Mittapalli Venkata Nageswara Rao

@gmrit.edu.in

Associate Dean(Academics) /Department of ECE
GMR Institute of Technology-Rajam



              

https://researchid.co/mvnr1968

RESEARCH INTERESTS

VLSI
Signal Processing

23

Scopus Publications

88

Scholar Citations

6

Scholar h-index

4

Scholar i10-index

Scopus Publications

  • Efficient Non-Local Similarity-based Image Dehazing: A Pixel-Level Approach for Enhanced Performance and Robustness
    A. Swetha Rani, K Venkata Lakshmi Keerthi, M. V. Nageswara Rao, G.V. Pradeep Kumar, V. V. Satyanarayana Tallapragada, and Kuruma Purnima
    IEEE
    This research study proposes an efficient approach for image dehazing using non-local similarity-based methods. Dehazing has roots in many applications, including image enhancement of underwater imagery, satellite aerial images, remote sensing imagery, and many others. The proposed method considers the challenges of single-image dehazing. The method involves the identification and recognition of haze lines. After identifying haze lines, a regularization process is introduced to consider the variance of the estimated haze lines. This regularization ensures that only the pixels adhering to the model's assumptions contribute to the reconstruction process. The proposed algorithm works at the pixel level rather than at the patch or regions of patch-level techniques. This results in improved speed, robustness, and reduced sensitivity to parameters like patch size and content. Performance analysis was done using several focus measures. Experimental results demonstrate the effectiveness of the proposed method. The simulation results prove that the proposed method outperforms the state-of-the-art methods.

  • Smart IoT-Enabled Deep Learning for Diagnosing Maize Leaf Diseases
    G. Ram Sundar, M. V. Nageswara Rao, R. Deepa, S. Selvanayaki, Vaanathi S, and K. Bala Karthik
    IEEE
    As a technology that will help alter agriculture, Convolutional Neural Networks (CNN) and the Internet of Things (IoT) are being integrated more and more by researchers. With the use of IoT, farmers will be able to make decisions and take action based on data about field conditions obtained from sensor nodes somewhat than only on their experience, which will reduce the amount of resource waste. However, CNN augments monitoring systems with duties like crop disease early detection or estimating the amount of useable means and supplies (water, manures) essential to rise yield. In order to observe environmental and physical characteristics, enable initial disease identification, and support precision agriculture, this study presents an IoT and CNN-based technology platform. The pretrained model used in this platform is the Inception v3 since it largely concentrates on using less processing power by altering the earlier Inception architectures. The IoT and CNN-based technology platform successfully identified early signs of crop diseases with an accuracy rate of 92%, compared to 75% with traditional methods. This improvement significantly reduces the time required for disease diagnosis and intervention. The study demonstrates that the IoT and CNN-based technology platform not only enhances early detection of crop diseases but also optimizes resource usage and improves yield prediction accuracy.

  • Wide Band Circularly Polarized Slot Antenna with Circular Stub for C-Band Applications


  • AI-Based Learning Techniques for Bladder Cancer Detection
    M.V.Nageswara Rao, Laith Jasim, Anil Pratap Singh, X.Mercilin Raajini, and Jothi E
    IEEE
    The early detection of bladder carcinoma is of paramount importance for improving patient outcomes. This research embarks on a transformative journey, leveraging cutting-edge artificial intelligence (AI) and machine learning techniques to revolutionize bladder cancer detection. Bladder cancer, a pervasive malignancy, disproportionately affects the aging male population. Traditional diagnostic methods exhibit limitations, resulting in diagnostic inaccuracies and treatment delays. The emergence of AI in healthcare offers a transformative opportunity to address these challenges. With the utilization of state-of-the-art AI-driven models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), this research aims to significantly enhance diagnostic accuracy and provide robust prognostic insights. Through a meticulous process of data preprocessing and feature engineering, ethical considerations, and the integration of advanced algorithms, this study endeavors to contribute to the evolving landscape of precision medicine. In this landscape, tailored, data-driven approaches redefine the realm of bladder cancer diagnosis, ultimately improving patient outcomes.

  • Design and Development of Efficient SRAM Cell Based on FinFET for Low Power Memory Applications
    M. V. Nageswara Rao, Mamidipaka Hema, Ramakrishna Raghutu, Ramakrishna S. S. Nuvvula, Polamarasetty P. Kumar, Ilhami Colak, and Baseem Khan
    Hindawi Limited
    Stationary random-access memory (SRAM) undergoes an expansion stage, to repel advanced process variation and support ultra-low power operation. Memories occupy more than 80% of the surface in today’s microdevices, and this trend is expected to continue. Metal oxide semiconductor field effect transistor (MOSFET) face a set of difficulties, that results in higher leakage current (Ileakage) at lower strategy collisions. Fin field effect transistor (FinFET) is a highly effective substitute to complementary metal oxide semiconductor (CMOS) under the 45 nm variant due to advanced stability. Memory cells are significant in the large-scale computation system. SRAM is the most commonly used memory type; SRAMs are thought to utilize more than 60% of the chip area. The proposed SRAM cell is developed with FinFETs at 16 nm knot. Power, delay, power delay product (PDP), Ileakage, and stationary noise margin (SNM) are compared with traditional 6T SRAM cells. The designed cell decreases leakage power, current, and read access time. While comparing 6T SRAM and earlier low power SRAM cells, FinFET-based 10T SRAM provides significant SNM with reduced access time. The proposed 10T SRAM based on FinFET provides an 80.80% PDP reduction in write mode and a 50.65% PDP reduction in read mode compared to MOSEFET models. There is an improvement of 22.20% in terms of SNM and 25.53% in terms of Ileakage.

  • Compression Techniques for Low Power Hardware Accelerator Design: Case Studies
    Govinda Rao Locharla, Pogiri Revathi, and M. V. Nageswara Rao


  • Design of Arrow Shaped Microstrip Patch Antenna for X and Ku- Band Applications
    Yuvaraj .D, Nageswara Rao M .V, Shanmuga priya P, and Naveneetha krishanan S
    The Electrochemical Society
    A small size printed patch antenna to cover applications of X and Ku band is suggested in this paper. The suggested antenna consists of an arrow shaped patch on front view of FR 4 substrate and a partial ground on the other side. Initial design started with a rectangular patch of dimensions 10.7 mm x 7 mm. Then it is modified into arrow shaped patch in order to improve the reflection coefficient at lower and higher frequencies.The measurement of the antenna is 12 x 15 x 1.6 mm3. The antenna parameters are optimized using FEM based HFSS software. The operating range of frequencies include from 6.75 GHz to 18.96 GHz. The proposed antenna displays a good omnidirectional radiation pattern and maximum gain of 7.41 dBi at 17 GHz. Simulated S11 parameters, VSWR, gain, and current distributions are offered in this paper, and S11 simulated parameters are in good agreement with measured parameters.

  • Design of UWB Rectangular Microstrip Antenna with Defected Ground Structure to Detect Breast Cancer
    A. Sudhakar, M. V. Nageswara Rao, and Telagarapu Prabhakar
    Springer Nature Singapore

  • Image compression based on adaptive image thresholding by maximising Shannon or fuzzy entropy using teaching learning based optimisation
    Karri Chiranjeevi, Umaranjan Jena, and M.V. Nageswara Rao
    Inderscience Publishers
    In this paper, teaching leaning based optimisation (TLBO) is used for maximising Shannon entropy or fuzzy entropy for effective image thresholding which leads to better image compression with higher peak signal to noise ratio (PSNR). The conventional multilevel thresholding methods are efficient when bi-level thresholding. However, they are computationally expensive extending to multilevel thresholding since they exhaustively search the optimal thresholds to optimise the objective functions. To overcome this drawback, a TLBO based multilevel image thresholding is proposed by maximising Shannon entropy or fuzzy entropy and results are compared with differential evolution, particle swarm optimisation and bat algorithm and proved better in standard deviation, PSNR, weighted PSNR and reconstructed image quality. The performance of the proposed algorithm is found better with fuzzy entropy compared to Shannon entropy.

  • Generating realistic blood-cell images using cycle-consistent generative adversial networks
    M. V. Nageswara Rao* and
    Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
    generative adversial networks are a neural-network based generative models , predominantly used for generating data-samples close to the data distribution they have been trained on .A model for generating realistic blood cell images based on cycle-consistent generative adversial networks is developed along with their corresponding segmentation masks

  • Asic implementation of 12-bit radix-8 booth multiplier
    Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
    Multipliers are playing a vital role in DSP and Neural Networks applications. Many methods have been introduced to work on multipliers that offer high speed, less power consumption and reduced area. Booth Algorithm demonstrates an efficient way of signed binary multiplication. In this paper, physical design of 12-bit radix-8 booth multiplier for signed multiplication is presented with an aim to improve the performance metrics such as power, area and delay. The performance of 12-bit radix-8 booth multiplier is compared with the 64-bit radix-16 booth multiplier.

  • Telugu text extraction and recognition using convolutional and recurrent neural networks


  • Design and implementation of speech to text conversion on raspberry Pi


  • A hybrid VLSI architecture of Manchester encoder for RFID applications


  • Implementation of humon with human intelligence


  • A hybrid companding transform technique for PAPR reduction of OFDM signals
    D. Durga Prasad and M. V. Nageswara Rao
    IEEE
    Orthogonal Frequency Division Multiplexing (OFDM) is an attractive technique for wireless communication applications. However, an OFDM signal has a large Peak-to-Average Power Ratio (PAPR), which can result in significant distortion when passed through a nonlinear device, such as a transmitter power amplifier. Number of techniques has been proposed for reducing the PAPR in OFDM systems. Among them Companding is a well-known technique for the PAPR reduction of OFDM signals. Recently, a piecewise linear companding technique is proposed to extenuate companding distortion. In this paper, a joint piecewise linear companding technique and Discrete Cosine transform (DCT) method is proposed to reduce peak-to-average of OFDM signal. Simulation results show that the proposed technique may obtain significant PAPR reduction while maintaining good performance in the Bit Error Rate (BER) and Power Spectral Density (PSD) compared to piecewise linear companding technique.

  • Removing of high density salt and pepper noise using fuzzy median filter
    B. Sravani and M. V. Nageswara Rao
    IEEE
    This paper presents the removal of high density salt and pepper noise in gray scale Images using fuzzy based median filter (FBMF) algorithm. FBMF replaces the noisy pixel by median value when 0's, 255's and other pixel values are present in the chosen window and when all pixel values are either 0's or 255's, or combination of these, then the noise pixel is replaced by fuzzy membership value of a selected window. FBMF algorithm is tested on different images and compared with the existing Median Filter (MF), adaptive median filter (AMF), Decision Based Algorithm (DBA), Modified decision based unsymmetrical trimmed median filter algorithm (MDBUTMF). Results shows that FBMF gives better Peak Signal to Noise Ratio (PSNR) and Image Enhancement Factor (IEF) compared with the existing algorithms in the literature.

  • Remote firmware update of networked data loggers


  • A novel traffic-tracking system using morphological and blob analysis
    Prabhakar Telagarapu, M. V. Nageswara Rao, and Gulivindala Suresh
    IEEE
    A vision-based pedestrian and car tracking system which is able to distinguish between car and pedestrian is possible using Morphological processing and Blob analysis. Videos are sequence of image frames. Here the algorithm is developed to analyse a frame and the same will be applied to all frames in a video. The unwanted objects in video frame can be removed by converting colour frames into a gray scale and by applying thresholding algorithm. Threshold can be set depending on the object to be detected. Gray scale image will be converted to binary during thresholding process. Morphological processing will be applied on binary image to remove small unwanted objects that are presented in a frame. A developed blob analysis technique for extracted binary image facilitates pedestrian and car detection. Processing blob's information of relative size and location leads to distinguishing between pedestrian and car. The threshold, morphological and blobs process is applied to all frames in a video and finally original video with tagged cars will be displayed.

  • Design of discrete frequency coded sequences using PSOCM for target detection with CAF


  • Design of polyphase sequences using PSOCM for target detection with cross ambiguity function


  • S-transform based pattern classifier for non-stationary signals
    B. Biswal, M. Biswal, and M. V. Nageswara Rao
    IEEE
    This paper presents a new approach for the classification of non-stationary signal patterns in an electric power network using a modified wavelet transform and neural network. The wavelet transform is phase corrected to yield a new transform known as S-transform, which has an excellent time-frequency resolution characteristic. The phase correction absolutely references the phase of the wavelet transform to the zero time point, thus assuring that the amplitude peaks are regions of stationary phase. Once the features of noisy time varying signal during steady state or transient condition are extracted using the S-transform, they are passed through either a feedforward neural network or a probabilistic neural network for pattern classification. The average classification accuracy of the noisy signals due to disturbances in the power network is of the order 95%. This document gives formatting instructions for authors preparing papers for publication in the Proceedings of an IEEE conference. The authors must follow the instructions given in the document for the papers to be published. You can use this document as both an instruction set and as a template into which you can type your own text.

  • TT-ACO based power signal classifier
    B. Biswal, M.K. Biswal, P.K. Dash, and M.V Nageswara Rao
    IEEE
    This paper intends to propose a novel clustering method based on ant colony (AC) algorithm. A new approach called TT-transform based time frequency analysis is used in processing the non-stationary power signal disturbances. The time-time transform is the inverse Fourier transform of S-transform. The proposed model is demonstrated using feature vector from the domain of power signal analysis, yielding promising results. Visual localization, detection and classification of non-stationary power signals problem is carried out through TT-transform to generate time-frequency contours for extracting relevant features and certain pertinent feature vectors are applied to the Fuzzy C-means Algorithm with ant colony optimization for power signal classification. From simulation results, it is shown that the proposed algorithm has superior performance when compared to particle swarm algorithm.

RECENT SCHOLAR PUBLICATIONS

  • Compression Techniques for Low Power Hardware Accelerator Design: Case Studies
    GR Locharla, P Revathi, MVN Rao
    Advances in Signal Processing and Communication Engineering: Select 2022

  • Design and Implementation of Speech to Text Conversion on Raspberry Pi
    MVNR A. Pardha Saradhi , A. Sai Kiran, A. Dileep Kumar, B. Srinivas
    International Journal of Innovative Technology and Exploring Engineering 8 2019

  • Telugu text extraction and recognition using convolutional and recurrent neural networks
    MVNR A. Ram Bharadwaj, A. Venugopal, Ch. Surya Kiran
    International Journal of Engineering and Advanced Technology 8 (5), 1449-51 2019

  • ASIC implementation of 12-bit radix-8 booth multiplier
    MVNR U.Geetalakshmi
    International Journal of Engineering and Advanced Technology 8 (2), 4013-16 2019

  • Generating Realistic Blood-Cell Images using Cycle-Consistent Generative Adversial Networks
    MVN Rao
    International Journal of Innovative Technology and Exploring Engineering 8 2019

  • Image compression based on adaptive image thresholding by maximising Shannon or fuzzy entropy using teaching learning based optimization
    MVNR K. Chiranjeevi
    Int. J. Advanced Intelligence Paradigms 75, 47-51 2018

  • Implementation Of Humon With Human Intelligence
    TGR M. V. Nageswara Rao, K. Chiranjeevi, L. Govinda Rao
    International Journal of Mechanical Engineering and Technology 9 (9), 882-88 2018

  • A hybrid VLSI architecture of Manchester Encoder for RFID applications
    MVNR M.Janaki
    International Journal of Mechanical Engineering and Technology 9 (9), 1208-1213 2018

  • ASIC Implementation of 4-Bit Montgomery Modular Multiplier
    MVNR B. Hyma
    Journal of Adv Research in Dynamical & Control Systems 10 (15), 441-47 2018

  • Design and Implementation 4-bit Flash ADC using XOR Encoder
    GMR S. Rajesh, M.V. Nageswara Rao
    Journal of Adv Research in Dynamical & Control Systems 10 (15), 454-590 2018

  • High Throughput FFT/IFFT Architecture for MIMO OFDM: A Review
    LGR M.V.Nageswara Rao, Pogiri Revathi
    International Journal of Engineering Applied Sciences and Technology 2 (6 2017

  • Performance analysis of Piecewise Linear Companding with various precoders for PAPR reduction of FDM signals
    MVN Rao, VJ Naveen, KK Kishore
    International Research Journal of Engineering and Technology 3 (10), 675-681 2016

  • Register Embedded Self Immunity using Reversible Logic Gates
    MVNR Suresh Reddy
    Indian Journal of Science and Technology 9 (1), 1-4 2016

  • Image Enhancement Recognized on Dual Tree Complex Wavelet transform based Noise Reduction
    MVNR B. Sivaramakrishna
    International Journal of Electrical and Electronics Engineering Research 8 2016

  • A hybrid companding transform technique for PAPR reduction of OFDM signals
    DD Prasad, MVN Rao
    2015 13th International Conference on Electromagnetic Interference and 2015

  • Remote Firmware Update of Networked Data loggers
    J Aditya, MVN Rao
    International Journal of Applied Engineering Research 10, 36061-36064 2015

  • Removing of high density salt and pepper noise using fuzzy median filter
    B Sravani, MVN Rao
    2014 International Conference on High Performance Computing and Applications 2014

  • 128-bit Advanced Encryption Standard Algorithm implementation on FPGA
    PR Rao, MVN Rao
    International Journal of Advanced Trends in Computer Science and Engg 3, 83-88 2014

  • Removal of low and high density salt and pepper noise using combination of Fuzzy logic and Median Filter
    B Sravani, MVN Rao
    International Journal of Advanced Trends in Computer Science and Engineering 2014

  • Design of 32-bit Carry Select Adder with Reduced Area
    Y Devi .Y, MVN Rao, GR Locharla
    International Journal of Computer Applications 75, 47-51 2013

MOST CITED SCHOLAR PUBLICATIONS

  • A novel traffic-tracking system using morphological and Blob analysis
    P Telagarapu, MVN Rao, G Suresh
    2012 International Conference on Computing, Communication and Applications, 1-4 2012
    Citations: 24

  • Design of 32-bit Carry Select Adder with Reduced Area
    Y Devi .Y, MVN Rao, GR Locharla
    International Journal of Computer Applications 75, 47-51 2013
    Citations: 14

  • Removing of high density salt and pepper noise using fuzzy median filter
    B Sravani, MVN Rao
    2014 International Conference on High Performance Computing and Applications 2014
    Citations: 10

  • Target Detection with Cross Ambiguity function using Binary Sequences with high Discrimination
    M.V.Nageswara Rao,K.Raja Rajeswari
    International Journal of Computer Applications 16, 8-12 2011
    Citations: 10

  • Architecture Design and FPGA Implementation of CORDIC Algorithm for Fingerprint Recognition Applications
    P Revathi, MVN Rao, GR Locharla
    Procedia Technology 1, 371-378 2012
    Citations: 8

  • A hybrid companding transform technique for PAPR reduction of OFDM signals
    DD Prasad, MVN Rao
    2015 13th International Conference on Electromagnetic Interference and 2015
    Citations: 7

  • TT-ACO Based power signal classifier
    B Biswal, PK Dash, MK Biswal, MVN Rao
    2009 World Congress on Nature & Biologically Inspired Computing (NaBIC 2009
    Citations: 5

  • Design of Ternary Sequences using PSOCM for Target Detection with CAF
    MVN Rao, KR Rajeswari
    International Journal of Wireless Communication 3, 188-192 2011
    Citations: 3

  • Performance analysis of Piecewise Linear Companding with various precoders for PAPR reduction of FDM signals
    MVN Rao, VJ Naveen, KK Kishore
    International Research Journal of Engineering and Technology 3 (10), 675-681 2016
    Citations: 2

  • Design of Polyphase Sequences using PSOCM for Target Detection with Cross Ambiguity Function
    MVN Rao, KR Rajeswari
    International Journal on Communications Antenna and Propagation 1, 182-188 2011
    Citations: 2

  • An Efficient Classification of Fiber Optic Sensors application to Avionics
    MS Rao, MVN Rao, R.Renuka
    Proceedings of the IEEE 1992 National Aerospace and Electronics Conference 1993
    Citations: 2

  • Telugu text extraction and recognition using convolutional and recurrent neural networks
    MVNR A. Ram Bharadwaj, A. Venugopal, Ch. Surya Kiran
    International Journal of Engineering and Advanced Technology 8 (5), 1449-51 2019
    Citations: 1