Characteristic spectrum, as the name implies, refers to the unique spectral pattern or fingerprint associated with a specific material or substance. In scientific research and analysis, characteristic spectra are utilized to identify and differentiate materials based on their distinct patterns of absorption, emission, or scattering of light. This is achieved through the use of various spectroscopic techniques such as Raman spectroscopy, infrared spectroscopy, and fluorescence spectroscopy. Characteristic spectra serve as a powerful tool in fields like chemistry, physics, and materials science, enabling researchers to gain valuable insights into the composition and properties of different substances. By analyzing characteristic spectra, scientists can elucidate the chemical bonds, molecular structures, and functional groups present in a material, thus facilitating its identification and characterization. This collection of English short sentences and example sentences aims to illustrate the diverse applications and significance of characteristic spectra in scientific research and analysis.
特征光谱,Characteristic spectrum
1)Characteristic spectrum特征光谱
英文短句/例句
1.A New Method of Caries Diagnosis Based on Fluorescence Spectrum基于荧光特征光谱的龋齿诊断新技术
2.Raman Spectra and PL Spectra of Ganoderma lucidum(Curtis)P. Karst灵芝特性的激光拉曼光谱和荧光光谱表征
3.The Analysis of the Characteristic Auto-fluorescence Spectra of Cancer Patients Serum;肿瘤患者血清特征自体荧光光谱分析
4.Study on High-Spectral Characteristics of Cotton Infected with Verticillium Wilt and Diagnosing Models by Spectrum;棉花黄萎病高光谱特征及其光谱诊断模型研究
5.Research on Compression Algorithm of Hyperspectral Remote Sensing Image Based on Spectral Feature基于光谱特征的超光谱遥感图像压缩算法研究
6.The Analyse and Abstraction of Mine Spectra Feature on Hyperspctral Image基于高光谱遥感的矿物光谱特征分析和提取
7.Feature extraction of hyperspectral remote sensing image based on adding window spectrum integral基于加窗光谱积分的高光谱遥感图像特征提取
8.Surface Spectral Measurement and Characteristics Analysis of Turbid Water in Hangzhou Bay杭州湾混浊水体表面光谱测量及光谱特征分析
9.Unique Optical Reflection Spectra of Bivalve Nacre and Its Origin珍珠层独特的反射光谱特征及其成因
10.Fractal Feature of Texture Characteristic Coding for Hyperspectral Images高光谱影像纹理特征编码分形特征研究
11.A detailed analysis of welding arc spectrum distribution characteristics to choose light sources of laser sensors电弧光谱分布特征及激光传感器的光源选择
12.Feature Extraction Based on Multifractal Spectrum for Hyperspectral Data基于多重分形谱的高光谱数据特征提取
13.Meanwhile, the spectrum is also related to the shock wave velocity.光谱的结构特征也依赖于激波的速度。
14.Synthesis of Quasi-one-dimensional SnO_2 Nanomaterials and Spectrum Performance;准一维纳米SnO_2材料制备与光谱特征
15.Study on the Growth and Spectra Characteristics of Doped BaWO_4 Crystal;掺杂BaWO_4晶体的生长及其光谱特征
16.Study of Infrared Feature of Oxygen-contained Compounds;含氧化合物红外光谱信息特征的研究
17.Study on the Growth and Spectra Characteristics of Doped GdVO_4 Crystal;掺杂GdVO_4晶体的生长及其光谱特征
18.Probe into the Luminescence Dating and the Characteristic Spectrum of Minerals with Selected Frequency Luminescence;选频释光测年及矿物特征图谱的探讨
相关短句/例句
characteristic spectrum特征光谱，特征谱
3)Spectral characteristics光谱特征
1.Research on spectral characteristics of aster image in black soil area of Songliao Plain and their automatic classification;松辽平原黑土区Aster数据光谱特征及自动分类研究
2.Study on seasonal variation of vegetation spectral characteristics at Dashanbao Nature Reserve大山包自然保护区植被光谱特征季节变化研究
3.By the rules of CART algorithm classification,selecting spectral characteristics,NDVI and textural characteristics as tes.运用基于CART算法的决策树分类方法,选用影像的光谱特征值、NDVI值以及纹理统计量值为测试变量,并通过计算确定决策树的节点规则,提取影像中主要地物信息。
4)spectrum characteristic光谱特征
1.The charge andspectrum characteristic of new prepared and six-hour-stored no-pickling chrome tanning solution were analyzed through ion exchange chromatography,and the possible composition and structure of the tanning solution was conjectured.根据鞣液的电荷性质及光谱特征,推测了鞣液可能的组份和结构。
2.Based on analyzing thespectrum characteristics of water, vegetation and soil etc.本文在分析地物光谱特征的基础上,利用TM影像的短波红外波段(TM5)和红光波段(TM3),构造了修订型归一化水体指数(RNDWI)来提取水库水面。
5)spectral feature光谱特征
1.Study on Spectral Feature of Soil Water and Rice Nitrogen Status;土壤水分与水稻氮素状况光谱特征研究
2.This paper summarizes thespectral features of oils in Liaohe oilfield and presents several interpretation charts and quantitative fluorescent log data.本文总结了辽河油田各种油质的光谱特征,并介绍了几种解释图板及定量荧光录井资料的影响因
6)Spectral features光谱特征
1.A study of spectral features of stands and their related factors with principal component analysis;用主成分法进行林分及相关因子光谱特征研究
2.On the basis of an in-depth analysis of the spectral features of mine waste water in the Dexing copper mine,the characteristic spectra of different types of waters(acid water,alkaline water and river water)were summed up and the pH information extraction from mine waste water was studied using the hyperspectral imaging data.在对德兴铜矿矿山废水的光谱特征深入分析研究的基础上,总结了不同类型水体(酸性水、碱性水以及河流水)的特征光谱,并利用地物谱特征开展矿山废水pH值污染指标提取研究。
3.Through analyzing the spectral features, the best time .通过对各时期棉花及主要农作物的地面测量光谱与TM图像光谱特征的差异性及规律性分析,确定了试验区棉花遥感识别的最佳时相期为9月中下旬,研究开发了基于光谱特征的棉花识别模型。
延伸阅读
偏微分算子的特征值与特征函数由边界固定的膜振动引出的拉普拉斯算子的特征值问题：是一个典型的偏微分算子的特征值问题,这里x=(x1,x2)；Ω是膜所占据的平面区域。使得问题有非平凡解（非零解）的参数λ的值,称为特征值；相应的解称为特征函数。当Ω有界且边界嬠Ω满足一定的正则条件时,存在可数无穷个特征值,相应的特征函数ψn(x)组成l2(Ω)上的完备正交系。乘以常因子来规范ψn(x)，使其l2(Ω)模为1,则Ω上的任意函数??(x)的特征展式可写为：当??可以"源形表达"，即??满足边界条件且Δ??平方可积时,展式在Ω一致收敛。当??平方可积时，展式平方平均收敛，且有帕舍伐尔公式：对膜振动问题的认识还是相当有限的。能够精确地知道特征值的，只限于矩形、圆盘等少数几种非常简单的区域。对椭圆和一般三角形的特征值精确值，还几乎毫无所知。其他情形就更谈不上了。将不超过 λ的特征值的个数记为N(λ)。特征值的渐近分布由N(λ)对大 λ的渐近式来刻画。这方面最早的结果是（C.H.）H.外尔在19得到的（外尔公式）：式中表示Ω的面积。R.库朗将余项改进为。对于多角形区域，又有人将余项改进到。各种情况下改进余项估计的工作至今绵延不绝。外尔猜测有一个更强的结果：式中｜嬠Ω｜是区域边界之长，但尚未被证出。与此密切相关的是下面的MP公式：(t→+0)取一个渐近项时，用陶伯型定理可由它推出N(λ)的外尔公式。第二渐近项与外尔猜想非常相象，但由此证不出外尔猜想。第三项迟至1966年才被M.卡茨导出，后来由H.P.麦基恩与I.M.辛格严格证明,其中h表示鼓膜Ω的洞数。特征值与膜振动频率有一个直接的换算关系，M.卡茨据此给MP公式一个非常生动的解释：可以"听出"鼓膜的面积|Ω|、周长｜嬠Ω｜和洞的个数h！由于1-h恰巧是Ω的欧拉-庞加莱示性数,是整体几何中颇受重视的一个不变量，"听出鼓形"或"谱的几何"问题立即引起人们的强烈兴趣，并导致一系列重要的研究。不过一般的特征值反问题,要求从特征值的谱完全恢复Ω,还远远没有解决。用陶伯型定理得出N(λ)渐近式的方法，由T.卡莱曼于1934年首创，他还得到谱函数的渐近式：(λ→∞)，式中δxy当x=y时为1，当x≠y时为0。上述关于拉普拉斯算子的结果，由L.戈尔丁和F.E.布劳德推广到 Rn的有界区域Ω上的m 阶椭圆算子。尽管推算繁杂，但结果十分简单整齐：；；式中 v(x) 表示集合{ξ｜｜A0(x,ξ)｜<1}的勒贝格测度，而是A的最高阶导数项相应的特征形式。特征展开定理亦由L.戈尔丁得出。对于奇异情形，例如薛定谔方程 的谱问题,可以证明存在谱函数S(x，y，λ),特征展式为。由于可能出现连续谱，S(x，y，λ)一般不一定能写成前述特征函数双线和的形式。判定奇（异）微分算子谱的离散性是很有意义的工作。已经出现各种充分条件。不过关于特征值与特征函数渐近性质的研究，还只是限于少数特例。在处理‖x‖→∞ 时V(x)→∞的情形，M.卡茨与D.雷等人曾创造了一种系统的概率方法，其中借助数学期望表出格林函数，有效地求出谱函数与特征值的渐近式： 。当算子A的系数不光滑,或非一致椭圆，或非自共轭，以及边条件带特征参数或带非定域项等等情形，都出现不少研究结果。还有人考察Au=λBu型的特征值问题,这里A、B都是椭圆算子。除上述问题外，特征展式的收敛性与求和法也一直受到人们的关注。
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