Pardhu Thottempudi

Pardhu Thottempudi became a member (M) of IEEE in 2015. Pardhu was born in Luxettipet village in Adilabad district in Telangana state, India. He completed Batchelor’s degree B.tech in the stream of electronics and communication engineering in 2011 from MLR Institute of Technology, Hyderabad, India. He has done his master’s degree M.Tech in embedded systems from Vignan’s University, Vadlamudi in 2013. He is pursuing Ph.D in the stream of RADAR signal processing from VIT University His Research Includes Human Motion Analysis Behind walls using Optimized Deep Learning Algorithms. His major fields of interests include Digital Signal Processing, RADAR communications, embedded systems, and implementation of signal processing on applications in FPGA. He is working as assistant professor of department of Electronics and Communication Engineering in BVRIT HYDERABAD College of Engineering for Women, Hyderabad, India since 2023.

EDUCATION

VIT University, VELLORE- Thesis Submitted (2023)
M.Tech- Vignan University, Vadlamudi- 2013
B.Tech- MLRIT, Hyderabad- 2011
Intermediate-2011
Tenth-2005

RESEARCH, TEACHING, or OTHER INTERESTS

Signal Processing, Artificial Intelligence, Communication

Scopus Publications

Digital health resilience: IoT solutions in pandemic response and future healthcare scenarios
Pardhu Thottempudi, Reddy Madhavi Konduru, Hima Bindu Valiveti, Swaraja Kuraparthi, and Vijay Kumar
Springer Science and Business Media LLC
Abstract This article explores the Internet of Things (IoT), an innovative technical advancement that utilizes the capabilities of billions of sensors across various applications. Sensors are critical to the IoT environment as they collect crucial data for complex analysis. The emergence of the Internet of Things (IoT) and its accompanying sensor technology has significant implications for various fields, including smart urban planning, advanced agriculture, online education, and healthcare. The Internet of Things (IoT) has played a crucial role in tackling worldwide health challenges, notably in the healthcare sector, with a particular emphasis on the recent COVID-19 pandemic. The epidemic has heightened the need for digital and home-based healthcare solutions. The Internet of Things (IoT) enhances healthcare services by providing precise patient monitoring over a unified digital network. This article examines the various uses, technical intricacies, and difficulties that exist in the healthcare field. A thorough investigation was conducted using reputable databases such as Google Scholar, Elsevier, PubMed, ACM, ResearchGate, Scopus, and Springer. Relevant keywords directed the search. The narrative highlights and emphasizes the significant impact of IoT on healthcare, explicitly identifying prospective research areas for doctors, scholars, and researchers to overcome obstacles in the field. As anticipated, the Internet of Things (IoT) serves as a guiding light for improved healthcare delivery. The Survey demonstrates that combining IoT with cutting-edge technology enhances computing capacities, emphasizing IoT’s widespread, advantageous, and extensive nature. To summarise, this discussion examines future difficulties and provides valuable solutions to strengthen the healthcare infrastructure led by the Internet of Things (IoT) during the COVID-19 crisis and future health catastrophes.


Advanced diabetes prediction: A comprehensive analysis of machine learning and deep learning techniques




Deep Kronecker LeNet for human motion classification with feature extraction
Thottempudi Pardhu, Vijay Kumar, and Kalyan C. Durbhakula
Springer Science and Business Media LLC
AbstractHuman motion classification is gaining more interest among researchers, and it is significant in various applications. Human motion classification and assessment play a significant role in health science and security. Technology-based human motion evaluation deploys motion sensors and infrared cameras for capturing essential portions of human motion and key facial elements. Nevertheless, the prime concern is providing effectual monitoring sensors amidst several stages with less privacy. To overcome this issue, we have developed a human motion categorization system called Deep Kronecker LeNet (DKLeNet), which uses a hybrid network.The system design of impulse radio Ultra-Wide Band (IR-UWB) through-wall radar (TWR) is devised, and the UWB radar acquires the signal. The acquired signal is passed through the gridding phase, and then the feature extraction unit is executed. A new module DKLeNet, which is tuned by Spotted Grey Wolf Optimizer (SGWO), wherein the layers of these networks are modified by applying the Fuzzy concept. In this model, the enhanced technique DKLeNet is unified by Deep Kronecker Network (DKN) and LeNet as well as the optimization modules SGWO is devised by Spotted Hyena Optimizer (SHO) and Grey Wolf Optimizer (GWO). The classified output of human motion is based on human walking, standing still, and empty. The analytic measures of DKLeNet_SGWO are Accuracy, True positive rate (TPR), True Negative rate (TNR), and Mean squared error (MSE) observed as 95.8%, 95.0%, 95.2%, and 38.5%, as well as the computational time observed less value in both training and testing data when compared to other modules with 4.099 min and 3.012 s.


High-Performance Real-Time Human Activity Recognition Using Machine Learning
Pardhu Thottempudi, Biswaranjan Acharya, and Fernando Moreira
MDPI AG
Human Activity Recognition (HAR) is a vital technology in domains such as healthcare, fitness, and smart environments. This paper presents an innovative HAR system that leverages machine-learning algorithms deployed on the B-L475E-IOT01A Discovery Kit, a highly efficient microcontroller platform designed for low-power, real-time applications. The system utilizes wearable sensors (accelerometers and gyroscopes) integrated with the kit to enable seamless data acquisition and processing. Our model achieves outstanding performance in classifying dynamic activities, including walking, walking upstairs, and walking downstairs, with high precision and recall, demonstrating its reliability and robustness. However, distinguishing between static activities, such as sitting and standing, remains a challenge, with the model showing a lower recall for sitting due to subtle postural differences. To address these limitations, we implement advanced feature extraction, data augmentation, and sensor fusion techniques, which significantly improve classification accuracy. The ease of use of the B-L475E-IOT01A kit allows for real-time activity classification, validated through the Tera Term interface, making the system ideal for practical applications in wearable devices and embedded systems. The novelty of our approach lies in the seamless integration of real-time processing capabilities with advanced machine-learning techniques, providing immediate, actionable insights. With an overall classification accuracy of 90%, this system demonstrates great potential for deployment in health monitoring, fitness tracking, and eldercare applications. Future work will focus on enhancing the system’s performance in distinguishing static activities and broadening its real-world applicability.


Enhanced Classification of Human Fall and Sit Motions Using Ultra-Wideband Radar and Hidden Markov Models
Thottempudi Pardhu, Vijay Kumar, Andreas Kanavos, Vassilis C. Gerogiannis, and Biswaranjan Acharya
MDPI AG
In this study, we address the challenge of accurately classifying human movements in complex environments using sensor data. We analyze both video and radar data to tackle this problem. From video sequences, we extract temporal characteristics using techniques such as motion history images (MHI) and Hu moments, which capture the dynamic aspects of movement. Radar data are processed through principal component analysis (PCA) to identify unique detection signatures. We refine these features using k-means clustering and employ them to train hidden Markov models (HMMs). These models are tailored to distinguish between distinct movements, specifically focusing on differentiating sitting from falling motions. Our experimental findings reveal that integrating video-derived and radar-derived features significantly improves the accuracy of motion classification. Specifically, the combined approach enhanced the precision of detecting sitting motions by over 10% compared to using single-modality data. This integrated method not only boosts classification accuracy but also extends the practical applicability of motion detection systems in diverse real-world scenarios, such as healthcare monitoring and emergency response systems.


The role of IoT in modern healthcare: Innovations and challenges in pandemic era
Pardhu Thottempudi and Vijay Kumar
IGI Global
This chapter delves into the internet of things, a pivotal technological innovation incorporating sensors across various fields. Sensors in IoT are essential for data collection and crucial for detailed analyticity's impact is profound in developing smart cities, modern agriculture, digital education, and healthcare, particularly noticeable during the COVID-19 pandemic, highlighting the need for digital healthcare solutions. IoT significantly enhances healthcare by enabling precise patient monitoring through a digital network. This study examines the applications of IoT, technological challenges, and barriers in healthcare, drawing on databases like Google Scholar, Elsevier, and PubMed. The discussion underscores the importance of IoT in healthcare, opening avenues for new research and tackling industry challenges. It shows IoT's role in improving healthcare services, combining it with advanced technologies to expand its beneficial impact. The chapter addresses potential challenges and offers strategies to improve IoT-based healthcare, focusing on COVID-19 and future health crises.


Hand gesture recognition in real time
Thottempudi Pardhu, Nagesh Deevi, and N. Srinivasa Rao
AIP Publishing



Revolutionizing wireless communication: A comprehensive study on modern antenna technologies
Pardhu Thottempudi and Vijay Kumar
IGI Global
In the rapidly evolving wireless communication landscape, antenna technology is indispensable. This chapter provides an in-depth review of recent advancements and trends in antenna technology transforming modern wireless communication systems. The authors delve into vital technological innovations, including massive MIMO, beamforming, metamaterial antennas, and reconfigurable intelligent surfaces, shedding light on their functions, potential benefits, and implications. The study underscores that the ongoing evolution in antenna technology holds immense potential to revolutionize wireless communication systems, enabling more efficient, high-speed, and sustainable networks. These insights will benefit communication engineers, researchers, and academicians, offering a broad understanding of antenna technology's current state and future trajectories in wireless communication systems.


EEG Artifact Removal Strategies for BCI Applications: A Survey




Precision Lunar Landscape Unveiled with Terrain Mapping and Advanced Techniques
Thottempudi Pardhu, Hemasree Jonnalagadda, Vijay Kumar, P. Sreeja, P. Bhavana, Ch. Sharon Rose, and N. Venkatesh
IEEE
The study introduces an innovative lunar exploration method employing deep neural networks, specifically Long Short-Term Memory (LSTM), to enhance Terrain Mapping Camera (TMC) data resolution to an unprecedented 5-meter precision. The primary objective is to surpass the capabilities of the Orthogonal High-Resolution Camera (OHRC). The proposed approach will undergo rigorous validation through k-fold cross-validation, aiming to provide unparalleled insights into lunar terrain. Leveraging the refined data, the study aims to create an advanced hazard map, promising a revolutionary impact on mission planning and ensuring safer lunar navigation. Going beyond, the methodology extends to real-time navigation techniques using the enhanced TMC dataset. By integrating high-resolution techniques, hazard mapping, real-time navigation, and robust model validation, this approach has the potential to establish a new benchmark for lunar missions. The anticipated outcomes may significantly advance our understanding of lunar landscapes, enhancing safety protocols and navigation strategies for future lunar exploration endeavors.


Advancements in UWB Based Human Motion Detection Through Wall: A Comprehensive Analysis
Thottempudi Pardhu, Vijay Kumar, Praveen Kumar, and Nagesh Deevi
Institute of Electrical and Electronics Engineers (IEEE)



Human motion classification using Impulse Radio Ultra Wide Band through-wall RADAR model
Thottempudi Pardhu and Vijay Kumar
Springer Science and Business Media LLC



Leveraging IoT and machine learning for improved health prediction systems
Pardhu Thottempudi
IGI Global
Machine learning (ML) is a powerful tool that unveils hidden insights from internet of things (IoT) data. These technologies enhance decision-making in education, security, business, and healthcare. In healthcare, they automate tasks such as maintaining records, predicting diagnoses, and monitoring patients in real time. However, different ML algorithms perform differently on various datasets, influencing results and clinical decisions. Understanding these ML algorithms and their application in handling IoT data in healthcare is crucial. This chapter highlights key ML algorithms for classification and prediction, providing an in-depth overview of their role in analyzing IoT medical data. The analysis reveals that different ML prediction algorithms have unique limitations, necessitating careful selection based on the dataset type for accurate healthcare predictions. The chapter also illustrates the use of IoT and ML in predicting future healthcare trends.


Face detection and recognition through live stream
Pardhu Thottempudi
IGI Global
This chapter delves into the development of a machine-based project aimed at detecting and identifying human faces, a process known as face recognition. This process not only discerns human faces but also determines whether the face is familiar or unfamiliar, with advanced iterations even providing the identified individual's name. OpenCV, a tool within the realm of image processing, is utilized to ascertain the detected face. The implementation of this method occurs in two phases: training and testing. The project's design primarily incorporates live-stream face detection, feature extraction, and recognition of detected faces from a stored database. This technology has potential applications in criminal identification and security surveillance in police investigations. The machine-based nature of the project significantly reduces the likelihood of errors compared to manual recognition. The chapter concludes by comparing the detected and processed faces with a database to recognize familiar faces, thereby verifying the individual's real identity.


Extraction and Matching of Fingerprint Features
Pardhu Thottempudi and Nagesh Deevi
IGI Global
The main objective of the project is to implement fingerprint recognition because it is one of the most popular and reliable methods for human identification and because it makes use of minutiae, which are unique features found in fingerprints. The fingerprint is another type of biometric that is employed to recognize individuals and verify their identities. Extraction of information from fingerprint scans is among the most important steps in fingerprint recognition and classification. The proposed approach for the project relies on utilizing a variety of methods and algorithms to identify fingerprints using the ROI method (i.e., threshold & centroid algorithm). Two human fingerprints can be compared using ROI to determine which has more detail. The main method for highlighting the minute details of the sample fingerprint's fingerprint is FFT extraction. A percentage score is produced as a result of the minute data, and it indicates whether or not two fingerprints match. It was written in MATLAB code.


Remote health prediction system: A machine learning-based approach
Pardhu Thottempudi, Nagesh Deevi, Amy Prasanna T., Srinivasarao N., and Mahesh Babu Katta
IGI Global
One of the many applications of machine learning in healthcare is the analysis of large amounts of data to reveal new therapeutic insights. Once doctors have this data, they can better serve their patients. Therefore, satisfaction can be raised by using deep learning to enhance the quality of care provided. This work aims to integrate machine learning and AI in healthcare into a single system. Predictive algorithms based on machine learning could revolutionize healthcare by allowing doctors to avoid unnecessary treatments. Various libraries, including those for machine learning algorithms, were used to develop this work. Because of its extensive library and user-friendliness, Python has emerged as the preferred language. syntax. The authors used various classification techniques to train machine learning models and then select the one that provided the best balance between accuracy and precision while avoiding prediction error and autocorrelation problems, the two main causes of bias and variance.


Design and implementation of automatic hand sanitizer dispenser using Arduino and ultrasonic sensor
D. V. S. Chandrababu, Pardhu Thottempudi, Ch. Babaiah, and G. Koushik
AIP Publishing



Self-adaptive TLC using verilog HDL
D. V. S. Chandrababu, Pardhu Thottempudi, Ch. Babaiah, and G. Koushik
AIP Publishing



A General Regression Neural Network based Blurred Image Restoration
Sreedhar Kollem, Katta Ramalinga Reddy, Sreejith S, Ch Rajendra Prasad, Srinivas Samala, and Thottempudi Pardhu
IEEE
Image distortion may result from a variety of factors, such as changes in electronic imaging equipment that create noise. The goal of blur image alignment is to determine which images are blurred and to restore them. Due to this, a unique blurred image restoration approach is developed, which consists of a point spread function, canny edge detection, GLCM extraction, and general regression neural network for identifying the type of blurred images, and a wiener filter for restoring the image. This technique uses a combination of the General Regression Neural Network (GRNN) and Deep Neural Network (DNN) to detect the kind of a blur and determine the calibration efficiency of the DNN and the degradation efficiency of the GRNN. The mean square error (MSE), the covariance factor, and the peak signal-to-noise ratio (PSNR) are used to assess the effectiveness of the proposed method. In comparison to conventional procedures, the proposed method yields superior outcomes.


Recognition of Moving Human Targets by Through the Wall Imaging RADAR Using RAMA and SIA Algorithms
Pardhu Thottempudi, Venkata Surya Chandra Babu Dasari, and Venkata Surya Prasad Sista
Springer Singapore



Experimental study of through the wall imaging for the detection of vital life signs using SFWR
Pardhu Thottempudi and Vijay Kumar
Institute of Advanced Engineering and Science
<p>Now a day’s defence applications associated to novel, army and military war fields are required wall imaging discrimination. As of now many wallimaging techniques are designed but didn’t identify the vital signs behind walls with accurate working. Therefore, a novel advance wall image tracking method is required identification of human target. An experimental study on through the wallimaging (TWI) to detect the life signs using sweep frequency continuous wave radar (SFCWR) is explained in this paper. The proposed system consists of agilent vector network analyzer (VNA) (Agilent E5071B ENA), horn antenna and a computer. The information of heart beat and the breathing can be a shift identification routine was used to collect information from the back scattering electric current. The outcomes of the procedure give the information of heart beat and breathing signs of real human being.</p>


Novel implementations of clutter and target discrimination using threshold skewness method
Thottempudi Pardhu and Vijay Kumar
International Information and Engineering Technology Association
Now a day’s defence applications associated to novel, army and military war fields are required wall imaging discrimination. As of now many wall-imaging techniques are designed but cannot discriminate the target and clutter with accurate working. Therefore, a novel advance wall image tracking method is required for differentiate the clutter and human target. In this research work single value decomposition technique is used to estimate the range bin behind the wall target. In order to track the target and clutter single-value-decomposition (SVD) is not sufficient, so that along this SVD, threshold skewness (TS) method has been presented. Combination of SVD-TS giving the accurate long range-bin sensing and directed the human’s targets. SVD-TS method is a statistical scheme, which can realise the amplitude ranges through large number of range-bin scans. This technique improves the accuracy by 98.6%, skewness by 8%, and normalised power by 98.9%. These SVD-TS method is more efficient and compete with existed techniques.


Design methodology to check the quality of the image in a mobile environment - State of the art
K Jyothi, Thottempudi Pardhu, R Karthik, and T S Arulananth
IEEE
This paper presents a methodology to measure the level of focus and to identify the areas of the skin with high levels of Specular-reflection that are uploaded to the BTBP mobile application for skin analysis. Measuring this information makes it possible to give the user feedback on the quality of their image capture, and how to improve future captures. Specular-Reflection is excessive reflection from the skin's surface has a detrimental effect on skin analysis. In order to avoid such problems we need to identify areas of intense Specular-Reflection and eliminate those areas from analysis. The concept used here is histogram analysis. Each time an image is captured, the camera's auto-focus is used to try and achieve optimal focus. Occasionally this can fail and result in an image that is out-of-focus and therefore undesirable In order to reject these images, focus level quantification is necessary. Referring to the edge information of the objects in the image, calculate the level of sharpness in the image. Filtering and image enhancement concepts will be used for this implementation. This will be implemented using the C# platform and will utilize the Open CV library. This includes learning about Image declarations, image data reading and assigning in C#.


An investigation on human identification behind the wall




Seperation of music and voice based on repeating pattern

RECENT SCHOLAR PUBLICATIONS

Resilient object detection for autonomous vehicles: Integrating deep learning and sensor fusion in adverse conditions
P Thottempudi, ABB Jambek, V Kumar, B Acharya, F Moreira
Engineering Applications of Artificial Intelligence 151, 110563 2025



Advanced Diabetes Prediction: A Comprehensive Analysis of Machine Learning and Deep Learning Techniques
T Pardhu, AB Pattan, V Kumar, U Desai, B Acharya
Decision Support System for Diabetes Healthcare: Advancements and 2025



Digital health resilience: IoT solutions in pandemic response and future healthcare scenarios
P Thottempudi, RM Konduru, HB Valiveti, S Kuraparthi, V Kumar
Discover Sustainability 6 (1), 144 2025



Deep Kronecker LeNet for human motion classification with feature extraction
T Pardhu, V Kumar, KC Durbhakula
Scientific Reports 14 (1), 29102 2024



High-performance real-time human activity recognition using machine learning
P Thottempudi, B Acharya, F Moreira
Mathematics 12 (22), 3622 2024



Enhanced classification of human fall and sit motions using ultra-wideband radar and hidden markov models
T Pardhu, V Kumar, A Kanavos, VC Gerogiannis, B Acharya
Mathematics 12 (15), 2314 2024



Hand gesture recognition in real time
T Pardhu, N Deevi, NS Rao
AIP Conference Proceedings 3028 (1) 2024



Precision Lunar Landscape Unveiled with Terrain Mapping and Advanced Techniques
T Pardhu, H Jonnalagadda, V Kumar, P Sreeja, P Bhavana, CS Rose, ...
2024 6th International Conference on Energy, Power and Environment (ICEPE), 1-7 2024



Advancements in UWB Based Human Motion Detection Through Wall: A Comprehensive Analysis
T Pardhu, V Kumar, P Kumar, N Deevi
IEEE Access 2024



EEG Artifact Removal Strategies for BCI Applications: A Survey
P Thottempudi, V Kumar, N Deevi
Majlesi Journal of Electrical Engineering 18 (1) 2024



The Role of IoT in Modern Healthcare: Innovations and Challenges in Pandemic Era
P Thottempudi, V Kumar
Technologies for Sustainable Healthcare Development, 57-80 2024



Revolutionizing Wireless Communication: A Comprehensive Study on Modern Antenna Technologies
P Thottempudi, V Kumar
Radar and RF Front End System Designs for Wireless Systems, 133-175 2024



Human motion classification using Impulse Radio Ultra Wide Band through-wall RADAR model
T Pardhu, V Kumar
Multimedia Tools and Applications 82 (24), 36769-36791 2023



Sustainable Science and Intelligent Technologies for Societal Development
BK Mishra
IGI Global 2023



Self-adaptive TLC using verilog HDL
DVS Chandrababu, P Thottempudi, C Babaiah, G Koushik
AIP Conference Proceedings 2492 (1) 2023



Design and implementation of automatic hand sanitizer dispenser using Arduino and ultrasonic sensor
DVS Chandrababu, P Thottempudi, C Babaiah, G Koushik
AIP Conference Proceedings 2492 (1) 2023



Remote health prediction system: a machine learning-based approach
P Thottempudi, N Deevi, MB Katta
Multi-Disciplinary Applications of Fog Computing: Responsiveness in Real 2023



Leveraging IoT and Machine Learning for Improved Health Prediction Systems
P Thottempudi
Sustainable Science and Intelligent Technologies for Societal Development 2023



Face Detection and Recognition Through Live Stream
P Thottempudi
Sustainable Science and Intelligent Technologies for Societal Development 2023



Extraction and Matching of Fingerprint Features
P Thottempudi, N Deevi
Intelligent Engineering Applications and Applied Sciences for Sustainability 2023

MOST CITED SCHOLAR PUBLICATIONS

Digital image watermarking in frequency domain
T Pardhu, BR Perli
2016 International Conference on Communication and Signal Processing (ICCSP 2016
Citations: 31



Reduction of clutter using TWI ultra wideband imaging
T Pardhu, V Kumar
International Journal of Ultra Wideband Communications and Systems 3 (2 2016
Citations: 11



Design of matched filter for radar applications
T Pardhu, AK Sree, K Tanuja
Electrical and Electronics Engineering: An International Journal (ELELIJ) Vol 3 2014
Citations: 10



A low power flash ADC with Wallace tree encoder
T Pardhu, S Manusha, K Sirisha
2014 Eleventh International Conference on Wireless and Optical 2014
Citations: 10



An investigation on human identification behind the wall
T Pardhu, V Kumar
J. Adv. Res. Dyn. Control Syst 10 (5), 122-129 2018
Citations: 8



Human motion classification using Impulse Radio Ultra Wide Band through-wall RADAR model
T Pardhu, V Kumar
Multimedia Tools and Applications 82 (24), 36769-36791 2023
Citations: 7



A general regression neural network based blurred image restoration
S Kollem, KR Reddy, CR Prasad, S Samala, T Pardhu
2022 Fourth International Conference on Emerging Research in Electronics 2022
Citations: 7



Implementation of TWI using UWB radar signals
T Pardhu, V Kumar
International Conference on Recent Trends in Engineering, Science 2016
Citations: 6



Design and simulation of digital frequency meter using VHDL
T Pardhu, S Harshitha
2014 International Conference on Communication and Signal Processing, 706-710 2014
Citations: 5



Generation of cryptographically secured pseudo random numbers using FPGA
P Thottempudi, NT Thottempudi, KN Bhushan, UR Neelakuditi
International journal of Electronics Communication Engineering and 2014
Citations: 5



Design of obstacle detection system for visually challenged people
T Pardhu, DVS Chandra Babu, E Amareshwar
International journal of recent technology and engineering 8 (5), 5-8 2020
Citations: 4



Novel random sequence generation and validation using fpga
T Pardhu, UR Nelakuditi, P Suresh
ICCNASP 6, 295-298 2013
Citations: 4



Advancements in UWB Based Human Motion Detection Through Wall: A Comprehensive Analysis
T Pardhu, V Kumar, P Kumar, N Deevi
IEEE Access 2024
Citations: 3



Experimental study of through the wall imaging for the detection of vital life signs using SFWR
VK Pardhu Thottempudi
Indonesian Journal of Electrical Engineering and Computer Science 24 (2 2021
Citations: 3



Novel implementations of clutter and target discrimination using threshold skewness method.
T Pardhu, V Kumar
Traitement du Signal 38 (4), 1079-1085 2021
Citations: 3



Low Power VLSI Compressors for Biomedical Applications
T Pardhu, S Manusha, K Sirisha
CS & IT Conference Proceedings 4 (7) 2014
Citations: 3



High-performance real-time human activity recognition using machine learning
P Thottempudi, B Acharya, F Moreira
Mathematics 12 (22), 3622 2024
Citations: 2



Face Detection and Recognition Through Live Stream
P Thottempudi
Sustainable Science and Intelligent Technologies for Societal Development 2023
Citations: 2



Recognition of moving human targets by through the wall imaging RADAR using RAMA and SIA algorithms
P Thottempudi, VSCB Dasari, VSP Sista
Advanced Techniques for IoT Applications: Proceedings of EAIT 2020, 544-563 2022
Citations: 2



Manusha
T Pardhu
S2, Katakam Sirisha.“A Low Power Flash ADC with Wallace Tree Encoder, 978-1 2014
Citations: 2

RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)

1.Power Efficient Compressor Using Full Adder Circuit
Inventor: Thottempudi Pardhu
Status: Published on 29/08/2014 pp:60
Application Number:3975/CHE/2014

2. WEED IDENTIFYING ROVER
Inventor: Thottempudi Pardhu
Status: Issued06/10/2021
Design Number: 347292-001

3.ARTIFICIAL INTELLIGENCE BASED HUMANOID ROBOT FOR SURVILLANCE AND SECURITY
Inventor: Thottempudi Pardhu
Status: Case is Amended with Controller
Application Number:377792-001

4.VARIABLE RATING ACCUMULATOR CHARGING STATION WITH TOOLS BOX
Inventor: Thottempudi Pardhu
Status: Granted
Design Number:6270282 (UK Design Patent)

5.DESIGN OF SENTRY ROBOT FOR SURVEILLANCE AND SECURITY
Inventor: Thottempudi Pardhu
Status: Granted
Design Number:6272417 (UK Design Patent)