SUMAN TURPATI

@drkvsrit.ac.in

Associate Professor and Dept of ECE
Dr. K.V.Subba Reddy Institute of Technology (Autonomous)

SUMAN TURPATI


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https://researchid.co/sumanturpati

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering, Signal Processing, Artificial Intelligence, Control and Systems Engineering
21

Scopus Publications

Scopus Publications

  • Outage performance of UAV-NOMA networks over rician faded channel with hardware impairments, channel estimation error, and SIC imperfection
    Sk Thaherbasha, S.D. Nageena Parveen, Sivasubramanyam Medasani, Suman Turpati, Tathababu Addepalli, Manish Sharma, Sameena Pathan
    Plos One, 2026
    The escalating demand for enhanced coverage and high data rates in wireless networks is driving the adoption of advanced technologies like unmanned aerial vehicles (UAVs). Integrating UAVs with non-orthogonal multiple access (NOMA) has emerged as a promising solution to boost spectral efficiency and user connectivity. However, the practical performance of these UAV-assisted NOMA systems is critically constrained by real-world imperfections, including hardware impairments, inaccurate channel state information (CSI), and non-ideal successive interference cancellation (SIC). To address this, a reliable system design necessitates a precise outage probability analysis, which quantifies the impact of these impairments on both reliability and user experience. This work derives closed-form expressions for the outage probability of a multi-user UAV-assisted NOMA system operating over Rician fading channels, explicitly incorporating the effects of the aforementioned impairments. Analytical results are obtained for a two-user UAV-assisted NOMA system by considering the detrimental effect of hardware impairments along with imperfect CSI and SIC on system performance. These analytical results are further validated by simulated results.
  • Assessment of a flexible Polyimide-Based Quad-Port MIMO antenna for wideband Sub-6 GHz 5G applications
    V.N. Koteswara Rao Devana, Arashpreet K. Sohi, A. Swetha, Dhana Raj Valluri, Suman Turpati, Z. Zakaria, A.J.A. Al-Gburi
    Ain Shams Engineering Journal, 2026
    This study delineates the design, simulation, and empirical evaluation of a compact, adaptable four-port multiple-input multiple-output (MIMO) antenna engineered for seamless incorporation into sophisticated portable and wearable 5G sub-6 GHz devices. The suggested MIMO antenna is made on a flexible polyimide substrate and is 46 × 46.5 × 0.6 mm 3 in size. The antenna has four nested-ring radiators that are fed by coplanar waveguides (CPWs). Each one has a fan-type structure that lets it work over a wide range of frequencies from 2.32 to 7.28 GHz (103.33% fractional bandwidth). Its peak gain and efficiency are 4.42 dBi and 82%, respectively. Partial metallic ground planes with an X-shaped decoupler in the middle work well to reduce cross-coupling effects between the radiating elements and improve impedance matching. The MIMO performance under mechanical deformation is evaluated through bending tests along the X- and Y-directions, demonstrating stable operation and adequate diversity performance. Several diversity parameters are examined and determined to meet standard thresholds, thereby validating the robust correlation between simulated and empirical results. These results show that the proposed antenna is good for sub-6 GHz New Radio (NR) mid-band 5G/IoT applications like n79, Wi-Fi 5/6, and Vehicle-to-Everything (V2X).
  • Review of 6G Wireless Communication System With Artificial Intelligence
    Suman Turpati, B. Geetha Rani, A. V. Prabu, Amrit Mukherjee, Sudan Jha, K. C. T. Swamy
    Internet Technology Letters, 2025
    Wireless communication has been in high demand over the last decades. Soon, the globe will be equipped with fifth‐generation (5G) communications, which provide an incredible number of additional capabilities compared to fourth‐generation communications. An innovative paradigm has evolved with the combination of artificial intelligence (AI) with sixth‐generation (6G) communication networks in response to the increasing need for intelligent communication and seamless connection. This integration enables optimum resource allocation and greater efficiency. It also enhances adaptive system performance by incorporating AI across multiple network layers. The next generation of wireless networks must address many fundamental issues, including increasing system capacity, data throughput, latency, security, and quality of service in comparison to 5G. This article provides a through review of the vision of future 6G network AI and wireless communication architecture, touching on their conceptual foundations, inherent difficulties, and potential fields for further study. Some new technologies discussed in this article include AI, terahertz communications, free‐space optical networks, blockchain, quantum communications, drones, mobile free communications, integrated sensing and communication, dynamic network slicing, big data analytics, and wireless optical technology. This could all be useful in ensuring the quality of service in the 6G architecture development. Furthermore, we provide a concise overview of the AI standardization process for wireless networks, focusing on essential achievements and current initiatives. We also examine the significant obstacles that 6G's AI and communication integration encountered. Lastly, we provide an overview of prospective future studies that ideally promote advancing and improving AI and 6G communications by describing possible obstacles and possibilities.
  • An Efficient Simulation Framework for 2D LiDAR-Based Navigation and Collision Detection of Autonomous Ground Vehicles to Enhance Safety in Warehouses
    Swamy KCT, Suman Turpati, Malika Anwar Siddiqu, Ahmed Anwer Jaafar, Towseef Ahmed Shaik, Sudan Jha
    Internet Technology Letters, 2025
    The increasing use of autonomous ground vehicles (AGVs) in warehouse automation necessitates effective and reliable collision detection systems to guarantee operational safety. The conventional real‐world evaluation of collision avoidance systems is resource‐demanding and hazardous, highlighting the necessity for dependable simulation tools. This article introduces an efficient simulation framework specifically developed for 2D LiDAR‐based collision detection, aimed at improving the safety performance of AGVs in warehouse settings. The proposed system simulates a realistic warehouse floor environment with multiple passages separated by obstacles. The framework's foundation leverages the cost‐effectiveness and simplicity of 2D LiDAR, employs ray‐casting methods and spatial filtering algorithms to accurately replicate 2D LiDAR scans and identify probable collisions with minimal processing expense. The simulation results indicate obstacle detection accuracy is very precise, using 2D LiDAR. This article offers a simulation environment for the immediate design and testing of 2D LiDAR‐based collision detection, significantly reducing the need for physical trials during the first development phases. The framework serves as a crucial tool for researchers and engineers aiming to improve AGV safety protocols in warehouse automation.
  • Adaptive noise reduction for efficient aviation operations
    Turpati Suman, Bandi Mahesh Babu, Posa Harsha Vardhan, Agavolla Naveen Kumar
    Digital Transformation in Aviation Industry Operations Innovations and Sustainable Solutions, 2025
    Efficient noise reduction is paramount in modern aviation systems to ensure reliability, safety, and optimal performance in complex operational environments. This chapter introduces the robust variable power fractional least mean square (RVP-FLMS) algorithm, a novel adaptive filtering method that addresses the limitations of traditional noise reduction techniques in dynamic and non-stationary conditions. By incorporating fractional calculus into adaptive filtering, the RVP-FLMS algorithm achieves enhanced convergence rates, superior steady-state error minimization, and resilience against non-Gaussian and high-frequency noise. The algorithm dynamically adjusts fractional power and step-size parameters, allowing it to adapt to fluctuating signal-to-noise ratios (SNRs) while maintaining stability and efficiency. Comparative analyses with traditional least mean square (LMS) and normalized LMS (NLMS) algorithms demonstrate the significant advantages of RVP-FLMS in terms of faster convergence, reduced oscillatory behavior, and robust performance under diverse noise scenarios. Simulations across SNR levels from 20 dB to 50 dB validate the algorithm’s ability to consistently outperform conventional methods, achieving up to a 35 percent improvement in convergence rates and a 50 percent reduction in steady-state error. The RVP-FLMS algorithm’s flexibility and robustness make it particularly suited for aviation applications, where real-time noise reduction is critical for communication systems, navigation, and signal processing. This chapter underscores the theoretical advances and practical implications of the RVP-FLMS algorithm, offering a transformative approach to adaptive noise reduction in aviation and paving the way for its integration into next-generation aerospace technologies.
  • The Use of the Internet of Things for Active Noise Control Technology
    Suman Turpati, Valli Bhasha, Ajay Roy
    Iot Potential for Green Energy Solutions, 2025
    Active noise cancellation (ANC) is a well-established field that involves the cancellation of noise in the surrounding environment by the generation of anti-noise signals in close proximity to the human ears. This technology is often used in products such as noise cancellation headphones. This research article introduces the integration of wireless communication and acoustics in order to include the Internet of Things (IoT) into active noise cancellation technology. The fundamental concept involves the deployment of IoT device inside a given location, which is designed to capture and process ambient sounds. Subsequently, the device transmits the recorded sound data through its wireless radio connectivity. The use of ANC technology has significant implications not just within the field of IoT, but also across several other domains such as system identification, noise suppression, communication, and signal management, among others. 42Due to the higher velocity of wireless signals compared to sound waves, the auditory apparatus in our ear detects the sound prior to its physical arrival. This provides a view into the future, referred to as look ahead, and is of significant importance for the real-time mitigation of noise, particularly for unpredictable and wide-band auditory stimuli such as music and speech. The aim of this study is to explore the practical uses of the ANC technique and assess the effectiveness of an adaptive algorithm. Additionally, this study presents the use cases of three noise cancelling filters in the context of the IoT. This chapter will conclude with a comprehensive performance evaluation of several ANC approaches, including recursive least square, variable step-size least mean square (LMS), leaky LMS, normalized least mean square, and LMS, based on the data obtained through simulations.
  • Correlation between rate of TEC index and positioning error during solar flares and geomagnetic storms using navigation with Indian constellation receiver measurements
    Katepogu C. T. Swamy, Venkata Ratnam Devanaboyina, Ramamurthy Nallagarla, Towseef Ahmed Shaik, Suman Turpati
    Journal of Applied Geodesy, 2025
    The real-time position accuracy of the Global Navigation Satellite System (GNSS)/Navigation with Indian Constellation (NavIC) receiver is limited by the dynamic behavior of the ionosphere, particularly in adverse conditions like solar flares and geomagnetic storms. The NavIC satellites broadcast dual coherent radio beacon signals on L5 (1,164.5 MHz) and S (2,472.5 MHz) bands for providing position, velocity, and timing services in all weather conditions. The Total Electron Content (TEC) and Rate of TEC Index (ROTI) are the potential indicators for characterizing the ionosphere and its irregularities. In this research work, the TEC and ROTI are computed from the code and carrier phase observations of the NavIC receiver located at Kurnool low latitude station (15.79° N, 78.07° E) with geomagnetic coordinates (7.30° N, 151.65° E). This paper presents a statistical study of TEC, ROTI, and the correlation between ROTI and NavIC positioning error during highly intense solar flares (X9.3 and X2.2) and geomagnetic storm conditions. Compared to quiet days mean TEC, the enhancement is 3 TECU due to X9.3 flares, and the maximum peak of TEC on storm day (September 8, 2017) is 80.92 TECU. Moreover, the correlation coefficient between ROTI and position error is 0.76 on a quiet day (September 4, 2017), 0.54 on an intense solar flares day (September 6, 2017), and 0.24 on a storm day (September 8, 2017), this indicates positional accuracy degradation on a geomagnetic storm day. The outcome of this research work would be helpful for investigating characteristics of the northern low latitude ionospheric irregularities and, in turn, useful for developing suitable ionospheric nowcasting/prediction models for GNSS applications.
  • Multistage Neural Network and Image Processing Based Plant Leaf Disease Diagnosis
    Syed Zahiruddin, Suman Turpati, Ajay Roy, MV Rajasekhar, Abdullatif Hakami, Boggiti Kezia
    2025 International Conference on Sustainability Innovation and Technology Icsit 2025, 2025
    Plants are essential for the human survival, providing needful resources such as food, medicine, clothing, and raw materials. The health of plants is directly correlated with agricultural productivity and global economic stability. However, plant diseases are a constant concern that causes significant losses in agricultural earnings and viability. Conventional disease identification techniques, mainly based on manual visual observation, are often inefficient, subjective, and unrealistic for wide range monitoring. To resolve these limitations, this manuscript proposes a systematic plant disease identification framework utilizing image processing techniques. The method uses k-means clustering to identify sick areas from healthy plant tissue. Next, a Support Vector Machine (SVM) classifier that uses learnt visual cues helps to correctly identify the type of disease. This technology makes it more easier and faster to complete manual exams, and it also makes them more accurate. The suggested method is a scalable and useful strategy to find plant illnesses early on. This can render farming more productive and protect crops from injury. The suggested plan would promote smart farming techniques that use computer vision and machine learning algorithms. This would boost the economy and make sure that everyone has enough food.
  • A Secure Image Encryption Using Shannon's Principles (Confusion and Diffusion)
    Suman Turpati, Shaik Kashif Hussain, Ajay Roy, B Suresh Babu, K Anil Kumar, Abdallah Hammad
    2025 International Conference on Sustainability Innovation and Technology Icsit 2025, 2025
    The development of secure and effective encryption techniques is required due to the widespread use of unsecure channels for multimedia data sharing. This paper suggests a new picture encryption method that combines multiple chaotic maps with the Key Addition and Averaging (KAA) map. The method uses bit diffusion and confusion to achieve encryption by putting Shannon's security concepts into practice. A pair of encryption keys generated from the 2D Logistic Sine Map (LSM), Linear Congruential Generator (LCG), Tent map, and Bernoulli map ensure confusion since the KAA map encourages diffusion. Entropy, NPCR, UACI, correlation coefficients, MSE, PSNR, and MAE will all be used in a thorough mathematical analysis to evaluate the algorithm's resilience to different attacks such as visual, statistical, and brute-force attacks.
  • SAR Image Colorization for Comprehensive Insight Using Deep Learning Model (h)
    Machiraju Jayalakshmi, Suman Turpati, Ajay Roy, L.Lakshmi Prasanna Kumar, Syed Zahiruddin, Abdullatif Hakami
    2025 International Conference on Sustainability Innovation and Technology Icsit 2025, 2025
    This This study investigates the colorization of engineered opening radar (SAR) pictures utilizing profound learning strategies to upgrade their interpretability and logical worth. By utilizing progressed brain network models, we change grayscale SAR pictures into lively, colorized portrayals. Consumers will be visually engaged and able to better understand the content's key arguments and examples through this interaction. The colorization approach considers the unique challenges of SAR imaging. The enhanced photographs are accessible to a wider audience, regardless of their level of technical knowledge, because of their versatility and wide variety of potential applications, such as in ecological monitoring and remote sensing. We show the method's true promise for future study and practical uses in various sectors by comparing its feasibility to industry norms. Finally, our effort intends to remove any obstacle that may exist between simplistic experiences and complicated SAR information.
  • Vector Decimation Harmonic Mean-Based Algorithm for Online Acoustic Feedback Active Noise Control
    Suman Turpati, Ajay Roy, Tathababu Addepalli, Mohammed Alkahtani, Abdullatif Hakami, Ahmad Faiz Minai, Abdallah Hammad
    IEEE Access, 2025
  • Robust Non-Linear Active Function-Based FxRLS for Impulsive Active Noise Control
    Suman Turpati, Ajay Roy, K. C. T. Swamy, Mohammad Khalid Imam Rahmani, Masood Ur Rehman, Sudan Jha, Sultan Ahmad, Jabeen Nazeer, Hikmat A. M. Abdeljaber, Yasser Eid Aljohani
    IEEE Access, 2024
  • Implementation of robust virtual sensing algorithm in active noise control to improve silence zone
    Suman Turpati, Venkatanarayana Moram
    International Journal of Speech Technology, 2023
  • Time-differenced double difference method for measurement of Navigation with Indian Constellation (NavIC) receiver differential phase bias
    K.C.T. Swamy, D. Venkata Ratnam, T. Suman, S. Towseef Ahmed
    Measurement Journal of the International Measurement Confederation, 2023
  • IoT based Social Device Network with Cloud Computing Architecture
    Turpati Suman, S. Kaliappan, L. Natrayan, Dinesh Chandra Dobhal
    Proceedings of the 2023 2nd International Conference on Electronics and Renewable Systems Icears 2023, 2023
  • Smart Notice Board Using the Internet of Things–Based NODEMCU ESP8266
    AI Aided Iot Technologies and Applications for Smart Business and Production, 2023
  • Active Noise Control for PVC Duct Using Robust Feedback Neutralization F×LMS Approach
    Turpati Suman, Moram Venkatanarayana
    Journal of Control Automation and Electrical Systems, 2021
  • Leaf disease detection and classification based on machine learning
    Sandeep Kumar, KMVV Prasad, A. Srilekha, T. Suman, B. Pranav Rao, J. Naga Vamshi Krishna
    Proceedings of the International Conference on Smart Technologies in Computing Electrical and Electronics Icstcee 2020, 2020
  • Improved performance of impulse active noise control using active function threshold with absolute harmonic variable step-size algorithm
    Suman Turpati, Venkatanarayana Moram
    Microprocessors and Microsystems, 2020
  • An efficient simulink model for active noise control using filtered-x adaptive algorithms
    Suman Turpati
    Journal of Advanced Research in Dynamical and Control Systems, 2020
  • Improved convergence rate with modified variant lms algorithms
    Journal of Advanced Research in Dynamical and Control Systems, 2019