bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatiotemporal and Multiscale Analysis of the Coupling Coordination Degree between Economic Development Equality and Eco-Environmental Quality in China from 2001 to 2020
Authors: Ji Jianwan , Tang Zhanzhong , Zhang Weiwei , Liu Wenliang , Jin Biao , Xi Xu , Wang Futao , Zhang Rui , Guo Bing , Xu Zhiyu , Shifaw Eshetu , Xiong Yibing , Wang Jinming , Xu Saiping , Wang Zhenqing
Published: February 2022

Evaluating and exploring regional eco-environmental quality (EEQ), economic development equality (EDE) and the coupling coordination degree (CCD) at multiple scales is important for realizing regional sustainable development goals. The CCD can reflect both the development level and the interaction relationship of two or more systems. However, relevant previous studies have ignored non-statistical data, lacked multiscale analyses, misused the coupling coordination degree model or have not sufficiently considered economic development equality. In response to these problems, this study integrated multisource remote sensing datasets to calculate and analyse the remote sensing ecological index (RSEI) and then used nighttime light data and population density data to calculate the proposed nighttime difference index (NTDI). Next, a modified coupling coordination degree (MCCD) index was proposed to analyse the MCCD between EEQ and EDE. Then, spatiotemporal and multiscale analyses at the county, city, province, urban agglomeration and country levels were performed. Global and local spatial autocorrelation and trend analyses were performed to evaluate the spatial aggregation degree and change trends from 2001 to 2020. The main conclusions are as follows: (1) The EEQ of China displayed a fluctuating upwards trend (0.0048 a−1), with average RSEI values of 0.5950, 0.6277, 0.6164, 0.6311 and 0.6173; the EDE of China showed an upwards trend (0.0298 a−1), with average NTDI values of 0.1271, 0.1635, 0.1642, 0.2181 and 0.2490; and China’s MCCD indicated an upwards trend (0.0220 a−1), with values of 0.4614, 0.5027, 0.4978, 0.5401 and 0.5525. (2) The highest global Moran’s I of NTDI and MCCD was achieved at the city scale, while the highest RSEI was achieved at the county scale. From 2001 to 2020, the spatial agglomeration effect of the RSEI decreased, while that of the NTDI and MCCD increased. (3) A power function relationship occurred between NTDI and MCCD at different scales. Furthermore, the NTDI had a higher contribution to improving the MCCD than the RSEI and the R2 of the fitted curve at different scales ranged from 0.8183 to 0.9915.
An In-Orbit Measurement Method for Elevation Antenna Pattern of MEO Synthetic Aperture Radar Based on Nano Calibration Satellite
Authors: Qiu Tian , Wang Yu , Hong Jun , Xing Kaichu , Du Shaoyan , Mu Jingwen
Published: February 2022

The medium-Earth-orbit synthetic aperture radar (MEO-SAR) is deployed at orbit altitude above low-Earth-orbit synthetic aperture radar (LEO-SAR, around 2000 km) and below the geosynchronous orbit SAR (GEO-SAR, near 35786 km) to cover a wide swath, which is four to five times larger than LEO-SAR. Therefore, the measurement method for the LEO-SAR elevation antenna pattern using the SAR data acquired over the Amazon tropical rainforest (ground-based method), where the typical width of rainforest area is approximately 150 km, can hardly meet the requirement of a wide swath to determine the MEO-SAR antenna elevation pattern. Moreover, several new MEO-SAR systems are now proposed that will use low frequency, and the low frequency penetration characteristics may affect the elevation antenna pattern determination using homogenous distributed targets such as the Amazon rainforest. This paper proposes a novel space-based method for the in-orbit measurement of the elevation antenna pattern of MEO-SAR based on one nano calibration satellite mounted with a receiver. Through appropriate orbit design, the nano calibration satellite can fly across the entire MEO-SAR swath along the range direction, and the elevation antenna pattern envelope can be extracted from the data recorded by the receiver. Simulation work is performed to verify the feasibility of the proposed space-based method, and the measurement accuracy of this method is analyzed.
Marine Mixed Layer Height Detection Using Ship-Borne Coherent Doppler Wind Lidar Based on Constant Turbulence Threshold
Authors: Wang Lu , Yuan Jinlong , Xia Haiyun , Zhao Lijie , Wu Yunbin
Published: February 2022

Marine mixed layer height (MLH) detection using a ship-borne coherent Doppler wind lidar (CDWL) based on a constant turbulent kinetic energy dissipation rate (TKEDR) threshold is realized and experimentally demonstrated. The MLH can be retrieved from the TKEDR estimated by the CDWL via setting an appropriate threshold. Here, the value of threshold is determined by a reference MLH retrieved from aerosol backscattered signal. The threshold of 10−4 m2 s−3 is found to be applicable in retrieving both inland and marine MLHs. In the experiments, to validate the reliability of the constant threshold, the MLH diurnal cycles at inland and marine sites are retrieved by using a ground-based CDWL. The MLH retrieval result at the marine site shows good agreement with radiosonde-derived MLH. After that, by using a ship-borne CDWL, the marine MLH along the ship’s route in South China Sea is successfully detected in real time.
AR-Based 3D Virtual Reconstruction of Brick Details
Authors: Shih Naai-Jung , Wu Yu-Chen
Published: February 2022

Building heritage contributes to the historical context and industrial history of a city. Brick warehouses, which comprise a systematic interface between components, demand an interactive manipulation of inspected parts to interpret their construction complexity. The documentation of brick details in augmented reality (AR) can be challenging when the relative location needs to be defined in 3D. This study aimed to compare brick details in AR, and to reconstruct the interacted result in the correct relative location. We applied photogrammetry modeling and smartphone AR for the first and secondary 3D reconstruction of brick warehouse details and compared the results. In total, 146 3D AR database models were created. The AR-based virtual reconstruction process applied multiple imagery resources from video conferencing and broadcast of models on the Augment® platform through a smartphone. Tests verified the virtual reconstruction in AR, and concluded the deviation between the final secondary reconstructed 3D model and the first reconstructed model had a standard deviation of less than 1 cm. AR enabled the study and documentation of cross-referenced results in comparison with the simplified reconstruction process, with structural detail and visual detail suitable for 3D color prints.
Remote Sensing Monitoring of Grasslands Based on Adaptive Feature Fusion with Multi-Source Data
Authors: Wang Weitao , Ma Qin , Huang Jianxi , Feng Quanlong , Zhao Yuanyuan , Guo Hao , Chen Boan , Li Chenxi , Zhang Yuxin
Published: February 2022

Grasslands, as an important part of terrestrial ecosystems, are facing serious threats of land degradation. Therefore, the remote monitoring of grasslands is an important tool to control degradation and protect grasslands. However, the existing methods are often disturbed by clouds and fog, which makes it difficult to achieve all-weather and all-time grassland remote sensing monitoring. Synthetic aperture radar (SAR) data can penetrate clouds, which is helpful for solving this problem. In this study, we verified the advantages of the fusion of multi-spectral (MS) and SAR data for improving classification accuracy, especially for cloud-covered areas. We also proposed an adaptive feature fusion method (the SK-like method) based on an attention mechanism, and tested two types of patch construction strategies, single-size and multi-size patches. Experiments have shown that the proposed SK-like method with single-size patches obtains the best results, with 93.12% accuracy and a 0.91 average f1-score, which is a 1.02% accuracy improvement and a 0.01 average f1-score improvement compared with the commonly used feature concatenation method. Our results show that the all-weather, all-time remote sensing monitoring of grassland is possible through the fusion of MS and SAR data with suitable feature fusion methods, which will effectively enhance the regulatory capability of grassland resources.
Sea Surface Salinity Variability in the Bering Sea in 2015–2020
Authors: Zhao Jian , Wang Yan , Liu Wenjing , Bi Hongsheng , Cokelet Edward D. , Mordy Calvin W. , Lawrence-Slavas Noah , Meinig Christian
Published: February 2022

Salinity in the Bering Sea is vital for the physical environment that is tied to the productive ecosystem and the properties of Pacific waters transported to the Arctic Ocean. Its salinity variability reflects many fundamental processes, including sea ice formation/melting and river runoff, but its spatial and temporal characteristics require better documentation. This study utilizes remote sensing products and in situ observations collected by saildrone missions to investigate Sea Surface Salinity (SSS) variability. All Satellite products resolve the large-scale pattern set up by the relatively salty deep basin and the fresh coastal region, but they can be inaccurate near the ice edge and near land. The SSS annual cycle exhibits seasonal maxima in winter to spring, and minima in summer to fall. The amplitude and timing of the seasonal cycle are variable, especially on the eastern Bering Sea shelf. SSS variability recorded by both saildrone, and satellite instruments provide unprecedented insights into short-term oceanic processes including sea ice melting, wind-driven currents during weather events, and river plumes etc. In particular, the Soil Moisture Active Passive (SMAP) satellite demonstrates encouraging skills in capturing the freshening signals induced by spring sea ice melting. The Yukon River plume is another source of intense SSS variability. Surface wind forcing plays an essential role in controlling the horizontal movement of plume water and thereby shaping the SSS seasonal cycle in local regions.
SSDBN: A Single-Side Dual-Branch Network with Encoder–Decoder for Building Extraction
Authors: Li Yang , Lu Hui , Liu Qi , Zhang Yonghong , Liu Xiaodong
Published: February 2022

In the field of building detection research, an accurate, state-of-the-art semantic segmentation model must be constructed to classify each pixel of the image, which has an important reference value for the statistical work of a building area. Recent research efforts have been devoted to semantic segmentation using deep learning approaches, which can be further divided into two aspects. In this paper, we propose a single-side dual-branch network (SSDBN) based on an encoder–decoder structure, where an improved Res2Net model is used at the encoder stage to extract the basic feature information of prepared images while a dual-branch module is deployed at the decoder stage. An intermediate framework was designed using a new feature information fusion methods to capture more semantic information in a small area. The dual-branch decoding module contains a deconvolution branch and a feature enhancement branch, which are responsible for capturing multi-scale information and enhancing high-level semantic details, respectively. All experiments were conducted using the Massachusetts Buildings Dataset and WHU Satellite Dataset I (global cities). The proposed model showed better performance than other recent approaches, achieving an F1-score of 87.69% and an IoU of 75.83% with a low network size volume (5.11 M), internal parameters (19.8 MB), and GFLOPs (22.54), on the Massachusetts Buildings Dataset.
A Spatial Downscaling Approach for WindSat Satellite Sea Surface Wind Based on Generative Adversarial Networks and Dual Learning Scheme
Authors: Liu Jia , Sun Yongjian , Ren Kaijun , Zhao Yanlai , Deng Kefeng , Wang Lizhe
Published: February 2022

Sea surface wind (SSW) is a crucial parameter for meteorological and oceanographic research, and accurate observation of SSW is valuable for a wide range of applications. However, most existing SSW data products are at a coarse spatial resolution, which is insufficient, especially for regional or local studies. Therefore, in this paper, to derive finer-resolution estimates of SSW, we present a novel statistical downscaling approach for satellite SSW based on generative adversarial networks and dual learning scheme, taking WindSat as a typical example. The dual learning scheme performs a primal task to reconstruct high resolution SSW, and a dual task to estimate the degradation kernels, which form a closed loop and are simultaneously learned, thus introducing an additional constraint to reduce the solution space. The integration of a dual learning scheme as the generator into the generative adversarial network structure further yield better downscaling performance by fine-tuning the generated SSW closer to high-resolution SSW. Besides, a model adaptation strategy was exploited to enhance the capacity for downscaling from low-resolution SSW without high-resolution ground truth. Comprehensive experiments were conducted on both the synthetic paired and unpaired SSW data. In the study areas of the East Coast of North America and the North Indian Ocean, in this work, the downscaling results to 0.25° (high resolution on the synthetic dataset), 0.03125° (8× downscaling), and 0.015625° (16× downscaling) of the proposed approach achieve the highest accuracy in terms of root mean square error and R-Square. The downscaling resolution can be enhanced by increasing the basic blocks in the generator. The highest downscaling reconstruction quality in terms of peak signal-to-noise ratio and structural similarity index was also achieved on the synthetic dataset with high-resolution ground truth. The experimental results demonstrate the effectiveness of the proposed downscaling network and the superior performance compared with the other typical advanced downscaling methods, including bicubic interpolation, DeepSD, dual regression networks, and adversarial DeepSD.
Assimilation of Satellite-Derived Soil Moisture and Brightness Temperature in Land Surface Models: A Review
Authors: Khandan Reza , Wigneron Jean-Pierre , Bonafoni Stefania , Biazar Arastoo Pour , Gholamnia Mehdi
Published: February 2022

The correction of Soil Moisture (SM) estimates in Land Surface Models (LSMs) is considered essential for improving the performance of numerical weather forecasting and hydrologic models used in weather and climate studies. Along with surface screen-level variables, the satellite data, including Brightness Temperature (BT) from passive microwave sensors, and retrieved SM from active, passive, or combined active–passive sensor products have been used as two critical inputs in improvements of the LSM. The present study reviewed the current status in correcting LSM SM estimates, evaluating the results with in situ measurements. Based on findings from previous studies, a detailed analysis of related issues in the assimilation of SM in LSM, including bias correction of satellite data, applied LSMs and in situ observations, input data from various satellite sensors, sources of errors, calibration (both LSM and radiative transfer model), are discussed. Moreover, assimilation approaches are compared, and considerations for assimilation implementation are presented. A quantitative representation of results from the literature review, including ranges and variability of improvements in LSMs due to assimilation, are analyzed for both surface and root zone SM. A direction for future studies is then presented.
Research on the Analytical Model of Improved Magnetic Flux Leakage Signal for the Local Stress Concentration Zone of Pipelines
Authors: Yang Lijian , Zheng Fuyin , Huang Ping , Bai Shi , Su Yuming
Published: February 2022

Local stress concentrations pose a significant hazard to the safe operation of pipelines. However, the classical analytical model of the magnetic flux leakage (MFL) signal is still unable to effectively quantitatively analyze and accurately evaluate the local stress concentration zone of a pipeline. In this paper, based on the Jiles–Atherton model of the magnetomechanical effect, the mathematical relationship between stress and the magnetization of ferromagnetic material under hysteresis conditions is introduced, and an improved analytical model of the MFL signal based on the magnetomechanical model is established. The influence law of stress intensity on the MFL signal in the local stress concentration zone of the pipeline is calculated and analyzed, and the theoretical calculation results are verified through experiments. Simulation and experimental results show that, considering the hysteresis condition, the stress causes a change in the hysteresis loop of the ferromagnetic material, and the magnetization strength of the material decreases with increasing stress; the effect of stress on the magnetization strength of ferromagnetic materials is most obvious when the external magnetic field is approximately 5 KA/m. The MFL signal on the surface of the local stress concentration zone of the pipe changes abruptly, and the amount of change in the axial amplitude and radial peak-to-peak value of the leakage signal of the pipe tends to increase with the increase in the stress intensity of the local stress concentration zone. A comparison of the analysis with the classical analytical model of the MFL signal shows that the improved analytical model of the MFL signal is more suitable for the quantification study of the local stress concentration zone of the pipeline.
A Wearable Multimodal Sensing System for Tracking Changes in Pulmonary Fluid Status, Lung Sounds, and Respiratory Markers
Authors: Sanchez-Perez Jesus Antonio , Berkebile John A. , Nevius Brandi N. , Ozmen Goktug C. , Nichols Christopher J. , Ganti Venu G. , Mabrouk Samer A. , Clifford Gari D. , Kamaleswaran Rishikesan , Wright David W. , Inan Omer T.
Published: February 2022

Heart failure (HF) exacerbations, characterized by pulmonary congestion and breathlessness, require frequent hospitalizations, often resulting in poor outcomes. Current methods for tracking lung fluid and respiratory distress are unable to produce continuous, holistic measures of cardiopulmonary health. We present a multimodal sensing system that captures bioimpedance spectroscopy (BIS), multi-channel lung sounds from four contact microphones, multi-frequency impedance pneumography (IP), temperature, and kinematics to track changes in cardiopulmonary status. We first validated the system on healthy subjects (n = 10) and then conducted a feasibility study on patients (n = 14) with HF in clinical settings. Three measurements were taken throughout the course of hospitalization, and parameters relevant to lung fluid status—the ratio of the resistances at 5 kHz to those at 150 kHz (K)—and respiratory timings (e.g., respiratory rate) were extracted. We found a statistically significant increase in K (p < 0.05) from admission to discharge and observed respiratory timings in physiologically plausible ranges. The IP-derived respiratory signals and lung sounds were sensitive enough to detect abnormal respiratory patterns (Cheyne–Stokes) and inspiratory crackles from patient recordings, respectively. We demonstrated that the proposed system is suitable for detecting changes in pulmonary fluid status and capturing high-quality respiratory signals and lung sounds in a clinical setting.
A Tool for High-Resolution Volumetric Optical Coherence Tomography by Compounding Radial-and Linear Acquired B-Scans Using Registration
Authors: Bosch Christian M. , Baumann Carmen , Dehghani Shervin , Sommersperger Michael , Johannigmann-Malek Navid , Kirchmair Katharina , Maier Mathias , Nasseri Mohammad Ali
Published: February 2022

Optical coherence tomography (OCT) is a medical imaging modality that is commonly used to diagnose retinal diseases. In recent years, linear and radial scanning patterns have been proposed to acquire three-dimensional OCT data. These patterns show differences in A-scan acquisition density across the generated volumes, and thus differ in their suitability for the diagnosis of retinal diseases. While radial OCT volumes exhibit a higher A-scan sampling rate around the scan center, linear scans contain more information in the peripheral scan areas. In this paper, we propose a method to combine a linearly and radially acquired OCT volume to generate a single compound volume, which merges the advantages of both scanning patterns to increase the information that can be gained from the three-dimensional OCT data. We initially generate 3D point clouds of the linearly and radially acquired OCT volumes and use an Iterative Closest Point (ICP) variant to register both volumes. After registration, the compound volume is created by selectively exploiting linear and radial scanning data, depending on the A-scan density of the individual scans. Fusing regions from both volumes with respect to their local A-scan sampling density, we achieve improved overall anatomical OCT information in a high-resolution compound volume. We demonstrate our method on linear and radial OCT volumes for the visualization and analysis of macular holes and the surrounding anatomical structures.
Detecting Bulbar Involvement in Patients with Amyotrophic Lateral Sclerosis Based on Phonatory and Time-Frequency Features
Authors: Tena Alberto , Clarià Francesc , Solsona Francesc , Povedano Mònica
Published: February 2022

The term “bulbar involvement” is employed in ALS to refer to deterioration of motor neurons within the corticobulbar area of the brainstem, which results in speech and swallowing dysfunctions. One of the primary symptoms is a deterioration of the voice. Early detection is crucial for improving the quality of life and lifespan of ALS patients suffering from bulbar involvement. The main objective, and the principal contribution, of this research, was to design a new methodology, based on the phonatory-subsystem and time-frequency characteristics for detecting bulbar involvement automatically. This study focused on providing a set of 50 phonatory-subsystem and time-frequency features to detect this deficiency in males and females through the utterance of the five Spanish vowels. Multivariant Analysis of Variance was then used to select the statistically significant features, and the most common supervised classifications models were analyzed. A set of statistically significant features was obtained for males and females to capture this dysfunction. To date, the accuracy obtained (98.01% for females and 96.10% for males employing a random forest) outperformed the models in the literature. Adding time-frequency features to more classical phonatory-subsystem features increases the prediction capabilities of the machine-learning models for detecting bulbar involvement. Studying men and women separately gives greater success. The proposed method can be deployed in any kind of recording device (i.e., smartphone).
Fused Projection-Based Point Cloud Segmentation
Authors: Kellner Maximilian , Stahl Bastian , Reiterer Alexander
Published: February 2022

Semantic segmentation is used to enable a computer to understand its surrounding environment. In image processing, images are partitioned into segments for this purpose. State-of-the-art methods make use of Convolutional Neural Networks to segment a 2D image. Compared to that, 3D approaches suffer from computational cost and are not applicable without any further steps. In this work, we focus on semantic segmentation based on 3D point clouds. We use the idea to project the 3D data into a 2D image to accelerate the segmentation process. Afterward, the processed image gets re-projected to receive the desired result. We investigate different projection views and compare them to clarify their strengths and weaknesses. To compensate for projection errors and the loss of geometrical information, we evolve the approach and show how to fuse different views. We have decided to fuse the bird’s-eye and the spherical projection as each of them achieves reasonable results, and the two perspectives complement each other best. For training and evaluation, we use the real-world datasets SemanticKITTI. Further, we use the ParisLille and synthetic data generated by the simulation framework Carla to analyze the approaches in more detail and clarify their strengths and weaknesses. Although these methods achieve reasonable and competitive results, they lack flexibility. They depend on the sensor used and the setup in which the sensor is used.
Determination of Gas Permeation Properties in Polymer Using Capacitive Electrode Sensors
Authors: Jung Jaekap , Kim Gyunghyun , Gim Gahyoun , Park Changyoung , Lee Jihun
Published: February 2022

The objective of this work was to develop an effective technique for characterizing the permeation properties of various gases, including H2, He, N2, and Ar, that are absorbed in polymers. Simultaneous three-channel real-time techniques for measuring the sorption content and diffusivity of gases emitted from polymers are developed after exposure to high pressure and the subsequent decompression of the corresponding gas. These techniques are based on the volumetric measurement of released gas combined with the capacitance measurement of the water content by both semi-cylindrical and coaxial-cylindrical electrodes. This minimizes the uncertainty due to the varying temperature and pressure of laboratory environments. The gas uptake and diffusivity are determined as a function of the exposed pressure and gas spices in nitrile butadiene rubber (NBR) and ethylene propylene diene monomer (EPDM) polymers. The pressure-dependent gas transport behaviors of four different gases are presented and compared with those obtained by different techniques. A linear correlation between the logarithmic diffusivity and kinetic diameter of molecules in the gas is found between the two polymers.
Enterprise Data Sharing with Privacy-Preserved Based on Hyperledger Fabric Blockchain in IIOT’s Application
Authors: Chen Chin-Ling , Yang Jiaxin , Tsaur Woei-Jiunn , Weng Wei , Wu Chih-Ming , Wei Xiaojun
Published: February 2022

Internet of Things (IoT) technology is now widely used in energy, healthcare, services, transportation, and other fields. With the increase in industrial equipment (e.g., smart mobile terminals, sensors, and other embedded devices) in the Internet of Things and the advent of Industry 4.0, there has been an explosion of data generated that is characterized by a high volume but small size. How to manage and protect sensitive private data in data sharing has become an urgent issue for enterprises. Traditional data sharing and storage relies on trusted third-party platforms or distributed cloud storage, but these approaches run the risk of single-node failure, and third parties and cloud storage providers can be vulnerable to attacks that can lead to data theft. To solve these problems, this paper proposes a Hyperledger Fabric blockchain-based secure data transfer scheme for enterprises in the Industrial Internet of Things (IIOT). We store raw data in the IIoT in the InterPlanetary File System (IPFS) network after encryption and store the Keyword-index table we designed in Hyperledger Fabric blockchain, and enterprises share the data by querying the Keyword-index table. We use Fabric’s channel mechanism combined with our designed Chaincode to achieve privacy protection and efficient data transmission while using the Elliptic Curve Digital Signature Algorithm (ECDSA) to ensure data integrity. Finally, we performed security analysis and experiments on the proposed scheme, and the results show that overall the data transfer performance in the IPFS network is generally better than the traditional network, In the case of transferring 5 MB file size data, the transmission speed and latency of IPFS are 19.23 mb/s and 0.26 s, respectively, and the IPFS network is almost 4 times faster than the TCP/IP network while taking only a quarter of the time, which is more advantageous when transferring small files, such as data in the IIOT. In addition, our scheme outperforms the blockchain systems mainly used today in terms of both throughput, latency, and system overhead. The average throughput of our solution can reach 110 tps (transactions are executed per second), and the minimum throughput in experimental tests can reach 101 tps.
Automatic Target Detection from Satellite Imagery Using Machine Learning
Authors: Tahir Arsalan , Munawar Hafiz , Akram Junaid , Adil Muhammad , Ali Shehryar , Kouzani Abbas , Mahmud M.
Published: February 2022

Object detection is a vital step in satellite imagery-based computer vision applications such as precision agriculture, urban planning and defense applications. In satellite imagery, object detection is a very complicated task due to various reasons including low pixel resolution of objects and detection of small objects in the large scale (a single satellite image taken by Digital Globe comprises over 240 million pixels) satellite images. Object detection in satellite images has many challenges such as class variations, multiple objects pose, high variance in object size, illumination and a dense background. This study aims to compare the performance of existing deep learning algorithms for object detection in satellite imagery. We created the dataset of satellite imagery to perform object detection using convolutional neural network-based frameworks such as faster RCNN (faster region-based convolutional neural network), YOLO (you only look once), SSD (single-shot detector) and SIMRDWN (satellite imagery multiscale rapid detection with windowed networks). In addition to that, we also performed an analysis of these approaches in terms of accuracy and speed using the developed dataset of satellite imagery. The results showed that SIMRDWN has an accuracy of 97% on high-resolution images, while Faster RCNN has an accuracy of 95.31% on the standard resolution (1000 × 600). YOLOv3 has an accuracy of 94.20% on standard resolution (416 × 416) while on the other hand SSD has an accuracy of 84.61% on standard resolution (300 × 300). When it comes to speed and efficiency, YOLO is the obvious leader. In real-time surveillance, SIMRDWN fails. When YOLO takes 170 to 190 milliseconds to perform a task, SIMRDWN takes 5 to 103 milliseconds.
High Precision Location Estimation in Mountainous Areas Using GPS
Authors: Kunisada Yugo , Premachandra Chinthaka
Published: February 2022

Outdoor recreation has become popular in recent years, against the backdrop of the new coronavirus epidemic that started in 2020. Mountaineering, in particular, has become a popular pastime for many people as an easy way to experience nature. However, the number of mountaineering accidents is increasing, owing to the inadequate knowledge and equipment for beginners. In particular, the lack of map-reading skills and experience often leads to the selection of wrong trails. The smartphones used for precise location information obtain correction information from radio waves from a base station, and the accuracy of using only the GPS in mountainous areas without radio waves is questionable. In general, the GPS position correction methods in the literature for such situations include complex processing of the GPS radio waves. Some of these methods have been proposed with complex hardware and are difficult to implement with portable hardware. In this study, we develop and demonstrate a method for obtaining accurate location information using GPS without the error correction of radio waves, even in mountainous areas. The multipath is the reason for most of the GPS errors in the mountains. In the mountains, depending on the locations, the correct GPS location can also be received. In the proposed method, the correct GPS data are used to detect the incorrect GPS locations. We present an experimental method for estimating the interrelationship between the GPS longitude and latitude data. Additionally, we demonstrate the effectiveness of our method by showing that the experimental mountain location data presented in this paper are more accurate than the GPS data alone.
Real-Time Georeferencing of Fire Front Aerial Images Using Iterative Ray-Tracing and the Bearings-Range Extended Kalman Filter
Authors: Santana Bernardo , Cherif El Khalil , Bernardino Alexandre , Ribeiro Ricardo
Published: February 2022

Although Aerial Vehicle images are a viable tool for observing large-scale patterns of fires and their impacts, its application is limited by the complex optical georeferencing procedure due to the lack of distinctive visual features in forest environments. For this reason, an exploratory study on rough and flat terrains was conducted to use and validate the Iterative Ray-Tracing method in combination with a Bearings-Range Extended Kalman Filter as a real-time forest fire georeferencing and filtering algorithm on images captured by an aerial vehicle. The Iterative Ray-Tracing method requires a vehicle equipped with a Global Positioning System (GPS), an Inertial Measurement Unit (IMU), a calibrated camera, and a Digital Elevation Map (DEM). The proposed method receives the real-time input of the GPS, IMU, and the image coordinates of the pixels to georeference (computed by a companion algorithm of fire front detection) and outputs the geographical coordinates corresponding to those pixels. The Unscented Transform B is proposed to characterize the Iterative Ray-Tracing uncertainty. A Bearings-Range filter measurement model is introduced in a sequential filtering architecture to reduce the noise in the measurements, assuming static targets. A performance comparison is done between the Bearings-Only and the Bearings-Range observation models, and between the Extended and Cubature Kalman Filters. In simulation studies with ground truth, without filtering we obtained a georeferencing Root Mean Squared Errors (RMSE) of 30.7 and 43.4 m for the rough and flat terrains respectively, while filtering with the proposed Bearings-Range Extended Kalman Filter showed the best results by reducing the previous RMSE to 11.7 and 19.8 m, respectively. In addition, the comparison of both filter algorithms showed a good performance of Bearings-Range filter which was slightly faster. Indeed, these experiments based on the real data conducted to results demonstrated the applicability of the proposed methodology for the real-time georeferencing forest fires.
Pre-Anodized Graphite Pencil Electrode Coated with a Poly(Thionine) Film for Simultaneous Sensing of 3-Nitrophenol and 4-Nitrophenol in Environmental Water Samples
Authors: Sree Vijaya Gopalan , Sohn Jung Inn , Im Hyunsik
Published: February 2022

A very simple, as well as sensitive and selective, sensing protocol was developed on a pre-anodized graphite pencil electrode surface coated using poly(thionine) (APGE/PTH). The poly(thionine) coated graphite pencil was then used for simultaneous sensing of 3-nitrophenol (3-NP) and 4-nitrophenol (4-NP). The poly(thionine) coated electrode exhibited an enhanced electrocatalytic property towards nitrophenol (3-NP and 4-NP) reduction. Redox peak potential and current of both nitrophenols were found well resolved and their simultaneous analysis was studied. Under optimized experimental conditions, APGE/PTH showed a long linear concentration range from 20 to 230 nM and 15 nM to 280 nM with a calculated limit of detection (LOD) of 4.5 and 4 nM and a sensitivity of 22.45 µA/nM and 27.12 µA/nM for 3-NP and 4-NP, respectively. Real sample analysis using the prepared sensor was tested with different environmental water samples and the sensors exhibited excellent recovery results in the range from 98.16 to 103.43%. Finally, the sensor exposed an promising selectivity, stability, and reproducibility towards sensing of 3-NP and 4-NP.
Optimization of Silicon Nitride Waveguide Platform for On-Chip Virus Detection
Authors: El Shamy Raghi S. , Swillam Mohamed A. , Li Xun
Published: February 2022

This work presents a rigorous and generic sensitivity analysis of silicon nitride on silicon dioxide strip waveguide for virus detection. In general, by functionalizing the waveguide surface with a specific antibodies layer, we make the optical sensor sensitive only to a particular virus. Unlike conventional virus detection methods such as polymerase chain reaction (PCR), integrated refractive index (RI) optical sensors offer cheap and mass-scale fabrication of compact devices for fast and straightforward detection with high sensitivity and selectivity. Our numerical analysis includes a wide range of wavelengths from visible to mid-infrared. We determined the strip waveguide’s single-mode dimensions and the optimum dimensions that maximize the sensitivity to the virus layer attached to its surface at each wavelength using finite difference eigenmode (FDE) solver. We also compared the strip waveguide with the widely used slot waveguide. Our theoretical study shows that silicon nitride strip waveguide working at lower wavelengths is the optimum choice for virus detection as it maximizes both the waveguide sensitivity (Swg) and the figure of merit (FOM) of the sensor. The optimized waveguides are well suited for a range of viruses with different sizes and refractive indices. Balanced Mach–Zehnder interferometer (MZI) sensors were designed using FDE solver and photonic circuit simulator at different wavelengths. The designed sensors show high FOM at λ = 450 nm ranging from 500 RIU−1 up to 1231 RIU−1 with LMZI = 500 µm. Different MZI configurations were also studied and compared. Finally, edge coupling from the fiber to the sensor was designed, showing insertion loss (IL) at λ = 450 nm of 4.1 dB for the design with FOM = 500 RIU−1. The obtained coupling efficiencies are higher than recently proposed fiber couplers.
Classification and Explanation for Intrusion Detection System Based on Ensemble Trees and SHAP Method
Authors: Le Thi-Thu-Huong , Kim Haeyoung , Kang Hyoeun , Kim Howon
Published: February 2022

In recent years, many methods for intrusion detection systems (IDS) have been designed and developed in the research community, which have achieved a perfect detection rate using IDS datasets. Deep neural networks (DNNs) are representative examples applied widely in IDS. However, DNN models are becoming increasingly complex in model architectures with high resource computing in hardware requirements. In addition, it is difficult for humans to obtain explanations behind the decisions made by these DNN models using large IoT-based IDS datasets. Many proposed IDS methods have not been applied in practical deployments, because of the lack of explanation given to cybersecurity experts, to support them in terms of optimizing their decisions according to the judgments of the IDS models. This paper aims to enhance the attack detection performance of IDS with big IoT-based IDS datasets as well as provide explanations of machine learning (ML) model predictions. The proposed ML-based IDS method is based on the ensemble trees approach, including decision tree (DT) and random forest (RF) classifiers which do not require high computing resources for training models. In addition, two big datasets are used for the experimental evaluation of the proposed method, NF-BoT-IoT-v2, and NF-ToN-IoT-v2 (new versions of the original BoT-IoT and ToN-IoT datasets), through the feature set of the net flow meter. In addition, the IoTDS20 dataset is used for experiments. Furthermore, the SHapley additive exPlanations (SHAP) is applied to the eXplainable AI (XAI) methodology to explain and interpret the classification decisions of DT and RF models; this is not only effective in interpreting the final decision of the ensemble tree approach but also supports cybersecurity experts in quickly optimizing and evaluating the correctness of their judgments based on the explanations of the results.
A Gesture Recognition Method with a Charge Induction Array of Nine Electrodes
Authors: Qian Hao , Chi Yangbin , Dong Zining , Yan Feng , Zhang Limin
Published: February 2022

In order to develop a non-contact and simple gesture recognition technology, a recognition method with a charge induction array of nine electrodes is proposed. Firstly, the principle of signal acquisition based on charge induction is introduced, and the whole system is given. Secondly, the recognition algorithms, including the pre-processing algorithm and back propagation neural network (BPNN) algorithm, are given to recognize three input modes of hand gestures, digital input, direction input and key input, respectively. Finally, experiments of three input modes of hand gestures are carried out, and the recognition accuracy is 97.2%, 94%, and 100% for digital input, direction input, and key input, respectively. The outstanding characteristic of this method is the real-time recognition of three hand gestures in the distance of 2 cm without the need of wearing any device, as well as being low cost and easy to implement.
Multipath Propagation of Acoustic Signal in a Swimming Pool—Source Localization Problem
Authors: Misiurewicz Jacek , Bruliński Konrad , Klembowski Wiesław , Kulpa Krzysztof Stefan , Pietrusiewicz Jan
Published: February 2022

This paper explores the problem of severe multipath propagation of underwater acoustic signals in a swimming pool. The problem appeared in a study that examined a system used to signal emergency situations (i.e., pre-drowning symptoms detected by a wearable device on a pool user’s wrist) and locate the signal source. A swimming pool acoustic environment is characterized by the presence of large flat reflecting planes surrounding a small volume of water. The reflections are numerous and much stronger than in typical hydroacoustic applications. In this paper, we attempted to create a model of the swimming pool response, one that is suitable for simulation experiments with detection and localization of emergency signals. Then, we explore the possible remedies for the localization system, applied on the transmit side (waveform design) and on the receive side (receiver placement and signal processing). Finally, we present an algorithm for object localization, considering the possible reflections with a multi-hypothesis approach.
BlockProof: A Framework for Verifying Authenticity and Integrity of Web Content
Authors: Avelino Meirylene , Rocha Antonio A. de A.
Published: February 2022

In the Literature, we can find several research works to help in the digital crime fight in order to prove integrity and authenticity of a published document, image or video. Among all the crimes, fake news certainly is among the most recurrent ones and needs to be mitigated. There are several Blockchain-based applications in order to make use of the benefits derived from technology, but little is found to verify the authenticity of Web content records as well as the history of all updates that have taken place in each Web content. Such kind of solution has become important nowadays as a way to cover the gap in the combat against fake news, for example. The purpose of this paper is to present BlockProof, a framework for verifying web content authenticity and integrity that offers a solution for content providers to register Web content, regardless of whether the page has dynamic or static content, in addition to enabling the consultation of the history of all records made for a given URL. We understand that such kind of solution may be useful to data producers/providers to provide evidence that they are in compliance with the fight against fake news, for instance.
Quantitative Evaluation System of Upper Limb Motor Function of Stroke Patients Based on Desktop Rehabilitation Robot
Authors: Zhang Mingliang , Chen Jing , Ling Zongquan , Zhang Bochao , Yan Yanxin , Xiong Daxi , Guo Liquan
Published: February 2022

Rehabilitation training and movement evaluation after stroke have become a research hotspot as stroke has become a very common and harmful disease. However, traditional rehabilitation training and evaluation are mainly conducted under the guidance of rehabilitation doctors. The evaluation process is time-consuming and the evaluation results are greatly influenced by doctors. In this study, a desktop upper limb rehabilitation robot was designed and a quantitative evaluation system of upper limb motor function for stroke patients was proposed. The kinematics and dynamics data of stroke patients during active training were collected by sensors. Combined with the scores of patients’ upper limb motor function by rehabilitation doctors using the Wolf Motor Function Test (WMFT) scale, three different quantitative evaluation models of upper limb motor function based on Back Propagation Neural Network (BPNN), K-Nearest Neighbors (KNN), and Support Vector Regression (SVR) algorithms were established. To verify the effectiveness of the quantitative evaluation system, 10 healthy subjects and 21 stroke patients were recruited for experiments. The experimental results show that the BPNN model has the best evaluation performance among the three quantitative evaluation models. The scoring accuracy of the BPNN model reached up to 87.1%. Moreover, there was a significant correlation between the models′ scores and the doctors′ scores. The proposed system can help doctors to quantitatively evaluate the upper limb motor function of stroke patients and accurately master the rehabilitation progress of patients.
Test-Retest Reliability of Kinematic and Temporal Outcome Measures for Clinical Gait and Stair Walking Tests, Based on Wearable Inertial Sensors
Authors: Nilsson Sofie , Ertzgaard Per , Lundgren Mikael , Grip Helena
Published: February 2022

It is important to assess gait function in neurological disorders. A common outcome measure from clinical walking tests is average speed, which is reliable but does not capture important kinematical and temporal aspects of gait function. An extended gait analysis must be time efficient and reliable to be included in the clinical routine. The aim of this study was to add an inertial sensor system to a gait test battery and analyze the test-retest reliability of kinematic and temporal outcome measures. Measurements and analyses were performed in the hospital environment by physiotherapists using customized software. In total, 22 healthy persons performed comfortable gait, fast gait, and stair walking, with 12 inertial sensors attached to the feet, shank, thigh, pelvis, thorax, and arms. Each person participated in 2 test sessions, with about 3–6 days between the sessions. Kinematics were calculated based on a sensor fusion algorithm. Sagittal peak angles, sagittal range of motion, and stride frequency were derived. Intraclass-correlation coefficients were determined to analyze the test-retest reliability, which was good to excellent for comfortable and fast gait, with exceptions for hip, knee, and ankle peak angles during fast gait, which showed moderate reliability, and fast gait stride frequency, which showed poor reliability. In stair walking, all outcome measures except shoulder extension showed good to excellent reliability. Inertial sensors have the potential to improve the clinical evaluation of gait function in neurological patients, but this must be verified in patient groups.