bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison of Adhesive Properties of Polyurethane Adhesive System and Wood-plastic Composites with Different Polymers after Mechanical, Chemical and Physical Surface Treatment
Authors: Nečasová Barbora , Liška Pavel , Kelar Jakub , Šlanhof Jiří
Published: March 2019

The cost of most primary materials is increasing, therefore, finding innovative solutions for the re-use of residual waste has become a topic discussed more intensely in recent years. WPCs certainly meet some of these demands. The presented study is focused on an experimental analysis of the effect of surface treatment on the adhesive properties of selected WPCs. Bonding of polymer-based materials is a rather complicated phenomenon and modification of the bonded area in order to improve the adhesive properties is required. Two traditional types of surface treatments and one entirely new approach have been used: mechanical with sandpaper, chemical with 10 wt % NaOH solution and physical modification of the surface by means of a MHSDBD plasma source. For comparison purposes, two high-density polyethylene based products and one polyvinyl-chloride based product with different component ratios were tested. A bonded joint was made using a moisture-curing permanently elastic one-component polyurethane pre-polymer adhesive. Standardized tensile and shear test methods were performed after surface treatment. All tested surface treatments resulted in an improvement of adhesive properties and an increase in bond strength, however, the MHSDBD plasma treatment was proven to be a more suitable surface modification for all selected WPCs.
Synthesis of Silver Nanoparticles Loaded onto Polymer-Inorganic Composite Materials and Their Regulated Catalytic Activity
Authors: Yan Sen , Jiang Chunge , Guo Jianwu , Fan Yinglan , Zhang Ying
Published: March 2019

We present a novel approach for the preparation of polymer-TiO2 composite microgels. These microgels were prepared by the in situ hydrolysis and condensation of titanium tetrabutoxide (TBOT) in a mixed ethanol/acetonitrile solvent system, using poly(styrene-co-N-isopropylacrylamide)/poly(N-isopropylacrylamide-co-methacrylic acid) (P(St-NIPAM/P(NIPAM-co-MAA)) as the core component. Silver nanoparticles (AgNPs) were controllably loaded onto the polymer-TiO2 composite microgels through the reduction of an ammoniacal silver solution in ethanol catalyzed by NaOH. The results showed that the P(St-NIPAM)/P(NIPAM-co-MAA)-TiO2 (polymer-TiO2) organic-inorganic composite microgels were less thermally sensitive than the polymer gels themselves, owing to rigid O–Ti–O chains introduced into the three-dimensional framework of the polymer microgels. The sizes of the AgNPs and their loading amount were controlled by adjusting the initial concentration of [Ag(NH3)2]+. The surface plasmon resonance (SPR) band of the P(St-NIPAM)/P(NIPAM-co-MAA)-TiO2/Ag (polymer-TiO2/Ag) composite microgels can be tuned by changing the temperature of the environment. The catalytic activities of the polymer-TiO2/Ag composite microgels were investigated in the NaBH4 reduction of 4-nitrophenol. It was demonstrated that the organic-inorganic network chains of the polymer microgels not only favor the mass transfer of the reactant but can also modulate the catalytic activities of the AgNPs by tuning the temperature.
Effects of pH on Nanofibrillation of TEMPO-Oxidized Paper Mulberry Bast Fibers
Authors: Park Jung Yoon , Park Chan-Woo , Han Song-Yi , Kwon Gu-Joong , Kim Nam-Hun , Lee Seung-Hwan
Published: March 2019

TEMPO oxidation was conducted as a pretreatment to achieve efficient nanofibrillation of long paper mulberry bast fibers (PMBFs). The pH dependency of nanofibrillation efficiency and the characteristics of the resulting cellulose nanofibrils (CNFs) were investigated. As the pH increased, the negative value of the zeta potential of TEMPO-oxidized fibers increased. The increase in electrostatic repulsion at pH values of greater than 9 prevented the entanglement of long PMBFs, which was a drawback for defibrillation at acidic pH. With increasing pH, the CNF production yield was increased. The crystallinity index of TEMPO-oxidized CNFs from PMBFs was 83.5%, which was higher than that of TEMPO-oxidized CNFs from softwood fibers in the same conditions. The tensile strength of nanopaper from TEMPO-oxidized PMBF CNFs was 110.18 MPa, which was approximately 30% higher than that (84.19 MPa) of the TEMPO-oxidized CNFs from softwood fibers.
Ecofaith: Reading Scripture in an Era of Ecological Crisis
Authors: Leese J. J. Johnson
Published: March 2019

This essay outlines the emerging field of ecological theology (ecotheology) with a primary focus on the methods of ecological hermeneutics developed by biblical scholars, ethicists, and theologians. This relatively new approach to reading ancient sacred texts has emerged in tandem with, and partially as a result of, increased public, political, and scientific consensus on the impacts of anthropogenic global warming and the ranging environmentally related effects (e.g., reduction of biodiversity and ecosystems, deforestation, loss of fertile lands, and so forth). The demands of our current context have challenged scholars to consider how religious anthropocentric worldviews have influenced historical readings of the Bible in ways that have contributed to the crisis and constricted the ecological contours of the ancient text. In order to place these developments within a broader historical context, the first section summarizes the history and trajectory of ecological hermeneutics over the past four decades. The main section of this work outlines and summarizes the different types of reading strategies being considered and debated among scholars today and includes promising examples of ecocritical readings of biblical texts. These readings are based on a constructive and critical engagement of ancient texts in light of the modern environmental challenges.
Comparison of Different Algorithms to Map Hydrothermal Alteration Zones Using ASTER Remote Sensing Data for Polymetallic Vein-Type Ore Exploration: Toroud–Chahshirin Magmatic Belt (TCMB), North Iran
Authors: Noori Lida , Pour Amin , Askari Ghasem , Taghipour Nader , Pradhan Biswajeet , Lee Chang-Wook , Honarmand Mehdi
Published: March 2019

Polymetallic vein-type ores are important sources of precious metal and a principal type of orebody for various base-metals. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data were used for mapping hydrothermal alteration zones associated with epithermal polymetallic vein-type mineralization in the Toroud–Chahshirin Magmatic Belt (TCMB), North of Iran. The TCMB is the largest known goldfield and base metals province in the central-north of Iran. Propylitic, phyllic, argillic, and advanced argillic alteration and silicification zones are typically associated with Au-Cu, Ag, and/or Pb-Zn mineralization in the TCMB. Specialized image processing techniques, namely Selective Principal Component Analysis (SPCA), Band Ratio Matrix Transformation (BRMT), Spectral Angle Mapper (SAM) and Mixture Tuned Matched Filtering (MTMF) were implemented and compared to map hydrothermal alteration minerals at the pixel and sub-pixel levels. Subtle differences between altered and non-altered rocks and hydrothermal alteration mineral assemblages were detected and mapped in the study area. The SPCA and BRMT spectral transformation algorithms discriminated the propylitic, phyllic, argillic and advanced argillic alteration and silicification zones as well as lithological units. The SAM and MTMF spectral mapping algorithms detected spectrally dominated mineral groups such as muscovite/montmorillonite/illite, hematite/jarosite, and chlorite/epidote/calcite mineral assemblages, systematically. Comprehensive fieldwork and laboratory analysis, including X-ray diffraction (XRD), petrographic study, and spectroscopy were conducted in the study area for verifying the remote sensing outputs. Results indicate several high potential zones of epithermal polymetallic vein-type mineralization in the northeastern and southwestern parts of the study area, which can be considered for future systematic exploration programs. The approach used in this research has great implications for the exploration of epithermal polymetallic vein-type mineralization in other base metals provinces in Iran and semi-arid regions around the world.
Changes in Snow Phenology from 1979 to 2016 over the Tianshan Mountains, Central Asia
Authors: Yang Tao , Li Qian , Ahmad Sajjad , Zhou Hongfei , Li Lanhai
Published: March 2019

Snowmelt from the Tianshan Mountains (TS) is a major contributor to the water resources of the Central Asian region. Thus, changes in snow phenology over the TS have significant implications for regional water supplies and ecosystem services. However, the characteristics of changes in snow phenology and their influences on the climate are poorly understood throughout the entire TS due to the lack of in situ observations, limitations of optical remote sensing due to clouds, and decentralized political landscapes. Using passive microwave remote sensing snow data from 1979 to 2016 across the TS, this study investigates the spatiotemporal variations of snow phenology and their attributes and implications. The results show that the mean snow onset day (Do), snow end day (De), snow cover duration days (Dd), and maximum snow depth (SDmax) from 1979 to 2016 were the 78.2nd day of hydrological year (DOY), 222.4th DOY, 146.2 days, and 16.1 cm over the TS, respectively. Dd exhibited a spatial distribution of days with a temperature of <0 °C derived from meteorological station observations. Anomalies of snow phenology displayed the regional diversities over the TS, with shortened Dd in high-altitude regions and the Fergana Valley but increased Dd in the Ili Valley and upper reaches of the Chu and Aksu Rivers. Increased SDmax was exhibited in the central part of the TS, and decreased SDmax was observed in the western and eastern parts of the TS. Changes in Dd were dominated by earlier De, which was caused by increased melt-season temperatures (Tm). Earlier De with increased accumulation of seasonal precipitation (Pa) influenced the hydrological processes in the snowmelt recharge basin, increasing runoff and earlier peak runoff in the spring, which intensified the regional water crisis.
Flood Inundation Mapping of the Sparsely Gauged Large-Scale Brahmaputra Basin Using Remote Sensing Products
Authors: Bhattacharya Biswa , Mazzoleni Maurizio , Ugay Reyne
Published: March 2019

Sustainable water management is one of the important priorities set out in the Sustainable Development Goals (SDGs) of the United Nations, which calls for efficient use of natural resources. Efficient water management nowadays depends a lot upon simulation models. However, the availability of limited hydro-meteorological data together with limited data sharing practices prohibits simulation modelling and consequently efficient flood risk management of sparsely gauged basins. Advances in remote sensing has significantly contributed to carrying out hydrological studies in ungauged or sparsely gauged basins. In particular, the global datasets of remote sensing observations (e.g., rainfall, evaporation, temperature, land use, terrain, etc.) allow to develop hydrological and hydraulic models of sparsely gauged catchments. In this research, we have considered large scale hydrological and hydraulic modelling, using freely available global datasets, of the sparsely gauged trans-boundary Brahmaputra basin, which has an enormous potential in terms of agriculture, hydropower, water supplies and other utilities. A semi-distributed conceptual hydrological model was developed using HEC-HMS (Hydrologic Modelling System from Hydrologic Engineering Centre). Rainfall estimates from Tropical Rainfall Measuring Mission (TRMM) was compared with limited gauge data and used in the simulation. The Nash Sutcliffe coefficient of the model with the uncorrected rainfall data in calibration and validation were 0.75 and 0.61 respectively whereas the similar values with the corrected rainfall data were 0.81 and 0.74. The output of the hydrological model was used as a boundary condition and lateral inflow to the hydraulic model. Modelling results obtained using uncorrected and corrected remotely sensed products of rainfall were compared with the discharge values at the basin outlet (Bahadurabad) and with altimetry data from Jason-2 satellite. The simulated flood inundation maps of the lower part of the Brahmaputra basin showed reasonably good match in terms of the probability of detection, success ratio and critical success index. Overall, this study demonstrated that reliable and robust results can be obtained in both hydrological and hydraulic modelling using remote sensing data as the only input to large scale and sparsely gauged basins.
Characterizing a New England Saltmarsh with NASA G-LiHT Airborne Lidar
Authors: Paynter Ian , Schaaf Crystal , Bowen Jennifer L. , Deegan Linda , Peri Francesco , Cook Bruce
Published: March 2019

Airborne lidar can observe saltmarshes on a regional scale, targeting phenological and tidal states to provide the information to more effectively utilize frequent multispectral satellite observations to monitor change. Airborne lidar observations from NASA Goddard Lidar Hyperspectral and Thermal (G-LiHT) of a well-studied region of saltmarsh (Plum Island, Massachusetts, United States) were acquired in multiple years (2014, 2015 and 2016). These airborne lidar data provide characterizations of important saltmarsh components, as well as specifications for effective surveys. The invasive Phragmites australis was observed to increase in extent from 8374 m2 in 2014, to 8882 m2 in 2015 (+6.1%), and again to 13,819 m2 in 2016 (+55.6%). Validation with terrestrial lidar supported this increase, but suggested the total extent was still underestimated. Estimates of Spartina alterniflora extent from airborne lidar were within 7% of those from terrestrial lidar, but overestimation of height of Spartina alterniflora was found to occur at the edges of creeks (+83.9%). Capturing algae was found to require observations within ±15° of nadir, and capturing creek structure required observations within ±10° of nadir. In addition, 90.33% of creeks and ditches were successfully captured in the airborne lidar data (8206.3 m out of 9084.3 m found in aerial imagery).
Adaptive Structured Light with Scatter Correction for High-Precision Underwater 3D Measurements
Authors: Risholm Petter , Kirkhus Trine , Thielemann Jens T. , Thorstensen Jostein
Published: March 2019

High-precision underwater 3D cameras are required to automate many of the traditional subsea inspection, maintenance and repair (IMR) operations. In this paper we introduce a novel multi-frequency phase stepping (structured light) method for high-precision 3D estimation even in turbid water. We introduce an adaptive phase-unwrapping procedure which uses the phase-uncertainty to determine the highest frequency that can be reliably unwrapped. Light scattering adversely affects the phase estimate. We propose to remove the effect of forward scatter with an unsharp filter and a model-based method to remove the backscatter effect. Tests in varying turbidity show that the scatter correction removes the adverse effect of scatter on the phase estimates. The adaptive frequency unwrapping with scatter correction results in images with higher accuracy and precision and less phase unwrap errors than the Gray-Code Phase Stepping (GCPS) approach.
An Improved Modified Universal Ultra-Wideband Antenna Designed for Step Frequency Continuous Wave Ground Penetrating Radar System
Authors: Wu Yuxuan , Shen Feng , Yuan Yue , Xu Dingjie
Published: March 2019

Step Frequency Continuous Wave Ground Penetrating Radar (SFCW-GPR), as a tool for nondestructive testing of shallow soil surface targets, the realization of the function of SFCW-GPR is mainly based on the theory of refraction, reflection and scattering of electromagnetic wave in the discontinuity of dielectric constant. So, the UWB antenna system, an important part of SFCW-GPR, becomes more indispensable. In this paper, an improved modified universal antenna is designed, simulated and fabricated. Based on a typical Bow-tie antenna, it is modified by the methods of lumped loads, cavity-backed loading and structure loading. The simulated and measured results show that the UWB antenna has 1.36 GHz bandwidth from 0.64 to 2.0 GHz with three resonant wavelength peaks, and having been modified and improved, the UWB antenna performances including voltage standing-wave ratio (VSWR), input impedance, the boresight gain and current distribution, are much better than the typical Bow-tie antenna. In addition, the results of verification experiment of Step Frequency Continuous Wave (SFCW) show that the antenna can be applied to the working scenarios of SFCW-GPR.
Boosting Texture-Based Classification by Describing Statistical Information of Gray-Levels Differences
Authors: García-Olalla Óscar , Fernández-Robles Laura , Alegre Enrique , Castejón-Limas Manuel , Fidalgo Eduardo
Published: March 2019

This paper presents a new texture descriptor booster, Complete Local Oriented Statistical Information Booster (CLOSIB), based on statistical information of the image. Our proposal uses the statistical information of the texture provided by the image gray-levels differences to increase the discriminative capability of Local Binary Patterns (LBP)-based and other texture descriptors. We demonstrated that Half-CLOSIB and M-CLOSIB versions are more efficient and precise than the general one. H-CLOSIB may eliminate redundant statistical information and the multi-scale version, M-CLOSIB, is more robust. We evaluated our method using four datasets: KTH TIPS (2-a) for material recognition, UIUC and USPTex for general texture recognition and JAFFE for face recognition. The results show that when we combine CLOSIB with well-known LBP-based descriptors, the hit rate increases in all the cases, introducing in this way the idea that CLOSIB can be used to enhance the description of texture in a significant number of situations. Additionally, a comparison with recent algorithms demonstrates that a combination of LBP methods with CLOSIB variants obtains comparable results to those of the state-of-the-art.
A Hybrid Path-Planning Strategy for Mobile Robots with Limited Sensor Capabilities
Authors: de Oliveira Guilherme Carlos R. , de Carvalho Kevin B. , Brandão Alexandre S.
Published: March 2019

This paper introduces a strategy for the path planning problem for platforms with limited sensor and processing capabilities. The proposed algorithm does not require any prior information but assumes that a mapping algorithm is used. If enough information is available, a global path planner finds sub-optimal collision-free paths within the known map. For the real time obstacle avoidance task, a simple and cost-efficient local planner is used, making the algorithm a hybrid global and local planning solution. The strategy was tested in a real, cluttered environment experiment using the Pioneer P3-DX and the Xbox 360 kinect sensor, to validate and evaluate the algorithm efficiency.
Identification and Compensation of Dynamic Interaction in a Non-Contact Dual-Stage Actuator System
Authors: Wang Shaokai , Hu Jinxin , Li Changqi , Tan Jiubin
Published: March 2019

Dynamic interaction seriously limits the overall performance of a Dual-Stage Actuator (DSA) system. This paper aims to identify and compensate for the dynamic interaction in a non-contact DSA system. The effects of the interaction in the non-contact DSA system are initially classified as non-contact position-dependent disturbance forces (PDDFs) and velocity-dependent disturbance forces (VDDFs). The PDDFs in the three degrees of freedom (DoFs) motion space between the two stages of the DSA system are directly identified in the time domain, and VDDFs are indirectly identified in the form of damping values in frequency domains. The feedforward networks of the force are subsequently applied to compensate the PDDFs and VDDFs, which are indexed with relative displacement and velocity, respectively. Experiments are finally conducted to investigate the effectiveness of compensation, which infers that the final positioning error in the time domain can be reduced from 260 nm to 130 nm with PDDFs and VDDFs compensation. The gain of the interaction transfer is decreased in the frequency range of up to 45 Hz with PDDFs and VDDFs compensation. With this method, some weak dynamic interaction can be completely compensated for by the force feedforward compensation, and the positioning accuracy of non-contact DSA systems can be greatly improved.
Local Strand-Breakage Detection in Multi-Strand Anchorage System Using an Impedance-Based Stress Monitoring Method—Feasibility Study
Authors: Dang Ngoc-Loi , Huynh Thanh-Canh , Kim Jeong-Tae
Published: March 2019

This study investigates the feasibility of impedance-based stress monitoring method for local-strand breakage detection in multi-strand anchorage systems. Firstly, stress fields of a multi-strand anchorage system are numerically analyzed to examine anchorage’s responses sensitive to local strand breakage. Secondly, an impedance-based stress monitoring technique via the PZT interface is outlined. Thirdly, a novel hoop-type PZT interface is designed for the multi-strands anchorage to monitor the stress variation induced by the strand breakage. Local dynamic responses of the hoop-type PZT interface are analyzed to predetermine the effective frequency ranges. Finally, the numerical feasibility of the proposed method is verified on a seven-strand anchorage system under various strand breakage cases. Variations in impedance responses are statistically quantified, and broken strands are localized by linear tomography analysis of damage indices. A lab-scale experiment is also conducted on a multi-strands anchorage to evaluate the realistic performance of the hoop PZT interface for impedance-based stress monitoring method.
A Data-Driven Approach for the Diagnosis of Mechanical Systems Using Trained Subtracted Signal Spectrograms
Authors: Huh Jiung , Pham Van Huan , Han Soonyoung , Choi Hae-Jin , Choi Seung-Kyum
Published: March 2019

Toward the prognostic and health management of mechanical systems, we propose and validate a novel effective, data-driven fault diagnosis method. In this method, we develop a trained subtracted spectrogram, the so called critical information map (CIM), identifying the difference between the signal spectrograms of normal and abnormal status. We believe this diagnosis process may be implemented in an autonomous manner so that an engineer employs it without expert knowledge in signal processing or mechanical analyses. Firstly, the CIM method applies sequential and autonomous procedures of time-synchronization, time frequency conversion, and spectral subtraction on raw signal. Secondly, the subtracted spectrogram is then trained to be a CIM for a specific mechanical system failure by finding out the optimal parameters and abstracted information of the spectrogram. Finally, the status of a system health can be monitored accurately by comparing the CIM with an acquired signal map in an automated and timely manner. The effectiveness of the proposed method is successfully validated by employing a diagnosis problem of six-degree-of-freedom industrial robot, which is the diagnosis of a non-stationary system with a small amount of training datasets.
Robust Distributed Collaborative Beamforming for Wireless Sensor Networks with Channel Estimation Impairments
Authors: Ben Smida Oussama , Zaidi Slim , Affes Sofiène , Valaee Shahrokh
Published: March 2019

We propose a new collaborative beamforming (CB) solution robust (i.e., RCB) against major channel estimation impairments over dual-hop transmissions through a wireless sensor network (WSN) of K nodes. The source first sends its signal to the WSN. Then, each node forwards its received signal after multiplying it by a properly selected beamforming weight. The latter aims to minimize the received noise power while maintaining the desired power equal to unity. These weights depend on some channel state information (CSI) parameters. Hence, they have to be estimated locally at each node, thereby, resulting in channel estimation errors that could severely hinder CB performance. Exploiting an efficient asymptotic approximation at large K, we develop alternative RCB solutions that adapt to different implementation scenarios and wireless propagation environments ranging from monochromatic (i.e., scattering-free) to polychromatic (i.e., scattered) ones. Besides, in contrast to existing techniques, our new RCB solutions are distributed (i.e., DCB) in that they do not require any information exchange among nodes, thereby dramatically improving both WSN spectral and power efficiencies. Simulation results confirm that the proposed robust DCB (RDCB) techniques are much more robust in terms of achieved signal-to-noise ratio (SNR) against channel estimation errors than best representative CB benchmarks.
Deep Submicron EGFET Based on Transistor Association Technique for Chemical Sensing
Authors: Pullano Salvatore A. , Tasneem Nishat T. , Mahbub Ifana , Shamsir Samira , Greco Marta , Islam Syed K. , Fiorillo Antonino S.
Published: March 2019

Extended-gate field-effect transistor (EGFET) is an electronic interface originally developed as a substitute for an ion-sensitive field-effect transistor (ISFET). Although the literature shows that commercial off-the-shelf components are widely used for biosensor fabrication, studies on electronic interfaces are still scarce (e.g., noise processes, scaling). Therefore, the incorporation of a custom EGFET can lead to biosensors with optimized performance. In this paper, the design and characterization of a transistor association (TA)-based EGFET was investigated. Prototypes were manufactured using a 130 nm standard complementary metal-oxide semiconductor (CMOS) process and compared with devices presented in recent literature. A DC equivalence with the counterpart involving a single equivalent transistor was observed. Experimental results showed a power consumption of 24.99 mW at 1.2 V supply voltage with a minimum die area of 0.685 × 1.2 mm2. The higher aspect ratio devices required a proportionally increased die area and power consumption. Conversely, the input-referred noise showed an opposite trend with a minimum of 176.4 nVrms over the 0.1 to 10 Hz frequency band for a higher aspect ratio. EGFET as a pH sensor presented further validation of the design with an average voltage sensitivity of 50.3 mV/pH, a maximum current sensitivity of 15.71 mA1/2/pH, a linearity higher than 99.9%, and the possibility of operating at a lower noise level with a compact design and a low complexity.
Adaptive Rate-Compatible Non-Binary LDPC Coding Scheme for the B5G Mobile System
Authors: Zhao Dan-feng , Tian Hai , Xue Rui
Published: March 2019

This paper studies an adaptive coding scheme for B5G (beyond 5th generation) mobile system-enhanced transmission technology. Different from the existing works, the authors develop a class of rate-compatible, non-binary, low-density parity check (RC-NB-LDPC) codes, which expresses the strong connection between the algebra-based and graph-theoretic-based constructions. The constructed codes can not only express rate-compatible (RC) features, but also possess a quasi-cyclic (QC) structure that facilitates the encoding implementation. Further, in order to achieve the code rate-adaptive allocation scheme, the authors propose using the K-means++ clustering algorithm to cluster different channel environments, considering various factors that affect channel characteristics. Finally, in order to present the advantages of the adaptive coding scheme, the authors construct a coding scheme for image transmission. The numerical results demonstrate that the developed code can obtain better waterfall performance in a larger code rate range, which is more suitable for data transmission; the adaptive coding transmission scheme can obtain higher reconstructed image quality compared to the fixed code rate-coding scheme. Moreover, when considering unequal error protection (UEP), the proposed scheme can further improve the reconstructed image quality.
Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
Authors: El-Sayed Hesham , Chaqfeh Moumena
Published: March 2019

Mobile edge computing (MEC) has been recently proposed to bring computing capabilities closer to mobile endpoints, with the aim of providing low latency and real-time access to network information via applications and services. Several attempts have been made to integrate MEC in intelligent transportation systems (ITS), including new architectures, communication frameworks, deployment strategies and applications. In this paper, we explore existing architecture proposals for integrating MEC in vehicular environments, which would allow the evolution of the next generation ITS in smart cities. Moreover, we classify the desired applications into four major categories. We rely on a MEC architecture with three layers to propose a data dissemination protocol, which can be utilized by traffic safety and travel convenience applications in vehicular networks. Furthermore, we provide a simulation-based prototype to evaluate the performance of our protocol. Simulation results show that our proposed protocol can significantly improve the performance of data dissemination in terms of data delivery, communication overhead and delay. In addition, we highlight challenges and open issues to integrate MEC in vehicular networking environments for further research.
On-Line Laser Triangulation Scanner for Wood Logs Surface Geometry Measurement
Authors: Siekański Piotr , Magda Krzysztof , Malowany Krzysztof , Rutkiewicz Jan , Styk Adam , Krzesłowski Jakub , Kowaluk Tomasz , Zagórski Andrzej
Published: March 2019

The paper presents the automated on-line system for wood logs 3D geometry scanning. The system consists of 6 laser triangulation scanners and is able to scan full wood logs which can have the diameter ranging from 250 mm to 500 mm and the length up to 4000 mm. The system was developed as a part of the BIOSTRATEG project aiming to optimize the cutting of logs in the process of wood planks manufacturing by intelligent positioning in sawmill operation. This paper illustrates the detailed description of scanner construction, full measurement process, system calibration and data processing schemes. The full 3D surface geometry of products and their applied portion of selected wood logs formed after cutting out the cant is also demonstrated.
Video-Based Actigraphy for Monitoring Wake and Sleep in Healthy Infants: A Laboratory Study
Authors: Long Xi , Otte Renée , Sanden Eric van der , Werth Jan , Tan Tao
Published: March 2019

Prolonged monitoring of infant sleep is paramount for parents and healthcare professionals for interpreting and evaluating infants’ sleep quality. Wake-sleep patterns are often studied to assess this. Video cameras have received a lot of attention in infant sleep monitoring because they are unobtrusive and easy to use at home. In this paper, we propose a method using motion data detected from infrared video frames (video-based actigraphy) to identify wake and sleep states. The motion, mostly caused by infant body movement, is known to be substantially associated with infant wake and sleep states. Two features were calculated from the video-based actigraphy, and a Bayesian-based linear discriminant classification model was employed to classify the two states. Leave-one-subject-out cross validation was performed to validate our proposed wake and sleep classification model. From a total of 11.6 h of infrared video recordings of 10 healthy term infants in a laboratory pilot study, we achieved a reliable classification performance with a Cohen’s kappa coefficient of 0.733 ± 0.204 (mean ± standard deviation) and an overall accuracy of 92.0% ± 4.6%.
A Sprayed Graphene Pattern-Based Flexible Strain Sensor with High Sensitivity and Fast Response
Authors: Xu Wei , Yang Tingting , Qin Feng , Gong Dongdong , Du Yijia , Dai Gang
Published: March 2019

Flexible strain sensors have a wide range of applications in biomedical science, aerospace industry, portable devices, precise manufacturing, etc. However, the manufacturing processes of most flexible strain sensors previously reported have usually required high manufacturing costs and harsh experimental conditions. Besides, research interests are often focused on improving a single attribute parameter while ignoring others. This work aims to propose a simple method of manufacturing flexible graphene-based strain sensors with high sensitivity and fast response. Firstly, oxygen plasma treats the substrate to improve the interfacial interaction between graphene and the substrate, thereby improving device performance. The graphene solution is then sprayed using a soft PET mask to define a pattern for making the sensitive layer. This flexible strain sensor exhibits high sensitivity (gauge factor ~100 at 1% strain), fast response (response time: 400–700 μs), good stability (1000 cycles), and low overshoot (<5%) as well. Those processes used are compatible with a variety of complexly curved substrates and is expected to broaden the application of flexible strain sensors.
High-Index Glass Ball Retroreflectors for Measuring Lateral Positions
Authors: Egidi Andrea , Balsamo Alessandro , Pisani Marco
Published: March 2019

This paper is concerned with backscattered luminous signals, coming from a particular class of dielectric spheres illuminated by a coherent source. The purpose is to measure the lateral position of the sphere serving as an optical target, to achieve an overall contactless sensor of lateral position in space. Traditional approaches and theories such as ray-tracing and Mie scattering—as implemented in dedicated software—are applied to investigate their fitness for purpose in this application. No previous literature was found dealing with this specific case. Unfortunately, our observations did not match the theories’ predictions to an acceptable degree, and these approaches proved to be unsatisfactory. The rest of the paper focusses then on the development and comparison of suitable algorithms to compute the image coordinates of a representative point, which was in fact the true motivation of this work. Two original algorithms are proposed and discussed. Their robustness and repeatability are benchmarked under noisy conditions and at different distances from the target, with simulated as well as real images. Both resulted capable of sub-pixel accuracy.
A Novel Calibration Method of Articulated Laser Sensor for Trans-Scale 3D Measurement
Authors: Kang Jiehu , Wu Bin , Duan Xiaodeng , Xue Ting
Published: March 2019

The articulated laser sensor is a new kind of trans-scale and non-contact measurement instrument in regular-size space and industrial applications. These sensors overcome many deficiencies and application limitations of traditional measurement methods. The articulated laser sensor consists of two articulated laser sensing modules, and each module is made up of two rotary tables and one collimated laser. The three axes represent a non-orthogonal shaft architecture. The calibration method of system parameters for traditional instruments is no longer suitable. A novel high-accuracy calibration method of an articulated laser sensor for trans-scale 3D measurement is proposed. Based on perspective projection models and image processing techniques, the calibration method of the laser beam is the key innovative aspect of this study and is introduced in detail. The experimental results show that a maximum distance error of 0.05 mm was detected with the articulated laser sensor. We demonstrate that the proposed high-accuracy calibration method is feasible and effective, particularly for the calibration of laser beams.
Detection of Unresolved Targets for Wideband Monopulse Radar
Authors: Tsai Tianyi , Liao Zhiqiang , Ding Zhiquan , Zhao Yuan , Tang Bin
Published: March 2019

Detecting unresolved targets is very important for radars in their target tracking phase. For wideband radars, the unresolved target detection algorithm should be fast and adaptive to different bandwidths. To meet the requirements, a detection algorithm for wideband monopulse radars is proposed, which can detect unresolved targets for each range profile sampling points. The algorithm introduces the Gaussian mixture model and uses a priori information to achieve high performance while keeping a low computational load, adaptive to different bandwidths. A comparison between the proposed algorithm and the latest unresolved target detection algorithm Joint Multiple Bin Processing Generalized Likelihood Ratio Test (JMBP GLRT) is carried out by simulation. On Rayleigh distributed echoes, the detection probability of the proposed algorithm is at most 0.5456 higher than the JMBP GLRT for different signal-to-noise ratios (SNRs), while the computation time of the proposed algorithm is no more than two 10,000ths of the JMBP GLRT computation time. On bimodal distributed echoes, the detection probability of the proposed algorithm is at most 0.7933 higher than the JMBP GLRT for different angular separations of two unresolved targets, while the computation time of the proposed algorithm is no more than one 10,000th of the JMBP GLRT computation time. To evaluate the performance of the proposed algorithm in a real wideband radar, an experiment on field test measured data was carried out, in which the proposed algorithm was compared with Blair GLRT. The results show that the proposed algorithm produces a higher detection probability and lower false alarm rate, and completes detections on a range profile within 0.22 ms.
Automatic Registration of Optical Images with Airborne LiDAR Point Cloud in Urban Scenes Based on Line-Point Similarity Invariant and Extended Collinearity Equations
Authors: Peng Shubiao , Ma Hongchao , Zhang Liang
Published: March 2019

This paper proposes a novel method to achieve the automatic registration of optical images and Light Detection and Ranging (LiDAR) points in urban areas. The whole procedure, which adopts a coarse-to-precise registration strategy, can be summarized as follows: Coarse registration is performed through a conventional point-feature-based method. The points needed can be extracted from both datasets through a matured point extractor, such as the Forster operator, followed by the extraction of straight lines. Considering that lines are mainly from building roof edges in urban scenes, and being aware of their inaccuracy when extracted from an irregularly spaced point cloud, an “infinitesimal feature analysis method” fully utilizing LiDAR scanning characteristics is proposed to refine edge lines. Points which are matched between the image and LiDAR data are then applied as guidance to search for matched lines via the line-point similarity invariant. Finally, a transformation function based on extended collinearity equations is applied to achieve precise registration. The experimental results show that the proposed method outperforms the conventional ones in terms of the registration accuracy and automation level.
Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning
Authors: Xu Gaowei , Liu Min , Jiang Zhuofu , Söffker Dirk , Shen Weiming
Published: March 2019

Recently, research on data-driven bearing fault diagnosis methods has attracted increasing attention due to the availability of massive condition monitoring data. However, most existing methods still have difficulties in learning representative features from the raw data. In addition, they assume that the feature distribution of training data in source domain is the same as that of testing data in target domain, which is invalid in many real-world bearing fault diagnosis problems. Since deep learning has the automatic feature extraction ability and ensemble learning can improve the accuracy and generalization performance of classifiers, this paper proposes a novel bearing fault diagnosis method based on deep convolutional neural network (CNN) and random forest (RF) ensemble learning. Firstly, time domain vibration signals are converted into two dimensional (2D) gray-scale images containing abundant fault information by continuous wavelet transform (CWT). Secondly, a CNN model based on LeNet-5 is built to automatically extract multi-level features that are sensitive to the detection of faults from the images. Finally, the multi-level features containing both local and global information are utilized to diagnose bearing faults by the ensemble of multiple RF classifiers. In particular, low-level features containing local characteristics and accurate details in the hidden layers are combined to improve the diagnostic performance. The effectiveness of the proposed method is validated by two sets of bearing data collected from reliance electric motor and rolling mill, respectively. The experimental results indicate that the proposed method achieves high accuracy in bearing fault diagnosis under complex operational conditions and is superior to traditional methods and standard deep learning methods.
Anchor Generation Optimization and Region of Interest Assignment for Vehicle Detection
Authors: Wang Ye , Liu Zhenyi , Deng Weiwen
Published: March 2019

Region proposal network (RPN) based object detection, such as Faster Regions with CNN (Faster R-CNN), has gained considerable attention due to its high accuracy and fast speed. However, it has room for improvements when used in special application situations, such as the on-board vehicle detection. Original RPN locates multiscale anchors uniformly on each pixel of the last feature map and classifies whether an anchor is part of the foreground or background with one pixel in the last feature map. The receptive field of each pixel in the last feature map is fixed in the original faster R-CNN and does not coincide with the anchor size. Hence, only a certain part can be seen for large vehicles and too much useless information is contained in the feature for small vehicles. This reduces detection accuracy. Furthermore, the perspective projection results in the vehicle bounding box size becoming related to the bounding box position, thereby reducing the effectiveness and accuracy of the uniform anchor generation method. This reduces both detection accuracy and computing speed. After the region proposal stage, many regions of interest (ROI) are generated. The ROI pooling layer projects an ROI to the last feature map and forms a new feature map with a fixed size for final classification and box regression. The number of feature map pixels in the projected region can also influence the detection performance but this is not accurately controlled in former works. In this paper, the original faster R-CNN is optimized, especially for the on-board vehicle detection. This paper tries to solve these above-mentioned problems. The proposed method is tested on the KITTI dataset and the result shows a significant improvement without too many tricky parameter adjustments and training skills. The proposed method can also be used on other objects with obvious foreshortening effects, such as on-board pedestrian detection. The basic idea of the proposed method does not rely on concrete implementation and thus, most deep learning based object detectors with multiscale feature maps can be optimized with it.
Identifying Communities within the Smart-Cultural City of Singapore: A Network Analysis Approach
Authors: von Richthofen Aurel , Tomarchio Ludovica , Costa Alberto
Published: March 2019

This article investigates the intersection and convergence of Smart Cities and Creative Cities that emerge with the availability of social media data, technology—smart technologies—and the shifting mode of cultural production—creative economies—forming a new nexus of Smart-Cultural Cities. It starts with a short review of literature surrounding Smart Cities and Creative Cities to establish domain criteria on Smart-Cultural Cities for Singapore. The article draws on a database of actors from authorities, industries, academia, and artists established by the research community in Singapore. Actors and domains are described using bipartite graphs and then analyzed by solving a deterministic optimization problem rather than computing a statistic. The result of this analysis reveals new clusters, nodes, and connections in the actor–domain network of the Singapore Smart-Cultural Cities discourse. The identified clusters are called “Urban Scenario Makers”, “Digital Cultural Transformers” and “Public Engagers”. The method gives significant insights on the number of clusters, the composition of each cluster, and the relationship between clusters that serve to locate and describe a next iteration of the Smart City that focusses on human interaction, culture, and technology.
Using Unilateral Strength, Power and Reactive Strength Tests to Detect the Magnitude and Direction of Asymmetry: A Test-Retest Design
Authors: Bishop Chris , Read Paul , Chavda Shyam , Jarvis Paul , Turner Anthony
Published: March 2019

The aims of the present study were to determine test-retest reliability for unilateral strength and power tests used to quantify asymmetry and determine the consistency of both the magnitude and direction of asymmetry between test sessions. Twenty-eight recreational trained sport athletes performed unilateral isometric squat, countermovement jump (CMJ) and drop jump (DJ) tests over two test sessions. Inter-limb asymmetry was calculated from both the best trial and as an average of three trials for each test. Test reliability was computed using the intraclass correlation coefficient (ICC), coefficient of variation (CV) and standard error of measurement (SEM). In addition, paired samples t-tests were used to determine systematic bias between test sessions and Kappa coefficients to report how consistently asymmetry favoured the same side. Within and between-session reliability ranged from moderate to excellent (ICC range = 0.70–0.96) and CV values ranged from 3.7–13.7% across tests. Significant differences in asymmetry between test sessions were seen for impulse during the isometric squat (p = 0.04; effect size = –0.60) but only when calculating from the best trial. When computing the direction of asymmetry across test sessions, levels of agreement were fair to substantial for the isometric squat (Kappa = 0.29–0.64), substantial for the CMJ (Kappa = 0.64–0.66) and fair to moderate for the DJ (Kappa = 0.36–0.56). These results show that when asymmetry is computed between test sessions, the group mean is generally devoid of systematic bias; however, the direction of asymmetry shows greater variability and is often inter-changeable. Thus, practitioners should consider both the direction and magnitude of asymmetry when monitoring inter-limb differences in healthy athlete populations.