bluuebunny/crossref_metadata_2025 huggingface dataset:
Wood Decay Detection in Norway Spruce Forests Based on Airborne Hyperspectral and ALS Data
Authors: Dalponte Michele , Kallio Alvar J. I. , Ørka Hans Ole , Næsset Erik , Gobakken Terje
Published: April 2022

Wood decay caused by pathogenic fungi in Norway spruce forests causes severe economic losses in the forestry sector, and currently no efficient methods exist to detect infected trees. The detection of wood decay could potentially lead to improvements in forest management and could help in reducing economic losses. In this study, airborne hyperspectral data were used to detect the presence of wood decay in the trees in two forest areas located in Etnedal (dataset I) and Gran (dataset II) municipalities, in southern Norway. The hyperspectral data used consisted of images acquired by two sensors operating in the VNIR and SWIR parts of the spectrum. Corresponding ground reference data were collected in Etnedal using a cut-to-length harvester while in Gran, field measurements were collected manually. Airborne laser scanning (ALS) data were used to detect the individual tree crowns (ITCs) in both sites. Different approaches to deal with pixels inside each ITC were considered: in particular, pixels were either aggregated to a unique value per ITC (i.e., mean, weighted mean, median, centermost pixel) or analyzed in an unaggregated way. Multiple classification methods were explored to predict rot presence: logistic regression, feed forward neural networks, and convolutional neural networks. The results showed that wood decay could be detected, even if with accuracy varying among the two datasets. The best results on the Etnedal dataset were obtained using a convolution neural network with the first five components of a principal component analysis as input (OA = 65.5%), while on the Gran dataset, the best result was obtained using LASSO with logistic regression and data aggregated using the weighted mean (OA = 61.4%). In general, the differences among aggregated and unaggregated data were small.
Modeling Post-Sunset Equatorial Spread-F Occurrence as a Function of Evening Upward Plasma Drift Using Logistic Regression, Deduced from Ionosondes in Southeast Asia
Authors: Abadi Prayitno , Ahmad Umar Ali , Otsuka Yuichi , Jamjareegulgarn Punyawi , Martiningrum Dyah Rahayu , Faturahman Agri , Perwitasari Septi , Saputra Randy Erfa , Septiawan Reza Rendian
Published: April 2022

The occurrence of post-sunset equatorial spread-F (ESF) could have detrimental effects on trans-ionospheric radio wave propagation used in modern communications systems. This problem calls for a simple but robust model that accurately predicts the occurrence of post-sunset ESF. Logistic regression was implemented to model the daily occurrence of post-sunset ESF as a function of the evening upward plasma drift (v). The use of logistic regression is formalized by y^=1/[1+exp(−z)], where y^ represents the probability of post-sunset ESF occurrence, and z is a linear function containing v. The value of v is derived from the vertical motion of the bottom side of the F-region in the evening equatorial ionosphere, which is observed by the ionosondes in Southeast Asia. Data points (938) of v and post-sunset ESF occurrence were collected in the equinox seasons from 2003 to 2016. The training set used 70% of the dataset to derive z and y^ and the remaining 30% was used to test the performance of y^. The expression z=−2.25+0.14v was obtained from the training set, and y^≥0.5 (v ≥ ~16.1 m/s) and y^<0.5 (v < ~16.1 m/s) represented the occurrence and non-occurrence of ESF, respectively, with an accuracy of ~0.8 and a true skill score (TSS) of ~0.6. Similarly, in the testing set, y^ shows an accuracy of ~0.8 and a TSS of ~0.6. Further analysis suggested that the performance of the z-function can be reliable in the daily F10.7 levels ranging from 60 to 140 solar flux units. The z-function implemented in the logistic regression (y^) found in this study is a novel technique to predict the post-sunset ESF occurrence. The performance consistency between the training set and the testing set concludes that the z-function and the y^ values of the proposed model could be a simple and robust mathematical model for daily nowcasting the occurrence or non-occurrence of post-sunset ESFs.
Potential Habitats and Their Conservation Status for Swan Geese (Anser cygnoides) along the East Asian Flyway
Authors: Wang Chunxiao , Yu Xiubo , Xia Shaoxia , Liu Yu , Huang Junlong , Zhao Wei
Published: April 2022

Habitats provide essential space for migratory birds to survive and reproduce. Identifying potential habitats in annual cycle stages and their influencing factors is indispensable for conservation along the flyway. In this study, we obtained satellite tracking of eight swan geese (Anser cygnoides) wintering at Poyang Lake (28°57′4.2″, 116°21′53.36″) from 2019 to 2020. Using the Maximum Entropy species distribution model, we investigated the potential habitats distribution of the swan geese during their migration cycle. We analyzed the relative contribution of various environmental factors to habitat suitability and conservation status for each potential habitat along the flyway. Our results show that the primary wintering grounds of swan geese are located in the middle and lower reaches of the Yangtze River. Stopover sites were widely distributed, mainly in the Bohai Rim, the middle reaches of the Yellow River, and the Northeast Plain, and extended westward to Inner Mongolia and Mongolia. Breeding grounds are mainly in Inner Mongolia and eastern Mongolia, while some are scattered in Mongolia’s central and western. The contribution rates of major environmental factors are different in breeding grounds, stopover sites, and wintering grounds. Breeding grounds were influenced by slope, elevation, and temperature. Slope, human footprint index, and temperature were the main factors that affected stopover sites. Wintering grounds were determined by land use, elevation, and precipitation. The conservation status of habitats is 9.6% for breeding grounds, 9.2% for wintering grounds, and 5.3% for stopover sites. Our findings thus provide a critically international assessment of potential habitats protection for geese species on the East Asian Flyway.
Beamspace Scene Classification Algorithm for Low-Angle Estimation in MIMO Radar
Authors: Chen Sheng , Zhao Yongbo , Hu Yili , Niu Ben
Published: April 2022

This paper discusses the issue of low-angle estimation in multiple-input, multiple-output (MIMO) radar. Due to the low amount of data transmission, storage, and computation required for beamspace super-resolution algorithms in low-angle estimation, the method has gained considerable interest in recent years. This paper develops a beamspace scene classification (BSC) algorithm to enhance the performance of low-angle estimation in MIMO radar. The proposed BSC algorithm solves an initial angle estimate and the multipath coefficient estimate using the 3D beamspace data, and further constructs the beamspace data required for transmitting and receiving sides to reduce the data dimensions. It is additionally used to provide three estimation schemes with closed-form solutions for three multipath scenes (obtaining two estimations for transmitting and receiving sides). Finally, it fuses the estimates of the two sides by the minimum variance criterion. As a result, the proposed method achieves high estimation accuracy while requiring few processing resources. Moreover, the computational complexity of the proposed algorithm is examined in this study, with the results demonstrating that the proposed method is superior for engineering applications.
A Smart Noise Jamming Suppression Method Based on Atomic Dictionary Parameter Optimization Decomposition
Authors: Liu Zhidong , Zhang Qun , Li Kaiming
Published: April 2022

Smart noise jamming is a new jamming style against radar, which plays an important role in modern radar electronic warfare. In order to solve the complex problem of convolutional smart noise jamming suppression with different time delay and jamming frequency modulation, a novel jamming suppression method based on the optimized atomic dictionary decomposition by estimating significant jamming parameters is proposed in this paper. First, the dual channel jamming signal information is obtained by designing twinning waveforms, then the independent estimation of time delay and jamming frequency parameters are established based on the spatial position sequences of the false targets. After that, the atomic dictionary is optimized by utilizing the obtained jamming parameters to achieve more effective decomposition. The performance of the new jamming suppression method is analyzed and verified by experimental simulations. The results show that this method can feasibly and practicably suppress the smart noise jamming with a deceptive effect, and the atomic decomposition efficiency is highly improved.
Spatiotemporal Dynamics of Land Surface Albedo and Its Influencing Factors in the Qilian Mountains, Northeastern Tibetan Plateau
Authors: Li Jichun , Pang Guojin , Wang Xuejia , Liu Fei , Zhang Yuting
Published: April 2022

Land surface albedo directly determines the distribution of radiant energy between the surface and the atmosphere, and it is a key parameter affecting the energy balance on the land surface. However, the spatiotemporal dynamics of land surface albedo and associated influencing factors in the Qilian Mountains (QM) have been rarely reported. By using the long-time series data products of MODIS shortwave albedo, normalized difference vegetation index (NDVI), and snow cover with a spatial resolution of 0.05° from 2001 to 2020, this paper analyzes the temporal and spatial variations of land surface albedo in the QM over the past 20 years and its influencing factors. The analysis results show that the multi-year average surface albedo in the QM has obvious differences in spatial distribution: it increases with the altitude, and it is high in the west (at the west of 98° E) and low in the east. Meanwhile, the surface albedo has different distribution characteristics in different seasons: the spatial distribution of surface albedo is similar in spring and autumn; the areas with a high surface albedo in summer are significantly fewer than those in other seasons. Besides, in the past 20 years, the annual average surface albedo has shown a weak growth trend in the QM, with a change rate of 5 × 10−3/10a, and the minimum and maximum values were reached in 2001 and 2019, respectively. In addition, the annual variation of the surface albedo in the QM showed a “U” shape, with the largest variation in January and the smallest variation in August. The annual variation of surface albedo is significantly positively correlated with snow cover (r = 0.96) and significantly negatively correlated with NDVI (r = −0.91). Moreover, the interannual variation of the surface albedo in the QM is closely related to land surface cover and is greatly affected by snow cover. Spatially, the annual variation of surface albedo in most areas of the QM is dominated by the change of snow cover, and the increase of surface albedo in the middle area is consistent with the increase of snow cover, while the decrease of albedo in the edge area is related to the improvement of vegetation cover. The results of this study provide a scientific basis for studying the climate and environmental changes caused by changes in the surface of the QM and making ecological environment restoration strategies.
Investigating the Potential of Sentinel-2 MSI in Early Crop Identification in Northeast China
Authors: Wei Mengfan , Wang Hongyan , Zhang Yuan , Li Qiangzi , Du Xin , Shi Guanwei , Ren Yiting
Published: April 2022

Early crop identification can provide timely and valuable information for agricultural planting management departments to make reasonable and correct decisions. At present, there is still a lack of systematic summary and analysis on how to obtain real-time samples in the early stage, what the optimal feature sets are, and what level of crop identification accuracy can be achieved at different stages. First, this study generated training samples with the help of historical crop maps in 2019 and remote sensing images in 2020. Then, a feature optimization method was used to obtain the optimal features in different stages. Finally, the differences of the four classifiers in identifying crops and the variation characteristics of crop identification accuracy at different stages were analyzed. These experiments were conducted at three sites in Heilongjiang Province to evaluate the reliability of the results. The results showed that the earliest identification time of corn can be obtained in early July (the seven leaves period) with an identification accuracy up to 86%. In the early stages, its accuracy was 40~79%, which was low, and could not reach the satisfied accuracy requirements. In the middle stages, a satisfactory recognition accuracy could be achieved, and its recognition accuracy was 79~100%. The late stage had a higher recognition accuracy, which was 90~100%. The accuracy of soybeans at each stage was similar to that of corn, and the earliest identification time of soybeans could also be obtained in early July (the blooming period) with an identification accuracy up to 87%. Its accuracy in the early growth stage was 35~71%; in the middle stage, it was 69~100%; and in the late stage, it was 92~100%. Unlike corn and soybeans, the earliest identification time of rice could be obtained at the end of April (the flooding period) with an identification accuracy up to 86%. In the early stage, its accuracy was 58~100%; in the middle stage, its accuracy was 93~100%; and in the late stage, its accuracy was 96~100%. In terms of crop identification accuracy in the whole growth stage, GBDT and RF performed better than other classifiers in our three study areas. This study systematically investigated the potential of early crop recognition in Northeast China, and the results are helpful for relevant applications and decision making of crop recognition in different crop growth stages.
Spatial-Temporal Neural Network for Rice Field Classification from SAR Images
Authors: Chang Yang-Lang , Tan Tan-Hsu , Chen Tsung-Hau , Chuah Joon Huang , Chang Lena , Wu Meng-Che , Tatini Narendra Babu , Ma Shang-Chih , Alkhaleefah Mohammad
Published: April 2022

Agriculture is an important regional economic industry in Asian regions. Ensuring food security and stabilizing the food supply are a priority. In response to the frequent occurrence of natural disasters caused by global warming in recent years, the Agriculture and Food Agency (AFA) in Taiwan has conducted agricultural and food surveys to address those issues. To improve the accuracy of agricultural and food surveys, AFA uses remote sensing technology to conduct surveys on the planting area of agricultural crops. Unlike optical images that are easily disturbed by rainfall and cloud cover, synthetic aperture radar (SAR) images will not be affected by climatic factors, which makes them more suitable for the forecast of crops production. This research proposes a novel spatial-temporal neural network called a convolutional long short-term memory rice field classifier (ConvLSTM-RFC) for rice field classification from Sentinel-1A SAR images of Yunlin and Chiayi counties in Taiwan. The proposed model ConvLSTM-RFC is implemented with multiple convolutional long short-term memory attentions blocks (ConvLSTM Att Block) and a bi-tempered logistic loss function (BiTLL). Moreover, a convolutional block attention module (CBAM) was added to the residual structure of the ConvLSTM Att Block to focus on rice detection in different periods on SAR images. The experimental results of the proposed model ConvLSTM-RFC have achieved the highest accuracy of 98.08% and the rice false positive is as low as 15.08%. The results indicate that the proposed ConvLSTM-RFC produces the highest area under curve (AUC) value of 88% compared with other related models.
Polarization Aberrations in High-Numerical-Aperture Lens Systems and Their Effects on Vectorial-Information Sensing
Authors: Shen Yuanxing , Chen Binguo , He Chao , He Honghui , Guo Jun , Wu Jian , Elson Daniel S. , Ma Hui
Published: April 2022

The importance of polarization aberrations has been recognized and studied in numerous optical systems and related applications. It is known that polarization aberrations are particularly crucial in certain photogrammetry and microscopy techniques that are related to vectorial information—such as polarization imaging, stimulated emission depletion microscopy, and structured illumination microscopy. Hence, a reduction in polarization aberrations would be beneficial to different types of optical imaging/sensing techniques with enhanced vectorial information. In this work, we first analyzed the intrinsic polarization aberrations induced by a high-NA lens theoretically and experimentally. The aberrations of depolarization, diattenuation, and linear retardance were studied in detail using the Mueller matrix polar-decomposition method. Based on an analysis of the results, we proposed strategies to compensate the polarization aberrations induced by high-NA lenses for hardware-based solutions. The preliminary imaging results obtained using a Mueller matrix polarimeter equipped with multiple coated aspheric lenses for polarization-aberration reduction confirmed that the conclusions and strategies proposed in this study had the potential to provide more precise polarization information of the targets for applications spanning across classical optics, remote sensing, biomedical imaging, photogrammetry, and vectorial optical-information extraction.
Evaluation and Comparison of MODIS C6 and C6.1 Deep Blue Aerosol Products in Arid and Semi-Arid Areas of Northwestern China
Authors: Yang Leiku , Tian Xinyao , Liu Chao , Ji Weiqian , Zheng Yu , Liu Huan , Lu Xiaofeng , Che Huizheng
Published: April 2022

The Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) algorithm was developed for aerosol retrieval on bright surfaces. Although the global validation accuracy of the DB product is satisfactory, there are still some regions found to have very low accuracy. To this end, DB has updated the surface database in the latest version of the Collection 6.1 (C6.1) algorithm. Some studies have shown that DB aerosol optical depth (AOD) of the old version Collection 6 (C6) has been seriously underestimated in Northwestern China. However, the status of the new version of the C6.1 product in this region is still unknown. This study aims to comprehensively evaluate the performance of the MODIS DB product in Northwestern China. The DB AOD with high quality (Quality Flag = 2 or 3) was selected to validate against the 23 sites from the China Aerosol Remote Sensing Network (CARSNET) and Aerosol Robotic Network (AERONET) during the period 2002–2014. By the overall analysis, the results indicate that both C6 and C6.1 show significant underestimation with a large fraction of more than 54% of collocations falling below the Expected Error (EE = ±(0.05 + 20% AODground)) envelope and with a large negative Mean Bias (MB) of less than −0.14. Furthermore, the new C6.1 products failed to achieve reasonable improvements in the region of Northwestern China. Besides, C6.1 has slightly fewer collocations than C6 due that some pixels with systematic biases have been removed from the new surface reflectance database. From the analysis of the site scale, the scatter plot of C6.1 is similar to that of C6 in most sites. Furthermore, a significant underestimation of DB AOD was observed at most sites, with the most severe underestimation at two sites located in the Taklimakan Desert region. Among 23 sites in Northwestern China, there are only two sites where C6.1 has largely improved the underestimation of C6. Furthermore, it is interesting to note that there are also two sites where the accuracy of the new C6.1 has declined. Moreover, it is surprising that there is one site where a large overestimation was observed in C6 and improved in C6.1. Additionally, we found a constant value of about 0.05 for both C6 and C6.1 at several sites with low aerosol loading, which is an obvious artifact. The significant improvements of C6.1 were observed in the Middle East and Central Asia but not in most sites of Northwestern China. The results of this study will be beneficial to further improvements in the MODIS DB algorithm.
Simulative Evaluation of the Underwater Geodetic Network Configuration on Kinematic Positioning Performance
Authors: Li Menghao , Liu Yang , Liu Yanxiong , Chen Guanxu , Tang Qiuhua , Han Yunfeng , Wen Yuanlan
Published: April 2022

The construction of underwater geodetic networks (UGN) is crucial in marine geodesy. To provide high-precision kinematic positioning for underwater submersibles, an underwater acoustic geodetic network configuration of three seafloor base stations, one subsurface buoy, and one sea surface buoy is proposed. The simulation results show that, for a 3 km-deep sea, based on the proposed UGN, the submersible positioning range and positioning accuracy are primarily affected by the size of the seafloor base station array, while the height of the subsurface buoy has a greater impact on the submersible positioning accuracy than the positioning range. Considering current acoustic ranging technology, the kinematic positioning performance of the UGN is optimal when the seafloor base stations are 9~13 km apart and the subsurface buoy is less than 2.5 km above the seafloor, which can achieve a submersible positioning accuracy of less than 30 m within an underwater space of 25 km × 25 km × 3 km. The proposed cost-effective UGN configuration can provide high-precision submersible kinematic positioning performance for seafloor surveying and ocean precision engineering. The impact of the underwater environment on the acoustic transmission characteristics should be further investigated.
BDS/GPS/UWB Adaptively Robust EKF Tightly Coupled Navigation Model Considering Pedestrian Motion Characteristics
Authors: Zhang Jian , Wang Jian , Cui Ximin , Yuan Debao
Published: April 2022

In the indoor and outdoor transition area, due to its poor availability in a complex positioning environment, the BDS/GPS SPP (single-point positioning by combining BeiDou Navigation Satellite System (BDS) and Global Positioning System (GPS)) is unable to provide an effective positioning service. In view of the poor positioning accuracy and low sampling rate of the BDS/GPS SPP and the gross error, such as the non-line-of-sight error of UWB (Ultra-Wide-Band), making the accuracy of positioning results poor, a BDS/GPS/UWB tightly coupled navigation model considering pedestrian motion characteristics is proposed to make positioning results more reliable and accurate in the transition area. The core content of this paper is divided into the following three parts: (1) Firstly, the dynamic model and positioning theories of BDS/GPS SPP and UWB are introduced, respectively. (2) Secondly, the BDS/GPS/UWB tightly coupled navigation model is proposed. An environment discrimination factor is introduced to adaptively adjust the variance factor of the system state. At the same time, the gross error detection factor is constructed by using the a posteriori residuals to make the variance factor of the measurement information of the combined positioning system able to be adjusted intelligently for the purpose of eliminating the interference of gross error observations on positioning results. On the other hand, pedestrian motion characteristics are introduced to establish the constraint equation to improve the consistency of positioning accuracy. (3) Thirdly, the actual measured data are used to demonstrate and analyze the reliability of the positioning model proposed by this paper. The experimental results show that the BDS/GPS/UWB tightly coupled navigation model can effectively improve the accuracy and availability of positioning. Compared with BDS/GPS SPP, the accuracy of this model is improved by 57.8%, 76.0% and 56.5% in the E, N and U directions, respectively.
Tropical Species Classification with Structural Traits Using Handheld Laser Scanning Data
Authors: Wang Meilian , Wong Man Sing , Abbas Sawaid
Published: April 2022

Information about tree species plays a pivotal role in sustainable forest management. Light detection and ranging (LiDAR) technology has demonstrated its potential to obtain species information using the structural features of trees. Several studies have explored the structural properties of boreal or temperate trees from terrestrial laser scanning (TLS) data and applied them to species classification, but the study of structural properties of tropical trees for species classification is rare. Compared to conventional static TLS, handheld laser scanning (HLS) is able to effectively capture point clouds of an individual tree with flexible movability. Therefore, in this study, we characterized the structural features of tropical species from HLS data as 23 LiDAR structural parameters, involving 6 branch, 11 crown and 6 entire tree parameters, and used these parameters to classify the species via 5 machine-learning (ML) models, respectively. The performance of each parameter was further evaluated and compared. Classification results showed that the employed parameters can achieve a classification accuracy of 84.09% using the support vector machine with a polynomial kernel. The evaluation of parameters indicated that it is insufficient to classify four species with only one and two parameters, but ten parameters were recommended in order to achieve satisfactory accuracy. The combination of different types of parameters, such as branch and crown parameters, can significantly improve classification accuracy. Finally, five sets of optimal parameters were suggested according to their importance and performance. This study also showed that the time- and cost-efficient HLS instrument could be a promising tool for tree-structure-related studies, such as structural parameter estimation, species classification, forest inventory, as well as sustainable tree management.
A Pre-Operational System Based on the Assimilation of MODIS Aerosol Optical Depth in the MOCAGE Chemical Transport Model
Authors: El Amraoui Laaziz , Plu Matthieu , Guidard Vincent , Cornut Flavien , Bacles Mickaël
Published: April 2022

In this study we present a pre-operational forecasting assimilation system of different types of aerosols. This system has been developed within the chemistry-transport model of Météo-France, MOCAGE, and uses the assimilation of the Aerosol Optical Depth (AOD) from MODIS (Moderate Resolution Imaging Spectroradiometer) onboard both Terra and Aqua. It is based on the AOD assimilation system within the MOCAGE model. It operates on a daily basis with a global configuration of 1∘×1∘ (longitude × latitude). The motivation of such a development is the capability to predict and anticipate extreme events and their impacts on the air quality and the aviation safety in the case of a huge volcanic eruption. The validation of the pre-operational system outputs has been done in terms of AOD compared against the global AERONET observations within two complete years (January 2018–December 2019). The comparison between both datasets shows that the correlation between the MODIS assimilated outputs and AERONET over the whole period of study is 0.77, whereas the biases and the RMSE (Root Mean Square Error) are 0.006 and 0.135, respectively. The ability of the pre-operational system to predict extreme events in near real time such as the desert dust transport and the propagation of the biomass burning was tested and evaluated. We particularly presented and documented the desert dust outbreak which occurred over Greece in late March 2018 as well as the wildfire event which happened on Australia between July 2019 and February 2020. We only presented these two events, but globally the assimilation chain has shown that it is capable of predicting desert dust events and biomass burning aerosols which happen all over the globe.
A Laser-Induced Breakdown Spectroscopy Experiment Platform for High-Degree Simulation of MarSCoDe In Situ Detection on Mars
Authors: Cui Zhicheng , Jia Liangchen , Li Luning , Liu Xiangfeng , Xu Weiming , Shu Rong , Xu Xuesen
Published: April 2022

The Zhurong rover of China’s Tianwen-1 mission started its inspection tour on Mars in May 2021. As a major scientific payload onboard the Zhurong rover, the Mars Surface Composition Detector (MarSCoDe) instrument adopts laser-induced breakdown spectroscopy (LIBS) to detect and analyze the chemical composition of Martian materials. This paper introduces an experimental platform capable of establishing a simulated Martian atmospheric environment, in which a duplicate model of the MarSCoDe flight model is placed. In the simulated environment, the limit vacuum degree can reach 10−5 Pa level, the temperature can change from −190 °C to +180 °C, and different gases can be filled and mixed according to desired proportion. Moreover, the sample stage can move along a track inside the vacuum chamber, enabling the detection distance to vary from 1.5 m to 7 m. Preliminary experimental results indicate that this platform is able to simulate the scenario of MarSCoDe in situ LIBS detection on Mars well.
Runtime ML-DL Hybrid Inference Platform Based on Multiplexing Adaptive Space-Time Resolution for Fast Car Incident Prevention in Low-Power Embedded Systems
Authors: Hong Sunghoon , Park Daejin
Published: April 2022

Forward vehicle detection is the key technique to preventing car incidents in front. Artificial intelligence (AI) techniques are used to more accurately detect vehicles, but AI-based vehicle detection takes a lot of processing time due to its high computational complexity. When there is a risk of collision with a vehicle in front, the slow detection speed of the vehicle may lead to an accident. To quickly detect a vehicle in real-time, a high-speed and lightweight vehicle detection technique with similar detection performance to that of an existing AI-based vehicle detection is required. In addition, to apply forward collision warning system (FCWS) technology to vehicles, it is important to provide high performance based on low-power embedded systems because the vehicle’s battery consumption must remain low. The vehicle detection algorithm occupies the most resources in FCWS. To reduce power consumption, it is important to reduce the computational complexity of an algorithm, that is, the amount of resources required to run it. This paper describes a method for fast, accurate forward vehicle detection using machine learning and deep learning. To detect a vehicle in consecutive images consistently, a Kalman filter is used to predict the bounding box based on the tracking algorithm and correct it based on the detection algorithm. As a result, its vehicle detection speed is about 25.85 times faster than deep-learning-based object detection is, and its detection accuracy is better than machine-learning-based object detection is.
Realization of a PEDOT:PSS/Graphene Oxide On-Chip Pseudo-Reference Electrode for Integrated ISFETs
Authors: Tintelott Marcel , Kremers Tom , Ingebrandt Sven , Pachauri Vivek , Vu Xuan Thang
Published: April 2022

A stable reference electrode (RE) plays a crucial role in the performance of an ion-sensitive field-effect transistor (ISFET) for bio/chemical sensing applications. There is a strong demand for the miniaturization of the RE for integrated sensor systems such as lab-on-a-chip (LoC) or point-of-care (PoC) applications. Out of several approaches presented so far to integrate an on-chip electrode, there exist critical limitations such as the effect of analyte composition on the electrode potential and drifts during the measurements. In this paper, we present a micro-scale solid-state pseudo-reference electrode (pRE) based on poly(3,4-ethylene dioxythiophene): poly(styrene sulfonic acid) (PEDOT:PSS) coated with graphene oxide (GO) to deploy with an ion-sensitive field-effect transistor (ISFET)-based sensor platform. The PEDOT:PSS was electropolymerized from its monomer on a micro size gold (Au) electrode and, subsequently, a thin GO layer was deposited on top. The stability of the electrical potential and the cross-sensitivity to the ionic strength of the electrolyte were investigated. The presented pRE exhibits a highly stable open circuit potential (OCP) for up to 10 h with a minimal drift of ~0.65 mV/h and low cross-sensitivity to the ionic strength of the electrolyte. pH measurements were performed using silicon nanowire field-effect transistors (SiNW-FETs), using the developed pRE to ensure good gating performance of electrolyte-gated FETs. The impact of ionic strength was investigated by measuring the transfer characteristic of a SiNW-FET in two electrolytes with different ionic strengths (1 mM and 100 mM) but the same pH. The performance of the PEDOT:PSS/GO electrode is similar to a commercial electrochemical Ag/AgCl reference electrode.
Effectiveness of Electrostatic Shielding in High-Frequency Electromagnetic Induction Soil Sensing
Authors: Špikić Dorijan , Švraka Matija , Vasić Darko
Published: April 2022

High-frequency electromagnetic induction (HFEMI) sensors, operating in the frequency range from 300 kHz to 30 MHz, have been proposed for the measurement of soil electrical conductivity and dielectric permittivity that are related to the physical and chemical properties of soil. Because of the high-frequency operation, the capacitive coupling between the sensor transmitter and receiver coils is comparable to inductive coupling, creating the need for electrostatic shielding. The remnant capacitive coupling after the implementation of shielding can lead to significant difficulties in the sensor signal interpretation, because both coupling mechanisms are highly dependent on the geometry of the HFEMI sensor and applied shield. In this paper, we introduce the discussion on the relationship between the sensor geometry, shielding and the coupling mechanisms for HFEMI soil sensing. We theoretically and experimentally evaluate a typical HFEMI sensor and its shielding in the frequency range of up to 20 MHz and propose a method for evaluating the effectiveness of a shield configuration. In the case study, we experimentally analyze the HFEMI sensor above a saline solution for two shield configurations. The results agree well with the results of a finite element method analysis.
Hypervelocity Impact Detection and Location for Stiffened Structures Using a Probabilistic Hyperbola Method
Authors: Yu Sunquan , Fan Chengguang , Zhao Yong
Published: April 2022

Hyper-velocity impact (HVI) caused by a collision between orbital debris and spacecraft exists widely in outer space, and it poses a threat to spacecraft. This paper proposes a probabilistic hyperbola method based on Lamb waves analysis to detect and locate the impact in stiffened aluminum (Al) plates. A hybrid model using finite element analysis (FEA) and smoothed particle hydrodynamics (SPH) was developed to gain an insight into characteristics of HVI-induced acoustic emission (AE) and shock wave propagation. In addition, an experimental validation was carried out with a two-stage light gas gun, giving an aluminum projectile a velocity of several kilometers per second. Then a quantitative agreement is obtained between numerical and experimental results, demonstrating the correctness of the hybrid model and facilitating the explanation of received AE signals in experiments. Signal analysis shows that the shock wave quickly converts to a Lamb wave as it propagates from the HVI spot, and the zeroth-order symmetric wave mode (S0) dominates wave signal energy. The S0 wave is dispersive and shows a wide frequency range, with dominant magnitudes below 500 kHz. Finally, the HVI experiment results obtained with a light gas gun showed that the average location error could be less than 1 cm with only four sensors for a 1-square-meter stiffened metal plate.
Review of Real-Time Biomechanical Feedback Systems in Sport and Rehabilitation
Authors: Hribernik Matevž , Umek Anton , Tomažič Sašo , Kos Anton
Published: April 2022

Real-time biomechanical feedback (BMF) is a relatively new area of research. The potential of using advanced technology to improve motion skills in sport and accelerate physical rehabilitation has been demonstrated in a number of studies. This paper provides a literature review of BMF systems in sports and rehabilitation. Our motivation was to examine the history of the field to capture its evolution over time, particularly how technologies are used and implemented in BMF systems, and to identify the most recent studies showing novel solutions and remarkable implementations. We searched for papers in three research databases: Scopus, Web of Science, and PubMed. The initial search yielded 1167 unique papers. After a rigorous and challenging exclusion process, 144 papers were eventually included in this report. We focused on papers describing applications and systems that implement a complete real-time feedback loop, which must include the use of sensors, real-time processing, and concurrent feedback. A number of research questions were raised, and the papers were studied and evaluated accordingly. We identified different types of physical activities, sensors, modalities, actuators, communications, settings and end users. A subset of the included papers, showing the most perspectives, was reviewed in depth to highlight and present their innovative research approaches and techniques. Real-time BMF has great potential in many areas. In recent years, sensors have been the main focus of these studies, but new types of processing devices, methods, and algorithms, actuators, and communication technologies and protocols will be explored in more depth in the future. This paper presents a broad insight into the field of BMF.
Label-Free Uric Acid Estimation of Spot Urine Using Portable Device Based on UV Spectrophotometry
Authors: Lin Tsung-Jui , Yen Kai-Ting , Chen Chien-Fan , Yan Shuo-Ting , Su Kuan-Wei , Chiang Ya-Ling
Published: April 2022

The maintenance of uric acid levels is crucial for the human body. In this study, the feasibility of using portable ultraviolet (UV) spectrophotometry to measure the uric acid of spot urine without the need to add reagents has been demonstrated for the first time. UV spectral analysis has been used to inspect the uric acid concentration in urine. It is found that the absorption spectrum of urine has a high correlation with the concentration of uric acid at a wavelength of around 290–300 nm. Uric acid levels measured with a spectral analyzer compared to uric acid concentrations measured with a traditional biochemical analysis showed good agreement. The portable prototype is label-free and capable of displaying the inspection result of each measurement within 10 s. In the long run, this device can assist people in checking uric acid levels of spot urine with higher frequency and can adjust diet or medication in real time for more efficient health management.
Polyvinyl Alcohol/Graphene Oxide Conductive Hydrogels via the Synergy of Freezing and Salting Out for Strain Sensors
Authors: Wei Jingjiang , Wang Rongjie , Pan Fei , Fu Zhengyi
Published: April 2022

Hydrogels of flexibility, strength, and conductivity have demonstrated broad applications in wearable electronics and soft robotics. However, it is still a challenge to fabricate conductive hydrogels with high strength massively and economically. Herein, a simple strategy is proposed to design a strong ionically conductive hydrogel. This ion-conducting hydrogel was obtained under the synergistic action by salting out the frozen mixture of polyvinyl alcohol (PVA) and graphene oxide (GO) using a high concentration of sodium chloride solution. The developed hydrogel containing only 5 wt% PVA manifests good tensile stress (65 kPa) and elongation (180%). Meanwhile, the PVA matrix doped with a small amount of GO formed uniformly porous ion channels after salting out, endowed the PVA/GO hydrogel with excellent ionic conductivity (up to 3.38 S m−1). Therefore, the fabricated PVA/GO hydrogel, anticipated for a strain sensor, exhibits good sensitivity (Gauge factor = 2.05 at 100% strain), satisfying working stability (stably cycled for 10 min), and excellent recognition ability. This facile method to prepare conductive hydrogels displays translational potential in flexible electronics for engineering applications.
Cable Monitoring Using Broadband Power Line Communication
Authors: Benesl Lukas , Mlynek Petr , Ptacek Michal , Vycital Vaclav , Misurec Jiri , Slacik Jan , Rusz Martin , Musil Petr
Published: April 2022

Power line communication (PLC) is considered one of the possible communication technologies for applications in the field of smart metering, smart substations, smart homes, and recently for the management of renewable resources or micro grid control. This article deals with the use of PLC technology to determine the technical condition of the cable. This coefficient can help distribution system operators (DSO) to assess the condition of their cable routes. In this way, possible cable breakdowns and subsequent power outages can be prevented. The resulting methodology for calculating the coefficient is presented in two specific examples of routes, in which a significant benefit for DSO’s can be found.
What Can 5G Do for Public Safety? Structural Health Monitoring and Earthquake Early Warning Scenarios
Authors: Franchi Fabio , Marotta Andrea , Rinaldi Claudia , Graziosi Fabio , Fratocchi Luciano , Parisse Massimo
Published: April 2022

The 5th generation of mobile networks has come to the market bringing the promise of disruptive performances as low latency, availability and reliability, imposing the development of the so-called “killer applications”. This contribution presents a 5G use case in the context of Structural Health Monitoring which guarantees an unprecedented level of reliability when exploited for public safety purposes as Earthquake Early Warning. The interest on this topic is at first justified through a deep market analysis, and subsequently declined in terms of public safety benefits. A specific sensor board, guaranteeing real-time processing and 5G connectivity, is presented as the foundation on which the architecture of the network is designed and developed. Advantages of 5G-enabled urban safety are then discussed and proven in the experimentation results, showing that the proposed architecture guarantees lower latency delays and overcome the impairments of cloud solutions especially in terms of delays variability.
PROSPECT-PMP+: Simultaneous Retrievals of Chlorophyll a and b, Carotenoids and Anthocyanins in the Leaf Optical Properties Model
Authors: Zhang Yao , Li Xinkai , Wang Chengjie , Zhang Rongxu , Jin Lisong , He Zongtai , Tian Shoupeng , Wu Kaihua , Wang Fumin
Published: April 2022

The PROSPECT leaf optical radiative transfer models, including PROSPECT-MP, have addressed the contributions of multiple photosynthetic pigments (chlorophyll a and b, and carotenoids) to leaf optical properties, but photo-protective pigment (anthocyanins), another important indicator of vegetation physiological and ecological functions, has not been simultaneously combined within a leaf optical model. Here, we present a new calibration and validation of PROSPECT-MP+ that separates the contributions of multiple photosynthetic and photo-protective pigments to leaf spectrum in the 400–800 nm range using a new empirical dataset that contains multiple photosynthetic and photo-protective pigments (LOPEX_ZJU dataset). We first provide multiple distinct in vivo individual photosynthetic and photo-protective pigment absorption coefficients and leaf average refractive index of the leaf interior using the LOPEX_ZJU dataset. Then, we evaluate the capabilities of PROSPECT-MP+ for forward modelling of leaf directional hemispherical reflectance and transmittance spectra and for retrieval of pigment concentrations by model inversion. The main result of this study is that the absorption coefficients of chlorophyll a and b, carotenoids, and anthocyanins display the physical principles of absorption spectra. Moreover, the validation result of this study demonstrates the potential of PROSPECT-MP+ for improving capabilities in remote sensing of leaf photosynthetic pigments (chlorophyll a and b, and carotenoids) and photo-protective pigment (anthocyanins).
Cladding Mode Fitting-Assisted Automatic Refractive Index Demodulation Optical Fiber Sensor Probe Based on Tilted Fiber Bragg Grating and SPR
Authors: Lin Wenwei , Huang Weiying , Liu Yingying , Chen Xiaoyong , Qu Hang , Hu Xuehao
Published: April 2022

In the paper based on surface plasmon resonance (SPR) in a tilted fiber Bragg grating (TFBG), a novel algorithm is proposed, which facilitates demodulation of surrounding refractive index (SRI) via cladding mode interrogation and accelerates calibration and measurement of SRI. Refractive indices with a tiny index step of 2.2 × 10−5 are prepared by the dilution of glucose aqueous solution for the test and the calibration of this fiber sensor probe. To accelerate the calibration process, automatic selection of the most sensitive cladding mode is demonstrated. First, peaks of transmitted spectrum are identified and numbered. Then, sensitivities of several potentially sensitive cladding modes in amplitude adjacent to the left of the SPR area are calculated and compared. After that, we focus on the amplitudes of the cladding modes as a function of a SRI, and the highest sensitivity of −6887 dB/RIU (refractive index unit) is obtained with a scanning time of 15.77 s in the range from 1520 nm to 1620 nm. To accelerate the scanning speed of the optical spectrum analyzer (OSA), the wavelength resolution is reduced from 0.028 nm to 0.07 nm, 0.14 nm, and 0.28 nm, and consequently the scanning time is shortened to 6.31 s, 3.15 s, and 1.58 s, respectively. However, compared to 0.028 nm, the SRI sensitivity for 0.07 nm, 0.14 nm, and 0.28 nm is reduced to −5685 dB/RIU (17.5% less), −5415 dB/RIU (21.4% less), and −4359 dB/RIU (36.7% less), respectively. Thanks to the calculation of parabolic equation and weighted Gauss fitting based on the original data, the sensitivity is improved to −6332 dB/RIU and −6721 dB/RIU, respectively, for 0.07 nm, and the sensitivity is increased to −5850 dB/RIU and −6228 dB/RIU, respectively, for 0.14 nm.
Musical Instrument Identification Using Deep Learning Approach
Authors: Blaszke Maciej , Kostek Bożena
Published: April 2022

The work aims to propose a novel approach for automatically identifying all instruments present in an audio excerpt using sets of individual convolutional neural networks (CNNs) per tested instrument. The paper starts with a review of tasks related to musical instrument identification. It focuses on tasks performed, input type, algorithms employed, and metrics used. The paper starts with the background presentation, i.e., metadata description and a review of related works. This is followed by showing the dataset prepared for the experiment and its division into subsets: training, validation, and evaluation. Then, the analyzed architecture of the neural network model is presented. Based on the described model, training is performed, and several quality metrics are determined for the training and validation sets. The results of the evaluation of the trained network on a separate set are shown. Detailed values for precision, recall, and the number of true and false positive and negative detections are presented. The model efficiency is high, with the metric values ranging from 0.86 for the guitar to 0.99 for drums. Finally, a discussion and a summary of the results obtained follows.