bluuebunny/crossref_metadata_2025 huggingface dataset:
Response Surface Methodology (RSM)-Based Prediction and Optimization of the Fenton Process in Landfill Leachate Decolorization
Authors: Roudi Anita Maslahati , Salem Sultan , Abedini Masoud , Maslahati Amin , Imran Muhammad
Published: December 2021

As an advanced oxidative processes, the Fenton process is receiving popularity as a wastewater treatment technique that can be used for hazardous landfill leachate. The treatment is simple, yet involves complex interactions between the affecting parameters including reaction time, H2O2/Fe2+ ratio, pH, and iron (II) ion concentration. Hence, the purpose of this present study was to analyze the factors affecting landfill leachate treatment as well as their interaction by means of response surface methodology (RSM) with central composite design. The independent variables were reaction time, H2O2/Fe2+ ratio, iron (II) ion concentration, and pH, and the dependent variable (response) was color-removal percentage. The optimum treatment conditions for pH, H2O2/Fe2+ ratio, Fe2+ concentration, and reaction time were 8.36, 3.32, 964.95 mg/L, and 50.15 min, respectively. The model predicted 100% color removal in optimum conditions, which was close to that obtained from the experiment (97.68%). In conclusion, the optimized Fenton process using the RSM approach promotes efficient landfill leachate treatment that is even higher than that already reported.
A Remote Sensing Image Destriping Model Based on Low-Rank and Directional Sparse Constraint
Authors: Wu Xiaobin , Qu Hongsong , Zheng Liangliang , Gao Tan , Zhang Ziyu
Published: December 2021

Stripe noise is a common condition that has a considerable impact on the quality of the images. Therefore, stripe noise removal (destriping) is a tremendously important step in image processing. Since the existing destriping models cause different degrees of ripple effects, in this paper a new model, based on total variation (TV) regularization, global low rank and directional sparsity constraints, is proposed for the removal of vertical stripes. TV regularization is used to preserve details, and the global low rank and directional sparsity are used to constrain stripe noise. The directional and structural characteristics of stripe noise are fully utilized to achieve a better removal effect. Moreover, we designed an alternating minimization scheme to obtain the optimal solution. Simulation and actual experimental data show that the proposed model has strong robustness and is superior to existing competitive destriping models, both subjectively and objectively.
Multimodal Remote Sensing Image Registration Methods and Advancements: A Survey
Authors: Zhang Xinyue , Leng Chengcai , Hong Yameng , Pei Zhao , Cheng Irene , Basu Anup
Published: December 2021

With rapid advancements in remote sensing image registration algorithms, comprehensive imaging applications are no longer limited to single-modal remote sensing images. Instead, multi-modal remote sensing (MMRS) image registration has become a research focus in recent years. However, considering multi-source, multi-temporal, and multi-spectrum input introduces significant nonlinear radiation differences in MMRS images for which researchers need to develop novel solutions. At present, comprehensive reviews and analyses of MMRS image registration methods are inadequate in related fields. Thus, this paper introduces three theoretical frameworks: namely, area-based, feature-based and deep learning-based methods. We present a brief review of traditional methods and focus on more advanced methods for MMRS image registration proposed in recent years. Our review or comprehensive analysis is intended to provide researchers in related fields with advanced understanding to achieve further breakthroughs and innovations.
Canopy Fluorescence Sensing for In-Season Maize Nitrogen Status Diagnosis
Authors: Dong Rui , Miao Yuxin , Wang Xinbing , Yuan Fei , Kusnierek Krzysztof
Published: December 2021

Accurate assessment of crop nitrogen (N) status and understanding the N demand are considered essential in precision N management. Chlorophyll fluorescence is unsusceptible to confounding signals from underlying bare soil and is closely related to plant photosynthetic activity. Therefore, fluorescence sensing is considered a promising technology for monitoring crop N status, even at an early growth stage. The objectives of this study were to evaluate the potential of using Multiplex® 3, a proximal canopy fluorescence sensor, to detect N status variability and to quantitatively estimate N status indicators at four key growth stages of maize. The sensor measurements were performed at different growth stages, and three different regression methods were compared to estimate plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). The results indicated that the induced differences in maize plant N status were detectable as early as the V6 growth stage. The first method based on simple regression (SR) and the Multiplex sensor indices normalized by growing degree days (GDD) or N sufficiency index (NSI) achieved acceptable estimation accuracy (R2 = 0.73–0.87), showing a good potential of canopy fluorescence sensing for N status estimation. The second method using multiple linear regression (MLR), fluorescence indices and GDDs had the lowest modeling accuracy (R2 = 0.46–0.79). The third tested method used a non-linear regression approach in the form of random forest regression (RFR) based on multiple sensor indices and GDDs. This approach achieved the best estimation accuracy (R2 = 0.84–0.93) and the most accurate diagnostic result.
Assessing Satellite, Land Surface Model and Reanalysis Evapotranspiration Products in the Absence of In-Situ in Central Asia
Authors: Ochege Friday Uchenna , Shi Haiyang , Li Chaofan , Ma Xiaofei , Igboeli Emeka Edwin , Luo Geping
Published: December 2021

Shortfalls in regular evapotranspiration (ET) monitoring and evaluation pose a huge challenge to agricultural water resource distribution in arid Central Asia (CA). In this study, a first detailed regional assessment of GLEAM, ERA5, MERRA2, CLSM and NOAH ET products in CA was performed by systematically implementing the triple collocation (TC) method, in which about 36,936 grid cells for each ET data (within a six-triplet design) were collocated, at 0.25° and with monthly resolutions during 2003–2020. The reliability of the strategy adopted was confirmed in four arid biomes using standard evaluation metrics (R, RMSE and BIAS), and by spatiotemporal cross-validation of the six ET triplets across CA. Results show that the systematic TC method produced more robust ET product assessment metrics with reduced RMSEs compared to the initial ET product validation using in-situ, which showed weak-positive correlation and high negative bias-range (i.e., −21.02 ≤ BIAS < 16 mm) in the four arid biomes of CA. The spatial cross-validation by TC showed that the magnitude of ET random errors significantly varies, and confirms the systematic biases with site-scale measurements. The highest ET uncertainties by CLSM (27.43%), NOAH (29.16%), MERRA2 (38.28%), ERA5 (36.75), and GLEAM (41%) were more evident in the shrubland, cropland, grassland, cropland again, and desert biomes, respectively. Moreover, error magnitudes in high altitudes (Tianshan Mountain range) are generally lower than in plain-desert areas. All ET products spatially captured ET dynamics over CA, but none simultaneously outperformed the other. These findings are invaluable in the utilization of the assessed ET products in supporting regional water resource management, particularly in CA.
Modeling Influence of Soil Properties in Different Gradients of Soil Moisture: The Case of the Valencia Anchor Station Validation Site, Spain
Authors: Carbó Ester , Juan Pablo , Añó Carlos , Chaudhuri Somnath , Diaz-Avalos Carlos , López-Baeza Ernesto
Published: December 2021

The prediction of spatial and temporal variation of soil water content brings numerous benefits in the studies of soil. However, it requires a considerable number of covariates to be included in the study, complicating the analysis. Integrated nested Laplace approximations (INLA) with stochastic partial differential equation (SPDE) methodology is a possible approach that allows the inclusion of covariates in an easy way. The current study has been conducted using INLA-SPDE to study soil moisture in the area of the Valencia Anchor Station (VAS), soil moisture validation site for the European Space Agency SMOS (Soil Moisture and Ocean Salinity). The data used were collected in a typical ecosystem of the semiarid Mediterranean conditions, subdivided into physio-hydrological units (SMOS units) which presents a certain degree of internal uniformity with respect to hydrological parameters and capture the spatial and temporal variation of soil moisture at the local fine scale. The paper advances the knowledge of the influence of hydrodynamic properties on VAS soil moisture (texture, porosity/bulk density and soil organic matter and land use). With the goal of understanding the factors that affect the variability of soil moisture in the SMOS pixel (50 km × 50 km), five states of soil moisture are proposed. We observed that the model with all covariates and spatial effect has the lowest DIC value. In addition, the correlation coefficient was close to 1 for the relationship between observed and predicted values. The methodology applied presents the possibility to analyze the significance of different covariates having spatial and temporal effects. This process is substantially faster and more effective than traditional kriging. The findings of this study demonstrate an advancement in that framework, demonstrating that it is faster than previous methodologies, provides significance of individual covariates, is reproducible, and is easy to compare with models.
Estimating Actual Evapotranspiration over Croplands Using Vegetation Index Methods and Dynamic Harvested Area
Authors: Abbasi Neda , Nouri Hamideh , Didan Kamel , Barreto-Muñoz Armando , Chavoshi Borujeni Sattar , Salemi Hamidreza , Opp Christian , Siebert Stefan , Nagler Pamela
Published: December 2021

Advances in estimating actual evapotranspiration (ETa) with remote sensing (RS) have contributed to improving hydrological, agricultural, and climatological studies. In this study, we evaluated the applicability of Vegetation-Index (VI) -based ETa (ET-VI) for mapping and monitoring drought in arid agricultural systems in a region where a lack of ground data hampers ETa work. To map ETa (2000–2019), ET-VIs were translated and localized using Landsat-derived 3- and 2-band Enhanced Vegetation Indices (EVI and EVI2) over croplands in the Zayandehrud River Basin (ZRB) in Iran. Since EVI and EVI2 were optimized for the MODerate Imaging Spectroradiometer (MODIS), using these VIs with Landsat sensors required a cross-sensor transformation to allow for their use in the ET-VI algorithm. The before- and after- impact of applying these empirical translation methods on the ETa estimations was examined. We also compared the effect of cropping patterns’ interannual change on the annual ETa rate using the maximum Normalized Difference Vegetation Index (NDVI) time series. The performance of the different ET-VIs products was then evaluated. Our results show that ETa estimates agreed well with each other and are all suitable to monitor ETa in the ZRB. Compared to ETc values, ETa estimations from MODIS-based continuity corrected Landsat-EVI (EVI2) (EVIMccL and EVI2MccL) performed slightly better across croplands than those of Landsat-EVI (EVI2) without transformation. The analysis of harvested areas and ET-VIs anomalies revealed a decline in the extent of cultivated areas and a loss of corresponding water resources downstream. The findings show the importance of continuity correction across sensors when using empirical algorithms designed and optimized for specific sensors. Our comprehensive ETa estimation of agricultural water use at 30 m spatial resolution provides an inexpensive monitoring tool for cropping areas and their water consumption.
Statistical Analysis for Tidal Flat Classification and Topography Using Multitemporal SAR Backscattering Coefficients
Authors: Kim Keunyong , Jung Hahn Chul , Choi Jong-Kuk , Ryu Joo-Hyung
Published: December 2021

Coastal zones are very dynamic natural systems that experience short-term and long-term morphological changes. Their highly dynamic behavior requires frequent monitoring. Tidal flat topography for a large spatial coverage has been generated mainly by the waterline extraction method from multitemporal remote sensing observations. Despite the efficiency and robustness of the waterline extraction method, the waterline-based digital elevation model (DEM) is limited to representing small scale topographic features, such as localized tidal tributaries. Tidal flats show a rapid increase in SAR backscattering coefficients when the tide height is lower than the tidal flat topography compared to when the tidal flat is covered by water. This leads to a tidal flat with a distinct statistical behavior on the temporal variability of our multitemporal SAR backscattering coefficients. Therefore, this study aims to suggest a new method that can overcome the constraints of the waterline-based method by using a pixel-based DEM generation algorithm. Jenks Natural Break (JNB) optimization was applied to distinguish the tidal flat from land and ocean using multitemporal Senitnel-1 SAR data for the years 2014–2020. We also implemented a logistic model to characterize the temporal evolution of the SAR backscattering coefficients along with the tide heights and estimated intertidal topography. The Sentinel-1 DEM from the JNB classification and logistic function was evaluated by an airborne Lidar DEM. Our pixel-based DEM outperformed the waterline-based Landsat DEM. This study demonstrates that our statistical approach to intertidal classification and topography serves to monitor the near real-time spatiotemporal distribution changes of tidal flats through continuous and stable SAR data collection on local and regional scales.
Statistical Study of Ionospheric Equivalent Slab Thickness at Guam Magnetic Equatorial Location
Authors: Zhang Yuqiang , Wu Zhensen , Feng Jian , Xu Tong , Deng Zhongxin , Ou Ming , Xiong Wen , Zhen Weimin
Published: December 2021

The ionospheric equivalent slab thickness (τ) is defined as the ratio of the total electron content (TEC) to the F2-layer peak electron density (NmF2), and it is a significant parameter representative of the ionosphere. In this paper, a comprehensive statistical analysis of the diurnal, seasonal, solar, and magnetic activity variations in the τ at Guam (144.86°E, 13.62°N, 5.54°N dip lat), which is located near the magnetic equator, is presented using the GPS-TEC and ionosonde NmF2 data during the years 2012–2017. It is found that, for geomagnetically quiet days, the τ reaches its maximum value in the noontime, and the peak value in winter and at the equinox are larger than that in summer. Moreover, there is a post-sunset peak observed in the winter and equinox, and the τ during the post-midnight period is smallest in equinox. The mainly diurnal and seasonal variation of τ can be explained within the framework of relative variation of TEC and NmF2 during different seasonal local time. The dependence of τ on the solar activity shows positive correlation during the daytime, and the opposite situation applies for the nighttime. Specifically, the disturbance index (DI), which can visually assess the relationship between instantaneous τ values and the median, is introduced in the paper to quantitatively describe the overall pattern of the geomagnetic storm effect on the τ variation. The results show that the geomagnetic storm seems to have positive effect on the τ during most of the storm-time period at Guam. An example, on the 1 June 2013, is also presented to analyze the physical mechanism. During the positive storms, the penetration electric field, along with storm time equator-ward neutral wind, tends to increase upward drift and uplift F region, causing the large increase in TEC, accompanied by a relatively small increase in NmF2. On the other hand, an enhanced equatorward wind tends to push more plasma, at low latitudes, into the topside ionosphere in the equatorial region, resulting in the TEC not undergoing severe depletion, as with NmF2, during the negative storms. The results would complement the analysis of τ behavior during quiet and disturbed conditions at equatorial latitudes in East Asia.
Automatic Identification System (AIS) Dynamic Data Integrity Monitoring and Trajectory Tracking Based on the Simultaneous Localization and Mapping (SLAM) Process Model
Authors: Jaskólski Krzysztof , Marchel Łukasz , Felski Andrzej , Jaskólski Marcin , Specht Mariusz
Published: December 2021

To enhance the safety of marine navigation, one needs to consider the involvement of the automatic identification system (AIS), an existing system designed for ship-to-ship and ship-to-shore communication. Previous research on the quality of AIS parameters revealed problems that the system experiences with sensor data exchange. In coastal areas, littoral AIS does not meet the expectations of operational continuity and system availability, and there are areas not covered by the system. Therefore, in this study, process models were designed to simulate the tracking of vessel trajectories, enabling system failure detection based on integrity monitoring. Three methods for system integrity monitoring, through hypotheses testing with regard to differences between model output and actual simulated vessel positions, were implemented, i.e., a Global Positioning System (GPS) ship position model, Dead Reckoning and RADAR Extended Kalman Filter (EKF)—Simultaneous localization and mapping (SLAM) based on distance and bearing to navigational aid. The designed process models were validated on simulated AIS dynamic data, i.e., in a simulated experiment in the area of Gdańsk Bay. The integrity of AIS information was determined using stochastic methods based on Markov chains. The research outcomes confirmed the usefulness of the proposed methods. The results of the research prove the high level (~99%) of integrity of the dynamic information of the AIS system for Dead Reckoning and the GPS process model, while the level of accuracy and integrity of the position varied depending on the distance to the navigation aid for the RADAR EKF-SLAM process model.
System for Tool-Wear Condition Monitoring in CNC Machines under Variations of Cutting Parameter Based on Fusion Stray Flux-Current Processing
Authors: Jaen-Cuellar Arturo Yosimar , Osornio-Ríos Roque Alfredo , Trejo-Hernández Miguel , Zamudio-Ramírez Israel , Díaz-Saldaña Geovanni , Pacheco-Guerrero José Pablo , Antonino-Daviu Jose Alfonso
Published: December 2021

The computer numerical control (CNC) machine has recently taken a fundamental role in the manufacturing industry, which is essential for the economic development of many countries. Current high quality production standards, along with the requirement for maximum economic benefits, demand the use of tool condition monitoring (TCM) systems able to monitor and diagnose cutting tool wear. Current TCM methodologies mainly rely on vibration signals, cutting force signals, and acoustic emission (AE) signals, which have the common drawback of requiring the installation of sensors near the working area, a factor that limits their application in practical terms. Moreover, as machining processes require the optimal tuning of cutting parameters, novel methodologies must be able to perform the diagnosis under a variety of cutting parameters. This paper proposes a novel non-invasive method capable of automatically diagnosing cutting tool wear in CNC machines under the variation of cutting speed and feed rate cutting parameters. The proposal relies on the sensor information fusion of spindle-motor stray flux and current signals by means of statistical and non-statistical time-domain parameters, which are then reduced by means of a linear discriminant analysis (LDA); a feed-forward neural network is then used to automatically classify the level of wear on the cutting tool. The proposal is validated with a Fanuc Oi mate Computer Numeric Control (CNC) turning machine for three different cutting tool wear levels and different cutting speed and feed rate values.
Extended Hierarchical Fuzzy Interpreted Petri Net
Authors: Markiewicz Michał , Gniewek Lesław , Warchoł Dawid
Published: December 2021

Petri nets (PNs) have many advantages such as graphical representation, formal description, and the possibility of sequential and concurrent control. An important aspect of using PNs is hierarchical modeling, which may be provided in different ways. In this paper, a new concept and definition of the hierarchical structure for Fuzzy Interpreted Petri Net (FIPN) are proposed. The concept of macroplace with several input, output, and input-output places is introduced to the net. The functionality of the macroplace instances and the hierarchy graph are also proposed. They are implemented in a computer simulator called HFIPN-SML. In this study, FIPN is employed since it allows the use of analogue sensors directly for process control. Better visualization and more precise control are among advantages of the introduced approach.
Micro-Lensed Negative-Curvature Fibre Probe for Raman Spectroscopy
Authors: Milenko Karolina , Yerolatsitis Stephanos , Aksnes Astrid , Hjelme Dag Roar , Stone James M.
Published: December 2021

We developed a novel miniature micro-lensed fibre probe for Raman spectroscopy. The fibre probe consists of a single negative-curvature fibre (NCF) and a spliced, cleaved, micro-lensed fibre cap. Using a single NCF, we minimized the Raman background generated from the silica and maintained the diameter of the probe at less than 0.5 mm. In addition, the cap provided fibre closure by blocking the sample from entering the hollow parts of the fibre, enabling the use of the probe in in vivo applications. Moreover, the micro-lensed cap offered an improved collection efficiency (1.5-times increase) compared to a cleaved end-cap. The sensing capabilities of the micro-lensed probe were demonstrated by measuring different concentrations of glucose in aqueous solutions.
A Study Protocol for Occupational Rehabilitation in Multiple Sclerosis
Authors: Trombini Marco , Ferraro Federica , Iaconi Giulia , Vestito Lucilla , Bandini Fabio , Mori Laura , Trompetto Carlo , Dellepiane Silvana
Published: December 2021

Digital medical solutions can be very helpful in restorative neurology, as they allow the patients to practice their rehabilitation activities remotely. This work discloses ReMoVES, an IoMT system providing telemedicine services, in the context of Multiple Sclerosis rehabilitation, within the frame of the project STORMS. A rehabilitative protocol of exercises can be provided as ReMoVES services and integrated into the Individual Rehabilitation Project as designed by a remote multidimensional medical team. In the present manuscript, the first phase of the study is described, including the definition of the needs to be addressed, the employed technology, the design and the development of the exergames, and the possible practical/professional and academic consequences. The STORMS project has been implemented with the aim to act as a starting point for the development of digital telerehabilitation solutions that support Multiple Sclerosis patients, improving their living conditions. This paper introduces a study protocol and it addresses pre-clinical research needs, where system issues can be studied and better understood how they might be addressed. It also includes tools to favor remote patient monitoring and to support the clinical staff.
Bio-Impedance Sensor for Real-Time Artery Diameter Waveform Assessment
Authors: Al-harosh Mugeb , Yangirov Marat , Kolesnikov Dmitry , Shchukin Sergey
Published: December 2021

The real-time artery diameter waveform assessment during cardio cycle can allow the measurement of beat-to-beat pressure change and the long-term blood pressure monitoring. The aim of this study is to develop a self-calibrated bio-impedance-based sensor, which can provide regular measurement of the blood-pressure-dependence time variable parameters such as the artery diameter waveform and the elasticity. This paper proposes an algorithm based on analytical models which need prior geometrical and physiological patient parameters for more appropriate electrode system selection and hence location to provide accurate blood pressure measurement. As a result of this study, the red cell orientation effect contribution was estimated and removed from the bio-impedance signal obtained from the artery to keep monitoring the diameter waveform correspondence to the change of blood pressure.
Enhancing AlN PMUTs’ Acoustic Responsivity within a MEMS-on-CMOS Process
Authors: Ledesma Eyglis , Zamora Ivan , Uranga Arantxa , Torres Francesc , Barniol Núria
Published: December 2021

In this paper, guidelines for the optimization of piezoelectrical micromachined ultrasound transducers (PMUTs) monolithically integrated over a CMOS technology are developed. Higher acoustic pressure is produced by PMUTs with a thin layer of AlN piezoelectrical material and Si3N4 as a passive layer, as is studied here with finite element modeling (FEM) simulations and experimental characterization. Due to the thin layers used, parameters such as residual stress become relevant as they produce a buckled structure. It has been reported that the buckling of the membrane due to residual stress, in general, reduces the coupling factor and consequently degrades the efficiency of the acoustic pressure production. In this paper, we show that this buckling can be beneficial and that the fabricated PMUTs exhibit enhanced performance depending on the placement of the electrodes. This behavior was demonstrated experimentally and through FEM. The acoustic characterization of the fabricated PMUTs shows the enhancement of the PMUT performance as a transmitter (with 5 kPa V−1 surface pressure for a single PMUT) and as a receiver (12.5 V MPa−1) in comparison with previously reported devices using the same MEMS-on-CMOS technology as well as state-of-the-art devices.
Design of Secure Microcontroller-Based Systems: Application to Mobile Robots for Perimeter Monitoring
Authors: Levshun Dmitry , Chechulin Andrey , Kotenko Igor
Published: December 2021

This paper describes an original methodology for the design of microcontroller-based physical security systems and its application for the system of mobile robots. The novelty of the proposed methodology lies in combining various design algorithms on the basis of abstract and detailed system representations. The suggested design approach, which is based on the methodology, is modular and extensible, takes into account the security of the physical layer of the system, works with the abstract system representation and is looking for a trade-off between the security of the final solution and the resources expended on it. Moreover, unlike existing solutions, the methodology has a strong focus on security. It is aimed at ensuring the protection of the system against attacks at the design stage, considers security components as an integral part of the system and checks if the system can be designed in accordance with given requirements and limitations. An experimental evaluation of the methodology was conducted with help of its software implementation that consists of Python script, PostgreSQL database, Tkinter interface and available for download on our GitHub. As a use case, the system of mobile robots for perimeter monitoring was chosen. During the experimental evaluation, the design time was measured depending on the parameters of the attacker against which system security must be ensured. Moreover, the software implementation of the methodology was analyzed in compliance with requirements and compared with analogues. The advantages and disadvantages of the methodology as well as future work directions are indicated.
Uncertainty Propagation for Inertial Navigation with Coning, Sculling, and Scrolling Corrections
Authors: Brouk James D. , DeMars Kyle J.
Published: December 2021

This paper investigates the propagation of estimation errors through a common coning, sculling, and scrolling architecture used in modern-day inertial navigation systems. Coning, sculling, and scrolling corrections often have an unaccounted for effect on the error statistics of inertial measurements used to describe the state and uncertainty propagation for position, velocity, and attitude estimates. Through the development of an error analysis for a set of coning, sculling, and scrolling algorithms, mappings of the measurement and estimation errors through the correction term are adaptively generated. Using the developed mappings, an efficient and consistent propagation of the state and uncertainty, within the multiplicative extended Kalman filter architecture, is achieved. Monte Carlo analysis is performed, and results show that the developed system has favorable attributes when compared to the traditional mechanization.
Multisensory Testing Framework for Advanced Driver Assistant Systems Supported by High-Quality 3D Simulation
Authors: Jabłoński Paweł , Iwaniec Joanna , Jabłoński Michał
Published: December 2021

ADAS and autonomous technologies in vehicles become more and more complex, which increases development time and expenses. This paper presents a new real-time ADAS multisensory validation system, which can speed up the development and implementation processes while lowering its cost. The proposed test system integrates a high-quality 3D CARLA simulator with a real-time-based automation platform. We present system experimental verifications on several types of sensors and testing system architectures. The first, open-loop experiment explains the real-time capabilities of the system based on the Mobileye 6 camera sensor detections. The second experiment runs a real-time closed-loop test of a lane-keeping algorithm (LKA) based on the Mobileye 6 line detection. The last experiment presents a simulation of Velodyne VLP-16 lidar, which runs a free space detection algorithm. Simulated lidar output is compared with the real lidar performance. We show that the platform generates reproducible results and allows closed-loop operation which, combined with a real-time collection of event information, promises good scalability toward complex ADAS or autonomous functionalities testing.
Hybrid Deflection of Spoiler Influencing Radar Cross-Section of Tailless Fighter
Authors: Zhou Zeyang , Huang Jun
Published: December 2021

With the continuous development of advanced fighters towards tailless and flying wing layouts, diverse control surfaces have become the mainstream design. To study the influence of spoiler control surface on the radar cross-section (RCS) of a tailless fighter, a calculation method is presented. The deflection angle of the spoiler is controlled by the fixed mode, linear mode, and smooth mode. The results show that the opening action of the spoiler will break the original stealth characteristics of the aircraft at the key azimuth angles of the head and tail. As the elevation angle increases, this adverse effect will spread to the side. The influence of the different dynamic deflection modes of the spoiler on the aircraft RCS is analyzed. Compared with the linear dynamic deflection mode, the smooth dynamic deflection mode is conducive to the reduction in the average RCS at the given head azimuth. The presented method is effective to study the influence of the spoiler deflection on the electromagnetic scattering characteristics of the tailless aircraft.
A Digital Twin Decision Support System for the Urban Facility Management Process
Authors: Bujari Armir , Calvio Alessandro , Foschini Luca , Sabbioni Andrea , Corradi Antonio
Published: December 2021

The ever increasing pace of IoT deployment is opening the door to concrete implementations of smart city applications, enabling the large-scale sensing and modeling of (near-)real-time digital replicas of physical processes and environments. This digital replica could serve as the basis of a decision support system, providing insights into possible optimizations of resources in a smart city scenario. In this article, we discuss an extension of a prior work, presenting a detailed proof-of-concept implementation of a Digital Twin solution for the Urban Facility Management (UFM) process. The Interactive Planning Platform for City District Adaptive Maintenance Operations (IPPODAMO) is a distributed geographical system, fed with and ingesting heterogeneous data sources originating from different urban data providers. The data are subject to continuous refinements and algorithmic processes, used to quantify and build synthetic indexes measuring the activity level inside an area of interest. IPPODAMO takes into account potential interference from other stakeholders in the urban environment, enabling the informed scheduling of operations, aimed at minimizing interference and the costs of operations.
Mechatronic Anti-Collision System for Electric Wheelchairs Based on 2D LiDAR Laser Scan
Authors: Szaj Wiesław , Fudali Paweł , Wojnarowska Wiktoria , Miechowicz Sławomir
Published: December 2021

Electric wheelchairs make it easier for disabled and elderly people to live, move, interact, and participate in society. Moving a wheelchair in open spaces is relatively easy, but in closed and small spaces, maneuvering is difficult. Solutions to such problems for people with disabilities are applicable to a relatively small group of recipients and are mostly custom-made solutions, whose considerable cost is a significant barrier to accessibility. New technologies can provide an opportunity to improve the quality of life of people with disabilities in this aspect. Using selected elements of complex automation and control systems, cost-effective solutions can be created that facilitate the functioning of people with disabilities. This paper presents an analysis of hazards and problems when maneuvering a wheelchair in narrow passageways, as well as the authors’ solution to this problem, and the concept and assumptions of a mechatronic anti-collision system based on 2D LiDAR laser scanners. This solution is composed of a proprietary 2D rotating scanner mechanism that ensures the acquisition of 3D images of the environment around the wheelchair. Preliminary tests of this solution yielded promising results. Further research will include miniaturization of the device.
Analysis of Energy Harvesting Enhancement in Piezoelectric Unimorph Cantilevers
Authors: Rahimzadeh Mohammad , Samadi Hamid , Mohammadi Nikta Shams
Published: December 2021

Environmental energy harvesting is a major operation in research and industries. Currently, researchers have started analyzing small-scale energy scavengers for the supply of energy in low-power electrical appliances. One area of interest is the use of piezoelectric materials, especially in the presence of mechanical vibrations. This study analyzed a unimorph cantilever beam in different modes by evaluating the effects of various parameters, such as geometry, piezoelectric material, lengths of layers, and the proof mass to the energy harvesting process. The finite element method was employed for analysis. The proposed model was designed and simulated in COMSOL Multiphysics, and the output parameters, i.e., natural frequencies and the output voltage, were then evaluated. The results suggested a considerable effect of geometrical and physical parameters on the energy harvesters and could lead to designing devices with a higher functional efficiency.
Factors Influencing Temperature Measurements from Miniaturized Thermal Infrared (TIR) Cameras: A Laboratory-Based Approach
Authors: Wan Quanxing , Brede Benjamin , Smigaj Magdalena , Kooistra Lammert
Published: December 2021

The workflow for estimating the temperature in agricultural fields from multiple sensors needs to be optimized upon testing each type of sensor’s actual user performance. In this sense, readily available miniaturized UAV-based thermal infrared (TIR) cameras can be combined with proximal sensors in measuring the surface temperature. Before the two types of cameras can be operationally used in the field, laboratory experiments are needed to fully understand their capabilities and all the influencing factors. We present the measurement results of laboratory experiments of UAV-borne WIRIS 2nd GEN and handheld FLIR E8-XT cameras. For these uncooled sensors, it took 30 to 60 min for the measured signal to stabilize and the sensor temperature drifted continuously. The drifting sensor temperature was strongly correlated to the measured signal. Specifically for WIRIS, the automated non-uniformity correction (NUC) contributed to extra uncertainty in measurements. Another problem was the temperature measurement dependency on various ambient environmental parameters. An increase in the measuring distance resulted in the underestimation of surface temperature, though the degree of change may also come from reflected radiation from neighboring objects, water vapor absorption, and the object size in the field of view (FOV). Wind and radiation tests suggested that these factors can contribute to the uncertainty of several Celsius degrees in measured results. Based on these indoor experiment results, we provide a list of suggestions on the potential practices for deriving accurate temperature data from radiometric miniaturized TIR cameras in actual field practices for (agro-)environmental research.
Cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction
Authors: Yu Kun , Qin Xizhong , Jia Zhenhong , Du Yan , Lin Mengmeng
Published: December 2021

Accurate traffic flow prediction is essential to building a smart transportation city. Existing research mainly uses a given single-graph structure as a model, only considers local and static spatial dependencies, and ignores the impact of dynamic spatio-temporal data diversity. To fully capture the characteristics of spatio-temporal data diversity, this paper proposes a cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network (CAFMGCN) model for traffic flow prediction. First, introduce GCN to model the historical traffic data’s three-time attributes (current, daily, and weekly) to extract time features. Second, consider the relationship between distance and traffic flow, constructing adjacency, connectivity, and regional similarity graphs to capture dynamic spatial topology information. To make full use of global information, a cross-attention mechanism is introduced to fuse temporal and spatial features separately to reduce prediction errors. Finally, the CAFMGCN model is evaluated, and the experimental results show that the prediction of this model is more accurate and effective than the baseline of other models.
Polyaniline Nanofibers-Embedded Gold Nanoparticles Obtained by Template-Free Procedure with Immobilization Prospects
Authors: Crespo-Rosa Joaquín Rafael , Sierra-Padilla Alfonso , García-Guzmán Juan José , López-Iglesias David , Bellido-Milla Dolores , Palacios-Santander José María , Cubillana-Aguilera Laura
Published: December 2021

In this work, template-free nanostructured conducting polymers (nCPs)-embedded gold nanoparticles (AuNPs) from aniline, thiophene and 3,4-ethylenedioxythiophene have been prepared via a one-pot sonochemical method. The synthesis of the nanocomposite (nCPs-AuNPs) was achieved in a short period of time (5–10 min), by applying high-energy ultrasound to an aqueous mixture of a CP precursor monomer and KAuCl4, in the presence of LiClO4 as dopant. The synthesis process is simpler, greener and faster in comparison to other procedures reported in the literature. Remarkably, bulk quantities of doped polyaniline PANI-AuNPs nanofibers were obtained. Subsequently, they were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDS) and Fourier transform infrared spectroscopy (FTIR), as well as by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). PANI-AuNPs nanofibers were also employed as immobilization matrix for a benchmark enzyme, glucose oxidase (GOX). Finally, glucose was determined in real samples of white and red wines by using the so-obtained GOX-PANI-AuNPs/Sonogel-Carbon biosensor, providing outstanding recoveries (99.54%). This work may offer important insights into the synthesis of nanostructured conducting polymers and also stimulates the exploration of the applications of these nanocomposites, especially in research fields such as (bio)sensors, catalysis and composite materials.
Intracavity Measurement Sensitivity Enhancement without Runaway Noise
Authors: Horstman Luke , Diels Jean-Claude
Published: December 2021

A method to increase the sensitivity of an intracavity differential phase measurement that is not made irrelevant by a larger increase of noise is explored. By introducing a phase velocity feedback by way of a resonant dispersive element in an active sensor in which two ultrashort pulses circulate, it is shown that the measurement sensitivity is elevated without significantly increasing the Petermann excess noise factor. This enhancement technique has considerable implications for any optical phase based measurement; from gyroscopes and accelerometers to magnetometers and optical index measurements. Here we describe the enhancement method in the context of past dispersion enhancement studies including the recent work surrounding non-Hermitian quantum mechanics, justify the method with a theoretical framework (including numerical simulations), and propose practical applications.
A Wideband Noise and Harmonic Distortion Canceling Low-Noise Amplifier for High-Frequency Ultrasound Transducers
Authors: Tang Yuxuan , Feng Yulang , Hu He , Fang Cheng , Deng Hao , Zhang Runxi , Zou Jun , Chen Jinghong
Published: December 2021

This paper presents a wideband low-noise amplifier (LNA) front-end with noise and distortion cancellation for high-frequency ultrasound transducers. The LNA employs a resistive shunt-feedback structure with a feedforward noise-canceling technique to accomplish both wideband impedance matching and low noise performance. A complementary CMOS topology was also developed to cancel out the second-order harmonic distortion and enhance the amplifier linearity. A high-frequency ultrasound (HFUS) and photoacoustic (PA) imaging front-end, including the proposed LNA and a variable gain amplifier (VGA), was designed and fabricated in a 180 nm CMOS process. At 80 MHz, the front-end achieves an input-referred noise density of 1.36 nV/sqrt (Hz), an input return loss (S11) of better than −16 dB, a voltage gain of 37 dB, and a total harmonic distortion (THD) of −55 dBc while dissipating a power of 37 mW, leading to a noise efficiency factor (NEF) of 2.66.
Efficient Online Object Tracking Scheme for Challenging Scenarios
Authors: Mehmood Khizer , Ali Ahmad , Jalil Abdul , Khan Baber , Cheema Khalid Mehmood , Murad Maria , Milyani Ahmad H.
Published: December 2021

Visual object tracking (VOT) is a vital part of various domains of computer vision applications such as surveillance, unmanned aerial vehicles (UAV), and medical diagnostics. In recent years, substantial improvement has been made to solve various challenges of VOT techniques such as change of scale, occlusions, motion blur, and illumination variations. This paper proposes a tracking algorithm in a spatiotemporal context (STC) framework. To overcome the limitations of STC based on scale variation, a max-pooling-based scale scheme is incorporated by maximizing over posterior probability. To avert target model from drift, an efficient mechanism is proposed for occlusion handling. Occlusion is detected from average peak to correlation energy (APCE)-based mechanism of response map between consecutive frames. On successful occlusion detection, a fractional-gain Kalman filter is incorporated for handling the occlusion. An additional extension to the model includes APCE criteria to adapt the target model in motion blur and other factors. Extensive evaluation indicates that the proposed algorithm achieves significant results against various tracking methods.
Sagittal and Vertical Growth of the Maxillo–Mandibular Complex in Untreated Children: A Longitudinal Study on Lateral Cephalograms Derived from Cone Beam Computed Tomography
Authors: Yi Leah , Jeon Hyeran Helen , Li Chenshuang , Boucher Normand , Chung Chun-Hsi
Published: December 2021

The aim of this longitudinal study was to evaluate the sagittal and vertical growth of the maxillo–mandibular complex in untreated children using orthogonal lateral cephalograms compressed from cone beam computed tomography (CBCT). Two sets of scans, on 12 males (mean 8.75 years at T1, and 11.52 years at T2) and 18 females (mean 9.09 years at T1, and 10.80 years at T2), were analyzed using Dolphin 3D imaging. The displacements of the landmarks and rotations of both jaws relative to the cranial base were measured using the cranial base, and the maxillary and mandibular core lines. From T1 to T2, relative to the cranial base, the nasion, orbitale, A-point, and B-point moved anteriorly and inferiorly. The porion moved posteriorly and inferiorly. The ANB and mandibular plane angle decreased. All but one subject had forward rotation in reference to the cranial base. The maxillary and mandibular superimpositions showed no sagittal change on the A-point and B-point. The U6 and U1 erupted at 0.94 and 1.01 mm/year (males) and 0.82 and 0.95 mm/year (females), respectively. The L6 and L1 erupted at 0.66 and 0.88 mm/year (males), and at 0.41 mm/year for both the L6 and the L1 (females), respectively.
Divergence-Based Segmentation Algorithm for Heavy-Tailed Acoustic Signals with Time-Varying Characteristics
Authors: Grzesiek Aleksandra , Gąsior Karolina , Wyłomańska Agnieszka , Zimroz Radosław
Published: December 2021

Many real-world systems change their parameters during the operation. Thus, before the analysis of the data, there is a need to divide the raw signal into parts that can be considered as homogeneous segments. In this paper, we propose a segmentation procedure that can be applied for the signal with time-varying characteristics. Moreover, we assume that the examined signal exhibits impulsive behavior, thus it corresponds to the so-called heavy-tailed class of distributions. Due to the specific behavior of the data, classical algorithms known from the literature cannot be used directly in the segmentation procedure. In the considered case, the transition between parts corresponding to homogeneous segments is smooth and non-linear. This causes that the segmentation algorithm is more complex than in the classical case. We propose to apply the divergence measures that are based on the distance between the probability density functions for the two examined distributions. The novel segmentation algorithm is applied to real acoustic signals acquired during coffee grinding. Justification of the methodology has been performed experimentally and using Monte-Carlo simulations for data from the model with heavy-tailed distribution (here the stable distribution) with time-varying parameters. Although the methodology is demonstrated for a specific case, it can be extended to any process with time-changing characteristics.