DR A BHUVANESHWARI
@deccancollege.ac.in
ASSOCIATE PROFESSOR ECE
DECCAN COLLEGE OF ENGINEERING AND TECHNOLOGY
Dr A.Bhuvaneshwari, obtained her B.E degree in the field of ECE, in 1995, M.Tech degree with specialisation in Digital Systems & Computer Electronics (D.S.C.E), and PhD in Wireless Communications from Jawaharlal Nehru Technological University, Hyderabad (J.N.T.U.H). She is working as an Associate Professor in Deccan College of Engineering and Technology, Hyderabad. She has 22 years of teaching experience, enriching research experience, published papers in standard journals and has guided several students. Her research interests include Wireless Mobile Communications, Deep Learning Neural Networks, Image, Video & Speech Processing and Computer Vision.
EDUCATION
BE, MTech (Digital Systems and Computer Electronics ) , Phd (Wireless Communication_
RESEARCH, TEACHING, or OTHER INTERESTS
Electrical and Electronic Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition, Signal Processing
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
A J Bhuvaneshwari and P Kaythry
IEEE
Many physical devices in everyday life have been connected to the global web since its inception. The network’s security improves as the number of items connected to it grows. However, current security measures make progress difficult. As a result, based on the BLAKE 2 hash algorithm, we propose a basic security mechanism. Our proposed method aims to improve the transfer of sensitive image data between nodes. The key issue is transmitting data across multiple nodes invisibly without being hacked. The proposed system’s primary objective is to maintain the picture secure and safe from third parties. It is accomplished by combining encryption and decryption into a lightweight image transport technique. It describes a technique for generating secret cryptographic keys from image pixels using the BLAKE 2 cryptographic hash that is image content adaptive. This scheme includes three encryption processes: DC coefficient encryption, AC coefficient encryption, and novel orthogonal transformation. The encrypted image is safely sent to another node over the network using an upgraded visual cryptosystem, and the decrypted image is successfully obtained at the receiver node.
A. J. Bhuvaneshwari and R. Nanthithaa Shree
Springer International Publishing
A. Bhuvaneshwari, R. Hemalatha, and T. Satya Savithri
Springer Science and Business Media LLC
A. Bhuvaneshwari, R. Hemalatha, and T. Satyasavithri
IEEE
The prediction of path loss for the mobile radio signals is an important part in the design phase of the wireless cellular networks. In the process of modelling the path loss, the GSM 900 MHz signals are collected experimentally using Test Mobile System (TEMS) tool in the dense urban environment of Hyderabad city. In this paper, the best suited Cost 231 Hata empirical propagation model is implemented using three major dynamic neural networks namely, Focused Time Delay Neural Network (FTDNN), Distributed Time Delay Neural Network (DTDNN) which are feed forward dynamic neural networks and Layer Recurrent Neural Network (LRNN) which is a feedback dynamic neural network. The aim of these implementations is to minimise the errors between simulations and measurements. The dynamic neural networks are trained using Levenberg-Marquardt and Scaled Conjugate Gradient training algorithms. Comparisons are made by varying the number of neurons in the hidden layer and changing the training epochs. The performance is analysed in terms of correlation with the measured data, standard deviation, mean error between the targets and outputs and computation times. From the results it is inferred that, the best correlation between simulations and measurements is 0.9972, standard deviation of error (0.04) and mean error (−5.379e-5) are least for Layer Recurrent Neural Network, trained by Levenberg method, but at the cost of increased computation time. With respect to the feed forward dynamic networks, the results show that FTDNN trained by Levenberg algorithm has a better performance compared to DTDNN.
A. Bhuvaneshwari, R. Hemalatha, and T. Satyasavithri
Elsevier BV
A. Bhuvaneshwari, R. Hemalatha, and T. Satyasavithri
IEEE
The performance of the wireless systems is significantly influenced by multiple reflections in addition to diffraction and scattering propagation effects. The geometric and dielectric properties of the obstacles vary to a large extent in the indoor environment and it is required to model these propagation effects accurately. In this paper, indoor mobile signal strengths are recorded at 2.4 GHz frequency for a wide corridor with glass partitions, in the premises of Deccan College of Engineering and Technology at Hyderabad. The data is collected within 10m from the source of the wireless router. Path loss is extracted from the measurements and comparisons are made with results derived by using two ray, four ray, six ray, and ten ray model. An N ray model is also implemented. Further a generalised ray tracing model is proposed by including diffraction and scattering effects. Diffraction losses due to the partitions are modelled using Fresnel-Kirchoff diffraction parameter and the spreading loss due to scattering is estimated using radar bi static equation. The performance of the proposed ray tracing model is evaluated by computing the error between the measurements and the proposed model. The least values of the error metrics for the proposed model indicate its accuracy in predicting the path loss for Wireless LAN mobile signals in the indoor environment.
A. Bhuvaneshwari and T. Sathyasavithri
IEEE
Path loss models are widely used in the planning and implementation of a mobile radio system, and to evaluate the quality of service. Path loss indicates the attenuation of the radio signal as it propagates through various terrain conditions. Empirical path loss models for mobile systems are largely used in research due to their high speed, and improved accuracy. This paper estimates the path loss of mobile signals, recorded during a set of experiments performed in the areas around Osmania University at Hyderabad city in southern India. The data is collected in the frequency range of 940-950 MHz, within the coverage area of the base stations, by using suitable outdoor equipment. The field strengths obtained, are used to calculate the path loss. Least square regression analysis is performed on the measured values, and the results are compared with path loss computed from standard empirical models such as Cost-231 Hata model and Stanford University Interim (SUI) channel model. The performance of the path loss models are evaluated in terms of mean prediction error, and average relative error. Compared to Least Square analysis, the average relative error is 5.56% for SUI model, and 39.72% for Cost 231-Hata model. The lesser values of mean prediction error and relative error of the SUI model, suggests that it is more suitable for the specified environment.
A. Bhuvaneshwari, R. Hemalatha, and T. Satyasavithri
IEEE
The main factor in cellular network planning is the accurate estimation of the received signal strength with a deeper understanding of the characterisation of a radio channel. A detailed planning is required which concentrates on parameter estimation with necessary field measurements. In this paper a drive test is conducted in the dense urban region of Hyderabad city (Southern India), using Test Mobile System (TEMS) tool to record the received GSM 900 MHz mobile signals. Processing the measured field strengths, the path loss exponents for the measurement sites are determined using Minimum Mean Square Error method. They are found to vary from 3.35 to 4.31, which suggest that it is a shadowed urban region. An empirical power model is developed to predict the received power. Further, path loss investigations are done by implementing three standard models namely, Cost-231 Hata, Stanford University Interim (SUI) model and ECC-33 model. The performances of the models are evaluated by comparing the predictions with the measured data. From the results, it is observed that the prediction error for Cost-231 Hata model is 5.98 and its relative error is 5.10% which are least compared to the implemented models. Various statistical metrics are compared for the measured and predicted models. Overall, this paper performs power modeling, investigates the path loss over a dense urban terrain and justifies that Cost-231 Hata model has a best fit with the experimentally measured data, compared to SUI and ECC-33 model for the specified region.
B. Manimegalai, A. Bhuvaneshwari, and S. Supraja
ACM
Cognitive Radio (CR) is a new paradigm which aims to make more efficient use of the frequency spectrum whilst guarding against interference and generally providing a better quality of service for the user. In order to work alongside existing users the CR must monitor the available frequency spectrum, and reconfigure to transmit on a different frequency when necessary. There are good technical arguments for using an ultra-wideband (UWB) antenna to perform the sensing function, whilst communicating via a narrowband antenna. This paper presents two different antennas which have been developed to address the specific demands of portable CR handsets. Each antenna combines UWB and narrowband functionality within a small space.