WorldWideScience

Sample records for convoluted shaped electrode

  1. Catoptric electrodes: transparent metal electrodes using shaped surfaces.

    Science.gov (United States)

    Kik, Pieter G

    2014-09-01

    An optical electrode design is presented that theoretically allows 100% optical transmission through an interdigitated metallic electrode at 50% metal areal coverage. This is achieved by redirection of light incident on embedded metal electrode lines to an angle beyond that required for total internal reflection. Full-field electromagnetic simulations using realistic material parameters demonstrate 84% frequency-averaged transmission for unpolarized illumination across the entire visible spectral range using a silver interdigitated electrode at 50% areal coverage. The redirection is achieved through specular reflection, making it nonresonant and arbitrarily broadband, provided the electrode width exceeds the optical wavelength. These findings could significantly improve the performance of photovoltaic devices and optical detectors that require high-conductivity top contacts.

  2. Modiolus-Hugging Intracochlear Electrode Array with Shape Memory Alloy

    Directory of Open Access Journals (Sweden)

    Kyou Sik Min

    2013-01-01

    Full Text Available In the cochlear implant system, the distance between spiral ganglia and the electrodes within the volume of the scala tympani cavity significantly affects the efficiency of the electrical stimulation in terms of the threshold current level and spatial selectivity. Because the spiral ganglia are situated inside the modiolus, the central axis of the cochlea, it is desirable that the electrode array hugs the modiolus to minimize the distance between the electrodes and the ganglia. In the present study, we propose a shape-memory-alloy-(SMA- embedded intracochlear electrode which gives a straight electrode a curved modiolus-hugging shape using the restoration force of the SMA as triggered by resistive heating after insertion into the cochlea. An eight-channel ball-type electrode array is fabricated with an embedded titanium-nickel SMA backbone wire. It is demonstrated that the electrode array changes its shape in a transparent plastic human cochlear model. To verify the safe insertion of the electrode array into the human cochlea, the contact pressures during insertion at the electrode tip and the contact pressures over the electrode length after insertion were calculated using a 3D finite element analysis. The results indicate that the SMA-embedded electrode is functionally and mechanically feasible for clinical applications.

  3. Zinc electrode shape change II. Process and mechanism

    NARCIS (Netherlands)

    Einerhand, R.E.F.; Visscher, W.; de Goeij, J.J.M.; Barendrecht, E.

    1991-01-01

    The process and mechanism of zinc electrode shape change is investigated with the radiotracer technique. It is shownthat during repeated cycling of the nickel oxide/zinc battery zinc material is transported over the zinc electrode via the battery electrolyte. During charge as well as during

  4. Determination of the Resistance of Cone-Shaped Solid Electrodes

    DEFF Research Database (Denmark)

    Frandsen, Henrik Lund; Hendriksen, Peter Vang; Koch, Søren

    2017-01-01

    during processing can be avoided. Newman's formula for current constriction in the electrolyte is then used to deduce the active contact area based on the ohmic resistance of the cell, and from this the surface specific electro-catalytic activity. However, for electrode materials with low electrical......A cone-shaped electrode pressed into an electrolyte can with advantage be utilized to characterize the electro-catalytic properties of the electrode, because it is less dependent on the electrode microstructure than e.g. thin porous composite electrodes, and reactions with the electrolyte occurring...... conductivity (like Ce1-xPrxO2-δ), the resistance of the cell is significantly influenced by the ohmic resistance of the cone electrode, wherefore it must be included. In this work the ohmic resistance of a cone is modelled analytically based on simplified geometries. The two analytical models only differ...

  5. Effect of electrode shape on grounding resistances - Part 2

    DEFF Research Database (Denmark)

    Tomaskovicova, Sonia; Ingeman-Nielsen, Thomas; Christiansen, Anders V.

    2016-01-01

    Although electric resistivity tomography (ERT) is now regarded as a standard tool in permafrost monitoring, high grounding resistances continue to limit the acquisition of time series over complete freeze-thaw cycles. In an attempt to alleviate the grounding resistance problem, we have tested three...... electrode designs featuring increasing sizes and surface area, in the laboratory and at three different field sites in Greenland. Grounding resistance measurements showed that changing the electrode shape (using plates instead of rods) reduced the grounding resistances at all sites by 28%-69% during...... unfrozen and frozen ground conditions. Using meshes instead of plates (the same rectangular shape and a larger effective surface area) further improved the grounding resistances by 29%-37% in winter. Replacement of rod electrodes of one entire permanent permafrost monitoring array by meshes resulted...

  6. The characterization of beam profile by modification of electrode shape

    International Nuclear Information System (INIS)

    Lee, Chan Young; Lee, Jae Sang

    2010-01-01

    Ion sources have been used for variety of industrial application over the past few decades and our research group has been studied about high current and large dimension ion source to meet the requirement from beam user. For a mass production in industry, a wide beam divergence and a beam profile of a broadly Gaussian shape is very needed. Generally, the production process like roll-to-roll or in-line system is need one-meter in diameter, ±5% in uniformity. Therefore it is difficult to apply with present system like 0.3-meter in diameter, ±20% in uniformity and needed new type ion source. In this study, it is approached with modification of electrode grid shape without fabrication of new type ion source. We modified from parallel type to hemispherical type electrode grid to secure large dimension ion beam and were discussed with respect to beam profile calculated with IGUN code simulation. Also, we identified beam profile before and after modification of electrode grid system(cathode, Acelldecel grid) with measurement of faraday cup

  7. Prostate segmentation in MRI using a convolutional neural network architecture and training strategy based on statistical shape models.

    Science.gov (United States)

    Karimi, Davood; Samei, Golnoosh; Kesch, Claudia; Nir, Guy; Salcudean, Septimiu E

    2018-05-15

    Most of the existing convolutional neural network (CNN)-based medical image segmentation methods are based on methods that have originally been developed for segmentation of natural images. Therefore, they largely ignore the differences between the two domains, such as the smaller degree of variability in the shape and appearance of the target volume and the smaller amounts of training data in medical applications. We propose a CNN-based method for prostate segmentation in MRI that employs statistical shape models to address these issues. Our CNN predicts the location of the prostate center and the parameters of the shape model, which determine the position of prostate surface keypoints. To train such a large model for segmentation of 3D images using small data (1) we adopt a stage-wise training strategy by first training the network to predict the prostate center and subsequently adding modules for predicting the parameters of the shape model and prostate rotation, (2) we propose a data augmentation method whereby the training images and their prostate surface keypoints are deformed according to the displacements computed based on the shape model, and (3) we employ various regularization techniques. Our proposed method achieves a Dice score of 0.88, which is obtained by using both elastic-net and spectral dropout for regularization. Compared with a standard CNN-based method, our method shows significantly better segmentation performance on the prostate base and apex. Our experiments also show that data augmentation using the shape model significantly improves the segmentation results. Prior knowledge about the shape of the target organ can improve the performance of CNN-based segmentation methods, especially where image features are not sufficient for a precise segmentation. Statistical shape models can also be employed to synthesize additional training data that can ease the training of large CNNs.

  8. Polymer-based candle-shaped microneedle electrodes for electroencephalography on hairy skin

    Science.gov (United States)

    Arai, Miyako; Kudo, Yuta; Miki, Norihisa

    2016-06-01

    In this paper, we report on the optimization of the shape of dry microneedle electrodes for electroencephalography (EEG) on hairy locations and compare the electrodes we developed with conventional wet electrodes. We propose the use of SU-8-based candle-shaped microneedle electrodes (CMEs), which have pillars of 1.0 mm height and 0.4 mm diameter with a gap of 0.43 mm between pillars. Microneedles are formed on the top of the pillars. The shape was determined by how well the pillars can avoid hairs and support the microneedles to penetrate through the stratum corneum. The skin-electrode contact impedances of the fabricated CMEs were found to be higher and less stable than those of conventional wet electrodes. However, the CMEs successfully acquired signals with qualities as good as those of conventional wet electrodes. Given the usability of the CMEs, which do not require skin preparation or gel, they are promising alternatives to conventional wet electrodes.

  9. The Effects of Different Electrode Types for Obtaining Surface Machining Shape on Shape Memory Alloy Using Electrochemical Machining

    Science.gov (United States)

    Choi, S. G.; Kim, S. H.; Choi, W. K.; Moon, G. C.; Lee, E. S.

    2017-06-01

    Shape memory alloy (SMA) is important material used for the medicine and aerospace industry due to its characteristics called the shape memory effect, which involves the recovery of deformed alloy to its original state through the application of temperature or stress. Consumers in modern society demand stability in parts. Electrochemical machining is one of the methods for obtained these stabilities in parts requirements. These parts of shape memory alloy require fine patterns in some applications. In order to machine a fine pattern, the electrochemical machining method is suitable. For precision electrochemical machining using different shape electrodes, the current density should be controlled precisely. And electrode shape is required for precise electrochemical machining. It is possible to obtain precise square holes on the SMA if the insulation layer controlled the unnecessary current between electrode and workpiece. If it is adjusting the unnecessary current to obtain the desired shape, it will be a great contribution to the medical industry and the aerospace industry. It is possible to process a desired shape to the shape memory alloy by micro controlling the unnecessary current. In case of the square electrode without insulation layer, it derives inexact square holes due to the unnecessary current. The results using the insulated electrode in only side show precise square holes. The removal rate improved in case of insulated electrode than others because insulation layer concentrate the applied current to the machining zone.

  10. Time-dependent simulation of plasma and electrodes in high-intensity discharge lamps with different electrode shapes

    CERN Document Server

    Flesch, P

    2003-01-01

    The subject of this paper is the modelling of d.c. and a.c. high-intensity Hg-discharge lamps with differently shaped electrodes. Different arc attachments on the electrodes are studied and insight for the development of new electrodes is gained. The model includes the entire discharge plasma (plasma column, hot plasma spots in front of electrodes, near-electrode non-LTE-plasma) as well as anode and cathode. No subdivision of the discharge space into different regions is necessary (like space charge layer, ionization zone, plasma column). This is achieved by using a differential equation for a non-LTE electrical conductivity which is applicable for local thermal equilibrium (LTE-)regions as well as for non-LTE plasma regions close to the electrodes in a high pressure plasma. Modelling results for a 0.6 MPa mercury discharge considering six different electrode shapes (anode and cathode) are presented and compared with experimental results. The electrodes have different diameters and different electrode tips, s...

  11. Effect of electrode shape on grounding resistances - Part 1

    DEFF Research Database (Denmark)

    Ingeman-Nielsen, Thomas; Tomaskovicova, Sonia; Dahlin, Torleif

    2016-01-01

    Electrode grounding resistance is a major factor affecting measurement quality in electric resistivity tomography (ERT) measurements for cryospheric applications. Still, little information is available on grounding resistances in the geophysical literature, mainly because it is difficult to measure....... The focus-one protocol is a new method for estimating single electrode grounding resistances by measuring the resistance between a single electrode in an ERT array and all the remaining electrodes connected in parallel. For large arrays, the measured resistance is dominated by the grounding resistance...... of the electrode under test, the focus electrode. We have developed an equivalent circuit model formulation for the resistance measured when applying the focus-one protocol. Our model depends on the individual grounding resistances of the electrodes of the array, the mutual resistances between electrodes...

  12. On the importance of electrode parameters for shaping electric field patterns generated by tDCS

    DEFF Research Database (Denmark)

    B. Saturnino, Guilherme; Antunes, André; Thielscher, Axel

    2015-01-01

    Transcranial direct current stimulation (tDCS) uses electrode pads placed on the head to deliver weak direct current to the brain and modulate neuronal excitability. The effects depend on the intensity and spatial distribution of the electric field. This in turn depends on the geometry and electric...... electrode modeling influences the calculated electric field in the brain. We take into account electrode shape, size, connector position and conductivities of different electrode materials (including saline solutions and electrode gels). These factors are systematically characterized to demonstrate...... their impact on the field distribution in the brain. The goals are to assess the effect of simplified electrode models; and to develop practical rules-of-thumb to achieve a stronger stimulation of the targeted brain regions underneath the electrode pads. We show that for standard rectangular electrode pads...

  13. Contact Resistance Reduction of ZnO Thin Film Transistors (TFTs) with Saw-Shaped Electrode

    KAUST Repository

    Park, Woojin

    2018-05-15

    We report a saw-shaped electrode architecture ZnO thin film transistor (TFT) for effectively increase channel width. Such a saw-shaped electrode has ~2 times longer contact line at the contact metal/ZnO channel junction. We experimentally observed an enhancement in the output drive current by 50% and reduction in the contact resistance by over 50%, when compared to a typical shaped electrode ZnO TFT consuming the same chip area. This performance enhancement is attributed to extension of channel width. This technique can contribute to device performance enhancement and especially reduction in the contact resistance which is a serious challenge.

  14. A Dynamic Mesh-Based Approach to Model Melting and Shape of an ESR Electrode

    Science.gov (United States)

    Karimi-Sibaki, E.; Kharicha, A.; Bohacek, J.; Wu, M.; Ludwig, A.

    2015-10-01

    This paper presents a numerical method to investigate the shape of tip and melt rate of an electrode during electroslag remelting process. The interactions between flow, temperature, and electromagnetic fields are taken into account. A dynamic mesh-based approach is employed to model the dynamic formation of the shape of electrode tip. The effect of slag properties such as thermal and electrical conductivities on the melt rate and electrode immersion depth is discussed. The thermal conductivity of slag has a dominant influence on the heat transfer in the system, hence on melt rate of electrode. The melt rate decreases with increasing thermal conductivity of slag. The electrical conductivity of slag governs the electric current path that in turn influences flow and temperature fields. The melting of electrode is a quite unstable process due to the complex interaction between the melt rate, immersion depth, and shape of electrode tip. Therefore, a numerical adaptation of electrode position in the slag has been implemented in order to achieve steady state melting. In fact, the melt rate, immersion depth, and shape of electrode tip are interdependent parameters of process. The generated power in the system is found to be dependent on both immersion depth and shape of electrode tip. In other words, the same amount of power was generated for the systems where the shapes of tip and immersion depth were different. Furthermore, it was observed that the shape of electrode tip is very similar for the systems running with the same ratio of power generation to melt rate. Comparison between simulations and experimental results was made to verify the numerical model.

  15. Effect of the number of electrodes on the reconstructed lung shape in electrical impedance tomography

    Directory of Open Access Journals (Sweden)

    Schullcke Benjamin

    2016-09-01

    Full Text Available Electrical impedance tomography (EIT is used to monitor the regional distribution of ventilation in a transversal plane of the thorax. In this manuscript we evaluate the impact of different quantities of electrodes used for current injection and voltage measurement on the reconstructed shape of the lungs. Results indicate that the shape of reconstructed impedance changes in the body depends on the number of electrodes. In this manuscript, we demonstrate that a higher number of electrodes do not necessarily increase the image quality. For the used stimulation pattern, utilizing neighboring electrodes for current injection and voltage measurement, we conclude that the shape of the lungs is best reconstructed if 16 electrodes are used.

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

    Directory of Open Access Journals (Sweden)

    Sung June Kim

    2008-09-01

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

  17. Optimization and analysis of shape of coaxial electrode for microwave plasma in water

    International Nuclear Information System (INIS)

    Hattori, Yoshiaki; Mukasa, Shinobu; Nomura, Shinfuku; Toyota, Hiromichi

    2010-01-01

    The effect of the shape of the electrode to generate 2.45 GHz microwave plasma in pure water is examined. Three variations of a common coaxial electrode are proposed, and compared according to the power required for plasma ignition and the position of plasma ignition in pure water at 6 kPa using a high-speed camera. These coaxial electrodes are calculated using three-dimensional finite-difference time-domain method calculations. The superior shape of coaxial electrode is found to be one with a flat plane on the tip of the inner electrode and dielectric substance located below the tip of the outer electrode. The position of the plasma ignition is related to the shape of the coaxial electrode. By solving the heat-conduction equation of water around the coaxial electrode taking into account the absorption of the microwave energy, the position of the plasma ignition is found to be not where electric field is the largest, but rather where temperature is maximized.

  18. Structural design of flexible Au electrode to enable shape memory polymer for electrical actuation

    Science.gov (United States)

    Lu, Haibao; Lei, Ming; Zhao, Chao; Xu, Ben; Leng, Jinsong; Fu, Y. Q.

    2015-04-01

    An effective resistive Joule heating approach was conducted to improve the electrical actuation and shape-recovery performance of a shape memory polymer (SMP) nanocomposite. Two types of gold (Au) film patterns were deposited to be used as electrodes to drive thermal-responsive SMPs and achieve a uniform temperature distribution during electro-activated shape recovery. Furthermore, the sensing capability of the Au electrode to both mechanical and thermal stimuli applied to the SMP nanocomposite was experimentally investigated and theoretically analyzed. It was found that the change in the electrical resistance of the Au electrode could be used as an indication of shape-recovery performance. The linear response of the electrical resistance to strain was identified mainly due to the opening/closing of microcracks and their propagations in the Au electrodes during out-of-plane deformations. With an increment of thermomechanical bending cycles, the electrical resistance was increased exponentially, but it returned back to the original reading when the SMP nanocomposite returned back to its permanent shape. Finally, the flexible Au electrode enabled the actuation of the SMP nanocomposite under an electric voltage of 13.4 V, with an improved shape-recovery performance and temperature distribution.

  19. Convolution based profile fitting

    International Nuclear Information System (INIS)

    Kern, A.; Coelho, A.A.; Cheary, R.W.

    2002-01-01

    Full text: In convolution based profile fitting, profiles are generated by convoluting functions together to form the observed profile shape. For a convolution of 'n' functions this process can be written as, Y(2θ)=F 1 (2θ)x F 2 (2θ)x... x F i (2θ)x....xF n (2θ). In powder diffractometry the functions F i (2θ) can be interpreted as the aberration functions of the diffractometer, but in general any combination of appropriate functions for F i (2θ) may be used in this context. Most direct convolution fitting methods are restricted to combinations of F i (2θ) that can be convoluted analytically (e.g. GSAS) such as Lorentzians, Gaussians, the hat (impulse) function and the exponential function. However, software such as TOPAS is now available that can accurately convolute and refine a wide variety of profile shapes numerically, including user defined profiles, without the need to convolute analytically. Some of the most important advantages of modern convolution based profile fitting are: 1) virtually any peak shape and angle dependence can normally be described using minimal profile parameters in laboratory and synchrotron X-ray data as well as in CW and TOF neutron data. This is possible because numerical convolution and numerical differentiation is used within the refinement procedure so that a wide range of functions can easily be incorporated into the convolution equation; 2) it can use physically based diffractometer models by convoluting the instrument aberration functions. This can be done for most laboratory based X-ray powder diffractometer configurations including conventional divergent beam instruments, parallel beam instruments, and diffractometers used for asymmetric diffraction. It can also accommodate various optical elements (e.g. multilayers and monochromators) and detector systems (e.g. point and position sensitive detectors) and has already been applied to neutron powder diffraction systems (e.g. ANSTO) as well as synchrotron based

  20. Effect of shape and resistivity of electrodes in a Faraday MHD duct

    International Nuclear Information System (INIS)

    Jayakumar, R.; Ghosh, S.

    1976-01-01

    The object of achieving uniform current distribution in the presence of high axial fields has prompted the use of resistive electrodes in flat and wedge geometries. In the case of flat geometry the technique involves the generation of voltage drop along the surface of the electrodes in the axial direction, due to the Faraday current collected by the electrode and flowing into a lead wire, to reduce or eliminate the discontinuity in the axial electrical field that would otherwise occur, say in case of metal electrodes. In the case of wedge shapes, higher resistance path is provided for the regions where current is likely to concentrate. In the case of flat geometry, the effect of the position of lead wire also influences the current distribution in the plasma and on the electrode surface. The resistive electrodes have been investigated for the actual current distribution by numerically solving the Laplace's equation for current stream function, arising out of Maxwell's equation and generalised Ohm's law. In the case of wedge electrode, the solution has been sought by numerical analysis of both plasma and electrode zones. It is shown that both geometries, the flat geometry with a lead wire shifted optimally from one edge and the wedge electrode can almost eliminate current concentration. (author)

  1. In situ Zn/ZnO mapping elucidating for "shape change" of zinc electrode

    Science.gov (United States)

    Nakata, Akiyoshi; Arai, Hajime; Murayama, Haruno; Fukuda, Katsutoshi; Yamane, Tomokazu; Hirai, Toshiro; Uchimoto, Yoshiharu; Yamaki, Jun-ichi; Ogumi, Zempachi

    2018-04-01

    For the use of the zinc anode in secondary batteries, it is necessary to solve the "shape change" deterioration issue in that zinc species agglomerate in the center of the electrode to fade the available capacity. The local chemical compositions of the zinc electrodes during "shape change" were precisely analyzed using the synchrotron X-ray diffraction mapping analysis of practical zinc-nickel cells in a non-destructive manner. The in situ Zn/ZnO mapping shows that metallic Zn deposition chiefly occurs in the periphery of ZnO while ZnO are left in the center of electrode like a hill on charging. On discharging, the ZnO hill grows to the perpendicular direction on the electrode while metallic zinc is oxidized and dissolved. These findings allow us to propose a mechanism for the shape change; thus dissolved zincate species are decomposed on the ZnO hill during discharging to be accumulated in the center of the electrode. It is suggested that suppressing zincate dissolution and non-uniform zinc deposition slow the growth rate of the ZnO hill to enhance the cyclability of zinc-based secondary batteries.

  2. Contact Resistance Reduction of ZnO Thin Film Transistors (TFTs) with Saw-Shaped Electrode

    KAUST Repository

    Park, Woojin; Shaikh, Sohail F.; Min, Jungwook; Lee, Sang Kyung; Lee, Byoung Hun; Hussain, Muhammad Mustafa

    2018-01-01

    an enhancement in the output drive current by 50% and reduction in the contact resistance by over 50%, when compared to a typical shaped electrode ZnO TFT consuming the same chip area. This performance enhancement is attributed to extension of channel width

  3. COMMUNICATION: Toward a self-deploying shape memory polymer neuronal electrode

    Science.gov (United States)

    Sharp, Andrew A.; Panchawagh, Hrishikesh V.; Ortega, Alicia; Artale, Ryan; Richardson-Burns, Sarah; Finch, Dudley S.; Gall, Ken; Mahajan, Roop L.; Restrepo, Diego

    2006-12-01

    The widespread application of neuronal probes for chronic recording of brain activity and functional stimulation has been slow to develop partially due to long-term biocompatibility problems with existing metallic and ceramic probes and the tissue damage caused during probe insertion. Stiff probes are easily inserted into soft brain tissue but cause astrocytic scars that become insulating sheaths between electrodes and neurons. In this communication, we explore the feasibility of a new approach to the composition and implantation of chronic electrode arrays. We demonstrate that softer polymer-based probes can be inserted into the olfactory bulb of a mouse and that slow insertion of the probes reduces astrocytic scarring. We further present the development of a micromachined shape memory polymer probe, which provides a vehicle to self-deploy an electrode at suitably slow rates and which can provide sufficient force to penetrate the brain. The deployment rate and composition of shape memory polymer probes can be tailored by polymer chemistry and actuator design. We conclude that it is feasible to fabricate shape memory polymer-based electrodes that would slowly self-implant compliant conductors into the brain, and both decrease initial trauma resulting from implantation and enhance long-term biocompatibility for long-term neuronal measurement and stimulation.

  4. Anti-3D Weapon Model Detection for Safe 3D Printing Based on Convolutional Neural Networks and D2 Shape Distribution

    Directory of Open Access Journals (Sweden)

    Giao N. Pham

    2018-03-01

    Full Text Available With the development of 3D printing, weapons are easily printed without any restriction from the production managers. Therefore, anti-3D weapon model detection is necessary issue in safe 3D printing to prevent the printing of 3D weapon models. In this paper, we would like to propose an anti-3D weapon model detection algorithm to prevent the printing of anti-3D weapon models for safe 3D printing based on the D2 shape distribution and an improved convolutional neural networks (CNNs. The purpose of the proposed algorithm is to detect anti-3D weapon models when they are used in 3D printing. The D2 shape distribution is computed from random points on the surface of a 3D weapon model and their geometric features in order to construct a D2 vector. The D2 vector is then trained by improved CNNs. The CNNs are used to detect anti-3D weapon models for safe 3D printing by training D2 vectors which have been constructed from the D2 shape distribution of 3D weapon models. Experiments with 3D weapon models proved that the D2 shape distribution of 3D weapon models in the same class is the same. Training and testing results also verified that the accuracy of the proposed algorithm is higher than the conventional works. The proposed algorithm is applied in a small application, and it could detect anti-3D weapon models for safe 3D printing.

  5. Impact of the electrode material and shape on performance of intrinsically tunable ferroelectric FBARs.

    Science.gov (United States)

    Vorobiev, Andrei; Gevorgian, Spartak

    2014-05-01

    Experiment-based analysis of losses in tunable ferroelectric xBiFeO3-(1-x)BaTiO3 (BF-BT) film bulk acoustic wave resonators (FBARs) is reported. The Q-factors, effective coupling coefficients, and tunabilities are considered as functions of surface roughness of the ferroelectric film, the acoustic impedance and shape of the electrodes/interconnecting strips, leakage of acoustic waves into the substrate via Bragg reflector, and the relative thicknesses of the electrodes and ferroelectric film. Compared with Al, the high acoustic impedance of Pt electrodes provides higher Q-factor, coupling coefficient, and tunability. However, using Pt in the interconnecting strips results in reduction of the Q-factor.

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

    International Nuclear Information System (INIS)

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

    2014-01-01

    This paper reports on a simple 3D metal electrode fabrication technique via inkjet printing onto a thermally contracting shape memory polymer (SMP) substrate. Inkjet printing allows for the direct patterning of structures from metal nanoparticle bearing liquid inks. After deposition, these inks require thermal curing steps to render a stable conductive film. By printing onto a SMP substrate, the metal nanoparticle ink can be cured and substrate shrunk simultaneously to create 3D metal microstructures, forming a large surface area topology well suited for energy applications. Polystyrene SMP shrinkage was characterized in a laboratory oven from 150-240°C, resulting in a size reduction of 1.97-2.58. Silver nanoparticle ink was patterned into electrodes, shrunk, and the topology characterized using scanning electron microscopy. Zinc-Silver Oxide microbatteries were fabricated to demonstrate the 3D electrodes compared to planar references. Characterization was performed using 10M potassium hydroxide electrolyte solution doped with zinc oxide (57g/L). After a 300s oxidation at 3Vdc, the 3D electrode battery demonstrated a 125% increased capacity over the reference cell. Reference cells degraded with longer oxidations, but the 3D electrodes were fully oxidized for 4 hours, and exhibited a capacity of 5.5mA-hr/cm 2 with stable metal performance

  7. Improved passive shunt vibration control of smart piezo-elastic beams using modal piezoelectric transducers with shaped electrodes

    International Nuclear Information System (INIS)

    Vasques, C M A

    2012-01-01

    Modal control and spatial filtering technologies for mitigation of vibration and/or structural acoustics radiation may be achieved through the use of distributed modal piezoelectric transducers with properly shaped electrodes. This approach filters out undesirable and uncontrollable modes over the bandwidth of interest in order to increase the robustness and stability of the controlled structural system, and may also yield higher values of the generalized modal electromechanical coupling coefficient, which is an important design parameter for achieving efficient passive shunt damping design. In this paper the improvements in passive shunt damping performance when using modal piezoelectric transducers with shaped electrodes are investigated for a two-layered resonant-shunted piezo-elastic smart beam structure. An electromechanical one-dimensional equivalent single-layer Euler–Bernoulli analytical model of two-layered smart piezo-elastic beams with arbitrary spatially shaped electrodes is established for modal and uniform electrode designs. The model is verified and validated by comparison with a one-dimensional discrete-layer (layerwise) finite element model, the damping performance of the shunted smart beam with shaped electrodes is investigated and assessed in terms of the generalized electromechanical coupling coefficient and frequency responses obtained when considering uniform and modally shaped electrodes and the underlying improved performance and advantages are assessed and discussed. (paper)

  8. Fundamentals of convolutional coding

    CERN Document Server

    Johannesson, Rolf

    2015-01-01

    Fundamentals of Convolutional Coding, Second Edition, regarded as a bible of convolutional coding brings you a clear and comprehensive discussion of the basic principles of this field * Two new chapters on low-density parity-check (LDPC) convolutional codes and iterative coding * Viterbi, BCJR, BEAST, list, and sequential decoding of convolutional codes * Distance properties of convolutional codes * Includes a downloadable solutions manual

  9. Electrode-shaping for the excitation and detection of permitted arbitrary modes in arbitrary geometries in piezoelectric resonators.

    Science.gov (United States)

    Pulskamp, Jeffrey S; Bedair, Sarah S; Polcawich, Ronald G; Smith, Gabriel L; Martin, Joel; Power, Brian; Bhave, Sunil A

    2012-05-01

    This paper reports theoretical analysis and experimental results on a numerical electrode shaping design technique that permits the excitation of arbitrary modes in arbitrary geometries for piezoelectric resonators, for those modes permitted to exist by the nonzero piezoelectric coefficients and electrode configuration. The technique directly determines optimal electrode shapes by assessing the local suitability of excitation and detection electrode placement on two-port resonators without the need for iterative numerical techniques. The technique is demonstrated in 61 different electrode designs in lead zirconate titanate (PZT) thin film on silicon RF micro electro-mechanical system (MEMS) plate, beam, ring, and disc resonators for out-of-plane flexural and various contour modes up to 200 MHz. The average squared effective electromechanical coupling factor for the designs was 0.54%, approximately equivalent to the theoretical maximum value of 0.53% for a fully electroded length-extensional mode beam resonator comprised of the same composite. The average improvement in S(21) for the electrode-shaped designs was 14.6 dB with a maximum improvement of 44.3 dB. Through this piezoelectric electrodeshaping technique, 95% of the designs showed a reduction in insertion loss.

  10. Fast Convolution Module (Fast Convolution Module)

    National Research Council Canada - National Science Library

    Bierens, L

    1997-01-01

    This report describes the design and realisation of a real-time range azimuth compression module, the so-called 'Fast Convolution Module', based on the fast convolution algorithm developed at TNO-FEL...

  11. Evaluation of LSF based SOFC Cathodes using Cone-shaped Electrodes

    DEFF Research Database (Denmark)

    Kammer Hansen, Kent; Mogensen, Mogens Bjerg

    2008-01-01

    Seven La1-xSrxFeO3-delta (x = 0, 0.05, 0.15, 0.25, 0.35, 0.50, 0.70) based perovskites were synthesized using the glycine-nitrate method. The La1-xSrxFeO3-delta compounds were characterized with powder X-ray diffraction and electrochemical impedance spectroscopy on cone-shaped electrodes using a Ce......(III) is the catalytic active specie towards the electrochemical reduction of oxygen in a solid oxide fuel cell on La1-xSrxFeO3-delta compounds. The results also show that oxide ion vacancies in the perovskite structure are important for the electrochemical reduction of oxygen. However, the effect of ordering of oxide...

  12. Air Damping in a Fan-Shaped Rotational Resonator with Comb Electrodes

    Science.gov (United States)

    Uchida, Yuki; Sugano, Koji; Tsuchiya, Toshiyuki; Tabata, Osamu; Ikehara, Tsuyoshi

    We theoretically and experimentally evaluated the damping effect in a rotational resonator with a comb-drive actuator and sensor. The resonator was fabricated from an SOI wafer and has a fan-shaped mass. The underlying substrate was removed using back side deep reactive ion etching. One set of comb electrodes was attached to each side of the mass: one for electrostatic driving and the other for capacitive detection. In our theoretical analysis, the dynamics of the resonator were simplified so that they could be represented by a lumped system. In this lumped system, the damping coefficient was estimated by assuming the damping to be slide film damping and the air flow to be a Stokes flow. The phase shift due to the slide film damping of thick air layers was included in the lumped system. In the experimental evaluation, one side of the rotational combs was removed step-by-step and a half of the mass using a laser trimming tool so that the individual damping effects caused by the comb electrodes and mass could be determined quantitatively. We compared the experimental results with the results of the theoretical analysis and found that the difference in the damping coefficients between the experimental results and results of the theoretical analysis was less than 40%.

  13. Fabrication and characterization of a micromachined swirl-shaped ionic polymer metal composite actuator with electrodes exhibiting asymmetric resistance.

    Science.gov (United States)

    Feng, Guo-Hua; Liu, Kim-Min

    2014-05-12

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

  14. On electrostatic acceleration of plasmas with the Hall effect using electrode shaping

    International Nuclear Information System (INIS)

    Wang, Zhehui; Barnes, Cris W.

    2001-01-01

    Resistive magnetohydrodynamics (MHD) is used to model the electromagnetic acceleration of plasmas in coaxial channels. When the Hall effect is considered, the inclusion of resistivity is necessary to obtain physically meaningful solutions. In resistive MHD with the Hall effect, if and only if the electric current and the plasma flow are orthogonal (J·U=0), then there is a conserved quantity, in the form of U 2 /2+w+eΦ/M, along the flow, where U is the flow velocity, Φ is the electric potential, w is the enthalpy, and M is the ion mass. New solutions suggest that in coaxial geometry the Hall effect along the axial plasma flow can be balanced by proper shaping of conducting electrodes, with acceleration then caused by an electrostatic potential drop along the streamlines of the flow. The Hall effect separation of ion and electron flow then just cancels the electrostatic charge separation. Assuming particle ionization increases with energy density in the system, the resulting particle flow rates (J p ) scales with accelerator bias (V bias ) as J p ∝V bias 2 , exceeding the Child--Langmuir limit. The magnitude of the Hall effect (as determined by the Morozov Hall parameter, Ξ, which is defined as the ratio of electric current to particle current) is related to the energy needed for the creation of each ion--electron pair

  15. Ultrasmooth, extremely deformable and shape recoverable Ag nanowire embedded transparent electrode.

    Science.gov (United States)

    Nam, Sanggil; Song, Myungkwan; Kim, Dong-Ho; Cho, Byungjin; Lee, Hye Moon; Kwon, Jung-Dae; Park, Sung-Gyu; Nam, Kee-Seok; Jeong, Yongsoo; Kwon, Se-Hun; Park, Yun Chang; Jin, Sung-Ho; Kang, Jae-Wook; Jo, Sungjin; Kim, Chang Su

    2014-04-25

    Transparent electrodes have been widely used in electronic devices such as solar cells, displays, and touch screens. Highly flexible transparent electrodes are especially desired for the development of next generation flexible electronic devices. Although indium tin oxide (ITO) is the most commonly used material for the fabrication of transparent electrodes, its brittleness and growing cost limit its utility for flexible electronic devices. Therefore, the need for new transparent conductive materials with superior mechanical properties is clear and urgent. Ag nanowire (AgNW) has been attracting increasing attention because of its effective combination of electrical and optical properties. However, it still suffers from several drawbacks, including large surface roughness, instability against oxidation and moisture, and poor adhesion to substrates. These issues need to be addressed before wide spread use of metallic NW as transparent electrodes can be realized. In this study, we demonstrated the fabrication of a flexible transparent electrode with superior mechanical, electrical and optical properties by embedding a AgNW film into a transparent polymer matrix. This technique can produce electrodes with an ultrasmooth and extremely deformable transparent electrode that have sheet resistance and transmittance comparable to those of an ITO electrode.

  16. Optimization of the shape of the HV electrode of the electrostatic deflectors for the Milan superconducting cyclotron

    International Nuclear Information System (INIS)

    De Martinis, C.; Ferrari, A.

    1987-01-01

    The electrostatic deflectors for the extraction of the beam from the Milan Superconducting Cyclotron are presently under development. The early tests showed that major troubles arise from the modifications induced in the discharge mechanism by the presence of the magnetic field, resulting in a drastic reduction of the deflector performances. Therefore a detailed analysis of the electric field configuration of the deflector has been carried out in order to improve its performances. In this paper the results so far obtained in the optimization of the shape of the electrode and insulator fixing are reported

  17. Shape resonances and EXAFS scattering in the $Pt L_{2,3}$ XANES from a Pt electrode

    CERN Document Server

    O'Grady, W E

    1999-01-01

    Atomic hydrogen and oxygen adsorption on a platinum electrode in H /sub 2/SO/sub 4/ and HClO/sub 4/ electrolytes were studied by Pt L /sub 23/ XANES. The Pt electrode was formed of highly dispersed 1.5-3.0 nm particles supported on $9 carbon. A difference procedure utilizing the L/sub 2/ and L/sub 3/ spectra at various applied voltages was used to isolate the electronic and geometric effects in the XANES spectra. At 0.54 V (relative to RHE) the Pt electrode in $9 HClO/sub 4/ is assumed to be "clean". By taking the difference between the spectra at 0.0 and 0.54 V, the Pt-H antibonding state (electronic effect) is isolated and found to have a Fano-resonance line shape. In addition, a $9 significant Pt-H EXAFS scattering (geometric effect) was found for photon energies 0 to 20 eV above the edge. The difference between the spectra at 1.14 and 0.54 V allows isolation of the Pt-O antibonding state and the Pt-O EXAFS $9 scattering. (7 refs).

  18. An enzymatic glucose biosensor based on a glassy carbon electrode modified with cylinder-shaped titanium dioxide nanorods

    International Nuclear Information System (INIS)

    Yang, Zhanjun; Xu, Youbao; Li, Juan; Jian, Zhiqin; Yu, Suhua; Zhang, Yongcai; Hu, Xiaoya; Dionysiou, Dionysios D.

    2015-01-01

    We describe a highly sensitive electrochemical enzymatic glucose biosensor. A glassy carbon electrode was modified with cylinder-shaped titanium dioxide nanorods (TiO 2 -NRs) for the immobilization of glucose oxidase. The modified nanorods and the enzyme biosensor were characterized by scanning electron microscopy, X-ray diffraction, Fourier transform infrared spectroscopy, electrochemical impedance spectroscopy and cyclic voltammetry. The glucose oxidase on the TiO 2 -NRs displays a high activity and undergoes fast surface-controlled electron transfer. A pair of well-defined quasi-reversible redox peaks was observed at −0.394 and −0.450 V. The TiO 2 -NRs provide a good microenvironment to facilitate the direct electron transfer between enzyme and electrode surface. The biosensor has two linear response ranges, viz. from 2.0 to 52 μM, and 0.052 to 2.3 mM. The lower detection limit is 0.5 μM, and the sensitivity is 68.58 mA M −1 cm −2 . The glucose biosensor is selective, well reproducible, and stable. In our perception, the cylindrically shaped TiO 2 -NRs provide a promising support for the immobilization of proteins and pave the way to the development of high-performance biosensors. (author)

  19. A theoretical model to determine the capacity performance of shape-specific electrodes

    Science.gov (United States)

    Yue, Yuan; Liang, Hong

    2018-06-01

    A theory is proposed to explain and predict the electrochemical process during reaction between lithium ions and electrode materials. In the model, the process of reaction is proceeded into two steps, surface adsorption and diffusion of lithium ions. The surface adsorption is an instantaneous process for lithium ions to adsorb onto the surface sites of active materials. The diffusion of lithium ions into particles is determined by the charge-discharge condition. A formula to determine the maximum specific capacity of active materials at different charging rates (C-rates) is derived. The maximum specific capacity is correlated to characteristic parameters of materials and cycling - such as size, aspect ratio, surface area, and C-rate. Analysis indicates that larger particle size or greater aspect ratio of active materials and faster C-rates can reduce maximum specific capacity. This suggests that reducing particle size of active materials and slowing the charge-discharge speed can provide enhanced electrochemical performance of a battery cell. Furthermore, the model is validated by published experimental results. This model brings new understanding in quantification of electrochemical kinetics and capacity performance. It enables development of design strategies for novel electrodes and future generation of energy storage devices.

  20. Supervised Convolutional Sparse Coding

    KAUST Repository

    Affara, Lama Ahmed

    2018-04-08

    Convolutional Sparse Coding (CSC) is a well-established image representation model especially suited for image restoration tasks. In this work, we extend the applicability of this model by proposing a supervised approach to convolutional sparse coding, which aims at learning discriminative dictionaries instead of purely reconstructive ones. We incorporate a supervised regularization term into the traditional unsupervised CSC objective to encourage the final dictionary elements to be discriminative. Experimental results show that using supervised convolutional learning results in two key advantages. First, we learn more semantically relevant filters in the dictionary and second, we achieve improved image reconstruction on unseen data.

  1. Direct growth of vanadium nitride nanosheets on carbon nanotube fibers as novel negative electrodes for high-energy-density wearable fiber-shaped asymmetric supercapacitors

    Science.gov (United States)

    Guo, Jiabin; Zhang, Qichong; Sun, Juan; Li, Chaowei; Zhao, Jingxin; Zhou, Zhenyu; He, Bing; Wang, Xiaona; Man, Ping; Li, Qiulong; Zhang, Jun; Xie, Liyan; Li, Mingxing; Yao, Yagang

    2018-04-01

    Significant efforts have been recently devoted to constructing high-performance fiber-shaped asymmetric supercapacitors. However, it is still a paramount challenge to develop high-energy-density fiber-shaped asymmetric supercapacitors for practical applications in portable and wearable electronics. This work reports a simple and efficient method to directly grow vanadium nitride nanosheets on carbon nanotube fibers as advanced negative electrodes with a high specific capacitance of 188 F/cm3 (564 mF/cm2). Taking advantage of their attractive structure, we successfully fabricated a fiber-shaped asymmetric supercapacitor device with a maximum operating voltage of 1.6 V by assembling the vanadium nitride/carbon nanotube fiber negative electrode with the Zinc-Nickel-Cobalt ternary oxides nanowire arrays positive electrode. Due to the excellent synergistic effects between positive and negative electrodes, a remarkable specific capacitance of 50 F/cm3 (150 mF/cm2) and an outstanding energy density of 17.78 mWh/cm3 (53.33 μWh/cm2) for our fiber-shaped asymmetric supercapacitor can be achieved. Furthermore, the as-assembled fiber-shaped asymmetric supercapacitor device has excellent mechanical flexibility in that 91% of the capacitance retained after bending 90° for 3000 times. Thus, this work exploits a pathway to construct high-energy-density fiber-shaped asymmetric supercapacitor for next-generation portable and wearable electronics.

  2. Microstrip electrode readout noise for load-dominated long shaping-time systems

    International Nuclear Information System (INIS)

    Collier, Kelsey; Cunnington, Taylor; Crosby, Sean; Fadeyev, Vitaliy; Martinez-McKinney, Forest; Mistry, Khilesh; Schumm, Bruce A.; Spencer, Edwin; Taylor, Aaron; Wilder, Max

    2013-01-01

    In cases such as that of the proposed International Linear Collider (ILC), for which the beam-delivery and detector-occupancy characteristics permit a long shaping-time readout of the microstrip sensors, it is possible to envision long (∼1 meter) daisy-chained ‘ladders’ of fine-pitch sensors read out by a single front-end amplifier. In this study, a long shaping-time (∼2μsec) front-end amplifier has been used to measure readout noise as a function of detector load. Comparing measured noise to that expected from lumped and distributed models of the load network, it is seen that network effects significantly mitigate the amount of readout noise contributed by the detector load. Further reduction in noise is demonstrated for the case that the sensor load is read out from its center rather than its end

  3. Microstrip electrode readout noise for load-dominated long shaping-time systems

    Energy Technology Data Exchange (ETDEWEB)

    Collier, Kelsey; Cunnington, Taylor; Crosby, Sean; Fadeyev, Vitaliy; Martinez-McKinney, Forest; Mistry, Khilesh; Schumm, Bruce A., E-mail: baschumm@ucsc.edu; Spencer, Edwin; Taylor, Aaron; Wilder, Max

    2013-11-21

    In cases such as that of the proposed International Linear Collider (ILC), for which the beam-delivery and detector-occupancy characteristics permit a long shaping-time readout of the microstrip sensors, it is possible to envision long (∼1 meter) daisy-chained ‘ladders’ of fine-pitch sensors read out by a single front-end amplifier. In this study, a long shaping-time (∼2μsec) front-end amplifier has been used to measure readout noise as a function of detector load. Comparing measured noise to that expected from lumped and distributed models of the load network, it is seen that network effects significantly mitigate the amount of readout noise contributed by the detector load. Further reduction in noise is demonstrated for the case that the sensor load is read out from its center rather than its end.

  4. Facile fabrication of plate-shaped hydrohausmannite as electrode material for supercapacitors

    Science.gov (United States)

    Liang, Jun; Chai, Yao; Li, Deli; Li, Meng; Lu, Jiaxue; Li, Li; Luo, Min

    2017-08-01

    A simple and one-step solvothermal synthesis method has been developed to prepare two-dimensional (2-D) hydrohausmannite ((Mn4-2xMnx)Mn8O16-x(OH)x) nanoplates with radial length of 300 nm and thickness of about 25 nm in a binary ethanediamine/water solvent system. The formation mechanism of hydrohausmannite is suggested. As an anode material for electrochemical capacitors, the plate-shaped hydrohausmannite not only displays a high specific capacity (215 at 0.1 A g-1) and good rate capability, but also shows good stable performance along with 94% specific capacity retained after 3000 cycle tests. The method can be easily controlled and expected to be applicable for the large-scale preparation of the 2-D hydrohausmannite.

  5. Convolution copula econometrics

    CERN Document Server

    Cherubini, Umberto; Mulinacci, Sabrina

    2016-01-01

    This book presents a novel approach to time series econometrics, which studies the behavior of nonlinear stochastic processes. This approach allows for an arbitrary dependence structure in the increments and provides a generalization with respect to the standard linear independent increments assumption of classical time series models. The book offers a solution to the problem of a general semiparametric approach, which is given by a concept called C-convolution (convolution of dependent variables), and the corresponding theory of convolution-based copulas. Intended for econometrics and statistics scholars with a special interest in time series analysis and copula functions (or other nonparametric approaches), the book is also useful for doctoral students with a basic knowledge of copula functions wanting to learn about the latest research developments in the field.

  6. Efficient convolutional sparse coding

    Science.gov (United States)

    Wohlberg, Brendt

    2017-06-20

    Computationally efficient algorithms may be applied for fast dictionary learning solving the convolutional sparse coding problem in the Fourier domain. More specifically, efficient convolutional sparse coding may be derived within an alternating direction method of multipliers (ADMM) framework that utilizes fast Fourier transforms (FFT) to solve the main linear system in the frequency domain. Such algorithms may enable a significant reduction in computational cost over conventional approaches by implementing a linear solver for the most critical and computationally expensive component of the conventional iterative algorithm. The theoretical computational cost of the algorithm may be reduced from O(M.sup.3N) to O(MN log N), where N is the dimensionality of the data and M is the number of elements in the dictionary. This significant improvement in efficiency may greatly increase the range of problems that can practically be addressed via convolutional sparse representations.

  7. Multithreaded implicitly dealiased convolutions

    Science.gov (United States)

    Roberts, Malcolm; Bowman, John C.

    2018-03-01

    Implicit dealiasing is a method for computing in-place linear convolutions via fast Fourier transforms that decouples work memory from input data. It offers easier memory management and, for long one-dimensional input sequences, greater efficiency than conventional zero-padding. Furthermore, for convolutions of multidimensional data, the segregation of data and work buffers can be exploited to reduce memory usage and execution time significantly. This is accomplished by processing and discarding data as it is generated, allowing work memory to be reused, for greater data locality and performance. A multithreaded implementation of implicit dealiasing that accepts an arbitrary number of input and output vectors and a general multiplication operator is presented, along with an improved one-dimensional Hermitian convolution that avoids the loop dependency inherent in previous work. An alternate data format that can accommodate a Nyquist mode and enhance cache efficiency is also proposed.

  8. Effects of the use of a flat wire electrode in gas metal arc welding and fuzzy logic model for the prediction of weldment shape profile

    Energy Technology Data Exchange (ETDEWEB)

    Karuthapandi, Sripriyan; Thyla, P. R. [PSG College of Technology, Coimbatore (India); Ramu, Murugan [Amrita University, Ettimadai (India)

    2017-05-15

    This paper describes the relationships between the macrostructural characteristics of weld beads and the welding parameters in Gas metal arc welding (GMAW) using a flat wire electrode. Bead-on-plate welds were produced with a flat wire electrode and different combinations of input parameters (i.e., welding current, welding speed, and flat wire electrode orientation). The macrostructural characteristics of the weld beads, namely, deposition, bead width, total bead width, reinforcement height, penetration depth, and depth of HAZ were investigated. A mapping technique was employed to measure these characteristics in various segments of the weldment zones. Results show that the use of a flat wire electrode improves the depth-to-width (D/W) ratio by 16.5 % on average compared with the D/W ratio when a regular electrode is used in GMAW. Furthermore, a fuzzy logic model was established to predict the effects of the use of a flat electrode on the weldment shape profile with varying input parameters. The predictions of the model were compared with the experimental results.

  9. Convolutional coding techniques for data protection

    Science.gov (United States)

    Massey, J. L.

    1975-01-01

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

  10. A yolk-shell V2O5 structure assembled from ultrathin nanosheets and coralline-shaped carbon as advanced electrodes for a high-performance asymmetric supercapacitor.

    Science.gov (United States)

    Xing, Ling-Li; Zhao, Gang-Gang; Huang, Ke-Jing; Wu, Xu

    2018-02-13

    Various V 2 O 5 three-dimensional nanostructures are synthesized using a facile template-free hydrothermal method and evaluated for use as supercapacitor electrode materials. As a result, the yolk-shell structure assembled from ultrathin nanosheets shows the best electrochemical performance, with a specific capacitance of 704.17 F g -1 at 1.0 A g -1 and a high capacity retention of 89% over 4000 cycles at 3.0 A g -1 . In addition, a continuous three-dimensional porous coralline-shaped carbon is synthesized from osmanthus and has a large Brunauer-Emmett-Teller surface area of 2840.88 m 2 g -1 . Then, an asymmetric supercapacitor is developed using the as-prepared yolk-shell V 2 O 5 as a positive electrode and the osmanthus derived coralline-shaped carbon as a negative electrode. This exhibits an energy density of 29.49 W h kg -1 at a power density of 800 W kg -1 with a good cycling performance that retains 90.6% of its initial capacity after 2000 cycles at 3.0 A g -1 . Furthermore, two cells in series can easily brightly light up a light-emitting diode (3 V), further demonstrating the great potential of the prepared materials for high-performance supercapacitor devices.

  11. The convolution transform

    CERN Document Server

    Hirschman, Isidore Isaac

    2005-01-01

    In studies of general operators of the same nature, general convolution transforms are immediately encountered as the objects of inversion. The relation between differential operators and integral transforms is the basic theme of this work, which is geared toward upper-level undergraduates and graduate students. It may be read easily by anyone with a working knowledge of real and complex variable theory. Topics include the finite and non-finite kernels, variation diminishing transforms, asymptotic behavior of kernels, real inversion theory, representation theory, the Weierstrass transform, and

  12. Separating Underdetermined Convolutive Speech Mixtures

    DEFF Research Database (Denmark)

    Pedersen, Michael Syskind; Wang, DeLiang; Larsen, Jan

    2006-01-01

    a method for underdetermined blind source separation of convolutive mixtures. The proposed framework is applicable for separation of instantaneous as well as convolutive speech mixtures. It is possible to iteratively extract each speech signal from the mixture by combining blind source separation...

  13. Strongly-MDS convolutional codes

    NARCIS (Netherlands)

    Gluesing-Luerssen, H; Rosenthal, J; Smarandache, R

    Maximum-distance separable (MDS) convolutional codes have the property that their free distance is maximal among all codes of the same rate and the same degree. In this paper, a class of MDS convolutional codes is introduced whose column distances reach the generalized Singleton bound at the

  14. Consensus Convolutional Sparse Coding

    KAUST Repository

    Choudhury, Biswarup

    2017-12-01

    Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high-dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaicing and 4D light field view synthesis.

  15. Consensus Convolutional Sparse Coding

    KAUST Repository

    Choudhury, Biswarup

    2017-04-11

    Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaickingand 4D light field view synthesis.

  16. Consensus Convolutional Sparse Coding

    KAUST Repository

    Choudhury, Biswarup; Swanson, Robin; Heide, Felix; Wetzstein, Gordon; Heidrich, Wolfgang

    2017-01-01

    Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high-dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaicing and 4D light field view synthesis.

  17. Relation Between Ni Particle Shape Change and Ni Migration in Ni–YSZ Electrodes – a Hypothesis

    DEFF Research Database (Denmark)

    Mogensen, Mogens Bjerg; Hauch, Anne; Sun, Xiufu

    2017-01-01

    This paper deals with degradation mechanisms of Ni–YSZ electrodes for solid oxide cells, mainly solid oxide electrolysis cells (SOECs), but also to some extent solid oxide fuel cells (SOFCs). Analysis of literature data reveals that several apparently different and even in one case apparently...... contradicting degradation phenomena are a consequence of interplay between loss of contact between the Ni–YSZ (and Ni–Ni particles) in the active fine-structured composite fuel electrode layer and migration of Ni via weakly oxidized Ni hydroxide species. A hypothesis that unravels the apparent contradiction...

  18. Relation between shape of Ni-particles and Ni migration in Ni-YSZ electrodes – a hypothesis

    DEFF Research Database (Denmark)

    Mogensen, Mogens Bjerg; Hauch, Anne; Sun, Xiufu

    2016-01-01

    This is an attempt to explain a phenomenon of total depletion of Ni next to the electrolyte in Ni-YSZ cermet electrodes in solid oxide electrolysis cells during electrolysis at high current density/overpotential. Intuitively, we would think that Ni would always migrate down the steam partial...

  19. Dealiased convolutions for pseudospectral simulations

    International Nuclear Information System (INIS)

    Roberts, Malcolm; Bowman, John C

    2011-01-01

    Efficient algorithms have recently been developed for calculating dealiased linear convolution sums without the expense of conventional zero-padding or phase-shift techniques. For one-dimensional in-place convolutions, the memory requirements are identical with the zero-padding technique, with the important distinction that the additional work memory need not be contiguous with the input data. This decoupling of data and work arrays dramatically reduces the memory and computation time required to evaluate higher-dimensional in-place convolutions. The memory savings is achieved by computing the in-place Fourier transform of the data in blocks, rather than all at once. The technique also allows one to dealias the n-ary convolutions that arise on Fourier transforming cubic and higher powers. Implicitly dealiased convolutions can be built on top of state-of-the-art adaptive fast Fourier transform libraries like FFTW. Vectorized multidimensional implementations for the complex and centered Hermitian (pseudospectral) cases have already been implemented in the open-source software FFTW++. With the advent of this library, writing a high-performance dealiased pseudospectral code for solving nonlinear partial differential equations has now become a relatively straightforward exercise. New theoretical estimates of computational complexity and memory use are provided, including corrected timing results for 3D pruned convolutions and further consideration of higher-order convolutions.

  20. Relation Between Ni Particle Shape Change and Ni Migration in Ni–YSZ Electrodes – a Hypothesis

    DEFF Research Database (Denmark)

    Mogensen, Mogens Bjerg; Hauch, Anne; Sun, Xiufu

    2017-01-01

    This paper deals with degradation mechanisms of Ni–YSZ electrodes for solid oxide cells, mainly solid oxide electrolysis cells (SOECs), but also to some extent solid oxide fuel cells (SOFCs). Analysis of literature data reveals that several apparently different and even in one case apparently con...... and explains qualitatively the phenomena is presented, and as a side effect, light has been shed on a degradation phenomenon in solid oxide fuel cells (SOFCs) that has been observed during a decade.......This paper deals with degradation mechanisms of Ni–YSZ electrodes for solid oxide cells, mainly solid oxide electrolysis cells (SOECs), but also to some extent solid oxide fuel cells (SOFCs). Analysis of literature data reveals that several apparently different and even in one case apparently...... contradicting degradation phenomena are a consequence of interplay between loss of contact between the Ni–YSZ (and Ni–Ni particles) in the active fine-structured composite fuel electrode layer and migration of Ni via weakly oxidized Ni hydroxide species. A hypothesis that unravels the apparent contradiction...

  1. Invariant moments based convolutional neural networks for image analysis

    Directory of Open Access Journals (Sweden)

    Vijayalakshmi G.V. Mahesh

    2017-01-01

    Full Text Available The paper proposes a method using convolutional neural network to effectively evaluate the discrimination between face and non face patterns, gender classification using facial images and facial expression recognition. The novelty of the method lies in the utilization of the initial trainable convolution kernels coefficients derived from the zernike moments by varying the moment order. The performance of the proposed method was compared with the convolutional neural network architecture that used random kernels as initial training parameters. The multilevel configuration of zernike moments was significant in extracting the shape information suitable for hierarchical feature learning to carry out image analysis and classification. Furthermore the results showed an outstanding performance of zernike moment based kernels in terms of the computation time and classification accuracy.

  2. Evaluation of strontium substituted lanthanum manganite-based solid oxide fuel cell cathodes using cone-shaped electrodes and electrochemical impedance spectroscopy

    Directory of Open Access Journals (Sweden)

    Kent Kammer Hansen

    2018-05-01

    Full Text Available Five La1-xSrxMnO3+d-based perovskites (x = 0, 0.05, 0.15, 0.25 and 0.50 were synthesized and investigated by powder XRD, dilatometry and electrochemical impedance spectroscopy measurements and cone-shaped electrode techniques. The thermal expansion coefficient increased with increasing strontium content. It was shown that the total polarization resistance was the lowest for the intermediate compound, La0.95Sr0.05MnO3+d. Two arcs were found in the impedance spectra. These arcs were attributed to two one-electron processes. The results indicate that either Mn(III is the catalytically active species or that the redox capacity is important for the activity of the compounds towards the reduction of oxygen in a solid oxide fuel cell. At higher temperatures, the oxide ionic conductivity may also play a role.

  3. Fabrication of 3D lawn-shaped N-doped porous carbon matrix/polyaniline nanocomposite as the electrode material for supercapacitors

    Science.gov (United States)

    Zhang, Xiuling; Ma, Li; Gan, Mengyu; Fu, Gang; Jin, Meng; Lei, Yao; Yang, Peishu; Yan, Maofa

    2017-02-01

    A facile approach to acquire electrode materials with prominent electrochemical property is pivotal to the progress of supercapacitors. 3D nitrogen-doped porous carbon matrix (PCM), with high specific surface area (SSA) up to 2720 m2 g-1, was obtained from the carbonization and activation of the nitrogen-enriched composite precursor (graphene/polyaniline). Then 3D lawn-shaped PCM/PANI composite was obtained by the simple in-situ polymerization. The morphology and structure of these resulting composites were characterized by combining SEM and TEM measurements, Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD) spectroscopy analyses and Raman spectroscope. The element content of all samples was evaluated using CHN analysis. The results of electrochemical testing indicated that the PCM/PANI composite displays a higher capacitance value of 527 F g-1 at 1 A g-1 compared to 338 F g-1 for pure PANI, and exhibits appreciable rate capability with a retention of 76% at 20 A g-1 as well as fine long-term cycling performance (with 88% retention of specific capacitance after 1000 cycles at 10 A g-1). Simultaneously, the excellent capacitance performance coupled with the facile synthesis of PCM/PANI indicates it is a promising electrode material for supercapacitors.

  4. Design of convolutional tornado code

    Science.gov (United States)

    Zhou, Hui; Yang, Yao; Gao, Hongmin; Tan, Lu

    2017-09-01

    As a linear block code, the traditional tornado (tTN) code is inefficient in burst-erasure environment and its multi-level structure may lead to high encoding/decoding complexity. This paper presents a convolutional tornado (cTN) code which is able to improve the burst-erasure protection capability by applying the convolution property to the tTN code, and reduce computational complexity by abrogating the multi-level structure. The simulation results show that cTN code can provide a better packet loss protection performance with lower computation complexity than tTN code.

  5. Trajectory Generation Method with Convolution Operation on Velocity Profile

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Geon [Hanyang Univ., Seoul (Korea, Republic of); Kim, Doik [Korea Institute of Science and Technology, Daejeon (Korea, Republic of)

    2014-03-15

    The use of robots is no longer limited to the field of industrial robots and is now expanding into the fields of service and medical robots. In this light, a trajectory generation method that can respond instantaneously to the external environment is strongly required. Toward this end, this study proposes a method that enables a robot to change its trajectory in real-time using a convolution operation. The proposed method generates a trajectory in real time and satisfies the physical limits of the robot system such as acceleration and velocity limit. Moreover, a new way to improve the previous method, which generates inefficient trajectories in some cases owing to the characteristics of the trapezoidal shape of trajectories, is proposed by introducing a triangle shape. The validity and effectiveness of the proposed method is shown through a numerical simulation and a comparison with the previous convolution method.

  6. Solutions to Arithmetic Convolution Equations

    Czech Academy of Sciences Publication Activity Database

    Glöckner, H.; Lucht, L.G.; Porubský, Štefan

    2007-01-01

    Roč. 135, č. 6 (2007), s. 1619-1629 ISSN 0002-9939 R&D Projects: GA ČR GA201/04/0381 Institutional research plan: CEZ:AV0Z10300504 Keywords : arithmetic functions * Dirichlet convolution * polynomial equations * analytic equations * topological algebras * holomorphic functional calculus Subject RIV: BA - General Mathematics Impact factor: 0.520, year: 2007

  7. Down image recognition based on deep convolutional neural network

    Directory of Open Access Journals (Sweden)

    Wenzhu Yang

    2018-06-01

    Full Text Available Since of the scale and the various shapes of down in the image, it is difficult for traditional image recognition method to correctly recognize the type of down image and get the required recognition accuracy, even for the Traditional Convolutional Neural Network (TCNN. To deal with the above problems, a Deep Convolutional Neural Network (DCNN for down image classification is constructed, and a new weight initialization method is proposed. Firstly, the salient regions of a down image were cut from the image using the visual saliency model. Then, these salient regions of the image were used to train a sparse autoencoder and get a collection of convolutional filters, which accord with the statistical characteristics of dataset. At last, a DCNN with Inception module and its variants was constructed. To improve the recognition accuracy, the depth of the network is deepened. The experiment results indicate that the constructed DCNN increases the recognition accuracy by 2.7% compared to TCNN, when recognizing the down in the images. The convergence rate of the proposed DCNN with the new weight initialization method is improved by 25.5% compared to TCNN. Keywords: Deep convolutional neural network, Weight initialization, Sparse autoencoder, Visual saliency model, Image recognition

  8. Adaptive Graph Convolutional Neural Networks

    OpenAIRE

    Li, Ruoyu; Wang, Sheng; Zhu, Feiyun; Huang, Junzhou

    2018-01-01

    Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and connectivity. The paper proposes a generalized and flexible graph CNN taking data of arbitrary graph structure as input. In that way a task-driven adaptive graph is learned for eac...

  9. Experimental study of current loss and plasma formation in the Z machine post-hole convolute

    Directory of Open Access Journals (Sweden)

    M. R. Gomez

    2017-01-01

    Full Text Available The Z pulsed-power generator at Sandia National Laboratories drives high energy density physics experiments with load currents of up to 26 MA. Z utilizes a double post-hole convolute to combine the current from four parallel magnetically insulated transmission lines into a single transmission line just upstream of the load. Current loss is observed in most experiments and is traditionally attributed to inefficient convolute performance. The apparent loss current varies substantially for z-pinch loads with different inductance histories; however, a similar convolute impedance history is observed for all load types. This paper details direct spectroscopic measurements of plasma density, temperature, and apparent and actual plasma closure velocities within the convolute. Spectral measurements indicate a correlation between impedance collapse and plasma formation in the convolute. Absorption features in the spectra show the convolute plasma consists primarily of hydrogen, which likely forms from desorbed electrode contaminant species such as H_{2}O, H_{2}, and hydrocarbons. Plasma densities increase from 1×10^{16}  cm^{−3} (level of detectability just before peak current to over 1×10^{17}  cm^{−3} at stagnation (tens of ns later. The density seems to be highest near the cathode surface, with an apparent cathode to anode plasma velocity in the range of 35–50  cm/μs. Similar plasma conditions and convolute impedance histories are observed in experiments with high and low losses, suggesting that losses are driven largely by load dynamics, which determine the voltage on the convolute.

  10. Combining morphometric features and convolutional networks fusion for glaucoma diagnosis

    Science.gov (United States)

    Perdomo, Oscar; Arevalo, John; González, Fabio A.

    2017-11-01

    Glaucoma is an eye condition that leads to loss of vision and blindness. Ophthalmoscopy exam evaluates the shape, color and proportion between the optic disc and physiologic cup, but the lack of agreement among experts is still the main diagnosis problem. The application of deep convolutional neural networks combined with automatic extraction of features such as: the cup-to-disc distance in the four quadrants, the perimeter, area, eccentricity, the major radio, the minor radio in optic disc and cup, in addition to all the ratios among the previous parameters may help with a better automatic grading of glaucoma. This paper presents a strategy to merge morphological features and deep convolutional neural networks as a novel methodology to support the glaucoma diagnosis in eye fundus images.

  11. Convolution of Distribution-Valued Functions. Applications.

    OpenAIRE

    BARGETZ, CHRISTIAN

    2011-01-01

    In this article we examine products and convolutions of vector-valued functions. For nuclear normal spaces of distributions Proposition 25 in [31,p. 120] yields a vector-valued product or convolution if there is a continuous product or convolution mapping in the range of the vector-valued functions. For specific spaces, we generalize this result to hypocontinuous bilinear maps at the expense of generality with respect to the function space. We consider holomorphic, meromorphic and differentia...

  12. Incomplete convolutions in production and inventory models

    NARCIS (Netherlands)

    Houtum, van G.J.J.A.N.; Zijm, W.H.M.

    1997-01-01

    In this paper, we study incomplete convolutions of continuous distribution functions, as they appear in the analysis of (multi-stage) production and inventory systems. Three example systems are discussed where these incomplete convolutions naturally arise. We derive explicit, nonrecursive formulae

  13. A convolutional approach to reflection symmetry

    DEFF Research Database (Denmark)

    Cicconet, Marcelo; Birodkar, Vighnesh; Lund, Mads

    2017-01-01

    We present a convolutional approach to reflection symmetry detection in 2D. Our model, built on the products of complex-valued wavelet convolutions, simplifies previous edge-based pairwise methods. Being parameter-centered, as opposed to feature-centered, it has certain computational advantages w...

  14. Symbol synchronization in convolutionally coded systems

    Science.gov (United States)

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

    1979-01-01

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

  15. The general theory of convolutional codes

    Science.gov (United States)

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

    1993-01-01

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

  16. Convolution-deconvolution in DIGES

    International Nuclear Information System (INIS)

    Philippacopoulos, A.J.; Simos, N.

    1995-01-01

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

  17. Model structure selection in convolutive mixtures

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai

    2006-01-01

    The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious represent......The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious...... representation in many practical mixtures. The new filter-CICAAR allows Bayesian model selection and can help answer questions like: ’Are we actually dealing with a convolutive mixture?’. We try to answer this question for EEG data....

  18. Convolutions

    Indian Academy of Sciences (India)

    President's Address to the Association of Mathematics Teachers of India, December 2011. I am expected to tell you, in 25 minutes, something that should interest you, excite you, pique your curiosity, and make you look for more. It is a tall order, but I will try. The word 'interactive' is in fashion these days. So I will leave a few ...

  19. Metal-free polymer/MWCNT composite fiber as an efficient counter electrode in fiber shape dye-sensitized solar cells

    Science.gov (United States)

    Ali, Abid; Mujtaba Shah, Syed; Bozar, Sinem; Kazici, Mehmet; Keskin, Bahadır; Kaleli, Murat; Akyürekli, Salih; Günes, Serap

    2016-09-01

    Highly aligned multiwall carbon nanotubes (MWCNT) as fiber were modified with a conducting polymer via a simple dip coating method. Modified MWCNT exhibited admirable improvement in electrocatalytic activity for the reduction of tri-iodide in dye sensitized solar cells. Scanning electron microscopy images confirm the successful deposition of polymer on MWCNT. Cyclic voltammetry, square wave voltammetry and electrochemical impedance spectroscopy studies were carried out to investigate the inner mechanism for the charge transfer behaviour. Results from bare and modified electrodes revealed that the MWCNT/(poly (3,4-ethylene dioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) composite electrode is much better at catalysing the {{{{I}}}3}-/{{{I}}}- redox couple compared to the pristine fiber electrode. The photoelectric conversion efficiency of 5.03% for the modified MWCNT electrodes was comparable with that of the conventional Pt-based electrode. The scientific results of this study reveal that MWCNT/PEDOT:PSS may be a better choice for the replacement of cost intensive electrode materials such as platinum. Good performance even after bending up to 90° and in-series connection to enhance the output voltage were also successfully achieved, highlighting the practical application of this novel device.

  20. Adaptive decoding of convolutional codes

    Directory of Open Access Journals (Sweden)

    K. Hueske

    2007-06-01

    Full Text Available 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.

  1. Adaptive decoding of convolutional codes

    Science.gov (United States)

    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.

  2. Convolutional Neural Network for Image Recognition

    CERN Document Server

    Seifnashri, Sahand

    2015-01-01

    The aim of this project is to use machine learning techniques especially Convolutional Neural Networks for image processing. These techniques can be used for Quark-Gluon discrimination using calorimeters data, but unfortunately I didn’t manage to get the calorimeters data and I just used the Jet data fromminiaodsim(ak4 chs). The Jet data was not good enough for Convolutional Neural Network which is designed for ’image’ recognition. This report is made of twomain part, part one is mainly about implementing Convolutional Neural Network on unphysical data such as MNIST digits and CIFAR-10 dataset and part 2 is about the Jet data.

  3. A Note on Cubic Convolution Interpolation

    OpenAIRE

    Meijering, E.; Unser, M.

    2003-01-01

    We establish a link between classical osculatory interpolation and modern convolution-based interpolation and use it to show that two well-known cubic convolution schemes are formally equivalent to two osculatory interpolation schemes proposed in the actuarial literature about a century ago. We also discuss computational differences and give examples of other cubic interpolation schemes not previously studied in signal and image processing.

  4. Correction of the tip convolution effects in the imaging of nanostructures studied through scanning force microscopy

    International Nuclear Information System (INIS)

    Canet-Ferrer, Josep; Coronado, Eugenio; Forment-Aliaga, Alicia; Pinilla-Cienfuegos, Elena

    2014-01-01

    AFM images are always affected by artifacts arising from tip convolution effects, resulting in a decrease in the lateral resolution of this technique. The magnitude of such effects is described by means of geometrical considerations, thereby providing better understanding of the convolution phenomenon. We demonstrate that for a constant tip radius, the convolution error is increased with the object height, mainly for the narrowest motifs. Certain influence of the object shape is observed between rectangular and elliptical objects with the same height. Such moderate differences are essentially expected among elongated objects; in contrast they are reduced as the object aspect ratio is increased. Finally, we propose an algorithm to study the influence of the size, shape and aspect ratio of different nanometric motifs on a flat substrate. Indeed, with this algorithm, convolution artifacts can be extended to any kind of motif including real surface roughness. From the simulation results we demonstrate that in most cases the real motif’s width can be estimated from AFM images without knowing its shape in detail. (paper)

  5. Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Fen Chen

    2018-03-01

    Full Text Available Fast and automatic detection of airports from remote sensing images is useful for many military and civilian applications. In this paper, a fast automatic detection method is proposed to detect airports from remote sensing images based on convolutional neural networks using the Faster R-CNN algorithm. This method first applies a convolutional neural network to generate candidate airport regions. Based on the features extracted from these proposals, it then uses another convolutional neural network to perform airport detection. By taking the typical elongated linear geometric shape of airports into consideration, some specific improvements to the method are proposed. These approaches successfully improve the quality of positive samples and achieve a better accuracy in the final detection results. Experimental results on an airport dataset, Landsat 8 images, and a Gaofen-1 satellite scene demonstrate the effectiveness and efficiency of the proposed method.

  6. Storage-battery electrodes. [preparation

    Energy Technology Data Exchange (ETDEWEB)

    1961-12-29

    Two incompatible thermoplastic resins are mixed with a powdered electrochemical active substance. The substance may be, for example, an oxide of cadmium, iron, lead, or zinc or nickel hydroxide. After the mixture is shaped into elements which are inserted into conducting sheaths for an electrode, the one resin is washed out to form a porous electrode. (RWR)

  7. Semantic segmentation of bioimages using convolutional neural networks

    CSIR Research Space (South Africa)

    Wiehman, S

    2016-07-01

    Full Text Available Convolutional neural networks have shown great promise in both general image segmentation problems as well as bioimage segmentation. In this paper, the application of different convolutional network architectures is explored on the C. elegans live...

  8. One weird trick for parallelizing convolutional neural networks

    OpenAIRE

    Krizhevsky, Alex

    2014-01-01

    I present a new way to parallelize the training of convolutional neural networks across multiple GPUs. The method scales significantly better than all alternatives when applied to modern convolutional neural networks.

  9. Gradient Flow Convolutive Blind Source Separation

    DEFF Research Database (Denmark)

    Pedersen, Michael Syskind; Nielsen, Chinton Møller

    2004-01-01

    Experiments have shown that the performance of instantaneous gradient flow beamforming by Cauwenberghs et al. is reduced significantly in reverberant conditions. By expanding the gradient flow principle to convolutive mixtures, separation in a reverberant environment is possible. By use...... of a circular four microphone array with a radius of 5 mm, and applying convolutive gradient flow instead of just applying instantaneous gradient flow, experimental results show an improvement of up to around 14 dB can be achieved for simulated impulse responses and up to around 10 dB for a hearing aid...

  10. Human Face Recognition Using Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Răzvan-Daniel Albu

    2009-10-01

    Full Text Available In this paper, I present a novel hybrid face recognition approach based on a convolutional neural architecture, designed to robustly detect highly variable face patterns. The convolutional network extracts successively larger features in a hierarchical set of layers. With the weights of the trained neural networks there are created kernel windows used for feature extraction in a 3-stage algorithm. I present experimental results illustrating the efficiency of the proposed approach. I use a database of 796 images of 159 individuals from Reims University which contains quite a high degree of variability in expression, pose, and facial details.

  11. CMOS Compressed Imaging by Random Convolution

    OpenAIRE

    Jacques, Laurent; Vandergheynst, Pierre; Bibet, Alexandre; Majidzadeh, Vahid; Schmid, Alexandre; Leblebici, Yusuf

    2009-01-01

    We present a CMOS imager with built-in capability to perform Compressed Sensing. The adopted sensing strategy is the random Convolution due to J. Romberg. It is achieved by a shift register set in a pseudo-random configuration. It acts as a convolutive filter on the imager focal plane, the current issued from each CMOS pixel undergoing a pseudo-random redirection controlled by each component of the filter sequence. A pseudo-random triggering of the ADC reading is finally applied to comp...

  12. Feedback equivalence of convolutional codes over finite rings

    Directory of Open Access Journals (Sweden)

    DeCastro-García Noemí

    2017-12-01

    Full Text Available The approach to convolutional codes from the linear systems point of view provides us with effective tools in order to construct convolutional codes with adequate properties that let us use them in many applications. In this work, we have generalized feedback equivalence between families of convolutional codes and linear systems over certain rings, and we show that every locally Brunovsky linear system may be considered as a representation of a code under feedback convolutional equivalence.

  13. Discrete convolution-operators and radioactive disintegration. [Numerical solution

    Energy Technology Data Exchange (ETDEWEB)

    Kalla, S L; VALENTINUZZI, M E [UNIVERSIDAD NACIONAL DE TUCUMAN (ARGENTINA). FACULTAD DE CIENCIAS EXACTAS Y TECNOLOGIA

    1975-08-01

    The basic concepts of discrete convolution and discrete convolution-operators are briefly described. Then, using the discrete convolution - operators, the differential equations associated with the process of radioactive disintegration are numerically solved. The importance of the method is emphasized to solve numerically, differential and integral equations.

  14. Negative differential resistance observation in complex convoluted fullerene junctions

    Science.gov (United States)

    Kaur, Milanpreet; Sawhney, Ravinder Singh; Engles, Derick

    2018-04-01

    In this work, we simulated the smallest fullerene molecule, C20 in a two-probe device model with gold electrodes. The gold electrodes comprised of (011) miller planes were carved to construct the novel geometry based four unique shapes, which were strung to fullerene molecules through mechanically controlled break junction techniques. The organized devices were later scrutinized using non-equilibrium Green's function based on the density functional theory to calculate their molecular orbitals, energy levels, charge transfers, and electrical parameters. After intense scrutiny, we concluded that five-edged and six-edged devices have the lowest and highest current-conductance values, which result from their electrode-dominating and electrode-subsidiary effects, respectively. However, an interesting observation was that the three-edged and four-edged electrodes functioned as semi-metallic in nature, allowing the C20 molecule to demonstrate its performance with the complementary effect of these electrodes in the electron conduction process of a two-probe device.

  15. Wet-process Fabrication of Low-cost All-solid Wire-shaped Solar Cells on Manganese-plated Electrodes

    International Nuclear Information System (INIS)

    Fan, Xing; Zhang, Xiaoying; Zhang, Nannan; Cheng, Li; Du, Jun; Tao, Changyuan

    2015-01-01

    Highlights: • All-solid wire-shaped flexible solar cells are firstly assembled on low-cost Mn-plated fibers. • Energy efficiency improved by >27% after coating a layer of Mn on various substrates. • The cell is fabricated via wet process under low temperature and mild pH conditions. • Stable flexible solar cells are realized on lightweight and low-cost polymer fiber. - Abstract: All-solid wire-shaped flexible solar cells are assembled for the first time on low-cost Mn-plated wires through wet-process fabrication under low temperature and mild pH conditions. With a price cheap as the steel, metal Mn can be easily plated on almost any substrates, and evidently promote the photovoltaic efficiency of wire-shaped solar cells on various traditional metal wire substrates, such as Fe and Ti, by 27% and 65%, respectively. Flexible solar cell with much lower cost and weight is assembled on Mn-plated polymer substrate, and is still capable of giving better performance than that on Fe or Ti substrate. Both its mechanical and chemical stability are good for future weaving applications. Owing to the wire-type structure, such low-cost metals as Mn, which are traditionally regarded as unsuitable for solar cells, may provide new opportunities for highly efficient solar cells

  16. Deformable image registration using convolutional neural networks

    NARCIS (Netherlands)

    Eppenhof, Koen A.J.; Lafarge, Maxime W.; Moeskops, Pim; Veta, Mitko; Pluim, Josien P.W.

    2018-01-01

    Deformable image registration can be time-consuming and often needs extensive parameterization to perform well on a specific application. We present a step towards a registration framework based on a three-dimensional convolutional neural network. The network directly learns transformations between

  17. Epileptiform spike detection via convolutional neural networks

    DEFF Research Database (Denmark)

    Johansen, Alexander Rosenberg; Jin, Jing; Maszczyk, Tomasz

    2016-01-01

    The EEG of epileptic patients often contains sharp waveforms called "spikes", occurring between seizures. Detecting such spikes is crucial for diagnosing epilepsy. In this paper, we develop a convolutional neural network (CNN) for detecting spikes in EEG of epileptic patients in an automated...

  18. Convolutional Neural Networks for SAR Image Segmentation

    DEFF Research Database (Denmark)

    Malmgren-Hansen, David; Nobel-Jørgensen, Morten

    2015-01-01

    Segmentation of Synthetic Aperture Radar (SAR) images has several uses, but it is a difficult task due to a number of properties related to SAR images. In this article we show how Convolutional Neural Networks (CNNs) can easily be trained for SAR image segmentation with good results. Besides...

  19. Convolutional Neural Networks - Generalizability and Interpretations

    DEFF Research Database (Denmark)

    Malmgren-Hansen, David

    from data despite it being limited in amount or context representation. Within Machine Learning this thesis focuses on Convolutional Neural Networks for Computer Vision. The research aims to answer how to explore a model's generalizability to the whole population of data samples and how to interpret...

  20. Towards dropout training for convolutional neural networks.

    Science.gov (United States)

    Wu, Haibing; Gu, Xiaodong

    2015-11-01

    Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in convolutional and pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this insight, we advocate employing our proposed probabilistic weighted pooling, instead of commonly used max-pooling, to act as model averaging at test time. Empirical evidence validates the superiority of probabilistic weighted pooling. We also empirically show that the effect of convolutional dropout is not trivial, despite the dramatically reduced possibility of over-fitting due to the convolutional architecture. Elaborately designing dropout training simultaneously in max-pooling and fully-connected layers, we achieve state-of-the-art performance on MNIST, and very competitive results on CIFAR-10 and CIFAR-100, relative to other approaches without data augmentation. Finally, we compare max-pooling dropout and stochastic pooling, both of which introduce stochasticity based on multinomial distributions at pooling stage. Copyright © 2015 Elsevier Ltd. All rights reserved.

  1. A locality aware convolutional neural networks accelerator

    NARCIS (Netherlands)

    Shi, R.; Xu, Z.; Sun, Z.; Peemen, M.C.J.; Li, A.; Corporaal, H.; Wu, D.

    2015-01-01

    The advantages of Convolutional Neural Networks (CNNs) with respect to traditional methods for visual pattern recognition have changed the field of machine vision. The main issue that hinders broad adoption of this technique is the massive computing workload in CNN that prevents real-time

  2. Applicability of the Fourier convolution theorem to the analysis of late-type stellar spectra

    International Nuclear Information System (INIS)

    Bruning, D.H.

    1981-01-01

    Solar flux and intensity measurements were obtained at Sacramento Peak Observatory to test the validity of the Fourier convolution method as a means of analyzing the spectral line shapes of late-type stars. Analysis of six iron lines near 6200A shows that, in general, the convolution method is not a suitable approximation for the calculation of the flux profile. The convolution method does reasonably reproduce the line shape for some lines which appear not to vary across the disk of the sun, but does not properly calculate the central line depth of these lines. Even if a central depth correction could be found, it is difficult to predict, especially for stars other than the sun, which lines have nearly constant shapes and could be used with the convolution method. Therefore, explicit disk integrations are promoted as the only reliable method of spectral line analysis for late-type stars. Several methods of performing the disk integration are investigated. Although the Abt (1957) prescription appears suitable for the limited case studied, methods using annuli of equal area, equal flux, or equal width (Soberblom, 1980) are considered better models. The model that is the easiest to use and most efficient computationally is the equal area model. Model atmosphere calculations yield values for the microturbulence and macroturbulence similar to those derived by observers. Since the depth dependence of the microturbulence is ignored in the calculations, the intensity profiles at disk center and the limb do not match the observed intensity profiles with only one set of velocity parameters. Use of these incorrectly calculated intensity profiles in the integration procedure to obtain the flux profile leads to incorrect estimates of the solar macroturbulence

  3. DCMDN: Deep Convolutional Mixture Density Network

    Science.gov (United States)

    D'Isanto, Antonio; Polsterer, Kai Lars

    2017-09-01

    Deep Convolutional Mixture Density Network (DCMDN) estimates probabilistic photometric redshift directly from multi-band imaging data by combining a version of a deep convolutional network with a mixture density network. The estimates are expressed as Gaussian mixture models representing the probability density functions (PDFs) in the redshift space. In addition to the traditional scores, the continuous ranked probability score (CRPS) and the probability integral transform (PIT) are applied as performance criteria. DCMDN is able to predict redshift PDFs independently from the type of source, e.g. galaxies, quasars or stars and renders pre-classification of objects and feature extraction unnecessary; the method is extremely general and allows the solving of any kind of probabilistic regression problems based on imaging data, such as estimating metallicity or star formation rate in galaxies.

  4. Gas Classification Using Deep Convolutional Neural Networks

    Science.gov (United States)

    Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

    2018-01-01

    In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP). PMID:29316723

  5. A convolutional neural network neutrino event classifier

    International Nuclear Information System (INIS)

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

    2016-01-01

    Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.

  6. Gas Classification Using Deep Convolutional Neural Networks.

    Science.gov (United States)

    Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

    2018-01-08

    In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).

  7. Quasi-cyclic unit memory convolutional codes

    DEFF Research Database (Denmark)

    Justesen, Jørn; Paaske, Erik; Ballan, Mark

    1990-01-01

    Unit memory convolutional codes with generator matrices, which are composed of circulant submatrices, are introduced. This structure facilitates the analysis of efficient search for good codes. Equivalences among such codes and some of the basic structural properties are discussed. In particular......, catastrophic encoders and minimal encoders are characterized and dual codes treated. Further, various distance measures are discussed, and a number of good codes, some of which result from efficient computer search and some of which result from known block codes, are presented...

  8. AC low-pressure plasmas generated by using annular-shaped electrodes for abatement of pollutants emitted during semiconductor manufacturing processes

    International Nuclear Information System (INIS)

    Hur, Min; Lee, Jae Ok; Song, Young Hoon

    2011-01-01

    A plasma abatement system operating at low pressures is set up with the aim of treating pollutants emitted by the semiconductor industry. The abatement device is characterized by using a tube-shaped reactor design and a bipolar alternating current, which allows an easy connection to pre-existing pipelines in the semiconductor industry and low installation cost, respectively. By using optical emission spectroscopy (OES) and Fourier transform infrared spectroscopy (FTIR), we analyzed the discharge characteristics and abatement efficiency with emphasis on the working pressure effect. In the case of CF 4 , the destruction and removal efficiency (DRE) is greatly reduced with increasing pressure. However, the pressure has a relatively small influence on the DRE for tetrakis(ethylmethylamino)zirconium (TEMAZ), which is significantly destroyed only with several hundred watts and without any liquefied byproducts. This difference is closely related to the spatial distribution of reactive species and to the chemical bond strengths of the pollutant's components. Finally, the applicability of the abatement device is discussed based on the experimental results.

  9. Applying Gradient Descent in Convolutional Neural Networks

    Science.gov (United States)

    Cui, Nan

    2018-04-01

    With the development of the integrated circuit and computer science, people become caring more about solving practical issues via information technologies. Along with that, a new subject called Artificial Intelligent (AI) comes up. One popular research interest of AI is about recognition algorithm. In this paper, one of the most common algorithms, Convolutional Neural Networks (CNNs) will be introduced, for image recognition. Understanding its theory and structure is of great significance for every scholar who is interested in this field. Convolution Neural Network is an artificial neural network which combines the mathematical method of convolution and neural network. The hieratical structure of CNN provides it reliable computer speed and reasonable error rate. The most significant characteristics of CNNs are feature extraction, weight sharing and dimension reduction. Meanwhile, combining with the Back Propagation (BP) mechanism and the Gradient Descent (GD) method, CNNs has the ability to self-study and in-depth learning. Basically, BP provides an opportunity for backwardfeedback for enhancing reliability and GD is used for self-training process. This paper mainly discusses the CNN and the related BP and GD algorithms, including the basic structure and function of CNN, details of each layer, the principles and features of BP and GD, and some examples in practice with a summary in the end.

  10. Phylogenetic convolutional neural networks in metagenomics.

    Science.gov (United States)

    Fioravanti, Diego; Giarratano, Ylenia; Maggio, Valerio; Agostinelli, Claudio; Chierici, Marco; Jurman, Giuseppe; Furlanello, Cesare

    2018-03-08

    Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architecture for the classification of metagenomics data based on the Convolutional Neural Networks, with the patristic distance defined on the phylogenetic tree being used as the proximity measure. The patristic distance between variables is used together with a sparsified version of MultiDimensional Scaling to embed the phylogenetic tree in a Euclidean space. Ph-CNN is tested with a domain adaptation approach on synthetic data and on a metagenomics collection of gut microbiota of 38 healthy subjects and 222 Inflammatory Bowel Disease patients, divided in 6 subclasses. Classification performance is promising when compared to classical algorithms like Support Vector Machines and Random Forest and a baseline fully connected neural network, e.g. the Multi-Layer Perceptron. Ph-CNN represents a novel deep learning approach for the classification of metagenomics data. Operatively, the algorithm has been implemented as a custom Keras layer taking care of passing to the following convolutional layer not only the data but also the ranked list of neighbourhood of each sample, thus mimicking the case of image data, transparently to the user.

  11. Image quality assessment using deep convolutional networks

    Science.gov (United States)

    Li, Yezhou; Ye, Xiang; Li, Yong

    2017-12-01

    This paper proposes a method of accurately assessing image quality without a reference image by using a deep convolutional neural network. Existing training based methods usually utilize a compact set of linear filters for learning features of images captured by different sensors to assess their quality. These methods may not be able to learn the semantic features that are intimately related with the features used in human subject assessment. Observing this drawback, this work proposes training a deep convolutional neural network (CNN) with labelled images for image quality assessment. The ReLU in the CNN allows non-linear transformations for extracting high-level image features, providing a more reliable assessment of image quality than linear filters. To enable the neural network to take images of any arbitrary size as input, the spatial pyramid pooling (SPP) is introduced connecting the top convolutional layer and the fully-connected layer. In addition, the SPP makes the CNN robust to object deformations to a certain extent. The proposed method taking an image as input carries out an end-to-end learning process, and outputs the quality of the image. It is tested on public datasets. Experimental results show that it outperforms existing methods by a large margin and can accurately assess the image quality on images taken by different sensors of varying sizes.

  12. Color encoding in biologically-inspired convolutional neural networks.

    Science.gov (United States)

    Rafegas, Ivet; Vanrell, Maria

    2018-05-11

    Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore how color is encoded in a trained artificial network. It is performed by estimating a color selectivity index for each neuron, which allows us to describe the neuron activity to a color input stimuli. The index allows us to classify whether they are color selective or not and if they are of a single or double color. We have determined that all five convolutional layers of the network have a large number of color selective neurons. Color opponency clearly emerges in the first layer, presenting 4 main axes (Black-White, Red-Cyan, Blue-Yellow and Magenta-Green), but this is reduced and rotated as we go deeper into the network. In layer 2 we find a denser hue sampling of color neurons and opponency is reduced almost to one new main axis, the Bluish-Orangish coinciding with the dataset bias. In layers 3, 4 and 5 color neurons are similar amongst themselves, presenting different type of neurons that detect specific colored objects (e.g., orangish faces), specific surrounds (e.g., blue sky) or specific colored or contrasted object-surround configurations (e.g. blue blob in a green surround). Overall, our work concludes that color and shape representation are successively entangled through all the layers of the studied network, revealing certain parallelisms with the reported evidences in primate brains that can provide useful insight into intermediate hierarchical spatio-chromatic representations. Copyright © 2018 Elsevier Ltd. All rights reserved.

  13. Observation of radio frequency ring-shaped hollow cathode discharge plasma with MgO and Al electrodes for plasma processing

    International Nuclear Information System (INIS)

    Ohtsu, Yasunori; Matsumoto, Naoki

    2014-01-01

    Various high-density plasma sources have been proposed for plasma processing. Especially, the hollow cathode discharge is one of the powerful ones. In this work, radio-frequency (RF) driven ring-shaped hollow cathode discharges with high secondary-electron emission have been investigated, using an aluminum (Al) cathode, coated or not with magnesium oxide (MgO). The thickness of MgO thin film is approximately 200 nm. The RF discharge voltage for the coated cathode is almost the same as that for the uncoated one, in a wide range of Ar gas pressure, from 5.3 to 53.2 Pa. The results reveal that the plasma density has a peak at an Ar gas pressure of 10.6 Pa for both cathodes. The plasma density for the coated cathode is about 1.5–3 times higher than that for the uncoated one, at various gas pressures. To the contrary, the electron temperature for the coated cathode is lower than temperature obtained with the uncoated cathode, at various gas pressures. Radial profiles of electron saturation current, which is proportional to plasma flux, are also examined for a wide range of gas pressure. Radial profiles of electron temperature at various axial positions are almost uniform for both cathodes so that the diffusion process due to density gradient is dominant for plasma transport. The secondary electrons emitted from the coated cathode contribute to the improvement of the plasma flux radial profile obtained using the uncoated cathode

  14. Liquid electrode

    Science.gov (United States)

    Ekechukwu, A.A.

    1994-07-05

    A dropping electrolyte electrode is described for use in electrochemical analysis of non-polar sample solutions, such as benzene or cyclohexane. The liquid electrode, preferably an aqueous salt solution immiscible in the sample solution, is introduced into the solution in dropwise fashion from a capillary. The electrolyte is introduced at a known rate, thus, the droplets each have the same volume and surface area. The electrode is used in making standard electrochemical measurements in order to determine properties of non-polar sample solutions. 2 figures.

  15. An Algorithm for the Convolution of Legendre Series

    KAUST Repository

    Hale, Nicholas; Townsend, Alex

    2014-01-01

    An O(N2) algorithm for the convolution of compactly supported Legendre series is described. The algorithm is derived from the convolution theorem for Legendre polynomials and the recurrence relation satisfied by spherical Bessel functions. Combining with previous work yields an O(N 2) algorithm for the convolution of Chebyshev series. Numerical results are presented to demonstrate the improved efficiency over the existing algorithm. © 2014 Society for Industrial and Applied Mathematics.

  16. The Urbanik generalized convolutions in the non-commutative ...

    Indian Academy of Sciences (India)

    −sν(dx) < ∞. Now we apply this construction to the Kendall convolution case, starting with the weakly stable measure δ1. Example 1. Let △ be the Kendall convolution, i.e. the generalized convolution with the probability kernel: δ1△δa = (1 − a)δ1 + aπ2 for a ∈ [0, 1] and π2 be the Pareto distribution with the density π2(dx) =.

  17. electrode array

    African Journals Online (AJOL)

    PROF EKWUEME

    A geoelectric investigation employing vertical electrical soundings (VES) using the Ajayi - Makinde Two-Electrode array and the ... arrangements used in electrical D.C. resistivity survey. These include ..... Refraction Tomography to Study the.

  18. On a Generalized Hankel Type Convolution of Generalized Functions

    Indian Academy of Sciences (India)

    Generalized Hankel type transformation; Parserval relation; generalized ... The classical generalized Hankel type convolution are defined and extended to a class of generalized functions. ... Proceedings – Mathematical Sciences | News.

  19. Enhanced online convolutional neural networks for object tracking

    Science.gov (United States)

    Zhang, Dengzhuo; Gao, Yun; Zhou, Hao; Li, Tianwen

    2018-04-01

    In recent several years, object tracking based on convolution neural network has gained more and more attention. The initialization and update of convolution filters can directly affect the precision of object tracking effective. In this paper, a novel object tracking via an enhanced online convolution neural network without offline training is proposed, which initializes the convolution filters by a k-means++ algorithm and updates the filters by an error back-propagation. The comparative experiments of 7 trackers on 15 challenging sequences showed that our tracker can perform better than other trackers in terms of AUC and precision.

  20. Unsupervised neural spike sorting for high-density microelectrode arrays with convolutive independent component analysis.

    Science.gov (United States)

    Leibig, Christian; Wachtler, Thomas; Zeck, Günther

    2016-09-15

    Unsupervised identification of action potentials in multi-channel extracellular recordings, in particular from high-density microelectrode arrays with thousands of sensors, is an unresolved problem. While independent component analysis (ICA) achieves rapid unsupervised sorting, it ignores the convolutive structure of extracellular data, thus limiting the unmixing to a subset of neurons. Here we present a spike sorting algorithm based on convolutive ICA (cICA) to retrieve a larger number of accurately sorted neurons than with instantaneous ICA while accounting for signal overlaps. Spike sorting was applied to datasets with varying signal-to-noise ratios (SNR: 3-12) and 27% spike overlaps, sampled at either 11.5 or 23kHz on 4365 electrodes. We demonstrate how the instantaneity assumption in ICA-based algorithms has to be relaxed in order to improve the spike sorting performance for high-density microelectrode array recordings. Reformulating the convolutive mixture as an instantaneous mixture by modeling several delayed samples jointly is necessary to increase signal-to-noise ratio. Our results emphasize that different cICA algorithms are not equivalent. Spike sorting performance was assessed with ground-truth data generated from experimentally derived templates. The presented spike sorter was able to extract ≈90% of the true spike trains with an error rate below 2%. It was superior to two alternative (c)ICA methods (≈80% accurately sorted neurons) and comparable to a supervised sorting. Our new algorithm represents a fast solution to overcome the current bottleneck in spike sorting of large datasets generated by simultaneous recording with thousands of electrodes. Copyright © 2016 Elsevier B.V. All rights reserved.

  1. Convolutional neural networks and face recognition task

    Science.gov (United States)

    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.

  2. Decoding LDPC Convolutional Codes on Markov Channels

    Directory of Open Access Journals (Sweden)

    Kashyap Manohar

    2008-01-01

    Full Text Available Abstract This paper describes a pipelined iterative technique for joint decoding and channel state estimation of LDPC convolutional codes over Markov channels. Example designs are presented for the Gilbert-Elliott discrete channel model. We also compare the performance and complexity of our algorithm against joint decoding and state estimation of conventional LDPC block codes. Complexity analysis reveals that our pipelined algorithm reduces the number of operations per time step compared to LDPC block codes, at the expense of increased memory and latency. This tradeoff is favorable for low-power applications.

  3. Decoding LDPC Convolutional Codes on Markov Channels

    Directory of Open Access Journals (Sweden)

    Chris Winstead

    2008-04-01

    Full Text Available This paper describes a pipelined iterative technique for joint decoding and channel state estimation of LDPC convolutional codes over Markov channels. Example designs are presented for the Gilbert-Elliott discrete channel model. We also compare the performance and complexity of our algorithm against joint decoding and state estimation of conventional LDPC block codes. Complexity analysis reveals that our pipelined algorithm reduces the number of operations per time step compared to LDPC block codes, at the expense of increased memory and latency. This tradeoff is favorable for low-power applications.

  4. Fourier transforms and convolutions for the experimentalist

    CERN Document Server

    Jennison, RC

    1961-01-01

    Fourier Transforms and Convolutions for the Experimentalist provides the experimentalist with a guide to the principles and practical uses of the Fourier transformation. It aims to bridge the gap between the more abstract account of a purely mathematical approach and the rule of thumb calculation and intuition of the practical worker. The monograph springs from a lecture course which the author has given in recent years and for which he has drawn upon a number of sources, including a set of notes compiled by the late Dr. I. C. Browne from a series of lectures given by Mr. J . A. Ratcliffe of t

  5. Target recognition based on convolutional neural network

    Science.gov (United States)

    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.

  6. QCDNUM: Fast QCD evolution and convolution

    Science.gov (United States)

    Botje, M.

    2011-02-01

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

  7. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs.

    Science.gov (United States)

    Chen, Liang-Chieh; Papandreou, George; Kokkinos, Iasonas; Murphy, Kevin; Yuille, Alan L

    2018-04-01

    In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or 'atrous convolution', as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed "DeepLab" system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online.

  8. Convolutive ICA for Spatio-Temporal Analysis of EEG

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Makeig, Scott; Hansen, Lars Kai

    2007-01-01

    in the convolutive model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving an EEG ICA subspace. Initial results suggest that in some cases convolutive mixing may be a more realistic model for EEG signals than the instantaneous ICA model....

  9. Modified Stieltjes Transform and Generalized Convolutions of Probability Distributions

    Directory of Open Access Journals (Sweden)

    Lev B. Klebanov

    2018-01-01

    Full Text Available The classical Stieltjes transform is modified in such a way as to generalize both Stieltjes and Fourier transforms. This transform allows the introduction of new classes of commutative and non-commutative generalized convolutions. A particular case of such a convolution for degenerate distributions appears to be the Wigner semicircle distribution.

  10. Nuclear norm regularized convolutional Max Pos@Top machine

    KAUST Repository

    Li, Qinfeng; Zhou, Xiaofeng; Gu, Aihua; Li, Zonghua; Liang, Ru-Ze

    2016-01-01

    , named as Pos@Top. Our proposed classification model has a convolutional structure that is composed by four layers, i.e., the convolutional layer, the activation layer, the max-pooling layer and the full connection layer. In this paper, we propose

  11. Cermet electrode

    Science.gov (United States)

    Maskalick, Nicholas J.

    1988-08-30

    Disclosed is a cermet electrode consisting of metal particles of nickel, cobalt, iron, or alloys or mixtures thereof immobilized by zirconia stabilized in cubic form which contains discrete deposits of about 0.1 to about 5% by weight of praseodymium, dysprosium, terbium, or a mixture thereof. The solid oxide electrode can be made by covering a substrate with particles of nickel, cobalt, iron, or mixtures thereof, growing a stabilized zirconia solid oxide skeleton around the particles thereby immobilizing them, contacting the skeleton with a compound of praseodymium, dysprosium, terbium, or a mixture thereof, and heating the skeleton to a temperature of at least 500.degree. C. The electrode can also be made by preparing a slurry of nickel, cobalt, iron, or mixture and a compound of praseodymium, dysprosium, terbium, or a mixture thereof, depositing the slurry on a substrate, heating the slurry to dryness, and growing a stabilized zirconia skeleton around the metal particles.

  12. Forecasting short-term data center network traffic load with convolutional neural networks

    Science.gov (United States)

    Ordozgoiti, Bruno; Gómez-Canaval, Sandra

    2018-01-01

    Efficient resource management in data centers is of central importance to content service providers as 90 percent of the network traffic is expected to go through them in the coming years. In this context we propose the use of convolutional neural networks (CNNs) to forecast short-term changes in the amount of traffic crossing a data center network. This value is an indicator of virtual machine activity and can be utilized to shape the data center infrastructure accordingly. The behaviour of network traffic at the seconds scale is highly chaotic and therefore traditional time-series-analysis approaches such as ARIMA fail to obtain accurate forecasts. We show that our convolutional neural network approach can exploit the non-linear regularities of network traffic, providing significant improvements with respect to the mean absolute and standard deviation of the data, and outperforming ARIMA by an increasingly significant margin as the forecasting granularity is above the 16-second resolution. In order to increase the accuracy of the forecasting model, we exploit the architecture of the CNNs using multiresolution input distributed among separate channels of the first convolutional layer. We validate our approach with an extensive set of experiments using a data set collected at the core network of an Internet Service Provider over a period of 5 months, totalling 70 days of traffic at the one-second resolution. PMID:29408936

  13. Forecasting short-term data center network traffic load with convolutional neural networks.

    Science.gov (United States)

    Mozo, Alberto; Ordozgoiti, Bruno; Gómez-Canaval, Sandra

    2018-01-01

    Efficient resource management in data centers is of central importance to content service providers as 90 percent of the network traffic is expected to go through them in the coming years. In this context we propose the use of convolutional neural networks (CNNs) to forecast short-term changes in the amount of traffic crossing a data center network. This value is an indicator of virtual machine activity and can be utilized to shape the data center infrastructure accordingly. The behaviour of network traffic at the seconds scale is highly chaotic and therefore traditional time-series-analysis approaches such as ARIMA fail to obtain accurate forecasts. We show that our convolutional neural network approach can exploit the non-linear regularities of network traffic, providing significant improvements with respect to the mean absolute and standard deviation of the data, and outperforming ARIMA by an increasingly significant margin as the forecasting granularity is above the 16-second resolution. In order to increase the accuracy of the forecasting model, we exploit the architecture of the CNNs using multiresolution input distributed among separate channels of the first convolutional layer. We validate our approach with an extensive set of experiments using a data set collected at the core network of an Internet Service Provider over a period of 5 months, totalling 70 days of traffic at the one-second resolution.

  14. Spherical convolutions and their application in molecular modelling

    DEFF Research Database (Denmark)

    Boomsma, Wouter; Frellsen, Jes

    2017-01-01

    Convolutional neural networks are increasingly used outside the domain of image analysis, in particular in various areas of the natural sciences concerned with spatial data. Such networks often work out-of-the box, and in some cases entire model architectures from image analysis can be carried over...... to other problem domains almost unaltered. Unfortunately, this convenience does not trivially extend to data in non-euclidean spaces, such as spherical data. In this paper, we introduce two strategies for conducting convolutions on the sphere, using either a spherical-polar grid or a grid based...... of spherical convolutions in the context of molecular modelling, by considering structural environments within proteins. We show that the models are capable of learning non-trivial functions in these molecular environments, and that our spherical convolutions generally outperform standard 3D convolutions...

  15. Improved technology for manufacture of carbon electrodes

    Indian Academy of Sciences (India)

    distribution, surface area, porosity, particle size distribution and type of pores. The .... the point from where the electrode sample has been drawn. ... In addition, qualitative information on the shape and the type of pores can be determined.

  16. Magnetohydrodynamic electrode

    International Nuclear Information System (INIS)

    1980-01-01

    The object of the invention is the provision of a material capable of withstanding a high-temperature, corrosive and erosive environment for use as a ceramic-metal composite electrode current collector in the channel of a magnetohydrodynamic generator. (U.K.)

  17. Electrode Processes in Porous Electrodes.

    Science.gov (United States)

    1985-11-26

    F104470 2.0 MASS SPECTROMETRY One part of activity for this year is an investigation of the behavior of silver electrodes through the distribution of...al. (2)). These, in some cases, involve tedious and time comsuming procedures and discrepencies of as much as 15% have been observed in the results. As

  18. Deformable image registration using convolutional neural networks

    Science.gov (United States)

    Eppenhof, Koen A. J.; Lafarge, Maxime W.; Moeskops, Pim; Veta, Mitko; Pluim, Josien P. W.

    2018-03-01

    Deformable image registration can be time-consuming and often needs extensive parameterization to perform well on a specific application. We present a step towards a registration framework based on a three-dimensional convolutional neural network. The network directly learns transformations between pairs of three-dimensional images. The outputs of the network are three maps for the x, y, and z components of a thin plate spline transformation grid. The network is trained on synthetic random transformations, which are applied to a small set of representative images for the desired application. Training therefore does not require manually annotated ground truth deformation information. The methodology is demonstrated on public data sets of inspiration-expiration lung CT image pairs, which come with annotated corresponding landmarks for evaluation of the registration accuracy. Advantages of this methodology are its fast registration times and its minimal parameterization.

  19. Codeword Structure Analysis for LDPC Convolutional Codes

    Directory of Open Access Journals (Sweden)

    Hua Zhou

    2015-12-01

    Full Text Available The codewords of a low-density parity-check (LDPC convolutional code (LDPC-CC are characterised into structured and non-structured. The number of the structured codewords is dominated by the size of the polynomial syndrome former matrix H T ( D , while the number of the non-structured ones depends on the particular monomials or polynomials in H T ( D . By evaluating the relationship of the codewords between the mother code and its super codes, the low weight non-structured codewords in the super codes can be eliminated by appropriately choosing the monomials or polynomials in H T ( D , resulting in improved distance spectrum of the mother code.

  20. An Improved Convolutional Neural Network on Crowd Density Estimation

    Directory of Open Access Journals (Sweden)

    Pan Shao-Yun

    2016-01-01

    Full Text Available In this paper, a new method is proposed for crowd density estimation. An improved convolutional neural network is combined with traditional texture feature. The data calculated by the convolutional layer can be treated as a new kind of features.So more useful information of images can be extracted by different features.In the meantime, the size of image has little effect on the result of convolutional neural network. Experimental results indicate that our scheme has adequate performance to allow for its use in real world applications.

  1. Pierce electrodes for a multigap accelerating system

    International Nuclear Information System (INIS)

    Davydenko, V.I.; Ivanov, A.A.; Kotelnikov, I.A.; Tiunov, M.A.

    2007-01-01

    A well-known Pierce's solution that allows to focus a beam of charged particles using properly shaped electrodes outside the beam is generalized to the case of multigap accelerating system. Simple parametric formulae for Pierce electrodes are derived for an accelerating system with current density, limited either by space charge or by emitting property of the cathode. As an example of general approach, Pierce electrodes shape is analyzed for a system with two accelerating gaps. It is shown that precise Pierce's solution exists if acceleration rate within second gap is lower than within first gap. In the opposite case quasi-Pierce solution can be implemented using non-equipotential electrode between the gaps, and guidelines, based on numerical simulations, for the design of equipotential focusing electrodes are given

  2. High Order Tensor Formulation for Convolutional Sparse Coding

    KAUST Repository

    Bibi, Adel Aamer; Ghanem, Bernard

    2017-01-01

    Convolutional sparse coding (CSC) has gained attention for its successful role as a reconstruction and a classification tool in the computer vision and machine learning community. Current CSC methods can only reconstruct singlefeature 2D images

  3. Adversarial training and dilated convolutions for brain MRI segmentation

    NARCIS (Netherlands)

    Moeskops, P.; Veta, M.; Lafarge, M.W.; Eppenhof, K.A.J.; Pluim, J.P.W.

    2017-01-01

    Convolutional neural networks (CNNs) have been applied to various automatic image segmentation tasks in medical image analysis, including brain MRI segmentation. Generative adversarial networks have recently gained popularity because of their power in generating images that are difficult to

  4. Classification of urine sediment based on convolution neural network

    Science.gov (United States)

    Pan, Jingjing; Jiang, Cunbo; Zhu, Tiantian

    2018-04-01

    By designing a new convolution neural network framework, this paper breaks the constraints of the original convolution neural network framework requiring large training samples and samples of the same size. Move and cropping the input images, generate the same size of the sub-graph. And then, the generated sub-graph uses the method of dropout, increasing the diversity of samples and preventing the fitting generation. Randomly select some proper subset in the sub-graphic set and ensure that the number of elements in the proper subset is same and the proper subset is not the same. The proper subsets are used as input layers for the convolution neural network. Through the convolution layer, the pooling, the full connection layer and output layer, we can obtained the classification loss rate of test set and training set. In the red blood cells, white blood cells, calcium oxalate crystallization classification experiment, the classification accuracy rate of 97% or more.

  5. Convolution of second order linear recursive sequences II.

    Directory of Open Access Journals (Sweden)

    Szakács Tamás

    2017-12-01

    Full Text Available We continue the investigation of convolutions of second order linear recursive sequences (see the first part in [1]. In this paper, we focus on the case when the characteristic polynomials of the sequences have common root.

  6. FPGA-based digital convolution for wireless applications

    CERN Document Server

    Guan, Lei

    2017-01-01

    This book presents essential perspectives on digital convolutions in wireless communications systems and illustrates their corresponding efficient real-time field-programmable gate array (FPGA) implementations. Covering these digital convolutions from basic concept to vivid simulation/illustration, the book is also supplemented with MS PowerPoint presentations to aid in comprehension. FPGAs or generic all programmable devices will soon become widespread, serving as the “brains” of all types of real-time smart signal processing systems, like smart networks, smart homes and smart cities. The book examines digital convolution by bringing together the following main elements: the fundamental theory behind the mathematical formulae together with corresponding physical phenomena; virtualized algorithm simulation together with benchmark real-time FPGA implementations; and detailed, state-of-the-art case studies on wireless applications, including popular linear convolution in digital front ends (DFEs); nonlinear...

  7. Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition

    OpenAIRE

    Zhang, Zewang; Sun, Zheng; Liu, Jiaqi; Chen, Jingwen; Huo, Zhao; Zhang, Xiao

    2016-01-01

    A deep learning approach has been widely applied in sequence modeling problems. In terms of automatic speech recognition (ASR), its performance has significantly been improved by increasing large speech corpus and deeper neural network. Especially, recurrent neural network and deep convolutional neural network have been applied in ASR successfully. Given the arising problem of training speed, we build a novel deep recurrent convolutional network for acoustic modeling and then apply deep resid...

  8. Traffic sign recognition with deep convolutional neural networks

    OpenAIRE

    Karamatić, Boris

    2016-01-01

    The problem of detection and recognition of traffic signs is becoming an important problem when it comes to the development of self driving cars and advanced driver assistance systems. In this thesis we will develop a system for detection and recognition of traffic signs. For the problem of detection we will use aggregate channel features and for the problem of recognition we will use a deep convolutional neural network. We will describe how convolutional neural networks work, how they are co...

  9. Convolutional Codes with Maximum Column Sum Rank for Network Streaming

    OpenAIRE

    Mahmood, Rafid; Badr, Ahmed; Khisti, Ashish

    2015-01-01

    The column Hamming distance of a convolutional code determines the error correction capability when streaming over a class of packet erasure channels. We introduce a metric known as the column sum rank, that parallels column Hamming distance when streaming over a network with link failures. We prove rank analogues of several known column Hamming distance properties and introduce a new family of convolutional codes that maximize the column sum rank up to the code memory. Our construction invol...

  10. Efficient and Invariant Convolutional Neural Networks for Dense Prediction

    OpenAIRE

    Gao, Hongyang; Ji, Shuiwang

    2017-01-01

    Convolutional neural networks have shown great success on feature extraction from raw input data such as images. Although convolutional neural networks are invariant to translations on the inputs, they are not invariant to other transformations, including rotation and flip. Recent attempts have been made to incorporate more invariance in image recognition applications, but they are not applicable to dense prediction tasks, such as image segmentation. In this paper, we propose a set of methods...

  11. Prediction of Electricity Usage Using Convolutional Neural Networks

    OpenAIRE

    Hansen, Martin

    2017-01-01

    Master's thesis Information- and communication technology IKT590 - University of Agder 2017 Convolutional Neural Networks are overwhelmingly accurate when attempting to predict numbers using the famous MNIST-dataset. In this paper, we are attempting to transcend these results for time- series forecasting, and compare them with several regression mod- els. The Convolutional Neural Network model predicted the same value through the entire time lapse in contrast with the other ...

  12. Research of convolutional neural networks for traffic sign recognition

    OpenAIRE

    Stadalnikas, Kasparas

    2017-01-01

    In this thesis the convolutional neural networks application for traffic sign recognition is analyzed. Thesis describes the basic operations, techniques that are commonly used to apply in the image classification using convolutional neural networks. Also, this paper describes the data sets used for traffic sign recognition, their problems affecting the final training results. The paper reviews most popular existing technologies – frameworks for developing the solution for traffic sign recogni...

  13. On the Fresnel sine integral and the convolution

    Directory of Open Access Journals (Sweden)

    Adem Kılıçman

    2003-01-01

    Full Text Available The Fresnel sine integral S(x, the Fresnel cosine integral C(x, and the associated functions S+(x, S−(x, C+(x, and C−(x are defined as locally summable functions on the real line. Some convolutions and neutrix convolutions of the Fresnel sine integral and its associated functions with x+r, xr are evaluated.

  14. Dispersion-convolution model for simulating peaks in a flow injection system.

    Science.gov (United States)

    Pai, Su-Cheng; Lai, Yee-Hwong; Chiao, Ling-Yun; Yu, Tiing

    2007-01-12

    A dispersion-convolution model is proposed for simulating peak shapes in a single-line flow injection system. It is based on the assumption that an injected sample plug is expanded due to a "bulk" dispersion mechanism along the length coordinate, and that after traveling over a distance or a period of time, the sample zone will develop into a Gaussian-like distribution. This spatial pattern is further transformed to a temporal coordinate by a convolution process, and finally a temporal peak image is generated. The feasibility of the proposed model has been examined by experiments with various coil lengths, sample sizes and pumping rates. An empirical dispersion coefficient (D*) can be estimated by using the observed peak position, height and area (tp*, h* and At*) from a recorder. An empirical temporal shift (Phi*) can be further approximated by Phi*=D*/u2, which becomes an important parameter in the restoration of experimental peaks. Also, the dispersion coefficient can be expressed as a second-order polynomial function of the pumping rate Q, for which D*(Q)=delta0+delta1Q+delta2Q2. The optimal dispersion occurs at a pumping rate of Qopt=sqrt[delta0/delta2]. This explains the interesting "Nike-swoosh" relationship between the peak height and pumping rate. The excellent coherence of theoretical and experimental peak shapes confirms that the temporal distortion effect is the dominating reason to explain the peak asymmetry in flow injection analysis.

  15. Radial Structure Scaffolds Convolution Patterns of Developing Cerebral Cortex

    Directory of Open Access Journals (Sweden)

    Mir Jalil Razavi

    2017-08-01

    Full Text Available Commonly-preserved radial convolution is a prominent characteristic of the mammalian cerebral cortex. Endeavors from multiple disciplines have been devoted for decades to explore the causes for this enigmatic structure. However, the underlying mechanisms that lead to consistent cortical convolution patterns still remain poorly understood. In this work, inspired by prior studies, we propose and evaluate a plausible theory that radial convolution during the early development of the brain is sculptured by radial structures consisting of radial glial cells (RGCs and maturing axons. Specifically, the regionally heterogeneous development and distribution of RGCs controlled by Trnp1 regulate the convex and concave convolution patterns (gyri and sulci in the radial direction, while the interplay of RGCs' effects on convolution and axons regulates the convex (gyral convolution patterns. This theory is assessed by observations and measurements in literature from multiple disciplines such as neurobiology, genetics, biomechanics, etc., at multiple scales to date. Particularly, this theory is further validated by multimodal imaging data analysis and computational simulations in this study. We offer a versatile and descriptive study model that can provide reasonable explanations of observations, experiments, and simulations of the characteristic mammalian cortical folding.

  16. Metaheuristic Algorithms for Convolution Neural Network.

    Science.gov (United States)

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

    2016-01-01

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

  17. Metaheuristic Algorithms for Convolution Neural Network

    Directory of Open Access Journals (Sweden)

    L. M. Rasdi Rere

    2016-01-01

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

  18. Do Convolutional Neural Networks Learn Class Hierarchy?

    Science.gov (United States)

    Bilal, Alsallakh; Jourabloo, Amin; Ye, Mao; Liu, Xiaoming; Ren, Liu

    2018-01-01

    Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the accuracy usually drops as the possibilities of confusion increase. Interestingly, the class confusion patterns follow a hierarchical structure over the classes. We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data. We found that this hierarchy not only dictates the confusion patterns between the classes, it furthermore dictates the learning behavior of CNNs. In particular, the early layers in these networks develop feature detectors that can separate high-level groups of classes quite well, even after a few training epochs. In contrast, the latter layers require substantially more epochs to develop specialized feature detectors that can separate individual classes. We demonstrate how these insights are key to significant improvement in accuracy by designing hierarchy-aware CNNs that accelerate model convergence and alleviate overfitting. We further demonstrate how our methods help in identifying various quality issues in the training data.

  19. Microaneurysm detection using fully convolutional neural networks.

    Science.gov (United States)

    Chudzik, Piotr; Majumdar, Somshubra; Calivá, Francesco; Al-Diri, Bashir; Hunter, Andrew

    2018-05-01

    Diabetic retinopathy is a microvascular complication of diabetes that can lead to sight loss if treated not early enough. Microaneurysms are the earliest clinical signs of diabetic retinopathy. This paper presents an automatic method for detecting microaneurysms in fundus photographies. A novel patch-based fully convolutional neural network with batch normalization layers and Dice loss function is proposed. Compared to other methods that require up to five processing stages, it requires only three. Furthermore, to the best of the authors' knowledge, this is the first paper that shows how to successfully transfer knowledge between datasets in the microaneurysm detection domain. The proposed method was evaluated using three publicly available and widely used datasets: E-Ophtha, DIARETDB1, and ROC. It achieved better results than state-of-the-art methods using the FROC metric. The proposed algorithm accomplished highest sensitivities for low false positive rates, which is particularly important for screening purposes. Performance, simplicity, and robustness of the proposed method demonstrates its suitability for diabetic retinopathy screening applications. Copyright © 2018 Elsevier B.V. All rights reserved.

  20. Multiscale Convolutional Neural Networks for Hand Detection

    Directory of Open Access Journals (Sweden)

    Shiyang Yan

    2017-01-01

    Full Text Available Unconstrained hand detection in still images plays an important role in many hand-related vision problems, for example, hand tracking, gesture analysis, human action recognition and human-machine interaction, and sign language recognition. Although hand detection has been extensively studied for decades, it is still a challenging task with many problems to be tackled. The contributing factors for this complexity include heavy occlusion, low resolution, varying illumination conditions, different hand gestures, and the complex interactions between hands and objects or other hands. In this paper, we propose a multiscale deep learning model for unconstrained hand detection in still images. Deep learning models, and deep convolutional neural networks (CNNs in particular, have achieved state-of-the-art performances in many vision benchmarks. Developed from the region-based CNN (R-CNN model, we propose a hand detection scheme based on candidate regions generated by a generic region proposal algorithm, followed by multiscale information fusion from the popular VGG16 model. Two benchmark datasets were applied to validate the proposed method, namely, the Oxford Hand Detection Dataset and the VIVA Hand Detection Challenge. We achieved state-of-the-art results on the Oxford Hand Detection Dataset and had satisfactory performance in the VIVA Hand Detection Challenge.

  1. Psoriasis skin biopsy image segmentation using Deep Convolutional Neural Network.

    Science.gov (United States)

    Pal, Anabik; Garain, Utpal; Chandra, Aditi; Chatterjee, Raghunath; Senapati, Swapan

    2018-06-01

    Development of machine assisted tools for automatic analysis of psoriasis skin biopsy image plays an important role in clinical assistance. Development of automatic approach for accurate segmentation of psoriasis skin biopsy image is the initial prerequisite for developing such system. However, the complex cellular structure, presence of imaging artifacts, uneven staining variation make the task challenging. This paper presents a pioneering attempt for automatic segmentation of psoriasis skin biopsy images. Several deep neural architectures are tried for segmenting psoriasis skin biopsy images. Deep models are used for classifying the super-pixels generated by Simple Linear Iterative Clustering (SLIC) and the segmentation performance of these architectures is compared with the traditional hand-crafted feature based classifiers built on popularly used classifiers like K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF). A U-shaped Fully Convolutional Neural Network (FCN) is also used in an end to end learning fashion where input is the original color image and the output is the segmentation class map for the skin layers. An annotated real psoriasis skin biopsy image data set of ninety (90) images is developed and used for this research. The segmentation performance is evaluated with two metrics namely, Jaccard's Coefficient (JC) and the Ratio of Correct Pixel Classification (RCPC) accuracy. The experimental results show that the CNN based approaches outperform the traditional hand-crafted feature based classification approaches. The present research shows that practical system can be developed for machine assisted analysis of psoriasis disease. Copyright © 2018 Elsevier B.V. All rights reserved.

  2. The application of convolution-based statistical model on the electrical breakdown time delay distributions in neon

    International Nuclear Information System (INIS)

    Maluckov, Cedomir A.; Karamarkovic, Jugoslav P.; Radovic, Miodrag K.; Pejovic, Momcilo M.

    2004-01-01

    The convolution-based model of the electrical breakdown time delay distribution is applied for statistical analysis of experimental results obtained in neon-filled diode tube at 6.5 mbar. At first, the numerical breakdown time delay density distributions are obtained by stochastic modeling as the sum of two independent random variables, the electrical breakdown statistical time delay with exponential, and discharge formative time with Gaussian distribution. Then, the single characteristic breakdown time delay distribution is obtained as the convolution of these two random variables with previously determined parameters. These distributions show good correspondence with the experimental distributions, obtained on the basis of 1000 successive and independent measurements. The shape of distributions is investigated, and corresponding skewness and kurtosis are plotted, in order to follow the transition from Gaussian to exponential distribution

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

    Science.gov (United States)

    Alidoost, F.; Arefi, H.

    2016-06-01

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

  4. Convolutional Dictionary Learning: Acceleration and Convergence

    Science.gov (United States)

    Chun, Il Yong; Fessler, Jeffrey A.

    2018-04-01

    Convolutional dictionary learning (CDL or sparsifying CDL) has many applications in image processing and computer vision. There has been growing interest in developing efficient algorithms for CDL, mostly relying on the augmented Lagrangian (AL) method or the variant alternating direction method of multipliers (ADMM). When their parameters are properly tuned, AL methods have shown fast convergence in CDL. However, the parameter tuning process is not trivial due to its data dependence and, in practice, the convergence of AL methods depends on the AL parameters for nonconvex CDL problems. To moderate these problems, this paper proposes a new practically feasible and convergent Block Proximal Gradient method using a Majorizer (BPG-M) for CDL. The BPG-M-based CDL is investigated with different block updating schemes and majorization matrix designs, and further accelerated by incorporating some momentum coefficient formulas and restarting techniques. All of the methods investigated incorporate a boundary artifacts removal (or, more generally, sampling) operator in the learning model. Numerical experiments show that, without needing any parameter tuning process, the proposed BPG-M approach converges more stably to desirable solutions of lower objective values than the existing state-of-the-art ADMM algorithm and its memory-efficient variant do. Compared to the ADMM approaches, the BPG-M method using a multi-block updating scheme is particularly useful in single-threaded CDL algorithm handling large datasets, due to its lower memory requirement and no polynomial computational complexity. Image denoising experiments show that, for relatively strong additive white Gaussian noise, the filters learned by BPG-M-based CDL outperform those trained by the ADMM approach.

  5. Lidar Cloud Detection with Fully Convolutional Networks

    Science.gov (United States)

    Cromwell, E.; Flynn, D.

    2017-12-01

    The vertical distribution of clouds from active remote sensing instrumentation is a widely used data product from global atmospheric measuring sites. The presence of clouds can be expressed as a binary cloud mask and is a primary input for climate modeling efforts and cloud formation studies. Current cloud detection algorithms producing these masks do not accurately identify the cloud boundaries and tend to oversample or over-represent the cloud. This translates as uncertainty for assessing the radiative impact of clouds and tracking changes in cloud climatologies. The Atmospheric Radiation Measurement (ARM) program has over 20 years of micro-pulse lidar (MPL) and High Spectral Resolution Lidar (HSRL) instrument data and companion automated cloud mask product at the mid-latitude Southern Great Plains (SGP) and the polar North Slope of Alaska (NSA) atmospheric observatory. Using this data, we train a fully convolutional network (FCN) with semi-supervised learning to segment lidar imagery into geometric time-height cloud locations for the SGP site and MPL instrument. We then use transfer learning to train a FCN for (1) the MPL instrument at the NSA site and (2) for the HSRL. In our semi-supervised approach, we pre-train the classification layers of the FCN with weakly labeled lidar data. Then, we facilitate end-to-end unsupervised pre-training and transition to fully supervised learning with ground truth labeled data. Our goal is to improve the cloud mask accuracy and precision for the MPL instrument to 95% and 80%, respectively, compared to the current cloud mask algorithms of 89% and 50%. For the transfer learning based FCN for the HSRL instrument, our goal is to achieve a cloud mask accuracy of 90% and a precision of 80%.

  6. Electron transfer reactions to probe the electrode/solution interface

    Energy Technology Data Exchange (ETDEWEB)

    Capitanio, F.; Guerrini, E.; Colombo, A.; Trasatti, S. [Milan Univ., Milan (Italy). Dept. of Physical Chemistry and Electrochemistry

    2008-07-01

    The reactions that occur at the interface between an electrode and an electrolyte were examined with particular reference to the interaction of different electrode surfaces with redox couples. A semi-integration or convolution technique was used to study the kinetics of electron transfer on different electrode materials with different hydrophilic behaviour, such as Boron-Doped-Diamond (BDD), Au and Pt. Standard reversible redox couples were also investigated, including (Fe3+/2+, Fe(CN)63-/4-, Ru(NH3)63+/2+, Co(NH3)63+/2+, Ir4+/3+, V4+/5+ and V3+/2+). The proposed method proved to be simple, straightforward and reliable since the obtained kinetic information was in good agreement with data in the literature. It was concluded that the kinetics of the electrode transfer reactions depend on the chemical nature of the redox couple and electrode material. The method should be further extended to irreversible couples and other electrode materials such as mixed oxide electrodes. 3 refs., 2 figs.

  7. Detecting atrial fibrillation by deep convolutional neural networks.

    Science.gov (United States)

    Xia, Yong; Wulan, Naren; Wang, Kuanquan; Zhang, Henggui

    2018-02-01

    Atrial fibrillation (AF) is the most common cardiac arrhythmia. The incidence of AF increases with age, causing high risks of stroke and increased morbidity and mortality. Efficient and accurate diagnosis of AF based on the ECG is valuable in clinical settings and remains challenging. In this paper, we proposed a novel method with high reliability and accuracy for AF detection via deep learning. The short-term Fourier transform (STFT) and stationary wavelet transform (SWT) were used to analyze ECG segments to obtain two-dimensional (2-D) matrix input suitable for deep convolutional neural networks. Then, two different deep convolutional neural network models corresponding to STFT output and SWT output were developed. Our new method did not require detection of P or R peaks, nor feature designs for classification, in contrast to existing algorithms. Finally, the performances of the two models were evaluated and compared with those of existing algorithms. Our proposed method demonstrated favorable performances on ECG segments as short as 5 s. The deep convolutional neural network using input generated by STFT, presented a sensitivity of 98.34%, specificity of 98.24% and accuracy of 98.29%. For the deep convolutional neural network using input generated by SWT, a sensitivity of 98.79%, specificity of 97.87% and accuracy of 98.63% was achieved. The proposed method using deep convolutional neural networks shows high sensitivity, specificity and accuracy, and, therefore, is a valuable tool for AF detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Video Super-Resolution via Bidirectional Recurrent Convolutional Networks.

    Science.gov (United States)

    Huang, Yan; Wang, Wei; Wang, Liang

    2018-04-01

    Super resolving a low-resolution video, namely video super-resolution (SR), is usually handled by either single-image SR or multi-frame SR. Single-Image SR deals with each video frame independently, and ignores intrinsic temporal dependency of video frames which actually plays a very important role in video SR. Multi-Frame SR generally extracts motion information, e.g., optical flow, to model the temporal dependency, but often shows high computational cost. Considering that recurrent neural networks (RNNs) can model long-term temporal dependency of video sequences well, we propose a fully convolutional RNN named bidirectional recurrent convolutional network for efficient multi-frame SR. Different from vanilla RNNs, 1) the commonly-used full feedforward and recurrent connections are replaced with weight-sharing convolutional connections. So they can greatly reduce the large number of network parameters and well model the temporal dependency in a finer level, i.e., patch-based rather than frame-based, and 2) connections from input layers at previous timesteps to the current hidden layer are added by 3D feedforward convolutions, which aim to capture discriminate spatio-temporal patterns for short-term fast-varying motions in local adjacent frames. Due to the cheap convolutional operations, our model has a low computational complexity and runs orders of magnitude faster than other multi-frame SR methods. With the powerful temporal dependency modeling, our model can super resolve videos with complex motions and achieve well performance.

  9. Fluctuations at electrode-YSZ interfaces

    DEFF Research Database (Denmark)

    Jacobsen, Torben; Hansen, Karin Vels; Skou, Eivind

    2005-01-01

    Current fluctuations at potentiostatically controlled point electrodes of Pt, La$_{0.85}$Sr$_{0.15}$MnO$_3$ and Ni on YSZ surfaces are determined at 1000$^\\circ$C. For the oxygen reduction process on Pt electrodes characteristic sawtooth shaped low frequency fluctuations are observed. At temperat......Current fluctuations at potentiostatically controlled point electrodes of Pt, La$_{0.85}$Sr$_{0.15}$MnO$_3$ and Ni on YSZ surfaces are determined at 1000$^\\circ$C. For the oxygen reduction process on Pt electrodes characteristic sawtooth shaped low frequency fluctuations are observed....../water atmosphere are presented for discussion. The origin of the observations is not known at present but it appears likely that they are related to the activation/deactivation mechanism of SOFCs....

  10. Multi electrode semiconductors detectors

    CERN Document Server

    Amendolia, S R; Bertolucci, Ennio; Bosisio, L; Bradaschia, C; Budinich, M; Fidecaro, F; Foà, L; Focardi, E; Giazotto, A; Giorgi, M A; Marrocchesi, P S; Menzione, A; Ristori, L; Rolandi, Luigi; Scribano, A; Stefanini, A; Vincelli, M L

    1981-01-01

    Detectors with very high space resolution have been built in this laboratory and tested at CERN in order to investigate their possible use in high energy physics experiments. These detectors consist of thin layers of silicon crystals acting as ionization chambers. Thin electrodes, structured in strips or in more fancy shapes are applied to their surfaces by metal coating. The space resolution which could be reached is of the order of a few microns. An interesting feature of these solid state detectors is that they can work under very high or low external pressure or at very low temperature. The use of these detectors would strongly reduce the dimensions and the cost of high energy experiments. (3 refs).

  11. Multi electrode semiconductor detectors

    International Nuclear Information System (INIS)

    Amendolia, S.R.; Batignani, G.; Bertolucci, E.; Bosisio, L.; Budinich, M.; Bradaschia, C.; Fidecaro, F.; Foa, L.; Focardi, E.; Giazotto, A.; Giorgi, M.A.; Marrocchesi, P.S.; Menzione, A.; Ristori, L.; Rolandi, L.; Scribano, A.; Stefanini, A.; Vincelli, M.L.

    1981-01-01

    Detectors with very high space resolution have been built in the laboratory and tested at CERN in order to investigate their possible use in high energy physics experiments. These detectors consist of thin layers of silicon crystals acting as ionization chambers. Thin electrodes, structured in strips or in more fancy shapes are applied to their surfaces by metal coating. The space resolution which could be reached is of the order of a few microns. An interesting feature of these solid state detectors is that they can work under very high or low external pressure or at very low temperature. The use of these detectors would strongly reduce the dimensions and the cost of high energy experiments. (Auth.)

  12. Single image super-resolution based on convolutional neural networks

    Science.gov (United States)

    Zou, Lamei; Luo, Ming; Yang, Weidong; Li, Peng; Jin, Liujia

    2018-03-01

    We present a deep learning method for single image super-resolution (SISR). The proposed approach learns end-to-end mapping between low-resolution (LR) images and high-resolution (HR) images. The mapping is represented as a deep convolutional neural network which inputs the LR image and outputs the HR image. Our network uses 5 convolution layers, which kernels size include 5×5, 3×3 and 1×1. In our proposed network, we use residual-learning and combine different sizes of convolution kernels at the same layer. The experiment results show that our proposed method performs better than the existing methods in reconstructing quality index and human visual effects on benchmarked images.

  13. A Data-Driven Response Virtual Sensor Technique with Partial Vibration Measurements Using Convolutional Neural Network

    Science.gov (United States)

    Sun, Shan-Bin; He, Yuan-Yuan; Zhou, Si-Da; Yue, Zhen-Jiang

    2017-01-01

    Measurement of dynamic responses plays an important role in structural health monitoring, damage detection and other fields of research. However, in aerospace engineering, the physical sensors are limited in the operational conditions of spacecraft, due to the severe environment in outer space. This paper proposes a virtual sensor model with partial vibration measurements using a convolutional neural network. The transmissibility function is employed as prior knowledge. A four-layer neural network with two convolutional layers, one fully connected layer, and an output layer is proposed as the predicting model. Numerical examples of two different structural dynamic systems demonstrate the performance of the proposed approach. The excellence of the novel technique is further indicated using a simply supported beam experiment comparing to a modal-model-based virtual sensor, which uses modal parameters, such as mode shapes, for estimating the responses of the faulty sensors. The results show that the presented data-driven response virtual sensor technique can predict structural response with high accuracy. PMID:29231868

  14. Using Convolutional Neural Network Filters to Measure Left-Right Mirror Symmetry in Images

    Directory of Open Access Journals (Sweden)

    Anselm Brachmann

    2016-12-01

    Full Text Available We propose a method for measuring symmetry in images by using filter responses from Convolutional Neural Networks (CNNs. The aim of the method is to model human perception of left/right symmetry as closely as possible. Using the Convolutional Neural Network (CNN approach has two main advantages: First, CNN filter responses closely match the responses of neurons in the human visual system; they take information on color, edges and texture into account simultaneously. Second, we can measure higher-order symmetry, which relies not only on color, edges and texture, but also on the shapes and objects that are depicted in images. We validated our algorithm on a dataset of 300 music album covers, which were rated according to their symmetry by 20 human observers, and compared results with those from a previously proposed method. With our method, human perception of symmetry can be predicted with high accuracy. Moreover, we demonstrate that the inclusion of features from higher CNN layers, which encode more abstract image content, increases the performance further. In conclusion, we introduce a model of left/right symmetry that closely models human perception of symmetry in CD album covers.

  15. A Data-Driven Response Virtual Sensor Technique with Partial Vibration Measurements Using Convolutional Neural Network.

    Science.gov (United States)

    Sun, Shan-Bin; He, Yuan-Yuan; Zhou, Si-Da; Yue, Zhen-Jiang

    2017-12-12

    Measurement of dynamic responses plays an important role in structural health monitoring, damage detection and other fields of research. However, in aerospace engineering, the physical sensors are limited in the operational conditions of spacecraft, due to the severe environment in outer space. This paper proposes a virtual sensor model with partial vibration measurements using a convolutional neural network. The transmissibility function is employed as prior knowledge. A four-layer neural network with two convolutional layers, one fully connected layer, and an output layer is proposed as the predicting model. Numerical examples of two different structural dynamic systems demonstrate the performance of the proposed approach. The excellence of the novel technique is further indicated using a simply supported beam experiment comparing to a modal-model-based virtual sensor, which uses modal parameters, such as mode shapes, for estimating the responses of the faulty sensors. The results show that the presented data-driven response virtual sensor technique can predict structural response with high accuracy.

  16. Glue detection based on teaching points constraint and tracking model of pixel convolution

    Science.gov (United States)

    Geng, Lei; Ma, Xiao; Xiao, Zhitao; Wang, Wen

    2018-01-01

    On-line glue detection based on machine version is significant for rust protection and strengthening in car production. Shadow stripes caused by reflect light and unevenness of inside front cover of car reduce the accuracy of glue detection. In this paper, we propose an effective algorithm to distinguish the edges of the glue and shadow stripes. Teaching points are utilized to calculate slope between the two adjacent points. Then a tracking model based on pixel convolution along motion direction is designed to segment several local rectangular regions using distance. The distance is the height of rectangular region. The pixel convolution along the motion direction is proposed to extract edges of gules in local rectangular region. A dataset with different illumination and complexity shape stripes are used to evaluate proposed method, which include 500 thousand images captured from the camera of glue gun machine. Experimental results demonstrate that the proposed method can detect the edges of glue accurately. The shadow stripes are distinguished and removed effectively. Our method achieves the 99.9% accuracies for the image dataset.

  17. Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN).

    Science.gov (United States)

    Iqbal, Sajid; Ghani, M Usman; Saba, Tanzila; Rehman, Amjad

    2018-04-01

    A tumor could be found in any area of the brain and could be of any size, shape, and contrast. There may exist multiple tumors of different types in a human brain at the same time. Accurate tumor area segmentation is considered primary step for treatment of brain tumors. Deep Learning is a set of promising techniques that could provide better results as compared to nondeep learning techniques for segmenting timorous part inside a brain. This article presents a deep convolutional neural network (CNN) to segment brain tumors in MRIs. The proposed network uses BRATS segmentation challenge dataset which is composed of images obtained through four different modalities. Accordingly, we present an extended version of existing network to solve segmentation problem. The network architecture consists of multiple neural network layers connected in sequential order with the feeding of Convolutional feature maps at the peer level. Experimental results on BRATS 2015 benchmark data thus show the usability of the proposed approach and its superiority over the other approaches in this area of research. © 2018 Wiley Periodicals, Inc.

  18. An Efficient Implementation of Deep Convolutional Neural Networks for MRI Segmentation.

    Science.gov (United States)

    Hoseini, Farnaz; Shahbahrami, Asadollah; Bayat, Peyman

    2018-02-27

    Image segmentation is one of the most common steps in digital image processing, classifying a digital image into different segments. The main goal of this paper is to segment brain tumors in magnetic resonance images (MRI) using deep learning. Tumors having different shapes, sizes, brightness and textures can appear anywhere in the brain. These complexities are the reasons to choose a high-capacity Deep Convolutional Neural Network (DCNN) containing more than one layer. The proposed DCNN contains two parts: architecture and learning algorithms. The architecture and the learning algorithms are used to design a network model and to optimize parameters for the network training phase, respectively. The architecture contains five convolutional layers, all using 3 × 3 kernels, and one fully connected layer. Due to the advantage of using small kernels with fold, it allows making the effect of larger kernels with smaller number of parameters and fewer computations. Using the Dice Similarity Coefficient metric, we report accuracy results on the BRATS 2016, brain tumor segmentation challenge dataset, for the complete, core, and enhancing regions as 0.90, 0.85, and 0.84 respectively. The learning algorithm includes the task-level parallelism. All the pixels of an MR image are classified using a patch-based approach for segmentation. We attain a good performance and the experimental results show that the proposed DCNN increases the segmentation accuracy compared to previous techniques.

  19. Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging.

    Science.gov (United States)

    Mohseni Salehi, Seyed Sadegh; Erdogmus, Deniz; Gholipour, Ali

    2017-11-01

    Brain extraction or whole brain segmentation is an important first step in many of the neuroimage analysis pipelines. The accuracy and the robustness of brain extraction, therefore, are crucial for the accuracy of the entire brain analysis process. The state-of-the-art brain extraction techniques rely heavily on the accuracy of alignment or registration between brain atlases and query brain anatomy, and/or make assumptions about the image geometry, and therefore have limited success when these assumptions do not hold or image registration fails. With the aim of designing an accurate, learning-based, geometry-independent, and registration-free brain extraction tool, in this paper, we present a technique based on an auto-context convolutional neural network (CNN), in which intrinsic local and global image features are learned through 2-D patches of different window sizes. We consider two different architectures: 1) a voxelwise approach based on three parallel 2-D convolutional pathways for three different directions (axial, coronal, and sagittal) that implicitly learn 3-D image information without the need for computationally expensive 3-D convolutions and 2) a fully convolutional network based on the U-net architecture. Posterior probability maps generated by the networks are used iteratively as context information along with the original image patches to learn the local shape and connectedness of the brain to extract it from non-brain tissue. The brain extraction results we have obtained from our CNNs are superior to the recently reported results in the literature on two publicly available benchmark data sets, namely, LPBA40 and OASIS, in which we obtained the Dice overlap coefficients of 97.73% and 97.62%, respectively. Significant improvement was achieved via our auto-context algorithm. Furthermore, we evaluated the performance of our algorithm in the challenging problem of extracting arbitrarily oriented fetal brains in reconstructed fetal brain magnetic

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

    Directory of Open Access Journals (Sweden)

    E. M. Waisman

    2014-12-01

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

  1. Spacings and pair correlations for finite Bernoulli convolutions

    International Nuclear Information System (INIS)

    Benjamini, Itai; Solomyak, Boris

    2009-01-01

    We consider finite Bernoulli convolutions with a parameter 1/2 N . These sequences are uniformly distributed with respect to the infinite Bernoulli convolution measure ν λ , as N → ∞. Numerical evidence suggests that for a generic λ, the distribution of spacings between appropriately rescaled points is Poissonian. We obtain some partial results in this direction; for instance, we show that, on average, the pair correlations do not exhibit attraction or repulsion in the limit. On the other hand, for certain algebraic λ the behaviour is totally different

  2. A New Reverberator Based on Variable Sparsity Convolution

    DEFF Research Database (Denmark)

    Holm-Rasmussen, Bo; Lehtonen, Heidi-Maria; Välimäki, Vesa

    2013-01-01

    FIR filter coefficients are selected from a velvet noise sequence, which consists of ones, minus ones, and zeros only. In this application, it is sufficient perceptually to use very sparse velvet noise sequences having only about 0.1 to 0.2% non-zero elements, with increasing sparsity along...... the impulse response. The algorithm yields a parametric approximation of the late part of the impulse response, which is more than 100 times more efficient computationally than the direct convolution. The computational load of the proposed algorithm is comparable to that of FFT-based partitioned convolution...

  3. Convolutional cylinder-type block-circulant cycle codes

    Directory of Open Access Journals (Sweden)

    Mohammad Gholami

    2013-06-01

    Full Text Available In this paper, we consider a class of column-weight two quasi-cyclic low-density paritycheck codes in which the girth can be large enough, as an arbitrary multiple of 8. Then we devote a convolutional form to these codes, such that their generator matrix can be obtained by elementary row and column operations on the parity-check matrix. Finally, we show that the free distance of the convolutional codes is equal to the minimum distance of their block counterparts.

  4. Weed Growth Stage Estimator Using Deep Convolutional Neural Networks

    DEFF Research Database (Denmark)

    Teimouri, Nima; Dyrmann, Mads; Nielsen, Per Rydahl

    2018-01-01

    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...... 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....

  5. Deep Convolutional Neural Networks: Structure, Feature Extraction and Training

    Directory of Open Access Journals (Sweden)

    Namatēvs Ivars

    2017-12-01

    Full Text Available Deep convolutional neural networks (CNNs are aimed at processing data that have a known network like topology. They are widely used to recognise objects in images and diagnose patterns in time series data as well as in sensor data classification. The aim of the paper is to present theoretical and practical aspects of deep CNNs in terms of convolution operation, typical layers and basic methods to be used for training and learning. Some practical applications are included for signal and image classification. Finally, the present paper describes the proposed block structure of CNN for classifying crucial features from 3D sensor data.

  6. Very deep recurrent convolutional neural network for object recognition

    Science.gov (United States)

    Brahimi, Sourour; Ben Aoun, Najib; Ben Amar, Chokri

    2017-03-01

    In recent years, Computer vision has become a very active field. This field includes methods for processing, analyzing, and understanding images. The most challenging problems in computer vision are image classification and object recognition. This paper presents a new approach for object recognition task. This approach exploits the success of the Very Deep Convolutional Neural Network for object recognition. In fact, it improves the convolutional layers by adding recurrent connections. This proposed approach was evaluated on two object recognition benchmarks: Pascal VOC 2007 and CIFAR-10. The experimental results prove the efficiency of our method in comparison with the state of the art methods.

  7. Spectral interpolation - Zero fill or convolution. [image processing

    Science.gov (United States)

    Forman, M. L.

    1977-01-01

    Zero fill, or augmentation by zeros, is a method used in conjunction with fast Fourier transforms to obtain spectral spacing at intervals closer than obtainable from the original input data set. In the present paper, an interpolation technique (interpolation by repetitive convolution) is proposed which yields values accurate enough for plotting purposes and which lie within the limits of calibration accuracies. The technique is shown to operate faster than zero fill, since fewer operations are required. The major advantages of interpolation by repetitive convolution are that efficient use of memory is possible (thus avoiding the difficulties encountered in decimation in time FFTs) and that is is easy to implement.

  8. Efficient forward propagation of time-sequences in convolutional neural networks using Deep Shifting

    NARCIS (Netherlands)

    K.L. Groenland (Koen); S.M. Bohte (Sander)

    2016-01-01

    textabstractWhen a Convolutional Neural Network is used for on-the-fly evaluation of continuously updating time-sequences, many redundant convolution operations are performed. We propose the method of Deep Shifting, which remembers previously calculated results of convolution operations in order

  9. Convolutional Sparse Coding for Static and Dynamic Images Analysis

    Directory of Open Access Journals (Sweden)

    B. A. Knyazev

    2014-01-01

    Full Text Available The objective of this work is to improve performance of static and dynamic objects recognition. For this purpose a new image representation model and a transformation algorithm are proposed. It is examined and illustrated that limitations of previous methods make it difficult to achieve this objective. Static images, specifically handwritten digits of the widely used MNIST dataset, are the primary focus of this work. Nevertheless, preliminary qualitative results of image sequences analysis based on the suggested model are presented.A general analytical form of the Gabor function, often employed to generate filters, is described and discussed. In this research, this description is required for computing parameters of responses returned by our algorithm. The recursive convolution operator is introduced, which allows extracting free shape features of visual objects. The developed parametric representation model is compared with sparse coding based on energy function minimization.In the experimental part of this work, errors of estimating the parameters of responses are determined. Also, parameters statistics and their correlation coefficients for more than 106 responses extracted from the MNIST dataset are calculated. It is demonstrated that these data correspond well with previous research studies on Gabor filters as well as with works on visual cortex primary cells of mammals, in which similar responses were observed. A comparative test of the developed model with three other approaches is conducted; speed and accuracy scores of handwritten digits classification are presented. A support vector machine with a linear or radial basic function is used for classification of images and their representations while principal component analysis is used in some cases to prepare data beforehand. High accuracy is not attained due to the specific difficulties of combining our model with a support vector machine (a 3.99% error rate. However, another method is

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

    Science.gov (United States)

    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.

  11. Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks

    Science.gov (United States)

    Roth, Holger; Oda, Masahiro; Shimizu, Natsuki; Oda, Hirohisa; Hayashi, Yuichiro; Kitasaka, Takayuki; Fujiwara, Michitaka; Misawa, Kazunari; Mori, Kensaku

    2018-03-01

    Pancreas segmentation in computed tomography imaging has been historically difficult for automated methods because of the large shape and size variations between patients. In this work, we describe a custom-build 3D fully convolutional network (FCN) that can process a 3D image including the whole pancreas and produce an automatic segmentation. We investigate two variations of the 3D FCN architecture; one with concatenation and one with summation skip connections to the decoder part of the network. We evaluate our methods on a dataset from a clinical trial with gastric cancer patients, including 147 contrast enhanced abdominal CT scans acquired in the portal venous phase. Using the summation architecture, we achieve an average Dice score of 89.7 +/- 3.8 (range [79.8, 94.8])% in testing, achieving the new state-of-the-art performance in pancreas segmentation on this dataset.

  12. Discrete singular convolution for the generalized variable-coefficient ...

    African Journals Online (AJOL)

    Numerical solutions of the generalized variable-coefficient Korteweg-de Vries equation are obtained using a discrete singular convolution and a fourth order singly diagonally implicit Runge-Kutta method for space and time discretisation, respectively. The theoretical convergence of the proposed method is rigorously ...

  13. Face recognition: a convolutional neural-network approach.

    Science.gov (United States)

    Lawrence, S; Giles, C L; Tsoi, A C; Back, A D

    1997-01-01

    We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.

  14. Training Convolutional Neural Networks for Translational Invariance on SAR ATR

    DEFF Research Database (Denmark)

    Malmgren-Hansen, David; Engholm, Rasmus; Østergaard Pedersen, Morten

    2016-01-01

    In this paper we present a comparison of the robustness of Convolutional Neural Networks (CNN) to other classifiers in the presence of uncertainty of the objects localization in SAR image. We present a framework for simulating simple SAR images, translating the object of interest systematically...

  15. An Interactive Graphics Program for Assistance in Learning Convolution.

    Science.gov (United States)

    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…

  16. Diffraction and Dirchlet problem for parameter-elliptic convolution ...

    African Journals Online (AJOL)

    In this paper we evaluate the difference between the inverse operators of a Dirichlet problem and of a diffraction problem for parameter-elliptic convolution operators with constant symbols. We prove that the inverse operator of a Dirichlet problem can be obtained as a limit case of such a diffraction problem. Quaestiones ...

  17. Review of the convolution algorithm for evaluating service integrated systems

    DEFF Research Database (Denmark)

    Iversen, Villy Bæk

    1997-01-01

    In this paper we give a review of the applicability of the convolution algorithm. By this we are able to evaluate communication networks end--to--end with e.g. BPP multi-ratetraffic models insensitive to the holding time distribution. Rearrangement, minimum allocation, and maximum allocation...

  18. A convolutional neural network to filter artifacts in spectroscopic MRI.

    Science.gov (United States)

    Gurbani, Saumya S; Schreibmann, Eduard; Maudsley, Andrew A; Cordova, James Scott; Soher, Brian J; Poptani, Harish; Verma, Gaurav; Barker, Peter B; Shim, Hyunsuk; Cooper, Lee A D

    2018-03-09

    Proton MRSI is a noninvasive modality capable of generating volumetric maps of in vivo tissue metabolism without the need for ionizing radiation or injected contrast agent. Magnetic resonance spectroscopic imaging has been shown to be a viable imaging modality for studying several neuropathologies. However, a key hurdle in the routine clinical adoption of MRSI is the presence of spectral artifacts that can arise from a number of sources, possibly leading to false information. A deep learning model was developed that was capable of identifying and filtering out poor quality spectra. The core of the model used a tiled convolutional neural network that analyzed frequency-domain spectra to detect artifacts. When compared with a panel of MRS experts, our convolutional neural network achieved high sensitivity and specificity with an area under the curve of 0.95. A visualization scheme was implemented to better understand how the convolutional neural network made its judgement on single-voxel or multivoxel MRSI, and the convolutional neural network was embedded into a pipeline capable of producing whole-brain spectroscopic MRI volumes in real time. The fully automated method for assessment of spectral quality provides a valuable tool to support clinical MRSI or spectroscopic MRI studies for use in fields such as adaptive radiation therapy planning. © 2018 International Society for Magnetic Resonance in Medicine.

  19. Deep convolutional neural networks for detection of rail surface defects

    NARCIS (Netherlands)

    Faghih Roohi, S.; Hajizadeh, S.; Nunez Vicencio, Alfredo; Babuska, R.; De Schutter, B.H.K.; Estevez, Pablo A.; Angelov, Plamen P.; Del Moral Hernandez, Emilio

    2016-01-01

    In this paper, we propose a deep convolutional neural network solution to the analysis of image data for the detection of rail surface defects. The images are obtained from many hours of automated video recordings. This huge amount of data makes it impossible to manually inspect the images and

  20. Symbol Stream Combining in a Convolutionally Coded System

    Science.gov (United States)

    Mceliece, R. J.; Pollara, F.; Swanson, L.

    1985-01-01

    Symbol stream combining has been proposed as a method for arraying signals received at different antennas. If convolutional coding and Viterbi decoding are used, it is shown that a Viterbi decoder based on the proposed weighted sum of symbol streams yields maximum likelihood decisions.

  1. Two-level convolution formula for nuclear structure function

    Science.gov (United States)

    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.

  2. Two-level convolution formula for nuclear structure function

    International Nuclear Information System (INIS)

    Ma Boqiang

    1990-01-01

    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

  3. Plant species classification using deep convolutional neural network

    DEFF Research Database (Denmark)

    Dyrmann, Mads; Karstoft, Henrik; Midtiby, Henrik Skov

    2016-01-01

    Information on which weed species are present within agricultural fields is important for site specific weed management. This paper presents a method that is capable of recognising plant species in colour images by using a convolutional neural network. The network is built from scratch trained an...

  4. Convoluted dislocation loops induced by helium irradiation in reduced-activation martensitic steel and their impact on mechanical properties

    Energy Technology Data Exchange (ETDEWEB)

    Luo, Fengfeng [Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, Hubei Nuclear Solid Physics Key Laboratory, School of Physics and Technology, Wuhan University, Wuhan 430072 (China); Yao, Z. [Department of Mechanical and Materials Engineering, Queen' s University, Kingston, ON, Canada K7L 3N6 (Canada); Guo, Liping, E-mail: guolp@whu.edu.cn [Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, Hubei Nuclear Solid Physics Key Laboratory, School of Physics and Technology, Wuhan University, Wuhan 430072 (China); Suo, Jinping [State Key Laboratory of Mould Technology, Institute of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China); Wen, Yongming [Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, Hubei Nuclear Solid Physics Key Laboratory, School of Physics and Technology, Wuhan University, Wuhan 430072 (China)

    2014-06-01

    Helium irradiation induced dislocation loops in reduced-activation martensitic steels were investigated using transmission electron microscopy. The specimens were irradiated with 100 keV helium ions to 0.8 dpa at 350 °C. Unexpectedly, very large dislocation loops were found, significantly larger than that induced by other types of irradiations under the same dose. Moreover, the large loops were convoluted and formed interesting flower-like shape. The large loops were determined as interstitial type. Loops with the Burgers vectors of b=〈100〉 were only observed. Furthermore, irradiation induced hardening caused by these large loops was observed using the nano-indentation technique.

  5. Morphological and electrochemical studies of spherical boron doped diamond electrodes

    Energy Technology Data Exchange (ETDEWEB)

    Mendes de Barros, R.C. [IQ/USP, Av. Lineu Prestes, 748, Bloco 2 Superior, Cidade Universitaria, Sao Paulo/SP, 05508-900 (Brazil); Ferreira, N.G. [LAS/INPE, Av. dos Astronautas, 1758, Jardim da Granja, Sao Jose dos Campos/SP, 12245-970 (Brazil); Azevedo, A.F. [LAS/INPE, Av. dos Astronautas, 1758, Jardim da Granja, Sao Jose dos Campos/SP, 12245-970 (Brazil); Corat, E.J. [LAS/INPE, Av. dos Astronautas, 1758, Jardim da Granja, Sao Jose dos Campos/SP, 12245-970 (Brazil); Sumodjo, P.T.A. [IQ/USP, Av. Lineu Prestes, 748, Bloco 2 Superior, Cidade Universitaria, Sao Paulo/SP, 05508-900 (Brazil); Serrano, S.H.P. [IQ/USP, Av. Lineu Prestes, 748, Bloco 2 Superior, Cidade Universitaria, Sao Paulo/SP, 05508-900 (Brazil)]. E-mail: shps@iq.usp.br

    2006-08-14

    Morphological and electrochemical characteristics of boron doped diamond electrode in new geometric shape are presented. The main purpose of this study is a comparison among voltammetric behavior of planar glassy carbon electrode (GCE), planar boron doped diamond electrode (PDDE) and spherical boron doped diamond electrode (SDDE), obtained from similar experimental parameters. SDDE was obtained by the growth of boron doped film on textured molybdenum tip. This electrode does not present microelectrode characteristics. However, its voltammetric peak current, determined at low scan rates, is largest associated to the smallest {delta}E {sub p} values for ferrocyanide system when compared with PDDE or GCE. In addition, the capacitance is about 200 times smaller than that for GCE. These results show that the analytical performance of boron doped diamond electrodes can be implemented just by the change of sensor geometry, from plane to spherical shape.

  6. High performance cermet electrodes

    Science.gov (United States)

    Isenberg, Arnold O.; Zymboly, Gregory E.

    1986-01-01

    Disclosed is a method of increasing the operating cell voltage of a solid oxide electrochemical cell having metal electrode particles in contact with an oxygen-transporting ceramic electrolyte. The metal electrode is heated with the cell, and oxygen is passed through the oxygen-transporting ceramic electrolyte to the surface of the metal electrode particles so that the metal electrode particles are oxidized to form a metal oxide layer between the metal electrode particles and the electrolyte. The metal oxide layer is then reduced to form porous metal between the metal electrode particles and the ceramic electrolyte.

  7. An Implementation of Error Minimization Data Transmission in OFDM using Modified Convolutional Code

    Directory of Open Access Journals (Sweden)

    Hendy Briantoro

    2016-04-01

    Full Text Available This paper presents about error minimization in OFDM system. In conventional system, usually using channel coding such as BCH Code or Convolutional Code. But, performance BCH Code or Convolutional Code is not good in implementation of OFDM System. Error bits of OFDM system without channel coding is 5.77%. Then, we used convolutional code with code rate 1/2, it can reduce error bitsonly up to 3.85%. So, we proposed OFDM system with Modified Convolutional Code. In this implementation, we used Software Define Radio (SDR, namely Universal Software Radio Peripheral (USRP NI 2920 as the transmitter and receiver. The result of OFDM system using Modified Convolutional Code with code rate is able recover all character received so can decrease until 0% error bit. Increasing performance of Modified Convolutional Code is about 1 dB in BER of 10-4 from BCH Code and Convolutional Code. So, performance of Modified Convolutional better than BCH Code or Convolutional Code. Keywords: OFDM, BCH Code, Convolutional Code, Modified Convolutional Code, SDR, USRP

  8. Light pulse shapes from plastic scintillators

    International Nuclear Information System (INIS)

    Moszynski, M.; Bengtson, B.

    1977-01-01

    A detailed study of the light pulse shape from the binary NE 111 and the ternary Pilot U, Naton 136, KL 236, NE 102A, NE 104 and NE 110 plastic scintillators was performed by the single photon method using XP 1021 and C 31024 photomultipliers. The analysis of the shape of the light pulses determined experimentally for several samples of different dimensions gave the following conclusions. The original light pulse shape from the binary NE 111 scintillator, as measured with a 5 mm thick polished sample is described analytically by the convolution integral of a Gaussian and an exponential function. The Gaussian function may reflect a deexcitation of several higher levels of the solvent molecules excited by nuclear particles preceding an intermolecular energy transfer in the scintillator. It may introduce a rather important limitation of the speed of plastic scintillators as the standard deviation of the Gaussian function is equal to 0.2 ns. The light pulse shape from the ternary plastics is described by the convolution integral of a Gaussian and two exponential functions. The Gaussian function presents the rate of energy transfer from nuclear particles to the primary solute as in the binary plastics. The exponential functions describe the energy transfer from the primary solute to the wavelength shifter and the final emission of the light. (Auth.)

  9. Micro-CAT with redundant electrodes (CATER)

    International Nuclear Information System (INIS)

    Berg, F.D. van den; Eijk, C.W.E. van; Hollander, R.W.; Sarro, P.M.

    2000-01-01

    High-rate X-ray or neutron counting introduces the problem of hit multiplicity when 2D position reconstruction is demanded. Implementation of a third readout electrode having a different angle than the anode or cathode allows to eliminate multiplicity problems. We present experimental results of a new type of gas-filled micro-patterned radiation detector, called 'Compteur a Trous a Electrodes Redondantes (CATER)', that disposes of such an extra readout channel in the form of a ring-shaped electrode that is positioned between the anode and the cathode. The ionic signal is shared between the ring-electrode and the cathode strip in a way that can be controlled by their potential difference. We observe a strong signal dependence on the drift field, which can be understood by the reduced transparency for the primary charge at high drift fields

  10. Electrode placement during electro-desalination of

    DEFF Research Database (Denmark)

    Ottosen, Lisbeth M.; Andersson, Lovisa C. H.

    2017-01-01

    Carved stone sculptures and ornaments can be severely damaged by salt induced decay. Often the irregular surfaces are decomposed, and the artwork is lost. The present paper is an experimental investigation on the possibility for using electro-desalination for treatment of stone with irregular shape....... Electro-desalination experiments were made with different duration to follow the progress. Successful desalination of the whole stone piece was obtained, showing that also parts not being placed directly between the electrodes were desalinated. This is important in case of salt damaged carved stones......, where the most fragile parts thus can be desalinated without physically placing electrodes on them. The Cl removal rate was higher in the areas closest to the electrodes and slowest in the part, which was not placed directly between the electrodes. This is important to incorporate in the monitoring...

  11. A Simple Hydrogen Electrode

    Science.gov (United States)

    Eggen, Per-Odd

    2009-01-01

    This article describes the construction of an inexpensive, robust, and simple hydrogen electrode, as well as the use of this electrode to measure "standard" potentials. In the experiment described here the students can measure the reduction potentials of metal-metal ion pairs directly, without using a secondary reference electrode. Measurements…

  12. The Composite Insertion Electrode

    DEFF Research Database (Denmark)

    Atlung, Sven; Zachau-Christiansen, Birgit; West, Keld

    1984-01-01

    The specific energy obtainable by discharge of porous insertion electrodes is limited by electrolyte depletion in thepores. This can be overcome using a solid ion conductor as electrolyte. The term "composite" is used to distinguishthese electrodes from porous electrodes with liquid electrolyte...

  13. Near-Electrode Imager

    Energy Technology Data Exchange (ETDEWEB)

    Rathke, Jerome W.; Klingler, Robert J.; Woelk, Klaus; Gerald, Rex E.,II

    1999-05-01

    An apparatus, near-electrode imager, for employing nuclear magnetic resonance imaging to provide in situ measurements of electrochemical properties of a sample as a function of distance from a working electrode. The near-electrode imager use the radio frequency field gradient within a cylindrical toroid cavity resonator to provide high-resolution nuclear magnetic resonance spectral information on electrolyte materials.

  14. Improving deep convolutional neural networks with mixed maxout units.

    Directory of Open Access Journals (Sweden)

    Hui-Zhen Zhao

    Full Text Available 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.

  15. Convolutional over Recurrent Encoder for Neural Machine Translation

    Directory of Open Access Journals (Sweden)

    Dakwale Praveen

    2017-06-01

    Full Text Available Neural machine translation is a recently proposed approach which has shown competitive results to traditional MT approaches. Standard neural MT is an end-to-end neural network where the source sentence is encoded by a recurrent neural network (RNN called encoder and the target words are predicted using another RNN known as decoder. Recently, various models have been proposed which replace the RNN encoder with a convolutional neural network (CNN. In this paper, we propose to augment the standard RNN encoder in NMT with additional convolutional layers in order to capture wider context in the encoder output. Experiments on English to German translation demonstrate that our approach can achieve significant improvements over a standard RNN-based baseline.

  16. Infimal Convolution Regularisation Functionals of BV and Lp Spaces

    KAUST Repository

    Burger, Martin

    2016-02-03

    We study a general class of infimal convolution type regularisation functionals suitable for applications in image processing. These functionals incorporate a combination of the total variation seminorm and Lp norms. A unified well-posedness analysis is presented and a detailed study of the one-dimensional model is performed, by computing exact solutions for the corresponding denoising problem and the case p=2. Furthermore, the dependency of the regularisation properties of this infimal convolution approach to the choice of p is studied. It turns out that in the case p=2 this regulariser is equivalent to the Huber-type variant of total variation regularisation. We provide numerical examples for image decomposition as well as for image denoising. We show that our model is capable of eliminating the staircasing effect, a well-known disadvantage of total variation regularisation. Moreover as p increases we obtain almost piecewise affine reconstructions, leading also to a better preservation of hat-like structures.

  17. 3D Medical Image Interpolation Based on Parametric Cubic Convolution

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In the process of display, manipulation and analysis of biomedical image data, they usually need to be converted to data of isotropic discretization through the process of interpolation, while the cubic convolution interpolation is widely used due to its good tradeoff between computational cost and accuracy. In this paper, we present a whole concept for the 3D medical image interpolation based on cubic convolution, and the six methods, with the different sharp control parameter, which are formulated in details. Furthermore, we also give an objective comparison for these methods using data sets with the different slice spacing. Each slice in these data sets is estimated by each interpolation method and compared with the original slice using three measures: mean-squared difference, number of sites of disagreement, and largest difference. According to the experimental results, we present a recommendation for 3D medical images under the different situations in the end.

  18. Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks.

    Science.gov (United States)

    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.

  19. Deep learning for steganalysis via convolutional neural networks

    Science.gov (United States)

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

    2015-03-01

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

  20. ID card number detection algorithm based on convolutional neural network

    Science.gov (United States)

    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.

  1. Airplane detection in remote sensing images using convolutional neural networks

    Science.gov (United States)

    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.

  2. Rock images classification by using deep convolution neural network

    Science.gov (United States)

    Cheng, Guojian; Guo, Wenhui

    2017-08-01

    Granularity analysis is one of the most essential issues in authenticate under microscope. To improve the efficiency and accuracy of traditional manual work, an convolutional neural network based method is proposed for granularity analysis from thin section image, which chooses and extracts features from image samples while build classifier to recognize granularity of input image samples. 4800 samples from Ordos basin are used for experiments under colour spaces of HSV, YCbCr and RGB respectively. On the test dataset, the correct rate in RGB colour space is 98.5%, and it is believable in HSV and YCbCr colour space. The results show that the convolution neural network can classify the rock images with high reliability.

  3. User-generated content curation with deep convolutional neural networks

    OpenAIRE

    Tous Liesa, Rubén; Wust, Otto; Gómez, Mauro; Poveda, Jonatan; Elena, Marc; Torres Viñals, Jordi; Makni, Mouna; Ayguadé Parra, Eduard

    2016-01-01

    In this paper, we report a work consisting in using deep convolutional neural networks (CNNs) for curating and filtering photos posted by social media users (Instagram and Twitter). The final goal is to facilitate searching and discovering user-generated content (UGC) with potential value for digital marketing tasks. The images are captured in real time and automatically annotated with multiple CNNs. Some of the CNNs perform generic object recognition tasks while others perform what we call v...

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

    OpenAIRE

    Grice, Jon R.; Meyer, David A.

    2014-01-01

    We present a quantum Viterbi algorithm (QVA) with better than classical performance under certain conditions. In this paper the proposed algorithm is applied to decoding classical convolutional codes, for instance; large constraint length $Q$ and short decode frames $N$. Other applications of the classical Viterbi algorithm where $Q$ is large (e.g. speech processing) could experience significant speedup with the QVA. The QVA exploits the fact that the decoding trellis is similar to the butter...

  5. Abnormality Detection in Mammography using Deep Convolutional Neural Networks

    OpenAIRE

    Xi, Pengcheng; Shu, Chang; Goubran, Rafik

    2018-01-01

    Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be tra...

  6. Quantifying Translation-Invariance in Convolutional Neural Networks

    OpenAIRE

    Kauderer-Abrams, Eric

    2017-01-01

    A fundamental problem in object recognition is the development of image representations that are invariant to common transformations such as translation, rotation, and small deformations. There are multiple hypotheses regarding the source of translation invariance in CNNs. One idea is that translation invariance is due to the increasing receptive field size of neurons in successive convolution layers. Another possibility is that invariance is due to the pooling operation. We develop a simple ...

  7. Fast convolutional sparse coding using matrix inversion lemma

    Czech Academy of Sciences Publication Activity Database

    Šorel, Michal; Šroubek, Filip

    2016-01-01

    Roč. 55, č. 1 (2016), s. 44-51 ISSN 1051-2004 R&D Projects: GA ČR GA13-29225S Institutional support: RVO:67985556 Keywords : Convolutional sparse coding * Feature learning * Deconvolution networks * Shift-invariant sparse coding Subject RIV: JD - Computer Applications, Robotics Impact factor: 2.337, year: 2016 http://library.utia.cas.cz/separaty/2016/ZOI/sorel-0459332.pdf

  8. Learning Convolutional Text Representations for Visual Question Answering

    OpenAIRE

    Wang, Zhengyang; Ji, Shuiwang

    2017-01-01

    Visual question answering is a recently proposed artificial intelligence task that requires a deep understanding of both images and texts. In deep learning, images are typically modeled through convolutional neural networks, and texts are typically modeled through recurrent neural networks. While the requirement for modeling images is similar to traditional computer vision tasks, such as object recognition and image classification, visual question answering raises a different need for textual...

  9. Shallow and deep convolutional networks for saliency prediction

    OpenAIRE

    Pan, Junting; Sayrol Clols, Elisa; Giró Nieto, Xavier; McGuinness, Kevin; O'Connor, Noel

    2016-01-01

    The prediction of salient areas in images has been traditionally addressed with hand-crafted features based on neuroscience principles. This paper, however, addresses the problem with a completely data-driven approach by training a convolutional neural network (convnet). The learning process is formulated as a minimization of a loss function that measures the Euclidean distance of the predicted saliency map with the provided ground truth. The recent publication of large datasets of saliency p...

  10. Production and reception of meaningful sound in Foville's 'encompassing convolution'.

    Science.gov (United States)

    Schiller, F

    1999-04-01

    In the history of neurology. Achille Louis Foville (1799-1879) is a name deserving to be remembered. In the course of time, his circonvolution d'enceinte of 1844 (surrounding the Sylvian fissure) became the 'convolution encompassing' every aspect of aphasiology, including amusia, ie., the localization in a coherent semicircle of semicircle of cerebral cortext serving the production and perception of language, song and instrumental music in health and disease.

  11. Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks

    OpenAIRE

    Shen, Li; Lin, Zhouchen; Huang, Qingming

    2015-01-01

    Learning deeper convolutional neural networks becomes a tendency in recent years. However, many empirical evidences suggest that performance improvement cannot be gained by simply stacking more layers. In this paper, we consider the issue from an information theoretical perspective, and propose a novel method Relay Backpropagation, that encourages the propagation of effective information through the network in training stage. By virtue of the method, we achieved the first place in ILSVRC 2015...

  12. Maximum likelihood convolutional decoding (MCD) performance due to system losses

    Science.gov (United States)

    Webster, L.

    1976-01-01

    A model for predicting the computational performance of a maximum likelihood convolutional decoder (MCD) operating in a noisy carrier reference environment is described. This model is used to develop a subroutine that will be utilized by the Telemetry Analysis Program to compute the MCD bit error rate. When this computational model is averaged over noisy reference phase errors using a high-rate interpolation scheme, the results are found to agree quite favorably with experimental measurements.

  13. General Dirichlet Series, Arithmetic Convolution Equations and Laplace Transforms

    Czech Academy of Sciences Publication Activity Database

    Glöckner, H.; Lucht, L.G.; Porubský, Štefan

    2009-01-01

    Roč. 193, č. 2 (2009), s. 109-129 ISSN 0039-3223 R&D Projects: GA ČR GA201/07/0191 Institutional research plan: CEZ:AV0Z10300504 Keywords : arithmetic function * Dirichlet convolution * polynomial equation * analytic equation * topological algebra * holomorphic functional calculus * implicit function theorem * Laplace transform * semigroup * complex measure Subject RIV: BA - General Mathematics Impact factor: 0.645, year: 2009 http://arxiv.org/abs/0712.3172

  14. Solving singular convolution equations using the inverse fast Fourier transform

    Czech Academy of Sciences Publication Activity Database

    Krajník, E.; Montesinos, V.; Zizler, P.; Zizler, Václav

    2012-01-01

    Roč. 57, č. 5 (2012), s. 543-550 ISSN 0862-7940 R&D Projects: GA AV ČR IAA100190901 Institutional research plan: CEZ:AV0Z10190503 Keywords : singular convolution equations * fast Fourier transform * tempered distribution Subject RIV: BA - General Mathematics Impact factor: 0.222, year: 2012 http://www.springerlink.com/content/m8437t3563214048/

  15. CICAAR - Convolutive ICA with an Auto-Regressive Inverse Model

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Hansen, Lars Kai

    2004-01-01

    We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least square...... estimation. We demonstrate the method on synthetic data and finally separate speech and music in a real room recording....

  16. AFM tip-sample convolution effects for cylinder protrusions

    Science.gov (United States)

    Shen, Jian; Zhang, Dan; Zhang, Fei-Hu; Gan, Yang

    2017-11-01

    A thorough understanding about the AFM tip geometry dependent artifacts and tip-sample convolution effect is essential for reliable AFM topographic characterization and dimensional metrology. Using rigid sapphire cylinder protrusions (diameter: 2.25 μm, height: 575 nm) as the model system, a systematic and quantitative study about the imaging artifacts of four types of tips-two different pyramidal tips, one tetrahedral tip and one super sharp whisker tip-is carried out through comparing tip geometry dependent variations in AFM topography of cylinders and constructing the rigid tip-cylinder convolution models. We found that the imaging artifacts and the tip-sample convolution effect are critically related to the actual inclination of the working cantilever, the tip geometry, and the obstructive contacts between the working tip's planes/edges and the cylinder. Artifact-free images can only be obtained provided that all planes and edges of the working tip are steeper than the cylinder sidewalls. The findings reported here will contribute to reliable AFM characterization of surface features of micron or hundreds of nanometers in height that are frequently met in semiconductor, biology and materials fields.

  17. Edgeworth Expansion Based Model for the Convolutional Noise pdf

    Directory of Open Access Journals (Sweden)

    Yonatan Rivlin

    2014-01-01

    Full Text Available Recently, the Edgeworth expansion up to order 4 was used to represent the convolutional noise probability density function (pdf in the conditional expectation calculations where the source pdf was modeled with the maximum entropy density approximation technique. However, the applied Lagrange multipliers were not the appropriate ones for the chosen model for the convolutional noise pdf. In this paper we use the Edgeworth expansion up to order 4 and up to order 6 to model the convolutional noise pdf. We derive the appropriate Lagrange multipliers, thus obtaining new closed-form approximated expressions for the conditional expectation and mean square error (MSE as a byproduct. Simulation results indicate hardly any equalization improvement with Edgeworth expansion up to order 4 when using optimal Lagrange multipliers over a nonoptimal set. In addition, there is no justification for using the Edgeworth expansion up to order 6 over the Edgeworth expansion up to order 4 for the 16QAM and easy channel case. However, Edgeworth expansion up to order 6 leads to improved equalization performance compared to the Edgeworth expansion up to order 4 for the 16QAM and hard channel case as well as for the case where the 64QAM is sent via an easy channel.

  18. Traffic sign recognition based on deep convolutional neural network

    Science.gov (United States)

    Yin, Shi-hao; Deng, Ji-cai; Zhang, Da-wei; Du, Jing-yuan

    2017-11-01

    Traffic sign recognition (TSR) is an important component of automated driving systems. It is a rather challenging task to design a high-performance classifier for the TSR system. In this paper, we propose a new method for TSR system based on deep convolutional neural network. In order to enhance the expression of the network, a novel structure (dubbed block-layer below) which combines network-in-network and residual connection is designed. Our network has 10 layers with parameters (block-layer seen as a single layer): the first seven are alternate convolutional layers and block-layers, and the remaining three are fully-connected layers. We train our TSR network on the German traffic sign recognition benchmark (GTSRB) dataset. To reduce overfitting, we perform data augmentation on the training images and employ a regularization method named "dropout". The activation function we employ in our network adopts scaled exponential linear units (SELUs), which can induce self-normalizing properties. To speed up the training, we use an efficient GPU to accelerate the convolutional operation. On the test dataset of GTSRB, we achieve the accuracy rate of 99.67%, exceeding the state-of-the-art results.

  19. Face recognition via Gabor and convolutional neural network

    Science.gov (United States)

    Lu, Tongwei; Wu, Menglu; Lu, Tao

    2018-04-01

    In recent years, the powerful feature learning and classification ability of convolutional neural network have attracted widely attention. Compared with the deep learning, the traditional machine learning algorithm has a good explanatory which deep learning does not have. Thus, In this paper, we propose a method to extract the feature of the traditional algorithm as the input of convolution neural network. In order to reduce the complexity of the network, the kernel function of Gabor wavelet is used to extract the feature from different position, frequency and direction of target image. It is sensitive to edge of image which can provide good direction and scale selection. The extraction of the image from eight directions on a scale are as the input of network that we proposed. The network have the advantage of weight sharing and local connection and texture feature of the input image can reduce the influence of facial expression, gesture and illumination. At the same time, we introduced a layer which combined the results of the pooling and convolution can extract deeper features. The training network used the open source caffe framework which is beneficial to feature extraction. The experiment results of the proposed method proved that the network structure effectively overcame the barrier of illumination and had a good robustness as well as more accurate and rapid than the traditional algorithm.

  20. Nuclear norm regularized convolutional Max Pos@Top machine

    KAUST Repository

    Li, Qinfeng

    2016-11-18

    In this paper, we propose a novel classification model for the multiple instance data, which aims to maximize the number of positive instances ranked before the top-ranked negative instances. This method belongs to a recently emerged performance, named as Pos@Top. Our proposed classification model has a convolutional structure that is composed by four layers, i.e., the convolutional layer, the activation layer, the max-pooling layer and the full connection layer. In this paper, we propose an algorithm to learn the convolutional filters and the full connection weights to maximize the Pos@Top measure over the training set. Also, we try to minimize the rank of the filter matrix to explore the low-dimensional space of the instances in conjunction with the classification results. The rank minimization is conducted by the nuclear norm minimization of the filter matrix. In addition, we develop an iterative algorithm to solve the corresponding problem. We test our method on several benchmark datasets. The experimental results show the superiority of our method compared with other state-of-the-art Pos@Top maximization methods.

  1. Relative location prediction in CT scan images using convolutional neural networks.

    Science.gov (United States)

    Guo, Jiajia; Du, Hongwei; Zhu, Jianyue; Yan, Ting; Qiu, Bensheng

    2018-07-01

    Relative location prediction in computed tomography (CT) scan images is a challenging problem. Many traditional machine learning methods have been applied in attempts to alleviate this problem. However, the accuracy and speed of these methods cannot meet the requirement of medical scenario. In this paper, we propose a regression model based on one-dimensional convolutional neural networks (CNN) to determine the relative location of a CT scan image both quickly and precisely. In contrast to other common CNN models that use a two-dimensional image as an input, the input of this CNN model is a feature vector extracted by a shape context algorithm with spatial correlation. Normalization via z-score is first applied as a pre-processing step. Then, in order to prevent overfitting and improve model's performance, 20% of the elements of the feature vectors are randomly set to zero. This CNN model consists primarily of three one-dimensional convolutional layers, three dropout layers and two fully-connected layers with appropriate loss functions. A public dataset is employed to validate the performance of the proposed model using a 5-fold cross validation. Experimental results demonstrate an excellent performance of the proposed model when compared with contemporary techniques, achieving a median absolute error of 1.04 cm and mean absolute error of 1.69 cm. The time taken for each relative location prediction is approximately 2 ms. Results indicate that the proposed CNN method can contribute to a quick and accurate relative location prediction in CT scan images, which can improve efficiency of the medical picture archiving and communication system in the future. Copyright © 2018 Elsevier B.V. All rights reserved.

  2. The characteristic of twin-electrode TIG coupling arc pressure

    International Nuclear Information System (INIS)

    Leng Xuesong; Zhang Guangjun; Wu Lin

    2006-01-01

    The coupling arc of twin-electrode TIG (T-TIG) is a particular kind of arc, which is achieved through the coupling of two arcs generated from two insulated electrodes in the same welding torch. It is therefore different from the single arc of conventional TIG in its physical characteristics. This paper studies the distribution of T-TIG coupling arc pressure, and analyses the influences of welding current, arc length, the distance between electrode tips and electrode shape upon arc pressure on the basis of experiment. It is expected that the T-TIG welding method can be applied in high efficiency welding according to its low arc pressure

  3. Convolution equations on lattices: periodic solutions with values in a prime characteristic field

    OpenAIRE

    Zaidenberg, Mikhail

    2006-01-01

    These notes are inspired by the theory of cellular automata. A linear cellular automaton on a lattice of finite rank or on a toric grid is a discrete dinamical system generated by a convolution operator with kernel concentrated in the nearest neighborhood of the origin. In the present paper we deal with general convolution operators. We propose an approach via harmonic analysis which works over a field of positive characteristic. It occurs that a standard spectral problem for a convolution op...

  4. System of fabricating a flexible electrode array

    Energy Technology Data Exchange (ETDEWEB)

    Krulevitch, Peter [Pleasanton, CA; Polla, Dennis L [Roseville, MN; Maghribi, Mariam N [Davis, CA; Hamilton, Julie [Tracy, CA; Humayun, Mark S [La Canada, CA; Weiland, James D [Valencia, CA

    2012-01-28

    An image is captured or otherwise converted into a signal in an artificial vision system. The signal is transmitted to the retina utilizing an implant. The implant consists of a polymer substrate made of a compliant material such as poly(dimethylsiloxane) or PDMS. The polymer substrate is conformable to the shape of the retina. Electrodes and conductive leads are embedded in the polymer substrate. The conductive leads and the electrodes transmit the signal representing the image to the cells in the retina. The signal representing the image stimulates cells in the retina.

  5. System of fabricating a flexible electrode array

    Science.gov (United States)

    Krulevitch, Peter; Polla, Dennis L.; Maghribi, Mariam N.; Hamilton, Julie; Humayun, Mark S.; Weiland, James D.

    2010-10-12

    An image is captured or otherwise converted into a signal in an artificial vision system. The signal is transmitted to the retina utilizing an implant. The implant consists of a polymer substrate made of a compliant material such as poly(dimethylsiloxane) or PDMS. The polymer substrate is conformable to the shape of the retina. Electrodes and conductive leads are embedded in the polymer substrate. The conductive leads and the electrodes transmit the signal representing the image to the cells in the retina. The signal representing the image stimulates cells in the retina.

  6. Performance Analysis of DPSK Signals with Selection Combining and Convolutional Coding in Fading Channel

    National Research Council Canada - National Science Library

    Ong, Choon

    1998-01-01

    The performance analysis of a differential phase shift keyed (DPSK) communications system, operating in a Rayleigh fading environment, employing convolutional coding and diversity processing is presented...

  7. Model selection for convolutive ICA with an application to spatiotemporal analysis of EEG

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai

    2007-01-01

    We present a new algorithm for maximum likelihood convolutive independent component analysis (ICA) in which components are unmixed using stable autoregressive filters determined implicitly by estimating a convolutive model of the mixing process. By introducing a convolutive mixing model...... for the components, we show how the order of the filters in the model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving a subspace of independent components in electroencephalography (EEG). Initial results suggest that in some cases, convolutive mixing may...

  8. Analytical studies of plasma extraction electrodes and ion beam formation

    International Nuclear Information System (INIS)

    Hassan, A.; Elsaftawy, A.; Zakhary, S. G.

    2007-01-01

    In this work a theoretical and computational study on the space charge dominated beams extracted from a plasma ion source through a spherical and planer electrode is simulated and optimized. The influence of some electrode parameters: axial position, electrode diameter, material and shape; on ion current extracted from a plasma source; were investigated and compared. The optimum values and conditions of the curvature of the plasma boundary, angular divergence, perveance, and the extraction gap were optimized to extract a high quality beams. It has shown that for a planar electrode system there is usually a minimum for optimum perveance versus angular divergence at about ? 0.6 for corresponding aspect ratios. This was assured by experimental data. The appropriate spherical electrode system focus the beam to a minimum value located at a distance equal to the focal length of the spherical extraction electrode.

  9. Electrode design for soil decontamination with Radio-Frequency heating

    Energy Technology Data Exchange (ETDEWEB)

    Roland, U.; Holzer, F.; Kraus, M.; Trommler, U.; Kopinke, F.D. [Helmholtz Centre for Environmental Research - UFZ, Department of Environmental Engineering, Leipzig (Germany)

    2011-10-15

    Radio-frequency heating to enhance soil decontamination requires adjusted solutions for the electrode design depending on scale and remediation technique. Parallel plate electrodes provide widely homogeneous field and temperature distributions and are, therefore, most suitable for supporting biodegradation processes. For thermally enhanced soil vapor extraction, certain temperature gradients can be accepted and, therefore, the less-demanding geometry of rod-shaped electrodes is usually applied. For electrode lengths of some meters, a design with an air gap has to be used in order to focus heating to the desired depth. Perforated rod electrodes may be simultaneously employed as extraction wells. Placing an oxidation catalyst in situ within the electrodes is an option for handling of highly loaded air flows. Coaxial antenna may be utilized to selectively heat soil compartments far from the surface of the soil. (Copyright copyright 2011 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

  10. Uncharged positive electrode composition

    Science.gov (United States)

    Kaun, Thomas D.; Vissers, Donald R.; Shimotake, Hiroshi

    1977-03-08

    An uncharged positive-electrode composition contains particulate lithium sulfide, another alkali metal or alkaline earth metal compound other than sulfide, e.g., lithium carbide, and a transition metal powder. The composition along with a binder, such as electrolytic salt or a thermosetting resin is applied onto an electrically conductive substrate to form a plaque. The plaque is assembled as a positive electrode within an electrochemical cell opposite to a negative electrode containing a material such as aluminum or silicon for alloying with lithium. During charging, lithium alloy is formed within the negative electrode and transition metal sulfide such as iron sulfide is produced within the positive electrode. Excess negative electrode capacity over that from the transition metal sulfide is provided due to the electrochemical reaction of the other than sulfide alkali metal or alkaline earth metal compound.

  11. Handbook of reference electrodes

    CERN Document Server

    Inzelt, György; Scholz, Fritz

    2013-01-01

    Reference Electrodes are a crucial part of any electrochemical system, yet an up-to-date and comprehensive handbook is long overdue. Here, an experienced team of electrochemists provides an in-depth source of information and data for the proper choice and construction of reference electrodes. This includes all kinds of applications such as aqueous and non-aqueous solutions, ionic liquids, glass melts, solid electrolyte systems, and membrane electrodes. Advanced technologies such as miniaturized, conducting-polymer-based, screen-printed or disposable reference electrodes are also covered. Essen

  12. Finding strong lenses in CFHTLS using convolutional neural networks

    Science.gov (United States)

    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.

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

    Science.gov (United States)

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

    2014-05-01

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

  14. Fast Convolutional Sparse Coding in the Dual Domain

    KAUST Repository

    Affara, Lama Ahmed

    2017-09-27

    Convolutional sparse coding (CSC) is an important building block of many computer vision applications ranging from image and video compression to deep learning. We present two contributions to the state of the art in CSC. First, we significantly speed up the computation by proposing a new optimization framework that tackles the problem in the dual domain. Second, we extend the original formulation to higher dimensions in order to process a wider range of inputs, such as color inputs, or HOG features. Our results show a significant speedup compared to the current state of the art in CSC.

  15. Phase transitions in glassy systems via convolutional neural networks

    Science.gov (United States)

    Fang, Chao

    Machine learning is a powerful approach commonplace in industry to tackle large data sets. Most recently, it has found its way into condensed matter physics, allowing for the first time the study of, e.g., topological phase transitions and strongly-correlated electron systems. The study of spin glasses is plagued by finite-size effects due to the long thermalization times needed. Here we use convolutional neural networks in an attempt to detect a phase transition in three-dimensional Ising spin glasses. Our results are compared to traditional approaches.

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

    Science.gov (United States)

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

    1996-01-01

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

  17. Fast Convolutional Sparse Coding in the Dual Domain

    KAUST Repository

    Affara, Lama Ahmed; Ghanem, Bernard; Wonka, Peter

    2017-01-01

    Convolutional sparse coding (CSC) is an important building block of many computer vision applications ranging from image and video compression to deep learning. We present two contributions to the state of the art in CSC. First, we significantly speed up the computation by proposing a new optimization framework that tackles the problem in the dual domain. Second, we extend the original formulation to higher dimensions in order to process a wider range of inputs, such as color inputs, or HOG features. Our results show a significant speedup compared to the current state of the art in CSC.

  18. Salient regions detection using convolutional neural networks and color volume

    Science.gov (United States)

    Liu, Guang-Hai; Hou, Yingkun

    2018-03-01

    Convolutional neural network is an important technique in machine learning, pattern recognition and image processing. In order to reduce the computational burden and extend the classical LeNet-5 model to the field of saliency detection, we propose a simple and novel computing model based on LeNet-5 network. In the proposed model, hue, saturation and intensity are utilized to extract depth cues, and then we integrate depth cues and color volume to saliency detection following the basic structure of the feature integration theory. Experimental results show that the proposed computing model outperforms some existing state-of-the-art methods on MSRA1000 and ECSSD datasets.

  19. Traffic sign classification with dataset augmentation and convolutional neural network

    Science.gov (United States)

    Tang, Qing; Kurnianggoro, Laksono; Jo, Kang-Hyun

    2018-04-01

    This paper presents a method for traffic sign classification using a convolutional neural network (CNN). In this method, firstly we transfer a color image into grayscale, and then normalize it in the range (-1,1) as the preprocessing step. To increase robustness of classification model, we apply a dataset augmentation algorithm and create new images to train the model. To avoid overfitting, we utilize a dropout module before the last fully connection layer. To assess the performance of the proposed method, the German traffic sign recognition benchmark (GTSRB) dataset is utilized. Experimental results show that the method is effective in classifying traffic signs.

  20. Tandem mass spectrometry data quality assessment by self-convolution

    Directory of Open Access Journals (Sweden)

    Tham Wai

    2007-09-01

    Full Text Available Abstract Background 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. Results 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. Conclusion 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

  1. The Use of Finite Fields and Rings to Compute Convolutions

    Science.gov (United States)

    1975-06-06

    showed in Ref. 1 that the convolution of two finite sequences of integers (a, ) and (b, ) for k = 1, 2, . . ., d can be obtained as the inverse transform of...since the T.’S are all distinct. Thus T~ exists and (7) can be solved as a = T A the inverse " transform .𔃻 Next let us impose on (7) the...the inverse transform d-1 Cn= (d) I Cka k=0 If an a can be found so that multiplications by powers of a are simple in hardware, the

  2. Tandem mass spectrometry data quality assessment by self-convolution.

    Science.gov (United States)

    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

  3. Classifying medical relations in clinical text via convolutional neural networks.

    Science.gov (United States)

    He, Bin; Guan, Yi; Dai, Rui

    2018-05-16

    Deep learning research on relation classification has achieved solid performance in the general domain. This study proposes a convolutional neural network (CNN) architecture with a multi-pooling operation for medical relation classification on clinical records and explores a loss function with a category-level constraint matrix. Experiments using the 2010 i2b2/VA relation corpus demonstrate these models, which do not depend on any external features, outperform previous single-model methods and our best model is competitive with the existing ensemble-based method. Copyright © 2018. Published by Elsevier B.V.

  4. Weed Growth Stage Estimator Using Deep Convolutional Neural Networks

    DEFF Research Database (Denmark)

    Teimouri, Nima; Dyrmann, Mads; Nielsen, Per Rydahl

    2018-01-01

    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 conditi...... 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....

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

    Science.gov (United States)

    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

  6. Electrode stabilizing materials

    Science.gov (United States)

    Amine, Khalil; Abouimrane, Ali; Moore, Jeffrey S.; Odom, Susan A.

    2015-11-03

    An electrolyte includes a polar aprotic solvent; an alkali metal salt; and an electrode stabilizing compound that is a monomer, which when polymerized forms an electrically conductive polymer. The electrode stabilizing compound is a thiophene, a imidazole, a anilines, a benzene, a azulene, a carbazole, or a thiol. Electrochemical devices may incorporate such electrolytes.

  7. Durable fuel electrode

    DEFF Research Database (Denmark)

    2017-01-01

    the composite. The invention also relates to the use of the composite as a fuel electrode, solid oxide fuel cell, and/or solid oxide electrolyser. The invention discloses a composite for an electrode, comprising a three-dimensional network of dispersed metal particles, stabilised zirconia particles and pores...

  8. Optical transmission testing based on asynchronous sampling techniques: images analysis containing chromatic dispersion using convolutional neural network

    Science.gov (United States)

    Mrozek, T.; Perlicki, K.; Tajmajer, T.; Wasilewski, P.

    2017-08-01

    The article presents an image analysis method, obtained from an asynchronous delay tap sampling (ADTS) technique, which is used for simultaneous monitoring of various impairments occurring in the physical layer of the optical network. The ADTS method enables the visualization of the optical signal in the form of characteristics (so called phase portraits) that change their shape under the influence of impairments such as chromatic dispersion, polarization mode dispersion and ASE noise. Using this method, a simulation model was built with OptSim 4.0. After the simulation study, data were obtained in the form of images that were further analyzed using the convolutional neural network algorithm. The main goal of the study was to train a convolutional neural network to recognize the selected impairment (distortion); then to test its accuracy and estimate the impairment for the selected set of test images. The input data consisted of processed binary images in the form of two-dimensional matrices, with the position of the pixel. This article focuses only on the analysis of images containing chromatic dispersion.

  9. Estimation of current density distribution under electrodes for external defibrillation

    Directory of Open Access Journals (Sweden)

    Papazov Sava P

    2002-12-01

    Full Text Available Abstract Background Transthoracic defibrillation is the most common life-saving technique for the restoration of the heart rhythm of cardiac arrest victims. The procedure requires adequate application of large electrodes on the patient chest, to ensure low-resistance electrical contact. The current density distribution under the electrodes is non-uniform, leading to muscle contraction and pain, or risks of burning. The recent introduction of automatic external defibrillators and even wearable defibrillators, presents new demanding requirements for the structure of electrodes. Method and Results Using the pseudo-elliptic differential equation of Laplace type with appropriate boundary conditions and applying finite element method modeling, electrodes of various shapes and structure were studied. The non-uniformity of the current density distribution was shown to be moderately improved by adding a low resistivity layer between the metal and tissue and by a ring around the electrode perimeter. The inclusion of openings in long-term wearable electrodes additionally disturbs the current density profile. However, a number of small-size perforations may result in acceptable current density distribution. Conclusion The current density distribution non-uniformity of circular electrodes is about 30% less than that of square-shaped electrodes. The use of an interface layer of intermediate resistivity, comparable to that of the underlying tissues, and a high-resistivity perimeter ring, can further improve the distribution. The inclusion of skin aeration openings disturbs the current paths, but an appropriate selection of number and size provides a reasonable compromise.

  10. A MacWilliams Identity for Convolutional Codes: The General Case

    OpenAIRE

    Gluesing-Luerssen, Heide; Schneider, Gert

    2008-01-01

    A MacWilliams Identity for convolutional codes will be established. It makes use of the weight adjacency matrices of the code and its dual, based on state space realizations (the controller canonical form) of the codes in question. The MacWilliams Identity applies to various notions of duality appearing in the literature on convolutional coding theory.

  11. Isointense infant brain MRI segmentation with a dilated convolutional neural network

    NARCIS (Netherlands)

    Moeskops, P.; Pluim, J.P.W.

    2017-01-01

    Quantitative analysis of brain MRI at the age of 6 months is difficult because of the limited contrast between white matter and gray matter. In this study, we use a dilated triplanar convolutional neural network in combination with a non-dilated 3D convolutional neural network for the segmentation

  12. An upper bound on the number of errors corrected by a convolutional code

    DEFF Research Database (Denmark)

    Justesen, Jørn

    2000-01-01

    The number of errors that a convolutional codes can correct in a segment of the encoded sequence is upper bounded by the number of distinct syndrome sequences of the relevant length.......The number of errors that a convolutional codes can correct in a segment of the encoded sequence is upper bounded by the number of distinct syndrome sequences of the relevant length....

  13. Linear diffusion-wave channel routing using a discrete Hayami convolution method

    Science.gov (United States)

    Li Wang; Joan Q. Wu; William J. Elliot; Fritz R. Feidler; Sergey. Lapin

    2014-01-01

    The convolution of an input with a response function has been widely used in hydrology as a means to solve various problems analytically. Due to the high computation demand in solving the functions using numerical integration, it is often advantageous to use the discrete convolution instead of the integration of the continuous functions. This approach greatly reduces...

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

    Science.gov (United States)

    Ormesher, Richard C [Albuquerque, NM; Mason, John J [Albuquerque, NM

    2010-01-26

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

  15. Classifying images using restricted Boltzmann machines and convolutional neural networks

    Science.gov (United States)

    Zhao, Zhijun; Xu, Tongde; Dai, Chenyu

    2017-07-01

    To improve the feature recognition ability of deep model transfer learning, we propose a hybrid deep transfer learning method for image classification based on restricted Boltzmann machines (RBM) and convolutional neural networks (CNNs). It integrates learning abilities of two models, which conducts subject classification by exacting structural higher-order statistics features of images. While the method transfers the trained convolutional neural networks to the target datasets, fully-connected layers can be replaced by restricted Boltzmann machine layers; then the restricted Boltzmann machine layers and Softmax classifier are retrained, and BP neural network can be used to fine-tuned the hybrid model. The restricted Boltzmann machine layers has not only fully integrated the whole feature maps, but also learns the statistical features of target datasets in the view of the biggest logarithmic likelihood, thus removing the effects caused by the content differences between datasets. The experimental results show that the proposed method has improved the accuracy of image classification, outperforming other methods on Pascal VOC2007 and Caltech101 datasets.

  16. Cloud Detection by Fusing Multi-Scale Convolutional Features

    Science.gov (United States)

    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.

  17. Real-Time Video Convolutional Face Finder on Embedded Platforms

    Directory of Open Access Journals (Sweden)

    Mamalet Franck

    2007-01-01

    Full Text Available A high-level optimization methodology is applied for implementing the well-known convolutional face finder (CFF algorithm for real-time applications on mobile phones, such as teleconferencing, advanced user interfaces, image indexing, and security access control. CFF is based on a feature extraction and classification technique which consists of a pipeline of convolutions and subsampling operations. The design of embedded systems requires a good trade-off between performance and code size due to the limited amount of available resources. The followed methodology copes with the main drawbacks of the original implementation of CFF such as floating-point computation and memory allocation, in order to allow parallelism exploitation and perform algorithm optimizations. Experimental results show that our embedded face detection system can accurately locate faces with less computational load and memory cost. It runs on a 275 MHz Starcore DSP at 35 QCIF images/s with state-of-the-art detection rates and very low false alarm rates.

  18. Enhancing neutron beam production with a convoluted moderator

    Energy Technology Data Exchange (ETDEWEB)

    Iverson, E.B., E-mail: iversoneb@ornl.gov [Spallation Neutron Source, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Baxter, D.V. [Center for the Exploration of Energy and Matter, Indiana University, Bloomington, IN 47408 (United States); Muhrer, G. [Lujan Neutron Scattering Center, Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM 87545 (United States); Ansell, S.; Dalgliesh, R. [ISIS Facility, Rutherford Appleton Laboratory, Chilton (United Kingdom); Gallmeier, F.X. [Spallation Neutron Source, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Kaiser, H. [Center for the Exploration of Energy and Matter, Indiana University, Bloomington, IN 47408 (United States); Lu, W. [Spallation Neutron Source, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States)

    2014-10-21

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

  19. Photon beam convolution using polyenergetic energy deposition kernels

    International Nuclear Information System (INIS)

    Hoban, P.W.; Murray, D.C.; Round, W.H.

    1994-01-01

    In photon beam convolution calculations where polyenergetic energy deposition kernels (EDKs) are used, the primary photon energy spectrum should be correctly accounted for in Monte Carlo generation of EDKs. This requires the probability of interaction, determined by the linear attenuation coefficient, μ, to be taken into account when primary photon interactions are forced to occur at the EDK origin. The use of primary and scattered EDKs generated with a fixed photon spectrum can give rise to an error in the dose calculation due to neglecting the effects of beam hardening with depth. The proportion of primary photon energy that is transferred to secondary electrons increases with depth of interaction, due to the increase in the ratio μ ab /μ as the beam hardens. Convolution depth-dose curves calculated using polyenergetic EDKs generated for the primary photon spectra which exist at depths of 0, 20 and 40 cm in water, show a fall-off which is too steep when compared with EGS4 Monte Carlo results. A beam hardening correction factor applied to primary and scattered 0 cm EDKs, based on the ratio of kerma to terma at each depth, gives primary, scattered and total dose in good agreement with Monte Carlo results. (Author)

  20. Multi-Branch Fully Convolutional Network for Face Detection

    KAUST Repository

    Bai, Yancheng

    2017-07-20

    Face detection is a fundamental problem in computer vision. It is still a challenging task in unconstrained conditions due to significant variations in scale, pose, expressions, and occlusion. In this paper, we propose a multi-branch fully convolutional network (MB-FCN) for face detection, which considers both efficiency and effectiveness in the design process. Our MB-FCN detector can deal with faces at all scale ranges with only a single pass through the backbone network. As such, our MB-FCN model saves computation and thus is more efficient, compared to previous methods that make multiple passes. For each branch, the specific skip connections of the convolutional feature maps at different layers are exploited to represent faces in specific scale ranges. Specifically, small faces can be represented with both shallow fine-grained and deep powerful coarse features. With this representation, superior improvement in performance is registered for the task of detecting small faces. We test our MB-FCN detector on two public face detection benchmarks, including FDDB and WIDER FACE. Extensive experiments show that our detector outperforms state-of-the-art methods on all these datasets in general and by a substantial margin on the most challenging among them (e.g. WIDER FACE Hard subset). Also, MB-FCN runs at 15 FPS on a GPU for images of size 640 x 480 with no assumption on the minimum detectable face size.

  1. Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

    Directory of Open Access Journals (Sweden)

    Haiyang Yu

    2017-06-01

    Full Text Available 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.

  2. Convolutional Neural Network for Histopathological Analysis of Osteosarcoma.

    Science.gov (United States)

    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.

  3. Multi-Input Convolutional Neural Network for Flower Grading

    Directory of Open Access Journals (Sweden)

    Yu Sun

    2017-01-01

    Full Text Available Flower grading is a significant task because it is extremely convenient for managing the flowers in greenhouse and market. With the development of computer vision, flower grading has become an interdisciplinary focus in both botany and computer vision. A new dataset named BjfuGloxinia contains three quality grades; each grade consists of 107 samples and 321 images. A multi-input convolutional neural network is designed for large scale flower grading. Multi-input CNN achieves a satisfactory accuracy of 89.6% on the BjfuGloxinia after data augmentation. Compared with a single-input CNN, the accuracy of multi-input CNN is increased by 5% on average, demonstrating that multi-input convolutional neural network is a promising model for flower grading. Although data augmentation contributes to the model, the accuracy is still limited by lack of samples diversity. Majority of misclassification is derived from the medium class. The image processing based bud detection is useful for reducing the misclassification, increasing the accuracy of flower grading to approximately 93.9%.

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

    Science.gov (United States)

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

    2016-01-01

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

  5. Transforming Musical Signals through a Genre Classifying Convolutional Neural Network

    Science.gov (United States)

    Geng, S.; Ren, G.; Ogihara, M.

    2017-05-01

    Convolutional neural networks (CNNs) have been successfully applied on both discriminative and generative modeling for music-related tasks. For a particular task, the trained CNN contains information representing the decision making or the abstracting process. One can hope to manipulate existing music based on this 'informed' network and create music with new features corresponding to the knowledge obtained by the network. In this paper, we propose a method to utilize the stored information from a CNN trained on musical genre classification task. The network was composed of three convolutional layers, and was trained to classify five-second song clips into five different genres. After training, randomly selected clips were modified by maximizing the sum of outputs from the network layers. In addition to the potential of such CNNs to produce interesting audio transformation, more information about the network and the original music could be obtained from the analysis of the generated features since these features indicate how the network 'understands' the music.

  6. Siamese convolutional networks for tracking the spine motion

    Science.gov (United States)

    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.

  7. Real-Time Video Convolutional Face Finder on Embedded Platforms

    Directory of Open Access Journals (Sweden)

    Franck Mamalet

    2007-03-01

    Full Text Available A high-level optimization methodology is applied for implementing the well-known convolutional face finder (CFF algorithm for real-time applications on mobile phones, such as teleconferencing, advanced user interfaces, image indexing, and security access control. CFF is based on a feature extraction and classification technique which consists of a pipeline of convolutions and subsampling operations. The design of embedded systems requires a good trade-off between performance and code size due to the limited amount of available resources. The followed methodology copes with the main drawbacks of the original implementation of CFF such as floating-point computation and memory allocation, in order to allow parallelism exploitation and perform algorithm optimizations. Experimental results show that our embedded face detection system can accurately locate faces with less computational load and memory cost. It runs on a 275 MHz Starcore DSP at 35 QCIF images/s with state-of-the-art detection rates and very low false alarm rates.

  8. Digital image correlation based on a fast convolution strategy

    Science.gov (United States)

    Yuan, Yuan; Zhan, Qin; Xiong, Chunyang; Huang, Jianyong

    2017-10-01

    In recent years, the efficiency of digital image correlation (DIC) methods has attracted increasing attention because of its increasing importance for many engineering applications. Based on the classical affine optical flow (AOF) algorithm and the well-established inverse compositional Gauss-Newton algorithm, which is essentially a natural extension of the AOF algorithm under a nonlinear iterative framework, this paper develops a set of fast convolution-based DIC algorithms for high-efficiency subpixel image registration. Using a well-developed fast convolution technique, the set of algorithms establishes a series of global data tables (GDTs) over the digital images, which allows the reduction of the computational complexity of DIC significantly. Using the pre-calculated GDTs, the subpixel registration calculations can be implemented efficiently in a look-up-table fashion. Both numerical simulation and experimental verification indicate that the set of algorithms significantly enhances the computational efficiency of DIC, especially in the case of a dense data sampling for the digital images. Because the GDTs need to be computed only once, the algorithms are also suitable for efficiently coping with image sequences that record the time-varying dynamics of specimen deformations.

  9. Convolutional neural network features based change detection in satellite images

    Science.gov (United States)

    Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

    2016-07-01

    With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

  10. Classification of stroke disease using convolutional neural network

    Science.gov (United States)

    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.

  11. Image Classification Based on Convolutional Denoising Sparse Autoencoder

    Directory of Open Access Journals (Sweden)

    Shuangshuang Chen

    2017-01-01

    Full Text Available Image classification aims to group images into corresponding semantic categories. Due to the difficulties of interclass similarity and intraclass variability, it is a challenging issue in computer vision. In this paper, an unsupervised feature learning approach called convolutional denoising sparse autoencoder (CDSAE is proposed based on the theory of visual attention mechanism and deep learning methods. Firstly, saliency detection method is utilized to get training samples for unsupervised feature learning. Next, these samples are sent to the denoising sparse autoencoder (DSAE, followed by convolutional layer and local contrast normalization layer. Generally, prior in a specific task is helpful for the task solution. Therefore, a new pooling strategy—spatial pyramid pooling (SPP fused with center-bias prior—is introduced into our approach. Experimental results on the common two image datasets (STL-10 and CIFAR-10 demonstrate that our approach is effective in image classification. They also demonstrate that none of these three components: local contrast normalization, SPP fused with center-prior, and l2 vector normalization can be excluded from our proposed approach. They jointly improve image representation and classification performance.

  12. Weed Growth Stage Estimator Using Deep Convolutional Neural Networks.

    Science.gov (United States)

    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.

  13. Convolutional neural networks for vibrational spectroscopic data analysis.

    Science.gov (United States)

    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.

  14. sEMG-Based Gesture Recognition with Convolution Neural Networks

    Directory of Open Access Journals (Sweden)

    Zhen Ding

    2018-06-01

    Full Text Available The traditional classification methods for limb motion recognition based on sEMG have been deeply researched and shown promising results. However, information loss during feature extraction reduces the recognition accuracy. To obtain higher accuracy, the deep learning method was introduced. In this paper, we propose a parallel multiple-scale convolution architecture. Compared with the state-of-art methods, the proposed architecture fully considers the characteristics of the sEMG signal. Larger sizes of kernel filter than commonly used in other CNN-based hand recognition methods are adopted. Meanwhile, the characteristics of the sEMG signal, that is, muscle independence, is considered when designing the architecture. All the classification methods were evaluated on the NinaPro database. The results show that the proposed architecture has the highest recognition accuracy. Furthermore, the results indicate that parallel multiple-scale convolution architecture with larger size of kernel filter and considering muscle independence can significantly increase the classification accuracy.

  15. Development of a morphological convolution operator for bearing fault detection

    Science.gov (United States)

    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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Lu Weiguo; Chen Mingli, E-mail: wlu@tomotherapy.co [TomoTherapy Inc., 1240 Deming Way, Madison, WI 53717 (United States)

    2010-12-07

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

  17. Deep multi-scale convolutional neural network for hyperspectral image classification

    Science.gov (United States)

    Zhang, Feng-zhe; Yang, Xia

    2018-04-01

    In this paper, we proposed a multi-scale convolutional neural network for hyperspectral image classification task. Firstly, compared with conventional convolution, we utilize multi-scale convolutions, which possess larger respective fields, to extract spectral features of hyperspectral image. We design a deep neural network with a multi-scale convolution layer which contains 3 different convolution kernel sizes. Secondly, to avoid overfitting of deep neural network, dropout is utilized, which randomly sleeps neurons, contributing to improve the classification accuracy a bit. In addition, new skills like ReLU in deep learning is utilized in this paper. We conduct experiments on University of Pavia and Salinas datasets, and obtained better classification accuracy compared with other methods.

  18. Object Detection Based on Fast/Faster RCNN Employing Fully Convolutional Architectures

    Directory of Open Access Journals (Sweden)

    Yun Ren

    2018-01-01

    Full Text Available Modern object detectors always include two major parts: a feature extractor and a feature classifier as same as traditional object detectors. The deeper and wider convolutional architectures are adopted as the feature extractor at present. However, many notable object detection systems such as Fast/Faster RCNN only consider simple fully connected layers as the feature classifier. In this paper, we declare that it is beneficial for the detection performance to elaboratively design deep convolutional networks (ConvNets of various depths for feature classification, especially using the fully convolutional architectures. In addition, this paper also demonstrates how to employ the fully convolutional architectures in the Fast/Faster RCNN. Experimental results show that a classifier based on convolutional layer is more effective for object detection than that based on fully connected layer and that the better detection performance can be achieved by employing deeper ConvNets as the feature classifier.

  19. Classification of C2C12 cells at differentiation by convolutional neural network of deep learning using phase contrast images.

    Science.gov (United States)

    Niioka, Hirohiko; Asatani, Satoshi; Yoshimura, Aina; Ohigashi, Hironori; Tagawa, Seiichi; Miyake, Jun

    2018-01-01

    In the field of regenerative medicine, tremendous numbers of cells are necessary for tissue/organ regeneration. Today automatic cell-culturing system has been developed. The next step is constructing a non-invasive method to monitor the conditions of cells automatically. As an image analysis method, convolutional neural network (CNN), one of the deep learning method, is approaching human recognition level. We constructed and applied the CNN algorithm for automatic cellular differentiation recognition of myogenic C2C12 cell line. Phase-contrast images of cultured C2C12 are prepared as input dataset. In differentiation process from myoblasts to myotubes, cellular morphology changes from round shape to elongated tubular shape due to fusion of the cells. CNN abstract the features of the shape of the cells and classify the cells depending on the culturing days from when differentiation is induced. Changes in cellular shape depending on the number of days of culture (Day 0, Day 3, Day 6) are classified with 91.3% accuracy. Image analysis with CNN has a potential to realize regenerative medicine industry.

  20. Cylindrical-shaped nanotube field effect transistor

    KAUST Repository

    Hussain, Muhammad Mustafa

    2015-12-29

    A cylindrical-shaped nanotube FET may be manufactured on silicon (Si) substrates as a ring etched into a gate stack and filled with semiconductor material. An inner gate electrode couples to a region of the gate stack inside the inner circumference of the ring. An outer gate electrode couples to a region of the gate stack outside the outer circumference of the ring. The multi-gate cylindrical-shaped nanotube FET operates in volume inversion for ring widths below 15 nanometers. The cylindrical-shaped nanotube FET demonstrates better short channel effect (SCE) mitigation and higher performance (I.sub.on/I.sub.off) than conventional transistor devices. The cylindrical-shaped nanotube FET may also be manufactured with higher yields and cheaper costs than conventional transistors.

  1. Cylindrical-shaped nanotube field effect transistor

    KAUST Repository

    Hussain, Muhammad Mustafa; Fahad, Hossain M.; Smith, Casey E.; Rojas, Jhonathan Prieto

    2015-01-01

    A cylindrical-shaped nanotube FET may be manufactured on silicon (Si) substrates as a ring etched into a gate stack and filled with semiconductor material. An inner gate electrode couples to a region of the gate stack inside the inner circumference of the ring. An outer gate electrode couples to a region of the gate stack outside the outer circumference of the ring. The multi-gate cylindrical-shaped nanotube FET operates in volume inversion for ring widths below 15 nanometers. The cylindrical-shaped nanotube FET demonstrates better short channel effect (SCE) mitigation and higher performance (I.sub.on/I.sub.off) than conventional transistor devices. The cylindrical-shaped nanotube FET may also be manufactured with higher yields and cheaper costs than conventional transistors.

  2. Offset-electrode profile acquisition strategy for electrical resistivity tomography

    Science.gov (United States)

    Robbins, Austin R.; Plattner, Alain

    2018-04-01

    We present an electrode layout strategy that allows electrical resistivity profiles to image the third dimension close to the profile plane. This "offset-electrode profile" approach involves laterally displacing electrodes away from the profile line in an alternating fashion and then inverting the resulting data using three-dimensional electrical resistivity tomography software. In our synthetic and field surveys, the offset-electrode method succeeds in revealing three-dimensional structures in the vicinity of the profile plane, which we could not achieve using three-dimensional inversions of linear profiles. We confirm and explain the limits of linear electrode profiles through a discussion of the three-dimensional sensitivity patterns: For a homogeneous starting model together with a linear electrode layout, all sensitivities remain symmetric with respect to the profile plane through each inversion step. This limitation can be overcome with offset-electrode layouts by breaking the symmetry pattern among the sensitivities. Thanks to freely available powerful three-dimensional resistivity tomography software and cheap modern computing power, the requirement for full three-dimensional calculations does not create a significant burden and renders the offset-electrode approach a cost-effective method. By offsetting the electrodes in an alternating pattern, as opposed to laying the profile out in a U-shape, we minimize shortening the profile length.

  3. Electrode-electrolyte interface model of tripolar concentric ring electrode and electrode paste.

    Science.gov (United States)

    Nasrollaholhosseini, Seyed Hadi; Steele, Preston; Besio, Walter G

    2016-08-01

    Electrodes are used to transform ionic currents to electrical currents in biological systems. Modeling the electrode-electrolyte interface could help to optimize the performance of the electrode interface to achieve higher signal to noise ratios. There are previous reports of accurate models for single-element biomedical electrodes. In this paper we develop a model for the electrode-electrolyte interface for tripolar concentric ring electrodes (TCRE) that are used to record brain signals.

  4. Bipolar Electrode Sample Preparation Devices

    Science.gov (United States)

    Wang, Yi (Inventor); Song, Hongjun (Inventor); Pant, Kapil (Inventor)

    2017-01-01

    An analyte selection device can include: a body defining a fluid channel having a channel inlet and channel outlet; a bipolar electrode (BPE) between the inlet and outlet; one of an anode or cathode electrically coupled with the BPE on a channel inlet side of the BPE and the other of the anode or cathode electrically coupled with the BPE on a channel outlet side of the BPE; and an electronic system operably coupled with the anode and cathode so as to polarize the BPE. The fluid channel can have any shape or dimension. The channel inlet and channel outlet can be longitudinal or lateral with respect to the longitudinal axis of the channel. The BPE can be any metallic member, such as a flat plate on a wall or mesh as a barrier BPE. The anode and cathode can be located at a position that polarizes the BPE.

  5. SVD-BASED TRANSMIT BEAMFORMING FOR VARIOUS MODULATIONS WITH CONVOLUTION ENCODING

    Directory of Open Access Journals (Sweden)

    M. Raja

    2011-09-01

    Full Text Available This paper present a new beamforming technique using singular value decomposition (SVD for closed loop Multiple-input, multiple-output (MIMO wireless systems with various modulation techniques such as BPSK, 16-QAM, 16-PSK, DPSK and PAM along with convolution encoder. The channel matrix is decomposed into a number of independent orthogonal modes of excitation, which refer to as eigenmodes of the channel. Transmit precoding is performed by multiplying the input symbols with unitary matrix to produce the transmit beamforming, and the precoded symbols are transmitted over Rayleigh fading channel. At the receiver, combining process is performed by using maximum ratio combiner (MRC, and the receiver shaping is performed to retrieve the original input symbols by multiplying the received signal with conjugate transpose of the unitary matrix. Furthermore, the expressions for average bit error rate (BER for M-PSK and average BER for M-QAM are derived. The superiority of the proposed work is proved by simulation results and the proposed work is compared to the other beamforming methods.

  6. Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis

    Directory of Open Access Journals (Sweden)

    Baidaa Al-Bander

    2018-03-01

    Full Text Available Glaucoma is a group of eye diseases which can cause vision loss by damaging the optic nerve. Early glaucoma detection is key to preventing vision loss yet there is a lack of noticeable early symptoms. Colour fundus photography allows the optic disc (OD to be examined to diagnose glaucoma. Typically, this is done by measuring the vertical cup-to-disc ratio (CDR; however, glaucoma is characterised by thinning of the rim asymmetrically in the inferior-superior-temporal-nasal regions in increasing order. Automatic delineation of the OD features has potential to improve glaucoma management by allowing for this asymmetry to be considered in the measurements. Here, we propose a new deep-learning-based method to segment the OD and optic cup (OC. The core of the proposed method is DenseNet with a fully-convolutional network, whose symmetric U-shaped architecture allows pixel-wise classification. The predicted OD and OC boundaries are then used to estimate the CDR on two axes for glaucoma diagnosis. We assess the proposed method’s performance using a large retinal colour fundus dataset, outperforming state-of-the-art segmentation methods. Furthermore, we generalise our method to segment four fundus datasets from different devices without further training, outperforming the state-of-the-art on two and achieving comparable results on the remaining two.

  7. Convolutional neural network guided blue crab knuckle detection for autonomous crab meat picking machine

    Science.gov (United States)

    Wang, Dongyi; Vinson, Robert; Holmes, Maxwell; Seibel, Gary; Tao, Yang

    2018-04-01

    The Atlantic blue crab is among the highest-valued seafood found in the American Eastern Seaboard. Currently, the crab processing industry is highly dependent on manual labor. However, there is great potential for vision-guided intelligent machines to automate the meat picking process. Studies show that the back-fin knuckles are robust features containing information about a crab's size, orientation, and the position of the crab's meat compartments. Our studies also make it clear that detecting the knuckles reliably in images is challenging due to the knuckle's small size, anomalous shape, and similarity to joints in the legs and claws. An accurate and reliable computer vision algorithm was proposed to detect the crab's back-fin knuckles in digital images. Convolutional neural networks (CNNs) can localize rough knuckle positions with 97.67% accuracy, transforming a global detection problem into a local detection problem. Compared to the rough localization based on human experience or other machine learning classification methods, the CNN shows the best localization results. In the rough knuckle position, a k-means clustering method is able to further extract the exact knuckle positions based on the back-fin knuckle color features. The exact knuckle position can help us to generate a crab cutline in XY plane using a template matching method. This is a pioneering research project in crab image analysis and offers advanced machine intelligence for automated crab processing.

  8. Deep convolutional neural networks for building extraction from orthoimages and dense image matching point clouds

    Science.gov (United States)

    Maltezos, Evangelos; Doulamis, Nikolaos; Doulamis, Anastasios; Ioannidis, Charalabos

    2017-10-01

    Automatic extraction of buildings from remote sensing data is an attractive research topic, useful for several applications, such as cadastre and urban planning. This is mainly due to the inherent artifacts of the used data and the differences in viewpoint, surrounding environment, and complex shape and size of the buildings. This paper introduces an efficient deep learning framework based on convolutional neural networks (CNNs) toward building extraction from orthoimages. In contrast to conventional deep approaches in which the raw image data are fed as input to the deep neural network, in this paper the height information is exploited as an additional feature being derived from the application of a dense image matching algorithm. As test sites, several complex urban regions of various types of buildings, pixel resolutions and types of data are used, located in Vaihingen in Germany and in Perissa in Greece. Our method is evaluated using the rates of completeness, correctness, and quality and compared with conventional and other "shallow" learning paradigms such as support vector machines. Experimental results indicate that a combination of raw image data with height information, feeding as input to a deep CNN model, provides potentials in building detection in terms of robustness, flexibility, and efficiency.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  11. A simple method for estimating the length density of convoluted tubular systems.

    Science.gov (United States)

    Ferraz de Carvalho, Cláudio A; de Campos Boldrini, Silvia; Nishimaru, Flávio; Liberti, Edson A

    2008-10-01

    We present a new method for estimating the length density (Lv) of convoluted tubular structures exhibiting an isotropic distribution. Although the traditional equation Lv=2Q/A is used, the parameter Q is obtained by considering the collective perimeters of tubular sections. This measurement is converted to a standard model of the structure, assuming that all cross-sections are approximately circular and have an average perimeter similar to that of actual circular cross-sections observed in the same material. The accuracy of this method was tested in eight experiments using hollow macaroni bent into helical shapes. After measuring the length of the macaroni segments, they were boiled and randomly packed into cylindrical volumes along with an aqueous suspension of gelatin and India ink. The solidified blocks were cut into slices 1.0 cm thick and 33.2 cm2 in area (A). The total perimeter of the macaroni cross-sections so revealed was stereologically estimated using a test system of straight parallel lines. Given Lv and the reference volume, the total length of macaroni in each section could be estimated. Additional corrections were made for the changes induced by boiling, and the off-axis position of the thread used to measure length. No statistical difference was observed between the corrected estimated values and the actual lengths. This technique is useful for estimating the length of capillaries, renal tubules, and seminiferous tubules.

  12. Lidar-based individual tree species classification using convolutional neural network

    Science.gov (United States)

    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.

  13. Nanostructured gold and platinum electrodes on silicon structures for biosensing

    International Nuclear Information System (INIS)

    Ogurtsov, V I; Sheehan, M M

    2005-01-01

    Gold and platinum metal electrodes on Si/SiO 2 having undergone anisotropic potassium hydroxide (KOH) etch treatment are considered. This treatment etches at different rates and directions in the material resulting in creation of numerous pyramid shaped holes in the silicon substrate. This surface is used to make metal electrodes with increased electrode efficiency. The electrodes can serve as the sensors or as the sensor substrates (for surface polymer modification) and because both gold and platinum are inert they have applications for food safety biosensing. Wine, an economically significant food product, was chosen as a matrix, and impedance spectroscopy (EIS) was selected as a method of investigation of electrode behaviour. Based on results of EIS, different complexity equivalent circuits were determined by applying fitting mean square root optimisation of sensor complex impedance measurements

  14. Electrodes for stochastic cooling of the FNAL antiproton source

    International Nuclear Information System (INIS)

    Voelker, F.

    1982-11-01

    AN electrode array for stochastic cooling is being developed for use on the FNAL antiproton source. With minor power handling modifications, the same electrodes can function as pickups or as kickers. When used as pickups, a large array is needed to increase the signal-to-noise ratio. Each electrode is one element of a pair of directional coupler loops that are mounted flush with the upper and lower walls of the beam chamber. The loops, fabricated from flat metal plates, are supported by specially shaped legs

  15. Effect of electrode geometry on photovoltaic performance of polymer solar cells

    International Nuclear Information System (INIS)

    Li, Meng; Ma, Heng; Liu, Hairui; Wu, Dongge; Niu, Heying; Cai, Wenjun

    2014-01-01

    This paper investigates the impact of electrode geometry on the performance of polymer solar cells (PSCs). The negative electrodes with equal area (0.09 cm 2 ) but different shape (round, oval, square and triangular) are evaluated with respect to short-circuit current density, open-circuit voltage, fill factor and power conversion efficiency of PSCs. The results show that the device with round electrodes gives the best photovoltaic performance; in contrast, the device with triangular electrodes reveals the worst properties. A maximum of almost a 19% increase in power conversion efficiency with a round electrode is obtained in the devices compared with that of the triangular electrode. To conclude, the electrode boundary curvature has a significant impact on the performance of PSCs. The larger curvature, i.e. sharper electrodes edges, perhaps has a negative effect on exciton separation and carrier transport in photoelectric conversion processes. (paper)

  16. Sensor employing internal reference electrode

    DEFF Research Database (Denmark)

    2013-01-01

    The present invention concerns a novel internal reference electrode as well as a novel sensing electrode for an improved internal reference oxygen sensor and the sensor employing same.......The present invention concerns a novel internal reference electrode as well as a novel sensing electrode for an improved internal reference oxygen sensor and the sensor employing same....

  17. Training strategy for convolutional neural networks in pedestrian gender classification

    Science.gov (United States)

    Ng, Choon-Boon; Tay, Yong-Haur; Goi, Bok-Min

    2017-06-01

    In this work, we studied a strategy for training a convolutional neural network in pedestrian gender classification with limited amount of labeled training data. Unsupervised learning by k-means clustering on pedestrian images was used to learn the filters to initialize the first layer of the network. As a form of pre-training, supervised learning for the related task of pedestrian classification was performed. Finally, the network was fine-tuned for gender classification. We found that this strategy improved the network's generalization ability in gender classification, achieving better test results when compared to random weights initialization and slightly more beneficial than merely initializing the first layer filters by unsupervised learning. This shows that unsupervised learning followed by pre-training with pedestrian images is an effective strategy to learn useful features for pedestrian gender classification.

  18. Accurate lithography simulation model based on convolutional neural networks

    Science.gov (United States)

    Watanabe, Yuki; Kimura, Taiki; Matsunawa, Tetsuaki; Nojima, Shigeki

    2017-07-01

    Lithography simulation is an essential technique for today's semiconductor manufacturing process. In order to calculate an entire chip in realistic time, compact resist model is commonly used. The model is established for faster calculation. To have accurate compact resist model, it is necessary to fix a complicated non-linear model function. However, it is difficult to decide an appropriate function manually because there are many options. This paper proposes a new compact resist model using CNN (Convolutional Neural Networks) which is one of deep learning techniques. CNN model makes it possible to determine an appropriate model function and achieve accurate simulation. Experimental results show CNN model can reduce CD prediction errors by 70% compared with the conventional model.

  19. An effective convolutional neural network model for Chinese sentiment analysis

    Science.gov (United States)

    Zhang, Yu; Chen, Mengdong; Liu, Lianzhong; Wang, Yadong

    2017-06-01

    Nowadays microblog is getting more and more popular. People are increasingly accustomed to expressing their opinions on Twitter, Facebook and Sina Weibo. Sentiment analysis of microblog has received significant attention, both in academia and in industry. So far, Chinese microblog exploration still needs lots of further work. In recent years CNN has also been used to deal with NLP tasks, and already achieved good results. However, these methods ignore the effective use of a large number of existing sentimental resources. For this purpose, we propose a Lexicon-based Sentiment Convolutional Neural Networks (LSCNN) model focus on Weibo's sentiment analysis, which combines two CNNs, trained individually base on sentiment features and word embedding, at the fully connected hidden layer. The experimental results show that our model outperforms the CNN model only with word embedding features on microblog sentiment analysis task.

  20. High Order Tensor Formulation for Convolutional Sparse Coding

    KAUST Repository

    Bibi, Adel Aamer

    2017-12-25

    Convolutional sparse coding (CSC) has gained attention for its successful role as a reconstruction and a classification tool in the computer vision and machine learning community. Current CSC methods can only reconstruct singlefeature 2D images independently. However, learning multidimensional dictionaries and sparse codes for the reconstruction of multi-dimensional data is very important, as it examines correlations among all the data jointly. This provides more capacity for the learned dictionaries to better reconstruct data. In this paper, we propose a generic and novel formulation for the CSC problem that can handle an arbitrary order tensor of data. Backed with experimental results, our proposed formulation can not only tackle applications that are not possible with standard CSC solvers, including colored video reconstruction (5D- tensors), but it also performs favorably in reconstruction with much fewer parameters as compared to naive extensions of standard CSC to multiple features/channels.

  1. Classification of decays involving variable decay chains with convolutional architectures

    CERN Multimedia

    CERN. Geneva

    2018-01-01

    Vidyo contribution We present a technique to perform classification of decays that exhibit decay chains involving a variable number of particles, which include a broad class of $B$ meson decays sensitive to new physics. The utility of such decays as a probe of the Standard Model is dependent upon accurate determination of the decay rate, which is challenged by the combinatorial background arising in high-multiplicity decay modes. In our model, each particle in the decay event is represented as a fixed-dimensional vector of feature attributes, forming an $n \\times k$ representation of the event, where $n$ is the number of particles in the event and $k$ is the dimensionality of the feature vector. A convolutional architecture is used to capture dependencies between the embedded particle representations and perform the final classification. The proposed model performs outperforms standard machine learning approaches based on Monte Carlo studies across a range of variable final-state decays with the Belle II det...

  2. CONEDEP: COnvolutional Neural network based Earthquake DEtection and Phase Picking

    Science.gov (United States)

    Zhou, Y.; Huang, Y.; Yue, H.; Zhou, S.; An, S.; Yun, N.

    2017-12-01

    We developed an automatic local earthquake detection and phase picking algorithm based on Fully Convolutional Neural network (FCN). The FCN algorithm detects and segments certain features (phases) in 3 component seismograms to realize efficient picking. We use STA/LTA algorithm and template matching algorithm to construct the training set from seismograms recorded 1 month before and after the Wenchuan earthquake. Precise P and S phases are identified and labeled to construct the training set. Noise data are produced by combining back-ground noise and artificial synthetic noise to form the equivalent scale of noise set as the signal set. Training is performed on GPUs to achieve efficient convergence. Our algorithm has significantly improved performance in terms of the detection rate and precision in comparison with STA/LTA and template matching algorithms.

  3. Computational optical tomography using 3-D deep convolutional neural networks

    Science.gov (United States)

    Nguyen, Thanh; Bui, Vy; Nehmetallah, George

    2018-04-01

    Deep convolutional neural networks (DCNNs) offer a promising performance for many image processing areas, such as super-resolution, deconvolution, image classification, denoising, and segmentation, with outstanding results. Here, we develop for the first time, to our knowledge, a method to perform 3-D computational optical tomography using 3-D DCNN. A simulated 3-D phantom dataset was first constructed and converted to a dataset of phase objects imaged on a spatial light modulator. For each phase image in the dataset, the corresponding diffracted intensity image was experimentally recorded on a CCD. We then experimentally demonstrate the ability of the developed 3-D DCNN algorithm to solve the inverse problem by reconstructing the 3-D index of refraction distributions of test phantoms from the dataset from their corresponding diffraction patterns.

  4. Drug-Drug Interaction Extraction via Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Shengyu Liu

    2016-01-01

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

  5. Truncation Depth Rule-of-Thumb for Convolutional Codes

    Science.gov (United States)

    Moision, Bruce

    2009-01-01

    In this innovation, it is shown that a commonly used rule of thumb (that the truncation depth of a convolutional code should be five times the memory length, m, of the code) is accurate only for rate 1/2 codes. In fact, the truncation depth should be 2.5 m/(1 - r), where r is the code rate. The accuracy of this new rule is demonstrated by tabulating the distance properties of a large set of known codes. This new rule was derived by bounding the losses due to truncation as a function of the code rate. With regard to particular codes, a good indicator of the required truncation depth is the path length at which all paths that diverge from a particular path have accumulated the minimum distance of the code. It is shown that the new rule of thumb provides an accurate prediction of this depth for codes of varying rates.

  6. Finding Neutrinos in LArTPCs using Convolutional Neural Networks

    Science.gov (United States)

    Wongjirad, Taritree

    2017-09-01

    Deep learning algorithms, which have emerged over the last decade, are opening up new ways to analyze data for many particle physics experiments. MicroBooNE, which is a neutrino experiment at Fermilab, has been exploring the use of such algorithms, in particular, convolutional neural networks (CNNS). CNNs are the state-of-the-art method for a large class of problems involving the analysis of images. This makes CNNs an attractive approach for MicroBooNE, whose detector, a liquid argon time projection chamber (LArTPC), produces high-resolution images of particle interactions. In this talk, I will discuss the ways CNNs can be applied to tasks like neutrino interaction detection and particle identification in MicroBooNE and LArTPCs.

  7. Radio frequency interference mitigation using deep convolutional neural networks

    Science.gov (United States)

    Akeret, J.; Chang, C.; Lucchi, A.; Refregier, A.

    2017-01-01

    We propose a novel approach for mitigating radio frequency interference (RFI) signals in radio data using the latest advances in deep learning. We employ a special type of Convolutional Neural Network, the U-Net, that enables the classification of clean signal and RFI signatures in 2D time-ordered data acquired from a radio telescope. We train and assess the performance of this network using the HIDE &SEEK radio data simulation and processing packages, as well as early Science Verification data acquired with the 7m single-dish telescope at the Bleien Observatory. We find that our U-Net implementation is showing competitive accuracy to classical RFI mitigation algorithms such as SEEK's SUMTHRESHOLD implementation. We publish our U-Net software package on GitHub under GPLv3 license.

  8. Forecasting Flare Activity Using Deep Convolutional Neural Networks

    Science.gov (United States)

    Hernandez, T.

    2017-12-01

    Current operational flare forecasting relies on human morphological analysis of active regions and the persistence of solar flare activity through time (i.e. that the Sun will continue to do what it is doing right now: flaring or remaining calm). In this talk we present the results of applying deep Convolutional Neural Networks (CNNs) to the problem of solar flare forecasting. CNNs operate by training a set of tunable spatial filters that, in combination with neural layer interconnectivity, allow CNNs to automatically identify significant spatial structures predictive for classification and regression problems. We will start by discussing the applicability and success rate of the approach, the advantages it has over non-automated forecasts, and how mining our trained neural network provides a fresh look into the mechanisms behind magnetic energy storage and release.

  9. Convolutional neural networks with balanced batches for facial expressions recognition

    Science.gov (United States)

    Battini Sönmez, Elena; Cangelosi, Angelo

    2017-03-01

    This paper considers the issue of fully automatic emotion classification on 2D faces. In spite of the great effort done in recent years, traditional machine learning approaches based on hand-crafted feature extraction followed by the classification stage failed to develop a real-time automatic facial expression recognition system. The proposed architecture uses Convolutional Neural Networks (CNN), which are built as a collection of interconnected processing elements to simulate the brain of human beings. The basic idea of CNNs is to learn a hierarchical representation of the input data, which results in a better classification performance. In this work we present a block-based CNN algorithm, which uses noise, as data augmentation technique, and builds batches with a balanced number of samples per class. The proposed architecture is a very simple yet powerful CNN, which can yield state-of-the-art accuracy on the very competitive benchmark algorithm of the Extended Cohn Kanade database.

  10. Network Intrusion Detection through Stacking Dilated Convolutional Autoencoders

    Directory of Open Access Journals (Sweden)

    Yang Yu

    2017-01-01

    Full Text Available Network intrusion detection is one of the most important parts for cyber security to protect computer systems against malicious attacks. With the emergence of numerous sophisticated and new attacks, however, network intrusion detection techniques are facing several significant challenges. The overall objective of this study is to learn useful feature representations automatically and efficiently from large amounts of unlabeled raw network traffic data by using deep learning approaches. We propose a novel network intrusion model by stacking dilated convolutional autoencoders and evaluate our method on two new intrusion detection datasets. Several experiments were carried out to check the effectiveness of our approach. The comparative experimental results demonstrate that the proposed model can achieve considerably high performance which meets the demand of high accuracy and adaptability of network intrusion detection systems (NIDSs. It is quite potential and promising to apply our model in the large-scale and real-world network environments.

  11. Fully Convolutional Network Based Shadow Extraction from GF-2 Imagery

    Science.gov (United States)

    Li, Z.; Cai, G.; Ren, H.

    2018-04-01

    There are many shadows on the high spatial resolution satellite images, especially in the urban areas. Although shadows on imagery severely affect the information extraction of land cover or land use, they provide auxiliary information for building extraction which is hard to achieve a satisfactory accuracy through image classification itself. This paper focused on the method of building shadow extraction by designing a fully convolutional network and training samples collected from GF-2 satellite imagery in the urban region of Changchun city. By means of spatial filtering and calculation of adjacent relationship along the sunlight direction, the small patches from vegetation or bridges have been eliminated from the preliminary extracted shadows. Finally, the building shadows were separated. The extracted building shadow information from the proposed method in this paper was compared with the results from the traditional object-oriented supervised classification algorihtms. It showed that the deep learning network approach can improve the accuracy to a large extent.

  12. Finger vein recognition based on convolutional neural network

    Directory of Open Access Journals (Sweden)

    Meng Gesi

    2017-01-01

    Full Text Available Biometric Authentication Technology has been widely used in this information age. As one of the most important technology of authentication, finger vein recognition attracts our attention because of its high security, reliable accuracy and excellent performance. However, the current finger vein recognition system is difficult to be applied widely because its complicated image pre-processing and not representative feature vectors. To solve this problem, a finger vein recognition method based on the convolution neural network (CNN is proposed in the paper. The image samples are directly input into the CNN model to extract its feature vector so that we can make authentication by comparing the Euclidean distance between these vectors. Finally, the Deep Learning Framework Caffe is adopted to verify this method. The result shows that there are great improvements in both speed and accuracy rate compared to the previous research. And the model has nice robustness in illumination and rotation.

  13. Fully convolutional network with cluster for semantic segmentation

    Science.gov (United States)

    Ma, Xiao; Chen, Zhongbi; Zhang, Jianlin

    2018-04-01

    At present, image semantic segmentation technology has been an active research topic for scientists in the field of computer vision and artificial intelligence. Especially, the extensive research of deep neural network in image recognition greatly promotes the development of semantic segmentation. This paper puts forward a method based on fully convolutional network, by cluster algorithm k-means. The cluster algorithm using the image's low-level features and initializing the cluster centers by the super-pixel segmentation is proposed to correct the set of points with low reliability, which are mistakenly classified in great probability, by the set of points with high reliability in each clustering regions. This method refines the segmentation of the target contour and improves the accuracy of the image segmentation.

  14. Real Time Eye Detector with Cascaded Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Bin Li

    2018-01-01

    Full Text Available An accurate and efficient eye detector is essential for many computer vision applications. In this paper, we present an efficient method to evaluate the eye location from facial images. First, a group of candidate regions with regional extreme points is quickly proposed; then, a set of convolution neural networks (CNNs is adopted to determine the most likely eye region and classify the region as left or right eye; finally, the center of the eye is located with other CNNs. In the experiments using GI4E, BioID, and our datasets, our method attained a detection accuracy which is comparable to existing state-of-the-art methods; meanwhile, our method was faster and adaptable to variations of the images, including external light changes, facial occlusion, and changes in image modality.

  15. Convolution product construction of interactions in probabilistic physical models

    International Nuclear Information System (INIS)

    Ratsimbarison, H.M.; Raboanary, R.

    2007-01-01

    This paper aims to give a probabilistic construction of interactions which may be relevant for building physical theories such as interacting quantum field theories. We start with the path integral definition of partition function in quantum field theory which recall us the probabilistic nature of this physical theory. From a Gaussian law considered as free theory, an interacting theory is constructed by nontrivial convolution product between the free theory and an interacting term which is also a probability law. The resulting theory, again a probability law, exhibits two proprieties already present in nowadays theories of interactions such as Gauge theory : the interaction term does not depend on the free term, and two different free theories can be implemented with the same interaction.

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

    Science.gov (United States)

    Rafaely, Boaz; Park, Munhum

    2004-05-01

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

  17. Deep learning with convolutional neural network in radiology.

    Science.gov (United States)

    Yasaka, Koichiro; Akai, Hiroyuki; Kunimatsu, Akira; Kiryu, Shigeru; Abe, Osamu

    2018-04-01

    Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.

  18. Static facial expression recognition with convolution neural networks

    Science.gov (United States)

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

    2018-03-01

    Facial expression recognition is a currently active research topic in the fields of computer vision, pattern recognition and artificial intelligence. In this paper, we have developed a convolutional neural networks (CNN) for classifying human emotions from static facial expression into one of the seven facial emotion categories. We pre-train our CNN model on the combined FER2013 dataset formed by train, validation and test set and fine-tune on the extended Cohn-Kanade database. In order to reduce the overfitting of the models, we utilized different techniques including dropout and batch normalization in addition to data augmentation. According to the experimental result, our CNN model has excellent classification performance and robustness for facial expression recognition.

  19. DeepNAT: Deep convolutional neural network for segmenting neuroanatomy.

    Science.gov (United States)

    Wachinger, Christian; Reuter, Martin; Klein, Tassilo

    2018-04-15

    We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground. Since patches lack spatial context, we augment them with coordinates. To this end, we introduce a novel intrinsic parameterization of the brain volume, formed by eigenfunctions of the Laplace-Beltrami operator. As network architecture, we use three convolutional layers with pooling, batch normalization, and non-linearities, followed by fully connected layers with dropout. The final segmentation is inferred from the probabilistic output of the network with a 3D fully connected conditional random field, which ensures label agreement between close voxels. The roughly 2.7million parameters in the network are learned with stochastic gradient descent. Our results show that DeepNAT compares favorably to state-of-the-art methods. Finally, the purely learning-based method may have a high potential for the adaptation to young, old, or diseased brains by fine-tuning the pre-trained network with a small training sample on the target application, where the availability of larger datasets with manual annotations may boost the overall segmentation accuracy in the future. Copyright © 2017 Elsevier Inc. All rights reserved.

  20. FULLY CONVOLUTIONAL NETWORKS FOR GROUND CLASSIFICATION FROM LIDAR POINT CLOUDS

    Directory of Open Access Journals (Sweden)

    A. Rizaldy

    2018-05-01

    Full Text Available Deep Learning has been massively used for image classification in recent years. The use of deep learning for ground classification from LIDAR point clouds has also been recently studied. However, point clouds need to be converted into an image in order to use Convolutional Neural Networks (CNNs. In state-of-the-art techniques, this conversion is slow because each point is converted into a separate image. This approach leads to highly redundant computation during conversion and classification. The goal of this study is to design a more efficient data conversion and ground classification. This goal is achieved by first converting the whole point cloud into a single image. The classification is then performed by a Fully Convolutional Network (FCN, a modified version of CNN designed for pixel-wise image classification. The proposed method is significantly faster than state-of-the-art techniques. On the ISPRS Filter Test dataset, it is 78 times faster for conversion and 16 times faster for classification. Our experimental analysis on the same dataset shows that the proposed method results in 5.22 % of total error, 4.10 % of type I error, and 15.07 % of type II error. Compared to the previous CNN-based technique and LAStools software, the proposed method reduces the total error and type I error (while type II error is slightly higher. The method was also tested on a very high point density LIDAR point clouds resulting in 4.02 % of total error, 2.15 % of type I error and 6.14 % of type II error.

  1. Fully Convolutional Networks for Ground Classification from LIDAR Point Clouds

    Science.gov (United States)

    Rizaldy, A.; Persello, C.; Gevaert, C. M.; Oude Elberink, S. J.

    2018-05-01

    Deep Learning has been massively used for image classification in recent years. The use of deep learning for ground classification from LIDAR point clouds has also been recently studied. However, point clouds need to be converted into an image in order to use Convolutional Neural Networks (CNNs). In state-of-the-art techniques, this conversion is slow because each point is converted into a separate image. This approach leads to highly redundant computation during conversion and classification. The goal of this study is to design a more efficient data conversion and ground classification. This goal is achieved by first converting the whole point cloud into a single image. The classification is then performed by a Fully Convolutional Network (FCN), a modified version of CNN designed for pixel-wise image classification. The proposed method is significantly faster than state-of-the-art techniques. On the ISPRS Filter Test dataset, it is 78 times faster for conversion and 16 times faster for classification. Our experimental analysis on the same dataset shows that the proposed method results in 5.22 % of total error, 4.10 % of type I error, and 15.07 % of type II error. Compared to the previous CNN-based technique and LAStools software, the proposed method reduces the total error and type I error (while type II error is slightly higher). The method was also tested on a very high point density LIDAR point clouds resulting in 4.02 % of total error, 2.15 % of type I error and 6.14 % of type II error.

  2. Fully convolutional neural networks improve abdominal organ segmentation

    Science.gov (United States)

    Bobo, Meg F.; Bao, Shunxing; Huo, Yuankai; Yao, Yuang; Virostko, Jack; Plassard, Andrew J.; Lyu, Ilwoo; Assad, Albert; Abramson, Richard G.; Hilmes, Melissa A.; Landman, Bennett A.

    2018-03-01

    Abdominal image segmentation is a challenging, yet important clinical problem. Variations in body size, position, and relative organ positions greatly complicate the segmentation process. Historically, multi-atlas methods have achieved leading results across imaging modalities and anatomical targets. However, deep learning is rapidly overtaking classical approaches for image segmentation. Recently, Zhou et al. showed that fully convolutional networks produce excellent results in abdominal organ segmentation of computed tomography (CT) scans. Yet, deep learning approaches have not been applied to whole abdomen magnetic resonance imaging (MRI) segmentation. Herein, we evaluate the applicability of an existing fully convolutional neural network (FCNN) designed for CT imaging to segment abdominal organs on T2 weighted (T2w) MRI's with two examples. In the primary example, we compare a classical multi-atlas approach with FCNN on forty-five T2w MRI's acquired from splenomegaly patients with five organs labeled (liver, spleen, left kidney, right kidney, and stomach). Thirty-six images were used for training while nine were used for testing. The FCNN resulted in a Dice similarity coefficient (DSC) of 0.930 in spleens, 0.730 in left kidneys, 0.780 in right kidneys, 0.913 in livers, and 0.556 in stomachs. The performance measures for livers, spleens, right kidneys, and stomachs were significantly better than multi-atlas (p < 0.05, Wilcoxon rank-sum test). In a secondary example, we compare the multi-atlas approach with FCNN on 138 distinct T2w MRI's with manually labeled pancreases (one label). On the pancreas dataset, the FCNN resulted in a median DSC of 0.691 in pancreases versus 0.287 for multi-atlas. The results are highly promising given relatively limited training data and without specific training of the FCNN model and illustrate the potential of deep learning approaches to transcend imaging modalities. 1

  3. A Shape Optimization Study for Tool Design in Resistance Welding

    DEFF Research Database (Denmark)

    Bogomolny, Michael; Bendsøe, Martin P.; Hattel, Jesper Henri

    2009-01-01

    The purpose of this study is to apply shape optimization tools for design of resistance welding electrodes. The numerical simulation of the welding process has been performed by a simplified FEM model implemented in COMSOL. The design process is formulated as an optimization problem where...... the objective is to prolong the life-time of the electrodes. Welding parameters like current, time and electrode shape parameters are selected to be the design variables while constraints are chosen to ensure a high quality of the welding. Surrogate models based on a Kriging approximation has been used in order...

  4. Limitations of a convolution method for modeling geometric uncertainties in radiation therapy. I. The effect of shift invariance

    International Nuclear Information System (INIS)

    Craig, Tim; Battista, Jerry; Van Dyk, Jake

    2003-01-01

    Convolution methods have been used to model the effect of geometric uncertainties on dose delivery in radiation therapy. Convolution assumes shift invariance of the dose distribution. Internal inhomogeneities and surface curvature lead to violations of this assumption. The magnitude of the error resulting from violation of shift invariance is not well documented. This issue is addressed by comparing dose distributions calculated using the Convolution method with dose distributions obtained by Direct Simulation. A comparison of conventional Static dose distributions was also made with Direct Simulation. This analysis was performed for phantom geometries and several clinical tumor sites. A modification to the Convolution method to correct for some of the inherent errors is proposed and tested using example phantoms and patients. We refer to this modified method as the Corrected Convolution. The average maximum dose error in the calculated volume (averaged over different beam arrangements in the various phantom examples) was 21% with the Static dose calculation, 9% with Convolution, and reduced to 5% with the Corrected Convolution. The average maximum dose error in the calculated volume (averaged over four clinical examples) was 9% for the Static method, 13% for Convolution, and 3% for Corrected Convolution. While Convolution can provide a superior estimate of the dose delivered when geometric uncertainties are present, the violation of shift invariance can result in substantial errors near the surface of the patient. The proposed Corrected Convolution modification reduces errors near the surface to 3% or less

  5. A frequency bin-wise nonlinear masking algorithm in convolutive mixtures for speech segregation.

    Science.gov (United States)

    Chi, Tai-Shih; Huang, Ching-Wen; Chou, Wen-Sheng

    2012-05-01

    A frequency bin-wise nonlinear masking algorithm is proposed in the spectrogram domain for speech segregation in convolutive mixtures. The contributive weight from each speech source to a time-frequency unit of the mixture spectrogram is estimated by a nonlinear function based on location cues. For each sound source, a non-binary mask is formed from the estimated weights and is multiplied to the mixture spectrogram to extract the sound. Head-related transfer functions (HRTFs) are used to simulate convolutive sound mixtures perceived by listeners. Simulation results show our proposed method outperforms convolutive independent component analysis and degenerate unmixing and estimation technique methods in almost all test conditions.

  6. Development and application of deep convolutional neural network in target detection

    Science.gov (United States)

    Jiang, Xiaowei; Wang, Chunping; Fu, Qiang

    2018-04-01

    With the development of big data and algorithms, deep convolution neural networks with more hidden layers have more powerful feature learning and feature expression ability than traditional machine learning methods, making artificial intelligence surpass human level in many fields. This paper first reviews the development and application of deep convolutional neural networks in the field of object detection in recent years, then briefly summarizes and ponders some existing problems in the current research, and the future development of deep convolutional neural network is prospected.

  7. Design of a new electrode array for cochlear implants

    International Nuclear Information System (INIS)

    Kha, H.; Chen, B.

    2010-01-01

    Full text: This study aims to design a new electrode array which can be precisely located beneath the basilar membrane within the cochlear scala tympani. This placement of the electrode array is beneficial for increasing the effectiveness of the electrical stimulation of the audi tory nerves and maximising the growth factors delivered into the cochlea for regenerating the progressively lost auditory neurons, thereby significantly improving performance of the cochlear implant systems. Methods The design process involved two steps. First, the biocom patible nitinol-based shape memory alloy, of which mechanical deformation can be controlled using electrical cUTents/fields act vated by body temperature, was selected. Second, five different designs of the electrode array with embedded nitinol actuators were studied (Table I). The finite element method was employed to predict final positions of these electrode arrays. Results The electrode array with three 6 mm actuators at 2-8, 8-J4 and 14-20 mm from the tip (Fig. I) was found to be located most closely to the basilar membrane, compared with those in the other four cases. Conclusions A new nitinol cochlear implant electrode array with three embedded nitinol actuators has been designed. This electrode array is expected to be located beneath the basilar membrane for maximising the delivery of growth factors. Future research will involve the manufacturing of a prototype of this electrode array for use in insertion experiments and neurotrophin release tests.

  8. Electroencephalogram measurement using polymer-based dry microneedle electrode

    Science.gov (United States)

    Arai, Miyako; Nishinaka, Yuya; Miki, Norihisa

    2015-06-01

    In this paper, we report a successful electroencephalogram (EEG) measurement using polymer-based dry microneedle electrodes. The electrodes consist of needle-shaped substrates of SU-8, a silver film, and a nanoporous parylene protective film. Differently from conventional wet electrodes, microneedle electrodes do not require skin preparation and a conductive gel. SU-8 is superior as a structural material to poly(dimethylsiloxane) (PDMS; Dow Corning Toray Sylgard 184) in terms of hardness, which was used in our previous work, and facilitates the penetration of needles through the stratum corneum. SU-8 microneedles can be successfully inserted into the skin without breaking and could maintain a sufficiently low skin-electrode contact impedance for EEG measurement. The electrodes successfully measured EEG from the frontal pole, and the quality of acquired signals was verified to be as high as those obtained using commercially available wet electrodes without any skin preparation or a conductive gel. The electrodes are readily applicable to record brain activities for a long period with little stress involved in skin preparation to the users.

  9. EFFECTS OF ELECTRODE DEFORMATION OF RESISTANCE SPOT WELDING ON 304 AUSTENITIC STAINLESS STEEL WELD GEOMETRY

    Directory of Open Access Journals (Sweden)

    Nachimani Charde

    2012-12-01

    Full Text Available The resistance spot welding process is accomplished by forcing huge amounts of current flow from the upper electrode tip through the base metals to the lower electrode tip, or vice versa or in both directions. A weld joint is established between the metal sheets through fusion, resulting in a strong bond between the sheets without occupying additional space. The growth of the weld nugget (bond between sheets is therefore determined from the welding current density; sufficient time for current delivery; reasonable electrode pressing force; and the area provided for current delivery (electrode tip. The welding current and weld time control the root penetration, while the electrode pressing force and electrode tips successfully accomplish the connection during the welding process. Although the welding current and weld time cause the heat generation at the areas concerned (electrode tip area, the electrode tips’ diameter and electrode pressing forces also directly influence the welding process. In this research truncated-electrode deformation and mushrooming effects are observed, which result in the welded areas being inconsistent due to the expulsion. The copper to chromium ratio is varied from the tip to the end of the electrode whilst the welding process is repeated. The welding heat affects the electrode and the electrode itself influences the shape of the weld geometry.

  10. Method for making carbon super capacitor electrode materials

    Science.gov (United States)

    Firsich, David W.; Ingersoll, David; Delnick, Frank M.

    1998-01-01

    A method for making near-net-shape, monolithic carbon electrodes for energy storage devices. The method includes the controlled pyrolysis and activation of a pressed shape of methyl cellulose powder with pyrolysis being carried out in two stages; pre-oxidation, preferably in air at a temperature between 200.degree.-250.degree. C., followed by carbonization under an inert atmosphere. An activation step to adjust the surface area of the carbon shape to a value desirable for the application being considered, including heating the carbon shape in an oxidizing atmosphere to a temperature of at least 300.degree. C., follows carbonization.

  11. Subsidence feature discrimination using deep convolutional neral networks in synthetic aperture radar imagery

    CSIR Research Space (South Africa)

    Schwegmann, Colin P

    2017-07-01

    Full Text Available International Geoscience and Remote Sensing Symposium (IGARSS), 23-28 July 2017, Fort Worth, TX, USA SUBSIDENCE FEATURE DISCRIMINATION USING DEEP CONVOLUTIONAL NEURAL NETWORKS IN SYNTHETIC APERTURE RADAR IMAGERY Schwegmann, Colin P Kleynhans, Waldo...

  12. A Study of Recurrent and Convolutional Neural Networks in the Native Language Identification Task

    KAUST Repository

    Werfelmann, Robert

    2018-01-01

    around the world. The neural network models consisted of Long Short-Term Memory and Convolutional networks using the sentences of each document as the input. Additional statistical features were generated from the text to complement the predictions

  13. Convolution of large 3D images on GPU and its decomposition

    Science.gov (United States)

    Karas, Pavel; Svoboda, David

    2011-12-01

    In this article, we propose a method for computing convolution of large 3D images. The convolution is performed in a frequency domain using a convolution theorem. The algorithm is accelerated on a graphic card by means of the CUDA parallel computing model. Convolution is decomposed in a frequency domain using the decimation in frequency algorithm. We pay attention to keeping our approach efficient in terms of both time and memory consumption and also in terms of memory transfers between CPU and GPU which have a significant inuence on overall computational time. We also study the implementation on multiple GPUs and compare the results between the multi-GPU and multi-CPU implementations.

  14. Single Image Super-Resolution Based on Multi-Scale Competitive Convolutional Neural Network.

    Science.gov (United States)

    Du, Xiaofeng; Qu, Xiaobo; He, Yifan; Guo, Di

    2018-03-06

    Deep convolutional neural networks (CNNs) are successful in single-image super-resolution. Traditional CNNs are limited to exploit multi-scale contextual information for image reconstruction due to the fixed convolutional kernel in their building modules. To restore various scales of image details, we enhance the multi-scale inference capability of CNNs by introducing competition among multi-scale convolutional filters, and build up a shallow network under limited computational resources. The proposed network has the following two advantages: (1) the multi-scale convolutional kernel provides the multi-context for image super-resolution, and (2) the maximum competitive strategy adaptively chooses the optimal scale of information for image reconstruction. Our experimental results on image super-resolution show that the performance of the proposed network outperforms the state-of-the-art methods.

  15. Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture.

    Science.gov (United States)

    Meszlényi, Regina J; Buza, Krisztian; Vidnyánszky, Zoltán

    2017-01-01

    Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network.

  16. A Revised Piecewise Linear Recursive Convolution FDTD Method for Magnetized Plasmas

    International Nuclear Information System (INIS)

    Liu Song; Zhong Shuangying; Liu Shaobin

    2005-01-01

    The piecewise linear recursive convolution (PLRC) finite-different time-domain (FDTD) method improves accuracy over the original recursive convolution (RC) FDTD approach and current density convolution (JEC) but retains their advantages in speed and efficiency. This paper describes a revised piecewise linear recursive convolution PLRC-FDTD formulation for magnetized plasma which incorporates both anisotropy and frequency dispersion at the same time, enabling the transient analysis of magnetized plasma media. The technique is illustrated by numerical simulations of the reflection and transmission coefficients through a magnetized plasma layer. The results show that the revised PLRC-FDTD method has improved the accuracy over the original RC FDTD method and JEC FDTD method

  17. Deep Fully Convolutional Networks for the Detection of Informal Settlements in VHR Images

    NARCIS (Netherlands)

    Persello, Claudio; Stein, Alfred

    2017-01-01

    This letter investigates fully convolutional networks (FCNs) for the detection of informal settlements in very high resolution (VHR) satellite images. Informal settlements or slums are proliferating in developing countries and their detection and classification provides vital information for

  18. Fourier transform and mean quadratic variation of Bernoulli convolution on homogeneous Cantor set

    Energy Technology Data Exchange (ETDEWEB)

    Yu Zuguo E-mail: yuzg@hotmail.comz.yu

    2004-07-01

    For the Bernoulli convolution on homogeneous Cantor set, under some condition, it is proved that the mean quadratic variation and the average of Fourier transform of this measure are bounded above and below.

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

    Science.gov (United States)

    Mishchenko, Michael I.

    2014-01-01

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

  20. The neuro vector engine : flexibility to improve convolutional net efficiency for wearable vision

    NARCIS (Netherlands)

    Peemen, M.C.J.; Shi, R.; Lal, S.; Juurlink, B.H.H.; Mesman, B.; Corporaal, H.

    2016-01-01

    Deep Convolutional Networks (ConvNets) are currently superior in benchmark performance, but the associated demands on computation and data transfer prohibit straightforward mapping on energy constrained wearable platforms. The computational burden can be overcome by dedicated hardware accelerators,

  1. Composite carbon foam electrode

    Science.gov (United States)

    Mayer, Steven T.; Pekala, Richard W.; Kaschmitter, James L.

    1997-01-01

    Carbon aerogels used as a binder for granularized materials, including other forms of carbon and metal additives, are cast onto carbon or metal fiber substrates to form composite carbon thin film sheets. The thin film sheets are utilized in electrochemical energy storage applications, such as electrochemical double layer capacitors (aerocapacitors), lithium based battery insertion electrodes, fuel cell electrodes, and electrocapacitive deionization electrodes. The composite carbon foam may be formed by prior known processes, but with the solid particles being added during the liquid phase of the process, i.e. prior to gelation. The other forms of carbon may include carbon microspheres, carbon powder, carbon aerogel powder or particles, graphite carbons. Metal and/or carbon fibers may be added for increased conductivity. The choice of materials and fibers will depend on the electrolyte used and the relative trade off of system resistivty and power to system energy.

  2. Porous electrode preparation method

    Science.gov (United States)

    Arons, R.M.; Dusek, J.T.

    1983-10-18

    A porous sintered plaque is provided with a bimodal porosity that is especially well suited for use as an electrode within a molten carbonate fuel cell. The coarse porosity is sufficient for admitting gases into contact with the reaction surfaces while the fine porosity is wetted with and retains molten electrolyte on the reaction sites. The electrode structure is prepared by providing a very fine powder of such as nickel oxide and blending the powder with a suitable decomposable binder to form a solid mass. The mass is comminuted into agglomerate size particles substantially larger than the fine oxide particles and formed into a cohesive compact for subsequent sintering. Sintering is carried out at sufficient conditions to bind the agglomerates together into a porous structure having both coarse and fine porosity. Where lithiated nickel oxide cathodes are prepared, the sintering conditions can be moderate enough to retain substantial quantities of lithium within the electrode for adequate conductivity. 2 figs.

  3. Estimate of the angular dimensions of objects and reconstruction of their shapes from the parameters of the fourth-order radiation correlation function

    International Nuclear Information System (INIS)

    Buryi, E V; Kosygin, A A

    2004-01-01

    It is shown that, when the angular resolution of a receiving optical system is insufficient, the angular dimensions of a located object can be estimated and its shape can be reconstructed by estimating the parameters of the fourth-order correlation function (CF) of scattered coherent radiation. The reliability of the estimates of CF counts obtained by the method of a discrete spatial convolution of the intensity-field counts, the possibility of estimating the CF profile counts by the method of one-dimensional convolution of intensity counts, and the applicability of the method for reconstructing the object shape are confirmed experimentally. (laser applications and other topics in quantum electronics)

  4. Method for assessing the probability of accumulated doses from an intermittent source using the convolution technique

    International Nuclear Information System (INIS)

    Coleman, J.H.

    1980-10-01

    A technique is discussed for computing the probability distribution of the accumulated dose received by an arbitrary receptor resulting from several single releases from an intermittent source. The probability density of the accumulated dose is the convolution of the probability densities of doses from the intermittent releases. Emissions are not assumed to be constant over the brief release period. The fast fourier transform is used in the calculation of the convolution

  5. Deep Galaxy: Classification of Galaxies based on Deep Convolutional Neural Networks

    OpenAIRE

    Khalifa, Nour Eldeen M.; Taha, Mohamed Hamed N.; Hassanien, Aboul Ella; Selim, I. M.

    2017-01-01

    In this paper, a deep convolutional neural network architecture for galaxies classification is presented. The galaxy can be classified based on its features into main three categories Elliptical, Spiral, and Irregular. The proposed deep galaxies architecture consists of 8 layers, one main convolutional layer for features extraction with 96 filters, followed by two principles fully connected layers for classification. It is trained over 1356 images and achieved 97.272% in testing accuracy. A c...

  6. Is Kinesio Taping to Generate Skin Convolutions Effective for Increasing Local Blood Circulation?

    OpenAIRE

    Yang, Jae-Man; Lee, Jung-Hoon

    2018-01-01

    Background It is unclear whether traditional application of Kinesio taping, which produces wrinkles in the skin, is effective for improving blood circulation. This study investigated local skin temperature changes after the application of an elastic therapeutic tape using convolution and non-convolution taping methods (CTM/NCTM). Material/Methods Twenty-eight pain-free men underwent CTM and NCTM randomly applied to the right and left sides of the lower back. Using infrared thermography, skin ...

  7. Isointense infant brain MRI segmentation with a dilated convolutional neural network

    OpenAIRE

    Moeskops, Pim; Pluim, Josien P. W.

    2017-01-01

    Quantitative analysis of brain MRI at the age of 6 months is difficult because of the limited contrast between white matter and gray matter. In this study, we use a dilated triplanar convolutional neural network in combination with a non-dilated 3D convolutional neural network for the segmentation of white matter, gray matter and cerebrospinal fluid in infant brain MR images, as provided by the MICCAI grand challenge on 6-month infant brain MRI segmentation.

  8. The convolution integral for the forward-backward asymmetry in e+e- annihilation

    International Nuclear Information System (INIS)

    Bardin, D.; Bilenky, M.; Chizhov, A.; Sazonov, A.; Sedykh, Yu.; Riemann, T.; Sachwitz, M.

    1989-01-01

    The complete convolution integral for the forward-backward asymmetry in A FB in e + e - annihilation is obtained in order O(α) with soft photon exponentiation. The influence of these QED corrections on A FB in the vicinity of the Z peak is discussed. The results are used to comment on a recent ad hoc ansatz using convolution weights derived for the total cross section. (orig.)

  9. A Convolution Tree with Deconvolution Branches: Exploiting Geometric Relationships for Single Shot Keypoint Detection

    OpenAIRE

    Kumar, Amit; Chellappa, Rama

    2017-01-01

    Recently, Deep Convolution Networks (DCNNs) have been applied to the task of face alignment and have shown potential for learning improved feature representations. Although deeper layers can capture abstract concepts like pose, it is difficult to capture the geometric relationships among the keypoints in DCNNs. In this paper, we propose a novel convolution-deconvolution network for facial keypoint detection. Our model predicts the 2D locations of the keypoints and their individual visibility ...

  10. Learning text representation using recurrent convolutional neural network with highway layers

    OpenAIRE

    Wen, Ying; Zhang, Weinan; Luo, Rui; Wang, Jun

    2016-01-01

    Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks. In this paper, we describe a staged hybrid model combining Recurrent Convolutional Neural Networks (RCNN) with highway layers. The highway network module is incorporated in the middle takes the output of the bi-directional Recurrent Neural Network (Bi-RNN) module in the first stage and provides the Convolutional Neural Network (CNN) module in the last stage with the i...

  11. Segmentation of Drosophila Heart in Optical Coherence Microscopy Images Using Convolutional Neural Networks

    OpenAIRE

    Duan, Lian; Qin, Xi; He, Yuanhao; Sang, Xialin; Pan, Jinda; Xu, Tao; Men, Jing; Tanzi, Rudolph E.; Li, Airong; Ma, Yutao; Zhou, Chao

    2018-01-01

    Convolutional neural networks are powerful tools for image segmentation and classification. Here, we use this method to identify and mark the heart region of Drosophila at different developmental stages in the cross-sectional images acquired by a custom optical coherence microscopy (OCM) system. With our well-trained convolutional neural network model, the heart regions through multiple heartbeat cycles can be marked with an intersection over union (IOU) of ~86%. Various morphological and dyn...

  12. Sandwich-type electrode

    Science.gov (United States)

    Lu, Wen-Tong P.; Garcia, Earl R.

    1983-01-01

    Disclosed is an improvement on a method of making an electrode wherein a suspension in a liquid is prepared of a powdered catalyst containing a noble metal, carbon powder and a binder, and the suspension is poured over a carbon substrate dried, compressed and sintered to form a solid catalyst layer bonded to the carbon substrate. The improvement is placing a carbon paper on the catalyst layer prior to compressing. The improved electrode can be used as either a cathode or an anode in a sulfur dioxide depolarized electrolyzer in a process for producing hydrogen from water.

  13. Ion-selective electrodes

    Energy Technology Data Exchange (ETDEWEB)

    Mikhelson, Konstantin N. [St. Petersburg State Univ. (Russian Federation). Ion-Selective Electrode Laboratory

    2013-06-01

    Ion-selective electrodes (ISEs) have a wide range of applications in clinical, environmental, food and pharmaceutical analysis as well as further uses in chemistry and life sciences. Based on his profound experience as a researcher in ISEs and a course instructor, the author summarizes current knowledge for advanced teaching and training purposes with a particular focus on ionophore-based ISEs. Coverage includes the basics of measuring with ISEs, essential membrane potential theory and a comprehensive overview of the various classes of ion-selective electrodes. The principles of constructing ISEs are outlined, and the transfer of methods into routine analysis is considered.

  14. Single Electrode Heat Effects

    DEFF Research Database (Denmark)

    Jacobsen, Torben; Broers, G. H. J.

    1977-01-01

    The heat evolution at a single irreversibly working electrode is treated onthe basis of the Brønsted heat principle. The resulting equation is analogous to the expression for the total heat evolution in a galvanic cellwith the exception that –DeltaS is substituted by the Peltier entropy, Delta......SP, of theelectrode reaction. eta is the overvoltage at the electrode. This equation is appliedto a high temperature carbonate fuel cell. It is shown that the Peltier entropyterm by far exceeds the heat production due to the irreversible losses, and thatthe main part of heat evolved at the cathode is reabsorbed...

  15. Ion-selective electrodes

    CERN Document Server

    Mikhelson, Konstantin N

    2013-01-01

    Ion-selective electrodes (ISEs) have a wide range of applications in clinical, environmental, food and pharmaceutical analysis as well as further uses in chemistry and life sciences. Based on his profound experience as a researcher in ISEs and a course instructor, the author summarizes current knowledge for advanced teaching and training purposes with a particular focus on ionophore-based ISEs. Coverage includes the basics of measuring with ISEs, essential membrane potential theory and a comprehensive overview of the various classes of ion-selective electrodes. The principles of constructing I

  16. In situ Microscopic Observation of Sodium Deposition/Dissolution on Sodium Electrode

    OpenAIRE

    Yuhki Yui; Masahiko Hayashi; Jiro Nakamura

    2016-01-01

    Electrochemical sodium deposition/dissolution behaviors in propylene carbonate-based electrolyte solution were observed by means of in situ light microscopy. First, granular sodium was deposited at pits in a sodium electrode in the cathodic process. Then, the sodium particles grew linearly from the electrode surface, becoming needle-like in shape. In the subsequent anodic process, the sodium dissolved near the base of the needles on the sodium electrode and the so-called ?dead sodium? broke a...

  17. Virtual electrodes for high-density electrode arrays

    Science.gov (United States)

    Cela, Carlos J.; Lazzi, Gianluca

    2015-10-13

    The present embodiments are directed to implantable electrode arrays having virtual electrodes. The virtual electrodes may improve the resolution of the implantable electrode array without the burden of corresponding complexity of electronic circuitry and wiring. In a particular embodiment, a virtual electrode may include one or more passive elements to help steer current to a specific location between the active electrodes. For example, a passive element may be a metalized layer on a substrate that is adjacent to, but not directly connected to an active electrode. In certain embodiments, an active electrode may be directly coupled to a power source via a conductive connection. Beneficially, the passive elements may help to increase the overall resolution of the implantable array by providing additional stimulation points without requiring additional wiring or driver circuitry for the passive elements.

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

    International Nuclear Information System (INIS)

    Houshmand, Monireh; Hosseini-Khayat, Saied

    2011-01-01

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

  19. On the Reduction of Computational Complexity of Deep Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Partha Maji

    2018-04-01

    Full Text Available Deep convolutional neural networks (ConvNets, which are at the heart of many new emerging applications, achieve remarkable performance in audio and visual recognition tasks. Unfortunately, achieving accuracy often implies significant computational costs, limiting deployability. In modern ConvNets it is typical for the convolution layers to consume the vast majority of computational resources during inference. This has made the acceleration of these layers an important research area in academia and industry. In this paper, we examine the effects of co-optimizing the internal structures of the convolutional layers and underlying implementation of fundamental convolution operation. We demonstrate that a combination of these methods can have a big impact on the overall speedup of a ConvNet, achieving a ten-fold increase over baseline. We also introduce a new class of fast one-dimensional (1D convolutions for ConvNets using the Toom–Cook algorithm. We show that our proposed scheme is mathematically well-grounded, robust, and does not require any time-consuming retraining, while still achieving speedups solely from convolutional layers with no loss in baseline accuracy.

  20. A convolution method for predicting mean treatment dose including organ motion at imaging

    International Nuclear Information System (INIS)

    Booth, J.T.; Zavgorodni, S.F.; Royal Adelaide Hospital, SA

    2000-01-01

    Full text: The random treatment delivery errors (organ motion and set-up error) can be incorporated into the treatment planning software using a convolution method. Mean treatment dose is computed as the convolution of a static dose distribution with a variation kernel. Typically this variation kernel is Gaussian with variance equal to the sum of the organ motion and set-up error variances. We propose a novel variation kernel for the convolution technique that additionally considers the position of the mobile organ in the planning CT image. The systematic error of organ position in the planning CT image can be considered random for each patient over a population. Thus the variance of the variation kernel will equal the sum of treatment delivery variance and organ motion variance at planning for the population of treatments. The kernel is extended to deal with multiple pre-treatment CT scans to improve tumour localisation for planning. Mean treatment doses calculated with the convolution technique are compared to benchmark Monte Carlo (MC) computations. Calculations of mean treatment dose using the convolution technique agreed with MC results for all cases to better than ± 1 Gy in the planning treatment volume for a prescribed 60 Gy treatment. Convolution provides a quick method of incorporating random organ motion (captured in the planning CT image and during treatment delivery) and random set-up errors directly into the dose distribution. Copyright (2000) Australasian College of Physical Scientists and Engineers in Medicine

  1. Application of structured support vector machine backpropagation to a convolutional neural network for human pose estimation.

    Science.gov (United States)

    Witoonchart, Peerajak; Chongstitvatana, Prabhas

    2017-08-01

    In this study, for the first time, we show how to formulate a structured support vector machine (SSVM) as two layers in a convolutional neural network, where the top layer is a loss augmented inference layer and the bottom layer is the normal convolutional layer. We show that a deformable part model can be learned with the proposed structured SVM neural network by backpropagating the error of the deformable part model to the convolutional neural network. The forward propagation calculates the loss augmented inference and the backpropagation calculates the gradient from the loss augmented inference layer to the convolutional layer. Thus, we obtain a new type of convolutional neural network called an Structured SVM convolutional neural network, which we applied to the human pose estimation problem. This new neural network can be used as the final layers in deep learning. Our method jointly learns the structural model parameters and the appearance model parameters. We implemented our method as a new layer in the existing Caffe library. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. Ignitor electrode system design for the pulses electron irradiators device

    International Nuclear Information System (INIS)

    Lely Susita RM; Ihwanul Aziz

    2016-01-01

    The designed ignitor electrode system is a system used to initiate the plasma discharge. It consists of two pieces which are placed on both side of the plasma vessel. Each of the ignitor electrode system consists of a cathode, an anode and insulator between the cathode and the anode. The best cathode material for ignitor electrode system is Mg due to its lowest ion erosion rate (γi =11.7 μg/C) and its low cohesive energy (1.51 eV). The specifications of ignitor electrode system designed for the pulse electron irradiators is as follow: Mg cathode materials in the form of rod having a diameter of 6.35 mm and length of 76.75 mm. Anode material are made of non magnetic of SS 304 cylinder shaped with an outer diameter of 88.53 mm, an inner diameter of 81.53 mm and a thickness of 3.50 mm. Insulating material between the cathode and the anode is made of teflon cylinder shaped, outer diameter of 9.50 mm, an inner diameter of 6.35 mm and a length of 30 mm. Based on the ignitor electrode system design, the next step is construction and function test of the ignitor electrode system. (author)

  3. Flexible electrode belt for EIT using nanofiber web dry electrodes.

    Science.gov (United States)

    Oh, Tong In; Kim, Tae Eui; Yoon, Sun; Kim, Kap Jin; Woo, Eung Je; Sadleir, Rosalind J

    2012-10-01

    Efficient connection of multiple electrodes to the body for impedance measurement and voltage monitoring applications is of critical importance to measurement quality and practicality. Electrical impedance tomography (EIT) experiments have generally required a cumbersome procedure to attach the multiple electrodes needed in EIT. Once placed, these electrodes must then maintain good contact with the skin during measurements that may last several hours. There is usually also the need to manage the wires that run between the electrodes and the EIT system. These problems become more severe as the number of electrodes increases, and may limit the practicality and portability of this imaging method. There have been several trials describing human-electrode interfaces using configurations such as electrode belts, helmets or rings. In this paper, we describe an electrode belt we developed for long-term EIT monitoring of human lung ventilation. The belt included 16 embossed electrodes that were designed to make good contact with the skin. The electrodes were fabricated using an Ag-plated PVDF nanofiber web and metallic threads. A large contact area and padding were used behind each electrode to improve subject comfort and reduce contact impedances. The electrodes were incorporated, equally spaced, into an elasticated fabric belt. We tested the electrode belt in conjunction with the KHU Mark1 multi-frequency EIT system, and demonstrate time-difference images of phantoms and human subjects during normal breathing and running. We found that the Ag-plated PVDF nanofiber web electrodes were suitable for long-term measurement because of their flexibility and durability. Moreover, the contact impedance and stability of the Ag-plated PVDF nanofiber web electrodes were found to be comparable to similarly tested Ag/AgCl electrodes.

  4. Effects of electrode material and configuration on the characteristics of planar resistive switching devices

    KAUST Repository

    Peng, H.Y.; Pu, L.; Wu, J.C.; Cha, Dong Kyu; Hong, J.H.; Lin, W.N.; Li, Yangyang; Ding, Junfeng; David, A.; Li, K.; Wu, Tao

    2013-01-01

    We report that electrode engineering, particularly tailoring the metal work function, measurement configuration and geometric shape, has significant effects on the bipolar resistive switching (RS) in lateral memory devices based on self-doped SrTiO3

  5. Solution processing of back electrodes for organic solar cells with inverted architecture

    NARCIS (Netherlands)

    Galagan, Y.; Shanmugam, S.; Teunissen, J.P.; Eggenhuisen, T.M.; Biezemans, A.F.K.V.; Van Gijseghem, T.; Groen, W.A.; Andriessen, R.

    2014-01-01

    Solution processing of the electrodes is a big challenge towards scaling up and R2R processing of organic solar cells. Inkjet printing is a non-contact printing method, it can be realized by solution processing at ambient condition and provides freedom of shape in the electrode pattern. The inkjet

  6. Pencil-shaped radiation detection ionization chamber

    International Nuclear Information System (INIS)

    Suzuki, A.

    1979-01-01

    A radiation detection ionization chamber is described. It consists of an elongated cylindrical pencil-shaped tubing forming an outer wall of the chamber and a center electrode disposed along the major axis of the tubing. The length of the chamber is substantially greater than the diameter. A cable connecting portion at one end of the chamber is provided for connecting the chamber to a triaxial cable. An end support portion is connected at the other end of the chamber for supporting and tensioning the center electrode. 17 claims

  7. Three-dimensional fingerprint recognition by using convolution neural network

    Science.gov (United States)

    Tian, Qianyu; Gao, Nan; Zhang, Zonghua

    2018-01-01

    With the development of science and technology and the improvement of social information, fingerprint recognition technology has become a hot research direction and been widely applied in many actual fields because of its feasibility and reliability. The traditional two-dimensional (2D) fingerprint recognition method relies on matching feature points. This method is not only time-consuming, but also lost three-dimensional (3D) information of fingerprint, with the fingerprint rotation, scaling, damage and other issues, a serious decline in robustness. To solve these problems, 3D fingerprint has been used to recognize human being. Because it is a new research field, there are still lots of challenging problems in 3D fingerprint recognition. This paper presents a new 3D fingerprint recognition method by using a convolution neural network (CNN). By combining 2D fingerprint and fingerprint depth map into CNN, and then through another CNN feature fusion, the characteristics of the fusion complete 3D fingerprint recognition after classification. This method not only can preserve 3D information of fingerprints, but also solves the problem of CNN input. Moreover, the recognition process is simpler than traditional feature point matching algorithm. 3D fingerprint recognition rate by using CNN is compared with other fingerprint recognition algorithms. The experimental results show that the proposed 3D fingerprint recognition method has good recognition rate and robustness.

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

    Science.gov (United States)

    Grice, Jon R.; Meyer, David A.

    2015-07-01

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

  9. On the Relationship between Visual Attributes and Convolutional Networks

    KAUST Repository

    Castillo, Victor; Ghanem, Bernard; Niebles, Juan Carlos

    2015-01-01

    One of the cornerstone principles of deep models is their abstraction capacity, i.e. their ability to learn abstract concepts from ‘simpler’ ones. Through extensive experiments, we characterize the nature of the relationship between abstract concepts (specifically objects in images) learned by popular and high performing convolutional networks (conv-nets) and established mid-level representations used in computer vision (specifically semantic visual attributes). We focus on attributes due to their impact on several applications, such as object description, retrieval and mining, and active (and zero-shot) learning. Among the findings we uncover, we show empirical evidence of the existence of Attribute Centric Nodes (ACNs) within a conv-net, which is trained to recognize objects (not attributes) in images. These special conv-net nodes (1) collectively encode information pertinent to visual attribute representation and discrimination, (2) are unevenly and sparsely distribution across all layers of the conv-net, and (3) play an important role in conv-net based object recognition.

  10. Generalization error analysis: deep convolutional neural network in mammography

    Science.gov (United States)

    Richter, Caleb D.; Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir; Cha, Kenny

    2018-02-01

    We conducted a study to gain understanding of the generalizability of deep convolutional neural networks (DCNNs) given their inherent capability to memorize data. We examined empirically a specific DCNN trained for classification of masses on mammograms. Using a data set of 2,454 lesions from 2,242 mammographic views, a DCNN was trained to classify masses into malignant and benign classes using transfer learning from ImageNet LSVRC-2010. We performed experiments with varying amounts of label corruption and types of pixel randomization to analyze the generalization error for the DCNN. Performance was evaluated using the area under the receiver operating characteristic curve (AUC) with an N-fold cross validation. Comparisons were made between the convergence times, the inference AUCs for both the training set and the test set of the original image patches without corruption, and the root-mean-squared difference (RMSD) in the layer weights of the DCNN trained with different amounts and methods of corruption. Our experiments observed trends which revealed that the DCNN overfitted by memorizing corrupted data. More importantly, this study improved our understanding of DCNN weight updates when learning new patterns or new labels. Although we used a specific classification task with the ImageNet as example, similar methods may be useful for analysis of the DCNN learning processes, especially those that employ transfer learning for medical image analysis where sample size is limited and overfitting risk is high.

  11. On the Relationship between Visual Attributes and Convolutional Networks

    KAUST Repository

    Castillo, Victor

    2015-06-02

    One of the cornerstone principles of deep models is their abstraction capacity, i.e. their ability to learn abstract concepts from ‘simpler’ ones. Through extensive experiments, we characterize the nature of the relationship between abstract concepts (specifically objects in images) learned by popular and high performing convolutional networks (conv-nets) and established mid-level representations used in computer vision (specifically semantic visual attributes). We focus on attributes due to their impact on several applications, such as object description, retrieval and mining, and active (and zero-shot) learning. Among the findings we uncover, we show empirical evidence of the existence of Attribute Centric Nodes (ACNs) within a conv-net, which is trained to recognize objects (not attributes) in images. These special conv-net nodes (1) collectively encode information pertinent to visual attribute representation and discrimination, (2) are unevenly and sparsely distribution across all layers of the conv-net, and (3) play an important role in conv-net based object recognition.

  12. A discrete convolution kernel for No-DC MRI

    International Nuclear Information System (INIS)

    Zeng, Gengsheng L; Li, Ya

    2015-01-01

    An analytical inversion formula for the exponential Radon transform with an imaginary attenuation coefficient was developed in 2007 (2007 Inverse Problems 23 1963–71). The inversion formula in that paper suggested that it is possible to obtain an exact MRI (magnetic resonance imaging) image without acquiring low-frequency data. However, this un-measured low-frequency region (ULFR) in the k-space (which is the two-dimensional Fourier transform space in MRI terminology) must be very small. This current paper derives a FBP (filtered backprojection) algorithm based on You’s formula by suggesting a practical discrete convolution kernel. A point spread function is derived for this FBP algorithm. It is demonstrated that the derived FBP algorithm can have a larger ULFR than that in the 2007 paper. The significance of this paper is that we present a closed-form reconstruction algorithm for a special case of under-sampled MRI data. Usually, under-sampled MRI data requires iterative (instead of analytical) algorithms with L 1 -norm or total variation norm to reconstruct the image. (paper)

  13. A deep convolutional neural network for recognizing foods

    Science.gov (United States)

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

    2015-12-01

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

  14. Forged Signature Distinction Using Convolutional Neural Network for Feature Extraction

    Directory of Open Access Journals (Sweden)

    Seungsoo Nam

    2018-01-01

    Full Text Available This paper proposes a dynamic verification scheme for finger-drawn signatures in smartphones. As a dynamic feature, the movement of a smartphone is recorded with accelerometer sensors in the smartphone, in addition to the moving coordinates of the signature. To extract high-level longitudinal and topological features, the proposed scheme uses a convolution neural network (CNN for feature extraction, and not as a conventional classifier. We assume that a CNN trained with forged signatures can extract effective features (called S-vector, which are common in forging activities such as hesitation and delay before drawing the complicated part. The proposed scheme also exploits an autoencoder (AE as a classifier, and the S-vector is used as the input vector to the AE. An AE has high accuracy for the one-class distinction problem such as signature verification, and is also greatly dependent on the accuracy of input data. S-vector is valuable as the input of AE, and, consequently, could lead to improved verification accuracy especially for distinguishing forged signatures. Compared to the previous work, i.e., the MLP-based finger-drawn signature verification scheme, the proposed scheme decreases the equal error rate by 13.7%, specifically, from 18.1% to 4.4%, for discriminating forged signatures.

  15. An improved multi-domain convolution tracking algorithm

    Science.gov (United States)

    Sun, Xin; Wang, Haiying; Zeng, Yingsen

    2018-04-01

    Along with the wide application of the Deep Learning in the field of Computer vision, Deep learning has become a mainstream direction in the field of object tracking. The tracking algorithm in this paper is based on the improved multidomain convolution neural network, and the VOT video set is pre-trained on the network by multi-domain training strategy. In the process of online tracking, the network evaluates candidate targets sampled from vicinity of the prediction target in the previous with Gaussian distribution, and the candidate target with the highest score is recognized as the prediction target of this frame. The Bounding Box Regression model is introduced to make the prediction target closer to the ground-truths target box of the test set. Grouping-update strategy is involved to extract and select useful update samples in each frame, which can effectively prevent over fitting. And adapt to changes in both target and environment. To improve the speed of the algorithm while maintaining the performance, the number of candidate target succeed in adjusting dynamically with the help of Self-adaption parameter Strategy. Finally, the algorithm is tested by OTB set, compared with other high-performance tracking algorithms, and the plot of success rate and the accuracy are drawn. which illustrates outstanding performance of the tracking algorithm in this paper.

  16. Convolutional networks for fast, energy-efficient neuromorphic computing.

    Science.gov (United States)

    Esser, Steven K; Merolla, Paul A; Arthur, John V; Cassidy, Andrew S; Appuswamy, Rathinakumar; Andreopoulos, Alexander; Berg, David J; McKinstry, Jeffrey L; Melano, Timothy; Barch, Davis R; di Nolfo, Carmelo; Datta, Pallab; Amir, Arnon; Taba, Brian; Flickner, Myron D; Modha, Dharmendra S

    2016-10-11

    Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware's underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.

  17. Convolution neural-network-based detection of lung structures

    Science.gov (United States)

    Hasegawa, Akira; Lo, Shih-Chung B.; Freedman, Matthew T.; Mun, Seong K.

    1994-05-01

    Chest radiography is one of the most primary and widely used techniques in diagnostic imaging. Nowadays with the advent of digital radiology, the digital medical image processing techniques for digital chest radiographs have attracted considerable attention, and several studies on the computer-aided diagnosis (CADx) as well as on the conventional image processing techniques for chest radiographs have been reported. In the automatic diagnostic process for chest radiographs, it is important to outline the areas of the lungs, the heart, and the diaphragm. This is because the original chest radiograph is composed of important anatomic structures and, without knowing exact positions of the organs, the automatic diagnosis may result in unexpected detections. The automatic extraction of an anatomical structure from digital chest radiographs can be a useful tool for (1) the evaluation of heart size, (2) automatic detection of interstitial lung diseases, (3) automatic detection of lung nodules, and (4) data compression, etc. Based on the clearly defined boundaries of heart area, rib spaces, rib positions, and rib cage extracted, one should be able to use this information to facilitate the tasks of the CADx on chest radiographs. In this paper, we present an automatic scheme for the detection of lung field from chest radiographs by using a shift-invariant convolution neural network. A novel algorithm for smoothing boundaries of lungs is also presented.

  18. Real-time hybrid simulation using the convolution integral method

    International Nuclear Information System (INIS)

    Kim, Sung Jig; Christenson, Richard E; Wojtkiewicz, Steven F; Johnson, Erik A

    2011-01-01

    This paper proposes a real-time hybrid simulation method that will allow complex systems to be tested within the hybrid test framework by employing the convolution integral (CI) method. The proposed CI method is potentially transformative for real-time hybrid simulation. The CI method can allow real-time hybrid simulation to be conducted regardless of the size and complexity of the numerical model and for numerical stability to be ensured in the presence of high frequency responses in the simulation. This paper presents the general theory behind the proposed CI method and provides experimental verification of the proposed method by comparing the CI method to the current integration time-stepping (ITS) method. Real-time hybrid simulation is conducted in the Advanced Hazard Mitigation Laboratory at the University of Connecticut. A seismically excited two-story shear frame building with a magneto-rheological (MR) fluid damper is selected as the test structure to experimentally validate the proposed method. The building structure is numerically modeled and simulated, while the MR damper is physically tested. Real-time hybrid simulation using the proposed CI method is shown to provide accurate results

  19. A Hierarchical Convolutional Neural Network for vesicle fusion event classification.

    Science.gov (United States)

    Li, Haohan; Mao, Yunxiang; Yin, Zhaozheng; Xu, Yingke

    2017-09-01

    Quantitative analysis of vesicle exocytosis and classification of different modes of vesicle fusion from the fluorescence microscopy are of primary importance for biomedical researches. In this paper, we propose a novel Hierarchical Convolutional Neural Network (HCNN) method to automatically identify vesicle fusion events in time-lapse Total Internal Reflection Fluorescence Microscopy (TIRFM) image sequences. Firstly, a detection and tracking method is developed to extract image patch sequences containing potential fusion events. Then, a Gaussian Mixture Model (GMM) is applied on each image patch of the patch sequence with outliers rejected for robust Gaussian fitting. By utilizing the high-level time-series intensity change features introduced by GMM and the visual appearance features embedded in some key moments of the fusion process, the proposed HCNN architecture is able to classify each candidate patch sequence into three classes: full fusion event, partial fusion event and non-fusion event. Finally, we validate the performance of our method on 9 challenging datasets that have been annotated by cell biologists, and our method achieves better performances when comparing with three previous methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Village Building Identification Based on Ensemble Convolutional Neural Networks

    Science.gov (United States)

    Guo, Zhiling; Chen, Qi; Xu, Yongwei; Shibasaki, Ryosuke; Shao, Xiaowei

    2017-01-01

    In this study, we present the Ensemble Convolutional Neural Network (ECNN), an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS) images. First, to optimize and mine the capability of CNN for village mapping and to ensure compatibility with our classification targets, a few state-of-the-art models were carefully optimized and enhanced based on a series of rigorous analyses and evaluations. Second, rather than directly implementing building identification by using these models, we exploited most of their advantages by ensembling their feature extractor parts into a stronger model called ECNN based on the multiscale feature learning method. Finally, the generated ECNN was applied to a pixel-level classification frame to implement object identification. The proposed method can serve as a viable tool for village building identification with high accuracy and efficiency. The experimental results obtained from the test area in Savannakhet province, Laos, prove that the proposed ECNN model significantly outperforms existing methods, improving overall accuracy from 96.64% to 99.26%, and kappa from 0.57 to 0.86. PMID:29084154

  1. Classification of crystal structure using a convolutional neural network.

    Science.gov (United States)

    Park, Woon Bae; Chung, Jiyong; Jung, Jaeyoung; Sohn, Keemin; Singh, Satendra Pal; Pyo, Myoungho; Shin, Namsoo; Sohn, Kee-Sun

    2017-07-01

    A deep machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal system, extinction group and space group. About 150 000 powder XRD patterns were collected and used as input for the CNN with no handcrafted engineering involved, and thereby an appropriate CNN architecture was obtained that allowed determination of the crystal system, extinction group and space group. In sharp contrast with the traditional use of powder XRD pattern analysis, the CNN never treats powder XRD patterns as a deconvoluted and discrete peak position or as intensity data, but instead the XRD patterns are regarded as nothing but a pattern similar to a picture. The CNN interprets features that humans cannot recognize in a powder XRD pattern. As a result, accuracy levels of 81.14, 83.83 and 94.99% were achieved for the space-group, extinction-group and crystal-system classifications, respectively. The well trained CNN was then used for symmetry identification of unknown novel inorganic compounds.

  2. Noisy Ocular Recognition Based on Three Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Min Beom Lee

    2017-12-01

    Full Text Available In recent years, the iris recognition system has been gaining increasing acceptance for applications such as access control and smartphone security. When the images of the iris are obtained under unconstrained conditions, an issue of undermined quality is caused by optical and motion blur, off-angle view (the user’s eyes looking somewhere else, not into the front of the camera, specular reflection (SR and other factors. Such noisy iris images increase intra-individual variations and, as a result, reduce the accuracy of iris recognition. A typical iris recognition system requires a near-infrared (NIR illuminator along with an NIR camera, which are larger and more expensive than fingerprint recognition equipment. Hence, many studies have proposed methods of using iris images captured by a visible light camera without the need for an additional illuminator. In this research, we propose a new recognition method for noisy iris and ocular images by using one iris and two periocular regions, based on three convolutional neural networks (CNNs. Experiments were conducted by using the noisy iris challenge evaluation-part II (NICE.II training dataset (selected from the university of Beira iris (UBIRIS.v2 database, mobile iris challenge evaluation (MICHE database, and institute of automation of Chinese academy of sciences (CASIA-Iris-Distance database. As a result, the method proposed by this study outperformed previous methods.

  3. Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Martin Längkvist

    2016-04-01

    Full Text Available The availability of high-resolution remote sensing (HRRS data has opened up the possibility for new interesting applications, such as per-pixel classification of individual objects in greater detail. This paper shows how a convolutional neural network (CNN can be applied to multispectral orthoimagery and a digital surface model (DSM of a small city for a full, fast and accurate per-pixel classification. The predicted low-level pixel classes are then used to improve the high-level segmentation. Various design choices of the CNN architecture are evaluated and analyzed. The investigated land area is fully manually labeled into five categories (vegetation, ground, roads, buildings and water, and the classification accuracy is compared to other per-pixel classification works on other land areas that have a similar choice of categories. The results of the full classification and segmentation on selected segments of the map show that CNNs are a viable tool for solving both the segmentation and object recognition task for remote sensing data.

  4. Bilinear Convolutional Neural Networks for Fine-grained Visual Recognition.

    Science.gov (United States)

    Lin, Tsung-Yu; RoyChowdhury, Aruni; Maji, Subhransu

    2017-07-04

    We present a simple and effective architecture for fine-grained recognition called Bilinear Convolutional Neural Networks (B-CNNs). These networks represent an image as a pooled outer product of features derived from two CNNs and capture localized feature interactions in a translationally invariant manner. B-CNNs are related to orderless texture representations built on deep features but can be trained in an end-to-end manner. Our most accurate model obtains 84.1%, 79.4%, 84.5% and 91.3% per-image accuracy on the Caltech-UCSD birds [66], NABirds [63], FGVC aircraft [42], and Stanford cars [33] dataset respectively and runs at 30 frames-per-second on a NVIDIA Titan X GPU. We then present a systematic analysis of these networks and show that (1) the bilinear features are highly redundant and can be reduced by an order of magnitude in size without significant loss in accuracy, (2) are also effective for other image classification tasks such as texture and scene recognition, and (3) can be trained from scratch on the ImageNet dataset offering consistent improvements over the baseline architecture. Finally, we present visualizations of these models on various datasets using top activations of neural units and gradient-based inversion techniques. The source code for the complete system is available at http://vis-www.cs.umass.edu/bcnn.

  5. Pneumothorax detection in chest radiographs using convolutional neural networks

    Science.gov (United States)

    Blumenfeld, Aviel; Konen, Eli; Greenspan, Hayit

    2018-02-01

    This study presents a computer assisted diagnosis system for the detection of pneumothorax (PTX) in chest radiographs based on a convolutional neural network (CNN) for pixel classification. Using a pixel classification approach allows utilization of the texture information in the local environment of each pixel while training a CNN model on millions of training patches extracted from a relatively small dataset. The proposed system uses a pre-processing step of lung field segmentation to overcome the large variability in the input images coming from a variety of imaging sources and protocols. Using a CNN classification, suspected pixel candidates are extracted within each lung segment. A postprocessing step follows to remove non-physiological suspected regions and noisy connected components. The overall percentage of suspected PTX area was used as a robust global decision for the presence of PTX in each lung. The system was trained on a set of 117 chest x-ray images with ground truth segmentations of the PTX regions. The system was tested on a set of 86 images and reached diagnosis accuracy of AUC=0.95. Overall preliminary results are promising and indicate the growing ability of CAD based systems to detect findings in medical imaging on a clinical level accuracy.

  6. Classification of breast cancer histology images using Convolutional Neural Networks.

    Directory of Open Access Journals (Sweden)

    Teresa Araújo

    Full Text Available Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. Conventional classification approaches rely on feature extraction methods designed for a specific problem based on field-knowledge. To overcome the many difficulties of the feature-based approaches, deep learning methods are becoming important alternatives. A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs is proposed. Images are classified in four classes, normal tissue, benign lesion, in situ carcinoma and invasive carcinoma, and in two classes, carcinoma and non-carcinoma. The architecture of the network is designed to retrieve information at different scales, including both nuclei and overall tissue organization. This design allows the extension of the proposed system to whole-slide histology images. The features extracted by the CNN are also used for training a Support Vector Machine classifier. Accuracies of 77.8% for four class and 83.3% for carcinoma/non-carcinoma are achieved. The sensitivity of our method for cancer cases is 95.6%.

  7. Image reconstruction in computerized tomography using the convolution method

    International Nuclear Information System (INIS)

    Oliveira Rebelo, A.M. de.

    1984-03-01

    In the present work an algoritin was derived, using the analytical convolution method (filtered back-projection) for two-dimensional or three-dimensional image reconstruction in computerized tomography applied to non-destructive testing and to the medical use. This mathematical model is based on the analytical Fourier transform method for image reconstruction. This model consists of a discontinuous system formed by an NxN array of cells (pixels). The attenuation in the object under study of a colimated gamma ray beam has been determined for various positions and incidence angles (projections) in terms of the interaction of the beam with the intercepted pixels. The contribution of each pixel to beam attenuation was determined using the weight function W ij which was used for simulated tests. Simulated tests using standard objects with attenuation coefficients in the range of 0,2 to 0,7 cm -1 were carried out using cell arrays of up to 25x25. One application was carried out in the medical area simulating image reconstruction of an arm phantom with attenuation coefficients in the range of 0,2 to 0,5 cm -1 using cell arrays of 41x41. The simulated results show that, in objects with a great number of interfaces and great variations of attenuation coefficients at these interfaces, a good reconstruction is obtained with the number of projections equal to the reconstruction matrix dimension. A good reconstruction is otherwise obtained with fewer projections. (author) [pt

  8. Classification of Two Comic Books based on Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Miki UENO

    2017-03-01

    Full Text Available Unphotographic images are the powerful representations described various situations. Thus, understanding intellectual products such as comics and picture books is one of the important topics in the field of artificial intelligence. Hence, stepwise analysis of a comic story, i.e., features of a part of the image, information features, features relating to continuous scene etc., was pursued. Especially, the length and each scene of four-scene comics are limited so as to ensure a clear interpretation of the contents.In this study, as the first step in this direction, the problem to classify two four-scene comics by the same artists were focused as the example. Several classifiers were constructed by utilizing a Convolutional Neural Network(CNN, and the results of classification by a human annotator and by a computational method were compared.From these experiments, we have clearly shown that CNN is efficient way to classify unphotographic gray scaled images and found that characteristic features of images to classify incorrectly.

  9. Bone age detection via carpogram analysis using convolutional neural networks

    Science.gov (United States)

    Torres, Felipe; Bravo, María. Alejandra; Salinas, Emmanuel; Triana, Gustavo; Arbeláez, Pablo

    2017-11-01

    Bone age assessment is a critical factor for determining delayed development in children, which can be a sign of pathologies such as endocrine diseases, growth abnormalities, chromosomal, neurological and congenital disorders among others. In this paper we present BoneNet, a methodology to assess automatically the skeletal maturity state in pediatric patients based on Convolutional Neural Networks. We train and evaluate our algorithm on a database of X-Ray images provided by the hospital Fundacion Santa Fe de Bogot ´ a with around 1500 images of patients between the ages 1 to 18. ´ We compare two different architectures to classify the given data in order to explore the generality of our method. To accomplish this, we define multiple binary age assessment problems, dividing the data by bone age and differentiating the patients by their gender. Thus, exploring several parameters, we develop BoneNet. Our approach is holistic, efficient, and modular, since it is possible for the specialists to use all the networks combined to determine how is the skeletal maturity of a patient. BoneNet achieves over 90% accuracy for most of the critical age thresholds, when differentiating the images between over or under a given age.

  10. Automated embolic signal detection using Deep Convolutional Neural Network.

    Science.gov (United States)

    Sombune, Praotasna; Phienphanich, Phongphan; Phuechpanpaisal, Sutanya; Muengtaweepongsa, Sombat; Ruamthanthong, Anuchit; Tantibundhit, Charturong

    2017-07-01

    This work investigated the potential of Deep Neural Network in detection of cerebral embolic signal (ES) from transcranial Doppler ultrasound (TCD). The resulting system is aimed to couple with TCD devices in diagnosing a risk of stroke in real-time with high accuracy. The Adaptive Gain Control (AGC) approach developed in our previous study is employed to capture suspected ESs in real-time. By using spectrograms of the same TCD signal dataset as that of our previous work as inputs and the same experimental setup, Deep Convolutional Neural Network (CNN), which can learn features while training, was investigated for its ability to bypass the traditional handcrafted feature extraction and selection process. Extracted feature vectors from the suspected ESs are later determined whether they are of an ES, artifact (AF) or normal (NR) interval. The effectiveness of the developed system was evaluated over 19 subjects going under procedures generating emboli. The CNN-based system could achieve in average of 83.0% sensitivity, 80.1% specificity, and 81.4% accuracy, with considerably much less time consumption in development. The certainly growing set of training samples and computational resources will contribute to high performance. Besides having potential use in various clinical ES monitoring settings, continuation of this promising study will benefit developments of wearable applications by leveraging learnable features to serve demographic differentials.

  11. Deep convolutional neural network for mammographic density segmentation

    Science.gov (United States)

    Wei, Jun; Li, Songfeng; Chan, Heang-Ping; Helvie, Mark A.; Roubidoux, Marilyn A.; Lu, Yao; Zhou, Chuan; Hadjiiski, Lubomir; Samala, Ravi K.

    2018-02-01

    Breast density is one of the most significant factors for cancer risk. In this study, we proposed a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammography (DM). The deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD). PD was calculated as the ratio of the dense area to the breast area based on the probability of each pixel belonging to dense region or fatty region at a decision threshold of 0.5. The DCNN estimate was compared to a feature-based statistical learning approach, in which gray level, texture and morphological features were extracted from each ROI and the least absolute shrinkage and selection operator (LASSO) was used to select and combine the useful features to generate the PMD. The reference PD of each image was provided by two experienced MQSA radiologists. With IRB approval, we retrospectively collected 347 DMs from patient files at our institution. The 10-fold cross-validation results showed a strong correlation r=0.96 between the DCNN estimation and interactive segmentation by radiologists while that of the feature-based statistical learning approach vs radiologists' segmentation had a correlation r=0.78. The difference between the segmentation by DCNN and by radiologists was significantly smaller than that between the feature-based learning approach and radiologists (p approach has the potential to replace radiologists' interactive thresholding in PD estimation on DMs.

  12. Vision-Based Fall Detection with Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Adrián Núñez-Marcos

    2017-01-01

    Full Text Available One of the biggest challenges in modern societies is the improvement of healthy aging and the support to older persons in their daily activities. In particular, given its social and economic impact, the automatic detection of falls has attracted considerable attention in the computer vision and pattern recognition communities. Although the approaches based on wearable sensors have provided high detection rates, some of the potential users are reluctant to wear them and thus their use is not yet normalized. As a consequence, alternative approaches such as vision-based methods have emerged. We firmly believe that the irruption of the Smart Environments and the Internet of Things paradigms, together with the increasing number of cameras in our daily environment, forms an optimal context for vision-based systems. Consequently, here we propose a vision-based solution using Convolutional Neural Networks to decide if a sequence of frames contains a person falling. To model the video motion and make the system scenario independent, we use optical flow images as input to the networks followed by a novel three-step training phase. Furthermore, our method is evaluated in three public datasets achieving the state-of-the-art results in all three of them.

  13. Reconstruction of Micropattern Detector Signals using Convolutional Neural Networks

    Science.gov (United States)

    Flekova, L.; Schott, M.

    2017-10-01

    Micropattern gaseous detector (MPGD) technologies, such as GEMs or MicroMegas, are particularly suitable for precision tracking and triggering in high rate environments. Given their relatively low production costs, MPGDs are an exemplary candidate for the next generation of particle detectors. Having acknowledged these advantages, both the ATLAS and CMS collaborations at the LHC are exploiting these new technologies for their detector upgrade programs in the coming years. When MPGDs are utilized for triggering purposes, the measured signals need to be precisely reconstructed within less than 200 ns, which can be achieved by the usage of FPGAs. In this work, we present a novel approach to identify reconstructed signals, their timing and the corresponding spatial position on the detector. In particular, we study the effect of noise and dead readout strips on the reconstruction performance. Our approach leverages the potential of convolutional neural network (CNNs), which have recently manifested an outstanding performance in a range of modeling tasks. The proposed neural network architecture of our CNN is designed simply enough, so that it can be modeled directly by an FPGA and thus provide precise information on reconstructed signals already in trigger level.

  14. Neonatal Seizure Detection Using Deep Convolutional Neural Networks.

    Science.gov (United States)

    Ansari, Amir H; Cherian, Perumpillichira J; Caicedo, Alexander; Naulaers, Gunnar; De Vos, Maarten; Van Huffel, Sabine

    2018-04-02

    Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.

  15. Cell dynamic morphology classification using deep convolutional neural networks.

    Science.gov (United States)

    Li, Heng; Pang, Fengqian; Shi, Yonggang; Liu, Zhiwen

    2018-05-15

    Cell morphology is often used as a proxy measurement of cell status to understand cell physiology. Hence, interpretation of cell dynamic morphology is a meaningful task in biomedical research. Inspired by the recent success of deep learning, we here explore the application of convolutional neural networks (CNNs) to cell dynamic morphology classification. An innovative strategy for the implementation of CNNs is introduced in this study. Mouse lymphocytes were collected to observe the dynamic morphology, and two datasets were thus set up to investigate the performances of CNNs. Considering the installation of deep learning, the classification problem was simplified from video data to image data, and was then solved by CNNs in a self-taught manner with the generated image data. CNNs were separately performed in three installation scenarios and compared with existing methods. Experimental results demonstrated the potential of CNNs in cell dynamic morphology classification, and validated the effectiveness of the proposed strategy. CNNs were successfully applied to the classification problem, and outperformed the existing methods in the classification accuracy. For the installation of CNNs, transfer learning was proved to be a promising scheme. © 2018 International Society for Advancement of Cytometry. © 2018 International Society for Advancement of Cytometry.

  16. Chinese Sentence Classification Based on Convolutional Neural Network

    Science.gov (United States)

    Gu, Chengwei; Wu, Ming; Zhang, Chuang

    2017-10-01

    Sentence classification is one of the significant issues in Natural Language Processing (NLP). Feature extraction is often regarded as the key point for natural language processing. Traditional ways based on machine learning can not take high level features into consideration, such as Naive Bayesian Model. The neural network for sentence classification can make use of contextual information to achieve greater results in sentence classification tasks. In this paper, we focus on classifying Chinese sentences. And the most important is that we post a novel architecture of Convolutional Neural Network (CNN) to apply on Chinese sentence classification. In particular, most of the previous methods often use softmax classifier for prediction, we embed a linear support vector machine to substitute softmax in the deep neural network model, minimizing a margin-based loss to get a better result. And we use tanh as an activation function, instead of ReLU. The CNN model improve the result of Chinese sentence classification tasks. Experimental results on the Chinese news title database validate the effectiveness of our model.

  17. Convolutional neural network with transfer learning for rice type classification

    Science.gov (United States)

    Patel, Vaibhav Amit; Joshi, Manjunath V.

    2018-04-01

    Presently, rice type is identified manually by humans, which is time consuming and error prone. Therefore, there is a need to do this by machine which makes it faster with greater accuracy. This paper proposes a deep learning based method for classification of rice types. We propose two methods to classify the rice types. In the first method, we train a deep convolutional neural network (CNN) using the given segmented rice images. In the second method, we train a combination of a pretrained VGG16 network and the proposed method, while using transfer learning in which the weights of a pretrained network are used to achieve better accuracy. Our approach can also be used for classification of rice grain as broken or fine. We train a 5-class model for classifying rice types using 4000 training images and another 2- class model for the classification of broken and normal rice using 1600 training images. We observe that despite having distinct rice images, our architecture, pretrained on ImageNet data boosts classification accuracy significantly.

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

    International Nuclear Information System (INIS)

    Barajas, L.; Powers, K.

    1989-01-01

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

  19. Classification of breast cancer cytological specimen using convolutional neural network

    Science.gov (United States)

    Żejmo, Michał; Kowal, Marek; Korbicz, Józef; Monczak, Roman

    2017-01-01

    The paper presents a deep learning approach for automatic classification of breast tumors based on fine needle cytology. The main aim of the system is to distinguish benign from malignant cases based on microscopic images. Experiment was carried out on cytological samples derived from 50 patients (25 benign cases + 25 malignant cases) diagnosed in Regional Hospital in Zielona Góra. To classify microscopic images, we used convolutional neural networks (CNN) of two types: GoogLeNet and AlexNet. Due to the very large size of images of cytological specimen (on average 200000 × 100000 pixels), they were divided into smaller patches of size 256 × 256 pixels. Breast cancer classification usually is based on morphometric features of nuclei. Therefore, training and validation patches were selected using Support Vector Machine (SVM) so that suitable amount of cell material was depicted. Neural classifiers were tuned using GPU accelerated implementation of gradient descent algorithm. Training error was defined as a cross-entropy classification loss. Classification accuracy was defined as the percentage ratio of successfully classified validation patches to the total number of validation patches. The best accuracy rate of 83% was obtained by GoogLeNet model. We observed that more misclassified patches belong to malignant cases.

  20. Processing of chromatic information in a deep convolutional neural network.

    Science.gov (United States)

    Flachot, Alban; Gegenfurtner, Karl R

    2018-04-01

    Deep convolutional neural networks are a class of machine-learning algorithms capable of solving non-trivial tasks, such as object recognition, with human-like performance. Little is known about the exact computations that deep neural networks learn, and to what extent these computations are similar to the ones performed by the primate brain. Here, we investigate how color information is processed in the different layers of the AlexNet deep neural network, originally trained on object classification of over 1.2M images of objects in their natural contexts. We found that the color-responsive units in the first layer of AlexNet learned linear features and were broadly tuned to two directions in color space, analogously to what is known of color responsive cells in the primate thalamus. Moreover, these directions are decorrelated and lead to statistically efficient representations, similar to the cardinal directions of the second-stage color mechanisms in primates. We also found, in analogy to the early stages of the primate visual system, that chromatic and achromatic information were segregated in the early layers of the network. Units in the higher layers of AlexNet exhibit on average a lower responsivity for color than units at earlier stages.

  1. Very Deep Convolutional Neural Networks for Morphologic Classification of Erythrocytes.

    Science.gov (United States)

    Durant, Thomas J S; Olson, Eben M; Schulz, Wade L; Torres, Richard

    2017-12-01

    Morphologic profiling of the erythrocyte population is a widely used and clinically valuable diagnostic modality, but one that relies on a slow manual process associated with significant labor cost and limited reproducibility. Automated profiling of erythrocytes from digital images by capable machine learning approaches would augment the throughput and value of morphologic analysis. To this end, we sought to evaluate the performance of leading implementation strategies for convolutional neural networks (CNNs) when applied to classification of erythrocytes based on morphology. Erythrocytes were manually classified into 1 of 10 classes using a custom-developed Web application. Using recent literature to guide architectural considerations for neural network design, we implemented a "very deep" CNN, consisting of >150 layers, with dense shortcut connections. The final database comprised 3737 labeled cells. Ensemble model predictions on unseen data demonstrated a harmonic mean of recall and precision metrics of 92.70% and 89.39%, respectively. Of the 748 cells in the test set, 23 misclassification errors were made, with a correct classification frequency of 90.60%, represented as a harmonic mean across the 10 morphologic classes. These findings indicate that erythrocyte morphology profiles could be measured with a high degree of accuracy with "very deep" CNNs. Further, these data support future efforts to expand classes and optimize practical performance in a clinical environment as a prelude to full implementation as a clinical tool. © 2017 American Association for Clinical Chemistry.

  2. Noisy Ocular Recognition Based on Three Convolutional Neural Networks.

    Science.gov (United States)

    Lee, Min Beom; Hong, Hyung Gil; Park, Kang Ryoung

    2017-12-17

    In recent years, the iris recognition system has been gaining increasing acceptance for applications such as access control and smartphone security. When the images of the iris are obtained under unconstrained conditions, an issue of undermined quality is caused by optical and motion blur, off-angle view (the user's eyes looking somewhere else, not into the front of the camera), specular reflection (SR) and other factors. Such noisy iris images increase intra-individual variations and, as a result, reduce the accuracy of iris recognition. A typical iris recognition system requires a near-infrared (NIR) illuminator along with an NIR camera, which are larger and more expensive than fingerprint recognition equipment. Hence, many studies have proposed methods of using iris images captured by a visible light camera without the need for an additional illuminator. In this research, we propose a new recognition method for noisy iris and ocular images by using one iris and two periocular regions, based on three convolutional neural networks (CNNs). Experiments were conducted by using the noisy iris challenge evaluation-part II (NICE.II) training dataset (selected from the university of Beira iris (UBIRIS).v2 database), mobile iris challenge evaluation (MICHE) database, and institute of automation of Chinese academy of sciences (CASIA)-Iris-Distance database. As a result, the method proposed by this study outperformed previous methods.

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

    Science.gov (United States)

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

    2016-01-11

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

  4. Understanding the Convolutional Neural Networks with Gradient Descent and Backpropagation

    Science.gov (United States)

    Zhou, XueFei

    2018-04-01

    With the development of computer technology, the applications of machine learning are more and more extensive. And machine learning is providing endless opportunities to develop new applications. One of those applications is image recognition by using Convolutional Neural Networks (CNNs). CNN is one of the most common algorithms in image recognition. It is significant to understand its theory and structure for every scholar who is interested in this field. CNN is mainly used in computer identification, especially in voice, text recognition and other aspects of the application. It utilizes hierarchical structure with different layers to accelerate computing speed. In addition, the greatest features of CNNs are the weight sharing and dimension reduction. And all of these consolidate the high effectiveness and efficiency of CNNs with idea computing speed and error rate. With the help of other learning altruisms, CNNs could be used in several scenarios for machine learning, especially for deep learning. Based on the general introduction to the background and the core solution CNN, this paper is going to focus on summarizing how Gradient Descent and Backpropagation work, and how they contribute to the high performances of CNNs. Also, some practical applications will be discussed in the following parts. The last section exhibits the conclusion and some perspectives of future work.

  5. Classifying magnetic resonance image modalities with convolutional neural networks

    Science.gov (United States)

    Remedios, Samuel; Pham, Dzung L.; Butman, John A.; Roy, Snehashis

    2018-02-01

    Magnetic Resonance (MR) imaging allows the acquisition of images with different contrast properties depending on the acquisition protocol and the magnetic properties of tissues. Many MR brain image processing techniques, such as tissue segmentation, require multiple MR contrasts as inputs, and each contrast is treated differently. Thus it is advantageous to automate the identification of image contrasts for various purposes, such as facilitating image processing pipelines, and managing and maintaining large databases via content-based image retrieval (CBIR). Most automated CBIR techniques focus on a two-step process: extracting features from data and classifying the image based on these features. We present a novel 3D deep convolutional neural network (CNN)- based method for MR image contrast classification. The proposed CNN automatically identifies the MR contrast of an input brain image volume. Specifically, we explored three classification problems: (1) identify T1-weighted (T1-w), T2-weighted (T2-w), and fluid-attenuated inversion recovery (FLAIR) contrasts, (2) identify pre vs postcontrast T1, (3) identify pre vs post-contrast FLAIR. A total of 3418 image volumes acquired from multiple sites and multiple scanners were used. To evaluate each task, the proposed model was trained on 2137 images and tested on the remaining 1281 images. Results showed that image volumes were correctly classified with 97.57% accuracy.

  6. Convolutional networks for fast, energy-efficient neuromorphic computing

    Science.gov (United States)

    Esser, Steven K.; Merolla, Paul A.; Arthur, John V.; Cassidy, Andrew S.; Appuswamy, Rathinakumar; Andreopoulos, Alexander; Berg, David J.; McKinstry, Jeffrey L.; Melano, Timothy; Barch, Davis R.; di Nolfo, Carmelo; Datta, Pallab; Amir, Arnon; Taba, Brian; Flickner, Myron D.; Modha, Dharmendra S.

    2016-01-01

    Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware’s underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer. PMID:27651489

  7. Mobile Stride Length Estimation With Deep Convolutional Neural Networks.

    Science.gov (United States)

    Hannink, Julius; Kautz, Thomas; Pasluosta, Cristian F; Barth, Jens; Schulein, Samuel; GaBmann, Karl-Gunter; Klucken, Jochen; Eskofier, Bjoern M

    2018-03-01

    Accurate estimation of spatial gait characteristics is critical to assess motor impairments resulting from neurological or musculoskeletal disease. Currently, however, methodological constraints limit clinical applicability of state-of-the-art double integration approaches to gait patterns with a clear zero-velocity phase. We describe a novel approach to stride length estimation that uses deep convolutional neural networks to map stride-specific inertial sensor data to the resulting stride length. The model is trained on a publicly available and clinically relevant benchmark dataset consisting of 1220 strides from 101 geriatric patients. Evaluation is done in a tenfold cross validation and for three different stride definitions. Even though best results are achieved with strides defined from midstance to midstance with average accuracy and precision of , performance does not strongly depend on stride definition. The achieved precision outperforms state-of-the-art methods evaluated on the same benchmark dataset by . Due to the independence of stride definition, the proposed method is not subject to the methodological constrains that limit applicability of state-of-the-art double integration methods. Furthermore, it was possible to improve precision on the benchmark dataset. With more precise mobile stride length estimation, new insights to the progression of neurological disease or early indications might be gained. Due to the independence of stride definition, previously uncharted diseases in terms of mobile gait analysis can now be investigated by retraining and applying the proposed method.

  8. Transfer Learning with Convolutional Neural Networks for SAR Ship Recognition

    Science.gov (United States)

    Zhang, Di; Liu, Jia; Heng, Wang; Ren, Kaijun; Song, Junqiang

    2018-03-01

    Ship recognition is the backbone of marine surveillance systems. Recent deep learning methods, e.g. Convolutional Neural Networks (CNNs), have shown high performance for optical images. Learning CNNs, however, requires a number of annotated samples to estimate numerous model parameters, which prevents its application to Synthetic Aperture Radar (SAR) images due to the limited annotated training samples. Transfer learning has been a promising technique for applications with limited data. To this end, a novel SAR ship recognition method based on CNNs with transfer learning has been developed. In this work, we firstly start with a CNNs model that has been trained in advance on Moving and Stationary Target Acquisition and Recognition (MSTAR) database. Next, based on the knowledge gained from this image recognition task, we fine-tune the CNNs on a new task to recognize three types of ships in the OpenSARShip database. The experimental results show that our proposed approach can obviously increase the recognition rate comparing with the result of merely applying CNNs. In addition, compared to existing methods, the proposed method proves to be very competitive and can learn discriminative features directly from training data instead of requiring pre-specification or pre-selection manually.

  9. Protected electrodes for plasma panels

    International Nuclear Information System (INIS)

    Hall, S.W.

    1984-01-01

    A metal oxide coating is applied between the conductive base and the magnesium oxide dielectric of the input and/or erase electrode(s) in a plasma display device to prevent break-down of the dielectric

  10. Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation

    Science.gov (United States)

    Qin, Wenjian; Wu, Jia; Han, Fei; Yuan, Yixuan; Zhao, Wei; Ibragimov, Bulat; Gu, Jia; Xing, Lei

    2018-05-01

    Segmentation of liver in abdominal computed tomography (CT) is an important step for radiation therapy planning of hepatocellular carcinoma. Practically, a fully automatic segmentation of liver remains challenging because of low soft tissue contrast between liver and its surrounding organs, and its highly deformable shape. The purpose of this work is to develop a novel superpixel-based and boundary sensitive convolutional neural network (SBBS-CNN) pipeline for automated liver segmentation. The entire CT images were first partitioned into superpixel regions, where nearby pixels with similar CT number were aggregated. Secondly, we converted the conventional binary segmentation into a multinomial classification by labeling the superpixels into three classes: interior liver, liver boundary, and non-liver background. By doing this, the boundary region of the liver was explicitly identified and highlighted for the subsequent classification. Thirdly, we computed an entropy-based saliency map for each CT volume, and leveraged this map to guide the sampling of image patches over the superpixels. In this way, more patches were extracted from informative regions (e.g. the liver boundary with irregular changes) and fewer patches were extracted from homogeneous regions. Finally, deep CNN pipeline was built and trained to predict the probability map of the liver boundary. We tested the proposed algorithm in a cohort of 100 patients. With 10-fold cross validation, the SBBS-CNN achieved mean Dice similarity coefficients of 97.31  ±  0.36% and average symmetric surface distance of 1.77  ±  0.49 mm. Moreover, it showed superior performance in comparison with state-of-art methods, including U-Net, pixel-based CNN, active contour, level-sets and graph-cut algorithms. SBBS-CNN provides an accurate and effective tool for automated liver segmentation. It is also envisioned that the proposed framework is directly applicable in other medical image segmentation scenarios.

  11. Segmentation of corneal endothelium images using a U-Net-based convolutional neural network.

    Science.gov (United States)

    Fabijańska, Anna

    2018-04-18

    Diagnostic information regarding the health status of the corneal endothelium may be obtained by analyzing the size and the shape of the endothelial cells in specular microscopy images. Prior to the analysis, the endothelial cells need to be extracted from the image. Up to today, this has been performed manually or semi-automatically. Several approaches to automatic segmentation of endothelial cells exist; however, none of them is perfect. Therefore this paper proposes to perform cell segmentation using a U-Net-based convolutional neural network. Particularly, the network is trained to discriminate pixels located at the borders between cells. The edge probability map outputted by the network is next binarized and skeletonized in order to obtain one-pixel wide edges. The proposed solution was tested on a dataset consisting of 30 corneal endothelial images presenting cells of different sizes, achieving an AUROC level of 0.92. The resulting DICE is on average equal to 0.86, which is a good result, regarding the thickness of the compared edges. The corresponding mean absolute percentage error of cell number is at the level of 4.5% which confirms the high accuracy of the proposed approach. The resulting cell edges are well aligned to the ground truths and require a limited number of manual corrections. This also results in accurate values of the cell morphometric parameters. The corresponding errors range from 5.2% for endothelial cell density, through 6.2% for cell hexagonality to 11.93% for the coefficient of variation of the cell size. Copyright © 2018 Elsevier B.V. All rights reserved.

  12. A flexible capacitive tactile sensing array with floating electrodes

    International Nuclear Information System (INIS)

    Cheng, M-Y; Huang, X-H; Ma, C-W; Yang, Y-J

    2009-01-01

    In this work, we present the development of a capacitive tactile sensing array realized by using MEMS fabrication techniques and flexible printed circuit board (FPCB) technologies. The sensing array, which consists of two micromachined polydimethlysiloxane (PDMS) structures and a FPCB, will be used as the artificial skin for robot applications. Each capacitive sensing element comprises two sensing electrodes and a common floating electrode. The sensing electrodes and the metal interconnect for signal scanning are implemented on the FPCB, while the floating electrode is patterned on one of the PDMS structures. This special design can effectively reduce the complexity of the device structure and thus makes the device highly manufacturable. The characteristics of the devices with different dimensions are measured and discussed. The corresponding scanning circuits are also designed and implemented. The tactile images induced by the PMMA stamps of different shapes are also successfully captured by a fabricated 8 × 8 array

  13. Ion-selective electrode reviews

    CERN Document Server

    Thomas, J D R

    1985-01-01

    Ion-Selective Electrode Reviews, Volume 7 is a collection of papers that covers the applications of electrochemical sensors, along with the versatility of ion-selective electrodes. The coverage of the text includes solid contact in membrane ion-selective electrodes; immobilized enzyme probes for determining inhibitors; potentiometric titrations based on ion-pair formation; and application of ion-selective electrodes in soil science, kinetics, and kinetic analysis. The text will be of great use to chemists and chemical engineers.

  14. Improvements and artifact analysis in conductivity images using multiple internal electrodes

    International Nuclear Information System (INIS)

    Farooq, Adnan; McEwan, Alistair Lee; Woo, Eung Je; Oh, Tong In; Tehrani, Joubin Nasehi

    2014-01-01

    Electrical impedance tomography is an attractive functional imaging method. It is currently limited in resolution and sensitivity due to the complexity of the inverse problem and the safety limits of introducing current. Recently, internal electrodes have been proposed for some clinical situations such as intensive care or RF ablation. This paper addresses the research question related to the benefit of one or more internal electrodes usage since these are invasive. Internal electrodes would be able to reduce the effect of insulating boundaries such as fat and bone and provide improved internal sensitivity. We found there was a measurable benefit with increased numbers of internal electrodes in saline tanks of a cylindrical and complex shape with up to two insulating boundary gel layers modeling fat and muscle. The internal electrodes provide increased sensitivity to internal changes, thereby increasing the amplitude response and improving resolution. However, they also present an additional challenge of increasing sensitivity to position and modeling errors. In comparison with previous work that used point sources for the internal electrodes, we found that it is important to use a detailed mesh of the internal electrodes with these voxels assigned to the conductivity of the internal electrode and its associated holder. A study of different internal electrode materials found that it is optimal to use a conductivity similar to the background. In the tank with a complex shape, the additional internal electrodes provided more robustness in a ventilation model of the lungs via air filled balloons. (paper)

  15. Development of a power electrode for plasma biasing on RFX

    International Nuclear Information System (INIS)

    Desideri, D.; Lorenzi, A. de; Zaccaria, P.

    1999-01-01

    A movable power electrode has been developed on the RFX experiment to modify the radial electric field at the edge of the plasma configuration. The electrode insertion head is a mushroom shaped limiter made of a carbon-carbon composite, and boron nitride is used as insulating material to be exposed to the plasma. The power electrode is designed to carry a 10 kA impulsive current and is insulated for 10 kV DC. The current into the electrode is driven by a power supply based on capacitor banks, and protective actions to cope with fault conditions have been implemented. The design of the electrode supporting structure has been done by using 3D finite element analyses, performed to evaluate the dynamic response of the system subjected to impulsive electromagnetic loads. The system has been used on the RFX experiment, showing the expected capability and flexibility. The current and voltage electrode waveforms are reported and discussed as far as the experimental results are concerned. Displacements of the electrode stiffening structure under electromagnetic load have been measured and compared to the numerical results. (orig.)

  16. Geometrical effects determinant of the Compton profile shape

    International Nuclear Information System (INIS)

    Sartori, Renzo; Mainardi, R.T.

    1987-01-01

    The main purpose of this work is to evaluate the influence of the experimental set up on the shape of the Compton line. In any scattering experiment, the scattering angle is not well defined due to the collimators aperture and thus, a distribution of angles is found for each set up. This, in turn, produces the energies' distribution of the scattered photons around a mean value. This contribution has been evaluated and found it to be significant for several cases. In order to do this evaluation, a response function, that is numerically generated for each experimental set up and convoluted with the Compton profile, was defined. (Author) [es

  17. Linear shaped charge

    Energy Technology Data Exchange (ETDEWEB)

    Peterson, David; Stofleth, Jerome H.; Saul, Venner W.

    2017-07-11

    Linear shaped charges are described herein. In a general embodiment, the linear shaped charge has an explosive with an elongated arrowhead-shaped profile. The linear shaped charge also has and an elongated v-shaped liner that is inset into a recess of the explosive. Another linear shaped charge includes an explosive that is shaped as a star-shaped prism. Liners are inset into crevices of the explosive, where the explosive acts as a tamper.

  18. Computer simulation and SERR detection of cytochrome c dynamics at SAM-coated electrodes

    International Nuclear Information System (INIS)

    Paggi, Damian Alvarez; Martin, Diego F.; Kranich, Anja; Hildebrandt, Peter; Marti, Marcelo A.; Murgida, Daniel H.

    2009-01-01

    In this paper we present a combined experimental and theoretical study of the heterogeneous electron transfer reaction of cytochrome c electrostatically adsorbed on metal electrodes coated with monolayers of 6-mercaptohexanoic acid. Molecular dynamics simulations and pathways calculations show that adsorption of the protein leads to a broad distribution of orientations and, thus, to a correspondingly broad distribution of electron transfer rate constants due to the orientation-dependence of the electronic coupling parameter. The adsorbed protein exhibits significant mobility and, therefore, the measured reaction rate is predicted to be a convolution of protein dynamics and tunnelling probabilities for each orientation. This prediction is confirmed by time-resolved surface enhanced resonance Raman which allows for the direct monitoring of protein (re-)orientation and electron transfer of the immobilised cytochrome c. The results provide a consistent explanation for the non-exponential distance-independence of electron transfer rates usually observed for proteins immobilized on electrodes.

  19. Computer simulation and SERR detection of cytochrome c dynamics at SAM-coated electrodes

    Energy Technology Data Exchange (ETDEWEB)

    Paggi, Damian Alvarez; Martin, Diego F. [Departamento de Quimica Inorganica, Analitica y Quimica Fisica/INQUIMAE-CONICET, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pab. 2, piso 1, C1428EHA Buenos Aires (Argentina); Kranich, Anja; Hildebrandt, Peter [Institut fuer Chemie, Technische Universitaet Berlin, Str. des 17, Juni 135, Sekr. PC14, D-10623 Berlin (Germany); Marti, Marcelo A. [Departamento de Quimica Inorganica, Analitica y Quimica Fisica/INQUIMAE-CONICET, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pab. 2, piso 1, C1428EHA Buenos Aires (Argentina)], E-mail: marcelo@qi.fcen.uba.ar; Murgida, Daniel H. [Departamento de Quimica Inorganica, Analitica y Quimica Fisica/INQUIMAE-CONICET, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pab. 2, piso 1, C1428EHA Buenos Aires (Argentina)], E-mail: dhmurgida@qi.fcen.uba.ar

    2009-09-01

    In this paper we present a combined experimental and theoretical study of the heterogeneous electron transfer reaction of cytochrome c electrostatically adsorbed on metal electrodes coated with monolayers of 6-mercaptohexanoic acid. Molecular dynamics simulations and pathways calculations show that adsorption of the protein leads to a broad distribution of orientations and, thus, to a correspondingly broad distribution of electron transfer rate constants due to the orientation-dependence of the electronic coupling parameter. The adsorbed protein exhibits significant mobility and, therefore, the measured reaction rate is predicted to be a convolution of protein dynamics and tunnelling probabilities for each orientation. This prediction is confirmed by time-resolved surface enhanced resonance Raman which allows for the direct monitoring of protein (re-)orientation and electron transfer of the immobilised cytochrome c. The results provide a consistent explanation for the non-exponential distance-independence of electron transfer rates usually observed for proteins immobilized on electrodes.

  20. Study of the Auger line shape of polyethylene and diamond

    Energy Technology Data Exchange (ETDEWEB)

    Dayan, M; Pepper, S V

    1984-03-01

    The KVV Auger electron line shapes of carbon in polyethylene and diamond have been studied. The spectra were obtained in derivative form by electron beam excitation. They were treated by background subtraction, integration and deconvolution to produce the intrinsic Auger line shape. Electron energy loss spectra provided the response function in the deconvolution procedure. The line shape from polyethylene is compared with spectra from linear alkanes and with a previous spectrum of Kelber et al. Both spectra are compared with the self-convolution of their full valence band densities of states and of their p-projected densities. The experimental spectra could not be understood in terms of existing theories. This is so even when correlation effects are qualitatively taken into account according to the theories of Cini and Sawatzky and Lenselink.

  1. Ion-selective electrode reviews

    CERN Document Server

    Thomas, J D R

    1983-01-01

    Ion-Selective Electrode Reviews, Volume 5 is a collection of articles that covers ion-speciation. The book aims to present the advancements of the range and capabilities of selective ion-sensors. The topics covered in the selection are neutral carrier based ion-selective electrodes; reference electrodes and liquid junction effects in ion-selective electrode potentiometry; ion transfer across water/organic phase boundaries and analytical; and carbon substrate ion-selective electrodes. The text will be of great use to chemists and chemical engineers.

  2. Particle Identification in Cherenkov Detectors using Convolutional Neural Networks

    CERN Document Server

    Theodore, Tomalty

    2016-01-01

    Cherenkov detectors are used for charged particle identification. When a charged particle moves through a medium faster than light can propagate in that medium, Cherenkov radiation is released in the shape of a cone in the direction of movement. The interior of the Cherenkov detector is instrumented with PMTs to detect this Cherenkov light. Particles, then, can be identified by the shapes of the images on the detector walls.

  3. Electrostatic Levitator Electrode Layout

    Science.gov (United States)

    1998-01-01

    Schematic of Electrostatic Levitator (ESL) electrodes and controls system. The ESL uses static electricity to suspend an object (about 2-3 mm in diameter) inside a vacuum chamber while a laser heats the sample until it melts. This lets scientists record a wide range of physical properties without the sample contacting the container or any instruments, conditions that would alter the readings. The Electrostatic Levitator is one of several tools used in NASA's microgravity materials science program.

  4. Two-stream Convolutional Neural Network for Methane Emissions Quantification

    Science.gov (United States)

    Wang, J.; Ravikumar, A. P.; McGuire, M.; Bell, C.; Tchapmi, L. P.; Brandt, A. R.

    2017-12-01

    Methane, a key component of natural gas, has a 25x higher global warming potential than carbon dioxide on a 100-year basis. Accurately monitoring and mitigating methane emissions require cost-effective detection and quantification technologies. Optical gas imaging, one of the most commonly used leak detection technology, adopted by Environmental Protection Agency, cannot estimate leak-sizes. In this work, we harness advances in computer science to allow for rapid and automatic leak quantification. Particularly, we utilize two-stream deep Convolutional Networks (ConvNets) to estimate leak-size by capturing complementary spatial information from still plume frames, and temporal information from plume motion between frames. We build large leak datasets for training and evaluating purposes by collecting about 20 videos (i.e. 397,400 frames) of leaks. The videos were recorded at six distances from the source, covering 10 -60 ft. Leak sources included natural gas well-heads, separators, and tanks. All frames were labeled with a true leak size, which has eight levels ranging from 0 to 140 MCFH. Preliminary analysis shows that two-stream ConvNets provides significant accuracy advantage over single steam ConvNets. Spatial stream ConvNet can achieve an accuracy of 65.2%, by extracting important features, including texture, plume area, and pattern. Temporal stream, fed by the results of optical flow analysis, results in an accuracy of 58.3%. The integration of the two-stream ConvNets gives a combined accuracy of 77.6%. For future work, we will split the training and testing datasets in distinct ways in order to test the generalization of the algorithm for different leak sources. Several analytic metrics, including confusion matrix and visualization of key features, will be used to understand accuracy rates and occurrences of false positives. The quantification algorithm can help to find and fix super-emitters, and improve the cost-effectiveness of leak detection and repair

  5. A convolution-superposition dose calculation engine for GPUs

    Energy Technology Data Exchange (ETDEWEB)

    Hissoiny, Sami; Ozell, Benoit; Despres, Philippe [Departement de genie informatique et genie logiciel, Ecole polytechnique de Montreal, 2500 Chemin de Polytechnique, Montreal, Quebec H3T 1J4 (Canada); Departement de radio-oncologie, CRCHUM-Centre hospitalier de l' Universite de Montreal, 1560 rue Sherbrooke Est, Montreal, Quebec H2L 4M1 (Canada)

    2010-03-15

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

  6. Automated segmentation of geographic atrophy using deep convolutional neural networks

    Science.gov (United States)

    Hu, Zhihong; Wang, Ziyuan; Sadda, SriniVas R.

    2018-02-01

    Geographic atrophy (GA) is an end-stage manifestation of the advanced age-related macular degeneration (AMD), the leading cause of blindness and visual impairment in developed nations. Techniques to rapidly and precisely detect and quantify GA would appear to be of critical importance in advancing the understanding of its pathogenesis. In this study, we develop an automated supervised classification system using deep convolutional neural networks (CNNs) for segmenting GA in fundus autofluorescene (FAF) images. More specifically, to enhance the contrast of GA relative to the background, we apply the contrast limited adaptive histogram equalization. Blood vessels may cause GA segmentation errors due to similar intensity level to GA. A tensor-voting technique is performed to identify the blood vessels and a vessel inpainting technique is applied to suppress the GA segmentation errors due to the blood vessels. To handle the large variation of GA lesion sizes, three deep CNNs with three varying sized input image patches are applied. Fifty randomly chosen FAF images are obtained from fifty subjects with GA. The algorithm-defined GA regions are compared with manual delineation by a certified grader. A two-fold cross-validation is applied to evaluate the algorithm performance. The mean segmentation accuracy, true positive rate (i.e. sensitivity), true negative rate (i.e. specificity), positive predictive value, false discovery rate, and overlap ratio, between the algorithm- and manually-defined GA regions are 0.97 +/- 0.02, 0.89 +/- 0.08, 0.98 +/- 0.02, 0.87 +/- 0.12, 0.13 +/- 0.12, and 0.79 +/- 0.12 respectively, demonstrating a high level of agreement.

  7. Automatic Seismic-Event Classification with Convolutional Neural Networks.

    Science.gov (United States)

    Bueno Rodriguez, A.; Titos Luzón, M.; Garcia Martinez, L.; Benitez, C.; Ibáñez, J. M.

    2017-12-01

    Active volcanoes exhibit a wide range of seismic signals, providing vast amounts of unlabelled volcano-seismic data that can be analyzed through the lens of artificial intelligence. However, obtaining high-quality labelled data is time-consuming and expensive. Deep neural networks can process data in their raw form, compute high-level features and provide a better representation of the input data distribution. These systems can be deployed to classify seismic data at scale, enhance current early-warning systems and build extensive seismic catalogs. In this research, we aim to classify spectrograms from seven different seismic events registered at "Volcán de Fuego" (Colima, Mexico), during four eruptive periods. Our approach is based on convolutional neural networks (CNNs), a sub-type of deep neural networks that can exploit grid structure from the data. Volcano-seismic signals can be mapped into a grid-like structure using the spectrogram: a representation of the temporal evolution in terms of time and frequency. Spectrograms were computed from the data using Hamming windows with 4 seconds length, 2.5 seconds overlapping and 128 points FFT resolution. Results are compared to deep neural networks, random forest and SVMs. Experiments show that CNNs can exploit temporal and frequency information, attaining a classification accuracy of 93%, similar to deep networks 91% but outperforming SVM and random forest. These results empirically show that CNNs are powerful models to classify a wide range of volcano-seismic signals, and achieve good generalization. Furthermore, volcano-seismic spectrograms contains useful discriminative information for the CNN, as higher layers of the network combine high-level features computed for each frequency band, helping to detect simultaneous events in time. Being at the intersection of deep learning and geophysics, this research enables future studies of how CNNs can be used in volcano monitoring to accurately determine the detection and

  8. Aerial Images and Convolutional Neural Network for Cotton Bloom Detection.

    Science.gov (United States)

    Xu, Rui; Li, Changying; Paterson, Andrew H; Jiang, Yu; Sun, Shangpeng; Robertson, Jon S

    2017-01-01

    Monitoring flower development can provide useful information for production management, estimating yield and selecting specific genotypes of crops. The main goal of this study was to develop a methodology to detect and count cotton flowers, or blooms, using color images acquired by an unmanned aerial system. The aerial images were collected from two test fields in 4 days. A convolutional neural network (CNN) was designed and trained to detect cotton blooms in raw images, and their 3D locations were calculated using the dense point cloud constructed from the aerial images with the structure from motion method. The quality of the dense point cloud was analyzed and plots with poor quality were excluded from data analysis. A constrained clustering algorithm was developed to register the same bloom detected from different images based on the 3D location of the bloom. The accuracy and incompleteness of the dense point cloud were analyzed because they affected the accuracy of the 3D location of the blooms and thus the accuracy of the bloom registration result. The constrained clustering algorithm was validated using simulated data, showing good efficiency and accuracy. The bloom count from the proposed method was comparable with the number counted manually with an error of -4 to 3 blooms for the field with a single plant per plot. However, more plots were underestimated in the field with multiple plants per plot due to hidden blooms that were not captured by the aerial images. The proposed methodology provides a high-throughput method to continuously monitor the flowering progress of cotton.

  9. A deep convolutional neural network model to classify heartbeats.

    Science.gov (United States)

    Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adam, Muhammad; Gertych, Arkadiusz; Tan, Ru San

    2017-10-01

    The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal individual heart beats, and their correct classification into different diagnoses, based on ECG morphology. Heartbeats can be sub-divided into five categories namely non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is challenging and time-consuming to distinguish these heartbeats on ECG as these signals are typically corrupted by noise. We developed a 9-layer deep convolutional neural network (CNN) to automatically identify 5 different categories of heartbeats in ECG signals. Our experiment was conducted in original and noise attenuated sets of ECG signals derived from a publicly available database. This set was artificially augmented to even out the number of instances the 5 classes of heartbeats and filtered to remove high-frequency noise. The CNN was trained using the augmented data and achieved an accuracy of 94.03% and 93.47% in the diagnostic classification of heartbeats in original and noise free ECGs, respectively. When the CNN was trained with highly imbalanced data (original dataset), the accuracy of the CNN reduced to 89.07%% and 89.3% in noisy and noise-free ECGs. When properly trained, the proposed CNN model can serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmic heartbeats. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Classifying Radio Galaxies with the Convolutional Neural Network

    International Nuclear Information System (INIS)

    Aniyan, A. K.; Thorat, K.

    2017-01-01

    We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff–Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ∼200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

  11. Convolutional neural networks for prostate cancer recurrence prediction

    Science.gov (United States)

    Kumar, Neeraj; Verma, Ruchika; Arora, Ashish; Kumar, Abhay; Gupta, Sanchit; Sethi, Amit; Gann, Peter H.

    2017-03-01

    Accurate prediction of the treatment outcome is important for cancer treatment planning. We present an approach to predict prostate cancer (PCa) recurrence after radical prostatectomy using tissue images. We used a cohort whose case vs. control (recurrent vs. non-recurrent) status had been determined using post-treatment follow up. Further, to aid the development of novel biomarkers of PCa recurrence, cases and controls were paired based on matching of other predictive clinical variables such as Gleason grade, stage, age, and race. For this cohort, tissue resection microarray with up to four cores per patient was available. The proposed approach is based on deep learning, and its novelty lies in the use of two separate convolutional neural networks (CNNs) - one to detect individual nuclei even in the crowded areas, and the other to classify them. To detect nuclear centers in an image, the first CNN predicts distance transform of the underlying (but unknown) multi-nuclear map from the input HE image. The second CNN classifies the patches centered at nuclear centers into those belonging to cases or controls. Voting across patches extracted from image(s) of a patient yields the probability of recurrence for the patient. The proposed approach gave 0.81 AUC for a sample of 30 recurrent cases and 30 non-recurrent controls, after being trained on an independent set of 80 case-controls pairs. If validated further, such an approach might help in choosing between a combination of treatment options such as active surveillance, radical prostatectomy, radiation, and hormone therapy. It can also generalize to the prediction of treatment outcomes in other cancers.

  12. Classifying Radio Galaxies with the Convolutional Neural Network

    Energy Technology Data Exchange (ETDEWEB)

    Aniyan, A. K.; Thorat, K. [Department of Physics and Electronics, Rhodes University, Grahamstown (South Africa)

    2017-06-01

    We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff–Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ∼200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

  13. Classifying Radio Galaxies with the Convolutional Neural Network

    Science.gov (United States)

    Aniyan, A. K.; Thorat, K.

    2017-06-01

    We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff-Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ˜200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

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

    Science.gov (United States)

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

    2015-11-01

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

  15. Deep convolutional networks for pancreas segmentation in CT imaging

    Science.gov (United States)

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

    2015-03-01

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

  16. Flexible transparent electrode

    Science.gov (United States)

    Demiryont, Hulya; Shannon, Kenneth C., III; Moorehead, David; Bratcher, Matthew

    2011-06-01

    This paper presents the properties of the EclipseTECTM transparent conductor. EclipseTECTM is a room temperature deposited nanostructured thin film coating system comprised of metal-oxide semiconductor elements. The system possesses metal-like conductivity and glass-like transparency in the visible region. These highly conductive TEC films exhibit high shielding efficiency (35dB at 1 to 100GHz). EclipseTECTM can be deposited on rigid or flexible substrates. For example, EclipseTECTM deposited on polyethylene terephthalate (PET) is extremely flexible that can be rolled around a 9mm diameter cylinder with little or no reduction in electrical conductivity and that can assume pre-extension states after an applied stress is relieved. The TEC is colorless and has been tailored to have high visible transmittance which matches the eye sensitivity curve and allows the viewing of true background colors through the coating. EclipseTECTM is flexible, durable and can be tailored at the interface for applications such as electron- or hole-injecting OLED electrodes as well as electrodes in flexible displays. Tunable work function and optical design flexibility also make EclipseTECTM well-suited as a candidate for grid electrode replacement in next-generation photovoltaic cells.

  17. Deep learning the dynamic appearance and shape of facial action units

    OpenAIRE

    Jaiswal, Shashank; Valstar, Michel F.

    2016-01-01

    Spontaneous facial expression recognition under uncontrolled conditions is a hard task. It depends on multiple factors including shape, appearance and dynamics of the facial features, all of which are adversely affected by environmental noise and low intensity signals typical of such conditions. In this work, we present a novel approach to Facial Action Unit detection using a combination of Convolutional and Bi-directional Long Short-Term Memory Neural Networks (CNN-BLSTM), which jointly lear...

  18. Convoluted laminations in waterlain sediments:three examples from Eastern Canada and their relevance to neotectonics

    International Nuclear Information System (INIS)

    Macdougall, D.A.; Broster, B.E.

    1995-10-01

    The catastrophic disturbance of unconsolidated sediment produces a wide variety of deformation structures, particularly if the sediment is water-saturated at the time of disturbance. Layers, originally deposited as sub-horizontal, can become stretched or distended resulting in convoluted laminations. Faulted beds, slumped units, or dewatering structures may also occur in association with the disturbance. Convolutions were studied in five examples of Pleistocene glaciomarine deltas, at three locations in eastern Canada. Results from this study indicate that similar structures were produced in each of the sediment deposits, but some are especially common in specific facies (e.g. bottomset, foreset, topset). However, the particular cause of the convolutions varied within each deposit, and the origin could be better assessed when studied in relationship to other structures. None of the convolutions found could be attributed, categorically, to a seismic origin. However, neither could a seismic origin be dismissed for structures associated with convolutions occurring in deposits at: St. George, New Brunswick; Economy Point, Nova Scotia; and Lanark, Ontario. Of these deposits, the deformed structures at Economy Point are apparently post-glacial. (author). 24 refs., 58 figs

  19. High Performance Implementation of 3D Convolutional Neural Networks on a GPU

    Science.gov (United States)

    Wang, Zelong; Wen, Mei; Zhang, Chunyuan; Wang, Yijie

    2017-01-01

    Convolutional neural networks have proven to be highly successful in applications such as image classification, object tracking, and many other tasks based on 2D inputs. Recently, researchers have started to apply convolutional neural networks to video classification, which constitutes a 3D input and requires far larger amounts of memory and much more computation. FFT based methods can reduce the amount of computation, but this generally comes at the cost of an increased memory requirement. On the other hand, the Winograd Minimal Filtering Algorithm (WMFA) can reduce the number of operations required and thus can speed up the computation, without increasing the required memory. This strategy was shown to be successful for 2D neural networks. We implement the algorithm for 3D convolutional neural networks and apply it to a popular 3D convolutional neural network which is used to classify videos and compare it to cuDNN. For our highly optimized implementation of the algorithm, we observe a twofold speedup for most of the 3D convolution layers of our test network compared to the cuDNN version. PMID:29250109

  20. High Performance Implementation of 3D Convolutional Neural Networks on a GPU.

    Science.gov (United States)

    Lan, Qiang; Wang, Zelong; Wen, Mei; Zhang, Chunyuan; Wang, Yijie

    2017-01-01

    Convolutional neural networks have proven to be highly successful in applications such as image classification, object tracking, and many other tasks based on 2D inputs. Recently, researchers have started to apply convolutional neural networks to video classification, which constitutes a 3D input and requires far larger amounts of memory and much more computation. FFT based methods can reduce the amount of computation, but this generally comes at the cost of an increased memory requirement. On the other hand, the Winograd Minimal Filtering Algorithm (WMFA) can reduce the number of operations required and thus can speed up the computation, without increasing the required memory. This strategy was shown to be successful for 2D neural networks. We implement the algorithm for 3D convolutional neural networks and apply it to a popular 3D convolutional neural network which is used to classify videos and compare it to cuDNN. For our highly optimized implementation of the algorithm, we observe a twofold speedup for most of the 3D convolution layers of our test network compared to the cuDNN version.

  1. Numerical analysis of the heat source characteristics of a two-electrode TIG arc

    International Nuclear Information System (INIS)

    Ogino, Y; Hirata, Y; Nomura, K

    2011-01-01

    Various kinds of multi-electrode welding processes are used to ensure high productivity in industrial fields such as shipbuilding, automotive manufacturing and pipe fabrication. However, it is difficult to obtain the optimum welding conditions for a specific product, because there are many operating parameters, and because welding phenomena are very complicated. In the present research, the heat source characteristics of a two-electrode TIG arc were numerically investigated using a 3D arc plasma model with a focus on the distance between the two electrodes. The arc plasma shape changed significantly, depending on the electrode spacing. The heat source characteristics, such as the heat input density and the arc pressure distribution, changed significantly when the electrode separation was varied. The maximum arc pressure of the two-electrode TIG arc was much lower than that of a single-electrode TIG. However, the total heat input of the two-electrode TIG arc was nearly constant and was independent of the electrode spacing. These heat source characteristics of the two-electrode TIG arc are useful for controlling the heat input distribution at a low arc pressure. Therefore, these results indicate the possibility of a heat source based on a two-electrode TIG arc that is capable of high heat input at low pressures.

  2. Modeling shape selection of buckled dielectric elastomers

    Science.gov (United States)

    Langham, Jacob; Bense, Hadrien; Barkley, Dwight

    2018-02-01

    A dielectric elastomer whose edges are held fixed will buckle, given a sufficiently applied voltage, resulting in a nontrivial out-of-plane deformation. We study this situation numerically using a nonlinear elastic model which decouples two of the principal electrostatic stresses acting on an elastomer: normal pressure due to the mutual attraction of oppositely charged electrodes and tangential shear ("fringing") due to repulsion of like charges at the electrode edges. These enter via physically simplified boundary conditions that are applied in a fixed reference domain using a nondimensional approach. The method is valid for small to moderate strains and is straightforward to implement in a generic nonlinear elasticity code. We validate the model by directly comparing the simulated equilibrium shapes with the experiment. For circular electrodes which buckle axisymetrically, the shape of the deflection profile is captured. Annular electrodes of different widths produce azimuthal ripples with wavelengths that match our simulations. In this case, it is essential to compute multiple equilibria because the first model solution obtained by the nonlinear solver (Newton's method) is often not the energetically favored state. We address this using a numerical technique known as "deflation." Finally, we observe the large number of different solutions that may be obtained for the case of a long rectangular strip.

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

    Science.gov (United States)

    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.

  4. Design and Implementation of Behavior Recognition System Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Yu Bo

    2017-01-01

    Full Text Available We build a set of human behavior recognition system based on the convolution neural network constructed for the specific human behavior in public places. Firstly, video of human behavior data set will be segmented into images, then we process the images by the method of background subtraction to extract moving foreground characters of body. Secondly, the training data sets are trained into the designed convolution neural network, and the depth learning network is constructed by stochastic gradient descent. Finally, the various behaviors of samples are classified and identified with the obtained network model, and the recognition results are compared with the current mainstream methods. The result show that the convolution neural network can study human behavior model automatically and identify human’s behaviors without any manually annotated trainings.

  5. Efficient airport detection using region-based fully convolutional neural networks

    Science.gov (United States)

    Xin, Peng; Xu, Yuelei; Zhang, Xulei; Ma, Shiping; Li, Shuai; Lv, Chao

    2018-04-01

    This paper presents a model for airport detection using region-based fully convolutional neural networks. To achieve fast detection with high accuracy, we shared the conv layers between the region proposal procedure and the airport detection procedure and used graphics processing units (GPUs) to speed up the training and testing time. For lack of labeled data, we transferred the convolutional layers of ZF net pretrained by ImageNet to initialize the shared convolutional layers, then we retrained the model using the alternating optimization training strategy. The proposed model has been tested on an airport dataset consisting of 600 images. Experiments show that the proposed method can distinguish airports in our dataset from similar background scenes almost real-time with high accuracy, which is much better than traditional methods.

  6. Space-Time Convolutional Codes over Finite Fields and Rings for Systems with Large Diversity Order

    Directory of Open Access Journals (Sweden)

    B. F. Uchôa-Filho

    2008-06-01

    Full Text Available We propose a convolutional encoder over the finite ring of integers modulo pk,ℤpk, where p is a prime number and k is any positive integer, to generate a space-time convolutional code (STCC. Under this structure, we prove three properties related to the generator matrix of the convolutional code that can be used to simplify the code search procedure for STCCs over ℤpk. Some STCCs of large diversity order (≥4 designed under the trace criterion for n=2,3, and 4 transmit antennas are presented for various PSK signal constellations.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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

  9. A digital pixel cell for address event representation image convolution processing

    Science.gov (United States)

    Camunas-Mesa, Luis; Acosta-Jimenez, Antonio; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabe

    2005-06-01

    Address Event Representation (AER) is an emergent neuromorphic interchip communication protocol that allows for real-time virtual massive connectivity between huge number of neurons located on different chips. By exploiting high speed digital communication circuits (with nano-seconds timings), synaptic neural connections can be time multiplexed, while neural activity signals (with mili-seconds timings) are sampled at low frequencies. Also, neurons generate events according to their information levels. Neurons with more information (activity, derivative of activities, contrast, motion, edges,...) generate more events per unit time, and access the interchip communication channel more frequently, while neurons with low activity consume less communication bandwidth. AER technology has been used and reported for the implementation of various type of image sensors or retinae: luminance with local agc, contrast retinae, motion retinae,... Also, there has been a proposal for realizing programmable kernel image convolution chips. Such convolution chips would contain an array of pixels that perform weighted addition of events. Once a pixel has added sufficient event contributions to reach a fixed threshold, the pixel fires an event, which is then routed out of the chip for further processing. Such convolution chips have been proposed to be implemented using pulsed current mode mixed analog and digital circuit techniques. In this paper we present a fully digital pixel implementation to perform the weighted additions and fire the events. This way, for a given technology, there is a fully digital implementation reference against which compare the mixed signal implementations. We have designed, implemented and tested a fully digital AER convolution pixel. This pixel will be used to implement a full AER convolution chip for programmable kernel image convolution processing.

  10. Porous carbonaceous electrode structure and method for secondary electrochemical cell

    Science.gov (United States)

    Kaun, Thomas D.

    1977-03-08

    Positive and negative electrodes are provided as rigid, porous carbonaceous matrices with particulate active material fixedly embedded. Active material such as metal chalcogenides, solid alloys of alkali metal or alkaline earth metals along with other metals and their oxides in particulate form are blended with a thermosetting resin and a solid volatile to form a paste mixture. Various electrically conductive powders or current collector structures can be blended or embedded into the paste mixture which can be molded to the desired electrode shape. The molded paste is heated to a temperature at which the volatile transforms into vapor to impart porosity as the resin begins to cure into a rigid solid structure.

  11. Tropical Cyclone Intensity Estimation Using Deep Convolutional Neural Networks

    Science.gov (United States)

    Maskey, Manil; Cecil, Dan; Ramachandran, Rahul; Miller, Jeffrey J.

    2018-01-01

    Estimating tropical cyclone intensity by just using satellite image is a challenging problem. With successful application of the Dvorak technique for more than 30 years along with some modifications and improvements, it is still used worldwide for tropical cyclone intensity estimation. A number of semi-automated techniques have been derived using the original Dvorak technique. However, these techniques suffer from subjective bias as evident from the most recent estimations on October 10, 2017 at 1500 UTC for Tropical Storm Ophelia: The Dvorak intensity estimates ranged from T2.3/33 kt (Tropical Cyclone Number 2.3/33 knots) from UW-CIMSS (University of Wisconsin-Madison - Cooperative Institute for Meteorological Satellite Studies) to T3.0/45 kt from TAFB (the National Hurricane Center's Tropical Analysis and Forecast Branch) to T4.0/65 kt from SAB (NOAA/NESDIS Satellite Analysis Branch). In this particular case, two human experts at TAFB and SAB differed by 20 knots in their Dvorak analyses, and the automated version at the University of Wisconsin was 12 knots lower than either of them. The National Hurricane Center (NHC) estimates about 10-20 percent uncertainty in its post analysis when only satellite based estimates are available. The success of the Dvorak technique proves that spatial patterns in infrared (IR) imagery strongly relate to tropical cyclone intensity. This study aims to utilize deep learning, the current state of the art in pattern recognition and image recognition, to address the need for an automated and objective tropical cyclone intensity estimation. Deep learning is a multi-layer neural network consisting of several layers of simple computational units. It learns discriminative features without relying on a human expert to identify which features are important. Our study mainly focuses on convolutional neural network (CNN), a deep learning algorithm, to develop an objective tropical cyclone intensity estimation. CNN is a supervised learning

  12. Deep convolutional neural networks as strong gravitational lens detectors

    Science.gov (United States)

    Schaefer, C.; Geiger, M.; Kuntzer, T.; Kneib, J.-P.

    2018-03-01

    Context. Future large-scale surveys with high-resolution imaging will provide us with approximately 105 new strong galaxy-scale lenses. These strong-lensing systems will be contained in large data amounts, however, which are beyond the capacity of human experts to visually classify in an unbiased way. Aim. We present a new strong gravitational lens finder based on convolutional neural networks (CNNs). The method was applied to the strong-lensing challenge organized by the Bologna Lens Factory. It achieved first and third place, respectively, on the space-based data set and the ground-based data set. The goal was to find a fully automated lens finder for ground-based and space-based surveys that minimizes human inspection. Methods: We compared the results of our CNN architecture and three new variations ("invariant" "views" and "residual") on the simulated data of the challenge. Each method was trained separately five times on 17 000 simulated images, cross-validated using 3000 images, and then applied to a test set with 100 000 images. We used two different metrics for evaluation, the area under the receiver operating characteristic curve (AUC) score, and the recall with no false positive (Recall0FP). Results: For ground-based data, our best method achieved an AUC score of 0.977 and a Recall0FP of 0.50. For space-based data, our best method achieved an AUC score of 0.940 and a Recall0FP of 0.32. Adding dihedral invariance to the CNN architecture diminished the overall score on space-based data, but achieved a higher no-contamination recall. We found that using committees of five CNNs produced the best recall at zero contamination and consistently scored better AUC than a single CNN. Conclusions: We found that for every variation of our CNN lensfinder, we achieved AUC scores close to 1 within 6%. A deeper network did not outperform simpler CNN models either. This indicates that more complex networks are not needed to model the simulated lenses. To verify this, more

  13. A Fast Numerical Method for Max-Convolution and the Application to Efficient Max-Product Inference in Bayesian Networks.

    Science.gov (United States)

    Serang, Oliver

    2015-08-01

    Observations depending on sums of random variables are common throughout many fields; however, no efficient solution is currently known for performing max-product inference on these sums of general discrete distributions (max-product inference can be used to obtain maximum a posteriori estimates). The limiting step to max-product inference is the max-convolution problem (sometimes presented in log-transformed form and denoted as "infimal convolution," "min-convolution," or "convolution on the tropical semiring"), for which no O(k log(k)) method is currently known. Presented here is an O(k log(k)) numerical method for estimating the max-convolution of two nonnegative vectors (e.g., two probability mass functions), where k is the length of the larger vector. This numerical max-convolution method is then demonstrated by performing fast max-product inference on a convolution tree, a data structure for performing fast inference given information on the sum of n discrete random variables in O(nk log(nk)log(n)) steps (where each random variable has an arbitrary prior distribution on k contiguous possible states). The numerical max-convolution method can be applied to specialized classes of hidden Markov models to reduce the runtime of computing the Viterbi path from nk(2) to nk log(k), and has potential application to the all-pairs shortest paths problem.

  14. Transparent Electrodes for Efficient Optoelectronics

    KAUST Repository

    Morales-Masis, Monica

    2017-03-30

    With the development of new generations of optoelectronic devices that combine high performance and novel functionalities (e.g., flexibility/bendability, adaptability, semi or full transparency), several classes of transparent electrodes have been developed in recent years. These range from optimized transparent conductive oxides (TCOs), which are historically the most commonly used transparent electrodes, to new electrodes made from nano- and 2D materials (e.g., metal nanowire networks and graphene), and to hybrid electrodes that integrate TCOs or dielectrics with nanowires, metal grids, or ultrathin metal films. Here, the most relevant transparent electrodes developed to date are introduced, their fundamental properties are described, and their materials are classified according to specific application requirements in high efficiency solar cells and flexible organic light-emitting diodes (OLEDs). This information serves as a guideline for selecting and developing appropriate transparent electrodes according to intended application requirements and functionality.

  15. Transparent Electrodes for Efficient Optoelectronics

    KAUST Repository

    Morales-Masis, Monica; De Wolf, Stefaan; Woods-Robinson, Rachel; Ager, Joel W.; Ballif, Christophe

    2017-01-01

    With the development of new generations of optoelectronic devices that combine high performance and novel functionalities (e.g., flexibility/bendability, adaptability, semi or full transparency), several classes of transparent electrodes have been developed in recent years. These range from optimized transparent conductive oxides (TCOs), which are historically the most commonly used transparent electrodes, to new electrodes made from nano- and 2D materials (e.g., metal nanowire networks and graphene), and to hybrid electrodes that integrate TCOs or dielectrics with nanowires, metal grids, or ultrathin metal films. Here, the most relevant transparent electrodes developed to date are introduced, their fundamental properties are described, and their materials are classified according to specific application requirements in high efficiency solar cells and flexible organic light-emitting diodes (OLEDs). This information serves as a guideline for selecting and developing appropriate transparent electrodes according to intended application requirements and functionality.

  16. Estimating the number of sources in a noisy convolutive mixture using BIC

    DEFF Research Database (Denmark)

    Olsson, Rasmus Kongsgaard; Hansen, Lars Kai

    2004-01-01

    The number of source signals in a noisy convolutive mixture is determined based on the exact log-likelihoods of the candidate models. In (Olsson and Hansen, 2004), a novel probabilistic blind source separator was introduced that is based solely on the time-varying second-order statistics of the s......The number of source signals in a noisy convolutive mixture is determined based on the exact log-likelihoods of the candidate models. In (Olsson and Hansen, 2004), a novel probabilistic blind source separator was introduced that is based solely on the time-varying second-order statistics...

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

    International Nuclear Information System (INIS)

    Acciarri, R.; Adams, C.; An, R.; Asaadi, J.; Auger, M.

    2017-01-01

    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 LArTPC at or near ground level.

  18. Alternate symbol inversion for improved symbol synchronization in convolutionally coded systems

    Science.gov (United States)

    Simon, M. K.; Smith, J. G.

    1980-01-01

    Inverting alternate symbols of the encoder output of a convolutionally coded system provides sufficient density of symbol transitions to guarantee adequate symbol synchronizer performance, a guarantee otherwise lacking. Although alternate symbol inversion may increase or decrease the average transition density, depending on the data source model, it produces a maximum number of contiguous symbols without transition for a particular class of convolutional codes, independent of the data source model. Further, this maximum is sufficiently small to guarantee acceptable symbol synchronizer performance for typical applications. Subsequent inversion of alternate detected symbols permits proper decoding.

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

    Energy Technology Data Exchange (ETDEWEB)

    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.; Fernandez, R. Castillo; Cavanna, F.; Chen, H.; Church, E.; Cianci, D.; Collin, G. H.; Conrad, J. M.; Convery, M.; Crespo-Anad?n, J. I.; Del Tutto, M.; Devitt, D.; Dytman, S.; Eberly, B.; Ereditato, A.; Sanchez, L. Escudero; 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.; Caicedo, D. A. Martinez; 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.; S?ldner-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.

  20. Convolutional Encoder and Viterbi Decoder Using SOPC For Variable Constraint Length

    DEFF Research Database (Denmark)

    Kulkarni, Anuradha; Dnyaneshwar, Mantri; Prasad, Neeli R.

    2013-01-01

    Convolution encoder and Viterbi decoder are the basic and important blocks in any Code Division Multiple Accesses (CDMA). They are widely used in communication system due to their error correcting capability But the performance degrades with variable constraint length. In this context to have...... detailed analysis, this paper deals with the implementation of convolution encoder and Viterbi decoder using system on programming chip (SOPC). It uses variable constraint length of 7, 8 and 9 bits for 1/2 and 1/3 code rates. By analyzing the Viterbi algorithm it is seen that our algorithm has a better...

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

    Science.gov (United States)

    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.

  2. Electrode for disintegrating metallic material

    International Nuclear Information System (INIS)

    Persang, J.C.

    1985-01-01

    A graphite electrode is provided for disintegrating and removing metallic material from a workpiece, e.g., such as portions of a nuclear reactor to be repaired while in an underwater and/or radioactive environment. The electrode is provided with a plurality of openings extending outwardly, and a manifold for supplying a mixture of water and compressed gas to be discharged through the openings for sweeping away the disintegrated metallic material during use of the electrode

  3. Ion-selective electrode reviews

    CERN Document Server

    Thomas, J D R

    1982-01-01

    Ion-Selective Electrode Reviews, Volume 3, provides a review of articles on ion-selective electrodes (ISEs). The volume begins with an article on methods based on titration procedures for surfactant analysis, which have been developed for discrete batch operation and for continuous AutoAnalyser use. Separate chapters deal with detection limits of ion-selective electrodes; the possibility of using inorganic ion-exchange materials as ion-sensors; and the effect of solvent on potentials of cells with ion-selective electrodes. Also included is a chapter on advances in calibration procedures, the d

  4. Field testing of sulphide electrodes

    International Nuclear Information System (INIS)

    Singh, P.R.; Gaonkar, K.B.; Gadiyar, H.S.

    1993-01-01

    Sulphide ion selective electrodes have been developed at BARC, for determination of Ag + and S - ions directly and Cl - and CN - ions indirectly. The electrodes were tested for their use in sulphide environments in the EAD (Effluent After Dilution) stream at the Heavy Water Plant, Kota. The electrodes are suitable in the concentration range of 16000 ppm to 0.002 ppm, with a slope of 29-31 mV per decade change in the sulphide ion concentration. The response time is less than 10 seconds. These electrodes are reliable for continuous on-line use for a long period. (author). 7 refs., 11 figs., 1 tab

  5. Coated carbon nanotube array electrodes

    Science.gov (United States)

    Ren, Zhifeng [Newton, MA; Wen, Jian [Newton, MA; Chen, Jinghua [Chestnut Hill, MA; Huang, Zhongping [Belmont, MA; Wang, Dezhi [Wellesley, MA

    2008-10-28

    The present invention provides conductive carbon nanotube (CNT) electrode materials comprising aligned CNT substrates coated with an electrically conducting polymer, and the fabrication of electrodes for use in high performance electrical energy storage devices. In particular, the present invention provides conductive CNTs electrode material whose electrical properties render them especially suitable for use in high efficiency rechargeable batteries. The present invention also provides methods for obtaining surface modified conductive CNT electrode materials comprising an array of individual linear, aligned CNTs having a uniform surface coating of an electrically conductive polymer such as polypyrrole, and their use in electrical energy storage devices.

  6. Microbial electrode sensor for alcohols

    Energy Technology Data Exchange (ETDEWEB)

    Hikuma, M [Ajinomoto Co., Inc., Kawasaki, Japan; Kubo, T; Yasuda, T; Karube, I; Suzuki, S

    1979-10-01

    A microbial electrode consisting of immobilized microorganisms, a gas permeable Teflon membrane, and an oxygen electrode was prepared for the continuous determination of methyl and ethyl alcohols. Immobilized Trichosporon brassicae was employed for a microbial electrode sensor for ethyl alcohol. When a sample solution containing ethyl alcohol was injected into a microbial electrode system, the current of the electrode decreased markedly with time until a steady state was reached. The response time was within 10 min by the steady state method and within 6 min by the pulse method. A linear relationship was observed between the current decrease and the concentration of ethyl alcohol below 22.5 mg/liter. The current was reproducible within +- 6% of the relative error when a sample solution containing 16.5 mg/liter ethyl alcohol. The standard deviation was 0.5 mg/liter in 40 experiments. The selectivity of the microbial electrode sensor for ethyl alcohol was satisfactory. The microbial electrode sensor was applied to a fermentation broth of yeasts and satisfactory comparative results were obtained (correlation coefficient 0.98). The current output of the microbial electrode sensor was almost constant for more than three weeks and 2100 assays. A microbial electrode sensor using immobilized bacteria for methyl alcohol was also described.

  7. Lithium alloy negative electrodes

    Science.gov (United States)

    Huggins, Robert A.

    The 1996 announcement by Fuji Photo Film of the development of lithium batteries containing convertible metal oxides has caused a great deal of renewed interest in lithium alloys as alternative materials for use in the negative electrode of rechargeable lithium cells. The earlier work on lithium alloys, both at elevated and ambient temperatures is briefly reviewed. Basic principles relating thermodynamics, phase diagrams and electrochemical properties under near-equilibrium conditions are discussed, with the Li-Sn system as an example. Second-phase nucleation, and its hindrance under dynamic conditions plays an important role in determining deviations from equilibrium behavior. Two general types of composite microstructure electrodes, those with a mixed-conducting matrix, and those with a solid electrolyte matrix, are discussed. The Li-Sn-Si system at elevated temperatures, and the Li-Sn-Cd at ambient temperatures are shown to be examples of mixed-conducting matrix microstructures. The convertible oxides are an example of the solid electrolyte matrix type. Although the reversible capacity can be very large in this case, the first cycle irreversible capacity required to convert the oxides to alloys may be a significant handicap.

  8. Evaluation of high-perimeter electrode designs for deep brain stimulation

    Science.gov (United States)

    Howell, Bryan; Grill, Warren M.

    2014-08-01

    Objective. Deep brain stimulation (DBS) is an effective treatment for movement disorders and a promising therapy for treating epilepsy and psychiatric disorders. Despite its clinical success, complications including infections and mis-programing following surgical replacement of the battery-powered implantable pulse generator adversely impact the safety profile of this therapy. We sought to decrease power consumption and extend battery life by modifying the electrode geometry to increase stimulation efficiency. The specific goal of this study was to determine whether electrode contact perimeter or area had a greater effect on increasing stimulation efficiency. Approach. Finite-element method (FEM) models of eight prototype electrode designs were used to calculate the electrode access resistance, and the FEM models were coupled with cable models of passing axons to quantify stimulation efficiency. We also measured in vitro the electrical properties of the prototype electrode designs and measured in vivo the stimulation efficiency following acute implantation in anesthetized cats. Main results. Area had a greater effect than perimeter on altering the electrode access resistance; electrode (access or dynamic) resistance alone did not predict stimulation efficiency because efficiency was dependent on the shape of the potential distribution in the tissue; and, quantitative assessment of stimulation efficiency required consideration of the effects of the electrode-tissue interface impedance. Significance. These results advance understanding of the features of electrode geometry that are important for designing the next generation of efficient DBS electrodes.

  9. Gas sensor with multiple internal reference electrodes and sensing electrodes

    DEFF Research Database (Denmark)

    2016-01-01

    The invention relates to a potentiometric gas sensor, or potentiometric gas detection element, with multiple internal reference electrodes and multiple sensing electrodes for determining the concentrations of gas components in a gaseous mixture. The sensor for gas detection comprises: a solid...

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

    Science.gov (United States)

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

    2014-03-01

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

  11. Simultaneous reconstruction of outer boundary shape and conductivity distribution in electrical impedance tomography

    KAUST Repository

    Hyvö nen, Nuutti

    2016-01-01

    The simultaneous retrieval of the exterior boundary shape and the interior admittivity distribution of an examined body in electrical impedance tomography is considered. The reconstruction method is built for the complete electrode model

  12. Exploring the effects of transducer models when training convolutional neural networks to eliminate reflection artifacts in experimental photoacoustic images

    Science.gov (United States)

    Allman, Derek; Reiter, Austin; Bell, Muyinatu

    2018-02-01

    We previously proposed a method of removing reflection artifacts in photoacoustic images that uses deep learning. Our approach generally relies on using simulated photoacoustic channel data to train a convolutional neural network (CNN) that is capable of distinguishing sources from artifacts based on unique differences in their spatial impulse responses (manifested as depth-based differences in wavefront shapes). In this paper, we directly compare a CNN trained with our previous continuous transducer model to a CNN trained with an updated discrete acoustic receiver model that more closely matches an experimental ultrasound transducer. These two CNNs were trained with simulated data and tested on experimental data. The CNN trained using the continuous receiver model correctly classified 100% of sources and 70.3% of artifacts in the experimental data. In contrast, the CNN trained using the discrete receiver model correctly classified 100% of sources and 89.7% of artifacts in the experimental images. The 19.4% increase in artifact classification accuracy indicates that an acoustic receiver model that closely mimics the experimental transducer plays an important role in improving the classification of artifacts in experimental photoacoustic data. Results are promising for developing a method to display CNN-based images that remove artifacts in addition to only displaying network-identified sources as previously proposed.

  13. Bearing Fault Diagnosis under Variable Speed Using Convolutional Neural Networks and the Stochastic Diagonal Levenberg-Marquardt Algorithm

    Directory of Open Access Journals (Sweden)

    Viet Tra

    2017-12-01

    Full Text Available This paper presents a novel method for diagnosing incipient bearing defects under variable operating speeds using convolutional neural networks (CNNs trained via the stochastic diagonal Levenberg-Marquardt (S-DLM algorithm. The CNNs utilize the spectral energy maps (SEMs of the acoustic emission (AE signals as inputs and automatically learn the optimal features, which yield the best discriminative models for diagnosing incipient bearing defects under variable operating speeds. The SEMs are two-dimensional maps that show the distribution of energy across different bands of the AE spectrum. It is hypothesized that the variation of a bearing’s speed would not alter the overall shape of the AE spectrum rather, it may only scale and translate it. Thus, at different speeds, the same defect would yield SEMs that are scaled and shifted versions of each other. This hypothesis is confirmed by the experimental results, where CNNs trained using the S-DLM algorithm yield significantly better diagnostic performance under variable operating speeds compared to existing methods. In this work, the performance of different training algorithms is also evaluated to select the best training algorithm for the CNNs. The proposed method is used to diagnose both single and compound defects at six different operating speeds.

  14. Light addressable gold electrodes

    Energy Technology Data Exchange (ETDEWEB)

    Khalid, Waqas

    2011-07-01

    The main objective carried out in this dissertation was to fabricate Light Amplified Potentiometric sensors (LAPS) based upon the semiconductor nanoparticles (quantum dots) instead of its bulk form. Quantum dots (QDs) were opted for this device fabrication because of their superior fluorescent, electric and catalytic properties. Also in comparison to their bulk counterparts they will make device small, light weighted and power consumption is much lower. QDs were immobilized on a Au substrate via 1,4 benzene dithiol (BDT) molecule. Initially a self-assembled monolayer (SAM) of BDT was established on Au substrate. Because of SAM, the conductivity of Au substrate decreased dramatically. Furthermore QDs were anchored with the help of BDT molecule on Au substrate. When QDs immobilized on Au substrate (QD/Au) via BDT molecule were irradiated with UV-visible light, electron-hole pairs were generated in QDs. The surface defect states in QDs trapped the excited electrons and long lived electron-hole pairs were formed. By the application of an appropriate bias potential on Au substrate the electrons could be supplied or extracted from the QDs via tunneling through BDT. Thus a cathodic or anodic current could be observed depending upon bias potential under illumination. However without light illumination the QD/Au electrode remained an insulator. To improve the device different modifications were made, including different substrates (Au evaporated on glass, Au evaporated on mica sheets and Au sputtered on SiO{sub 2}/Si) and different dithiol molecules (capped and uncapped biphenyl 4,4' dithiol and capped and uncapped 4,4' dimercaptostilbenes) were tried. Also different QD immobilization techniques (normal incubation, spin coating, layer by layer assembly (LbL) of polyelectrolytes and heat immobilization) were employed. This device was able to detect electrochemically different analytes depending upon the QDs incorporated. For example CdS QDs were able to detect 4

  15. Automated mammographic breast density estimation using a fully convolutional network.

    Science.gov (United States)

    Lee, Juhun; Nishikawa, Robert M

    2018-03-01

    The purpose of this study was to develop a fully automated algorithm for mammographic breast density estimation using deep learning. Our algorithm used a fully convolutional network, which is a deep learning framework for image segmentation, to segment both the breast and the dense fibroglandular areas on mammographic images. Using the segmented breast and dense areas, our algorithm computed the breast percent density (PD), which is the faction of dense area in a breast. Our dataset included full-field digital screening mammograms of 604 women, which included 1208 mediolateral oblique (MLO) and 1208 craniocaudal (CC) views. We allocated 455, 58, and 91 of 604 women and their exams into training, testing, and validation datasets, respectively. We established ground truth for the breast and the dense fibroglandular areas via manual segmentation and segmentation using a simple thresholding based on BI-RADS density assessments by radiologists, respectively. Using the mammograms and ground truth, we fine-tuned a pretrained deep learning network to train the network to segment both the breast and the fibroglandular areas. Using the validation dataset, we evaluated the performance of the proposed algorithm against radiologists' BI-RADS density assessments. Specifically, we conducted a correlation analysis between a BI-RADS density assessment of a given breast and its corresponding PD estimate by the proposed algorithm. In addition, we evaluated our algorithm in terms of its ability to classify the BI-RADS density using PD estimates, and its ability to provide consistent PD estimates for the left and the right breast and the MLO and CC views of the same women. To show the effectiveness of our algorithm, we compared the performance of our algorithm against a state of the art algorithm, laboratory for individualized breast radiodensity assessment (LIBRA). The PD estimated by our algorithm correlated well with BI-RADS density ratings by radiologists. Pearson's rho values of

  16. DEFORMATION INFLUENCE ON A LIFETIME OF WELDING ELECTRODE TIPS

    Directory of Open Access Journals (Sweden)

    Ján Viňáš

    2009-02-01

    Full Text Available The contribution deals with the influence of welding electrode tips deformation on their lifetime. The influence of material properties, production technology and the intensity of welding electrodes load on their lifetime are presented. The electrode tips of the most used type of CuCr1Zr alloy of three basic standard shapes before and after the process of welding are evaluated. The process of welding is realized with low, middle and maximum welding parameters on programmable pneumatic spot welding machine VTS BPK 20. The influence of welding parameters on chosen material characteristics of welding tips is observed. Through the use of upsetting test, dependency of forming strength and deformation of material on used technology of welding tip production is observed.

  17. EDM Electrode for Internal Grooves

    Science.gov (United States)

    Ramani, V.; Werner, A.

    1985-01-01

    Electroerosive process inexpensive alternative to broaching. Hollow brass electrodes, soldered at one end to stainless-steel holding ring, held in grooves in mandrel. These electrodes used to machine grooves electrically in stainless-steel tube three-eights inch (9.5 millimeters) in diameter. Tool used on tubes already in place in equipment.

  18. Making EDM Electrodes By Stereolithography

    Science.gov (United States)

    Barlas, Philip A.

    1988-01-01

    Stereolithography is computer-aided manufacturing technique. Used to make models and molds of electrodes for electrical-discharge machining (EDM). Eliminates intermediate steps in fabrication of plastic model of object used in making EDM electrode to manufacture object or mold for object.

  19. Surface-modified electrodes (SME)

    NARCIS (Netherlands)

    Schreurs, J.P.G.M.; Barendrecht, E.

    1984-01-01

    This review deals with the literature (covered up to August 1983), the characterization and the applications of Surface-Modified Electrodes (SME). As a special class of SME's, the Enzyme-Modified Electrode (EME) is introduced. Three types of modification procedures are distinguished; i.e. covalent

  20. Electrochemical photovoltaic cells and electrodes

    Science.gov (United States)

    Skotheim, Terje A.

    1984-01-01

    Improved electrochemical photovoltaic cells and electrodes for use therein, particularly electrodes employing amorphous silicon or polyacetylene coating are produced by a process which includes filling pinholes or porous openings in the coatings by electrochemical oxidation of selected monomers to deposit insulating polymer in the openings.