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3rd IEEE International Conference on Industrial electronics: Developments and Applications, ICIDeA 2025

作者： Saini, Amrita Talukdar, Bhaskar Jyoti Dash, Rudra Narayan Rao, D Chandrasekhar Das, Bikramaditya BPUT Dept. of Computer Science and Engineering Odisha Rourkela India VSSUT Dept. of Electronics and Telecommunication Engineering Odisha Burla India School of Electrical Engineering KIIT Deemed to be University Bhubaneswar India BPUT Dept. of Electronics and Communication Engineering Odisha Rourkela India

ISBN: (纸本)9798331533205

This study's main objective is to look at strategies, for avoiding underwater collisions and planning trajectories while navigating around static obstacles. Traditional Artificial Potential Fields (APF) face challenges such as getting stuck in local minima, which diminishes the path's optimality. Additionally, conventional APF often results in trajectories characterized by sharp turns or sudden changes in direction, particularly in environments populated with numerous obstacles. These obstacles can significantly hinder Autonomous Underwater Vehicles (AUVs) that depend on smooth trajectories for maintaining stability and control. To address these drawbacks, we present the Hybrid A∗ - Artificial Potential Field (HA-APF) algorithm. When using AUVs for collision avoidance duties, adherence to safety procedures is crucial. We propose an enhanced HA-APF algorithm to address this issue for efficient path planning and collision avoidance. Additionally, the A∗ algorithm, which has been altered to mitigate the innate problems with local optimality in the conventional APF approach, is used in conjunction with the improved APF technique. Finally, validations are conducted in three different regions with different beginning and finishing positions. These tests show how feasible and successful the suggested algorithm is in assisting AUVs in avoiding collisions while also carrying out path-planning duties. Through this work, we provide a robust tool for advancing AUV capabilities in real-world applications, ultimately allowing for more effective underwater exploration and intervention. © 2025 IEEE.

关键词： Motion planning

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Precise Identification and Segmentation of Anomalies in MR Brain Images Using Emperor Penguin Optimization Based Weighted Fuzzy C-Means Clustering

Precise Identification and Segmentation of Anomalies in MR B...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： Mathankumar K Sumathi R Kottaimalai Ramaraj Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

In numerous applications, segmentation of images is essential to image comprehension, extraction of features, evaluation, and understanding. It is widely used in healthcare for a variety of purposes, including tissue categorization, tumor location identification, tumor size calculation, blood cell separation, preparation for surgery, atlas comparison, and registration of images. One of the widely used algorithms for clustering in segmentation of medical images is fuzzy c-means (FCM). However, FCM is extremely susceptible to noise since it ignores spatial information when segmenting images. The limitations of FCM might be overcome by adding weighted FCM, which takes into account the spatial and intensity information of the surrounding pixels in this situation. A population-centered meta-heuristic technique called Emperor Penguin Optimizer (EPO) was created currently to mimic the huddling manners of emperor penguins. Regarding how well EPO performs when handling universal issues with optimization, different outcomes were noted. In order to achieve an appropriate equilibrium among exploration and exploitation, two variables belonging to the EPO must be adjusted. The EPO uses the emperor penguins to symbolize the potential solution, the huddle to signify the area of search, which is a two-dimensional L-shaped polygon plane, and the viable option to be represented by the emperor penguins arranged arbitrary. Determining the efficient mover that embodies the global best option among all the emperor penguins. The fact that EPO has been successfully applied to a wide variety of optimization problems has allowed it to gradually gather energy up to this point. It is suggested that EPO and WFCM be integrated to recognize and separate the abnormalities seen in brain magnetic resonance imaging (MRI). The effectiveness of the algorithm that has been established is confirmed through contrasting the dissection results with the corresponding ground truth images and by looking at spec

关键词： Measurement Image segmentation Magnetic resonance imaging Metaheuristics Noise Surgery Feature extraction

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GUI Based Modeling and Simulation of Maximal Calcium Ion Bifurcation on Neuron Response and its Applications

GUI Based Modeling and Simulation of Maximal Calcium Ion Bif...

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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： Taslima Ahmed Zaiba Ishrat Satvik Vats Sandhya Bharadwaj Hitendra Singh Department of Electronics and Communication Engineering IIMT College of Engineering Greater Noida India Department of Electronics and Communication Engineering Meerut Institute of Technology Meerut India Department of Computer and Science Engineering Graphic Era Hill University Dehradun India Department of ECE IIMT College of Engineering Greater Noida India Delhi Technological University Delhi India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

In spite of sodium, potassium and chloride ions, calcium, magnesium and cobalt ions also plays a vital role for complex signaling in brain. Calcium ions (Ca 2+ ) regulate neuronal activity in homo- and hetero cellular control brain physiology. Without calcium ion signaling, all transcriptional networks, and ion exchangers in brain may cause sudden stop of all physiological functions. Wrong interactions between Ca 2+ and reactive other species signaling coordinate in neurodegenerative disorders, perturbed energy metabolism, and alterations of disease-related synaptic dysfunction. Therefore, in this paper, review of Ca 2+ has been modeled with original H-H equations and discusses deregulation of Ca 2+ for different neurological related diseases as Alzheimer, Parkinson, coma etc. These channels activate due to depolarization and inactivate very little and slowly during the whole action potential generation phase. Calcium ion bifurcation was first characterized by Fatt and Katz (1953). From researcher’s simulation and formulation it was concluded, there are various subtypes channel in neuronal cells .These sub-channels serve a vital role in cellular signaling in neuron cell. Based on the neural functionality, calcium ion bifurcation is depended on calcium channel types L ca , T ca and N ca type. L ca -type currents are the main calcium ion response observed in muscular cells, where they initiate function of the respective cells. T ca type calcium ion response is activated by depolarization of ion. N ca type calcium ion channel provided only neuronal *** paper represents the bifurcation level of calcium ion response on the neuron functionality and an investigation on the properties of Ca 2+ ion response has been observed. This paper also explains the extension of original H-H model for neuron at excitable state and is analyzed with GUI panel with proper simulated version.

关键词： Pathology Reviews Calcium Sodium Neurons Bifurcation Ions

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A Novel Peak-to-Average Power Ratio Reduction for 5G Advanced Waveforms

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computers, Materials & Continua 2022年第10期73卷 1637-1648页

作者： Rajneesh Pareek Karthikeyan Rajagopal Himanshu Sharma Nidhi Gour Arun Kumar Sami Althahabi Haya Mesfer Alshahrani Mohamed Mousa Manar Ahmed Hamza Department of Computer Science&Engineering JECRC UniversityJaipur303905India Centre for Nonlinear Systems Chennai Institute of TechnologyChennaiIndia Department of Electronics and Communication JECRC UniversityJaipur303905India Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Electrical Engineering Department Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Multi and single carrier waveforms are utilized in cellular systems for high-speed data *** The Fifth Generation(5G)system,several waveform techniques based on multi carrier waveforms are ***,the Peak to Average Power Ratio(PAPR)is seen as one of the significant concerns in advanced waveforms as it degrades the efficiency of the *** proposed article documents the study,progress,and implementation of PAPR reduction algorithms for the 5G radio *** compare the PAPR algorithm performance for advanced and conventional *** simulation results reveal that the advanced Partial Transmission Sequence(PTS)and Selective Mapping(SLM)methods enhanced the throughput and gain of the 5G ***,we have also analyzed the performance of Orthogonal Time Frequency Space Modulation(OTFSM)based on a single carrier system and found that PAPR is significantly low and is best suited to fading *** is seen that the conventional algorithms lower the PAPR but increase the *** proposed PTS and SLM have shown good performance with low computational complexity.

关键词： PAPR 5G OFDM NOMA OTFSM PTS SLM

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Traffic Classification Using CNN-LSTM Based on Multimedia communication System 3

Traffic Classification Using CNN-LSTM Based on Multimedia Co...

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3rd International Conference on Integrated Circuits and communication Systems, ICICACS 2025

作者： Jagannathan, Srinivasan Muhsnhasan, Montater Kavitha, Panjagari Vigneshwaran, K.S. Francis, Neethu C. Madanapalle Institute of Technology & Science Department of Computer Applications Madanapalle India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Al Diwaniyah Iraq Cvr College of Engineering Department of Electronics and Communication Engineering Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Dhanalakshmi Srinivasan College of Engineering Technology Department of Electronics and Communication Engineering Mamallapuram India

ISBN: (纸本)9798331508456

Network traffic classification is the process of determining and categorizing network data by identifying various kinds of services, applications, and threats. However, the challenge lies in recognizing and classifying the various class of network traffic among high volume of network traffic data. The classification with high network traffic data struggles from imbalanced data and noise. This research proposes an effective Convolution Neural Network - Long Short-Term Memory (CNN-LSTM) model to classify the network traffic based on multimedia communication system. Initially, the proposed CNN-LSTM is evaluated using Information Security centre for Excellence (ISCX) Virtual Private Network (VPN) and non-VPN traffic dataset to determine the model performance. After obtaining data, the min-max normalization is applied to transform numerical values in a standardized range followed by feature extraction using CNN to capture spatial relationships in network patterns. Finally, the proposed CNN-LSTM is performed to classify the network traffic effectively by utilizing CNN for spatial feature extraction and LSTM to learn sequential dependencies of data effectively in traffic flow. While compared to existing methods like Advanced Density-Based Spatial Clustering of Applications with Noise (A-DBSCAN), the proposed method obtains a high accuracy of 0.9876 using ISCX VPN-non-VPN traffic dataset. © 2025 IEEE.

关键词： Multimedia services

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TRFI-Net: Modified TRFI-Based Channel Estimation for Internet of Vehicles

TRFI-Net: Modified TRFI-Based Channel Estimation for Interne...

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International Symposium on Advanced Networks and Telecommunication Systems (ANTS)

作者： Dipanjan Sar Debanjan Das Rajarshi Mahapatra Venkanna Uduthalapally Department of Electronics and Communication Engineering IIIT Naya Raipur India IIT Kharagpur Centre of Excellence in Affordable Healthcare India Department of Computer Science and Engineering NIT Warangal India

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

In the evolving landscape of the Internet of Vehicles, ensuring dependable Vehicle-to-Everything communication is imperative for Intelligent Transportation Systems. The prevailing IEEE 802.11p protocol presents formidable obstacles to accurate channel estimation owing to its standardized pilot patterns. Conventional solutions, such as data pilot-aided methods, are encumbered by error propagation issues stemming from pilot limitations, particularly pronounced at higher speeds. To address these challenges, this paper introduces TRFI-Net, a novel residual network-based channel estimation method with timedomain and frequency-domain interpolation, demonstrating up to a 3 dB gain in high SNR conditions. By harnessing advanced deep learning methodologies and ResNet architecture, TRFI-Net enhances IoV communication resilience. Benchmarking against six channel models and assessing BER and NMSE in the “Roadside unit to vehicle Urban Canyon” scenario highlights TRFI-Net’s efficacy, marking a significant stride in dynamic vehicular channel estimation research.

关键词： Interpolation Protocols Channel estimation Telecommunications Vehicle dynamics Vehicle-to-everything Intelligent transportation systems Internet of Vehicles Signal to noise ratio Resilience

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IoT based Smart U-Turn Vehicle Accident Prevention System

IoT based Smart U-Turn Vehicle Accident Prevention System

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Sustainable Computing and Data communication Systems (ICSCDS), International Conference on

作者： G Pradeepkumar G Praveen Santhoshkumar C Rohith Bhat M Jeyalakshmi T Muthukumar Neelam Sanjeev Kumar Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering Nandha Engineering College Erode Tamilnadu India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Electronics and Communication Engineering SSM Institute of Engineering and Technology Dindigul Department of Electrical and Electronics Engineering Kongunadu College of Engineering and Technology Department of Biomedical Engineering Saveetha School of Engineering SIMATS Chennai India

Unintentional deaths occur at a very high rate in developing countries. Curved roads have significantly more fatalities than straight roads. This occurs mainly on U-turns, hairnin turns, and narrow mountain roads. Drivers in this position cannot see the vehicle approaching from the opposite direction. As a result, thousands of people are killed in car accidents every year. The best way to avoid further accidents is to alert the car driver approaching from the side. Place the ultrasonic range detection sensor on one side of the road before the bend and the light indicator system on the opposite side after the bend. When a vehicle approaches from afar, an ultrasonic sensor on one side of the road sends a signal to the other side of the road via a light system. In response to a warning, the driver may stop the car until the other vehicle has passed. A buzzer will also be used to warn the driver of the car that is approaching.

关键词： Earth Roads Developing countries Acoustics Automobiles Data communication Monitoring

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Network Intrusion Detection in Autonomous Vehicles

Network Intrusion Detection in Autonomous Vehicles

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Multimedia Processing, communication & Information Technology (MPCIT), International Conference on

作者： Namrata Wadekar Huda Shaikh Ashlesh Shetty Kirankumar Kumbar Bharati Chilad Arun Tigadi Department of Computer Science and Engineering KLE Dr M S Sheshgiri College of Engineering and Technology Belagavi India Department of Electronics and Communication Engineering KLE Dr M S Sheshgiri College of Engineering and Technology Belagavi India

ISBN: (数字)9798350375466

ISBN: (纸本)9798350375473

Autonomous vehicles promise safer and more efficient transportation, yet they face cybersecurity risks due to their reliance on interconnected systems. This paper emphasizes and sizes the critical role of intrusion detection systems (IDS) in safeguarding these vehicles. Various Intrusion Detection System (IDS) methods are explored and designed to meet the specific security needs of Controller Area Networks (CAN) in autonomous cars, emphasizing machine learning techniques. Our paper provides significant performance improvements in IDS through feature selection and hyperparameter optimization. Experimental results show a boost in the F1 score from 98.861 % to 99.57%, with a 70.2% reduction in execution time. Additionally, the IDS achieves 99.57% accuracy in binary classification. These findings highlight the importance of optimizing IDS for robust security in autonomous vehicle environments.

关键词： Accuracy Protocols Transportation Network intrusion detection Hyperparameter optimization Vectors Controller area networks Protection Autonomous vehicles Random forests

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Interweaving Artificial Intelligence and Bio-Signals in Mental Fatigue: Unveiling Dynamics and Future Pathways 20

Interweaving Artificial Intelligence and Bio-Signals in Ment...

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20th International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Parveen, Saba Bin Heyat, Md Belal Akhtar, Faijan Parveen, Saima Asrafali, Barkathulla Singh, Bandana Ali, Kazim Huang, Lie Li, Xiao Peng Jabbar, Ayesha Asad, Maimoona Li, Jian Ping College of Electronics and Information Engineering Shenzhen University Shenzhen China CenBRAIN Neurotech Center of Excellence School of Engineering Westlake University Zhejiang Hangzhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Shah Abdul Latif University Department of Mathematics Sindh Khairpur Pakistan College of Physics and Optoelectronics Engineering Shenzhen University Shenzhen China School of Media and Communication Shenzhen University Shenzhen China

ISBN: (纸本)9798350318982

This study conducts a thorough examination of machine learning models within Artificial Intelligence (AI)- integrated mental fatigue research, focusing on their identification and assessment. Investigating various biomedical signals linked to mental fatigue-such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR) this research sheds light on their roles in deciphering cognitive states and emotional responses. Through integration, these signals facilitate the creation of network visualizations, unveiling the intricate relationship between physiological markers and cognitive aspects during mental fatigue scenarios. Additionally, this study identifies gaps in current mental fatigue research with AI, emphasizing the need for an integrated approach across diverse domains and populations. It outlines future directions, suggesting avenues like multi-domain integration, personalized interventions, ethical considerations, longitudinal studies, and interdisciplinary collaborations to propel advancements in mental fatigue research, offering promising prospects for innovative AI-driven solutions in mental health and well-being. © 2023 IEEE.

关键词： Electrophysiology

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FAMC: A FPGA Implemented Automatic Modulation Classification via Deep Learning for IoV Applications 21

FAMC: A FPGA Implemented Automatic Modulation Classification...

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21st IEEE India Council International Conference, INDICON 2024

作者： Sar, Dipanjan Das, Debanjan Mahapatra, Rajarshi Udutalapally, Venkanna Iiit Naya Raipur Department of Electronics and Communication Engineering India Iit Kharagpur Centre of Excellence in Affordable Healthcare India Nit Warangal Department of Computer Science and Engineering India

ISBN: (纸本)9798350391282

The Internet of Vehicles (IoV) is revolutionizing intelligent transportation systems by enabling real-time data exchange and decision-making to enhance traffic safety and reduce congestion. However, effective spectrum management in dynamic vehicular environments presents a significant challenge, particularly for Automatic Modulation Classification (AMC) in the presence of noise and high mobility. Traditional AMC methods, reliant on manual feature extraction and classical algorithms, often struggle under these conditions due to limited accuracy and high computational demands. Existing deep learning solutions, while promising, face difficulties in real-time implementation on resource-constrained devices like FP-GAs. Our work introduces a novel deep learning-based AMC framework optimized for FPGA deployment named 'fAMC', addressing these limitations. The proposed 'fAMC' achieves a classification accuracy of 96.00% on the FPGA platform, with real-time processing capabilities of 15.7 frames per second. This demonstrates significant advancements in accuracy and efficiency, providing a robust solution for IoV systems. © 2024 IEEE.

关键词： Convolutional neural networks

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