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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Murugadass, Arthi Kumaravelu, Vinoth Babu Kennedy, Helen Sheeba John Anjana, B.S. Dipinkrishnan, R. Bilim, Mehmet Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Electronics Engineering Department of Communication Engineering Vellore India Nuh Naci Yazgan University Department of Electrical and Electronics Engineering Kayseri Turkey

ISBN: (纸本)9798350370249

The traditional cooperative non-orthogonal multiple access (CNOMA) technique improves the outage and data rate performance for cell-edge users, albeit at the expense of near users. Due to the ability to control the propagation environment, introducing intelligent reflective surfaces (IRS) into a multi-user network can reduce costs, signal processing complexity, and energy consumption. In this work, a comprehensive downlink framework for a blind-IRS-supported CNOMA system is established to enhance outage performance for both cell-edge and near users. The closed-form analytical expressions for the outage probability of the near and cell-edge users within this proposed system are derived. Furthermore, these analytical expressions are validated through Monte-Carlo simulations. The cell-edge user of our proposed model outperforms traditional NOMA and IRS-assisted NOMA variants. To achieve a target outage probability of 10-3, our proposed system requires ∼ 4 dB lower signal-to-noise ratio (SNR) compared to other IRS-assisted NOMA variants. © 2024 IEEE.

关键词： Image coding

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On Applying Preprocessing Techniques on Gallbladder Ultrasound Images 8

On Applying Preprocessing Techniques on Gallbladder Ultrasou...

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8th IEEE International Conference on Information and Communication technology, CICT 2024

作者： Suji, R. Jenkin Godfrey, W. Wilfred Madhav Institute of Technology and Science Department of Electronics Engineering Gwalior India ABV-Indian Institute of Information Technology and Management Department of Computer Science and Engineering Gwalior India

ISBN: (纸本)9798331531539

Ultrasound imaging is a common and non-invasive method for diagnosing gallbladder diseases, including gallbladder cancer (GBC). However, the inherent challenges of ultrasound images-such as noise, low contrast, and variable image quality-necessitate the use of preprocessing techniques to enhance image clarity and improve diagnostic accuracy. In this study, we apply a series of preprocessing methods, including median filtering, Gaussian filtering, bilateral filtering, adaptive histogram equalization, and wavelet transforms, to gallbladder ultrasound images. We evaluate the effectiveness of these techniques using a variety of quality metrics such as sharpness, signal-to-noise ratio (SNR), entropy, structural similarity index (SSIM), and perceptual image quality evaluator (PIQE). A detailed analysis of the preprocessing methods was conducted, where the performance of each technique was systematically compared. Additionally, we highlight the statistical variations in these metrics across different preprocessing approaches to identify the most suitable methods for enhancing GBC detection. The results show that specific preprocessing methods improve image quality and feature clarity, aiding accurate gallbladder disease detection models. This work is an initial groundwork for enhancing the diagnostic value of ultrasound images for GBC. It suggests future directions for integrating these methods into machine-learning pipelines for automated disease detection. © 2024 IEEE.

关键词： Wavelet transforms

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Optimizing Packet Delivery in Underwater Wireless Sensor Networks: A Comparative Study of Genetic Bee Colony and Ant Colony Algorithms 9

Optimizing Packet Delivery in Underwater Wireless Sensor Net...

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9th International Conference on Communication and electronics Systems, ICCES 2024

作者： Kannappan, A. Kolagani, Nagesh Kandaswamy, Vijay Anand Vishnupriyan, J. Saveetha School of Engineering Simats Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India Chennai Institute of Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350377972

Physically Underwater communication adopt the bio inspired genetic bee colony formed network communication structures. Underwater environment condition Communication is established between the Transmitting node and in between several clustering relay nodes to reach receiver nodes. Transmitting nodes are placed on sea bed areas at certain depth and clustering relay nodes are placed in various underwater depth conditions dipping the sensor nodes. Also, underwater battery life is very hard and battery replacement is a very tough task. Hence, an alternate solution of optimization battery capacity utilization is used. This optimization algorithm improves Packet delivery ratio by comparing genetic bee algorithm with Ant colony algorithm. Hence, equivalence of two algorithms implemented in underwater conditions. The genetic bee colony Algorithm produces better output results more than 4 to 6 percentage compared to ant colony parameter output. The genetic bee colony algorithm offers a maximum clustering node technique compared to ant colony algorithm. Its greater support for saving energy consumption extending battery life. © 2024 IEEE.

关键词： Ant colony optimization

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Investigation of the Temperature Impact on the Performance Characteristics of the Field-Plated Recessed Gate III-Nitride HEMT on β-Ga2 O3 Substrate 3rd

Investigation of the Temperature Impact on the Performance C...

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3rd International Conference on Micro and Nanoelectronics Devices, Circuits and Systems, MNDCS 2023

作者： Purnachandra Rao, G. Lenka, Trupti Ranjan Boukortt, Nour El. I. Nguyen, Hieu Pham Trung Department of Electronics and Communication Engineering National Institute of Technology Assam Silchar India Department of Electronics and Communication Engineering Kuwait College of Science and Technology Doha Kuwait Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

ISBN: (纸本)9789819944941

In this chapter, a field-plated recessed gate III-nitride High Electron Mobility Transistor (HEMT) grown on β-Ga2O3 substrate is designed. The electrical characteristics of the proposed HEMT is investigated by using the thermal models of ATLAS TCAD simulations. The impact of temperature on the transport properties is studied. Influence of the substrate thickness with temperature changes on drain characteristics are also discussed. A field-plate and gate length of 20 nm each with 30 nm recessed depth is considered for the analysis. Self-heating effect in drain current characteristics are investigated with temperature changes. The maximum drain saturation current observed with 180 nm (230 nm) substrate thickness are 1.1 (1.06) A/mm, 0.708 (0.705) A/mm and 0.502 (0.499) A/mm for 300 K, 550 K and 800 K, respectively. The influence of substrate layer thickness on drain current is less apparent at a higher temperature. The findings demonstrated that scattering processes that emerge when temperature increases above a particular amount cause both the mobility and the carrier concentration of 2DEG to decrease. Furthermore, A kink effect is seen in the drain current–voltage characteristics for gate voltage VGSâ€‰=â€‰− 2 V. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： High electron mobility transistors

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Fusion of Forward and Backward LSTMs for Effective Occlusion Reconstruction in Gait Sequences

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SN computer Science 2025年第3期6卷 1-20页

作者： Jain, Manav Chattopadhyay, Pratik Paul, Abhishek Jain, Jinesh Department of Computer Science and Engineering Indian Institute of Technology (Banaras Hindu University) Uttar Pradesh Varanasi 221005 India Department of Electronics and Communication Engineering National Institute of Technology West Bengal Durgapur 713209 India

Gait-based person identification in the presence of occlusion is a challenging problem and research work in this domain is still in its infancy. Some methods that have been developed in the past are based on certain assumptions such as gait features over a cycle following a Gaussian, or a complete cycle can be reconstructed from multiple occluded cycles. Recent deep neural network-based approaches to occlusion handling also mostly focus on making feature-level reconstruction instead of frame-level reconstruction, and thus their effectiveness is likely to suffer if several frames in a cycle are corrupted due to occlusion. There exists a single work on LSTM-based occlusion reconstruction which predicts frames based on the previous unoccluded/reconstructed frames only and does not utilize the complete sequence information effectively. In this paper, we improve the existing work on occlusion reconstruction by performing a two-way prediction using LSTMs and finally combining the two predictions. Our reconstruction model is based on (i) a forward LSTM that performs reconstruction in the forward direction by predicting each occluded frame from a few previous unoccluded or already reconstructed frames and (ii) a backward LSTM that carries out reconstruction in the backward direction by predicting each occluded frame from a few succeeding unoccluded or already reconstructed frames. To train the LSTMs, an extensive gallery set is constructed from the CASIA-B and the OU-ISIR LP data, and the mean-square loss between the corresponding generated and target frames is minimized. Next, a fusion network combines the predictions from the two LSTMs to generate the final reconstructed frame corresponding to each occluded frame. Evaluation of our approach has been performed using synthetically occluded sequences generated from the OU-ISIR LP, OU-ISIR MVLP and CASIA-B data and real occluded sequences present in the TUM-IITKGP and GREW data. The effectiveness of the proposed reconstructio

关键词： Gait recognition Long short term memory Occlusion detection and reconstruction Residual block

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Innovative Approach for Early Heart Disease Prediction: Gaussian Mixture Model and K-Nearest Neighbour 3

Innovative Approach for Early Heart Disease Prediction: Gaus...

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

作者： Surendran, R. Anandaraj, B. Deepa, P. Mageshwari, M. Balamurugan, K.S. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Madnapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer science and engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India Department of Electronics and communication engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

An Electrocardiogram (ECG) contributes significantly to early diagnosis and classification of heart diseases, arrhythmia which means irregular heart rate. Regrettably, the process became difficult due to asymmetric and minimal amplitude from recordings of ECG and cannot do the extraction from multiple signals. By using machine learning technology, it can classify and extract multiple samples, but cannot extract the data features from complex data structures. To overcome the limitations, previously introduced metaheuristic optimization (MHO) to improve the classification and extraction. Proposed a method which supports and K-Nearest Neighbour (KNN) Gaussian Mixture Model (GMM) algorithms for determine the abnormalities, predicting cardiac events, and improve diagnostic accuracy. As a result, this can help us to get results more quickly and accurately with sensitivity 99.7% and accuracy 99.2%, for helping healthcare professionals in diagnosing arrythmias, myocardial infarction and any other heart conditions. © 2025 IEEE.

关键词： Adversarial machine learning

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Revolutionizing Home Connectivity with IoT-Enabled Smart Mirrors for Internet Browsing and Smart Home Integration 5

Revolutionizing Home Connectivity with IoT-Enabled Smart Mir...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Prabha, M. Manicka Jegadeesan, S. Jayavathi, S.D. Rajkumar, G. Vinoth Jothi, J. Nirmal Krishnan, R. Santhana SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Information Technology Madurai India JP College of Engineering Department of Electronics and Communication Engineering Ayikudi Tenkasi India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India

ISBN: (纸本)9798350395235

This research study explores the transformative potential of IoT-Enabled Smart Mirrors to redefine home connectivity by enabling seamless internet browsing and smart home integration. It introduces a novel model algorithm, IoT-ViSense (IVS), which leverages IoT sensor data fusion with vision-based Convolutional Neural Networks (CNN) for real-time environmental monitoring and security. A comprehensive simulation analysis of IoT-ViSense is conducted and compared against existing algorithms using pertinent simulation metrics. IoT-Enabled Smart Mirrors serve as multifunctional interfaces, integrating personalized weather updates, news, calendar schedules, and real-time compliments. They also incorporate features such as a built-in camera for capturing high-quality selfies, on-demand workout sessions facilitated by fitness experts, and the ability to browse the internet, make calls, and interact with other connected devices. The proposed IoT-ViSense (IVS) algorithm combines IoT sensor data with Vision-based CNN analysis to enhance environmental monitoring and security. It continuously processes data from IoT sensors and camera feeds, detecting anomalies and patterns in real-time. By fusing these data streams, IVS enhances security by identifying unauthorized access and suspicious activities while concurrently optimizing resource usage and ensuring comfortable living and working environments. The simulation analysis of IoT-ViSense (IVS) is compared with suitable existing algorithms, assessing its efficiency in terms of accuracy, response time, and resource optimization. Results reveal that IoT-ViSense (IVS) outperforms other algorithms, demonstrating its prowess in achieving a harmonious blend of smart home integration and real-time environmental monitoring. © 2024 IEEE.

关键词： Convolutional neural networks

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IoT-Based Smart Learning Management System on Cloud 3

IoT-Based Smart Learning Management System on Cloud

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Chandak, Eshant Kothalkar, Mahesh Raghatwan, Pranali Rao, Y. Srinivasa Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India Sardar Patel Institute of Technology Department of Electronics and Telecommunication Mumbai India

ISBN: (纸本)9798350381931

In this pioneering research endeavor, we present an advanced online learning management system meticulously crafted for hands-on hardware experiments, heralding a transformative paradigm in circuit-related education. Exploiting the potent capabilities of Internet of Things (IoT) technology, our system not only facilitates remote monitoring but also ensures secure data storage in cloud platforms, contributing a unique and impactful dimension to the educational landscape. The focal point of our innovation lies in the systematic evaluation of student performance against predefined faculty criteria, fostering an environment that is dynamic and deeply *** by its provision of real-time, data-driven feedback to both students and educators, our system aspires to significantly enhance educational outcomes, marking a monumental stride in the realm of online education. This innovative approach empowers students with practical learning experiences, making a noteworthy contribution to the field of hardware experiment ***, the system's significance transcends traditional methodologies through the seamless incorporation of IoT connectivity. This integration not only facilitates the collection of valuable data on student progress and experiment outcomes but also enriches performance assessment, fostering a learning environment that is more interactive and personalized. Students can actively engage in hands-on learning experiences remotely, liberating education from the confines of traditional classroom *** inherent promise of this project lies in its capacity to seamlessly amalgamate cutting-edge technology, remote accessibility, and continuous performance assessment, thereby significantly elevating the quality of circuit-related education. This initiative represents a pivotal advancement in the ever-evolving landscape of online learning, promising a future where education is not only accessible but also tailored to the diverse needs of

关键词： Internet of things

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Leveraging Data Analytics to Enhance Smart Home Efficiency and Security 2

Leveraging Data Analytics to Enhance Smart Home Efficiency a...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Vivekanandan, Karthi Kaleeswari, B. Natchiammai, P.L. Anthonisamy, Arun Vijayabharathi, R. Alagarsamy, Manjunathan Builders Engineering College Department of Electrical and Electronics Engineering Tirupur India Department of Electronics and Communication Engineering Chennai India Mookambigai College of Engineering Department of Electronics and Communication Engineering Pudukkottai India Panimalar Engineering College Department of Computer Science and Business System Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India K. Ramakrishnan College of Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798331534950

Smart home technology has made daily life easier, automated, and managed. IoT devices like smart speakers, cameras, thermostats, and security systems are increasingly integrated, raising security and privacy concerns. This study examines smart home devices' energy consumption, use trends, device failures, and system efficiency. This research uses smart home devices to identify consumption trends and risks. To better understand smart home ecosystems, the study will evaluate energy consumption, device usage hours, and malfunctions that can affect functionality and security. Using predictive analytics and machine learning, the researchers predict excessive energy usage and device problems. These methods identify trends and irregularities in smart home device operation to improve performance, efficiency, and security. This study investigates how user decisions, usage hours, and device age affect energy consumption, as well as how to identify optimization opportunities. The data analysis study contains statistical summaries and visualisations of device type, usage hours, energy consumption, user preferences, malfunction incidents, and device age. Machine learning algorithms such as linear regression and anomaly detection predict energy usage and locate anomalies. The data illustrate energy and device efficiency trends and recommend improvements to smart home security and effectiveness. This study addresses smart home device vulnerabilities and inefficiencies to improve smart home system optimization. The results offer smart home technology manufacturers and customers ways to improve energy efficiency, device performance, and system security. © 2024 IEEE.

关键词： Smart manufacturing

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Collective Diagnostic Prototypical in Internet of Medical Things for Depression Identification using Deep Learning Algorithm

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Recent Patents on engineering 2024年第9期18卷 24-33页

作者： Selvarajan, Shitharth Thirumalaisamy, Manikandan Manoharan, Hariprasath Devendiran, Ramkumar Lakshmi Narayanan, S. Kumar, Ashwani Department of Computer Science Kebri Dehar University Kebri Dehar250 Ethiopia School of Computer Science Engineering and technology Bennett University U.P India Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Poonamallee Tamil Nadu Chennai600 123 India School of Computer Science and Engineering VIT-AP UNIVERSITY Andhra Pradesh Amaravathi522237 India Gojan School of Business and Technology Chennai India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

Background: The majority of wearable technology that is present in various patents for Internet of Medical Things (IoMT) health monitoring systems is introduced to recognize various bodily indicators. The enumerated patents indicate that monitored values are sent to a central server, where they are all treated by experts at the appropriate moment. Therefore, a new patent technique by expanding the use of wireless devices, has been discovered that such communication technologies can recognize specific depression traits and mood swings. Objectives: The major objective of the proposed method is to analyze the disputes that arise in the characteristics of an individual by observing the leveling periods that are identified from the processed image. In addition, the rate of data transfer in case of any dispute is maximized therefore recognition problem is solved at a minimized distance. Further, the steady state probability values are achieved at low delay thus minimizing the dropout packets in the monitored system using IoMT and LSTM. Methods: A balanced record with four distinct parameters—such as livelihood, self-reliance, correlation, and precision—is employed with the projected model on IoMT for depression identification. As a result, high data transfer rates and low distance separation are used to process the identification framework. Additionally, by combining an original matrix representation with the input feature set using LSTM, a novel framework with great efficiency is created. Results: In order to assess the results of IoMT using LSTM, four situations are split apart and their probability ratios are calculated. The results of each situation are then contrasted with the current methodology, and it is found that when there is a low dropout ratio, depression in a person is quickly diagnosed. Conclusion: The comparison analysis demonstrates that the proposed method, when compared to the current method, offers the best-compromised outcomes at roughly 64%. © 2024 B

关键词： Data transfer

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