Shweta B. Thomas

Bio: Shweta B. Thomas is an academic researcher from National Institute of Technology, Rourkela. The author has contributed to research in topics: Ground-penetrating radar & Support vector machine. The author has an hindex of 2, co-authored 4 publications receiving 11 citations.

Signal processing for coal layer thickness estimation using high-resolution time delay estimation methods

Abstract: The coal mining research technology is gaining popularity in terms of thickness measurement of coal mining horizon. The main challenge is to improve a robust sensing method for estimating the coal layer thickness left on mine-haulage way roofs for mine safety. This study addresses this challenge by step frequency continuous wave ground penetrating radar (GPR), whose resolution is dependent on bandwidth. To improve the time resolution of GPR signal, this study adopted four high-resolution algorithms, which are also known as subspace methods, namely, estimation of signal parameter via rotational invariance techniques, multiple-signal classification (MUSIC) algorithm, polynomial version of MUSIC, i.e. root-MUSIC and root-Min-Norm. The performance of all these algorithms is compared with synthetic data generated by the plane wave model and full wave model. The results are presented in terms of resolution power as well as relative root-mean-square error on the estimated thickness.

Thin coal layer thickness estimation using MUSIC algorithm

Abstract: In coal mining, thickness of thin coal layer is measured for maintaining a defined coal mining horizon. Researchers working in the geotechnical field for detection and thickness measurement of near surface interface address challenges using recent development in radar signal processing. This paper addresses challenge in measuring thickness of thin coal layer left on mine haulage way roof for mine safety. Here, step frequency continuous wave ground penetrating radar (SFCW GPR) signal processing is given for measuring thickness of thin coal layer in presence of interfaces as coal-shale and coal-shale-clay. We use multiple signal classification (MUSIC) algorithm for detecting the interfaces of dissimilar material. In order to improve the resolving power, MUSIC with spatial smoothing process (SSP) and modified spatial smoothing process (MSSP) are applied. Experimental results on thickness measurement using synthetic data models, full wave model (FWM), plane wave model (PWM) and modified plane wave model (MPWM) are demonstrated to compare the effectiveness of estimation algorithms.

Microstructure and Mechanical Characterization of Al6061 Based Composite and Nanocomposites Prepared via Conventional and Ultrasonic-assisted Melt-stirring Techniques

Abstract: In the current work, Al6061-based composites (2 wt% SiC) and nanocomposites (2 wt% BN, TiO2, Al2O3, MWCNT and Graphene) were prepared via a conventional melt stirring and ultrasonic-assisted melt-stirring approach. In this study, an effort has been made to reduce the agglomeration and clustering of reinforcing particles in the matrix slurry. Hence, reinforcing particles were pre-processed employing ultrasonic liquid processing (ULP) and ball milling (BM) approach prior to mixing in molten matrix slurry. Further, microstructure and mechanical characterization of the specimens fabricated via conventional melt stirring and ultrasonic-assisted melt-stirring approaches were performed and compared. The microscopic analysis (optical microscopy and scanning electron microscopy), microhardness, tensile strength, flexural strength and fractography were performed to analyze the influence of conventional melt-stirring and ultrasonic-assisted melt-stirring on the resulting properties of the fabricated composite and nanocomposite specimens. Microstructure investigations demonstrated better dispersal of reinforcing particles in the specimens prepared via a combined effort of ultrasonic-assisted melt-stirring and reinforcement pre-processing approach. Also, mechanical properties investigation revealed that the specimens prepared via the ultrasonic-assisted melt-stirring approach have superior strength and hardness over the specimens fabricated via the conventional melt-stirring technique. The ultimate tensile and flexural strength of Al6061-2 wt%BN nanocomposite prepared via ultrasonic-assisted melt-stirring reached 246.01 and 498.03, which was 143.57 % and 116.26 % higher than that of Al6061 specimen prepared via conventional melt-stirring. Al6061-2 wt%MWCNT nanocomposite prepared via ultrasonic-assisted melt-stirring has a maximum microhardness (Vickers’s) of 117.1 HV, which was 74.25 % higher than that of Al6061 prepared via conventional melt-stirring. Moreover, Al6061-2 wt%SiC nanocomposite prepared via ultrasonic-assisted melt-stirring has a maximum (Rockwell) of 89.8 HR, which was 105.02 % higher than that of Al6061 prepared via conventional melt-stirring.

Gpr data regression and clustering by the fuzzy support vector machine and regression

Abstract: In this paper, the problem of determining the depth and radius of a circular pipe along with the soil characteristics is studied, using electromagnetic waves with a fuzzy support vector machine as well as a fuzzy support vector machine. To this end, three neural network based fuzzy support vectors are used to determine the soil, depth, and dimensions. Also, using the 2D time domain numerical simulations of electromagnetic field scattering, along with MATLAB software, 1030 data are generated for training as well as neural network verification. Given the fact that for each of the three parameters the nature of the problem is different, separate neural networks are considered with different parameters, thus the number of different data for the network training is considered. In all three cases, the neural network parameters are optimized using genetic algorithm to reduce the error and also reduce the number of support vectors. It should be noted that the objective function of the genetic algorithm consists of two components of the error, as well as the number of membership functions, which can be determined by determining a control parameter. For soil permittivity, the algorithm can accurately predict 93% of permittivities, and it decreases to 89.8 for the pipe depth determination. For diameter it is seen that for 69.3 of the cases the algorithm can correctly classify the pipes.

A Modified Min-Norm for Time Delay and Interface Roughness Estimation by Ground Penetrating Radar: Experimental Results

Abstract: The development of methods and tools for the road infrastructure sustainable management is a research challenge, especially for nondestructive testing methods. This letter focuses on the estimation of the thickness of civil engineering structures, like pavements, and more precisely, the time delay and interface roughness. We propose a modified Min-Norm algorithm which allows efficiently estimating the time delay and interface roughness without the eigenvalue decomposition. Therefore, it has a smaller computational load compared with subspace-based methods. The experimental results show the efficiency of the proposed algorithm.

Microstructure and Mechanical Characterization of Al6061 Based Composite and Nanocomposites Prepared via Conventional and Ultrasonic-assisted Melt-stirring Techniques

Abstract: In the current work, Al6061-based composites (2 wt% SiC) and nanocomposites (2 wt% BN, TiO2, Al2O3, MWCNT and Graphene) were prepared via a conventional melt stirring and ultrasonic-assisted melt-stirring approach. In this study, an effort has been made to reduce the agglomeration and clustering of reinforcing particles in the matrix slurry. Hence, reinforcing particles were pre-processed employing ultrasonic liquid processing (ULP) and ball milling (BM) approach prior to mixing in molten matrix slurry. Further, microstructure and mechanical characterization of the specimens fabricated via conventional melt stirring and ultrasonic-assisted melt-stirring approaches were performed and compared. The microscopic analysis (optical microscopy and scanning electron microscopy), microhardness, tensile strength, flexural strength and fractography were performed to analyze the influence of conventional melt-stirring and ultrasonic-assisted melt-stirring on the resulting properties of the fabricated composite and nanocomposite specimens. Microstructure investigations demonstrated better dispersal of reinforcing particles in the specimens prepared via a combined effort of ultrasonic-assisted melt-stirring and reinforcement pre-processing approach. Also, mechanical properties investigation revealed that the specimens prepared via the ultrasonic-assisted melt-stirring approach have superior strength and hardness over the specimens fabricated via the conventional melt-stirring technique. The ultimate tensile and flexural strength of Al6061-2 wt%BN nanocomposite prepared via ultrasonic-assisted melt-stirring reached 246.01 and 498.03, which was 143.57 % and 116.26 % higher than that of Al6061 specimen prepared via conventional melt-stirring. Al6061-2 wt%MWCNT nanocomposite prepared via ultrasonic-assisted melt-stirring has a maximum microhardness (Vickers’s) of 117.1 HV, which was 74.25 % higher than that of Al6061 prepared via conventional melt-stirring. Moreover, Al6061-2 wt%SiC nanocomposite prepared via ultrasonic-assisted melt-stirring has a maximum (Rockwell) of 89.8 HR, which was 105.02 % higher than that of Al6061 prepared via conventional melt-stirring.

Air Void Detection Using Variational Mode Decomposition With Low Rank

Abstract: This paper presents an air-void detection technique for air-coupled radar, which emits electromagnetic waves to interrogate an air-void inside a medium or between two media. The reflections from the air-medium interfaces are usually corrupted by air-coupling, antenna ringing, and internal reflections, rendering air-void detection very difficult or, in certain cases, impossible. The proposed method exploits the low-rank structure of the background clutter to suppress these nuisance signals. A variational mode decomposition model is developed to extract the backscattering at different air-medium interfaces as signal modes. Real experiments are conducted using a stepped frequency radar. The experimental results show that the proposed method can detect air-gap between two sand blocks.