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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Xiran Xiong Weibo Zheng Shulei Wang Yuanzhe Qu Shiqi Chen Yongchun Yuan Wentao Li Yiming Hong Bingyao Cao Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Engineering No.1 Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai China Engineering No.1 Shanghai Institute of Technical Physics Chinese Academy of Sciences University of Chinese Academy of Sciences Shanghai China

We propose a low-delay modified OLSR scheme for FANET. Through hardware testing, compared with the OLSR protocol, EED and RO have decreased by 100 times and 8% respectively, and PDR has increased by 3%.

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

关键词： Protocols Asia Routing Optical fiber communication Hardware Delays Photonics Testing

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Smart Warehouse Solutions for Efficient Onion Buffer Stock Management System 7

Smart Warehouse Solutions for Efficient Onion Buffer Stock M...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Raman, Ramakrishnan Ramalingam, L. Kishorbhai Sutaria, Kamal Kamthan, Manika Sangeetha, S. Murugan, S. Maharashtra Pune India Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Parul Institute of Engineering and Technology Faculty of Engineering and Technology Parul University Gujarat Vadodara India Symbiosis Law School Maharashtra Pune India Karpagam College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350340600

This research study presents an innovative smart warehouse system for onion buffer stock management. The system includes an RFID reader, a central controlling unit, a displaying unit, a temperature sensor, a moisture sensor, a relay, a cooling fan, storage nodes, and a cloud server. The RFID reader automatically tracks onion crates or pallets in the warehouse, enabling real-time visibility and traceability. The central controlling unit coordinates activities and collects data from sensors. Onions are stored at appropriate temperatures and moisture levels via sensors. Warehouse workers can monitor this data in real-time on the displaying unit once the central controlling unit processes it. Maintaining temperature and humidity need the relay and cooling fan. Based on sensor data, the main controlling unit activates the cooling fan to avoid onion spoiling. Storage nodes in the warehouse store onion bags and pallets. These nodes have RFID tags or sensors for convenient tracking of stock. data analysis, storage, and integration depend on the cloud server. It allows advanced data analytics, forecasting, and decision-making. It also helps suppliers, distributors, and retailers communicate and distribute products efficiently. Real-time visibility, smart monitoring, and proactive management of storage conditions advance efficient operations, prevent spoiling, and improve onion supply chain efficiency. © 2023 IEEE.

关键词： Decision making

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An Intelligent Method for Train Accident Prevention and Detection Using ResNet and Long Short-Term Memory Network

An Intelligent Method for Train Accident Prevention and Dete...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Jeyaselvi, M. Kavitha, V. Kalaiarasi, S. Sekhar, Savanam Chandra Girdharwal, Nitin Sindhu, K. Srm Institute of Science and Technology Department of Networking and Communications Chennai Kattankulathur India Vardhaman College of Engineering Department of Mathematics Hyderabad India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Data Science Chennai India Koneru Lakshmaiah Education Foundation Kl University Department of Bba Telangana Vijayawada India Faculty of Management Indore India M.Kumarasamy College of Engineering Master of Computer Applications Tamilnadu Karur India

ISBN: (纸本)9798350300857

At unattended railroad crossings, numerous technologies have been developed to monitor trains, identify objects, and automatically open and close gates. In order to prevent accidents at the crossing, the currently planned systems may effectively close and open the gates upon the arrival and departure of trains. However, there are frequently incidents at railroad crossings caused by the unexpected arrival of trains. Indulging in headphone use is a leading cause of accidents on railroad tracks. The literature review revealed that no viable approach for preventing such mishaps had been developed. The purpose of this proposed system is to provide a comprehensive analysis of the current systems dedicated to the prevention of accidents in real time. Preprocessing, feature selection, and model performance evaluation are all used in the suggested method. The DWT method is used for preprocessing in the suggested method. PCA and LDA are used for feature selection. The suggested method employs ResNet LS TM to assess the efficiency of the model. Two conventional approaches, LSTM and GRU, are contrasted with the proposed methodology. The proposed method outperforms the other two. © 2023 IEEE.

关键词： Discrete wavelet transforms

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Cloud Computing-Enhanced and Raspberry Pi-powered Solution for Remote Ear, Nose, and Throat Consultations with AI-Driven Diagnostics 5

Cloud Computing-Enhanced and Raspberry Pi-powered Solution f...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Giriprasad, S. Karpagam, T. Nalini, V. Mishra, Nilamadhab Mohankumar, N. Malathi, N. Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Tamil Nadu Chennai India Department of Mathematics Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Tamil Nadu Chennai India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Electronics and Communication Engineering B.V. Raju Institute of Technology Telangana India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350361155

The cutting-edge telemedicine program presents a game-changing alternative for patients seeking ENT (ear, nose, and throat) consultations from a distant location. Utilizing the capabilities of Raspberry Pi devices, powerful artificial intelligence, and cloud computing, the system allows otoscopic tests to be performed without interruption while securely transmitting data to the cloud. On a web-based platform, interactive patient-professional consultations are made possible via real-time communication, which is made possible by WebRTC. Cloud services are very important because of their ability to provide scalable storage as well as AI-driven diagnostics for accurate evaluations. The system complies with the requirements for healthcare compliance, emphasizes security and privacy, and features encryption from beginning to finish. This integrated approach promises scalability, reliability, and advanced diagnostic capabilities, and it reshapes the landscape of remote ENT care with accessible and technologically advanced healthcare solutions. With a user-friendly interface for patients and a cloud-hosted application for healthcare professionals, this integrated approach provides patients with advanced healthcare solutions. © 2024 IEEE.

关键词： Artificial intelligence

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Concurrent Pipeline Rendering Scheme based on GPU multi-queue and partitioning images

Concurrent Pipeline Rendering Scheme based on GPU multi-queu...

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2023 International Conference on Optics and Machine Vision, ICOMV 2023

作者： Qiu, Zixia Ma, Junchao Zhang, Haitang Sibahee, Mustafa A.Al. Abduljabbar, Zaid Ameen Nyangaresi, Vincent Omollo College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China College of Applied Technology Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61001 Iraq Technical Computer Engineering Department Al-Kunooze University College Basrah61001 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518063 China Faculty of Biological & Physical Sciences Tom Mboya University Homabay40300 Kenya

ISBN: (纸本)9781510664777

Compared with rasterization rendering, ray tracing rendering can improve the image’s visual effect and make the image look more realistic. Real-time ray tracing requires very high computing power of graphics processing unit (GPU). When the number of GPUs is limited and the performance of a single GPU cannot be fully utilized, high rendering latency will occur. In this paper, we propose Multi-Queue Concurrent Pipeline Rendering (MQCPR), a novel ray-tracing parallel rendering scheme based on GPU multi-queue. This scheme divides the image area into multiple parts and uses multi-queue of GPU to enable the computation and transmission tasks in the rendering process to be executed simultaneously, which can maximize the performance of a single GPU and improve the graphics rendering speed. MQCPR may keep the GPU busy to make full use of the GPU resources. Experiments illustrate that in the case of a single GPU, compared with the single queue serial rendering scheme, the number of frames per second (FPS) is increased by 1.5 times after using MQCPR. © 2023 SPIE. All rights reserved.

关键词： Graphics processing unit

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SCIM MILQ: An HPC Quantum Scheduler

arXiv

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arXiv 2024年

作者： Seitz, Philipp Geiger, Manuel Ufrecht, Christian Plinge, Axel Mutschler, Christopher Scherer, Daniel D. Mendl, Christian B. Technical University of Munich TUM School of Computation Information and Technology Department of Computer Science Boltzmannstraße 3 Garching85748 Germany Fraunhofer IIS Fraunhofer Institute for Integrated Circuits IIS Division Positioning and Networks Nordostpark 84 Nürnberg90411 Germany Technical University of Munich TUM Institute for Advanced Study Germany

With the increasing sophistication and capability of quantum hardware, its integration, and employment in high performance computing (HPC) infrastructure becomes relevant. This opens largely unexplored access models and scheduling questions in such quantum-classical computing environments, going beyond the current cloud access model. SCIM MILQ is a scheduler for quantum tasks in HPC infrastructure. It combines well-established scheduling techniques with methods unique to quantum computing, such as circuit cutting. SCIM MILQ can schedule tasks while minimizing the makespan, i.e., the time that elapses from the start of work to the end, improving on average by 25 %. Additionally, it reduces the noise in the circuit by up to 10 %, increasing the outcome's reliability. We compare it against an existing baseline and show its viability in an HPC environment. Copyright © 2024, The Authors. All rights reserved.

关键词： Qubits

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Numerical simulation of endovascular treatment options for cerebral aneurysms

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GAMM Mitteilungen 2024年第3期47卷

作者： Frank, Martin Holzberger, Fabian Horvat, Medeea Kirschke, Jan Mayr, Matthias Muhr, Markus Nebulishvili, Natalia Popp, Alexander Schwarting, Julian Wohlmuth, Barbara University of the Bundeswehr Munich Institute for Mathematics and Computer-Based Simulation Neubiberg Germany Technical University of Munich School of Computation Information and Technology Department of Mathematics Chair for Numerical Mathematics Garching Germany Department of Neuroradiology Technical University of Munich Munich Germany University of the Bundeswehr Munich Data Science & Computing Lab Neubiberg Germany

Predicting the long-term success of endovascular interventions in the clinical management of cerebral aneurysms requires detailed insight into the patient-specific physiological conditions. In this work, we not only propose numerical representations of endovascular medical devices such as coils, flow diverters or Woven EndoBridge but also outline numerical models for the prediction of blood flow patterns in the aneurysm cavity right after a surgical intervention. Detailed knowledge about the postsurgical state then lays the basis to assess the chances of a stable occlusion of the aneurysm required for a long-term treatment success. To this end, we propose mathematical and mechanical models of endovascular medical devices made out of thin metal wires. These can then be used for fully resolved flow simulations of the postsurgical blood flow, which in this work will be performed by means of a Lattice Boltzmann method applied to the incompressible Navier–Stokes equations and patient-specific geometries. To probe the suitability of homogenized models, we also investigate poro-elastic models to represent such medical devices. In particular, we examine the validity of this modeling approach for flow diverter placement across the opening of the aneurysm cavity. For both approaches, physiologically meaningful boundary conditions are provided from reduced-order models of the vascular system. The present study demonstrates our capabilities to predict the postsurgical state and lays a solid foundation to tackle the prediction of thrombus formation and, thus, the aneurysm occlusion in a next step. © 2024 The Authors. GAMM - Mitteilungen published by Wiley-VCH GmbH.

关键词： Forecasting

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FedRBE – a decentralized privacy-preserving federated batch effect correction tool for omics data based on limma

arXiv

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arXiv 2024年

作者： Burankova, Yuliya Klemm, Julian Lohmann, Jens J.G. Taheri, Ahmad Probul, Niklas Baumbach, Jan Zolotareva, Olga Institute for Computational Systems Biology University of Hamburg Hamburg Germany TUM School of Life Sciences Technical University of Munich Freising Germany Department of Mathematics and Computer Science University of Southern Denmark Odense Denmark Data Science in Systems Biology TUM School of Life Sciences Technical University of Munich Freising Germany

Batch effects in omics data obscure true biological signals and constitute a major challenge for privacy-preserving analyses of distributed patient data. Existing batch effect correction methods either require data centralization, which may easily conflict with privacy requirements, or lack support for missing values and automated workflows. To bridge this gap, we developed fedRBE, a federated implementation of limma’s removeBatchEffect method. We implemented it as an app for the FeatureCloud platform. Unlike its existing analogs, fedRBE effectively handles data with missing values and offers an automated, user-friendly online user interface (https://***/app/fedrbe). Leveraging secure multi-party computation provides enhanced security guarantees over classical federated learning approaches. We evaluated our fedRBE algorithm on simulated and real omics data, achieving performance comparable to the centralized method with negligible differences (no greater than 3.6E-13). By enabling collaborative correction without data sharing, fedRBE facilitates large-scale omics studies where batch effect correction is crucial. © 2024, CC BY.

关键词： Federated learning

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Integration of AI and Cloud-Native Technologies for Personalized Mobile Banking Experiences

Integration of AI and Cloud-Native Technologies for Personal...

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Recent Innovation in Smart and Sustainable Technology (ICRISST), International Conference on

作者： N. Legapriyadharshini T. Thirumalaikumari K. Senbagam R. Sarasu H. Anwer Basha P. Malathi Department of Computer Application Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram Tamil Nadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical And Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

In today's rapidly evolving digital landscape, using Artificial Intelligence (AI) and Cloud-Native Technologies is essential for improving personalized mobile banking experiences. The limitations and errors of existing financial systems necessitate such integration. Existing systems often struggle to adapt to modern consumers dynamic tastes and expectations, resulting in unsatisfactory customer experiences. Identifying the gap, the proposed system revolutionizes mobile banking applications by leveraging AI algorithms such as machine learning and natural language processing within a microservices-based cloud infrastructure. As a result, the proposed system solves the limitations of outdated architectures by providing personalized and efficient services while addressing security issues. The study indicates considerable gains in terms of user pleasure, system scalability, and security robustness through an in-depth evaluation of the results. The results highlight AI's and Cloud-Native Technologies transformational potential in revolutionizing the landscape of tailored mobile banking experiences. The study provides helpful information to financial institutions looking to adopt innovation and fulfill the evolving needs of modern consumers.

关键词： Technological innovation Scalability Microservice architectures Banking Predictive models User experience Robustness Natural language processing Security Artificial intelligence

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A Novel Method for Finger-Vein Recognition Based on MFAGNET Approach

A Novel Method for Finger-Vein Recognition Based on MFAGNET ...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Rajasree R Amit V Kadam C Mahesh S. Kaliappan K. Kartheeban H Anwer Basha Department of Computer Science (Artificial Intelligence & Data Science) Karpagam Academy of Higher Education Coimbatore India Department of Mechanical Engineering Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Division of Research and Development Lovely Professional University Phagwara Punjab India Department of Computer Science and Engineering Krishnankoil Srivilliputtur Tamilnadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai India

Biometrics based on veins are gaining prominence due to their higher level of accuracy and security. A more advanced biometric solution to the problems with fingerprint systems is the use of finger veins. A promising biometric pattern for personal identification, vein-based authentication offers both security and convenience. To acquire the vein patterns, one must use a live subject. Thus, by means of natural and convincing evidence, it is demonstrated that the patient whose veins were successfully extracted is alive. Be careful to maintain the importance order while completing preprocessing, feature selection, and training the model. The proposed approach employ Harris Corner in preprocessing. In images, corners are areas where there are noticeable changes in brightness in more than one direction. Assuming there is enough data, the objective of LLE in feature selection is to locate a low-dimensional embedding. Unified MFGANET models need to be trained after feature selection. Current state-of-the-art methods, CNN and LSTM, are outdone by the suggested methodology. Following the technique's use, accuracy improved by 98.16%.

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