SATHEESH.S

EDUCATION

M.E. (

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Artificial Intelligence

Scopus Publications

Mathematical model for swarm optimization in multimodal biomedical images
Swarm Optimization for Biomedical Applications, 2025
Bivariate Bicycle Code-Enhanced Quantum Consciousness Networks: A qLDPC Framework for Brain-Quantum Interface Computing with 200-Logical-Qubit Scalability
B Dinesh, M. Thangamani, S. Tamizharasu, S. Satheesh, M. Moorthy, M. Anandraj
Proceedings of International Conference on Sustainable Communication Networks and Application Icscn 2025, 2025
This paper introduces a computer science framework for large-scale brain–quantum interface computing by integrating bivariate bicycle code quantum low-density parity-check (qLDPC) codes with a neural spike encoding pipeline. The proposed system achieves 200 logical qubits using sparse circulant constructions, maintaining fault tolerance and constant encoding rate under realistic noise models. We present a mathematical mapping from real-time neural spike streams to logical registers, implement a hardware-aware belief-propagation decoder, and optimize quantum circuit placement for minimal routing overhead. Real-time emulation using live neural surrogate data and calibrated quantum noise confirms a logical error probability below 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−8</sup> per cycle and end-to-end information retention exceeding 99.7%. These results demonstrate the feasibility and scalability of robust, low-latency neural signal processing on quantum hardware, paving the way for dependable quantum brain–computer interfaces.
Deep Learning-Driven Novel Energy Conversion Frameworks for Smart Grid Applications
Venkataramana Guntreddi, S. Satheesh, Badugu Praveena, Neves Binza Tunga, Hushein R, Dillip Kumar Sahoo
13th IEEE International Conference on Smart Grid Icsmartgrid 2025, 2025
A deep learning-driven approach is advanced by researchers in order to increase energy conversion rate and energy efficiencies in environments of smart grids. The proposed system includes state-of-the art models like CNNLSTM, BiLSTM-AE, and Transformers to accurately forecast both photovoltaic(PV) and wind power generation figures. The Transformer model proved superior showing an RMSE of 1.84, MAE of 1.41, and MAPE of 3.97 in PV forecasting. Energy storage management was another area for exploration. Compared to traditional methods, DL-based adjustment algorithms could lead to battery state of charge (SOC) becoming more stable. Responding to the needs of the power grid with simulations showed large peak shaving and valley filling operations--these helped balance loads and reduce stress on the system,Demand response simulations clearly showed significant peak-shaving and valley-filling operations that could balance the load and reduce grid stress. In addition, DL-driven optimization improved the energy conversion efficiency of PV, wind and hybrid systems to reach 92.6%; at the same time it also enhanced grid stability by restraining voltage and frequency deviations and harmonics impendence. Economic assessments showed that this proposed approach can produce significant benefits in financial terms, with savings of more than $25000 available through energy conservation, lower costs for batteries and penalties averted. In conclusion, the DL-enabled framework not only boosts prediction and control but also upgrades reliability of the grid economic performance--meaning that this is a leading candidate for next generation smart grid deployment.
TinyML on Microcontrollers: Enabling Energy-Efficient, Real-Time, Privacy-Preserving Incremental Learning for Embedded Systems
Govarthan V, M. Thangamani, R. Aarthi, S. Satheesh, M. Moorthy, Kavitha V. Kakade
Proceedings of International Conference on Sustainable Communication Networks and Application Icscn 2025, 2025
TinyML is transforming embedded systems by enabling machine learning models to perform real-time inference and incremental learning directly on highly constrained microcontrollers. This paper explores algorithms and architectures that empower microcontrollers with TinyML capabilities, focusing on resource-efficient incremental model updates without cloud dependency. We propose a lightweight framework enabling on-device real-time adaptation and demonstrate its efficacy on standard sensor datasets. Experimental results highlight up to 2% improvement in accuracy and approximately 65% reduction in energy consumption compared to traditional cloud-dependent approaches, validating the efficacy of the proposed on-device learning framework.
Integrating Deep Learning into VLSI Technology: Challenges and Opportunities
Veera Boopathy E, Sasikala C, Vigneash L, Satheesh S, Gomalavalli R, Rajeshwaran K
International Research Journal of Multidisciplinary Scope, 2024
This paper conducts a comprehensive review and analysis of the difficulties and possibilities related to integrating deep learning algorithms into the future of VLSI design and technology. The area of integrated circuit design is becoming increasingly complex as transistors become smaller and the expectations for enhanced reliability and environmental sustainability increase. Analysts are looking into novel techniques that involve deep learning, as traditional techniques find it challenging to tackle these issues. In particular, deep neural networks possess the ability to improve various aspects of integrated circuit design, including timing assessment, layout enhancement, fault detection, and energy utilization minimization. Deep learning has become a viable solution for addressing a range of VLSI challenges, providing opportunities for automated processes, enhancement, and creativity at several phases of the development and fabrication cycle. The incorporation of deep learning into system acceleration, identifying defects, layout synthesis, and future repairs is investigated in this article. It also draws attention to the challenges and opportunities associated with incorporating neural networks into VLSI, highlighting the necessity of multidisciplinary cooperation and creativity to realize their maximum potential. By surmounting these challenges and capitalizing on the prospects presented by deep computing, the integrated circuit sector might unleash unprecedented heights of efficiency, productivity, and inventiveness in integrated circuit innovations.
Secure medical sensor monitoring framework using novel optimal encryption algorithm driven by Internet of Things
J. Lekha, K. Sandhya, Uriti Archana, Chunduru Anilkumar, Saini Jacob Soman, S. Satheesh
Measurement Sensors, 2023
Recently, healthcare monitoring systems have emerged as significant tolls for constant monitoring of patient's physiological characteristics. These systems use implanted sensors. IoT (Internet of Things) have revolutionized healthcare systems where health care equipment's are equipped with many sensors that actively collect data from patients and pass it on to cloud based storages using gateway sensors. Securing data have been significant barriers in many applications as false information get injected, or important information are modified or stolen at different phases of health care systems dependent on IoT. The attacks can also result in fatalities making it imperative to secure IoT based health care systems. A Hybrid technique combining MOAES (Modified Optimal Advanced Encryption Standard) with CM (Chaotic Map) Encryptions called HMOAES-CM technique is proposed. This technique can be helpful in securely accessing the patient data over online mode, and in addition, the data sharing can be performed in an encrypted form for the necessary targets of stakeholders. The proposed authentication approach is aimed at IoT, which is resilient to all kinds of network attacks and its implementation is also simpler. Comparing the suggested work to similar works, the level of evaluation is much improved.
GLCM Features and Fuzzy C Means Clustering-Based Brain Tumor Detection in MR Images
Bhuvaneswari S, Surendiran R, Satheesh S, Kavitha V Kakade, Thangamani M, Thangaraj P
Ssrg International Journal of Electronics and Communication Engineering, 2023
Identifying and categorizing human brain tumors are labour-intensive activities, yet they are crucial for any doctor. There is a growing trend toward using computer-assisted diagnosis (CAD) to improve diagnostic abilities and raise detection accuracy to the highest possible levels. Variability in picture modality, contrast, tumor kind, and other characteristics makes brain tumor segmentation a difficult problem to solve despite years of study. Though there are many excellent works accessible, there is still a need for the development of efficient and precise ways for tumor segmentation with MR brain images. To address this gap in the literature, researchers have developed a new method for detecting human brain cancers that combines a template-based K-means (TK) algorithm, superpixels, and principal component analysis (PCA) to achieve faster detection rates while requiring less processing time overall. Super pixels and PCA can be utilized to identify the most useful information for the early detection of brain cancers. Then, a filter is used on the enhanced image to boost precision. Finally, the TK-means grouping technique is considered for subdividing the pictures for brain tumor identification. Super pixel-based feature extraction, on the other hand, leads to subpar segmentation results since it relies on region-based feature calculation rather than attempting to extract every possible feature from the brain pictures. Once the images have been improved by converting colour into grayscale, the Gray Level Co-Occurrence Matrix (GLCM) approach is utilized to extract the five statistical texture parameter features. Reduce the file size of an image by using a dimensionality reduction technique like the Independent Component Analysis (ICA) model. Finally, brain tumors are identified, and their segmentation is performed with the help of Fuzzy C Means clustering (FCM). The results obtained from a broad set of images further demonstrate the usefulness of the suggested model for recognizing the sizes and shapes of brain tumors.
Construction and Integration of Knowledge Grid in Agricultural Information Management Services
Rajasekhara Babu L, Thangamani M, Surendiran R, Ganthimathi M, Gomathi B, Satheesh S
International Journal of Engineering Trends and Technology, 2023
Agriculture is a major employment source in the world. In India, 55% of the population is employed in the agriculture and allied sectors. The Gross Domestic Production (GDP) contribution of agriculture is 15% levels. Managing crops, soil, climate, irrigation, fertilisers, disease, pest, market, and trade information is essential to guide the farmers and other industries. Data collection, analysis, organisation and presentation are the key operations of the knowledge management structures. The knowledge grid is a graph or network formed by element entities and relational links between element entities. The concepts, events and relationships are represented in the knowledge grids. The schema layer and data layers are used in the knowledge grids.The knowledge representation, extraction, fusion and reasoning operations are applied knowledge grid models. The crop disease and pest information are managed under the knowledge grids. The knowledge grid is utilised with expert structures and crop query answering models. The Agriculture Information Management Services (AIMS) are building with knowledge grids. The knowledge grid construction process is enhanced with crop, soil, season, fertiliser and disease and pest information. Food manufacturing was hypercritical action in which every single country desired to have their own sustenance. Our country, India, is the largest Autotroph of the nutrition corpuscle in the biosphere. In our country, close to seventy percentage of agricultural family stagnant be contingent on farming for their living. Being farm growers blessed mostly essential in our country by way of agriculturalists making a huge elect-vote group which leaders challenge, not spoil. All together, Administrations are necessary to stabilise the involvement of agriculturalists with patrons, the mediator, and then the social group at huge. The entire farming body is extremely statistics serious. Even with tremendous information gathering and quantities from different administration areas, proceed to be statistics gaps. In this section, sensing the Societal Statistics Organization Supporting structure will assist in examining the agronomic segment and modifying the similar using a holistic approach. The automatic knowledge extraction, knowledge map quality enhancement and entity alignment methods are combined in the knowledge grid process. The Machine Learning (ML) based crop pest prediction models are integrated with the knowledge grids. The Java language and MongoDB are used for the structure development process.
Brain Tumor Detection in MRI Images using Convolutional Neural Network Technique
Tamilaruvi R, Vijayalakshmi R, Ganthimathi M, Surendiran R, Thangamani M, Satheesh S
Ssrg International Journal of Electrical and Electronics Engineering, 2022
A brain tumor is a type of cancer that is difficult to detect. As a result, it is more important for care to evaluate nodules swiftly and appropriately for both men and women. As a result, numerous approaches for detecting brain tumors in their early stages have been developed. A comparative comparison of multiple strategies based on machine learning and deep learning for brain tumor identification has been offered in this procedure. There have been far too many approaches for diagnosing brain tumors developed in recent years, the majority of which rely on MRI images. In addition, several classifier methods are used in conjunction with threshold segmentation algorithms to locate tumors using picture recognition. MRI gray scale images have been discovered to be more suitable for obtaining accurate results because of this method. As a result, most MRI scan images are used to detect tumors in the brain. Furthermore, the findings obtained from approaches based on machine learning and deep learning techniques were more accurate than those obtained from methods based on traditional deep learning techniques. The deep learning method was proposed using the Convolutional neural network to predict the outcome with high accuracy.
MIS, MVC and MDS in Cyclic Layered Graphs
Bhadrachalam Chitturi, Srijith Balachander, Sandeep Satheesh, Krithic Puthiyoppil
Proceedings of the 8th International Advance Computing Conference Iacc 2018, 2018
Graphs are discrete objects with myriad applications in science and engineering. Several graph theoretic problems are shown to be hard. However, for restricted versions of graphs based on the type of restriction the problems that are hard to solve for a general graph become tractable. Layered graphs have been defined and are shown to have applications in social networks and computational molecular biology. We define a new class of graphs called cyclic layered graphs that are related to layered graphs. We pose three problems that can be modeled as graph theoretic problems on cyclic layered graphs. We design efficient algorithms for these problems.
Layered graphs: Applications and algorithms
Bhadrachalam Chitturi, Srijith Balachander, Sandeep Satheesh, Krithic Puthiyoppil
Algorithms, 2018

RECENT SCHOLAR PUBLICATIONS

TinyML on Microcontrollers: Enabling Energy-Efficient, Real-Time, Privacy-Preserving Incremental Learning for Embedded Systems
V Govarthan, M Thangamani, R Aarthi, S Satheesh, M Moorthy, ...
2025 International Conference on Sustainable Communication Networks and … , 2025
2025.0
Citations: 3
Bivariate Bicycle Code-Enhanced Quantum Consciousness Networks: A qLDPC Framework for Brain–Quantum Interface Computing with 200-Logical-Qubit Scalability
B Dinesh, M Thangamani, S Tamizharasu, S Satheesh, M Moorthy, ...
2025 International Conference on Sustainable Communication Networks and … , 2025
2025.0
Mathematical Model for Swarm Optimization in Multimodal Biomedical Images
M Thangamani, S Satheesh, R Lingisetty, S Rajendran, BD Shivahare
Swarm Optimization for Biomedical Applications, 86-107 , 2025
2025.0
Citations: 25
Integrating Deep Learning into VLSI Technology: Challenges and Opportunities
SC S.Satheesh, Dr.Vigneash
International Research journal of Multidisciplinary Scope (IRJMS) 5 (4), 689 … , 2024
2024.0
Secure medical sensor monitoring framework using novel optimal encryption algorithm driven by Internet of Things
J Lekha, K Sandhya, U Archana, C Anilkumar, SJ Soman, S Satheesh
Measurement: Sensors 30, 100929 , 2023
2023.0
Citations: 9
Brain Tumor Detection in MRI Images using Convolutional Neural Network Technique
R Tamilaruvi, R Vijayalakshmi, M Ganthimathi, R Surendiran, ...
SSRG International Journal of Electrical and Electronics Engineering 9 (12 … , 2022
2022.0
Citations: 7
Construction and Integration of Knowledge Grid in Agricultural Information Management Services
LR Babu, M Thangamani, R Surendiran, M Ganthimathi, B Gomathi, ...
GLCM Features and Fuzzy C Means Clustering-Based Brain Tumor Detection in MR Images
S Bhuvaneswari, R Surendiran, S Satheesh, KV Kakade, M Thangamani, ...
Citations: 1

MOST CITED SCHOLAR PUBLICATIONS

Mathematical Model for Swarm Optimization in Multimodal Biomedical Images
M Thangamani, S Satheesh, R Lingisetty, S Rajendran, BD Shivahare
Swarm Optimization for Biomedical Applications, 86-107 , 2025
2025.0
Citations: 25
Secure medical sensor monitoring framework using novel optimal encryption algorithm driven by Internet of Things
J Lekha, K Sandhya, U Archana, C Anilkumar, SJ Soman, S Satheesh
Measurement: Sensors 30, 100929 , 2023
2023.0
Citations: 9
Brain Tumor Detection in MRI Images using Convolutional Neural Network Technique
R Tamilaruvi, R Vijayalakshmi, M Ganthimathi, R Surendiran, ...
SSRG International Journal of Electrical and Electronics Engineering 9 (12 … , 2022
2022.0
Citations: 7
TinyML on Microcontrollers: Enabling Energy-Efficient, Real-Time, Privacy-Preserving Incremental Learning for Embedded Systems
V Govarthan, M Thangamani, R Aarthi, S Satheesh, M Moorthy, ...
2025 International Conference on Sustainable Communication Networks and … , 2025
2025.0
Citations: 3
GLCM Features and Fuzzy C Means Clustering-Based Brain Tumor Detection in MR Images
S Bhuvaneswari, R Surendiran, S Satheesh, KV Kakade, M Thangamani, ...
Citations: 1
Bivariate Bicycle Code-Enhanced Quantum Consciousness Networks: A qLDPC Framework for Brain–Quantum Interface Computing with 200-Logical-Qubit Scalability
B Dinesh, M Thangamani, S Tamizharasu, S Satheesh, M Moorthy, ...
2025 International Conference on Sustainable Communication Networks and … , 2025
2025.0
Integrating Deep Learning into VLSI Technology: Challenges and Opportunities
SC S.Satheesh, Dr.Vigneash
International Research journal of Multidisciplinary Scope (IRJMS) 5 (4), 689 … , 2024
2024.0
Construction and Integration of Knowledge Grid in Agricultural Information Management Services
LR Babu, M Thangamani, R Surendiran, M Ganthimathi, B Gomathi, ...