SENSORS (ISSN 1424-8220) 为传感器和生物传感器的科学和技术提供了一个高级论坛。它发表评论（包括对完整传感器产品的综合评论）、定期研究论文和简报。我们的目标是鼓励科学家尽可能详细地发表他们的实验和理论结果。论文的长度没有限制。必须提供完整的实验细节，以便重现结果。

"Sensors" (ISSN 1424-8220) is a high-level forum dedicated to the science and technology of sensors and biosensors. The journal publishes various types of articles, including comprehensive reviews of complete sensor products, regular research papers, and brief communications. Our aim is to encourage scientists to share their experimental and theoretical results in as much detail as possible. There is no limit on the length of the papers, but complete experimental details must be provided to allow for the reproducibility of results.

《Sensors》（ISSN 1424-8220）是一个专注于传感器和生物传感器科学与技术的高级论坛。期刊接受各种类型的文章，包括对完整传感器产品的综合评论、定期研究论文和简报。我们的目标是鼓励科学家尽可能详细地分享他们的实验和理论结果。论文的长度没有限制，要求提供完整的实验细节，以便他人能够重现结果。

Sensors

This paper presents the development of a visual-perception system on a dual-arm mobile robot for human-robot interaction. This visual system integrates three subsystems. Hand gesture recognition is utilized to trigger human-robot interaction. Engagement and intention of the participants are detected and quantified through a cognitive system. Visual servoing uses YOLO to identify the object to be tracked and hybrid, model-based tracking to follow the object’s geometry. The proposed visual-perception system is implemented in the developed dual-arm mobile robot, and experiments are conducted to validate the proposed method’s effects on human-robot interaction applications.

Wei-Ting Weng

Han-Pang Huang

Yu-Lin Zhao

Chun-Yeon Lin

Development of a Visual Perception System on a Dual-Arm Mobile Robot for Human-Robot Interaction

The marine environment presents a unique set of challenges for human-robot interaction. Communicating with gestures is a common way for interacting between the diver and autonomous underwater vehicles (AUVs). However, underwater gesture recognition is a challenging visual task for AUVs due to light refraction and wavelength color attenuation issues. Current gesture recognition methods classify the whole image directly or locate the hand position first and then classify the hand features. Among these purely visual approaches, textual information is largely ignored. This paper proposes a visual-textual model for underwater hand gesture recognition (VT-UHGR). The VT-UHGR model encodes the underwater diver's image as visual features, the category text as textual features, and generates visual-textual features through multimodal interactions. We guide AUVs to use image-text matching for learning and inference. The proposed method achieves better performance than most existing purely visual methods on the dataset CADDY, demonstrating the effectiveness of using textual patterns for underwater gesture recognition.

Yongji Zhang

Yu Jiang

Hong Qi

Minghao Zhao

Yuehang Wang

Kai Wang

Fenglin Wei

An Underwater Human–Robot Interaction Using a Visual–Textual Model for Autonomous Underwater Vehicles

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

Sk. Tanzir Mehedi

Adnan Anwar

Ziaur Rahman

Kawsar Ahmed

Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Irrigation systems are becoming increasingly important, owing to the increase in human population, global warming, and food demand. This study aims to design a low-cost autonomous sensor interface to automate the monitoring and control of irrigation systems in remote locations, and to optimize water use for irrigation farming. An internet of things-based irrigation monitoring and control system, employing sensors and actuators, is designed to facilitate the autonomous supply of adequate water from a reservoir to domestic crops in a smart irrigation systems. System development lifecycle and waterfall model design methodologies have been employed in the development paradigm. The Proteus 8.5 design suite, Arduino integrated design environment, and embedded C programming language are commonly used to develop and implement a real working prototype. A pumping mechanism has been used to supply the water required by the soil. The prototype provides power supply, sensing, monitoring and control, and internet connectivity capabilities. Experimental and simulation results demonstrate the flexibility and practical applicability of the proposed system, and are of paramount importance, not only to farmers, but also for the expansion of economic activity. Furthermore, this system reduces the high level of supervision required to supply irrigation water, enabling remote monitoring and control.

None Abba

None Namkusong

None Lee

None Crespo

Design and Performance Evaluation of a Low-Cost Autonomous Sensor Interface for a Smart IoT-Based Irrigation Monitoring and Control System

Surface subsidence hazards in mining areas are common geological disasters involving issues such as vegetation degradation and ground collapse during the mining process, which also raise safety concerns. To address the accuracy issues of traditional prediction models and study methods for predicting subsidence in open-pit mining areas, this study first employed 91 scenes of Sentinel-1A ascending and descending orbits images to monitor long-term deformations of a phosphate mine in Anning City, Yunnan Province, southwestern China. It obtained annual average subsidence rates and cumulative surface deformation values for the study area. Subsequently, a two-dimensional deformation decomposition was conducted using a time-series registration interpolation method to determine the distribution of vertical and east-west deformations. Finally, three prediction models were employed: Back Propagation Neural Network (BPNN), BPNN optimized by Genetic Algorithm (GA-BP), and BPNN optimized by Artificial Bee Colony Algorithm (ABC-BP). These models were used to forecast six selected time series points. The results indicate that the BPNN model had Mean Absolute Errors (MAE) and Root Mean Squared Errors (RMSE) within 7.6 mm, while the GA-BP model errors were within 3.5 mm, and the ABC-BP model errors were within 3.7 mm. Both optimized models demonstrated significantly improved accuracy and good predictive capabilities.

Kangtai Chang

Zhifang Zhao

Dingyi Zhou

Zhuyu Tian

Chang Wang

Prediction of Surface Subsidence in Mining Areas Based on Ascending-Descending Orbits Small Baseline Subset InSAR and Neural Network Optimization Models

We present a benchmark dataset for evaluating physical human activity recognition methods from wrist-worn sensors, for the specific setting of basketball training, drills, and games. Basketball activities lend themselves well for measurement by wrist-worn inertial sensors, and systems that are able to detect such sport-relevant activities could be used in applications of game analysis, guided training, and personal physical activity tracking. The dataset was recorded from two teams in separate countries (USA and Germany) with a total of 24 players who wore an inertial sensor on their wrist, during both a repetitive basketball training session and a game. Particular features of this dataset include an inherent variance through cultural differences in game rules and styles as the data was recorded in two countries, as well as different sport skill levels since the participants were heterogeneous in terms of prior basketball experience. We illustrate the dataset’s features in several time-series analyses and report on a baseline classification performance study with two state-of-the-art deep learning architectures.

Alexander Hoelzemann

Julia Lee Romero

Marius Bock

Kristof Van Laerhoven

Qin Lv

Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition Using Wrist-Worn Inertial Sensors

In the medical field, guidance to follow the surgical plan is crucial. Image overlay projection is a solution to link the surgical plan with the patient. It realizes augmented reality (AR) by projecting computer-generated image on the surface of the target through a projector, which can visualize additional information to the scene. By overlaying anatomical information or surgical plans on the surgery area, projection helps to enhance the surgeon’s understanding of the anatomical structure, and intuitively visualizes the surgical target and key structures of the operation, and avoid the surgeon’s sight diversion between monitor and patient. However, it still remains a challenge to project the surgical navigation information on the target precisely and efficiently. In this study, we propose a projector-based surgical navigation system. Through the gray code-based calibration method, the projector can be calibrated with a camera and then be integrated with an optical spatial locator, so that the navigation information of the operation can be accurately projected onto the target area. We validated the projection accuracy of the system through back projection, with average projection error of 3.37 pixels in x direction and 1.51 pixels in y direction, and model projection with an average position error of 1.03 ± 0.43 mm, and carried out puncture experiments using the system with correct rate of 99%, and qualitatively analyzed the system’s performance through the questionnaire. The results demonstrate the efficacy of our proposed AR system.

Yuan Gao

Yuyun Zhao

Le Xie

Guoyan Zheng

A Projector-Based Augmented Reality Navigation System for Computer-Assisted Surgery

The change from face-to-face work to teleworking caused by the pandemic has induced multiple workers to spend more time than usual in front of a computer; in addition, the sudden installation of workstations in homes means that not all of them meet the necessary characteristics for the worker to be able to position himself/herself comfortably with the correct posture in front of their computer. Furthermore, from the point of view of the medical personnel in charge of occupational risk prevention, an automated tool able to quantify the degree of incorrectness of a postural habit in a worker is needed. For this purpose, in this work, a system based on the postural detection of the worker is designed, implemented and tested, using a specialized hardware system that processes video in real time through convolutional neural networks. This system is capable of detecting the posture of the neck, shoulders and arms, providing recommendations to the worker in order to prevent possible health problems, due to poor posture. The results of the proposed system show that this video processing can be carried out in real time (up to 25 processed frames/sec) with a low power consumption (less than 10 watts) using specialized hardware, obtaining an accuracy of over 80% in terms of the pattern detected.

Enrique Piñero-Fuentes

Salvador Canas-Moreno

Antonio Rios-Navarro

Manuel Domínguez-Morales

José Luis Sevillano

Alejandro Linares-Barranco

A Deep-Learning Based Posture Detection System for Preventing Telework-Related Musculoskeletal Disorders

Soil organic matter (SOM) is one of the best indicators to assess soil health and understand soil productivity and fertility. Therefore, measuring SOM content is a fundamental practice in soil science and agricultural research. The traditional approach (oven-dry) of measuring SOM is a costly, arduous, and time-consuming process. However, the integration of cutting-edge technology can significantly aid in the prediction of SOM, presenting a promising alternative to traditional methods. In this study, we tested the hypothesis that an accurate estimate of SOM might be obtained by combining the ground-based sensor-captured soil parameters and soil analysis data along with drone images of the farm. The data are gathered using three different methods: ground-based sensors detect soil parameters such as temperature, pH, humidity, nitrogen, phosphorous, and potassium of the soil; aerial photos taken by UAVs display the vegetative index (NDVI); and the Haney test of soil analysis reports measured in a lab from collected samples. Our datasets combined the soil parameters collected using ground-based sensors, soil analysis reports, and NDVI content of farms to perform the data analysis to predict SOM using different machine learning algorithms. We incorporated regression and ANOVA for analyzing the dataset and explored seven different machine learning algorithms, such as linear regression, Ridge regression, Lasso regression, random forest regression, Elastic Net regression, support vector machine, and Stochastic Gradient Descent regression to predict the soil organic matter content using other parameters as predictors.

Md Jasim Uddin

Jordan Sherrell

Anahita Emami

Meysam Khaleghian

Application of Artificial Intelligence and Sensor Fusion for Soil Organic Matter Prediction

Bridge health monitoring is increasingly relevant for the maintenance of existing structures or new structures with innovative concepts that require validation of design predictions. In the United States there are more than 600,000 highway bridges. Nearly half of them (46.4%) are rated as fair while about 1 out of 13 (7.6%) is rated in poor condition. As such, the United States is one of those countries in which bridge health monitoring systems are installed in order to complement conventional periodic nondestructive inspections. This paper reviews the challenges associated with bridge health monitoring related to the detection of specific bridge characteristics that may be indicators of anomalous behavior. The methods used to detect loss of stiffness, time-dependent and temperature-dependent deformations, fatigue, corrosion, and scour are discussed. Owing to the extent of the existing scientific literature, this review focuses on systems installed in U.S. bridges over the last 20 years. These are all major factors that contribute to long-term degradation of bridges. Issues related to wireless sensor drifts are discussed as well. The scope of the paper is to help newcomers, practitioners, and researchers at navigating the many methodologies that have been proposed and developed in order to identify damage using data collected from sensors installed in real structures.

Piervincenzo Rizzo

Alireza Enshaeian

Challenges in Bridge Health Monitoring: A Review

短名
Journal Impact	3.20
国际分区	INSTRUMENTS & INSTRUMENTATION(Q2)

ISSN	1424-3210, 1424-8220
h-index	245

涉及主题	计算机科学工程类物理量子力学数学人工智能生物操作系统电气工程材料科学电信化学计算机视觉光学统计地理地质学程序设计语言医学心理学热力学机器学习计算机网络复合材料有机化学
出版信息	出版商: Multidisciplinary Digital Publishing Institute (MDPI)，出版周期: Monthly，期刊类型: journal，开源期刊: 非开源
基本数据	创刊年份: 2001，原创研究文献占比: 93.85%，自引率:11.80%， Gold OA占比: 99.60%
平均审稿周期	期刊官网数据：平均21.03天网友分享经验：平均1.7个月
平均录用比例	网友分享经验：80%

Sensors

阅读文献时，研飞即可帮你快速了解期刊，查询影响因子与分区，智能高亮期刊预警！

期刊引文格式

只有1位作者的期刊

有2位作者的期刊

有3位作者的期刊

有5位以上作者的期刊

书籍引用格式

学位论文引用格式

网页引用格式

专利引用格式

手工熬夜修改参考文献？研飞自动匹配期刊，一键轻松成稿，支持 Word/WPS

投稿经验分享