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检索条件"任意字段=7th International Conference on Real Time Computing Systems and Applications"

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Development of An Automated IoT-based Fish Tank Maintenance Assistive System 7

Development of An Automated IoT-based Fish Tank Maintenance ...

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7th international conference on Intelligent computing and Control systems, ICICCS 2023

作者： theagarajan, Padma Padmanaban, Barath Palanisamy, Kishore Raja, Pavithra Poomagan, Praveenkumar Sona College of Technology Department of Computer Applications Salem India

ISBN: (纸本)9798350397253

the proposed prototype of a fish tank maintenance automation system intends to support aquarists in terms of easy up keep of the fish tank. Keeping a household fish tank is associated with significant health benefits including lessening stress, sleep quality improvement, lowered blood pressure, intense focus, and soothing effects for Alzheimer's patients, etc. A predominant part of keeping fish healthy is ensuring that their habitat remains healthy. As part of the routine maintenance of a fishtank some of the vital parameters that are to be keenly assessed are nitrate, nitrite, pH, carbonate hardness, salinity, etc. Since fish live in entirely diverse environments compared to humans and other pets, they are dependent on humans for all their necessities including food, water, and proper habitat. Fish tanks are relatively different from natural habitat since they are closed systems with a limited capacity of water. thus, the entire process of the natural method of cleaning a water body has to be performed manually in the fish tank in addition to cleaning the wastes produced regularly by fish such as sediments, sludge, etc. therefore, to ease aquarists towards maintaining fish tanks, this paper proposes an automation system using IoT technology and also guards environmental sustainability. If the observed data such as pH, temperature, and dissolved oxygen are beyond the threshold limits then the attached beeper will be aware of the aquarists to instant care of the fish tank. the live data is provided through a web-based application and Mobile app for real-time monitoring and decision-making. © 2023 IEEE.

关键词： Microcontrollers

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FPGA based real-time hand gesture recognition system 15

FPGA based real-time hand gesture recognition system

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Chopde, Abhay Joshi, Shushwet Surve, Supriya Chavanke, Shraddha Vit Department of EnTC Maharashtra Pune411037 India

ISBN: (纸本)9798350370249

Hand gesture recognition is a technology that enables computers to interpret and understand human hand movements, typically through machine learning and image processing algorithms. the proposed system employs contour detection for real-time conversion of input images into binary images, facilitating accurate identification of fingers and thumbs. Rather than relying on other methods, it computes features from detected contours, such as length, convexity defects, and aspect ratio, to categorize 5 different hand gestures. these features serve as valuable inputs for machine learning algorithms, allowing the system to categorize a diverse range of hand gestures effectively. this enables seamless human-machine communication. the system's unique capability to recognize hand gestures in real time enhances its adaptability across various applications. this approach not only enhances accuracy up to 84.7 percent but also ensures real-world applicability without encountering performance issues like lag or delays. proposed system is compact easy to use and efficient in real time as it reduces the processing time as compared to existing systems. © 2024 IEEE.

关键词： FPGA Hand Gesture Recognition VHDL Xillinx

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A real-time Machine Learning Framework for Smart Home-based Yoga Teaching System 7

A Real-Time Machine Learning Framework for Smart Home-based ...

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7th international conference on Machine Vision and Information Technology, CMVIT 2023

作者： Sunney, Jothika Jilani, Musfira Pathak, Pramod Stynes, Paul National College of Ireland School of Computing Dublin Ireland Technological University Dublin Faculty of Digital and Data Dublin Ireland

ISBN: (纸本)9781665464857

Practicing yoga poses in a home-based environment has increased due to Covid19. Yoga poses without a trainer can be challenging, and incorrect yoga poses can cause muscle damage. Smart home-based yoga teaching systems may aid in performing accurate yoga poses. However, the challenge with such systems is the computational time required to detect yoga poses. this research proposes a real-time machine learning framework for teaching accurate yoga poses. It combines a pose estimation model, a pose classification model, and a real-time feedback mechanism. the dataset consists of five popular yoga poses namely the downdog pose, the tree pose, the goddess pose, the plank pose, and the warrior pose. the BlazePose model was used for yoga pose estimation which transforms the image data into 3D landmark points. the output of the pose estimation model was then passed to the pose classification model for yoga pose detection. Four machine learning classifiers namely, Random Forest, Support Vector Machine, XGBoost, Decision Tree, and two neural network classifiers LSTM and CNN were evaluated based on accuracy, latency and size. Results demonstrate that XGBoost outperforms other models with an accuracy of 95.14 percentage, latency of 8 ms, and size of 513 KB. the output of the XGBoost Classifier was then used to correct yoga poses by displaying real-time feedback to the user. this novel framework has the potential to be integrated into mobile applications which can be used by people for the unsupervised practice of yoga at home. © 2023 IEEE.

关键词： Decision trees

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DVFS Virtualization for Energy Minimization of Mixed-Criticality Dual-OS Platforms 28

DVFS Virtualization for Energy Minimization of Mixed-Critica...

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28th IEEE international conference on Embedded and real-time computing systems and applications (RTCSA)

作者： Komori, Takumi Masuda, Yutaka Ishihara, Tohru Nagoya Univ Grad Sch Informat Chikusa Ku Furo Cho Nagoya Aichi 4648603 Japan

ISBN: (数字)9781665453448

ISBN: (纸本)9781665453448

A dual-OS platform can efficiently implement emerging mixed-criticality systems by consolidating a real-time OS (RTOS) and a general-purpose OS (GPOS). Although the dual-OS platform attracts increasing attention, it often suffers from energy inefficiency in the GPOS for guaranteeing real-time responses of the RTOS. this paper proposes an energy minimization method called DVFS virtualization, which allows running multiple DVFS policies dedicated to the RTOS and GPOS, respectively. the experimental evaluation using a commercial processor showed that the proposed hardware could change the supply voltage within 500 ns and reduce the energy consumption of typical applications by 60% in the best case compared to conventional dual-OS platforms.

关键词： Dynamic voltage scaling Virtual machine monitors real-time systems Mixed-criticality Embedded software

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Smart Wearable Devices for Prediction Using Machine Learning Techniques: Depth Exploration 7

Smart Wearable Devices for Prediction Using Machine Learning...

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Mary, L. William Antony Raj, S. Albert SRM Institute of Science and Technology Department of Computer Applications India

ISBN: (纸本)9798350372816

the emergence of the COVID-19 epidemic has emphasized the continuous need for innovative and discreet methods to monitor and evaluate the progression of the disease. Wearable technology, equipped with an array of sensors and powered by advanced machine learning algorithms, presents a highly promising solution. these devices make use of sensors like heart rate monitors, pulse oximeters, temperature sensors, and accelerometers to continuously gather real-time data. this research explores how wearables, including fitness trackers and smartwatches, can be used to continuously monitor the COVID-19 disease and capture the data. It also explores applications in assessing COVID-19 severity through the utilization of machine learning techniques. Machine learning techniques including decision trees, support vector machines, and deep neural networks are used to process this data. this includes the early detection of warning signs, evaluating disease progression, and distinguishing between mild, moderate, and severe cases. the integration of patient-reported symptoms with wearable data further enhances the precision of assessing COVID-19 severity. © 2024 IEEE.

关键词： COVID-19

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Federated Learning Deployments of Industrial applications on Cloud, Fog, and Edge Resources 8

Federated Learning Deployments of Industrial Applications on...

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8th IEEE international conference on Fog and Edge computing (ICFEC)

作者： Blumauer-Hiessl, thomas Schulte, Stefan Lakani, Safoura Rezapour Keusch, Alexander Pinter, Elias Kaufmann, thomas Schall, Daniel TU Wien Vienna Austria Siemens Technol Vienna Austria TU Hamburg Christian Doppler Lab Blockchain Technol Internet Inst Data Engn Hamburg Germany

ISBN: (纸本)9798350361360;9798350361353

Federated Learning (FL) has gained prominence as a method for facilitating collaborative and privacy-preserving model training across multiple heterogeneous devices in recent years. In most approaches, the clients are closely deployed to the data source. However, as FL systems are implemented in the industry, multiple platform options can be considered in the design phase. In this paper, we present a novel approach for deploying FL clients to multiple locations considering a multi-platform strategy with cloud, fog, and edge resources. We provide a FL architecture that integrates mechanisms for building cohorts of similar clients and a client selection algorithm for optimizing the performance of all clients with respect to energy consumption, model performance, and FL completion time. We evaluate seven deployment strategies in three scenarios given a real-world use case from the electronics industry and heterogeneous hardware capabilities. Our results show that our approach can improve model performance by up to 15%, while energy consumption and completion time converge to the relatively best deployment.

关键词： Cloud computing Deployment Strategies Edge computing Federated Learning Fog computing Industrial Use Case Optimization Personalized Federated Learning

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A real time Obstacle Detection Using Camera and Laser Line Generator 7

A Real Time Obstacle Detection Using Camera and Laser Line G...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Dehankar, Sushank Dandawate, Akshay Gawade, Avishkar Bansode, Omkar Pawar, Atul PCCOE Dept. Computer Engineering Pune India

ISBN: (纸本)9798350304268

Detecting obstacles has been the major focus nowadays in the technological era. It has the most research subject over the last few decades. there are many applications which are using computer vision techniques such as security, surveillance, medical applications etc. Researchers have focused on coloured images where lighting is an important factor for images. As per their *** a scene and detecting objects is critical and camera and laser is used in this case. In this paper we will be able to take in live web cam image and highlights space where there are obstacles in the path. And the black area will demonstrate the free space available. By the help of laser line generator to assist in detecting the accuracy of obstacles through devices. the proposed model present in this paper present an effective model for obstacle detection using a camera and a laser line generator, on that system we can mount various devices such as robots, cars, helmets to test our model how effectively they are performing © 2023 IEEE.

关键词： Camera CNN Computer Vision Image Processing Laser Line Obstacle Detection

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Optimal Deployment of Cloud-native applications with Fault-Tolerance and time-Critical End-to-End Constraints 16

Optimal Deployment of Cloud-native Applications with Fault-T...

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16th IEEE/ACM international conference on Utility and Cloud computing (UCC) / 10th IEEE/ACM international conference on Big Data computing, applications and Technologies (BDCAT)

作者： Andreoli, Remo Gustafsson, Harald Abeni, Luca Mini, Raquel Cucinotta, Tommaso Scuola Super Sant Anna Pisa Italy Ericsson Res Lund Sweden

ISBN: (纸本)9798400702341

Cloud environments are becoming increasingly interesting to host time-critical use cases with far more stringent latency requirements than conventional cloud-native applications, such as smart industrial control systems or cloud-enabled autonomous vehicles. In these emerging domains, fault tolerance mechanisms play a critical role, due to the catastrophic consequences a fault might lead to, in the real world. this work presents a formal model for designing and deploying time-critical, cloud-native applications under fault conditions. Our model considers the interactions and interferences among service components and the possible occurrence of faults. We present an optimization framework to solve the deployment problem of minimizing the resources needed to achieve fault-tolerance under precise end-to-end deadline constraints. the ability of the optimizer to deliver precise temporal and fault-tolerance guarantees is validated through extensive simulations.

关键词： time-Critical Cloud Fault Tolerance Capacity Planning Resource Management Optimization

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9th international Skin Imaging Collaboration Workshop, ISIC 2024, 7th international Workshop on Interpretability of Machine Intelligence in Medical Image computing, iMIMIC 2024, Embodied AI and Robotics for Healthcare Workshop, EARth 2024 and 5th MICCAI Workshop on Distributed, Collaborative and Federated Learning, DeCaF 2024 held at 27th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

9th International Skin Imaging Collaboration Workshop, ISIC ...

引用

9th international Skin Imaging Collaboration Workshop, ISIC 2024, 7th international Workshop on Interpretability of Machine Intelligence in Medical Image computing, iMIMIC 2024, Embodied AI and Robotics for Healthcare Workshop, EARth 2024 and 5th MICCAI Workshop on Distributed, Collaborative and Federated Learning, DeCaF 2024 held at 27th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031776090

the proceedings contain 23 papers. the special focus in this conference is on Skin Imaging Collaboration, Interpretability of Machine Intelligence in Medical Image computing, Embodied AI and Robotics for Healthcare Workshop and MICCAI Workshop on Distributed, Collaborative and Federated Learning. the topics include: DeCaF 2024 Preface;i2M2Net: Inter/Intra-modal Feature Masking Self-distillation for Incomplete Multimodal Skin Lesion Diagnosis;from Majority to Minority: A Diffusion-Based Augmentation for Underrepresented Groups in Skin Lesion Analysis;segmentation Style Discovery: Application to Skin Lesion Images;a Vision Transformer with Adaptive Cross-Image and Cross-Resolution Attention;lesion Elevation Prediction from Skin Images Improves Diagnosis;DWARF: Disease-Weighted Network for Attention Map Refinement;PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans;Detecting Unforeseen Data Properties with Diffusion Autoencoder Embeddings Using Spine MRI Data;interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis;TextCAVs: Debugging Vision Models Using Text;evaluating Visual Explanations of Attention Maps for Transformer-Based Medical Imaging;Exploiting XAI Maps to Improve MS Lesion Segmentation and Detection in MRI;EndoGS: Deformable Endoscopic Tissues Reconstruction with Gaussian Splatting;VISAGE: Video Synthesis Using Action Graphs for Surgery;a Review of 3D Reconstruction Techniques for Deformable Tissues in Robotic Surgery;SurgTrack: CAD-Free 3D Tracking of real-World Surgical Instruments;MUTUAL: Towards Holistic Sensing and Inference in the Operating Room;Complex-Valued Federated Learning with Differential Privacy and MRI applications;enhancing Privacy in Federated Learning: Secure Aggregation for real-World Healthcare applications;federated Impression for Learning with Distributed Heterogeneous Data;A Federated Learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation;probing the Effic

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HuMachineSensing: A Novel Mobile Crowdsensing Framework with Robust Task Allocation Algorithm 23

HuMachineSensing: A Novel Mobile Crowdsensing Framework with...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Luo, Yixuan Yu, Zhiwen Ren, Jiaju Guo, Bin School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781665494571

Mobile crowdsensing (MCS) is a novel and innovative sensing model. Instead of traditional methods of sensing, MCS uses intelligent mobile devices carried by people to perform different sensing tasks. With the development of robotics and artificial intelligence technologies, many MCS studies with pervasive machines [1] (e.g., robots, smart vehicles, drones, etc.) as participants have emerged in recent years. Robot participants can perform dangerous, tedious tasks with a high degree of control. In contrast, human participants can perform various complex tasks flexibly with human intelligence. therefore, this paper proposes a novel framework of MCS, i.e., HuMachineSensing, which combines human participant and robot participant, and investigates the task assignment problem of this system. the key to the combined human-robot task assignment problem is how to determine whether a task is assigned to a human participant or a robot participant. In this paper, we propose the concept of sense information map (SIM), which can reflect the performance of different participants performing sensing tasks at different locations and times, and provide guidance for task assignment. the SIM is a real-time optimized model that can become more accurate as participants perform tasks, as we propose a map-based Gaussian process algorithm to continuously update the map. To further improve the robustness of the system, we propose a self-repairing task assignment algorithm, which can realize the self-repairing and reassignment of the task assignment scheme after the participants encounter abnormal situations. through experiments, it is demonstrated that the sensing information graph and the map-based self-repairing task assignment algorithm can effectively improve the task coverage. © 2021 IEEE.

关键词： Performance evaluation Crowdsensing Smart cities Gaussian processes Robot sensing systems Robustness real-time systems

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