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Titlebook: High Resolution Spectral Atlas of Nitrogen Dioxide 559–597 nm; Kiyoji Uehara,Hiroyuki Sasada Book 1985 Springer-Verlag Berlin Heidelberg 1

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發(fā)表于 2025-3-21 19:32:14 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱High Resolution Spectral Atlas of Nitrogen Dioxide 559–597 nm
編輯Kiyoji Uehara,Hiroyuki Sasada
視頻videohttp://file.papertrans.cn/427/426486/426486.mp4
叢書名稱Springer Series in Chemical Physics
圖書封面Titlebook: High Resolution Spectral Atlas of Nitrogen Dioxide 559–597 nm;  Kiyoji Uehara,Hiroyuki Sasada Book 1985 Springer-Verlag Berlin Heidelberg 1
描述Nitrogen dioxide (N0 ) is notorious for its complex visible spectrum and has 2 frustrated many chemists and physicists. Despite their intense investigations the molecule still resists complete analysis of its spectrum. Because of the high density of lines, it is apparent that high-resolution measurements are essential to a thorough understanding of the spectrum. The major part of this book consists of an atlas, described in Chap. 2, of the absorption spectrum and the Stark modulation spectrum of N0 mea- 2 sured using a cw dye laser. The narrow spectral width as well as the wide scan range of the dye laser made it possible to record the spectra contin- uously over a wide range with Doppler-limited resolution. In the spectral range 16751-17885 em-I, about 13300 peaks have been observed in the absorption spectrum and their wave numbers, calibrated against Ar lines, are listed in the tables. The Stark modulation spectrum also shown provides useful information about the energy level structures complementary to that obtained from the absorption spectrum.
出版日期Book 1985
關(guān)鍵詞Absorption; Dioxide; Nitrogen; laser; molecule; spectra
版次1
doihttps://doi.org/10.1007/978-3-642-82409-8
isbn_softcover978-3-642-82411-1
isbn_ebook978-3-642-82409-8Series ISSN 0172-6218 Series E-ISSN 2364-9003
issn_series 0172-6218
copyrightSpringer-Verlag Berlin Heidelberg 1985
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High Resolution Spectral Atlas of Nitrogen Dioxide 559–597 nm
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Kiyoji Uehara Ph.D.,Hiroyuki Sasada Ph.D. the smart systems, such as at smart homes, is connected by network technologies to gather contextual information of vital signs and behavior of users via the sensors. The very common approaches for user monitoring are based on machine vision. However, other sensors (e.g., wearables, motion, radar,
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Kiyoji Uehara Ph.D.,Hiroyuki Sasada Ph.D.also discuss how deep learning can help a sport data scientist, and vice versa, how we can improve our models by knowing a few physics principles. Classical mechanics is a reliable method of movement analysis, and it’s a valuable tool if you’re planning to build any practical sport machine learning
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cher, you are likely to train quite a few models, changing your training script, experimenting with data, before making them available to your customers. This chapter is about making AI a high-quality, automated process that makes it easy to manage your code, publish models, and consume them. You’ll
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