bluuebunny/crossref_metadata_2025 huggingface dataset:
A Deep Reinforcement-Learning Approach for Inverse Kinematics Solution of a High Degree of Freedom Robotic Manipulator
Authors: Malik Aryslan , Lischuk Yevgeniy , Henderson Troy , Prazenica Richard
Published: April 2022

The foundation and emphasis of robotic manipulator control is Inverse Kinematics (IK). Due to the complexity of derivation, difficulty of computation, and redundancy, traditional IK solutions pose numerous challenges to the operation of a variety of robotic manipulators. This paper develops a Deep Reinforcement Learning (RL) approach for solving the IK problem of a 7-Degree of Freedom (DOF) robotic manipulator using Product of Exponentials (PoE) as a Forward Kinematics (FK) computation tool and the Deep Q-Network (DQN) as an IK solver. The selected approach is architecturally simpler, making it faster and easier to implement, as well as more stable, because it is less sensitive to hyperparameters than continuous action spaces algorithms. The algorithm is designed to produce joint-space trajectories from a given end-effector trajectory. Different network architectures were explored and the output of the DQN was implemented experimentally on a Sawyer robotic arm. The DQN was able to find different trajectories corresponding to a specified Cartesian path of the end-effector. The network agent was able to learn random Bézier and straight-line end-effector trajectories in a reasonable time frame with good accuracy, demonstrating that even though DQN is mainly used in discrete solution spaces, it could be applied to generate joint space trajectories.
High-Efficiency Optimization Algorithm of PMEPR for OFDM Integrated Radar and Communication Waveform Based on Conjugate Gradient
Authors: Rong Juan , Liu Feifeng , Miao Yingjie
Published: April 2022

The tone reservation (TR) approach is mainly adopted to reduce the peak-to-mean envelope power ratio (PMEPR) of orthogonal frequency division multiplexing (OFDM) waveform with low TR ratio (TRR) in classic 4G communication systems. However, for the OFDM integrated radar and communication waveform, high TRR is necessary to simultaneously maintain the radar detection performance as well as communication performance. For cases with high TRR, the traditional waveform optimization algorithms have low execution efficiency and a poor PMEPR convergence level, and thus a new algorithm is needed. This paper proposes a new PMEPR optimization algorithm based on conjugate gradient. Firstly, by introducing the concept of Lp-norm, the PMEPR of the OFDM waveform is accurately remodeled as the objective function of the waveform optimization problem. Secondly, the conjugate gradient of the objective function is analytically derived to form the basis of the efficient PMEPR optimization. Finally, a PMEPR optimization algorithm based on the Polak–Ribière–Polyak (PRP) conjugate gradient is proposed. The simulation results verified the proposed algorithm in terms of optimization efficiency, as well as convergence level, and the initial experimental results suggested the practicality of the proposed algorithm.
Changes in Long-Term PM2.5 Pollution in the Urban and Suburban Areas of China’s Three Largest Urban Agglomerations from 2000 to 2020
Authors: Zhang Lili , Zhao Na , Zhang Wenhao , Wilson John P.
Published: April 2022

Particulate matter (PM2.5) is a significant public health concern in China, and the Chinese government has implemented a series of laws, policies, regulations, and standards to improve air quality. This study documents the changes in PM2.5 and evaluates the effects of industrial transformation and clean air policies on PM2.5 levels in urban and suburban areas of China’s three largest urban agglomerations, Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) based on a new degree of urbanization classification method. We used high-resolution PM2.5 concentration and population datasets to quantify the differences in PM2.5 concentrations in urban and suburban areas of these three urban agglomerations. From 2000 to 2020, the urban areas have expanded while the suburban areas have shrunk. PM2.5 concentrations in urban areas were approximately 32, 10, and 7 μg/m3 higher than those in suburban areas from 2000 to 2020 in BTH, YRD, and PRD, respectively. Since 2013, the PM2.5 concentrations in the urban regions of BTH, YRD, and PRD have declined at average annual rates of 7.30, 5.50, and 5.03 μg/m3/year, respectively, while PM2.5 concentrations in suburban areas have declined at average annual rates of 3.11, 4.23 and 4.69 μg/m3/year, respectively. By 2018, all of the urban and suburban areas of BTH, YRD, and PRD satisfied their specific targets in the Air Pollution and Control Action Plan. By 2020, the PM2.5 declines of BTH, YRD, and PRD exceeded the targets by two, three, and four times, respectively. However, the PM2.5 exposure risks in urban areas are 10–20 times higher than those in suburban areas. China will need to implement more robust air pollution mitigation policies to achieve the World Health Organization’s Air Quality Guideline (WHO-AQG) and reduce long-term PM2.5 exposure health risks.
A Spatiotemporal Network Model for Global Ionospheric TEC Forecasting
Authors: Lin Xu , Wang Hongyue , Zhang Qingqing , Yao Chaolong , Chen Changxin , Cheng Lin , Li Zhaoxiong
Published: April 2022

In the Global Navigation Satellite System, ionospheric delay is a significant source of error. The magnitude of the ionosphere total electron content (TEC) directly impacts the magnitude of the ionospheric delay. Correcting the ionospheric delay and improving the accuracy of satellite navigation positioning can both benefit from the accurate modeling and forecasting of ionospheric TEC. The majority of current ionospheric TEC forecasting research only considers the temporal or spatial dimensions, ignoring the ionospheric TEC’s spatial and temporal autocorrelation. Therefore, we constructed a spatiotemporal network model with two modules: (i) global spatiotemporal characteristics extraction via forwarding spatiotemporal characteristics transfer and (ii) regional spatiotemporal characteristics correction via reverse spatiotemporal characteristics transfer. This model can realize the complementarity of TEC global spatiotemporal characteristics and regional spatiotemporal characteristics. It also ensures that the global spatiotemporal characteristics of the global ionospheric TEC are transferred to each other in both temporal and spatial domains at the same time. The spatiotemporal network model thus achieves a spatiotemporal prediction of global ionospheric TEC. The Huber loss function is also used to suppress the gross error and noise in the ionospheric TEC data to improve the forecasting accuracy of global ionospheric TEC. We compare the results of the spatiotemporal network model with the Center for Orbit Determination in Europe (CODE), the convolutional Long Short-Term Memory (convLSTM) model and the Predictive Recurrent Neural Network (PredRNN) model for one-day forecasts of global ionospheric TEC under different conditions of time and solar activity, respectively. With internal data validation, the average root mean square error (RMSE) of our proposed algorithm increased by 21.19, 15.75, and 9.67%, respectively, during the maximum solar activity period. During the minimum solar activity period, the RMSE improved by 38.69, 38.02, and 13.54%, respectively. This algorithm can effectively be applied to ionospheric delay error correction and can improve the accuracy of satellite navigation and positioning.
Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
Authors: White Corey T. , Reckling William , Petrasova Anna , Meentemeyer Ross K. , Mitasova Helena
Published: April 2022

As rapid urbanization occurs in cities worldwide, the importance of maintaining updated digital elevation models (DEM) will continue to increase. However, due to the cost of generating high-resolution DEM over large spatial extents, the temporal resolution of DEMs is coarse in many regions. Low-cost unmanned aerial vehicles (UAS) and DEM data fusion provide a partial solution to improving the temporal resolution of DEM but do not identify which areas of a DEM require updates. We present Rapid-DEM, a framework that identifies and prioritizes locations with a high likelihood of an urban topographic change to target UAS data acquisition and fusion to provide up-to-date DEM. The framework uses PlanetScope 3 m satellite imagery, Google Earth Engine, and OpenStreetMap for land cover classification. GRASS GIS generates a contextualized priority queue from the land cover data and outputs polygons for UAS flight planning. Low-cost UAS fly the identified areas, and WebODM generates a DEM from the UAS survey data. The UAS data is fused with an existing DEM and uploaded to a public data repository. To demonstrate Rapid-DEM a case study in the Walnut Creek Watershed in Wake County, North Carolina is presented. Two land cover classification models were generated using random forests with an overall accuracy of 89% (kappa 0.86) and 91% (kappa 0.88). The priority queue identified 109 priority locations representing 1.5% area of the watershed. Large forest clearings were the highest priority locations, followed by newly constructed buildings. The highest priority site was a 0.5 km2 forest clearing that was mapped with UAS, generating a 15 cm DEM. The UAS DEM was resampled to 3 m resolution and fused with USGS NED 1/9 arc-second DEM data. Surface water flow was simulated over the original and updated DEM to illustrate the impact of the topographic change on flow patterns and highlight the importance of timely DEM updates.
Evaluating Several Vegetation Indices Derived from Sentinel-2 Imagery for Quantifying Localized Overgrazing in a Semi-Arid Region of South Africa
Authors: Harmse Christiaan J. , Gerber Hannes , van Niekerk Adriaan
Published: April 2022

Rangeland monitoring aims to determine whether grazing management strategies meet the goals of sustainable resource utilization. The development of sustainable grazing management strategies requires an understanding of the manner in which grazing animals utilize available vegetation. In this study, we made use of livestock tracking, in situ observations and Sentinel-2 imagery to make rangeland scale observations of vegetation conditions in a semi-arid environment, to better understand the spatial relationships between vegetation conditions and sheep movement patterns. We hypothesized that sheep graze more selectively under low stocking rates—resulting in localized overgrazing. We also assessed the importance of image spatial resolution, as it was assumed localized effects of grazing will be best explained by higher resolution imagery. The results showed that livestock tend to congregate along drainage lines where soils are deeper. The findings demonstrate how the spatial analysis of remotely sensed data can provide a landscape-scale overview of livestock movement patterns. This study illustrates that high-resolution normalized difference vegetation index (NDVI) data can be used as a grazing management tool to determine the spatial variability of productive areas across the semi-arid Upper Karoo rangelands and identify preferred grazing areas.
Hyperspectral Indices for Predicting Nitrogen Use Efficiency in Maize Hybrids
Authors: Olson Monica B. , Crawford Melba M. , Vyn Tony J.
Published: April 2022

Enhancing the nitrogen (N) efficiency of maize hybrids is a common goal of researchers, but involves repeated field and laboratory measurements that are laborious and costly. Hyperspectral remote sensing has recently been investigated for measuring and predicting biomass, N content, and grain yield in maize. We hypothesized that vegetation indices (HSI) obtained mid-season through hyperspectral remote sensing could predict whole-plant biomass per unit of N taken up by plants (i.e., N conversion efficiency: NCE) and grain yield per unit of plant N (i.e., N internal efficiency: NIE). Our objectives were to identify the best mid-season HSI for predicting end-of-season NCE and NIE, rank hybrids by the selected HSI, and evaluate the effect of decreased spatial resolution on the HSI values and hybrid rankings. Analysis of 20 hyperspectral indices from imaging at V16/18 and R1/R2 by manned aircraft and UAVs over three site-years using mixed models showed that two indices, HBSI1 and HBS2, were predictive of NCE, and two indices, HBCI8 and HBCI9, were predictive of NIE for actual data collected from five to nine hybrids at maturity. Statistical differentiation of hybrids in their NCE or NIE performance was possible based on the models with the greatest accuracy obtained for NIE. Lastly, decreasing the spatial resolution changed the HSI values, but an effect on hybrid differentiation was not evident.
Desert Locust Cropland Damage Differentiated from Drought, with Multi-Source Remote Sensing in Ethiopia
Authors: Alemu Woubet G. , Neigh Christopher S. R.
Published: April 2022

In 2020, Ethiopia had the worst desert locust outbreak in 25 years, leading to food insecurity. Locust research has typically focused on predicting the paths and breeding grounds based on ground surveys and remote sensing of outbreak factors. In this study, we hypothesized that it is possible to detect desert locust cropland damage through the analysis of fine-scale (5–10 m) resolution satellite remote sensing datasets. We performed our analysis on 121 swarm point locations on croplands derived from the Food and Agriculture Organization (FAO) of the United Nations, and 94 ‘non-affected’ random cropland sample points generated for this study that are distributed within 20–25 km from the ‘center’ of swarm affected sample locations. Integrated Drought Condition Indices (IDCIs) and Vegetation Health Indices (VHIs) calculated for the affected sample locations for 2000–2020 were strongly correlated (R2 > 0.90) with that of the corresponding non-affected group of sample sites. Drought indices were strongly correlated with the evaluation Standardized Precipitation Evapotranspiration Indices (SPEIs), and showed that 2020 was the wettest year since 2000. In 2020, the NDVI and backscatter coefficient of cropland phenologies from the affected versus non-affected cropland sample sites showed a slightly wider, but significant gap in March (short growing season) and August-October (long growing season). Thus, slightly wider gaps in cropland phenologies between the affected and non-affected sites were likely induced from the locust damage, not drought, with fine scale data representing a larger gap.
Satellite-Based Evidences to Improve Cropland Productivity on the High-Standard Farmland Project Regions in Henan Province, China
Authors: Yan Huimin , Du Wenpeng , Zhou Ying , Luo Liang , Niu Zhong’en
Published: April 2022

Under the pressure of limited arable land and increasing demand for food, improving the quality of existing arable land has become a priority to ensure food security. The Chinese government gives great importance to improving cropland productivity by focusing on the construction of high-standard farmland (HSF). The government puts forward the goal of constructing 1.2 billion mu (100 mu ≈ 6.67 hectares) of HSF by 2030. Therefore, how to apply remote sensing to monitor the ability to increase and stabilize yields in HSF project regions has become an essential task for proving the efficiency of HSF construction. Based on HSF project distribution data, Moderate Resolution Imaging Spectroradiometer (MODIS) data and Landsat-8 Operational Land Imager (Landsat8-OLI) data, this study develops a method to monitor cropland productivity improvement by measuring cropland productivity level (CPL), disaster resistance ability (DRA) and homogeneous yield degree (HYD) in the HSF project region. Taking China’s largest grain production province (Henan Province) as a case study area, research shows that a light use efficiency model that includes multiple cropping data can effectively detect changes in cropland productivity before and after HSF construction. Furthermore, integrated Landsat8-OLI and MODIS data can detect changes in DRA and HYD before and after HSF construction with higher temporal and spatial resolution. In 109 HSF project regions concentrated and distributed in contiguous regions in Henan Province, the average cropland productivity increased by 145 kg/mu; among the eight sample project regions, DRA was improved in seven sample project regions; and the HYD in all eight sample project regions was greatly improved (the degree of increase is more than 75%). This evidence from satellites proves that the Chinese HSF project has significantly improved the CPL, DRA and HYD of cropland, while this study also verifies the practicability of the three indices to monitor the efficiency of HSF construction.
Spatiotemporal Analysis of NO2 Production Using TROPOMI Time-Series Images and Google Earth Engine in a Middle Eastern Country
Authors: Rabiei-Dastjerdi Hamidreza , Mohammadi Shahin , Saber Mohsen , Amini Saeid , McArdle Gavin
Published: April 2022

Like many developing countries, Iran faces air pollution, especially in its metropolises and industrial cities. Nitrogen dioxide (NO2) is one of the significant air pollutants; therefore, this study aims to investigate the spatiotemporal variability of NO2 using Tropospheric Monitoring Instrument (TROPOMI) sensor mounted on the Sentinel-5P (S5P) satellite and the Google Earth Engine (GEE) platform over Iran. In addition, we used ground truth data to assess the correlation between data acquired by this sensor and ground stations. The results show that there is a strong correlation between products of the TROPOMI sensor and data provided by the Air Quality Monitoring Organization of Iran. The results also display that the correlation coefficient (R) of NO2 between ground truth data and the TROPOMI sensor varies in the range of 0.4 to 0.92, over three years. Over an annual period (2018 to 2021) and wide area, these data can become valuable points of reference for NO2 monitoring. In addition, this study proved that the tropospheric NO2 concentrations are generally located over the northern part of Iran. According to the time and season, the concentration of the tropospheric NO2 column shows higher values during winter than in the summertime. The results show that a higher concentration of the tropospheric NO2 column is in winter while in some southern and central parts of the country more NO2 concentration can be seen in the summertime. This study indicates that these urban areas are highly polluted, which proves the impact of pollutants such as NO2 on the people living there. In other words, small parts of Iran are classified as high and very highly polluted areas, but these areas are the primary location of air pollution in Iran. We provide a code repository that allows spatiotemporal analysis of NO2 estimation using TROPOMI time-series images within GEE. This method can be applied to other regions of interest for NO2 mapping.
Normalized Burn Ratio Plus (NBR+): A New Index for Sentinel-2 Imagery
Authors: Alcaras Emanuele , Costantino Domenica , Guastaferro Francesca , Parente Claudio , Pepe Massimiliano
Published: April 2022

The monitoring of burned areas can easily be performed using satellite multispectral images: several indices are available in the literature for highlighting the differences between healthy vegetation areas and burned areas, in consideration of their different signatures. However, these indices may have limitations determined, for example, by the presence of clouds or water bodies that produce false alarms. To avoid these inaccuracies and optimize the results, this work proposes a new index for detecting burned areas named Normalized Burn Ratio Plus (NBR+), based on the involvement of Sentinel-2 bands. The efficiency of this index is verified by comparing it with five other existing indices, all applied on an area with a surface of about 500 km2 and covering the north-eastern part of Sicily (Italy). To achieve this aim, both a uni-temporal approach (single date image) and a bi-temporal approach (two date images) are adopted. The maximum likelihood classifier (MLC) is applied to each resulting index map to define the threshold separating burned pixels from non-burned ones. To evaluate the efficiency of the indices, confusion matrices are constructed and compared with each other. The NBR+ shows excellent results, especially because it excludes a large part of the areas incorrectly classified as burned by other indices, despite being clouds or water bodies.
A Serverless-Based, On-the-Fly Computing Framework for Remote Sensing Image Collection
Authors: Wu Jin , Wu Mingbo , Li Haiyan , Li Lijuan , Li Leilei
Published: April 2022

The rapid growth of remote sensing data calls for the construction of new computational models for algorithmic exploration, which requires on-demand execution, instant response, and multitenancy. We call this model on-the-fly computing, which could reduce the complexity of cloud programming for remote sensing data analysis and benefit from efficient multiplexing. As an advancement of cloud computing, serverless computing makes it possible to realize the on-the-fly computational model. In the study, the concise definition of an on-the-fly computing model for remote sensing data analysis and the corresponding software architecture based on the serverless computing commodities are presented. The proof-of-concept experiments have suggested that the on-the-fly computing model for remote sensing data analysis can be efficiently implemented as a serverless software. The response time is mainly related to the tile reading operation and data structure conversion. In the case of high concurrency, the system can scale to hundreds of instances in seconds.
Offshore Oil Platform Detection in Polarimetric SAR Images Using Level Set Segmentation of Limited Initial Region and Convolutional Neural Network
Authors: Liu Chun , Yang Jian , Ou Jianghong , Fan Dahua
Published: April 2022

Offshore oil platforms are difficult to detect due to the complex sea state, the sparseness of target distribution, and the similarity of targets with ships. In this paper, we propose an oil platform detection method in polarimetric synthetic aperture radar (PolSAR) images using level set segmentation of a limited initial region and a convolutional neural network (CNN). Firstly, to reduce the interference of sea clutter, the offshore strong scattering targets were initially detected by the generalized optimization of polarimetric contrast enhancement (GOPCE) detector. Secondly, to accurately locate the contour of targets and eliminate false alarms, the coarse results were refined using an improved level set segmentation method. An algorithm for splitting and merging the smallest enclosing circle (SMSEC) was proposed to cover the coarse results and obtain the initial level set function. Finally, the LeNet-5 CNN model was used to classify the oil platforms and ships. Experimental results using multiple sets of polarimetric SAR data acquired by RADARSAT-2 show that the performance of the proposed method, including the detection rate, the false alarm rate, and the Intersection over Union (IOU) index between the extracted ROI and the ground truth, is better than the performance of a method that combines a GOPCE detector and a support vector machine classifier.
Temporal Synergies Detection in Gait Cyclograms Using Wearable Technology
Authors: Gavrilović Marija M. , Janković Milica M.
Published: April 2022

The human gait can be described as the synergistic activity of all individual components of the sensory–motor system. The central nervous system (CNS) develops synergies to execute endpoint motion by coordinating muscle activity to reflect the global goals of the endpoint trajectory. This paper proposes a new method for assessing temporal dynamic synergies. Principal component analysis (PCA) has been applied on the signals acquired by wearable sensors (inertial measurement units, IMU and ground reaction force sensors, GRF mounted on feet) to detect temporal synergies in the space of two-dimensional PCA cyclograms. The temporal synergy results for different gait speeds in healthy subjects and stroke patients before and after the therapy were compared. The hypothesis of invariant temporal synergies at different gait velocities was statistically confirmed, without the need to record and analyze muscle activity. A significant difference in temporal synergies was noticed in hemiplegic gait compared to healthy gait. Finally, the proposed PCA-based cyclogram method provided the therapy follow-up information about paretic leg gait in stroke patients that was not available by observing conventional parameters, such as temporal and symmetry gait measures.
A Study on Double Inputs Direct Contact and Single Output Capacitively Coupled Conductivity Detector
Authors: Zhang Shuangfei , Yuan Hongyan , Xiao Dan
Published: April 2022

In this paper, an improved double inputs direct contact and single output capacitively coupled conductivity detector (DISODCD) based on traditional contactless capacitively coupled conductivity detector (C4D) is developed. The sensor uses double inputs of the contact electrode and capacitively coupled output of the contactless electrode and a lock-in amplifier to reduce interfering noise signals and amplify gain. Parallel circuit counteracts the part of the adverse capacitance reactance introduced by electrode polarization and reduces the effect of the impedance caused by the coupled wall capacitance to measure the resistance of solution. The sensor reduces limit of detection (LOD) of analyte and improves the sensitivity of the device. The LOD of the potassium chloride solution is 1 nM, and the detection range is 0.01 μM to 10 mM in actual testing for a single sample. The ratio of the response of potassium chloride solution to background ultrapure water at low concentrations is better than that of double input capacitively coupled contactless conductivity detector (DIC4D) and direct contact conductivity detection (DCD) under the same condition. In the case that the test cell is contaminated with impurities, pollution of impurities has little effect on the response of DISODCD. In practical application, it has a good service life.
Microstructured PVDF Film with Improved Performance as Flexible Infrared Sensor
Authors: Guan Hongjian , Li Weizhi , Yang Ruilin , Su Yuanjie , Li Hang
Published: April 2022

Polyvinylidene fluoride (PVDF) is a very promising material for fabricating flexible infrared sensors due to its ferroelectricity as well as excellent flexibility and low fabrication cost. This work focuses on improving PVDF’s pyroelectric performance by creating microstructures in the film. Simulation results suggest that the pyroelectric response of PVDF film can be improved if micro groove, square-pit or sinusoidal patterns are created on the film surface, with the grooved film showing the best pyroelectric performance. Suggested by the simulation results, flexible PVDF samples with groove structure are prepared by casting the precursor solution on the mold with designed patterns. Measurement results demonstrate that the optimal microstructured PVDF film can improve its pyroelectric performance by as high as 146%, which is in good agreement with the simulations. This work provides an innovative way of achieving flexible infrared sensor devices with promoted performance based on pyroelectric polymers.
Technical, Regulatory, Economic, and Trust Issues Preventing Successful Integration of Sensors into the Mainstream Consumer Wearables Market
Authors: Devine Jaime K. , Schwartz Lindsay P. , Hursh Steven R.
Published: April 2022

Sensors that track physiological biomarkers of health must be successfully incorporated into a fieldable, wearable device if they are to revolutionize the management of remote patient care and preventative medicine. This perspective article discusses logistical considerations that may impede the process of adapting a body-worn laboratory sensor into a commercial-integrated health monitoring system with a focus on examples from sleep tracking technology.
Leveraging Artificial Intelligence and Fleet Sensor Data towards a Higher Resolution Road Weather Model
Authors: Bogaerts Toon , Watelet Sylvain , De Bruyne Niko , Thoen Chris , Coopman Tom , Van den Bergh Joris , Reyniers Maarten , Seynaeve Dirck , Casteels Wim , Latré Steven , Hellinckx Peter
Published: April 2022

Road weather conditions such as ice, snow, or heavy rain can have a significant impact on driver safety. In this paper, we present an approach to continuously monitor the road conditions in real time by equipping a fleet of vehicles with sensors. Based on the observed conditions, a physical road weather model is used to forecast the conditions for the following hours. This can be used to deliver timely warnings to drivers about potentially dangerous road conditions. To optimally process the large data volumes, we show how artificial intelligence is used to (1) calibrate the sensor measurements and (2) to retrieve relevant weather information from camera images. The output of the road weather model is compared to forecasts at road weather station locations to validate the approach.
Event-Triggered Dynamic Coverage Control for Multiple Stratospheric Airships
Authors: Zhang Yifei , Zhu Ming , Chen Tian
Published: April 2022

This article first investigates the dynamic coverage control problem for the multiple stratospheric airships (MSAs) system considering its practical application scenarios. A dynamic coverage control framework is put forward, in which the MSA system can be guided and controlled to fully cover the observation target region. Once a specific target is detected, the coverage target can be switched. First, the location information of the monitored target is predicted by an autoregressive model against processing delay. Second, the coverage control scheme consists of two layers: a novel potential field-based virtual control law to generate the desired velocity and angular velocity and an adaptive tracking controller to track them. In the virtual control law, a dynamic artificial potential field is introduced to adapt to the dynamic scenarios. In the tracking controller, which is combined with the adaptive control technique and the saturation compensator theory, the external disturbances and input saturation are addressed. Third, the event-triggered mechanism is designed to reduce the control frequency to prolong the actuator life. The simulation results are given to substantiate the capability of the proposed dynamic coverage control framework.
Validity and Reliability of Kinect v2 for Quantifying Upper Body Kinematics during Seated Reaching
Authors: Faity Germain , Mottet Denis , Froger Jérôme
Published: April 2022

Kinematic analysis of the upper limbs is a good way to assess and monitor recovery in individuals with stroke, but it remains little used in clinical routine due to its low feasibility. The aim of this study is to assess the validity and reliability of the Kinect v2 for the analysis of upper limb reaching kinematics. Twenty-six healthy participants performed seated hand-reaching tasks while holding a dumbbell to induce behaviour similar to that of stroke survivors. With the Kinect v2 and with the VICON, 3D upper limb and trunk motions were simultaneously recorded. The Kinect assesses trunk compensations, hand range of motion, movement time and mean velocity with a moderate to excellent reliability. In contrast, elbow and shoulder range of motion, time to peak velocity and path length ratio have a poor to moderate reliability. Finally, instantaneous hand and elbow tracking are not precise enough to reliably assess the number of velocity peaks and the peak hand velocity. Thanks to its ease of use and markerless properties, the Kinect can be used in clinical routine for semi-automated quantitative diagnostics guiding individualised rehabilitation of the upper limb. However, engineers and therapists must bear in mind the tracking limitations of the Kinect.
Farm Vehicle Following Distance Estimation Using Deep Learning and Monocular Camera Images
Authors: Arabi Saeed , Sharma Anuj , Reyes Michelle , Hamann Cara , Peek-Asa Corinne
Published: April 2022

This paper presents a comprehensive solution for distance estimation of the following vehicle solely based on visual data from a low-resolution monocular camera. To this end, a pair of vehicles were instrumented with real-time kinematic (RTK) GPS, and the lead vehicle was equipped with custom devices that recorded video of the following vehicle. Forty trials were recorded with a sedan as the following vehicle, and then the procedure was repeated with a pickup truck in the following position. Vehicle detection was then conducted by employing a deep-learning-based framework on the video footage. Finally, the outputs of the detection were used for following distance estimation. In this study, three main methods for distance estimation were considered and compared: linear regression model, pinhole model, and artificial neural network (ANN). RTK GPS was used as the ground truth for distance estimation. The output of this study can contribute to the methodological base for further understanding of driver following behavior with a long-term goal of reducing rear-end collisions.
Extraction of Energy Characteristics of Blue Whale Vocalizations Based on Empirical Mode Decomposition
Authors: Wen Chai-Sheng , Lin Chin-Feng , Chang Shun-Hsyung
Published: April 2022

This study extracts the energy characteristic distributions of the intrinsic mode functions (IMFs) and residue functions (RF) for a blue whale sound signal, with empirical mode decomposition (EMD) as the basic theoretical framework. A high-resolution marginal frequency characteristics extraction method, based on EMD with energy density intensity (EDI) parameters for blue B call vocalizations, was proposed. The extraction algorithm included six steps: EMD, energy analysis, marginal frequency (MF) analysis with EDI parameters, feature extraction (FE), classification, and Hilbert spectrum (HS) analysis. The blue whale sound sources were obtained from the website of the Scripps Whale Acoustics Lab of the University of California, San Diego, USA. The source is a type of B call with a time duration of 46.65 s, from which 59 analysis samples with a time duration of 180 ms were taken. The average energy distribution ratios of the IMF1, IMF2, IMF3, IMF4, and RF are 49.06%, 20.58%, 13.51%, 10.94% and 3.84%, respectively. New classification criteria and EDI parameters were proposed to extract the blue whale B call vocalization (BWBCV) characteristics. The analysis results show that the main frequency bands of the signal are distributed at 41–43 Hz in the MF of IMF1 for Class I BWBCV and 11–13 Hz in the MF of IMF2 for Class II BWBCV, respectively.
Damper Winding for Noise and Vibration Reduction of a Permanent Magnet Synchronous Machine
Authors: Ni Sijie , Bauw Grégory , Romary Raphaël , Cassoret Bertrand , Le Besnerais Jean
Published: April 2022

In this paper, a passive method for the noise reduction of the PMSM (Permanent Magnet Synchronous Machine) is presented. The principle is to add an auxiliary three-phase winding into the same slots as the initial stator winding, short-circuited via three capacitors of suitable values. The aim is to create a damping effect for flux density harmonic components, especially high-frequency harmonics from the PWM (PulseWidth Modulation), in the air gap in order to reduce the noise and vibration of the PMSM. The method can significantly reduce the global sound pressure level and vibrations for specific frequencies. Because of passive features, the additional winding effectively mitigates magnetic noise without greatly increasing the complexity of design and manufacturing, which also extends its applicability to different PMSMs.
Geometric Calibration for Cameras with Inconsistent Imaging Capabilities
Authors: Wang Ke , Liu Chuhao , Shen Shaojie
Published: April 2022

Traditional calibration methods rely on the accurate localization of the chessboard points in images and their maximum likelihood estimation (MLE)-based optimization models implicitly require all detected points to have an identical uncertainty. The uncertainties of the detected control points are mainly determined by camera pose, the slant of the chessboard and the inconsistent imaging capabilities of the camera. The negative influence of the uncertainties that are induced by the two former factors can be eliminated by adequate data sampling. However, the last factor leads to the detected control points from some sensor areas having larger uncertainties than those from other sensor areas. This causes the final calibrated parameters to overfit the control points that are located at the poorer sensor areas. In this paper, we present a method for measuring the uncertainties of the detected control points and incorporating these measured uncertainties into the optimization model of the geometric calibration. The new model suppresses the influence from the control points with large uncertainties while amplifying the contributions from points with small uncertainties for the final convergence. We demonstrate the usability of the proposed method by first using eight cameras to collect a calibration dataset and then comparing our method to other recent works and the calibration module in OpenCV using that dataset.
Inter-Comparison of UT1-UTC from 24-Hour, Intensives, and VGOS Sessions during CONT17
Authors: Raut Shrishail , Heinkelmann Robert , Modiri Sadegh , Belda Santiago , Balidakis Kyriakos , Schuh Harald
Published: April 2022

This work focuses on the assessment of UT1-UTC estimates from various types of sessions during the CONT17 campaign. We chose the CONT17 campaign as it provides 15 days of continuous, high-quality VLBI data from two legacy networks (S/X band), i.e., Legacy-1 (IVS) and Legacy-2 (VLBA) (having different network geometry and are non-overlapping), two types of Intensive sessions, i.e., IVS and Russian Intensives, and five days of new-generation, broadband VGOS sessions. This work also investigates different approaches to optimally compare dUT1 from Intensives with respect to the 24 h sessions given the different parameterization adopted for analyzing Intensives and different session lengths. One approach includes the estimation of dUT1 from pseudo Intensives, which are created from the 24 h sessions having their epochs synchronized with respect to the Intensive sessions. Besides, we assessed the quality of the dUT1 estimated from VGOS sessions at daily and sub-daily resolution. The study suggests that a different approach should be adopted when comparing the dUT1 from the Intensives, i.e., comparison of dUT1 value at the mean epoch of an Intensive session. The initial results regarding the VGOS sessions show that the dUT1 estimated from VGOS shows good agreement with the legacy network despite featuring fewer observations and stations. In the case of sub-daily dUT1 from VGOS sessions, we found that estimating dUT1 with 6 h resolution is superior to other sub-daily resolutions. Moreover, we introduced a new concept of sub-daily dUT1-tie to improve the estimation of dUT1 from the Intensive sessions. We observed an improvement of up to 20% with respect to the dUT1 from the 24 h sessions.
Dilute Polymerization of Aniline on PDMS Substrate via Surface Modification Using (3-Aminopropyl)Triethoxysilane for Stretchable Strain Sensor
Authors: Yim Chang-Joo , Choy Ji-Yeon , Youi Hae-Kyung , Hwang Jung-Hoon , Jo Eun-Bee , Lee Jun-Ho , Kim Hyun-Seok
Published: April 2022

Stretchable strain sensors are capable of acquiring data when in contact with human skin or equipment and are widely used in wearable applications. Most strain sensors have tensile properties of less than 20% and have limitations regarding body motion linkage, complex sensor structure, and motion nonreliability. To address these problems, we developed a high tension and high sensitivity sensor with a gauge factor over 40 and tensile stress about 50%. Polydimethylsiloxane (PDMS) was selected as the flexible substrate to ensure tensile strength, and polyaniline (PANI) was used to measure the resistance changes in the sensor. In particular, problems regarding poor uniformity of PANI on PDMS were resolved by surface treatment of the PDMS, wherein PANI polymerization was performed sequentially after forming a self-assembled monolayer (SAM) on the PDMS substrate. O2 plasma and (3-aminopropyl)triethoxysilane were used to form the SAM. It is expected that this sensor can obtain stable characteristics even under high tensile stress through the evenly formed PANI films on the surface-treated PDMS substrate and may be used in various flexible sensor applications.
Miniaturized Sensors for Detection of Ethanol in Water Based on Electrical Impedance Spectroscopy and Resonant Perturbation Method—A Comparative Study
Authors: Leo Angelo , Monteduro Anna Grazia , Rizzato Silvia , Milone Angelo , Maruccio Giuseppe
Published: April 2022

The development of highly sensitive, portable and low-cost sensors for the evaluation of ethanol content in liquid is particularly important in several monitoring processes, from the food industry to the pharmaceutical industry. In this respect, we report the optimization of two sensing approaches based on electrical impedance spectroscopy (EIS) and complementary double split ring resonators (CDSRRs) for the detection of ethanol in water. Miniaturized EIS sensors were realized with interdigitated electrodes, and the ethanol sensing was carried out in liquid solutions without any functionalization of the electrodes. Impedance fitting analysis, with an equivalent circuit over a frequency range from 100 Hz to 1 MHz, was performed to estimate the electric parameters, which allowed us to evaluate the amount of ethanol in water solutions. On the other hand, complementary double split ring resonators (CDSRRs) were optimized by adjusting the device geometry to achieve higher quality factors while operating at a low fundamental frequency despite the small size (useful for compact electronic packaging). Both sensors were found to be efficient for the detection of low amounts of ethanol in water, even in the presence of salts. In particular, EIS sensors proved to be effective in performing a broadband evaluation of ethanol concentration and are convenient when low cost is the priority. On the other end, the employment of split ring resonators allowed us to achieve a very low limit of detection of 0.2 v/v%, and provides specific advantages in the case of known environments where they can enable fast real-time single-frequency measurements.
Quantification and Visualization of Reliable Hemodynamics Evaluation Based on Non-Contact Arteriovenous Fistula Measurement
Authors: Iwai Rumi , Shimazaki Takunori , Kawakubo Yoshifumi , Fukami Kei , Ata Shingo , Yokoyama Takeshi , Hitosugi Takashi , Otsuka Aki , Hayashi Hiroyuki , Tsurumoto Masanobu , Yokoyama Reiko , Yoshida Tetsuya , Hirono Shinya , Anzai Daisuke
Published: April 2022

The condition of arteriovenous fistula (AVF) blood flow is typically checked by using auscultation; however, auscultation should require a qualitative judgment dependent on the skills of doctors, and further attention to contact infection is required. For these reasons, this study developed a non-contact and non-invasive medical device to measure the pulse wave of AVFs by applying optical imaging technology. As a first step toward realization of the quantification judgment based on non-contact AVF measurement, we experimentally validated the developed system, whereby the hemodynamics of 168 subjects were visually and quantitatively evaluated based on clinical tests. Based on the evaluation results, the fundamental statistical characteristics of the non-contact measurement, including the average and median values, and distribution of measured signal-to-noise power ratio, were demonstrated. The clinical test results contributed to the future construction of quantified criteria for the AVF condition with the non-contact measurement.
Study on Multiple Fractal Analysis and Response Characteristics of Acoustic Emission Signals from Goaf Rock Bodies
Authors: Xie Xuebin , Li Shaoqian , Guo Jiang
Published: April 2022

Based on the actual monitoring data of the acoustic emission (AE) ground pressure monitoring and positioning system, this paper introduces fractal theory and the multifractal detrended fluctuation analysis (MF-DFA) method to estimate the waveform multifractal spectrum of goaf rock acoustic emission signals and investigate multifractal time-varying response characteristics. The research results show that the wavelet hard thresholding method has the best noise reduction effect on the original signal, and the box counting dimension has a strong waveform identification effect. Before deformation damage occurs, fractal spectral width Δα shows an increase and then decrease and the fluctuation scale factor Δf(α) decreases and then increases. When damage occurs, fractal spectral width Δα decreases and then stabilizes and concentrates. Simultaneously, the fluctuation scale factor Δf(α) keeps decreasing until the lowest point, and then shows an increasing trend until it reaches a stable state. This study is of great significance to the stability evaluation and disaster early warning of mine goaf.
A Case Study in Breast Density Evaluation Using Bioimpedance Measurements
Authors: Gutiérrez-Lopez Marcos , Prado-Olivarez Juan , Matheus-Troconis Carolina , Padilla-Medina Alfredo , Barranco-Gutiérrez Alejandro I. , Espinosa-Calderon Alejandro , Herrera-Ramírez Carlos A. , Diaz-Carmona Javier
Published: April 2022

(1) Background: As breast cancer studies suggest, a high percentage of breast density (PBD) may be related to breast cancer incidence. Although PBD screening is one of the strongest predictors of breast cancer risk, X-ray-based mammography evaluation is subjective. Therefore, new objective PBD measuring techniques are of interest. A case study analyzing the PBD of thirteen female participants using a bioimpedance-based method, the anomalies tracking circle (ATC), is described in this paper. (2) Methods: In the first stage, the breast bioimpedance of each participant was measured. Then, the participant breast density was determined by applying a mammogram just after the breast bioimpedance measurement stage. In the third stage, the ATC algorithm was applied to the measured bioimpedance data for each participant, and a results analysis was done. (3) Results: An ATC variation according to the breast density was observed from the obtained data, this allowed the use of classification techniques to determine the PBD. (4) Conclusions: The described breast density method is a promising approach that might be applied as an auxiliary tool to the mammography in order to obtain precise and objective results for evaluation of breast density and with that determine potential breast cancer risk.
Control Design for Uncertain Higher-Order Networked Nonlinear Systems via an Arbitrary Order Finite-Time Sliding Mode Control Law
Authors: Munir Maryam , Khan Qudrat , Ullah Safeer , Syeda Tayyaba Maryam , Algethami Abdullah A.
Published: April 2022

The authors proposed an arbitrary order finite-time sliding mode control (SMC) design for a networked of uncertain higher-order nonlinear systems. A network of n+1 nodes, connected via a directed graph (with fixed topology), is considered. The nodes are considered to be uncertain in nature. A consensus error-based canonical form of the error dynamics is developed and a new arbitrary order distributed control protocol design strategy is proposed, which not only ensures the sliding mode enforcement in finite time but also confirms the finite time error dynamics stability. Rigorous stability analysis, in closed-loop, is presented, and a simulation example is given, which demonstrates the results developed in this work.