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Sensors, Volume 24, Issue 2 (January-2 2024) – 406 articles

Cover Story (view full-size image): Indoor localization has become increasingly important in modern and strategic applications, such as navigation, industrial, medical and entertainment. Bluetooth Low Energy ensures low energy consumption and significant diffusion in modern mobile devices. Although the scientific literature proposes various solutions for BLE-based indoor localization, it is not yet clear which combination of solutions is the most effective for obtaining accurate and reliable performance. In this work, a comparative analysis was performed to provide a better understanding of the most effective and reliable solutions for achieving more accurate BLE-based indoor localization. The proposed methodology could help designers of indoor localization systems to identify which techniques should be used to meet the performance requirements of specific applications. View this paper
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14 pages, 616 KB  
Article
Mood Disorder Severity and Subtype Classification Using Multimodal Deep Neural Network Models
by Joo Hun Yoo, Harim Jeong, Ji Hyun An and Tai-Myoung Chung
Sensors 2024, 24(2), 715; https://doi.org/10.3390/s24020715 - 22 Jan 2024
Cited by 6 | Viewed by 3637
Abstract
The subtype diagnosis and severity classification of mood disorder have been made through the judgment of verified assistance tools and psychiatrists. Recently, however, many studies have been conducted using biomarker data collected from subjects to assist in diagnosis, and most studies use heart [...] Read more.
The subtype diagnosis and severity classification of mood disorder have been made through the judgment of verified assistance tools and psychiatrists. Recently, however, many studies have been conducted using biomarker data collected from subjects to assist in diagnosis, and most studies use heart rate variability (HRV) data collected to understand the balance of the autonomic nervous system on statistical analysis methods to perform classification through statistical analysis. In this research, three mood disorder severity or subtype classification algorithms are presented through multimodal analysis of data on the collected heart-related data variables and hidden features from the variables of time and frequency domain of HRV. Comparing the classification performance of the statistical analysis widely used in existing major depressive disorder (MDD), anxiety disorder (AD), and bipolar disorder (BD) classification studies and the multimodality deep neural network analysis newly proposed in this study, it was confirmed that the severity or subtype classification accuracy performance of each disease improved by 0.118, 0.231, and 0.125 on average. Through the study, it was confirmed that deep learning analysis of biomarker data such as HRV can be applied as a primary identification and diagnosis aid for mental diseases, and that it can help to objectively diagnose psychiatrists in that it can confirm not only the diagnosed disease but also the current mood status. Full article
(This article belongs to the Special Issue Advanced Machine Intelligence for Biomedical Signal Processing)
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14 pages, 2632 KB  
Article
A Compact Broadside Coupled Stripline 2-D Beamforming Network and Its Application to a 2-D Beam Scanning Array Antenna Using Panasonic Megtron 6 Substrate
by Jean Temga, Takashi Shiba and Noriharu Suematsu
Sensors 2024, 24(2), 714; https://doi.org/10.3390/s24020714 - 22 Jan 2024
Cited by 1 | Viewed by 1995
Abstract
This article presents a 4-way 2-D butler matrix (BM)-based beamforming network (BFN) using a multilayer substrate broadside coupled stripline (BCS). To achieve the characteristics of a compact, wide-bandwidth, high-gain phased array, a BCS coupler is implemented using the Megtron 6 substrate. The compact [...] Read more.
This article presents a 4-way 2-D butler matrix (BM)-based beamforming network (BFN) using a multilayer substrate broadside coupled stripline (BCS). To achieve the characteristics of a compact, wide-bandwidth, high-gain phased array, a BCS coupler is implemented using the Megtron 6 substrate. The compact 2-D BFN is formed by combining planarly two horizontal BCS couplers and two vertical BCS couplers. The BFN is proposed without a crossover and without a phase shifter, generating phase responses of ±90° in the x- and y-directions, respectively. The proposed BFN exhibits a wide operating band of 66.7% (3–7 GHz) and a compact physical area of just 0.25 λ0 × 0.25 λ0 × 0.04 λ0. The planar 2-D BFN is easily integrated with the patch antenna radiation elements to construct a 2-D multibeam array antenna that generates four fixed beams, one in each quadrant, at an elevation angle of 30° from the broadside to the array axis when the element separation is 0.6 λ0. The physical area of the 2-D multibeam array antenna is just 0.8 λ0 × 0.8 λ0 × 0.04 λ0. The prototypes of the BCS coupler, the 2-D BFN, and the 2-D multibeam array antenna were fabricated and measured. The measured and simulated results were in good agreement. A gain of 9.1 to 9.9 dBi was measured. Full article
(This article belongs to the Special Issue Advanced Millimeter Wave Antenna Systems for 5G and beyond 5G Wireless Communications)
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30 pages, 4027 KB  
Article
Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms
by Esra Altulaihan, Mohammed Amin Almaiah and Ahmed Aljughaiman
Sensors 2024, 24(2), 713; https://doi.org/10.3390/s24020713 - 22 Jan 2024
Cited by 129 | Viewed by 11371
Abstract
Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT) systems are causing a wide range of problems for individuals and organizations. The IoT is self-configuring and open, making it vulnerable to insider and outsider attacks. In the IoT, devices are designed to [...] Read more.
Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT) systems are causing a wide range of problems for individuals and organizations. The IoT is self-configuring and open, making it vulnerable to insider and outsider attacks. In the IoT, devices are designed to self-configure, enabling them to connect to networks autonomously without extensive manual configuration. By using various protocols, technologies, and automated processes, self-configuring IoT devices are able to seamlessly connect to networks, discover services, and adapt their configurations without requiring manual intervention or setup. Users’ security and privacy may be compromised by attackers seeking to obtain access to their personal information, create monetary losses, and spy on them. A Denial of Service (DoS) attack is one of the most devastating attacks against IoT systems because it prevents legitimate users from accessing services. A cyberattack of this type can significantly damage IoT services and smart environment applications in an IoT network. As a result, securing IoT systems has become an increasingly significant concern. Therefore, in this study, we propose an IDS defense mechanism to improve the security of IoT networks against DoS attacks using anomaly detection and machine learning (ML). Anomaly detection is used in the proposed IDS to continuously monitor network traffic for deviations from normal profiles. For that purpose, we used four types of supervised classifier algorithms, namely, Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (kNN), and Support Vector Machine (SVM). In addition, we utilized two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA) and compared their performances. We also utilized the IoTID20 dataset, one of the most recent for detecting anomalous activity in IoT networks, to train our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by GA. However, other metrics, such as training and testing times, showed that DT was superior. Full article
(This article belongs to the Special Issue Data Security Approaches for Autonomous Systems, IoT, and Smart Sensing Systems)
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20 pages, 12695 KB  
Article
IIoT Low-Cost ZigBee-Based WSN Implementation for Enhanced Production Efficiency in a Solar Protection Curtains Manufacturing Workshop
by Hicham Klaina, Imanol Picallo, Peio Lopez-Iturri, Aitor Biurrun, Ana V. Alejos, Leyre Azpilicueta, Abián B. Socorro-Leránoz and Francisco Falcone
Sensors 2024, 24(2), 712; https://doi.org/10.3390/s24020712 - 22 Jan 2024
Cited by 4 | Viewed by 2450
Abstract
Nowadays, the Industry 4.0 concept and the Industrial Internet of Things (IIoT) are considered essential for the implementation of automated manufacturing processes across various industrial settings. In this regard, wireless sensor networks (WSN) are crucial due to their inherent mobility, easy deployment and [...] Read more.
Nowadays, the Industry 4.0 concept and the Industrial Internet of Things (IIoT) are considered essential for the implementation of automated manufacturing processes across various industrial settings. In this regard, wireless sensor networks (WSN) are crucial due to their inherent mobility, easy deployment and maintenance, scalability, and low power consumption, among other benefits. In this context, the presented paper proposes an optimized and low-cost WSN based on ZigBee communication technology for the monitoring of a real manufacturing facility. The company designs and manufactures solar protection curtains and aims to integrate the deployed WSN into the Enterprise Resource Planning (ERP) system in order to optimize their production processes and enhance production efficiency and cost estimation capabilities. To achieve this, radio propagation measurements and 3D ray launching simulations were conducted to characterize the wireless channel behavior and facilitate the development of an optimized WSN system that can operate in the complex industrial environment presented and validated through on-site wireless channel measurements, as well as interference analysis. Then, a low-cost WSN was implemented and deployed to acquire real-time data from different machinery and workstations, which will be integrated into the ERP system. Multiple data streams have been collected and processed from the shop floor of the factory by means of the prototype wireless nodes implemented. This integration will enable the company to optimize its production processes, fabricate products more efficiently, and enhance its cost estimation capabilities. Moreover, the proposed system provides a scalable platform, enabling the integration of new sensors as well as information processing capabilities. Full article
(This article belongs to the Collection Wireless Sensor Networks towards the Internet of Things)
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17 pages, 8867 KB  
Article
Intuitive Cell Manipulation Microscope System with Haptic Device for Intracytoplasmic Sperm Injection Simplification
by Kazuya Sakamoto, Tadayoshi Aoyama, Masaru Takeuchi and Yasuhisa Hasegawa
Sensors 2024, 24(2), 711; https://doi.org/10.3390/s24020711 - 22 Jan 2024
Cited by 5 | Viewed by 2119
Abstract
In recent years, the demand for effective intracytoplasmic sperm injection (ICSI) for the treatment of male infertility has increased. The ICSI operation is complicated as it involves delicate organs and requires a high level of skill. Several cell manipulation systems that do not [...] Read more.
In recent years, the demand for effective intracytoplasmic sperm injection (ICSI) for the treatment of male infertility has increased. The ICSI operation is complicated as it involves delicate organs and requires a high level of skill. Several cell manipulation systems that do not require such skills have been proposed; notably, several automated methods are available for cell rotation. However, these methods are unfeasible for the delicate ICSI medical procedure because of safety issues. Thus, this study proposes a microscopic system that enables intuitive micropipette manipulation using a haptic device that safely and efficiently performs the entire ICSI procedure. The proposed system switches between field-of-view expansion and three-dimensional image presentation to present images according to the operational stage. In addition, the system enables intuitive pipette manipulation using a haptic device. Experiments were conducted on microbeads instead of oocytes. The results confirmed that the time required for the experimental task was improved by 52.6%, and the injection error was improved by 75.3% compared to those observed in the conventional system. Full article
(This article belongs to the Section Biomedical Sensors)
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16 pages, 3360 KB  
Article
Assessment of Foot Strike Angle and Forward Propulsion with Wearable Sensors in People with Stroke
by Carmen J. Ensink, Cheriel Hofstad, Theo Theunissen and Noël L. W. Keijsers
Sensors 2024, 24(2), 710; https://doi.org/10.3390/s24020710 - 22 Jan 2024
Cited by 6 | Viewed by 2558
Abstract
Effective retraining of foot elevation and forward propulsion is a critical aspect of gait rehabilitation therapy after stroke, but valuable feedback to enhance these functions is often absent during home-based training. To enable feedback at home, this study assesses the validity of an [...] Read more.
Effective retraining of foot elevation and forward propulsion is a critical aspect of gait rehabilitation therapy after stroke, but valuable feedback to enhance these functions is often absent during home-based training. To enable feedback at home, this study assesses the validity of an inertial measurement unit (IMU) to measure the foot strike angle (FSA), and explores eight different kinematic parameters as potential indicators for forward propulsion. Twelve people with stroke performed walking trials while equipped with five IMUs and markers for optical motion analysis (the gold standard). The validity of the IMU-based FSA was assessed via Bland–Altman analysis, ICC, and the repeatability coefficient. Eight different kinematic parameters were compared to the forward propulsion via Pearson correlation. Analyses were performed on a stride-by-stride level and within-subject level. On a stride-by-stride level, the mean difference between the IMU-based FSA and OMCS-based FSA was 1.4 (95% confidence: −3.0; 5.9) degrees, with ICC = 0.97, and a repeatability coefficient of 5.3 degrees. The mean difference for the within-subject analysis was 1.5 (95% confidence: −1.0; 3.9) degrees, with a mean repeatability coefficient of 3.1 (SD: 2.0) degrees. Pearson’s r value for all the studied parameters with forward propulsion were below 0.75 for the within-subject analysis, while on a stride-by-stride level the foot angle upon terminal contact and maximum foot angular velocity could be indicative for the peak forward propulsion. In conclusion, the FSA can accurately be assessed with an IMU on the foot in people with stroke during regular walking. However, no suitable kinematic indicator for forward propulsion was identified based on foot and shank movement that could be used for feedback in people with stroke. Full article
(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)
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20 pages, 12344 KB  
Article
Vehicle–Bridge Interaction Modelling Using Precise 3D Road Surface Analysis
by Maja Kreslin, Peter Češarek, Aleš Žnidarič, Darko Kokot, Jan Kalin and Rok Vezočnik
Sensors 2024, 24(2), 709; https://doi.org/10.3390/s24020709 - 22 Jan 2024
Cited by 4 | Viewed by 2516
Abstract
Uneven road surfaces are the primary source of excitation in the dynamic interaction between a bridge and a vehicle and can lead to errors in bridge weigh-in-motion (B-WIM) systems. In order to correctly reproduce this interaction in a numerical model of a bridge, [...] Read more.
Uneven road surfaces are the primary source of excitation in the dynamic interaction between a bridge and a vehicle and can lead to errors in bridge weigh-in-motion (B-WIM) systems. In order to correctly reproduce this interaction in a numerical model of a bridge, it is essential to know the magnitude and location of the various roadway irregularities. This paper presents a methodology for measuring the 3D road surface using static terrestrial laser scanning and a numerical model for simulating vehicle passage over a bridge with a measured road surface. This model allows the evaluation of strain responses in the time domain at any bridge location considering different parameters such as vehicle type, lateral position and speed, road surface unevenness, bridge type, etc. Since the time domain strains are crucial for B-WIM algorithms, the proposed approach facilitates the analysis of the different factors affecting the B-WIM results. The first validation of the proposed methodology was carried out on a real bridge, where extensive measurements were performed using different sensors, including measurements of the road surface, the response of the bridge when crossed by a test vehicle and the dynamic properties of the bridge and vehicle. The comparison between the simulated and measured bridge response marks a promising step towards investigating the influence of unevenness on the results of B-WIM. Full article
(This article belongs to the Special Issue Sensors in Civil Structural Health Monitoring)
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28 pages, 1300 KB  
Review
A Review of IoT Firmware Vulnerabilities and Auditing Techniques
by Taimur Bakhshi, Bogdan Ghita and Ievgeniia Kuzminykh
Sensors 2024, 24(2), 708; https://doi.org/10.3390/s24020708 - 22 Jan 2024
Cited by 36 | Viewed by 15562
Abstract
In recent years, the Internet of Things (IoT) paradigm has been widely applied across a variety of industrial and consumer areas to facilitate greater automation and increase productivity. Higher dependability on connected devices led to a growing range of cyber security threats targeting [...] Read more.
In recent years, the Internet of Things (IoT) paradigm has been widely applied across a variety of industrial and consumer areas to facilitate greater automation and increase productivity. Higher dependability on connected devices led to a growing range of cyber security threats targeting IoT-enabled platforms, specifically device firmware vulnerabilities, often overlooked during development and deployment. A comprehensive security strategy aiming to mitigate IoT firmware vulnerabilities would entail auditing the IoT device firmware environment, from software components, storage, and configuration, to delivery, maintenance, and updating, as well as understanding the efficacy of tools and techniques available for this purpose. To this effect, this paper reviews the state-of-the-art technology in IoT firmware vulnerability assessment from a holistic perspective. To help with the process, the IoT ecosystem is divided into eight categories: system properties, access controls, hardware and software re-use, network interfacing, image management, user awareness, regulatory compliance, and adversarial vectors. Following the review of individual areas, the paper further investigates the efficiency and scalability of auditing techniques for detecting firmware vulnerabilities. Beyond the technical aspects, state-of-the-art IoT firmware architectures and respective evaluation platforms are also reviewed according to their technical, regulatory, and standardization challenges. The discussion is accompanied also by a review of the existing auditing tools, the vulnerabilities addressed, the analysis method used, and their abilities to scale and detect unknown attacks. The review also proposes a taxonomy of vulnerabilities and maps them with their exploitation vectors and with the auditing tools that could help in identifying them. Given the current interest in analysis automation, the paper explores the feasibility and impact of evolving machine learning and blockchain applications in securing IoT firmware. The paper concludes with a summary of ongoing and future research challenges in IoT firmware to facilitate and support secure IoT development. Full article
(This article belongs to the Special Issue IoT Cybersecurity)
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24 pages, 12639 KB  
Article
Cooperative Safe Trajectory Planning for Quadrotor Swarms
by Yahui Zhang, Peng Yi and Yiguang Hong
Sensors 2024, 24(2), 707; https://doi.org/10.3390/s24020707 - 22 Jan 2024
Cited by 4 | Viewed by 2472
Abstract
In this paper, we propose a novel distributed algorithm based on model predictive control and alternating direction multiplier method (DMPC-ADMM) for cooperative trajectory planning of quadrotor swarms. First, a receding horizon trajectory planning optimization problem is constructed, in which the differential flatness property [...] Read more.
In this paper, we propose a novel distributed algorithm based on model predictive control and alternating direction multiplier method (DMPC-ADMM) for cooperative trajectory planning of quadrotor swarms. First, a receding horizon trajectory planning optimization problem is constructed, in which the differential flatness property is used to deal with the nonlinear dynamics of quadrotors while we design a relaxed form of the discrete-time control barrier function (DCBF) constraint to balance feasibility and safety. Then, we decompose the original trajectory planning problem by ADMM and solve it in a fully distributed manner with peer-to-peer communication, which induces the quadrotors within the communication range to reach a consensus on their future trajectories to enhance safety. In addition, an event-triggered mechanism is designed to reduce the communication overhead. The simulation results verify that the trajectories generated by our method are real-time, safe, and smooth. A comprehensive comparison with the centralized strategy and several other distributed strategies in terms of real-time, safety, and feasibility verifies that our method is more suitable for the trajectory planning of large-scale quadrotor swarms. Full article
(This article belongs to the Special Issue Advances and Applications in Aerial Unmanned Robots: Sensing, Planning, and Control)
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23 pages, 10644 KB  
Article
Relationship of Magnetic Domain and Permeability for Clustered Soft Magnetic Narrow Strips with In-Plane Inclined Magnetization Easy Axis on Distributed Magnetic Field
by Tomoo Nakai
Sensors 2024, 24(2), 706; https://doi.org/10.3390/s24020706 - 22 Jan 2024
Cited by 3 | Viewed by 2376
Abstract
A unique functionality was reported for a thin-film soft magnetic strip with a certain angle of inclined magnetic anisotropy. It can switch magnetic domain by applying a surface normal field with a certain distribution on the element. The domain switches between a single [...] Read more.
A unique functionality was reported for a thin-film soft magnetic strip with a certain angle of inclined magnetic anisotropy. It can switch magnetic domain by applying a surface normal field with a certain distribution on the element. The domain switches between a single domain and a multi-domain. Our previous study shows that this phenomenon appears even in the case of the adjacent configuration of multiple narrow strips. It was also reported that the magnetic permeability for the alternating current (AC) magnetic field changes drastically in the frequency range from 10 kHz to 10 MHz as a function of the strength of the distributed magnetic field. In this paper, the correspondence of AC permeability and the magnetic domain as a function of the intensity of the distributed field is investigated. It was confirmed that the extension of the area of the Landau–Lifshitz-like multi-domain on the clustered narrow strips was observed as a function of the intensity of the distributed magnetic field, and this domain extension was matched with the permeability variation. The result leads to the application of this phenomenon to a tunable inductor, electromagnetic shielding, or a sensor for detecting and memorizing the existence of a distributed magnetic field generated by a magnetic nanoparticle in the vicinity of the sensor. Full article
(This article belongs to the Special Issue Challenges and Future Trends of Magnetic Sensors)
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12 pages, 2562 KB  
Article
Triangle-Shaped Cerium Tungstate Nanoparticles Used to Modify Carbon Paste Electrode for Sensitive Hydroquinone Detection in Water Samples
by Vesna Stanković, Slađana Đurđić, Miloš Ognjanović, Gloria Zlatić and Dalibor Stanković
Sensors 2024, 24(2), 705; https://doi.org/10.3390/s24020705 - 22 Jan 2024
Cited by 8 | Viewed by 2638
Abstract
In this study, we propose an eco-friendly method for synthesizing cerium tungstate nanoparticles using hydrothermal techniques. We used scanning, transmission electron microscopy, and X-ray diffraction to analyze the morphology of the synthesized nanoparticles. The results showed that the synthesized nanoparticles were uniform and [...] Read more.
In this study, we propose an eco-friendly method for synthesizing cerium tungstate nanoparticles using hydrothermal techniques. We used scanning, transmission electron microscopy, and X-ray diffraction to analyze the morphology of the synthesized nanoparticles. The results showed that the synthesized nanoparticles were uniform and highly crystalline, with a particle size of about 50 nm. The electrocatalytic properties of the nanoparticles were then investigated using cyclic voltammetry and electrochemical impedance spectroscopy. We further used the synthesized nanoparticles to develop an electrochemical sensor based on a carbon paste electrode that can detect hydroquinone. By optimizing the differential pulse voltammetric method, a wide linearity range of 0.4 to 45 µM and a low detection limit of 0.06 µM were obtained. The developed sensor also expressed excellent repeatability (RSD up to 3.8%) and reproducibility (RSD below 5%). Interferences had an insignificant impact on the determination of analytes, making it possible to use this method for monitoring hydroquinone concentrations in tap water. This study introduces a new approach to the chemistry of materials and the environment and demonstrates that a careful selection of components can lead to new horizons in analytical chemistry. Full article
(This article belongs to the Special Issue Chemical Sensors for Toxic Chemical Detection)
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14 pages, 8472 KB  
Technical Note
Experimental Investigation of Pore Pressure on Sandy Seabed around Submarine Pipeline under Irregular Wave Loading
by Changjing Fu, Jinguo Wang and Tianlong Zhao
Sensors 2024, 24(2), 704; https://doi.org/10.3390/s24020704 - 22 Jan 2024
Cited by 5 | Viewed by 1723
Abstract
The propagation of shallow-water waves may cause liquefaction of the seabed, thereby reducing its support capacity for pipelines and potentially leading to pipeline settlement or deformation. To ensure the stability of buried pipelines, it is crucial to consider the excess pore pressure induced [...] Read more.
The propagation of shallow-water waves may cause liquefaction of the seabed, thereby reducing its support capacity for pipelines and potentially leading to pipeline settlement or deformation. To ensure the stability of buried pipelines, it is crucial to consider the excess pore pressure induced by irregular waves thoroughly. This paper presents the findings of an experimental study on excess pore pressure caused by irregular waves on a sandy seabed. A series of two-dimensional wave flume experiments investigated the excess pore pressure generated by irregular waves. Based on the experimental results, this study examined the influences of irregular wave characteristics and pipeline proximity on excess pore pressure. Using test data, the signal analysis method was employed to categorize different modes of excess pore-water pressure growth into two types and explore the mechanism underlying pore pressure development under the influence of irregular waves. Full article
(This article belongs to the Section Environmental Sensing)
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13 pages, 7250 KB  
Article
OS-BREEZE: Oil Spills Boundary Red Emission Zone Estimation Using Unmanned Surface Vehicles
by Oren Elmakis, Semion Polinov, Tom Shaked, Gabi Gordon and Amir Degani
Sensors 2024, 24(2), 703; https://doi.org/10.3390/s24020703 - 22 Jan 2024
Viewed by 2281
Abstract
Maritime transport, responsible for delivering over eighty percent of the world’s goods, is the backbone of the global delivery industry. However, it also presents considerable environmental risks, particularly regarding aquatic contamination. Nearly ninety percent of marine oil spills near shores are attributed to [...] Read more.
Maritime transport, responsible for delivering over eighty percent of the world’s goods, is the backbone of the global delivery industry. However, it also presents considerable environmental risks, particularly regarding aquatic contamination. Nearly ninety percent of marine oil spills near shores are attributed to human activities, highlighting the urgent need for continuous and effective surveillance. To address this pressing issue, this paper introduces a novel technique named OS-BREEZE. This method employs an Unmanned Surface Vehicle (USV) for assessing the extent of oil pollution on the sea surface. The OS-BREEZE algorithm directs the USV along the spill edge, facilitating rapid and accurate assessment of the contaminated area. The key contribution of this paper is the development of this novel approach for monitoring and managing marine pollution, which significantly reduces the path length required for mapping and estimating the size of the contaminated area. Furthermore, this paper presents a scale model experiment executed at the Coastal and Marine Engineering Research Institute (CAMERI). This experiment demonstrated the method’s enhanced speed and efficiency compared to traditional monitoring techniques. The experiment was methodically conducted across four distinct scenarios: the initial and advanced stages of an oil spill at the outer anchoring, as well as scenarios at the inner docking on both the stern and port sides. Full article
(This article belongs to the Special Issue Remote Sensing Application for Environmental Monitoring)
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15 pages, 9761 KB  
Article
Proximity-Based Optical Camera Communication with Multiple Transmitters Using Deep Learning
by Muhammad Rangga Aziz Nasution, Herfandi Herfandi, Ones Sanjerico Sitanggang, Huy Nguyen and Yeong Min Jang
Sensors 2024, 24(2), 702; https://doi.org/10.3390/s24020702 - 22 Jan 2024
Cited by 4 | Viewed by 2873
Abstract
In recent years, optical camera communication (OCC) has garnered attention as a research focus. OCC uses optical light to transmit data by scattering the light in various directions. Although this can be advantageous with multiple transmitter scenarios, there are situations in which only [...] Read more.
In recent years, optical camera communication (OCC) has garnered attention as a research focus. OCC uses optical light to transmit data by scattering the light in various directions. Although this can be advantageous with multiple transmitter scenarios, there are situations in which only a single transmitter is permitted to communicate. Therefore, this method is proposed to fulfill the latter requirement using 2D object size to calculate the proximity of the objects through an AI object detection model. This approach enables prioritization among transmitters based on the transmitter proximity to the receiver for communication, facilitating alternating communication with multiple transmitters. The image processing employed when receiving the signals from transmitters enables communication to be performed without the need to modify the camera parameters. During the implementation, the distance between the transmitter and receiver varied between 1.0 and 5.0 m, and the system demonstrated a maximum data rate of 3.945 kbps with a minimum BER of 4.2×10−3. Additionally, the system achieved high accuracy from the refined YOLOv8 detection algorithm, reaching 0.98 mAP at a 0.50 IoU. Full article
(This article belongs to the Topic Machine Learning in Internet of Things)
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12 pages, 565 KB  
Article
Efficient Cumulant-Based Automatic Modulation Classification Using Machine Learning
by Ben Dgani and Israel Cohen
Sensors 2024, 24(2), 701; https://doi.org/10.3390/s24020701 - 22 Jan 2024
Cited by 5 | Viewed by 3131
Abstract
This paper introduces a new technique for automatic modulation classification (AMC) in Cognitive Radio (CR) networks. The method employs a straightforward classifier that utilizes high-order cumulant for training. It focuses on the statistical behavior of both analog modulation and digital schemes, which have [...] Read more.
This paper introduces a new technique for automatic modulation classification (AMC) in Cognitive Radio (CR) networks. The method employs a straightforward classifier that utilizes high-order cumulant for training. It focuses on the statistical behavior of both analog modulation and digital schemes, which have received limited attention in previous works. The simulation results show that the proposed method performs well with different signal-to-noise ratios (SNRs) and channel conditions. The classifier’s performance is superior to that of complex deep learning methods, making it suitable for deployment in CR networks’ end units, especially in military and emergency service applications. The proposed method offers a cost-effective and high-quality solution for AMC that meets the strict demands of these critical applications. Full article
(This article belongs to the Special Issue Cognitive Radio Networks: Technologies, Challenges and Applications)
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17 pages, 5864 KB  
Article
Deep Reinforcement Learning for Autonomous Driving with an Auxiliary Actor Discriminator
by Qiming Gao, Fangle Chang, Jiahong Yang, Yu Tao, Longhua Ma and Hongye Su
Sensors 2024, 24(2), 700; https://doi.org/10.3390/s24020700 - 22 Jan 2024
Cited by 4 | Viewed by 3860
Abstract
In the research of robot systems, path planning and obstacle avoidance are important research directions, especially in unknown dynamic environments where flexibility and rapid decision makings are required. In this paper, a state attention network (SAN) was developed to extract features to represent [...] Read more.
In the research of robot systems, path planning and obstacle avoidance are important research directions, especially in unknown dynamic environments where flexibility and rapid decision makings are required. In this paper, a state attention network (SAN) was developed to extract features to represent the interaction between an intelligent robot and its obstacles. An auxiliary actor discriminator (AAD) was developed to calculate the probability of a collision. Goal-directed and gap-based navigation strategies were proposed to guide robotic exploration. The proposed policy was trained through simulated scenarios and updated by the Soft Actor-Critic (SAC) algorithm. The robot executed the action depending on the AAD output. Heuristic knowledge (HK) was developed to prevent blind exploration of the robot. Compared to other methods, adopting our approach in robot systems can help robots converge towards an optimal action strategy. Furthermore, it enables them to explore paths in unknown environments with fewer moving steps (showing a decrease of 33.9%) and achieve higher average rewards (showning an increase of 29.15%). Full article
(This article belongs to the Special Issue Advances in Sensing, Imaging and Computing for Autonomous Driving)
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17 pages, 1671 KB  
Article
Simple Scalable Multimodal Semantic Segmentation Model
by Yuchang Zhu and Nanfeng Xiao
Sensors 2024, 24(2), 699; https://doi.org/10.3390/s24020699 - 22 Jan 2024
Viewed by 3393
Abstract
Visual perception is a crucial component of autonomous driving systems. Traditional approaches for autonomous driving visual perception often rely on single-modal methods, and semantic segmentation tasks are accomplished by inputting RGB images. However, for semantic segmentation tasks in autonomous driving visual perception, a [...] Read more.
Visual perception is a crucial component of autonomous driving systems. Traditional approaches for autonomous driving visual perception often rely on single-modal methods, and semantic segmentation tasks are accomplished by inputting RGB images. However, for semantic segmentation tasks in autonomous driving visual perception, a more effective strategy involves leveraging multiple modalities, which is because different sensors of the autonomous driving system bring diverse information, and the complementary features among different modalities enhance the robustness of the semantic segmentation modal. Contrary to the intuitive belief that more modalities lead to better accuracy, our research reveals that adding modalities to traditional semantic segmentation models can sometimes decrease precision. Inspired by the residual thinking concept, we propose a multimodal visual perception model which is capable of maintaining or even improving accuracy with the addition of any modality. Our approach is straightforward, using RGB as the main branch and employing the same feature extraction backbone for other modal branches. The modals score module (MSM) evaluates channel and spatial scores of all modality features, measuring their importance for overall semantic segmentation. Subsequently, the modal branches provide additional features to the RGB main branch through the features complementary module (FCM). Leveraging the residual thinking concept further enhances the feature extraction capabilities of all the branches. Through extensive experiments, we derived several conclusions. The integration of certain modalities into traditional semantic segmentation models tends to result in a decline in segmentation accuracy. In contrast, our proposed simple and scalable multimodal model demonstrates the ability to maintain segmentation precision when accommodating any additional modality. Moreover, our approach surpasses some state-of-the-art multimodal semantic segmentation models. Additionally, we conducted ablation experiments on the proposed model, confirming that the application of the proposed MSM, FCM, and the incorporation of residual thinking contribute significantly to the enhancement of the model. Full article
(This article belongs to the Section Vehicular Sensing)
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16 pages, 6375 KB  
Article
Using Diffraction Deep Neural Networks for Indirect Phase Recovery Based on Zernike Polynomials
by Fang Yuan, Yang Sun, Yuting Han, Hairong Chu, Tianxiang Ma and Honghai Shen
Sensors 2024, 24(2), 698; https://doi.org/10.3390/s24020698 - 22 Jan 2024
Cited by 2 | Viewed by 2812
Abstract
The phase recovery module is dedicated to acquiring phase distribution information within imaging systems, enabling the monitoring and adjustment of a system’s performance. Traditional phase inversion techniques exhibit limitations, such as the speed of the sensor and complexity of the system. Therefore, we [...] Read more.
The phase recovery module is dedicated to acquiring phase distribution information within imaging systems, enabling the monitoring and adjustment of a system’s performance. Traditional phase inversion techniques exhibit limitations, such as the speed of the sensor and complexity of the system. Therefore, we propose an indirect phase retrieval approach based on a diffraction neural network. By utilizing non-source diffraction through multiple layers of diffraction units, this approach reconstructs coefficients based on Zernike polynomials from incident beams with distorted phases, thereby indirectly synthesizing interference phases. Through network training and simulation testing, we validate the effectiveness of this approach, showcasing the trained network’s capacity for single-order phase recognition and multi-order composite phase inversion. We conduct an analysis of the network’s generalization and evaluate the impact of the network depth on the restoration accuracy. The test results reveal an average root mean square error of 0.086λ for phase inversion. This research provides new insights and methodologies for the development of the phase recovery component in adaptive optics systems. Full article
(This article belongs to the Section Optical Sensors)
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28 pages, 1447 KB  
Article
Analytical Model of the Connection Handoff in 5G Mobile Networks with Call Admission Control Mechanisms
by Mariusz Głąbowski, Maciej Sobieraj and Maciej Stasiak
Sensors 2024, 24(2), 697; https://doi.org/10.3390/s24020697 - 22 Jan 2024
Cited by 5 | Viewed by 2152
Abstract
Handoff mechanisms are very important in fifth-generation (5G) mobile networks because of the cellular architecture employed to maximize spectrum utilization. Together with call admission control (CAC) mechanisms, they enable better optimization of bandwidth use. The primary objective of the research presented in this [...] Read more.
Handoff mechanisms are very important in fifth-generation (5G) mobile networks because of the cellular architecture employed to maximize spectrum utilization. Together with call admission control (CAC) mechanisms, they enable better optimization of bandwidth use. The primary objective of the research presented in this article is to analyze traffic levels, aiming to optimize traffic management and handling. This article considers the two most popular CAC mechanisms: the resource reservation mechanism and the threshold mechanism. It presents an analytical approach to occupancy distribution and blocking probability calculation in 5G mobile networks, incorporating connection handoff and CAC mechanisms for managing multiple traffic streams generated by multi-service sources. Due to the fact that the developed analytical model is an approximate model, its accuracy was also examined. For this purpose, the results of analytical calculations of the blocking probability in a group of 5G cells are compared with the simulation data. This paper is an extended version of our paper published in 17th ConTEL 2023. Full article
(This article belongs to the Special Issue Recent Trends and Advances in Telecommunications and Sensing)
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20 pages, 4612 KB  
Article
Buck Converter with Cubic Static Conversion Ratio
by Delia-Anca Botila, Ioana-Monica Pop-Calimanu and Dan Lascu
Sensors 2024, 24(2), 696; https://doi.org/10.3390/s24020696 - 22 Jan 2024
Cited by 1 | Viewed by 2057
Abstract
The paper introduces a step-down converter that exhibits a static conversion ratio of cubic nature, providing an output voltage which is much closer to the input voltage, and at the same duty cycle, compared to a wide class of one-transistor buck-type topologies. Although [...] Read more.
The paper introduces a step-down converter that exhibits a static conversion ratio of cubic nature, providing an output voltage which is much closer to the input voltage, and at the same duty cycle, compared to a wide class of one-transistor buck-type topologies. Although the proposed topology contains many components, its control is still simple, as it employs only one transistor. A dc analysis is performed, the semiconductor stresses are derived in terms of input and output voltages and output power, revealing that the semiconductor voltage stresses remain acceptable and anyway lower than in other cubic buck topology. All detailed design equations are provided. The state-space approach is used to analyze the converter in the presence of conduction losses and a procedure for calculating the individual power dissipation is provided. The feasibility of the proposed cubic buck topology is first validated by computer simulation and finally confirmed by an experimental 12 V–10 W prototype. Full article
(This article belongs to the Special Issue Selected Papers from International Symposium on Electronics and Telecommunications ISETC 2022)
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20 pages, 24482 KB  
Article
Knee Angle Estimation with Dynamic Calibration Using Inertial Measurement Units for Running
by Matthew B. Rhudy, Joseph M. Mahoney and Allison R. Altman-Singles
Sensors 2024, 24(2), 695; https://doi.org/10.3390/s24020695 - 22 Jan 2024
Cited by 5 | Viewed by 4410
Abstract
The knee flexion angle is an important measurement for studies of the human gait. Running is a common activity with a high risk of knee injury. Studying the running gait in realistic situations is challenging because accurate joint angle measurements typically come from [...] Read more.
The knee flexion angle is an important measurement for studies of the human gait. Running is a common activity with a high risk of knee injury. Studying the running gait in realistic situations is challenging because accurate joint angle measurements typically come from optical motion-capture systems constrained to laboratory settings. This study considers the use of shank and thigh inertial sensors within three different filtering algorithms to estimate the knee flexion angle for running without requiring sensor-to-segment mounting assumptions, body measurements, specific calibration poses, or magnetometers. The objective of this study is to determine the knee flexion angle within running applications using accelerometer and gyroscope information only. Data were collected for a single test participant (21-year-old female) at four different treadmill speeds and used to validate the estimation results for three filter variations with respect to a Vicon optical motion-capture system. The knee flexion angle filtering algorithms resulted in root-mean-square errors of approximately three degrees. The results of this study indicate estimation results that are within acceptable limits of five degrees for clinical gait analysis. Specifically, a complementary filter approach is effective for knee flexion angle estimation in running applications. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis)
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12 pages, 3603 KB  
Article
Self-Biased Magneto-Electric Antenna for Very-Low-Frequency Communications: Exploiting Magnetization Grading and Asymmetric Structure-Induced Resonance
by Chung Ming Leung, Haoran Zheng, Jing Yang, Tao Wang and Feifei Wang
Sensors 2024, 24(2), 694; https://doi.org/10.3390/s24020694 - 22 Jan 2024
Cited by 2 | Viewed by 3425
Abstract
VLF magneto-electric (ME) antennas have gained attention for their compact size and high radiation efficiency in lossy conductive environments. However, the need for a large DC magnetic field bias presents challenges for miniaturization, limiting portability. This study introduces a self-biased ME antenna with [...] Read more.
VLF magneto-electric (ME) antennas have gained attention for their compact size and high radiation efficiency in lossy conductive environments. However, the need for a large DC magnetic field bias presents challenges for miniaturization, limiting portability. This study introduces a self-biased ME antenna with an asymmetric design using two magneto materials, inducing a magnetization grading effect that reduces the resonant frequency during bending. Operating principles are explored, and performance parameters, including the radiation mechanism, intensity and driving power, are experimentally assessed. Leveraging its excellent direct and converse magneto-electric effect, the antenna proves adept at serving as both a transmitter and a receiver. The results indicate that, at 2.09 mW and a frequency of 24.47 kHz, the antenna has the potential to achieve a 2.44 pT magnetic flux density at a 3 m distance. A custom modulation–demodulation circuit is employed, applying 2ASK and 2PSK to validate communication capability at baseband signals of 10 Hz and 100 Hz. This approach offers a practical strategy for the lightweight and compact design of VLF communication systems. Full article
(This article belongs to the Special Issue Multiferroics and Magnetoelectrics for Sensors, Low-F Antenna and Other Related Emerging Devices)
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20 pages, 25820 KB  
Article
Enhanced Out-of-Stock Detection in Retail Shelf Images Based on Deep Learning
by Franko Šikić, Zoran Kalafatić, Marko Subašić and Sven Lončarić
Sensors 2024, 24(2), 693; https://doi.org/10.3390/s24020693 - 22 Jan 2024
Cited by 3 | Viewed by 7770
Abstract
The term out-of-stock (OOS) describes a problem that occurs when shoppers come to a store and the product they are seeking is not present on its designated shelf. Missing products generate huge sales losses and may lead to a declining reputation or the [...] Read more.
The term out-of-stock (OOS) describes a problem that occurs when shoppers come to a store and the product they are seeking is not present on its designated shelf. Missing products generate huge sales losses and may lead to a declining reputation or the loss of loyal customers. In this paper, we propose a novel deep-learning (DL)-based OOS-detection method that utilizes a two-stage training process and a post-processing technique designed for the removal of inaccurate detections. To develop the method, we utilized an OOS detection dataset that contains a commonly used fully empty OOS class and a novel class that represents the frontal OOS. We present a new image augmentation procedure in which some existing OOS instances are enlarged by duplicating and mirroring themselves over nearby products. An object-detection model is first pre-trained using only augmented shelf images and, then, fine-tuned on the original data. During the inference, the detected OOS instances are post-processed based on their aspect ratio. In particular, the detected instances are discarded if their aspect ratio is higher than the maximum or lower than the minimum instance aspect ratio found in the dataset. The experimental results showed that the proposed method outperforms the existing DL-based OOS-detection methods and detects fully empty and frontal OOS instances with 86.3% and 83.7% of the average precision, respectively. Full article
(This article belongs to the Special Issue Deep Learning-Based Neural Networks for Sensing and Imaging)
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19 pages, 7287 KB  
Article
Preliminary Characterization of an Active CMOS Pad Detector for Tracking and Dosimetry in HDR Brachytherapy
by Thi Ngoc Hang Bui, Matthew Large, Joel Poder, Joseph Bucci, Edoardo Bianco, Raffaele Aaron Giampaolo, Angelo Rivetti, Manuel Da Rocha Rolo, Zeljko Pastuovic, Thomas Corradino, Lucio Pancheri and Marco Petasecca
Sensors 2024, 24(2), 692; https://doi.org/10.3390/s24020692 - 22 Jan 2024
Cited by 2 | Viewed by 2099
Abstract
We assessed the accuracy of a prototype radiation detector with a built in CMOS amplifier for use in dosimetry for high dose rate brachytherapy. The detectors were fabricated on two substrates of epitaxial high resistivity silicon. The radiation detection performance of prototypes has [...] Read more.
We assessed the accuracy of a prototype radiation detector with a built in CMOS amplifier for use in dosimetry for high dose rate brachytherapy. The detectors were fabricated on two substrates of epitaxial high resistivity silicon. The radiation detection performance of prototypes has been tested by ion beam induced charge (IBIC) microscopy using a 5.5 MeV alpha particle microbeam. We also carried out the HDR Ir-192 radiation source tracking at different depths and angular dose dependence in a water equivalent phantom. The detectors show sensitivities spanning from (5.8 ± 0.021) × 10−8 to (3.6 ± 0.14) × 10−8 nC Gy−1 mCi−1 mm−2. The depth variation of the dose is within 5% with that calculated by TG-43. Higher discrepancies are recorded for 2 mm and 7 mm depths due to the scattering of secondary particles and the perturbation of the radiation field induced in the ceramic/golden package. Dwell positions and dwell time are reconstructed within ±1 mm and 20 ms, respectively. The prototype detectors provide an unprecedented sensitivity thanks to its monolithic amplification stage. Future investigation of this technology will include the optimisation of the packaging technique. Full article
(This article belongs to the Special Issue Integrated Circuits and CMOS Sensors)
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15 pages, 559 KB  
Article
A Dynamic Framework for Internet-Based Network Time Protocol
by Kelum A. A. Gamage, Asher Sajid, Omar S. Sonbul, Muhammad Rashid and Amar Y. Jaffar
Sensors 2024, 24(2), 691; https://doi.org/10.3390/s24020691 - 22 Jan 2024
Cited by 4 | Viewed by 2436
Abstract
Time synchronization is vital for accurate data collection and processing in sensor networks. Sensors in these networks often operate under fluctuating conditions. However, an accurate timekeeping mechanism is critical even in varying network conditions. Consequently, a synchronization method is required in sensor networks [...] Read more.
Time synchronization is vital for accurate data collection and processing in sensor networks. Sensors in these networks often operate under fluctuating conditions. However, an accurate timekeeping mechanism is critical even in varying network conditions. Consequently, a synchronization method is required in sensor networks to ensure reliable timekeeping for correlating data accurately across the network. In this research, we present a novel dynamic NTP (Network Time Protocol) algorithm that significantly enhances the precision and reliability of the generalized NTP protocol. It incorporates a dynamic mechanism to determine the Round-Trip Time (RTT), which allows accurate timekeeping even in varying network conditions. The proposed approach has been implemented on an FPGA and a comprehensive performance analysis has been made, comparing three distinct NTP methods: dynamic NTP (DNTP), static NTP (SNTP), and GPS-based NTP (GNTP). As a result, key performance metrics such as variance, standard deviation, mean, and median accuracy have been evaluated. Our findings demonstrate that DNTP is markedly superior in dynamic network scenarios, a common characteristic in sensor networks. This adaptability is important for sensors installed in time-critical networks, such as real-time industrial IoTs, where precise and reliable time synchronization is necessary. Full article
(This article belongs to the Special Issue Advanced Wireless Sensor Network Deployment in Smart Cities, Industry 4.0, and Agriculture 4.0)
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1 pages, 132 KB  
Correction
Correction: Masi et al. Stress and Workload Assessment in Aviation—A Narrative Review. Sensors 2023, 23, 3556
by Giulia Masi, Gianluca Amprimo, Claudia Ferraris and Lorenzo Priano
Sensors 2024, 24(2), 690; https://doi.org/10.3390/s24020690 - 22 Jan 2024
Viewed by 1432
Abstract
The published publication [...] Full article
(This article belongs to the Special Issue Physiological Signal Sensing for Mental Health Monitoring and Management)
19 pages, 13040 KB  
Article
A Framework for Determining the Optimal Vibratory Frequency of Graded Gravel Fillers Using Hammering Modal Approach and ANN
by Xianpu Xiao, Taifeng Li, Feng Lin, Xinzhi Li, Zherui Hao and Jiashen Li
Sensors 2024, 24(2), 689; https://doi.org/10.3390/s24020689 - 22 Jan 2024
Cited by 4 | Viewed by 1683
Abstract
To address the uncertainty of optimal vibratory frequency fov of high-speed railway graded gravel (HRGG) and achieve high-precision prediction of the fov, the following research was conducted. Firstly, commencing with vibratory compaction experiments and the hammering modal analysis [...] Read more.
To address the uncertainty of optimal vibratory frequency fov of high-speed railway graded gravel (HRGG) and achieve high-precision prediction of the fov, the following research was conducted. Firstly, commencing with vibratory compaction experiments and the hammering modal analysis method, the resonance frequency f0 of HRGG fillers, varying in compactness K, was initially determined. The correlation between f0 and fov was revealed through vibratory compaction experiments conducted at different vibratory frequencies. This correlation was established based on the compaction physical–mechanical properties of HRGG fillers, encompassing maximum dry density ρdmax, stiffness Krd, and bearing capacity coefficient K20. Secondly, the gray relational analysis algorithm was used to determine the key feature influencing the fov based on the quantified relationship between the filler feature and fov. Finally, the key features influencing the fov were used as input parameters to establish the artificial neural network prediction model (ANN-PM) for fov. The predictive performance of ANN-PM was evaluated from the ablation study, prediction accuracy, and prediction error. The results showed that the ρdmax, Krd, and K20 all obtained optimal states when fov was set as f0 for different gradation HRGG fillers. Furthermore, it was found that the key features influencing the fov were determined to be the maximum particle diameter dmax, gradation parameters b and m, flat and elongated particles in coarse aggregate Qe, and the Los Angeles abrasion of coarse aggregate LAA. Among them, the influence of dmax on the ANN-PM predictive performance was the most significant. On the training and testing sets, the goodness-of-fit R2 of ANN-PM all exceeded 0.95, and the prediction errors were small, which indicated that the accuracy of ANN-PM predictions was relatively high. In addition, it was clear that the ANN-PM exhibited excellent robust performance. The research results provide a novel method for determining the fov of subgrade fillers and provide theoretical guidance for the intelligent construction of high-speed railway subgrades. Full article
(This article belongs to the Topic AI-Enabled Sustainable Computing for Digital Infrastructures: Challenges and Innovations)
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14 pages, 1962 KB  
Article
Distributed Sequential Detection for Cooperative Spectrum Sensing in Cognitive Internet of Things
by Jun Wu, Zhaoyang Qiu, Mingyuan Dai, Jianrong Bao, Xiaorong Xu and Weiwei Cao
Sensors 2024, 24(2), 688; https://doi.org/10.3390/s24020688 - 22 Jan 2024
Cited by 7 | Viewed by 1817
Abstract
The rapid development of wireless communication technology has led to an increasing number of internet of thing (IoT) devices, and the demand for spectrum for these devices and their related applications is also increasing. However, spectrum scarcity has become an increasingly serious problem. [...] Read more.
The rapid development of wireless communication technology has led to an increasing number of internet of thing (IoT) devices, and the demand for spectrum for these devices and their related applications is also increasing. However, spectrum scarcity has become an increasingly serious problem. Therefore, we introduce a collaborative spectrum sensing (CSS) framework in this paper to identify available spectrum resources so that IoT devices can access them and, meanwhile, avoid causing harmful interference to the normal communication of the primary user (PU). However, in the process of sensing the PUs signal in IoT devices, the issue of sensing time and decision cost (the cost of determining whether the signal state of the PU is correct or incorrect) arises. To this end, we propose a distributed cognitive IoT model, which includes two IoT devices independently using sequential decision rules to detect the PU. On this basis, we define the sensing time and cost functions for IoT devices and formulate an average cost optimization problem in CSS. To solve this problem, we further regard the optimal sensing time problem as a finite horizon problem and solve the threshold of the optimal decision rule by person-by-person optimization (PBPO) methodology and dynamic programming. At last, numerical simulation results demonstrate the correctness of our proposal in terms of the global false alarm and miss detection probability, and it always achieves minimal average cost under various costs of each observation taken and thresholds. Full article
(This article belongs to the Special Issue Cognitive Radio for Wireless Sensor Networks)
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17 pages, 8343 KB  
Article
An Efficient Attentional Image Dehazing Deep Network Using Two Color Space (ADMC2-net)
by Samia Haouassi and Di Wu
Sensors 2024, 24(2), 687; https://doi.org/10.3390/s24020687 - 22 Jan 2024
Cited by 8 | Viewed by 3060
Abstract
Image dehazing has become a crucial prerequisite for most outdoor computer applications. The majority of existing dehazing models can achieve the haze removal problem. However, they fail to preserve colors and fine details. Addressing this problem, we introduce a novel high-performing attention-based dehazing [...] Read more.
Image dehazing has become a crucial prerequisite for most outdoor computer applications. The majority of existing dehazing models can achieve the haze removal problem. However, they fail to preserve colors and fine details. Addressing this problem, we introduce a novel high-performing attention-based dehazing model (ADMC2-net)that successfully incorporates both RGB and HSV color spaces to maintain color properties. This model consists of two parallel densely connected sub-models (RGB and HSV) followed by a new efficient attention module. This attention module comprises pixel-attention and channel-attention mechanisms to get more haze-relevant features. Experimental results analyses can validate that our proposed model (ADMC2-net) can achieve superior results on synthetic and real-world datasets and outperform most of state-of-the-art methods. Full article
(This article belongs to the Section Sensing and Imaging)
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15 pages, 2566 KB  
Article
A Low-Cost Inertial Measurement Unit Motion Capture System for Operation Posture Collection and Recognition
by Mingyue Yin, Jianguang Li and Tiancong Wang
Sensors 2024, 24(2), 686; https://doi.org/10.3390/s24020686 - 21 Jan 2024
Cited by 7 | Viewed by 4524
Abstract
In factories, human posture recognition facilitates human–machine collaboration, human risk management, and workflow improvement. Compared to optical sensors, inertial sensors have the advantages of portability and resistance to obstruction, making them suitable for factories. However, existing product-level inertial sensing solutions are generally expensive. [...] Read more.
In factories, human posture recognition facilitates human–machine collaboration, human risk management, and workflow improvement. Compared to optical sensors, inertial sensors have the advantages of portability and resistance to obstruction, making them suitable for factories. However, existing product-level inertial sensing solutions are generally expensive. This paper proposes a low-cost human motion capture system based on BMI 160, a type of six-axis inertial measurement unit (IMU). Based on WIFI communication, the collected data are processed to obtain the displacement of human joints’ rotation angles around XYZ directions and the displacement in XYZ directions, then the human skeleton hierarchical relationship was combined to calculate the real-time human posture. Furthermore, the digital human model was been established on Unity3D to synchronously visualize and present human movements. We simulated assembly operations in a virtual reality environment for human posture data collection and posture recognition experiments. Six inertial sensors were placed on the chest, waist, knee joints, and ankle joints of both legs. There were 16,067 labeled samples obtained for posture recognition model training, and the accumulated displacement and the rotation angle of six joints in the three directions were used as input features. The bi-directional long short-term memory (BiLSTM) model was used to identify seven common operation postures: standing, slightly bending, deep bending, half-squatting, squatting, sitting, and supine, with an average accuracy of 98.24%. According to the experiment result, the proposed method could be used to develop a low-cost and effective solution to human posture recognition for factory operation. Full article
(This article belongs to the Special Issue Advanced Sensors for Real-Time Monitoring Applications ‖)
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