Vijayalaxmi Biradar

Completed PhD in 2016 from GITAM University
Area of Research: Image Processing and Computer Vision, Human Computer Interaction, Geo-Science and Remote Sensing, Embedded Systems, IOT
M.Tech in 2008 from JNTU, Hyderabad
B.E in 2003 from VTU,Belgaum

Presently working as Associate Professor and Incharge IQAC at Kalinga University, Raipur. Total 16 years of Teaching Experience.

Selected as Volunteer for IEEE WIE International Leadership Conference, 2017 held at San Jose, USA.

Selected as Exemplary past Volunteer for IEEE WIE International Leadership Conference, 2018 held at San Jose, USA.


Published 50 Research papers in various reputed journals and conferences.

Received Early Career Research (ECR) grant from Science & Engineering Research Board (SERB), Govt. of India of 26.68 Lakhs.

Received $3,000 from EPICS in IEEE for the Project Titled “Smart Security System for an Orphanage”.

Project Investigator for the project selected for final presentation at R10 IEEE HTC 2019 at Depok I

EDUCATION

Completed Ph.D from GITAM University in the year 2016.
Thesis topic “High Performance and Low Power Driver Fatigue Detection using Image
Processing Technique”, for Ph.D to GITAM University,Vishakapatnam,AP.
Guide: 1. Rani, GITAM University, Vishakapatnam
Ø Completed M.Tech (Digital Electronics & Communication Engineering) from JNTU
Hyderabad in June 2008.
Ø Completed B.E(Electronics & Communication Engineering) from VTU,Belgaum in June
2003.
Ø Received Certification on Big data Engineering with Hadoop & Spark, Data Science
Specialization, Deep Learning, Machine Learning, Python for Machine Learning
from IIT Roorkee.

RESEARCH INTERESTS

Internet of Things
Embedded Systems
Robotics
Image Processing
Computer Vision
Python Programming
AI & ML

FUTURE PROJECTS

Scopus Publications

Intelligence-Enabled Conventional Control of DC Motor
Vikash Bhardwaj and Vijayalaxmi Biradar
Springer Nature Singapore



Messages
IEEE



An Empirical Analysis of Pose Correction Techniques for Multidomain Applications from a Statistical Perspective
Manas Ramteke and Vijayalaxmi Biradar
Springer Nature Singapore



Intelligent Fuzzy Logic-Controlled DC Machine
Vikash Bhardwaj and Vijayalaxmi Biradar
Springer Nature Singapore



Secure and Efficient Data Hiding in the Cloud with Quantum-Resistant Encryption
Salma Banu
Science Research Society
Traditional steganographic methods in multi-cloud environments face significant challenges in cloud security, including complex key management, high computational overhead, and vulnerabilities to various attacks regaring secure data concealment. This paper presents a novel steganographic mechanism tailored for single cloud environments that address these issues. The proposed mechanism leverages a combination of homomorphic encryption, elliptic curve cryptography (ECC), and lattice-based cryptographic techniques to ensure robust security against classical and quantum attacks. Confining operations to a single cloud simplifies credential and key management, reducing operational complexity and enhancing system efficiency. The proposed approach mitigates the risks associated with multi-cloud environments and offers strong resistance to brute-force, differential, and steganalysis attacks. The implementation and validation of the mechanism demonstrate its efficacy in maintaining data integrity, confidentiality, and availability while minimizing computational resources. The results indicate that this approach significantly improves the security and performance of steganographic operations in cloud storage environments, setting a new standard for secure data concealment in cloud computing.


A Study on Grid-Connected PV System Using Sliding Mode Control Technique to Improve Power Quality and Energy Efficiency
Jnanaranjan Nayak, Vijayalaxmi Biradar, and Madhab Chandra Jena
Informa UK Limited



Real-Time Electric Vehicle Battery SOC Estimation Using Advanced Optimization Filtering Techniques
, Sanjay Sanjay, , , , Srikiran Chinta, Vijayalaxmi Biradar, and Sanjay Kumar Suman
American Scientific Publishing Group (ASPG) LLC
Improving the Extended Kalman Filter's (EKF) State of Charge (SOC) prediction for EV battery packs is the primary goal of this section. Optimised batteries management procedures rely on SOC estimate that is both accurate and reliable. The EKF is a popular tool for estimating nonlinear states, but how well it works relies heavily on which noise coefficient matrices are used (Q and R). Experimental testing and other conventional approaches of calibrating these matrix systems are extremely costly and time-consuming. In order to tackle this, the section delves into the integration of four state-of-the-art metaheuristic optimisation methods: GA, PSO, SFO, and HHO. By minimising the mean square error (MSE) among the real and expected SOC, these techniques optimise the Q and R matrices. When looking at preciseness, converging speed, and resilience, SFO-EKF comes out on top in both static and dynamic comparisons. By greatly improving the reliability of SOC estimations, the numerical results show that SFO-EKF obtains the lowest MSE RMSE. This study advances electric car batteries by providing a realistic scheme for combining optimisation methods with EKF to offer highly effective and exact SOC estimates. When as opposed to TR-EKF, GA-EKF, PSO-EKF, and HHO-EKF, the SFO-EKF approach shows the best accuracy, with an improvement of over 94%. This is a result of the suggested model's exceptional efficiency in SOC estimates.


Enhanced SOC Estimation Performance Through Noise Covariance Analysis in Nonlinear Kalman Filters
Hari Prasad Bhupathi, Srikiran Chinta, Swarna Kumari Yeditha, Vijayalaxmi Biradar, L. Bhagyalakshmi, and Sanjay Kumar Suman
IEEE



Optimized Battery Modeling Using Adaptive Forgetting Factor in Recursive Least Squares Algorithm
Srikiran Chinta, Hari Prasad Bhupathi, Swarna Kumari Yeditha, Vijayalaxmi Biradar, Sanjay Kumar Suman, and L. Bhagyalakshmi
IEEE



Application of DSTATCOM for Harmonic Compensation in Distribution System
Mamrata Mishra, Shailesh Deshmukh, Vijayalaxhmi Biradar, S. P. Gawande, S. P. Adhau, and Jagdish Yadav
AIP Publishing



AI-enabled DC motor speed controlling
Vikash Bhardwaj and Vijayalaxmi Biradar
AIP Publishing



Optimized Solar Potential Maximization Model for Improved Power Stability in Photovoltaic systems
Vijayalaxmi Biradar
EDP Sciences
The power stability in power distribution systems are well studied. There exist numbers of models towards maintaining the power stability which consider the residual energy of PV systems. However, there are not efficient in maintaining the power stability and suffer to achieve higher efficiency in potential maximization. Towards maintaining higher potential maximization, an efficient Optimized Solar Potential Maximization Model (OSPMM) is presented in this article. The model considers the factors like Mean Voltage Generation, Mean Voltage Supply and Residual Voltage as the key in the selection of PV system towards power stability. To perform this, the method monitors the incoming voltage produced by various PV systems and collects such factors mentioned above. According to the factors identified, the method estimates Potential Maximization Support (PMS) for various PV systems. Based on the value of PMS, the method selects the PV system towards maintaining power stability. The proposed approach improves the performance of solar potential maximization and power stability maximization.


An Energy Efficient Requirement Optimization Model Based Power Switching for Improved Performance in Smart Grids
Vijayalaxmi Biradar
EDP Sciences
Power switching in smart grid has been identified as the key issue in literature. There exist number of models to handle this problem which consider the residual energy of grids in performing power switching. However, the performances of the models are not up to the expected rate. To handle this issue, an efficient Requirement Optimization based Power Switching Model (ROPSM) is presented in this article. The model focused on optimizing the selection of power grids towards maintaining the power stability in smart grids. To perform this, the model monitors the incoming voltage level and maintains the voltage production capability of various grids at all the duty cycles. Based on that, the method computes Power Requirement Support (PRS) value for various grid units. According to the value of PRS, a subset of grids are selected to optimize the power stability and allowed to regulate the power. The proposed model improves the performance of power stability in smart grid environment.


Monitoring and control system for the detection of crop health in agricultural application through an ensemble based deep learning strategy
Subhash Y. Kamdi and Vijayalaxmi Biradar
Springer Science and Business Media LLC



Message
IEEE



Advancing Pose Correction Efficiency Through Video Analysis and Incremental Learning in Diverse Domains
Manas Ramteke and Vijayalaxmi Biradar
Springer Nature Singapore



Energy Efficient Adaptive LTE Selection Model for Improved QoS Performance in Wireless Networks
Rotherham Press



An Intelligent Microgrid Energy Management System Using a Deep Reinforcement Approach to Handle the Flexible Demands
Rotherham Press



Smart Grid Energy Management using Improved Swarm Optimization Algorithm
Rajani S. Hardas, Vijayalaxmi Biradar, and M. R. Tarambale
IEEE
From an administrative standpoint, a Smart Grid (SG) is emerging as a future power grid that will support a smart energy distribution system. Challenges in this context include the strategic maneuvers used by parties during bidding, the exchange of energy, the appropriate planning and monitoring, and the essential protective systems. The presence of non-dispatchable actors, such as distant producing units and consumers with changing loads, in distribution networks (DNs) results in an increase in trading and management challenges. This research study presented innovative contributions to enhance the performance of smart grid systems, in order to tackle the issues faced by existing smart grid management strategies. This study presented an innovative approach and analyzed its effectiveness based on cost and energy usage factors. We have implemented the optimization technique known as Enhanced Glowworm Swarm Optimization (EGSO). The EGSO algorithm is an enhanced iteration of the existing GSO technique. The GSO technique is a derivative-free algorithm that uses a meta-heuristic approach to mimic the glowing behavior of glow-worms. It can efficiently capture all the highest multimodal functionalities. The EGSO algorithm is employed for the management of load distribution mechanism in order to assign locally accessible energy to consumers in a proportionate manner. The simulation results demonstrate the superior efficiency of the suggested models in comparison to existing methodologies.


Frequency Regulation Improves Power Systems using Renewable Energy
Shilpa D Joshi, Vijayalaxmi Biradar, and M. R. Tarambale
IEEE
Improving the power system performance connected with renewable energy sources is challenging research problem. Researchers have addressed the challenges related to load frequency control (LFC) by employing various methods, such as the automated gain control (AGC) regulator, designing excitation controllers, and implementing control strategies, due to uncertainties in parameters. In a modern power system, a blackout can have a broader impact on a bigger area because of the variability in renewable energy and unforeseen shifts in demand. In this scenario, a more advanced method of managing or regulating is necessary. Controllers utilizing optimization algorithms are gaining popularity due to their user-friendly nature, rapid convergence, and the absence of a requirement for a physical system model in most optimization-based controllers. For optimal cost management and LFC problem solving, this method employs two natural solutions: the Firefly Algorithm (FA) and the Atom Search Optimization (ASO). Our first major contribution is a model of a power system that incorporates renewable energy sources and makes use of frequency regularization. As a component of this strategy, we have been working toward the objective of reducing steady-state errors to enhance the overall accuracy and performance of the power system under steady-state conditions.


Smart Microgrid System Performance Awareness Management using Multi-Stakeholder Techique
Rajani S Hardas, Vijayalaxmi Biradar, and M.R. Tarambale
IEEE
From a trade and administrative standpoint, a Smart Grid (SG) is emerging as a future power grid that will support a Smart Distribution System. Distribution Networks (DNs) profit economically from Distributed Energy Resources (DERs), notably renewable energy resources (RERs), and electrical policy deregulation. Distribution network planning, construction, operation, and administration have been reevaluated due to multiple-party integration, load management advances, smart appliances, and higher consumer expectations. Distribution automation issues arise from these methods. In this environment, issues include strategic bidding manoeuvres, energy exchange, effective planning and monitoring, and vital protective systems. Distribution networks (DNs) with non-dispatchable players like scattered producing units and consumers with changing loads increase trading and management challenges. This research presented unique smart grid system performance improvements to overcome the limitations of existing smart grid management strategies. This research made two contributions with its cost and energy consumption analysis. We presented a multi-stakeholder method to improve smart microgrid performance in our initial contribution. Few microgrids have more than one operator, owner, and user. We think multi-stakeholder microgrids that use renewable and conventional power sources to combat environmental risks would work best. Stakeholders improve generation source variety, economies of scale, and operational opportunities. The extra stakeholders present governance, financial, and technological issues that may hinder multi-stakeholder adoption. We propose multi-stakeholder microgrids as an environmental solution and examine their development challenges. Simulation results demonstrate the suggested models' efficiency over existing techniques.


Regulating Frequencies Enhances Renewable Energy Power Systems
Shilpa D Joshi, Vijaylaxmi Biradar, and M. R. Tarambale
IEEE
It is a tough topic of research to investigate how to improve the efficiency of the power system by utilizing renewable energy sources. The load frequency control (LFC) issues have been addressed by researchers through the use of several methods, such as the automated gain control (AGC) regulator, excitation controllers, and control plans, all in response to parameter uncertainties. A blackout in a contemporary power grid might affect a larger region due to unanticipated changes in demand and the unpredictability of renewable energy. In this case, a more sophisticated approach to controlling or regulating is required. The increasing popularity of controllers that employ optimization algorithms can be attributed to their ease of use, quick convergence, and the fact that most optimization-based controllers do not require a physical system model. To guarantee an optimal solution for LFC problems and cost optimization, this method makes use of two natural solutions: the Atom Search Optimization (ASO) and the Firefly Algorithm (FA). In our initial contribution, we used frequency regularization to construct a power system model that is related to renewable energy sources. Using this method, we have attempted to lower the steady-state errors to raise the power system's overall steady-state accuracy and performance.


Power Quality Issues in Power System & Load Voltage Compensation for Boosting Performance
Anu G Pillai, Vijayalaxmi Biradar, Sarat Chandra Mohanty, Anup Kumar Jana, Anita Verma, and Rupal Gupta
IEEE
Electrical power system performance is often evaluated through the lens of power quality, which encompasses a wide array of factors. This term encapsulates the reliability and effectiveness of the system in delivering electricity while meeting specified standards. Electric power is one of the most efficient and indispensable forms of energy across the globe. Its ubiquitous presence underscores its critical role in modern life, as many aspects of daily living is dependent on a consistent supply of electricity. In today's advancing society, the focus extends beyond merely providing electricity; ensuring a consistent and high-quality power supply is paramount. With human reliance on electricity being so profound, the reliability and quality of the power system are crucial. The various components of the electrical network within a power system work together to generate, transmit, distribute, and utilize energy according to the needs of diverse consumers. Simultaneously, maintaining the excellence and continuity of electrical energy is vital for the optimal functioning of consumer appliances and equipment. The power system connects hundreds of generators to load centers like cities. Issues like voltage fluctuations or interruptions can affect consumers. These problems can damage electronics, disrupt services, and pose safety risks. Improving power quality requires maintenance, infrastructure upgrades, and advanced monitoring to ensure efficient and reliable electricity supply. Indeed, maintaining high-quality power is crucial for ensuring reliable and efficient electricity supply.


Message
IEEE



Nature-Inspired Driven Multiuser Multi Service Scheduling Protocol for Wireless Sensor Networks
Vidya N. Abdulpur, Vijayalaxmi Biradar, and Mhalskant M Sardeshmukh
IEEE
In order to enhance the energy efficiency of wireless sensor networks, the authors of this research suggest the Nature-Inspired Driven Multiuser Multi Service Scheduling Prootocol (NID-MMSSP). Load balancing is a method used in cloud computing to accomplish the goal of optimum resource use. The load is spread among available resources using a method inspired by natural systems. As a result, a plethora of NID-MMSSP methods are put out to ensure optimal performance. Nonetheless, there is not a comprehensive and exhaustive research examining and investigating the key essential concerns in this sector, despite the relevance of the NID-MMSSP methodologies for tackling the problem of the Nature-Inspired in the cloud environment. Consequently, this study provides extensive treatment of the NID-MMSSP methods used in the field of Nature-Inspired cloud computing. This paper's primary objective is to emphasize the importance of algorithms and the advantages they give in resolving Nature-Inspired problems. Furthermore, the benefits and drawbacks of the NID-MMSSP algorithms have been studied, and their substantial obstacles are taken into account when suggesting new strategies that are more successful at solving the Nature-Inspired issue in cloud settings.

RECENT SCHOLAR PUBLICATIONS

GRANT DETAILS

Received total funding of Rs. 50L till now.
Ø Co-Principal Investigator for DST SEED STI-Hub Project “STI-HUB for Income Generation and Livelihood Improvement of Scheduled Caste community of
Yadadri-Bhuvangiri, Telangana”, worth INR 225.7Lakhs. (Waiting for Result).
Ø Principal Investigator for the Project funded by Science and Engineering Research Board(SERB), Govt. of India. “Development of Non-Intrusive Driver Fatigue Detection and Warning System to avoid on road Accidents”, INR 26.68 Lakhs (2017 to 2020).
Ø Principal Investigator for the project funded by IEEE- EPICS (Engineering Projects in Community Service). “Smart Security system for an Orphanage”,USD 3,000 in 2018.
Ø Project Investigator for the project selected for final presentation at R10 IEEE HTC 2019 at Depok Indonesia on 12th – 14th November, 2019. USD 7,500.
“IOT based online Telemedicine system for old age home”.Ø Project received funding from IEEE SIGHT/HAC special call for Covid-19 “Tele Health
Monitoring System in Rural Areas through Primary Health Center using IOT for Covid-19”, USD 5051 in 2020.
Ø Project received funding from Unnat Bharat Abhiyaan “IOT medical kit for rural development”, INR: 25,000 in 2020.
Ø Received Project funding from IEEE CSS Outreach fund “Hands on workshop on IOT Training: Exposing Rural Students to Opportunities in STEM”, USD 10,650 in 2020.
Ø Received Project funding from Unnat Bharat Abhiyaan for “Village Survey”, INR: 50,000 in 2019

RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)

Software:
Python, OpenCV, MATLAB,Scilab,Simulink.
Comfortable with Windows and Linux Operating System
HARDWARE
Ø Beagle Board C4, Beagle Board Xm, BeagleBone, Wandboard, Raspberry Pi, DM6437
and Xilinx 7000 board.

Patents
1. High performance low power (HPLP) computer vision based real-time driver fatigue detection method and alert system
201841020769
2. Real-Time NonIntrusive Driver Fatigue Detection And Alert System
202041044246

Products
1. Hand Gesture controlled Robot
2. Driver Fatigue Detection system
3. Bluetooth Controlled Robot
4. Tele Health Monitoring system
5. IOT medical kit
6. CCTV camera using Raspberry PI

CONSULTANCY

Received Project funding from IEEMA-ELECRAMA- B.Tech and M.Tech Projects worth in 2014.
Received funding from AICTE ATAL for conducting Online FDP on “Wearable Devices”, worth Rs. 93,000 in 2020

1 Sustainable Practices IEEE R10 3 Months USD 100
2 Small and Medium Scale Enterprise IEEE R10 3 Months USD 100
3 WIE Congress IEEE R10 6 Months USD 200
4 Warangal WIE Zonal Congress IEEE WIE R10 6 Months USD 500
5 Imparting Interest in Engineering skills among Rural Girl Students. (IIERGS) IEEE WIE R10 6 Months USD 300
6 WIE National Leadership Conference IEEE WIE R10 6 Months USD 400
7 Insight of Technology or Youngsters IEEE WIE R10 6 Months USD 200
8 Imparting Enlightenment in engineering education in Tribal Girl students IEEE WIE R10 6 Months USD 300
9 IEEE Social Ideas Challenge Approved funding USD 200
10 Workshop on “Teacher Science Holistically: Rural School Teachers” Approved funding USD 150
11 Teacher’s Congress- Towards Capacity Building Approved funding USD 250
12 Development of a video in Hindi Language of IEEE Resume Lab and to build a professional resume Approved funding USD 200

Industry, Institute, or Organisation Collaboration

Foreign Collaborations:
Five MoU's with Foreign universities from USA and Sweden
Completed one research proposal in collaboration with Saint Peter's University, USA
26 students got selected directly for MS programs

Industry:
28 MOU's with Industry from IOT, Robotics, etc.,
Conducted various workshops
Sent students fof internships
Established IOT lab incollaboration with Red Pine Signals

SOCIAL, ECONOMIC, or ACADEMIC BENEFITS

Social:
1 Sustainable Practices IEEE R10 3 Months USD 100
2 Small and Medium Scale Enterprise IEEE R10 3 Months USD 100
3 WIE Congress IEEE R10 6 Months USD 200
4 Warangal WIE Zonal Congress IEEE WIE R10 6 Months USD 500
5 Imparting Interest in Engineering skills among Rural Girl Students. (IIERGS) IEEE WIE R10 6 Months USD 300
6 WIE National Leadership Conference IEEE WIE R10 6 Months USD 400
7 Insight of Technology or Youngsters IEEE WIE R10 6 Months USD 200
8 Imparting Enlightenment in engineering education in Tribal Girl students IEEE WIE R10 6 Months USD 300
9 IEEE Social Ideas Challenge Approved funding USD 200
10 Workshop on “Teacher Science Holistically: Rural School Teachers” Approved funding USD 150
11 Teacher’s Congress- Towards Capacity Building Approved funding USD 250
12 Development of a video in Hindi Language of IEEE Resume Lab and to build a professional resume Approved funding USD 200
13 Principal Investigator for the project funded by IEEE- EPICS (Engineering Projects in Community Service). “Smart Security system for an Orphanage”,USD 3,000 in 2018.