Discriminative feature analysis and selection for document classification

Classification of a large document collection involves dealing with a huge feature space where each distinct word is a feature. In such an environment, classification is a costly task both in terms of running time and computing resources. Further it will not guarantee optimal results because it is likely to overfit by considering every feature for classification. In such a context, feature selection is inevitable. This work analyses the feature selection methods, explores the relations among them and attempts to find a minimal subset of features which are discriminative for document classification.

Analysis of integrated optofluidic lab-on-a-chip sensor based on refractive index and absorbance sensing

The analysis of a fully integrated optofluidic lab-on-a-chip sensor is presented in this paper. This device is comprised of collinear input and output waveguides that are separated by a microfluidic channel. When light is passed through the analyte contained in the fluidic gap, optical power loss occurs owing to absorption of light. Apart from absorption, a mode-mismatch between the input and output waveguides occurs when the light propagates through the fluidic gap. The degree of mode-mismatch and quantum of optical power loss due to absorption of light by the fluid form the basis of our analysis. This sensor can detect changes in refractive index and changes in concentration of species contained in the analyte. The sensitivity to detect minute changes depends on many parameters. The parameters that influence the sensitivity of the sensor are mode spot size, refractive index of the fluid, molar concentration of the species contained in the analyte, width of the fluidic gap, and waveguide geometry. By correlating various parameters, an optimal fluidic gap distance corresponding to a particular mode spot size that achieves the best sensitivity is determined both for refractive index and absorbance-based sensing.

Vibration spectrum analyzer using stretched membranes of polymer piezoelectrics for sensor networks

The key problem tackled in this paper is the development of a stand-alone self-powered sensor to directly sense the spectrum of mechanical vibrations. Such a sensor could be deployed in wide area sensor networks to monitor structural vibrations of large machines (e. g. aircrafts) and initiate corrective action if the structure approaches resonance. In this paper, we study the feasibility of using stretched membranes of polymer piezoelectric polyvinlidene fluoride for low-frequency vibration spectrum sensing. We design and evaluate a low-frequency vibration spectrum sensor that accepts an incoming vibration and directly provides the spectrum of the vibration as the output.

Influence of rock depth on seismic site classification for shallow bedrock regions

Seismic site classifications are used to represent site effects for estimating hazard parameters (response spectral ordinates) at the soil surface. Seismic site classifications have generally been carried out using average shear wave velocity and/or standard penetration test n-values of top 30-m soil layers, according to the recommendations of the National Earthquake Hazards Reduction Program (NEHRP) or the International Building Code (IBC). The site classification system in the NEHRP and the IBC is based on the studies carried out in the United States where soil layers extend up to several hundred meters before reaching any distinct soil-bedrock interface and may not be directly applicable to other regions, especially in regions having shallow geological deposits. This paper investigates the influence of rock depth on site classes based on the recommendations of the NEHRP and the IBC. For this study, soil sites having a wide range of average shear wave velocities (or standard penetration test n-values) have been collected from different parts of Australia, China, and India. Shear wave velocities of rock layers underneath soil layers have also been collected at depths from a few meters to 180 m. It is shown that a site classification system based on the top 30-m soil layers often represents stiffer site classes for soil sites having shallow rock depths (rock depths less than 25 m from the soil surface). A new site classification system based on average soil thickness up to engineering bedrock has been proposed herein, which is considered more representative for soil sites in shallow bedrock regions. It has been observed that response spectral ordinates, amplification factors, and site periods estimated using one-dimensional shear wave analysis considering the depth of engineering bedrock are different from those obtained considering top 30-m soil layers.

Green synthesis of polysaccharide/gold nanoparticle nanocomposite: An efficient ammonia sensor

A low cost eco-friendly method for the synthesis of gold nanoparticles (AuNPs) using guar gum (GG) as a reducing agent is reported. The nanoparticles obtained are characterized by UV-vis spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X-ray diffraction (XRD). Based on these results, a potential mechanism for this method of AuNPs synthesis is discussed. GG/AuNPs nanocomposite (GG/AuNPs NC) was exploited for optical sensor for detection of aqueous ammonia based on surface plasmon resonance (SPR). It was found to have good reproducibility, response times of similar to 10 s and excellent sensitivity with a detection limit of 1 ppb (parts-per-billion). This system allows the rapid production of an ultra-low-cost GG/AuNPs NC-based aqueous ammonia sensor.

Efficient classification using phrases generated by topic models

There are many popular models available for classification of documents like Naïve Bayes Classifier, k-Nearest Neighbors and Support Vector Machine. In all these cases, the representation is based on the “Bag of words” model. This model doesn't capture the actual semantic meaning of a word in a particular document. Semantics are better captured by proximity of words and their occurrence in the document. We propose a new “Bag of Phrases” model to capture this discriminative power of phrases for text classification. We present a novel algorithm to extract phrases from the corpus using the well known topic model, Latent Dirichlet Allocation(LDA), and to integrate them in vector space model for classification. Experiments show a better performance of classifiers with the new Bag of Phrases model against related representation models.

AEROSAT - a space-borne sensor for continental aerosols: evaluation of the conceptual model

Even though satellite observations are the most effective means to gather global information in a short span of time, the challenges in this field still remain over continental landmass, despite most of the aerosol sources being land-based. This is a hurdle in global and regional aerosol climate forcing assessment. Retrieval of aerosol properties over land is complicated due to irregular terrain characteristics and the high and largely uncertain surface reflection which acts as `noise' to the much smaller amount of radiation scattered by aerosols, which is the `signal'. In this paper, we describe a satellite sensor the - `Aerosol Satellite (AEROSAT)', which is capable of retrieving aerosols over land with much more accuracy and reduced dependence on models. The sensor, utilizing a set of multi-spectral and multi-angle measurements of polarized components of radiation reflected from the Earth's surface, along with measurements of thermal infrared broadband radiance, results in a large reduction of the `noise' component (compared to the `signal). A conceptual engineering model of AEROSAT has been designed, developed and used to measure the land-surface features in the visible spectral band. Analysing the received signals using a polarization radiative transfer approach, we demonstrate the superiority of this method. It is expected that satellites carrying sensors following the AEROSAT concept would be `self-sufficient', to obtain all the relevant information required for aerosol retrieval from its own measurements.

Crop stage classification of hyperspectral data using unsupervised techniques

The presence of a large number of spectral bands in the hyperspectral images increases the capability to distinguish between various physical structures. However, they suffer from the high dimensionality of the data. Hence, the processing of hyperspectral images is applied in two stages: dimensionality reduction and unsupervised classification techniques. The high dimensionality of the data has been reduced with the help of Principal Component Analysis (PCA). The selected dimensions are classified using Niche Hierarchical Artificial Immune System (NHAIS). The NHAIS combines the splitting method to search for the optimal cluster centers using niching procedure and the merging method is used to group the data points based on majority voting. Results are presented for two hyperspectral images namely EO-1 Hyperion image and Indian pines image. A performance comparison of this proposed hierarchical clustering algorithm with the earlier three unsupervised algorithms is presented. From the results obtained, we deduce that the NHAIS is efficient.

Gene expression programming-fuzzy logic method for crop type classification

Crop type classification using remote sensing data plays a vital role in planning cultivation activities and for optimal usage of the available fertile land. Thus a reliable and precise classification of agricultural crops can help improve agricultural productivity. Hence in this paper a gene expression programming based fuzzy logic approach for multiclass crop classification using Multispectral satellite image is proposed. The purpose of this work is to utilize the optimization capabilities of GEP for tuning the fuzzy membership functions. The capabilities of GEP as a classifier is also studied. The proposed method is compared to Bayesian and Maximum likelihood classifier in terms of performance evaluation. From the results we can conclude that the proposed method is effective for classification.

Process control of cantilever deflection for sensor application based on optical waveguides

This paper reports on the fabrication of cantilever silicon-on-insulator (SOI) optical waveguides and presents solutions to the challenges of using a very thin 260-nm active silicon layer in the SOI structure to enable single-transverse-mode operation of the waveguide with minimal optical transmission losses. In particular, to ameliorate the anchor effect caused by the mean stress difference between the active silicon layer and buried oxide layer, a cantilever flattening process based on Ar plasma treatment is developed and presented. Vertical deflections of 0.5 mu m for 70-mu m-long cantilevers are mitigated to within few nanometers. Experimental investigations of cantilever mechanical resonance characteristics confirm the absence of significant detrimental side effects. Optical and mechanical modeling is extensively used to supplement experimental observations. This approach can satisfy the requirements for on-chip simultaneous readout of many integrated cantilever sensors in which the displacement or resonant frequency changes induced by analyte absorption are measured using an optical-waveguide-based division multiplexed system.

Robust formulations for clustering-based large-scale classification

Chebyshev-inequality-based convex relaxations of Chance-Constrained Programs (CCPs) are shown to be useful for learning classifiers on massive datasets. In particular, an algorithm that integrates efficient clustering procedures and CCP approaches for computing classifiers on large datasets is proposed. The key idea is to identify high density regions or clusters from individual class conditional densities and then use a CCP formulation to learn a classifier on the clusters. The CCP formulation ensures that most of the data points in a cluster are correctly classified by employing a Chebyshev-inequality-based convex relaxation. This relaxation is heavily dependent on the second-order statistics. However, this formulation and in general such relaxations that depend on the second-order moments are susceptible to moment estimation errors. One of the contributions of the paper is to propose several formulations that are robust to such errors. In particular a generic way of making such formulations robust to moment estimation errors is illustrated using two novel confidence sets. An important contribution is to show that when either of the confidence sets is employed, for the special case of a spherical normal distribution of clusters, the robust variant of the formulation can be posed as a second-order cone program. Empirical results show that the robust formulations achieve accuracies comparable to that with true moments, even when moment estimates are erroneous. Results also illustrate the benefits of employing the proposed methodology for robust classification of large-scale datasets.

Calibration of etched fiber bragg grating sensor arrays for measurement of molecular surface adsorption

Etched Fiber Bragg Grating (EFBG) sensors are attractive from the point of the inherently high multiplexing ability of fiber based sensors. However, the strong dependence of the sensitivity of EFBG sensors on the fiber diameter requires robust methods for calibration when used for distributed sensing in a large array format. Using experimental data and numerical modelling, we show that knowledge of the wavelength shift during the etch process is necessary for high-fidelity calibration of EFBG arrays. However as this approach requires the monitoring of every element of the sensor array during etching, we also proposed and demonstrated a calibration scheme using data from bulk refractometry measurements conducted post-fabrication without needing any information about the etching process. Although this approach is not as precise as the first one, it may be more practical as there is no requirement to monitor each element of the sensor array. We were able to calibrate the response of the sensors to within 3% with the approach using information acquired during etching and to within 5% using the post-fabrication bulk refractometry approach in spite of the sensitivities of the array element differing by more than a factor of 4. These two approaches present a tradeoff between accuracy and practicality.

Nanocomposite based flexible ultrasensitive resistive gas sensor for chemical reactions studies

Room temperature operation, low detection limit and fast response time are highly desirable for a wide range of gas sensing applications. However, the available gas sensors suffer mainly from high temperature operation or external stimulation for response/recovery. Here, we report an ultrasensitive-flexible-silver-nanoparticle based nanocomposite resistive sensor for ammonia detection and established the sensing mechanism. We show that the nanocomposite can detect ammonia as low as 500 parts-per-trillion at room temperature in a minute time. Furthermore, the evolution of ammonia from different chemical reactions has been demonstrated using the nanocomposite sensor as an example. Our results demonstrate the proof-of-concept for the new detector to be used in several applications including homeland security, environmental pollution and leak detection in research laboratories and many others.

Efficient method of moving shadow detection and vehicle classification

Moving shadow detection and removal from the extracted foreground regions of video frames, aim to limit the risk of misconsideration of moving shadows as a part of moving objects. This operation thus enhances the rate of accuracy in detection and classification of moving objects. With a similar reasoning, the present paper proposes an efficient method for the discrimination of moving object and moving shadow regions in a video sequence, with no human intervention. Also, it requires less computational burden and works effectively under dynamic traffic road conditions on highways (with and without marking lines), street ways (with and without marking lines). Further, we have used scale-invariant feature transform-based features for the classification of moving vehicles (with and without shadow regions), which enhances the effectiveness of the proposed method. The potentiality of the method is tested with various data sets collected from different road traffic scenarios, and its superiority is compared with the existing methods. (C) 2013 Elsevier GmbH. All rights reserved.

Optical read-out scheme based on Grated Waveguide Cantilever cavity resonance for interrogation of cantilever sensor arrays

The design and analysis of an optical read-out scheme based on a grated waveguide (GWG) resonator for interrogating microcantilever sensor arrays is presented. The optical system consisting of a micro cantilever monolithically integrated in proximity to a grated waveguide (GWG), is realized in silicon optical bench platform. The mathematical analysis of the optical system is performed using a Fabry-Perot interferometer model with a lossy cavity formed between the cantilever and the GWG and an analytical expression is derived for the optical power transmission as a function of the cantilever deflection which corresponds to cavity width variation. The intensity transmission of the optical system for different cantilever deflections estimated using the analytical expression captures the essential features exhibited by a FDTD numerical model.