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Robust Self-Localization of Wireless Acoustic Sensor Networks

IEEE Internet of Things Journal 2025年

作者： Wang, Xu Hu, De Liu, Rui Bao, Feilong Inner Mongolia University College of Computer Science Hohhot010021 China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot 010021 China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot 010021 China

Wireless acoustic sensor networks (WASNs), or the so-called internet of audio things (IoAuTs), have attracted increasing attention in the IoT community. As the geometry structure of WASNs is required in audio/speech processing tasks like source localization or acoustic beamforming, automatic self-localization of sensors is necessary. However, most of the existing approaches suffer from poor stability, as their constructed cost functions involve non-convex programming. To address this issue, we investigate the robust self-localization (or geometry calibration) of WASNs in this paper. Specifically, a rough self-localization (RSL) method is first presented based on measurements including time-difference-of-arrivals (TDoAs), direction-of-arrivals (DoA), and energy-rates (ERs), and its closed-form solution is further derived. As ER estimates are sensitive to acoustic environments, the performance of the RSL method is somewhat limited. Therefore, a precise self-localization (PSL) method is then developed by building a weighted (and non-convex) TDoA-DoA cost function, after regarding the RSL approach as an initialization step. As the RSL offers better initial values compared with existing initialization strategies, the combination of RSL and PSL methods (named as RSL-PSL method) shows stronger robustness and stability. In addition, computational complexity of both RSL and PSL methods are analyzed in details. Finally, the Cramér-Rao Bound (CRB) of the PSL method is derived to show its theoretical lower bound. The proposed RSL-PSL method outperforms the state-of-the-arts in terms of stability and accuracy, which is confirmed by numerical real-world and simulation experiments. © 2014 IEEE.

关键词： Cost functions

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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Morphotectonic Analyses of LiDAR-Derived DEMs:Insights into Tectonic Activity of the Xinhua Fault within the Three Gorges Area(Central China)

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Journal of Earth Science 2025年第2期36卷 823-829页

作者： Xiaofeng Wang Xiaohan Yin Tonghui Liu Xuan Li Hongming Wang Yaqi Zhong Gang Rao School of Computer and the Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences(Wuhan)Wuhan 430078China School of Geoscience and Technology Southwest Petroleum UniversityChengdu 610500China School of Earth Sciences China University of Geosciences(Wuhan)Wuhan 430074China Hubei Communications Planning and Design Institute Co. Ltd.Wuhan 430051China School of Economics and Management Shandong Jiaotong UniversityJinan 250357China

0 *** global availability of digital elevation model(DEM)data,such as 90-m Shuttle Radar Topography Mission(SRTM)DEM and 30-m Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model(ASTER GDEM),has been extensively utilized in morphotectonic analyses(e.g.,Wang et al.,2024;Cheng et al.,2018;Pérez-Pe?a et al.,2010;El Hamdouni et al.,2008).

关键词： xinhua fault morphotectonic analyses egwang three gorges area digital elevation model aster lidar derived dems tectonic activity morphotectonic analyses digital elevation model dem datasuch

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On the -differential spectra of two classes of power functions over finite fields

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Applicable Algebra in Engineering, Communication and Computing 2025年 1-30页

作者： Liu, Qian Huang, Zhiwei Chen, Zhixiong Jiang, Rong Zhang, Liupiao Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou University Fuzhou China Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Fujian Key Laboratory of Financial Information Processing Putian University Putian China School of Computer Science and Technology Dongguan University of Technology Dongguan China

Recently, a new concept called multiplicative differential was introduced by Ellingsen et al. [7]. As an extension of the differential uniformity, it is theoretically appealing to determine the properties of c-differential uniformity and the corresponding c-differential spectrum. In this paper, based on certain quadratic character sums and two special elliptic curves over $$\mathbb {F}_p$$ , the $$(-1)$$ -differential spectra of the following two classes of power functions over $$\mathbb {F}_{p^n}$$ is completely determined: (1) $$f_1(x)=x^{\frac{p^n+3}{2}}$$ , where $$p>3$$ and $$p\equiv 3\pmod 4$$ ; (2) $$f_2(x)=x^{p^n-3}$$ , where $$p>3$$ . The obtained result shows that the $$(-1)$$ -differential spectra of $$f_1(x)$$ and $$f_2(x)$$ can be expressed explicitly in terms of n. Moreover, an upper bound of the c-differential uniformity of $$f_2(x)$$ is given.

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Multi-Modal Anomalous Driving Behavior Detection With Adaptive Masking

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Concurrency and Computation: Practice and Experience 2025年第12-14期37卷

作者： Zeng, Kun Peng, Zhonghua Li, Zuoyong Chen, Yun Chen, Feng Wu, Nanbing Fujan Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Data Science Minjiang University Fuzhou China Fujian Communications Planning & Design Institute Co. LTD Fuzhou China Fujian Provincial Highway Development Center Fuzhou China

Intelligent Transportation Systems are tasked with enhancing road safety, a crucial challenge given that approximately 1.35 million fatalities occur globally each year, with 15%–27% of these deaths attributed to Anomalous Driving Behaviors (ADBs). Detecting these behaviors in real time is vital for preventing accidents and improving traffic safety. However, the complexity of driving environments, characterized by diverse scenarios, drivers, and vehicle conditions, makes ADB detection a challenging task. This article proposes a novel approach for ADB detection, leveraging the advantages of multimodal data, adaptive masking, and multihead self-attention mechanisms. The proposed method first employs an adaptive masking technique based on the Softmax function to sparsify input features, effectively reducing the influence of irrelevant information. By focusing on key features, the model becomes more resilient to noise, such as background clutter or irrelevant driver actions, which might otherwise interfere with the detection of abnormal behaviors. To further enhance feature integration across different data modalities (e.g., visual, infrared, and depth data), a multihead self-attention mechanism is incorporated. This mechanism enables the model to prioritize important information from various sensor inputs, fostering more effective multimodal fusion and better decision-making for behavior classification. In addition, a supervised contrastive learning strategy is utilized to mitigate memory usage, a common challenge in real-time systems where computational resources are limited. This approach ensures efficient learning by emphasizing the distinction between normal and abnormal behaviors while minimizing the memory footprint of the model. Extensive experiments on two benchmark datasets, 3MDAD and DAD, demonstrate the proposed method's superior performance in detecting ADBs. The results indicate a significant improvement in detection accuracy and robustness, highlighting th

关键词： Highway accidents

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Inference-Aware State Reconstruction for Industrial Metaverse under Synchronous/Asynchronous Short-Packet Transmission

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IEEE Transactions on Communications 2025年

作者： Xiong, Qinqin Cao, Jie Zhu, Xu Jiang, Yufei Pappas, Nikolaos Harbin Institute of Technology School of Electronic and Information Engineering Shenzhen518055 China The Shenzhen Key Laboratory of Wireless AloT Communications Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen Guangdong China Linkoping University Department of Computer and Information Science Linköping58183 Sweden

We consider a real-time state reconstruction system for industrial metaverse. The time-varying physical process states in real space are captured by multiple sensors via wireless links, and then reconstructed in virtual space. In this paper, we use the spatial-temporal correlation of the sensor data of interest to infer the real-time data of the target sensor to reduce the mean squared error (MSE) of reconstruction for industrial metaverse under short-packet transmission (SPT). Both synchronous and asynchronous transmission modes for multiple sensors are considered. It is proved that the average reconstruction MSE and average block error probability (BLEP) have a positive correlation under inference with synchronous transmission scheme, whereas they have a negative correlation under inference with asynchronous transmission scheme in certain conditions. Additionally, the average reconstruction MSE decreases monotonically with the mean squared spatial correlation (MSSC), which characterizes the global spatial correlation level. With a high BLEP or long transmission period, even under weak MSSC, the inference scheme still significantly reduces the average reconstruction MSE compared to the no inference case. Moreover, closed-form MSSC thresholds are derived for the superiority regions of the inference with synchronous transmission and inference with asynchronous transmission schemes, respectively. Adaptations of blocklength and time shift of asynchronous transmission are conducted to minimize the average reconstruction MSE. Simulation results show that the two inference schemes outperform the no inference case, with an average MSE reduction of more than 50%. © 1972-2012 IEEE.

关键词： Packet networks

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Digital Human Techniques for Education Reform 24

Digital Human Techniques for Education Reform

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7th International Conference on Educational technology Management, ICETM 2024

作者： Guo, Peirong Zhang, Qi Tian, Chunwei Xue, Wanli Feng, Xiaocheng School of Software Northwestern Polytechnical University Shaanxi Xi’an China School of Economics and Management Harbin Institute of Technology at Weihai Shandong Weihai China Shenzhen Research Institute of Northwestern Polytechnical University Northwestern Polytechnical University Guangdong Shenzhen China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology Heilongjiang Harbin China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400717468

The rapid evolution of artificial intelligence, big data, and generative AI models has ushered in significant transformations across various sectors, including education. Digital Human Technique, an innovative technology grounded in advanced computer science and artificial intelligence, is reshaping educational paradigms by enabling virtual humans to simulate human behavior, express emotions, and interact with users. This paper explores the application of Digital Human Technique in education reform, focusing on creating immersive, intelligent classroom experiences that foster meaningful interactions between teachers and students. We define Digital Human Technique and delve into its key technical components such as character modeling and rendering, natural language processing, computer vision, and augmented reality technologies. Our methodology involves analyzing the role of educational digital humans created through these technologies, assessing their impact on educational processes, and examining various application scenarios in educational reform. Results indicate that Digital Human Technique significantly enhances the learning experience by enabling personalized teaching, increasing engagement, and fostering emotional connections. Educational digital humans serve as virtual teachers, interactive learning aids, and facilitators of emotional interaction, effectively addressing the challenges of traditional educational methods. They also promote a deeper understanding of complex concepts through simulated environments and interactive digital content. © 2024 Copyright held by the owner/author(s).

关键词： Digital elevation model

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High-Fidelity Editable Portrait Synthesis with 3D GAN Inversion

High-Fidelity Editable Portrait Synthesis with 3D GAN Invers...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jindong Xie Jiachen Liu Yupei Lin Jinbao Wang Xianxu Hou Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of Information Engineering Guangdong University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Information Processing School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The 3D generative adversarial network (GAN) inversion converts an image into 3D representation to attain high-fidelity reconstruction and facilitate realistic image manipulation within the 3D latent space. However, previous approaches face challenges regarding the trade-off between the reconstruction ability and editability. That is, reversing a real-world image to a low-dimensional latent code would inevitably lead to information loss, and achieving a near-perfect reconstruction using high-rate triplane representation often limits the ability to manipulate the image freely in the latent space. To address these issues, we propose a novel latent conditioning encoder-based framework with the alignment between the low-dimensional latent and high-dimensional triplane. A non-semantic guided editing strategy bridges the intrinsic relation between the latent condition and triplane generation, making it possible to edit the high-dimensional representation by latent manipulation. As a result, our method can achieve high-fidelity reconstruction and editing simultaneously by directly controlling the latent code. Experimental results demonstrate that our approach excels in reconstruction and editing quality compared to previous 3D inversion methods. Furthermore, our method can also edit even real faces with large poses and out-of-domain cases.

关键词： Bridges Three-dimensional displays Codes Signal processing Generative adversarial networks Vectors Acoustics Speech processing Image reconstruction Faces

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Enhancing NR-IQA Model Robustness through Simple Image Compression Techniques

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Liu, Yujia Yang, Chenxi Yu, Zhaofei Huang, Tiejun Peking University National Engineering Research Center of Visual Technology State Key Laboratory of Multimedia Information Processing School of Computer Science Beijing China Peking University School of Mathematical Sciences Beijing China China Institution for Artificial Intelligence Peking University National Engineering Research Center of Visual Technology State Key Laboratory of Multimedia Information Processing China

No-Reference Image Quality Assessment (NR-IQA) plays a crucial role in various real-world applications by predicting image quality scores without the need for reference images. Despite the impressive performance of deep learning-based NR-IQA models, they remain vulnerable to adversarial attacks, which introduce imperceptible perturbations to input images, causing significant changes in predicted scores. In this study, we explore the use of simple JPEG compression techniques, as well as their combination with norm regularization training, to defend against these adversarial attacks. Our results demonstrate that image compression is an effective method to enhance model robustness, and it can further improve the robustness of NR-IQA models when combined with appropriate training strategies. Since excessive compression may reduce performance on clean images, it is essential to strike a balance. This work provides valuable insights into designing effective image compression methods for NR-IQA models. © 1991-2012 IEEE.

关键词： Image compression

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Multi-photon neuron embedded bionic skin for high-precision complex texture and object reconstruction perception research

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Opto-Electronic Advances 2025年第2期8卷 29-44页

作者： Hongyu Zhou Chao Zhang Hengchang Nong Junjie Weng Dongying Wang Yang Yu Jianfa Zhang Chaofan Zhang Jinran Yu Zhaojian Zhang Huan Chen Zhenrong Zhang Junbo Yang College of Science National University of Defense TechnologyChangsha 410073China Key Laboratory of Multimedia Communication and Network Technology in Guangxi School of ComputerElectronics and InformationGuangxi UniversityNanning 530004China College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410073China College of Meteorology and Oceanography National University of Defense TechnologyChangsha 410073China Hunan Provincial Key Laboratory of Novel Nano-Optoelectronic Information Materials and DevicesNational University of Defense TechnologyChangsha 410073China College of Advanced Interdisciplinary Studies National University of Defense TechnologyChangsha 410073China

Attributable to the complex distribution of tactile vesicles under the skin and the ability of the brain to process specific tactile parameters(shape,hardness,and surface texture),human skin can have the capacity for tactile spatial reconstruction and visualization of complex object geometry and surface ***,current haptic sensor technologies are predominantly point sensors,which do not have an interlaced distribution structure similar to that of haptic vesicles,limiting their potential in human-computer interaction ***,we report an optical microfiber array skin(OMAS)imitating tactile vesicle interlaced structures for tactile visualization and object reconstruction *** device is characterized by high sensitivity(−0.83 N/V)and fast response time(38 ms).We demonstrate that combining the signals collected by the OMAS with appropriate artificial intelligence algorithms enables the recognition of objects with different hardnesses and shapes with 100%*** also allows for the classification of fabrics with different surface textures with 98.5%accuracy and Braille patterns with 99%*** a proof-of-concept,we integrated OMAS into a robot arm to select mahjong among six common objects and successfully recognize its suits by touch,which provides a new solution for tactile sensory processing for human-computer interaction.

关键词： multiphoton neurons human-computer interaction tactile sensing tactile imaging tactile spatial reconstruction

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