期刊名：Sensors
期刊主页：https://www.mdpi.com/journal/sensors
Sensors创刊于2001年，是一个国际性、经过同行评审的开放获取期刊，专注于传感器科学技术领域的研究。在过去的25年里，传感技术取得了显著的进展——从基础传感器设计发展到如今在物联网和人工智能驱动系统中广泛应用智能传感技术。为纪念这一重要发展节点，我们从期刊海量已发表文献中精选推出专题文集，收录25篇代表性研究论文与25篇综述文章。该批文章由主编及编委会团队遴选，遴选标准为研究具备突出影响力，且在多学科传感器研究的关键发展节点中起到重要奠基作用。
(一) 传感原理、材料与器件
>研究论文
1. Electrochemical Impedance Spectroscopy (EIS): Principles, Construction, and Biosensing Applications
电化学阻抗谱法：原理、实现方式及生物传感应用
Hend S. Magar, Rabeay Y. A. Hassan and Ashok Mulchandani
https://www.mdpi.com/1424-8220/21/19/6578
Magar, H.S.; Hassan, R.Y.A.; Mulchandani, A. Electrochemical Impedance Spectroscopy (EIS): Principles, Construction, and Biosensing Applications.Sensors2021,21, 6578.
>综述论文
2. Electrochemical Biosensors - Sensor Principles and Architectures
电化学生物传感器——传感器原理与结构
Dorothee Grieshaber, Robert MacKenzie, Janos Vörös and Erik Reimhult
https://www.mdpi.com/1424-8220/8/3/1400
Grieshaber, D.; MacKenzie, R.; Vörös, J.; Reimhult, E. Electrochemical Biosensors - Sensor Principles and Architectures.Sensors2008,8, 1400-1458.https://doi.org/10.3390/s80314000
3. Metal Oxide Gas Sensors: Sensitivity and Influencing Factors
金属氧化物气体传感器：灵敏度及影响因素
Chengxiang Wang, Longwei Yin, Luyuan Zhang, Dong Xiang and Rui Gao
https://www.mdpi.com/1424-8220/10/3/2088
Wang, C.; Yin, L.; Zhang, L.; Xiang, D.; Gao, R. Metal Oxide Gas Sensors: Sensitivity and Influencing Factors.Sensors2010,10, 2088-2106.
4. Metal Oxide Semi-Conductor Gas Sensors in Environmental Monitoring
用于环境监测的金属氧化物半导体气体传感器
George F. Fine, Leon M. Cavanagh, Ayo Afonja and Russell Binions
https://www.mdpi.com/1424-8220/10/6/5469
Fine, G.F.; Cavanagh, L.M.; Afonja, A.; Binions, R. Metal Oxide Semi-Conductor Gas Sensors in Environmental Monitoring.Sensors2010,10, 5469-5502.
5. Metal Oxide Nanostructures and Their Gas Sensing Properties: A Review
金属氧化物纳米结构及其气体传感特性：综述
Yu-Feng Sun, Shao-Bo Liu, Fan-Li Meng, Jin-Yun Liu, Zhen Jin, Ling-Tao Kong and Jin-Huai Liu
https://www.mdpi.com/1424-8220/12/3/2610
Sun, Y.-F.; Liu, S.-B.; Meng, F.-L.; Liu, J.-Y.; Jin, Z.; Kong, L.-T.; Liu, J.-H. Metal Oxide Nanostructures and Their Gas Sensing Properties: A Review.Sensors2012,12, 2610-2631.
6. A Survey on Gas Sensing Technology
气体传感技术研究概述
Xiao Liu, Sitian Cheng, Hong Liu, Sha Hu, Daqiang Zhang and Huansheng Ning
https://www.mdpi.com/1424-8220/12/7/9635
Liu, X.; Cheng, S.; Liu, H.; Hu, S.; Zhang, D.; Ning, H. A Survey on Gas Sensing Technology.Sensors2012,12, 9635-9665.
7. Humidity Sensors Principle, Mechanism, and Fabrication Technologies: A Comprehensive Review
湿度传感器的工作原理、机制及制造技术：全面综述
Hamid Farahani, Rahman Wagiran and Mohd Nizar Hamidon
https://www.mdpi.com/1424-8220/14/5/7881
Farahani, H.; Wagiran, R.; Hamidon, M.N. Humidity Sensors Principle, Mechanism, and Fabrication Technologies: A Comprehensive Review.Sensors2014,14, 7881-7939.
8. Surface Plasmon Resonance: A Versatile Technique for Biosensor Applications
表面等离子体共振：一种适用于生物传感器应用的多功能技术
Hoang Hiep Nguyen, Jeho Park, Sebyung Kang and Moonil Kim
https://www.mdpi.com/1424-8220/15/5/10481
Nguyen, H.H.; Park, J.; Kang, S.; Kim, M. Surface Plasmon Resonance: A Versatile Technique for Biosensor Applications.Sensors2015,15, 10481-10510.
9. A Review on Biosensors and Recent Development of Nanostructured Materials-Enabled Biosensors
生物传感器综述及基于纳米结构材料的生物传感器最新发展
Varnakavi. Naresh and Nohyun Lee
https://www.mdpi.com/1424-8220/21/4/1109
Naresh, V.; Lee, N. A Review on Biosensors and Recent Development of Nanostructured Materials-Enabled Biosensors.Sensors2021,21, 1109.
（二）可穿戴设备、生理传感器与生物医学传感器
>研究论文
10. Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers
利用体感加速度计对人类身体活动进行分类的机器学习方法
Andrea Mannini and Angelo Maria Sabatini
https://www.mdpi.com/1424-8220/10/2/1154
Mannini, A.; Sabatini, A.M. Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers.Sensors2010,10, 1154-1175.
11. IMU-Based Joint Angle Measurement for Gait Analysis
基于IMU的步态分析关节角度测量技术
Thomas Seel, Jörg Raisch and Thomas Schauer
https://www.mdpi.com/1424-8220/14/4/6891
Seel, T.; Raisch, J.; Schauer, T. IMU-Based Joint Angle Measurement for Gait Analysis.Sensors2014,14, 6891-6909.
12. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
用于多模态可穿戴设备活动识别的深度卷积神经网络与 LSTM 循环神经网络
Francisco Javier Ordóñez and Daniel Roggen
https://www.mdpi.com/1424-8220/16/1/115
Ordóñez, F.J.; Roggen, D. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition.Sensors2016,16, 115.
13. A Validation of Six Wearable Devices for Estimating Sleep, Heart Rate and Heart Rate Variability in Healthy Adults
六种用于评估健康成人睡眠、心率及心率变异性的可穿戴设备的验证研究
Dean J. Miller, Charli Sargent and Gregory D. Roach
https://www.mdpi.com/1424-8220/22/16/6317
Miller, D.J.; Sargent, C.; Roach, G.D. A Validation of Six Wearable Devices for Estimating Sleep, Heart Rate and Heart Rate Variability in Healthy Adults.Sensors2022,22, 6317.
>综述论文
14. Brain Computer Interfaces, a Review
脑机接口综述
Luis Fernando Nicolas-Alonso and Jaime Gomez-Gil
https://www.mdpi.com/1424-8220/12/2/1211
Nicolas-Alonso, L.F.; Gomez-Gil, J. Brain Computer Interfaces, a Review.Sensors2012,12, 1211-1279.
15. Wearable Electronics and Smart Textiles: A Critical Review
可穿戴电子器件与智能纺织品：评述
Matteo Stoppa and Alessandro Chiolerio
https://www.mdpi.com/1424-8220/14/7/11957
Stoppa, M.; Chiolerio, A. Wearable Electronics and Smart Textiles: A Critical Review.Sensors2014,14, 11957-11992.
16. Wearable Sensors for Remote Health Monitoring
用于远程健康监测的可穿戴传感器
Sumit Majumder, Tapas Mondal and M. Jamal Deen
https://www.mdpi.com/1424-8220/17/1/130
Majumder, S.; Mondal, T.; Deen, M.J. Wearable Sensors for Remote Health Monitoring.Sensors2017,17, 130.
17. Biomarkers in Cancer Detection, Diagnosis, and Prognosis
癌症检测、诊断与预后的生物标志物研究
Sreyashi Das, Mohan Kumar Dey, Ram Devireddy and Manas Ranjan Gartia
https://www.mdpi.com/1424-8220/24/1/37
Das, S.; Dey, M.K.; Devireddy, R.; Gartia, M.R. Biomarkers in Cancer Detection, Diagnosis, and Prognosis.Sensors2024,24, 37.
18. Transformers in EEG Analysis: A Review of Architectures and Applications in Motor Imagery, Seizure, and Emotion Classification
脑电图分析中的转换技术：关于运动想象、癫痫发作及情绪分类中各种架构与应用的综述
Elnaz Vafaei and Mohammad Hosseini
https://www.mdpi.com/1424-8220/25/5/1293
Vafaei, E.; Hosseini, M. Transformers in EEG Analysis: A Review of Architectures and Applications in Motor Imagery, Seizure, and Emotion Classification.Sensors2025,25, 1293.
19. Wearable and Flexible Sensor Devices: Recent Advances in Designs, Fabrication Methods, and Applications
可穿戴与柔性传感器设备：设计、制造方法及应用领域的最新进展
Shahid Muhammad Ali, Sima Noghanian, Zia Ullah Khan, Saeed Alzahrani, Saad Alharbi, Mohammad Alhartomi, and Ruwaybih Alsulami
https://www.mdpi.com/1424-8220/25/5/1377
Ali, S.M.; Noghanian, S.; Khan, Z.U.; Alzahrani, S.; Alharbi, S.; Alhartomi, M.; Alsulami, R. Wearable and Flexible Sensor Devices: Recent Advances in Designs, Fabrication Methods, and Applications.Sensors2025,25, 1377.
（三）传感器融合与计算机视觉
>研究论文
20. Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications
面向室内地图绘制的Kinect深度数据精度与分辨率研究
Kourosh Khoshelham and Sander Oude Elberink
https://www.mdpi.com/1424-8220/12/2/1437
Khoshelham, K.; Elberink, S.O. Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications.Sensors2012,12, 1437-1454.
21. Analysis of the Accuracy and Robustness of the Leap Motion Controller
Leap Motion 控制器的准确性和鲁棒性分析
Frank Weichert, Daniel Bachmann, Bartholomäus Rudak and Denis Fisseler
https://www.mdpi.com/1424-8220/13/5/6380
Weichert, F.; Bachmann, D.; Rudak, B.; Fisseler, D. Analysis of the Accuracy and Robustness of the Leap Motion Controller.Sensors2013,13, 6380-6393.
22. Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras
基于可见光和热成像摄像头拍摄的身体图像组合的人体识别系统
Dat Tien Nguyen, Hyung Gil Hong, Ki Wan Kim and Kang Ryoung Park
https://www.mdpi.com/1424-8220/17/3/605
Nguyen, D.T.; Hong, H.G.; Kim, K.W.; Park, K.R. Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras.Sensors2017,17, 605.
23. GelSight: High-Resolution Robot Tactile Sensors for Estimating Geometry and Force
GelSight：用于几何形貌与接触力估算的高分辨率机器人触觉传感器
Wenzhen Yuan, Siyuan Dong and Edward H. Adelson
https://www.mdpi.com/1424-8220/17/12/2762
Yuan, W.; Dong, S.; Adelson, E.H. GelSight: High-Resolution Robot Tactile Sensors for Estimating Geometry and Force.Sensors2017,17, 2762.
24. SECOND: Sparsely Embedded Convolutional Detection
SECOND：稀疏嵌入卷积检测
Yan Yan, Yuxing Mao and Bo Li
https://www.mdpi.com/1424-8220/18/10/3337
Yan, Y.; Mao, Y.; Li, B. SECOND: Sparsely Embedded Convolutional Detection.Sensors2018,18, 3337.
25. Comparing YOLOv3, YOLOv4 and YOLOv5 for Autonomous Landing Spot Detection in Faulty UAVs
比较 YOLOv3、YOLOv4 和 YOLOv5 在故障无人机自主着陆检测中的应用
Upesh Nepal and Hossein Eslamiat
https://www.mdpi.com/1424-8220/22/2/464
Nepal, U.; Eslamiat, H. Comparing YOLOv3, YOLOv4 and YOLOv5 for Autonomous Landing Spot Detection in Faulty UAVs.Sensors2022,22, 464.
>综述论文
26. 自动驾驶车辆中的传感器与传感器融合技术：综述
Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review
De Jong Yeong, Gustavo Velasco-Hernandez, John Barry and Joseph Walsh
https://www.mdpi.com/1424-8220/21/6/2140
Yeong, D.J.; Velasco-Hernandez, G.; Barry, J.; Walsh, J. Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review.Sensors2021,21, 2140.
(四) 工业与结构状态的监测与故障诊断
>研究论文
27. 非侵入式负载监测方法在独立能源感知中的应用：综述
Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey
Ahmed Zoha, Alexander Gluhak, Muhammad Ali Imran and Sutharshan Rajasegarar
https://www.mdpi.com/1424-8220/12/12/16838
Zoha, A.; Gluhak, A.; Imran, M.A.; Rajasegarar, S. Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey.Sensors2012,12, 16838-16866.
28. 学习如何使用卷积双向 LSTM 网络来监控机器健康状况
Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks
Rui Zhao, Ruqiang Yan, Jinjiang Wang and Kezhi Mao
https://www.mdpi.com/1424-8220/17/2/273
Zhao, R.; Yan, R.; Wang, J.; Mao, K. Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks.Sensors2017,17, 273.
29. 一种面向原始振动信号、具有良好抗噪与域自适应能力的故障诊断深度学习新模型
A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals
Wei Zhang, Gaoliang Peng, Chuanhao Li, Yuanhang Chen and Zhujun Zhang
https://www.mdpi.com/1424-8220/17/2/425
Zhang, W.; Peng, G.; Li, C.; Chen, Y.; Zhang, Z. A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals.Sensors2017,17, 425.
>综述论文
30. 无损检测与结构健康监测先进传感技术的系统评估
A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring
Sahar Hassani and Ulrike Dackermann
https://www.mdpi.com/1424-8220/23/4/2204
Hassani, S.; Dackermann, U. A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring.Sensors2023,23, 2204.
31. 增材制造：全面综述
Additive Manufacturing: A Comprehensive Review
Longfei Zhou, Jenna Miller, Jeremiah Vezza, Maksim Mayster, Muhammad Raffay, Quentin Justice, Zainab Al Tamimi, Gavyn Hansotte, Lavanya Devi Sunkara and Jessica Bernat
https://www.mdpi.com/1424-8220/24/9/2668
Zhou, L.; Miller, J.; Vezza, J.; Mayster, M.; Raffay, M.; Justice, Q.; Al Tamimi, Z.; Hansotte, G.; Sunkara, L.D.; Bernat, J. Additive Manufacturing: A Comprehensive Review.Sensors2024,24, 2668.
32. 工业 4.0 中的故障检测与诊断：挑战与机遇综述
Fault Detection and Diagnosis in Industry 4.0: A Review on Challenges and Opportunities
Denis Leite, Emmanuel Andrade, Diego Rativa and Alexandre M. A. Maciel
https://www.mdpi.com/1424-8220/25/1/60
Leite, D.; Andrade, E.; Rativa, D.; Maciel, A.M.A. Fault Detection and Diagnosis in Industry 4.0: A Review on Challenges and Opportunities.Sensors2025,25, 60.
33. 结构健康监测的传感技术：关于性能标准与新一代技术的先进综述
Sensing Techniques for Structural Health Monitoring: A State-of-the-Art Review on Performance Criteria and New-Generation Technologies
Ali Mardanshahi, Abhilash Sreekumar, Xin Yang, Swarup Kumar Barman and Dimitrios Chronopoulos
https://www.mdpi.com/1424-8220/25/5/1424
Mardanshahi, A.; Sreekumar, A.; Yang, X.; Barman, S.K.; Chronopoulos, D. Sensing Techniques for Structural Health Monitoring: A State-of-the-Art Review on Performance Criteria and New-Generation Technologies.Sensors2025,25, 1424.
(五) 农业与遥感技术
>研究论文
34. 增强型植被指数（EVI）和标准化差异植被指数（NDVI）对地形效应的敏感性：以高密度蒲葵林为例的研究
Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic Effects: A Case Study in High-density Cypress Forest
Bunkei Matsushita, Wei Yang, Jin Chen, Yuyichi Onda and Guoyu Qiu
https://www.mdpi.com/1424-8220/7/11/2636
Matsushita, B.; Yang, W.; Chen, J.; Onda, Y.; Qiu, G. Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic Effects: A Case Study in High-density Cypress Forest.Sensors2007,7, 2636-2651.
35. DeepFruits：一种利用深度神经网络进行水果识别的系统
DeepFruits: A Fruit Detection System Using Deep Neural Networks
Inkyu Sa, Zongyuan Ge, Feras Dayoub, Ben Upcroft, Tristan Perez and Chris McCool
https://www.mdpi.com/1424-8220/16/8/1222
Sa, I.; Ge, Z.; Dayoub, F.; Upcroft, B.; Perez, T.; McCool, C. DeepFruits: A Fruit Detection System Using Deep Neural Networks.Sensors2016,16, 1222.
>综述论文
36. 利用遥感技术估算水质参数的综合评估
A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques
Mohammad Haji Gholizadeh, Assefa M. Melesse and Lakshmi Reddi
https://www.mdpi.com/1424-8220/16/8/1298
Gholizadeh, M.H.; Melesse, A.M.; Reddi, L. A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques.Sensors2016,16, 1298.
37. 农业中的机器学习：综述
Machine Learning in Agriculture: A Review
Konstantinos G. Liakos, Patrizia Busato, Dimitrios Moshou, Simon Pearson and Dionysis Bochtis
https://www.mdpi.com/1424-8220/18/8/2674
Liakos, K.G.; Busato, P.; Moshou, D.; Pearson, S.; Bochtis, D. Machine Learning in Agriculture: A Review.Sensors2018,18, 2674.
38. 关于在智能农业中应用多模态数据的 CNN 技术综述
A Review of CNN Applications in Smart Agriculture Using Multimodal Data
Mohammad El Sakka, Mihai Ivanovici, Lotfi Chaari and Josiane Mothe
https://www.mdpi.com/1424-8220/25/2/472
El Sakka, M.; Ivanovici, M.; Chaari, L.; Mothe, J. A Review of CNN Applications in Smart Agriculture Using Multimodal Data.Sensors2025,25, 472.
39. 物联网与人工智能在农业领域的应用：现在是实施智能传感技术的好时机—一项关于智能传感技术的系统综述
The IoT and AI in Agriculture: The Time Is Now—A Systematic Review of Smart Sensing Technologies
Tymoteusz Miller, Grzegorz Mikiciuk, Irmina Durlik, Ma?gorzata Mikiciuk, Adrianna Lobodzińska and Marek ?nieg
https://www.mdpi.com/1424-8220/25/12/3583
Miller, T.; Mikiciuk, G.; Durlik, I.; Mikiciuk, M.; ?obodzińska, A.; Snieg, M. The IoT and AI in Agriculture: The Time Is Now—A Systematic Review of Smart Sensing Technologies.Sensors2025,25, 3583.
（六）无线通信与物联网传感器网络
>研究论文
40. 蓝牙低功耗技术概述与评估：一种新兴的低功耗无线技术
Overview and Evaluation of Bluetooth Low Energy: An Emerging Low-Power Wireless Technology
Carles Gomez, Joaquim Oller and Josep Paradells
https://www.mdpi.com/1424-8220/12/9/11734
Gomez, C.; Oller, J.; Paradells, J. Overview and Evaluation of Bluetooth Low Energy: An Emerging Low-Power Wireless Technology.Sensors2012,12, 11734-11753.
41. LoRa研究：面向物联网的长距离低功耗网络
A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
Aloÿs Augustin, Jiazi Yi, Thomas Clausen and William Mark Townsley
https://www.mdpi.com/1424-8220/16/9/1466
Augustin, A.; Yi, J.; Clausen, T.; Townsley, W.M. A Study of LoRa: Long Range & Low Power Networks for the Internet of Things.Sensors2016,16, 1466.
42. 通过图像学习交通情况：一种用于大规模交通网络速度预测的深度卷积神经网络
Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction
Xiaolei Ma, Zhuang Dai, Zhengbing He, Jihui Ma, Yong Wang and Yunpeng Wang
https://www.mdpi.com/1424-8220/17/4/818
Ma, X.; Dai, Z.; He, Z.; Ma, J.; Wang, Y.; Wang, Y. Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.Sensors2017,17, 818.
43. CICIoT2023：一种用于物联网环境大规模攻击的实时数据集与基准
CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment
Euclides Carlos Pinto Neto, Sajjad Dadkhah, Raphael Ferreira, Alireza Zohourian, Rongxing Lu and Ali A. Ghorbani
https://www.mdpi.com/1424-8220/23/13/5941
Neto, E.C.P.; Dadkhah, S.; Ferreira, R.; Zohourian, A.; Lu, R.; Ghorbani, A.A. CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment.Sensors2023,23, 5941.
（七）基于人工智能的感知与数据分析技术
>研究论文
44. 一种基于深度学习的鲁棒番茄病害与害虫实时检测器
A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition
Alvaro Fuentes, Sook Yoon, Sang Cheol Kim and Dong Sun Park
https://www.mdpi.com/1424-8220/17/9/2022
Fuentes, A.; Yoon, S.; Kim, S.C.; Park, D.S. A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition.Sensors2017,17, 2022.
45. Sentinel-2影像土地覆盖分类中随机森林、k-NN与SVM分类器的性能比较
Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery
Phan Thanh Noi and Martin Kappas
https://www.mdpi.com/1424-8220/18/1/18
Thanh Noi, P.; Kappas, M. Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery.Sensors2018,18, 18.
46. 用于智能城市颗粒物（PM2.5）预测的深度 CNN-LSTM 模型
A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities
Chiou-Jye Huang and Ping-Huan Kuo
https://www.mdpi.com/1424-8220/18/7/2220
Huang, C.-J.; Kuo, P.-H. A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities.Sensors2018,18, 2220.
47. UAV-YOLOv8：基于改进YOLOv8的无人机航拍小目标检测模型
UAV-YOLOv8: A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Photography Scenarios
Gang Wang, Yanfei Chen, Pei An, Hanyu Hong, Jinghu Hu and Tiange Huang
https://www.mdpi.com/1424-8220/23/16/7190
Wang, G.; Chen, Y.; An, P.; Hong, H.; Hu, J.; Huang, T. UAV-YOLOv8: A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Photography Scenarios.Sensors2023,23, 7190.
48. PC-YOLO11s：用于小目标图像检测的轻量高效特征提取方法
PC-YOLO11s: A Lightweight and Effective Feature Extraction Method for Small Target Image Detection
Zhou Wang, Yuting Su, Feng Kang, Lijin Wang, Yaohua Lin, Qingshou Wu, Huicheng Li and Zhiling Cai
https://www.mdpi.com/1424-8220/25/2/348
Wang, Z.; Su, Y.; Kang, F.; Wang, L.; Lin, Y.; Wu, Q.; Li, H.; Cai, Z. PC-YOLO11s: A Lightweight and Effective Feature Extraction Method for Small Target Image Detection.Sensors2025,25, 348.
>综述论文
49. 可解释的人工智能技术在医疗领域的应用研究
Survey of Explainable AI Techniques in Healthcare
Ahmad Chaddad, Jihao Peng, Jian Xu and Ahmed Bouridane
https://www.mdpi.com/1424-8220/23/2/634
Chaddad, A.; Peng, J.; Xu, J.; Bouridane, A. Survey of Explainable AI Techniques in Healthcare.Sensors2023,23, 634.
50. 从传感器到数据智能：利用物联网、云计算和边缘计算技术结合人工智能技术
From Sensors to Data Intelligence: Leveraging IoT, Cloud, and Edge Computing with AI
Ilenia Ficili, Maurizio Giacobbe, Giuseppe Tricomi and Antonio Puliafito
https://www.mdpi.com/1424-8220/25/6/1763
Ficili, I.; Giacobbe, M.; Tricomi, G.; Puliafito, A. From Sensors to Data Intelligence: Leveraging IoT, Cloud, and Edge Computing with AI.Sensors2025,25, 1763.
我们向多年来为期刊发展贡献力量的全体作者、审稿专家以及编委会成员致以诚挚谢意。衷心希望本文集既能回望致敬过往成果，也能够激励新一代传感技术创新不断涌现。
Sensors编辑部
期刊介绍
主编：Vittorio M. N. Passaro, Politecnico di Bari, Italy
期刊涵盖所有传感器科学和技术研究领域，例如物理传感器、智能传感器、传感网络、生物传感器、化学传感器、雷达、可穿戴电子设备和先进的传感材料及其在物联网、工业、农业、环境、遥感、导航、通信、车辆、成像、生物医药等领域的应用。目前期刊已被Science Citation Index Expanded (SCIE)、PubMed、Ei Compendex、Scopus等数据库收录。
2025 Impact Factor：4.0
2025 CiteScore：9.4
Time to First Decision：17.8 Days
Acceptance to Publication：2.8 Days
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