bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America
Authors: Howe Alexander A. , Parks Sean A. , Harvey Brian J. , Saberi Saba J. , Lutz James A. , Yocom Larissa L.
Published: October 2022

Accurate assessment of burn severity is a critical need for an improved understanding of fire behavior and ecology and effective post-fire management. Although NASA Landsat satellites have a long history of use for remotely sensed mapping of burn severity, the recently launched (2015 and 2017) European Space Agency Sentinel-2 satellite constellation offers increased temporal and spatial resolution with global coverage, combined with free data access. Evaluations of burn severity derived from Landsat and Sentinel generally show comparable results, but these studies only assessed a small number of fires with limited field data. We used 912 ground calibration plots from 26 fires that burned between 2016 and 2019 in western North America to compare Sentinel- and Landsat-derived burn severity estimates with the field-based composite burn index. We mapped burn severity using two methods; the well-established paired scene approach, in which a single pre- and post-fire scene are selected for each fire, and also a mean image compositing approach that automatically integrates multiple scenes using the cloud-based remote sensing platform Google Earth Engine. We found that Sentinel generally performed as well or better than Landsat for four spectral indices of burn severity, particularly when using atmospherically corrected Sentinel imagery. Additionally, we tested the effects of mapping burn severity at Sentinel’s finer spatial resolution (10 m) on estimates of the spatial complexity of stand-replacing fire, resulting in a 5% average reduction per-fire in area mapped as high-severity patch interiors (24,273 ha total) compared to mapping at the resolution of Landsat (30 m). These findings suggest Sentinel may improve ecological discrimination of fine-scale fire effects, but also warrant caution when comparing estimates of burn severity spatial patterns derived at different resolutions. Overall, these results indicate that burn severity mapping will benefit substantially from the integration of Sentinel imagery through increased imagery availability, and that Sentinel’s higher spatial resolution improves opportunities for examining finer-scale fire effects across ecosystems.
A Black Ice Detection Method Based on 1-Dimensional CNN Using mmWave Sensor Backscattering
Authors: Kim Jaewook , Kim Eunkyung , Kim Dongwan
Published: October 2022

Black ice on the road can be dangerous, as it renders the road slippery and is difficult to identify, owing to its transparency. Although studies on black ice detection using cameras, optical sensors, and infrared sensors have been conducted, these sensors have limitations, as they are affected by low light conditions and sunlight. To detect black ice regardless of low light conditions or sunlight, in this study, we incorporate a mmWave sensor that is consistent with varying light conditions. In the proposed method, a frequency modulated continuous wave is transmitted to the surface by the mmWave sensor, and the mmWave sensor backscattering is modulated by the surface medium and roughness. The proposed method also includes preprocessing to calculate the Range-FFT result of the mmWave sensor backscattering and a classification based on a 1-dimensional convolutional neural network to precisely detect the presence of black ice from the Range-FFT result. As a result of the indoor experiment, the proposed black ice detection method achieves an accuracy of 98.2% on dry, wet, and black ice surfaces. Additionally, under low light conditions and in an outdoor environment with sunlight, the proposed method achieves accuracies of 95.6% and 98.5%, respectively.
ExtractEO, a Pipeline for Disaster Extent Mapping in the Context of Emergency Management
Authors: Maxant Jérôme , Braun Rémi , Caspard Mathilde , Clandillon Stephen
Published: October 2022

Rapid mapping of disasters using any kind of satellite imagery is a challenge. The faster the response, the better the service is for the end users who are managing the emergency activities. Indeed, production rapidity is crucial whatever the satellite data in input. However, the speed of delivery must not be at the expense of crisis information quality. The automated flood and fire extraction pipelines, presented in this technical note, make it possible to take full advantage of advanced algorithms in short timeframes, and leave enough time for an expert operator to validate the results and correct any unmanaged thematic errors. Although automated algorithms aren’t flawless, they greatly facilitate and accelerate the detection and mapping of crisis information, especially for floods and fires. ExtractEO is a pipeline developed by SERTIT and dedicated to disaster mapping. It brings together automatic data download and pre-processing, along with highly accurate flood and fire detection chains. Indeed, the thematic quality assessment revealed F1-score values of 0.91 and 0.88 for burnt area and flooded area detection, respectively, from various kinds of high- and very-high- resolution data (optical and SAR).
ShuffleCloudNet: A Lightweight Composite Neural Network-Based Method for Cloud Computation in Remote-Sensing Images
Authors: Wang Gang , Lu Zhiying , Wang Ping
Published: October 2022

The occlusion of cloud layers affects the accurate acquisition of ground object information and causes a large amount of useless remote-sensing data transmission and processing, wasting storage, as well as computing resources. Therefore, in this paper, we designed a lightweight composite neural network model to calculate the cloud amount in high-resolution visible remote-sensing images by training the model using thumbnail images and browsing images in remote-sensing images. The training samples were established using paired thumbnail images and browsing images, and the cloud-amount calculation model was obtained by training a proposed composite neural network. The strategy used the thumbnail images for preliminary judgment and the browsing images for accurate calculation, and this combination can quickly determine the cloud amount. The multi-scale confidence fusion module and bag-of-words loss function were redesigned to achieve fast and accurate calculation of cloud-amount data from remote-sensing images. This effectively alleviates the problem of low cloud-amount calculation, thin clouds not being counted as clouds, and that of ice and clouds being confused as in existing methods. Furthermore, a complete dataset of cloud-amount calculation for remote-sensing images, CTI_RSCloud, was constructed for training and testing. The experimental results show that, with less than 13 MB of parameters, the proposed lightweight network model greatly improves the timeliness of cloud-amount calculation, with a runtime is in the millisecond range. In addition, the calculation accuracy is better than the classic lightweight networks and backbone networks of the best cloud-detection models.
Evaluating Optical Remote Sensing Methods for Estimating Leaf Area Index for Corn and Soybean
Authors: Nandan Rohit , Bandaru Varaprasad , He Jiaying , Daughtry Craig , Gowda Prasanna , Suyker Andrew E.
Published: October 2022

The leaf area index (LAI) is a key crop biophysical variable influencing many vegetation processes. Spatial LAI estimates are essential to develop and improve spatial modeling tools to monitor vegetation conditions at large regional scales. Numerous optical remote sensing methods have been explored to retrieve crop-specific LAI at a regional scale using satellite observations. However, a major challenge is selecting a method that performance well under various conditions without local scale calibration. As such, we assessed the performance of existing statistical and physical approaches, developed based on parametric, non-parametric and radiative transfer model (RTM)-look-up-table based inversion, using field observations from two geographically distant locations and Landsat 5, 7, and 8 satellite observations. These methods were implemented for corn and soybeans cultivated at two locations in the U.S (i.e., Mead, Nebraska, and Bushland, Texas). The evaluation metrics (i.e., Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2)) were used to study the performance of each method, and then the methods were ranked based on these metrics. Our study showed that overall parametric methods outperformed other methods. The RMSE (MAE) for the top five methods was less than 1.3 (0.95) for corn and 1.0 (0.8) for soybeans, irrespective of location. Even though they outperformed, parametric methods exhibited inconsistency in their performance. For instance, the SR_CA_cross method ranked 1 for corn, however, it performed poorly for soybean (ranked 15). The non-parametric methods showed moderate accuracy partly due to the availability of a smaller number of observations for training. The RTM-LUT inversion physical-based approach was found to perform reasonably well RMSE (MAE) less than 1.5 (1.0) consistently irrespective of location and crop, implying that this approach is more suitable for regional-scale LAI estimation. The results of this study highlighted the drawbacks and advantages of available optical remote sensing approaches to estimate LAI for corn and soybean crops using Landsat imagery. These results are of interest for remote sensing and modeling communities developing spatial-scale approaches to model and monitor agricultural vegetation.
Compromised Improvement of Poor Visibility Due to PM Chemical Composition Changes in South Korea
Authors: Jeong Jaein I. , Seo Jisu , Park Rokjin J.
Published: October 2022

Fine particulate matter (PM) significantly affects visibility, a sensitive indicator of air pollution. Despite a continuous decrease in the PM concentrations in South Korea, the public generally believes that PM air pollution has worsened over the past years. To explain this disparity, we analyzed the characteristics of recent visibility changes using observations of visibility and PM component data observed in Seoul, South Korea, from 2012 to 2018. A significant negative correlation (R = −0.96) existed between visibility and concentrations of PM, with an aerodynamic diameter ≤ 2.5 μm (PM2.5); a high PM2.5 concentration was the most important contributor to poor visibility. Annual mean PM2.5 concentrations in Seoul decreased by −5.1% yr−1 during 2012–2018, whereas annual mean visibility improved by 2.1% yr−1. We found that a lower improvement in visibility was associated with changes in the PM component. Among the PM components affecting poor visibility, contributions of ammonium nitrate (NH4NO3) significantly increased during 2012–2018 (from 48% in 2012 to 59% in 2018). Increases in NO3− aerosol concentrations were owing to SOx emission reduction and the resulting decreases in SO42− aerosol concentrations, which led to an increase in NH3 available for additional NH4NO3 production in the atmosphere. Despite decreased PM concentrations in Seoul, the change of PM components has compromised visibility improvement; thus, NO3− concentrations need to be reduced.
Convolutional Long-Short Term Memory Network with Multi-Head Attention Mechanism for Traffic Flow Prediction
Authors: Wei Yupeng , Liu Hongrui
Published: October 2022

Accurate predictive modeling of traffic flow is critically important as it allows transportation users to make wise decisions to circumvent traffic congestion regions. The advanced development of sensing technology makes big data more affordable and accessible, meaning that data-driven methods have been increasingly adopted for traffic flow prediction. Although numerous data-driven methods have been introduced for traffic flow predictions, existing data-driven methods cannot consider the correlation of the extracted high-dimensional features and cannot use the most relevant part of the traffic flow data to make predictions. To address these issues, this work proposes a decoder convolutional LSTM network, where the convolutional operation is used to consider the correlation of the high-dimensional features, and the LSTM network is used to consider the temporal correlation of traffic flow data. Moreover, the multi-head attention mechanism is introduced to use the most relevant portion of the traffic data to make predictions so that the prediction performance can be improved. A traffic flow dataset collected from the Caltrans Performance Measurement System (PeMS) database is used to demonstrate the effectiveness of the proposed method.
Validity of Ultra-Short-Term HRV Analysis Using PPG—A Preliminary Study
Authors: Taoum Aline , Bisiaux Alexis , Tilquin Florian , Le Guillou Yann , Carrault Guy
Published: October 2022

Continuous measurement of heart rate variability (HRV) in the short and ultra-short-term using wearable devices allows monitoring of physiological status and prevention of diseases. This study aims to evaluate the agreement of HRV features between a commercial device (Bora Band, Biosency) measuring photoplethysmography (PPG) and reference electrocardiography (ECG) and to assess the validity of ultra-short-term HRV as a surrogate for short-term HRV features. PPG and ECG recordings were acquired from 5 healthy subjects over 18 nights in total. HRV features include time-domain, frequency-domain, nonlinear, and visibility graph features and are extracted from 5 min 30 s and 1 min 30 s duration PPG recordings. The extracted features are compared with reference features of 5 min 30 s duration ECG recordings using repeated-measures correlation, Bland–Altman plots with 95% limits of agreements, Cliff’s delta, and an equivalence test. Results showed agreement between PPG recordings and ECG reference recordings for 37 out of 48 HRV features in short-term durations. Sixteen of the forty-eight HRV features were valid and retained very strong correlations, negligible to small bias, with statistical equivalence in the ultra-short recordings (1 min 30 s). The current study concludes that the Bora Band provides valid and reliable measurement of HRV features in short and ultra-short duration recordings.
Modeling Match Performance in Elite Volleyball Players: Importance of Jump Load and Strength Training Characteristics
Authors: de Leeuw Arie-Willem , van Baar Rick , Knobbe Arno , van der Zwaard Stephan
Published: October 2022

In this study, we investigated the relationships between training load, perceived wellness and match performance in professional volleyball by applying the machine learning techniques XGBoost, random forest regression and subgroup discovery. Physical load data were obtained by manually logging all physical activities and using wearable sensors. Daily wellness of players was monitored using questionnaires. Match performance was derived from annotated actions by a video scout during matches. We identified conditions of predictor variables that related to attack and pass performance (p < 0.05). Better attack performance is related to heavy weights of lower-body strength training exercises in the preceding four weeks. However, worse attack performance is linked to large variations in weights of full-body strength training exercises, excessively heavy upper-body strength training, low jump heights and small variations in the number of high jumps in the four weeks prior to competition. Lower passing performance was associated with small variations in the number of high jumps in the preceding week and an excessive amount of high jumps performed, on average, in the two weeks prior to competition. Differences in findings with respect to passing and attack performance suggest that elite volleyball players can improve their performance if training schedules are adapted to the position of a player.
Comparison of High-Speed Polarization Imaging Methods for Biological Tissues
Authors: Wu Xianyu , Pankow Mark , Onuma Taka , Huang Hsiao-Ying Shadow , Peters Kara
Published: October 2022

We applied a polarization filter array and high-speed camera to the imaging of biological tissues during large, dynamic deformations at 7000 frames per second. The results are compared to previous measurements of similar specimens using a rotating polarizer imaging system. The polarization filter eliminates motion blur and temporal bias from the reconstructed collagen fiber alignment angle and retardation images. The polarization imaging configuration dose pose additional challenges due to the need for calibration of the polarization filter array for a given sample in the same lighting conditions as during the measurement.
Comparison of the Usability of Apple M1 Processors for Various Machine Learning Tasks
Authors: Kasperek David , Podpora Michal , Kawala-Sterniuk Aleksandra
Published: October 2022

In this paper, the authors have compared all of the currently available Apple MacBook Pro laptops, in terms of their usability for basic machine learning research applications (text-based, vision-based, tabular). The paper presents four tests/benchmarks, comparing four Apple Macbook Pro laptop versions: Intel based (i5) and three Apple based (M1, M1 Pro and M1 Max). A script in the Swift programming language was prepared, whose goal was to conduct the training and evaluation process for four machine learning (ML) models. It used the Create ML framework—Apple’s solution dedicated to ML model creation on macOS devices. The training and evaluation processes were performed three times. While running, the script performed measurements of their performance, including the time results. The results were compared with each other in tables, which allowed to compare and discuss the performance of individual devices and the benefits of the specificity of their hardware architectures.
Soil Nitrogen Content Detection Based on Near-Infrared Spectroscopy
Authors: Tan Baohua , You Wenhao , Tian Shihao , Xiao Tengfei , Wang Mengchen , Zheng Beitian , Luo Lina
Published: October 2022

Traditional soil nitrogen detection methods have the characteristics of being time-consuming and having an environmental pollution effect. We urgently need a rapid, easy-to-operate, and non-polluting soil nitrogen detection technology. In order to quickly measure the nitrogen content in soil, a new method for detecting the nitrogen content in soil is presented by using a near-infrared spectrum technique and random forest regression (RF). Firstly, the experiment took the soil by the Xunsi River in the area of Hubei University of Technology as the research object, and a total of 143 soil samples were collected. Secondly, NIR spectral data from 143 soil samples were acquired, and chemical and physical methods were used to determine the content of nitrogen in the soil. Thirdly, the raw spectral data of soil samples were denoised by preprocessing. Finally, a forecast model for the soil nitrogen content was developed by using the measured values of components and modeling algorithms. The model was optimized by adjusting the changes in the model parameters and Gini coefficient (∆Gini), and the model was compared with the back propagation (BP) and support vector machine (SVM) models. The results show that: the RF model modeling set prediction R2C is 0.921, the RMSEC is 0.115, the test set R2P is 0.83, and the RMSEP is 0.141; the detection of the soil nitrogen content can be realized by using a near-infrared spectrum technique and random forest algorithm, and its prediction accuracy is better than that of the BP and SVM models; using ∆ Gini to optimize the RF modeling data, the spectral information of the soil nitrogen content can be extracted, and the data redundancy can be reduced effectively.
A Novel Pallet Detection Method for Automated Guided Vehicles Based on Point Cloud Data
Authors: Shao Yiping , Fan Zhengshuai , Zhu Baochang , Zhou Minlong , Chen Zhihui , Lu Jiansha
Published: October 2022

Automated guided vehicles are widely used in warehousing environments for automated pallet handling, which is one of the fundamental parts to construct intelligent logistics systems. Pallet detection is a critical technology for automated guided vehicles, which directly affects production efficiency. A novel pallet detection method for automated guided vehicles based on point cloud data is proposed, which consists of five modules including point cloud preprocessing, key point extraction, feature description, surface matching and point cloud registration. The proposed method combines the color with the geometric features of the pallet point cloud and constructs a new Adaptive Color Fast Point Feature Histogram (ACFPFH) feature descriptor by selecting the optimal neighborhood adaptively. In addition, a new surface matching method called the Bidirectional Nearest Neighbor Distance Ratio-Approximate Congruent Triangle Neighborhood (BNNDR-ACTN) is proposed. The proposed method overcomes the problems of current methods such as low efficiency, poor robustness, random parameter selection, and being time-consuming. To verify the performance, the proposed method is compared with the traditional and modified Iterative Closest Point (ICP) methods in two real-world cases. The results show that the Root Mean Square Error (RMSE) is reduced to 0.009 and the running time is reduced to 0.989 s, which demonstrates that the proposed method has faster registration speed while maintaining higher registration accuracy.
Development of Augmented-Reality-Based Magnetic Field Visualization System as an Educational Tool
Authors: Nakamura Hisahide , Mizuno Yukio
Published: October 2022

Electromagnetism is a difficult subject to learn because the phenomenon is not observed directly and does not have a perceivable, concrete image. The visualization of the phenomenon will greatly help beginners understand electromagnetics. This paper proposes an augmented-reality-based visualization system of the magnetic flux density around current-generating objects. The system can be realized with a portable magnetic field sensor and a familiar device—a smartphone. The effectiveness of the system is verified through experiments, and our findings suggest that the system can effectively help an operator gain an intuitive understanding of the magnetic flux density.
A Deep Learning Framework for Accurate Vehicle Yaw Angle Estimation from a Monocular Camera Based on Part Arrangement
Authors: Huang Wenjun , Li Wenbo , Tang Luqi , Zhu Xiaoming , Zou Bin
Published: October 2022

An accurate object pose is essential to assess its state and predict its movements. In recent years, scholars have often predicted object poses by matching an image with a virtual 3D model or by regressing the six-degree-of-freedom pose of the target directly from the pixel data via deep learning methods. However, these approaches may ignore a fact that was proposed in the early days of computer vision research, i.e., that object parts are strongly represented in the object pose. In this study, we propose a novel and lightweight deep learning framework, YAEN (yaw angle estimation network), for accurate object yaw angle prediction from a monocular camera based on the arrangement of parts. YAEN uses an encoding–decoding structure for vehicle yaw angle prediction. The vehicle part arrangement information is extracted by the part-encoding network, and the yaw angle is extracted from vehicle part arrangement information by the yaw angle decoding network. Because vehicle part information is refined by the encoder, the decoding network structure is lightweight; the YAEN model has low hardware requirements and can reach a detection speed of 97FPS on a 2070s graphics cards. To improve the performance of our model, we used asymmetric convolution and SSE (sum of squared errors) loss functions of adding the sign. To verify the effectiveness of this model, we constructed an accurate yaw angle dataset under real-world conditions with two vehicles equipped with high-precision positioning devices. Experimental results prove that our method can achieve satisfactory prediction performance in scenarios in which vehicles do not obscure each other, with an average prediction error of less than 3.1° and an accuracy of 96.45% for prediction errors of less than 10° in real driving scenarios.
Effects of Resistance Training as a Behavioural Preventive Measure on Musculoskeletal Complaints, Maximum Strength and Ergonomic Risk in Dentists and Dental Assistants
Authors: Holzgreve Fabian , Fraeulin Laura , Maurer-Grubinger Christian , Betz Werner , Erbe Christina , Weis Tim , Janssen Keno , Schulte Lisa , de Boer Amaya , Nienhaus Albert , Groneberg David A. , Ohlendorf Daniela
Published: October 2022

Introduction: For dental professionals, musculoskeletal disorders (MSD) are common health hazards and resistance training programmes have been promising approaches in the quest for a reduction in the pain intensity of these professionals. Therefore, the aim of the current study was to investigate the effect of a trunk-oriented 10-week resistance training programme. Method: In total, the study was conducted with 17 dentists and dental assistants (3 m/14 f) over a course of 10 weeks, with workouts being performed 2 times a week using a 60 min intervention programme consisting of 11 resistance training exercises. The outcome values that were collected were the pain intensity (visual analogue scale (VAS) combined with a modified version of the Nordic Questionnaire), the MVIC and the rapid upper limb assessment (RULA) score (based on data from inertial motion units) during a standardised dental treatment protocol. Results: A significant reduction in pain intensity was found for each queried body region: the neck, upper back, lower back and the right and left shoulders. The maximum voluntary isometric contraction (MVIC) improved significantly in all outcome measures: flexion, extension, right and left lateral flexion and right and left rotation. Conclusions: A 10-week resistance training programme for dentists and dental assistants had significant effects on pain intensity reduction and the MVIC of the musculature of the trunk and is, therefore, suitable as a behavioural preventive measure against MSD in dental professionals.
E-Cardiac Care: A Comprehensive Systematic Literature Review
Authors: Umar Umara , Nayab Sanam , Irfan Rabia , Khan Muazzam A. , Umer Amna
Published: October 2022

The Internet of Things (IoT) is a complete ecosystem encompassing various communication technologies, sensors, hardware, and software. IoT cutting-edge technologies and Artificial Intelligence (AI) have enhanced the traditional healthcare system considerably. The conventional healthcare system faces many challenges, including avoidable long wait times, high costs, a conventional method of payment, unnecessary long travel to medical centers, and mandatory periodic doctor visits. A Smart healthcare system, Internet of Things (IoT), and AI are arguably the best-suited tailor-made solutions for all the flaws related to traditional healthcare systems. The primary goal of this study is to determine the impact of IoT, AI, various communication technologies, sensor networks, and disease detection/diagnosis in Cardiac healthcare through a systematic analysis of scholarly articles. Hence, a total of 104 fundamental studies are analyzed for the research questions purposefully defined for this systematic study. The review results show that deep learning emerges as a promising technology along with the combination of IoT in the domain of E-Cardiac care with enhanced accuracy and real-time clinical monitoring. This study also pins down the key benefits and significant challenges for E-Cardiology in the domains of IoT and AI. It further identifies the gaps and future research directions related to E-Cardiology, monitoring various Cardiac parameters, and diagnosis patterns.
Towards East Asian Facial Expression Recognition in the Real World: A New Database and Deep Recognition Baseline
Authors: Li Shanshan , Guo Liang , Liu Jianya
Published: October 2022

In recent years, the focus of facial expression recognition (FER) has gradually shifted from laboratory settings to challenging natural scenes. This requires a great deal of real-world facial expression data. However, most existing real-world databases are based on European-American cultures, and only one is for Asian cultures. This is mainly because the data on European-American expressions are more readily accessed and publicly available online. Owing to the diversity of huge data, FER in European-American cultures has recently developed rapidly. In contrast, the development of FER in Asian cultures is limited by the data. To narrow this gap, we construct a challenging real-world East Asian facial expression (EAFE) database, which contains 10,000 images collected from 113 Chinese, Japanese, and Korean movies and five search engines. We apply three neural network baselines including VGG-16, ResNet-50, and Inception-V3 to classify the images in EAFE. Then, we conduct two sets of experiments to find the optimal learning rate schedule and loss function. Finally, by training with the cosine learning rate schedule and island loss, ResNet-50 can achieve the best accuracy of 80.53% on the testing set, proving that the database is challenging. In addition, we used the Microsoft Cognitive Face API to extract facial attributes in EAFE, so that the database can also be used for facial recognition and attribute analysis. The release of the EAFE can encourage more research on Asian FER in natural scenes and can also promote the development of FER in cross-cultural domains.
A Comprehensive Survey on the Non-Invasive Passive Side-Channel Analysis
Authors: Socha Petr , Miškovský Vojtěch , Novotný Martin
Published: October 2022

Side-channel analysis has become a widely recognized threat to the security of cryptographic implementations. Different side-channel attacks, as well as countermeasures, have been proposed in the literature. Such attacks pose a severe threat to both hardware and software cryptographic implementations, especially in the IoT environment where the attacker may easily gain physical access to a device, leaving it vulnerable to tampering. In this paper, we provide a comprehensive survey regarding the non-invasive passive side-channel analysis. We describe both non-profiled and profiled attacks, related security metrics, countermeasures against such attacks, and leakage-assessment methodologies, as available in the literature of more than twenty years of research.
Estimation and Classification of NLFM Signals Based on the Time–Chirp Representation
Authors: Swiercz Ewa , Janczak Dariusz , Konopko Krzysztof
Published: October 2022

A new approach to the estimation and classification of nonlinear frequency modulated (NLFM) signals is presented in the paper. These problems are crucial in electronic reconnaissance systems whose role is to indicate what signals are being received and recognized by the intercepting receiver. NLFM signals offer a variety of useful properties not available for signals with linear frequency modulation (LFM). In particular, NLFM signals can ensure the desired reduction of sidelobes of an autocorrelation (AC) function and desired power spectral density (PSD); therefore, such signals are more frequently used in modern radar and echolocation systems. Due to their nonlinear properties, the discussed signals are difficult to recognize and therefore require sophisticated methods of analysis, estimation and classification. NLFM signals with frequency content varying with time are mainly analyzed by time–frequency algorithms. However, the methods presented in the paper belong to time–chirp domain, which is relatively rarely cited in the literature. It is proposed to use polynomial approximations of nonlinear frequency and phase functions describing signals. This allows for applying the cubic phase function (CPF) as an estimator of phase polynomial coefficients. Originally, the CPF involved only third-order nonlinearities of the phase function. The extension of the CPF using nonuniform sampling is used to analyse the higher order polynomial phase. In this paper, a sixth order polynomial is considered. It is proposed to estimate the instantaneous frequency using a polynomial with coefficients calculated from the coefficients of the phase polynomial obtained by CPF. The determined coefficients also constitute the set of distinctive features for a classification task. The proposed CPF-based classification method was examined for three common NLFM signals and one LFM signal. Two types of neural network classifiers: learning vector quantization (LVQ) and multilayer perceptron (MLP) are considered for such defined classification problem. The performance of both the estimation and classification processes was analyzed using Monte Carlo simulation studies for different SNRs. The results of the simulation research revealed good estimation performance and error-free classification for the SNR range encountered in practical applications.
HoloKinect: Holographic 3D Video Conferencing
Authors: Siemonsma Stephen , Bell Tyler
Published: October 2022

Recent world events have caused a dramatic rise in the use of video conferencing solutions such as Zoom and FaceTime. Although 3D capture and display technologies are becoming common in consumer products (e.g., Apple iPhone TrueDepth sensors, Microsoft Kinect devices, and Meta Quest VR headsets), 3D telecommunication has not yet seen any appreciable adoption. Researchers have made great progress in developing advanced 3D telepresence systems, but often with burdensome hardware and network requirements. In this work, we present HoloKinect, an open-source, user-friendly, and GPU-accelerated platform for enabling live, two-way 3D video conferencing on commodity hardware and a standard broadband internet connection. A Microsoft Azure Kinect serves as the capture device and a Looking Glass Portrait multiscopically displays the final reconstructed 3D mesh for a hologram-like effect. HoloKinect packs color and depth information into a single video stream, leveraging multiwavelength depth (MWD) encoding to store depth maps in standard RGB video frames. The video stream is compressed with highly optimized and hardware-accelerated video codecs such as H.264. A search of the depth and video encoding parameter space was performed to analyze the quantitative and qualitative losses resulting from HoloKinect’s lossy compression scheme. Visual results were acceptable at all tested bitrates (3–30 Mbps), while the best results were achieved with higher video bitrates and full 4:4:4 chroma sampling. RMSE values of the recovered depth measurements were low across all settings permutations.
Path Planning of Laser Soldering System Based on Intelligent Algorithm
Authors: Zhang Cong , Fan Zige , Dai Yaonan , Chen Hang , Wang Sikai , Chen Xubing
Published: October 2022

Laser soldering has been gradually applied to the soldering of electronic components due to the rapid development of microelectronics. However, it is inefficient to use a mechanical shaft to move a laser beam. Here, a laser soldering system is constructed using galvanometer scanning, and an intelligent algorithm is also introduced to optimize the soldering path. Firstly, a laser soldering system for scanning of galvanometers is established, and the functions of visual monitoring, motion planning and parameter integration are presented. Secondly, the position of the laser beam and the corresponding soldering spot are determined, and the coordinate information is provided to plan a route by camera calibration and coordinate system transformation. Finally, the problem of path planning in this system is decomposed into the generation of the soldering point full coverage processing frame, and the route optimization of processing platform and laser beam motion. Furthermore, an improved clustering algorithm, based on the characteristics of system structure, and a hybrid optimization algorithm are designed to deal with the generation of the soldering point full coverage processing frame, the route optimization of processing platform and laser beam motion. In addition, the simulations and experiments are verified by test board. These findings shown that the established system and designed optimization algorithm can promote the efficiency of laser soldering.
Design and Verification of an Integrated Panoramic Sun Sensor atop a Small Spherical Satellite
Authors: Zhang Qi , Zhang Yulin
Published: October 2022

This paper proposes an integrated panoramic sun sensor (IPSS) for the small spherical satellite Q-SAT that has been working in orbit since 2020. IPSS is essentially a set of temperature-compensated photoelectric cells distributed on the spherical surface of Q-SAT. Compared with traditional sun sensors, IPSS has full spherical coverage of 4π so that the sun vector from any direction can be inversed. The mechatronic design and mathematical model of the proposed IPSS are presented. In-depth error analyses in terms of albedo effect, sampling error, parameter deviation, etc. are carried out. IPSS can provide a sun vector inversion accuracy of 1.5∘ where albedo disturbance does not dominate. Simulation results show that the measurement of IPSS together with a COTS magnetometer can support the three-axis attitude determination of satellites in various orbits and can adapt to the seasonal variations of subpolar points. Ground experimental results and on-orbit data have also verified the feasibility and performance of IPSS. Although the panoramic sun sensor is designed for the small spherical Q-SAT, it can also be applied to other satellites with limited power budgets.
Model for Managing the Integration of a Vehicle-to-Home Unit into an Intelligent Home Energy Management System
Authors: Almughram Ohoud , Ben Slama Sami , Zafar Bassam
Published: October 2022

Integration of vehicle-to-home (V2H) centralized photovoltaic (HCPV) systems is a requested and potentially fruitful research topic for both industry and academia. Renewable energy sources, such as wind turbines and solar photovoltaic panels, alleviate energy deficits. Furthermore, energy storage technologies, such as batteries, thermal, and electric vehicles, are indispensable. Consequently, in this article, we examine the impact of solar photovoltaic (SPV), microgrid (MG) storage, and an electric vehicle (EV) on maximum sun radiation hours. As a result, an HCPV scheduling algorithm is developed and applied to maximize energy sustainability in a smart home (SH). The suggested algorithm can manage energy demand between the MG and SPV systems, as well as the EV as a mobile storage system. The model is based on several limitations to meet households’ electrical needs during sunny and cloudy weather. A multi-agent system (MAS) is undertaken to ensure proper system operation and meet the power requirements of various devices. An experimental database for weather and appliances is deployed to evaluate and control energy consumption and production cost parameters. The obtained results illustrate the benefits of V2H technology as a prospective unit storage solution.
Modification of Taxifolin Properties by Spray Drying
Authors: Taldaev Amir , Terekhov Roman P. , Selivanova Irina A. , Pankov Denis I. , Anurova Maria N. , Markovina Irina Yu. , Cong Zhaoqing , Ma Siqi , Dong Zhengqi , Yang Feifei , Liao Yonghong
Published: October 2022

Taxifolin is known as an active pharmaceutical ingredient (API) and food supplement due to its high antioxidant activity, multiple pharmacological effects, and good safety profile. Previously, taxifolin spheres (TS) were obtained from industrially produced API taxifolin in Russia (RT). In our work, we perform a pharmaceutical analysis of this new taxifolin material versus RT. TS is an amorphous material; however, it is stable without the polymer carrier, as confirmed by Fourier transform infrared spectroscopy. Both RT and TS demonstrate high safety profiles and are assigned to Class 1 of the Biopharmaceutical Classification System based on the results of experiments with MDCK cells. The water solubility of the new taxifolin form was 2.225 times higher compared with RT. Hausner ratios for RT and TS were 1.421 and 1.219, respectively, while Carr indices were 29.63% and 19.00%, respectively. Additionally, TS demonstrated sustained release from tablets compared with RT: the half-life values of tablets were 14.56 min and 20.63 min for RT and TS, respectively. Thus, TS may be a promising object for developing oral antiseptics in the form of orally dispersed tablets with sustained release patterns because of its anti-inflammatory, -protozoal, and -viral activities.
Ecological Effects of Surface Water Evolution in the Yellow River Delta
Authors: Li Yunlong , Huang Shuping , Kong Xianglun , Han Mei , Wang Min , Hui Hongkuan
Published: October 2022

With the ecological protection and high-quality development of the Yellow River Basin rising to China’s national strategy, the Yellow River Delta is facing a historic development opportunity, and the surface water problems in this region are becoming more and more severe. Owing to the dual effects of the swing of the Yellow River’s channel and human activities, the surface water in the Yellow River Delta is in an evolving state. Consequently, it is important to pay attention to the ecosystem response caused by surface water evolution for the ecological protection and high-quality development of the Yellow River Delta. Drawing on ecological, economic, and network analysis, in this study, the ecological service and landscape effects of the surface water evolution in the Yellow River Delta from 1986 to 2019 are explored using remote sensing and socioeconomic data. The results are as follows: (1) The surface water in the Yellow River Delta has evolved significantly in the last 35 years. Artificial water accounted for the dominant proportion of the total water, and the composition of water tended to be remarkably heterogeneous. (2) The ecological services of the surface water in the delta increased significantly during the study period. The ecological services of the surface water improved to varying degrees except for sedimentary land reclamation. Tourism, materials production, and water supply became the leading service functions of the surface water in the Yellow River Delta. The proportion of cultural functions rose rapidly, and the share of regulatory functions shrank significantly. (3) The evolution of the surface water had an evident impact on the landscape pattern of the Yellow River Delta, which was manifested as physical cutting and spatial attraction. This demonstrates the comprehensive effect of protective constraints, spatial attraction, and spatial exclusion on the landscape pattern of regional land types. The results of this study have a certain guiding significance for the development and management of the water resources in the Yellow River Delta and also provide information for ecological protection in this region.
Societal Trust Related to COVID-19 Vaccination: Evidence from Western Balkans
Authors: Cvjetkovic Smiljana , Jeremic Stojkovic Vida , Mandic-Rajcevic Stefan , Matovic-Miljanovic Sanja , Jankovic Janko , Jovic Vranes Aleksandra , Stevanovic Aleksandar , Stamenkovic Zeljka
Published: October 2022

The lower rates of COVID-19 vaccination in Western Balkans countries could be partially explained by societal distrust of its citizens, jeopardizing the sustainability of COVID-19 vaccination programs. The aim of the study was to determine the level and predictors of societal trust in five countries of the region. Using an online questionnaire, data were obtained from 1157 respondents from Albania, Bosnia and Herzegovina, Montenegro, North Macedonia, and Serbia. The instrument included a socio-demographic questionnaire, a measure of vaccination behavior, and a scale measuring societal trust. Being a significant determinant of the COVID-19 vaccination behavior in all countries, societal trust considerably varied from country to country (F (24, 4002) = 7.574, p < 0.001). It was highest in North Macedonia (Mean = 3.74, SD = 0.99), and lowest in Albania (Mean = 3.21, SD = 1.03). Younger, female, less religious, and higher educated tended to have more pronounced societal trust in Serbia. In North Macedonia, younger age and lower health literacy predicted societal trust, while in Bosnia and Herzegovina, educational level was the single predictor. In Montenegro and Albania, higher societal trust was significantly predicted by lower health literacy only. The results provide evidence that the determinants of societal trust in Western Balkans vary across countries, indicating the need for different approaches in communication campaigns.
Exploring the Spatio-Temporal and Behavioural Variations in Taxi Travel Based on Big Data during the COVID-19 Pandemic: A Case Study of New York City
Authors: Li Sen , Bao Shitai , Yao Ceyi , Zhang Lan
Published: October 2022

The COVID-19 pandemic has caused severe social and economic chaos worldwide. To explore the impact of the COVID-19 pandemic on the travel patterns of residents, we analysed taxi trajectory data and COVID-19 pandemic data from New York City. Pearson coefficients, which were −0.7139, −0.8041, and −0.7046 during the three waves of the COVID-19, revealed a significant negative correlation between confirmed cases and taxi trips. Moran’s I was higher in drop-off areas than in pick-up areas, indicating a stronger spatial autocorrelation in drop-off areas during the study period. The hotspots of travel destinations had changed by spatial clustering, and variations in origin–destination distribution were obvious after the pandemic. Comparison of temporal and spatial dimensions before and after the pandemic revealed that strict epidemic policies directly affected travel. For instance, a week after the restrictions the taxi journeys plummeted by 95.3%, and their spatial and temporal patterns also changed. Once the anti-epidemic policy was eased or lifted, the taxi travel recovered, whereas, notably the new Omicron wave did not cause dramatic changes in taxi journeys. Despite this, travel spatial and temporal patterns did not return to pre-pandemic levels by the end of March 2022, the taxi journeys remained below half the pre-pandemic level. This study identified the profound impact of the COVID-19 outbreak on travel patterns and revealed distinct variations in behavioural responses during the pandemic and in response to subsequent policies. Strengthening targeted epidemic prevention and control measures are required to improve the balance between anti-epidemic policies and implementation efforts, that will facilitate the recovery of urban transport, work, and lifestyle of residents.