检索结果-内蒙古大学图书馆

Editorial: Acquisition and application of multimodal sensing information, volume II

FRONTIERS IN PHYSICS 2025年 13卷

作者： Yin, Xukun Jiang, Changhui Zheng, Huadan Sampaolo, Angelo Xu, Kaijie Xidian Univ Sch Optoelect Engn Xian Peoples R China Finnish Geospatial Res Inst Dept Photogrammetry & Remote Sensing Masala Finland Jinan Univ Guangdong Higher Educ Inst Key Lab Optoelect Informat & Sensing Technol Dept Optoelect Engn Guangzhou Peoples R China Univ & Politecn Bari Dipartimento Interateneo Fis PolySense Lab Bari Italy Xidian Univ Sch Elect Engn Xian Peoples R China

With advancements in sensor technology, these devices are now essential in diverse fields from industrial production and space exploration to healthcare and environmental monitoring. As the foundation of data acquisition, sensors measure physical parameters (e.g., temperature, pressure, speed) and convert them into electrical signals. High-performance sensors with high sensitivity, resolution, and real-time capabilities are highly sought after. Coupled with computer-based signal processing, multi-sensor systems enhance applications like intelligent transportation, smart cities, and UAVs. Optical trace gas sensor as a super-sensitive detection method has been developed in many fields, such as non-invasive medical diagnosis, manned spacecraft, chip manufacturing and smart grid (Jing et al., 2023;Sun et al., 2025;Yin et al. 2020;Wu et al., 2019). Futhermore, the sensors based on neural networks have become a research hotspot (Xu et al., 2022;Hu et al., 2024). This Research Topic-Volume II comprises several original research and review works, which are summarized below:In the first article, Jing et al. proposed a non-resonant photoacoustic SO2 gas sensor utilizing a 273 nm deep-ultraviolet light-emitting diode. Due to its divergent beam, the LED has a output power of ~10 mW with an emission angle of 60 o , which is unsuitable for the traditional photoacoustic gas sensors such as a resonant photoacoustic cell or a quartz tuning fork. The sensor achieved a minimum detection limit of 725 ppbv with a 1s integration *** the second study, Cheng et al. developed a Molybdenum disulfide (MoS2) nanosheets sensor for NO2 gas sensing. Engineering MoS2 nanosheets into a 3D network microstructure through the self-assembling of MoS2/SiO2 microspheres effectively mitigated the stacking effect. Compared to pure 2D MoS2 filim (single-or few-layer), MoS2 nanosheets had increased its surface area per footprint with a sensitivity of 0.297 ppmv, which was 6.15 times greater than that of

关键词： optical sensor laser spectroscopy signal processing LiDAR point cloud processing 3D geospatial sensing

来源：评论

学校读者我要写书评

暂无评论

On cross-attention-based graph neural networks for fault diagnosis using multi-sensor measurement

引用

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2025年

作者： Ren, Zhenxing Zhou, Yu Taiyuan Univ Technol Coll Comp Sci & Technol Daxue St 209 Jinzhong 030024 Shanxi Peoples R China Taiyuan Univ Technol Coll Data Sci Daxue St 209 Jinzhong 030024 Shanxi Peoples R China

Rotating machinery fault diagnostics has received a lot of attention in recent years. As a result, there has been an increase in research interest in rotating machine intelligent fault detection, especially when using measurement from multi-sensors. However, an accurate fault diagnosis is still challenged based on nonlinear and non-stationary vibration signals. On the other hand, not enough research has been done on structural information fusion from multi-sensor measurement due to the complexity of spatial-temporal correlation. This paper explores the use of vibration signals in multi-sensor measurement fusion for rotating machinery fault diagnostics, and a method using a cross-attention-based dual-branch graph neural network (CA-GNN) is proposed. First, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used to decompose vibration signals. Variational mode decomposition is then used to further deconstruct the highest-frequency subsequence after its parameters have been optimized using the whale optimization algorithm. Next, we build the CA-GNN, which contains two space branches for high- and low-frequency signals after initially creating the graph using multiple sensor data sets. information across both branches for high- and low-frequency components can then be efficiently fused. Lastly, two experimental scenarios are used to illustrate the suggested technique and assess its viability and accuracy for rotating machinery fault diagnosis. Results indicate that the proposed method can diagnose rotating machinery health issues with an average accuracy of up to 99%, indicating that the method's performance can fulfill real-world requirements.

关键词： Fault diagnosis rotating machinery signal processing graph neural network cross-attention module

来源：评论

学校读者我要写书评

暂无评论

Improved DOA Estimation of Sound Source of Small Amplitudes Using a Single Acoustic Vector sensor 19th

Improved DOA Estimation of Sound Source of Small Amplitudes ...

引用

19th National conference on Man-Machine Speech Communication

作者： Zhu, Bing Zhang, Wen Northwestern Polytech Univ Sch Marine Sci & Technol Ctr Intelligent Acoust & Immers Commun Xian Peoples R China

ISBN: (纸本)9789819610440;9789819610457

The localization of the sound source is crucial in audio signal processing, especially when using acoustic vector sensors. Traditional methods typically utilize active intensity vector information to estimate the direction of arrival (DOA). However, these methods often fail to estimate the DOA of small-amplitude sound sources in the presence of high interference. This study introduces an innovative approach that uses the reactive intensity vector as a clue to enhance the estimation of DOA for sound sources. The simulation results confirm that our proposed method consistently outperforms traditional techniques in estimating DOA for small-amplitude sound sources.

关键词： Active intensity vector Reactive intensity vector DOA estimation

来源：评论

学校读者我要写书评

暂无评论

Transmission scheme based on collaborative AUV communication 16

Transmission scheme based on collaborative AUV communication

引用

16th International conference on Signal processing Systems, ICSPS 2024

作者： Sun, Jiahao Qi, Chao Liu, Zengli Zhao, Xuanzhi Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Computer Center Kunming University of Science and Technology Kunming China

ISBN: (纸本)9781510689251

Underwater wireless sensor networks can monitor ocean information, which provides a new approach to marine environmental monitoring, disaster warning and resource exploration. However, the development of underwater wireless sensing networks is limited by factors such as ocean currents and limited energy of the sensors themselves. Based on this situation, this paper proposes a transmission scheme based on collaborative communication with autonomous underwater vehicles (AUVs), in which autonomous underwater vehicles (AUVs) are used as a tool for distributed data collection and forwarding, which can solve the problem of imbalanced energy consumption of sensor nodes in the traditional multi-hop underwater communication network. At the same time, we consider the influence of ocean currents on the node positions to maximize the imitation of the real underwater environment, and the simulation results show that the proposed transmission scheme for collaborative communication with AUVs can significantly prolong the service life of underwater sensor networks. © 2025 SPIE.

关键词： Autonomous underwater vehicles

来源：评论

学校读者我要写书评

暂无评论

Multiple intelligent Reflecting Surfaces Collaborative Wireless Localization System

引用

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第1期24卷 134-148页

作者： Zhang, Ziheng Chen, Wen Wu, Qingqing Li, Zhendong Zhu, Xusheng Chen, Jingfeng Cheng, Nan Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Peoples R China Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China

This paper studies a multiple intelligent reflecting surfaces (IRSs) collaborative localization system where multiple semi-passive IRSs are deployed in the network to locate one or more targets based on time-of-arrival. It is assumed that each semi-passive IRS is equipped with reflective elements and sensors, which are used to establish the line-of-sight links from the base station (BS) to multiple targets and process echo signals, respectively. Based on the above model, we derive the Fisher information matrix of the echo signal with respect to the time delay. By employing the chain rule and exploiting the geometric relationship between time delay and position, the Cram & eacute;r-Rao bound (CRB) for estimating the target's Cartesian coordinate position is derived. Then, we propose a two-stage algorithmic framework to minimize CRB in single- and multi-target localization systems by joint optimizing active beamforming at BS, passive beamforming at multiple IRSs and IRS selection. For the single-target case, we derive the optimal closed-form solution for multiple IRSs coefficients design and propose a low-complexity algorithm based on alternating direction method of multipliers to obtain the optimal solution for active beaming design. For the multi-target case, alternating optimization is used to transform the original problem into two subproblems where semi-definite relaxation and successive convex approximation are applied to tackle the quadraticity and indefiniteness in the CRB expression, respectively. Finally, numerical simulation results validate the effectiveness of the proposed algorithm for multiple IRSs collaborative localization system compared to other benchmark schemes as well as the significant performance gains.

关键词： sensors Location awareness Collaboration Array signal processing Estimation Optimization Interference Wireless sensor networks Vectors Transforms intelligent reflecting surface collaborative localization time-of-arrival Cramer-Rao bound

来源：评论

学校读者我要写书评

暂无评论

Infrastructure Assisted Autonomous Driving: Research, Challenges, and Opportunities

IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY

引用

IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY 2025年 6卷 662-716页

作者： George, Roshan Clancy, Joseph Brophy, Tim Sistu, Ganesh O'Grady, William Chandra, Sunil Collins, Fiachra Mullins, Darragh Jones, Edward Deegan, Brian Glavin, Martin Univ Galway Sch Engn Galway H91TK33 Ireland Univ Galway Ryan Inst Galway H91TK33 Ireland Valeo Tuam Co Galway H91TK33 Ireland

Despite advancements in perception technology, achieving full autonomy in vehicles remains challenging partly due to limited situational awareness. Even with their sophisticated sensor arrays, autonomous vehicles often struggle to comprehend complex real-world environments due to the challenges associated with occlusion. A possible solution for addressing this limitation lies in the concept of vehicle-to-infrastructure cooperative driving, which enables vehicles to interact with various sensors implemented in the surrounding infrastructure. The infrastructure can share real-time data, such as traffic conditions, road hazards, and weather updates, facilitating safer and more efficient navigation. Within this framework, cooperative sensing is a crucial component, augmenting the onboard sensing capabilities of autonomous vehicles. Cooperative sensing surpasses traditional onboard sensors by leveraging a shared sensor network among vehicles and infrastructure. This approach mitigates challenges posed by occlusion, where objects are obscured from a vehicle's direct view. By pooling information from multiple sources, autonomous vehicles can gain a more comprehensive understanding of their surroundings, leading to enhanced safety and performance on the road. This study addresses a literature gap regarding information flow from real-world scenes to environmental models for cooperative V2I systems. It explores three core concepts essential for understanding the environment: sensing, perception, and mapping. This paper identifies the specific information required from infrastructure nodes, proposes an optimized sensor suite, discusses data processing algorithms, and investigates effective spatial model representations for cooperative sensing. This research informs the reader about the different challenges and opportunities associated with a V2I cooperative sensing system.

关键词： sensors Pedestrians Robot sensing systems Autonomous vehicles Roads Safety Vehicle-to-infrastructure Robot kinematics Accidents Vehicular ad hoc networks Cooperative intelligent transportation systems (C-ITS) infrastructure sensing map fusion roadside units V2I V2X

来源：评论

学校读者我要写书评

暂无评论

Active Sensing for Multiuser Beam Tracking With Reconfigurable intelligent Surface

引用

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第1期24卷 540-554页

作者： Han, Han Jiang, Tao Yu, Wei Univ Toronto Edward S Rogers Sr Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada Marvell Semicond Canada Inc Toronto ON M5H 1J9 Canada Qualcomm Technol Inc Santa Clara CA 95051 USA

This paper studies a beam tracking problem in which an access point (AP), in collaboration with a reconfigurable intelligent surface (RIS), dynamically adjusts its downlink beamformers and the reflection pattern at the RIS in order to maintain reliable communications with multiple mobile user equipments (UEs). Specifically, the mobile UEs send uplink pilots to the AP periodically during the channel sensing intervals, the AP then adaptively configures the beamformers and the RIS reflection coefficients for subsequent data transmission based on the received pilots. This is an active sensing problem, because channel sensing involves configuring the RIS coefficients during the pilot stage and the optimal sensing strategy should exploit the trajectory of channel state information (CSI) from previously received pilots. Analytical solution to such an active sensing problem is very challenging. In this paper, we propose a deep learning framework utilizing a recurrent neural network (RNN) to automatically summarize the time-varying CSI obtained from the periodically received pilots into state vectors. These state vectors are then mapped to the AP beamformers and RIS reflection coefficients for subsequent downlink data transmissions, as well as the RIS reflection coefficients for the next round of uplink channel sensing. The mappings from the state vectors to the downlink beamformers and the RIS reflection coefficients for both channel sensing and downlink data transmission are performed using graph neural networks (GNNs) to account for the interference among the UEs. Simulations demonstrate significant and interpretable performance improvement of the proposed approach over the existing data-driven methods with nonadaptive channel sensing schemes.

关键词： sensors Reconfigurable intelligent surfaces Reflection coefficient Vectors Downlink Correlation Data communication Adaptation models Reflection Long short term memory Active sensing beam tracking deep learning reconfigurable intelligent surface graph neural network recurrent neural network long short-term memory (LSTM)

来源：评论

学校读者我要写书评

暂无评论

Bionic Olfactory Neuron with In-sensor Reservoir Computing for intelligent Gas Recognition

引用

ADVANCED MATERIALS 2025年第13期37卷 e2419159页

作者： Wu, Xiaosong Shi, Shuhui Jiang, Jingyan Lin, Dedong Song, Jian Wang, Zhongrui Huang, Weiguo Chinese Acad Sci State Key Lab Struct Chem Fujian Inst Res Struct Matter Fuzhou 350002 Fujian Peoples R China Fujian Sci & Technol Innovat Lab Optoelect Informa Fuzhou 350002 Fujian Peoples R China Univ Chinese Acad Sci 19A Yuquan Rd Beijing 100049 Peoples R China Univ Hong Kong Dept Elect & Elect Engn Pokfulam Rd Hong Kong Peoples R China Shenzhen Technol Univ Coll Big data & Internet Shenzhen 518118 Peoples R China Shanghai Univ Sch Microelect Shanghai 201800 Peoples R China

Gas sensing and recognition are closely related to the sustainable development of human society, current electronic noses (e-noses) typically focus on detecting specific gases, with only a few capable of recognizing complex odor mixtures. Further, these sensors often struggle to distinguish between isomers and homologs, as these compounds usually have similar physical and chemical properties, yielding nearly identical sensor responses. Even the mammalian olfactory systems consisting of a large variety of receptor cells and efficient neuron networks sometimes fail in this task. The bottleneck stems from the inability to extract the fingerprints of these compounds and the inefficiency of signal processing. To address these limitations, a material-device-algorithm co-design strategy is proposed that integrates an organic field-effect transistor (OFET) array with in-sensor reservoir computing (RC) and the k-nearest neighbors (KNN) algorithm. Organic semiconductors diversify responses to different gases, while RC efficiently extracts spatiotemporal features with lower training costs and reduced energy overhead. This synergy achieves 100% classification accuracy for eight gases and 99.04% accuracy for a library of 26 gases, including mixtures, isomers, and homologs-among the highest reported accuracies. This work provides a groundbreaking hardware solution for bionic olfactory neurons with edge artificial intelligence (AI) functions, surpassing traditional e-noses.

关键词： bionic olfactory neuron in-sensor reservoir computing intelligent gas recognition isomers and homologs discrimination pollutant analysis in underground water

来源：评论

学校读者我要写书评

暂无评论

An Optimized Edge Platform for 3D Object Detection in Autonomous Driving

An Optimized Edge Platform for 3D Object Detection in Autono...

引用

2025 International conference on Electronics, information, and Communication, ICEIC 2025

作者： Bok, Kwonseung Kim, Yumi Kim, Aeri Lee, Sang-Seol Kim, Kyungho Intelligent Image Processing Research Center Korea Electronics Technology Institute Gyeonggi-do Korea Republic of

ISBN: (纸本)9798331510756

Autonomous driving technology has been developed to enhance the safety of vehicles and drivers by recognizing surrounding objects and making appropriate situational judgments using sophisticated sensors. In particular, 3D object detection networks utilizing LiDAR within deep learning models have advanced to achieve accurate object detection. However, performing 3D object detection using LiDAR and advanced sensors requires processing a massive amount of data, making it challenging to handle directly within the vehicle. While external computational resources such as cloud servers can be employed, this approach introduces difficulties in guaranteeing low data exchange latency and ensuring data stability and security. In this paper, we propose a method to process 3D object detection on an edge platform, enabling it to be handled locally for autonomous driving applications. The proposed edge platform consists of a hardware architecture optimized for processing large volumes of point cloud data and a quantized, lightweight network. This enables the edge platform to handle the entire process of acquiring point cloud data via LiDAR, performing 3D object detection, and executing image post-processing tasks directly on the device. © 2025 IEEE.

关键词： 3D-object detection Autonomous driving Edge platform

来源：评论

学校读者我要写书评

暂无评论

In-depth development of temperature insensitive refractive index Fabry-Perot optical sensor 2

In-depth development of temperature insensitive refractive i...

引用

2nd International conference on Optical Communication and Optical information processing, OCOIP 2024

作者： Gao, Yuda Luo, Yanhua Institute of Fiber Optics Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China

ISBN: (数字)9781510688995

ISBN: (纸本)9781510688988

sensors based on optical fiber interference structures offer benefits such as high sensitivity, simple fabrication and good compatibility, and are widely used in fields like biological monitoring, chemical engineering, and environmental monitoring. However, these sensors often face unavoidable cross-sensitivity issues between temperature and refractive index. Therefore, developing a new optical fiber refractive index sensor that is insensitive to temperature and has a broad refractive index measurement range remains a significant research issue. In this work, proposing a temperature-insensitive refractive index sensor featuring a two-stage Fabry-Perot (F-P) cavity made from single-mode fiber (SMF), capillary fiber (CF), and no core fiber (NCF) based on the principle of mode interference. The research experimentally investigates the sensor's sensitivity to temperature and refractive index. Results indicate that the sensor's amplitude correlates linearly with the refractive index in both optical and Fourier domains, covering a refractive index range of 1.3164 to 1.4215. In the Fourier domains, sensitivities of - 0.7184/refractive index unit (RIU) and - 0.2672/RIU are recorded at two distinct positions, respectively. A direct relationship between the amplitude and the refractive index is established. Additionally, the proposed sensor is almost insensitive to temperature in both the wavelength and Fourier domains. At the same time, experimental validation using water, ethanol, isopropanol, and glycerin solutions with varying refractive indexes, yielding results that align well with the established refractive index relationship. © 2025 SPIE.

关键词： Fabry-Perot interferometers

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：