A convolution-superposition dose calculation engine for GPUs
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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.
The denoising of Monte Carlo dose distributions using convolution superposition calculations
International Nuclear Information System (INIS)
El Naqa, I; Cui, J; Lindsay, P; Olivera, G; Deasy, J O
2007-01-01
Monte Carlo (MC) dose calculations can be accurate but are also computationally intensive. In contrast, convolution superposition (CS) offers faster and smoother results but by making approximations. We investigated MC denoising techniques, which use available convolution superposition results and new noise filtering methods to guide and accelerate MC calculations. Two main approaches were developed to combine CS information with MC denoising. In the first approach, the denoising result is iteratively updated by adding the denoised residual difference between the result and the MC image. Multi-scale methods were used (wavelets or contourlets) for denoising the residual. The iterations are initialized by the CS data. In the second approach, we used a frequency splitting technique by quadrature filtering to combine low frequency components derived from MC simulations with high frequency components derived from CS components. The rationale is to take the scattering tails as well as dose levels in the high-dose region from the MC calculations, which presumably more accurately incorporates scatter; high-frequency details are taken from CS calculations. 3D Butterworth filters were used to design the quadrature filters. The methods were demonstrated using anonymized clinical lung and head and neck cases. The MC dose distributions were calculated by the open-source dose planning method MC code with varying noise levels. Our results indicate that the frequency-splitting technique for incorporating CS-guided MC denoising is promising in terms of computational efficiency and noise reduction. (note)
NOTE: The denoising of Monte Carlo dose distributions using convolution superposition calculations
El Naqa, I.; Cui, J.; Lindsay, P.; Olivera, G.; Deasy, J. O.
2007-09-01
Monte Carlo (MC) dose calculations can be accurate but are also computationally intensive. In contrast, convolution superposition (CS) offers faster and smoother results but by making approximations. We investigated MC denoising techniques, which use available convolution superposition results and new noise filtering methods to guide and accelerate MC calculations. Two main approaches were developed to combine CS information with MC denoising. In the first approach, the denoising result is iteratively updated by adding the denoised residual difference between the result and the MC image. Multi-scale methods were used (wavelets or contourlets) for denoising the residual. The iterations are initialized by the CS data. In the second approach, we used a frequency splitting technique by quadrature filtering to combine low frequency components derived from MC simulations with high frequency components derived from CS components. The rationale is to take the scattering tails as well as dose levels in the high-dose region from the MC calculations, which presumably more accurately incorporates scatter; high-frequency details are taken from CS calculations. 3D Butterworth filters were used to design the quadrature filters. The methods were demonstrated using anonymized clinical lung and head and neck cases. The MC dose distributions were calculated by the open-source dose planning method MC code with varying noise levels. Our results indicate that the frequency-splitting technique for incorporating CS-guided MC denoising is promising in terms of computational efficiency and noise reduction.
International Nuclear Information System (INIS)
Lu Weiguo; Olivera, Gustavo H; Chen Mingli; Reckwerdt, Paul J; Mackie, Thomas R
2005-01-01
Convolution/superposition (C/S) is regarded as the standard dose calculation method in most modern radiotherapy treatment planning systems. Different implementations of C/S could result in significantly different dose distributions. This paper addresses two major implementation issues associated with collapsed cone C/S: one is how to utilize the tabulated kernels instead of analytical parametrizations and the other is how to deal with voxel size effects. Three methods that utilize the tabulated kernels are presented in this paper. These methods differ in the effective kernels used: the differential kernel (DK), the cumulative kernel (CK) or the cumulative-cumulative kernel (CCK). They result in slightly different computation times but significantly different voxel size effects. Both simulated and real multi-resolution dose calculations are presented. For simulation tests, we use arbitrary kernels and various voxel sizes with a homogeneous phantom, and assume forward energy transportation only. Simulations with voxel size up to 1 cm show that the CCK algorithm has errors within 0.1% of the maximum gold standard dose. Real dose calculations use a heterogeneous slab phantom, both the 'broad' (5 x 5 cm 2 ) and the 'narrow' (1.2 x 1.2 cm 2 ) tomotherapy beams. Various voxel sizes (0.5 mm, 1 mm, 2 mm, 4 mm and 8 mm) are used for dose calculations. The results show that all three algorithms have negligible difference (0.1%) for the dose calculation in the fine resolution (0.5 mm voxels). But differences become significant when the voxel size increases. As for the DK or CK algorithm in the broad (narrow) beam dose calculation, the dose differences between the 0.5 mm voxels and the voxels up to 8 mm (4 mm) are around 10% (7%) of the maximum dose. As for the broad (narrow) beam dose calculation using the CCK algorithm, the dose differences between the 0.5 mm voxels and the voxels up to 8 mm (4 mm) are around 1% of the maximum dose. Among all three methods, the CCK algorithm
Directory of Open Access Journals (Sweden)
Tamer Dawod
2015-01-01
Full Text Available Purpose: This work investigated the accuracy of prowess treatment planning system (TPS in dose calculation in a homogenous phantom for symmetric and asymmetric field sizes using collapse cone convolution / superposition algorithm (CCCS. Methods: The measurements were carried out at source-to-surface distance (SSD set to 100 cm for 6 and 10 MV photon beams. Data for a full set of measurements for symmetric fields and asymmetric fields, including inplane and crossplane profiles at various depths and percentage depth doses (PDDs were obtained during measurements on the linear accelerator.Results: The results showed that the asymmetric collimation dose lead to significant errors (up to approximately 7% in dose calculations if changes in primary beam intensity and beam quality. It is obvious that the most difference in the isodose curves was found in buildup and the penumbra regions. Conclusion: The results showed that the dose calculation using Prowess TPS based on CCCS algorithm is generally in excellent agreement with measurements.
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Stathakis, Sotirios [Department of Radiation Oncology, University of Texas Health Science Center San Antonio, 7979 Wurzbach Rd, San Antonio, TX 78229 (United States)], E-mail: stathakis@uthscsa.edu; Esquivel, Carlos; Gutierrez, Alonso N.; Shi, ChengYu; Papanikolaou, Niko [Department of Radiation Oncology, University of Texas Health Science Center San Antonio, 7979 Wurzbach Rd, San Antonio, TX 78229 (United States)
2009-10-15
Purpose: In this paper, we present an alternative to the originally proposed technique for the delivery of spatially fractionated radiation therapy (GRID) using multi-leaf collimator (MLC) shaped fields. We employ the MLC to deliver various pattern GRID treatments to large solid tumors and dosimetrically characterize the GRID fields. Methods and materials: The GRID fields were created with different open to blocked area ratios and with variable separation between the openings using a MLC. GRID designs were introduced into the Pinnacle{sup 3} treatment planning system, and the dose was calculated in a water phantom. Ionization chamber and film measurements using both Kodak EDR2 and Gafchromic EBT film were performed in a SolidWater phantom to determine the relative output of each GRID design as well as its spatial dosimetric characteristics. Results: Agreement within 5.0% was observed between the Pinnacle{sup 3} predicted dose distributions and the measurements for the majority of experiments performed. A higher magnitude of discrepancy (15%) was observed using a high photon beam energy (18 MV) and small GRID opening. Skin dose at the GRID openings was higher than the corresponding open field by a factor as high as three for both photon energies and was found to be independent of the open-to-blocked area ratio. Conclusion: In summary, we reaffirm that the MLC can be used to deliver spatially fractionated GRID therapy and show that various GRID patterns may be generated. The Pinnacle{sup 3} TPS can accurately calculate the dose of the different GRID patterns in our study to within 5% for the majority of the cases based on film and ion chamber measurements. Disadvantages of MLC-based GRID therapy are longer treatment times and higher surface doses.
Ultrafast convolution/superposition using tabulated and exponential kernels on GPU
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Chen Quan; Chen Mingli; Lu Weiguo [TomoTherapy Inc., 1240 Deming Way, Madison, Wisconsin 53717 (United States)
2011-03-15
Purpose: Collapsed-cone convolution/superposition (CCCS) dose calculation is the workhorse for IMRT dose calculation. The authors present a novel algorithm for computing CCCS dose on the modern graphic processing unit (GPU). Methods: The GPU algorithm includes a novel TERMA calculation that has no write-conflicts and has linear computation complexity. The CCCS algorithm uses either tabulated or exponential cumulative-cumulative kernels (CCKs) as reported in literature. The authors have demonstrated that the use of exponential kernels can reduce the computation complexity by order of a dimension and achieve excellent accuracy. Special attentions are paid to the unique architecture of GPU, especially the memory accessing pattern, which increases performance by more than tenfold. Results: As a result, the tabulated kernel implementation in GPU is two to three times faster than other GPU implementations reported in literature. The implementation of CCCS showed significant speedup on GPU over single core CPU. On tabulated CCK, speedups as high as 70 are observed; on exponential CCK, speedups as high as 90 are observed. Conclusions: Overall, the GPU algorithm using exponential CCK is 1000-3000 times faster over a highly optimized single-threaded CPU implementation using tabulated CCK, while the dose differences are within 0.5% and 0.5 mm. This ultrafast CCCS algorithm will allow many time-sensitive applications to use accurate dose calculation.
Fluence-convolution broad-beam (FCBB) dose calculation
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Lu Weiguo; Chen Mingli, E-mail: wlu@tomotherapy.co [TomoTherapy Inc., 1240 Deming Way, Madison, WI 53717 (United States)
2010-12-07
IMRT optimization requires a fast yet relatively accurate algorithm to calculate the iteration dose with small memory demand. In this paper, we present a dose calculation algorithm that approaches these goals. By decomposing the infinitesimal pencil beam (IPB) kernel into the central axis (CAX) component and lateral spread function (LSF) and taking the beam's eye view (BEV), we established a non-voxel and non-beamlet-based dose calculation formula. Both LSF and CAX are determined by a commissioning procedure using the collapsed-cone convolution/superposition (CCCS) method as the standard dose engine. The proposed dose calculation involves a 2D convolution of a fluence map with LSF followed by ray tracing based on the CAX lookup table with radiological distance and divergence correction, resulting in complexity of O(N{sup 3}) both spatially and temporally. This simple algorithm is orders of magnitude faster than the CCCS method. Without pre-calculation of beamlets, its implementation is also orders of magnitude smaller than the conventional voxel-based beamlet-superposition (VBS) approach. We compared the presented algorithm with the CCCS method using simulated and clinical cases. The agreement was generally within 3% for a homogeneous phantom and 5% for heterogeneous and clinical cases. Combined with the 'adaptive full dose correction', the algorithm is well suitable for calculating the iteration dose during IMRT optimization.
International Nuclear Information System (INIS)
Sterpin, E.; Salvat, F.; Olivera, G.; Vynckier, S.
2009-01-01
The reliability of the convolution/superposition (C/S) algorithm of the Hi-Art tomotherapy system is evaluated by using the Monte Carlo model TomoPen, which has been already validated for homogeneous phantoms. The study was performed in three stages. First, measurements with EBT Gafchromic film for a 1.25x2.5 cm 2 field in a heterogeneous phantom consisting of two slabs of polystyrene separated with Styrofoam were compared to simulation results from TomoPen. The excellent agreement found in this comparison justifies the use of TomoPen as the reference for the remaining parts of this work. Second, to allow analysis and interpretation of the results in clinical cases, dose distributions calculated with TomoPen and C/S were compared for a similar phantom geometry, with multiple slabs of various densities. Even in conditions of lack of lateral electronic equilibrium, overall good agreement was obtained between C/S and TomoPen results, with deviations within 3%/2 mm, showing that the C/S algorithm accounts for modifications in secondary electron transport due to the presence of a low density medium. Finally, calculations were performed with TomoPen and C/S of dose distributions in various clinical cases, from large bilateral head and neck tumors to small lung tumors with diameter of <3 cm. To ensure a ''fair'' comparison, identical dose calculation grid and dose-volume histogram calculator were used. Very good agreement was obtained for most of the cases, with no significant differences between the DVHs obtained from both calculations. However, deviations of up to 4% for the dose received by 95% of the target volume were found for the small lung tumors. Therefore, the approximations in the C/S algorithm slightly influence the accuracy in small lung tumors even though the C/S algorithm of the tomotherapy system shows very good overall behavior.
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Sterpin, E.; Salvat, F.; Olivera, G.; Vynckier, S. [Department of Radiotherapy, Saint-Luc University Hospital, Universite Catholique de Louvain, 10 Avenue Hippocrate, 1200 Brussels (Belgium); Facultat de Fisica (ECM), Universitat de Barcelona, Diagonal 647, 08028 Barcelona (Spain); Tomotherapy Inc., 1240 Deming Way, Madison, Wisconsin 53717 and Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin 53705 (United States); Department of Radiotherapy, Saint-Luc University Hospital, Universite Catholique de Louvain, 10 Avenue Hippocrate, 1200 Brussels (Belgium)
2009-05-15
The reliability of the convolution/superposition (C/S) algorithm of the Hi-Art tomotherapy system is evaluated by using the Monte Carlo model TomoPen, which has been already validated for homogeneous phantoms. The study was performed in three stages. First, measurements with EBT Gafchromic film for a 1.25x2.5 cm{sup 2} field in a heterogeneous phantom consisting of two slabs of polystyrene separated with Styrofoam were compared to simulation results from TomoPen. The excellent agreement found in this comparison justifies the use of TomoPen as the reference for the remaining parts of this work. Second, to allow analysis and interpretation of the results in clinical cases, dose distributions calculated with TomoPen and C/S were compared for a similar phantom geometry, with multiple slabs of various densities. Even in conditions of lack of lateral electronic equilibrium, overall good agreement was obtained between C/S and TomoPen results, with deviations within 3%/2 mm, showing that the C/S algorithm accounts for modifications in secondary electron transport due to the presence of a low density medium. Finally, calculations were performed with TomoPen and C/S of dose distributions in various clinical cases, from large bilateral head and neck tumors to small lung tumors with diameter of <3 cm. To ensure a ''fair'' comparison, identical dose calculation grid and dose-volume histogram calculator were used. Very good agreement was obtained for most of the cases, with no significant differences between the DVHs obtained from both calculations. However, deviations of up to 4% for the dose received by 95% of the target volume were found for the small lung tumors. Therefore, the approximations in the C/S algorithm slightly influence the accuracy in small lung tumors even though the C/S algorithm of the tomotherapy system shows very good overall behavior.
A nonvoxel-based dose convolution/superposition algorithm optimized for scalable GPU architectures
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Neylon, J., E-mail: jneylon@mednet.ucla.edu; Sheng, K.; Yu, V.; Low, D. A.; Kupelian, P.; Santhanam, A. [Department of Radiation Oncology, University of California Los Angeles, 200 Medical Plaza, #B265, Los Angeles, California 90095 (United States); Chen, Q. [Department of Radiation Oncology, University of Virginia, 1300 Jefferson Park Avenue, Charlottesville, California 22908 (United States)
2014-10-15
, respectively. Accuracy was investigated using three distinct phantoms with varied geometries and heterogeneities and on a series of 14 segmented lung CT data sets. Performance gains were calculated using three 256 mm cube homogenous water phantoms, with isotropic voxel dimensions of 1, 2, and 4 mm. Results: The nonvoxel-based GPU algorithm was independent of the data size and provided significant computational gains over the CPU algorithm for large CT data sizes. The parameter search analysis also showed that the ray combination of 8 zenithal and 8 azimuthal angles along with 1 mm radial sampling and 2 mm parallel ray spacing maintained dose accuracy with greater than 99% of voxels passing the γ test. Combining the acceleration obtained from GPU parallelization with the sampling optimization, the authors achieved a total performance improvement factor of >175 000 when compared to our voxel-based ground truth CPU benchmark and a factor of 20 compared with a voxel-based GPU dose convolution method. Conclusions: The nonvoxel-based convolution method yielded substantial performance improvements over a generic GPU implementation, while maintaining accuracy as compared to a CPU computed ground truth dose distribution. Such an algorithm can be a key contribution toward developing tools for adaptive radiation therapy systems.
A nonvoxel-based dose convolution/superposition algorithm optimized for scalable GPU architectures
International Nuclear Information System (INIS)
Neylon, J.; Sheng, K.; Yu, V.; Low, D. A.; Kupelian, P.; Santhanam, A.; Chen, Q.
2014-01-01
, respectively. Accuracy was investigated using three distinct phantoms with varied geometries and heterogeneities and on a series of 14 segmented lung CT data sets. Performance gains were calculated using three 256 mm cube homogenous water phantoms, with isotropic voxel dimensions of 1, 2, and 4 mm. Results: The nonvoxel-based GPU algorithm was independent of the data size and provided significant computational gains over the CPU algorithm for large CT data sizes. The parameter search analysis also showed that the ray combination of 8 zenithal and 8 azimuthal angles along with 1 mm radial sampling and 2 mm parallel ray spacing maintained dose accuracy with greater than 99% of voxels passing the γ test. Combining the acceleration obtained from GPU parallelization with the sampling optimization, the authors achieved a total performance improvement factor of >175 000 when compared to our voxel-based ground truth CPU benchmark and a factor of 20 compared with a voxel-based GPU dose convolution method. Conclusions: The nonvoxel-based convolution method yielded substantial performance improvements over a generic GPU implementation, while maintaining accuracy as compared to a CPU computed ground truth dose distribution. Such an algorithm can be a key contribution toward developing tools for adaptive radiation therapy systems
Point kernels and superposition methods for scatter dose calculations in brachytherapy
International Nuclear Information System (INIS)
Carlsson, A.K.
2000-01-01
Point kernels have been generated and applied for calculation of scatter dose distributions around monoenergetic point sources for photon energies ranging from 28 to 662 keV. Three different approaches for dose calculations have been compared: a single-kernel superposition method, a single-kernel superposition method where the point kernels are approximated as isotropic and a novel 'successive-scattering' superposition method for improved modelling of the dose from multiply scattered photons. An extended version of the EGS4 Monte Carlo code was used for generating the kernels and for benchmarking the absorbed dose distributions calculated with the superposition methods. It is shown that dose calculation by superposition at and below 100 keV can be simplified by using isotropic point kernels. Compared to the assumption of full in-scattering made by algorithms currently in clinical use, the single-kernel superposition method improves dose calculations in a half-phantom consisting of air and water. Further improvements are obtained using the successive-scattering superposition method, which reduces the overestimates of dose close to the phantom surface usually associated with kernel superposition methods at brachytherapy photon energies. It is also shown that scatter dose point kernels can be parametrized to biexponential functions, making them suitable for use with an effective implementation of the collapsed cone superposition algorithm. (author)
International Nuclear Information System (INIS)
Mihaylov, I. B.; Siebers, J. V.
2008-01-01
The purpose of this study is to evaluate dose prediction errors (DPEs) and optimization convergence errors (OCEs) resulting from use of a superposition/convolution dose calculation algorithm in deliverable intensity-modulated radiation therapy (IMRT) optimization for head-and-neck (HN) patients. Thirteen HN IMRT patient plans were retrospectively reoptimized. The IMRT optimization was performed in three sequential steps: (1) fast optimization in which an initial nondeliverable IMRT solution was achieved and then converted to multileaf collimator (MLC) leaf sequences; (2) mixed deliverable optimization that used a Monte Carlo (MC) algorithm to account for the incident photon fluence modulation by the MLC, whereas a superposition/convolution (SC) dose calculation algorithm was utilized for the patient dose calculations; and (3) MC deliverable-based optimization in which both fluence and patient dose calculations were performed with a MC algorithm. DPEs of the mixed method were quantified by evaluating the differences between the mixed optimization SC dose result and a MC dose recalculation of the mixed optimization solution. OCEs of the mixed method were quantified by evaluating the differences between the MC recalculation of the mixed optimization solution and the final MC optimization solution. The results were analyzed through dose volume indices derived from the cumulative dose-volume histograms for selected anatomic structures. Statistical equivalence tests were used to determine the significance of the DPEs and the OCEs. Furthermore, a correlation analysis between DPEs and OCEs was performed. The evaluated DPEs were within ±2.8% while the OCEs were within 5.5%, indicating that OCEs can be clinically significant even when DPEs are clinically insignificant. The full MC-dose-based optimization reduced normal tissue dose by as much as 8.5% compared with the mixed-method optimization results. The DPEs and the OCEs in the targets had correlation coefficients greater
Starting SCF Calculations by Superposition of Atomic Densities
van Lenthe, J.H.; Zwaans, R.; van Dam, H.J.J.; Guest, M.F.
2006-01-01
We describe the procedure to start an SCF calculation of the general type from a sum of atomic electron densities, as implemented in GAMESS-UK. Although the procedure is well-known for closed-shell calculations and was already suggested when the Direct SCF procedure was proposed, the general
Energy Technology Data Exchange (ETDEWEB)
Lee, K; Leung, R; Law, G; Wong, M; Lee, V; Tung, S; Cheung, S; Chan, M [Tuen Mun Hospital, Hong Kong (Hong Kong)
2016-06-15
Background: Commercial treatment planning system Pinnacle3 (Philips, Fitchburg, WI, USA) employs a convolution-superposition algorithm for volumetric-modulated arc radiotherapy (VMAT) optimization and dose calculation. Study of Monte Carlo (MC) dose recalculation of VMAT plans for advanced-stage nasopharyngeal cancers (NPC) is currently limited. Methods: Twenty-nine VMAT prescribed 70Gy, 60Gy, and 54Gy to the planning target volumes (PTVs) were included. These clinical plans achieved with a CS dose engine on Pinnacle3 v9.0 were recalculated by the Monaco TPS v5.0 (Elekta, Maryland Heights, MO, USA) with a XVMC-based MC dose engine. The MC virtual source model was built using the same measurement beam dataset as for the Pinnacle beam model. All MC recalculation were based on absorbed dose to medium in medium (Dm,m). Differences in dose constraint parameters per our institution protocol (Supplementary Table 1) were analyzed. Results: Only differences in maximum dose to left brachial plexus, left temporal lobe and PTV54Gy were found to be statistically insignificant (p> 0.05). Dosimetric differences of other tumor targets and normal organs are found in supplementary Table 1. Generally, doses outside the PTV in the normal organs are lower with MC than with CS. This is also true in the PTV54-70Gy doses but higher dose in the nasal cavity near the bone interfaces is consistently predicted by MC, possibly due to the increased backscattering of short-range scattered photons and the secondary electrons that is not properly modeled by the CS. The straight shoulders of the PTV dose volume histograms (DVH) initially resulted from the CS optimization are merely preserved after MC recalculation. Conclusion: Significant dosimetric differences in VMAT NPC plans were observed between CS and MC calculations. Adjustments of the planning dose constraints to incorporate the physics differences from conventional CS algorithm should be made when VMAT optimization is carried out directly
FAST-PT: a novel algorithm to calculate convolution integrals in cosmological perturbation theory
Energy Technology Data Exchange (ETDEWEB)
McEwen, Joseph E.; Fang, Xiao; Hirata, Christopher M.; Blazek, Jonathan A., E-mail: mcewen.24@osu.edu, E-mail: fang.307@osu.edu, E-mail: hirata.10@osu.edu, E-mail: blazek@berkeley.edu [Center for Cosmology and AstroParticle Physics, Department of Physics, The Ohio State University, 191 W Woodruff Ave, Columbus OH 43210 (United States)
2016-09-01
We present a novel algorithm, FAST-PT, for performing convolution or mode-coupling integrals that appear in nonlinear cosmological perturbation theory. The algorithm uses several properties of gravitational structure formation—the locality of the dark matter equations and the scale invariance of the problem—as well as Fast Fourier Transforms to describe the input power spectrum as a superposition of power laws. This yields extremely fast performance, enabling mode-coupling integral computations fast enough to embed in Monte Carlo Markov Chain parameter estimation. We describe the algorithm and demonstrate its application to calculating nonlinear corrections to the matter power spectrum, including one-loop standard perturbation theory and the renormalization group approach. We also describe our public code (in Python) to implement this algorithm. The code, along with a user manual and example implementations, is available at https://github.com/JoeMcEwen/FAST-PT.
Superposition of configurations in semiempirical calculation of iron group ion spectra
International Nuclear Information System (INIS)
Kantseryavichyus, A.Yu.; Ramonas, A.A.
1976-01-01
The energy spectra of ions from the iron group in the dsup(N), dsup(N)s, dsup(N)p configurations are studied. A semiempirical method is used in which the effective hamiltonian contains configuration superposition. The sdsup(N+1), psup(4)dsup(N+2) quasidegenerated configurations, as well as configurations which differ by one electron are taken as correction configurations. It follows from the calculations that the most important role among the quasidegenerate configurations is played by the sdsup(N+1) correctional configuration. When it is taken into account, the introduction of the psup(4)dsup(N+2) correctional configuration practically does not affect the results. Account of the dsup(N-1)s configuration in the second order of the perturbation theory is equivalent to that of sdsup(N+1) in the sense that it results in the identical mean square deviation. As follows from the comparison of the results of the approximate and complete account of the configuration superposition, in many cases one can be satisfied with its approximate and complete account of the configuration superposition, in many cases one can be satisfied with its approximate version. The results are presented in the form of tables including the values of empirical parameters, radial integrals, mean square errors, etc
International Nuclear Information System (INIS)
Faddegon, B.A.; Villarreal-Barajas, J.E.
2005-01-01
The Final Aperture Superposition Technique (FAST) is described and applied to accurate, near instantaneous calculation of the relative output factor (ROF) and central axis percentage depth dose curve (PDD) for clinical electron beams used in radiotherapy. FAST is based on precalculation of dose at select points for the two extreme situations of a fully open final aperture and a final aperture with no opening (fully shielded). This technique is different than conventional superposition of dose deposition kernels: The precalculated dose is differential in position of the electron or photon at the downstream surface of the insert. The calculation for a particular aperture (x-ray jaws or MLC, insert in electron applicator) is done with superposition of the precalculated dose data, using the open field data over the open part of the aperture and the fully shielded data over the remainder. The calculation takes explicit account of all interactions in the shielded region of the aperture except the collimator effect: Particles that pass from the open part into the shielded part, or visa versa. For the clinical demonstration, FAST was compared to full Monte Carlo simulation of 10x10,2.5x2.5, and 2x8 cm 2 inserts. Dose was calculated to 0.5% precision in 0.4x0.4x0.2 cm 3 voxels, spaced at 0.2 cm depth intervals along the central axis, using detailed Monte Carlo simulation of the treatment head of a commercial linear accelerator for six different electron beams with energies of 6-21 MeV. Each simulation took several hours on a personal computer with a 1.7 Mhz processor. The calculation for the individual inserts, done with superposition, was completed in under a second on the same PC. Since simulations for the pre calculation are only performed once, higher precision and resolution can be obtained without increasing the calculation time for individual inserts. Fully shielded contributions were largest for small fields and high beam energy, at the surface, reaching a maximum
SU-F-T-620: Development of a Convolution/Superposition Dose Engine for CyberKnife System
Energy Technology Data Exchange (ETDEWEB)
Li, Y; Liu, B; Liang, B; Xu, X; Guo, B; Wei, R; Zhou, F [Beihang University, Beijing, Beijing (China); Song, T [Southern Medical University, Guangzhou, Guangdong (China); Xu, S [PLA General Hospital, Beijing, Beijing (China); Piao, J [302 Military Hospital, Beijing, Beijing (China)
2016-06-15
Purpose: Current CyberKnife treatment planning system (TPS) provided two dose calculation algorithms: Ray-tracing and Monte Carlo. Ray-tracing algorithm is fast, but less accurate, and also can’t handle irregular fields since a multi-leaf collimator system was recently introduced to CyberKnife M6 system. Monte Carlo method has well-known accuracy, but the current version still takes a long time to finish dose calculations. The purpose of this paper is to develop a GPU-based fast C/S dose engine for CyberKnife system to achieve both accuracy and efficiency. Methods: The TERMA distribution from a poly-energetic source was calculated based on beam’s eye view coordinate system, which is GPU friendly and has linear complexity. The dose distribution was then computed by inversely collecting the energy depositions from all TERMA points along 192 collapsed-cone directions. EGSnrc user code was used to pre-calculate energy deposition kernels (EDKs) for a series of mono-energy photons The energy spectrum was reconstructed based on measured tissue maximum ratio (TMR) curve, the TERMA averaged cumulative kernels was then calculated. Beam hardening parameters and intensity profiles were optimized based on measurement data from CyberKnife system. Results: The difference between measured and calculated TMR are less than 1% for all collimators except in the build-up regions. The calculated profiles also showed good agreements with the measured doses within 1% except in the penumbra regions. The developed C/S dose engine was also used to evaluate four clinical CyberKnife treatment plans, the results showed a better dose calculation accuracy than Ray-tracing algorithm compared with Monte Carlo method for heterogeneous cases. For the dose calculation time, it takes about several seconds for one beam depends on collimator size and dose calculation grids. Conclusion: A GPU-based C/S dose engine has been developed for CyberKnife system, which was proven to be efficient and accurate
International Nuclear Information System (INIS)
Wu, Vincent W.C.; Tse, Teddy K.H.; Ho, Cola L.M.; Yeung, Eric C.Y.
2013-01-01
Monte Carlo (MC) simulation is currently the most accurate dose calculation algorithm in radiotherapy planning but requires relatively long processing time. Faster model-based algorithms such as the anisotropic analytical algorithm (AAA) by the Eclipse treatment planning system and multigrid superposition (MGS) by the XiO treatment planning system are 2 commonly used algorithms. This study compared AAA and MGS against MC, as the gold standard, on brain, nasopharynx, lung, and prostate cancer patients. Computed tomography of 6 patients of each cancer type was used. The same hypothetical treatment plan using the same machine and treatment prescription was computed for each case by each planning system using their respective dose calculation algorithm. The doses at reference points including (1) soft tissues only, (2) bones only, (3) air cavities only, (4) soft tissue-bone boundary (Soft/Bone), (5) soft tissue-air boundary (Soft/Air), and (6) bone-air boundary (Bone/Air), were measured and compared using the mean absolute percentage error (MAPE), which was a function of the percentage dose deviations from MC. Besides, the computation time of each treatment plan was recorded and compared. The MAPEs of MGS were significantly lower than AAA in all types of cancers (p<0.001). With regards to body density combinations, the MAPE of AAA ranged from 1.8% (soft tissue) to 4.9% (Bone/Air), whereas that of MGS from 1.6% (air cavities) to 2.9% (Soft/Bone). The MAPEs of MGS (2.6%±2.1) were significantly lower than that of AAA (3.7%±2.5) in all tissue density combinations (p<0.001). The mean computation time of AAA for all treatment plans was significantly lower than that of the MGS (p<0.001). Both AAA and MGS algorithms demonstrated dose deviations of less than 4.0% in most clinical cases and their performance was better in homogeneous tissues than at tissue boundaries. In general, MGS demonstrated relatively smaller dose deviations than AAA but required longer computation time
Energy Technology Data Exchange (ETDEWEB)
Kays, W M; Hossaini-Hashemi, F [Stanford Univ., Palo Alto, CA (USA). Dept. of Mechanical Engineering; Busch, J S [Kaiser Engineers, Oakland, CA (USA)
1982-02-01
A linearized transient thermal conduction model was developed to economically determine media temperatures in geologic repositories for nuclear wastes. Individual canisters containing either high-level waste or spent fuel assemblies are represented as finite-length line sources in a continuous medium. The combined effects of multiple canisters in a representative storage pattern can be established in the medium at selected point of interest by superposition of the temperature rises calculated for each canister. A mathematical solution of the calculation for each separate source is given in this article, permitting a slow hand calculation. The full report, ONWI-94, contains the details of the computer code FLLSSM and its use, yielding the total solution in one computer output.
Chang, Li-Na; Luo, Shun-Long; Sun, Yuan
2017-11-01
The principle of superposition is universal and lies at the heart of quantum theory. Although ever since the inception of quantum mechanics a century ago, superposition has occupied a central and pivotal place, rigorous and systematic studies of the quantification issue have attracted significant interests only in recent years, and many related problems remain to be investigated. In this work we introduce a figure of merit which quantifies superposition from an intuitive and direct perspective, investigate its fundamental properties, connect it to some coherence measures, illustrate it through several examples, and apply it to analyze wave-particle duality. Supported by Science Challenge Project under Grant No. TZ2016002, Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing, Key Laboratory of Random Complex Structures and Data Science, Chinese Academy of Sciences, Grant under No. 2008DP173182
Brandenburg, Jan Gerit; Alessio, Maristella; Civalleri, Bartolomeo; Peintinger, Michael F; Bredow, Thomas; Grimme, Stefan
2013-09-26
We extend the previously developed geometrical correction for the inter- and intramolecular basis set superposition error (gCP) to periodic density functional theory (DFT) calculations. We report gCP results compared to those from the standard Boys-Bernardi counterpoise correction scheme and large basis set calculations. The applicability of the method to molecular crystals as the main target is tested for the benchmark set X23. It consists of 23 noncovalently bound crystals as introduced by Johnson et al. (J. Chem. Phys. 2012, 137, 054103) and refined by Tkatchenko et al. (J. Chem. Phys. 2013, 139, 024705). In order to accurately describe long-range electron correlation effects, we use the standard atom-pairwise dispersion correction scheme DFT-D3. We show that a combination of DFT energies with small atom-centered basis sets, the D3 dispersion correction, and the gCP correction can accurately describe van der Waals and hydrogen-bonded crystals. Mean absolute deviations of the X23 sublimation energies can be reduced by more than 70% and 80% for the standard functionals PBE and B3LYP, respectively, to small residual mean absolute deviations of about 2 kcal/mol (corresponding to 13% of the average sublimation energy). As a further test, we compute the interlayer interaction of graphite for varying distances and obtain a good equilibrium distance and interaction energy of 6.75 Å and -43.0 meV/atom at the PBE-D3-gCP/SVP level. We fit the gCP scheme for a recently developed pob-TZVP solid-state basis set and obtain reasonable results for the X23 benchmark set and the potential energy curve for water adsorption on a nickel (110) surface.
SU-E-T-423: Fast Photon Convolution Calculation with a 3D-Ideal Kernel On the GPU
Energy Technology Data Exchange (ETDEWEB)
Moriya, S; Sato, M [Komazawa University, Setagaya, Tokyo (Japan); Tachibana, H [National Cancer Center Hospital East, Kashiwa, Chiba (Japan)
2015-06-15
Purpose: The calculation time is a trade-off for improving the accuracy of convolution dose calculation with fine calculation spacing of the KERMA kernel. We investigated to accelerate the convolution calculation using an ideal kernel on the Graphic Processing Units (GPU). Methods: The calculation was performed on the AMD graphics hardware of Dual FirePro D700 and our algorithm was implemented using the Aparapi that convert Java bytecode to OpenCL. The process of dose calculation was separated with the TERMA and KERMA steps. The dose deposited at the coordinate (x, y, z) was determined in the process. In the dose calculation running on the central processing unit (CPU) of Intel Xeon E5, the calculation loops were performed for all calculation points. On the GPU computation, all of the calculation processes for the points were sent to the GPU and the multi-thread computation was done. In this study, the dose calculation was performed in a water equivalent homogeneous phantom with 150{sup 3} voxels (2 mm calculation grid) and the calculation speed on the GPU to that on the CPU and the accuracy of PDD were compared. Results: The calculation time for the GPU and the CPU were 3.3 sec and 4.4 hour, respectively. The calculation speed for the GPU was 4800 times faster than that for the CPU. The PDD curve for the GPU was perfectly matched to that for the CPU. Conclusion: The convolution calculation with the ideal kernel on the GPU was clinically acceptable for time and may be more accurate in an inhomogeneous region. Intensity modulated arc therapy needs dose calculations for different gantry angles at many control points. Thus, it would be more practical that the kernel uses a coarse spacing technique if the calculation is faster while keeping the similar accuracy to a current treatment planning system.
Yi, Xingwen; Xu, Bo; Zhang, Jing; Lin, Yun; Qiu, Kun
2014-12-15
Digital coherent superposition (DCS) of optical OFDM subcarrier pairs with Hermitian symmetry can reduce the inter-carrier-interference (ICI) noise resulted from phase noise. In this paper, we show two different implementations of DCS-OFDM that have the same performance in the presence of laser phase noise. We complete the theoretical calculation on ICI reduction by using the model of pure Wiener phase noise. By Taylor expansion of the ICI, we show that the ICI power is cancelled to the second order by DCS. The fourth order term is further derived out and only decided by the ratio of laser linewidth to OFDM subcarrier symbol rate, which can greatly simplify the system design. Finally, we verify our theoretical calculations in simulations and use the analytical results to predict the system performance. DCS-OFDM is expected to be beneficial to certain optical fiber transmissions.
International Nuclear Information System (INIS)
Tachibana, Masayuki; Noguchi, Yoshitaka; Fukunaga, Jyunichi; Hirano, Naomi; Yoshidome, Satoshi; Hirose, Takaaki
2009-01-01
The monitor unit (MU) was calculated by pencil beam convolution (inhomogeneity correction algorithm: batho power law) [PBC (BPL)] which is the dose calculation algorithm based on measurement in the past in the stereotactic lung irradiation study. The recalculation was done by analytical anisotropic algorithm (AAA), which is the dose calculation algorithm based on theory data. The MU calculated by PBC (BPL) and AAA was compared for each field. In the result of the comparison of 1031 fields in 136 cases, the MU calculated by PBC (BPL) was about 2% smaller than that calculated by AAA. This depends on whether one does the calculation concerning the extension of the second electrons. In particular, the difference in the MU is influenced by the X-ray energy. With the same X-ray energy, when the irradiation field size is small, the lung pass length is long, the lung pass length percentage is large, and the CT value of the lung is low, and the difference of MU is increased. (author)
Kruse, Holger; Grimme, Stefan
2012-04-21
A semi-empirical counterpoise-type correction for basis set superposition error (BSSE) in molecular systems is presented. An atom pair-wise potential corrects for the inter- and intra-molecular BSSE in supermolecular Hartree-Fock (HF) or density functional theory (DFT) calculations. This geometrical counterpoise (gCP) denoted scheme depends only on the molecular geometry, i.e., no input from the electronic wave-function is required and hence is applicable to molecules with ten thousands of atoms. The four necessary parameters have been determined by a fit to standard Boys and Bernadi counterpoise corrections for Hobza's S66×8 set of non-covalently bound complexes (528 data points). The method's target are small basis sets (e.g., minimal, split-valence, 6-31G*), but reliable results are also obtained for larger triple-ζ sets. The intermolecular BSSE is calculated by gCP within a typical error of 10%-30% that proves sufficient in many practical applications. The approach is suggested as a quantitative correction in production work and can also be routinely applied to estimate the magnitude of the BSSE beforehand. The applicability for biomolecules as the primary target is tested for the crambin protein, where gCP removes intramolecular BSSE effectively and yields conformational energies comparable to def2-TZVP basis results. Good mutual agreement is also found with Jensen's ACP(4) scheme, estimating the intramolecular BSSE in the phenylalanine-glycine-phenylalanine tripeptide, for which also a relaxed rotational energy profile is presented. A variety of minimal and double-ζ basis sets combined with gCP and the dispersion corrections DFT-D3 and DFT-NL are successfully benchmarked on the S22 and S66 sets of non-covalent interactions. Outstanding performance with a mean absolute deviation (MAD) of 0.51 kcal/mol (0.38 kcal/mol after D3-refit) is obtained at the gCP-corrected HF-D3/(minimal basis) level for the S66 benchmark. The gCP-corrected B3LYP-D3/6-31G* model
Kruse, Holger; Grimme, Stefan
2012-04-01
A semi-empirical counterpoise-type correction for basis set superposition error (BSSE) in molecular systems is presented. An atom pair-wise potential corrects for the inter- and intra-molecular BSSE in supermolecular Hartree-Fock (HF) or density functional theory (DFT) calculations. This geometrical counterpoise (gCP) denoted scheme depends only on the molecular geometry, i.e., no input from the electronic wave-function is required and hence is applicable to molecules with ten thousands of atoms. The four necessary parameters have been determined by a fit to standard Boys and Bernadi counterpoise corrections for Hobza's S66×8 set of non-covalently bound complexes (528 data points). The method's target are small basis sets (e.g., minimal, split-valence, 6-31G*), but reliable results are also obtained for larger triple-ζ sets. The intermolecular BSSE is calculated by gCP within a typical error of 10%-30% that proves sufficient in many practical applications. The approach is suggested as a quantitative correction in production work and can also be routinely applied to estimate the magnitude of the BSSE beforehand. The applicability for biomolecules as the primary target is tested for the crambin protein, where gCP removes intramolecular BSSE effectively and yields conformational energies comparable to def2-TZVP basis results. Good mutual agreement is also found with Jensen's ACP(4) scheme, estimating the intramolecular BSSE in the phenylalanine-glycine-phenylalanine tripeptide, for which also a relaxed rotational energy profile is presented. A variety of minimal and double-ζ basis sets combined with gCP and the dispersion corrections DFT-D3 and DFT-NL are successfully benchmarked on the S22 and S66 sets of non-covalent interactions. Outstanding performance with a mean absolute deviation (MAD) of 0.51 kcal/mol (0.38 kcal/mol after D3-refit) is obtained at the gCP-corrected HF-D3/(minimal basis) level for the S66 benchmark. The gCP-corrected B3LYP-D3/6-31G* model
Superposition and macroscopic observation
International Nuclear Information System (INIS)
Cartwright, N.D.
1976-01-01
The principle of superposition has long plagued the quantum mechanics of macroscopic bodies. In at least one well-known situation - that of measurement - quantum mechanics predicts a superposition. It is customary to try to reconcile macroscopic reality and quantum mechanics by reducing the superposition to a mixture. To establish consistency with quantum mechanics, values for the apparatus after a measurement are to be distributed in the way predicted by the superposition. The distributions observed, however, are those of the mixture. The statistical predictions of quantum mechanics, it appears, are not borne out by observation in macroscopic situations. It has been shown that, insofar as specific ergodic hypotheses apply to the apparatus after the interaction, the superposition which evolves is experimentally indistinguishable from the corresponding mixture. In this paper an idealized model of the measuring situation is presented in which this consistency can be demonstrated. It includes a simplified version of the measurement solution proposed by Daneri, Loinger, and Prosperi (1962). The model should make clear the kind of statistical evidence required to carry of this approach, and the role of the ergodic hypotheses assumed. (Auth.)
Network class superposition analyses.
Directory of Open Access Journals (Sweden)
Carl A B Pearson
Full Text Available Networks are often used to understand a whole system by modeling the interactions among its pieces. Examples include biomolecules in a cell interacting to provide some primary function, or species in an environment forming a stable community. However, these interactions are often unknown; instead, the pieces' dynamic states are known, and network structure must be inferred. Because observed function may be explained by many different networks (e.g., ≈ 10(30 for the yeast cell cycle process, considering dynamics beyond this primary function means picking a single network or suitable sample: measuring over all networks exhibiting the primary function is computationally infeasible. We circumvent that obstacle by calculating the network class ensemble. We represent the ensemble by a stochastic matrix T, which is a transition-by-transition superposition of the system dynamics for each member of the class. We present concrete results for T derived from boolean time series dynamics on networks obeying the Strong Inhibition rule, by applying T to several traditional questions about network dynamics. We show that the distribution of the number of point attractors can be accurately estimated with T. We show how to generate Derrida plots based on T. We show that T-based Shannon entropy outperforms other methods at selecting experiments to further narrow the network structure. We also outline an experimental test of predictions based on T. We motivate all of these results in terms of a popular molecular biology boolean network model for the yeast cell cycle, but the methods and analyses we introduce are general. We conclude with open questions for T, for example, application to other models, computational considerations when scaling up to larger systems, and other potential analyses.
Engineering mesoscopic superpositions of superfluid flow
International Nuclear Information System (INIS)
Hallwood, D. W.; Brand, J.
2011-01-01
Modeling strongly correlated atoms demonstrates the possibility to prepare quantum superpositions that are robust against experimental imperfections and temperature. Such superpositions of vortex states are formed by adiabatic manipulation of interacting ultracold atoms confined to a one-dimensional ring trapping potential when stirred by a barrier. Here, we discuss the influence of nonideal experimental procedures and finite temperature. Adiabaticity conditions for changing the stirring rate reveal that superpositions of many atoms are most easily accessed in the strongly interacting, Tonks-Girardeau, regime, which is also the most robust at finite temperature. NOON-type superpositions of weakly interacting atoms are most easily created by adiabatically decreasing the interaction strength by means of a Feshbach resonance. The quantum dynamics of small numbers of particles is simulated and the size of the superpositions is calculated based on their ability to make precision measurements. The experimental creation of strongly correlated and NOON-type superpositions with about 100 atoms seems feasible in the near future.
International Nuclear Information System (INIS)
Joosten, Andreas; Matzinger, Oscar; Jeanneret-Sozzi, Wendy; Bochud, François; Moeckli, Raphaël
2013-01-01
Background and purpose: To make a comprehensive evaluation of organ-specific out-of-field doses using Monte Carlo (MC) simulations for different breast cancer irradiation techniques and to compare results with a commercial treatment planning system (TPS). Materials and methods: Three breast radiotherapy techniques using 6MV tangential photon beams were compared: (a) 2DRT (open rectangular fields), (b) 3DCRT (conformal wedged fields), and (c) hybrid IMRT (open conformal + modulated fields). Over 35 organs were contoured in a whole-body CT scan and organ-specific dose distributions were determined with MC and the TPS. Results: Large differences in out-of-field doses were observed between MC and TPS calculations, even for organs close to the target volume such as the heart, the lungs and the contralateral breast (up to 70% difference). MC simulations showed that a large fraction of the out-of-field dose comes from the out-of-field head scatter fluence (>40%) which is not adequately modeled by the TPS. Based on MC simulations, the 3DCRT technique using external wedges yielded significantly higher doses (up to a factor 4–5 in the pelvis) than the 2DRT and the hybrid IMRT techniques which yielded similar out-of-field doses. Conclusions: In sharp contrast to popular belief, the IMRT technique investigated here does not increase the out-of-field dose compared to conventional techniques and may offer the most optimal plan. The 3DCRT technique with external wedges yields the largest out-of-field doses. For accurate out-of-field dose assessment, a commercial TPS should not be used, even for organs near the target volume (contralateral breast, lungs, heart)
International Nuclear Information System (INIS)
Doroudi, A.; Emampour, M.; Emampour, M.
2012-01-01
In this paper a combination of the method of multiple scales and the method of Lindstedt-Poincare which is a perturbative technique is used for calculation of axial secular frequencies of a nonlinear ion trap in the presence of second ,third, fourth and fifth order nonlinear terms of the potential distribution within the trap. The frequencies are calculated. The calculated frequencies are compared with the results of multiple scales method and the exact results.
Superposition Enhanced Nested Sampling
Directory of Open Access Journals (Sweden)
Stefano Martiniani
2014-08-01
Full Text Available The theoretical analysis of many problems in physics, astronomy, and applied mathematics requires an efficient numerical exploration of multimodal parameter spaces that exhibit broken ergodicity. Monte Carlo methods are widely used to deal with these classes of problems, but such simulations suffer from a ubiquitous sampling problem: The probability of sampling a particular state is proportional to its entropic weight. Devising an algorithm capable of sampling efficiently the full phase space is a long-standing problem. Here, we report a new hybrid method for the exploration of multimodal parameter spaces exhibiting broken ergodicity. Superposition enhanced nested sampling combines the strengths of global optimization with the unbiased or athermal sampling of nested sampling, greatly enhancing its efficiency with no additional parameters. We report extensive tests of this new approach for atomic clusters that are known to have energy landscapes for which conventional sampling schemes suffer from broken ergodicity. We also introduce a novel parallelization algorithm for nested sampling.
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
Generating superpositions of higher order bessel beams [Conference paper
CSIR Research Space (South Africa)
Vasilyeu, R
2009-10-01
Full Text Available An experimental setup to generate a superposition of higher-order Bessel beams by means of a spatial light modulator and ring aperture is presented. The experimentally produced fields are in good agreement with those calculated theoretically....
Dealiased convolutions for pseudospectral simulations
International Nuclear Information System (INIS)
Roberts, Malcolm; Bowman, John C
2011-01-01
Efficient algorithms have recently been developed for calculating dealiased linear convolution sums without the expense of conventional zero-padding or phase-shift techniques. For one-dimensional in-place convolutions, the memory requirements are identical with the zero-padding technique, with the important distinction that the additional work memory need not be contiguous with the input data. This decoupling of data and work arrays dramatically reduces the memory and computation time required to evaluate higher-dimensional in-place convolutions. The memory savings is achieved by computing the in-place Fourier transform of the data in blocks, rather than all at once. The technique also allows one to dealias the n-ary convolutions that arise on Fourier transforming cubic and higher powers. Implicitly dealiased convolutions can be built on top of state-of-the-art adaptive fast Fourier transform libraries like FFTW. Vectorized multidimensional implementations for the complex and centered Hermitian (pseudospectral) cases have already been implemented in the open-source software FFTW++. With the advent of this library, writing a high-performance dealiased pseudospectral code for solving nonlinear partial differential equations has now become a relatively straightforward exercise. New theoretical estimates of computational complexity and memory use are provided, including corrected timing results for 3D pruned convolutions and further consideration of higher-order convolutions.
International Nuclear Information System (INIS)
Fairbanks, Leandro R.; Barbi, Gustavo L.; Silva, Wiliam T.; Reis, Eduardo G.F.; Borges, Leandro F.; Bertucci, Edenyse C.; Maciel, Marina F.; Amaral, Leonardo L.
2011-01-01
Since the cross-section for various radiation interactions is dependent upon tissue material, the presence of heterogeneities affects the final dose delivered. This paper aims to analyze how different treatment planning algorithms (Fast Fourier Transform, Convolution, Superposition, Fast Superposition and Clarkson) work when heterogeneity corrections are used. To that end, a farmer-type ionization chamber was positioned reproducibly (during the time of CT as well as irradiation) inside several phantoms made of aluminum, bone, cork and solid water slabs. The percent difference between the dose measured and calculated by the various algorithms was less than 5%.The convolution method shows better results for high density materials (difference ∼1 %), whereas the Superposition algorithm is more accurate for low densities (around 1,1%). (author)
Fast Convolution Module (Fast Convolution Module)
National Research Council Canada - National Science Library
Bierens, L
1997-01-01
This report describes the design and realisation of a real-time range azimuth compression module, the so-called 'Fast Convolution Module', based on the fast convolution algorithm developed at TNO-FEL...
Optimal simultaneous superpositioning of multiple structures with missing data.
Theobald, Douglas L; Steindel, Phillip A
2012-08-01
Superpositioning is an essential technique in structural biology that facilitates the comparison and analysis of conformational differences among topologically similar structures. Performing a superposition requires a one-to-one correspondence, or alignment, of the point sets in the different structures. However, in practice, some points are usually 'missing' from several structures, for example, when the alignment contains gaps. Current superposition methods deal with missing data simply by superpositioning a subset of points that are shared among all the structures. This practice is inefficient, as it ignores important data, and it fails to satisfy the common least-squares criterion. In the extreme, disregarding missing positions prohibits the calculation of a superposition altogether. Here, we present a general solution for determining an optimal superposition when some of the data are missing. We use the expectation-maximization algorithm, a classic statistical technique for dealing with incomplete data, to find both maximum-likelihood solutions and the optimal least-squares solution as a special case. The methods presented here are implemented in THESEUS 2.0, a program for superpositioning macromolecular structures. ANSI C source code and selected compiled binaries for various computing platforms are freely available under the GNU open source license from http://www.theseus3d.org. dtheobald@brandeis.edu Supplementary data are available at Bioinformatics online.
Nonlinear superposition of monopoles
International Nuclear Information System (INIS)
Forgacs, P.; Horvath, Z.; Palla, L.
1981-04-01
With the aid of Baecklund transformations the authors construct exact multimonopole solutions of the axially and mirror-symmetric Bogomolny equations. The explicit form of the length of the Higgs field is given and is studied both analytically and numerically. The energy density for monopoles with charges 2,3,4,5 is also calculated. (author)
Energy Technology Data Exchange (ETDEWEB)
Fairbanks, L.R.; Barbi, G.L.; Silva, W.T. da; Reis, E.G.F. dos; Borges, L.F.; Bertucci, E.C.; Maciel, M.F.; Amaral, L.L. do, E-mail: lefairbanks@yahoo.com.b [Universidade de Sao Paulo (USP), Ribeirao Preto, SP (Brazil). Hospital das Clinicas. Servico de Radioterapia
2010-07-01
Since the cross-section for various radiation interactions is dependent upon tissue material, the presence of heterogeneities affects the final dose delivered. This paper aims to analyze how different treatment planning algorithms (Fast Fourier Transform, Convolution, Superposition, Fast Superposition and Clarkson) work when heterogeneity corrections are used. To that end, a farmer-type ionization chamber was positioned reproducibly (during the time of CT as well as irradiation) inside several phantoms made of aluminum, bone, cork and solid water slabs. The percent difference between the dose measured and calculated by the various algorithms was less than 5%; This is in accordance with the recommendation of several references.The convolution method shows better results for high density materials (difference {approx}1 %), whereas the Superposition algorithm is more accurate for low densities (around 1,1%).
Energy Technology Data Exchange (ETDEWEB)
Fairbanks, Leandro R.; Barbi, Gustavo L.; Silva, Wiliam T.; Reis, Eduardo G.F.; Borges, Leandro F.; Bertucci, Edenyse C.; Maciel, Marina F.; Amaral, Leonardo L., E-mail: lefairbanks@yahoo.com.b [Universidade de Sao Paulo (HCRP/USP), Ribeirao Preto, SP (Brazil). Hospital das Clinicas. Servico de Radioterapia
2011-07-01
Since the cross-section for various radiation interactions is dependent upon tissue material, the presence of heterogeneities affects the final dose delivered. This paper aims to analyze how different treatment planning algorithms (Fast Fourier Transform, Convolution, Superposition, Fast Superposition and Clarkson) work when heterogeneity corrections are used. To that end, a farmer-type ionization chamber was positioned reproducibly (during the time of CT as well as irradiation) inside several phantoms made of aluminum, bone, cork and solid water slabs. The percent difference between the dose measured and calculated by the various algorithms was less than 5%.The convolution method shows better results for high density materials (difference {approx}1 %), whereas the Superposition algorithm is more accurate for low densities (around 1,1%). (author)
A superposition principle in quantum logics
International Nuclear Information System (INIS)
Pulmannova, S.
1976-01-01
A new definition of the superposition principle in quantum logics is given which enables us to define the sectors. It is shown that the superposition principle holds only in the irreducible quantum logics. (orig.) [de
Superposition Attacks on Cryptographic Protocols
DEFF Research Database (Denmark)
Damgård, Ivan Bjerre; Funder, Jakob Løvstad; Nielsen, Jesper Buus
2011-01-01
of information. In this paper, we introduce a fundamentally new model of quantum attacks on classical cryptographic protocols, where the adversary is allowed to ask several classical queries in quantum superposition. This is a strictly stronger attack than the standard one, and we consider the security......Attacks on classical cryptographic protocols are usually modeled by allowing an adversary to ask queries from an oracle. Security is then defined by requiring that as long as the queries satisfy some constraint, there is some problem the adversary cannot solve, such as compute a certain piece...... of several primitives in this model. We show that a secret-sharing scheme that is secure with threshold $t$ in the standard model is secure against superposition attacks if and only if the threshold is lowered to $t/2$. We use this result to give zero-knowledge proofs for all of NP in the common reference...
Classification of high-resolution remote sensing images based on multi-scale superposition
Wang, Jinliang; Gao, Wenjie; Liu, Guangjie
2017-07-01
Landscape structures and process on different scale show different characteristics. In the study of specific target landmarks, the most appropriate scale for images can be attained by scale conversion, which improves the accuracy and efficiency of feature identification and classification. In this paper, the authors carried out experiments on multi-scale classification by taking the Shangri-la area in the north-western Yunnan province as the research area and the images from SPOT5 HRG and GF-1 Satellite as date sources. Firstly, the authors upscaled the two images by cubic convolution, and calculated the optimal scale for different objects on the earth shown in images by variation functions. Then the authors conducted multi-scale superposition classification on it by Maximum Likelyhood, and evaluated the classification accuracy. The results indicates that: (1) for most of the object on the earth, the optimal scale appears in the bigger scale instead of the original one. To be specific, water has the biggest optimal scale, i.e. around 25-30m; farmland, grassland, brushwood, roads, settlement places and woodland follows with 20-24m. The optimal scale for shades and flood land is basically as the same as the original one, i.e. 8m and 10m respectively. (2) Regarding the classification of the multi-scale superposed images, the overall accuracy of the ones from SPOT5 HRG and GF-1 Satellite is 12.84% and 14.76% higher than that of the original multi-spectral images, respectively, and Kappa coefficient is 0.1306 and 0.1419 higher, respectively. Hence, the multi-scale superposition classification which was applied in the research area can enhance the classification accuracy of remote sensing images .
International Nuclear Information System (INIS)
Tajaldeen, A; Ramachandran, P; Geso, M
2015-01-01
Purpose: The purpose of this study was to investigate and quantify the variation in dose distributions in small field lung cancer radiotherapy using seven different dose calculation algorithms. Methods: The study was performed in 21 lung cancer patients who underwent Stereotactic Ablative Body Radiotherapy (SABR). Two different methods (i) Same dose coverage to the target volume (named as same dose method) (ii) Same monitor units in all algorithms (named as same monitor units) were used for studying the performance of seven different dose calculation algorithms in XiO and Eclipse treatment planning systems. The seven dose calculation algorithms include Superposition, Fast superposition, Fast Fourier Transform ( FFT) Convolution, Clarkson, Anisotropic Analytic Algorithm (AAA), Acurous XB and pencil beam (PB) algorithms. Prior to this, a phantom study was performed to assess the accuracy of these algorithms. Superposition algorithm was used as a reference algorithm in this study. The treatment plans were compared using different dosimetric parameters including conformity, heterogeneity and dose fall off index. In addition to this, the dose to critical structures like lungs, heart, oesophagus and spinal cord were also studied. Statistical analysis was performed using Prism software. Results: The mean±stdev with conformity index for Superposition, Fast superposition, Clarkson and FFT convolution algorithms were 1.29±0.13, 1.31±0.16, 2.2±0.7 and 2.17±0.59 respectively whereas for AAA, pencil beam and Acurous XB were 1.4±0.27, 1.66±0.27 and 1.35±0.24 respectively. Conclusion: Our study showed significant variations among the seven different algorithms. Superposition and AcurosXB algorithms showed similar values for most of the dosimetric parameters. Clarkson, FFT convolution and pencil beam algorithms showed large differences as compared to superposition algorithms. Based on our study, we recommend Superposition and AcurosXB algorithms as the first choice of
Energy Technology Data Exchange (ETDEWEB)
Tajaldeen, A [RMIT university, Docklands, Vic (Australia); Ramachandran, P [Peter MacCallum Cancer Centre, Bendigo (Australia); Geso, M [RMIT University, Bundoora, Melbourne (Australia)
2015-06-15
Purpose: The purpose of this study was to investigate and quantify the variation in dose distributions in small field lung cancer radiotherapy using seven different dose calculation algorithms. Methods: The study was performed in 21 lung cancer patients who underwent Stereotactic Ablative Body Radiotherapy (SABR). Two different methods (i) Same dose coverage to the target volume (named as same dose method) (ii) Same monitor units in all algorithms (named as same monitor units) were used for studying the performance of seven different dose calculation algorithms in XiO and Eclipse treatment planning systems. The seven dose calculation algorithms include Superposition, Fast superposition, Fast Fourier Transform ( FFT) Convolution, Clarkson, Anisotropic Analytic Algorithm (AAA), Acurous XB and pencil beam (PB) algorithms. Prior to this, a phantom study was performed to assess the accuracy of these algorithms. Superposition algorithm was used as a reference algorithm in this study. The treatment plans were compared using different dosimetric parameters including conformity, heterogeneity and dose fall off index. In addition to this, the dose to critical structures like lungs, heart, oesophagus and spinal cord were also studied. Statistical analysis was performed using Prism software. Results: The mean±stdev with conformity index for Superposition, Fast superposition, Clarkson and FFT convolution algorithms were 1.29±0.13, 1.31±0.16, 2.2±0.7 and 2.17±0.59 respectively whereas for AAA, pencil beam and Acurous XB were 1.4±0.27, 1.66±0.27 and 1.35±0.24 respectively. Conclusion: Our study showed significant variations among the seven different algorithms. Superposition and AcurosXB algorithms showed similar values for most of the dosimetric parameters. Clarkson, FFT convolution and pencil beam algorithms showed large differences as compared to superposition algorithms. Based on our study, we recommend Superposition and AcurosXB algorithms as the first choice of
Student Ability to Distinguish between Superposition States and Mixed States in Quantum Mechanics
Passante, Gina; Emigh, Paul J.; Shaffer, Peter S.
2015-01-01
Superposition gives rise to the probabilistic nature of quantum mechanics and is therefore one of the concepts at the heart of quantum mechanics. Although we have found that many students can successfully use the idea of superposition to calculate the probabilities of different measurement outcomes, they are often unable to identify the…
Linear superposition solutions to nonlinear wave equations
International Nuclear Information System (INIS)
Liu Yu
2012-01-01
The solutions to a linear wave equation can satisfy the principle of superposition, i.e., the linear superposition of two or more known solutions is still a solution of the linear wave equation. We show in this article that many nonlinear wave equations possess exact traveling wave solutions involving hyperbolic, triangle, and exponential functions, and the suitable linear combinations of these known solutions can also constitute linear superposition solutions to some nonlinear wave equations with special structural characteristics. The linear superposition solutions to the generalized KdV equation K(2,2,1), the Oliver water wave equation, and the k(n, n) equation are given. The structure characteristic of the nonlinear wave equations having linear superposition solutions is analyzed, and the reason why the solutions with the forms of hyperbolic, triangle, and exponential functions can form the linear superposition solutions is also discussed
Superposition as a logical glue
Directory of Open Access Journals (Sweden)
Andrea Asperti
2011-03-01
Full Text Available The typical mathematical language systematically exploits notational and logical abuses whose resolution requires not just the knowledge of domain specific notation and conventions, but not trivial skills in the given mathematical discipline. A large part of this background knowledge is expressed in form of equalities and isomorphisms, allowing mathematicians to freely move between different incarnations of the same entity without even mentioning the transformation. Providing ITP-systems with similar capabilities seems to be a major way to improve their intelligence, and to ease the communication between the user and the machine. The present paper discusses our experience of integration of a superposition calculus within the Matita interactive prover, providing in particular a very flexible, "smart" application tactic, and a simple, innovative approach to automation.
Supervised Convolutional Sparse Coding
Affara, Lama Ahmed
2018-04-08
Convolutional Sparse Coding (CSC) is a well-established image representation model especially suited for image restoration tasks. In this work, we extend the applicability of this model by proposing a supervised approach to convolutional sparse coding, which aims at learning discriminative dictionaries instead of purely reconstructive ones. We incorporate a supervised regularization term into the traditional unsupervised CSC objective to encourage the final dictionary elements to be discriminative. Experimental results show that using supervised convolutional learning results in two key advantages. First, we learn more semantically relevant filters in the dictionary and second, we achieve improved image reconstruction on unseen data.
Directory of Open Access Journals (Sweden)
Ammar Guellab
2018-01-01
Full Text Available We propose an efficient finite difference time domain (FDTD method based on the piecewise linear recursive convolution (PLRC technique to evaluate the human body exposure to electromagnetic (EM radiation. The source of radiation considered in this study is a high-power antenna, mounted on a military vehicle, covering a broad band of frequency (100 MHz–3 GHz. The simulation is carried out using a nonhomogeneous human body model which takes into consideration most of the internal body tissues. The human tissues are modeled by a four-pole Debye model which is derived from experimental data by using particle swarm optimization (PSO. The human exposure to EM radiation is evaluated by computing the local and whole-body average specific absorption rate (SAR for each occupant. The higher in-tissue electric field intensity points are localized, and the SAR values are compared with the crew safety standard recommendations. The accuracy of the proposed PLRC-FDTD approach and the matching of the Debye model with the experimental data are verified in this study.
Decoherence of superposition states in trapped ions
CSIR Research Space (South Africa)
Uys, H
2010-09-01
Full Text Available This paper investigates the decoherence of superpositions of hyperfine states of 9Be+ ions due to spontaneous scattering of off-resonant light. It was found that, contrary to conventional wisdom, elastic Raleigh scattering can have major...
Convolution based profile fitting
International Nuclear Information System (INIS)
Kern, A.; Coelho, A.A.; Cheary, R.W.
2002-01-01
Full text: In convolution based profile fitting, profiles are generated by convoluting functions together to form the observed profile shape. For a convolution of 'n' functions this process can be written as, Y(2θ)=F 1 (2θ)x F 2 (2θ)x... x F i (2θ)x....xF n (2θ). In powder diffractometry the functions F i (2θ) can be interpreted as the aberration functions of the diffractometer, but in general any combination of appropriate functions for F i (2θ) may be used in this context. Most direct convolution fitting methods are restricted to combinations of F i (2θ) that can be convoluted analytically (e.g. GSAS) such as Lorentzians, Gaussians, the hat (impulse) function and the exponential function. However, software such as TOPAS is now available that can accurately convolute and refine a wide variety of profile shapes numerically, including user defined profiles, without the need to convolute analytically. Some of the most important advantages of modern convolution based profile fitting are: 1) virtually any peak shape and angle dependence can normally be described using minimal profile parameters in laboratory and synchrotron X-ray data as well as in CW and TOF neutron data. This is possible because numerical convolution and numerical differentiation is used within the refinement procedure so that a wide range of functions can easily be incorporated into the convolution equation; 2) it can use physically based diffractometer models by convoluting the instrument aberration functions. This can be done for most laboratory based X-ray powder diffractometer configurations including conventional divergent beam instruments, parallel beam instruments, and diffractometers used for asymmetric diffraction. It can also accommodate various optical elements (e.g. multilayers and monochromators) and detector systems (e.g. point and position sensitive detectors) and has already been applied to neutron powder diffraction systems (e.g. ANSTO) as well as synchrotron based
Efficient forward propagation of time-sequences in convolutional neural networks using Deep Shifting
K.L. Groenland (Koen); S.M. Bohte (Sander)
2016-01-01
textabstractWhen a Convolutional Neural Network is used for on-the-fly evaluation of continuously updating time-sequences, many redundant convolution operations are performed. We propose the method of Deep Shifting, which remembers previously calculated results of convolution operations in order
International Nuclear Information System (INIS)
Moral, F. del; Ramos, A.; Salgado, M.; Andrade, B; Munoz, V.
2010-01-01
In this work an analysis of the influence of the choice of the algorithm or planning system, on the calculus of the same treatment plan is introduced. For this purpose specific software has been developed for comparing plans of a series of IMRT cases of prostate and head and neck cancer calculated using the convolution, superposition and fast superposition algorithms implemented in the XiO 4.40 planning system (CMS). It has also been used for the comparison of the same treatment plan for lung pathology calculated in XiO with the mentioned algorithms, and calculated in the Plan 4.1 planning system (Brainlab) using its pencil beam algorithm. Differences in dose among the treatment plans have been quantified using a set of metrics. The recommendation for the dosimetrist of a careful choice of the algorithm has been numerically confirmed. (Author).
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
Wohlberg, Brendt
2017-06-20
Computationally efficient algorithms may be applied for fast dictionary learning solving the convolutional sparse coding problem in the Fourier domain. More specifically, efficient convolutional sparse coding may be derived within an alternating direction method of multipliers (ADMM) framework that utilizes fast Fourier transforms (FFT) to solve the main linear system in the frequency domain. Such algorithms may enable a significant reduction in computational cost over conventional approaches by implementing a linear solver for the most critical and computationally expensive component of the conventional iterative algorithm. The theoretical computational cost of the algorithm may be reduced from O(M.sup.3N) to O(MN log N), where N is the dimensionality of the data and M is the number of elements in the dictionary. This significant improvement in efficiency may greatly increase the range of problems that can practically be addressed via convolutional sparse representations.
Multithreaded implicitly dealiased convolutions
Roberts, Malcolm; Bowman, John C.
2018-03-01
Implicit dealiasing is a method for computing in-place linear convolutions via fast Fourier transforms that decouples work memory from input data. It offers easier memory management and, for long one-dimensional input sequences, greater efficiency than conventional zero-padding. Furthermore, for convolutions of multidimensional data, the segregation of data and work buffers can be exploited to reduce memory usage and execution time significantly. This is accomplished by processing and discarding data as it is generated, allowing work memory to be reused, for greater data locality and performance. A multithreaded implementation of implicit dealiasing that accepts an arbitrary number of input and output vectors and a general multiplication operator is presented, along with an improved one-dimensional Hermitian convolution that avoids the loop dependency inherent in previous work. An alternate data format that can accommodate a Nyquist mode and enhance cache efficiency is also proposed.
Exclusion of identification by negative superposition
Directory of Open Access Journals (Sweden)
Takač Šandor
2012-01-01
Full Text Available The paper represents the first report of negative superposition in our country. Photo of randomly selected young, living woman was superimposed on the previously discovered female skull. Computer program Adobe Photoshop 7.0 was used in work. Digitilized photographs of the skull and face, after uploaded to computer, were superimposed on each other and displayed on the monitor in order to assess their possible similarities or differences. Special attention was payed to matching the same anthropometrical points of the skull and face, as well as following their contours. The process of fitting the skull and the photograph is usually started by setting eyes in correct position relative to the orbits. In this case, lower jaw gonions go beyond the face contour and gnathion is highly placed. By positioning the chin, mouth and nose their correct anatomical position cannot be achieved. All the difficulties associated with the superposition were recorded, with special emphasis on critical evaluation of work results in a negative superposition. Negative superposition has greater probative value (exclusion of identification than positive (possible identification. 100% negative superposition is easily achieved, but 100% positive - almost never. 'Each skull is unique and viewed from different perspectives is always a new challenge'. From this point of view, identification can be negative or of high probability.
Experimental superposition of orders of quantum gates
Procopio, Lorenzo M.; Moqanaki, Amir; Araújo, Mateus; Costa, Fabio; Alonso Calafell, Irati; Dowd, Emma G.; Hamel, Deny R.; Rozema, Lee A.; Brukner, Časlav; Walther, Philip
2015-01-01
Quantum computers achieve a speed-up by placing quantum bits (qubits) in superpositions of different states. However, it has recently been appreciated that quantum mechanics also allows one to ‘superimpose different operations'. Furthermore, it has been shown that using a qubit to coherently control the gate order allows one to accomplish a task—determining if two gates commute or anti-commute—with fewer gate uses than any known quantum algorithm. Here we experimentally demonstrate this advantage, in a photonic context, using a second qubit to control the order in which two gates are applied to a first qubit. We create the required superposition of gate orders by using additional degrees of freedom of the photons encoding our qubits. The new resource we exploit can be interpreted as a superposition of causal orders, and could allow quantum algorithms to be implemented with an efficiency unlikely to be achieved on a fixed-gate-order quantum computer. PMID:26250107
The principle of superposition in human prehension.
Zatsiorsky, Vladimir M; Latash, Mark L; Gao, Fan; Shim, Jae Kun
2004-03-01
The experimental evidence supports the validity of the principle of superposition for multi-finger prehension in humans. Forces and moments of individual digits are defined by two independent commands: "Grasp the object stronger/weaker to prevent slipping" and "Maintain the rotational equilibrium of the object". The effects of the two commands are summed up.
Generation of picosecond pulsed coherent state superpositions
DEFF Research Database (Denmark)
Dong, Ruifang; Tipsmark, Anders; Laghaout, Amine
2014-01-01
We present the generation of approximated coherent state superpositions-referred to as Schrodinger cat states-by the process of subtracting single photons from picosecond pulsed squeezed states of light. The squeezed vacuum states are produced by spontaneous parametric down-conversion (SPDC...... which exhibit non-Gaussian behavior. (C) 2014 Optical Society of America...
An Improved Convolutional Neural Network on Crowd Density Estimation
Directory of Open Access Journals (Sweden)
Pan Shao-Yun
2016-01-01
Full Text Available In this paper, a new method is proposed for crowd density estimation. An improved convolutional neural network is combined with traditional texture feature. The data calculated by the convolutional layer can be treated as a new kind of features.So more useful information of images can be extracted by different features.In the meantime, the size of image has little effect on the result of convolutional neural network. Experimental results indicate that our scheme has adequate performance to allow for its use in real world applications.
On the superposition principle and its physics content
International Nuclear Information System (INIS)
Roos, M.
1984-01-01
What is commonly denoted the superposition principle is shown to consist of three different physical assumptions: conservation of probability, completeness, and some phase conditions. The latter conditions form the physical assumptions of the superposition principle. These phase conditions are exemplified by the Kobayashi-Maskawa matrix. Some suggestions for testing the superposition principle are given. (Auth.)
Projective measurement onto arbitrary superposition of weak coherent state bases
DEFF Research Database (Denmark)
Izumi, Shuro; Takeoka, Masahiro; Wakui, Kentaro
2018-01-01
One of the peculiar features in quantum mechanics is that a superposition of macroscopically distinct states can exist. In optical system, this is highlighted by a superposition of coherent states (SCS), i.e. a superposition of classical states. Recently this highly nontrivial quantum state and i...
Toward quantum superposition of living organisms
International Nuclear Information System (INIS)
Romero-Isart, Oriol; Cirac, J Ignacio; Juan, Mathieu L; Quidant, Romain
2010-01-01
The most striking feature of quantum mechanics is the existence of superposition states, where an object appears to be in different situations at the same time. The existence of such states has been previously tested with small objects, such as atoms, ions, electrons and photons (Zoller et al 2005 Eur. Phys. J. D 36 203-28), and even with molecules (Arndt et al 1999 Nature 401 680-2). More recently, it has been shown that it is possible to create superpositions of collections of photons (Deleglise et al 2008 Nature 455 510-14), atoms (Hammerer et al 2008 arXiv:0807.3358) or Cooper pairs (Friedman et al 2000 Nature 406 43-6). Very recent progress in optomechanical systems may soon allow us to create superpositions of even larger objects, such as micro-sized mirrors or cantilevers (Marshall et al 2003 Phys. Rev. Lett. 91 130401; Kippenberg and Vahala 2008 Science 321 1172-6; Marquardt and Girvin 2009 Physics 2 40; Favero and Karrai 2009 Nature Photon. 3 201-5), and thus to test quantum mechanical phenomena at larger scales. Here we propose a method to cool down and create quantum superpositions of the motion of sub-wavelength, arbitrarily shaped dielectric objects trapped inside a high-finesse cavity at a very low pressure. Our method is ideally suited for the smallest living organisms, such as viruses, which survive under low-vacuum pressures (Rothschild and Mancinelli 2001 Nature 406 1092-101) and optically behave as dielectric objects (Ashkin and Dziedzic 1987 Science 235 1517-20). This opens up the possibility of testing the quantum nature of living organisms by creating quantum superposition states in very much the same spirit as the original Schroedinger's cat 'gedanken' paradigm (Schroedinger 1935 Naturwissenschaften 23 807-12, 823-8, 844-9). We anticipate that our paper will be a starting point for experimentally addressing fundamental questions, such as the role of life and consciousness in quantum mechanics.
Toward quantum superposition of living organisms
Energy Technology Data Exchange (ETDEWEB)
Romero-Isart, Oriol; Cirac, J Ignacio [Max-Planck-Institut fuer Quantenoptik, Hans-Kopfermann-Strasse 1, D-85748, Garching (Germany); Juan, Mathieu L; Quidant, Romain [ICFO-Institut de Ciencies Fotoniques, Mediterranean Technology Park, Castelldefels, Barcelona 08860 (Spain)], E-mail: oriol.romero-isart@mpq.mpg.de
2010-03-15
The most striking feature of quantum mechanics is the existence of superposition states, where an object appears to be in different situations at the same time. The existence of such states has been previously tested with small objects, such as atoms, ions, electrons and photons (Zoller et al 2005 Eur. Phys. J. D 36 203-28), and even with molecules (Arndt et al 1999 Nature 401 680-2). More recently, it has been shown that it is possible to create superpositions of collections of photons (Deleglise et al 2008 Nature 455 510-14), atoms (Hammerer et al 2008 arXiv:0807.3358) or Cooper pairs (Friedman et al 2000 Nature 406 43-6). Very recent progress in optomechanical systems may soon allow us to create superpositions of even larger objects, such as micro-sized mirrors or cantilevers (Marshall et al 2003 Phys. Rev. Lett. 91 130401; Kippenberg and Vahala 2008 Science 321 1172-6; Marquardt and Girvin 2009 Physics 2 40; Favero and Karrai 2009 Nature Photon. 3 201-5), and thus to test quantum mechanical phenomena at larger scales. Here we propose a method to cool down and create quantum superpositions of the motion of sub-wavelength, arbitrarily shaped dielectric objects trapped inside a high-finesse cavity at a very low pressure. Our method is ideally suited for the smallest living organisms, such as viruses, which survive under low-vacuum pressures (Rothschild and Mancinelli 2001 Nature 406 1092-101) and optically behave as dielectric objects (Ashkin and Dziedzic 1987 Science 235 1517-20). This opens up the possibility of testing the quantum nature of living organisms by creating quantum superposition states in very much the same spirit as the original Schroedinger's cat 'gedanken' paradigm (Schroedinger 1935 Naturwissenschaften 23 807-12, 823-8, 844-9). We anticipate that our paper will be a starting point for experimentally addressing fundamental questions, such as the role of life and consciousness in quantum mechanics.
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.
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
International Nuclear Information System (INIS)
Sharma, Subhash; Ott, Joseph; Williams, Jamone; Dickow, Danny
2011-01-01
Monte Carlo dose calculation algorithms have the potential for greater accuracy than traditional model-based algorithms. This enhanced accuracy is particularly evident in regions of lateral scatter disequilibrium, which can develop during treatments incorporating small field sizes and low-density tissue. A heterogeneous slab phantom was used to evaluate the accuracy of several commercially available dose calculation algorithms, including Monte Carlo dose calculation for CyberKnife, Analytical Anisotropic Algorithm and Pencil Beam convolution for the Eclipse planning system, and convolution-superposition for the Xio planning system. The phantom accommodated slabs of varying density; comparisons between planned and measured dose distributions were accomplished with radiochromic film. The Monte Carlo algorithm provided the most accurate comparison between planned and measured dose distributions. In each phantom irradiation, the Monte Carlo predictions resulted in gamma analysis comparisons >97%, using acceptance criteria of 3% dose and 3-mm distance to agreement. In general, the gamma analysis comparisons for the other algorithms were <95%. The Monte Carlo dose calculation algorithm for CyberKnife provides more accurate dose distribution calculations in regions of lateral electron disequilibrium than commercially available model-based algorithms. This is primarily because of the ability of Monte Carlo algorithms to implicitly account for tissue heterogeneities, density scaling functions; and/or effective depth correction factors are not required.
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...
Strongly-MDS convolutional codes
Gluesing-Luerssen, H; Rosenthal, J; Smarandache, R
Maximum-distance separable (MDS) convolutional codes have the property that their free distance is maximal among all codes of the same rate and the same degree. In this paper, a class of MDS convolutional codes is introduced whose column distances reach the generalized Singleton bound at the
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.
Consensus Convolutional Sparse Coding
Choudhury, Biswarup
2017-12-01
Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high-dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaicing and 4D light field view synthesis.
Consensus Convolutional Sparse Coding
Choudhury, Biswarup
2017-04-11
Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaickingand 4D light field view synthesis.
Consensus Convolutional Sparse Coding
Choudhury, Biswarup; Swanson, Robin; Heide, Felix; Wetzstein, Gordon; Heidrich, Wolfgang
2017-01-01
Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high-dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaicing and 4D light field view synthesis.
International Nuclear Information System (INIS)
Craig, Tim; Battista, Jerry; Van Dyk, Jake
2003-01-01
Convolution methods have been used to model the effect of geometric uncertainties on dose delivery in radiation therapy. Convolution assumes shift invariance of the dose distribution. Internal inhomogeneities and surface curvature lead to violations of this assumption. The magnitude of the error resulting from violation of shift invariance is not well documented. This issue is addressed by comparing dose distributions calculated using the Convolution method with dose distributions obtained by Direct Simulation. A comparison of conventional Static dose distributions was also made with Direct Simulation. This analysis was performed for phantom geometries and several clinical tumor sites. A modification to the Convolution method to correct for some of the inherent errors is proposed and tested using example phantoms and patients. We refer to this modified method as the Corrected Convolution. The average maximum dose error in the calculated volume (averaged over different beam arrangements in the various phantom examples) was 21% with the Static dose calculation, 9% with Convolution, and reduced to 5% with the Corrected Convolution. The average maximum dose error in the calculated volume (averaged over four clinical examples) was 9% for the Static method, 13% for Convolution, and 3% for Corrected Convolution. While Convolution can provide a superior estimate of the dose delivered when geometric uncertainties are present, the violation of shift invariance can result in substantial errors near the surface of the patient. The proposed Corrected Convolution modification reduces errors near the surface to 3% or less
International Nuclear Information System (INIS)
Dubrovsky, V. G.; Topovsky, A. V.
2013-01-01
New exact solutions, nonstationary and stationary, of Veselov-Novikov (VN) equation in the forms of simple nonlinear and linear superpositions of arbitrary number N of exact special solutions u (n) , n= 1, …, N are constructed via Zakharov and Manakov ∂-dressing method. Simple nonlinear superpositions are represented up to a constant by the sums of solutions u (n) and calculated by ∂-dressing on nonzero energy level of the first auxiliary linear problem, i.e., 2D stationary Schrödinger equation. It is remarkable that in the zero energy limit simple nonlinear superpositions convert to linear ones in the form of the sums of special solutions u (n) . It is shown that the sums u=u (k 1 ) +...+u (k m ) , 1 ⩽k 1 2 m ⩽N of arbitrary subsets of these solutions are also exact solutions of VN equation. The presented exact solutions include as superpositions of special line solitons and also superpositions of plane wave type singular periodic solutions. By construction these exact solutions represent also new exact transparent potentials of 2D stationary Schrödinger equation and can serve as model potentials for electrons in planar structures of modern electronics.
Energy Technology Data Exchange (ETDEWEB)
Dubrovsky, V. G.; Topovsky, A. V. [Novosibirsk State Technical University, Karl Marx prosp. 20, Novosibirsk 630092 (Russian Federation)
2013-03-15
New exact solutions, nonstationary and stationary, of Veselov-Novikov (VN) equation in the forms of simple nonlinear and linear superpositions of arbitrary number N of exact special solutions u{sup (n)}, n= 1, Horizontal-Ellipsis , N are constructed via Zakharov and Manakov {partial_derivative}-dressing method. Simple nonlinear superpositions are represented up to a constant by the sums of solutions u{sup (n)} and calculated by {partial_derivative}-dressing on nonzero energy level of the first auxiliary linear problem, i.e., 2D stationary Schroedinger equation. It is remarkable that in the zero energy limit simple nonlinear superpositions convert to linear ones in the form of the sums of special solutions u{sup (n)}. It is shown that the sums u=u{sup (k{sub 1})}+...+u{sup (k{sub m})}, 1 Less-Than-Or-Slanted-Equal-To k{sub 1} < k{sub 2} < Horizontal-Ellipsis < k{sub m} Less-Than-Or-Slanted-Equal-To N of arbitrary subsets of these solutions are also exact solutions of VN equation. The presented exact solutions include as superpositions of special line solitons and also superpositions of plane wave type singular periodic solutions. By construction these exact solutions represent also new exact transparent potentials of 2D stationary Schroedinger equation and can serve as model potentials for electrons in planar structures of modern electronics.
Use of the modal superposition technique for piping system blowdown analyses
International Nuclear Information System (INIS)
Ware, A.G.; Macek, R.W.
1983-01-01
A standard method of solving for the seismic response of piping systems is the modal superposition technique. Only a limited number of structural modes are considered (typically those up to 33 Hz in the U.S.), since the effect on the calculated response due to higher modes is generally small, and the method can result in considerable computer cost savings over the direct integration method. The modal superposition technique has also been applied to piping response problems in which the forcing functions are due to fluid excitation. Application of the technique to this case is somewhat more difficult, because a well defined cutoff frequency for determining structural modes to be included has not been established. This paper outlines a method for higher mode corrections, and suggests methods to determine suitable cutoff frequencies for piping system blowdown analyses. A numerical example illustrates how uncorrected modal superposition results can produce erroneous stress results
Energy Technology Data Exchange (ETDEWEB)
Hubbard, C.R.; Babich, M.W.; Jacobson, R.A.
1977-01-01
A new system of three programs written in PL/1 can calculate symmetry and Patterson superposition maps for triclinic, monoclinic, and orthorhombic space groups as well as any space group reducible to one of these three. These programs are based on a system of FORTRAN programs developed at Ames Laboratory, but are more general and have expanded utility, especially with regard to large unit cells. The program PLIGEN calculates a direct access data set, SYMPL1 calculates a direct access symmetry map, and ALSPL1 calculates a superposition map using one or multiple superpositions. A detailed description of the use of these programs including symbolic program listings is included. 2 tables.
Energy Technology Data Exchange (ETDEWEB)
Moral, F. del; Ramos, A.; Salgado, M.; Andrade, B; Munoz, V.
2010-07-01
In this work an analysis of the influence of the choice of the algorithm or planning system, on the calculus of the same treatment plan is introduced. For this purpose specific software has been developed for comparing plans of a series of IMRT cases of prostate and head and neck cancer calculated using the convolution, superposition and fast superposition algorithms implemented in the XiO 4.40 planning system (CMS). It has also been used for the comparison of the same treatment plan for lung pathology calculated in XiO with the mentioned algorithms, and calculated in the Plan 4.1 planning system (Brainlab) using its pencil beam algorithm. Differences in dose among the treatment plans have been quantified using a set of metrics. The recommendation for the dosimetry of a careful choice of the algorithm has been numerically confirmed. (Author).
Thermalization as an Invisibility Cloak for Fragile Quantum Superpositions
Hahn, Walter; Fine, Boris V.
2017-01-01
We propose a method for protecting fragile quantum superpositions in many-particle systems from dephasing by external classical noise. We call superpositions "fragile" if dephasing occurs particularly fast, because the noise couples very differently to the superposed states. The method consists of letting a quantum superposition evolve under the internal thermalization dynamics of the system, followed by a time reversal manipulation known as Loschmidt echo. The thermalization dynamics makes t...
On the superposition principle in interference experiments.
Sinha, Aninda; H Vijay, Aravind; Sinha, Urbasi
2015-05-14
The superposition principle is usually incorrectly applied in interference experiments. This has recently been investigated through numerics based on Finite Difference Time Domain (FDTD) methods as well as the Feynman path integral formalism. In the current work, we have derived an analytic formula for the Sorkin parameter which can be used to determine the deviation from the application of the principle. We have found excellent agreement between the analytic distribution and those that have been earlier estimated by numerical integration as well as resource intensive FDTD simulations. The analytic handle would be useful for comparing theory with future experiments. It is applicable both to physics based on classical wave equations as well as the non-relativistic Schrödinger equation.
Authentication Protocol using Quantum Superposition States
Energy Technology Data Exchange (ETDEWEB)
Kanamori, Yoshito [University of Alaska; Yoo, Seong-Moo [University of Alabama, Huntsville; Gregory, Don A. [University of Alabama, Huntsville; Sheldon, Frederick T [ORNL
2009-01-01
When it became known that quantum computers could break the RSA (named for its creators - Rivest, Shamir, and Adleman) encryption algorithm within a polynomial-time, quantum cryptography began to be actively studied. Other classical cryptographic algorithms are only secure when malicious users do not have sufficient computational power to break security within a practical amount of time. Recently, many quantum authentication protocols sharing quantum entangled particles between communicators have been proposed, providing unconditional security. An issue caused by sharing quantum entangled particles is that it may not be simple to apply these protocols to authenticate a specific user in a group of many users. An authentication protocol using quantum superposition states instead of quantum entangled particles is proposed. The random number shared between a sender and a receiver can be used for classical encryption after the authentication has succeeded. The proposed protocol can be implemented with the current technologies we introduce in this paper.
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
Generation of optical coherent state superpositions for quantum information processing
DEFF Research Database (Denmark)
Tipsmark, Anders
2012-01-01
I dette projektarbejde med titlen “Generation of optical coherent state superpositions for quantum information processing” har målet været at generere optiske kat-tilstande. Dette er en kvantemekanisk superpositions tilstand af to koherente tilstande med stor amplitude. Sådan en tilstand er...
Teleportation of Unknown Superpositions of Collective Atomic Coherent States
Institute of Scientific and Technical Information of China (English)
ZHENG ShiBiao
2001-01-01
We propose a scheme to teleport an unknown superposition of two atomic coherent states with different phases. Our scheme is based on resonant and dispersive atom-field interaction. Our scheme provides a possibility of teleporting macroscopic superposition states of many atoms first time.``
THESEUS: maximum likelihood superpositioning and analysis of macromolecular structures.
Theobald, Douglas L; Wuttke, Deborah S
2006-09-01
THESEUS is a command line program for performing maximum likelihood (ML) superpositions and analysis of macromolecular structures. While conventional superpositioning methods use ordinary least-squares (LS) as the optimization criterion, ML superpositions provide substantially improved accuracy by down-weighting variable structural regions and by correcting for correlations among atoms. ML superpositioning is robust and insensitive to the specific atoms included in the analysis, and thus it does not require subjective pruning of selected variable atomic coordinates. Output includes both likelihood-based and frequentist statistics for accurate evaluation of the adequacy of a superposition and for reliable analysis of structural similarities and differences. THESEUS performs principal components analysis for analyzing the complex correlations found among atoms within a structural ensemble. ANSI C source code and selected binaries for various computing platforms are available under the GNU open source license from http://monkshood.colorado.edu/theseus/ or http://www.theseus3d.org.
QCDNUM: Fast QCD evolution and convolution
Botje, M.
2011-02-01
The QCDNUM program numerically solves the evolution equations for parton densities and fragmentation functions in perturbative QCD. Un-polarised parton densities can be evolved up to next-to-next-to-leading order in powers of the strong coupling constant, while polarised densities or fragmentation functions can be evolved up to next-to-leading order. Other types of evolution can be accessed by feeding alternative sets of evolution kernels into the program. A versatile convolution engine provides tools to compute parton luminosities, cross-sections in hadron-hadron scattering, and deep inelastic structure functions in the zero-mass scheme or in generalised mass schemes. Input to these calculations are either the QCDNUM evolved densities, or those read in from an external parton density repository. Included in the software distribution are packages to calculate zero-mass structure functions in un-polarised deep inelastic scattering, and heavy flavour contributions to these structure functions in the fixed flavour number scheme. Program summaryProgram title: QCDNUM version: 17.00 Catalogue identifier: AEHV_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEHV_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU Public Licence No. of lines in distributed program, including test data, etc.: 45 736 No. of bytes in distributed program, including test data, etc.: 911 569 Distribution format: tar.gz Programming language: Fortran-77 Computer: All Operating system: All RAM: Typically 3 Mbytes Classification: 11.5 Nature of problem: Evolution of the strong coupling constant and parton densities, up to next-to-next-to-leading order in perturbative QCD. Computation of observable quantities by Mellin convolution of the evolved densities with partonic cross-sections. Solution method: Parametrisation of the parton densities as linear or quadratic splines on a discrete grid, and evolution of the spline
Design of convolutional tornado code
Zhou, Hui; Yang, Yao; Gao, Hongmin; Tan, Lu
2017-09-01
As a linear block code, the traditional tornado (tTN) code is inefficient in burst-erasure environment and its multi-level structure may lead to high encoding/decoding complexity. This paper presents a convolutional tornado (cTN) code which is able to improve the burst-erasure protection capability by applying the convolution property to the tTN code, and reduce computational complexity by abrogating the multi-level structure. The simulation results show that cTN code can provide a better packet loss protection performance with lower computation complexity than tTN code.
Thermalization as an invisibility cloak for fragile quantum superpositions
Hahn, Walter; Fine, Boris V.
2017-07-01
We propose a method for protecting fragile quantum superpositions in many-particle systems from dephasing by external classical noise. We call superpositions "fragile" if dephasing occurs particularly fast, because the noise couples very differently to the superposed states. The method consists of letting a quantum superposition evolve under the internal thermalization dynamics of the system, followed by a time-reversal manipulation known as Loschmidt echo. The thermalization dynamics makes the superposed states almost indistinguishable during most of the above procedure. We validate the method by applying it to a cluster of spins ½.
Empirical Evaluation of Superposition Coded Multicasting for Scalable Video
Chun Pong Lau
2013-03-01
In this paper we investigate cross-layer superposition coded multicast (SCM). Previous studies have proven its effectiveness in exploiting better channel capacity and service granularities via both analytical and simulation approaches. However, it has never been practically implemented using a commercial 4G system. This paper demonstrates our prototype in achieving the SCM using a standard 802.16 based testbed for scalable video transmissions. In particular, to implement the superposition coded (SPC) modulation, we take advantage a novel software approach, namely logical SPC (L-SPC), which aims to mimic the physical layer superposition coded modulation. The emulation results show improved throughput comparing with generic multicast method.
Solutions to Arithmetic Convolution Equations
Czech Academy of Sciences Publication Activity Database
Glöckner, H.; Lucht, L.G.; Porubský, Štefan
2007-01-01
Roč. 135, č. 6 (2007), s. 1619-1629 ISSN 0002-9939 R&D Projects: GA ČR GA201/04/0381 Institutional research plan: CEZ:AV0Z10300504 Keywords : arithmetic functions * Dirichlet convolution * polynomial equations * analytic equations * topological algebras * holomorphic functional calculus Subject RIV: BA - General Mathematics Impact factor: 0.520, year: 2007
Measurement-Induced Macroscopic Superposition States in Cavity Optomechanics
DEFF Research Database (Denmark)
Hoff, Ulrich Busk; Kollath-Bönig, Johann; Neergaard-Nielsen, Jonas Schou
2016-01-01
A novel protocol for generating quantum superpositions of macroscopically distinct states of a bulk mechanical oscillator is proposed, compatible with existing optomechanical devices operating in the bad-cavity limit. By combining a pulsed optomechanical quantum nondemolition (QND) interaction...
Testing the quantum superposition principle: matter waves and beyond
Ulbricht, Hendrik
2015-05-01
New technological developments allow to explore the quantum properties of very complex systems, bringing the question of whether also macroscopic systems share such features, within experimental reach. The interest in this question is increased by the fact that, on the theory side, many suggest that the quantum superposition principle is not exact, departures from it being the larger, the more macroscopic the system. Testing the superposition principle intrinsically also means to test suggested extensions of quantum theory, so-called collapse models. We will report on three new proposals to experimentally test the superposition principle with nanoparticle interferometry, optomechanical devices and by spectroscopic experiments in the frequency domain. We will also report on the status of optical levitation and cooling experiments with nanoparticles in our labs, towards an Earth bound matter-wave interferometer to test the superposition principle for a particle mass of one million amu (atomic mass unit).
Empirical Evaluation of Superposition Coded Multicasting for Scalable Video
Chun Pong Lau; Shihada, Basem; Pin-Han Ho
2013-01-01
In this paper we investigate cross-layer superposition coded multicast (SCM). Previous studies have proven its effectiveness in exploiting better channel capacity and service granularities via both analytical and simulation approaches. However
Quantum State Engineering Via Coherent-State Superpositions
Janszky, Jozsef; Adam, P.; Szabo, S.; Domokos, P.
1996-01-01
The quantum interference between the two parts of the optical Schrodinger-cat state makes possible to construct a wide class of quantum states via discrete superpositions of coherent states. Even a small number of coherent states can approximate the given quantum states at a high accuracy when the distance between the coherent states is optimized, e. g. nearly perfect Fock state can be constructed by discrete superpositions of n + 1 coherent states lying in the vicinity of the vacuum state.
International Nuclear Information System (INIS)
Lu Yanyun; Gu Shenjie; Lou Tianyang
2014-01-01
Background: As nuclear grade cable must endure harsh environment within design life, it is critical to predict cable thermal life accurately owing to thermal aging, which is one of dominant factors of aging mechanism. Purpose: Using time temperature superposition (TTS) method, the aim is to construct nuclear grade cable thermal life model, predict cable residual life and develop life model interactive interface under Matlab GUI. Methods: According to TTS, nuclear grade cable thermal life model can be constructed by shifting data groups at various temperatures to preset reference temperature with translation factor which is determined by non linear programming optimization. Interactive interface of cable thermal life model developed under Matlab GUI consists of superposition mode and standard mode which include features such as optimization of translation factor, calculation of activation energy, construction of thermal aging curve and analysis of aging mechanism., Results: With calculation result comparison between superposition and standard method, the result with TTS has better accuracy than that with standard method. Furthermore, confidence level of nuclear grade cable thermal life with TTS is higher than that with standard method. Conclusion: The results show that TTS methodology is applicable to thermal life prediction of nuclear grade cable. Interactive Interface under Matlab GUI achieves anticipated functionalities. (authors)
Guérin, Philippe Allard; Feix, Adrien; Araújo, Mateus; Brukner, Časlav
2016-09-01
In communication complexity, a number of distant parties have the task of calculating a distributed function of their inputs, while minimizing the amount of communication between them. It is known that with quantum resources, such as entanglement and quantum channels, one can obtain significant reductions in the communication complexity of some tasks. In this work, we study the role of the quantum superposition of the direction of communication as a resource for communication complexity. We present a tripartite communication task for which such a superposition allows for an exponential saving in communication, compared to one-way quantum (or classical) communication; the advantage also holds when we allow for protocols with bounded error probability.
Energy Technology Data Exchange (ETDEWEB)
Cheong, Kwang-Ho; Suh, Tae-Suk; Lee, Hyoung-Koo; Choe, Bo-Young [The Catholic Univ. of Korea, Seoul (Korea, Republic of); Kim, Hoi-Nam; Yoon, Sei-Chul [Kangnam St. Mary' s Hospital, Seoul (Korea, Republic of)
2002-07-01
Accurate dose calculation in radiation treatment planning is most important for successful treatment. Since human body is composed of various materials and not an ideal shape, it is not easy to calculate the accurate effective dose in the patients. Many methods have been proposed to solve inhomogeneity and surface contour problems. Monte Carlo simulations are regarded as the most accurate method, but it is not appropriate for routine planning because it takes so much time. Pencil beam kernel based convolution/superposition methods were also proposed to correct those effects. Nowadays, many commercial treatment planning systems have adopted this algorithm as a dose calculation engine. The purpose of this study is to verify the accuracy of the dose calculated from pencil beam kernel based treatment planning system comparing to Monte Carlo simulations and measurements especially in inhomogeneous region. Home-made inhomogeneous phantom, Helax-TMS ver. 6.0 and Monte Carlo code BEAMnrc and DOSXYZnrc were used in this study. In homogeneous media, the accuracy was acceptable but in inhomogeneous media, the errors were more significant. However in general clinical situation, pencil beam kernel based convolution algorithm is thought to be a valuable tool to calculate the dose.
Adaptive Graph Convolutional Neural Networks
Li, Ruoyu; Wang, Sheng; Zhu, Feiyun; Huang, Junzhou
2018-01-01
Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and connectivity. The paper proposes a generalized and flexible graph CNN taking data of arbitrary graph structure as input. In that way a task-driven adaptive graph is learned for eac...
International Nuclear Information System (INIS)
Coleman, J.H.
1980-10-01
A technique is discussed for computing the probability distribution of the accumulated dose received by an arbitrary receptor resulting from several single releases from an intermittent source. The probability density of the accumulated dose is the convolution of the probability densities of doses from the intermittent releases. Emissions are not assumed to be constant over the brief release period. The fast fourier transform is used in the calculation of the convolution
Non-coaxial superposition of vector vortex beams.
Aadhi, A; Vaity, Pravin; Chithrabhanu, P; Reddy, Salla Gangi; Prabakar, Shashi; Singh, R P
2016-02-10
Vector vortex beams are classified into four types depending upon spatial variation in their polarization vector. We have generated all four of these types of vector vortex beams by using a modified polarization Sagnac interferometer with a vortex lens. Further, we have studied the non-coaxial superposition of two vector vortex beams. It is observed that the superposition of two vector vortex beams with same polarization singularity leads to a beam with another kind of polarization singularity in their interaction region. The results may be of importance in ultrahigh security of the polarization-encrypted data that utilizes vector vortex beams and multiple optical trapping with non-coaxial superposition of vector vortex beams. We verified our experimental results with theory.
Entanglement and quantum superposition induced by a single photon
Lü, Xin-You; Zhu, Gui-Lei; Zheng, Li-Li; Wu, Ying
2018-03-01
We predict the occurrence of single-photon-induced entanglement and quantum superposition in a hybrid quantum model, introducing an optomechanical coupling into the Rabi model. Originally, it comes from the photon-dependent quantum property of the ground state featured by the proposed hybrid model. It is associated with a single-photon-induced quantum phase transition, and is immune to the A2 term of the spin-field interaction. Moreover, the obtained quantum superposition state is actually a squeezed cat state, which can significantly enhance precision in quantum metrology. This work offers an approach to manipulate entanglement and quantum superposition with a single photon, which might have potential applications in the engineering of new single-photon quantum devices, and also fundamentally broaden the regime of cavity QED.
Robust mesoscopic superposition of strongly correlated ultracold atoms
International Nuclear Information System (INIS)
Hallwood, David W.; Ernst, Thomas; Brand, Joachim
2010-01-01
We propose a scheme to create coherent superpositions of annular flow of strongly interacting bosonic atoms in a one-dimensional ring trap. The nonrotating ground state is coupled to a vortex state with mesoscopic angular momentum by means of a narrow potential barrier and an applied phase that originates from either rotation or a synthetic magnetic field. We show that superposition states in the Tonks-Girardeau regime are robust against single-particle loss due to the effects of strong correlations. The coupling between the mesoscopically distinct states scales much more favorably with particle number than in schemes relying on weak interactions, thus making particle numbers of hundreds or thousands feasible. Coherent oscillations induced by time variation of parameters may serve as a 'smoking gun' signature for detecting superposition states.
Convolution of Distribution-Valued Functions. Applications.
BARGETZ, CHRISTIAN
2011-01-01
In this article we examine products and convolutions of vector-valued functions. For nuclear normal spaces of distributions Proposition 25 in [31,p. 120] yields a vector-valued product or convolution if there is a continuous product or convolution mapping in the range of the vector-valued functions. For specific spaces, we generalize this result to hypocontinuous bilinear maps at the expense of generality with respect to the function space. We consider holomorphic, meromorphic and differentia...
Improving deep convolutional neural networks with mixed maxout units.
Directory of Open Access Journals (Sweden)
Hui-Zhen Zhao
Full Text Available Motivated by insights from the maxout-units-based deep Convolutional Neural Network (CNN that "non-maximal features are unable to deliver" and "feature mapping subspace pooling is insufficient," we present a novel mixed variant of the recently introduced maxout unit called a mixout unit. Specifically, we do so by calculating the exponential probabilities of feature mappings gained by applying different convolutional transformations over the same input and then calculating the expected values according to their exponential probabilities. Moreover, we introduce the Bernoulli distribution to balance the maximum values with the expected values of the feature mappings subspace. Finally, we design a simple model to verify the pooling ability of mixout units and a Mixout-units-based Network-in-Network (NiN model to analyze the feature learning ability of the mixout models. We argue that our proposed units improve the pooling ability and that mixout models can achieve better feature learning and classification performance.
Superposition of helical beams by using a Michelson interferometer.
Gao, Chunqing; Qi, Xiaoqing; Liu, Yidong; Weber, Horst
2010-01-04
Orbital angular momentum (OAM) of a helical beam is of great interests in the high density optical communication due to its infinite number of eigen-states. In this paper, an experimental setup is realized to the information encoding and decoding on the OAM eigen-states. A hologram designed by the iterative method is used to generate the helical beams, and a Michelson interferometer with two Porro prisms is used for the superposition of two helical beams. The experimental results of the collinear superposition of helical beams and their OAM eigen-states detection are presented.
EPR, optical and superposition model study of Mn2+ doped L+ glutamic acid
Kripal, Ram; Singh, Manju
2015-12-01
Electron paramagnetic resonance (EPR) study of Mn2+ doped L+ glutamic acid single crystal is done at room temperature. Four interstitial sites are observed and the spin Hamiltonian parameters are calculated with the help of large number of resonant lines for various angular positions of external magnetic field. The optical absorption study is also done at room temperature. The energy values for different orbital levels are calculated, and observed bands are assigned as transitions from 6A1g(s) ground state to various excited states. With the help of these assigned bands, Racah inter-electronic repulsion parameters B = 869 cm-1, C = 2080 cm-1 and cubic crystal field splitting parameter Dq = 730 cm-1 are calculated. Zero field splitting (ZFS) parameters D and E are calculated by the perturbation formulae and crystal field parameters obtained using superposition model. The calculated values of ZFS parameters are in good agreement with the experimental values obtained by EPR.
Macroscopic superposition states and decoherence by quantum telegraph noise
Energy Technology Data Exchange (ETDEWEB)
Abel, Benjamin Simon
2008-12-19
In the first part of the present thesis we address the question about the size of superpositions of macroscopically distinct quantum states. We propose a measure for the ''size'' of a Schroedinger cat state, i.e. a quantum superposition of two many-body states with (supposedly) macroscopically distinct properties, by counting how many single-particle operations are needed to map one state onto the other. We apply our measure to a superconducting three-junction flux qubit put into a superposition of clockwise and counterclockwise circulating supercurrent states and find this Schroedinger cat to be surprisingly small. The unavoidable coupling of any quantum system to many environmental degrees of freedom leads to an irreversible loss of information about an initially prepared superposition of quantum states. This phenomenon, commonly referred to as decoherence or dephasing, is the subject of the second part of the thesis. We have studied the time evolution of the reduced density matrix of a two-level system (qubit) subject to quantum telegraph noise which is the major source of decoherence in Josephson charge qubits. We are able to derive an exact expression for the time evolution of the reduced density matrix. (orig.)
Linear Plasma Oscillation Described by Superposition of Normal Modes
DEFF Research Database (Denmark)
Pécseli, Hans
1974-01-01
The existence of steady‐state solutions to the linearized ion and electron Vlasov equation is demonstrated for longitudinal waves in an initially stable plasma. The evolution of an arbitrary initial perturbation can be described by superposition of these solutions. Some common approximations...
Macroscopic superposition states and decoherence by quantum telegraph noise
International Nuclear Information System (INIS)
Abel, Benjamin Simon
2008-01-01
In the first part of the present thesis we address the question about the size of superpositions of macroscopically distinct quantum states. We propose a measure for the ''size'' of a Schroedinger cat state, i.e. a quantum superposition of two many-body states with (supposedly) macroscopically distinct properties, by counting how many single-particle operations are needed to map one state onto the other. We apply our measure to a superconducting three-junction flux qubit put into a superposition of clockwise and counterclockwise circulating supercurrent states and find this Schroedinger cat to be surprisingly small. The unavoidable coupling of any quantum system to many environmental degrees of freedom leads to an irreversible loss of information about an initially prepared superposition of quantum states. This phenomenon, commonly referred to as decoherence or dephasing, is the subject of the second part of the thesis. We have studied the time evolution of the reduced density matrix of a two-level system (qubit) subject to quantum telegraph noise which is the major source of decoherence in Josephson charge qubits. We are able to derive an exact expression for the time evolution of the reduced density matrix. (orig.)
Generating superpositions of higher–order Bessel beams [Journal article
CSIR Research Space (South Africa)
Vasilyeu, R
2009-12-01
Full Text Available The authors report the first experimental generation of the superposition of higher-order Bessel beams, by means of a spatial light modulator (SLM) and a ring slit aperture. They present illuminating a ring slit aperture with light which has...
Spectral properties of superpositions of Ornstein-Uhlenbeck type processes
DEFF Research Database (Denmark)
Barndorff-Nielsen, Ole Eiler; Leonenko, N.N.
2005-01-01
Stationary processes with prescribed one-dimensional marginal laws and long-range dependence are constructed. The asymptotic properties of the spectral densities are studied. The possibility of Mittag-Leffler decay in the autocorrelation function of superpositions of Ornstein-Uhlenbeck type...... processes is proved....
On some properties of the superposition operator on topological manifolds
Directory of Open Access Journals (Sweden)
Janusz Dronka
2010-01-01
Full Text Available In this paper the superposition operator in the space of vector-valued, bounded and continuous functions on a topological manifold is considered. The acting conditions and criteria of continuity and compactness are established. As an application, an existence result for the nonlinear Hammerstein integral equation is obtained.
Incomplete convolutions in production and inventory models
Houtum, van G.J.J.A.N.; Zijm, W.H.M.
1997-01-01
In this paper, we study incomplete convolutions of continuous distribution functions, as they appear in the analysis of (multi-stage) production and inventory systems. Three example systems are discussed where these incomplete convolutions naturally arise. We derive explicit, nonrecursive formulae
A convolutional approach to reflection symmetry
DEFF Research Database (Denmark)
Cicconet, Marcelo; Birodkar, Vighnesh; Lund, Mads
2017-01-01
We present a convolutional approach to reflection symmetry detection in 2D. Our model, built on the products of complex-valued wavelet convolutions, simplifies previous edge-based pairwise methods. Being parameter-centered, as opposed to feature-centered, it has certain computational advantages w...
Symbol synchronization in convolutionally coded systems
Baumert, L. D.; Mceliece, R. J.; Van Tilborg, H. C. A.
1979-01-01
Alternate symbol inversion is sometimes applied to the output of convolutional encoders to guarantee sufficient richness of symbol transition for the receiver symbol synchronizer. A bound is given for the length of the transition-free symbol stream in such systems, and those convolutional codes are characterized in which arbitrarily long transition free runs occur.
The general theory of convolutional codes
Mceliece, R. J.; Stanley, R. P.
1993-01-01
This article presents a self-contained introduction to the algebraic theory of convolutional codes. This introduction is partly a tutorial, but at the same time contains a number of new results which will prove useful for designers of advanced telecommunication systems. Among the new concepts introduced here are the Hilbert series for a convolutional code and the class of compact codes.
A convolution method for predicting mean treatment dose including organ motion at imaging
International Nuclear Information System (INIS)
Booth, J.T.; Zavgorodni, S.F.; Royal Adelaide Hospital, SA
2000-01-01
Full text: The random treatment delivery errors (organ motion and set-up error) can be incorporated into the treatment planning software using a convolution method. Mean treatment dose is computed as the convolution of a static dose distribution with a variation kernel. Typically this variation kernel is Gaussian with variance equal to the sum of the organ motion and set-up error variances. We propose a novel variation kernel for the convolution technique that additionally considers the position of the mobile organ in the planning CT image. The systematic error of organ position in the planning CT image can be considered random for each patient over a population. Thus the variance of the variation kernel will equal the sum of treatment delivery variance and organ motion variance at planning for the population of treatments. The kernel is extended to deal with multiple pre-treatment CT scans to improve tumour localisation for planning. Mean treatment doses calculated with the convolution technique are compared to benchmark Monte Carlo (MC) computations. Calculations of mean treatment dose using the convolution technique agreed with MC results for all cases to better than ± 1 Gy in the planning treatment volume for a prescribed 60 Gy treatment. Convolution provides a quick method of incorporating random organ motion (captured in the planning CT image and during treatment delivery) and random set-up errors directly into the dose distribution. Copyright (2000) Australasian College of Physical Scientists and Engineers in Medicine
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.
Entanglement and discord of the superposition of Greenberger-Horne-Zeilinger states
International Nuclear Information System (INIS)
Parashar, Preeti; Rana, Swapan
2011-01-01
We calculate the analytic expression for geometric measure of entanglement for arbitrary superposition of two N-qubit canonical orthonormal Greenberger-Horne-Zeilinger (GHZ) states and the same for two W states. In the course of characterizing all kinds of nonclassical correlations, an explicit formula for quantum discord (via relative entropy) for the former class of states has been presented. Contrary to the GHZ state, the closest separable state to the W state is not classical. Therefore, in this case, the discord is different from the relative entropy of entanglement. We conjecture that the discord for the N-qubit W state is log 2 N.
Convolution-deconvolution in DIGES
International Nuclear Information System (INIS)
Philippacopoulos, A.J.; Simos, N.
1995-01-01
Convolution and deconvolution operations is by all means a very important aspect of SSI analysis since it influences the input to the seismic analysis. This paper documents some of the convolution/deconvolution procedures which have been implemented into the DIGES code. The 1-D propagation of shear and dilatational waves in typical layered configurations involving a stack of layers overlying a rock is treated by DIGES in a similar fashion to that of available codes, e.g. CARES, SHAKE. For certain configurations, however, there is no need to perform such analyses since the corresponding solutions can be obtained in analytic form. Typical cases involve deposits which can be modeled by a uniform halfspace or simple layered halfspaces. For such cases DIGES uses closed-form solutions. These solutions are given for one as well as two dimensional deconvolution. The type of waves considered include P, SV and SH waves. The non-vertical incidence is given special attention since deconvolution can be defined differently depending on the problem of interest. For all wave cases considered, corresponding transfer functions are presented in closed-form. Transient solutions are obtained in the frequency domain. Finally, a variety of forms are considered for representing the free field motion both in terms of deterministic as well as probabilistic representations. These include (a) acceleration time histories, (b) response spectra (c) Fourier spectra and (d) cross-spectral densities
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....
Indian Academy of Sciences (India)
President's Address to the Association of Mathematics Teachers of India, December 2011. I am expected to tell you, in 25 minutes, something that should interest you, excite you, pique your curiosity, and make you look for more. It is a tall order, but I will try. The word 'interactive' is in fashion these days. So I will leave a few ...
Dose discrepancies in the buildup region and their impact on dose calculations for IMRT fields
International Nuclear Information System (INIS)
Hsu, Shu-Hui; Moran, Jean M.; Chen Yu; Kulasekere, Ravi; Roberson, Peter L.
2010-01-01
Purpose: Dose accuracy in the buildup region for radiotherapy treatment planning suffers from challenges in both measurement and calculation. This study investigates the dosimetry in the buildup region at normal and oblique incidences for open and IMRT fields and assesses the quality of the treatment planning calculations. Methods: This study was divided into three parts. First, percent depth doses and profiles (for 5x5, 10x10, 20x20, and 30x30 cm 2 field sizes at 0 deg., 45 deg., and 70 deg. incidences) were measured in the buildup region in Solid Water using an Attix parallel plate chamber and Kodak XV film, respectively. Second, the parameters in the empirical contamination (EC) term of the convolution/superposition (CVSP) calculation algorithm were fitted based on open field measurements. Finally, seven segmental head-and-neck IMRT fields were measured on a flat phantom geometry and compared to calculations using γ and dose-gradient compensation (C) indices to evaluate the impact of residual discrepancies and to assess the adequacy of the contamination term for IMRT fields. Results: Local deviations between measurements and calculations for open fields were within 1% and 4% in the buildup region for normal and oblique incidences, respectively. The C index with 5%/1 mm criteria for IMRT fields ranged from 89% to 99% and from 96% to 98% at 2 mm and 10 cm depths, respectively. The quality of agreement in the buildup region for open and IMRT fields is comparable to that in nonbuildup regions. Conclusions: The added EC term in CVSP was determined to be adequate for both open and IMRT fields. Due to the dependence of calculation accuracy on (1) EC modeling, (2) internal convolution and density grid sizes, (3) implementation details in the algorithm, and (4) the accuracy of measurements used for treatment planning system commissioning, the authors recommend an evaluation of the accuracy of near-surface dose calculations as a part of treatment planning commissioning.
Adaptive decoding of convolutional codes
Directory of Open Access Journals (Sweden)
K. Hueske
2007-06-01
Full Text Available Convolutional codes, which are frequently used as error correction codes in digital transmission systems, are generally decoded using the Viterbi Decoder. On the one hand the Viterbi Decoder is an optimum maximum likelihood decoder, i.e. the most probable transmitted code sequence is obtained. On the other hand the mathematical complexity of the algorithm only depends on the used code, not on the number of transmission errors. To reduce the complexity of the decoding process for good transmission conditions, an alternative syndrome based decoder is presented. The reduction of complexity is realized by two different approaches, the syndrome zero sequence deactivation and the path metric equalization. The two approaches enable an easy adaptation of the decoding complexity for different transmission conditions, which results in a trade-off between decoding complexity and error correction performance.
Adaptive decoding of convolutional codes
Hueske, K.; Geldmacher, J.; Götze, J.
2007-06-01
Convolutional codes, which are frequently used as error correction codes in digital transmission systems, are generally decoded using the Viterbi Decoder. On the one hand the Viterbi Decoder is an optimum maximum likelihood decoder, i.e. the most probable transmitted code sequence is obtained. On the other hand the mathematical complexity of the algorithm only depends on the used code, not on the number of transmission errors. To reduce the complexity of the decoding process for good transmission conditions, an alternative syndrome based decoder is presented. The reduction of complexity is realized by two different approaches, the syndrome zero sequence deactivation and the path metric equalization. The two approaches enable an easy adaptation of the decoding complexity for different transmission conditions, which results in a trade-off between decoding complexity and error correction performance.
A fast dose calculation method based on table lookup for IMRT optimization
International Nuclear Information System (INIS)
Wu Qiuwen; Djajaputra, David; Lauterbach, Marc; Wu Yan; Mohan, Radhe
2003-01-01
This note describes a fast dose calculation method that can be used to speed up the optimization process in intensity-modulated radiotherapy (IMRT). Most iterative optimization algorithms in IMRT require a large number of dose calculations to achieve convergence and therefore the total amount of time needed for the IMRT planning can be substantially reduced by using a faster dose calculation method. The method that is described in this note relies on an accurate dose calculation engine that is used to calculate an approximate dose kernel for each beam used in the treatment plan. Once the kernel is computed and saved, subsequent dose calculations can be done rapidly by looking up this kernel. Inaccuracies due to the approximate nature of the kernel in this method can be reduced by performing scheduled kernel updates. This fast dose calculation method can be performed more than two orders of magnitude faster than the typical superposition/convolution methods and therefore is suitable for applications in which speed is critical, e.g., in an IMRT optimization that requires a simulated annealing optimization algorithm or in a practical IMRT beam-angle optimization system. (note)
Transforming spatial point processes into Poisson processes using random superposition
DEFF Research Database (Denmark)
Møller, Jesper; Berthelsen, Kasper Klitgaaard
with a complementary spatial point process Y to obtain a Poisson process X∪Y with intensity function β. Underlying this is a bivariate spatial birth-death process (Xt,Yt) which converges towards the distribution of (X,Y). We study the joint distribution of X and Y, and their marginal and conditional distributions....... In particular, we introduce a fast and easy simulation procedure for Y conditional on X. This may be used for model checking: given a model for the Papangelou intensity of the original spatial point process, this model is used to generate the complementary process, and the resulting superposition is a Poisson...... process with intensity function β if and only if the true Papangelou intensity is used. Whether the superposition is actually such a Poisson process can easily be examined using well known results and fast simulation procedures for Poisson processes. We illustrate this approach to model checking...
Coherent inflation for large quantum superpositions of levitated microspheres
Romero-Isart, Oriol
2017-12-01
We show that coherent inflation (CI), namely quantum dynamics generated by inverted conservative potentials acting on the center of mass of a massive object, is an enabling tool to prepare large spatial quantum superpositions in a double-slit experiment. Combined with cryogenic, extreme high vacuum, and low-vibration environments, we argue that it is experimentally feasible to exploit CI to prepare the center of mass of a micrometer-sized object in a spatial quantum superposition comparable to its size. In such a hitherto unexplored parameter regime gravitationally-induced decoherence could be unambiguously falsified. We present a protocol to implement CI in a double-slit experiment by letting a levitated microsphere traverse a static potential landscape. Such a protocol could be experimentally implemented with an all-magnetic scheme using superconducting microspheres.
Improved superposition schemes for approximate multi-caloron configurations
International Nuclear Information System (INIS)
Gerhold, P.; Ilgenfritz, E.-M.; Mueller-Preussker, M.
2007-01-01
Two improved superposition schemes for the construction of approximate multi-caloron-anti-caloron configurations, using exact single (anti-)caloron gauge fields as underlying building blocks, are introduced in this paper. The first improvement deals with possible monopole-Dirac string interactions between different calorons with non-trivial holonomy. The second one, based on the ADHM formalism, improves the (anti-)selfduality in the case of small caloron separations. It conforms with Shuryak's well-known ratio-ansatz when applied to instantons. Both superposition techniques provide a higher degree of (anti-)selfduality than the widely used sum-ansatz, which simply adds the (anti)caloron vector potentials in an appropriate gauge. Furthermore, the improved configurations (when discretized onto a lattice) are characterized by a higher stability when they are exposed to lattice cooling techniques
Interplay of gravitation and linear superposition of different mass eigenstates
International Nuclear Information System (INIS)
Ahluwalia, D.V.
1998-01-01
The interplay of gravitation and the quantum-mechanical principle of linear superposition induces a new set of neutrino oscillation phases. These ensure that the flavor-oscillation clocks, inherent in the phenomenon of neutrino oscillations, redshift precisely as required by Einstein close-quote s theory of gravitation. The physical observability of these phases in the context of the solar neutrino anomaly, type-II supernova, and certain atomic systems is briefly discussed. copyright 1998 The American Physical Society
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.
Single-Atom Gating of Quantum State Superpositions
Energy Technology Data Exchange (ETDEWEB)
Moon, Christopher
2010-04-28
The ultimate miniaturization of electronic devices will likely require local and coherent control of single electronic wavefunctions. Wavefunctions exist within both physical real space and an abstract state space with a simple geometric interpretation: this state space - or Hilbert space - is spanned by mutually orthogonal state vectors corresponding to the quantized degrees of freedom of the real-space system. Measurement of superpositions is akin to accessing the direction of a vector in Hilbert space, determining an angle of rotation equivalent to quantum phase. Here we show that an individual atom inside a designed quantum corral1 can control this angle, producing arbitrary coherent superpositions of spatial quantum states. Using scanning tunnelling microscopy and nanostructures assembled atom-by-atom we demonstrate how single spins and quantum mirages can be harnessed to image the superposition of two electronic states. We also present a straightforward method to determine the atom path enacting phase rotations between any desired state vectors. A single atom thus becomes a real-space handle for an abstract Hilbert space, providing a simple technique for coherent quantum state manipulation at the spatial limit of condensed matter.
Edgeworth Expansion Based Model for the Convolutional Noise pdf
Directory of Open Access Journals (Sweden)
Yonatan Rivlin
2014-01-01
Full Text Available Recently, the Edgeworth expansion up to order 4 was used to represent the convolutional noise probability density function (pdf in the conditional expectation calculations where the source pdf was modeled with the maximum entropy density approximation technique. However, the applied Lagrange multipliers were not the appropriate ones for the chosen model for the convolutional noise pdf. In this paper we use the Edgeworth expansion up to order 4 and up to order 6 to model the convolutional noise pdf. We derive the appropriate Lagrange multipliers, thus obtaining new closed-form approximated expressions for the conditional expectation and mean square error (MSE as a byproduct. Simulation results indicate hardly any equalization improvement with Edgeworth expansion up to order 4 when using optimal Lagrange multipliers over a nonoptimal set. In addition, there is no justification for using the Edgeworth expansion up to order 6 over the Edgeworth expansion up to order 4 for the 16QAM and easy channel case. However, Edgeworth expansion up to order 6 leads to improved equalization performance compared to the Edgeworth expansion up to order 4 for the 16QAM and hard channel case as well as for the case where the 64QAM is sent via an easy channel.
A Note on Cubic Convolution Interpolation
Meijering, E.; Unser, M.
2003-01-01
We establish a link between classical osculatory interpolation and modern convolution-based interpolation and use it to show that two well-known cubic convolution schemes are formally equivalent to two osculatory interpolation schemes proposed in the actuarial literature about a century ago. We also discuss computational differences and give examples of other cubic interpolation schemes not previously studied in signal and image processing.
Validation of GPU based TomoTherapy dose calculation engine.
Chen, Quan; Lu, Weiguo; Chen, Yu; Chen, Mingli; Henderson, Douglas; Sterpin, Edmond
2012-04-01
The graphic processing unit (GPU) based TomoTherapy convolution/superposition(C/S) dose engine (GPU dose engine) achieves a dramatic performance improvement over the traditional CPU-cluster based TomoTherapy dose engine (CPU dose engine). Besides the architecture difference between the GPU and CPU, there are several algorithm changes from the CPU dose engine to the GPU dose engine. These changes made the GPU dose slightly different from the CPU-cluster dose. In order for the commercial release of the GPU dose engine, its accuracy has to be validated. Thirty eight TomoTherapy phantom plans and 19 patient plans were calculated with both dose engines to evaluate the equivalency between the two dose engines. Gamma indices (Γ) were used for the equivalency evaluation. The GPU dose was further verified with the absolute point dose measurement with ion chamber and film measurements for phantom plans. Monte Carlo calculation was used as a reference for both dose engines in the accuracy evaluation in heterogeneous phantom and actual patients. The GPU dose engine showed excellent agreement with the current CPU dose engine. The majority of cases had over 99.99% of voxels with Γ(1%, 1 mm) engine also showed similar degree of accuracy in heterogeneous media as the current TomoTherapy dose engine. It is verified and validated that the ultrafast TomoTherapy GPU dose engine can safely replace the existing TomoTherapy cluster based dose engine without degradation in dose accuracy.
JaSTA-2: Second version of the Java Superposition T-matrix Application
Halder, Prithish; Das, Himadri Sekhar
2017-12-01
In this article, we announce the development of a new version of the Java Superposition T-matrix App (JaSTA-2), to study the light scattering properties of porous aggregate particles. It has been developed using Netbeans 7.1.2, which is a java integrated development environment (IDE). The JaSTA uses double precision superposition T-matrix codes for multi-sphere clusters in random orientation, developed by Mackowski and Mischenko (1996). The new version consists of two options as part of the input parameters: (i) single wavelength and (ii) multiple wavelengths. The first option (which retains the applicability of older version of JaSTA) calculates the light scattering properties of aggregates of spheres for a single wavelength at a given instant of time whereas the second option can execute the code for a multiple numbers of wavelengths in a single run. JaSTA-2 provides convenient and quicker data analysis which can be used in diverse fields like Planetary Science, Atmospheric Physics, Nanoscience, etc. This version of the software is developed for Linux platform only, and it can be operated over all the cores of a processor using the multi-threading option.
International Nuclear Information System (INIS)
Schlegel, R.
1975-01-01
With the interaction interpretation, the Lorentz transformation of a system arises with selection from a superposition of its states in an observation-interaction. Integration of momentum states of a mass over all possible velocities gives the rest-mass energy. Static electrical and magnetic fields are not found to form such a superposition and are to be taken as irreducible elements. The external superposition consists of those states that are reached only by change of state of motion, whereas the internal superposition contains all the states available to an observer in a single inertial coordinate system. The conjecture is advanced that states of superposition may only be those related by space-time transformations (Lorentz transformations plus space inversion and charge conjugation). The continuum of external and internal superpositions is examined for various masses, and an argument for the unity of the superpositions is presented
Quantum-mechanical Green's functions and nonlinear superposition law
International Nuclear Information System (INIS)
Nassar, A.B.; Bassalo, J.M.F.; Antunes Neto, H.S.; Alencar, P. de T.S.
1986-01-01
The quantum-mechanical Green's function is derived for the problem of a time-dependent variable mass particle subject to a time-dependent forced harmonic oscillator potential by taking direct recourse of the corresponding Schroedinger equation. Through the usage of the nonlinear superposition law of Ray and Reid, it is shown that such a Green's function can be obtained from that for the problem of a particle with unit (constant) mass subject to either a forced harmonic potential with constant frequency or only to a time-dependent linear field. (Author) [pt
Quantum-mechanical Green's function and nonlinear superposition law
International Nuclear Information System (INIS)
Nassar, A.B.; Bassalo, J.M.F.; Antunes Neto, H.S.; Alencar, P.T.S.
1986-01-01
It is derived the quantum-mechanical Green's function for the problem of a time-dependent variable mass particle subject to a time-dependent forced harmonic-oscillator potential by taking direct recourse of the corresponding Schroedinger equation. Through the usage of the nonlinear superposition law of Ray and Reid, it is shown that such a Green's function can be obtained from that for the problem of a particle with unit (constant) mass subject to either a forced harmonic potential with constant frequency or only to a time-dependent linear field
Efficient Power Allocation for Video over Superposition Coding
Lau, Chun Pong
2013-03-01
In this paper we consider a wireless multimedia system by mapping scalable video coded (SVC) bit stream upon superposition coded (SPC) signals, referred to as (SVC-SPC) architecture. Empirical experiments using a software-defined radio(SDR) emulator are conducted to gain a better understanding of its efficiency, specifically, the impact of the received signal due to different power allocation ratios. Our experimental results show that to maintain high video quality, the power allocated to the base layer should be approximately four times higher than the power allocated to the enhancement layer.
Quantum superposition of massive objects and collapse models
International Nuclear Information System (INIS)
Romero-Isart, Oriol
2011-01-01
We analyze the requirements to test some of the most paradigmatic collapse models with a protocol that prepares quantum superpositions of massive objects. This consists of coherently expanding the wave function of a ground-state-cooled mechanical resonator, performing a squared position measurement that acts as a double slit, and observing interference after further evolution. The analysis is performed in a general framework and takes into account only unavoidable sources of decoherence: blackbody radiation and scattering of environmental particles. We also discuss the limitations imposed by the experimental implementation of this protocol using cavity quantum optomechanics with levitating dielectric nanospheres.
Quantum superposition of massive objects and collapse models
Energy Technology Data Exchange (ETDEWEB)
Romero-Isart, Oriol [Max-Planck-Institut fuer Quantenoptik, Hans-Kopfermann-Str. 1, D-85748 Garching (Germany)
2011-11-15
We analyze the requirements to test some of the most paradigmatic collapse models with a protocol that prepares quantum superpositions of massive objects. This consists of coherently expanding the wave function of a ground-state-cooled mechanical resonator, performing a squared position measurement that acts as a double slit, and observing interference after further evolution. The analysis is performed in a general framework and takes into account only unavoidable sources of decoherence: blackbody radiation and scattering of environmental particles. We also discuss the limitations imposed by the experimental implementation of this protocol using cavity quantum optomechanics with levitating dielectric nanospheres.
On Kolmogorov's superpositions and Boolean functions
Energy Technology Data Exchange (ETDEWEB)
Beiu, V.
1998-12-31
The paper overviews results dealing with the approximation capabilities of neural networks, as well as bounds on the size of threshold gate circuits. Based on an explicit numerical (i.e., constructive) algorithm for Kolmogorov's superpositions they will show that for obtaining minimum size neutral networks for implementing any Boolean function, the activation function of the neurons is the identity function. Because classical AND-OR implementations, as well as threshold gate implementations require exponential size (in the worst case), it will follow that size-optimal solutions for implementing arbitrary Boolean functions require analog circuitry. Conclusions and several comments on the required precision are ending the paper.
Push-pull optical pumping of pure superposition states
International Nuclear Information System (INIS)
Jau, Y.-Y.; Miron, E.; Post, A.B.; Kuzma, N.N.; Happer, W.
2004-01-01
A new optical pumping method, 'push-pull pumping', can produce very nearly pure, coherent superposition states between the initial and the final sublevels of the important field-independent 0-0 clock resonance of alkali-metal atoms. The key requirement for push-pull pumping is the use of D1 resonant light which alternates between left and right circular polarization at the Bohr frequency of the state. The new pumping method works for a wide range of conditions, including atomic beams with almost no collisions, and atoms in buffer gases with pressures of many atmospheres
Photon beam convolution using polyenergetic energy deposition kernels
International Nuclear Information System (INIS)
Hoban, P.W.; Murray, D.C.; Round, W.H.
1994-01-01
In photon beam convolution calculations where polyenergetic energy deposition kernels (EDKs) are used, the primary photon energy spectrum should be correctly accounted for in Monte Carlo generation of EDKs. This requires the probability of interaction, determined by the linear attenuation coefficient, μ, to be taken into account when primary photon interactions are forced to occur at the EDK origin. The use of primary and scattered EDKs generated with a fixed photon spectrum can give rise to an error in the dose calculation due to neglecting the effects of beam hardening with depth. The proportion of primary photon energy that is transferred to secondary electrons increases with depth of interaction, due to the increase in the ratio μ ab /μ as the beam hardens. Convolution depth-dose curves calculated using polyenergetic EDKs generated for the primary photon spectra which exist at depths of 0, 20 and 40 cm in water, show a fall-off which is too steep when compared with EGS4 Monte Carlo results. A beam hardening correction factor applied to primary and scattered 0 cm EDKs, based on the ratio of kerma to terma at each depth, gives primary, scattered and total dose in good agreement with Monte Carlo results. (Author)
SUPERPOSITION OF STOCHASTIC PROCESSES AND THE RESULTING PARTICLE DISTRIBUTIONS
International Nuclear Information System (INIS)
Schwadron, N. A.; Dayeh, M. A.; Desai, M.; Fahr, H.; Jokipii, J. R.; Lee, M. A.
2010-01-01
Many observations of suprathermal and energetic particles in the solar wind and the inner heliosheath show that distribution functions scale approximately with the inverse of particle speed (v) to the fifth power. Although there are exceptions to this behavior, there is a growing need to understand why this type of distribution function appears so frequently. This paper develops the concept that a superposition of exponential and Gaussian distributions with different characteristic speeds and temperatures show power-law tails. The particular type of distribution function, f ∝ v -5 , appears in a number of different ways: (1) a series of Poisson-like processes where entropy is maximized with the rates of individual processes inversely proportional to the characteristic exponential speed, (2) a series of Gaussian distributions where the entropy is maximized with the rates of individual processes inversely proportional to temperature and the density of individual Gaussian distributions proportional to temperature, and (3) a series of different diffusively accelerated energetic particle spectra with individual spectra derived from observations (1997-2002) of a multiplicity of different shocks. Thus, we develop a proof-of-concept for the superposition of stochastic processes that give rise to power-law distribution functions.
Unveiling the curtain of superposition: Recent gedanken and laboratory experiments
Cohen, E.; Elitzur, A. C.
2017-08-01
What is the true meaning of quantum superposition? Can a particle genuinely reside in several places simultaneously? These questions lie at the heart of this paper which presents an updated survey of some important stages in the evolution of the three-boxes paradox, as well as novel conclusions drawn from it. We begin with the original thought experiment of Aharonov and Vaidman, and proceed to its non-counterfactual version. The latter was recently realized by Okamoto and Takeuchi using a quantum router. We then outline a dynamic version of this experiment, where a particle is shown to “disappear” and “re-appear” during the time evolution of the system. This surprising prediction based on self-cancellation of weak values is directly related to our notion of Quantum Oblivion. Finally, we present the non-counterfactual version of this disappearing-reappearing experiment. Within the near future, this last version of the experiment is likely to be realized in the lab, proving the existence of exotic hitherto unknown forms of superposition. With the aid of Bell’s theorem, we prove the inherent nonlocality and nontemporality underlying such pre- and post-selected systems, rendering anomalous weak values ontologically real.
Evolution of superpositions of quantum states through a level crossing
International Nuclear Information System (INIS)
Torosov, B. T.; Vitanov, N. V.
2011-01-01
The Landau-Zener-Stueckelberg-Majorana (LZSM) model is widely used for estimating transition probabilities in the presence of crossing energy levels in quantum physics. This model, however, makes the unphysical assumption of an infinitely long constant interaction, which introduces a divergent phase in the propagator. This divergence remains hidden when estimating output probabilities for a single input state insofar as the divergent phase cancels out. In this paper we show that, because of this divergent phase, the LZSM model is inadequate to describe the evolution of pure or mixed superposition states across a level crossing. The LZSM model can be used only if the system is initially in a single state or in a completely mixed superposition state. To this end, we show that the more realistic Demkov-Kunike model, which assumes a hyperbolic-tangent level crossing and a hyperbolic-secant interaction envelope, is free of divergences and is a much more adequate tool for describing the evolution through a level crossing for an arbitrary input state. For multiple crossing energies which are reducible to one or more effective two-state systems (e.g., by the Majorana and Morris-Shore decompositions), similar conclusions apply: the LZSM model does not produce definite values of the populations and the coherences, and one should use the Demkov-Kunike model instead.
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...
One weird trick for parallelizing convolutional neural networks
Krizhevsky, Alex
2014-01-01
I present a new way to parallelize the training of convolutional neural networks across multiple GPUs. The method scales significantly better than all alternatives when applied to modern convolutional neural networks.
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.
Gradient Flow Convolutive Blind Source Separation
DEFF Research Database (Denmark)
Pedersen, Michael Syskind; Nielsen, Chinton Møller
2004-01-01
Experiments have shown that the performance of instantaneous gradient flow beamforming by Cauwenberghs et al. is reduced significantly in reverberant conditions. By expanding the gradient flow principle to convolutive mixtures, separation in a reverberant environment is possible. By use...... of a circular four microphone array with a radius of 5 mm, and applying convolutive gradient flow instead of just applying instantaneous gradient flow, experimental results show an improvement of up to around 14 dB can be achieved for simulated impulse responses and up to around 10 dB for a hearing aid...
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.
CMOS Compressed Imaging by Random Convolution
Jacques, Laurent; Vandergheynst, Pierre; Bibet, Alexandre; Majidzadeh, Vahid; Schmid, Alexandre; Leblebici, Yusuf
2009-01-01
We present a CMOS imager with built-in capability to perform Compressed Sensing. The adopted sensing strategy is the random Convolution due to J. Romberg. It is achieved by a shift register set in a pseudo-random configuration. It acts as a convolutive filter on the imager focal plane, the current issued from each CMOS pixel undergoing a pseudo-random redirection controlled by each component of the filter sequence. A pseudo-random triggering of the ADC reading is finally applied to comp...
Feedback equivalence of convolutional codes over finite rings
Directory of Open Access Journals (Sweden)
DeCastro-García Noemí
2017-12-01
Full Text Available The approach to convolutional codes from the linear systems point of view provides us with effective tools in order to construct convolutional codes with adequate properties that let us use them in many applications. In this work, we have generalized feedback equivalence between families of convolutional codes and linear systems over certain rings, and we show that every locally Brunovsky linear system may be considered as a representation of a code under feedback convolutional equivalence.
International Nuclear Information System (INIS)
Fogliata, Antonella; Nicolini, Giorgia; Vanetti, Eugenio; Clivio, Alessandro; Cozzi, Luca
2006-01-01
In July 2005 a new algorithm was released by Varian Medical Systems for the Eclipse planning system and installed in our institute. It is the anisotropic analytical algorithm (AAA) for photon dose calculations, a convolution/superposition model for the first time implemented in a Varian planning system. It was therefore necessary to perform validation studies at different levels with a wide investigation approach. To validate the basic performances of the AAA, a detailed analysis of data computed by the AAA configuration algorithm was carried out and data were compared against measurements. To better appraise the performance of AAA and the capability of its configuration to tailor machine-specific characteristics, data obtained from the pencil beam convolution (PBC) algorithm implemented in Eclipse were also added in the comparison. Since the purpose of the paper is to address the basic performances of the AAA and of its configuration procedures, only data relative to measurements in water will be reported. Validation was carried out for three beams: 6 MV and 15 MV from a Clinac 2100C/D and 6 MV from a Clinac 6EX. Generally AAA calculations reproduced very well measured data, and small deviations were observed, on average, for all the quantities investigated for open and wedged fields. In particular, percentage depth-dose curves showed on average differences between calculation and measurement smaller than 1% or 1 mm, and computed profiles in the flattened region matched measurements with deviations smaller than 1% for all beams, field sizes, depths and wedges. Percentage differences in output factors were observed as small as 1% on average (with a range smaller than ±2%) for all conditions. Additional tests were carried out for enhanced dynamic wedges with results comparable to previous results. The basic dosimetric validation of the AAA was therefore considered satisfactory
Capacity-Approaching Superposition Coding for Optical Fiber Links
DEFF Research Database (Denmark)
Estaran Tolosa, Jose Manuel; Zibar, Darko; Tafur Monroy, Idelfonso
2014-01-01
We report on the first experimental demonstration of superposition coded modulation (SCM) for polarization-multiplexed coherent-detection optical fiber links. The proposed coded modulation scheme is combined with phase-shifted bit-to-symbol mapping (PSM) in order to achieve geometric and passive......-SCM) is employed in the framework of bit-interleaved coded modulation with iterative decoding (BICM-ID) for forward error correction. The fiber transmission system is characterized in terms of signal-to-noise ratio for back-to-back case and correlated with simulated results for ideal transmission over additive...... white Gaussian noise channel. Thereafter, successful demodulation and decoding after dispersion-unmanaged transmission over 240-km standard single mode fiber of dual-polarization 6-Gbaud 16-, 32- and 64-ary SCM-PSM is experimentally demonstrated....
Superposition of Stress Fields in Diametrically Compressed Cylinders
Directory of Open Access Journals (Sweden)
João Augusto de Lima Rocha
Full Text Available Abstract The theoretical analysis for the Brazilian test is a classical plane stress problem of elasticity theory, where a vertical force is applied to a horizontal plane, the boundary of a semi-infinite medium. Hypothesizing a normal radial stress field, the results of that model are correct. Nevertheless, the superposition of three stress fields, with two being based on prior results and the third based on a hydrostatic stress field, is incorrect. Indeed, this work shows that the Cauchy vectors (tractions are non-vanishing in the parallel planes in which the two opposing vertical forces are applied. The aim of this work is to detail the process used in the construction of the theoretical model for the three stress fields used, with the objective being to demonstrate the inconsistency often stated in the literature.
Simulation Analysis of DC and Switching Impulse Superposition Circuit
Zhang, Chenmeng; Xie, Shijun; Zhang, Yu; Mao, Yuxiang
2018-03-01
Surge capacitors running between the natural bus and the ground are affected by DC and impulse superposition voltage during operation in the converter station. This paper analyses the simulation aging circuit of surge capacitors by PSCAD electromagnetic transient simulation software. This paper also analyses the effect of the DC voltage to the waveform of the impulse voltage generation. The effect of coupling capacitor to the test voltage waveform is also studied. Testing results prove that the DC voltage has little effect on the waveform of the output of the surge voltage generator, and the value of the coupling capacitor has little effect on the voltage waveform of the sample. Simulation results show that surge capacitor DC and impulse superimposed aging test is feasible.
Decoherence bypass of macroscopic superpositions in quantum measurement
International Nuclear Information System (INIS)
Spehner, Dominique; Haake, Fritz
2008-01-01
We study a class of quantum measurement models. A microscopic object is entangled with a macroscopic pointer such that a distinct pointer position is tied to each eigenvalue of the measured object observable. Those different pointer positions mutually decohere under the influence of an environment. Overcoming limitations of previous approaches we (i) cope with initial correlations between pointer and environment by considering them initially in a metastable local thermal equilibrium, (ii) allow for object-pointer entanglement and environment-induced decoherence of distinct pointer readouts to proceed simultaneously, such that mixtures of macroscopically distinct object-pointer product states arise without intervening macroscopic superpositions, and (iii) go beyond the Markovian treatment of decoherence. (fast track communication)
Adiabatic rotation, quantum search, and preparation of superposition states
International Nuclear Information System (INIS)
Siu, M. Stewart
2007-01-01
We introduce the idea of using adiabatic rotation to generate superpositions of a large class of quantum states. For quantum computing this is an interesting alternative to the well-studied 'straight line' adiabatic evolution. In ways that complement recent results, we show how to efficiently prepare three types of states: Kitaev's toric code state, the cluster state of the measurement-based computation model, and the history state used in the adiabatic simulation of a quantum circuit. We also show that the method, when adapted for quantum search, provides quadratic speedup as other optimal methods do with the advantages that the problem Hamiltonian is time independent and that the energy gap above the ground state is strictly nondecreasing with time. Likewise the method can be used for optimization as an alternative to the standard adiabatic algorithm
Discrete convolution-operators and radioactive disintegration. [Numerical solution
Energy Technology Data Exchange (ETDEWEB)
Kalla, S L; VALENTINUZZI, M E [UNIVERSIDAD NACIONAL DE TUCUMAN (ARGENTINA). FACULTAD DE CIENCIAS EXACTAS Y TECNOLOGIA
1975-08-01
The basic concepts of discrete convolution and discrete convolution-operators are briefly described. Then, using the discrete convolution - operators, the differential equations associated with the process of radioactive disintegration are numerically solved. The importance of the method is emphasized to solve numerically, differential and integral equations.
Polyphony: superposition independent methods for ensemble-based drug discovery.
Pitt, William R; Montalvão, Rinaldo W; Blundell, Tom L
2014-09-30
Structure-based drug design is an iterative process, following cycles of structural biology, computer-aided design, synthetic chemistry and bioassay. In favorable circumstances, this process can lead to the structures of hundreds of protein-ligand crystal structures. In addition, molecular dynamics simulations are increasingly being used to further explore the conformational landscape of these complexes. Currently, methods capable of the analysis of ensembles of crystal structures and MD trajectories are limited and usually rely upon least squares superposition of coordinates. Novel methodologies are described for the analysis of multiple structures of a protein. Statistical approaches that rely upon residue equivalence, but not superposition, are developed. Tasks that can be performed include the identification of hinge regions, allosteric conformational changes and transient binding sites. The approaches are tested on crystal structures of CDK2 and other CMGC protein kinases and a simulation of p38α. Known interaction - conformational change relationships are highlighted but also new ones are revealed. A transient but druggable allosteric pocket in CDK2 is predicted to occur under the CMGC insert. Furthermore, an evolutionarily-conserved conformational link from the location of this pocket, via the αEF-αF loop, to phosphorylation sites on the activation loop is discovered. New methodologies are described and validated for the superimposition independent conformational analysis of large collections of structures or simulation snapshots of the same protein. The methodologies are encoded in a Python package called Polyphony, which is released as open source to accompany this paper [http://wrpitt.bitbucket.org/polyphony/].
Macroscopicity of quantum superpositions on a one-parameter unitary path in Hilbert space
Volkoff, T. J.; Whaley, K. B.
2014-12-01
We analyze quantum states formed as superpositions of an initial pure product state and its image under local unitary evolution, using two measurement-based measures of superposition size: one based on the optimal quantum binary distinguishability of the branches of the superposition and another based on the ratio of the maximal quantum Fisher information of the superposition to that of its branches, i.e., the relative metrological usefulness of the superposition. A general formula for the effective sizes of these states according to the branch-distinguishability measure is obtained and applied to superposition states of N quantum harmonic oscillators composed of Gaussian branches. Considering optimal distinguishability of pure states on a time-evolution path leads naturally to a notion of distinguishability time that generalizes the well-known orthogonalization times of Mandelstam and Tamm and Margolus and Levitin. We further show that the distinguishability time provides a compact operational expression for the superposition size measure based on the relative quantum Fisher information. By restricting the maximization procedure in the definition of this measure to an appropriate algebra of observables, we show that the superposition size of, e.g., NOON states and hierarchical cat states, can scale linearly with the number of elementary particles comprising the superposition state, implying precision scaling inversely with the total number of photons when these states are employed as probes in quantum parameter estimation of a 1-local Hamiltonian in this algebra.
Deformable image registration using convolutional neural networks
Eppenhof, Koen A.J.; Lafarge, Maxime W.; Moeskops, Pim; Veta, Mitko; Pluim, Josien P.W.
2018-01-01
Deformable image registration can be time-consuming and often needs extensive parameterization to perform well on a specific application. We present a step towards a registration framework based on a three-dimensional convolutional neural network. The network directly learns transformations between
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...
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...
Convolutional Neural Networks - Generalizability and Interpretations
DEFF Research Database (Denmark)
Malmgren-Hansen, David
from data despite it being limited in amount or context representation. Within Machine Learning this thesis focuses on Convolutional Neural Networks for Computer Vision. The research aims to answer how to explore a model's generalizability to the whole population of data samples and how to interpret...
Towards dropout training for convolutional neural networks.
Wu, Haibing; Gu, Xiaodong
2015-11-01
Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in convolutional and pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this insight, we advocate employing our proposed probabilistic weighted pooling, instead of commonly used max-pooling, to act as model averaging at test time. Empirical evidence validates the superiority of probabilistic weighted pooling. We also empirically show that the effect of convolutional dropout is not trivial, despite the dramatically reduced possibility of over-fitting due to the convolutional architecture. Elaborately designing dropout training simultaneously in max-pooling and fully-connected layers, we achieve state-of-the-art performance on MNIST, and very competitive results on CIFAR-10 and CIFAR-100, relative to other approaches without data augmentation. Finally, we compare max-pooling dropout and stochastic pooling, both of which introduce stochasticity based on multinomial distributions at pooling stage. Copyright © 2015 Elsevier Ltd. All rights reserved.
A locality aware convolutional neural networks accelerator
Shi, R.; Xu, Z.; Sun, Z.; Peemen, M.C.J.; Li, A.; Corporaal, H.; Wu, D.
2015-01-01
The advantages of Convolutional Neural Networks (CNNs) with respect to traditional methods for visual pattern recognition have changed the field of machine vision. The main issue that hinders broad adoption of this technique is the massive computing workload in CNN that prevents real-time
International Nuclear Information System (INIS)
Carrasco, P.; Jornet, N.; Duch, M. A.; Panettieri, V.; Weber, L.; Eudaldo, T.; Ginjaume, M.; Ribas, M.
2007-01-01
To evaluate the dose values predicted by several calculation algorithms in two treatment planning systems, Monte Carlo (MC) simulations and measurements by means of various detectors were performed in heterogeneous layer phantoms with water- and bone-equivalent materials. Percentage depth doses (PDDs) were measured with thermoluminescent dosimeters (TLDs), metal-oxide semiconductor field-effect transistors (MOSFETs), plane parallel and cylindrical ionization chambers, and beam profiles with films. The MC code used for the simulations was the PENELOPE code. Three different field sizes (10x10, 5x5, and 2x2 cm 2 ) were studied in two phantom configurations and a bone equivalent material. These two phantom configurations contained heterogeneities of 5 and 2 cm of bone, respectively. We analyzed the performance of four correction-based algorithms and one based on convolution superposition. The correction-based algorithms were the Batho, the Modified Batho, the Equivalent TAR implemented in the Cadplan (Varian) treatment planning system (TPS), and the Helax-TMS Pencil Beam from the Helax-TMS (Nucletron) TPS. The convolution-superposition algorithm was the Collapsed Cone implemented in the Helax-TMS. All the correction-based calculation algorithms underestimated the dose inside the bone-equivalent material for 18 MV compared to MC simulations. The maximum underestimation, in terms of root-mean-square (RMS), was about 15% for the Helax-TMS Pencil Beam (Helax-TMS PB) for a 2x2 cm 2 field inside the bone-equivalent material. In contrast, the Collapsed Cone algorithm yielded values around 3%. A more complex behavior was found for 6 MV where the Collapsed Cone performed less well, overestimating the dose inside the heterogeneity in 3%-5%. The rebuildup in the interface bone-water and the penumbra shrinking in high-density media were not predicted by any of the calculation algorithms except the Collapsed Cone, and only the MC simulations matched the experimental values within
Carrasco, P; Jornet, N; Duch, M A; Panettieri, V; Weber, L; Eudaldo, T; Ginjaume, M; Ribas, M
2007-08-01
To evaluate the dose values predicted by several calculation algorithms in two treatment planning systems, Monte Carlo (MC) simulations and measurements by means of various detectors were performed in heterogeneous layer phantoms with water- and bone-equivalent materials. Percentage depth doses (PDDs) were measured with thermoluminescent dosimeters (TLDs), metal-oxide semiconductor field-effect transistors (MOSFETs), plane parallel and cylindrical ionization chambers, and beam profiles with films. The MC code used for the simulations was the PENELOPE code. Three different field sizes (10 x 10, 5 x 5, and 2 x 2 cm2) were studied in two phantom configurations and a bone equivalent material. These two phantom configurations contained heterogeneities of 5 and 2 cm of bone, respectively. We analyzed the performance of four correction-based algorithms and one based on convolution superposition. The correction-based algorithms were the Batho, the Modified Batho, the Equivalent TAR implemented in the Cadplan (Varian) treatment planning system (TPS), and the Helax-TMS Pencil Beam from the Helax-TMS (Nucletron) TPS. The convolution-superposition algorithm was the Collapsed Cone implemented in the Helax-TMS. All the correction-based calculation algorithms underestimated the dose inside the bone-equivalent material for 18 MV compared to MC simulations. The maximum underestimation, in terms of root-mean-square (RMS), was about 15% for the Helax-TMS Pencil Beam (Helax-TMS PB) for a 2 x 2 cm2 field inside the bone-equivalent material. In contrast, the Collapsed Cone algorithm yielded values around 3%. A more complex behavior was found for 6 MV where the Collapsed Cone performed less well, overestimating the dose inside the heterogeneity in 3%-5%. The rebuildup in the interface bone-water and the penumbra shrinking in high-density media were not predicted by any of the calculation algorithms except the Collapsed Cone, and only the MC simulations matched the experimental values
Accurate lithography simulation model based on convolutional neural networks
Watanabe, Yuki; Kimura, Taiki; Matsunawa, Tetsuaki; Nojima, Shigeki
2017-07-01
Lithography simulation is an essential technique for today's semiconductor manufacturing process. In order to calculate an entire chip in realistic time, compact resist model is commonly used. The model is established for faster calculation. To have accurate compact resist model, it is necessary to fix a complicated non-linear model function. However, it is difficult to decide an appropriate function manually because there are many options. This paper proposes a new compact resist model using CNN (Convolutional Neural Networks) which is one of deep learning techniques. CNN model makes it possible to determine an appropriate model function and achieve accurate simulation. Experimental results show CNN model can reduce CD prediction errors by 70% compared with the conventional model.
Dose-calculation algorithms in the context of inhomogeneity corrections for high energy photon beams
International Nuclear Information System (INIS)
Papanikolaou, Niko; Stathakis, Sotirios
2009-01-01
Radiation therapy has witnessed a plethora of innovations and developments in the past 15 years. Since the introduction of computed tomography for treatment planning there has been a steady introduction of new methods to refine treatment delivery. Imaging continues to be an integral part of the planning, but also the delivery, of modern radiotherapy. However, all the efforts of image guided radiotherapy, intensity-modulated planning and delivery, adaptive radiotherapy, and everything else that we pride ourselves in having in the armamentarium can fall short, unless there is an accurate dose-calculation algorithm. The agreement between the calculated and delivered doses is of great significance in radiation therapy since the accuracy of the absorbed dose as prescribed determines the clinical outcome. Dose-calculation algorithms have evolved greatly over the years in an effort to be more inclusive of the effects that govern the true radiation transport through the human body. In this Vision 20/20 paper, we look back to see how it all started and where things are now in terms of dose algorithms for photon beams and the inclusion of tissue heterogeneities. Convolution-superposition algorithms have dominated the treatment planning industry for the past few years. Monte Carlo techniques have an inherent accuracy that is superior to any other algorithm and as such will continue to be the gold standard, along with measurements, and maybe one day will be the algorithm of choice for all particle treatment planning in radiation therapy.
Convolutional Neural Network for Histopathological Analysis of Osteosarcoma.
Mishra, Rashika; Daescu, Ovidiu; Leavey, Patrick; Rakheja, Dinesh; Sengupta, Anita
2018-03-01
Pathologists often deal with high complexity and sometimes disagreement over osteosarcoma tumor classification due to cellular heterogeneity in the dataset. Segmentation and classification of histology tissue in H&E stained tumor image datasets is a challenging task because of intra-class variations, inter-class similarity, crowded context, and noisy data. In recent years, deep learning approaches have led to encouraging results in breast cancer and prostate cancer analysis. In this article, we propose convolutional neural network (CNN) as a tool to improve efficiency and accuracy of osteosarcoma tumor classification into tumor classes (viable tumor, necrosis) versus nontumor. The proposed CNN architecture contains eight learned layers: three sets of stacked two convolutional layers interspersed with max pooling layers for feature extraction and two fully connected layers with data augmentation strategies to boost performance. The use of a neural network results in higher accuracy of average 92% for the classification. We compare the proposed architecture with three existing and proven CNN architectures for image classification: AlexNet, LeNet, and VGGNet. We also provide a pipeline to calculate percentage necrosis in a given whole slide image. We conclude that the use of neural networks can assure both high accuracy and efficiency in osteosarcoma classification.
Fast, large-scale hologram calculation in wavelet domain
Shimobaba, Tomoyoshi; Matsushima, Kyoji; Takahashi, Takayuki; Nagahama, Yuki; Hasegawa, Satoki; Sano, Marie; Hirayama, Ryuji; Kakue, Takashi; Ito, Tomoyoshi
2018-04-01
We propose a large-scale hologram calculation using WAvelet ShrinkAge-Based superpositIon (WASABI), a wavelet transform-based algorithm. An image-type hologram calculated using the WASABI method is printed on a glass substrate with the resolution of 65 , 536 × 65 , 536 pixels and a pixel pitch of 1 μm. The hologram calculation time amounts to approximately 354 s on a commercial CPU, which is approximately 30 times faster than conventional methods.
Validation of a dose-point kernel convolution technique for internal dosimetry
International Nuclear Information System (INIS)
Giap, H.B.; Macey, D.J.; Bayouth, J.E.; Boyer, A.L.
1995-01-01
The objective of this study was to validate a dose-point kernel convolution technique that provides a three-dimensional (3D) distribution of absorbed dose from a 3D distribution of the radionuclide 131 I. A dose-point kernel for the penetrating radiations was calculated by a Monte Carlo simulation and cast in a 3D rectangular matrix. This matrix was convolved with the 3D activity map furnished by quantitative single-photon-emission computed tomography (SPECT) to provide a 3D distribution of absorbed dose. The convolution calculation was performed using a 3D fast Fourier transform (FFT) technique, which takes less than 40 s for a 128 x 128 x 16 matrix on an Intel 486 DX2 (66 MHz) personal computer. The calculated photon absorbed dose was compared with values measured by thermoluminescent dosimeters (TLDS) inserted along the diameter of a 22 cm diameter annular source of 131 I. The mean and standard deviation of the percentage difference between the measurements and the calculations were equal to -1% and 3.6% respectively. This convolution method was also used to calculate the 3D dose distribution in an Alderson abdominal phantom containing a liver, a spleen, and a spherical tumour volume loaded with various concentrations of 131 I. By averaging the dose calculated throughout the liver, spleen, and tumour the dose-point kernel approach was compared with values derived using the MIRD formalism, and found to agree to better than 15%. (author)
Intra-cavity generation of superpositions of Laguerre-Gaussian beams
CSIR Research Space (South Africa)
Naidoo, Darryl
2012-01-01
Full Text Available In this paper we demonstrate experimentally the intra-cavity generation of a coherent superposition of Laguerre–Gaussian modes of zero radial order but opposite azimuthal order. The superposition is created with a simple intra-cavity stop...
Energy Technology Data Exchange (ETDEWEB)
Alexeyev, Alexander A [Laboratory of Computer Physics and Mathematical Simulation, Research Division, Room 247, Faculty of Phys.-Math. and Natural Sciences, Peoples' Friendship University of Russia, 6 Miklukho-Maklaya street, Moscow 117198 (Russian Federation) and Department of Mathematics 1, Faculty of Cybernetics, Moscow State Institute of Radio Engineering, Electronics and Automatics, 78 Vernadskogo Avenue, Moscow 117454 (Russian Federation)
2004-11-26
In the framework of a multidimensional superposition principle a series of computer experiments with integrable and nonintegrable models are carried out with the goal of verifying the existence of switching effect and superposition in soliton-perturbation interactions for a wide class of nonlinear PDEs. (letter to the editor)
Fogliata, Antonella; Vanetti, Eugenio; Albers, Dirk; Brink, Carsten; Clivio, Alessandro; Knöös, Tommy; Nicolini, Giorgia; Cozzi, Luca
2007-03-01
A comparative study was performed to reveal differences and relative figures of merit of seven different calculation algorithms for photon beams when applied to inhomogeneous media. The following algorithms were investigated: Varian Eclipse: the anisotropic analytical algorithm, and the pencil beam with modified Batho correction; Nucletron Helax-TMS: the collapsed cone and the pencil beam with equivalent path length correction; CMS XiO: the multigrid superposition and the fast Fourier transform convolution; Philips Pinnacle: the collapsed cone. Monte Carlo simulations (MC) performed with the EGSnrc codes BEAMnrc and DOSxyznrc from NRCC in Ottawa were used as a benchmark. The study was carried out in simple geometrical water phantoms (ρ = 1.00 g cm-3) with inserts of different densities simulating light lung tissue (ρ = 0.035 g cm-3), normal lung (ρ = 0.20 g cm-3) and cortical bone tissue (ρ = 1.80 g cm-3). Experiments were performed for low- and high-energy photon beams (6 and 15 MV) and for square (13 × 13 cm2) and elongated rectangular (2.8 × 13 cm2) fields. Analysis was carried out on the basis of depth dose curves and transverse profiles at several depths. Assuming the MC data as reference, γ index analysis was carried out distinguishing between regions inside the non-water inserts or inside the uniform water. For this study, a distance to agreement was set to 3 mm while the dose difference varied from 2% to 10%. In general all algorithms based on pencil-beam convolutions showed a systematic deficiency in managing the presence of heterogeneous media. In contrast, complicated patterns were observed for the advanced algorithms with significant discrepancies observed between algorithms in the lighter materials (ρ = 0.035 g cm-3), enhanced for the most energetic beam. For denser, and more clinical, densities a better agreement among the sophisticated algorithms with respect to MC was observed.
DCMDN: Deep Convolutional Mixture Density Network
D'Isanto, Antonio; Polsterer, Kai Lars
2017-09-01
Deep Convolutional Mixture Density Network (DCMDN) estimates probabilistic photometric redshift directly from multi-band imaging data by combining a version of a deep convolutional network with a mixture density network. The estimates are expressed as Gaussian mixture models representing the probability density functions (PDFs) in the redshift space. In addition to the traditional scores, the continuous ranked probability score (CRPS) and the probability integral transform (PIT) are applied as performance criteria. DCMDN is able to predict redshift PDFs independently from the type of source, e.g. galaxies, quasars or stars and renders pre-classification of objects and feature extraction unnecessary; the method is extremely general and allows the solving of any kind of probabilistic regression problems based on imaging data, such as estimating metallicity or star formation rate in galaxies.
Gas Classification Using Deep Convolutional Neural Networks
Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin
2018-01-01
In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP). PMID:29316723
A convolutional neural network neutrino event classifier
International Nuclear Information System (INIS)
Aurisano, A.; Sousa, A.; Radovic, A.; Vahle, P.; Rocco, D.; Pawloski, G.; Himmel, A.; Niner, E.; Messier, M.D.; Psihas, F.
2016-01-01
Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.
Gas Classification Using Deep Convolutional Neural Networks.
Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin
2018-01-08
In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).
Quasi-cyclic unit memory convolutional codes
DEFF Research Database (Denmark)
Justesen, Jørn; Paaske, Erik; Ballan, Mark
1990-01-01
Unit memory convolutional codes with generator matrices, which are composed of circulant submatrices, are introduced. This structure facilitates the analysis of efficient search for good codes. Equivalences among such codes and some of the basic structural properties are discussed. In particular......, catastrophic encoders and minimal encoders are characterized and dual codes treated. Further, various distance measures are discussed, and a number of good codes, some of which result from efficient computer search and some of which result from known block codes, are presented...
Quantifying the interplay effect in prostate IMRT delivery using a convolution-based method
International Nuclear Information System (INIS)
Li, Haisen S.; Chetty, Indrin J.; Solberg, Timothy D.
2008-01-01
The authors present a segment-based convolution method to account for the interplay effect between intrafraction organ motion and the multileaf collimator position for each particular segment in intensity modulated radiation therapy (IMRT) delivered in a step-and-shoot manner. In this method, the static dose distribution attributed to each segment is convolved with the probability density function (PDF) of motion during delivery of the segment, whereas in the conventional convolution method (''average-based convolution''), the static dose distribution is convolved with the PDF averaged over an entire fraction, an entire treatment course, or even an entire patient population. In the case of IMRT delivered in a step-and-shoot manner, the average-based convolution method assumes that in each segment the target volume experiences the same motion pattern (PDF) as that of population. In the segment-based convolution method, the dose during each segment is calculated by convolving the static dose with the motion PDF specific to that segment, allowing both intrafraction motion and the interplay effect to be accounted for in the dose calculation. Intrafraction prostate motion data from a population of 35 patients tracked using the Calypso system (Calypso Medical Technologies, Inc., Seattle, WA) was used to generate motion PDFs. These were then convolved with dose distributions from clinical prostate IMRT plans. For a single segment with a small number of monitor units, the interplay effect introduced errors of up to 25.9% in the mean CTV dose compared against the planned dose evaluated by using the PDF of the entire fraction. In contrast, the interplay effect reduced the minimum CTV dose by 4.4%, and the CTV generalized equivalent uniform dose by 1.3%, in single fraction plans. For entire treatment courses delivered in either a hypofractionated (five fractions) or conventional (>30 fractions) regimen, the discrepancy in total dose due to interplay effect was negligible
Fully Convolutional Network Based Shadow Extraction from GF-2 Imagery
Li, Z.; Cai, G.; Ren, H.
2018-04-01
There are many shadows on the high spatial resolution satellite images, especially in the urban areas. Although shadows on imagery severely affect the information extraction of land cover or land use, they provide auxiliary information for building extraction which is hard to achieve a satisfactory accuracy through image classification itself. This paper focused on the method of building shadow extraction by designing a fully convolutional network and training samples collected from GF-2 satellite imagery in the urban region of Changchun city. By means of spatial filtering and calculation of adjacent relationship along the sunlight direction, the small patches from vegetation or bridges have been eliminated from the preliminary extracted shadows. Finally, the building shadows were separated. The extracted building shadow information from the proposed method in this paper was compared with the results from the traditional object-oriented supervised classification algorihtms. It showed that the deep learning network approach can improve the accuracy to a large extent.
Applying Gradient Descent in Convolutional Neural Networks
Cui, Nan
2018-04-01
With the development of the integrated circuit and computer science, people become caring more about solving practical issues via information technologies. Along with that, a new subject called Artificial Intelligent (AI) comes up. One popular research interest of AI is about recognition algorithm. In this paper, one of the most common algorithms, Convolutional Neural Networks (CNNs) will be introduced, for image recognition. Understanding its theory and structure is of great significance for every scholar who is interested in this field. Convolution Neural Network is an artificial neural network which combines the mathematical method of convolution and neural network. The hieratical structure of CNN provides it reliable computer speed and reasonable error rate. The most significant characteristics of CNNs are feature extraction, weight sharing and dimension reduction. Meanwhile, combining with the Back Propagation (BP) mechanism and the Gradient Descent (GD) method, CNNs has the ability to self-study and in-depth learning. Basically, BP provides an opportunity for backwardfeedback for enhancing reliability and GD is used for self-training process. This paper mainly discusses the CNN and the related BP and GD algorithms, including the basic structure and function of CNN, details of each layer, the principles and features of BP and GD, and some examples in practice with a summary in the end.
Phylogenetic convolutional neural networks in metagenomics.
Fioravanti, Diego; Giarratano, Ylenia; Maggio, Valerio; Agostinelli, Claudio; Chierici, Marco; Jurman, Giuseppe; Furlanello, Cesare
2018-03-08
Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architecture for the classification of metagenomics data based on the Convolutional Neural Networks, with the patristic distance defined on the phylogenetic tree being used as the proximity measure. The patristic distance between variables is used together with a sparsified version of MultiDimensional Scaling to embed the phylogenetic tree in a Euclidean space. Ph-CNN is tested with a domain adaptation approach on synthetic data and on a metagenomics collection of gut microbiota of 38 healthy subjects and 222 Inflammatory Bowel Disease patients, divided in 6 subclasses. Classification performance is promising when compared to classical algorithms like Support Vector Machines and Random Forest and a baseline fully connected neural network, e.g. the Multi-Layer Perceptron. Ph-CNN represents a novel deep learning approach for the classification of metagenomics data. Operatively, the algorithm has been implemented as a custom Keras layer taking care of passing to the following convolutional layer not only the data but also the ranked list of neighbourhood of each sample, thus mimicking the case of image data, transparently to the user.
Image quality assessment using deep convolutional networks
Li, Yezhou; Ye, Xiang; Li, Yong
2017-12-01
This paper proposes a method of accurately assessing image quality without a reference image by using a deep convolutional neural network. Existing training based methods usually utilize a compact set of linear filters for learning features of images captured by different sensors to assess their quality. These methods may not be able to learn the semantic features that are intimately related with the features used in human subject assessment. Observing this drawback, this work proposes training a deep convolutional neural network (CNN) with labelled images for image quality assessment. The ReLU in the CNN allows non-linear transformations for extracting high-level image features, providing a more reliable assessment of image quality than linear filters. To enable the neural network to take images of any arbitrary size as input, the spatial pyramid pooling (SPP) is introduced connecting the top convolutional layer and the fully-connected layer. In addition, the SPP makes the CNN robust to object deformations to a certain extent. The proposed method taking an image as input carries out an end-to-end learning process, and outputs the quality of the image. It is tested on public datasets. Experimental results show that it outperforms existing methods by a large margin and can accurately assess the image quality on images taken by different sensors of varying sizes.
Acral melanoma detection using a convolutional neural network for dermoscopy images.
Yu, Chanki; Yang, Sejung; Kim, Wonoh; Jung, Jinwoong; Chung, Kee-Yang; Lee, Sang Wook; Oh, Byungho
2018-01-01
Acral melanoma is the most common type of melanoma in Asians, and usually results in a poor prognosis due to late diagnosis. We applied a convolutional neural network to dermoscopy images of acral melanoma and benign nevi on the hands and feet and evaluated its usefulness for the early diagnosis of these conditions. A total of 724 dermoscopy images comprising acral melanoma (350 images from 81 patients) and benign nevi (374 images from 194 patients), and confirmed by histopathological examination, were analyzed in this study. To perform the 2-fold cross validation, we split them into two mutually exclusive subsets: half of the total image dataset was selected for training and the rest for testing, and we calculated the accuracy of diagnosis comparing it with the dermatologist's and non-expert's evaluation. The accuracy (percentage of true positive and true negative from all images) of the convolutional neural network was 83.51% and 80.23%, which was higher than the non-expert's evaluation (67.84%, 62.71%) and close to that of the expert (81.08%, 81.64%). Moreover, the convolutional neural network showed area-under-the-curve values like 0.8, 0.84 and Youden's index like 0.6795, 0.6073, which were similar score with the expert. Although further data analysis is necessary to improve their accuracy, convolutional neural networks would be helpful to detect acral melanoma from dermoscopy images of the hands and feet.
Applicability of the Fourier convolution theorem to the analysis of late-type stellar spectra
International Nuclear Information System (INIS)
Bruning, D.H.
1981-01-01
Solar flux and intensity measurements were obtained at Sacramento Peak Observatory to test the validity of the Fourier convolution method as a means of analyzing the spectral line shapes of late-type stars. Analysis of six iron lines near 6200A shows that, in general, the convolution method is not a suitable approximation for the calculation of the flux profile. The convolution method does reasonably reproduce the line shape for some lines which appear not to vary across the disk of the sun, but does not properly calculate the central line depth of these lines. Even if a central depth correction could be found, it is difficult to predict, especially for stars other than the sun, which lines have nearly constant shapes and could be used with the convolution method. Therefore, explicit disk integrations are promoted as the only reliable method of spectral line analysis for late-type stars. Several methods of performing the disk integration are investigated. Although the Abt (1957) prescription appears suitable for the limited case studied, methods using annuli of equal area, equal flux, or equal width (Soberblom, 1980) are considered better models. The model that is the easiest to use and most efficient computationally is the equal area model. Model atmosphere calculations yield values for the microturbulence and macroturbulence similar to those derived by observers. Since the depth dependence of the microturbulence is ignored in the calculations, the intensity profiles at disk center and the limb do not match the observed intensity profiles with only one set of velocity parameters. Use of these incorrectly calculated intensity profiles in the integration procedure to obtain the flux profile leads to incorrect estimates of the solar macroturbulence
The superposition of the states and the logic approach to quantum mechanics
International Nuclear Information System (INIS)
Zecca, A.
1981-01-01
An axiomatic approach to quantum mechanics is proposed in terms of a 'logic' scheme satisfying a suitable set of axioms. In this context the notion of pure, maximal, and characteristic state as well as the superposition relation and the superposition principle for the states are studied. The role the superposition relation plays in the reversible and in the irreversible dynamics is investigated and its connection with the tensor product is studied. Throughout the paper, the W*-algebra model, is used to exemplify results and properties of the general scheme. (author)
International Nuclear Information System (INIS)
Suzuki, Shigenari; Takeoka, Masahiro; Sasaki, Masahide; Andersen, Ulrik L.; Kannari, Fumihiko
2006-01-01
We present a simple protocol to purify a coherent-state superposition that has undergone a linear lossy channel. The scheme constitutes only a single beam splitter and a homodyne detector, and thus is experimentally feasible. In practice, a superposition of coherent states is transformed into a classical mixture of coherent states by linear loss, which is usually the dominant decoherence mechanism in optical systems. We also address the possibility of producing a larger amplitude superposition state from decohered states, and show that in most cases the decoherence of the states are amplified along with the amplitude
An Algorithm for the Convolution of Legendre Series
Hale, Nicholas; Townsend, Alex
2014-01-01
An O(N2) algorithm for the convolution of compactly supported Legendre series is described. The algorithm is derived from the convolution theorem for Legendre polynomials and the recurrence relation satisfied by spherical Bessel functions. Combining with previous work yields an O(N 2) algorithm for the convolution of Chebyshev series. Numerical results are presented to demonstrate the improved efficiency over the existing algorithm. © 2014 Society for Industrial and Applied Mathematics.
The Urbanik generalized convolutions in the non-commutative ...
Indian Academy of Sciences (India)
−sν(dx) < ∞. Now we apply this construction to the Kendall convolution case, starting with the weakly stable measure δ1. Example 1. Let △ be the Kendall convolution, i.e. the generalized convolution with the probability kernel: δ1△δa = (1 − a)δ1 + aπ2 for a ∈ [0, 1] and π2 be the Pareto distribution with the density π2(dx) =.
On a Generalized Hankel Type Convolution of Generalized Functions
Indian Academy of Sciences (India)
Generalized Hankel type transformation; Parserval relation; generalized ... The classical generalized Hankel type convolution are defined and extended to a class of generalized functions. ... Proceedings – Mathematical Sciences | News.
Enhanced online convolutional neural networks for object tracking
Zhang, Dengzhuo; Gao, Yun; Zhou, Hao; Li, Tianwen
2018-04-01
In recent several years, object tracking based on convolution neural network has gained more and more attention. The initialization and update of convolution filters can directly affect the precision of object tracking effective. In this paper, a novel object tracking via an enhanced online convolution neural network without offline training is proposed, which initializes the convolution filters by a k-means++ algorithm and updates the filters by an error back-propagation. The comparative experiments of 7 trackers on 15 challenging sequences showed that our tracker can perform better than other trackers in terms of AUC and precision.
Liu, Wanjun; Liang, Xuejian; Qu, Haicheng
2017-11-01
Hyperspectral image (HSI) classification is one of the most popular topics in remote sensing community. Traditional and deep learning-based classification methods were proposed constantly in recent years. In order to improve the classification accuracy and robustness, a dimensionality-varied convolutional neural network (DVCNN) was proposed in this paper. DVCNN was a novel deep architecture based on convolutional neural network (CNN). The input of DVCNN was a set of 3D patches selected from HSI which contained spectral-spatial joint information. In the following feature extraction process, each patch was transformed into some different 1D vectors by 3D convolution kernels, which were able to extract features from spectral-spatial data. The rest of DVCNN was about the same as general CNN and processed 2D matrix which was constituted by by all 1D data. So that the DVCNN could not only extract more accurate and rich features than CNN, but also fused spectral-spatial information to improve classification accuracy. Moreover, the robustness of network on water-absorption bands was enhanced in the process of spectral-spatial fusion by 3D convolution, and the calculation was simplified by dimensionality varied convolution. Experiments were performed on both Indian Pines and Pavia University scene datasets, and the results showed that the classification accuracy of DVCNN improved by 32.87% on Indian Pines and 19.63% on Pavia University scene than spectral-only CNN. The maximum accuracy improvement of DVCNN achievement was 13.72% compared with other state-of-the-art HSI classification methods, and the robustness of DVCNN on water-absorption bands noise was demonstrated.
A cute and highly contrast-sensitive superposition eye : The diurnal owlfly Libelloides macaronius
Belušič, Gregor; Pirih, Primož; Stavenga, Doekele G.
The owlfly Libelloides macaronius (Insecta: Neuroptera) has large bipartite eyes of the superposition type. The spatial resolution and sensitivity of the photoreceptor array in the dorsofrontal eye part was studied with optical and electrophysiological methods. Using structured illumination
Czech Academy of Sciences Publication Activity Database
Červený, V.; Pšenčík, Ivan
2015-01-01
Roč. 25, - (2015), s. 109-155 ISSN 2336-3827 Institutional support: RVO:67985530 Keywords : integral superposition of paraxial Gaussian beams * inhomogeneous anisotropic media * S waves in weakly anisotropic media Subject RIV: DC - Siesmology, Volcanology, Earth Structure
Collapsing a perfect superposition to a chosen quantum state without measurement.
Directory of Open Access Journals (Sweden)
Ahmed Younes
Full Text Available Given a perfect superposition of [Formula: see text] states on a quantum system of [Formula: see text] qubits. We propose a fast quantum algorithm for collapsing the perfect superposition to a chosen quantum state [Formula: see text] without applying any measurements. The basic idea is to use a phase destruction mechanism. Two operators are used, the first operator applies a phase shift and a temporary entanglement to mark [Formula: see text] in the superposition, and the second operator applies selective phase shifts on the states in the superposition according to their Hamming distance with [Formula: see text]. The generated state can be used as an excellent input state for testing quantum memories and linear optics quantum computers. We make no assumptions about the used operators and applied quantum gates, but our result implies that for this purpose the number of qubits in the quantum register offers no advantage, in principle, over the obvious measurement-based feedback protocol.
On the L-characteristic of nonlinear superposition operators in lp-spaces
International Nuclear Information System (INIS)
Dedagic, F.
1995-04-01
In this paper we describe the L-characteristic of the nonlinear superposition operator F(x) f(s,x(s)) between two Banach spaces of functions x from N to R. It was shown that L-characteristic of the nonlinear superposition operator which acts between two Lebesgue spaces has so-called Σ-convexity property. In this paper we show that L-characteristic of the operator F (between two Banach spaces) has the convexity property. It means that the classical interpolation theorem of Reisz-Thorin for a linear operator holds for the nonlinear operator superposition which acts between two Banach spaces of sequences. Moreover, we consider the growth function of the operator superposition in mentioned spaces and we show that one has the logarithmically convexity property. (author). 7 refs
Quantum Experiments and Graphs: Multiparty States as Coherent Superpositions of Perfect Matchings
Krenn, Mario; Gu, Xuemei; Zeilinger, Anton
2017-12-01
We show a surprising link between experimental setups to realize high-dimensional multipartite quantum states and graph theory. In these setups, the paths of photons are identified such that the photon-source information is never created. We find that each of these setups corresponds to an undirected graph, and every undirected graph corresponds to an experimental setup. Every term in the emerging quantum superposition corresponds to a perfect matching in the graph. Calculating the final quantum state is in the #P-complete complexity class, thus it cannot be done efficiently. To strengthen the link further, theorems from graph theory—such as Hall's marriage problem—are rephrased in the language of pair creation in quantum experiments. We show explicitly how this link allows one to answer questions about quantum experiments (such as which classes of entangled states can be created) with graph theoretical methods, and how to potentially simulate properties of graphs and networks with quantum experiments (such as critical exponents and phase transitions).
Probing the conductance superposition law in single-molecule circuits with parallel paths.
Vazquez, H; Skouta, R; Schneebeli, S; Kamenetska, M; Breslow, R; Venkataraman, L; Hybertsen, M S
2012-10-01
According to Kirchhoff's circuit laws, the net conductance of two parallel components in an electronic circuit is the sum of the individual conductances. However, when the circuit dimensions are comparable to the electronic phase coherence length, quantum interference effects play a critical role, as exemplified by the Aharonov-Bohm effect in metal rings. At the molecular scale, interference effects dramatically reduce the electron transfer rate through a meta-connected benzene ring when compared with a para-connected benzene ring. For longer conjugated and cross-conjugated molecules, destructive interference effects have been observed in the tunnelling conductance through molecular junctions. Here, we investigate the conductance superposition law for parallel components in single-molecule circuits, particularly the role of interference. We synthesize a series of molecular systems that contain either one backbone or two backbones in parallel, bonded together cofacially by a common linker on each end. Single-molecule conductance measurements and transport calculations based on density functional theory show that the conductance of a double-backbone molecular junction can be more than twice that of a single-backbone junction, providing clear evidence for constructive interference.
Quantum Experiments and Graphs: Multiparty States as Coherent Superpositions of Perfect Matchings.
Krenn, Mario; Gu, Xuemei; Zeilinger, Anton
2017-12-15
We show a surprising link between experimental setups to realize high-dimensional multipartite quantum states and graph theory. In these setups, the paths of photons are identified such that the photon-source information is never created. We find that each of these setups corresponds to an undirected graph, and every undirected graph corresponds to an experimental setup. Every term in the emerging quantum superposition corresponds to a perfect matching in the graph. Calculating the final quantum state is in the #P-complete complexity class, thus it cannot be done efficiently. To strengthen the link further, theorems from graph theory-such as Hall's marriage problem-are rephrased in the language of pair creation in quantum experiments. We show explicitly how this link allows one to answer questions about quantum experiments (such as which classes of entangled states can be created) with graph theoretical methods, and how to potentially simulate properties of graphs and networks with quantum experiments (such as critical exponents and phase transitions).
A study of radiative properties of fractal soot aggregates using the superposition T-matrix method
International Nuclear Information System (INIS)
Li Liu; Mishchenko, Michael I.; Patrick Arnott, W.
2008-01-01
We employ the numerically exact superposition T-matrix method to perform extensive computations of scattering and absorption properties of soot aggregates with varying state of compactness and size. The fractal dimension, D f , is used to quantify the geometrical mass dispersion of the clusters. The optical properties of soot aggregates for a given fractal dimension are complex functions of the refractive index of the material m, the number of monomers N S , and the monomer radius a. It is shown that for smaller values of a, the absorption cross section tends to be relatively constant when D f f >2. However, a systematic reduction in light absorption with D f is observed for clusters with sufficiently large N S , m, and a. The scattering cross section and single-scattering albedo increase monotonically as fractals evolve from chain-like to more densely packed morphologies, which is a strong manifestation of the increasing importance of scattering interaction among spherules. Overall, the results for soot fractals differ profoundly from those calculated for the respective volume-equivalent soot spheres as well as for the respective external mixtures of soot monomers under the assumption that there are no electromagnetic interactions between the monomers. The climate-research implications of our results are discussed
Experimental Demonstration of Capacity-Achieving Phase-Shifted Superposition Modulation
DEFF Research Database (Denmark)
Estaran Tolosa, Jose Manuel; Zibar, Darko; Caballero Jambrina, Antonio
2013-01-01
We report on the first experimental demonstration of phase-shifted superposition modulation (PSM) for optical links. Successful demodulation and decoding is obtained after 240 km transmission for 16-, 32- and 64-PSM.......We report on the first experimental demonstration of phase-shifted superposition modulation (PSM) for optical links. Successful demodulation and decoding is obtained after 240 km transmission for 16-, 32- and 64-PSM....
Efstratiadis, Stella; Baumrind, Sheldon; Shofer, Frances; Jacobsson-Hunt, Ulla; Laster, Larry; Ghafari, Joseph
2005-11-01
The aims of this study were (1) to evaluate cephalometric changes in subjects with Class II Division 1 malocclusion who were treated with headgear (HG) or Fränkel function regulator (FR) and (2) to compare findings from regional superpositions of cephalometric structures with those from conventional cephalometric measurements. Cephalographs were taken at baseline, after 1 year, and after 2 years of 65 children enrolled in a prospective randomized clinical trial. The spatial location of the landmarks derived from regional superpositions was evaluated in a coordinate system oriented on natural head position. The superpositions included the best anatomic fit of the anterior cranial base, maxillary base, and mandibular structures. Both the HG and the FR were effective in correcting the distoclusion, and they generated enhanced differential growth between the jaws. Differences between cranial and maxillary superpositions regarding mandibular displacement (Point B, pogonion, gnathion, menton) were noted: the HG had a more horizontal vector on maxillary superposition that was also greater (.0001 < P < .05) than the horizontal displacement observed with the FR. This discrepancy appeared to be related to (1) the clockwise (backward) rotation of the palatal and mandibular planes observed with the HG; the palatal plane's rotation, which was transferred through the occlusion to the mandibular plane, was factored out on maxillary superposition; and (2) the interaction between the inclination of the maxillary incisors and the forward movement of the mandible during growth. Findings from superpositions agreed with conventional angular and linear measurements regarding the basic conclusions for the primary effects of HG and FR. However, the results suggest that inferences of mandibular displacement are more reliable from maxillary than cranial superposition when evaluating occlusal changes during treatment.
International Nuclear Information System (INIS)
Spagnolo, Nicolo; Sciarrino, Fabio; De Martini, Francesco
2010-01-01
We show that the quantum states generated by universal optimal quantum cloning of a single photon represent a universal set of quantum superpositions resilient to decoherence. We adopt the Bures distance as a tool to investigate the persistence of quantum coherence of these quantum states. According to this analysis, the process of universal cloning realizes a class of quantum superpositions that exhibits a covariance property in lossy configuration over the complete set of polarization states in the Bloch sphere.
Convolutional neural networks and face recognition task
Sochenkova, A.; Sochenkov, I.; Makovetskii, A.; Vokhmintsev, A.; Melnikov, A.
2017-09-01
Computer vision tasks are remaining very important for the last couple of years. One of the most complicated problems in computer vision is face recognition that could be used in security systems to provide safety and to identify person among the others. There is a variety of different approaches to solve this task, but there is still no universal solution that would give adequate results in some cases. Current paper presents following approach. Firstly, we extract an area containing face, then we use Canny edge detector. On the next stage we use convolutional neural networks (CNN) to finally solve face recognition and person identification task.
Decoding LDPC Convolutional Codes on Markov Channels
Directory of Open Access Journals (Sweden)
Kashyap Manohar
2008-01-01
Full Text Available Abstract This paper describes a pipelined iterative technique for joint decoding and channel state estimation of LDPC convolutional codes over Markov channels. Example designs are presented for the Gilbert-Elliott discrete channel model. We also compare the performance and complexity of our algorithm against joint decoding and state estimation of conventional LDPC block codes. Complexity analysis reveals that our pipelined algorithm reduces the number of operations per time step compared to LDPC block codes, at the expense of increased memory and latency. This tradeoff is favorable for low-power applications.
Decoding LDPC Convolutional Codes on Markov Channels
Directory of Open Access Journals (Sweden)
Chris Winstead
2008-04-01
Full Text Available This paper describes a pipelined iterative technique for joint decoding and channel state estimation of LDPC convolutional codes over Markov channels. Example designs are presented for the Gilbert-Elliott discrete channel model. We also compare the performance and complexity of our algorithm against joint decoding and state estimation of conventional LDPC block codes. Complexity analysis reveals that our pipelined algorithm reduces the number of operations per time step compared to LDPC block codes, at the expense of increased memory and latency. This tradeoff is favorable for low-power applications.
Target recognition based on convolutional neural network
Wang, Liqiang; Wang, Xin; Xi, Fubiao; Dong, Jian
2017-11-01
One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.
Chen, Liang-Chieh; Papandreou, George; Kokkinos, Iasonas; Murphy, Kevin; Yuille, Alan L
2018-04-01
In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or 'atrous convolution', as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed "DeepLab" system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online.
International Nuclear Information System (INIS)
Deng, Li; Niu, Yueping; Jin, Luling; Gong, Shangqing
2010-01-01
The coherent superposition state of the lower two levels in non-degenerate three-level Λ atoms is investigated using the accumulative effects of non-resonant pulse trains when the repetition period is smaller than the decay time of the upper level. First, using a rectangular pulse train, the accumulative effects are re-examined in the non-resonant two-level atoms and the modified constructive accumulation equation is analytically given. The equation shows that the relative phase and the repetition period are important in the accumulative effect. Next, under the modified equation in the non-degenerate three-level Λ atoms, we show that besides the constructive accumulation effect, the use of the partial constructive accumulation effect can also achieve the steady state of the maximum coherent superposition state of the lower two levels and the latter condition is relatively easier to manipulate. The analysis is verified by numerical calculations. The influence of the external levels in such a case is also considered and we find that it can be avoided effectively. The above analysis is also applicable to pulse trains with arbitrary envelopes.
International Nuclear Information System (INIS)
Carrasco, P.; Jornet, N.; Duch, M.A.; Weber, L.; Ginjaume, M.; Eudaldo, T.; Jurado, D.; Ruiz, A.; Ribas, M.
2004-01-01
An extensive set of benchmark measurement of PDDs and beam profiles was performed in a heterogeneous layer phantom, including a lung equivalent heterogeneity, by means of several detectors and compared against the predicted dose values by different calculation algorithms in two treatment planning systems. PDDs were measured with TLDs, plane parallel and cylindrical ionization chambers and beam profiles with films. Additionally, Monte Carlo simulations by meansof the PENELOPE code were performed. Four different field sizes (10x10, 5x5, 2x2, and1x1 cm 2 ) and two lung equivalent materials (CIRS, ρ e w =0.195 and St. Bartholomew Hospital, London, ρ e w =0.244-0.322) were studied. The performance of four correction-based algorithms and one based on convolution-superposition was analyzed. The correction-based algorithms were the Batho, the Modified Batho, and the Equivalent TAR implemented in the Cadplan (Varian) treatment planning system and the TMS Pencil Beam from the Helax-TMS (Nucletron) treatment planning system. The convolution-superposition algorithm was the Collapsed Cone implemented in the Helax-TMS. The only studied calculation methods that correlated successfully with the measured values with a 2% average inside all media were the Collapsed Cone and the Monte Carlo simulation. The biggest difference between the predicted and the delivered dose in the beam axis was found for the EqTAR algorithm inside the CIRS lung equivalent material in a 2x2 cm 2 18 MV x-ray beam. In these conditions, average and maximum difference against the TLD measurements were 32% and 39%, respectively. In the water equivalent part of the phantom every algorithm correctly predicted the dose (within 2%) everywhere except very close to the interfaces where differences up to 24% were found for 2x2 cm 2 18 MV photon beams. Consistent values were found between the reference detector (ionization chamber in water and TLD in lung) and Monte Carlo simulations, yielding minimal differences (0
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....
Modified Stieltjes Transform and Generalized Convolutions of Probability Distributions
Directory of Open Access Journals (Sweden)
Lev B. Klebanov
2018-01-01
Full Text Available The classical Stieltjes transform is modified in such a way as to generalize both Stieltjes and Fourier transforms. This transform allows the introduction of new classes of commutative and non-commutative generalized convolutions. A particular case of such a convolution for degenerate distributions appears to be the Wigner semicircle distribution.
Nuclear norm regularized convolutional Max Pos@Top machine
Li, Qinfeng; Zhou, Xiaofeng; Gu, Aihua; Li, Zonghua; Liang, Ru-Ze
2016-01-01
, named as Pos@Top. Our proposed classification model has a convolutional structure that is composed by four layers, i.e., the convolutional layer, the activation layer, the max-pooling layer and the full connection layer. In this paper, we propose
Spherical convolutions and their application in molecular modelling
DEFF Research Database (Denmark)
Boomsma, Wouter; Frellsen, Jes
2017-01-01
Convolutional neural networks are increasingly used outside the domain of image analysis, in particular in various areas of the natural sciences concerned with spatial data. Such networks often work out-of-the box, and in some cases entire model architectures from image analysis can be carried over...... to other problem domains almost unaltered. Unfortunately, this convenience does not trivially extend to data in non-euclidean spaces, such as spherical data. In this paper, we introduce two strategies for conducting convolutions on the sphere, using either a spherical-polar grid or a grid based...... of spherical convolutions in the context of molecular modelling, by considering structural environments within proteins. We show that the models are capable of learning non-trivial functions in these molecular environments, and that our spherical convolutions generally outperform standard 3D convolutions...
A scatter model for fast neutron beams using convolution of diffusion kernels
International Nuclear Information System (INIS)
Moyers, M.F.; Horton, J.L.; Boyer, A.L.
1988-01-01
A new model is proposed to calculate dose distributions in materials irradiated with fast neutron beams. Scattered neutrons are transported away from the point of production within the irradiated material in the forward, lateral and backward directions, while recoil protons are transported in the forward and lateral directions. The calculation of dose distributions, such as for radiotherapy planning, is accomplished by convolving a primary attenuation distribution with a diffusion kernel. The primary attenuation distribution may be quickly calculated for any given set of beam and material conditions as it describes only the magnitude and distribution of first interaction sites. The calculation of energy diffusion kernels is very time consuming but must be calculated only once for a given energy. Energy diffusion distributions shown in this paper have been calculated using a Monte Carlo type of program. To decrease beam calculation time, convolutions are performed using a Fast Fourier Transform technique. (author)
Testing of the analytical anisotropic algorithm for photon dose calculation
International Nuclear Information System (INIS)
Esch, Ann van; Tillikainen, Laura; Pyykkonen, Jukka; Tenhunen, Mikko; Helminen, Hannu; Siljamaeki, Sami; Alakuijala, Jyrki; Paiusco, Marta; Iori, Mauro; Huyskens, Dominique P.
2006-01-01
The analytical anisotropic algorithm (AAA) was implemented in the Eclipse (Varian Medical Systems) treatment planning system to replace the single pencil beam (SPB) algorithm for the calculation of dose distributions for photon beams. AAA was developed to improve the dose calculation accuracy, especially in heterogeneous media. The total dose deposition is calculated as the superposition of the dose deposited by two photon sources (primary and secondary) and by an electron contamination source. The photon dose is calculated as a three-dimensional convolution of Monte-Carlo precalculated scatter kernels, scaled according to the electron density matrix. For the configuration of AAA, an optimization algorithm determines the parameters characterizing the multiple source model by optimizing the agreement between the calculated and measured depth dose curves and profiles for the basic beam data. We have combined the acceptance tests obtained in three different departments for 6, 15, and 18 MV photon beams. The accuracy of AAA was tested for different field sizes (symmetric and asymmetric) for open fields, wedged fields, and static and dynamic multileaf collimation fields. Depth dose behavior at different source-to-phantom distances was investigated. Measurements were performed on homogeneous, water equivalent phantoms, on simple phantoms containing cork inhomogeneities, and on the thorax of an anthropomorphic phantom. Comparisons were made among measurements, AAA, and SPB calculations. The optimization procedure for the configuration of the algorithm was successful in reproducing the basic beam data with an overall accuracy of 3%, 1 mm in the build-up region, and 1%, 1 mm elsewhere. Testing of the algorithm in more clinical setups showed comparable results for depth dose curves, profiles, and monitor units of symmetric open and wedged beams below d max . The electron contamination model was found to be suboptimal to model the dose around d max , especially for physical
Deformable image registration using convolutional neural networks
Eppenhof, Koen A. J.; Lafarge, Maxime W.; Moeskops, Pim; Veta, Mitko; Pluim, Josien P. W.
2018-03-01
Deformable image registration can be time-consuming and often needs extensive parameterization to perform well on a specific application. We present a step towards a registration framework based on a three-dimensional convolutional neural network. The network directly learns transformations between pairs of three-dimensional images. The outputs of the network are three maps for the x, y, and z components of a thin plate spline transformation grid. The network is trained on synthetic random transformations, which are applied to a small set of representative images for the desired application. Training therefore does not require manually annotated ground truth deformation information. The methodology is demonstrated on public data sets of inspiration-expiration lung CT image pairs, which come with annotated corresponding landmarks for evaluation of the registration accuracy. Advantages of this methodology are its fast registration times and its minimal parameterization.
Codeword Structure Analysis for LDPC Convolutional Codes
Directory of Open Access Journals (Sweden)
Hua Zhou
2015-12-01
Full Text Available The codewords of a low-density parity-check (LDPC convolutional code (LDPC-CC are characterised into structured and non-structured. The number of the structured codewords is dominated by the size of the polynomial syndrome former matrix H T ( D , while the number of the non-structured ones depends on the particular monomials or polynomials in H T ( D . By evaluating the relationship of the codewords between the mother code and its super codes, the low weight non-structured codewords in the super codes can be eliminated by appropriately choosing the monomials or polynomials in H T ( D , resulting in improved distance spectrum of the mother code.
Two new proofs of the test particle superposition principle of plasma kinetic theory
International Nuclear Information System (INIS)
Krommes, J.A.
1975-12-01
The test particle superposition principle of plasma kinetic theory is discussed in relation to the recent theory of two-time fluctuations in plasma given by Williams and Oberman. Both a new deductive and a new inductive proof of the principle are presented. The fundamental observation is that two-time expectations of one-body operators are determined completely in terms of the (x,v) phase space density autocorrelation, which to lowest order in the discreteness parameter obeys the linearized Vlasov equation with singular initial condition. For the deductive proof, this equation is solved formally using time-ordered operators, and the solution then rearranged into the superposition principle. The inductive proof is simpler than Rostoker's, although similar in some ways; it differs in that first order equations for pair correlation functions need not be invoked. It is pointed out that the superposition principle is also applicable to the short-time theory of neutral fluids
Towards quantum superposition of a levitated nanodiamond with a NV center
Li, Tongcang
2015-05-01
Creating large Schrödinger's cat states with massive objects is one of the most challenging goals in quantum mechanics. We have previously achieved an important step of this goal by cooling the center-of-mass motion of a levitated microsphere from room temperature to millikelvin temperatures with feedback cooling. To generate spatial quantum superposition states with an optical cavity, however, requires a very strong quadratic coupling that is difficult to achieve. We proposed to optically trap a nanodiamond with a nitrogen-vacancy (NV) center in vacuum, and generate large spatial superposition states using the NV spin-optomechanical coupling in a strong magnetic gradient field. The large spatial superposition states can be used to study objective collapse theories of quantum mechanics. We have optically trapped nanodiamonds in air and are working towards this goal.
Calculation methods for dissolution rate of multicomponent alloys during electrochemical machining
International Nuclear Information System (INIS)
Dikusar, A.I.; Petrenko, V.I.; Dikusar, G.K.; Ehngel'gardt, G.R.; Michukova, N.Yu.
1981-01-01
The possibility of theoretical calculation of metal dissolution rate during electrochemical mashining is considered. Two calculation techniques are compared at the example of two-component W-Re, Ni-W, Mo-Re alloys, namely: ''charge superposition'' and ''weight percents''. It is concluded that the technique of ''charge superposition'' is the only grounded calculation technique of specific rates of dissolution for alloys [ru
Ijpma, G; Al-Jumaily, A M; Cairns, S P; Sieck, G C
2010-12-01
We present a systematic quantitative analysis of power-law force relaxation and investigate logarithmic superposition of force response in relaxed porcine airway smooth muscle (ASM) strips in vitro. The term logarithmic superposition describes linear superposition on a logarithmic scale, which is equivalent to multiplication on a linear scale. Additionally, we examine whether the dynamic response of contracted and relaxed muscles is dominated by cross-bridge cycling or passive dynamics. The study shows the following main findings. For relaxed ASM, the force response to length steps of varying amplitude (0.25-4% of reference length, both lengthening and shortening) are well-fitted with power-law functions over several decades of time (10⁻² to 10³ s), and the force response after consecutive length changes is more accurately fitted assuming logarithmic superposition rather than linear superposition. Furthermore, for sinusoidal length oscillations in contracted and relaxed muscles, increasing the oscillation amplitude induces greater hysteresivity and asymmetry of force-length relationships, whereas increasing the frequency dampens hysteresivity but increases asymmetry. We conclude that logarithmic superposition is an important feature of relaxed ASM, which may facilitate a more accurate prediction of force responses in the continuous dynamic environment of the respiratory system. In addition, the single power-function response to length changes shows that the dynamics of cross-bridge cycling can be ignored in relaxed muscle. The similarity in response between relaxed and contracted states implies that the investigated passive dynamics play an important role in both states and should be taken into account.
The general use of the time-temperature-pressure superposition principle
DEFF Research Database (Denmark)
Rasmussen, Henrik Koblitz
This note is a supplement to Dynamic of Polymeric Liquids (DPL) section 3.6(a). DPL do only concern material functions and only the effect of the temperature on these. This is a short introduction to the general use of the time-temperature-pressure superposition principle.......This note is a supplement to Dynamic of Polymeric Liquids (DPL) section 3.6(a). DPL do only concern material functions and only the effect of the temperature on these. This is a short introduction to the general use of the time-temperature-pressure superposition principle....
International Nuclear Information System (INIS)
Jack, B.; Leach, J.; Franke-Arnold, S.; Ireland, D. G.; Padgett, M. J.; Yao, A. M.; Barnett, S. M.; Romero, J.
2010-01-01
We use spatial light modulators (SLMs) to measure correlations between arbitrary superpositions of orbital angular momentum (OAM) states generated by spontaneous parametric down-conversion. Our technique allows us to fully access a two-dimensional OAM subspace described by a Bloch sphere, within the higher-dimensional OAM Hilbert space. We quantify the entanglement through violations of a Bell-type inequality for pairs of modal superpositions that lie on equatorial, polar, and arbitrary great circles of the Bloch sphere. Our work shows that SLMs can be used to measure arbitrary spatial states with a fidelity sufficient for appropriate quantum information processing systems.
High Order Tensor Formulation for Convolutional Sparse Coding
Bibi, Adel Aamer; Ghanem, Bernard
2017-01-01
Convolutional sparse coding (CSC) has gained attention for its successful role as a reconstruction and a classification tool in the computer vision and machine learning community. Current CSC methods can only reconstruct singlefeature 2D images
Adversarial training and dilated convolutions for brain MRI segmentation
Moeskops, P.; Veta, M.; Lafarge, M.W.; Eppenhof, K.A.J.; Pluim, J.P.W.
2017-01-01
Convolutional neural networks (CNNs) have been applied to various automatic image segmentation tasks in medical image analysis, including brain MRI segmentation. Generative adversarial networks have recently gained popularity because of their power in generating images that are difficult to
Classification of urine sediment based on convolution neural network
Pan, Jingjing; Jiang, Cunbo; Zhu, Tiantian
2018-04-01
By designing a new convolution neural network framework, this paper breaks the constraints of the original convolution neural network framework requiring large training samples and samples of the same size. Move and cropping the input images, generate the same size of the sub-graph. And then, the generated sub-graph uses the method of dropout, increasing the diversity of samples and preventing the fitting generation. Randomly select some proper subset in the sub-graphic set and ensure that the number of elements in the proper subset is same and the proper subset is not the same. The proper subsets are used as input layers for the convolution neural network. Through the convolution layer, the pooling, the full connection layer and output layer, we can obtained the classification loss rate of test set and training set. In the red blood cells, white blood cells, calcium oxalate crystallization classification experiment, the classification accuracy rate of 97% or more.
Convolution of second order linear recursive sequences II.
Directory of Open Access Journals (Sweden)
Szakács Tamás
2017-12-01
Full Text Available We continue the investigation of convolutions of second order linear recursive sequences (see the first part in [1]. In this paper, we focus on the case when the characteristic polynomials of the sequences have common root.
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...
Deep convolutional neural network for mammographic density segmentation
Wei, Jun; Li, Songfeng; Chan, Heang-Ping; Helvie, Mark A.; Roubidoux, Marilyn A.; Lu, Yao; Zhou, Chuan; Hadjiiski, Lubomir; Samala, Ravi K.
2018-02-01
Breast density is one of the most significant factors for cancer risk. In this study, we proposed a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammography (DM). The deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD). PD was calculated as the ratio of the dense area to the breast area based on the probability of each pixel belonging to dense region or fatty region at a decision threshold of 0.5. The DCNN estimate was compared to a feature-based statistical learning approach, in which gray level, texture and morphological features were extracted from each ROI and the least absolute shrinkage and selection operator (LASSO) was used to select and combine the useful features to generate the PMD. The reference PD of each image was provided by two experienced MQSA radiologists. With IRB approval, we retrospectively collected 347 DMs from patient files at our institution. The 10-fold cross-validation results showed a strong correlation r=0.96 between the DCNN estimation and interactive segmentation by radiologists while that of the feature-based statistical learning approach vs radiologists' segmentation had a correlation r=0.78. The difference between the segmentation by DCNN and by radiologists was significantly smaller than that between the feature-based learning approach and radiologists (p approach has the potential to replace radiologists' interactive thresholding in PD estimation on DMs.
Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition
Zhang, Zewang; Sun, Zheng; Liu, Jiaqi; Chen, Jingwen; Huo, Zhao; Zhang, Xiao
2016-01-01
A deep learning approach has been widely applied in sequence modeling problems. In terms of automatic speech recognition (ASR), its performance has significantly been improved by increasing large speech corpus and deeper neural network. Especially, recurrent neural network and deep convolutional neural network have been applied in ASR successfully. Given the arising problem of training speed, we build a novel deep recurrent convolutional network for acoustic modeling and then apply deep resid...
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...
Convolutional Codes with Maximum Column Sum Rank for Network Streaming
Mahmood, Rafid; Badr, Ahmed; Khisti, Ashish
2015-01-01
The column Hamming distance of a convolutional code determines the error correction capability when streaming over a class of packet erasure channels. We introduce a metric known as the column sum rank, that parallels column Hamming distance when streaming over a network with link failures. We prove rank analogues of several known column Hamming distance properties and introduce a new family of convolutional codes that maximize the column sum rank up to the code memory. Our construction invol...
Efficient and Invariant Convolutional Neural Networks for Dense Prediction
Gao, Hongyang; Ji, Shuiwang
2017-01-01
Convolutional neural networks have shown great success on feature extraction from raw input data such as images. Although convolutional neural networks are invariant to translations on the inputs, they are not invariant to other transformations, including rotation and flip. Recent attempts have been made to incorporate more invariance in image recognition applications, but they are not applicable to dense prediction tasks, such as image segmentation. In this paper, we propose a set of methods...
Prediction of Electricity Usage Using Convolutional Neural Networks
Hansen, Martin
2017-01-01
Master's thesis Information- and communication technology IKT590 - University of Agder 2017 Convolutional Neural Networks are overwhelmingly accurate when attempting to predict numbers using the famous MNIST-dataset. In this paper, we are attempting to transcend these results for time- series forecasting, and compare them with several regression mod- els. The Convolutional Neural Network model predicted the same value through the entire time lapse in contrast with the other ...
Research of convolutional neural networks for traffic sign recognition
Stadalnikas, Kasparas
2017-01-01
In this thesis the convolutional neural networks application for traffic sign recognition is analyzed. Thesis describes the basic operations, techniques that are commonly used to apply in the image classification using convolutional neural networks. Also, this paper describes the data sets used for traffic sign recognition, their problems affecting the final training results. The paper reviews most popular existing technologies – frameworks for developing the solution for traffic sign recogni...
On the Fresnel sine integral and the convolution
Directory of Open Access Journals (Sweden)
Adem Kılıçman
2003-01-01
Full Text Available The Fresnel sine integral S(x, the Fresnel cosine integral C(x, and the associated functions S+(x, S−(x, C+(x, and C−(x are defined as locally summable functions on the real line. Some convolutions and neutrix convolutions of the Fresnel sine integral and its associated functions with x+r, xr are evaluated.
Radial Structure Scaffolds Convolution Patterns of Developing Cerebral Cortex
Directory of Open Access Journals (Sweden)
Mir Jalil Razavi
2017-08-01
Full Text Available Commonly-preserved radial convolution is a prominent characteristic of the mammalian cerebral cortex. Endeavors from multiple disciplines have been devoted for decades to explore the causes for this enigmatic structure. However, the underlying mechanisms that lead to consistent cortical convolution patterns still remain poorly understood. In this work, inspired by prior studies, we propose and evaluate a plausible theory that radial convolution during the early development of the brain is sculptured by radial structures consisting of radial glial cells (RGCs and maturing axons. Specifically, the regionally heterogeneous development and distribution of RGCs controlled by Trnp1 regulate the convex and concave convolution patterns (gyri and sulci in the radial direction, while the interplay of RGCs' effects on convolution and axons regulates the convex (gyral convolution patterns. This theory is assessed by observations and measurements in literature from multiple disciplines such as neurobiology, genetics, biomechanics, etc., at multiple scales to date. Particularly, this theory is further validated by multimodal imaging data analysis and computational simulations in this study. We offer a versatile and descriptive study model that can provide reasonable explanations of observations, experiments, and simulations of the characteristic mammalian cortical folding.
Energy Technology Data Exchange (ETDEWEB)
Cates, J; Drzymala, R [Washington Univ, Saint Louis, MO (United States)
2015-06-15
Purpose: The purpose of this study was to develop and use a novel phantom to evaluate the accuracy and usefulness of the Leskell Gamma Plan convolution-based dose calculation algorithm compared with the current TMR10 algorithm. Methods: A novel phantom was designed to fit the Leskell Gamma Knife G Frame which could accommodate various materials in the form of one inch diameter, cylindrical plugs. The plugs were split axially to allow EBT2 film placement. Film measurements were made during two experiments. The first utilized plans generated on a homogeneous acrylic phantom setup using the TMR10 algorithm, with various materials inserted into the phantom during film irradiation to assess the effect on delivered dose due to unplanned heterogeneities upstream in the beam path. The second experiment utilized plans made on CT scans of different heterogeneous setups, with one plan using the TMR10 dose calculation algorithm and the second using the convolution-based algorithm. Materials used to introduce heterogeneities included air, LDPE, polystyrene, Delrin, Teflon, and aluminum. Results: The data shows that, as would be expected, having heterogeneities in the beam path does induce dose delivery error when using the TMR10 algorithm, with the largest errors being due to the heterogeneities with electron densities most different from that of water, i.e. air, Teflon, and aluminum. Additionally, the Convolution algorithm did account for the heterogeneous material and provided a more accurate predicted dose, in extreme cases up to a 7–12% improvement over the TMR10 algorithm. The convolution algorithm expected dose was accurate to within 3% in all cases. Conclusion: This study proves that the convolution algorithm is an improvement over the TMR10 algorithm when heterogeneities are present. More work is needed to determine what the heterogeneity size/volume limits are where this improvement exists, and in what clinical and/or research cases this would be relevant.
Measuring the band structures of periodic beams using the wave superposition method
Junyi, L.; Ruffini, V.; Balint, D.
2016-11-01
Phononic crystals and elastic metamaterials are artificially engineered periodic structures that have several interesting properties, such as negative effective stiffness in certain frequency ranges. An interesting property of phononic crystals and elastic metamaterials is the presence of band gaps, which are bands of frequencies where elastic waves cannot propagate. The presence of band gaps gives this class of materials the potential to be used as vibration isolators. In many studies, the band structures were used to evaluate the band gaps. The presence of band gaps in a finite structure is commonly validated by measuring the frequency response as there are no direct methods of measuring the band structures. In this study, an experiment was conducted to determine the band structure of one dimension phononic crystals with two wave modes, such as a bi-material beam, using the frequency response at only 6 points to validate the wave superposition method (WSM) introduced in a previous study. A bi-material beam and an aluminium beam with varying geometry were studied. The experiment was performed by hanging the beams freely, exciting one end of the beams, and measuring the acceleration at consecutive unit cells. The measured transfer function of the beams agrees with the analytical solutions but minor discrepancies. The band structure was then determined using WSM and the band structure of one set of the waves was found to agree well with the analytical solutions. The measurements taken for the other set of waves, which are the evanescent waves in the bi-material beams, were inaccurate and noisy. The transfer functions at additional points of one of the beams were calculated from the measured band structure using WSM. The calculated transfer function agrees with the measured results except at the frequencies where the band structure was inaccurate. Lastly, a study of the potential sources of errors was also conducted using finite element modelling and the errors in
GPU-Based Point Cloud Superpositioning for Structural Comparisons of Protein Binding Sites.
Leinweber, Matthias; Fober, Thomas; Freisleben, Bernd
2018-01-01
In this paper, we present a novel approach to solve the labeled point cloud superpositioning problem for performing structural comparisons of protein binding sites. The solution is based on a parallel evolution strategy that operates on large populations and runs on GPU hardware. The proposed evolution strategy reduces the likelihood of getting stuck in a local optimum of the multimodal real-valued optimization problem represented by labeled point cloud superpositioning. The performance of the GPU-based parallel evolution strategy is compared to a previously proposed CPU-based sequential approach for labeled point cloud superpositioning, indicating that the GPU-based parallel evolution strategy leads to qualitatively better results and significantly shorter runtimes, with speed improvements of up to a factor of 1,500 for large populations. Binary classification tests based on the ATP, NADH, and FAD protein subsets of CavBase, a database containing putative binding sites, show average classification rate improvements from about 92 percent (CPU) to 96 percent (GPU). Further experiments indicate that the proposed GPU-based labeled point cloud superpositioning approach can be superior to traditional protein comparison approaches based on sequence alignments.
Superpositions of higher-order bessel beams and nondiffracting speckle fields
CSIR Research Space (South Africa)
Dudley, Angela L
2009-08-01
Full Text Available speckle fields. The paper reports on illuminating a ring slit aperture with light which has an azimuthal phase dependence, such that the field produced is a superposition of two higher-order Bessel beams. In the case that the phase dependence of the light...
Chaos and Complexities Theories. Superposition and Standardized Testing: Are We Coming or Going?
Erwin, Susan
2005-01-01
The purpose of this paper is to explore the possibility of using the principle of "superposition of states" (commonly illustrated by Schrodinger's Cat experiment) to understand the process of using standardized testing to measure a student's learning. Comparisons from literature, neuroscience, and Schema Theory will be used to expound upon the…
Superposition of Planckian spectra and the distortions of the cosmic microwave background radiation
International Nuclear Information System (INIS)
Alexanian, M.
1982-01-01
A fit of the spectrum of the cosmic microwave background radiation (CMB) by means of a positive linear superposition of Planckian spectra implies an upper bound to the photon spectrum. The observed spectrum of the CMB gives a weighting function with a normalization greater than unity
Teleportation of a Superposition of Three Orthogonal States of an Atom via Photon Interference
Institute of Scientific and Technical Information of China (English)
ZHENG Shi-Biao
2006-01-01
We propose a scheme to teleport a superposition of three states of an atom trapped in a cavity to a second atom trapped in a remote cavity. The scheme is based on the detection of photons leaking from the cavities after the atom-cavity interaction.
On a computational method for modelling complex ecosystems by superposition procedure
International Nuclear Information System (INIS)
He Shanyu.
1986-12-01
In this paper, the Superposition Procedure is concisely described, and a computational method for modelling a complex ecosystem is proposed. With this method, the information contained in acceptable submodels and observed data can be utilized to maximal degree. (author). 1 ref
Using Musical Intervals to Demonstrate Superposition of Waves and Fourier Analysis
LoPresto, Michael C.
2013-01-01
What follows is a description of a demonstration of superposition of waves and Fourier analysis using a set of four tuning forks mounted on resonance boxes and oscilloscope software to create, capture and analyze the waveforms and Fourier spectra of musical intervals.
Superpositions of higher-order bessel beams and nondiffracting speckle fields - (SAIP 2009)
CSIR Research Space (South Africa)
Dudley, Angela L
2009-07-01
Full Text Available speckle fields. The paper reports on illuminating a ring slit aperture with light which has an azimuthal phase dependence, such that the field produced is a superposition of two higher-order Bessel beams. In the case that the phase dependence of the light...
Metaheuristic Algorithms for Convolution Neural Network.
Rere, L M Rasdi; Fanany, Mohamad Ivan; Arymurthy, Aniati Murni
2016-01-01
A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent).
Metaheuristic Algorithms for Convolution Neural Network
Directory of Open Access Journals (Sweden)
L. M. Rasdi Rere
2016-01-01
Full Text Available A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN, a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent.
Do Convolutional Neural Networks Learn Class Hierarchy?
Bilal, Alsallakh; Jourabloo, Amin; Ye, Mao; Liu, Xiaoming; Ren, Liu
2018-01-01
Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the accuracy usually drops as the possibilities of confusion increase. Interestingly, the class confusion patterns follow a hierarchical structure over the classes. We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data. We found that this hierarchy not only dictates the confusion patterns between the classes, it furthermore dictates the learning behavior of CNNs. In particular, the early layers in these networks develop feature detectors that can separate high-level groups of classes quite well, even after a few training epochs. In contrast, the latter layers require substantially more epochs to develop specialized feature detectors that can separate individual classes. We demonstrate how these insights are key to significant improvement in accuracy by designing hierarchy-aware CNNs that accelerate model convergence and alleviate overfitting. We further demonstrate how our methods help in identifying various quality issues in the training data.
Microaneurysm detection using fully convolutional neural networks.
Chudzik, Piotr; Majumdar, Somshubra; Calivá, Francesco; Al-Diri, Bashir; Hunter, Andrew
2018-05-01
Diabetic retinopathy is a microvascular complication of diabetes that can lead to sight loss if treated not early enough. Microaneurysms are the earliest clinical signs of diabetic retinopathy. This paper presents an automatic method for detecting microaneurysms in fundus photographies. A novel patch-based fully convolutional neural network with batch normalization layers and Dice loss function is proposed. Compared to other methods that require up to five processing stages, it requires only three. Furthermore, to the best of the authors' knowledge, this is the first paper that shows how to successfully transfer knowledge between datasets in the microaneurysm detection domain. The proposed method was evaluated using three publicly available and widely used datasets: E-Ophtha, DIARETDB1, and ROC. It achieved better results than state-of-the-art methods using the FROC metric. The proposed algorithm accomplished highest sensitivities for low false positive rates, which is particularly important for screening purposes. Performance, simplicity, and robustness of the proposed method demonstrates its suitability for diabetic retinopathy screening applications. Copyright © 2018 Elsevier B.V. All rights reserved.
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.
Noise-based logic hyperspace with the superposition of 2N states in a single wire
International Nuclear Information System (INIS)
Kish, Laszlo B.; Khatri, Sunil; Sethuraman, Swaminathan
2009-01-01
In the introductory paper [L.B. Kish, Phys. Lett. A 373 (2009) 911], about noise-based logic, we showed how simple superpositions of single logic basis vectors can be achieved in a single wire. The superposition components were the N orthogonal logic basis vectors. Supposing that the different logic values have 'on/off' states only, the resultant discrete superposition state represents a single number with N bit accuracy in a single wire, where N is the number of orthogonal logic vectors in the base. In the present Letter, we show that the logic hyperspace (product) vectors defined in the introductory paper can be generalized to provide the discrete superposition of 2 N orthogonal system states. This is equivalent to a multi-valued logic system with 2 2 N logic values per wire. This is a similar situation to quantum informatics with N qubits, and hence we introduce the notion of noise-bit. This system has major differences compared to quantum informatics. The noise-based logic system is deterministic and each superposition element is instantly accessible with the high digital accuracy, via a real hardware parallelism, without decoherence and error correction, and without the requirement of repeating the logic operation many times to extract the probabilistic information. Moreover, the states in noise-based logic do not have to be normalized, and non-unitary operations can also be used. As an example, we introduce a string search algorithm which is O(√(M)) times faster than Grover's quantum algorithm (where M is the number of string entries), while it has the same hardware complexity class as the quantum algorithm.
Noise-based logic hyperspace with the superposition of 2 states in a single wire
Kish, Laszlo B.; Khatri, Sunil; Sethuraman, Swaminathan
2009-05-01
In the introductory paper [L.B. Kish, Phys. Lett. A 373 (2009) 911], about noise-based logic, we showed how simple superpositions of single logic basis vectors can be achieved in a single wire. The superposition components were the N orthogonal logic basis vectors. Supposing that the different logic values have “on/off” states only, the resultant discrete superposition state represents a single number with N bit accuracy in a single wire, where N is the number of orthogonal logic vectors in the base. In the present Letter, we show that the logic hyperspace (product) vectors defined in the introductory paper can be generalized to provide the discrete superposition of 2 orthogonal system states. This is equivalent to a multi-valued logic system with 2 logic values per wire. This is a similar situation to quantum informatics with N qubits, and hence we introduce the notion of noise-bit. This system has major differences compared to quantum informatics. The noise-based logic system is deterministic and each superposition element is instantly accessible with the high digital accuracy, via a real hardware parallelism, without decoherence and error correction, and without the requirement of repeating the logic operation many times to extract the probabilistic information. Moreover, the states in noise-based logic do not have to be normalized, and non-unitary operations can also be used. As an example, we introduce a string search algorithm which is O(√{M}) times faster than Grover's quantum algorithm (where M is the number of string entries), while it has the same hardware complexity class as the quantum algorithm.
Reducing dose calculation time for accurate iterative IMRT planning
International Nuclear Information System (INIS)
Siebers, Jeffrey V.; Lauterbach, Marc; Tong, Shidong; Wu Qiuwen; Mohan, Radhe
2002-01-01
A time-consuming component of IMRT optimization is the dose computation required in each iteration for the evaluation of the objective function. Accurate superposition/convolution (SC) and Monte Carlo (MC) dose calculations are currently considered too time-consuming for iterative IMRT dose calculation. Thus, fast, but less accurate algorithms such as pencil beam (PB) algorithms are typically used in most current IMRT systems. This paper describes two hybrid methods that utilize the speed of fast PB algorithms yet achieve the accuracy of optimizing based upon SC algorithms via the application of dose correction matrices. In one method, the ratio method, an infrequently computed voxel-by-voxel dose ratio matrix (R=D SC /D PB ) is applied for each beam to the dose distributions calculated with the PB method during the optimization. That is, D PB xR is used for the dose calculation during the optimization. The optimization proceeds until both the IMRT beam intensities and the dose correction ratio matrix converge. In the second method, the correction method, a periodically computed voxel-by-voxel correction matrix for each beam, defined to be the difference between the SC and PB dose computations, is used to correct PB dose distributions. To validate the methods, IMRT treatment plans developed with the hybrid methods are compared with those obtained when the SC algorithm is used for all optimization iterations and with those obtained when PB-based optimization is followed by SC-based optimization. In the 12 patient cases studied, no clinically significant differences exist in the final treatment plans developed with each of the dose computation methodologies. However, the number of time-consuming SC iterations is reduced from 6-32 for pure SC optimization to four or less for the ratio matrix method and five or less for the correction method. Because the PB algorithm is faster at computing dose, this reduces the inverse planning optimization time for our implementation
Mercan, Kadir; Demir, Çiǧdem; Civalek, Ömer
2016-01-01
In the present manuscript, free vibration response of circular cylindrical shells with functionally graded material (FGM) is investigated. The method of discrete singular convolution (DSC) is used for numerical solution of the related governing equation of motion of FGM cylindrical shell. The constitutive relations are based on the Love's first approximation shell theory. The material properties are graded in the thickness direction according to a volume fraction power law indexes. Frequency values are calculated for different types of boundary conditions, material and geometric parameters. In general, close agreement between the obtained results and those of other researchers has been found.
Directory of Open Access Journals (Sweden)
Vladimir I. Volchikhin
2017-12-01
Full Text Available Introduction: The aim of the study is to accelerate the solution of neural network biometrics inverse problem on an ordinary desktop computer. Materials and Methods: To speed up the calculations, the artificial neural network is introduced into the dynamic mode of “jittering” of the states of all 256 output bits. At the same time, too many output states of the neural network are logarithmically folded by transitioning to the Hamming distance space between the code of the image “Own” and the codes of the images “Alien”. From the database of images of “Alien” 2.5 % of the most similar images are selected. In the next generation, 97.5 % of the discarded images are restored with GOST R 52633.2-2010 procedures by crossing parent images and obtaining descendant images from them. Results: Over a period of about 10 minutes, 60 generations of directed search for the solution of the inverse problem can be realized that allows inversing matrices of neural network functionals of dimension 416 inputs to 256 outputs with restoration of up to 97 % information on unknown biometric parameters of the image “Own”. Discussion and Conclusions: Supporting for 10 minutes of computer time the 256 qubit quantum superposition allows on a conventional computer to bypass the actual infinity of analyzed states in 5050 (50 to 50 times more than the same computer could process realizing the usual calculations. The increase in the length of the supported quantum superposition by 40 qubits is equivalent to increasing the processor clock speed by about a billion times. It is for this reason that it is more profitable to increase the number of quantum superpositions supported by the software emulator in comparison with the creation of a more powerful processor.
Convolutional Dictionary Learning: Acceleration and Convergence
Chun, Il Yong; Fessler, Jeffrey A.
2018-04-01
Convolutional dictionary learning (CDL or sparsifying CDL) has many applications in image processing and computer vision. There has been growing interest in developing efficient algorithms for CDL, mostly relying on the augmented Lagrangian (AL) method or the variant alternating direction method of multipliers (ADMM). When their parameters are properly tuned, AL methods have shown fast convergence in CDL. However, the parameter tuning process is not trivial due to its data dependence and, in practice, the convergence of AL methods depends on the AL parameters for nonconvex CDL problems. To moderate these problems, this paper proposes a new practically feasible and convergent Block Proximal Gradient method using a Majorizer (BPG-M) for CDL. The BPG-M-based CDL is investigated with different block updating schemes and majorization matrix designs, and further accelerated by incorporating some momentum coefficient formulas and restarting techniques. All of the methods investigated incorporate a boundary artifacts removal (or, more generally, sampling) operator in the learning model. Numerical experiments show that, without needing any parameter tuning process, the proposed BPG-M approach converges more stably to desirable solutions of lower objective values than the existing state-of-the-art ADMM algorithm and its memory-efficient variant do. Compared to the ADMM approaches, the BPG-M method using a multi-block updating scheme is particularly useful in single-threaded CDL algorithm handling large datasets, due to its lower memory requirement and no polynomial computational complexity. Image denoising experiments show that, for relatively strong additive white Gaussian noise, the filters learned by BPG-M-based CDL outperform those trained by the ADMM approach.
Lidar Cloud Detection with Fully Convolutional Networks
Cromwell, E.; Flynn, D.
2017-12-01
The vertical distribution of clouds from active remote sensing instrumentation is a widely used data product from global atmospheric measuring sites. The presence of clouds can be expressed as a binary cloud mask and is a primary input for climate modeling efforts and cloud formation studies. Current cloud detection algorithms producing these masks do not accurately identify the cloud boundaries and tend to oversample or over-represent the cloud. This translates as uncertainty for assessing the radiative impact of clouds and tracking changes in cloud climatologies. The Atmospheric Radiation Measurement (ARM) program has over 20 years of micro-pulse lidar (MPL) and High Spectral Resolution Lidar (HSRL) instrument data and companion automated cloud mask product at the mid-latitude Southern Great Plains (SGP) and the polar North Slope of Alaska (NSA) atmospheric observatory. Using this data, we train a fully convolutional network (FCN) with semi-supervised learning to segment lidar imagery into geometric time-height cloud locations for the SGP site and MPL instrument. We then use transfer learning to train a FCN for (1) the MPL instrument at the NSA site and (2) for the HSRL. In our semi-supervised approach, we pre-train the classification layers of the FCN with weakly labeled lidar data. Then, we facilitate end-to-end unsupervised pre-training and transition to fully supervised learning with ground truth labeled data. Our goal is to improve the cloud mask accuracy and precision for the MPL instrument to 95% and 80%, respectively, compared to the current cloud mask algorithms of 89% and 50%. For the transfer learning based FCN for the HSRL instrument, our goal is to achieve a cloud mask accuracy of 90% and a precision of 80%.
Detecting atrial fibrillation by deep convolutional neural networks.
Xia, Yong; Wulan, Naren; Wang, Kuanquan; Zhang, Henggui
2018-02-01
Atrial fibrillation (AF) is the most common cardiac arrhythmia. The incidence of AF increases with age, causing high risks of stroke and increased morbidity and mortality. Efficient and accurate diagnosis of AF based on the ECG is valuable in clinical settings and remains challenging. In this paper, we proposed a novel method with high reliability and accuracy for AF detection via deep learning. The short-term Fourier transform (STFT) and stationary wavelet transform (SWT) were used to analyze ECG segments to obtain two-dimensional (2-D) matrix input suitable for deep convolutional neural networks. Then, two different deep convolutional neural network models corresponding to STFT output and SWT output were developed. Our new method did not require detection of P or R peaks, nor feature designs for classification, in contrast to existing algorithms. Finally, the performances of the two models were evaluated and compared with those of existing algorithms. Our proposed method demonstrated favorable performances on ECG segments as short as 5 s. The deep convolutional neural network using input generated by STFT, presented a sensitivity of 98.34%, specificity of 98.24% and accuracy of 98.29%. For the deep convolutional neural network using input generated by SWT, a sensitivity of 98.79%, specificity of 97.87% and accuracy of 98.63% was achieved. The proposed method using deep convolutional neural networks shows high sensitivity, specificity and accuracy, and, therefore, is a valuable tool for AF detection. Copyright © 2017 Elsevier Ltd. All rights reserved.
Video Super-Resolution via Bidirectional Recurrent Convolutional Networks.
Huang, Yan; Wang, Wei; Wang, Liang
2018-04-01
Super resolving a low-resolution video, namely video super-resolution (SR), is usually handled by either single-image SR or multi-frame SR. Single-Image SR deals with each video frame independently, and ignores intrinsic temporal dependency of video frames which actually plays a very important role in video SR. Multi-Frame SR generally extracts motion information, e.g., optical flow, to model the temporal dependency, but often shows high computational cost. Considering that recurrent neural networks (RNNs) can model long-term temporal dependency of video sequences well, we propose a fully convolutional RNN named bidirectional recurrent convolutional network for efficient multi-frame SR. Different from vanilla RNNs, 1) the commonly-used full feedforward and recurrent connections are replaced with weight-sharing convolutional connections. So they can greatly reduce the large number of network parameters and well model the temporal dependency in a finer level, i.e., patch-based rather than frame-based, and 2) connections from input layers at previous timesteps to the current hidden layer are added by 3D feedforward convolutions, which aim to capture discriminate spatio-temporal patterns for short-term fast-varying motions in local adjacent frames. Due to the cheap convolutional operations, our model has a low computational complexity and runs orders of magnitude faster than other multi-frame SR methods. With the powerful temporal dependency modeling, our model can super resolve videos with complex motions and achieve well performance.
Aerial Images and Convolutional Neural Network for Cotton Bloom Detection.
Xu, Rui; Li, Changying; Paterson, Andrew H; Jiang, Yu; Sun, Shangpeng; Robertson, Jon S
2017-01-01
Monitoring flower development can provide useful information for production management, estimating yield and selecting specific genotypes of crops. The main goal of this study was to develop a methodology to detect and count cotton flowers, or blooms, using color images acquired by an unmanned aerial system. The aerial images were collected from two test fields in 4 days. A convolutional neural network (CNN) was designed and trained to detect cotton blooms in raw images, and their 3D locations were calculated using the dense point cloud constructed from the aerial images with the structure from motion method. The quality of the dense point cloud was analyzed and plots with poor quality were excluded from data analysis. A constrained clustering algorithm was developed to register the same bloom detected from different images based on the 3D location of the bloom. The accuracy and incompleteness of the dense point cloud were analyzed because they affected the accuracy of the 3D location of the blooms and thus the accuracy of the bloom registration result. The constrained clustering algorithm was validated using simulated data, showing good efficiency and accuracy. The bloom count from the proposed method was comparable with the number counted manually with an error of -4 to 3 blooms for the field with a single plant per plot. However, more plots were underestimated in the field with multiple plants per plot due to hidden blooms that were not captured by the aerial images. The proposed methodology provides a high-throughput method to continuously monitor the flowering progress of cotton.
Nonclassical thermal-state superpositions: Analytical evolution law and decoherence behavior
Meng, Xiang-guo; Goan, Hsi-Sheng; Wang, Ji-suo; Zhang, Ran
2018-03-01
Employing the integration technique within normal products of bosonic operators, we present normal product representations of thermal-state superpositions and investigate their nonclassical features, such as quadrature squeezing, sub-Poissonian distribution, and partial negativity of the Wigner function. We also analytically and numerically investigate their evolution law and decoherence characteristics in an amplitude-decay model via the variations of the probability distributions and the negative volumes of Wigner functions in phase space. The results indicate that the evolution formulas of two thermal component states for amplitude decay can be viewed as the same integral form as a displaced thermal state ρ(V , d) , but governed by the combined action of photon loss and thermal noise. In addition, the larger values of the displacement d and noise V lead to faster decoherence for thermal-state superpositions.
A numerical dressing method for the nonlinear superposition of solutions of the KdV equation
International Nuclear Information System (INIS)
Trogdon, Thomas; Deconinck, Bernard
2014-01-01
In this paper we present the unification of two existing numerical methods for the construction of solutions of the Korteweg–de Vries (KdV) equation. The first method is used to solve the Cauchy initial-value problem on the line for rapidly decaying initial data. The second method is used to compute finite-genus solutions of the KdV equation. The combination of these numerical methods allows for the computation of exact solutions that are asymptotically (quasi-)periodic finite-gap solutions and are a nonlinear superposition of dispersive, soliton and (quasi-)periodic solutions in the finite (x, t)-plane. Such solutions are referred to as superposition solutions. We compute these solutions accurately for all values of x and t. (paper)
Optical threshold secret sharing scheme based on basic vector operations and coherence superposition
Deng, Xiaopeng; Wen, Wei; Mi, Xianwu; Long, Xuewen
2015-04-01
We propose, to our knowledge for the first time, a simple optical algorithm for secret image sharing with the (2,n) threshold scheme based on basic vector operations and coherence superposition. The secret image to be shared is firstly divided into n shadow images by use of basic vector operations. In the reconstruction stage, the secret image can be retrieved by recording the intensity of the coherence superposition of any two shadow images. Compared with the published encryption techniques which focus narrowly on information encryption, the proposed method can realize information encryption as well as secret sharing, which further ensures the safety and integrality of the secret information and prevents power from being kept centralized and abused. The feasibility and effectiveness of the proposed method are demonstrated by numerical results.
Analysis of magnetic damping problem by the coupled mode superposition method
International Nuclear Information System (INIS)
Horie, Tomoyoshi; Niho, Tomoya
1997-01-01
In this paper we describe the coupled mode superposition method for the magnetic damping problem, which is produced by the coupled effect between the deformation and the induced eddy current of the structures for future fusion reactors and magnetically levitated vehicles. The formulation of the coupled mode superposition method is based on the matrix equation for the eddy current and the structure using the coupled mode vectors. Symmetric form of the coupled matrix equation is obtained. Coupled problems of a thin plate are solved to verify the formulation and the computer code. These problems are solved efficiently by this method using only a few coupled modes. Consideration of the coupled mode vectors shows that the coupled effects are included completely in each coupled mode. (author)
Optical information encryption based on incoherent superposition with the help of the QR code
Qin, Yi; Gong, Qiong
2014-01-01
In this paper, a novel optical information encryption approach is proposed with the help of QR code. This method is based on the concept of incoherent superposition which we introduce for the first time. The information to be encrypted is first transformed into the corresponding QR code, and thereafter the QR code is further encrypted into two phase only masks analytically by use of the intensity superposition of two diffraction wave fields. The proposed method has several advantages over the previous interference-based method, such as a higher security level, a better robustness against noise attack, a more relaxed work condition, and so on. Numerical simulation results and actual smartphone collected results are shown to validate our proposal.
International Nuclear Information System (INIS)
Daoud, M.; Ahl Laamara, R.
2012-01-01
We give the explicit expressions of the pairwise quantum correlations present in superpositions of multipartite coherent states. A special attention is devoted to the evaluation of the geometric quantum discord. The dynamics of quantum correlations under a dephasing channel is analyzed. A comparison of geometric measure of quantum discord with that of concurrence shows that quantum discord in multipartite coherent states is more resilient to dissipative environments than is quantum entanglement. To illustrate our results, we consider some special superpositions of Weyl–Heisenberg, SU(2) and SU(1,1) coherent states which interpolate between Werner and Greenberger–Horne–Zeilinger states. -- Highlights: ► Pairwise quantum correlations multipartite coherent states. ► Explicit expression of geometric quantum discord. ► Entanglement sudden death and quantum discord robustness. ► Generalized coherent states interpolating between Werner and Greenberger–Horne–Zeilinger states
On Multiple Users Scheduling Using Superposition Coding over Rayleigh Fading Channels
Zafar, Ammar
2013-02-20
In this letter, numerical results are provided to analyze the gains of multiple users scheduling via superposition coding with successive interference cancellation in comparison with the conventional single user scheduling in Rayleigh blockfading broadcast channels. The information-theoretic optimal power, rate and decoding order allocation for the superposition coding scheme are considered and the corresponding histogram for the optimal number of scheduled users is evaluated. Results show that at optimality there is a high probability that only two or three users are scheduled per channel transmission block. Numerical results for the gains of multiple users scheduling in terms of the long term throughput under hard and proportional fairness as well as for fixed merit weights for the users are also provided. These results show that the performance gain of multiple users scheduling over single user scheduling increases when the total number of users in the network increases, and it can exceed 10% for high number of users
Energy Technology Data Exchange (ETDEWEB)
Daoud, M., E-mail: m_daoud@hotmail.com [Department of Physics, Faculty of Sciences, University Ibnou Zohr, Agadir (Morocco); Ahl Laamara, R., E-mail: ahllaamara@gmail.com [LPHE-Modeling and Simulation, Faculty of Sciences, University Mohammed V, Rabat (Morocco); Centre of Physics and Mathematics, CPM, CNESTEN, Rabat (Morocco)
2012-07-16
We give the explicit expressions of the pairwise quantum correlations present in superpositions of multipartite coherent states. A special attention is devoted to the evaluation of the geometric quantum discord. The dynamics of quantum correlations under a dephasing channel is analyzed. A comparison of geometric measure of quantum discord with that of concurrence shows that quantum discord in multipartite coherent states is more resilient to dissipative environments than is quantum entanglement. To illustrate our results, we consider some special superpositions of Weyl–Heisenberg, SU(2) and SU(1,1) coherent states which interpolate between Werner and Greenberger–Horne–Zeilinger states. -- Highlights: ► Pairwise quantum correlations multipartite coherent states. ► Explicit expression of geometric quantum discord. ► Entanglement sudden death and quantum discord robustness. ► Generalized coherent states interpolating between Werner and Greenberger–Horne–Zeilinger states.
Sanchez-Garcia, Manuel; Gardin, Isabelle; Lebtahi, Rachida; Dieudonné, Arnaud
2015-10-21
Two collapsed cone (CC) superposition algorithms have been implemented for radiopharmaceutical dosimetry of photon emitters. The straight CC (SCC) superposition method uses a water energy deposition kernel (EDKw) for each electron, positron and photon components, while the primary and scatter CC (PSCC) superposition method uses different EDKw for primary and once-scattered photons. PSCC was implemented only for photons originating from the nucleus, precluding its application to positron emitters. EDKw are linearly scaled by radiological distance, taking into account tissue density heterogeneities. The implementation was tested on 100, 300 and 600 keV mono-energetic photons and (18)F, (99m)Tc, (131)I and (177)Lu. The kernels were generated using the Monte Carlo codes MCNP and EGSnrc. The validation was performed on 6 phantoms representing interfaces between soft-tissues, lung and bone. The figures of merit were γ (3%, 3 mm) and γ (5%, 5 mm) criterions corresponding to the computation comparison on 80 absorbed doses (AD) points per phantom between Monte Carlo simulations and CC algorithms. PSCC gave better results than SCC for the lowest photon energy (100 keV). For the 3 isotopes computed with PSCC, the percentage of AD points satisfying the γ (5%, 5 mm) criterion was always over 99%. A still good but worse result was found with SCC, since at least 97% of AD-values verified the γ (5%, 5 mm) criterion, except a value of 57% for the (99m)Tc with the lung/bone interface. The CC superposition method for radiopharmaceutical dosimetry is a good alternative to Monte Carlo simulations while reducing computation complexity.
International Nuclear Information System (INIS)
Gajnutdinov, R.Kh.
1983-01-01
Possibility is studied to build the nonrelativistic scattering theory on the base of the general physical principles: causality, superposition, and unitarity, making no use of the Schroedinger formalism. The suggested approach is shown to be more general than the nonrelativistic scattering theory based on the Schroedinger equation. The approach is applied to build a model ofthe scattering theory for a system which consists of heavy nonrelativistic particles and a light relativistic particle
Sanchez-Garcia, Manuel; Gardin, Isabelle; Lebtahi, Rachida; Dieudonné, Arnaud
2015-10-01
Two collapsed cone (CC) superposition algorithms have been implemented for radiopharmaceutical dosimetry of photon emitters. The straight CC (SCC) superposition method uses a water energy deposition kernel (EDKw) for each electron, positron and photon components, while the primary and scatter CC (PSCC) superposition method uses different EDKw for primary and once-scattered photons. PSCC was implemented only for photons originating from the nucleus, precluding its application to positron emitters. EDKw are linearly scaled by radiological distance, taking into account tissue density heterogeneities. The implementation was tested on 100, 300 and 600 keV mono-energetic photons and 18F, 99mTc, 131I and 177Lu. The kernels were generated using the Monte Carlo codes MCNP and EGSnrc. The validation was performed on 6 phantoms representing interfaces between soft-tissues, lung and bone. The figures of merit were γ (3%, 3 mm) and γ (5%, 5 mm) criterions corresponding to the computation comparison on 80 absorbed doses (AD) points per phantom between Monte Carlo simulations and CC algorithms. PSCC gave better results than SCC for the lowest photon energy (100 keV). For the 3 isotopes computed with PSCC, the percentage of AD points satisfying the γ (5%, 5 mm) criterion was always over 99%. A still good but worse result was found with SCC, since at least 97% of AD-values verified the γ (5%, 5 mm) criterion, except a value of 57% for the 99mTc with the lung/bone interface. The CC superposition method for radiopharmaceutical dosimetry is a good alternative to Monte Carlo simulations while reducing computation complexity.
Seismic analysis of structures of nuclear power plants by Lanczos mode superposition method
International Nuclear Information System (INIS)
Coutinho, A.L.G.A.; Alves, J.L.D.; Landau, L.; Lima, E.C.P. de; Ebecken, N.F.F.
1986-01-01
The Lanczos Mode Superposition Method is applied in the seismic analysis of nuclear power plants. The coordinate transformation matrix is generated by the Lanczos algorithm. It is shown that, through a convenient choice of the starting vector of the algorithm, modes with participation factors are automatically selected. It is performed the Response Spectra analysis of a typical reactor building. The obtained results are compared with those determined by the classical aproach stressing the remarkable computer effectiveness of the proposed methodology. (Author) [pt
Wu, Kaifeng; Lim, Jaehoon; Klimov, Victor I
2017-08-22
Application of colloidal semiconductor quantum dots (QDs) in optical and optoelectronic devices is often complicated by unintentional generation of extra charges, which opens fast nonradiative Auger recombination pathways whereby the recombination energy of an exciton is quickly transferred to the extra carrier(s) and ultimately dissipated as heat. Previous studies of Auger recombination have primarily focused on neutral and, more recently, negatively charged multicarrier states. Auger dynamics of positively charged species remains more poorly explored due to difficulties in creating, stabilizing, and detecting excess holes in the QDs. Here we apply photochemical doping to prepare both negatively and positively charged CdSe/CdS QDs with two distinct core/shell interfacial profiles ("sharp" versus "smooth"). Using neutral and charged QD samples we evaluate Auger lifetimes of biexcitons, negative and positive trions (an exciton with an extra electron or a hole, respectively), and multiply negatively charged excitons. Using these measurements, we demonstrate that Auger decay of both neutral and charged multicarrier states can be presented as a superposition of independent elementary three-particle Auger events. As one of the manifestations of the superposition principle, we observe that the biexciton Auger decay rate can be presented as a sum of the Auger rates for independent negative and positive trion pathways. By comparing the measurements on the QDs with the "sharp" versus "smooth" interfaces, we also find that while affecting the absolute values of Auger lifetimes, manipulation of the shape of the confinement potential does not lead to violation of the superposition principle, which still allows us to accurately predict the biexciton Auger lifetimes based on the measured negative and positive trion dynamics. These findings indicate considerable robustness of the superposition principle as applied to Auger decay of charged and neutral multicarrier states
Energy Technology Data Exchange (ETDEWEB)
Surovtsev, A P; Golovanenko, S A; Sukhanov, V E; Kazantsev, V F
1983-12-01
Investigation results of kinetics and quality of carbon steel joints with the steel 12Kh18N10T, obtained by pressure welding with superposition of ultrasonic oscillations with the frequency 16.5-18.0 kHz are given. The effect of ultrasonic oscillations on the process of physical contact development of the surfaces welded, formation of microstructure and impact viscosity of the compound, is shown.
Invariant moments based convolutional neural networks for image analysis
Directory of Open Access Journals (Sweden)
Vijayalakshmi G.V. Mahesh
2017-01-01
Full Text Available The paper proposes a method using convolutional neural network to effectively evaluate the discrimination between face and non face patterns, gender classification using facial images and facial expression recognition. The novelty of the method lies in the utilization of the initial trainable convolution kernels coefficients derived from the zernike moments by varying the moment order. The performance of the proposed method was compared with the convolutional neural network architecture that used random kernels as initial training parameters. The multilevel configuration of zernike moments was significant in extracting the shape information suitable for hierarchical feature learning to carry out image analysis and classification. Furthermore the results showed an outstanding performance of zernike moment based kernels in terms of the computation time and classification accuracy.
Single image super-resolution based on convolutional neural networks
Zou, Lamei; Luo, Ming; Yang, Weidong; Li, Peng; Jin, Liujia
2018-03-01
We present a deep learning method for single image super-resolution (SISR). The proposed approach learns end-to-end mapping between low-resolution (LR) images and high-resolution (HR) images. The mapping is represented as a deep convolutional neural network which inputs the LR image and outputs the HR image. Our network uses 5 convolution layers, which kernels size include 5×5, 3×3 and 1×1. In our proposed network, we use residual-learning and combine different sizes of convolution kernels at the same layer. The experiment results show that our proposed method performs better than the existing methods in reconstructing quality index and human visual effects on benchmarked images.
Two new proofs of the test particle superposition principle of plasma kinetic theory
International Nuclear Information System (INIS)
Krommes, J.A.
1976-01-01
The test particle superposition principle of plasma kinetic theory is discussed in relation to the recent theory of two-time fluctuations in plasma given by Williams and Oberman. Both a new deductive and a new inductive proof of the principle are presented; the deductive approach appears here for the first time in the literature. The fundamental observation is that two-time expectations of one-body operators are determined completely in terms of the (x,v) phase space density autocorrelation, which to lowest order in the discreteness parameter obeys the linearized Vlasov equation with singular initial condition. For the deductive proof, this equation is solved formally using time-ordered operators, and the solution is then re-arranged into the superposition principle. The inductive proof is simpler than Rostoker's although similar in some ways; it differs in that first-order equations for pair correlation functions need not be invoked. It is pointed out that the superposition principle is also applicable to the short-time theory of neutral fluids
Sagnac interferometry with coherent vortex superposition states in exciton-polariton condensates
Moxley, Frederick Ira; Dowling, Jonathan P.; Dai, Weizhong; Byrnes, Tim
2016-05-01
We investigate prospects of using counter-rotating vortex superposition states in nonequilibrium exciton-polariton Bose-Einstein condensates for the purposes of Sagnac interferometry. We first investigate the stability of vortex-antivortex superposition states, and show that they survive at steady state in a variety of configurations. Counter-rotating vortex superpositions are of potential interest to gyroscope and seismometer applications for detecting rotations. Methods of improving the sensitivity are investigated by targeting high momentum states via metastable condensation, and the application of periodic lattices. The sensitivity of the polariton gyroscope is compared to its optical and atomic counterparts. Due to the large interferometer areas in optical systems and small de Broglie wavelengths for atomic BECs, the sensitivity per detected photon is found to be considerably less for the polariton gyroscope than with competing methods. However, polariton gyroscopes have an advantage over atomic BECs in a high signal-to-noise ratio, and have other practical advantages such as room-temperature operation, area independence, and robust design. We estimate that the final sensitivities including signal-to-noise aspects are competitive with existing methods.
Spacings and pair correlations for finite Bernoulli convolutions
International Nuclear Information System (INIS)
Benjamini, Itai; Solomyak, Boris
2009-01-01
We consider finite Bernoulli convolutions with a parameter 1/2 N . These sequences are uniformly distributed with respect to the infinite Bernoulli convolution measure ν λ , as N → ∞. Numerical evidence suggests that for a generic λ, the distribution of spacings between appropriately rescaled points is Poissonian. We obtain some partial results in this direction; for instance, we show that, on average, the pair correlations do not exhibit attraction or repulsion in the limit. On the other hand, for certain algebraic λ the behaviour is totally different
A New Reverberator Based on Variable Sparsity Convolution
DEFF Research Database (Denmark)
Holm-Rasmussen, Bo; Lehtonen, Heidi-Maria; Välimäki, Vesa
2013-01-01
FIR filter coefficients are selected from a velvet noise sequence, which consists of ones, minus ones, and zeros only. In this application, it is sufficient perceptually to use very sparse velvet noise sequences having only about 0.1 to 0.2% non-zero elements, with increasing sparsity along...... the impulse response. The algorithm yields a parametric approximation of the late part of the impulse response, which is more than 100 times more efficient computationally than the direct convolution. The computational load of the proposed algorithm is comparable to that of FFT-based partitioned convolution...
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.
Weed Growth Stage Estimator Using Deep Convolutional Neural Networks
DEFF Research Database (Denmark)
Teimouri, Nima; Dyrmann, Mads; Nielsen, Per Rydahl
2018-01-01
conditions with regards to soil types, resolution and light settings. Then, 9649 of these images were used for training the computer, which automatically divided the weeds into nine growth classes. The performance of this proposed convolutional neural network approach was evaluated on a further set of 2516...... in estimating the number of leaves and 96% accuracy when accepting a deviation of two leaves. These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species....
Deep Convolutional Neural Networks: Structure, Feature Extraction and Training
Directory of Open Access Journals (Sweden)
Namatēvs Ivars
2017-12-01
Full Text Available Deep convolutional neural networks (CNNs are aimed at processing data that have a known network like topology. They are widely used to recognise objects in images and diagnose patterns in time series data as well as in sensor data classification. The aim of the paper is to present theoretical and practical aspects of deep CNNs in terms of convolution operation, typical layers and basic methods to be used for training and learning. Some practical applications are included for signal and image classification. Finally, the present paper describes the proposed block structure of CNN for classifying crucial features from 3D sensor data.
Very deep recurrent convolutional neural network for object recognition
Brahimi, Sourour; Ben Aoun, Najib; Ben Amar, Chokri
2017-03-01
In recent years, Computer vision has become a very active field. This field includes methods for processing, analyzing, and understanding images. The most challenging problems in computer vision are image classification and object recognition. This paper presents a new approach for object recognition task. This approach exploits the success of the Very Deep Convolutional Neural Network for object recognition. In fact, it improves the convolutional layers by adding recurrent connections. This proposed approach was evaluated on two object recognition benchmarks: Pascal VOC 2007 and CIFAR-10. The experimental results prove the efficiency of our method in comparison with the state of the art methods.
Spectral interpolation - Zero fill or convolution. [image processing
Forman, M. L.
1977-01-01
Zero fill, or augmentation by zeros, is a method used in conjunction with fast Fourier transforms to obtain spectral spacing at intervals closer than obtainable from the original input data set. In the present paper, an interpolation technique (interpolation by repetitive convolution) is proposed which yields values accurate enough for plotting purposes and which lie within the limits of calibration accuracies. The technique is shown to operate faster than zero fill, since fewer operations are required. The major advantages of interpolation by repetitive convolution are that efficient use of memory is possible (thus avoiding the difficulties encountered in decimation in time FFTs) and that is is easy to implement.
Approaches to reducing photon dose calculation errors near metal implants
Energy Technology Data Exchange (ETDEWEB)
Huang, Jessie Y.; Followill, David S.; Howell, Rebecca M.; Mirkovic, Dragan; Kry, Stephen F., E-mail: sfkry@mdanderson.org [Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030 and Graduate School of Biomedical Sciences, The University of Texas Health Science Center Houston, Houston, Texas 77030 (United States); Liu, Xinming [Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030 and Graduate School of Biomedical Sciences, The University of Texas Health Science Center Houston, Houston, Texas 77030 (United States); Stingo, Francesco C. [Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, Texas 77030 and Graduate School of Biomedical Sciences, The University of Texas Health Science Center Houston, Houston, Texas 77030 (United States)
2016-09-15
Purpose: Dose calculation errors near metal implants are caused by limitations of the dose calculation algorithm in modeling tissue/metal interface effects as well as density assignment errors caused by imaging artifacts. The purpose of this study was to investigate two strategies for reducing dose calculation errors near metal implants: implementation of metal-based energy deposition kernels in the convolution/superposition (C/S) dose calculation method and use of metal artifact reduction methods for computed tomography (CT) imaging. Methods: Both error reduction strategies were investigated using a simple geometric slab phantom with a rectangular metal insert (composed of titanium or Cerrobend), as well as two anthropomorphic phantoms (one with spinal hardware and one with dental fillings), designed to mimic relevant clinical scenarios. To assess the dosimetric impact of metal kernels, the authors implemented titanium and silver kernels in a commercial collapsed cone C/S algorithm. To assess the impact of CT metal artifact reduction methods, the authors performed dose calculations using baseline imaging techniques (uncorrected 120 kVp imaging) and three commercial metal artifact reduction methods: Philips Healthcare’s O-MAR, GE Healthcare’s monochromatic gemstone spectral imaging (GSI) using dual-energy CT, and GSI with metal artifact reduction software (MARS) applied. For the simple geometric phantom, radiochromic film was used to measure dose upstream and downstream of metal inserts. For the anthropomorphic phantoms, ion chambers and radiochromic film were used to quantify the benefit of the error reduction strategies. Results: Metal kernels did not universally improve accuracy but rather resulted in better accuracy upstream of metal implants and decreased accuracy directly downstream. For the clinical cases (spinal hardware and dental fillings), metal kernels had very little impact on the dose calculation accuracy (<1.0%). Of the commercial CT artifact
Approaches to reducing photon dose calculation errors near metal implants
International Nuclear Information System (INIS)
Huang, Jessie Y.; Followill, David S.; Howell, Rebecca M.; Mirkovic, Dragan; Kry, Stephen F.; Liu, Xinming; Stingo, Francesco C.
2016-01-01
Purpose: Dose calculation errors near metal implants are caused by limitations of the dose calculation algorithm in modeling tissue/metal interface effects as well as density assignment errors caused by imaging artifacts. The purpose of this study was to investigate two strategies for reducing dose calculation errors near metal implants: implementation of metal-based energy deposition kernels in the convolution/superposition (C/S) dose calculation method and use of metal artifact reduction methods for computed tomography (CT) imaging. Methods: Both error reduction strategies were investigated using a simple geometric slab phantom with a rectangular metal insert (composed of titanium or Cerrobend), as well as two anthropomorphic phantoms (one with spinal hardware and one with dental fillings), designed to mimic relevant clinical scenarios. To assess the dosimetric impact of metal kernels, the authors implemented titanium and silver kernels in a commercial collapsed cone C/S algorithm. To assess the impact of CT metal artifact reduction methods, the authors performed dose calculations using baseline imaging techniques (uncorrected 120 kVp imaging) and three commercial metal artifact reduction methods: Philips Healthcare’s O-MAR, GE Healthcare’s monochromatic gemstone spectral imaging (GSI) using dual-energy CT, and GSI with metal artifact reduction software (MARS) applied. For the simple geometric phantom, radiochromic film was used to measure dose upstream and downstream of metal inserts. For the anthropomorphic phantoms, ion chambers and radiochromic film were used to quantify the benefit of the error reduction strategies. Results: Metal kernels did not universally improve accuracy but rather resulted in better accuracy upstream of metal implants and decreased accuracy directly downstream. For the clinical cases (spinal hardware and dental fillings), metal kernels had very little impact on the dose calculation accuracy (<1.0%). Of the commercial CT artifact
International Nuclear Information System (INIS)
He, Cenlin; Takano, Yoshi; Liou, Kuo-Nan; Yang, Ping; Li, Qinbin; Mackowski, Daniel W.
2016-01-01
We perform a comprehensive intercomparison of the geometric-optics surface-wave (GOS) approach, the superposition T-matrix method, and laboratory measurements for optical properties of fresh and coated/aged black carbon (BC) particles with complex structures. GOS and T-matrix calculations capture the measured optical (i.e., extinction, absorption, and scattering) cross sections of fresh BC aggregates, with 5–20% differences depending on particle size. We find that the T-matrix results tend to be lower than the measurements, due to uncertainty in theoretical approximations of realistic BC structures, particle property measurements, and numerical computations in the method. On the contrary, the GOS results are higher than the measurements (hence the T-matrix results) for BC radii 100 nm. We find good agreement (differences 100 nm. We find small deviations (≤10%) in asymmetry factors computed from the two methods for most BC coating structures and sizes, but several complex structures have 10–30% differences. This study provides the foundation for downstream application of the GOS approach in radiative transfer and climate studies. - Highlights: • The GOS and T-matrix methods capture laboratory measurements of BC optical properties. • The GOS results are consistent with the T-matrix results for BC optical properties. • BC optical properties vary remarkably with coating structures and sizes during aging.
Zaima, Kazunori; Sasaki, Koichi
2016-01-01
We investigated the transient phenomena in a premixed burner flame with the superposition of a pulsed dielectric barrier discharge (DBD). The length of the flame was shortened by the superposition of DBD, indicating the activation of combustion chemical reactions with the help of the plasma. In addition, we observed the modulation of the top position of the unburned gas region and the formations of local minimums in the axial distribution of the optical emission intensity of OH. These experim...
Discrete singular convolution for the generalized variable-coefficient ...
African Journals Online (AJOL)
Numerical solutions of the generalized variable-coefficient Korteweg-de Vries equation are obtained using a discrete singular convolution and a fourth order singly diagonally implicit Runge-Kutta method for space and time discretisation, respectively. The theoretical convergence of the proposed method is rigorously ...
Down image recognition based on deep convolutional neural network
Directory of Open Access Journals (Sweden)
Wenzhu Yang
2018-06-01
Full Text Available Since of the scale and the various shapes of down in the image, it is difficult for traditional image recognition method to correctly recognize the type of down image and get the required recognition accuracy, even for the Traditional Convolutional Neural Network (TCNN. To deal with the above problems, a Deep Convolutional Neural Network (DCNN for down image classification is constructed, and a new weight initialization method is proposed. Firstly, the salient regions of a down image were cut from the image using the visual saliency model. Then, these salient regions of the image were used to train a sparse autoencoder and get a collection of convolutional filters, which accord with the statistical characteristics of dataset. At last, a DCNN with Inception module and its variants was constructed. To improve the recognition accuracy, the depth of the network is deepened. The experiment results indicate that the constructed DCNN increases the recognition accuracy by 2.7% compared to TCNN, when recognizing the down in the images. The convergence rate of the proposed DCNN with the new weight initialization method is improved by 25.5% compared to TCNN. Keywords: Deep convolutional neural network, Weight initialization, Sparse autoencoder, Visual saliency model, Image recognition
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.
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...
An Interactive Graphics Program for Assistance in Learning Convolution.
Frederick, Dean K.; Waag, Gary L.
1980-01-01
A program has been written for the interactive computer graphics facility at Rensselaer Polytechnic Institute that is designed to assist the user in learning the mathematical technique of convolving two functions. Because convolution can be represented graphically by a sequence of steps involving folding, shifting, multiplying, and integration, it…
Diffraction and Dirchlet problem for parameter-elliptic convolution ...
African Journals Online (AJOL)
In this paper we evaluate the difference between the inverse operators of a Dirichlet problem and of a diffraction problem for parameter-elliptic convolution operators with constant symbols. We prove that the inverse operator of a Dirichlet problem can be obtained as a limit case of such a diffraction problem. Quaestiones ...
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...
A convolutional neural network to filter artifacts in spectroscopic MRI.
Gurbani, Saumya S; Schreibmann, Eduard; Maudsley, Andrew A; Cordova, James Scott; Soher, Brian J; Poptani, Harish; Verma, Gaurav; Barker, Peter B; Shim, Hyunsuk; Cooper, Lee A D
2018-03-09
Proton MRSI is a noninvasive modality capable of generating volumetric maps of in vivo tissue metabolism without the need for ionizing radiation or injected contrast agent. Magnetic resonance spectroscopic imaging has been shown to be a viable imaging modality for studying several neuropathologies. However, a key hurdle in the routine clinical adoption of MRSI is the presence of spectral artifacts that can arise from a number of sources, possibly leading to false information. A deep learning model was developed that was capable of identifying and filtering out poor quality spectra. The core of the model used a tiled convolutional neural network that analyzed frequency-domain spectra to detect artifacts. When compared with a panel of MRS experts, our convolutional neural network achieved high sensitivity and specificity with an area under the curve of 0.95. A visualization scheme was implemented to better understand how the convolutional neural network made its judgement on single-voxel or multivoxel MRSI, and the convolutional neural network was embedded into a pipeline capable of producing whole-brain spectroscopic MRI volumes in real time. The fully automated method for assessment of spectral quality provides a valuable tool to support clinical MRSI or spectroscopic MRI studies for use in fields such as adaptive radiation therapy planning. © 2018 International Society for Magnetic Resonance in Medicine.
Deep convolutional neural networks for detection of rail surface defects
Faghih Roohi, S.; Hajizadeh, S.; Nunez Vicencio, Alfredo; Babuska, R.; De Schutter, B.H.K.; Estevez, Pablo A.; Angelov, Plamen P.; Del Moral Hernandez, Emilio
2016-01-01
In this paper, we propose a deep convolutional neural network solution to the analysis of image data for the detection of rail surface defects. The images are obtained from many hours of automated video recordings. This huge amount of data makes it impossible to manually inspect the images and
Symbol Stream Combining in a Convolutionally Coded System
Mceliece, R. J.; Pollara, F.; Swanson, L.
1985-01-01
Symbol stream combining has been proposed as a method for arraying signals received at different antennas. If convolutional coding and Viterbi decoding are used, it is shown that a Viterbi decoder based on the proposed weighted sum of symbol streams yields maximum likelihood decisions.
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-level convolution formula for nuclear structure function
International Nuclear Information System (INIS)
Ma Boqiang
1990-01-01
A two-level convolution formula for the nuclear structure function is derived in considering the nucleus as a composite system of baryon-mesons which are also composite systems of quark-gluons again. The results show that the European Muon Colaboration effect can not be explained by the nuclear effects as nucleon Fermi motion and nuclear binding contributions
Plant species classification using deep convolutional neural network
DEFF Research Database (Denmark)
Dyrmann, Mads; Karstoft, Henrik; Midtiby, Henrik Skov
2016-01-01
Information on which weed species are present within agricultural fields is important for site specific weed management. This paper presents a method that is capable of recognising plant species in colour images by using a convolutional neural network. The network is built from scratch trained an...
The impact of dose calculation algorithms on partial and whole breast radiation treatment plans
International Nuclear Information System (INIS)
Basran, Parminder S; Zavgorodni, Sergei; Berrang, Tanya; Olivotto, Ivo A; Beckham, Wayne
2010-01-01
This paper compares the calculated dose to target and normal tissues when using pencil beam (PBC), superposition/convolution (AAA) and Monte Carlo (MC) algorithms for whole breast (WBI) and accelerated partial breast irradiation (APBI) treatment plans. Plans for 10 patients who met all dosimetry constraints on a prospective APBI protocol when using PBC calculations were recomputed with AAA and MC, keeping the monitor units and beam angles fixed. Similar calculations were performed for WBI plans on the same patients. Doses to target and normal tissue volumes were tested for significance using the paired Student's t-test. For WBI plans the average dose to target volumes when using PBC calculations was not significantly different than AAA calculations, the average PBC dose to the ipsilateral breast was 10.5% higher than the AAA calculations and the average MC dose to the ipsilateral breast was 11.8% lower than the PBC calculations. For ABPI plans there were no differences in dose to the planning target volume, ipsilateral breast, heart, ipsilateral lung, or contra-lateral lung. Although not significant, the maximum PBC dose to the contra-lateral breast was 1.9% higher than AAA and the PBC dose to the clinical target volume was 2.1% higher than AAA. When WBI technique is switched to APBI, there was significant reduction in dose to the ipsilateral breast when using PBC, a significant reduction in dose to the ipsilateral lung when using AAA, and a significant reduction in dose to the ipsilateral breast and lung and contra-lateral lung when using MC. There is very good agreement between PBC, AAA and MC for all target and most normal tissues when treating with APBI and WBI and most of the differences in doses to target and normal tissues are not clinically significant. However, a commonly used dosimetry constraint, as recommended by the ASTRO consensus document for APBI, that no point in the contra-lateral breast volume should receive >3% of the prescribed dose needs
International Nuclear Information System (INIS)
Song, William; Battista, Jerry; Van Dyk, Jake
2004-01-01
The convolution method can be used to model the effect of random geometric uncertainties into planned dose distributions used in radiation treatment planning. This is effectively done by linearly adding infinitesimally small doses, each with a particular geometric offset, over an assumed infinite number of fractions. However, this process inherently ignores the radiobiological dose-per-fraction effect since only the summed physical dose distribution is generated. The resultant potential error on predicted radiobiological outcome [quantified in this work with tumor control probability (TCP), equivalent uniform dose (EUD), normal tissue complication probability (NTCP), and generalized equivalent uniform dose (gEUD)] has yet to be thoroughly quantified. In this work, the results of a Monte Carlo simulation of geometric displacements are compared to those of the convolution method for random geometric uncertainties of 0, 1, 2, 3, 4, and 5 mm (standard deviation). The α/β CTV ratios of 0.8, 1.5, 3, 5, and 10 Gy are used to represent the range of radiation responses for different tumors, whereas a single α/β OAR ratio of 3 Gy is used to represent all the organs at risk (OAR). The analysis is performed on a four-field prostate treatment plan of 18 MV x rays. The fraction numbers are varied from 1-50, with isoeffective adjustments of the corresponding dose-per-fractions to maintain a constant tumor control, using the linear-quadratic cell survival model. The average differences in TCP and EUD of the target, and in NTCP and gEUD of the OAR calculated from the convolution and Monte Carlo methods reduced asymptotically as the total fraction number increased, with the differences reaching negligible levels beyond the treatment fraction number of ≥20. The convolution method generally overestimates the radiobiological indices, as compared to the Monte Carlo method, for the target volume, and underestimates those for the OAR. These effects are interconnected and attributed
Glue detection based on teaching points constraint and tracking model of pixel convolution
Geng, Lei; Ma, Xiao; Xiao, Zhitao; Wang, Wen
2018-01-01
On-line glue detection based on machine version is significant for rust protection and strengthening in car production. Shadow stripes caused by reflect light and unevenness of inside front cover of car reduce the accuracy of glue detection. In this paper, we propose an effective algorithm to distinguish the edges of the glue and shadow stripes. Teaching points are utilized to calculate slope between the two adjacent points. Then a tracking model based on pixel convolution along motion direction is designed to segment several local rectangular regions using distance. The distance is the height of rectangular region. The pixel convolution along the motion direction is proposed to extract edges of gules in local rectangular region. A dataset with different illumination and complexity shape stripes are used to evaluate proposed method, which include 500 thousand images captured from the camera of glue gun machine. Experimental results demonstrate that the proposed method can detect the edges of glue accurately. The shadow stripes are distinguished and removed effectively. Our method achieves the 99.9% accuracies for the image dataset.
Liu, Jia; Gong, Maoguo; Qin, Kai; Zhang, Puzhao
2018-03-01
We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on different dates. Most existing change detection methods are based on homogeneous images. Due to the complementary properties of optical and radar sensors, there is an increasing interest in change detection based on heterogeneous images. The proposed network is symmetric with each side consisting of one convolutional layer and several coupling layers. The two input images connected with the two sides of the network, respectively, are transformed into a feature space where their feature representations become more consistent. In this feature space, the different map is calculated, which then leads to the ultimate detection map by applying a thresholding algorithm. The network parameters are learned by optimizing a coupling function. The learning process is unsupervised, which is different from most existing change detection methods based on heterogeneous images. Experimental results on both homogenous and heterogeneous images demonstrate the promising performance of the proposed network compared with several existing approaches.
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
Nucleus-nucleus collision as superposition of nucleon-nucleus collisions
International Nuclear Information System (INIS)
Orlova, G.I.; Adamovich, M.I.; Aggarwal, M.M.
1999-01-01
Angular distributions of charged particles produced in 16 O and 32 S collisions with nuclear track emulsion were studied at momenta 4.5 and 200 A GeV/c. Comparison with the angular distributions of charged particles produced in proton-nucleus collisions at the same momentum allows to draw the conclusion, that the angular distributions in nucleus-nucleus collisions can be seen as superposition of the angular distributions in nucleon-nucleus collisions taken at the same impact parameter b NA , that is mean impact parameter between the participating projectile nucleons and the center of the target nucleus. (orig.)
Constructing petal modes from the coherent superposition of Laguerre-Gaussian modes
Naidoo, Darryl; Forbes, Andrew; Ait-Ameur, Kamel; Brunel, Marc
2011-03-01
An experimental approach in generating Petal-like transverse modes, which are similar to what is seen in porro-prism resonators, has been successfully demonstrated. We hypothesize that the petal-like structures are generated from a coherent superposition of Laguerre-Gaussian modes of zero radial order and opposite azimuthal order. To verify this hypothesis, visually based comparisons such as petal peak to peak diameter and the angle between adjacent petals are drawn between experimental data and simulated data. The beam quality factor of the Petal-like transverse modes and an inner product interaction is also experimentally compared to numerical results.
Experimental generation and application of the superposition of higher-order Bessel beams
CSIR Research Space (South Africa)
Dudley, Angela L
2009-07-01
Full Text Available Academy of Sciences of Belarus 4 School of Physics, University of Stellenbosch Presented at the 2009 South African Institute of Physics Annual Conference University of KwaZulu-Natal Durban, South Africa 6-10 July 2009 Page 2 © CSIR 2008... www.csir.co.za Generation of Bessel Fields: • METHOD 1: Ring Slit Aperture • METHOD 2: Axicon Adaptation of method 1 to produce superpositions of higher-order Bessel beams: J. Durnin, J.J. Miceli and J.H. Eberly, Phys. Rev. Lett. 58 1499...
Strategies for reducing basis set superposition error (BSSE) in O/AU and O/Ni
Shuttleworth, I.G.
2015-01-01
© 2015 Elsevier Ltd. All rights reserved. The effect of basis set superposition error (BSSE) and effective strategies for the minimisation have been investigated using the SIESTA-LCAO DFT package. Variation of the energy shift parameter ΔEPAO has been shown to reduce BSSE for bulk Au and Ni and across their oxygenated surfaces. Alternative strategies based on either the expansion or contraction of the basis set have been shown to be ineffective in reducing BSSE. Comparison of the binding energies for the surface systems obtained using LCAO were compared with BSSE-free plane wave energies.
Nucleus-Nucleus Collision as Superposition of Nucleon-Nucleus Collisions
International Nuclear Information System (INIS)
Orlova, G.I.; Adamovich, M.I.; Aggarwal, M.M.; Alexandrov, Y.A.; Andreeva, N.P.; Badyal, S.K.; Basova, E.S.; Bhalla, K.B.; Bhasin, A.; Bhatia, V.S.; Bradnova, V.; Bubnov, V.I.; Cai, X.; Chasnikov, I.Y.; Chen, G.M.; Chernova, L.P.; Chernyavsky, M.M.; Dhamija, S.; Chenawi, K.El; Felea, D.; Feng, S.Q.; Gaitinov, A.S.; Ganssauge, E.R.; Garpman, S.; Gerassimov, S.G.; Gheata, A.; Gheata, M.; Grote, J.; Gulamov, K.G.; Gupta, S.K.; Gupta, V.K.; Henjes, U.; Jakobsson, B.; Kanygina, E.K.; Karabova, M.; Kharlamov, S.P.; Kovalenko, A.D.; Krasnov, S.A.; Kumar, V.; Larionova, V.G.; Li, Y.X.; Liu, L.S.; Lokanathan, S.; Lord, J.J.; Lukicheva, N.S.; Lu, Y.; Luo, S.B.; Mangotra, L.K.; Manhas, I.; Mittra, I.S.; Musaeva, A.K.; Nasyrov, S.Z.; Navotny, V.S.; Nystrand, J.; Otterlund, I.; Peresadko, N.G.; Qian, W.Y.; Qin, Y.M.; Raniwala, R.; Rao, N.K.; Roeper, M.; Rusakova, V.V.; Saidkhanov, N.; Salmanova, N.A.; Seitimbetov, A.M.; Sethi, R.; Singh, B.; Skelding, D.; Soderstrem, K.; Stenlund, E.; Svechnikova, L.N.; Svensson, T.; Tawfik, A.M.; Tothova, M.; Tretyakova, M.I.; Trofimova, T.P.; Tuleeva, U.I.; Vashisht, Vani; Vokal, S.; Vrlakova, J.; Wang, H.Q.; Wang, X.R.; Weng, Z.Q.; Wilkes, R.J.; Yang, C.B.; Yin, Z.B.; Yu, L.Z.; Zhang, D.H.; Zheng, P.Y.; Zhokhova, S.I.; Zhou, D.C.
1999-01-01
Angular distributions of charged particles produced in 16 O and 32 S collisions with nuclear track emulsion were studied at momenta 4.5 and 200 A GeV/c. Comparison with the angular distributions of charged particles produced in proton-nucleus collisions at the same momentum allows to draw the conclusion, that the angular distributions in nucleus-nucleus collisions can be seen as superposition of the angular distributions in nucleon-nucleus collisions taken at the same impact parameter b NA , that is mean impact parameter between the participating projectile nucleons and the center of the target nucleus
Nucleus-Nucleus Collision as Superposition of Nucleon-Nucleus Collisions
Energy Technology Data Exchange (ETDEWEB)
Orlova, G I; Adamovich, M I; Aggarwal, M M; Alexandrov, Y A; Andreeva, N P; Badyal, S K; Basova, E S; Bhalla, K B; Bhasin, A; Bhatia, V S; Bradnova, V; Bubnov, V I; Cai, X; Chasnikov, I Y; Chen, G M; Chernova, L P; Chernyavsky, M M; Dhamija, S; Chenawi, K El; Felea, D; Feng, S Q; Gaitinov, A S; Ganssauge, E R; Garpman, S; Gerassimov, S G; Gheata, A; Gheata, M; Grote, J; Gulamov, K G; Gupta, S K; Gupta, V K; Henjes, U; Jakobsson, B; Kanygina, E K; Karabova, M; Kharlamov, S P; Kovalenko, A D; Krasnov, S A; Kumar, V; Larionova, V G; Li, Y X; Liu, L S; Lokanathan, S; Lord, J J; Lukicheva, N S; Lu, Y; Luo, S B; Mangotra, L K; Manhas, I; Mittra, I S; Musaeva, A K; Nasyrov, S Z; Navotny, V S; Nystrand, J; Otterlund, I; Peresadko, N G; Qian, W Y; Qin, Y M; Raniwala, R; Rao, N K; Roeper, M; Rusakova, V V; Saidkhanov, N; Salmanova, N A; Seitimbetov, A M; Sethi, R; Singh, B; Skelding, D; Soderstrem, K; Stenlund, E; Svechnikova, L N; Svensson, T; Tawfik, A M; Tothova, M; Tretyakova, M I; Trofimova, T P; Tuleeva, U I; Vashisht, Vani; Vokal, S; Vrlakova, J; Wang, H Q; Wang, X R; Weng, Z Q; Wilkes, R J; Yang, C B; Yin, Z B; Yu, L Z; Zhang, D H; Zheng, P Y; Zhokhova, S I; Zhou, D C
1999-03-01
Angular distributions of charged particles produced in {sup 16}O and {sup 32}S collisions with nuclear track emulsion were studied at momenta 4.5 and 200 A GeV/c. Comparison with the angular distributions of charged particles produced in proton-nucleus collisions at the same momentum allows to draw the conclusion, that the angular distributions in nucleus-nucleus collisions can be seen as superposition of the angular distributions in nucleon-nucleus collisions taken at the same impact parameter b{sub NA}, that is mean impact parameter between the participating projectile nucleons and the center of the target nucleus.
Double-contrast examination of the gastric antrum without Duodenal superposition
International Nuclear Information System (INIS)
Treugut, H.; Isper, J.
1980-01-01
By using a modified technique of double-contrast examination of the stomach it was possible in 75% to perform a study without superposition of the duodenum and jejunum on the distal stomach compared to 36% with the usual method. In this technique a small amount (50 ml) of Barium-suspension is given to the patient in left decubitus position by a straw or gastric tube after antiperistaltic medication. There was no difference in the quality of mucosa-coating compared to the technique using higher volumes of Barium. (orig.) [de
Teleportation of a Coherent Superposition State Via a nonmaximally Entangled Coherent Xhannel
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
@@ We investigate the problemm of teleportation of a superposition coherent state with nonmaximally entangled coherent channel. Two strategies are considered to complete the task. The first one uses entanglement concentration to purify the channel to a maximally entangled one. The second one teleports the state through the nonmaximally entangled coherent channel directly. We find that the probabilities of successful teleportations for the two strategies are depend on the amplitudes of the coherent states and the mean fidelity of teleportation using the first strategy is always less than that of the second strategy.
Long-term creep modeling of wood using time temperature superposition principle
Gamalath, Sandhya Samarasinghe
1991-01-01
Long-term creep and recovery models (master curves) were developed from short-term data using the time temperature superposition principle (TTSP) for kiln-dried southern pine loaded in compression parallel-to-grain and exposed to constant environmental conditions (~70Â°F, ~9%EMC). Short-term accelerated creep (17 hour) and recovery (35 hour) data were collected for each specimen at a range of temperature (70Â°F-150Â°F) and constant moisture condition of 9%. The compressive stra...
International Nuclear Information System (INIS)
Chernichenko, Yu.D.
2005-01-01
Within the relativistic quasipotential approach to quantum field theory, the relativistic inverse scattering problem is solved for the case where the total quasipotential describing the interaction of two relativistic spinless particles having different masses is a superposition of a nonlocal separable and a local quasipotential. It is assumed that the local component of the total quasipotential is known and that there exist bound states in this local component. It is shown that the nonlocal separable component of the total interaction can be reconstructed provided that the local component, an increment of the phase shift, and the energies of bound states are known
Strategies for reducing basis set superposition error (BSSE) in O/AU and O/Ni
Shuttleworth, I.G.
2015-11-01
© 2015 Elsevier Ltd. All rights reserved. The effect of basis set superposition error (BSSE) and effective strategies for the minimisation have been investigated using the SIESTA-LCAO DFT package. Variation of the energy shift parameter ΔEPAO has been shown to reduce BSSE for bulk Au and Ni and across their oxygenated surfaces. Alternative strategies based on either the expansion or contraction of the basis set have been shown to be ineffective in reducing BSSE. Comparison of the binding energies for the surface systems obtained using LCAO were compared with BSSE-free plane wave energies.
International Nuclear Information System (INIS)
Martini, F. de; Giuseppe, G. di
2001-01-01
A multiparticle quantum superposition state has been generated by a novel phase-selective parametric amplifier of an entangled two-photon state. This realization is expected to open a new field of investigations on the persistence of the validity of the standard quantum theory for systems of increasing complexity, in a quasi decoherence-free environment. Because of its nonlocal structure the new system is expected to play a relevant role in the modern endeavor on quantum information and in the basic physics of entanglement. (orig.)
Linear dynamic analysis of arbitrary thin shells modal superposition by using finite element method
International Nuclear Information System (INIS)
Goncalves Filho, O.J.A.
1978-11-01
The linear dynamic behaviour of arbitrary thin shells by the Finite Element Method is studied. Plane triangular elements with eighteen degrees of freedom each are used. The general equations of movement are obtained from the Hamilton Principle and solved by the Modal Superposition Method. The presence of a viscous type damping can be considered by means of percentages of the critical damping. An automatic computer program was developed to provide the vibratory properties and the dynamic response to several types of deterministic loadings, including temperature effects. The program was written in FORTRAN IV for the Burroughs B-6700 computer. (author)
International Nuclear Information System (INIS)
Streltsov, Alexej I.; Alon, Ofir E.; Cederbaum, Lorenz S.
2009-01-01
Scattering in one dimension of an attractive ultracold bosonic cloud from a barrier can lead to the formation of two nonoverlapping clouds. Once formed, the clouds travel with constant velocity, in general different in magnitude from that of the incoming cloud, and do not disperse. The phenomenon and its mechanism - transformation of kinetic energy to internal energy of the scattered cloud - are obtained by solving the time-dependent many-boson Schroedinger equation. The analysis of the wave function shows that the object formed corresponds to a quantum superposition state of two distinct wave packets traveling through real space.
International Nuclear Information System (INIS)
De Martini, Francesco; Sciarrino, Fabio; Spagnolo, Nicolo
2009-01-01
The high resilience to decoherence shown by a recently discovered macroscopic quantum superposition (MQS) generated by a quantum-injected optical parametric amplifier and involving a number of photons in excess of 5x10 4 motivates the present theoretical and numerical investigation. The results are analyzed in comparison with the properties of the MQS based on |α> and N-photon maximally entangled states (NOON), in the perspective of the comprehensive theory of the subject by Zurek. In that perspective the concepts of 'pointer state' and 'environment-induced superselection' are applied to the new scheme.
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
Directory of Open Access Journals (Sweden)
Takahashi Wataru
2012-02-01
Full Text Available Abstract Background The purpose of this study was to compare dose distributions from three different algorithms with the x-ray Voxel Monte Carlo (XVMC calculations, in actual computed tomography (CT scans for use in stereotactic radiotherapy (SRT of small lung cancers. Methods Slow CT scan of 20 patients was performed and the internal target volume (ITV was delineated on Pinnacle3. All plans were first calculated with a scatter homogeneous mode (SHM which is compatible with Clarkson algorithm using Pinnacle3 treatment planning system (TPS. The planned dose was 48 Gy in 4 fractions. In a second step, the CT images, structures and beam data were exported to other treatment planning systems (TPSs. Collapsed cone convolution (CCC from Pinnacle3, superposition (SP from XiO, and XVMC from Monaco were used for recalculating. The dose distributions and the Dose Volume Histograms (DVHs were compared with each other. Results The phantom test revealed that all algorithms could reproduce the measured data within 1% except for the SHM with inhomogeneous phantom. For the patient study, the SHM greatly overestimated the isocenter (IC doses and the minimal dose received by 95% of the PTV (PTV95 compared to XVMC. The differences in mean doses were 2.96 Gy (6.17% for IC and 5.02 Gy (11.18% for PTV95. The DVH's and dose distributions with CCC and SP were in agreement with those obtained by XVMC. The average differences in IC doses between CCC and XVMC, and SP and XVMC were -1.14% (p = 0.17, and -2.67% (p = 0.0036, respectively. Conclusions Our work clearly confirms that the actual practice of relying solely on a Clarkson algorithm may be inappropriate for SRT planning. Meanwhile, CCC and SP were close to XVMC simulations and actual dose distributions obtained in lung SRT.
Automatic superposition of drug molecules based on their common receptor site
Kato, Yuichi; Inoue, Atsushi; Yamada, Miho; Tomioka, Nobuo; Itai, Akiko
1992-10-01
We have prevously developed a new rational method for superposing molecules in terms of submolecular physical and chemical properties, but not in terms of atom positions or chemical structures as has been done in the conventional methods. The program was originally developed for interactive use on a three-dimensional graphic display, providing goodness-of-fit indices on molecular shape, hydrogen bonds, electrostatic interactions and others. Here, we report a new unbiased searching method for the best superposition of molecules, covering all the superposing modes and conformational freedom, as an additional function of the program. The function is based on a novel least-squares method which superposes the expected positions and orientations of hydrogen bonding partners in the receptor that are deduced from both molecules. The method not only gives reliability and reproducibility to the result of the superposition, but also allows us to save labor and time. It is demonstrated that this method is very efficient for finding the correct superposing mode in such systems where hydrogen bonds play important roles.
Noise-based logic: Binary, multi-valued, or fuzzy, with optional superposition of logic states
Energy Technology Data Exchange (ETDEWEB)
Kish, Laszlo B. [Texas A and M University, Department of Electrical and Computer Engineering, College Station, TX 77843-3128 (United States)], E-mail: laszlo.kish@ece.tamu.edu
2009-03-02
A new type of deterministic (non-probabilistic) computer logic system inspired by the stochasticity of brain signals is shown. The distinct values are represented by independent stochastic processes: independent voltage (or current) noises. The orthogonality of these processes provides a natural way to construct binary or multi-valued logic circuitry with arbitrary number N of logic values by using analog circuitry. Moreover, the logic values on a single wire can be made a (weighted) superposition of the N distinct logic values. Fuzzy logic is also naturally represented by a two-component superposition within the binary case (N=2). Error propagation and accumulation are suppressed. Other relevant advantages are reduced energy dissipation and leakage current problems, and robustness against circuit noise and background noises such as 1/f, Johnson, shot and crosstalk noise. Variability problems are also non-existent because the logic value is an AC signal. A similar logic system can be built with orthogonal sinusoidal signals (different frequency or orthogonal phase) however that has an extra 1/N type slowdown compared to the noise-based logic system with increasing number of N furthermore it is less robust against time delay effects than the noise-based counterpart.
Noise-based logic: Binary, multi-valued, or fuzzy, with optional superposition of logic states
International Nuclear Information System (INIS)
Kish, Laszlo B.
2009-01-01
A new type of deterministic (non-probabilistic) computer logic system inspired by the stochasticity of brain signals is shown. The distinct values are represented by independent stochastic processes: independent voltage (or current) noises. The orthogonality of these processes provides a natural way to construct binary or multi-valued logic circuitry with arbitrary number N of logic values by using analog circuitry. Moreover, the logic values on a single wire can be made a (weighted) superposition of the N distinct logic values. Fuzzy logic is also naturally represented by a two-component superposition within the binary case (N=2). Error propagation and accumulation are suppressed. Other relevant advantages are reduced energy dissipation and leakage current problems, and robustness against circuit noise and background noises such as 1/f, Johnson, shot and crosstalk noise. Variability problems are also non-existent because the logic value is an AC signal. A similar logic system can be built with orthogonal sinusoidal signals (different frequency or orthogonal phase) however that has an extra 1/N type slowdown compared to the noise-based logic system with increasing number of N furthermore it is less robust against time delay effects than the noise-based counterpart
Noise-based logic: Binary, multi-valued, or fuzzy, with optional superposition of logic states
Kish, Laszlo B.
2009-03-01
A new type of deterministic (non-probabilistic) computer logic system inspired by the stochasticity of brain signals is shown. The distinct values are represented by independent stochastic processes: independent voltage (or current) noises. The orthogonality of these processes provides a natural way to construct binary or multi-valued logic circuitry with arbitrary number N of logic values by using analog circuitry. Moreover, the logic values on a single wire can be made a (weighted) superposition of the N distinct logic values. Fuzzy logic is also naturally represented by a two-component superposition within the binary case ( N=2). Error propagation and accumulation are suppressed. Other relevant advantages are reduced energy dissipation and leakage current problems, and robustness against circuit noise and background noises such as 1/f, Johnson, shot and crosstalk noise. Variability problems are also non-existent because the logic value is an AC signal. A similar logic system can be built with orthogonal sinusoidal signals (different frequency or orthogonal phase) however that has an extra 1/N type slowdown compared to the noise-based logic system with increasing number of N furthermore it is less robust against time delay effects than the noise-based counterpart.
Directory of Open Access Journals (Sweden)
M. Saphiannikova
2012-06-01
Full Text Available The theoretical description of electrical properties of polymer melts, filled with attractively interacting conductive particles, represents a great challenge. Such filler particles tend to build a network-like structure which is very fragile and can be easily broken in a shear flow with shear rates of about 1 s–1. In this study, measured shear-induced changes in electrical conductivity of polymer composites are described using a superposition approach, in which the filler particles are separated into a highly conductive percolating and low conductive non-percolating phases. The latter is represented by separated well-dispersed filler particles. It is assumed that these phases determine the effective electrical properties of composites through a type of mixing rule involving the phase volume fractions. The conductivity of the percolating phase is described with the help of classical percolation theory, while the conductivity of non-percolating phase is given by the matrix conductivity enhanced by the presence of separate filler particles. The percolation theory is coupled with a kinetic equation for a scalar structural parameter which describes the current state of filler network under particular flow conditions. The superposition approach is applied to transient shear experiments carried out on polycarbonate composites filled with multi-wall carbon nanotubes.
Combining morphometric features and convolutional networks fusion for glaucoma diagnosis
Perdomo, Oscar; Arevalo, John; González, Fabio A.
2017-11-01
Glaucoma is an eye condition that leads to loss of vision and blindness. Ophthalmoscopy exam evaluates the shape, color and proportion between the optic disc and physiologic cup, but the lack of agreement among experts is still the main diagnosis problem. The application of deep convolutional neural networks combined with automatic extraction of features such as: the cup-to-disc distance in the four quadrants, the perimeter, area, eccentricity, the major radio, the minor radio in optic disc and cup, in addition to all the ratios among the previous parameters may help with a better automatic grading of glaucoma. This paper presents a strategy to merge morphological features and deep convolutional neural networks as a novel methodology to support the glaucoma diagnosis in eye fundus images.
Convolutional over Recurrent Encoder for Neural Machine Translation
Directory of Open Access Journals (Sweden)
Dakwale Praveen
2017-06-01
Full Text Available Neural machine translation is a recently proposed approach which has shown competitive results to traditional MT approaches. Standard neural MT is an end-to-end neural network where the source sentence is encoded by a recurrent neural network (RNN called encoder and the target words are predicted using another RNN known as decoder. Recently, various models have been proposed which replace the RNN encoder with a convolutional neural network (CNN. In this paper, we propose to augment the standard RNN encoder in NMT with additional convolutional layers in order to capture wider context in the encoder output. Experiments on English to German translation demonstrate that our approach can achieve significant improvements over a standard RNN-based baseline.
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.
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.
Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks.
Yu, Haiyang; Wu, Zhihai; Wang, Shuqin; Wang, Yunpeng; Ma, Xiaolei
2017-06-26
Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.
Deep learning for steganalysis via convolutional neural networks
Qian, Yinlong; Dong, Jing; Wang, Wei; Tan, Tieniu
2015-03-01
Current work on steganalysis for digital images is focused on the construction of complex handcrafted features. This paper proposes a new paradigm for steganalysis to learn features automatically via deep learning models. We novelly propose a customized Convolutional Neural Network for steganalysis. The proposed model can capture the complex dependencies that are useful for steganalysis. Compared with existing schemes, this model can automatically learn feature representations with several convolutional layers. The feature extraction and classification steps are unified under a single architecture, which means the guidance of classification can be used during the feature extraction step. We demonstrate the effectiveness of the proposed model on three state-of-theart spatial domain steganographic algorithms - HUGO, WOW, and S-UNIWARD. Compared to the Spatial Rich Model (SRM), our model achieves comparable performance on BOSSbase and the realistic and large ImageNet database.
ID card number detection algorithm based on convolutional neural network
Zhu, Jian; Ma, Hanjie; Feng, Jie; Dai, Leiyan
2018-04-01
In this paper, a new detection algorithm based on Convolutional Neural Network is presented in order to realize the fast and convenient ID information extraction in multiple scenarios. The algorithm uses the mobile device equipped with Android operating system to locate and extract the ID number; Use the special color distribution of the ID card, select the appropriate channel component; Use the image threshold segmentation, noise processing and morphological processing to take the binary processing for image; At the same time, the image rotation and projection method are used for horizontal correction when image was tilting; Finally, the single character is extracted by the projection method, and recognized by using Convolutional Neural Network. Through test shows that, A single ID number image from the extraction to the identification time is about 80ms, the accuracy rate is about 99%, It can be applied to the actual production and living environment.
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.
Airplane detection in remote sensing images using convolutional neural networks
Ouyang, Chao; Chen, Zhong; Zhang, Feng; Zhang, Yifei
2018-03-01
Airplane detection in remote sensing images remains a challenging problem and has also been taking a great interest to researchers. In this paper we propose an effective method to detect airplanes in remote sensing images using convolutional neural networks. Deep learning methods show greater advantages than the traditional methods with the rise of deep neural networks in target detection, and we give an explanation why this happens. To improve the performance on detection of airplane, we combine a region proposal algorithm with convolutional neural networks. And in the training phase, we divide the background into multi classes rather than one class, which can reduce false alarms. Our experimental results show that the proposed method is effective and robust in detecting airplane.
Rock images classification by using deep convolution neural network
Cheng, Guojian; Guo, Wenhui
2017-08-01
Granularity analysis is one of the most essential issues in authenticate under microscope. To improve the efficiency and accuracy of traditional manual work, an convolutional neural network based method is proposed for granularity analysis from thin section image, which chooses and extracts features from image samples while build classifier to recognize granularity of input image samples. 4800 samples from Ordos basin are used for experiments under colour spaces of HSV, YCbCr and RGB respectively. On the test dataset, the correct rate in RGB colour space is 98.5%, and it is believable in HSV and YCbCr colour space. The results show that the convolution neural network can classify the rock images with high reliability.
User-generated content curation with deep convolutional neural networks
Tous Liesa, Rubén; Wust, Otto; Gómez, Mauro; Poveda, Jonatan; Elena, Marc; Torres Viñals, Jordi; Makni, Mouna; Ayguadé Parra, Eduard
2016-01-01
In this paper, we report a work consisting in using deep convolutional neural networks (CNNs) for curating and filtering photos posted by social media users (Instagram and Twitter). The final goal is to facilitate searching and discovering user-generated content (UGC) with potential value for digital marketing tasks. The images are captured in real time and automatically annotated with multiple CNNs. Some of the CNNs perform generic object recognition tasks while others perform what we call v...
A quantum algorithm for Viterbi decoding of classical convolutional codes
Grice, Jon R.; Meyer, David A.
2014-01-01
We present a quantum Viterbi algorithm (QVA) with better than classical performance under certain conditions. In this paper the proposed algorithm is applied to decoding classical convolutional codes, for instance; large constraint length $Q$ and short decode frames $N$. Other applications of the classical Viterbi algorithm where $Q$ is large (e.g. speech processing) could experience significant speedup with the QVA. The QVA exploits the fact that the decoding trellis is similar to the butter...
Abnormality Detection in Mammography using Deep Convolutional Neural Networks
Xi, Pengcheng; Shu, Chang; Goubran, Rafik
2018-01-01
Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be tra...
Quantifying Translation-Invariance in Convolutional Neural Networks
Kauderer-Abrams, Eric
2017-01-01
A fundamental problem in object recognition is the development of image representations that are invariant to common transformations such as translation, rotation, and small deformations. There are multiple hypotheses regarding the source of translation invariance in CNNs. One idea is that translation invariance is due to the increasing receptive field size of neurons in successive convolution layers. Another possibility is that invariance is due to the pooling operation. We develop a simple ...
Fast convolutional sparse coding using matrix inversion lemma
Czech Academy of Sciences Publication Activity Database
Šorel, Michal; Šroubek, Filip
2016-01-01
Roč. 55, č. 1 (2016), s. 44-51 ISSN 1051-2004 R&D Projects: GA ČR GA13-29225S Institutional support: RVO:67985556 Keywords : Convolutional sparse coding * Feature learning * Deconvolution networks * Shift-invariant sparse coding Subject RIV: JD - Computer Applications, Robotics Impact factor: 2.337, year: 2016 http://library.utia.cas.cz/separaty/2016/ZOI/sorel-0459332.pdf
Learning Convolutional Text Representations for Visual Question Answering
Wang, Zhengyang; Ji, Shuiwang
2017-01-01
Visual question answering is a recently proposed artificial intelligence task that requires a deep understanding of both images and texts. In deep learning, images are typically modeled through convolutional neural networks, and texts are typically modeled through recurrent neural networks. While the requirement for modeling images is similar to traditional computer vision tasks, such as object recognition and image classification, visual question answering raises a different need for textual...
Shallow and deep convolutional networks for saliency prediction
Pan, Junting; Sayrol Clols, Elisa; Giró Nieto, Xavier; McGuinness, Kevin; O'Connor, Noel
2016-01-01
The prediction of salient areas in images has been traditionally addressed with hand-crafted features based on neuroscience principles. This paper, however, addresses the problem with a completely data-driven approach by training a convolutional neural network (convnet). The learning process is formulated as a minimization of a loss function that measures the Euclidean distance of the predicted saliency map with the provided ground truth. The recent publication of large datasets of saliency p...
Production and reception of meaningful sound in Foville's 'encompassing convolution'.
Schiller, F
1999-04-01
In the history of neurology. Achille Louis Foville (1799-1879) is a name deserving to be remembered. In the course of time, his circonvolution d'enceinte of 1844 (surrounding the Sylvian fissure) became the 'convolution encompassing' every aspect of aphasiology, including amusia, ie., the localization in a coherent semicircle of semicircle of cerebral cortext serving the production and perception of language, song and instrumental music in health and disease.
Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks
Shen, Li; Lin, Zhouchen; Huang, Qingming
2015-01-01
Learning deeper convolutional neural networks becomes a tendency in recent years. However, many empirical evidences suggest that performance improvement cannot be gained by simply stacking more layers. In this paper, we consider the issue from an information theoretical perspective, and propose a novel method Relay Backpropagation, that encourages the propagation of effective information through the network in training stage. By virtue of the method, we achieved the first place in ILSVRC 2015...
Maximum likelihood convolutional decoding (MCD) performance due to system losses
Webster, L.
1976-01-01
A model for predicting the computational performance of a maximum likelihood convolutional decoder (MCD) operating in a noisy carrier reference environment is described. This model is used to develop a subroutine that will be utilized by the Telemetry Analysis Program to compute the MCD bit error rate. When this computational model is averaged over noisy reference phase errors using a high-rate interpolation scheme, the results are found to agree quite favorably with experimental measurements.
General Dirichlet Series, Arithmetic Convolution Equations and Laplace Transforms
Czech Academy of Sciences Publication Activity Database
Glöckner, H.; Lucht, L.G.; Porubský, Štefan
2009-01-01
Roč. 193, č. 2 (2009), s. 109-129 ISSN 0039-3223 R&D Projects: GA ČR GA201/07/0191 Institutional research plan: CEZ:AV0Z10300504 Keywords : arithmetic function * Dirichlet convolution * polynomial equation * analytic equation * topological algebra * holomorphic functional calculus * implicit function theorem * Laplace transform * semigroup * complex measure Subject RIV: BA - General Mathematics Impact factor: 0.645, year: 2009 http://arxiv.org/abs/0712.3172
Solving singular convolution equations using the inverse fast Fourier transform
Czech Academy of Sciences Publication Activity Database
Krajník, E.; Montesinos, V.; Zizler, P.; Zizler, Václav
2012-01-01
Roč. 57, č. 5 (2012), s. 543-550 ISSN 0862-7940 R&D Projects: GA AV ČR IAA100190901 Institutional research plan: CEZ:AV0Z10190503 Keywords : singular convolution equations * fast Fourier transform * tempered distribution Subject RIV: BA - General Mathematics Impact factor: 0.222, year: 2012 http://www.springerlink.com/content/m8437t3563214048/
CICAAR - Convolutive ICA with an Auto-Regressive Inverse Model
DEFF Research Database (Denmark)
Dyrholm, Mads; Hansen, Lars Kai
2004-01-01
We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least square...... estimation. We demonstrate the method on synthetic data and finally separate speech and music in a real room recording....
Convolution Model of a Queueing System with the cFIFO Service Discipline
Directory of Open Access Journals (Sweden)
Sławomir Hanczewski
2016-01-01
Full Text Available This article presents an approximate convolution model of a multiservice queueing system with the continuous FIFO (cFIFO service discipline. The model makes it possible to service calls sequentially with variable bit rate, determined by unoccupied (free resources of the multiservice server. As compared to the FIFO discipline, the cFIFO queue utilizes the resources of a multiservice server more effectively. The assumption in the model is that the queueing system is offered a mixture of independent multiservice Bernoulli-Poisson-Pascal (BPP call streams. The article also discusses the results of modelling a number of queueing systems to which different, non-Poissonian, call streams are offered. To verify the accuracy of the model, the results of the analytical calculations are compared with the results of simulation experiments for a number of selected queueing systems. The study has confirmed the accuracy of all adopted theoretical assumptions for the proposed analytical model.
Xie, Tian; Grossman, Jeffrey C.
2018-04-01
The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with 1 04 data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.
AFM tip-sample convolution effects for cylinder protrusions
Shen, Jian; Zhang, Dan; Zhang, Fei-Hu; Gan, Yang
2017-11-01
A thorough understanding about the AFM tip geometry dependent artifacts and tip-sample convolution effect is essential for reliable AFM topographic characterization and dimensional metrology. Using rigid sapphire cylinder protrusions (diameter: 2.25 μm, height: 575 nm) as the model system, a systematic and quantitative study about the imaging artifacts of four types of tips-two different pyramidal tips, one tetrahedral tip and one super sharp whisker tip-is carried out through comparing tip geometry dependent variations in AFM topography of cylinders and constructing the rigid tip-cylinder convolution models. We found that the imaging artifacts and the tip-sample convolution effect are critically related to the actual inclination of the working cantilever, the tip geometry, and the obstructive contacts between the working tip's planes/edges and the cylinder. Artifact-free images can only be obtained provided that all planes and edges of the working tip are steeper than the cylinder sidewalls. The findings reported here will contribute to reliable AFM characterization of surface features of micron or hundreds of nanometers in height that are frequently met in semiconductor, biology and materials fields.
Traffic sign recognition based on deep convolutional neural network
Yin, Shi-hao; Deng, Ji-cai; Zhang, Da-wei; Du, Jing-yuan
2017-11-01
Traffic sign recognition (TSR) is an important component of automated driving systems. It is a rather challenging task to design a high-performance classifier for the TSR system. In this paper, we propose a new method for TSR system based on deep convolutional neural network. In order to enhance the expression of the network, a novel structure (dubbed block-layer below) which combines network-in-network and residual connection is designed. Our network has 10 layers with parameters (block-layer seen as a single layer): the first seven are alternate convolutional layers and block-layers, and the remaining three are fully-connected layers. We train our TSR network on the German traffic sign recognition benchmark (GTSRB) dataset. To reduce overfitting, we perform data augmentation on the training images and employ a regularization method named "dropout". The activation function we employ in our network adopts scaled exponential linear units (SELUs), which can induce self-normalizing properties. To speed up the training, we use an efficient GPU to accelerate the convolutional operation. On the test dataset of GTSRB, we achieve the accuracy rate of 99.67%, exceeding the state-of-the-art results.
Face recognition via Gabor and convolutional neural network
Lu, Tongwei; Wu, Menglu; Lu, Tao
2018-04-01
In recent years, the powerful feature learning and classification ability of convolutional neural network have attracted widely attention. Compared with the deep learning, the traditional machine learning algorithm has a good explanatory which deep learning does not have. Thus, In this paper, we propose a method to extract the feature of the traditional algorithm as the input of convolution neural network. In order to reduce the complexity of the network, the kernel function of Gabor wavelet is used to extract the feature from different position, frequency and direction of target image. It is sensitive to edge of image which can provide good direction and scale selection. The extraction of the image from eight directions on a scale are as the input of network that we proposed. The network have the advantage of weight sharing and local connection and texture feature of the input image can reduce the influence of facial expression, gesture and illumination. At the same time, we introduced a layer which combined the results of the pooling and convolution can extract deeper features. The training network used the open source caffe framework which is beneficial to feature extraction. The experiment results of the proposed method proved that the network structure effectively overcame the barrier of illumination and had a good robustness as well as more accurate and rapid than the traditional algorithm.
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.
Qian, Kun; Zhou, Huixin; Wang, Bingjian; Song, Shangzhen; Zhao, Dong
2017-11-01
Infrared dim and small target tracking is a great challenging task. The main challenge for target tracking is to account for appearance change of an object, which submerges in the cluttered background. An efficient appearance model that exploits both the global template and local representation over infrared image sequences is constructed for dim moving target tracking. A Sparsity-based Discriminative Classifier (SDC) and a Convolutional Network-based Generative Model (CNGM) are combined with a prior model. In the SDC model, a sparse representation-based algorithm is adopted to calculate the confidence value that assigns more weights to target templates than negative background templates. In the CNGM model, simple cell feature maps are obtained by calculating the convolution between target templates and fixed filters, which are extracted from the target region at the first frame. These maps measure similarities between each filter and local intensity patterns across the target template, therefore encoding its local structural information. Then, all the maps form a representation, preserving the inner geometric layout of a candidate template. Furthermore, the fixed target template set is processed via an efficient prior model. The same operation is applied to candidate templates in the CNGM model. The online update scheme not only accounts for appearance variations but also alleviates the migration problem. At last, collaborative confidence values of particles are utilized to generate particles' importance weights. Experiments on various infrared sequences have validated the tracking capability of the presented algorithm. Experimental results show that this algorithm runs in real-time and provides a higher accuracy than state of the art algorithms.
Some kinematics and dynamics from a superposition of two axisymmetric stellar systems
International Nuclear Information System (INIS)
Cubarsi i Morera, R.
1990-01-01
Some kinematic and dynamic implications of a superposition of two stellar systems are studied. In the general case of a stellar system in nonsteady states, Chandrasekhar's axially symmetrical model has been adopted for each one of the subsystems. The solution obtained for the potential function provides some kinematical constraints between the subsystems. These relationships are derived using the partial centered moments of the velocity distribution and the subcentroid velocities in order to study the velocity distribution. These relationships are used to prove that, only in a stellar system where the potential function is assumed to be stationary, the relative movement of the local subcentroids (not only in rotation), the vertex deviation phenomenon, and the whole set of the second-order-centered moments may be explained. A qualitative verification with three stellar samples in the solar neighborhood is carried out. 41 refs
Yin, H-L; Cao, W-F; Fu, Y; Tang, Y-L; Liu, Y; Chen, T-Y; Chen, Z-B
2014-09-15
Measurement-device-independent quantum key distribution (MDI-QKD) with decoy-state method is believed to be securely applied to defeat various hacking attacks in practical quantum key distribution systems. Recently, the coherent-state superpositions (CSS) have emerged as an alternative to single-photon qubits for quantum information processing and metrology. Here, in this Letter, CSS are exploited as the source in MDI-QKD. We present an analytical method that gives two tight formulas to estimate the lower bound of yield and the upper bound of bit error rate. We exploit the standard statistical analysis and Chernoff bound to perform the parameter estimation. Chernoff bound can provide good bounds in the long-distance MDI-QKD. Our results show that with CSS, both the security transmission distance and secure key rate are significantly improved compared with those of the weak coherent states in the finite-data case.
Energy Technology Data Exchange (ETDEWEB)
Lee, Su-Yong; Kim, Ho-Joon [Department of Physics, Texas A and M University at Qatar, P.O. Box 23874, Doha (Qatar); Ji, Se-Wan [School of Computational Sciences, Korea Institute for Advanced Study, Seoul 130-012 (Korea, Republic of); Nha, Hyunchul [Department of Physics, Texas A and M University at Qatar, P.O. Box 23874, Doha (Qatar); Institute fuer Quantenphysik, Universitaet Ulm, D-89069 Ulm (Germany)
2011-07-15
We investigate how the entanglement properties of a two-mode state can be improved by performing a coherent superposition operation ta+ra{sup {dagger}} of photon subtraction and addition, proposed by Lee and Nha [Phys. Rev. A 82, 053812 (2010)], on each mode. We show that the degree of entanglement, the Einstein-Podolsky-Rosen-type correlation, and the performance of quantum teleportation can be all enhanced for the output state when the coherent operation is applied to a two-mode squeezed state. The effects of the coherent operation are more prominent than those of the mere photon subtraction a and the addition a{sup {dagger}} particularly in the small-squeezing regime, whereas the optimal operation becomes the photon subtraction (case of r=0) in the large-squeezing regime.
A millimeter wave linear superposition oscillator in 0.18 μm CMOS technology
International Nuclear Information System (INIS)
Yan Dong; Mao Luhong; Su Qiujie; Xie Sheng; Zhang Shilin
2014-01-01
This paper presents a millimeter wave (mm-wave) oscillator that generates signal at 36.56 GHz. The mm-wave oscillator is realized in a UMC 0.18 μm CMOS process. The linear superposition (LS) technique breaks through the limit of cut-off frequency (f T ), and realizes a much higher oscillation than f T . Measurement results show that the LS oscillator produces a calibrated −37.17 dBm output power when biased at 1.8 V; the output power of fundamental signal is −10.85 dBm after calibration. The measured phase noise at 1 MHz frequency offset is −112.54 dBc/Hz at the frequency of 9.14 GHz. This circuit can be properly applied to mm-wave communication systems with advantages of low cost and high integration density. (semiconductor integrated circuits)
Superposition of two optical vortices with opposite integer or non-integer orbital angular momentum
Directory of Open Access Journals (Sweden)
Carlos Fernando Díaz Meza
2016-01-01
Full Text Available This work develops a brief proposal to achieve the superposition of two opposite vortex beams, both with integer or non-integer mean value of the orbital angular momentum. The first part is about the generation of this kind of spatial light distributions through a modified Brown and Lohmann’s hologram. The inclusion of a simple mathematical expression into the pixelated grid’s transmittance function, based in Fourier domain properties, shifts the diffraction orders counterclockwise and clockwise to the same point and allows the addition of different modes. The strategy is theoretically and experimentally validated for the case of two opposite rotation helical wavefronts.
Proportional fair scheduling with superposition coding in a cellular cooperative relay system
DEFF Research Database (Denmark)
Kaneko, Megumi; Hayashi, Kazunori; Popovski, Petar
2013-01-01
Many works have tackled on the problem of throughput and fairness optimization in cellular cooperative relaying systems. Considering firstly a two-user relay broadcast channel, we design a scheme based on superposition coding (SC) which maximizes the achievable sum-rate under a proportional...... fairness constraint. Unlike most relaying schemes where users are allocated orthogonally, our scheme serves the two users simultaneously on the same time-frequency resource unit by superposing their messages into three SC layers. The optimal power allocation parameters of each SC layer are derived...... by analysis. Next, we consider the general multi-user case in a cellular relay system, for which we design resource allocation algorithms based on proportional fair scheduling exploiting the proposed SC-based scheme. Numerical results show that the proposed algorithms allowing simultaneous user allocation...
Yi, Xingwen; Chen, Xuemei; Sharma, Dinesh; Li, Chao; Luo, Ming; Yang, Qi; Li, Zhaohui; Qiu, Kun
2014-06-02
Digital coherent superposition (DCS) provides an approach to combat fiber nonlinearities by trading off the spectrum efficiency. In analogy, we extend the concept of DCS to the optical OFDM subcarrier pairs with Hermitian symmetry to combat the linear and nonlinear phase noise. At the transmitter, we simply use a real-valued OFDM signal to drive a Mach-Zehnder (MZ) intensity modulator biased at the null point and the so-generated OFDM signal is Hermitian in the frequency domain. At receiver, after the conventional OFDM signal processing, we conduct DCS of the optical OFDM subcarrier pairs, which requires only conjugation and summation. We show that the inter-carrier-interference (ICI) due to phase noise can be reduced because of the Hermitain symmetry. In a simulation, this method improves the tolerance to the laser phase noise. In a nonlinear WDM transmission experiment, this method also achieves better performance under the influence of cross phase modulation (XPM).
The study on the Sensorless PMSM Control using the Superposition Theory
Energy Technology Data Exchange (ETDEWEB)
Hong, Joung Pyo [Changwon National University, Changwon (Korea); Kwon, Soon Jae [Pukung National University, Seoul (Korea); Kim, Gyu Seob; Sohn, Mu Heon; Kim, Jong Dal [Dongmyung College, Pusan (Korea)
2002-07-01
This study presents a solution to control a Permanent Magnet Synchronous Motor without sensors. The control method is the presented superposition principle. This method of sensorless theory is very simple to compute estimated angle. Therefore computing time to estimate angle is shorter than other sensorless method. The use of this system yields enhanced operations, fewer system components, lower system cost, energy efficient control system design and increased deficiency. A practical solution is described and results are given in this Study. The performance of a Sensorless architecture allows an intelligent approach to reduce the complete system costs of digital motion control applications using cheaper electrical motors without sensors. This paper deals with an overview of sensorless solutions in PMSM control applications whereby the focus will be the new controller without sensors and its applications. (author). 6 refs., 16 figs., 1 tab.
Yeom, Jeong Seon; Chu, Eunmi; Jung, Bang Chul; Jin, Hu
2018-02-10
In this paper, we propose a novel low-complexity multi-user superposition transmission (MUST) technique for 5G downlink networks, which allows multiple cell-edge users to be multiplexed with a single cell-center user. We call the proposed technique diversity-controlled MUST technique since the cell-center user enjoys the frequency diversity effect via signal repetition over multiple orthogonal frequency division multiplexing (OFDM) sub-carriers. We assume that a base station is equipped with a single antenna but users are equipped with multiple antennas. In addition, we assume that the quadrature phase shift keying (QPSK) modulation is used for users. We mathematically analyze the bit error rate (BER) of both cell-edge users and cell-center users, which is the first theoretical result in the literature to the best of our knowledge. The mathematical analysis is validated through extensive link-level simulations.
Strong-field effects in Rabi oscillations between a single state and a superposition of states
International Nuclear Information System (INIS)
Zhdanovich, S.; Milner, V.; Hepburn, J. W.
2011-01-01
Rabi oscillations of quantum population are known to occur in two-level systems driven by spectrally narrow laser fields. In this work we study Rabi oscillations induced by shaped broadband femtosecond laser pulses. Due to the broad spectral width of the driving field, the oscillations are initiated between a ground state and a coherent superposition of excited states, or a ''wave packet,'' rather than a single excited state. Our experiments reveal an intricate dependence of the wave-packet phase on the intensity of the laser field. We confirm numerically that the effect is associated with the strong-field nature of the interaction and provide a qualitative picture by invoking a simple theoretical model.
Quantum tele-amplification with a continuous-variable superposition state
DEFF Research Database (Denmark)
Neergaard-Nielsen, Jonas S.; Eto, Yujiro; Lee, Chang-Woo
2013-01-01
-enhanced functions such as coherent-state quantum computing (CSQC), quantum metrology and a quantum repeater could be realized in the networks. Optical cat states are now routinely generated in laboratories. An important next challenge is to use them for implementing the aforementioned functions. Here, we......Optical coherent states are classical light fields with high purity, and are essential carriers of information in optical networks. If these states could be controlled in the quantum regime, allowing for their quantum superposition (referred to as a Schrödinger-cat state), then novel quantum...... demonstrate a basic CSQC protocol, where a cat state is used as an entanglement resource for teleporting a coherent state with an amplitude gain. We also show how this can be extended to a loss-tolerant quantum relay of multi-ary phase-shift keyed coherent states. These protocols could be useful in both...
Convolution equations on lattices: periodic solutions with values in a prime characteristic field
Zaidenberg, Mikhail
2006-01-01
These notes are inspired by the theory of cellular automata. A linear cellular automaton on a lattice of finite rank or on a toric grid is a discrete dinamical system generated by a convolution operator with kernel concentrated in the nearest neighborhood of the origin. In the present paper we deal with general convolution operators. We propose an approach via harmonic analysis which works over a field of positive characteristic. It occurs that a standard spectral problem for a convolution op...
International Nuclear Information System (INIS)
Oshtrakh, M. I.; Semionkin, V. A.
2004-01-01
Moessbauer spectra of hemoglobins have some features in the range of liquid nitrogen temperature: a non-Lorentzian asymmetric line shape for oxyhemoglobins and symmetric Lorentzian line shape for deoxyhemoglobins. A comparison of the approximation of the hemoglobin Moessbauer spectra by a superposition of two quadrupole doublets and by a distribution of the quadrupole splitting demonstrates that a superposition of two quadrupole doublets is more reliable and may reflect the non-equivalent iron electronic structure and the stereochemistry in the α- and β-subunits of hemoglobin tetramers.
SU-E-J-60: Efficient Monte Carlo Dose Calculation On CPU-GPU Heterogeneous Systems
Energy Technology Data Exchange (ETDEWEB)
Xiao, K; Chen, D. Z; Hu, X. S [University of Notre Dame, Notre Dame, IN (United States); Zhou, B [Altera Corp., San Jose, CA (United States)
2014-06-01
Purpose: It is well-known that the performance of GPU-based Monte Carlo dose calculation implementations is bounded by memory bandwidth. One major cause of this bottleneck is the random memory writing patterns in dose deposition, which leads to several memory efficiency issues on GPU such as un-coalesced writing and atomic operations. We propose a new method to alleviate such issues on CPU-GPU heterogeneous systems, which achieves overall performance improvement for Monte Carlo dose calculation. Methods: Dose deposition is to accumulate dose into the voxels of a dose volume along the trajectories of radiation rays. Our idea is to partition this procedure into the following three steps, which are fine-tuned for CPU or GPU: (1) each GPU thread writes dose results with location information to a buffer on GPU memory, which achieves fully-coalesced and atomic-free memory transactions; (2) the dose results in the buffer are transferred to CPU memory; (3) the dose volume is constructed from the dose buffer on CPU. We organize the processing of all radiation rays into streams. Since the steps within a stream use different hardware resources (i.e., GPU, DMA, CPU), we can overlap the execution of these steps for different streams by pipelining. Results: We evaluated our method using a Monte Carlo Convolution Superposition (MCCS) program and tested our implementation for various clinical cases on a heterogeneous system containing an Intel i7 quad-core CPU and an NVIDIA TITAN GPU. Comparing with a straightforward MCCS implementation on the same system (using both CPU and GPU for radiation ray tracing), our method gained 2-5X speedup without losing dose calculation accuracy. Conclusion: The results show that our new method improves the effective memory bandwidth and overall performance for MCCS on the CPU-GPU systems. Our proposed method can also be applied to accelerate other Monte Carlo dose calculation approaches. This research was supported in part by NSF under Grants CCF
National Research Council Canada - National Science Library
Ong, Choon
1998-01-01
The performance analysis of a differential phase shift keyed (DPSK) communications system, operating in a Rayleigh fading environment, employing convolutional coding and diversity processing is presented...
Model selection for convolutive ICA with an application to spatiotemporal analysis of EEG
DEFF Research Database (Denmark)
Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai
2007-01-01
We present a new algorithm for maximum likelihood convolutive independent component analysis (ICA) in which components are unmixed using stable autoregressive filters determined implicitly by estimating a convolutive model of the mixing process. By introducing a convolutive mixing model...... for the components, we show how the order of the filters in the model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving a subspace of independent components in electroencephalography (EEG). Initial results suggest that in some cases, convolutive mixing may...
HETERO code, heterogeneous procedure for reactor calculation
International Nuclear Information System (INIS)
Jovanovic, S.M.; Raisic, N.M.
1966-11-01
This report describes the procedure for calculating the parameters of heterogeneous reactor system taking into account the interaction between fuel elements related to established geometry. First part contains the analysis of single fuel element in a diffusion medium, and criticality condition of the reactor system described by superposition of elements interactions. the possibility of performing such analysis by determination of heterogeneous system lattice is described in the second part. Computer code HETERO with the code KETAP (calculation of criticality factor η n and flux distribution) is part of this report together with the example of RB reactor square lattice
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.
Czech Academy of Sciences Publication Activity Database
Červený, V.; Pšenčík, Ivan
2016-01-01
Roč. 26 (2016), s. 131-153 ISSN 2336-3827 R&D Projects: GA ČR(CZ) GA16-05237S Institutional support: RVO:67985530 Keywords : elastodynamic Green function * inhomogeneous anisotropic media * integral superposition of Gaussian beams Subject RIV: DC - Siesmology, Volcanology, Earth Structure
Wei, Jianing; Bouman, Charles A; Allebach, Jan P
2014-05-01
Many imaging applications require the implementation of space-varying convolution for accurate restoration and reconstruction of images. Here, we use the term space-varying convolution to refer to linear operators whose impulse response has slow spatial variation. In addition, these space-varying convolution operators are often dense, so direct implementation of the convolution operator is typically computationally impractical. One such example is the problem of stray light reduction in digital cameras, which requires the implementation of a dense space-varying deconvolution operator. However, other inverse problems, such as iterative tomographic reconstruction, can also depend on the implementation of dense space-varying convolution. While space-invariant convolution can be efficiently implemented with the fast Fourier transform, this approach does not work for space-varying operators. So direct convolution is often the only option for implementing space-varying convolution. In this paper, we develop a general approach to the efficient implementation of space-varying convolution, and demonstrate its use in the application of stray light reduction. Our approach, which we call matrix source coding, is based on lossy source coding of the dense space-varying convolution matrix. Importantly, by coding the transformation matrix, we not only reduce the memory required to store it; we also dramatically reduce the computation required to implement matrix-vector products. Our algorithm is able to reduce computation by approximately factoring the dense space-varying convolution operator into a product of sparse transforms. Experimental results show that our method can dramatically reduce the computation required for stray light reduction while maintaining high accuracy.
Fast Convolutional Sparse Coding in the Dual Domain
Affara, Lama Ahmed
2017-09-27
Convolutional sparse coding (CSC) is an important building block of many computer vision applications ranging from image and video compression to deep learning. We present two contributions to the state of the art in CSC. First, we significantly speed up the computation by proposing a new optimization framework that tackles the problem in the dual domain. Second, we extend the original formulation to higher dimensions in order to process a wider range of inputs, such as color inputs, or HOG features. Our results show a significant speedup compared to the current state of the art in CSC.
Phase transitions in glassy systems via convolutional neural networks
Fang, Chao
Machine learning is a powerful approach commonplace in industry to tackle large data sets. Most recently, it has found its way into condensed matter physics, allowing for the first time the study of, e.g., topological phase transitions and strongly-correlated electron systems. The study of spin glasses is plagued by finite-size effects due to the long thermalization times needed. Here we use convolutional neural networks in an attempt to detect a phase transition in three-dimensional Ising spin glasses. Our results are compared to traditional approaches.
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.
Fast Convolutional Sparse Coding in the Dual Domain
Affara, Lama Ahmed; Ghanem, Bernard; Wonka, Peter
2017-01-01
Convolutional sparse coding (CSC) is an important building block of many computer vision applications ranging from image and video compression to deep learning. We present two contributions to the state of the art in CSC. First, we significantly speed up the computation by proposing a new optimization framework that tackles the problem in the dual domain. Second, we extend the original formulation to higher dimensions in order to process a wider range of inputs, such as color inputs, or HOG features. Our results show a significant speedup compared to the current state of the art in CSC.
Salient regions detection using convolutional neural networks and color volume
Liu, Guang-Hai; Hou, Yingkun
2018-03-01
Convolutional neural network is an important technique in machine learning, pattern recognition and image processing. In order to reduce the computational burden and extend the classical LeNet-5 model to the field of saliency detection, we propose a simple and novel computing model based on LeNet-5 network. In the proposed model, hue, saturation and intensity are utilized to extract depth cues, and then we integrate depth cues and color volume to saliency detection following the basic structure of the feature integration theory. Experimental results show that the proposed computing model outperforms some existing state-of-the-art methods on MSRA1000 and ECSSD datasets.
Traffic sign classification with dataset augmentation and convolutional neural network
Tang, Qing; Kurnianggoro, Laksono; Jo, Kang-Hyun
2018-04-01
This paper presents a method for traffic sign classification using a convolutional neural network (CNN). In this method, firstly we transfer a color image into grayscale, and then normalize it in the range (-1,1) as the preprocessing step. To increase robustness of classification model, we apply a dataset augmentation algorithm and create new images to train the model. To avoid overfitting, we utilize a dropout module before the last fully connection layer. To assess the performance of the proposed method, the German traffic sign recognition benchmark (GTSRB) dataset is utilized. Experimental results show that the method is effective in classifying traffic signs.
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
The Use of Finite Fields and Rings to Compute Convolutions
1975-06-06
showed in Ref. 1 that the convolution of two finite sequences of integers (a, ) and (b, ) for k = 1, 2, . . ., d can be obtained as the inverse transform of...since the T.’S are all distinct. Thus T~ exists and (7) can be solved as a = T A the inverse " transform . Next let us impose on (7) the...the inverse transform d-1 Cn= (d) I Cka k=0 If an a can be found so that multiplications by powers of a are simple in hardware, the
Tandem mass spectrometry data quality assessment by self-convolution.
Choo, Keng Wah; Tham, Wai Mun
2007-09-20
Many algorithms have been developed for deciphering the tandem mass spectrometry (MS) data sets. They can be essentially clustered into two classes. The first performs searches on theoretical mass spectrum database, while the second based itself on de novo sequencing from raw mass spectrometry data. It was noted that the quality of mass spectra affects significantly the protein identification processes in both instances. This prompted the authors to explore ways to measure the quality of MS data sets before subjecting them to the protein identification algorithms, thus allowing for more meaningful searches and increased confidence level of proteins identified. The proposed method measures the qualities of MS data sets based on the symmetric property of b- and y-ion peaks present in a MS spectrum. Self-convolution on MS data and its time-reversal copy was employed. Due to the symmetric nature of b-ions and y-ions peaks, the self-convolution result of a good spectrum would produce a highest mid point intensity peak. To reduce processing time, self-convolution was achieved using Fast Fourier Transform and its inverse transform, followed by the removal of the "DC" (Direct Current) component and the normalisation of the data set. The quality score was defined as the ratio of the intensity at the mid point to the remaining peaks of the convolution result. The method was validated using both theoretical mass spectra, with various permutations, and several real MS data sets. The results were encouraging, revealing a high percentage of positive prediction rates for spectra with good quality scores. We have demonstrated in this work a method for determining the quality of tandem MS data set. By pre-determining the quality of tandem MS data before subjecting them to protein identification algorithms, spurious protein predictions due to poor tandem MS data are avoided, giving scientists greater confidence in the predicted results. We conclude that the algorithm performs well
Classifying medical relations in clinical text via convolutional neural networks.
He, Bin; Guan, Yi; Dai, Rui
2018-05-16
Deep learning research on relation classification has achieved solid performance in the general domain. This study proposes a convolutional neural network (CNN) architecture with a multi-pooling operation for medical relation classification on clinical records and explores a loss function with a category-level constraint matrix. Experiments using the 2010 i2b2/VA relation corpus demonstrate these models, which do not depend on any external features, outperform previous single-model methods and our best model is competitive with the existing ensemble-based method. Copyright © 2018. Published by Elsevier B.V.
Weed Growth Stage Estimator Using Deep Convolutional Neural Networks
DEFF Research Database (Denmark)
Teimouri, Nima; Dyrmann, Mads; Nielsen, Per Rydahl
2018-01-01
This study outlines a new method of automatically estimating weed species and growth stages (from cotyledon until eight leaves are visible) of in situ images covering 18 weed species or families. Images of weeds growing within a variety of crops were gathered across variable environmental conditi...... in estimating the number of leaves and 96% accuracy when accepting a deviation of two leaves. These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species....
CPU architecture for a fast and energy-saving calculation of convolution neural networks
Knoll, Florian J.; Grelcke, Michael; Czymmek, Vitali; Holtorf, Tim; Hussmann, Stephan
2017-06-01
One of the most difficult problem in the use of artificial neural networks is the computational capacity. Although large search engine companies own specially developed hardware to provide the necessary computing power, for the conventional user only remains the state of the art method, which is the use of a graphic processing unit (GPU) as a computational basis. Although these processors are well suited for large matrix computations, they need massive energy. Therefore a new processor on the basis of a field programmable gate array (FPGA) has been developed and is optimized for the application of deep learning. This processor is presented in this paper. The processor can be adapted for a particular application (in this paper to an organic farming application). The power consumption is only a fraction of a GPU application and should therefore be well suited for energy-saving applications.
A deterministic partial differential equation model for dose calculation in electron radiotherapy.
Duclous, R; Dubroca, B; Frank, M
2010-07-07
High-energy ionizing radiation is a prominent modality for the treatment of many cancers. The approaches to electron dose calculation can be categorized into semi-empirical models (e.g. Fermi-Eyges, convolution-superposition) and probabilistic methods (e.g.Monte Carlo). A third approach to dose calculation has only recently attracted attention in the medical physics community. This approach is based on the deterministic kinetic equations of radiative transfer. We derive a macroscopic partial differential equation model for electron transport in tissue. This model involves an angular closure in the phase space. It is exact for the free streaming and the isotropic regime. We solve it numerically by a newly developed HLLC scheme based on Berthon et al (2007 J. Sci. Comput. 31 347-89) that exactly preserves the key properties of the analytical solution on the discrete level. We discuss several test cases taken from the medical physics literature. A test case with an academic Henyey-Greenstein scattering kernel is considered. We compare our model to a benchmark discrete ordinate solution. A simplified model of electron interactions with tissue is employed to compute the dose of an electron beam in a water phantom, and a case of irradiation of the vertebral column. Here our model is compared to the PENELOPE Monte Carlo code. In the academic example, the fluences computed with the new model and a benchmark result differ by less than 1%. The depths at half maximum differ by less than 0.6%. In the two comparisons with Monte Carlo, our model gives qualitatively reasonable dose distributions. Due to the crude interaction model, these so far do not have the accuracy needed in clinical practice. However, the new model has a computational cost that is less than one-tenth of the cost of a Monte Carlo simulation. In addition, simulations can be set up in a similar way as a Monte Carlo simulation. If more detailed effects such as coupled electron-photon transport, bremsstrahlung
A deterministic partial differential equation model for dose calculation in electron radiotherapy
Duclous, R.; Dubroca, B.; Frank, M.
2010-07-01
High-energy ionizing radiation is a prominent modality for the treatment of many cancers. The approaches to electron dose calculation can be categorized into semi-empirical models (e.g. Fermi-Eyges, convolution-superposition) and probabilistic methods (e.g. Monte Carlo). A third approach to dose calculation has only recently attracted attention in the medical physics community. This approach is based on the deterministic kinetic equations of radiative transfer. We derive a macroscopic partial differential equation model for electron transport in tissue. This model involves an angular closure in the phase space. It is exact for the free streaming and the isotropic regime. We solve it numerically by a newly developed HLLC scheme based on Berthon et al (2007 J. Sci. Comput. 31 347-89) that exactly preserves the key properties of the analytical solution on the discrete level. We discuss several test cases taken from the medical physics literature. A test case with an academic Henyey-Greenstein scattering kernel is considered. We compare our model to a benchmark discrete ordinate solution. A simplified model of electron interactions with tissue is employed to compute the dose of an electron beam in a water phantom, and a case of irradiation of the vertebral column. Here our model is compared to the PENELOPE Monte Carlo code. In the academic example, the fluences computed with the new model and a benchmark result differ by less than 1%. The depths at half maximum differ by less than 0.6%. In the two comparisons with Monte Carlo, our model gives qualitatively reasonable dose distributions. Due to the crude interaction model, these so far do not have the accuracy needed in clinical practice. However, the new model has a computational cost that is less than one-tenth of the cost of a Monte Carlo simulation. In addition, simulations can be set up in a similar way as a Monte Carlo simulation. If more detailed effects such as coupled electron-photon transport, bremsstrahlung
Study of dose calculation and beam parameters optimization with genetic algorithm in IMRT
International Nuclear Information System (INIS)
Chen Chaomin; Tang Mutao; Zhou Linghong; Lv Qingwen; Wang Zhuoyu; Chen Guangjie
2006-01-01
Objective: To study the construction of dose calculation model and the method of automatic beam parameters selection in IMRT. Methods: The three-dimension convolution dose calculation model of photon was constructed with the methods of Fast Fourier Transform. The objective function based on dose constrain was used to evaluate the fitness of individuals. The beam weights were optimized with genetic algorithm. Results: After 100 iterative analyses, the treatment planning system produced highly conformal and homogeneous dose distributions. Conclusion: the throe-dimension convolution dose calculation model of photon gave more accurate results than the conventional models; genetic algorithm is valid and efficient in IMRT beam parameters optimization. (authors)
Noise-based logic hyperspace with the superposition of 2{sup N} states in a single wire
Energy Technology Data Exchange (ETDEWEB)
Kish, Laszlo B. [Texas A and M University, Department of Electrical and Computer Engineering, College Station, TX 77843-3128 (United States)], E-mail: laszlo.kish@ece.tamu.edu; Khatri, Sunil; Sethuraman, Swaminathan [Texas A and M University, Department of Electrical and Computer Engineering, College Station, TX 77843-3128 (United States)
2009-05-11
In the introductory paper [L.B. Kish, Phys. Lett. A 373 (2009) 911], about noise-based logic, we showed how simple superpositions of single logic basis vectors can be achieved in a single wire. The superposition components were the N orthogonal logic basis vectors. Supposing that the different logic values have 'on/off' states only, the resultant discrete superposition state represents a single number with N bit accuracy in a single wire, where N is the number of orthogonal logic vectors in the base. In the present Letter, we show that the logic hyperspace (product) vectors defined in the introductory paper can be generalized to provide the discrete superposition of 2{sup N} orthogonal system states. This is equivalent to a multi-valued logic system with 2{sup 2{sup N}} logic values per wire. This is a similar situation to quantum informatics with N qubits, and hence we introduce the notion of noise-bit. This system has major differences compared to quantum informatics. The noise-based logic system is deterministic and each superposition element is instantly accessible with the high digital accuracy, via a real hardware parallelism, without decoherence and error correction, and without the requirement of repeating the logic operation many times to extract the probabilistic information. Moreover, the states in noise-based logic do not have to be normalized, and non-unitary operations can also be used. As an example, we introduce a string search algorithm which is O({radical}(M)) times faster than Grover's quantum algorithm (where M is the number of string entries), while it has the same hardware complexity class as the quantum algorithm.
Energy Technology Data Exchange (ETDEWEB)
Oyewale, S [Cancer Centers of Southwest Oklahoma, Lawton, OK (United States); Pokharel, S [21st Century Oncology, Naples, FL (United States); Rana, S [ProCure Proton Therapy Center, Oklahoma City, OK (United States)
2015-06-15
Purpose: To compare the percentage depth dose (PDD) computational accuracy of Adaptive Convolution (AC) and Collapsed Cone Convolution (CCC) algorithms in the presence of air gaps. Methods: A 30×30×30 cm{sup 3} solid water phantom with two 5cm air gaps was scanned with a CT simulator unit and exported into the Phillips Pinnacle™ treatment planning system. PDDs were computed using the AC and CCC algorithms. Photon energy of 6 MV was used with field sizes of 3×3 cm{sup 2}, 5×5 cm{sup 2}, 10×10 cm{sup 2}, 15×15 cm{sup 2}, and 20×20 cm{sup 2}. Ionization chamber readings were taken at different depths in water for all the field sizes. The percentage differences in the PDDs were computed with normalization to the depth of maximum dose (dmax). The calculated PDDs were then compared with measured PDDs. Results: In the first buildup region, both algorithms overpredicted the dose for all field sizes and under-predicted for all other subsequent buildup regions. After dmax in the three water media, AC under-predicted the dose for field sizes 3×3 and 5×5 cm{sup 2} and overpredicted for larger field sizes, whereas CCC under-predicted for all field sizes. Upon traversing the first air gap, AC showed maximum differences of –3.9%, −1.4%, 2.0%, 2.5%, 2.9% and CCC had maximum differences of −3.9%, −3.0%,–3.1%, −2.7%, −1.8% for field sizes 3×3, 5×5, 10×10, 15×15, and 20×20 cm{sup 2} respectively. Conclusion: The effect of air gaps causes a significant difference in the PDDs computed by both the AC and CCC algorithms in secondary build-up regions. AC computed larger values for the PDDs except at smaller field sizes. For CCC, the size of the errors in prediction of the PDDs has an inverse relationship with respect to field size. These effects should be considered in treatment planning where significant air gaps are encountered.
A MacWilliams Identity for Convolutional Codes: The General Case
Gluesing-Luerssen, Heide; Schneider, Gert
2008-01-01
A MacWilliams Identity for convolutional codes will be established. It makes use of the weight adjacency matrices of the code and its dual, based on state space realizations (the controller canonical form) of the codes in question. The MacWilliams Identity applies to various notions of duality appearing in the literature on convolutional coding theory.
Isointense infant brain MRI segmentation with a dilated convolutional neural network
Moeskops, P.; Pluim, J.P.W.
2017-01-01
Quantitative analysis of brain MRI at the age of 6 months is difficult because of the limited contrast between white matter and gray matter. In this study, we use a dilated triplanar convolutional neural network in combination with a non-dilated 3D convolutional neural network for the segmentation
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....
Linear diffusion-wave channel routing using a discrete Hayami convolution method
Li Wang; Joan Q. Wu; William J. Elliot; Fritz R. Feidler; Sergey. Lapin
2014-01-01
The convolution of an input with a response function has been widely used in hydrology as a means to solve various problems analytically. Due to the high computation demand in solving the functions using numerical integration, it is often advantageous to use the discrete convolution instead of the integration of the continuous functions. This approach greatly reduces...
Using convolutional decoding to improve time delay and phase estimation in digital communications
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.
Classifying images using restricted Boltzmann machines and convolutional neural networks
Zhao, Zhijun; Xu, Tongde; Dai, Chenyu
2017-07-01
To improve the feature recognition ability of deep model transfer learning, we propose a hybrid deep transfer learning method for image classification based on restricted Boltzmann machines (RBM) and convolutional neural networks (CNNs). It integrates learning abilities of two models, which conducts subject classification by exacting structural higher-order statistics features of images. While the method transfers the trained convolutional neural networks to the target datasets, fully-connected layers can be replaced by restricted Boltzmann machine layers; then the restricted Boltzmann machine layers and Softmax classifier are retrained, and BP neural network can be used to fine-tuned the hybrid model. The restricted Boltzmann machine layers has not only fully integrated the whole feature maps, but also learns the statistical features of target datasets in the view of the biggest logarithmic likelihood, thus removing the effects caused by the content differences between datasets. The experimental results show that the proposed method has improved the accuracy of image classification, outperforming other methods on Pascal VOC2007 and Caltech101 datasets.
Cloud Detection by Fusing Multi-Scale Convolutional Features
Li, Zhiwei; Shen, Huanfeng; Wei, Yancong; Cheng, Qing; Yuan, Qiangqiang
2018-04-01
Clouds detection is an important pre-processing step for accurate application of optical satellite imagery. Recent studies indicate that deep learning achieves best performance in image segmentation tasks. Aiming at boosting the accuracy of cloud detection for multispectral imagery, especially for those that contain only visible and near infrared bands, in this paper, we proposed a deep learning based cloud detection method termed MSCN (multi-scale cloud net), which segments cloud by fusing multi-scale convolutional features. MSCN was trained on a global cloud cover validation collection, and was tested in more than ten types of optical images with different resolution. Experiment results show that MSCN has obvious advantages over the traditional multi-feature combined cloud detection method in accuracy, especially when in snow and other areas covered by bright non-cloud objects. Besides, MSCN produced more detailed cloud masks than the compared deep cloud detection convolution network. The effectiveness of MSCN make it promising for practical application in multiple kinds of optical imagery.
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.
Enhancing neutron beam production with a convoluted moderator
Energy Technology Data Exchange (ETDEWEB)
Iverson, E.B., E-mail: iversoneb@ornl.gov [Spallation Neutron Source, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Baxter, D.V. [Center for the Exploration of Energy and Matter, Indiana University, Bloomington, IN 47408 (United States); Muhrer, G. [Lujan Neutron Scattering Center, Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM 87545 (United States); Ansell, S.; Dalgliesh, R. [ISIS Facility, Rutherford Appleton Laboratory, Chilton (United Kingdom); Gallmeier, F.X. [Spallation Neutron Source, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Kaiser, H. [Center for the Exploration of Energy and Matter, Indiana University, Bloomington, IN 47408 (United States); Lu, W. [Spallation Neutron Source, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States)
2014-10-21
We describe a new concept for a neutron moderating assembly resulting in the more efficient production of slow neutron beams. The Convoluted Moderator, a heterogeneous stack of interleaved moderating material and nearly transparent single-crystal spacers, is a directionally enhanced neutron beam source, improving beam emission over an angular range comparable to the range accepted by neutron beam lines and guides. We have demonstrated gains of 50% in slow neutron intensity for a given fast neutron production rate while simultaneously reducing the wavelength-dependent emission time dispersion by 25%, both coming from a geometric effect in which the neutron beam lines view a large surface area of moderating material in a relatively small volume. Additionally, we have confirmed a Bragg-enhancement effect arising from coherent scattering within the single-crystal spacers. We have not observed hypothesized refractive effects leading to additional gains at long wavelength. In addition to confirmation of the validity of the Convoluted Moderator concept, our measurements provide a series of benchmark experiments suitable for developing simulation and analysis techniques for practical optimization and eventual implementation at slow neutron source facilities.
Multi-Branch Fully Convolutional Network for Face Detection
Bai, Yancheng
2017-07-20
Face detection is a fundamental problem in computer vision. It is still a challenging task in unconstrained conditions due to significant variations in scale, pose, expressions, and occlusion. In this paper, we propose a multi-branch fully convolutional network (MB-FCN) for face detection, which considers both efficiency and effectiveness in the design process. Our MB-FCN detector can deal with faces at all scale ranges with only a single pass through the backbone network. As such, our MB-FCN model saves computation and thus is more efficient, compared to previous methods that make multiple passes. For each branch, the specific skip connections of the convolutional feature maps at different layers are exploited to represent faces in specific scale ranges. Specifically, small faces can be represented with both shallow fine-grained and deep powerful coarse features. With this representation, superior improvement in performance is registered for the task of detecting small faces. We test our MB-FCN detector on two public face detection benchmarks, including FDDB and WIDER FACE. Extensive experiments show that our detector outperforms state-of-the-art methods on all these datasets in general and by a substantial margin on the most challenging among them (e.g. WIDER FACE Hard subset). Also, MB-FCN runs at 15 FPS on a GPU for images of size 640 x 480 with no assumption on the minimum detectable face size.
Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks
Directory of Open Access Journals (Sweden)
Haiyang Yu
2017-06-01
Full Text Available Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs, for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs and long short-term memory (LSTM neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.
Multi-Input Convolutional Neural Network for Flower Grading
Directory of Open Access Journals (Sweden)
Yu Sun
2017-01-01
Full Text Available Flower grading is a significant task because it is extremely convenient for managing the flowers in greenhouse and market. With the development of computer vision, flower grading has become an interdisciplinary focus in both botany and computer vision. A new dataset named BjfuGloxinia contains three quality grades; each grade consists of 107 samples and 321 images. A multi-input convolutional neural network is designed for large scale flower grading. Multi-input CNN achieves a satisfactory accuracy of 89.6% on the BjfuGloxinia after data augmentation. Compared with a single-input CNN, the accuracy of multi-input CNN is increased by 5% on average, demonstrating that multi-input convolutional neural network is a promising model for flower grading. Although data augmentation contributes to the model, the accuracy is still limited by lack of samples diversity. Majority of misclassification is derived from the medium class. The image processing based bud detection is useful for reducing the misclassification, increasing the accuracy of flower grading to approximately 93.9%.
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
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.
Siamese convolutional networks for tracking the spine motion
Liu, Yuan; Sui, Xiubao; Sun, Yicheng; Liu, Chengwei; Hu, Yong
2017-09-01
Deep learning models have demonstrated great success in various computer vision tasks such as image classification and object tracking. However, tracking the lumbar spine by digitalized video fluoroscopic imaging (DVFI), which can quantitatively analyze the motion mode of spine to diagnose lumbar instability, has not yet been well developed due to the lack of steady and robust tracking method. In this paper, we propose a novel visual tracking algorithm of the lumbar vertebra motion based on a Siamese convolutional neural network (CNN) model. We train a full-convolutional neural network offline to learn generic image features. The network is trained to learn a similarity function that compares the labeled target in the first frame with the candidate patches in the current frame. The similarity function returns a high score if the two images depict the same object. Once learned, the similarity function is used to track a previously unseen object without any adapting online. In the current frame, our tracker is performed by evaluating the candidate rotated patches sampled around the previous frame target position and presents a rotated bounding box to locate the predicted target precisely. Results indicate that the proposed tracking method can detect the lumbar vertebra steadily and robustly. Especially for images with low contrast and cluttered background, the presented tracker can still achieve good tracking performance. Further, the proposed algorithm operates at high speed for real time tracking.
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.
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.
Classification of stroke disease using convolutional neural network
Marbun, J. T.; Seniman; Andayani, U.
2018-03-01
Stroke is a condition that occurs when the blood supply stop flowing to the brain because of a blockage or a broken blood vessel. A symptoms that happen when experiencing stroke, some of them is a dropped consciousness, disrupted vision and paralyzed body. The general examination is being done to get a picture of the brain part that have stroke using Computerized Tomography (CT) Scan. The image produced from CT will be manually checked and need a proper lighting by doctor to get a type of stroke. That is why it needs a method to classify stroke from CT image automatically. A method proposed in this research is Convolutional Neural Network. CT image of the brain is used as the input for image processing. The stage before classification are image processing (Grayscaling, Scaling, Contrast Limited Adaptive Histogram Equalization, then the image being classified with Convolutional Neural Network. The result then showed that the method significantly conducted was able to be used as a tool to classify stroke disease in order to distinguish the type of stroke from CT image.
Image Classification Based on Convolutional Denoising Sparse Autoencoder
Directory of Open Access Journals (Sweden)
Shuangshuang Chen
2017-01-01
Full Text Available Image classification aims to group images into corresponding semantic categories. Due to the difficulties of interclass similarity and intraclass variability, it is a challenging issue in computer vision. In this paper, an unsupervised feature learning approach called convolutional denoising sparse autoencoder (CDSAE is proposed based on the theory of visual attention mechanism and deep learning methods. Firstly, saliency detection method is utilized to get training samples for unsupervised feature learning. Next, these samples are sent to the denoising sparse autoencoder (DSAE, followed by convolutional layer and local contrast normalization layer. Generally, prior in a specific task is helpful for the task solution. Therefore, a new pooling strategy—spatial pyramid pooling (SPP fused with center-bias prior—is introduced into our approach. Experimental results on the common two image datasets (STL-10 and CIFAR-10 demonstrate that our approach is effective in image classification. They also demonstrate that none of these three components: local contrast normalization, SPP fused with center-prior, and l2 vector normalization can be excluded from our proposed approach. They jointly improve image representation and classification performance.
Weed Growth Stage Estimator Using Deep Convolutional Neural Networks.
Teimouri, Nima; Dyrmann, Mads; Nielsen, Per Rydahl; Mathiassen, Solvejg Kopp; Somerville, Gayle J; Jørgensen, Rasmus Nyholm
2018-05-16
This study outlines a new method of automatically estimating weed species and growth stages (from cotyledon until eight leaves are visible) of in situ images covering 18 weed species or families. Images of weeds growing within a variety of crops were gathered across variable environmental conditions with regards to soil types, resolution and light settings. Then, 9649 of these images were used for training the computer, which automatically divided the weeds into nine growth classes. The performance of this proposed convolutional neural network approach was evaluated on a further set of 2516 images, which also varied in term of crop, soil type, image resolution and light conditions. The overall performance of this approach achieved a maximum accuracy of 78% for identifying Polygonum spp. and a minimum accuracy of 46% for blackgrass. In addition, it achieved an average 70% accuracy rate in estimating the number of leaves and 96% accuracy when accepting a deviation of two leaves. These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species.
Convolutional neural networks for vibrational spectroscopic data analysis.
Acquarelli, Jacopo; van Laarhoven, Twan; Gerretzen, Jan; Tran, Thanh N; Buydens, Lutgarde M C; Marchiori, Elena
2017-02-15
In this work we show that convolutional neural networks (CNNs) can be efficiently used to classify vibrational spectroscopic data and identify important spectral regions. CNNs are the current state-of-the-art in image classification and speech recognition and can learn interpretable representations of the data. These characteristics make CNNs a good candidate for reducing the need for preprocessing and for highlighting important spectral regions, both of which are crucial steps in the analysis of vibrational spectroscopic data. Chemometric analysis of vibrational spectroscopic data often relies on preprocessing methods involving baseline correction, scatter correction and noise removal, which are applied to the spectra prior to model building. Preprocessing is a critical step because even in simple problems using 'reasonable' preprocessing methods may decrease the performance of the final model. We develop a new CNN based method and provide an accompanying publicly available software. It is based on a simple CNN architecture with a single convolutional layer (a so-called shallow CNN). Our method outperforms standard classification algorithms used in chemometrics (e.g. PLS) in terms of accuracy when applied to non-preprocessed test data (86% average accuracy compared to the 62% achieved by PLS), and it achieves better performance even on preprocessed test data (96% average accuracy compared to the 89% achieved by PLS). For interpretability purposes, our method includes a procedure for finding important spectral regions, thereby facilitating qualitative interpretation of results. Copyright © 2016 Elsevier B.V. All rights reserved.
sEMG-Based Gesture Recognition with Convolution Neural Networks
Directory of Open Access Journals (Sweden)
Zhen Ding
2018-06-01
Full Text Available The traditional classification methods for limb motion recognition based on sEMG have been deeply researched and shown promising results. However, information loss during feature extraction reduces the recognition accuracy. To obtain higher accuracy, the deep learning method was introduced. In this paper, we propose a parallel multiple-scale convolution architecture. Compared with the state-of-art methods, the proposed architecture fully considers the characteristics of the sEMG signal. Larger sizes of kernel filter than commonly used in other CNN-based hand recognition methods are adopted. Meanwhile, the characteristics of the sEMG signal, that is, muscle independence, is considered when designing the architecture. All the classification methods were evaluated on the NinaPro database. The results show that the proposed architecture has the highest recognition accuracy. Furthermore, the results indicate that parallel multiple-scale convolution architecture with larger size of kernel filter and considering muscle independence can significantly increase the classification accuracy.
Development of a morphological convolution operator for bearing fault detection
Li, Yifan; Liang, Xihui; Liu, Weiwei; Wang, Yan
2018-05-01
This paper presents a novel signal processing scheme, namely morphological convolution operator (MCO) lifted morphological undecimated wavelet (MUDW), for rolling element bearing fault detection. In this scheme, a MCO is first designed to fully utilize the advantage of the closing & opening gradient operator and the closing-opening & opening-closing gradient operator for feature extraction as well as the merit of excellent denoising characteristics of the convolution operator. The MCO is then introduced into MUDW for the purpose of improving the fault detection ability of the reported MUDWs. Experimental vibration signals collected from a train wheelset test rig and the bearing data center of Case Western Reserve University are employed to evaluate the effectiveness of the proposed MCO lifted MUDW on fault detection of rolling element bearings. The results show that the proposed approach has a superior performance in extracting fault features of defective rolling element bearings. In addition, comparisons are performed between two reported MUDWs and the proposed MCO lifted MUDW. The MCO lifted MUDW outperforms both of them in detection of outer race faults and inner race faults of rolling element bearings.
Deep multi-scale convolutional neural network for hyperspectral image classification
Zhang, Feng-zhe; Yang, Xia
2018-04-01
In this paper, we proposed a multi-scale convolutional neural network for hyperspectral image classification task. Firstly, compared with conventional convolution, we utilize multi-scale convolutions, which possess larger respective fields, to extract spectral features of hyperspectral image. We design a deep neural network with a multi-scale convolution layer which contains 3 different convolution kernel sizes. Secondly, to avoid overfitting of deep neural network, dropout is utilized, which randomly sleeps neurons, contributing to improve the classification accuracy a bit. In addition, new skills like ReLU in deep learning is utilized in this paper. We conduct experiments on University of Pavia and Salinas datasets, and obtained better classification accuracy compared with other methods.
Object Detection Based on Fast/Faster RCNN Employing Fully Convolutional Architectures
Directory of Open Access Journals (Sweden)
Yun Ren
2018-01-01
Full Text Available Modern object detectors always include two major parts: a feature extractor and a feature classifier as same as traditional object detectors. The deeper and wider convolutional architectures are adopted as the feature extractor at present. However, many notable object detection systems such as Fast/Faster RCNN only consider simple fully connected layers as the feature classifier. In this paper, we declare that it is beneficial for the detection performance to elaboratively design deep convolutional networks (ConvNets of various depths for feature classification, especially using the fully convolutional architectures. In addition, this paper also demonstrates how to employ the fully convolutional architectures in the Fast/Faster RCNN. Experimental results show that a classifier based on convolutional layer is more effective for object detection than that based on fully connected layer and that the better detection performance can be achieved by employing deeper ConvNets as the feature classifier.
International Nuclear Information System (INIS)
Pozza, Nicola Dalla; Wiseman, Howard M; Huntington, Elanor H
2015-01-01
The preparation stage of optical qubits is an essential task in all the experimental setups employed for the test and demonstration of quantum optics principles. We consider a deterministic protocol for the preparation of qubits as a superposition of vacuum and one photon number states, which has the advantage to reduce the amount of resources required via phase-sensitive measurements using a local oscillator (‘dyne detection’). We investigate the performances of the protocol using different phase measurement schemes: homodyne, heterodyne, and adaptive dyne detection (involving a feedback loop). First, we define a suitable figure of merit for the prepared state and we obtain an analytical expression for that in terms of the phase measurement considered. Further, we study limitations that the phase measurement can exhibit, such as delay or limited resources in the feedback strategy. Finally, we evaluate the figure of merit of the protocol for different mode-shapes handily available in an experimental setup. We show that even in the presence of such limitations simple feedback algorithms can perform surprisingly well, outperforming the protocols when simple homodyne or heterodyne schemes are employed. (paper)
A Bethe ansatz solvable model for superpositions of Cooper pairs and condensed molecular bosons
International Nuclear Information System (INIS)
Hibberd, K.E.; Dunning, C.; Links, J.
2006-01-01
We introduce a general Hamiltonian describing coherent superpositions of Cooper pairs and condensed molecular bosons. For particular choices of the coupling parameters, the model is integrable. One integrable manifold, as well as the Bethe ansatz solution, was found by Dukelsky et al. [J. Dukelsky, G.G. Dussel, C. Esebbag, S. Pittel, Phys. Rev. Lett. 93 (2004) 050403]. Here we show that there is a second integrable manifold, established using the boundary quantum inverse scattering method. In this manner we obtain the exact solution by means of the algebraic Bethe ansatz. In the case where the Cooper pair energies are degenerate we examine the relationship between the spectrum of these integrable Hamiltonians and the quasi-exactly solvable spectrum of particular Schrodinger operators. For the solution we derive here the potential of the Schrodinger operator is given in terms of hyperbolic functions. For the solution derived by Dukelsky et al., loc. cit. the potential is sextic and the wavefunctions obey PT-symmetric boundary conditions. This latter case provides a novel example of an integrable Hermitian Hamiltonian acting on a Fock space whose states map into a Hilbert space of PT-symmetric wavefunctions defined on a contour in the complex plane
Effect of the superposition of a dielectric barrier discharge onto a premixed gas burner flame
Zaima, Kazunori; Takada, Noriharu; Sasaki, Koichi
2011-10-01
We are investigating combustion control with the help of nonequilibrium plasma. In this work, we examined the effect of dielectric barrier discharge (DBD) on a premixed burner flame with CH4/O2/Ar gas mixture. The premixed burner flame was covered with a quartz tube. A copper electrode was attached on the outside of the quartz tube, and it was connected to a high-voltage power supply. DBD inside the quartz tube was obtained between the copper electrode and the grounded nozzle of the burner which was placed at the bottom of the quartz tube. We clearly observed that the flame length was shortened by superposing DBD onto the bottom part of the flame. The shortened flame length indicates the enhancement of the burning velocity. We measured the optical emission spectra from the bottom region of the flame. As a result, we observed clear line emissions from Ar, which were never observed from the flame without DBD. We evaluated the rotational temperatures of OH and CH radicals by spectral fitting. As a result, the rotational temperature of CH was not changed, and the rotational temperature of OH was decreased by the superposition of DBD. According to these results, it is considered that the enhancement of the burning velocity is not caused by gas heating. New reaction pathways are suggested.
Konakondla, Sanjay; Brimley, Cameron J; Sublett, Jesna Mathew; Stefanowicz, Edward; Flora, Sarah; Mongelluzzo, Gino; Schirmer, Clemens M
2017-09-29
Whole brain tractography using diffusion tensor imaging (DTI) sequences can be used to map cerebral connectivity; however, this can be time-consuming due to the manual component of image manipulation required, calling for the need for a standardized, automated, and accurate fiber tracking protocol with automatic whole brain tractography (AWBT). Interpreting conventional two-dimensional (2D) images, such as computed tomography (CT) and magnetic resonance imaging (MRI), as an intraoperative three-dimensional (3D) environment is a difficult task with recognized inter-operator variability. Three-dimensional printing in neurosurgery has gained significant traction in the past decade, and as software, equipment, and practices become more refined, trainee education, surgical skills, research endeavors, innovation, patient education, and outcomes via valued care is projected to improve. We describe a novel multimodality 3D superposition (MMTS) technique, which fuses multiple imaging sequences alongside cerebral tractography into one patient-specific 3D printed model. Inferences on cost and improved outcomes fueled by encouraging patient engagement are explored.
Level crossings and excess times due to a superposition of uncorrelated exponential pulses
Theodorsen, A.; Garcia, O. E.
2018-01-01
A well-known stochastic model for intermittent fluctuations in physical systems is investigated. The model is given by a superposition of uncorrelated exponential pulses, and the degree of pulse overlap is interpreted as an intermittency parameter. Expressions for excess time statistics, that is, the rate of level crossings above a given threshold and the average time spent above the threshold, are derived from the joint distribution of the process and its derivative. Limits of both high and low intermittency are investigated and compared to previously known results. In the case of a strongly intermittent process, the distribution of times spent above threshold is obtained analytically. This expression is verified numerically, and the distribution of times above threshold is explored for other intermittency regimes. The numerical simulations compare favorably to known results for the distribution of times above the mean threshold for an Ornstein-Uhlenbeck process. This contribution generalizes the excess time statistics for the stochastic model, which find applications in a wide diversity of natural and technological systems.
Superposition of elliptic functions as solutions for a large number of nonlinear equations
International Nuclear Information System (INIS)
Khare, Avinash; Saxena, Avadh
2014-01-01
For a large number of nonlinear equations, both discrete and continuum, we demonstrate a kind of linear superposition. We show that whenever a nonlinear equation admits solutions in terms of both Jacobi elliptic functions cn(x, m) and dn(x, m) with modulus m, then it also admits solutions in terms of their sum as well as difference. We have checked this in the case of several nonlinear equations such as the nonlinear Schrödinger equation, MKdV, a mixed KdV-MKdV system, a mixed quadratic-cubic nonlinear Schrödinger equation, the Ablowitz-Ladik equation, the saturable nonlinear Schrödinger equation, λϕ 4 , the discrete MKdV as well as for several coupled field equations. Further, for a large number of nonlinear equations, we show that whenever a nonlinear equation admits a periodic solution in terms of dn 2 (x, m), it also admits solutions in terms of dn 2 (x,m)±√(m) cn (x,m) dn (x,m), even though cn(x, m)dn(x, m) is not a solution of these nonlinear equations. Finally, we also obtain superposed solutions of various forms for several coupled nonlinear equations
Identification of distant drug off-targets by direct superposition of binding pocket surfaces.
Schumann, Marcel; Armen, Roger S
2013-01-01
Correctly predicting off-targets for a given molecular structure, which would have the ability to bind a large range of ligands, is both particularly difficult and important if they share no significant sequence or fold similarity with the respective molecular target ("distant off-targets"). A novel approach for identification of off-targets by direct superposition of protein binding pocket surfaces is presented and applied to a set of well-studied and highly relevant drug targets, including representative kinases and nuclear hormone receptors. The entire Protein Data Bank is searched for similar binding pockets and convincing distant off-target candidates were identified that share no significant sequence or fold similarity with the respective target structure. These putative target off-target pairs are further supported by the existence of compounds that bind strongly to both with high topological similarity, and in some cases, literature examples of individual compounds that bind to both. Also, our results clearly show that it is possible for binding pockets to exhibit a striking surface similarity, while the respective off-target shares neither significant sequence nor significant fold similarity with the respective molecular target ("distant off-target").
Deng, Bo; Shi, Yaoyao
2017-11-01
The tape winding technology is an effective way to fabricate rotationally composite materials. Nevertheless, some inevitable defects will seriously influence the performance of winding products. One of the crucial ways to identify the quality of fiber-reinforced composite material products is examining its void content. Significant improvement in products' mechanical properties can be achieved by minimizing the void defect. Two methods were applied in this study, finite element analysis and experimental testing, respectively, to investigate the mechanism of how void forming in composite tape winding processing. Based on the theories of interlayer intimate contact and Domain Superposition Technique (DST), a three-dimensional model of prepreg tape void with SolidWorks has been modeled in this paper. Whereafter, ABAQUS simulation software was used to simulate the void content change with pressure and temperature. Finally, a series of experiments were performed to determine the accuracy of the model-based predictions. The results showed that the model is effective for predicting the void content in the composite tape winding process.
International Nuclear Information System (INIS)
Suzuki, Hisashi; Isozaki, Yukio; Itaya, Tetsumaru.
1990-01-01
Weakly metamorphosed pre-Cenozoic accretionary complex in the northern part of the Chichibu Belt in Kamikatsu Town, eastern Shikoku, consists of two distinct geologic units; the Northern Unit and Southern Unit. The Northern Unit is composed mainly of phyllitic pelites and basic tuff with allochthonous blocks of chert and limestone, and possesses mineral paragenesis of the glaucophane schist facies. The Southern Unit is composed mainly of phyllitic pelites with allochthonous blocks of sandstone, limestone, massive green rocks, and chert, and possesses mineral paragenesis of the pumpellyite-actinolite facies. The Southern Unit tectonically overlies the Northern Univ by the south-dipping Jiganji Fault. K-Ar ages were dated for the recrystallized white micas from 11 samples of pelites and basic tuff in the Northern Unit, and from 6 samples of pelites in the Southern Unit. The K-Ar ages of the samples from the Northern Unit range in 129-112 Ma, and those from the Southern Unit in 225-194 Ma. In terms of metamorphic ages, the Northern Unit and Southern Unit are referred to the constituents of the Sanbagawa Metamorphic Belt, and to those of the Kurosegawa Terrane, respectively. Thus, tectonic superposition of these two units in the study area suggests that the Kurosegawa Terrane occurs in a higher structural position over the Sanbagawa Metamorphic Belt in eastern Shikoku. (author)
International Nuclear Information System (INIS)
Kim, Y. J.; Kim, W. T.; Lee, Y. S.
2006-01-01
Full text: Full text: Due to the potentiality of accidents, the transportation safety of radioactive material has become extremely important in these days. The most important means of accomplishing the safety in transportation for radioactive material is the integrity of cask. The cask for spent fuel consists of a cask body and two impact limiters generally. The impact limiters are attached at the upper and the lower of the cask body. The cask comprises general requirements and test requirements for normal transport conditions and hypothetical accident conditions in accordance with IAEA regulations. Among the test requirements for hypothetical accident conditions, the 9 m drop test of dropping the cask from 9 m height to unyielding surface to get maximum damage becomes very important requirement because it can affect the structural soundness of the cask. So far the impact response analysis for 9 m drop test has been obtained by finite element method with complex computational procedure. In this study, the empirical equations of the impact forces for 9 m drop test are formulated by dimensional analysis. And then using the empirical equations the characteristics of material used for impact limiters are analysed. Also the dynamic impact response of the cask body is analysed using the mode superposition method and the analysis method is proposed. The results are also validated by comparing with previous experimental results and finite element analysis results. The present method is simpler than finite element method and can be used to predict the impact response of the cask
Motion Estimation Using the Single-row Superposition-type Planar Compound-like Eye
Directory of Open Access Journals (Sweden)
Gwo-Long Lin
2007-06-01
Full Text Available How can the compound eye of insects capture the prey so accurately andquickly? This interesting issue is explored from the perspective of computer vision insteadof from the viewpoint of biology. The focus is on performance evaluation of noiseimmunity for motion recovery using the single-row superposition-type planar compound-like eye (SPCE. The SPCE owns a special symmetrical framework with tremendousamount of ommatidia inspired by compound eye of insects. The noise simulates possibleambiguity of image patterns caused by either environmental uncertainty or low resolutionof CCD devices. Results of extensive simulations indicate that this special visualconfiguration provides excellent motion estimation performance regardless of themagnitude of the noise. Even when the noise interference is serious, the SPCE is able todramatically reduce errors of motion recovery of the ego-translation without any type offilters. In other words, symmetrical, regular, and multiple vision sensing devices of thecompound-like eye have statistical averaging advantage to suppress possible noises. Thisdiscovery lays the basic foundation in terms of engineering approaches for the secret of thecompound eye of insects.
Hersch, Roger David; Crete, Frederique
2005-01-01
Dot gain is different when dots are printed alone, printed in superposition with one ink or printed in superposition with two inks. In addition, the dot gain may also differ depending on which solid ink the considered halftone layer is superposed. In a previous research project, we developed a model for computing the effective surface coverage of a dot according to its superposition conditions. In the present contribution, we improve the Yule-Nielsen modified Neugebauer model by integrating into it our effective dot surface coverage computation model. Calibration of the reproduction curves mapping nominal to effective surface coverages in every superposition condition is carried out by fitting effective dot surfaces which minimize the sum of square differences between the measured reflection density spectra and reflection density spectra predicted according to the Yule-Nielsen modified Neugebauer model. In order to predict the reflection spectrum of a patch, its known nominal surface coverage values are converted into effective coverage values by weighting the contributions from different reproduction curves according to the weights of the contributing superposition conditions. We analyze the colorimetric prediction improvement brought by our extended dot surface coverage model for clustered-dot offset prints, thermal transfer prints and ink-jet prints. The color differences induced by the differences between measured reflection spectra and reflection spectra predicted according to the new dot surface estimation model are quantified on 729 different cyan, magenta, yellow patches covering the full color gamut. As a reference, these differences are also computed for the classical Yule-Nielsen modified spectral Neugebauer model incorporating a single halftone reproduction curve for each ink. Taking into account dot surface coverages according to different superposition conditions considerably improves the predictions of the Yule-Nielsen modified Neugebauer model. In
Roy, Dipankar; Marianski, Mateusz; Maitra, Neepa T.; Dannenberg, J. J.
2012-10-01
We compare dispersion and induction interactions for noble gas dimers and for Ne, methane, and 2-butyne with HF and LiF using a variety of functionals (including some specifically parameterized to evaluate dispersion interactions) with ab initio methods including CCSD(T) and MP2. We see that inductive interactions tend to enhance dispersion and may be accompanied by charge-transfer. We show that the functionals do not generally follow the expected trends in interaction energies, basis set superposition errors (BSSE), and interaction distances as a function of basis set size. The functionals parameterized to treat dispersion interactions often overestimate these interactions, sometimes by quite a lot, when compared to higher level calculations. Which functionals work best depends upon the examples chosen. The B3LYP and X3LYP functionals, which do not describe pure dispersion interactions, appear to describe dispersion mixed with induction about as accurately as those parametrized to treat dispersion. We observed significant differences in high-level wavefunction calculations in a basis set larger than those used to generate the structures in many of the databases. We discuss the implications for highly parameterized functionals based on these databases, as well as the use of simple potential energy for fitting the parameters rather than experimentally determinable thermodynamic state functions that involve consideration of vibrational states.
International Nuclear Information System (INIS)
Monnier, L.; Collet, H.; Suquet, P.; Mirouze, J.
1975-01-01
The intestinal absorption of calcium was measured by a double isotopic labelling method, the results being obtained by a mathematical deconvolution technique. This analytical method was compared with the simple measurement of the plasma radioactivity ratio for the two isotopes administered orally and intraveinously respectively. The study covered 29 determinations. It was possible to estimate the total fractional absorption of calcium (TFACa) by calculating the average of the 47 Ca/ 45 Ca quotients measured on the 3rd and 8th hour after simultaneous administration of 45 Ca intraveinously and 47 Ca by mouth. The advantages of this method are obvious: need for only two blood samplings, simplicity of calculations which nevertheless give TFACa values comparable to those obtained by deconvolution analysis. However the only information supplied by the quotients method is the total fractional absorption, whereas inverse convolution analysis provides several interesting parameters such as the maximum absorption and the mean transit time of radiocalcium through the intestinal wall [fr
von Spiczak, Jochen; Mannil, Manoj; Peters, Benjamin; Hickethier, Tilman; Baer, Matthias; Henning, André; Schmidt, Bernhard; Flohr, Thomas; Manka, Robert; Maintz, David; Alkadhi, Hatem
2018-05-23
The aims of this study were to assess the value of a dedicated sharp convolution kernel for photon counting detector (PCD) computed tomography (CT) for coronary stent imaging and to evaluate to which extent iterative reconstructions can compensate for potential increases in image noise. For this in vitro study, a phantom simulating coronary artery stenting was prepared. Eighteen different coronary stents were expanded in plastic tubes of 3 mm diameter. Tubes were filled with diluted contrast agent, sealed, and immersed in oil calibrated to an attenuation of -100 HU simulating epicardial fat. The phantom was scanned in a modified second generation 128-slice dual-source CT scanner (SOMATOM Definition Flash, Siemens Healthcare, Erlangen, Germany) equipped with both a conventional energy integrating detector and PCD. Image data were acquired using the PCD part of the scanner with 48 × 0.25 mm slices, a tube voltage of 100 kVp, and tube current-time product of 100 mAs. Images were reconstructed using a conventional convolution kernel for stent imaging with filtered back-projection (B46) and with sinogram-affirmed iterative reconstruction (SAFIRE) at level 3 (I463). For comparison, a dedicated sharp convolution kernel with filtered back-projection (D70) and SAFIRE level 3 (Q703) and level 5 (Q705) was used. The D70 and Q70 kernels were specifically designed for coronary stent imaging with PCD CT by optimizing the image modulation transfer function and the separation of contrast edges. Two independent, blinded readers evaluated subjective image quality (Likert scale 0-3, where 3 = excellent), in-stent diameter difference, in-stent attenuation difference, mathematically defined image sharpness, and noise of each reconstruction. Interreader reliability was calculated using Goodman and Kruskal's γ and intraclass correlation coefficients (ICCs). Differences in image quality were evaluated using a Wilcoxon signed-rank test. Differences in in-stent diameter difference, in
International Nuclear Information System (INIS)
Huang, J; Followill, D; Howell, R; Liu, X; Mirkovic, D; Stingo, F; Kry, S
2015-01-01
Purpose: To investigate two strategies for reducing dose calculation errors near metal implants: use of CT metal artifact reduction methods and implementation of metal-based energy deposition kernels in the convolution/superposition (C/S) method. Methods: Radiochromic film was used to measure the dose upstream and downstream of titanium and Cerrobend implants. To assess the dosimetric impact of metal artifact reduction methods, dose calculations were performed using baseline, uncorrected images and metal artifact reduction Methods: Philips O-MAR, GE’s monochromatic gemstone spectral imaging (GSI) using dual-energy CT, and GSI imaging with metal artifact reduction software applied (MARs).To assess the impact of metal kernels, titanium and silver kernels were implemented into a commercial collapsed cone C/S algorithm. Results: The CT artifact reduction methods were more successful for titanium than Cerrobend. Interestingly, for beams traversing the metal implant, we found that errors in the dimensions of the metal in the CT images were more important for dose calculation accuracy than reduction of imaging artifacts. The MARs algorithm caused a distortion in the shape of the titanium implant that substantially worsened the calculation accuracy. In comparison to water kernel dose calculations, metal kernels resulted in better modeling of the increased backscatter dose at the upstream interface but decreased accuracy directly downstream of the metal. We also found that the success of metal kernels was dependent on dose grid size, with smaller calculation voxels giving better accuracy. Conclusion: Our study yielded mixed results, with neither the metal artifact reduction methods nor the metal kernels being globally effective at improving dose calculation accuracy. However, some successes were observed. The MARs algorithm decreased errors downstream of Cerrobend by a factor of two, and metal kernels resulted in more accurate backscatter dose upstream of metals. Thus
Zaima, Kazunori; Sasaki, Koichi
2016-08-01
We investigated the transient phenomena in a premixed burner flame with the superposition of a pulsed dielectric barrier discharge (DBD). The length of the flame was shortened by the superposition of DBD, indicating the activation of combustion chemical reactions with the help of the plasma. In addition, we observed the modulation of the top position of the unburned gas region and the formations of local minimums in the axial distribution of the optical emission intensity of OH. These experimental results reveal the oscillation of the rates of combustion chemical reactions as a response to the activation by pulsed DBD. The cycle of the oscillation was 0.18-0.2 ms, which could be understood as the eigenfrequency of the plasma-assisted combustion reaction system.
Some calculations of the failure statistics of coated fuel particles
International Nuclear Information System (INIS)
Martin, D.G.; Hobbs, J.E.
1977-03-01
Statistical variations of coated fuel particle parameters were considered in stress model calculations and the resulting particle failure fraction versus burn-up evaluated. Variations in the following parameters were considered simultaneously: kernel diameter and porosity, thickness of the buffer, seal, silicon carbide and inner and outer pyrocarbon layers, which were all assumed to be normally distributed, and the silicon carbide fracture stress which was assumed to follow a Weibull distribution. Two methods, based respectively on random sampling and convolution of the variations were employed and applied to particles manufactured by Dragon Project and RFL Springfields. Convolution calculations proved the more satisfactory. In the present calculations variations in the silicon carbide fracture stress caused the greatest spread in burn-up for a given change in failure fraction; kernel porosity is the next most important parameter. (author)
Energy Technology Data Exchange (ETDEWEB)
Kinugawa, Tohru, E-mail: kinugawa@phoenix.kobe-u.ac.jp [Institute for Promotion of Higher Education, Kobe University, Kobe 657-8501 (Japan)
2014-02-15
This paper presents a simple but nontrivial generalization of Abel's mechanical problem, based on the extended isochronicity condition and the superposition principle. There are two primary aims. The first one is to reveal the linear relation between the transit-time T and the travel-length X hidden behind the isochronicity problem that is usually discussed in terms of the nonlinear equation of motion (d{sup 2}X)/(dt{sup 2}) +(dU)/(dX) =0 with U(X) being an unknown potential. Second, the isochronicity condition is extended for the possible Abel-transform approach to designing the isochronous trajectories of charged particles in spectrometers and/or accelerators for time-resolving experiments. Our approach is based on the integral formula for the oscillatory motion by Landau and Lifshitz [Mechanics (Pergamon, Oxford, 1976), pp. 27–29]. The same formula is used to treat the non-periodic motion that is driven by U(X). Specifically, this unknown potential is determined by the (linear) Abel transform X(U) ∝ A[T(E)], where X(U) is the inverse function of U(X), A=(1/√(π))∫{sub 0}{sup E}dU/√(E−U) is the so-called Abel operator, and T(E) is the prescribed transit-time for a particle with energy E to spend in the region of interest. Based on this Abel-transform approach, we have introduced the extended isochronicity condition: typically, τ = T{sub A}(E) + T{sub N}(E) where τ is a constant period, T{sub A}(E) is the transit-time in the Abel type [A-type] region spanning X > 0 and T{sub N}(E) is that in the Non-Abel type [N-type] region covering X < 0. As for the A-type region in X > 0, the unknown inverse function X{sub A}(U) is determined from T{sub A}(E) via the Abel-transform relation X{sub A}(U) ∝ A[T{sub A}(E)]. In contrast, the N-type region in X < 0 does not ensure this linear relation: the region is covered with a predetermined potential U{sub N}(X) of some arbitrary choice, not necessarily obeying the Abel-transform relation. In
Hamza, Doha R.
2015-02-13
We propose a three-message superposition coding scheme in a cognitive radio relay network exploiting active cooperation between primary and secondary users. The primary user is motivated to cooperate by substantial benefits it can reap from this access scenario. Specifically, the time resource is split into three transmission phases: The first two phases are dedicated to primary communication, while the third phase is for the secondary’s transmission. We formulate two throughput maximization problems for the secondary network subject to primary user rate constraints and per-node power constraints with respect to the time durations of primary transmission and the transmit power of the primary and the secondary users. The first throughput maximization problem assumes a partial power constraint such that the secondary power dedicated to primary cooperation, i.e. for the first two communication phases, is fixed apriori. In the second throughput maximization problem, a total power constraint is assumed over the three phases of communication. The two problems are difficult to solve analytically when the relaying channel gains are strictly greater than each other and strictly greater than the direct link channel gain. However, mathematically tractable lowerbound and upperbound solutions can be attained for the two problems. For both problems, by only using the lowerbound solution, we demonstrate significant throughput gains for both the primary and the secondary users through this active cooperation scheme. We find that most of the throughput gains come from minimizing the second phase transmission time since the secondary nodes assist the primary communication during this phase. Finally, we demonstrate the superiority of our proposed scheme compared to a number of reference schemes that include best relay selection, dual-hop routing, and an interference channel model.
Hamza, Doha R.; Park, Kihong; Alouini, Mohamed-Slim; Aissa, Sonia
2015-01-01
We propose a three-message superposition coding scheme in a cognitive radio relay network exploiting active cooperation between primary and secondary users. The primary user is motivated to cooperate by substantial benefits it can reap from this access scenario. Specifically, the time resource is split into three transmission phases: The first two phases are dedicated to primary communication, while the third phase is for the secondary’s transmission. We formulate two throughput maximization problems for the secondary network subject to primary user rate constraints and per-node power constraints with respect to the time durations of primary transmission and the transmit power of the primary and the secondary users. The first throughput maximization problem assumes a partial power constraint such that the secondary power dedicated to primary cooperation, i.e. for the first two communication phases, is fixed apriori. In the second throughput maximization problem, a total power constraint is assumed over the three phases of communication. The two problems are difficult to solve analytically when the relaying channel gains are strictly greater than each other and strictly greater than the direct link channel gain. However, mathematically tractable lowerbound and upperbound solutions can be attained for the two problems. For both problems, by only using the lowerbound solution, we demonstrate significant throughput gains for both the primary and the secondary users through this active cooperation scheme. We find that most of the throughput gains come from minimizing the second phase transmission time since the secondary nodes assist the primary communication during this phase. Finally, we demonstrate the superiority of our proposed scheme compared to a number of reference schemes that include best relay selection, dual-hop routing, and an interference channel model.
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.
An effective convolutional neural network model for Chinese sentiment analysis
Zhang, Yu; Chen, Mengdong; Liu, Lianzhong; Wang, Yadong
2017-06-01
Nowadays microblog is getting more and more popular. People are increasingly accustomed to expressing their opinions on Twitter, Facebook and Sina Weibo. Sentiment analysis of microblog has received significant attention, both in academia and in industry. So far, Chinese microblog exploration still needs lots of further work. In recent years CNN has also been used to deal with NLP tasks, and already achieved good results. However, these methods ignore the effective use of a large number of existing sentimental resources. For this purpose, we propose a Lexicon-based Sentiment Convolutional Neural Networks (LSCNN) model focus on Weibo's sentiment analysis, which combines two CNNs, trained individually base on sentiment features and word embedding, at the fully connected hidden layer. The experimental results show that our model outperforms the CNN model only with word embedding features on microblog sentiment analysis task.
High Order Tensor Formulation for Convolutional Sparse Coding
Bibi, Adel Aamer
2017-12-25
Convolutional sparse coding (CSC) has gained attention for its successful role as a reconstruction and a classification tool in the computer vision and machine learning community. Current CSC methods can only reconstruct singlefeature 2D images independently. However, learning multidimensional dictionaries and sparse codes for the reconstruction of multi-dimensional data is very important, as it examines correlations among all the data jointly. This provides more capacity for the learned dictionaries to better reconstruct data. In this paper, we propose a generic and novel formulation for the CSC problem that can handle an arbitrary order tensor of data. Backed with experimental results, our proposed formulation can not only tackle applications that are not possible with standard CSC solvers, including colored video reconstruction (5D- tensors), but it also performs favorably in reconstruction with much fewer parameters as compared to naive extensions of standard CSC to multiple features/channels.
Classification of decays involving variable decay chains with convolutional architectures
CERN. Geneva
2018-01-01
Vidyo contribution We present a technique to perform classification of decays that exhibit decay chains involving a variable number of particles, which include a broad class of $B$ meson decays sensitive to new physics. The utility of such decays as a probe of the Standard Model is dependent upon accurate determination of the decay rate, which is challenged by the combinatorial background arising in high-multiplicity decay modes. In our model, each particle in the decay event is represented as a fixed-dimensional vector of feature attributes, forming an $n \\times k$ representation of the event, where $n$ is the number of particles in the event and $k$ is the dimensionality of the feature vector. A convolutional architecture is used to capture dependencies between the embedded particle representations and perform the final classification. The proposed model performs outperforms standard machine learning approaches based on Monte Carlo studies across a range of variable final-state decays with the Belle II det...
CONEDEP: COnvolutional Neural network based Earthquake DEtection and Phase Picking
Zhou, Y.; Huang, Y.; Yue, H.; Zhou, S.; An, S.; Yun, N.
2017-12-01
We developed an automatic local earthquake detection and phase picking algorithm based on Fully Convolutional Neural network (FCN). The FCN algorithm detects and segments certain features (phases) in 3 component seismograms to realize efficient picking. We use STA/LTA algorithm and template matching algorithm to construct the training set from seismograms recorded 1 month before and after the Wenchuan earthquake. Precise P and S phases are identified and labeled to construct the training set. Noise data are produced by combining back-ground noise and artificial synthetic noise to form the equivalent scale of noise set as the signal set. Training is performed on GPUs to achieve efficient convergence. Our algorithm has significantly improved performance in terms of the detection rate and precision in comparison with STA/LTA and template matching algorithms.
Computational optical tomography using 3-D deep convolutional neural networks
Nguyen, Thanh; Bui, Vy; Nehmetallah, George
2018-04-01
Deep convolutional neural networks (DCNNs) offer a promising performance for many image processing areas, such as super-resolution, deconvolution, image classification, denoising, and segmentation, with outstanding results. Here, we develop for the first time, to our knowledge, a method to perform 3-D computational optical tomography using 3-D DCNN. A simulated 3-D phantom dataset was first constructed and converted to a dataset of phase objects imaged on a spatial light modulator. For each phase image in the dataset, the corresponding diffracted intensity image was experimentally recorded on a CCD. We then experimentally demonstrate the ability of the developed 3-D DCNN algorithm to solve the inverse problem by reconstructing the 3-D index of refraction distributions of test phantoms from the dataset from their corresponding diffraction patterns.
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%.
Truncation Depth Rule-of-Thumb for Convolutional Codes
Moision, Bruce
2009-01-01
In this innovation, it is shown that a commonly used rule of thumb (that the truncation depth of a convolutional code should be five times the memory length, m, of the code) is accurate only for rate 1/2 codes. In fact, the truncation depth should be 2.5 m/(1 - r), where r is the code rate. The accuracy of this new rule is demonstrated by tabulating the distance properties of a large set of known codes. This new rule was derived by bounding the losses due to truncation as a function of the code rate. With regard to particular codes, a good indicator of the required truncation depth is the path length at which all paths that diverge from a particular path have accumulated the minimum distance of the code. It is shown that the new rule of thumb provides an accurate prediction of this depth for codes of varying rates.
Finding Neutrinos in LArTPCs using Convolutional Neural Networks
Wongjirad, Taritree
2017-09-01
Deep learning algorithms, which have emerged over the last decade, are opening up new ways to analyze data for many particle physics experiments. MicroBooNE, which is a neutrino experiment at Fermilab, has been exploring the use of such algorithms, in particular, convolutional neural networks (CNNS). CNNs are the state-of-the-art method for a large class of problems involving the analysis of images. This makes CNNs an attractive approach for MicroBooNE, whose detector, a liquid argon time projection chamber (LArTPC), produces high-resolution images of particle interactions. In this talk, I will discuss the ways CNNs can be applied to tasks like neutrino interaction detection and particle identification in MicroBooNE and LArTPCs.
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.
Forecasting Flare Activity Using Deep Convolutional Neural Networks
Hernandez, T.
2017-12-01
Current operational flare forecasting relies on human morphological analysis of active regions and the persistence of solar flare activity through time (i.e. that the Sun will continue to do what it is doing right now: flaring or remaining calm). In this talk we present the results of applying deep Convolutional Neural Networks (CNNs) to the problem of solar flare forecasting. CNNs operate by training a set of tunable spatial filters that, in combination with neural layer interconnectivity, allow CNNs to automatically identify significant spatial structures predictive for classification and regression problems. We will start by discussing the applicability and success rate of the approach, the advantages it has over non-automated forecasts, and how mining our trained neural network provides a fresh look into the mechanisms behind magnetic energy storage and release.
Convolutional neural networks with balanced batches for facial expressions recognition
Battini Sönmez, Elena; Cangelosi, Angelo
2017-03-01
This paper considers the issue of fully automatic emotion classification on 2D faces. In spite of the great effort done in recent years, traditional machine learning approaches based on hand-crafted feature extraction followed by the classification stage failed to develop a real-time automatic facial expression recognition system. The proposed architecture uses Convolutional Neural Networks (CNN), which are built as a collection of interconnected processing elements to simulate the brain of human beings. The basic idea of CNNs is to learn a hierarchical representation of the input data, which results in a better classification performance. In this work we present a block-based CNN algorithm, which uses noise, as data augmentation technique, and builds batches with a balanced number of samples per class. The proposed architecture is a very simple yet powerful CNN, which can yield state-of-the-art accuracy on the very competitive benchmark algorithm of the Extended Cohn Kanade database.
Network Intrusion Detection through Stacking Dilated Convolutional Autoencoders
Directory of Open Access Journals (Sweden)
Yang Yu
2017-01-01
Full Text Available Network intrusion detection is one of the most important parts for cyber security to protect computer systems against malicious attacks. With the emergence of numerous sophisticated and new attacks, however, network intrusion detection techniques are facing several significant challenges. The overall objective of this study is to learn useful feature representations automatically and efficiently from large amounts of unlabeled raw network traffic data by using deep learning approaches. We propose a novel network intrusion model by stacking dilated convolutional autoencoders and evaluate our method on two new intrusion detection datasets. Several experiments were carried out to check the effectiveness of our approach. The comparative experimental results demonstrate that the proposed model can achieve considerably high performance which meets the demand of high accuracy and adaptability of network intrusion detection systems (NIDSs. It is quite potential and promising to apply our model in the large-scale and real-world network environments.
Finger vein recognition based on convolutional neural network
Directory of Open Access Journals (Sweden)
Meng Gesi
2017-01-01
Full Text Available Biometric Authentication Technology has been widely used in this information age. As one of the most important technology of authentication, finger vein recognition attracts our attention because of its high security, reliable accuracy and excellent performance. However, the current finger vein recognition system is difficult to be applied widely because its complicated image pre-processing and not representative feature vectors. To solve this problem, a finger vein recognition method based on the convolution neural network (CNN is proposed in the paper. The image samples are directly input into the CNN model to extract its feature vector so that we can make authentication by comparing the Euclidean distance between these vectors. Finally, the Deep Learning Framework Caffe is adopted to verify this method. The result shows that there are great improvements in both speed and accuracy rate compared to the previous research. And the model has nice robustness in illumination and rotation.
Fully convolutional network with cluster for semantic segmentation
Ma, Xiao; Chen, Zhongbi; Zhang, Jianlin
2018-04-01
At present, image semantic segmentation technology has been an active research topic for scientists in the field of computer vision and artificial intelligence. Especially, the extensive research of deep neural network in image recognition greatly promotes the development of semantic segmentation. This paper puts forward a method based on fully convolutional network, by cluster algorithm k-means. The cluster algorithm using the image's low-level features and initializing the cluster centers by the super-pixel segmentation is proposed to correct the set of points with low reliability, which are mistakenly classified in great probability, by the set of points with high reliability in each clustering regions. This method refines the segmentation of the target contour and improves the accuracy of the image segmentation.
Real Time Eye Detector with Cascaded Convolutional Neural Networks
Directory of Open Access Journals (Sweden)
Bin Li
2018-01-01
Full Text Available An accurate and efficient eye detector is essential for many computer vision applications. In this paper, we present an efficient method to evaluate the eye location from facial images. First, a group of candidate regions with regional extreme points is quickly proposed; then, a set of convolution neural networks (CNNs is adopted to determine the most likely eye region and classify the region as left or right eye; finally, the center of the eye is located with other CNNs. In the experiments using GI4E, BioID, and our datasets, our method attained a detection accuracy which is comparable to existing state-of-the-art methods; meanwhile, our method was faster and adaptable to variations of the images, including external light changes, facial occlusion, and changes in image modality.
Convolution product construction of interactions in probabilistic physical models
International Nuclear Information System (INIS)
Ratsimbarison, H.M.; Raboanary, R.
2007-01-01
This paper aims to give a probabilistic construction of interactions which may be relevant for building physical theories such as interacting quantum field theories. We start with the path integral definition of partition function in quantum field theory which recall us the probabilistic nature of this physical theory. From a Gaussian law considered as free theory, an interacting theory is constructed by nontrivial convolution product between the free theory and an interacting term which is also a probability law. The resulting theory, again a probability law, exhibits two proprieties already present in nowadays theories of interactions such as Gauge theory : the interaction term does not depend on the free term, and two different free theories can be implemented with the same interaction.
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.
Deep learning with convolutional neural network in radiology.
Yasaka, Koichiro; Akai, Hiroyuki; Kunimatsu, Akira; Kiryu, Shigeru; Abe, Osamu
2018-04-01
Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.
Static facial expression recognition with convolution neural networks
Zhang, Feng; Chen, Zhong; Ouyang, Chao; Zhang, Yifei
2018-03-01
Facial expression recognition is a currently active research topic in the fields of computer vision, pattern recognition and artificial intelligence. In this paper, we have developed a convolutional neural networks (CNN) for classifying human emotions from static facial expression into one of the seven facial emotion categories. We pre-train our CNN model on the combined FER2013 dataset formed by train, validation and test set and fine-tune on the extended Cohn-Kanade database. In order to reduce the overfitting of the models, we utilized different techniques including dropout and batch normalization in addition to data augmentation. According to the experimental result, our CNN model has excellent classification performance and robustness for facial expression recognition.
Chmiel, Malgorzata; Roux, Philippe; Herrmann, Philippe; Rondeleux, Baptiste; Wathelet, Marc
2018-05-01
We investigated the construction of diffraction kernels for surface waves using two-point convolution and/or correlation from land active seismic data recorded in the context of exploration geophysics. The high density of controlled sources and receivers, combined with the application of the reciprocity principle, allows us to retrieve two-dimensional phase-oscillation diffraction kernels (DKs) of surface waves between any two source or receiver points in the medium at each frequency (up to 15 Hz, at least). These DKs are purely data-based as no model calculations and no synthetic data are needed. They naturally emerge from the interference patterns of the recorded wavefields projected on the dense array of sources and/or receivers. The DKs are used to obtain multi-mode dispersion relations of Rayleigh waves, from which near-surface shear velocity can be extracted. Using convolution versus correlation with a grid of active sources is an important step in understanding the physics of the retrieval of surface wave Green's functions. This provides the foundation for future studies based on noise sources or active sources with a sparse spatial distribution.
DeepNAT: Deep convolutional neural network for segmenting neuroanatomy.
Wachinger, Christian; Reuter, Martin; Klein, Tassilo
2018-04-15
We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground. Since patches lack spatial context, we augment them with coordinates. To this end, we introduce a novel intrinsic parameterization of the brain volume, formed by eigenfunctions of the Laplace-Beltrami operator. As network architecture, we use three convolutional layers with pooling, batch normalization, and non-linearities, followed by fully connected layers with dropout. The final segmentation is inferred from the probabilistic output of the network with a 3D fully connected conditional random field, which ensures label agreement between close voxels. The roughly 2.7million parameters in the network are learned with stochastic gradient descent. Our results show that DeepNAT compares favorably to state-of-the-art methods. Finally, the purely learning-based method may have a high potential for the adaptation to young, old, or diseased brains by fine-tuning the pre-trained network with a small training sample on the target application, where the availability of larger datasets with manual annotations may boost the overall segmentation accuracy in the future. Copyright © 2017 Elsevier Inc. All rights reserved.
FULLY CONVOLUTIONAL NETWORKS FOR GROUND CLASSIFICATION FROM LIDAR POINT CLOUDS
Directory of Open Access Journals (Sweden)
A. Rizaldy
2018-05-01
Full Text Available Deep Learning has been massively used for image classification in recent years. The use of deep learning for ground classification from LIDAR point clouds has also been recently studied. However, point clouds need to be converted into an image in order to use Convolutional Neural Networks (CNNs. In state-of-the-art techniques, this conversion is slow because each point is converted into a separate image. This approach leads to highly redundant computation during conversion and classification. The goal of this study is to design a more efficient data conversion and ground classification. This goal is achieved by first converting the whole point cloud into a single image. The classification is then performed by a Fully Convolutional Network (FCN, a modified version of CNN designed for pixel-wise image classification. The proposed method is significantly faster than state-of-the-art techniques. On the ISPRS Filter Test dataset, it is 78 times faster for conversion and 16 times faster for classification. Our experimental analysis on the same dataset shows that the proposed method results in 5.22 % of total error, 4.10 % of type I error, and 15.07 % of type II error. Compared to the previous CNN-based technique and LAStools software, the proposed method reduces the total error and type I error (while type II error is slightly higher. The method was also tested on a very high point density LIDAR point clouds resulting in 4.02 % of total error, 2.15 % of type I error and 6.14 % of type II error.
Fully Convolutional Networks for Ground Classification from LIDAR Point Clouds
Rizaldy, A.; Persello, C.; Gevaert, C. M.; Oude Elberink, S. J.
2018-05-01
Deep Learning has been massively used for image classification in recent years. The use of deep learning for ground classification from LIDAR point clouds has also been recently studied. However, point clouds need to be converted into an image in order to use Convolutional Neural Networks (CNNs). In state-of-the-art techniques, this conversion is slow because each point is converted into a separate image. This approach leads to highly redundant computation during conversion and classification. The goal of this study is to design a more efficient data conversion and ground classification. This goal is achieved by first converting the whole point cloud into a single image. The classification is then performed by a Fully Convolutional Network (FCN), a modified version of CNN designed for pixel-wise image classification. The proposed method is significantly faster than state-of-the-art techniques. On the ISPRS Filter Test dataset, it is 78 times faster for conversion and 16 times faster for classification. Our experimental analysis on the same dataset shows that the proposed method results in 5.22 % of total error, 4.10 % of type I error, and 15.07 % of type II error. Compared to the previous CNN-based technique and LAStools software, the proposed method reduces the total error and type I error (while type II error is slightly higher). The method was also tested on a very high point density LIDAR point clouds resulting in 4.02 % of total error, 2.15 % of type I error and 6.14 % of type II error.
Color encoding in biologically-inspired convolutional neural networks.
Rafegas, Ivet; Vanrell, Maria
2018-05-11
Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore how color is encoded in a trained artificial network. It is performed by estimating a color selectivity index for each neuron, which allows us to describe the neuron activity to a color input stimuli. The index allows us to classify whether they are color selective or not and if they are of a single or double color. We have determined that all five convolutional layers of the network have a large number of color selective neurons. Color opponency clearly emerges in the first layer, presenting 4 main axes (Black-White, Red-Cyan, Blue-Yellow and Magenta-Green), but this is reduced and rotated as we go deeper into the network. In layer 2 we find a denser hue sampling of color neurons and opponency is reduced almost to one new main axis, the Bluish-Orangish coinciding with the dataset bias. In layers 3, 4 and 5 color neurons are similar amongst themselves, presenting different type of neurons that detect specific colored objects (e.g., orangish faces), specific surrounds (e.g., blue sky) or specific colored or contrasted object-surround configurations (e.g. blue blob in a green surround). Overall, our work concludes that color and shape representation are successively entangled through all the layers of the studied network, revealing certain parallelisms with the reported evidences in primate brains that can provide useful insight into intermediate hierarchical spatio-chromatic representations. Copyright © 2018 Elsevier Ltd. All rights reserved.
Fully convolutional neural networks improve abdominal organ segmentation
Bobo, Meg F.; Bao, Shunxing; Huo, Yuankai; Yao, Yuang; Virostko, Jack; Plassard, Andrew J.; Lyu, Ilwoo; Assad, Albert; Abramson, Richard G.; Hilmes, Melissa A.; Landman, Bennett A.
2018-03-01
Abdominal image segmentation is a challenging, yet important clinical problem. Variations in body size, position, and relative organ positions greatly complicate the segmentation process. Historically, multi-atlas methods have achieved leading results across imaging modalities and anatomical targets. However, deep learning is rapidly overtaking classical approaches for image segmentation. Recently, Zhou et al. showed that fully convolutional networks produce excellent results in abdominal organ segmentation of computed tomography (CT) scans. Yet, deep learning approaches have not been applied to whole abdomen magnetic resonance imaging (MRI) segmentation. Herein, we evaluate the applicability of an existing fully convolutional neural network (FCNN) designed for CT imaging to segment abdominal organs on T2 weighted (T2w) MRI's with two examples. In the primary example, we compare a classical multi-atlas approach with FCNN on forty-five T2w MRI's acquired from splenomegaly patients with five organs labeled (liver, spleen, left kidney, right kidney, and stomach). Thirty-six images were used for training while nine were used for testing. The FCNN resulted in a Dice similarity coefficient (DSC) of 0.930 in spleens, 0.730 in left kidneys, 0.780 in right kidneys, 0.913 in livers, and 0.556 in stomachs. The performance measures for livers, spleens, right kidneys, and stomachs were significantly better than multi-atlas (p < 0.05, Wilcoxon rank-sum test). In a secondary example, we compare the multi-atlas approach with FCNN on 138 distinct T2w MRI's with manually labeled pancreases (one label). On the pancreas dataset, the FCNN resulted in a median DSC of 0.691 in pancreases versus 0.287 for multi-atlas. The results are highly promising given relatively limited training data and without specific training of the FCNN model and illustrate the potential of deep learning approaches to transcend imaging modalities. 1
A frequency bin-wise nonlinear masking algorithm in convolutive mixtures for speech segregation.
Chi, Tai-Shih; Huang, Ching-Wen; Chou, Wen-Sheng
2012-05-01
A frequency bin-wise nonlinear masking algorithm is proposed in the spectrogram domain for speech segregation in convolutive mixtures. The contributive weight from each speech source to a time-frequency unit of the mixture spectrogram is estimated by a nonlinear function based on location cues. For each sound source, a non-binary mask is formed from the estimated weights and is multiplied to the mixture spectrogram to extract the sound. Head-related transfer functions (HRTFs) are used to simulate convolutive sound mixtures perceived by listeners. Simulation results show our proposed method outperforms convolutive independent component analysis and degenerate unmixing and estimation technique methods in almost all test conditions.
Development and application of deep convolutional neural network in target detection
Jiang, Xiaowei; Wang, Chunping; Fu, Qiang
2018-04-01
With the development of big data and algorithms, deep convolution neural networks with more hidden layers have more powerful feature learning and feature expression ability than traditional machine learning methods, making artificial intelligence surpass human level in many fields. This paper first reviews the development and application of deep convolutional neural networks in the field of object detection in recent years, then briefly summarizes and ponders some existing problems in the current research, and the future development of deep convolutional neural network is prospected.
Winters, Andrew C.
Careful observational work has demonstrated that the tropopause is typically characterized by a three-step pole-to-equator structure, with each break between steps in the tropopause height associated with a jet stream. While the two jet streams, the polar and subtropical jets, typically occupy different latitude bands, their separation can occasionally vanish, resulting in a vertical superposition of the two jets. A cursory examination of a number of historical and recent high-impact weather events over North America and the North Atlantic indicates that superposed jets can be an important component of their evolution. Consequently, this dissertation examines two recent jet superposition cases, the 18--20 December 2009 Mid-Atlantic Blizzard and the 1--3 May 2010 Nashville Flood, in an effort (1) to determine the specific influence that a superposed jet can have on the development of a high-impact weather event and (2) to illuminate the processes that facilitated the production of a superposition in each case. An examination of these cases from a basic-state variable and PV inversion perspective demonstrates that elements of both the remote and local synoptic environment are important to consider while diagnosing the development of a jet superposition. Specifically, the process of jet superposition begins with the remote production of a cyclonic (anticyclonic) tropopause disturbance at high (low) latitudes. The cyclonic circulation typically originates at polar latitudes, while organized tropical convection can encourage the development of an anticyclonic circulation anomaly within the tropical upper-troposphere. The concurrent advection of both anomalies towards middle latitudes subsequently allows their individual circulations to laterally displace the location of the individual tropopause breaks. Once the two circulation anomalies position the polar and subtropical tropopause breaks in close proximity to one another, elements within the local environment, such as
Energy Technology Data Exchange (ETDEWEB)
Prajapati, S [M D Anderson Cancer Center, Houston, TX (United States); Mo, X; Bednarz, B; Lawless, M; Hammer, C; Jeraj, R; Mackie, T [University of Wisconsin- Madison, Madison, WI (United States); Flynn, R [University of Iowa Hospitals and Clinics, Iowa City, IA (United States); Westerly, D [University of Colorado Denver, Aurora, CO (United States)
2016-06-15
Purpose: An open-source, convolution/superposition based kV-treatment planning system(TPS) was developed for small animal radiotherapy from previously existed in-house MV-TPS. It is flexible and applicable to both step and shoot and helical tomotherapy treatment delivery. For initial commissioning process, the dose calculation from kV-TPS was compared with measurements and Monte Carlo(MC) simulations. Methods: High resolution, low energy kernels were simulated using EGSnrc user code EDKnrc, which was used as an input in kV-TPS together with MC-simulated x-ray beam spectrum. The Blue Water™ homogeneous phantom (with film inserts) and heterogeneous phantom (with film and TLD inserts) were fabricated. Phantom was placed at 100cm SSD, and was irradiated with 250 kVp beam for 10mins with 1.1cm × 1.1cm open field (at 100cm) created by newly designed binary micro-MLC assembly positioned at 90cm SSD. Gafchromic™ EBT3 film was calibrated in-phantom following AAPM TG-61 guidelines, and were used for measurement at 5 different depths in phantom. Calibrated TLD-100s were obtained from ADCL. EGS and MNCP5 simulation were used to model experimental irradiation set up calculation of dose in phantom. Results: Using the homogeneous phantom, dose difference between film and kV-TPS was calculated: mean(x)=0.9%; maximum difference(MD)=3.1%; standard deviation(σ)=1.1%. Dose difference between MCNP5 and kV-TPS was: x=1.5%; MD=4.6%; σ=1.9%. Dose difference between EGS and kV-TPS was: x=0.8%; MD=1.9%; σ=0.8%. Using the heterogeneous phantom, dose difference between film and kV-TPS was: x=2.6%; MD=3%; σ=1.1%; and dose difference between TLD and kV-TPS was: x=2.9%; MD=6.4%; σ=2.5%. Conclusion: The inhouse, open-source kV-TPS dose calculation system was comparable within 5% of measurements and MC simulations in both homogeneous and heterogeneous phantoms. The dose calculation system of the kV-TPS is validated as a part of initial commissioning process for small animal radiotherapy
National Oceanic and Atmospheric Administration, Department of Commerce — Declination is calculated using the current International Geomagnetic Reference Field (IGRF) model. Declination is calculated using the current World Magnetic Model...
CSIR Research Space (South Africa)
Schwegmann, Colin P
2017-07-01
Full Text Available International Geoscience and Remote Sensing Symposium (IGARSS), 23-28 July 2017, Fort Worth, TX, USA SUBSIDENCE FEATURE DISCRIMINATION USING DEEP CONVOLUTIONAL NEURAL NETWORKS IN SYNTHETIC APERTURE RADAR IMAGERY Schwegmann, Colin P Kleynhans, Waldo...
A Study of Recurrent and Convolutional Neural Networks in the Native Language Identification Task
Werfelmann, Robert
2018-01-01
around the world. The neural network models consisted of Long Short-Term Memory and Convolutional networks using the sentences of each document as the input. Additional statistical features were generated from the text to complement the predictions
Convolution of large 3D images on GPU and its decomposition
Karas, Pavel; Svoboda, David
2011-12-01
In this article, we propose a method for computing convolution of large 3D images. The convolution is performed in a frequency domain using a convolution theorem. The algorithm is accelerated on a graphic card by means of the CUDA parallel computing model. Convolution is decomposed in a frequency domain using the decimation in frequency algorithm. We pay attention to keeping our approach efficient in terms of both time and memory consumption and also in terms of memory transfers between CPU and GPU which have a significant inuence on overall computational time. We also study the implementation on multiple GPUs and compare the results between the multi-GPU and multi-CPU implementations.
Single Image Super-Resolution Based on Multi-Scale Competitive Convolutional Neural Network.
Du, Xiaofeng; Qu, Xiaobo; He, Yifan; Guo, Di
2018-03-06
Deep convolutional neural networks (CNNs) are successful in single-image super-resolution. Traditional CNNs are limited to exploit multi-scale contextual information for image reconstruction due to the fixed convolutional kernel in their building modules. To restore various scales of image details, we enhance the multi-scale inference capability of CNNs by introducing competition among multi-scale convolutional filters, and build up a shallow network under limited computational resources. The proposed network has the following two advantages: (1) the multi-scale convolutional kernel provides the multi-context for image super-resolution, and (2) the maximum competitive strategy adaptively chooses the optimal scale of information for image reconstruction. Our experimental results on image super-resolution show that the performance of the proposed network outperforms the state-of-the-art methods.
Meszlényi, Regina J; Buza, Krisztian; Vidnyánszky, Zoltán
2017-01-01
Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network.
A Revised Piecewise Linear Recursive Convolution FDTD Method for Magnetized Plasmas
International Nuclear Information System (INIS)
Liu Song; Zhong Shuangying; Liu Shaobin
2005-01-01
The piecewise linear recursive convolution (PLRC) finite-different time-domain (FDTD) method improves accuracy over the original recursive convolution (RC) FDTD approach and current density convolution (JEC) but retains their advantages in speed and efficiency. This paper describes a revised piecewise linear recursive convolution PLRC-FDTD formulation for magnetized plasma which incorporates both anisotropy and frequency dispersion at the same time, enabling the transient analysis of magnetized plasma media. The technique is illustrated by numerical simulations of the reflection and transmission coefficients through a magnetized plasma layer. The results show that the revised PLRC-FDTD method has improved the accuracy over the original RC FDTD method and JEC FDTD method
Deep Fully Convolutional Networks for the Detection of Informal Settlements in VHR Images
Persello, Claudio; Stein, Alfred
2017-01-01
This letter investigates fully convolutional networks (FCNs) for the detection of informal settlements in very high resolution (VHR) satellite images. Informal settlements or slums are proliferating in developing countries and their detection and classification provides vital information for
Fourier transform and mean quadratic variation of Bernoulli convolution on homogeneous Cantor set
Energy Technology Data Exchange (ETDEWEB)
Yu Zuguo E-mail: yuzg@hotmail.comz.yu
2004-07-01
For the Bernoulli convolution on homogeneous Cantor set, under some condition, it is proved that the mean quadratic variation and the average of Fourier transform of this measure are bounded above and below.
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.
The neuro vector engine : flexibility to improve convolutional net efficiency for wearable vision
Peemen, M.C.J.; Shi, R.; Lal, S.; Juurlink, B.H.H.; Mesman, B.; Corporaal, H.
2016-01-01
Deep Convolutional Networks (ConvNets) are currently superior in benchmark performance, but the associated demands on computation and data transfer prohibit straightforward mapping on energy constrained wearable platforms. The computational burden can be overcome by dedicated hardware accelerators,