In the presence of optically thin clouds (usually ice clouds), an index o n column watervapor amount above and within cirrus clouds can be established. Our recent analysis of AVIRIS data acquired over water surfaces indicates that the ratio of the radiance from the 0.94- µ m channel against the radiance from the 0.865- µ m channel provides information about watervapor above and within cirrus clouds. Because the water surface away from sun glint regions is black above 0.8 µ m, it has no contribution to the 2-channel ratio. The sensitivities of MODIS IR channels near 14 µ m are not sufficiently high for reliable detection of thin cirrus clouds with effective IR emissivities less than about 0.04. The estimation of the heights of such thin cirrus clouds using CO2 slicing techniques is certainly not possible. For slightly thicker cirrus clouds, the CO2-slicing technique can sometimes successively estimate the heights of such clouds, but often has a tendency to underestimate the height of such clouds due to intrinsic absorption, emission, and partial transparent nature of cirrus clouds. Based on these considerations, we have concluded that thin cirrus cloud heights will have to be assumed at a few discrete height levels based o n climatology. From the 2-channel ratios and the assumed cirrus heights, the upper level watervapor index can be established.
IR bands of the MODerate resolution Imaging Spectroradiometer (MODIS) onboard Aqua satellite are used to detect dust over East Asia. MYD02, level 1B calibrated radiance data, and MYD03, geo-location data, are used in this study. MODIS is a 36-band spectrometer covering spectral range from the visible to infrared onboard sun synchronous satellites, with an equatorial crossing time of 1:30 PM for Aqua. The IR bands have a spatial resolution of 1 km × 1 km ( http://modis.gsfc. nasa.gov/about/speci ﬁcations.php ). There are 16 bands in IR (bands 20 to 36, except band 26), including the atmospheric window channels, bands 29 (8.400 –8.700 μm), 31 (10.78–11.28 μm) and 32 (11.77– 12.27 μm). Most of the signals for the three bands in the atmospheric window are from the surface (cf. Tang & Li, 2008 ). Therefore, the Bright- ness Temperatures (BTs) of these three bands are used to detect dust effectively, because the dust layer is mostly located in the lower-half of the troposphere. Among the three bands, however, band 29 is in ﬂuenced by the weak absorption of sulfur dioxide. For this reason, bands 31 and 32 are used as the primary, and band 29 is used as an ancillary band. In addition to the conventional method for screening clouds by utilizing band 31, a watervaporabsorptionband (band 28, 7.175 –7.475 μm) is used to improve the cloud masking. In this study, the analysis area was selected over East Asia, de ﬁned by the area from 100°E to 160°E in longitudes, and from 25°N to 60°N in latitudes. The MODIS IR data from Aqua are used for the period from January to June, 2006.
Abstract. A detection method for high-level clouds, such as ice clouds, is developed using the watervapor saturated chan- nels of the solar reflected spectrum observed by the Green- house gases Observing SATellite (GOSAT) Thermal And Near-infrared Sensor for carbon Observation Fourier Trans- form Spectrometer (TANSO-FTS). The clouds detected by this method are optically relatively thin (0.01 or less) and located at high altitude. Approximately 85 % of the results from this method for clouds with cloud-top altitude above 5 km agree with the Cloud-Aerosol Lidar with Orthogo- nal Polarization (CALIOP) cloud classification. GOSAT has been operating since April 2009 with a 3-day repeat cycle for a pointwise geolocation pattern, providing a spectral data record that exceeds 9 years. Cloud information derived from GOSAT TANSO-FTS spectra could be powerful data for un- derstanding the variability in cirrus cloud on temporal scales from synoptic to interannual.
Abstract. Previous bi-spectral imager retrievals of cloud op- tical thickness (COT) and effective particle radius (CER) based on the Nakajima and King (1990) approach, such as those of the operational MODIScloud optical property retrieval product (MOD06), have typically paired a non- absorbing visible or near-infrared wavelength, sensitive to COT, with an absorbing shortwave or mid-wave infrared wavelength sensitive to CER. However, in practice it is only necessary to select two spectral channels that exhibit a strong contrast in cloud particle absorption. Here it is shown, using eMAS observations obtained during NASA’s SEAC 4 RS field campaign, that selecting two absorbing wavelength channels within the broader 1.88 µm watervaporabsorptionband, namely the 1.83 and 1.93 µm channels that have sufficient differences in ice crystal single scattering albedo, can yield COT and CER retrievals for thin to moderately thick single- layer cirrus that are reasonably consistent with other solar and IR imager-based and lidar-based retrievals. A distinct ad- vantage of this channel selection for cirrus cloud retrievals is that the below-cloudwatervaporabsorption minimizes the surface contribution to measured cloudy top-of-atmosphere reflectance, in particular compared to the solar window chan- nels used in heritage retrievals such as MOD06. This reduces
Abstract. Quantitative knowledge of watervapor absorp- tion is crucial for accurate climate simulations. An open sci- ence question in this context concerns the strength of the watervapor continuum in the nearinfrared (NIR) at atmo- spheric temperatures, which is still to be quantified by mea- surements. This issue can be addressed with radiative clo- sure experiments using solar absorption spectra. However, the spectra used for watervapor continuum quantification have to be radiometrically calibrated. We present for the first time a method that yields sufficient calibration accuracy for NIR watervapor continuum quantification in an atmo- spheric closure experiment. Our method combines the Lan- gley method with spectral radiance measurements of a high- temperature blackbody calibration source ( < 2000 K). The calibration scheme is demonstrated in the spectral range 2500 to 7800 cm −1 , but minor modifications to the method enable calibration also throughout the remainder of the NIR spectral range. The resulting uncertainty (2σ ) excluding the contribu- tion due to inaccuracies in the extra-atmospheric solar spec- trum (ESS) is below 1 % in window regions and up to 1.7 % within absorption bands. The overall radiometric accuracy of the calibration depends on the ESS uncertainty, on which at present no firm consensus has been reached in the NIR. How- ever, as is shown in the companion publication Reichert and Sussmann (2016), ESS uncertainty is only of minor impor- tance for the specific aim of this study, i.e., the quantification of the watervapor continuum in a closure experiment. The calibration uncertainty estimate is substantiated by the inves- tigation of calibration self-consistency, which yields com- patible results within the estimated errors for 91.1 % of the
2 denotes a chi-square distribution with two DOF and P k is the
mean spectrum at the wavelet scale s. Once a background spec- trum is chosen, the 95% confidence level for χ 2 can be calcu- lated by Eq. ( 6 ) and then the 95% confidence contour lines can be drawn by comparing the local wavelet power spectrum against P k χ 2 2 ∕2. The contour can be used to identify the region where a significant power of the fNIRS channel data is located. 2.4 Activation Detection by Wavelet Coherence fNIRS provides the oxy and deoxyhemoglobin concentrations, which provide functional information about certain underlying brain sensorimotor or cognitive tasks. Hence, their concentra- tions should reflect correlations between brain neurophysiology and behavioral paradigms. The correlations between these have been confirmed by both functional magnetic resonance imaging and fNIRS. 39 In our case, the coherence, i.e., the time-locked correlation in the frequency domain, is used to evaluate the level of each channel correlated with the task paradigm.
Abstract: We propose a novel plasmon nano-optical antenna that consists of three vertically superimposed discs with different materials. Two symmetrical nanometer-scale Yagi antenna elements with gold-material are embedded into the surface of the middle disc with silicon dioxide. Based on plasmon theory and Yagi antenna principle, we explore the properties of the far-field radiation and near-field transmission of the antenna in nearinfraredband. It is found that the pattern of the xoy plane in the antenna can be adjusted in arbitrary direction. The far-field pattern and near-field transmission spectrum can be reconfigured by changing the angle α of the Yagi antenna elements in the middle disc. Moreover, we also discuss the influences of the widths and lengths of the elements on the near-field transmission spectrum when α is fixed at 0 degree.
Abstract— Cloud is one of the most common interferers in Moderate Resolution Imaging Spectrum-radiometer (MODIS) remote sensing imagery. Because of cloud interference, much important and useful information covered by cloud cannot be recovered well. How to detect and remove cloud from MODIS imagery is an important issue for wide application of remote sensing data. In general, cloud can be roughly divided into the two types, namely, thin cloud and thick cloud. In order to effectively detect and eliminate cloud, an automatic algorithm of clouddetection and removal is proposed in this paper. Firstly, several necessary preprocessing works need to be done for MODIS L1B data, including geometric precision correction, bowtie effect elimination and stripe noise removal. Furthermore, through analyzing the cloud spectral characters derived from the thirty-six bands of MODIS data, it can be found the spectral reflections of ground and cloud are different in various MODIS bands. Hence, cloud and ground can be respectively identified based on the analysis of multispectral characters derived from MODIS imagery. Cloud removal processing mainly aims at cloud region rather than whole image, which can improve processing efficiency. As for thin cloud and thick cloud regions, the corresponding cloud removal algorithms are proposed in this paper. Experimental results demonstrate that the proposed algorithms can effectively detect and remove cloud from MODIS imagery, which can meet the demands of post- processing of remote sensing imagery.
The inherent problem of shadow detection methods that use segmentation, such as Guo et al., is that if the segmentation fails to segment regions that are in the shadow from regions that are not in the shadow, the shadow detection will not be correct, as illustrated in Fig. 7b, where the black sweater is completely missed. Tian et al.’s method, on the other hand, labels the whole sweater as being in shadow. Segmentation-based shadow detection algorithms also tend to have problems in highly textured regions, which are quite common, especially in natural outdoor scenes. This is also reflected in our results, where Guo et al. performed better on the images of scenes that contain simpler shadows, such as in Fig. 7a, d, and g, and fails for more complex ones such as Fig. 7b, c, and f. While Tian et al.’s method is almost on par with our method for outdoor images and performs slightly better on the uncontrolled indoor images, our method outperforms them on the flash indoor images. This is due to the fact that their tricolor attenuation model, which forms the basis for their shadow detection algorithm, is computed based on outdoor light sources. The reason their method is working very well on the uncontrolled indoor image set is probably that the most predominant light source in these scenes is still the sun, as exemplified in Fig. 7g.
Landsat 8 has 11 spectral bands that comprise instru- ments OLI and Thermal Infrared Sensor. However, only bands 2 to 8 of OLI were considered in this study. Figure 4 shows the spectral signatures of non-water and water bodies. Bands 2 to 4 are the visible spectrum of blue, green and red, respectively, that ranges from 0.45 to 0.68 μm. On the other hand, bands 5 to 7 are invisible spectrum of NIR, SWIR1 and SWIR2 that ranges from 0.845 to 2.3 μm. As shown in Fig. 4, water absorbs more energy in NIR (band 5) and SWIR (bands 6 & 7) wavelengths, while non-water reflects more energy [8, 11, 16, 25]. Thus, single-band method chooses between NIR and mid infrared bands . Examining closely Fig. 4, from left to right, reflectance difference or gap between water and non-water bodies is increasing from band 2 to band 5 and decreasing thereafter. In other words, band 5 has the largest difference in reflectance values between water and non-water bodies. Thus, NIR band was chosen to be an effective single bandwater index that can efficiently delineate water from non-water bodies.
11 of spice and adulterant. For example, when papaya seed adulterated black pepper kernels are immersed in carbon tetrachloride (CCl 4 ) the pure black pepper kernels will settle while the black papaya seeds will float. Adulteration of ground spices may be identified by sprinkling the suspect sample into water; saw dust and powdered bran will float on the surface while the ground spices will settle. Identification of visual differences between adulterant and spice forms the basis of microscopic probing. Pure turmeric is yellow coloured, relatively large in size and has an angular structure when viewed microscopically. Added foreign starches appear colourless and small in size compared to pure turmeric starch. The final group of methods, chemical analyses, is varied and several examples are subsequently given. Turmeric, chilli and curry powder is extracted with petroleum ether and 13N sulphuric acid (H2SO4) added to the extract. The presence of added dyes will be indicated by the appearance of a red colour. When distilled water is added to the mixture and the red colour disappears, it can be concluded that the sample is not adulterated. Alternatively, turmeric can be ashed, dissolved in a 1:7 dilution of H 2 SO 4 and filtered. The formation of a pink colour upon the addition of one or two drops of 0.1% diphenylcarbazide to the solution is indicative of the presence of lead chromate. With the addition of a few drops of concentrated hydrochloric acid (HCl) to the sample, a violet colour will appear instantly. If the sample is pure turmeric, this colour will disappear if the sample is diluted with water. If the colour does not disappear, metanil yellow is present. Coriander powder adulterated with salt can be detected through adding a few drops of silver nitrate (AgNO 3 ) to 5 mL of the sample. The adulteration is confirmed by the occurrence of a white precipitate.
Very little is know about the pulsar IR emission. Only 5 out of the 12 pulsars identiﬁed in the optical, have been also detected in the nIR. These are the young pulsars Crab (e.g., Sandberg & Sollerman 2009; Tziamtzis et al. 2009), Vela (Shibanov et al. 2003), and PSR B1509−58 (Kaplan & Moon 2006), and the middle-aged pulsars PSR B0656+14 and Geminga (Koptsevich et al. 2001). Moreover, the Crab and Vela pulsars have been also clearly detected in the mIR by Spitzer (Temim et al. 2009; Danilenko et al. 2011), with a possible marginal in- dication of detection also for Geminga (Danilenko et al. 2011). Spitzer also observed PSR J1124−5916 (Zyuzin et al. 2009), PSR J0205+6449 (Slane et al. 2008), and PSR J1833−1034 (Zajczyk et al. 2011) but it could only de- tect the surrounding pulsar-wind nebulae (PWNe). Spitzer also detected the Crab PWN, which is the only one de- tected both in the nIR (Sandberg & Sollerman 2009) and in the mIR (Temim et al. 2009), together with the PSR J1833−1034 PWN (Zajczyk et al. 2011). A nIR source has been recently associated with the 38.5 ms X-ray pul- sar IGR J18490−0000 (Gotthelf et al. 2011; Ratti et al. 2010; Curran et al. 2011). However, the identiﬁcation with the pulsar has never been conﬁrmed so far. Detecting more pulsars in the IR is crucial to search for continuity or spec- tral breaks at wavelengths longer than the optical band and, in turn, to constrain the properties of particles in the pulsar magnetosphere on a broader spectral range. Obviously, young and energetic pulsars are the best tar- gets.
In the new absorber design, the thin film of LiBr solution is constrained by hydrophobic porous membranes and the inner wall of cooling water channel. The LiBr solution is held outside of the membrane by a meniscus formed over the pores of the membrane. The solution is cooled by water running in the cooling channel. The heights of solution and cooling water channels are very small: on the scales of less than 100µm. The membrane based absorber has been proposed and studied by Ali (2010). In that paper, the results of experimental work are compared with a 1-D model results. The correlations for mass and heat transfer used in that paper are from Martinez and Maroto (2007). The correlations were derived for the mass transfer inside a shell where hollow fibers are woven and uneven flows were suspected. Clearly, the flow patterns are much different between the membrane based absorber and the shell distiller. In the membrane based absorber, the flow is very similar to the Poiseuille flow with very small disturbance in the cross flow direction. While in the distillation module (Martinez and Maroto, 2007), the flow is much complicated in all velocity components and much larger velocity disturbances are from the fibers. Thus, it is not clear whether the correlations in Martinez and Maroto (2007) can be applied in the membrane based absorber.
Dyadic output of a normal person shows no variation as there is no turbid medium inside the sinus cavity in a fully aerated condition. However, in case of acute or chronic condition the output obtained varies due to fluid filled condition and turbid medium inside the sinus cavity undergoes reflection, refraction, scattering and absorption. Due to all these phenomena, there will be a change in the magnitude with the moving layers of the turbid medium. Sometimes, the signal is fully absorbed or scattered due to which the output will be very less. Similarly, the signals recorded were also analyzed with the help of regression analysis to validate the output obtained using hardware.
It can be seen from Table 4 that the SVR quantitative prediction model of sesame oil has a good correlation with the calibration set and the error was small, which indicate that the SVR can quantitatively predict its content. As shown in the above table, the correlation coefficient of each model after pretreatment of the raw spectra of the experimental sample was improved, and the mean square error was reduced, indicating the necessity of the pretreatment in NIRS modeling. The spectral data was optimized. The spectral data was optimized with CARS and SiPLS, the best wavelength was selected. The characteristic band model with CARS optimization was slightly lower than the full band model. However, after the optimization, the modeling speed was fast and the required time was short, which was suitable for rapid detection. After applying the SiPLS preferred variables, the model parameters were basically unchanged, and the modeling variables were reduced, indicating that the application of SiPLS can select the best feature modeling variables. Therefore, the SNV-SiPLS-SVR model is the optimal model. Its SVR modeling parameter (C, g) has a g value of 4, which was small. The correlation coefficient R and the mean square error MSE of the correction set and the prediction set were 99.5234% and 99.0609%, 0.0416 and 0.0842, respectively (Figure 5 and Figure 6), the correlation degree was high, the MSE was small and the difference was small, and the model prediction ability was good.
Microscopic characterization of the continuum band. To learn more about the origin of the continuum band in protonated water
clusters, we compare in Fig. 3 the simulated axial spectrum of a narrow protonated water chain including contributions from vibrations of the nuclei and electron polarization ﬂuctuations (denoted as n + e and already shown in Fig. 1 a, red line) with the spectrum obtained from the nuclear motion alone (denoted by n, broken line); for the latter, we assign effective charges q H = +e and q O = −2e to protons and oxygens. This choice overestimates the water dipole moment and thus exaggerates the water vibration bands but gives the excess proton the correct charge (see Sup- plementary Note 1 for details). All spectra are plotted to scale and thus can be compared with each other. Figure 3 shows that in the wavenumber range ν ≈ 2000–3000 cm −1 , the amplitude of the continuum band in the full n + e spectrum is doubled compared to the n spectrum, indicating a sizable electronic contribution to the continuum band. We also compute the spectral contribution of the excess proton together with its two neighboring water molecules, which together make up the Zundel complex O 2 H þ 5 (see Supplementary Note 2 for details on the projection formal- ism 27 , 50 ). Note that the Zundel complex is the dominant solva- tion state of the excess proton in water chains 28 , in contrast to discs and bulk water, where the Eigen state dominates (see Sup- plementary Note 3 for a comparison of Zundel occupation probabilities in the different water cluster geometries). In Fig. 3 , the contribution of the Zundel complex to the axial spectrum is shown including contributions from nuclear motion and elec- tronic polarization (denoted n + e, violet line) and nuclear motion alone (denoted n, yellow line). Interestingly, the nuclear motion spectrum of the Zundel complex (yellow line) is sufﬁcient to describe the nuclear motion spectrum of the entire chain (dashed black line) in the range between ν ≈ 2000–3000 cm −1 . At the same time, the difference between the Zundel spectrum including only nuclear motion (yellow line) and the Zundel spectrum including also the electronic contribution (violet line) is minimal in this frequency range. This means that the nuclear contribution to the continuum band comes from a very localized region around the excess proton, while the electrons in the Zundel complex con- tribute little to the spectrum. We conclude that the axial con- tinuum band in protonated water chains is caused by local nuclear polarization ﬂuctuations in the immediate vicinity of the excess proton, which predominantly corresponds to a Zundel complex. The nuclear polarization ﬂuctuations are ampliﬁed by the rather long-ranged electronic polarizability, which extends further out to next-nearest neighboring water molecules.
The characterization of the optoelectron response property for the fabricated device demonstrates that the designed photodetector is able to work in the NIR region. The experi- mental comparison of the photo-electron responsivity be- tween the devices with and without the resonator provides us with a strong evidence for the resonant absorption of the light in NIR, leading to the internal photon emission (IPE) in the Au grating/Si Schottky interfaces. When the generated hot carriers gain sufficient energy to overcome the Schottky barrier, extra photocurrent is collected by the silicon sub- strate. The measured responsivity, however, is still below the average value comparing with conventional detectors. Fur- ther improvement should be made by reducing the top Au layer thickness down to 30 nm so that most of the generated hot electrons are able to diffuse into the silicon, considering the diffuse length of them is ~ 35 nm .
The basics of light-matter interaction by particles and the response of positive and negative charges to incident electromagnetic fields was discussed in Chapter 1. The response of every single electric dipole contributes in the overall response of a particle, but as was mentioned, calculating and considering all the microscopic responses even in a micrometre scale particle and by having powerful computation facilities is not practical. Therefore we use macroscopic analysis to investigate the scattering and absorption of light by particles. Mie-theory for analysing spherical particles was first introduced in 1908 by Gustav Mie  to explain the colourful eﬀects of colloidal gold solutions. In Mie-theory, the Maxwell’s equations are used for mathematically derivation of the incident, scattered and internal fields. These expressions take the form of an infinite series expansion of vector spherical harmonics [6, 89–91]. Nowadays, the interest in Mies theory is much broader, from interstellar dust, near-field optics and plasmonics to engineering subjects such as optical particle characterisation or nano-medical applications. Mie theory can be applied in many areas as scattering particles are often considered as homogeneous isotropic structures or can be approximated in such a way. However, I developed a novel semi- analytical approach based on Mie solution for highly nonlinear structures which experience inhomogeneous correction in refractive index . This approach is separately discussed in Chapter 5.
Abstract— The importance of determining skin hydration has over the years prompt the development of many instruments and methods, specifically designed to assess this parameter or water contents especially in the stratum corneum, and have greatly matured to suit different anatomical sites and measure multiple attributes. Of those, NearInfrared Spectroscopy (NIRS) has gained wide interest as a precise, safe, fast and noninvasive technique for determining skin hydration due to its high sensitivity to hydrogen bonding and ability to measure the amount of water in skin directly using the intensities of overtone and combination bands of OH and HOH water bonds occurring in the NIR region, that are good indicators of the state of skin hydration. This paper reports nearinfrared spectrophotometric measurements using a highly sophisticated spectrophotometer in the region of 1000-2500 nm to study the water uptake and dehydration properties of skin in vitro using samples of porcine skin. Initial results of pure liquid water and skin samples have clearly displayed the prominent bands associated with water content, and desorption tests have been able to verify changes in these bands associated with water content, although a clear correlation between the rates of weight loss and absorbance loss at various hydration periods has not yet been established. These preliminary results are expected to further explain the relationship between water and skin, and its role within, in hope to aid the future development of a portable instrument based on nearinfrared spectroscopy that would be capable of directly measuring skin hydration and/or water content in a fast and noninvasive manner.