Sensors

14 pages, 2292 KiB

Open AccessArticle

Accurately Measuring the Infrared Spectral Emissivity of Inconel 601, Inconel 625, and Inconel 718 Alloys during the Oxidation Process

by Longfei Li, Fayu Wang, Jiaying Gao, Kun Yu, Lan Wang and Yufang Liu

Sensors 2024, 24(18), 5906; https://doi.org/10.3390/s24185906 (registering DOI) - 11 Sep 2024

Abstract

Accurate measurement of the infrared spectral emissivity of nickel-based alloys is significant for applications in aerospace. The low thermal conductivity of these alloys limits the accuracy of direct emissivity measurement, especially during the oxidation process. To improve measurement accuracy, a surface temperature correction [...] Read more.

Accurate measurement of the infrared spectral emissivity of nickel-based alloys is significant for applications in aerospace. The low thermal conductivity of these alloys limits the accuracy of direct emissivity measurement, especially during the oxidation process. To improve measurement accuracy, a surface temperature correction method based on two thermocouples was proposed to eliminate the effect of thermal conductivity changes on emissivity measurement. By using this method, the infrared spectral emissivity of Inconel 601, Inconel 625, and Inconel 718 alloys was accurately measured during the oxidation process, with a temperature range of 673–873 K, a wavelength range of 3–20 μm, and a zenith angle range of 0–80°. The results show that the emissivity of the three alloys is similar in value and variation law; the emissivity of Inconel 718 is slightly less than that of Inconel 601 and Inconel 625; and the spectral emissivity of the three alloys strongly increases in the first hour, whereafter it grows gradually with the increase in oxidation time. Finally, Inconel 601 has a lower emissivity growth rate, which illustrates that it possesses stronger oxidation resistance and thermal stability. The maximum relative uncertainty of the emissivity measurement of the three alloys does not exceed 2.6%, except for the atmospheric absorption wavebands. Full article

(This article belongs to the Section Optical Sensors)

18 pages, 3557 KiB

Open AccessArticle

Field Test of the MiniRadMeter Gamma and Neutron Detector for the EU Project CLEANDEM

by Marco Ripani, Fabio Rossi, Luigi Cosentino, Fabio Longhitano, Paolo Musico, Mikhail Osipenko, Gaetano Elio Poma and Paolo Finocchiaro

Sensors 2024, 24(18), 5905; https://doi.org/10.3390/s24185905 - 11 Sep 2024

Abstract

In the framework of the H2020 CLEANDEM project, a small robotic vehicle was equipped with a series of different sensors that were developed for the preliminary inspection of areas possibly contaminated by radiation. Such unmanned inspection allows to identify dangerous locations prior to [...] Read more.

In the framework of the H2020 CLEANDEM project, a small robotic vehicle was equipped with a series of different sensors that were developed for the preliminary inspection of areas possibly contaminated by radiation. Such unmanned inspection allows to identify dangerous locations prior to the possible start of human operations. One of the developed devices, named the MiniRadMeter, is a compact low-cost sensor that performs gamma and neutron radiation field mapping in the environment. The MiniRadMeter was successfully tested in a simulated field mission with four “hidden” radioactive sources and a neutron generator. In this work, we describe the test procedure and the results, which were supported by the outcome of dedicated Monte Carlo simulations. Full article

(This article belongs to the Special Issue Radiation Sensors and Detectors: Materials, Principles and Applications (2nd Edition))

20 pages, 6529 KiB

Open AccessArticle

Gas Detection and Classification Using Multimodal Data Based on Federated Learning

by Ashutosh Sharma, Vikas Khullar, Isha Kansal, Gunjan Chhabra, Priya Arora, Renu Popli and Rajeev Kumar

Sensors 2024, 24(18), 5904; https://doi.org/10.3390/s24185904 - 11 Sep 2024

Abstract

The identification of gas leakages is a significant factor to be taken into consideration in various industries such as coal mines, chemical industries, etc., as well as in residential applications. In order to reduce damage to the environment as well as human lives, [...] Read more.

The identification of gas leakages is a significant factor to be taken into consideration in various industries such as coal mines, chemical industries, etc., as well as in residential applications. In order to reduce damage to the environment as well as human lives, early detection and gas type identification are necessary. The main focus of this paper is multimodal gas data that were obtained simultaneously by using multiple sensors for gas detection and a thermal imaging camera. As the reliability and sensitivity of low-cost sensors are less, they are not suitable for gas detection over long distances. In order to overcome the drawbacks of relying just on sensors to identify gases, a thermal camera capable of detecting temperature changes is also used in the collection of the current multimodal dataset The multimodal dataset comprises 6400 samples, including smoke, perfume, a combination of both, and neutral environments. In this paper, convolutional neural networks (CNNs) are trained on thermal image data, utilizing variants such as bidirectional long–short-term memory (Bi-LSTM), dense LSTM, and a fusion of both datasets to effectively classify comma separated value (CSV) data from gas sensors. The dataset can be used as a valuable source for research scholars and system developers to improvise their artificial intelligence (AI) models used for gas leakage detection. Furthermore, in order to ensure the privacy of the client’s data, this paper explores the implementation of federated learning for privacy-protected gas leakage classification, demonstrating comparable accuracy to traditional deep learning approaches. Full article

(This article belongs to the Special Issue Advanced Sensor Fusion in Industry 4.0)

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22 pages, 11254 KiB

Open AccessArticle

WoodenCube: An Innovative Dataset for Object Detection in Concealed Industrial Environments

by Chao Wu, Shilong Li, Tao Xie, Xiangdong Wang and Jiali Zhou

Sensors 2024, 24(18), 5903; https://doi.org/10.3390/s24185903 - 11 Sep 2024

Abstract

With the rapid advancement of intelligent manufacturing technologies, the operating environments of modern robotic arms are becoming increasingly complex. In addition to the diversity of objects, there is often a high degree of similarity between the foreground and the background. Although traditional RGB-based [...] Read more.

With the rapid advancement of intelligent manufacturing technologies, the operating environments of modern robotic arms are becoming increasingly complex. In addition to the diversity of objects, there is often a high degree of similarity between the foreground and the background. Although traditional RGB-based object-detection models have achieved remarkable success in many fields, they still face the challenge of effectively detecting targets with textures similar to the background. To address this issue, we introduce the WoodenCube dataset, which contains over 5000 images of 10 different types of blocks. All images are densely annotated with object-level categories, bounding boxes, and rotation angles. Additionally, a new evaluation metric, Cube-mAP, is proposed to more accurately assess the detection performance of cube-like objects. In addition, we have developed a simple, yet effective, framework for WoodenCube, termed CS-SKNet, which captures strong texture features in the scene by enlarging the network’s receptive field. The experimental results indicate that our CS-SKNet achieves the best performance on the WoodenCube dataset, as evaluated by the Cube-mAP metric. We further evaluate the CS-SKNet on the challenging DOTAv1.0 dataset, with the consistent enhancement demonstrating its strong generalization capability. Full article

(This article belongs to the Section Sensing and Imaging)

25 pages, 6731 KiB

Open AccessArticle

Experimental Validation of Realistic Measurement Setup for Quantitative UWB-Guided Hyperthermia Temperature Monitoring

by Alexandra Prokhorova and Marko Helbig

Sensors 2024, 24(18), 5902; https://doi.org/10.3390/s24185902 - 11 Sep 2024

Abstract

Hyperthermia induces slight temperature increase of 4–8 °C inside the tumor, making it more responsive to radiation and drugs, thereby improving the outcome of the oncological treatment. To verify the level of heat in the tumor and to avoid damage of the healthy [...] Read more.

Hyperthermia induces slight temperature increase of 4–8 °C inside the tumor, making it more responsive to radiation and drugs, thereby improving the outcome of the oncological treatment. To verify the level of heat in the tumor and to avoid damage of the healthy tissue, methods for non-invasive temperature monitoring are needed. Temperature estimation by means of microwave imaging is of great interest among the scientific community. In this paper, we present the results of experiments based on ultra-wideband (UWB) M-sequence technology. Our temperature estimation approach uses temperature dependency of tissue dielectric properties and relation of UWB images to the reflection coefficient on the boundary between tissue types. The realistic measurement setup for neck cancer hyperthermia considers three antenna arrangements. Data are processed with Delay and Sum beamforming and Truncated Singular Value Decomposition. Two types of experiments are presented in this paper. In the first experiment, relative permittivity of subsequently replaced tumor mimicking material is estimated, and in the second experiment, real temperature change in the tumor imitate is monitored. The results showed that the presented approach allows for qualitative as well as quantitative permittivity and temperature estimation. The frequency range for temperature estimation, preferable antenna configurations, and limitations of the method are indicated. Full article

(This article belongs to the Special Issue Microwaves for Biomedical Applications and Sensing)

16 pages, 623 KiB

Open AccessArticle

Enhancing Reliability and Stability of BLE Mesh Networks: A Multipath Optimized AODV Approach

by Muhammad Rizwan Ghori, Tat-Chee Wan, Gian Chand Sodhy, Mohammad Aljaidi, Amna Rizwan, Ali Safaa Sadiq and Omprakash Kaiwartya

Sensors 2024, 24(18), 5901; https://doi.org/10.3390/s24185901 - 11 Sep 2024

Abstract

Bluetooth Low Energy (BLE) mesh networks provide flexible and reliable communication among low-power sensor-enabled Internet of Things (IoT) devices, enabling them to communicate in a flexible and robust manner. Nonetheless, the majority of existing BLE-based mesh protocols operate as flooding-based piconet or scatternet [...] Read more.

Bluetooth Low Energy (BLE) mesh networks provide flexible and reliable communication among low-power sensor-enabled Internet of Things (IoT) devices, enabling them to communicate in a flexible and robust manner. Nonetheless, the majority of existing BLE-based mesh protocols operate as flooding-based piconet or scatternet overlays on top of existing Bluetooth star topologies. In contrast, the Ad hoc On-Demand Distance Vector (AODV) protocol used primarily in wireless ad hoc networks (WAHNs) is forwarding-based and therefore more efficient, with lower overheads. However, the packet delivery ratio (PDR) and link recovery time for AODV performs worse compared to flooding-based BLE protocols when encountering link disruptions. We propose the Multipath Optimized AODV (M-O-AODV) protocol to address these issues, with improved PDR and link robustness compared with other forwarding-based protocols. In addition, M-O-AODV achieved a PDR of 88%, comparable to the PDR of 92% for flooding-based BLE, unlike protocols such as Reverse-AODV (R-AODV). Also, M-O-AODV was able to perform link recovery within 3700 ms in the case of node failures, compared with other forwarding-based protocols that require 4800 ms to 6000 ms. Consequently, M-O-AODV-based BLE mesh networks are more efficient for wireless sensor-enabled IoT environments. Full article

(This article belongs to the Special Issue Energy Harvesting and Self-Powered Sensors)

16 pages, 1444 KiB

Open AccessArticle

A Deep Learning Method for Human Sleeping Pose Estimation with Millimeter Wave Radar

by Zisheng Li, Ken Chen and Yaoqin Xie

Sensors 2024, 24(18), 5900; https://doi.org/10.3390/s24185900 - 11 Sep 2024

Abstract

Recognizing sleep posture is crucial for the monitoring of people with sleeping disorders. Existing contact-based systems might interfere with sleeping, while camera-based systems may raise privacy concerns. In contrast, radar-based sensors offer a promising solution with high penetration ability and the capability to [...] Read more.

Recognizing sleep posture is crucial for the monitoring of people with sleeping disorders. Existing contact-based systems might interfere with sleeping, while camera-based systems may raise privacy concerns. In contrast, radar-based sensors offer a promising solution with high penetration ability and the capability to detect vital bio-signals. This study propose a deep learning method for human sleep pose recognition from signals acquired from single-antenna Frequency-Modulated Continuous Wave (FMCW) radar device. To capture both frequency features and sequential features, we introduce ResTCN, an effective architecture combining Residual blocks and Temporal Convolution Network (TCN) to recognize different sleeping postures, from augmented statistical motion features of the radar time series. We rigorously evaluated our method with an experimentally acquired data set which contains sleeping radar sequences from 16 volunteers. We report a classification accuracy of 82.74% on average, which outperforms the state-of-the-art methods. Full article

(This article belongs to the Section Radar Sensors)

17 pages, 4811 KiB

Open AccessArticle

An Algorithm for Strapdown Airborne Gravity Disturbance Vector Measurement Based on High-Precision Navigation and EGM2008

by Ke Fang and Tijing Cai

Sensors 2024, 24(18), 5899; https://doi.org/10.3390/s24185899 - 11 Sep 2024

Abstract

Attitude errors, accelerometer bias, the gravity disturbance vector, and their coupling are the primary factors obstructing strapdown airborne vector gravimetry. This paper takes the geocentric inertial frame as a reference and solves the kinematic equations of its motion and its errors of the [...] Read more.

Attitude errors, accelerometer bias, the gravity disturbance vector, and their coupling are the primary factors obstructing strapdown airborne vector gravimetry. This paper takes the geocentric inertial frame as a reference and solves the kinematic equations of its motion and its errors of the body frame and local geographic frame in the Lie group, respectively; the attitude accuracy is improved through a high-precision navigation algorithm. The constant accelerometer bias is estimated through Kalman filtering and is deducted from the accelerometer output to eliminate its influence. Based on the EGM2008 model, the low-frequency components of the gravity disturbance vector are corrected. The gravity disturbance vectors after model data fusion were low-pass filtered to obtain the ultimate results. This method was applied to flight experimental data in the South China Sea, and a gravity anomaly accuracy of better than 0.5 mGal, a northward gravity disturbance accuracy of 0.85 mGal, and an eastward gravity disturbance accuracy of 4.0 mGal were obtained, with a spatial resolution of approximately 4.8 km. Full article

(This article belongs to the Section Navigation and Positioning)

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29 pages, 60872 KiB

Open AccessArticle

Hybrid Optimization Path Planning Method for AGV Based on KGWO

by Zhengjiang Guo, Yingkai Xia, Jiawei Li, Jiajun Liu and Kan Xu

Sensors 2024, 24(18), 5898; https://doi.org/10.3390/s24185898 - 11 Sep 2024

Abstract

To address the path planning problem for automated guided vehicles (AGVs) in challenging and complex industrial environments, a hybrid optimization approach is proposed, integrating a Kalman filter with grey wolf optimization (GWO), as well as incorporating partially matched crossover (PMX) mutation operations and [...] Read more.

To address the path planning problem for automated guided vehicles (AGVs) in challenging and complex industrial environments, a hybrid optimization approach is proposed, integrating a Kalman filter with grey wolf optimization (GWO), as well as incorporating partially matched crossover (PMX) mutation operations and roulette wheel selection. Paths are first optimized using GWO, then refined with Kalman filter corrections every ten iterations. Moreover, roulette wheel selection guides robust parent path selection, while an elite strategy and partially matched crossover (PMX) with mutation generate diverse offspring. Extensive simulations and experiments were carried out under a densely packed goods scenario and complex indoor layout scenario, within a fully automated warehouse environment. The results showed that this hybrid method not only enhanced the various optimization metrics but also ensured more predictable and collision-free navigation paths, particularly in environments with complex obstacles. These improvements lead to increased operational efficiency and safety, highlighting the method’s potential in real-world applications. Full article

(This article belongs to the Section Sensors and Robotics)

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11 pages, 5474 KiB

Open AccessArticle

Current Mirror Improved Potentiostat (CMIPot) for a Three Electrode Electrochemical Cell

by Alexandre Kennedy Pinto Souza, Carlos Augusto de Moraes Cruz, Élvio Carlos Dutra e Silva Júnior and Fagnaldo Braga Pontes

Sensors 2024, 24(18), 5897; https://doi.org/10.3390/s24185897 - 11 Sep 2024

Abstract

This work presents a novel compact CMOS potentiostat-designed circuit for an electrochemical cell. The proposed topology functions as a circuit interface, controlling the polarization of voltage signals at the sensor electrodes and facilitating current measurement during the oxidation–reduction process of an analyzed solution. [...] Read more.

This work presents a novel compact CMOS potentiostat-designed circuit for an electrochemical cell. The proposed topology functions as a circuit interface, controlling the polarization of voltage signals at the sensor electrodes and facilitating current measurement during the oxidation–reduction process of an analyzed solution. The potentiostat, designed for CMOS technology, comprises a two-stage amplifier, two current mirror blocks coupled to this amplifier, and a CMOS push–pull output stage. The electrochemical method of cyclic voltammetry is employed, operating within a voltage range of ±0.8 V and scan rates of 10 mV/s, 25 mV/s, 100 mV/s, and 250 mV/s. The circuit is capable of reading currents ranging from 10 µA to 500 µA. Experimental results were obtained using a potassium ferrocyanide K₃[Fe(CN)₆] redox solution with concentrations of 10, 15, and 20 mmol/L, and their corresponding voltammograms were evaluated. The experimental results from a discrete circuit demonstrate that the proposed potentiostat topology produces outcomes consistent with those of classical topologies presented in the literature and industrial equipment. Full article

(This article belongs to the Section Chemical Sensors)

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25 pages, 9089 KiB

Open AccessArticle

Remotely Powered Two-Wire Cooperative Sensors for Bioimpedance Imaging Wearables

by Olivier Chételat, Michaël Rapin, Benjamin Bonnal, André Fivaz, Benjamin Sporrer, James Rosenthal and Josias Wacker

Sensors 2024, 24(18), 5896; https://doi.org/10.3390/s24185896 - 11 Sep 2024

Abstract

Bioimpedance imaging aims to generate a 3D map of the resistivity and permittivity of biological tissue from multiple impedance channels measured with electrodes applied to the skin. When the electrodes are distributed around the body (for example, by delineating a cross section of [...] Read more.

Bioimpedance imaging aims to generate a 3D map of the resistivity and permittivity of biological tissue from multiple impedance channels measured with electrodes applied to the skin. When the electrodes are distributed around the body (for example, by delineating a cross section of the chest or a limb), bioimpedance imaging is called electrical impedance tomography (EIT) and results in functional 2D images. Conventional EIT systems rely on individually cabling each electrode to master electronics in a star configuration. This approach works well for rack-mounted equipment; however, the bulkiness of the cabling is unsuitable for a wearable system. Previously presented cooperative sensors solve this cabling problem using active (dry) electrodes connected via a two-wire parallel bus. The bus can be implemented with two unshielded wires or even two conductive textile layers, thus replacing the cumbersome wiring of the conventional star arrangement. Prior research demonstrated cooperative sensors for measuring bioimpedances, successfully realizing a measurement reference signal, sensor synchronization, and data transfer though still relying on individual batteries to power the sensors. Subsequent research using cooperative sensors for biopotential measurements proposed a method to remove batteries from the sensors and have the central unit supply power over the two-wire bus. Building from our previous research, this paper presents the application of this method to the measurement of bioimpedances. Two different approaches are discussed, one using discrete, commercially available components, and the other with an application-specific integrated circuit (ASIC). The initial experimental results reveal that both approaches are feasible, but the ASIC approach offers advantages for medical safety, as well as lower power consumption and a smaller size. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring—2nd Edition)

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16 pages, 12495 KiB

Open AccessArticle

Design and Analysis of a Hand-Held Surgical Forceps with a Force-Holding Function

by Yang Bai, Yang Yu and Zhenbang Xu

Sensors 2024, 24(18), 5895; https://doi.org/10.3390/s24185895 - 11 Sep 2024

Abstract

Physiological hand tremors, twitching, and the nonlinear characteristics of the relationship between surgical forceps clamping force and operating force seriously affect the clamping accuracy of surgical instruments. To address this problem, a new type of surgical forceps with a force-holding function was developed [...] Read more.

Physiological hand tremors, twitching, and the nonlinear characteristics of the relationship between surgical forceps clamping force and operating force seriously affect the clamping accuracy of surgical instruments. To address this problem, a new type of surgical forceps with a force-holding function was developed to replace traditional forceps, which was studied in terms of structural design, statics, and dynamics. The overall structure of the surgical forceps was designed based on the lever principle, the kinematic model of the clamping part of the surgical forceps was established by the geometrical method, and the correctness of the kinematic model was verified by ADAMS. To address the clamping accuracy of the surgical forceps, a stress analysis was performed, its dynamics model was established, a finite element simulation was performed, the modal of the forceps was optimized using the Box–Behnken method, and, finally, an experimental platform was built to perform the accuracy test. The results demonstrate that the designed surgical forceps exhibit high clamping accuracy and fulfill the design specifications for surgical operations. Full article

(This article belongs to the Section Biomedical Sensors)

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13 pages, 4067 KiB

Open AccessArticle

Tiny Security Hole: First-Order Vulnerability of Masked SEED and Its Countermeasure

by Ju-Hwan Kim and Dong-Guk Han

Sensors 2024, 24(18), 5894; https://doi.org/10.3390/s24185894 - 11 Sep 2024

Abstract

Side-channel analysis is a type of cryptanalysis that utilizes the physical leakage of a cryptographic device. An adversary exploits the relationship between a physical leakage and the secret intermediate value of an encryption algorithm. In order to prevent side-channel analysis, the masking method [...] Read more.

Side-channel analysis is a type of cryptanalysis that utilizes the physical leakage of a cryptographic device. An adversary exploits the relationship between a physical leakage and the secret intermediate value of an encryption algorithm. In order to prevent side-channel analysis, the masking method was proposed. Several masking methods of the ISO/IEC 18033-3 standard encryption algorithm SEED have been proposed, as the Korean financial IC (integrated circuit) card standard (CFIP.ST.FINIC-01-2021) mandates using a robust implementation of SEED as an encryption algorithm against side-channel analyses. However, vulnerabilities were reported, except for with only one masking method. This study proposes the first-order vulnerability of that masking method. That is, an adversary is able to perform a side-channel analysis with the same complexity as an unprotected implementation. In order to fix this vulnerability, we revise the masking method with negligible additional overhead. Its vulnerability and security are theoretically verified and experimentally demonstrated. The round key of the existing masking method is revealed with only 210 power consumption traces, while that of the proposed masking method is not disclosed with 10,000 traces. Full article

(This article belongs to the Special Issue Security, and Privacy in IoT and 6G Sensor Network)

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17 pages, 110874 KiB

Open AccessArticle

RT-CBAM: Refined Transformer Combined with Convolutional Block Attention Module for Underwater Image Restoration

by Renchuan Ye, Yuqiang Qian and Xinming Huang

Sensors 2024, 24(18), 5893; https://doi.org/10.3390/s24185893 - 11 Sep 2024

Abstract

Recently, transformers have demonstrated notable improvements in natural advanced visual tasks. In the field of computer vision, transformer networks are beginning to supplant conventional convolutional neural networks (CNNs) due to their global receptive field and adaptability. Although transformers excel in capturing global features, [...] Read more.

Recently, transformers have demonstrated notable improvements in natural advanced visual tasks. In the field of computer vision, transformer networks are beginning to supplant conventional convolutional neural networks (CNNs) due to their global receptive field and adaptability. Although transformers excel in capturing global features, they lag behind CNNs in handling fine local features, especially when dealing with underwater images containing complex and delicate structures. In order to tackle this challenge, we propose a refined transformer model by improving the feature blocks (dilated transformer block) to more accurately compute attention weights, enhancing the capture of both local and global features. Subsequently, a self-supervised method (a local and global blind-patch network) is embedded in the bottleneck layer, which can aggregate local and global information to enhance detail recovery and improve texture restoration quality. Additionally, we introduce a multi-scale convolutional block attention module (MSCBAM) to connect encoder and decoder features; this module enhances the feature representation of color channels, aiding in the restoration of color information in images. We plan to deploy this deep learning model onto the sensors of underwater robots for real-world underwater image-processing and ocean exploration tasks. Our model is named the refined transformer combined with convolutional block attention module (RT-CBAM). This study compares two traditional methods and six deep learning methods, and our approach achieved the best results in terms of detail processing and color restoration. Full article

(This article belongs to the Section Sensing and Imaging)

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20 pages, 10736 KiB

Open AccessArticle

Application of Hyperspectral Imaging and Multi-Module Joint Hierarchical Residual Network in Seed Cotton Foreign Fiber Recognition

by Yunlong Zhang, Laigang Zhang, Zhijun Guo and Ran Zhang

Sensors 2024, 24(18), 5892; https://doi.org/10.3390/s24185892 - 11 Sep 2024

Abstract

Due to the difficulty in distinguishing transparent and white foreign fibers from seed cotton in RGB images and in order to improve the recognition ability of deep learning (DL) algorithms for white, transparent, and multi-class mixed foreign fibers with different sizes in seed [...] Read more.

Due to the difficulty in distinguishing transparent and white foreign fibers from seed cotton in RGB images and in order to improve the recognition ability of deep learning (DL) algorithms for white, transparent, and multi-class mixed foreign fibers with different sizes in seed cotton, this paper proposes a method of combining hyperspectral imaging technology with a multi-module joint hierarchical residue network (MJHResNet). Firstly, a series of preprocessing methods are performed on the hyperspectral image (HSI) to reduce the interference of noise. Secondly, a double-hierarchical residual (DHR) structure is designed, which can not only obtain multi-scale information, but also avoid gradient vanishing to some extent. After that, a squeeze-and-excitation network (SENet) is integrated to reduce redundant information, improve the expression of model features, and improve the accuracy of foreign fiber identification in seed cotton. Finally, by analyzing the experimental results with advanced classifiers, this method has significant advantages. The average accuracy is 98.71% and the overall accuracy is 99.28%. This method has great potential for application in the field of foreign fiber identification in seed cotton. Full article

(This article belongs to the Topic Hyperspectral Imaging and Signal Processing)

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9 pages, 5085 KiB

Open AccessCommunication

Research of a 0.14 THz Dual-Cavity Parallel Structure Extended Interaction Oscillator

by Chuanhong Xiao, Ruizhe Ren, Zhenhua Wu, Yijun Li, Qing You, Zongjun Shi, Kaichun Zhang, Xiaoxing Chen, Mingzhou Zhan, Diwei Liu, Renbin Zhong and Shenggang Liu

Sensors 2024, 24(18), 5891; https://doi.org/10.3390/s24185891 - 11 Sep 2024

Abstract

This paper presents a method to enhance extended interaction oscillator (EIO) output power based on a dual-cavity parallel structure (DCPS). This stucture consists of two conventional ladder-line structures in parallel through a connecting structure, which improves the coupling efficiency between the cavities. The [...] Read more.

This paper presents a method to enhance extended interaction oscillator (EIO) output power based on a dual-cavity parallel structure (DCPS). This stucture consists of two conventional ladder-line structures in parallel through a connecting structure, which improves the coupling efficiency between the cavities. The dual output power fusion structure employs an H-T type combiner as the output coupler, which can effectively combine the two input waves in phase to further increase the output power. The dispersion characteristics, coupling impedance, and field distribution of the DCPS are investigated through numerical and simulation calculations, and the optimal operating parameters and output structure are obtained by PIC simulation. At an operating voltage of 12.6 kV, current density of 200 A/

{cm}^{2}

, and longitudinal magnetic field of 0.5 T, the DCPS EIO exhibits an output power exceeding 600 W at a frequency of 140.6 GHz. This represents a nearly three-fold enhancement compared with the 195 W output of the conventional ladder-line EIO structure. These findings demonstrate the significant improvement in output power and interaction efficiency achieved by the DCPS for the EIO device. Full article

(This article belongs to the Special Issue Millimeter Wave and Terahertz Source, Sensing and Imaging)

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33 pages, 2094 KiB

Open AccessReview

Applications of Artificial Intelligence for Heat Stress Management in Ruminant Livestock

by Ebenezer Binuni Rebez, Veerasamy Sejian, Mullakkalparambil Velayudhan Silpa, Gajendirane Kalaignazhal, Duraisamy Thirunavukkarasu, Chinnasamy Devaraj, Kumar Tej Nikhil, Jacob Ninan, Artabandhu Sahoo, Nicola Lacetera and Frank Rowland Dunshea

Sensors 2024, 24(18), 5890; https://doi.org/10.3390/s24185890 - 11 Sep 2024

Abstract

Heat stress impacts ruminant livestock production on varied levels in this alarming climate breakdown scenario. The drastic effects of the global climate change-associated heat stress in ruminant livestock demands constructive evaluation of animal performance bordering on effective monitoring systems. In this climate-smart digital [...] Read more.

Heat stress impacts ruminant livestock production on varied levels in this alarming climate breakdown scenario. The drastic effects of the global climate change-associated heat stress in ruminant livestock demands constructive evaluation of animal performance bordering on effective monitoring systems. In this climate-smart digital age, adoption of advanced and developing Artificial Intelligence (AI) technologies is gaining traction for efficient heat stress management. AI has widely penetrated the climate sensitive ruminant livestock sector due to its promising and plausible scope in assessing production risks and the climate resilience of ruminant livestock. Significant improvement has been achieved alongside the adoption of novel AI algorithms to evaluate the performance of ruminant livestock. These AI-powered tools have the robustness and competence to expand the evaluation of animal performance and help in minimising the production losses associated with heat stress in ruminant livestock. Advanced heat stress management through automated monitoring of heat stress in ruminant livestock based on behaviour, physiology and animal health responses have been widely accepted due to the evolution of technologies like machine learning (ML), neural networks and deep learning (DL). The AI-enabled tools involving automated data collection, pre-processing, data wrangling, development of appropriate algorithms, and deployment of models assist the livestock producers in decision-making based on real-time monitoring and act as early-stage warning systems to forecast disease dynamics based on prediction models. Due to the convincing performance, precision, and accuracy of AI models, the climate-smart livestock production imbibes AI technologies for scaled use in the successful reducing of heat stress in ruminant livestock, thereby ensuring sustainable livestock production and safeguarding the global economy. Full article

(This article belongs to the Section Smart Agriculture)

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26 pages, 7999 KiB

Open AccessArticle

Multimodal Social Sensing for the Spatio-Temporal Evolution and Assessment of Nature Disasters

by Chen Yu and Zhiguo Wang

Sensors 2024, 24(18), 5889; https://doi.org/10.3390/s24185889 - 11 Sep 2024

Abstract

Social sensing, using humans as sensors to collect disaster data, has emerged as a timely, cost-effective, and reliable data source. However, research has focused on the textual data. With advances in information technology, multimodal data such as images and videos are now shared [...] Read more.

Social sensing, using humans as sensors to collect disaster data, has emerged as a timely, cost-effective, and reliable data source. However, research has focused on the textual data. With advances in information technology, multimodal data such as images and videos are now shared on media platforms, aiding in-depth analysis of social sensing systems. This study proposed an analytical framework to extract disaster-related spatiotemporal information from multimodal social media data. Using a pre-trained multimodal neural network and a location entity recognition model, the framework integrates disaster semantics with spatiotemporal information, enhancing situational awareness. A case study of the April 2024 heavy rain event in Guangdong, China, using Weibo data, demonstrates that multimodal content correlates more strongly with rainfall patterns than textual data alone, offering a dynamic perception of disasters. These findings confirm the utility of multimodal social media data and offer a foundation for future research. The proposed framework offers valuable applications for emergency response, disaster relief, risk assessment, and witness discovery, and presents a viable approach for safety risk monitoring and early warning systems. Full article

(This article belongs to the Special Issue Advances in Wireless Sensor Networks for Smart City)

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15 pages, 4552 KiB

Open AccessCommunication

Research on On-Line Monitoring of Grinding Wheel Wear Based on Multi-Sensor Fusion

by Jingsong Duan, Guohua Cao, Guoqing Ma, Zhenglin Yu and Changshun Shao

Sensors 2024, 24(18), 5888; https://doi.org/10.3390/s24185888 - 11 Sep 2024

Abstract

The state of a grinding wheel directly affects the surface quality of the workpiece. The monitoring of grinding wheel wear state can allow one to efficiently identify grinding wheel wear information and to timely and effectively trim the grinding wheel. At present, on-line [...] Read more.

The state of a grinding wheel directly affects the surface quality of the workpiece. The monitoring of grinding wheel wear state can allow one to efficiently identify grinding wheel wear information and to timely and effectively trim the grinding wheel. At present, on-line monitoring technology using specific sensor signals can detect abnormal grinding wheel wear in a timely manner. However, due to the non-linearity and complexity of the grinding wheel wear process, as well as the interference and noise of the sensor signal, the accuracy and reliability of on-line monitoring technology still need to be improved. In this paper, an intelligent monitoring system based on multi-sensor fusion is established, and this system can be used for precise grinding wheel wear monitoring. The proposed system focuses on titanium alloy, a typical difficult-to-process aerospace material, and addresses the issue of low on-line monitoring accuracy found in traditional single-sensor systems. Additionally, a multi-eigenvalue fusion algorithm based on an improved support vector machine (SVM) is proposed. In this study, the mean square value of the wavelet packet decomposition coefficient of the acoustic emission signal, the grinding force ratio of the force signal, and the effective value of the vibration signal were taken as inputs for the improved support vector machine, and the recognition strategy was adjusted using the entropy weight evaluation method. A high-precision grinding machine was used to carry out multiple sets of grinding wheel wear experiments. After being processed by the multi-sensor integrated precision grinding wheel wear intelligent monitoring system, the collected signals can accurately reflect the grinding wheel wear state, and the monitoring accuracy can reach more than 92%. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

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