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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 8958

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Special Issue Editors

Prof. Dr. Peter W. McCarthy

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Faculty of Life Sciences and Education, University of South Wales, Pontypridd, UK
Interests: humidity sensor
Special Issues, Collections and Topics in MDPI journals

Dr. Vincenzo Cascioli

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Murdoch University Chiropractic Clinic, Perth, WA, Australia
Interests: evaluation of sitting comfort and discomfort; signal measurement at the user–seat interface
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Prof. Dr. Zhuofu Liu

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The Higher Educational Key Laboratory for Measuring and Control Technology and Instrumentations of Heilongjiang Province, Harbin University of Science and Technology, Harbin, China
Interests: data analysis; signal measurement and detection; medical information processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advancements in material science and biochemical technology have led to great progress in sensor design and development. Highly integrated and multifunctional sensors have boosted the rapid growth in research and applications across various fields from machine failure detection, food quality control and pharmaceutical manufacture to human health monitoring. To achieve reliable, accurate and useful outcomes, however, requires advanced algorithms. Such algorithms play vital roles at both the measurement and the control stages. They can be used in the display of information, and their calculations and compensations can be used to handle the vagaries of the sensors. It is the algorithm that turns sensor output into presentable information (displays) that is understandable to patients/users.

The aim of this Special Issue is to illustrate recent achievements in sensing technique development and to present both measurement control applications and state-of-the-art algorithms, including deep/machine learning and artificial intelligence, in order to produce an effective, reliable and simple user interface. Articles involving an in-depth discussion of specific problems, such as a clinical/medical data analysis, will also be considered, as these are rarely published yet are important to indicate potential pitfalls to avoid and methods to compensate for them.

We welcome original research papers and review articles on sensing technology and its applications in measurement and control.

Prof. Dr. Peter W. McCarthy
Dr. Vincenzo Cascioli
Prof. Dr. Zhuofu Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

sensors
measurement and control
signal processing
computer vision
artificial intelligence
machine/deep learning algorithms
clinical/medical data analysis

Published Papers (7 papers)

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Research

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23 pages, 7332 KiB

Open AccessArticle

A Vascular Feature Detection and Matching Method Based on Dual-Branch Fusion and Structure Enhancement

by Kaiyang Xu, Haibin Wu, Yuji Iwahori, Xiaoyu Yu, Zeyu Hu and Aili Wang

Sensors 2024, 24(6), 1880; https://doi.org/10.3390/s24061880 - 15 Mar 2024

Viewed by 599

Abstract

How to obtain internal cavity features and perform image matching is a great challenge for laparoscopic 3D reconstruction. This paper proposes a method for detecting and associating vascular features based on dual-branch weighted fusion vascular structure enhancement. Our proposed method is divided into three stages, including analyzing various types of minimally invasive surgery (MIS) images and designing a universal preprocessing framework to make our method generalized. We propose a Gaussian weighted fusion vascular structure enhancement algorithm using the dual-branch Frangi measure and MFAT (multiscale fractional anisotropic tensor) to address the structural measurement differences and uneven responses between venous vessels and microvessels, providing effective structural information for vascular feature extraction. We extract vascular features through dual-circle detection based on branch point characteristics, and introduce NMS (non-maximum suppression) to reduce feature point redundancy. We also calculate the ZSSD (zero sum of squared differences) and perform feature matching on the neighboring blocks of feature points extracted from the front and back frames. The experimental results show that the proposed method has an average accuracy and repeatability score of 0.7149 and 0.5612 in the Vivo data set, respectively. By evaluating the quantity, repeatability, and accuracy of feature detection, our method has more advantages and robustness than the existing methods. Full article

(This article belongs to the Special Issue Advanced Sensing and Measurement Control Applications)

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31 pages, 6375 KiB

Open AccessArticle

Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks

by Yi Jiang, Jinlin Zhu and Kexin Yang

Sensors 2024, 24(1), 40; https://doi.org/10.3390/s24010040 - 20 Dec 2023

Viewed by 618

Abstract

With the rapid development of the intelligent transportation system (ITS), routing in vehicular ad hoc networks (VANETs) has become a popular research topic. The high mobility of vehicles in urban streets poses serious challenges to routing protocols and has a significant impact on network performance. Existing topology-based routing is not suitable for highly dynamic VANETs, thereby making location-based routing protocols the preferred choice due to their scalability. However, the working environment of VANETs is complex and interference-prone. In wireless-network communication, the channel contention introduced by the high density of vehicles, coupled with urban structures, significantly increases the difficulty of designing high-quality communication protocols. In this context, compared to topology-based routing protocols, location-based geographic routing is widely employed in VANETs due to its avoidance of the route construction and maintenance phases. Considering the characteristics of VANETs, this paper proposes a novel environment-aware adaptive reinforcement routing (EARR) protocol aimed at establishing reliable connections between source and destination nodes. The protocol adopts periodic beacons to perceive and explore the surrounding environment, thereby constructing a local topology. By applying reinforcement learning to the vehicle network’s route selection, it adaptively adjusts the Q table through the perception of multiple metrics from beacons, including vehicle speed, available bandwidth, signal-reception strength, etc., thereby assisting the selection of relay vehicles and alleviating the challenges posed by the high dynamics, shadow fading, and limited bandwidth in VANETs. The combination of reinforcement learning and beacons accelerates the establishment of end-to-end routes, thereby guiding each vehicle to choose the optimal next hop and forming suboptimal routes throughout the entire communication process. The adaptive adjustment feature of the protocol enables it to address sudden link interruptions, thereby enhancing communication reliability. In experiments, the EARR protocol demonstrates significant improvements across various performance metrics compared to existing routing protocols. Throughout the simulation process, the EARR protocol maintains a consistently high packet-delivery rate and throughput compared to other protocols, as well as demonstrates stable performance across various scenarios. Finally, the proposed protocol demonstrates relatively consistent standardized latency and low overhead in all experiments. Full article

(This article belongs to the Special Issue Advanced Sensing and Measurement Control Applications)

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26 pages, 10383 KiB

Open AccessArticle

Observing Liquid Sloshing Based on a Multi-Degree-of-Freedom Pendulum Model and Free Surface Fluctuation Sensor

by Xiaojing Qi, Yingchao Zhang and Bolin Gao

Sensors 2023, 23(21), 8831; https://doi.org/10.3390/s23218831 - 30 Oct 2023

Viewed by 930

Abstract

Rollover prevention of partially filled tank trucks is an ongoing challenge in the road transportation industry, with the core challenge being real-time perception and observation of the liquid state inside the tank. In order to realize reliable observation of a sloshing liquid, this article first proposes a sloshing modeling method based on a multi-degree-of-freedom pendulum model and derives the double mass trammel pendulum model (DMTP, 2DOF) accordingly, which accurately reflects the sloshing dynamics under wider operating conditions. Second, a free surface fluctuation sensor is designed based on magnetostriction, capable of measuring the inclination and height of the liquid level inside tanks filled with hazardous chemicals. Finally, the unscented Kalman filter (UKF) is utilized to synthesize the information of the two, establishing a credible real-time observation of the sloshing liquid. Verified using a vehicle–fluid coupled co-simulation, under the condition of a consecutive double lane change, the observation error of the proposed method is only 25.9% of that of the open-loop calculation, providing a secure guarantee for the observation of the state variables of the single pendulum model (SP) used for most kinds of anti-rollover control. Full article

(This article belongs to the Special Issue Advanced Sensing and Measurement Control Applications)

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13 pages, 2397 KiB

Open AccessArticle

DOA Estimation for Coherent Sources Based on Uniformly Distributed Two Concentric Rings Array

by Chuang Han, Shenghong Guo, Ning Yan, Jingwei Dong and Bowen Xing

Sensors 2023, 23(20), 8408; https://doi.org/10.3390/s23208408 - 12 Oct 2023

Viewed by 781

Abstract

The direction estimation of the coherent source in a uniform circular array is an essential part of the signal processing area of the array, but the traditional uniform circular array algorithm has a low localization accuracy and a poor localization effect on the coherent source. To solve this problem, this paper proposes a two-dimensional direction of arrival (DOA) estimation for the coherent source in broadband. Firstly, the central frequency of the coherent sound source is estimated using the frequency estimation method of the delayed data, and a real-valued beamformer is constructed using the concept of the multiloop phase mode. Then, the cost function in the beam space is obtained. Finally, the cost function is searched in two dimensions to locate the sound source. In this paper, we simulate the DOA of the sound source at different frequencies and signal-to-noise ratios and analyze the resolution of the circular array. The simulation results show that the proposed algorithm can estimate the direction of arrival with high precision and achieve the desired results. Full article

(This article belongs to the Special Issue Advanced Sensing and Measurement Control Applications)

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15 pages, 5098 KiB

Open AccessArticle

A Novel Evaluation Method for SLAM-Based 3D Reconstruction of Lumen Panoramas

by Xiaoyu Yu, Jianbo Zhao, Haibin Wu and Aili Wang

Sensors 2023, 23(16), 7188; https://doi.org/10.3390/s23167188 - 15 Aug 2023

Viewed by 870

Abstract

Laparoscopy is employed in conventional minimally invasive surgery to inspect internal cavities by viewing two-dimensional images on a monitor. This method has a limited field of view and provides insufficient information for surgeons, increasing surgical complexity. Utilizing simultaneous localization and mapping (SLAM) technology to reconstruct laparoscopic scenes can offer more comprehensive and intuitive visual feedback. Moreover, the precision of the reconstructed models is a crucial factor for further applications of surgical assistance systems. However, challenges such as data scarcity and scale uncertainty hinder effective assessment of the accuracy of endoscopic monocular SLAM reconstructions. Therefore, this paper proposes a technique that incorporates existing knowledge from calibration objects to supplement metric information and resolve scale ambiguity issues, and it quantifies the endoscopic reconstruction accuracy based on local alignment metrics. The experimental results demonstrate that the reconstructed models restore realistic scales and enable error analysis for laparoscopic SLAM reconstruction systems. This suggests that for the evaluation of monocular SLAM three-dimensional (3D) reconstruction accuracy in minimally invasive surgery scenarios, our proposed scheme for recovering scale factors is viable, and our evaluation outcomes can serve as criteria for measuring reconstruction precision. Full article

(This article belongs to the Special Issue Advanced Sensing and Measurement Control Applications)

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19 pages, 552 KiB

Open AccessArticle

AODV-EOCW: An Energy-Optimized Combined Weighting AODV Protocol for Mobile Ad Hoc Networks

by Yi Jiang, Hui Sun and Muyan Yang

Sensors 2023, 23(15), 6759; https://doi.org/10.3390/s23156759 - 28 Jul 2023

Cited by 2 | Viewed by 972

Abstract

The Ad Hoc On-demand Distance Vector (AODV) is a routing protocol for mobile ad hoc networks (MANETs) and other wireless ad hoc networks. The vanilla AODV protocol is simple and easy to implement because it only uses the hop count as a routing metric. Single-metric route determination also causes problems, such as network congestion and energy exhaustion, which limit the usage of AODV in resource-limited applications. To solve these problems, the authors propose a new routing protocol that combines the analytic hierarchy process (AHP), the entropy weight method (EWM), and AODV. The proposed protocol uses energy, congestion, and the hop count as metrics and weights these three metrics using AHP and EWM. To address the importance of energy in applications, such as drones, the proposed protocol chooses different comparison matrices for AHP at different node residual energy levels. Finally, the node chooses the best route link according to the score (sum of weighted metrics). It is also suitable for wireless sensor networks because the proposed protocol considers the residual energy of the node. The simulation results show that the improved routing protocol can effectively reduce the average end-to-end delay and energy consumption and prolong the lifetime of the whole network. Full article

(This article belongs to the Special Issue Advanced Sensing and Measurement Control Applications)

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Review

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39 pages, 18882 KiB

Open AccessReview

Wearable Optical Fiber Sensors in Medical Monitoring Applications: A Review

by Xuhui Zhang, Chunyang Wang, Tong Zheng, Haibin Wu, Qing Wu and Yunzheng Wang

Sensors 2023, 23(15), 6671; https://doi.org/10.3390/s23156671 - 25 Jul 2023

Cited by 9 | Viewed by 3253

Abstract

Wearable optical fiber sensors have great potential for development in medical monitoring. With the increasing demand for compactness, comfort, accuracy, and other features in new medical monitoring devices, the development of wearable optical fiber sensors is increasingly meeting these requirements. This paper reviews the latest evolution of wearable optical fiber sensors in the medical field. Three types of wearable optical fiber sensors are analyzed: wearable optical fiber sensors based on Fiber Bragg grating, wearable optical fiber sensors based on light intensity changes, and wearable optical fiber sensors based on Fabry–Perot interferometry. The innovation of wearable optical fiber sensors in respiration and joint monitoring is introduced in detail, and the main principles of three kinds of wearable optical fiber sensors are summarized. In addition, we discuss their advantages, limitations, directions to improve accuracy and the challenges they face. We also look forward to future development prospects, such as the combination of wireless networks which will change how medical services are provided. Wearable optical fiber sensors offer a viable technology for prospective continuous medical surveillance and will change future medical benefits. Full article

(This article belongs to the Special Issue Advanced Sensing and Measurement Control Applications)

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