy concentrator

    NASA Technical Reports Server (NTRS)

    Miller, C. G.; Phol, J. H.

    1979-01-01

    Proposed low cost three dimensional tracking solar concentrator fabricated from lightweight, flexible polymeric film membrane is controlled in shape by differential pressure loading. Fine adjustments to shape could be made by mounting electrets or magnets on membrane or applying electric or magnetic field.

  1. De-convoluting mixed crude oil in Prudhoe Bay Field, North Slope, Alaska

    USGS Publications Warehouse

    Peters, K.E.; Scott, Ramos L.; Zumberge, J.E.; Valin, Z.C.; Bird, K.J.

    2008-01-01

    Seventy-four crude oil samples from the Barrow arch on the North Slope of Alaska were studied to assess the relative volumetric contributions from different source rocks to the giant Prudhoe Bay Field. We applied alternating least squares to concentration data (ALS-C) for 46 biomarkers in the range C19-C35 to de-convolute mixtures of oil generated from carbonate rich Triassic Shublik Formation and clay rich Jurassic Kingak Shale and Cretaceous Hue Shale-gamma ray zone (Hue-GRZ) source rocks. ALS-C results for 23 oil samples from the prolific Ivishak Formation reservoir of the Prudhoe Bay Field indicate approximately equal contributions from Shublik Formation and Hue-GRZ source rocks (37% each), less from the Kingak Shale (26%), and little or no contribution from other source rocks. These results differ from published interpretations that most oil in the Prudhoe Bay Field originated from the Shublik Formation source rock. With few exceptions, the relative contribution of oil from the Shublik Formation decreases, while that from the Hue-GRZ increases in reservoirs along the Barrow arch from Point Barrow in the northwest to Point Thomson in the southeast (???250 miles or 400 km). The Shublik contribution also decreases to a lesser degree between fault blocks within the Ivishak pool from west to east across the Prudhoe Bay Field. ALS-C provides a robust means to calculate the relative amounts of two or more oil types in a mixture. Furthermore, ALS-C does not require that pure end member oils be identified prior to analysis or that laboratory mixtures of these oils be prepared to evaluate mixing. ALS-C of biomarkers reliably de-convolutes mixtures because the concentrations of compounds in mixtures vary as linear functions of the amount of each oil type. ALS of biomarker ratios (ALS-R) cannot be used to de-convolute mixtures because compound ratios vary as nonlinear functions of the amount of each oil type.

  2. Convolutions of Rayleigh functions and their application to semi-linear equations in circular domains

    NASA Astrophysics Data System (ADS)

    Varlamov, Vladimir

    2007-03-01

    Rayleigh functions [sigma]l([nu]) are defined as series in inverse powers of the Bessel function zeros [lambda][nu],n[not equal to]0, where ; [nu] is the index of the Bessel function J[nu](x) and n=1,2,... is the number of the zeros. Convolutions of Rayleigh functions with respect to the Bessel index, are needed for constructing global-in-time solutions of semi-linear evolution equations in circular domains [V. Varlamov, On the spatially two-dimensional Boussinesq equation in a circular domain, Nonlinear Anal. 46 (2001) 699-725; V. Varlamov, Convolution of Rayleigh functions with respect to the Bessel index, J. Math. Anal. Appl. 306 (2005) 413-424]. The study of this new family of special functions was initiated in [V. Varlamov, Convolution of Rayleigh functions with respect to the Bessel index, J. Math. Anal. Appl. 306 (2005) 413-424], where the properties of R1(m) were investigated. In the present work a general representation of Rl(m) in terms of [sigma]l([nu]) is deduced. On the basis of this a representation for the function R2(m) is obtained in terms of the [psi]-function. An asymptotic expansion is computed for R2(m) as m-->[infinity]. Such asymptotics are needed for establishing function spaces for solutions of semi-linear equations in bounded domains with periodicity conditions in one coordinate. As an example of application of Rl(m) a forced Boussinesq equationutt-2b[Delta]ut=-[alpha][Delta]2u+[Delta]u+[beta][Delta](u2)+f with [alpha],b=const>0 and [beta]=const[set membership, variant]R is considered in a unit disc with homogeneous boundary and initial data. Construction of its global-in-time solutions involves the use of the functions R1(m) and R2(m) which are responsible for the nonlinear smoothing effect.

  3. Automatic breast density classification using a convolutional neural network architecture search procedure

    NASA Astrophysics Data System (ADS)

    Fonseca, Pablo; Mendoza, Julio; Wainer, Jacques; Ferrer, Jose; Pinto, Joseph; Guerrero, Jorge; Castaneda, Benjamin

    2015-03-01

    Breast parenchymal density is considered a strong indicator of breast cancer risk and therefore useful for preventive tasks. Measurement of breast density is often qualitative and requires the subjective judgment of radiologists. Here we explore an automatic breast composition classification workflow based on convolutional neural networks for feature extraction in combination with a support vector machines classifier. This is compared to the assessments of seven experienced radiologists. The experiments yielded an average kappa value of 0.58 when using the mode of the radiologists' classifications as ground truth. Individual radiologist performance against this ground truth yielded kappa values between 0.56 and 0.79.

  4. Processing circuit with asymmetry corrector and convolutional encoder for digital data

    NASA Technical Reports Server (NTRS)

    Pfiffner, Harold J. (Inventor)

    1987-01-01

    A processing circuit is provided for correcting for input parameter variations, such as data and clock signal symmetry, phase offset and jitter, noise and signal amplitude, in incoming data signals. An asymmetry corrector circuit performs the correcting function and furnishes the corrected data signals to a convolutional encoder circuit. The corrector circuit further forms a regenerated clock signal from clock pulses in the incoming data signals and another clock signal at a multiple of the incoming clock signal. These clock signals are furnished to the encoder circuit so that encoded data may be furnished to a modulator at a high data rate for transmission.

  5. Real-time minimal bit error probability decoding of convolutional codes

    NASA Technical Reports Server (NTRS)

    Lee, L. N.

    1973-01-01

    A recursive procedure is derived for decoding of rate R=1/n binary convolutional codes which minimizes the probability of the individual decoding decisions for each information bit subject to the constraint that the decoding delay be limited to Delta branches. This new decoding algorithm is similar to, but somewhat more complex than, the Viterbi decoding algorithm. A real-time, i.e. fixed decoding delay, version of the Viterbi algorithm is also developed and used for comparison to the new algorithm on simulated channels. It is shown that the new algorithm offers advantages over Viterbi decoding in soft-decision applications such as in the inner coding system for concatenated coding.

  6. Parity retransmission hybrid ARQ using rate 1/2 convolutional codes on a nonstationary channel

    NASA Technical Reports Server (NTRS)

    Lugand, Laurent R.; Costello, Daniel J., Jr.; Deng, Robert H.

    1989-01-01

    A parity retransmission hybrid automatic repeat request (ARQ) scheme is proposed which uses rate 1/2 convolutional codes and Viterbi decoding. A protocol is described which is capable of achieving higher throughputs than previously proposed parity retransmission schemes. The performance analysis is based on a two-state Markov model of a nonstationary channel. This model constitutes a first approximation to a nonstationary channel. The two-state channel model is used to analyze the throughput and undetected error probability of the protocol presented when the receiver has both an infinite and a finite buffer size. It is shown that the throughput improves as the channel becomes more bursty.

  7. The use of interleaving for reducing radio loss in convolutionally coded systems

    NASA Technical Reports Server (NTRS)

    Divsalar, D.; Simon, M. K.; Yuen, J. H.

    1989-01-01

    The use of interleaving after convolutional coding and deinterleaving before Viterbi decoding is proposed. This effectively reduces radio loss at low-loop Signal to Noise Ratios (SNRs) by several decibels and at high-loop SNRs by a few tenths of a decibel. Performance of the coded system can further be enhanced if the modulation index is optimized for this system. This will correspond to a reduction of bit SNR at a certain bit error rate for the overall system. The introduction of interleaving/deinterleaving into communication systems designed for future deep space missions does not substantially complicate their hardware design or increase their system cost.

  8. Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding

    PubMed Central

    Johnson, Rie; Zhang, Tong

    2016-01-01

    This paper presents a new semi-supervised framework with convolutional neural networks (CNNs) for text categorization. Unlike the previous approaches that rely on word embeddings, our method learns embeddings of small text regions from unlabeled data for integration into a supervised CNN. The proposed scheme for embedding learning is based on the idea of two-view semi-supervised learning, which is intended to be useful for the task of interest even though the training is done on unlabeled data. Our models achieve better results than previous approaches on sentiment classification and topic classification tasks. PMID:27087766

  9. Introducing electron capture into the unitary-convolution-approximation energy-loss theory at low velocities

    SciTech Connect

    Schiwietz, G.; Grande, P. L.

    2011-11-15

    Recent developments in the theoretical treatment of electronic energy losses of bare and screened ions in gases are presented. Specifically, the unitary-convolution-approximation (UCA) stopping-power model has proven its strengths for the determination of nonequilibrium effects for light as well as heavy projectiles at intermediate to high projectile velocities. The focus of this contribution will be on the UCA and its extension to specific projectile energies far below 100 keV/u, by considering electron-capture contributions at charge-equilibrium conditions.

  10. Vibration analysis of FG cylindrical shells with power-law index using discrete singular convolution technique

    NASA Astrophysics Data System (ADS)

    Mercan, Kadir; Demir, Çiğdem; Civalek, Ömer

    2016-01-01

    In the present manuscript, free vibration response of circular cylindrical shells with functionally graded material (FGM) is investigated. The method of discrete singular convolution (DSC) is used for numerical solution of the related governing equation of motion of FGM cylindrical shell. The constitutive relations are based on the Love's first approximation shell theory. The material properties are graded in the thickness direction according to a volume fraction power law indexes. Frequency values are calculated for different types of boundary conditions, material and geometric parameters. In general, close agreement between the obtained results and those of other researchers has been found.

  11. Some optimal partial-unit-memory codes. [time-invariant binary convolutional codes

    NASA Technical Reports Server (NTRS)

    Lauer, G. S.

    1979-01-01

    A class of time-invariant binary convolutional codes is defined, called partial-unit-memory codes. These codes are optimal in the sense of having maximum free distance for given values of R, k (the number of encoder inputs), and mu (the number of encoder memory cells). Optimal codes are given for rates R = 1/4, 1/3, 1/2, and 2/3, with mu not greater than 4 and k not greater than mu + 3, whenever such a code is better than previously known codes. An infinite class of optimal partial-unit-memory codes is also constructed based on equidistant block codes.

  12. Performance of DPSK with convolutional encoding on time-varying fading channels

    NASA Technical Reports Server (NTRS)

    Mui, S. Y.; Modestino, J. W.

    1977-01-01

    The bit error probability performance of a differentially-coherent phase-shift keyed (DPSK) modem with convolutional encoding and Viterbi decoding on time-varying fading channels is examined. Both the Rician and the lognormal channels are considered. Bit error probability upper bounds on fully-interleaved (zero-memory) fading channels are derived and substantiated by computer simulation. It is shown that the resulting coded system performance is a relatively insensitive function of the choice of channel model provided that the channel parameters are related according to the correspondence developed as part of this paper. Finally, a comparison of DPSK with a number of other modulation strategies is provided.

  13. Cygrid: Cython-powered convolution-based gridding module for Python

    NASA Astrophysics Data System (ADS)

    Winkel, B.; Lenz, D.; Flöer, L.

    2016-06-01

    The Python module Cygrid grids (resamples) data to any collection of spherical target coordinates, although its typical application involves FITS maps or data cubes. The module supports the FITS world coordinate system (WCS) standard; its underlying algorithm is based on the convolution of the original samples with a 2D Gaussian kernel. A lookup table scheme allows parallelization of the code and is combined with the HEALPix tessellation of the sphere for fast neighbor searches. Cygrid's runtime scales between O(n) and O(nlog n), with n being the number of input samples.

  14. Using convolutional neural networks for human activity classification on micro-Doppler radar spectrograms

    NASA Astrophysics Data System (ADS)

    Jordan, Tyler S.

    2016-05-01

    This paper presents the findings of using convolutional neural networks (CNNs) to classify human activity from micro-Doppler features. An emphasis on activities involving potential security threats such as holding a gun are explored. An automotive 24 GHz radar on chip was used to collect the data and a CNN (normally applied to image classification) was trained on the resulting spectrograms. The CNN achieves an error rate of 1.65 % on classifying running vs. walking, 17.3 % error on armed walking vs. unarmed walking, and 22 % on classifying six different actions.

  15. Diffuse dispersive delay and the time convolution/attenuation of transients

    NASA Technical Reports Server (NTRS)

    Bittner, Burt J.

    1991-01-01

    Test data and analytic evaluations are presented to show that relatively poor 100 KHz shielding of 12 Db can effectively provide an electromagnetic pulse transient reduction of 100 Db. More importantly, several techniques are shown for lightning surge attenuation as an alternative to crowbar, spark gap, or power zener type clipping which simply reflects the surge. A time delay test method is shown which allows CW testing, along with a convolution program to define transient shielding effectivity where the Fourier phase characteristics of the transient are known or can be broadly estimated.

  16. Triaxial Fabrics

    NASA Technical Reports Server (NTRS)

    1981-01-01

    Gentax Corporation's triaxal fabrics are woven from three separate yarn sets whose intersections form equilateral triangles. This type of weave, derived from space shuttle pressure suits, assures practically equal strength in every direction; has essentially no bias, or weak dimension offering greater resistance to tear and shear along with significant weight reduction. Applications of the Triax line include inflatable equipment, life vests, aircraft evacuation slides, helicopter flotation devices, tension structures, safety clothing and sailcloth for boats. Ability to accept compound curvatures with no distortion of the weave configuration makes it useful in manufacturing molded composites.

  17. A Cable-Shaped Lithium Sulfur Battery.

    PubMed

    Fang, Xin; Weng, Wei; Ren, Jing; Peng, Huisheng

    2016-01-20

    A carbon nanostructured hybrid fiber is developed by integrating mesoporous carbon and graphene oxide into aligned carbon nanotubes. This hybrid fiber is used as a 1D cathode to fabricate a new cable-shaped lithium-sulfur battery. The fiber cathode exhibits a decent specific capacity and lifespan, which makes the cable-shaped lithium-sulfur battery rank far ahead of other fiber-shaped batteries. PMID:26585740

  18. Post polymerization cure shape memory polymers

    DOEpatents

    Wilson, Thomas S; Hearon, Michael Keith; Bearinger, Jane P

    2014-11-11

    This invention relates to chemical polymer compositions, methods of synthesis, and fabrication methods for devices regarding polymers capable of displaying shape memory behavior (SMPs) and which can first be polymerized to a linear or branched polymeric structure, having thermoplastic properties, subsequently processed into a device through processes typical of polymer melts, solutions, and dispersions and then crossed linked to a shape memory thermoset polymer retaining the processed shape.

  19. Effects of Convoluted Divergent Flap Contouring on the Performance of a Fixed-Geometry Nonaxisymmetric Exhaust Nozzle

    NASA Technical Reports Server (NTRS)

    Asbury, Scott C.; Hunter, Craig A.

    1999-01-01

    An investigation was conducted in the model preparation area of the Langley 16-Foot Transonic Tunnel to determine the effects of convoluted divergent-flap contouring on the internal performance of a fixed-geometry, nonaxisymmetric, convergent-divergent exhaust nozzle. Testing was conducted at static conditions using a sub-scale nozzle model with one baseline and four convoluted configurations. All tests were conducted with no external flow at nozzle pressure ratios from 1.25 to approximately 9.50. Results indicate that baseline nozzle performance was dominated by unstable, shock-induced, boundary-layer separation at overexpanded conditions. Convoluted configurations were found to significantly reduce, and in some cases totally alleviate separation at overexpanded conditions. This result was attributed to the ability of convoluted contouring to energize and improve the condition of the nozzle boundary layer. Separation alleviation offers potential for installed nozzle aeropropulsive (thrust-minus-drag) performance benefits by reducing drag at forward flight speeds, even though this may reduce nozzle thrust ratio as much as 6.4% at off-design conditions. At on-design conditions, nozzle thrust ratio for the convoluted configurations ranged from 1% to 2.9% below the baseline configuration; this was a result of increased skin friction and oblique shock losses inside the nozzle.

  20. Shape-Morphing Nanocomposite Origami

    PubMed Central

    2015-01-01

    Nature provides a vast array of solid materials that repeatedly and reversibly transform in shape in response to environmental variations. This property is essential, for example, for new energy-saving technologies, efficient collection of solar radiation, and thermal management. Here we report a similar shape-morphing mechanism using differential swelling of hydrophilic polyelectrolyte multilayer inkjets deposited on an LBL carbon nanotube (CNT) composite. The out-of-plane deflection can be precisely controlled, as predicted by theoretical analysis. We also demonstrate a controlled and stimuli-responsive twisting motion on a spiral-shaped LBL nanocomposite. By mimicking the motions achieved in nature, this method offers new opportunities for the design and fabrication of functional stimuli-responsive shape-morphing nanoscale and microscale structures for a variety of applications. PMID:24689908

  1. Shape-morphing nanocomposite origami.

    PubMed

    Andres, Christine M; Zhu, Jian; Shyu, Terry; Flynn, Connor; Kotov, Nicholas A

    2014-05-20

    Nature provides a vast array of solid materials that repeatedly and reversibly transform in shape in response to environmental variations. This property is essential, for example, for new energy-saving technologies, efficient collection of solar radiation, and thermal management. Here we report a similar shape-morphing mechanism using differential swelling of hydrophilic polyelectrolyte multilayer inkjets deposited on an LBL carbon nanotube (CNT) composite. The out-of-plane deflection can be precisely controlled, as predicted by theoretical analysis. We also demonstrate a controlled and stimuli-responsive twisting motion on a spiral-shaped LBL nanocomposite. By mimicking the motions achieved in nature, this method offers new opportunities for the design and fabrication of functional stimuli-responsive shape-morphing nanoscale and microscale structures for a variety of applications. PMID:24689908

  2. Lexan Linear Shaped Charge Holder with Magnets and Backing Plate

    NASA Technical Reports Server (NTRS)

    Maples, Matthew W.; Dutton, Maureen L.; Hacker, Scott C.; Dean, Richard J.; Kidd, Nicholas; Long, Chris; Hicks, Robert C.

    2013-01-01

    A method was developed for cutting a fabric structural member in an inflatable module, without damaging the internal structure of the module, using linear shaped charge. Lexan and magnets are used in a charge holder to precisely position the linear shaped charge over the desired cut area. Two types of charge holders have been designed, each with its own backing plate. One holder cuts fabric straps in the vertical configuration, and the other charge holder cuts fabric straps in the horizontal configuration.

  3. NOTE: Verification of lung dose in an anthropomorphic phantom calculated by the collapsed cone convolution method

    NASA Astrophysics Data System (ADS)

    Butson, Martin J.; Elferink, Rebecca; Cheung, Tsang; Yu, Peter K. N.; Stokes, Michael; You Quach, Kim; Metcalfe, Peter

    2000-11-01

    Verification of calculated lung dose in an anthropomorphic phantom is performed using two dosimetry media. Dosimetry is complicated by factors such as variations in density at slice interfaces and appropriate position on CT scanning slice to accommodate these factors. Dose in lung for a 6 MV and 10 MV anterior-posterior field was calculated with a collapsed cone convolution method using an ADAC Pinnacle, 3D planning system. Up to 5% variations between doses calculated at the centre and near the edge of the 2 cm phantom slice positioned at the beam central axis were seen, due to the composition of each phantom slice. Validation of dose was performed with LiF thermoluminescent dosimeters (TLDs) and X-Omat V radiographic film. Both dosimetry media produced dose results which agreed closely with calculated results nearest their physical positioning in the phantom. The collapsed cone convolution method accurately calculates dose within inhomogeneous lung regions at 6 MV and 10 MV x-ray energy.

  4. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition.

    PubMed

    Ordóñez, Francisco Javier; Roggen, Daniel

    2016-01-01

    Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters' influence on performance to provide insights about their optimisation. PMID:26797612

  5. The Probabilistic Convolution Tree: Efficient Exact Bayesian Inference for Faster LC-MS/MS Protein Inference

    PubMed Central

    Serang, Oliver

    2014-01-01

    Exact Bayesian inference can sometimes be performed efficiently for special cases where a function has commutative and associative symmetry of its inputs (called “causal independence”). For this reason, it is desirable to exploit such symmetry on big data sets. Here we present a method to exploit a general form of this symmetry on probabilistic adder nodes by transforming those probabilistic adder nodes into a probabilistic convolution tree with which dynamic programming computes exact probabilities. A substantial speedup is demonstrated using an illustration example that can arise when identifying splice forms with bottom-up mass spectrometry-based proteomics. On this example, even state-of-the-art exact inference algorithms require a runtime more than exponential in the number of splice forms considered. By using the probabilistic convolution tree, we reduce the runtime to and the space to where is the number of variables joined by an additive or cardinal operator. This approach, which can also be used with junction tree inference, is applicable to graphs with arbitrary dependency on counting variables or cardinalities and can be used on diverse problems and fields like forward error correcting codes, elemental decomposition, and spectral demixing. The approach also trivially generalizes to multiple dimensions. PMID:24626234

  6. Muon Neutrino Disappearance in NOvA with a Deep Convolutional Neural Network Classifier

    NASA Astrophysics Data System (ADS)

    Rocco, Dominick Rosario

    The NuMI Off-axis Neutrino Appearance Experiment (NOvA) is designed to study neutrino oscillation in the NuMI (Neutrinos at the Main Injector) beam. NOvA observes neutrino oscillation using two detectors separated by a baseline of 810 km; a 14 kt Far Detector in Ash River, MN and a functionally identical 0.3 kt Near Detector at Fermilab. The experiment aims to provide new measurements of $[special characters omitted]. and theta23 and has potential to determine the neutrino mass hierarchy as well as observe CP violation in the neutrino sector. Essential to these analyses is the classification of neutrino interaction events in NOvA detectors. Raw detector output from NOvA is interpretable as a pair of images which provide orthogonal views of particle interactions. A recent advance in the field of computer vision is the advent of convolutional neural networks, which have delivered top results in the latest image recognition contests. This work presents an approach novel to particle physics analysis in which a convolutional neural network is used for classification of particle interactions. The approach has been demonstrated to improve the signal efficiency and purity of the event selection, and thus physics sensitivity. Early NOvA data has been analyzed (2.74 x 1020 POT, 14 kt equivalent) to provide new best-fit measurements of sin2(theta23) = 0.43 (with a statistically-degenerate compliment near 0.60) and [special characters omitted]..

  7. Efficient pedestrian detection from aerial vehicles with object proposals and deep convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Minnehan, Breton; Savakis, Andreas

    2016-05-01

    As Unmanned Aerial Systems grow in numbers, pedestrian detection from aerial platforms is becoming a topic of increasing importance. By providing greater contextual information and a reduced potential for occlusion, the aerial vantage point provided by Unmanned Aerial Systems is highly advantageous for many surveillance applications, such as target detection, tracking, and action recognition. However, due to the greater distance between the camera and scene, targets of interest in aerial imagery are generally smaller and have less detail. Deep Convolutional Neural Networks (CNN's) have demonstrated excellent object classification performance and in this paper we adopt them to the problem of pedestrian detection from aerial platforms. We train a CNN with five layers consisting of three convolution-pooling layers and two fully connected layers. We also address the computational inefficiencies of the sliding window method for object detection. In the sliding window configuration, a very large number of candidate patches are generated from each frame, while only a small number of them contain pedestrians. We utilize the Edge Box object proposal generation method to screen candidate patches based on an "objectness" criterion, so that only regions that are likely to contain objects are processed. This method significantly reduces the number of image patches processed by the neural network and makes our classification method very efficient. The resulting two-stage system is a good candidate for real-time implementation onboard modern aerial vehicles. Furthermore, testing on three datasets confirmed that our system offers high detection accuracy for terrestrial pedestrian detection in aerial imagery.

  8. Calcium-activated chloride currents in primary cultures of rabbit distal convoluted tubule.

    PubMed

    Bidet, M; Tauc, M; Rubera, I; de Renzis, G; Poujeol, C; Bohn, M T; Poujeol, P

    1996-10-01

    Chloride (Cl-) conductances were studied in primary cultures of rabbit distal convoluted tubule (very early distal "bright" convoluted tubule, DCTb) by the whole cell patch-clamp technique. We identified a Cl- current activated by 2 microM extracellular ionomycin. The kinetics of the macroscopic current were time dependent for depolarizing potentials with a slow developing component. The steady state current presented outward rectification, and the ion selectivity sequence was I- > Br- > > Cl > glutamate. The current was inhibited by 0.1 mM 5-nitro-2-(3-phenylpropyl-amino)benzoic acid, 1 mM 4,4'-diisothiocyanostilbene-2,2'-disulfonic acid, and 1 mM diphenylamine-2-carboxylate. To identify the location of the Cl- conductance, 6-methoxy-N-(3-sulfopropyl)quinolinium fluorescence experiments were carried out in confluent cultures developed on collagen-coated permeable filters. Cl- removal from the apical solution induced a Cl- efflux that was stimulated by 10 microM forskolin. Forskolin had no effect on the basolateral Cl- permeability Cl- substitution in the basolateral solution induced an efflux stimulated by 2 microM ionomycin or 50 microM extracellular ATP Ionomycin had no effect on the apical Cl- fluxes. Thus cultured DCTb cells exhibit Ca(2+)-activated Cl- channels located in the basolateral membrane. This Cl- permeability was active at a resting membrane potential and could participate in the Cl- reabsorption across the DCTb in control conditions. PMID:8898026

  9. Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network.

    PubMed

    Liu, Ziyi; Gao, Junfeng; Yang, Guoguo; Zhang, Huan; He, Yong

    2016-01-01

    We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to compute a saliency map for localizing pest insect objects. Bounding squares containing targets were then extracted, resized to a fixed size, and used to construct a large standard database called Pest ID. This database was then utilized for self-learning of local image features which were, in turn, used for classification by DCNN. DCNN learning optimized the critical parameters, including size, number and convolutional stride of local receptive fields, dropout ratio and the final loss function. To demonstrate the practical utility of using DCNN, we explored different architectures by shrinking depth and width, and found effective sizes that can act as alternatives for practical applications. On the test set of paddy field images, our architectures achieved a mean Accuracy Precision (mAP) of 0.951, a significant improvement over previous methods. PMID:26864172

  10. Performance of convolutional codes on fading channels typical of planetary entry missions

    NASA Technical Reports Server (NTRS)

    Modestino, J. W.; Mui, S. Y.; Reale, T. J.

    1974-01-01

    The performance of convolutional codes in fading channels typical of the planetary entry channel is examined in detail. The signal fading is due primarily to turbulent atmospheric scattering of the RF signal transmitted from an entry probe through a planetary atmosphere. Short constraint length convolutional codes are considered in conjunction with binary phase-shift keyed modulation and Viterbi maximum likelihood decoding, and for longer constraint length codes sequential decoding utilizing both the Fano and Zigangirov-Jelinek (ZJ) algorithms are considered. Careful consideration is given to the modeling of the channel in terms of a few meaningful parameters which can be correlated closely with theoretical propagation studies. For short constraint length codes the bit error probability performance was investigated as a function of E sub b/N sub o parameterized by the fading channel parameters. For longer constraint length codes the effect was examined of the fading channel parameters on the computational requirements of both the Fano and ZJ algorithms. The effects of simple block interleaving in combatting the memory of the channel is explored, using the analytic approach or digital computer simulation.

  11. Large patch convolutional neural networks for the scene classification of high spatial resolution imagery

    NASA Astrophysics Data System (ADS)

    Zhong, Yanfei; Fei, Feng; Zhang, Liangpei

    2016-04-01

    The increase of the spatial resolution of remote-sensing sensors helps to capture the abundant details related to the semantics of surface objects. However, it is difficult for the popular object-oriented classification approaches to acquire higher level semantics from the high spatial resolution remote-sensing (HSR-RS) images, which is often referred to as the "semantic gap." Instead of designing sophisticated operators, convolutional neural networks (CNNs), a typical deep learning method, can automatically discover intrinsic feature descriptors from a large number of input images to bridge the semantic gap. Due to the small data volume of the available HSR-RS scene datasets, which is far away from that of the natural scene datasets, there have been few reports of CNN approaches for HSR-RS image scene classifications. We propose a practical CNN architecture for HSR-RS scene classification, named the large patch convolutional neural network (LPCNN). The large patch sampling is used to generate hundreds of possible scene patches for the feature learning, and a global average pooling layer is used to replace the fully connected network as the classifier, which can greatly reduce the total parameters. The experiments confirm that the proposed LPCNN can learn effective local features to form an effective representation for different land-use scenes, and can achieve a performance that is comparable to the state-of-the-art on public HSR-RS scene datasets.

  12. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

    PubMed Central

    Ordóñez, Francisco Javier; Roggen, Daniel

    2016-01-01

    Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation. PMID:26797612

  13. Noise-induced bias for convolution-based interpolation in digital image correlation.

    PubMed

    Su, Yong; Zhang, Qingchuan; Gao, Zeren; Xu, Xiaohai

    2016-01-25

    In digital image correlation (DIC), the noise-induced bias is significant if the noise level is high or the contrast of the image is low. However, existing methods for the estimation of the noise-induced bias are merely applicable to traditional interpolation methods such as linear and cubic interpolation, but are not applicable to generalized interpolation methods such as BSpline and OMOMS. Both traditional interpolation and generalized interpolation belong to convolution-based interpolation. Considering the widely use of generalized interpolation, this paper presents a theoretical analysis of noise-induced bias for convolution-based interpolation. A sinusoidal approximate formula for noise-induced bias is derived; this formula motivates an estimating strategy which is with speed, ease, and accuracy; furthermore, based on this formula, the mechanism of sophisticated interpolation methods generally reducing noise-induced bias is revealed. The validity of the theoretical analysis is established by both numerical simulations and actual subpixel translation experiment. Compared to existing methods, formulae provided by this paper are simpler, briefer, and more general. In addition, a more intuitionistic explanation of the cause of noise-induced bias is provided by quantitatively characterized the position-dependence of noise variability in the spatial domain. PMID:26832501

  14. Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network

    PubMed Central

    Liu, Ziyi; Gao, Junfeng; Yang, Guoguo; Zhang, Huan; He, Yong

    2016-01-01

    We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to compute a saliency map for localizing pest insect objects. Bounding squares containing targets were then extracted, resized to a fixed size, and used to construct a large standard database called Pest ID. This database was then utilized for self-learning of local image features which were, in turn, used for classification by DCNN. DCNN learning optimized the critical parameters, including size, number and convolutional stride of local receptive fields, dropout ratio and the final loss function. To demonstrate the practical utility of using DCNN, we explored different architectures by shrinking depth and width, and found effective sizes that can act as alternatives for practical applications. On the test set of paddy field images, our architectures achieved a mean Accuracy Precision (mAP) of 0.951, a significant improvement over previous methods. PMID:26864172

  15. Region-Based Convolutional Networks for Accurate Object Detection and Segmentation.

    PubMed

    Girshick, Ross; Donahue, Jeff; Darrell, Trevor; Malik, Jitendra

    2016-01-01

    Object detection performance, as measured on the canonical PASCAL VOC Challenge datasets, plateaued in the final years of the competition. The best-performing methods were complex ensemble systems that typically combined multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 50 percent relative to the previous best result on VOC 2012-achieving a mAP of 62.4 percent. Our approach combines two ideas: (1) one can apply high-capacity convolutional networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data are scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, boosts performance significantly. Since we combine region proposals with CNNs, we call the resulting model an R-CNN or Region-based Convolutional Network. Source code for the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn. PMID:26656583

  16. Concatenated coding systems employing a unit-memory convolutional code and a byte-oriented decoding algorithm

    NASA Technical Reports Server (NTRS)

    Lee, L. N.

    1976-01-01

    Concatenated coding systems utilizing a convolutional code as the inner code and a Reed-Solomon code as the outer code are considered. In order to obtain very reliable communications over a very noisy channel with relatively small coding complexity, it is proposed to concatenate a byte oriented unit memory convolutional code with an RS outer code whose symbol size is one byte. It is further proposed to utilize a real time minimal byte error probability decoding algorithm, together with feedback from the outer decoder, in the decoder for the inner convolutional code. The performance of the proposed concatenated coding system is studied, and the improvement over conventional concatenated systems due to each additional feature is isolated.

  17. Concatenated coding systems employing a unit-memory convolutional code and a byte-oriented decoding algorithm

    NASA Technical Reports Server (NTRS)

    Lee, L.-N.

    1977-01-01

    Concatenated coding systems utilizing a convolutional code as the inner code and a Reed-Solomon code as the outer code are considered. In order to obtain very reliable communications over a very noisy channel with relatively modest coding complexity, it is proposed to concatenate a byte-oriented unit-memory convolutional code with an RS outer code whose symbol size is one byte. It is further proposed to utilize a real-time minimal-byte-error probability decoding algorithm, together with feedback from the outer decoder, in the decoder for the inner convolutional code. The performance of the proposed concatenated coding system is studied, and the improvement over conventional concatenated systems due to each additional feature is isolated.

  18. Revision of the theory of tracer transport and the convolution model of dynamic contrast enhanced magnetic resonance imaging

    PubMed Central

    Bammer, Roland; Stollberger, Rudolf

    2012-01-01

    Counterexamples are used to motivate the revision of the established theory of tracer transport. Then dynamic contrast enhanced magnetic resonance imaging in particular is conceptualized in terms of a fully distributed convection–diffusion model from which a widely used convolution model is derived using, alternatively, compartmental discretizations or semigroup theory. On this basis, applications and limitations of the convolution model are identified. For instance, it is proved that perfusion and tissue exchange states cannot be identified on the basis of a single convolution equation alone. Yet under certain assumptions, particularly that flux is purely convective at the boundary of a tissue region, physiological parameters such as mean transit time, effective volume fraction, and volumetric flow rate per unit tissue volume can be deduced from the kernel. PMID:17429633

  19. Scanning tunneling microscopy on rough surfaces: Tip-shape-limited resolution

    NASA Astrophysics Data System (ADS)

    Reiss, G.; Vancea, J.; Wittmann, H.; Zweck, J.; Hoffmann, H.

    1990-02-01

    This paper discusses the reliability of scanning tunneling microscopy (STM) images of mesoscopically rough surfaces. The specific structure of these images represents a convolution between the real surface topography and the shape of the tip. In order to interpret these images quantitatively, the line scans of steep and high steps can be used to obtain an image of the tip itself. This image shows tip radii ranging typically from 5 to 15 nm and cone angles of about 30° over a length of 80 nm, and can in turn be used to recognize the limits of STM resolution on a rough surface: High-resolution transmission electron microscopy cross-section images of Au island films on a Au-Nb double layer are convoluted with the experimentally observed tip shape; the resulting line scans correspond very well with STM graphs of the same samples. Finally an overall criterion for the resolution of the STM on such surfaces is proposed.

  20. Tally modifying of MCNP and post processing of pile-up simulation with time convolution method in PGNAA

    NASA Astrophysics Data System (ADS)

    Asghar Mowlavi, Ali; Koohi-Fayegh, Rahim

    2005-11-01

    Time convolution method has been employed for pile-up simulation in prompt gamma neutron activation analysis with an Am-Be neutron source and a 137Cs gamma source. A TALLYX subroutine has been written to design a new tally in the MCNP code. This tally records gamma particle information for the detector cell into an output file to be processed later. The times at which the particles are emitted by the source have been randomly generated following an exponential decay time distribution. A time convolution program was written to process the data produced and simulate more realistic pile-up. This method can be applied in optimization studies.

  1. On the application of a fast polynomial transform and the Chinese remainder theorem to compute a two-dimensional convolution

    NASA Technical Reports Server (NTRS)

    Truong, T. K.; Lipes, R.; Reed, I. S.; Wu, C.

    1980-01-01

    A fast algorithm is developed to compute two dimensional convolutions of an array of d sub 1 X d sub 2 complex number points, where d sub 2 = 2(M) and d sub 1 = 2(m-r+) for some 1 or = r or = m. This algorithm requires fewer multiplications and about the same number of additions as the conventional fast fourier transform method for computing the two dimensional convolution. It also has the advantage that the operation of transposing the matrix of data can be avoided.

  2. Study of the Auger line shape of polyethylene and diamond

    NASA Technical Reports Server (NTRS)

    Dayan, M.; Pepper, S. V.

    1984-01-01

    The KVV Auger electron line shapes of carbon in polyethylene and diamond have been studied. The spectra were obtained in derivative form by electron beam excitation. They were treated by background subtraction, integration and deconvolution to produce the intrinsic Auger line shape. Electron energy loss spectra provided the response function in the deconvolution procedure. The line shape from polyethylene is compared with spectra from linear alkanes and with a previous spectrum of Kelber et al. Both spectra are compared with the self-convolution of their full valence band densities of states and of their p-projected densities. The experimental spectra could not be understood in terms of existing theories. This is so even when correlation effects are qualitatively taken into account account to the theories of Cini and Sawatzky and Lenselink.

  3. Nonimaging active system determination of target shape through turbulent medium

    NASA Astrophysics Data System (ADS)

    Chandler, Susan M.; Lukesh, Gordon W.

    2001-01-01

    Image reconstruction techniques for atmospheric applications often work best with an initial estimate of the object support. This paper examines the ability of a non-imaging laser pointing system to obtain an estimate of target size and shape based on the statistics of the return signal. Fundamental limits on system pointing, such as the tracking errors, corrupt a simple raster scan that would provide gross object shape form the convolution of the far-field pattern with the target. Using techniques developed previously for the estimation of pointing performance, it is possible to distinguish between simple shapes such as bars, circles and T's based on the statistics of the received time signal. Simulated space objects, such as those illuminated during field experiments, may also be distinguished.

  4. Equilibrium Shaping

    NASA Astrophysics Data System (ADS)

    Izzo, Dario; Petazzi, Lorenzo

    2006-08-01

    We present a satellite path planning technique able to make identical spacecraft aquire a given configuration. The technique exploits a behaviour-based approach to achieve an autonomous and distributed control over the relative geometry making use of limited sensorial information. A desired velocity is defined for each satellite as a sum of different contributions coming from generic high level behaviours: forcing the final desired configuration the behaviours are further defined by an inverse dynamic calculation dubbed Equilibrium Shaping. We show how considering only three different kind of behaviours it is possible to acquire a number of interesting formations and we set down the theoretical framework to find the entire set. We find that allowing a limited amount of communication the technique may be used also to form complex lattice structures. Several control feedbacks able to track the desired velocities are introduced and discussed. Our results suggest that sliding mode control is particularly appropriate in connection with the developed technique.

  5. Convolution-based estimation of organ dose in tube current modulated CT.

    PubMed

    Tian, Xiaoyu; Segars, W Paul; Dixon, Robert L; Samei, Ehsan

    2016-05-21

    Estimating organ dose for clinical patients requires accurate modeling of the patient anatomy and the dose field of the CT exam. The modeling of patient anatomy can be achieved using a library of representative computational phantoms (Samei et al 2014 Pediatr. Radiol. 44 460-7). The modeling of the dose field can be challenging for CT exams performed with a tube current modulation (TCM) technique. The purpose of this work was to effectively model the dose field for TCM exams using a convolution-based method. A framework was further proposed for prospective and retrospective organ dose estimation in clinical practice. The study included 60 adult patients (age range: 18-70 years, weight range: 60-180 kg). Patient-specific computational phantoms were generated based on patient CT image datasets. A previously validated Monte Carlo simulation program was used to model a clinical CT scanner (SOMATOM Definition Flash, Siemens Healthcare, Forchheim, Germany). A practical strategy was developed to achieve real-time organ dose estimation for a given clinical patient. CTDIvol-normalized organ dose coefficients ([Formula: see text]) under constant tube current were estimated and modeled as a function of patient size. Each clinical patient in the library was optimally matched to another computational phantom to obtain a representation of organ location/distribution. The patient organ distribution was convolved with a dose distribution profile to generate [Formula: see text] values that quantified the regional dose field for each organ. The organ dose was estimated by multiplying [Formula: see text] with the organ dose coefficients ([Formula: see text]). To validate the accuracy of this dose estimation technique, the organ dose of the original clinical patient was estimated using Monte Carlo program with TCM profiles explicitly modeled. The discrepancy between the estimated organ dose and dose simulated using TCM Monte Carlo program was quantified. We further compared the

  6. Real-time dose computation: GPU-accelerated source modeling and superposition/convolution

    SciTech Connect

    Jacques, Robert; Wong, John; Taylor, Russell; McNutt, Todd

    2011-01-15

    Purpose: To accelerate dose calculation to interactive rates using highly parallel graphics processing units (GPUs). Methods: The authors have extended their prior work in GPU-accelerated superposition/convolution with a modern dual-source model and have enhanced performance. The primary source algorithm supports both focused leaf ends and asymmetric rounded leaf ends. The extra-focal algorithm uses a discretized, isotropic area source and models multileaf collimator leaf height effects. The spectral and attenuation effects of static beam modifiers were integrated into each source's spectral function. The authors introduce the concepts of arc superposition and delta superposition. Arc superposition utilizes separate angular sampling for the total energy released per unit mass (TERMA) and superposition computations to increase accuracy and performance. Delta superposition allows single beamlet changes to be computed efficiently. The authors extended their concept of multi-resolution superposition to include kernel tilting. Multi-resolution superposition approximates solid angle ray-tracing, improving performance and scalability with a minor loss in accuracy. Superposition/convolution was implemented using the inverse cumulative-cumulative kernel and exact radiological path ray-tracing. The accuracy analyses were performed using multiple kernel ray samplings, both with and without kernel tilting and multi-resolution superposition. Results: Source model performance was <9 ms (data dependent) for a high resolution (400{sup 2}) field using an NVIDIA (Santa Clara, CA) GeForce GTX 280. Computation of the physically correct multispectral TERMA attenuation was improved by a material centric approach, which increased performance by over 80%. Superposition performance was improved by {approx}24% to 0.058 and 0.94 s for 64{sup 3} and 128{sup 3} water phantoms; a speed-up of 101-144x over the highly optimized Pinnacle{sup 3} (Philips, Madison, WI) implementation. Pinnacle{sup 3

  7. NCSX Vacuum Vessel Fabrication

    SciTech Connect

    Viola, M. E.; Brown, T.; Heitzenroeder, P.; Malinowski, F.; Reiersen, W.; Sutton, L.; Goranson, P.; Nelson, B.; Cole, M.; Manuel, M.; McCorkle, D.

    2005-10-07

    The National Compact Stellarator Experiment (NCSX) is being constructed at the Princeton Plasma Physics Laboratory (PPPL) in conjunction with the Oak Ridge National Laboratory (ORNL). The goal of this experiment is to develop a device which has the steady state properties of a traditional stellarator along with the high performance characteristics of a tokamak. A key element of this device is its highly shaped Inconel 625 vacuum vessel. This paper describes the manufacturing of the vessel. The vessel is being fabricated by Major Tool and Machine, Inc. (MTM) in three identical 120º vessel segments, corresponding to the three NCSX field periods, in order to accommodate assembly of the device. The port extensions are welded on, leak checked, cut off within 1" of the vessel surface at MTM and then reattached at PPPL, to accommodate assembly of the close-fitting modular coils that surround the vessel. The 120º vessel segments are formed by welding two 60º segments together. Each 60º segment is fabricated by welding ten press-formed panels together over a collapsible welding fixture which is needed to precisely position the panels. The vessel is joined at assembly by welding via custom machined 8" (20.3 cm) wide spacer "spool pieces." The vessel must have a total leak rate less than 5 X 10-6 t-l/s, magnetic permeability less than 1.02μ, and its contours must be within 0.188" (4.76 mm). It is scheduled for completion in January 2006.

  8. Free form fabrication of thermoplastic composites

    SciTech Connect

    Kaufman, S.G.; Spletzer, B.L.; Guess, T.R.

    1998-02-01

    This report describes the results of composites fabrication research sponsored by the Laboratory Directed Research and Development (LDRD) program at Sandia National Laboratories. They have developed, prototyped, and demonstrated the feasibility of a novel robotic technique for rapid fabrication of composite structures. Its chief innovation is that, unlike all other available fabrication methods, it does not require a mold. Instead, the structure is built patch by patch, using a rapidly reconfigurable forming surface, and a robot to position the evolving part. Both of these components are programmable, so only the control software needs to be changed to produce a new shape. Hence it should be possible to automatically program the system to produce a shape directly from an electronic model of it. It is therefore likely that the method will enable faster and less expensive fabrication of composites.

  9. Error-Trellis Construction for Convolutional Codes Using Shifted Error/Syndrome-Subsequences

    NASA Astrophysics Data System (ADS)

    Tajima, Masato; Okino, Koji; Miyagoshi, Takashi

    In this paper, we extend the conventional error-trellis construction for convolutional codes to the case where a given check matrix H(D) has a factor Dl in some column (row). In the first case, there is a possibility that the size of the state space can be reduced using shifted error-subsequences, whereas in the second case, the size of the state space can be reduced using shifted syndrome-subsequences. The construction presented in this paper is based on the adjoint-obvious realization of the corresponding syndrome former HT(D). In the case where all the columns and rows of H(D) are delay free, the proposed construction is reduced to the conventional one of Schalkwijk et al. We also show that the proposed construction can equally realize the state-space reduction shown by Ariel et al. Moreover, we clarify the difference between their construction and that of ours using examples.

  10. A high-order fast method for computing convolution integral with smooth kernel

    SciTech Connect

    Qiang, Ji

    2009-09-28

    In this paper we report on a high-order fast method to numerically calculate convolution integral with smooth non-periodic kernel. This method is based on the Newton-Cotes quadrature rule for the integral approximation and an FFT method for discrete summation. The method can have an arbitrarily high-order accuracy in principle depending on the number of points used in the integral approximation and a computational cost of O(Nlog(N)), where N is the number of grid points. For a three-point Simpson rule approximation, the method has an accuracy of O(h{sup 4}), where h is the size of the computational grid. Applications of the Simpson rule based algorithm to the calculation of a one-dimensional continuous Gauss transform and to the calculation of a two-dimensional electric field from a charged beam are also presented.

  11. DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields

    PubMed Central

    Wang, Sheng; Weng, Shunyan; Ma, Jianzhu; Tang, Qingming

    2015-01-01

    Intrinsically disordered proteins or protein regions are involved in key biological processes including regulation of transcription, signal transduction, and alternative splicing. Accurately predicting order/disorder regions ab initio from the protein sequence is a prerequisite step for further analysis of functions and mechanisms for these disordered regions. This work presents a learning method, weighted DeepCNF (Deep Convolutional Neural Fields), to improve the accuracy of order/disorder prediction by exploiting the long-range sequential information and the interdependency between adjacent order/disorder labels and by assigning different weights for each label during training and prediction to solve the label imbalance issue. Evaluated by the CASP9 and CASP10 targets, our method obtains 0.855 and 0.898 AUC values, which are higher than the state-of-the-art single ab initio predictors. PMID:26230689

  12. A Shortest Dependency Path Based Convolutional Neural Network for Protein-Protein Relation Extraction.

    PubMed

    Hua, Lei; Quan, Chanqin

    2016-01-01

    The state-of-the-art methods for protein-protein interaction (PPI) extraction are primarily based on kernel methods, and their performances strongly depend on the handcraft features. In this paper, we tackle PPI extraction by using convolutional neural networks (CNN) and propose a shortest dependency path based CNN (sdpCNN) model. The proposed method (1) only takes the sdp and word embedding as input and (2) could avoid bias from feature selection by using CNN. We performed experiments on standard Aimed and BioInfer datasets, and the experimental results demonstrated that our approach outperformed state-of-the-art kernel based methods. In particular, by tracking the sdpCNN model, we find that sdpCNN could extract key features automatically and it is verified that pretrained word embedding is crucial in PPI task. PMID:27493967

  13. Short, unit-memory, Byte-oriented, binary convolutional codes having maximal free distance

    NASA Technical Reports Server (NTRS)

    Lee, L. N.

    1975-01-01

    It is shown that (n sub 0, k sub 0) convolutional codes with unit memory always achieve the largest free distance among all codes of the same rate k sub 0/n sub 0 and same number 2MK sub 0 of encoder states, where M is the encoder memory. A unit-memory code with maximal free distance is given at each place where this free distance exceeds that of the best code with k sub 0 and n sub 0 relatively prime, for all Mk sub 0 less than or equal to 6 and for R = 1/2, 1/3, 1/4, 2/3. It is shown that the unit-memory codes are byte-oriented in such a way as to be attractive for use in concatenated coding systems.

  14. Variable-Rate Ring Convolutional Coded Continuous Phase Modulation Using Puncturing

    NASA Astrophysics Data System (ADS)

    Zhang, Lei; Wu, Jun; Zhu, Aiming

    2013-01-01

    In this paper, puncturing technique is used to establish variable-rate ring convolutional coded continuous phase modulation (CPM) systems. Maximum likelihood sequence detectors over both AWGN channels and Rayleigh flat-fading channels are considered. The suggested system provides us with different rates and performance when simple adjustment is taken to the puncturing matrix. Since the performance of the first error event of this system is represented by normalized minimum squared Euclidean distance (NMSED), some typical codes with maximum NMSED are searched and given. The performance of symbol error rate for the suggested system is simulated using computer software, and the results show that this system provides good performance of symbol error rate with variable-rate capabilities in time varying channels. Furthermore, simulation results also prove that the transmission efficiency increases when code rate is decreasing.

  15. Strategies for Effectively Visualizing a 3D Flow Using Volume Line Integral Convolution

    NASA Technical Reports Server (NTRS)

    Interrante, Victoria; Grosch, Chester

    1997-01-01

    This paper discusses strategies for effectively portraying 3D flow using volume line integral convolution. Issues include defining an appropriate input texture, clarifying the distinct identities and relative depths of the advected texture elements, and selectively highlighting regions of interest in both the input and output volumes. Apart from offering insights into the greater potential of 3D LIC as a method for effectively representing flow in a volume, a principal contribution of this work is the suggestion of a technique for generating and rendering 3D visibility-impeding 'halos' that can help to intuitively indicate the presence of depth discontinuities between contiguous elements in a projection and thereby clarify the 3D spatial organization of elements in the flow. The proposed techniques are applied to the visualization of a hot, supersonic, laminar jet exiting into a colder, subsonic coflow.

  16. Imaging in scattering media using correlation image sensors and sparse convolutional coding.

    PubMed

    Heide, Felix; Xiao, Lei; Kolb, Andreas; Hullin, Matthias B; Heidrich, Wolfgang

    2014-10-20

    Correlation image sensors have recently become popular low-cost devices for time-of-flight, or range cameras. They usually operate under the assumption of a single light path contributing to each pixel. We show that a more thorough analysis of the sensor data from correlation sensors can be used can be used to analyze the light transport in much more complex environments, including applications for imaging through scattering and turbid media. The key of our method is a new convolutional sparse coding approach for recovering transient (light-in-flight) images from correlation image sensors. This approach is enabled by an analysis of sparsity in complex transient images, and the derivation of a new physically-motivated model for transient images with drastically improved sparsity. PMID:25401666

  17. A Shortest Dependency Path Based Convolutional Neural Network for Protein-Protein Relation Extraction

    PubMed Central

    Quan, Chanqin

    2016-01-01

    The state-of-the-art methods for protein-protein interaction (PPI) extraction are primarily based on kernel methods, and their performances strongly depend on the handcraft features. In this paper, we tackle PPI extraction by using convolutional neural networks (CNN) and propose a shortest dependency path based CNN (sdpCNN) model. The proposed method (1) only takes the sdp and word embedding as input and (2) could avoid bias from feature selection by using CNN. We performed experiments on standard Aimed and BioInfer datasets, and the experimental results demonstrated that our approach outperformed state-of-the-art kernel based methods. In particular, by tracking the sdpCNN model, we find that sdpCNN could extract key features automatically and it is verified that pretrained word embedding is crucial in PPI task. PMID:27493967

  18. An Efficient Robust Eye Localization by Learning the Convolution Distribution Using Eye Template

    PubMed Central

    Li, Xuan; Dou, Yong; Niu, Xin; Xu, Jiaqing; Xiao, Ruorong

    2015-01-01

    Eye localization is a fundamental process in many facial analyses. In practical use, it is often challenged by illumination, head pose, facial expression, occlusion, and other factors. It remains great difficulty to achieve high accuracy with short prediction time and low training cost at the same time. This paper presents a novel eye localization approach which explores only one-layer convolution map by eye template using a BP network. Results showed that the proposed method is robust to handle many difficult situations. In experiments, accuracy of 98% and 96%, respectively, on the BioID and LFPW test sets could be achieved in 10 fps prediction rate with only 15-minute training cost. In comparison with other robust models, the proposed method could obtain similar best results with greatly reduced training time and high prediction speed. PMID:26504460

  19. A nonvoxel-based dose convolution/superposition algorithm optimized for scalable GPU architectures

    SciTech Connect

    Neylon, J. Sheng, K.; Yu, V.; Low, D. A.; Kupelian, P.; Santhanam, A.; Chen, Q.

    2014-10-15

    Purpose: Real-time adaptive planning and treatment has been infeasible due in part to its high computational complexity. There have been many recent efforts to utilize graphics processing units (GPUs) to accelerate the computational performance and dose accuracy in radiation therapy. Data structure and memory access patterns are the key GPU factors that determine the computational performance and accuracy. In this paper, the authors present a nonvoxel-based (NVB) approach to maximize computational and memory access efficiency and throughput on the GPU. Methods: The proposed algorithm employs a ray-tracing mechanism to restructure the 3D data sets computed from the CT anatomy into a nonvoxel-based framework. In a process that takes only a few milliseconds of computing time, the algorithm restructured the data sets by ray-tracing through precalculated CT volumes to realign the coordinate system along the convolution direction, as defined by zenithal and azimuthal angles. During the ray-tracing step, the data were resampled according to radial sampling and parallel ray-spacing parameters making the algorithm independent of the original CT resolution. The nonvoxel-based algorithm presented in this paper also demonstrated a trade-off in computational performance and dose accuracy for different coordinate system configurations. In order to find the best balance between the computed speedup and the accuracy, the authors employed an exhaustive parameter search on all sampling parameters that defined the coordinate system configuration: zenithal, azimuthal, and radial sampling of the convolution algorithm, as well as the parallel ray spacing during ray tracing. The angular sampling parameters were varied between 4 and 48 discrete angles, while both radial sampling and parallel ray spacing were varied from 0.5 to 10 mm. The gamma distribution analysis method (γ) was used to compare the dose distributions using 2% and 2 mm dose difference and distance-to-agreement criteria

  20. Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks

    PubMed Central

    Ypsilantis, Petros-Pavlos; Siddique, Musib; Sohn, Hyon-Mok; Davies, Andrew; Cook, Gary; Goh, Vicky; Montana, Giovanni

    2015-01-01

    Imaging of cancer with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) has become a standard component of diagnosis and staging in oncology, and is becoming more important as a quantitative monitor of individual response to therapy. In this article we investigate the challenging problem of predicting a patient’s response to neoadjuvant chemotherapy from a single 18F-FDG PET scan taken prior to treatment. We take a “radiomics” approach whereby a large amount of quantitative features is automatically extracted from pretherapy PET images in order to build a comprehensive quantification of the tumor phenotype. While the dominant methodology relies on hand-crafted texture features, we explore the potential of automatically learning low- to high-level features directly from PET scans. We report on a study that compares the performance of two competing radiomics strategies: an approach based on state-of-the-art statistical classifiers using over 100 quantitative imaging descriptors, including texture features as well as standardized uptake values, and a convolutional neural network, 3S-CNN, trained directly from PET scans by taking sets of adjacent intra-tumor slices. Our experimental results, based on a sample of 107 patients with esophageal cancer, provide initial evidence that convolutional neural networks have the potential to extract PET imaging representations that are highly predictive of response to therapy. On this dataset, 3S-CNN achieves an average 80.7% sensitivity and 81.6% specificity in predicting non-responders, and outperforms other competing predictive models. PMID:26355298

  1. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning.

    PubMed

    Shin, Hoo-Chang; Roth, Holger R; Gao, Mingchen; Lu, Le; Xu, Ziyue; Nogues, Isabella; Yao, Jianhua; Mollura, Daniel; Summers, Ronald M

    2016-05-01

    Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks. In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computer-aided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks. PMID:26886976

  2. Deep MRI brain extraction: A 3D convolutional neural network for skull stripping.

    PubMed

    Kleesiek, Jens; Urban, Gregor; Hubert, Alexander; Schwarz, Daniel; Maier-Hein, Klaus; Bendszus, Martin; Biller, Armin

    2016-04-01

    Brain extraction from magnetic resonance imaging (MRI) is crucial for many neuroimaging workflows. Current methods demonstrate good results on non-enhanced T1-weighted images, but struggle when confronted with other modalities and pathologically altered tissue. In this paper we present a 3D convolutional deep learning architecture to address these shortcomings. In contrast to existing methods, we are not limited to non-enhanced T1w images. When trained appropriately, our approach handles an arbitrary number of modalities including contrast-enhanced scans. Its applicability to MRI data, comprising four channels: non-enhanced and contrast-enhanced T1w, T2w and FLAIR contrasts, is demonstrated on a challenging clinical data set containing brain tumors (N=53), where our approach significantly outperforms six commonly used tools with a mean Dice score of 95.19. Further, the proposed method at least matches state-of-the-art performance as demonstrated on three publicly available data sets: IBSR, LPBA40 and OASIS, totaling N=135 volumes. For the IBSR (96.32) and LPBA40 (96.96) data set the convolutional neuronal network (CNN) obtains the highest average Dice scores, albeit not being significantly different from the second best performing method. For the OASIS data the second best Dice (95.02) results are achieved, with no statistical difference in comparison to the best performing tool. For all data sets the highest average specificity measures are evaluated, whereas the sensitivity displays about average results. Adjusting the cut-off threshold for generating the binary masks from the CNN's probability output can be used to increase the sensitivity of the method. Of course, this comes at the cost of a decreased specificity and has to be decided application specific. Using an optimized GPU implementation predictions can be achieved in less than one minute. The proposed method may prove useful for large-scale studies and clinical trials. PMID:26808333

  3. Reducing weight precision of convolutional neural networks towards large-scale on-chip image recognition

    NASA Astrophysics Data System (ADS)

    Ji, Zhengping; Ovsiannikov, Ilia; Wang, Yibing; Shi, Lilong; Zhang, Qiang

    2015-05-01

    In this paper, we develop a server-client quantization scheme to reduce bit resolution of deep learning architecture, i.e., Convolutional Neural Networks, for image recognition tasks. Low bit resolution is an important factor in bringing the deep learning neural network into hardware implementation, which directly determines the cost and power consumption. We aim to reduce the bit resolution of the network without sacrificing its performance. To this end, we design a new quantization algorithm called supervised iterative quantization to reduce the bit resolution of learned network weights. In the training stage, the supervised iterative quantization is conducted via two steps on server - apply k-means based adaptive quantization on learned network weights and retrain the network based on quantized weights. These two steps are alternated until the convergence criterion is met. In this testing stage, the network configuration and low-bit weights are loaded to the client hardware device to recognize coming input in real time, where optimized but expensive quantization becomes infeasible. Considering this, we adopt a uniform quantization for the inputs and internal network responses (called feature maps) to maintain low on-chip expenses. The Convolutional Neural Network with reduced weight and input/response precision is demonstrated in recognizing two types of images: one is hand-written digit images and the other is real-life images in office scenarios. Both results show that the new network is able to achieve the performance of the neural network with full bit resolution, even though in the new network the bit resolution of both weight and input are significantly reduced, e.g., from 64 bits to 4-5 bits.

  4. Efficient training algorithms for a class of shunting inhibitory convolutional neural networks.

    PubMed

    Tivive, Fok Hing Chi; Bouzerdoum, Abdesselam

    2005-05-01

    This article presents some efficient training algorithms, based on first-order, second-order, and conjugate gradient optimization methods, for a class of convolutional neural networks (CoNNs), known as shunting inhibitory convolution neural networks. Furthermore, a new hybrid method is proposed, which is derived from the principles of Quickprop, Rprop, SuperSAB, and least squares (LS). Experimental results show that the new hybrid method can perform as well as the Levenberg-Marquardt (LM) algorithm, but at a much lower computational cost and less memory storage. For comparison sake, the visual pattern recognition task of face/nonface discrimination is chosen as a classification problem to evaluate the performance of the training algorithms. Sixteen training algorithms are implemented for the three different variants of the proposed CoNN architecture: binary-, Toeplitz- and fully connected architectures. All implemented algorithms can train the three network architectures successfully, but their convergence speed vary markedly. In particular, the combination of LS with the new hybrid method and LS with the LM method achieve the best convergence rates in terms of number of training epochs. In addition, the classification accuracies of all three architectures are assessed using ten-fold cross validation. The results show that the binary- and Toeplitz-connected architectures outperform slightly the fully connected architecture: the lowest error rates across all training algorithms are 1.95% for Toeplitz-connected, 2.10% for the binary-connected, and 2.20% for the fully connected network. In general, the modified Broyden-Fletcher-Goldfarb-Shanno (BFGS) methods, the three variants of LM algorithm, and the new hybrid/LS method perform consistently well, achieving error rates of less than 3% averaged across all three architectures. PMID:15940985

  5. Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks.

    PubMed

    Ypsilantis, Petros-Pavlos; Siddique, Musib; Sohn, Hyon-Mok; Davies, Andrew; Cook, Gary; Goh, Vicky; Montana, Giovanni

    2015-01-01

    Imaging of cancer with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) has become a standard component of diagnosis and staging in oncology, and is becoming more important as a quantitative monitor of individual response to therapy. In this article we investigate the challenging problem of predicting a patient's response to neoadjuvant chemotherapy from a single 18F-FDG PET scan taken prior to treatment. We take a "radiomics" approach whereby a large amount of quantitative features is automatically extracted from pretherapy PET images in order to build a comprehensive quantification of the tumor phenotype. While the dominant methodology relies on hand-crafted texture features, we explore the potential of automatically learning low- to high-level features directly from PET scans. We report on a study that compares the performance of two competing radiomics strategies: an approach based on state-of-the-art statistical classifiers using over 100 quantitative imaging descriptors, including texture features as well as standardized uptake values, and a convolutional neural network, 3S-CNN, trained directly from PET scans by taking sets of adjacent intra-tumor slices. Our experimental results, based on a sample of 107 patients with esophageal cancer, provide initial evidence that convolutional neural networks have the potential to extract PET imaging representations that are highly predictive of response to therapy. On this dataset, 3S-CNN achieves an average 80.7% sensitivity and 81.6% specificity in predicting non-responders, and outperforms other competing predictive models. PMID:26355298

  6. A simulation study of the performance of the NASA (2,1,6) convolutional code on RFI/burst channels

    NASA Technical Reports Server (NTRS)

    Perez, Lance C.; Costello, Daniel J., Jr.

    1993-01-01

    In an earlier report, the LINKABIT Corporation studied the performance of the (2,1,6) convolutional code on the radio frequency interference (RFI)/burst channel using analytical methods. Using an R(sub 0) analysis, the report concluded that channel interleaving was essential to achieving reliable performance. In this report, Monte Carlo simulation techniques are used to study the performance of the convolutional code on the RFI/burst channel in more depth. The basic system model under consideration is shown. The convolutional code is the NASA standard code with generators g(exp 1) = 1+D(exp 2)+D(exp 3)+D(exp 5)+D(exp 6) and g(exp 2) = 1+D+D(exp 2)+D(exp 3)+D(exp 6) and d(sub free) = 10. The channel interleaver is of the convolutional or periodic type. The binary output of the channel interleaver is transmitted across the channel using binary phase shift keying (BPSK) modulation. The transmitted symbols are corrupted by an RFI/burst channel consisting of a combination of additive white Gaussian noise (AWGN) and RFI pulses. At the receiver, a soft-decision Viterbi decoder with no quantization and variable truncation length is used to decode the deinterleaved sequence.

  7. ASIC-based architecture for the real-time computation of 2D convolution with large kernel size

    NASA Astrophysics Data System (ADS)

    Shao, Rui; Zhong, Sheng; Yan, Luxin

    2015-12-01

    Bidimensional convolution is a low-level processing algorithm of interest in many areas, but its high computational cost constrains the size of the kernels, especially in real-time embedded systems. This paper presents a hardware architecture for the ASIC-based implementation of 2-D convolution with medium-large kernels. Aiming to improve the efficiency of storage resources on-chip, reducing off-chip bandwidth of these two issues, proposed construction of a data cache reuse. Multi-block SPRAM to cross cached images and the on-chip ping-pong operation takes full advantage of the data convolution calculation reuse, design a new ASIC data scheduling scheme and overall architecture. Experimental results show that the structure can achieve 40× 32 size of template real-time convolution operations, and improve the utilization of on-chip memory bandwidth and on-chip memory resources, the experimental results show that the structure satisfies the conditions to maximize data throughput output , reducing the need for off-chip memory bandwidth.

  8. Modeling of forced CVI for tube fabrication

    SciTech Connect

    Starr, T.L.; Smith, A.W.

    1994-05-01

    The forced flow/thermal gradient chemical vapor infiltration process (FCVI) can be used for fabrication of tube-shaped components of ceramic matrix composites. Recent experimental work at Oak Ridge National Laboratory (ORNL) includes process and materials development studies using a small tube reactor for tubes 20 cm long and 2.5 cm ID. Adaption of FCVI for this geometry involves significant changes in fixturing as compared to disk-shaped preforms previously fabricated. The authors have used this computer model of the CVI process to simulate tube densification and to identify process modifications that will decrease processing time.

  9. Dip-molded t-shaped cannula

    NASA Technical Reports Server (NTRS)

    Broyles, H. F.; Cuddihy, E. F.; Moacanin, J.

    1978-01-01

    Cannula, fabricated out of polyetherurethane, has been designed for long-term service. Improved cannula is T-shaped to collect blood from both directions, thus replacing two conventional cannulas that are usually required and eliminating need for large surgical wound. It is fabricated by using dip-molding process that can be adapted to other elastomeric objects having complex shapes. Dimensions of cannula were chosen to optimize its blood-flow properties and to reduce danger of excessive clotting, making it suitable for continuous service up to 21 days in vein or artery of patient.

  10. Solid Freeform Fabrication Using the Wirefeed Process

    SciTech Connect

    Buchheit, T.E.; Crenshaw, T.B.; Ensz, M.T.; Greene, D.L.; Griffith, M.L.; Harwell, L.D.; Reckaway, D.E.; Romero, J.A.; Tikare, V.

    1999-07-22

    Direct metal deposition technologies produce complex, near net shape components from CAD solid models. Most of these techniques fabricate a component by melting powder in a laser weld pool, rastering this weld bead to form a layer, and additively constructing subsequent layers. This talk describes a new direct metal deposition process, known as WireFeed, whereby a small diameter wire is used instead of powder as the feed material to fabricate components. Currently, parts are being fabricated from stainless steel. Microscopy studies show the WireFeed parts to be fully dense with fine microstructural features. Initial mechanical tests show stainless steel parts to have good strength values with retained ductility.

  11. Superordinate Shape Classification Using Natural Shape Statistics

    ERIC Educational Resources Information Center

    Wilder, John; Feldman, Jacob; Singh, Manish

    2011-01-01

    This paper investigates the classification of shapes into broad natural categories such as "animal" or "leaf". We asked whether such coarse classifications can be achieved by a simple statistical classification of the shape skeleton. We surveyed databases of natural shapes, extracting shape skeletons and tabulating their parameters within each…

  12. Material cutting, shaping, and forming: A compilation

    NASA Technical Reports Server (NTRS)

    1974-01-01

    Information is presented concerning cutting, shaping, and forming of materials, and the equipment and techniques required for utilizing these materials. The use of molds, electrical fields, and mechanical devices are related to forming materials. Material cutting methods by devices including borers and slicers are presented along with chemical techniques. Shaping and fabrication techniques are described for tubing, honeycomb panels, and ceramic structures. The characteristics of the materials are described. Patent information is included.

  13. Shape Determination for Deformed Electromagnetic Cavities

    SciTech Connect

    Akcelik, Volkan; Ko, Kwok; Lee, Lie-Quan; Li, Zhenghai; Ng, Cho-Kuen; Xiao, Liling; /SLAC

    2007-12-10

    The measured physical parameters of a superconducting cavity differ from those of the designed ideal cavity. This is due to shape deviations caused by both loose machine tolerances during fabrication and by the tuning process for the accelerating mode. We present a shape determination algorithm to solve for the unknown deviations from the ideal cavity using experimentally measured cavity data. The objective is to match the results of the deformed cavity model to experimental data through least-squares minimization. The inversion variables are unknown shape deformation parameters that describe perturbations of the ideal cavity. The constraint is the Maxwell eigenvalue problem. We solve the nonlinear optimization problem using a line-search based reduced space Gauss-Newton method where we compute shape sensitivities with a discrete adjoint approach. We present two shape determination examples, one from synthetic and the other from experimental data. The results demonstrate that the proposed algorithm is very effective in determining the deformed cavity shape.

  14. Non-spherical particle generation from 4D optofluidic fabrication.

    PubMed

    Paulsen, Kevin S; Chung, Aram J

    2016-08-01

    Particles with non-spherical shapes can exhibit properties which are not available from spherical shaped particles. Complex shaped particles can provide unique benefits for areas such as drug delivery, tissue engineering, structural materials, and self-assembly building blocks. Current methods of creating complex shaped particles such as 3D printing, photolithography, and imprint lithography are limited by either slow speeds, shape limitations, or expensive processes. Previously, we presented a novel microfluidic flow lithography fabrication scheme combined with fluid inertia called optofluidic fabrication for the creation of complex shaped three-dimensional (3D) particles. This process was able to address the aforementioned limits and overcome two-dimensional shape limitations faced by traditional flow lithography methods; however, all of the created 3D particle shapes displayed top-down symmetry. Here, by introducing the time dimension into our existing optofluidic fabrication process, we break this top-down symmetry, generating fully asymmetric 3D particles where we termed the process: four-dimensional (4D) optofluidic fabrication. This 4D optofluidic fabrication is comprised of three sequential procedures. First, density mismatched precursor fluids flow past pillars within fluidic channels to manipulate the flow cross sections via fluid inertia. Next, the time dimension is incorporated by stopping the flow and allowing the denser fluids to settle by gravity to create asymmetric flow cross sections. Finally, the fluids are exposed to patterned ultraviolet (UV) light in order to polymerize fully asymmetric 3D-shaped particles. By varying inertial flow shaping, gravity-induced flow shaping, and UV light patterns, 4D optofluidic fabrication can create an infinite set of complex shaped asymmetric particles. PMID:27092661

  15. Seismic body wave separation in volcano-tectonic activity inferred by the Convolutive Independent Component Analysis

    NASA Astrophysics Data System (ADS)

    Capuano, Paolo; De Lauro, Enza; De Martino, Salvatore; Falanga, Mariarosaria; Petrosino, Simona

    2015-04-01

    One of the main challenge in volcano-seismological literature is to locate and characterize the source of volcano/tectonic seismic activity. This passes through the identification at least of the onset of the main phases, i.e. the body waves. Many efforts have been made to solve the problem of a clear separation of P and S phases both from a theoretical point of view and developing numerical algorithms suitable for specific cases (see, e.g., Küperkoch et al., 2012). Recently, a robust automatic procedure has been implemented for extracting the prominent seismic waveforms from continuously recorded signals and thus allowing for picking the main phases. The intuitive notion of maximum non-gaussianity is achieved adopting techniques which involve higher-order statistics in frequency domain., i.e, the Convolutive Independent Component Analysis (CICA). This technique is successful in the case of the blind source separation of convolutive mixtures. In seismological framework, indeed, seismic signals are thought as the convolution of a source function with path, site and the instrument response. In addition, time-delayed versions of the same source exist, due to multipath propagation typically caused by reverberations from some obstacle. In this work, we focus on the Volcano Tectonic (VT) activity at Campi Flegrei Caldera (Italy) during the 2006 ground uplift (Ciaramella et al., 2011). The activity was characterized approximately by 300 low-magnitude VT earthquakes (Md < 2; for the definition of duration magnitude, see Petrosino et al. 2008). Most of them were concentrated in distinct seismic sequences with hypocenters mainly clustered beneath the Solfatara-Accademia area, at depths ranging between 1 and 4 km b.s.l.. The obtained results show the clear separation of P and S phases: the technique not only allows the identification of the S-P time delay giving the timing of both phases but also provides the independent waveforms of the P and S phases. This is an enormous

  16. Polymorphous computing fabric

    DOEpatents

    Wolinski, Christophe Czeslaw; Gokhale, Maya B.; McCabe, Kevin Peter

    2011-01-18

    Fabric-based computing systems and methods are disclosed. A fabric-based computing system can include a polymorphous computing fabric that can be customized on a per application basis and a host processor in communication with said polymorphous computing fabric. The polymorphous computing fabric includes a cellular architecture that can be highly parameterized to enable a customized synthesis of fabric instances for a variety of enhanced application performances thereof. A global memory concept can also be included that provides the host processor random access to all variables and instructions associated with the polymorphous computing fabric.

  17. Detailed investigation of Long-Period activity at Campi Flegrei by Convolutive Independent Component Analysis

    NASA Astrophysics Data System (ADS)

    Capuano, P.; De Lauro, E.; De Martino, S.; Falanga, M.

    2016-04-01

    This work is devoted to the analysis of seismic signals continuously recorded at Campi Flegrei Caldera (Italy) during the entire year 2006. The radiation pattern associated with the Long-Period energy release is investigated. We adopt an innovative Independent Component Analysis algorithm for convolutive seismic series adapted and improved to give automatic procedures for detecting seismic events often buried in the high-level ambient noise. The extracted waveforms characterized by an improved signal-to-noise ratio allows the recognition of Long-Period precursors, evidencing that the seismic activity accompanying the mini-uplift crisis (in 2006), which climaxed in the three days from 26-28 October, had already started at the beginning of the month of October and lasted until mid of November. Hence, a more complete seismic catalog is then provided which can be used to properly quantify the seismic energy release. To better ground our results, we first check the robustness of the method by comparing it with other blind source separation methods based on higher order statistics; secondly, we reconstruct the radiation patterns of the extracted Long-Period events in order to link the individuated signals directly to the sources. We take advantage from Convolutive Independent Component Analysis that provides basic signals along the three directions of motion so that a direct polarization analysis can be performed with no other filtering procedures. We show that the extracted signals are mainly composed of P waves with radial polarization pointing to the seismic source of the main LP swarm, i.e. a small area in the Solfatara, also in the case of the small-events, that both precede and follow the main activity. From a dynamical point of view, they can be described by two degrees of freedom, indicating a low-level of complexity associated with the vibrations from a superficial hydrothermal system. Our results allow us to move towards a full description of the complexity of

  18. Chemically enabled nanostructure fabrication

    NASA Astrophysics Data System (ADS)

    Huo, Fengwei

    The first part of the dissertation explored ways of chemically synthesizing new nanoparticles and biologically guided assembly of nanoparticle building blocks. Chapter two focuses on synthesizing three-layer composite magnetic nanoparticles with a gold shell which can be easily functionalized with other biomolecules. The three-layer magnetic nanoparticles, when functionalized with oligonucleotides, exhibit the surface chemistry, optical properties, and cooperative DNA binding properties of gold nanoparticle probes, while maintaining the magnetic properties of the Fe3O4 inner shell. Chapter three describes a new method for synthesizing nanoparticles asymmetrically functionalized with oligonucleotides and the use of these novel building blocks to create satellite structures. This synthetic capability allows one to introduce valency into such structures and then use that valency to direct particle assembly events. The second part of the thesis explored approaches of nanostructure fabrication on substrates. Chapter four focuses on the development of a new scanning probe contact printing method, polymer pen lithography (PPL), which combines the advantages of muCp and DPN to achieve high-throughput, flexible molecular printing. PPL uses a soft elastomeric tip array, rather than tips mounted on individual cantilevers, to deliver inks to a surface in a "direct write" manner. Arrays with as many as ˜11 million pyramid-shaped pens can be brought into contact with substrates and readily leveled optically in order to insure uniform pattern development. Chapter five describes gel pen lithography, which uses a gel to fabricate pen array. Gel pen lithography is a low-cost, high-throughput nanolithography method especially useful for biomaterials patterning and aqueous solution patterning which makes it a supplement to DPN and PPL. Chapter 6 shows a novel form of optical nanolithography, Beam Pen Lithography (BPL), which uses an array of NSOM pens to do nanoscale optical

  19. Fabrication of zein nanostructure

    NASA Astrophysics Data System (ADS)

    Luecha, Jarupat

    resins. The soft lithography technique was mainly used to fabricate micro and nanostructures on zein films. Zein material well-replicated small structures with the smallest size at sub micrometer scale that resulted in interesting photonic properties. The bonding method was also developed for assembling portable zein microfluidic devices with small shape distortion. Zein-zein and zein-glass microfluidic devices demonstrated sufficient strength to facilitate fluid flow in a complex microfluidic design with no leakage. Aside from the fabrication technique development, several potential applications of this environmentally friendly microfluidic device were investigated. The concentration gradient manipulation of Rhodamine B solution in zein-glass microfluidic devices was demonstrated. The diffusion of small molecules such as fluorescent dye into the wall of the zein microfluidic channels was observed. However, with this formulation, zein microfluidic devices were not suitable for cell culture applications. This pioneer study covered a wide spectrum of the implementation of the two nanotechnology approaches to advance zein biomaterial which provided proof of fundamental concepts as well as presenting some limitations. The findings in this study can lead to several innovative research opportunities of advanced zein biomaterials with broad applications. The information from the study of zein nanocomposite structure allows the packaging industry to develop the low cost biodegradable materials with physical property improvement. The information from the study of the zein microfluidic devices allows agro-industry to develop the nanotechnology-enabled microfluidic sensors fabricated entirely from biodegradable polymer for on-site disease or contaminant detection in the fields of food and agriculture.

  20. Simplified Fabrication of Helical Copper Antennas

    NASA Technical Reports Server (NTRS)

    Petro, Andrew

    2006-01-01

    A simplified technique has been devised for fabricating helical antennas for use in experiments on radio-frequency generation and acceleration of plasmas. These antennas are typically made of copper (for electrical conductivity) and must have a specific helical shape and precise diameter.

  1. Shape measurement biases from underfitting and ellipticity gradients

    SciTech Connect

    Bernstein, Gary M.

    2010-08-21

    With this study, precision weak gravitational lensing experiments require measurements of galaxy shapes accurate to <1 part in 1000. We investigate measurement biases, noted by Voigt and Bridle (2009) and Melchior et al. (2009), that are common to shape measurement methodologies that rely upon fitting elliptical-isophote galaxy models to observed data. The first bias arises when the true galaxy shapes do not match the models being fit. We show that this "underfitting bias" is due, at root, to these methods' attempts to use information at high spatial frequencies that has been destroyed by the convolution with the point-spread function (PSF) and/or by sampling. We propose a new shape-measurement technique that is explicitly confined to observable regions of k-space. A second bias arises for galaxies whose ellipticity varies with radius. For most shape-measurement methods, such galaxies are subject to "ellipticity gradient bias". We show how to reduce such biases by factors of 20–100 within the new shape-measurement method. The resulting shear estimator has multiplicative errors < 1 part in 103 for high-S/N images, even for highly asymmetric galaxies. Without any training or recalibration, the new method obtains Q = 3000 in the GREAT08 Challenge of blind shear reconstruction on low-noise galaxies, several times better than any previous method.

  2. Shape measurement biases from underfitting and ellipticity gradients

    DOE PAGESBeta

    Bernstein, Gary M.

    2010-08-21

    With this study, precision weak gravitational lensing experiments require measurements of galaxy shapes accurate to <1 part in 1000. We investigate measurement biases, noted by Voigt and Bridle (2009) and Melchior et al. (2009), that are common to shape measurement methodologies that rely upon fitting elliptical-isophote galaxy models to observed data. The first bias arises when the true galaxy shapes do not match the models being fit. We show that this "underfitting bias" is due, at root, to these methods' attempts to use information at high spatial frequencies that has been destroyed by the convolution with the point-spread function (PSF)more » and/or by sampling. We propose a new shape-measurement technique that is explicitly confined to observable regions of k-space. A second bias arises for galaxies whose ellipticity varies with radius. For most shape-measurement methods, such galaxies are subject to "ellipticity gradient bias". We show how to reduce such biases by factors of 20–100 within the new shape-measurement method. The resulting shear estimator has multiplicative errors < 1 part in 103 for high-S/N images, even for highly asymmetric galaxies. Without any training or recalibration, the new method obtains Q = 3000 in the GREAT08 Challenge of blind shear reconstruction on low-noise galaxies, several times better than any previous method.« less

  3. Ceramic for Silicon-Shaping Dies

    NASA Technical Reports Server (NTRS)

    Sekercioglu, I.; Wills, R. R.

    1982-01-01

    Silicon beryllium oxynitride (SiBON) is a promising candidate material for manufacture of shaping dies used in fabricating ribbons or sheets of silicon. It is extremely stable, resists thermal shock, and has excellent resistance to molten silicon. SiBON is a solid solution of beryllium silicate in beta-silicon nitride.

  4. Effects of glucose on water and sodium reabsorption in the proximal convoluted tubule of rat kidney.

    PubMed Central

    Bishop, J H; Green, R; Thomas, S

    1978-01-01

    1. The effects of glucose on sodium and water reabsorption by rat renal proximal tubules was investigated by in situ microperfusion of segments of proximal tubules with solutions containing glucose or no glucose, with and without phlorizin. 2. Absence of glucose did not significantly alter net water flux. Sodium flux was reduced by about 10% but this was not statistically significant. 3. In the absence of glucose in the perfusion fluid net secretion of glucose occurred. 4. Phlorizin reduced either net reabsorption or net secretion of glucose; and net water flux. 5. The data suggest that in later parts of the proximal convoluted tubule some sodium may be co-transported with glucose, but that this represents only a small fraction of the total sodium reabsorption. 6. It is suggested that the glucose carrier is reversible and in appropriate circumstances could cause glucose secretion. 7. Although phlorizin alters net water flux the underlying mechanisms are not clear. 8. The calculated osmolality of the reabsorbate was significantly greater than the perfusate osmolality and greater than plasma osmolality although this was not quite significant statistically. PMID:633143

  5. A Systems Level Analysis of Vasopressin-mediated Signaling Networks in Kidney Distal Convoluted Tubule Cells

    PubMed Central

    Cheng, Lei; Wu, Qi; Kortenoeven, Marleen L. A.; Pisitkun, Trairak; Fenton, Robert A.

    2015-01-01

    The kidney distal convoluted tubule (DCT) plays an essential role in maintaining body sodium balance and blood pressure. The major sodium reabsorption pathway in the DCT is the thiazide-sensitive NaCl cotransporter (NCC), whose functions can be modulated by the hormone vasopressin (VP) acting via uncharacterized signaling cascades. Here we use a systems biology approach centered on stable isotope labeling by amino acids in cell culture (SILAC) based quantitative phosphoproteomics of cultured mouse DCT cells to map global changes in protein phosphorylation upon acute treatment with a VP type II receptor agonist 1-desamino-8-D-arginine vasopressin (dDAVP). 6330 unique proteins, containing 12333 different phosphorylation sites were identified. 185 sites were altered in abundance following dDAVP. Basophilic motifs were preferential targets for upregulated sites upon dDAVP stimulation, whereas proline-directed motifs were prominent for downregulated sites. Kinase prediction indicated that dDAVP increased AGC and CAMK kinase families’ activities and decreased activity of CDK and MAPK families. Network analysis implicated phosphatidylinositol-4,5-bisphosphate 3-kinase or CAMKK dependent pathways in VP-mediated signaling; pharmacological inhibition of which significantly reduced dDAVP induced increases in phosphorylated NCC at an activating site. In conclusion, this study identifies unique VP signaling cascades in DCT cells that may be important for regulating blood pressure. PMID:26239621

  6. Applying Convolution-Based Processing Methods To A Dual-Channel, Large Array Artificial Olfactory Mucosa

    NASA Astrophysics Data System (ADS)

    Taylor, J. E.; Che Harun, F. K.; Covington, J. A.; Gardner, J. W.

    2009-05-01

    Our understanding of the human olfactory system, particularly with respect to the phenomenon of nasal chromatography, has led us to develop a new generation of novel odour-sensitive instruments (or electronic noses). This novel instrument is in need of new approaches to data processing so that the information rich signals can be fully exploited; here, we apply a novel time-series based technique for processing such data. The dual-channel, large array artificial olfactory mucosa consists of 3 arrays of 300 sensors each. The sensors are divided into 24 groups, with each group made from a particular type of polymer. The first array is connected to the other two arrays by a pair of retentive columns. One channel is coated with Carbowax 20 M, and the other with OV-1. This configuration partly mimics the nasal chromatography effect, and partly augments it by utilizing not only polar (mucus layer) but also non-polar (artificial) coatings. Such a device presents several challenges to multi-variate data processing: a large, redundant dataset, spatio-temporal output, and small sample space. By applying a novel convolution approach to this problem, it has been demonstrated that these problems can be overcome. The artificial mucosa signals have been classified using a probabilistic neural network and gave an accuracy of 85%. Even better results should be possible through the selection of other sensors with lower correlation.

  7. The Luminous Convolution Model-The light side of dark matter

    NASA Astrophysics Data System (ADS)

    Cisneros, Sophia; Oblath, Noah; Formaggio, Joe; Goedecke, George; Chester, David; Ott, Richard; Ashley, Aaron; Rodriguez, Adrianna

    2014-03-01

    We present a heuristic model for predicting the rotation curves of spiral galaxies. The Luminous Convolution Model (LCM) utilizes Lorentz-type transformations of very small changes in the photon's frequencies from curved space-times to construct a dynamic mass model of galaxies. These frequency changes are derived using the exact solution to the exterior Kerr wave equation, as opposed to a linearized treatment. The LCM Lorentz-type transformations map between the emitter and the receiver rotating galactic frames, and then to the associated flat frames in each galaxy where the photons are emitted and received. This treatment necessarily rests upon estimates of the luminous matter in both the emitter and the receiver galaxies. The LCM is tested on a sample of 22 randomly chosen galaxies, represented in 33 different data sets. LCM fits are compared to the Navarro, Frenk & White (NFW) Dark Matter Model and to the Modified Newtonian Dynamics (MOND) model when possible. The high degree of sensitivity of the LCM to the initial assumption of a luminous mass to light ratios (M/L), of the given galaxy, is demonstrated. We demonstrate that the LCM is successful across a wide range of spiral galaxies for predicting the observed rotation curves. Through the generous support of the MIT Dr. Martin Luther King Jr. Fellowship program.

  8. Crosswell electromagnetic modeling from impulsive source: Optimization strategy for dispersion suppression in convolutional perfectly matched layer.

    PubMed

    Fang, Sinan; Pan, Heping; Du, Ting; Konaté, Ahmed Amara; Deng, Chengxiang; Qin, Zhen; Guo, Bo; Peng, Ling; Ma, Huolin; Li, Gang; Zhou, Feng

    2016-01-01

    This study applied the finite-difference time-domain (FDTD) method to forward modeling of the low-frequency crosswell electromagnetic (EM) method. Specifically, we implemented impulse sources and convolutional perfectly matched layer (CPML). In the process to strengthen CPML, we observed that some dispersion was induced by the real stretch κ, together with an angular variation of the phase velocity of the transverse electric plane wave; the conclusion was that this dispersion was positively related to the real stretch and was little affected by grid interval. To suppress the dispersion in the CPML, we first derived the analytical solution for the radiation field of the magneto-dipole impulse source in the time domain. Then, a numerical simulation of CPML absorption with high-frequency pulses qualitatively amplified the dispersion laws through wave field snapshots. A numerical simulation using low-frequency pulses suggested an optimal parameter strategy for CPML from the established criteria. Based on its physical nature, the CPML method of simply warping space-time was predicted to be a promising approach to achieve ideal absorption, although it was still difficult to entirely remove the dispersion. PMID:27585538

  9. Low impedance z-pinch drivers without post-hole convolute current adders.

    SciTech Connect

    Savage, Mark Edward; Seidel, David Bruce; Mendel, Clifford Will, Jr.

    2009-09-01

    Present-day pulsed-power systems operating in the terawatt regime typically use post-hole convolute current adders to operate at sufficiently low impedance. These adders necessarily involve magnetic nulls that connect the positive and negative electrodes. The resultant loss of magnetic insulation results in electron losses in the vicinity of the nulls that can severely limit the efficiency of the delivery of the system's energy to a load. In this report, we describe an alternate transformer-based approach to obtaining low impedance. The transformer consists of coils whose windings are in parallel rather than in series, and does not suffer from the presence of magnetic nulls. By varying the pitch of the coils windings, the current multiplication ratio can be varied, leading to a more versatile driver. The coupling efficiency of the transformer, its behavior in the presence of electron flow, and its mechanical strength are issues that need to be addressed to evaluate the potential of transformer-based current multiplication as a viable alternative to conventional current adder technology.

  10. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks.

    PubMed

    Kelley, David R; Snoek, Jasper; Rinn, John L

    2016-07-01

    The complex language of eukaryotic gene expression remains incompletely understood. Despite the importance suggested by many noncoding variants statistically associated with human disease, nearly all such variants have unknown mechanisms. Here, we address this challenge using an approach based on a recent machine learning advance-deep convolutional neural networks (CNNs). We introduce the open source package Basset to apply CNNs to learn the functional activity of DNA sequences from genomics data. We trained Basset on a compendium of accessible genomic sites mapped in 164 cell types by DNase-seq, and demonstrate greater predictive accuracy than previous methods. Basset predictions for the change in accessibility between variant alleles were far greater for Genome-wide association study (GWAS) SNPs that are likely to be causal relative to nearby SNPs in linkage disequilibrium with them. With Basset, a researcher can perform a single sequencing assay in their cell type of interest and simultaneously learn that cell's chromatin accessibility code and annotate every mutation in the genome with its influence on present accessibility and latent potential for accessibility. Thus, Basset offers a powerful computational approach to annotate and interpret the noncoding genome. PMID:27197224

  11. Successful therapy of convoluted T-lymphoblastic lymphoma in the adult

    SciTech Connect

    Levine, A.M.; Forman, S.J.; Meyer, P.R.; Koehler, S.C.; Liebman, H.; Paganini-Hill, A.; Pockros, A.; Lukes, R.J.; Feinstein, D.I.

    1983-01-01

    Fifteen adult patients with biopsy-proven convoluted T-lymphoblastic lymphoma were treated with an aggressive regimen, modified from the LSA2-L2 protocol used for childhood lymphoma. The treatment schema consisted of induction phase, including cyclophosphamide, vincristine, prednisone, adriamycin, and 2000 rads to mediastinum, as well as intrathecal methotrexate. Consolidation phase included cytosine arabinoside, 6-thioguanine, L-asparaginase, and CCNU, along with cranial irradiation and further intrathecal methotrexate. Maintenance consisted of cyclical chemotherapy and intrathecal methotrexate, continuing for a total of 3 yr. Median age in the group was 25 yr (range 16-73). There were 8 males and 7 females. At diagnosis, 9 patients had mediastinal involvement, and 9 had bone marrow involvement. Five of these demonstrated malignant cells in the peripheral blood. Complete clinical response was attained in 11 patients. Three patients achieved partial response. Four complete responders have relapsed, 1 in the central nervous system at 6 mo. and 1 in nodal sites at 3 mo, 1 in multiple sites at 24 mo. and 1 in bone marrow at 42 mo while off all chemotherapy for 6 mos. At this time, median survival of all patients is 28.3 mo. and median relapse-free survival is 21 mo. The median survival for complete responders in excess of 71 mo. while the median relapse-free survival for this group is 41 mo.

  12. 2D image classification for 3D anatomy localization: employing deep convolutional neural networks

    NASA Astrophysics Data System (ADS)

    de Vos, Bob D.; Wolterink, Jelmer M.; de Jong, Pim A.; Viergever, Max A.; Išgum, Ivana

    2016-03-01

    Localization of anatomical regions of interest (ROIs) is a preprocessing step in many medical image analysis tasks. While trivial for humans, it is complex for automatic methods. Classic machine learning approaches require the challenge of hand crafting features to describe differences between ROIs and background. Deep convolutional neural networks (CNNs) alleviate this by automatically finding hierarchical feature representations from raw images. We employ this trait to detect anatomical ROIs in 2D image slices in order to localize them in 3D. In 100 low-dose non-contrast enhanced non-ECG synchronized screening chest CT scans, a reference standard was defined by manually delineating rectangular bounding boxes around three anatomical ROIs -- heart, aortic arch, and descending aorta. Every anatomical ROI was automatically identified using a combination of three CNNs, each analyzing one orthogonal image plane. While single CNNs predicted presence or absence of a specific ROI in the given plane, the combination of their results provided a 3D bounding box around it. Classification performance of each CNN, expressed in area under the receiver operating characteristic curve, was >=0.988. Additionally, the performance of ROI localization was evaluated. Median Dice scores for automatically determined bounding boxes around the heart, aortic arch, and descending aorta were 0.89, 0.70, and 0.85 respectively. The results demonstrate that accurate automatic 3D localization of anatomical structures by CNN-based 2D image classification is feasible.

  13. Axial heterogeneity of intracellular pH in rat proximal convoluted tubule.

    PubMed Central

    Pastoriza-Munoz, E; Harrington, R M; Graber, M L

    1987-01-01

    In the proximal convoluted tubule (PT), the HCO3- reabsorptive rate is higher in early (EPS) compared with late proximal segments (LPS). To examine the mechanism of this HCO3- reabsorption profile, intracellular pH (pHi) was measured along the superficial PT of the rat under free-flow and stationary microperfusion using the pH-sensitive fluorescence of 4-methylumbelliferone (4MU). With 4MU superfusion, pHi was found to decline along the PT. Observation with 365-nm excitation revealed that EPS were brightly fluorescent and always emerged away from their star vessel. Midproximal segments were darker and closer to the star vessel which was surrounded by the darkest LPS. Decreasing luminal HCO3- from 15 to 0 mM lowered pHi in both EPS and LPS, but pHi remained more alkaline in EPS with both perfusates. Thus the axial decline in pHi along the PT is due to both luminal factors and intrinsic differences in luminal H+ extrusion in PT cells. PMID:3036912

  14. Robust visual track using an ensemble cascade of convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Hu, Dan; Zhou, Xingshe; Yu, Xiaohao; Hou, Zhiqiang

    2015-12-01

    Convolutional Neural Networks (CNN) have dramatically boosted the performance of various computer vision tasks except visual tracking due to the lack of training data. In this paper, we pre-train a deep CNN offline to classify the 1 million images from 256 classes with very leaky non-saturating neurons for training acceleration, which is transformed to a discriminative classifier by adding an additional classification layer. In addition, we propose a novel approach for combining increasingly our CNN classifiers in a "cascade" structure through a modification of the AdaBoost framework, and then transfer the selected discriminative features from the ensemble of CNN classifiers to the robust visual tracking task, by updating online to robustly discard the background regions from promising object-like region to cope with appearance changes of the target. Extensive experimental evaluations on an open tracker benchmark demonstrate outstanding performance of our tracker by improving tracking success rate and tracking precision on an average of 9.2% and 13.9% at least over other state-of-the-art trackers.

  15. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks.

    PubMed

    Huynh, Benjamin Q; Li, Hui; Giger, Maryellen L

    2016-07-01

    Convolutional neural networks (CNNs) show potential for computer-aided diagnosis (CADx) by learning features directly from the image data instead of using analytically extracted features. However, CNNs are difficult to train from scratch for medical images due to small sample sizes and variations in tumor presentations. Instead, transfer learning can be used to extract tumor information from medical images via CNNs originally pretrained for nonmedical tasks, alleviating the need for large datasets. Our database includes 219 breast lesions (607 full-field digital mammographic images). We compared support vector machine classifiers based on the CNN-extracted image features and our prior computer-extracted tumor features in the task of distinguishing between benign and malignant breast lesions. Five-fold cross validation (by lesion) was conducted with the area under the receiver operating characteristic (ROC) curve as the performance metric. Results show that classifiers based on CNN-extracted features (with transfer learning) perform comparably to those using analytically extracted features [area under the ROC curve [Formula: see text

  16. Discriminative boosted forest with convolutional neural network-based patch descriptor for object detection

    NASA Astrophysics Data System (ADS)

    Xiang, Tao; Li, Tao; Ye, Mao; Li, Xudong

    2016-01-01

    Object detection with intraclass variations is challenging. The existing methods have not achieved the optimal combinations of classifiers and features, especially features learned by convolutional neural networks (CNNs). To solve this problem, we propose an object-detection method based on improved random forest and local image patches represented by CNN features. First, we compute CNN-based patch descriptors for each sample by modified CNNs. Then, the random forest is built whose split functions are defined by patch selector and linear projection learned by linear support vector machine. To improve the classification accuracy, the split functions in each depth of the forest make up a local classifier, and all local classifiers are assembled in a layer-wise manner by a boosting algorithm. The main contributions of our approach are summarized as follows: (1) We propose a new local patch descriptor based on CNN features. (2) We define a patch-based split function which is optimized with maximum class-label purity and minimum classification error over the samples of the node. (3) Each local classifier is assembled by minimizing the global classification error. We evaluate the method on three well-known challenging datasets: TUD pedestrians, INRIA pedestrians, and UIUC cars. The experiments demonstrate that our method achieves state-of-the-art or competitive performance.

  17. A method for medulloblastoma tumor differentiation based on convolutional neural networks and transfer learning

    NASA Astrophysics Data System (ADS)

    Cruz-Roa, Angel; Arévalo, John; Judkins, Alexander; Madabhushi, Anant; González, Fabio

    2015-12-01

    Convolutional neural networks (CNN) have been very successful at addressing different computer vision tasks thanks to their ability to learn image representations directly from large amounts of labeled data. Features learned from a dataset can be used to represent images from a different dataset via an approach called transfer learning. In this paper we apply transfer learning to the challenging task of medulloblastoma tumor differentiation. We compare two different CNN models which were previously trained in two different domains (natural and histopathology images). The first CNN is a state-of-the-art approach in computer vision, a large and deep CNN with 16-layers, Visual Geometry Group (VGG) CNN. The second (IBCa-CNN) is a 2-layer CNN trained for invasive breast cancer tumor classification. Both CNNs are used as visual feature extractors of histopathology image regions of anaplastic and non-anaplastic medulloblastoma tumor from digitized whole-slide images. The features from the two models are used, separately, to train a softmax classifier to discriminate between anaplastic and non-anaplastic medulloblastoma image regions. Experimental results show that the transfer learning approach produce competitive results in comparison with the state of the art approaches for IBCa detection. Results also show that features extracted from the IBCa-CNN have better performance in comparison with features extracted from the VGG-CNN. The former obtains 89.8% while the latter obtains 76.6% in terms of average accuracy.

  18. Convolution Comparison Pattern: An Efficient Local Image Descriptor for Fingerprint Liveness Detection

    PubMed Central

    Gottschlich, Carsten

    2016-01-01

    We present a new type of local image descriptor which yields binary patterns from small image patches. For the application to fingerprint liveness detection, we achieve rotation invariant image patches by taking the fingerprint segmentation and orientation field into account. We compute the discrete cosine transform (DCT) for these rotation invariant patches and attain binary patterns by comparing pairs of two DCT coefficients. These patterns are summarized into one or more histograms per image. Each histogram comprises the relative frequencies of pattern occurrences. Multiple histograms are concatenated and the resulting feature vector is used for image classification. We name this novel type of descriptor convolution comparison pattern (CCP). Experimental results show the usefulness of the proposed CCP descriptor for fingerprint liveness detection. CCP outperforms other local image descriptors such as LBP, LPQ and WLD on the LivDet 2013 benchmark. The CCP descriptor is a general type of local image descriptor which we expect to prove useful in areas beyond fingerprint liveness detection such as biological and medical image processing, texture recognition, face recognition and iris recognition, liveness detection for face and iris images, and machine vision for surface inspection and material classification. PMID:26844544

  19. Convolutional neural networks for P300 detection with application to brain-computer interfaces.

    PubMed

    Cecotti, Hubert; Gräser, Axel

    2011-03-01

    A Brain-Computer Interface (BCI) is a specific type of human-computer interface that enables the direct communication between human and computers by analyzing brain measurements. Oddball paradigms are used in BCI to generate event-related potentials (ERPs), like the P300 wave, on targets selected by the user. A P300 speller is based on this principle, where the detection of P300 waves allows the user to write characters. The P300 speller is composed of two classification problems. The first classification is to detect the presence of a P300 in the electroencephalogram (EEG). The second one corresponds to the combination of different P300 responses for determining the right character to spell. A new method for the detection of P300 waves is presented. This model is based on a convolutional neural network (CNN). The topology of the network is adapted to the detection of P300 waves in the time domain. Seven classifiers based on the CNN are proposed: four single classifiers with different features set and three multiclassifiers. These models are tested and compared on the Data set II of the third BCI competition. The best result is obtained with a multiclassifier solution with a recognition rate of 95.5 percent, without channel selection before the classification. The proposed approach provides also a new way for analyzing brain activities due to the receptive field of the CNN models. PMID:20567055

  20. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks

    PubMed Central

    Kelley, David R.; Snoek, Jasper; Rinn, John L.

    2016-01-01

    The complex language of eukaryotic gene expression remains incompletely understood. Despite the importance suggested by many noncoding variants statistically associated with human disease, nearly all such variants have unknown mechanisms. Here, we address this challenge using an approach based on a recent machine learning advance—deep convolutional neural networks (CNNs). We introduce the open source package Basset to apply CNNs to learn the functional activity of DNA sequences from genomics data. We trained Basset on a compendium of accessible genomic sites mapped in 164 cell types by DNase-seq, and demonstrate greater predictive accuracy than previous methods. Basset predictions for the change in accessibility between variant alleles were far greater for Genome-wide association study (GWAS) SNPs that are likely to be causal relative to nearby SNPs in linkage disequilibrium with them. With Basset, a researcher can perform a single sequencing assay in their cell type of interest and simultaneously learn that cell's chromatin accessibility code and annotate every mutation in the genome with its influence on present accessibility and latent potential for accessibility. Thus, Basset offers a powerful computational approach to annotate and interpret the noncoding genome. PMID:27197224

  1. Single-Image Super Resolution for Multispectral Remote Sensing Data Using Convolutional Neural Networks

    NASA Astrophysics Data System (ADS)

    Liebel, L.; Körner, M.

    2016-06-01

    In optical remote sensing, spatial resolution of images is crucial for numerous applications. Space-borne systems are most likely to be affected by a lack of spatial resolution, due to their natural disadvantage of a large distance between the sensor and the sensed object. Thus, methods for single-image super resolution are desirable to exceed the limits of the sensor. Apart from assisting visual inspection of datasets, post-processing operations—e.g., segmentation or feature extraction—can benefit from detailed and distinguishable structures. In this paper, we show that recently introduced state-of-the-art approaches for single-image super resolution of conventional photographs, making use of deep learning techniques, such as convolutional neural networks (CNN), can successfully be applied to remote sensing data. With a huge amount of training data available, end-to-end learning is reasonably easy to apply and can achieve results unattainable using conventional handcrafted algorithms. We trained our CNN on a specifically designed, domain-specific dataset, in order to take into account the special characteristics of multispectral remote sensing data. This dataset consists of publicly available SENTINEL-2 images featuring 13 spectral bands, a ground resolution of up to 10m, and a high radiometric resolution and thus satisfying our requirements in terms of quality and quantity. In experiments, we obtained results superior compared to competing approaches trained on generic image sets, which failed to reasonably scale satellite images with a high radiometric resolution, as well as conventional interpolation methods.

  2. A Systems Level Analysis of Vasopressin-mediated Signaling Networks in Kidney Distal Convoluted Tubule Cells.

    PubMed

    Cheng, Lei; Wu, Qi; Kortenoeven, Marleen L A; Pisitkun, Trairak; Fenton, Robert A

    2015-01-01

    The kidney distal convoluted tubule (DCT) plays an essential role in maintaining body sodium balance and blood pressure. The major sodium reabsorption pathway in the DCT is the thiazide-sensitive NaCl cotransporter (NCC), whose functions can be modulated by the hormone vasopressin (VP) acting via uncharacterized signaling cascades. Here we use a systems biology approach centered on stable isotope labeling by amino acids in cell culture (SILAC) based quantitative phosphoproteomics of cultured mouse DCT cells to map global changes in protein phosphorylation upon acute treatment with a VP type II receptor agonist 1-desamino-8-D-arginine vasopressin (dDAVP). 6330 unique proteins, containing 12333 different phosphorylation sites were identified. 185 sites were altered in abundance following dDAVP. Basophilic motifs were preferential targets for upregulated sites upon dDAVP stimulation, whereas proline-directed motifs were prominent for downregulated sites. Kinase prediction indicated that dDAVP increased AGC and CAMK kinase families' activities and decreased activity of CDK and MAPK families. Network analysis implicated phosphatidylinositol-4,5-bisphosphate 3-kinase or CAMKK dependent pathways in VP-mediated signaling; pharmacological inhibition of which significantly reduced dDAVP induced increases in phosphorylated NCC at an activating site. In conclusion, this study identifies unique VP signaling cascades in DCT cells that may be important for regulating blood pressure. PMID:26239621

  3. Convoluted Plasma Membrane Domains in the Green Alga Chara are Depleted of Microtubules and Actin Filaments

    PubMed Central

    Sommer, Aniela; Hoeftberger, Margit; Hoepflinger, Marion C.; Schmalbrock, Sarah; Bulychev, Alexander; Foissner, Ilse

    2015-01-01

    Charasomes are convoluted plasma membrane domains in the green alga Chara australis. They harbor H+-ATPases involved in acidification of the medium, which facilitates carbon uptake required for photosynthesis. In this study we investigated the distribution of cortical microtubules and cortical actin filaments in relation to the distribution of charasomes. We found that microtubules and actin filaments were largely lacking beneath the charasomes, suggesting the absence of nucleating and/or anchoring complexes or an inhibitory effect on polymerization. We also investigated the influence of cytoskeleton inhibitors on the light-dependent growth and the darkness-induced degradation of charasomes. Inhibition of cytoplasmic streaming by cytochalasin D significantly inhibited charasome growth and delayed charasome degradation, whereas depolymerization of microtubules by oryzalin or stabilization of microtubules by paclitaxel had no effect. Our data indicate that the membrane at the cytoplasmic surface of charasomes has different properties in comparison with the smooth plasma membrane. We show further that the actin cytoskeleton is necessary for charasome growth and facilitates charasome degradation presumably via trafficking of secretory and endocytic vesicles, respectively. However, microtubules are required neither for charasome growth nor for charasome degradation. PMID:26272553

  4. Convoluted Plasma Membrane Domains in the Green Alga Chara are Depleted of Microtubules and Actin Filaments.

    PubMed

    Sommer, Aniela; Hoeftberger, Margit; Hoepflinger, Marion C; Schmalbrock, Sarah; Bulychev, Alexander; Foissner, Ilse

    2015-10-01

    Charasomes are convoluted plasma membrane domains in the green alga Chara australis. They harbor H(+)-ATPases involved in acidification of the medium, which facilitates carbon uptake required for photosynthesis. In this study we investigated the distribution of cortical microtubules and cortical actin filaments in relation to the distribution of charasomes. We found that microtubules and actin filaments were largely lacking beneath the charasomes, suggesting the absence of nucleating and/or anchoring complexes or an inhibitory effect on polymerization. We also investigated the influence of cytoskeleton inhibitors on the light-dependent growth and the darkness-induced degradation of charasomes. Inhibition of cytoplasmic streaming by cytochalasin D significantly inhibited charasome growth and delayed charasome degradation, whereas depolymerization of microtubules by oryzalin or stabilization of microtubules by paclitaxel had no effect. Our data indicate that the membrane at the cytoplasmic surface of charasomes has different properties in comparison with the smooth plasma membrane. We show further that the actin cytoskeleton is necessary for charasome growth and facilitates charasome degradation presumably via trafficking of secretory and endocytic vesicles, respectively. However, microtubules are required neither for charasome growth nor for charasome degradation. PMID:26272553

  5. Toward content-based image retrieval with deep convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Sklan, Judah E. S.; Plassard, Andrew J.; Fabbri, Daniel; Landman, Bennett A.

    2015-03-01

    Content-based image retrieval (CBIR) offers the potential to identify similar case histories, understand rare disorders, and eventually, improve patient care. Recent advances in database capacity, algorithm efficiency, and deep Convolutional Neural Networks (dCNN), a machine learning technique, have enabled great CBIR success for general photographic images. Here, we investigate applying the leading ImageNet CBIR technique to clinically acquired medical images captured by the Vanderbilt Medical Center. Briefly, we (1) constructed a dCNN with four hidden layers, reducing dimensionality of an input scaled to 128x128 to an output encoded layer of 4x384, (2) trained the network using back-propagation 1 million random magnetic resonance (MR) and computed tomography (CT) images, (3) labeled an independent set of 2100 images, and (4) evaluated classifiers on the projection of the labeled images into manifold space. Quantitative results were disappointing (averaging a true positive rate of only 20%); however, the data suggest that improvements would be possible with more evenly distributed sampling across labels and potential re-grouping of label structures. This preliminary effort at automated classification of medical images with ImageNet is promising, but shows that more work is needed beyond direct adaptation of existing techniques.

  6. Dose convolution filter: Incorporating spatial dose information into tissue response modeling

    SciTech Connect

    Huang Yimei; Joiner, Michael; Zhao Bo; Liao Yixiang; Burmeister, Jay

    2010-03-15

    Purpose: A model is introduced to integrate biological factors such as cell migration and bystander effects into physical dose distributions, and to incorporate spatial dose information in plan analysis and optimization. Methods: The model consists of a dose convolution filter (DCF) with single parameter {sigma}. Tissue response is calculated by an existing NTCP model with DCF-applied dose distribution as input. The authors determined {sigma} of rat spinal cord from published data. The authors also simulated the GRID technique, in which an open field is collimated into many pencil beams. Results: After applying the DCF, the NTCP model successfully fits the rat spinal cord data with a predicted value of {sigma}=2.6{+-}0.5 mm, consistent with 2 mm migration distances of remyelinating cells. Moreover, it enables the appropriate prediction of a high relative seriality for spinal cord. The model also predicts the sparing of normal tissues by the GRID technique when the size of each pencil beam becomes comparable to {sigma}. Conclusions: The DCF model incorporates spatial dose information and offers an improved way to estimate tissue response from complex radiotherapy dose distributions. It does not alter the prediction of tissue response in large homogenous fields, but successfully predicts increased tissue tolerance in small or highly nonuniform fields.

  7. Analyzing astrophysical neutrino signals using realistic nuclear structure calculations and the convolution procedure

    NASA Astrophysics Data System (ADS)

    Tsakstara, V.; Kosmas, T. S.

    2011-12-01

    Convoluted differential and total cross sections of inelastic ν scattering on 128,130Te isotopes are computed from the original cross sections calculated previously using the quasiparticle random-phase approximation. We adopt various spectral distributions for the neutrino energy spectra such as the common two-parameter Fermi-Dirac and power-law distributions appropriate to explore nuclear detector responses to supernova neutrino spectra. We also concentrate on the use of low-energy β-beam neutrinos, originating from boosted β--radioactive 6He ions, to decompose original supernova (anti)neutrino spectra that are subsequently employed to simulate total cross sections of the reactions 130Te(ν˜,ν˜')130Te*. The concrete nuclear regimes selected, 128,130Te, are contents of the multipurpose CUORE and COBRA rare event detectors. Our present investigation may provide useful information about the efficiency of the Te detector medium of the above experiments in their potential use in supernova neutrino searches.

  8. Scattering theory for the radial H˙1/2-critical wave equation with a cubic convolution

    NASA Astrophysics Data System (ADS)

    Miao, Changxing; Zhang, Junyong; Zheng, Jiqiang

    2015-12-01

    In this paper, we study the global well-posedness and scattering for the wave equation with a cubic convolution ∂t2 u - Δu = ± (| x | - 3 *| u | 2) u in dimensions d ≥ 4. We prove that if the radial solution u with life-span I obeys (u, ut) ∈ Lt∞ (I ; H˙x 1 / 2 (Rd) × H˙x - 1 / 2 (Rd)), then u is global and scatters. By the strategy derived from concentration compactness, we show that the proof of the global well-posedness and scattering is reduced to disprove the existence of two scenarios: soliton-like solution and high to low frequency cascade. Making use of the No-waste Duhamel formula and double Duhamel trick, we deduce that these two scenarios enjoy the additional regularity by the bootstrap argument of [7]. This together with virial analysis implies the energy of such two scenarios is zero and so we get a contradiction.

  9. Crosswell electromagnetic modeling from impulsive source: Optimization strategy for dispersion suppression in convolutional perfectly matched layer

    PubMed Central

    Fang, Sinan; Pan, Heping; Du, Ting; Konaté, Ahmed Amara; Deng, Chengxiang; Qin, Zhen; Guo, Bo; Peng, Ling; Ma, Huolin; Li, Gang; Zhou, Feng

    2016-01-01

    This study applied the finite-difference time-domain (FDTD) method to forward modeling of the low-frequency crosswell electromagnetic (EM) method. Specifically, we implemented impulse sources and convolutional perfectly matched layer (CPML). In the process to strengthen CPML, we observed that some dispersion was induced by the real stretch κ, together with an angular variation of the phase velocity of the transverse electric plane wave; the conclusion was that this dispersion was positively related to the real stretch and was little affected by grid interval. To suppress the dispersion in the CPML, we first derived the analytical solution for the radiation field of the magneto-dipole impulse source in the time domain. Then, a numerical simulation of CPML absorption with high-frequency pulses qualitatively amplified the dispersion laws through wave field snapshots. A numerical simulation using low-frequency pulses suggested an optimal parameter strategy for CPML from the established criteria. Based on its physical nature, the CPML method of simply warping space-time was predicted to be a promising approach to achieve ideal absorption, although it was still difficult to entirely remove the dispersion. PMID:27585538

  10. Classification of human activity on water through micro-Dopplers using deep convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Kim, Youngwook; Moon, Taesup

    2016-05-01

    Detecting humans and classifying their activities on the water has significant applications for surveillance, border patrols, and rescue operations. When humans are illuminated by radar signal, they produce micro-Doppler signatures due to moving limbs. There has been a number of research into recognizing humans on land by their unique micro-Doppler signatures, but there is scant research into detecting humans on water. In this study, we investigate the micro-Doppler signatures of humans on water, including a swimming person, a swimming person pulling a floating object, and a rowing person in a small boat. The measured swimming styles were free stroke, backstroke, and breaststroke. Each activity was observed to have a unique micro-Doppler signature. Human activities were classified based on their micro-Doppler signatures. For the classification, we propose to apply deep convolutional neural networks (DCNN), a powerful deep learning technique. Rather than using conventional supervised learning that relies on handcrafted features, we present an alternative deep learning approach. We apply the DCNN, one of the most successful deep learning algorithms for image recognition, directly to a raw micro-Doppler spectrogram of humans on the water. Without extracting any explicit features from the micro-Dopplers, the DCNN can learn the necessary features and build classification boundaries using the training data. We show that the DCNN can achieve accuracy of more than 87.8% for activity classification using 5- fold cross validation.

  11. Convolution Comparison Pattern: An Efficient Local Image Descriptor for Fingerprint Liveness Detection.

    PubMed

    Gottschlich, Carsten

    2016-01-01

    We present a new type of local image descriptor which yields binary patterns from small image patches. For the application to fingerprint liveness detection, we achieve rotation invariant image patches by taking the fingerprint segmentation and orientation field into account. We compute the discrete cosine transform (DCT) for these rotation invariant patches and attain binary patterns by comparing pairs of two DCT coefficients. These patterns are summarized into one or more histograms per image. Each histogram comprises the relative frequencies of pattern occurrences. Multiple histograms are concatenated and the resulting feature vector is used for image classification. We name this novel type of descriptor convolution comparison pattern (CCP). Experimental results show the usefulness of the proposed CCP descriptor for fingerprint liveness detection. CCP outperforms other local image descriptors such as LBP, LPQ and WLD on the LivDet 2013 benchmark. The CCP descriptor is a general type of local image descriptor which we expect to prove useful in areas beyond fingerprint liveness detection such as biological and medical image processing, texture recognition, face recognition and iris recognition, liveness detection for face and iris images, and machine vision for surface inspection and material classification. PMID:26844544

  12. Multimodal integration of micro-Doppler sonar and auditory signals for behavior classification with convolutional networks.

    PubMed

    Dura-Bernal, Salvador; Garreau, Guillaume; Georgiou, Julius; Andreou, Andreas G; Denham, Susan L; Wennekers, Thomas

    2013-10-01

    The ability to recognize the behavior of individuals is of great interest in the general field of safety (e.g. building security, crowd control, transport analysis, independent living for the elderly). Here we report a new real-time acoustic system for human action and behavior recognition that integrates passive audio and active micro-Doppler sonar signatures over multiple time scales. The system architecture is based on a six-layer convolutional neural network, trained and evaluated using a dataset of 10 subjects performing seven different behaviors. Probabilistic combination of system output through time for each modality separately yields 94% (passive audio) and 91% (micro-Doppler sonar) correct behavior classification; probabilistic multimodal integration increases classification performance to 98%. This study supports the efficacy of micro-Doppler sonar systems in characterizing human actions, which can then be efficiently classified using ConvNets. It also demonstrates that the integration of multiple sources of acoustic information can significantly improve the system's performance. PMID:23924412

  13. Detecting text in natural scene images with conditional clustering and convolution neural network

    NASA Astrophysics Data System (ADS)

    Zhu, Anna; Wang, Guoyou; Dong, Yangbo; Iwana, Brian Kenji

    2015-09-01

    We present a robust method of detecting text in natural scenes. The work consists of four parts. First, automatically partition the images into different layers based on conditional clustering. The clustering operates in two sequential ways. One has a constrained clustering center and conditional determined cluster numbers, which generate small-size subregions. The other has fixed cluster numbers, which generate full-size subregions. After the clustering, we obtain a bunch of connected components (CCs) in each subregion. In the second step, the convolutional neural network (CNN) is used to classify those CCs to character components or noncharacter ones. The output score of the CNN can be transferred to the postprobability of characters. Then we group the candidate characters into text strings based on the probability and location. Finally, we use a verification step. We choose a multichannel strategy to evaluate the performance on the public datasets: ICDAR2011 and ICDAR2013. The experimental results demonstrate that our algorithm achieves a superior performance compared with the state-of-the-art text detection algorithms.

  14. Resting State EEG-based biometrics for individual identification using convolutional neural networks.

    PubMed

    Lan Ma; Minett, James W; Blu, Thierry; Wang, William S-Y

    2015-08-01

    Biometrics is a growing field, which permits identification of individuals by means of unique physical features. Electroencephalography (EEG)-based biometrics utilizes the small intra-personal differences and large inter-personal differences between individuals' brainwave patterns. In the past, such methods have used features derived from manually-designed procedures for this purpose. Another possibility is to use convolutional neural networks (CNN) to automatically extract an individual's best and most unique neural features and conduct classification, using EEG data derived from both Resting State with Open Eyes (REO) and Resting State with Closed Eyes (REC). Results indicate that this CNN-based joint-optimized EEG-based Biometric System yields a high degree of accuracy of identification (88%) for 10-class classification. Furthermore, rich inter-personal difference can be found using a very low frequency band (0-2Hz). Additionally, results suggest that the temporal portions over which subjects can be individualized is less than 200 ms. PMID:26736885

  15. Spatial-Spectral Classification Based on the Unsupervised Convolutional Sparse Auto-Encoder for Hyperspectral Remote Sensing Imagery

    NASA Astrophysics Data System (ADS)

    Han, Xiaobing; Zhong, Yanfei; Zhang, Liangpei

    2016-06-01

    Current hyperspectral remote sensing imagery spatial-spectral classification methods mainly consider concatenating the spectral information vectors and spatial information vectors together. However, the combined spatial-spectral information vectors may cause information loss and concatenation deficiency for the classification task. To efficiently represent the spatial-spectral feature information around the central pixel within a neighbourhood window, the unsupervised convolutional sparse auto-encoder (UCSAE) with window-in-window selection strategy is proposed in this paper. Window-in-window selection strategy selects the sub-window spatial-spectral information for the spatial-spectral feature learning and extraction with the sparse auto-encoder (SAE). Convolution mechanism is applied after the SAE feature extraction stage with the SAE features upon the larger outer window. The UCSAE algorithm was validated by two common hyperspectral imagery (HSI) datasets - Pavia University dataset and the Kennedy Space Centre (KSC) dataset, which shows an improvement over the traditional hyperspectral spatial-spectral classification methods.

  16. Deep Neural Networks as a Computational Model for Human Shape Sensitivity

    PubMed Central

    Op de Beeck, Hans P.

    2016-01-01

    Theories of object recognition agree that shape is of primordial importance, but there is no consensus about how shape might be represented, and so far attempts to implement a model of shape perception that would work with realistic stimuli have largely failed. Recent studies suggest that state-of-the-art convolutional ‘deep’ neural networks (DNNs) capture important aspects of human object perception. We hypothesized that these successes might be partially related to a human-like representation of object shape. Here we demonstrate that sensitivity for shape features, characteristic to human and primate vision, emerges in DNNs when trained for generic object recognition from natural photographs. We show that these models explain human shape judgments for several benchmark behavioral and neural stimulus sets on which earlier models mostly failed. In particular, although never explicitly trained for such stimuli, DNNs develop acute sensitivity to minute variations in shape and to non-accidental properties that have long been implicated to form the basis for object recognition. Even more strikingly, when tested with a challenging stimulus set in which shape and category membership are dissociated, the most complex model architectures capture human shape sensitivity as well as some aspects of the category structure that emerges from human judgments. As a whole, these results indicate that convolutional neural networks not only learn physically correct representations of object categories but also develop perceptually accurate representational spaces of shapes. An even more complete model of human object representations might be in sight by training deep architectures for multiple tasks, which is so characteristic in human development. PMID:27124699

  17. Deep Neural Networks as a Computational Model for Human Shape Sensitivity.

    PubMed

    Kubilius, Jonas; Bracci, Stefania; Op de Beeck, Hans P

    2016-04-01

    Theories of object recognition agree that shape is of primordial importance, but there is no consensus about how shape might be represented, and so far attempts to implement a model of shape perception that would work with realistic stimuli have largely failed. Recent studies suggest that state-of-the-art convolutional 'deep' neural networks (DNNs) capture important aspects of human object perception. We hypothesized that these successes might be partially related to a human-like representation of object shape. Here we demonstrate that sensitivity for shape features, characteristic to human and primate vision, emerges in DNNs when trained for generic object recognition from natural photographs. We show that these models explain human shape judgments for several benchmark behavioral and neural stimulus sets on which earlier models mostly failed. In particular, although never explicitly trained for such stimuli, DNNs develop acute sensitivity to minute variations in shape and to non-accidental properties that have long been implicated to form the basis for object recognition. Even more strikingly, when tested with a challenging stimulus set in which shape and category membership are dissociated, the most complex model architectures capture human shape sensitivity as well as some aspects of the category structure that emerges from human judgments. As a whole, these results indicate that convolutional neural networks not only learn physically correct representations of object categories but also develop perceptually accurate representational spaces of shapes. An even more complete model of human object representations might be in sight by training deep architectures for multiple tasks, which is so characteristic in human development. PMID:27124699

  18. Automated laser fabrication of cemented carbide components

    NASA Astrophysics Data System (ADS)

    Paul, C. P.; Khajepour, A.

    2008-07-01

    Automated Laser Fabrication (ALFa) is one of the most rapidly growing rapid-manufacturing technologies. It is similar to laser cladding at process level with different end applications. In general, laser cladding technique is used to deposit materials on the substrate either to improve the surface properties or to refurbish the worn-out parts, while ALFa is capable of near net shaping the components by layer-by-layer deposition of the material directly from CAD model. This manufacturing method is very attractive for low volume manufacturing of hard materials, as near net shaping minimizes machining of hard material and subsequently brings significant savings in time and costly material. To date, many researchers have used this technology to fabricate components using various alloy steels, nickel-based alloys and cobalt-based alloys. In the present study, the work is extended to tungsten carbide cobalt (WC-Co) composites. A set of comprehensive experiments was carried out to study the effect of processing parameters during multi-layer fabrication. The process parameters were optimized for the component-level fabrication. Fabricated components were subjected to dye-penetrant testing, three-point flexural testing, hardness measurement, optical and scanning electron microscopy and X-ray diffraction analysis. The test results revealed that the laser-fabricated material was defect free and more ductile in nature. Thus, ALFa technology, not only produced the quality components, but also minimized machining of hard material and brought significant saving of time and costly WC-Co material.

  19. EIT-Based Fabric Pressure Sensing

    PubMed Central

    Yao, A.; Yang, C. L.; Seo, J. K.; Soleimani, M.

    2013-01-01

    This paper presents EIT-based fabric sensors that aim to provide a pressure mapping using the current carrying and voltage sensing electrodes attached to the boundary of the fabric patch. Pressure-induced shape change over the sensor area makes a change in the conductivity distribution which can be conveyed to the change of boundary current-voltage data. This boundary data is obtained through electrode measurements in EIT system. The corresponding inverse problem is to reconstruct the pressure and deformation map from the relationship between the applied current and the measured voltage on the fabric boundary. Taking advantage of EIT in providing dynamical images of conductivity changes due to pressure induced shape change, the pressure map can be estimated. In this paper, the EIT-based fabric sensor was presented for circular and rectangular sensor geometry. A stretch sensitive fabric was used in circular sensor with 16 electrodes and a pressure sensitive fabric was used in a rectangular sensor with 32 electrodes. A preliminary human test was carried out with the rectangular sensor for foot pressure mapping showing promising results. PMID:23533538

  20. Coordinated regulation of TRPV5-mediated Ca²⁺ transport in primary distal convolution cultures.

    PubMed

    van der Hagen, Eline A E; Lavrijsen, Marla; van Zeeland, Femke; Praetorius, Jeppe; Bonny, Olivier; Bindels, René J M; Hoenderop, Joost G J

    2014-11-01

    Fine-tuning of renal calcium ion (Ca(2+)) reabsorption takes place in the distal convoluted and connecting tubules (distal convolution) of the kidney via transcellular Ca(2+) transport, a process controlled by the epithelial Ca(2+) channel Transient Receptor Potential Vanilloid 5 (TRPV5). Studies to delineate the molecular mechanism of transcellular Ca(2+) transport are seriously hampered by the lack of a suitable cell model. The present study describes the establishment and validation of a primary murine cell model of the distal convolution. Viable kidney tubules were isolated from mice expressing enhanced Green Fluorescent Protein (eGFP) under the control of a TRPV5 promoter (pTRPV5-eGFP), using Complex Object Parametric Analyser and Sorting (COPAS) technology. Tubules were grown into tight monolayers on semi-permeable supports. Radioactive (45)Ca(2+) assays showed apical-to-basolateral transport rates of 13.5 ± 1.2 nmol/h/cm(2), which were enhanced by the calciotropic hormones parathyroid hormone and 1,25-dihydroxy vitamin D3. Cell cultures lacking TRPV5, generated by crossbreeding pTRPV5-eGFP with TRPV5 knockout mice (TRPV5(-/-)), showed significantly reduced transepithelial Ca(2+) transport (26 % of control), for the first time directly confirming the key role of TRPV5. Most importantly, using this cell model, a novel molecular player in transepithelial Ca(2+) transport was identified: mRNA analysis revealed that ATP-dependent Ca(2+)-ATPase 4 (PMCA4) instead of PMCA1 was enriched in isolated tubules and downregulated in TRPV5(-/-) material. Immunohistochemical stainings confirmed co-localization of PMCA4 with TRPV5 in the distal convolution. In conclusion, a novel primary cell model with TRPV5-dependent Ca(2+) transport characteristics was successfully established, enabling comprehensive studies of transcellular Ca(2+) transport. PMID:24557712