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5th IEEE International Conference on Smart Internet of Things, SmartIoT 2021

作者： Piperigkos, Nikos Lalos, Aris S. Berberidis, Kostas Computer Engineering and Informatics Department University of Patras Greece Industrial Systems Institute Athena Research Center Patras Greece

ISBN: (纸本)9781665445115

Cooperative autonomous driving in 5G and smart cities environment is expected to further improve safety, security and efficiency of transportation systems. To this end, involved vehicles is imperative to have accurate knowledge of both their own and neighboring vehicles’ location, a task known as cooperative awareness. In this paper, we have formulated two novel distributed localization and tracking schemes, based on Gradient Descent and Extended Kalman Filter algorithms, to cope with erroneous GPS location. Sensor-rich vehicles exploit Vehicle-to-Vehicle communications and a multitude of integrated sensors, like LIDAR and Cameras, to generate and fuse heterogeneous data. Each vehicle interacts only with its own connected neighboring vehicles, formulating individual star topologies. Extensive simulation studies using CARLA autonomous driving simulator, verify the significant reduction of GPS error achieved by the two methods in various experimental conditions. Distributed tracking proves to be much superior than Gradient descent algorithm, both in the case of self (58% reduction of GPS error) and neighboring vehicles location estimation (38% reduction of average GPS error). © 2021 IEEE.

关键词： 5G mobile communication systems

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MECG-E: Mamba-based ECG Enhancer for Baseline Wander Removal

MECG-E: Mamba-based ECG Enhancer for Baseline Wander Removal

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IEEE International Conference on Big Data

作者： Kuo-Hsuan Hung Kuan-Chen Wang Kai-Chun Liu Wei-Lun Chen Xugang Lu Yu Tsao Chii-Wann Lin Department of Biomedical Engineering National Taiwan University Taiwan Research Center for Information Technology Innovation Academic Sinica Taiwan College of Information and Computer Sciences University of Massachusetts Amherst USA National Institute of Information and Communications Technology Japan

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Electrocardiogram (ECG) is an important non-invasive method for diagnosing cardiovascular disease. However, ECG signals are susceptible to noise contamination, such as electrical interference or signal wandering, which reduces diagnostic accuracy. Various ECG denoising methods have been proposed, but most existing methods yield suboptimal performance under very noisy conditions or require several steps during inference, leading to latency during online processing. In this paper, we propose a novel ECG denoising model, namely Mamba-based ECG Enhancer (MECG-E), which leverages the Mamba architecture known for its fast inference and outstanding nonlinear mapping capabilities. Experimental results indicate that MECG-E surpasses several well-known existing models across multiple metrics under different noise conditions. Additionally, MECG-E requires less inference time than state-of-the-art diffusion-based ECG denoisers, demonstrating the model’s functionality and efficiency.

关键词： Electric potential Noise reduction Interference Electrocardiography Real-time systems Data models Noise measurement Cardiovascular diseases Contamination Signal to noise ratio

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Data Driven Methods to Predict Vertical Profiles of the Rails: A Case Study

Data Driven Methods to Predict Vertical Profiles of the Rail...

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International Conference on ICT For Smart Society (ICISS)

作者： Ade Chandra Nugraha Suhono Harso Supangkat IGB Baskara Nugraha Yunendar Aryo Handoko Awan Hermawan Purwadinata Dimas Wisnu Saputro Department of Computer Engineering and Informatics Bandung State of Polytechnic School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Smart City and Community Innovation Center School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Faculty of Mechanical and Aerospace Engineering Bandung Institute of Technology Bandung Indonesia Managing Director of Infrastructure PT Kereta Api Indonesia Bandung Indonesia Department of Computer Engineering and Informatics Bandung State of Polytechnic Bandung Indonesia

Predicting the value of railroad track geometry is needed to reduce the cost and time of rail condition measurements that have been carried out periodically every three months by railroad operators in Indonesia (PT Kereta Api Indonesia/PT KAI). Four track geometry parameters - vertical profile, horizontal alignment, twist, and gauge - are the reference measurements that will be mapped with alternative measurement results using accelerometers installed on regular trains (in-service trains). In this paper, eight prediction algorithms from the machine learning model are applied to map the vertical profile with 73 kilometers of measurement data in the Jakarta to Padalarang railroad segment, precisely on the Jatinegara-Cikampek measuring trip conducted from April 2019 to June 2022. The evaluation performance of the prediction results has been measured with the GadientBoosting Regressor algorithm is the best algorithm with MSE = 0.791, R^2 = 0.507 and feature importance on the Train speed and Az attributes. The research can be continued to detect the correlation of three other track geometry parameters referring to the dataset of rail geometry condition measurement results through accelerometers on regular trains (in-service trains).

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Pending Interest Table (PIT) Performance Analysis in Named Data Networking on Palapa Ring Topology 16

Pending Interest Table (PIT) Performance Analysis in Named D...

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16th International Conference on Telecommunication Systems Services and Applications, TSSA 2022

作者： Sucipto, Adi Jupriyadi Ahdan, Syaiful Arifin, Hasan Nur Zaki Hamidi, Eki Ahmad Syambas, Nana Rachmana School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Lampung35132 Indonesia Uin Sunan Gunung Djati Department of Electrical Engineering Bandung40132 Indonesia

ISBN: (纸本)9781665455947

The concept of the internet in the future will prioritize content, by reducing delays in data transmission. Named Data Networking (NDN) is a content-based future internet concept that changes the paradigm of using IP. Inside the NDN router, there are three data structures, namely Content Store (CS), Pending Interest Table (PIT), and Forwarding Information Base (FIB). Pending Interest Table (PIT) contains a list of unfulfilled interests. This condition occurs when the node has not received a response after the interest forwarding process. Measurable and fast PIT performance is a challenge in Named Data Networks. In this study, we will try to do a simulation to measure and analyze the performance of PIT in NDN in the Palapa Ring topology. The research was conducted using the NDNSim simulator, to see the performance in the PIT. The simulation and analysis of the results show that the granularity of a prefix has an effect on In Satisfied Interest in an NDN network. At the number of interests of 100, the result obtained from the simulation is that there is a decrease in the percentage of interest data served, amounting to more than 20%. At the amount of interest in 1000 about more than 30%. The length of the prefix and the number of interest sent by the consumer affect the performance of the PIT, seen from the number of In Satisfied Interests. © 2022 IEEE.

关键词： Topology

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A Framework for Democratizing Open-Source Decision-Making using Decentralized Autonomous Organization

A Framework for Democratizing Open-Source Decision-Making us...

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Computing and informatics (ICCI), International Conference on

作者： Khalifa H. Afifi Khaled M. Fouad Heba K. Aslan Faculty of Information Technology and Computer Science Nile University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Faculty of Computers and Engineering New Mansoura University Egypt Informatics Department Electronics Research Institute (ERI) Cairo Egypt Center of Informatics Science Faculty of Information Technology and Computer Science Nile University Cairro Egypt

The open Source Software (OSS) became the backbone of the most heavily used technologies, including operating systems, cloud computing, AI, Blockchain, Bigdata Systems, IoT, and many more. Although the OSS individual contributors are the primary power for developing the OSS projects, they do not contribute to the OSS project's decisionmaking as much as their contributions in the OSS Projects development. This paper proposes a framework to democratize the OSS Project's decision-making using a blockchain-related technology called Decentralized Autonomous Organization (DAO). Using DAO, contributors get incentive tokens as a reward in return for contributions. The earned tokens can be used in the process of decision-making governance. This governance model tends to allow OSS individual contributors to have a public voice in the OSS project's development roadmap in particular, and the OSS decision making governance in general.

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The Role of Non-Invasive Neural Devices in Modern Neurorehabilitation and Mental Health Treatment

The Role of Non-Invasive Neural Devices in Modern Neurorehab...

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International Conference on Trends in Electronics and informatics (ICEI)

作者： Adesh Pawar Palash Gourshettiwar Pradnyawant M. Gote Vishal Kumar Choudhary Prajyot Yesankar Shrikant Khadse Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (D.U) Wardha Maharashtra India Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (D.U.) Wardha Maharashtra India Department of Artificial Intelligence &Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (D.U.) Wardha Maharashtra India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Modern neurorehabilitation and mental health treatment have received transformative tools through non-invasive neural devices that offer effective and patient-friendly alternatives to traditional interventions. Technologies in this regard include transcranial magnetic stimulation, transcranial direct current stimulation, EEG-based neurofeedback, and functional near-infrared spectroscopy, where advances in neuroscience and bioengineering are utilised in modulating brain activity without risks associated with surgical procedures. Their applications in neurorehabilitation range from post-stroke motor recovery to cognitive rehabilitation and treatment of mental health disorders, including depression, anxiety, and PTSD. Focusing on personalised medicine, machine learning, and big data could be used in non-invasive neural devices to fine-tune outcomes tailored to an individual's needs. These are seminal challenges, such as accessibility, variability in patient responses, and regulatory hurdles, in the face of these devices offering much promise regarding enhancing the quality of life and improving cognitive functions, besides providing sustainable, long-term mental health. This paper underlines their potential and discusses future directions in integration.

关键词： Neuroplasticity Transcranial magnetic stimulation Surgery Mental health Machine learning Motors Market research Neurorehabilitation Transcranial direct current stimulation Neurofeedback

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Prediction of Favorability of Maxillary Canine Impaction Using Artificial Intelligence Algorithm

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Journal of Indian Orthodontic Society 2024年第3期58卷 291 - 302页

作者： Dilip Kumar M. Bhadrinath S. Balika J. Chelliah Umamageshwari A. Deepa S. Department of Orthodontics and Dentofacial Orthopaedics Sri Ramachandra Institute of Higher Education and Research (SRIHER) Chennai Tamil Nadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India

Objective To test the accuracy of an artificial intelligence (AI) algorithm in predicting the favorability of maxillary canine impaction as compared to the conventional manual tracing method using orthopantomograms. Materials and Methods Orthopantomograms of 437 canine impactions were included in this study. Six parameters (sector classification, three angular, and two linear) on orthopantomograms were used to assess the severity of the maxillary canine impaction. The most advanced convolutional neural network model implemented using MATLAB program was used in assessing the favorability of maxillary canine impaction. The outcome of the parameters assessed by the implemented AI model and conventional manual tracing were compared. Receiver operating characteristic (ROC) curves, diagnostic accuracy, sensitivity, specificity, and positive and negative predictive values were used to assess the performance of AI model in comparison to manual tracing for the angular and sector parameters. Paired t-tests were used for linear measurements. Results The overall clinical performance exceeded 90% for all the angular parameters including sector classification except for the angle between long axis of canine with lateral incisor which had a specificity score of 55%. The value and the area under the ROC curve were more than 0.9 for all the parameters. The distance from the canine cusp tip to the occlusal plane and midline was statistically significant between the groups (p = .000). Conclusion The proposed AI algorithm had higher accuracy in predicting the favorability of eruption in maxillary canine impactions compared to conventional manual tracing.

关键词： Maxillary canine impaction diagnosis accuracy artificial intelligence

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Design, Analysis and Fabrication of Dual Band Microstrip Patch Antenna for (L2) Band GPS and WiFi Applications

Design, Analysis and Fabrication of Dual Band Microstrip Pat...

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2022 International Conference for Advancement in technology, ICONAT 2022

作者： Oo, Wai Mar Tun, Hla Myo Nway, Tint May Pradhan, Devasis Sahu, Prasanna Kumar Naing, Zaw Min Yangon Technological University Department of Electronic Engineering Yangon Myanmar Yangon Technological University Faculty of Electrical and Computer Engineering Yangon Myanmar Acharya Institute of Technology Department of Electronics and Communication Engineering Bengaluru India National Institute of Technology Department of Electrical Engineering Odisha Rourkela India Ministry of Science and Technology Department of Research and Innovation Yangon Myanmar

ISBN: (纸本)9781665425773

Dual-frequency antennas are popular for many applications in communication systems. This work proposes a dual-band microstrip patch antenna design based on two-layer stacked patch for GPS and WiFi purposes. The targeted antenna is created to maneuver at the frequency range of 1.227GHz GPS L2 band (1.215GHz to 1.239GHz) and the 2.4GHz WiFi band frequency (2.4GHz to 2.48GHz). The antenna is fabricated on substrate of FR4 which dielectric constant 4.4 and the substrate material's height is 1.6mm. The created two-layer stacked patch antenna is compared with a conformist single patch antenna and analyzed the parameters of performance specifications for return loss (RL), bandwidth, VSWR, gain and pattern of radiation. It is recognized that the performance of the fabricated antenna improves when compared to the single-layer conventional antenna. © 2022 IEEE.

关键词： Global positioning system

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The Realization Factors of Taiwan's Low Altitude Airspace Economy and UAM: Analysis and Evaluation 20

The Realization Factors of Taiwan's Low Altitude Airspace Ec...

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20th IEEE International Conference on e-Business engineering, ICEBE 2024

作者： Wu, Tung-Ling Kung, Chih-Ming Lin, Guan-Ting Tsai, Kuen-Yu Meng, Guang-Yun Huang, Yu-Che Cheng, Wan-Shu Institute of Transportation at Ministry of Transportation and Communications Taiwan Shin Chin Univeristy Kaohsiung Campus Dep. of Information Technology&Communication Taiwan Providence University Dep. of Computer Science and Information Management Taichung Taiwan National Taiwan Univeristy Department of Electrical Engineering Taipei Taiwan Executive Officer Center for Research on High-Performance Remote Sensing and Urban Informatics Ntu Taiwan

ISBN: (纸本)9798350365856

Low altitude airspace economy applications will be of highly effective in a wide range of civil activities and will be a critical element in the smart cities of the future. In view of the main features of unmanned aerial vehicle (UAV), such as automatic piloting system, access to uninhabited areas, low cost compared to road and manned aircraft, shorter delivery time, and the ability to carry heavy payloads, it is important to recognize that UAVs can be used for a wide range of civil activities. With the development of these applications in full swing around the world, in addition to advanced communications technology and other related infrastructure, business demand and a favorable investment climate, clarity, definiteness and feasible regulations in the Taiwanese environment are also essential. Based on the premise of local economic strength, the technical level of the telecommunication industry, and commercial demands, Taiwan should establish a rational standard for the UAV regulatory system as soon as possible, so as to facilitate the development of UAM and to ensure the international competitiveness of the related technologies for market development © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Optimizing complex event forecasting 22

Optimizing complex event forecasting

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16th ACM International Conference on Distributed and Event-Based Systems, DEBS 2022

作者： Stavropoulos, Vasileios Alevizos, Elias Giatrakos, Nikos Artikis, Alexander Institute of Informatics and Telecommunications Ncsr Demokritos Greece School of Electrical and Computer Engineering Technical University of Crete Greece Athena Research Center Institute for Management of Information Systems Greece Department of Maritime Studies University of Piraeus Greece

ISBN: (纸本)9781450393089

In Complex Event Recognition (CER), applications express business rules in the form of patterns and deploy them in a CER Engine which seeks the occurrence of such patterns on incoming streams. This is useful for practical applications which rely on the timely detection of patterns to support critical decisions. One step further, stakeholders want to act proactively, accurately forecasting the occurrence of patterns on raw streams well ahead of time to better schedule their decisions. This calls for making the transition from CER to Complex Event Forecasting (CEF). In CEF, stochastic models of future behavior are embedded into the event processing loop to project into the future the sequence of events that have occurred so far and to estimate the likelihood of the imminent occurrence of more complex patterns. CEF performance engages the stochastic model's training speed and forecast accuracy. In turn, these performance dimensions are affected by few parameters. However, CEF parameter tuning so that optimal CEF performance is achieved is a non-trivial task. This is due to the fact that there is an infinite number of possible parameter combinations, each affecting CEF performance in ways which are hard to predict. In this work, we introduce the first CEF Optimizer that gracefully automates CEF parameter tuning decisions, rapidly cherry picking good CEF configurations. We detail the novel internal architecture of our CEF Optimizer and present an elaborate empirical analysis on two applications that illustrates the effectiveness of our optimization approach. © 2022 ACM.

关键词： Stochastic models

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