bluuebunny/crossref_metadata_2025 huggingface dataset:
A Simple Preparation Route for Bio-Phenol MQ Silicone Resin via the Hydrosilylation Method and its Autonomic Antibacterial Property
Authors: Ji Jianye , Ge Xin , Liang Weijie , Liang Ruiyuan , Pang Xiaoyan , Liu Ruoling , Wen Shuyi , Sun Jiaqi , Chen Xunjun , Ge Jianfang
Published: August 2019

MQ silicone resins represent a broad range of hydrolytic condensation products of monofunctional silane (M units) and tetrafunctional silane (Q units). In this work, a Bio-Phenol MQ silicone resin (BPMQ) was designed and synthesized by the hydrosilylation of hydrogen containing MQ silicone resin and eugenol in the presence of chloroplatinic acid. The structure, thermal property, and antibacterial property against Escherichia coli of the modified MQ silicone resin were investigated. The results showed that BPMQ has been prepared successfully, and the thermal stability of this modified polymer improved significantly because of the introduction of phenyl in eugenol. The temperature at the maximum degradation rate increased from 250 °C to 422.5 °C, and the residual yields mass left at 600 °C were increased from 2.0% to 28.3%. In addition, its antibacterial property against Escherichia coli was also enhanced markedly without adding any other antimicrobial agents. This improved performance is ascribed to special functional groups in the structure of eugenol. The BPMQ polymer is expected to be applied to pressure-sensitive adhesives and silicone rubber products for the biomedical field due to its reinforcing effect and antioxidant quality.
CuS@Corn Stalk/Chitin Composite Hydrogel for Photodegradation and Antibacterial
Authors: Xiong Yutong , Luo Bichong , Chen Guixin , Cai Jihai , Jiang Qimeng , Gu Bin , Wang Xiaoying
Published: August 2019

Copper sulfide nanoparticles (CuS NPs) have recently attracted extensive attention in various fields due to their excellent optical and electrical properties. However, CuS NPs are easy to agglomerate in their preparation on account of the high surface activity. In this study, uniform dispersion of CuS NPs were fabricated with corn stalk as a template and stabilizer, further CuS@corn stalk/chitin composite hydrogel was obtained by crosslinking with chitin. The results reveal that the CuS NPs were evenly dispersed into the composite hydrogels with a three-dimensional network structure, which were verified by the UV-vis spectrum, XRD, FT-IR spectra and SEM. In addition, the as-prepared composite hydrogel with the traits of peroxidase-like activity can convert H2O2 into an extremely oxidative and toxic ·OH, which manifested good effects for photodegradation of RhB and antibacterial against Escherichia coli and Staphylococcus aureus. Hence, the composite hydrogels could be used for photocatalytic treatment and sterilization of wastewater, which provides a new idea for the functional application of CuS NPs.
Mechanical Characterization and Impact Damage Assessment of Hybrid Three-Dimensional Five-Directional Composites
Authors: Wu Liwei , Wang Wei , Jiang Qian , Xiang Chunjie , Lou Ching-Wen
Published: August 2019

The effects of braided architecture and co-braided hybrid structure on low-velocity response of carbon-aramid hybrid three-dimensional five-directional (3D5d) braided composites were experimentally investigated in this study. Low-velocity impact was conducted on two types of hybridization and one pure carbon fiber braided reinforced composites under three velocities. Damage morphologies after low-velocity impact were detected by microscopy and ultrasonic nondestructive testing. Interior damages of composites were highly dependent on yarn type and alignment. Impact damage tolerance was introduced to evaluate the ductility of hybrid composites. Maximum impact load and toughness changed with impact velocity and constituent materials of the composites. The composite with aramid fiber as axial yarn and carbon fiber as braiding yarn showed the best impact resistance due to the synergistic effect of both materials. Wavelet transform was applied in frequency and time domain analyses to reflect the failure mode and mechanism of hybrid 3D5d braided composites. Aramid fibers were used either as axial yarns or braiding yarns, aiding in the effective decrease in the level of initial damage. In particular, when used as axial yarns, aramid fibers effectively mitigate the level of damage during damage evolution.
Enhanced Solubility, Stability, and Herbicidal Activity of the Herbicide Diuron by Complex Formation with β-Cyclodextrin
Authors: Gao Shuang , Jiang Jing-Yu , Liu Yan-Yan , Fu Ying , Zhao Li-Xia , Li Chun-Yan , Ye Fei
Published: August 2019

The herbicide diuron is hardly soluble in water and most organic solvents and is usually made into a wettable powder or mixed with soil when used, which causes environmental risk and a reduction in herbicidal efficacy. In this study, the physicochemical properties were changed by using β-cyclodextrin (β-CD) to encapsulate diuron to form an inclusion complex. Some key technologies, including X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), differential scanning calorimetry (DSC), and nuclear magnetic resonance (1H NMR), were used to characterize the inclusion complex. The stoichiometry of the inclusion complex was determined by recording the 1H NMR spectrum or by using a diagram of inclusion ratios. A phase solubility study proved that the formed inclusion complex exhibited higher water solubility. Thermogravimetric analysis (TGA) demonstrated that the formed inclusion complex exhibited better thermal stability. Biological activity studies indicated that the herbicidal activity, in terms of herbicide removal, of the formed inclusion complex was higher than that of the original diuron. In general, the formation of the inclusion complex could reduce the environmental damage caused by diuron and enhance its herbicidal activity, providing an environmentally friendly method for using diuron.
Pore Network Simulation of Gas-Liquid Distribution in Porous Transport Layers
Authors: Vorhauer Nicole , Altaf Haashir , Tsotsas Evangelos , Vidakovic-Koch Tanja
Published: August 2019

Pore network models are powerful tools to simulate invasion and transport processes in porous media. They are widely applied in the field of geology and the drying of porous media, and have recently also received attention in fuel cell applications. Here we want to describe and discuss how pore network models can be used as a prescriptive tool for future water electrolysis technologies. In detail, we suggest in a first approach a pore network model of drainage for the prediction of the oxygen and water invasion process inside the anodic porous transport layer at high current densities. We neglect wetting liquid films and show that, in this situation, numerous isolated liquid clusters develop when oxygen invades the pore network. In the simulation with narrow pore size distribution, the volumetric ratio of the liquid transporting clusters connected between the catalyst layer and the water supply channel is only around 3% of the total liquid volume contained inside the pore network at the moment when the water supply route through the pore network is interrupted; whereas around 40% of the volume is occupied by the continuous gas phase. The majority of liquid clusters are disconnected from the water supply routes through the pore network if liquid films along the walls of the porous transport layer are disregarded. Moreover, these clusters hinder the countercurrent oxygen transport. A higher ratio of liquid transporting clusters was obtained for greater pore size distribution. Based on the results of pore network drainage simulations, we sketch a new route for the extraction of transport parameters from Monte Carlo simulations, incorporating pore scale flow computations and Darcy flow.
Global Sensitivity Analysis of a Spray Drying Process
Authors: Bhonsale Satyajeet , Muñoz López Carlos André , Van Impe Jan
Published: August 2019

Spray drying is a key unit operation used to achieve particulate products of required properties. Despite its widespread use, the product and process design, as well as the process control remain highly empirical and depend on trial and error experiments. Studying the effect of operational parameters experimentally is tedious, time consuming, and expensive. In this paper, we carry out a model-based global sensitivity analysis (GSA) of the process. Such an exercise allows us to quantify the impact of different process parameters, many of which interact with each other, on the product properties and conditions that have an impact on the functionality of the final drug product. Moreover, classical sensitivity analysis using the Sobol-based sensitivity indices was supplemented by a polynomial chaos-based sensitivity analysis, which proved to be an efficient method to reduce the computational cost of the GSA. The results obtained demonstrate the different response dependencies of the studied variables, which helps to identify possible control strategies that can result in major robustness for the spray drying process.
The Simulation of Vortex Structures Induced by Different Local Vibrations at the Wall in a Flat-Plate Laminar Boundary Layer
Authors: Cao Weidong , Jia Zhixiang , Zhang Qiqi
Published: August 2019

The compact finite difference scheme on non-uniform meshes and the Fourier spectral hybrid method are used to directly simulate the evolution of vortex structures in a laminar boundary layer over a flat plate. To this end, two initial local vibration disturbances, namely, the positive–negative and the negative–positive models, at the wall were adopted. The numerical results show that the maximum amplitudes of vortex structures experience a process of linear growth and nonlinear rapid growth. The vertical disturbance velocity and mean flow shear and the derivative term of the stream-wise disturbance velocity and the span-wise disturbance velocity, are important factors for vortex structure development; the high- and low-speed stripe and the stream-wise vortex are consistent with structures seen in full turbulence. The maximum amplitude of the negative–positive model grows more quickly than that of the negative–positive model, and the detailed vortex structures are different for the two models. The mean flow profiles both become plump, which leads to the instability of the laminar boundary layer. The way in which the disturbance is generated with different local vibrations influences the dynamics of vortex structures in a laminar boundary layer.
Adsorption Process and Properties Analyses of a Pure Magadiite and a Modified Magadiite on Rhodamine-B from an Aqueous Solution
Authors: Ge Mingliang , Xi Zhuangzhuang , Zhu Caiping , Liang Guodong , Yang Yinye , Hu Guoqing , Jamal Lafifa , S.M. Jahangir Alam
Published: August 2019

The result of an adsorption experiment indicated that the pure magadiite (MAG) and the modified MAG via cetyltrimethylammonium-bromide (CTAB-MAG) possessed pronounced affinity to the Rhodamine-B (Rh-B) dye molecules. CTAB-MAG was synthesized with an ion-exchange method between MAG and cetyltrimethylammonium-bromide (CTAB) in an aqueous solution. The adsorption capacities of CTAB-MAG and MAG on Rh-B were 67.19 mg/g and 48.13 mg/g, respectively; while the pH and the time were 7 and 60 min, respectively; however, the initial concentration of Rh-B was 100 mg/L, and adsorbent dosage was 1 g/L. Whereas, the adsorption capacity of CTAB-MAG was increased by 40% over MAG which indicated that CTAB-MAG can be used as an efficient low-cost adsorbent. Adsorption kinetics were consistent with the pseudo-second-order kinetic equation; the adsorption processes were dominated by film diffusion process which belonged to monomolecular layer adsorption.
Grouping Method of Semiconductor Bonding Equipment Based on Clustering by Fast Search and Find of Density Peaks for Dynamic Matching According to Processing Tasks
Authors: Gao Zhijun , Si Wen , Han Zhonghua , Peng Jiayu , Qiao Feng
Published: August 2019

Given that the equipment for the semiconductor packaging line adopts the fixed grouping production method, thus failing to dynamically match the processing task demand capacity, in the present study, we proposed a semiconductor bonding equipment-grouping method based on processing task matching. This method sets the device group closed position constraint and the matching constraint between the device type and the processing type and uses the graph theory method to establish the device grouping model. The dynamic grouping of equipment under the capacity demand of different processing tasks was achieved by changing the relationship matrix between devices. The drawback of this grouping method is rather large grouping deviation, which we tried to solve with the clustering by fast search and find of density peaks (CFSFDP) that was added to cluster the sets of attribute information of the devices so as to obtain the maximum number of grouping groups obtained to reduce the grouping deviation. Simulation comparison experiments were carried out under different circumstances considering the size of the formation, the distribution of demand capacity, and the coefficient of difference in demand capacity. Compared with the standard device grouping method, the grouping method based on semiconductor bonding equipment and CFSFDP algorithm for dynamic matching according to processing tasks had better performance in solving the dynamic grouping problem.
Integrated Hyperspectral and Geochemical Study of Sediment-Hosted Disseminated Gold at the Goldstrike District, Utah
Authors: Sun Lei , Khan Shuhab , Shabestari Peter
Published: August 2019

The Goldstrike district in southwest Utah is similar to Carlin-type gold deposits in Nevada that are characterized by sediment-hosted disseminated gold. Suitable structural and stratigraphic conditions facilitated precipitation of gold in arsenian pyrite grains from ascending gold-bearing fluids. This study used ground-based hyperspectral imaging to study a core drilled in the Goldstrike district covering the basal Claron Formation and Callville Limestone. Spectral modeling of absorptions at 2340, 2200, and 500 nm allowed the extraction of calcite, clay minerals, and ferric iron abundances and identification of lithology. This study integrated remote sensing and geochemistry data and identified an optimum stratigraphic combination of limestone above and siliciclastic rocks below in the basal Claron Formation, as well as decarbonatization, argillization, and pyrite oxidation in the Callville Limestone, that are related with gold mineralization. This study shows an example of utilizing ground-based hyperspectral imaging in geological characterization, which can be broadly applied in the determination of mining interests and classification of ore grades. The utilization of this new terrestrial remote sensing technique has great potentials in resource exploration and exploitation.
Bringing Lunar LiDAR Back Down to Earth: Mapping Our Industrial Heritage through Deep Transfer Learning
Authors: Gallwey Jane , Eyre Matthew , Tonkins Matthew , Coggan John
Published: August 2019

This article presents a novel deep learning method for semi-automated detection of historic mining pits using aerial LiDAR data. The recent emergence of national scale remotely sensed datasets has created the potential to greatly increase the rate of analysis and recording of cultural heritage sites. However, the time and resources required to process these datasets in traditional desktop surveys presents a near insurmountable challenge. The use of artificial intelligence to carry out preliminary processing of vast areas could enable experts to prioritize their prospection focus; however, success so far has been hindered by the lack of large training datasets in this field. This study develops an innovative transfer learning approach, utilizing a deep convolutional neural network initially trained on Lunar LiDAR datasets and reapplied here in an archaeological context. Recall rates of 80% and 83% were obtained on the 0.5 m and 0.25 m resolution datasets respectively, with false positive rates maintained below 20%. These results are state of the art and demonstrate that this model is an efficient, effective tool for semi-automated object detection for this type of archaeological objects. Further tests indicated strong potential for detection of other types of archaeological objects when trained accordingly.
Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model over the Southern US
Authors: He Liming , Mostovoy Georgy
Published: August 2019

High-resolution data with nearly global coverage from Sentinel-2 mission open a new era for crop growth monitoring and yield estimation from remote sensing. The objective of this study is to demonstrate the potential of using Sentinel-2 biophysical data combined with an ecosystem modeling approach for estimation of cotton yield in the southern United States (US). The Boreal Ecosystems Productivity Simulator (BEPS) ecosystem model was used to simulate the cotton gross primary production (GPP) over three Sentinel-2 tiles located in Mississippi, Georgia, and Texas in 2017. Leaf area index (LAI) derived from Sentinel-2 measurements and hourly meteorological data from Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis were used to drive the ecosystem model. The simulated GPP values at 20-m grid spacing were aggregated to the county level (17 counties in total) and compared to the cotton lint yield estimates at the county level which are available from National Agricultural Statistics Service in the United States Department of Agriculture. The results of the comparison show that the BEPS-simulated cotton GPP explains 85% of variation in cotton yield. Our study suggests that the integration of Sentinel-2 LAI time series into the ecosystem model results in reliable estimates of cotton yield.
Hyperspectral Remote Sensing of Phytoplankton Species Composition Based on Transfer Learning
Authors: Zhu Qing , Shen Fang , Shang Pei , Pan Yanqun , Li Mengyu
Published: August 2019

Phytoplankton species composition research is key to understanding phytoplankton ecological and biogeochemical functions. Hyperspectral optical sensor technology allows us to obtain detailed information about phytoplankton species composition. In the present study, a transfer learning method to inverse phytoplankton species composition using in situ hyperspectral remote sensing reflectance and hyperspectral satellite imagery was presented. By transferring the general knowledge learned from the first few layers of a deep neural network (DNN) trained by a general simulation dataset, and updating the last few layers with an in situ dataset, the requirement for large numbers of in situ samples for training the DNN to predict phytoplankton species composition in natural waters was lowered. This method was established from in situ datasets and validated with datasets collected in different ocean regions in China with considerable accuracy (R2 = 0.88, mean absolute percentage error (MAPE) = 26.08%). Application of the method to Hyperspectral Imager for the Coastal Ocean (HICO) imagery showed that spatial distributions of dominant phytoplankton species and associated compositions could be derived. These results indicated the feasibility of species composition inversion from hyperspectral remote sensing, highlighting the advantages of transfer learning algorithms, which can bring broader application prospects for phytoplankton species composition and phytoplankton functional type research.
Microphysical Characteristics of Three Convective Events with Intense Rainfall Observed by Polarimetric Radar and Disdrometer in Eastern China
Authors: Chen Gang , Zhao Kun , Wen Long , Wang Mengyao , Huang Hao , Wang Mingjun , Yang Zhengwei , Zhang Guifu , Zhang Pengfei , Lee Wen-Chau
Published: August 2019

Polarimetric radar and disdrometer observations obtained during the 2014 Observation, Prediction, and Analysis of Severe Convection of China (OPACC) field campaign are used in this study to investigate the microphysical characteristics of three primary types of organized intense rainfall events (meiyu rainband, typhoon outer rainband, and squall line) in eastern China. Drop size distributions (DSDs) of these three events on the ground are derived from measurements of a surface disdrometer, while the corresponding three-dimensional microphysical structures are obtained from the Nanjing University C-band polarimetric radar (NJU-CPOL). Although the environmental moisture and instability conditions are different, all three events possess relatively high freezing level favorable for warm-rain processes where the high medium to small raindrop concentration at low levels is consistent with the high surface rainfall rates. Convection is tallest in the squall line where abundant ice-phase processes generate large amounts of rimed particles (graupel and hail) above the freezing level and the largest surface raindrops are present among these three events. The storm tops of both the typhoon and meiyu rainbands are lower than that in the squall line, composed of less active ice processes above the freezing level. The typhoon rainrate is more intense than that of meiyu, enhanced by higher coalescence efficiency. A revised generalized intercept parameter versus mass-weighted mean diameter (Nw-Dm) space diagram is constructed to describe the DSD distributions over the three events and illustrate the relative DSD positions for heavy precipitation. DSDs of these intense rainfall convections observed in this midlatitude region of eastern Asia somewhat represent the typical DSD characteristics in low latitudes, suggesting that the parameterization of microphysical characteristics in eastern China in numerical models needs to be further investigated to improve rain fall forecasts in these heavy rainfall events.
Study of a High Spectral Resolution Hyperspectral LiDAR in Vegetation Red Edge Parameters Extraction
Authors: Jiang Changhui , Chen Yuwei , Wu Haohao , Li Wei , Zhou Hui , Bo Yuming , Shao Hui , Song Shaojing , Puttonen Eetu , Hyyppä Juha
Published: August 2019

Non-contact and active vegetation or plant parameters extraction using hyperspectral information is a prospective research direction among the remote sensing community. Hyperspectral LiDAR (HSL) is an instrument capable of acquiring spectral and spatial information actively, which could mitigate the environmental illumination influence on the spectral information collection. However, HSL usually has limited spectral resolution and coverage, which is vital for vegetation parameter extraction. In this paper, to broaden the HSL spectral range and increase the spectral resolution, an Acousto-optical Tunable Filter based Hyperspectral LiDAR (AOTF-HSL) with 10 nm spectral resolution, consecutively covering from 500–1000 nm, was designed. The AOTF-HSL was employed and evaluated for vegetation parameters extraction. “Red Edge” parameters of four different plants with green and yellow leaves were extracted in the lab experiments for evaluating the HSL vegetation parameter extraction capacity. The experiments were composed of two parts. Firstly, the first-order derivative of the spectral reflectance was employed to extract the “Red Edge” position (REP), “Red Edge” slope (RES) and “Red Edge” area (REA) of these green and yellow leaves. The results were compared with the referenced value from a standard SVC© HR-1024 spectrometer for validation. Green leaf parameter differences between HSL and SVC results were minor, which supported that notion the HSL was practical for extracting the employed parameter as an active method. Secondly, another two different REP extraction methods, Linear Four-point Interpolation technology (LFPIT) and Linear Extrapolation technology (LET), were utilized for further evaluation of using the AOTF-HSL spectral profile to determine the REP value. The differences between the plant green leaves’ REP results extracted using the three methods were all below 10%, and the some of them were below 1%, which further demonstrated that the spectral data collected from HSL with this spectral range and resolution settings was applicable for “Red Edge” parameters extraction.
Sensitivity of Piezoelectric-Based Smart Interfaces to Structural Damage in Bolted Connections
Authors: Huynh Thanh-Canh , Ho Duc-Duy , Dang Ngoc-Loi , Kim Jeong-Tae
Published: August 2019

This study presents a set of experimental and numerical investigations to study the sensitivity of the piezoelectric-based smart interface device to structural damage in a bolted connection. The study aims to identify the proper geometric sizes of smart interfaces for damage detection tasks. First, the fundamentals of the damage monitoring technique via lead zirconate titanate (PZT) interface is briefly described for a bolted connection. Second, a lab-scaled girder connection is selected as the test structure for the experimental investigation. PZT interface prototypes with varying geometric sizes are designed for the test connection. Under the bolt-loosening inflicted in the connection, the impedance responses of the PZT interfaces are analyzed to understand the effect of geometric parameters on the damage sensitivity of the impedance responses. Subsequently, the bolt-loosening detection capabilities of the PZT interfaces are comparatively evaluated for identifying the proper geometric sizes of the devices. Finally, a finite element model of the PZT interface-bolted connection system is established for the numerical investigation. The damage sensitivity of the numerical impedance responses is compared with the experimental results for the verification.
Retrospective Continuous-Time Blood Glucose Estimation in Free Living Conditions with a Non-Invasive Multisensor Device
Authors: Acciaroli Giada , Zanon Mattia , Facchinetti Andrea , Caduff Andreas , Sparacino Giovanni
Published: August 2019

Even if still at an early stage of development, non-invasive continuous glucose monitoring (NI-CGM) sensors represent a promising technology for optimizing diabetes therapy. Recent studies showed that the Multisensor provides useful information about glucose dynamics with a mean absolute relative difference (MARD) of 35.4% in a fully prospective setting. Here we propose a method that, exploiting the same Multisensor measurements, but in a retrospective setting, achieves a much better accuracy. Data acquired by the Multisensor during a long-term study are retrospectively processed following a two-step procedure. First, the raw data are transformed to a blood glucose (BG) estimate by a multiple linear regression model. Then, an enhancing module is applied in cascade to the regression model to improve the accuracy of the glucose estimation by retrofitting available BG references through a time-varying linear model. MARD between the retrospectively reconstructed BG time-series and reference values is 20%. Here, 94% of values fall in zone A or B of the Clarke Error Grid. The proposed algorithm achieved a level of accuracy that could make this device a potential complementary tool for diabetes management and also for guiding prediabetic or nondiabetic users through life-style changes.
Application of Near-Infrared Optical Feedback Cavity Enhanced Absorption Spectroscopy (OF-CEAS) to the Detection of Ammonia in Exhaled Human Breath
Authors: Luo Zhifu , Tan Zhongqi , Long Xingwu
Published: August 2019

The qualitative and quantitative analysis to trace gas in exhaled human breath has become a promising technique in biomedical applications such as disease diagnosis and health status monitoring. This paper describes an application of a high spectral resolution optical feedback cavity enhanced absorption spectroscopy (OF-CEAS) for ammonia detection in exhaled human breath, and the main interference of gases such as CO2 and H2O are approximately eliminated at the same time. With appropriate optical feedback, a fibered distributed feedback (DFB) diode laser emitting at 1531.6 nm is locked to the resonance of a V-shaped cavity with a free spectral range (FSR) of 300 MHz and a finesse of 14,610. A minimum detectable absorption coefficient of αmin = 2.3 × 10−9 cm−1 is achieved in a single scan within 5 s, yielding a detection limit of 17 ppb for NH3 in breath gas at low pressure, and this stable system allows the detection limit down to 4.5 ppb when the spectra to be averaged over 16 laser scans. Different from typical CEAS with a static cavity, which is limited by the FSR in frequency space, the attainable spectral resolution of our experimental setup can be up to 0.002 cm−1 owing to the simultaneous laser frequency tuning and cavity dither. Hence, the absorption line profile is more accurate, which is most suitable for low-pressure trace gas detection. This work has great potential for accurate selectivity and high sensitivity applications in human breath analysis and atmosphere sciences.
Monitoring Dust Storms in Iraq Using Satellite Data
Authors: Albarakat Reyadh , Lakshmi Venkataraman
Published: August 2019

Dust storms can suspend large quantities of sand and cause haze in the boundary layer over local and regional scales. Iraq is one of the countries that is often impacted to a large degree by the occurrences of dust storms. The time between June 29 to July 8, 2009 is considered one of the worst dust storm periods of all times and many Iraq is suffered medical problems as a result. We used data from the Moderate Resolution Imaging Spectroradiometer (MODIS). MODIS Surface Reflectance Daily L2G Global 1 km and 500 m data were utilized to calculate the Normalized Difference Dust Index (NDDI). The MYD09GA V006 product was used to monitor, map, and assess the development and spread of dust storms over the arid and semi-arid territories of Iraq. We set thresholds for NDDI to distinguish between water and/or ice cloud and ground features and dust storms. In addition; brightness temperature data (TB) from the Aqua /MODIS thermal band 31 were analyzed to distinguish sand on the land surface from atmospheric dust. We used the MODIS level 2 MYD04 deep blue 550 nm Aerosol Option Depth (AOD) data that maintains accuracy even over bright desert surfaces. We found NDDI values lower than 0.05 represent clouds and water bodies, while NDDI greater than 0.18 correspond to dust storm regions. The threshold of TB of 310.5 K was used to distinguish aerosols from the sand on the ground. Approximately 75% of the territory was covered by a dust storm in 5 July 2009 due to strong and dry northwesterly winds.
An Energy-Efficient Method for Human Activity Recognition with Segment-Level Change Detection and Deep Learning
Authors: Jeong Chi Yoon , Kim Mooseop
Published: August 2019

Human activity recognition (HAR), which is important in context awareness services, needs to occur continuously in daily life, owing to which an energy-efficient method is needed. However, because human activities have a longer cycle than HAR methods, which have analysis cycles of a few seconds, continuous classification of human activities using these methods is computationally and energy inefficient. Therefore, we propose segment-level change detection to identify activity change with very low computational complexity. Additionally, a fully convolutional network (FCN) with a high recognition rate is used to classify the activity only when activity change occurs. We compared the accuracy and energy consumption of the proposed method with that of a method based on a convolutional neural network (CNN) by using a public dataset on different embedded platforms. The experimental results showed that, although the recognition rate of the proposed FCN model is similar to that of the CNN model, the former requires only 10% of the network parameters of the CNN model. In addition, our experiments to measure the energy consumption on the embedded platforms showed that the proposed method uses as much as 6.5 times less energy than the CNN-based method when only HAR energy consumption is compared.
Endpoints-Clipping CSI Amplitude for SVM-Based Indoor Localization
Authors: Hao Zhanjun , Yan Yan , Dang Xiaochao , Shao Chenguang
Published: August 2019

With the wide application of Channel State Information (CSI) in the field of sensing, the accuracy of positioning accuracy of indoor fingerprint positioning is increasingly necessary. The flexibility of the CSI signals may lead to an increase in fingerprint noise and inaccurate data classification. This paper presents an indoor localization algorithm based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Endpoints-Clipping (EC) CSI amplitude, and Support Vector Machine (EC-SVM). In the offline phase, the CSI amplitude information collected through the three channels is combined and clipped using the EC, and then a fingerprint database is obtained. In the online phase, the SVM is used to train the data in the fingerprint database, and the corresponding relationship is found with the CSI data collected in real time to perform matching and positioning. The experimental results show that the positioning accuracy of the EC-SVM algorithm is superior to the state-of-art indoor CSI-based localization technique.
Estimation of Knee Joint Forces in Sport Movements Using Wearable Sensors and Machine Learning
Authors: Stetter Bernd J. , Ringhof Steffen , Krafft Frieder C. , Sell Stefan , Stein Thorsten
Published: August 2019

Knee joint forces (KJF) are biomechanical measures used to infer the load on knee joint structures. The purpose of this study is to develop an artificial neural network (ANN) that estimates KJF during sport movements, based on data obtained by wearable sensors. Thirteen participants were equipped with two inertial measurement units (IMUs) located on the right leg. Participants performed a variety of movements, including linear motions, changes of direction, and jumps. Biomechanical modelling was carried out to determine KJF. An ANN was trained to model the association between the IMU signals and the KJF time series. The ANN-predicted KJF yielded correlation coefficients that ranged from 0.60 to 0.94 (vertical KJF), 0.64 to 0.90 (anterior–posterior KJF) and 0.25 to 0.60 (medial–lateral KJF). The vertical KJF for moderate running showed the highest correlation (0.94 ± 0.33). The summed vertical KJF and peak vertical KJF differed between calculated and predicted KJF across all movements by an average of 5.7% ± 5.9% and 17.0% ± 13.6%, respectively. The vertical and anterior–posterior KJF values showed good agreement between ANN-predicted outcomes and reference KJF across most movements. This study supports the use of wearable sensors in combination with ANN for estimating joint reactions in sports applications.
Predicting Depression, Anxiety, and Stress Levels from Videos Using the Facial Action Coding System
Authors: Gavrilescu Mihai , Vizireanu Nicolae
Published: August 2019

We present the first study in the literature that has aimed to determine Depression Anxiety Stress Scale (DASS) levels by analyzing facial expressions using Facial Action Coding System (FACS) by means of a unique noninvasive architecture on three layers designed to offer high accuracy and fast convergence: in the first layer, Active Appearance Models (AAM) and a set of multiclass Support Vector Machines (SVM) are used for Action Unit (AU) classification; in the second layer, a matrix is built containing the AUs’ intensity levels; and in the third layer, an optimal feedforward neural network (FFNN) analyzes the matrix from the second layer in a pattern recognition task, predicting the DASS levels. We obtained 87.2% accuracy for depression, 77.9% for anxiety, and 90.2% for stress. The average prediction time was 64 s, and the architecture could be used in real time, allowing health practitioners to evaluate the evolution of DASS levels over time. The architecture could discriminate with 93% accuracy between healthy subjects and those affected by Major Depressive Disorder (MDD) or Post-traumatic Stress Disorder (PTSD), and 85% for Generalized Anxiety Disorder (GAD). For the first time in the literature, we determined a set of correlations between DASS, induced emotions, and FACS, which led to an increase in accuracy of 5%. When tested on AVEC 2014 and ANUStressDB, the method offered 5% higher accuracy, sensitivity, and specificity compared to other state-of-the-art methods.
Ground Moving Target 2-D Velocity Estimation and Refocusing for Multichannel Maneuvering SAR with Fixed Acceleration
Authors: Tang Xinxin , Zhang Xiaoling , Shi Jun , Wei Shunjun , Tian Bokun
Published: August 2019

It is difficult for multichannel maneuvering synthetic aperture radar (SAR) to achieve ground moving target 2D velocity estimation and refocusing. In this paper, a novel method based on back projection (BP) and velocity SAR (VSAR) is proposed to cope with the issues. First, the static scene is reconstructed by BP to solve the imaging problem of multichannel maneuvering SAR. Then, the static clutter is suppressed, and the range velocity is estimated via VSAR processing. As for azimuth velocity estimation and refocusing, a velocity search method based on velocity-aided BP (VA-BP) and VSAR is proposed to accomplish them. First, each azimuth velocity in the search and the estimated range velocity are used to image the moving target in a small-sized subimage space by VA-BP, i.e., matching the range history and the Doppler phase of the moving target in the image processing. Then, multiple sets of multichannel SAR subimages corresponding to different azimuth velocities are generated, and the clutter of each set of multichannel SAR subimages is also suppressed by VSAR processing. After that, the azimuth velocity is estimated by searching the clutter-suppressed subimage of the first spatial receiving channel in each set of multichannel SAR subimages with the best refocusing quality measured by the minimum entropy. Simulation results show the proposed method can reach high accuracy in moving target 2D velocity estimation and refocusing with the absolute error of 2D velocity estimation smaller than 0.1 m/s.
Locating Ultrasonic Signals Employing MEMS-On-Fiber Sensors
Authors: Si Wenrong , Fu Chenzhao , Li Haoyong , Lv Jiaming , Xiong Chaoyu , Yuan Peng , Yu Yiting
Published: August 2019

Sound sensing finds wide applications in various fields, such as underwater detection, structural health monitoring, and medical diagnosis, to name just a few. Based on our previously developed MEMS-on-fiber sensors, showing the advantages of low cost, small volume, and high performance, a three-dimensional ultrasonic localization system employing four such sensors was established in this work. A time difference of arrival (TDOA) algorithm was utilized to analyze the acquired data and then calculate the accurate position of the ultrasonic signal source. Plenty of practical measurements were performed, and the derived localization deviation in the region of 2 m × 2 m × 1 m was about 2–5 mm. Outside this region, the deviation tended to increase due to the directional sensitivity existing in these sensors. As a result, for a more accurate localization requirement, more sensing probes are needed in order to depict a completely suitable application situation for MEMS technology.
MW-PPG Sensor: An on-Chip Spectrometer Approach
Authors: Chang Cheng-Chun , Wu Chien-Ta , Choi Byung Il , Fang Tong-Jing
Published: August 2019

Multi-wavelength photoplethysmography (MW-PPG) sensing technology has been known to be superior to signal-wavelength photoplethysmography (SW-PPG) sensing technology. However, limited by the availability of sensing detectors, many prior studies can only use conventional bulky and pricy spectrometers as the detectors, and hence cannot bring the MW-PPG technology to daily-life applications. In this study we developed a chip-scale MW-PPG sensor using innovative on-chip spectrometers, aimed at wearable applications. Also in this paper we present signal processing methods for robustly extracting the PPG signals, in which an increase of up to 50% in the signal-to-noise ratio (S/N) was observed. Example measurements of saturation of peripheral blood oxygen (SpO2) and blood pressure were conducted.
SLAM in Dynamic Environments: A Deep Learning Approach for Moving Object Tracking Using ML-RANSAC Algorithm
Authors: Bahraini Masoud S. , Rad Ahmad B. , Bozorg Mohammad
Published: August 2019

The important problem of Simultaneous Localization and Mapping (SLAM) in dynamic environments is less studied than the counterpart problem in static settings. In this paper, we present a solution for the feature-based SLAM problem in dynamic environments. We propose an algorithm that integrates SLAM with multi-target tracking (SLAMMTT) using a robust feature-tracking algorithm for dynamic environments. A novel implementation of RANdomSAmple Consensus (RANSAC) method referred to as multilevel-RANSAC (ML-RANSAC) within the Extended Kalman Filter (EKF) framework is applied for multi-target tracking (MTT). We also apply machine learning to detect features from the input data and to distinguish moving from stationary objects. The data stream from LIDAR and vision sensors are fused in real-time to detect objects and depth information. A practical experiment is designed to verify the performance of the algorithm in a dynamic environment. The unique feature of this algorithm is its ability to maintain tracking of features even when the observations are intermittent whereby many reported algorithms fail in such situations. Experimental validation indicates that the algorithm is able to perform consistent estimates in a fast and robust manner suggesting its feasibility for real-time applications.
The Impact of the Land Cover Dynamics on Surface Urban Heat Island Variations in Semi-Arid Cities: A Case Study in Ahmedabad City, India, Using Multi-Sensor/Source Data
Authors: Mohammad Pir , Goswami Ajanta , Bonafoni Stefania
Published: August 2019

This study examines the behavior of land surface temperature (LST) and surface urban heat island (SUHI) from MODIS data over Ahmedabad city, Gujarat state (India), from 2003 to 2018. Summer and winter LST patterns were analyzed, both daytime and nighttime. Ahmedabad, one of the fastest growing metropolitan cities in India, is characterized by a semi-arid climate. The investigation focuses on the SUHI variations due to warming or cooling trends of both urban and rural areas, providing quantitative interpretations by means of multi-sensor/source data. Land cover maps, normalized differential vegetation index, surface albedo, evapotranspiration, urban population, and groundwater level were analyzed across the years to assess their impact on SUHI variations. Moreover, a field campaign was carried out in summer 2018 to measure LST in several rural and urban sites. During summer daytime, the rural zone exhibits a higher average LST than the urban area, resulting in a mean negative SUHI, typical of arid cities, while a slight positive SUHI (mean intensity of 0.4 °C) during winter daytime is present. An evident positive SUHI is found only during summer (1.8 °C) and winter nighttime (3.2 °C). The negative SUHI intensity is due to the low vegetation presence in the rural area, dominated by croplands turning into bare land surfaces during the pre-monsoon summer season. Higher LST values in the rural area than in the urban area are also confirmed by the field campaign, with an average difference of about 5 °C. Therefore, the impact of the rural LST in biasing the SUHI is evident, and a careful biophysical interpretation is needed. For instance, within the urban area, the yearly intensity of the summer daytime SUHI is not correlated with the evapotranspiration, while the correspondent summer daytime LST exhibits a significant negative correlation (−0.73) with evapotranspiration. Furthermore, despite the city growth across the years, the urban area does not generally reveal a temporal increase of the magnitude of the heat island but an enlargement of its spatial footprint.
CPG-Based Gait Generation of the Curved-Leg Hexapod Robot with Smooth Gait Transition
Authors: Bai Long , Hu Hao , Chen Xiaohong , Sun Yuanxi , Ma Chaoyang , Zhong Yuanhong
Published: August 2019

This paper presents a novel CPG-based gait generation of the curved-leg hexapod robot that can enable smooth gait transitions between multi-mode gaits. First, the locomotion of the curved leg and instability during the gait transitions are analyzed. Then, a modified Hopf oscillator is applied in the CPG control, which can realize multiple gaits by adjusting a simple parameter. In addition, a smooth gait switching method is also proposed via smooth gait transition functions and gait planning. Tripod gait, quadruped gait, and wave gait are planned for the hexapod robot to achieve quick and stable gait transitions smoothly and continuously. MATLAB and ADAMS simulations and corresponding practical experiments are conducted. The results show that the proposed method can achieve smooth and continuous mutual gait transitions, which proves the effectiveness of the proposed CPG-based hexapod robot control.
An FPGA-Based Ultra-High-Speed Object Detection Algorithm with Multi-Frame Information Fusion
Authors: Long Xianlei , Hu Shenhua , Hu Yiming , Gu Qingyi , Ishii Idaku
Published: August 2019

An ultra-high-speed algorithm based on Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) for hardware implementation at 10,000 frames per second (FPS) under complex backgrounds is proposed for object detection. The algorithm is implemented on the field-programmable gate array (FPGA) in the high-speed-vision platform, in which 64 pixels are input per clock cycle. The high pixel parallelism of the vision platform limits its performance, as it is difficult to reduce the strides between detection windows below 16 pixels, thus introduce non-negligible deviation of object detection. In addition, limited by the transmission bandwidth, only one frame in every four frames can be transmitted to PC for post-processing, that is, 75% image information is wasted. To overcome the mentioned problem, a multi-frame information fusion model is proposed in this paper. Image data and synchronization signals are first regenerated according to image frame numbers. The maximum HOG feature value and corresponding coordinates of each frame are stored in the bottom of the image with that of adjacent frames’. The compensated ones will be obtained through information fusion with the confidence of continuous frames. Several experiments are conducted to demonstrate the performance of the proposed algorithm. As the evaluation result shows, the deviation is reduced with our proposed method compared with the existing one.
Drinking Water Risk Management Plan: A Case Study in the Ore Processing Industry
Authors: Susanto Arif , Purwanto Purwanto , Putro Edi K. , Yuliasari Fanny
Published: August 2019

All of the water supply system development needs to be safe and meet health requirements. Due to the expansion of the ore processing industry, water supply providers are required to identify the risks that may arise from dangerous and hazardous events. The purpose of this study was to build a structured approach to drinking water risk management plans (RMPs). We used risk assessments with reference to the environmental management system, risk management, and the food safety management system. The results of the risk assessment show two critical control points (CCPs) of high risk: Dam (catchment) and pipe reticulation. Some CCPs were categorized as posing very high risks of hazardous events due to pathogen contamination, including the clean water tank, the disinfectant injector, and pipe reticulation. The RMPs recorded and identified various preventive measures that could be taken to reduce and eliminate the risks. Daily operational requirements can be implemented to prepare for these hazards and risks that have the potential to negatively affect the quality of the drinking water supplied to the workers. Risk assessments were conducted in two stages: Preventive measures and operational monitoring. The involvement of all departments and authority agencies was crucial to ensure the success in the development and review of the operational aspects of these RMPs.