bluuebunny/crossref_metadata_2025 huggingface dataset:
A Review of Artificial Intelligence and Remote Sensing for Archaeological Research
Authors: Argyrou Argyro , Agapiou Athos
Published: November 2022

The documentation and protection of archaeological and cultural heritage (ACH) using remote sensing, a non-destructive tool, is increasingly popular for experts around the world, as it allows rapid searching and mapping at multiple scales, rapid analysis of multi-source data sets, and dynamic monitoring of ACH sites and their environments. The exploitation of remote sensing data and their products have seen an increased use in recent years in the fields of archaeological science and cultural heritage. Different spatial and spectral analysis datasets have been applied to distinguish archaeological remains and detect changes in the landscape over time, and, in the last decade, archaeologists have adopted more thoroughly automated object detection approaches for potential sites. These approaches included, among others, object detection methods, such as those of machine learning (ML) and deep learning (DL) algorithms, as well as convolutional neural networks (CNN) and deep learning (DL) models using aerial and satellite images, airborne and spaceborne remote sensing (ASRS), multispectral, hyperspectral images, and active methods (synthetic aperture radar (SAR) and light detection and ranging radar (LiDAR)). Researchers also refer to the potential for archaeologists to explore such artificial intelligence (AI) approaches in various ways, such as identifying archaeological features and classifying them. Here, we present a review study related to the contributions of remote sensing (RS) and artificial intelligence in archaeology. However, a main question remains open in the field of research: the rate of positive contribution of remote sensing and artificial intelligence techniques in archaeological research. The scope of this study is to summarize the state of the art related to AI and RS for archaeological research and provide some further insights into the existing literature.
Improved Spatiotemporal Information Fusion Approach Based on Bayesian Decision Theory for Land Cover Classification
Authors: Jin Yan , Guan Xudong , Ge Yong , Jia Yan , Li Wenmei
Published: November 2022

High-spatial-resolution (HSR) images and high-temporal-resolution (HTR) images have their unique advantages and can be replenished by each other effectively. For land cover classification, a series of spatiotemporal fusion algorithms were developed to acquire a high-resolution land cover map. The fusion processes focused on the single level, especially the pixel level, could ignore the different phenology changes and land cover changes. Based on Bayesian decision theory, this paper proposes a novel decision-level fusion for multisensor data to classify the land cover. The proposed Bayesian fusion (PBF) combines the classification accuracy of results and the class allocation uncertainty of classifiers in the estimation of conditional probability, which consider the detailed spectral information as well as the various phenology information. To deal with the scale inconsistency problem at the decision level, an object layer and an area factor are employed for unifying the spatial resolution of distinct images, which would be applied for evaluating the classification uncertainty related to the conditional probability inference. The approach was verified on two cases to obtain the HSR land cover maps, in comparison with the implementation of two single-source classification methods and the benchmark fusion methods. Analyses and comparisons of the different classification results showed that PBF outperformed the best performance. The overall accuracy of PBF for two cases rose by an average of 27.8% compared with two single-source classifications, and an average of 13.6% compared with two fusion classifications. This analysis indicated the validity of the proposed method for a large area of complex surfaces, demonstrating the high potential for land cover classification.
Effects of Mining on Urban Environmental Change: A Case Study of Panzhihua
Authors: Dai Xiaoai , Li Wenyu , Liu Zhilong , Tong Chenbo , Li Cheng , Zeng Jianwen , Ye Yakang , Li Weile , Shan Yunfeng , Zhou Jiayun , Zhang Junjun , Xu Li , Jiang Xiaoli , Ruan Huihua , Zhang Jinbiao , Huang Wei
Published: November 2022

The environment supplies water, land, biological resources, and climate resources for people’s daily life and development, dramatically affecting the subsistence and development of human beings. Panzhihua City is a representative resource-based industrial city of southwestern China. The abundant mineral resources provide the material basis for the city’s development. However, while the overdevelopment of the past decades has provided the preconditions for its rapid economic growth, it has also inevitably had a huge impact on its environmental quality and land use structure. In this study, the landsat remote sensing images, terrain data, socio-economic data, and mining resources exploitation data of Panzhihua were used to extract the NDVI (Normalized Difference Vegetation Index), NDBSI (Normalized Difference Build and Soil Index) and LST (Land Surface Temperature) of the past 20 years at 5-year intervals. We normalized four indicators by Principal Component Analysis to derive a remote sensing ecological index of each factor and build the Remote Sensing-based Ecological Index evaluation model. This research quantified the changes in environmental quality in the past 20 years through the range method, showing that the environmental quality of Panzhihua City first declined and then increased slowly. This research also analyzed the influence of land use types, terrain, mining area, and socio-economy on the environmental quality of Panzhihua City by grey relational analysis and buffer analysis. It is found that with the influence of its unique topographical factors and economic aspects, the environmental quality of Panzhihua City changed to varying degrees. The results provide a reliable basis for the future environmental planning of Panzhihua City and a reference for the ecological restoration of mining areas with different mineral species accurately.
A Novel Technique Using Planar Area and Ground Shadows Calculated from UAV RGB Imagery to Estimate Pistachio Tree (Pistacia vera L.) Canopy Volume
Authors: Vélez Sergio , Vacas Rubén , Martín Hugo , Ruano-Rosa David , Álvarez Sara
Published: November 2022

Interest in pistachios has increased in recent years due to their healthy nutritional profile and high profitability. In pistachio trees, as in other woody crops, the volume of the canopy is a key factor that affects the pistachio crop load, water requirements, and quality. However, canopy/crown monitoring is time-consuming and labor-intensive, as it is traditionally carried out by measuring tree dimensions in the field. Therefore, methods for rapid tree canopy characterization are needed for providing accurate information that can be used for management decisions. The present study focuses on developing a new, fast, and low-cost technique, based on two main steps, for estimating the canopy volume in pistachio trees. The first step is based on adequately planning the UAV (unmanned aerial vehicle) flight according to light conditions and segmenting the RGB (Red, Green, Blue) imagery using machine learning methods. The second step is based on measuring vegetation planar area and ground shadows using two methodological approaches: a pixel-based classification approach and an OBIA (object-based image analysis) approach. The results show statistically significant linear relationships (p < 0.05) between the ground-truth data and the estimated volume of pistachio tree crowns, with R2 > 0.8 (pixel-based classification) and R2 > 0.9 (OBIA). The proposed methodologies show potential benefits for accurately monitoring the vegetation of the trees. Moreover, the method is compatible with other remote sensing techniques, usually performed at solar noon, so UAV operators can plan a flexible working day. Further research is needed to verify whether these results can be extrapolated to other woody crops.
Analyzing Driving Factors of Drought in Growing Season in the Inner Mongolia Based on Geodetector and GWR Models
Authors: Ji Bowen , Qin Yanbin , Zhang Tingbin , Zhou Xiaobing , Yi Guihua , Zhang Mengting , Li Menglin
Published: November 2022

As an important ecological security barrier in northern China, the Inner Mongolia Autonomous Region (hereinafter referred to as Inner Mongolia) is seriously affected by drought. It is of great significance to characterize the spatial distribution of drought and identify the influencing factors of drought. Due to complex interactions among drought driving factors, it is difficult to quantify the contribution of each driving factor to drought using linear correlation analysis alone. In this study, we used the Standardized Precipitation Evapotranspiration Index (SPEI) as a quantitative indicator of drought to discuss the spatiotemporal variation of drought during growing seasons in the Inner Mongolia from 2000 to 2018. We quantitatively characterized mode, scope, and intensity of changes in SPEI caused by drought-influencing factors such as weather, water, topography, soil, and human activities using the Geodetector and Geographically Weighted Regression (GWR) models. We concluded that about 20.3% of the region showed a downward trend in SPEI, with the fastest rate of decline in the central and western Inner Mongolia. Air temperature, precipitation, elevation, and distance to rivers are the main controlling factors in drought change, and the factor interactions showed nonlinear enhancement. The drought driving effect was obvious in Alxa League, Wuhai City, Ulanqab City, and Baotou City. The results will help us to understand the effects of the driving factors on drought and eventually help policymakers with water-resource management.
Generating Salt-Affected Irrigated Cropland Map in an Arid and Semi-Arid Region Using Multi-Sensor Remote Sensing Data
Authors: Wuyun Deji , Bao Junwei , Crusiol Luís Guilherme Teixeira , Wulan Tuya , Sun Liang , Wu Shangrong , Xin Qingqiang , Sun Zheng , Chen Ruiqing , Peng Jingyu , Xu Hongtao , Wu Nitu , Hou Anhong , Wu Lan , Ren Tingting
Published: November 2022

Soil salinization is a widespread environmental hazard and a major abiotic constraint affecting global food production and threatening food security. Salt-affected cropland is widely distributed in China, and the problem of salinization in the Hetao Irrigation District (HID) in the Inner Mongolia Autonomous Region is particularly prominent. The salt-affected soil in Inner Mongolia is 1.75 million hectares, accounting for 14.8% of the total land. Therefore, mapping saline cropland in the irrigation district of Inner Mongolia could evaluate the impacts of cropland soil salinization on the environment and food security. This study hypothesized that a reasonably accurate regional map of salt-affected cropland would result from a ground sampling approach based on PlanetScope images and the methodology developed by Sentinel multi-sensor images employing the machine learning algorithm in the cloud computing platform. Thus, a model was developed to create the salt-affected cropland map of HID in 2021 based on the modified cropland base map, valid saline and non-saline samples through consistency testing, and various spectral parameters, such as reflectance bands, published salinity indices, vegetation indices, and texture information. Additionally, multi-sensor data of Sentinel from dry and wet seasons were used to determine the best solution for mapping saline cropland. The results imply that combining the Sentinel-1 and Sentinel-2 data could map the soil salinity in HID during the dry season with reasonable accuracy and close to real time. Then, the indicators derived from the confusion matrix were used to validate the established model. As a result, the combined dataset, which included reflectance bands, spectral indices, vertical transmit–vertical receive (VV) and vertical transmit–horizontal receive (VH) polarization, and texture information, outperformed the highest overall accuracy at 0.8938, while the F1 scores for saline cropland and non-saline cropland are 0.8687 and 0.9109, respectively. According to the analyses conducted for this study, salt-affected cropland can be detected more accurately during the dry season by using just Sentinel images from March to April. The findings of this study provide a clear explanation of the efficiency and standardization of salt-affected cropland mapping in arid and semi-arid regions, with significant potential for applicability outside the current study area.
UAV Low-Altitude Aerial Image Stitching Based on Semantic Segmentation and ORB Algorithm for Urban Traffic
Authors: Zhang Gengxin , Qin Danyang , Yang Jiaqiang , Yan Mengying , Tang Huapeng , Bie Haoze , Ma Lin
Published: November 2022

UAVs are flexible in action, changeable in shooting angles, and complex and changeable in the shooting environment. Most of the existing stitching algorithms are suitable for images collected by UAVs in static environments, but the images are in fact being captured dynamically, especially in low-altitude flights. Considering that the great changes of the object position may cause the low-altitude aerial images to be affected by the moving foreground during stitching, so as to result in quality problems, such as splicing misalignment and tearing, a UAV aerial image stitching algorithm is proposed based on semantic segmentation and ORB. In the image registration, the algorithm introduces a semantic segmentation network to separate the foreground and background of the image and obtains the foreground semantic information. At the same time, it uses the quadtree decomposition idea and the classical ORB algorithm to extract feature points. By comparing the feature point information with the foreground semantic information, the foreground feature points can be deleted to realize feature point matching. Based on the accurate image registration, the image stitching and fusion will be achieved by the homography matrix and the weighted fusion algorithm. The proposed algorithm not only preserves the details of the original image, but also improves the four objective data points of information entropy, average gradient, peak signal-to-noise ratio and root mean square error. It can solve the problem of splicing misalignment tearing during background stitching caused by dynamic foreground and improves the stitching quality of UAV low-altitude aerial images.
Ratio of Land Consumption Rate to Population Growth Rate in the Major Metropolitan Areas of Romania
Authors: Holobâcă Iulian-Horia , Benedek József , Ursu Cosmina-Daniela , Alexe Mircea , Temerdek-Ivan Kinga
Published: November 2022

In 2015, the 2030 Agenda for Sustainable Development was adopted by all United Nations Member States and includes a set of 17 Sustainable Development Goals. The indicator, “Ratio of land consumption rate to population growth rate” (indicator 11.3.1) was proposed for the monitoring of urban development. The present study proposes the analysis of the built-up space evolution in relation to the demographic growth in the main metropolitan areas of Romania using the 11.3.1 indicator. Land consumption rate and population growth rate (LCRPGR) is used to assess the sustainability of urban growth, which takes into account both the change in the built-up area and in the population. LCRPGR is calculated as the ratio of land consumption rate (LCR) and the population growth rate (PGR). The analysis was conducted at the metropolitan area level for the 2006–2009, 2009–2015 and 2015–2020 periods. LCR and PGR proved to be very useful indicators for the monitoring of the intensity of built-up changes in the eight metropolitan areas both in time and in space and are useful for the local and central administrations, in both the context of achieving the sustainable development targets and goals and in conducting urban design and planning.
Autoencoder Neural Network-Based STAP Algorithm for Airborne Radar with Inadequate Training Samples
Authors: Liu Jing , Liao Guisheng , Xu Jingwei , Zhu Shengqi , Juwono Filbert H. , Zeng Cao
Published: November 2022

Clutter suppression is a key problem for airborne radar, and space-time adaptive processing (STAP) is a core technology for clutter suppression and moving target detection. However, in practical applications, the non-uniform time-varying environments including clutter range dependence for non-side-looking radar lead to the training samples being unable to satisfy the sample requirements of STAP that they should be independent identical distributed (IID) and that their number should be greater than twice the system’s degree of freedom (DOF). The lack of sufficient IID training samples causes difficulty in the convergence of STAP and further results in a serious degeneration of performance. To overcome this problem, this paper proposes a novel autoencoder neural network for clutter suppression with a unique matrix designed to be decoded and encoded. The main challenges are improving the accuracy of the estimation of the clutter-plus-noise covariance matrix (CNCM) for STAP convergence, designing the form of the data input to the network, and making the network successfully explored to the improvement of CNCM. For these challenges, the main proposed solutions include designing a unique matrix with a certain dimension and a series of covariance data selections and matrix transformations. Consequently, the proposed method compresses and retains the characteristics of the covariances, and abandons the deviations caused by the non-uniformity and the deficiency of training samples. Specifically, the proposed method firstly develops a unique matrix whose dimension is less than half of the DOF, meanwhile, it is based on a processing of the selected clutter-plus-noise covariances. Then, an autoencoder neural network with l2 regularization and the sparsity regularization is proposed for the unique matrix to be decoded and encoded. The training of the proposed autoencoder can be achieved by reducing the total loss function with the gradient descent iterations. Finally, an inverted processing for the autoencoder output is designed for the reconstruct ion of the clutter-plus-noise covariances. Simulation results are used to verify the effectiveness and advantages of the proposed method. It performs obviously superior clutter suppression for both side-looking and non-side-looking radars with strong clutter, and can deal with the insufficient and the non-uniform training samples. For these conditions, the proposed method provides the relatively narrowest and deepest IF notch. Furthermore, on average it improves the improvement factor (IF) by 10 dB more than the ADC, DW, JDL, and original STAP methods.
Ellipse Crater Recognition for Lost-in-Space Scenario
Authors: Xu Liheng , Jiang Jie , Ma Yan
Published: November 2022

In the field of deep space exploration, a crater recognition algorithm is key to landing navigation based on craters. When there is only visual information, determining how to identify the crater and provide the initial pose of the lander for a lost-in-space (LIS) scenario is a difficulty in terrain relative navigation (TRN). In this paper, a fast and robust crater recognition method for absolute pose estimation based on projective invariants is proposed, which can provide an accurate initial pose for tracking navigation. First, the method selects navigation craters to establish a small-capacity and high-efficiency crater database, and crater pair serial numbers and projective invariants are stored. Second, our method uses a dynamic threshold to solve the problem that the projective invariants are sensitive to noise. Then, an iterative pyramid algorithm is proposed to quickly filter redundancies. Using a dynamic threshold, the matching rate was increased by at least 10%, and the average processing speed was increased by 40%. When the detection errors of the major and minor axes of the ellipse reached 5%, the detection error of the center point reached 1 pixel, and the tilt angle error reached 5°; the matching rate was still >80%. Finally, the pose was estimated by solving the perspective-n-point (PNP) problem based on the recognized craters. The initial pose error in the simulation environment was less than 2°, and the position error was less than 44 m.
The Impact of Systematic Attitude Error on the Measurement of Interferometric Radar Altimeter
Authors: Tang Haoyan , Wang Yunhua , Zhao Chaofang , Sun Daozhong , Chen Ge , Sun Hanwei
Published: November 2022

Altimetric error has always been the significant performance parameter of the interferometric radar altimeter (IRA), particularly in the observation of sub-mesoscale ocean dynamic processes in which a higher accuracy of sea surface elevation (SSE) measurement is needed. The systematic attitude error of IRA associated with platform altitude, roll, pitch, and yaw errors is a remarkable source of altimetric error. However, the coupling attitude altimetric error is still less discussed up to now. In this paper, we focus on the study of the coupling attitude altimetric error and its related position-shifting, which are all induced by the attitude errors. The theoretical formulas of the coupling attitude altimetric error were derived, and the theoretical analysis demonstrates that the coupling attitude altimetric error is no longer along the range direction of the IRA image rigorously due to the change of the radar beam pointing. Based on theoretical formulas proposed by this study, the coupling attitude altimetric error and its related position-shifting are simulated and verified by using attitude data recorded by an airborne position and orientation system (POS) of three airborne experiments. The experimental results illustrate that the simulated coupling attitude altimetric errors are consistent with the measurements of the airborne experiments.
Analysis of Precise Orbit Determination of BDS-3 MEO and IGSO Satellites Based on Several Dual-Frequency Measurement Combinations
Authors: Tan Bingfeng , Ai Qingsong , Yuan Yunbin
Published: November 2022

The Chinese BeiDou-3 navigation satellite system (BDS-3) is capable of transmitting both old B1I, B3I signals and new B1C, B2a, B2b signals. Current BDS-3 precise orbits are generally calculated using a B1I/B3I combination considering overlap with the BeiDou-2 navigation satellite system (BDS-2). In this contribution, the observation quality of BDS-3 medium earth orbit (MEO) satellites and inclined geosynchronous orbit (IGSO) satellites are analyzed based on three aspects, i.e., carrier to noise ratio (C/N0), pseudo-range noise and pseudo-range multipath (MP). The C/N0 of the MEO satellite is 2~3 dB higher than that of the IGSO satellite at the same elevation angle. Meanwhile, the order of the Root Mean Square (RMS) values of both pseudo-range noise and MP is B1I < B1C < B3I < B2a ≈ B2b. Three kinds of combinations, i.e., B1CB2a, B1CB2b and B1IB3I, are selected for the BDS-3 precise orbit determination (POD) experiment. Orbits are assessed by the orbit-only signal-in-space range error (SISRE) computed between pairs of the three kinds of combinations in this contribution, CODE and GFZ final orbits. Orbit-only SISRE assessment shows that B1CB2a/CODE, B1CB2b/CODE, B1CB2a/GFZ and B1CB2b/GFZ are at the same level with CODE/GFZ, and the orbit-only SISRE is at the level of 5 cm for MEOs and 9 cm for IGSOs, respectively. Meanwhile, B1IB3I/CODE and B1IB3I/GFZ are about 1–2 cm worse. Inter-solution comparison between B1CB2a, B1CB2b and B1IB3I also indicate that B1CB2a and B1CB2b have good consistency, while B1IB3I shows poor performance. Satellite laser ranging (SLR) residuals indicate that the mean RMS is 3–4 cm for the four BDS-3 MEOs for CODE final orbit, GFZ final orbit, B1CB2a and B1CB2b combinations, while the mean RMS value for B1IB3I combination is a few millimeters worse, at approximately 4–5 cm.
Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control
Authors: Nazar Khola , Saeed Yousaf , Ali Abid , Algarni Abeer D. , Soliman Naglaa F. , Ateya Abdelhamied A. , Muthanna Mohammed Saleh Ali , Jamil Faisal
Published: November 2022

In vehicular ad hoc networks (VANETs), content pre-caching is a significant technology that improves network performance and lowers network response delay. VANET faces network congestion when multiple requests for the same content are generated. Location-based dependency requirements make the system more congested. Content pre-caching is an existing challenge in VANET; pre-caching involves the content’s early delivery to the requested vehicles to avoid network delays and control network congestion. Early content prediction saves vehicles from accidents and road disasters in urban environments. Periodic data dissemination without considering the state of the road and surrounding vehicles are considered in this research. The content available at a specified time poses considerable challenges in VANET for content delivery. To address these challenges, we propose a machine learning-based, zonal/context-aware-equipped content pre-caching strategy in this research. The proposed model improves content placement and content management in the pre-caching mode for VANET. Content caching is achieved through machine learning, which significantly improves content prediction by pre-caching the content early to the desired vehicles that are part of the zone. In this paper, three algorithms are presented, the first is zone selection using the customized algorithm, the second is the content dissemination algorithm, and the third is the content pre-caching decision algorithm using supervised machine learning that improves the early content prediction accuracy by 99.6%. The cache hit ratio for the proposed technique improves by 13% from the previous techniques. The prediction accuracy of the proposed technique is compared with CCMP, MLCP, and PCZS+PCNS on the number of vehicles from 10 to 150, with an improved average of 16%. Finally, the average delay reduces over time compared with the state-of-the-art techniques of RPSS, MLCP, CCMP, and PCZS+PCNS. Finally, the average delay shows that the proposed method effectively reduces the delay when the number of nodes increases. The proposed solution improves the content delivery request while comparing it with existing techniques. The results show improved pre-caching in VANET to avoid network congestion.
Measurements, Analysis, Classification, and Detection of Gunshot and Gunshot-like Sounds
Authors: Singh Rajesh Baliram , Zhuang Hanqi
Published: November 2022

Gun violence has been on the rise in recent years. To help curb the downward spiral of this negative influence in communities, machine learning strategies on gunshot detection can be developed and deployed. After outlining the procedure by which a typical type of gunshot-like sounds were measured, this paper focuses on the analysis of feature importance pertaining to gunshot and gunshot-like sounds. The random forest mean decrease in impurity and the SHapley Additive exPlanations feature importance analysis were employed for this task. From the feature importance analysis, feature reduction was then carried out. Via the Mel-frequency cepstral coefficients feature extraction process on 1-sec audio clips, these extracted features were then reduced to a more manageable quantity using the above-mentioned feature reduction processes. These reduced features were sent to a random forest classifier. The SHapley Additive exPlanations feature importance output was compared to that of the mean decrease in impurity feature importance. The results show what Mel-frequency cepstral coefficients features are important in discriminating gunshot sounds and various gunshot-like sounds. Together with the feature importance/reduction processes, the recent uniform manifold approximation and projection method was used to compare the closeness of various gunshot-like sounds to gunshot sounds in the feature space. Finally, the approach presented in this paper provides people with a viable means to make gunshot sounds more discernible from other sounds.
Comparative Analyses of Unsupervised PCA K-Means Change Detection Algorithm from the Viewpoint of Follow-Up Plan
Authors: Kılıç Deniz Kenan , Nielsen Peter
Published: November 2022

In this study, principal component analysis and k-means clustering (PCAKM) methods for synthetic aperture radar (SAR) data are analyzed to reduce the sensitivity caused by changes in the parameters and input images of the algorithm, increase the accuracy, and make an improvement in the computation time, which are advantageous for scoring in the follow-up plan. Although there are many supervised methods described in the literature, unsupervised methods may be more appropriate in terms of computing time, data scarcity, and explainability in order to supply a trustworthy system. We consider the PCAKM algorithm, which is used as a benchmark method in many studies when making comparisons. Error metrics, computing times, and utility functions are calculated for 22 modified PCAKM regarding difference images and filtering methods. Various images with different characteristics affect the results of the configurations. However, it is evident that the PCAKM becomes less sensitive and more accurate for both the overall results and image results. Scoring by utilizing these results and other map information is a gap and innovation. Obtaining a change map in a fast, explainable, more robust and less sensitive way is one of the aims of our studies on scoring points in the follow-up plan.
Broadband and Efficient Envelope Amplifier for Envelope Elimination and Restoration/Envelope Tracking Higher-Efficiency Power Amplifiers
Authors: Varlamov Oleg , Nguyen Dang Canh , Grebennikov Andrei
Published: November 2022

Increasing the efficiency of transmitters, as the largest consumers of energy, is relevant for any wireless communication devices. For higher efficiency, a number of methods are used, including envelope tracking and envelope elimination and restoration. Increasing the bandwidth of used frequencies requires expanding envelope modulators bandwidth up to 250–500 MHz or more. The possibility of using amplifiers with input signal quantization (AISQ), as an alternative to the most common hybrid envelope tracking modulators, is considered. An approach has been developed for optimizing AISQ characteristics according to the criterion of minimum loss when amplifying modern telecommunication signals with Rayleigh envelope distribution. The optimal quantization levels are determined and the energy characteristics of AISQ are calculated. AISQ loss power is shown to decrease by 1.66 times with two-level quantization, by 2.4 times with three-level quantization, and by a factor of 3.0–3.7 for four–five quantization levels compared to a class B amplifier. With these parameters, AISQ becomes competitive with respect to hybrid envelope tracking modulators but does not have electromagnetic interference from the pulse width modulation (PWM) path.
Analysis of the Influence of the Geometrical Parameters of the Body Scanner on the Accuracy of Reconstruction of the Human Figure Using the Photogrammetry Technique
Authors: Trojnacki Maciej , Dąbek Przemysław , Jaroszek Piotr
Published: November 2022

This article concerns the research of the HUBO full-body scanner, which includes the analysis and selection of the scanner’s geometrical parameters in order to obtain the highest possible accuracy of the reconstruction of a human figure. In the scanner version analyzed in this paper, smartphone cameras are used as sensors. In order to process the collected photos into a 3D model, the photogrammetry technique is applied. As part of the work, dependencies between the geometrical parameters of the scanner are derived, which allows to significantly reduce the number of degrees of freedom in the selection of its geometrical parameters. Based on these dependencies, a numerical analysis is carried out, as a result of which the initial values of the geometrical parameters are pre-selected and distribution of scanner cameras is visualized. As part of the experimental research, the influence of selected scanner parameters on the scanning accuracy is analyzed. For the experimental research, a specially prepared dummy was used instead of the participation of a real human, which allowed to ensure the constancy of the scanned object. The accuracy of the object reconstruction was assessed in relation to the reference 3D model obtained with a scanner of superior measurement uncertainty. On the basis of the conducted research, a method for the selection of the scanner’s geometrical parameters was finally verified, leading to the arrangement of cameras around a human, which guarantees high accuracy of the reconstruction. Additionally, to quantify the results, the quality rates were used, taking into account not only the obtained measurement uncertainty of the scanner, but also the processing time and the resulting efficiency.
Pavement Distress Estimation via Signal on Graph Processing
Authors: Bruno Salvatore , Colonnese Stefania , Scarano Gaetano , Del Serrone Giulia , Loprencipe Giuseppe
Published: November 2022

A comprehensive representation of the road pavement state of health is of great interest. In recent years, automated data collection and processing technology has been used for pavement inspection. In this paper, a new signal on graph (SoG) model of road pavement distresses is presented with the aim of improving automatic pavement distress detection systems. A novel nonlinear Bayesian estimator in recovering distress metrics is also derived. The performance of the methodology was evaluated on a large dataset of pavement distress values collected in field tests conducted in Kazakhstan. The application of the proposed methodology is effective in recovering acquisition errors, improving road failure detection. Moreover, the output of the Bayesian estimator can be used to identify sections where the measurement acquired by the 3D laser technology is unreliable. Therefore, the presented model could be used to schedule road section maintenance in a better way.
A Sampling-Based Algorithm with the Metropolis Acceptance Criterion for Robot Motion Planning
Authors: Liu Yiyang , Zhao Yang , Yan Shuaihua , Song Chunhe , Li Fei
Published: November 2022

Motion planning is one of the important research topics of robotics. As an improvement of Rapidly exploring Random Tree (RRT), the RRT* motion planning algorithm is widely used because of its asymptotic optimality. However, the running time of RRT* increases rapidly with the number of potential path vertices, resulting in slow convergence or even an inability to converge, which seriously reduces the performance and practical value of RRT*. To solve this issue, this paper proposes a two-phase motion planning algorithm named Metropolis RRT* (M-RRT*) based on the Metropolis acceptance criterion. First, to efficiently obtain the initial path and start the optimal path search phase earlier, an asymptotic vertex acceptance criterion is defined in the initial path estimation phase of M-RRT*. Second, to improve the convergence rate of the algorithm, a nonlinear dynamic vertex acceptance criterion is defined in the optimal path search phase, which preferentially accepts vertices that may improve the current path. The effectiveness of M-RRT* is verified by comparing it with existing algorithms through the simulation results in three test environments.
New Method to Calculate the Angular Weighting Function for a Scattering Instrument: Application to a Dust Sensor on Mars
Authors: Santalices David , de Castro Antonio J. , Briz Susana
Published: November 2022

Suspended dust above the Martian surface is an important element in Martian climatology. In the frame of the Exomars’22 mission, we developed a dust sensor instrument, designed to provide size parameters of dust particles suspended in Mars surface from the light scattered by the particles. Thus, to interpret the data of the dust sensor, we need a method to calculate the theoretical optical power dispersed by the particles and, therefore, the theoretical signal obtained by the instrument. This signal depends on the suspended particles and on the instrument configuration. In this paper, we present a new method to calculate the angular weighting function (Wf) for scattering sensors. Wf encompasses the scattering angles measured by the sensor and depends only on the instrument and not on the suspended particles. To calculate this Wf, we use fundamental radiometry principles and an appropriate coordinate system, where one coordinate is the scattering angle. The method is applied to the dust sensor instrument and compared with other methods. The comparison highlights the advantages of the proposed method since it avoids using an ideal sampling volume, preserves the radiometric meaning, and avoids instrument calibration. The effectiveness of the method makes it a valuable tool for the design of scattering instruments and also for the interpretation of their data.
Smart Air Quality Monitoring IoT-Based Infrastructure for Industrial Environments
Authors: García Laura , Garcia-Sanchez Antonio-Javier , Asorey-Cacheda Rafael , Garcia-Haro Joan , Zúñiga-Cañón Claudia-Liliana
Published: November 2022

Deficient air quality in industrial environments creates a number of problems that affect both the staff and the ecosystems of a particular area. To address this, periodic measurements must be taken to monitor the pollutant substances discharged into the atmosphere. However, the deployed system should also be adapted to the specific requirements of the industry. This paper presents a complete air quality monitoring infrastructure based on the IoT paradigm that is fully integrable into current industrial systems. It includes the development of two highly precise compact devices to facilitate real-time monitoring of particulate matter concentrations and polluting gases in the air. These devices are able to collect other information of interest, such as the temperature and humidity of the environment or the Global Positioning System (GPS) location of the device. Furthermore, machine learning techniques have been applied to the Big Data collected by this system. The results identify that the Gaussian Process Regression is the technique with the highest accuracy among the air quality data sets gathered by the devices. This provides our solution with, for instance, the intelligence to predict when safety levels might be surpassed.
Electromagnetic Spectrum Allocation Method for Multi-Service Irregular Frequency-Using Devices in the Space–Air–Ground Integrated Network
Authors: Meng Yongchao , Qi Peihan , Lei Qian , Zhang Zhengyu , Ren Jinyang , Zhou Xiaoyu
Published: November 2022

The management and allocation of electromagnetic spectrum resources is the inner driving force of the construction of the space–air–ground integrated network. Existing spectrum allocation methods are difficult to adapt to the scenario where the working bandwidth of multi-service frequency-using devices is irregular and the working priorities are different. In this paper, an orthogonal genetic algorithm based on the idea of mixed niches is proposed to transform the problem of frequency allocation into the optimization problem of minimizing the electromagnetic interference between frequency-using devices in the integrated network. At the same time, a system model is constructed that takes the minimum interference effect of low-priority-to-high-priority devices as the objective function and takes the protection frequency and natural frequency as the constraint conditions. In this paper, we not only introduce the thought of niches to improve the diversity of the population but also use an orthogonal uniform crossover operator to improve the search efficiency. At the same time, we use a standard genetic algorithm and a micro genetic algorithm to optimize the model. The global searchability and local search precision of the proposed algorithm are all improved. Simulation results show that compared with the existing methods, the proposed algorithm has the advantages of fast convergence, strong stability and good optimization effect.
IRS-Enabled Ultra-Low-Power Wireless Sensor Networks: Scheduling and Transmission Schemes
Authors: Alwazani Hibatallah , Chaaban Anas
Published: November 2022

Passive technologies, including intelligent reflecting surfaces (IRS), are gaining traction thanks to their ability to enhance communication systems while maintaining minimal cost and low complexity. They can assist a wireless sensor network (WSN) by achieving low power requirements for sensors and aid communication needs in many applications, for instance, environmental monitoring. In this paper, we propose an IRS-equipped WSN which describes sensors equipped with IRSs instead of active radio frequency (RF) electronics. The IRS sensor node (ISN) intercepts a dedicated signal from a power source such as a base station (BS) and modulates the transmission of that signal to an intended recipient. In order to enable multiple sensors to transmit to the receiver, we study opportunistic scheduling (OS) utilizing multi-sensor diversity while considering blind IRS operation, and compare it with round-robin (RR), proportional fairness (PF), and a theoretical upper bound. We study the effect of the choice of the number of IRS elements N and number of ISNs L on the average throughput of the system under OS. Finally, we provide pertinent comparisons for the different scheduling schemes and IRS configurations under relevant system performance metrics, highlighting different scenarios in which each scheme performs better.
Machine Learning-Based Air-to-Ground Channel Model Selection Method for UAV Communications Using Digital Surface Model Data
Authors: Kang Young-Eun , Jung Young-Ho
Published: November 2022

This paper proposes an automatic air-to-ground (A2G) channel model selection method based on machine learning (ML) using digital surface model (DSM) terrain data. In order to verify whether a communication network for a new non-terrestrial user service such as Urban Air Mobility (UAM) satisfies the required performance, it is necessary to perform a simulation reflecting the characteristics of the corresponding terrain environments as accurately as possible. For this simulation, A2G channel models corresponding to various terrain environments and a method of automatically classifying the terrain type of the simulation area must be provided. Many A2G channel models based on actual measurement results exist, but the practical automatic topography classification method still needs to be developed. This paper proposes the first practical automatic topography classification method using a two-step neural network-based classifier utilizing various geographic feature data as input. Since there is no open topography dataset to evaluate the accuracy of the proposed method, we built a new dataset for five topography classes that reflect the characteristics of Korea’s topography, which is also a contribution of our study. The simulation results using the new data set show that the proposed ML-based method could increase the selection accuracy compared to the technique for direct classification by humans or the existing cross-correlation-based classification method. Since the proposed method utilizes the DSM data, open to the public, it can easily reflect the different terrain characteristics of each country. Therefore, the proposed method can be effectively used in the realistic performance evaluation of new non-terrestrial communication networks utilizing vast airspace such as UAM or 6G mobile communications.
Secure Edge-Based Energy Management Protocol in Smart Grid Environments with Correlation Analysis
Authors: Rehman Amjad , Haseeb Khalid , Jeon Gwanggil , Bahaj Saeed Ali
Published: November 2022

For the monitoring and processing of network data, wireless systems are widely used in many industrial applications. With the assistance of wireless sensor networks (WSNs) and the Internet of Things (IoT), smart grids are being explored in many distributed communication systems. They collect data from the surrounding environment and transmit it with the support of a multi-hop system. However, there is still a significant research gap in energy management for IoT devices and smart sensors. Many solutions have been proposed by researchers to cope with efficient routing schemes in smart grid applications. But, reducing energy holes and offering intelligent decisions for forwarding data are remain major problems. Moreover, the management of network traffic on grid nodes while balancing the communication overhead on the routing paths is an also demanding challenge. In this research work, we propose a secure edge-based energy management protocol for a smart grid environment with the support of multi-route management. It strengthens the ability to predict the data forwarding process and improves the management of IoT devices by utilizing a technique of correlation analysis. Moreover, the proposed protocol increases the system’s reliability and achieves security goals by employing lightweight authentication with sink coordination. To demonstrate the superiority of our proposed protocol over the chosen existing work, extensive experiments were performed on various network parameters.
Simultaneous Gut-Brain Electrophysiology Shows Cognition and Satiety Specific Coupling
Authors: Balasubramani Pragathi Priyadharsini , Walke Anuja , Grennan Gillian , Perley Andrew , Purpura Suzanna , Ramanathan Dhakshin , Coleman Todd P. , Mishra Jyoti
Published: November 2022

Recent studies, using high resolution magnetoencephalography (MEG) and electrogastrography (EGG), have shown that during resting state, rhythmic gastric physiological signals are linked with cortical brain oscillations. Yet, gut-brain coupling has not been investigated with electroencephalography (EEG) during cognitive brain engagement or during hunger-related gut engagement. In this study in 14 young adults (7 females, mean ± SD age 25.71 ± 8.32 years), we study gut-brain coupling using simultaneous EEG and EGG during hunger and satiety states measured in separate visits, and compare responses both while resting as well as during a cognitively demanding working memory task. We find that EGG-EEG phase-amplitude coupling (PAC) differs based on both satiety state and cognitive effort, with greater PAC modulation observed in the resting state relative to working memory. We find a significant interaction between gut satiation levels and cognitive states in the left fronto-central brain region, with larger cognitive demand based differences in the hunger state. Furthermore, strength of PAC correlated with behavioral performance during the working memory task. Altogether, these results highlight the role of gut-brain interactions in cognition and demonstrate the feasibility of these recordings using scalable sensors.