Gangireddy Harinatha Reddy

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering, Biomedical Engineering, Computer Vision and Pattern Recognition, Artificial Intelligence

Scopus Publications

IMPROVING THE PERFORMANCE OF ORTHOGONAL MULTIPLEXING FREQUENCY DIVISION USING EFFICIENT CHANNEL ESTIMATION MODEL
Journal of Theoretical and Applied Information Technology, 2024
Quality of Service Aided Power Aware Resource Allocation Optimization with Channel Estimation in Wireless Sensor Network
Aswani. Lalitha, G. Harinatha Reddy
Proceedings of the 2023 2nd International Conference on Augmented Intelligence and Sustainable Systems Icaiss 2023, 2023
Wireless Sensor Networks (WSN) have long relied on resource allocation technologies, however most of the existing methods are designed with a single network interface. The Multi-Input Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) methodology is a relatively new channel estimation method used for varying Quality of Service considerations and for various service types, MIMO employs several parallel channels to transmit these services in a decoupled manner. Constant Bit Rate (CBR) traffic is a high-priority service category that has reduced delay than other types of traffic. Performance indicators such as outage probability, throughput, and access probability of both classes of service are evaluated numerically, and the analysis is offered to determine whether or not the power was allocated appropriately. The channel estimation method is used to analyze the distribution of wireless communication resources. Relay selection, channel assignment, and relay assignment are all used as part of a data-aided estimate procedure to boost overall throughput. The proposed research considers a Quality of Service aided power aware Resource Allocation Optimization Model with Channel Estimation (QoS-PARAO-CE) model for accurate resource allocation with channel estimation to enhance the QoS in WSNs. The proposed model when contrasted with the traditional models performance better.
FPGA Accelerated Track to Track Association and Fusion for ADAS Distributed Sensors
B Gopala Swamy, G Harinatha Reddy, Pathipati Srihari, U. Shripathi Acharya, Bethi Pardhasaradhi
Proceedings 2023 IEEE International Symposium on Smart Electronic Systems Ises 2023, 2023
The integration and amalgamation of sensor data in the automotive domain play a pivotal role in informing real-time decision-making for advanced driver assistance and safety (ADAS) systems. In a distributed architecture, the track-to-track association (T2TA) modules are responsible for associating the correct track pairs and subsequently fusion modules fuses the information. The T2TA and fusion modules operate within the CPU framework, often leading to elevated latency across the system. This paper introduces digital signal processing (DSP) architectures for the T2TA and fusion modules, designed to meet stringent constraints in terms of both area and latency. These modules encompass critical operations such as matrix inversion, vector-to-matrix multiplications, and matrix-to-matrix multiplications. The challenge of vector-to-matrix multiplications is effectively addressed through the utilization of the constant co-efficient multiplication technique. Additionally, matrix-to-matrix multiplication is performed by employing a vector-to-vector multiplication architecture with Block RAMs (BRAMs). Further-more, matrix inversion is realized through the LU decomposition method. Moreover, this paper presents an innovative approach to expedite the T2TA and fusion modules by harnessing folded DSP architecture within a system-on-chip (SOC) framework. The results of simulations substantiate that the proposed architectures exhibit a remarkable suitability for applications necessitating low area, low power consumption, and high throughput capabilities.
A Modified Strassen Algorithm based DSP Accelerated 3D Kalman Filter
Vivaksha Mohalia, Pathipati Srihari, Sreenivasula Reddy, G Harinatha Reddy, Bethi Pardhasaradhi
Proceedings 2023 12th IEEE International Conference on Communication Systems and Network Technologies Csnt 2023, 2023
The high-speed Kalman filter (KF) algorithms are essential for robotics, autonomous vehicle, target tracking, and other applications. The dimensions of the state vector and traditional matrix multiplication (complexity of order $\\mathcal{O}\\left(n^{3}\\right)$) are the two main reasons for the computational time of the $K F$ algorithm. Hence, a matrix multiplication accelerator module is needed to accelerate the KF algorithm for higher dimensions of the state vector. In this paper, modified Strassen matrix multiplication (complexity of order $\\mathcal{O}\\left(n^{2.80}\\right)$) is utilized to increase the computational efficiency of the KF algorithm. The number of cycles is evaluated against the dimensions of the KF algorithm to illustrate the proposed methodology. After that, 2D-KF and 3D-KF algorithms targeted on DSP processor TMS320C6678 using C language to ensure real-time processing. The $3 D-K F$ with a state of nine consumes $19.962 \\mathrm{~ms}, 30.47 \\mathrm{~ms}$, and $40.04 \\mathrm{~ms}$ of time by employing the hybrid Strassen, Strassen, and conventional matrix multiplication. It is observed that the usage of hybrid Strassen takes only half of the time provided by conventional multiplications.
Improved Target Tracking and Fusion Using Optimally Quantized Measurement Channels
B N Balarami Reddy, G Harinatha Reddy, T Sreenivasula Reddy, Pathipati Srihari, Bethi Pardhasaradhi
Proceedings 2023 IEEE International Symposium on Smart Electronic Systems Ises 2023, 2023
Nowadays, autonomous underwater vehicle (AUV) technologies provide localization of AUV s, high-precision 3D measurement mapping, and underwater target tracking. Usually, the AUV consists of various sensors to acquire the dense measurements of the underwater scene to perform target tracking and functionalities. In in-water mobility, the bandwidth is a significant bottleneck, allowing communication with other AUV s and performing centralized target tracking and fusion. Since the communication modules within the AUV are compact, low power, and have low bandwidth, the quantized measurements are transmitted to the fusion center (FC). The sensing devices provide different measurements like range, range rate, azimuth, elevation, and directional cosines corresponding to the scene. Whereas the range measurements are in meters, azimuth measurements range from 0 to 360°• Hence, using a single quantizer with a predefined step size leads to tremendous errors. This paper proposes to deploy an optimal quantizer for every measurement channel and then transmit it to the FC. To explicitly study the quantization effect, we have used linear and optimal quantization techniques which can adaptively choose the levels of the measurements. The extended Kalman filter (EKF) in combination with correlation-free covariance intersection (CI) fusion algorithm is used to attain the global track information. The performance of the proposed method is quantified using the position root mean square error (PRMSE) and compared with the no-quantization state-of-art.
Blind Channel Estimation Using Enhanced Independent Component Analysis for MIMO-OFDM System
Aswani. Lalitha, G. Reddy
International Journal of Intelligent Engineering and Systems, 2022
: Blind Source Separation (BSS) is a process of separating a set of source signals from mixed-signal without the help of information of source signals. In some noisy acoustic surroundings, instinctive class detection is completely dependent on vocalization which remains a stimulating task. To identify the definite classes easily, the source signals have to be detached from the mixed signals and this separation procedure is considered as a substantial pre-processing phase before the detection procedure takes place. This research mainly focuses on the issues of BSS in bio-acoustic mixed signals. Independent Component Analysis (ICA) is current technique in the area of BSS that can discrete the mixed-signal and also utilizes Negentropy as its objective function. However, this method is penetrating to the separation matrix and it cannot diverge. So, the bootstrap ICA procedures with Fast and Robust Bootstrap (FRB) method is developed which is applicable for all the signals. The quality of separated source signals using Enhanced-ICA and other algorithms are compared and evaluated according to MATLAB toolbox metrics. The results show that Enhanced-ICA with negentropy is used for finding a maximum non-gaussianity which achieves the BER performances of 0.00019 which is better than existing Discrete Wavelet Transform based BSS (DWT-BSS) and Modified Newton with Improved Animal Migration Optimization (MN-IAMO).
Optimized Scale-Invariant Feature Transform with Local Tri-directional Patterns for Facial Expression Recognition with Deep Learning Model
Gurukumar Lokku, G Harinatha Reddy, M N Giri Prasad
Computer Journal, 2022
Facial expression recognition (FER) is the process of identifying human expressions. People vary in their accuracy at recognizing the emotions of others. Use of technology to help people with emotion recognition is a relatively important research area. Various works have been conducted on automating the recognition of facial expressions. The main intent of this paper is to plan for the FER model with the aid of intelligent techniques. The proposed models consist of steps like data collection, face detection, optimized feature extraction and emotion recognition. Initially, the standard benchmark facial emotion dataset is collected, and it is subjected to face detection. The optimized scale-invariant feature transform (OSIFT) is adopted for feature extraction, in which the key points that are giving unique information are optimized by the hybrid meta-heuristic algorithm. Two meta-heuristic algorithms like spotted hyena optimization and beetle swarm optimization (BSO) are merged to form the proposed spotted hyena-based BSO (SH-BSO). Also, the local tri-directional pattern is extracted, which is further combined with optimized SIFT. Here, the proposed SH-BSO is utilized for optimizing the number of hidden neurons of both deep neural network and convolutional neural network in such a way that the recognition accuracy could attain maximum.
OPFaceNet: OPtimized Face Recognition Network for noise and occlusion affected face images using Hyperparameters tuned Convolutional Neural Network
Gurukumar Lokku, G. Harinatha Reddy, M.N. Giri Prasad
Applied Soft Computing, 2022
An integrated signal allocation model with effective collision resolution model for performance enhancement of wireless sensor networks
Aswani Lalitha, Gangireddy Harinatha Reddy
Traitement Du Signal, 2021
A Wireless Sensor Network (WSN) differs from conventional wireless or wired networks in that it interacts with the environment. Orthogonal Frequency Division Multiplexing (OFDM) was investigated as a possible interface technology for making effective use of bandwidth. Such networks have been proposed for a variety of purposes such as search and rescue, disaster assistance, and smart positioning systems. These applications often require a large number of wireless sensors that are powered by batteries and are designed for long-term, human-free deployment. Collisions between network nodes can significantly degrade performance in WSNs. Although increased bandwidth facilitates wireless access to high data frequencies, it is prohibitively expensive to increase due to spectrum limits. This necessitates making good use of the available bandwidth. OFDM has been considered as a possible interface mechanism for efficiently utilising bandwidth. While many signals available in WSN technology can be employed to mitigate collisions, multi-signal allocations may have a significant impact on the efficiency of multistage communications. Real-time multimedia flow raises the chance of sensor network failures and congestion, which reduces the efficiency of Quality of Service (QoS). The main goal of the Signal Allocation Scheme is to allocate an appropriate number of signals to any node in order to use professional bandwidth and assure QoS. Load balancing is intended to measure and prevent collisions caused by the number of available slots in the frame. Preparation is another important component in preventing collisions because it decreases delay and optimises energy utilisation. In this paper, an Integrated Signal Allocation Model with Effective Collision Resolution Model (ICAM-ECR) is used to deploy non-overlapping signals dynamically for varying application loads based on expected bandwidth estimation. The suggested model is compared to standard methods, and the findings reveal that the proposed model outperforms existing models.
A Robust Face Recognition model using Deep Transfer Metric Learning built on AlexNet Convolutional Neural Network
Gurukumar Lokku, G. Harinatha Reddy, M. N. Giri Prasad
Iccisc 2021 2021 International Conference on Communication Control and Information Sciences Proceedings, 2021
Exertion of Transfer Learning for Convolutional Neural Networks (CNNs) describes a dynamic solution for face recognition instead of building and training a neutral network from scratch. Transfer Learning gains and stores the knowledge while solving one problem, and utilizes it for a different related problem. So, choosing a suitable CNN for Deep Face Recognition among the state of art CNN is a challenging task. Deep face recognition requires a large density of datasets with standard subjects. Subjects with parameters like pose, illumination and expression are major constraints for frontal facial images. Ignoring the parameters in training as well as testing phase will result to significant decrease in recognition rate; also show indelible impact on accuracy. Training the network with improper illumination facial data base can lead to biased classifier in deep CNN. In this paper, we propose AlexNet-CNN architecture-based transfer learning framework to enlarge the facial feature space on the constrained face dataset. The proposed framework is self-possessed with input layer, convolutional layers, activation layers, pooling layers, fully connected layer connected to a classifier layer and runs on standard CUHK (China University of Hong Kong) and ORL (Olivetti Research Laboratory) database in a standard frontal pose faces, under ordinary illumination state and with unbiassed expression ensuing to excellent accuracy in facial recognizing tasks.
Real Time Remote Monitoring, Control and Reporting Dashboard System to Avoid Industrial Disasters Using Industrial IOT
Lokku Guru Kumar, Gangireddy Harinatha Reddy, Payasam Venkata Sai, Sk. Irfan, K. Pushpa Pujitha
Advances in Science and Technology, 2021
Automatic face recognition for various expressions and facial details
Mr Guru, Gurukumar Lokku, G. Harinatha Reddy, M Giri Prasad, M Abuzneid, et al.
International Journal of Innovative Technology and Exploring Engineering, 2019
Survey on channel estimation of the orthogonal frequency division multiplexing
International Journal of Recent Technology and Engineering, 2019

RECENT SCHOLAR PUBLICATIONS

Discriminative Feature Learning Framework for Face Recognition using Deep Convolution Neural Network
GPMN Gurukumar Lokku, Harinatha Reddy G
Solid State Technology 63 (6), 18103-18115 , 2020
2020
Citations: 6
Shadow Suppression for Enlightenment Face Recognition
DGHR Shaik Yasmeen
International Journal of Analytical and experimental modal analysis 12 , 2020
2020
Automatic Face Recognition for Various Expressions and Facial Details
DMNGP L. Gurukumar, Dr. G. Harinatha Reddy
International Journal of Innovative Technology and Exploring Engineering 8 … , 2019
2019
Citations: 4
Survey on Channel Estimation of the Orthogonal Frequency Division Multiplexing
DGHR Aswani Lalitha
International Journal of Recent Technology and Engineering 7, 312-318 , 2019
2019
A New Filter for Remote Sensing Images with Enhancement and De-Noising
DGHR M. Chethana
The international journal of analytical and experimental Modal Analysis 11 … , 2019
2019
Robotic Arm Control through Video Surveillance of a Robot using Raspberry pi3
KAGH Reddy
Journal of Emerging Technologies and Innovative Research 5, 95-98 , 2018
2018
Adaptive Human Machine Interface Approach for Face Recognition
DGHR Mr. L. Guru Kumar
International Journal of Engineering Research in Electronics and … , 2017
2017
An Assimilated Face Recognition System with effective Gender Recognition Rate
DGHR K Akhil Srinivasulu
International Research Journal of Engineering and Technology 4, 2060-2065 , 2017
2017
Identification of Intra-Cardiac Masses based on Advanced ACM Segmentation using Multiclass SVM Classifier
DGHR M. Archana
International Journal for Scientific Research & Development 4 (5), 387-391 , 2016
2016
Detection and Classification of Exudates using Enhanced Digital Funds Photography
GH Reddy
International Journal of Advanced and Innovative Research 2, 65-71 , 2015
2015
Detection and Classification of Diabetic Retinopathy using Retinal Images
GHR Nagarjuna
International Journal of Emerging Technologies in Engineering Research 3 , 2015
2015
Detection of masses in digital mammogram using gabor based edge detection method
JK Jangala, GH Reddy
International Journal of Industrial Electronics and Electrical Engineering 2, 12 , 2014
2014
Citations: 1
Improved snr of mst radar signals: Chebyshev window parameters
GH Reddy
International Journal of Electronics and Communication Engineering 1 (1) , 2009
2009
Citations: 3
The Effect of ‘’in Kaiser Window on the Signal to Noise Ratio (SNR) of MST Radar Signals
GH Reddy
Asian Journal of Scientific Research 1 (3), 203-212 , 2008
2008
Citations: 4
Effect of Windowing on SNR of MST Radar Signals
G Reddy Harnadh
Engineering Today 9 (11), 22-26 , 2007
2007
Citations: 2

MOST CITED SCHOLAR PUBLICATIONS

Discriminative Feature Learning Framework for Face Recognition using Deep Convolution Neural Network
GPMN Gurukumar Lokku, Harinatha Reddy G
Solid State Technology 63 (6), 18103-18115 , 2020
2020
Citations: 6
Automatic Face Recognition for Various Expressions and Facial Details
DMNGP L. Gurukumar, Dr. G. Harinatha Reddy
International Journal of Innovative Technology and Exploring Engineering 8 … , 2019
2019
Citations: 4
The Effect of ‘’in Kaiser Window on the Signal to Noise Ratio (SNR) of MST Radar Signals
GH Reddy
Asian Journal of Scientific Research 1 (3), 203-212 , 2008
2008
Citations: 4
Improved snr of mst radar signals: Chebyshev window parameters
GH Reddy
International Journal of Electronics and Communication Engineering 1 (1) , 2009
2009
Citations: 3
Effect of Windowing on SNR of MST Radar Signals
G Reddy Harnadh
Engineering Today 9 (11), 22-26 , 2007
2007
Citations: 2
Detection of masses in digital mammogram using gabor based edge detection method
JK Jangala, GH Reddy
International Journal of Industrial Electronics and Electrical Engineering 2, 12 , 2014
2014
Citations: 1
Shadow Suppression for Enlightenment Face Recognition
DGHR Shaik Yasmeen
International Journal of Analytical and experimental modal analysis 12 , 2020
2020
Survey on Channel Estimation of the Orthogonal Frequency Division Multiplexing
DGHR Aswani Lalitha
International Journal of Recent Technology and Engineering 7, 312-318 , 2019
2019
A New Filter for Remote Sensing Images with Enhancement and De-Noising
DGHR M. Chethana
The international journal of analytical and experimental Modal Analysis 11 … , 2019
2019
Robotic Arm Control through Video Surveillance of a Robot using Raspberry pi3
KAGH Reddy
Journal of Emerging Technologies and Innovative Research 5, 95-98 , 2018
2018
Adaptive Human Machine Interface Approach for Face Recognition
DGHR Mr. L. Guru Kumar
International Journal of Engineering Research in Electronics and … , 2017
2017
An Assimilated Face Recognition System with effective Gender Recognition Rate
DGHR K Akhil Srinivasulu
International Research Journal of Engineering and Technology 4, 2060-2065 , 2017
2017
Identification of Intra-Cardiac Masses based on Advanced ACM Segmentation using Multiclass SVM Classifier
DGHR M. Archana
International Journal for Scientific Research & Development 4 (5), 387-391 , 2016
2016
Detection and Classification of Exudates using Enhanced Digital Funds Photography
GH Reddy
International Journal of Advanced and Innovative Research 2, 65-71 , 2015
2015
Detection and Classification of Diabetic Retinopathy using Retinal Images
GHR Nagarjuna
International Journal of Emerging Technologies in Engineering Research 3 , 2015
2015