bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimal Estimation Inversion of Ionospheric Electron Density from GNSS-POD Limb Measurements: Part I-Algorithm and Morphology
Authors: Wu Dong L. , Swarnalingam Nimalan , Salinas Cornelius Csar Jude H. , Emmons Daniel J. , Summers Tyler C. , Gardiner-Garden Robert
Published: June 2023

GNSS-LEO radio links from Precise Orbital Determination (POD) and Radio Occultation (RO) antennas have been used increasingly in characterizing the global 3D distribution and variability of ionospheric electron density (Ne). In this study, we developed an optimal estimation (OE) method to retrieve Ne profiles from the slant total electron content (hTEC) measurements acquired by the GNSS-POD links at negative elevation angles (ε < 0°). Although both OE and onion-peeling (OP) methods use the Abel weighting function in the Ne inversion, they are significantly different in terms of performance in the lower ionosphere. The new OE results can overcome the large Ne oscillations, sometimes negative values, seen in the OP retrievals in the E-region ionosphere. In the companion paper in this Special Issue, the HmF2 and NmF2 from the OE retrieval are validated against ground-based ionosondes and radar observations, showing generally good agreements in NmF2 from all sites. Nighttime hmF2 measurements tend to agree better than the daytime when the ionosonde heights tend to be slightly lower. The OE algorithm has been applied to all GNSS-POD data acquired from the COSMIC-1 (2006–2019), COSMIC-2 (2019–present), and Spire (2019–present) constellations, showing a consistent ionospheric Ne morphology. The unprecedented spatiotemporal sampling of the ionosphere from these constellations now allows a detailed analysis of the frequency–wavenumber spectra for the Ne variability at different heights. In the lower ionosphere (~150 km), we found significant spectral power in DE1, DW6, DW4, SW5, and SE4 wave components, in addition to well-known DW1, SW2, and DE3 waves. In the upper ionosphere (~450 km), additional wave components are still present, including DE4, DW4, DW6, SE4, and SW4. The co-existence of eastward- and westward-propagating wave4 components implies the presence of a stationary wave4 (SPW4), as suggested by other earlier studies. Further improvements to the OE method are proposed, including a tomographic inversion technique that leverages the asymmetric sampling about the tangent point associated with GNSS-LEO links.
Small Object Detection Based on Deep Learning for Remote Sensing: A Comprehensive Review
Authors: Wang Xuan , Wang Aoran , Yi Jinglei , Song Yongchao , Chehri Abdellah
Published: June 2023

With the accelerated development of artificial intelligence, remote-sensing image technologies have gained widespread attention in smart cities. In recent years, remote sensing object detection research has focused on detecting and counting small dense objects in large remote sensing scenes. Small object detection, as a branch of object detection, remains a significant challenge in research due to the image resolution, size, number, and orientation of objects, among other factors. This paper examines object detection based on deep learning and its applications for small object detection in remote sensing. This paper aims to provide readers with a thorough comprehension of the research objectives. Specifically, we aggregate the principal datasets and evaluation methods extensively employed in recent remote sensing object detection techniques. We also discuss the irregularity problem of remote sensing image object detection and overview the small object detection methods in remote sensing images. In addition, we select small target detection methods with excellent performance in recent years for experiments and analysis. Finally, the challenges and future work related to small object detection in remote sensing are highlighted.
Airborne Radar Staggered PRF Coherent Processing Method for Down-Looking Target Detection
Authors: Sun Xiping , Wu Xipeng , Zhou Qinquan , Zhang Lei , Wu Jianxin
Published: June 2023

Airborne radar plays a crucial role in detecting aerial targets, but its detection capability can be significantly affected by strong ground clutter when operating in down-looking mode. The range-Doppler (RD) results of ground clutter in airborne radar may overlap the range-Doppler results of the targets, causing target occlusion and rendering them undetectable. In addition to target detection, accurate extraction of target parameters is also of great importance. Due to the high-speed movement of the airborne platform, targets may exhibit range-Doppler ambiguity, necessitating the use of appropriate methods to resolve the range-Doppler ambiguity. Aiming at the problem of target occlusion by clutter with range-Doppler two-dimensional ambiguity during airborne radar down-looking target detection, this paper conducts a study on the detection of airborne radar down-looking targets based on orthogonal signals combined with pulse repetition frequency (PRF) staggered transmission. An optimization method is employed to design multiple sets of PRF values, aiming to reduce the radar blind zone. The range-Doppler two-dimensional ambiguity of targets is resolved using multiple sets of PRF, and coherent processing is performed on the corresponding echoes using the transmission intervals, maximizing the utilization of radar transmission energy. This approach enhances the signal-to-noise ratio (SNR) and improves the detection performance and parameter estimation accuracy, particularly for low SNR targets. The superiority of the proposed approach is demonstrated through simulation experiments.
A Novel Approach to Characterizing Crown Vertical Profile Shapes Using Terrestrial Laser Scanning (TLS)
Authors: Wang Fan , Sun Yuman , Jia Weiwei , Li Dandan , Zhang Xiaoyong , Tang Yiren , Guo Haotian
Published: June 2023

Crown vertical profiles (CVP) play an essential role in stand biomass and forest fire prediction. Traditionally, due to measurement difficulties, CVP models developed based on a small number of individual trees are not convincing. Terrestrial laser scanning (TLS) provides new insights for researching trees’ CVPs. However, there is a limited understanding of the ability to accurately describe CVPs with TLS. In this study, we propose a new approach to automatically extract the crown radius (CR) at different heights and confirm the correctness and effectiveness of the proposed approach with field measurement data from 30 destructively harvested sample trees. We then applied the approach to extract the CR from 283 trees in 6 sample plots to develop a two-level nonlinear mixed-effects (NLME) model for the CVP. The results of the study showed that the average extraction accuracy of the CR when the proposed approach was applied was 90.12%, with differences in the extraction accuracies at different relative depths into the crown (RDINC) ranges. The TLS-based extracted CR strongly correlated with the field-measured CR, with an R2 of 0.93. Compared with the base model, the two-level NLME model has significantly improved the prediction accuracy, with Ra2 increasing by 13.8% and RMSE decreasing by 23.46%. All our research has demonstrated that TLS has great potential for accurately extracting CRs, which would provide a novel way to nondestructively measure the crown structure. Moreover, our research lays the foundation for the future development of CVP models using TLS at a regional scale.
LACC2.0: Improving the LACC Algorithm for Reconstructing Satellite-Derived Time Series of Vegetation Biochemical Parameters
Authors: Xu Mingzhu , Shang Rong , Chen Jing M. , Zeng Lingfang
Published: June 2023

The locally adjusted cubic-spline capping (LACC) algorithm is well recognized for its effectiveness in the global time series reconstruction of vegetation biophysical and biochemical parameters. However, in its application, we often encounter issues, such as identifying positively biased outliers for vegetation biochemical parameters and reducing the influence of long consecutive gaps. In this study, we improved the LACC algorithm to address the above two issues by (1) incorporating a procedure to remove outliers and (2) integrating the spatial information of neighboring pixels for large data gap filling. To evaluate the performance of the new version of LACC (namely LACC2.0), leaf chlorophyll content (LCC) was taken as an example. A reference LCC curve was generated for each pixel of the global map as the true value for global evaluation, and a time series of LCC with real gaps in the original data for each pixel was created by adding Gaussian noises into observations for testing the effectiveness of time series reconstruction algorithms. Results showed that the percentage of pixels with an RMSE smaller than 5 μg/cm2 was improved from 81.2% in LACC to 96.4% in LACC2.0, demonstrating that LACC2.0 had the potential to provide a better reconstruction of global daily satellite-derived vegetation biochemical parameters. This finding highlights the significance of outlier removal and spatial-temporal fusion to enhance the accuracy and reliability of time series reconstruction.
Paving the Way for Motor Imagery-Based Tele-Rehabilitation through a Fully Wearable BCI System
Authors: Arpaia Pasquale , Coyle Damien , Esposito Antonio , Natalizio Angela , Parvis Marco , Pesola Marisa , Vallefuoco Ersilia
Published: June 2023

The present study introduces a brain–computer interface designed and prototyped to be wearable and usable in daily life. Eight dry electroencephalographic sensors were adopted to acquire the brain activity associated with motor imagery. Multimodal feedback in extended reality was exploited to improve the online detection of neurological phenomena. Twenty-seven healthy subjects used the proposed system in five sessions to investigate the effects of feedback on motor imagery. The sample was divided into two equal-sized groups: a “neurofeedback” group, which performed motor imagery while receiving feedback, and a “control” group, which performed motor imagery with no feedback. Questionnaires were administered to participants aiming to investigate the usability of the proposed system and an individual’s ability to imagine movements. The highest mean classification accuracy across the subjects of the control group was about 62% with 3% associated type A uncertainty, and it was 69% with 3% uncertainty for the neurofeedback group. Moreover, the results in some cases were significantly higher for the neurofeedback group. The perceived usability by all participants was high. Overall, the study aimed at highlighting the advantages and the pitfalls of using a wearable brain–computer interface with dry sensors. Notably, this technology can be adopted for safe and economically viable tele-rehabilitation.
Does Smartphone Use Affect a Subsequent Swimming Training Session? Preliminary Results in Amateur Triathletes
Authors: Quagliarotti Claudio , Coloretti Vittorio , Dello Stritto Emanuele , Cuccurullo Sarah , Acalai Jessica , Lepers Romuald , Fantozzi Silvia , Cortesi Matteo , Piacentini Maria Francesca
Published: June 2023

To date, the literature has failed to individuate a clear motivation for the performance decrement after a mental fatigue-inducing task. This study aimed to evaluate biomechanical and perceptual variables during a swimming training session in different mental fatigue states. Seven amateur triathletes watched a documentary, utilized a smartphone, or performed an AX-CPT for 45 min randomly on three different days. After, they performed a 15-min warm-up followed by 6 × 200 m at constant pre-set speed plus one 200 m at maximal effort. The mental fatigue status was assessed by the visual analog scale (VAS) and short-Stroop task results before, post-mental task, and post-swimming session. The biomechanical and motor coordination variables during swimming were assessed using five IMU sensors and video analysis. The heart rate and rate of perceived exertion were monitored during the task. No differences in biomechanical and perceptual variables were found between and within conditions. Higher mental fatigue was found only in the AX-CPT condition at post task by VAS. In this preliminary study, no changes in swimming biomechanics were highlighted by mental fatigue, but the warm-up performed may have counteracted its negative effects. Further studies are recommended.
Ion-Selective Electrodes with Solid Contact Based on Composite Materials: A Review
Authors: Wardak Cecylia , Pietrzak Karolina , Morawska Klaudia , Grabarczyk Malgorzata
Published: June 2023

Potentiometric sensors are the largest and most commonly used group of electrochemical sensors. Among them, ion-selective electrodes hold a prominent place. Since the end of the last century, their re-development has been observed, which is a consequence of the introduction of solid contact constructions, i.e., electrodes without an internal electrolyte solution. Research carried out in the field of potentiometric sensors primarily focuses on developing new variants of solid contact in order to obtain devices with better analytical parameters, and at the same time cheaper and easier to use, which has been made possible thanks to the achievements of material engineering. This paper presents an overview of new materials used as a solid contact in ion-selective electrodes over the past several years. These are primarily composite and hybrid materials that are a combination of carbon nanomaterials and polymers, as well as those obtained from carbon and polymer nanomaterials in combination with others, such as metal nanoparticles, metal oxides, ionic liquids and many others. Composite materials often have better mechanical, thermal, electrical, optical and chemical properties than the original components. With regard to their use in the construction of ion-selective electrodes, it is particularly important to increase the capacitance and surface area of the material, which makes them more effective in the process of charge transfer between the polymer membrane and the substrate material. This allows to obtain sensors with better analytical and operational parameters. Brief characteristics of electrodes with solid contact, their advantages and disadvantages, as well as research methods used to assess their parameters and analytical usefulness were presented. The work was divided into chapters according to the type of composite material, while the data in the table were arranged according to the type of ion. Selected basic analytical parameters of the obtained electrodes have been collected and summarized in order to better illustrate and compare the achievements that have been described till now in this field of analytical chemistry, which is potentiometry. This comprehensive review is a compendium of knowledge in the research area of functional composite materials and state-of-the-art SC-ISE construction technologies.
Mechatronic Design and Experimental Research of an Automated Photogrammetry-Based Human Body Scanner
Authors: Trojnacki Maciej , Dąbek Przemysław , Jaroszek Piotr
Published: June 2023

The article concerns the mechatronic design and experimental investigations of the HUBO automated human body scanning system. Functional problems that should be solved by using the developed scanning system are defined. These include reducing the number of sensors used while eliminating the need to rotate a human and ensuring the automation of the scanning process. Research problems that should be the subject of experimental research are defined. The current state of the art in the field of systems and techniques for scanning the human figure is described. The functional and technical assumptions for the HUBO scanning system are formulated. The mechanical design of the scanner, the hardware and information system architectures as well as the user’s mobile application are presented. The method of operation of the scanning system and its innovative features are discussed. It is demonstrated that the developed solution of the scanning system allows the identified problems to be overcome. The methodology of the experimental research of the scanning system based on the photogrammetry technique is described. The results of laboratory studies with the use of dummies and experimental research with human participation are presented. The scope of the research carried out allows answers to the identified research problems related to the scanning of the human figure using the photogrammetry technique to be obtained. As part of laboratory tests using a measuring dummy, a mean error of 0.65 mm and standard deviation of the mean of 0.65 mm were obtained for the best scanner configuration. Research with human participation was carried out for the scanner version, in which the scanning time was 30 s, with the possibility of its reduction to 15 s. The results of studies using realistic dummies and with human participation were compared using the root mean square error parameter (RMSE) provided by the AliceVision framework, which was available for all analyzed objects. As a result, it was observed that these results are comparable, i.e., the RMSE parameter is equal to about 1 px.
Soft-Computing-Based Estimation of a Static Load for an Overhead Crane
Authors: Kusznir Tom , Smoczek Jaroslaw
Published: June 2023

Payload weight detection plays an important role in condition monitoring and automation of cranes. Crane cells and scales are commonly used in industrial practice; however, when their installation to the hoisting equipment is not possible or costly, an alternative solution is to derive information about the load weight indirectly from other sensors. In this paper, a static payload weight is estimated based on the local strain of a crane’s girder and the current position of the trolley. Soft-computing-based techniques are used to derive a nonlinear input–output relationship between the measured signals and the estimated payload mass. Data-driven identification is performed using a novel variant of genetic programming named grammar-guided genetic programming with sparse regression, multi-gene genetic programming, and subtractive fuzzy clustering method combined with the least squares algorithm on experimental data obtained from a laboratory overhead crane. A comparative analysis of the methods showed that multi-gene genetic programming and grammar-guided genetic programming with sparse regression performed similarly in terms of accuracy and both performed better than subtractive fuzzy clustering. The novel approach was able to find a more parsimonious model with its direct implantation having a lower execution time.
Parking Time Violation Tracking Using YOLOv8 and Tracking Algorithms
Authors: Sharma Nabin , Baral Sushish , Paing May Phu , Chawuthai Rathachai
Published: June 2023

The major problem in Thailand related to parking is time violation. Vehicles are not allowed to park for more than a specified amount of time. Implementation of closed-circuit television (CCTV) surveillance cameras along with human labor is the present remedy. However, this paper presents an approach that can introduce a low-cost time violation tracking system using CCTV, Deep Learning models, and object tracking algorithms. This approach is fairly new because of its appliance of the SOTA detection technique, object tracking approach, and time boundary implementations. YOLOv8, along with the DeepSORT/OC-SORT algorithm, is utilized for the detection and tracking that allows us to set a timer and track the time violation. Using the same apparatus along with Deep Learning models and algorithms has produced a better system with better performance. The performance of both tracking algorithms was well depicted in the results, obtaining MOTA scores of (1.0, 1.0, 0.96, 0.90) and (1, 0.76, 0.90, 0.83) in four different surveillance data for DeepSORT and OC-SORT, respectively.
Wearable Technologies for Electrodermal and Cardiac Activity Measurements: A Comparison between Fitbit Sense, Empatica E4 and Shimmer GSR3+
Authors: Ronca Vincenzo , Martinez-Levy Ana C. , Vozzi Alessia , Giorgi Andrea , Aricò Pietro , Capotorto Rossella , Borghini Gianluca , Babiloni Fabio , Di Flumeri Gianluca
Published: June 2023

The capability of measuring specific neurophysiological and autonomic parameters plays a crucial role in the objective evaluation of a human’s mental and emotional states. These human aspects are commonly known in the scientific literature to be involved in a wide range of processes, such as stress and arousal. These aspects represent a relevant factor especially in real and operational environments. Neurophysiological autonomic parameters, such as Electrodermal Activity (EDA) and Photoplethysmographic data (PPG), have been usually investigated through research-graded devices, therefore resulting in a high degree of invasiveness, which could negatively interfere with the monitored user’s activity. For such a reason, in the last decade, recent consumer-grade wearable devices, usually designed for fitness-tracking purposes, are receiving increasing attention from the scientific community, and are characterized by a higher comfort, ease of use and, therefore, by a higher compatibility with daily-life environments. The present preliminary study was aimed at assessing the reliability of a consumer wearable device, i.e., the Fitbit Sense, with respect to a research-graded wearable, i.e., the Empatica E4 wristband, and a laboratory device, i.e., the Shimmer GSR3+. EDA and PPG data were collected among 12 participants while they performed multiple resting conditions. The results demonstrated that the EDA- and PPG-derived features computed through the wearable and research devices were positively and significantly correlated, while the reliability of the consumer device was significantly lower.
Improvement of Tiny Object Segmentation Accuracy in Aerial Images for Asphalt Pavement Pothole Detection
Authors: Kim Sujong , Seo Dongmahn , Jeon Soobin
Published: June 2023

In this study, we propose an algorithm to improve the accuracy of tiny object segmentation for precise pothole detection on asphalt pavements. The approach comprises a three-step process: MOED, VAPOR, and Exception Processing, designed to extract pothole edges, validate the results, and manage detected abnormalities. The proposed algorithm addresses the limitations of previous methods and offers several advantages, including wider coverage. We experimentally evaluated the performance of the proposed algorithm by filming roads in various regions of South Korea using a UAV at high altitudes of 30–70 m. The results show that our algorithm outperforms previous methods in terms of instance segmentation performance for small objects such as potholes. Our study offers a practical and efficient solution for pothole detection and contributes to road safety maintenance and monitoring.
A Compact Broadband Common-Mode Suppression Filter That Integrates Series-Mushroom into Defected Corrugated Reference Plane Structures
Authors: Yu Chung-Ke , Lin Ding-Bing , Lin Hsin-Piao , Pramudita Aloysius Adya , Adiprabowo Tjahjo
Published: June 2023

This paper proposes a common-mode noise suppression filter scheme for use in the servers and computer systems of high-speed buses such as SATA Express, HDMI 2.0, USB 3.2, and PCI Express 5.0. The filter uses a novel series-mushroom-defected corrugated reference plane (SMDCRP) structure. The measured results are similar to the full-wave simulation results. In the frequency domain, the measured insertion loss of the SMDCRP structure filter in differential mode (DM) can be kept below −4.838 dB from DC to 32 GHz and can maintain signal integrity characteristics. The common-mode (CM) suppression performance can suppress more than −10 dB from 8.81 GHz to 32.65 GHz. Fractional bandwidth can be increased to 115%, and CM noise can be ameliorated by 55.2%. In the time domain, using eye diagram verification, the filter shows complete differential signal transmission capability and supports a transmission rate of 32 Gb/s for high-speed buses. The SMDCRP structure filter reduces the electromagnetic interference (EMI) problem and meets the quality requirements for the controllers and sensors used in the server and computer systems of high-speed buses.
A Light-Weight Artificial Neural Network for Recognition of Activities of Daily Living
Authors: Mohamed Samer A. , Martinez-Hernandez Uriel
Published: June 2023

Human activity recognition (HAR) is essential for the development of robots to assist humans in daily activities. HAR is required to be accurate, fast and suitable for low-cost wearable devices to ensure portable and safe assistance. Current computational methods can achieve accurate recognition results but tend to be computationally expensive, making them unsuitable for the development of wearable robots in terms of speed and processing power. This paper proposes a light-weight architecture for recognition of activities using five inertial measurement units and four goniometers attached to the lower limb. First, a systematic extraction of time-domain features from wearable sensor data is performed. Second, a small high-speed artificial neural network and line search method for cost function optimization are used for activity recognition. The proposed method is systematically validated using a large dataset composed of wearable sensor data from seven activities (sitting, standing, walking, stair ascent/descent, ramp ascent/descent) associated with eight healthy subjects. The accuracy and speed results are compared against methods commonly used for activity recognition including deep neural networks, convolutional neural networks, long short-term memory and convolutional–long short-term memory hybrid networks. The experiments demonstrate that the light-weight architecture can achieve a high recognition accuracy of 98.60%, 93.10% and 84.77% for seen data from seen subjects, unseen data from seen subjects and unseen data from unseen subjects, respectively, and an inference time of 85 μs. The results show that the proposed approach can perform accurate and fast activity recognition with a reduced computational complexity suitable for the development of portable assistive devices.
Image Mapping Accuracy Evaluation Using UAV with Standalone, Differential (RTK), and PPP GNSS Positioning Techniques in an Abandoned Mine Site
Authors: Kim Hanjin , Hyun Chang-Uk , Park Hyeong-Dong , Cha Jongmun
Published: June 2023

Global navigation satellite systems (GNSSs) provide a common positioning method that utilizes satellite signals to determine the spatial location of a receiver. However, there are several error factors in standalone GNSS positioning due to instrumental, procedural, and environmental factors that arise during the signal transmission process, and the final positioning error can be up to several meters or greater in length. Thus, real-time kinematic (RTK) correction and post-mission precise point positioning (PPP) processing technologies are proposed to improve accuracy and accomplish precise position measurements. To evaluate the geolocation accuracy of mosaicked UAV images of an abandoned mine site, we compared each orthomosaic image and digital elevation model obtained using standalone GNSS positioning, differential (RTK) GNSS positioning, and post-mission PPP processing techniques. In the three types of error evaluation measure (i.e., relative camera location error, ground control points-based absolute image mapping error, and volumetric difference of mine tailings), we found that the RTK GNSS positioning method obtained the best performance in terms of the relative camera location error and the absolute image mapping error evaluations, and the PPP post-processing correction effectively reduced the error (69.5% of the average total relative camera location error and 59.3% of the average total absolute image mapping error) relative to the standalone GNSS positioning method. Although differential (RTK) GNSS positioning is widely used in positioning applications that require very high accuracy, post-mission PPP processing can also be used in various fields in which it is either not feasible to operate expensive equipment to receive RTK GNSS signals or network RTK services are unavailable.
A Vectorial Current Density Imaging Method Based on Magnetic Gradient Tensor
Authors: Wu Yangjing , Zhang Mingji , Peng Chengyuan , Zhang Zehuang , He Yichen , Zhang Wenwei , Chang Liang
Published: June 2023

Magnetic current imaging is deemed an emerging powerful technique for visualizing electrical currents in electronic devices. However, the existing magnetic-field-based Fourier Transform back-evolution method is limited by its mono-function of imaging the magnitude of current density in devices under test, and subject to background noise distortion. Here, we developed a novel vectorial current density imaging method based on the detection of the magnetic field gradient generated by current carrying conductors. A closed form solution of current density inversion was analytically derived and numerically verified. Experiments were conducted by scanning tri-axial fluxgate sensor over different shapes of electrical wires. The results show that a current density resolution of 24.15 mA/mm2, probe-to-sample separation of 2 mm, and spatial resolution of 0.69 mm were achieved over a maximum scanning area of 300 mm × 300 mm. Such a method is verified to be capable of simultaneously imaging both magnitude and directions of current density, which is a promising technique for in situ noninvasive inspection for the power electronic and semiconductor industry.
Temporal Evaluation of the Surface Area of Treated Skin Ulcers Caused by Cutaneous Leishmaniasis and Relation with Optical Parameters in an Animal Model: A Proof of Concept
Authors: Londoño Sergio , Viloria Carolina , Pérez-Buitrago Sandra , Murillo Javier , Botina Deivid , Zarzycki Artur , Garzón Johnson , Torres-Madronero Maria C. , Robledo Sara M. , Marzani Franck , Treuillet Sylvie , Castaneda Benjamin , Galeano July
Published: June 2023

Cutaneous leishmaniasis (CL) is a neglected disease caused by an intracellular parasite of the Leishmania genus. CL lacks tools that allow its understanding and treatment follow-up. This article presents the use of metrical and optical tools for the analysis of the temporal evolution of treated skin ulcers caused by CL in an animal model. Leishmania braziliensis and L. panamensis were experimentally inoculated in golden hamsters, which were treated with experimental and commercial drugs. The temporal evolution was monitored by means of ulcers’ surface areas, as well as absorption and scattering optical parameters. Ulcers’ surface areas were obtained via photogrammetry, which is a procedure that allowed for 3D modeling of the ulcer using specialized software. Optical parameters were obtained from a spectroscopy study, representing the cutaneous tissue’s biological components. A one-way ANOVA analysis was conducted to identify relationships between both the ulcers’ areas and optical parameters. As a result, ulcers’ surface areas were found to be related to the following optical parameters: epidermis thickness, collagen, keratinocytes, volume-fraction of blood, and oxygen saturation. This study is a proof of concept that shows that optical parameters could be associated with metrical ones, giving a more reliable concept during the assessment of a skin ulcer’s healing.
Application of Machine Learning Algorithms to Classify Peruvian Pisco Varieties Using an Electronic Nose
Authors: De-La-Cruz Celso , Trevejo-Pinedo Jorge , Bravo Fabiola , Visurraga Karina , Peña-Echevarría Joseph , Pinedo Angela , Rojas Freddy , Sun-Kou María R.
Published: June 2023

Pisco is an alcoholic beverage obtained from grape juice distillation. Considered the flagship drink of Peru, it is produced following strict and specific quality standards. In this work, sensing results for volatile compounds in pisco, obtained with an electronic nose, were analyzed through the application of machine learning algorithms for the differentiation of pisco varieties. This differentiation aids in verifying beverage quality, considering the parameters established in its Designation of Origin”. For signal processing, neural networks, multiclass support vector machines and random forest machine learning algorithms were implemented in MATLAB. In addition, data augmentation was performed using a proposed procedure based on interpolation–extrapolation. All algorithms trained with augmented data showed an increase in performance and more reliable predictions compared to those trained with raw data. From the comparison of these results, it was found that the best performance was achieved with neural networks.
Spatial Smoothing Effect on Group-Level Functional Connectivity during Resting and Task-Based fMRI
Authors: Candemir Cemre
Published: June 2023

Spatial smoothing is a preprocessing step applied to neuroimaging data to enhance data quality by reducing noise and artifacts. However, selecting an appropriate smoothing kernel size can be challenging as it can lead to undesired alterations in final images and functional connectivity networks. However, there is no sufficient information about the effects of the Gaussian kernel size on group-level results for different cases yet. This study investigates the influence of kernel size on functional connectivity networks and network parameters in whole-brain rs-fMRI and tb-fMRI analyses of healthy adults. The analysis includes {0, 2, 4, 6, 8, 10} mm kernels, commonly used in practical analyses, covering all major brain networks. Graph theoretical measures such as betweenness centrality, global/local efficiency, clustering coefficient, and average path length are examined for each kernel. Additionally, principal component analysis (PCA) and independent component analysis (ICA) parameters, namely kurtosis and skewness, are evaluated for the functional images. The findings demonstrate that kernel size directly affects node connections, resulting in modifications to functional network structures and PCA/ICA parameters. However, network metrics exhibit greater resilience to these changes.
Robust Virtual Sensing of the Vehicle Sideslip Angle through the Cross-Combination of Multiple Filters Using a Decision Tree Algorithm
Authors: Atheupe Gaël P. , El Mrhasli Younesse , Emabou Ulrich , Monsuez Bruno , Bordignon Kenneth , Tapus Adriana
Published: June 2023

This paper presents a state-of-the-art estimation technique by cross-combining a number n of filters for high-precision, reliable and robust vehicle sideslip angle state estimation, over a full range of vehicle operations irrespective of the driving mission and disruptions that may occur in the system. A machine-learning algorithm based on decision trees connects several filters together to switch between them according to the driving context, ensuring the best possible state estimate for relatively small and large sideslip angle values. In conjunction with the above-mentioned aspects, a seamless transition between different vehicle models is attained by observing the key parameters characterizing the lateral motion of the vehicle. The tests conducted using a prototype vehicle on a snow-covered track confirm the effectiveness and reliability of the proposed approach.
Quantification of Structural Defects Using Pixel Level Spatial Information from Photogrammetry
Authors: Guo Youheng , Shen Xuesong , Linke James , Wang Zihao , Barati Khalegh
Published: June 2023

Aging infrastructure has drawn increased attention globally, as its collapse would be destructive economically and socially. Precise quantification of minor defects is essential for identifying issues before structural failure occurs. Most studies measured the dimension of defects at image level, ignoring the third-dimensional information available from close-range photogrammetry. This paper aims to develop an efficient approach to accurately detecting and quantifying minor defects on complicated infrastructures. Pixel sizes of inspection images are estimated using spatial information generated from three-dimensional (3D) point cloud reconstruction. The key contribution of this research is to obtain the actual pixel size within the grided small sections by relating spatial information. To automate the process, deep learning technology is applied to detect and highlight the cracked area at the pixel level. The adopted convolutional neural network (CNN) achieves an F1 score of 0.613 for minor crack extraction. After that, the actual crack dimension can be derived by multiplying the pixel number with the pixel size. Compared with the traditional approach, defects distributed on a complex structure can be estimated with the proposed approach. A pilot case study was conducted on a concrete footpath with cracks distributed on a selected 1500 mm × 1500 mm concrete road section. Overall, 10 out of 88 images are selected for validation; average errors ranging from 0.26 mm to 0.71 mm were achieved for minor cracks under 5 mm, which demonstrates a promising result of the proposed study.
Design and Evaluation of a Potential Non-Invasive Neurostimulation Strategy for Treating Persistent Anosmia in Post-COVID-19 Patients
Authors: Gracia Desirée I. , Ortiz Mario , Candela Tatiana , Iáñez Eduardo , Sánchez Rosa M. , Díaz Carmina , Azorín José M.
Published: June 2023

A new pandemic was declared at the end of 2019 because of coronavirus disease 2019 (COVID-19). One of the effects of COVID-19 infection is anosmia (i.e., a loss of smell). Unfortunately, this olfactory dysfunction is persistent in around 5% of the world’s population, and there is not an effective treatment for it yet. The aim of this paper is to describe a potential non-invasive neurostimulation strategy for treating persistent anosmia in post-COVID-19 patients. In order to design the neurostimulation strategy, 25 subjects who experienced anosmia due to COVID-19 infection underwent an olfactory assessment while their electroencephalographic (EEG) signals were recorded. These signals were used to investigate the activation of brain regions during the olfactory process and identify which regions would be suitable for neurostimulation. Afterwards, 15 subjects participated in the evaluation of the neurostimulation strategy, which was based on applying transcranial direct current stimulation (tDCS) in selected brain regions related to olfactory function. The results showed that subjects with lower scores in the olfactory assessment obtained greater improvement than the other subjects. Thus, tDCS could be a promising option for people who have not fully regained their sense of smell following COVID-19 infection.
Co-Design Dedicated System for Efficient Object Tracking Using Swarm Intelligence-Oriented Search Strategies
Authors: Nedjah Nadia , Cardoso Alexandre V. , Tavares Yuri M. , Mourelle Luiza de Macedo , Gupta Brij Booshan , Arya Varsha
Published: June 2023

The template matching technique is one of the most applied methods to find patterns in images, in which a reduced-size image, called a target, is searched within another image that represents the overall environment. In this work, template matching is used via a co-design system. A hardware coprocessor is designed for the computationally demanding step of template matching, which is the calculation of the normalized cross-correlation coefficient. This computation allows invariance in the global brightness changes in the images, but it is computationally more expensive when using images of larger dimensions, or even sets of images. Furthermore, we investigate the performance of six different swarm intelligence techniques aiming to accelerate the target search process. To evaluate the proposed design, the processing time, the number of iterations, and the success rate were compared. The results show that it is possible to obtain approaches capable of processing video images at 30 frames per second with an acceptable average success rate for detecting the tracked target. The search strategies based on PSO, ABC, FFA, and CS are able to meet the processing time of 30 frame/s, yielding average accuracy rates above 80% for the pipelined co-design implementation. However, FWA, EHO, and BFOA could not achieve the required timing restriction, and they achieved an acceptance rate around 60%. Among all the investigated search strategies, the PSO provides the best performance, yielding an average processing time of 16.22 ms coupled with a 95% success rate.
Next-Hop Relay Selection for Ad Hoc Network-Assisted Train-to-Train Communications in the CBTC System
Authors: Ma Sixing , Li Meng , Yang Ruizhe , Sun Yang , Wang Zhuwei , Si Pengbo
Published: June 2023

In the communication-based train control (CBTC) system, traditional modes such as LTE or WLAN in train-to-train (T2T) communication face the problem of a complex and costly deployment of base stations and ground core networks. Therefore, the multi-hop ad hoc network, which has the characteristics of being relatively flexible and cheap, is considered for CBTC. However, because of the high mobility of the train, it is likely to move out of the communication range of wayside nodes. Moreover, some wayside nodes are heavily congested, resulting in long packet queuing delays that cannot meet the transmission requirements. To solve these problems, in this paper, we investigate the next-hop relay selection problem in multi-hop ad hoc networks to minimize transmission time, enhance the network throughput, and ensure the channel quality. In addition, we propose a multiagent dueling deep Q learning (DQN) algorithm to optimize the delay and throughput of the entire link by selecting the next-hop relay node. The simulation results show that, compared with the existing routing algorithms, it has obvious improvement in the aspects of delay, throughput, and packet loss rate.
Theoretical Upper and Lower Limits for Normalized Bandwidth of Digital Phase-Locked Loop in GNSS Receivers
Authors: Song Young-Jin , Pany Thomas , Won Jong-Hoon
Published: June 2023

Determining the loop noise bandwidth and the coherent integration time is essential and important for the design of a reliable digital phase-locked loop (DPLL) in global navigation satellite system (GNSS) receivers. In general, designers set such parameters approximately by utilizing the well-known fact that the DPLL is stable if the normalized bandwidth, which is the product of the integration time and the noise bandwidth, is much less than one. However, actual limit points are not fixed at exactly one, and they vary with the loop filter order and implementation method. Furthermore, a lower limit on the normalized bandwidth may exist. This paper presents theoretical upper and lower limits for the normalized bandwidth of DPLL in GNSS receivers. The upper limit was obtained by examining the stability of DPLL with a special emphasis on the digital integration methods. The stability was investigated in terms of z-plane root loci with and without the consideration of the computational delay, which is a delay induced by the calculation of the discriminator and the loop filter. The lower limit was analyzed using the DPLL measurement error composed of the thermal noise, oscillator phase noise, and dynamic stress error. By utilizing the carrier-to-noise density ratio threshold which indicates the crossing point between the measurement error and the corresponding threshold, the lower limit of the normalized bandwidth is obtained.
A Novel Data-Driven Fault Detection Method Based on Stable Kernel Representation for Dynamic Systems
Authors: Wang Qiang , Peng Bo , Xie Pu , Cheng Chao
Published: June 2023

With the steady improvement of advanced manufacturing processes and big data technologies, modern industrial systems have become large-scale. To enhance the sensitivity of fault detection (FD) and overcome the drawbacks of the centralized FD framework in dynamic systems, a new data-driven FD method based on Hellinger distance and subspace techniques is proposed for dynamic systems. Specifically, the proposed approach uses only system input/output data collected via sensor networks, and the distributed residual signals can be generated directly through the stable kernel representation of the process. Based on this, each sensor node can obtain the identical residual signal and test statistic through the average consensus algorithms. In addition, this paper integrates the Hellinger distance into the residual signal analysis for improving the FD performance. Finally, the effectiveness and accuracy of the proposed method have been verified in a real multiphase flow facility.
Efficient Super-Resolution Method for Targets Observed by Satellite SAR
Authors: Lee Seung-Jae , Lee Sun-Gu
Published: June 2023

This study presents an efficient super-resolution (SR) method for targets observed by satellite synthetic aperture radar (SAR). First, a small target image is extracted from a large-scale SAR image and undergoes proper preprocessing. The preprocessing step is adaptively designed depending on the types of movements of targets. Next, the principal scattering centers of targets are extracted using the compressive sensing technique. Subsequently, an impulse response function (IRF) of the satellite SAR system (IRF-S) is generated using a SAR image of a corner reflector located at the calibration site. Then, the spatial resolution of the IRF-S is improved by the spectral estimation technique. Finally, according to the SAR signal model, the super-resolved IRF-S is combined with the extracted scattering centers to generate a super-resolved target image. In our experiments, the SR capabilities for various targets were investigated using quantitative and qualitative analysis. Compared with conventional SAR SR methods, the proposed scheme exhibits greater robustness towards improvement of the spatial resolution of the target image when the degrees of SR are high. Additionally, the proposed scheme has faster computation time (CT) than other SR algorithms, irrespective of the degree of SR. The novelties of this study can be summarized as follows: (1) the practical design of an efficient SAR SR scheme that has robustness at a high SR degree; (2) the application of proper preprocessing considering the types of movements of targets (i.e., stationary, moderate motion, and complex motion) in SAR SR processing; (3) the effective evaluation of SAR SR capability using various metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), focus quality parameters, and CT, as well as qualitative analysis.
The Control Method of Autonomous Flight Avoidance Barriers of UAVs in Confined Environments
Authors: Dong Tiantian , Zhang Yonghong , Xiao Qianyu , Huang Yi
Published: June 2023

This paper proposes an improved 3D-Vector Field Histogram (3D-VFH) algorithm for autonomous flight and local obstacle avoidance of multi-rotor unmanned aerial vehicles (UAVs) in a confined environment. Firstly, the method employs a target point coordinate system based on polar coordinates to convert the point cloud data, considering that long-range point cloud information has no effect on local obstacle avoidance by UAVs. This enables UAVs to effectively utilize obstacle information for obstacle avoidance and improves the real-time performance of the algorithm. Secondly, a sliding window algorithm is used to estimate the optimal flight path of the UAV and implement obstacle avoidance control, thereby maintaining the attitude stability of the UAV during obstacle avoidance flight. Finally, experimental analysis is conducted, and the results show that the UAV has good attitude stability during obstacle avoidance flight, can autonomously follow the expected trajectory, and can avoid dynamic obstacles, achieving precise obstacle avoidance.