检索结果-内蒙古大学图书馆

20th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Chu, Chu Fu, Ning Xu, Rui College of Computer Science Sichuan Normal University Chengdu610101 China The Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu610101 China School of Network and Communication Engineering Chengdu Technological University Chengdu611730 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798350318982

Radio Frequency Identification (RFID) technology is greatly supporting a variety of life and industry. Enhanced by sensors, RFID tags can monitor real-time information about attached objects and the environment, making efficient collection of tag information an important task. However, most works focus on collecting all tags but lack effective mechanisms to avoid interfering from irrelevant tags when the application only cares about a part of the tags in some cases. Besides, in more common scenarios with multiple RFID readers, existing works fail to effectively determine the surrounding tags for each reader, increasing the execution time. To conquer these defects, we propose a new solution called the efficient Vector-based target tag Information Collection (VIC) protocol to accelerate the collection rate. First, we introduce a composite vector to silence irrelevant tags and determine the local tags. During this process, the expected empty slots are skipped and the useless information transmitted by tags is reduced. Second, we use an allocation vector to collect tag information by resolving tag collision, thus improving the time efficiency. Finally, we provide theoretical analyses to optimize the parameter settings. Extensive experiments prove that VIC outperforms existing solutions by up to 32% increase in time efficiency. © 2023 IEEE.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

Concurrent Charging with Wave Interference

Concurrent Charging with Wave Interference

引用

IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Yuzhuo Ma Dié Wu Meixuan Ren Jian Peng Jilin Yang Tang Liu College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Lab Sichuan Normal University Chengdu China College of Computer Science Sichuan University Chengdu China

To improve the charging performance, employing multiple wireless chargers to charge sensors concurrently is an effective way. In such charging scenarios, the radio waves radiated from multiple chargers will interfere with each other. Though a few work have realized the wave interference, they do not fully utilize the high power caused by constructive interference while avoiding the negative impacts brought by the destructive interference. In this paper, we aim to investigate the power distribution regularity of concurrent charging and take full advantage of the high power to enhance the charging efficiency. Specifically, we formulate a concurrent charGing utility mAxImizatioN (GAIN) problem and build a practical charging model with wave interference. Further, we propose a concurrent charging scheme, which not only can improve the power of interference enhanced regions by deploying chargers, but also find a set of points with the highest power to locate sensors. Finally, we conduct both simulations and field experiments to evaluate the proposed scheme. The results demonstrate that our scheme outperforms the comparison algorithms by 40.48% on average.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Utilizing the Neglected Back Lobe for Mobile Charging

Utilizing the Neglected Back Lobe for Mobile Charging

引用

IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Meixuan Ren Dié Wu Jing Xue Wenzheng Xu Jian Peng Tang Liu College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Lab Sichuan Normal University Chengdu China College of Computer Science Sichuan University Chengdu China

Benefitting from the breakthrough of wireless power transfer technology, the lifetime of Wireless Sensor Networks (WSNs) can be significantly prolonged by scheduling a mobile charger (MC) to charge sensors. Compared with omnidirectional charging, the MC equipped with directional antenna can concentrate energy in the intended direction, making charging more efficient. However, all prior arts ignore the considerable energy leakage behind the directional antenna (i.e., back lobe), resulting in energy wasted in vain. To address this issue, we study a fundamental problem of how to utilize the neglected back lobe and schedule the directional MC efficiently. Towards this end, we first build and verify a directional charging model considering both main and back lobes. Then, we focus on jointly optimizing the number of dead sensors and energy usage effectiveness. We achieve these by introducing a scheduling scheme that utilizes both main and back lobes to charge multiple sensors simultaneously. Finally, extensive simulations and field experiments demonstrate that our scheme reduces the number of dead sensors by 49.5% and increases the energy usage effectiveness by 10.2% on average as compared with existing algorithms.

关键词：

来源：评论

学校读者我要写书评

暂无评论

ReconGait: Giant Panda Gait Recognition Based on Spatio-Temporal Feature Reconstruction

ReconGait: Giant Panda Gait Recognition Based on Spatio-Temp...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Peng Min Han Su Mengnan He Rong Hou Pinjia Que Peng Chen School of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu China Sichuan Key Laboratory of Conservation Biology for Endangered Wildlife Chengdu Research Base of Giant Panda Breeding Chengdu China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

As an endangered species, protecting and identifying individual giant pandas has always been a focus. However, traditional methods based on feces and molecular biology have inherent limitations. With the progress of deep learning, image analysis methods can deliver good results, but they rely on high-quality frontal facial images, which are challenging to acquire in the wild. Furthermore, these approaches have neglected the fact that a substantial portion of data from the wild is predominantly in the form of video, and have inadequately exploited the temporal information. Therefore, we apply gait recognition to identify individual giant pandas. By examining the unique walking patterns of individuals in the video, gait recognition allows accurate identification from various angles, long ranges, and without subject cooperation. This makes it ideal to identify wild individual giant pandas, but it also introduces new challenges, such as quadrupeds having more complex body gestures compared to humans, and the generally lower image quality taken by cameras in the wild. To tackle issues in natural environments, we design ReconGait, a giant panda gait recognition model capable of aligning multi-view images and restoring low-quality regions effectively. Experiments shows state-of-the-art results, proving the effectiveness of using gait analysis for individual giant panda identification. Through our work, we strive to contribute significantly to the preservation of this rare species and set a precedent for the integration of gait recognition in wildlife conservation efforts.

关键词： Endangered species Wildlife Neural networks Image restoration Quadrupedal robots Object recognition Environmental management

来源：评论

学校读者我要写书评

暂无评论

Age Estimation of Giant Pandas Based on Narrowed Window Ordered Regression

Age Estimation of Giant Pandas Based on Narrowed Window Orde...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Zihao Zhang Han Su Rong Hou Pengcheng Wu Ping Xu Peng Chen School of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu China Sichuan Key Laboratory of Conservation Biology for Endangered Wildlife Chengdu Research Base of Giant Panda Breeding Chengdu China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The age estimation of giant pandas is a crucial aspect of panda conservation efforts. Traditional methods for giant panda age estimation are time-consuming and expensive. Previous research has explored computer vision approaches for this task, primarily focusing on standardized frontal images of giant pandas. However, capturing clear frontal photos in the wild is challenging. In this study, we proposed a method based on Deep Convolutional Neural Networks (DCNN) for age estimation using multi-angle facial images of giant pandas. We expanded the original giant panda frontal age dataset into a multi-angle dataset, consisting of 13,524 facial images from 285 pandas. To enhance the model’s ability to fit multi-angle data, we incorporated deformable convolutions during feature extraction. We introduced a narrowed window ordered regression (NWR), and comparisons across multiple images to reduce spatial distances between feature vectors from different angles. The final model achieved a Mean Absolute Error (MAE) of 3.38. demonstrating superior performance even on frontal data. The results indicate the effectiveness of our approach in utilizing multi-angle facial data for wild giant panda age estimation. Computer vision technologies showcase their potential for meaningful contributions to wildlife conservation efforts.

关键词： Deformable models Computer vision Wildlife Neural networks Estimation Focusing Feature extraction

来源：评论

学校读者我要写书评

暂无评论

G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks

G-MAP: General Memory-Augmented Pre-trained Language Model f...

引用

2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Wan, Zhongwei Yin, Yichun Zhang, Wei Shi, Jiaxin Shang, Lifeng Chen, Guangyong Jiang, Xin Liu, Qun Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Science China University of Chinese Academy of Sciences China Huawei Noah's Ark Lab Huawei Cloud Computing Zhejiang Lab China

Recently, domain-specific PLMs have been proposed to boost the task performance of specific domains (e.g., biomedical and computer science) by continuing to pre-train general PLMs with domain-specific corpora. However, this Domain-Adaptive Pre-Training (DAPT;Gururangan et al. (2020)) tends to forget the previous general knowledge acquired by general PLMs, which leads to a catastrophic forgetting phenomenon and sub-optimal performance. To alleviate this problem, we propose a new framework of General Memory-Augmented Pre-trained Language Model (G-MAP), which augments the domain-specific PLM by a memory representation built from the frozen general PLM without losing any general knowledge. Specifically, we propose a new memory-augmented layer, and based on it, different augmented strategies are explored to build the memory representation and then adaptively fuse it into the domain-specific PLM. We demonstrate the effectiveness of G-MAP on various domains (biomedical and computer science publications, news, and reviews) and different kinds (text classification, QA, NER) of tasks, and the extensive results show that the proposed G-MAP can achieve SOTA results on all tasks. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Dynamic Power Distribution Controlling for Directional Chargers

Dynamic Power Distribution Controlling for Directional Charg...

引用

IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Yuzhuo Ma Dié Wu Jing Gao Wen Sun Jilin Yang Tang Liu College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Lab Sichuan Normal University Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Cyberspace Security Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Recently, deploying static chargers to construct timely and robust Wireless Rechargeable Sensor Networks (WRSNs) has become an important research issue for solving the limited energy problem of wireless sensor networks. However, the established fixed power distribution lacks flexibility in response to dynamic charging requests from sensors and may render some sensors to be continuously impacted by destructive wave interference. This results in a gap between energy supply and practical demand, making the charging process less efficient. In this paper, we focus on the real-time sensor charging requests and formulate a dynamic power disTributIon controlling for Directional chargErs (TIDE) problem to maximize the overall charging utility. To solve the problem, we first build a charging model for directional chargers while considering wave interference and extract the candidate charging orientations from the continuous search space. Then we propose the neighbor set division method to narrow the scope of calculation. Finally, we design a dynamic power distribution controlling algorithm to update the neighbor sets timely and select optimal orientations for chargers. Our experimental results demonstrate the effectiveness and efficiency of the proposed scheme, it outperforms the comparison algorithms by 142.62% on average.

关键词： Wireless communication Wireless sensor networks Heuristic algorithms Computational modeling Power distribution Interference Search problems

来源：评论

学校读者我要写书评

暂无评论

IC-CViT: Inverse-Consistent Convolutional Vision Transformer for Diffeomorphic Image Registration

IC-CViT: Inverse-Consistent Convolutional Vision Transformer...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Tao Xu Ting Jiang Xiaoning Li College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu China Sichuan 2011 Collaborative Innovation Center for Educational Big Data Chengdu China

Diffeomorphic registration plays a crucial role in medical image analysis due to the invertible and one-to-one mapping transformation. In recent years, with the development of deep learning technology, convolutional neural networks (CNNs) have been a broad focus of research in medical image registration, and CNN-based methods have made great progress. However, the results of most existing methods generally are not necessarily diffeomorphic, generating implausibly bijective mappings between images due to the interpolation and discrete representation. Furthermore, the performances of CNNs may be limited by a lack of precise comprehension of global and long-range cross-image spatial relevance. Vision Transformer (ViT) is capable of enhancing the long-distance information interaction ability to identify the semantically anatomically correspondences of medical images. Compared with CNN, ViT has weak local feature extraction ability due to less inductive bias, especially in small-scale training datasets, meaning that the samples between adjacent pixels cannot be exploited adequately. To address the above challenges, we propose a novel Inverse-Consistent Convolutional Vision Transformer (IC-CViT) network for diffeomorphic image registration. Specifically, image pairs can explicitly conduct bi-directional registration through the predicted deformation filed, generated within the diffeomorphic mappings space and restricted by the proposed inverse consistent loss term. We verify our method on two 3D brain MRI scan datasets including OASIS and LPBA40. Comprehensive results demonstrate that IC-CViT achieves state-of-the-art registration accuracy while maintaining desired diffeomorphic properties.

关键词：

来源：评论

学校读者我要写书评

暂无评论

S2WAT: Image Style Transfer via Hierarchical Vision Transformer Using Strips Window Attention

arXiv

引用

arXiv 2022年

作者： Zhang, Chiyu Xu, Xiaogang Wang, Lei Dai, Zaiyan Yang, Jun Sichuan Normal University China Nanjing University of Aeronautics and Astronautics China Zhejiang Lab China Zhejiang University China Visual Computing and Virtual Reality Key Laboratory of Sichuan Provience China

Transformer's recent integration into style transfer leverages its proficiency in establishing long-range dependencies, albeit at the expense of attenuated local modeling. This paper introduces Strips Window Attention Transformer (S2WAT), a novel hierarchical vision transformer designed for style transfer. S2WAT employs attention computation in diverse window shapes to capture both short- and long-range dependencies. The merged dependencies utilize the "Attn Merge" strategy, which adaptively determines spatial weights based on their relevance to the target. Extensive experiments on representative datasets show the proposed method's effectiveness compared to state-of-the-art (SOTA) transformer-based and other approaches. The code and pre-trained models are available at https://***/AlienZhang1996/S2WAT. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Utilizing Multipath Effects for Mobile Charging

引用

IEEE Transactions on Mobile computing 2025年

作者： Gao, Jing Wu, Die Zhang, Linglin Li, Jingwen Yang, Jin Peng, Jian Liu, Tang Sichuan Normal University College of Computer Science Sichuan Chengdu610101 China Sichuan University School of Cyber Science and Engineering Chengdu610065 China Sichuan University College of Computer Science Chengdu610065 China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Chengdu610068 China

Recently, Wireless Rechargeable Sensor Networks (WRSNs) have emerged as a promising solution to address the energy limitations of wireless sensor networks. In practical applications of WRSNs, environmental objects are ubiquitous, reflecting radio waves and causing them to reach sensors via multiple paths. These multipath effects significantly impact the power intensity received by sensors. In this paper, we study a fundamental issue of charGing schEduling with mulTipath effectS (GETS), that is, how to schedule a mobile charger by comprehensively considering the multipath effects to maximize the overall charging utility. To this end, we first establish a charging model with environmental objects to investigate the impact of multipath effects on power distribution. Then, we propose a charging scheduling scheme that not only selects a series of sojourn locations for the MC (Mobile Charger) to maximize the total power received by nearby sensors but also construct a charging path that avoids environmental objects. We conduct extensive simulations as well as indoor and outdoor field experiments to evaluate the performance of our scheme. The results demonstrate that, on average, our scheme outperforms baseline algorithms by 48.87%. © 2002-2012 IEEE.

关键词： Charging stations

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：