bluuebunny/crossref_metadata_2025 huggingface dataset:
Intercalibration of MERIS, MODIS, and OLCI Satellite Imagers for Construction of Past, Present, and Future Cyanobacterial Biomass Time Series
Authors: Wynne Timothy T. , Mishra Sachidananda , Meredith Andrew , Litaker R. Wayne , Stumpf Richard P.
Published: June 2021

Satellite imagery has been used to monitor and assess Harmful Algal Blooms (HABs), specifically, cyanobacterial blooms in Lake Erie (the USA and Canada) for over twelve years. In recent years, imagery has been applied to the other Great Lakes as well as other U.S. lakes. The key algorithm used in this monitoring system is the cyanobacterial index (CI), a measure of the chlorophyll found in cyanobacterial blooms. The CI is a “spectral shape” (or curvature) algorithm, which is a form of the second derivative around the 681 nm (MERIS/OLCI) or 678 nm (MODIS) band, which is robust and implicitly includes an atmospheric correction, allowing reliable use for many more scenes than analytical algorithms. Monitoring of cyanobacterial blooms with the CI began with the European Space Agency’s (ESA) Medium Resolution Imaging Spectrometer (MERIS) sensor (2002–2012). With the loss of data from MERIS in the spring of 2012, the monitoring system shifted to using NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). MODIS has bands that allow computation of a CI product, which was intercalibrated with MERIS at the time to establish a conversion of MODIS CI to MERIS CI. In 2016, ESA launched the Ocean and Land Color Imager (OLCI), the replacement for MERIS, on the Sentinel-3 spacecraft. MODIS can serve two purposes. It can provide a critical data set for the blooms of 2012–2015, and it offers a bridge from MERIS to OLCI. We propose a basin-wide integrated technique for intercalibrating the CI algorithm from MODIS to both MERIS and OLCI. This method allowed us to intercalibrate OLCI CI to MERIS CI, which would then allow the production of a 20-year and ongoing record of cyanobacterial bloom activity. This approach also allows updates as sensor calibrations change or new sensors are launched, and it could be readily applied to spectral shape algorithms.
Modeling Diameter Distributions with Six Probability Density Functions in Pinus halepensis Mill. Plantations Using Low-Density Airborne Laser Scanning Data in Aragón (Northeast Spain)
Authors: Gorgoso-Varela J. Javier , Ponce Rafael Alonso , Rodríguez-Puerta Francisco
Published: June 2021

The diameter distributions of trees in 50 temporary sample plots (TSPs) established in Pinus halepensis Mill. stands were recovered from LiDAR metrics by using six probability density functions (PDFs): the Weibull (2P and 3P), Johnson’s SB, beta, generalized beta and gamma-2P functions. The parameters were recovered from the first and the second moments of the distributions (mean and variance, respectively) by using parameter recovery models (PRM). Linear models were used to predict both moments from LiDAR data. In recovering the functions, the location parameters of the distributions were predetermined as the minimum diameter inventoried, and scale parameters were established as the maximum diameters predicted from LiDAR metrics. The Kolmogorov–Smirnov (KS) statistic (Dn), number of acceptances by the KS test, the Cramér von Misses (W2) statistic, bias and mean square error (MSE) were used to evaluate the goodness of fits. The fits for the six recovered functions were compared with the fits to all measured data from 58 TSPs (LiDAR metrics could only be extracted from 50 of the plots). In the fitting phase, the location parameters were fixed at a suitable value determined according to the forestry literature (0.75·dmin). The linear models used to recover the two moments of the distributions and the maximum diameters determined from LiDAR data were accurate, with R2 values of 0.750, 0.724 and 0.873 for dg, dmed and dmax. Reasonable results were obtained with all six recovered functions. The goodness-of-fit statistics indicated that the beta function was the most accurate, followed by the generalized beta function. The Weibull-3P function provided the poorest fits and the Weibull-2P and Johnson’s SB also yielded poor fits to the data.
Evaluating Short-Term Tidal Flat Evolution Through UAV Surveys: A Case Study in the Po Delta (Italy)
Authors: Brunetta Riccardo , Duo Enrico , Ciavola Paolo
Published: June 2021

The use of Unmanned Aerial Vehicles (UAV) on wetlands is becoming a common survey technique that is extremely useful for understanding tidal flats and salt marshes. However, its implementation is not straightforward because of the complexity of the environment and fieldwork conditions. This paper presents the morphological evolution of the Po della Pila tidal flat in the municipality of Porto Tolle (Italy) and discusses the reliability of UAV-derived Digital Surface Models (DSMs) for such environments. Four UAV surveys were performed between October 2018 and February 2020 on an 8 ha young tidal flat that was generated, amongst others, as a consequence of the massive sediment injection into the Po Delta system due to the floods of the 1950s and 1960s. The DSM accuracy was tested by processing (i.e., photogrammetry) diverse sets of pictures taken at different altitudes during the same survey day. The DSMs and the orthophotos show that the tidal flat is characterised by several crevasse splays and that the sediment provision depends strictly on the river. During the study period, the sediment budget was positive (gaining 800 m3/year and an average rate of vertical changes of 1.3 cm/year). Comparisons of DSMs demonstrated that neither lower flight altitudes (i.e., 20–100 m) nor the combination of more photos from different flights during the same surveys necessarily reduce the error in such environments. However, centimetric errors (i.e., RMSEs) are achievable flying at 80–100 m, as the increase of GCP (Ground Control Point) density is the most effective solution for enhancing the resolution. Guidelines are suggested for implementing high-quality UAV surveys in wetlands.
High-Resolution Mapping of Aerosol Optical Depth and Ground Aerosol Coefficients for Mainland China
Authors: Li Lianfa
Published: June 2021

Aerosols play an important role in climate change, and ground aerosols (e.g., fine particulate matter, abbreviated as PM2.5) are associated with a variety of health problems. Due to clouds and high reflectance conditions, satellite-derived aerosol optical depth (AOD) products usually have large percentages of missing values (e.g., on average greater than 60% for mainland China), which limits their applicability. In this study, we generated grid maps of high-resolution, daily complete AOD and ground aerosol coefficients for the large study area of mainland China from 2015 to 2018. Based on the AOD retrieved using the recent Multi-Angle Implementation of Atmospheric Correction advanced algorithm, we added a geographic zoning factor to account for variability in meteorology, and developed an adaptive method based on the improved full residual deep network (with attention layers) to impute extensively missing AOD in the whole study area consistently and reliably. Furthermore, we generated high-resolution grid maps of complete AOD and ground aerosol coefficients. Overall, compared with the original residual model, in the independent test of 20% samples, our daily models achieved an average test R2 of 0.90 (an improvement of approximately 5%) with a range of 0.75–0.97 (average test root mean square error: 0.075). This high test performance shows the validity of AOD imputation. In the evaluation using the ground AOD data from six Aerosol Robotic Network monitoring stations, our method obtained an R2 of 0.78, which further illustrated the reliability of the dataset. In addition, ground aerosol coefficients were generated to provide an improved correlation with PM2.5. With the complete AOD data and ground coefficients, we presented and interpreted their spatiotemporal variations in mainland China. This study has important implications for using satellite-derived AOD to estimate aerosol air pollutants.
Effects of Canopy and Multi-Epoch Observations on Single-Point Positioning Errors of a GNSS in Coniferous and Broadleaved Forests
Authors: Feng Tong , Chen Shilin , Feng Zhongke , Shen Chaoyong , Tian Yi
Published: June 2021

Global navigation satellite systems (GNSS) can quickly, efficiently, and accurately provide precise coordinates of points, lines, and surface elements, plus complete surveys and determine various boundary lines in forest investigations and management. The system has become a powerful tool for dynamic forest resource investigations and monitoring. GNSS technology plays a unique and important role in estimating timber volume, calculating timber cutting area, and determining the location of virgin forest roads and individual trees in forests. In this study, we quantitatively analyzed the influence of crown size and observation time on the single-point positioning accuracy of GNSS receivers for different forest types. The GNSS located single points for different forest types and crown sizes, enabling the collection of data. The locating time for each tree was more than 10 min. Statistical methods were used to analyze the positioning accuracy of multi-epoch data, and a model was developed to estimate the maximum positioning errors under different forest conditions in a certain positioning time. The results showed that for a continuous positioning time of approximately 10 min, the maximum positioning accuracies in coniferous and broadleaf forests were obtained, which were 12.13 and 15.11 m, respectively. The size of a single canopy had no obvious influence on the single-point positioning error of the GNSS, and canopy density was proven to be closely related to the positioning accuracy of a GNSS. The determination coefficients (R2) in the regression analysis of the general model, coniferous forest model, and broadleaved forest model that were developed in this study were 0.579, 0.701, and 0.544, respectively. These results indicated that the model could effectively predict the maximum positioning error in a certain period of time under different forest types and crown conditions at middle altitudes, which has important guiding significance for forest resource inventories and precise forest management.
Editorial for Special Issue “UAV Photogrammetry and Remote Sensing”
Authors: Carvajal-Ramírez Fernando , Agüera-Vega Francisco , Martínez-Carricondo Patricio
Published: June 2021

The concept of Remote Sensing as a way of capturing information from an object without making contact with it has, until recently, been exclusively focused on the use of earth observation satellites [...]
Near-Real-Time Flood Mapping Using Off-the-Shelf Models with SAR Imagery and Deep Learning
Authors: Katiyar Vaibhav , Tamkuan Nopphawan , Nagai Masahiko
Published: June 2021

Timely detection of flooding is paramount for saving lives as well as evaluating levels of damage. Floods generally occur under specific weather conditions, such as excessive precipitation, which makes the presence of clouds very likely. For this reason, radar-based sensors are most suitable for near-real-time flood mapping. The public dataset Sen1Floods11 recently released by the Cloud to Street is one example of ongoing beneficial initiatives to employ deep learning for flood detection with synthetic aperture radar. The present study used this dataset to improve flood detection using well-known segmentation architectures, such as SegNet and UNet, as networks. In addition, this study provided a deeper understanding of which set of polarized band combination is more suitable for distinguishing permanent water, as well as flooded areas from the SAR image. The overall performance of the models with various kinds of labels and a combination of bands to detect all surface water areas were also assessed. Finally, the trained models were tested on a completely different location at Kerala, India, during the 2018 flood for verifying their performance in the real-world situation of a flood event outside of the given test set in the dataset. The results prove that trained models can be used as off-the-shelf models to achieve an intersection over union (IoU) as high as 0.88 in comparison with optical images. The omission and commission error were less than 6%. However, the most important result is that the processing time for the whole satellite image was less than 1 min. This will help significantly for providing analysis and near-real-time flood mapping services to first responder organizations during flooding disasters.
Defect Imaging Enhancement through Optimized Shape Factors of the RAPID Algorithm Based on Guided Wave Beam Pattern Analysis
Authors: Lee Yonghee , Cho Younho
Published: June 2021

In this study, a modified imaging algorithm was implemented to improve the imaging accuracy for defects located on a structure. Based on analysis of the Lamb wave mode, a guided ultrasonic wave inspection technique was applied, which was able to illustrate images of defects in a 6 mm steel plate simulating containment liner plate (CLP) in nuclear power plants. The dominant Lamb wave mode was determined through short-time Fourier transform waveform analysis and imaging verification. Following tomography verification, limitations of the antisymmetric mode in the thick steel plate were identified. In addition, a modified shape factor, based on the energy distribution factor according to the beam pattern and beam width, was suggested for field applications and improved imaging accuracy. Results of the analysis revealed a beam skewing phenomenon for the Lamb wave mode. In the case of S0 2.7 MHz·mm, skewing as well as distortion effects are not observed in the experiment, while the S0 modes at 2.64 and 2.74 MHz·mm show either of them. Considering skewing width, the size of the shape function was modified. Application of the modified shape function allows us to obtain more accurate image to actual defect shape.
Design of a Payload Adjustment Device for an Unpowered Lower-Limb Exoskeleton
Authors: Yun Junghwan , Kang Ohhyun , Joe Hyun-Min
Published: June 2021

This paper proposes a device that can change the payload of an unpowered lower-limb exoskeleton supporting the weights of humans and loads. Our previous exoskeletons used a cam–follower structure with a spring applied to the hip joint. This exoskeleton showed satisfying performance within the payload; however, the performance decreased when the payload was exceeded. Therefore, a payload adjustment device that can adjust the wearer’s required torque by easily applying it to the cam–follower structure was developed. An exoskeleton dynamic equation that can calculate a person’s required joint torque given the required payload and the wearer’s posture was derived. This dynamic equation provides a guideline for designing a device that can adjust the allowable joint torque range of an unpowered exoskeleton. In the Adams simulation environment, the payload adjustment device is applied to the cam–follower structure to show that the payload of the exoskeleton can be changed. User convenience and mass production were taken into account in the design of this device. This payload adjustment device should flexibly change the payload of the level desired by the wearer because it can quickly change the payload of the exoskeleton.
Printed Graphene, Nanotubes and Silver Electrodes Comparison for Textile and Structural Electronics Applications
Authors: Tabaczyńska Agnieszka , Dąbrowska Anna , Słoma Marcin
Published: June 2021

Due to the appearance of smart textiles and wearable electronics, the need for electro-conductive textiles and electro-conductive paths on textiles has become clear. In this article the results of a test of developed textile electro-conductive paths obtained by applying the method of screen printing pastes containing silver nanoparticles and carbon (graphene, nanotubes, graphite) are presented. Conducted research included analysis of the adhesion test, as well as evaluation of the surface resistance before and after the washing and bending cycles. Obtained results indicated that the samples with the content of carbon nanotubes 3% by weight in PMMA on substrate made of aramid fibers (surface mass of 260 g/m2) were characterized by the best adhesion and the best resistance to washing and bending cycles. Such electro-conductive paths have potential to be used in smart clothing applications.
Vision-Based Tunnel Lining Health Monitoring via Bi-Temporal Image Comparison and Decision-Level Fusion of Change Maps
Authors: Attard Leanne , Debono Carl James , Valentino Gianluca , Di Castro Mario
Published: June 2021

Tunnel structural health inspections are predominantly done through periodic visual observations, requiring humans to be physically present on-site, possibly exposing them to hazardous environments. These surveys are subjective (relying on the surveyor experience), time-consuming, and may demand operation shutdown. These issues can be mitigated through accurate automatic monitoring and inspection systems. In this work, we propose a remotely operated machine vision change detection application to improve the structural health monitoring of tunnels. The vision-based sensing system acquires the data from a rig of cameras hosted on a robotic platform that is driven parallel to the tunnel walls. These data are then pre-processed using image processing and deep learning techniques to reduce nuisance changes caused by light variations. Image fusion techniques are then applied to identify the changes occurring in the tunnel structure. Different pixel-based change detection approaches are used to generate temporal change maps. Decision-level fusion methods are then used to combine these change maps to obtain a more reliable detection of the changes that occur between surveys. A quantitative analysis of the results achieved shows that the proposed change detection system achieved a recall value of 81%, a precision value of 93% and an F1-score of 86.7%.
Aerodynamic Characteristics and Lateral Displacements of a Set of Two Buildings in a Linked Tall Building System
Authors: Chen Zengshun , Kim Bubryur , Lee Dong-Eun
Published: June 2021

This study evaluates the aerodynamic characteristics and lateral displacements of two staggered buildings in a linked-building (LB) system. Particle image velocimetry and pressure measurements are employed, and the lateral displacement is evaluated using a 3-dimensional analytical model. When the gap distance between two non-linked buildings is small, the wind flows in a narrow jet, and a strong suction is generated on the inner surfaces of the two buildings, leading to a large cross-wind-induced response. However, the cross-wind-induced response is significantly reduced when a link is installed, because the suction forces generated from the buildings are in opposite directions and have a negative aerodynamic correlation. Conversely, with a large gap distance, the buildings at the front obstruct the wind blowing toward the rear buildings. Therefore, while the pressure distribution, wind-force coefficients, and wind-induced responses of the front and rear buildings show similar trends, the magnitude of impact on the front building is larger than that on the rear building. Installing a link is demonstrated to reduce the wind-induced response of the buildings in an LB system. However, the reduction in the along-wind-induced response is less than that in the cross-wind-induced response when the gap distance is small.
Keeping Track of Phaeodactylum tricornutum (Bacillariophyta) Culture Contamination by Potentiometric E-Tongue
Authors: Savio Saverio , di Natale Corrado , Paolesse Roberto , Lvova Larisa , Congestri Roberta
Published: June 2021

The large-scale cultivation of microalgae provides a wide spectrum of marketable bioproducts, profitably used in many fields, from the preparation of functional health products and feed supplement in aquaculture and animal husbandry to biofuels and green chemistry agents. The commercially successful algal biomass production requires effective strategies to maintain the process at desired productivity and stability levels. Hence, the development of effective early warning methods to timely indicate remedial actions and to undertake countermeasures is extremely important to avoid culture collapse and consequent economic losses. With the aim to develop an early warning method of algal contamination, the potentiometric E-tongue was applied to record the variations in the culture environments, over the whole growth process, of two unialgal cultures, Phaeodactylum tricornutum and a microalgal contaminant, along with those of their mixed culture. The E-tongue system ability to distinguish the cultures and to predict their growth stage, through the application of multivariate data analysis, was shown. A PLS regression method applied to the E-tongue output data allowed a good prediction of culture growth time, expressed as growth days, with R2 values in a range from 0.913 to 0.960 and RMSEP of 1.97–2.38 days. Moreover, the SIMCA and PLS-DA techniques were useful for cultures contamination monitoring. The constructed PLS-DA model properly discriminated 67% of cultures through the analysis of their growth media, i.e., environments, thus proving the potential of the E-tongue system for a real time monitoring of contamination in microalgal intensive cultivation.
HyAdamC: A New Adam-Based Hybrid Optimization Algorithm for Convolution Neural Networks
Authors: Kim Kyung-Soo , Choi Yong-Suk
Published: June 2021

As the performance of devices that conduct large-scale computations has been rapidly improved, various deep learning models have been successfully utilized in various applications. Particularly, convolution neural networks (CNN) have shown remarkable performance in image processing tasks such as image classification and segmentation. Accordingly, more stable and robust optimization methods are required to effectively train them. However, the traditional optimizers used in deep learning still have unsatisfactory training performance for the models with many layers and weights. Accordingly, in this paper, we propose a new Adam-based hybrid optimization method called HyAdamC for training CNNs effectively. HyAdamC uses three new velocity control functions to adjust its search strength carefully in term of initial, short, and long-term velocities. Moreover, HyAdamC utilizes an adaptive coefficient computation method to prevent that a search direction determined by the first momentum is distorted by any outlier gradients. Then, these are combined into one hybrid method. In our experiments, HyAdamC showed not only notable test accuracies but also significantly stable and robust optimization abilities when training various CNN models. Furthermore, we also found that HyAdamC could be applied into not only image classification and image segmentation tasks.
UAV-Based and WebRTC-Based Open Universal Framework to Monitor Urban and Industrial Areas
Authors: Chodorek Agnieszka , Chodorek Robert Ryszard , Sitek Paweł
Published: June 2021

Nowadays, we are observing a rapid development of UAV-based monitoring systems, which are faced with more and more new tasks, such as high temporal resolution and high spatial resolution of measurements, or Artificial Intelligence on board. This paper presents the open universal framework intended for fast prototyping or building a short series of specialized flying monitoring systems able to work in urban and industrial areas. The proposed framework combines mobility of UAV with IoT measurements and full-stack WebRTC communications. WebRTC offers simultaneous transmission of both a real-time video stream and the flow of data coming from sensors, and ensures a kind of protection of data flow, which leads to preserving its near-real-time character and enables contextual communication. Addition of the AI accelerator hardware makes this system AI-ready, i.e., the IoT communication hub, which is the air component of our system, is able to perform tasks of AI-supported computing. The exemplary prototype of this system was evaluated in terms of the ability to work with fast-response sensors, the ability to work with high temporal and high spatial resolutions, video information in poor visibility conditions and AI-readiness. Results show that prototypes based on the proposed framework are able to meet the challenges of monitoring systems in smart cities and industrial areas.
A Fusion Algorithm for Estimating Time-Independent/-Dependent Parameters and States
Authors: Zhang Zheshuo , Zhang Jie , Dai Jiawen , Zhang Bangji , Qi Hengmin
Published: June 2021

Vehicle parameters are essential for dynamic analysis and control systems. One problem of the current estimation algorithm for vehicles’ parameters is that: real-time estimation methods only identify parts of vehicle parameters, whereas other parameters such as suspension damping coefficients and suspension and tire stiffnesses are assumed to be known in advance by means of an inertial parameter measurement device (IPMD). In this study, a fusion algorithm is proposed for identifying comprehensive vehicle parameters without the help of an IPMD, and vehicle parameters are divided into time-independent parameters (TIPs) and time-dependent parameters (TDPs) based on whether they change over time. TIPs are identified by a hybrid-mass state-variable (HMSV). A dual unscented Kalman filter (DUKF) is applied to update both TDPs and online states. The experiment is conducted on a real two-axle vehicle and the test data are used to estimate both TIPs and TDPs to validate the accuracy of the proposed algorithm. Numerical simulations are performed to further investigate the algorithm’s performance in terms of sprung mass variation, model error because of linearization and various road conditions. The results from both the experiment and simulation show that the proposed algorithm can estimate TIPs as well as update TDPs and online states with high accuracy and quick convergence, and no requirement of road information.
ZnO@ZIF-8 Core-Shell Structure Gas Sensors with Excellent Selectivity to H2
Authors: Lv Ruonan , Zhang Qinyi , Wang Wei , Lin Yaojun , Zhang Shunping
Published: June 2021

As the energy crisis becomes worse, hydrogen as a clean energy source is more and more widely used in industrial production and people’s daily life. However, there are hidden dangers in hydrogen storage and transportation, because of its flammable and explosive features. Gas detection is the key to solving this problem. High quality sensors with more practical and commercial value must be able to accurately detect target gases in the environment. Emerging porous metal-organic framework (MOF) materials can effectively improve the selectivity of sensors as a result of high surface area and coordinated pore structure. The application of MOFs for surface modification to improve the selectivity and sensitivity of metal oxides sensors to hydrogen has been widely investigated. However, the influence of MOF modified film thickness on the selectivity of hydrogen sensors is seldom studied. Moreover, the mechanism of the selectivity improvement of the sensors with MOF modified film is still unclear. In this paper, we prepared nano-sized ZnO particles by a homogeneous precipitation method. ZnO nanoparticle (NP) gas sensors were prepared by screen printing technology. Then a dense ZIF-8 film was grown on the surface of the gas sensor by hydrothermal synthesis. The morphology, the composition of the elements and the characters of the product were analyzed by X-ray diffraction analysis (XRD), transmission electron microscope (TEM), scanning electron microscope (SEM), energy dispersive spectrometer (EDS), Brunauer-Emmett-Teller (BET) and differential scanning calorimetry (DSC). It is found that the ZIF-8 film grown for 4 h cannot form a dense core-shell structure. The thickness of ZIF-8 reaches 130 nm at 20 h. Through the detection and analysis of hydrogen (1000 ppm), ethanol (100 ppm) and acetone (50 ppm) from 150 °C to 290 °C, it is found that the response of the ZnO@ZIF-8 sensors to hydrogen has been significantly improved, while the response to ethanol and acetone was decreased. By comparing the change of the response coefficient, when the thickness of ZIF-8 is 130 nm, the gas sensor has a significantly improved selectivity to hydrogen at 230 °C. The continuous increase of the thickness tends to inhibit selectivity. The mechanism of selectivity improvement of the sensors with different thickness of the ZIF-8 films is discussed.
An Explainable AI-Based Fault Diagnosis Model for Bearings
Authors: Hasan Md Junayed , Sohaib Muhammad , Kim Jong-Myon
Published: June 2021

In this paper, an explainable AI-based fault diagnosis model for bearings is proposed with five stages, i.e., (1) a data preprocessing method based on the Stockwell Transformation Coefficient (STC) is proposed to analyze the vibration signals for variable speed and load conditions, (2) a statistical feature extraction method is introduced to capture the significance from the invariant pattern of the analyzed data by STC, (3) an explainable feature selection process is proposed by introducing a wrapper-based feature selector—Boruta, (4) a feature filtration method is considered on the top of the feature selector to avoid the multicollinearity problem, and finally, (5) an additive Shapley explanation followed by k-NN is proposed to diagnose and to explain the individual decision of the k-NN classifier for debugging the performance of the diagnosis model. Thus, the idea of explainability is introduced for the first time in the field of bearing fault diagnosis in two steps: (a) incorporating explainability to the feature selection process, and (b) interpretation of the classifier performance with respect to the selected features. The effectiveness of the proposed model is demonstrated on two different datasets obtained from separate bearing testbeds. Lastly, an assessment of several state-of-the-art fault diagnosis algorithms in rotating machinery is included.
Capacitive Measurements of SiO2 Films of Different Thicknesses Using a MOSFET-Based SPM Probe
Authors: Lee Hoontaek , Shin Kumjae , Moon Wonkyu
Published: June 2021

We utilized scanning probe microscopy (SPM) based on a metal-oxide-silicon field-effect transistor (MOSFET) to image interdigitated electrodes covered with oxide films that were several hundred nanometers in thickness. The signal varied depending on the thickness of the silicon dioxide film covering the electrodes. We deposited a 400- or 500-nm-thick silicon dioxide film on each sample electrode. Thick oxide films are difficult to analyze using conventional probes because of their low capacitance. In addition, we evaluated linearity and performed frequency response measurements; the measured frequency response reflected the electrical characteristics of the system, including the MOSFET, conductive tip, and local sample area. Our technique facilitated analysis of the passivation layers of integrated circuits, especially those of the back-end-of-line (BEOL) process, and can be used for subsurface imaging of various dielectric layers.
Piezo-Resistive Properties of Bio-Based Sensor Yarn Made with Sisal Fibre
Authors: Abed Ahmed , Samouh Zineb , Cochrane Cédric , Boussu Francois , Cherkaoui Omar , El Moznine Reddad , Vieillard Julien
Published: June 2021

In this work, a sensor yarn based on a natural sisal yarn containing a non-electro-conductive core impregnated with PVA polymer and coated by PEDOT:PSS polymer as an electro-conductive sheath was investigated. The main objectives include the development of this new sensor yarn as a first step. Then, we look towards the insertion of this sensor yarn into different woven structures followed by the monitoring of the mechanical behaviour of composite materials made with these fibrous reinforcements. The combined effect of the structural geometry and the number of PEDOT:PSS coating layers on the properties of the sensor yarns was investigated. It was found that the number of PEDOT:PSS coating layers could strongly influence the electromechanical behaviours of the sensor yarns. Different methods of characterization were employed on strain-sensor yarns with two and four coating layers of PEDOT:PSS. The piezo-resistive strain-sensor properties of these selected coating layers were evaluated. Cyclic stretching-releasing tests were also performed to investigate the dynamic strain-sensing behavior. The obtained results indicated that gauge factor values can be extracted in three strain regions for two and four coating layers, respectively. Moreover, these strain-sensor yarns showed accurate and stable sensor responses under cyclic conditions. Furthers works are in progress to investigate the mechanism behind these first results of these sisal fibre-based sensors.
Bionic Birdlike Imaging Using a Multi-Hyperuniform LED Array
Authors: Zhao Xin-Yu , Li Li-Jing , Cao Lei , Sun Ming-Jie
Published: June 2021

Digital cameras obtain color information of the scene using a chromatic filter, usually a Bayer filter, overlaid on a pixelated detector. However, the periodic arrangement of both the filter array and the detector array introduces frequency aliasing in sampling and color misregistration during demosaicking process which causes degradation of image quality. Inspired by the biological structure of the avian retinas, we developed a chromatic LED array which has a geometric arrangement of multi-hyperuniformity, which exhibits an irregularity on small-length scales but a quasi-uniformity on large scales, to suppress frequency aliasing and color misregistration in full color image retrieval. Experiments were performed with a single-pixel imaging system using the multi-hyperuniform chromatic LED array to provide structured illumination, and 208 fps frame rate was achieved at 32 × 32 pixel resolution. By comparing the experimental results with the images captured with a conventional digital camera, it has been demonstrated that the proposed imaging system forms images with less chromatic moiré patterns and color misregistration artifacts. The concept proposed verified here could provide insights for the design and the manufacturing of future bionic imaging sensors.
An Optimized Vector Tracking Architecture for Pseudo-Random Pulsing CDMA Signals
Authors: Tao Lin , Li Guangchen , Sun Junren , Zhu Bocheng
Published: June 2021

The vector tracking loop (VTL) has high tracking accuracy and a superior ability to track weak signals in GNSS. However, traditional VTL architecture is established on continuous Code Division Multiple Access (CDMA) signal and is incompatible with pseudolite positioning systems (PLPS) because PLPS generally adopts a pseudo-random pulsing CDMA signal structure to mitigate the near-far effect. Therefore, this paper proposes an optimized VTL architecture for pseudo-random pulsing CDMA signals. To avoid estimation biases in PLPS, the proposed VTL adopts irregular update periods (IUP) pre-filters which adjust the update cycles according to the active timeslot intervals. Meanwhile, as the active timeslots of different pseudolites do not overlap, the sampling time of the navigation filter inputs is inconsistent and time-varying, causing jitter degradation. Thus, the proposed VTL predicts the measurements so that they can be sampled at the same time, which improves tracking accuracy. Simulation is carried out to evaluate the performance of the proposed VTL. The results suggest that the proposed VTL outperforms the traditional pre-filter-based VTL and IUP pre-filter-based VTL.
AdaMM: Adaptive Object Movement and Motion Tracking in Hierarchical Edge Computing System
Authors: Kim Jingyeom , Lee Joohyung , Kim Taeyeon
Published: June 2021

This paper presents a novel adaptive object movement and motion tracking (AdaMM) framework in a hierarchical edge computing system for achieving GPU memory footprint reduction of deep learning (DL)-based video surveillance services. DL-based object movement and motion tracking requires a significant amount of resources, such as (1) GPU processing power for the inference phase and (2) GPU memory for model loading. Despite the absence of an object in the video, if the DL model is loaded, the GPU memory must be kept allocated for the loaded model. Moreover, in several cases, video surveillance tries to capture events that rarely occur (e.g., abnormal object behaviors); therefore, such standby GPU memory might be easily wasted. To alleviate this problem, the proposed AdaMM framework categorizes the tasks used for the object movement and motion tracking procedure in an increasing order of the required processing and memory resources as task (1) frame difference calculation, task (2) object detection, and task (3) object motion and movement tracking. The proposed framework aims to adaptively release the unnecessary standby object motion and movement tracking model to save GPU memory by utilizing light tasks, such as frame difference calculation and object detection in a hierarchical manner. Consequently, object movement and motion tracking are adaptively triggered if the object is detected within the specified threshold time; otherwise, the GPU memory for the model of task (3) can be released. Moreover, object detection is also adaptively performed if the frame difference over time is greater than the specified threshold. We implemented the proposed AdaMM framework using commercial edge devices by considering a three-tier system, such as the 1st edge node for both tasks (1) and (2), the 2nd edge node for task (3), and the cloud for sending a push alarm. A measurement-based experiment reveals that the proposed framework achieves a maximum GPU memory reduction of 76.8% compared to the baseline system, while requiring a 2680 ms delay for loading the model for object movement and motion tracking.
Transmission Quality Classification with Use of Fusion of Neural Network and Genetic Algorithm in Pay&Require Multi-Agent Managed Network
Authors: Żelasko Dariusz , Książek Wojciech , Pławiak Paweł
Published: June 2021

Modern computer systems practically cannot function without a computer network. New concepts of data transmission are emerging, e.g., programmable networks. However, the development of computer networks entails the need for development in one more aspect, i.e., the quality of the data transmission through the network. The data transmission quality can be described using parameters, i.e., delay, bandwidth, packet loss ratio and jitter. On the basis of the obtained values, specialists are able to state how measured parameters impact on the overall quality of the provided service. Unfortunately, for a non-expert user, understanding of these parameters can be too complex. Hence, the problem of translation of the parameters describing the transmission quality appears understandable to the user. This article presents the concept of using Machine Learning (ML) to solve the above-mentioned problem, i.e., a dynamic classification of the measured parameters describing the transmission quality in a certain scale. Thanks to this approach, describing the quality will become less complex and more understandable for the user. To date, some studies have been conducted. Therefore, it was decided to use different approaches, i.e., fusion of a neural network (NN) and a genetic algorithm (GA). GA’s were choosen for the selection of weights replacing the classic gradient descent algorithm. For learning purposes, 100 samples were obtained, each of which was described by four features and the label, which describes the quality. In the reasearch carried out so far, single classifiers and ensemble learning have been used. The current result compared to the previous ones is better. A relatively high quality of the classification was obtained when we have used 10-fold stratified cross-validation, i.e., SEN = 95% (overall accuracy). The incorrect classification was 5/100, which is a better result compared to previous studies.
GoRG: Towards a GPU-Accelerated Multiview Hyperspectral Depth Estimation Tool for Medical Applications
Authors: Sancho Jaime , Sutradhar Pallab , Rosa Gonzalo , Chavarrías Miguel , Perez-Nuñez Angel , Salvador Rubén , Lagares Alfonso , Juárez Eduardo , Sanz César
Published: June 2021

HyperSpectral (HS) images have been successfully used for brain tumor boundary detection during resection operations. Nowadays, these classification maps coexist with other technologies such as MRI or IOUS that improve a neurosurgeon’s action, with their incorporation being a neurosurgeon’s task. The project in which this work is framed generates an unified and more accurate 3D immersive model using HS, MRI, and IOUS information. To do so, the HS images need to include 3D information and it needs to be generated in real-time operating room conditions, around a few seconds. This work presents Graph cuts Reference depth estimation in GPU (GoRG), a GPU-accelerated multiview depth estimation tool for HS images also able to process YUV images in less than 5.5 s on average. Compared to a high-quality SoA algorithm, MPEG DERS, GoRG YUV obtain quality losses of −0.93 dB, −0.6 dB, and −1.96% for WS-PSNR, IV-PSNR, and VMAF, respectively, using a video synthesis processing chain. For HS test images, GoRG obtains an average RMSE of 7.5 cm, with most of its errors in the background, needing around 850 ms to process one frame and view. These results demonstrate the feasibility of using GoRG during a tumor resection operation.
Disjoint and Functional Principal Component Analysis for Infected Cases and Deaths Due to COVID-19 in South American Countries with Sensor-Related Data
Authors: Martin-Barreiro Carlos , Ramirez-Figueroa John A. , Cabezas Xavier , Leiva Víctor , Galindo-Villardón M. Purificación
Published: June 2021

In this paper, we group South American countries based on the number of infected cases and deaths due to COVID-19. The countries considered are: Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Peru, Paraguay, Uruguay, and Venezuela. The data used are collected from a database of Johns Hopkins University, an institution that is dedicated to sensing and monitoring the evolution of the COVID-19 pandemic. A statistical analysis, based on principal components with modern and recent techniques, is conducted. Initially, utilizing the correlation matrix, standard components and varimax rotations are calculated. Then, by using disjoint components and functional components, the countries are grouped. An algorithm that allows us to keep the principal component analysis updated with a sensor in the data warehouse is designed. As reported in the conclusions, this grouping changes depending on the number of components considered, the type of principal component (standard, disjoint or functional) and the variable to be considered (infected cases or deaths). The results obtained are compared to the k-means technique. The COVID-19 cases and their deaths vary in the different countries due to diverse reasons, as reported in the conclusions.
Aluminium-Based Plasmonic Sensors in Ultraviolet
Authors: Karpiński Karol , Zielińska-Raczyńska Sylwia , Ziemkiewicz David
Published: June 2021

We theoretically investigate the surface plasmon polaritons (SPPs) generated on an Al film covered by an Al2O3 layer in the context of their application as refractive index sensors. The calculated reflection spectra indicate SPP resonance excited by ultraviolet light, which was affected by the thickness of both the metal and the oxide layers on the surface. With optimized geometry, the system can work as a tunable sensor with a wide UV wavelength range λ∼ 150–300 nm. We report a quality factor of up to 10 and a figure of merit on the order of 9, and these are comparable to the performance of more complicated UV plasmonic nanostructures and allow for the detection of a 1% change of the refraction index. The sensor can operate on the basis of either the incidence angle or wavelength changes. The effect of oxide surface roughness is also investigated with an emphasis on amplitude-based refraction index sensing.
Analysis of Default Mode Network in Social Anxiety Disorder: EEG Resting-State Effective Connectivity Study
Authors: Al-Ezzi Abdulhakim , Kamel Nidal , Faye Ibrahima , Gunaseli Esther
Published: June 2021

Recent brain imaging findings by using different methods (e.g., fMRI and PET) have suggested that social anxiety disorder (SAD) is correlated with alterations in regional or network-level brain function. However, due to many limitations associated with these methods, such as poor temporal resolution and limited number of samples per second, neuroscientists could not quantify the fast dynamic connectivity of causal information networks in SAD. In this study, SAD-related changes in brain connections within the default mode network (DMN) were investigated using eight electroencephalographic (EEG) regions of interest. Partial directed coherence (PDC) was used to assess the causal influences of DMN regions on each other and indicate the changes in the DMN effective network related to SAD severity. The DMN is a large-scale brain network basically composed of the mesial prefrontal cortex (mPFC), posterior cingulate cortex (PCC)/precuneus, and lateral parietal cortex (LPC). The EEG data were collected from 88 subjects (22 control, 22 mild, 22 moderate, 22 severe) and used to estimate the effective connectivity between DMN regions at different frequency bands: delta (1–3 Hz), theta (4–8 Hz), alpha (8–12 Hz), low beta (13–21 Hz), and high beta (22–30 Hz). Among the healthy control (HC) and the three considered levels of severity of SAD, the results indicated a higher level of causal interactions for the mild and moderate SAD groups than for the severe and HC groups. Between the control and the severe SAD groups, the results indicated a higher level of causal connections for the control throughout all the DMN regions. We found significant increases in the mean PDC in the delta (p = 0.009) and alpha (p = 0.001) bands between the SAD groups. Among the DMN regions, the precuneus exhibited a higher level of causal influence than other regions. Therefore, it was suggested to be a major source hub that contributes to the mental exploration and emotional content of SAD. In contrast to the severe group, HC exhibited higher resting-state connectivity at the mPFC, providing evidence for mPFC dysfunction in the severe SAD group. Furthermore, the total Social Interaction Anxiety Scale (SIAS) was positively correlated with the mean values of the PDC of the severe SAD group, r (22) = 0.576, p = 0.006 and negatively correlated with those of the HC group, r (22) = −0.689, p = 0.001. The reported results may facilitate greater comprehension of the underlying potential SAD neural biomarkers and can be used to characterize possible targets for further medication.
Antihyperuricemic, Anti-Inflammatory and Antihypertensive Effect of a Dry Extract from Solidago virgaurea L. (Asteraceae)
Authors: Tămaş Mircea , Vostinaru Oliviu , Soran Loredana , Lung Ildiko , Opris Ocsana , Toiu Anca , Gavan Alexandru , Dinte Elena , Mogosan Cristina
Published: June 2021

Solidago virgaurea L. is a perennial plant used in European traditional medicine as a diuretic or a remedy for inflammatory conditions of the urinary tract but also for gout, especially in the Balkans. The present study was focused on a preclinical, in vivo evaluation of antihyperuricemic, anti-inflammatory, and antihypertensive effects of a dry extract from S. virgaurea L. (ESV). Colorimetric and HPLC–MS techniques were used to identify the main chemical constituents of ESV. Antihyperuricemic effect of ESV was assessed in a rat model of hyperuricemia induced by the administration of potassium oxonate. Antihypertensive effect of ESV was evaluated in hyperuricemic rats by monitoring systolic blood pressure with a non-invasive blood-pressure recording system. The anti-inflammatory effect of ESV was tested using a rat model of paw edema. The main chemical constituents of ESV were rutin and phenolic acids represented by chlorogenic and caffeic acid. ESV demonstrated significant antihyperuricemic effects in rats due to an uricosuric mechanism. Additionally, ESV reduced the progression of arterial hypertension in hyperuricemic rats and also showed anti-inflammatory properties slightly inferior to diclofenac. The results suggest that ESV could be a natural remedy for the treatment of gout and protection against endothelial dysfunction caused by hyperuricemia.