Browsing by Author "Chaturvedi, A."

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A geographical location aware energy efficient routing scheme for query based Wireless Sensor Networks
(2013) Kumar, P.; Chaturvedi, A.; Shrivastava, S.
In this paper, a geographical location based routing scheme is proposed that works effectively for different hierarchical networks and thus supports an important aspect of network scalability. Further, herein a heuristic is proposed that minimizes the occurrence of hot spot to improve the overall life time of Wireless Sensor Networks (WSNs). This utilizes residual energy estimation based on energy of each sensor node. Its implementation mainly comprises of the geographical location of each sensor in Binary Location Index (BLI) representation form, precise updates about the link with shortest path, and coverage of the each sensor; the method which is without change of cluster head is compared with the change of cluster head based on BLI for fixed single sink.
A Robust Pansharpening Algorithm Based on Convolutional Sparse Coding for Spatial Enhancement
(Institute of Electrical and Electronics Engineers, 2019) Gogineni, R.; Chaturvedi, A.
Pansharpening (PS) is a prominent remote sensing image fusion technique. It yields high-resolution multispectral (HRMS) images, which are imperative for the applications, such as recognition and detection. The PS methods based on conventional sparse representation induce blurring effects and are unable to preserve the essential spatial details in the fused outcome. In this article, to overcome these drawbacks, a robust fusion scheme is proposed based on convolutional sparse coding (CSC). The source images are decomposed into its constituent texture and cartoon components. The sparse coefficient maps are acquired from texture components by adapting CSC. Texture components are fused using activity level measurement, whereas averaging mechanism is used to fuse the cartoon components. The HRMS image is reconstructed by combining the fused components in proportion to the gradient information. Impact of number of filters on quality metrics estimation is analyzed. Comprehensive experiments are performed on the images acquired from distinct sensors. The proposed method is evaluated in terms of visual analysis and the quantitative metrics with reduced-scale and full-scale experiments. Extensive evaluations manifest the capability of the proposed method of maintaining the balanced tradeoff and retaining the desired spatial and spectral details. © 2008-2012 IEEE.
A variational pan-sharpening algorithm to enhance the spectral and spatial details
(Taylor and Francis Ltd., 2021) Gogineni, R.; Chaturvedi, A.; Daya Sagar, B.S.
Pan-sharpening is a remote sensing image fusion technique that generates a high-resolution multispectral (HRMS) image on combining a low resolution multispectral (MS) image and a panchromatic (PAN) image. In this paper, a new optimisation model is proposed for pan-sharpening. The proposed model consists of three terms: (i) a data synthesis fidelity term formulated on inferring the relationship between source MS image and fused image to preserve the spectral information, (ii) a total generalised variation-based prior term to inject the significant spatial details from PAN image to pan-sharpened image, and (iii) a spectral distortion reduction term that exploits the correlation between multispectral image bands. To solve the resultant convex optimisation problem, an efficient and convergence guaranteed operator splitting framework based on the alternating direction method of multipliers (ADMM) algorithm is formulated. Finally, the proposed model is experimentally validated using full-resolution and reduced-resolution data. The pan-sharpened outcomes exhibit the potential of the proposed method in enhancing the spatial and spectral quality. © 2020 Informa UK Limited, trading as Taylor & Francis Group.
An energy efficient algorithm to avoid hot spot effects in Wireless Sensor Networks
(IEEE Computer Society help@computer.org, 2013) Kumar, P.; Chaturvedi, A.
In this paper, a novel approach is proposed to minimize the hot spot effect to improve the life time of Wireless Sensor Networks (WSNs) based on energy of each sensor node. To implement the proposed approach, the spatial locations of geographical area under surveillance are motioned using binary location index. The simulation work is carried out for two different case studies; in first case the sink/base station is remains stationary during entire observation, whereas in other case the sink is reallocated to appropriate locations at suitable time instants. Timely varying pattern of residual energy of all network nodes and total number of queries supported by entire network till it attains targeted life time is presented and discussed. Â© 2013 IEEE.
An improved mechanism of leaf node identification for radial distribution networks
(2011) Sharma, D.P.; Chaturvedi, A.; Purohit, G.; Prasad, G.
Optimal operational & control aspects of distribution networks have been a thrust research area in academics as well as in industries since last two-three decades. In day to day practice, every one of us uses services offered by public utility distribution networks namely, water distribution network, electrical power distribution network etc. Operational topology of power distribution networks are radial in nature and hence are termed as radial distribution networks (RDNs). Network reconfiguration has been exercised as one of the prime and widely adopted approach for operational, maintenance and control activities of an RDN. Since last two decades, researchers have been using evolutionary computation based techniques (Genetic Algorithm [1], Simulating Annealing etc) for optimal network reconfiguration. The choices of network topology for the specific purpose/application requires a careful analysis of its merits and demerits. In RDNs, the ultimate performance of a specific network topology is usually assessed by an iterative algorithm known as Load flow analysis (LFA) and its execution results in estimation of voltages, currents and losses profiles which in turn decides, whether the obtained network topology is good or bad. Thus, an obvious need is in favor of developing a conceptual frame work for faster load flow algorithm especially to meet near real-time operation requirements. In this paper, a simple and computationally efficient method for terminal (leaf) nodes identification is presented and thus, by integrating this subroutine in LFA definitely leads to an efficient and faster LFA algorithm. Â© 2011 IEEE.
Analysis of symbol error probability in GFDM under generalized ????? channels: An approach based on probability density function for beyond 5G wireless applications
(Elsevier GmbH, 2025) Bodempudi, N.S.P.; Chaturvedi, A.; Rashmi, R.; Kulkarni, M.
Generalized Frequency Division Multiplexing (GFDM) is a flexible multicarrier modulation scheme tailored to meet the diverse requirements of 5G and beyond (B5G) wireless systems. It provides flexibility to select subsymbols, subcarriers, and pulse-shaping filters, making it suitable for various B5G communication needs. Moreover, GFDM includes orthogonal frequency division multiplexing (OFDM) and single carrier frequency domain equalization (SC-FDE) as special cases. Since fading conditions greatly affect communication reliability, this paper derives a closed-form expression for the symbol error probability/symbol error rate (SEP/SER) of GFDM using M-QAM and M-PSK in generalized ????? fading channels. The study examines how fading parameters (?,?,?), pulse-shaping filters, roll-off factor, and subsymbol count influence SER performance. Results show these factors have a significant impact on error probability. This comprehensive analysis provides important insights for evaluating GFDM performance under complex fading scenarios, making it a valuable reference for GFDM system design for B5G wireless applications. © 2025
Convolutional neural networks for medical image analysis
(CRC Press, 2022) Gogineni, R.; Chaturvedi, A.
The enormous advancement of Deep Learning (DL) algorithms in various fields has been adopted by many perspectives of medical image analysis. In the context of deep learning, convolutional neural networks (CNNs) are widely used models for medical imaging tasks and lead to a huge progress in computer aided diagnosis. CNNs are being profoundly absorbed in the research of medical imaging because of their pertinent features in preserving local image relations and dimensionality reduction. CNNs demonstrate excellent performance in lesion detection and other vision tasks. This chapter presents the modules of convolutional neural networks and their prominent architectures employed for medical image analysis. The commonly used variants of CNNs in medical image analysis including ResNet, GoogleNet, and fully convolutional neural networks are analyzed. The principal research areas and applications of medical image registration, segmentation, detection and classification are discussed. The overview of the above mentioned tasks in a few of the usual diagnosis areas of the human body such as brain, lungs and eye is presented in this chapter. The crucial challenge in medical image analysis using CNNs is the availability of training data sets. This problem is addressed in this chapter, by providing the existing medical image datasets for different application areas like chest, diabetic retinopathy, lungs and heart. Finally, the chapter is concluded by discussing the challenges associated with CNN architectures and promising future research directions © 2023 Şaban öztürk. All rights reserved.
Correlation analysis and statistical characterization of heterogeneous sensor data in environmental sensor networks
(Elsevier B.V., 2019) Rajesh, G.; Chaturvedi, A.
In wireless sensor networks, missing data is an inevitable phenomenon due to the inherent limitations of the sensor nodes, such as battery power constraints of nodes, missing communication links, bandwidth limitation, etc. Missing data adversely affects the quality of data received by the sink node. Since the data acquired by the sensor nodes in a multimodal environmental sensor network are spatially and temporally correlated, these correlations play a pivotal role in missing data recovery and data prediction. This paper proposes an analytical framework to characterize the correlation between two different pairs of modalities in an environmental sensor network using a set of classical and robust measures of correlation coefficient estimates. Monte Carlo simulation is performed to approximately model sensed environmental data characteristics. Three classical estimates (Pearson's correlation coefficient, Spearman's rank correlation coefficient, and Kendall's-tau rank correlation coefficient), and four robust estimates of correlation coefficients are used to establish the correlation between different pairs of sensed modalities in the data characteristics. The efficacy of these estimates is obtained using the two performance metrics, mean-squared error (MSE) and relative estimation efficiency (RE). Stationarity analysis among the acquired environmental variables shed light upon the best estimates of the correlation coefficient, which could be used for prediction of temperature modality in a known region of slope/stationarity in the data characteristics. The robustness of the correlation coefficient estimates in the presence of outliers present in the data due to noise, errors, low residual battery power of sensor nodes, etc. is also investigated. © 2019 Elsevier B.V.
Correlation Analysis of Multimodal Sensor Data in Environmental Sensor Networks
(IEEE Computer Society help@computer.org, 2019) Rajesh, G.; Chaturvedi, A.
In this paper, the performance analysis of the classical measures of correlation coefficients, i.e., Pearson's correlation coefficient r-{P}, Spearman's rank correlation coefficient, r-{S} and Kendall-\tau rank correlation coefficient, r-{K}, and four robust correlation coefficients, is carried out in a specific scenario of multimodal environmental sensor network. The correlation coefficients were compared based on their estimated values with true values calculated using the slope of the regression line of the data points. The analysis is performed for two typical multivariate environmental sensor network scenarios, viz, positive correlation and negative correlation. The simulation results shed light on the required sample size of multimodal data and the class of the correlation coefficient required to give the best performance while establishing the correlation between the multimodal variables in environmental sensor data. Â© 2019 IEEE.
Data Reconstruction in Heterogeneous Environmental Wireless Sensor Networks Using Robust Tensor Principal Component Analysis
(Institute of Electrical and Electronics Engineers Inc., 2021) Rajesh, G.; Chaturvedi, A.
The major challenges during the data acquisition process in an environmental wireless sensor network (EWSN) architecture are the presence of outliers and missing data. Outliers and missing data are ubiquitous in EWSN due to sensor failures, external noise, power dwindling, communication failures etc. Robust tensor principal component analysis (RTPCA) decomposes a noisy data tensor into a low-rank tensor and a sparse tensor, which can be exploited for the data recovery in EWSNs, where the low-rank component represents the intrinsic data tensor and the sparse component represents the gross outlier tensor. In this paper, a novel probabilistic outlier modelling scheme using multivariate Chebyshev's inequality hypothesis is proposed, which maps the sample population and the associated magnitudes of outliers with the spatio-temporal correlations in the acquired data. The inherent spatio-temporal and multi-attribute correlations in the EWSN data are established using singular value methods. A tensor nuclear norm (TNN) which extracts more temporal and multi-attribute correlations in the sensory data through block circulant matricization is used as the minimization function in RTPCA. In the forest surveillance scenario, usage of RTPCA results in a reconstruction accuracy of approximately 90% for a dataset with a high missing ratio of 0.95 and the outliers having largest possible magnitudes considered. For oceanographic data, in which the variance is lower, the simulations show similar performance for different outlier contaminations, although RPTCA performs better than other matrix and tensor data completion methods in terms of reconstruction accuracy. © 2015 IEEE.
Design and analysis of microstrip elliptical low pass filter
(2009) Kumar, P.; Chaturvedi, A.
Design and analysis of a stepped impedance elliptical microstrip low pass filter has been described in this paper. To improve the frequency response, fractals have been proposed in the conventional design. Conventional geometry and fractalized geometry have been simulated and thus obtained results are reported and compared.
Design and analysis of microstrip elliptical low pass filter
(IEEE Computer Society, 2009) Kumar, P.; Chaturvedi, A.
Design and analysis of a stepped impedance elliptical microstrip low pass filter has been described in this paper. To improve the frequency response, fractals have been proposed in the conventional design. Conventional geometry and fractalized geometry have been simulated and thus obtained results are reported and compared.
Design and Development of Single & Dual Resonant Frequency Antennas for Moisture Content Measurement
(Springer, 2020) Kumar, P.; Chaturvedi, A.
The microstrip antenna is designed for the measurement of soil moisture content for agriculture applications. In this paper, the single and dual-band microstrip antennas are designed, simulated and fabricated. The simulated results are compared and analysed with measured results for both designed for different types of feeds. It is found that the simulated and measured results are similar and better. Therefore, the fabricated antennas can be used for the soil moisture content measurement for agriculture applications. The dual resonant frequency antennas results are better than the single band antenna, it covers wide band thus, and it can be applied for wide applications. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
Distributed load flow analysis using graph theory
(2011) Sharma, D.P.; Chaturvedi, A.; Purohit, G.; Shivarudraswamy, R.
In today scenario, to meet enhanced demand imposed by domestic, commercial and industrial consumers, various operational & control activities of Radial Distribution Network (RDN) requires a focused attention. Irrespective of sub-domains research aspects of RDN like network reconfiguration, reactive power compensation and economic load scheduling etc, network performance parameters are usually estimated by an iterative process and is commonly known as load (power) flow algorithm. In this paper, a simple mechanism is presented to implement the load flow analysis (LFA) algorithm. The reported algorithm utilizes graph theory principles and is tested on a 69- bus RDN.
Distributed load flow analysis using graph theory
(2011) Sharma, D.P.; Chaturvedi, A.; Purohit, G.; Shivarudraswamy, R.
In today scenario, to meet enhanced demand imposed by domestic, commercial and industrial consumers, various operational & control activities of Radial Distribution Network (RDN) requires a focused attention. Irrespective of sub-domains research aspects of RDN like network reconfiguration, reactive power compensation and economic load scheduling etc, network performance parameters are usually estimated by an iterative process and is commonly known as load (power) flow algorithm. In this paper, a simple mechanism is presented to implement the load flow analysis (LFA) algorithm. The reported algorithm utilizes graph theory principles and is tested on a 69- bus RDN.
Edge detection of femur bone - A comparative study
(2010) Kumar, Jr., H.; Chaturvedi, A.
Edge detection of femur in X - ray images is an important pre processing step in segmentation and 3 - D reconstruction of femur. A typical femur image is generally very noisy. A lot of edges caused by the muscles and other bones can easily mislead the edge detection algorithm. Particularly, the femoral head overlapping the pelvic bone makes it very difficult to get a clear edge of the femur head. The edge caused by the abdominal muscles around the femur shaft can also mislead the edge detection algorithm. These extraneous edges and noise make edge detection very difficult and challenging, which is not well solved. Classical edge detectors fail miserably due to the high inhomogeneous nature of the femur X - ray images. This paper compares a new approach to edge detection of femur X - ray images using Wavelet transforms with classical edge detectors. The Wavelet based edge detection algorithm combines the coefficients of wavelet transforms on a series of scales and significantly improves the result. It is found that Wavelet based technique works much better than classical edge detectors. �2010 IEEE.
Edge detection of femur bone - A comparative study
(2010) Harish Kumar, H.; Chaturvedi, A.
Edge detection of femur in X - ray images is an important pre processing step in segmentation and 3 - D reconstruction of femur. A typical femur image is generally very noisy. A lot of edges caused by the muscles and other bones can easily mislead the edge detection algorithm. Particularly, the femoral head overlapping the pelvic bone makes it very difficult to get a clear edge of the femur head. The edge caused by the abdominal muscles around the femur shaft can also mislead the edge detection algorithm. These extraneous edges and noise make edge detection very difficult and challenging, which is not well solved. Classical edge detectors fail miserably due to the high inhomogeneous nature of the femur X - ray images. This paper compares a new approach to edge detection of femur X - ray images using Wavelet transforms with classical edge detectors. The Wavelet based edge detection algorithm combines the coefficients of wavelet transforms on a series of scales and significantly improves the result. It is found that Wavelet based technique works much better than classical edge detectors. Â©2010 IEEE.
An energy efficient algorithm to avoid hot spot effects in Wireless Sensor Networks
(2013) Kumar, P.; Chaturvedi, A.
In this paper, a novel approach is proposed to minimize the hot spot effect to improve the life time of Wireless Sensor Networks (WSNs) based on energy of each sensor node. To implement the proposed approach, the spatial locations of geographical area under surveillance are motioned using binary location index. The simulation work is carried out for two different case studies; in first case the sink/base station is remains stationary during entire observation, whereas in other case the sink is reallocated to appropriate locations at suitable time instants. Timely varying pattern of residual energy of all network nodes and total number of queries supported by entire network till it attains targeted life time is presented and discussed. � 2013 IEEE.
Ergodic Capacity and Outage Probability Analysis for Multiple Intelligent Reflecting Surface Assisted Wireless Networks
(Springer, 2024) DhruvaKumar, T.; Chaturvedi, A.
Due to location-specific geometrical properties and time-dependent vehicular traffic, many urban area segments exhibit diverse fading patterns. This research examines the ergodic capacity and outage probability estimate for these segments. Several Intelligent Reflecting Surface (IRS) panels installed on roadside infrastructure are present in these geographical regions. Various application scenarios are examined when the receiver is exposed to reflected and direct Line-of-Sight links, which are defined by ?-? shadowed fading. An approximation analysis of the studied ?-? shadowed fading channel via Nakagami-m fading model and an exact study of the impact of ?-? shadowed fading on the ergodic capacity and outage probability of the wireless link are presented using Monte Carlo simulation. In order to confirm the effectiveness of IRS panel deployment, one of the governing factors is the average signal-to-noise ratio. When accounting for the dynamics of the vehicle traffic, an improved ergodic capacity and outage probability estimate show the value of placing IRS panels at any point within the examined geographic segments. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
Estimation of degradation of performance of optical network due to crosstalk
(2011) Gupta, M.L.; Chaturvedi, A.; Bhomia, Y.
The aim of this paper is to carry out an analysis of optical network by calculating bit error rate (BER) as a function of signal to noise ratio by using Hermitian polynomial for different homodyne crosstalk levels. The results have been plotted for different crosstalk level for BER as a function of single to noise ratio (SNR) by using exact analysis and Gaussian approximation and the results are compared. Simulations have been carried out in MAT Lab Version 7.4. We present an exact analytical probability density function and a closed form bit error rate formula for optical network which performance is degraded by homodyne crosstalk. In homodyne (intrachannel) crosstalk, the interfering signal is at same wavelength as the desired signal. This effect is more severe than interchannel crosstalk (in which interfering signal operates at different wavelength), since the interference falls completely within the receiver bandwidth. � 2011 IEEE.