Shape measurement biases from underfitting and ellipticity gradients

DOE PAGES

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 10 3 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

Active Tube-Shaped Actuator with Embedded Square Rod-Shaped Ionic Polymer-Metal Composites for Robotic-Assisted Manipulation

PubMed Central

Liu, Jiayu; Zhu, Denglin; Chen, Hualing

2018-01-01

This paper reports a new technique involving the design, fabrication, and characterization of an ionic polymer-metal composite- (IPMC-) embedded active tube, which can achieve multidegree-of-freedom (MODF) bending motions desirable in many applications, such as a manipulator and an active catheter. However, traditional strip-type IPMC actuators are limited in only being able to generate 1-dimensional bending motion. So, in this paper, we try to develop an approach which involves molding or integrating rod-shaped IPMC actuators into a soft silicone rubber structure to create an active tube. We modified the Nafion solution casting method and developed a complete sequence of a fabrication process for rod-shaped IPMCs with square cross sections and four insulated electrodes on the surface. The silicone gel was cured at a suitable temperature to form a flexible tube using molds fabricated by 3D printing technology. By applying differential voltages to the four electrodes of each IPMC rod-shaped actuator, MDOF bending motions of the active tube can be generated. Experimental results show that such IPMC-embedded tube designs can be used for developing robotic-assisted manipulation. PMID:29770160

Active Tube-Shaped Actuator with Embedded Square Rod-Shaped Ionic Polymer-Metal Composites for Robotic-Assisted Manipulation.

PubMed

Wang, Yanjie; Liu, Jiayu; Zhu, Denglin; Chen, Hualing

2018-01-01

This paper reports a new technique involving the design, fabrication, and characterization of an ionic polymer-metal composite- (IPMC-) embedded active tube, which can achieve multidegree-of-freedom (MODF) bending motions desirable in many applications, such as a manipulator and an active catheter. However, traditional strip-type IPMC actuators are limited in only being able to generate 1-dimensional bending motion. So, in this paper, we try to develop an approach which involves molding or integrating rod-shaped IPMC actuators into a soft silicone rubber structure to create an active tube. We modified the Nafion solution casting method and developed a complete sequence of a fabrication process for rod-shaped IPMCs with square cross sections and four insulated electrodes on the surface. The silicone gel was cured at a suitable temperature to form a flexible tube using molds fabricated by 3D printing technology. By applying differential voltages to the four electrodes of each IPMC rod-shaped actuator, MDOF bending motions of the active tube can be generated. Experimental results show that such IPMC-embedded tube designs can be used for developing robotic-assisted manipulation.

No-reference image quality assessment based on statistics of convolution feature maps

NASA Astrophysics Data System (ADS)

Lv, Xiaoxin; Qin, Min; Chen, Xiaohui; Wei, Guo

2018-04-01

We propose a Convolutional Feature Maps (CFM) driven approach to accurately predict image quality. Our motivation bases on the finding that the Nature Scene Statistic (NSS) features on convolution feature maps are significantly sensitive to distortion degree of an image. In our method, a Convolutional Neural Network (CNN) is trained to obtain kernels for generating CFM. We design a forward NSS layer which performs on CFM to better extract NSS features. The quality aware features derived from the output of NSS layer is effective to describe the distortion type and degree an image suffered. Finally, a Support Vector Regression (SVR) is employed in our No-Reference Image Quality Assessment (NR-IQA) model to predict a subjective quality score of a distorted image. Experiments conducted on two public databases demonstrate the promising performance of the proposed method is competitive to state of the art NR-IQA methods.

Fabrication of tough epoxy with shape memory effects by UV-assisted direct-ink write printing.

PubMed

Chen, Kaijuan; Kuang, Xiao; Li, Vincent; Kang, Guozheng; Qi, H Jerry

2018-03-07

3D printing of epoxy-based shape memory polymers with high mechanical strength, excellent thermal stability and chemical resistance is highly desirable for practical applications. However, thermally cured epoxy in general is difficult to print directly. There have been limited numbers of successes in printing epoxy but they suffer from relatively poor mechanical properties. Here, we present an ultraviolet (UV)-assisted 3D printing of thermally cured epoxy composites with high tensile toughness via a two-stage curing approach. The ink containing UV curable resin and epoxy oligomer is used for UV-assisted direct-ink write (DIW)-based 3D printing followed by thermal curing of the part containing the epoxy oligomer. The UV curable resin forms a network by photo polymerization after the 1st stage of UV curing, which can maintain the printed architecture at an elevated temperature. The 2nd stage thermal curing of the epoxy oligomer yields an interpenetrating polymer network (IPN) composite with highly enhanced mechanical properties. It is found that the printed IPN epoxy composites enabled by the two-stage curing show isotropic mechanical properties and high tensile toughness. We demonstrated that the 3D-printed high-toughness epoxy composites show good shape memory properties. This UV-assisted DIW 3D printing via a two-stage curing method can broaden the application of 3D printing to fabricate thermoset materials with enhanced tensile toughness and tunable properties for high-performance and functional applications.

Ship detection in optical remote sensing images based on deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Yao, Yuan; Jiang, Zhiguo; Zhang, Haopeng; Zhao, Danpei; Cai, Bowen

2017-10-01

Automatic ship detection in optical remote sensing images has attracted wide attention for its broad applications. Major challenges for this task include the interference of cloud, wave, wake, and the high computational expenses. We propose a fast and robust ship detection algorithm to solve these issues. The framework for ship detection is designed based on deep convolutional neural networks (CNNs), which provide the accurate locations of ship targets in an efficient way. First, the deep CNN is designed to extract features. Then, a region proposal network (RPN) is applied to discriminate ship targets and regress the detection bounding boxes, in which the anchors are designed by intrinsic shape of ship targets. Experimental results on numerous panchromatic images demonstrate that, in comparison with other state-of-the-art ship detection methods, our method is more efficient and achieves higher detection accuracy and more precise bounding boxes in different complex backgrounds.

Enhanced line integral convolution with flow feature detection

DOT National Transportation Integrated Search

1995-01-01

Prepared ca. 1995. 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 [Cabral & Leedom '93]. The method produces a flow texture imag...

A Novel Shape Memory Alloy Annuloplasty Ring for Minimally Invasive Surgery: Design, Fabrication, and Evaluation

PubMed Central

Purser, Molly F.; Richards, Andrew L.; Cook, Richard C.; Osborne, Jason A.; Cormier, Denis R.; Buckner, Gregory D.

2013-01-01

A novel annuloplasty ring with a shape memory alloy core has been developed to facilitate minimally invasive mitral valve repair. In its activated (austenitic) phase, this prototype ring has comparable mechanical properties to commercial semi-rigid rings. In its pre-activated (martensitic) phase, this ring is flexible enough to be introduced through an 8-mm trocar and easily manipulated with robotic instruments within the confines of a left atrial model. The core is constructed of 0.50 mm diameter NiTi, which is maintained below its martensitic transition temperature (24 °C) during deployment and suturing. After suturing, the ring is heated above its austenitic transition temperature (37 °C, normal human body temperature) enabling the NiTi core to attain its optimal geometry and stiffness characteristics indefinitely. This article summarizes the design, fabrication, and evaluation of this prototype ring. Experimental results suggest that the NiTi core ring could be a viable alternative to flexible bands in robot-assisted minimally invasive mitral valve repair. PMID:20652747

Superhydrophobic NiTi shape memory alloy surfaces fabricated by anodization and surface mechanical attrition treatment

NASA Astrophysics Data System (ADS)

Ou, Shih-Fu; Wang, Kuang-Kuo; Hsu, Yen-Chi

2017-12-01

This paper describes the fabrication of superhydrophobic NiTi shape memory alloy (SMA) surfaces using an environmentally friendly method based on an economical anodizing process. Perfluorooctyltriethoxysilane was used to reduce the surface energy of the anodized surfaces. The wettability, morphology, composition, and microstructure of the surfaces were investigated by scanning electron microscopy, transmission electron microscopy, and x-ray photoelectron spectroscopy. The surface of the treated NiTi SMA exhibited superhydrophobicity, with a water contact angle of 150.6° and sliding angle of 8°. The anodic film on the NiTi SMA comprised of TiO2 and NiO, as well as traces of TiCl3. In addition, before the NiTi SMA was anodized, it underwent a surface mechanical attrition treatment to grain-refine its surface. This method efficiently enhanced the growth rate of the anodic oxide film, and improved the hydrophobic uniformity of the anodized NiTi-SMA-surface.

Transformers: Shape-Changing Space Systems Built with Robotic Textiles

NASA Technical Reports Server (NTRS)

Stoica, Adrian

2013-01-01

Prior approaches to transformer-like robots had only very limited success. They suffer from lack of reliability, ability to integrate large surfaces, and very modest change in overall shape. Robots can now be built from two-dimensional (2D) layers of robotic fabric. These transformers, a new kind of robotic space system, are dramatically different from current systems in at least two ways. First, the entire transformer is built from a single, thin sheet; a flexible layer of a robotic fabric (ro-fabric); or robotic textile (ro-textile). Second, the ro-textile layer is foldable to small volume and self-unfolding to adapt shape and function to mission phases.

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.

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.

Beryllium fabrication/cost assessment for ITER (International Thermonuclear Experimental Reactor)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beeston, J.M.; Longhurst, G.R.; Parsonage, T.

1990-06-01

A fabrication and cost estimate of three possible beryllium shapes for the International Thermonuclear Experimental Reactor (ITER) blanket is presented. The fabrication method by hot pressing (HP), cold isostatic pressing plus sintering (CIP+S), cold isostatic pressing plus sintering plus hot isostatic pressing (CIP+S+HIP), and sphere production by atomization or rotary electrode will be discussed. Conventional hot pressing blocks of beryllium with subsequent machining to finished shapes can be more expensive than production of a net shape by cold isostatic pressing and sintering. The three beryllium shapes to be considered here and proposed for ITER are: (1) cubic blocks (3 tomore » 17 cm on an edge), (2) tubular cylinders (33 to 50 mm i.d. by 62 mm o.d. by 8 m long), and (3) spheres (1--5 mm dia.). A rough cost estimate of the basic shape is presented which would need to be refined if the surface finish and tolerances required are better than the sintering process produces. The final cost of the beryllium in the blanket will depend largely on the machining and recycling of beryllium required to produce the finished product. The powder preparation will be discussed before shape fabrication. 10 refs., 6 figs.« less

Tactile Fabric Panel in an Eight Zones Structure

PubMed Central

Alsina, Maria; Escudero, Francesc; Margalef, Jordi; Luengo, Sonia

2007-01-01

By introducing a percentage of conductive material during the manufacture of sewing thread, it is possible to obtain a fabric which is able to detect variations in pressure in certain areas. In previous experiments the existence of resistance variations has been demonstrated, although some constrains of cause and effect were found in the fabric. The research has been concentrated in obtaining a fabric that allows electronic detection of its shape changes. Additionally, and because a causal behavior is needed, it is necessary that the fabric recovers its original shape when the external forces cease. The structure of the fabric varies with the type of deformation applied. Two kinds of deformation are described: those caused by stretching and those caused by pressure. This last type of deformation gives different responses depending on the conductivity of the object used to cause the pressure. This effect is related to the type of thread used to manufacture the fabric. So, if the pressure is caused by a finger the response is different compared to the response caused by a conductive object. Another fact that has to be mentioned is that a pressure in a specific point of the fabric can affect other detection points depending on the force applied. This effect is related to the fabric structure. The goals of this article are validating the structure of the fabric used, as well as the study of the two types of deformation mentioned before. PMID:28903272

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. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Enhancement of digital radiography image quality using a convolutional neural network.

PubMed

Sun, Yuewen; Li, Litao; Cong, Peng; Wang, Zhentao; Guo, Xiaojing

2017-01-01

Digital radiography system is widely used for noninvasive security check and medical imaging examination. However, the system has a limitation of lower image quality in spatial resolution and signal to noise ratio. In this study, we explored whether the image quality acquired by the digital radiography system can be improved with a modified convolutional neural network to generate high-resolution images with reduced noise from the original low-quality images. The experiment evaluated on a test dataset, which contains 5 X-ray images, showed that the proposed method outperformed the traditional methods (i.e., bicubic interpolation and 3D block-matching approach) as measured by peak signal to noise ratio (PSNR) about 1.3 dB while kept highly efficient processing time within one second. Experimental results demonstrated that a residual to residual (RTR) convolutional neural network remarkably improved the image quality of object structural details by increasing the image resolution and reducing image noise. Thus, this study indicated that applying this RTR convolutional neural network system was useful to improve image quality acquired by the digital radiography system.

The unitary convolution approximation for heavy ions

NASA Astrophysics Data System (ADS)

Grande, P. L.; Schiwietz, G.

2002-10-01

The convolution approximation for the impact-parameter dependent energy loss is reviewed with emphasis on the determination of the stopping force for heavy projectiles. In this method, the energy loss in different impact-parameter regions is well determined and interpolated smoothly. The physical inputs of the model are the projectile-screening function (in the case of dressed ions), the electron density and oscillators strengths of the target atoms. Moreover, the convolution approximation, in the perturbative mode (called PCA), yields remarkable agreement with full semi-classical-approximation (SCA) results for bare as well as for screened ions at all impact parameters. In the unitary mode (called UCA), the method contains some higher-order effects (yielding in some cases rather good agreement with full coupled-channel calculations) and approaches the classical regime similar as the Bohr model for large perturbations ( Z/ v≫1). The results are then used to compare with experimental values of the non-equilibrium stopping force as a function of the projectile charge as well as with the equilibrium energy loss under non-aligned and channeling conditions.

Tandem mass spectrometry data quality assessment by self-convolution.

PubMed

Choo, Keng Wah; Tham, Wai Mun

2007-09-20

Many algorithms have been developed for deciphering the tandem mass spectrometry (MS) data sets. They can be essentially clustered into two classes. The first performs searches on theoretical mass spectrum database, while the second based itself on de novo sequencing from raw mass spectrometry data. It was noted that the quality of mass spectra affects significantly the protein identification processes in both instances. This prompted the authors to explore ways to measure the quality of MS data sets before subjecting them to the protein identification algorithms, thus allowing for more meaningful searches and increased confidence level of proteins identified. The proposed method measures the qualities of MS data sets based on the symmetric property of b- and y-ion peaks present in a MS spectrum. Self-convolution on MS data and its time-reversal copy was employed. Due to the symmetric nature of b-ions and y-ions peaks, the self-convolution result of a good spectrum would produce a highest mid point intensity peak. To reduce processing time, self-convolution was achieved using Fast Fourier Transform and its inverse transform, followed by the removal of the "DC" (Direct Current) component and the normalisation of the data set. The quality score was defined as the ratio of the intensity at the mid point to the remaining peaks of the convolution result. The method was validated using both theoretical mass spectra, with various permutations, and several real MS data sets. The results were encouraging, revealing a high percentage of positive prediction rates for spectra with good quality scores. We have demonstrated in this work a method for determining the quality of tandem MS data set. By pre-determining the quality of tandem MS data before subjecting them to protein identification algorithms, spurious protein predictions due to poor tandem MS data are avoided, giving scientists greater confidence in the predicted results. We conclude that the algorithm performs well

Design of fabric preforms for double diaphragm forming

NASA Technical Reports Server (NTRS)

Luby, Steven; Bernardon, Edward

1992-01-01

Resin Transfer Molding (RTM) has the potential of becoming one of the most cost effective ways of producing composite structures since the raw materials used, resin and dry fabric, are less costly than prepregs. Unfortunately these low material costs are offset by the high labor costs incurred to layup the dry fabric into 3D shapes. To reduce the layup costs, double diaphragm forming is being investigated as a potential technique for creating a complex 3D preform from a simple flat layup. As part of our effort to develop double diaphragm forming into a production capable process, we have undertaken a series of experiments to investigate the interactions between process parameters, mold geometry, fabric weave, tow size, and the quality of the formed part. The results of these tests will be used to determine the forming geometry limitations of double diaphragm forming and to characterize the formability of fabric configurations. An important part of this work was the development of methods to measure and analyze fiber orientations, deformation angles, tow spreading, and shape conformation of the formed parts. This paper will describe the methods used to mark plies, the double diaphragm forming process, the techniques used to measure the formed parts, and the calculation of the parameters of interest. The results can be displayed as 3D contour plots. These experimental results have also been used to verify and improve a computer model which simulates the draping of fabrics over 3D mold shapes.

DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations.

PubMed

Kruthiventi, Srinivas S S; Ayush, Kumar; Babu, R Venkatesh

2017-09-01

Understanding and predicting the human visual attention mechanism is an active area of research in the fields of neuroscience and computer vision. In this paper, we propose DeepFix, a fully convolutional neural network, which models the bottom-up mechanism of visual attention via saliency prediction. Unlike classical works, which characterize the saliency map using various hand-crafted features, our model automatically learns features in a hierarchical fashion and predicts the saliency map in an end-to-end manner. DeepFix is designed to capture semantics at multiple scales while taking global context into account, by using network layers with very large receptive fields. Generally, fully convolutional nets are spatially invariant-this prevents them from modeling location-dependent patterns (e.g., centre-bias). Our network handles this by incorporating a novel location-biased convolutional layer. We evaluate our model on multiple challenging saliency data sets and show that it achieves the state-of-the-art results.

Net-Shape HIP Powder Metallurgy Components for Rocket Engines

NASA Technical Reports Server (NTRS)

Bampton, Cliff; Goodin, Wes; VanDaam, Tom; Creeger, Gordon; James, Steve

2005-01-01

True net shape consolidation of powder metal (PM) by hot isostatic pressing (HIP) provides opportunities for many cost, performance and life benefits over conventional fabrication processes for large rocket engine structures. Various forms of selectively net-shape PM have been around for thirty years or so. However, it is only recently that major applications have been pursued for rocket engine hardware fabricated in the United States. The method employs sacrificial metallic tooling (HIP capsule and shaped inserts), which is removed from the part after HIP consolidation of the powder, by selective acid dissolution. Full exploitation of net-shape PM requires innovative approaches in both component design and materials and processing details. The benefits include: uniform and homogeneous microstructure with no porosity, irrespective of component shape and size; elimination of welds and the associated quality and life limitations; removal of traditional producibility constraints on design freedom, such as forgeability and machinability, and scale-up to very large, monolithic parts, limited only by the size of existing HIP furnaces. Net-shape PM HIP also enables fabrication of complex configurations providing additional, unique functionalities. The progress made in these areas will be described. Then critical aspects of the technology that still require significant further development and maturation will be discussed from the perspective of an engine systems builder and end-user of the technology.

Fabrication, Characterization and Cytotoxicity of Spherical-Shaped Conjugated Gold-Cockle Shell Derived Calcium Carbonate Nanoparticles for Biomedical Applications

NASA Astrophysics Data System (ADS)

Kiranda, Hanan Karimah; Mahmud, Rozi; Abubakar, Danmaigoro; Zakaria, Zuki Abubakar

2018-01-01

The evolution of nanomaterial in science has brought about a growing increase in nanotechnology, biomedicine, and engineering fields. This study was aimed at fabrication and characterization of conjugated gold-cockle shell-derived calcium carbonate nanoparticles (Au-CSCaCO3NPs) for biomedical application. The synthetic technique employed used gold nanoparticle citrate reduction method and a simple precipitation method coupled with mechanical use of a Programmable roller-ball mill. The synthesized conjugated nanomaterial was characterized for its physicochemical properties using transmission electron microscope (TEM), field emission scanning electron microscope (FESEM) equipped with energy dispersive X-ray (EDX) and Fourier transform infrared spectroscopy (FTIR). However, the intricacy of cellular mechanisms can prove challenging for nanomaterial like Au-CSCaCO3NPs and thus, the need for cytotoxicity assessment. The obtained spherical-shaped nanoparticles (light-green purplish) have an average diameter size of 35 ± 16 nm, high carbon and oxygen composition. The conjugated nanomaterial, also possesses a unique spectra for aragonite polymorph and carboxylic bond significantly supporting interactions between conjugated nanoparticles. The negative surface charge and spectra absorbance highlighted their stability. The resultant spherical shaped conjugated Au-CSCaCO3NPs could be a great nanomaterial for biomedical applications.

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.

CAD/CAM transtibial prosthetic sockets from central fabrication facilities: How accurate are they?

PubMed Central

Sanders, Joan E.; Rogers, Ellen L.; Sorenson, Elizabeth A.; Lee, Gregory S.; Abrahamson, Daniel C.

2014-01-01

This research compares transtibial prosthetic sockets made by central fabrication facilities with their corresponding American Academy of Orthotists and Prosthetists (AAOP) electronic shape files and assesses the central fabrication process. We ordered three different socket shapes from each of 10 manufacturers. Then we digitized the sockets using a very accurate custom mechanical digitizer. Results showed that quality varied considerably among the different manufacturers. Four of the companies consistently made sockets within +/−1.1% volume (approximately 1 sock ply) of the AAOP electronic shape file, while six other companies did not. Six of the companies showed consistent undersizing or oversizing in their sockets, which suggests a consistent calibration or manufacturing error. Other companies showed inconsistent sizing or shape distortion, a difficult problem that represents a most challenging limitation for central fabrication facilities. PMID:18247236

Keypoint Density-Based Region Proposal for Fine-Grained Object Detection and Classification Using Regions with Convolutional Neural Network Features

DTIC Science & Technology

2015-12-15

Keypoint Density-based Region Proposal for Fine-Grained Object Detection and Classification using Regions with Convolutional Neural Network ... Convolutional Neural Networks (CNNs) enable them to outperform conventional techniques on standard object detection and classification tasks, their...detection accuracy and speed on the fine-grained Caltech UCSD bird dataset (Wah et al., 2011). Recently, Convolutional Neural Networks (CNNs), a deep

Rethinking Skin Lesion Segmentation in a Convolutional Classifier.

PubMed

Burdick, Jack; Marques, Oge; Weinthal, Janet; Furht, Borko

2017-10-18

Melanoma is a fatal form of skin cancer when left undiagnosed. Computer-aided diagnosis systems powered by convolutional neural networks (CNNs) can improve diagnostic accuracy and save lives. CNNs have been successfully used in both skin lesion segmentation and classification. For reasons heretofore unclear, previous works have found image segmentation to be, conflictingly, both detrimental and beneficial to skin lesion classification. We investigate the effect of expanding the segmentation border to include pixels surrounding the target lesion. Ostensibly, segmenting a target skin lesion will remove inessential information, non-lesion skin, and artifacts to aid in classification. Our results indicate that segmentation border enlargement produces, to a certain degree, better results across all metrics of interest when using a convolutional based classifier built using the transfer learning paradigm. Consequently, preprocessing methods which produce borders larger than the actual lesion can potentially improve classifier performance, more than both perfect segmentation, using dermatologist created ground truth masks, and no segmentation altogether.

Cloud Detection by Fusing Multi-Scale Convolutional Features

NASA Astrophysics Data System (ADS)

Li, Zhiwei; Shen, Huanfeng; Wei, Yancong; Cheng, Qing; Yuan, Qiangqiang

2018-04-01

Clouds detection is an important pre-processing step for accurate application of optical satellite imagery. Recent studies indicate that deep learning achieves best performance in image segmentation tasks. Aiming at boosting the accuracy of cloud detection for multispectral imagery, especially for those that contain only visible and near infrared bands, in this paper, we proposed a deep learning based cloud detection method termed MSCN (multi-scale cloud net), which segments cloud by fusing multi-scale convolutional features. MSCN was trained on a global cloud cover validation collection, and was tested in more than ten types of optical images with different resolution. Experiment results show that MSCN has obvious advantages over the traditional multi-feature combined cloud detection method in accuracy, especially when in snow and other areas covered by bright non-cloud objects. Besides, MSCN produced more detailed cloud masks than the compared deep cloud detection convolution network. The effectiveness of MSCN make it promising for practical application in multiple kinds of optical imagery.

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

Robust Vacuum-/Air-Dried Graphene Aerogels and Fast Recoverable Shape-Memory Hybrid Foams.

PubMed

Li, Chenwei; Qiu, Ling; Zhang, Baoqing; Li, Dan; Liu, Chen-Yang

2016-02-17

New graphene aerogels can be fabricated by vacuum/air drying, and because of the mechanical robustness of the graphene aerogels, shape-memory polymer/graphene hybrid foams can be fabricated by a simple infiltration-air-drying-crosslinking method. Due to the superelasticity, high strength, and good electrical conductivity of the as-prepared graphene aerogels, the shape-memory hybrid foams exhibit excellent thermotropical and electrical shape-memory properties, outperforming previously reported shape-memory polymer foams. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Fabric-based alkaline direct formate microfluidic fuel cells.

PubMed

Domalaon, Kryls; Tang, Catherine; Mendez, Alex; Bernal, Franky; Purohit, Krutarth; Pham, Linda; Haan, John; Gomez, Frank A

2017-04-01

Fabric-based microfluidic fuel cells (MFCs) serve as a novel, cost-efficient alternative to traditional FCs and batteries, since fluids naturally travel across fabric via capillary action, eliminating the need for an external pump and lowering production and operation costs. Building on previous research with Y-shaped paper-based MFCs, fabric-based MFCs mitigate fragility and durability issues caused by long periods of fuel immersion. In this study, we describe a microfluidic fabric-based direct formate fuel cell, with 5 M potassium formate and 30% hydrogen peroxide as the anode fuel and cathode oxidant, respectively. Using a two-strip, stacked design, the optimized parameters include the type of encasement, the barrier, and the fabric type. Surface contact of the fabric and laminate sheet expedited flow and respective chemical reactions. The maximum current (22.83 mA/cm 2 ) and power (4.40 mW/cm 2 ) densities achieved with a 65% cotton/35% polyester blend material are a respective 8.7% and 32% higher than previous studies with Y-shaped paper-based MFCs. In series configuration, the MFCs generate sufficient energy to power a handheld calculator, a thermometer, and a spectrum of light-emitting diodes. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Producing data-based sensitivity kernels from convolution and correlation in exploration geophysics.

NASA Astrophysics Data System (ADS)

Chmiel, M. J.; Roux, P.; Herrmann, P.; Rondeleux, B.

2016-12-01

Many studies have shown that seismic interferometry can be used to estimate surface wave arrivals by correlation of seismic signals recorded at a pair of locations. In the case of ambient noise sources, the convergence towards the surface wave Green's functions is obtained with the criterion of equipartitioned energy. However, seismic acquisition with active, controlled sources gives more possibilities when it comes to interferometry. The use of controlled sources makes it possible to recover the surface wave Green's function between two points using either correlation or convolution. We investigate the convolutional and correlational approaches using land active-seismic data from exploration geophysics. The data were recorded on 10,710 vertical receivers using 51,808 sources (seismic vibrator trucks). The sources spacing is the same in both X and Y directions (30 m) which is known as a "carpet shooting". The receivers are placed in parallel lines with a spacing 150 m in the X direction and 30 m in the Y direction. Invoking spatial reciprocity between sources and receivers, correlation and convolution functions can thus be constructed between either pairs of receivers or pairs of sources. Benefiting from the dense acquisition, we extract sensitivity kernels from correlation and convolution measurements of the seismic data. These sensitivity kernels are subsequently used to produce phase-velocity dispersion curves between two points and to separate the higher mode from the fundamental mode for surface waves. Potential application to surface wave cancellation is also envisaged.

One-step fabrication of multifunctional micromotors

NASA Astrophysics Data System (ADS)

Gao, Wenlong; Liu, Mei; Liu, Limei; Zhang, Hui; Dong, Bin; Li, Christopher Y.

2015-08-01

Although artificial micromotors have undergone tremendous progress in recent years, their fabrication normally requires complex steps or expensive equipment. In this paper, we report a facile one-step method based on an emulsion solvent evaporation process to fabricate multifunctional micromotors. By simultaneously incorporating various components into an oil-in-water droplet, upon emulsification and solidification, a sphere-shaped, asymmetric, and multifunctional micromotor is formed. Some of the attractive functions of this model micromotor include autonomous movement in high ionic strength solution, remote control, enzymatic disassembly and sustained release. This one-step, versatile fabrication method can be easily scaled up and therefore may have great potential in mass production of multifunctional micromotors for a wide range of practical applications.Although artificial micromotors have undergone tremendous progress in recent years, their fabrication normally requires complex steps or expensive equipment. In this paper, we report a facile one-step method based on an emulsion solvent evaporation process to fabricate multifunctional micromotors. By simultaneously incorporating various components into an oil-in-water droplet, upon emulsification and solidification, a sphere-shaped, asymmetric, and multifunctional micromotor is formed. Some of the attractive functions of this model micromotor include autonomous movement in high ionic strength solution, remote control, enzymatic disassembly and sustained release. This one-step, versatile fabrication method can be easily scaled up and therefore may have great potential in mass production of multifunctional micromotors for a wide range of practical applications. Electronic supplementary information (ESI) available: Videos S1-S4 and Fig. S1-S3. See DOI: 10.1039/c5nr03574k

A water-responsive shape memory ionomer with permanent shape reconfiguration ability

NASA Astrophysics Data System (ADS)

Bai, Yongkang; Zhang, Jiwen; Tian, Ran; Chen, Xin

2018-04-01

In this work, a water-responsive shape memory ionomer with high toughness was fabricated by cross-linking hyaluronic acid sodium (HAS) and polyvinyl alcohol (PVA) through coordination interactions. The strong Fe3+-carboxyl (from HAS) coordination interactions served as main physical cross-linking points for the performance of water-responsive shape memory, which associated with the flexibility of PVA chain producing excellent mechanical properties of this ionomer. The optimized ionomer was not only able to recover to its original shape within just 22 s by exposing to water, but exhibited high tensile strength up to 35.4 MPa and 4 times higher tractility than the ionomer without PVA. Moreover, the ionomers can be repeatedly programed to various new permanent shapes on demand due to the reversible physical interactions, which still performed complete and fast geometric recovery under stimuli even after 4 cycles of reprograming with 3 different shapes. The excellent shape memory and strong mechanical behaviors make our ionomers significant and promising smart materials for variety of applications.

Focused ion beam-assisted technology in sub-picolitre micro-dispenser fabrication

NASA Astrophysics Data System (ADS)

Lopez, M. J.; Caballero, D.; Campo, E. M.; Perez-Castillejos, R.; Errachid, A.; Esteve, J.; Plaza, J. A.

2008-07-01

Novel medical and biological applications are driving increased interest in the fabrication of micropipette or micro-dispensers. Reduced volume samples and drug dosages are prime motivators in this effort. We have combined microfabrication technology with ion beam milling techniques to successfully produce cantilever-type polysilicon micro-dispensers with 3D enclosed microchannels. The microfabrication technology described here allows for the designing of nozzles with multiple shapes. The contribution of ion beam milling has had a large impact on the fabrication process and on further customizing shapes of nozzles and inlet ports. Functionalization tests were conducted to prove the viability of ion beam-fabricated micro-dispensers. Self-assembled monolayers were successfully formed when a gold surface was patterned with a thiol solution dispensed by the fabricated micro-dispensers.

Fully convolutional neural networks for polyp segmentation in colonoscopy

NASA Astrophysics Data System (ADS)

Brandao, Patrick; Mazomenos, Evangelos; Ciuti, Gastone; Caliò, Renato; Bianchi, Federico; Menciassi, Arianna; Dario, Paolo; Koulaouzidis, Anastasios; Arezzo, Alberto; Stoyanov, Danail

2017-03-01

Colorectal cancer (CRC) is one of the most common and deadliest forms of cancer, accounting for nearly 10% of all forms of cancer in the world. Even though colonoscopy is considered the most effective method for screening and diagnosis, the success of the procedure is highly dependent on the operator skills and level of hand-eye coordination. In this work, we propose to adapt fully convolution neural networks (FCN), to identify and segment polyps in colonoscopy images. We converted three established networks into a fully convolution architecture and fine-tuned their learned representations to the polyp segmentation task. We validate our framework on the 2015 MICCAI polyp detection challenge dataset, surpassing the state-of-the-art in automated polyp detection. Our method obtained high segmentation accuracy and a detection precision and recall of 73.61% and 86.31%, respectively.

Simulation of ICD-9 to ICD-10-CM Transition for Family Medicine: Simple or Convoluted?

PubMed

Grief, Samuel N; Patel, Jesal; Kochendorfer, Karl M; Green, Lee A; Lussier, Yves A; Li, Jianrong; Burton, Michael; Boyd, Andrew D

2016-01-01

The objective of this study was to examine the impact of the transition from International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM), to Interactional Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM), on family medicine and to identify areas where additional training might be required. Family medicine ICD-9-CM codes were obtained from an Illinois Medicaid data set (113,000 patient visits and $5.5 million in claims). Using the science of networks, we evaluated each ICD-9-CM code used by family medicine physicians to determine whether the transition was simple or convoluted. A simple transition is defined as 1 ICD-9-CM code mapping to 1 ICD-10-CM code, or 1 ICD-9-CM code mapping to multiple ICD-10-CM codes. A convoluted transition is where the transitions between coding systems is nonreciprocal and complex, with multiple codes for which definitions become intertwined. Three family medicine physicians evaluated the most frequently encountered complex mappings for clinical accuracy. Of the 1635 diagnosis codes used by family medicine physicians, 70% of the codes were categorized as simple, 27% of codes were convoluted, and 3% had no mapping. For the visits, 75%, 24%, and 1% corresponded with simple, convoluted, and no mapping, respectively. Payment for submitted claims was similarly aligned. Of the frequently encountered convoluted codes, 3 diagnosis codes were clinically incorrect, but they represent only <0.1% of the overall diagnosis codes. The transition to ICD-10-CM is simple for 70% or more of diagnosis codes, visits, and reimbursement for a family medicine physician. However, some frequently used codes for disease management are convoluted and incorrect, and for which additional resources need to be invested to ensure a successful transition to ICD-10-CM. © Copyright 2016 by the American Board of Family Medicine.

Simulation of ICD-9 to ICD-10-CM transition for family medicine: simple or convoluted?

PubMed Central

Grief, Samuel N.; Patel, Jesal; Lussier, Yves A.; Li, Jianrong; Burton, Michael; Boyd, Andrew D.

2017-01-01

Objectives The objective of this study was to examine the impact of the transition from International Classification of Disease Version Nine Clinical Modification (ICD-9-CM) to Interactional Classification of Disease Version Ten Clinical Modification (ICD-10-CM) on family medicine and identify areas where additional training might be required. Methods Family medicine ICD-9-CM codes were obtained from an Illinois Medicaid data set (113,000 patient visits and $5.5 million dollars in claims). Using the science of networks we evaluated each ICD-9-CM code used by family medicine physicians to determine if the transition was simple or convoluted.1 A simple translation is defined as one ICD-9-CM code mapping to one ICD-10-CM code or one ICD-9-CM code mapping to multiple ICD-10-CM codes. A convoluted transition is where the transitions between coding systems is non-reciprocal and complex with multiple codes where definitions become intertwined. Three family medicine physicians evaluated the most frequently encountered complex mappings for clinical accuracy. Results Of the 1635 diagnosis codes used by the family medicine physicians, 70% of the codes were categorized as simple, 27% of the diagnosis codes were convoluted and 3% were found to have no mapping. For the visits, 75%, 24%, and 1% corresponded with simple, convoluted, and no mapping, respectively. Payment for submitted claims were similarly aligned. Of the frequently encountered convoluted codes, 3 diagnosis codes were clinically incorrect, but they represent only < 0.1% of the overall diagnosis codes. Conclusions The transition to ICD-10-CM is simple for 70% or more of diagnosis codes, visits, and reimbursement for a family medicine physician. However, some frequently used codes for disease management are convoluted and incorrect, where additional resources need to be invested to ensure a successful transition to ICD-10-CM. PMID:26769875

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.

Shape-programmable magnetic soft matter

PubMed Central

Lum, Guo Zhan; Ye, Zhou; Dong, Xiaoguang; Marvi, Hamid; Erin, Onder; Hu, Wenqi; Sitti, Metin

2016-01-01

Shape-programmable matter is a class of active materials whose geometry can be controlled to potentially achieve mechanical functionalities beyond those of traditional machines. Among these materials, magnetically actuated matter is particularly promising for achieving complex time-varying shapes at small scale (overall dimensions smaller than 1 cm). However, previous work can only program these materials for limited applications, as they rely solely on human intuition to approximate the required magnetization profile and actuating magnetic fields for their materials. Here, we propose a universal programming methodology that can automatically generate the required magnetization profile and actuating fields for soft matter to achieve new time-varying shapes. The universality of the proposed method can therefore inspire a vast number of miniature soft devices that are critical in robotics, smart engineering surfaces and materials, and biomedical devices. Our proposed method includes theoretical formulations, computational strategies, and fabrication procedures for programming magnetic soft matter. The presented theory and computational method are universal for programming 2D or 3D time-varying shapes, whereas the fabrication technique is generic only for creating planar beams. Based on the proposed programming method, we created a jellyfish-like robot, a spermatozoid-like undulating swimmer, and an artificial cilium that could mimic the complex beating patterns of its biological counterpart. PMID:27671658

Shape-programmable magnetic soft matter.

PubMed

Lum, Guo Zhan; Ye, Zhou; Dong, Xiaoguang; Marvi, Hamid; Erin, Onder; Hu, Wenqi; Sitti, Metin

2016-10-11

Shape-programmable matter is a class of active materials whose geometry can be controlled to potentially achieve mechanical functionalities beyond those of traditional machines. Among these materials, magnetically actuated matter is particularly promising for achieving complex time-varying shapes at small scale (overall dimensions smaller than 1 cm). However, previous work can only program these materials for limited applications, as they rely solely on human intuition to approximate the required magnetization profile and actuating magnetic fields for their materials. Here, we propose a universal programming methodology that can automatically generate the required magnetization profile and actuating fields for soft matter to achieve new time-varying shapes. The universality of the proposed method can therefore inspire a vast number of miniature soft devices that are critical in robotics, smart engineering surfaces and materials, and biomedical devices. Our proposed method includes theoretical formulations, computational strategies, and fabrication procedures for programming magnetic soft matter. The presented theory and computational method are universal for programming 2D or 3D time-varying shapes, whereas the fabrication technique is generic only for creating planar beams. Based on the proposed programming method, we created a jellyfish-like robot, a spermatozoid-like undulating swimmer, and an artificial cilium that could mimic the complex beating patterns of its biological counterpart.

Shape-programmable magnetic soft matter

NASA Astrophysics Data System (ADS)

Zhan Lum, Guo; Ye, Zhou; Dong, Xiaoguang; Marvi, Hamid; Erin, Onder; Hu, Wenqi; Sitti, Metin

2016-10-01

Shape-programmable matter is a class of active materials whose geometry can be controlled to potentially achieve mechanical functionalities beyond those of traditional machines. Among these materials, magnetically actuated matter is particularly promising for achieving complex time-varying shapes at small scale (overall dimensions smaller than 1 cm). However, previous work can only program these materials for limited applications, as they rely solely on human intuition to approximate the required magnetization profile and actuating magnetic fields for their materials. Here, we propose a universal programming methodology that can automatically generate the required magnetization profile and actuating fields for soft matter to achieve new time-varying shapes. The universality of the proposed method can therefore inspire a vast number of miniature soft devices that are critical in robotics, smart engineering surfaces and materials, and biomedical devices. Our proposed method includes theoretical formulations, computational strategies, and fabrication procedures for programming magnetic soft matter. The presented theory and computational method are universal for programming 2D or 3D time-varying shapes, whereas the fabrication technique is generic only for creating planar beams. Based on the proposed programming method, we created a jellyfish-like robot, a spermatozoid-like undulating swimmer, and an artificial cilium that could mimic the complex beating patterns of its biological counterpart.

a Novel Deep Convolutional Neural Network for Spectral-Spatial Classification of Hyperspectral Data

NASA Astrophysics Data System (ADS)

Li, N.; Wang, C.; Zhao, H.; Gong, X.; Wang, D.

2018-04-01

Spatial and spectral information are obtained simultaneously by hyperspectral remote sensing. Joint extraction of these information of hyperspectral image is one of most import methods for hyperspectral image classification. In this paper, a novel deep convolutional neural network (CNN) is proposed, which extracts spectral-spatial information of hyperspectral images correctly. The proposed model not only learns sufficient knowledge from the limited number of samples, but also has powerful generalization ability. The proposed framework based on three-dimensional convolution can extract spectral-spatial features of labeled samples effectively. Though CNN has shown its robustness to distortion, it cannot extract features of different scales through the traditional pooling layer that only have one size of pooling window. Hence, spatial pyramid pooling (SPP) is introduced into three-dimensional local convolutional filters for hyperspectral classification. Experimental results with a widely used hyperspectral remote sensing dataset show that the proposed model provides competitive performance.

Sensitivity Kernels for the Cross-Convolution Measure: Eliminate the Source in Waveform Tomography

NASA Astrophysics Data System (ADS)

Menke, W. H.

2017-12-01

We use the adjoint method to derive sensitivity kernels for the cross-convolution measure, a goodness-of-fit criterion that is applicable to seismic data containing closely-spaced multiple arrivals, such as reverberating compressional waves and split shear waves. In addition to a general formulation, specific expressions for sensitivity with respect to density, Lamé parameter and shear modulus are derived for a isotropic elastic solid. As is typical of adjoint methods, the kernels depend upon an adjoint field, the source of which, in this case, is the reference displacement field, pre-multiplied by a matrix of cross-correlations of components of the observed field. We use a numerical simulation to evaluate the resolving power of a topographic inversion that employs the cross-convolution measure. The estimated resolving kernel shows is point-like, indicating that the cross-convolution measure will perform well in waveform tomography settings.

Accurate Segmentation of Cervical Cytoplasm and Nuclei Based on Multiscale Convolutional Network and Graph Partitioning.

PubMed

Song, Youyi; Zhang, Ling; Chen, Siping; Ni, Dong; Lei, Baiying; Wang, Tianfu

2015-10-01

In this paper, a multiscale convolutional network (MSCN) and graph-partitioning-based method is proposed for accurate segmentation of cervical cytoplasm and nuclei. Specifically, deep learning via the MSCN is explored to extract scale invariant features, and then, segment regions centered at each pixel. The coarse segmentation is refined by an automated graph partitioning method based on the pretrained feature. The texture, shape, and contextual information of the target objects are learned to localize the appearance of distinctive boundary, which is also explored to generate markers to split the touching nuclei. For further refinement of the segmentation, a coarse-to-fine nucleus segmentation framework is developed. The computational complexity of the segmentation is reduced by using superpixel instead of raw pixels. Extensive experimental results demonstrate that the proposed cervical nucleus cell segmentation delivers promising results and outperforms existing methods.

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

PubMed

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

2008-05-01

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.

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.

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.

Airplane detection in remote sensing images using convolutional neural networks

NASA Astrophysics Data System (ADS)

Ouyang, Chao; Chen, Zhong; Zhang, Feng; Zhang, Yifei

2018-03-01

Airplane detection in remote sensing images remains a challenging problem and has also been taking a great interest to researchers. In this paper we propose an effective method to detect airplanes in remote sensing images using convolutional neural networks. Deep learning methods show greater advantages than the traditional methods with the rise of deep neural networks in target detection, and we give an explanation why this happens. To improve the performance on detection of airplane, we combine a region proposal algorithm with convolutional neural networks. And in the training phase, we divide the background into multi classes rather than one class, which can reduce false alarms. Our experimental results show that the proposed method is effective and robust in detecting airplane.

Convolute laminations — a theoretical analysis: example of a Pennsylvanian sandstone

NASA Astrophysics Data System (ADS)

Visher, Glenn S.; Cunningham, Russ D.

1981-03-01

Data from an outcropping laminated interval were collected and analyzed to test the applicability of a theoretical model describing instability of layered systems. Rayleigh—Taylor wave perturbations result at the interface between fluids of contrasting density, viscosity, and thickness. In the special case where reverse density and viscosity interlaminations are developed, the deformation response produces a single wave with predictable amplitudes, wavelengths, and amplification rates. Physical measurements from both the outcropping section and modern sediments suggest the usefulness of the model for the interpretation of convolute laminations. Internal characteristics of the stratigraphic interval, and the developmental sequence of convoluted beds, are used to document the developmental history of these structures.

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.

Direct single-layered fabrication of 3D concavo convex patterns in nano-stereolithography

NASA Astrophysics Data System (ADS)

Lim, T. W.; Park, S. H.; Yang, D. Y.; Kong, H. J.; Lee, K. S.

2006-09-01

A nano-surfacing process (NSP) is proposed to directly fabricate three-dimensional (3D) concavo convex-shaped microstructures such as micro-lens arrays using two-photon polymerization (TPP), a promising technique for fabricating arbitrary 3D highly functional micro-devices. In TPP, commonly utilized methods for fabricating complex 3D microstructures to date are based on a layer-by-layer accumulating technique employing two-dimensional sliced data derived from 3D computer-aided design data. As such, this approach requires much time and effort for precise fabrication. In this work, a novel single-layer exposure method is proposed in order to improve the fabricating efficiency for 3D concavo convex-shaped microstructures. In the NSP, 3D microstructures are divided into 13 sub-regions horizontally with consideration of the heights. Those sub-regions are then expressed as 13 characteristic colors, after which a multi-voxel matrix (MVM) is composed with the characteristic colors. Voxels with various heights and diameters are generated to construct 3D structures using a MVM scanning method. Some 3D concavo convex-shaped microstructures were fabricated to estimate the usefulness of the NSP, and the results show that it readily enables the fabrication of single-layered 3D microstructures.

Local dynamic range compensation for scanning electron microscope imaging system by sub-blocking multiple peak HE with convolution.

PubMed

Sim, K S; Teh, V; Tey, Y C; Kho, T K

2016-11-01

This paper introduces new development technique to improve the Scanning Electron Microscope (SEM) image quality and we name it as sub-blocking multiple peak histogram equalization (SUB-B-MPHE) with convolution operator. By using this new proposed technique, it shows that the new modified MPHE performs better than original MPHE. In addition, the sub-blocking method consists of convolution operator which can help to remove the blocking effect for SEM images after applying this new developed technique. Hence, by using the convolution operator, it effectively removes the blocking effect by properly distributing the suitable pixel value for the whole image. Overall, the SUB-B-MPHE with convolution outperforms the rest of methods. SCANNING 38:492-501, 2016. © 2015 Wiley Periodicals, Inc. © Wiley Periodicals, Inc.

Computational analysis of current-loss mechanisms in a post-hole convolute driven by magnetically insulated transmission lines

DOE PAGES

Rose, D.  V.; Madrid, E.  A.; Welch, D.  R.; ...

2015-03-04

Numerical simulations of a vacuum post-hole convolute driven by magnetically insulated vacuum transmission lines (MITLs) are used to study current losses due to charged particle emission from the MITL-convolute-system electrodes. This work builds on the results of a previous study [E.A. Madrid et al. Phys. Rev. ST Accel. Beams 16, 120401 (2013)] and adds realistic power pulses, Ohmic heating of anode surfaces, and a model for the formation and evolution of cathode plasmas. The simulations suggest that modestly larger anode-cathode gaps in the MITLs upstream of the convolute result in significantly less current loss. In addition, longer pulse durations leadmore » to somewhat greater current loss due to cathode-plasma expansion. These results can be applied to the design of future MITL-convolute systems for high-current pulsed-power systems.« less

Fabricating Composite-Material Structures Containing SMA Ribbons

NASA Technical Reports Server (NTRS)

Turner, Travis L.; Cano, Roberto J.; Lach, Cynthia L.

2003-01-01

An improved method of designing and fabricating laminated composite-material (matrix/fiber) structures containing embedded shape-memory-alloy (SMA) actuators has been devised. Structures made by this method have repeatable, predictable properties, and fabrication processes can readily be automated. Such structures, denoted as shape-memory-alloy hybrid composite (SMAHC) structures, have been investigated for their potential to satisfy requirements to control the shapes or thermoelastic responses of themselves or of other structures into which they might be incorporated, or to control noise and vibrations. Much of the prior work on SMAHC structures has involved the use SMA wires embedded within matrices or within sleeves through parent structures. The disadvantages of using SMA wires as the embedded actuators include (1) complexity of fabrication procedures because of the relatively large numbers of actuators usually needed; (2) sensitivity to actuator/ matrix interface flaws because voids can be of significant size, relative to wires; (3) relatively high rates of breakage of actuators during curing of matrix materials because of sensitivity to stress concentrations at mechanical restraints; and (4) difficulty of achieving desirable overall volume fractions of SMA wires when trying to optimize the integration of the wires by placing them in selected layers only.

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

Alcoholism Detection by Data Augmentation and Convolutional Neural Network with Stochastic Pooling.

PubMed

Wang, Shui-Hua; Lv, Yi-Ding; Sui, Yuxiu; Liu, Shuai; Wang, Su-Jing; Zhang, Yu-Dong

2017-11-17

Alcohol use disorder (AUD) is an important brain disease. It alters the brain structure. Recently, scholars tend to use computer vision based techniques to detect AUD. We collected 235 subjects, 114 alcoholic and 121 non-alcoholic. Among the 235 image, 100 images were used as training set, and data augmentation method was used. The rest 135 images were used as test set. Further, we chose the latest powerful technique-convolutional neural network (CNN) based on convolutional layer, rectified linear unit layer, pooling layer, fully connected layer, and softmax layer. We also compared three different pooling techniques: max pooling, average pooling, and stochastic pooling. The results showed that our method achieved a sensitivity of 96.88%, a specificity of 97.18%, and an accuracy of 97.04%. Our method was better than three state-of-the-art approaches. Besides, stochastic pooling performed better than other max pooling and average pooling. We validated CNN with five convolution layers and two fully connected layers performed the best. The GPU yielded a 149× acceleration in training and a 166× acceleration in test, compared to CPU.

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.

The influence of grating shape formation fluctuation on DFB laser diode threshold condition

NASA Astrophysics Data System (ADS)

Bao, Shiwei; Song, Qinghai; Xie, Chunmei

2018-03-01

Not only the grating material refractive index itself but also the Bragg grating physical shape formation affects the coupling strength greatly. The Bragg grating shape includes three factors, namely grating depth, duty ratio and grating angle. During the lithography and wet etching process, there always will be some fluctuation between the target and real grating shape formation after fabrication process. This grating shape fluctuation will affect the DFB coupling coefficient κ , and then consequently threshold current and corresponding wavelength. This paper studied the grating shape formation fluctuation influence to improve the DFB fabrication yield. A truncated normal random distribution fluctuation is considered in this paper. The simulation results conclude that it is better to choose relative thicker grating depth with lower refractive index to obtain a better fabrication tolerance, while not quite necessary to spend too much effort on improving lithography and wet etching process to get a precisely grating duty ratio and grating angle.

The influence of grating shape formation fluctuation on DFB laser diode threshold condition

NASA Astrophysics Data System (ADS)

Bao, Shiwei; Song, Qinghai; Xie, Chunmei

2018-06-01

Not only the grating material refractive index itself but also the Bragg grating physical shape formation affects the coupling strength greatly. The Bragg grating shape includes three factors, namely grating depth, duty ratio and grating angle. During the lithography and wet etching process, there always will be some fluctuation between the target and real grating shape formation after fabrication process. This grating shape fluctuation will affect the DFB coupling coefficient κ, and then consequently threshold current and corresponding wavelength. This paper studied the grating shape formation fluctuation influence to improve the DFB fabrication yield. A truncated normal random distribution fluctuation is considered in this paper. The simulation results conclude that it is better to choose relative thicker grating depth with lower refractive index to obtain a better fabrication tolerance, while not quite necessary to spend too much effort on improving lithography and wet etching process to get a precisely grating duty ratio and grating angle.

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.

Mechanical design of a shape memory alloy actuated prosthetic hand.

PubMed

De Laurentis, Kathryn J; Mavroidis, Constantinos

2002-01-01

This paper presents the mechanical design for a new five fingered, twenty degree-of-freedom dexterous hand patterned after human anatomy and actuated by Shape Memory Alloy artificial muscles. Two experimental prototypes of a finger, one fabricated by traditional means and another fabricated by rapid prototyping techniques, are described and used to evaluate the design. An important aspect of the Rapid Prototype technique used here is that this multi-articulated hand will be fabricated in one step, without requiring assembly, while maintaining its desired mobility. The use of Shape Memory Alloy actuators combined with the rapid fabrication of the non-assembly type hand, reduce considerably its weight and fabrication time. Therefore, the focus of this paper is the mechanical design of a dexterous hand that combines Rapid Prototype techniques and smart actuators. The type of robotic hand described in this paper can be utilized for applications requiring low weight, compactness, and dexterity such as prosthetic devices, space and planetary exploration.

Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.

PubMed

Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep

Collaborative identification method for sea battlefield target based on deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Zheng, Guangdi; Pan, Mingbo; Liu, Wei; Wu, Xuetong

2018-03-01

The target identification of the sea battlefield is the prerequisite for the judgment of the enemy in the modern naval battle. In this paper, a collaborative identification method based on convolution neural network is proposed to identify the typical targets of sea battlefields. Different from the traditional single-input/single-output identification method, the proposed method constructs a multi-input/single-output co-identification architecture based on optimized convolution neural network and weighted D-S evidence theory. The simulation results show that

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.

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.

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.

Cellular Structure Fabricated on Ni Wire by a Simple and Cost-Effective Direct-Flame Approach and Its Application in Fiber-Shaped Supercapacitors.

PubMed

Wang, Zhihong; Cao, Fenhui; Chen, Kongfa; Yan, Yingming; Chen, Yifu; Zhang, Yaohui; Zhu, Xingbao; Wei, Bo; Xiong, Yueping; Lv, Zhe

2018-03-09

Cellular metals with the large surface/volume ratios and excellent electrical conductivity are widely applicable and have thus been studied extensively. It is highly desirable to develop a facile and cost-effective process for fabrication of porous metallic structures, and yet more so for micro/nanoporous structures. A direct-flame strategy is developed for in situ fabrication of micron-scale cellular architecture on a Ni metal precursor. The flame provides the required heat and also serves as a fuel reformer, which provides a gas mixture of H 2 , CO, and O 2 for redox treatment of metallic Ni. The redox processes at elevated temperatures allow fast reconstruction of the metal, leading to a cellular structure on Ni wire. This process is simple and clean and avoids the use of sacrificial materials or templates. Furthermore, nanocrystalline MnO 2 is coated on the microporous Ni wire (MPNW) to form a supercapacitor electrode. The MnO 2 /MPNW electrode and the corresponding fiber-shaped supercapacitor exhibit high specific capacitance and excellent cycling stability. Moreover, this work provides a novel strategy for the fabrication of cellular metals and alloys for a variety of applications, including catalysis, energy storage and conversion, and chemical sensing. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

A model of traffic signs recognition with convolutional neural network

NASA Astrophysics Data System (ADS)

Hu, Haihe; Li, Yujian; Zhang, Ting; Huo, Yi; Kuang, Wenqing

2016-10-01

In real traffic scenes, the quality of captured images are generally low due to some factors such as lighting conditions, and occlusion on. All of these factors are challengeable for automated recognition algorithms of traffic signs. Deep learning has provided a new way to solve this kind of problems recently. The deep network can automatically learn features from a large number of data samples and obtain an excellent recognition performance. We therefore approach this task of recognition of traffic signs as a general vision problem, with few assumptions related to road signs. We propose a model of Convolutional Neural Network (CNN) and apply the model to the task of traffic signs recognition. The proposed model adopts deep CNN as the supervised learning model, directly takes the collected traffic signs image as the input, alternates the convolutional layer and subsampling layer, and automatically extracts the features for the recognition of the traffic signs images. The proposed model includes an input layer, three convolutional layers, three subsampling layers, a fully-connected layer, and an output layer. To validate the proposed model, the experiments are implemented using the public dataset of China competition of fuzzy image processing. Experimental results show that the proposed model produces a recognition accuracy of 99.01 % on the training dataset, and yield a record of 92% on the preliminary contest within the fourth best.

Finding strong lenses in CFHTLS using convolutional neural networks

NASA Astrophysics Data System (ADS)

Jacobs, C.; Glazebrook, K.; Collett, T.; More, A.; McCarthy, C.

2017-10-01

We train and apply convolutional neural networks, a machine learning technique developed to learn from and classify image data, to Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) imaging for the identification of potential strong lensing systems. An ensemble of four convolutional neural networks was trained on images of simulated galaxy-galaxy lenses. The training sets consisted of a total of 62 406 simulated lenses and 64 673 non-lens negative examples generated with two different methodologies. An ensemble of trained networks was applied to all of the 171 deg2 of the CFHTLS wide field image data, identifying 18 861 candidates including 63 known and 139 other potential lens candidates. A second search of 1.4 million early-type galaxies selected from the survey catalogue as potential deflectors, identified 2465 candidates including 117 previously known lens candidates, 29 confirmed lenses/high-quality lens candidates, 266 novel probable or potential lenses and 2097 candidates we classify as false positives. For the catalogue-based search we estimate a completeness of 21-28 per cent with respect to detectable lenses and a purity of 15 per cent, with a false-positive rate of 1 in 671 images tested. We predict a human astronomer reviewing candidates produced by the system would identify 20 probable lenses and 100 possible lenses per hour in a sample selected by the robot. Convolutional neural networks are therefore a promising tool for use in the search for lenses in current and forthcoming surveys such as the Dark Energy Survey and the Large Synoptic Survey Telescope.

A unitary convolution approximation for the impact-parameter dependent electronic energy loss

NASA Astrophysics Data System (ADS)

Schiwietz, G.; Grande, P. L.

1999-06-01

In this work, we propose a simple method to calculate the impact-parameter dependence of the electronic energy loss of bare ions for all impact parameters. This perturbative convolution approximation (PCA) is based on first-order perturbation theory, and thus, it is only valid for fast particles with low projectile charges. Using Bloch's stopping-power result and a simple scaling, we get rid of the restriction to low charge states and derive the unitary convolution approximation (UCA). Results of the UCA are then compared with full quantum-mechanical coupled-channel calculations for the impact-parameter dependent electronic energy loss.

Fabrication of micro T-shaped tubular components by hydroforming process

NASA Astrophysics Data System (ADS)

Manabe, Ken-ichi; Itai, Kenta; Tada, Kazuo

2017-10-01

This paper deals with a T-shape micro tube hydroforming (MTHF) process for 500 µm outer diameter copper microtube. The MTHF experiments were carried out using a MTHF system utilizing ultrahigh pressure. The fundamental micro hydroforming characteristics as well as forming limits are examined experimentally and numerically. From the results, a process window diagram for micro T-shape hydroforming process is created, and a suitable "success" region is revealed.

Method for fabricating multi-strand superconducting cable

DOEpatents

Borden, A.R.

1985-04-01

Multi-strand superconducting cables adapted to be used, for example, to wind a magnet are fabricated by directing wire strands inwardly from spools disposed on the perimeter of a rotating disk and wrapping them diagonally around a tapered mandrel with a flattened cross-sectional shape with a core having a wedge-shaped channel. As the cable is pulled axially, flexibly coupled wedge-shaped pieces are continuously passed through the channel in the mandrel and inserted into the cable as an internal support therefor.

Classification of stroke disease using convolutional neural network

NASA Astrophysics Data System (ADS)

Marbun, J. T.; Seniman; Andayani, U.

2018-03-01

Stroke is a condition that occurs when the blood supply stop flowing to the brain because of a blockage or a broken blood vessel. A symptoms that happen when experiencing stroke, some of them is a dropped consciousness, disrupted vision and paralyzed body. The general examination is being done to get a picture of the brain part that have stroke using Computerized Tomography (CT) Scan. The image produced from CT will be manually checked and need a proper lighting by doctor to get a type of stroke. That is why it needs a method to classify stroke from CT image automatically. A method proposed in this research is Convolutional Neural Network. CT image of the brain is used as the input for image processing. The stage before classification are image processing (Grayscaling, Scaling, Contrast Limited Adaptive Histogram Equalization, then the image being classified with Convolutional Neural Network. The result then showed that the method significantly conducted was able to be used as a tool to classify stroke disease in order to distinguish the type of stroke from CT image.

Offline signature verification using convolution Siamese network

NASA Astrophysics Data System (ADS)

Xing, Zi-Jian; Yin, Fei; Wu, Yi-Chao; Liu, Cheng-Lin

2018-04-01

This paper presents an offline signature verification approach using convolutional Siamese neural network. Unlike the existing methods which consider feature extraction and metric learning as two independent stages, we adopt a deepleaning based framework which combines the two stages together and can be trained end-to-end. The experimental results on two offline public databases (GPDSsynthetic and CEDAR) demonstrate the superiority of our method on the offline signature verification problem.

Two-level convolution formula for nuclear structure function

NASA Astrophysics Data System (ADS)

Ma, Boqiang

1990-05-01

A two-level convolution formula for the nuclear structure function is derived in considering the nucleus as a composite system of baryon-mesons which are also composite systems of quark-gluons again. The results show that the European Muon Colaboration effect can not be explained by the nuclear effects as nucleon Fermi motion and nuclear binding contributions.

DSN telemetry system performance with convolutionally code data

NASA Technical Reports Server (NTRS)

Mulhall, B. D. L.; Benjauthrit, B.; Greenhall, C. A.; Kuma, D. M.; Lam, J. K.; Wong, J. S.; Urech, J.; Vit, L. D.

1975-01-01

The results obtained to date and the plans for future experiments for the DSN telemetry system were presented. The performance of the DSN telemetry system in decoding convolutionally coded data by both sequential and maximum likelihood techniques is being determined by testing at various deep space stations. The evaluation of performance models is also an objective of this activity.

Comparison of the convolution quadrature method and enhanced inverse FFT with application in elastodynamic boundary element method

NASA Astrophysics Data System (ADS)

Schanz, Martin; Ye, Wenjing; Xiao, Jinyou

2016-04-01

Transient problems can often be solved with transformation methods, where the inverse transformation is usually performed numerically. Here, the discrete Fourier transform in combination with the exponential window method is compared with the convolution quadrature method formulated as inverse transformation. Both are inverse Laplace transforms, which are formally identical but use different complex frequencies. A numerical study is performed, first with simple convolution integrals and, second, with a boundary element method (BEM) for elastodynamics. Essentially, when combined with the BEM, the discrete Fourier transform needs less frequency calculations, but finer mesh compared to the convolution quadrature method to obtain the same level of accuracy. If further fast methods like the fast multipole method are used to accelerate the boundary element method the convolution quadrature method is better, because the iterative solver needs much less iterations to converge. This is caused by the larger real part of the complex frequencies necessary for the calculation, which improves the conditions of system matrix.

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.

Improving deep convolutional neural networks with mixed maxout units.

PubMed

Zhao, Hui-Zhen; Liu, Fu-Xian; Li, Long-Yue

2017-01-01

Motivated by insights from the maxout-units-based deep Convolutional Neural Network (CNN) that "non-maximal features are unable to deliver" and "feature mapping subspace pooling is insufficient," we present a novel mixed variant of the recently introduced maxout unit called a mixout unit. Specifically, we do so by calculating the exponential probabilities of feature mappings gained by applying different convolutional transformations over the same input and then calculating the expected values according to their exponential probabilities. Moreover, we introduce the Bernoulli distribution to balance the maximum values with the expected values of the feature mappings subspace. Finally, we design a simple model to verify the pooling ability of mixout units and a Mixout-units-based Network-in-Network (NiN) model to analyze the feature learning ability of the mixout models. We argue that our proposed units improve the pooling ability and that mixout models can achieve better feature learning and classification performance.

Improving deep convolutional neural networks with mixed maxout units

PubMed Central

Liu, Fu-xian; Li, Long-yue

2017-01-01

Motivated by insights from the maxout-units-based deep Convolutional Neural Network (CNN) that “non-maximal features are unable to deliver” and “feature mapping subspace pooling is insufficient,” we present a novel mixed variant of the recently introduced maxout unit called a mixout unit. Specifically, we do so by calculating the exponential probabilities of feature mappings gained by applying different convolutional transformations over the same input and then calculating the expected values according to their exponential probabilities. Moreover, we introduce the Bernoulli distribution to balance the maximum values with the expected values of the feature mappings subspace. Finally, we design a simple model to verify the pooling ability of mixout units and a Mixout-units-based Network-in-Network (NiN) model to analyze the feature learning ability of the mixout models. We argue that our proposed units improve the pooling ability and that mixout models can achieve better feature learning and classification performance. PMID:28727737

Chemical-induced disease relation extraction via convolutional neural network.

PubMed

Gu, Jinghang; Sun, Fuqing; Qian, Longhua; Zhou, Guodong

2017-01-01

This article describes our work on the BioCreative-V chemical-disease relation (CDR) extraction task, which employed a maximum entropy (ME) model and a convolutional neural network model for relation extraction at inter- and intra-sentence level, respectively. In our work, relation extraction between entity concepts in documents was simplified to relation extraction between entity mentions. We first constructed pairs of chemical and disease mentions as relation instances for training and testing stages, then we trained and applied the ME model and the convolutional neural network model for inter- and intra-sentence level, respectively. Finally, we merged the classification results from mention level to document level to acquire the final relations between chemical and disease concepts. The evaluation on the BioCreative-V CDR corpus shows the effectiveness of our proposed approach. http://www.biocreative.org/resources/corpora/biocreative-v-cdr-corpus/. © The Author(s) 2017. Published by Oxford University Press.

Shape-Reprogrammable Polymers: Encoding, Erasing, and Re-Encoding (Postprint)

DTIC Science & Technology

2014-11-01

printing , is a layer-by-layer technology for producing 3D objects directly from a digital model. While 3D printing allows the fabrication of increasingly...one linear shape-translation processes often increase rapidly with shape complexity. Additive manufacturing, also called three-dimensional ( 3D

Transfer Learning with Convolutional Neural Networks for Classification of Abdominal Ultrasound Images.

PubMed

Cheng, Phillip M; Malhi, Harshawn S

2017-04-01

The purpose of this study is to evaluate transfer learning with deep convolutional neural networks for the classification of abdominal ultrasound images. Grayscale images from 185 consecutive clinical abdominal ultrasound studies were categorized into 11 categories based on the text annotation specified by the technologist for the image. Cropped images were rescaled to 256 × 256 resolution and randomized, with 4094 images from 136 studies constituting the training set, and 1423 images from 49 studies constituting the test set. The fully connected layers of two convolutional neural networks based on CaffeNet and VGGNet, previously trained on the 2012 Large Scale Visual Recognition Challenge data set, were retrained on the training set. Weights in the convolutional layers of each network were frozen to serve as fixed feature extractors. Accuracy on the test set was evaluated for each network. A radiologist experienced in abdominal ultrasound also independently classified the images in the test set into the same 11 categories. The CaffeNet network classified 77.3% of the test set images accurately (1100/1423 images), with a top-2 accuracy of 90.4% (1287/1423 images). The larger VGGNet network classified 77.9% of the test set accurately (1109/1423 images), with a top-2 accuracy of VGGNet was 89.7% (1276/1423 images). The radiologist classified 71.7% of the test set images correctly (1020/1423 images). The differences in classification accuracies between both neural networks and the radiologist were statistically significant (p < 0.001). The results demonstrate that transfer learning with convolutional neural networks may be used to construct effective classifiers for abdominal ultrasound images.

A pre-trained convolutional neural network based method for thyroid nodule diagnosis.

PubMed

Ma, Jinlian; Wu, Fa; Zhu, Jiang; Xu, Dong; Kong, Dexing

2017-01-01

In ultrasound images, most thyroid nodules are in heterogeneous appearances with various internal components and also have vague boundaries, so it is difficult for physicians to discriminate malignant thyroid nodules from benign ones. In this study, we propose a hybrid method for thyroid nodule diagnosis, which is a fusion of two pre-trained convolutional neural networks (CNNs) with different convolutional layers and fully-connected layers. Firstly, the two networks pre-trained with ImageNet database are separately trained. Secondly, we fuse feature maps learned by trained convolutional filters, pooling and normalization operations of the two CNNs. Finally, with the fused feature maps, a softmax classifier is used to diagnose thyroid nodules. The proposed method is validated on 15,000 ultrasound images collected from two local hospitals. Experiment results show that the proposed CNN based methods can accurately and effectively diagnose thyroid nodules. In addition, the fusion of the two CNN based models lead to significant performance improvement, with an accuracy of 83.02%±0.72%. These demonstrate the potential clinical applications of this method. Copyright © 2016 Elsevier B.V. All rights reserved.

Accelerating Convolutional Sparse Coding for Curvilinear Structures Segmentation by Refining SCIRD-TS Filter Banks.

PubMed

Annunziata, Roberto; Trucco, Emanuele

2016-11-01

Deep learning has shown great potential for curvilinear structure (e.g., retinal blood vessels and neurites) segmentation as demonstrated by a recent auto-context regression architecture based on filter banks learned by convolutional sparse coding. However, learning such filter banks is very time-consuming, thus limiting the amount of filters employed and the adaptation to other data sets (i.e., slow re-training). We address this limitation by proposing a novel acceleration strategy to speed-up convolutional sparse coding filter learning for curvilinear structure segmentation. Our approach is based on a novel initialisation strategy (warm start), and therefore it is different from recent methods improving the optimisation itself. Our warm-start strategy is based on carefully designed hand-crafted filters (SCIRD-TS), modelling appearance properties of curvilinear structures which are then refined by convolutional sparse coding. Experiments on four diverse data sets, including retinal blood vessels and neurites, suggest that the proposed method reduces significantly the time taken to learn convolutional filter banks (i.e., up to -82%) compared to conventional initialisation strategies. Remarkably, this speed-up does not worsen performance; in fact, filters learned with the proposed strategy often achieve a much lower reconstruction error and match or exceed the segmentation performance of random and DCT-based initialisation, when used as input to a random forest classifier.

An Interactive Graphics Program for Assistance in Learning Convolution.

ERIC Educational Resources Information Center

Frederick, Dean K.; Waag, Gary L.

1980-01-01

A program has been written for the interactive computer graphics facility at Rensselaer Polytechnic Institute that is designed to assist the user in learning the mathematical technique of convolving two functions. Because convolution can be represented graphically by a sequence of steps involving folding, shifting, multiplying, and integration, it…

Applications of deep convolutional neural networks to digitized natural history collections.

PubMed

Schuettpelz, Eric; Frandsen, Paul B; Dikow, Rebecca B; Brown, Abel; Orli, Sylvia; Peters, Melinda; Metallo, Adam; Funk, Vicki A; Dorr, Laurence J

2017-01-01

Natural history collections contain data that are critical for many scientific endeavors. Recent efforts in mass digitization are generating large datasets from these collections that can provide unprecedented insight. Here, we present examples of how deep convolutional neural networks can be applied in analyses of imaged herbarium specimens. We first demonstrate that a convolutional neural network can detect mercury-stained specimens across a collection with 90% accuracy. We then show that such a network can correctly distinguish two morphologically similar plant families 96% of the time. Discarding the most challenging specimen images increases accuracy to 94% and 99%, respectively. These results highlight the importance of mass digitization and deep learning approaches and reveal how they can together deliver powerful new investigative tools.

Applications of deep convolutional neural networks to digitized natural history collections

PubMed Central

Frandsen, Paul B.; Dikow, Rebecca B.; Brown, Abel; Orli, Sylvia; Peters, Melinda; Metallo, Adam; Funk, Vicki A.; Dorr, Laurence J.

2017-01-01

Abstract Natural history collections contain data that are critical for many scientific endeavors. Recent efforts in mass digitization are generating large datasets from these collections that can provide unprecedented insight. Here, we present examples of how deep convolutional neural networks can be applied in analyses of imaged herbarium specimens. We first demonstrate that a convolutional neural network can detect mercury-stained specimens across a collection with 90% accuracy. We then show that such a network can correctly distinguish two morphologically similar plant families 96% of the time. Discarding the most challenging specimen images increases accuracy to 94% and 99%, respectively. These results highlight the importance of mass digitization and deep learning approaches and reveal how they can together deliver powerful new investigative tools. PMID:29200929

Lidar-based individual tree species classification using convolutional neural network

NASA Astrophysics Data System (ADS)

Mizoguchi, Tomohiro; Ishii, Akira; Nakamura, Hiroyuki; Inoue, Tsuyoshi; Takamatsu, Hisashi

2017-06-01

Terrestrial lidar is commonly used for detailed documentation in the field of forest inventory investigation. Recent improvements of point cloud processing techniques enabled efficient and precise computation of an individual tree shape parameters, such as breast-height diameter, height, and volume. However, tree species are manually specified by skilled workers to date. Previous works for automatic tree species classification mainly focused on aerial or satellite images, and few works have been reported for classification techniques using ground-based sensor data. Several candidate sensors can be considered for classification, such as RGB or multi/hyper spectral cameras. Above all candidates, we use terrestrial lidar because it can obtain high resolution point cloud in the dark forest. We selected bark texture for the classification criteria, since they clearly represent unique characteristics of each tree and do not change their appearance under seasonable variation and aged deterioration. In this paper, we propose a new method for automatic individual tree species classification based on terrestrial lidar using Convolutional Neural Network (CNN). The key component is the creation step of a depth image which well describe the characteristics of each species from a point cloud. We focus on Japanese cedar and cypress which cover the large part of domestic forest. Our experimental results demonstrate the effectiveness of our proposed method.

One-step fabrication of multifunctional micromotors.

PubMed

Gao, Wenlong; Liu, Mei; Liu, Limei; Zhang, Hui; Dong, Bin; Li, Christopher Y

2015-09-07

Although artificial micromotors have undergone tremendous progress in recent years, their fabrication normally requires complex steps or expensive equipment. In this paper, we report a facile one-step method based on an emulsion solvent evaporation process to fabricate multifunctional micromotors. By simultaneously incorporating various components into an oil-in-water droplet, upon emulsification and solidification, a sphere-shaped, asymmetric, and multifunctional micromotor is formed. Some of the attractive functions of this model micromotor include autonomous movement in high ionic strength solution, remote control, enzymatic disassembly and sustained release. This one-step, versatile fabrication method can be easily scaled up and therefore may have great potential in mass production of multifunctional micromotors for a wide range of practical applications.

Additive manufacturing of near-net-shape bonded magnets: Prospects and challenges

DOE PAGES

Li, Ling; Post, Brian; Kunc, Vlastimil; ...

2017-01-03

Additive manufacturing (AM) or 3D printing is well known for producing arbitrary shaped parts without any tooling required, offering a promising alternative to the conventional injection molding method to fabricate near-net-shaped magnets. In order to determine their applicability in the fabrication of Nd-Fe-B bondedmagnets, we compare two 3D printing technologies, namely binder jetting and material extrusion. Some prospects and challenges of these state-of-the-art technologies for large-scale industrial applications will be discussed.

Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network.

PubMed

Yoon, Jaehong; Lee, Jungnyun; Whang, Mincheol

2018-01-01

Feature of event-related potential (ERP) has not been completely understood and illiteracy problem remains unsolved. To this end, P300 peak has been used as the feature of ERP in most brain-computer interface applications, but subjects who do not show such peak are common. Recent development of convolutional neural network provides a way to analyze spatial and temporal features of ERP. Here, we train the convolutional neural network with 2 convolutional layers whose feature maps represented spatial and temporal features of event-related potential. We have found that nonilliterate subjects' ERP show high correlation between occipital lobe and parietal lobe, whereas illiterate subjects only show correlation between neural activities from frontal lobe and central lobe. The nonilliterates showed peaks in P300, P500, and P700, whereas illiterates mostly showed peaks in around P700. P700 was strong in both subjects. We found that P700 peak may be the key feature of ERP as it appears in both illiterate and nonilliterate subjects.

Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network

PubMed Central

2018-01-01

Feature of event-related potential (ERP) has not been completely understood and illiteracy problem remains unsolved. To this end, P300 peak has been used as the feature of ERP in most brain–computer interface applications, but subjects who do not show such peak are common. Recent development of convolutional neural network provides a way to analyze spatial and temporal features of ERP. Here, we train the convolutional neural network with 2 convolutional layers whose feature maps represented spatial and temporal features of event-related potential. We have found that nonilliterate subjects' ERP show high correlation between occipital lobe and parietal lobe, whereas illiterate subjects only show correlation between neural activities from frontal lobe and central lobe. The nonilliterates showed peaks in P300, P500, and P700, whereas illiterates mostly showed peaks in around P700. P700 was strong in both subjects. We found that P700 peak may be the key feature of ERP as it appears in both illiterate and nonilliterate subjects.

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.

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.

Multichannel Convolutional Neural Network for Biological Relation Extraction.

PubMed

Quan, Chanqin; Hua, Lei; Sun, Xiao; Bai, Wenjun

2016-01-01

The plethora of biomedical relations which are embedded in medical logs (records) demands researchers' attention. Previous theoretical and practical focuses were restricted on traditional machine learning techniques. However, these methods are susceptible to the issues of "vocabulary gap" and data sparseness and the unattainable automation process in feature extraction. To address aforementioned issues, in this work, we propose a multichannel convolutional neural network (MCCNN) for automated biomedical relation extraction. The proposed model has the following two contributions: (1) it enables the fusion of multiple (e.g., five) versions in word embeddings; (2) the need for manual feature engineering can be obviated by automated feature learning with convolutional neural network (CNN). We evaluated our model on two biomedical relation extraction tasks: drug-drug interaction (DDI) extraction and protein-protein interaction (PPI) extraction. For DDI task, our system achieved an overall f -score of 70.2% compared to the standard linear SVM based system (e.g., 67.0%) on DDIExtraction 2013 challenge dataset. And for PPI task, we evaluated our system on Aimed and BioInfer PPI corpus; our system exceeded the state-of-art ensemble SVM system by 2.7% and 5.6% on f -scores.

Esophagus segmentation in CT via 3D fully convolutional neural network and random walk.

PubMed

Fechter, Tobias; Adebahr, Sonja; Baltas, Dimos; Ben Ayed, Ismail; Desrosiers, Christian; Dolz, Jose

2017-12-01

Precise delineation of organs at risk is a crucial task in radiotherapy treatment planning for delivering high doses to the tumor while sparing healthy tissues. In recent years, automated segmentation methods have shown an increasingly high performance for the delineation of various anatomical structures. However, this task remains challenging for organs like the esophagus, which have a versatile shape and poor contrast to neighboring tissues. For human experts, segmenting the esophagus from CT images is a time-consuming and error-prone process. To tackle these issues, we propose a random walker approach driven by a 3D fully convolutional neural network (CNN) to automatically segment the esophagus from CT images. First, a soft probability map is generated by the CNN. Then, an active contour model (ACM) is fitted to the CNN soft probability map to get a first estimation of the esophagus location. The outputs of the CNN and ACM are then used in conjunction with a probability model based on CT Hounsfield (HU) values to drive the random walker. Training and evaluation were done on 50 CTs from two different datasets, with clinically used peer-reviewed esophagus contours. Results were assessed regarding spatial overlap and shape similarity. The esophagus contours generated by the proposed algorithm showed a mean Dice coefficient of 0.76 ± 0.11, an average symmetric square distance of 1.36 ± 0.90 mm, and an average Hausdorff distance of 11.68 ± 6.80, compared to the reference contours. These results translate to a very good agreement with reference contours and an increase in accuracy compared to existing methods. Furthermore, when considering the results reported in the literature for the publicly available Synapse dataset, our method outperformed all existing approaches, which suggests that the proposed method represents the current state-of-the-art for automatic esophagus segmentation. We show that a CNN can yield accurate estimations of esophagus location, and that

EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation

PubMed Central

Amidi, Afshine; Megalooikonomou, Vasileios; Paragios, Nikos

2018-01-01

During the past decade, with the significant progress of computational power as well as ever-rising data availability, deep learning techniques became increasingly popular due to their excellent performance on computer vision problems. The size of the Protein Data Bank (PDB) has increased more than 15-fold since 1999, which enabled the expansion of models that aim at predicting enzymatic function via their amino acid composition. Amino acid sequence, however, is less conserved in nature than protein structure and therefore considered a less reliable predictor of protein function. This paper presents EnzyNet, a novel 3D convolutional neural networks classifier that predicts the Enzyme Commission number of enzymes based only on their voxel-based spatial structure. The spatial distribution of biochemical properties was also examined as complementary information. The two-layer architecture was investigated on a large dataset of 63,558 enzymes from the PDB and achieved an accuracy of 78.4% by exploiting only the binary representation of the protein shape. Code and datasets are available at https://github.com/shervinea/enzynet. PMID:29740518

EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation.

PubMed

Amidi, Afshine; Amidi, Shervine; Vlachakis, Dimitrios; Megalooikonomou, Vasileios; Paragios, Nikos; Zacharaki, Evangelia I

2018-01-01

During the past decade, with the significant progress of computational power as well as ever-rising data availability, deep learning techniques became increasingly popular due to their excellent performance on computer vision problems. The size of the Protein Data Bank (PDB) has increased more than 15-fold since 1999, which enabled the expansion of models that aim at predicting enzymatic function via their amino acid composition. Amino acid sequence, however, is less conserved in nature than protein structure and therefore considered a less reliable predictor of protein function. This paper presents EnzyNet, a novel 3D convolutional neural networks classifier that predicts the Enzyme Commission number of enzymes based only on their voxel-based spatial structure. The spatial distribution of biochemical properties was also examined as complementary information. The two-layer architecture was investigated on a large dataset of 63,558 enzymes from the PDB and achieved an accuracy of 78.4% by exploiting only the binary representation of the protein shape. Code and datasets are available at https://github.com/shervinea/enzynet.

Methods of fabricating a conductor assembly having a curvilinear arcuate shape

DOEpatents

Meinke, Rainer [Melbourne, FL

2011-08-23

A method for manufacture of a conductor assembly along a curvilinear axis. The assembly may be of the type which, when conducting current, generates a magnetic field or in which, in the presence of a changing magnetic field, a voltage is induced. In one example, the assembly includes a structure having a curved shape extending along the axis. A surface of the structure is positioned for formation of a channel along the curved shape. The structure is rotated about a second axis. While rotating the structure, a channel is formed in the surface that results in a helical shape in the structure. The channel extends both around and along the first axis.

Spectral characteristics of convolutionally coded digital signals

NASA Technical Reports Server (NTRS)

Divsalar, D.

1979-01-01

The power spectral density of the output symbol sequence of a convolutional encoder is computed for two different input symbol stream source models, namely, an NRZ signaling format and a first order Markov source. In the former, the two signaling states of the binary waveform are not necessarily assumed to occur with equal probability. The effects of alternate symbol inversion on this spectrum are also considered. The mathematical results are illustrated with many examples corresponding to optimal performance codes.

Fabrication of unique 3D microparticles in non-rectangular microchannels with flow lithography

NASA Astrophysics Data System (ADS)

Nam, Sung Min; Kim, Kibeom; Park, Wook; Lee, Wonhee

Invention of flow lithography has offered a simple yet effective method of fabricating micro-particles. However particles produced with conventional techniques were largely limited to 2-dimensional shapes projected to form a column. We proposed inexpensive and simple soft-lithography techniques to fabricate micro-channels with various cross-sectional shapes. The non-rectangular channels are then used to fabricate micro-particles using flow lithography resulting in interesting 3D shapes such as tetrahedrals or half-pyramids. In addition, a microfluidic device capable of fabricating multi-layered micro-particles was developed. On-chip PDMS valves are used to trap and position the particle at the precise location in microchannel with varying cross-section. Multilayer particles are generated by sequential monomer exchange and polymerization along the channel. While conventional multi-layered particles made with droplet generators require their layer materials be dissolved in immiscible fluids, the new method allows diverse choice of materials, not limited to their diffusibility. The multilayer 3D particles can be applied in areas such as drug delivery and tissue engineering.

Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology.

PubMed

Sharma, Harshita; Zerbe, Norman; Klempert, Iris; Hellwich, Olaf; Hufnagl, Peter

2017-11-01

Deep learning using convolutional neural networks is an actively emerging field in histological image analysis. This study explores deep learning methods for computer-aided classification in H&E stained histopathological whole slide images of gastric carcinoma. An introductory convolutional neural network architecture is proposed for two computerized applications, namely, cancer classification based on immunohistochemical response and necrosis detection based on the existence of tumor necrosis in the tissue. Classification performance of the developed deep learning approach is quantitatively compared with traditional image analysis methods in digital histopathology requiring prior computation of handcrafted features, such as statistical measures using gray level co-occurrence matrix, Gabor filter-bank responses, LBP histograms, gray histograms, HSV histograms and RGB histograms, followed by random forest machine learning. Additionally, the widely known AlexNet deep convolutional framework is comparatively analyzed for the corresponding classification problems. The proposed convolutional neural network architecture reports favorable results, with an overall classification accuracy of 0.6990 for cancer classification and 0.8144 for necrosis detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

ID card number detection algorithm based on convolutional neural network

NASA Astrophysics Data System (ADS)

Zhu, Jian; Ma, Hanjie; Feng, Jie; Dai, Leiyan

2018-04-01

In this paper, a new detection algorithm based on Convolutional Neural Network is presented in order to realize the fast and convenient ID information extraction in multiple scenarios. The algorithm uses the mobile device equipped with Android operating system to locate and extract the ID number; Use the special color distribution of the ID card, select the appropriate channel component; Use the image threshold segmentation, noise processing and morphological processing to take the binary processing for image; At the same time, the image rotation and projection method are used for horizontal correction when image was tilting; Finally, the single character is extracted by the projection method, and recognized by using Convolutional Neural Network. Through test shows that, A single ID number image from the extraction to the identification time is about 80ms, the accuracy rate is about 99%, It can be applied to the actual production and living environment.

A 3D Self-Shaping Strategy for Nanoresolution Multicomponent Architectures.

PubMed

Su, Meng; Huang, Zhandong; Li, Yifan; Qian, Xin; Li, Zheng; Hu, Xiaotian; Pan, Qi; Li, Fengyu; Li, Lihong; Song, Yanlin

2018-01-01

3D printing or fabrication pursues the essential surface behavior manipulation of droplets or a liquid for rapidly and precisely constructing 3D multimaterial architectures. Further development of 3D fabrication desires a self-shaping strategy that can heterogeneously integrate functional materials with disparate electrical or optical properties. Here, a 3D liquid self-shaping strategy is reported for rapidly patterning materials over a series of compositions and accurately achieving micro- and nanoscale structures. The predesigned template selectively pins the droplet, and the surface energy minimization drives the self-shaping processing. The as-prepared 3D circuits assembled by silver nanoparticles carry a current of 208-448 µA at 0.01 V impressed voltage, while the 3D architectures achieved by two different quantum dots show noninterfering optical properties with feature resolution below 3 µm. This strategy can facilely fabricate micro-nanogeometric patterns without a modeling program, which will be of great significance for the development of 3D functional devices. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Method for Fabricating Composite Structures Including Continuous Press Forming and Pultrusion Processing

NASA Technical Reports Server (NTRS)

Farley, Gary L. (Inventor)

1995-01-01

A method for fabricating composite structures at a low-cost, moderate-to-high production rate is disclosed. A first embodiment of the method includes employing a continuous press forming fabrication process. A second embodiment of the method includes employing a pultrusion process for obtaining composite structures. The methods include coating yarns with matrix material, weaving the yarn into fabric to produce a continuous fabric supply, and feeding multiple layers of net-shaped fabrics having optimally oriented fibers into a debulking tool to form an undebulked preform. The continuous press forming fabrication process includes partially debulking the preform, cutting the partially debulked preform, and debulking the partially debulked preform to form a netshape. An electron-beam or similar technique then cures the structure. The pultrusion fabric process includes feeding the undebulked preform into a heated die and gradually debulking the undebulked preform. The undebulked preform in the heated die changes dimension until a desired cross-sectional dimension is achieved. This process further includes obtaining a net-shaped infiltrated uncured preform, cutting the uncured preform to a desired length, and electron-beam curing (or similar technique) the uncured preform. These fabrication methods produce superior structures formed at higher production rates, resulting in lower cost and high structural performance.

Detecting of foreign object debris on airfield pavement using convolution neural network

NASA Astrophysics Data System (ADS)

Cao, Xiaoguang; Gu, Yufeng; Bai, Xiangzhi

2017-11-01

It is of great practical significance to detect foreign object debris (FOD) timely and accurately on the airfield pavement, because the FOD is a fatal threaten for runway safety in airport. In this paper, a new FOD detection framework based on Single Shot MultiBox Detector (SSD) is proposed. Two strategies include making the detection network lighter and using dilated convolution, which are proposed to better solve the FOD detection problem. The advantages mainly include: (i) the network structure becomes lighter to speed up detection task and enhance detection accuracy; (ii) dilated convolution is applied in network structure to handle smaller FOD. Thus, we get a faster and more accurate detection system.

Video-based convolutional neural networks for activity recognition from robot-centric videos

NASA Astrophysics Data System (ADS)

Ryoo, M. S.; Matthies, Larry

2016-05-01

In this evaluation paper, we discuss convolutional neural network (CNN)-based approaches for human activity recognition. In particular, we investigate CNN architectures designed to capture temporal information in videos and their applications to the human activity recognition problem. There have been multiple previous works to use CNN-features for videos. These include CNNs using 3-D XYT convolutional filters, CNNs using pooling operations on top of per-frame image-based CNN descriptors, and recurrent neural networks to learn temporal changes in per-frame CNN descriptors. We experimentally compare some of these different representatives CNNs while using first-person human activity videos. We especially focus on videos from a robots viewpoint, captured during its operations and human-robot interactions.

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. The

Shape forming by thermal expansion mismatch and shape memory locking in polymer/elastomer laminates

NASA Astrophysics Data System (ADS)

Yuan, Chao; Ding, Zhen; Wang, T. J.; Dunn, Martin L.; Qi, H. Jerry

2017-10-01

This paper studies a novel method to fabricate three-dimensional (3D) structure from 2D thermo-responsive shape memory polymer (SMP)/elastomer bilayer laminate. In this method, the shape change is actuated by the thermal mismatch strain between the SMP and the elastomer layers upon heating. However, the glass transition behavior of the SMP locks the material into a new 3D shape that is stable even upon cooling. Therefore, the second shape becomes a new permanent shape of the laminate. A theoretical model that accounts for the temperature-dependent thermomechanical behavior of the SMP material and thermal mismatch strain between the two layers is developed to better understand the underlying physics. Model predictions and experiments show good agreement and indicate that the theoretical model can well predict the bending behavior of the bilayer laminate. The model is then used in the optimal design of geometrical configuration and material selection. The latter also illustrates the requirement of thermomechanical behaviors of the SMP to lock the shape. Based on the fundamental understandings, several self-folding structures are demonstrated by the bilayer laminate design.

Electroencephalography Based Fusion Two-Dimensional (2D)-Convolution Neural Networks (CNN) Model for Emotion Recognition System.

PubMed

Kwon, Yea-Hoon; Shin, Sae-Byuk; Kim, Shin-Dug

2018-04-30

The purpose of this study is to improve human emotional classification accuracy using a convolution neural networks (CNN) model and to suggest an overall method to classify emotion based on multimodal data. We improved classification performance by combining electroencephalogram (EEG) and galvanic skin response (GSR) signals. GSR signals are preprocessed using by the zero-crossing rate. Sufficient EEG feature extraction can be obtained through CNN. Therefore, we propose a suitable CNN model for feature extraction by tuning hyper parameters in convolution filters. The EEG signal is preprocessed prior to convolution by a wavelet transform while considering time and frequency simultaneously. We use a database for emotion analysis using the physiological signals open dataset to verify the proposed process, achieving 73.4% accuracy, showing significant performance improvement over the current best practice models.

Transfer Function Bounds for Partial-unit-memory Convolutional Codes Based on Reduced State Diagram

NASA Technical Reports Server (NTRS)

Lee, P. J.

1984-01-01

The performance of a coding system consisting of a convolutional encoder and a Viterbi decoder is analytically found by the well-known transfer function bounding technique. For the partial-unit-memory byte-oriented convolutional encoder with m sub 0 binary memory cells and (k sub 0 m sub 0) inputs, a state diagram of 2(K) (sub 0) was for the transfer function bound. A reduced state diagram of (2 (m sub 0) +1) is used for easy evaluation of transfer function bounds for partial-unit-memory codes.

2D net shape weaving for cost effective manufacture of textile reinforced composites

NASA Astrophysics Data System (ADS)

Vo, D. M. P.; Kern, M.; Hoffmann, G.; Cherif, C.

2017-10-01

Despite significant weight and performance advantages over metal parts, the today’s demand for fibre-reinforced polymer composites (FRPC) has been limited mainly by their large manufacturing cost. The combination of dry textile preforms and low-cost consolidation processes such as resin transfer molding (RTM) has been appointed as a promising approach to low-cost FRPC manufacture. At the current state of the art, tooling and impregnation technology is well understood whereas preform fabrication technology has not been developed effectively. This paper presents an advanced 2D net shape weaving technology developed with the aim to establish a more cost effective system for the manufacture of dry textile preforms for FRPC. 2D net shape weaving is developed based on open reed weave (ORW) technology and enables the manufacture of 2D contoured woven fabrics with firm edge, so that oversize cutting and hand trimming after molding are no longer required. The introduction of 2D net shape woven fabrics helps to reduce material waste, cycle time and preform manufacturing cost significantly. Furthermore, higher grade of automation in preform fabrication can be achieved.

Shape-matching soft mechanical metamaterials.

PubMed

Mirzaali, M J; Janbaz, S; Strano, M; Vergani, L; Zadpoor, A A

2018-01-17

Architectured materials with rationally designed geometries could be used to create mechanical metamaterials with unprecedented or rare properties and functionalities. Here, we introduce "shape-matching" metamaterials where the geometry of cellular structures comprising auxetic and conventional unit cells is designed so as to achieve a pre-defined shape upon deformation. We used computational models to forward-map the space of planar shapes to the space of geometrical designs. The validity of the underlying computational models was first demonstrated by comparing their predictions with experimental observations on specimens fabricated with indirect additive manufacturing. The forward-maps were then used to devise the geometry of cellular structures that approximate the arbitrary shapes described by random Fourier's series. Finally, we show that the presented metamaterials could match the contours of three real objects including a scapula model, a pumpkin, and a Delft Blue pottery piece. Shape-matching materials have potential applications in soft robotics and wearable (medical) devices.

Non-spherical micro- and nanoparticles: fabrication, characterization and drug delivery applications.

PubMed

Mathaes, Roman; Winter, Gerhard; Besheer, Ahmed; Engert, Julia

2015-03-01

Micro- and nanoparticles in drug and vaccine delivery have opened up new possibilities in pharmaceutics. In the past, researchers focused mainly on particle size, surface chemistry and the use of various materials to control particle characteristics and functions. Lately, shape has been acknowledged as an important design parameter having an impact on the interaction with biological systems. In this review, we report on the latest developments in fabrication methods to tailor particle geometry, summarize analytical techniques for non-spherical particles and highlight the most important findings regarding their interaction with biological systems and their potential applications in drug delivery. The impact of shape on particle internalization into different cell types and particle biodistribution has been extensively studied in the past. Current research focuses on shape-dependent uptake mechanisms and applications for tumour therapy and vaccination. Different fabrication methods can be used to produce a variety of different particle types and shapes. Key challenges will be the transfer of new non-spherical particle fabrication methods from lab-scale to industrial large-scale production. Not all techniques may be scalable for the production of high quantities of particles. It will also be challenging to transfer the promising in vitro findings to suitable in vivo models.

Nanofibers-based nanoweb promise superhydrophobic polyaniline: from star-shaped to leaf-shaped structures.

PubMed

Fan, Haosen; Wang, Hao; Guo, Jing; Zhao, Ning; Xu, Jian

2013-11-01

Star-shaped and leaf-shaped polyaniline (PANI) hierarchical structures with interlaced nanofibers on the surface were successfully prepared by chemical polymerization of aniline in the presence of lithium triflate (LT). Chemical structure and composition of the star-like PANI obtained were characterized by FTIR and UV-vis spectra. PANI 2D architectures can be tailored from star-shaped to leaf-shaped structures by change the concentration of LT. The synthesized star-like and leaf-like polyaniline show good superhydrophobicity with water contact angles of both above 150° due to the combination of the rough nanoweb structure and the low surface tension of fluorinated chain of dopant. This method is a facile and applicable strategy for a large-scale fabrication of 2D PANI micro/nanostructures. Many potential applications such as self-cleaning and antifouling coating can be expected based on the superhydrophobic PANI micro/nanostructures. Crown Copyright © 2013. Published by Elsevier Inc. All rights reserved.

Generalized type II hybrid ARQ scheme using punctured convolutional coding

NASA Astrophysics Data System (ADS)

Kallel, Samir; Haccoun, David

1990-11-01

A method is presented to construct rate-compatible convolutional (RCC) codes from known high-rate punctured convolutional codes, obtained from best-rate 1/2 codes. The construction method is rather simple and straightforward, and still yields good codes. Moreover, low-rate codes can be obtained without any limit on the lowest achievable code rate. Based on the RCC codes, a generalized type-II hybrid ARQ scheme, which combines the benefits of the modified type-II hybrid ARQ strategy of Hagenauer (1988) with the code-combining ARQ strategy of Chase (1985), is proposed and analyzed. With the proposed generalized type-II hybrid ARQ strategy, the throughput increases as the starting coding rate increases, and as the channel degrades, it tends to merge with the throughput of rate 1/2 type-II hybrid ARQ schemes with code combining, thus allowing the system to be flexible and adaptive to channel conditions, even under wide noise variations and severe degradations.

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

PubMed Central

Yu, Haiyang; Wu, Zhihai; Wang, Shuqin; Wang, Yunpeng; Ma, Xiaolei

2017-01-01

Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction. PMID:28672867

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks.

PubMed

Yu, Haiyang; Wu, Zhihai; Wang, Shuqin; Wang, Yunpeng; Ma, Xiaolei

2017-06-26

Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

Ni-Mn-Ga shape memory nanoactuation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kohl, M., E-mail: manfred.kohl@kit.edu; Schmitt, M.; Krevet, B.

2014-01-27

To probe finite size effects in ferromagnetic shape memory nanoactuators, double-beam structures with minimum dimensions down to 100 nm are designed, fabricated, and characterized in-situ in a scanning electron microscope with respect to their coupled thermo-elastic and electro-thermal properties. Electrical resistance and mechanical beam bending tests demonstrate a reversible thermal shape memory effect down to 100 nm. Electro-thermal actuation involves large temperature gradients along the nanobeam in the order of 100 K/μm. We discuss the influence of surface and twin boundary energies and explain why free-standing nanoactuators behave differently compared to constrained geometries like films and nanocrystalline shape memory alloys.

Ni-Mn-Ga shape memory nanoactuation

NASA Astrophysics Data System (ADS)

Kohl, M.; Schmitt, M.; Backen, A.; Schultz, L.; Krevet, B.; Fähler, S.

2014-01-01

To probe finite size effects in ferromagnetic shape memory nanoactuators, double-beam structures with minimum dimensions down to 100 nm are designed, fabricated, and characterized in-situ in a scanning electron microscope with respect to their coupled thermo-elastic and electro-thermal properties. Electrical resistance and mechanical beam bending tests demonstrate a reversible thermal shape memory effect down to 100 nm. Electro-thermal actuation involves large temperature gradients along the nanobeam in the order of 100 K/μm. We discuss the influence of surface and twin boundary energies and explain why free-standing nanoactuators behave differently compared to constrained geometries like films and nanocrystalline shape memory alloys.

Forging of metallic nano-objects for the fabrication of submicron-size components

NASA Astrophysics Data System (ADS)

Rösler, J.; Mukherji, D.; Schock, K.; Kleindiek, S.

2007-03-01

In recent years, nanoscale fabrication has developed considerably, but the fabrication of free-standing nanosize components is still a great challenge. The fabrication of metallic nanocomponents utilizing three basic steps is demonstrated here. First, metallic alloys are used as factories to produce a metallic raw stock of nano-objects/nanoparticles in large numbers. These objects are then isolated from the powder containing thousands of such objects inside a scanning electron microscope using manipulators, and placed on a micro-anvil or a die. Finally, the shape of the individual nano-object is changed by nanoforging using a microhammer. In this way free-standing, high-strength, metallic nano-objects may be shaped into components with dimensions in the 100 nm range. By assembling such nanocomponents, high-performance microsystems can be fabricated, which are truly in the micrometre scale (the size ratio of a system to its component is typically 10:1).

The Convolutional Visual Network for Identification and Reconstruction of NOvA Events

DOE Office of Scientific and Technical Information (OSTI.GOV)

Psihas, Fernanda

In 2016 the NOvA experiment released results for the observation of oscillations in the vμ and ve channels as well as ve cross section measurements using neutrinos from Fermilab’s NuMI beam. These and other measurements in progress rely on the accurate identification and reconstruction of the neutrino flavor and energy recorded by our detectors. This presentation describes the first application of convolutional neural network technology for event identification and reconstruction in particle detectors like NOvA. The Convolutional Visual Network (CVN) Algorithm was developed for identification, categorization, and reconstruction of NOvA events. It increased the selection efficiency of the ve appearancemore » signal by 40% and studies show potential impact to the vμ disappearance analysis.« less

Image inpainting and super-resolution using non-local recursive deep convolutional network with skip connections

NASA Astrophysics Data System (ADS)

Liu, Miaofeng

2017-07-01

In recent years, deep convolutional neural networks come into use in image inpainting and super-resolution in many fields. Distinct to most of the former methods requiring to know beforehand the local information for corrupted pixels, we propose a 20-depth fully convolutional network to learn an end-to-end mapping a dataset of damaged/ground truth subimage pairs realizing non-local blind inpainting and super-resolution. As there often exist image with huge corruptions or inpainting on a low-resolution image that the existing approaches unable to perform well, we also share parameters in local area of layers to achieve spatial recursion and enlarge the receptive field. To avoid the difficulty of training this deep neural network, skip-connections between symmetric convolutional layers are designed. Experimental results shows that the proposed method outperforms state-of-the-art methods for diverse corrupting and low-resolution conditions, it works excellently when realizing super-resolution and image inpainting simultaneously

Fabrication of high quality aspheric microlens array by dose-modulated lithography and surface thermal reflow

NASA Astrophysics Data System (ADS)

Huang, Shengzhou; Li, Mujun; Shen, Lianguan; Qiu, Jinfeng; Zhou, Youquan

2018-03-01

A novel fabrication method for high quality aspheric microlens array (MLA) was developed by combining the dose-modulated DMD-based lithography and surface thermal reflow process. In this method, the complex shape of aspheric microlens is pre-modeled via dose modulation in a digital micromirror device (DMD) based maskless projection lithography. And the dose modulation mainly depends on the distribution of exposure dose of photoresist. Then the pre-shaped aspheric microlens is polished by a following non-contact thermal reflow (NCTR) process. Different from the normal process, the reflow process here is investigated to improve the surface quality while keeping the pre-modeled shape unchanged, and thus will avoid the difficulties in generating the aspheric surface during reflow. Fabrication of a designed aspheric MLA with this method was demonstrated in experiments. Results showed that the obtained aspheric MLA was good in both shape accuracy and surface quality. The presented method may be a promising approach in rapidly fabricating high quality aspheric microlens with complex surface.

Automated Detection of Obstructive Sleep Apnea Events from a Single-Lead Electrocardiogram Using a Convolutional Neural Network.

PubMed

Urtnasan, Erdenebayar; Park, Jong-Uk; Joo, Eun-Yeon; Lee, Kyoung-Joung

2018-04-23

In this study, we propose a method for the automated detection of obstructive sleep apnea (OSA) from a single-lead electrocardiogram (ECG) using a convolutional neural network (CNN). A CNN model was designed with six optimized convolution layers including activation, pooling, and dropout layers. One-dimensional (1D) convolution, rectified linear units (ReLU), and max pooling were applied to the convolution, activation, and pooling layers, respectively. For training and evaluation of the CNN model, a single-lead ECG dataset was collected from 82 subjects with OSA and was divided into training (including data from 63 patients with 34,281 events) and testing (including data from 19 patients with 8571 events) datasets. Using this CNN model, a precision of 0.99%, a recall of 0.99%, and an F 1 -score of 0.99% were attained with the training dataset; these values were all 0.96% when the CNN was applied to the testing dataset. These results show that the proposed CNN model can be used to detect OSA accurately on the basis of a single-lead ECG. Ultimately, this CNN model may be used as a screening tool for those suspected to suffer from OSA.

Universal FFM Hydrogen Spectral Line Shapes Applied to Ions and Electrons

NASA Astrophysics Data System (ADS)

Mossé, C.; Calisti, A.; Ferri, S.; Talin, B.; Bureyeva, L. A.; Lisitsa, V. S.

2008-10-01

We present a method for the calculation of hydrogen spectral line shapes based on two combined approaches: Universal Model and FFM procedure. We start with the analytical functions for the intensities of the Stark components of radiative transitions between highly excited atomic states with large values of principal quantum numbers n,n'γ1, with Δn = n-n'≪n for the specific cases of Hn-α line (Δn = 1) and Hn-β line (Δn = 2). The FFM line shape is obtained by averaging on the electric field of the Hooper's field distribution for ion and electron perturber dynamics and by mixing the Stark components with a jumping frequency rate ve (vi) where v = N1/3u (N is electron density and u is the ion or electron thermal velocity). Finally, the total line shape is given by convolution of ion and electron line shapes. Hydrogen line shape calculations for Balmer Hα and Hβ lines are compared to experimental results in low density plasma (Ne˜1016-1017cm-3) and low electron temperature in order of 10 000K. This method relying on analytic expressions permits fast calculation of Hn-α and Hn-β lines of hydrogen and could be used in the study of the Stark broadening of radio recombination lines for high principal quantum number.

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.

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.

Convolutional Neural Network for Histopathological Analysis of Osteosarcoma.

PubMed

Mishra, Rashika; Daescu, Ovidiu; Leavey, Patrick; Rakheja, Dinesh; Sengupta, Anita

2018-03-01

Pathologists often deal with high complexity and sometimes disagreement over osteosarcoma tumor classification due to cellular heterogeneity in the dataset. Segmentation and classification of histology tissue in H&E stained tumor image datasets is a challenging task because of intra-class variations, inter-class similarity, crowded context, and noisy data. In recent years, deep learning approaches have led to encouraging results in breast cancer and prostate cancer analysis. In this article, we propose convolutional neural network (CNN) as a tool to improve efficiency and accuracy of osteosarcoma tumor classification into tumor classes (viable tumor, necrosis) versus nontumor. The proposed CNN architecture contains eight learned layers: three sets of stacked two convolutional layers interspersed with max pooling layers for feature extraction and two fully connected layers with data augmentation strategies to boost performance. The use of a neural network results in higher accuracy of average 92% for the classification. We compare the proposed architecture with three existing and proven CNN architectures for image classification: AlexNet, LeNet, and VGGNet. We also provide a pipeline to calculate percentage necrosis in a given whole slide image. We conclude that the use of neural networks can assure both high accuracy and efficiency in osteosarcoma classification.

Metric learning with spectral graph convolutions on brain connectivity networks.

PubMed

Ktena, Sofia Ira; Parisot, Sarah; Ferrante, Enzo; Rajchl, Martin; Lee, Matthew; Glocker, Ben; Rueckert, Daniel

2018-04-01

Graph representations are often used to model structured data at an individual or population level and have numerous applications in pattern recognition problems. In the field of neuroscience, where such representations are commonly used to model structural or functional connectivity between a set of brain regions, graphs have proven to be of great importance. This is mainly due to the capability of revealing patterns related to brain development and disease, which were previously unknown. Evaluating similarity between these brain connectivity networks in a manner that accounts for the graph structure and is tailored for a particular application is, however, non-trivial. Most existing methods fail to accommodate the graph structure, discarding information that could be beneficial for further classification or regression analyses based on these similarities. We propose to learn a graph similarity metric using a siamese graph convolutional neural network (s-GCN) in a supervised setting. The proposed framework takes into consideration the graph structure for the evaluation of similarity between a pair of graphs, by employing spectral graph convolutions that allow the generalisation of traditional convolutions to irregular graphs and operates in the graph spectral domain. We apply the proposed model on two datasets: the challenging ABIDE database, which comprises functional MRI data of 403 patients with autism spectrum disorder (ASD) and 468 healthy controls aggregated from multiple acquisition sites, and a set of 2500 subjects from UK Biobank. We demonstrate the performance of the method for the tasks of classification between matching and non-matching graphs, as well as individual subject classification and manifold learning, showing that it leads to significantly improved results compared to traditional methods. Copyright © 2017 Elsevier Inc. All rights reserved.

A colour-tunable, weavable fibre-shaped polymer light-emitting electrochemical cell

NASA Astrophysics Data System (ADS)

Zhang, Zhitao; Guo, Kunping; Li, Yiming; Li, Xueyi; Guan, Guozhen; Li, Houpu; Luo, Yongfeng; Zhao, Fangyuan; Zhang, Qi; Wei, Bin; Pei, Qibing; Peng, Huisheng

2015-04-01

The emergence of wearable electronics and optoelectronics requires the development of devices that are not only highly flexible but can also be woven into textiles to offer a truly integrated solution. Here, we report a colour-tunable, weavable fibre-shaped polymer light-emitting electrochemical cell (PLEC). The fibre-shaped PLEC is fabricated using all-solution-based processes that can be scaled up for practical applications. The design has a coaxial structure comprising a modified metal wire cathode and a conducting aligned carbon nanotube sheet anode, with an electroluminescent polymer layer sandwiched between them. The fibre shape offers unique and promising advantages. For example, the luminance is independent of viewing angle, the fibre-shaped PLEC can provide a variety of different and tunable colours, it is lightweight, flexible and wearable, and it can potentially be woven into light-emitting clothes for the creation of smart fabrics.

Fabrication of High Temperature Cermet Materials for Nuclear Thermal Propulsion

NASA Technical Reports Server (NTRS)

Hickman, Robert; Panda, Binayak; Shah, Sandeep

2005-01-01

Processing techniques are being developed to fabricate refractory metal and ceramic cermet materials for Nuclear Thermal Propulsion (NTP). Significant advances have been made in the area of high-temperature cermet fuel processing since RoverNERVA. Cermet materials offer several advantages such as retention of fission products and fuels, thermal shock resistance, hydrogen compatibility, high conductivity, and high strength. Recent NASA h d e d research has demonstrated the net shape fabrication of W-Re-HfC and other refractory metal and ceramic components that are similar to UN/W-Re cermet fuels. This effort is focused on basic research and characterization to identify the most promising compositions and processing techniques. A particular emphasis is being placed on low cost processes to fabricate near net shape parts of practical size. Several processing methods including Vacuum Plasma Spray (VPS) and conventional PM processes are being evaluated to fabricate material property samples and components. Surrogate W-Re/ZrN cermet fuel materials are being used to develop processing techniques for both coated and uncoated ceramic particles. After process optimization, depleted uranium-based cermets will be fabricated and tested to evaluate mechanical, thermal, and hot H2 erosion properties. This paper provides details on the current results of the project.

Apparatus and method for fabricating multi-strand superconducting cable

DOEpatents

Borden, Albert R.

1986-01-01

Multi-strand superconducting cables adapted to be used, for example, to wind a magnet is fabricated by directing wire strands inwardly from spools disposed on the perimeter of a rotating disk and wrapping them diagonally around a tapered mandrel with a flattened cross-sectional shape with a core having a wedge-shaped channel. As the cable is pulled axially, flexibly coupled wedge-shaped pieces are continuously passed through the channel in the mandrel and inserted into the cable as an internal support therefor.

Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks.

PubMed

Zhong, Jiandan; Lei, Tao; Yao, Guangle

2017-11-24

Vehicle detection in aerial images is an important and challenging task. Traditionally, many target detection models based on sliding-window fashion were developed and achieved acceptable performance, but these models are time-consuming in the detection phase. Recently, with the great success of convolutional neural networks (CNNs) in computer vision, many state-of-the-art detectors have been designed based on deep CNNs. However, these CNN-based detectors are inefficient when applied in aerial image data due to the fact that the existing CNN-based models struggle with small-size object detection and precise localization. To improve the detection accuracy without decreasing speed, we propose a CNN-based detection model combining two independent convolutional neural networks, where the first network is applied to generate a set of vehicle-like regions from multi-feature maps of different hierarchies and scales. Because the multi-feature maps combine the advantage of the deep and shallow convolutional layer, the first network performs well on locating the small targets in aerial image data. Then, the generated candidate regions are fed into the second network for feature extraction and decision making. Comprehensive experiments are conducted on the Vehicle Detection in Aerial Imagery (VEDAI) dataset and Munich vehicle dataset. The proposed cascaded detection model yields high performance, not only in detection accuracy but also in detection speed.

Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks

PubMed Central

Zhong, Jiandan; Lei, Tao; Yao, Guangle

2017-01-01

Vehicle detection in aerial images is an important and challenging task. Traditionally, many target detection models based on sliding-window fashion were developed and achieved acceptable performance, but these models are time-consuming in the detection phase. Recently, with the great success of convolutional neural networks (CNNs) in computer vision, many state-of-the-art detectors have been designed based on deep CNNs. However, these CNN-based detectors are inefficient when applied in aerial image data due to the fact that the existing CNN-based models struggle with small-size object detection and precise localization. To improve the detection accuracy without decreasing speed, we propose a CNN-based detection model combining two independent convolutional neural networks, where the first network is applied to generate a set of vehicle-like regions from multi-feature maps of different hierarchies and scales. Because the multi-feature maps combine the advantage of the deep and shallow convolutional layer, the first network performs well on locating the small targets in aerial image data. Then, the generated candidate regions are fed into the second network for feature extraction and decision making. Comprehensive experiments are conducted on the Vehicle Detection in Aerial Imagery (VEDAI) dataset and Munich vehicle dataset. The proposed cascaded detection model yields high performance, not only in detection accuracy but also in detection speed. PMID:29186756

Automated Fabrication Technologies for High Performance Polymer Composites

NASA Technical Reports Server (NTRS)

Shuart , M. J.; Johnston, N. J.; Dexter, H. B.; Marchello, J. M.; Grenoble, R. W.

1998-01-01

New fabrication technologies are being exploited for building high graphite-fiber-reinforced composite structure. Stitched fiber preforms and resin film infusion have been successfully demonstrated for large, composite wing structures. Other automatic processes being developed include automated placement of tacky, drapable epoxy towpreg, automated heated head placement of consolidated ribbon/tape, and vacuum-assisted resin transfer molding. These methods have the potential to yield low cost high performance structures by fabricating composite structures to net shape out-of-autoclave.

Simple and Fast Method for Fabrication of Endoscopic Implantable Sensor Arrays

PubMed Central

Tahirbegi, I. Bogachan; Alvira, Margarita; Mir, Mònica; Samitier, Josep

2014-01-01

Here we have developed a simple method for the fabrication of disposable implantable all-solid-state ion-selective electrodes (ISE) in an array format without using complex fabrication equipment or clean room facilities. The electrodes were designed in a needle shape instead of planar electrodes for a full contact with the tissue. The needle-shape platform comprises 12 metallic pins which were functionalized with conductive inks and ISE membranes. The modified microelectrodes were characterized with cyclic voltammetry, scanning electron microscope (SEM), and optical interferometry. The surface area and roughness factor of each microelectrode were determined and reproducible values were obtained for all the microelectrodes on the array. In this work, the microelectrodes were modified with membranes for the detection of pH and nitrate ions to prove the reliability of the fabricated sensor array platform adapted to an endoscope. PMID:24971473

Simple and fast method for fabrication of endoscopic implantable sensor arrays.

PubMed

Tahirbegi, I Bogachan; Alvira, Margarita; Mir, Mònica; Samitier, Josep

2014-06-26

Here we have developed a simple method for the fabrication of disposable implantable all-solid-state ion-selective electrodes (ISE) in an array format without using complex fabrication equipment or clean room facilities. The electrodes were designed in a needle shape instead of planar electrodes for a full contact with the tissue. The needle-shape platform comprises 12 metallic pins which were functionalized with conductive inks and ISE membranes. The modified microelectrodes were characterized with cyclic voltammetry, scanning electron microscope (SEM), and optical interferometry. The surface area and roughness factor of each microelectrode were determined and reproducible values were obtained for all the microelectrodes on the array. In this work, the microelectrodes were modified with membranes for the detection of pH and nitrate ions to prove the reliability of the fabricated sensor array platform adapted to an endoscope.

Advanced Near Net Shape Technology

NASA Technical Reports Server (NTRS)

Vickers, John

2015-01-01

The objective of the Advanced Near Net Shape Technology (ANNST) project is to radically improve near net shape manufacturing methods from the current Technology/ Manufacturing Readiness Levels (TRL/MRL 3-4) to the point where they are viable candidates (TRL/ MRL-6) for shortening the time and cost for insertion of new aluminum alloys and revolutionary manufacturing methods into the development/improvement of space structures. Conventional cyrotank manufacturing processes require fabrication of multiple pieces welded together to form a complete tank. A variety of near net shape manufacturing processes has demonstrated excellent potential for enabling single-piece construction of components such as domes, barrels, and ring frames. Utilization of such processes can dramatically reduce the extent of welding and joining needed to construct cryogenic tanks and other aerospace structures. The specific focus of this project is to successfully mature the integrally stiffened cylinder (ISC) process in which a single-piece cylinder with integral stiffeners is formed in one spin/flow forming process. Structural launch vehicle components, like cryogenic fuel tanks (e.g., space shuttle external tank), are currently fabricated via multipiece assembly of parts produced through subtractive manufacturing techniques. Stiffened structural panels are heavily machined from thick plate, which results in excessive scrap rates. Multipiece construction requires welds to assemble the structure, which increases the risk for defects and catastrophic failures.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Akabani, G.; Hawkins, W.G.; Eckblade, M.B.

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 Transformmore » (CHT) algorithm.« less

Zebrafish tracking using convolutional neural networks.

PubMed

Xu, Zhiping; Cheng, Xi En

2017-02-17

Keeping identity for a long term after occlusion is still an open problem in the video tracking of zebrafish-like model animals, and accurate animal trajectories are the foundation of behaviour analysis. We utilize the highly accurate object recognition capability of a convolutional neural network (CNN) to distinguish fish of the same congener, even though these animals are indistinguishable to the human eye. We used data augmentation and an iterative CNN training method to optimize the accuracy for our classification task, achieving surprisingly accurate trajectories of zebrafish of different size and age zebrafish groups over different time spans. This work will make further behaviour analysis more reliable.

Zebrafish tracking using convolutional neural networks

NASA Astrophysics Data System (ADS)

Xu, Zhiping; Cheng, Xi En

2017-02-01

Keeping identity for a long term after occlusion is still an open problem in the video tracking of zebrafish-like model animals, and accurate animal trajectories are the foundation of behaviour analysis. We utilize the highly accurate object recognition capability of a convolutional neural network (CNN) to distinguish fish of the same congener, even though these animals are indistinguishable to the human eye. We used data augmentation and an iterative CNN training method to optimize the accuracy for our classification task, achieving surprisingly accurate trajectories of zebrafish of different size and age zebrafish groups over different time spans. This work will make further behaviour analysis more reliable.

The decoding of majority-multiplexed signals by means of dyadic convolution

NASA Astrophysics Data System (ADS)

Losev, V. V.

1980-09-01

The maximum likelihood method can often not be used for the decoding of majority-multiplexed signals because of the large number of computations required. This paper describes a fast dyadic convolution transform which can be used to reduce the number of computations.

Spectral-spatial classification of hyperspectral image using three-dimensional convolution network

NASA Astrophysics Data System (ADS)

Liu, Bing; Yu, Xuchu; Zhang, Pengqiang; Tan, Xiong; Wang, Ruirui; Zhi, Lu

2018-01-01

Recently, hyperspectral image (HSI) classification has become a focus of research. However, the complex structure of an HSI makes feature extraction difficult to achieve. Most current methods build classifiers based on complex handcrafted features computed from the raw inputs. The design of an improved 3-D convolutional neural network (3D-CNN) model for HSI classification is described. This model extracts features from both the spectral and spatial dimensions through the application of 3-D convolutions, thereby capturing the important discrimination information encoded in multiple adjacent bands. The designed model views the HSI cube data altogether without relying on any pre- or postprocessing. In addition, the model is trained in an end-to-end fashion without any handcrafted features. The designed model was applied to three widely used HSI datasets. The experimental results demonstrate that the 3D-CNN-based method outperforms conventional methods even with limited labeled training samples.

Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network.

PubMed

Kang, Eunhee; Chang, Won; Yoo, Jaejun; Ye, Jong Chul

2018-06-01

Model-based iterative reconstruction algorithms for low-dose X-ray computed tomography (CT) are computationally expensive. To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the textures were not fully recovered. To address this problem, here we propose a novel framelet-based denoising algorithm using wavelet residual network which synergistically combines the expressive power of deep learning and the performance guarantee from the framelet-based denoising algorithms. The new algorithms were inspired by the recent interpretation of the deep CNN as a cascaded convolution framelet signal representation. Extensive experimental results confirm that the proposed networks have significantly improved performance and preserve the detail texture of the original images.

Weed Growth Stage Estimator Using Deep Convolutional Neural Networks.

PubMed

Teimouri, Nima; Dyrmann, Mads; Nielsen, Per Rydahl; Mathiassen, Solvejg Kopp; Somerville, Gayle J; Jørgensen, Rasmus Nyholm

2018-05-16

This study outlines a new method of automatically estimating weed species and growth stages (from cotyledon until eight leaves are visible) of in situ images covering 18 weed species or families. Images of weeds growing within a variety of crops were gathered across variable environmental conditions with regards to soil types, resolution and light settings. Then, 9649 of these images were used for training the computer, which automatically divided the weeds into nine growth classes. The performance of this proposed convolutional neural network approach was evaluated on a further set of 2516 images, which also varied in term of crop, soil type, image resolution and light conditions. The overall performance of this approach achieved a maximum accuracy of 78% for identifying Polygonum spp. and a minimum accuracy of 46% for blackgrass. In addition, it achieved an average 70% accuracy rate in estimating the number of leaves and 96% accuracy when accepting a deviation of two leaves. These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species.

Plasmonic nanofocusing with a metallic pyramid and an integrated C-shaped aperture

NASA Astrophysics Data System (ADS)

Lindquist, Nathan C.; Johnson, Timothy W.; Nagpal, Prashant; Norris, David J.; Oh, Sang-Hyun

2013-05-01

We demonstrate the design, fabrication and characterization of a near-field plasmonic nanofocusing probe with a hybrid tip-plus-aperture design. By combining template stripping with focused ion beam lithography, a variety of aperture-based near-field probes can be fabricated with high optical performance. In particular, the combination of large transmission through a C-shaped aperture aligned to the sharp apex (<10 nm radius) of a template-stripped metallic pyramid allows the efficient delivery of light--via the C-shaped aperture--while providing a nanometric hotspot determined by the sharpness of the tip itself.

Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.

PubMed

Chang, P; Grinband, J; Weinberg, B D; Bardis, M; Khy, M; Cadena, G; Su, M-Y; Cha, S; Filippi, C G; Bota, D; Baldi, P; Poisson, L M; Jain, R; Chow, D

2018-05-10

The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation. MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify isocitrate dehydrogenase 1 ( IDH1 ) mutation status, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase ( MGMT ) promotor methylation status. Principal component analysis of the final convolutional neural network layer was used to extract the key imaging features critical for successful classification. Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. Each genetic category was also associated with distinctive imaging features such as definition of tumor margins, T1 and FLAIR suppression, extent of edema, extent of necrosis, and textural features. Our results indicate that for The Cancer Imaging Archives dataset, machine-learning approaches allow classification of individual genetic mutations of both low- and high-grade gliomas. We show that relevant MR imaging features acquired from an added dimensionality-reduction technique demonstrate that neural networks are capable of learning key imaging components without prior feature selection or human-directed training. © 2018 by American Journal of Neuroradiology.

Photothermal fabrication of microscale patterned DNA hydrogels

NASA Astrophysics Data System (ADS)

Shimomura, Suguru; Nishimura, Takahiro; Ogura, Yusuke; Tanida, Jun

2018-02-01

This paper introduces a method for fabricating microscale DNA hydrogels using irradiation with patterned light. Optical fabrication allows for the flexible and tunable formation of DNA hydrogels without changing the environmental conditions. Our scheme is based on local heat generation via the photothermal effect, which is induced by light irradiation on a quenching species. We demonstrate experimentally that, depending on the power and irradiation time, light irradiation enables the creation of local microscale DNA hydrogels, while the shapes of the DNA hydrogels are controlled by the irradiation patterns.

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.

Defect detection and classification of galvanized stamping parts based on fully convolution neural network

NASA Astrophysics Data System (ADS)

Xiao, Zhitao; Leng, Yanyi; Geng, Lei; Xi, Jiangtao

2018-04-01

In this paper, a new convolution neural network method is proposed for the inspection and classification of galvanized stamping parts. Firstly, all workpieces are divided into normal and defective by image processing, and then the defective workpieces extracted from the region of interest (ROI) area are input to the trained fully convolutional networks (FCN). The network utilizes an end-to-end and pixel-to-pixel training convolution network that is currently the most advanced technology in semantic segmentation, predicts result of each pixel. Secondly, we mark the different pixel values of the workpiece, defect and background for the training image, and use the pixel value and the number of pixels to realize the recognition of the defects of the output picture. Finally, the defect area's threshold depended on the needs of the project is set to achieve the specific classification of the workpiece. The experiment results show that the proposed method can successfully achieve defect detection and classification of galvanized stamping parts under ordinary camera and illumination conditions, and its accuracy can reach 99.6%. Moreover, it overcomes the problem of complex image preprocessing and difficult feature extraction and performs better adaptability.

Fabrication of cell container arrays with overlaid surface topographies.

PubMed

Truckenmüller, Roman; Giselbrecht, Stefan; Escalante-Marun, Maryana; Groenendijk, Max; Papenburg, Bernke; Rivron, Nicolas; Unadkat, Hemant; Saile, Volker; Subramaniam, Vinod; van den Berg, Albert; van Blitterswijk, Clemens; Wessling, Matthias; de Boer, Jan; Stamatialis, Dimitrios

2012-02-01

This paper presents cell culture substrates in the form of microcontainer arrays with overlaid surface topographies, and a technology for their fabrication. The new fabrication technology is based on microscale thermoforming of thin polymer films whose surfaces are topographically prepatterned on a micro- or nanoscale. For microthermoforming, we apply a new process on the basis of temporary back moulding of polymer films and use the novel concept of a perforated-sheet-like mould. Thermal micro- or nanoimprinting is applied for prepatterning. The novel cell container arrays are fabricated from polylactic acid (PLA) films. The thin-walled microcontainer structures have the shape of a spherical calotte merging into a hexagonal shape at their upper circumferential edges. In the arrays, the cell containers are arranged densely packed in honeycomb fashion. The inner surfaces of the highly curved container walls are provided with various topographical micro- and nanopatterns. For a first validation of the microcontainer arrays as in vitro cell culture substrates, C2C12 mouse premyoblasts are cultured in containers with microgrooved surfaces and shown to align along the grooves in the three-dimensional film substrates. In future stem-cell-biological and tissue engineering applications, microcontainers fabricated using the proposed technology may act as geometrically defined artificial microenvironments or niches.

Weaving multi-layer fabrics for reinforcement of engineering components

NASA Technical Reports Server (NTRS)

Hill, B. J.; Mcilhagger, R.; Mclaughlin, P.

1993-01-01

The performance of interlinked, multi-layer fabrics and near net shape preforms for engineering applications, woven on a 48 shaft dobby loom using glass, aramid, and carbon continuous filament yarns is assessed. The interlinking was formed using the warp yarns. Two basic types of structure were used. The first used a single warp beam and hence each of the warp yarns followed a similar path to form four layer interlinked reinforcements and preforms. In the second two warp beams were used, one for the interlinking yarns which pass from the top to the bottom layer through-the-thickness of the fabric and vice versa, and the other to provide 'straight' yarns in the body of the structure to carry the axial loading. Fabrics up to 15mm in thickness were constructed with varying amounts of through-the-thickness reinforcement. Tapered T and I sections were also woven, with the shaping produced by progressive removal of ends during construction. These fabrics and preforms were impregnated with resin and cured to form composite samples for testing. Using these two basic types of construction, the influence of reinforcement construction and the proportion and type of interlinking yarn on the performance of the composite was assessed.

Methods for freeform fabrication of structures

DOEpatents

Kaufman, Stephen G.; Spletzer, Barry L.

2000-01-01

Rapid prototyping methods and apparatuses that produce structures made of continuous-fiber polymer-matrix composites without the use of molds. Instead of using molds, the composite structure is fabricated patch by patch in layers or wraps, using a two- or three-axis stage connected to a rapidly-reconfigurable forming surface, and a robot arm to position the evolving composite structure, which are both programmable devices. Because programmable devices are included, i.e., a robot and a two- or three-axis stage connected to the reconfigurable forming surface, the control program needed to produce a desired shape can be easily modified to automatically generate the desired shape from an electronic model (e.g., using a CAD/CAM system) of the desired (predetermined) shape.

Fabrication of tissue engineered tympanic membrane patches using computer-aided design and injection molding.

PubMed

Hott, Morgan E; Megerian, Cliff A; Beane, Rich; Bonassar, Lawrence J

2004-07-01

The goal of the current study was to use computer-aided design and injection molding technologies to tissue engineer precisely shaped cartilage in the shape of butterfly tympanic membrane patches out of chondrocyte-seeded calcium alginate gels. Molds were designed on SolidWorks 2000 and built out of acrylonitrile butadiene styrene (ABS) using fused deposition modeling (FDM). Tympanic membrane patches were fabricated using bovine articular chondrocytes seeded at 50 x 10 cells/mL in 2% calcium alginate gels. Molded patches were cultured in vitro for up to 10 weeks and assessed biochemically, morphologically, and histologically. Unmolded patches demonstrated outstanding dimensional fidelity, with a volumetric precision of at least 3 microL, and maintained their shape well for up to 10 weeks of in vitro culture. Glycosaminoglycan and collagen content increased steadily over 10 weeks in culture, demonstrating continual deposition of new extracellular matrix consistent with new tissue development. The use of computer-aided design and injection molding technologies allows for the fabrication of very small, precisely shaped chondrocyte-seeded calcium alginate structures that faithfully maintain their shape during in vitro culture. In vitro fabrication of tympanic membrane patches with a precisely controlled geometry may have the potential to provide a minimally invasive alternative to traditional methods for the repair of chronic tympanic membrane perforations.

Micro-fabrication method of graphite mesa microdevices based on optical lithography technology

NASA Astrophysics Data System (ADS)

Zhang, Cheng; Wen, Donghui; Zhu, Huamin; Zhang, Xiaorui; Yang, Xing; Shi, Yunsheng; Zheng, Tianxiang

2017-12-01

Graphite mesa microdevices have incommensurate contact nanometer interfaces, superlubricity, high-speed self-retraction, and other characteristics, which have potential applications in high-performance oscillators and micro-scale switches, memory devices, and gyroscopes. However, the current method of fabricating graphite mesa microdevices is mainly based on high-cost, low efficiency electron beam lithography technology. In this paper, the processing technologies of graphite mesa microdevices with various shapes and sizes were investigated by a low-cost micro-fabrication method, which was mainly based on optical lithography technology. The characterization results showed that the optical lithography technology could realize a large-area of patterning on the graphite surface, and the graphite mesa microdevices, which have a regular shape, neat arrangement, and high verticality could be fabricated in large batches through optical lithography technology. The experiments and analyses showed that the graphite mesa microdevices fabricated through optical lithography technology basically have the same self-retracting characteristics as those fabricated through electron beam lithography technology, and the maximum size of the graphite mesa microdevices with self-retracting phenomenon can reach 10 µm  ×  10 µm. Therefore, the proposed method of this paper can realize the high-efficiency and low-cost processing of graphite mesa microdevices, which is significant for batch fabrication and application of graphite mesa microdevices.

Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber

NASA Astrophysics Data System (ADS)

Acciarri, R.; Adams, C.; An, R.; Asaadi, J.; Auger, M.; Bagby, L.; Baller, B.; Barr, G.; Bass, M.; Bay, F.; Bishai, M.; Blake, A.; Bolton, T.; Bugel, L.; Camilleri, L.; Caratelli, D.; Carls, B.; Castillo Fernandez, R.; Cavanna, F.; Chen, H.; Church, E.; Cianci, D.; Collin, G. H.; Conrad, J. M.; Convery, M.; Crespo-Anadón, J. I.; Del Tutto, M.; Devitt, D.; Dytman, S.; Eberly, B.; Ereditato, A.; Escudero Sanchez, L.; Esquivel, J.; Fleming, B. T.; Foreman, W.; Furmanski, A. P.; Garvey, G. T.; Genty, V.; Goeldi, D.; Gollapinni, S.; Graf, N.; Gramellini, E.; Greenlee, H.; Grosso, R.; Guenette, R.; Hackenburg, A.; Hamilton, P.; Hen, O.; Hewes, J.; Hill, C.; Ho, J.; Horton-Smith, G.; James, C.; de Vries, J. Jan; Jen, C.-M.; Jiang, L.; Johnson, R. A.; Jones, B. J. P.; Joshi, J.; Jostlein, H.; Kaleko, D.; Karagiorgi, G.; Ketchum, W.; Kirby, B.; Kirby, M.; Kobilarcik, T.; Kreslo, I.; Laube, A.; Li, Y.; Lister, A.; Littlejohn, B. R.; Lockwitz, S.; Lorca, D.; Louis, W. C.; Luethi, M.; Lundberg, B.; Luo, X.; Marchionni, A.; Mariani, C.; Marshall, J.; Martinez Caicedo, D. A.; Meddage, V.; Miceli, T.; Mills, G. B.; Moon, J.; Mooney, M.; Moore, C. D.; Mousseau, J.; Murrells, R.; Naples, D.; Nienaber, P.; Nowak, J.; Palamara, O.; Paolone, V.; Papavassiliou, V.; Pate, S. F.; Pavlovic, Z.; Porzio, D.; Pulliam, G.; Qian, X.; Raaf, J. L.; Rafique, A.; Rochester, L.; von Rohr, C. Rudolf; Russell, B.; Schmitz, D. W.; Schukraft, A.; Seligman, W.; Shaevitz, M. H.; Sinclair, J.; Snider, E. L.; Soderberg, M.; Söldner-Rembold, S.; Soleti, S. R.; Spentzouris, P.; Spitz, J.; St. John, J.; Strauss, T.; Szelc, A. M.; Tagg, N.; Terao, K.; Thomson, M.; Toups, M.; Tsai, Y.-T.; Tufanli, S.; Usher, T.; Van de Water, R. G.; Viren, B.; Weber, M.; Weston, J.; Wickremasinghe, D. A.; Wolbers, S.; Wongjirad, T.; Woodruff, K.; Yang, T.; Zeller, G. P.; Zennamo, J.; Zhang, C.

2017-03-01

We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.

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.

Periodic Cellular Structure Technology for Shape Memory Alloys

NASA Technical Reports Server (NTRS)

Chen, Edward Y.

2015-01-01

Shape memory alloys are being considered for a wide variety of adaptive components for engine and airframe applications because they can undergo large amounts of strain and then revert to their original shape upon heating or unloading. Transition45 Technologies, Inc., has developed an innovative periodic cellular structure (PCS) technology for shape memory alloys that enables fabrication of complex bulk configurations, such as lattice block structures. These innovative structures are manufactured using an advanced reactive metal casting technology that offers a relatively low cost and established approach for constructing near-net shape aerospace components. Transition45 is continuing to characterize these structures to determine how best to design a PCS to better exploit the use of shape memory alloys in aerospace applications.

Shape memory polymer network with thermally distinct elasticity and plasticity.

PubMed

Zhao, Qian; Zou, Weike; Luo, Yingwu; Xie, Tao

2016-01-01

Stimuli-responsive materials with sophisticated yet controllable shape-changing behaviors are highly desirable for real-world device applications. Among various shape-changing materials, the elastic nature of shape memory polymers allows fixation of temporary shapes that can recover on demand, whereas polymers with exchangeable bonds can undergo permanent shape change via plasticity. We integrate the elasticity and plasticity into a single polymer network. Rational molecular design allows these two opposite behaviors to be realized at different temperature ranges without any overlap. By exploring the cumulative nature of the plasticity, we demonstrate easy manipulation of highly complex shapes that is otherwise extremely challenging. The dynamic shape-changing behavior paves a new way for fabricating geometrically complex multifunctional devices.

Shape memory polymer network with thermally distinct elasticity and plasticity

PubMed Central

Zhao, Qian; Zou, Weike; Luo, Yingwu; Xie, Tao

2016-01-01

Stimuli-responsive materials with sophisticated yet controllable shape-changing behaviors are highly desirable for real-world device applications. Among various shape-changing materials, the elastic nature of shape memory polymers allows fixation of temporary shapes that can recover on demand, whereas polymers with exchangeable bonds can undergo permanent shape change via plasticity. We integrate the elasticity and plasticity into a single polymer network. Rational molecular design allows these two opposite behaviors to be realized at different temperature ranges without any overlap. By exploring the cumulative nature of the plasticity, we demonstrate easy manipulation of highly complex shapes that is otherwise extremely challenging. The dynamic shape-changing behavior paves a new way for fabricating geometrically complex multifunctional devices. PMID:26824077

Multi-focus image fusion with the all convolutional neural network

NASA Astrophysics Data System (ADS)

Du, Chao-ben; Gao, She-sheng

2018-01-01

A decision map contains complete and clear information about the image to be fused, which is crucial to various image fusion issues, especially multi-focus image fusion. However, in order to get a satisfactory image fusion effect, getting a decision map is very necessary and usually difficult to finish. In this letter, we address this problem with convolutional neural network (CNN), aiming to get a state-of-the-art decision map. The main idea is that the max-pooling of CNN is replaced by a convolution layer, the residuals are propagated backwards by gradient descent, and the training parameters of the individual layers of the CNN are updated layer by layer. Based on this, we propose a new all CNN (ACNN)-based multi-focus image fusion method in spatial domain. We demonstrate that the decision map obtained from the ACNN is reliable and can lead to high-quality fusion results. Experimental results clearly validate that the proposed algorithm can obtain state-of-the-art fusion performance in terms of both qualitative and quantitative evaluations.

Multineuron spike train analysis with R-convolution linear combination kernel.

PubMed

Tezuka, Taro

2018-06-01

A spike train kernel provides an effective way of decoding information represented by a spike train. Some spike train kernels have been extended to multineuron spike trains, which are simultaneously recorded spike trains obtained from multiple neurons. However, most of these multineuron extensions were carried out in a kernel-specific manner. In this paper, a general framework is proposed for extending any single-neuron spike train kernel to multineuron spike trains, based on the R-convolution kernel. Special subclasses of the proposed R-convolution linear combination kernel are explored. These subclasses have a smaller number of parameters and make optimization tractable when the size of data is limited. The proposed kernel was evaluated using Gaussian process regression for multineuron spike trains recorded from an animal brain. It was compared with the sum kernel and the population Spikernel, which are existing ways of decoding multineuron spike trains using kernels. The results showed that the proposed approach performs better than these kernels and also other commonly used neural decoding methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

Development of a morphological convolution operator for bearing fault detection

NASA Astrophysics Data System (ADS)

Li, Yifan; Liang, Xihui; Liu, Weiwei; Wang, Yan

2018-05-01

This paper presents a novel signal processing scheme, namely morphological convolution operator (MCO) lifted morphological undecimated wavelet (MUDW), for rolling element bearing fault detection. In this scheme, a MCO is first designed to fully utilize the advantage of the closing & opening gradient operator and the closing-opening & opening-closing gradient operator for feature extraction as well as the merit of excellent denoising characteristics of the convolution operator. The MCO is then introduced into MUDW for the purpose of improving the fault detection ability of the reported MUDWs. Experimental vibration signals collected from a train wheelset test rig and the bearing data center of Case Western Reserve University are employed to evaluate the effectiveness of the proposed MCO lifted MUDW on fault detection of rolling element bearings. The results show that the proposed approach has a superior performance in extracting fault features of defective rolling element bearings. In addition, comparisons are performed between two reported MUDWs and the proposed MCO lifted MUDW. The MCO lifted MUDW outperforms both of them in detection of outer race faults and inner race faults of rolling element bearings.

Numerical Simulation with Experimental Validation of the Draping Behavior of Woven Fabrics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rodgers, William; Pasupuleti, Praveen; Zhao, Selina

Woven fabric composites are extensively used in molding complex geometrical shapes due to their high conformability compared to other fabrics. Preforming is an important step in the overall process. In this step, the two-dimensional fabric is draped to become the three-dimensional shape of the part prior to resin injection. During preforming, the orientation of the tows may change significantly compared to the initial orientations. Accurate prediction of the tow orientations after molding is important for evaluating the structural performance of the final part. This paper investigates the fiber angle changes for carbon fiber woven fabrics during draping over a truncatedmore » pyramid tool designed and fabricated at the General Motors Research Labs. This aspect of study is a subset of the broad study conducted under the purview of a Department of Energy project funded to GM in developing state of the art computational tools for integrated manufacturing and structural performance prediction of carbon fiber composites. Fabric bending, picture frame testing, and bias-extension evaluations were carried out to determine the material parameters for these fabrics. The PAM-FORM computer program was used to model the draping behavior of these fabrics. Following deformation, fiber angle changes at different locations on the truncated pyramid were measured experimentally. The predicted angles matched the experimental results well as measured along the centerline and at several different locations on the deformed fabric. Details of the test methods used as well as the numerical results with various simulation parameters will be provided.« less

Video-based face recognition via convolutional neural networks

NASA Astrophysics Data System (ADS)

Bao, Tianlong; Ding, Chunhui; Karmoshi, Saleem; Zhu, Ming

2017-06-01

Face recognition has been widely studied recently while video-based face recognition still remains a challenging task because of the low quality and large intra-class variation of video captured face images. In this paper, we focus on two scenarios of video-based face recognition: 1)Still-to-Video(S2V) face recognition, i.e., querying a still face image against a gallery of video sequences; 2)Video-to-Still(V2S) face recognition, in contrast to S2V scenario. A novel method was proposed in this paper to transfer still and video face images to an Euclidean space by a carefully designed convolutional neural network, then Euclidean metrics are used to measure the distance between still and video images. Identities of still and video images that group as pairs are used as supervision. In the training stage, a joint loss function that measures the Euclidean distance between the predicted features of training pairs and expanding vectors of still images is optimized to minimize the intra-class variation while the inter-class variation is guaranteed due to the large margin of still images. Transferred features are finally learned via the designed convolutional neural network. Experiments are performed on COX face dataset. Experimental results show that our method achieves reliable performance compared with other state-of-the-art methods.

Adaptive Correlation Model for Visual Tracking Using Keypoints Matching and Deep Convolutional Feature.

PubMed

Li, Yuankun; Xu, Tingfa; Deng, Honggao; Shi, Guokai; Guo, Jie

2018-02-23

Although correlation filter (CF)-based visual tracking algorithms have achieved appealing results, there are still some problems to be solved. When the target object goes through long-term occlusions or scale variation, the correlation model used in existing CF-based algorithms will inevitably learn some non-target information or partial-target information. In order to avoid model contamination and enhance the adaptability of model updating, we introduce the keypoints matching strategy and adjust the model learning rate dynamically according to the matching score. Moreover, the proposed approach extracts convolutional features from a deep convolutional neural network (DCNN) to accurately estimate the position and scale of the target. Experimental results demonstrate that the proposed tracker has achieved satisfactory performance in a wide range of challenging tracking scenarios.

Evaluation of human exposure to single electromagnetic pulses of arbitrary shape.

PubMed

Jelínek, Lukás; Pekárek, Ludĕk

2006-03-01

Transient current density J(t) induced in the body of a person exposed to a single magnetic pulse of arbitrary shape or to a magnetic jump is filtered by a convolution integral containing in its kernel the frequency and phase dependence of the basic limit value adopted in a way similar to that used for reference values in the International Commission on Non-lonising Radiation Protection statement. From the obtained time-dependent dimensionless impact function W(J)(t) can immediately be determined whether the exposure to the analysed single event complies with the basic limit. For very slowly varying field, the integral kernel is extended to include the softened ICNIRP basic limit for frequencies lower than 4 Hz.

Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images.

PubMed

Li, Wei; Cao, Peng; Zhao, Dazhe; Wang, Junbo

2016-01-01

Computer aided detection (CAD) systems can assist radiologists by offering a second opinion on early diagnosis of lung cancer. Classification and feature representation play critical roles in false-positive reduction (FPR) in lung nodule CAD. We design a deep convolutional neural networks method for nodule classification, which has an advantage of autolearning representation and strong generalization ability. A specified network structure for nodule images is proposed to solve the recognition of three types of nodules, that is, solid, semisolid, and ground glass opacity (GGO). Deep convolutional neural networks are trained by 62,492 regions-of-interest (ROIs) samples including 40,772 nodules and 21,720 nonnodules from the Lung Image Database Consortium (LIDC) database. Experimental results demonstrate the effectiveness of the proposed method in terms of sensitivity and overall accuracy and that it consistently outperforms the competing methods.

Energy-beam-driven rapid fabrication system

DOEpatents

Keicher, David M.; Atwood, Clinton L.; Greene, Donald L.; Griffith, Michelle L.; Harwell, Lane D.; Jeantette, Francisco P.; Romero, Joseph A.; Schanwald, Lee P.; Schmale, David T.

2002-01-01

An energy beam driven rapid fabrication system, in which an energy beam strikes a growth surface to form a molten puddle thereon. Feed powder is then injected into the molten puddle from a converging flow of feed powder. A portion of the feed powder becomes incorporated into the molten puddle, forcing some of the puddle contents to freeze on the growth surface, thereby adding an additional layer of material. By scanning the energy beam and the converging flow of feed powder across the growth surface, complex three-dimensional shapes can be formed, ready or nearly ready for use. Nearly any class of material can be fabricated using this system.

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

NASA Technical Reports Server (NTRS)

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

2016-01-01

Low Technology Readiness Levels (TRLs) and high levels of uncertainty make it challenging to develop cost estimates of new technologies in the R&D phase. It is however essential for NASA to understand the costs and benefits associated with novel concepts, in order to prioritize research investments and evaluate the potential for technology transfer and commercialization. This paper proposes a framework to perform a cost-benefit analysis of a technology in the R&D phase. This framework was developed and used to assess the Advanced Near Net Shape Technology (ANNST) manufacturing process for fabricating integrally stiffened cylinders. The ANNST method was compared with the conventional multi-piece metallic construction and composite processes for fabricating integrally stiffened cylinders. Following the definition of a case study for a cryogenic tank cylinder of specified geometry, data was gathered through interviews with Subject Matter Experts (SMEs), with particular focus placed on production costs and process complexity. This data served as the basis to produce process flowcharts and timelines, mass estimates, and rough order-of-magnitude cost and schedule estimates. The scalability of the results was subsequently investigated to understand the variability of the results based on tank size. Lastly, once costs and benefits were identified, the Analytic Hierarchy Process (AHP) was used to assess the relative value of these achieved benefits for potential stakeholders. These preliminary, rough order-of-magnitude results predict 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. Compared to the composite manufacturing technique, these results predict cost savings of 35 to 58 percent; 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

High-strain slide-ring shape-memory polycaprolactone-based polyurethane.

PubMed

Wu, Ruiqing; Lai, Jingjuan; Pan, Yi; Zheng, Zhaohui; Ding, Xiaobin

2018-06-06

To enable shape-memory polymer networks to achieve recoverable high deformability with a simultaneous high shape-fixity ratio and shape-recovery ratio, novel semi-crystalline slide-ring shape-memory polycaprolactone-based polyurethane (SR-SMPCLU) with movable net-points constructed by a topologically interlocked slide-ring structure was designed and fabricated. The SR-SMPCLU not only exhibited good shape fixity, almost complete shape recovery, and a fast shape-recovery speed, it also showed an outstanding recoverable high-strain capacity with 95.83% Rr under a deformation strain of 1410% due to the pulley effect of the topological slide-ring structure. Furthermore, the SR-SMPCLU system maintained excellent shape-memory performance with increasing the training cycle numbers at 45% and even 280% deformation strain. The effects of the slide-ring cross-linker content, deformation strain, and successive shape-memory cycles on the shape-memory performance were investigated. A possible mechanism for the shape-memory effect of the SR-SMPCLU system is proposed.

Optical Properties of the Crescent–Shaped Nanohole Antenna

PubMed Central

Wu, Liz Y.; Ross, Benjamin M.; Lee, Luke P.

2009-01-01

We present the first optical study of large–area random arrays of crescent–shaped nanoholes. The crescent–shaped nanohole antennae, fabricated using wafer–scale nanosphere lithography, provide a complement to crescent–shaped nanostructures, called nanocrescents, which have been established as powerful plasmonic biosensors. With both systematic experimental and computational analysis, we characterize the optical properties of crescent–shaped nanohole antennae, and demonstrate tunability of their optical response by varying all key geometric parameters. Crescent–shaped nanoholes have reproducible sub–10 nm tips and are sharper than corresponding nanocrescents, resulting in higher local field enhancement (LFE), which is predicted to be |E|/|E0| = 1500. In addition, the crescent–shaped nanohole hole–based geometry offers increased integratability and the potential to nanoconfine analyte in “hot–spot” regions—increasing biomolecular sensitivity and allowing localized nanoscale optical control of biological functions. PMID:19354226

Target recognition based on convolutional neural network

NASA Astrophysics Data System (ADS)

Wang, Liqiang; Wang, Xin; Xi, Fubiao; Dong, Jian

2017-11-01

One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.

Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding

PubMed Central

Min, Xu; Zeng, Wanwen; Chen, Ning; Chen, Ting; Jiang, Rui

2017-01-01

Abstract Motivation: Experimental techniques for measuring chromatin accessibility are expensive and time consuming, appealing for the development of computational approaches to predict open chromatin regions from DNA sequences. Along this direction, existing methods fall into two classes: one based on handcrafted k-mer features and the other based on convolutional neural networks. Although both categories have shown good performance in specific applications thus far, there still lacks a comprehensive framework to integrate useful k-mer co-occurrence information with recent advances in deep learning. Results: We fill this gap by addressing the problem of chromatin accessibility prediction with a convolutional Long Short-Term Memory (LSTM) network with k-mer embedding. We first split DNA sequences into k-mers and pre-train k-mer embedding vectors based on the co-occurrence matrix of k-mers by using an unsupervised representation learning approach. We then construct a supervised deep learning architecture comprised of an embedding layer, three convolutional layers and a Bidirectional LSTM (BLSTM) layer for feature learning and classification. We demonstrate that our method gains high-quality fixed-length features from variable-length sequences and consistently outperforms baseline methods. We show that k-mer embedding can effectively enhance model performance by exploring different embedding strategies. We also prove the efficacy of both the convolution and the BLSTM layers by comparing two variations of the network architecture. We confirm the robustness of our model to hyper-parameters by performing sensitivity analysis. We hope our method can eventually reinforce our understanding of employing deep learning in genomic studies and shed light on research regarding mechanisms of chromatin accessibility. Availability and implementation: The source code can be downloaded from https://github.com/minxueric/ismb2017_lstm. Contact: tingchen@tsinghua.edu.cn or ruijiang

Residual Highway Convolutional Neural Networks for in-loop Filtering in HEVC.

PubMed

Zhang, Yongbing; Shen, Tao; Ji, Xiangyang; Zhang, Yun; Xiong, Ruiqin; Dai, Qionghai

2018-08-01

High efficiency video coding (HEVC) standard achieves half bit-rate reduction while keeping the same quality compared with AVC. However, it still cannot satisfy the demand of higher quality in real applications, especially at low bit rates. To further improve the quality of reconstructed frame while reducing the bitrates, a residual highway convolutional neural network (RHCNN) is proposed in this paper for in-loop filtering in HEVC. The RHCNN is composed of several residual highway units and convolutional layers. In the highway units, there are some paths that could allow unimpeded information across several layers. Moreover, there also exists one identity skip connection (shortcut) from the beginning to the end, which is followed by one small convolutional layer. Without conflicting with deblocking filter (DF) and sample adaptive offset (SAO) filter in HEVC, RHCNN is employed as a high-dimension filter following DF and SAO to enhance the quality of reconstructed frames. To facilitate the real application, we apply the proposed method to I frame, P frame, and B frame, respectively. For obtaining better performance, the entire quantization parameter (QP) range is divided into several QP bands, where a dedicated RHCNN is trained for each QP band. Furthermore, we adopt a progressive training scheme for the RHCNN where the QP band with lower value is used for early training and their weights are used as initial weights for QP band of higher values in a progressive manner. Experimental results demonstrate that the proposed method is able to not only raise the PSNR of reconstructed frame but also prominently reduce the bit-rate compared with HEVC reference software.

Robust visual tracking based on deep convolutional neural networks and kernelized correlation filters

NASA Astrophysics Data System (ADS)

Yang, Hua; Zhong, Donghong; Liu, Chenyi; Song, Kaiyou; Yin, Zhouping

2018-03-01

Object tracking is still a challenging problem in computer vision, as it entails learning an effective model to account for appearance changes caused by occlusion, out of view, plane rotation, scale change, and background clutter. This paper proposes a robust visual tracking algorithm called deep convolutional neural network (DCNNCT) to simultaneously address these challenges. The proposed DCNNCT algorithm utilizes a DCNN to extract the image feature of a tracked target, and the full range of information regarding each convolutional layer is used to express the image feature. Subsequently, the kernelized correlation filters (CF) in each convolutional layer are adaptively learned, the correlation response maps of that are combined to estimate the location of the tracked target. To avoid the case of tracking failure, an online random ferns classifier is employed to redetect the tracked target, and a dual-threshold scheme is used to obtain the final target location by comparing the tracking result with the detection result. Finally, the change in scale of the target is determined by building scale pyramids and training a CF. Extensive experiments demonstrate that the proposed algorithm is effective at tracking, especially when evaluated using an index called the overlap rate. The DCNNCT algorithm is also highly competitive in terms of robustness with respect to state-of-the-art trackers in various challenging scenarios.

Capillary Origami Inspired Fabrication of Complex 3D Hydrogel Constructs.

PubMed

Li, Moxiao; Yang, Qingzhen; Liu, Hao; Qiu, Mushu; Lu, Tian Jian; Xu, Feng

2016-09-01

Hydrogels have found broad applications in various engineering and biomedical fields, where the shape and size of hydrogels can profoundly influence their functions. Although numerous methods have been developed to tailor 3D hydrogel structures, it is still challenging to fabricate complex 3D hydrogel constructs. Inspired by the capillary origami phenomenon where surface tension of a droplet on an elastic membrane can induce spontaneous folding of the membrane into 3D structures along with droplet evaporation, a facile strategy is established for the fabrication of complex 3D hydrogel constructs with programmable shapes and sizes by crosslinking hydrogels during the folding process. A mathematical model is further proposed to predict the temporal structure evolution of the folded 3D hydrogel constructs. Using this model, precise control is achieved over the 3D shapes (e.g., pyramid, pentahedron, and cube) and sizes (ranging from hundreds of micrometers to millimeters) through tuning membrane shape, dimensionless parameter of the process (elastocapillary number Ce ), and evaporation time. This work would be favorable to multiple areas, such as flexible electronics, tissue regeneration, and drug delivery. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber

DOE PAGES

Acciarri, R.; Adams, C.; An, R.; ...

2017-03-14

Here, we present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. Lastly, we also address technical issues that arise when applying this technique to data from a large LArTPCmore » at or near ground level.« less

Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data

NASA Astrophysics Data System (ADS)

Anirudh, Rushil; Thiagarajan, Jayaraman J.; Bremer, Timo; Kim, Hyojin

2016-03-01

Early detection of lung nodules is currently the one of the most effective ways to predict and treat lung cancer. As a result, the past decade has seen a lot of focus on computer aided diagnosis (CAD) of lung nodules, whose goal is to efficiently detect, segment lung nodules and classify them as being benign or malignant. Effective detection of such nodules remains a challenge due to their arbitrariness in shape, size and texture. In this paper, we propose to employ 3D convolutional neural networks (CNN) to learn highly discriminative features for nodule detection in lieu of hand-engineered ones such as geometric shape or texture. While 3D CNNs are promising tools to model the spatio-temporal statistics of data, they are limited by their need for detailed 3D labels, which can be prohibitively expensive when compared obtaining 2D labels. Existing CAD methods rely on obtaining detailed labels for lung nodules, to train models, which is also unrealistic and time consuming. To alleviate this challenge, we propose a solution wherein the expert needs to provide only a point label, i.e., the central pixel of of the nodule, and its largest expected size. We use unsupervised segmentation to grow out a 3D region, which is used to train the CNN. Using experiments on the SPIE-LUNGx dataset, we show that the network trained using these weak labels can produce reasonably low false positive rates with a high sensitivity, even in the absence of accurate 3D labels.

Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval.

PubMed

Wei, Xiu-Shen; Luo, Jian-Hao; Wu, Jianxin; Zhou, Zhi-Hua

2017-06-01

Deep convolutional neural network models pre-trained for the ImageNet classification task have been successfully adopted to tasks in other domains, such as texture description and object proposal generation, but these tasks require annotations for images in the new domain. In this paper, we focus on a novel and challenging task in the pure unsupervised setting: fine-grained image retrieval. Even with image labels, fine-grained images are difficult to classify, letting alone the unsupervised retrieval task. We propose the selective convolutional descriptor aggregation (SCDA) method. The SCDA first localizes the main object in fine-grained images, a step that discards the noisy background and keeps useful deep descriptors. The selected descriptors are then aggregated and the dimensionality is reduced into a short feature vector using the best practices we found. The SCDA is unsupervised, using no image label or bounding box annotation. Experiments on six fine-grained data sets confirm the effectiveness of the SCDA for fine-grained image retrieval. Besides, visualization of the SCDA features shows that they correspond to visual attributes (even subtle ones), which might explain SCDA's high-mean average precision in fine-grained retrieval. Moreover, on general image retrieval data sets, the SCDA achieves comparable retrieval results with the state-of-the-art general image retrieval approaches.

Semantic segmentation of mFISH images using convolutional networks.

PubMed

Pardo, Esteban; Morgado, José Mário T; Malpica, Norberto

2018-04-30

Multicolor in situ hybridization (mFISH) is a karyotyping technique used to detect major chromosomal alterations using fluorescent probes and imaging techniques. Manual interpretation of mFISH images is a time consuming step that can be automated using machine learning; in previous works, pixel or patch wise classification was employed, overlooking spatial information which can help identify chromosomes. In this work, we propose a fully convolutional semantic segmentation network for the interpretation of mFISH images, which uses both spatial and spectral information to classify each pixel in an end-to-end fashion. The semantic segmentation network developed was tested on samples extracted from a public dataset using cross validation. Despite having no labeling information of the image it was tested on, our algorithm yielded an average correct classification ratio (CCR) of 87.41%. Previously, this level of accuracy was only achieved with state of the art algorithms when classifying pixels from the same image in which the classifier has been trained. These results provide evidence that fully convolutional semantic segmentation networks may be employed in the computer aided diagnosis of genetic diseases with improved performance over the current image analysis methods. © 2018 International Society for Advancement of Cytometry. © 2018 International Society for Advancement of Cytometry.

Building Extraction from Remote Sensing Data Using Fully Convolutional Networks

NASA Astrophysics Data System (ADS)

Bittner, K.; Cui, S.; Reinartz, P.

2017-05-01

Building detection and footprint extraction are highly demanded for many remote sensing applications. Though most previous works have shown promising results, the automatic extraction of building footprints still remains a nontrivial topic, especially in complex urban areas. Recently developed extensions of the CNN framework made it possible to perform dense pixel-wise classification of input images. Based on these abilities we propose a methodology, which automatically generates a full resolution binary building mask out of a Digital Surface Model (DSM) using a Fully Convolution Network (FCN) architecture. The advantage of using the depth information is that it provides geometrical silhouettes and allows a better separation of buildings from background as well as through its invariance to illumination and color variations. The proposed framework has mainly two steps. Firstly, the FCN is trained on a large set of patches consisting of normalized DSM (nDSM) as inputs and available ground truth building mask as target outputs. Secondly, the generated predictions from FCN are viewed as unary terms for a Fully connected Conditional Random Fields (FCRF), which enables us to create a final binary building mask. A series of experiments demonstrate that our methodology is able to extract accurate building footprints which are close to the buildings original shapes to a high degree. The quantitative and qualitative analysis show the significant improvements of the results in contrast to the multy-layer fully connected network from our previous work.

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.

Hierarchical graphical-based human pose estimation via local multi-resolution convolutional neural network

NASA Astrophysics Data System (ADS)

Zhu, Aichun; Wang, Tian; Snoussi, Hichem

2018-03-01

This paper addresses the problems of the graphical-based human pose estimation in still images, including the diversity of appearances and confounding background clutter. We present a new architecture for estimating human pose using a Convolutional Neural Network (CNN). Firstly, a Relative Mixture Deformable Model (RMDM) is defined by each pair of connected parts to compute the relative spatial information in the graphical model. Secondly, a Local Multi-Resolution Convolutional Neural Network (LMR-CNN) is proposed to train and learn the multi-scale representation of each body parts by combining different levels of part context. Thirdly, a LMR-CNN based hierarchical model is defined to explore the context information of limb parts. Finally, the experimental results demonstrate the effectiveness of the proposed deep learning approach for human pose estimation.

Tip-Based Nanofabrication of Arbitrary Shapes of Graphene Nanoribbons for Device Applications

PubMed Central

Estrada, David; Bashir, Rashid; King, William P.

2015-01-01

Graphene nanoribbons (GNRs) have promising applications in future nanoelectronics, chemical sensing and electrical interconnects. Although there are quite a few GNR nanofabrication methods reported, a rapid and low-cost fabrication method that is capable of fabricating arbitrary shapes of GNRs with good-quality is still in demand for using GNRs for device applications. In this paper, we present a tip-based nanofabrication method capable of fabricating arbitrary shapes of GNRs. A heated atomic force microscope (AFM) tip deposits polymer nanowires atop a CVD-grown graphene surface. The polymer nanowires serve as an etch mask to define GNRs through one step of oxygen plasma etching similar to a photoresist in conventional photolithography. Various shapes of GNRs with either linear or curvilinear features are demonstrated. The width of the GNR is around 270 nm and is determined by the width of the depositing polymer nanowire, which we estimate can be scaled down 15 nms. We characterize our TBN-fabricated GNRs using Raman spectroscopy and I-V measurements. The measured sheet resistances of our GNRs fall within the range of 1.65 kΩ/□−1 – 2.64 kΩ/□−1, in agreement with previously reported values. Furthermore, we determined the high-field breakdown current density of GNRs to be approximately 2.94×108 A/cm2. This TBN process is seamlessly compatible with existing nanofabrication processes, and is particularly suitable for fabricating GNR based electronic devices including next generation DNA sequencing technologies and beyond silicon field effect transistors. PMID:26257891

Finessing filter scarcity problem in face recognition via multi-fold filter convolution

NASA Astrophysics Data System (ADS)

Low, Cheng-Yaw; Teoh, Andrew Beng-Jin

2017-06-01

The deep convolutional neural networks for face recognition, from DeepFace to the recent FaceNet, demand a sufficiently large volume of filters for feature extraction, in addition to being deep. The shallow filter-bank approaches, e.g., principal component analysis network (PCANet), binarized statistical image features (BSIF), and other analogous variants, endure the filter scarcity problem that not all PCA and ICA filters available are discriminative to abstract noise-free features. This paper extends our previous work on multi-fold filter convolution (ℳ-FFC), where the pre-learned PCA and ICA filter sets are exponentially diversified by ℳ folds to instantiate PCA, ICA, and PCA-ICA offspring. The experimental results unveil that the 2-FFC operation solves the filter scarcity state. The 2-FFC descriptors are also evidenced to be superior to that of PCANet, BSIF, and other face descriptors, in terms of rank-1 identification rate (%).

Deep Convolutional Neural Networks for Classifying Body Constitution Based on Face Image.

PubMed

Huan, Er-Yang; Wen, Gui-Hua; Zhang, Shi-Jun; Li, Dan-Yang; Hu, Yang; Chang, Tian-Yuan; Wang, Qing; Huang, Bing-Lin

2017-01-01

Body constitution classification is the basis and core content of traditional Chinese medicine constitution research. It is to extract the relevant laws from the complex constitution phenomenon and finally build the constitution classification system. Traditional identification methods have the disadvantages of inefficiency and low accuracy, for instance, questionnaires. This paper proposed a body constitution recognition algorithm based on deep convolutional neural network, which can classify individual constitution types according to face images. The proposed model first uses the convolutional neural network to extract the features of face image and then combines the extracted features with the color features. Finally, the fusion features are input to the Softmax classifier to get the classification result. Different comparison experiments show that the algorithm proposed in this paper can achieve the accuracy of 65.29% about the constitution classification. And its performance was accepted by Chinese medicine practitioners.

Convolutional Neural Networks for 1-D Many-Channel Data

DTIC Science & Technology

Deep convolutional neural networks (CNNs) represent the state of the art in image recognition. The same properties that led to their success in that... crack detection ( 8,000 data points, 72 channels). Though the models predictive ability is limited to fitting the trend , its partial success suggests that...originally written to classify digits in the MNIST database (28 28 pixels, 1 channel), for use on 1-D acoustic data taken from experiments focused on

Quantitative fabric analysis of eclogite facies mylonites: texture and microtomography

NASA Astrophysics Data System (ADS)

Gomez Barreiro, J.; Voltolini, M.; Martinez-Catalan, J. R.; Benitez-Perez, J. M.; Diez-fernandez, R.; Wenk, H. R.; Vogel, S. C.; Mancini, L.

2014-12-01

Understanding the flow of rock deformed under eclogite facies conditions is crucial to constraint the dynamics of a subducting slab. Prograde metamorphism during burial in a subduction zone proceeds across several lithologies, resulting in heterogeneous eclogitization and potentially different processes. In order to explore the expression of such a variety in terms of a deformative fabric, we have analyzed texture and shape fabric of eclogites and eclogitic orthogneisses from the Malpica-Tui unit (NW Spain). We explore the same rock volumes with TOF-neutron diffraction (HIPPO @ LANSCE) and synchrotron microtomography (SYRMEP @Elettra). Orientation distribution functions were extracted after Rietveld refinement in MAUD and morphometric data (size, aspect ratio, orientation) were obtained after image processing with FIJI, Blob3D and MATLAB. Shape fabric reflects the macroscopic foliation and lineation and correlates with texture. Garnet fabric is particularly important because of the rheological implications of its mechanical behavior. Garnet shows little elongation in both samples, and texture is significant, what probably points to a relatively dry deformative environment, with diffusion-assisted dislocation. This eclogites could represent a rigidification stage in the subduction channel preserved during the exhumation at high-P and high-T documented in the Malpica-Tui unit during the Variscan orogeny.

Siamese convolutional networks for tracking the spine motion

NASA Astrophysics Data System (ADS)

Liu, Yuan; Sui, Xiubao; Sun, Yicheng; Liu, Chengwei; Hu, Yong

2017-09-01

Deep learning models have demonstrated great success in various computer vision tasks such as image classification and object tracking. However, tracking the lumbar spine by digitalized video fluoroscopic imaging (DVFI), which can quantitatively analyze the motion mode of spine to diagnose lumbar instability, has not yet been well developed due to the lack of steady and robust tracking method. In this paper, we propose a novel visual tracking algorithm of the lumbar vertebra motion based on a Siamese convolutional neural network (CNN) model. We train a full-convolutional neural network offline to learn generic image features. The network is trained to learn a similarity function that compares the labeled target in the first frame with the candidate patches in the current frame. The similarity function returns a high score if the two images depict the same object. Once learned, the similarity function is used to track a previously unseen object without any adapting online. In the current frame, our tracker is performed by evaluating the candidate rotated patches sampled around the previous frame target position and presents a rotated bounding box to locate the predicted target precisely. Results indicate that the proposed tracking method can detect the lumbar vertebra steadily and robustly. Especially for images with low contrast and cluttered background, the presented tracker can still achieve good tracking performance. Further, the proposed algorithm operates at high speed for real time tracking.

Convolutional neural networks for vibrational spectroscopic data analysis.

PubMed

Acquarelli, Jacopo; van Laarhoven, Twan; Gerretzen, Jan; Tran, Thanh N; Buydens, Lutgarde M C; Marchiori, Elena

2017-02-15

In this work we show that convolutional neural networks (CNNs) can be efficiently used to classify vibrational spectroscopic data and identify important spectral regions. CNNs are the current state-of-the-art in image classification and speech recognition and can learn interpretable representations of the data. These characteristics make CNNs a good candidate for reducing the need for preprocessing and for highlighting important spectral regions, both of which are crucial steps in the analysis of vibrational spectroscopic data. Chemometric analysis of vibrational spectroscopic data often relies on preprocessing methods involving baseline correction, scatter correction and noise removal, which are applied to the spectra prior to model building. Preprocessing is a critical step because even in simple problems using 'reasonable' preprocessing methods may decrease the performance of the final model. We develop a new CNN based method and provide an accompanying publicly available software. It is based on a simple CNN architecture with a single convolutional layer (a so-called shallow CNN). Our method outperforms standard classification algorithms used in chemometrics (e.g. PLS) in terms of accuracy when applied to non-preprocessed test data (86% average accuracy compared to the 62% achieved by PLS), and it achieves better performance even on preprocessed test data (96% average accuracy compared to the 89% achieved by PLS). For interpretability purposes, our method includes a procedure for finding important spectral regions, thereby facilitating qualitative interpretation of results. Copyright © 2016 Elsevier B.V. All rights reserved.

Flexible Metal-Fabric Radiators

NASA Technical Reports Server (NTRS)

Cross, Cynthia; Nguyen, Hai D.; Ruemmele, Warren; Andish, Kambiz K.; McCalley, Sean

2005-01-01

Flexible metal-fabric radiators have been considered as alternative means of dissipating excess heat from spacecraft and space suits. The radiators also may be useful in such special terrestrial applications as rejecting heat from space-suit-like protective suits worn in hot work environments. In addition to flexibility and consequent ease of deployment and installation on objects of varying sizes and shapes, the main advantages of these radiators over conventional rigid radiators are that they weigh less and occupy less volume for a given amount of cooling capacity. A radiator of this type includes conventional stainless-steel tubes carrying a coolant fluid. The main radiating component consists of a fabric of interwoven aluminum-foil strips bonded to the tubes by use of a proprietary process. The strip/tube bonds are strong and highly thermally conductive. Coolant is fed to and from the tubes via flexible stainless-steel manifolds designed to accommodate flexing of, and minimize bending forces on, the fabric. The manifolds are sized to minimize pressure drops and distribute the flow of coolant evenly to all the tubes. The tubes and manifolds are configured in two independent flow loops for operational flexibility and protective redundancy.

Fabrication of PDMS through-holes using the MIMIC method and the surface treatment by atmospheric-pressure CH4/He RF plasma

NASA Astrophysics Data System (ADS)

Choi, Jongchan; Lee, Kyeong-Hwan; Yang, Sung

2011-09-01

This note presents a simple fabrication process for patterning micro through-holes in a PDMS layer by a combination of the micromolding in capillaries (MIMIC) method and the surface treatment by atmospheric-pressure CH4/He RF plasma. The fabrication process is confirmed by forming micro through-holes with various shapes including circle, C-shape, open microfluidic channel and hemisphere. All micro through-holes of various shapes in a wide range of diameters and heights are well fabricated by the proposed method. Also, a 3D micromixer containing a PDMS micro through-hole layer formed by the proposed method is built and its performance is tested as another practical demonstration of the proposed fabrication method. Therefore, we believe that the proposed fabrication process will build a PDMS micro through-hole layer in a simple and easy way and will contribute to developing highly efficient multi-layered microfluidic systems, which may require PDMS micro through-hole layers.

Rate-compatible punctured convolutional codes (RCPC codes) and their applications

NASA Astrophysics Data System (ADS)

Hagenauer, Joachim

1988-04-01

The concept of punctured convolutional codes is extended by punctuating a low-rate 1/N code periodically with period P to obtain a family of codes with rate P/(P + l), where l can be varied between 1 and (N - 1)P. A rate-compatibility restriction on the puncturing tables ensures that all code bits of high rate codes are used by the lower-rate codes. This allows transmission of incremental redundancy in ARQ/FEC (automatic repeat request/forward error correction) schemes and continuous rate variation to change from low to high error protection within a data frame. Families of RCPC codes with rates between 8/9 and 1/4 are given for memories M from 3 to 6 (8 to 64 trellis states) together with the relevant distance spectra. These codes are almost as good as the best known general convolutional codes of the respective rates. It is shown that the same Viterbi decoder can be used for all RCPC codes of the same M. The application of RCPC codes to hybrid ARQ/FEC schemes is discussed for Gaussian and Rayleigh fading channels using channel-state information to optimize throughput.

Thermalnet: a Deep Convolutional Network for Synthetic Thermal Image Generation

NASA Astrophysics Data System (ADS)

Kniaz, V. V.; Gorbatsevich, V. S.; Mizginov, V. A.

2017-05-01

Deep convolutional neural networks have dramatically changed the landscape of the modern computer vision. Nowadays methods based on deep neural networks show the best performance among image recognition and object detection algorithms. While polishing of network architectures received a lot of scholar attention, from the practical point of view the preparation of a large image dataset for a successful training of a neural network became one of major challenges. This challenge is particularly profound for image recognition in wavelengths lying outside the visible spectrum. For example no infrared or radar image datasets large enough for successful training of a deep neural network are available to date in public domain. Recent advances of deep neural networks prove that they are also capable to do arbitrary image transformations such as super-resolution image generation, grayscale image colorisation and imitation of style of a given artist. Thus a natural question arise: how could be deep neural networks used for augmentation of existing large image datasets? This paper is focused on the development of the Thermalnet deep convolutional neural network for augmentation of existing large visible image datasets with synthetic thermal images. The Thermalnet network architecture is inspired by colorisation deep neural networks.

Fabrication of Nonperiodic Metasurfaces by Microlens Projection Lithography.

PubMed

Gonidec, Mathieu; Hamedi, Mahiar M; Nemiroski, Alex; Rubio, Luis M; Torres, Cesar; Whitesides, George M

2016-07-13

This paper describes a strategy that uses template-directed self-assembly of micrometer-scale microspheres to fabricate arrays of microlenses for projection photolithography of periodic, quasiperiodic, and aperiodic infrared metasurfaces. This method of "template-encoded microlens projection lithography" (TEMPL) enables rapid prototyping of planar, multiscale patterns of similarly shaped structures with critical dimensions down to ∼400 nm. Each of these structures is defined by local projection lithography with a single microsphere acting as a lens. This paper explores the use of TEMPL for the fabrication of a broad range of two-dimensional lattices with varying types of nonperiodic spatial distribution. The matching optical spectra of the fabricated and simulated metasurfaces confirm that TEMPL can produce structures that conform to expected optical behavior.

Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks.

PubMed

Men, Kuo; Dai, Jianrong; Li, Yexiong

2017-12-01

Delineation of the clinical target volume (CTV) and organs at risk (OARs) is very important for radiotherapy but is time-consuming and prone to inter-observer variation. Here, we proposed a novel deep dilated convolutional neural network (DDCNN)-based method for fast and consistent auto-segmentation of these structures. Our DDCNN method was an end-to-end architecture enabling fast training and testing. Specifically, it employed a novel multiple-scale convolutional architecture to extract multiple-scale context features in the early layers, which contain the original information on fine texture and boundaries and which are very useful for accurate auto-segmentation. In addition, it enlarged the receptive fields of dilated convolutions at the end of networks to capture complementary context features. Then, it replaced the fully connected layers with fully convolutional layers to achieve pixel-wise segmentation. We used data from 278 patients with rectal cancer for evaluation. The CTV and OARs were delineated and validated by senior radiation oncologists in the planning computed tomography (CT) images. A total of 218 patients chosen randomly were used for training, and the remaining 60 for validation. The Dice similarity coefficient (DSC) was used to measure segmentation accuracy. Performance was evaluated on segmentation of the CTV and OARs. In addition, the performance of DDCNN was compared with that of U-Net. The proposed DDCNN method outperformed the U-Net for all segmentations, and the average DSC value of DDCNN was 3.8% higher than that of U-Net. Mean DSC values of DDCNN were 87.7% for the CTV, 93.4% for the bladder, 92.1% for the left femoral head, 92.3% for the right femoral head, 65.3% for the intestine, and 61.8% for the colon. The test time was 45 s per patient for segmentation of all the CTV, bladder, left and right femoral heads, colon, and intestine. We also assessed our approaches and results with those in the literature: our system showed superior

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.

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.

3D Printing of Shape Memory Polymers for Flexible Electronic Devices.

PubMed

Zarek, Matt; Layani, Michael; Cooperstein, Ido; Sachyani, Ela; Cohn, Daniel; Magdassi, Shlomo

2016-06-01

The formation of 3D objects composed of shape memory polymers for flexible electronics is described. Layer-by-layer photopolymerization of methacrylated semicrystalline molten macromonomers by a 3D digital light processing printer enables rapid fabrication of complex objects and imparts shape memory functionality for electrical circuits. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Lithographically fabricated gold nanowire waveguides for plasmonic routers and logic gates.

PubMed

Gao, Long; Chen, Li; Wei, Hong; Xu, Hongxing

2018-06-14

Fabricating plasmonic nanowire waveguides and circuits by lithographic fabrication methods is highly desired for nanophotonic circuitry applications. Here we report an approach for fabricating metal nanowire networks by using electron beam lithography and metal film deposition techniques. The gold nanowire structures are fabricated on quartz substrates without using any adhesion layer but coated with a thin layer of Al2O3 film for immobilization. The thermal annealing during the Al2O3 deposition process decreases the surface plasmon loss. In a Y-shaped gold nanowire network, the surface plasmons can be routed to different branches by controlling the polarization of the excitation light, and the routing behavior is dependent on the length of the main nanowire. Simulated electric field distributions show that the zigzag distribution of the electric field in the nanowire network determines the surface plasmon routing. By using two laser beams to excite surface plasmons in a Y-shaped nanowire network, the output intensity can be modulated by the interference of surface plasmons, which can be used to design Boolean logic gates. We experimentally demonstrate that AND, OR, XOR and NOT gates can be realized in three-terminal nanowire networks, and NAND, NOR and XNOR gates can be realized in four-terminal nanowire networks. This work takes a step toward the fabrication of on-chip integrated plasmonic circuits.

Fabrication and Characterization of SMA Hybrid Composites

NASA Technical Reports Server (NTRS)

Turner, Travis L.; Lach, Cynthia L.; Cano, Robert J.

2001-01-01

Results from an effort to fabrication shape memory alloy hybrid composite (SMAHC) test specimens and characterize the material system are presented in this study. The SMAHC specimens are conventional composite structures with an embedded SMA constituent. The fabrication and characterization work was undertaken to better understand the mechanics of the material system, address fabrication issues cited in the literature, and provide specimens for experimental validation of a recently developed thermomechanical model for SMAHC structures. Processes and hardware developed for fabrication of the SMAHC specimens are described. Fabrication of a SMA14C laminate with quasi-isotropic lamination and ribbon-type Nitinol actuators embedded in the 0' layers is presented. Beam specimens are machined from the laminate and are the focus of recent work, but the processes and hardware are readily extensible to more practical structures. Results of thermomechanical property testing on the composite matrix and Nitinol ribbon are presented. Test results from the Nitinol include stress-strain behavior, modulus versus temperature. and constrained recovery stress versus temperature and thermal cycle. Complex thermomechanical behaviors of the Nitinol and composite matrix are demonstrated, which have significant implications for modeling of SMAHC structures.

Long-term Recurrent Convolutional Networks for Visual Recognition and Description

DTIC Science & Technology

2014-11-17

deep???, are effective for tasks involving sequences, visual and otherwise. We develop a novel recurrent convolutional architecture suitable for large...models which are also recurrent, or “temporally deep”, are effective for tasks involving sequences, visual and otherwise. We develop a novel recurrent...limitation of simple RNN models which strictly integrate state information over time is known as the “vanishing gradient” effect : the ability to

Shape-morphing composites with designed micro-architectures

DOE PAGES

Rodriguez, Jennifer N.; Zhu, Cheng; Duoss, Eric B.; ...

2016-06-15

Shape memory polymers (SMPs) are attractive materials due to their unique mechanical properties, including high deformation capacity and shape recovery. SMPs are easier to process, lightweight, and inexpensive compared to their metallic counterparts, shape memory alloys. However, SMPs are limited to relatively small form factors due to their low recovery stresses. Lightweight, micro-architected composite SMPs may overcome these size limitations and offer the ability to combine functional properties (e.g., electrical conductivity) with shape memory behavior. Fabrication of 3D SMP thermoset structures via traditional manufacturing methods is challenging, especially for designs that are composed of multiple materials within porous microarchitectures designedmore » for specific shape change strategies, e.g. sequential shape recovery. We report thermoset SMP composite inks containing some materials from renewable resources that can be 3D printed into complex, multi-material architectures that exhibit programmable shape changes with temperature and time. Through addition of fiber-based fillers, we demonstrate printing of electrically conductive SMPs where multiple shape states may induce functional changes in a device and that shape changes can be actuated via heating of printed composites. As a result, the ability of SMPs to recover their original shapes will be advantageous for a broad range of applications, including medical, aerospace, and robotic devices.« less

Shape-morphing composites with designed micro-architectures

NASA Astrophysics Data System (ADS)

Rodriguez, Jennifer N.; Zhu, Cheng; Duoss, Eric B.; Wilson, Thomas S.; Spadaccini, Christopher M.; Lewicki, James P.

2016-06-01

Shape memory polymers (SMPs) are attractive materials due to their unique mechanical properties, including high deformation capacity and shape recovery. SMPs are easier to process, lightweight, and inexpensive compared to their metallic counterparts, shape memory alloys. However, SMPs are limited to relatively small form factors due to their low recovery stresses. Lightweight, micro-architected composite SMPs may overcome these size limitations and offer the ability to combine functional properties (e.g., electrical conductivity) with shape memory behavior. Fabrication of 3D SMP thermoset structures via traditional manufacturing methods is challenging, especially for designs that are composed of multiple materials within porous microarchitectures designed for specific shape change strategies, e.g. sequential shape recovery. We report thermoset SMP composite inks containing some materials from renewable resources that can be 3D printed into complex, multi-material architectures that exhibit programmable shape changes with temperature and time. Through addition of fiber-based fillers, we demonstrate printing of electrically conductive SMPs where multiple shape states may induce functional changes in a device and that shape changes can be actuated via heating of printed composites. The ability of SMPs to recover their original shapes will be advantageous for a broad range of applications, including medical, aerospace, and robotic devices.

Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks.

PubMed

Längkvist, Martin; Jendeberg, Johan; Thunberg, Per; Loutfi, Amy; Lidén, Mats

2018-06-01

Computed tomography (CT) is the method of choice for diagnosing ureteral stones - kidney stones that obstruct the ureter. The purpose of this study is to develop a computer aided detection (CAD) algorithm for identifying a ureteral stone in thin slice CT volumes. The challenge in CAD for urinary stones lies in the similarity in shape and intensity of stones with non-stone structures and how to efficiently deal with large high-resolution CT volumes. We address these challenges by using a Convolutional Neural Network (CNN) that works directly on the high resolution CT volumes. The method is evaluated on a large data base of 465 clinically acquired high-resolution CT volumes of the urinary tract with labeling of ureteral stones performed by a radiologist. The best model using 2.5D input data and anatomical information achieved a sensitivity of 100% and an average of 2.68 false-positives per patient on a test set of 88 scans. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

A low-power, high-throughput maximum-likelihood convolutional decoder chip for NASA's 30/20 GHz program

NASA Technical Reports Server (NTRS)

Mccallister, R. D.; Crawford, J. J.

1981-01-01

It is pointed out that the NASA 30/20 GHz program will place in geosynchronous orbit a technically advanced communication satellite which can process time-division multiple access (TDMA) information bursts with a data throughput in excess of 4 GBPS. To guarantee acceptable data quality during periods of signal attenuation it will be necessary to provide a significant forward error correction (FEC) capability. Convolutional decoding (utilizing the maximum-likelihood techniques) was identified as the most attractive FEC strategy. Design trade-offs regarding a maximum-likelihood convolutional decoder (MCD) in a single-chip CMOS implementation are discussed.

Handling of computational in vitro/in vivo correlation problems by Microsoft Excel: III. Convolution and deconvolution.

PubMed

Langenbucher, Frieder

2003-11-01

Convolution and deconvolution are the classical in-vitro-in-vivo correlation tools to describe the relationship between input and weighting/response in a linear system, where input represents the drug release in vitro, weighting/response any body response in vivo. While functional treatment, e.g. in terms of polyexponential or Weibull distribution, is more appropriate for general survey or prediction, numerical algorithms are useful for treating actual experimental data. Deconvolution is not considered an algorithm by its own, but the inversion of a corresponding convolution. MS Excel is shown to be a useful tool for all these applications.

Convolutional neural networks and face recognition task

NASA Astrophysics Data System (ADS)

Sochenkova, A.; Sochenkov, I.; Makovetskii, A.; Vokhmintsev, A.; Melnikov, A.

2017-09-01

Computer vision tasks are remaining very important for the last couple of years. One of the most complicated problems in computer vision is face recognition that could be used in security systems to provide safety and to identify person among the others. There is a variety of different approaches to solve this task, but there is still no universal solution that would give adequate results in some cases. Current paper presents following approach. Firstly, we extract an area containing face, then we use Canny edge detector. On the next stage we use convolutional neural networks (CNN) to finally solve face recognition and person identification task.

A versatile technique for fabrication of SiC SPM probes

NASA Astrophysics Data System (ADS)

Therrien, Joel; Schmidt, Daniel; Barrot, Sheetal; Patel, Bhavin

2008-03-01

To date SPM probes have largely been fabricated via methods borrowed from the semiconductor industry for fabricating Micro Electro Mechanical Systems. Although these techniques have enabled SPM to see widespread use, the processes put significant limitations on what structures can be made. We report our progress on fabricating SPM cantilevers composed of Silicon Carbide using polymer molding techniques. A pre-ceramic polymer is molded into the desired probe shape and then converted to SiC via pyrolisys. We will also report on progress in using photo-sterolithography for fabrication of even more complex geometries. In addition to opening up a much larger set of probe structures, the use of SiC leads to improved wear resistance of the resulting probes. Among the potential applications, this method enables the fabrication of low spring constant, high resonant frequency cantilevers via cross sectional geometries not accessible to standard fabrication techniques. Such probes are required for high speed tapping and non-contact imaging.

Detection of bars in galaxies using a deep convolutional neural network

NASA Astrophysics Data System (ADS)

Abraham, Sheelu; Aniyan, A. K.; Kembhavi, Ajit K.; Philip, N. S.; Vaghmare, Kaustubh

2018-06-01

We present an automated method for the detection of bar structure in optical images of galaxies using a deep convolutional neural network that is easy to use and provides good accuracy. In our study, we use a sample of 9346 galaxies in the redshift range of 0.009-0.2 from the Sloan Digital Sky Survey (SDSS), which has 3864 barred galaxies, the rest being unbarred. We reach a top precision of 94 per cent in identifying bars in galaxies using the trained network. This accuracy matches the accuracy reached by human experts on the same data without additional information about the images. Since deep convolutional neural networks can be scaled to handle large volumes of data, the method is expected to have great relevance in an era where astronomy data is rapidly increasing in terms of volume, variety, volatility, and velocity along with other V's that characterize big data. With the trained model, we have constructed a catalogue of barred galaxies from SDSS and made it available online.

View-invariant gait recognition method by three-dimensional convolutional neural network

NASA Astrophysics Data System (ADS)

Xing, Weiwei; Li, Ying; Zhang, Shunli

2018-01-01

Gait as an important biometric feature can identify a human at a long distance. View change is one of the most challenging factors for gait recognition. To address the cross view issues in gait recognition, we propose a view-invariant gait recognition method by three-dimensional (3-D) convolutional neural network. First, 3-D convolutional neural network (3DCNN) is introduced to learn view-invariant feature, which can capture the spatial information and temporal information simultaneously on normalized silhouette sequences. Second, a network training method based on cross-domain transfer learning is proposed to solve the problem of the limited gait training samples. We choose the C3D as the basic model, which is pretrained on the Sports-1M and then fine-tune C3D model to adapt gait recognition. In the recognition stage, we use the fine-tuned model to extract gait features and use Euclidean distance to measure the similarity of gait sequences. Sufficient experiments are carried out on the CASIA-B dataset and the experimental results demonstrate that our method outperforms many other methods.

Combining convolutional neural networks and Hough Transform for classification of images containing lines

NASA Astrophysics Data System (ADS)

Sheshkus, Alexander; Limonova, Elena; Nikolaev, Dmitry; Krivtsov, Valeriy

2017-03-01

In this paper, we propose an expansion of convolutional neural network (CNN) input features based on Hough Transform. We perform morphological contrasting of source image followed by Hough Transform, and then use it as input for some convolutional filters. Thus, CNNs computational complexity and the number of units are not affected. Morphological contrasting and Hough Transform are the only additional computational expenses of introduced CNN input features expansion. Proposed approach was demonstrated on the example of CNN with very simple structure. We considered two image recognition problems, that were object classification on CIFAR-10 and printed character recognition on private dataset with symbols taken from Russian passports. Our approach allowed to reach noticeable accuracy improvement without taking much computational effort, which can be extremely important in industrial recognition systems or difficult problems utilising CNNs, like pressure ridge analysis and classification.

Seismic signal auto-detecing from different features by using Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Huang, Y.; Zhou, Y.; Yue, H.; Zhou, S.

2017-12-01

We try Convolutional Neural Network to detect some features of seismic data and compare their efficience. The features include whether a signal is seismic signal or noise and the arrival time of P and S phase and each feature correspond to a Convolutional Neural Network. We first use traditional STA/LTA to recongnize some events and then use templete matching to find more events as training set for the Neural Network. To make the training set more various, we add some noise to the seismic data and make some synthetic seismic data and noise. The 3-component raw signal and time-frequancy ananlyze are used as the input data for our neural network. Our Training is performed on GPUs to achieve efficient convergence. Our method improved the precision in comparison with STA/LTA and template matching. We will move to recurrent neural network to see if this kind network is better in detect P and S phase.

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.

Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network.

PubMed

Yang, Zhongliang; Huang, Yongfeng; Jiang, Yiran; Sun, Yuxi; Zhang, Yu-Jin; Luo, Pengcheng

2018-04-20

Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.

LensFlow: A Convolutional Neural Network in Search of Strong Gravitational Lenses

NASA Astrophysics Data System (ADS)

Pourrahmani, Milad; Nayyeri, Hooshang; Cooray, Asantha

2018-03-01

In this work, we present our machine learning classification algorithm for identifying strong gravitational lenses from wide-area surveys using convolutional neural networks; LENSFLOW. We train and test the algorithm using a wide variety of strong gravitational lens configurations from simulations of lensing events. Images are processed through multiple convolutional layers that extract feature maps necessary to assign a lens probability to each image. LENSFLOW provides a ranking scheme for all sources that could be used to identify potential gravitational lens candidates by significantly reducing the number of images that have to be visually inspected. We apply our algorithm to the HST/ACS i-band observations of the COSMOS field and present our sample of identified lensing candidates. The developed machine learning algorithm is more computationally efficient and complimentary to classical lens identification algorithms and is ideal for discovering such events across wide areas from current and future surveys such as LSST and WFIRST.

Adaptive slit beam shaping for direct laser written waveguides.

PubMed

Salter, P S; Jesacher, A; Spring, J B; Metcalf, B J; Thomas-Peter, N; Simmonds, R D; Langford, N K; Walmsley, I A; Booth, M J

2012-02-15

We demonstrate an improved method for fabricating optical waveguides in bulk materials by means of femtosecond laser writing. We use an LC spatial light modulator (SLM) to shape the beam focus by generating adaptive slit illumination in the pupil of the objective lens. A diffraction grating is applied in a strip across the SLM to simulate a slit, with the first diffracted order mapped onto the pupil plane of the objective lens while the zeroth order is blocked. This technique enables real-time control of the beam-shaping parameters during writing, facilitating the fabrication of more complicated structures than is possible using nonadaptive methods. Waveguides are demonstrated in fused silica with a coupling loss to single-mode fibers in the range of 0.2 to 0.5 dB and propagation loss <0.4 dB/cm.

Convolutional coding results for the MVM '73 X-band telemetry experiment

NASA Technical Reports Server (NTRS)

Layland, J. W.

1978-01-01

Results of simulation of several short-constraint-length convolutional codes using a noisy symbol stream obtained via the turnaround ranging channels of the MVM'73 spacecraft are presented. First operational use of this coding technique is on the Voyager mission. The relative performance of these codes in this environment is as previously predicted from computer-based simulations.

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.

An Observation of Diamond-Shaped Particle Structure in a Soya Phosphatidylcohline and Bacteriorhodopsin Composite Langmuir Blodgett Film Fabricated by Multilayer Molecular Thin Film Method

NASA Astrophysics Data System (ADS)

Tsujiuchi, Y.; Makino, Y.

A composite film of soya phosphatidylcohline (soya PC) and bacteriorhodopsin (BR) was fabricated by the multilayer molecular thin film method using fatty acid and lipid on a quartz substrate. Direct Force Microscopy (DFM), UV absorption spectra and IR absorption spectra of the film were characterized on the detail of surface structure of the film. The DFM data revealed that many rhombus (diamond-shaped) particles were observed in the film. The spectroscopic data exhibited the yield of M-intermediate of BR in the film. On our modelling of molecular configuration indicate that the coexistence of the strong inter-molecular interaction and the strong inter-molecular interaction between BR trimmers attributed to form the particles.

Detection of high-grade small bowel obstruction on conventional radiography with convolutional neural networks.

PubMed

Cheng, Phillip M; Tejura, Tapas K; Tran, Khoa N; Whang, Gilbert

2018-05-01

The purpose of this pilot study is to determine whether a deep convolutional neural network can be trained with limited image data to detect high-grade small bowel obstruction patterns on supine abdominal radiographs. Grayscale images from 3663 clinical supine abdominal radiographs were categorized into obstructive and non-obstructive categories independently by three abdominal radiologists, and the majority classification was used as ground truth; 74 images were found to be consistent with small bowel obstruction. Images were rescaled and randomized, with 2210 images constituting the training set (39 with small bowel obstruction) and 1453 images constituting the test set (35 with small bowel obstruction). Weight parameters for the final classification layer of the Inception v3 convolutional neural network, previously trained on the 2014 Large Scale Visual Recognition Challenge dataset, were retrained on the training set. After training, the neural network achieved an AUC of 0.84 on the test set (95% CI 0.78-0.89). At the maximum Youden index (sensitivity + specificity-1), the sensitivity of the system for small bowel obstruction is 83.8%, with a specificity of 68.1%. The results demonstrate that transfer learning with convolutional neural networks, even with limited training data, may be used to train a detector for high-grade small bowel obstruction gas patterns on supine radiographs.

BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment.

PubMed

Kawahara, Jeremy; Brown, Colin J; Miller, Steven P; Booth, Brian G; Chau, Vann; Grunau, Ruth E; Zwicker, Jill G; Hamarneh, Ghassan

2017-02-01

We propose BrainNetCNN, a convolutional neural network (CNN) framework to predict clinical neurodevelopmental outcomes from brain networks. In contrast to the spatially local convolutions done in traditional image-based CNNs, our BrainNetCNN is composed of novel edge-to-edge, edge-to-node and node-to-graph convolutional filters that leverage the topological locality of structural brain networks. We apply the BrainNetCNN framework to predict cognitive and motor developmental outcome scores from structural brain networks of infants born preterm. Diffusion tensor images (DTI) of preterm infants, acquired between 27 and 46 weeks gestational age, were used to construct a dataset of structural brain connectivity networks. We first demonstrate the predictive capabilities of BrainNetCNN on synthetic phantom networks with simulated injury patterns and added noise. BrainNetCNN outperforms a fully connected neural-network with the same number of model parameters on both phantoms with focal and diffuse injury patterns. We then apply our method to the task of joint prediction of Bayley-III cognitive and motor scores, assessed at 18 months of age, adjusted for prematurity. We show that our BrainNetCNN framework outperforms a variety of other methods on the same data. Furthermore, BrainNetCNN is able to identify an infant's postmenstrual age to within about 2 weeks. Finally, we explore the high-level features learned by BrainNetCNN by visualizing the importance of each connection in the brain with respect to predicting the outcome scores. These findings are then discussed in the context of the anatomy and function of the developing preterm infant brain. Copyright © 2016 Elsevier Inc. All rights reserved.

Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding.

PubMed

Min, Xu; Zeng, Wanwen; Chen, Ning; Chen, Ting; Jiang, Rui

2017-07-15

Experimental techniques for measuring chromatin accessibility are expensive and time consuming, appealing for the development of computational approaches to predict open chromatin regions from DNA sequences. Along this direction, existing methods fall into two classes: one based on handcrafted k -mer features and the other based on convolutional neural networks. Although both categories have shown good performance in specific applications thus far, there still lacks a comprehensive framework to integrate useful k -mer co-occurrence information with recent advances in deep learning. We fill this gap by addressing the problem of chromatin accessibility prediction with a convolutional Long Short-Term Memory (LSTM) network with k -mer embedding. We first split DNA sequences into k -mers and pre-train k -mer embedding vectors based on the co-occurrence matrix of k -mers by using an unsupervised representation learning approach. We then construct a supervised deep learning architecture comprised of an embedding layer, three convolutional layers and a Bidirectional LSTM (BLSTM) layer for feature learning and classification. We demonstrate that our method gains high-quality fixed-length features from variable-length sequences and consistently outperforms baseline methods. We show that k -mer embedding can effectively enhance model performance by exploring different embedding strategies. We also prove the efficacy of both the convolution and the BLSTM layers by comparing two variations of the network architecture. We confirm the robustness of our model to hyper-parameters by performing sensitivity analysis. We hope our method can eventually reinforce our understanding of employing deep learning in genomic studies and shed light on research regarding mechanisms of chromatin accessibility. The source code can be downloaded from https://github.com/minxueric/ismb2017_lstm . tingchen@tsinghua.edu.cn or ruijiang@tsinghua.edu.cn. Supplementary materials are available at

Quantitative analysis of nucleolar chromatin distribution in the complex convoluted nucleoli of Didinium nasutum (Ciliophora).

PubMed

Leonova, Olga G; Karajan, Bella P; Ivlev, Yuri F; Ivanova, Julia L; Skarlato, Sergei O; Popenko, Vladimir I

2013-01-01

We have earlier shown that the typical Didinium nasutum nucleolus is a complex convoluted branched domain, comprising a dense fibrillar component located at the periphery of the nucleolus and a granular component located in the central part. Here our main interest was to study quantitatively the spatial distribution of nucleolar chromatin structures in these convoluted nucleoli. There are no "classical" fibrillar centers in D.nasutum nucleoli. The spatial distribution of nucleolar chromatin bodies, which play the role of nucleolar organizers in the macronucleus of D.nasutum, was studied using 3D reconstructions based on serial ultrathin sections. The relative number of nucleolar chromatin bodies was determined in macronuclei of recently fed, starved D.nasutum cells and in resting cysts. This parameter is shown to correlate with the activity of the nucleolus. However, the relative number of nucleolar chromatin bodies in different regions of the same convoluted nucleolus is approximately the same. This finding suggests equal activity in different parts of the nucleolar domain and indicates the existence of some molecular mechanism enabling it to synchronize this activity in D. nasutum nucleoli. Our data show that D. nasutum nucleoli display bipartite structure. All nucleolar chromatin bodies are shown to be located outside of nucleoli, at the periphery of the fibrillar component.

Self-powered textile for wearable electronics by hybridizing fiber-shaped nanogenerators, solar cells, and supercapacitors.

PubMed

Wen, Zhen; Yeh, Min-Hsin; Guo, Hengyu; Wang, Jie; Zi, Yunlong; Xu, Weidong; Deng, Jianan; Zhu, Lei; Wang, Xin; Hu, Chenguo; Zhu, Liping; Sun, Xuhui; Wang, Zhong Lin

2016-10-01

Wearable electronics fabricated on lightweight and flexible substrate are believed to have great potential for portable devices, but their applications are limited by the life span of their batteries. We propose a hybridized self-charging power textile system with the aim of simultaneously collecting outdoor sunshine and random body motion energies and then storing them in an energy storage unit. Both of the harvested energies can be easily converted into electricity by using fiber-shaped dye-sensitized solar cells (for solar energy) and fiber-shaped triboelectric nanogenerators (for random body motion energy) and then further stored as chemical energy in fiber-shaped supercapacitors. Because of the all-fiber-shaped structure of the entire system, our proposed hybridized self-charging textile system can be easily woven into electronic textiles to fabricate smart clothes to sustainably operate mobile or wearable electronics.

ITER Central Solenoid Module Fabrication

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, John

The fabrication of the modules for the ITER Central Solenoid (CS) has started in a dedicated production facility located in Poway, California, USA. The necessary tools have been designed, built, installed, and tested in the facility to enable the start of production. The current schedule has first module fabrication completed in 2017, followed by testing and subsequent shipment to ITER. The Central Solenoid is a key component of the ITER tokamak providing the inductive voltage to initiate and sustain the plasma current and to position and shape the plasma. The design of the CS has been a collaborative effort betweenmore » the US ITER Project Office (US ITER), the international ITER Organization (IO) and General Atomics (GA). GA’s responsibility includes: completing the fabrication design, developing and qualifying the fabrication processes and tools, and then completing the fabrication of the seven 110 tonne CS modules. The modules will be shipped separately to the ITER site, and then stacked and aligned in the Assembly Hall prior to insertion in the core of the ITER tokamak. A dedicated facility in Poway, California, USA has been established by GA to complete the fabrication of the seven modules. Infrastructure improvements included thick reinforced concrete floors, a diesel generator for backup power, along with, cranes for moving the tooling within the facility. The fabrication process for a single module requires approximately 22 months followed by five months of testing, which includes preliminary electrical testing followed by high current (48.5 kA) tests at 4.7K. The production of the seven modules is completed in a parallel fashion through ten process stations. The process stations have been designed and built with most stations having completed testing and qualification for carrying out the required fabrication processes. The final qualification step for each process station is achieved by the successful production of a prototype coil. Fabrication of

Control of cell nucleus shapes via micropillar patterns.

PubMed

Pan, Zhen; Yan, Ce; Peng, Rong; Zhao, Yingchun; He, Yao; Ding, Jiandong

2012-02-01

We herein report a material technique to control the shapes of cell nuclei by the design of the microtopography of substrates to which the cells adhere. Poly(D,L-lactide-co-glycolide) (PLGA) micropillars or micropits of a series of height or depth were fabricated, and some surprising self deformation of the nuclei of bone marrow stromal cells (BMSCs) was found in the case of micropillars with a sufficient height. Despite severe nucleus deformation, BMSCs kept the ability of proliferation and differentiation. We further demonstrated that the shapes of cell nuclei could be regulated by the appropriate micropillar patterns. Besides circular and elliptoid shapes, some unusual nucleus shapes of BMSCs have been achieved, such as square, cross, dumbbell, and asymmetric sphere-protrusion. Crown Copyright © 2011. Published by Elsevier Ltd. All rights reserved.

Fabrication and characterization of an SU-8 gripper actuated by a shape memory alloy thin film

NASA Astrophysics Data System (ADS)

Roch, I.; Bidaud, Ph; Collard, D.; Buchaillot, L.

2003-03-01

In this paper, we present the fabrication process of a shape memory alloy (SMA) thin film in both monolithic and hybrid configurations. This provides an effective actuation part for a gripper made of SU-8 thick photoresist. We also extensively describe and discuss the assembly of the SMA thin film with the SU-8 mechanism. Measurements show that the SU-8 gripper is able to achieve an opening action of 500 mum in amplitude at a frequency of 1 Hz. Finite element model simulations indicate that a force of 50 mN, corresponding to 400 mum of opening amplitude, should be produced by the SMA actuator. Although the assembly of the TiNi SMA thin film with the SU-8 mechanism is demonstrated, the bond reliability needs further development in order to improve the thermal behavior of the interface. In this paper, we show that SU-8 is well suited as a structural material for microelectromechanical systems (MEMS) applications. An attractive feature in the MEMS design is that the SMA generated force is well matched with the elastic properties of SU-8. From the application point of view, a SMA-actuated SU-8 high-aspect-ratio microgripper can serve as a secure means to transport microelectronics device, because it provides good grasping and safe insulation. This is also a preliminary result for the future development of biogrippers.

Fabrication of flexible piezoelectric PZT/fabric composite.

PubMed

Chen, Caifeng; Hong, Daiwei; Wang, Andong; Ni, Chaoying

2013-01-01

Flexible piezoelectric PZT/fabric composite material is pliable and tough in nature which is in a lack of traditional PZT patches. It has great application prospect in improving the sensitivity of sensor/actuator made by piezoelectric materials especially when they are used for curved surfaces or complicated conditions. In this paper, glass fiber cloth was adopted as carrier to grow PZT piezoelectric crystal particles by hydrothermal method, and the optimum conditions were studied. The results showed that the soft glass fiber cloth was an ideal kind of carrier. A large number of cubic-shaped PZT nanocrystallines grew firmly in the carrier with a dense and uniform distribution. The best hydrothermal condition was found to be pH 13, reaction time 24 h, and reaction temperature 200°C.

Fabrication of Flexible Piezoelectric PZT/Fabric Composite

PubMed Central

Chen, Caifeng; Hong, Daiwei; Wang, Andong; Ni, Chaoying

2013-01-01

Flexible piezoelectric PZT/fabric composite material is pliable and tough in nature which is in a lack of traditional PZT patches. It has great application prospect in improving the sensitivity of sensor/actuator made by piezoelectric materials especially when they are used for curved surfaces or complicated conditions. In this paper, glass fiber cloth was adopted as carrier to grow PZT piezoelectric crystal particles by hydrothermal method, and the optimum conditions were studied. The results showed that the soft glass fiber cloth was an ideal kind of carrier. A large number of cubic-shaped PZT nanocrystallines grew firmly in the carrier with a dense and uniform distribution. The best hydrothermal condition was found to be pH 13, reaction time 24 h, and reaction temperature 200°C. PMID:24348194

Fatigue design curve of a TiNi/Al shape memory alloy composite for aircraft stringer design

NASA Astrophysics Data System (ADS)

Park, Young-Chul; Jo, Young-Jik; Baek, Seok-Heum; Furuya, Yasubumi

2009-05-01

In this study, a TiNi/Al6061 shape memory alloy (SMA) composite was fabricated by the hot press method, and pressed by a roller for its strength improvement using the shape memory fiber shrinkage phenomenon. These two kinds of specimens were fabricated with 0% and 5% volume ratio and 0%, 10 % and 20% reduction ratio of TiNi alloy fiber, respectively. A fatigue test has been performed to evaluate the fatigue life for the fabricated TiNi/Al SMA composite as an S-N curve. The results from the Goodman diagram is able to illustrate the failure criterion and fatigue limit between tensile and bending fatigue strength in the fatigue characterization of TiNi/Al SMA composites.

Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading.

PubMed

Li, Siqi; Jiang, Huiyan; Pang, Wenbo

2017-05-01

Accurate cell grading of cancerous tissue pathological image is of great importance in medical diagnosis and treatment. This paper proposes a joint multiple fully connected convolutional neural network with extreme learning machine (MFC-CNN-ELM) architecture for hepatocellular carcinoma (HCC) nuclei grading. First, in preprocessing stage, each grayscale image patch with the fixed size is obtained using center-proliferation segmentation (CPS) method and the corresponding labels are marked under the guidance of three pathologists. Next, a multiple fully connected convolutional neural network (MFC-CNN) is designed to extract the multi-form feature vectors of each input image automatically, which considers multi-scale contextual information of deep layer maps sufficiently. After that, a convolutional neural network extreme learning machine (CNN-ELM) model is proposed to grade HCC nuclei. Finally, a back propagation (BP) algorithm, which contains a new up-sample method, is utilized to train MFC-CNN-ELM architecture. The experiment comparison results demonstrate that our proposed MFC-CNN-ELM has superior performance compared with related works for HCC nuclei grading. Meanwhile, external validation using ICPR 2014 HEp-2 cell dataset shows the good generalization of our MFC-CNN-ELM architecture. Copyright © 2017 Elsevier Ltd. All rights reserved.

Optical device fabrication using femtosecond laser processing with glass-hologram

NASA Astrophysics Data System (ADS)

Suzuki, Jun'ichi; Arima, Yasunori; Tanaka, Shuhei

2011-03-01

Using femtosecond laser processing with glass-hologram, fabrication of 1cm-long straight waveguide and X-coupler is reported in this paper. We design and fabricate 4-level glass-hologram which generates 1cm-long straight line intensity. We fabricate 1cm-long waveguides inside fused silica at one shot exposure with the glass-hologram. We investigate the waveguide performance of near field pattern and propagation loss at wavelength of 1550nm. The near field pattern is almost circular shape. The propagation loss at 1550nm is estimated to be < 1.0 dB/cm. As an example of an optical device consisting of straight waveguides, we fabricate X-coupler or 2x2 coupler using straight line waveguides, and observe the output power ratio depending on crossing angle.

Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image.

PubMed

Xu, Kele; Feng, Dawei; Mi, Haibo

2017-11-23

The automatic detection of diabetic retinopathy is of vital importance, as it is the main cause of irreversible vision loss in the working-age population in the developed world. The early detection of diabetic retinopathy occurrence can be very helpful for clinical treatment; although several different feature extraction approaches have been proposed, the classification task for retinal images is still tedious even for those trained clinicians. Recently, deep convolutional neural networks have manifested superior performance in image classification compared to previous handcrafted feature-based image classification methods. Thus, in this paper, we explored the use of deep convolutional neural network methodology for the automatic classification of diabetic retinopathy using color fundus image, and obtained an accuracy of 94.5% on our dataset, outperforming the results obtained by using classical approaches.

Topology reduction in deep convolutional feature extraction networks

NASA Astrophysics Data System (ADS)

Wiatowski, Thomas; Grohs, Philipp; Bölcskei, Helmut

2017-08-01

Deep convolutional neural networks (CNNs) used in practice employ potentially hundreds of layers and 10,000s of nodes. Such network sizes entail significant computational complexity due to the large number of convolutions that need to be carried out; in addition, a large number of parameters needs to be learned and stored. Very deep and wide CNNs may therefore not be well suited to applications operating under severe resource constraints as is the case, e.g., in low-power embedded and mobile platforms. This paper aims at understanding the impact of CNN topology, specifically depth and width, on the network's feature extraction capabilities. We address this question for the class of scattering networks that employ either Weyl-Heisenberg filters or wavelets, the modulus non-linearity, and no pooling. The exponential feature map energy decay results in Wiatowski et al., 2017, are generalized to O(a-N), where an arbitrary decay factor a > 1 can be realized through suitable choice of the Weyl-Heisenberg prototype function or the mother wavelet. We then show how networks of fixed (possibly small) depth N can be designed to guarantee that ((1 - ɛ) · 100)% of the input signal's energy are contained in the feature vector. Based on the notion of operationally significant nodes, we characterize, partly rigorously and partly heuristically, the topology-reducing effects of (effectively) band-limited input signals, band-limited filters, and feature map symmetries. Finally, for networks based on Weyl-Heisenberg filters, we determine the prototype function bandwidth that minimizes - for fixed network depth N - the average number of operationally significant nodes per layer.

X-ray mirror prototype based on cold shaping of thin glass foils

NASA Astrophysics Data System (ADS)

Basso, Stefano; Civitani, Marta; Ghigo, Mauro; Hołyszko, Joanna; Pareschi, Giovanni; Salmaso, Bianca; Vecchi, Gabriele; Burwitz, Vadim; Pelliciari, Carlo; Hartner, Gisela D.; Breunig, Elias

2017-08-01

The Slumping Glass Optics technology for the fabrication of astronomical X-ray mirrors has been developed in recent years in USA and Europe. The process has been used for making the mirrors of the Nustar, mission. The process starts with very thin glass foils hot formed to copy the profile of replication moulds. At INAF - Osservatorio Astronomico di Brera a process based on cold shaping is being developed, based on an integration method involving the use of interconnecting ribs for making stacks. Each glass foil in the stack is shaped onto a very precise integration mould and the correct shape is frozen by means of glued ribs that act as spacers between one layer and the next one (the first layers being attached to a thick substrate). Therefore, the increasing availability of flexible glass foils with a thickness of a few tens of microns (driven by electronic market for ultra-thin displays) opens new possibilities for the fabrication of X-ray mirrors. This solution appears interesting especially for the fabrication of mirrors for hard X-rays (with energy > 10 keV) based on multilayer coatings, taking advantage from the intrinsic low roughness of the glass foils that should grant a low scattering level. The stress frozen on the glass due to the cold shaping is not negligible, but it is kept into account in the errors of the X-ray optics design. As an exercise, we have considered the requirements and specs of the FORCE hard Xray mission concept (being studied by JAXA) and we have designed the mirror modules assuming the cold slumping as a fabrication method. In the meantime, a prototype (representative of the FORCE mirror modules) is being design and integrated in order to demonstrate the feasibility and the capacity to reach good angular resolution.

Recoverable Wire-Shaped Supercapacitors with Ultrahigh Volumetric Energy Density for Multifunctional Portable and Wearable Electronics.

PubMed

Shi, Minjie; Yang, Cheng; Song, Xuefeng; Liu, Jing; Zhao, Liping; Zhang, Peng; Gao, Lian

2017-05-24

Wire-shaped supercapacitors (SCs) based on shape memory materials are of considerable interest for next-generation portable and wearable electronics. However, the bottleneck in this field is how to develop the devices with excellent electrochemical performance while well-maintaining recoverability and flexibility. Herein, a unique asymmetric electrode concept is put forward to fabricate smart wire-shaped SCs with ultrahigh energy density, which is realized by using porous carbon dodecahedra coated on NiTi alloy wire and flexible graphene fiber as yarn electrodes. Notably, the wire-shaped SCs not only exhibit high flexibility that can be readily woven into real clothing but also represent the available recoverable ability. When irreversible plastic deformations happen, the deformed shape of the devices can automatically resume the initial predesigned shape in a warm environment (about 35 °C). More importantly, the wire-shaped SCs act as efficient energy storage devices, which display high volumetric energy density (8.9 mWh/cm 3 ), volumetric power density (1080 mW/cm 3 ), strong durability in multiple mechanical states, and steady electrochemical behavior after repeated shape recovery processes. Considering their relative facile fabrication technology and excellent electrochemical performance, this asymmetric electrode strategy produced smart wire-shaped supercapacitors desirable for multifunctional portable and wearable electronics.

A low-cost, high-yield fabrication method for producing optimized biomimetic dry adhesives

NASA Astrophysics Data System (ADS)

Sameoto, D.; Menon, C.

2009-11-01

We present a low-cost, large-scale method of fabricating biomimetic dry adhesives. This process is useful because it uses all photosensitive polymers with minimum fabrication costs or complexity to produce molds for silicone-based dry adhesives. A thick-film lift-off process is used to define molds using AZ 9260 photoresist, with a slow acting, deep UV sensitive material, PMGI, used as both an adhesion promoter for the AZ 9260 photoresist and as an undercutting material to produce mushroom-shaped fibers. The benefits to this process are ease of fabrication, wide range of potential layer thicknesses, no special surface treatment requirements to demold silicone adhesives and easy stripping of the full mold if process failure does occur. Sylgard® 184 silicone is used to cast full sheets of biomimetic dry adhesives off 4" diameter wafers, and different fiber geometries are tested for normal adhesion properties. Additionally, failure modes of the adhesive during fabrication are noted and strategies for avoiding these failures are discussed. We use this fabrication method to produce different fiber geometries with varying cap diameters and test them for normal adhesion strengths. The results indicate that the cap diameters relative to post diameters for mushroom-shaped fibers dominate the adhesion properties.

Fabric panel clean change-out frame

DOEpatents

Brown, Ronald M.

1995-01-31

A fabric panel clean change-out frame, for use on a containment structure having rigid walls, is formed of a compression frame and a closure panel. The frame is formed of elongated spacers, each carrying a plurality of closely spaced flat springs, and each having a hooked lip extending on the side of the spring facing the spacer. The closure panel is includes a perimeter frame formed of flexible, wedge-shaped frame members that are receivable under the springs to deflect the hooked lips. A groove on the flexible frame members engages the hooked lips and locks the frame members in place under the springs. A flexible fabric panel is connected to the flexible frame members and closes its center.

D-Shaped Polarization Maintaining Fiber Sensor for Strain and Temperature Monitoring.

PubMed

Qazi, Hummad Habib; Mohammad, Abu Bakar; Ahmad, Harith; Zulkifli, Mohd Zamani

2016-09-15

A D-shaped polarization-maintaining fiber (PMF) as fiber optic sensor for the simultaneous monitoring of strain and the surrounding temperature is presented. A mechanical end and edge polishing system with aluminum oxide polishing film is utilized to perform sequential polishing on one side (lengthwise) of the PMF in order to fabricate a D-shaped cross-section. Experimental results show that the proposed sensor has high sensitivity of 46 pm/µε and 130 pm/°C for strain and temperature, respectively, which is significantly higher than other recently reported work (mainly from 2013) related to fiber optic sensors. The easy fabrication method, high sensitivity, and good linearity make this sensing device applicable in various applications such as health monitoring and spatial analysis of engineering structures.

Robust and adjustable C-shaped electron vortex beams

NASA Astrophysics Data System (ADS)

Mousley, M.; Thirunavukkarasu, G.; Babiker, M.; Yuan, J.

2017-06-01

Wavefront engineering is an important quantum technology, often applied to the production of states carrying orbital angular momentum (OAM). Here, we demonstrate the design and production of robust C-shaped beam states carrying OAM, in which the usual doughnut-shaped transverse intensity structure of the vortex beam contains an adjustable gap. We find that the presence of the vortex lines in the core of the beam is crucial for maintaining the stability of the C-shape structure during beam propagation. The topological charge of the vortex core controls mainly the size of the C-shape, while its opening angle is related to the presence of vortex-anti-vortex loops. We demonstrate the generation and characterisation of C-shaped electron vortex beams, although the result is equally applicable to other quantum waves. C-shaped electron vortex beams have potential applications in nanoscale fabrication of planar split-ring structures and three-dimensional chiral structures as well as depth sensing and magnetic field determination through rotation of the gap in the C-shape.