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检索条件"任意字段=8th IEEE International Enterprise Distributed Object Computing Conference"

共 1278 条记录，以下是21-30 订阅

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Transformer Tracker Based on Clipped SwinV2 Network 8

Transformer Tracker Based on Clipped SwinV2 Network

引用

8th international conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Li, Na Gu, Longyu Gao, Yun Yunnan University School of Information Science and Engineering Kunming China

ISBN: (纸本)9798350302455

As Transformer technology continues to show its advantages in the field of computer vision, object tracking technology based on the Transformer network has also attracted much attention. In this paper, a Transformer object tracking algorithm based on an improved SwinV2 backbone network is proposed. First, the SwinV2 network is suitably trimmed to fit the object tracking application requirements by combining the requirements of feature dimensionality and computational speed of the object tracking application and retaining the advantages of the SwinV2 Transformer network in terms of the visual model. then, the trimmed SwinV2 network is migrated to the SwinTrack base algorithm as the backbone network. Comparison experiments are conducted with the recent mainstream tracking algorithms based on three public test sets, LaSOT, GOT-10k, and OTB100, and the results show that the algorithm in this paper has better tracking performance. © 2023 ieee.

关键词： Siamese network Transformer Visual object tracking

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Detection of corn leaf blight Based on Improved YOLOv5 8

Detection of corn leaf blight Based on Improved YOLOv5

引用

8th international conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Leng, Shengjie Musha, Yasenjiang Xinjiang University School of Mechanical Engineering Urumqi China

ISBN: (纸本)9798350302455

In order to rapidly detect the occurrence of corn leaf blight in complex environments and take early control measures, this paper proposes a corn leaf blight detection method based on YOLOV5. First, we use an attentional feature fusion module based on sub-pixel convolution to use the semantic information at different scales fully. Second, To enhance the model's capability to understand the information in complex scenes, we added the Residual Interactive Information Blocks to the YOLOv5 head. With an input image size of 640∗640, the model proposed in this paper improves the accuracy over the baseline model by 5.2%, providing our model more advantages in parameters and detection speed compared to other object detection models. © 2023 ieee.

关键词： object detection

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Augmented Reality localisation using 6 DoF phantom head Pose Estimation-based generalisable Deep Learning model 8

Augmented Reality localisation using 6 DoF phantom head Pose...

引用

8th ieee international conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Amara, Kahina Guerroudji, Mohamed Amine Zenati, Nadia Kerdjidj, Oussama Atalla, Shadi Mansoor, Wathiq Ramzan, Naeem Centre for Development of Advanced Technologies Algeria University of Dubai College of Engineering and IT Dubai United Arab Emirates UWS School of Computing Engineering and Physical Sciences United Kingdom

ISBN: (纸本)9798350309249

Localisation and the estimation of the six degrees of freedom (6 DoF) object's position and orientation are essential tasks in numerous applications, such as augmented and virtual reality (AR and VR). It enables machines to gain a 3D understanding of their surroundings, empowering them to execute tasks like identifying objects, tracking their movement, interacting with them, and manipulating them. there is a growing trend in developing generalisable '6DoF' object pose estimators, which possess the ability to estimate the position and orientation of an unfamiliar object without the need for specific training or finetuning on that particular object during testing. By comparison, the previous 6 DoF object pose estimators focused primarily on specific categories of objects. this paper addresses the challenge of 6 DoF phantom head pose estimation from a single RGB image for augmented reality localisation. Using a local Magnetic Resonance Imaging (MRI) dataset, we reconstruct and print a 3D phantom head. this later is the target object for our AR localisation. Based on the comparison study including the accuracy and inference runtime scores, the Gen6D model achieves promising performance. Implementing 6 DoF pose estimation in deep learning stands to enhance and prove valuable in facilitating more effective interactions within the realm of augmented reality. © 2024 ieee.

关键词： Augmented reality

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Analysis of Sparse Reconstruction Effect on Compressive Imaging Space object 8

Analysis of Sparse Reconstruction Effect on Compressive Imag...

引用

8th international conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Yin, Hang Liu, Yuqing Zhang, Jiaqi Wang, Shuge Space Engineering University Beijing China

ISBN: (纸本)9798350302455

Sparse reconstruction is the key factor of Compressive Sensing (CS) theory that can overcome the Nyquist sampling limitation and sense large amount of data in low measurement. Based on the admirable sparse representation of space object scene, CS is promising in the compressive imaging of space observation. the main process of CS is to sparsely reconstruct the original signal from the low-dimensional measurements, where the reconstruction quality is the principal consideration to guarantee the application of CS in this field. this paper investigates the sparse reconstruction effect on space object images by different algorithms such as convex optimization and greedy. the numerical simulations are performed in increasing compression ratios with respect to different datasets. the reconstruction effect is evaluated by image quality indexes in different cases. the statistical results show that the sparse reconstruction of space object can keep the image quality to a large extent. the conclusion verifies the feasibility for compressive imaging application in space observation. © 2023 ieee.

关键词： Compressed sensing

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2023 8th international conference on Cloud computing and Big Data Analytics, ICCCBDA 2023

2023 8th International Conference on Cloud Computing and Big...

引用

8th international conference on Cloud computing and Big Data Analytics, ICCCBDA 2023

ISBN: (纸本)9781665455336

the proceedings contain 108 papers. the topics discussed include: knowledge map analysis of digital empowerment theme under the background of big data;evaluation of ecological protection importance in Yangtze River delta based on geospatial big data;the influence of family involvement on enterprise innovation based on big data analysis;housing profile and recommendation for housing provident fund using two-tower neural collaborative filtering;an approximation computation approach to big data analysis with a case analysis of PV system;research on intelligent perception and supervision for data circulation security based on block-chain;intra-company pay gap and enterprise performance: an empirical analysis based on panel data of listed Chinese manufacturing companies;evaluating off-chain transaction queueing delay to ensure data integrity by blockchain;and a data augmentation method for wound infection prediction.

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Real-Time Deep Learning Based Safe Autonomous Navigation 8

Real-Time Deep Learning Based Safe Autonomous Navigation

引用

8th international conference on Robotics, Control and Automation (ICRCA)

作者： Dutta, Tuhin Reddy, D. Santhosh Rajalakshmi, P. IIT Hyderabad Dept Artificial Intelligence Hyderabad Telangana India IIT Hyderabad NMICPS TiHAN Hyderabad Telangana India

ISBN: (纸本)9798350344738;9798350344721

With the rapid advancements in artificial intelligence and sensor technology, autonomous vehicles are becoming increasingly closer to reality. To ensure the safety and reliability of autonomous navigation, robust perception and control systems are crucial. this paper presents a novel integrated module for collision-free autonomous navigation that leverages the power of deep learning and edge computing. By synergistically combining Nvidia's DriveNet for object detection and OpenRoadNet for drive-able free space detection using the DriveWorks SDK, the module enables precise perception capabilities and dynamic control of the vehicle's speed based on the calculated probability of collision, resulting in collision-free navigation. Additionally, a customized Pure-Pursuit algorithm ensures smooth and accurate steering angle prediction. the system is implemented on Nvidia Drive Pegasus for perception and control and dSPACE MicroAutoBox-III for actuation, achieving a remarkable real-time processing speed of 30 frames per second (FPS). this research significantly advances Advanced Driver Assistance Systems (ADAS), offering robust navigation suitable for controlled environment deployment.

关键词： deep learning collision detection autonomous navigation object detection freespace detection edge device deployment

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the Low-RCS Drone: A Compact, Cost-Effective Covert Drone for Efficient Surveillance 8

The Low-RCS Drone: A Compact, Cost-Effective Covert Drone fo...

引用

8th ieee international conference on computing, Communication, Control and Automation, ICCUBEA 2024

作者： Chitale, Atharva Abhijeet Srivastava, Vandna Walke, Adveith Meena, Sachin Richhariya, Mukti Chakrabartty, Shubhro D.Y. Patil International University School of Computer Science Engineering & Applications Maharashtra Akurdi India D.Y. Patil International University School of Interdisciplinary Studies and Research Maharashtra Akurdi India Hindustan College of Science and Technology Farah Mathura India

ISBN: (纸本)9798350391770

this paper introduces a cost-effective covert drone prototype designed to evade detection in an era of heightened surveillance. Inspired by ancient texts like the Vaimānika Shāstra, the project integrates historical knowledge with modern technology, achieving a 60-70% reduction in manufacturing costs through innovative engineering. the drone features a single BLDC motor powered by a commercially available drone battery, housed in a radar-evading aerodynamic structure. Compact and lightweight, it measures under 30 cm and weighs less than 500 grams, making it easily portable. Mathematical formulations show that the torus-shaped object exhibits significantly lower Radar Cross Section (RCS) at different angles compared to other systems, offering potential benefits for military applications. © 2024 ieee.

关键词： Radar cross section

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Customizing of ERP in Microservice SaaS Architecture: An Overview of Intrusive & Non-Intrusive Approach 8

Customizing of ERP in Microservice SaaS Architecture: An Ove...

引用

8th ieee international conference on Software Engineering and Computer Systems, ICSECS 2023

作者： Ying Hong, Ding Kwee Teck, See Tong Ming, Lim Hoong Jack, Chang Tunku Abdul Rahman University of Management and Technology Faculty of Computing and Information Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798350310931

In Industry Revolution 4.0, industries are aiming towards a direction of digitalization whereby enterprise system will need to be adopted widely in Small Medium enterprise to improve their competitive power. Customization of enterprise system is commonly adopted as it is necessary to meet the specific needs of the organization. However, customization for enterprise system would take a large effort for modifying underlying code and with the increasing adoption of microservice-based Software-as-a-Service (SaaS) architectures, the process of customization has become even more complicated. By understanding the tradeoffs between these approaches and carefully evaluating their customization needs, organizations can make informed decisions that meet their unique business requirements while minimizing the risks associated with customization. this paper discusses the two approaches to customize enterprise Resource Planning system in microservice SaaS architecture - intrusive and non-intrusive approaches, and the advantages and the disadvantages of each approach. In this paper, it shows that non-intrusive approach is more sounding approach in implementation for enterprise system. © 2023 ieee.

关键词： enterprise resource planning

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Dual Attention and Edge Refinement Network for Camouflaged object Detection 8

Dual Attention and Edge Refinement Network for Camouflaged O...

引用

8th international conference on Image, Vision and computing, ICIVC 2023

作者： Fan, Bo Cong, Kuochen Zou, Wenjie School of Electrical Engineering and Automation Anhui University China

ISBN: (纸本)9798350335231

Camouflaged object detection (COD) aims to identify and segment objects similar to the background. However, existing methods suffer from unsatisfactory camouflaged object identifying or segmenting. In this paper, we propose a novel Dual Attention and Edge Refinement Network (DAERNet) to boost the performance of COD. Specifically, a Dual Attention Mechanism (DAM) is proposed to capture the scale diversities of camouflaged objects, which mainly includes Spotlight Attention Module (SAM) and Modulation Attention Module (MAM). the SAM aims to obtain multi-scale refinement features which contain discriminative semantic information, while the MAM aims to obtain the refined features with specific semantic information. then, we propose a Boundary Extraction Module (BEM) to obtain edge information. Finally, a Feature Aggregation Module (FAM) is designed to fuse the refined multi-scale features obtained by DAM with the help of edge information to achieve accurate camouflage object prediction. Extensive experiments on four datasets demonstrate that the proposed DAERNet performs comparably against other SOTA methods. © 2023 ieee.

关键词： object detection

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Identification of Tumor using Segmentation & object Detection: A Comparison and an Analysis 8

Identification of Tumor using Segmentation & Object Detectio...

引用

8th ieee international conference on computing, Communication, Control and Automation, ICCUBEA 2024

作者： Swain, Debabrata Ramanuj, Ashwini Shah, Vaishvi Kumar, Manish Pandit Deendayal Energy University Department of Computer Science and Engineering Gandhinagar India Pandit Deendayal Energy University Department of Electronics and Communication Engineering Gandhinagar India

ISBN: (纸本)9798350391770

Abnormal cells proliferate in the brain to form brain tumors, which frequently cause death. Localising and segmenting brain tumors using MRI is a difficult task in medical analysis, yet it is necessary for many applications. Accurate segmentation of brain tumor images is required for the identification and treatment of patients with these tumors. Even though they achieved promising segmentation results on the BRATS 2020 dataset, several of them need more testing and training time due to their complicated structure. In order to help doctors identify tumors in their early stages with high accuracy, we have proposed a two-step deep learning method for the diagnosis of three different types of tumors: necrotic, edema, enhancing, as well as brains free of tumors, using MRI images. the metrics used for evaluating the performance of these models are intersection over union, recall, and precision. the research indicates the precision of YOLO model to be 0.97 showcasing the accurate tumor detections, while recall and IoU of UNet segmentation to be 0.99 and 0.83 respectively. In this study, we addressed the issue of inaccuracy and latency in current artificial diagnosis systems by proposing a robust method for classifying brain tumors using deep learning techniques. © 2024 ieee.

关键词： Magnetic resonance imaging

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