Sensors

23 pages, 1793 KiB

Open AccessArticle

Prototype of a New Head Grabber for Robotic Strawberry Harvesting with a Vision System

by Zygmunt Sobol, Sławomir Kurpaska, Piotr Nawara, Norbert Pedryc, Grzegorz Basista, Janusz Tabor, Tomasz Hebda and Marcin Tomasik

Sensors 2024, 24(20), 6628; https://doi.org/10.3390/s24206628 (registering DOI) - 14 Oct 2024

Abstract

This paper presents the design of a strawberry fruit head gripper unit, together with the concept of a control system for the operation of its mechanisms and vision system. The developed design consists of three specialised mechanisms: positioning, grasping, and cutting off of [...] Read more.

This paper presents the design of a strawberry fruit head gripper unit, together with the concept of a control system for the operation of its mechanisms and vision system. The developed design consists of three specialised mechanisms: positioning, grasping, and cutting off of the fruit. A Finite Element Method (FEM) model was developed for the described design. Next, calculations were carried out, based on which the construction materials were selected. The key performance parameters of the functional model, built on the basis of the developed design concept, were verified under laboratory conditions. In tests carried out on the possible hematoma caused by exceeding the breaking stress induced by the pressure of the encompassing jaws on the fruit, it was found that none of the fruit tested suffered mechanical damage as a result of the sensor triggering force, and the average length of the trimmed stalk was approximately 14 mm. The designed head gripper, together with the proposed automation system, will contribute to improving harvesting precision, and this will favour a reduction in the quantitative and qualitative losses of the harvested crop. The experimental tests conducted under harvesting conditions showed a high efficiency of 95% in identifying ripe fruit, and the harvesting efficiency of the robotic arm was 90%. Full article

(This article belongs to the Section Sensors and Robotics)

27 pages, 5307 KiB

Open AccessArticle

A Case Study on the Integration of Powerline Communications and Visible Light Communications from a Power Electronics Perspective

by Felipe Loose, Juan Ramón Garcia-Meré, Adrion Andrei Rosanelli, Carlos Henrique Barriquello, José Antonio Fernandez Alvárez, Juan Rodríguez and Diego González Lamar

Sensors 2024, 24(20), 6627; https://doi.org/10.3390/s24206627 (registering DOI) - 14 Oct 2024

Abstract

This paper presents a dual-purpose LED driver system that functions as both a lighting source and a Visible Light Communication (VLC) transmitter integrated with a Powerline Communication (PLC) network under the PRIME G3 standard. The system decodes PLC messages from the powerline grid [...] Read more.

This paper presents a dual-purpose LED driver system that functions as both a lighting source and a Visible Light Communication (VLC) transmitter integrated with a Powerline Communication (PLC) network under the PRIME G3 standard. The system decodes PLC messages from the powerline grid and transmits the information via LED light to an optical receiver under a binary phase shift keying (BPSK) modulation. The load design targets a light flux of 800 lumens, suitable for LED light bulb applications up to 10 watts, ensuring practicality and energy efficiency. The Universal Asynchronous Receiver-Transmitter (UART) module enables communication between the PLC and VLC systems, allowing for an LED driver with dynamic control and real-time operation. Key signal processing stages are commented and developed, including a hybrid buck converter with modulation capabilities and a nonlinear optical receiver to regenerate the BPSK reference signal for VLC. Results show a successful prototype working under a laboratory environment. Experimental validation shows successful transmission of bit streams from the PLC grid to the VLC setup. A design guideline is presented in order to dictate the design of the electronic devices involved in the experiment. Finally, this research highlights the feasibility of integrating PLC and VLC technologies, offering an efficient and cost-effective solution for data transmission over existing infrastructure. Full article

(This article belongs to the Special Issue Challenges and Future Trends in Optical Communications)

19 pages, 7109 KiB

Open AccessArticle

Design and Implementation of Digital Calibration Certificate for RFID Tag Storage

by Kan Kan, Shuaizhe Wang, Zilong Liu and Xingchuang Xiong

Sensors 2024, 24(20), 6626; https://doi.org/10.3390/s24206626 (registering DOI) - 14 Oct 2024

Abstract

The digital transformation of metrology is one of the most active activities in the field of metrology at present, where the digital calibration certificate (DCC) being developed is the topic receiving the most concern in the current digital transformation. In practical industrial applications, [...] Read more.

The digital transformation of metrology is one of the most active activities in the field of metrology at present, where the digital calibration certificate (DCC) being developed is the topic receiving the most concern in the current digital transformation. In practical industrial applications, the issue of storage carrier for the DCC plays a crucial role in its promotion and implementation. To address this issue, a DCC meta-model called DCC-Lite schema has been designed along with a corresponding processing method. This solution involves compressing and segmenting the DCC to make it suitable for storage using RFID tags. These RFID tags are affixed to the instruments and accompany them throughout their usage. Additionally, the DCC-RFID processing system has been developed to validate the effectiveness of the DCC meta-model and its corresponding processing method within a wireless temperature acquisition system. Experimental results demonstrate that the system successfully reads and writes the DCC stored within the RFID tag group. Furthermore, it enables automated parsing of the DCC calibration data by the machine and real-time compensation of measurement data to reduce measurement errors. Full article

(This article belongs to the Section Internet of Things)

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13 pages, 4083 KiB

Open AccessArticle

Tensor Based Semi-Blind Channel Estimation for Reconfigurable Intelligent Surface-Aided Multiple-Input Multiple-Output Communication Systems

by Ni Li, Honggui Deng, Fuxin Xu, Yitao Zheng, Mingkang Qu, Wanqing Fu and Nanqing Zhou

Sensors 2024, 24(20), 6625; https://doi.org/10.3390/s24206625 (registering DOI) - 14 Oct 2024

Abstract

Reconfigurable intelligent surfaces (RISs) are a promising technology for sixth-generation (6G) wireless networks. However, a fully passive RIS cannot independently process signals. Wireless systems equipped with it often encounter the challenge of large channel matrix dimensions when acquiring channel state information using pilot-assisted [...] Read more.

Reconfigurable intelligent surfaces (RISs) are a promising technology for sixth-generation (6G) wireless networks. However, a fully passive RIS cannot independently process signals. Wireless systems equipped with it often encounter the challenge of large channel matrix dimensions when acquiring channel state information using pilot-assisted algorithms, resulting in high pilot overhead. To address this issue, this article proposes a semi-blind joint channel and symbol estimation receiver without a pilot training stage for RIS-aided multiple-input multiple-output (MIMO) (including massive MIMO) communication systems. In a semi-blind system, a transmission symbol matrix and two channel matrices are coupled within the received signals at the base station (BS). We decouple them by building two parallel factor (PARAFAC) tensor models. Leveraging PARAFAC tensor decomposition, we transform the joint channel and symbol estimation problem into least square (LS) problems, which can be solved by Alternating Least Squares (ALSs). Our proposed scheme allows duplex communication. Compared to recently proposed pilot-based methods and semi-blind receivers, our results demonstrate the superior performance of our proposed algorithm in estimation accuracy and speed. Full article

(This article belongs to the Topic Advanced Array Signal Processing for B5G/6G: Models, Algorithms, and Applications)

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18 pages, 913 KiB

Open AccessArticle

Validation of Automated Countermovement Vertical Jump Analysis: Markerless Pose Estimation vs. 3D Marker-Based Motion Capture System

by Jelena Aleksic, Dmitry Kanevsky, David Mesaroš, Olivera M. Knezevic, Dimitrije Cabarkapa, Branislav Bozovic and Dragan M. Mirkov

Sensors 2024, 24(20), 6624; https://doi.org/10.3390/s24206624 (registering DOI) - 14 Oct 2024

Abstract

This study aimed to validate the automated temporal analysis of countermovement vertical jump (CMJ) using MMPose, a markerless pose estimation framework, by comparing it with the gold-standard 3D marker-based motion capture system. Twelve participants performed five CMJ trials, which were simultaneously recorded using [...] Read more.

This study aimed to validate the automated temporal analysis of countermovement vertical jump (CMJ) using MMPose, a markerless pose estimation framework, by comparing it with the gold-standard 3D marker-based motion capture system. Twelve participants performed five CMJ trials, which were simultaneously recorded using the marker-based system and two smartphone cameras capturing both sides of the body. Key kinematic points, including center of mass (CoM) and toe trajectories, were analyzed to determine jump phases and temporal variables. The agreement between methods was assessed using Bland–Altman analysis, root mean square error (RMSE), and Pearson’s correlation coefficient (r), while consistency was evaluated via intraclass correlation coefficient (ICC 3,1) and two-way repeated-measures ANOVA. Cohen’s effect size (d) quantified the practical significance of differences. Results showed strong agreement (r > 0.98) with minimal bias and narrow limits of agreement for most variables. The markerless system slightly overestimated jump height and CoM vertical velocity, but ICC values (ICC > 0.91) confirmed strong reliability. Cohen’s d values were near zero, indicating trivial differences, and no variability due to recording side was observed. Overall, MMPose proved to be a reliable alternative for in-field CMJ analysis, supporting its broader application in sports and rehabilitation settings. Full article

(This article belongs to the Special Issue Sensor Techniques and Methods for Sports Science)

20 pages, 11032 KiB

Open AccessArticle

A Novel Defect Quantification Method Utilizing Multi-Sensor Magnetic Flux Leakage Signal Fusion

by Wenlong Liu, Lemei Ren and Guansan Tian

Sensors 2024, 24(20), 6623; https://doi.org/10.3390/s24206623 (registering DOI) - 14 Oct 2024

Abstract

In the assessment of pipeline integrity using magnetic flux leakage (MFL) detection, it is crucial to quantify defects accurately and efficiently using MFL signals. However, in complex detection environments, traditional defect inversion methods exhibit low quantification accuracy and efficiency due to the complexity [...] Read more.

In the assessment of pipeline integrity using magnetic flux leakage (MFL) detection, it is crucial to quantify defects accurately and efficiently using MFL signals. However, in complex detection environments, traditional defect inversion methods exhibit low quantification accuracy and efficiency due to the complexity of their algorithms or excessive reliance on a priori knowledge and expert experience. To address these issues, this study presents a novel defect quantification method based on multi-sensor signal fusion (MSSF). The method employs a multi-sensor probe to fuse the MFL signals under multiple lift-off values, enhancing the diversity of defect information. This enables defect-opening profile recognition using the characteristic approximation approach (CAA). Subsequently, the MSSF method is based on a 3D magnetic dipole model and integrates the structural features of multi-sensor probes to develop an algorithm. This algorithm iteratively determines the defect depth at multiple data acquisition points within the defect region to obtain the maximum defect depth. The feasibility of the MSSF quantification method is validated through finite element simulation and physical experiments. The results demonstrate that the proposed method achieves accurate defect quantification while enhancing efficiency, with the number of iterations for each defect depth calculation point consistently requiring fewer than 15 iterations. For rectangular metal loss, perforation, and conical defects, quantification errors are less than 10%, meeting practical inspection requirements. Full article

(This article belongs to the Topic Advances in Non-Destructive Testing Methods, 2nd Volume)

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20 pages, 2358 KiB

Open AccessArticle

The Square-Root Unscented and the Square-Root Cubature Kalman Filters on Manifolds

by Joachim Clemens and Constantin Wellhausen

Sensors 2024, 24(20), 6622; https://doi.org/10.3390/s24206622 (registering DOI) - 14 Oct 2024

Abstract

Estimating the state of a system by fusing sensor data is a major prerequisite in many applications. When the state is time-variant, derivatives of the Kalman filter are a popular choice for solving that task. Two variants are the square-root unscented Kalman filter [...] Read more.

Estimating the state of a system by fusing sensor data is a major prerequisite in many applications. When the state is time-variant, derivatives of the Kalman filter are a popular choice for solving that task. Two variants are the square-root unscented Kalman filter (SRUKF) and the square-root cubature Kalman filter (SCKF). In contrast to the unscented Kalman filter (UKF) and the cubature Kalman filter (CKF), they do not operate on the covariance matrix but on its square root. In this work, we modify the SRUKF and the SCKF for use on manifolds. This is particularly relevant for many state estimation problems when, for example, an orientation is part of a state or a measurement. In contrast to other approaches, our solution is both generic and mathematically coherent. It has the same theoretical complexity as the UKF and CKF on manifolds, but we show that the practical implementation can be faster. Furthermore, it gains the improved numerical properties of the classical SRUKF and SCKF. We compare the SRUKF and the SCKF on manifolds to the UKF and the CKF on manifolds, using the example of odometry estimation for an autonomous car. It is demonstrated that all algorithms have the same localization performance, but our SRUKF and SCKF have lower computational demands. Full article

(This article belongs to the Section Vehicular Sensing)

16 pages, 7320 KiB

Open AccessArticle

Use of Low-Cost Sensors to Study Atmospheric Particulate Matter Concentrations: Limitations and Benefits Discussed through the Analysis of Three Case Studies in Palermo, Sicily

by Filippo Brugnone, Luciana Randazzo and Sergio Calabrese

Sensors 2024, 24(20), 6621; https://doi.org/10.3390/s24206621 (registering DOI) - 14 Oct 2024

Abstract

The paper discusses the results of the concentrations of atmospheric particulate matter, in the PM_2.5 and PM₁₀ fractions, acquired by two low-cost sensors. The research was carried out from 1 July 2023 to 30 June 2024, in Palermo, Sicily. The results [...] Read more.

The paper discusses the results of the concentrations of atmospheric particulate matter, in the PM_2.5 and PM₁₀ fractions, acquired by two low-cost sensors. The research was carried out from 1 July 2023 to 30 June 2024, in Palermo, Sicily. The results obtained from two systems equipped with the same sensor model were compared. Excellent linear correlation was observed between the results, with differences in measurements falling within instrumental accuracy. Two instruments equipped with different sensors, models Novasense SDS011 and Plantower PMSA003, were placed at the same site. These were complemented by a weather station to measure meteorological parameters. Upon comparing the atmospheric particulate matter concentrations measured by the two instruments, it was observed that there was a good linear correlation for PM_2.5 and a poor linear correlation for PM₁₀. Additionally, the PMSA003 sensor appeared to consistently record higher concentrations than the SDS011 sensor. During periods influenced by natural sources and/or anthropogenic activities at the regional and/or local scale, i.e., the dispersal of Saharan sands, forest fires, and local events using fireworks, abnormal concentrations of atmospheric particulate matter were detected. Despite the inherent limitations in precision and accuracy, both low-cost instruments were able to identify periods with abnormal concentrations of atmospheric particulate matter, regardless of their source or type. Full article

(This article belongs to the Special Issue Feature Papers in Remote Sensors 2024)

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20 pages, 1215 KiB

Open AccessReview

Review of Photodetectors for Space Lidars

by Xiaoli Sun

Sensors 2024, 24(20), 6620; https://doi.org/10.3390/s24206620 (registering DOI) - 14 Oct 2024

Abstract

Photodetectors play a critical role in space lidars designed for scientific investigations from orbit around planetary bodies. The detectors must be highly sensitive due to the long range of measurements and tight constraints on the size, weight, and power of the instrument. The [...] Read more.

Photodetectors play a critical role in space lidars designed for scientific investigations from orbit around planetary bodies. The detectors must be highly sensitive due to the long range of measurements and tight constraints on the size, weight, and power of the instrument. The detectors must also be space radiation tolerant over multi-year mission lifetimes with no significant performance degradation. Early space lidars used diode-pumped Nd:YAG lasers with a single beam for range and atmospheric backscattering measurements at 1064 nm or its frequency harmonics. The photodetectors used were single-element photomultiplier tubes and infrared performance-enhanced silicon avalanche photodiodes. Space lidars have advanced to multiple beams for surface topographic mapping and active infrared spectroscopic measurements of atmospheric species and surface composition, which demand increased performance and new capabilities for lidar detectors. Higher sensitivity detectors are required so that multi-beam and multi-wavelength measurements can be performed without increasing the laser and instrument power. Pixelated photodetectors are needed so that a single detector assembly can be used for simultaneous multi-channel measurements. Photon-counting photodetectors are needed for active spectroscopy measurements from short-wave infrared to mid-wave infrared. HgCdTe avalanche photodiode arrays have emerged recently as a promising technology to fill these needs. This paper gives a review of the photodetectors used in past and present lidars and the development and outlook of HgCdTe APD arrays for future space lidars. Full article

(This article belongs to the Section Remote Sensors)

12 pages, 601 KiB

Open AccessArticle

Enhanced Infant Movement Analysis Using Transformer-Based Fusion of Diverse Video Features For Neurodevelopmental Monitoring

by Alexander Turner and Don Sharkey

Sensors 2024, 24(20), 6619; https://doi.org/10.3390/s24206619 (registering DOI) - 14 Oct 2024

Abstract

Neurodevelopment is a highly intricate process, and early detection of abnormalities is critical for optimizing outcomes through timely intervention. Accurate and cost-effective diagnostic methods for neurological disorders, particularly in infants, remain a significant challenge due to the heterogeneity of data and the variability [...] Read more.

Neurodevelopment is a highly intricate process, and early detection of abnormalities is critical for optimizing outcomes through timely intervention. Accurate and cost-effective diagnostic methods for neurological disorders, particularly in infants, remain a significant challenge due to the heterogeneity of data and the variability in neurodevelopmental conditions. This study recruited twelve parent–infant pairs, with infants aged 3 to 12 months. Approximately 25 minutes of 2D video footage was captured, documenting natural play interactions between the infants and toys. We developed a novel, open-source method to classify and analyse infant movement patterns using deep learning techniques, specifically employing a transformer-based fusion model that integrates multiple video features within a unified deep neural network. This approach significantly outperforms traditional methods reliant on individual video features, achieving an accuracy of over 90%. Furthermore, a sensitivity analysis revealed that the pose estimation contributed far less to the model’s output than the pre-trained transformer and convolutional neural network (CNN) components, providing key insights into the relative importance of different feature sets. By providing a more robust, accurate and low-cost analysis of movement patterns, our work aims to enhance the early detection and potential prediction of neurodevelopmental delays, whilst providing insight into the functioning of the transformer-based fusion models of diverse video features. Full article

(This article belongs to the Section Sensing and Imaging)

15 pages, 3766 KiB

Open AccessArticle

Smart Vision Transparency: Efficient Ocular Disease Prediction Model Using Explainable Artificial Intelligence

by Sagheer Abbas, Adnan Qaisar, Muhammad Sajid Farooq, Muhammad Saleem, Munir Ahmad and Muhammad Adnan Khan

Sensors 2024, 24(20), 6618; https://doi.org/10.3390/s24206618 (registering DOI) - 14 Oct 2024

Abstract

The early prediction of ocular disease is certainly an obligatory concern in the domain of ophthalmic medicine. Although modern scientific discoveries have shown the potential to treat eye diseases by using artificial intelligence (AI) and machine learning, explainable AI remains a crucial challenge [...] Read more.

The early prediction of ocular disease is certainly an obligatory concern in the domain of ophthalmic medicine. Although modern scientific discoveries have shown the potential to treat eye diseases by using artificial intelligence (AI) and machine learning, explainable AI remains a crucial challenge confronting this area of research. Although some traditional methods put in significant effort, they cannot accurately predict the proper ocular diseases. However, incorporating AI into diagnosing eye diseases in healthcare complicates the situation as the decision-making process of AI demonstrates complexity, which is a significant concern, especially in major sectors like ocular disease prediction. The lack of transparency in the AI models may hinder the confidence and trust of the doctors and the patients, as well as their perception of the AI and its abilities. Accordingly, explainable AI is significant in ensuring trust in the technology, enhancing clinical decision-making ability, and deploying ocular disease detection. This research proposed an efficient transfer learning model for eye disease prediction to transform smart vision potential in the healthcare sector and meet conventional approaches’ challenges while integrating explainable artificial intelligence (XAI). The integration of XAI in the proposed model ensures the transparency of the decision-making process through the comprehensive provision of rationale. This proposed model provides promising results with 95.74% accuracy and explains the transformative potential of XAI in advancing ocular healthcare. This significant milestone underscores the effectiveness of the proposed model in accurately determining various types of ocular disease. It is clearly shown that the proposed model is performing better than the previously published methods. Full article

(This article belongs to the Special Issue Application of Transfer Learning and Ensembling Techniques for Cyber Security, Medicine, and Education Using Sensing Data)

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44 pages, 14459 KiB

Open AccessReview

A Review: Laser Interference Lithography for Diffraction Gratings and Their Applications in Encoders and Spectrometers

by Linbin Luo, Shuonan Shan and Xinghui Li

Sensors 2024, 24(20), 6617; https://doi.org/10.3390/s24206617 (registering DOI) - 14 Oct 2024

Abstract

The unique diffractive properties of gratings have made them essential in a wide range of applications, including spectral analysis, precision measurement, optical data storage, laser technology, and biomedical imaging. With advancements in micro- and nanotechnologies, the demand for more precise and efficient grating [...] Read more.

The unique diffractive properties of gratings have made them essential in a wide range of applications, including spectral analysis, precision measurement, optical data storage, laser technology, and biomedical imaging. With advancements in micro- and nanotechnologies, the demand for more precise and efficient grating fabrication has increased. This review discusses the latest advancements in grating manufacturing techniques, particularly highlighting laser interference lithography, which excels in sub-beam generation through wavefront and amplitude division. Techniques such as Lloyd’s mirror configurations produce stable interference fringe fields for grating patterning in a single exposure. Orthogonal and non-orthogonal, two-axis Lloyd’s mirror interferometers have advanced the fabrication of two-dimensional gratings and large-area gratings, respectively, while laser interference combined with concave lenses enables the creation of concave gratings. Grating interferometry, utilizing optical interference principles, allows for highly precise measurements of minute displacements at the nanometer to sub-nanometer scale. This review also examines the application of grating interferometry in high-precision, absolute, and multi-degree-of-freedom measurement systems. Progress in grating fabrication has significantly advanced spectrometer technology, with integrated structures such as concave gratings, Fresnel gratings, and grating–microlens arrays driving the miniaturization of spectrometers and expanding their use in compact analytical instruments. Full article

(This article belongs to the Special Issue Feature Papers in Optical Sensors 2024)

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26 pages, 2083 KiB

Open AccessReview

Recent Advances in Nanozyme-Based Sensing Technology for Antioxidant Detection

by Xin Cao, Tianyu Liu, Xianping Wang, Yueting Yu, Yangguang Li and Lu Zhang

Sensors 2024, 24(20), 6616; https://doi.org/10.3390/s24206616 (registering DOI) - 14 Oct 2024

Abstract

Antioxidants are substances that have the ability to resist or delay oxidative damage. Antioxidants can be used not only for the diagnosis and prevention of vascular diseases, but also for food preservation and industrial production. However, due to the excessive use of antioxidants, [...] Read more.

Antioxidants are substances that have the ability to resist or delay oxidative damage. Antioxidants can be used not only for the diagnosis and prevention of vascular diseases, but also for food preservation and industrial production. However, due to the excessive use of antioxidants, it can cause environmental pollution and endanger human health. It can be seen that the development of antioxidant detection technology is important for environment/health maintenance. It is found that traditional detection methods, including high performance liquid chromatography, gas chromatography, etc., have shortcomings such as cumbersome operation and high cost. In contrast, the nanozyme-based detection method features advantages of low cost, simple operation, and rapidity, which has been widely used in the detection of various substances such as glucose and antioxidants. This article focuses on the latest research progress of nanozymes for antioxidant detection. Nanozymes for antioxidant detection are classified according to enzyme-like types. Different types of nanozyme-based sensing strategies and detection devices are summarized. Based on the summary and analysis, one can find that the development of commercial nanozyme-based devices for the practical detection of antioxidants is still challenging. Some emerging technologies (such as artificial intelligence) should be fully utilized to improve the detection sensitivity and accuracy. This article aims to emphasize the application prospects of nanozymes in antioxidant detection and to provide new ideas and inspiration for the development of detection methods. Full article

(This article belongs to the Special Issue Advanced Sensors in Atomic Level)

22 pages, 5579 KiB

Open AccessArticle

Experimental Study on LTE Mobile Network Performance Parameters for Controlled Drone Flights

by Janis Braunfelds, Gints Jakovels, Ints Murans, Anna Litvinenko, Ugis Senkans, Rudolfs Rumba, Andis Onzuls, Guntis Valters, Elina Lidere and Evija Plone

Sensors 2024, 24(20), 6615; https://doi.org/10.3390/s24206615 (registering DOI) - 14 Oct 2024

Abstract

This paper analyzes the quantitative quality parameters of a mobile communication network in a controlled drone logistic use-case scenario. Based on the analysis of standards and recommendations, the values of key performance indicators (KPIs) are set. As the main network-impacting parameters, reference signal [...] Read more.

This paper analyzes the quantitative quality parameters of a mobile communication network in a controlled drone logistic use-case scenario. Based on the analysis of standards and recommendations, the values of key performance indicators (KPIs) are set. As the main network-impacting parameters, reference signal received power (RSRP), reference signal received quality (RSRQ), and signal to interference and noise ratio (SINR) were selected. Uplink (UL), downlink (DL), and ping parameters were chosen as the secondary ones, as they indicate the quality of the link depending on primary parameters. The analysis is based on experimental measurements performed using a Latvian mobile operator’s “LMT” JSC infrastructure in a real-life scenario. To evaluate the altitude impact on the selected network parameters, the measurements were performed using a drone as transport for the following altitude values: 40, 60, 90, and 110 m. Network parameter measurements were implemented in automatic mode, allowing switching between LTE4–LTE2 standards, providing the opportunity for more complex analysis. Based on the analysis made, the recommendations for the future mobile networks employed in controlled drone flights should correspond to the following KPI and their values: −100 dBm for RSRP, −16 dB for RSRQ, −5 dB for SINR, 4096 kbps for downlink, 4096 kbps for uplink, and 50 ms for ping. Lastly, recommendations for a network coverage digital twin (DT) model with integrated KPIs are also provided. Full article

(This article belongs to the Section Navigation and Positioning)

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24 pages, 4763 KiB

Open AccessArticle

Impact of Mask Type as Training Target for Speech Intelligibility and Quality in Cochlear-Implant Noise Reduction

by Fergal Henry, Martin Glavin, Edward Jones and Ashkan Parsi

Sensors 2024, 24(20), 6614; https://doi.org/10.3390/s24206614 (registering DOI) - 14 Oct 2024

Abstract

The selection of a target when training deep neural networks for speech enhancement is an important consideration. Different masks have been shown to exhibit different performance characteristics depending on the application and the conditions. This paper presents a comprehensive comparison of several different [...] Read more.

The selection of a target when training deep neural networks for speech enhancement is an important consideration. Different masks have been shown to exhibit different performance characteristics depending on the application and the conditions. This paper presents a comprehensive comparison of several different masks for noise reduction in cochlear implants. The study incorporated three well-known masks, namely the Ideal Binary Mask (IBM), Ideal Ratio Mask (IRM) and the Fast Fourier Transform Mask (FFTM), as well as two newly proposed masks, based on existing masks, called the Quantized Mask (QM) and the Phase-Sensitive plus Ideal Ratio Mask (PSM+). These five masks are used to train networks to estimate masks for the purpose of separating speech from noisy mixtures. A vocoder was used to simulate the behavior of a cochlear implant. Short-time Objective Intelligibility (STOI) and Perceptual Evaluation of Speech Quality (PESQ) scores indicate that the two new masks proposed in this study (QM and PSM+) perform best for normal speech intelligibility and quality in the presence of stationary and non-stationary noise over a range of signal-to-noise ratios (SNRs). The Normalized Covariance Measure (NCM) and similarity scores indicate that they also perform best for speech intelligibility/gauging the similarity of vocoded speech. The Quantized Mask performs better than the Ideal Binary Mask due to its better resolution as it approximates the Wiener Gain Function. The PSM+ performs better than the three existing benchmark masks (IBM, IRM, and FFTM) as it incorporates both magnitude and phase information. Full article

(This article belongs to the Section Intelligent Sensors)

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18 pages, 4703 KiB

Open AccessArticle

A Brain Network Analysis Model for Motion Sickness in Electric Vehicles Based on EEG and fNIRS Signal Fusion

by Bin Ren, Pengyu Ren, Wenfa Luo and Jingze Xin

Sensors 2024, 24(20), 6613; https://doi.org/10.3390/s24206613 (registering DOI) - 14 Oct 2024

Abstract

Motion sickness is a common issue in electric vehicles, significantly impacting passenger comfort. This study aims to develop a functional brain network analysis model by integrating electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals to evaluate motion sickness symptoms. During real-world testing with [...] Read more.

Motion sickness is a common issue in electric vehicles, significantly impacting passenger comfort. This study aims to develop a functional brain network analysis model by integrating electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals to evaluate motion sickness symptoms. During real-world testing with the Feifan F7 series of new energy-electric vehicles from SAIC Motor Corp, data were collected from 32 participants. The EEG signals were divided into four frequency bands: delta-range, theta-range, alpha-range, and beta-range, and brain oxygenation variation was calculated from the fNIRS signals. Functional connectivity between brain regions was measured to construct functional brain network models for motion sickness analysis. A motion sickness detection model was developed using a graph convolutional network (GCN) to integrate EEG and fNIRS data. Our results show significant differences in brain functional connectivity between participants in motion and non-motion sickness states. The model that combined fNIRS data with high-frequency EEG signals achieved the best performance, improving the F1 score by 11.4% compared to using EEG data alone and by 8.2% compared to using fNIRS data alone. These results highlight the effectiveness of integrating EEG and fNIRS signals using GCN for motion sickness detection. They demonstrate the model’s superiority over single-modality approaches, showcasing its potential for real-world applications in electric vehicles. Full article

(This article belongs to the Section Biosensors)

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15 pages, 5533 KiB

Open AccessArticle

A Crowded Object Counting System with Self-Attention Mechanism

by Cheng-Chang Lien and Pei-Chen Wu

Sensors 2024, 24(20), 6612; https://doi.org/10.3390/s24206612 (registering DOI) - 14 Oct 2024

Abstract

Traditional object counting systems use object detection methods to count objects. However, when objects are small, crowded, and dense, object detection may fail, leading to inaccuracies in counting. To address this issue, we propose a crowded object counting system based on density map [...] Read more.

Traditional object counting systems use object detection methods to count objects. However, when objects are small, crowded, and dense, object detection may fail, leading to inaccuracies in counting. To address this issue, we propose a crowded object counting system based on density map estimation. While most density map estimation models employ encoder–decoder or multi-branch approaches to generate feature maps at different scales for obtaining an accurate density map, improving the accuracy of crowded object counting remains a challenge. In this paper, we propose a novel model that can generate more accurate density maps, utilizing the context-aware network as the primary structure and integrating the self-attention mechanism. There are three main contributions in this paper. Firstly, the self-attention mechanism is employed to improve the accuracy of density map estimation. Secondly, the missing vehicle labels in the TRANCOS database are relabeled, ensuring that the ground truth data are more complete than the original TRANCOS database, thus enabling the proposed novel model to have higher crowded object counting accuracy. Thirdly, the parameters of the self-attention mechanism are analyzed to obtain the optimum parameter combination. The experimental results demonstrate that the accuracy of crowded object counting can reach 85.9%, 90.0%, 83.4%, and 92.6% for the TRANCOS, relabeled TRANCOS, ShanghaiTech Part A, and Part B datasets, respectively. Furthermore, the ablation study for the context-aware network with self-attention mechanism analyzes the optimum parameter combination. Full article

(This article belongs to the Topic Innovation, Communication and Engineering)

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24 pages, 2431 KiB

Open AccessArticle

Identifying Tampered Radio-Frequency Transmissions in LoRa Networks Using Machine Learning

by Nurettin Selcuk Senol, Amar Rasheed, Mohamed Baza and Maazen Alsabaan

Sensors 2024, 24(20), 6611; https://doi.org/10.3390/s24206611 (registering DOI) - 14 Oct 2024

Abstract

Long-range networks, renowned for their long-range, low-power communication capabilities, form the backbone of many Internet of Things systems, enabling efficient and reliable data transmission. However, detecting tampered frequency signals poses a considerable challenge due to the vulnerability of LoRa devices to radio-frequency interference [...] Read more.

Long-range networks, renowned for their long-range, low-power communication capabilities, form the backbone of many Internet of Things systems, enabling efficient and reliable data transmission. However, detecting tampered frequency signals poses a considerable challenge due to the vulnerability of LoRa devices to radio-frequency interference and signal manipulation, which can undermine both data integrity and security. This paper presents an innovative method for identifying tampered radio frequency transmissions by employing five sophisticated anomaly detection algorithms—Local Outlier Factor, Isolation Forest, Variational Autoencoder, traditional Autoencoder, and Principal Component Analysis within the framework of a LoRa-based Internet of Things network structure. The novelty of this work lies in applying image-based tampered frequency techniques with these algorithms, offering a new perspective on securing LoRa transmissions. We generated a dataset of over 26,000 images derived from real-world experiments with both normal and manipulated frequency signals by splitting video recordings of LoRa transmissions into frames to thoroughly assess the performance of each algorithm. Our results demonstrate that Local Outlier Factor achieved the highest accuracy of 97.78%, followed by Variational Autoencoder, traditional Autoencoder and Principal Component Analysis at 97.27%, and Isolation Forest at 84.49%. These findings highlight the effectiveness of these methods in detecting tampered frequencies, underscoring their potential for enhancing the reliability and security of LoRa networks. Full article

(This article belongs to the Special Issue AI-Driven Cybersecurity in IoT-Based Systems)

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11 pages, 4151 KiB

Open AccessArticle

Identifying the Vertical Stratification of Sediment Samples by Visible and Near-Infrared Spectroscopy

by Pingping Fan, Zongchao Jia, Huimin Qiu, Hongru Wang and Yang Gao

Sensors 2024, 24(20), 6610; https://doi.org/10.3390/s24206610 (registering DOI) - 14 Oct 2024

Abstract

Vertical stratification in marine sediment profiles indicates physical and chemical sedimentary processes and, thus, is the first step in sedimentary research and in studying their relationship with global climate change. Traditional technologies for studying vertical stratification have low efficiency; thus, new technologies are [...] Read more.

Vertical stratification in marine sediment profiles indicates physical and chemical sedimentary processes and, thus, is the first step in sedimentary research and in studying their relationship with global climate change. Traditional technologies for studying vertical stratification have low efficiency; thus, new technologies are highly needed. Recently, visible and near-infrared spectroscopy (VNIR) has been explored to rapidly determine sediment parameters, such as clay content, particle size, total carbon (TC), total nitrogen (TN), and so on. Here, we explored vertical stratification in a sediment column in the South China Sea using VNIR. The sediment column was 160 cm and divided into 160 samples by 1 cm intervals. All samples were classified into three layers by depth, that is, 0–50 cm (the upper layer), 50–100 cm (the middle layer), and 100–160 cm (the bottom layer). Concentrations of TC and TN in each sample were measured by Elementa Vario EL III. Visible and near-infrared reflectance spectra of each sample were collected by Agilent Cary 5000. A global model and several classification models for vertical stratification in sediments were established by a Support Vector Machine (SVM) after the characteristic spectra were identified using Competitive Adaptive Reweighted Sampling. In the classification models, K-means clustering and Density Peak Clustering (DPC) were employed as the unsupervised clustering algorithms. The results showed that the stratification was successful by VNIR, especially when using the combination of unsupervised clustering and machine learning algorithms. The correct classification rate (CCR) was much higher in the classification models than in the global model. And the classification models had a higher CCR using K-means combined with SVM (94.8%) and using DPC combined with SVM (96.0%). The higher CCR might be derived from the chemical classification. Indeed, similar results were also found in the chemical stratification. This study provided a theoretical basis for the rapid and synchronous measurement of chemical and physical parameters in sediment profiles by VNIR. Full article

(This article belongs to the Section Optical Sensors)

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