Multispectral Remote Sensing of Saline Seeps

An aircraft experiment was conducted in early summer of 1981 to determine the feasibility of optical and microwave remote sensing techniques for the detection of fully developed and incipient saline seeps in South Dakota and Montana. The NASA C-130 earth resources aircraft was used to acquire L-band and C-band scatterometer data (backscattering coefficient profiles), radiometer data (brightness temperature profiles), and color-infrared photography; additional passive microwave data and thermal images were acquired by the L- band radiometer on the Beechcraft D-18 aircraft operated by South Dakota State University. Intensive soil moisture and salinity data were collected on a uniform 20-m grid spacing and at several depths for the 600 × 600 m South Dakota site. The two Montana sites were over-flown with flight lines several kilometers in length, and ground truth information was obtained by identifying known geological and geohydrological units with varying soil salinities on a regional basis. The C-130 radiometers (both L- and C-bands) were effective in detecting wet soil areas including fully developed seeps; however, incipient seeps were not accurately detected by the radiometers. The D-18 L-band radiometer data did not appear to be sensitive to soil wetness. The C-130 scatterometer data profiles, although showing some sensitivity to soil moisture, were greatly influenced by surface roughness and appear to be ineffective in accurately delineating either fully developed or incipient seeps. Thermal-IR scanner data acquired by the D-18 aircraft did not appear to provide a reliable means for identifying potential seeps.     

High temperature dynamic viscosity sensor for engine oil applications

This paper presents a new high temperature dynamic viscosity sensor for in situ condition monitoring of engine lubricants. The sensor is used to measure the variation in the quality factor of a vibrating piezoelectric cantilever beam due to viscous damping. The sensor was used to measure the dynamic viscosity of various single and multi-grade engines oils up to 180 cP from 25 °C to 60 °C. The sensor is capable of detecting degradation and dilution of engine oil for both new and used samples of 5W-30 and 10W-40 and diluted SAE 30 engine oils. All of the viscosity measurements presented are within 0.13-9.8% of the results obtained using the standard Walther equation at various temperatures. An equation relating dynamic viscosity of an oil sample to the quality factor of the beam is presented. The quality factor measurement circuit presented in this research can be implemented in automotive applications for in situ condition monitoring of lubricant viscosity.     

Multi-class brain tumor classification using residual network and global average pooling

Multimedia Tools and Applications - A rapid increase in brain tumor cases mandates researchers for the automation of brain tumor detection and diagnosis. Multi-tumor brain image classification...     

Image segmentation using fuzzy competitive learning based counter propagation network

Image segmentation is the method of partitioning an image into some homogenous regions that are more meaningful for its better understanding and examination. Soft computing methods having the capabilities of achieving artificial intelligence are predominately used to perform the task of segmentation. Due to the variability and the uncertainty present in natural scenes, segmentation is a complicated task to perform with the help of conventional image segmentation techniques. Therefore, in this article a hybrid Fuzzy Competitive Learning based Counter Propagation Network (FCPN) is proposed for the segmentation of natural scene images. This method compromises of the uncertainty handling capabilities of the fuzzy system and proficiency of parallel learning ability of neural network. To identify the number of clusters automatically in less computational time, the instar layer of Counter propagation network (CPN) has been trained by using Fuzzy competitive learning (FCL). The outstar layer of counter propagation network is trained by using Grossberg learning for obtaining the desired output. Region growing method having the tendency to correctly identify edges with simplicity is used for initial seed point selection. Then, the most similar regions in the image are clustered and the number of clusters is estimated automatically. Finally, by identifying the cluster centers the images are segmented. Bacterial foraging algorithm is used to initialize the initial weights to the network, which helps the proposed method in achieving low convergence ratio with higher accuracy. Results validated the higher performance of proposed FCPN method when compared with other states-of-the-art methods. For future work, some other adaptive methods like the fuzzy model-based network can be used to identify multiple object regions and classifying them among separate clusters.

     

Speech to speech interaction system using Multimedia Tools and Partially Observable Markov Decision Process for visually impaired students

Multimedia Tools and Applications - In general, visually impaired students need of another person’s to teach them with the help of computers and book. However, a number of students are not...     

Meander line MIMO antenna for 5.8 GHz WLAN application

A four port compact low profile planar MIMO antenna with meander line radiators and with polarization diversity effect has been proposed to cover 5.8 GHz wireless local area network application. The proposed MIMO antenna has ?10 dB impedance bandwidth of 1.4 GHz (5.3–6.7 GHz) along with the compact size of 38 × 38 mm² and an envelope correlation coefficient (ECC) of less than 4 × 10^?4 in the whole band. The proposed antenna resonates at 5.8 GHz frequency, having return loss of ?43.2 dB. The isolation between diagonal and opposite ports is more than 10 and 12 dB, respectively, in the presented frequency band. The total active reflection coefficient frequency response shows more than 1.0 GHz of bandwidth in the whole band. The antenna gain is more than 4.0 dBi in the operating frequency band. The radiating elements are very close to each other to make the design very compact.     

Microwave irradiation of guar seed flour: Effect on anti-nutritional factors,phytochemicals, in vitro protein digestibility,thermo-pasting,structural, and functional attributes

Guar seed flour (GSF) has a high amount of carbohydrates, proteins, phytochemicals, and anti-nutritional factors (ANFs), which limits its use. To address this issue, the current study was undertaken to understand the effect of microwave (MW) irradiation on ANFs, phytochemicals, in vitro protein digestibility (IVPD), and functional attributes of GSF at varying power density (P_d: 1–3 W/g) and duration (3–9 min). The ANFs were determined using a colorimetric assay and a Fourier transform infrared spectrum. At 3 P_d-9 min, the maximum reduction in ANFs (tannin, phytic acid, saponin, and trypsin inhibitor activity) was observed. Higher P_d and treatment duration increased antioxidant activity and total phenolic content, except for total flavonoid content. Furthermore, compared to the control sample (78.38%), the IVPD of the GSF samples increased to 3.28% (3 P_d-9 min). An increase in P_d and duration of MW treatment improved the thermal and pasting properties of GSF samples up to 2 P_d-9 min. Due to inter- and intramolecular hydrogen bonding degradation, the relative crystallinity of the 3 P_d-9 min treated GSF sample was 30.58%, which was lower than that of the control (40.08%). In MW-treated samples, SEM images revealed smaller clusters with rough and porous structures. However, no noticeable color (ΔE) changes were observed in MW-treated samples. Aside from water absorption capacity and water solubility index, MW treatment reduced oil absorption capacity, foaming capacity, and emulsifying capacity. As demonstrated by principal component analysis, MW irradiation with moderate P_d (2–3) was more effective in reducing ANFs, retaining nutritional contents, and improving the digestible properties of GSF, which could be a potential ingredient for developing gluten-free products.     

Radiative transfer modeling of filter-based measurements of light absorption by particles: Importance of particle size dependent penetration depth

We present new physical models to interpret the response characteristics of filter-based measurements of aerosol light absorption. They were motivated by a recently found particle size dependence of a systematic bias in absorption measurements that cannot be interpreted by any models reported thus far. A theory of particle filtration by fibrous filter is applied to reproduce the penetration depth of particles into a filter matrix, and the light transmissivity of the matrix is calculated by the use of radiative transfer theory for plane-parallel layers. Optical properties of individual layers are calculated from microphysical properties and the number density of filter fibers and deposited particles. The size-dependent systematic bias in absorption measurement is successfully explained by the particle size dependence of penetration depth into a filter matrix. In practice the physical models developed here are useful for selecting operating conditions and filter matrices for instruments with fewer systematic biases.     

Protecting copper from electrochemical degradation by graphene coating

Graphene coating on copper (Cu) is shown to increase the resistance of the metal to electrochemical degradation by one and half orders of magnitude. Detailed electrochemical characterization in aggressive chloride environment shows the impedance of Cu increasing dramatically and the anodic and cathodic current densities of the coated Cu becoming nearly 1–2 orders of magnitude smaller when coated with graphene. The observations are counterintuitive as graphite in contact with metals increases metallic corrosion. The results can bring paradigm changes in the development of anti-corrosion coatings using conformal, ultrathin graphene films.