bluuebunny/crossref_metadata_2025 huggingface dataset:
Methodology for Eddy Current Losses Calculation in Linear Variable Differential Transformers (LVDTs)
Authors: Drandić Ana , Frljić Stjepan , Trkulja Bojan
Published: February 2023

Linear variable differential transformer (LVDT) is a commonly used linear displacement sensor because of its good measurement characteristics. When using laminated ferromagnetic cores in LVDTs, it is very important to take eddy currents into the account during design phase of the sensor. Particularity of the open-type core means that the eddy currents induced by the stray magnetic flux that flow in large loops tangential to the lamination surfaces take on significant values. Due to the open-type core a typical LVDT has, depending on the core material, it is, therefore, very important to take eddy currents into the account when designing the sensor. This paper’s goal is to present a methodology for calculating LVDT eddy current losses that can be applied to LVDT design in order to optimize the dimensions and help with selection of materials of the LVDTs, in order to achieve the highest measurement accuracy. Presented approach using an AτA-formulation with elimination of redundant degrees of freedom exhibits rapid convergence. In order to calculate the relationship between eddy current losses and core displacement, frequency, and material characteristics, a number of 3D finite element method (FEM) simulations was performed. Analysis of the obtained results using presented methodology for eddy current losses calculation in LVDTs enables the designer optimize the design of the LVDT.
A Novel Method for Pose and Position Calibration of Laser Displacement Sensors
Authors: Han Liya , Yu Long , Zhu Xusheng
Published: February 2023

Laser displacement sensors are widely used in the aviation industry for the purpose of surface normal measurements. The measurement of a surface normal depends on prior knowledge of the poses and positions of the sensors, which are obtained through calibration. This paper introduces a new parameter to the traditional calibration procedure, to reduce the calibration error, and explores the factors affecting calibration using the Monte Carlo method. In the experiment, the normal measurement error of the probe consisted of four sensors after calibration was less than 0.1∘, which satisfied the established requirements. This paper indicates the boundary conditions for a successful calibration and validates the proposed method, which provides a new method for the pose and position calibration of laser displacement sensors and other similar sensors.
Denoising of BOTDR Dynamic Strain Measurement Using Convolutional Neural Networks
Authors: Li Bo , Jiang Ningjun , Han Xiaole
Published: February 2023

The Brillouin optical time domain reflectometry (BOTDR) system measures the distributed strain and temperature information along the optic fibre by detecting the Brillouin gain spectra (BGS) and finding the Brillouin frequency shift profiles. By introducing small gain stimulated Brillouin scattering (SBS), dynamic measurement using BOTDR can be realized, but the performance is limited due to the noise of the detected information. An image denoising method using the convolutional neural network (CNN) is applied to the derived Brillouin gain spectrum images to enhance the performance of the Brillouin frequency shift detection and the strain vibration measurement of the BOTDR system. By reducing the noise of the BGS images along the length of the fibre under test with different network depths and epoch numbers, smaller frequency uncertainties are obtained, and the sine-fitting R-squared values of the detected strain vibration profiles are also higher. The Brillouin frequency uncertainty is improved by 24% and the sine-fitting R-squared value of the obtained strain vibration profile is enhanced to 0.739, with eight layers of total depth and 200 epochs.
Investigating Visual Perception Impairments through Serious Games and Eye Tracking to Anticipate Handwriting Difficulties
Authors: Piazzalunga Chiara , Dui Linda Greta , Termine Cristiano , Bortolozzo Marisa , Matteucci Matteo , Ferrante Simona
Published: February 2023

Dysgraphia is a learning disability that causes handwritten production below expectations. Its diagnosis is delayed until the completion of handwriting development. To allow a preventive training program, abilities not directly related to handwriting should be evaluated, and one of them is visual perception. To investigate the role of visual perception in handwriting skills, we gamified standard clinical visual perception tests to be played while wearing an eye tracker at three difficulty levels. Then, we identified children at risk of dysgraphia through the means of a handwriting speed test. Five machine learning models were constructed to predict if the child was at risk, using the CatBoost algorithm with Nested Cross-Validation, with combinations of game performance, eye-tracking, and drawing data as predictors. A total of 53 children participated in the study. The machine learning models obtained good results, particularly with game performances as predictors (F1 score: 0.77 train, 0.71 test). SHAP explainer was used to identify the most impactful features. The game reached an excellent usability score (89.4 ± 9.6). These results are promising to suggest a new tool for dysgraphia early screening based on visual perception skills.
An Explainable Spatial-Temporal Graphical Convolutional Network to Score Freezing of Gait in Parkinsonian Patients
Authors: Kwon Hyeokhyen , Clifford Gari D. , Genias Imari , Bernhard Doug , Esper Christine D. , Factor Stewart A. , McKay J. Lucas
Published: February 2023

Freezing of gait (FOG) is a poorly understood heterogeneous gait disorder seen in patients with parkinsonism which contributes to significant morbidity and social isolation. FOG is currently measured with scales that are typically performed by movement disorders specialists (i.e., MDS-UPDRS), or through patient completed questionnaires (N-FOG-Q) both of which are inadequate in addressing the heterogeneous nature of the disorder and are unsuitable for use in clinical trials The purpose of this study was to devise a method to measure FOG objectively, hence improving our ability to identify it and accurately evaluate new therapies. A major innovation of our study is that it is the first study of its kind that uses the largest sample size (>30 h, N = 57) in order to apply explainable, multi-task deep learning models for quantifying FOG over the course of the medication cycle and at varying levels of parkinsonism severity. We trained interpretable deep learning models with multi-task learning to simultaneously score FOG (cross-validated F1 score 97.6%), identify medication state (OFF vs. ON levodopa; cross-validated F1 score 96.8%), and measure total PD severity (MDS-UPDRS-III score prediction error ≤ 2.7 points) using kinematic data of a well-characterized sample of N = 57 patients during levodopa challenge tests. The proposed model was able to explain how kinematic movements are associated with each FOG severity level that were highly consistent with the features, in which movement disorders specialists are trained to identify as characteristics of freezing. Overall, we demonstrate that deep learning models’ capability to capture complex movement patterns in kinematic data can automatically and objectively score FOG with high accuracy. These models have the potential to discover novel kinematic biomarkers for FOG that can be used for hypothesis generation and potentially as clinical trial outcome measures.
Three Shaft Industrial Gas Turbine Transient Performance Analysis
Authors: Salilew Waleligne Molla , Abdul Karim Zainal Ambri , Lemma Tamiru Alemu , Fentaye Amare Desalegn , Kyprianidis Konstantinos G.
Published: February 2023

The power demand from gas turbines in electrical grids is becoming more dynamic due to the rising demand for power generation from renewable energy sources. Therefore, including the transient data in the fault diagnostic process is important when the steady-state data are limited and if some component faults are more observable in the transient condition than in the steady-state condition. This study analyses the transient behaviour of a three-shaft industrial gas turbine engine in clean and degraded conditions with consideration of the secondary air system and variable inlet guide vane effects. Different gas path faults are simulated to demonstrate how magnified the transient measurement deviations are compared with the steady-state measurement deviations. The results show that some of the key measurement deviations are considerably higher in the transient mode than in the steady state. This confirms the importance of considering transient measurements for early fault detection and more accurate diagnostic solutions.
Water Surface Targets Detection Based on the Fusion of Vision and LiDAR
Authors: Wang Lin , Xiao Yufeng , Zhang Baorui , Liu Ran , Zhao Bin
Published: February 2023

The use of vision for the recognition of water targets is easily influenced by reflections and ripples, resulting in misidentification. This paper proposed a detection method based on the fusion of 3D point clouds and visual information to detect and locate water surface targets. The point clouds help to reduce the impact of ripples and reflections, and the recognition accuracy is enhanced by visual information. This method consists of three steps: Firstly, the water surface target is detected using the CornerNet-Lite network, and then the candidate target box and camera detection confidence are determined. Secondly, the 3D point cloud is projected onto the two-dimensional pixel plane, and the confidence of LiDAR detection is calculated based on the ratio between the projected area of the point clouds and the pixel area of the bounding box. The target confidence is calculated with the camera detection and LiDAR detection confidence, and the water surface target is determined by combining the detection thresholds. Finally, the bounding box is used to determine the 3D point clouds of the target and estimate its 3D coordinates. The experiment results showed this method reduced the misidentification rate and had 15.5% higher accuracy compared with traditional CornerNet-Lite network. By combining the depth information from LiDAR, the position of the target relative to the detection coordinate system origin could be accurately estimated.
The Effect of Air Turbulence on Vortex Beams in Nonlinear Propagation
Authors: Zhu Di , Li Chunhua , Sun Xiaodong , Liu Yali , Zhang Yuqi , Gao Hui
Published: February 2023

Vortex beams with orthogonality can be widely used in atmospheric applications. We numerically analyzed the statistical regularities of vortex beams propagating through a lens or an axicon with different series of turbulent air phase screens. The simulative results revealed that the distortion of the transverse intensity was sensitive to the location and the structure constant of the turbulence screen. In addition, the axicon can be regarded as a very useful optical device, since it can not only suppress the turbulence but also maintain a stable beam pattern. We further confirmed that a vortex beam with a large topological charge can suppress the influence of air turbulence. Our outcomes are valuable for many applications in the atmospheric air, especially for optical communication and remote sensing.
Stretchable Strain Sensor with Small but Sufficient Adhesion to Skin
Authors: Nishikawa Takaaki , Yamane Hisaya , Matsuhisa Naoji , Miki Norihisa
Published: February 2023

Stretchable strain sensors that use a liquid metal (eutectic gallium–indium alloy; E-GaIn) and flexible silicone rubber (Ecoflex) as the support and adhesive layers, respectively, are demonstrated. The flexibility of Ecoflex and the deformability of E-GaIn enable the sensors to be stretched by 100%. Ecoflex gel has sufficiently large adhesion force to skin, even though the adhesion force is smaller than that for commercially available adhesives. This enables the sensor to be used for non-invasive monitoring of human motion. The mechanical and electrical properties of the sensor are experimentally evaluated. The effectiveness of the proposed sensors is demonstrated by monitoring joint movements, facial expressions, and respiration.
Laser-Generated Scholte Waves in Floating Microparticles
Authors: Ranjan Abhishek , Ahmad Azeem , Ahluwalia Balpreet Singh , Melandsø Frank
Published: February 2023

This study aims to demonstrate the generation and detection of Scholte waves inside polystyrene microparticles. This was proven using both experimental analysis and COMSOL simulation. Microspheres of different sizes were excited optically with a pulsed laser (532 nm), and the acoustic signals were detected using a transducer (40 MHz). On analyzing the laser-generated ultrasound signals, the results obtained experimentally and from COMSOL are in close agreement both in the time and frequency domain. A simplified analysis of Scholte wave generation by laser irradiation for homogeneous, isotropic microspheres is presented. The theoretical wave velocity of the Scholte wave was calculated and found close to our experimental results. A representation of pressure wave motion showing the Scholte wave generation is presented at different times.
PPG2EMG: Estimating Upper-Arm Muscle Activities and EMG from Wrist PPG Values
Authors: Okamoto Masahiro , Murao Kazuya
Published: February 2023

The electromyogram (EMG) is a waveform representation of the action potential generated by muscle cells using electrodes. EMG acquired using surface electrodes is called surface EMG (sEMG), and it is the acquisition of muscle action potentials transmitted by volume conduction from the skin. Surface electrodes require disposable conductive gel or adhesive tape to be attached to the skin, which is costly to run, and the tape is hard on the skin when it is removed. Muscle activity can be evaluated by acquiring muscle potentials and analyzing quantitative, temporal, and frequency factors. It is also possible to evaluate muscle fatigue because the frequency of the EMG becomes lower as the muscle becomes fatigued. Research on human activity recognition from EMG signals has been actively conducted and applied to systems that support arm and hand functions. This paper proposes a method for recognizing the muscle activity state of the arm using pulse wave data (PPG: Photoplethysmography) and a method for estimating EMG using pulse wave data. This paper assumes that the PPG sensor is worn on the user’s wrist to measure the heart rate. The user also attaches an elastic band to the upper arm, and when the user exerts a force on the arm, the muscles of the upper arm contract. The arteries are then constricted, and the pulse wave measured at the wrist becomes weak. From the change in the pulse wave, the muscle activity of the arm can be recognized and the number of action potentials of the muscle can be estimated. From the evaluation experiment with five subjects, three types of muscle activity were recognized with 80+%, and EMG was estimated with approximately 20% error rate.
Monkeypox Detection Using CNN with Transfer Learning
Authors: Altun Murat , Gürüler Hüseyin , Özkaraca Osman , Khan Faheem , Khan Jawad , Lee Youngmoon
Published: February 2023

Monkeypox disease is caused by a virus that causes lesions on the skin and has been observed on the African continent in the past years. The fatal consequences caused by virus infections after the COVID pandemic have caused fear and panic among the public. As a result of COVID reaching the pandemic dimension, the development and implementation of rapid detection methods have become important. In this context, our study aims to detect monkeypox disease in case of a possible pandemic through skin lesions with deep-learning methods in a fast and safe way. Deep-learning methods were supported with transfer learning tools and hyperparameter optimization was provided. In the CNN structure, a hybrid function learning model was developed by customizing the transfer learning model together with hyperparameters. Implemented on the custom model MobileNetV3-s, EfficientNetV2, ResNET50, Vgg19, DenseNet121, and Xception models. In our study, AUC, accuracy, recall, loss, and F1-score metrics were used for evaluation and comparison. The optimized hybrid MobileNetV3-s model achieved the best score, with an average F1-score of 0.98, AUC of 0.99, accuracy of 0.96, and recall of 0.97. In this study, convolutional neural networks were used in conjunction with optimization of hyperparameters and a customized hybrid function transfer learning model to achieve striking results when a custom CNN model was developed. The custom CNN model design we have proposed is proof of how successfully and quickly the deep learning methods can achieve results in classification and discrimination.
Condition-Based Failure-Free Time Estimation of a Pump
Authors: Ćwikła Grzegorz , Paprocka Iwona
Published: February 2023

Reliable and continuous operation of the equipment is expected in the wastewater treatment plant, as any perturbations can lead to environmental pollution and the need to pay penalties. Optimization and minimization of operating costs of the pump station cannot, therefore, lead to a reduction in reliability but rather should be based on preventive works, the necessity of which should be foreseen. The purpose of this paper is to develop an accurate model to predict a pump’s mean time to failure, allowing for rational planning of maintenance. The pumps operate under the supervision of the automatic control system and SCADA, which is the source of historical data on pump operation parameters. This enables the research and development of various methods and algorithms for optimizing service activities. In this case, a multiple linear regression model is developed to describe the impact of historical data on pump operation for pump maintenance. In the literature, the least squares method is used to estimate unknown regression coefficients for this data. The original value of the paper is the application of the genetic algorithm to estimate coefficient values of the multiple linear regression model of failure-free time of the pump. Necessary analysis and simulations are performed on the data collected for submersible pumps in a sewage pumping station. As a result, an improvement in the adequacy of the presented model was identified.
Locating Partial Discharges in Power Transformers with Convolutional Iterative Filtering
Authors: Wang Jonathan , Wu Kesheng , Sim Alex , Hwangbo Seongwook
Published: February 2023

The most common source of transformer failure is in the insulation, and the most prevalent warning signal for insulation weakness is partial discharge (PD). Locating the positions of these partial discharges would help repair the transformer to prevent failures. This work investigates algorithms that could be deployed to locate the position of a PD event using data from ultra-high frequency (UHF) sensors inside the transformer. These algorithms typically proceed in two steps: first determining the signal arrival time, and then locating the position based on time differences. This paper reviews available methods for each task and then propose new algorithms: a convolutional iterative filter with thresholding (CIFT) to determine the signal arrival time and a reference table of travel times to resolve the source location. The effectiveness of these algorithms are tested with a set of laboratory-triggered PD events and two sets of simulated PD events inside transformers in production use. Tests show the new approach provides more accurate locations than the best-known data analysis algorithms, and the difference is particularly large, 3.7X, when the signal sources are far from sensors.
UltrasonicGS: A Highly Robust Gesture and Sign Language Recognition Method Based on Ultrasonic Signals
Authors: Wang Yuejiao , Hao Zhanjun , Dang Xiaochao , Zhang Zhenyi , Li Mengqiao
Published: February 2023

With the global spread of the novel coronavirus, avoiding human-to-human contact has become an effective way to cut off the spread of the virus. Therefore, contactless gesture recognition becomes an effective means to reduce the risk of contact infection in outbreak prevention and control. However, the recognition of everyday behavioral sign language of a certain population of deaf people presents a challenge to sensing technology. Ubiquitous acoustics offer new ideas on how to perceive everyday behavior. The advantages of a low sampling rate, slow propagation speed, and easy access to the equipment have led to the widespread use of acoustic signal-based gesture recognition sensing technology. Therefore, this paper proposed a contactless gesture and sign language behavior sensing method based on ultrasonic signals—UltrasonicGS. The method used Generative Adversarial Network (GAN)-based data augmentation techniques to expand the dataset without human intervention and improve the performance of the behavior recognition model. In addition, to solve the problem of inconsistent length and difficult alignment of input and output sequences of continuous gestures and sign language gestures, we added the Connectionist Temporal Classification (CTC) algorithm after the CRNN network. Additionally, the architecture can achieve better recognition of sign language behaviors of certain people, filling the gap of acoustic-based perception of Chinese sign language. We have conducted extensive experiments and evaluations of UltrasonicGS in a variety of real scenarios. The experimental results showed that UltrasonicGS achieved a combined recognition rate of 98.8% for 15 single gestures and an average correct recognition rate of 92.4% and 86.3% for six sets of continuous gestures and sign language gestures, respectively. As a result, our proposed method provided a low-cost and highly robust solution for avoiding human-to-human contact.
Super-Resolution and Wide-Field-of-View Imaging Based on Large-Angle Deflection with Risley Prisms
Authors: Gui Chao , Wang Detian , Huang Xiwang , Wu Chunyan , Chen Xin , Huang Huachuan
Published: February 2023

A novel single camera combined with Risley prisms is proposed to achieve a super-resolution (SR) imaging and field-of-view extension (FOV) imaging method. We develop a mathematical model to consider the imaging aberrations caused by large-angle beam deflection and propose an SR reconstruction scheme that uses a beam backtracking method for image correction combined with a sub-pixel shift alignment technique. For the FOV extension, we provide a new scheme for the scanning position path of the Risley prisms and the number of image acquisitions, which improves the acquisition efficiency and reduces the complexity of image stitching. Simulation results show that the method can increase the image resolution to the diffraction limit of the optical system for imaging systems where the resolution is limited by the pixel size. Experimental results and analytical verification yield that the resolution of the image can be improved by a factor of 2.5, and the FOV extended by a factor of 3 at a reconstruction factor of 5. The FOV extension is in general agreement with the simulation results. Risley prisms can provide a more general, low-cost, and efficient method for SR reconstruction, FOV expansion, central concave imaging, and various scanning imaging.
Deep Learning Solution for Quantification of Fluorescence Particles on a Membrane
Authors: Sellam Abdellah Zakaria , Benlamoudi Azeddine , Cid Clément Antoine , Dobelle Leopold , Slama Amina , El Hillali Yassin , Taleb-Ahmed Abdelmalik
Published: February 2023

The detection and quantification of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus particles in ambient waters using a membrane-based in-gel loop-mediated isothermal amplification (mgLAMP) method can play an important role in large-scale environmental surveillance for early warning of potential outbreaks. However, counting particles or cells in fluorescence microscopy is an expensive, time-consuming, and tedious task that only highly trained technicians and researchers can perform. Although such objects are generally easy to identify, manually annotating cells is occasionally prone to fatigue errors and arbitrariness due to the operator’s interpretation of borderline cases. In this research, we proposed a method to detect and quantify multiscale and shape variant SARS-CoV-2 fluorescent cells generated using a portable (mgLAMP) system and captured using a smartphone camera. The proposed method is based on the YOLOv5 algorithm, which uses CSPnet as its backbone. CSPnet is a recently proposed convolutional neural network (CNN) that duplicates gradient information within the network using a combination of Dense nets and ResNet blocks, and bottleneck convolution layers to reduce computation while at the same time maintaining high accuracy. In addition, we apply the test time augmentation (TTA) algorithm in conjunction with YOLO’s one-stage multihead detection heads to detect all cells of varying sizes and shapes. We evaluated the model using a private dataset provided by the Linde + Robinson Laboratory, California Institute of Technology, United States. The model achieved a mAP@0.5 score of 90.3 in the YOLOv5-s6.
Evaluation of Electric Muscle Stimulation Method for Haptic Augmented Reality
Authors: Ishimaru Takaya , Saga Satoshi
Published: February 2023

Currently, visual Augmented Reality (AR) technology is widespread among the public. Similarly, haptic AR technology is also widely practiced in the academic field. However, conventional haptic AR devices are not suitable for interacting with real objects. These devices are often held by the users, and they contact the real object via the devices. Thus, they prevent direct contact between the user and real objects. To solve this problem, we proposed employing Electrical Muscle Stimulation (EMS) technology. EMS technology does not interfere with the interaction between the user and the real object, and the user can wear the device. First, we examined proper stimulus waveforms for EMS, in addition to pulse waveforms. At the same time, we examined the appropriate frequency and pulse width. The waveforms that we used this time were a sawtooth wave, a reverse sawtooth wave, and a sine wave. Second, to clarify the characteristic of the force presented by the EMS, we measured the relationship between the input voltage and the force presented and obtained the point of subjective equality using the constant method. Subsequently, we presented the bump sensation using EMS to the participants and verified its effectiveness by comparing it with the existing methods.
Performance of a Full-Scale Upstream MAPS-Based Verification Device for Radiotherapy
Authors: Velthuis Jaap , Li Yutong , Pritchard Jordan , De Sio Chiara , Beck Lana , Hugtenburg Richard
Published: February 2023

Intensity-modulated radiotherapy is a widely used technique for accurately targeting cancerous tumours in difficult locations using dynamically shaped beams. This is ideally accompanied by real-time independent verification. Monolithic active pixel sensors are a viable candidate for providing upstream beam monitoring during treatment. We have already demonstrated that a Monolithic Active Pixel Sensor (MAPS)-based system can fulfill all clinical requirements except for the minimum required size. Here, we report the performance of a large-scale demonstrator system consisting of a matrix of 2 × 2 sensors, which is large enough to cover almost all radiotherapy treatment fields when affixed to the shadow tray of the LINAC head. When building a matrix structure, a small dead area is inevitable. Here, we report that with a newly developed position algorithm, leaf positions can be reconstructed over the entire range with a position resolution of below ∼200 μm in the centre of the sensor, which worsens to just below 300 μm in the middle of the gap between two sensors. A leaf position resolution below 300 μm results in a dose error below 2%, which is good enough for clinical deployment.
A Review of Subsurface Electrical Conductivity Anomalies in Magnetotelluric Imaging
Authors: Lin Wule , Yang Bo , Han Bo , Hu Xiangyun
Published: February 2023

After 70 years of development, magnetotelluric (MT), a remote sensing technique for subsurface electrical resistivity imaging, has been widely applied in resource exploration and the deep tectonic evolution of the Earth. The electrical resistivity anomalies and their quantitative interpretation are closely related to or even controlled by the interconnected high-conductivity phases, which are frequently associated with tectonic activity. Based on representative electrical resistivity studies mainly of the deep crust and mantle, we reviewed principal electrical conduction mechanisms, generally used conductivity mixing models, and potential causes of high-conductivity including the saline fluid, partial melting, graphite, sulfide, and hydrogen in nominally anhydrous minerals, and the general methods to infer the water content of the upper mantle through electrical anomaly revealed by MT.
Cross Dataset Analysis for Generalizability of HRV-Based Stress Detection Models
Authors: Benchekroun Mouna , Velmovitsky Pedro Elkind , Istrate Dan , Zalc Vincent , Morita Plinio Pelegrini , Lenne Dominique
Published: February 2023

Stress is an increasingly prevalent mental health condition across the world. In Europe, for example, stress is considered one of the most common health problems, and over USD 300 billion are spent on stress treatments annually. Therefore, monitoring, identification and prevention of stress are of the utmost importance. While most stress monitoring is carried out through self-reporting, there are now several studies on stress detection from physiological signals using Artificial Intelligence algorithms. However, the generalizability of these models is only rarely discussed. The main goal of this work is to provide a monitoring proof-of-concept tool exploring the generalization capabilities of Heart Rate Variability-based machine learning models. To this end, two Machine Learning models are used, Logistic Regression and Random Forest to analyze and classify stress in two datasets differing in terms of protocol, stressors and recording devices. First, the models are evaluated using leave-one-subject-out cross-validation with train and test samples from the same dataset. Next, a cross-dataset validation of the models is performed, that is, leave-one-subject-out models trained on a Multi-modal Dataset for Real-time, Continuous Stress Detection from Physiological Signals dataset and validated using the University of Waterloo stress dataset. While both logistic regression and random forest models achieve good classification results in the independent dataset analysis, the random forest model demonstrates better generalization capabilities with a stable F1 score of 61%. This indicates that the random forest can be used to generalize HRV-based stress detection models, which can lead to better analyses in the mental health and medical research field through training and integrating different models.
An Improved CatBoost-Based Classification Model for Ecological Suitability of Blueberries
Authors: Chang Wenfeng , Wang Xiao , Yang Jing , Qin Tao
Published: February 2023

Selecting the best planting area for blueberries is an essential issue in agriculture. To better improve the effectiveness of blueberry cultivation, a machine learning-based classification model for blueberry ecological suitability was proposed for the first time and its validation was conducted by using multi-source environmental features data in this paper. The sparrow search algorithm (SSA) was adopted to optimize the CatBoost model and classify the ecological suitability of blueberries based on the selection of data features. Firstly, the Borderline-SMOTE algorithm was used to balance the number of positive and negative samples. The Variance Inflation Factor and information gain methods were applied to filter out the factors affecting the growth of blueberries. Subsequently, the processed data were fed into the CatBoost for training, and the parameters of the CatBoost were optimized to obtain the optimal model using SSA. Finally, the SSA-CatBoost model was adopted to classify the ecological suitability of blueberries and output the suitability types. Taking a study on a blueberry plantation in Majiang County, Guizhou Province, China as an example, the findings demonstrate that the AUC value of the SSA-CatBoost-based blueberry ecological suitability model is 0.921, which is 2.68% higher than that of the CatBoost (AUC = 0.897) and is significantly higher than Logistic Regression (AUC = 0.855), Support Vector Machine (AUC = 0.864), and Random Forest (AUC = 0.875). Furthermore, the ecological suitability of blueberries in Majiang County is mapped according to the classification results of different models. When comparing the actual blueberry cultivation situation in Majiang County, the classification results of the SSA-CatBoost model proposed in this paper matches best with the real blueberry cultivation situation in Majiang County, which is of a high reference value for the selection of blueberry cultivation sites.
Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
Authors: Alhoraibi Lamia , Alghazzawi Daniyal , Alhebshi Reemah , Rabie Osama Bassam J.
Published: February 2023

The physical layer security of wireless networks is becoming increasingly important because of the rapid development of wireless communications and the increasing security threats. In addition, because of the open nature of the wireless channel, authentication is a critical issue in wireless communications. Physical layer authentication (PLA) is based on distinctive features to provide information-theory security and low complexity. However, although many researchers are interested in the PLA and how it might be used to improve wireless security, there is surprisingly little literature on the subject, with no systematic overview of the current state-of-the-art PLA and the main foundations involved. Therefore, this paper aims to determine and systematically compare existing studies in the physical layer authentication. This study showed whether machine learning approaches in physical layer authentication models increased wireless network security performance and demonstrated the latest techniques used in PLA. Moreover, it identified issues and suggested directions for future research. This study is valuable for researchers and security model developers interested in using machine learning (ML) and deep learning (DL) approaches for PLA in wireless communication systems in future research and designs.
Pedestrian Augmented Reality Navigator
Authors: Mahapatra Tanmaya , Tsiamitros Nikolaos , Rohr Anton Moritz , K Kailashnath , Pipelidis Georgios
Published: February 2023

Navigation is often regarded as one of the most-exciting use cases for Augmented Reality (AR). Current AR Head-Mounted Displays (HMDs) are rather bulky and cumbersome to use and, therefore, do not offer a satisfactory user experience for the mass market yet. However, the latest-generation smartphones offer AR capabilities out of the box, with sometimes even pre-installed apps. Apple’s framework ARKit is available on iOS devices, free to use for developers. Android similarly features a counterpart, ARCore. Both systems work well for small spatially confined applications, but lack global positional awareness. This is a direct result of one limitation in current mobile technology. Global Navigation Satellite Systems (GNSSs) are relatively inaccurate and often cannot work indoors due to the restriction of the signal to penetrate through solid objects, such as walls. In this paper, we present the Pedestrian Augmented Reality Navigator (PAReNt) iOS app as a solution to this problem. The app implements a data fusion technique to increase accuracy in global positioning and showcases AR navigation as one use case for the improved data. ARKit provides data about the smartphone’s motion, which is fused with GNSS data and a Bluetooth indoor positioning system via a Kalman Filter (KF). Four different KFs with different underlying models have been implemented and independently evaluated to find the best filter. The evaluation measures the app’s accuracy against a ground truth under controlled circumstances. Two main testing methods were introduced and applied to determine which KF works best. Depending on the evaluation method, this novel approach improved the accuracy by 57% (when GPS and AR were used) or 32% (when Bluetooth and AR were used) over the raw sensor data.
Efficacy of Specific Trunk Exercises in the Balance Dysfunction of Patients with Parkinson’s Disease: A Systematic Review and Meta-Analysis
Authors: López-Liria Remedios , Vega-Tirado Sofía , Valverde-Martínez María Ángeles , Calvache-Mateo Andrés , Martínez-Martínez Ana María , Rocamora-Pérez Patricia
Published: February 2023

Parkinson’s disease (PD) is a neurodegenerative pathology classified as a movement disorder. Physical exercise within a physiotherapy program is an important element to improve postural stability, balance and mobility in order to reduce falls in people with PD. The aim of this work was to determine the efficacy of specific balance and trunk mobility exercises, as well as their benefits for and effects on patients with idiopathic PD. A systematic review and meta-analysis was conducted according to PRISMA standards. The search was performed in five databases: Cochrane Library, SciELO, PEDro, Scopus and PubMed, in February 2022 with the following descriptors: Parkinson’s disease, trunk, exercise, therapy and physical therapy. The inclusion criteria were randomized controlled trials (RCTs) over the last ten years. A meta-analysis on static and dynamic balance was conducted with the software Review Manager. Nine articles met the objectives and inclusion criteria, with a total of 240 participants. The trials had moderate methodological quality according to the PEDro scale. The studies included differed with regard to intervention protocol and outcome measures. Finally, eight studies were included in a quantitative analysis in which it was shown that trunk-specific exercises interventions did not significantly improve static balance (SMD = −0.10, 95% CI= −0.29, 0.08; p = 0.28) or dynamic balance (SMD = 0.64 95% CI= −0.24, 1.52; p = 0.15). However, significant differences were found in static balance measured subjectively using the Berg Balance Scale (SMD = −0.52, 95% CI= −1.01, −0.02; p = 0.04). Although some differences were not significant, the studies included in this systematic review consider that specific trunk exercises or balance training combined with muscle strengthening in patients with idiopathic PD should be a complement to pharmacological treatment for improving balance dysfunction and postural instability, preventing falls and promoting wellness.
Single Seed Near-Infrared Hyperspectral Imaging for Classification of Perennial Ryegrass Seed
Authors: Reddy Priyanka , Panozzo Joe , Guthridge Kathryn M. , Spangenberg German C. , Rochfort Simone J.
Published: February 2023

The detection of beneficial microbes living within perennial ryegrass seed causing no apparent defects is challenging, even with the most sensitive and conventional methods, such as DNA genotyping. Using a near-infrared hyperspectral imaging system (NIR-HSI), we were able to discriminate not only the presence of the commercial NEA12 fungal endophyte strain but perennial ryegrass cultivars of diverse seed age and batch. A total of 288 wavebands were extracted for individual seeds from hyperspectral images. The optimal pre-processing methods investigated yielded the best partial least squares discriminant analysis (PLS-DA) classification model to discriminate NEA12 and without endophyte (WE) perennial ryegrass seed with a classification accuracy of 89%. Effective wavelength (EW) selection based on GA-PLS-DA resulted in the selection of 75 wavebands yielding 88.3% discrimination accuracy using PLS-DA. For cultivar identification, the artificial neural network discriminant analysis (ANN-DA) was the best-performing classification model, resulting in >90% classification accuracy for Trojan, Alto, Rohan, Governor and Bronsyn. EW selection using GA-PLS-DA resulted in 87 wavebands, and the PLS-DA model performed the best, with no extensive compromise in performance, resulting in >89.1% accuracy. The study demonstrates the use of NIR-HSI reflectance data to discriminate, for the first time, an associated beneficial fungal endophyte and five cultivars of perennial ryegrass seed, irrespective of seed age and batch. Furthermore, the negligible effects on the classification errors using EW selection improve the capability and deployment of optimized methods for real-time analysis, such as the use of low-cost multispectral sensors for single seed analysis and automated seed sorting devices.
Minimal Risk Maneuvers of Automated Vehicles: Effects of a Contact Analog Head-Up Display Supporting Driver Decisions and Actions in Transition Phases
Authors: Karakaya Burak , Bengler Klaus
Published: February 2023

Minimal risk maneuvers (MRMs), as part of highly automated systems, aim at minimizing the risk during a transition phase from automated to manual driving. Previous studies show that many drivers have an urge to intervene in transition phases despite the system’s capability to safely come to a standstill. A human–machine interface (HMI) concept was developed to support driver decisions by providing environmental information and action recommendations. This was investigated in a static driving simulator experiment with 36 participants. Two scenarios that differed in the traffic on the adjacent left lane were implemented and the HMI concept displayed the content accordingly. Results of the study again show a high intervention rate of drivers overtaking the obstacle from the left, even if the lane is occupied by other vehicles. The HMI concept had a positive influence on the manner of intervention by encouraging a standstill in the shoulder lane. Nevertheless, negative consequences included accidents and dangerous situations, but at lower frequencies and proportions during drives with the HMI concept. In conclusion, the risk during the transition phase was reduced. Furthermore, the results showed a significant decrease in the subjective workload and a positive influence on the drivers’ understanding and predictability of the automated system.
Removal of Ni(II) from Aqueous Solution by Novel Lycopersicon esculentum Peel and Brassica botrytis Leaves Adsorbents
Authors: Din Israf Ud , Shah Qadeer Ullah , Tasleem Syed , Naeem Abdul , I. Alharthi Abdulrahman , Ayad Alotaibi Mshari
Published: February 2023

The current work reports adsorption of Ni(II) using Brassica botrytis leaves (BBL), Brassica botrytis leaves-activated carbon (BBL-AC), Lycopersicon esculentum peel (LEP) and Lycopersicon esculentum peel-activated carbon (LEP-AC). The adsorption of Ni(II) was tested in batch experiments by varying different parameters such as pH, initial metal ion concentration, temperature, adsorbent dosage, and contact time. Thermodynamics and kinetics investigations were performed for Ni removal. The adsorption of Ni(II) was improved by incorporation of activated carbon to the parental Brassica botrytis leaves and Lycopersicon esculentum peel adsorbents. The studies revealed 40 min of equilibrium time for Ni(II) adsorption by different adsorbents. Adsorption of Ni was drastically declined by temperature with a minimum adsorption of 53% observed for BBL. Similarly, solution pH also played a vital role in Ni(II) adsorption by different adsorbents. A 95% adsorption of Ni was recorded in the case of LEP-AC at pH 7. The study concluded with the application of Lycopersicon esculentum peel and Brassica botrytis leaves as active adsorbents for Ni(II) adsorption from aqueous solution.
Overlooked Impacts of Urban Environments on the Air Quality in Naturally Ventilated Schools Amid the COVID-19 Pandemic
Authors: Lavtižar Kristijan , Fikfak Alenka , Fink Rok
Published: February 2023

The density, form, and dimensions of urban morphology are important for healthy living conditions in cities, especially if they are related to the climate and air pollution. Morphology and environmental conditions determine the relationship between open and built space, the width of street spaces, the aerodynamic characteristics of wind currents, albedo, and the retention of pollutants, as well as determining the radiative exchange with the atmosphere. Studies on the COVID-19 pandemic have focused on the assumption of a possible relationship between the spread of the SARS-CoV-2 virus and the presence and concentration of airborne particulate matter (PM10 and PM2.5). This paper focuses on the research of indoor air quality (IAQ) in two schools with naturally ventilated classrooms in Ljubljana, Slovenia. The presence of particulate matter (PM2.5 and PM10) and the concentration of CO2 were studied, along with other microclimatic conditions, e.g., ambient temperature, relative humidity, air pressure, and wind conditions. These were compared and assessed via analysis of variance (ANOVA) and Duncan’s post hoc test. The main concern was to see how effective different ventilation strategies are, as well as how the openings in the classroom impact the concentrations of CO2 relative to the concentrations of PM2.5 and PM10 particles as a side effect of these ventilation strategies. The inconsistent application of recommended COVID-19 ventilation strategies suggests that IAQ in naturally ventilated classrooms is highly determined by individual perceptions of indoor air quality. The results also suggest that the IAQ is significantly affected by the schools’ urban environment; however, this is not considered within the national COVID-19 ventilation recommendations. Future ventilation guidelines for pandemics should also include the urban environment as a risk factor for inadequate IAQ, instead of focusing solely on pathogen characteristics.
Methodology to Estimate the Impact of the DC to AC Power Ratio, Azimuth, and Slope on Clipping Losses of Solar Photovoltaic Inverters: Application to a PV System Located in Valencia Spain
Authors: Díaz-Bello Dácil , Vargas-Salgado Carlos , Águila-León Jesus , Lara-Vargas Fabián
Published: February 2023

Renewable power capacity sets records annually, driven by solar photovoltaic power, which accounts for more than half of all renewable power expansion in 2021. In this sense, photovoltaic system design must be correctly defined before system installation to generate the maximum quantity of energy at the lowest possible cost. The proposed study analyses the oversizing of the solar array vs. the capacity of the solar inverter, seeking low clipping losses in the inverter. A real 4.2 kWp residential PV installation was modelled and validated using the software SAM and input data from different sources, such as a weather station for weather conditions, ESIOS for electricity rates, and FusionSolar to obtain energy data from the PV installation. Once data were validated through SAM, the DC to AC ratio was varied between 0.9 and 2.1. The azimuth and slope sensitivity analyses were performed regarding clipping inverter losses. Results have been evaluated through the energy generated and the discounted payback period, showing that, depending on the weather conditions, slope, and azimuth, among others, it is advisable to increase the DC to AC ratio to values between 1.63 and 1.87, implying low discounted payback periods of about 8 to 9 years. In addition, it was observed that inverter clipping losses significantly vary depending on the defined azimuth and slope.