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)

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.

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...

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)

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

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.

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.

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.

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.

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

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

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

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.

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.

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.

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.

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.

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.

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...

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.

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.

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...

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.

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.

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.

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

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....

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

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%.

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...

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...

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.

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.

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.

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.

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....

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...

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).

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...

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.

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.

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.

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.

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.)

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)

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.

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

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

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.

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.

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...

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.

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

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) =.

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.

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.

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.

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.

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

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)

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.

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.

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.

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

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.

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.

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...

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.

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

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....

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

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.

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...

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.

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.

Zinc electrode - its behaviour in the nickel oxide-zinc accumulator

Energy Technology Data Exchange (ETDEWEB)

1984-01-01

Certain aspects of zinc electrode reaction and behavior are investigated in view of their application to batteries. The properties of the zinc electrode in a battery system are discussed, emphasizing porous structure. Shape change is emphasized as the most important factor leading to limited battery cycle life. It is shown that two existing models of shape change based on electroosmosis and current distribution are unable to consistently describe observed phenomena. The first stages of electrocrystallization are studied and the surface reactions between the silver substrate and the deposited zinc layer are investigated. The reaction mechanism of zinc and amalgamated zinc in an alkaline electrolyte is addressed, and the batter system is studied to obtain information on cycling behavior and on the shape change phenomenon. The effect on cycle behavior of diferent amalgamation techniques of the zinc electrode and several additives is addressed. Impedance measurements on zinc electrodes are considered, and battery behavior is correlated with changes in the zinc electrode during cycling. 193 references.

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

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.

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...

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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

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 ...

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.

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.

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...

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.

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.

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.

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.

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.

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)

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.

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.

Fabrication and characterization of a nanometer-sized optical fiber electrode based on selective chemical etching for scanning electrochemical/optical microscopy.

Science.gov (United States)

Maruyama, Kenichi; Ohkawa, Hiroyuki; Ogawa, Sho; Ueda, Akio; Niwa, Osamu; Suzuki, Koji

2006-03-15

We have already reported a method for fabricating ultramicroelectrodes (Suzuki, K. JP Patent, 2004-45394, 2004). This method is based on the selective chemical etching of optical fibers. In this work, we undertake a detailed investigation involving a combination of etched optical fibers with various types of tapered tip (protruding-shape, double- (or pencil-) shape and triple-tapered electrode) and insulation with electrophoretic paint. Our goal is to establish a method for fabricating nanometer-sized optical fiber electrodes with high reproducibility. As a result, we realized pencil-shaped and triple-tapered electrodes that had radii in the nanometer range with high reproducibility. These nanometer-sized electrodes showed well-defined sigmoidal curves and stable diffusion-limited responses with cyclic voltammetry. The pencil-shaped optical fiber, which has a conical tip with a cone angle of 20 degrees , was effective for controlling the electrode radius. The pencil-shaped electrodes had higher reproducibility and smaller electrode radii (r(app) etched optical fiber electrodes. By using a pencil-shaped electrode with a 105-nm radius as a probe, we obtained simultaneous electrochemical and optical images of an implantable interdigitated array electrode. We achieved nanometer-scale resolution with a combination of scanning electrochemical microscopy SECM and optical microscopy. The resolution of the electrochemical and optical images indicated sizes of 300 and 930 nm, respectively. The neurites of living PC12 cells were also successfully imaged on a 1.6-microm scale by using the negative feedback mode of an SECM.

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...

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...

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.

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

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.

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.

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.

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...

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

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...

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.

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.

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.

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

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

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

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.

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...

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.

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.

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

Off-resonance artifacts correction with convolution in k-space (ORACLE).

Science.gov (United States)

Lin, Wei; Huang, Feng; Simonotto, Enrico; Duensing, George R; Reykowski, Arne

2012-06-01

Off-resonance artifacts hinder the wider applicability of echo-planar imaging and non-Cartesian MRI methods such as radial and spiral. In this work, a general and rapid method is proposed for off-resonance artifacts correction based on data convolution in k-space. The acquired k-space is divided into multiple segments based on their acquisition times. Off-resonance-induced artifact within each segment is removed by applying a convolution kernel, which is the Fourier transform of an off-resonance correcting spatial phase modulation term. The field map is determined from the inverse Fourier transform of a basis kernel, which is calibrated from data fitting in k-space. The technique was demonstrated in phantom and in vivo studies for radial, spiral and echo-planar imaging datasets. For radial acquisitions, the proposed method allows the self-calibration of the field map from the imaging data, when an alternating view-angle ordering scheme is used. An additional advantage for off-resonance artifacts correction based on data convolution in k-space is the reusability of convolution kernels to images acquired with the same sequence but different contrasts. Copyright © 2011 Wiley-Liss, Inc.

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.

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.

The applications of carbon nanomaterials in fiber-shaped energy storage devices

Science.gov (United States)

Wu, Jingxia; Hong, Yang; Wang, Bingjie

2018-01-01

As a promising candidate for future demand, fiber-shaped electrochemical energy storage devices, such as supercapacitors and lithium-ion batteries have obtained considerable attention from academy to industry. Carbon nanomaterials, such as carbon nanotube and graphene, have been widely investigated as electrode materials due to their merits of light weight, flexibility and high capacitance. In this review, recent progress of carbon nanomaterials in flexible fiber-shaped energy storage devices has been summarized in accordance with the development of fibrous electrodes, including the diversified electrode preparation, functional and intelligent device structure, and large-scale production of fibrous electrodes or devices. Project supported by the National Natural Science Foundation of China (Nos. 21634003, 21604012).

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.

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

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...

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.

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

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

Experimental investigation of cathode spots and plasma jets behavior subjected to two kinds of axial magnetic field electrodes

International Nuclear Information System (INIS)

Wang, Lijun; Deng, Jie; Zhou, Xin; Jia, Shenli; Qian, Zhonghao; Shi, Zongqian

2016-01-01

In this paper, cathode spot plasma jet (CSPJ) rotation and cathode spots behavior subjected to two kinds of large diameter axial magnetic field (AMF) electrode (cup-shaped and coil-shaped) are studied and analyzed based on experiments. The influence of gap distances on the CSPJ rotational behavior is analyzed. Experimental results show that CSPJ rotational phenomena extensively exist in the vacuum interrupters, and CSPJ rotational direction is along the direction of composite magnetic field (mainly the combination of the axial and azimuthal components). For coil-shaped and cup-shaped AMF electrodes, the rotational or inclination phenomena before the current peak value are much more significant than that after current peak value (for the same arc current), which is related to the larger ratio of azimuthal magnetic field B_t and AMF B_z (B_t/B_z). With the increase of the gap distance, the AMF strength decreases, when the arc current is kept as constant, the azimuthal magnetic field is kept invariable, the ratio between azimuthal magnetic field and AMF is increased, which results in the increase of rotational effect. For cathode spots motion, compared with cup-shaped electrode, coil-shaped electrode has the inverse AMF direction. The Robson drift direction of cathode spots of coil-shaped electrode is opposite to that of cup-shaped electrode. With the increase of gap distance, the Robson angle is decreased, which is associated with the reduced AMF strength. Erosion imprints of anode and cathode are also related to the CSPJ rotational phenomena and cathode spots behavior. The noise of arc voltage in the initial arcing stage is related to the weaker AMF.

Experimental investigation of cathode spots and plasma jets behavior subjected to two kinds of axial magnetic field electrodes

Energy Technology Data Exchange (ETDEWEB)

Wang, Lijun; Deng, Jie; Zhou, Xin; Jia, Shenli; Qian, Zhonghao; Shi, Zongqian [State Key Laboratory of Electrical Insulation and Power Equipment, Xi' an Jiaotong University, Xi' an 710049 (China)

2016-04-15

In this paper, cathode spot plasma jet (CSPJ) rotation and cathode spots behavior subjected to two kinds of large diameter axial magnetic field (AMF) electrode (cup-shaped and coil-shaped) are studied and analyzed based on experiments. The influence of gap distances on the CSPJ rotational behavior is analyzed. Experimental results show that CSPJ rotational phenomena extensively exist in the vacuum interrupters, and CSPJ rotational direction is along the direction of composite magnetic field (mainly the combination of the axial and azimuthal components). For coil-shaped and cup-shaped AMF electrodes, the rotational or inclination phenomena before the current peak value are much more significant than that after current peak value (for the same arc current), which is related to the larger ratio of azimuthal magnetic field B{sub t} and AMF B{sub z} (B{sub t}/B{sub z}). With the increase of the gap distance, the AMF strength decreases, when the arc current is kept as constant, the azimuthal magnetic field is kept invariable, the ratio between azimuthal magnetic field and AMF is increased, which results in the increase of rotational effect. For cathode spots motion, compared with cup-shaped electrode, coil-shaped electrode has the inverse AMF direction. The Robson drift direction of cathode spots of coil-shaped electrode is opposite to that of cup-shaped electrode. With the increase of gap distance, the Robson angle is decreased, which is associated with the reduced AMF strength. Erosion imprints of anode and cathode are also related to the CSPJ rotational phenomena and cathode spots behavior. The noise of arc voltage in the initial arcing stage is related to the weaker AMF.

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)

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

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).

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...

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...

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.

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

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.

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.

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.

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...

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)

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....

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.

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.

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

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

Description of corrections on electrode polarization impedance using isopotential interface factor

Directory of Open Access Journals (Sweden)

John Alexander Gomez Sanchez

2012-08-01

Full Text Available In this paper, we propose an equation and define the Isopotential Interface Factor (IIF to quantify the contribution of electrode polarization impedance in two tetrapolar electrode shapes. The first tetrapolar electrode geometry shape was adjacent and the second axial concentric, both probes were made of stainless steel (AISI 304. The experiments were carried out with an impedance analyzer (Solartron 1260 using a frequency range between 0.1 Hz and 8 MHz. Based on a theoretical simplification, the experimental results show a lower value of the IIF in the axial concentric tetrapolar electrode system which caused a lower correction of interface value. The higher value of the IIF in the adjacent electrode system was KEEI (1Hz, 0.28 mS/cm = 1.41 and decreased when the frequency and conductance were increased, whereas in the axial concentric electrode system was KEEI (1Hz, 0.28 mS/cm = 0.08. The average isopotential interface factor throughout the whole range of conductivities and frequencies was 0.23 in the adjacent electrode system and 0.02 in the axial concentric electrode system. The index of inherent electrical anisotropy (IEA was used to present an analysis of electrical anisotropy of biceps brachii muscle in vitro using the corrections of both tetrapolar electrode systems. A higher IEA was present in lower frequency where the variation below 1 kHz was 15 % in adjacent electrode configuration and 26 % in the axial concentric probe with respect to full range. The IIF is then shown that it can be used to describe the quality of an electrode system.

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

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...

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.

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...

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.

Carbon nanotube-coated macroporous sponge for microbial fuel cell electrodes

KAUST Repository

Xie, Xing

2012-01-01

The materials that are used to make electrodes and their internal structures significantly affect microbial fuel cell (MFC) performance. In this study, we describe a carbon nanotube (CNT)-sponge composite prepared by coating a sponge with CNTs. Compared to the CNT-coated textile electrodes evaluated in prior studies, CNT-sponge electrodes had lower internal resistance, greater stability, more tunable and uniform macroporous structure (pores up to 1 mm in diameter), and improved mechanical properties. The CNT-sponge composite also provided a three-dimensional scaffold that was favorable for microbial colonization and catalytic decoration. Using a batch-fed H-shaped MFC outfitted with CNT-sponge electrodes, an areal power density of 1.24 W m -2 was achieved when treating domestic wastewater. The maximum volumetric power density of a continuously fed plate-shaped MFC was 182 W m -3. To our knowledge, these are the highest values obtained to date for MFCs fed domestic wastewater: 2.5 times the previously reported maximum areal power density and 12 times the previously reported maximum volumetric power density. © 2011 The Royal Society of Chemistry.

Electrolyte solutions at curved electrodes. II. Microscopic approach.

Science.gov (United States)

Reindl, Andreas; Bier, Markus; Dietrich, S

2017-04-21

Density functional theory is used to describe electrolyte solutions in contact with electrodes of planar or spherical shape. For the electrolyte solutions, we consider the so-called civilized model, in which all species present are treated on equal footing. This allows us to discuss the features of the electric double layer in terms of the differential capacitance. The model provides insight into the microscopic structure of the electric double layer, which goes beyond the mesoscopic approach studied in Paper I. This enables us to judge the relevance of microscopic details, such as the radii of the particles forming the electrolyte solutions or the dipolar character of the solvent particles, and to compare the predictions of various models. Similar to Paper I, a general behavior is observed for small radii of the electrode in that in this limit the results become independent of the surface charge density and of the particle radii. However, for large electrode radii, non-trivial behaviors are observed. Especially the particle radii and the surface charge density strongly influence the capacitance. From the comparison with the Poisson-Boltzmann approach, it becomes apparent that the shape of the electrode determines whether the microscopic details of the full civilized model have to be taken into account or whether already simpler models yield acceptable predictions.

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

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.

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.

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

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.

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.

Tissue Damage, Temperature, and pH Induced by Different Electrode Arrays on Potato Pieces (Solanum tuberosum L.

Directory of Open Access Journals (Sweden)

Maraelys Morales González

2018-04-01

Full Text Available One of the most challenging problems of electrochemical therapy is the design and selection of suitable electrode array for cancer. The aim is to determine how two-dimensional spatial patterns of tissue damage, temperature, and pH induced in pieces of potato (Solanum tuberosum L., var. Mondial depend on electrode array with circular, elliptical, parabolic, and hyperbolic shape. The results show the similarity between the shapes of spatial patterns of tissue damage and electric field intensity, which, like temperature and pH take the same shape of electrode array. The adequate selection of suitable electrodes array requires an integrated analysis that involves, in a unified way, relevant information about the electrochemical process, which is essential to perform more efficiently way the therapeutic planning and the personalized therapy for patients with a cancerous tumor.

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)

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.

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

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.

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...

A Newly Developed Perfused Umbrella Electrode for Radiofrequency Ablation: An Ex Vivo Evaluation Study in Bovine Liver

International Nuclear Information System (INIS)

Bruners, Philipp; Pfeffer, Jochen; Kazim, Rana M.; Guenther, Rolf W.; Schmitz-Rode, Thomas; Mahnken, Andreas H.

2007-01-01

The purpose of this study was to evaluate the effectiveness of a newly developed perfused monopolar radiofrequency (RF) probe with an umbrella-shaped array. A perfused umbrella-shaped monopolar RF probe based on a LeVeen electrode (Boston Scientific Corp., Natick, MA, USA) with a 3-cm array diameter was developed. Five different configurations of this electrode were tested: (a) perfusion channel/endhole, (b) perfusion channel/endhole + sideholes, (c) 1 cm insulation removed at the tip, (d) 1 cm insulation removed at the tip + perfusion channel/endhole, and (e) 1 cm insulation removed at the tip + perfusion channel/endhole + sideholes. An unmodified LeVeen electrode served as a reference standard. RF ablations were performed in freshly excised bovine liver using a commercial monopolar RF system with a 200-W generator (RF 3000; Boston Scientific Corp.). Each electrode was tested 10 times applying the vendor's recommended ablation protocol combined with the preinjection of 2 ml 0.9% saline. Volumes and shapes of the lesions were compared. Lesions generated with the original LeVeen electrode showed a mean volume of 12.74 ± 0.52 cm 3 . Removing parts of the insulation led to larger coagulation volumes (22.65 ± 2.12 cm 3 ). Depending on the configuration, saline preinjection resulted in a further increase in coagulation volume (25.22 ± 3.37 to 31.28 ± 2.32 cm 3 ). Besides lesion volume, the shape of the ablation zone was influenced by the configuration of the electrode used. We conclude that saline preinjection in combination with increasing the active tip length of the umbrella-shaped LeVeen RF probe allows the reliable ablation of larger volumes in comparison to the originally configured electrode

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.

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...

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...

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.

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...

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.

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.

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.

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.

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)

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.

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.

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.

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.

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...

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

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.

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.)

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

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.

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.

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

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.

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

Science.gov (United States)

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

2017-05-24

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

A new approach for the evaluation of the effective electrode spacing in spherical ion chambers

Energy Technology Data Exchange (ETDEWEB)

Maghraby, Ahmed M., E-mail: maghrabism@yahoo.com [National Institute of Standards (NIS), Ionizing Radiation Metrology Laboratory, Tersa Street 12211, Giza P.O. Box: 136 (Egypt); Shqair, Mohammed [Physics Department, Faculty of Science and Humanities, Sattam Bin Abdul Aziz University, Alkharj (Saudi Arabia)

2016-10-21

Proper determination of the effective electrode spacing (d{sub eff}) of an ion chamber ensures proper determination of its collection efficiency either in continuous or in pulsed radiation in addition to the proper evaluation of the transit time. Boag's method for the determination of d{sub eff} assumes the spherical shape of the internal electrode of the spherical ion chambers which is not always true, except for some cases, its common shape is cylindrical. Current work provides a new approach for the evaluation of the effective electrode spacing in spherical ion chambers considering the cylindrical shape of the internal electrode. Results indicated that d{sub eff} values obtained through current work are less than those obtained using Boag's method by factors ranging from 12.1% to 26.9%. Current method also impacts the numerically evaluated collection efficiency (f) where values obtained differ by factors up to 3% at low potential (V) values while at high V values minor differences were noticed. Additionally, impacts on the evaluation of the transit time (τ{sub i}) were obtained. It is concluded that approximating the internal electrode as a sphere may result in false values of d{sub eff}, f, and τ{sub i}.

Hourglass-ShapeNetwork Based Semantic Segmentation for High Resolution Aerial Imagery

Directory of Open Access Journals (Sweden)

Yu Liu

2017-05-01

Full Text Available A new convolution neural network (CNN architecture for semantic segmentation of high resolution aerial imagery is proposed in this paper. The proposed architecture follows an hourglass-shaped network (HSN design being structured into encoding and decoding stages. By taking advantage of recent advances in CNN designs, we use the composed inception module to replace common convolutional layers, providing the network with multi-scale receptive areas with rich context. Additionally, in order to reduce spatial ambiguities in the up-sampling stage, skip connections with residual units are also employed to feed forward encoding-stage information directly to the decoder. Moreover, overlap inference is employed to alleviate boundary effects occurring when high resolution images are inferred from small-sized patches. Finally, we also propose a post-processing method based on weighted belief propagation to visually enhance the classification results. Extensive experiments based on the Vaihingen and Potsdam datasets demonstrate that the proposed architectures outperform three reference state-of-the-art network designs both numerically and visually.

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.

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

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.

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.

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.

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...

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.

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.

Dimensionality-varied convolutional neural network for spectral-spatial classification of hyperspectral data

Science.gov (United States)

Liu, Wanjun; Liang, Xuejian; Qu, Haicheng

2017-11-01

Hyperspectral image (HSI) classification is one of the most popular topics in remote sensing community. Traditional and deep learning-based classification methods were proposed constantly in recent years. In order to improve the classification accuracy and robustness, a dimensionality-varied convolutional neural network (DVCNN) was proposed in this paper. DVCNN was a novel deep architecture based on convolutional neural network (CNN). The input of DVCNN was a set of 3D patches selected from HSI which contained spectral-spatial joint information. In the following feature extraction process, each patch was transformed into some different 1D vectors by 3D convolution kernels, which were able to extract features from spectral-spatial data. The rest of DVCNN was about the same as general CNN and processed 2D matrix which was constituted by by all 1D data. So that the DVCNN could not only extract more accurate and rich features than CNN, but also fused spectral-spatial information to improve classification accuracy. Moreover, the robustness of network on water-absorption bands was enhanced in the process of spectral-spatial fusion by 3D convolution, and the calculation was simplified by dimensionality varied convolution. Experiments were performed on both Indian Pines and Pavia University scene datasets, and the results showed that the classification accuracy of DVCNN improved by 32.87% on Indian Pines and 19.63% on Pavia University scene than spectral-only CNN. The maximum accuracy improvement of DVCNN achievement was 13.72% compared with other state-of-the-art HSI classification methods, and the robustness of DVCNN on water-absorption bands noise was demonstrated.

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.

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

Cascaded K-means convolutional feature learner and its application to face recognition

Science.gov (United States)

Zhou, Daoxiang; Yang, Dan; Zhang, Xiaohong; Huang, Sheng; Feng, Shu

2017-09-01

Currently, considerable efforts have been devoted to devise image representation. However, handcrafted methods need strong domain knowledge and show low generalization ability, and conventional feature learning methods require enormous training data and rich parameters tuning experience. A lightened feature learner is presented to solve these problems with application to face recognition, which shares similar topology architecture as a convolutional neural network. Our model is divided into three components: cascaded convolution filters bank learning layer, nonlinear processing layer, and feature pooling layer. Specifically, in the filters learning layer, we use K-means to learn convolution filters. Features are extracted via convoluting images with the learned filters. Afterward, in the nonlinear processing layer, hyperbolic tangent is employed to capture the nonlinear feature. In the feature pooling layer, to remove the redundancy information and incorporate the spatial layout, we exploit multilevel spatial pyramid second-order pooling technique to pool the features in subregions and concatenate them together as the final representation. Extensive experiments on four representative datasets demonstrate the effectiveness and robustness of our model to various variations, yielding competitive recognition results on extended Yale B and FERET. In addition, our method achieves the best identification performance on AR and labeled faces in the wild datasets among the comparative methods.

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.

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....

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.

Paper-based potentiometric pH sensor using carbon electrode drawn by pencil

Science.gov (United States)

Kawahara, Ryotaro; Sahatiya, Parikshit; Badhulika, Sushmee; Uno, Shigeyasu

2018-04-01

A flexible and disposable paper-based pH sensor fabricated with a pencil-drawn working electrode and a Ag/AgCl paste reference electrode is demonstrated for the first time to show pH response by the potentiometric principle. The sensor substrate is made of chromatography paper with a wax-printed hydrophobic area, and various types of carbon pencils are tested as working electrodes. The pH sensitivities of the electrodes drawn by carbon pencils with different hardnesses range from 16.5 to 26.9 mV/pH. The proposed sensor is expected to be more robust against shape change in electrodes on a flexible substrate than other types of chemiresistive/amperometric pH sensors.

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.

Electrochemistry of thulium on inert electrodes and electrochemical formation of a Tm-Al alloy from molten chlorides

International Nuclear Information System (INIS)

Castrillejo, Y.; Fernandez, P.; Bermejo, M.R.; Barrado, E.; Martinez, A.M.

2009-01-01

The electrochemical behaviour of TmCl 3 solutions was studied in the eutectic LiCl-KCl in the temperature range 673-823 K using inert and reactive electrodes, i.e. W and Al, respectively. On an inert electrode, Tm(III) ions are reduced to metallic thulium through two consecutive steps: Tm(III) + 1e ↔ Tm(II) and Tm(II) + 2e ↔ Tm(0) The electroreduction of Tm(III) to Tm(II) was found to be quasi-reversible. The intrinsic rate constant of charge transfer, k 0 , as well as of the charge transfer coefficient, α, have been calculated by simulation of the cyclic voltammograms and logarithmic analysis of the convoluted curves. Electrocrystallization of thulium plays an important role in the electrodeposition process, being the nucleation mode affected by temperature. The diffusion coefficients of Tm(III) and Tm(II) ions have been found to be equal. The validity of the Arrhenius law was verified by plotting the variation of the logarithm of the diffusion coefficients vs. 1/T. The electrode reactions of Tm(III) solutions at an Al electrode were also investigated. The results showed that for the extraction of thulium from molten chlorides, the use of a reactive electrode made of aluminium leading to Al-Tm alloys seems to be a pertinent route. Potentiometric titrations of Tm(III) solutions with oxide donors, using a ytria stabilized zirconia electrode 'YSZE' as a pO 2- indicator electrode, have shown the formation of thulium oxychloride and thulium oxide and their corresponding solubility products have been determined at 723 K (pk s (TmOCl) = 8.0 ± 0.3 pk s (Tm 2 O 3 ) = 18.8 ± 0.7).

Evolutionary image simplification for lung nodule classification with convolutional neural networks.

Science.gov (United States)

Lückehe, Daniel; von Voigt, Gabriele

2018-05-29

Understanding decisions of deep learning techniques is important. Especially in the medical field, the reasons for a decision in a classification task are as crucial as the pure classification results. In this article, we propose a new approach to compute relevant parts of a medical image. Knowing the relevant parts makes it easier to understand decisions. In our approach, a convolutional neural network is employed to learn structures of images of lung nodules. Then, an evolutionary algorithm is applied to compute a simplified version of an unknown image based on the learned structures by the convolutional neural network. In the simplified version, irrelevant parts are removed from the original image. In the results, we show simplified images which allow the observer to focus on the relevant parts. In these images, more than 50% of the pixels are simplified. The simplified pixels do not change the meaning of the images based on the learned structures by the convolutional neural network. An experimental analysis shows the potential of the approach. Besides the examples of simplified images, we analyze the run time development. Simplified images make it easier to focus on relevant parts and to find reasons for a decision. The combination of an evolutionary algorithm employing a learned convolutional neural network is well suited for the simplification task. From a research perspective, it is interesting which areas of the images are simplified and which parts are taken as relevant.

Acral melanoma detection using a convolutional neural network for dermoscopy images.

Science.gov (United States)

Yu, Chanki; Yang, Sejung; Kim, Wonoh; Jung, Jinwoong; Chung, Kee-Yang; Lee, Sang Wook; Oh, Byungho

2018-01-01

Acral melanoma is the most common type of melanoma in Asians, and usually results in a poor prognosis due to late diagnosis. We applied a convolutional neural network to dermoscopy images of acral melanoma and benign nevi on the hands and feet and evaluated its usefulness for the early diagnosis of these conditions. A total of 724 dermoscopy images comprising acral melanoma (350 images from 81 patients) and benign nevi (374 images from 194 patients), and confirmed by histopathological examination, were analyzed in this study. To perform the 2-fold cross validation, we split them into two mutually exclusive subsets: half of the total image dataset was selected for training and the rest for testing, and we calculated the accuracy of diagnosis comparing it with the dermatologist's and non-expert's evaluation. The accuracy (percentage of true positive and true negative from all images) of the convolutional neural network was 83.51% and 80.23%, which was higher than the non-expert's evaluation (67.84%, 62.71%) and close to that of the expert (81.08%, 81.64%). Moreover, the convolutional neural network showed area-under-the-curve values like 0.8, 0.84 and Youden's index like 0.6795, 0.6073, which were similar score with the expert. Although further data analysis is necessary to improve their accuracy, convolutional neural networks would be helpful to detect acral melanoma from dermoscopy images of the hands and feet.

Influence of Electrodes Characteristics on The Performance of a Microbial Fuel Cell

Directory of Open Access Journals (Sweden)

Muhammad Hadi Radi

2017-07-01

Full Text Available A single chamber microbial fuel cell is designed incorporating microorganism as catalyst with Escherichia coli, Staphylococcus, Kelbssila bacteria as an electrolyte at pH =7 and an operating temperature of 30 C0 in batch mode. The electrodes are made of three different types of materials, namely; aluminum, copper and zinc. Each material is configurated at three different shape (circle, rectangle and square in three different cross sectional areas of (3.14,7.065and 12.56cm2. The distance between anode and cathode is fixed at different values of 0.5,1,2,4 and 6cm. Results indicate that electrodes of circular shape show the best performance among other shapes investigated in this study, however the area of the anode is found to affect the cell performance more than its shape. Using zinc as an anode material and copper as a cathode in circular shape with cross sectional area of 12.56 cm2 and a 2 cm distance between them output the best performance in comparison to other combinations investigated in this study.

REAL-TIME VIDEO SCALING BASED ON CONVOLUTION NEURAL NETWORK ARCHITECTURE

Directory of Open Access Journals (Sweden)

S Safinaz

2017-08-01

Full Text Available In recent years, video super resolution techniques becomes mandatory requirements to get high resolution videos. Many super resolution techniques researched but still video super resolution or scaling is a vital challenge. In this paper, we have presented a real-time video scaling based on convolution neural network architecture to eliminate the blurriness in the images and video frames and to provide better reconstruction quality while scaling of large datasets from lower resolution frames to high resolution frames. We compare our outcomes with multiple exiting algorithms. Our extensive results of proposed technique RemCNN (Reconstruction error minimization Convolution Neural Network shows that our model outperforms the existing technologies such as bicubic, bilinear, MCResNet and provide better reconstructed motioning images and video frames. The experimental results shows that our average PSNR result is 47.80474 considering upscale-2, 41.70209 for upscale-3 and 36.24503 for upscale-4 for Myanmar dataset which is very high in contrast to other existing techniques. This results proves our proposed model real-time video scaling based on convolution neural network architecture’s high efficiency and better performance.

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

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

Science.gov (United States)

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

2016-01-01

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

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.

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...

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.)

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.

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.

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

Directory of Open Access Journals (Sweden)

Yanjie Wang

2018-01-01

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

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

Science.gov (United States)

Liu, Jiayu; Zhu, Denglin; Chen, Hualing

2018-01-01

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

Design and Implementation of Convolutional Encoder and Viterbi Decoder Using FPGA.

Directory of Open Access Journals (Sweden)

Riham Ali Zbaid

2018-01-01

Full Text Available Keeping  the  fineness of data is the most significant thing in communication.There are many factors that affect the accuracy of the data when it is transmitted over the communication channel such as noise etc. to overcome these effects are encoding channels encryption.In this paper is used for one type of channel coding is convolutional codes. Convolution encoding is a Forward Error Correction (FEC method used in incessant one-way and real time communication links .It can offer a great development in the error bit rates so that small, low energy, and devices cheap transmission when used in applications such as satellites. In this paper highlight the design, simulation and implementation of convolution encoder and Viterbi decoder by using MATLAB- program (2011. SIMULINK HDL coder is used to convert MATLAB-SIMULINK models to VHDL using plates Altera Cyclone II code DE2-70. Simulation and evaluation of the implementation of the results coincided with the results of the design show the coinciding with the designed results.

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

Science.gov (United States)

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

2017-01-01

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

High-voltage electrode optimization towards uniform surface treatment by a pulsed volume discharge

International Nuclear Information System (INIS)

Ponomarev, A V; Pedos, M S; Scherbinin, S V; Mamontov, Y I; Ponomarev, S V

2015-01-01

In this study, the shape and material of the high-voltage electrode of an atmospheric pressure plasma generation system were optimised. The research was performed with the goal of achieving maximum uniformity of plasma treatment of the surface of the low-voltage electrode with a diameter of 100 mm. In order to generate low-temperature plasma with the volume of roughly 1 cubic decimetre, a pulsed volume discharge was used initiated with a corona discharge. The uniformity of the plasma in the region of the low-voltage electrode was assessed using a system for measuring the distribution of discharge current density. The system's low-voltage electrode - collector - was a disc of 100 mm in diameter, the conducting surface of which was divided into 64 radially located segments of equal surface area. The current at each segment was registered by a high-speed measuring system controlled by an ARM™-based 32-bit microcontroller. To facilitate the interpretation of results obtained, a computer program was developed to visualise the results. The program provides a 3D image of the current density distribution on the surface of the low-voltage electrode. Based on the results obtained an optimum shape for a high-voltage electrode was determined. Uniformity of the distribution of discharge current density in relation to distance between electrodes was studied. It was proven that the level of non-uniformity of current density distribution depends on the size of the gap between electrodes. Experiments indicated that it is advantageous to use graphite felt VGN-6 (Russian abbreviation) as the material of the high-voltage electrode's emitting surface. (paper)

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

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

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 ...

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.

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

Science.gov (United States)

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

2017-09-01

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

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...

The cataphoretic emitter effect exhibited in high intensity discharge lamp electrodes

Science.gov (United States)

Mentel, Juergen

2018-01-01

A mono-layer of atoms, electropositive with respect to the substrate atoms, forms a dipole layer, reducing its work function. Such a layer is generated by diffusion of emitter material from the interior of the substrate, by vapour deposition or by deposition of emitter material onto arc electrodes by cataphoresis. This cataphoretic emitter effect is investigated within metal halide lamps with transparent YAG ceramic burners, and within model lamps. Within the YAG lamps, arcs are operated with switched-dc current between rod shaped tungsten electrodes in high pressure Hg vapour seeded with metal iodides. Within the model lamps, dc arcs are operated between rod-shaped tungsten electrodes—one doped—in atmospheric pressure Ar. Electrode temperatures are determined by 1λ -pyrometry, combined with simulation of the electrode heat balance. Plasma temperatures, atom and ion densities of emitter material are determined by emission and absorption spectroscopy. Phase resolved measurements in YAG lamps seeded with CeI3, CsI, DyI3, TmI3 and LaI3 show, within the cathodic half period, a reduction of the electrode temperature and an enhanced metal ion density in front of the electrode, and an opposite behavior after phase reversal. With increasing operating frequency, the state of the cathode overlaps onto the anodic phase—except for Cs, being low in adsorption energy. Generally, the phase averaged electrode tip temperature is reduced by seeding a lamp with emitter material; its height depends on admixtures. Measurements at tungsten electrodes doped with ThO2, La2O3 and Ce2O3 within the model lamp show that evaporated emitter material is redeposited by an emitter ion current onto the electrode surface. It reduces the work function of tungsten cathodes above the evaporation temperature of the emitter material, too; and also of cold anodes, indicating a field reversal in front of them. The formation of an emitter spot at low cathode temperature and high emitter material

Two-dimensional nickel hydroxide nanosheets as high performance pseudo-capacitor electrodes

Science.gov (United States)

Bhat, Karthik S.; Nagaraja, H. S.

2018-04-01

Electrochemical supercapacitor is a vital technology for the progress of consistent energy harvesting devices. Herein, we report the fabrication of supercapacitor electrodes based on nickel hydroxide nanosheets synthesized via one-pot hydrothermal method. Structure and shape of synthesized materials were analyzed with XRD and SEM measurements. Pseudo-capacitive performances of the fabricated electrodes were evaluated through cyclic voltammetry and galvanostatic charge-discharge measurements with three-electrode configurations. Results indicated the specific capacitance of l80 F g-1 at 5 mV s-1 scan rate and complimented with capacitance retention of 76% for l500 cycles.

Improving the Separability of Deep Features with Discriminative Convolution Filters for RSI Classification

Directory of Open Access Journals (Sweden)

Na Liu

2018-03-01

Full Text Available The extraction of activation vectors (or deep features from the fully connected layers of a convolutional neural network (CNN model is widely used for remote sensing image (RSI representation. In this study, we propose to learn discriminative convolution filter (DCF based on class-specific separability criteria for linear transformation of deep features. In particular, two types of pretrained CNN called CaffeNet and VGG-VD16 are introduced to illustrate the generality of the proposed DCF. The activation vectors extracted from the fully connected layers of a CNN are rearranged into the form of an image matrix, from which a spatial arrangement of local patches is extracted using sliding window strategy. DCF learning is then performed on each local patch individually to obtain the corresponding discriminative convolution kernel through generalized eigenvalue decomposition. The proposed DCF learning characterizes that a convolutional kernel with small size (e.g., 3 × 3 pixels can be effectively learned on a small-size local patch (e.g., 8 × 8 pixels, thereby ensuring that the linear transformation of deep features can maintain low computational complexity. Experiments on two RSI datasets demonstrate the effectiveness of DCF in improving the classification performances of deep features without increasing dimensionality.

Isofocusing lens with cylindrical electrodes for charged particle beam with finite emittance

International Nuclear Information System (INIS)

Shpak, E.V.; Smirnova, A.A.

1995-01-01

An axially symmetric lens, consisting of three cylindrical electrodes and designed for shaping the beams of charged particles with final emittance, is studied. The potentials on the lens electrodes, which ensure the maintenance of the crossover formed by the lens, are calculated. The dependences of the ratios of potentials on the lens electrodes are analyzed for different values of R 0 /R 0 1 ratios, where R 0 and R 1 are maximum values of initial values of coordinates and the slopes in the crossover, respectively. 4 refs.; 3 figs

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...

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.

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 ...

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...

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

Solid-state, polymer-based fiber solar cells with carbon nanotube electrodes.

Science.gov (United States)

Liu, Dianyi; Zhao, Mingyan; Li, Yan; Bian, Zuqiang; Zhang, Luhui; Shang, Yuanyuan; Xia, Xinyuan; Zhang, Sen; Yun, Daqin; Liu, Zhiwei; Cao, Anyuan; Huang, Chunhui

2012-12-21

Most previous fiber-shaped solar cells were based on photoelectrochemical systems involving liquid electrolytes, which had issues such as device encapsulation and stability. Here, we deposited classical semiconducting polymer-based bulk heterojunction layers onto stainless steel wires to form primary electrodes and adopted carbon nanotube thin films or densified yarns to replace conventional metal counter electrodes. The polymer-based fiber cells with nanotube film or yarn electrodes showed power conversion efficiencies in the range 1.4% to 2.3%, with stable performance upon rotation and large-angle bending and during long-time storage without further encapsulation. Our fiber solar cells consisting of a polymeric active layer sandwiched between steel and carbon electrodes have potential in the manufacturing of low-cost, liquid-free, and flexible fiber-based photovoltaics.

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.

Flower-shaped gold nanoparticles: Preparation, characterization, and electro

Directory of Open Access Journals (Sweden)

Islam M. Al-Akraa

2017-09-01

Full Text Available The modification of a glassy carbon electrode with gold nanoparticles was pursued, characterized, and examined for electrocatalytic applications. The fabrication process of this electrode involved assembling the gold nanoparticles atop of amino group grafted glassy carbon electrode. The scanning electron microscopy indicated the deposition of gold nanoparticles in flower-shaped nanostructures with an average particle size of ca. 150 nm. Interestingly, the electrode exhibited outstanding enhancement in the electrocatalytic activity toward the oxygen evolution reaction, which reflected from the large negative shift (ca. 0.8 V in its onset potential, in comparison with that observed at the bulk unmodified glassy carbon and gold electrodes. Alternatively, the Tafel plot of the modified electrode revealed a significant increase (∼one order of magnitude in the apparent exchange current density of the oxygen evolution reaction upon the modification, which infers a faster charge transfer. Kinetically, gold nanoparticles are believed to facilitate a favorable adsorption of OH− (fundamental step in oxygen evolution reaction, which allows the charge transfer at reasonably lower anodic polarizations.

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.

Wrinkled Graphene–AgNWs Hybrid Electrodes for Smart Window

Directory of Open Access Journals (Sweden)

Ki-Woo Jun

2017-02-01

Full Text Available Over the past few years, there has been an increasing demand for stretchable electrodes for flexible and soft electronic devices. An electrode in such devices requires special functionalities to be twisted, bent, stretched, and deformed into variable shapes and also will need to have the capacity to be restored to the original state. In this study, we report uni- or bi-axially wrinkled graphene–silver nanowire hybrid electrodes comprised of chemical vapor deposition (CVD-grown graphene and silver nanowires. A CVD-grown graphene on a Cu-foil was transferred onto a biaxially pre-strained elastomer substrate and silver nanowires were sprayed on the transferred graphene surface. The pre-strained film was relaxed uni-(or bi-axially to produce a wrinkled structure. The bi-axially wrinkled graphene and silver nanowires hybrid electrodes were very suitable for high actuating performance of electro-active dielectric elastomers compared with the wrinkle-free case. Present results show that the optical transparency of the highly stretchable electrode can be successfully tuned by modulating input voltages.

Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network

Directory of Open Access Journals (Sweden)

Abdullah-Al Nahid

2018-01-01

Full Text Available Identification of the malignancy of tissues from Histopathological images has always been an issue of concern to doctors and radiologists. This task is time-consuming, tedious and moreover very challenging. Success in finding malignancy from Histopathological images primarily depends on long-term experience, though sometimes experts disagree on their decisions. However, Computer Aided Diagnosis (CAD techniques help the radiologist to give a second opinion that can increase the reliability of the radiologist’s decision. Among the different image analysis techniques, classification of the images has always been a challenging task. Due to the intense complexity of biomedical images, it is always very challenging to provide a reliable decision about an image. The state-of-the-art Convolutional Neural Network (CNN technique has had great success in natural image classification. Utilizing advanced engineering techniques along with the CNN, in this paper, we have classified a set of Histopathological Breast-Cancer (BC images utilizing a state-of-the-art CNN model containing a residual block. Conventional CNN operation takes raw images as input and extracts the global features; however, the object oriented local features also contain significant information—for example, the Local Binary Pattern (LBP represents the effective textural information, Histogram represent the pixel strength distribution, Contourlet Transform (CT gives much detailed information about the smoothness about the edges, and Discrete Fourier Transform (DFT derives frequency-domain information from the image. Utilizing these advantages, along with our proposed novel CNN model, we have examined the performance of the novel CNN model as Histopathological image classifier. To do so, we have introduced five cases: (a Convolutional Neural Network Raw Image (CNN-I; (b Convolutional Neural Network CT Histogram (CNN-CH; (c Convolutional Neural Network CT LBP (CNN-CL; (d Convolutional

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...

The Application of Real Convolution for Analytically Evaluating Fermi-Dirac-Type and Bose-Einstein-Type Integrals

Directory of Open Access Journals (Sweden)

Jerry P. Selvaggi

2018-01-01

Full Text Available The Fermi-Dirac-type or Bose-Einstein-type integrals can be transformed into two convergent real-convolution integrals. The transformation simplifies the integration process and may ultimately produce a complete analytical solution without recourse to any mathematical approximations. The real-convolution integrals can either be directly integrated or be transformed into the Laplace Transform inversion integral in which case the full power of contour integration becomes available. Which method is employed is dependent upon the complexity of the real-convolution integral. A number of examples are introduced which will illustrate the efficacy of the analytical approach.

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

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.

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

International Nuclear Information System (INIS)

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

2008-01-01

The authors present a segment-based convolution method to account for the interplay effect between intrafraction organ motion and the multileaf collimator position for each particular segment in intensity modulated radiation therapy (IMRT) delivered in a step-and-shoot manner. In this method, the static dose distribution attributed to each segment is convolved with the probability density function (PDF) of motion during delivery of the segment, whereas in the conventional convolution method (''average-based convolution''), the static dose distribution is convolved with the PDF averaged over an entire fraction, an entire treatment course, or even an entire patient population. In the case of IMRT delivered in a step-and-shoot manner, the average-based convolution method assumes that in each segment the target volume experiences the same motion pattern (PDF) as that of population. In the segment-based convolution method, the dose during each segment is calculated by convolving the static dose with the motion PDF specific to that segment, allowing both intrafraction motion and the interplay effect to be accounted for in the dose calculation. Intrafraction prostate motion data from a population of 35 patients tracked using the Calypso system (Calypso Medical Technologies, Inc., Seattle, WA) was used to generate motion PDFs. These were then convolved with dose distributions from clinical prostate IMRT plans. For a single segment with a small number of monitor units, the interplay effect introduced errors of up to 25.9% in the mean CTV dose compared against the planned dose evaluated by using the PDF of the entire fraction. In contrast, the interplay effect reduced the minimum CTV dose by 4.4%, and the CTV generalized equivalent uniform dose by 1.3%, in single fraction plans. For entire treatment courses delivered in either a hypofractionated (five fractions) or conventional (>30 fractions) regimen, the discrepancy in total dose due to interplay effect was negligible

Concatenated coding systems employing a unit-memory convolutional code and a byte-oriented decoding algorithm

Science.gov (United States)

Lee, L.-N.

1977-01-01

Concatenated coding systems utilizing a convolutional code as the inner code and a Reed-Solomon code as the outer code are considered. In order to obtain very reliable communications over a very noisy channel with relatively modest coding complexity, it is proposed to concatenate a byte-oriented unit-memory convolutional code with an RS outer code whose symbol size is one byte. It is further proposed to utilize a real-time minimal-byte-error probability decoding algorithm, together with feedback from the outer decoder, in the decoder for the inner convolutional code. The performance of the proposed concatenated coding system is studied, and the improvement over conventional concatenated systems due to each additional feature is isolated.

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.

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.

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.

Method of preparing an electrode material of lithium-aluminum alloy

Science.gov (United States)

Settle, Jack L.; Myles, Kevin M.; Battles, James E.

1976-01-01

A solid compact having a uniform alloy composition of lithium and aluminum is prepared as a negative electrode for an electrochemical cell. Lithium losses during preparation are minimized by dissolving aluminum within a lithium-rich melt at temperatures near the liquidus temperatures. The desired alloy composition is then solidified and fragmented. The fragments are homogenized to a uniform composition by annealing at a temperature near the solidus temperature. After comminuting to fine particles, the alloy material can be blended with powdered electrolyte and pressed into a solid compact having the desired electrode shape. In the preparation of some electrodes, an electrically conductive metal mesh is embedded into the compact as a current collector.

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.

Development of liquid film thickness measurement technique by high-density multipoint electrodes method

International Nuclear Information System (INIS)

Arai, Takahiro; Furuya, Masahiro; Kanai, Taizo

2010-01-01

High-density multipoint electrode method was developed to measure a liquid film thickness transient on a curved surface. The devised method allows us to measure spatial distribution of liquid film with its conductance between electrodes. The sensor was designed and fabricated as a multilayer print circuit board, where electrode pairs were distributed in reticular pattern with narrow interval. In order to measure a lot of electrode pairs at a high sampling rate, signal-processing method used by the wire mesh sensor measurement system was applied. An electrochemical impedance spectrometry concludes that the sampling rate of 1000 slices/s is feasible without signal distortion by electric double layer. The method was validated with two experimental campaigns: (1) a droplet impingement on a flat film and (2) a jet impingement on a rod-shape sensor surface. In the former experiment, a water droplet having 4 mm in diameter impinged onto the 1 mm thick film layer. A visual observation study with high-speed video camera shows after the liquid impingement, the water layer thinning process was clearly demonstrated with the sensor. For the latter experiment, the flexible circuit board was bended to form a cylindrical shape to measure water film on a simulated fuel rod in bundle geometry. A water jet having 3 mm in diameter impinged onto the rod-shape sensor surface. The process of wetting area enlargement on the rod surface was demonstrated in the same manner that the video-frames showed. (author)

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.

Anisotropic D-EAP Electrodes and their Application in Spring Roll Actuators

Science.gov (United States)

Fang, Xiaomeng

Electroactive polymers (EAPs) exhibit shape change when subjected to an electric field. They are lightweight, soft, and inexpensive, while they are easy to process, shape, and tune to offer a broad range of mechanical and electrical properties. Dielectric electroactive polymers (DEAP) constitute a class of EAPs with great potential. D-EAPs consist of physically or chemically cross-linked macromolecular networks and are mechanically isotopic. Therefore, in most actuator applications that require directional electromechanical response, it is necessary to use other complex means to direct the stress/strain in the preferred direction. In this work, a simple carbon nanotube (CNT) based electrode for D-EAP actuators is demonstrated that vastly improves directional strain response originating from the mechanical anisotropy of the electrode material. Using this novel approach, the mechanical anisotropy, defined as the ratio of initial modulus in fiber direction and that in cross-fiber direction, of the CNT electroded VHB actuators, ranges from 7.9 to 11.2. Hence, the CNT-VHB flat film actuators show high directed linear actuation strain in cross-fiber direction of greater than 25% meanwhile almost no strain in fiber direction at a relatively low electric field (120 V mum-1). The morphology of the CNT sheets has critical influence on their mechanical properties and resultant actuator performance. The results demonstrate the efficacy of microcombing and selective laser etching processes to improve the CNT fiber alignment to produce pure unidirectional strain of 33% at a relatively moderate electric field. Unidirectional D-EAP composite laminates using polyurethane and polyamide monofilaments are also employed in spring roll actuators to investigate their directional mechanical and electromechanical properties. While CNT electroded D-EAP spring roll actuators were found to have about the same performance as actuators with carbon grease electrodes (6.5% strain in CNT

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

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

Science.gov (United States)

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

2017-11-17

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

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

Science.gov (United States)

Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

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

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 ...

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.

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

Science.gov (United States)

Cheng, Phillip M; Malhi, Harshawn S

2017-04-01

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

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...

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.

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.

Lattice model of ionic liquid confined by metal electrodes

Science.gov (United States)

Girotto, Matheus; Malossi, Rodrigo M.; dos Santos, Alexandre P.; Levin, Yan

2018-05-01

We study, using Monte Carlo simulations, the density profiles and differential capacitance of ionic liquids confined by metal electrodes. To compute the electrostatic energy, we use the recently developed approach based on periodic Green's functions. The method also allows us to easily calculate the induced charge on the electrodes permitting an efficient implementation of simulations in a constant electrostatic potential ensemble. To speed up the simulations further, we model the ionic liquid as a lattice Coulomb gas and precalculate the interaction potential between the ions. We show that the lattice model captures the transition between camel-shaped and bell-shaped capacitance curves—the latter characteristic of ionic liquids (strong coupling limit) and the former of electrolytes (weak coupling). We observe the appearance of a second peak in the differential capacitance at ≈0.5 V for 2:1 ionic liquids, as the packing fraction is increased. Finally, we show that ionic size asymmetry decreases substantially the capacitance maximum, when all other parameters are kept fixed.

Method of electrode printing on one or more surfaces of a dielectric substrate

KAUST Repository

Neophytou, Marios

2017-09-14

Described herein is a method for printing electrodes surfaces of a dielectric substrate. Provided herein is a new method of depositing electrically conductive electrodes of any shape on flexible and/or rigid dielectric substrates/surfaces and devices so produced. In various embodiments, the devices can generate ionic wind, for example to remove dust or other debris or contaminants or to remove ice or humidity from a surface.

Method of electrode printing on one or more surfaces of a dielectric substrate

KAUST Repository

Neophytou, Marios; Kirkus, Mindaugas; Lacoste, Deanna A.

2017-01-01

Described herein is a method for printing electrodes surfaces of a dielectric substrate. Provided herein is a new method of depositing electrically conductive electrodes of any shape on flexible and/or rigid dielectric substrates/surfaces and devices so produced. In various embodiments, the devices can generate ionic wind, for example to remove dust or other debris or contaminants or to remove ice or humidity from a surface.

Effect of Electrode Geometry on the Classification Performance of Rapid Evaporative Ionization Mass Spectrometric (REIMS) Bacterial Identification

Science.gov (United States)

Bodai, Zsolt; Cameron, Simon; Bolt, Frances; Simon, Daniel; Schaffer, Richard; Karancsi, Tamas; Balog, Julia; Rickards, Tony; Burke, Adam; Hardiman, Kate; Abda, Julia; Rebec, Monica; Takats, Zoltan

2018-01-01

The recently developed automated, high-throughput monopolar REIMS platform is suited for the identification of clinically important microorganisms. Although already comparable to the previously reported bipolar forceps method, optimization of the geometry of monopolar electrodes, at the heart of the system, holds the most scope for further improvements to be made. For this, sharp tip and round shaped electrodes were optimized to maximize species-level classification accuracy. Following optimization of the distance between the sample contact point and tube inlet with the sharp tip electrodes, the overall cross-validation accuracy improved from 77% to 93% in negative and from 33% to 63% in positive ion detection modes, compared with the original 4 mm distance electrode. As an alternative geometry, round tube shaped electrodes were developed. Geometry optimization of these included hole size, number, and position, which were also required to prevent plate pick-up due to vacuum formation. Additional features, namely a metal "X"-shaped insert and a pin in the middle were included to increase the contact surface with a microbial biomass to maximize aerosol production. Following optimization, cross-validation scores showed improvement in classification accuracy from 77% to 93% in negative and from 33% to 91% in positive ion detection modes. Supervised models were also built, and after the leave 20% out cross-validation, the overall classification accuracy was 98.5% in negative and 99% in positive ion detection modes. This suggests that the new generation of monopolar REIMS electrodes could provide substantially improved species level identification accuracies in both polarity detection modes. [Figure not available: see fulltext.

Numerical Characterization of Intraoperative and Chronic Electrodes in Deep Brain Stimulation

Directory of Open Access Journals (Sweden)

Alessandra ePaffi

2015-02-01

Full Text Available Intraoperative electrode is used in the Deep Brain stimulation (DBS technique to pinpoint the brain target and to choose the best parameters for the stimulating signal. However, when the intraoperative electrode is replaced with the chronic one, the observed effects do not always coincide with predictions.To investigate the causes of such discrepancies, in this work, a 3D model of the basal ganglia has been considered and realistic models of both intraoperative and chronic electrodes have been developed and numerically solved.Results of simulations on the electric potential and the activating function along neuronal fibers show that the different geometries and sizes of the two electrodes do not change shapes and polarities of these functions, but only the amplitudes. A similar effect is caused by the presence of different tissue layers (edema or glial tissue in the peri-electrode space. On the contrary, a not accurate positioning of the chronic electrode with respect to the intraoperative one (electric centers not coincident may induce a complete different electric stimulation on some groups of fibers.

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

KAUST Repository

Peng, H.Y.

2013-11-13

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 (STO) single crystals. Metals with different work functions (Ti and Pt) and their combinations are used to control the junction transport (either ohmic or Schottky-like). We find that the electric bias is effective in manipulating the concentration of oxygen vacancies at the metal/STO interface, influencing the RS characteristics. Furthermore, we show that the geometric shapes of electrodes (e.g., rectangular, circular, or triangular) affect the electric field distribution at the metal/oxide interface, thus plays an important role in RS. These systematic results suggest that electrode engineering should be deemed as a powerful approach toward controlling and improving the characteristics of RS memories. 2013 Author(s).

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.

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.

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.

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%.

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 ...

Damped button electrode for B-Factory BPM system

Energy Technology Data Exchange (ETDEWEB)

Shintake, T; Akasaka, N; Obina, T; Chin, Y H [National Lab. for High Energy Physics, Tsukuba, Ibaraki (Japan)

1996-08-01

A new concept of damping of resonances in a button electrode has been proposed and tested in the BPM system for the B-Factory project at KEK (KEKB). Since a very high current beam has to be stored in the machine, even a small resonance in the ring will result in losing a beam due to multi-bunch instabilities. In a conventional button electrode used in BPMs, a TE110 mode resonance can be trapped in the gap between the electrode and the vacuum chamber. In order to damp this mode, the diameter of the electrode has been chosen to be small to increase the resonance frequency and to radiate the power into the beam pipe. In addition, an asymmetric structure is applied to extract the EM energy of the TE110 mode into the coaxial cable as the propagating TEM mode which has no cut-off frequency. Results of the computer simulations and tests with cold models are reported. The quality factor of the TE110 mode was small enough due to the radiation into the beam pipe even in the conventional electrode and the mode coupling effect due to the asymmetric shape was significant on a cavity-like TE111 mode. (author)

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

The Influence of Anion Shape on the Electrical Double Layer Microstructure and Capacitance of Ionic Liquids-Based Supercapacitors by Molecular Simulations

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Ming Chen

2017-02-01

Full Text Available Room-temperature ionic liquids (RTILs are an emerging class of electrolytes for supercapacitors. In this work, we investigate the effects of different supercapacitor models and anion shape on the electrical double layers (EDLs of two different RTILs: 1-ethyl-3-methylimidazolium bis(trifluoromethanesulfonylimide ([Emim][Tf2N] and 1-ethyl-3-methylimidazolium 2-(cyanopyrrolide ([Emim][CNPyr] by molecular dynamics (MD simulation. The EDL microstructure is represented by number densities of cations and anions, and the potential drop near neutral and charged electrodes reveal that the supercapacitor model with a single electrode has the same EDL structure as the model with two opposite electrodes. Nevertheless, the employment of the one-electrode model without tuning the bulk density of RTILs is more time-saving in contrast to the two-electrode one. With the one-electrode model, our simulation demonstrated that the shapes of anions significantly imposed effects on the microstructure of EDLs. The EDL differential capacitance vs. potential (C-V curves of [Emim][CNPyr] electrolyte exhibit higher differential capacitance at positive potentials. The modeling study provides microscopic insight into the EDLs structure of RTILs with different anion shapes.

Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification

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Qingshan Liu

2017-12-01

Full Text Available This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM network to automatically learn the spectral-spatial features from hyperspectral images (HSIs. In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it. Meanwhile, inspired from the widely used convolutional neural network (CNN, a convolution operator across the spatial domain is incorporated into the network to extract the spatial feature. In addition, to sufficiently capture the spectral information, a bidirectional recurrent connection is proposed. In the classification phase, the learned features are concatenated into a vector and fed to a Softmax classifier via a fully-connected operator. To validate the effectiveness of the proposed Bi-CLSTM framework, we compare it with six state-of-the-art methods, including the popular 3D-CNN model, on three widely used HSIs (i.e., Indian Pines, Pavia University, and Kennedy Space Center. The obtained results show that Bi-CLSTM can improve the classification performance by almost 1.5 % as compared to 3D-CNN.

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

Science.gov (United States)

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

2017-01-01

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

Strain response of stretchable micro-electrodes: Controlling sensitivity with serpentine designs and encapsulation

International Nuclear Information System (INIS)

Gutruf, Philipp; Walia, Sumeet; Nur Ali, Md; Sriram, Sharath; Bhaskaran, Madhu

2014-01-01

The functionality of flexible electronics relies on stable performance of thin film micro-electrodes. This letter investigates the behavior of gold thin films on polyimide, a prevalent combination in flexible devices. The dynamic behavior of gold micro-electrodes has been studied by subjecting them to stress while monitoring their resistance in situ. The shape of the electrodes was systematically varied to examine resistive strain sensitivity, while an additional encapsulation was applied to characterize multilayer behavior. The realized designs show remarkable tolerance to repetitive strain, demonstrating that curvature and encapsulation are excellent approaches for minimizing resistive strain sensitivity to enable durable flexible electronics

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…

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 ...

Applications of deep convolutional neural networks to digitized natural history collections

Directory of Open Access Journals (Sweden)

Eric Schuettpelz

2017-11-01

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

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

Science.gov (United States)

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

2017-01-01

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

A mixed-scale dense convolutional neural network for image analysis

NARCIS (Netherlands)

D.M. Pelt (Daniël); J.A. Sethian (James)

2016-01-01

textabstractDeep convolutional neural networks have been successfully applied to many image-processing problems in recent works. Popular network architectures often add additional operations and connections to the standard architecture to enable training deeper networks. To achieve accurate results

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.

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

Fiber-shaped energy harvesting and storage devices

CERN Document Server

Peng, Huisheng

2015-01-01

This comprehensive book covers flexible fiber-shaped devices in the area of energy conversion and storage. The first part of the book introduces recently developed materials, particularly, various nanomaterials and composite materials based on nanostructured carbon such as carbon nanotubes and graphene, metals and polymers for the construction of fiber electrodes. The second part of the book focuses on two typical twisted and coaxial architectures of fiber-shaped devices for energy conversion and storage. The emphasis is placed on dye-sensitized solar cells, polymer solar cells, lithium-ion b

Dual Approach to Amplify Anodic Stripping Voltammetric Signals Recorded Using Screen Printed Electrodes

Directory of Open Access Journals (Sweden)

Agnieszka KRÓLICKA

2016-12-01

Full Text Available Screen printed electrodes plated with bismuth were used to record anodic stripping voltammograms of Pb(II, In(III and Cd(II. Using two bismuth precursors: Bi2O3 dispersed in the electrode body and Bi(III ions spiked into the tested solution it was possible to deposit bismuth layers, demonstrating exceptional ability to accumulate metals forming alloys with bismuth. The voltammetric signals were amplified by adjusting the electrode location with respect to rotating magnetic field. The electrode response was influenced by vertical and horizontal distance between the magnet center and the sensing area of screen printed electrode as well as the angle between the magnet surface and the electrode. When the electrode was moved away from the magnet center the recorded peaks were increasingly smaller and almost not affected by the presence of bismuth ions. It was shown that to obtain well-shaped signals a favourable morphology of bismuth deposits is of key importance. Hypotheses explaining processes responsible for the amplification of voltammetric signals were proposed.

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

Science.gov (United States)

Yoon, Jaehong; Lee, Jungnyun; Whang, Mincheol

2018-01-01

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

Renormalized G-convolution of n-point functions in quantum field theory. I. The Euclidean case

International Nuclear Information System (INIS)

Bros, Jacques; Manolessou-Grammaticou, Marietta.

1977-01-01

The notion of Feynman amplitude associated with a graph G in perturbative quantum field theory admits a generalized version in which each vertex v of G is associated with a general (non-perturbative) nsub(v)-point function Hsup(nsub(v)), nsub(v) denoting the number of lines which are incident to v in G. In the case where no ultraviolet divergence occurs, this has been performed directly in complex momentum space through Bros-Lassalle's G-convolution procedure. The authors propose a generalization of G-convolution which includes the case when the functions Hsup(nsub(v)) are not integrable at infinity but belong to a suitable class of slowly increasing functions. A finite part of the G-convolution integral is then defined through an algorithm which closely follows Zimmermann's renormalization scheme. The case of Euclidean four-momentum configurations is only treated

The time course of activation of object shape and shape+colour representations during memory retrieval.

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Toby J Lloyd-Jones

Full Text Available Little is known about the timing of activating memory for objects and their associated perceptual properties, such as colour, and yet this is important for theories of human cognition. We investigated the time course associated with early cognitive processes related to the activation of object shape and object shape+colour representations respectively, during memory retrieval as assessed by repetition priming in an event-related potential (ERP study. The main findings were as follows: (1 we identified a unique early modulation of mean ERP amplitude during the N1 that was associated with the activation of object shape independently of colour; (2 we also found a subsequent early P2 modulation of mean amplitude over the same electrode clusters associated with the activation of object shape+colour representations; (3 these findings were apparent across both familiar (i.e., correctly coloured - yellow banana and novel (i.e., incorrectly coloured - blue strawberry objects; and (4 neither of the modulations of mean ERP amplitude were evident during the P3. Together the findings delineate the timing of object shape and colour memory systems and support the notion that perceptual representations of object shape mediate the retrieval of temporary shape+colour representations for familiar and novel objects.

The time course of activation of object shape and shape+colour representations during memory retrieval.

Science.gov (United States)

Lloyd-Jones, Toby J; Roberts, Mark V; Leek, E Charles; Fouquet, Nathalie C; Truchanowicz, Ewa G

2012-01-01

Little is known about the timing of activating memory for objects and their associated perceptual properties, such as colour, and yet this is important for theories of human cognition. We investigated the time course associated with early cognitive processes related to the activation of object shape and object shape+colour representations respectively, during memory retrieval as assessed by repetition priming in an event-related potential (ERP) study. The main findings were as follows: (1) we identified a unique early modulation of mean ERP amplitude during the N1 that was associated with the activation of object shape independently of colour; (2) we also found a subsequent early P2 modulation of mean amplitude over the same electrode clusters associated with the activation of object shape+colour representations; (3) these findings were apparent across both familiar (i.e., correctly coloured - yellow banana) and novel (i.e., incorrectly coloured - blue strawberry) objects; and (4) neither of the modulations of mean ERP amplitude were evident during the P3. Together the findings delineate the timing of object shape and colour memory systems and support the notion that perceptual representations of object shape mediate the retrieval of temporary shape+colour representations for familiar and novel objects.

High voltage performance of a dc photoemission electron gun with centrifugal barrel-polished electrodes

Science.gov (United States)

Hernandez-Garcia, C.; Bullard, D.; Hannon, F.; Wang, Y.; Poelker, M.

2017-09-01

The design and fabrication of electrodes for direct current (dc) high voltage photoemission electron guns can significantly influence their performance, most notably in terms of maximum achievable bias voltage. Proper electrostatic design of the triple-point junction shield electrode minimizes the risk of electrical breakdown (arcing) along the insulator-cable plug interface, while the electrode shape is designed to maintain work, we describe a centrifugal barrel-polishing technique commonly used for polishing the interior surface of superconducting radio frequency cavities but implemented here for the first time to polish electrodes for dc high voltage photoguns. The technique reduced polishing time from weeks to hours while providing surface roughness comparable to that obtained with diamond-paste polishing and with unprecedented consistency between different electrode samples. We present electrode design considerations and high voltage conditioning results to 360 kV (˜11 MV/m), comparing barrel-polished electrode performance to that of diamond-paste polished electrodes. Tests were performed using a dc high voltage photogun with an inverted-geometry ceramic insulator design.

Activation and discharge kinetics of metal hydride electrodes

Energy Technology Data Exchange (ETDEWEB)

Johnsen, Stein Egil

2003-07-01

Potential step chronoamperometry and Electrochemical Impedance Spectroscopy (eis) measurements were performed on single metal hydride particles. For the {alpha}-phase, the bulk diffusion coefficient and the absorption/adsorption rate parameters were determined. Materials produced by atomisation, melt spinning and conventional casting were investigated. The melt spun and conventional cast materials were identical and the atomised material similar in composition. The particles from the cast and the melt spun material were shaped like parallelepipeds. A corresponding equation, for this geometry, for diffusion coupled to an absorption/adsorption reaction was developed. It was found that materials produced by melt spinning exhibited lower bulk diffusion (1.7E-14 m2/s) and absorption/adsorption reaction rate (1.0E-8 m/s), compared to materials produced by conventionally casting (1.1E-13 m2/s and 5.5E-8 m/s respectively). In addition, the influence of particle active surface and relative diffusion length were discussed. It was concluded that there are uncertainties connected to these properties, which may explain the large distribution in the kinetic parameters measured on metal hydride particles. Activation of metal hydride forming materials has been studied and an activation procedure, for porous electrodes, was investigated. Cathodic polarisation of the electrode during a hot alkaline surface treatment gave the maximum discharge capacity on the first discharge of the electrode. The studied materials were produced by gas atomisation and the spherical shape was retained during the activation. Both an AB{sub 5} and an AB{sub 2} alloy was successfully activated and discharge rate properties determined. The AB{sub 2} material showed a higher maximum discharge capacity, but poor rate properties, compared to the AB{sub 5} material. Reduction of surface oxides, and at the same time protection against corrosion of active metallic nickel, can explain the satisfying results of

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

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.

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

Energy Technology Data Exchange (ETDEWEB)

Avdic, A; Lugstein, A; Bertagnolli, E [Solid State Electronics Institute, Vienna University of Technology, Floragasse 7, 1040 Vienna (Austria); Wu, M; Gollas, B [Competence Centre for Electrochemical Surface Technology, Viktor Kaplan Strasse 2, 2700 Wiener Neustadt (Austria); Pobelov, I; Wandlowski, T [Department of Chemistry and Biochemistry, University of Bern, Freiestrasse 3, 3012 Bern (Switzerland); Leonhardt, K; Denuault, G, E-mail: alois.lugstein@tuwien.ac.at [School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ (United Kingdom)

2011-04-08

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

Complex calculation and improvement of beam shaping and accelerating system of the ''Sokol'' small-size electrostatic accelerator

International Nuclear Information System (INIS)

Simonenko, A.V.; Pistryak, V.M.; Zats, A.V.; Levchenko, Yu.Z.; Kuz'menko, V.V.

1987-01-01

Features of charged particle accelerated beam shaping in the electrostatic part of the ''Sokol'' small-size accelerator are considered in complex taking into account the electrode real geometry. Effect of the extracting, accelerating electorde potential and accelerator total voltage on beam behaviour is investigated. A modified variation of the beam shaping system, allowing to decrease 2 times the required interval of accelerating electrode potential adjustment and to decrease the beam size in the starting acceleration region, is presented. It permits to simplify the construction and to improve accelerator operation. Comparison of experimental and calculational data on the beam in the improved accelerator variation is carried out. Effect of peripheral parts of accelerating tube electrodes on the beam is investigated

Performance and applications of the ORNL local electrode atom probe

International Nuclear Information System (INIS)

Miller, M.K.; Russell, K.F.

2004-01-01

Full text: The commercial introduction in 2003 of the local electrode atom probe (LEAP) developed by Imago Scientific Instruments has made dramatic, orders of magnitude improvements in the data acquisition rate and the size of the analyzed volume compared to previous types of three-dimensional atom probes and other scanning atom probes. This state-of-the-art instrument may be used for the analysis of traditional needle-shaped specimens and specimens fabricated from 'flat' specimens with focused ion beam (FIB) techniques. The advantage of this local electrode configuration is that significantly lower (∼50 %) standing and pulse voltages are required to produce the field strength required to field evaporate ions from the specimen. New high speed (200 kHz) pulse generators coupled with crossed delay line detectors and faster timing systems also enable significantly faster (up to 300 times) data acquisition rates to be achieved. This new design also permits a significantly larger field of view to be analyzed and results in data sets containing up to 10 8 atoms. In the local electrode atom probe, a ∼10-50 μm diameter aperture is typically positioned approximately one aperture diameter in front of the specimen. In order to accurately align the specimen to the aperture in the funnel-shaped electrode, the specimen is mounted on a three axis nanopositioning stage. An approximate alignment is performed while viewing the relative positions of the specimen and the local electrode with a pair of low magnification video cameras and then a pair of higher magnification video cameras attached to long range microscopes. The final alignment is performed with the use of the field evaporated ions from the specimen. A discussion on the alignment of the specimen with the local electrode, the effects of the fields on the specimen, and the effects of aperture size on aperture lifetime will be presented. The performance of the ORNL local electrode atom probe will be described. The

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

Science.gov (United States)

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

2017-11-01

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

3D Medical Image Interpolation Based on Parametric Cubic Convolution

Institute of Scientific and Technical Information of China (English)

无

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.

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.

Ultra-Thin Optically Transparent Carbon Electrodes Produced from Layers of Adsorbed Proteins

Science.gov (United States)

Alharthi, Sarah A.; Benavidez, Tomas E.; Garcia, Carlos D.

2013-01-01

This work describes a simple, versatile, and inexpensive procedure to prepare optically transparent carbon electrodes, using proteins as precursors. Upon adsorption, the protein-coated substrates were pyrolyzed under reductive conditions (5% H2) to form ultra-thin, conductive electrodes. Because proteins spontaneously adsorb to interfaces forming uniform layers, the proposed method does not require a precise control of the preparation conditions, specialized instrumentation, or expensive precursors. The resulting electrodes were characterized by a combination of electrochemical, optical, and spectroscopic means. As a proof-of-concept, the optically-transparent electrodes were also used as substrate for the development of an electrochemical glucose biosensor. The proposed films represent a convenient alternative to more sophisticated, and less available, carbon-based nanomaterials. Furthermore, these films could be formed on a variety of substrates, without classical limitations of size or shape. PMID:23421732

Convolution-based estimation of organ dose in tube current modulated CT

Science.gov (United States)

Tian, Xiaoyu; Segars, W. Paul; Dixon, Robert L.; Samei, Ehsan

2016-05-01

Estimating organ dose for clinical patients requires accurate modeling of the patient anatomy and the dose field of the CT exam. The modeling of patient anatomy can be achieved using a library of representative computational phantoms (Samei et al 2014 Pediatr. Radiol. 44 460-7). The modeling of the dose field can be challenging for CT exams performed with a tube current modulation (TCM) technique. The purpose of this work was to effectively model the dose field for TCM exams using a convolution-based method. A framework was further proposed for prospective and retrospective organ dose estimation in clinical practice. The study included 60 adult patients (age range: 18-70 years, weight range: 60-180 kg). Patient-specific computational phantoms were generated based on patient CT image datasets. A previously validated Monte Carlo simulation program was used to model a clinical CT scanner (SOMATOM Definition Flash, Siemens Healthcare, Forchheim, Germany). A practical strategy was developed to achieve real-time organ dose estimation for a given clinical patient. CTDIvol-normalized organ dose coefficients ({{h}\\text{Organ}} ) under constant tube current were estimated and modeled as a function of patient size. Each clinical patient in the library was optimally matched to another computational phantom to obtain a representation of organ location/distribution. The patient organ distribution was convolved with a dose distribution profile to generate {{≤ft(\\text{CTD}{{\\text{I}}\\text{vol}}\\right)}\\text{organ, \\text{convolution}}} values that quantified the regional dose field for each organ. The organ dose was estimated by multiplying {{≤ft(\\text{CTD}{{\\text{I}}\\text{vol}}\\right)}\\text{organ, \\text{convolution}}} with the organ dose coefficients ({{h}\\text{Organ}} ). To validate the accuracy of this dose estimation technique, the organ dose of the original clinical patient was estimated using Monte Carlo program with TCM profiles explicitly modeled. The

A computation study on the interplay between surface morphology and electrochemical performance of patterned thin film electrodes for Li-ion batteries

Science.gov (United States)

Gur, Sourav; Frantziskonis, George N.; Aifantis, Katerina E.

2017-08-01

Recent experiments illustrate that the morphology of the electrode surface impacts the voltage - capacity curves and long term cycling performance of Li-ion batteries. The present study systematically explores the role of the electrode surface morphology and uncertainties in the reactions that occur during electrochemical cycling, by performing kinetic Monte Carlo (kMC) simulations using the lattice Boltzmann method (LBM). This allows encoding of the inherent stochasticity at discrete microscale reaction events over the deterministic mean field reaction dynamics that occur in Li-ion cells. The electrodes are taken to be dense thin films whose surfaces are patterned with conical, trapezoidal, dome-shaped, or pillar-shaped structures. It is shown that the inherent perturbations in the reactions together with the characteristics of the electrode surface configuration can significantly improve battery performance, mainly because patterned surfaces, as opposed to flat surfaces, result in a smaller voltage drop. The most efficient pattern was the trapezoidal, which is consistent with experimental evidence on Si patterned electrodes.

Study of imploding liner-electrode wall interaction

Energy Technology Data Exchange (ETDEWEB)

Chernyshev, V K; Zharinov, E I; Mokhov, V N [All-Russian Scientific Research Institute of Experimental Physics, Sarov (Russian Federation)

1997-12-31

Acceleration of solid aluminium liners and their interaction with electrodes is studied experimentally. One of the main goal of the experiments is to find the method of improving the contact between the liner and the electrode during the acceleration process. Two independent liners connected in series in one discharge circuit are used. This arrangement makes it possible to record two different liner positions simultaneously at one discharge current. As an energy source, a helical explosive magnetic generator of the length of 0.7 m and 100 mm in diameter is used. The shape of liners at various stages of acceleration is recorded by using a flash radiographic facility. The measured liner flight velocity and the compression radius are compared with the results of MHD model calculations. (J.U.). 21 figs., 7 refs.

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

Directory of Open Access Journals (Sweden)

Yea-Hoon Kwon

2018-04-01

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

Carbon composite micro- and nano-tubes-based electrodes for detection of nucleic acids

Directory of Open Access Journals (Sweden)

Huska Dalibor

2011-01-01

Full Text Available Abstract The first aim of this study was to fabricate vertically aligned multiwalled carbon nanotubes (MWCNTs. MWCNTs were successfully prepared by using plasma enhanced chemical vapour deposition. Further, three carbon composite electrodes with different content of carbon particles with various shapes and sizes were prepared and tested on measuring of nucleic acids. The dependences of adenine peak height on the concentration of nucleic acid sample were measured. Carbon composite electrode prepared from a mixture of glassy and spherical carbon powder and MWCNTs had the highest sensitivity to nucleic acids. Other interesting result is the fact that we were able to distinguish signals for all bases using this electrode.

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.

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...

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...

Fabrication and Characterization of 3D-Printed Highly-Porous 3D LiFePO₄ Electrodes by Low Temperature Direct Writing Process.

Science.gov (United States)

Liu, Changyong; Cheng, Xingxing; Li, Bohan; Chen, Zhangwei; Mi, Shengli; Lao, Changshi

2017-08-10

LiFePO₄ (LFP) is a promising cathode material for lithium-ion batteries. In this study, low temperature direct writing (LTDW)-based 3D printing was used to fabricate three-dimensional (3D) LFP electrodes for the first time. LFP inks were deposited into a low temperature chamber and solidified to maintain the shape and mechanical integrity of the printed features. The printed LFP electrodes were then freeze-dried to remove the solvents so that highly-porous architectures in the electrodes were obtained. LFP inks capable of freezing at low temperature was developed by adding 1,4 dioxane as a freezing agent. The rheological behavior of the prepared LFP inks was measured and appropriate compositions and ratios were selected. A LTDW machine was developed to print the electrodes. The printing parameters were optimized and the printing accuracy was characterized. Results showed that LTDW can effectively maintain the shape and mechanical integrity during the printing process. The microstructure, pore size and distribution of the printed LFP electrodes was characterized. In comparison with conventional room temperature direct ink writing process, improved pore volume and porosity can be obtained using the LTDW process. The electrochemical performance of LTDW-fabricated LFP electrodes and conventional roller-coated electrodes were conducted and compared. Results showed that the porous structure that existed in the printed electrodes can greatly improve the rate performance of LFP electrodes.

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...

Donut-shaped Co_3O_4 nanoflakes grown on nickel foam with enhanced supercapacitive performances

International Nuclear Information System (INIS)

Han, Zhicheng; Zheng, Xin; Yao, Shunyu; Xiao, Huanhao; Qu, Fengyu; Wu, Xiang

2016-01-01

Graphical abstract: The as-synthesized product exhibits a high initial discharge capacitance of 518 mF/cm"2 at a current density of 1 mA cm"−"2 and can maintain 75% capacitance retention even after 6000 charge–discharge cycles. Electrochemical results revealed that the prepared Co_3O_4 nanoflakes possess a remarkable performance in supercapacitor applications. - Highlights: • Donut-shaped Co_3O_4 nanoflakes were first fabricated by a solution approach. • The tests show high discharge areal capacitance and long cycle life stability. • Co_3O_4 nanoflakes might be promising supercapacitor electrode materials. - Abstract: Donut-shaped Co_3O_4 nanoflakes grown on nickel foam were successfully fabricated by a simple one-pot hydrothermal approach. The prepared products were functionalized as the supercapacitors electrodes. Electrochemical performance of the as-prepared products demonstrated high specific capacitance (518 mF cm"−"2) and excellent cycling stability (∼25% loss) after 6000 repetitive cycles at a charge–discharge current density of 1 mA cm"−"2. The superior electrochemical performance may be ascribed into two reasons: one is the unique spatial structures which possess many active sites and provide enhanced combination between the electrode and nickel foam to support fast ion and electron transfer, the other is that donut-shaped Co_3O_4 nanoflakes electrodes show relatively lower resistances. It is expected that the as-obtained donut-shaped Co_3O_4 nanoflakes could have potential applications in portable electronics and electrical vehicles.

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

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...

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.

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.

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.

Sequential flow membraneless microfluidic fuel cell with porous electrodes

Energy Technology Data Exchange (ETDEWEB)

Salloum, Kamil S.; Posner, Jonathan D. [Department of Mechanical and Aerospace Engineering, Arizona State University, Tempe, AZ 85287-6106 (United States); Hayes, Joel R.; Friesen, Cody A. [School of Materials, Arizona State University, Tempe, AZ 85287-8706 (United States)

2008-05-15

A novel convective flow membraneless microfluidic fuel cell with porous disk electrodes is described. In this fuel cell design, the fuel flows radially outward through a thin disk shaped anode and across a gap to a ring shaped cathode. An oxidant is introduced into the gap between anode and cathode and advects radially outward to the cathode. This fuel cell differs from previous membraneless designs in that the fuel and the oxidant flow in series, rather than in parallel, enabling independent control over the fuel and oxidant flow rate and the electrode areas. The cell uses formic acid as a fuel and potassium permanganate as the oxidant, both contained in a sulfuric acid electrolyte. The flow velocity field is examined using microscale particle image velocimetry and shown to be nearly axisymmetric and steady. The results show that increasing the electrolyte concentration reduces the cell Ohmic resistance, resulting in larger maximum currents and peak power densities. Increasing the flow rate delays the onset of mass transport and reduces Ohmic losses resulting in larger maximum currents and peak power densities. An average open circuit potential of 1.2 V is obtained with maximum current and power densities of 5.35 mA cm{sup -2} and 2.8 mW cm{sup -2}, respectively (cell electrode area of 4.3 cm{sup 2}). At a flow rate of 100 {mu}L min{sup -1} a fuel utilization of 58% is obtained. (author)

Validation of a dose-point kernel convolution technique for internal dosimetry

International Nuclear Information System (INIS)

Giap, H.B.; Macey, D.J.; Bayouth, J.E.; Boyer, A.L.

1995-01-01

The objective of this study was to validate a dose-point kernel convolution technique that provides a three-dimensional (3D) distribution of absorbed dose from a 3D distribution of the radionuclide 131 I. A dose-point kernel for the penetrating radiations was calculated by a Monte Carlo simulation and cast in a 3D rectangular matrix. This matrix was convolved with the 3D activity map furnished by quantitative single-photon-emission computed tomography (SPECT) to provide a 3D distribution of absorbed dose. The convolution calculation was performed using a 3D fast Fourier transform (FFT) technique, which takes less than 40 s for a 128 x 128 x 16 matrix on an Intel 486 DX2 (66 MHz) personal computer. The calculated photon absorbed dose was compared with values measured by thermoluminescent dosimeters (TLDS) inserted along the diameter of a 22 cm diameter annular source of 131 I. The mean and standard deviation of the percentage difference between the measurements and the calculations were equal to -1% and 3.6% respectively. This convolution method was also used to calculate the 3D dose distribution in an Alderson abdominal phantom containing a liver, a spleen, and a spherical tumour volume loaded with various concentrations of 131 I. By averaging the dose calculated throughout the liver, spleen, and tumour the dose-point kernel approach was compared with values derived using the MIRD formalism, and found to agree to better than 15%. (author)

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

Directory of Open Access Journals (Sweden)

Jaehong Yoon

2018-01-01

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

A Viable Electrode Material for Use in Microbial Fuel Cells for Tropical Regions

Directory of Open Access Journals (Sweden)

Felix Offei

2016-01-01

Full Text Available Electrode materials are critical for microbial fuel cells (MFC since they influence the construction and operational costs. This study introduces a simple and efficient electrode material in the form of palm kernel shell activated carbon (AC obtained in tropical regions. The novel introduction of this material is also targeted at introducing an inexpensive and durable electrode material, which can be produced in rural communities to improve the viability of MFCs. The maximum voltage and power density obtained (under 1000 Ω load using an H-shaped MFC with AC as both anode and cathode electrode material was 0.66 V and 1.74 W/m3, respectively. The power generated by AC was as high as 86% of the value obtained with the extensively used carbon paper. Scanning electron microscopy and Denaturing Gradient Gel Electrophoresis (DGGE analysis of AC anode biofilms confirmed that electrogenic bacteria were present on the electrode surface for substrate oxidation and the formation of nanowires.

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...

The Convolutional Visual Network for Identification and Reconstruction of NOvA Events

Energy Technology Data Exchange (ETDEWEB)

Psihas, Fernanda [Indiana U.

2017-11-22

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

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

Science.gov (United States)

Liu, Miaofeng

2017-07-01

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

Detection and recognition of bridge crack based on convolutional neural network

Directory of Open Access Journals (Sweden)

Honggong LIU

2016-10-01

Full Text Available Aiming at the backward artificial visual detection status of bridge crack in China, which has a great danger coefficient, a digital and intelligent detection method of improving the diagnostic efficiency and reducing the risk coefficient is studied. Combing with machine vision and convolutional neural network technology, Raspberry Pi is used to acquire and pre-process image, and the crack image is analyzed; the processing algorithm which has the best effect in detecting and recognizing is selected; the convolutional neural network(CNN for crack classification is optimized; finally, a new intelligent crack detection method is put forward. The experimental result shows that the system can find all cracks beyond the maximum limit, and effectively identify the type of fracture, and the recognition rate is above 90%. The study provides reference data for engineering detection.

The fabrication of front electrodes of Si solar cell by dispensing printing

International Nuclear Information System (INIS)

Kim, Do-Hyung; Ryu, Sung-Soo; Shin, Dongwook; Shin, Jung-Han; Jeong, Jwa-Jin; Kim, Hyeong-Jun; Chang, Hyo Sik

2012-01-01

Highlights: ► We propose the process for the front silver electrode by employing dispensing method. ► The dispensing method is a non-contact printing process. ► The electrode by dispensing method has more uniform and narrower shape. ► The dispensing method helped to enhance the efficiency of solar cell by 0.8% absolute. - Abstract: The dispensing printing was applied to fabricate the front electrodes of silicon solar cell. In this method, a micro channel nozzle and normal Ag paste were employed. The aspect ratio and line width of electrodes could be controlled by the process variables such as the inner diameter of nozzle, dispensing speed, discharge pressure, and the gap between wafer and nozzle. For the nozzle with the inner diameter of 50 μm, the line width and aspect ratio of electrode were under 90 μm and more than ∼0.2, respectively. When comparing the efficiency of solar cell prepared by conventional screen printing and the dispensing printing, the latter exhibited 19.1%, which is 0.8% absolute higher than the former even with the same Ag paste. This is because the electrode by dispensing printing has uniform aspect ratio and narrow line width over the length of electrode.

Electrode configuration for extreme-UV electrical discharge source

Science.gov (United States)

Spence, Paul Andrew; Fornaciari, Neal Robert; Chang, Jim Jihchyun

2002-01-01

It has been demonstrated that debris generation within an electric capillary discharge source, for generating extreme ultraviolet and soft x-ray, is dependent on the magnitude and profile of the electric field that is established along the surfaces of the electrodes. An electrode shape that results in uniform electric field strength along its surface has been developed to minimize sputtering and debris generation. The electric discharge plasma source includes: (a) a body that defines a circular capillary bore that has a proximal end and a distal end; (b) a back electrode positioned around and adjacent to the distal end of the capillary bore wherein the back electrode has a channel that is in communication with the distal end and that is defined by a non-uniform inner surface which exhibits a first region which is convex, a second region which is concave, and a third region which is convex wherein the regions are viewed outwardly from the inner surface of the channel that is adjacent the distal end of the capillary bore so that the first region is closest to the distal end; (c) a front electrode positioned around and adjacent to the proximal end of the capillary bore wherein the front electrode has an opening that is communication with the proximal end and that is defined by a non-uniform inner surface which exhibits a first region which is convex, a second region which is substantially linear, and third region which is convex wherein the regions are viewed outwardly from the inner surface of the opening that is adjacent the proximal end of the capillary bore so that the first region is closest to the proximal end; and (d) a source of electric potential that is connected across the front and back electrodes.

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

Science.gov (United States)

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

2018-04-23

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

A MacWilliams Identity for Convolutional Codes : The General Case

NARCIS (Netherlands)

Gluesing-Luerssen, Heide; Schneider, Gert

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

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

Universal FFM Hydrogen Spectral Line Shapes Applied to Ions and Electrons

Science.gov (United States)

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

2008-10-01

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

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)

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.

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.

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

Science.gov (United States)

Zhong, Jiandan; Lei, Tao; Yao, Guangle

2017-11-24

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

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...

Optimized parallel convolutions for non-linear fluid models of tokamak ηi turbulence

International Nuclear Information System (INIS)

Milovich, J.L.; Tomaschke, G.; Kerbel, G.D.

1993-01-01

Non-linear computational fluid models of plasma turbulence based on spectral methods typically spend a large fraction of the total computing time evaluating convolutions. Usually these convolutions arise from an explicit or semi implicit treatment of the convective non-linearities in the problem. Often the principal convective velocity is perpendicular to magnetic field lines allowing a reduction of the convolution to two dimensions in an appropriate geometry, but beyond this, different models vary widely in the particulars of which mode amplitudes are selectively evolved to get the most efficient representation of the turbulence. As the number of modes in the problem, N, increases, the amount of computation required for this part of the evolution algorithm then scales as N 2 /timestep for a direct or analytic method and N ln N/timestep for a pseudospectral method. The constants of proportionality depend on the particulars of mode selection and determine the size problem for which the method will perform equally. For large enough N, the pseudospectral method performance is always superior, though some problems do not require correspondingly high resolution. Further, the Courant condition for numerical stability requires that the timestep size must decrease proportionately as N increases, thus accentuating the need to have fast methods for larger N problems. The authors have developed a package for the Cray system which performs these convolutions for a rather arbitrary mode selection scheme using either method. The package is highly optimized using a combination of macro and microtasking techniques, as well as vectorization and in some cases assembly coded routines. Parts of the package have also been developed and optimized for the CM200 and CM5 system. Performance comparisons with respect to problem size, parallelization, selection schemes and architecture are presented

Amperometric Detection in Microchip Electrophoresis Devices: Effect of Electrode Material and Alignment on Analytical Performance

Science.gov (United States)

Fischer, David J.; Hulvey, Matthew K.; Regel, Anne R.; Lunte, Susan M.

2012-01-01

The fabrication and evaluation of different electrode materials and electrode alignments for microchip electrophoresis with electrochemical (EC) detection is described. The influences of electrode material, both metal and carbon-based, on sensitivity and limits of detection (LOD) were examined. In addition, the effects of working electrode alignment on analytical performance (in terms of peak shape, resolution, sensitivity, and LOD) were directly compared. Using dopamine (DA), norepinephrine (NE), and catechol (CAT) as test analytes, it was found that pyrolyzed photoresist electrodes with end-channel alignment yielded the lowest limit of detection (35 nM for DA). In addition to being easier to implement, end-channel alignment also offered better analytical performance than off-channel alignment for the detection of all three analytes. In-channel electrode alignment resulted in a 3.6-fold reduction in peak skew and reduced peak tailing by a factor of 2.1 for catechol in comparison to end-channel alignment. PMID:19802847

Manufacture of SOFC electrodes by wet powder spraying

Energy Technology Data Exchange (ETDEWEB)

Wilkenhoener, R.; Mallener, W.; Buchkremer, H.P. [Forschungszentrum Juelich GmbH (Germany)] [and others

1996-12-31

The reproducible and commercial manufacturing of electrodes with enhanced electrochemical performance is of central importance for a successful technical realization of Solid Oxide Fuel Cell (SOFC) systems. The route of electrode fabrication for the SOFC by Wet Powder Spraying (WPS) is presented. Stabilized suspensions of the powder materials for the electrodes were sprayed onto a substrate by employing a spray gun. After drying of the layers, binder removal and sintering are performed in one step. The major advantage of this process is its applicability for a large variety of materials and its flexibility with regard to layer shape and thickness. Above all, flat or curved substrates of any size can be coated, thus opening up the possibility of {open_quotes}up-scaling{close_quotes} SOFC technology. Electrodes with an enhanced electrochemical performance were developed by gradually optimizing the different process steps. For example an optimized SOFC cathode of the composition La{sub 0.65}Sr{sub 0.3}MnO{sub 3} with 40% 8YSZ showed a mean overpotential of about -50 mV at a current density of -0.8 A/cm{sup 2}, with a standard deviation amounting to 16 mV (950{degrees}C, air). Such optimized electrodes can be manufactured with a high degree of reproducibility, as a result of employing a computer-controlled X-Y system for moving the spray gun. Several hundred sintered composites, comprising the substrate anode and the electrolyte, of 100x 100 mm{sup 2} were coated with the cathode by WPS and used for stack integration. The largest manufactured electrodes were 240x240 mm{sup 2}, and data concerning their thickness homogeneity and electrochemical performance are given.

Discrete singular convolution method for the analysis of Mindlin plates on elastic foundations

International Nuclear Information System (INIS)

Civalek, Omer; Acar, Mustafa Hilmi

2007-01-01

The method of discrete singular convolution (DSC) is used for the bending analysis of Mindlin plates on two-parameter elastic foundations for the first time. Two different realizations of singular kernels, such as the regularized Shannon's delta (RSD) kernel and Lagrange delta sequence (LDS) kernel, are selected as singular convolution to illustrate the present algorithm. The methodology and procedures are presented and bending problems of thick plates on elastic foundations are studied for different boundary conditions. The influence of foundation parameters and shear deformation on the stress resultants and deflections of the plate have been investigated. Numerical studies are performed and the DSC results are compared well with other analytical solutions and some numerical results

The quick convolution of galaxy profiles, with application to power-law intensity distributions

International Nuclear Information System (INIS)

Bailey, M.E.; Sparks, W.B.

1983-01-01

The two-dimensional convolution of a circularly symmetric galaxy model with a Gaussian point-spread function of dispersion σ reduces to a single integral. This is solved analytically for models with power-law intensity distributions and results are given which relate the apparent core radius to σ and the power-law index k. The convolution integral is also simplified for the case of a point-spread function corresponding to a circular aperture. Models of galactic nuclei with stellar density cusps can only be distinguished from alternatives with small core radii if both the brightness and seeing profiles are measured accurately. The results are applied to data on the light distribution at the Galactic Centre. (author)

Paediatric frontal chest radiograph screening with fine-tuned convolutional neural networks

CSIR Research Space (South Africa)

Gerrand, Jonathan D

2017-07-01

Full Text Available of fine-tuned convolutional neural networks (CNN). We use two popular CNN models that are pre-trained on a large natural image dataset and two distinct datasets containing paediatric and adult radiographs respectively. Evaluation is performed using a 5...

Convolution quotients in the production of heat in an infinite cylinder

Energy Technology Data Exchange (ETDEWEB)

Battig, A; Kalla, S L [Universidad Nacional de Tucuman (Argentina). Facultad de Ciencias Exactas y Tecnologia

1974-12-01

A solution of the problem of heat production in an infinite cylinder is considered by an appeal to the concept of convolution quotients and finite Hankel transforms. The result given by Erdelyi follows as a particular case of the result established here.

A novel fabrication method of carbon electrodes using 3D printing and chemical modification process.

Science.gov (United States)

Tian, Pan; Chen, Chaoyang; Hu, Jie; Qi, Jin; Wang, Qianghua; Chen, Jimmy Ching-Ming; Cavanaugh, John; Peng, Yinghong; Cheng, Mark Ming-Cheng

2017-11-23

Three-dimensional (3D) printing is an emerging technique in the field of biomedical engineering and electronics. This paper presents a novel biofabrication method of implantable carbon electrodes with several advantages including fast prototyping, patient-specific and miniaturization without expensive cleanroom. The method combines stereolithography in additive manufacturing and chemical modification processes to fabricate electrically conductive carbon electrodes. The stereolithography allows the structures to be 3D printed with very fine resolution and desired shapes. The resin is then chemically modified to carbon using pyrolysis to enhance electrochemical performance. The electrochemical characteristics of 3D printing carbon electrodes are assessed by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The specific capacitance of 3D printing carbon electrodes is much higher than the same sized platinum (Pt) electrode. In-vivo electromyography (EMG) recording, 3D printing carbon electrodes exhibit much higher signal-to-noise ratio (40.63 ± 7.73) than Pt electrodes (14.26 ± 6.83). The proposed biofabrication method is envisioned to enable 3D printing in many emerging applications in biomedical engineering and electronics.

Orientation- and position-controlled alignment of asymmetric silicon microrod on a substrate with asymmetric electrodes

Science.gov (United States)

Shibata, Akihide; Watanabe, Keiji; Sato, Takuya; Kotaki, Hiroshi; Schuele, Paul J.; Crowder, Mark A.; Zhan, Changqing; Hartzell, John W.; Nakatani, Ryoichi

2014-03-01

In this paper, we demonstrate the orientation-controlled alignment of asymmetric Si microrods on a glass substrate with an asymmetric pair of electrodes. The Si microrods have the shape of a paddle with a blade and a shaft part, and the pair of electrodes consists of a narrow electrode and a wide electrode. By applying AC bias to the electrodes, the Si microrods suspended in a fluid align in such a way to settle across the electrode pair, and over 80% of the aligned Si microrods have an orientation with the blade and the shaft of the paddle on the wide and the narrow electrodes, respectively. When Si microrods have a shell of dielectric film and its thickness on the top face is thicker than that on the bottom face, 97.8% of the Si microrods are aligned with the top face facing upwards. This technique is useful for orientation-controlled alignment of nano- and microsized devices that have polarity or a distinction between the top and bottom faces.

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.

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.

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

Science.gov (United States)

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

2018-01-01

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

A Novel Image Tag Completion Method Based on Convolutional Neural Transformation

KAUST Repository

Geng, Yanyan; Zhang, Guohui; Li, Weizhi; Gu, Yi; Liang, Ru-Ze; Liang, Gaoyuan; Wang, Jingbin; Wu, Yanbin; Patil, Nitin; Wang, Jing-Yan

2017-01-01

In the problems of image retrieval and annotation, complete textual tag lists of images play critical roles. However, in real-world applications, the image tags are usually incomplete, thus it is important to learn the complete tags for images. In this paper, we study the problem of image tag complete and proposed a novel method for this problem based on a popular image representation method, convolutional neural network (CNN). The method estimates the complete tags from the convolutional filtering outputs of images based on a linear predictor. The CNN parameters, linear predictor, and the complete tags are learned jointly by our method. We build a minimization problem to encourage the consistency between the complete tags and the available incomplete tags, reduce the estimation error, and reduce the model complexity. An iterative algorithm is developed to solve the minimization problem. Experiments over benchmark image data sets show its effectiveness.

A Novel Image Tag Completion Method Based on Convolutional Neural Transformation

KAUST Repository

Geng, Yanyan

2017-10-24

In the problems of image retrieval and annotation, complete textual tag lists of images play critical roles. However, in real-world applications, the image tags are usually incomplete, thus it is important to learn the complete tags for images. In this paper, we study the problem of image tag complete and proposed a novel method for this problem based on a popular image representation method, convolutional neural network (CNN). The method estimates the complete tags from the convolutional filtering outputs of images based on a linear predictor. The CNN parameters, linear predictor, and the complete tags are learned jointly by our method. We build a minimization problem to encourage the consistency between the complete tags and the available incomplete tags, reduce the estimation error, and reduce the model complexity. An iterative algorithm is developed to solve the minimization problem. Experiments over benchmark image data sets show its effectiveness.

Conformally encapsulated multi-electrode arrays with seamless insulation

Energy Technology Data Exchange (ETDEWEB)

Tabada, Phillipe J.; Shah, Kedar G.; Tolosa, Vanessa; Pannu, Satinderall S.; Tooker, Angela; Delima, Terri; Sheth, Heeral; Felix, Sarah

2016-11-22

Thin-film multi-electrode arrays (MEA) having one or more electrically conductive beams conformally encapsulated in a seamless block of electrically insulating material, and methods of fabricating such MEAs using reproducible, microfabrication processes. One or more electrically conductive traces are formed on scaffold material that is subsequently removed to suspend the traces over a substrate by support portions of the trace beam in contact with the substrate. By encapsulating the suspended traces, either individually or together, with a single continuous layer of an electrically insulating material, a seamless block of electrically insulating material is formed that conforms to the shape of the trace beam structure, including any trace backings which provide suspension support. Electrical contacts, electrodes, or leads of the traces are exposed from the encapsulated trace beam structure by removing the substrate.

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.

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.

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.

Fabrication and characterization of three-dimensional carbon electrodes for lithium-ion batteries

Science.gov (United States)

Teixidor, Genis Turon; Zaouk, Rabih B.; Park, Benjamin Y.; Madou, Marc J.

This paper presents fabrication and testing results of three-dimensional carbon anodes for lithium-ion batteries, which are fabricated through the pyrolysis of lithographically patterned epoxy resins. This technique, known as Carbon-MEMS, provides great flexibility and an unprecedented dimensional control in shaping carbon microstructures. Variations in the pattern density and in the pyrolysis conditions result in anodes with different specific and gravimetric capacities, with a three to six times increase in specific capacity with respect to the current thin-film battery technology. Newly designed cross-shaped Carbon-MEMS arrays have a much higher mechanical robustness (as given by their moment of inertia) than the traditionally used cylindrical posts, but the gravimetric analysis suggests that new designs with thinner features are required for better carbon utilization. Pyrolysis at higher temperatures and slower ramping up schedules reduces the irreversible capacity of the carbon electrodes. We also analyze the addition of Meso-Carbon Micro-Beads (MCMB) particles on the reversible and irreversible capacities of new three-dimensional, hybrid electrodes. This combination results in a slight increase in reversible capacity and a big increase in the irreversible capacity of the carbon electrodes, mostly due to the non-complete attachment of the MCMB particles.

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

Science.gov (United States)

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

2018-05-10

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

Compensation for geometric modeling errors by positioning of electrodes in electrical impedance tomography

DEFF Research Database (Denmark)

Hyvönen, N.; Majander, H.; Staboulis, Stratos

2017-01-01

Electrical impedance tomography aims at reconstructing the conductivity inside a physical body from boundary measurements of current and voltage at a finite number of contact electrodes. In many practical applications, the shape of the imaged object is subject to considerable uncertainties...

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

Science.gov (United States)

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

2018-04-01

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

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.

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.

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.

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.

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.

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

Science.gov (United States)

Tezuka, Taro

2018-06-01

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

Multi-focus image fusion with the all convolutional neural network

Science.gov (United States)

Du, Chao-ben; Gao, She-sheng

2018-01-01

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

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).

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.)

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

Fabrication and Characterization of 3D-Printed Highly-Porous 3D LiFePO4 Electrodes by Low Temperature Direct Writing Process

Directory of Open Access Journals (Sweden)

Changyong Liu

2017-08-01

Full Text Available LiFePO4 (LFP is a promising cathode material for lithium-ion batteries. In this study, low temperature direct writing (LTDW-based 3D printing was used to fabricate three-dimensional (3D LFP electrodes for the first time. LFP inks were deposited into a low temperature chamber and solidified to maintain the shape and mechanical integrity of the printed features. The printed LFP electrodes were then freeze-dried to remove the solvents so that highly-porous architectures in the electrodes were obtained. LFP inks capable of freezing at low temperature was developed by adding 1,4 dioxane as a freezing agent. The rheological behavior of the prepared LFP inks was measured and appropriate compositions and ratios were selected. A LTDW machine was developed to print the electrodes. The printing parameters were optimized and the printing accuracy was characterized. Results showed that LTDW can effectively maintain the shape and mechanical integrity during the printing process. The microstructure, pore size and distribution of the printed LFP electrodes was characterized. In comparison with conventional room temperature direct ink writing process, improved pore volume and porosity can be obtained using the LTDW process. The electrochemical performance of LTDW-fabricated LFP electrodes and conventional roller-coated electrodes were conducted and compared. Results showed that the porous structure that existed in the printed electrodes can greatly improve the rate performance of LFP electrodes.

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.

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.

Automatic segmentation of MR brain images with a convolutional neural network

NARCIS (Netherlands)

Moeskops, P.; Viergever, M.A.; Mendrik, A.M.; de Vries, L.S.; Benders, M.J.N.L.; Išgum, I.

2016-01-01

Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure

Silicon/copper dome-patterned electrodes for high-performance hybrid supercapacitors

Science.gov (United States)

Liu, Xuyan; Jung, Hun-Gi; Kim, Sang-Ok; Choi, Ho-Suk; Lee, Sangwha; Moon, Jun Hyuk; Lee, Joong Kee

2013-12-01

This study proposes a method for manufacturing high-performance electrode materials in which controlling the shape of the current collector and electrode material for a Li-ion capacitor (LIC). In particular, the proposed LIC manufacturing method maintains the high voltage of a cell by using a microdome-patterned electrode material, allowing for reversible reactions between the Li-ion and the active material for an extended period of time. As a result, the LICs exhibit initial capacities of approximately 42 F g-1, even at 60 A g-1. The LICs also exhibit good cycle performance up to approximately 15,000 cycles. In addition, these advancements allow for a considerably higher energy density than other existing capacitor systems. The energy density of the proposed LICs is approximately nine, two, and 1.5 times higher than those of the electrochemical double layer capacitor (EDLC), AC/LiMn2O4 hybrid capacitor, and intrinsic Si/AC LIC, respectively.

Simulation study of dielectrophoretic assembly of nanowire between electrode pairs

Energy Technology Data Exchange (ETDEWEB)

Tao, Quan, E-mail: taq3@pitt.edu; Lan, Fei; Jiang, Minlin [University of Pittsburgh, The Department of Electrical and Computer Engineering (United States); Wei, Fanan [Chinese Academy of Sciences, State Key Laboratory of Robotics, Shenyang Institute of Automation (China); Li, Guangyong, E-mail: gul6@pitt.edu [University of Pittsburgh, The Department of Electrical and Computer Engineering (United States)

2015-07-15

Dielectrophoresis (DEP) of rod-shaped nanostructures is attractive because of its exceptional capability to fabricate nanowire-based electronic devices. This efficient manipulation method, however, has a common side effect of assembling a certain number of nanowires at undesired positions. It is therefore essential to understand the underlying physics of DEP of nanowires in order to better guide the assembly. In this work, we propose theoretical methods to characterize the dielectrophoretic force and torque as well as the hydrodynamic drag force and torque on the nanowire (typical length: 10 μm). The trajectory of the nanowire is then simulated based on rigid body dynamics. The nanowire is predicted to either bridge the electrodes or attach on the surface of one electrode. A neighborhood in which the nanowire is more likely to bridge electrodes is found, which is conducive to successful assembly. The simulation study in this work provides us not only a better understanding of the underlying physics but also practical guidance on nanowire assembly by DEP.

Tracking boundary movement and exterior shape modelling in lung EIT imaging

International Nuclear Information System (INIS)

Biguri, A; Soleimani, M; Grychtol, B; Adler, A

2015-01-01

Electrical impedance tomography (EIT) has shown significant promise for lung imaging. One key challenge for EIT in this application is the movement of electrodes during breathing, which introduces artefacts in reconstructed images. Various approaches have been proposed to compensate for electrode movement, but no comparison of these approaches is available. This paper analyses boundary model mismatch and electrode movement in lung EIT. The aim is to evaluate the extent to which various algorithms tolerate movement, and to determine if a patient specific model is required for EIT lung imaging. Movement data are simulated from a CT-based model, and image analysis is performed using quantitative figures of merit. The electrode movement is modelled based on expected values of chest movement and an extended Jacobian method is proposed to make use of exterior boundary tracking. Results show that a dynamical boundary tracking is the most robust method against any movement, but is computationally more expensive. Simultaneous electrode movement and conductivity reconstruction algorithms show increased robustness compared to only conductivity reconstruction. The results of this comparative study can help develop a better understanding of the impact of shape model mismatch and electrode movement in lung EIT. (paper)

Tracking boundary movement and exterior shape modelling in lung EIT imaging.

Science.gov (United States)

Biguri, A; Grychtol, B; Adler, A; Soleimani, M

2015-06-01

Electrical impedance tomography (EIT) has shown significant promise for lung imaging. One key challenge for EIT in this application is the movement of electrodes during breathing, which introduces artefacts in reconstructed images. Various approaches have been proposed to compensate for electrode movement, but no comparison of these approaches is available. This paper analyses boundary model mismatch and electrode movement in lung EIT. The aim is to evaluate the extent to which various algorithms tolerate movement, and to determine if a patient specific model is required for EIT lung imaging. Movement data are simulated from a CT-based model, and image analysis is performed using quantitative figures of merit. The electrode movement is modelled based on expected values of chest movement and an extended Jacobian method is proposed to make use of exterior boundary tracking. Results show that a dynamical boundary tracking is the most robust method against any movement, but is computationally more expensive. Simultaneous electrode movement and conductivity reconstruction algorithms show increased robustness compared to only conductivity reconstruction. The results of this comparative study can help develop a better understanding of the impact of shape model mismatch and electrode movement in lung EIT.

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/

The effects of electrode cleaning and conditioning on the performance of high-energy, pulsed-power devices

Energy Technology Data Exchange (ETDEWEB)

Cuneo, M.E.

1998-09-01

High-energy pulsed-power devices routinely access field strengths above those at which broad-area, cathode-initiated, high-voltage vacuum-breakdown occur (> 1e7--3e7 V/m). Examples include magnetically-insulated-transmission-lines and current convolutes, high-current-density electron and ion diodes, high-power microwave devices, and cavities and other structures for electrostatic and RF accelerators. Energy deposited in anode surfaces may exceed anode plasma thermal-desorption creation thresholds on the time-scale of the pulse. Stimulated desorption by electron or photon bombardment can also lead to plasma formation on electrode or insulator surfaces. Device performance is limited above these thresholds, particularly in pulse length and energy, by the formation and expansion of plasmas formed primarily from electrode contaminants. In-situ conditioning techniques to modify and eliminate the contaminants through multiple high-voltage pulses, low base pressures, RF discharge cleaning, heating, surface coatings, and ion- and electron-beam surface treatment allow access to new regimes of performance through control of plasma formation and modification of the plasma properties. Experimental and theoretical progress from a variety of devices and small scale experiments with a variety of treatment methods will be reviewed and recommendations given for future work.

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...

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

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....

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

Synthetic bootstrapping of convolutional neural networks for semantic plant part segmentation

NARCIS (Netherlands)

Barth, R.; IJsselmuiden, J.; Hemming, J.; Henten, Van E.J.

2017-01-01

A current bottleneck of state-of-the-art machine learning methods for image segmentation in agriculture, e.g. convolutional neural networks (CNNs), is the requirement of large manually annotated datasets on a per-pixel level. In this paper, we investigated how related synthetic images can be used to

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.

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

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

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.

Multi-Scale Residual Convolutional Neural Network for Haze Removal of Remote Sensing Images

Directory of Open Access Journals (Sweden)

Hou Jiang

2018-06-01

Full Text Available Haze removal is a pre-processing step that operates on at-sensor radiance data prior to the physically based image correction step to enhance hazy imagery visually. Most current haze removal methods focus on point-to-point operations and utilize information in the spectral domain, without taking consideration of the multi-scale spatial information of haze. In this paper, we propose a multi-scale residual convolutional neural network (MRCNN for haze removal of remote sensing images. MRCNN utilizes 3D convolutional kernels to extract spatial–spectral correlation information and abstract features from surrounding neighborhoods for haze transmission estimation. It takes advantage of dilated convolution to aggregate multi-scale contextual information for the purpose of improving its prediction accuracy. Meanwhile, residual learning is utilized to avoid the loss of weak information while deepening the network. Our experiments indicate that MRCNN performs accurately, achieving an extremely low validation error and testing error. The haze removal results of several scenes of Landsat 8 Operational Land Imager (OLI data show that the visibility of the dehazed images is significantly improved, and the color of recovered surface is consistent with the actual scene. Quantitative analysis proves that the dehazed results of MRCNN are superior to the traditional methods and other networks. Additionally, a comparison to haze-free data illustrates the spectral consistency after haze removal and reveals the changes in the vegetation index.

Compressed multiwall carbon nanotube composite electrodes provide enhanced electroanalytical performance for determination of serotonin

International Nuclear Information System (INIS)

Fagan-Murphy, Aidan; Patel, Bhavik Anil

2014-01-01

Serotonin (5-HT) is an important neurochemical that is present in high concentrations within the intestinal tract. Carbon fibre and boron-doped diamond based electrodes have been widely used to date for monitoring 5-HT, however these electrodes are prone to fouling and are difficult to fabricate in certain sizes and geometries. Carbon nanotubes have shown potential as a suitable material for electroanalytical monitoring of 5-HT but can be difficult to manipulate into a suitable form. The fabrication of composite electrodes is an approach that can shape conductive materials into practical electrode geometries suitable for biological environments. This work investigated how compression of multiwall carbon nanotubes (MWCNTs) epoxy composite electrodes can influence their electroanalytical performance. Highly compressed composite electrodes displayed significant improvements in their electrochemical properties along with decreased internal and charge transfer resistance, reproducible behaviour and improved batch to batch variability when compared to non-compressed composite electrodes. Compression of MWCNT epoxy composite electrodes resulted in an increased current response for potassium ferricyanide, ruthenium hexaammine and dopamine, by preferentially removing the epoxy during compression and increasing the electrochemical active surface of the final electrode. For the detection of serotonin, compressed electrodes have a lower limit of detection and improved sensitivity compared to non-compressed electrodes. Fouling studies were carried out in 10 μM serotonin where the MWCNT compressed electrodes were shown to be less prone to fouling than non-compressed electrodes. This work indicates that the compression of MWCNT carbon-epoxy can result in a highly conductive material that can be moulded to various geometries, thus providing scope for electroanalytical measurements and the production of a wide range of analytical devices for a variety of systems

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...

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.

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.

Convolutional neural networks for segmentation and object detection of human semen

DEFF Research Database (Denmark)

Nissen, Malte Stær; Krause, Oswin; Almstrup, Kristian

2017-01-01

We compare a set of convolutional neural network (CNN) architectures for the task of segmenting and detecting human sperm cells in an image taken from a semen sample. In contrast to previous work, samples are not stained or washed to allow for full sperm quality analysis, making analysis harder due...

The main features of electrical stimulation of biological tissues by implant electrodes: study from engineering perspective and equipment development to produce

International Nuclear Information System (INIS)

Suarez Bagnasco, D.; Alvarez Alonso, J.; Suarez Antola, R.

2004-08-01

The main features of electrical stimulation of biological tissues by implant electrodes are studied.These electrodes are applied in neural prostheses and cardiac pacing.Threshold phenomena are stressed and some aspects related with implant electrode design are discussed. A fairly through theoretical research about the optimal pulse shape for electrical stimulation of biological tissues is done.The excitation functional is introduced as a criterium to identify threshold pulses of electric current. We obtain the optimal pulse shapes that minimize the energy dissipated in tissues, or the energy taken by the load seen by the pulse generator, amongst other criteria.We show how these pulse shapes can be determined from experimentally measured strength-duration (S-D) curves using rectangular pulses of current. The development of a prototype of a new equipment is described.The equipment may be used to measure S-D curves and with this information it is able to syntetize the abovementioned optimal pulse shapes. The top-down design process is presented, involving both hardware and software.The construction and assembling of the prototype, as well as the implementation of software are described.Some testing and measures with the prototype, including test with biological tissues are described and assessed

A Convolution-LSTM-Based Deep Neural Network for Cross-Domain MOOC Forum Post Classification

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Xiaocong Wei

2017-07-01

Full Text Available Learners in a massive open online course often express feelings, exchange ideas and seek help by posting questions in discussion forums. Due to the very high learner-to-instructor ratios, it is unrealistic to expect instructors to adequately track the forums, find all of the issues that need resolution and understand their urgency and sentiment. In this paper, considering the biases among different courses, we propose a transfer learning framework based on a convolutional neural network and a long short-term memory model, called ConvL, to automatically identify whether a post expresses confusion, determine the urgency and classify the polarity of the sentiment. First, we learn the feature representation for each word by considering the local contextual feature via the convolution operation. Second, we learn the post representation from the features extracted through the convolution operation via the LSTM model, which considers the long-term temporal semantic relationships of features. Third, we investigate the possibility of transferring parameters from a model trained on one course to another course and the subsequent fine-tuning. Experiments on three real-world MOOC courses confirm the effectiveness of our framework. This work suggests that our model can potentially significantly increase the effectiveness of monitoring MOOC forums in real time.

Cobalt phthalocyanine modified electrodes utilised in electroanalysis: nano-structured modified electrodes vs. bulk modified screen-printed electrodes.

Science.gov (United States)

Foster, Christopher W; Pillay, Jeseelan; Metters, Jonathan P; Banks, Craig E

2014-11-19

Cobalt phthalocyanine (CoPC) compounds have been reported to provide electrocatalytic performances towards a substantial number of analytes. In these configurations, electrodes are typically constructed via drop casting the CoPC onto a supporting electrode substrate, while in other cases the CoPC complex is incorporated within the ink of a screen-printed sensor, providing a one-shot economical and disposable electrode configuration. In this paper we critically compare CoPC modified electrodes prepared by drop casting CoPC nanoparticles (nano-CoPC) onto a range of carbon based electrode substrates with that of CoPC bulk modified screen-printed electrodes in the sensing of the model analytes L-ascorbic acid, oxygen and hydrazine. It is found that no "electrocatalysis" is observed towards L-ascorbic acid using either of these CoPC modified electrode configurations and that the bare underlying carbon electrode is the origin of the obtained voltammetric signal, which gives rise to useful electroanalytical signatures, providing new insights into literature reports where "electrocatalysis" has been reported with no clear control experiments undertaken. On the other hand true electrocatalysis is observed towards hydrazine, where no such voltammetric features are witnessed on the bare underlying electrode substrate.

Shape estimation of the buried body from the ground surface potential distributions generated by current injection; Tsuryu ni yoru chihyomen den`i bunpu wo riyoshita maizobutsu keijo no suitei

Energy Technology Data Exchange (ETDEWEB)

Takahashi, Y; Okamoto, Y [Chiba Institute of Technology, Chiba (Japan); Noguchi, K [Waseda University, Tokyo (Japan); Teramachi, Y [University of Industrial Technology, Kanagawa (Japan); Akabane, H; Agu, M [Ibaraki University, Ibaraki (Japan)

1996-10-01

Ground surface potential distribution generated by current injection was studied to estimate the shape of buried bodies. Since the uniform ground system including a homogeneous buried body is perfectly determined with the surface shape of a buried body and resistivities in/around a buried body, inversion is easy if the surface shape is described with some parameters. N electrodes are arranged in 2-D grid manner on the ground, and two electrodes among them are used for current injection, while the others for measurement of potentials. M times of measurements are repeated while changing combination of electrodes for current injection. The potential distribution measured by the mth electrode pair is represented by N-2 dimensional vectors. The square error between this distribution and calculated one is the function of k parameters on the surface shape and resistivities on a buried body. Both shape and resistivities can be estimated by solving an optimum value problem using the square error as evaluation function. Analysis is easy for a spherical body with 6 unknown parameters, however, it is difficult for more complex bodies than elliptical one or more than two bodies. 5 refs., 9 figs.

Embedded Ultrathin Cluster Electrodes for Long-Term Recordings in Deep Brain Centers.

Directory of Open Access Journals (Sweden)

Leila Etemadi

Full Text Available Neural interfaces which allow long-term recordings in deep brain structures in awake freely moving animals have the potential of becoming highly valuable tools in neuroscience. However, the recording quality usually deteriorates over time, probably at least partly due to tissue reactions caused by injuries during implantation, and subsequently micro-forces due to a lack of mechanical compliance between the tissue and neural interface. To address this challenge, we developed a gelatin embedded neural interface comprising highly flexible electrodes and evaluated its long term recording properties. Bundles of ultrathin parylene C coated platinum electrodes (N = 29 were embedded in a hard gelatin based matrix shaped like a needle, and coated with Kollicoat™ to retard dissolution of gelatin during the implantation. The implantation parameters were established in an in vitro model of the brain (0.5% agarose. Following a craniotomy in the anesthetized rat, the gelatin embedded electrodes were stereotactically inserted to a pre-target position, and after gelatin dissolution the electrodes were further advanced and spread out in the area of the subthalamic nucleus (STN. The performance of the implanted electrodes was evaluated under anesthesia, during 8 weeks. Apart from an increase in the median-noise level during the first 4 weeks, the electrode impedance and signal-to-noise ratio of single-units remained stable throughout the experiment. Histological postmortem analysis confirmed implantation in the area of STN in most animals. In conclusion, by combining novel biocompatible implantation techniques and ultra-flexible electrodes, long-term neuronal recordings from deep brain structures with no significant deterioration of electrode function were achieved.

A Conditional Fourier-Feynman Transform and Conditional Convolution Product with Change of Scales on a Function Space II

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Dong Hyun Cho

2017-01-01

Full Text Available Using a simple formula for conditional expectations over continuous paths, we will evaluate conditional expectations which are types of analytic conditional Fourier-Feynman transforms and conditional convolution products of generalized cylinder functions and the functions in a Banach algebra which is the space of generalized Fourier transforms of the measures on the Borel class of L2[0,T]. We will then investigate their relationships. Particularly, we prove that the conditional transform of the conditional convolution product can be expressed by the product of the conditional transforms of each function. Finally we will establish change of scale formulas for the conditional transforms and the conditional convolution products. In these evaluation formulas and change of scale formulas, we use multivariate normal distributions so that the conditioning function does not contain present positions of the paths.

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

Science.gov (United States)

Zhu, Aichun; Wang, Tian; Snoussi, Hichem

2018-03-01

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

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

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Yuankun Li

2018-02-01

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

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

Directory of Open Access Journals (Sweden)

Aichun Zhu

2018-03-01

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

A New Missing Values Estimation Algorithm in Wireless Sensor Networks Based on Convolution

Directory of Open Access Journals (Sweden)

Feng Liu

2013-04-01

Full Text Available Nowadays, with the rapid development of Internet of Things (IoT applications, data missing phenomenon becomes very common in wireless sensor networks. This problem can greatly and directly threaten the stability and usability of the Internet of things applications which are constructed based on wireless sensor networks. How to estimate the missing value has attracted wide interest, and some solutions have been proposed. Different with the previous works, in this paper, we proposed a new convolution based missing value estimation algorithm. The convolution theory, which is usually used in the area of signal and image processing, can also be a practical and efficient way to estimate the missing sensor data. The results show that the proposed algorithm in this paper is practical and effective, and can estimate the missing value accurately.

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

Science.gov (United States)

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

2017-01-01

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

Mapping the temporal pole with a specialized electrode array: technique and preliminary results

International Nuclear Information System (INIS)

Abel, Taylor J; Rhone, Ariane E; Nourski, Kirill V; Oya, Hiroyuki; Kawasaki, Hiroto; Howard, Matthew A III; Granner, Mark A; Tranel, Daniel T; Griffiths, Timothy D

2014-01-01

Temporopolar cortex plays a crucial role in the pathogenesis of temporal lobe epilepsy and subserves important cognitive functions. Because of its shape and position in the middle cranial fossa, complete electrode coverage of the temporal pole (TP) is difficult to achieve using existing devices. We designed a novel TP electrode array that conforms to the surface of temporopolar cortex and achieves dense electrode coverage of this important brain region. A multi-pronged electrode array was designed that can be placed over the surface of the TP using a straightforward insertion technique. Twelve patients with medically intractable epilepsy were implanted with the TP electrode array for purposes of seizure localization. Select patients underwent cognitive mapping by electrocorticographic (ECoG) recording from the TP during a naming task. Use of the array resulted in excellent TP electrode coverage in all patients. High quality ECoG data were consistently obtained for purposes of delineating seizure activity and functional mapping. During a naming task, significant increases in ECoG power were observed within localized subregions of the TP. One patient developed a transient neurological deficit thought to be related to the mass effect of multiple intracranial recording arrays, including the TP array. This deficit resolved following removal of all electrodes. The TP electrode array overcomes limitations of existing devices and enables clinicians and researchers to obtain optimal multi-site recordings from this important brain region. (paper)

Electrode spanning with partial tripolar stimulation mode in cochlear implants.

Science.gov (United States)

Wu, Ching-Chih; Luo, Xin

2014-12-01

The perceptual effects of electrode spanning (i.e., the use of nonadjacent return electrodes) in partial tripolar (pTP) mode were tested on a main electrode EL8 in five cochlear implant (CI) users. Current focusing was controlled by σ (the ratio of current returned within the cochlea), and current steering was controlled by α (the ratio of current returned to the basal electrode). Experiment 1 tested whether asymmetric spanning with α = 0.5 can create additional channels around standard pTP stimuli. It was found that in general, apical spanning (i.e., returning current to EL6 rather than EL7) elicited a pitch between those of standard pTP stimuli on main electrodes EL8 and EL9, while basal spanning (i.e., returning current to EL10 rather than EL9) elicited a pitch between those of standard pTP stimuli on main electrodes EL7 and EL8. The pitch increase caused by apical spanning was more salient than the pitch decrease caused by basal spanning. To replace the standard pTP channel on the main electrode EL8 when EL7 or EL9 is defective, experiment 2 tested asymmetrically spanned pTP stimuli with various α, and experiment 3 tested symmetrically spanned pTP stimuli with various σ. The results showed that pitch increased with decreasing α in asymmetric spanning, or with increasing σ in symmetric spanning. Apical spanning with α around 0.69 and basal spanning with α around 0.38 may both elicit a similar pitch as the standard pTP stimulus. With the same σ, the symmetrically spanned pTP stimulus was higher in pitch than the standard pTP stimulus. A smaller σ was thus required for symmetric spanning to match the pitch of the standard pTP stimulus. In summary, electrode spanning is an effective field-shaping technique that is useful for adding spectral channels and handling defective electrodes with CIs.

Joint Multi-scale Convolution Neural Network for Scene Classification of High Resolution Remote Sensing Imagery

Directory of Open Access Journals (Sweden)

ZHENG Zhuo

2018-05-01

Full Text Available High resolution remote sensing imagery scene classification is important for automatic complex scene recognition, which is the key technology for military and disaster relief, etc. In this paper, we propose a novel joint multi-scale convolution neural network (JMCNN method using a limited amount of image data for high resolution remote sensing imagery scene classification. Different from traditional convolutional neural network, the proposed JMCNN is an end-to-end training model with joint enhanced high-level feature representation, which includes multi-channel feature extractor, joint multi-scale feature fusion and Softmax classifier. Multi-channel and scale convolutional extractors are used to extract scene middle features, firstly. Then, in order to achieve enhanced high-level feature representation in a limit dataset, joint multi-scale feature fusion is proposed to combine multi-channel and scale features using two feature fusions. Finally, enhanced high-level feature representation can be used for classification by Softmax. Experiments were conducted using two limit public UCM and SIRI datasets. Compared to state-of-the-art methods, the JMCNN achieved improved performance and great robustness with average accuracies of 89.3% and 88.3% on the two datasets.

Donut-shaped Co{sub 3}O{sub 4} nanoflakes grown on nickel foam with enhanced supercapacitive performances

Energy Technology Data Exchange (ETDEWEB)

Han, Zhicheng; Zheng, Xin; Yao, Shunyu; Xiao, Huanhao; Qu, Fengyu; Wu, Xiang, E-mail: wuxiang05@163.com

2016-03-01

Graphical abstract: The as-synthesized product exhibits a high initial discharge capacitance of 518 mF/cm{sup 2} at a current density of 1 mA cm{sup −2} and can maintain 75% capacitance retention even after 6000 charge–discharge cycles. Electrochemical results revealed that the prepared Co{sub 3}O{sub 4} nanoflakes possess a remarkable performance in supercapacitor applications. - Highlights: • Donut-shaped Co{sub 3}O{sub 4} nanoflakes were first fabricated by a solution approach. • The tests show high discharge areal capacitance and long cycle life stability. • Co{sub 3}O{sub 4} nanoflakes might be promising supercapacitor electrode materials. - Abstract: Donut-shaped Co{sub 3}O{sub 4} nanoflakes grown on nickel foam were successfully fabricated by a simple one-pot hydrothermal approach. The prepared products were functionalized as the supercapacitors electrodes. Electrochemical performance of the as-prepared products demonstrated high specific capacitance (518 mF cm{sup −2}) and excellent cycling stability (∼25% loss) after 6000 repetitive cycles at a charge–discharge current density of 1 mA cm{sup −2}. The superior electrochemical performance may be ascribed into two reasons: one is the unique spatial structures which possess many active sites and provide enhanced combination between the electrode and nickel foam to support fast ion and electron transfer, the other is that donut-shaped Co{sub 3}O{sub 4} nanoflakes electrodes show relatively lower resistances. It is expected that the as-obtained donut-shaped Co{sub 3}O{sub 4} nanoflakes could have potential applications in portable electronics and electrical vehicles.

Optical breast shape capture and finite-element mesh generation for electrical impedance tomography

International Nuclear Information System (INIS)

Forsyth, J; Borsic, A; Halter, R J; Hartov, A; Paulsen, K D

2011-01-01

X-ray mammography is the standard for breast cancer screening. The development of alternative imaging modalities is desirable because mammograms expose patients to ionizing radiation. Electrical impedance tomography (EIT) may be used to determine tissue conductivity, a property which is an indicator of cancer presence. EIT is also a low-cost imaging solution and does not involve ionizing radiation. In breast EIT, impedance measurements are made using electrodes placed on the surface of the patient's breast. The complex conductivity of the volume of the breast is estimated by a reconstruction algorithm. EIT reconstruction is a severely ill-posed inverse problem. As a result, noisy instrumentation and incorrect modelling of the electrodes and domain shape produce significant image artefacts. In this paper, we propose a method that has the potential to reduce these errors by accurately modelling the patient breast shape. A 3D hand-held optical scanner is used to acquire the breast geometry and electrode positions. We develop methods for processing the data from the scanner and producing volume meshes accurately matching the breast surface and electrode locations, which can be used for image reconstruction. We demonstrate this method for a plaster breast phantom and a human subject. Using this approach will allow patient-specific finite-element meshes to be generated which has the potential to improve the clinical value of EIT for breast cancer diagnosis

Efficiently GPU-accelerating long kernel convolutions in 3-D DIRECT TOF PET reconstruction via memory cache optimization

Energy Technology Data Exchange (ETDEWEB)

Ha, Sungsoo; Mueller, Klaus [Stony Brook Univ., NY (United States). Center for Visual Computing; Matej, Samuel [Pennsylvania Univ., Philadelphia, PA (United States). Dept. of Radiology

2011-07-01

The DIRECT represents a novel approach for 3-D Time-of-Flight (TOF) PET reconstruction. Its novelty stems from the fact that it performs all iterative predictor-corrector operations directly in image space. The projection operations now amount to convolutions in image space, using long TOF (resolution) kernels. While for spatially invariant kernels the computational complexity can be algorithmically overcome by replacing spatial convolution with multiplication in Fourier space, spatially variant kernels cannot use this shortcut. Therefore in this paper, we describe a GPU-accelerated approach for this task. However, the intricate parallel architecture of GPUs poses its own challenges, and careful memory and thread management is the key to obtaining optimal results. As convolution is mainly memory-bound we focus on the former, proposing two types of memory caching schemes that warrant best cache memory re-use by the parallel threads. In contrast to our previous two-stage algorithm, the schemes presented here are both single-stage which is more accurate. (orig.)

Convolutional auto-encoder for image denoising of ultra-low-dose CT

Directory of Open Access Journals (Sweden)

Mizuho Nishio

2017-08-01

Conclusion: Neural network with convolutional auto-encoder could be trained using pairs of standard-dose and ultra-low-dose CT image patches. According to the visual assessment by radiologists and technologists, the performance of our proposed method was superior to that of large-scale nonlocal mean and block-matching and 3D filtering.

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.

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.

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...

Strabismus Recognition Using Eye-Tracking Data and Convolutional Neural Networks

Directory of Open Access Journals (Sweden)

Zenghai Chen

2018-01-01

Full Text Available Strabismus is one of the most common vision diseases that would cause amblyopia and even permanent vision loss. Timely diagnosis is crucial for well treating strabismus. In contrast to manual diagnosis, automatic recognition can significantly reduce labor cost and increase diagnosis efficiency. In this paper, we propose to recognize strabismus using eye-tracking data and convolutional neural networks. In particular, an eye tracker is first exploited to record a subject’s eye movements. A gaze deviation (GaDe image is then proposed to characterize the subject’s eye-tracking data according to the accuracies of gaze points. The GaDe image is fed to a convolutional neural network (CNN that has been trained on a large image database called ImageNet. The outputs of the full connection layers of the CNN are used as the GaDe image’s features for strabismus recognition. A dataset containing eye-tracking data of both strabismic subjects and normal subjects is established for experiments. Experimental results demonstrate that the natural image features can be well transferred to represent eye-tracking data, and strabismus can be effectively recognized by our proposed method.

Yarn-dyed fabric defect classification based on convolutional neural network

Science.gov (United States)

Jing, Junfeng; Dong, Amei; Li, Pengfei; Zhang, Kaibing

2017-09-01

Considering that manual inspection of the yarn-dyed fabric can be time consuming and inefficient, we propose a yarn-dyed fabric defect classification method by using a convolutional neural network (CNN) based on a modified AlexNet. CNN shows powerful ability in performing feature extraction and fusion by simulating the learning mechanism of human brain. The local response normalization layers in AlexNet are replaced by the batch normalization layers, which can enhance both the computational efficiency and classification accuracy. In the training process of the network, the characteristics of the defect are extracted step by step and the essential features of the image can be obtained from the fusion of the edge details with several convolution operations. Then the max-pooling layers, the dropout layers, and the fully connected layers are employed in the classification model to reduce the computation cost and extract more precise features of the defective fabric. Finally, the results of the defect classification are predicted by the softmax function. The experimental results show promising performance with an acceptable average classification rate and strong robustness on yarn-dyed fabric defect classification.

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

Cobalt Phthalocyanine Modified Electrodes Utilised in Electroanalysis: Nano-Structured Modified Electrodes vs. Bulk Modified Screen-Printed Electrodes

Directory of Open Access Journals (Sweden)

Christopher W. Foster

2014-11-01

Full Text Available Cobalt phthalocyanine (CoPC compounds have been reported to provide electrocatalytic performances towards a substantial number of analytes. In these configurations, electrodes are typically constructed via drop casting the CoPC onto a supporting electrode substrate, while in other cases the CoPC complex is incorporated within the ink of a screen-printed sensor, providing a one-shot economical and disposable electrode configuration. In this paper we critically compare CoPC modified electrodes prepared by drop casting CoPC nanoparticles (nano-CoPC onto a range of carbon based electrode substrates with that of CoPC bulk modified screen-printed electrodes in the sensing of the model analytes L-ascorbic acid, oxygen and hydrazine. It is found that no “electrocatalysis” is observed towards L-ascorbic acid using either of these CoPC modified electrode configurations and that the bare underlying carbon electrode is the origin of the obtained voltammetric signal, which gives rise to useful electroanalytical signatures, providing new insights into literature reports where “electrocatalysis” has been reported with no clear control experiments undertaken. On the other hand true electrocatalysis is observed towards hydrazine, where no such voltammetric features are witnessed on the bare underlying electrode substrate.

A tripolar current-steering stimulator ASIC for field shaping in deep brain stimulation.

Science.gov (United States)

Valente, Virgilio; Demosthenous, Andreas; Bayford, Richard

2012-06-01

A significant problem with clinical deep brain stimulation (DBS) is the high variability of its efficacy and the frequency of side effects, related to the spreading of current beyond the anatomical target area. This is the result of the lack of control that current DBS systems offer on the shaping of the electric potential distribution around the electrode. This paper presents a stimulator ASIC with a tripolar current-steering output stage, aiming at achieving more selectivity and field shaping than current DBS systems. The ASIC was fabricated in a 0.35-μ m CMOS technology occupying a core area of 0.71 mm(2). It consists of three current sourcing/sinking channels. It is capable of generating square and exponential-decay biphasic current pulses with five different time constants up to 28 ms and delivering up to 1.85 mA of cathodic current, in steps of 4 μA, from a 12 V power supply. Field shaping was validated by mapping the potential distribution when injecting current pulses through a multicontact DBS electrode in saline.

Multimodal Classification of Violent Online Political Extremism Content with Graph Convolutional Networks

NARCIS (Netherlands)

Rudinac, S.; Gornishka, I.; Worring, M.

2017-01-01

In this paper we present a multimodal approach to categorizing user posts based on their discussion topic. To integrate heterogeneous information extracted from the posts, i.e. text, visual content and the information about user interactions with the online platform, we deploy graph convolutional

A viable electrode material for use in microbial fuel cells for tropical regions

DEFF Research Database (Denmark)

Offei, Felix; Thygesen, Anders; Mensah, Moses

2016-01-01

of this material is also targeted at introducing an inexpensive and durable electrode material, which can be produced in rural communities to improve the viability of MFCs. The maximum voltage and power density obtained (under 1000 Ω load) using an H-shaped MFC with AC as both anode and cathode electrode material...... was 0.66 V and 1.74 W/m3, respectively. The power generated by AC was as high as 86% of the value obtained with the extensively used carbon paper. Scanning electron microscopy and Denaturing Gradient Gel Electrophoresis (DGGE) analysis of AC anode biofilms confirmed that electrogenic bacteria were...

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.

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

Science.gov (United States)

Langenbucher, Frieder

2003-11-01

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

Convolutional neural networks and face recognition task

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.

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.

Fine-grained vehicle type recognition based on deep convolution neural networks

Directory of Open Access Journals (Sweden)

Hongcai CHEN

2017-12-01

Full Text Available Public security and traffic department put forward higher requirements for real-time performance and accuracy of vehicle type recognition in complex traffic scenes. Aiming at the problems of great plice forces occupation, low retrieval efficiency, and lacking of intelligence for dealing with false license, fake plate vehicles and vehicles without plates, this paper proposes a vehicle type fine-grained recognition method based GoogleNet deep convolution neural networks. The filter size and numbers of convolution neural network are designed, the activation function and vehicle type classifier are optimally selected, and a new network framework is constructed for vehicle type fine-grained recognition. The experimental results show that the proposed method has 97% accuracy for vehicle type fine-grained recognition and has greater improvement than the original GoogleNet model. Moreover, the new model effectively reduces the number of training parameters, and saves computer memory. Fine-grained vehicle type recognition can be used in intelligent traffic management area, and has important theoretical research value and practical significance.

Detection of bars in galaxies using a deep convolutional neural network

Science.gov (United States)

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

2018-06-01

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

Multi-scale Fully Convolutional Network for Face Detection in the Wild

KAUST Repository

Bai, Yancheng

2017-08-24

Face detection is a classical problem in computer vision. It is still a difficult task due to many nuisances that naturally occur in the wild. In this paper, we propose a multi-scale fully convolutional network for face detection. To reduce computation, the intermediate convolutional feature maps (conv) are shared by every scale model. We up-sample and down-sample the final conv map to approximate K levels of a feature pyramid, leading to a wide range of face scales that can be detected. At each feature pyramid level, a FCN is trained end-to-end to deal with faces in a small range of scale change. Because of the up-sampling, our method can detect very small faces (10×10 pixels). We test our MS-FCN detector on four public face detection datasets, including FDDB, WIDER FACE, AFW and PASCAL FACE. Extensive experiments show that it outperforms state-of-the-art methods. Also, MS-FCN runs at 23 FPS on a GPU for images of size 640×480 with no assumption on the minimum detectable face size.

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

Science.gov (United States)

Xing, Weiwei; Li, Ying; Zhang, Shunli

2018-01-01

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

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...

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

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,

Initial-value problems for first-order differential recurrence equations with auto-convolution

Directory of Open Access Journals (Sweden)

Mircea Cirnu

2011-01-01

Full Text Available A differential recurrence equation consists of a sequence of differential equations, from which must be determined by recurrence a sequence of unknown functions. In this article, we solve two initial-value problems for some new types of nonlinear (quadratic first order homogeneous differential recurrence equations, namely with discrete auto-convolution and with combinatorial auto-convolution of the unknown functions. In both problems, all initial values form a geometric progression, but in the second problem the first initial value is exempted and has a prescribed form. Some preliminary results showing the importance of the initial conditions are obtained by reducing the differential recurrence equations to algebraic type. Final results about solving the considered initial value problems, are shown by mathematical induction. However, they can also be shown by changing the unknown functions, or by the generating function method. So in a remark, we give a proof of the first theorem by the generating function method.

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

Science.gov (United States)

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

2017-12-01

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

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

Science.gov (United States)

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

2018-04-20

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

Eye and sheath folds in turbidite convolute lamination: Aberystwyth Grits Group, Wales

Science.gov (United States)

McClelland, H. L. O.; Woodcock, N. H.; Gladstone, C.

2011-07-01

Eye and sheath folds are described from the turbidites of the Aberystwyth Group, in the Silurian of west Wales. They have been studied at outcrop and on high resolution optical scans of cut surfaces. The folds are not tectonic in origin. They occur as part of the convolute-laminated interval of each sand-mud turbidite bed. The thickness of this interval is most commonly between 20 and 100 mm. Lamination patterns confirm previous interpretations that convolute lamination nucleated on ripples and grew during continued sedimentation of the bed. The folds amplified vertically and were sheared horizontally by continuing turbidity flow, but only to average values of about γ = 1. The strongly curvilinear fold hinges are due not to high shear strains, but to nucleation on sinuous or linguoid ripples. The Aberystwyth Group structures provide a warning that not all eye folds in sedimentary or metasedimentary rocks should be interpreted as sections through high shear strain sheath folds.

Servo scanning 3D micro EDM for array micro cavities using on-machine fabricated tool electrodes

Science.gov (United States)

Tong, Hao; Li, Yong; Zhang, Long

2018-02-01

Array micro cavities are useful in many fields including in micro molds, optical devices, biochips and so on. Array servo scanning micro electro discharge machining (EDM), using array micro electrodes with simple cross-sectional shape, has the advantage of machining complex 3D micro cavities in batches. In this paper, the machining errors caused by offline-fabricated array micro electrodes are analyzed in particular, and then a machining process of array servo scanning micro EDM is proposed by using on-machine fabricated array micro electrodes. The array micro electrodes are fabricated on-machine by combined procedures including wire electro discharge grinding, array reverse copying and electrode end trimming. Nine-array tool electrodes with Φ80 µm diameter and 600 µm length are obtained. Furthermore, the proposed process is verified by several machining experiments for achieving nine-array hexagonal micro cavities with top side length of 300 µm, bottom side length of 150 µm, and depth of 112 µm or 120 µm. In the experiments, a chip hump accumulates on the electrode tips like the built-up edge in mechanical machining under the conditions of brass workpieces, copper electrodes and the dielectric of deionized water. The accumulated hump can be avoided by replacing the water dielectric by an oil dielectric.

Note: a novel vacuum ultraviolet light source assembly with aluminum-coated electrodes for enhancing the ionization efficiency of photoionization mass spectrometry.

Science.gov (United States)

Zhu, Zhixiang; Wang, Jian; Qiu, Keqing; Liu, Chengyuan; Qi, Fei; Pan, Yang

2014-04-01

A novel vacuum ultraviolet (VUV) light source assembly (VUVLSA) for enhancing the ionization efficiency of photoionization mass spectrometer has been described. The VUVLSA composes of a Krypton lamp and a pair of disk electrodes with circular center cavities. The two interior surfaces that face the photoionization region were aluminum-coated. VUV light can be reflected back and forth in the photoionization region between the electrodes, thus the photoionization efficiency can be greatly enhanced. The performances of two different shaped electrodes, the coated double flat electrodes (DFE), and double conical electrodes, were studied. We showed that the signal amplification of coated DFE is around 4 times higher than that of uncoated electrodes without VUV light reflection. The relationship between the pressure of ionization chamber and mass signal enhancement has also been studied.

Comparison of expandable electrodes in percutaneous radiofrequency ablation of renal cell carcinoma

International Nuclear Information System (INIS)

Gulesserian, Talin; Mahnken, Andreas H.; Schernthaner, Ruediger; Memarsadeghi, Mazda; Weber, Michael; Tacke, A.; Kettenbach, Joachim

2006-01-01

Objective: To compare two different expandable electrodes in radiofrequency ablation of renal cell carcinoma. Methods: Percutaneous ablation was performed at two centers using either an expandable 7F umbrella-shaped LeVeen TM probe (diameter 2-4 cm) and a 200-W generator (group A), or an expandable Starburst XL TM electrode with a 150-W generator (group B). From each center, eight patients with one tumor each were matched retrospectively with regard to tumor volume, which was 9.71 ± 6.43 cm 3 for group A and 8.74 ± 4.35 cm 3 for group B (mean tumor diameter: 2.47 ± 0.9 cm versus 2.50 ± 0.4 cm, respectively). An unpaired t-test showed no significant difference in tumor volume between the two groups (p = 0.820). Results: Sixteen patients with 16 tumors were treated. The primary technical success of radiofrequency ablation was 94% (15 of 16 patients). After retreatment of residual tumor in one patient from group B, secondary technical success was 100%. No major complications were observed. The resulting mean volume of the almost spherical necroses was 21.1 ± 9.1 cm 3 versus 14.6 ± 6.7 cm 3 for groups A and B (diameter of necrosis: 3.5 ± 0.7 cm versus 3.1 ± 0.6 cm, respectively). A Mann-Whitney U-test showed no significant difference in necrosis volume between the two groups (CI [-0.215; 0.471]; p = 0.2892). The calculated shape value of S (ratio of length to height of the coagulation necrosis) was 0.9 ± 0.1 and 1.0 ± 0.1 for groups A and B, respectively. No local recurrence was observed during a mean follow-up of 14.8 ± 11.6 months, while extrarenal tumor progression occurred in three patients. Conclusions: No significant differences in coagulation volume and shape were found after RF ablation of renal cell carcinoma using two different expandable electrodes. To avoid local recurrence, however, accurate placement of probes and appropriate expansion of the electrode is necessary

Extreme-value limit of the convolution of exponential and multivariate normal distributions: Link to the Hüsler–Reiß distribution

KAUST Repository

Krupskii, Pavel

2017-11-02

The multivariate Hüsler–Reiß copula is obtained as a direct extreme-value limit from the convolution of a multivariate normal random vector and an exponential random variable multiplied by a vector of constants. It is shown how the set of Hüsler–Reiß parameters can be mapped to the parameters of this convolution model. Assuming there are no singular components in the Hüsler–Reiß copula, the convolution model leads to exact and approximate simulation methods. An application of simulation is to check if the Hüsler–Reiß copula with different parsimonious dependence structures provides adequate fit to some data consisting of multivariate extremes.

Extreme-value limit of the convolution of exponential and multivariate normal distributions: Link to the Hüsler–Reiß distribution

KAUST Repository

Krupskii, Pavel; Joe, Harry; Lee, David; Genton, Marc G.

2017-01-01

The multivariate Hüsler–Reiß copula is obtained as a direct extreme-value limit from the convolution of a multivariate normal random vector and an exponential random variable multiplied by a vector of constants. It is shown how the set of Hüsler–Reiß parameters can be mapped to the parameters of this convolution model. Assuming there are no singular components in the Hüsler–Reiß copula, the convolution model leads to exact and approximate simulation methods. An application of simulation is to check if the Hüsler–Reiß copula with different parsimonious dependence structures provides adequate fit to some data consisting of multivariate extremes.

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