An infrared spectral signature of human lymphocyte subpopulations from peripheral blood.

Metastatic melanomas are frequently refractory to most adjuvant therapies such as chemotherapies and radiotherapies. Recently, immunotherapies have shown good results in the treatment of some metastatic melanomas. Immune cell infiltration in the tumor has been associated with successful immunotherapy. More generally, tumor infiltrating lymphocytes (TILs) in the primary tumor and in metastases of melanoma patients have been demonstrated to correlate positively with favorable clinical outcomes. Altogether, these findings suggest the importance of being able to identify, quantify and characterize immune infiltration at the tumor site for a better diagnostic and treatment choice. In this paper, we used Fourier Transform Infrared (FTIR) imaging to identify and quantify different subpopulations of T cells: the cytotoxic T cells (CD8+), the helper T cells (CD4+) and the regulatory T cells (T reg). As a proof of concept, we investigated pure populations isolated from human peripheral blood from 6 healthy donors. These subpopulations were isolated from blood samples by magnetic labeling and purities were assessed by Fluorescence Activated Cell Sorting (FACS). The results presented here show that Fourier Transform Infrared (FTIR) imaging followed by supervised Partial Least Square Discriminant Analysis (PLS-DA) allows an accurate identification of CD4+ T cells and CD8+ T cells (>86%). We then developed a PLS regression allowing the quantification of T reg in a different mix of immune cells (e.g. Peripheral Blood Mononuclear Cells (PBMCs)). Altogether, these results demonstrate the sensitivity of infrared imaging to detect the low biological variability observed in T cell subpopulations.

Design and Development of Spectral Signature Based Chipless RFID Tags

The main objective of this thesis is to design and develop spectral signature based chipless RFID tags Multiresonators are essential component of spectral signature based chipless tags. To enhance the data coding capacity in spectral signature based tags require large number of resonances in a limited bandwidth. The frequency of the resonators have to be close to each other. To achieve this condition, the quality factor of each resonance needs to be high. The thesis discusses about various types of multiresonators, their practical implementation and how they can be used in design. Encoding of data into spectral domain is another challenge in chipless tag design. Here, the technique used is the presence or absence encoding technique. The presence of a resonance is used to encode Logic 1 and absence of a speci c resonance is used to encode Logic 0. Di erent types of multiresonators such as open stub multiresonators, coupled bunch hairpin resonators and shorted slot ground ring resonator are proposed in this thesis.

VIS/NIR spectral signature for the identification of peanut contamination of powder foods

Visible-near infrared reflectance spectra are proposed for the characterization of IRMM 481 peanuts variety in comparison to powder food materials: wheat flour, milk and cocoa. Multidimensional analysis of reflectance spectra of powder samples shows a specific NIR band centred at 1200 nm that identifies peanut compared to the rest of food ingredients, regardless compaction level and temperature. Spectral range of 400-1000 nm is not robust for identification of blanched peanut. The visible range has shown to be reliable for the identification of pre-treatment and processing of unknown commercial peanut samples. A spectral index is proposed based on the combination of three wavelengths around 1200 nm that is 100% robust against pre-treatment (raw or blanched) and roasting (various temperatures and treatment duration).

Spectral Signature Secured Retrodirective Array

A novel retrodirective array (RDA) architecture is proposed which utilises a special case spectral signature embedded within the data payload as pilot signals. With the help of a pair of phase-locked-loop (PLL) based phase conjugators (PCs) the RDA’s response to other unwanted and/or unfriendly interrogating signals can be disabled, leading to enhanced secrecy performance directly in the wireless physical layer. The effectiveness of the proposed RDA system is experimentally demonstrated.

Spectral Signature Activity Data

Resources created at the University of Southampton for the module Remote Sensing for Earth Observation

Evaluating effects of spectral training data distribution on continuous field mapping performance

Continuous field mapping has to address two conflicting remote sensing requirements when collecting training data. On one hand, continuous field mapping trains fractional land cover and thus favours mixed training pixels. On the other hand, the spectral signature has to be preferably distinct and thus favours pure training pixels. The aim of this study was to evaluate the sensitivity of training data distribution along fractional and spectral gradients on the resulting mapping performance. We derived four continuous fields (tree, shrubherb, bare, water) from aerial photographs as response variables and processed corresponding spectral signatures from multitemporal Landsat 5 TM data as explanatory variables. Subsequent controlled experiments along fractional cover gradients were then based on generalised linear models. Resulting fractional and spectral distribution differed between single continuous fields, but could be satisfactorily trained and mapped. Pixels with fractional or without respective cover were much more critical than pure full cover pixels. Error distribution of continuous field models was non-uniform with respect to horizontal and vertical spatial distribution of target fields. We conclude that a sampling for continuous field training data should be based on extent and densities in the fractional and spectral, rather than the real spatial space. Consequently, adequate training plots are most probably not systematically distributed in the real spatial space, but cover the gradient and covariate structure of the fractional and spectral space well. (C) 2009 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

Spectral invariant behavior of zenith radiance around cloud edges observed by ARM SWS

The ARM Shortwave Spectrometer (SWS) measures zenith radiance at 418 wavelengths between 350 and 2170 nm. Because of its 1-sec sampling resolution, the SWS provides a unique capability to study the transition zone between cloudy and clear sky areas. A spectral invariant behavior is found between ratios of zenith radiance spectra during the transition from cloudy to cloud-free. This behavior suggests that the spectral signature of the transition zone is a linear mixture between the two extremes (definitely cloudy and definitely clear). The weighting function of the linear mixture is a wavelength-independent characteristic of the transition zone. It is shown that the transition zone spectrum is fully determined by this function and zenith radiance spectra of clear and cloudy regions. An important result of these discoveries is that high temporal resolution radiance measurements in the clear-to-cloud transition zone can be well approximated by lower temporal resolution measurements plus linear interpolation.

Vertex component analysis: a fast algorithm to extract endmembers spectra from hyperspectral data

Chapter in Book Proceedings with Peer Review First Iberian Conference, IbPRIA 2003, Puerto de Andratx, Mallorca, Spain, JUne 4-6, 2003. Proceedings

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.

Variation des biomarqueurs dans le spectre visible non résolu de la Terre

L’évolution rapide des technologies de détection et de caractérisation des exoplanètes depuis le début des années 1990 permet de croire que de nouveaux instruments du type Terrestrial Planet Finder (TPF) pourront prendre les premiers spectres d’exoplanètes semblables à la Terre d’ici une ou deux décennies. Dans ce contexte, l’étude du spectre de la seule planète habitée connue, la Terre, est essentielle pour concevoir ces instruments et analyser leurs résultats. Cette recherche présente les spectres de la Terre dans le visible (390-900 nm), acquis lors de 8 nuits d’observation étalées sur plus d’un an. Ces spectres ont été obtenus en observant la lumière cendrée de la Lune avec le télescope de 1.6 m de l’Observatoire du Mont-Mégantic (OMM). La surface de la Lune réfléchissant de manière diffuse la lumière provenant d’une portion de la Terre, ces spectres sont non résolus spatialement. L’évolution de ces spectres en fonction de la lumière réfléchie à différentes phases de Terre est analogue à celle du spectre d’une exoplanète, dont la phase change selon sa position autour de l’étoile. L'eau, l'oxygène et l'ozone de l’atmosphère, détectés dans tous nos spectres, sont des biomarqueurs dont la présence suggère l’habitabilité de la planète et/ou la présence d’une activité biologique. Le Vegetation Red Edge (VRE), une autre biosignature spectrale, dû aux organismes photosynthétiques à la surface, est caractérisé par l’augmentation de la réflectivité autour de 700 nm. Pour les spectres de 5 nuits, cette augmentation a été évaluée entre -5 et 15% ±~5%, après que les contributions de la diffusion de Rayleigh, des aérosols et d’une large bande moléculaire de l’ozone aient été enlevées. Les valeurs mesurées sont cohérentes avec la présence de végétation dans la phase de la Terre contribuant au spectre, mais s’étendent sur une plage de variations plus large que celles trouvées dans la littérature (0-10%). Cela pourrait s’expliquer par des choix faits lors de la réduction des données et du calcul du VRE, ou encore par la présence d’autres éléments de surface ou de l’atmosphère dont la contribution spectrale autour de 700 nm serait variable.

Étude des propriétés plasmoniques des réseaux de nanotrous

Les réseaux de nanotrous sont des structures plasmoniques ayant un énorme potentiel en tant que transducteurs pour la conception de biocapteurs. De telles structures sont prometteuses pour l’élaboration de biocapteurs capable d’effectuer du criblage à haut débit. L’intérêt de travailler avec des réseaux de nanotrous est dû à la simplicité d’excitation des polaritons de plasmons de surface en transmission directe, à la sensibilité et à la facilité de fabrication de ces senseurs. L’architecture de tels réseaux métalliques permet la conception de nanostructures ayant de multiples propriétés plasmoniques. L’intensité, la signature spectrale et la sensibilité du signal plasmonique sont grandement affectées par l’aspect physique du réseau de nanotrous. L’optimisation du signal plasmonique nécessite ainsi un ajustement du diamètre des trous, de la périodicité et de la composition métallique du réseau. L'agencement de l'ensemble de ces paramètres permet d'identifier une structure optimale possédant une périodicité de 1000 nm, un diamètre des nanotrous de 600-650 nm et un film métallique ayant une épaisseur de 125 nm d'or. Ce type de transducteur a une sensibilité en solution de 500-600 nm/RIU pour des bandes plasmoniques situées entre 600-700 nm. L'intérêt de travailler avec cette structure est la possibilité d'exciter les plasmons de polaritons de surface (SPPs) selon deux modes d'excitation : en transmission exaltée (EOT) ou en réflexion totale interne par résonance des plasmons de surface (SPR). Une comparaison entre les propriétés plasmoniques des senseurs selon les modes d'excitation permet de déterminer expérimentalement que le couplage de la lumière avec les ondes de SPP de Bloch (BW-SPPs) en transmission directe résulte en un champ électromagnétique davantage propagatif que localisé. D'un point de vue analytique, la biodétection de l'IgG en SPR est 6 fois plus sensible par rapport au mode EOT pour une même structure. Une étude du signal plasmonique associé au BW-SPP pour un certain mode de diffraction démontre que la distance de pénétration de ces structures en EOT est d'environ 140 nm. La limite de détection de l'IgG humain pour un réseau de nanotrous de 1000 nm de périodicité est d'environ 50 nM en EOT. Ce mémoire démontre la viabilité des réseaux de nanotrous pour effectuer de la biodétection par criblage à haut débit lors de prochaines recherches. L'investigation de l'effet de l'angle d'excitation en transmission exaltée par rapport au signal plasmonique associé au mode (1,0) d'un réseau de nanotrous de 820 nm d'or démontre que la sensibilité en solution n'est pas proportionnelle à la sensibilité en surface du senseur. En fait, une optimisation de l'angle d'incidence pour le mode (1,0) de diffraction des BW-SPP permet d'amplifier la sensibilité en surface du senseur jusqu'à 3-fois pour un angle de 13,3°. Ce mémoire démontre ainsi la nécessité d'optimiser l'angle d'excitation et les propriétés physiques du senseur afin de développer un transducteur de grande sensibilité basé sur l'excitation en transmission de réseaux de nanotrous.

Frequency Coded Chipless RFID Tag using Spurline Resonators

A novel compact chipless RFID tag using spurline resonators is discussed in this paper. The detection of the tag's ID is using the spectral signature of a spurline resonator circuit. The tag has a data capacity of 8-bits in the range 2.38 to 4.04 GHz. The tag consists of a spurline multiresonating circuit and two cross polarized antennas. The prototype of the tag is fabricated on a substrate CMET/ LK4.3 of dielectric constant 4.3 and loss tangent 0.0018. The measured results show that group delay response can also be used to decode the tag’s identity

Abrasion control on dune colour: Muleshoe Dunes, SW USA

The Muleshoe Dunes, an east-west trending dunefield on the border separating Texas and New Mexico, consist of two distinct components: a white (carbonate rich) component and an overlying pink (quartz rich) component. The pink component exhibits significant spatial variation in redness. The reddest sands, in the western part of the dunefield, decrease in redness towards the east. This gradient is thought to result from abrasion of all iron-rich, red clay coating as the sediments were transported eastward by Late Quaternary aeolian processes. The effects of aeolian abrasion on the spectral signature and surface texture of the sediments were examined using laboratory abrasion experiments. Changes in spectral reflectance of abrasion samples from the laboratory were compared to field samples that were abraded naturally because of sediment transport. The changes resulting from increased time of abrasion are similar to those observed with increased distance downwind in the dunefield. These results suggest that downwind abrasion can explain the pattern of dune colour in the Muleshoe Dunes, although this does not preclude other possible causes. (C) 2008 Elsevier B.V. All rights reserved.