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Guest Editorial: Internet of Drones: Novel Applications, Recent Deployments, and Integration

IEEE Internet of Things Magazine 2021年第4期4卷 8-10页

作者： Chamola, Vinay Yu, F. Richard Sikdar, Biplab Kanhere, Salil Guizani, Mohsen Internet of Things Lab at BITS-Pilani India Carleton University Ottawa Canada Department of Electrical and Computer Engineering Singapore Singapore School of Computer Science and Engineering University of New South Wales Sydney Australia Qatar University Computer Science and Engineering Department Qatar

Unmanned aerial vehicles (UAVs), also called drones, are extensively being adopted for a wide variety of applications such as traffic surveillance, disaster management, rescue operations, and environment monitoring. With the adoption of the Internet of Things (IoT), UAV networks are quickly transforming into the Internet of Drones (IoD) paradigm. This has opened up opportunities for the exploration of more sophisticated UAV applications. © 2018 IEEE.

关键词： Drones

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Joint alignment and steering to manage interference

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Digital Communications and Networks 2024年第2期10卷 429-438页

作者： Zhao Li Xiujuan Liang Yinghou Liu Jia Liu Zheng Yan School of Cyber Engineering Xidian UniversityXi'an710126China Technol.on Commun.Net.Lab. The 54th Research Inst.of China Elec.Technol.Group Corp.Shijiazhuang050081China School of Computer Science and Technology Xidian UniversityXi'an710126China The Department of Communications and Networking(Comnet) Aalto University02150EspooFinland

In wireless communication networks,mobile users in overlapping areas may experience severe interference,therefore,designing effective Interference Management(IM)methods is crucial to improving network ***,when managing multiple disturbances from the same source,it may not be feasible to use existing IM methods such as Interference Alignment(IA)and Interference Steering(IS)*** is because with IA,the aligned interference becomes indistinguishable at its desired Receiver(Rx)under the cost constraint of Degrees-of-Freedom(DoF),while with IS,more transmit power will be consumed in the direct and repeated application of IS to each *** remedy these deficiencies,Interference Alignment Steering(IAS)is proposed by incorporating IA and IS and exploiting their advantages in *** IAS,the interfering Transmitter(Tx)first aligns one interference incurred by the transmission of one data stream to a one-dimensional subspace orthogonal to the desired transmission at the interfered Rx,and then the remaining interferences are treated as a whole and steered to the same subspace as the aligned ***,two improved versions of IAS,i.e.,IAS with Full Adjustment at the Interfering Tx(IAS-FAIT)and Interference Steering and Alignment(ISA),are *** former considers the influence of IA on the interfering user-pair's *** orthogonality between the desired signals at the interfered Rx can be maintained by adjusting the spatial characteristics of all interferences and the aligned interference components,thus ensuring the Spectral Efficiency(SE)of the interfering communication *** ISA,the power cost for IS at the interfered Tx is minimized,hence improving SE performance of the interfered *** the proposed methods are realized at the interfering and interfered Txs cooperatively,the expenses of IM are shared by both *** in-depth simulation results show that joint use of IA and

关键词： Interference Interference management Interference alignment Interference steering Spectral efficiency

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HexTileConnect: Achieving Connected k-Coverage in Planar Wireless Sensor Networks Using Irregular Hexagonal Tessellation

HexTileConnect: Achieving Connected k-Coverage in Planar Wir...

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2024 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2024

作者： Maddala, Dakshanya Ammari, Habib M. Frank H. Dotterweich College of Engineering Texas A&m University-Kingsville WiSeMAN Research Lab Department of Electrical Engineering and Computer Science KingsvilleTX78363 United States School of Engineering College of Arts and Sciences Texas A&m International University WiSeMAN Research Lab LaredoTX78041 United States

ISBN: (纸本)9798350389838

In wireless sensor networks (WSNs), achieving network connectivity and sensing coverage (or simply coverage) simultaneously is crucial for ensuring the desired quality of service of the underlying sensing application. The two main functions of sensors in WSNs are sensing and communication, which are assessed using the coverage and connectivity metrics. This study focuses on the problem of connected k -coverage in planar WSNs (PWSNs), denoted by (CCkP), where every point in a planar field of interest (PFoI) is covered by at least k sensors at the same time, with k ≥ 1, and all the participating sensors are mutually connected. To address this problem, our study follows a series of steps: First, we investigate the sensor placement problem, and compute the required sensor density for k-coverage of a PFoI. Second, we consider tiling a field using planar convex polygonal tiles with no gaps or overlaps, ensuring effective coverage. Third, we identify the necessary relationship between the sensors' communication and sensing ranges to ensure network connectivity. Fourth, we propose an energy-efficient k- coverage protocol using the 'best' planar convex polygonal tile. Our goal is to extend the network lifespan while achieving k- coverage of a PFoI with a minimum number of sensors. We support our analysis with simulation results. © 2024 IEEE.

关键词： Wireless sensor networks

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Kanji Workbook: A Writing-Based Intelligent Tutoring System for Learning Proper Japanese Kanji Writing Technique with Instructor-Emulated Assessment

arXiv

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arXiv 2025年

作者： Taele, Paul Koh, Jung In Hammond, Tracy Sketch Recognition Lab Department of Computer Science & Engineering Texas A&M University United States

Kanji script writing is a skill that is often introduced to novice Japanese foreign language students for achieving Japanese writing mastery, but often poses difficulties to students with primarily English fluency due to their its vast differences with written English. Instructors often introduce various pedagogical methods—such as visual structure and written techniques—to assist students in kanji study, but may lack availability providing direct feedback on students’ writing outside of class. Current educational applications are also limited due to lacking richer instructor-emulated feedback. We introduce Kanji Workbook, a writing-based intelligent tutoring system for students to receive intelligent assessment that emulates human instructor feedback. Our interface not only leverages students’ computing devices for allowing them to learn, practice, and review the writing of prompted characters from their course’s kanji script lessons, but also provides a diverse set of writing assessment metrics—derived from instructor interviews and classroom observation insights—through intelligent scoring and visual animations. We deployed our interface onto novice- and intermediate-level university courses over an entire academic year, and observed that interface users on average achieved higher course grades than their peers and also reacted positively to our interface’s various features. © 2025, CC BY-NC-ND.

关键词： Students

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Speech Separation for Low-Resource Languages

Speech Separation for Low-Resource Languages

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Borsdorf, Marvin Pan, Zexu Himmelmann, Pascal Li, Haizhou Schultz, Tanja Machine Listening Lab University of Bremen Bremen Germany Alibaba Group Singapore Department of Mathematics and Computer Science FernUniversität in Hagen Hagen Germany School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore Cognitive Systems Lab University of Bremen Bremen Germany

ISBN: (纸本)9798350368741

Speech separation aims to equip machines with the human ability of selective listening, i.e. to focus attention on specific information in spoken communication. Studies have shown that the language spoken in a cocktail party scenario matters. While the development of speech separation models can leverage extensive databases, for the majority of languages only very limited data is available. This work presents the very first study on speech separation for low-resource languages. We choose blind source separation as the task to be studied and analyze three strategies to overcome the data scarcity of two low-resource languages from the GlobalPhoneMS2 database. We show that data from other languages can be used to develop models that work for low-resource languages. Finetuning additionally boosts the performance, and training on multiple languages increases both performance and robustness. We show that dynamic mixing in the development helps to find a trade-off between performance and development time. © 2025 IEEE.

关键词： cocktail party problem GlobalPhone low-resource language multilingual speech separation

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TREEBON: ENHANCING INFERENCE-TIME ALIGNMENT WITH SPECULATIVE TREE-SEARCH AND BEST-OF-N SAMPLING

arXiv

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arXiv 2024年

作者： Qiu, Jiahao Lu, Yifu Zeng, Yifan Guo, Jiacheng Geng, Jiayi Wang, Huazheng Huang, Kaixuan Wu, Yue Wang, Mengdi Department of Electrical & Computer Engineering Princeton University United States Department of Electrical Engineering & Computer Science University of Michigan United States Department of Computer Science Princeton University United States School of Electrical Engineering and Computer Science Oregon State University United States AI Lab Princeton United States

Inference-time alignment enhances the performance of large language models without requiring additional training or fine-tuning but presents challenges due to balancing computational efficiency with high-quality output. Best-of-N (BoN) sampling, as a simple yet powerful approach, generates multiple responses and selects the best one, achieving improved performance but with a high computational cost. We propose TreeBoN, a novel framework that integrates a speculative tree-search strategy into Best-of-N (BoN) Sampling. TreeBoN maintains a set of parent nodes, iteratively branching and pruning low-quality responses, thereby reducing computational overhead while maintaining high output quality. Our approach also leverages token-level rewards from Direct Preference Optimization (DPO) to guide tree expansion and prune low-quality paths. We evaluate TreeBoN using AlpacaFarm, HH-RLHF, UltraFeedback, GSM8K, and TutorEval datasets, demonstrating consistent improvements. Specifically, TreeBoN achieves the highest win rate of 65% on TutorEval and around 60% win rates across other different datasets, outperforming standard BoN with the same computational cost and showcasing its scalability and alignment efficacy. © 2024, CC BY.

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MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficient Crowdsourcing for Video Analytics 40

MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficien...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Chen, Yu Zhang, Sheng Zhou, Ziying Wang, Xiaokun Liang, Yu Chen, Ning Yan, Yuting Xiao, Mingjun Wu, Jie Qian, Zhuzhong Xu, Harry Nanjing University State Key Lab. for Novel Software Technology China School of Systems Science Beijing Normal University China School of Computer and Electronic Information School of Artificial Intelligence Nanjing Normal University China School of Computer Science and Technology University of Science and Technology of China China Temple University Center for Networked Computing United States Samueli School of Engineering University of California Computer Science Department Los Angeles United States

ISBN: (纸本)9798350317152

In recent years, many crowdsourcing platforms have emerged, using the resources of recruited workers to perform diverse outsourcing tasks, where the video analytics attracts much attention due to its practical implications. For maximum profits, platforms carefully choose the workers and determine the video analytics configurations to ensure accuracy;meanwhile, workers possess the flexibility to tailor the configurations for their indivi-dual gains, which makes it hard for platforms to optimize their profits considering the platform-worker conflicts. In this paper, we design an incentive mechanism for Multi-leader game-based video Analytics upon CROwdsourcing, named MACRO, to over-come the above situation. Under that mechanism, we first formu-late the utility optimization problems for platforms and workers, respectively. We then propose a dual ascent-based method to op-timally determine the video analytics configurations for a multi-platform game, ensuring Pareto efficiency. Moreover, in the context of a multi-leader game involving platform-worker conflicts, we design an incentive function with its incentive factor update strategy and propose an ADMM-based approach for maximizing incentives that motivate workers to contribute to the platforms' profits. Rigorous proofs demonstrate the linear convergence of the MACRO to the multi-leader Stackelberg equilibrium. Trace-driven experiments show that MACRO improves the Pareto efficiency by 26.3%, outperforming other approaches. © 2024 IEEE.

关键词： Profitability

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ED-DCFNet: an unsupervised encoder-decoder neural model for event-driven feature extraction and object tracking

ED-DCFNet: an unsupervised encoder-decoder neural model for ...

引用

IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Raz Ramon Hadar Cohen-Duwek Elishai Ezra Tsur Department of Mathematics and Computer Science Neuro-Biomorphic Engineering Lab (NBEL) The Open University of Israel

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neuromorphic cameras feature asynchronous event-based pixel-level processing and are particularly useful for object tracking in dynamic environments. Current approaches for feature extraction and optical flow with high-performing hybrid RGB-events vision systems require large computational models and supervised learning, which impose challenges for embedded vision and require annotated datasets. In this work, we propose ED-DCFNet, a small and efficient (< 72k) unsupervised multi-domain learning framework, which extracts events-frames shared features without requiring annotations, with comparable performance. Furthermore, we introduce an open-sourced event and frame-based dataset that captures indoor scenes with various lighting and motion-type conditions in realistic scenarios, which can be used for model building and evaluation. The dataset is available at https://***/NBElab/UnsupervisedTracking.

关键词： Training Neuromorphics Computational modeling Machine vision Supervised learning Feature extraction Pattern recognition

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6G-Enabled Internet of Things:Vision,Techniques,and Open Issues

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computer Modeling in engineering & sciences 2022年第12期133卷 509-556页

作者： Mehdi Hosseinzadeh Atefeh Hemmati Amir Masoud Rahmani Pattern Recognition and Machine Learning Lab Gachon UniversitySeongnam13120Korea Department of Computer Engineering Science and Research BranchIslamic Azad UniversityTehran***Iran Future Technology Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

There are changes in the development of wireless technology systems every decade.6G(sixth generation)wireless networks improve on previous generations by increasing dependability,accelerating networks,increasing available bandwidth,decreasing latency,and increasing data transmission speed to standardize communication *** purpose of this article is to comprehend the current directions in 6G studies and their relationship to the Internet of Things(IoT).Also,this paper discusses the impacts of 6G on IoT,critical requirements and trends for 6G-enabled IoT,new service classes of 6G and IoT technologies,and current 6G-enabled IoT studies selected by the systematic literature review(SLR)method published from 2018 to *** addition,we present a technical taxonomy for the classification of 6G-enabled IoT,which includes self-organization systems,energy efficiency,channel assessment,and ***,according to the articles reviewed,we consider the evaluation factors in this domain,including data transmission,delay,energy consumption,and ***,we focus on open issues and future research challenges in 6G-enabled *** mention important future challenges and directions,we can point to migration,data storage,data resource,data security,data sharing,data offloading,availability,scalability,portability,user experience,reliability,authentication,and authorization.

关键词： Internet of Things 6G wireless network machine learning artificial intelligence

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Improving open-ended text generation via adaptive decoding 24

Improving open-ended text generation via adaptive decoding

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Proceedings of the 41st International Conference on Machine Learning

作者： Wenhong Zhu Hongkun Hao Zhiwei He Yiming Ai Rui Wang MT Lab Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

Current language models decode text token by token according to probabilistic distribution, and determining the appropriate candidates for the next token is crucial to ensure generation quality. This study introduces adaptive decoding, a mechanism that dynamically empowers language models to ascertain a sensible candidate set during generation. Specifically, we introduce an entropy-based metric called confidence and conceptualize determining the optimal candidate set as a confidence-increasing process. The rationality of including a token in the candidate set is assessed by leveraging the increment of confidence. Experimental results reveal that our method balances diversity and coherence well. The human evaluation shows that our method can generate human-preferred text. Additionally, our method can potentially improve the reasoning ability of language models.

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