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To Split or not to Split? A Simulation Study on the Network Convergence Duration of Multi-Area OSPF 46

To Split or not to Split? A Simulation Study on the Network ...

引用

46th International Conference on Telecommunications and Signal Processing, TSP 2023

作者： Sachinidis, Theofilos Politis, Anastasios C. Hilas, Constantinos S. International Hellenic University Dept. of Computer Informatics and Telecommunications Engineering Serres Greece

ISBN: (纸本)9798350303964

Internetworks with a large number of routing devices that utilise the Open Shortest Path First (OSPF) routing protocol, benefit greatly if they are designed in a hierarchical manner. The multi-area feature of OSPF leads to reduced network overhead, low processing and memory requirements and smaller routing tables. This, however, comes at the cost of increased configuration complexity, especially at the Area Border Routers. The threshold after which splitting the OSPF domain into multiple areas, is an ongoing debate among network administrators. A well known rule-of-thumb is recommended by large vendors of networking devices setting that threshold to 50 routing devices. The objective of this preliminary work is to investigate that threshold by conducting a simulation-based study using the OPNET network simulation tool. To assess the network performance, we select the network convergence duration as the quantitative Key Performance Indicator (KPI) of our study. Results indicate that the above mentioned threshold does not appear to be appropriate and a revision of that empirical rule should be considered. © 2023 IEEE.

关键词： Routing protocols

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Non-Invasive Oral Cancer Detection Using Hyperspectral Imaging and Advanced Spectral Unmixing Models

Non-Invasive Oral Cancer Detection Using Hyperspectral Imagi...

引用

2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Ayyapa, Valluri Kothamasu, Kesava Sriram Killaru, Priyusha Muddana, Saadhivik Aala, Suresh Gutha, Vamsi Kumar, Mukkara Prasana Chinnadurai, Sunil SRM University Dept.of Electronics and Communication Engineering AP India SRM University Dept. of Computer Science and Engineering AP India SRM University Dept. of Computer Science AP India

ISBN: (纸本)9798331508432

Oral cancer is a significant global health concern, often leading to high mortality rates due to late-stage diagnosis and the lack of effective early detection methods. Despite advances in medical science, the absence of reliable early diagnostic tools remains a critical challenge. Hyperspectral imaging (HSI) has emerged as a powerful noninvasive technology, capturing detailed spectral information across a wide range of wavelengths. This allows for accurate differentiation between cancerous and healthy tissues, improving early detection and enhancing treatment outcomes. In this study, we propose the use of HSI for early oral cancer diagnosis. To address the scarcity of labeled data, we developed a synthetic hyperspectral dataset that includes spectral signatures of both normal and cancerous tissues. The dataset was generated using a bilinear mixing model, with key spectral features extracted through Vertex Component Analysis (VCA) and abundances computed using Non-Negative Least Squares (NNLS). The model's performance was evaluated using Spectral angle distance (SAD) and Root mean square error (RMSE) metrics. Our findings demonstrate that HSI significantly improves the accuracy of early oral cancer detection, outperforming traditional methods. This work highlights the potential of advanced imaging technologies in revolutionizing cancer diagnosis, offering a robust framework for non-invasive detection and showcasing the effectiveness of synthetic datasets in medical imaging research. © 2024 IEEE.

关键词： Deep Learning Hyperspectral Imaging (HSI) Non-Negative Least Squares (NNLS) Oral Cancer Detection Root Mean Square Error (RMSE) Spectral Angle Distance (SAD) Synthetic Dataset Vertex Component Analysis (VCA)

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Machine Learning Aided Spectrum Sensing

Machine Learning Aided Spectrum Sensing

引用

2023 IEEE International Conference on Electrical, electronics, Communication and computers, ELEXCOM 2023

作者： Jabbal, Raunaq Singh Srivastava, Vishwas Prasad, Binod Dept. of Computer Science and Engineering MP Gwalior India Dept. of Electrical and Electronics Engineering MP Gwalior India

ISBN: (纸本)9798350305111

Spectrum sensing (SS) is a key concept that determines the effectiveness of cognitive radio (CR). Although multiple sensing practices have been proposed in the literature, out of them energy detector (ED) have been widely adopted. However, the performance of ED relies on pre-existing knowledge of noise distribution and threshold. Therefore, efficient spectrum sensing remains a challenge in wireless communication technologies. This paper aims to analyze the sensing performance of various machine learning (ML) models in different fading scenarios. We carefully model a spectrum sensing environment and generate a large-scale comprehensive dataset. We then train and test several well-established classical and ML techniques for spectrum sensing. The performance of classical spectrum sensing and ML-based spectrum sensing are compared under various parameters like the number of secondary user (SU), the distance between primary user (PU) and SU;and different fading environments such as Rayleigh, Rician, and Nakagami. The Simulation result shows that machine learning models outperform classical sensing techniques. Furthermore, it is found that support vector machine (SVM) outperformed other ML techniques in predicting channel availability. © 2023 IEEE.

关键词： Cognitive radio

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The Impact of Weather Conditions on Renewable Energy Production: A Machine Learning Approach

The Impact of Weather Conditions on Renewable Energy Product...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Özbay, Recep Çelebi, Merve Yavanoǧlu, Uraz Gazi University Graduate School of Informatics Dept. Of Computer Forensics Ankara Turkey Hatay Mustafa Kemal University Faculty of Education Dept. Of Computer Education and Instructional Technologies Hatay Turkey Gazi University Faculty of Engineering Dept. of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350362480

In recent decades, obtaining energy from wind, solar, and hydro energy has brought hope for a sustainable future. Machine learning (ML) significantly impacts the field of energy systems. ML techniques have emerged as a powerful tool for analyzing and optimizing energy systems. This approach can potentially reduce the cost of analyzing a device by focusing solely on the data generated during its operation. This paper investigates the application of regression models to predict renewable energy production based on weather conditions, aiming to enhance the reliability and efficiency of renewable energy systems. Experiments are performed using Decision Tree (DT), k-nearest Neighbors (KNN), Multiple Linear (MLR), Polynomial (PR), Random Forest (RF), and Support Vector (SVR) Regression algorithms. The performances of ML models are compared based on the evaluations obtained through the Coefficient of Determination (R2), and error measures namely Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE). RF is the most successful algorithm in predicting renewable energy production depending on weather conditions as it has the highest R2 value and the lowest error measures. © 2024 IEEE.

关键词： Clean energy

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Fringing Effects in a Capacitive Micromachined Ultrasonic Transducer with a SiC Actuation Layer Insulated by High Dielectric Material 3

Fringing Effects in a Capacitive Micromachined Ultrasonic Tr...

引用

3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Ahamed, Sk. Sohel Pal, Moumita Chakraborty, Pritam Neogi, Biswarup Maity, Niladri Das, Reshmi Future Institute of Engineering and Management Dept. of electronics and communication kolkata India JIS College of Engineering Dept. of electronics and communication Kalyani India Institute of Engineering and Management Dept. of computer science and application Kolkata India NITTTR Dept. of technical education Kolkata India Mizoram University Dept. of electronics and communication Mizoram India

ISBN: (纸本)9798350374643

Sealed capacitive micromachined ultrasonic transducer (CMUT) mostly consists of two parallel electrodes separated by a structural layer of silicon nitride (Si3N4) and a vacuum cavity. Biasing the transducer element generates electrostatic force of attraction where the lines in the field bulge at the boundaries of the layers contributing to fringing effects. These fringing capacitances result in overall increment of the device capacitance. To capture this effect a very simple model of Landau and Lifschitz is incorporated, where the analytical model outputs are well matched with commercially available PZFlex FEM simulated results. Because silicon carbide (SiC) has a higher Young's modulus and lower residual stress than Si3N4, it is used as a structural material in the CMUT. The improvement in device capacitance is approximately 13.1% through adjustments in the thickness of gaps and insulation. However, factoring in the relative dielectric constant and the electrode radius, this improvement reduces to around 7.9%. Additionally, introducing a high-k insulating layer enables the upper electrode to be structured on the lower face of the SiC layer. This modification leads to an augmentation in the device's total capacitance. This article also examines HfO2 materials of varied thicknesses. © 2024 IEEE.

关键词： Silicon nitride

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Detection of Cardiovascular Activities of Multiple Patients using MIMO mmWave Radar

Detection of Cardiovascular Activities of Multiple Patients ...

引用

2024 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2024

作者： Jena, Paramananda Sahu, Kedar Nath Behera, Sagarika Electronics and Radar Development Establishment DRDO Bengaluru India Dept. of Electronics and Communication Engineering Stanley College of Engineering and Technology for Women Hyderabad India Dept. of Computer Science and Engineering CMR Institute of Technology Bengaluru India

ISBN: (纸本)9798350379693

This paper presents the detection and analysis of cardiovascular activities for localisation of multiple patients using Doppler signals associated with their chest wall motion, heart beat and sleep disorder. The modeling of the vital signs has been carried out considering the vibrating surface of human body illuminated by a radar. The main challenge of a medical radar is the noncontact diagnosis of multiple people. MIMO radar has enormous potential in two areas: micro Doppler detection and fine angular resolution. In this work, MIMO radar is designed and analysis of micro Doppler due to (i) chest movement, (ii) heartbeat and (iii) tidal volume of patients in their sleeping conditions has been carried out using MATLAB models. © 2024 IEEE.

关键词： Doppler radar

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CRISP - DM Process in Smart Building Management 11

CRISP - DM Process in Smart Building Management

引用

11th International Conference on Electrical and electronics engineering, ICEEE 2024

作者： Dursun, Mahir Sabyrzhan, Moldir Gazi University Faculty of Technology Dept. of Electrical and Electronics Engineering Ankara Turkey Graduate School of Informatics Gazi University Department of Computer Science Ankara Turkey

ISBN: (纸本)9798350362541

Smart Buildings are buildings that integrate and combine intelligence, management, control, materials, and construction as a single system to meet all the needs of the building, such as comfort, energy efficiency, and durability. In universities, laboratories are used less frequently than other facilities and classrooms. Courses and students may change in the same classroom, and the period of use of such classrooms is much more extended than in laboratories. However, classrooms may only be used for classes during long breaks or for short periods of non-use between exams. In addition, there is no provision for when the heating system will start to heat up before classes or when it will be turned off after classes. By planning heating and lightning, energy and costs will be saved. This paper presents data acquisition methods for preparing a dataset to create a machine learning (ML) model to save energy loss in university laboratories. The developed model will predict energy consumption in classrooms and laboratories based on weather conditions. The study is being conducted in two stages. The first stage describes how to collect data using electronic devices such as Internet of Things (IoT) controllers of Smart Building Management Systems. The second stage gave possibilities for data analysis and preparation using the phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM). © 2024 IEEE.

关键词： Information management

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JARVIS - Smart AI Support System 3

JARVIS - Smart AI Support System

引用

3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Ramya Manaswi, V. Saketh, Meduri Ravi Tejha, Mandumula Sai Karthikeya, Atluri Sai Prakash, Dharani Chandra Alkhayyat, Ahmad Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science Engineering Hyderabad India Gitam University Dept. of Computer Science Engineering with Specialisation of Cybersecurity Hyderabad India K.G Reddy College of Engineering and Technology Dept. of Electronics & Communication Engineering Telangana Hyderabad India College of Technical Engineering The Islamic University Dept. of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350354379

The JARVIS AI Support System represents a remarkable fusion of modern technology, blending a sophisticated GUI design, seamless voice control, and inventive features like the captivating 'Air Canvas' facilitated by OpenCV. This AI-driven virtual assistant offers users a natural and intuitive experience, allowing them to effortlessly perform tasks such as browsing the web, interacting with a chatbot, and executing dynamic voice- controlled actions. Moreover, the system showcases advanced capabilities including motion detection and facial recognition with an accuracy of 95% in multiple runs. Leveraging the power of computer vision, the Air Canvas feature empowers users to express creativity through fluid hand gestures, while voice commands effortlessly manage diverse tasks. This innovative project presents an approachable way to interact with technology in the world of AI. © 2024 IEEE.

关键词： Face recognition

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IoT-Driven Smart Car Parking System: Optimizing Urban Mobility and Reducing Environmental Impact 3

IoT-Driven Smart Car Parking System: Optimizing Urban Mobili...

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

作者： Tanvi, Y. Lakshmi Sowmya, B. Prashanth, B.N. Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The rapid increase in urban vehicle numbers has significantly worsened traffic congestion, particularly in public parking spaces, where conventional parking systems often prove inefficient, leading to wasted time, excessive fuel consumption, and increased environmental pollution. A Smart Car Parking System is proposed to address these challenges by integrating advanced technologies, including the Internet of Things (IoT), to optimize parking operations. The system employs sensors embedded in parking spaces to detect vehicle presence and provide real-time data on availability. This enables drivers to locate free parking spaces efficiently, improving traffic flow. Additionally, the system offers valuable insights to city planners and facility managers, supporting optimal space allocation and dynamic pricing models. By reducing vehicle idling and unnecessary driving while searching for parking, the system helps minimize emissions, contributing to environmental sustainability. The study builds an automated smart car parking system for parking slot monitoring and gate control. The system ensures correct slot occupancy reading, status displayed in real time, and smooth operation of an opening and closing system. It manages additional features such as real-time sensor calibration and effective gate control with a high degree of accuracy and accountability. The proposed Smart Car Parking System represents a key innovation in urban mobility, offering a user-friendly, efficient, and ecofriendly solution to one of the most persistent challenges faced by modern cities. © 2025 IEEE.

关键词： Traffic congestion

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Speech Recognition Paradigms: A Comparative Evaluation of SpeechBrain, Whisper and Wav2Vec2 Models 9

Speech Recognition Paradigms: A Comparative Evaluation of Sp...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Yerramreddy, Dhanvanth Reddy Marasani, Jayasurya Gowtham, Ponnuru Sathwik Venkata Harshit, Guduru Anjali Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Engineering Amritapuri India Amrita Vishwa Vidyapeetham School of Engineering Dept. of Electronics & Communication Engineering Amritapuri India

ISBN: (纸本)9798350394474

Speech recognition plays a pivotal role in the realm of natural language processing that deals in converting the language into the written text, providing human-computer interaction and enables us to use it widely for applications starting with voice assistants and delving upto the transcription services. Due to the complexity present in performing the task of speech recognition has led to the development of various models to enhance accuracy and efficiency. Our project mainly delves into three prominent speech recognition models that are Whisper, Wav2Vec2 and Speechbrain each of them representing distinct approaches of transcribing spoken language. The significance of the models used lies in their potential to perform better for realtime applications by improving the accuracy and reliability of speech recognition. To evaluate the best model that performs effectively, an array of metrics are used including levenshtein distance and it's similarity percentage, jaccard similarity along with semantic similarity that provides an additional layer of evaluation describing the model's understanding of contextual meaning in spoken language. Out of the three models, Speech Brain model outperformed all the other models through the calculation of Word Error Rate (WER), Character Error rate (CER) and BLEU score. The results have shown the model's efficiency in converting spoken language(audio files) into precise and contextually relevant text. The results shows that these models contribute to the field of speech recognition highlighting the strengths of each approach and among them considering speechbrain as an ideal solution for accurate and meaningful transcription. © 2024 IEEE.

关键词： Speech recognition

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