electrode of an arbitrary shape
Directory of Open Access Journals (Sweden)
P. A. Krutitskii
1999-01-01
Full Text Available A problem on electric current in a semiconductor film from an electrode of an arbitrary shape is studied in the presence of a magnetic field. This situation describes the Hall effect, which indicates the deflection of electric, current from electric field in a semiconductor. From mathematical standpoint we consider the skew derivative problem for harmonic functions in the exterior of an open arc in a plane. By means of potential theory the problem is reduced to the Cauchy singular integral equation and next to the Fredholm equation of the 2nd kind which is uniquely solvable. The solution of the integral equation can be computed by standard codes by discretization and inversion of the matrix. The uniqueness and existence theorems are formulated.
Modiolus-Hugging Intracochlear Electrode Array with Shape Memory Alloy
Directory of Open Access Journals (Sweden)
Kyou Sik Min
2013-01-01
Full Text Available In the cochlear implant system, the distance between spiral ganglia and the electrodes within the volume of the scala tympani cavity significantly affects the efficiency of the electrical stimulation in terms of the threshold current level and spatial selectivity. Because the spiral ganglia are situated inside the modiolus, the central axis of the cochlea, it is desirable that the electrode array hugs the modiolus to minimize the distance between the electrodes and the ganglia. In the present study, we propose a shape-memory-alloy-(SMA- embedded intracochlear electrode which gives a straight electrode a curved modiolus-hugging shape using the restoration force of the SMA as triggered by resistive heating after insertion into the cochlea. An eight-channel ball-type electrode array is fabricated with an embedded titanium-nickel SMA backbone wire. It is demonstrated that the electrode array changes its shape in a transparent plastic human cochlear model. To verify the safe insertion of the electrode array into the human cochlea, the contact pressures during insertion at the electrode tip and the contact pressures over the electrode length after insertion were calculated using a 3D finite element analysis. The results indicate that the SMA-embedded electrode is functionally and mechanically feasible for clinical applications.
Modiolus-hugging intracochlear electrode array with shape memory alloy.
Min, Kyou Sik; Jun, Sang Beom; Lim, Yoon Seob; Park, Se-Ik; Kim, Sung June
2013-01-01
In the cochlear implant system, the distance between spiral ganglia and the electrodes within the volume of the scala tympani cavity significantly affects the efficiency of the electrical stimulation in terms of the threshold current level and spatial selectivity. Because the spiral ganglia are situated inside the modiolus, the central axis of the cochlea, it is desirable that the electrode array hugs the modiolus to minimize the distance between the electrodes and the ganglia. In the present study, we propose a shape-memory-alloy-(SMA-) embedded intracochlear electrode which gives a straight electrode a curved modiolus-hugging shape using the restoration force of the SMA as triggered by resistive heating after insertion into the cochlea. An eight-channel ball-type electrode array is fabricated with an embedded titanium-nickel SMA backbone wire. It is demonstrated that the electrode array changes its shape in a transparent plastic human cochlear model. To verify the safe insertion of the electrode array into the human cochlea, the contact pressures during insertion at the electrode tip and the contact pressures over the electrode length after insertion were calculated using a 3D finite element analysis. The results indicate that the SMA-embedded electrode is functionally and mechanically feasible for clinical applications.
Modiolus-Hugging Intracochlear Electrode Array with Shape Memory Alloy
Min, Kyou Sik; Lim, Yoon Seob; Park, Se-Ik; Kim, Sung June
2013-01-01
In the cochlear implant system, the distance between spiral ganglia and the electrodes within the volume of the scala tympani cavity significantly affects the efficiency of the electrical stimulation in terms of the threshold current level and spatial selectivity. Because the spiral ganglia are situated inside the modiolus, the central axis of the cochlea, it is desirable that the electrode array hugs the modiolus to minimize the distance between the electrodes and the ganglia. In the present study, we propose a shape-memory-alloy-(SMA-) embedded intracochlear electrode which gives a straight electrode a curved modiolus-hugging shape using the restoration force of the SMA as triggered by resistive heating after insertion into the cochlea. An eight-channel ball-type electrode array is fabricated with an embedded titanium-nickel SMA backbone wire. It is demonstrated that the electrode array changes its shape in a transparent plastic human cochlear model. To verify the safe insertion of the electrode array into the human cochlea, the contact pressures during insertion at the electrode tip and the contact pressures over the electrode length after insertion were calculated using a 3D finite element analysis. The results indicate that the SMA-embedded electrode is functionally and mechanically feasible for clinical applications. PMID:23762181
Geometrical Shape Investigation For Electrodes in Silent Discharge Chamber
Facta, Mochammad; Ayu Ketut Umiati, Ngurah; Warsito, Agung
2017-04-01
Silent discharge is the most prominent method to carry out plasma reaction because. discharge is easily initiated by injecting alternating current in high voltage to the pair of separated electrodes. The electron emission from surface of dielectric placed on instantaneous cathode is stimulated by ion induced electron emission. In this method, spark is avoided by placing insulation material to either one or both of the electrodes. In practical, it is very difficult to determine the exact limit of the voltage that initiate the discharge by mathematical analysis because it depends on many factors, namely dimensions, type and geometrical shapes of electrode, thickness of insulation, and type of electric field inside the discharge gap. To get lower initial voltage for discharge, it is important to find the best geometrical shape of electrode in relation to skin effect that trigger electron emission. This work investigates the behaviour of charges, current, electric field and voltage surrounding electrodes with various geometrical shape.
MHD modeling of dense plasma focus electrode shape variation
McLean, Harry; Hartman, Charles; Schmidt, Andrea; Tang, Vincent; Link, Anthony; Ellsworth, Jen; Reisman, David
2013-10-01
The dense plasma focus (DPF) is a very simple device physically, but results to date indicate that very extensive physics is needed to understand the details of operation, especially during the final pinch where kinetic effects become very important. Nevertheless, the overall effects of electrode geometry, electrode size, and drive circuit parameters can be informed efficiently using MHD fluid codes, especially in the run-down phase before the final pinch. These kinds of results can then guide subsequent, more detailed fully kinetic modeling efforts. We report on resistive 2-d MHD modeling results applying the TRAC-II code to the DPF with an emphasis on varying anode and cathode shape. Drive circuit variations are handled in the code using a self-consistent circuit model for the external capacitor bank since the device impedance is strongly coupled to the internal plasma physics. Electrode shape is characterized by the ratio of inner diameter to outer diameter, length to diameter, and various parameterizations for tapering. This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.
Improvement of Dielectric Barrier Discharge Plasma Reactor for Ozone Generation by Electrode Shape
Shimizu, Masaki; Sato, Tohru; Kato, Shoji; Mukaigawa, Seiji; Takaki, Koichi; Fujiwara, Tamiya
An effect of electrode shape on ozone generation in dielectric barrier discharge reactor is described in this article. Three different shape electrodes were employed as ground electrodes. A plane electrode is 6 cm in width, and 20 cm in length. A trench electrode has large number of knife-edge rails. A multipoint electrode has large number of four-sided pyramid projections on the plane. A high voltage plane electrode is covered with 0.5 mm thickness alumina layer worked as dielectric barrier. The experimental results show that the breakdown for the multipoint electrode occurs at 7.0 kVpp. This value is lower than 8.4 kVpp that is the breakdown voltage of the plane electrode. The ozone yield increases from 80 g/kWh to 130 g/kWh by changing the electrode shape from the plane to the multipoint. The ozone generation efficiency decreased with increase of the ozone concentration.
Balzeau, Antoine; Gilissen, Emmanuel; Holloway, Ralph L; Prima, Sylvain; Grimaud-Hervé, Dominique
2014-11-01
The study of brain structural asymmetries as anatomical substrates of functional asymmetries in extant humans, great apes, and fossil hominins is of major importance in understanding the structural basis of modern human cognition. We propose methods to quantify the variation in size, shape and bilateral asymmetries of the third frontal convolution (or posterior inferior frontal gyrus) among recent modern humans, bonobos and chimpanzees, and fossil hominins using actual and virtual endocasts. These methodological improvements are necessary to extend previous qualitative studies of these features. We demonstrate both an absolute and relative bilateral increase in the size of the third frontal convolution in width and length between Pan species, as well as in hominins. We also observed a global bilateral increase in the size of the third frontal convolution across all species during hominin evolution, but also non-allometric intra-group variations independent of brain size within the fossil samples. Finally, our results show that the commonly accepted leftward asymmetry of Broca's cap is biased by qualitative observation of individual specimens. The trend during hominin evolution seems to be a reduction in size on the left compared with the right side, and also a clearer definition of the area. The third frontal convolution considered as a whole projects more laterally and antero-posteriorly in the right hemisphere. As a result, the left 'Broca's cap' looks more globular and better defined. Our results also suggest that the pattern of brain asymmetries is similar between Pan paniscus and hominins, leaving the gradient of the degree of asymmetry as the only relevant structural parameter. As the anatomical substrate related to brain asymmetry has been present since the appearance of the hominin lineage, it is not possible to prove a direct relationship between the extent of variations in the size, shape, and asymmetries of the third frontal convolution and the origin of
Effect of electrode shape on grounding resistances - Part 1
DEFF Research Database (Denmark)
Ingeman-Nielsen, Thomas; Tomaskovicova, Sonia; Dahlin, Torleif
2016-01-01
. 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......, and the instrument input impedance. Using analytical formulations for the potentials around prolate and oblate spheroidal electrode models (as approximations for rod and plate electrodes), we have investigated the performance and accuracy of the focus-one protocol in estimating single-electrode grounding resistances...
Choi, S. G.; Kim, S. H.; Choi, W. K.; Moon, G. C.; Lee, E. S.
2017-06-01
Shape memory alloy (SMA) is important material used for the medicine and aerospace industry due to its characteristics called the shape memory effect, which involves the recovery of deformed alloy to its original state through the application of temperature or stress. Consumers in modern society demand stability in parts. Electrochemical machining is one of the methods for obtained these stabilities in parts requirements. These parts of shape memory alloy require fine patterns in some applications. In order to machine a fine pattern, the electrochemical machining method is suitable. For precision electrochemical machining using different shape electrodes, the current density should be controlled precisely. And electrode shape is required for precise electrochemical machining. It is possible to obtain precise square holes on the SMA if the insulation layer controlled the unnecessary current between electrode and workpiece. If it is adjusting the unnecessary current to obtain the desired shape, it will be a great contribution to the medical industry and the aerospace industry. It is possible to process a desired shape to the shape memory alloy by micro controlling the unnecessary current. In case of the square electrode without insulation layer, it derives inexact square holes due to the unnecessary current. The results using the insulated electrode in only side show precise square holes. The removal rate improved in case of insulated electrode than others because insulation layer concentrate the applied current to the machining zone.
A guide to convolution arithmetic for deep learning
Dumoulin, Vincent; Visin, Francesco
2016-01-01
We introduce a guide to help deep learning practitioners understand and manipulate convolutional neural network architectures. The guide clarifies the relationship between various properties (input shape, kernel shape, zero padding, strides and output shape) of convolutional, pooling and transposed convolutional layers, as well as the relationship between convolutional and transposed convolutional layers. Relationships are derived for various cases, and are illustrated in order to make them i...
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...
Novel active comb-shaped dry electrode for EEG measurement in hairy site.
Huang, Yan-Jun; Wu, Chung-Yu; Wong, Alice May-Kuen; Lin, Bor-Shyh
2015-01-01
Electroencephalography (EEG) is an important biopotential, and has been widely applied in clinical applications. The conventional EEG electrode with conductive gels is usually used for measuring EEG. However, the use of conductive gel also encounters with the issue of drying and hardening. Recently, many dry EEG electrodes based on different conductive materials and techniques were proposed to solve the previous issue. However, measuring EEG in the hairy site is still a difficult challenge. In this study, a novel active comb-shaped dry electrode was proposed to measure EEG in hairy site. Different form other comb-shaped or spike-shaped dry electrodes, it can provide more excellent performance of avoiding the signal attenuation, phase distortion, and the reduction of common mode rejection ratio. Even under walking motion, it can effectively acquire EEG in hairy site. Finally, the experiments for alpha rhythm and steady-state visually evoked potential were also tested to validate the proposed electrode.
Takino, Hideo; Yamamura, Kazuya; Sano, Yasuhisa; Mori, Yuzo
2012-01-20
We propose a plasma chemical vaporization machining device with a hemispherical tip electrode for optical fabrication. Radio-frequency plasma is generated close to the electrode under atmospheric conditions, and a workpiece is scanned relative to the stationary electrode under three-axis motion control to remove target areas on a workpiece surface. Experimental results demonstrate that surface removal progresses although process gas is not forcibly supplied to the plasma. The correction of shape errors on conventionally polished spheres is performed. As a result, highly accurate smooth surfaces with the desired rms shape accuracy of 3 nm are successfully obtained, which confirms that the device is effective for the fabrication of optics.
Tai, Zhixin; Yan, Xingbin; Xue, Qunji
2012-09-01
In this paper, a graphene/shape-memory polyurethane (PU) composite film, used for a supercapacitor electrode, is fabricated by a simple bonding method. In the composite, formerly prepared graphene paper is closely bonded on the surface of the PU slice, forming a bi-layered composite film. Based on the good flexibility of graphene paper and the outstanding shape holding capacity of PU phase, the resulting composite film can be changed into various shapes. Also, the composite film shows excellent shape recovery ability. The graphene/PU composite film used as the electrode maintains a satisfactory electrochemical capacitance of graphene material and there is no decay in the specific capacitance after long-cycle testing, making it attractive for novel supercapacitors with special shapes and shape-memory ability.
Sareni, Bruno; Krähenbühl, Laurent; Muller, Daniel
1998-01-01
In this paper, we present a new approach for automatic design of electrodes. The investigated method consists in identifying an optimal shape from an optimal equipotential resulting from a system of point charges. The electric field and potential are computed using the point charge simulation method. Niching genetic algorithms and constrained optimization techniques are applied to the electrode benchmark in order to find multiple optimal profiles.
Polymer-stabilized blue phase liquid crystal display with slanted wall-shaped electrodes
Institute of Scientific and Technical Information of China (English)
Feng Zhou; Qionghua Wang; Di Wu; Jianpeng Cui
2012-01-01
A polymer-stabilized blue phase liquid crystal display (BPLCD) with slanted wall-shaped electrodes is proposed. Compared with the traditional BPLCD with wall-shaped electrodes, the electrodes of the proposed BPLCD are slightly angled to obtain phase retardation in the entire cell even at the position of electrodes. The proposed BPLCD demonstrates a relatively higher average transmittance and overall brightness than the traditional BPLCD.%A polymer-stabilized blue phase liquid crystal display (BPLCD) with slanted wall-shaped electrodes is proposed.Compared with the traditional BPLCD with wall-shaped electrodes,the electrodes of the proposed BPLCD are slightly angled to obtain phase retardation in the entire cell even at the position of electrodes.The proposed BPLCD demonstrates a relatively higher average transmittance and overall brightness than the traditional BPLCD.Owing to the continuous improvement in image quality of liquid crystal displays (LCDs),they have been widely employed in desktop monitors,TVs,and mobile displays at present[1-5].With the development of LCDs the polymer-stabilized blue phase LCDs (BPLCDs)[6-11]can replace the conventional LCDs and become the nextgeneration display technology.The polymer-stabilized BPLCDs have numerous attractive features,such as submillisecond gray-to-gray response time,alignmentlayer-free process optically isotropic dark state and cell gap insensitivity[12-14].Because of these advantages,the fabrication processes of the BPLCDs are simplified,motion-image blurs are reduced,and color-sequential displays using RGB LEDs are enabled.
Takino, Hideo; Hosaka, Takahiro
2016-06-20
We propose a method for fabricating a spherical lens array mold by electrical discharge machining (EDM) with a ball-type electrode. The electrode is constructed by arranging conductive spherical balls in an array. To fundamentally examine the applicability of the proposed EDM method to the fabrication of lens array molds, we use an electrode having a single ball to shape a lens array mold made of stainless steel with 16 spherical elements, each having a maximum depth of 0.5 mm. As a result, a mold surface is successfully shaped with a peak-to-valley shape accuracy of approximately 10 μm, and an average surface roughness of 0.85 μm.
Takino, Hideo; Hosaka, Takahiro
2014-11-20
We propose a method for fabricating a lens array mold by electrical discharge machining (EDM). In this method, the tips of rods are machined individually to form a specific surface, and then a number of the machined rods are arranged to construct an electrode for EDM. The repetition of the EDM process using the electrode enables a number of lens elements to be produced on the mold surface. The effectiveness of our proposed method is demonstrated by shaping a lens array mold made of stainless steel with 16 spherical elements, in which the EDM process with a single rod electrode is repeatedly conducted.
Design of blade-shaped-electrode linear ion traps with reduced anharmonic contributions
Energy Technology Data Exchange (ETDEWEB)
Deng, K.; Che, H.; Ge, Y. P.; Xu, Z. T.; Yuan, W. H.; Zhang, J.; Lu, Z. H., E-mail: zehuanglu@mail.hust.edu.cn [MOE Key Laboratory of Fundamental Physical Quantities Measurement, School of Physics, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074 (China); Lan, Y. [MOE Key Laboratory of Fundamental Physical Quantities Measurement, School of Physics, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074 (China); Department of Physics and Astronomy, University of British Columbia, Vancouver, BC V6T 1Z1 (Canada)
2015-09-21
RF quadrupole linear Paul traps are versatile tools in quantum physics experiments. Linear Paul traps with blade-shaped electrodes have the advantages of larger solid angles for fluorescence collection. But with these kinds of traps, the existence of higher-order anharmonic terms of the trap potentials can cause large heating rate for the trapped ions. In this paper, we theoretically investigate the dependence of higher-order terms of trap potentials on the geometry of blade-shaped traps, and offer an optimized design. A modified blade electrodes trap is proposed to further reduce higher-order anharmonic terms while still retaining large fluorescence collection angle.
Fundamentals of convolutional coding
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
Directory of Open Access Journals (Sweden)
Li Xu
2016-12-01
Full Text Available We theoretically analyze the properties of thermoelectric transport through a T-shaped DQD connected to ferromagnetic and superconducting electrodes by means of nonequilibrium Green function formalism. The influences of the superconducting gap, interdot tunneling coupling and asymmetry parameter on the thermoelectric properties are discussed. The large thermoelectric efficiency can be obtained by choosing small polarization of ferromagnetic electrode, small asymmetry parameter (<1, appropriately large gap and appropriately interdot coupling, which can be used as the optimal schemes for obtaining high thermoelectric efficiency in the device.
Son, ByungDae; Seong, IlWon; Lee, JunKyu; Shin, JooHyun; Lee, Heon; Yoon, WooYoung
2017-05-04
A nanopillar-patterned Si substrate was fabricated by photolithography, and its potential as an anode material for Li ion secondary batteries was investigated. The Si nanopillar electrode showed a capacity of ∼3000 mAh g(-1) during 100 charging/discharging cycles, with 98.3% capacity retention, and it was revealed that the nanopillars underwent delithiation via a process similar to shape-memory behavior. Despite the tensile stress and structural fractures resulting from repeated lithiation, the nanoscale size and residual crystalline tip of the pillar (influenced by the bulk crystalline Si base) enabled recrystallization and transformation into a single-crystalline phase. To the best of our knowledge, this observation of shape memory recrystallization mechanism observation was not reported before for Si used as the active material in Li ion battery applications; these findings are expected to provide new insights into electrode materials for rechargeable batteries.
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 ...
Roberts, R. C.; Wu, J.; Hau, N. Y.; Chang, Y. H.; Feng, S. P.; Li, D. C.
2014-11-01
This paper reports on a simple 3D metal electrode fabrication technique via inkjet printing onto a thermally contracting shape memory polymer (SMP) substrate. Inkjet printing allows for the direct patterning of structures from metal nanoparticle bearing liquid inks. After deposition, these inks require thermal curing steps to render a stable conductive film. By printing onto a SMP substrate, the metal nanoparticle ink can be cured and substrate shrunk simultaneously to create 3D metal microstructures, forming a large surface area topology well suited for energy applications. Polystyrene SMP shrinkage was characterized in a laboratory oven from 150-240°C, resulting in a size reduction of 1.97-2.58. Silver nanoparticle ink was patterned into electrodes, shrunk, and the topology characterized using scanning electron microscopy. Zinc-Silver Oxide microbatteries were fabricated to demonstrate the 3D electrodes compared to planar references. Characterization was performed using 10M potassium hydroxide electrolyte solution doped with zinc oxide (57g/L). After a 300s oxidation at 3Vdc, the 3D electrode battery demonstrated a 125% increased capacity over the reference cell. Reference cells degraded with longer oxidations, but the 3D electrodes were fully oxidized for 4 hours, and exhibited a capacity of 5.5mA-hr/cm2 with stable metal performance.
Quasi-Convolution Pyramidal Blurring
Kraus, Martin
2008-01-01
Efficient image blurring techniques based on the pyramid algorithm can be implemented on modern graphics hardware; thus, image blurring with arbitrary blur width is possible in real time even for large images. However, pyramidal blurring methods do not achieve the image quality provided by convolution filters; in particular, the shape of the corresponding filter kernel varies locally, which potentially results in objectionable rendering artifacts. In this work, a new analysis filter is design...
Palys, M.J.; Palys, M.; Bos, M.; van der Linden, W.E.
1991-01-01
An improved expert system for the automatic elucidation of electrode reaction mechanisms is presented. The system is coupled to the electrochemical instrument, designs the experiment, controls it and collects the results automatically. The approximate logic concept is used to handle situations when
Energy Technology Data Exchange (ETDEWEB)
Kim, Miyoung; Kim, Min Su; Kang, Byeong Gyun; Kim, Mi-Kyung; Yoon, Sukin; Lee, Seung Hee [Polymer BIN Fusion Research Center, Department of Polymer Nano-Science and Technology, Chonbuk National University, Chonju, Chonbuk 561-756 (Korea, Republic of); Ge Zhibing; Rao Linghui; Gauza, Sebastian; Wu, Shin-Tson, E-mail: lsh1@chonbuk.ac.k, E-mail: swu@creol.ucf.ed [College of Optics and Photonics, University of Central Florida, Orlando, FL 32816 (United States)
2009-12-07
Polymer-stabilized blue phase liquid crystal displays based on the Kerr effect are emerging due to their submillisecond response time, wide view and simple fabrication process. However, the conventional in-plane switching device exhibits a relatively high operating voltage because the electric fields are restricted in the vicinity of the electrode surface. To overcome this technical barrier, we propose a partitioned wall-shaped electrode configuration so that the induced birefringence is uniform between electrodes throughout the entire cell gap. Consequently, the operating voltage is reduced by {approx} 2.8x with two transistors. The responsible physical mechanisms are explained.
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.
Applications of convolution voltammetry in electroanalytical chemistry.
Bentley, Cameron L; Bond, Alan M; Hollenkamp, Anthony F; Mahon, Peter J; Zhang, Jie
2014-02-18
The robustness of convolution voltammetry for determining accurate values of the diffusivity (D), bulk concentration (C(b)), and stoichiometric number of electrons (n) has been demonstrated by applying the technique to a series of electrode reactions in molecular solvents and room temperature ionic liquids (RTILs). In acetonitrile, the relatively minor contribution of nonfaradaic current facilitates analysis with macrodisk electrodes, thus moderate scan rates can be used without the need to perform background subtraction to quantify the diffusivity of iodide [D = 1.75 (±0.02) × 10(-5) cm(2) s(-1)] in this solvent. In the RTIL 1-ethyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide, background subtraction is necessary at a macrodisk electrode but can be avoided at a microdisk electrode, thereby simplifying the analytical procedure and allowing the diffusivity of iodide [D = 2.70 (±0.03) × 10(-7) cm(2) s(-1)] to be quantified. Use of a convolutive procedure which simultaneously allows D and nC(b) values to be determined is also demonstrated. Three conditions under which a technique of this kind may be applied are explored and are related to electroactive species which display slow dissolution kinetics, undergo a single multielectron transfer step, or contain multiple noninteracting redox centers using ferrocene in an RTIL, 1,4-dinitro-2,3,5,6-tetramethylbenzene, and an alkynylruthenium trimer, respectively, as examples. The results highlight the advantages of convolution voltammetry over steady-state techniques such as rotating disk electrode voltammetry and microdisk electrode voltammetry, as it is not restricted by the mode of diffusion (planar or radial), hence removing limitations on solvent viscosity, electrode geometry, and voltammetric scan rate.
Compressing Convolutional Neural Networks
Chen, Wenlin; Wilson, James T.; Tyree, Stephen; Weinberger, Kilian Q.; Chen, Yixin
2015-01-01
Convolutional neural networks (CNN) are increasingly used in many areas of computer vision. They are particularly attractive because of their ability to "absorb" great quantities of labeled data through millions of parameters. However, as model sizes increase, so do the storage and memory requirements of the classifiers. We present a novel network architecture, Frequency-Sensitive Hashed Nets (FreshNets), which exploits inherent redundancy in both convolutional layers and fully-connected laye...
Sadighi-Bonabi, R.; Pasandideh, K.; Zand, M.; Nazari Mahroo, H.
2017-03-01
Using an appropriate design of electrodes and adjustment of the thyratron decoupling circuit as a high-repetition-rate and high-voltage switch, very stable operation of a copper vapor laser at high pressures was obtained. This was achieved by canceling the intense filamentation in the laser plasma at the higher pressures. The transverse grooves on the inner surface of the funnel-shaped copper electrodes permit operation of the laser up to 100 torr. This design reduces the cathode-fall voltage, and as a result reduces the thermal loading in the cathode-fall region. The optimum pressure was 80 torr. At this condition the output power was more than that observed with expensive molybdenum electrodes in a similar laser system.
Institute of Scientific and Technical Information of China (English)
FU Yubin; LIU Jia; SU Jia; ZHAO Zhongkai; LIU Yang; XU Qian
2012-01-01
Microbial fuel cell (MFC) on the ocean floor is a kind of novel energy- harvesting device that can be developed to drive small instruments to work continuously.The shape of electrode has a great effect on the performance of the MFC.In this paper,several shapes of electrode and cell structure were designed,and their performance in MFC were compared in pairs:Mesh (cell-1) vs.flat plate (cell-2),branch (cell-3) vs.cylinder (cell-4),and forest (cell-5) vs.disk (cell-6) FC.Our results showed that the maximum power densities were 16.50,14.20,19.30,15.00,14.64,and 9.95 mWm-2 for cell-l,2,3,4,5 and 6 respectively.And the corresponding diffusion-limited currents were 7.16,2.80,18.86,10.50,18.00,and 6.900 mA.The mesh and branch anodes showed higher power densities and much higher diffusion-limited currents than the flat plate and the cylinder anodes respectively due to the low diffusion hindrance with the former anodes.The forest cathode improved by 47％ of the power density and by 161％ of diffusionlimited current than the disk cathode due to the former's extended solid/liquid/gas three-phase boundary.These results indicated that the shape of electrode is a major parameter that determining the diffusion-limited current of an MFC,and the differences in the electrode shape lead to the differences in cell performance.These results would be useful for MFC structure design in practical applications.
Muscle twitch responses for shaping the multi-pad electrode for functional electrical stimulation
Malešević Nebojša; Popović Lana; Bijelić Goran; Kvaščev Goran
2010-01-01
In this paper we present a method for optimization of multi-pad electrode spatial selectivity during transcutaneous Functional Electrical Stimulation (FES) of hand. The presented method is based on measurement of individual muscle twitch responses during low frequency electrical stimulation via pads within multi-pad electrode. Twitch responses are recorded by Micro-Electro-Mechanical Systems (MEMS) accelerometers. The aim of this methodology is to substitute bulky sensors, torque sensor...
Convolution copula econometrics
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.
Efficient convolutional sparse coding
Energy Technology Data Exchange (ETDEWEB)
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.
McCoul, David; Rosset, Samuel; Besse, Nadine; Shea, Herbert
2017-02-01
Dielectric elastomer actuators (DEAs) are an attractive form of electromechanical transducer, possessing high energy densities, an efficient design, mechanical flexibility, high speed, and noiseless operation. They have been incorporated into a variety of elegant devices, such as microfluidic devices, tunable optics, haptic displays, and minimum-energy grippers. Dielectric elastomer minimum energy structures (DEMESs) take advantage of the prestretch of the DEA to bend a non-stretchable but flexible component to perform mechanical work. The gripper is perhaps the most intuitive type of DEMES, capable of grasping objects but with only small to moderate forces. We present a novel configuration of a DEA using electrodes made of a conductive shape-memory polymer (SMP), incorporated into the design of a gripper. The SMP electrodes allow the DEA to be rigid in the cold state, offering greater holding force than a conventional gripper. Joule heating applied to the SMP electrodes softens them, allowing for electrostatic actuation. Cooling then locks in the actuated position without the need for continued power to be supplied. Additionally, the Joule heating voltage is at least one order of magnitude less than electrostatic actuation voltages, allowing for addressing of multiple actuator elements using commercially available transistors. The shape memory gripper incorporates this addressing into its design, enabling the three segments of each finger to be controlled independently.
Convolution Operators on Groups
Derighetti, Antoine
2011-01-01
This volume is devoted to a systematic study of the Banach algebra of the convolution operators of a locally compact group. Inspired by classical Fourier analysis we consider operators on Lp spaces, arriving at a description of these operators and Lp versions of the theorems of Wiener and Kaplansky-Helson.
Directory of Open Access Journals (Sweden)
Herb Silverman
1996-01-01
Full Text Available The radius of univalence is found for the convolution f∗g of functions f∈S (normalized univalent functions and g∈C (close-to-convex functions. A lower bound for the radius of univalence is also determined when f and g range over all of S. Finally, a characterization of C provides an inclusion relationship.
Institute of Scientific and Technical Information of China (English)
Fang Wang; Yan-chun Lou; Rui Chen; Zhao-wei Song; Bao-kuan Li
2015-01-01
The vibrating electrode method was proposed in the electro-slag remelting (ESR) process in this paper, and the effect of vibrating electrode on the solidiifcation structure of ingot was studied. A transient three-dimensional (3D) coupled mathematical model was established to simulate the electromagnetic phenomenon, fluid flow as well as pool shape in the ESR process with the vibrating electrode. The finite element volume method is developed to solve the electromagnetic field using ANSYS mechanical APDL software. Moreover, the electromagnetic force and Joule heating are interpolated as the source term of the momentum and energy equations. The multi-physical fields have been investigated and compared between the traditional electrode and the vibrating electrode in the ESR process. The results show that the drop process of metal droplets with the traditional electrode is scattered randomly. However, the drop process of metal droplets with the vibrating electrode is periodic. The highest temperature of slag layer with the vibrating electrode is higher than that with the traditional electrode, which can increase the melting rate due to the enhanced heat transfer in the vicinity of the electrode tip. The results also show that when the amplitude and frequency of the vibrating electrode increase, the cycle of drop process of metal droplets decreases signiifcantly.
Directory of Open Access Journals (Sweden)
Fang Wang
2015-07-01
Full Text Available The vibrating electrode method was proposed in the electro-slag remelting (ESR process in this paper, and the effect of vibrating electrode on the solidification structure of ingot was studied. A transient three-dimensional (3D coupled mathematical model was established to simulate the electromagnetic phenomenon, fluid flow as well as pool shape in the ESR process with the vibrating electrode. The finite element volume method is developed to solve the electromagnetic field using ANSYS mechanical APDL software. Moreover, the electromagnetic force and Joule heating are interpolated as the source term of the momentum and energy equations. The multi-physical fields have been investigated and compared between the traditional electrode and the vibrating electrode in the ESR process. The results show that the drop process of metal droplets with the traditional electrode is scattered randomly. However, the drop process of metal droplets with the vibrating electrode is periodic. The highest temperature of slag layer with the vibrating electrode is higher than that with the traditional electrode, which can increase the melting rate due to the enhanced heat transfer in the vicinity of the electrode tip. The results also show that when the amplitude and frequency of the vibrating electrode increase, the cycle of drop process of metal droplets decreases significantly.
Shape resonances and EXAFS scattering in the $Pt L_{2,3}$ XANES from a Pt electrode
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).
Nomura, Fumimasa; Kaneko, Tomoyuki; Hamada, Tomoyo; Hattori, Akihiro; Yasuda, Kenji
2013-06-01
To predict the risk of fatal arrhythmia induced by cardiotoxicity in the highly complex human heart system, we have developed a novel quasi-in vivo electrophysiological measurement assay, which combines a ring-shaped human cardiomyocyte network and a set of two electrodes that form a large single ring-shaped electrode for the direct measurement of irregular cell-to-cell conductance occurrence in a cardiomyocyte network, and a small rectangular microelectrode for forced pacing of cardiomyocyte beating and for acquiring the field potential waveforms of cardiomyocytes. The advantages of this assay are as follows. The electrophysiological signals of cardiomyocytes in the ring-shaped network are superimposed directly on a single loop-shaped electrode, in which the information of asynchronous behavior of cell-to-cell conductance are included, without requiring a set of huge numbers of microelectrode arrays, a set of fast data conversion circuits, or a complex analysis in a computer. Another advantage is that the small rectangular electrode can control the position and timing of forced beating in a ring-shaped human induced pluripotent stem cell (hiPS)-derived cardiomyocyte network and can also acquire the field potentials of cardiomyocytes. First, we constructed the human iPS-derived cardiomyocyte ring-shaped network on the set of two electrodes, and acquired the field potential signals of particular cardiomyocytes in the ring-shaped cardiomyocyte network during simultaneous acquisition of the superimposed signals of whole-cardiomyocyte networks representing cell-to-cell conduction. Using the small rectangular electrode, we have also evaluated the response of the cell network to electrical stimulation. The mean and SD of the minimum stimulation voltage required for pacing (VMin) at the small rectangular electrode was 166+/-74 mV, which is the same as the magnitude of amplitude for the pacing using the ring-shaped electrode (179+/-33 mV). The results showed that the
Invariant Scattering Convolution Networks
Bruna, Joan
2012-01-01
A wavelet scattering network computes a translation invariant image representation, which is stable to deformations and preserves high frequency information for classification. It cascades wavelet transform convolutions with non-linear modulus and averaging operators. The first network layer outputs SIFT-type descriptors whereas the next layers provide complementary invariant information which improves classification. The mathematical analysis of wavelet scattering networks explains important properties of deep convolution networks for classification. A scattering representation of stationary processes incorporates higher order moments and can thus discriminate textures having the same Fourier power spectrum. State of the art classification results are obtained for handwritten digits and texture discrimination, using a Gaussian kernel SVM and a generative PCA classifier.
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
Convolutional Goppa codes defined on fibrations
Curto, J I Iglesias; Martín, F J Plaza; Sotelo, G Serrano
2010-01-01
We define a new class of Convolutional Codes in terms of fibrations of algebraic varieties generalizaing our previous constructions of Convolutional Goppa Codes. Using this general construction we can give several examples of Maximum Distance Separable (MDS) Convolutional Codes.
Convolutional coding techniques for data protection
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.
Consensus Convolutional Sparse Coding
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.
Strongly-MDS convolutional codes
Gluesing-Luerssen, H; Rosenthal, J; Smarandache, R
2006-01-01
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 earlies
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...
Topological convolution algebras
Alpay, Daniel
2012-01-01
In this paper we introduce a new family of topological convolution algebras of the form $\\bigcup_{p\\in\\mathbb N} L_2(S,\\mu_p)$, where $S$ is a Borel semi-group in a locally compact group $G$, which carries an inequality of the type $\\|f*g\\|_p\\le A_{p,q}\\|f\\|_q\\|g\\|_p$ for $p > q+d$ where $d$ pre-assigned, and $A_{p,q}$ is a constant. We give a sufficient condition on the measures $\\mu_p$ for such an inequality to hold. We study the functional calculus and the spectrum of the elements of these algebras, and present two examples, one in the setting of non commutative stochastic distributions, and the other related to Dirichlet series.
Khalil, Rania; Homaeigohar, Shahin; Häußler, Dietrich; Elbahri, Mady
2016-09-01
In this study, the transparent conducting polymer of poly (3,4-ethylenendioxythiophene): poly(styrene sulphonate) (PEDOT:PSS) was nanohybridized via inclusion of gold nanofillers including nanospheres (NSs) and nanorods (NRs). Such nanocomposite thin films offer not only more optimum conductivity than the pristine polymer but also excellent resistivity against volatile organic compounds (VOCs). Interestingly, such amazing properties are achieved in the diluted regimes of the nanofillers and depend on the characteristics of the interfacial region of the polymer and nanofillers, i.e. the aspect ratio of the latter component. Accordingly, a shape dependent response is made that is more desirable in case of using the Au nanorods with a much larger aspect ratio than their nanosphere counterparts. This transparent nanocomposite thin film with an optimized conductivity and very low sensitivity to organic gases is undoubtedly a promising candidate material for the touch screen panel production industry. Considering PEDOT as a known material for integrated electrodes in energy saving applications, we believe that our strategy might be an important progress in the field.
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.
Blind recognition of punctured convolutional codes
Institute of Scientific and Technical Information of China (English)
LU Peizhong; LI Shen; ZOU Yan; LUO Xiangyang
2005-01-01
This paper presents an algorithm for blind recognition of punctured convolutional codes which is an important problem in adaptive modulation and coding. For a given finite sequence of convolutional code, the parity check matrix of the convolutional code is first computed by solving a linear system with adequate error tolerance. Then a minimal basic encoding matrix of the original convolutional code and its puncturing pattern are determined according to the known parity check matrix of the punctured convolutional code.
Chen, Liang; Dai, Hui; Zhou, Yong; Hu, Yingjie; Yu, Tao; Liu, Jianguo; Zou, Zhigang
2014-11-28
An excellent, platinum free fiber counter electrode (CE) was successfully fabricated, consisting of porous, single crystalline titanium nitride (TiN) nanoplates grown on carbon fibers (CF). The fiber-shaped dye-sensitized solar cells (FDSSCs) based on the TiN-CF CE show a high conversion efficiency of 7.20%, comparable or even superior to that of the Pt wire (6.23%).
Matrix convolution operators on groups
Chu, Cho-Ho
2008-01-01
In the last decade, convolution operators of matrix functions have received unusual attention due to their diverse applications. This monograph presents some new developments in the spectral theory of these operators. The setting is the Lp spaces of matrix-valued functions on locally compact groups. The focus is on the spectra and eigenspaces of convolution operators on these spaces, defined by matrix-valued measures. Among various spectral results, the L2-spectrum of such an operator is completely determined and as an application, the spectrum of a discrete Laplacian on a homogeneous graph is computed using this result. The contractivity properties of matrix convolution semigroups are studied and applications to harmonic functions on Lie groups and Riemannian symmetric spaces are discussed. An interesting feature is the presence of Jordan algebraic structures in matrix-harmonic functions.
General logarithmic image processing convolution.
Palomares, Jose M; González, Jesús; Ros, Eduardo; Prieto, Alberto
2006-11-01
The logarithmic image processing model (LIP) is a robust mathematical framework, which, among other benefits, behaves invariantly to illumination changes. This paper presents, for the first time, two general formulations of the 2-D convolution of separable kernels under the LIP paradigm. Although both formulations are mathematically equivalent, one of them has been designed avoiding the operations which are computationally expensive in current computers. Therefore, this fast LIP convolution method allows to obtain significant speedups and is more adequate for real-time processing. In order to support these statements, some experimental results are shown in Section V.
A REMARK ON CERTAIN CONVOLUTION OPERATOR
Institute of Scientific and Technical Information of China (English)
刘金林
1993-01-01
A certain operator D(a+p-1) defined by convolutions (or Hadamard products) is introduced. The object of this paper is to give an application of the convolution operator D(a+p-1) to the differential inequalities.
Size and Shape Control of Gold Nanodeposits in an Array of Silica Nanowells on a Gold Electrode
Rue, Amy E.; Collinson, Maryanne M.
2012-01-01
Ordered arrays of hemispherical nanowells were formed in a sol-gel-derived silica film on a gold electrode using 500 nm diameter polystyrene latex spheres as templates. The conductive domain located at the bottom of each nanowell upon template removal was enlarged via electroless deposition from a gold plating solution. The structured electrodes thus formed were characterized using scanning electron microscopy and atomic force microscopy. Depending on the method used to make the films, the ex...
Fast Algorithms for Convolutional Neural Networks
Lavin, Andrew; Gray, Scott
2015-01-01
Deep convolutional neural networks take GPU days of compute time to train on large data sets. Pedestrian detection for self driving cars requires very low latency. Image recognition for mobile phones is constrained by limited processing resources. The success of convolutional neural networks in these situations is limited by how fast we can compute them. Conventional FFT based convolution is fast for large filters, but state of the art convolutional neural networks use small, 3x3 filters. We ...
Engineering Multirate Convolutions for Radar Imaging
Bierens, L.H.J.; Deprettere, E.F.
1996-01-01
We present a schematic design methodology for multirate convolution systems, based on combined algorithmic development and architecture design. It allows us to map the algebraic specification of a long convolution algorithm directly onto efficient fast convolution hardware based on short FFT process
Reed-Solomon convolutional codes
Gluesing-Luerssen, H; Schmale, W
2005-01-01
In this paper we will introduce a specific class of cyclic convolutional codes. The construction is based on Reed-Solomon block codes. The algebraic parameters as well as the distance of these codes are determined. This shows that some of these codes are optimal or near optimal.
Electroformed Electrodes for Electrical-Discharge Machining
Werner, A.; Cassidenti, M.
1984-01-01
Copper electrodes replace graphite electrodes in many instances of electrical-discharge machining (EDM) of complex shapes. Copper electrodes wear longer and cause less contamination of EDM dielectric fluid than do graphite electrodes.
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.
Energy Technology Data Exchange (ETDEWEB)
Chen Xianlan [Fujian Key Lab of Medical Instrument and Pharmaceutical Technology, Yishan Campus, Fuzhou University, Fuzhou, Fujian 350002 (China); Institute of Research for Functional Materials, Fuzhou University, Yishan Campus, Fuzhou University, Fuzhou, Fujian 350108 (China); College of Chemistry and Chemical Engineering, Qishan Campus, Fuzhou University, Fuzhou, Fujian 350108 (China); Pan Hiabo, E-mail: hbpan@fzu.edu.c [Fujian Key Lab of Medical Instrument and Pharmaceutical Technology, Yishan Campus, Fuzhou University, Fuzhou, Fujian 350002 (China); State Key Laboratory Breeding Base of Photocatalysis, Yishan Campus, Fuzhou University, Fuzhou, Fujian 350002 (China); Institute of Research for Functional Materials, Fuzhou University, Yishan Campus, Fuzhou University, Fuzhou, Fujian 350108 (China); College of Chemistry and Chemical Engineering, Qishan Campus, Fuzhou University, Fuzhou, Fujian 350108 (China); Liu Hongfang [Fujian Key Lab of Medical Instrument and Pharmaceutical Technology, Yishan Campus, Fuzhou University, Fuzhou, Fujian 350002 (China); College of Chemistry and Chemical Engineering, Qishan Campus, Fuzhou University, Fuzhou, Fujian 350108 (China); Du Min [Fujian Key Lab of Medical Instrument and Pharmaceutical Technology, Yishan Campus, Fuzhou University, Fuzhou, Fujian 350002 (China)
2010-12-30
A novel nonenzymatic glucose sensor based on flower-shaped (FS) Au -Pd core-shell nanoparticles-ionic liquids (ILs i.e., trihexyltetradecylphosphonium bis(trifluoromethylsulfonyl) imide, [P(C{sub 6}){sub 3}C{sub 14}][Tf{sub 2}N]) composite film modified glassy carbon electrodes (GCE) was reported. The Au-Pd nanocatalysts were prepared by seed-mediated growth method, forming the three-dimensional FS nanoparticles, where tens of small Pd nanoparticles ({approx}3 nm) aggregated on gold seeds ({approx}20 nm). The FS Au-Pd nanoparticle was a good candidate for the catalytic efficiency of nanometallic surfaces because of its flower-shaped nature, which has greater adsorption capacity. XPS analysis and zeta potential indicated that the surface of Pd atoms is positively charged, profiting the oxidation process of glucose. And ILs acted as bridge connecting Au-Pd one another and bucky gel as platform within the whole nanocomposite. So the modified electrode has higher sensitivity and selectivity owing to intrinsic synergistic effects of this nanocomposite. Amperometric measurements allow observation of the electrochemical oxidation of glucose at 0.0 V (vs. Ag/AgCl), the glucose oxidation current is linear to its concentration in the range of 5 nM-0.5 {mu}M, and the detection limit was found to be 1.0 nM (S/N = 3). The as-prepared nonenzyme glucose sensor exhibited excellent stability, repeatability, and selectivity.
Size and Shape Control of Gold Nanodeposits in an Array of Silica Nanowells on a Gold Electrode
Directory of Open Access Journals (Sweden)
Amy E. Rue
2012-01-01
Full Text Available Ordered arrays of hemispherical nanowells were formed in a sol-gel-derived silica film on a gold electrode using 500 nm diameter polystyrene latex spheres as templates. The conductive domain located at the bottom of each nanowell upon template removal was enlarged via electroless deposition from a gold plating solution. The structured electrodes thus formed were characterized using scanning electron microscopy and atomic force microscopy. Depending on the method used to make the films, the extent of the long-range packing and the size of the conductive domain changed. Electroless deposition in the nanowells produced (near sphere-like nanostructures of gold, the size of which depended on the incubation time in the plating solution and the size of the conductive domain. Longer exposure times yielded nanostructures that filled the nanowell, whereas smaller exposure time yielded much smaller structures. Significantly larger, rougher deposits were formed in nanowells with large conductive domains. The electrochemical response observed at these electrodes was strongly dependent on the extent of long-range packing, the presence of defect sites in the film and their relative spacing, and the redox species in solution.
Cantilever tilt causing amplitude related convolution in dynamic mode atomic force microscopy.
Wang, Chunmei; Sun, Jielin; Itoh, Hiroshi; Shen, Dianhong; Hu, Jun
2011-01-01
It is well known that the topography in atomic force microscopy (AFM) is a convolution of the tip's shape and the sample's geometry. The classical convolution model was established in contact mode assuming a static probe, but it is no longer valid in dynamic mode AFM. It is still not well understood whether or how the vibration of the probe in dynamic mode affects the convolution. Such ignorance complicates the interpretation of the topography. Here we propose a convolution model for dynamic mode by taking into account the typical design of the cantilever tilt in AFMs, which leads to a different convolution from that in contact mode. Our model indicates that the cantilever tilt results in a dynamic convolution affected by the absolute value of the amplitude, especially in the case that corresponding contact convolution has sharp edges beyond certain angle. The effect was experimentally demonstrated by a perpendicular SiO(2)/Si super-lattice structure. Our model is useful for quantitative characterizations in dynamic mode, especially in probe characterization and critical dimension measurements.
Relation between shape of Ni-particles and Ni migration in Ni-YSZ electrodes – a hypothesis
DEFF Research Database (Denmark)
Mogensen, Mogens Bjerg; Hauch, Anne; Sun, Xiufu
2016-01-01
pressure (pH2O) gradient as previously observed [1], but in the present cases Ni seems to migrate up the pH2O gradient. However, it is also observed that there is a preceding phase in this Ni-YSZ electrode degradation, namely that the Ni-particles closest to the YSZ electrolyte loose contact to each other....... This means that the active three phase boundary (TPB) moves away from the electrolyte and causes a significant increase in the ohmic resistance as is also observed in electrochemical impedance spectra....
Convolutive Blind Source Separation Methods
DEFF Research Database (Denmark)
Pedersen, Michael Syskind; Larsen, Jan; Kjems, Ulrik
2008-01-01
During the past decades, much attention has been given to the separation of mixed sources, in particular for the blind case where both the sources and the mixing process are unknown and only recordings of the mixtures are available. In several situations it is desirable to recover all sources from....... This may help practitioners and researchers new to the area of convolutive source separation obtain a complete overview of the field. Hopefully those with more experience in the field can identify useful tools, or find inspiration for new algorithms....
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.
Oury, Alexandre; Kirchev, Angel; Bultel, Yann
2014-01-01
A novel reactor design is proposed for the soluble lead-acid flow battery (SLFB), in which a three-dimensional honeycomb-shaped positive PbO2-electrode is sandwiched between two planar negative electrodes. A two-dimensional stationary model is developed to predict the electrochemical behaviour of the cell, especially the current distribution over the positive structure and the cell voltage, as a function of the honeycomb dimensions and the electrolyte composition. The model includes several experimentally-based parameters measured over a wide range of electrolyte compositions. The results show that the positive current distribution is almost entirely determined by geometrical effects, with little influence from the hydrodynamic. It is also suggested that an increase in the electrolyte acidity diminishes the overvoltage during discharge but leads at the same time to a more heterogeneous reaction rate distribution on account of the faster kinetics of PbO2 dissolution. Finally, the cycling of experimental mono-cells is performed and the voltage response is in fairly good accordance with the model predictions.
Incomplete convolutions in production and inventory models
Houtum, van G.J.; 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 f
Independent Component Analysis in a convoluted world
DEFF Research Database (Denmark)
Dyrholm, Mads
2006-01-01
instantaneousICA, then select a physiologically interesting subspace, then remove the delayed temporal dependencies among the instantaneous ICA components by using convolutive ICA. By Bayesian model selection, in a real world EEG data set, it is shown that convolutive ICA is a better model for EEG than...
Model structure selection in convolutive mixtures
DEFF Research Database (Denmark)
Dyrholm, Mads; Makeig, Scott; 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 parsimoneous 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 parsimoneous...... 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....
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....
Two-dimensional cubic convolution.
Reichenbach, Stephen E; Geng, Frank
2003-01-01
The paper develops two-dimensional (2D), nonseparable, piecewise cubic convolution (PCC) for image interpolation. Traditionally, PCC has been implemented based on a one-dimensional (1D) derivation with a separable generalization to two dimensions. However, typical scenes and imaging systems are not separable, so the traditional approach is suboptimal. We develop a closed-form derivation for a two-parameter, 2D PCC kernel with support [-2,2] x [-2,2] that is constrained for continuity, smoothness, symmetry, and flat-field response. Our analyses, using several image models, including Markov random fields, demonstrate that the 2D PCC yields small improvements in interpolation fidelity over the traditional, separable approach. The constraints on the derivation can be relaxed to provide greater flexibility and performance.
Convolutional Neural Network for Image Recognition
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.
Gao, Libo; Surjadi, James Utama; Cao, Ke; Zhang, Hongti; Li, Peifeng; Xu, Shang; Jiang, Chenchen; Song, Jian; Sun, Dong; Lu, Yang
2017-02-15
Flexible fiber-shaped supercapacitors (FSSCs) are recently of extensive interest for portable and wearable electronic gadgets. Yet the lack of industrial-scale flexible fibers with high conductivity and capacitance and low cost greatly limits its practical engineering applications. To this end, we here present pristine twisted carbon fibers (CFs) coated with a thin metallic layer via electroless deposition route, which exhibits exceptional conductivity with ∼300% enhancement and superior mechanical strength (∼1.8 GPa). Subsequently, the commercially available conductive pen ink modified high conductive composite fibers, on which uniformly covered ultrathin nickel-cobalt double hydroxides (Ni-Co DHs) were introduced to fabricate flexible FSSCs. The synthesized functionalized hierarchical flexible fibers exhibit high specific capacitance up to 1.39 F·cm(-2) in KOH aqueous electrolyte. The asymmetric solid-state FSSCs show maximum specific capacitance of 28.67 mF·cm(-2) and energy density of 9.57 μWh·cm(-2) at corresponding power density as high as 492.17 μW·cm(-2) in PVA/KOH gel electrolyte, with demonstrated high flexibility during stretching, demonstrating their potential in flexible electronic devices and wearable energy systems.
PROPERTIES OF THE CONVOLUTION WITH PRESTARLIKE FUNCTIONS
Institute of Scientific and Technical Information of China (English)
Jacek DZIOK
2013-01-01
In the paper we investigate convolution properties related to the prestarlike functions and various inclusion relationships between defined classes of functions. Interest-ing applications involving the well-known classes of functions defined by linear operators are also considered.
Inf-convolution of G-expectations
Institute of Scientific and Technical Information of China (English)
BUCKDAHN; Rainer
2010-01-01
In this paper we will discuss the optimal risk transfer problems when risk measures are generated by G-expectations,and we present the relationship between inf-convolution of G-expectations and the infconvolution of drivers G.
Convolution kernels for multi-wavelength imaging
National Research Council Canada - National Science Library
Boucaud, Alexandre; Bocchio, Marco; Abergel, Alain; Orieux, François; Dole, Hervé; Hadj-Youcef, Mohamed Amine
2016-01-01
.... Given the knowledge of the PSF in each band, a straightforward way of processing images is to homogenise them all to a target PSF using convolution kernels, so that they appear as if they had been...
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 circ......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...
Charge Transfer in Nanocrystalline Semiconductor Electrodes
Directory of Open Access Journals (Sweden)
M. Bouroushian
2013-01-01
Full Text Available Nanocrystalline electrodes in liquid junction devices possess a number of unique properties arising from their convoluted structure and the dimensions of their building units. The light-induced charge separation and transport in photoelectrochemical systems using nanocrystalline/nanoporous semiconductor electrodes is discussed here in connection with the basic principles of the (Schottky barrier theory. Recent models for charge transfer kinetics in normal and unipolar (dye-sensitized cells are reviewed, and novel concepts and materials are considered.
Sharifi, Ensiyeh; Salimi, Abdollah; Shams, Esmaeil; Noorbakhsh, Abdollah; Amini, Mohammad K
2014-06-15
Herein we describe improved electron transfer properties and catalytic activity of nickel oxide nanoparticles (NiONPs) via the electrochemical deposition on DNA modified glassy carbon electrode (DNA/GCE) surface. NiONPs deposited on the bare and DNA-coated GCE showed different morphologies, electrochemical kinetics and catalytic activities. The atomic force microscopy (AFM) images revealed the formation of triangular NPs on the DNA/GCE that followed the shape produced by the DNA template, while the electrodeposition of NiONPs on the bare GCE surface led to the formation of spherical nanoparticles. Electrochemical impedance spectroscopy (EIS) measurements revealed lower charge-transfer resistance (Rct) of triangular NiONPs compared to spherical NPs. Furthermore, the electrocatalytic activity of triangular NiONPs compared to spherical NPs toward glucose oxidation in alkaline media was significantly improved. The amperometric oxidation of glucose at NiONP-DNA/GCE, yielded a very high sensitivity of 17.32 mA mM(-1)cm(-2) and an unprecedented detection limit of 17 nM. The enhanced electron transfer properties and electrocatalytic activity of NiONP-DNA/GCE can be attributed to the higher fraction of sharp corners and edges present in the triangular NiONPs compared to the spherical NPs. The developed sensor was successfully applied to the determination of glucose in serum samples.
Anatomically informed convolution kernels for the projection of fMRI data on the cortical surface.
Operto, Grégory; Bulot, Rémy; Anton, Jean-Luc; Coulon, Olivier
2006-01-01
We present here a method that aims at producing representations of functional brain data on the cortical surface from functional MRI volumes. Such representations are required for subsequent cortical-based functional analysis. We propose a projection technique based on the definition, around each node of the grey/white matter interface mesh, of convolution kernels whose shape and distribution rely on the geometry of the local anatomy. For one anatomy, a set of convolution kernels is computed that can be used to project any functional data registered with this anatomy. The method is presented together with experiments on synthetic data and real statistical t-maps.
Trainable Convolution Filters and Their Application to Face Recognition.
Kumar, Ritwik; Banerjee, Arunava; Vemuri, Baba C; Pfister, Hanspeter
2012-07-01
In this paper, we present a novel image classification system that is built around a core of trainable filter ensembles that we call Volterra kernel classifiers. Our system treats images as a collection of possibly overlapping patches and is composed of three components: (1) A scheme for a single patch classification that seeks a smooth, possibly nonlinear, functional mapping of the patches into a range space, where patches of the same class are close to one another, while patches from different classes are far apart-in the L_2 sense. This mapping is accomplished using trainable convolution filters (or Volterra kernels) where the convolution kernel can be of any shape or order. (2) Given a corpus of Volterra classifiers with various kernel orders and shapes for each patch, a boosting scheme for automatically selecting the best weighted combination of the classifiers to achieve higher per-patch classification rate. (3) A scheme for aggregating the classification information obtained for each patch via voting for the parent image classification. We demonstrate the effectiveness of the proposed technique using face recognition as an application area and provide extensive experiments on the Yale, CMU PIE, Extended Yale B, Multi-PIE, and MERL Dome benchmark face data sets. We call the Volterra kernel classifiers applied to face recognition Volterrafaces. We show that our technique, which falls into the broad class of embedding-based face image discrimination methods, consistently outperforms various state-of-the-art methods in the same category.
Directory of Open Access Journals (Sweden)
Abbasi Mahdi
2012-06-01
Full Text Available Abstract Background Electrical Impedance Tomography (EIT is used as a fast clinical imaging technique for monitoring the health of the human organs such as lungs, heart, brain and breast. Each practical EIT reconstruction algorithm should be efficient enough in terms of convergence rate, and accuracy. The main objective of this study is to investigate the feasibility of precise empirical conductivity imaging using a sinc-convolution algorithm in D-bar framework. Methods At the first step, synthetic and experimental data were used to compute an intermediate object named scattering transform. Next, this object was used in a two-dimensional integral equation which was precisely and rapidly solved via sinc-convolution algorithm to find the square root of the conductivity for each pixel of image. For the purpose of comparison, multigrid and NOSER algorithms were implemented under a similar setting. Quality of reconstructions of synthetic models was tested against GREIT approved quality measures. To validate the simulation results, reconstructions of a phantom chest and a human lung were used. Results Evaluation of synthetic reconstructions shows that the quality of sinc-convolution reconstructions is considerably better than that of each of its competitors in terms of amplitude response, position error, ringing, resolution and shape-deformation. In addition, the results confirm near-exponential and linear convergence rates for sinc-convolution and multigrid, respectively. Moreover, the least degree of relative errors and the most degree of truth were found in sinc-convolution reconstructions from experimental phantom data. Reconstructions of clinical lung data show that the related physiological effect is well recovered by sinc-convolution algorithm. Conclusions Parametric evaluation demonstrates the efficiency of sinc-convolution to reconstruct accurate conductivity images from experimental data. Excellent results in phantom and clinical
Interpolation by two-dimensional cubic convolution
Shi, Jiazheng; Reichenbach, Stephen E.
2003-08-01
This paper presents results of image interpolation with an improved method for two-dimensional cubic convolution. Convolution with a piecewise cubic is one of the most popular methods for image reconstruction, but the traditional approach uses a separable two-dimensional convolution kernel that is based on a one-dimensional derivation. The traditional, separable method is sub-optimal for the usual case of non-separable images. The improved method in this paper implements the most general non-separable, two-dimensional, piecewise-cubic interpolator with constraints for symmetry, continuity, and smoothness. The improved method of two-dimensional cubic convolution has three parameters that can be tuned to yield maximal fidelity for specific scene ensembles characterized by autocorrelation or power-spectrum. This paper illustrates examples for several scene models (a circular disk of parametric size, a square pulse with parametric rotation, and a Markov random field with parametric spatial detail) and actual images -- presenting the optimal parameters and the resulting fidelity for each model. In these examples, improved two-dimensional cubic convolution is superior to several other popular small-kernel interpolation methods.
Lund, Gordon F. (Inventor)
1982-01-01
A low-noise electrode suited for sensing electrocardiograms when chronically and subcutaneously implanted in a free-ranging subject. The electrode comprises a pocket-shaped electrically conductive member with a single entrance adapted to receive body fluids. The exterior of the member and the entrance region is coated with electrical insulation so that the only electrolyte/electrode interface is within the member remote from artifact-generating tissue. Cloth straps are bonded to the member to permit the electrode to be sutured to tissue and to provide electrical lead flexure relief.
ESTIMATING LOSS SEVERITY DISTRIBUTION: CONVOLUTION APPROACH
Directory of Open Access Journals (Sweden)
Ro J. Pak
2014-01-01
Full Text Available Financial loss can be classified into two types such as expected loss and unexpected loss. A current definition seeks to separate two losses from a total loss. In this article, however, we redefine a total loss as the sum of expected and unexpended losses; then the distribution of loss can be considered as the convolution of the distributions of both expected and unexpended losses. We propose to use a convolution of normal and exponential distribution for modelling a loss distribution. Subsequently, we compare its performance with other commonly used loss distributions. The examples of property insurance claim data are analyzed to show the applicability of this normal-exponential convolution model. Overall, we claim that the proposed model provides further useful information with regard to losses compared to existing models. We are able to provide new statistical quantities which are very critical and useful.
Uncertainty estimation by convolution using spatial statistics.
Sanchez-Brea, Luis Miguel; Bernabeu, Eusebio
2006-10-01
Kriging has proven to be a useful tool in image processing since it behaves, under regular sampling, as a convolution. Convolution kernels obtained with kriging allow noise filtering and include the effects of the random fluctuations of the experimental data and the resolution of the measuring devices. The uncertainty at each location of the image can also be determined using kriging. However, this procedure is slow since, currently, only matrix methods are available. In this work, we compare the way kriging performs the uncertainty estimation with the standard statistical technique for magnitudes without spatial dependence. As a result, we propose a much faster technique, based on the variogram, to determine the uncertainty using a convolutional procedure. We check the validity of this approach by applying it to one-dimensional images obtained in diffractometry and two-dimensional images obtained by shadow moire.
Astronomical Image Subtraction by Cross-Convolution
Yuan, Fang; Akerlof, Carl W.
2008-04-01
In recent years, there has been a proliferation of wide-field sky surveys to search for a variety of transient objects. Using relatively short focal lengths, the optics of these systems produce undersampled stellar images often marred by a variety of aberrations. As participants in such activities, we have developed a new algorithm for image subtraction that no longer requires high-quality reference images for comparison. The computational efficiency is comparable with similar procedures currently in use. The general technique is cross-convolution: two convolution kernels are generated to make a test image and a reference image separately transform to match as closely as possible. In analogy to the optimization technique for generating smoothing splines, the inclusion of an rms width penalty term constrains the diffusion of stellar images. In addition, by evaluating the convolution kernels on uniformly spaced subimages across the total area, these routines can accommodate point-spread functions that vary considerably across the focal plane.
Indian Academy of Sciences (India)
Barbara Jasiulis-Gołdyn; Anna Kula
2012-08-01
The paper deals with the notions of weak stability and weak generalized convolution with respect to a generalized convolution, introduced by Kucharczak and Urbanik. We study properties of such objects and give examples of weakly stable measures with respect to the Kendall convolution. Moreover, we show that in the context of non-commutative probability, two operations: the -convolution and the (,1)-convolution satisfy the Urbanik’s conditions for a generalized convolution, interpreted on the set of moment sequences. The weak stability reveals the relation between two operations.
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.
Spectral classification using convolutional neural networks
Hála, Pavel
2014-01-01
There is a great need for accurate and autonomous spectral classification methods in astrophysics. This thesis is about training a convolutional neural network (ConvNet) to recognize an object class (quasar, star or galaxy) from one-dimension spectra only. Author developed several scripts and C programs for datasets preparation, preprocessing and postprocessing of the data. EBLearn library (developed by Pierre Sermanet and Yann LeCun) was used to create ConvNets. Application on dataset of more than 60000 spectra yielded success rate of nearly 95%. This thesis conclusively proved great potential of convolutional neural networks and deep learning methods in astrophysics.
SAR ATR Based on Convolutional Neural Network
Directory of Open Access Journals (Sweden)
Tian Zhuangzhuang
2016-06-01
Full Text Available This study presents a new method of Synthetic Aperture Radar (SAR image target recognition based on a convolutional neural network. First, we introduce a class separability measure into the cost function to improve this network’s ability to distinguish between categories. Then, we extract SAR image features using the improved convolutional neural network and classify these features using a support vector machine. Experimental results using moving and stationary target acquisition and recognition SAR datasets prove the validity of this method.
On a Generalized Hankel Type Convolution of Generalized Functions
Indian Academy of Sciences (India)
S P Malgonde; G S Gaikawad
2001-11-01
The classical generalized Hankel type convolution are defined and extended to a class of generalized functions. Algebraic properties of the convolution are explained and the existence and significance of an identity element are discussed.
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...
A note on maximal estimates for stochastic convolutions
Veraar, M.; Weis, L.
2011-01-01
In stochastic partial differential equations it is important to have pathwise regularity properties of stochastic convolutions. In this note we present a new sufficient condition for the pathwise continuity of stochastic convolutions in Banach spaces.
Rojas-Villabona, Alvaro; Kitchen, Neil; Paddick, Ian
2016-11-01
, but consistent, dose shift compared to the TMR 10 algorithm traditionally used for GKR. A reduction of the prescription dose may be necessary to obtain the same clinical effect with the convolution algorithm. Head shape definition using CT outlining can reduce treatment uncertainty from head shape approximations. PACS number(s): 87.53.-j; 87.55.D; 87.55.kd. © 2016 The Authors.
Multiple deep convolutional neural networks averaging for face alignment
Zhang, Shaohua; Yang, Hua; Yin, Zhouping
2015-05-01
Face alignment is critical for face recognition, and the deep learning-based method shows promise for solving such issues, given that competitive results are achieved on benchmarks with additional benefits, such as dispensing with handcrafted features and initial shape. However, most existing deep learning-based approaches are complicated and quite time-consuming during training. We propose a compact face alignment method for fast training without decreasing its accuracy. Rectified linear unit is employed, which allows all networks approximately five times faster convergence than a tanh neuron. An eight learnable layer deep convolutional neural network (DCNN) based on local response normalization and a padding convolutional layer (PCL) is designed to provide reliable initial values during prediction. A model combination scheme is presented to further reduce errors, while showing that only two network architectures and hyperparameter selection procedures are required in our approach. A three-level cascaded system is ultimately built based on the DCNNs and model combination mode. Extensive experiments validate the effectiveness of our method and demonstrate comparable accuracy with state-of-the-art methods on BioID, labeled face parts in the wild, and Helen datasets.
Parallel Multi Channel Convolution using General Matrix Multiplication
VASUDEVAN, ARAVIND; Anderson, Andrew; Gregg, David
2017-01-01
Convolutional neural networks (CNNs) have emerged as one of the most successful machine learning technologies for image and video processing. The most computationally intensive parts of CNNs are the convolutional layers, which convolve multi-channel images with multiple kernels. A common approach to implementing convolutional layers is to expand the image into a column matrix (im2col) and perform Multiple Channel Multiple Kernel (MCMK) convolution using an existing parallel General Matrix Mul...
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...
Discrete Fresnel Transform and Its Circular Convolution
Ouyang, Xing; Gunning, Fatima; Zhang, Hongyu; Guan, Yong Liang
2015-01-01
Discrete trigonometric transformations, such as the discrete Fourier and cosine/sine transforms, are important in a variety of applications due to their useful properties. For example, one well-known property is the convolution theorem for Fourier transform. In this letter, we derive a discrete Fresnel transform (DFnT) from the infinitely periodic optical gratings, as a linear trigonometric transform. Compared to the previous formulations of DFnT, the DFnT in this letter has no degeneracy, which hinders its mathematic applications, due to destructive interferences. The circular convolution property of the DFnT is studied for the first time. It is proved that the DFnT of a circular convolution of two sequences equals either one circularly convolving with the DFnT of the other. As circular convolution is a fundamental process in discrete systems, the DFnT not only gives the coefficients of the Talbot image, but can also be useful for optical and digital signal processing and numerical evaluation of the Fresnel ...
Properties of derivations on some convolution algebras
DEFF Research Database (Denmark)
Pedersen, Thomas Vils
2014-01-01
For all convolution algebras L1[0; 1); L1 loc and A(!) = T n L1(!n), the derivations are of the form Dμf = Xf μ for suitable measures μ, where (Xf)(t) = tf(t). We describe the (weakly) compact as well as the (weakly) Montel derivations on these algebras in terms of properties of the measure μ...
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 fash...
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...
Convolutions with the Continuous Primitive Integral
Directory of Open Access Journals (Sweden)
Erik Talvila
2009-01-01
I⊂ℝ. When g∈L1, the estimate is ‖f∗g‖≤‖f‖‖g‖1. There are results on differentiation and integration of convolutions. A type of Fubini theorem is proved for the continuous primitive integral.
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.
Convolution theorems: partitioning the space of integral transforms
Lindsey, Alan R.; Suter, Bruce W.
1999-03-01
Investigating a number of different integral transforms uncovers distinct patterns in the type of translation convolution theorems afforded by each. It is shown that transforms based on separable kernels (aka Fourier, Laplace and their relatives) have a form of the convolution theorem providing for a transform domain product of the convolved functions. However, transforms based on kernels not separable in the function and transform variables mandate a convolution theorem of a different type; namely in the transform domain the convolution becomes another convolution--one function with the transform of the other.
Decoding of Convolutional Codes over the Erasure Channel
Tomás, Virtudes; Smarandache, Roxana
2010-01-01
In this paper we study the decoding capabilities of convolutional codes over the erasure channel. Of special interest will be maximum distance profile (MDP) convolutional codes. These are codes which have a maximum possible column distance increase. We show how this strong minimum distance condition of MDP convolutional codes help us to solve error situations that maximum distance separable (MDS) block codes fail to solve. Towards this goal, we define two subclasses of MDP codes: reverse-MDP convolutional codes and complete-MDP convolutional codes. Reverse-MDP codes have the capability to recover a maximum number of erasures using an algorithm which runs backward in time. Complete-MDP convolutional codes are both MDP and reverse-MDP codes. They are capable to recover the state of the decoder under the mildest condition. We show that complete-MDP convolutional codes perform in certain sense better than MDS block codes of the same rate over the erasure channel.
A convolutional neural network neutrino event classifier
Aurisano, A.; Radovic, A.; Rocco, D.; Himmel, A.; Messier, M. D.; Niner, E.; Pawloski, G.; Psihas, F.; Sousa, A.; Vahle, P.
2016-09-01
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.
Transition Mean Values of Shifted Convolution Sums
Petrow, Ian
2011-01-01
Let f be a classical holomorphic cusp form for SL_2(Z) of weight k which is a normalized eigenfunction for the Hecke algebra, and let \\lambda(n) be its eigenvalues. In this paper we study "shifted convolution sums" of the eigenvalues \\lambda(n) after averaging over many shifts h and obtain asymptotic estimates. The result is somewhat surprising: one encounters a transition region depending on the ratio of the square of the length of the average over h to the length of the shifted convolution sum. The phenomenon is similar to that encountered by Conrey, Farmer and Soundararajan in their 2000 paper Transition Mean Values of Real Characters, and the connection of both results to Eisenstein series and multiple Dirichlet series is discussed.
A Convolutional Neural Network Neutrino Event Classifier
Aurisano, A; Rocco, D; Himmel, A; Messier, M D; Niner, E; Pawloski, G; Psihas, F; Sousa, A; Vahle, P
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.
Rational Convolution Roots of Isobaric Polynomials
Conci, Aura; Li, Huilan; MacHenry, Trueman
2014-01-01
In this paper, we exhibit two matrix representations of the rational roots of generalized Fibonacci polynomials (GFPs) under convolution product, in terms of determinants and permanents, respectively. The underlying root formulas for GFPs and for weighted isobaric polynomials (WIPs), which appeared in an earlier paper by MacHenry and Tudose, make use of two types of operators. These operators are derived from the generating functions for Stirling numbers of the first kind and second kind. Hen...
A Generative Model for Deep Convolutional Learning
Pu, Yunchen; Yuan, Xin; Carin, Lawrence
2015-01-01
A generative model is developed for deep (multi-layered) convolutional dictionary learning. A novel probabilistic pooling operation is integrated into the deep model, yielding efficient bottom-up (pretraining) and top-down (refinement) probabilistic learning. Experimental results demonstrate powerful capabilities of the model to learn multi-layer features from images, and excellent classification results are obtained on the MNIST and Caltech 101 datasets.
Convolutional Neural Network Based dem Super Resolution
Chen, Zixuan; Wang, Xuewen; Xu, Zekai; Hou, Wenguang
2016-06-01
DEM super resolution is proposed in our previous publication to improve the resolution for a DEM on basis of some learning examples. Meanwhile, the nonlocal algorithm is introduced to deal with it and lots of experiments show that the strategy is feasible. In our publication, the learning examples are defined as the partial original DEM and their related high measurements due to this way can avoid the incompatibility between the data to be processed and the learning examples. To further extent the applications of this new strategy, the learning examples should be diverse and easy to obtain. Yet, it may cause the problem of incompatibility and unrobustness. To overcome it, we intend to investigate a convolutional neural network based method. The input of the convolutional neural network is a low resolution DEM and the output is expected to be its high resolution one. A three layers model will be adopted. The first layer is used to detect some features from the input, the second integrates the detected features to some compressed ones and the final step transforms the compressed features as a new DEM. According to this designed structure, some learning DEMs will be taken to train it. Specifically, the designed network will be optimized by minimizing the error of the output and its expected high resolution DEM. In practical applications, a testing DEM will be input to the convolutional neural network and a super resolution will be obtained. Many experiments show that the CNN based method can obtain better reconstructions than many classic interpolation methods.
The convolution theorem for two-dimensional continuous wavelet transform
Institute of Scientific and Technical Information of China (English)
ZHANG CHI
2013-01-01
In this paper , application of two -dimensional continuous wavelet transform to image processes is studied. We first show that the convolution and correlation of two continuous wavelets satisfy the required admissibility and regularity conditions ,and then we derive the convolution and correlation theorem for two-dimensional continuous wavelet transform. Finally, we present numerical example showing the usefulness of applying the convolution theorem for two -dimensional continuous wavelet transform to perform image restoration in the presence of additive noise.
An Algorithm for the Convolution of Legendre Series
Hale, Nicholas
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.
1985-07-01
adherent, and showed excellent physical stability. U -12- 4. RESULTS AND DISCUSSION 4.1 CYCLIC VOLTAMETRY AT A SILVER DISK ELECTRODE Silver screens are... cyclic voltammetry, :*.,. chronopotentiometry and chronocoulometry. A significant reduction in the rate of zinc deposition in the pre- sence of lead was... cyclic voltammetry, chronopotentiometry and chronocoulometry. A significant reduction in the rate of zinc deposition in the pre- sence of lead was
1991-05-01
that the most advantageous method of evaluating electrochemical kinetics with microelectrode cyclic voltametry would be to choose conditions so to...PROGRAM PROJECT TASK Vt.LRk UNII Arlington , VA 22217 ELEMENT NO. NO. NO ACCISSION NO II :IILE (W’.clde Secutit Cl4asoatfon I Cyclic Volammetric Wave... cyclic voltaoograms at microiink electrodes, double- layer capacitance, electrochemical kinetics, resistive/ canaci tance dstnr- inna p 19 AISIRAC1
BERNOULLI CONVOLUTIONS ASSOCIATED WITH CERTAIN NON-PISOT NUMBERS
Institute of Scientific and Technical Information of China (English)
Feng Dejun; Wang Yang
2003-01-01
The Bernoulli convolution vλ measure is shown to be absolutely continuous with L2 density for almost all 1/2<λ<1,and singular if λ-1 is a Pisot number.It is an open question whether the Pisot type Bernoulli convolutions are the only singular ones.In this paper,we construct a family of non-Pisot type Bernoulli convolutions vλ such that their density functions,if they excist,are not L2.We also construct other Bernolulli convolutions whose density functions,if they exist,behave rather badly.
Convolutions Induced Discrete Probability Distributions and a New Fibonacci Constant
Rajan, Arulalan; Rao, Vittal; Rao, Ashok
2010-01-01
This paper proposes another constant that can be associated with Fibonacci sequence. In this work, we look at the probability distributions generated by the linear convolution of Fibonacci sequence with itself, and the linear convolution of symmetrized Fibonacci sequence with itself. We observe that for a distribution generated by the linear convolution of the standard Fibonacci sequence with itself, the variance converges to 8.4721359... . Also, for a distribution generated by the linear convolution of symmetrized Fibonacci sequences, the variance converges in an average sense to 17.1942 ..., which is approximately twice that we get with common Fibonacci sequence.
ARKCoS: Artifact-Suppressed Accelerated Radial Kernel Convolution on the Sphere
Elsner, Franz
2011-01-01
We describe a hybrid Fourier/direct space convolution algorithm for compact radial (azimuthally symmetric) kernels on the sphere. For high resolution maps covering a large fraction of the sky, our implementation takes advantage of the inexpensive massive parallelism afforded by consumer graphics processing units (GPUs). Applications involve modeling of instrumental beam shapes in terms of compact kernels, computation of fine-scale wavelet transformations, and optimal filtering for the detection of point sources. Our algorithm works for any pixelization where pixels are grouped into isolatitude rings. Even for kernels that are not bandwidth limited, ringing features are completely absent on an ECP grid. We demonstrate that they can be highly suppressed on the popular HEALPix pixelization, for which we develop a freely available implementation of the algorithm. As an example application, we show that running on a high-end consumer graphics card our method speeds up beam convolution for simulations of a characte...
Melchert, O; Roth, B
2016-01-01
We discuss efficient algorithms for the accurate forward and reverse evaluation of the discrete Fourier-Bessel transform (dFBT) as numerical tools to assist in the 2D polar convolution of two radially symmetric functions, relevant, e.g., to applications in computational biophotonics. In our survey of the numerical procedure we account for the circumstance that the objective function might result from a more complex measurement process and is, in the worst case, known on a finite sequence of coordinate values, only. We contrast the performance of the resulting algorithms with a procedure based on a straight forward numerical quadrature of the underlying integral transform and asses its efficienty for two benchmark Fourier-Bessel pairs. An application to the problem of finite-size beam-shape convolution in polar coordinates, relevant in the context of tissue optics and optoacoustics, is used to illustrate the versatility and computational efficiency of the numerical procedure.
Institute of Scientific and Technical Information of China (English)
El-Hallag S Ibrahim
2009-01-01
The electrochemical behaviour of the heterobimetallic complex [Pt(C≡C tol)_2(dppm)_2-Ir(CO)_2]~+PF_6~-was studied via cyclic voltammetry, convolutive voltammetry and chronopotentiometry at glassy carbon electrode in dichloromethane solution. The electrochemical parameters calculated from experimental data were tested and confirmed by matching the experimental cyclic voltammograms with the simu-lated data. It was found that convolutive voltammetry provided higher sensitivity, better resolution and more accurate method for determination of the electrochemical parameters than ordinary cyclic volt-ammetry.
Convolution neural networks for ship type recognition
Rainey, Katie; Reeder, John D.; Corelli, Alexander G.
2016-05-01
Algorithms to automatically recognize ship type from satellite imagery are desired for numerous maritime applications. This task is difficult, and example imagery accurately labeled with ship type is hard to obtain. Convolutional neural networks (CNNs) have shown promise in image recognition settings, but many of these applications rely on the availability of thousands of example images for training. This work attempts to under- stand for which types of ship recognition tasks CNNs might be well suited. We report the results of baseline experiments applying a CNN to several ship type classification tasks, and discuss many of the considerations that must be made in approaching this problem.
Fourier transforms and convolutions for the experimentalist
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
Zebrafish tracking using convolutional neural networks
XU, Zhiping; Cheng, Xi En
2017-01-01
Keeping identity for a long term after occlusion is still an open problem in the video tracking of zebrafish-like model animals, and accurate animal trajectories are the foundation of behaviour analysis. We utilize the highly accurate object recognition capability of a convolutional neural network (CNN) to distinguish fish of the same congener, even though these animals are indistinguishable to the human eye. We used data augmentation and an iterative CNN training method to optimize the accuracy for our classification task, achieving surprisingly accurate trajectories of zebrafish of different size and age zebrafish groups over different time spans. This work will make further behaviour analysis more reliable. PMID:28211462
Zebrafish tracking using convolutional neural networks
Xu, Zhiping; Cheng, Xi En
2017-02-01
Keeping identity for a long term after occlusion is still an open problem in the video tracking of zebrafish-like model animals, and accurate animal trajectories are the foundation of behaviour analysis. We utilize the highly accurate object recognition capability of a convolutional neural network (CNN) to distinguish fish of the same congener, even though these animals are indistinguishable to the human eye. We used data augmentation and an iterative CNN training method to optimize the accuracy for our classification task, achieving surprisingly accurate trajectories of zebrafish of different size and age zebrafish groups over different time spans. This work will make further behaviour analysis more reliable.
Chen, Liang-Chieh; Papandreou, George; Kokkinos, Iasonas; Murphy, Kevin; Yuille, Alan L
2017-04-27
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% 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.
Boquist, Carl W.; Marchant, David D.
1978-01-01
A ceramic-metal composite suitable for use in a high-temperature environment consists of a refractory ceramic matrix containing 10 to 50 volume percent of a continuous high-temperature metal reinforcement. In a specific application of the composite, as an electrode in a magnetohydrodynamic generator, the one surface of the electrode which contacts the MHD fluid may have a layer of varying thickness of nonreinforced refractory ceramic for electrode temperature control. The side walls of the electrode may be coated with a refractory ceramic insulator. Also described is an electrode-insulator system for a MHD channel.
One dimensional Convolutional Goppa Codes over the projective line
Pérez, J A Domínguez; Sotelo, G Serrano
2011-01-01
We give a general method to construct MDS one-dimensional convolutional codes. Our method generalizes previous constructions of H. Gluesing-Luerssen and B. Langfeld. Moreover we give a classification of one-dimensional Convolutional Goppa Codes and propose a characterization of MDS codes of this type.
Explicit solutions of fractional diffusion equations via Generalized Gamma Convolution
D'Ovidio, Mirko
2010-01-01
In this paper we deal with Mellin convolution of generalized Gamma densities which brings to integrals of modified Bessel functions of the second kind. Such convolutions allow us to write explicitly the solutions of the time-fractional diffusion equations involving the adjoint operators of a square Bessel process and a Bessel process.
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....
Convolution of Lorentz Invariant Ultradistributions and Field Theory
Bollini, C G
2003-01-01
In this work, a general definition of convolution between two arbitrary four dimensional Lorentz invariant (fdLi) Tempered Ultradistributions is given, in both: Minkowskian and Euclidean Space (Spherically symmetric tempered ultradistributions). The product of two arbitrary fdLi distributions of exponential type is defined via the convolution of its corresponding Fourier Transforms. Several examples of convolution of two fdLi Tempered Ultradistributions are given. In particular we calculate exactly the convolution of two Feynman's massless propagators. An expression for the Fourier Transform of a Lorentz invariant Tempered Ultradistribution in terms of modified Bessel distributions is obtained in this work (Generalization of Bochner's formula to Minkowskian space). At the same time, and in a previous step used for the deduction of the convolution formula, we obtain the generalization to the Minkowskian space, of the dimensional regularization of the perturbation theory of Green Functions in the Euclidean conf...
Accurate segmentation of lung fields on chest radiographs using deep convolutional networks
Arbabshirani, Mohammad R.; Dallal, Ahmed H.; Agarwal, Chirag; Patel, Aalpan; Moore, Gregory
2017-02-01
Accurate segmentation of lung fields on chest radiographs is the primary step for computer-aided detection of various conditions such as lung cancer and tuberculosis. The size, shape and texture of lung fields are key parameters for chest X-ray (CXR) based lung disease diagnosis in which the lung field segmentation is a significant primary step. Although many methods have been proposed for this problem, lung field segmentation remains as a challenge. In recent years, deep learning has shown state of the art performance in many visual tasks such as object detection, image classification and semantic image segmentation. In this study, we propose a deep convolutional neural network (CNN) framework for segmentation of lung fields. The algorithm was developed and tested on 167 clinical posterior-anterior (PA) CXR images collected retrospectively from picture archiving and communication system (PACS) of Geisinger Health System. The proposed multi-scale network is composed of five convolutional and two fully connected layers. The framework achieved IOU (intersection over union) of 0.96 on the testing dataset as compared to manual segmentation. The suggested framework outperforms state of the art registration-based segmentation by a significant margin. To our knowledge, this is the first deep learning based study of lung field segmentation on CXR images developed on a heterogeneous clinical dataset. The results suggest that convolutional neural networks could be employed reliably for lung field segmentation.
Compressed imaging by sparse random convolution.
Marcos, Diego; Lasser, Theo; López, Antonio; Bourquard, Aurélien
2016-01-25
The theory of compressed sensing (CS) shows that signals can be acquired at sub-Nyquist rates if they are sufficiently sparse or compressible. Since many images bear this property, several acquisition models have been proposed for optical CS. An interesting approach is random convolution (RC). In contrast with single-pixel CS approaches, RC allows for the parallel capture of visual information on a sensor array as in conventional imaging approaches. Unfortunately, the RC strategy is difficult to implement as is in practical settings due to important contrast-to-noise-ratio (CNR) limitations. In this paper, we introduce a modified RC model circumventing such difficulties by considering measurement matrices involving sparse non-negative entries. We then implement this model based on a slightly modified microscopy setup using incoherent light. Our experiments demonstrate the suitability of this approach for dealing with distinct CS scenarii, including 1-bit CS.
Relationships among transforms, convolutions, and first variations
Directory of Open Access Journals (Sweden)
Jeong Gyoo Kim
1999-01-01
Full Text Available In this paper, we establish several interesting relationships involving the Fourier-Feynman transform, the convolution product, and the first variation for functionals F on Wiener space of the form F(x=f(〈α1,x〉,…,〈αn,x〉, (* where 〈αj,x〉 denotes the Paley-Wiener-Zygmund stochastic integral ∫0Tαj(tdx(t.
Robust Convolutional Neural Networks for Image Recognition
Directory of Open Access Journals (Sweden)
Hayder M. Albeahdili
2015-11-01
Full Text Available Recently image recognition becomes vital task using several methods. One of the most interesting used methods is using Convolutional Neural Network (CNN. It is widely used for this purpose. However, since there are some tasks that have small features that are considered an essential part of a task, then classification using CNN is not efficient because most of those features diminish before reaching the final stage of classification. In this work, analyzing and exploring essential parameters that can influence model performance. Furthermore different elegant prior contemporary models are recruited to introduce new leveraging model. Finally, a new CNN architecture is proposed which achieves state-of-the-art classification results on the different challenge benchmarks. The experimented are conducted on MNIST, CIFAR-10, and CIFAR-100 datasets. Experimental results showed that the results outperform and achieve superior results comparing to the most contemporary approaches.
An exactly solvable self-convolutive recurrence
Martin, Richard J
2011-01-01
We consider a self-convolutive recurrence whose solution is the sequence of coefficients in the asymptotic expansion of the logarithmic derivative of the confluent hypergeometic function $U(a,b,z)$. By application of the Hilbert transform we convert this expression into an explicit, non-recursive solution in which the $n$th coefficient is expressed as the $(n-1)$th moment of a measure, and also as the trace of the $(n-1)$th iterate of a linear operator. Applications of these sequences, and hence of the explicit solution provided, are found in quantum field theory as the number of Feynman diagrams of a certain type and order, in Brownian motion theory, and in combinatorics.
Robust smile detection using convolutional neural networks
Bianco, Simone; Celona, Luigi; Schettini, Raimondo
2016-11-01
We present a fully automated approach for smile detection. Faces are detected using a multiview face detector and aligned and scaled using automatically detected eye locations. Then, we use a convolutional neural network (CNN) to determine whether it is a smiling face or not. To this end, we investigate different shallow CNN architectures that can be trained even when the amount of learning data is limited. We evaluate our complete processing pipeline on the largest publicly available image database for smile detection in an uncontrolled scenario. We investigate the robustness of the method to different kinds of geometric transformations (rotation, translation, and scaling) due to imprecise face localization, and to several kinds of distortions (compression, noise, and blur). To the best of our knowledge, this is the first time that this type of investigation has been performed for smile detection. Experimental results show that our proposal outperforms state-of-the-art methods on both high- and low-quality images.
Building Extraction from Remote Sensing Data Using Fully Convolutional Networks
Bittner, K.; Cui, S.; Reinartz, P.
2017-05-01
Building detection and footprint extraction are highly demanded for many remote sensing applications. Though most previous works have shown promising results, the automatic extraction of building footprints still remains a nontrivial topic, especially in complex urban areas. Recently developed extensions of the CNN framework made it possible to perform dense pixel-wise classification of input images. Based on these abilities we propose a methodology, which automatically generates a full resolution binary building mask out of a Digital Surface Model (DSM) using a Fully Convolution Network (FCN) architecture. The advantage of using the depth information is that it provides geometrical silhouettes and allows a better separation of buildings from background as well as through its invariance to illumination and color variations. The proposed framework has mainly two steps. Firstly, the FCN is trained on a large set of patches consisting of normalized DSM (nDSM) as inputs and available ground truth building mask as target outputs. Secondly, the generated predictions from FCN are viewed as unary terms for a Fully connected Conditional Random Fields (FCRF), which enables us to create a final binary building mask. A series of experiments demonstrate that our methodology is able to extract accurate building footprints which are close to the buildings original shapes to a high degree. The quantitative and qualitative analysis show the significant improvements of the results in contrast to the multy-layer fully connected network from our previous work.
BUILDING EXTRACTION FROM REMOTE SENSING DATA USING FULLY CONVOLUTIONAL NETWORKS
Directory of Open Access Journals (Sweden)
K. Bittner
2017-05-01
Full Text Available Building detection and footprint extraction are highly demanded for many remote sensing applications. Though most previous works have shown promising results, the automatic extraction of building footprints still remains a nontrivial topic, especially in complex urban areas. Recently developed extensions of the CNN framework made it possible to perform dense pixel-wise classification of input images. Based on these abilities we propose a methodology, which automatically generates a full resolution binary building mask out of a Digital Surface Model (DSM using a Fully Convolution Network (FCN architecture. The advantage of using the depth information is that it provides geometrical silhouettes and allows a better separation of buildings from background as well as through its invariance to illumination and color variations. The proposed framework has mainly two steps. Firstly, the FCN is trained on a large set of patches consisting of normalized DSM (nDSM as inputs and available ground truth building mask as target outputs. Secondly, the generated predictions from FCN are viewed as unary terms for a Fully connected Conditional Random Fields (FCRF, which enables us to create a final binary building mask. A series of experiments demonstrate that our methodology is able to extract accurate building footprints which are close to the buildings original shapes to a high degree. The quantitative and qualitative analysis show the significant improvements of the results in contrast to the multy-layer fully connected network from our previous work.
Gearbox Fault Identification and Classification with Convolutional Neural Networks
Directory of Open Access Journals (Sweden)
ZhiQiang Chen
2015-01-01
Full Text Available Vibration signals of gearbox are sensitive to the existence of the fault. Based on vibration signals, this paper presents an implementation of deep learning algorithm convolutional neural network (CNN used for fault identification and classification in gearboxes. Different combinations of condition patterns based on some basic fault conditions are considered. 20 test cases with different combinations of condition patterns are used, where each test case includes 12 combinations of different basic condition patterns. Vibration signals are preprocessed using statistical measures from the time domain signal such as standard deviation, skewness, and kurtosis. In the frequency domain, the spectrum obtained with FFT is divided into multiple bands, and the root mean square (RMS value is calculated for each one so the energy maintains its shape at the spectrum peaks. The achieved accuracy indicates that the proposed approach is highly reliable and applicable in fault diagnosis of industrial reciprocating machinery. Comparing with peer algorithms, the present method exhibits the best performance in the gearbox fault diagnosis.
Multi-modal vertebrae recognition using Transformed Deep Convolution Network.
Cai, Yunliang; Landis, Mark; Laidley, David T; Kornecki, Anat; Lum, Andrea; Li, Shuo
2016-07-01
Automatic vertebra recognition, including the identification of vertebra locations and naming in multiple image modalities, are highly demanded in spinal clinical diagnoses where large amount of imaging data from various of modalities are frequently and interchangeably used. However, the recognition is challenging due to the variations of MR/CT appearances or shape/pose of the vertebrae. In this paper, we propose a method for multi-modal vertebra recognition using a novel deep learning architecture called Transformed Deep Convolution Network (TDCN). This new architecture can unsupervisely fuse image features from different modalities and automatically rectify the pose of vertebra. The fusion of MR and CT image features improves the discriminativity of feature representation and enhances the invariance of the vertebra pattern, which allows us to automatically process images from different contrast, resolution, protocols, even with different sizes and orientations. The feature fusion and pose rectification are naturally incorporated in a multi-layer deep learning network. Experiment results show that our method outperforms existing detection methods and provides a fully automatic location+naming+pose recognition for routine clinical practice. Copyright © 2016 Elsevier Ltd. All rights reserved.
Image quality of mixed convolution kernel in thoracic computed tomography.
Neubauer, Jakob; Spira, Eva Maria; Strube, Juliane; Langer, Mathias; Voss, Christian; Kotter, Elmar
2016-11-01
The mixed convolution kernel alters his properties geographically according to the depicted organ structure, especially for the lung. Therefore, we compared the image quality of the mixed convolution kernel to standard soft and hard kernel reconstructions for different organ structures in thoracic computed tomography (CT) images.Our Ethics Committee approved this prospective study. In total, 31 patients who underwent contrast-enhanced thoracic CT studies were included after informed consent. Axial reconstructions were performed with hard, soft, and mixed convolution kernel. Three independent and blinded observers rated the image quality according to the European Guidelines for Quality Criteria of Thoracic CT for 13 organ structures. The observers rated the depiction of the structures in all reconstructions on a 5-point Likert scale. Statistical analysis was performed with the Friedman Test and post hoc analysis with the Wilcoxon rank-sum test.Compared to the soft convolution kernel, the mixed convolution kernel was rated with a higher image quality for lung parenchyma, segmental bronchi, and the border between the pleura and the thoracic wall (P kernel, the mixed convolution kernel was rated with a higher image quality for aorta, anterior mediastinal structures, paratracheal soft tissue, hilar lymph nodes, esophagus, pleuromediastinal border, large and medium sized pulmonary vessels and abdomen (P kernel cannot fully substitute the standard CT reconstructions. Hard and soft convolution kernel reconstructions still seem to be mandatory for thoracic CT.
Terminated LDPC Convolutional Codes over GF(2^p)
Uchikawa, Hironori; Sakaniwa, Kohichi
2010-01-01
In this paper, we present a construction method of terminated non-binary low-density parity-check (LDPC) convolutional codes. Our construction method is an expansion of Felstrom and Zigangirov construction for non-binary LDPC convolutional codes. The rate-compatibility of the non-binary LDPC convolutional codes is also discussed. The proposed rate-compatible code is designed from one single mother (2,4)-regular non-binary LDPC convolutional code of rate 1/2. Higher-rate codes are produced by puncturing the mother code and lower-rate codes are produced by multiplicatively repeating the mother code. For moderate values of the syndrome former memory, simulation results show that mother non-binary LDPC convolutional code outperform binary LDPC convolutional codes with comparable constraint bit length. And the derived low-rate and high-rate non-binary LDPC convolutional codes exhibit good decoding performance without loss of large gap to the Shannon limits.
The Law of Large Numbers for the Free Multiplicative Convolution
DEFF Research Database (Denmark)
Haagerup, Uffe; Möller, Sören
2013-01-01
In classical probability the law of large numbers for the multiplicative convolution follows directly from the law for the additive convolution. In free probability this is not the case. The free additive law was proved by D. Voiculescu in 1986 for probability measures with bounded support...... for the case of bounded support. In contrast to the classical multiplicative convolution case, the limit measure for the free multiplicative law of large numbers is not a Dirac measure, unless the original measure is a Dirac measure. We also show that the mean value of lnx is additive with respect to the free...
Convolution kernel design and efficient algorithm for sampling density correction.
Johnson, Kenneth O; Pipe, James G
2009-02-01
Sampling density compensation is an important step in non-cartesian image reconstruction. One of the common techniques to determine weights that compensate for differences in sampling density involves a convolution. A new convolution kernel is designed for sampling density attempting to minimize the error in a fully reconstructed image. The resulting weights obtained using this new kernel are compared with various previous methods, showing a reduction in reconstruction error. A computationally efficient algorithm is also presented that facilitates the calculation of the convolution of finite kernels. Both the kernel and the algorithm are extended to 3D. Copyright 2009 Wiley-Liss, Inc.
Interpolating and filtering decoding algorithm for convolution codes
Directory of Open Access Journals (Sweden)
O. O. Shpylka
2010-01-01
Full Text Available There has been synthesized interpolating and filtering decoding algorithm for convolution codes on maximum of a posteriori probability criterion, in which combined filtering coder state and interpolation of information signs on sliding interval are processed
FPGA-based digital convolution for wireless applications
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...
Model Convolution: A Computational Approach to Digital Image Interpretation
Gardner, Melissa K.; Sprague, Brian L.; Pearson, Chad G.; Cosgrove, Benjamin D.; Bicek, Andrew D.; Bloom, Kerry; Salmon, E. D.
2010-01-01
Digital fluorescence microscopy is commonly used to track individual proteins and their dynamics in living cells. However, extracting molecule-specific information from fluorescence images is often limited by the noise and blur intrinsic to the cell and the imaging system. Here we discuss a method called “model-convolution,” which uses experimentally measured noise and blur to simulate the process of imaging fluorescent proteins whose spatial distribution cannot be resolved. We then compare model-convolution to the more standard approach of experimental deconvolution. In some circumstances, standard experimental deconvolution approaches fail to yield the correct underlying fluorophore distribution. In these situations, model-convolution removes the uncertainty associated with deconvolution and therefore allows direct statistical comparison of experimental and theoretical data. Thus, if there are structural constraints on molecular organization, the model-convolution method better utilizes information gathered via fluorescence microscopy, and naturally integrates experiment and theory. PMID:20461132
Multipath Convolutional-Recursive Neural Networks for Object Recognition
2014-01-01
Part 8: Pattern Recognition; International audience; Extracting good representations from images is essential for many computer vision tasks. While progress in deep learning shows the importance of learning hierarchical features, it is also important to learn features through multiple paths. This paper presents Multipath Convolutional-Recursive Neural Networks(M-CRNNs), a novel scheme which aims to learn image features from multiple paths using models based on combination of convolutional and...
Approximation of integral operators using product-convolution expansions
Escande, Paul; Weiss, Pierre
2016-01-01
We consider a class of linear integral operators with impulse responses varying regularly in time or space. These operators appear in a large number of applications ranging from signal/image processing to biology. Evaluating their action on functions is a computationally intensive problem necessary for many practical problems. We analyze a technique called product-convolution expansion: the operator is locally approximated by a convolution, allowing to design fast numerical algorithms ba...
Approximation of integral operators using convolution-product expansions
Escande, Paul; Weiss, Pierre
2016-01-01
We consider a class of linear integral operators with impulse responses varying regularly in time or space. These operators appear in a large number of applications ranging from signal/image processing to biology. Evaluating their action on functions is a computation-ally intensive problem necessary for many practical problems. We analyze a technique called convolution-product expansion: the operator is locally approximated by a convolution, allowing to design fast numerical algorithms based ...
Two-dimensional Block of Spatial Convolution Algorithm and Simulation
Mussa Mohamed Ahmed
2012-01-01
This paper proposes an algorithm based on sub image-segmentation strategy. The proposed scheme divides a grayscale image into overlapped 6×6 blocks each of which is segmented into four small 3x3 non-overlapped sub-images. A new spatial approach for efficiently computing 2-dimensional linear convolution or cross-correlation between suitable flipped and fixed filter coefficients (sub image for cross-correlation) and corresponding input sub image is presented. Computation of convolution is itera...
Traffic sign recognition with deep convolutional neural networks
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...
Multiscale Convolutional Neural Networks for Hand Detection
Directory of Open Access Journals (Sweden)
Shiyang Yan
2017-01-01
Full Text Available Unconstrained hand detection in still images plays an important role in many hand-related vision problems, for example, hand tracking, gesture analysis, human action recognition and human-machine interaction, and sign language recognition. Although hand detection has been extensively studied for decades, it is still a challenging task with many problems to be tackled. The contributing factors for this complexity include heavy occlusion, low resolution, varying illumination conditions, different hand gestures, and the complex interactions between hands and objects or other hands. In this paper, we propose a multiscale deep learning model for unconstrained hand detection in still images. Deep learning models, and deep convolutional neural networks (CNNs in particular, have achieved state-of-the-art performances in many vision benchmarks. Developed from the region-based CNN (R-CNN model, we propose a hand detection scheme based on candidate regions generated by a generic region proposal algorithm, followed by multiscale information fusion from the popular VGG16 model. Two benchmark datasets were applied to validate the proposed method, namely, the Oxford Hand Detection Dataset and the VIVA Hand Detection Challenge. We achieved state-of-the-art results on the Oxford Hand Detection Dataset and had satisfactory performance in the VIVA Hand Detection Challenge.
Metaheuristic Algorithms for Convolution Neural Network
Fanany, Mohamad Ivan; Arymurthy, Aniati Murni
2016-01-01
A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent). PMID:27375738
Convolution kernels for multi-wavelength imaging
Boucaud, Alexandre; Abergel, Alain; Orieux, François; Dole, Hervé; Hadj-Youcef, Mohamed Amine
2016-01-01
Astrophysical images issued from different instruments and/or spectral bands often require to be processed together, either for fitting or comparison purposes. However each image is affected by an instrumental response, also known as PSF, that depends on the characteristics of the instrument as well as the wavelength and the observing strategy. Given the knowledge of the PSF in each band, a straightforward way of processing images is to homogenise them all to a target PSF using convolution kernels, so that they appear as if they had been acquired by the same instrument. We propose an algorithm that generates such PSF-matching kernels, based on Wiener filtering with a tunable regularisation parameter. This method ensures all anisotropic features in the PSFs to be taken into account. We compare our method to existing procedures using measured Herschel/PACS and SPIRE PSFs and simulated JWST/MIRI PSFs. Significant gains up to two orders of magnitude are obtained with respect to the use of kernels computed assumin...
Event Discrimination using Convolutional Neural Networks
Menon, Hareesh; Hughes, Richard; Daling, Alec; Winer, Brian
2017-01-01
Convolutional Neural Networks (CNNs) are computational models that have been shown to be effective at classifying different types of images. We present a method to use CNNs to distinguish events involving the production of a top quark pair and a Higgs boson from events involving the production of a top quark pair and several quark and gluon jets. To do this, we generate and simulate data using MADGRAPH and DELPHES for a general purpose LHC detector at 13 TeV. We produce images using a particle flow algorithm by binning the particles geometrically based on their position in the detector and weighting the bins by the energy of each particle within each bin, and by defining channels based on particle types (charged track, neutral hadronic, neutral EM, lepton, heavy flavor). Our classification results are competitive with standard machine learning techniques. We have also looked into the classification of the substructure of the events, in a process known as scene labeling. In this context, we look for the presence of boosted objects (such as top quarks) with substructure encompassed within single jets. Preliminary results on substructure classification will be presented.
Convolution kernels for multi-wavelength imaging
Boucaud, A.; Bocchio, M.; Abergel, A.; Orieux, F.; Dole, H.; Hadj-Youcef, M. A.
2016-12-01
Astrophysical images issued from different instruments and/or spectral bands often require to be processed together, either for fitting or comparison purposes. However each image is affected by an instrumental response, also known as point-spread function (PSF), that depends on the characteristics of the instrument as well as the wavelength and the observing strategy. Given the knowledge of the PSF in each band, a straightforward way of processing images is to homogenise them all to a target PSF using convolution kernels, so that they appear as if they had been acquired by the same instrument. We propose an algorithm that generates such PSF-matching kernels, based on Wiener filtering with a tunable regularisation parameter. This method ensures all anisotropic features in the PSFs to be taken into account. We compare our method to existing procedures using measured Herschel/PACS and SPIRE PSFs and simulated JWST/MIRI PSFs. Significant gains up to two orders of magnitude are obtained with respect to the use of kernels computed assuming Gaussian or circularised PSFs. A software to compute these kernels is available at https://github.com/aboucaud/pypher
Do Convolutional Neural Networks Learn Class Hierarchy?
Alsallakh, Bilal; Jourabloo, Amin; Ye, Mao; Liu, Xiaoming; Ren, Liu
2017-08-29
Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the accuracy usually drops as the possibilities of confusion increase. Interestingly, the class confusion patterns follow a hierarchical structure over the classes. We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data. We found that this hierarchy not only dictates the confusion patterns between the classes, it furthermore dictates the learning behavior of CNNs. In particular, the early layers in these networks develop feature detectors that can separate high-level groups of classes quite well, even after a few training epochs. In contrast, the latter layers require substantially more epochs to develop specialized feature detectors that can separate individual classes. We demonstrate how these insights are key to significant improvement in accuracy by designing hierarchy-aware CNNs that accelerate model convergence and alleviate overfitting. We further demonstrate how our methods help in identifying various quality issues in the training data.
Colonoscopic polyp detection using convolutional neural networks
Park, Sun Young; Sargent, Dusty
2016-03-01
Computer aided diagnosis (CAD) systems for medical image analysis rely on accurate and efficient feature extraction methods. Regardless of which type of classifier is used, the results will be limited if the input features are not diagnostically relevant and do not properly discriminate between the different classes of images. Thus, a large amount of research has been dedicated to creating feature sets that capture the salient features that physicians are able to observe in the images. Successful feature extraction reduces the semantic gap between the physician's interpretation and the computer representation of images, and helps to reduce the variability in diagnosis between physicians. Due to the complexity of many medical image classification tasks, feature extraction for each problem often requires domainspecific knowledge and a carefully constructed feature set for the specific type of images being classified. In this paper, we describe a method for automatic diagnostic feature extraction from colonoscopy images that may have general application and require a lower level of domain-specific knowledge. The work in this paper expands on our previous CAD algorithm for detecting polyps in colonoscopy video. In that work, we applied an eigenimage model to extract features representing polyps, normal tissue, diverticula, etc. from colonoscopy videos taken from various viewing angles and imaging conditions. Classification was performed using a conditional random field (CRF) model that accounted for the spatial and temporal adjacency relationships present in colonoscopy video. In this paper, we replace the eigenimage feature descriptor with features extracted from a convolutional neural network (CNN) trained to recognize the same image types in colonoscopy video. The CNN-derived features show greater invariance to viewing angles and image quality factors when compared to the eigenimage model. The CNN features are used as input to the CRF classifier as before. We report
Noise-enhanced convolutional neural networks.
Audhkhasi, Kartik; Osoba, Osonde; Kosko, Bart
2016-06-01
Injecting carefully chosen noise can speed convergence in the backpropagation training of a convolutional neural network (CNN). The Noisy CNN algorithm speeds training on average because the backpropagation algorithm is a special case of the generalized expectation-maximization (EM) algorithm and because such carefully chosen noise always speeds up the EM algorithm on average. The CNN framework gives a practical way to learn and recognize images because backpropagation scales with training data. It has only linear time complexity in the number of training samples. The Noisy CNN algorithm finds a special separating hyperplane in the network's noise space. The hyperplane arises from the likelihood-based positivity condition that noise-boosts the EM algorithm. The hyperplane cuts through a uniform-noise hypercube or Gaussian ball in the noise space depending on the type of noise used. Noise chosen from above the hyperplane speeds training on average. Noise chosen from below slows it on average. The algorithm can inject noise anywhere in the multilayered network. Adding noise to the output neurons reduced the average per-iteration training-set cross entropy by 39% on a standard MNIST image test set of handwritten digits. It also reduced the average per-iteration training-set classification error by 47%. Adding noise to the hidden layers can also reduce these performance measures. The noise benefit is most pronounced for smaller data sets because the largest EM hill-climbing gains tend to occur in the first few iterations. This noise effect can assist random sampling from large data sets because it allows a smaller random sample to give the same or better performance than a noiseless sample gives.
Chen, Liang; Yin, Hexing; Zhou, Yong; Dai, Hui; Yu, Tao; Liu, Jianguo; Zou, Zhigang
2016-01-28
Highly crystalline metal (Co, Ni) selenium (Co0.85Se or Ni0.85Se) nanosheets were in situ grown on metal (Co, Ni) fibers (M-M0.85Se). Both M-M0.85Se (Co-Co0.85Se and Ni-Ni0.85Se) fibers prove to function as excellent, low-cost counter electrodes (CEs) in fiber-shaped dye-sensitized solar cells (FDSSCs) with high power conversion efficiency (Co-Co0.85Se 6.55% and Ni-Ni0.85Se 7.07%), comparable or even superior to a Pt fiber CE (6.54%). The good performance of the present Pt-free CE-based solar cell was believed to originate from: (1) the intrinsic electrocatalytic properties of the single-crystalline M-M0.85Se; (2) the enough void space among M0.85Se nanosheets that allows easier redox ion diffusion; (3) the two-dimensional morphology that provides a large contact area between the CE catalytic material and electrolyte; (4) in situ direct growth of the M0.85Se on metal fibers that renders good electrical contact between the active material and the electron collector.
Evaluation of convolutional neural networks for visual recognition.
Nebauer, C
1998-01-01
Convolutional neural networks provide an efficient method to constrain the complexity of feedforward neural networks by weight sharing and restriction to local connections. This network topology has been applied in particular to image classification when sophisticated preprocessing is to be avoided and raw images are to be classified directly. In this paper two variations of convolutional networks--neocognitron and a modification of neocognitron--are compared with classifiers based on fully connected feedforward layers (i.e., multilayer perceptron, nearest neighbor classifier, auto-encoding network) with respect to their visual recognition performance. Beside the original neocognitron a modification of the neocognitron is proposed which combines neurons from perceptron with the localized network structure of neocognitron. Instead of training convolutional networks by time-consuming error backpropagation, in this work a modular procedure is applied whereby layers are trained sequentially from the input to the output layer in order to recognize features of increasing complexity. For a quantitative experimental comparison with standard classifiers two very different recognition tasks have been chosen: handwritten digit recognition and face recognition. In the first example on handwritten digit recognition the generalization of convolutional networks is compared to fully connected networks. In several experiments the influence of variations of position, size, and orientation of digits is determined and the relation between training sample size and validation error is observed. In the second example recognition of human faces is investigated under constrained and variable conditions with respect to face orientation and illumination and the limitations of convolutional networks are discussed.
Brain and art: illustrations of the cerebral convolutions. A review.
Lazić, D; Marinković, S; Tomić, I; Mitrović, D; Starčević, A; Milić, I; Grujičić, M; Marković, B
2014-08-01
Aesthetics and functional significance of the cerebral cortical relief gave us the idea to find out how often the convolutions are presented in fine art, and in which techniques, conceptual meaning and pathophysiological aspect. We examined 27,614 art works created by 2,856 authors and presented in art literature, and in Google images search. The cerebral gyri were shown in 0.85% of the art works created by 2.35% of the authors. The concept of the brain was first mentioned in ancient Egypt some 3,700 years ago. The first artistic drawing of the convolutions was made by Leonardo da Vinci, and the first colour picture by an unknown Italian author. Rembrandt van Rijn was the first to paint the gyri. Dozens of modern authors, who are professional artists, medical experts or designers, presented the cerebralc onvolutions in drawings, paintings, digital works or sculptures, with various aesthetic, symbolic and metaphorical connotation. Some artistic compositions and natural forms show a gyral pattern. The convolutions, whose cortical layers enable the cognitive functions, can be affected by various disorders. Some artists suffered from those disorders, and some others presented them in their artworks. The cerebral convolutions or gyri, thanks to their extensive cortical mantle, are the specific morphological basis for the human mind, but also the structures with their own aesthetics. Contemporary authors relatively often depictor model the cerebral convolutions, either from the aesthetic or conceptual aspect. In this way, they make a connection between the neuroscience and fineart.
Model-based optoacoustic inversion with arbitrary-shape detectors.
Rosenthal, Amir; Ntziachristos, Vasilis; Razansky, Daniel
2011-07-01
Optoacoustic imaging enables mapping the optical absorption of biological tissue using optical excitation and acoustic detection. Although most image-reconstruction algorithms are based on the assumption of a detector with an isotropic sensitivity, the geometry of the detector often leads to a response with spatially dependent magnitude and bandwidth. This effect may lead to attenuation or distortion in the recorded signal and, consequently, in the reconstructed image. Herein, an accurate numerical method for simulating the spatially dependent response of an arbitrary-shape acoustic transducer is presented. The method is based on an analytical solution obtained for a two-dimensional line detector. The calculated response is incorporated in the forward model matrix of an optoacoustic imaging setup using temporal convolution, and image reconstruction is performed by inverting the matrix relation. The method was numerically and experimentally demonstrated in two dimensions for both flat and focused transducers and compared to the spatial-convolution method. In forward simulations, the developed method did not suffer from the numerical errors exhibited by the spatial-convolution method. In reconstruction simulations and experiments, the use of both temporal-convolution and spatial-convolution methods lead to an enhancement in resolution compared to a reconstruction with a point detector model. However, because of its higher modeling accuracy, the temporal-convolution method achieved a noise figure approximated three times lower than the spatial-convolution method. The demonstrated performance of the spatial-convolution method shows it is a powerful tool for reducing reconstruction artifacts originating from the detector finite size and improving the quality of optoacoustic reconstructions. Furthermore, the method may be used for assessing new system designs. Specifically, detectors with nonstandard shapes may be investigated.
Directory of Open Access Journals (Sweden)
F. Alidoost
2016-06-01
Full Text Available 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.
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.
Glaucoma detection based on deep convolutional neural network.
Xiangyu Chen; Yanwu Xu; Damon Wing Kee Wong; Tien Yin Wong; Jiang Liu
2015-08-01
Glaucoma is a chronic and irreversible eye disease, which leads to deterioration in vision and quality of life. In this paper, we develop a deep learning (DL) architecture with convolutional neural network for automated glaucoma diagnosis. Deep learning systems, such as convolutional neural networks (CNNs), can infer a hierarchical representation of images to discriminate between glaucoma and non-glaucoma patterns for diagnostic decisions. The proposed DL architecture contains six learned layers: four convolutional layers and two fully-connected layers. Dropout and data augmentation strategies are adopted to further boost the performance of glaucoma diagnosis. Extensive experiments are performed on the ORIGA and SCES datasets. The results show area under curve (AUC) of the receiver operating characteristic curve in glaucoma detection at 0.831 and 0.887 in the two databases, much better than state-of-the-art algorithms. The method could be used for glaucoma detection.
Two dimensional convolute integers for machine vision and image recognition
Edwards, Thomas R.
1988-01-01
Machine vision and image recognition require sophisticated image processing prior to the application of Artificial Intelligence. Two Dimensional Convolute Integer Technology is an innovative mathematical approach for addressing machine vision and image recognition. This new technology generates a family of digital operators for addressing optical images and related two dimensional data sets. The operators are regression generated, integer valued, zero phase shifting, convoluting, frequency sensitive, two dimensional low pass, high pass and band pass filters that are mathematically equivalent to surface fitted partial derivatives. These operators are applied non-recursively either as classical convolutions (replacement point values), interstitial point generators (bandwidth broadening or resolution enhancement), or as missing value calculators (compensation for dead array element values). These operators show frequency sensitive feature selection scale invariant properties. Such tasks as boundary/edge enhancement and noise or small size pixel disturbance removal can readily be accomplished. For feature selection tight band pass operators are essential. Results from test cases are given.
Improving displayed resolution in convolution reconstruction of digital holograms
Institute of Scientific and Technical Information of China (English)
FAN Qi; ZHAO Jian-lin; ZHANG Yan-cao; WANG Jun; DI Jiang-lei
2006-01-01
In digital holographic microscopy,when the object is placed near the CCD,the Fresnel approximation is no longer valid and the convolution approach has to be applied.With this approach,the sampling spacing of the reconstructed image plane is equal to the pixel size of the CCD.If the lateral resolution of the reconstructed image is higher than that of the CCD,Nyquist sampling criterion is violated and aliasing errors will be introduced.In this Letter,a new method is proposed to solve this problem by investigating convolution reconstruction of digital holograms.By appending enough zeros to the angular spectrum between the two FFT's in convolution reconstruction of digital holograms,the displayed resolution of the reconstructed image can be improved.Experimental results show a good agreement with theoretical analysis.
Throughput Scaling Of Convolution For Error-Tolerant Multimedia Applications
Anam, Mohammad Ashraful
2012-01-01
Convolution and cross-correlation are the basis of filtering and pattern or template matching in multimedia signal processing. We propose two throughput scaling options for any one-dimensional convolution kernel in programmable processors by adjusting the imprecision (distortion) of computation. Our approach is based on scalar quantization, followed by two forms of tight packing in floating-point (one of which is proposed in this paper) that allow for concurrent calculation of multiple results. We illustrate how our approach can operate as an optional pre- and post-processing layer for off-the-shelf optimized convolution routines. This is useful for multimedia applications that are tolerant to processing imprecision and for cases where the input signals are inherently noisy (error tolerant multimedia applications). Indicative experimental results with a digital music matching system and an MPEG-7 audio descriptor system demonstrate that the proposed approach offers up to 175% increase in processing throughput...
Spatially variant convolution with scaled B-splines.
Muñoz-Barrutia, Arrate; Artaechevarria, Xabier; Ortiz-de-Solorzano, Carlos
2010-01-01
We present an efficient algorithm to compute multidimensional spatially variant convolutions--or inner products--between N-dimensional signals and B-splines--or their derivatives--of any order and arbitrary sizes. The multidimensional B-splines are computed as tensor products of 1-D B-splines, and the input signal is expressed in a B-spline basis. The convolution is then computed by using an adequate combination of integration and scaled finite differences as to have, for moderate and large scale values, a computational complexity that does not depend on the scaling factor. To show in practice the benefit of using our spatially variant convolution approach, we present an adaptive noise filter that adjusts the kernel size to the local image characteristics and a high sensitivity local ridge detector.
1985-09-25
Microvoltammetric Electrodes, J. 0. Howell, R. M. Wightman, Anal. Chem., 56, 524-529 (1984). 2. Flow Rate Independent Amperometric Cell , W. L. Caudill...Electroanal. Chem., 182, 113-122 (1985). C. List of all publications 1. Flow Rate Independent Amperometric Cell , W. L. Caudill, J. 0. Howell, R. M
The Existence of Strongly-MDS Convolutional Codes
Hutchinson, Ryan
2008-01-01
It is known that maximum distance separable and maximum distance profile convolutional codes exist over large enough finite fields of any characteristic for all parameters $(n,k,\\delta)$. It has been conjectured that the same is true for convolutional codes that are strongly maximum distance separable. Using methods from linear systems theory, we resolve this conjecture by showing that, over a large enough finite field of any characteristic, codes which are simultaneously maximum distance profile and strongly maximum distance separable exist for all parameters $(n,k,\\delta)$.
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.
Inferring low-dimensional microstructure representations using convolutional neural networks
Lubbers, Nicholas; Barros, Kipton
2016-01-01
We apply recent advances in machine learning and computer vision to a central problem in materials informatics: The statistical representation of microstructural images. We use activations in a pre-trained convolutional neural network to provide a high-dimensional characterization of a set of synthetic microstructural images. Next, we use manifold learning to obtain a low-dimensional embedding of this statistical characterization. We show that the low-dimensional embedding extracts the parameters used to generate the images. According to a variety of metrics, the convolutional neural network method yields dramatically better embeddings than the analogous method derived from two-point correlations alone.
Detection of phase transition via convolutional neural network
Tanaka, Akinori
2016-01-01
We design a Convolutional Neural Network (CNN) which studies correlation between discretized inverse temperature and spin configuration of 2D Ising model and show that it can find a feature of the phase transition without teaching any a priori information for it. We also define a new order parameter via the CNN and show that it provides well approximated critical inverse temperature. In addition, we compare the activation functions for convolution layer and find that the Rectified Linear Unit (ReLU) is important to detect the phase transition of 2D Ising model.
Directory of Open Access Journals (Sweden)
Kılıç Emrah
2016-12-01
Full Text Available In this paper, we consider Gauthier’s generalized convolution and then define its binomial analogue as well as alternating binomial analogue. We formulate these convolutions and give some applications of them.
Projection of fMRI data onto the cortical surface using anatomically-informed convolution kernels.
Operto, G; Bulot, R; Anton, J-L; Coulon, O
2008-01-01
As surface-based data analysis offer an attractive approach for intersubject matching and comparison, the projection of voxel-based 3D volumes onto the cortical surface is an essential problem. We present here a method that aims at producing representations of functional brain data on the cortical surface from functional MRI volumes. Such representations are for instance required for subsequent cortical-based functional analysis. We propose a projection technique based on the definition, around each node of the gray/white matter interface mesh, of convolution kernels whose shape and distribution rely on the geometry of the local anatomy. For one anatomy, a set of convolution kernels is computed that can be used to project any functional data registered with this anatomy. Therefore resulting in anatomically-informed projections of data onto the cortical surface, this kernel-based approach offers better sensitivity, specificity than other classical methods and robustness to misregistration errors. Influences of mesh and volumes spatial resolutions were also estimated for various projection techniques, using simulated functional maps.
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
Radio Signal Augmentation for Improved Training of a Convolutional Neural Network
2016-09-01
parameters of the network. Examples of these parameters include: • Input data dimensions and channels (e.g., image size and colors) • Size of convolutional ...filters • Number of convolutional filters • Pooling/downsampling size and method (e.g., max-pool or average) • Number of convolution and pooling...TECHNICAL REPORT 3055 September 2016 Radio Signal Augmentation for Improved Training of a Convolutional Neural Network Daniel
Efficient Partitioning of Algorithms for Long Convolutions and their Mapping onto Architectures
Bierens, L.; Deprettere, E.
1998-01-01
We present an efficient approach for the partitioning of algorithms implementing long convolutions. The dependence graph (DG) of a convolution algorithm is locally sequential globally parallel (LSGP) partitioned into smaller, less complex convolution algorithms. The LSGP partitioned DG is mapped ont
General Purpose Convolution Algorithm in S4 Classes by Means of FFT
Directory of Open Access Journals (Sweden)
Peter Ruckdeschel
2014-08-01
By means of object orientation this default algorithm is overloaded by more specific algorithms where possible, in particular where explicit convolution formulae are available. Our focus is on R package distr which implements this approach, overloading operator + for convolution; based on this convolution, we define a whole arithmetics of mathematical operations acting on distribution objects, comprising operators +, -, *, /, and ^.
Convolutional Sparse Coding for Static and Dynamic Images Analysis
Directory of Open Access Journals (Sweden)
B. A. Knyazev
2014-01-01
Full Text Available The objective of this work is to improve performance of static and dynamic objects recognition. For this purpose a new image representation model and a transformation algorithm are proposed. It is examined and illustrated that limitations of previous methods make it difficult to achieve this objective. Static images, specifically handwritten digits of the widely used MNIST dataset, are the primary focus of this work. Nevertheless, preliminary qualitative results of image sequences analysis based on the suggested model are presented.A general analytical form of the Gabor function, often employed to generate filters, is described and discussed. In this research, this description is required for computing parameters of responses returned by our algorithm. The recursive convolution operator is introduced, which allows extracting free shape features of visual objects. The developed parametric representation model is compared with sparse coding based on energy function minimization.In the experimental part of this work, errors of estimating the parameters of responses are determined. Also, parameters statistics and their correlation coefficients for more than 106 responses extracted from the MNIST dataset are calculated. It is demonstrated that these data correspond well with previous research studies on Gabor filters as well as with works on visual cortex primary cells of mammals, in which similar responses were observed. A comparative test of the developed model with three other approaches is conducted; speed and accuracy scores of handwritten digits classification are presented. A support vector machine with a linear or radial basic function is used for classification of images and their representations while principal component analysis is used in some cases to prepare data beforehand. High accuracy is not attained due to the specific difficulties of combining our model with a support vector machine (a 3.99% error rate. However, another method is
Robust Fusion of Irregularly Sampled Data Using Adaptive Normalized Convolution
Pham, T.Q.; Van Vliet, L.J.; Schutte, K.
2006-01-01
We present a novel algorithm for image fusion from irregularly sampled data. The method is based on the framework of normalized convolution (NC), in which the local signal is approximated through a projection onto a subspace. The use of polynomial basis functions in this paper makes NC equivalent to
A single Chip Implementation for Fast Convolution of Long Sequences
Zwartenkot, H.T.J.; Boerrigter, M.J.G.; Bierens, L.H.J.; Smit, J.
1996-01-01
Usually, long convolutions are computed by programmable DSP boards using long FFTs. Typical operational requirements such as minimum power dissipation, minimum volume and high dynamic range/accuracy, make this solution often inefficient and even unacceptable. In this paper we present a single chip f
Unsupervised pre-training for fully convolutional neural networks
Wiehman, Stiaan; Kroon, Steve; Villiers, De Hendrik
2017-01-01
Unsupervised pre-training of neural networks has been shown to act as a regularization technique, improving performance and reducing model variance. Recently, fully convolutional networks (FCNs) have shown state-of-the-art results on various semantic segmentation tasks. Unfortunately, there is no ef
Face recognition: a convolutional neural-network approach.
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.
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 squares...
Real-time rendering of optical effects using spatial convolution
Rokita, Przemyslaw
1998-03-01
Simulation of special effects such as: defocus effect, depth-of-field effect, raindrops or water film falling on the windshield, may be very useful in visual simulators and in all computer graphics applications that need realistic images of outdoor scenery. Those effects are especially important in rendering poor visibility conditions in flight and driving simulators, but can also be applied, for example, in composing computer graphics and video sequences- -i.e. in Augmented Reality systems. This paper proposes a new approach to the rendering of those optical effects by iterative adaptive filtering using spatial convolution. The advantage of this solution is that the adaptive convolution can be done in real-time by existing hardware. Optical effects mentioned above can be introduced into the image computed using conventional camera model by applying to the intensity of each pixel the convolution filter having an appropriate point spread function. The algorithms described in this paper can be easily implemented int the visualization pipeline--the final effect may be obtained by iterative filtering using a single hardware convolution filter or with the pipeline composed of identical 3 X 3 filters placed as the stages of this pipeline. Another advantage of the proposed solution is that the extension based on proposed algorithm can be added to the existing rendering systems as a final stage of the visualization pipeline.
Convolution operators defined by singular measures on the motion group
Brandolini, Luca; Thangavelu, Sundaram; Travaglini, Giancarlo
2010-01-01
This paper contains an $L^{p}$ improving result for convolution operators defined by singular measures associated to hypersurfaces on the motion group. This needs only mild geometric properties of the surfaces, and it extends earlier results on Radon type transforms on $\\mathbb{R}^{n}$. The proof relies on the harmonic analysis on the motion group.
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 are ...
Behaviour at infinity of solutions of twisted convolution equations
Energy Technology Data Exchange (ETDEWEB)
Volchkov, Valerii V; Volchkov, Vitaly V [Donetsk National University, Donetsk (Ukraine)
2012-02-28
We obtain a precise characterization of the minimal rate of growth at infinity of non-trivial solutions of twisted convolution equations in unbounded domains of C{sup n}. As an application, we obtain definitive versions of the two-radii theorem for twisted spherical means.
Two-level convolution formula for nuclear structure function
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-Dimensional Tail-Biting Convolutional Codes
Alfandary, Liam
2011-01-01
The multidimensional convolutional codes are an extension of the notion of convolutional codes (CCs) to several dimensions of time. This paper explores the class of two-dimensional convolutional codes (2D CCs) and 2D tail-biting convolutional codes (2D TBCCs), in particular, from several aspects. First, we derive several basic algebraic properties of these codes, applying algebraic methods in order to find bijective encoders, create parity check matrices and to inverse encoders. Next, we discuss the minimum distance and weight distribution properties of these codes. Extending an existing tree-search algorithm to two dimensions, we apply it to find codes with high minimum distance. Word-error probability asymptotes for sample codes are given and compared with other codes. The results of this approach suggest that 2D TBCCs can perform better than comparable 1D TBCCs or other codes. We then present several novel iterative suboptimal algorithms for soft decoding 2D CCs, which are based on belief propagation. Two ...
Yetter－Drinfel‘d Module and Convolution Module
Institute of Scientific and Technical Information of China (English)
张良云; 王栓宏
2002-01-01
In this paper,we first give a sufficent and necessary condition for a Hopf algebra to be a Yetter-Drinfel'd module,and prove that the finite dual of a Yetter-Drinfel'd module is still a Yetter-Drinfel'd module,Finally,we introduce a concept of convolution module.
On the generalized Hamming weights of convolutional codes
Rosenthal, J.; York, E.V.
1995-01-01
Motivated by applications in cryptology K. Wei introduced in 1991 the concept of a generalized Hamming weight for a linear block code. In this paper we define generalized Hamming weights for the class of convolutional codes and we derive several of their basic properties. By restricting to convoluti
Maximum-likelihood estimation of circle parameters via convolution.
Zelniker, Emanuel E; Clarkson, I Vaughan L
2006-04-01
The accurate fitting of a circle to noisy measurements of circumferential points is a much studied problem in the literature. In this paper, we present an interpretation of the maximum-likelihood estimator (MLE) and the Delogne-Kåsa estimator (DKE) for circle-center and radius estimation in terms of convolution on an image which is ideal in a certain sense. We use our convolution-based MLE approach to find good estimates for the parameters of a circle in digital images. In digital images, it is then possible to treat these estimates as preliminary estimates into various other numerical techniques which further refine them to achieve subpixel accuracy. We also investigate the relationship between the convolution of an ideal image with a "phase-coded kernel" (PCK) and the MLE. This is related to the "phase-coded annulus" which was introduced by Atherton and Kerbyson who proposed it as one of a number of new convolution kernels for estimating circle center and radius. We show that the PCK is an approximate MLE (AMLE). We compare our AMLE method to the MLE and the DKE as well as the Cramér-Rao Lower Bound in ideal images and in both real and synthetic digital images.
Robust Fusion of Irregularly Sampled Data Using Adaptive Normalized Convolution
Pham, T.Q.; Vliet, L.J. van; Schutte, K.
2006-01-01
We present a novel algorithm for image fusion from irregularly sampled data. The method is based on the framework of normalized convolution (NC), in which the local signal is approximated through a projection onto a subspace. The use of polynomial basis functions in this paper makes NC equivalent to
A single Chip Implementation for Fast Convolution of Long Sequences
Zwartenkot, H.T.J.; Boerrigter, M.J.G.; Bierens, L.H.J.; Smit, J.
1996-01-01
Usually, long convolutions are computed by programmable DSP boards using long FFTs. Typical operational requirements such as minimum power dissipation, minimum volume and high dynamic range/accuracy, make this solution often inefficient and even unacceptable. In this paper we present a single chip
iElectrodes: A Comprehensive Open-Source Toolbox for Depth and Subdural Grid Electrode Localization
Blenkmann, Alejandro O.; Phillips, Holly N.; Princich, Juan P.; Rowe, James B.; Bekinschtein, Tristan A.; Muravchik, Carlos H.; Kochen, Silvia
2017-01-01
The localization of intracranial electrodes is a fundamental step in the analysis of invasive electroencephalography (EEG) recordings in research and clinical practice. The conclusions reached from the analysis of these recordings rely on the accuracy of electrode localization in relationship to brain anatomy. However, currently available techniques for localizing electrodes from magnetic resonance (MR) and/or computerized tomography (CT) images are time consuming and/or limited to particular electrode types or shapes. Here we present iElectrodes, an open-source toolbox that provides robust and accurate semi-automatic localization of both subdural grids and depth electrodes. Using pre- and post-implantation images, the method takes 2–3 min to localize the coordinates in each electrode array and automatically number the electrodes. The proposed pre-processing pipeline allows one to work in a normalized space and to automatically obtain anatomical labels of the localized electrodes without neuroimaging experts. We validated the method with data from 22 patients implanted with a total of 1,242 electrodes. We show that localization distances were within 0.56 mm of those achieved by experienced manual evaluators. iElectrodes provided additional advantages in terms of robustness (even with severe perioperative cerebral distortions), speed (less than half the operator time compared to expert manual localization), simplicity, utility across multiple electrode types (surface and depth electrodes) and all brain regions. PMID:28303098
Blanco-Pillado, Jose J; Shlaer, Benjamin
2015-01-01
We analyze the shapes of cosmic string loops found in large-scale simulations of an expanding-universe string network. The simulation does not include gravitational back reaction, but we model that process by smoothing the loop using Lorentzian convolution. We find that loops at formation consist of generally straight segments separated by kinks. We do not see cusps or any cusp-like structure at the scale of the entire loop, although we do see very small regions of string that move with large Lorentz boosts. However, smoothing of the string almost always introduces two cusps on each loop. The smoothing process does not lead to any significant fragmentation of loops that were in non-self-intersecting trajectories before smoothing.
Sammons, Daniel; Winfree, William P.; Burke, Eric; Ji, Shuiwang
2016-02-01
Nondestructive evaluation (NDE) utilizes a variety of techniques to inspect various materials for defects without causing changes to the material. X-ray computed tomography (CT) produces large volumes of three dimensional image data. Using the task of identifying delaminations in carbon fiber reinforced polymer (CFRP) composite CT, this work shows that it is possible to automate the analysis of these large volumes of CT data using a machine learning model known as a convolutional neural network (CNN). Further, tests on simulated data sets show that with a robust set of experimental data, it may be possible to go beyond just identification and instead accurately characterize the size and shape of the delaminations with CNNs.
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
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.
Institute of Scientific and Technical Information of China (English)
EL-DALY, Samy A; EL-HALLAG,Ibrahirn S; EBEED, Ezeiny M; GHONEIM, Mohamed M
2009-01-01
Electrochemical properties of two diolefinic laser dyes namely 1,4-bis[2-(2-pyddyl)-vinyl] benzene (2PVB) and 1,4-bis[2-(4-pyridyl) vinyl] benzene (4PVB) have been investigated using cyclic voltammetry and convolutive voltammetry combined with digital simulation at a platinum electrode in 0.1 mol/L tetrabutyl ammonium perchlo-rate (TBAP) in the two different solvents acetonitrile (CH3CN) and dimethylformamide (DMF). The species were reduced via consumption of two sequential electrons to form radical anion and dianion. In switching the potential to positive direction, the two compounds were oxidized by loss of one electron, which was followed by a fast isomeri-sation process. The electrode reaction pathway and the electrochemical parameters of the investigated compounds were determined using cyclic voltammetry. The extracted electrochemical parameters were verified and confirmed via digital simulation and convolutive voltammetry methods.
Image Super-Resolution Using Deep Convolutional Networks.
Dong, Chao; Loy, Chen Change; He, Kaiming; Tang, Xiaoou
2016-02-01
We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality.
The analysis of VERITAS muon images using convolutional neural networks
Feng, Qi
2016-01-01
Imaging atmospheric Cherenkov telescopes (IACTs) are sensitive to rare gamma-ray photons, buried in the background of charged cosmic-ray (CR) particles, the flux of which is several orders of magnitude greater. The ability to separate gamma rays from CR particles is important, as it is directly related to the sensitivity of the instrument. This gamma-ray/CR-particle classification problem in IACT data analysis can be treated with the rapidly-advancing machine learning algorithms, which have the potential to outperform the traditional box-cut methods on image parameters. We present preliminary results of a precise classification of a small set of muon events using a convolutional neural networks model with the raw images as input features. We also show the possibility of using the convolutional neural networks model for regression problems, such as the radius and brightness measurement of muon events, which can be used to calibrate the throughput efficiency of IACTs.
Convolution theorems for the linear canonical transform and their applications
Institute of Scientific and Technical Information of China (English)
DENG Bing; TAO Ran; WANG Yue
2006-01-01
As generalization of the fractional Fourier transform (FRFT), the linear canonical transform (LCT) has been used in several areas, including optics and signal processing. Many properties for this transform are already known, but the convolution theorems, similar to the version of the Fourier transform, are still to be determined. In this paper, the authors derive the convolution theorems for the LCT, and explore the sampling theorem and multiplicative filter for the band limited signal in the linear canonical domain. Finally, the sampling and reconstruction formulas are deduced, together with the construction methodology for the above mentioned multiplicative filter in the time domain based on fast Fourier transform (FFT), which has much lower computational load than the construction method in the linear canonical domain.
Star-galaxy Classification Using Deep Convolutional Neural Networks
Kim, Edward J
2016-01-01
Most existing star-galaxy classifiers use the reduced summary information from catalogs, requiring careful feature extraction and selection. The latest advances in machine learning that use deep convolutional neural networks allow a machine to automatically learn the features directly from data, minimizing the need for input from human experts. We present a star-galaxy classification framework that uses deep convolutional neural networks (ConvNets) directly on the reduced, calibrated pixel values. Using data from the Sloan Digital Sky Survey (SDSS) and the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS), we demonstrate that ConvNets are able to produce accurate and well-calibrated probabilistic classifications that are competitive with conventional machine learning techniques. Future advances in deep learning may bring more success with current and forthcoming photometric surveys, such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope (LSST), because deep neural networks require...
The analysis of VERITAS muon images using convolutional neural networks
Feng, Qi; Lin, Tony T. Y.; VERITAS Collaboration
2017-06-01
Imaging atmospheric Cherenkov telescopes (IACTs) are sensitive to rare gamma-ray photons, buried in the background of charged cosmic-ray (CR) particles, the flux of which is several orders of magnitude greater. The ability to separate gamma rays from CR particles is important, as it is directly related to the sensitivity of the instrument. This gamma-ray/CR-particle classification problem in IACT data analysis can be treated with the rapidly-advancing machine learning algorithms, which have the potential to outperform the traditional box-cut methods on image parameters. We present preliminary results of a precise classification of a small set of muon events using a convolutional neural networks model with the raw images as input features. We also show the possibility of using the convolutional neural networks model for regression problems, such as the radius and brightness measurement of muon events, which can be used to calibrate the throughput efficiency of IACTs.
a Convolutional Network for Semantic Facade Segmentation and Interpretation
Schmitz, Matthias; Mayer, Helmut
2016-06-01
In this paper we present an approach for semantic interpretation of facade images based on a Convolutional Network. Our network processes the input images in a fully convolutional way and generates pixel-wise predictions. We show that there is no need for large datasets to train the network when transfer learning is employed, i. e., a part of an already existing network is used and fine-tuned, and when the available data is augmented by using deformed patches of the images for training. The network is trained end-to-end with patches of the images and each patch is augmented independently. To undo the downsampling for the classification, we add deconvolutional layers to the network. Outputs of different layers of the network are combined to achieve more precise pixel-wise predictions. We demonstrate the potential of our network based on results for the eTRIMS (Korč and Förstner, 2009) dataset reduced to facades.
Self-Taught convolutional neural networks for short text clustering.
Xu, Jiaming; Xu, Bo; Wang, Peng; Zheng, Suncong; Tian, Guanhua; Zhao, Jun; Xu, Bo
2017-01-12
Short text clustering is a challenging problem due to its sparseness of text representation. Here we propose a flexible Self-Taught Convolutional neural network framework for Short Text Clustering (dubbed STC(2)), which can flexibly and successfully incorporate more useful semantic features and learn non-biased deep text representation in an unsupervised manner. In our framework, the original raw text features are firstly embedded into compact binary codes by using one existing unsupervised dimensionality reduction method. Then, word embeddings are explored and fed into convolutional neural networks to learn deep feature representations, meanwhile the output units are used to fit the pre-trained binary codes in the training process. Finally, we get the optimal clusters by employing K-means to cluster the learned representations. Extensive experimental results demonstrate that the proposed framework is effective, flexible and outperform several popular clustering methods when tested on three public short text datasets.
On New Bijective Convolution Operator Act for Analytic Functions
Directory of Open Access Journals (Sweden)
Oqlah Al-Refai
2009-01-01
Full Text Available Problem statement: We introduced a new bijective convolution linear operator defined on the class of normalized analytic functions. This operator was motivated by many researchers namely Srivastava, Owa, Ruscheweyh and many others. The operator was essential to obtain new classes of analytic functions. Approach: Simple technique of Ruscheweyh was used in our preliminary approach to create new bijective convolution linear operator. The preliminary concept of Hadamard products was mentioned and the concept of subordination was given to give sharp proofs for certain sufficient conditions of the linear operator aforementioned. In fact, the subordinating factor sequence was used to derive different types of subordination results. Results: Having the linear operator, subordination theorems were established by using standard concept of subordination. The results reduced to well-known results studied by various researchers. Coefficient bounds and inclusion properties, growth and closure theorems for some subclasses were also obtained. Conclusion: Therefore, many interesting results could be obtained and some applications could be gathered.
Fibonacci Sequence, Recurrence Relations, Discrete Probability Distributions and Linear Convolution
Rajan, Arulalan; Rao, Ashok; Jamadagni, H S
2012-01-01
The classical Fibonacci sequence is known to exhibit many fascinating properties. In this paper, we explore the Fibonacci sequence and integer sequences generated by second order linear recurrence relations with positive integer coe?cients from the point of view of probability distributions that they induce. We obtain the generalizations of some of the known limiting properties of these probability distributions and present certain optimal properties of the classical Fibonacci sequence in this context. In addition, we also look at the self linear convolution of linear recurrence relations with positive integer coefficients. Analysis of self linear convolution is focused towards locating the maximum in the resulting sequence. This analysis, also highlights the influence that the largest positive real root, of the "characteristic equation" of the linear recurrence relations with positive integer coefficients, has on the location of the maximum. In particular, when the largest positive real root is 2,the locatio...
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.
Infimal Convolution Regularisation Functionals of BV and Lp Spaces
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.
Protein secondary structure prediction using deep convolutional neural fields
Sheng Wang; Jian Peng; Jianzhu Ma; Jinbo Xu
2015-01-01
Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF)...
Interleaved Convolutional Code and Its Viterbi Decoder Architecture
2003-01-01
We propose an area-efficient high-speed interleaved Viterbi decoder architecture, which is based on the state-parallel architecture with register exchange path memory structure, for interleaved convolutional code. The state-parallel architecture uses as many add-compare-select (ACS) units as the number of trellis states. By replacing each delay (or storage) element in state metrics memory (or path metrics memory) and path memory (or survival memory) with delays, interleaved Viterbi decoder ...
Design of SVD/SGK Convolution Filters for Image Processing
1980-01-01
of filters by transforming one-dimensional linear phase filters * into two-dimensional linear phase filters . By assuming that the prototype filter is a...linear phase filter , his algorithm transforms a one-dimensional filter h(u) into a two-dimensional filter W (u,v) by means of transformation given by...significance of their implementation of the designed filter is that a large two-dimensional convolution *A linear phase filter implies symmetry of the filter. 13
Efficient Convolutional Neural Network with Binary Quantization Layer
Ravanbakhsh, Mahdyar; Mousavi, Hossein; Nabi, Moin; Marcenaro, Lucio; Regazzoni, Carlo
2016-01-01
In this paper we introduce a novel method for segmentation that can benefit from general semantics of Convolutional Neural Network (CNN). Our segmentation proposes visually and semantically coherent image segments. We use binary encoding of CNN features to overcome the difficulty of the clustering on the high-dimensional CNN feature space. These binary encoding can be embedded into the CNN as an extra layer at the end of the network. This results in real-time segmentation. To the best of our ...
Fusing Deep Convolutional Networks for Large Scale Visual Concept Classification
Ergun, Hilal; Sert, Mustafa
2016-01-01
Deep learning architectures are showing great promise in various computer vision domains including image classification, object detection, event detection and action recognition. In this study, we investigate various aspects of convolutional neural networks (CNNs) from the big data perspective. We analyze recent studies and different network architectures both in terms of running time and accuracy. We present extensive empirical information along with best practices for big data practitioners...
An obstruction for q-deformation of the convolution product
Van Leeuwen, H; van Leeuwen, Hans; Maassen, Hans
1995-01-01
We consider two independent q-Gaussian random variables X and Y and a function f chosen in such a way that f(X) and X have the same distribution. For 0 < q < 1 we find that at least the fourth moments of X + Y and f(X) + Y are different. We conclude that no q-deformed convolution product can exist for functions of independent q-Gaussian random variables.
Contour Detection Using Cost-Sensitive Convolutional Neural Networks
Hwang, Jyh-Jing; Liu, Tyng-Luh
2014-01-01
We address the problem of contour detection via per-pixel classifications of edge point. To facilitate the process, the proposed approach leverages with DenseNet, an efficient implementation of multiscale convolutional neural networks (CNNs), to extract an informative feature vector for each pixel and uses an SVM classifier to accomplish contour detection. The main challenge lies in adapting a pre-trained per-image CNN model for yielding per-pixel image features. We propose to base on the Den...
Image interpolation by two-dimensional parametric cubic convolution.
Shi, Jiazheng; Reichenbach, Stephen E
2006-07-01
Cubic convolution is a popular method for image interpolation. Traditionally, the piecewise-cubic kernel has been derived in one dimension with one parameter and applied to two-dimensional (2-D) images in a separable fashion. However, images typically are statistically nonseparable, which motivates this investigation of nonseparable cubic convolution. This paper derives two new nonseparable, 2-D cubic-convolution kernels. The first kernel, with three parameters (designated 2D-3PCC), is the most general 2-D, piecewise-cubic interpolator defined on [-2, 2] x [-2, 2] with constraints for biaxial symmetry, diagonal (or 90 degrees rotational) symmetry, continuity, and smoothness. The second kernel, with five parameters (designated 2D-5PCC), relaxes the constraint of diagonal symmetry, based on the observation that many images have rotationally asymmetric statistical properties. This paper also develops a closed-form solution for determining the optimal parameter values for parametric cubic-convolution kernels with respect to ensembles of scenes characterized by autocorrelation (or power spectrum). This solution establishes a practical foundation for adaptive interpolation based on local autocorrelation estimates. Quantitative fidelity analyses and visual experiments indicate that these new methods can outperform several popular interpolation methods. An analysis of the error budgets for reconstruction error associated with blurring and aliasing illustrates that the methods improve interpolation fidelity for images with aliased components. For images with little or no aliasing, the methods yield results similar to other popular methods. Both 2D-3PCC and 2D-5PCC are low-order polynomials with small spatial support and so are easy to implement and efficient to apply.
GPU Acceleration of Image Convolution using Spatially-varying Kernel
Hartung, Steven; Shukla, Hemant; Miller, J. Patrick; Pennypacker, Carlton
2012-01-01
Image subtraction in astronomy is a tool for transient object discovery such as asteroids, extra-solar planets and supernovae. To match point spread functions (PSFs) between images of the same field taken at different times a convolution technique is used. Particularly suitable for large-scale images is a computationally intensive spatially-varying kernel. The underlying algorithm is inherently massively parallel due to unique kernel generation at every pixel location. The spatially-varying k...
Nuclear norm regularized convolutional Max Pos@Top machine
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.
Fine-grained representation learning in convolutional autoencoders
Luo, Chang; Wang, Jie
2016-03-01
Convolutional autoencoders (CAEs) have been widely used as unsupervised feature extractors for high-resolution images. As a key component in CAEs, pooling is a biologically inspired operation to achieve scale and shift invariances, and the pooled representation directly affects the CAEs' performance. Fine-grained pooling, which uses small and dense pooling regions, encodes fine-grained visual cues and enhances local characteristics. However, it tends to be sensitive to spatial rearrangements. In most previous works, pooled features were obtained by empirically modulating parameters in CAEs. We see the CAE as a whole and propose a fine-grained representation learning law to extract better fine-grained features. This representation learning law suggests two directions for improvement. First, we probabilistically evaluate the discrimination-invariance tradeoff with fine-grained granularity in the pooled feature maps, and suggest the proper filter scale in the convolutional layer and appropriate whitening parameters in preprocessing step. Second, pooling approaches are combined with the sparsity degree in pooling regions, and we propose the preferable pooling approach. Experimental results on two independent benchmark datasets demonstrate that our representation learning law could guide CAEs to extract better fine-grained features and performs better in multiclass classification task. This paper also provides guidance for selecting appropriate parameters to obtain better fine-grained representation in other convolutional neural networks.
Deep Convolutional Neural Network for Inverse Problems in Imaging
Jin, Kyong Hwan; McCann, Michael T.; Froustey, Emmanuel; Unser, Michael
2017-09-01
In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyper parameter selection. The starting point of our work is the observation that unrolled iterative methods have the form of a CNN (filtering followed by point-wise non-linearity) when the normal operator (H*H, the adjoint of H times H) of the forward model is a convolution. Based on this observation, we propose using direct inversion followed by a CNN to solve normal-convolutional inverse problems. The direct inversion encapsulates the physical model of the system, but leads to artifacts when the problem is ill-posed; the CNN combines multiresolution decomposition and residual learning in order to learn to remove these artifacts while preserving image structure. We demonstrate the performance of the proposed network in sparse-view reconstruction (down to 50 views) on parallel beam X-ray computed tomography in synthetic phantoms as well as in real experimental sinograms. The proposed network outperforms total variation-regularized iterative reconstruction for the more realistic phantoms and requires less than a second to reconstruct a 512 x 512 image on GPU.
A model of traffic signs recognition with convolutional neural network
Hu, Haihe; Li, Yujian; Zhang, Ting; Huo, Yi; Kuang, Wenqing
2016-10-01
In real traffic scenes, the quality of captured images are generally low due to some factors such as lighting conditions, and occlusion on. All of these factors are challengeable for automated recognition algorithms of traffic signs. Deep learning has provided a new way to solve this kind of problems recently. The deep network can automatically learn features from a large number of data samples and obtain an excellent recognition performance. We therefore approach this task of recognition of traffic signs as a general vision problem, with few assumptions related to road signs. We propose a model of Convolutional Neural Network (CNN) and apply the model to the task of traffic signs recognition. The proposed model adopts deep CNN as the supervised learning model, directly takes the collected traffic signs image as the input, alternates the convolutional layer and subsampling layer, and automatically extracts the features for the recognition of the traffic signs images. The proposed model includes an input layer, three convolutional layers, three subsampling layers, a fully-connected layer, and an output layer. To validate the proposed model, the experiments are implemented using the public dataset of China competition of fuzzy image processing. Experimental results show that the proposed model produces a recognition accuracy of 99.01 % on the training dataset, and yield a record of 92% on the preliminary contest within the fourth best.
Electrode geometry for electrostatic generators and motors
Energy Technology Data Exchange (ETDEWEB)
Post, Richard F.
2016-02-23
An electrostatic (ES) device is described with electrodes that improve its performance metrics. Devices include ES generators and ES motors, which are comprised of one or more stators (stationary members) and one or more rotors (rotatable members). The stator and rotors are configured as a pair of concentric cylindrical structures and aligned about a common axis. The stator and rotor are comprised of an ensemble of discrete, longitudinal electrodes, which are axially oriented in an annular arrangement. The shape of the electrodes described herein enables the ES device to function at voltages significantly greater than that of the existing art, resulting in devices with greater power-handling capability and overall efficiency. Electrode shapes include, but are not limited to, rods, corrugated sheets and emulations thereof.
Super-resolution reconstruction algorithm based on adaptive convolution kernel size selection
Gao, Hang; Chen, Qian; Sui, Xiubao; Zeng, Junjie; Zhao, Yao
2016-09-01
Restricted by the detector technology and optical diffraction limit, the spatial resolution of infrared imaging system is difficult to achieve significant improvement. Super-Resolution (SR) reconstruction algorithm is an effective way to solve this problem. Among them, the SR algorithm based on multichannel blind deconvolution (MBD) estimates the convolution kernel only by low resolution observation images, according to the appropriate regularization constraints introduced by a priori assumption, to realize the high resolution image restoration. The algorithm has been shown effective when each channel is prime. In this paper, we use the significant edges to estimate the convolution kernel and introduce an adaptive convolution kernel size selection mechanism, according to the uncertainty of the convolution kernel size in MBD processing. To reduce the interference of noise, we amend the convolution kernel in an iterative process, and finally restore a clear image. Experimental results show that the algorithm can meet the convergence requirement of the convolution kernel estimation.
Preparation of Electrode Array by Electrochemical Etching Based on FEM
Institute of Scientific and Technical Information of China (English)
Minghuan WANG; Di ZHU; Lei WANG
2008-01-01
Process technology of multiple cylindrical micro-pins by wire-electrical discharge machining (wire-EDM) and electrochemical etching was presented.A row of rectangular micro-columns were machined by wire-EDM and then machined into cylindrical shape by electrochemical etching.However,the shape of the multiple electrodes and the consistent sizes of the electrodes row are not easy to be controlled.In the electrochemical process,the shape of the cathode electrode determines the current density distribution on the anode and so the forming of multiple electrodes.This paper proposes a finite element method (FEM) to accurately optimize the electrode profile.The microelectrodes row with uniformity diameters with size from hundreds micrometers to several decades could be fabricated,and mathematical model controlling the shape and diameter of multiple microelectrodes was provided.Furthermore,a good agreement between experimental and theoretical results was confirmed.
Low impedance z-pinch drivers without post-hole convolute current adders.
Energy Technology Data Exchange (ETDEWEB)
Savage, Mark Edward; Seidel, David Bruce; Mendel, Clifford Will, Jr.
2009-09-01
Present-day pulsed-power systems operating in the terawatt regime typically use post-hole convolute current adders to operate at sufficiently low impedance. These adders necessarily involve magnetic nulls that connect the positive and negative electrodes. The resultant loss of magnetic insulation results in electron losses in the vicinity of the nulls that can severely limit the efficiency of the delivery of the system's energy to a load. In this report, we describe an alternate transformer-based approach to obtaining low impedance. The transformer consists of coils whose windings are in parallel rather than in series, and does not suffer from the presence of magnetic nulls. By varying the pitch of the coils windings, the current multiplication ratio can be varied, leading to a more versatile driver. The coupling efficiency of the transformer, its behavior in the presence of electron flow, and its mechanical strength are issues that need to be addressed to evaluate the potential of transformer-based current multiplication as a viable alternative to conventional current adder technology.
The double Mellin-Barnes type integrals and their applications to convolution theory
Hai, Nguyen Thanh
1992-01-01
This book presents new results in the theory of the double Mellin-Barnes integrals popularly known as the general H-function of two variables.A general integral convolution is constructed by the authors and it contains Laplace convolution as a particular case and possesses a factorization property for one-dimensional H-transform. Many examples of convolutions for classical integral transforms are obtained and they can be applied for the evaluation of series and integrals.
An area-efficient 2-D convolution implementation on FPGA for space applications
Gambardella, Giulio; Tiotto, Gabriele; Prinetto, Paolo Ernesto; Di Carlo, Stefano; Indaco, Marco; Rolfo, Daniele
2011-01-01
The 2-D Convolution is an algorithm widely used in image and video processing. Although its computation is simple, its implementation requires a high computational power and an intensive use of memory. Field Programmable Gate Arrays (FPGA) architectures were proposed to accelerate calculations of 2-D Convolution and the use of buffers implemented on FPGAs are used to avoid direct memory access. In this paper we present an implementation of the 2-D Convolution algorithm on a FPGA architecture ...
Rank Based Selection of Electrode Positions for a Multi-Lead ECG Electrode Array.
Hintermuller, Christoph; Fischer, Gerald; Seger, Michael; Pfeifer, Bernhard; Modre, Robert; Tilg, Bernhard
2005-01-01
Methods for noninvasive imaging of electrical function of the heart seem to become a clinical standard procedure the next years. Thus, the overall procedure has to meet clinical requirements as easy and fast application. In this study we propose a new electrode array meeting clinical requirements such as easy to apply and compatibility with routine leads. Within body surface regions of high sensitivity, identified in a prior, information content based study, the number of required electrodes was optimized using effort-gain plots. These plots were generated by applying a so called type one detector criterion. The optimal array was selected from a set of 12 electrode arrays. Each of them consists of two L-shaped regular spaced parts. The optimal array was found by comparing several layouts and electrode densities to the electrode array we use for clinical studies. It consists of 125 electrodes with a regular spacing between 2cm and 3cm.
IMAGE DE-BLURRING USING WIENER DE-CONVOLUTION AND WAVELET FOR DIFFERENT BLURRING KERNEL
M.Tech Research Scholar Shuchi Singh*, Asst Professor Vipul Awasthi, Asst Professor NitinSahu
2016-01-01
Image de-convolution is an active research area of recovering a sharp image after blurring by a convolution. One of the problems in image de-convolution is how to preserve the texture structures while removing blur in presence of noise. Various methods have been used for such as gradient based methods, sparsity based methods, and nonlocal self-similarity methods. In this thesis, we have used the conventional non-blind method of Wiener de-convolution. Further Wavelet denoising has been used to...
Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data
Anirudh, Rushil; Thiagarajan, Jayaraman J.; Bremer, Timo; Kim, Hyojin
2016-03-01
Early detection of lung nodules is currently the one of the most effective ways to predict and treat lung cancer. As a result, the past decade has seen a lot of focus on computer aided diagnosis (CAD) of lung nodules, whose goal is to efficiently detect, segment lung nodules and classify them as being benign or malignant. Effective detection of such nodules remains a challenge due to their arbitrariness in shape, size and texture. In this paper, we propose to employ 3D convolutional neural networks (CNN) to learn highly discriminative features for nodule detection in lieu of hand-engineered ones such as geometric shape or texture. While 3D CNNs are promising tools to model the spatio-temporal statistics of data, they are limited by their need for detailed 3D labels, which can be prohibitively expensive when compared obtaining 2D labels. Existing CAD methods rely on obtaining detailed labels for lung nodules, to train models, which is also unrealistic and time consuming. To alleviate this challenge, we propose a solution wherein the expert needs to provide only a point label, i.e., the central pixel of of the nodule, and its largest expected size. We use unsupervised segmentation to grow out a 3D region, which is used to train the CNN. Using experiments on the SPIE-LUNGx dataset, we show that the network trained using these weak labels can produce reasonably low false positive rates with a high sensitivity, even in the absence of accurate 3D labels.
Finding strong lenses in CFHTLS using convolutional neural networks
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.
Learning Contextual Dependence With Convolutional Hierarchical Recurrent Neural Networks
Zuo, Zhen; Shuai, Bing; Wang, Gang; Liu, Xiao; Wang, Xingxing; Wang, Bing; Chen, Yushi
2016-07-01
Existing deep convolutional neural networks (CNNs) have shown their great success on image classification. CNNs mainly consist of convolutional and pooling layers, both of which are performed on local image areas without considering the dependencies among different image regions. However, such dependencies are very important for generating explicit image representation. In contrast, recurrent neural networks (RNNs) are well known for their ability of encoding contextual information among sequential data, and they only require a limited number of network parameters. General RNNs can hardly be directly applied on non-sequential data. Thus, we proposed the hierarchical RNNs (HRNNs). In HRNNs, each RNN layer focuses on modeling spatial dependencies among image regions from the same scale but different locations. While the cross RNN scale connections target on modeling scale dependencies among regions from the same location but different scales. Specifically, we propose two recurrent neural network models: 1) hierarchical simple recurrent network (HSRN), which is fast and has low computational cost; and 2) hierarchical long-short term memory recurrent network (HLSTM), which performs better than HSRN with the price of more computational cost. In this manuscript, we integrate CNNs with HRNNs, and develop end-to-end convolutional hierarchical recurrent neural networks (C-HRNNs). C-HRNNs not only make use of the representation power of CNNs, but also efficiently encodes spatial and scale dependencies among different image regions. On four of the most challenging object/scene image classification benchmarks, our C-HRNNs achieve state-of-the-art results on Places 205, SUN 397, MIT indoor, and competitive results on ILSVRC 2012.
Continuous speech recognition based on convolutional neural network
Zhang, Qing-qing; Liu, Yong; Pan, Jie-lin; Yan, Yong-hong
2015-07-01
Convolutional Neural Networks (CNNs), which showed success in achieving translation invariance for many image processing tasks, are investigated for continuous speech recognitions in the paper. Compared to Deep Neural Networks (DNNs), which have been proven to be successful in many speech recognition tasks nowadays, CNNs can reduce the NN model sizes significantly, and at the same time achieve even better recognition accuracies. Experiments on standard speech corpus TIMIT showed that CNNs outperformed DNNs in the term of the accuracy when CNNs had even smaller model size.
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...... and testing the classification performance. Our results show that where other classification methods are very sensitive to even small translations, CNN is quite robust to translational variance, making it much more useful in relation to Automatic Target Recognition (ATR) in a real life context....
Convolution Algebra for Fluid Modes with Finite Energy
1992-04-01
PHILLIPS LABORATORY AIR FORCE SYSTEMS COMMAND UNITED STATES AIR FORCE HANSCOM AIR FORCE BASE, MASSACHIUSETTS 01731-5000 94-22604 "This technical report ’-as...with finite spatial and temporal extents. At Boston University, we have developed a full form of wavelet expansion which has the advantage over more...distribution: 00 bX =00 0l if, TZ< VPf (X) = V •a,,,’(x) = E bnb (x) where b, =otherwise (34) V=o ,i=o a._, otherwise 7 The convolution of two
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...... stabilisation and illumination, and images shot with hand-held mobile phones in fields with changing lighting conditions and different soil types. For these 22 species, the network is able to achieve a classification accuracy of 86.2%....
Convolutional neural networks for synthetic aperture radar classification
Profeta, Andrew; Rodriguez, Andres; Clouse, H. Scott
2016-05-01
For electro-optical object recognition, convolutional neural networks (CNNs) are the state-of-the-art. For large datasets, CNNs are able to learn meaningful features used for classification. However, their application to synthetic aperture radar (SAR) has been limited. In this work we experimented with various CNN architectures on the MSTAR SAR dataset. As the input to the CNN we used the magnitude and phase (2 channels) of the SAR imagery. We used the deep learning toolboxes CAFFE and Torch7. Our results show that we can achieve 93% accuracy on the MSTAR dataset using CNNs.
A Fortran 90 code for magnetohydrodynamics. Part 1, Banded convolution
Energy Technology Data Exchange (ETDEWEB)
Walker, D.W.
1992-03-01
This report describes progress in developing a Fortran 90 version of the KITE code for studying plasma instabilities in Tokamaks. In particular, the evaluation of convolution terms appearing in the numerical solution is discussed, and timing results are presented for runs performed on an 8k processor Connection Machine (CM-2). Estimates of the performance on a full-size 64k CM-2 are given, and range between 100 and 200 Mflops. The advantages of having a Fortran 90 version of the KITE code are stressed, and the future use of such a code on the newly announced CM5 and Paragon computers, from Thinking Machines Corporation and Intel, is considered.
Image reconstruction of simulated specimens using convolution back projection
Directory of Open Access Journals (Sweden)
Mohd. Farhan Manzoor
2001-04-01
Full Text Available This paper reports about the reconstruction of cross-sections of composite structures. The convolution back projection (CBP algorithm has been used to capture the attenuation field over the specimen. Five different test cases have been taken up for evaluation. These cases represent varying degrees of complexity. In addition, the role of filters on the nature of the reconstruction errors has also been discussed. Numerical results obtained in the study reveal that CBP algorithm is a useful tool for qualitative as well as quantitative assessment of composite regions encountered in engineering applications.
Faster GPU-based convolutional gridding via thread coarsening
Merry, Bruce
2016-01-01
Convolutional gridding is a processor-intensive step in interferometric imaging. While it is possible to use graphics processing units (GPUs) to accelerate this operation, existing methods use only a fraction of the available flops. We apply thread coarsening to improve the efficiency of an existing algorithm, and observe performance gains of up to $3.2\\times$ for single-polarization gridding and $1.9\\times$ for quad-polarization gridding on a GeForce GTX 980, and smaller but still significant gains on a Radeon R9 290X.
Convolution seal for transition duct in turbine system
Energy Technology Data Exchange (ETDEWEB)
Flanagan, James Scott; LeBegue, Jeffrey Scott; McMahan, Kevin Weston; Dillard, Daniel Jackson; Pentecost, Ronnie Ray
2015-03-10
A turbine system is disclosed. In one embodiment, the turbine system includes a transition duct. The transition duct includes an inlet, an outlet, and a passage extending between the inlet and the outlet and defining a longitudinal axis, a radial axis, and a tangential axis. The outlet of the transition duct is offset from the inlet along the longitudinal axis and the tangential axis. The transition duct further includes an interface member for interfacing with a turbine section. The turbine system further includes a convolution seal contacting the interface member to provide a seal between the interface member and the turbine section.
Convolution seal for transition duct in turbine system
Energy Technology Data Exchange (ETDEWEB)
Flanagan, James Scott; LeBegue, Jeffrey Scott; McMahan, Kevin Weston; Dillard, Daniel Jackson; Pentecost, Ronnie Ray
2015-05-26
A turbine system is disclosed. In one embodiment, the turbine system includes a transition duct. The transition duct includes an inlet, an outlet, and a passage extending between the inlet and the outlet and defining a longitudinal axis, a radial axis, and a tangential axis. The outlet of the transition duct is offset from the inlet along the longitudinal axis and the tangential axis. The transition duct further includes an interface feature for interfacing with an adjacent transition duct. The turbine system further includes a convolution seal contacting the interface feature to provide a seal between the interface feature and the adjacent transition duct.
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
Visualizing Vector Fields Using Line Integral Convolution and Dye Advection
Shen, Han-Wei; Johnson, Christopher R.; Ma, Kwan-Liu
1996-01-01
We present local and global techniques to visualize three-dimensional vector field data. Using the Line Integral Convolution (LIC) method to image the global vector field, our new algorithm allows the user to introduce colored 'dye' into the vector field to highlight local flow features. A fast algorithm is proposed that quickly recomputes the dyed LIC images. In addition, we introduce volume rendering methods that can map the LIC texture on any contour surface and/or translucent region defined by additional scalar quantities, and can follow the advection of colored dye throughout the volume.
Medical image fusion using the convolution of Meridian distributions.
Agrawal, Mayank; Tsakalides, Panagiotis; Achim, Alin
2010-01-01
The aim of this paper is to introduce a novel non-Gaussian statistical model-based approach for medical image fusion based on the Meridian distribution. The paper also includes a new approach to estimate the parameters of generalized Cauchy distribution. The input images are first decomposed using the Dual-Tree Complex Wavelet Transform (DT-CWT) with the subband coefficients modelled as Meridian random variables. Then, the convolution of Meridian distributions is applied as a probabilistic prior to model the fused coefficients, and the weights used to combine the source images are optimised via Maximum Likelihood (ML) estimation. The superior performance of the proposed method is demonstrated using medical images.
Faster GPU-based convolutional gridding via thread coarsening
Merry, B.
2016-07-01
Convolutional gridding is a processor-intensive step in interferometric imaging. While it is possible to use graphics processing units (GPUs) to accelerate this operation, existing methods use only a fraction of the available flops. We apply thread coarsening to improve the efficiency of an existing algorithm, and observe performance gains of up to 3.2 × for single-polarization gridding and 1.9 × for quad-polarization gridding on a GeForce GTX 980, and smaller but still significant gains on a Radeon R9 290X.
Low-dose CT denoising with convolutional neural network
Chen, Hu; Zhang, Weihua; Liao, Peixi; Li, Ke; Zhou, Jiliu; Wang, Ge
2016-01-01
To reduce the potential radiation risk, low-dose CT has attracted much attention. However, simply lowering the radiation dose will lead to significant deterioration of the image quality. In this paper, we propose a noise reduction method for low-dose CT via deep neural network without accessing original projection data. A deep convolutional neural network is trained to transform low-dose CT images towards normal-dose CT images, patch by patch. Visual and quantitative evaluation demonstrates a competing performance of the proposed method.
Digital image correlation based on a fast convolution strategy
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.
Convolutional neural network architectures for predicting DNA–protein binding
Zeng, Haoyang; Edwards, Matthew D.; Liu, Ge; Gifford, David K.
2016-01-01
Motivation: Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA–protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications. Results: We present a systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets. We identify the best-performing architectures by varying CNN width, depth and pooling designs. We find that adding convolutional kernels to a network is important for motif-based tasks. We show the benefits of CNNs in learning rich higher-order sequence features, such as secondary motifs and local sequence context, by comparing network performance on multiple modeling tasks ranging in difficulty. We also demonstrate how careful construction of sequence benchmark datasets, using approaches that control potentially confounding effects like positional or motif strength bias, is critical in making fair comparisons between competing methods. We explore how to establish the sufficiency of training data for these learning tasks, and we have created a flexible cloud-based framework that permits the rapid exploration of alternative neural network architectures for problems in computational biology. Availability and Implementation: All the models analyzed are available at http://cnn.csail.mit.edu. Contact: gifford@mit.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307608
Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks.
Dosovitskiy, Alexey; Fischer, Philipp; Springenberg, Jost Tobias; Riedmiller, Martin; Brox, Thomas
2016-09-01
Deep convolutional networks have proven to be very successful in learning task specific features that allow for unprecedented performance on various computer vision tasks. Training of such networks follows mostly the supervised learning paradigm, where sufficiently many input-output pairs are required for training. Acquisition of large training sets is one of the key challenges, when approaching a new task. In this paper, we aim for generic feature learning and present an approach for training a convolutional network using only unlabeled data. To this end, we train the network to discriminate between a set of surrogate classes. Each surrogate class is formed by applying a variety of transformations to a randomly sampled 'seed' image patch. In contrast to supervised network training, the resulting feature representation is not class specific. It rather provides robustness to the transformations that have been applied during training. This generic feature representation allows for classification results that outperform the state of the art for unsupervised learning on several popular datasets (STL-10, CIFAR-10, Caltech-101, Caltech-256). While features learned with our approach cannot compete with class specific features from supervised training on a classification task, we show that they are advantageous on geometric matching problems, where they also outperform the SIFT descriptor.
Multichannel Convolutional Neural Network for Biological Relation Extraction
Quan, Chanqin; Sun, Xiao; Bai, Wenjun
2016-01-01
The plethora of biomedical relations which are embedded in medical logs (records) demands researchers' attention. Previous theoretical and practical focuses were restricted on traditional machine learning techniques. However, these methods are susceptible to the issues of “vocabulary gap” and data sparseness and the unattainable automation process in feature extraction. To address aforementioned issues, in this work, we propose a multichannel convolutional neural network (MCCNN) for automated biomedical relation extraction. The proposed model has the following two contributions: (1) it enables the fusion of multiple (e.g., five) versions in word embeddings; (2) the need for manual feature engineering can be obviated by automated feature learning with convolutional neural network (CNN). We evaluated our model on two biomedical relation extraction tasks: drug-drug interaction (DDI) extraction and protein-protein interaction (PPI) extraction. For DDI task, our system achieved an overall f-score of 70.2% compared to the standard linear SVM based system (e.g., 67.0%) on DDIExtraction 2013 challenge dataset. And for PPI task, we evaluated our system on Aimed and BioInfer PPI corpus; our system exceeded the state-of-art ensemble SVM system by 2.7% and 5.6% on f-scores. PMID:28053977
Robust Visual Tracking via Convolutional Networks Without Training.
Kaihua Zhang; Qingshan Liu; Yi Wu; Ming-Hsuan Yang
2016-04-01
Deep networks have been successfully applied to visual tracking by learning a generic representation offline from numerous training images. However, the offline training is time-consuming and the learned generic representation may be less discriminative for tracking specific objects. In this paper, we present that, even without offline training with a large amount of auxiliary data, simple two-layer convolutional networks can be powerful enough to learn robust representations for visual tracking. In the first frame, we extract a set of normalized patches from the target region as fixed filters, which integrate a series of adaptive contextual filters surrounding the target to define a set of feature maps in the subsequent frames. These maps measure similarities between each filter and useful local intensity patterns across the target, thereby encoding its local structural information. Furthermore, all the maps together form a global representation, via which the inner geometric layout of the target is also preserved. A simple soft shrinkage method that suppresses noisy values below an adaptive threshold is employed to de-noise the global representation. Our convolutional networks have a lightweight structure and perform favorably against several state-of-the-art methods on the recent tracking benchmark data set with 50 challenging videos.
Convolutional neural network features based change detection in satellite images
Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong
2016-07-01
With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.
Convolution approach to the piNN system
Blankleider, B
1994-01-01
The unitary NN-piNN model contains a serious theoretical flaw: unitarity is obtained at the price of having to use an effective piNN coupling constant that is smaller than the experimental one. This is but one aspect of a more general renormalization problem whose origin lies in the truncation of Hilbert space used to derive the equations. Here we present a new theoretical approach to the piNN problem where unitary equations are obtained without having to truncate Hilbert space. Indeed, the only approximation made is the neglect of connected three-body forces. As all possible dressings of one-particle propagators and vertices are retained in our model, we overcome the renormalization problems inherent in previous piNN theories. The key element of our derivation is the use of convolution integrals that have enabled us to sum all the possible disconnected time-ordered graphs. We also discuss how the convolution method can be extended to sum all the time orderings of a connected graph. This has enabled us to cal...
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.
A Mathematical Motivation for Complex-Valued Convolutional Networks.
Tygert, Mark; Bruna, Joan; Chintala, Soumith; LeCun, Yann; Piantino, Serkan; Szlam, Arthur
2016-05-01
A complex-valued convolutional network (convnet) implements the repeated application of the following composition of three operations, recursively applying the composition to an input vector of nonnegative real numbers: (1) convolution with complex-valued vectors, followed by (2) taking the absolute value of every entry of the resulting vectors, followed by (3) local averaging. For processing real-valued random vectors, complex-valued convnets can be viewed as data-driven multiscale windowed power spectra, data-driven multiscale windowed absolute spectra, data-driven multiwavelet absolute values, or (in their most general configuration) data-driven nonlinear multiwavelet packets. Indeed, complex-valued convnets can calculate multiscale windowed spectra when the convnet filters are windowed complex-valued exponentials. Standard real-valued convnets, using rectified linear units (ReLUs), sigmoidal (e.g., logistic or tanh) nonlinearities, or max pooling, for example, do not obviously exhibit the same exact correspondence with data-driven wavelets (whereas for complex-valued convnets, the correspondence is much more than just a vague analogy). Courtesy of the exact correspondence, the remarkably rich and rigorous body of mathematical analysis for wavelets applies directly to (complex-valued) convnets.
Convolutional Network Coding Based on Matrix Power Series Representation
Guo, Wangmei; Sun, Qifu Tyler
2011-01-01
In this paper, convolutional network coding is formulated by means of matrix power series representation of the local encoding kernel (LEK) matrices and global encoding kernel (GEK) matrices to establish its theoretical fundamentals for practical implementations. From the encoding perspective, the GEKs of a convolutional network code (CNC) are shown to be uniquely determined by its LEK matrix $K(z)$ if $K_0$, the constant coefficient matrix of $K(z)$, is nilpotent. This will simplify the CNC design because a nilpotent $K_0$ suffices to guarantee a unique set of GEKs. Besides, the relation between coding topology and $K(z)$ is also discussed. From the decoding perspective, the main theme is to justify that the first $L+1$ terms of the GEK matrix $F(z)$ at a sink $r$ suffice to check whether the code is decodable at $r$ with delay $L$ and to start decoding if so. The concomitant decoding scheme avoids dealing with $F(z)$, which may contain infinite terms, as a whole and hence reduces the complexity of decodabil...
Fluence-convolution broad-beam (FCBB) dose calculation.
Lu, Weiguo; Chen, Mingli
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(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.
Classification of Histology Sections via Multispectral Convolutional Sparse Coding.
Zhou, Yin; Chang, Hang; Barner, Kenneth; Spellman, Paul; Parvin, Bahram
2014-06-01
Image-based classification of histology sections plays an important role in predicting clinical outcomes. However this task is very challenging due to the presence of large technical variations (e.g., fixation, staining) and biological heterogeneities (e.g., cell type, cell state). In the field of biomedical imaging, for the purposes of visualization and/or quantification, different stains are typically used for different targets of interest (e.g., cellular/subcellular events), which generates multi-spectrum data (images) through various types of microscopes and, as a result, provides the possibility of learning biological-component-specific features by exploiting multispectral information. We propose a multispectral feature learning model that automatically learns a set of convolution filter banks from separate spectra to efficiently discover the intrinsic tissue morphometric signatures, based on convolutional sparse coding (CSC). The learned feature representations are then aggregated through the spatial pyramid matching framework (SPM) and finally classified using a linear SVM. The proposed system has been evaluated using two large-scale tumor cohorts, collected from The Cancer Genome Atlas (TCGA). Experimental results show that the proposed model 1) outperforms systems utilizing sparse coding for unsupervised feature learning (e.g., PSD-SPM [5]); 2) is competitive with systems built upon features with biological prior knowledge (e.g., SMLSPM [4]).
Convolutional Neural Network Based Fault Detection for Rotating Machinery
Janssens, Olivier; Slavkovikj, Viktor; Vervisch, Bram; Stockman, Kurt; Loccufier, Mia; Verstockt, Steven; Van de Walle, Rik; Van Hoecke, Sofie
2016-09-01
Vibration analysis is a well-established technique for condition monitoring of rotating machines as the vibration patterns differ depending on the fault or machine condition. Currently, mainly manually-engineered features, such as the ball pass frequencies of the raceway, RMS, kurtosis an crest, are used for automatic fault detection. Unfortunately, engineering and interpreting such features requires a significant level of human expertise. To enable non-experts in vibration analysis to perform condition monitoring, the overhead of feature engineering for specific faults needs to be reduced as much as possible. Therefore, in this article we propose a feature learning model for condition monitoring based on convolutional neural networks. The goal of this approach is to autonomously learn useful features for bearing fault detection from the data itself. Several types of bearing faults such as outer-raceway faults and lubrication degradation are considered, but also healthy bearings and rotor imbalance are included. For each condition, several bearings are tested to ensure generalization of the fault-detection system. Furthermore, the feature-learning based approach is compared to a feature-engineering based approach using the same data to objectively quantify their performance. The results indicate that the feature-learning system, based on convolutional neural networks, significantly outperforms the classical feature-engineering based approach which uses manually engineered features and a random forest classifier. The former achieves an accuracy of 93.61 percent and the latter an accuracy of 87.25 percent.
Asymptotic formula for the moments of Bernoulli convolutions
Directory of Open Access Journals (Sweden)
E. A. Timofeev
2016-01-01
Full Text Available Abstract. Asymptotic Formula for the Moments of Bernoulli Convolutions Timofeev E. A. Received February 8, 2016 For each λ, 0 < λ < 1, we define a random variable ∞ Yλ =(1−λξnλn, n=0 where ξn are independent random variables with P{ξn =0}=P{ξn =1}= 1. 2 The distribution of Yλ is called a symmetric Bernoulli convolution. The main result of this paper is Mn =EYλn =nlogλ22logλ(1−λ+0.5logλ2−0.5eτ(−logλn1+O(n−0.99, where is a 1-periodic function, 1k2πikx τ(x= kα −lnλ e k̸=0 1 (1 − λ2πit(1 − 22πitπ−2πit2−2πitζ(2πit, 2i sh(π2t α(t = − and ζ(z is the Riemann zeta function. The article is published in the author’s wording.
Transforming Musical Signals through a Genre Classifying Convolutional Neural Network
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.
Photon beam convolution using polyenergetic energy deposition kernels
Energy Technology Data Exchange (ETDEWEB)
Hoban, P.W.; Murray, D.C.; Round, W.H. (Waikato Univ., Hamilton (New Zealand). Dept. of Physics)
1994-04-01
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, [mu], 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 [mu][sub ab]/[mu] 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).
Single-Cell Phenotype Classification Using Deep Convolutional Neural Networks.
Dürr, Oliver; Sick, Beate
2016-10-01
Deep learning methods are currently outperforming traditional state-of-the-art computer vision algorithms in diverse applications and recently even surpassed human performance in object recognition. Here we demonstrate the potential of deep learning methods to high-content screening-based phenotype classification. We trained a deep learning classifier in the form of convolutional neural networks with approximately 40,000 publicly available single-cell images from samples treated with compounds from four classes known to lead to different phenotypes. The input data consisted of multichannel images. The construction of appropriate feature definitions was part of the training and carried out by the convolutional network, without the need for expert knowledge or handcrafted features. We compare our results against the recent state-of-the-art pipeline in which predefined features are extracted from each cell using specialized software and then fed into various machine learning algorithms (support vector machine, Fisher linear discriminant, random forest) for classification. The performance of all classification approaches is evaluated on an untouched test image set with known phenotype classes. Compared to the best reference machine learning algorithm, the misclassification rate is reduced from 8.9% to 6.6%.
Enhancing Neutron Beam Production with a Convoluted Moderator
Energy Technology Data Exchange (ETDEWEB)
Iverson, Erik B [ORNL; Baxter, David V [Center for the Exploration of Energy and Matter, Indiana University; Muhrer, Guenter [Los Alamos National Laboratory (LANL); Ansell, Stuart [ISIS Facility, Rutherford Appleton Laboratory (ISIS); Gallmeier, Franz X [ORNL; Dalgliesh, Robert [ISIS Facility, Rutherford Appleton Laboratory (ISIS); Lu, Wei [ORNL; Kaiser, Helmut [Center for the Exploration of Energy and Matter, Indiana University
2014-10-01
We describe a new concept for a neutron moderating assembly resulting in the more efficient production of slow neutron beams. The Convoluted Moderator, a heterogeneous stack of interleaved moderating material and nearly transparent single-crystal spacers, is a directionally-enhanced neutron beam source, improving beam effectiveness over an angular range comparable to the range accepted by neutron beam lines and guides. We have demonstrated gains of 50% in slow neutron intensity for a given fast neutron production rate while simultaneously reducing the wavelength-dependent emission time dispersion by 25%, both coming from a geometric effect in which the neutron beam lines view a large surface area of moderating material in a relatively small volume. Additionally, we have confirmed a Bragg-enhancement effect arising from coherent scattering within the single-crystal spacers. We have not observed hypothesized refractive effects leading to additional gains at long wavelength. In addition to confirmation of the validity of the Convoluted Moderator concept, our measurements provide a series of benchmark experiments suitable for developing simulation and analysis techniques for practical optimization and eventual implementation at slow neutron source facilities.
Classifications of multispectral colorectal cancer tissues using convolution neural network
Directory of Open Access Journals (Sweden)
Hawraa Haj-Hassan
2017-01-01
Full Text Available Background: Colorectal cancer (CRC is the third most common cancer among men and women. Its diagnosis in early stages, typically done through the analysis of colon biopsy images, can greatly improve the chances of a successful treatment. This paper proposes to use convolution neural networks (CNNs to predict three tissue types related to the progression of CRC: benign hyperplasia (BH, intraepithelial neoplasia (IN, and carcinoma (Ca. Methods: Multispectral biopsy images of thirty CRC patients were retrospectively analyzed. Images of tissue samples were divided into three groups, based on their type (10 BH, 10 IN, and 10 Ca. An active contour model was used to segment image regions containing pathological tissues. Tissue samples were classified using a CNN containing convolution, max-pooling, and fully-connected layers. Available tissue samples were split into a training set, for learning the CNN parameters, and test set, for evaluating its performance. Results: An accuracy of 99.17% was obtained from segmented image regions, outperforming existing approaches based on traditional feature extraction, and classification techniques. Conclusions: Experimental results demonstrate the effectiveness of CNN for the classification of CRC tissue types, in particular when using presegmented regions of interest.
An optimal nonorthogonal separation of the anisotropic Gaussian convolution filter.
Lampert, Christoph H; Wirjadi, Oliver
2006-11-01
We give an analytical and geometrical treatment of what it means to separate a Gaussian kernel along arbitrary axes in R(n), and we present a separation scheme that allows us to efficiently implement anisotropic Gaussian convolution filters for data of arbitrary dimensionality. Based on our previous analysis we show that this scheme is optimal with regard to the number of memory accesses and interpolation operations needed. The proposed method relies on nonorthogonal convolution axes and works completely in image space. Thus, it avoids the need for a fast Fourier transform (FFT)-subroutine. Depending on the accuracy and speed requirements, different interpolation schemes and methods to implement the one-dimensional Gaussian (finite impulse response and infinite impulse response) can be integrated. Special emphasis is put on analyzing the performance and accuracy of the new method. In particular, we show that without any special optimization of the source code, it can perform anisotropic Gaussian filtering faster than methods relying on the FFT.
Thermalnet: a Deep Convolutional Network for Synthetic Thermal Image Generation
Kniaz, V. V.; Gorbatsevich, V. S.; Mizginov, V. A.
2017-05-01
Deep convolutional neural networks have dramatically changed the landscape of the modern computer vision. Nowadays methods based on deep neural networks show the best performance among image recognition and object detection algorithms. While polishing of network architectures received a lot of scholar attention, from the practical point of view the preparation of a large image dataset for a successful training of a neural network became one of major challenges. This challenge is particularly profound for image recognition in wavelengths lying outside the visible spectrum. For example no infrared or radar image datasets large enough for successful training of a deep neural network are available to date in public domain. Recent advances of deep neural networks prove that they are also capable to do arbitrary image transformations such as super-resolution image generation, grayscale image colorisation and imitation of style of a given artist. Thus a natural question arise: how could be deep neural networks used for augmentation of existing large image datasets? This paper is focused on the development of the Thermalnet deep convolutional neural network for augmentation of existing large visible image datasets with synthetic thermal images. The Thermalnet network architecture is inspired by colorisation deep neural networks.
Video-based face recognition via convolutional neural networks
Bao, Tianlong; Ding, Chunhui; Karmoshi, Saleem; Zhu, Ming
2017-06-01
Face recognition has been widely studied recently while video-based face recognition still remains a challenging task because of the low quality and large intra-class variation of video captured face images. In this paper, we focus on two scenarios of video-based face recognition: 1)Still-to-Video(S2V) face recognition, i.e., querying a still face image against a gallery of video sequences; 2)Video-to-Still(V2S) face recognition, in contrast to S2V scenario. A novel method was proposed in this paper to transfer still and video face images to an Euclidean space by a carefully designed convolutional neural network, then Euclidean metrics are used to measure the distance between still and video images. Identities of still and video images that group as pairs are used as supervision. In the training stage, a joint loss function that measures the Euclidean distance between the predicted features of training pairs and expanding vectors of still images is optimized to minimize the intra-class variation while the inter-class variation is guaranteed due to the large margin of still images. Transferred features are finally learned via the designed convolutional neural network. Experiments are performed on COX face dataset. Experimental results show that our method achieves reliable performance compared with other state-of-the-art methods.
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.
Deep Convolutional Neural Networks for large-scale speech tasks.
Sainath, Tara N; Kingsbury, Brian; Saon, George; Soltau, Hagen; Mohamed, Abdel-rahman; Dahl, George; Ramabhadran, Bhuvana
2015-04-01
Convolutional Neural Networks (CNNs) are an alternative type of neural network that can be used to reduce spectral variations and model spectral correlations which exist in signals. Since speech signals exhibit both of these properties, we hypothesize that CNNs are a more effective model for speech compared to Deep Neural Networks (DNNs). In this paper, we explore applying CNNs to large vocabulary continuous speech recognition (LVCSR) tasks. First, we determine the appropriate architecture to make CNNs effective compared to DNNs for LVCSR tasks. Specifically, we focus on how many convolutional layers are needed, what is an appropriate number of hidden units, what is the best pooling strategy. Second, investigate how to incorporate speaker-adapted features, which cannot directly be modeled by CNNs as they do not obey locality in frequency, into the CNN framework. Third, given the importance of sequence training for speech tasks, we introduce a strategy to use ReLU+dropout during Hessian-free sequence training of CNNs. Experiments on 3 LVCSR tasks indicate that a CNN with the proposed speaker-adapted and ReLU+dropout ideas allow for a 12%-14% relative improvement in WER over a strong DNN system, achieving state-of-the art results in these 3 tasks.
Coronary artery calcification (CAC) classification with deep convolutional neural networks
Liu, Xiuming; Wang, Shice; Deng, Yufeng; Chen, Kuan
2017-03-01
Coronary artery calcification (CAC) is a typical marker of the coronary artery disease, which is one of the biggest causes of mortality in the U.S. This study evaluates the feasibility of using a deep convolutional neural network (DCNN) to automatically detect CAC in X-ray images. 1768 posteroanterior (PA) view chest X-Ray images from Sichuan Province Peoples Hospital, China were collected retrospectively. Each image is associated with a corresponding diagnostic report written by a trained radiologist (907 normal, 861 diagnosed with CAC). Onequarter of the images were randomly selected as test samples; the rest were used as training samples. DCNN models consisting of 2,4,6 and 8 convolutional layers were designed using blocks of pre-designed CNN layers. Each block was implemented in Theano with Graphics Processing Units (GPU). Human-in-the-loop learning was also performed on a subset of 165 images with framed arteries by trained physicians. The results from the DCNN models were compared to the diagnostic reports. The average diagnostic accuracies for models with 2,4,6,8 layers were 0.85, 0.87, 0.88, and 0.89 respectively. The areas under the curve (AUC) were 0.92, 0.95, 0.95, and 0.96. As the model grows deeper, the AUC or diagnostic accuracies did not have statistically significant changes. The results of this study indicate that DCNN models have promising potential in the field of intelligent medical image diagnosis practice.
Using convolutional decoding to improve time delay and phase estimation in digital communications
Energy Technology Data Exchange (ETDEWEB)
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.
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....
Scattering correction based on regularization de-convolution for Cone-Beam CT
Xie, Shi-peng
2016-01-01
In Cone-Beam CT (CBCT) imaging systems, the scattering phenomenon has a significant impact on the reconstructed image and is a long-lasting research topic on CBCT. In this paper, we propose a simple, novel and fast approach for mitigating scatter artifacts and increasing the image contrast in CBCT, belonging to the category of convolution-based method in which the projected data is de-convolved with a convolution kernel. A key step in this method is how to determine the convolution kernel. Compared with existing methods, the estimation of convolution kernel is based on bi-l1-l2-norm regularization imposed on both the intermediate the known scatter contaminated projection images and the convolution kernel. Our approach can reduce the scatter artifacts from 12.930 to 2.133.
The effect of whitening transformation on pooling operations in convolutional autoencoders
Li, Zuhe; Fan, Yangyu; Liu, Weihua
2015-12-01
Convolutional autoencoders (CAEs) are unsupervised feature extractors for high-resolution images. In the pre-processing step, whitening transformation has widely been adopted to remove redundancy by making adjacent pixels less correlated. Pooling is a biologically inspired operation to reduce the resolution of feature maps and achieve spatial invariance in convolutional neural networks. Conventionally, pooling methods are mainly determined empirically in most previous work. Therefore, our main purpose is to study the relationship between whitening processing and pooling operations in convolutional autoencoders for image classification. We propose an adaptive pooling approach based on the concepts of information entropy to test the effect of whitening on pooling in different conditions. Experimental results on benchmark datasets indicate that the performance of pooling strategies is associated with the distribution of feature activations, which can be affected by whitening processing. This provides guidance for the selection of pooling methods in convolutional autoencoders and other convolutional neural networks.
Complete electrode model in EEG: relationship and differences to the point electrode model
Pursiainen, S.; Lucka, F.; Wolters, C. H.
2012-02-01
In electroencephalography (EEG) source analysis, a primary current density generated by the neural activity of the brain is reconstructed from external electrode voltage measurements. This paper focuses on accurate and effective simulations of EEG through the complete electrode model (CEM). The CEM allows for the incorporation of the electrode size, shape and effective contact impedance into the forward simulation. Both neural currents in the brain and shunting currents between the electrodes and the skin can affect the measured voltages in the CEM. The goal of this study was to investigate the CEM by comparing it with the point electrode model (PEM), which is the current standard electrode model for EEG. We used a three-dimensional, realistic and high-resolution finite element head model as the reference computational domain in the comparison. The PEM could be formulated as a limit of the CEM, in which the effective impedance of each electrode goes to infinity and the size tends to zero. Numerical results concerning the forward and inverse errors and electrode voltage strengths with different impedances and electrode sizes are presented. Based on the results obtained, limits for extremely high and low impedance values of the shunting currents are suggested.
Method for manufacturing magnetohydrodynamic electrodes
Killpatrick, Don H.; Thresh, Henry R.
1982-01-01
A method of manufacturing electrodes for use in a magnetohydrodynamic (MHD) generator comprising the steps of preparing a billet having a core 10 of a first metal, a tubular sleeve 12 of a second metal, and an outer sheath 14, 16, 18 of an extrusile metal; evacuating the space between the parts of the assembled billet; extruding the billet; and removing the outer jacket 14. The extruded bar may be made into electrodes by cutting and bending to the shape required for an MDH channel frame. The method forms a bond between the first metal of the core 10 and the second metal of the sleeve 12 strong enough to withstand a hot and corrosive environment.
FUEL CELL ELECTRODE MATERIALS. RAW MATERIAL SELECTION INFLUENCES POLARIZATION BUT IS NOT A SINGLE CONTROLLING FACTOR. AVAILABLE...DATA INDICATES THAT AN INTERRELATIONSHIP OF POROSITY, AVERAGE PORE VOLUME, AND PERMEABILITY CONTRIBUTES TO ELECTRODE FUEL CELL BEHAVIOR.
Materials analyses and electrochemical impedance of implantable metal electrodes.
Howlader, Matiar M R; Ul Alam, Arif; Sharma, Rahul P; Deen, M Jamal
2015-04-21
Implantable electrodes with high flexibility, high mechanical fixation and low electrochemical impedance are desirable for neuromuscular activation because they provide safe, effective and stable stimulation. In this paper, we report on detailed materials and electrical analyses of three metal implantable electrodes - gold (Au), platinum (Pt) and titanium (Ti) - using X-ray photoelectron spectroscopy (XPS), scanning acoustic microscopy, drop shape analysis and electrochemical impedance spectroscopy. We investigated the cause of changes in electrochemical impedance of long-term immersed Au, Pt and Ti electrodes on liquid crystal polymers (LCPs) in phosphate buffered saline (PBS). We analyzed the surface wettability, surface and interface defects and the elemental depth profile of the electrode-adhesion layers on the LCP. The impedance of the electrodes decreased at lower frequencies, but increased at higher frequencies compared with that of the short-term immersion. The increase of impedances was influenced by the oxidation of the electrode/adhesion-layers that affected the double layer capacitance behavior of the electrode/PBS. The oxidation of the adhesion layer for all the electrodes was confirmed by XPS. Alkali ions (sodium) were adsorbed on the Au and Pt surfaces, but diffused into the Ti electrode and LCPs. The Pt electrode showed a higher sensitivity to surface and interface defects than that of Ti and Au electrodes. These findings may be useful when designing electrodes for long-term implantable devices.
An electrode for a chemical power source
Energy Technology Data Exchange (ETDEWEB)
Matsumoto, I.; Ivaki, T.; Yanashkara, N.
1983-07-29
A metal which has a spongy structure is used as the electrode base for the chemical current source (KhIT). The majority of pores has a spindle shaped form and is identically oriented along the direction of the long axis. The size of the pores along the short diameter is 5 to 30 percent of the pore size along the long diameter. The length of a pore along the long axis is 100 to 500 micrometers. In making the electrode the pores are filled with an active mass. The electrode is manufactured in the form of a strip which is turned in a direction perpendicular to the long axis of the pores. The electrodes have excellent characteristics.
Antiferroelectric Shape Memory Ceramics
Directory of Open Access Journals (Sweden)
Kenji Uchino
2016-05-01
Full Text Available Antiferroelectrics (AFE can exhibit a “shape memory function controllable by electric field”, with huge isotropic volumetric expansion (0.26% associated with the AFE to Ferroelectric (FE phase transformation. Small inverse electric field application can realize the original AFE phase. The response speed is quick (2.5 ms. In the Pb0.99Nb0.02[(Zr0.6Sn0.41-yTiy]0.98O3 (PNZST system, the shape memory function is observed in the intermediate range between high temperature AFE and low temperature FE, or low Ti-concentration AFE and high Ti-concentration FE in the composition. In the AFE multilayer actuators (MLAs, the crack is initiated in the center of a pair of internal electrodes under cyclic electric field, rather than the edge area of the internal electrodes in normal piezoelectric MLAs. The two-sublattice polarization coupling model is proposed to explain: (1 isotropic volume expansion during the AFE-FE transformation; and (2 piezoelectric anisotropy. We introduce latching relays and mechanical clampers as possible unique applications of shape memory ceramics.
Microresonator electrode design
Olsson, III, Roy H.; Wojciechowski, Kenneth; Branch, Darren W.
2016-05-10
A microresonator with an input electrode and an output electrode patterned thereon is described. The input electrode includes a series of stubs that are configured to isolate acoustic waves, such that the waves are not reflected into the microresonator. Such design results in reduction of spurious modes corresponding to the microresonator.
Eggen, Per-Odd
2009-01-01
This article describes the construction of an inexpensive, robust, and simple hydrogen electrode, as well as the use of this electrode to measure "standard" potentials. In the experiment described here the students can measure the reduction potentials of metal-metal ion pairs directly, without using a secondary reference electrode. Measurements…
The Composite Insertion Electrode
DEFF Research Database (Denmark)
Atlung, Sven; Zachau-Christiansen, Birgit; West, Keld
1984-01-01
The specific energy obtainable by discharge of porous insertion electrodes is limited by electrolyte depletion in thepores. This can be overcome using a solid ion conductor as electrolyte. The term "composite" is used to distinguishthese electrodes from porous electrodes with liquid electrolyte...
Eggen, Per-Odd
2009-01-01
This article describes the construction of an inexpensive, robust, and simple hydrogen electrode, as well as the use of this electrode to measure "standard" potentials. In the experiment described here the students can measure the reduction potentials of metal-metal ion pairs directly, without using a secondary reference electrode. Measurements…
Portnoy, W. M.; David, R. M.
1973-01-01
Insulated, capacitively coupled electrode does not require electrolyte paste for attachment. Other features of electrode include wide range of nontoxic material that may be employed for dielectric because of sputtering technique used. Also, electrode size is reduced because there is no need for external compensating networks with FET operational amplifier.
A convolution model of rock bed thermal storage units
Sowell, E. F.; Curry, R. L.
1980-01-01
A method is presented whereby a packed-bed thermal storage unit is dynamically modeled for bi-directional flow and arbitrary input flow stream temperature variations. The method is based on the principle of calculating the output temperature as the sum of earlier input temperatures, each multiplied by a predetermined 'response factor', i.e., discrete convolution. A computer implementation of the scheme, in the form of a subroutine for a widely used solar simulation program (TRNSYS) is described and numerical results compared with other models. Also, a method for efficient computation of the required response factors is described; this solution is for a triangular input pulse, previously unreported, although the solution method is also applicable for other input functions. This solution requires a single integration of a known function which is easily carried out numerically to the required precision.
Drug-Drug Interaction Extraction via Convolutional Neural Networks
Directory of Open Access Journals (Sweden)
Shengyu Liu
2016-01-01
Full Text Available Drug-drug interaction (DDI extraction as a typical relation extraction task in natural language processing (NLP has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM with a large number of manually defined features. Recently, convolutional neural networks (CNN, a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extraction. Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction. The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%.
Online multipath convolutional coding for real-time transmission
Thai, Tuan Tran; Lacan, Jerome
2012-01-01
Most of multipath multimedia streaming proposals use Forward Error Correction (FEC) approach to protect from packet losses. However, FEC does not sustain well burst of losses even when packets from a given FEC block are spread over multiple paths. In this article, we propose an online multipath convolutional coding for real-time multipath streaming based on an on-the-fly coding scheme called Tetrys. We evaluate the benefits brought out by this coding scheme inside an existing FEC multipath load splitting proposal known as Encoded Multipath Streaming (EMS). We demonstrate that Tetrys consistently outperforms FEC in both uniform and burst losses with EMS scheme. We also propose a modification of the standard EMS algorithm that greatly improves the performance in terms of packet recovery. Finally, we analyze different spreading policies of the Tetrys redundancy traffic between available paths and observe that the longer propagation delay path should be preferably used to carry repair packets.
Drug-Drug Interaction Extraction via Convolutional Neural Networks.
Liu, Shengyu; Tang, Buzhou; Chen, Qingcai; Wang, Xiaolong
2016-01-01
Drug-drug interaction (DDI) extraction as a typical relation extraction task in natural language processing (NLP) has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM) with a large number of manually defined features. Recently, convolutional neural networks (CNN), a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extraction. Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction. The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%.
Convolutional Neural Networks Applied to House Numbers Digit Classification
Sermanet, Pierre; LeCun, Yann
2012-01-01
We classify digits of real-world house numbers using convolutional neural networks (ConvNets). ConvNets are hierarchical feature learning neural networks whose structure is biologically inspired. Unlike many popular vision approaches that are hand-designed, ConvNets can automatically learn a unique set of features optimized for a given task. We augmented the traditional ConvNet architecture by learning multi-stage features and by using Lp pooling and establish a new state-of-the-art of 94.85% accuracy on the SVHN dataset (45.2% error improvement). Furthermore, we analyze the benefits of different pooling methods and multi-stage features in ConvNets. The source code and a tutorial are available at eblearn.sf.net.
INVARIANT DESCRIPTOR LEARNING USING A SIAMESE CONVOLUTIONAL NEURAL NETWORK
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L. Chen
2016-06-01
Full Text Available In this paper we describe learning of a descriptor based on the Siamese Convolutional Neural Network (CNN architecture and evaluate our results on a standard patch comparison dataset. The descriptor learning architecture is composed of an input module, a Siamese CNN descriptor module and a cost computation module that is based on the L2 Norm. The cost function we use pulls the descriptors of matching patches close to each other in feature space while pushing the descriptors for non-matching pairs away from each other. Compared to related work, we optimize the training parameters by combining a moving average strategy for gradients and Nesterov's Accelerated Gradient. Experiments show that our learned descriptor reaches a good performance and achieves state-of-art results in terms of the false positive rate at a 95 % recall rate on standard benchmark datasets.
Hybrid Algorithm for the Optimization of Training Convolutional Neural Network
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Hayder M. Albeahdili
2015-10-01
Full Text Available The training optimization processes and efficient fast classification are vital elements in the development of a convolution neural network (CNN. Although stochastic gradient descend (SGD is a Prevalence algorithm used by many researchers for the optimization of training CNNs, it has vast limitations. In this paper, it is endeavor to diminish and tackle drawbacks inherited from SGD by proposing an alternate algorithm for CNN training optimization. A hybrid of genetic algorithm (GA and particle swarm optimization (PSO is deployed in this work. In addition to SGD, PSO and genetic algorithm (PSO-GA are also incorporated as a combined and efficient mechanism in achieving non trivial solutions. The proposed unified method achieves state-of-the-art classification results on the different challenge benchmark datasets such as MNIST, CIFAR-10, and SVHN. Experimental results showed that the results outperform and achieve superior results to most contemporary approaches.
Facial Expression Recognition Using 3D Convolutional Neural Network
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Young-Hyen Byeon
2014-12-01
Full Text Available This paper is concerned with video-based facial expression recognition frequently used in conjunction with HRI (Human-Robot Interaction that can naturally interact between human and robot. For this purpose, we design a 3D-CNN(3D Convolutional Neural Networks by augmenting dimensionality reduction methods such as PCA(Principal Component Analysis and TMPCA(Tensor-based Multilinear Principal Component Analysis to recognize simultaneously the successive frames with facial expression images obtained through video camera. The 3D-CNN can achieve some degree of shift and deformation invariance using local receptive fields and spatial subsampling through dimensionality reduction of redundant CNN’s output. The experimental results on video-based facial expression database reveal that the presented method shows a good performance in comparison to the conventional methods such as PCA and TMPCA.
FPGA Prototyping of RNN Decoder for Convolutional Codes
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Salcic Zoran
2006-01-01
Full Text Available This paper presents prototyping of a recurrent type neural network (RNN convolutional decoder using system-level design specification and design flow that enables easy mapping to the target FPGA architecture. Implementation and the performance measurement results have shown that an RNN decoder for hard-decision decoding coupled with a simple hard-limiting neuron activation function results in a very low complexity, which easily fits into standard Altera FPGA. Moreover, the design methodology allowed modeling of complete testbed for prototyping RNN decoders in simulation and real-time environment (same FPGA, thus enabling evaluation of BER performance characteristics of the decoder for various conditions of communication channel in real time.
Fast convolution with free-space Green's functions
Vico, Felipe; Greengard, Leslie; Ferrando, Miguel
2016-10-01
We introduce a fast algorithm for computing volume potentials - that is, the convolution of a translation invariant, free-space Green's function with a compactly supported source distribution defined on a uniform grid. The algorithm relies on regularizing the Fourier transform of the Green's function by cutting off the interaction in physical space beyond the domain of interest. This permits the straightforward application of trapezoidal quadrature and the standard FFT, with superalgebraic convergence for smooth data. Moreover, the method can be interpreted as employing a Nystrom discretization of the corresponding integral operator, with matrix entries which can be obtained explicitly and rapidly. This is of use in the design of preconditioners or fast direct solvers for a variety of volume integral equations. The method proposed permits the computation of any derivative of the potential, at the cost of an additional FFT.
Structured learning via convolutional neural networks for vehicle detection
Maqueda, Ana I.; del Blanco, Carlos R.; Jaureguizar, Fernando; García, Narciso
2017-05-01
One of the main tasks in a vision-based traffic monitoring system is the detection of vehicles. Recently, deep neural networks have been successfully applied to this end, outperforming previous approaches. However, most of these works generally rely on complex and high-computational region proposal networks. Others employ deep neural networks as a segmentation strategy to achieve a semantic representation of the object of interest, which has to be up-sampled later. In this paper, a new design for a convolutional neural network is applied to vehicle detection in highways for traffic monitoring. This network generates a spatially structured output that encodes the vehicle locations. Promising results have been obtained in the GRAM-RTM dataset.
SOME ASYMPTOTIC PROPERTIES OF THE CONVOLUTION TRANSFORMS OF FRACTAL MEASURES
Institute of Scientific and Technical Information of China (English)
Cao Li
2012-01-01
We study the asymptotic behavior near the boundary of u(x,y) =Ky * μ (x),defined on the half-space R+ × RN by the convolution of an approximate identity Ky (.) (y ＞0) and a measure μ on RN.The Poisson and the heat kernel are unified as special cases in our setting.We are mainly interested in the relationship between the rate of growth at boundary of u and the s-density of a singular measure μ.Then a boundary limit theorem of Fatou's type for singular measures is proved.Meanwhile,the asymptotic behavior of a quotient of Kμ and Kv is also studied,then the corresponding Fatou-Doob's boundary relative limit is obtained.In particular,some results about the singular boundary behavior of harmonic and heat functions can be deduced simultaneously from ours.At the end,an application in fractal geometry is given.
Plane-wave decomposition by spherical-convolution microphone array
Rafaely, Boaz; Park, Munhum
2001-05-01
Reverberant sound fields are widely studied, as they have a significant influence on the acoustic performance of enclosures in a variety of applications. For example, the intelligibility of speech in lecture rooms, the quality of music in auditoria, the noise level in offices, and the production of 3D sound in living rooms are all affected by the enclosed sound field. These sound fields are typically studied through frequency response measurements or statistical measures such as reverberation time, which do not provide detailed spatial information. The aim of the work presented in this seminar is the detailed analysis of reverberant sound fields. A measurement and analysis system based on acoustic theory and signal processing, designed around a spherical microphone array, is presented. Detailed analysis is achieved by decomposition of the sound field into waves, using spherical Fourier transform and spherical convolution. The presentation will include theoretical review, simulation studies, and initial experimental results.
Convolution Equivalent L\\'evy Processes and First Passage Times
Griffin, Philip S
2012-01-01
We investigate the behaviour of L\\'{e}vy processes with convolution equivalent L\\'evy measures, up to the time of first passage over a high level $u$. Such problems arise naturally in the context of insurance risk where $u$ is the initial reserve. We obtain a precise asymptotic estimate on the probability of first passage occurring by time $T$. This result is then used to study the process conditioned on first passage by time $T$. The existence of a limiting process as $u\\to \\infty$ is demonstrated, which leads to precise estimates for the probability of other events relating to first passage, such as the overshoot. A discussion of these results, as they relate to insurance risk, is also given.
Star-galaxy classification using deep convolutional neural networks
Kim, Edward J.; Brunner, Robert J.
2017-02-01
Most existing star-galaxy classifiers use the reduced summary information from catalogues, requiring careful feature extraction and selection. The latest advances in machine learning that use deep convolutional neural networks (ConvNets) allow a machine to automatically learn the features directly from the data, minimizing the need for input from human experts. We present a star-galaxy classification framework that uses deep ConvNets directly on the reduced, calibrated pixel values. Using data from the Sloan Digital Sky Survey and the Canada-France-Hawaii Telescope Lensing Survey, we demonstrate that ConvNets are able to produce accurate and well-calibrated probabilistic classifications that are competitive with conventional machine learning techniques. Future advances in deep learning may bring more success with current and forthcoming photometric surveys, such as the Dark Energy Survey and the Large Synoptic Survey Telescope, because deep neural networks require very little, manual feature engineering.
Invariant Descriptor Learning Using a Siamese Convolutional Neural Network
Chen, L.; Rottensteiner, F.; Heipke, C.
2016-06-01
In this paper we describe learning of a descriptor based on the Siamese Convolutional Neural Network (CNN) architecture and evaluate our results on a standard patch comparison dataset. The descriptor learning architecture is composed of an input module, a Siamese CNN descriptor module and a cost computation module that is based on the L2 Norm. The cost function we use pulls the descriptors of matching patches close to each other in feature space while pushing the descriptors for non-matching pairs away from each other. Compared to related work, we optimize the training parameters by combining a moving average strategy for gradients and Nesterov's Accelerated Gradient. Experiments show that our learned descriptor reaches a good performance and achieves state-of-art results in terms of the false positive rate at a 95 % recall rate on standard benchmark datasets.
Exploiting Narrowband Efficiency for Broadband Convolutive Blind Source Separation
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Aichner Robert
2007-01-01
Full Text Available Based on a recently presented generic broadband blind source separation (BSS algorithm for convolutive mixtures, we propose in this paper a novel algorithm combining advantages of broadband algorithms with the computational efficiency of narrowband techniques. By selective application of the Szegö theorem which relates properties of Toeplitz and circulant matrices, a new normalization is derived as a special case of the generic broadband algorithm. This results in a computationally efficient and fast converging algorithm without introducing typical narrowband problems such as the internal permutation problem or circularity effects. Moreover, a novel regularization method for the generic broadband algorithm is proposed and subsequently also derived for the proposed algorithm. Experimental results in realistic acoustic environments show improved performance of the novel algorithm compared to previous approximations.
Learning Building Extraction in Aerial Scenes with Convolutional Networks.
Yuan, Jiangye
2017-09-11
Extracting buildings from aerial scene images is an important task with many applications. However, this task is highly difficult to automate due to extremely large variations of building appearances, and still heavily relies on manual work. To attack this problem, we design a deep convolutional network with a simple structure that integrates activation from multiple layers for pixel-wise prediction, and introduce the signed distance function of building boundaries as the output representation, which has an enhanced representation power. To train the network, we leverage abundant building footprint data from geographic information systems (GIS) to generate large amounts of labeled data. The trained model achieves a superior performance on datasets that are significantly larger and more complex than those used in prior work, demonstrating that the proposed method provides a promising and scalable solution for automating this labor-intensive task.
Training strategy for convolutional neural networks in pedestrian gender classification
Ng, Choon-Boon; Tay, Yong-Haur; Goi, Bok-Min
2017-06-01
In this work, we studied a strategy for training a convolutional neural network in pedestrian gender classification with limited amount of labeled training data. Unsupervised learning by k-means clustering on pedestrian images was used to learn the filters to initialize the first layer of the network. As a form of pre-training, supervised learning for the related task of pedestrian classification was performed. Finally, the network was fine-tuned for gender classification. We found that this strategy improved the network's generalization ability in gender classification, achieving better test results when compared to random weights initialization and slightly more beneficial than merely initializing the first layer filters by unsupervised learning. This shows that unsupervised learning followed by pre-training with pedestrian images is an effective strategy to learn useful features for pedestrian gender classification.
XOR-FREE Implementation of Convolutional Encoder for Reconfigurable Hardware
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Gaurav Purohit
2016-01-01
Full Text Available This paper presents a novel XOR-FREE algorithm to implement the convolutional encoder using reconfigurable hardware. The approach completely removes the XOR processing of a chosen nonsystematic, feedforward generator polynomial of larger constraint length. The hardware (HW implementation of new architecture uses Lookup Table (LUT for storing the parity bits. The design implements architectural reconfigurability by modifying the generator polynomial of the same constraint length and code rate to reduce the design complexity. The proposed architecture reduces the dynamic power up to 30% and improves the hardware cost and propagation delay up to 20% and 32%, respectively. The performance of the proposed architecture is validated in MATLAB Simulink and tested on Zynq-7 series FPGA.
Forward Error Correction Convolutional Codes for RTAs' Networks: An Overview
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Salehe I. Mrutu
2014-06-01
Full Text Available For more than half a century, Forward Error Correction Convolutional Codes (FEC-CC have been in use to provide reliable data communication over various communication networks. The recent high increase of mobile communication services that require both bandwidth intensive and interactive Real Time Applications (RTAs impose an increased demand for fast and reliable wireless communication networks. Transmission burst errors; data decoding complexity and jitter are identified as key factors influencing the quality of service of RTAs implementation over wireless transmission media. This paper reviews FEC-CC as one of the most commonly used algorithm in Forward Error Correction for the purpose of improving its operational performance. Under this category, we have analyzed various previous works for their strengths and weaknesses in decoding FEC-CC. A comparison of various decoding algorithms is made based on their decoding computational complexity.
Radio Frequency Interference mitigation using deep convolutional neural networks
Akeret, Joel; Lucchi, Aurelien; Refregier, Alexandre
2016-01-01
We propose a novel approach for mitigating radio frequency interference (RFI) signals in radio data using the latest advances in deep learning. We employ a special type of Convolutional Neural Network, the U-Net, that enables the classification of clean signal and RFI signatures in 2D time-ordered data acquired from a radio telescope. We train and assess the performance of this network using the HIDE & SEEK radio data simulation and processing packages, as well as data collected at the Bleien Observatory. We find that our U-Net implementation can outperform classical RFI mitigation algorithms such as SEEK's SumThreshold implementation. We publish our U-Net software package on GitHub under GPLv3 license.
Radio frequency interference mitigation using deep convolutional neural networks
Akeret, J.; Chang, C.; Lucchi, A.; Refregier, A.
2017-01-01
We propose a novel approach for mitigating radio frequency interference (RFI) signals in radio data using the latest advances in deep learning. We employ a special type of Convolutional Neural Network, the U-Net, that enables the classification of clean signal and RFI signatures in 2D time-ordered data acquired from a radio telescope. We train and assess the performance of this network using the HIDE &SEEK radio data simulation and processing packages, as well as early Science Verification data acquired with the 7m single-dish telescope at the Bleien Observatory. We find that our U-Net implementation is showing competitive accuracy to classical RFI mitigation algorithms such as SEEK's SUMTHRESHOLD implementation. We publish our U-Net software package on GitHub under GPLv3 license.
Interleaved Convolutional Code and Its Viterbi Decoder Architecture
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Jun Jin Kong
2003-12-01
Full Text Available We propose an area-efficient high-speed interleaved Viterbi decoder architecture, which is based on the state-parallel architecture with register exchange path memory structure, for interleaved convolutional code. The state-parallel architecture uses as many add-compare-select (ACS units as the number of trellis states. By replacing each delay (or storage element in state metrics memory (or path metrics memory and path memory (or survival memory with I delays, interleaved Viterbi decoder is obtained where I is the interleaving degree. The decoding speed of this decoder architecture is as fast as the operating clock speed. The latency of proposed interleaved Viterbi decoder is Ã‚Â“decoding depth (DD ÃƒÂ— interleaving degree (I+Ã¢Â€Â‰extra delaysÃ¢Â€Â‰(A,Ã‚Â” which increases linearly with the interleaving degree I.
Rapid Exact Signal Scanning With Deep Convolutional Neural Networks
Thom, Markus; Gritschneder, Franz
2017-03-01
A rigorous formulation of the dynamics of a signal processing scheme aimed at dense signal scanning without any loss in accuracy is introduced and analyzed. Related methods proposed in the recent past lack a satisfactory analysis of whether they actually fulfill any exactness constraints. This is improved through an exact characterization of the requirements for a sound sliding window approach. The tools developed in this paper are especially beneficial if Convolutional Neural Networks are employed, but can also be used as a more general framework to validate related approaches to signal scanning. The proposed theory helps to eliminate redundant computations and renders special case treatment unnecessary, resulting in a dramatic boost in efficiency particularly on massively parallel processors. This is demonstrated both theoretically in a computational complexity analysis and empirically on modern parallel processors.
Convolutional Neural Networks for patient-specific ECG classification.
Kiranyaz, Serkan; Ince, Turker; Hamila, Ridha; Gabbouj, Moncef
2015-01-01
We propose a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system using an adaptive implementation of 1D Convolutional Neural Networks (CNNs) that can fuse feature extraction and classification into a unified learner. In this way, a dedicated CNN will be trained for each patient by using relatively small common and patient-specific training data and thus it can also be used to classify long ECG records such as Holter registers in a fast and accurate manner. Alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. The experimental results demonstrate that the proposed system achieves a superior classification performance for the detection of ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB).
Plane-wave decomposition by spherical-convolution microphone array
Rafaely, Boaz; Park, Munhum
2004-05-01
Reverberant sound fields are widely studied, as they have a significant influence on the acoustic performance of enclosures in a variety of applications. For example, the intelligibility of speech in lecture rooms, the quality of music in auditoria, the noise level in offices, and the production of 3D sound in living rooms are all affected by the enclosed sound field. These sound fields are typically studied through frequency response measurements or statistical measures such as reverberation time, which do not provide detailed spatial information. The aim of the work presented in this seminar is the detailed analysis of reverberant sound fields. A measurement and analysis system based on acoustic theory and signal processing, designed around a spherical microphone array, is presented. Detailed analysis is achieved by decomposition of the sound field into waves, using spherical Fourier transform and spherical convolution. The presentation will include theoretical review, simulation studies, and initial experimental results.
Enhanced Line Integral Convolution with Flow Feature Detection
Lane, David; Okada, Arthur
1996-01-01
The Line Integral Convolution (LIC) method, which blurs white noise textures along a vector field, is an effective way to visualize overall flow patterns in a 2D domain. The method produces a flow texture image based on the input velocity field defined in the domain. Because of the nature of the algorithm, the texture image tends to be blurry. This sometimes makes it difficult to identify boundaries where flow separation and reattachments occur. We present techniques to enhance LIC texture images and use colored texture images to highlight flow separation and reattachment boundaries. Our techniques have been applied to several flow fields defined in 3D curvilinear multi-block grids and scientists have found the results to be very useful.
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...... and tested on a total of 10,413 images containing 22 weed and crop species at early growth stages. These images originate from six different data sets, which have variations with respect to lighting, resolution, and soil type. This includes images taken under controlled conditions with regard to camera...... stabilisation and illumination, and images shot with hand-held mobile phones in fields with changing lighting conditions and different soil types. For these 22 species, the network is able to achieve a classification accuracy of 86.2%....
Robust Fusion of Irregularly Sampled Data Using Adaptive Normalized Convolution
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Schutte Klamer
2006-01-01
Full Text Available We present a novel algorithm for image fusion from irregularly sampled data. The method is based on the framework of normalized convolution (NC, in which the local signal is approximated through a projection onto a subspace. The use of polynomial basis functions in this paper makes NC equivalent to a local Taylor series expansion. Unlike the traditional framework, however, the window function of adaptive NC is adapted to local linear structures. This leads to more samples of the same modality being gathered for the analysis, which in turn improves signal-to-noise ratio and reduces diffusion across discontinuities. A robust signal certainty is also adapted to the sample intensities to minimize the influence of outliers. Excellent fusion capability of adaptive NC is demonstrated through an application of super-resolution image reconstruction.
SVD-BASED TRANSMIT BEAMFORMING FOR VARIOUS MODULATIONS WITH CONVOLUTION ENCODING
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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.
Classification of mitotic figures with convolutional neural networks and seeded blob features
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Christopher D Malon
2013-01-01
Full Text Available Background: The mitotic figure recognition contest at the 2012 International Conference on Pattern Recognition (ICPR challenges a system to identify all mitotic figures in a region of interest of hematoxylin and eosin stained tissue, using each of three scanners (Aperio, Hamamatsu, and multispectral. Methods: Our approach combines manually designed nuclear features with the learned features extracted by convolutional neural networks (CNN. The nuclear features capture color, texture, and shape information of segmented regions around a nucleus. The use of a CNN handles the variety of appearances of mitotic figures and decreases sensitivity to the manually crafted features and thresholds. Results : On the test set provided by the contest, the trained system achieves F1 scores up to 0.659 on color scanners and 0.589 on multispectral scanner. Conclusions : We demonstrate a powerful technique combining segmentation-based features with CNN, identifying the majority of mitotic figures with a fair precision. Further, we show that the approach accommodates information from the additional focal planes and spectral bands from a multi-spectral scanner without major redesign.
Cygrid: A fast Cython-powered convolution-based gridding module for Python
Winkel, B; Flöer, L
2016-01-01
Data gridding is a common task in astronomy and many other science disciplines. It refers to the resampling of irregularly sampled data to a regular grid. We present cygrid, a library module for the general purpose programming language Python. Cygrid can be used to resample data to any collection of target coordinates, although its typical application involves FITS maps or data cubes. The FITS world coordinate system standard is supported. The regridding algorithm is based on the convolution of the original samples with a kernel of arbitrary shape. We introduce a lookup table scheme that allows us to parallelize the gridding and combine it with the HEALPix tessellation of the sphere for fast neighbor searches. We show that for $n$ input data points, cygrids runtime scales between O(n) and O(n log n) and analyze the performance gain that is achieved using multiple CPU cores. We also compare the gridding speed with other techniques, such as nearest-neighbor, and linear and cubic spline interpolation. Cygrid is ...
Cygrid: A fast Cython-powered convolution-based gridding module for Python
Winkel, B.; Lenz, D.; Flöer, L.
2016-06-01
Context. Data gridding is a common task in astronomy and many other science disciplines. It refers to the resampling of irregularly sampled data to a regular grid. Aims: We present cygrid, a library module for the general purpose programming language Python. Cygrid can be used to resample data to any collection of target coordinates, although its typical application involves FITS maps or data cubes. The FITS world coordinate system standard is supported. Methods: The regridding algorithm is based on the convolution of the original samples with a kernel of arbitrary shape. We introduce a lookup table scheme that allows us to parallelize the gridding and combine it with the HEALPix tessellation of the sphere for fast neighbor searches. Results: We show that for n input data points, cygrids runtime scales between O(n) and O(nlog n) and analyze the performance gain that is achieved using multiple CPU cores. We also compare the gridding speed with other techniques, such as nearest-neighbor, and linear and cubic spline interpolation. Conclusions: Cygrid is a very fast and versatile gridding library that significantly outperforms other third-party Python modules, such as the linear and cubic spline interpolation provided by SciPy. http://https://github.com/bwinkel/cygrid
Intervertebral disc segmentation in MR images with 3D convolutional networks
Korez, Robert; Ibragimov, Bulat; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž
2017-02-01
The vertebral column is a complex anatomical construct, composed of vertebrae and intervertebral discs (IVDs) supported by ligaments and muscles. During life, all components undergo degenerative changes, which may in some cases cause severe, chronic and debilitating low back pain. The main diagnostic challenge is to locate the pain generator, and degenerated IVDs have been identified to act as such. Accurate and robust segmentation of IVDs is therefore a prerequisite for computer-aided diagnosis and quantification of IVD degeneration, and can be also used for computer-assisted planning and simulation in spinal surgery. In this paper, we present a novel fully automated framework for supervised segmentation of IVDs from three-dimensional (3D) magnetic resonance (MR) spine images. By considering global intensity appearance and local shape information, a landmark-based approach is first used for the detection of IVDs in the observed image, which then initializes the segmentation of IVDs by coupling deformable models with convolutional networks (ConvNets). For this purpose, a 3D ConvNet architecture was designed that learns rich high-level appearance representations from a training repository of IVDs, and then generates spatial IVD probability maps that guide deformable models towards IVD boundaries. By applying the proposed framework to 15 3D MR spine images containing 105 IVDs, quantitative comparison of the obtained against reference IVD segmentations yielded an overall mean Dice coefficient of 92.8%, mean symmetric surface distance of 0.4 mm and Hausdorff surface distance of 3.7 mm.
Modeling Task fMRI Data via Deep Convolutional Autoencoder.
Huang, Heng; Hu, Xintao; Zhao, Yu; Makkie, Milad; Dong, Qinglin; Zhao, Shijie; Guo, Lei; Liu, Tianming
2017-06-15
Task-based fMRI (tfMRI) has been widely used to study functional brain networks under task performance. Modeling tfMRI data is challenging due to at least two problems: the lack of the ground truth of underlying neural activity and the highly complex intrinsic structure of tfMRI data. To better understand brain networks based on fMRI data, data-driven approaches have been proposed, for instance, Independent Component Analysis (ICA) and Sparse Dictionary Learning (SDL). However, both ICA and SDL only build shallow models, and they are under the strong assumption that original fMRI signal could be linearly decomposed into time series components with their corresponding spatial maps. As growing evidence shows that human brain function is hierarchically organized, new approaches that can infer and model the hierarchical structure of brain networks are widely called for. Recently, deep convolutional neural network (CNN) has drawn much attention, in that deep CNN has proven to be a powerful method for learning high-level and mid-level abstractions from low-level raw data. Inspired by the power of deep CNN, in this study, we developed a new neural network structure based on CNN, called Deep Convolutional Auto-Encoder (DCAE), in order to take the advantages of both data-driven approach and CNN's hierarchical feature abstraction ability for the purpose of learning mid-level and high-level features from complex, large-scale tfMRI time series in an unsupervised manner. The DCAE has been applied and tested on the publicly available human connectome project (HCP) tfMRI datasets, and promising results are achieved.
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.
He, Kaiming; Zhang, Xiangyu; Ren, Shaoqing; Sun, Jian
2015-09-01
Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224 × 224) input image. This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with another pooling strategy, "spatial pyramid pooling", to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based image classification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures despite their different designs. On the Pascal VOC 2007 and Caltech101 datasets, SPP-net achieves state-of-the-art classification results using a single full-image representation and no fine-tuning. The power of SPP-net is also significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method is 24-102 × faster than the R-CNN method, while achieving better or comparable accuracy on Pascal VOC 2007. In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our methods rank #2 in object detection and #3 in image classification among all 38 teams. This manuscript also introduces the improvement made for this competition.
Institute of Scientific and Technical Information of China (English)
LI Ke-Ping; YU Chao-Fan; GAO Zi-You; LIANG Guo-Dong; YU Xiao-Min
2008-01-01
Based on the picture of nonlinear and non-parabolic symmetry response, I.e., △n2( I) ≈ p(αo -α1x- α2x2), we propose a model for the transversal beam intensity distribution of the nonlocal spatial soliton. In this model, as a convolution response with non-parabolic symmetry, △n2( I) ≈ p(b0+b1 f - b2 f2 with b2/b1 > 0 is assumed. Furthermore, instead of the wave function Ψ, the high-order nonlinear equation for the beam intensity distribution f has been derived and the bell-shaped soliton solution with the envelope form has been obtained. The results demonstrate that, since the existence of the terms of non-parabolic response, the nonlocal spatial soliton has the bistable state solution. If thefrequency shift of wave number β satisfies 0 0 has been demonstrated.
Some physical factors influencing the accuracy of convolution scatter correction in SPECT.
Msaki, P; Axelsson, B; Larsson, S A
1989-03-01
Some important physical factors influencing the accuracy of convolution scatter correction techniques in SPECT are presented. In these techniques scatter correction in the projection relies on filter functions, QF, evaluated by Fourier transforms, from measured scatter functions, Qp, obtained from point spread functions. The spatial resolution has a marginal effect on Qp. Thus a single QF can be used in the scatter correction of SPECT measurements acquired with the low energy high resolution or the low energy general purpose collimators and over a wide range of patient-collimator distances. However, it is necessary to examine the details of the shape of point spread functions during evaluation of Qp. QF is completely described by scatter amplitude AF, slope BF and filter sum SF. SF is obtained by summation of the values of QF occupying a 31 x 31 pixels matrix. Regardless of differences in amplitude and slope, two filter functions are shown to be equivalent in terms of scatter correction ability, whenever their sums are equal. On the basis of filter sum, the observed small influence of ellipticity on QF implies that an average function can be used in scatter correcting SPECT measurements conducted with elliptic objects. SF is shown to increase with a decrease in photon energy and with an increase in window size. Thus, scatter correction by convolution may be severely hampered by photon statistics when SPECT imaging is done with low-energy photons. It is pointless to use unnecessarily large discriminator windows, in the hope of improving photon statistics, since most of the extra events acquired will eventually be subtracted during scatter correction. Regardless of the observed moderate reduction in SF when a lung-equivalent material replaces a portion of a water phantom, further studies are needed to develop a technique that is capable of handling attenuation and scatter corrections simultaneously. Whenever superficial and inner radioactive distributions coexist the
Convoluted cells as a marker for maternal cell contamination in CVS cultures
DEFF Research Database (Denmark)
Hertz, Jens Michael; Jensen, P K; Therkelsen, A J
1987-01-01
In order to identify cells of maternal origin in CVS cultures, tissue from 1st trimester abortions were cultivated and the cultures stained in situ for X-chromatin. Convoluted cells and maternal fibroblasts were found to be positive. By chromosome analysis of cultures from 105 diagnostic placenta...... biopsies, obtained by the transabdominal route, metaphases of maternal origin were found in nine cases. In eight of these cases colonies of convoluted cells were observed. We conclude that convoluted cells are of maternal origin and are a reliable marker for maternal cell contamination in CVS cultures....
Scattering correction based on regularization de-convolution for Cone-Beam CT
Xie, Shi-peng; Yan, Rui-ju
2016-01-01
In Cone-Beam CT (CBCT) imaging systems, the scattering phenomenon has a significant impact on the reconstructed image and is a long-lasting research topic on CBCT. In this paper, we propose a simple, novel and fast approach for mitigating scatter artifacts and increasing the image contrast in CBCT, belonging to the category of convolution-based method in which the projected data is de-convolved with a convolution kernel. A key step in this method is how to determine the convolution kernel. Co...
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
We study the effect of electrodes with varying thickness on thickness-twist vibration of a piezoelectric plate resonator of crystals of 6 mm symmetry. An exact theoretical analysis is performed. Results show that non-uniform electrodes have a strong effect on mode shapes, and suggest the possibility of using nonuniform electrodes for strong energy trapping.
Michaelidou, U.; Heijne, ter A.; Euverink, G.J.W.; Hamelers, H.V.M.; Stams, A.J.M.; Geelhoed, J.S.
2011-01-01
Four types of titanium (Ti)-based electrodes were tested in the same microbial fuel cell (MFC) anodic compartment. Their electrochemical performances and the dominant microbial communities of the electrode biofilms were compared. The electrodes were identical in shape, macroscopic surface area, and
Efficient Terahertz Photoconductive Emitters with Improved Electrode Structures
Institute of Scientific and Technical Information of China (English)
Ying-Xin Wang; Yi-Jie Niu; Wei Cheng; Zhi-Qiang Li; Zi-Ran Zhao
2014-01-01
We present the design, fabrication, and characterization of two new types of terahertz photoconductive emitters. One has an asymmetric four-contact electrode structure and the other has an arc-shaped electrode structure, which are all modified from a traditional strip line antenna. Numerical simulations and real experiments confirm the good performance of the proposed antennas. An amplitude increase of about 40% is experimentally observed for the terahertz signals generated from the new structures. The special electrode structure and its induced local bias field enhancement are responsible for this radiation efficiency improvement. Our work demonstrates the feasibility of developing highly efficient terahertz photoconductive emitters by optimizing the electrode structure.
Suzuki, S; Arai, H
1990-04-01
In single-photon emission computed tomography (SPECT) and X-ray CT one-dimensional (1-D) convolution method is used for their image reconstruction from projections. The method makes a 1-D convolution filtering on projection data with a 1-D filter in the space domain, and back projects the filtered data for reconstruction. Images can also be reconstructed by first forming the 2-D backprojection images from projections and then convoluting them with a 2-D space-domain filter. This is the reconstruction by the 2-D convolution method, and it has the opposite reconstruction process to the 1-D convolution method. Since the 2-D convolution method is inferior to the 1-D convolution method in speed in reconstruction, it has no practical use. In the actual reconstruction by the 2-D convolution method, convolution is made on a finite plane which is called convolution window. A convolution window of size N X N needs a 2-D discrete filter of the same size. If better reconstructions are achieved with small convolution windows, the reconstruction time for the 2-D convolution method can be reduced. For this purpose, 2-D filters of a simple function form are proposed which can give good reconstructions with small convolution windows. They are here defined on a finite plane, depending on the window size used, although a filter function is usually defined on the infinite plane. They are however set so that they better approximate the property of a 2-D filter function defined on the infinite plane. Filters of size N X N are thus determined. Their value varies with window size. The filters are applied to image reconstructions of SPECT.(ABSTRACT TRUNCATED AT 250 WORDS)
A new convolution algorithm for loss probablity analysis in multiservice networks
DEFF Research Database (Denmark)
Huang, Qian; Ko, King-Tim; Iversen, Villy Bæk
2011-01-01
Performance analysis in multiservice loss systems generally focuses on accurate and efficient calculation methods for traffic loss probability. Convolution algorithm is one of the existing efficient numerical methods. Exact loss probabilities are obtainable from the convolution algorithm in systems...... where the bandwidth is fully shared by all traffic classes; but not available for systems with trunk reservation, i.e. part of the bandwidth is reserved for a special class of traffic. A proposal known as asymmetric convolution algorithm (ACA) has been made to overcome the deficiency of the convolution...... algorithm. It obtains an approximation of the channel occupancy distribution in multiservice systems with trunk reservation. However, the ACA approximation is only accurate with two traffic flows; increased approximation errors are observed for systems with three or more traffic flows. In this paper, we...
Time-Domain Convolutive Blind Source Separation Employing Selective-Tap Adaptive Algorithms
Directory of Open Access Journals (Sweden)
Pan Qiongfeng
2007-01-01
Full Text Available We investigate novel algorithms to improve the convergence and reduce the complexity of time-domain convolutive blind source separation (BSS algorithms. First, we propose MMax partial update time-domain convolutive BSS (MMax BSS algorithm. We demonstrate that the partial update scheme applied in the MMax LMS algorithm for single channel can be extended to multichannel time-domain convolutive BSS with little deterioration in performance and possible computational complexity saving. Next, we propose an exclusive maximum selective-tap time-domain convolutive BSS algorithm (XM BSS that reduces the interchannel coherence of the tap-input vectors and improves the conditioning of the autocorrelation matrix resulting in improved convergence rate and reduced misalignment. Moreover, the computational complexity is reduced since only half of the tap inputs are selected for updating. Simulation results have shown a significant improvement in convergence rate compared to existing techniques.
Mishchenko, Michael I.
2014-01-01
This Essay traces the centuries-long history of the phenomenological disciplines of directional radiometry and radiative transfer in turbid media, discusses their fundamental weaknesses, and outlines the convoluted process of their conversion into legitimate branches of physical optics.
ICA if fMRI based on a convolutive mixture model
DEFF Research Database (Denmark)
Hansen, Lars Kai
2003-01-01
mixing relevant for spatial ICA. Convolutive ICA has many computational problems and no standard solution is available. In this study a new predictive estimation method is used for finding the mixing coefficients and the source signals of a convolutive mixture and it is applied in temporal mode...... challenge with previous independent component analyses is the convolutive nature of the mixing process in fMRI. In temporal ICA we assume that the measured fMRI response is an instantaneous, spatially varying, mixture of independent time functions. However, the convolutive structure of the hemodynamic....... The mixing is represented by “mixture coefficient images” quantifying the local response to a given source at a certain time lag. This is the first communication to address this important issue in the context of fMRI ICA. Data: A single slice holding 128x128 pixels and passing through primary visual cortex...
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
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.
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)
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.
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.
Flexible electrode array for artifical vision
Krulevitch, Peter; Polla, Dennis L.; Maghribi, Mariam N.; Hamilton, Julie
2006-12-05
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.
Numerical simulation of seismic wave propagation in complex media by convolutional differentiator
Institute of Scientific and Technical Information of China (English)
LI Xin-fu; LI Xiao-fan
2008-01-01
We apply the forward modeling algorithm constituted by the convolutional Forsyte polynomial differentiator pro- posed by former worker to seismic wave simulation of complex heterogeneous media and compare the efficiency and accuracy between this method and other seismic simulation methods such as finite difference and pseudospec- tral method. Numerical experiments demonstrate that the algorithm constituted by convolutional Forsyte polyno- mial differentiator has high efficiency and accuracy and needs less computational resources, so it is a numerical modeling method with much potential.
Learning text representation using recurrent convolutional neural network with highway layers
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...
Relative n-widths of periodic convolution classes with NCVD-kernel and B-kernel
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
In this paper,we consider the relative n-widths of two kinds of periodic convolution classes,Kp(K) and Bp(G),whose convolution kernels are NCVD-kernel K and B-kernel G. The asymptotic estimations of Kn(Kp(K),Kp(K))q and Kn(Bp(G),Bp(G))q are obtained for p=1 and ∞,1≤ q≤∞.
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.
Examples of minimal-memory, non-catastrophic quantum convolutional encoders
Wilde, Mark M; Hosseini-Khayat, Saied
2010-01-01
One of the most important open questions in the theory of quantum convolutional coding is to determine a minimal-memory, non-catastrophic, polynomial-depth convolutional encoder for an arbitrary quantum convolutional code. Here, we present a technique that finds quantum convolutional encoders with such desirable properties for several example quantum convolutional codes (an exposition of our technique in full generality will appear elsewhere). We first show how to encode the well-studied Forney-Grassl-Guha (FGG) code with an encoder that exploits just one memory qubit (the former Grassl-Roetteler encoder requires 15 memory qubits). We then show how our technique can find an online decoder corresponding to this encoder, and we also detail the operation of our technique on a different example of a quantum convolutional code. Finally, the reduction in memory for the FGG encoder makes it feasible to simulate the performance of a quantum turbo code employing it, and we present the results of such simulations.
Directory of Open Access Journals (Sweden)
Cătălin LUPU
2014-12-01
Full Text Available This article presents the development of optimal filters through covolution methods, necessary for restoring, correcting and improving fingerprints acquired from a sensor, able to provide the most ideal image in the output. After the image was binarized and equalized, Canny filter is applied in order to: eliminate the noise (filtering the image with a Gaussian filter, non-maxima suppression, module gradient adaptive binarization and extension edge points edges by hysteresis. The resulting image after applying Canny filter is not ideal. It is possible that the result will be an image with very fragmented edges and many pores in ridge. For the resulting image, a bank of convolution filters are applied one after another (Kirsch, Laplace, Roberts, Prewitt, Sobel, Frei-Chen, averaging convolution filter, circular convolution filter, lapacian convolution filter, gaussian convolution filter, LoG convolution filter, DoG, inverted filters, Wiener, the filter of ”equalization of the power spectrum” (intermediary filter between the Wiener filter and the inverted filter, the geometrical average filter , etc. with different features.
Handbook of reference electrodes
Inzelt, György; Scholz, Fritz
2013-01-01
Reference Electrodes are a crucial part of any electrochemical system, yet an up-to-date and comprehensive handbook is long overdue. Here, an experienced team of electrochemists provides an in-depth source of information and data for the proper choice and construction of reference electrodes. This includes all kinds of applications such as aqueous and non-aqueous solutions, ionic liquids, glass melts, solid electrolyte systems, and membrane electrodes. Advanced technologies such as miniaturized, conducting-polymer-based, screen-printed or disposable reference electrodes are also covered. Essen
A deep convolutional neural network for recognizing foods
Jahani Heravi, Elnaz; Habibi Aghdam, Hamed; Puig, Domenec
2015-12-01
Controlling the food intake is an efficient way that each person can undertake to tackle the obesity problem in countries worldwide. This is achievable by developing a smartphone application that is able to recognize foods and compute their calories. State-of-art methods are chiefly based on hand-crafted feature extraction methods such as HOG and Gabor. Recent advances in large-scale object recognition datasets such as ImageNet have revealed that deep Convolutional Neural Networks (CNN) possess more representation power than the hand-crafted features. The main challenge with CNNs is to find the appropriate architecture for each problem. In this paper, we propose a deep CNN which consists of 769; 988 parameters. Our experiments show that the proposed CNN outperforms the state-of-art methods and improves the best result of traditional methods 17%. Moreover, using an ensemble of two CNNs that have been trained two different times, we are able to improve the classification performance 21:5%.
A quantum algorithm for Viterbi decoding of classical convolutional codes
Grice, Jon R.; Meyer, David A.
2015-07-01
We present a quantum Viterbi algorithm (QVA) with better than classical performance under certain conditions. In this paper, the proposed algorithm is applied to decoding classical convolutional codes, for instance, large constraint length and short decode frames . Other applications of the classical Viterbi algorithm where is large (e.g., speech processing) could experience significant speedup with the QVA. The QVA exploits the fact that the decoding trellis is similar to the butterfly diagram of the fast Fourier transform, with its corresponding fast quantum algorithm. The tensor-product structure of the butterfly diagram corresponds to a quantum superposition that we show can be efficiently prepared. The quantum speedup is possible because the performance of the QVA depends on the fanout (number of possible transitions from any given state in the hidden Markov model) which is in general much less than . The QVA constructs a superposition of states which correspond to all legal paths through the decoding lattice, with phase as a function of the probability of the path being taken given received data. A specialized amplitude amplification procedure is applied one or more times to recover a superposition where the most probable path has a high probability of being measured.
Very Deep Convolutional Neural Networks for Morphologic Classification of Erythrocytes.
Durant, Thomas J S; Olson, Eben M; Schulz, Wade L; Torres, Richard
2017-09-06
Morphologic profiling of the erythrocyte population is a widely used and clinically valuable diagnostic modality, but one that relies on a slow manual process associated with significant labor cost and limited reproducibility. Automated profiling of erythrocytes from digital images by capable machine learning approaches would augment the throughput and value of morphologic analysis. To this end, we sought to evaluate the performance of leading implementation strategies for convolutional neural networks (CNNs) when applied to classification of erythrocytes based on morphology. Erythrocytes were manually classified into 1 of 10 classes using a custom-developed Web application. Using recent literature to guide architectural considerations for neural network design, we implemented a "very deep" CNN, consisting of >150 layers, with dense shortcut connections. The final database comprised 3737 labeled cells. Ensemble model predictions on unseen data demonstrated a harmonic mean of recall and precision metrics of 92.70% and 89.39%, respectively. Of the 748 cells in the test set, 23 misclassification errors were made, with a correct classification frequency of 90.60%, represented as a harmonic mean across the 10 morphologic classes. These findings indicate that erythrocyte morphology profiles could be measured with a high degree of accuracy with "very deep" CNNs. Further, these data support future efforts to expand classes and optimize practical performance in a clinical environment as a prelude to full implementation as a clinical tool. © 2017 American Association for Clinical Chemistry.
Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks.
Schlegl, Thomas; Waldstein, Sebastian M; Vogl, Wolf-Dieter; Schmidt-Erfurth, Ursula; Langs, Georg
2015-01-01
Learning representative computational models from medical imaging data requires large training data sets. Often, voxel-level annotation is unfeasible for sufficient amounts of data. An alternative to manual annotation, is to use the enormous amount of knowledge encoded in imaging data and corresponding reports generated during clinical routine. Weakly supervised learning approaches can link volume-level labels to image content but suffer from the typical label distributions in medical imaging data where only a small part consists of clinically relevant abnormal structures. In this paper we propose to use a semantic representation of clinical reports as a learning target that is predicted from imaging data by a convolutional neural network. We demonstrate how we can learn accurate voxel-level classifiers based on weak volume-level semantic descriptions on a set of 157 optical coherence tomography (OCT) volumes. We specifically show how semantic information increases classification accuracy for intraretinal cystoid fluid (IRC), subretinal fluid (SRF) and normal retinal tissue, and how the learning algorithm links semantic concepts to image content and geometry.
Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks
Directory of Open Access Journals (Sweden)
Martin Längkvist
2016-04-01
Full Text Available The availability of high-resolution remote sensing (HRRS data has opened up the possibility for new interesting applications, such as per-pixel classification of individual objects in greater detail. This paper shows how a convolutional neural network (CNN can be applied to multispectral orthoimagery and a digital surface model (DSM of a small city for a full, fast and accurate per-pixel classification. The predicted low-level pixel classes are then used to improve the high-level segmentation. Various design choices of the CNN architecture are evaluated and analyzed. The investigated land area is fully manually labeled into five categories (vegetation, ground, roads, buildings and water, and the classification accuracy is compared to other per-pixel classification works on other land areas that have a similar choice of categories. The results of the full classification and segmentation on selected segments of the map show that CNNs are a viable tool for solving both the segmentation and object recognition task for remote sensing data.
Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields
Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo
2016-01-01
Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.
On the Relationship between Visual Attributes and Convolutional Networks
Castillo, Victor
2015-06-02
One of the cornerstone principles of deep models is their abstraction capacity, i.e. their ability to learn abstract concepts from ‘simpler’ ones. Through extensive experiments, we characterize the nature of the relationship between abstract concepts (specifically objects in images) learned by popular and high performing convolutional networks (conv-nets) and established mid-level representations used in computer vision (specifically semantic visual attributes). We focus on attributes due to their impact on several applications, such as object description, retrieval and mining, and active (and zero-shot) learning. Among the findings we uncover, we show empirical evidence of the existence of Attribute Centric Nodes (ACNs) within a conv-net, which is trained to recognize objects (not attributes) in images. These special conv-net nodes (1) collectively encode information pertinent to visual attribute representation and discrimination, (2) are unevenly and sparsely distribution across all layers of the conv-net, and (3) play an important role in conv-net based object recognition.
Innervation of the renal proximal convoluted tubule of the rat
Energy Technology Data Exchange (ETDEWEB)
Barajas, L.; Powers, K. (Harbor-UCLA Medical Center, Torrance (USA))
1989-12-01
Experimental data suggest the proximal tubule as a major site of neurogenic influence on tubular function. The functional and anatomical axial heterogeneity of the proximal tubule prompted this study of the distribution of innervation sites along the early, mid, and late proximal convoluted tubule (PCT) of the rat. Serial section autoradiograms, with tritiated norepinephrine serving as a marker for monoaminergic nerves, were used in this study. Freehand clay models and graphic reconstructions of proximal tubules permitted a rough estimation of the location of the innervation sites along the PCT. In the subcapsular nephrons, the early PCT (first third) was devoid of innervation sites with most of the innervation occurring in the mid (middle third) and in the late (last third) PCT. Innervation sites were found in the early PCT in nephrons located deeper in the cortex. In juxtamedullary nephrons, innervation sites could be observed on the PCT as it left the glomerulus. This gradient of PCT innervation can be explained by the different tubulovascular relationships of nephrons at different levels of the cortex. The absence of innervation sites in the early PCT of subcapsular nephrons suggests that any influence of the renal nerves on the early PCT might be due to an effect of neurotransmitter released from renal nerves reaching the early PCT via the interstitium and/or capillaries.
Image reconstruction from incomplete convolution data via total variation regularization
Directory of Open Access Journals (Sweden)
Zhida Shen
2015-02-01
Full Text Available Variational models with Total Variation (TV regularization have long been known to preserve image edges and produce high quality reconstruction. On the other hand, recent theory on compressive sensing has shown that it is feasible to accurately reconstruct images from a few linear measurements via TV regularization. However, in general TV models are difficult to solve due to the nondifferentiability and the universal coupling of variables. In this paper, we propose the use of alternating direction method for image reconstruction from highly incomplete convolution data, where an image is reconstructed as a minimizer of an energy function that sums a TV term for image regularity and a least squares term for data fitting. Our algorithm, called RecPK, takes advantage of problem structures and has an extremely low per-iteration cost. To demonstrate the efficiency of RecPK, we compare it with TwIST, a state-of-the-art algorithm for minimizing TV models. Moreover, we also demonstrate the usefulness of RecPK in image zooming.
Convolutional networks for fast, energy-efficient neuromorphic computing.
Esser, Steven K; Merolla, Paul A; Arthur, John V; Cassidy, Andrew S; Appuswamy, Rathinakumar; Andreopoulos, Alexander; Berg, David J; McKinstry, Jeffrey L; Melano, Timothy; Barch, Davis R; di Nolfo, Carmelo; Datta, Pallab; Amir, Arnon; Taba, Brian; Flickner, Myron D; Modha, Dharmendra S
2016-10-11
Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware's underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.
Method for Veterbi decoding of large constraint length convolutional codes
Hsu, In-Shek; Truong, Trieu-Kie; Reed, Irving S.; Jing, Sun
1988-05-01
A new method of Viterbi decoding of convolutional codes lends itself to a pipline VLSI architecture using a single sequential processor to compute the path metrics in the Viterbi trellis. An array method is used to store the path information for NK intervals where N is a number, and K is constraint length. The selected path at the end of each NK interval is then selected from the last entry in the array. A trace-back method is used for returning to the beginning of the selected path back, i.e., to the first time unit of the interval NK to read out the stored branch metrics of the selected path which correspond to the message bits. The decoding decision made in this way is no longer maximum likelihood, but can be almost as good, provided that constraint length K in not too small. The advantage is that for a long message, it is not necessary to provide a large memory to store the trellis derived information until the end of the message to select the path that is to be decoded; the selection is made at the end of every NK time unit, thus decoding a long message in successive blocks.
Classification of breast cancer cytological specimen using convolutional neural network
Żejmo, Michał; Kowal, Marek; Korbicz, Józef; Monczak, Roman
2017-01-01
The paper presents a deep learning approach for automatic classification of breast tumors based on fine needle cytology. The main aim of the system is to distinguish benign from malignant cases based on microscopic images. Experiment was carried out on cytological samples derived from 50 patients (25 benign cases + 25 malignant cases) diagnosed in Regional Hospital in Zielona Góra. To classify microscopic images, we used convolutional neural networks (CNN) of two types: GoogLeNet and AlexNet. Due to the very large size of images of cytological specimen (on average 200000 × 100000 pixels), they were divided into smaller patches of size 256 × 256 pixels. Breast cancer classification usually is based on morphometric features of nuclei. Therefore, training and validation patches were selected using Support Vector Machine (SVM) so that suitable amount of cell material was depicted. Neural classifiers were tuned using GPU accelerated implementation of gradient descent algorithm. Training error was defined as a cross-entropy classification loss. Classification accuracy was defined as the percentage ratio of successfully classified validation patches to the total number of validation patches. The best accuracy rate of 83% was obtained by GoogLeNet model. We observed that more misclassified patches belong to malignant cases.
Infimal convolution of total generalized variation functionals for dynamic MRI.
Schloegl, Matthias; Holler, Martin; Schwarzl, Andreas; Bredies, Kristian; Stollberger, Rudolf
2017-07-01
To accelerate dynamic MR applications using infimal convolution of total generalized variation functionals (ICTGV) as spatio-temporal regularization for image reconstruction. ICTGV comprises a new image prior tailored to dynamic data that achieves regularization via optimal local balancing between spatial and temporal regularity. Here it is applied for the first time to the reconstruction of dynamic MRI data. CINE and perfusion scans were investigated to study the influence of time dependent morphology and temporal contrast changes. ICTGV regularized reconstruction from subsampled MR data is formulated as a convex optimization problem. Global solutions are obtained by employing a duality based non-smooth optimization algorithm. The reconstruction error remains on a low level with acceleration factors up to 16 for both CINE and dynamic contrast-enhanced MRI data. The GPU implementation of the algorithm suites clinical demands by reducing reconstruction times of one dataset to less than 4 min. ICTGV based dynamic magnetic resonance imaging reconstruction allows for vast undersampling and therefore enables for very high spatial and temporal resolutions, spatial coverage and reduced scan time. With the proposed distinction of model and regularization parameters it offers a new and robust method of flexible decomposition into components with different degrees of temporal regularity. Magn Reson Med 78:142-155, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.
Synthesising Primary Reflections by Marchenko Redatuming and Convolutional Interferometry
Curtis, A.
2015-12-01
Standard active-source seismic processing and imaging steps such as velocity analysis and reverse time migration usually provide best results when all reflected waves in the input data are primaries (waves that reflect only once). Multiples (recorded waves that reflect multiple times) represent a source of coherent noise in data that must be suppressed to avoid imaging artefacts. Consequently, multiple-removal methods have been a primcipal direction of active-source seismic research for decades. We describe a new method to estimate primaries directly, which obviates the need for multiple removal. Primaries are constructed within convolutional interferometry by combining first arriving events of up-going and direct wave down-going Green's functions to virtual receivers in the subsurface. The required up-going wavefields to virtual receivers along discrete subsurface boundaries can be constructed using Marchenko redatuming. Crucially, this is possible without detailed models of the Earth's subsurface velocity structure: similarly to most migration techniques, the method only requires surface reflection data and estimates of direct (non-reflected) arrivals between subsurface sources and the acquisition surface. The method is demonstrated on a stratified synclinal model. It is shown both to improve reverse time migration compared to standard methods, and to be particularly robust against errors in the reference velocity model used.
New Parallel Interference Cancellation for Convolutionally Coded CDMA Systems
Institute of Scientific and Technical Information of China (English)
Xu Guo-xiong; Gan Liang-cai; Huang Tian-xi
2004-01-01
Based on BCJR algorithm proposed by Bahl et al and linear soft decision feedback, a reduced-complexity parallel interference cancellation (simplified PIC) for convolutionally coded DS CDMA systems is proposed. By computer simulation, we compare the simplified PIC with the exact PIC. It shows that the simplified PIC can achieve the performance close to the exact PIC if the mean values of coded symbols are linearly computed in terms of the sum of initial a prior log-likelihood rate (LLR) and updated a prior LLR, while a significant performance loss will occur if the mean values of coded symbols are linearly computed in terms of the updated a prior LLR only. Meanwhile, we also compare the simplified PIC with MF receiver and conventional PICs. The simulation results show that the simplified PIC dominantly outperforms the MF receiver and conventional PICs, at signal-noise rate (SNR) of 7 dB, for example, the bit error rate is about 10-4 for the simplified PIC, which is far below that of matched-filter receiver and conventional PIC.
Toward an optimal convolutional neural network for traffic sign recognition
Habibi Aghdam, Hamed; Jahani Heravi, Elnaz; Puig, Domenec
2015-12-01
Convolutional Neural Networks (CNN) beat the human performance on German Traffic Sign Benchmark competition. Both the winner and the runner-up teams trained CNNs to recognize 43 traffic signs. However, both networks are not computationally efficient since they have many free parameters and they use highly computational activation functions. In this paper, we propose a new architecture that reduces the number of the parameters 27% and 22% compared with the two networks. Furthermore, our network uses Leaky Rectified Linear Units (ReLU) as the activation function that only needs a few operations to produce the result. Specifically, compared with the hyperbolic tangent and rectified sigmoid activation functions utilized in the two networks, Leaky ReLU needs only one multiplication operation which makes it computationally much more efficient than the two other functions. Our experiments on the Gertman Traffic Sign Benchmark dataset shows 0:6% improvement on the best reported classification accuracy while it reduces the overall number of parameters 85% compared with the winner network in the competition.
O'Connell, Emily
2009-01-01
This article describes a lesson on Schapiro Shapes. Schapiro Shapes is based on the art of Miriam Schapiro, who created a number of works of figures in action. Using the basic concepts of this project, students learn to create their own figures and styles. (Contains 1 online resource.)
Effect of Counter Electrode in Electroformation of Giant Vesicles
Directory of Open Access Journals (Sweden)
Shuuhei Oana
2011-11-01
Full Text Available Electroformation of cell-sized lipid membrane vesicles (giant vesicles, GVs, from egg yolk phosphatidylcholine, was examined varying the shape of the counter electrode. Instead of a planar ITO (indium tin oxide electrode commonly used, platinum wire mesh was employed as a counter electrode facing lipid deposit on a planar formation electrode. The modification did not significantly alter GV formation, and many GVs of 30–50 µm, some as large as 100 µm, formed as with the standard setup, indicating that a counter electrode does not have to be a complete plane. When the counter electrode was reduced to a set of two parallel platinum wires, GV formation deteriorated. Some GVs formed, but only in close proximity to the counter electrode. Lower electric voltage with this setup no longer yielded GVs. Instead, a large onion-like multilamellar structure was observed. The deteriorated GV formation and the formation of a multilamellar structure seemed to indicate the weakened effect of the electric field on lipid deposit due to insufficient coverage with a small counter electrode. Irregular membranous objects formed by spontaneous swelling of lipid without electric voltage gradually turned into multilamellar structure upon following application of voltage. No particular enhancement of GV formation was observed when lipid deposit on a wire formation electrode was used in combination with a large planar counter electrode.
Effects of electrode surface structure on the mechanoelectrical transduction of IPMC sensors
Palmre, Viljar; Pugal, David; Kim, Kwang
2014-03-01
This study investigates the effects of electrode surface structure on the mechanoelectrical transduction of IPMC sensors. A physics-based mechanoelectrical transduction model was developed that takes into account the electrode surface profile (shape) by describing the polymer-electrode interface as a Koch fractal structure. Based on the model, the electrode surface effects were experimentally investigated in case of IPMCs with Pd-Pt electrodes. IPMCs with different electrode surface structures were fabricated through electroless plating process by appropriately controlling the synthesis parameters and conditions. The changes in the electrode surface morphology and the corresponding effects on the IPMC mechanoelectrical transduction were examined. Our experimental results indicate that increasing the dispersion of Pd particles near the membrane surface, and thus the polymer-electrode interfacial area, leads to a higher peak mechanoelectrically induced voltage of IPMC. However, the overall effect of the electrode surface structure is relatively low compared to the electromechanical transduction, which is in good agreement with theoretical prediction.
DEFF Research Database (Denmark)
2017-01-01
the composite. The invention also relates to the use of the composite as a fuel electrode, solid oxide fuel cell, and/or solid oxide electrolyser. The invention discloses a composite for an electrode, comprising a three-dimensional network of dispersed metal particles, stabilised zirconia particles and pores...
Rechnitz, Garry A.
1975-01-01
Describes the design of ion selective electrodes coupled with immobilized enzymes which operate either continuously or on drop-sized samples. Cites techniques for urea, L-phenylalanine and amygdalin. Micro size electrodes for use in single cells are discussed. (GH)
Rechnitz, Garry A.
1975-01-01
Describes the design of ion selective electrodes coupled with immobilized enzymes which operate either continuously or on drop-sized samples. Cites techniques for urea, L-phenylalanine and amygdalin. Micro size electrodes for use in single cells are discussed. (GH)
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.
QingJie, Wei; WenBin, Wang
2017-06-01
In this paper, the image retrieval using deep convolutional neural network combined with regularization and PRelu activation function is studied, and improves image retrieval accuracy. Deep convolutional neural network can not only simulate the process of human brain to receive and transmit information, but also contains a convolution operation, which is very suitable for processing images. Using deep convolutional neural network is better than direct extraction of image visual features for image retrieval. However, the structure of deep convolutional neural network is complex, and it is easy to over-fitting and reduces the accuracy of image retrieval. In this paper, we combine L1 regularization and PRelu activation function to construct a deep convolutional neural network to prevent over-fitting of the network and improve the accuracy of image retrieval
A semiconductor based electrode
Energy Technology Data Exchange (ETDEWEB)
Khamatani, A.; Kobayasi, K.
1983-03-30
The semiconductor electrode is submerged into an electrolyte which is held in the illuminated chamber. The other electrode is placed in a dark chamber connected with the channel to be illuminated, which has a partition in the form of a membrane. An electric current flows in the external circuit of the element with illumination of the first electrode. The illuminated electrode is covered with a thin film of a substance which is stable with the action of the electrolyte. The film is made of Si02, A1203, GaN or A1N. The protective coating makes it possible to use materials less stable than Ti02 in a rutile modification, but which have higher characteristics than the GaP, GaAs, CdS and InP, for making the electrode.
A convolution-superposition dose calculation engine for GPUs
Energy Technology Data Exchange (ETDEWEB)
Hissoiny, Sami; Ozell, Benoit; Despres, Philippe [Departement de genie informatique et genie logiciel, Ecole polytechnique de Montreal, 2500 Chemin de Polytechnique, Montreal, Quebec H3T 1J4 (Canada); Departement de radio-oncologie, CRCHUM-Centre hospitalier de l' Universite de Montreal, 1560 rue Sherbrooke Est, Montreal, Quebec H2L 4M1 (Canada)
2010-03-15
Purpose: Graphic processing units (GPUs) are increasingly used for scientific applications, where their parallel architecture and unprecedented computing power density can be exploited to accelerate calculations. In this paper, a new GPU implementation of a convolution/superposition (CS) algorithm is presented. Methods: This new GPU implementation has been designed from the ground-up to use the graphics card's strengths and to avoid its weaknesses. The CS GPU algorithm takes into account beam hardening, off-axis softening, kernel tilting, and relies heavily on raytracing through patient imaging data. Implementation details are reported as well as a multi-GPU solution. Results: An overall single-GPU acceleration factor of 908x was achieved when compared to a nonoptimized version of the CS algorithm implemented in PlanUNC in single threaded central processing unit (CPU) mode, resulting in approximatively 2.8 s per beam for a 3D dose computation on a 0.4 cm grid. A comparison to an established commercial system leads to an acceleration factor of approximately 29x or 0.58 versus 16.6 s per beam in single threaded mode. An acceleration factor of 46x has been obtained for the total energy released per mass (TERMA) calculation and a 943x acceleration factor for the CS calculation compared to PlanUNC. Dose distributions also have been obtained for a simple water-lung phantom to verify that the implementation gives accurate results. Conclusions: These results suggest that GPUs are an attractive solution for radiation therapy applications and that careful design, taking the GPU architecture into account, is critical in obtaining significant acceleration factors. These results potentially can have a significant impact on complex dose delivery techniques requiring intensive dose calculations such as intensity-modulated radiation therapy (IMRT) and arc therapy. They also are relevant for adaptive radiation therapy where dose results must be obtained rapidly.
A convolutional neural network approach for objective video quality assessment.
Le Callet, Patrick; Viard-Gaudin, Christian; Barba, Dominique
2006-09-01
This paper describes an application of neural networks in the field of objective measurement method designed to automatically assess the perceived quality of digital videos. This challenging issue aims to emulate human judgment and to replace very complex and time consuming subjective quality assessment. Several metrics have been proposed in literature to tackle this issue. They are based on a general framework that combines different stages, each of them addressing complex problems. The ambition of this paper is not to present a global perfect quality metric but rather to focus on an original way to use neural networks in such a framework in the context of reduced reference (RR) quality metric. Especially, we point out the interest of such a tool for combining features and pooling them in order to compute quality scores. The proposed approach solves some problems inherent to objective metrics that should predict subjective quality score obtained using the single stimulus continuous quality evaluation (SSCQE) method. This latter has been adopted by video quality expert group (VQEG) in its recently finalized reduced referenced and no reference (RRNR-TV) test plan. The originality of such approach compared to previous attempts to use neural networks for quality assessment, relies on the use of a convolutional neural network (CNN) that allows a continuous time scoring of the video. Objective features are extracted on a frame-by-frame basis on both the reference and the distorted sequences; they are derived from a perceptual-based representation and integrated along the temporal axis using a time-delay neural network (TDNN). Experiments conducted on different MPEG-2 videos, with bit rates ranging 2-6 Mb/s, show the effectiveness of the proposed approach to get a plausible model of temporal pooling from the human vision system (HVS) point of view. More specifically, a linear correlation criteria, between objective and subjective scoring, up to 0.92 has been obtained on
Text-Attentional Convolutional Neural Network for Scene Text Detection
He, Tong; Huang, Weilin; Qiao, Yu; Yao, Jian
2016-06-01
Recent deep learning models have demonstrated strong capabilities for classifying text and non-text components in natural images. They extract a high-level feature computed globally from a whole image component (patch), where the cluttered background information may dominate true text features in the deep representation. This leads to less discriminative power and poorer robustness. In this work, we present a new system for scene text detection by proposing a novel Text-Attentional Convolutional Neural Network (Text-CNN) that particularly focuses on extracting text-related regions and features from the image components. We develop a new learning mechanism to train the Text-CNN with multi-level and rich supervised information, including text region mask, character label, and binary text/nontext information. The rich supervision information enables the Text-CNN with a strong capability for discriminating ambiguous texts, and also increases its robustness against complicated background components. The training process is formulated as a multi-task learning problem, where low-level supervised information greatly facilitates main task of text/non-text classification. In addition, a powerful low-level detector called Contrast- Enhancement Maximally Stable Extremal Regions (CE-MSERs) is developed, which extends the widely-used MSERs by enhancing intensity contrast between text patterns and background. This allows it to detect highly challenging text patterns, resulting in a higher recall. Our approach achieved promising results on the ICDAR 2013 dataset, with a F-measure of 0.82, improving the state-of-the-art results substantially.
Deep convolutional networks for pancreas segmentation in CT imaging
Roth, Holger R.; Farag, Amal; Lu, Le; Turkbey, Evrim B.; Summers, Ronald M.
2015-03-01
Automatic organ segmentation is an important prerequisite for many computer-aided diagnosis systems. The high anatomical variability of organs in the abdomen, such as the pancreas, prevents many segmentation methods from achieving high accuracies when compared to state-of-the-art segmentation of organs like the liver, heart or kidneys. Recently, the availability of large annotated training sets and the accessibility of affordable parallel computing resources via GPUs have made it feasible for "deep learning" methods such as convolutional networks (ConvNets) to succeed in image classification tasks. These methods have the advantage that used classification features are trained directly from the imaging data. We present a fully-automated bottom-up method for pancreas segmentation in computed tomography (CT) images of the abdomen. The method is based on hierarchical coarse-to-fine classification of local image regions (superpixels). Superpixels are extracted from the abdominal region using Simple Linear Iterative Clustering (SLIC). An initial probability response map is generated, using patch-level confidences and a two-level cascade of random forest classifiers, from which superpixel regions with probabilities larger 0.5 are retained. These retained superpixels serve as a highly sensitive initial input of the pancreas and its surroundings to a ConvNet that samples a bounding box around each superpixel at different scales (and random non-rigid deformations at training time) in order to assign a more distinct probability of each superpixel region being pancreas or not. We evaluate our method on CT images of 82 patients (60 for training, 2 for validation, and 20 for testing). Using ConvNets we achieve maximum Dice scores of an average 68% +/- 10% (range, 43-80%) in testing. This shows promise for accurate pancreas segmentation, using a deep learning approach and compares favorably to state-of-the-art methods.
Convolutional neural networks for prostate cancer recurrence prediction
Kumar, Neeraj; Verma, Ruchika; Arora, Ashish; Kumar, Abhay; Gupta, Sanchit; Sethi, Amit; Gann, Peter H.
2017-03-01
Accurate prediction of the treatment outcome is important for cancer treatment planning. We present an approach to predict prostate cancer (PCa) recurrence after radical prostatectomy using tissue images. We used a cohort whose case vs. control (recurrent vs. non-recurrent) status had been determined using post-treatment follow up. Further, to aid the development of novel biomarkers of PCa recurrence, cases and controls were paired based on matching of other predictive clinical variables such as Gleason grade, stage, age, and race. For this cohort, tissue resection microarray with up to four cores per patient was available. The proposed approach is based on deep learning, and its novelty lies in the use of two separate convolutional neural networks (CNNs) - one to detect individual nuclei even in the crowded areas, and the other to classify them. To detect nuclear centers in an image, the first CNN predicts distance transform of the underlying (but unknown) multi-nuclear map from the input HE image. The second CNN classifies the patches centered at nuclear centers into those belonging to cases or controls. Voting across patches extracted from image(s) of a patient yields the probability of recurrence for the patient. The proposed approach gave 0.81 AUC for a sample of 30 recurrent cases and 30 non-recurrent controls, after being trained on an independent set of 80 case-controls pairs. If validated further, such an approach might help in choosing between a combination of treatment options such as active surveillance, radical prostatectomy, radiation, and hormone therapy. It can also generalize to the prediction of treatment outcomes in other cancers.
Classifying Radio Galaxies with the Convolutional Neural Network
Aniyan, A. K.; Thorat, K.
2017-06-01
We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff-Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ˜200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.
Text-Attentional Convolutional Neural Network for Scene Text Detection.
He, Tong; Huang, Weilin; Qiao, Yu; Yao, Jian
2016-06-01
Recent deep learning models have demonstrated strong capabilities for classifying text and non-text components in natural images. They extract a high-level feature globally computed from a whole image component (patch), where the cluttered background information may dominate true text features in the deep representation. This leads to less discriminative power and poorer robustness. In this paper, we present a new system for scene text detection by proposing a novel text-attentional convolutional neural network (Text-CNN) that particularly focuses on extracting text-related regions and features from the image components. We develop a new learning mechanism to train the Text-CNN with multi-level and rich supervised information, including text region mask, character label, and binary text/non-text information. The rich supervision information enables the Text-CNN with a strong capability for discriminating ambiguous texts, and also increases its robustness against complicated background components. The training process is formulated as a multi-task learning problem, where low-level supervised information greatly facilitates the main task of text/non-text classification. In addition, a powerful low-level detector called contrast-enhancement maximally stable extremal regions (MSERs) is developed, which extends the widely used MSERs by enhancing intensity contrast between text patterns and background. This allows it to detect highly challenging text patterns, resulting in a higher recall. Our approach achieved promising results on the ICDAR 2013 data set, with an F-measure of 0.82, substantially improving the state-of-the-art results.
Text-Attentional Convolutional Neural Networks for Scene Text Detection.
He, Tong; Huang, Weilin; Qiao, Yu; Yao, Jian
2016-03-28
Recent deep learning models have demonstrated strong capabilities for classifying text and non-text components in natural images. They extract a high-level feature computed globally from a whole image component (patch), where the cluttered background information may dominate true text features in the deep representation. This leads to less discriminative power and poorer robustness. In this work, we present a new system for scene text detection by proposing a novel Text-Attentional Convolutional Neural Network (Text-CNN) that particularly focuses on extracting text-related regions and features from the image components. We develop a new learning mechanism to train the Text-CNN with multi-level and rich supervised information, including text region mask, character label, and binary text/nontext information. The rich supervision information enables the Text-CNN with a strong capability for discriminating ambiguous texts, and also increases its robustness against complicated background components. The training process is formulated as a multi-task learning problem, where low-level supervised information greatly facilitates main task of text/non-text classification. In addition, a powerful low-level detector called Contrast- Enhancement Maximally Stable Extremal Regions (CE-MSERs) is developed, which extends the widely-used MSERs by enhancing intensity contrast between text patterns and background. This allows it to detect highly challenging text patterns, resulting in a higher recall. Our approach achieved promising results on the ICDAR 2013 dataset, with a F-measure of 0.82, improving the state-of-the-art results substantially.
Single-trial EEG RSVP classification using convolutional neural networks
Shamwell, Jared; Lee, Hyungtae; Kwon, Heesung; Marathe, Amar R.; Lawhern, Vernon; Nothwang, William
2016-05-01
Traditionally, Brain-Computer Interfaces (BCI) have been explored as a means to return function to paralyzed or otherwise debilitated individuals. An emerging use for BCIs is in human-autonomy sensor fusion where physiological data from healthy subjects is combined with machine-generated information to enhance the capabilities of artificial systems. While human-autonomy fusion of physiological data and computer vision have been shown to improve classification during visual search tasks, to date these approaches have relied on separately trained classification models for each modality. We aim to improve human-autonomy classification performance by developing a single framework that builds codependent models of human electroencephalograph (EEG) and image data to generate fused target estimates. As a first step, we developed a novel convolutional neural network (CNN) architecture and applied it to EEG recordings of subjects classifying target and non-target image presentations during a rapid serial visual presentation (RSVP) image triage task. The low signal-to-noise ratio (SNR) of EEG inherently limits the accuracy of single-trial classification and when combined with the high dimensionality of EEG recordings, extremely large training sets are needed to prevent overfitting and achieve accurate classification from raw EEG data. This paper explores a new deep CNN architecture for generalized multi-class, single-trial EEG classification across subjects. We compare classification performance from the generalized CNN architecture trained across all subjects to the individualized XDAWN, HDCA, and CSP neural classifiers which are trained and tested on single subjects. Preliminary results show that our CNN meets and slightly exceeds the performance of the other classifiers despite being trained across subjects.
Output-sensitive 3D line integral convolution.
Falk, Martin; Weiskopf, Daniel
2008-01-01
We propose an output-sensitive visualization method for 3D line integral convolution (LIC) whose rendering speed is largely independent of the data set size and mostly governed by the complexity of the output on the image plane. Our approach of view-dependent visualization tightly links the LIC generation with the volume rendering of the LIC result in order to avoid the computation of unnecessary LIC points: early-ray termination and empty-space leaping techniques are used to skip the computation of the LIC integral in a lazy-evaluation approach; both ray casting and texture slicing can be used as volume-rendering techniques. The input noise is modeled in object space to allow for temporal coherence under object and camera motion. Different noise models are discussed, covering dense representations based on filtered white noise all the way to sparse representations similar to oriented LIC. Aliasing artifacts are avoided by frequency control over the 3D noise and by employing a 3D variant of MIPmapping. A range of illumination models is applied to the LIC streamlines: different codimension-2 lighting models and a novel gradient-based illumination model that relies on precomputed gradients and does not require any direct calculation of gradients after the LIC integral is evaluated. We discuss the issue of proper sampling of the LIC and volume-rendering integrals by employing a frequency-space analysis of the noise model and the precomputed gradients. Finally, we demonstrate that our visualization approach lends itself to a fast graphics processing unit (GPU) implementation that supports both steady and unsteady flow. Therefore, this 3D LIC method allows users to interactively explore 3D flow by means of high-quality, view-dependent, and adaptive LIC volume visualization. Applications to flow visualization in combination with feature extraction and focus-and-context visualization are described, a comparison to previous methods is provided, and a detailed performance
Particle Identification in Cherenkov Detectors using Convolutional Neural Networks
Theodore, Tomalty
2016-01-01
Cherenkov detectors are used for charged particle identification. When a charged particle moves through a medium faster than light can propagate in that medium, Cherenkov radiation is released in the shape of a cone in the direction of movement. The interior of the Cherenkov detector is instrumented with PMTs to detect this Cherenkov light. Particles, then, can be identified by the shapes of the images on the detector walls.
Inkjet printed multiwall carbon nanotube electrodes for dielectric elastomer actuators
Baechler, Curdin; Gardin, Samuele; Abuhimd, Hatem; Kovacs, Gabor
2016-05-01
Dielectric elastomers (DE’s) offer promising applications as soft and light-weight electromechanical actuators. It is known that beside the dielectric material, the electrode properties are of particular importance regarding the DE performance. Therefore, in recent years various studies have focused on the optimization of the electrode in terms of conductivity, stretchability and reliability. However, less attention was given to efficient electrode processing and deposition methods. In the present study, digital inkjet printing was used to deposit highly conductive and stretchable electrodes on silicone. Inkjet printing is a versatile and cost effective deposition method, which allows depositing complex-shaped electrode patterns with high precision. The electrodes were printed using an ink based on industrial low-cost MWCNT. Experiments have shown that the strain-conductivity properties of the printed electrode are strongly depended on the deposition parameters like drop-spacing and substrate temperature. After the optimization of the printing parameters, thin film electrodes could be deposited showing conductivities of up to 30 S cm-1 without the need of any post-treatment. In addition, electromechanical tests with fabricated DE actuators have revealed that the inkjet printed MWCNT electrodes are capable to self-clear in case of a dielectric breakdown.
Convolution effect on TCR log response curve and the correction method for it
Chen, Q.; Liu, L. J.; Gao, J.
2016-09-01
Through-casing resistivity (TCR) logging has been successfully used in production wells for the dynamic monitoring of oil pools and the distribution of the residual oil, but its vertical resolution has limited its efficiency in identification of thin beds. The vertical resolution is limited by the distortion phenomenon of vertical response of TCR logging. The distortion phenomenon was studied in this work. It was found that the vertical response curve of TCR logging is the convolution of the true formation resistivity and the convolution function of TCR logging tool. Due to the effect of convolution, the measurement error at thin beds can reach 30% or even bigger. Thus the information of thin bed might be covered up very likely. The convolution function of TCR logging tool was obtained in both continuous and discrete way in this work. Through modified Lyle-Kalman deconvolution method, the true formation resistivity can be optimally estimated, so this inverse algorithm can correct the error caused by the convolution effect. Thus it can improve the vertical resolution of TCR logging tool for identification of thin beds.
A fast convolution-based methodology to simulate 2-D/3-D cardiac ultrasound images.
Gao, Hang; Choi, Hon Fai; Claus, Piet; Boonen, Steven; Jaecques, Siegfried; Van Lenthe, G Harry; Van der Perre, Georges; Lauriks, Walter; D'hooge, Jan
2009-02-01
This paper describes a fast convolution-based methodology for simulating ultrasound images in a 2-D/3-D sector format as typically used in cardiac ultrasound. The conventional convolution model is based on the assumption of a space-invariant point spread function (PSF) and typically results in linear images. These characteristics are not representative for cardiac data sets. The spatial impulse response method (IRM) has excellent accuracy in the linear domain; however, calculation time can become an issue when scatterer numbers become significant and when 3-D volumetric data sets need to be computed. As a solution to these problems, the current manuscript proposes a new convolution-based methodology in which the data sets are produced by reducing the conventional 2-D/3-D convolution model to multiple 1-D convolutions (one for each image line). As an example, simulated 2-D/3-D phantom images are presented along with their gray scale histogram statistics. In addition, the computation time is recorded and contrasted to a commonly used implementation of IRM (Field II). It is shown that COLE can produce anatomically plausible images with local Rayleigh statistics but at improved calculation time (1200 times faster than the reference method).
Data convolution and combination operation (COCOA) for motion ghost artifacts reduction.
Huang, Feng; Lin, Wei; Börnert, Peter; Li, Yu; Reykowski, Arne
2010-07-01
A novel method, data convolution and combination operation, is introduced for the reduction of ghost artifacts due to motion or flow during data acquisition. Since neighboring k-space data points from different coil elements have strong correlations, a new "synthetic" k-space with dispersed motion artifacts can be generated through convolution for each coil. The corresponding convolution kernel can be self-calibrated using the acquired k-space data. The synthetic and the acquired data sets can be checked for consistency to identify k-space areas that are motion corrupted. Subsequently, these two data sets can be combined appropriately to produce a k-space data set showing a reduced level of motion induced error. If the acquired k-space contains isolated error, the error can be completely eliminated through data convolution and combination operation. If the acquired k-space data contain widespread errors, the application of the convolution also significantly reduces the overall error. Results with simulated and in vivo data demonstrate that this self-calibrated method robustly reduces ghost artifacts due to swallowing, breathing, or blood flow, with a minimum impact on the image signal-to-noise ratio. (c) 2010 Wiley-Liss, Inc.
Chacko, Nikhil; Liebling, Michael; Blu, Thierry
2013-10-01
Discretization of continuous (analog) convolution operators by direct sampling of the convolution kernel and use of fast Fourier transforms is highly efficient. However, it assumes the input and output signals are band-limited, a condition rarely met in practice, where signals have finite support or abrupt edges and sampling is nonideal. Here, we propose to approximate signals in analog, shift-invariant function spaces, which do not need to be band-limited, resulting in discrete coefficients for which we derive discrete convolution kernels that accurately model the analog convolution operator while taking into account nonideal sampling devices (such as finite fill-factor cameras). This approach retains the efficiency of direct sampling but not its limiting assumption. We propose fast forward and inverse algorithms that handle finite-length, periodic, and mirror-symmetric signals with rational sampling rates. We provide explicit convolution kernels for computing coherent wave propagation in the context of digital holography. When compared to band-limited methods in simulations, our method leads to fewer reconstruction artifacts when signals have sharp edges or when using nonideal sampling devices.
Iterative sinc-convolution method for solving planar D-bar equation with application to EIT.
Abbasi, Mahdi; Naghsh-Nilchi, Ahmad-Reza
2012-08-01
The numerical solution of D-bar integral equations is the key in inverse scattering solution of many complex problems in science and engineering including conductivity imaging. Recently, a couple of methodologies were considered for the numerical solution of D-bar integral equation, namely product integrals and multigrid. The first one involves high computational complexity and other one has low convergence rate disadvantages. In this paper, a new and efficient sinc-convolution algorithm is introduced to solve the two-dimensional D-bar integral equation to overcome both of these disadvantages and to resolve the singularity problem not tackled before effectively. The method of sinc-convolution is based on using collocation to replace multidimensional convolution-form integrals- including the two-dimensional D-bar integral equations - by a system of algebraic equations. Separation of variables in the proposed method allows elimination of the formulation of the huge full matrices and therefore reduces the computational complexity drastically. In addition, the sinc-convolution method converges exponentially with a convergence rate of O(e-cN). Simulation results on solving a test electrical impedance tomography problem confirm the efficiency of the proposed sinc-convolution-based algorithm. Copyright © 2012 John Wiley & Sons, Ltd.
1980-10-01
shortcut is available; note that on the right-hand side of Equation (26) the first term leads to Eular Convolution and the second to Mean Value...Convolution. Eular Convolution and Mean Value Convolution are just special cases of R-K(2,a) Convolution (see Table 2). TABLE 2. SPECIAL CASES OF R-K(2,a)C...Convolution Eular 0 Mean Value for 1/2 1/2 Trapezoidal I For a single real pole filter, F(s) - 1 (28) and any input, G(s), the approximation using R-K(2
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 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...
Nanostructured gold and platinum electrodes on silicon structures for biosensing
Ogurtsov, V. I.; Sheehan, M. M.
2005-01-01
Gold and platinum metal electrodes on Si/SiO2 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.
Note: electrode polarization of Galinstan electrodes for liquid impedance spectroscopy.
Mellor, Brett L; Kellis, Nathan A; Mazzeo, Brian A
2011-04-01
Electrode polarization is a significant obstacle in the impedance measurements of ionic liquids. An atomically smooth electrode surface could potentially reduce unwanted impedance contributions from electrode polarization. Liquid metal electrodes were formed by adhering Galinstan to acrylic plates in a parallel-plate capacitor arrangement. Electrode polarization was compared to a similar cell with stainless steel electrodes. The impedance of salt and protein solutions (β-lactoglobulin) was measured from 40 Hz to 110 MHz. Because of oxide layer formation, the performance of the Galinstan electrode is significantly different than the theoretical ideal.
1980-07-01
representation and analysis for their observed current distributions. Simonsson won the young author’s award of the Electrochemical Society for his paper...and T. Katan, Proc. Symp. Energy Storage and Conversion, the Electrochemical Society 77-6, 770 (1977) The optimum thickness of porous electrodes is...Chloride Electrodes; Surface Morphology on Charging and Dis- charging," T. Katan, S. Szpak, and D. N. Bennion, The Electrochemical Society , 143rd National
Minimal memory requirements for pearl necklace encoders of quantum convolutional codes
Houshmand, Monireh; Wilde, Mark M
2010-01-01
One of the major goals in quantum computer science is to reduce the overhead associated with the implementation of quantum computers, and inevitably, routines for quantum error correction will account for most of this overhead. A particular technique for quantum error correction that may be useful in the outer layers of a concatenated scheme for fault tolerance is quantum convolutional coding. The encoder for a quantum convolutional code has a representation as a convolutional encoder or as a "pearl necklace" encoder. In the pearl necklace representation, it has not been particularly clear in the research literature how much quantum memory such an encoder would require for implementation. Here, we offer an algorithm that answers this question. The algorithm first constructs a weighted, directed acyclic graph where each vertex of the graph corresponds to a gate string in the pearl necklace encoder, and each path through the graph represents a non-commutative path through gates in the encoder. We show that the ...
Deep Manifold Learning Combined With Convolutional Neural Networks for Action Recognition.
Chen, Xin; Weng, Jian; Lu, Wei; Xu, Jiaming; Weng, Jiasi
2017-09-15
Learning deep representations have been applied in action recognition widely. However, there have been a few investigations on how to utilize the structural manifold information among different action videos to enhance the recognition accuracy and efficiency. In this paper, we propose to incorporate the manifold of training samples into deep learning, which is defined as deep manifold learning (DML). The proposed DML framework can be adapted to most existing deep networks to learn more discriminative features for action recognition. When applied to a convolutional neural network, DML embeds the previous convolutional layer's manifold into the next convolutional layer; thus, the discriminative capacity of the next layer can be promoted. We also apply the DML on a restricted Boltzmann machine, which can alleviate the overfitting problem. Experimental results on four standard action databases (i.e., UCF101, HMDB51, KTH, and UCF sports) show that the proposed method outperforms the state-of-the-art methods.
Image retrieval method based on metric learning for convolutional neural network
Wang, Jieyuan; Qian, Ying; Ye, Qingqing; Wang, Biao
2017-09-01
At present, the research of content-based image retrieval (CBIR) focuses on learning effective feature for the representations of origin images and similarity measures. The retrieval accuracy and efficiency are crucial to a CBIR. With the rise of deep learning, convolutional network is applied in the domain of image retrieval and achieved remarkable results, but the image visual feature extraction of convolutional neural network exist high dimension problems, this problem makes the image retrieval and speed ineffective. This paper uses the metric learning for the image visual features extracted from the convolutional neural network, decreased the feature redundancy, improved the retrieval performance. The work in this paper is also a necessary part for further implementation of feature hashing to the approximate-nearest-neighbor (ANN) retrieval method.
Kudekar, Shrinivas; Urbanke, Ruediger
2010-01-01
Convolutional LDPC ensembles, introduced by Felstrom and Zigangirov, have excellent thresholds and these thresholds are rapidly increasing as a function of the average degree. Several variations on the basic theme have been proposed to date, all of which share the good performance characteristics of convolutional LDPC ensembles. We describe the fundamental mechanism which explains why "convolutional-like" or "spatially coupled" codes perform so well. In essence, the spatial coupling of the individual code structure has the effect of increasing the belief-propagation (BP) threshold of the new ensemble to its maximum possible value, namely the maximum-a-posteriori (MAP) threshold of the underlying ensemble. For this reason we call this phenomenon "threshold saturation." This gives an entirely new way of approaching capacity. One significant advantage of such a construction is that one can create capacity-approaching ensembles with an error correcting radius which is increasing in the blocklength. Our proof make...
Wu, Xuecheng; Wu, Yingchun; Yang, Jing; Wang, Zhihua; Zhou, Binwu; Gréhan, Gérard; Cen, Kefa
2013-05-20
Application of the modified convolution method to reconstruct digital inline holography of particle illuminated by an elliptical Gaussian beam is investigated. Based on the analysis on the formation of particle hologram using the Collins formula, the convolution method is modified to compensate the astigmatism by adding two scaling factors. Both simulated and experimental holograms of transparent droplets and opaque particles are used to test the algorithm, and the reconstructed images are compared with that using FRFT reconstruction. Results show that the modified convolution method can accurately reconstruct the particle image. This method has an advantage that the reconstructed images in different depth positions have the same size and resolution with the hologram. This work shows that digital inline holography has great potential in particle diagnostics in curvature containers.
Techniques of Electrode Fabrication
Guo, Liang; Li, Xinyong; Chen, Guohua
Electrochemical applications using many kinds of electrode materials as an advanced oxidation/reduction technique have been a focus of research by a number of groups during the last two decades. The electrochemical approach has been adopted successfully to develop various environmental applications, mainly including water and wastewater treatment, aqueous system monitoring, and solid surface analysis. In this chapter, a number of methods for the fabrication of film-structured electrode materials were selectively reviewed. Firstly, the thermal decomposition method is briefly described, followed by introducing chemical vapor deposition (CVD) strategy. Especially, much attention was focused on introducing the methods to produce diamond novel film electrode owing to its unique physical and chemical properties. The principle and influence factors of hot filament CVD and plasma enhanced CVD preparation were interpreted by refereeing recent reports. Finally, recent developments that address electro-oxidation/reduction issues and novel electrodes such as nano-electrode and boron-doped diamond electrode (BDD) are presented in the overview.
Cylindrical-shaped nanotube field effect transistor
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.
Sensor employing internal reference electrode
DEFF Research Database (Denmark)
2013-01-01
The present invention concerns a novel internal reference electrode as well as a novel sensing electrode for an improved internal reference oxygen sensor and the sensor employing same.......The present invention concerns a novel internal reference electrode as well as a novel sensing electrode for an improved internal reference oxygen sensor and the sensor employing same....
Measuring the PSF from aperture images of arbitrary shape--an algorithm.
Nakashima, P N H; Johnson, A W S
2003-01-01
A new algorithm for determining the point spread function (PSF) of digital imaging systems is presented. The input is an image of an aperture whose shape need not be regular. The aperture shape is refined to an effective sub-pixel resolution and the PSF of the system is determined by de-convolution, assuming uniform illumination and a step function edge. The method has been tested on theoretical aperture images of varying shape and PSF, with and without noise. Depending on the degree of noise, a known PSF can be recovered to an accuracy of between 0.2 and 0.8%. Some typical results are given for a Gatan Image Filter with a 794 YAG multiscan camera on a Philips EM 430 transmission electron microscope at 200 and 300 kV. An example of a de-convoluted convergent beam electron diffraction pattern is included. The algorithm tolerates a small amount of de-focus.
Measuring the PSF from aperture images of arbitrary shape--an algorithm
Energy Technology Data Exchange (ETDEWEB)
Nakashima, P.N.H.; Johnson, A.W.S
2003-02-15
A new algorithm for determining the point spread function (PSF) of digital imaging systems is presented. The input is an image of an aperture whose shape need not be regular. The aperture shape is refined to an effective sub-pixel resolution and the PSF of the system is determined by de-convolution, assuming uniform illumination and a step function edge. The method has been tested on theoretical aperture images of varying shape and PSF, with and without noise. Depending on the degree of noise, a known PSF can be recovered to an accuracy of between 0.2 and 0.8%. Some typical results are given for a Gatan Image Filter with a 794 YAG multiscan camera on a Philips EM 430 transmission electron microscope at 200 and 300 kV. An example of a de-convoluted convergent beam electron diffraction pattern is included. The algorithm tolerates a small amount of de-focus.
Perez-Carrasco, Jose Antonio; Acha, Begona; Serrano, Carmen; Camunas-Mesa, Luis; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabe
2010-04-01
Address-event representation (AER) is an emergent hardware technology which shows a high potential for providing in the near future a solid technological substrate for emulating brain-like processing structures. When used for vision, AER sensors and processors are not restricted to capturing and processing still image frames, as in commercial frame-based video technology, but sense and process visual information in a pixel-level event-based frameless manner. As a result, vision processing is practically simultaneous to vision sensing, since there is no need to wait for sensing full frames. Also, only meaningful information is sensed, communicated, and processed. Of special interest for brain-like vision processing are some already reported AER convolutional chips, which have revealed a very high computational throughput as well as the possibility of assembling large convolutional neural networks in a modular fashion. It is expected that in a near future we may witness the appearance of large scale convolutional neural networks with hundreds or thousands of individual modules. In the meantime, some research is needed to investigate how to assemble and configure such large scale convolutional networks for specific applications. In this paper, we analyze AER spiking convolutional neural networks for texture recognition hardware applications. Based on the performance figures of already available individual AER convolution chips, we emulate large scale networks using a custom made event-based behavioral simulator. We have developed a new event-based processing architecture that emulates with AER hardware Manjunath's frame-based feature recognition software algorithm, and have analyzed its performance using our behavioral simulator. Recognition rate performance is not degraded. However, regarding speed, we show that recognition can be achieved before an equivalent frame is fully sensed and transmitted.
Institute of Scientific and Technical Information of China (English)
亚玲
2007-01-01
<正>Teenagers have been of a new shape these days. They are about 20 pounds heavier than teenagers were 60 years ago. They are about four inches taller, too. These facts come from J. M. Tanner, a professor in England.
Change of Scale Formulas for Wiener Integrals Related to Fourier-Feynman Transform and Convolution
Directory of Open Access Journals (Sweden)
Bong Jin Kim
2014-01-01
Full Text Available Cameron and Storvick discovered change of scale formulas for Wiener integrals of functionals in Banach algebra S on classical Wiener space. Yoo and Skoug extended these results for functionals in the Fresnel class F(B and in a generalized Fresnel class FA1,A2 on abstract Wiener space. We express Fourier-Feynman transform and convolution product of functionals in S as limits of Wiener integrals. Moreover we obtain change of scale formulas for Wiener integrals related to Fourier-Feynman transform and convolution product of these functionals.
A convolutional learning system for object classification in 3-D Lidar data.
Prokhorov, Danil
2010-05-01
In this brief, a convolutional learning system for classification of segmented objects represented in 3-D as point clouds of laser reflections is proposed. Several novelties are discussed: (1) extension of the existing convolutional neural network (CNN) framework to direct processing of 3-D data in a multiview setting which may be helpful for rotation-invariant consideration, (2) improvement of CNN training effectiveness by employing a stochastic meta-descent (SMD) method, and (3) combination of unsupervised and supervised training for enhanced performance of CNN. CNN performance is illustrated on a two-class data set of objects in a segmented outdoor environment.
Pindza, Edson; Maré, Eben
2017-03-01
A modified discrete singular convolution method is proposed. The method is based on the single (SE) and double (DE) exponential transformation to speed up the convergence of the existing methods. Numerical computations are performed on a wide variety of singular boundary value and singular perturbed problems in one and two dimensions. The obtained results from discrete singular convolution methods based on single and double exponential transformations are compared with each other, and with the existing methods too. Numerical results confirm that these methods are considerably efficient and accurate in solving singular and regular problems. Moreover, the method can be applied to a wide class of nonlinear partial differential equations.
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...
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.
Meda Inequality for Rearrangements of the Convolution on the Heisenberg Group and Some Applications
Directory of Open Access Journals (Sweden)
V. S. Guliyev
2009-01-01
Full Text Available The Meda inequality for rearrangements of the convolution operator on the Heisenberg group ℍn is proved. By using the Meda inequality, an O'Neil-type inequality for the convolution is obtained. As applications of these results, some sufficient and necessary conditions for the boundedness of the fractional maximal operator MΩ,α and fractional integral operator IΩ,α with rough kernels in the spaces Lp(ℍn are found. Finally, we give some comments on the extension of our results to the case of homogeneous groups.
Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber
Acciarri, R; 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-Anadón, 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; Söldner-Rembold, S; Soleti, S R; Spentzouris, P; Spitz, J; John, J St; 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
2016-01-01
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.
Nascov, Victor; Logofătu, Petre Cătălin
2009-08-01
We describe a fast computational algorithm able to evaluate the Rayleigh-Sommerfeld diffraction formula, based on a special formulation of the convolution theorem and the fast Fourier transform. What is new in our approach compared to other algorithms is the use of a more general type of convolution with a scale parameter, which allows for independent sampling intervals in the input and output computation windows. Comparison between the calculations made using our algorithm and direct numeric integration show a very good agreement, while the computation speed is increased by orders of magnitude.
Implementation of large kernel 2-D convolution in limited FPGA resource
Zhong, Sheng; Li, Yang; Yan, Luxin; Zhang, Tianxu; Cao, Zhiguo
2007-12-01
2-D Convolution is a simple mathematical operation which is fundamental to many common image processing operators. Using FPGA to implement the convolver can greatly reduce the DSP's heavy burden in signal processing. But with the limit resource the FPGA can implement a convolver with small 2-D kernel. In this paper, An FIFO type line delayer is presented to serve as the data buffer for convolution to reduce the data fetching operation. A finite state machine is applied to control the reuse of multipliers and adders arrays. With these two techniques, a resource limited FPGA can be used to implement a larger kernel convolver which is commonly used in image process systems.
Transfer Function Bounds for Partial-unit-memory Convolutional Codes Based on Reduced State Diagram
Lee, P. J.
1984-01-01
The performance of a coding system consisting of a convolutional encoder and a Viterbi decoder is analytically found by the well-known transfer function bounding technique. For the partial-unit-memory byte-oriented convolutional encoder with m sub 0 binary memory cells and (k sub 0 m sub 0) inputs, a state diagram of 2(K) (sub 0) was for the transfer function bound. A reduced state diagram of (2 (m sub 0) +1) is used for easy evaluation of transfer function bounds for partial-unit-memory codes.
Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber
Acciarri, R.; Adams, C.; An, R.; Asaadi, J.; Auger, M.; Bagby, L.; Baller, B.; Barr, G.; Bass, M.; Bay, F.; Bishai, M.; Blake, A.; Bolton, T.; Bugel, L.; Camilleri, L.; Caratelli, D.; Carls, B.; Castillo Fernandez, R.; Cavanna, F.; Chen, H.; Church, E.; Cianci, D.; Collin, G. H.; Conrad, J. M.; Convery, M.; Crespo-Anadón, J. I.; Del Tutto, M.; Devitt, D.; Dytman, S.; Eberly, B.; Ereditato, A.; Escudero Sanchez, L.; Esquivel, J.; Fleming, B. T.; Foreman, W.; Furmanski, A. P.; Garvey, G. T.; Genty, V.; Goeldi, D.; Gollapinni, S.; Graf, N.; Gramellini, E.; Greenlee, H.; Grosso, R.; Guenette, R.; Hackenburg, A.; Hamilton, P.; Hen, O.; Hewes, J.; Hill, C.; Ho, J.; Horton-Smith, G.; James, C.; de Vries, J. Jan; Jen, C.-M.; Jiang, L.; Johnson, R. A.; Jones, B. J. P.; Joshi, J.; Jostlein, H.; Kaleko, D.; Karagiorgi, G.; Ketchum, W.; Kirby, B.; Kirby, M.; Kobilarcik, T.; Kreslo, I.; Laube, A.; Li, Y.; Lister, A.; Littlejohn, B. R.; Lockwitz, S.; Lorca, D.; Louis, W. C.; Luethi, M.; Lundberg, B.; Luo, X.; Marchionni, A.; Mariani, C.; Marshall, J.; Martinez Caicedo, D. A.; Meddage, V.; Miceli, T.; Mills, G. B.; Moon, J.; Mooney, M.; Moore, C. D.; Mousseau, J.; Murrells, R.; Naples, D.; Nienaber, P.; Nowak, J.; Palamara, O.; Paolone, V.; Papavassiliou, V.; Pate, S. F.; Pavlovic, Z.; Porzio, D.; Pulliam, G.; Qian, X.; Raaf, J. L.; Rafique, A.; Rochester, L.; von Rohr, C. Rudolf; Russell, B.; Schmitz, D. W.; Schukraft, A.; Seligman, W.; Shaevitz, M. H.; Sinclair, J.; Snider, E. L.; Soderberg, M.; Söldner-Rembold, S.; Soleti, S. R.; Spentzouris, P.; Spitz, J.; St. John, J.; Strauss, T.; Szelc, A. M.; Tagg, N.; Terao, K.; Thomson, M.; Toups, M.; Tsai, Y.-T.; Tufanli, S.; Usher, T.; Van de Water, R. G.; Viren, B.; Weber, M.; Weston, J.; Wickremasinghe, D. A.; Wolbers, S.; Wongjirad, T.; Woodruff, K.; Yang, T.; Zeller, G. P.; Zennamo, J.; Zhang, C.
2017-03-01
We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.
Patient-specific dosimetry based on quantitative SPECT imaging and 3D-DFT convolution
Energy Technology Data Exchange (ETDEWEB)
Akabani, G.; Hawkins, W.G.; Eckblade, M.B.; Leichner, P.K. [Univ. of Nebraska Medical Center, Omaha, NE (United States)
1999-01-01
The objective of this study was to validate the use of a 3-D discrete Fourier Transform (3D-DFT) convolution method to carry out the dosimetry for I-131 for soft tissues in radioimmunotherapy procedures. To validate this convolution method, mathematical and physical phantoms were used as a basis of comparison with Monte Carlo transport (MCT) calculations which were carried out using the EGS4 system code. The mathematical phantom consisted of a sphere containing uniform and nonuniform activity distributions. The physical phantom consisted of a cylinder containing uniform and nonuniform activity distributions. Quantitative SPECT reconstruction was carried out using the Circular Harmonic Transform (CHT) algorithm.
Positive convolution structure for a class of Heckman-Opdam hypergeometric functions of type BC
Rösler, Margit
2009-01-01
In this paper, we derive explicit product formulas and positive convolution structures for three continuous classes of Heckman-Opdam hypergeometric functions of type $BC$. For specific discrete series of multiplicities these hypergeometric functions occur as the spherical functions of non-compact Grassmann manifolds $G/K$ over one of the (skew) fields $\\mathbb F= \\mathbb R, \\mathbb C, \\mathbb H.$ We write the product formula of these spherical functions in an explicit form which allows analytic continuation with respect to the parameters. In each of the three cases, we obtain a series of hypergroup algebras which include the commutative convolution algebras of $K$-biinvariant functions on $G$.
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.
FEM numerical model study of electrosurgical dispersive electrode design parameters.
Pearce, John A
2015-01-01
Electrosurgical dispersive electrodes must safely carry the surgical current in monopolar procedures, such as those used in cutting, coagulation and radio frequency ablation (RFA). Of these, RFA represents the most stringent design constraint since ablation currents are often more than 1 to 2 Arms (continuous) for several minutes depending on the size of the lesion desired and local heat transfer conditions at the applicator electrode. This stands in contrast to standard surgical activations, which are intermittent, and usually less than 1 Arms, but for several seconds at a time. Dispersive electrode temperature rise is also critically determined by the sub-surface skin anatomy, thicknesses of the subcutaneous and supra-muscular fat, etc. Currently, we lack fundamental engineering design criteria that provide an estimating framework for preliminary designs of these electrodes. The lack of a fundamental design framework means that a large number of experiments must be conducted in order to establish a reasonable design. Previously, an attempt to correlate maximum temperatures in experimental work with the average current density-time product failed to yield a good match. This paper develops and applies a new measure of an electrode stress parameter that correlates well with both the previous experimental data and with numerical models of other electrode shapes. The finite element method (FEM) model work was calibrated against experimental RF lesions in porcine skin to establish the fundamental principle underlying dispersive electrode performance. The results can be used in preliminary electrode design calculations, experiment series design and performance evaluation.
Electroencephalogram measurement using polymer-based dry microneedle electrode
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.
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.
Dudley, G.; Mattle, T.
2011-10-01
Individual electrode EMFs of new and cycled Sony 18650 HC cells have been measured with the help of a lithium reference electrode inserted into complete cells. Results have revealed the relative contribution of each electrode to voltage hysteresis (the difference in cell EMF between charge and discharge at the same state of charge).They have also shown changes to the shape of the positive electrode EMF versus state of charge in cycled compared to beginning of life cells.
2015-12-15
convolution, activation functions, and pooling. For a model trained on classes, the output from the classification layer comprises + 1...Keypoint Density-based Region Proposal for Fine-Grained Object Detection and Classification using Regions with Convolutional Neural Network...Convolutional Neural Networks (CNNs) enable them to outperform conventional techniques on standard object detection and classification tasks, their
Wang, Haibo; Cruz-Roa, Angel; Basavanhally, Ajay; Gilmore, Hannah; Shih, Natalie; Feldman, Mike; Tomaszewski, John; Gonzalez, Fabio; Madabhushi, Anant
2014-10-01
Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is the mitotic count, which involves quantifying the number of cells in the process of dividing (i.e., undergoing mitosis) at a specific point in time. Currently, mitosis counting is done manually by a pathologist looking at multiple high power fields (HPFs) on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical, or textural attributes of mitoses or features learned with convolutional neural networks (CNN). Although handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely supervised feature generation methods, there is an appeal in attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. We present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color, and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing the performance
Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection
Wang, Haibo; Cruz-Roa, Angel; Basavanhally, Ajay; Gilmore, Hannah; Shih, Natalie; Feldman, Mike; Tomaszewski, John; Gonzalez, Fabio; Madabhushi, Anant
2014-03-01
Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is mitotic count, which involves quantifying the number of cells in the process of dividing (i.e. undergoing mitosis) at a specific point in time. Currently mitosis counting is done manually by a pathologist looking at multiple high power fields on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical or textural attributes of mitoses or features learned with convolutional neural networks (CNN). While handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely unsupervised feature generation methods, there is an appeal to attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. In this paper, we present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing performance by
Composite carbon foam electrode
Energy Technology Data Exchange (ETDEWEB)
Mayer, S.T.; Pekala, R.W.; Kaschmitter, J.L.
1997-05-06
Carbon aerogels used as a binder for granulated materials, including other forms of carbon and metal additives, are cast onto carbon or metal fiber substrates to form composite carbon thin film sheets. The thin film sheets are utilized in electrochemical energy storage applications, such as electrochemical double layer capacitors (aerocapacitors), lithium based battery insertion electrodes, fuel cell electrodes, and electrocapacitive deionization electrodes. The composite carbon foam may be formed by prior known processes, but with the solid particles being added during the liquid phase of the process, i.e. prior to gelation. The other forms of carbon may include carbon microspheres, carbon powder, carbon aerogel powder or particles, graphite carbons. Metal and/or carbon fibers may be added for increased conductivity. The choice of materials and fibers will depend on the electrolyte used and the relative trade off of system resistivity and power to system energy. 1 fig.
Composite carbon foam electrode
Energy Technology Data Exchange (ETDEWEB)
Mayer, Steven T. (San Leandro, CA); Pekala, Richard W. (Pleasant Hill, CA); Kaschmitter, James L. (Pleasanton, CA)
1997-01-01
Carbon aerogels used as a binder for granularized materials, including other forms of carbon and metal additives, are cast onto carbon or metal fiber substrates to form composite carbon thin film sheets. The thin film sheets are utilized in electrochemical energy storage applications, such as electrochemical double layer capacitors (aerocapacitors), lithium based battery insertion electrodes, fuel cell electrodes, and electrocapacitive deionization electrodes. The composite carbon foam may be formed by prior known processes, but with the solid particles being added during the liquid phase of the process, i.e. prior to gelation. The other forms of carbon may include carbon microspheres, carbon powder, carbon aerogel powder or particles, graphite carbons. Metal and/or carbon fibers may be added for increased conductivity. The choice of materials and fibers will depend on the electrolyte used and the relative trade off of system resistivty and power to system energy.
Error Analysis of Padding Schemes for DFT’s of Convolutions and Derivatives
2012-01-31
spectral techniques for geoid computations over large regions. Journal of Geodesy , 70(6), 357-373. Tziavos IN, Sideris MG, Forsberg R, Schwarz KP...362- 378. Zhang C (1995) A general formula and its inverse formula for gravimetric transformations by use of convolution and deconvolution techniques. Journal of Geodesy , 70(1-2), 51-64. 24
Shifting and Variational Properties for Fourier-Feynman Transform and Convolution
Directory of Open Access Journals (Sweden)
Byoung Soo Kim
2015-01-01
Full Text Available Shifting, scaling, modulation, and variational properties for Fourier-Feynman transform of functionals in a Banach algebra S are given. Cameron and Storvick's translation theorem can be obtained as a corollary of our result. We also study shifting, scaling, and modulation properties for the convolution product of functionals in S.
Ferre, S.; Hoenderop, J.G.J.; Bindels, R.J.M.
2011-01-01
In healthy individuals, Mg(2+) homeostasis is tightly regulated by the concerted action of intestinal absorption, exchange with bone, and renal excretion. The kidney, more precisely the distal convoluted tubule (DCT), is the final determinant of plasma Mg(2+) concentrations. Positional cloning strat
John Michael Salgado Cebola
2016-01-01
Comparative study between the performance of Convolutional Networks using pretrained models and statistical generative models on tasks of image classification in semi-supervised enviroments.Study of multiple ensembles using these techniques and generated data from estimated pdfs.Pretrained Convents, LDA, pLSA, Fisher Vectors, Sparse-coded SPMs, TSVMs being the key models worked upon.
Upper bounds on the number of errors corrected by a convolutional code
DEFF Research Database (Denmark)
Justesen, Jørn
2004-01-01
We derive upper bounds on the weights of error patterns that can be corrected by a convolutional code with given parameters, or equivalently we give bounds on the code rate for a given set of error patterns. The bounds parallel the Hamming bound for block codes by relating the number of error pat...
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
2014-11-17
scription task only requires a single convolutional network since the input consists of a single image. A variety of deep and multi- modal models [8...architectures for video description (see Figure 4). For each architecture, we assume we have predictions of objects, subjects, and verbs present in the video from
Strahl, Stefan B; Ramekers, Dyan; Nagelkerke, Marjolijn M B; Schwarz, Konrad E; Spitzer, Philipp; Klis, Sjaak F L; Grolman, Wilko; Versnel, Huib
2016-01-01
The electrically evoked compound action potential (eCAP) is a routinely performed measure of the auditory nerve in cochlear implant users. Using a convolution model of the eCAP, additional information about the neural firing properties can be obtained, which may provide relevant information about th
Fast 2D Convolutions and Cross-Correlations Using Scalable Architectures.
Carranza, Cesar; Llamocca, Daniel; Pattichis, Marios
2017-05-01
The manuscript describes fast and scalable architectures and associated algorithms for computing convolutions and cross-correlations. The basic idea is to map 2D convolutions and cross-correlations to a collection of 1D convolutions and cross-correlations in the transform domain. This is accomplished through the use of the discrete periodic radon transform for general kernels and the use of singular value decomposition -LU decompositions for low-rank kernels. The approach uses scalable architectures that can be fitted into modern FPGA and Zynq-SOC devices. Based on different types of available resources, for P×P blocks, 2D convolutions and cross-correlations can be computed in just O(P) clock cycles up to O(P(2)) clock cycles. Thus, there is a trade-off between performance and required numbers and types of resources. We provide implementations of the proposed architectures using modern programmable devices (Virtex-7 and Zynq-SOC). Based on the amounts and types of required resources, we show that the proposed approaches significantly outperform current methods.
New molecular players facilitating Mg(2+) reabsorption in the distal convoluted tubule.
Glaudemans, B.; Knoers, N.V.A.M.; Hoenderop, J.G.J.; Bindels, R.J.M.
2010-01-01
The renal distal convoluted tubule (DCT) has an essential role in maintaining systemic magnesium (Mg(2+)) concentration. The DCT is the final determinant of plasma Mg(2+) levels, as the more distal nephron segments are largely impermeable to Mg(2+). In the past decade, positional candidate strategie
Pawara, Pornntiwa; Okafor, Emmanuel; Surinta, Olarik; Schomaker, Lambertus; Wiering, Marco
2017-01-01
The use of machine learning and computer vision methods for recognizing different plants from images has attracted lots of attention from the community. This paper aims at comparing local feature descriptors and bags of visual words with different classifiers to deep convolutional neural networks (C
Convolutional neural network based sensor fusion for forward looking ground penetrating radar
Sakaguchi, Rayn; Crosskey, Miles; Chen, David; Walenz, Brett; Morton, Kenneth
2016-05-01
Forward looking ground penetrating radar (FLGPR) is an alternative buried threat sensing technology designed to offer additional standoff compared to downward looking GPR systems. Due to additional flexibility in antenna configurations, FLGPR systems can accommodate multiple sensor modalities on the same platform that can provide complimentary information. The different sensor modalities present challenges in both developing informative feature extraction methods, and fusing sensor information in order to obtain the best discrimination performance. This work uses convolutional neural networks in order to jointly learn features across two sensor modalities and fuse the information in order to distinguish between target and non-target regions. This joint optimization is possible by modifying the traditional image-based convolutional neural network configuration to extract data from multiple sources. The filters generated by this process create a learned feature extraction method that is optimized to provide the best discrimination performance when fused. This paper presents the results of applying convolutional neural networks and compares these results to the use of fusion performed with a linear classifier. This paper also compares performance between convolutional neural networks architectures to show the benefit of fusing the sensor information in different ways.
Yetter-Drinfel'd模与卷积Hopf模%Yetter-Drinfel'd Module and Convolution Module
Institute of Scientific and Technical Information of China (English)
张良云; 王栓宏
2002-01-01
In this paper, we first give a sufficient and necessary condition for a Hopf algebra to be a Yetter-Drinfe]'d module, and prove that the finite dual of a YetterDrinfel'd module is still a Yetter-Drinfel'd module. Finally, we introduce a concept of convolution module.
A generalized recursive convolution method for time-domain propagation in porous media.
Dragna, Didier; Pineau, Pierre; Blanc-Benon, Philippe
2015-08-01
An efficient numerical method, referred to as the auxiliary differential equation (ADE) method, is proposed to compute convolutions between relaxation functions and acoustic variables arising in sound propagation equations in porous media. For this purpose, the relaxation functions are approximated in the frequency domain by rational functions. The time variation of the convolution is thus governed by first-order differential equations which can be straightforwardly solved. The accuracy of the method is first investigated and compared to that of recursive convolution methods. It is shown that, while recursive convolution methods are first or second-order accurate in time, the ADE method does not introduce any additional error. The ADE method is then applied for outdoor sound propagation using the equations proposed by Wilson et al. in the ground [(2007). Appl. Acoust. 68, 173-200]. A first one-dimensional case is performed showing that only five poles are necessary to accurately approximate the relaxation functions for typical applications. Finally, the ADE method is used to compute sound propagation in a three-dimensional geometry over an absorbing ground. Results obtained with Wilson's equations are compared to those obtained with Zwikker and Kosten's equations and with an impedance surface for different flow resistivities.
Institute of Scientific and Technical Information of China (English)
WUMianbin; XIALiming; 等
2002-01-01
Continuous hydrolysis of chitosan was performed in a convoluted fibrous bed bioreactor (CFBB) with immobilized T. reesei. At dilution rate of 0.4d-1 and substrate concentration of 2% (mass vs. volume), the average degree of polymerization of hydrolysate can be kept at 1.25-1.35, which can be easily regulated by changing dilution rate or inlet chitosan concentration.
The beta-binomial convolution model for 2 × 2 tables with missing cell counts
Eisinga, Rob
2009-01-01
This paper considers the beta-binomial convolution model for the analysis of 2×2 tables with missing cell counts.We discuss maximumlikelihood (ML) parameter estimation using the expectation–maximization algorithm and study information loss relative to complete data estimators. We also examine bias o
A new family of windows--convolution windows and their applications
Institute of Scientific and Technical Information of China (English)
ZHANG; Jieqiu; LIANG; Changhong; CHEN; Yanpu
2005-01-01
A new family of windows is constructed by convolutions via a few rectangular windows with same time width and is thus referred to as convolution windows. The expressions of the second-order up to the eighth-order convolution windows in both the time and frequency domains are derived. Their applications in high accuracy harmonic analysis of periodic signals are investigated. Comparisons between the proposed windows and some known windows with the same width shows that, when the synchronous deviation of data sampling is slight, the proposed ones have the least effect of spectral leakage. Therefore, the new windows are well suited for high accuracy harmonic analysis and parameter estimation for periodic signals. The error analysis and computer simulations show that the estimation errors, corresponding to frequency,amplitude and phase of every harmonic component of a signal, are proportional to thepth power of the relative frequency deviation in case of the pth-order convolution window is applied to windowing signal of approximately p cycles. By introducing real time adjustment in sampling interval, the proposed algorithm can adaptively trace signal frequency and lead to less sampling synchronous deviation. The proposed approach has the advantages of easy implementation and high measure precision and can be used in harmonic analysis of quasi-periodic signals whose fundamental frequency drifts slowly with time.
Inverse Problems for a Parabolic Integrodifferential Equation in a Convolutional Weak Form
Directory of Open Access Journals (Sweden)
Kairi Kasemets
2013-01-01
Full Text Available We deduce formulas for the Fréchet derivatives of cost functionals of several inverse problems for a parabolic integrodifferential equation in a weak formulation. The method consists in the application of an integrated convolutional form of the weak problem and all computations are implemented in regular Sobolev spaces.
Schuh, Fabian
2012-01-01
In this paper we propose a matched decoding scheme for convolutionally encoded transmission over intersymbol interference (ISI) channels and devise a nonlinear trellis description. As an application we show that for coded continuous phase modulation (CPM) using a non-coherent receiver the number of states of the super trellis can be significantly reduced by means of a matched non-linear trellis encoder.
Mapping the Relationship between Cortical Convolution and Intelligence: Effects of Gender
Luders, Eileen; Narr, Katherine L.; Bilder, Robert M.; Szeszko, Philip R.; Gurbani, Mala N.; Hamilton, Liberty; Gaser, Christian
2008-01-01
The pronounced convolution of the human cortex may be a morphological substrate that supports some of our species’ most distinctive cognitive abilities. Therefore, individual intelligence within humans might be modulated by the degree of folding in certain cortical regions. We applied advanced methods to analyze cortical convolution at high spatial resolution and correlated those measurements with intelligence quotients. Within a large sample of healthy adult subjects (n = 65), we detected the most prominent correlations in the left medial hemisphere. More specifically, intelligence scores were positively associated with the degree of folding in the temporo-occipital lobe, particularly in the outermost section of the posterior cingulate gyrus (retrosplenial areas). Thus, this region might be an important contributor toward individual intelligence, either via modulating pathways to (pre)frontal regions or by serving as a location for the convergence of information. Prominent gender differences within the right frontal cortex were observed; females showed uncorrected significant positive correlations and males showed a nonsignificant trend toward negative correlations. It is possible that formerly described gender differences in regional convolution are associated with differences in the underlying architecture. This might lead to the development of sexually dimorphic information processing strategies and affect the relationship between intelligence and cortical convolution. PMID:18089578
Off-resonance artifacts correction with convolution in k-space (ORACLE).
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.
Producing data-based sensitivity kernels from convolution and correlation in exploration geophysics.
Chmiel, M. J.; Roux, P.; Herrmann, P.; Rondeleux, B.
2016-12-01
Many studies have shown that seismic interferometry can be used to estimate surface wave arrivals by correlation of seismic signals recorded at a pair of locations. In the case of ambient noise sources, the convergence towards the surface wave Green's functions is obtained with the criterion of equipartitioned energy. However, seismic acquisition with active, controlled sources gives more possibilities when it comes to interferometry. The use of controlled sources makes it possible to recover the surface wave Green's function between two points using either correlation or convolution. We investigate the convolutional and correlational approaches using land active-seismic data from exploration geophysics. The data were recorded on 10,710 vertical receivers using 51,808 sources (seismic vibrator trucks). The sources spacing is the same in both X and Y directions (30 m) which is known as a "carpet shooting". The receivers are placed in parallel lines with a spacing 150 m in the X direction and 30 m in the Y direction. Invoking spatial reciprocity between sources and receivers, correlation and convolution functions can thus be constructed between either pairs of receivers or pairs of sources. Benefiting from the dense acquisition, we extract sensitivity kernels from correlation and convolution measurements of the seismic data. These sensitivity kernels are subsequently used to produce phase-velocity dispersion curves between two points and to separate the higher mode from the fundamental mode for surface waves. Potential application to surface wave cancellation is also envisaged.
New molecular players facilitating Mg(2+) reabsorption in the distal convoluted tubule.
Glaudemans, B.; Knoers, N.V.A.M.; Hoenderop, J.G.J.; Bindels, R.J.M.
2010-01-01
The renal distal convoluted tubule (DCT) has an essential role in maintaining systemic magnesium (Mg(2+)) concentration. The DCT is the final determinant of plasma Mg(2+) levels, as the more distal nephron segments are largely impermeable to Mg(2+). In the past decade, positional candidate strategie
The zinc electrode - Its behaviour in the nickel oxide-zinc accumulator
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.
Mikhelson, Konstantin N
2013-01-01
Ion-selective electrodes (ISEs) have a wide range of applications in clinical, environmental, food and pharmaceutical analysis as well as further uses in chemistry and life sciences. Based on his profound experience as a researcher in ISEs and a course instructor, the author summarizes current knowledge for advanced teaching and training purposes with a particular focus on ionophore-based ISEs. Coverage includes the basics of measuring with ISEs, essential membrane potential theory and a comprehensive overview of the various classes of ion-selective electrodes. The principles of constructing I
DEFF Research Database (Denmark)
Jacobsen, Torben; Broers, G. H. J.
1977-01-01
SP, of theelectrode reaction. eta is the overvoltage at the electrode. This equation is appliedto a high temperature carbonate fuel cell. It is shown that the Peltier entropyterm by far exceeds the heat production due to the irreversible losses, and thatthe main part of heat evolved at the cathode is reabsorbed......The heat evolution at a single irreversibly working electrode is treated onthe basis of the Brønsted heat principle. The resulting equation is analogous to the expression for the total heat evolution in a galvanic cellwith the exception that –DeltaS is substituted by the Peltier entropy, Delta...
Reference Electrodes in Metal Corrosion
Directory of Open Access Journals (Sweden)
S. Szabó
2010-01-01
Full Text Available With especial regard to hydrogen electrode, the theoretical fundamentals of electrode potential, the most important reference electrodes and the electrode potential measurement have been discussed. In the case of the hydrogen electrode, it have been emphasised that there is no equilibrium between the hydrogen molecule (H2 and the hydrogen (H+, hydronium (H3O+ ion in the absence of a suitable catalyst. Taking into account the practical aspects as well, the theorectical basis of working of hydrogen, copper-copper sulphate, mercury-mercurous halide, silver-silver halide, metal-metal oxide, metal-metal sulphate and “Thalamid” electrodes, has been discussed.
Atmospheric Pressure Glow Discharge with Liquid Electrode
Tochikubo, Fumiyoshi
2013-09-01
Nonthermal atmospheric pressure plasmas in contact with liquid are widely studied aiming variety of plasma applications. DC glow discharge with liquid electrode is an easy method to obtain simple and stable plasma-liquid interface. When we focus attention on liquid-phase reaction, the discharge system is considered as electrolysis with plasma electrode. The plasma electrode will supply electrons and positive ions to the liquid surface in a different way from the conventional metal electrode. However, the phenomena at plasma-liquid interface have not been understood well. In this work, we studied physical and chemical effect in liquid induced by dc atmospheric pressure glow discharge with liquid electrode. The experiment was carried out using H-shaped Hoffman electrolysis apparatus filled with electrolyte, to separate the anodic and cathodic reactions. Two nozzle electrodes made of stainless steel are set about 2 mm above the liquid surface. By applying a dc voltage between the nozzle electrodes, dc glow discharges as plasma electrodes are generated in contact with liquid. As electrolyte, we used aqueous solutions of NaCl, Na2SO4, AgNO3 and HAuCl4. AgNO3 and HAuCl4 are to discuss the reduction process of metal ions for synthesis of nanoparticles (NPs). OH radical generation yield in liquid was measured by chemical probe method using terephthalic acid. Discharge-induced liquid flow was visualized by Schlieren method. Electron irradiation to liquid surface (plasma cathode) generated OH- and OH radical in liquid while positive ion irradiation (plasma anode) generated H+ and OH radical. The generation efficiency of OH radical was better with plasma anode. Both Ag NPs in AgNO3 and Au NPs in HAuCl4 were synthesized with plasma cathode while only Au NPs were generated with plasma anode. Possible reaction process is qualitatively discussed. The discharge-induced liquid flow such as convection pattern was strongly influenced by the gas flow on the liquid surface. This work
Virtual electrodes for high-density electrode arrays
Energy Technology Data Exchange (ETDEWEB)
Cela, Carlos Jose; Lazzi, Gianluca
2017-05-23
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.
Electrochemical and mechanical polishing and shaping method and system
Engelhaupt, Darell E. (Inventor); Gubarev, Mikhail V. (Inventor); Jones, William David (Inventor); Ramsey, Brian D. (Inventor); Benson, Carl M. (Inventor)
2011-01-01
A method and system are provided for the shaping and polishing of the surface of a material selected from the group consisting of electrically semi-conductive materials and conductive materials. An electrically non-conductive polishing lap incorporates a conductive electrode such that, when the polishing lap is placed on the material's surface, the electrode is placed in spaced-apart juxtaposition with respect to the material's surface. A liquid electrolyte is disposed between the material's surface and the electrode. The electrolyte has an electrochemical stability constant such that cathodic material deposition on the electrode is not supported when a current flows through the electrode, the electrolyte and the material. As the polishing lap and the material surface experience relative movement, current flows through the electrode based on (i) adherence to Faraday's Law, and (ii) a pre-processing profile of the surface and a desired post-processing profile of the surface.
Submicron electrode gaps fabricated by gold electrodeposition at interdigitated electrodes
Megen, M.J.J; Olthuis, W.; Berg, van den A.
2014-01-01
Electrodes with submicron gaps are desired for achieving high amplification redox cycling sensors. In this contribution we report the use of electrodeposition of gold in order to decrease the inter-electrode spacing at interdigitated electrodes. Using this method submicron spacings can be obtained w
Solution processing of back electrodes for organic solar cells with inverted architecture
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
Design of a MHD conduction machine with frame-type electrodes
Energy Technology Data Exchange (ETDEWEB)
Gel' fgat, Yu.M.; Gorbunov, L.A.
1977-01-01
An examination is made of a spatial channel model of a MHD conduction machine with frame type electrodes. The design was performed by the finite differences method. Relationships were obtained between the channel's basic magnetohydrodynamic characteristics and its form and the shape of the frame electrodes.
DEFF Research Database (Denmark)
Jacobsen, Torben; Broers, G. H. J.
1977-01-01
for the oxygen electrode reaction is estimatedfrom thermodynamic data and reasonable agreement with the experimentalresults is found. It is concluded that the main contribution to the Peltierentropy arises from the transition from gaseous to liquid state, whereas thetransfer entropies of the ionic species...
Directory of Open Access Journals (Sweden)
M. A. Lopez-Gordo
2014-07-01
Full Text Available Electroencephalography (EEG emerged in the second decade of the 20th century as a technique for recording the neurophysiological response. Since then, there has been little variation in the physical principles that sustain the signal acquisition probes, otherwise called electrodes. Currently, new advances in technology have brought new unexpected fields of applications apart from the clinical, for which new aspects such as usability and gel-free operation are first order priorities. Thanks to new advances in materials and integrated electronic systems technologies, a new generation of dry electrodes has been developed to fulfill the need. In this manuscript, we review current approaches to develop dry EEG electrodes for clinical and other applications, including information about measurement methods and evaluation reports. We conclude that, although a broad and non-homogeneous diversity of approaches has been evaluated without a consensus in procedures and methodology, their performances are not far from those obtained with wet electrodes, which are considered the gold standard, thus enabling the former to be a useful tool in a variety of novel applications.
Electroanalysis with carbon paste electrodes
Svancara, Ivan; Walcarius, Alain; Vytras, Karel
2011-01-01
Introduction to Electrochemistry and Electroanalysis with Carbon Paste-Based ElectrodesHistorical Survey and GlossaryField in Publication Activities and LiteratureCarbon Pastes and Carbon Paste ElectrodesCarbon Paste as the Binary MixtureClassification of Carbon Pastes and Carbon Paste ElectrodesConstruction of Carbon Paste HoldersCarbon Paste as the Electrode MaterialPhysicochemical Properties of Carbon PastesElectrochemical Characteristics of Carbon PastesTesting of Unmodified CPEsIntera
Ion-selective electrode reviews
Thomas, J D R
1985-01-01
Ion-Selective Electrode Reviews, Volume 7 is a collection of papers that covers the applications of electrochemical sensors, along with the versatility of ion-selective electrodes. The coverage of the text includes solid contact in membrane ion-selective electrodes; immobilized enzyme probes for determining inhibitors; potentiometric titrations based on ion-pair formation; and application of ion-selective electrodes in soil science, kinetics, and kinetic analysis. The text will be of great use to chemists and chemical engineers.
Implementation of Convolution Encoder and Viterbi Decoder for Constraint Length 7 and Bit Rate 1/2
Directory of Open Access Journals (Sweden)
Mr. Sandesh Y.M
2013-11-01
Full Text Available Convolutional codes are non blocking codes that can be designed to either error detecting or correcting. Convolution coding has been used in communication systems including deep space communication and wireless communication. At the receiver end the original message sequence is obtained from the received data using Viterbi decoder. It implements Viterbi Algorithm which is a maximum likelihood algorithm, based on the minimum cumulative hamming distance it decides the optimal trellis path that is most likely followed at the encoder. In this paper I present the convolution encoder and Viterbi decoder for constraint length 7 and bit rate 1/2.
Structural effects on water adsorption on gold electrodes
Garcia-Araez, N.; Rodriguez, P.; Navarro, V.; Bakker, H.J.; Koper, M.T.M.
2011-01-01
We study the molecular properties of the interface formed between aqueous sulfuric acid solutions and gold electrodes by means of surface-enhanced infrared absorption spectroscopy (SEIRAS). The shape of the SEIRAS spectra is observed to be strongly dependent on the deposition rate with which the gol
Structural effects on water adsorption on gold electrodes
Garcia-Araez, N.; Rodriguez, P.; Navarro, V.; Bakker, H.J.; Koper, M.T.M.
2011-01-01
We study the molecular properties of the interface formed between aqueous sulfuric acid solutions and gold electrodes by means of surface-enhanced infrared absorption spectroscopy (SEIRAS). The shape of the SEIRAS spectra is observed to be strongly dependent on the deposition rate with which the
Spontaneous branching of anode-directed streamers between planar electrodes
Arrayás, M.; Ebert, U.; Hundsdorfer, W.
2001-01-01
Non-ionized media subject to strong fields can become locally ionized by penetration of finger-shaped streamers. We study negative streamers between planar electrodes in a simple deterministic continuum approximation. We observe that for sufficiently large fields, the streamer tip can split. Th
Improvements and artifact analysis in conductivity images using multiple internal electrodes.
Farooq, Adnan; Tehrani, Joubin Nasehi; McEwan, Alistair Lee; Woo, Eung Je; Oh, Tong In
2014-06-01
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.
General bounds for electrode mislocation on the EEG inverse problem.
Beltrachini, L; von Ellenrieder, N; Muravchik, C H
2011-07-01
We analyze the effect of electrode mislocation on the electroencephalography (EEG) inverse problem using the Cramér-Rao bound (CRB) for single dipolar source parameters. We adopt a realistic head shape model, and solve the forward problem using the Boundary Element Method; the use of the CRB allows us to obtain general results which do not depend on the algorithm used for solving the inverse problem. We consider two possible causes for the electrode mislocation, errors in the measurement of the electrode positions and an imperfect registration between the electrodes and the scalp surfaces. For 120 electrodes placed in the scalp according to the 10-20 standard, and errors on the electrode location with a standard deviation of 5mm, the lower bound on the standard deviation in the source depth estimation is approximately 1mm in the worst case. Therefore, we conclude that errors in the electrode location may be tolerated since their effect on the EEG inverse problem are negligible from a practical point of view. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.
Medeiros, Maria C. R.; Mestre, Ana L. G.; Inácio, Pedro M. C.; Santos, José M. L.; Araujo, Inês M.; Bragança, José; Biscarini, Fabio; Gomes, Henrique L.
2016-09-01
Conducting polymer electrodes based on poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) are used to record extracellular signals from autonomous cardiac contractile cells and glioma cell cultures. The performance of these conducting polymer electrodes is compared with Au electrodes. A small-signal impedance analysis shows that in the presence of an electrolyte, both Au and polymer electrodes establish high capacitive double-layers. However, the polymer/electrolyte interfacial resistance is 3 orders of magnitude lower than the resistance of the metal/electrolyte interface. The polymer low interfacial resistance minimizes the intrinsic thermal noise and increases the system sensitivity. However, when measurements are carried out in current mode a low interfacial resistance partially acts as a short circuit of the interfacial capacitance, this affects the signal shape.
Electrode models in electrical impedance tomography
Institute of Scientific and Technical Information of China (English)
WANG M.
2005-01-01
This paper presents different views on electrode modelling, which include electrode electrochemistry models for modelling the effects of electrode-electrolyte interface, electric field electrode models for modelling electrode geometry, and electrode models for modelling the effects of electrode common mode voltage and double layer capacitance. Taking the full electrode models into consideration .in electrical impedance tomography (EIT) will greatly help the optimised approach to a good solution and further understanding of the measurement principle.
Ion-selective electrode reviews
Thomas, J D R
1983-01-01
Ion-Selective Electrode Reviews, Volume 5 is a collection of articles that covers ion-speciation. The book aims to present the advancements of the range and capabilities of selective ion-sensors. The topics covered in the selection are neutral carrier based ion-selective electrodes; reference electrodes and liquid junction effects in ion-selective electrode potentiometry; ion transfer across water/organic phase boundaries and analytical; and carbon substrate ion-selective electrodes. The text will be of great use to chemists and chemical engineers.
Magnetohydrodynamic generator electrode
Marchant, David D.; Killpatrick, Don H.; Herman, Harold; Kuczen, Kenneth D.
1979-01-01
An improved electrode for use as a current collector in the channel of a magnetohydrodynamid (MHD) generator utilizes an elongated monolithic cap of dense refractory material compliantly mounted to the MHD channel frame for collecting the current. The cap has a central longitudinal channel which contains a first layer of porous refractory ceramic as a high-temperature current leadout from the cap and a second layer of resilient wire mesh in contact with the first layer as a low-temperature current leadout between the first layer and the frame. Also described is a monolithic ceramic insulator compliantly mounted to the frame parallel to the electrode by a plurality of flexible metal strips.
Electrocatalysts for oxygen electrodes
Energy Technology Data Exchange (ETDEWEB)
Yeager, E.B. (Case Western Reserve Univ., Cleveland, OH (United States))
1991-10-01
The objectives of the research were: to develop further understanding of the factors controlling O{sub 2} reduction and generation on various electrocatalysts, including transition metal macrocycles and oxides: to use this understanding to identify and develop much higher activity catalysts, both monofunction and bifunction; and to establish how catalytic activity for a given O{sub 2} electrocatalyst depends on catalyst-support interactions and to identify stable catalyst supports for bifunctional electrodes.
Composite electrodes for lithium batteries.
Energy Technology Data Exchange (ETDEWEB)
Hackney, S. A.; Johnson, C. S.; Kahaian, A. J.; Kepler, K. D.; Shao-Horn, Y.; Thackeray, M. M.; Vaughey, J. T.
1999-02-03
The stability of composite positive and negative electrodes for rechargeable lithium batteries is discussed. Positive electrodes with spinel-type structures that are derived from orthorhombic-LiMnO{sub 2} and layered-MnO{sub 2} are significantly more stable than standard spinel Li[Mn{sub 2}]O{sub 4} electrodes when cycled electrochemically over both the 4-V and 3-V plateaus in lithium cells. Transmission electron microscope data of cycled electrodes have indicated that a composite domain structure accounts for this greater electrochemical stability. The performance of composite Cu{sub x}Sn materials as alternative negative electrodes to amorphous SnO{sub x} electrodes for lithium-ion batteries is discussed in terms of the importance of the concentration of the electrochemically inactive copper component in the electrode.
Sá, Ana Cravo; Coelho, Carina Marques; Monsanto, Fátima
2014-01-01
Objectivo do estudo: comparar o desempenho dos algoritmos Pencil Beam Convolution (PBC) e do Analytical Anisotropic Algorithm (AAA) no planeamento do tratamento de tumores de mama com radioterapia conformacional a 3D.
National Research Council Canada - National Science Library
Cang, Zixuan; Wei, Guowei
2017-01-01
.... This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet...
Sim, K S; Teh, V; Tey, Y C; Kho, T K
2016-11-01
This paper introduces new development technique to improve the Scanning Electron Microscope (SEM) image quality and we name it as sub-blocking multiple peak histogram equalization (SUB-B-MPHE) with convolution operator. By using this new proposed technique, it shows that the new modified MPHE performs better than original MPHE. In addition, the sub-blocking method consists of convolution operator which can help to remove the blocking effect for SEM images after applying this new developed technique. Hence, by using the convolution operator, it effectively removes the blocking effect by properly distributing the suitable pixel value for the whole image. Overall, the SUB-B-MPHE with convolution outperforms the rest of methods. SCANNING 38:492-501, 2016. © 2015 Wiley Periodicals, Inc. © Wiley Periodicals, Inc.
Calculation of the reactor neutron time of flight spectrum by convolution technique
Institute of Scientific and Technical Information of China (English)
Cheng Jin-Xing; Ouyang Xiao-Ping; Zheng Yi; Zhang An-Hui; Ouyang Mao-Jie
2008-01-01
It is a very complex and tlme-consuming process to simulate the nuclear reactor neutron spectrum from the reactor core to the export channel by applying a Monte Carlo program. This paper presents a new method to calculate the neutron spectrum by using the convolution technique which considers the channel transportation as a linear system and the transportation scattering as the response function. It also applies Monte Carlo Neutron and Photon Transport Code (MCNP) to simulate the response function numerically. With the application of convolution technique to calculate thespectrum distribution from the core to the channel, the process is then much more convenient only with the simple numerical integral numeration. This saves computer time and reduces some trouble in re-writing of the MCNP program.
Shkolyar, Anat; Gefen, Amit; Benayahu, Dafna; Greenspan, Hayit
2015-08-01
We propose a semi-automated pipeline for the detection of possible cell divisions in live-imaging microscopy and the classification of these mitosis candidates using a Convolutional Neural Network (CNN). We use time-lapse images of NIH3T3 scratch assay cultures, extract patches around bright candidate regions that then undergo segmentation and binarization, followed by a classification of the binary patches into either containing or not containing cell division. The classification is performed by training a Convolutional Neural Network on a specially constructed database. We show strong results of AUC = 0.91 and F-score = 0.89, competitive with state-of-the-art methods in this field.
Video-based convolutional neural networks for activity recognition from robot-centric videos
Ryoo, M. S.; Matthies, Larry
2016-05-01
In this evaluation paper, we discuss convolutional neural network (CNN)-based approaches for human activity recognition. In particular, we investigate CNN architectures designed to capture temporal information in videos and their applications to the human activity recognition problem. There have been multiple previous works to use CNN-features for videos. These include CNNs using 3-D XYT convolutional filters, CNNs using pooling operations on top of per-frame image-based CNN descriptors, and recurrent neural networks to learn temporal changes in per-frame CNN descriptors. We experimentally compare some of these different representatives CNNs while using first-person human activity videos. We especially focus on videos from a robots viewpoint, captured during its operations and human-robot interactions.
Multi-Task Learning for Food Identification and Analysis with Deep Convolutional Neural Networks
Institute of Scientific and Technical Information of China (English)
Xi-Jin Zhang; Yi-Fan Lu; Song-Hai Zhang
2016-01-01
In this paper, we proposed a multi-task system that can identify dish types, food ingredients, and cooking methods from food images with deep convolutional neural networks. We built up a dataset of 360 classes of different foods with at least 500 images for each class. To reduce the noises of the data, which was collected from the Internet, outlier images were detected and eliminated through a one-class SVM trained with deep convolutional features. We simultaneously trained a dish identifier, a cooking method recognizer, and a multi-label ingredient detector. They share a few low-level layers in the deep network architecture. The proposed framework shows higher accuracy than traditional method with handcrafted features, and the cooking method recognizer and ingredient detector can be applied to dishes which are not included in the training dataset to provide reference information for users.
On the Convolution Equation Related to the Diamond Klein-Gordon Operator
Directory of Open Access Journals (Sweden)
Amphon Liangprom
2011-01-01
Full Text Available We study the distribution eαx(♢+m2kδ for m≥0, where (♢+m2k is the diamond Klein-Gordon operator iterated k times, δ is the Dirac delta distribution, x=(x1,x2,…,xn is a variable in ℝn, and α=(α1,α2,…,αn is a constant. In particular, we study the application of eαx(♢+m2kδ for solving the solution of some convolution equation. We find that the types of solution of such convolution equation, such as the ordinary function and the singular distribution, depend on the relationship between k and M.