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Готові списки джерел за темами / ENHANCED CORRELATION / Статті в журналах

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Статті в журналах з теми "ENHANCED CORRELATION"

Автор: Grafiati

Опубліковано: 16 січня 2024

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1

Xie, Boya, and Sheng Feng. "Heterodyne detection enhanced by quantum correlation." Chinese Optics Letters 19, no. 7 (2021): 072701. http://dx.doi.org/10.3788/col202119.072701.

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2

Sanchis, Charlotte, and Alfred Hanssen. "Enhanced local correlation stacking method." GEOPHYSICS 76, no. 3 (May 2011): V33—V45. http://dx.doi.org/10.1190/1.3552687.

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Анотація:

Stacking is a common technique to improve the signal-to-noise ratio (S/N) and the imaging quality of seismic data. Conventional stacking that averages equally a collection of normal moveout corrected or migrated shot gathers with a common reflection point is not always satisfactory. Instead, we propose a novel time-dependent weighted average stacking method that utilizes local correlation between each individual trace and a chosen reference trace as a measure of weight and a new weight normalization scheme that ensures meaningful amplitudes of the output. Three different reference traces have been proposed. These are based on conventional stacking, S/N estimation, and Kalman filtering. The outputs of the enhanced stacking methods, as well as their reference traces, were compared on both synthetic data and real marine migrated subsalt data. We conclude that both S/N estimation and Kalman reference stacking methods as well as the output of the enhanced stacking method yield consistently better results than conventional stacking. They exhibit cleaner and better defined reflection events and a larger number of reflections. We found that the Kalman reference method produces the best overall seismic image contrast and reveals many more reflected events, but at the cost of a higher noise level and a longer processing time. Thus, enhanced stacking using S/N estimation as reference method is a possible alternative that has the advantages of running faster, but also emphasizes some reflected events under the subsalt structure.

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3

Alam, M. S., and M. A. Karim. "Enhanced correlation discrimination using binary joint transform correlation with feedback." Microwave and Optical Technology Letters 5, no. 14 (December 20, 1992): 752–57. http://dx.doi.org/10.1002/mop.4650051415.

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4

Wang, Hongfei, and Kun He. "Sub‐population prediction using enhanced correlation filters." Electronics Letters 54, no. 13 (June 2018): 831–33. http://dx.doi.org/10.1049/el.2018.0338.

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5

Jian, Muwei, Yue Jin, and Hui Yu. "Enhanced Temporal Correlation for Universal Lesion Detection." Computer Modeling in Engineering & Sciences 138, no. 3 (2024): 3051–63. http://dx.doi.org/10.32604/cmes.2023.030236.

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6

Zeng, Chunhua, Tao Yang, Qinglin Han, Chun Zhang, Dong Tian, and Hua Wang. "Noises-induced toggle switch and stability in a gene regulation network." International Journal of Modern Physics B 28, no. 31 (December 8, 2014): 1450223. http://dx.doi.org/10.1142/s0217979214502233.

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It is well-known that noises are inevitable in gene regulatory networks due to the low-copy numbers of molecules and environmental fluctuations. In this paper, we investigate the stationary probability distribution (SPD) between both low (OFF state) and high (ON state) protein levels and mean first passage time (MFPT) in an abstract model of the Myc/E2F/miR-17-92 network presented by Aguda et al., PNAS 105, 19678 (2008), where the gene expression is assumed to be disturbed simultaneously by intrinsic and extrinsic noises that were correlated. Our results show that (i) the OFF state is enhanced by the extrinsic noise (D), while the ON state is enhanced by the intrinsic noise (Q) or cross-correlation between two noises (λ); (ii) for the cases of negative or no cross-correlation (λ⩽0.0), the increase of the noise intensity (D or Q) leads to a decline of the MFPT and enhances the probability of toggle switch to the OFF state; (iii) but for the case of positive cross-correlation (λ>0.0), the MFPT as a function of the noise intensity (D or Q) exhibits a maximum, this maximum for MFPT identifies the characteristic of noise enhanced stability of the ON state and (iv) the cross-correlation between two noises can enhance stability of the ON state.

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7

Amirruddin, Melaty, Mohd Rafi Adzman, Nur Adyani Mohd Affendi, Muhd Hafizi Idris, and Syahirah Abd Halim. "Arcing fault diagnosis using enhanced cross-correlation technique." Journal of Physics: Conference Series 2312, no. 1 (August 1, 2022): 012066. http://dx.doi.org/10.1088/1742-6596/2312/1/012066.

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Abstract This study explored the potential use of cross-correlation as a technique for detecting arcing faults in a power system distribution network. The cross-correlation technique was employed to investigate the effect of each antenna placement as a detection device, time difference of arrival (TDOA), time delay, and correlation magnitude of arcing signals detected during on-line arcing fault measurement. The arcing fault was detected using four antennas that had been set up around the arc source point in a high voltage (HV) laboratory. The measurements were taken using a digital oscilloscope. For precise results, the Discrete Wavelet Transform (DWT) denoising technique combined with cross-correlation (CC) technique were applied using MATLAB software to identify the arcing signals detected in order to diagnose the differentiation between noisy and real arcing fault signals. Further assessment was carried out by performing a cross-correlation technique on the real arcing signals obtained to find the similarities and arrival time’s delay between single arcing signals’ placement. The outcome shows that all measurements including the time difference of arrival (TDOA), correlation magnitude, time delay, and antennas’ placement towards the arcing source point are valuable in determining the arcing signals detected precisely.

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8

Ma, Hong-Mei, Li-Qing Chen, and Chun-Hua Yuan. "Cascade correlation-enhanced Raman scattering in atomic vapors." Chinese Physics B 25, no. 12 (November 29, 2016): 124206. http://dx.doi.org/10.1088/1674-1056/25/12/124206.

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9

Evangelidis, G. D., and E. Z. Psarakis. "Parametric Image Alignment Using Enhanced Correlation Coefficient Maximization." IEEE Transactions on Pattern Analysis and Machine Intelligence 30, no. 10 (October 2008): 1858–65. http://dx.doi.org/10.1109/tpami.2008.113.

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10

Langguth, Lutz, and A. Femius Koenderink. "Simple model for plasmon enhanced fluorescence correlation spectroscopy." Optics Express 22, no. 13 (June 17, 2014): 15397. http://dx.doi.org/10.1364/oe.22.015397.

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11

Magill, J. V., and J. H. R. Clarke. "Photon correlation spectroscopy using resonance-enhanced light scattering." Journal of Physical Chemistry 89, no. 5 (February 1985): 734–37. http://dx.doi.org/10.1021/j100251a003.

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12

Barbara, A., F. Dubois, P. Quémerais, and L. Eng. "Non-resonant and non-enhanced Raman Correlation Spectroscopy." Optics Express 21, no. 13 (June 20, 2013): 15418. http://dx.doi.org/10.1364/oe.21.015418.

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13

Chen, Kai, Wenbing Tao, and Shoudong Han. "Visual object tracking via enhanced structural correlation filter." Information Sciences 394-395 (July 2017): 232–45. http://dx.doi.org/10.1016/j.ins.2017.02.012.

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14

Wang, Yang, Luxiang Xu, Yu Sui, Xianjie Wang, Jinguang Cheng, and Wenhui Su. "Enhanced electron correlation in rare-earth doped Ca3Co4O9." Applied Physics Letters 97, no. 6 (August 9, 2010): 062114. http://dx.doi.org/10.1063/1.3479923.

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15

Tyburn, Jean-Max, Ian M. Brereton, and David M. Doddrell. "Coherence selection in gradient-enhanced, heteronuclear correlation spectroscopy." Journal of Magnetic Resonance (1969) 97, no. 2 (April 1992): 305–12. http://dx.doi.org/10.1016/0022-2364(92)90315-x.

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16

Li, Y., T. Sato, and K. L. Ishikawa. "High-harmonic generation enhanced by dynamical electron correlation." Journal of Physics: Conference Series 1412 (January 2020): 072012. http://dx.doi.org/10.1088/1742-6596/1412/7/072012.

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17

Sunian, Xiaobing, Wei Liu, Lei Chen, Zhihao Zhou, Dongyi Shen, Yuehan Liu, and Wenjie Wan. "Resolution enhanced photothermal imaging by high-order correlation." Optics Letters 45, no. 20 (October 9, 2020): 5696. http://dx.doi.org/10.1364/ol.396780.

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18

Ave, M., L. Cazon, J. Cronin, J. R. T. de Mello Neto, A. V. Olinto, V. Pavlidou, P. Privitera, B. B. Siffert, F. Schmidt, and T. Venters. "The 2pt+: an enhanced 2 point correlation function." Journal of Cosmology and Astroparticle Physics 2009, no. 07 (July 15, 2009): 023. http://dx.doi.org/10.1088/1475-7516/2009/07/023.

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19

Mai, Vu M., Sean Tutton, Pottumarthi V. Prasad, Qun Chen, Wei Li, Chi Chen, Benjamin Liu, Jason Polzin, Saban Kurucay, and Robert R. Edelman. "Computing oxygen-enhanced ventilation maps using correlation analysis." Magnetic Resonance in Medicine 49, no. 3 (February 14, 2003): 591–94. http://dx.doi.org/10.1002/mrm.10395.

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20

Li, Huiyun, Keke Wu, and Fengqi Yu. "Enhanced correlation power analysis attack against trusted systems." Security and Communication Networks 4, no. 1 (December 29, 2010): 3–10. http://dx.doi.org/10.1002/sec.176.

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21

Dykstra, Clifford E., and Ernest R. Davidson. "Enhanced second-order treatment of electron pair correlation." International Journal of Quantum Chemistry 78, no. 4 (2000): 226–36. http://dx.doi.org/10.1002/(sici)1097-461x(2000)78:4<226::aid-qua4>3.0.co;2-n.

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22

Liang, Xinyun, Zhifei Yu, Chun-Hua Yuan, Weiping Zhang, and Liqing Chen. "Phase Sensitivity Improvement in Correlation-Enhanced Nonlinear Interferometers." Symmetry 14, no. 12 (December 19, 2022): 2684. http://dx.doi.org/10.3390/sym14122684.

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Анотація:

Interferometers are widely used as sensors in precision measurement. Compared with a conventional Mach–Zehnder interferometer, the sensitivity of a correlation-enhanced nonlinear interferometer can break the standard quantum limit. Phase sensitivity plays a significant role in the enhanced performance. In this paper, we review improvement in phase estimation technologies in correlation-enhanced nonlinear interferometers, including SU(1,1) interferometer and SU(1,1)-SU(2) hybrid interferometer, and so on, and the applications in quantum metrology and quantum sensing networks.

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23

Shaye Alghofeli, Mashael. "The Correlation between Supply Chain Performance and Information Technology." Tehnički glasnik 17, no. 1 (February 1, 2023): 81–87. http://dx.doi.org/10.31803/tg-20220826130310.

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Анотація:

Supply Chain Performance is regarded as a crucial organizational performance indicators. Much effort has been made to enhance it. So, effective and efficient supply chains are recognized as a crucial strategic aspect for achieving several essential corporate objectives, such as improved customer service, enhanced efficiency, and enhanced competitiveness. Therefore, the objective of this study is to analyze the role of information technology on supply chain performance with information technology support as a mediator. The study has been designed based on a descriptive approach using a survey to collect data by questionnaire from Saudi logistics professionals. The potential respondents were members of Hyper Panda's top level of management and executive personnel who were familiar with the company's operations and supply chain procedures. There are 83 valid responses. The research result has several findings and outcomes that will inform future research. The study findings will shed light on choosing the most effective information technology with a significant influence on supply chain performance.

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24

Liu, Lei, Genwen Fang, Jun Wang, Shuai Wang, Chun Wang, Longfeng Shen, Kongfen Zhu, and Silas N. Melo. "Consistent Weighted Correlation-Based Attention for Transformer Tracking." Electronics 12, no. 22 (November 15, 2023): 4648. http://dx.doi.org/10.3390/electronics12224648.

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Attention mechanism takes a crucial role among the key technologies in transformer-based visual tracking. However, the current methods for attention computing neglect the correlation between the query and the key, which results in erroneous correlations. To address this issue, a CWCTrack framework is proposed in this study for transformer visual tracking. To balance the weights of the attention module and enhance the feature extraction of the search region and template region, a consistent weighted correlation (CWC) module is introduced into the cross-attention block. The CWC module computes the correlation score between each query and all keys. Then, the correlation multiplies the consistent weights of the other query–key pairs to acquire the final attention weights. The weights of consistency are computed by the relevance of the query–key pairs. The correlation is enhanced for the relevant query–key pair and suppressed for the irrelevant query–key pair. Experimental results conducted on four prevalent benchmarks demonstrate that the proposed CWCTrack yields preferable performances.

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25

Ma, Cheng, Kai Sun, Lei Chang, and Zhijian Qu. "Enhanced Information Graph Recursive Network for Traffic Forecasting." Electronics 12, no. 11 (June 2, 2023): 2519. http://dx.doi.org/10.3390/electronics12112519.

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Accurate traffic forecasting is crucial for the advancement of smart cities. Although there have been many studies on traffic forecasting, the accurate forecasting of traffic volume is still a challenge. To effectively capture the spatio-temporal correlations of traffic data, a deep learning-based traffic volume forecasting model called the Enhanced Information Graph Recursive Network (EIGRN) is presented in this paper. The model consists of three main parts: a Graph Embedding Adaptive Graph Convolution Network (GE-AGCN), a Modified Gated Recursive Unit (MGRU), and a local information enhancement module. The local information enhancement module is composed of a convolutional neural network (CNN), a transposed convolutional neural network, and an attention mechanism. In the EIGRN, the GE-AGCN is used to capture the spatial correlation of the traffic network by adaptively learning the hidden information of the complex topology, the MGRU is employed to capture the temporal correlation by learning the time change of the traffic volume, and the local information enhancement module is employed to capture the global and local correlations of the traffic volume. The EIGRN was evaluated using the real datasets PEMS-BAY and PeMSD7(M) to assess its predictive performance The results indicate that the forecasting performance of the EIGRN is better than the comparison models.

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26

Chen, Xu, Hong-Yi Su, Zhen-Peng Xu, Yu-Chun Wu, and Jing-Ling Chen. "Quantum nonlocality enhanced by homogenization." International Journal of Quantum Information 12, no. 06 (September 2014): 1450040. http://dx.doi.org/10.1142/s0219749914500403.

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Homogenization proposed in [Y.-C Wu and M. Żukowski, Phys. Rev. A 85 (2012) 022119] is a procedure to transform a tight Bell inequality with partial correlations into a full-correlation form that is also tight. In this paper, we check the homogenizations of two families of n-partite Bell inequalities: the Hardy inequality and the tight Bell inequality without quantum violation. For Hardy's inequalities, their homogenizations bear stronger quantum violation for the maximally entangled state; the tight Bell inequalities without quantum violation give the boundary of quantum and supra-quantum, but their homogenizations do not have the similar properties. We find their homogenization are violated by the maximally entangled state. Numerically computation shows the the domains of quantum violation of homogenized Hardy's inequalities for the generalized GHZ states are smaller than those of Hardy's inequalities.

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27

Alsarhan, Tamam, Osama Harfoushi, Ahmed Younes Shdefat, Nour Mostafa, Mohammad Alshinwan, and Ahmad Ali. "Improved Graph Convolutional Network with Enriched Graph Topology Representation for Skeleton-Based Action Recognition." Electronics 12, no. 4 (February 9, 2023): 879. http://dx.doi.org/10.3390/electronics12040879.

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Lately, skeleton-based action recognition has drawn remarkable attention to graph convolutional networks (GCNs). Recent methods have focused on graph learning because graph topology is the key to GCNs. We propose to align graph learning on the channel level by introducing graph convolution with enriched topology based on careful channel-wise correlations, namely the attentive channel-wise correlation graph convolution (ACC-GC). For the model to learn channel-wise enriched topologies, ACC-GC learns a shared graph topology spanning many channels and enhances it with careful channel-wise correlations. Encoding the intra-correlation between various nodes within each channel, boosting informative channel-wise correlations, and suppressing trivial ones generates attentive channel-wise correlations. Our enhanced ACC-GCN is created by substituting our ACC-GC for the GC in a standard GCN. Extensive experiments on NTURGB60 and Northwestern-UCLA datasets demonstrate that our proposed ACC-GCN performs comparably to state-of-the-art methods while reducing the computational cost.

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28

Wen Hwooi, Stephen Khor, and Aznul Qalid Md Sabri. "INVESTIGATION OF IMAGE STITCHING REFINEMENT WITH ENHANCED CORRELATION COEFFICIENT." Malaysian Journal of Computer Science 33, no. 1 (January 31, 2020): 22–34. http://dx.doi.org/10.22452/mjcs.vol33no1.2.

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Image stitching combines multiple overlapping scenes into a panorama. It can be applied in video stitching, super-resolution, and medical imaging. Post-processing routines such as exposure compensation, seam line adjustment, and blending are often performed to increase its visual appeal. Our aim is to increase the registration accuracy between adjacent image pairs in a stitching algorithm. This is done by adding an area-based registration step, namely Enhanced Correlation Coefficient (ECC), which refines the original feature-based image registration. The performance of registrations in the stitching algorithm is evaluated using root-mean-squared error of control points, structural similarity index, and universal image quality index. The Boat and Graffiti datasets from the Oxford Robotics Database are used for experiments. It is found that ECC largely improves registrations in stitching except for a slight increase in root-mean-squared error of control points in the Boat dataset. In addition, ECC’s enhancement makes the registration outperform the ground truth in one image of both datasets.

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29

Rossides, George, Alan Hunter, and Benjamin Metcalfe. "Source Localisation Using Wavefield Correlation-Enhanced Particle Swarm Optimisation." Robotics 11, no. 2 (April 18, 2022): 52. http://dx.doi.org/10.3390/robotics11020052.

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Particle swarm optimisation (PSO) is a swarm intelligence algorithm used for controlling robotic swarms in applications such as source localisation. However, conventional PSO algorithms consider only the intensity of the received signal. Wavefield signals, such as propagating underwater acoustic waves, permit the measurement of higher order statistics that can be used to provide additional information about the location of the source and thus improve overall swarm performance. Wavefield correlation techniques that make use of such information are already used in multi-element hydrophone array systems for the localisation of underwater marine sources. Additionally, the simplest model of a multi-element array (a two-element array) is characterised by operational simplicity and low-cost, which matches the ethos of robotic swarms. Thus, in this paper, three novel approaches are introduced that enable PSO to consider the higher order statistics available in wavefield measurements. In simulations, they are shown to outperform the standard intensity-based PSO in terms of robustness to low signal-to-noise ratio (SNR) and convergence speed. The best performing approach, cross-correlation bearing PSO (XB-PSO), is capable of converging to the source from as low as −5 dB initial SNR. The original PSO algorithm only manages to converge at 10 dB and at this SNR, XB-PSO converges 4 times faster.

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30

Classen, Anton, Xinghua Liu, Aleksei M. Zheltikov, and Girish S. Agarwal. "Analysis of intensity correlation enhanced plasmonic structured illumination microscopy." Optics Letters 46, no. 7 (March 19, 2021): 1554. http://dx.doi.org/10.1364/ol.418292.

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31

Gusakov, E. Z., and A. V. Surkov. "Microwave enhanced-scattering correlation diagnostics of a turbulent plasma." Plasma Physics Reports 29, no. 1 (January 2003): 42–52. http://dx.doi.org/10.1134/1.1538501.

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32

Shrivastava, Ruchi, and Dr Krishna Teerth Chaturvedi. "Correlation Enhanced Machine Learning Approach based Wave Height Prediction." SMART MOVES JOURNAL IJOSCIENCE 4, no. 5 (May 26, 2018): 10. http://dx.doi.org/10.24113/ijoscience.v4i5.136.

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The prediction of wave height is one of the major problems of coastal engineering and coastal structures. In recent years, advances in the prediction of significant wave height have been considerably developed using flexible calculation techniques. In addition to the traditional prediction of significant wave height, soft computing has explored a new way of predicting significant wave heights. This research was conducted in the direction of forecasting a significant wave height using machine learning approaches. In this paper, a problem of significant wave height prediction problem has been tackled by using wave parameters such as wave spectral density. This prediction of significant wave height helps in wave energy converters as well as in ship navigation system. This research will optimize wave parameters for a fast and efficient wave height prediction. For this Pearson’s, Kendall’s and Spearman’s Correlation Coefficients and Particle Swarm Optimization feature reduction techniques are used. So reduced features are taken into consideration for prediction of wave height using neural network. In this work, performance evaluation metrics such as MSE and RMSE values are decreased and gives better performance of classification that is compared with existing research’s implemented methodology. From the experimental results, it is observed that proposed algorithm gives the better prediction as compared to PSO feature reduction technique. So, it is also concluded that Co-relation enhanced neural network is better as compared to PSO based neural network with increased number of features.

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33

Keat Ong, Chor, and Parvathy Rajendran. "Kernelized Correlation Filters Parameters Optimization for Enhanced Visual Tracking." Journal of Engineering and Applied Sciences 14, no. 1 (December 10, 2019): 293–307. http://dx.doi.org/10.36478/jeasci.2019.293.307.

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34

Boyer, E. P., and T. G. Webb. "Ethics and diversity: a correlation enhanced through corporate communication." IEEE Transactions on Professional Communication 35, no. 1 (March 1992): 38–43. http://dx.doi.org/10.1109/47.126938.

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35

Meda, A., E. Losero, N. Samantaray, F. Scafirimuto, S. Pradyumna, A. Avella, I. Ruo-Berchera, and M. Genovese. "Photon-number correlation for quantum enhanced imaging and sensing." Journal of Optics 19, no. 9 (August 11, 2017): 094002. http://dx.doi.org/10.1088/2040-8986/aa7b27.

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36

Hsu, Chia Wei, Seng Fatt Liew, Arthur Goetschy, Hui Cao, and A. Douglas Stone. "Correlation-enhanced control of wave focusing in disordered media." Nature Physics 13, no. 5 (February 13, 2017): 497–502. http://dx.doi.org/10.1038/nphys4036.

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37

Cincotti, Gabriella, Gianluca Manzacca, Xu Wang, Tetsuya Miyazaki, Naoya Wada, and Ken-ichi Kitayama. "Reconfigurable Multiport Optical Encoder/Decoder With Enhanced Auto-Correlation." IEEE Photonics Technology Letters 20, no. 2 (January 2008): 168–70. http://dx.doi.org/10.1109/lpt.2007.912482.

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38

Zhang, Jing, Mei Chen, Han Sun, Dongdong Li, and Zhe Wang. "Object semantics sentiment correlation analysis enhanced image sentiment classification." Knowledge-Based Systems 191 (March 2020): 105245. http://dx.doi.org/10.1016/j.knosys.2019.105245.

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39

Ruiz-Cabello, Jesús, Geerten W. Vuister, Chrit T. W. Moonen, Peter van Gelderen, Jack S. Cohen, and Peter C. M. van Zijl. "Gradient-enhanced heteronuclear correlation spectroscopy: Theory and experimental aspects." Journal of Magnetic Resonance 213, no. 2 (December 2011): 446–66. http://dx.doi.org/10.1016/j.jmr.2011.09.006.

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40

Pelupessy, Philippe, Guillermo Minguez Espallargas, and Geoffrey Bodenhausen. "Symmetrical reconversion: measuring cross-correlation rates with enhanced accuracy." Journal of Magnetic Resonance 161, no. 2 (April 2003): 258–64. http://dx.doi.org/10.1016/s1090-7807(02)00190-8.

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41

Gusakov, E. Z., N. M. Kaganskaya, M. Krämer, and V. L. Selenin. "Correlation enhanced-scattering diagnostics of small scale plasma turbulence." Plasma Physics and Controlled Fusion 42, no. 10 (October 1, 2000): 1033–47. http://dx.doi.org/10.1088/0741-3335/42/10/303.

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42

Asiala, Steven M., and Zachary D. Schultz. "Surface Enhanced Raman Correlation Spectroscopy of Particles in Solution." Analytical Chemistry 86, no. 5 (February 17, 2014): 2625–32. http://dx.doi.org/10.1021/ac403882h.

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Williamson, Daniel, and Ad Bax. "Resolution-enhanced correlation of 1H and 31P chemical shifts." Journal of Magnetic Resonance (1969) 76, no. 1 (January 1988): 174–77. http://dx.doi.org/10.1016/0022-2364(88)90213-2.

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Ruiz-Cabello, Jesus, Geerten W. Vuister, Chrit T. W. Moonen, Peter van Gelderen, Jack S. Cohen, and Peter C. M. van Zijl. "Gradient-enhanced heteronuclear correlation spectroscopy. Theory and experimental aspects." Journal of Magnetic Resonance (1969) 100, no. 2 (November 1992): 282–302. http://dx.doi.org/10.1016/0022-2364(92)90262-6.

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Crupi, F., G. Giusi, C. Ciofi, and C. Pace. "Enhanced Sensitivity Cross-Correlation Method for Voltage Noise Measurements." IEEE Transactions on Instrumentation and Measurement 55, no. 4 (August 2006): 1143–47. http://dx.doi.org/10.1109/tim.2006.876392.

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Zhang, M., Z. Sun, and T. Tan. "Perturbation-enhanced feature correlation filter for robust iris recognition." IET Biometrics 1, no. 1 (2012): 37. http://dx.doi.org/10.1049/iet-bmt.2012.0002.

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Banerjee, Partha P., Esam Gad, Tracy Hudson, Deanna McMillen, Hossin Abdeldayem, Donald Frazier, and Kenji Matsushita. "Edge enhancement and edge-enhanced correlation with photorefractive polymers." Applied Optics 39, no. 29 (October 10, 2000): 5337. http://dx.doi.org/10.1364/ao.39.005337.

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Bax, Ad, and Sankaran Subramanian. "Sensitivity-enhanced two-dimensional heteronuclear shift correlation NMR spectroscopy." Journal of Magnetic Resonance (1969) 67, no. 3 (May 1986): 565–69. http://dx.doi.org/10.1016/0022-2364(86)90395-1.

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Zhu, Guang, Xiangming Kong, and Konghong Sze. "Gradient- and Sensitivity-Enhanced Heteronuclear Multiple-Quantum Correlation Spectroscopy." Journal of Magnetic Resonance 135, no. 1 (November 1998): 232–35. http://dx.doi.org/10.1006/jmre.1998.1558.

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Iida, Hiroya, Tsukasa Aihara, Shinichi Ikuta, and Naoki Yamanaka. "Usefulness of Gadolinium Ethoxybenzyl Diethylenetriamine Pentaacetic Acid–Enhanced Magnetic Resonance Imaging for the Evaluation of Hepatic Functional Reserve." International Surgery 107, no. 1 (January 1, 2023): 31–37. http://dx.doi.org/10.9738/intsurg-d-17-00034.1.

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Анотація:

Background To determine the usefulness of the enhanced rate of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid–enhanced magnetic resonance imaging (EOB-MRI) during the hepatobiliary phase as a new parameter for hepatic functional reserve. Aim We retrospectively analyzed 153 patients who underwent EOB-MRI before radiofrequency ablation or hepatic resection between January 2008 and December 2012. The enhanced rate during the hepatobiliary phase was estimated using the ratio of regions of interest in the liver parenchyma to that in the paraspinal muscles. The enhanced rate was examined along with liver function tests and fibrosis levels for any correlations. Results A high correlation was found between the enhanced rate and indocyanine green retention rate at 15 minutes (r = −0.66, P < 0.0001). Additionally, a moderate correlation was discovered between the enhanced rate and serum albumin (r = 0.57, P < 0.0001), and fibrosis level (r = −0.48, P < 0.0001). The enhanced rate also weakly correlated with platelet count (r = 0.28, P = 0.0005), aspartate aminotransferase (r = −0.37, P < 0.0001), total bilirubin (r = −0.26, P = 0.0014), and prothrombin activity (r = 0.27, P = 0.0014). Conclusions The EOB-MRI–enhanced rate during the hepatobiliary phase strongly correlates with hepatic functional reserve.

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