Virtual sensors: using data mining techniques to efficiently estimate remote sensing spectra

Various instruments are used to create images of the earth and other objects in the universe in a diverse set of wavelength bands with the aim of understanding natural phenomena. Sometimes these instruments are built in a phased approach, with additional measurement capabilities added in later phases. In other cases, technology may mature to the point that the instrument offers new measurement capabilities that were not planned in the original design of the instrument. In still other cases, high-resolution spectral measurements may be too costly to perform on a large sample, and therefore, lower resolution spectral instruments are used to take the majority of measurements. Many applied science questions that are relevant to the earth science remote sensing community require analysis of enormous amounts of data that were generated by instruments with disparate measurement capabilities. This work addresses this problem using virtual sensors: a method that uses models trained on spectrally rich (high spectral resolution) data to "fill in" unmeasured spectral channels in spectrally poor (low spectral resolution) data. The models we use Are multilayer perceptrons, support vector machines (SVMs) with radial basis function kernels, and SVMs with mixture density Mercer kernels. We demonstrate this method by using models trained on the high spectral resolution Terra Moderate Resolution Imaging Spectrometer (MODIS) instrument to estimate what the equivalent of the MODIS 1.6-/spl mu/m channel would be for the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (AVHRR/2) instrument. The scientific motivation for the simulation of the 1.6-/spl mu/m channel is to improve the ability of the AVHRR/2 sensor to detect clouds over snow and ice.     

An Evaluation of Smecta as a Tablet Disintegrant and Dissolution Aid

Smecta is a nonfibrous Attapulgite (NFA), mostly composed of smectite. It was evaluated as a disintegrant in tablets made by direct compression as well as by wet granulation and using lactose and dicalcium phosphate as water soluble and water insoluble fillers, respectively. An inorganic clay, magnesium aluminum silicate (Veegum), a modified starch (Starch 1500), a cross-linked carboxymethyl cellulose (Ac-Di-Sol), and a cross-linked polyvinylpyrrolidone (Polyplasdone XL) were used for comparative evaluation. Smecta performed well as a disintegrant in tablets made by either method. It was superior to Veegum and Starch 1500, but inferior to Ac-Di-Sol and Polyplasdone XL. In tablets with Smecta, dissolution of hydrochlorothiazide (HCTZ) was superior to those with Ac-Di-Sol.     

Detection of Choroidal Neovascularization (CNV) in Retina OCT Images Using VGG16 and DenseNet CNN

Wireless Personal Communications - In this study, we intend to diagnose Choroidal Neovascularization in retinal Optical Coherence Tomography (OCT) images using Deep Learning Architectures. OCT...     

MWCNT-Modified Gold Electrode Sensor for the Determination of Propyl Gallate in Vegetable Oils

A differential pulse voltammetric method for the determination of the synthetic food antioxidant, propyl gallate (PG) was developed. Determination of PG was carried out using multiwalled carbon nanotube (MWCNT)-modified gold electrode (GE) in a solution of 0.1 M acetate buffer (pH 7) as supporting electrolyte. The developed sensor showed an excellent catalytic activity and stability for PG oxidation. Effect of common interfering ions, amount of MWCNT–Nafion suspension, pH of the supporting electrolyte, and scan rate were studied. Under optimized conditions, PG concentration as low as 6.3?×?10^?7 M can be determined in food samples. The determination of PG in vegetable oils with the proposed sensor was in good agreement with those obtained by reported spectrophotometric method.     

Green synthesis of biocompatible carbon dots using aqueous extract of Trapa bispinosa peel

We are reporting highly economical plant based method for the production of luminescent water soluble carbon dots (C-dot) using Indian water plant Trapa bispinosa peel extract without adding any external oxidizing agent at 90 °C. C-dots ranging from 5 to 10 nm were found in the solution with a prominent green fluorescence under UV-light (λ_ex = 365 nm). UV–vis spectra recorded at different time intervals (30–120 min) displayed signature absorption of C-dots between 400 and 600 nm. Fluorescence spectra of the dispersion after 120 min of synthesis exhibited characteristic emission peaks of C-dots when excited at 350, 400, 450 and 500 nm. C-dots were further analyzed using X-ray diffraction (XRD), Raman Spectroscopy and Thermo-Gravimetric Analysis (TGA). Structure of the C-dots was found to be turbostratic when studied using XRD. C-dots synthesized by our method were found to be exceptionally biocompatible against MDCK cells.     

Test Data Volume Comparison: Monolithic vs. Modular SoC Testing

Editor's note:Containing production cost is a major concern for today's complex SoCs. One of the key contributors to production cost is test time and test data volume, for which numerous compression techniques were proposed. This article introduces a different approach to test data volume reduction, namely the use of modular test based on IEEE Std 1500 architecture, and it provides modeling, analysis, and quantification to support the proposed approach.—Yervant Zorian, VirageLogic     

Input decimated ensembles

Using an ensemble of classifiers instead of a single classifier has been shown to improve generalization performance in many pattern recognition problems. However, the extent of such improvement depends greatly on the amount of correlation among the errors of the base classifiers. Therefore, reducing those correlations while keeping the classifiers’ performance levels high is an important area of research. In this article, we explore Input Decimation (ID), a method which selects feature subsets for their ability to discriminate among the classes and uses these subsets to decouple the base classifiers. We provide a summary of the theoretical benefits of correlation reduction, along with results of our method on two underwater sonar data sets, three benchmarks from the Probenl/UCI repositories, and two synthetic data sets. The results indicate that input decimated ensembles outperform ensembles whose base classifiers use all the input features; randomly selected subsets of features; and features created using principal components analysis, on a wide range of domains. ID="A1"Correspondance and offprint requests to: Kagan Tumer, NASA Ames Research Center, Moffett Field, CA, USA     

Molecular weights and conductance studies of some organic poly(aminiumphosphate)s

Summary Long chain poly(aminiumphosphate)s of general composition (BHPO₃)_n (where BH = organic cations) were prepared by precipitation technique. The composition was established by the analysis of these derivatives for N and P. The polymeric nature was confirmed by their molecular weight determinations by end group titration and viscosity methods. Conductance studies at different concentrations confirmed their polyelectrolytic behaviour.     

Facing the reality of data stream classification: coping with scarcity of labeled data

Recent approaches for classifying data streams are mostly based on supervised learning algorithms, which can only be trained with labeled data. Manual labeling of data is both costly and time consuming. Therefore, in a real streaming environment where large volumes of data appear at a high speed, only a small fraction of the data can be labeled. Thus, only a limited number of instances will be available for training and updating the classification models, leading to poorly trained classifiers. We apply a novel technique to overcome this problem by utilizing both unlabeled and labeled instances to train and update the classification model. Each classification model is built as a collection of micro-clusters using semi-supervised clustering, and an ensemble of these models is used to classify unlabeled data. Empirical evaluation of both synthetic and real data reveals that our approach outperforms state-of-the-art stream classification algorithms that use ten times more labeled data than our approach.