Vijaya Kumar Padarti received his B.Tech. degree in Electronics and
Communication Engineering in Bapatla Engineering College, Bapatla
, M.Tech. degree in VR Siddhartha Engineering College,
Vijayawada and PhD in Acharya Nagarjuna University. Currently working as Assistant Professor, Department of
ECE, in VR Siddhartha Engineering College, Vijayawada, India.
Qualified in UGC-NET for lectureship in DEC-2012
Qualified in GATE (2009,2012,2014,2016,2017,2018)
Achieved ALL INDIA GATE RANK 923 in GATE 2018
Received stipend of worth 2,00,000 during M.Tech from MHRD.
His research
interests include Wireless Communication, Antenna and Wave
Propagation.
EDUCATION
Ph. D (Wireless Communication)
2022 Acharya Nagarjuna University, Guntur
M.Tech (Commuinications and Signal Processing)-78%
2011, VR Siddhartha Engineering College
B.Tech (Electronics and Communication Engineering)-85%
2009, Bapatla Engineering College
Intermediate (MPC)-95.5%
ANTI-TRIANGLE MICROSTRIP TEXTILE ANTENNA WITH DEFECTED GROUND STRUCTURE FOR WEARABLE APPLICATIONS Bhagya Lakshmi, saritha vanka, Vijaya eduri, P Vijaya kumar, poojitha k, nikitha B, Amrootha Sai, G Sahitya Telecommunications and Radio Engineering English Translation of Elektrosvyaz and Radiotekhnika, 2026 This paper investigates the performance of a miniaturized wearable jute textile antenna operating across three distinct frequency bands (3.8-6.9, 8.6-9.3, 12.5-13.5 GHz). Despite its compact form factor of 16 &times; 16 &times; 1.0 mm<sup>3</sup>, the antenna exhibits remarkable return loss characteristics and achieves a gain of 1.06 dB. Through comprehensive assessments of operating frequencies, right-hand circular polarization (RHCP), and gains, this paper emphasizes the antenna's efficacy for wearable applications. Moreover, its proven effectiveness at 5.8 GHz for wireless medical telemetry service (WMTS) applications signifies its potential to significantly enhance healthcare monitoring systems. The antenna underwent durability and functionality testing, simulating human sweat patterns with artificial sweat solutions. Testing occurred under dry, partially wet, and completely wet conditions, crucial for assessing antenna performance in wearable applications prone to sweat exposure. Jute's high absorption properties helped mimic sweat absorption during testing, enabling a comprehensive evaluation of the antenna's suitability for such applications. This research contributes valuable insights into the development of wearable antenna technology, highlighting its versatility and promising implications for communication and healthcare sectors alike.
Enhanced targeted attacks on Graph Neural Networks via Average Gradient and Perturbation Optimization Yang Chen, Bin Zhou, Haixing Zhao, Padarti Vijaya Kumar Engineering Applications of Artificial Intelligence, 2025 Graph Neural Networks (GNNs) are vulnerable to adversarial attacks that cause performance degradation by adding small perturbations to the graph. Gradient-based attacks are among the most widely used methods and have demonstrated strong performance across various attack scenarios. However, most gradient attacks use greedy strategies to generate perturbations, which tend to fall into local optima, leading to underperformance of the attack. To address the above problem, we propose an attack (Average Gradient and Perturbation Optimization Attack, AGPOA) on GNNs, which consists of an average gradient calculation and a perturbation optimization module. In the average gradient calculation module, we compute the average of the gradient information over all moments to guide the attack to generate perturbed edges, which stabilizes the direction of the attack update and gets rid of undesirable local maxima. We use a perturbation optimization module to limit the attack budget and further improve performance. Furthermore, we demonstrate the theoretical superiority of AGPOA over traditional gradient-based attack methods through attack loss variance. The experimental results show that AGPOA improves the misclassification rate by 2%–8% compared to other state-of-the-art models in the node classification task.
AI-based routing algorithms improve energy efficiency, latency, and data reliability in wireless sensor networks Rahul Priyadarshi, Ravi Ranjan Kumar, Rakesh Ranjan, Padarti Vijaya Kumar Scientific Reports, 2025 This paper proposes a modular Artificial Intelligence (AI)-based routing framework for Wireless Sensor Networks (WSNs) that integrates reinforcement learning (RL), supervised learning, and swarm intelligence techniques such as genetic algorithms (GA) and particle swarm optimization (PSO). Unlike conventional approaches that rely on static or standalone algorithms, the proposed framework employs a structured decision-making pipeline that dynamically adapts to real-time changes in network topology, traffic, and energy conditions. Each AI module plays a distinct role-RL handles local routing decisions, while GA and PSO are invoked for global optimization under resource constraints. Simulations conducted in MATLAB R2021b validate the framework's effectiveness, demonstrating improvements in packet delivery ratio, end-to-end latency, and energy efficiency when compared to traditional protocols. While this study is based on synthetic evaluations, it outlines the architectural groundwork for future real-world implementation and discusses deployment challenges such as scalability, resource usage, and security. The results highlight the potential of hybrid AI-based routing strategies to enhance the reliability, adaptability, and sustainability of WSNs in dynamic and resource-limited environments.
POWER SIGNAL PROCESSING AND FEATURE EXTRACTION ALGORITHMS BASED ON TIME-FREQUENCY ANALYSIS Supraja Veerabomma, K. Chinna Kullayappa, Kethepalli Mallikarjuna, Putta Brundavani, P. Vijaya Kumar, Bepar Abdul Raheem Transactions of the Royal Institution of Naval Architects Part A International Journal of Maritime Engineering, 2025 Feature extraction in power signal processing plays a crucial role in accurately identifying and classifying various power quality disturbances. Power signals are often non-stationary and complex, containing both transient and steady-state components, which necessitates the extraction of meaningful features that capture their underlying characteristics. In this process, features are derived from multiple domains—time, frequency, and time-frequency—to ensure a holistic representation of the signal behavior. Time-domain features such as mean, standard deviation, skewness, kurtosis, root mean square (RMS), and entropy help in capturing statistical variations and signal energy fluctuations. Frequency-domain features like Total Harmonic Distortion (THD), spectral centroid, and spectral entropy provide insights into harmonic content and frequency distribution, which are critical for detecting distortions and resonances in the power system. This paper proposes an efficient and intelligent framework for power signal classification using a Stacked Whale Optimization-based Machine Learning (SWO-ML) model. The approach combines robust feature extraction from time, frequency, and time-frequency domains with advanced optimization and classification techniques to enhance power quality assessment. A total of 13 features were extracted, including statistical, spectral, and wavelet-based parameters, from different signal conditions such as normal, fault, transient, harmonic distortion, and load switching. The SWO algorithm was employed to select the most informative 18 features out of the initial pool, significantly reducing dimensionality while maintaining high discriminative performance. The proposed SVM + SWO model achieved a classification accuracy of 96.8%, precision of 96.2%, recall of 95.9%, and an F1-score of 96.0%, outperforming baseline models such as SVM without optimization (90.2%), SVM + PSO (93.1%), and SVM + GA (92.4%). In addition, the training time was reduced to 1.85 seconds, showcasing the computational efficiency of the system. Performance evaluation over 100 training epochs showed stable learning with final validation accuracy reaching 98.2% and a minimal loss of 0.05. The results confirm that the SWO-ML framework is highly effective for intelligent, real-time classification of power signals, offering promising applications in power system monitoring, smart grid stability, and fault diagnosis.
An Efficient Clipping Method for PAPR Reduction in OFDM Systems Vijaya Kumar Padarti, Chandini Dasari, P V Venu Gopal Dokala, Venkata Sravya Chowtapalli, L V Rama Kowshik Avula 5th International Conference on Inventive Computation Technologies Icict 2022 Proceedings, 2022
A Novel Method for ICI Cancellation in OFDM Systems P. Vijaya Kumar, N. Venkateswara Rao 3rd International Conference on Electrical Electronics Communication Computer Technologies and Optimization Techniques Iceeccot 2018, 2018
