bluuebunny/crossref_metadata_2025 huggingface dataset:
An Effective, Economical and Ultra-Fast Method for Hydrophobic Modification of NCC Using Poly(Methylhydrogen)Siloxane
Authors: You Xueqing , Hu Qingjian , Hu Xiaoyong , Chen Hanxian , Yang Wenbin , Zhang Xinxiang
Published: June 2019

Poor compatibility between nanocellulose crystals (NCCs) and major polymers has limited the application of NCC as bio-reinforcements. In this work, an effective and ultra-fast method was investigated to significantly improve the hydrophobicity of NCC by using poly(methylhydrogen)siloxane (PMHS) as modifier. PMHS possessed amounts of reactive –Si–H groups and hydrophobic –CH3 groups. The former groups were reactive with the hydroxyl groups of NCC, while the latter groups afforded NCC very low surface energy. As the weight ratio of PMHS to NCC was only 0.0005%, the hydrophobicity of NCC was significantly improved by increasing the water contact angle of NCC from 0° to 134°. The effect of weight ratio of PMHS to NCC and the hydrogen content of –Si–H in PMHS on the hydrophobicity and thermal stability was investigated in detail by Fourier transform infrared spectroscopy (FTIR), (X-ray Diffraction) XRD and (thermogravimetric analysis) TGA. The results indicated that PMHS chains were covalently grafted onto NCC and PMHS modification improved the thermal stability of NCC.
Formability and Failure Mechanisms of Woven CF/PEEK Composite Sheet in Solid-State Thermoforming
Authors: Zheng Bing , Gao Xiping , Li Maoyuan , Deng Tianzhengxiong , Huang Zhigao , Zhou Huamin , Li Dequn
Published: June 2019

In this study, the formability of woven carbon-fiber (CF)-reinforced polyether-ether-ketone (PEEK) composite sheets in the solid-state thermoforming process were investigated, and the failure mechanisms were discussed. The formability of the woven CF/PEEK sheets were analyzed using flexural tests, Erichsen test, and microscopic observation. The results show that the formability of CF/PEEK sheets significantly increases as the temperature rises from 165 to 325 °C, and slightly decreases as the deformation speed rises from 2 to 120 mm/min. The deformation of the sheets is caused by plastic deformation, shear deformation and squeeze deformation, without plastic thinning and fiber slippage, which is due to the restriction of the solid matrix and locked fibers. Moreover, the wrinkles will cause fiber fracture at lower temperatures and delamination at higher temperatures. At higher temperatures, the wrinkles mainly occur at the position with [0°/90°] fibers due to the squeezing of the matrix and fibers.
Inhibition Effect of Graphene Nanoplatelets on Electrical Degradation in Silicone Rubber
Authors: Han Tao , Du Boxue , Su Jingang , Gao Yu , Xing Yunqi , Fang Shengchen , Li Chuanyang , Lei Zhipeng
Published: June 2019

Silicone rubber (SIR) is widely used as an insulation material in high voltage cable accessories. Electrical tree is a typical electrical degradation and is easily initiated because of the distorted electric field. In this study, graphene nanoplatelets at contents of 0.001–0.010 wt % (0.00044–0.00436 vol %) were added into SIR to improve the electrical tree inhibiting ability. Scanning electron microscopy, conductivity and surface potential decay tests were conducted to analyze the characteristics of graphene/SIR nanocomposites. The typical electrical treeing experiment was employed to observe the electrical tree inhibition of graphene in SIR. The results show that graphene nanoplatelets were well dispersed in SIR. The conductivity was higher after the addition of graphene nanoplatelets, and the trap distribution was affected by graphene nanoplatelets. The tree was changed from a bush-branch structure to a bush structure after the addition of graphene. Tree inception voltage improved and reached the highest mean value at 0.003 wt %. The tree length was inhibited at 0.001 to 0.007 wt % and the lowest tree length occurred at 0.005 wt %.
Adsorption, Antibacterial and Antioxidant Properties of Tannic Acid on Silk Fiber
Authors: Zhang Wen , Yang Zhi-Yi , Cheng Xian-Wei , Tang Ren-Cheng , Qiao Yi-Fan
Published: June 2019

Natural bioactive compounds have received increasing attention in the functional modification of textiles. In this work, tannic acid was used to impart antibacterial and antioxidant functions to silk using an adsorption technique, and the adsorption properties of tannic acid on silk were studied. The adsorption quantity of tannic acid on silk increased with decreasing pH in the range of 3–7. The rates of the uptake of tannic acid by silk were well correlated to the pseudo-second-order kinetic model, and the calculated activation energy of adsorption was 93.49 kJ/mol. The equilibrium adsorption isotherms followed the Langmuir model. The adsorption rate and isotherm studies demonstrated that the chemical adsorption of tannic acid on silk occurred through the ion-ion interaction between tannic acid and silk. Tannic acid displayed good building-up properties on silk. The silk fabric treated with 0.5% tannic acid (relative to fabric weight) exhibited excellent and durable antibacterial properties. Moreover, the silk fabrics treated with 2% and 5% tannic acid had good and durable antioxidant properties. The treatment by tannic acid had less impact on the whiteness of the silk fabric. In summary, tannic acid can be used as a functional agent for preparing healthy and hygienic silk materials.
Opposite Effects of SiO2 Nanoparticles on the Local α and Larger-Scale α’ Segmental Relaxation Dynamics of PMMA Nanocomposites
Authors: Wang Na , Wu Xuebang , Liu C.S.
Published: June 2019

The segmental relaxation dynamics of poly(methyl methacrylate)/silica (PMMA/SiO2) nanocomposites with different compositions ( ϕ SiO 2 ) near and above the glass transition temperature were investigated by mechanical spectroscopy. At ϕ SiO 2 ≤ 0.5%, the α peak temperature hardly changes with ϕ SiO 2 , but that of α’ relaxation composed of Rouse and sub-Rouse modes decreases by 15 °C due to the increase of free volume. At ϕ SiO 2 ≥ 0.7%, both α and α’ relaxations shift to high temperatures because of the steric hindrance introduced by nanoparticle agglomeration. On the other hand, with increasing ϕ SiO 2 , the peak height for α relaxation increases at ϕ SiO 2 ≤ 0.5% and then decreases at ϕ SiO 2 ≥ 0.7%, but that for α’ relaxation shows an opposite behavior. This is because at low ϕ SiO 2 , the short-chain segments related to α relaxation can easily bypass the particles, but the longer-chain segments related to α’ relaxation cannot. At high ϕ SiO 2 , the polymer chains were bound to the nanoparticles due to the physical adsorption effect, leading to the decrease of relaxation unit concentration involved in α relaxation. However, the dissociation of those bonds with heating and the concentration heterogeneity of polymer chains result in the increase of peak height for α’ relaxation.
Development of Hydrophilic Drug Encapsulation and Controlled Release Using a Modified Nanoprecipitation Method
Authors: Xu Jiang , Chen Yuyan , Jiang Xizhi , Gui Zhongzheng , Zhang Lei
Published: June 2019

The improvement of the loading content of hydrophilic drugs by polymer nanoparticles (NPs) recently has received increased attention from the field of controlled release. We developed a novel, simply modified, drop-wise nanoprecipitation method which separated hydrophilic drugs and polymers into aqueous phase (continuous phase) and organic phase (dispersed phase), both individually and involving a mixing process. Using this method, we produced ciprofloxacin-loaded NPs by Poly (d,l-lactic acid)-Dextran (PLA-DEX) and Poly lactic acid-co-glycolic acid-Polyethylene glycol (PLGA-PEG) successfully, with a considerable drug-loading ability up to 27.2 wt% and an in vitro sustained release for up to six days. Drug content with NPs can be precisely tuned by changing the initial drug feed concentration of ciprofloxacin. These studies suggest that this modified nanoprecipitation method is a rapid, facile, and reproducible technique for making nano-scale drug delivery carriers with high drug-loading abilities
Simulation Optimization for Complex Multi-Domain Physical Systems Based on Partial Resolving
Authors: Hou Kexi , Li Yaohui
Published: June 2019

The iterative process of simulation optimization is a time-consuming task, as it involves executing the main simulation program in order to evaluate the optimal constraints and objective functions repeatedly according to the values of tuner parameters. Parameter optimization for a model of a multi-domain physical system based on Modelica is a typical simulation optimization problem. Traditionally, each simulation during each iterative step needs resolve all the variables in all the mass differential-algebraic equations (DAE) generated from the simulation model through constructing and traversing the solving dependency graph of the model. In order to improve the efficiency of the simulation optimization process, a new method named partial simulation resolving algorithm based on the set of input parameters and output variables for complex simulation model was proposed. By using this algorithm, a minimum solving graph (MSG) of the simulation model was built according to the set of parameters, constraints, and objective functions of the optimization model. The simulation during the optimization iterative process needs only to resolve the variables on the MSG, and therefore this method could decrease the simulation time greatly during every iterative step of the optimization process. As an example, the parameter optimization on economy of fuel for a heavy truck was realized to demonstrate the efficiency of this solving strategy. This method has been implemented in MWorks—a Modelica-based simulation platform.
Double-Branch Multi-Attention Mechanism Network for Hyperspectral Image Classification
Authors: Ma Wenping , Yang Qifan , Wu Yue , Zhao Wei , Zhang Xiangrong
Published: June 2019

Recently, Hyperspectral Image (HSI) classification has gradually been getting attention from more and more researchers. HSI has abundant spectral and spatial information; thus, how to fuse these two types of information is still a problem worth studying. In this paper, to extract spectral and spatial feature, we propose a Double-Branch Multi-Attention mechanism network (DBMA) for HSI classification. This network has two branches to extract spectral and spatial feature respectively which can reduce the interference between the two types of feature. Furthermore, with respect to the different characteristics of these two branches, two types of attention mechanism are applied in the two branches respectively, which ensures to extract more discriminative spectral and spatial feature. The extracted features are then fused for classification. A lot of experiment results on three hyperspectral datasets shows that the proposed method performs better than the state-of-the-art method.
Ensemble-Based Cascaded Constrained Energy Minimization for Hyperspectral Target Detection
Authors: Zhao Rui , Shi Zhenwei , Zou Zhengxia , Zhang Zhou
Published: June 2019

Ensemble learning is an important group of machine learning techniques that aim to enhance the nonlinearity and generalization ability of a learning system by aggregating multiple learners. We found that ensemble techniques show great potential for improving the performance of traditional hyperspectral target detection algorithms, while at present, there are few previous works have been done on this topic. To this end, we propose an Ensemble based Constrained Energy Minimization (E-CEM) detector for hyperspectral image target detection. Classical hyperspectral image target detection algorithms like Constrained Energy Minimization (CEM), matched filter (MF) and adaptive coherence/cosine estimator (ACE) are usually designed based on constrained least square regression methods or hypothesis testing methods with Gaussian distribution assumption. However, remote sensing hyperspectral data captured in a real-world environment usually shows strong nonlinearity and non-Gaussianity, which will lead to performance degradation of these classical detection algorithms. Although some hierarchical detection models are able to learn strong nonlinear discrimination of spectral data, due to the spectrum changes, these models usually suffer from the instability in detection tasks. The proposed E-CEM is designed based on the classical CEM detection algorithm. To improve both of the detection nonlinearity and generalization ability, the strategies of “cascaded detection”, “random averaging” and “multi-scale scanning” are specifically designed. Experiments on one synthetic hyperspectral image and two real hyperspectral images demonstrate the effectiveness of our method. E-CEM outperforms the traditional CEM detector and other state-of-the-art detection algorithms. Our code will be made publicly available.
Design and Implementation of a Multi-Band Active Radar Calibrator for SAR
Authors: Li Liang , Liu Gukun , Hong Jun , Ming Feng , Wang Yu
Published: June 2019

Over the past decade, IECAS (Institute of Electronics, Chinese Academy of Sciences) has developed a set of L-, S-, C-, and X-band active radar calibrators that are deployed during the calibration campaigns for HJ1C synthetic aperture radar (SAR), Gaofen-3 SAR, and so on. In the near future, P-band and Ka-band spaceborne SARs will be launched. We found that it is not convenient to develop special active radar calibrators (ARCs) for a specific SAR or a specific frequency band SAR, and the acquired experience could help in the design and development of a multi-band ARC. This paper describes the design and implementation of a multi-band active radar calibrator which can operate in the L-, C-, X-, and Ka-bands. Moreover, laboratory measurements are performed to characterize the performance of the multi-band ARC, paying particular attention to the gain stability, the system transfer function, the gain flatness, and the linearity of the ARC receiver. Three such ARCs are developed, and to our knowledge, the multi-band ARC is the first of its kind in China or even in the world, and it can be used to implement the calibration campaigns of the Chinese Gaofen-3 SAR, Shenzhen-1 SAR, Luojia-2 SAR, and so on.
An Unsupervised SAR Change Detection Method Based on Stochastic Subspace Ensemble Learning
Authors: Cui Bin , Zhang Yonghong , Yan Li , Wei Jujie , Wu Hong’an
Published: June 2019

As synthetic aperture radar (SAR) is playing an increasingly important role in Earth observations, many new methods and technologies have been proposed for change detection using multi-temporal SAR images. Especially with the development of deep learning, numerous methods have been proposed in recent years. However, the requirement to have a certain number of high-quality samples has become one of the main reasons for the limited development of these methods. Thus, in this paper, we propose an unsupervised SAR change detection method that is based on stochastic subspace ensemble learning. The proposed method consists of two stages: The first stage involves the automatic determination of high-confidence samples, which includes a fusion strategy and a refinement process; and the second stage entails using the stochastic subspace ensemble learning module, which contains three steps: obtaining the subsample sets, establishing and training a two-channel network, and applying the prediction results and an ensemble strategy. The subsample sets are used to solve the problem of imbalanced samples. The two-channel networks are used to extract high-dimensional features and learn the relationship between the neighborhood of the pixels in the original images and the labels. Finally, by using an ensemble strategy, the results predicted by all patches reclassified in each network are integrated as the detection result. The experimental results of different SAR datasets prove the effectiveness and the feasibility of the proposed method.
Landsat-Based Estimation of Seasonal Water Cover and Change in Arid and Semi-Arid Central Asia (2000–2015)
Authors: Che Xianghong , Feng Min , Sexton Joe , Channan Saurabh , Sun Qing , Ying Qing , Liu Jiping , Wang Yong
Published: June 2019

Surface water is of great importance to ecosystems and economies. Crucial to understanding hydrological variability and its relationships to human activities at large scales, open-access satellite datasets and big-data computational methods are now enabling the global mapping of the distribution and changes of inland water over time. A machine-learning algorithm, previously used only to map water at single points in time, was applied over 16 years of the USGS Landsat archive to detect and map surface water over central Asia from 2000 to 2015 at a 30-m, monthly resolution. The resulting dataset had an overall classification accuracy of 99.59% (±0.32% standard error), 98.24% (±1.02%) user’s accuracy, and 87.12% (±3.21%) producer’s accuracy for water class. This study describes the temporal extension of the algorithm and the application of the dataset to present patterns of regional surface water cover and change. The findings indicate that smaller water bodies are dramatically changing in two specific ecological zones: the Kazakh Steppe and the Tian Shan Montane Steppe and Meadows. Both the maximum and minimum extent of water bodies have decreased over the 16-year period, but the rate of decrease of the maxima was double that of the minima. Coverage decreased in each month from April to October, and a significant decrease in water area was found in April and May. These results indicate that the dataset can provide insights into the behavior of surface water across central Asia through time, and that the method can be further developed for regional and global applications.
A Fast Global Interpolation Method for Digital Terrain Model Generation from Large LiDAR-Derived Data
Authors: Chen Chuanfa , Li Yanyan
Published: June 2019

Airborne light detection and ranging (LiDAR) datasets with a large volume pose a great challenge to the traditional interpolation methods for the production of digital terrain models (DTMs). Thus, a fast, global interpolation method based on thin plate spline (TPS) is proposed in this paper. In the methodology, a weighted version of finite difference TPS is first developed to deal with the problem of missing data in the grid-based surface construction. Then, the interpolation matrix of the weighted TPS is deduced and found to be largely sparse. Furthermore, the values and positions of each nonzero element in the matrix are analytically determined. Finally, to make full use of the sparseness of the interpolation matrix, the linear system is solved with an iterative manner. These make the new method not only fast, but also require less random-access memory. Tests on six simulated datasets indicate that compared to recently developed discrete cosine transformation (DCT)-based TPS, the proposed method has a higher speed and accuracy, lower memory requirement, and less sensitivity to the smoothing parameter. Real-world examples on 10 public and 1 private dataset demonstrate that compared to the DCT-based TPS and the locally weighted interpolation methods, such as linear, natural neighbor (NN), inverse distance weighting (IDW), and ordinary kriging (OK), the proposed method produces visually good surfaces, which overcome the problems of peak-cutting, coarseness, and discontinuity of the aforementioned interpolators. More importantly, the proposed method has a similar performance to the simple interpolation methods (e.g., IDW and NN) with respect to computing time and memory cost, and significantly outperforms OK. Overall, the proposed method with low memory requirement and computing cost offers great potential for the derivation of DTMs from large-scale LiDAR datasets.
A Convolutional Neural Network with Fletcher–Reeves Algorithm for Hyperspectral Image Classification
Authors: Chen Chen , Ma Yi , Ren Guangbo
Published: June 2019

Deep learning models, especially the convolutional neural networks (CNNs), are very active in hyperspectral remote sensing image classification. In order to better apply the CNN model to hyperspectral classification, we propose a CNN model based on Fletcher–Reeves algorithm (F–R CNN), which uses the Fletcher–Reeves (F–R) algorithm for gradient updating to optimize the convergence performance of the model in classification. In view of the fact that there are fewer optional training samples in practical applications, we further propose a method of increasing the number of samples by adding a certain degree of perturbed samples, which can also test the anti-interference ability of classification methods. Furthermore, we analyze the anti-interference and convergence performance of the proposed model in terms of different training sample data sets, different batch training sample numbers and iteration time. In this paper, we describe the experimental process in detail and comprehensively evaluate the proposed model based on the classification of CHRIS hyperspectral imagery covering coastal wetlands, and further evaluate it on a commonly used hyperspectral image benchmark dataset. The experimental results show that the accuracy of the two models after increasing training samples and adjusting the number of batch training samples is improved. When the number of batch training samples is continuously increased to 350, the classification accuracy of the proposed method can still be maintained above 80.7%, which is 2.9% higher than the traditional one. And its time consumption is less than that of the traditional one while ensuring classification accuracy. It can be concluded that the proposed method has anti-interference ability and outperforms the traditional CNN in terms of batch computing adaptability and convergence speed.
SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact
Authors: Rodríguez-Fernández Nemesio , de Rosnay Patricia , Albergel Clement , Richaume Philippe , Aires Filipe , Prigent Catherine , Kerr Yann
Published: June 2019

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised-Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if local biases can remain. Experiments performing joint data assimilation (DA) of NNSM, 2 m air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April–September, while NNSM alone has a significant positive effect in July–September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 h lead time.
Utilizing Satellite Surface Soil Moisture Data in Calibrating a Distributed Hydrological Model Applied in Humid Regions Through a Multi-Objective Bayesian Hierarchical Framework
Authors: Yang Han , Xiong Lihua , Ma Qiumei , Xia Jun , Chen Jie , Xu Chong-Yu
Published: June 2019

The traditional calibration objective of hydrological models is to optimize streamflow simulations. To identify the value of satellite soil moisture data in calibrating hydrological models, a new objective of optimizing soil moisture simulations has been added to bring in satellite data. However, it leads to problems: (i) how to consider the trade-off between various objectives; (ii) how to consider the uncertainty these satellite data bring in. Among existing methods, the multi-objective Bayesian calibration framework has the potential to solve both problems but is more suitable for lumped models since it can only deal with constant variances (in time and space) of model residuals. In this study, to investigate the utilization of a soil moisture product from the Soil Moisture Active Passive (SMAP) satellite in calibrating a distributed hydrological model, the DEM (Digital Elevation Model) -based Distributed Rainfall-Runoff Model (DDRM), a multi-objective Bayesian hierarchical framework is employed in two humid catchments of southwestern China. This hierarchical framework is superior to the non-hierarchical framework when applied to distributed models since it considers the spatial and temporal residual heteroscedasticity of distributed model simulations. Taking the streamflow-based single objective calibration as the benchmark, results of adding satellite soil moisture data in calibration show that (i) there is less uncertainty in streamflow simulations and better performance of soil moisture simulations either in time and space; (ii) streamflow simulations are largely affected, while soil moisture simulations are slightly affected by weights of objectives. Overall, the introduction of satellite soil moisture data in addition to observed streamflow in calibration and putting more weights on the streamflow calibration objective lead to better hydrological performance. The multi-objective Bayesian hierarchical framework implemented here successfully provides insights into the value of satellite soil moisture data in distributed model calibration.
Vulnerability Assessment of Sensor Systems
Authors: Bialas Andrzej
Published: June 2019

There are more and more applications of sensors in today’s world. Moreover, sensor systems are getting more complex and they are used for many high-risk security-critical purposes. Security assurance is a key issue for sensors and for other information technology (IT) products. Still, sensor security facilities and methodologies are relatively poor compared to other IT products. That is why a methodical approach to the sensor IT security is needed, i.e., risk management, implementation of countermeasures, vulnerability removal, and security evaluation and certification. The author proposes to apply the main security assurance methodology specified in ISO/IEC 15408 Common Criteria to solve specific security problems of sensors. A new Common Criteria compliant method is developed which specifies the vulnerability assessment process and related data in a structured way. The input/output data of the introduced elementary evaluation processes are modeled as ontology classes to work out knowledge bases. The validation shows that sensor-specific knowledge can be acquired during the vulnerability assessment process and then placed in knowledge bases and used. The method can be applied in different IT products, especially those with few certifications, such as sensors. The presented methodology will be implemented in a software tool in the future.
Indirect Force Control of a Cable-Driven Parallel Robot: Tension Estimation using Artificial Neural Network trained by Force Sensor Measurements
Authors: Piao Jinlong , Kim Eui-Sun , Choi Hongseok , Moon Chang-Bae , Choi Eunpyo , Park Jong-Oh , Kim Chang-Sei
Published: June 2019

In a cable-driven parallel robot (CDPR), force sensors are utilized at each winch motor to measure the cable tension in order to obtain the force distribution at the robot end-effector. However, because of the effects of friction in the pulleys and the unmodeled cable properties of the robot, the measured cable tensions are often inaccurate, which causes force-control difficulties. To overcome this issue, this paper presents an artificial neural network (ANN)-based indirect end-effector force-estimation method, and its application to CDPR force control. The pulley friction and other unmodeled effects are considered as black-box uncertainties, and the tension at the end-effector is estimated by compensating for these uncertainties using an ANN that is developed using the training datasets from CDPR experiments. The estimated cable tensions at the end-effector are used to design a P-controller to track the desired force. The performance of the proposed ANN model is verified through comparisons with the forces measured directly at the end-effector. Furthermore, cable force control is implemented based on the compensated tensions to evaluate the performance of the CDPR in wrench space. The experimental results show that the proposed friction-compensation method is suitable for application in CDPRs to control the cable force.
A Novel Semi-Soft Decision Scheme for Cooperative Spectrum Sensing in Cognitive Radio Networks
Authors: Mi Yin , Lu Guangyue , Li Yuxin , Bao Zhiqiang
Published: June 2019

Spectrum sensing (SS) is an essential part of cognitive radio (CR) technology, and cooperative spectrum sensing (CSS) could efficiently improve the detection performance in environments with fading and shadowing effects, solving hidden terminal problems. Hard and Soft decision detection are usually employed at the fusion center (FC) to detect the presence or absence of the primary user (PU). However, soft decision detection achieves better sensing performance than hard decision detection at the expense of the local transmission band. In this paper, we propose a tradeoff scheme between the sensing performance and band cost. The sensing strategy is designed based on three modules. Firstly, a local detection module is used to detect the PU signal by energy detection (ED) and send decision results in terms of 1-bit or 2-bit information. Secondly, and most importantly, the FC estimates the received decision data through a data reconstruction module based on the statistical distribution such that the extra thresholds are not needed. Finally, a global decision module is in charge of fusing the estimated data and making a final decision. The results from a simulation show that the detection performance of the proposed scheme outperforms that of other algorithms. Moreover, savings on the transmission band cost can be made compared with soft decision detection.
Vision-Based Obstacle Avoidance Strategies for MAVs Using Optical Flows in 3-D Textured Environments
Authors: Cho Gangik , Kim Jongyun , Oh Hyondong
Published: June 2019

Due to payload restrictions for micro aerial vehicles (MAVs), vision-based approaches have been widely studied with their light weight characteristics and cost effectiveness. In particular, optical flow-based obstacle avoidance has proven to be one of the most efficient methods in terms of obstacle avoidance capabilities and computational load; however, existing approaches do not consider 3-D complex environments. In addition, most approaches are unable to deal with situations where there are wall-like frontal obstacles. Although some algorithms consider wall-like frontal obstacles, they cause a jitter or unnecessary motion. To address these limitations, this paper proposes a vision-based obstacle avoidance algorithm for MAVs using the optical flow in 3-D textured environments. The image obtained from a monocular camera is first split into two horizontal and vertical half planes. The desired heading direction and climb rate are then determined by comparing the sum of optical flows between half planes horizontally and vertically, respectively, for obstacle avoidance in 3-D environments. Besides, the proposed approach is capable of avoiding wall-like frontal obstacles by considering the divergence of the optical flow at the focus of expansion and navigating to the goal position using a sigmoid weighting function. The performance of the proposed algorithm was validated through numerical simulations and indoor flight experiments in various situations.
A Deep Learning Approach for MIMO-NOMA Downlink Signal Detection
Authors: Lin Chuan , Chang Qing , Li Xianxu
Published: June 2019

As a key candidate technique for fifth-generation (5G) mobile communication systems, non-orthogonal multiple access (NOMA) has attracted considerable attention in the field of wireless communication. Successive interference cancellation (SIC) is the main NOMA detection method applied at receivers for both uplink and downlink NOMA transmissions. However, SIC is limited by the receiver complex and error propagation problems. Toward this end, we explore a high-performance, high-efficiency tool—deep learning (DL). In this paper, we propose a learning method that automatically analyzes the channel state information (CSI) of the communication system and detects the original transmit sequences. In contrast to existing SIC schemes, which must search for the optimal order of the channel gain and remove the signal with higher power allocation factor while detecting a signal with a lower power allocation factor, the proposed deep learning method can combine the channel estimation process with recovery of the desired signal suffering from channel distortion and multiuser signal superposition. Extensive performance simulations were conducted for the proposed MIMO-NOMA-DL system, and the results were compared with those of the conventional SIC method. According to our simulation results, the deep learning method can successfully address channel impairment and achieve good detection performance. In contrast to implementing well-designed detection algorithms, MIMO-NOMA-DL searches for the optimal solution via a neural network (NN). Consequently, deep learning is a powerful and effective tool for NOMA signal detection.
Improving the Classification Effectiveness of Intrusion Detection by Using Improved Conditional Variational AutoEncoder and Deep Neural Network
Authors: Yang Yanqing , Zheng Kangfeng , Wu Chunhua , Yang Yixian
Published: June 2019

Intrusion detection systems play an important role in preventing security threats and protecting networks from attacks. However, with the emergence of unknown attacks and imbalanced samples, traditional machine learning methods suffer from lower detection rates and higher false positive rates. We propose a novel intrusion detection model that combines an improved conditional variational AutoEncoder (ICVAE) with a deep neural network (DNN), namely ICVAE-DNN. ICVAE is used to learn and explore potential sparse representations between network data features and classes. The trained ICVAE decoder generates new attack samples according to the specified intrusion categories to balance the training data and increase the diversity of training samples, thereby improving the detection rate of the imbalanced attacks. The trained ICVAE encoder is not only used to automatically reduce data dimension, but also to initialize the weight of DNN hidden layers, so that DNN can easily achieve global optimization through back propagation and fine tuning. The NSL-KDD and UNSW-NB15 datasets are used to evaluate the performance of the ICVAE-DNN. The ICVAE-DNN is superior to the three well-known oversampling methods in data augmentation. Moreover, the ICVAE-DNN outperforms six well-known models in detection performance, and is more effective in detecting minority attacks and unknown attacks. In addition, the ICVAE-DNN also shows better overall accuracy, detection rate and false positive rate than the nine state-of-the-art intrusion detection methods.
A Target Identification Method for the Millimeter Wave Seeker via Correlation Matching and Beam Pointing
Authors: Chen Shichao , Liu Ming , Lu Fugang , Xing Mengdao
Published: June 2019

Target identification is a challenging task under land backgrounds for the millimeter wave (MMW) seeker, especially under complex backgrounds. Focusing on the problem, an effective method combining correlation matching and beam pointing is proposed in this paper. In the beginning, seeker scanning for target detection is conducted in two rounds, and target information of the detected targets is stored for correlation matching. Point or body feature judgment is implemented by using high resolution range profile (HRRP). Then, the error distribution zone is constructed with the beam pointing as the origin. In the end, we identify the target by searching the one which lies in the closest error distribution from the beam pointing center. The effectiveness of the proposed method is verified by using mooring test-fly and real flight data.
Design of a Local Information Incentive Mechanism for Mobile Crowdsensing
Authors: Nava Auza Jose Mauricio , Boisson de Marca Jose Roberto , Lima Siqueira Glaucio
Published: June 2019

The world of telecommunications has seen the growing popularity of mobile devices and its massive technological advancements and innovations (e.g., smartphones, smart watches, among others). One critical particularity is that these devices have a series of built-in sensors and continuous network connectivity. Therefore, they present a great opportunity to perform large-scale sensing of different activities in the physical world. This new sensor application, better known as Mobile crowd-sensing (MCS), has lately become a focus of research. One of the challenges when developing a MCS-based network is to attract and convince users to participate. In this paper, we present a framework for MCS that includes a model to represent the behavior of the users and a novel incentive mechanism. The model aims to characterize the behavior of users considering the availability of their resources and the non-homogeneity of their responses. The incentive mechanism proposed assigns different values of incentives and in it the users only consider their local information to decide their participation in the framework. The performance of the proposed framework is evaluated through simulations. The results allow us to prove the uncertainty of participation of the users and that they react in different ways to the incentives offered. They also prove that the incentive mechanism estimates satisfactorily the type of users and the incentive that will be offered to each user. In addition, we show the advantages of an incentive mechanism that considers different values of payments.
Point-of-Care Surface Plasmon Resonance Biosensor for Stroke Biomarkers NT-proBNP and S100β Using a Functionalized Gold Chip with Specific Antibody
Authors: Harpaz Dorin , Koh Brescia , Marks Robert S. , Seet Raymond C.S. , Abdulhalim Ibrahim , Tok Alfred I.Y.
Published: June 2019

Surface-plasmon-resonance (SPR) is a quantum-electromagnetic phenomenon arising from the interaction of light with free electrons at a metal-dielectric interface. At a specific angle/wavelength of light, the photon’s energy is transferred to excite the oscillation of the free electrons on the surface. A change in the refractive-index (RI) may occur, which is influenced by the analyte concentration in the medium in close contact with the metal surface. SPR has been widely used for the detection of gaseous, liquid, or solid samples. In this study, a functionalized specific SPR chip was designed and used in a novel point-of-care SPR module (PhotonicSys SPR H5) for the detection of the stroke biomarkers NT-proBNP and S100β. These biomarkers have proven to be good for stroke diagnosis, with sensitivity and specificity of >85%. Specific detection was done by binding a biomolecular-recognizing antibody onto the Au SPR-chip. Detection was tested in water and plasma samples. NT-proBNP and S100β were detected in a range of concentrations for stroke, from 0.1 ng/mL to 10 ng/mL. The RI of the blank plasma samples was 1.362412, and the lowest concentration tested for both biomarkers showed a prominent shift in the RI signal (0.25 ng/mL NT-proBNP (1.364215) and S100β (1.364024)). The sensor demonstrated a clinically relevant limit-of-detection of less than ng/mL.
Joint Resource Optimization in Simultaneous Wireless Information and Power Transfer (SWIPT) Enabled Multi-Relay Internet of Things (IoT) System
Authors: Lu Weidang , Liu Guangzhe , Si Peiyuan , Zhang Guanghua , Li Bo , Peng Hong
Published: June 2019

The internet of things (IoT) is becoming more indispensable in modern society as the further development and maturity of information technology progresses. However the exponential growth of IoT devices leads to severe energy consumption. As a technology with broad application prospects, simultaneous wireless information and power transfer (SWIPT) enables IoT devices to harvest energy from receiving radio frequency (RF) signals while ensuring information transmission. In this paper, we investigate the transmission rate optimization problem for a dual-hop multi-relay IoT system, where a decode-and-forward (DF) relay supports the SWIPT technique. We jointly optimize the resource including power and subcarrier allocation, to maximize the system transmission rate. The time-sharing strategy and Lagrange dual method are used to solve this optimization problem. Simulation results reveal that the proposed algorithm has a larger transmission rate than other benchmark algorithms when ensuring each relay has no additional energy supply. Specifically, the proposed algorithm improves the information transmission rate by 2.8%, 3.4% and 43% compared with other algorithms in the case of five relays when the source’s power is equal to 0.5 W, respectively.
Recycling of 1,2-Dimethyl-3-propylimidazolium bis(trifluoromethylsulfonyl)imide Ionic Liquid by Stacked Cation and Anion Exchange Adsorption-Desorption
Authors: Corley Cynthia A. , Iacono Scott T.
Published: June 2019

There are many advantages to using ionic liquids as solvents or catalysts in chemical processes. Their non-volatile characteristic and high cost, however, can pose economic, environmental, and long-term health concerns. As such, the recovery and recycling of ionic liquids have become essential to mitigate their environmental impact and to reduce costs. Numerous recovery and recycling methods have been reported, including distillation, extraction, membrane separation (a.k.a. filtration), adsorption, crystallization, gravity, and electrochemical separation. Whereas most of these methods recover both cations and anions of the ionic liquid as ion pairs, recycling methods such as single-phase ion exchange or mixed-ion exchange/non-ionic adsorption methods recover only one of the ionic liquid ions, typically the cation. These methods are frequently used for the recycling of ionic liquids having simple anions such as chloride or acetate, but are seldom employed for ionic liquids consisting of larger and more complex anions due to the added time and reagent costs necessary for the regeneration of the original ionic liquid. Herein, a combined cation and anion exchange adsorption-desorption method is presented that can effectively separate 1,2-dimethyl-3-propylimidazolium bis(trifluoromethylsulfonimide) [DMPIm][NTf2] ionic liquid from neutral impurities. More importantly, the method is capable of recovery and recycling of the original ionic liquid. Concomitant desorption of both ionic liquid ions was achieved using 0.1 M NaCl: methanol (90:10 v/v) eluent followed by isolation using liquid–liquid extraction to afford high purity products and yields of approximately 60%.
Evaluating Sustainability in Traditional Silvopastoral Systems (caívas): Looking Beyond the Impact of Animals on Biodiversity
Authors: Hanisch Ana Lúcia , Negrelle Raquel R. B. , Bonatto Rafael Araújo , Nimmo Evelyn Roberta , Lacerda André Eduardo Biscaia
Published: June 2019

Caívas are traditional silvopastoral systems that occur in the Araucaria Forest biome, Southern Brazil, in which animal production and erva-mate extraction are integrated. Participatory research was conducted in caívas in the Northern Plateau, Santa Catarina State, to identify strategies to intensify pasture use and increase animal productivity. To better understand the outcomes of these strategies, a sustainability assessment was conducted in properties that participated in the research (improved caívas; IC) and those that did not (traditional caívas; TC). The Sustainability Assessment of Food and Agriculture Systems (SAFA) tool 2.0.0 for smallholders was chosen as it evaluates the productive unit as a whole using environmental, social, economic, and governance indicators and is tailored for small-scale production. All evaluated indicators showed higher scores for IC properties in relation to TC. In general, the SAFA analysis showed that when evaluated as productive systems, TCs are a strategic option for rural development, as 65% of their indicators were evaluated as good. With the support of rural outreach and research and the adoption of appropriate technologies, this percentage increased to 86% in ICs. These results confirm that with adequate support caívas can significantly contribute to the development of more sustainable livestock farming in Southern Brazil.
Identifying Human-Induced Spatial Differences of Soil Erosion Change in a Hilly Red Soil Region of Southern China
Authors: Huang Dong , Yang Xiaohuan , Cai Hongyan , Xiao Zuolin , Han Dongrui
Published: June 2019

Soil erosion (SE) processes are closely related to natural conditions and human activities, posing a threat to environment and society. Identifying the human impact on regional SE changes is increasingly essential for pertinent SE management. Jiangxi province is studied here as a representative area of hilly-red-soil regions within southern China. The main objectives of this study were to investigate the changing trend of SE within Jiangxi and identify human impacts on regional SE change from the perspective of spatial differences, through a new approach based on a gravity-center model. Our results showed that SE status presented an overall amelioration from 1990 to 2015, while the average soil erosion modulus (SEM) declined from 864 to 281 Mg/(km2·a). Compared to the situation under human and natural impacts, human-induced spatial differences of SE change demonstrated that the western and northwest regions showed stronger negative effects; the southern region shifted towards negative effects; the northeast region presented a much weaker negative effect. Our results indicated that 4 cities with strong negative effects need more attention in further SE management suited to their local conditions and development, and also suggested that the approach based on a gravity-center has potential for identifying the human impact on regional SE change from the perspective of spatial patterns.
Activities of Environmental Convention-Secretariats: Laws, Functions and Discretions
Authors: Mauerhofer Volker
Published: June 2019

Multilateral Environmental Agreements—MEAs—are indispensable legal frameworks for environmental sustainability and also define the operating rules of their implementation bodies (“Secretariats”). The contribution assesses in how far the norms defining Secretariats’ functions differ and also reflect on actual functions for three MEAs, namely (1) the Convention on International Trade in Endangered Species of Wild Fauna and Flora-CITES (1973), (2) the Convention on Biological Diversity—CBD (1992), and (3) the Convention on Migratory Species—CMS (1979). It does so by comparative legal interpretation of the main norms of these MEAs laying down the functions of its respective Secretariats as well as an in-depth review of academic literature about these functions. The results for these three conventions divide into nine functional areas and show an unexpectedly wide range of different functions laid down in the conventions as well as extensive variety in the discretion for many of these functional areas. Some potential explanations of these formal differences are provided. The paper further finds that actually executed functions may not be fully covered by the underlying legal norms but rather by “flexible” highest governing bodies of MEAs and concludes that occasionally an unusual legislative style was chosen, and shows potential solutions and future research directions.