Keshava P. Singh

Computational scalability with Apache Flume and Mahout for large scale round the clock analysis of sensor network data

In this paper, a typical scenario has been considered wherein gas sensor array responses from a WAN deployed sensor network are being received hourly, 24×7. From every sensor node, we are retrieving Static as well as Dynamic Responses with 16 sensing elements generating a .csv file of 9 MB size. Considering 1000 sensor nodes, the data received at the Hadoop Cluster at our Data Centre would be about 9 GB, which can be even more if more number of nodes, over larger geographical area and/or higher density of nodes is considered. Hence, (i) to receive and store such a huge data from a sensor network and (ii) to analyse the received data, we explored the suitability of Apache Flume and Apache Mahout to deliver high performance computational scalability on Hadoop Distributed File System. In this work, an implementation methodology for realization of such a scalable system has been presented by considering a sensor network for air pollution observation over a large geographical area, as an example.

Accelerated collective detection technique for weak GNSS signal environment

Collective Detection is an Assisted-GNSS (A-GNSS) technique for direct positioning, where the information from all satellites in view is combined to enable rapid acquisition and a direct navigation solution. This technique is shown to perform effectively in weak signal environments like indoor navigation, reducing the required signal strength by 10 to 20 dB-Hz. When the signal from satellites cannot be acquired individually, Collective Detection constructively adds information from each satellite together, thus improving sensitivity and directly leading to a position solution. In a sense, the vector-based approach used generally in tracking, is extended to the acquisition stage. However, the existing Collective Detection techniques are computationally intensive and thus have limited practical applications. Also, the transmit-time assistance provided to the receiver is assumed to be of sub-millisecond accuracy, which is not a feasible assumption. This paper looks at these limitations of Collective Detection and aims to mitigate them under the assumption that at least one satellite can be acquired individually. It has been shown that the proposed Accelerated Collective Detection is faster and more efficient than the traditional scheme, and performs equally well in terms of accuracy.

Optimization of image processing techniques to detect and reconstruct the image of concealed blade for MMW imaging system

The concealed weapon, like blade, detection and identification is one of the most puzzling task faces by security agency. Researchers have demonstrated MMW imaging systems to detect concealed targets like gun, knife and scissors but detection of small size target like blade with different orientation is still challenging due to resolution limitation of MMW imaging system. The success of small size concealed target detection depends upon scanning step size of imaging system and dielectric property of covering cloths and hidden object. Therefore, resolution enhancement techniques may play a very important role for small size concealed target detection. To perceive such challenges, active V-band MMW radar conjunction with image processing techniques has been demonstrated for detection and identification of concealed blade and obtained two dimensional good quality of images of concealed blade under different cloths at various angle. For this purpose, a critical analysis of various signal and image processing has been carried out and integrated following algorithms like singular value decomposition (SVD) for clutter reduction, discrete wavelet transform (DWT) for resolution enhancement, thresholding for target detection and in last artificial neural network (ANN) based algorithm for rotation invariant target identification. An image processing based methodology has been proposed by which the concealed target like blade can be successfully detected.

An efficient use of random forest technique for SAR data classification

In the past SAR data has been proven as a great source for land cover characterization. For classification purpose many individual methods has been used, but single method are likely to undergo high variance or biasness depending on the base used for classification. Hence, in this paper random forest classification technique has been used for SAR data classification into different land cover classes (urban, water, vegetation and bare soil) which minimizes the diversity amongst the fragile classifiers and produce more accurate predictions. In this regard, an attempt has been made to fuse, four types of measures, namely texture features, SAR observable, statistical features and color features using random forest classifier for land cover classification. The results show that the resultant classified image has better accuracy in comparison to the individual method.

Development of electromagnetic approach for early breast tumor detection

Survival from breast cancer strongly linked to the size of the tumor at the detection stage. Thus, the early stage detection of tumor of size as minimum as 1.0 mm radius is of great research interest. Currently used techniques for breast cancer detection fails in 10-30% cases and it gives any positive results when the tumor grows in to a size more than 10.0 mm, this reduces the possibility for an early stage detection and thus the survival rate. Thus, in this paper an alternate method of breast cancer detection through microwave imaging is studied. A dielectric mixing model is used to compute the dielectric constant of the breast tissue with and without the malignant tissue and the proposed model is verified through the simulation in CST. Free space transmission and metal back method are used for the measurement of dielectric constant of the phantom containing one, two, three and four tumors of radius 1.0 mm each. The proposed dielectric mixing model can be applied to detect the changes in the dielectric constant of the tumor affected tissue of radius 1.0 mm which is not possible through any other existing methods.

Pattern analysis of MiniSAR data for differentiation of icy craters in lunar surface

Classification of water ice region on lunar surface with Mini-SAR data is quite challenging. Therefore, a probability density function (pdf) based pattern analysis approach has been applied to classify lunar surface. This paper represents the pattern analysis approach to fit data points to a distribution function for understanding the distribution behaviour of Mini-SAR data which helps in developing a method based on density functions to differentiate two types of craters namely icy (type-I) and non-icy (type-II) craters. Circular polarization ratio (CPR) is a very important parameter in study of lunar surface. More specifically, the criterion CPR>1 is used to determine possible presence of water-ice deposits on lunar surface So, its important to study distribution behaviour of CPR pixels and to determine best fitted distribution function representing this behaviour. Therefore, in this paper, pattern analysis techniques have been applied to differentiate two crater types based on the distribution behaviour of CPR. The best fitted function for CPR has been obtained as Generalized Extreme Value function which clearly differentiate type-I and type-II craters.

Critical analysis of deorientation effect on various land covers: An application of POLSAR data

The aim of model based decomposition is to express coherency matrix in terms of various scattering components (like, volume, surface, double bounce, and helix). In spite of this decomposition, ambiguity occurs in scattering response from various land covers, like urban and vegetation. Deorientation process is believed to remove this ambiguity. However, there is a need to check whether decomposition methods and deorientation helps in identification of different land covers in terms of scattering mechanisms. To fulfil this task, in this paper, a study of four D decomposition methods with and without deorientation has been performed. The purpose of this study is to visualize the effect of deorientation on various land covers like, urban, vegetation, bare soil, water, and subsidence, in Jharia region, one of the major coal fields of India. Both visual and quantitative analysis have been performed for comprehensive evaluation of deorientation effect.

Radiative transfer based cross-calibration of passive microwave measurements obtained from satellites

A number of Earth observing satellite missions carrying onboard passive microwave instruments operate in frequency channels in close proximity in terms of frequency, polarization, incidence angle, system noise, etc., which, however, results in difference in their measurements. In order to remove such differences in the measurements from two or more sensors, cross-calibration of brightness temperature may be desired. Present study provides a procedure to calibrate such measurements from one sensor with another while they do not need to have any common period of operation. Study demonstrates the technique using measurements from AMSR-2 and TMI onboard GCOM-W1 and TRMM. Relationships between their corresponding channels are established using radiative transfer simulations. The relationship thus established when applied to their actual near-concurrent observations found to have the root mean square error of 3.15 K to 6.18 K between them compared to 3.71 K to 10.4 K found without calibration.

Analysis and retrieval of soil parameters with specular scattering data at different incidence angle

In this paper, we have analyzed the angular response of specular scattering coefficient for different soil texture fields while varying soil moisture and surface roughness at C-band. An approach based on multi-incidence angle data has been developed to retrieve soil texture, soil moisture and surface roughness. An empirical relationship has been developed between normalized specular scattering coefficient and surface roughness parameters. This empirical relationship has been utilized along with the Kirchhoff Scalar Approximation to retrieve soil texture, soil moisture and surface roughness. Obtained results are in good agreement with ground truth data.