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Journal articles on the topic 'Spectal Processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Carlsohn, Matthias F. "Special issue on spectral imaging: Real-time processing of hyperspectral data." Journal of Real-Time Image Processing 1, no. 2 (2006): 99–100. http://dx.doi.org/10.1007/s11554-006-0020-y.

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2

Ostrikov, V. N., O. V. Plakhotnikov, and A. V. Kirienko. "Application of aviation video-spectral survey to search for fragments of separating parts of launch vehicles on the ground." Исследования Земли из Космоса, no. 2 (May 21, 2019): 45–54. http://dx.doi.org/10.31857/s0205-96142019245-54.

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The possibility of detecting debris fragments of separating parts of launch vehicles on the ground on the basis of video spectral (hyperspectral) aerial survey data processing in the range of 0.4-1.0 µm is considered. Evaluation of the quality of special methods pre-correction when comparing, during thematic processing, measures the spectral vicinity – the special delta-vector metric, modified metric Terebizh, factor correlations, and sub-pixel method.

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3

Zhang, H., O. Van Kaick, and R. Dyer. "Spectral Mesh Processing." Computer Graphics Forum 29, no. 6 (2010): 1865–94. http://dx.doi.org/10.1111/j.1467-8659.2010.01655.x.

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4

Görlitz, L., B. H. Menze, B. M. Kelm, and F. A. Hamprecht. "Processing spectral data." Surface and Interface Analysis 41, no. 8 (2009): 636–44. http://dx.doi.org/10.1002/sia.3066.

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5

Tovée, Martin J. "Is Face Processing Special?" Neuron 21, no. 6 (1998): 1239–42. http://dx.doi.org/10.1016/s0896-6273(00)80644-3.

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6

He, Dr Lizhong, and Anton Middelberg. "Special Issue—Downstream Processing." Food and Bioproducts Processing 84, no. 1 (2006): 1. http://dx.doi.org/10.1205/fpb.ed.0601.

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7

Cheung, Gene, Enrico Magli, Yuichi Tanaka, and Michael K. Ng. "Graph Spectral Image Processing." Proceedings of the IEEE 106, no. 5 (2018): 907–30. http://dx.doi.org/10.1109/jproc.2018.2799702.

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8

Rosa, A. L. R., and T. J. Ulrych. "Processing via spectral modeling." GEOPHYSICS 56, no. 8 (1991): 1244–51. http://dx.doi.org/10.1190/1.1443144.

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The widespread occurrence of subtle trap accumulations offshore Brazil has led to the need for the development of a high resolution processing scheme that helps the delineation of these features. The process consists of three stages, the first of which is deterministic and stochastic deconvolution. The second stage is the deconvolution of the residual wavelet by means of spectral modeling. The last stage consists of the correction of the color of the reflectivity function using a model developed for the area. An important conclusion that is drawn from the model is that the acoustic impedance is not white. Rather it is as red as the corresponding reflectivity is blue. Successful results from the application of the proposed technique to real data indicate that the color compensation is of second order importance as compared with the first two stages of the proposed scheme.

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9

Wensheng Zhai, Wensheng Zhai, Xinlu Gao Xinlu Gao, Wenjing Xu Wenjing Xu, et al. "Microwave photonic phase shifter with spectral separation processing using a linear chirped fiber Bragg grating." Chinese Optics Letters 14, no. 4 (2016): 040601–40604. http://dx.doi.org/10.3788/col201614.040601.

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10

Toriwaki, Jun-ichiro. "Special Issue Image Processing. 1. Image Processing. Introduction." Journal of the Institute of Television Engineers of Japan 46, no. 11 (1992): 1386–92. http://dx.doi.org/10.3169/itej1978.46.1386.

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11

Saberi, Kourosh, Yoshifumi Takahashi, and Haleh Farahbod. "Spectral integration in binaural processing." Journal of the Acoustical Society of America 105, no. 2 (1999): 1390. http://dx.doi.org/10.1121/1.426571.

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12

Wakeman, Richard. "Special Topic Issue—Particle Processing." Chemical Engineering Research and Design 75, no. 3 (1997): 269. http://dx.doi.org/10.1205/026387697523633.

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13

Looney, Kieran. "Special Topic Issue—Materials Processing." Chemical Engineering Research and Design 76, no. 1 (1998): 1–2. http://dx.doi.org/10.1205/026387698524479.

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14

Sarra, Scott A. "The spectral signal processing suite." ACM Transactions on Mathematical Software 29, no. 2 (2003): 195–217. http://dx.doi.org/10.1145/779359.779364.

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15

Pesti, Jaan A. "Special Feature Issue: Continuous Processing." Organic Process Research & Development 18, no. 11 (2014): 1284–85. http://dx.doi.org/10.1021/op500323a.

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16

Song, Ran, Yonghuai Liu, Ralph R. Martin, and Paul L. Rosin. "Mesh saliency via spectral processing." ACM Transactions on Graphics 33, no. 1 (2014): 1–17. http://dx.doi.org/10.1145/2530691.

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17

Brown, Elaine C. "Editorial: Polymer Processing Special Issue." Transactions of the Institute of Measurement and Control 29, no. 5 (2007): 381–82. http://dx.doi.org/10.1177/0142331207086636.

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18

Looney, Kieran. "Special Topic Issue—Materials Processing." Chemical Engineering Research and Design 78, no. 1 (2000): 3–4. http://dx.doi.org/10.1205/026387600526997.

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19

Looney, Kieran. "Special Topic Issue—Materials Processing." Chemical Engineering Research and Design 80, no. 5 (2002): 421. http://dx.doi.org/10.1205/026387602320223996.

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20

Freund, R., and V. Sunderam. "Special Issue on Heterogeneous Processing." Journal of Parallel and Distributed Computing 21, no. 3 (1994): 255–56. http://dx.doi.org/10.1006/jpdc.1994.1057.

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21

Ying, Jackie Y., Alan W. Weimer, and Matthew V. Tirrell. "Special issue on ceramics processing." AIChE Journal 43, S11 (1997): 2609. http://dx.doi.org/10.1002/aic.690431302.

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22

Iiguni, Y. "Special Section on Signal Processing." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E91-A, no. 8 (2008): 1857. http://dx.doi.org/10.1093/ietfec/e91-a.8.1857.

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23

De Cuyper, J. "Special issue on mineral processing." International Journal of Mineral Processing 31, no. 3-4 (1991): 305–6. http://dx.doi.org/10.1016/0301-7516(91)90033-f.

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24

Zaichenko, K. V., and B. S. Gurevich. "Spectral Processing of Bioelectric Signals." Biomedical Engineering 55, no. 1 (2021): 17–20. http://dx.doi.org/10.1007/s10527-021-10062-6.

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25

Borzov, S. M., and O. I. Potaturkin. "Increasing the classification efficiency of hyperspectral images due to multi-scale spatial processing." Computer Optics 44, no. 6 (2020): 937–43. http://dx.doi.org/10.18287/2412-6179-co-779.

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Classification of the land cover types from multi- and hyperspectral (HS) imagery is traditionally carried out on the basis of analysis of scatter plots of pixel values in a multidimensional feature space, which are used as brightness in individual channels. To increase the reliability of HS image classification, approaches are used based on simultaneously accounting for the characteristics of each pixel and the nearest-neighbor pixels, i.e., on the joint analysis of spectral and spatial features. The pixel neighborhood analysis is performed at various stages of the classification process. In this work, using a test hyperspectral image, the efficiency of spectral-spatial data classification methods that take into account spatial information at various stages of processing is studied. Special attention is paid to selecting the size of the spatial processing core. It is shown that the best results are obtained by combining pre-processing of raw data before performing the procedures of pixel-by-pixel spectral classification and post-processing of the resulting maps. Prospects of multi-scale smoothing of initial images, with the increase of the number of spectral-spatial features being multiple of the number of the scales, are shown.

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26

Sakudo, Noriyuki. "Technical Information-Special Issue-Micro Material Processing. Ion Processing." Journal of the Japan Welding Society 61, no. 6 (1992): 501–6. http://dx.doi.org/10.2207/qjjws1943.61.6_501.

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27

Kaeli, David R., and Miriam Leeser. "Special issue: General-purpose processing using graphics processing units." Journal of Parallel and Distributed Computing 68, no. 10 (2008): 1305–6. http://dx.doi.org/10.1016/j.jpdc.2008.07.002.

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28

Lah, Ji Young. "Users’ Responses to a Non-processing Message by a Dialogue System: HCI." Korean Journal of Applied Linguistics 37, Special (2021): 43–74. http://dx.doi.org/10.17154/kjal.2021.7.37.special.43.

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29

Wang, Li Jun, Dai Qin Tao, Zhi Yong Yin, and Hui Zhao. "Multiband Techniques of LED and UV Materials on Visualization of Needle Marks." Advanced Materials Research 463-464 (February 2012): 1382–86. http://dx.doi.org/10.4028/www.scientific.net/amr.463-464.1382.

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Critical optical techniques on visualization and extraction about needle marks have been developed based on the material properties of the speedometer. It has developed the multi-band waves and special image processing to do multi-spectral analysis on the suspected needle marks locations. The in-depth discussion has been taken especially for UV fluorescence traces, and alternatives have been established for multi-spectral laser with the multi-wavelength LED light source. Issues have been solved such as size and power consumption to meet the needs of image acquisition and processing. It should choose the right light waves based on the optical properties for various materials of the needle and the gauge plate.

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30

Gajic, Dusan, and Radomir Stankovic. "GPU accelerated computation of fast spectral transforms." Facta universitatis - series: Electronics and Energetics 24, no. 3 (2011): 483–99. http://dx.doi.org/10.2298/fuee1103483g.

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This paper discusses techniques for accelerated computation of several fast spectral transforms on graphics processing units (GPUs) using the Open Computing Language (OpenCL). We present a reformulation of fast algorithms which takes into account peculiar properties of transforms to make them suitable for the GPU implementation. A special attention is paid to the organization of computations, memory transfer reductions, impact of integer and Boolean arithmetic, different structure of algorithms, etc. Performance of the GPU implementations is compared with the classical C/C++ implementations for the central processing unit (CPU). Experiments confirm that, even though the spectral transforms considered involve only simple arithmetic, significant speedups are achieved by implementing the algorithms in OpenCL and performing them on the GPU.

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31

Fujisaki, Hiroya. "Special edition. Recent speech processing techniques. Introduction to speech processing." Journal of the Institute of Television Engineers of Japan 41, no. 8 (1987): 698–706. http://dx.doi.org/10.3169/itej1978.41.698.

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32

Watson, Kenneth. "Introduction to the Special Issue on remote sensing." GEOPHYSICS 52, no. 7 (1987): 839–40. http://dx.doi.org/10.1190/1.1442355.

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In 1977, the first Special Issue on remote sensing published by Geophysics contained papers selected from two special sessions at the 45th Annual International SEG Meeting, October 12–16, 1975, in Denver, Colorado. That first Special Issue consisted of eight papers: four are primarily tutorial (image processing, spectral signatures in the visible and near infrared, microwave spectra of layered media, and factor analysis of gamma‐ray spectrometry), two involve structural interpretations with implications for mineral exploration and seismicity, and two examine multispectral reflectance data for detecting hydrothermal alteration and for uranium exploration. Although these papers indicate the importance of physical properties and models in the interpretation of remote sensing data, the studies were constrained by the instruments that collected the data and by the availability of image‐processing software. Circumstances have changed significantly in the intervening decade, as illustrated in recent review papers (Watson, 1985; Goetz et al., 1983) and demonstrated by the papers in this Special Issue.

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33

Griesser, Thomas, and Hans Richner. "Multipeakverarbeitungsverfahren zur Erkennung des atmosphärischen Signals in Dopplerradar Windprofiler Spektren." Meteorologische Zeitschrift 7, no. 6 (1998): 292–302. http://dx.doi.org/10.1127/metz/7/1998/292.

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34

Gaffard, Catherine, Laura Bianco, Vladislav Klaus, and Monica Matabuena. "Evaluation of moments calculated from wind profiler spectra: A comparison between five different processing techniques." Meteorologische Zeitschrift 15, no. 1 (2006): 73–85. http://dx.doi.org/10.1127/0941-2948/2006/0101.

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35

Cubon, Juraj. "PROTEIN DEGRADATION AND FAT OXIDATION CHANGES IN SALTED MEAT PROCESSING." Journal of Microbiology, Biotechnology and Food Sciences 9, Special issue (2019): 376–79. http://dx.doi.org/10.15414/jmbfs.2019.9.special.376-379.

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36

Piro, Joseph M. "Talent, Special Ability, and Hemispheric Processing." Perceptual and Motor Skills 77, no. 2 (1993): 701–2. http://dx.doi.org/10.2466/pms.1993.77.2.701.

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The role talent training and special ability may play in hemispheric functioning is discussed. Further research on hemispheric processing from the perspective of cognitive strength as well as deficit is suggested.

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37

Yamnenko, Yuliia Serhiivna, and Tetiana Oleksandrivna Tereshchenko. "Spectral methods for processing biotelemetrical data." Electronics and Communications 21, no. 4 (2016): 38–43. http://dx.doi.org/10.20535/2312-1807.2016.21.4.81904.

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38

DE BOT, KEES, and CAROL JAENSCH. "What is special about L3 processing?" Bilingualism: Language and Cognition 18, no. 2 (2013): 130–44. http://dx.doi.org/10.1017/s1366728913000448.

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While research on third language (L3) and multilingualism has recently shown remarkable growth, the fundamental question of what makes trilingualism special compared to bilingualism, and indeed monolingualism, continues to be evaded. In this contribution we consider whether there is such a thing as a true monolingual, and if there is a difference between dialects, styles, registers and languages. While linguistic and psycholinguistic studies suggest differences in the processing of a third, compared to the first or second language, neurolinguistic research has shown that generally the same areas of the brain are activated during language use in proficient multilinguals. It is concluded that while from traditional linguistic and psycholinguistic perspectives there are grounds to differentiate monolingual, bilingual and multilingual processing, a more dynamic perspective on language processing in which development over time is the core issue, leads to a questioning of the notion of languages as separate entities in the brain.

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39

Asari, Vijayan K., and M. Nazrul Islam. "Special issue on optical image processing." Optics & Laser Technology 57 (April 2014): 227–29. http://dx.doi.org/10.1016/j.optlastec.2013.11.007.

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40

Nairne, James S., and Josefa N. S. Pandeirada. "Adaptive memory: Is survival processing special?" Journal of Memory and Language 59, no. 3 (2008): 377–85. http://dx.doi.org/10.1016/j.jml.2008.06.001.

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41

Vallet, B., and B. Lévy. "Spectral Geometry Processing with Manifold Harmonics." Computer Graphics Forum 27, no. 2 (2008): 251–60. http://dx.doi.org/10.1111/j.1467-8659.2008.01122.x.

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42

Gotsman, Craig, and Leif Kobbelt. "Special Issue on Geometric Mesh Processing." Computer Aided Geometric Design 21, no. 4 (2004): 422. http://dx.doi.org/10.1016/s0167-8396(04)00037-8.

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43

Gotsman, Craig, and Leif Kobbelt. "Special Issue on Geometric Mesh Processing." Computer Aided Geometric Design 21, no. 5 (2004): 530. http://dx.doi.org/10.1016/s0167-8396(04)00056-1.

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44

Gotsman, Craig, and Leif Kobbelt. "Special Issue on Geometric Mesh Processing." Computer Aided Geometric Design 21, no. 6 (2004): 637. http://dx.doi.org/10.1016/s0167-8396(04)00073-1.

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45

Brandt, Christopher, Leonardo Scandolo, Elmar Eisemann, and Klaus Hildebrandt. "Spectral Processing of Tangential Vector Fields." Computer Graphics Forum 36, no. 6 (2016): 338–53. http://dx.doi.org/10.1111/cgf.12942.

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46

Grainger, Jonathan, and Thomas Hannagan. "What is special about orthographic processing?" Written Language and Literacy 17, no. 2 (2014): 225–52. http://dx.doi.org/10.1075/wll.17.2.03gra.

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Starting from a generic architecture for reading words in alphabetic scripts, we examine the special status of letters as the building block of single word reading. After briefly describing the overall architecture that defines the interaction between orthographic and phonological processes during silent reading for meaning, we then focus on orthographic processing. We describe the nature of orthographic representations as hypothesized in our approach and we discuss how such representations might be learned during reading acquisition. We present the hypothesis that such learning involves the adaptation of basic object identification mechanisms to the specific constraints of reading, and we provide examples of this adaptation. In the light of this, we then compare the function of letters as constituents of written words relative to the role of object parts in other kinds of familiar visual stimuli (e.g. faces, numbers). We explain why we think letters must have a special status and we provide some preliminary empirical evidence in favor of this special status for letters as parts of words. Keywords: reading; orthography; visual word recognition; orthographic learning; letter strings; object identification

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47

Yan, Hong, Jesse S. Jin, and Zhi-qiang Liu. "Special section on visual information processing." Pattern Recognition 40, no. 8 (2007): 2127–28. http://dx.doi.org/10.1016/j.patcog.2006.12.017.

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48

Peyré, Gabriel. "Image Processing with Nonlocal Spectral Bases." Multiscale Modeling & Simulation 7, no. 2 (2008): 703–30. http://dx.doi.org/10.1137/07068881x.

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49

Yamashita, Kohei, Shin'Ya Ogata, and Tetsuya Shimamura. "Improved spectral subtraction utilizing iterative processing." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 90, no. 4 (2006): 39–51. http://dx.doi.org/10.1002/ecjc.20242.

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50

Fageth, R., U. Jäger, and W. G. Allen. "Fast image processing with special chips." Journal of Microcomputer Applications 16, no. 4 (1993): 369–84. http://dx.doi.org/10.1006/jmca.1993.1037.

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