Modeling and Performance analysis of Capacitive Pressure Sensor with Hexagonal Diaphragm using COMSOL Multiphysics Rakesh Bhavanasi, Shaik Sai Sadhik, Bhargav Alla, S. Teena Mrudula 2025 5th International Conference on Artificial Intelligence and Signal Processing Aisp 2025, 2025 This study describes the simulation-based design and optimization of a MEMS capacitive pressure sensor with an emphasis on enhancing performance through sophisticated material selection and diaphragm geometry modification. The conventional square diaphragm is replaced with a hexagonal structure to improve stress distribution and deflection uniformity under applied pressure. Molybdenum disulfide (MOS<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>) is utilized as the diaphragm material in parallel because of its exceptional mechanical flexibility and high sensitivity to deformation. COMSOL Multiphysics simulations are used to assess the displacement and capacitance characteristics of the sensor under varied pressure loads. The results indicate that the hexagonal MOS<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>-based diaphragm performs better than traditional designs in terms of consistency and sensitivity, which makes it a viable choice for biomedical and wearable sensing applications.
Multi-Task Learning Approach for Detecting Multiple Tumors Using CT and MRI Images S. Teena Mrudula, D. Jayaprakash Narayana, Sk. Muzammil Shareef, G. Leela Krishna 2025 International Conference on Emerging Smart Computing and Informatics Esci 2025, 2025 This paper presents a multi-task learning approach for detection of brain and lung tumors from CT and MRI images. The Inception v3 model, which is a CNN that has an efficient feature extraction process with low computational complexity, was used to classify and locate tumors. The model utilized its multi-scale feature extraction ability to achieve high accuracy and robustness in tumor region differentiation across different datasets. Preprocessing steps such as normalization and data augmentation were found to enhance model generalization, while segmentation techniques improved the accuracy of localizing tumors. Experimental results show that the model was effective, with over 85% accuracy in tumor classification tasks. The proposed system aims to streamline diagnostic workflows, assist radiologists, and enhance healthcare outcomes by providing reliable, automated tumor detection. Future developments include expanding the system to detect additional tumor types and integrating real-time processing capabilities for clinical deployment..
Internet of things and optimized knn based intelligent transportation system for traffic flow prediction in smart cities Sunkara Teena Mrudula, Meenakshi, Mahyudin Ritonga, S. Sivakumar, Malik Jawarneh, Sammy F, T. Keerthika, Kantilal Pitambar Rane, Bhaskar Roy Measurement Sensors, 2024 The rapid expansion of urban areas and the increasing number of vehicles on the road have resulted in accidents, traffic congestion, economic repercussions, environmental deterioration, and excessive fuel consumption. A dependable traffic management system is necessary to anticipate and regulate urban traffic patterns. Traffic forecast aids in the prevention of traffic issues. Urban traffic predictions often utilise historical and current traffic flow data to forecast road conditions. This article presents a traffic flow prediction system that utilises the Internet of Things (IoT), machine learning, and feature selection. Internet of Things (IoT) devices located on highways or within cars gather sensor data in real-time. The input data set comprises both real-time Internet of Things (IoT) data and historical traffic statistics. The input data is stored in a centralized cloud. The data is subjected to preprocessing in order to eliminate any unwanted interference and identify any exceptional values. The accuracy and root mean square error are contingent upon the process of feature selection. Particle swarm optimization identifies and extracts crucial features from input data. The classification model is constructed using K Nearest Neighbor, Multi layer Perceptron, and Bayes network approaches. The UCI traffic data is used for conducting experiments. The dataset has 47 attributes and 2102 occurrences. The accuracy of traffic flow prediction using PSO KNN is 96 %. The PSO KNN algorithm achieved a Mean Square Error (MSE) of 0.00289 and a Root Mean Square Error (RMSE) of 0.0595.
Adaptive Huffman Coding with Memory Optimization Ramyasri Maturi, Raju Javvadi, Vignesh Naga Manikanta Sunkara, Sunkara Teena Mrudula Proceedings of the 5th International Conference on Smart Electronics and Communication Icosec 2024, 2024 Adaptive Huffman coding (AHC) is an entropy encoding algorithm used for lossless data compression. This paper explores the effectiveness of Adaptive Huffman coding in achieving efficient data compression. The primary goal is to present and analyze the performance of AHC, focusing on its compression efficiency. The study integrates MATLAB code and block diagrams to evaluate the algorithm’s effectiveness. By leveraging parallel processing and optimization techniques, our approach aims to enhance compression efficiency. The paper highlights the significance of AHC in data compression and examines its implementation for improved performance.
Design of Automated Smart Attendance System Using Deep Learning Based Face Recognition Ritonga Mahyudin, Domenic T. Sanchez, S Teena Mrudula, Ravi Kishore Veluri, Jawarneh Malik, Abhishek Raghuvanshi Proceedings of 9th International Conference on Science Technology Engineering and Mathematics the Role of Emerging Technologies in Digital Transformation Iconstem 2024, 2024 The process of taking attendance in the traditional manner is one that is both laborious and time-consuming. Face recognition and identification technology was developed with the primary objective of providing a timesaving automated solution for tracking attendance. This article presents Design of automated smart attendance system using deep learning based face recognition. Camera IoT devices are used to acquire images. These images are stored in cloud via IoT gateway. Images contain many noises. To remove or reduce these noises, adaptive median filter are used. Once noise is removed, then images quality is enhanced by particle swarm optimization. Enhanced images are classified by CNN, CNN VGG 16 and Xception CNN deep learning techniques. Performance is compared on the basis of metrics like- accuracy, specificity and recall. Xception CNN is outperforming other techniques used in the framework. Accuracy, Specificity and Recall rate of Xception CNN is 99.33%, 98.66% and 99.33 percent respectively.
Multiplication free Fast-Adaptive Binary Range Coder using ISW Sunkara Teena Mrudula, K.E. Srinivasa Murthy, M.N. Giri Prasad International Journal of Electrical and Electronics Research, 2023 Data compression is defined as the process of encoding, converting and modifying the bits-structures of data in such a way that reduces less-spaces on the disk. Fast-ABRC, a new context ABRC for compressing the image and video. This paper introduces novel hardware F-ABRC (Fast-adaptive binary range coder) and architecture of VLSI, as it doesn’t have requirement of LUTs (Look-up-Tables) and also it is completely multiplication free. To get the result, we will combine the utilization of simple operation to compute the approximation after encoding every single symbol and the PE (probability estimation) on the basis of ISW (Imaginary Sliding Window) with approximation of the multiplication. We have represented our introduced algorithm, which is faster and in comparison, to the existing model it gives superior compression efficiency and the comparison takes place on the basis of two parameters such as power dissipation (Dynamic and Static) and device utilization.
Optimized Context-Adaptive Binary Arithmetic Coder in Video Compression Standard Without Probability Estimation S.T. Mrudula, K.E. Srinivasa Murthy, M.N. Giri Prasad Mathematical Modelling of Engineering Problems, 2022 CABAC is a Context Adaptive Binary Arithmetic Coder utilized in novel AVC/H.264 of video standard. AC (arithmetic coding) permits important enhancement in the compression. However, the complexity of implementation is main drawback because of slowness and hardware cost. In this paper, we propose the implementation of MPEG4/H-264 AVC against M-decoder without PE (Probability Estimation). Furthermore, in order to estimate an algorithm, we have compared many existing methods, and the comparison takes place based on power dissipation and device utilization.
Experimental Analysis and Improvements of a Visible Spectrophotometer for Detection of Nano Materials Pamula Rajakumari, Polaiah Bojja, Smitha Chowdary Ch, Sunkara Teena Mrudula, Krishnam Raju Putta, Amsalu Gosuadigo International Journal of Chemical Engineering, 2022 As the field of nanotechnology advances, there is an increasing need for green nanomaterial identification devices. Recently, a few new studies have reminded us that as nanotechnology gets better and better, so will natural phenomena. As we grow closer to and finally reach the nanoscale, it is feasible that new physical expertise will develop. Developments in the future may allow for new technical advancements. It is the ability of nanotechnology to construct human constructs at the nanoscale that distinguishes it from other fields of science and engineering. Various components, including high-dissociation electron microscopy, centre-ion beam milling tools, and scanner probes, have made this practical. Spectrometers, sometimes known as spectrometers, are used in fabric identification machines. To conclude this inquiry, a nanoparticle scatter spectrometer was devised and built artificially. This study focused on the visible spectrum of spectroscopy because there are a broad number of programmes available for visible optical instruments.
