bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantitative Analysis of Land Use and Land Cover Dynamics using Geoinformatics Techniques: A Case Study on Kolkata Metropolitan Development Authority (KMDA) in West Bengal, India
Authors: Ray Ratnadeep , Das Abhinandan , Hasan Mohd Sayeed Ul , Aldrees Ali , Islam Saiful , Khan Mohammad Amir , Lama Giuseppe Francesco Cesare
Published: February 2023

One of the most valuable approaches in spatial analysis for a better understanding of the hydrological response of a region or a watershed is certainly the analysis of the well-known land use land cover (LULC) dynamicity. The present case study delves deeper into the analysis of LULC dynamicity by using digital Landsat TM and Landsat OLI data to classify the Kolkata Metropolitan Development Authority (KMDA) into seven classes with over 90% classification accuracy for decadal level assessments of 30 years (for the years 1989, 1999, 2009, and 2019). The change index, the Dematel method for analyzing the cause-effect relationship among the LULC classes, the Jaccard Similarity Index for measuring the nature of similarity among the LULC classes, and the Adherence Index for measuring the consistency of the LULC classes after the transition was used in this study to analyze the LULC transformation. In more detail, the present study considers how urban land use is altering at the expense of other land uses. Besides the shifting pattern of mean centers of the LULC classes through time, also gives a very significant insight into the LULC dynamics over 30 years of span. The current study of LULC dynamicity and transformation patterns over the 30 years of the KMDA area is expected to assist land and urban planners, engineers, and administrators in sustainable decisions and policies to ensure inclusive urbanization that accommodates population growth while minimizing the impact on potential natural resources within the whole study area.
Real-Time 3D Mapping in Complex Environments Using a Spinning Actuated LiDAR System
Authors: Yan Li , Dai Jicheng , Zhao Yinghao , Chen Changjun
Published: February 2023

LiDAR is a crucial sensor for 3D environment perception. However, limited by the field of view of the LiDAR, it is sometimes difficult to achieve complete coverage of the environment with a single LiDAR. In this paper, we designed a spinning actuated LiDAR mapping system that is compatible with both UAV and backpack platforms and propose a tightly coupled laser–inertial SLAM algorithm for it. In our algorithm, edge and plane features in the point cloud are first extracted. Then, for the significant changes in the distribution of point cloud features between two adjacent scans caused by the continuous rotation of the LiDAR, we employed an adaptive scan accumulation method to improve the stability and accuracy of point cloud registration. After feature matching, the LiDAR feature factors and IMU pre-integration factor are added to the factor graph and jointly optimized to output the trajectory. In addition, an improved loop closure detection algorithm based on the Cartographer algorithm is used to reduce the drift. We conducted exhaustive experiments to evaluate the performance of the proposed algorithm in complex indoor and outdoor scenarios. The results showed that our algorithm is more accurate than the state-of-the-art algorithms LIO-SAM and FAST-LIO2 for the spinning actuated LiDAR system, and it can achieve real-time performance.
Backscattering Statistics of Indoor Full-Polarization Scatterometric and Synthetic Aperture Radar Measurements of a Rice Field
Authors: Liu Xiangchen , Shao Yun , Li Kun , Liu Zhiqu , Liu Long , Xiao Xiulai
Published: February 2023

The backscattering coefficient σ0 of a rice field is closely related to the amplitude, power, and phase of its radar backscattered signals. An investigation of the statistics of indoor full-polarization scatterometric and synthetic aperture radar (SAR) measurements on rice fields in the Laboratory of Target Microwave Properties (LAMP) is implemented in terms of the amplitude, power, and phase difference of backscattered signals. The validity and accuracy of LAMP measured data are studied and confirmed for the first time. The Rayleigh fading model and phase difference statistical model are both validated by the experimental data. Continuous microwave spectrum is obtained after spatial and frequency averaging over N independent scatterometric samples and full-polarization images are generated by applying a focusing algorithm to the SAR data. Comparisons between scatterometric results and SAR images with three resolutions of rice field scene are conducted with respect to amplitude and co-pol phase difference (CPD) statistics, as well as backscattering coefficients. The results show that the measured statistics of a rice field scene are in good agreement with those calculated by theoretical formulas. Spatial and frequency averaging of scatterometric data can increase N and thus improve the estimation accuracy of the backscattering coefficients. SAR images show a shift to the near range due to the intrinsic height of the rice plants and the probable existence of the double bounce scattering between vertical rice stems and the water surface considering the measurement geometry. The measured amplitude statistics of the SAR images approach a Rayleigh distribution with reduction of the resolution cell size while the size has little effect on the CPD statistics. The differences between backscattering coefficients extracted from the scatterometric data and SAR images confirm a 1-dB calibration accuracy in power of the LAMP measurement system.
Radiative Effects and Costing Assessment of Arctic Sea Ice Albedo Changes
Authors: Hao Hairui , Su Bo , Liu Shiwei , Zhuo Wenqin
Published: February 2023

The rapid loss of Arctic Sea ice cover and thickness diminishes the surface albedo, which increases the ocean’s absorption of solar heat and exacerbates the Arctic amplification effect. According to the most recent research from the Intergovernmental Panel on Climate Change, the Sixth Assessment Report (IPCC, AR6), the extent of summer sea ice is anticipated to decrease below 1 million km2 by the 2050s as a result of the extreme climate. Nevertheless, past and future changes in sea ice albedo radiative forcing and the resulting economic cost remain to be explored in systematic and multi-disciplinary manners. In this study, we first analyze the evolution of Arctic sea ice radiative forcing (SIRF) from 1982 to 2100 using a radiative kernel method based on albedo data from the Polar Pathfinder-Extent (APP-x) and Coupled Model Intercomparison Project 5 (CMIP5). Then, the SIRF is converted to CO2 equivalent emissions via the Dynamic Integrated Model of Climate and Economy (DICE) model. Finally, the associated costs are calculated using the substitute cost method, based on the social cost of carbon to achieve the Paris Agreement targets. The results show that the average Arctic SIRF was −0.75 ± 0.1 W·m−2 between 1982 and 2020, and increased by 0.12 W·m−2 during this period. The SIRF in April–June accounts for nearly 77% of the average annual value, with a maximum absolute value of –3.2 W·m−2 in May. Through model transformation, it is shown that the Arctic SIRF rising leads to global warming comparable to the effect of an increase of 34.5 Gt of CO2 in the atmosphere relative to pre-industrialization, and results in a loss of 24.4–48.8 trillion USD for climate regulation service (CRS). From 2020 to 2100, in the representative concentration pathway (RCP) 8.5, the Arctic SIRF is projected to increase by 0.31 W·m−2. Combined with the discount rate, the estimated average annual cost over the period ranges from 6.7–13.3 trillion USD. These findings provide a systematic understanding of the radiative effect of Arctic sea ice change on the global climate and the corresponding economic cost.
Power Line Extraction and Reconstruction Methods from Laser Scanning Data: A Literature Review
Authors: Munir Nosheen , Awrangjeb Mohammad , Stantic Bela
Published: February 2023

Electricity has become an indispensable source of energy, and power lines play a crucial role in the functioning of modern societies. It is essential to inspect power lines promptly and precisely in order to ensure the safe and secure delivery of electricity. In steep and mountainous terrain, traditional surveying methods cannot inspect power lines precisely due to their nature. Remote sensing platforms, such as satellite and aerial images, thermal images, and light detection and ranging (LiDAR) points, were utilised for the detection and inspection of power lines. Nevertheless, with the advancements of remote sensing technologies, in recent years, LiDAR surveying has been favoured for power line corridor (PLC) inspection due to active and weather-independent nature of laser scanning. Laser ranging data and the precise location of the LiDAR can be used to generate a three-dimensional (3D) image of the PLC. The resulting 3D point cloud enables accurate extraction of power lines and measurement of their distances from the forest below. In the literature, there have been many proposals for power line extraction and reconstruction for PLC modelling. This article examines the pros and cons of each domain method, providing researchers involved in three-dimensional modelling of power lines using innovative LiDAR scanning systems with useful guidelines. To achieve these objectives, research papers were analysed, focusing primarily on geoscience-related journals and conferences for the extraction and reconstruction of power lines. There has been a growing interest in examining the extraction and reconstruction of power line spans with single and multi-conductor configurations using different image and point-based techniques. Our study provides a comprehensive overview of the methodologies offered by various approaches using laser scanning data from the perspective of power line extraction applications, as well as to discuss the benefits and drawbacks of each approach. The comparison revealed that, despite the tremendous potential of aerial and mobile laser scanning systems, human intervention and post-processing actions are still required to achieve the desired results. In addition, the majority of the methods have been evaluated on the small datasets, and very few methods have been focused on multi-conductor extraction and reconstruction for power lines modelling. These barriers hinder the automated extraction and reconstruction of power line using LiDAR data and point to unexplored areas for further research and serve as useful guidelines for future research directions. Several promising directions for future LiDAR experiments using deep learning methods are outlined in the hope that they will pave the way for applications of PLC modelling and assessment at a finer scale and on a larger scale.
A Physically Interpretable Rice Field Extraction Model for PolSAR Imagery
Authors: Ge Ji , Zhang Hong , Xu Lu , Sun Chunling , Duan Haoxuan , Guo Zihuan , Wang Chao
Published: February 2023

Reliable and timely rice distribution information is of great value for real-time, quantitative, and localized control of rice production information. Synthetic aperture radar (SAR) has all-weather and all-day observation capability to monitor rice distribution in tropical and subtropical areas. To improve the physical interpretability and spatial interpretability of the deep learning model for SAR rice field extraction, a new SHapley Additive exPlanation (SHAP) value-guided explanation model (SGEM) for polarimetric SAR (PolSAR) data was proposed. First, a rice sample set was produced based on field survey and optical data, and the physical characteristics were extracted using decomposition of polarimetric scattering. Then a SHAP-based Physical Feature Interpretable Module (SPFIM) combing the long short-term memory (LSTM) model and SHAP values was designed to analyze the importance of physical characteristics, a credible physical interpretation associated with rice phenology was provided, and the weight of physical interpretation was combined with the weight of original PolSAR data. Moreover, a SHAP-guided spatial interpretation network (SSEN) was constructed to internalize the spatial interpretation values into the network layer to optimize the spatial refinement of the extraction results. Shanwei City, Guangdong Province, China, was chosen as the study area. The experimental results showed that the physical explanation provided by the proposed method had a high correlation with the rice phenology, and spatial self-interpretation for finer extraction results. The overall accuracy of the rice mapping results was 95.73%, and the kappa coefficient reached 0.9143. The proposed method has a high interpretability and practical value compared with other methods.
Tropical Forest Top Height by GEDI: From Sparse Coverage to Continuous Data
Authors: Ngo Yen-Nhi , Ho Tong Minh Dinh , Baghdadi Nicolas , Fayad Ibrahim
Published: February 2023

Estimating consistent large-scale tropical forest height using remote sensing is essential for understanding forest-related carbon cycles. The Global Ecosystem Dynamics Investigation (GEDI) light detection and ranging (LiDAR) instrument employed on the International Space Station has collected unique vegetation structure data since April 2019. Our study shows the potential value of using remote-sensing (RS) data (i.e., optical Sentinel-2, radar Sentinel-1, and radar PALSAR-2) to extrapolate GEDI footprint-level forest canopy height model (CHM) measurements. We show that selected RS features can estimate vegetation heights with high precision by analyzing RS data, spaceborne GEDI LiDAR, and airborne LiDAR at four tropical forest sites in South America and Africa. We found that the GEDI relative height (RH) metric is the best at 98% (RH98), filtered by full-power shots with a sensitivity greater than 98%. We found that the optical Sentinel-2 indices are dominant with respect to radar from 77 possible features. We proposed the nine essential optical Sentinel-2 and the radar cross-polarization HV PALSAR-2 features in CHM estimation. Using only ten optimal indices for the regression problems can avoid unimportant features and reduce the computational effort. The predicted CHM was compared to the available airborne LiDAR data, resulting in an error of around 5 m. Finally, we tested cross-validation error values between South America and Africa, including around 40% from validation data in training to obtain a similar performance. We recommend that GEDI data be extracted from all continents to maintain consistent performance on a global scale. Combining GEDI and RS data is a promising method to advance our capability in mapping CHM values.
Synergism of Multi-Modal Data for Mapping Tree Species Distribution—A Case Study from a Mountainous Forest in Southwest China
Authors: Zheng Pengfei , Fang Panfei , Wang Leiguang , Ou Guanglong , Xu Weiheng , Dai Fei , Dai Qinling
Published: February 2023

Accurately mapping tree species is crucial for forest management and conservation. Most previous studies relied on features derived from optical imagery, and digital elevation data and the potential of synthetic aperture radar (SAR) imagery and other environmental factors have, generally, been underexplored. Therefore, the aim of this study is to evaluate the potential of fusing freely available multi-modal data for accurately mapping tree species. Sentinel-2, Sentinel-1, and various environmental datasets over a large mountainous forest in Southwest China were obtained and analyzed using Google Earth Engine (GEE). Seven data cases considering the individual or joint performance of different features, and four additional cases considering a novel clustering-based feature selection method, were analyzed. All 11 cases were assessed using three machine learning algorithms, including random forest (RF), support vector machine (SVM), and extreme gradient boosting tree (XGBoost). The best performance, with an overall accuracy of 77.98%, was attained from the case with all features and the random forest classifier. Sentinel-2 data alone exhibited similar performance as environmental data in terms of overall accuracy. Similar species, such as oak and birch, cannot be spectrally discriminated based on Sentinel-2-based features alone. The addition of SAR features improved discrimination, especially when distinguishing between some coniferous and deciduous species, but also decreased accuracy for oak. The analysis based on different data cases and feature importance rankings indicated that environmental features are important. The random forest outperformed other models, and a better prediction was achieved for planted tree species compared to that for the natural forest. These results suggest that accurately mapping tree species over large mountainous areas is feasible with freely accessible multi-modal data, especially when considering environmental factors.
Analysis of China’s Coastline Changes during 1990–2020
Authors: Li Kaixin , Zhang Li , Chen Bowei , Zuo Jian , Yang Fan , Li Li
Published: February 2023

As the boundary between the sea and the land, information on the location and type of coastline constantly changes with environmental changes in coastal zones. Monitoring of coastline changes in long time series becomes important for the monitoring and assessment of the coastal zone environment. In this study, Landsat series images from five time periods (1990, 2000, 2010, 2015, and 2020) were selected for monitoring and analyzing the changes in coastline length, sea–land pattern, the index of coastline diversity, and fractal dimension characteristics. Our conclusions are as follows: (1) The lengths of the entire coastline and the artificial coastline of mainland China increased from 30,041.22 km and 10,022.49 km in 1990 to 32,977.34 km and 17,660.84 km in 2020, with annual change rates of 97.87 km/year and 254.61 km/year, respectively. From 1990 to 2020, the rate of natural coastline decreased from 66.68% to 42.29%, and the artificial coastline increased from 33.32% to 57.71%. (2) The length of natural coastline decreased from 20,018.73 km to 15,316.5 km; among the types of natural coastline, the length of sandy coastline and bedrock coastline decreased the most, at 2062.95 km and 1815.8 km, respectively. (3) The coastal zone of mainland China had a significant increase in land area, with a net increase of about 10,902.55 km2. (4) The index of coastline diversity continued to decrease, and the coastline structure tended to be simple. The fractal dimension of the mainland coastline was consistent with the trend of the length of the coastline, which basically shows an increasing trend. Therefore, the length of the mainland coastline and artificial coastline displayed an upward trend between 1990 and 2020, which also led to simpler coastline diversity and more complex coastline shapes. Since the first year of the SDGs (2015), the growth rate of the artificial coastline has decreased by 158.32 km/year compared with that between 2010 and 2015. In recent years, China has enacted a number of laws, regulations, and action plans to protect its coastline, and it has proposed that by 2020, the proportion of natural coastline will be no less than 35%. The rapid development of China’s coastal areas drives the construction of coastal zone cities but also creates a variety of challenges for the ecological environment of the coastal zone, and the management and sustainable use of the mainland coastline resources should be further strengthened.
SquconvNet: Deep Sequencer Convolutional Network for Hyperspectral Image Classification
Authors: Li Bing , Wang Qi-Wen , Liang Jia-Hong , Zhu En-Ze , Zhou Rong-Qian
Published: February 2023

The application of Transformer in computer vision has had the most significant influence of all the deep learning developments over the past five years. In addition to the exceptional performance of convolutional neural networks (CNN) in hyperspectral image (HSI) classification, Transformer has begun to be applied to HSI classification. However, for the time being, Transformer has not produced satisfactory results in HSI classification. Recently, in the field of image classification, the creators of Sequencer have proposed a Sequencer structure that substitutes the Transformer self-attention layer with a BiLSTM2D layer and achieves satisfactory results. As a result, this paper proposes a unique network called SquconvNet, that combines CNN with Sequencer block to improve hyperspectral classification. In this paper, we conducted rigorous HSI classification experiments on three relevant baseline datasets to evaluate the performance of the proposed method. The experimental results show that our proposed method has clear advantages in terms of classification accuracy and stability.
Reducing the Influence of Systematic Errors in Interference Core of Stepped Micro-Mirror Imaging Fourier Transform Spectrometer: A Novel Calibration Method
Authors: Zhao Baixuan , Liang Jingqiu , Lv Jinguang , Zheng Kaifeng , Zhao Yingze , Chen Yupeng , Sheng Kaiyang , Qin Yuxin , Wang Weibiao
Published: February 2023

The stepped micro-mirror imaging Fourier transform spectrometer (SIFTS) has the advantages of high throughput, compactness, and stability. However, the systematic errors in the interference core of the SIFTS have a significant impact on the interferogram and the reconstructed spectrum. In order to reduce the influence of systematic errors, a transfer error model of the systematic errors in the interference core of the SIFTS is established, and an interferogram and spectrum calibration method is presented, which combines the least squares fitting calibration and the row-by-row fast Fourier transform-inverse fast Fourier transform (FFT-IFFT) flat-field calibration. The experimental results show that the methods can sufficiently reduce the influence of systematic errors in the interference core of the SIFTS, such as the interferogram fringe tilt, the peak position shift of the reconstructed spectrum, and the error of spectral response.
Weakly Supervised Semantic Segmentation in Aerial Imagery via Cross-Image Semantic Mining
Authors: Zhou Ruixue , Yuan Zhiqiang , Rong Xuee , Ma Weicong , Sun Xian , Fu Kun , Zhang Wenkai
Published: February 2023

Weakly Supervised Semantic Segmentation (WSSS) with only image-level labels reduces the annotation burden and has been rapidly developed in recent years. However, current mainstream methods only employ a single image’s information to localize the target and do not account for the relationships across images. When faced with Remote Sensing (RS) images, limited to complex backgrounds and multiple categories, it is challenging to locate and differentiate between the categories of targets. As opposed to previous methods that mostly focused on single-image information, we propose CISM, a novel cross-image semantic mining WSSS framework. CISM explores cross-image semantics in multi-category RS scenes for the first time with two novel loss functions: the Common Semantic Mining (CSM) loss and the Non-common Semantic Contrastive (NSC) loss. In particular, prototype vectors and the Prototype Interactive Enhancement (PIE) module were employed to capture semantic similarity and differences across images. To overcome category confusions and closely related background interferences, we integrated the Single-Label Secondary Classification (SLSC) task and the corresponding single-label loss into our framework. Furthermore, a Multi-Category Sample Generation (MCSG) strategy was devised to balance the distribution of samples among various categories and drastically increase the diversity of images. The above designs facilitated the generation of more accurate and higher-granularity Class Activation Maps (CAMs) for each category of targets. Our approach is superior to the RS dataset based on extensive experiments and is the first WSSS framework to explore cross-image semantics in multi-category RS scenes and obtain cutting-edge state-of-the-art results on the iSAID dataset by only using image-level labels. Experiments on the PASCAL VOC2012 dataset also demonstrated the effectiveness and competitiveness of the algorithm, which pushes the mean Intersection-Over-Union (mIoU) to 67.3% and 68.5% on the validation and test sets of PASCAL VOC2012, respectively.
A Real-Time Application for the Analysis of Multi-Purpose Vending Machines with Machine Learning
Authors: Cao Yu , Ikenoya Yudai , Kawaguchi Takahiro , Hashimoto Seiji , Morino Takayuki
Published: February 2023

With the development of mobile payment, the Internet of Things (IoT) and artificial intelligence (AI), smart vending machines, as a kind of unmanned retail, are moving towards a new future. However, the scarcity of data in vending machine scenarios is not conducive to the development of its unmanned services. This paper focuses on using machine learning on small data to detect the placement of the spiral rack indicated by the end of the spiral rack, which is the most crucial factor in causing a product potentially to get stuck in vending machines during the dispensation. To this end, we propose a k-means clustering-based method for splitting small data that is unevenly distributed both in number and in features due to real-world constraints and design a remarkably lightweight convolutional neural network (CNN) as a classifier model for the benefit of real-time application. Our proposal of data splitting along with the CNN is visually interpreted to be effective in that the trained model is robust enough to be unaffected by changes in products and reaches an accuracy of 100%. We also design a single-board computer-based handheld device and implement the trained model to demonstrate the feasibility of a real-time application.
Optical Hydrogen Sensing Properties of e-Beam WO3 Films Decorated with Gold Nanoparticles
Authors: Colusso Elena , Rigon Michele , Corso Alain Jody , Pelizzo Maria Guglielmina , Martucci Alessandro
Published: February 2023

Tungsten oxide thin films with different thicknesses, crystallinity and morphology were synthesized by e-beam deposition followed by thermal treatment and acid boiling. The films with different surface morphologies were coated with gold nanoparticles and tested as optical sensing materials towards hydrogen. X-ray diffraction, scanning electron microscopy, ellipsometry and UV-VIS spectroscopy were employed to characterize the structural, morphological and optical properties of the film. We demonstrated a good response towards hydrogen in air, reaching a good selectivity among other common reducing gases, such as ammonia and carbon monoxide. The sensitivity has been proven to be highly dependent on the thickness and crystallinity of the samples.
A Morphing Point-to-Point Displacement Control Based on Long Short-Term Memory for a Coplanar XXY Stage
Authors: Ma Ming-Yu , Huang Yi-Cheng , Wu Yu-Tso
Published: February 2023

In this study, visual recognition with a charge-coupled device (CCD) image feedback control system was used to record the movement of a coplanar XXY stage. The position of the stage is fedback through the image positioning method, and the positioning compensation of the stage is performed by the image compensation control parameter. The image resolution was constrained and resulted in an average positioning error of the optimized control parameter of 6.712 µm, with the root mean square error being 2.802 µm, and the settling time being approximately 7 s. The merit of a long short-term memory (LSTM) deep learning model is that it can identify long-term dependencies and sequential state data to determine the next control signal. As for improving the positioning performance, LSTM was used to develop a training model for stage motion with an additional dial indicator with an accuracy of 1 μm being used to record the XXY position information. After removing the assisting dial indicator, a new LSTM-based XXY feedback control system was subsequently constructed to reduce the positioning error. In other words, the morphing control signals are dependent not only on time, but also on the iterations of the LSTM learning process. Point-to-point commanded forward, backward and repeated back-and-forth repetitive motions were conducted. Experimental results revealed that the average positioning error achieved after using the LSTM model was 2.085 µm, with the root mean square error being 2.681 µm, and a settling time of 2.02 s. With the assistance of LSTM, the stage exhibited a higher control accuracy and less settling time than did the CCD imaging system according to three positioning indices.
FRMDB: Face Recognition Using Multiple Points of View
Authors: Contardo Paolo , Sernani Paolo , Tomassini Selene , Falcionelli Nicola , Martarelli Milena , Castellini Paolo , Dragoni Aldo Franco
Published: February 2023

Although face recognition technology is currently integrated into industrial applications, it has open challenges, such as verification and identification from arbitrary poses. Specifically, there is a lack of research about face recognition in surveillance videos using, as reference images, mugshots taken from multiple Points of View (POVs) in addition to the frontal picture and the right profile traditionally collected by national police forces. To start filling this gap and tackling the scarcity of databases devoted to the study of this problem, we present the Face Recognition from Mugshots Database (FRMDB). It includes 28 mugshots and 5 surveillance videos taken from different angles for 39 distinct subjects. The FRMDB is intended to analyze the impact of using mugshots taken from multiple points of view on face recognition on the frames of the surveillance videos. To validate the FRMDB and provide a first benchmark on it, we ran accuracy tests using two CNNs, namely VGG16 and ResNet50, pre-trained on the VGGFace and VGGFace2 datasets for the extraction of face image features. We compared the results to those obtained from a dataset from the related literature, the Surveillance Cameras Face Database (SCFace). In addition to showing the features of the proposed database, the results highlight that the subset of mugshots composed of the frontal picture and the right profile scores the lowest accuracy result among those tested. Therefore, additional research is suggested to understand the ideal number of mugshots for face recognition on frames from surveillance videos.
Performance Analysis of Turbo Codes, LDPC Codes, and Polar Codes over an AWGN Channel in the Presence of Inter Symbol Interference
Authors: Cuc Adriana-Maria , Morgoș Florin Lucian , Grava Cristian
Published: February 2023

This paper discusses the results of simulations relating to the performances of turbo codes, low density parity check (LDPC) codes, and polar codes over an additive white Gaussian noise (AWGN) channel in the presence of inter symbol interference, denoting the disturbances that altered the original signal. To eliminate the negative effects of inter symbol interference (ISI), an equalizer was used at the level of the receiver. Practically, two types of equalizers were used: zero forcing (ZF) and minimum mean square error (MMSE), considering the case of perfect channel estimation and the case of estimation using the least square algorithm. The performance measure used was the modification of the bit error rate compared to a given signal to noise ratio; in this sense, the MMSE equalizer offered a higher performance than the ZF equalizer. The aspect of channel equalization considered here is not novel, but there have been very few works that dealt with equalization in the context of the use of turbo codes, especially LDPC codes and polar codes for channel coding. In this respect, this research can be considered a contribution to the field of digital communications.
Ambient Dose and Dose Rate Measurement in SNOLAB Underground Laboratory at Sudbury, Ontario, Canada
Authors: Golovko Victor V. , Kamaev Oleg , Sun Jiansheng , Jillings Chris J. , Gorel Pierre , Vázquez-Jáuregui Eric
Published: February 2023

The paper describes a system and experimental procedure that use integrating passive detectors, such as thermoluminescent dosimeters (TLDs), for the measurement of ultra-low-level ambient dose equivalent rate values at the underground SNOLAB facility located in Sudbury, Ontario, Canada. Because these detectors are passive and can be exposed for relatively long periods of time, they can provide better sensitivity for measuring ultra-low activity levels. The final characterization of ultra-low-level ambient dose around water shielding for ongoing direct dark matter search experiments in Cube Hall at SNOLAB underground laboratory is given. The conclusion is that TLDs provide reliable results in the measurement of the ultra-low-level environmental radiation background.
Parametric Damage Mechanics Empowering Structural Health Monitoring of 3D Woven Composites
Authors: Jacot Maurine , Champaney Victor , Chinesta Francisco , Cortial Julien
Published: February 2023

This paper presents a data-driven structural health monitoring (SHM) method by the use of so-called reduced-order models relying on an offline training/online use for unidirectional fiber and matrix failure detection in a 3D woven composite plate. During the offline phase (or learning) a dataset of possible damage localization, fiber and matrix failure ratios is generated through high-fidelity simulations (ABAQUS software). Then, a reduced model in a lower-dimensional approximation subspace based on the so-called sparse proper generalized decomposition (sPGD) is constructed. The parametrized approach of the sPGD method reduces the computational burden associated with a high-fidelity solver and allows a faster evaluation of all possible failure configurations. However, during the testing phase, it turns out that classical sPGD fails to capture the influence of the damage localization on the solution. To alleviate the just-referred difficulties, the present work proposes an adaptive sPGD. First, a change of variable is carried out to place all the damage areas on the same reference region, where an adapted interpolation can be done. During the online use, an optimization algorithm is employed with numerical experiments to evaluate the damage localization and damage ratio which allow us to define the health state of the structure.
Channel Selectivity of Satellite Transponders with the Antenna Combined with a Size-Reduced Metallic Waveguide Bandpass Filter Having Thin Metamaterial Resonators
Authors: Cho Junghyun , Seo Yejune , Shin Wonjae , Lee Eungdon , Kahng Sungtek
Published: February 2023

Global and intercontinental networking relies on satellite communication. Its wireless communication system always has antennas and their feed assembly comprising waveguides. This makes the satellite payload heavy and costly. In this paper, a novel method is proposed to effectively reduce the size of a waveguide bandpass filter (BPF). Because the metallic cavities make the conventional waveguide end up with a large geometry, especially for high-order BPFs, very compact waveguide-type resonators having metamaterial zeroth-order resonance (WG-ZOR) are designed on the cross-section of the waveguide and substituted for the cavities. While the cavities are half-wavelength resonators, the WG-ZOR is shorter than one eighth of a wavelength. A substantial reduction in size and weight of the waveguide filter is observed as the resonators are cascaded in series through coupling elements in the X-band much longer than K- or Ka-band. An X-band of 7.25~7.75 GHz is chosen to verify the method as the passband with attenuation of 40 dB at 7.00 GHz and 8.00 GHz as the roll-off in the stopband. The BPF is manufactured using the CNC milling technique. The design is carried out with geometrical parameters, not of the level of 10 μm, but the level of 100 μm, which is good for manufacturers but a big challenge for component designers. The measurement of the manufactured metal waveguide filter reveals that the passband has about ≤1 dB and ≤−15 dB as insertion loss and reflection coefficient and the stopband has ≤−40 dB as attenuation, which are in good agreement with the results of the circuit and simulation. The proposed filter has a length of 3.5 λg as the eighth-order BPF, but the conventional waveguide is 5 λg as the seventh-order BPF for the same area of the cross-section. This metamaterial BPF is combined with a horn antenna. The filter enables the wide-band antenna to distinguish the band of transmission from that of noise suppression. This channel selectivity is obviously observed by the filter integrated antenna test.
Multilayer Model in Soil Moisture Content Retrieval Using GNSS Interferometric Reflectometry
Authors: Li Jie , Hong Xuebao , Wang Feng , Yang Lei , Yang Dongkai
Published: February 2023

The global navigation satellite system–interferometric reflectometry (GNSS-IR) was developed more than a decade ago to monitor soil moisture content (SMC); a system that is essentially finished has emerged. The standard GNSS-IR model typically considers soil to be a single layer of medium and measures the average SMC between 1 and 10 cm below the soil surface. The majority of the SMC is not distributed uniformly along the longitudinal axis. This study is based on a simulation platform and suggests a SMC-stratified measurement model that can be used to recover the SMC at different depths in the sink and reverse osmosis to address the issue that conventional techniques cannot accurately measure soil moisture at different depths. The soil moisture of each layer was assessed by utilizing the GNSS signals reflected by various soil layers, and this study employed total transmission when the vertical linearly polarized component of the electromagnetic wave was conveyed by the GNSS signal reflected by the soil. This work employed the Hilbert transform to obtain the interference signal envelope, which increases the visibility of the interference signal’s “notch” and reduces the burr impact of the interference signal brought on by ambient noise. The accuracy of the SMC measurement at the bottom declines due to the soil’s attenuation of the GNSS signal power, but the correlation between the predetermined value and SMC retrieved by the GNSS-IR multilayer SMC measurement model similarly approached 0.92.
An End-to-End Steel Surface Classification Approach Based on EDCGAN and MobileNet V2
Authors: Jin Ge , Liu Yanghe , Qin Peiliang , Hong Rongjing , Xu Tingting , Lu Guoyu
Published: February 2023

In the production process of steel products, it is very important to find defects, which can not only reduce the failure rate of industrial production but also can reduce economic losses. All deep learning-based methods need many labeled samples for training. However, in the industrial field, there is a lack of sufficient training samples, especially in steel surface defects. It is almost impossible to collect enough samples that can be used for training. To solve this kind of problem, different from traditional data enhancement methods, this paper constructed a data enhancement model dependent on GAN, using our designed EDCGAN to generate abundant samples that can be used for training. Finally, we mixed different proportions of the generated samples with the original samples and tested them through the MobileNet V2 classification model. The test results showed that if we added the samples generated by EDCGAN to the original samples, the classification results would gradually improve. When the ratio reaches 80%, the overall classification result reaches the highest, achieving an accuracy rate of more than 99%. The experimental process proves the effectiveness of this method and can improve the quality of steel processing.
Decoupling Transmission and Transduction for Improved Durability of Highly Stretchable, Soft Strain Sensing: Applications in Human Health Monitoring
Authors: Kight Ali , Pirozzi Ileana , Liang Xinyi , McElhinney Doff B. , Han Amy Kyungwon , Dual Seraina A. , Cutkosky Mark
Published: February 2023

This work presents a modular approach to the development of strain sensors for large deformations. The proposed method separates the extension and signal transduction mechanisms using a soft, elastomeric transmission and a high-sensitivity microelectromechanical system (MEMS) transducer. By separating the transmission and transduction, they can be optimized independently for application-specific mechanical and electrical performance. This work investigates the potential of this approach for human health monitoring as an implantable cardiac strain sensor for measuring global longitudinal strain (GLS). The durability of the sensor was evaluated by conducting cyclic loading tests over one million cycles, and the results showed negligible drift. To account for hysteresis and frequency-dependent effects, a lumped-parameter model was developed to represent the viscoelastic behavior of the sensor. Multiple model orders were considered and compared using validation and test data sets that mimic physiologically relevant dynamics. Results support the choice of a second-order model, which reduces error by 73% compared to a linear calibration. In addition, we evaluated the suitability of this sensor for the proposed application by demonstrating its ability to operate on compliant, curved surfaces. The effects of friction and boundary conditions are also empirically assessed and discussed.
Multi-Core Time-Triggered OCBP-Based Scheduling for Mixed Criticality Periodic Task Systems
Authors: Baciu Marian D. , Capota Eugenia A. , Stângaciu Cristina S. , Curiac Daniel-Ioan , Micea Mihai V.
Published: February 2023

Mixed criticality systems are one of the relatively new directions of development for the classical real-time systems. As the real-time embedded systems become more and more complex, incorporating different tasks with different criticality levels, the continuous development of mixed criticality systems is only natural. These systems have practically entered every field where embedded systems are present: avionics, automotive, medical systems, wearable devices, home automation, industry and even the Internet of Things. While scheduling techniques have already been proposed in the literature for different types of mixed criticality systems, the number of papers addressing multiprocessor platforms running in a time-triggered mixed criticality environment is relatively low. These algorithms are easier to certify due to their complete determinism and isolation between components of different criticalities. Our research has centered on the problem of real-time scheduling on multiprocessor platforms for periodic tasks in a time-triggered mixed criticality environment. A partitioned, non-preemptive, table-driven scheduling algorithm was proposed, called Partitioned Time-Triggered Own Criticality Based Priority, based on a uniprocessor mixed criticality method. Furthermore, an analysis of the scheduling algorithm is provided in terms of success ratio by comparing it against an event-driven and a time-triggered method.
Performance Comparison of Tungsten-Halogen Light and Phosphor-Converted NIR LED in Soluble Solid Content Estimation of Apple
Authors: Lee Hoyoung , Cho Sungho , Lim Jongguk , Lee Ahyeong , Kim Giyoung , Song Doo-Jin , Chun Seung-Woo , Kim Min-Jee , Mo Changyeun
Published: February 2023

A Tungsten-Halogen (TH) lamp is the most popular light source in NIR spectroscopy and hyperspectral imaging, which requires a warm-up to reach very high temperatures of up to 250 °C and take a long time for radiation stabilization. Consequently, it has a large enough volume to enable heat dissipation to prevent the thermal runaway of the electric circuit and turn out its power efficiency very low. These are major barriers for miniaturizing spectral systems and hyperspectral imaging devices. However, TH lamps can be replaced by pc-NIR LEDs in order to avoid high temperature and large volume. We compared the spectral emission of the available commercial pc-NIR LEDs under the same condition. As a replacement for the TH lamp, the VIS + NIR LED module was developed to combine a warm-white LED and pc-NIR LEDs. In order to feature out the availability of the VIS + NIR LED module against the TH lamp, they were used as the light source for evaluating the Soluble Solid Content (SSC) of an apple through VIS-NIR spectroscopy. The results show a remarkable feasibility in the performance of the partial least square (PLS) model using the VIS + NIR LED module; during PLS calibration, the correlation coefficient (R) values are 0.664 and 0.701, and the Mean Square Error (MSE) values are 0.681 and 0.602 for the TH lamp and VIS + NIR LED module, respectively. In VIS-NIR spectroscopy, this study indicates that the TH lamp could be replaceable with a warm-white LED and pc-NIR LEDs.
Autonomous Vehicles Enabled by the Integration of IoT, Edge Intelligence, 5G, and Blockchain
Authors: Biswas Anushka , Wang Hwang-Cheng
Published: February 2023

The wave of modernization around us has put the automotive industry on the brink of a paradigm shift. Leveraging the ever-evolving technologies, vehicles are steadily transitioning towards automated driving to constitute an integral part of the intelligent transportation system (ITS). The term autonomous vehicle has become ubiquitous in our lives, owing to the extensive research and development that frequently make headlines. Nonetheless, the flourishing of AVs hinges on many factors due to the extremely stringent demands for safety, security, and reliability. Cutting-edge technologies play critical roles in tackling complicated issues. Assimilating trailblazing technologies such as the Internet of Things (IoT), edge intelligence (EI), 5G, and Blockchain into the AV architecture will unlock the potential of an efficient and sustainable transportation system. This paper provides a comprehensive review of the state-of-the-art in the literature on the impact and implementation of the aforementioned technologies into AV architectures, along with the challenges faced by each of them. We also provide insights into the technological offshoots concerning their seamless integration to fulfill the requirements of AVs. Finally, the paper sheds light on future research directions and opportunities that will spur further developments. Exploring the integration of key enabling technologies in a single work will serve as a valuable reference for the community interested in the relevant issues surrounding AV research.
A Comprehensive Study of Cyber Attack Mitigation with the Exchange of Frequency Containment Reserves Control in a Multi-Infeed Direct Current Power System
Authors: Ramadhan Umar Fitra , Lee Jaehyeong , Yoon Minhan
Published: February 2023

By 2040, the Korean government aims for a penetration rate of 30–35% of the total power from renewable sources. Due to a lack of inertia, particularly in remote systems such as those on Jeju Island, these circumstances will reduce network stability. To maintain the diversity and unpredictability of RES penetration, HVDC systems with an exchange of frequency containment reserve control are utilized. An exchange of frequency containment reserves control (E-FCR) is one of the balancing arrangement concepts of HVDC systems. However, the development of E-FCR concepts is vulnerable to cyber attacks because this concept only considers one wide-area measurement for data exchange. This study established a simultaneous cyber attack operation, i.e., an attack was set at the same time as a contingency operation that affects the balancing arrangement between two regions. Multiple possibilities of cyber attack and mitigation operations were suggested according to their ability to access information in the MIDC system. Then, a cyber detection strategy was proposed through a normalized correlation concept to activate mitigation control that could enhance the frequency stability by adjusting the value of the ramp-rate deviation between two HVDC types. By simulating the Korean power system model that was implemented in PSS/E, along with a Python script, simulation results demonstrated that a cyber attack on missing data can cause severe low-frequency nadir responses, and the proposed methodology can practically detect and mitigate cyber attacks.