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Identification of Fiber Type Proportions in Gastrocnemius Lateralis and Soleus Muscles Using Surface EMG Signals and Concentration Measure Derived From Rihaczek Distribution

Identification of Fiber Type Proportions in Gastrocnemius La...

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2025 International Conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： Nair, Remya R Sidharth, N. Venugopal, G. Biomedical Instrumentation and Signal Processing Lab Department of Instrumentation and Control Engineering Kerala Palakkad India Department of Electronics and Communication Engineering Kerala Palakkad India

ISBN: (纸本)9798331507541

Fiber type proportion influences the fatigue characteristics and force production in skeletal muscles. In this study, surface electromyography signals and concentration measure derived from Rihaczek distribution are used to identify the fiber type proportions in gastrocnemius lateralis (GL) and soleus (SOL) muscles. Signals are acquired using standard protocols until the subjects experience muscle fatigue and are processed using Rihaczek distribution. Further, a comparative study is performed to analyze the advantages of energy-based features over complexity, statistical, and spectral frequency-based features in analyzing the fiber type proportions. Features such as renyi information, spectral skewness, spectral flatness, and peak frequency are also extracted from the distributions of non-fatigue (NF) and fatigue (F) segments. Results indicate that the extracted features showed a decrease during fatigue in the signals of GL and an increase in the case of SOL, reflecting their distinct fiber composition. The highest statistical significance of p equal to 4.26E-05 and 6.47E-05 respectively is obtained using concentration measure for differentiating the signals of GL and SOL during NF and F states. The lowest significance is observed for renyi information in discriminating the signals. The energy-based features like concentration measure and spectral flatness are found highly sensitive to fiber-type variations compared to other features considered in this study. Thus, the proposed method using Rihaczek distribution is found suitable for identifying the fiber type differences among GL and SOL. This approach may be utilized for identifying the fiber typology in different skeletal muscles which finds application in sports science. © 2025 IEEE.

关键词： Creatinine

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AI Based on Inception v3 Architecture for Fault Diagnosis in paddy crop using image recognition and Transfer Learning 4

AI Based on Inception v3 Architecture for Fault Diagnosis in...

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4th International Conference on Power, Energy, control and Transmission Systems, ICPECTS 2024

作者： Jayanthi, G. Brindha, S. Sri Sairam Engineering College Department of Instrumentation and Control Engineering India Sri Sairam Engineering College Department of Electronics and Communication Engineering India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Plant diseases is a serious effect that greatly affects crop yield due to these diseases. The optimal diagnosis of this plant disease will prevent us from the loss in crop yield. In analyzing automatic diagnosis of the plant, disease by implementing a smartphone application established. Artificial intelligence is used is machine learning framework to detect the plant disease in time. The basic preventive measure taken using machine learning algorithms that works on an example model in which the module integrated to predict the plant diseases. The proposed structure uses a tuned Inception v3 architecture, which is a typical deep convolutional neural network with multiple convolution layers, consisting of nearly 6 convolution layers. This architecture, which use for classifying the plant disease, supports nearly 12 layers whose performance is an outstanding one for image network. The result shows that this model built using this Framework works very well in timely diagnosis of the plant diseases. Places like the learning rate position f- measure recall and disease recognition with accuracy performances investigated. Total accuracy attained using the built model is 76.56 % with maximum area of the curve. © 2024 IEEE.

关键词： Adversarial machine learning

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Effect of Time and Frequency Windows in Analyzing the Muscle Typology of Adductor Pollicis and Triceps Brachii Muscles Using Surface EMG Signals and Smoothed Pseudo-Wigner-Ville Distribution

Effect of Time and Frequency Windows in Analyzing the Muscle...

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2025 International Conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： Sidharth, N. Nair, Remya R Venugopal, G. Department of Electronics and Communication Engineering Kerala Palakkad India Ieee Biomedical Instrumentation and Signal Processing Lab Department of Instrumentation and Control Engineering Kerala Palakkad India

ISBN: (纸本)9798331507541

The selection of time and frequency windows is having a significant impact on the time-frequency (TF) resolution and interference suppression of smoothed pseudo-Wigner-Ville distribution (SPWVD). In this study, the effect of time and frequency windows in demonstrating the fiber type differences among adductor pollicis (AP) and triceps brachii (TB) muscles is investigated using surface electromyography signals and SPWVD-derived features. The first and last two seconds of the pre-processed signals are considered non-fatigue (NF) and fatigue (FT) segments, respectively. The median frequency (MDF) and mean power of the TF spectrum (MPS) are extracted from the obtained spectra. The statistical significance of SPWVD-derived features in differentiating the signals of AP and TB during NF and FT states are evaluated for different time and frequency windows, using the Mann-Whitney test. Results indicate that the frequency-based feature showed greater statistical significance compared to the energy-based feature for all the different time and frequency window lengths taken. MDF is found to decrease for both muscles, whereas MPS increases for TB and decreases in the case of AP. The highest significant value of 5.39E-27 is observed for MDF with time and frequency window length of 201 and 2001, respectively, for differentiating the signals during the fatigue state. Moreover, all the extracted features showed high statistical significance with p © 2025 IEEE.

关键词： Wigner-Ville distribution

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Advancing Underwater Object Identification Using Aerial Hyperspectral Imaging 4

Advancing Underwater Object Identification Using Aerial Hype...

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4th International Conference on Power, Energy, control and Transmission Systems, ICPECTS 2024

作者： Sujith, R. Ganapathy, V.S. Sathya Prasanna, S. Mohanraj, K. Balaji, N. Vignesh, A. Sri Sairam Engineering College Department of Instrumentation and Control Engineering India Sri Sairam Engineering College Department of Instumentation and Control Engineering India Sri Sairam Engineering College Department of Electronics and Communication Enginnering India Sri Sairam Engineering College Department of Electronics and Control Enginnering India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

This This paper proposes a novel approach of Underwater object identification is a critical aspect of marine exploration, environmental monitoring, and underwater resource management. However, traditional methods face challenges such as limited visibility and time-consuming manual processes. This paper proposes an innovative approach to enhance underwater object identification using aerial hyperspectral imaging coupled with artificial intelligence (AI) techniques. Leveraging the advantages of aerial platforms equipped with hyperspectral sensors, this research aims to improve the efficiency and accuracy of underwater object detection and classification. The methodology involves data acquisition through aerial platforms, preprocessing of hyperspectral data to enhance quality, and the application of AI algorithms for automated object identification. An experimental study is conducted to evaluate the proposed approach, demonstrating its effectiveness in accurately identifying underwater objects across different environments. The results highlight significant advancements over conventional methods, showcasing the potential of aerial hyperspectral imaging and AI for underwater applications. This research contributes to the advancement of underwater sensing technologies and underscores the importance of integrating aerial platforms and AI for improved underwater object identification. © 2024 IEEE.

关键词： Underwater imaging

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Smart Assistive E-Vehicle for Enhanced Mobility of Physically Challenged Individuals 4

Smart Assistive E-Vehicle for Enhanced Mobility of Physicall...

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4th International Conference on Power, Energy, control and Transmission Systems, ICPECTS 2024

作者： Jayanthi, G. Ganapathy, V.S. Vijayalakshmi, S. Sujith, R. Vallarasu, M. Vignesh, A. Sri Sairam Engineering College Department of Instrumentation and Control Engineering India R.M.K Engineering College Department of Electronics and Communication Engineering India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Mobility is essential to freedom and a high quality of life in the fast-paced world of today, especially for those with physical disabilities. Even while they work well, traditional cars frequently fall short of meeting the unique requirements of this population, which makes navigation and autonomy challenging. In order to tackle this issue, we suggest creating a Smart Assistive E-Vehicle that is intended to improve mobility for those with physical disabilities. This cutting-edge car is an electric four-wheel drive vehicle with cutting-edge technology that offers convenient, safe, and effective transportation. The YOLO (You Only Look Once) algorithm, a cutting-edge deep learning model that enables real-time traffic light monitoring, is the brains behind this system. Because of the great efficiency of the YOLO algorithm, the car can recognize traffic lights and signals with great speed and accuracy, which is essential for safe urban navigation. Because of the great efficiency of the YOLO algorithm, the car can recognize traffic lights and signals with great speed and accuracy, which is essential for safe urban navigation. © 2024 IEEE.

关键词： Traffic signals

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Intelligent Agricultural Drones Utilizing Nano-Fertilizer Dispensation for Precision Farming

Intelligent Agricultural Drones Utilizing Nano-Fertilizer Di...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Jayanthi, G. Nancy, W. Umamaheswari, B. Chithrakkannan, R. Sujith, R. Sathya Prasanna, S. Sri Sairam Engineering College Department Of Instrumentation And Control Engineering India Jeppiaar Institute Of Technology Department Of Electronics And Communication Engineering India Sri Sairam Engineering College Department Of Electronics And Communication Engineering India

ISBN: (纸本)9798350352689

An innovative approach to precision agriculture through the integration of artificial intelligence (AI) and nano-fertilizer technology in an agricultural drone system. The proposed system aims to optimize crop yield, reduce environmental impact, and enhance resource efficiency by employing advanced AI algorithms for crop health assessment and nano-fertilizers for targeted nutrient delivery. The drone's real-time decision-making capabilities, coupled with precise dispensation of nano-sized fertilizers, contribute to sustainable farming practices. Field trials demonstrate promising results in terms of improved crop productivity, resource utilization, and minimized environmental footprint. © 2024 IEEE.

关键词： Crops

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Seamless Integration of Legacy Industrial Systems with OPC UA for Enhanced Digital Transformation 4

Seamless Integration of Legacy Industrial Systems with OPC U...

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4th International Conference on Power, Energy, control and Transmission Systems, ICPECTS 2024

作者： Sujith, R. Ganapathy, V.S. Ilayaraja, R. Durgadevi, K. Balaji, N. Vignesh, A. Sri Sairam Engineering College Department of Instrumentation & Control Engineering Chennai India Sri Sairam Engineering College Department of Electronics & Communication Engineering Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

The integration of legacy systems with modern control technologies is a significant challenge in the digital transformation of industrial environments. Many facilities still rely on outdated equipment that lacks compatibility with contemporary communication standards like OPC UA (Open Platform Communications Unified Architecture). This paper proposes a comprehensive solution to bridge this gap by developing OPC UA wrappers and adapters that facilitate communication between legacy systems and modern control infrastructures. The approach involves translating data from various legacy protocols into the OPC UA format, enabling seamless data exchange and interoperability. A central OPC UA server is implemented to aggregate and normalize data, providing a unified and secure platform for real-time monitoring and control. The proposed solution was evaluated in a simulated industrial environment, demonstrating effective data integration, enhanced performance, robust security, and high operator satisfaction. The results validate the feasibility of using OPC UA to integrate legacy systems, supporting digital transformation and fostering a more connected industrial ecosystem. © 2024 IEEE.

关键词： Interoperability

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Smart Ventilation Bag with Adjustable Oxygen Range and Automated Pressure Regulation 3

Smart Ventilation Bag with Adjustable Oxygen Range and Autom...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Saisukirtha, S. Omprakash, M. Shane Steffy, S. Dilip Kumar, S. Department of Instrumentation and Control engineering Sri Krishna College of Technology Coimbatore India Department of Electrical and Electronics engineering Sri Krishna College of Technology Coimbatore India

ISBN: (纸本)9798331527549

The necessity of effective and scalable respiratory support systems in emergency and critical care settings was brought to light by the COVID-19 pandemic. Despite their widespread use, manual ventilation bags pose difficulties in guaranteeing steady airflow, tidal volume, and real-time patient needs adaptation, particularly when healthcare professionals use them for extended periods of time. Manual Ambu bags limitations in sustaining a steady oxygen supply and pressure. This paper presents a new design for a" Smart Ambu Bag," which combines an automated pressure regulation system with an adjustable oxygen concentration range. The system seeks to improve accuracy and patient outcomes by addressing the difficulties associated with manual ventilation in emergency situations. Creating a Smart Ventilation Bag with automated pressure adjustments and real-time oxygen monitoring is the primary goal. The Smart Ventilation Bag an oxygen sensor is integrated into the air mix to measure the oxygen content according to the patient’s needs. The Smart Ventilation Bag has the potential to close the gap between accessibility and performance, especially in environments with limited resources, according to a comparison with current manual and automated ventilators during COVID-19 scenarios. Results from simulations and experiments show better patient outcomes, less caregiver fatigue, and more consistent ventilation delivery. The study emphasizes how the Smart Ventilation Bag can help increase the capacity for respiratory care in emergency situations, opening the door for more affordable and scalable respiratory support options. © 2025 IEEE.

关键词： Oxygen sensors

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Identification of batch reactor process using Extreme Learning Machine based Hammerstein and Hammerstein-Wiener Models

Identification of batch reactor process using Extreme Learni...

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2024 International Conference on Thermofluids and Manufacturing Science, ICTMS 2024

作者： Balakrishnan, Murugan Rajendran, Vinodha Prajwal, Shettigar J. Indiran, Thirunavukkarasu Department of Electronics and Instrumentation Engineering Annamalai University Annamalai Nagar Tamilnadu India Department of Instrumentation and Control Engineering Institution Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India

This paper concentrates on system identification using neural networks, specifically avoiding the need for gradient calculation. In our proposed approach, the parameters of the nonlinear static blocks in both Hammerstein and Hammerstein-Wiener models are represented as Single Hidden Layer Feed forward Networks (SLFNs). The identification of both nonlinear and linear parameters is accomplished through the application of Extreme Learning Machine (ELM). In ELM, the hidden layer weights and biases are generated randomly and remain fixed, whereas the output weights are computed using linear regression. The training process involves a forward pass, in which the hidden layer activations are computed, followed by the computation of the output weights and backward pass is exempted, where iterative optimization based weight updates are done. To show the efficacy of the ELM based block oriented models, intense nonlinear batch reactor process is identified with its input output data. One can take away the best model fit for controller design from this ELM based modelling. © Published under licence by IOP Publishing Ltd.

关键词： Batch data processing

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Enhancement in Smart Operation of Greenhouse Environment Using Intelligent Biomimetic control Framework

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SN Computer Science 2024年第3期5卷 287页

作者： Revathi, S. Sivakumaran, N. Radhakrishnan, T.K. Department of Electrical and Electronics Engineering National Institute of Technology Puducherry India Department of Instrumentation and Control Engineering National Institute of Technology Tiruchirappalli India Department of Chemical Engineering National Institute of Technology Tiruchirappalli India

The greenhouse is a complex nonlinear system, which provides a suitable growing environment for plants. Realizing a comprehensive control for such systems is more challenging in the premise of the presence of model parameter uncertainties and variable time delay. This work presents, develops, and tests the methodology entrenched from the computational inspiration of the biologic immune concept. Mostly, in contention to the widely used classical approach of greenhouse control, an essential facet of the bioinspired algorithm is integrated that synergizes its strength. In this work, the study considers proportional integral and pseudo-derivative-feedback controllers and the artificial immune feature supplements it for the proposal of a new unifying control framework. The non-intelligent techniques are more classical, whereas intelligent techniques involving resistant feature is still relatively new to the control arena. The main distinguishing feature is the superficial resemblance to the conventional controller, but it is inherently more flexible and sophisticated and can exploit the power of heuristics. To deal with the system realistically and to ensure robustness, the Kharitonov theorem is used to synthesize the controller by elucidating the relationship between coefficient perturbations, set of test eight specially constructed vortex polynomials and its stability. Further, Hardware in Loop simulation is performed to verify the performance near physical operating conditions and the results validate the efficacy of the proposed controller frameworks with improvement in dynamic performance and robustness. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Artificial immune feedback Complex Kharitonov polynomial Greenhouse environment Hardware in loop simulation

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