Spectrophotometric study in the near-ir of a sample of MIPS selected galaxies at Z~2

Our main objective is to determine what kind of galaxies dominate the cosmic SFR density at z~2. Our sample consists of 24 galaxies in Chandra Deep Field South, a unique field for the study of galaxy evolution (12 observed with GNIRS/GEMINI and 12 with ISAAC/VLT). We use H alpha together with the already merged X-ray, ultraviolet, optical, near and mid-infrared imaging data to obtain estimations of SFRs, metallicities, stellar and dynamical masses, AGN activity, and extinction properties. We have obtained 15 Hα detections, 4 rotation curves, and SFR relationship for 7 galaxies. The metallicities obtained for 8 galaxies of the sample are compatible with the metallicities of local galaxies.

Discovery of "Warm Dust" galaxies in clusters at z ~ 0.3: evidence for stripping of cool dust in the dense environment?

Using far-infrared imaging from the "Herschel Lensing Survey," we derive dust properties of spectroscopically confirmed cluster member galaxies within two massive systems at z ~ 0.3: the merging Bullet Cluster and the more relaxed MS2137.3-2353. Most star-forming cluster sources (~90%) have characteristic dust temperatures similar to local field galaxies of comparable infrared (IR) luminosity (T_dust ~ 30 K). Several sub-luminous infrared galaxy (LIRG; L_IR < 10^11 L_☉) Bullet Cluster members are much warmer (T_dust > 37 K) with far-infrared spectral energy distribution (SED) shapes resembling LIRG-type local templates. X-ray and mid-infrared data suggest that obscured active galactic nuclei do not contribute significantly to the infrared flux of these "warm dust" galaxies. Sources of comparable IR luminosity and dust temperature are not observed in the relaxed cluster MS2137, although the significance is too low to speculate on an origin involving recent cluster merging. "Warm dust" galaxies are, however, statistically rarer in field samples (>3σ), indicating that the responsible mechanism may relate to the dense environment. The spatial distribution of these sources is similar to the whole far-infrared bright population, i.e., preferentially located in the cluster periphery, although the galaxy hosts tend toward lower stellar masses (M_* < 10^10 M_☉). We propose dust stripping and heating processes which could be responsible for the unusually warm characteristic dust temperatures. A normal star-forming galaxy would need 30%-50% of its dust removed (preferentially stripped from the outer reaches, where dust is typically cooler) to recover an SED similar to a "warm dust" galaxy. These progenitors would not require a higher IR luminosity or dust mass than the currently observed normal star-forming population.

Signal subspace identification in hyperspectral imagery

Terrestrial remote sensing imagery involves the acquisition of information from the Earth's surface without physical contact with the area under study. Among the remote sensing modalities, hyperspectral imaging has recently emerged as a powerful passive technology. This technology has been widely used in the fields of urban and regional planning, water resource management, environmental monitoring, food safety, counterfeit drugs detection, oil spill and other types of chemical contamination detection, biological hazards prevention, and target detection for military and security purposes [2-9]. Hyperspectral sensors sample the reflected solar radiation from the Earth surface in the portion of the spectrum extending from the visible region through the near-infrared and mid-infrared (wavelengths between 0.3 and 2.5 µm) in hundreds of narrow (of the order of 10 nm) contiguous bands [10]. This high spectral resolution can be used for object detection and for discriminating between different objects based on their spectral xharacteristics [6]. However, this huge spectral resolution yields large amounts of data to be processed. For example, the Airbone Visible/Infrared Imaging Spectrometer (AVIRIS) [11] collects a 512 (along track) X 614 (across track) X 224 (bands) X 12 (bits) data cube in 5 s, corresponding to about 140 MBs. Similar data collection ratios are achieved by other spectrometers [12]. Such huge data volumes put stringent requirements on communications, storage, and processing. The problem of signal sbspace identification of hyperspectral data represents a crucial first step in many hypersctral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction (DR) yelding gains in data storage and retrieval and in computational time and complexity. Additionally, DR may also improve algorithms performance since it reduce data dimensionality without losses in the useful signal components. The computation of statistical estimates is a relevant example of the advantages of DR, since the number of samples required to obtain accurate estimates increases drastically with the dimmensionality of the data (Hughes phnomenon) [13].

The SHIVir Survey: A Dynamical Catalogue of Virgo Cluster Galaxies and their Scaling Relations

By virtue of its proximity and richness, the Virgo galaxy cluster is a perfect testing ground to expand our understanding of structure formation in the Universe. Here, we present a comprehensive dynamical catalogue based on 190 Virgo cluster galaxies (VCGs) in the "Spectroscopy and H-band Imaging of the Virgo cluster" (SHIVir) survey, including kinematics and dynamical masses. Spectroscopy collected over a multi-year campaign on 4-8m telescopes was joined with optical and near-infrared imaging to create a cosmologically-representative overview of parameter distributions and scaling relations describing galaxy evolution in a rich cluster environment. The use of long-slit spectroscopy has allowed the extraction and systematic analysis of resolved kinematic profiles: Halpha rotation curves for late-type galaxies (LTGs), and velocity dispersion profiles for early-type galaxies (ETGs). The latter are shown to span a wide range of profile shapes which correlate with structural, morphological, and photometric parameters. A study of the distributions of surface brightnesses and circular velocities for ETGs and LTGs considered separately show them all to be strongly bimodal, hinting at the existence of dynamically unstable modes where the baryon and dark matter fractions may be comparable within the inner regions of galaxies. Both our Tully-Fisher relation for LTGs and Fundamental Plane analysis for ETGs exhibit the smallest scatter when a velocity metric probing the galaxy at larger radii (where the baryonic fraction becomes sub-dominant) is used: rotational velocity measured in the outer disc at the 23.5 i-mag arcsec^{-2} level, and velocity dispersion measured within an aperture of 2 effective radii, respectively. Dynamical estimates for gas-poor and gas-rich VCGs are merged into a joint analysis of the stellar-to-total mass relation (STMR), stellar TFR, and Mass-Size relation. These relations are all found to contain strong bimodalities or dichotomies between the ETG and LTG samples, alluding to a "mixed scenario'' evolutionary sequence between morphological/dynamical classes that involves both quenching and dry mergers. The unmistakable differentiation between these two galaxy classes appears robust against different classification schemes, and supports the notion that they are driven by different evolutionary histories. Future observations using integral field spectroscopy and including lower-mass galaxies should solidify this hypothesis.

Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis.

The use of near infrared (NIR) hyperspectral imaging and hyperspectral image analysis for distinguishing between hard, intermediate and soft maize kernels from inbred lines was evaluated. NIR hyperspectral images of two sets (12 and 24 kernels) of whole maize kernels were acquired using a Spectral Dimensions MatrixNIR camera with a spectral range of 960-1662 nm and a sisuChema SWIR (short wave infrared) hyperspectral pushbroom imaging system with a spectral range of 1000-2498 nm. Exploratory principal component analysis (PCA) was used on absorbance images to remove background, bad pixels and shading. On the cleaned images. PCA could be used effectively to find histological classes including glassy (hard) and floury (soft) endosperm. PCA illustrated a distinct difference between glassy and floury endosperm along principal component (PC) three on the MatrixNIR and PC two on the sisuChema with two distinguishable clusters. Subsequently partial least squares discriminant analysis (PLS-DA) was applied to build a classification model. The PLS-DA model from the MatrixNIR image (12 kernels) resulted in root mean square error of prediction (RMSEP) value of 0.18. This was repeated on the MatrixNIR image of the 24 kernels which resulted in RMSEP of 0.18. The sisuChema image yielded RMSEP value of 0.29. The reproducible results obtained with the different data sets indicate that the method proposed in this paper has a real potential for future classification uses.

Assessment of signs of foot infection in diabetes patients using photographic foot imaging and infrared thermography

Background Patients with diabetic foot disease require frequent screening to prevent complications and may be helped through telemedical home monitoring. Within this context, the goal was to determine the validity and reliability of assessing diabetic foot infection using photographic foot imaging and infrared thermography. Subjects and Methods For 38 patients with diabetes who presented with a foot infection or were admitted to the hospital with a foot-related complication, photographs of the plantar foot surface using a photographic imaging device and temperature data from six plantar regions using an infrared thermometer were obtained. A temperature difference between feet of > 2.2 °C defined a ''hotspot.'' Two independent observers assessed each foot for presence of foot infection, both live (using the Perfusion-Extent-Depth- Infection-Sensation classification) and from photographs 2 and 4 weeks later (for presence of erythema and ulcers). Agreement in diagnosis between live assessment and (the combination of ) photographic assessment and temperature recordings was calculated. Results Diagnosis of infection from photographs was specific (> 85%) but not very sensitive (< 60%). Diagnosis based on hotspots present was sensitive (> 90%) but not very specific (<25%). Diagnosis based on the combination of photographic and temperature assessments was both sensitive (> 60%) and specific (> 79%). Intra-observer agreement between photographic assessments was good (Cohen's j = 0.77 and 0.52 for both observers). Conclusions Diagnosis of foot infection in patients with diabetes seems valid and reliable using photographic imaging in combination with infrared thermography. This supports the intended use of these modalities for the home monitoring of high-risk patients with diabetes to facilitate early diagnosis of signs of foot infection.

Mesh Simplification Based on Edge Collapsing Could Improve Computational Efficiency in Near Infrared Optical Tomographic Imaging

The diffusion equation-based modeling of near infrared light propagation in tissue is achieved by using finite-element mesh for imaging real-tissue types, such as breast and brain. The finite-element mesh size (number of nodes) dictates the parameter space in the optical tomographic imaging. Most commonly used finite-element meshing algorithms do not provide the flexibility of distinct nodal spacing in different regions of imaging domain to take the sensitivity of the problem into consideration. This study aims to present a computationally efficient mesh simplification method that can be used as a preprocessing step to iterative image reconstruction, where the finite-element mesh is simplified by using an edge collapsing algorithm to reduce the parameter space at regions where the sensitivity of the problem is relatively low. It is shown, using simulations and experimental phantom data for simple meshes/domains, that a significant reduction in parameter space could be achieved without compromising on the reconstructed image quality. The maximum errors observed by using the simplified meshes were less than 0.27% in the forward problem and 5% for inverse problem.

Improved infrared thermal imaging of a CMOS MEMS device

We report a technique which can be used to improve the accuracy of infrared (IR) surface temperature measurements made on MEMS (Micro-Electro-Mechanical- Systems) devices. The technique was used to thermally characterize a SOI (Silicon-On-Insulator) CMOS (Complementary Metal Oxide Semiconductor) MEMS thermal flow sensor. Conventional IR temperature measurements made on the sensor were shown to give significant surface temperature errors, due to the optical transparency of the SiO 2 membrane layers and low emissivity/high reflectivity of the metal. By making IR measurements on radiative carbon micro-particles placed in isothermal contact with the device, the accuracy of the surface temperature measurement was significantly improved. © 2010 EDA Publishing/THERMINIC.

PHOTOREFRACTIVE CRYSTAL-BASED ACOUSTO-OPTIC IMAGING IN THE NEAR-INFRARED AND ITS APPLICATIONS

Acousto-optic (AO) sensing and imaging (AOI) is a dual-wave modality that combines ultrasound with diffusive light to measure and/or image the optical properties of optically diffusive media, including biological tissues such as breast and brain. The light passing through a focused ultrasound beam undergoes a phase modulation at the ultrasound frequency that is detected using an adaptive interferometer scheme employing a GaAs photorefractive crystal (PRC). The PRC-based AO system operating at 1064 nm is described, along with the underlying theory, validating experiments, characterization, and optimization of this sensing and imaging apparatus. The spatial resolution of AO sensing, which is determined by spatial dimensions of the ultrasound beam or pulse, can be sub-millimeter for megahertz-frequency sound waves.A modified approach for quantifying the optical properties of diffuse media with AO sensing employs the ratio of AO signals generated at two different ultrasound focal pressures. The resulting “pressure contrast signal” (PCS), once calibrated for a particular set of pressure pulses, yields a direct measure of the spatially averaged optical transport attenuation coefficient within the interaction volume between light and sound. This is a significant improvement over current AO sensing methods since it produces a quantitative measure of the optical properties of optically diffuse media without a priori knowledge of the background illumination. It can also be used to generate images based on spatial variations in both optical scattering and absorption. Finally, the AO sensing system is modified to monitor the irreversible optical changes associated with the tissue heating from high intensity focused ultrasound (HIFU) therapy, providing a powerful method for noninvasively sensing the onset and growth of thermal lesions in soft tissues. A single HIFU transducer is used to simultaneously generate tissue damage and pump the AO interaction. Experimental results performed in excised chicken breast demonstrate that AO sensing can identify the onset and growth of lesion formation in real time and, when used as feedback to guide exposure parameters, results in more predictable lesion formation.

The effect of environment on endosperm cell-wall development in Triticum aestivum during grain filling: an infrared spectroscopic imaging study

One of the major factors contributing to the failure of new wheat varieties is seasonal variability in end-use quality. Consequently, it is important to produce varieties which are robust and stable over a range of environmental conditions. Recently developed sample preparation methods have allowed the application of FT-IR spectroscopic imaging methods to the analysis of wheat endosperm cell wall composition, allowing the spatial distribution of structural components to be determined without the limitations of conventional chemical analysis. The advantages of the methods, described in this paper, are that they determine the composition of endosperm cell walls in situ and with minimal modification during preparation. Two bread-making wheat cultivars, Spark and Rialto, were selected to determine the impact of environmental conditions on the cell-wall composition of the starchy endosperm of the developing and mature grain, focusing on the period of grain filling (starting at about 14 days after anthesis). Studies carried out over two successive seasons show that the structure of the arabinoxylans in the endosperm cell walls changes from a highly branched form to a less branched form. Furthermore, during development the rate of restructuring was faster when the plants were grown at higher temperature with restricted water availability from 14 days after anthesis with differences in the rate of restructuring occurring between the two cultivars.

CHANDRA AND SPITZER IMAGING OF THE INFRARED CLUSTER IN NGC 2071

We present results of a sensitive Chandra X-ray observation and Spitzer mid-infrared (mid-IR) observations of the IR cluster lying north of the NGC 2071 reflection nebula in the Orion B molecular cloud. We focus on the dense cluster core known as NGC 2071-IR, which contains at least nine IR sources within a 40 `` x 40 `` region. This region shows clear signs of active star formation including powerful molecular outflows, Herbig-Haro objects, and both OH and H(2)O masers. We use Spitzer Infrared Array Camera (IRAC) images to aid in X-ray source identification and to determine young stellar object (YSO) classes using mid-IR colors. Spitzer IRAC colors show that the luminous source IRS 1 is a class I protostar. IRS 1 is believed to be driving a powerful bipolar molecular outflow and may be an embedded B-type star or its progenitor. Its X-ray spectrum reveals a fluorescent Fe emission line at 6.4 keV, arising in cold material near the protostar. The line is present even in the absence of large flares, raising questions about the nature of the ionizing mechanism responsible for producing the 6.4 keV fluorescent line. Chandra also detects X-ray sources at or near the positions of IRS 2, IRS 3, IRS 4, and IRS 6 and a variable X-ray source coincident with the radio source VLA 1, located just 2 `` north of IRS 1. No IR data are yet available to determine a YSO classification for VLA 1, but its high X-ray absorption shows that it is even more deeply embedded than IRS 1, suggesting that it could be an even younger, less-evolved protostar.

A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology

This year marks the 20th anniversary of functional near-infrared spectroscopy and imaging (fNIRS/fNIRI). As the vast majority of commercial instruments developed until now are based on continuous wave technology, the aim of this publication is to review the current state of instrumentation and methodology of continuous wave fNIRI. For this purpose we provide an overview of the commercially available instruments and address instrumental aspects such as light sources, detectors and sensor arrangements. Methodological aspects, algorithms to calculate the concentrations of oxy- and deoxyhemoglobin and approaches for data analysis are also reviewed. From the single-location measurements of the early years, instrumentation has progressed to imaging initially in two dimensions (topography) and then three (tomography). The methods of analysis have also changed tremendously, from the simple modified Beer-Lambert law to sophisticated image reconstruction and data analysis methods used today. Due to these advances, fNIRI has become a modality that is widely used in neuroscience research and several manufacturers provide commercial instrumentation. It seems likely that fNIRI will become a clinical tool in the foreseeable future, which will enable diagnosis in single subjects.

Sentinel node mapping using hysteroscopic injection of indocyanine green and laparoscopic near-infrared fluorescence imaging in endometrial cancer staging.

Herein is presented a technique for minimally invasive sentinel node mapping. The patient had apparently early stage endometrial cancer. Sentinel node mapping was performed using a hysteroscopic injection of indocyanine green followed by laparoscopic sentinel node detection via near-infrared fluorescence. This technique ensures delineation of lymphatic drainage from the tumor area, thus achieving accurate detection of sentinel nodes.

Detection and quantification of peanut traces in wheat flour by near infrared hyperspectral imaging spectroscopy using principal-component analysis

The use of a common environment for processing different powder foods in the industry has increased the risk of finding peanut traces in powder foods. The analytical methods commonly used for detection of peanut such as enzyme-linked immunosorbent assay (ELISA) and real-time polymerase chain reaction (RT-PCR) represent high specificity and sensitivity but are destructive and time-consuming, and require highly skilled experimenters. The feasibility of NIR hyperspectral imaging (HSI) is studied for the detection of peanut traces down to 0.01% by weight. A principal-component analysis (PCA) was carried out on a dataset of peanut and flour spectra. The obtained loadings were applied to the HSI images of adulterated wheat flour samples with peanut traces. As a result, HSI images were reduced to score images with enhanced contrast between peanut and flour particles. Finally, a threshold was fixed in score images to obtain a binary classification image, and the percentage of peanut adulteration was compared with the percentage of pixels identified as peanut particles. This study allowed the detection of traces of peanut down to 0.01% and quantification of peanut adulteration from 10% to 0.1% with a coefficient of determination (r2) of 0.946. These results show the feasibility of using HSI systems for the detection of peanut traces in conjunction with chemical procedures, such as RT-PCR and ELISA to facilitate enhanced quality-control surveillance on food-product processing lines.