the Go programming language has seen a rise in popularity because of its straightforward nature and efficient performance. However, it still lacks a dependable and fully featured object Document Mapper (ODM) for worki...
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the proceedings contain 15 papers. the special focus in this conference is on Cloud computing and Artificial Intelligence: Technologies and Applications. the topics include: CAReNet: A Promising AI Architecture for...
ISBN:
(纸本)9783031786976
the proceedings contain 15 papers. the special focus in this conference is on Cloud computing and Artificial Intelligence: Technologies and Applications. the topics include: CAReNet: A Promising AI Architecture for Low Data Regime Mixing Convolutions and Attention;Designing Converged Middleware for HPC, AI, and Big Data: Challenges and Opportunities;key Mechanisms and Emerging Issues in Cloud Identity Systems;Power Consumption in HPC-AI Systems;integrated Architecture for Cloud and IoT with Logical Sensors and Actuators - Logical IoT Cloud;the Need for HPC in AI Solutions;facts and Issues of Neural Networks for Numerical Simulation;scalable Deep Learning for Industry 4.0: Speedup withdistributed Deep Learning and Environmental Sustainability Considerations;multi-domain Dataset for Moroccan Arabic Dialect Sentiment Analysis in Social Networks;on the Challenges of Migrating to Microservices Architectures for Better Cloud Solutions;missing Data Imputation Approach for IoT Using Machine Learning;holistic Approach for Enhanced object Recognition in Complex Environments;analyzing Sentiment in Arabic Tweets: A Study Using Machine Learning and Deep Learning Techniques.
Autonomous robots that interact withtheir environment require a detailed semantic scene model. For this, volumetric semantic maps are frequently used. the scene understanding can further be improved by including obje...
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ISBN:
(纸本)9781665472609
Autonomous robots that interact withtheir environment require a detailed semantic scene model. For this, volumetric semantic maps are frequently used. the scene understanding can further be improved by including object-level information in the map. In this work, we extend a multi-view 3D semantic mapping system consisting of a network of distributed smart edge sensors withobject-level information, to enable downstream tasks that need object-level input. objects are represented in the map via their 3D mesh model or as an object-centric volumetric sub-map that can model arbitrary object geometry when no detailed 3D model is available. We propose a keypoint-based approach to estimate object poses via PnP and refinement via ICP alignment of the 3D object model withthe observed point cloud segments. object instances are tracked to integrate observations over time and to be robust against temporary occlusions. Our method is evaluated on the public Behave dataset where it shows pose estimation accuracy within a few centimeters and in real-world experiments withthe sensor network in a challenging lab environment where multiple chairs and a table are tracked through the scene online, in real time even under high occlusions.
the proceedings contain 78 papers. the topics discussed include: multimodal data collection system for UAV-based precision agriculture applications;towards gesture-based cooperation with cargo handling unmanned aerial...
ISBN:
(纸本)9781665472609
the proceedings contain 78 papers. the topics discussed include: multimodal data collection system for UAV-based precision agriculture applications;towards gesture-based cooperation with cargo handling unmanned aerial vehicles: a conceptual approach;real-time learning of wing motion correction in an unconstrained flapping-wing air vehicle;a comparative analysis of collaborative robots for autonomous mobile depalletizing tasks;mechanical exploration of the design of tactile fingertips via finite element analysis;a flexible MATLAB/Simulink simulator for robotic floating-base systems in contact withthe ground;distributed computation and dynamic load balancing in modular edge robotics;gaze-based object detection in the wild;experimental assessment of feature-based lidar odometry and mapping;and beacon-based indoor fire evacuation system using augmented reality and machine learning.
the last mile in smart grid comprise of gateways and electricity meters. these further can be connected to households (Residential), industrial and commercial Units or grid itself. Utility through network management s...
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the Go programming language has seen a rise in popularity because of its straightforward nature and efficient performance. However, it still lacks a dependable and fully featured object Document Mapper (ODM) for worki...
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ISBN:
(数字)9798331517878
ISBN:
(纸本)9798331517885
the Go programming language has seen a rise in popularity because of its straightforward nature and efficient performance. However, it still lacks a dependable and fully featured object Document Mapper (ODM) for working with document databases like MongoDB. Many existing ODMs either miss critical features or have fallen out of active development, which can lead to messy code and added complexity. To address these issues, this study presents “Elemental,” a robust and optimized ODM built specifically for Go. Elemental is equipped with a powerful query builder, seamless middleware integration, and advanced data aggregation tools that work across multiple database connections. Test results have shown that Elemental outperforms the official MongoDB driver in both performance and maintainability, making it a suitable choice for Go enterprise applications. the obtained benchmarks and metrics indicate that the Elemental ODM provides shorter execution times, reduces cyclomatic complexity, and has a higher maintainability index. this study concludes that Elemental ODM significantly improves the developer experience and efficiency of software development by providing a well refined set of tools.
the deep learning techniques have been shown to make a traffic objects classification system for V2V communications to ensure traffic safety and traffic flow prediction. the robust classifier on the base of MLP and Po...
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ISBN:
(纸本)9781450399050
the deep learning techniques have been shown to make a traffic objects classification system for V2V communications to ensure traffic safety and traffic flow prediction. the robust classifier on the base of MLP and PointNet are explored to recognize the traffic objects from lidar point clouds. the features of a lidar sensor, the lidar point cloud coordinate system and its complex properties for creation a smart traffic object detection and recognition model are described. the best configuration of PointNet architecture with hyperparameters are shown, which is more efficient and robust with respect to input perturbation and corruption of lidar point clouds.
A research domain that concentrates on looking at the interactions and phenomena between information technology and enterprise organizations. It is necessary to study the level of acceptance of the use of Global Posit...
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the proportion of CEOs with financial experience in listed companies is increasing year by year, and CEOs with financial backgrounds make financial decisions for companies different from those without financial backgr...
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As the era of IoT (Internet of things) and Edge computing emerges, there is a rising need for real-time applications in the domain of computer vision. the increase in hardware computing capabilities gave rise to appli...
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ISBN:
(纸本)9781665404785
As the era of IoT (Internet of things) and Edge computing emerges, there is a rising need for real-time applications in the domain of computer vision. the increase in hardware computing capabilities gave rise to applications of neural networks in various fields. Implementing IoT with neural networks in domains like image and video recognition has shown promising performance when deployed in complex environments. there is an emerging demand for applications that require data computation in real-time with low latency. To address these issues, while keeping in mind the computing capabilities of IoT devices, we seek to develop a framework for efficient object detection on a distributed constrained platform system. We employed PYNQ Z1 AP-SoC (All Programmable System-on-Chip) as the IoT edge node platform and integrated state-of-the-art algorithms for object detection. the distributed architecture is robust and exploits the heterogeneous computing capability of the PYNQ platform. the proposed work is on a wireless distributed network with minimal communication latency. We demonstrate the framework for low frame rate applications where the scenery is not changing rapidly. We were able to achieve 19.23 frames per second withthree IoT nodes with Binarized Neural Network (BNN) image classification algorithm. the frames per second rate is directly proportional to the number of nodes in the network. the communication latency can be offset by the scalability offered by the distributed framework.
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