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2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Kaba, Vanita Patil, Rajendra Dept. of Electronics and Communication Engineering Kalaburagi India Gsss Institute of Engineering and Technology for Women Dept. of Electronics and Communication Engineering Mysore India

ISBN: (纸本)9798350309140

The requirements of the wireless communication system have progressed consistently over previous several years. The growing fame and utilization of wireless multimedia approaches have driven the advancement of the wireless system. The fifth generation (5G) wireless communication has evolved to serve users with enhanced proficiency, low latency, reliable communication, and lesser battery exhausts. The non-Orthogonal Multiple Access (NOMA) technique is an efficient multiple access scheme to fulfil the requirements of a 5G mobile system. NOMA permits the allocation of multiple users with similar frequency, code and time domain multiplexing. NOMA enables a remarkable enhancement in system performance as well as the ability to connect devices. This paper presents, a Zadoff Chu non-orthogonal spreading sequence to enhance the NOMA systems performance. Here transmitting symbols are precoded using the Zadoff Chu spreading sequence. Simulation results represent the enhancement in the bit error rate (BER) performance of the NOMA system over fading channels compared to the traditional NOMA system. © 2024 IEEE.

关键词： Bit error rate

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Overcurrent Relays Coordination of Interconnected Power system using Modified Objective Functions and Multiple Standardized Tripping Curves 1

Overcurrent Relays Coordination of Interconnected Power Syst...

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1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Godwal, Shanker D. Pandya, Kartik S. Vora, Santosh C. Mehta, Chintan R. Rajput, Vipul N. Institute of Technology Nirma University Dept. Electrical Engineering Ahmedabad India Parul Institute of Technology Parul University Dept. of Electrical Engineering Vadodara India Institute of Technology Dr. Subhash University Dept. Electrical Engineering Junagarh India

ISBN: (纸本)9798331520540

Over the last few decades, many researchers opt various techniques to achieve optimum time for coordination of overcurrent relays. The main motive of these techniques is to avoid mal operation among the main and secondary relays and to reduce time of operation of each relay as per required criteria. Some researchers have suggested modification in relay characteristics and objective functions to avoid mis-coordination. Three objective functions from the literature are examined in this paper, and new modified objective functions are the suggested after modification. A comparison is made between the standard objective function with modified obje ctive functions. Out of three modified objective functions, the best modified objective function is proposed. Further, three IEC standard relay characteristics have been adopted to analyze & compare the effectiveness & robustness of said objective functions. For the said comparison IEEE 14-bus modified distribution system is considered and a Harmony search algorithm is used. © 2025 IEEE.

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AttRes-UNet: A Dual-Model Approach for Brain Tumor Segmentation 4

AttRes-UNet: A Dual-Model Approach for Brain Tumor Segmentat...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Kolekar, Samruddhi Maheshkumar Yadav, Agnesh Chandra Yadav, Suchita Dash, Daba Prasad Thaper Institue of Engineering and Technology Dept. Electrical Engineering Patiala India India Instite of Technology Patna Dept. Electrical Engineering Patna India Bennett University Dept. of Computer Science and Engineering Gr. Noida India

ISBN: (纸本)9798350364729

Accurate segmentation of brain tumors in MRI images is crucial for diagnosis, treatment planning, and disease monitoring. Traditional manual segmentation methods are time- consuming and prone to variability, while standard deep learning architectures such as U-Net face challenges in capturing the complex, heterogeneous structures of brain tumors. In this study, we proposed AttRes-UNet, a novel dual-model architecture combining Attention U-Net and Residual U-Net to enhance segmentation accuracy. Attention Gates allow the model to focus on critical tumor regions, while Residual Blocks improve deep feature learning, enabling the model to handle diverse tumor morphologies with greater precision. We evaluated the model using the BraTS 2021 dataset, achieving high performance with an accuracy of 0.996, a dice coefficient of 0.8340, and an Intersection over Union of 0.8210. These results demonstrate the effectiveness of AttRes-UNet in improving segmentation accuracy, making it a valuable tool for automated brain tumor delineation, ultimately reducing manual workload and improving clinical outcomes. © 2024 IEEE.

关键词： Magnetic resonance imaging

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EXPERIMENTAL STUDY ON BEHAVIOR OF COMPOSITE BEAM IN LARGE DEFORMATION RANGE UNDER HA ORIZONO T FAL UNIDIRECTIONAL LOA G

Journal of Structural and Construction Engineering

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Journal of Structural and Construction engineering 2025年第827期90卷 133-143页

作者： Mukaide, Seiji Yamamoto, Daiki Dept. of Architecture Faculty of Engineering Osaka Institute of Technology Japan Okabe Co. Ltd Former Osaka Institute of Technology Japan

Loading tests have been conducted on subassemblage with wide composite beams to investigate the bearing capacity degradation of composite beams due to slab damage in the large deformation range. The experimental results show that the slab transfers stress over a wide range and that the axial force acting on the beam affects the occurrence of local buckling. Furthermore, an analytical model is proposed that takes into account its bearing capacity degradation characteristics. © 2025 Architectural Institute of Japan. All rights reserved.

关键词： Local buckling

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3D-manufactured soft haptic actuators utilizing electrostatically driven pneumatic valves

3D-manufactured soft haptic actuators utilizing electrostati...

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Soft Mechatronics and Wearable systems 2024

作者： Chien, I-Hui He, I-Lun Su, Yu-Chuan Cheng, Chih-Cheng Yang, Chen-Tsai Dept. of Engineering & System Science National Tsing Hua University Taiwan Electronic and Optoelectronic System Research Laboratories Industrial Technology Research Institute Taiwan

ISBN: (数字)9781510672031

ISBN: (纸本)9781510672024

We have successfully demonstrated a diaphragm-type pneumatic valve capable of amplifying electrostatic actuation and attenuating air pressure applied onto the diaphragm. When a high voltage is applied between the flexible electrode coated on top of the diaphragm and an underneath ground electrode, the electrostatic attraction induces a zipping motion of the diaphragm. The actuation of the pneumatic valve can seal an air chamber and maintain its internal pressure at a high level, which drives a circular elastomer membrane to form a bump and stimulate the skin. If the applied voltage falls below a certain threshold, air bypasses the diaphragm to the atmosphere and causes a sharp drop of the inter pressure, whose magnitude is controlled by a dedicated fluidic resistor located upstream. By manipulating pressure, the electrically-driven valve can control the movement of the membrane and the stimulation to the skin. In the prototype demonstration, structures are 3D manufactured, selectively coated with silver electrodes, and integrated to realize the proposed actuator with 1 cm3 in volume. The fabricated circular elastomer membrane of 3 mm in diameter and 0.1 mm in thickness forms a bump of 0.5 mm in height under an air pressure of 10 kPa, which requires a voltage of >472 V for the valve to maintain. While the voltage is lower than 472 V, the pressure drops and the membrane moves down. Once a voltage of >814 V is applied, it seals the chamber and forms the bump again. Therefore, haptic actuation can be generated as desired. © 2024 SPIE.

关键词： Diaphragms

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Image Enhancement for Iris Analysis Using Deep Learning Algorithms - A Brief Survey 2

Image Enhancement for Iris Analysis Using Deep Learning Algo...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Poovayar Priya, M. Ezhilarasan, M. Pondicherry Engineering College Dept. of Computer Science and Engineering Puducherry India Pondicherry Engineering College Dept. of Information Technology Puducherry India

ISBN: (纸本)9798350328202

With applications including medical image analysis, picture sharpening and restoration, robot vision, pattern recognition, and video processing, among many others, image enhancement is the main topic in image processing and computer vision. There have been many algorithms for improving images established in the past. More extensive work on image-enhancing techniques has been done utilizing deep learning models in recent years, as these algorithms have achieved their peak in vision applications. In this study, we present a thorough analysis of machine learning, deep learning, and literature. The literature on image enhancement is then summarised, and future approaches for iris analysis are discussed. © 2024 IEEE.

关键词： Image enhancement

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Enhancing Solar Energy Efficiency: A Deep Learning Approach for Dust Detection

Enhancing Solar Energy Efficiency: A Deep Learning Approach ...

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2024 International Conference on Smart Electronics and Communication systems, ISENSE 2024

作者： Vigna Harish, R. Ramya, A. Siva, S. Santhi, S. Kalaiselvi, S. Anitha, V. National Engineering College Dept. of Information Technology Tuticorin India National Engineering College Dept. of Computer Science and Engineering Tuticorin India

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528126

In recent decades, power generation from PV systems has become increasingly popular. However, several environmental variables, such as dust deposition on PV panels, have significantly reduced PV energy production. Several approaches are under investigation to identify dust and decrease power loss. These include thermal imaging, image processing, sensors, and IoT devices, machine learning, and deep learning. The orientation of a solar panel and the angular inclination relative to the horizontal plane affect one of the environmental factors that impact its performance: since they have an impact on how much solar energy that enters into the photovoltaic panel's surface. Dust, for one, is another example of environmental factors: the quantity of energy output can be influenced by coming into contact with dust. Dust particles lying on the panel's surface have the potential to reduce light from reaching the solar modules, which would drastically reduces the energy output. Cleaning or reducing modules is critical, but these procedures must be continuously monitored and assessed for their effects. Image processing algorithms can be used to detect dust to optimize solar panels. In this paper, a novel dataset from Kaggle comprising images of dusty and clean solar panels is presented, aiming to facilitate the research on automated dust detection algorithms. The dataset is utilized to evaluate the performance of existing state-of-the-art (SOTA) image classification methods. Subsequently, a new Convolutional Neural Network (CNN) architecture, ResNet-50 with an additional attention mechanism, is proposed, specifically designed for solar panel dust detection. The experimental results shows that the proposed method outperforms other SOTA algorithms, achieving an accuracy level of 96.2%. © 2024 IEEE.

关键词： Thermography (imaging)

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Deep Learning-Driven Cloud Intrusion Detection: Internet of Things and Cloud Networks 1

Deep Learning-Driven Cloud Intrusion Detection: Internet of ...

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1st International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Moon, Iffath Tanjim Islam Mondal, Md. Nazrul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331518578

In this paper, we have utilized deep learning approaches to detect cloud intrusion for the Internet of Things (IoT). The emerging growth of IoT and cloud environments has revolutionized many industries by allowing real-time data sharing and storage. Cloud platforms offer the essential infrastructure for managing the massive volumes of data produced by IoT devices. However, due to several flaws, including insufficient encryption, insecure APIs, and weak authentication, intruders can exploit the vulnerabilities to interrupt services and obtain unauthorized access. Therefore, deep learning can be an effective solution to detect cloud intrusion in IoT. In this research, we have developed individual convolutional neural network (CNN) and long short-term memory (LSTM) models, along with a hybrid CNN-LSTM model for multiclass classification and binary classification to detect cloud intrusion. In addition, we have examined which models perform the best for cloud intrusion detection for IoT among the individual and hybrid models. The proposed models are effective in detecting cloud intrusion, as CNN can extract spatial features, and LSTM can capture temporal dependencies from network traffic data. Furthermore, we have utilized one of the most recent and comprehensive datasets for IoT, the ToN-IoT dataset, containing real-world data. In multiclass classification, the hybrid CNN-LSTM has given the best performance, achieving an accuracy of 97.65%, precision of 97.72%, recall of 97.65%, f1 score of 97.67%, and AUC of 99.93%. In binary classification, all CNN, LSTM, and hybrid CNN-LSTM have demonstrated high performance, where LSTM has achieved the highest accuracy of 99.97%. Moreover, we have evaluated the performance of multiclass classification for detecting nine distinct kinds of attacks. In this instance, CNN has shown better performance in detecting backdoor attacks. Thus, this research can make the IoT platform more secure by efficiently detecting cloud intrusion. © 2025

关键词： Network intrusion

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Alzheimer’s Disease Detection and Classification using Modified Hybrid CNN-LSTM Network 6

Alzheimer’s Disease Detection and Classification using Modi...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Nisha, A.V. Rajeev, S.K. Soorya, M. Shaheer, S. Dept. of Electronics and Communication Engineering Younus College of Engineering and Technology Kerala Kollam India

ISBN: (纸本)9798331522667

Alzheimer’s Disease (AD) is an escalating neurodegenerative disorder and the leading cause of dementia, with early detection being essential for prompt action and slowing disease progression. However, due to subtle and overlapping brain deterioration patterns, early AD diagnosis is difficult. This study proposes a hybrid deep learning model that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to automatically detect and classify AD from axial brain magnetic resonance imaging (MRI) images. The model uses VGG16, pre-trained on ImageNet, for feature extraction, with an LSTM layer to capture temporal dependencies in MRI slices. The dataset includes three categories: AD, Cognitively Impaired (CI), and Normal Control (NC) subjects. To improve generalization, data augmentation techniques such as median blurring and horizontal flipping were used. The model, which was trained with the Adam optimizer, achieved a test accuracy of 0.9942 and a loss of 0.030. High sensitivity and specificity were shown by the performance evaluation which included a classification report and evaluation matrix, particularly in distinguishing AD from CI cases. This hybrid CNN-LSTM model effectively leverages spatial and temporal information for early AD detection, highlighting its potential to improve automated diagnostic tools and enable timely intervention. © 2025 IEEE.

关键词： Convolutional neural networks

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Cryptosight: Decrypting Future Trends with LSTM, PyCaret and Jinja 2.0 1

Cryptosight: Decrypting Future Trends with LSTM, PyCaret and...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Mhatre, Soham Motiram Mishra, Brajesh Kumar Raja, Ishan Kumar Goswami, Bigit Krishna Agrawal, Sneha Upadhyay, Pranjal National Institute of Technology Dept. of Electronics and Communication Engineering Nagaland Dimapur India National Institute of Technology Dept. of Mechanical Engineering Nagaland Dimapur India National Institute of Technology Dept. of Electrical & Electronics Engineering Nagaland Dimapur India National Institute of Technology Dept. of Computer Science and Engineering Nagaland Dimapur India

ISBN: (纸本)9798350377002

The primary objective of this research is to develop and evaluate combined cryptocurrency price prediction models using Long Short-Term Memory (LSTM) neural networks along with traditional time series models and machine learning techniques like Random Forest and Linear Regression. The LSTM models will capture long-term dependencies in sequential cryptocurrency price data, while the other models provide complementary predictive capabilities. This multi-model approach aims to leverage the strengths of different techniques to improve overall prediction accuracy. Additionally, this research seeks to explore the practical application of these predictions in real-world cryptocurrency trading scenarios. The study will collect historical price data and relevant news for major cryptocurrencies from reputable sources. Data preprocessing, feature engineering, model training, hyper-parameter tuning, and evaluation using metrics like RMSE, MAE, and R-squared will be performed. The predictive performance of the LSTM-based models will be compared against baseline models, with statistical significance testing. Interpretability analysis will provide insights into the factors driving price movements. Furthermore, the research will investigate integrating the predictions into trading algorithms and evaluate their efficacy through back-testing on historical data. Limitations, challenges, and future research directions in cryptocurrency price prediction will also be discussed. © 2024 IEEE.

关键词： Prediction models

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