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Understanding Wireless Charger Networks: Concepts, Current Research, and Future Directions

IEEE Communications Surveys and Tutorials 2024年

作者： Ren, Meixuan Dai, Haipeng Liu, Tang Deng, Xianjun Dou, Wanchun Yang, Yuanyuan Chen, Guihai Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China Sichuan Normal University College of Computer Science Sichuan Chengdu610101 China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Chengdu610068 China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China Stony Brook University Department of Electrical and Computer Engineering NY11794 United States

Wireless Charger Network (WCN) emerges as a promising networking paradigm, employing wireless chargers with Wireless Power Transfer (WPT) technology to provide long-term and sustainable energy supply for future networks. Although extensive research has been conducted in this area over the last decade, there is currently no comprehensive survey to compile the latest literature and provide insights into future research directions. To fill this gap, our survey explores the recent developments in the active research area of WCNs. This paper starts by providing a framework of WCNs in detail, covering aspects of network architecture, various charging models, network design issues, and typical applications of WCNs. Then, we give an overview of charger deployment schemes, focusing on omnidirectional, directional, non-radiative, and heterogeneous charger deployments. We also provide an overview of charging scheduling schemes, encompassing power control, time allocation, energy beamforming, and multi-resource scheduling. Moreover, we explore communication optimization schemes, including Medium Access Control (MAC) protocols, routing protocols, broadcast transmission, and data collection. Finally, we highlight some future research directions and present corresponding open issues to advance the research on WCNs. © 1998-2012 IEEE.

关键词： Inductive power transmission

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Enabling Generalized Zero-Shot Vulnerability Classification

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Hu, Jinghao Guo, Jinsong Luo, Chen Hu, Yang Lanzinger, Matthias Li, Zhanshan Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Software Changchun130012 China University College London Department of Computer Science United Kingdom Amazon United States United Kingdom University of Oxford Department of Computer Science United Kingdom Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Changchun130012 China

Regarding computer security, the growth of code vulnerability types presents a persistent challenge. These vulnerabilities, which may cause severe consequences, necessitate precise classification for effective mitigation. However, the rapid emergence of new vulnerability types complicates the classification process. Traditional methodologies, which often involve human expertise and the manual labeling or generation of example instances, are not only resource-intensive but also struggle to adapt to the dynamic nature of these vulnerabilities. This paper introduces VulnSense, an innovative method that harnesses the capabilities of Generalized Zero-Shot Learning (GZSL) to address the vulnerability classification problem. VulnSense learns about unseen vulnerability classes from the descriptions of these unseen classes, while not requiring to see any instances of these unseen classes. Specifically, VulnSense learns from three main resources: (1) seen classes with labeled code instances;(2) descriptions of these seen classes;and (3) descriptions of "unseen"classes which have no labeled instances. Our experiments underscore VulnSense's superiority over existing GZSL methods in classifying instances of unseen classes. Concurrently, it maintains a performance parity with traditional labeled-example based learning methods in classifying instances of seen vulnerabilities. VulnSense demonstrates the potential of using GZSL for vulnerability classification, while also highlighting challenges that inspire future work. © 2004-2012 IEEE.

关键词： Zero-shot learning

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Heterogeneous Laplace Distribution Noise Background Modeling for Sperm Detection in Microscopic Images 9

Heterogeneous Laplace Distribution Noise Background Modeling...

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9th International Conference on computer and Communications, ICCC 2023

作者： Tian, Manqing Zhao, Yang Zeng, Ni Kang, Xiaomei Pei, Jihong Yang, Xuan Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen China Shenzhen University College of Computer Science and Software Engineering Shenzhen China Huazhong University of Science and Technology Tongji Medical College Department of Reproductive Maternal and Child Health Hospital of Hubei Province Wuhan China

ISBN: (纸本)9798350317251

Semen quality assessment is a very important tool for the timely detection and treatment of infertility disorders. Many artificial intelligence methods for sperm image analysis have emerged in recent years. Sperm detection in sperm microscopy is the first step in semen quality assessment. However, the acquisition of sperm electron microscopy images can produce a variety of different background lighting situations. The performance of established methods is susceptible to changes in background illumination. Therefore, a sperm detection method with heterogeneous Laplacian distribution noise background modeling (HLDNBM) is proposed. In this paper the method uses background illumination surface fitting to eliminate the effect of illumination in a single sperm image, and realizes the segmentation of sperm head and sperm tail with different grayscale by upper and lower segmentation threshold surfaces to achieve complete sperm image segmentation and multi-target detection in a single image. This method effectively avoids the degradation of target detection performance caused by sudden changes in illumination in other background modeling methods. Moreover, this method shows strong adaptability for images with different lighting backgrounds, and is friendly to small datasets as well. The experimental results show that the accuracy and sensitivity of the algorithm for sperm detection in images with different grayscale characteristics are very promising, with high universality and robustness. © 2023 IEEE.

关键词： Lighting

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Visual Style Prompt Learning Using Diffusion Models for Blind Face Restoration

arXiv

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arXiv 2024年

作者： Lu, Wanglong Wang, Jikai Wang, Tao Zhang, Kaihao Jiang, Xianta Zhao, Hanli Key Laboratory of Intelligent Informatics for Safety and Emergency of Zhejiang Province Wenzhou University China Department of Computer Science Memorial University of Newfoundland St. John’s Canada State Key Laboratory for Novel Software Technology Nanjing University China College of Engineering and Computer Science Australian National University Australia

Blind face restoration aims to recover high-quality facial images from various unidentified sources of degradation, posing significant challenges due to the minimal information retrievable from the degraded images. Prior knowledge-based methods, leveraging geometric priors and facial features, have led to advancements in face restoration but often fall short of capturing fine details. To address this, we introduce a visual style prompt learning framework that utilizes diffusion probabilistic models to explicitly generate visual prompts within the latent space of pre-trained generative models. These prompts are designed to guide the restoration process. To fully utilize the visual prompts and enhance the extraction of informative and rich patterns, we introduce a style-modulated aggregation transformation layer. Extensive experiments and applications demonstrate the superiority of our method in achieving high-quality blind face restoration. The source code is available at https://***/LonglongaaaGo/*** Codes 68U10 © 2024, CC BY.

关键词： Generative adversarial networks

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An incentive mechanism for crowdsourcing markets with social welfare maximization in cloud-edge computing

An incentive mechanism for crowdsourcing markets with social...

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作者： Xu, Xiaolong Cai, Qing Zhang, Guoming Zhang, Jie Tian, Wei Zhang, Xiaorui Liu, Alex X. School of Computer and Software Nanjing University of Information Science and Technology Jiangsu China Jiangsu Engineering Centre of Network Monitoring Nanjing University of Information Science and Technology Jiangsu China State Key Laboratory for Novel Software Technology Nanjing University Jiangsu China Department of Computer Science and Engineering Michigan State University MI United States

Crowdsourcing is emerging as a powerful paradigm that utilizes the distributed devices to sense, collect, and upload data to satisfy the requirements of the users. Currently, with the popularity of edge computing, edge device users can act as recruiters or participants to publish or perform crowdsourcing tasks and share feedback. However, due to the individual selfishness, it is still a challenge to maximize the social welfare distribution of all the participants and the recruiters for the crowdsourcing market. In view of this challenge, an incentive mechanism for the crowdsourcing market with social welfare maximization in the cloud-edge computing is designed in this paper. Technically, a double action model under the cloud-edge computing framework is proposed first, which aims to maximize the social welfare maximization and meanwhile meet the demands of incentive compatibility, individual rationality, market clearing, and budget constraint. Secondly, a corresponding incentive mechanism is designed based on the double auction model to achieve the market fairness. Experimental evaluation and comparison analysis are conducted to validate the efficiency and effectiveness of the mechanism. © 2018 John Wiley & Sons, Ltd.

关键词： Crowdsourcing

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Nimbus: Toward Speed Up Function Signature Recovery via Input Resizing and Multi-Task Learning

arXiv

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arXiv 2022年

作者： Qian, Yi Chen, Ligeng Wang, Yuyang Mao, Bing State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China

Function signature recovery is important for many binary analysis tasks such as control-flow integrity enforcement, clone detection, and bug finding. Existing works try to substitute learning-based methods with rule-based methods to reduce human *** made considerable efforts to enhance the system’s performance, which also bring the side effect of higher resource consumption. However, recovering the function signature is more about providing information for subsequent tasks, and both efficiency and performance are significant. In this paper, we first propose a method called Nimbus for efficient function signature recovery that furthest reduces the whole-process resource consumption without performance loss. Thanks to information bias and task relation (i.e., the relation between parameter count and parameter type recovery), we utilize selective inputs and introduce multi-task learning (MTL) structure for function signature recovery to reduce computational resource consumption, and fully leverage mutual information. Our experimental results show that, with only about the one-eighth processing time of the state-of-the-art method, we even achieve about 1% more prediction accuracy over all function signature recovery tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Minimizing Link Generation in Constraint Checking for Context Inconsistency Detection

Minimizing Link Generation in Constraint Checking for Contex...

引用

International Symposium on software Reliability Engineering (ISSRE)

作者： Chuyang Chen Huiyan Wang Lingyu Zhang Chang Xu Ping Yu State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (数字)9781665451321

ISBN: (纸本)9781665451338

Adaptive applications rely on conditions about their environments (or contexts) to deliver smart services, e.g., location-aware services. Due to inherent noises in environmental sensing and interpretation, there is an increasing demand for guarding the consistency of contexts to avoid application misbehavior, and at the same time minimizing the guarding cost. Existing work has tried to reduce the cost by speeding up the kernel constraint checking module inside the consistency guarding process. Most efforts have been spent on reusing previous checking results or checking constraints in parallel, while leaving untouched one central problem of link generation, the step that consumes a substantially large part of the total time cost for explaining why constraints have been violated. In this paper, we propose a novel technique, MG, to automatically identify and remove redundant link generation, without harming any checking result. We show that MG is sound (always checking correctly) and complete (removing all redundancy). Our experiments with synthesized and real-world consistency constraints reported that compared with existing work, MG achieved significant efficiency improvements on the link generation (tens to hundreds times speedup), and could reduce the total constraint checking time up to 45.4%.

关键词： Costs Runtime Redundancy software reliability Sensors Time factors Kernel

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Online Neural Fiber Classification Method based on Lightweight Neural Network 7

Online Neural Fiber Classification Method based on Lightweig...

引用

7th International Conference on Image, Vision and Computing, ICIVC 2022

作者： Xu, Baolei Li, Xiaojie Shi, Canghong Sichuan University of Arts and Sciences Key Laboratory of Exploitation and Study of Distinctive Plants in Education Department of Sichuan Province Dazhou China Chengdu University of Information Technology School of Computer Science Chengdu China Xihua University School of Computer Science and Software Engineering Chengdu China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

Traditional neural fiber classification methods are generally difficult to balance classification accuracy and algorithm complexity, and there is a problem of relying on manual intervention, mainly including manual drawing of region of interest (ROI) and manual screening of target fiber bundles, resulting in uncertainty of classification results and large data error. To reduce manual intervention, balance accuracy and algorithm complexity, a simple and effective BP neural network model combined with fast image processing algorithm is proposed. Based on the neural fiber map information, this method uses the fiber coordinate information to train the classification network structure and realize the online fiber classification. While improving the classification accuracy, it also reduces the complexity of the algorithm and does not need to know the distribution of fibers in advance. Compared with several typical classification algorithms, BP neural network model has strong robustness and can effectively prevent over-fitting. The experimental results show that the method performs well in several classifiers, and the classification accuracy is optimal. The experimental training accuracy is 0.9824, and the test accuracy is 0.9774. © 2022 IEEE.

关键词： Neural networks

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Service offloading oriented edge server placement in smart farming

Service offloading oriented edge server placement in smart f...

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作者： Zhang, Jie Li, Xiang Zhang, Xuyun Xue, Yuan Srivastava, Gautam Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Department of Computing Macquarie University SydneyNSW Australia Department of Mathematics and Computer Science Brandon University BrandonMB Canada

Currently, smart farming has been established to realize agriculture automation by leveraging sensors to gather the growth and environmental data for crops, and realizing multiple intelligent controls, such as irrigation, fertilization, and so on, to increase the crop yields. To support real-time intelligent controls, edge computing is introduced to smart farming by endowing computing and storage capacities to edge devices nearby the geographically distributed sensors. However, the farmers are relatively willing to purchase and deploy a small quantity of edge servers (ESs) in the farm from the perspective of expenditure saving, thereby leading to a key challenge to guarantee the performance of the real-time controls and the overall edge services. In view of this challenge, a service offloading-oriented ES placement method for supporting smart farming, called SOP, is proposed to optimize the data transmission delay from sensors to ESs and the load balance among ESs. More precisely, the corresponding service range of a certain ES is ascertained according to the specific analysis of the farming service requirements. Subsequently, the layout policies for the trade-offs of the ES performance and service efficiency are acquired. Then the most balanced policy is determined as the final ES placement strategy. Eventually, we evaluate the performance of the whole ES system and the service execution efficiency with SOP. © 2020 John Wiley & Sons, Ltd.

关键词： Edge computing

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Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases

Domain Adaptive Code Completion via Language Models and Deco...

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IEEE International Conference on Automated software Engineering (ASE)

作者： Ze Tang Jidong Ge Shangqing Liu Tingwei Zhu Tongtong Xu Liguo Huang Bin Luo National Key Laboratory for Novel Software Technology Nanjing University China Nanyang Technological University Singapore Huawei Software Engineering Application Technology China Department of Computer Science Southern Methodist University USA

Large Language Models (LLMs) have demonstrated remarkable performance in code completion. However, due to the lack of domain-specific knowledge, they may not be optimal in completing code that requires intensive domain knowledge for example completing the library names. Although there are several works that have confirmed the effectiveness of fine-tuning techniques to adapt language models for code completion in specific domains. They are limited by the need for constant fine-tuning of the model when the project is in constant iteration. To address this limitation, in this paper, we propose $k$ NM-LM, a retrieval-augmented language model (R-LM), that integrates domain knowledge into language models without fine-tuning. Different from previous techniques, our approach is able to automatically adapt to different language models and domains. Specifically, it utilizes the in-domain code to build the retrieval-based database decoupled from LM, and then combines it with LM through Bayesian inference to complete the code. The extensive experiments on the completion of intra-project and intra-scenario have confirmed that $k$ NM-LM brings about appreciable enhancements when compared to CodeGPT and UnixCoder. A deep analysis of our tool including the responding speed, storage usage, specific type code completion, and API invocation completion has confirmed that $k$ NM-LM provides satisfactory performance, which renders it highly appropriate for domain adaptive code completion. Furthermore, our approach operates without the requirement for direct access to the language model's parameters. As a result, it can seamlessly integrate with black-box code completion models, making it easy to integrate our approach as a plugin to further enhance the performance of these models.

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