bluuebunny/crossref_metadata_2025 huggingface dataset:
Antimicrobial LDPE/EVOH Layered Films Containing Carvacrol Fabricated by Multiplication Extrusion
Authors: Krepker Max , Zhang Cong , Nitzan Nadav , Prinz-Setter Ofer , Massad-Ivanir Naama , Olah Andrew , Baer Eric , Segal Ester
Published: August 2018

This work describes the fabrication of antimicrobial multilayered polymeric films containing carvacrol (used as a model essential oil) by co-extrusion and multiplication technique. The microlayering process was utilized to produce films, with up to 65 alternating layers, of carvacrol-containing low-density polyethylene (LDPE) and ethylene vinyl alcohol copolymer (EVOH). Carvacrol was melt compounded with LDPE or loaded into halloysite nanotubes (HNTs) in a pre-compounding step prior film production. The detailed nanostructure and composition (in terms of carvacrol content) of the films were characterized and correlated to their barrier properties, carvacrol release rate, and antibacterial and antifungal activity. The resulting films exhibit high carvacrol content despite the harsh processing conditions (temperature of 200 °C and long processing time), regardless of the number of layers or the presence of HNTs. The multilayered films exhibit superior oxygen transmission rates and carvacrol diffusivity values that are more than two orders of magnitude lower in comparison to single-layered carvacrol-containing films (i.e., LDPE/carvacrol and LDPE/(HNTs/carvacrol)) produced by conventional cast extrusion. The (LDPE/carvacrol)/EVOH and (LDPE/[HNTs/carvacrol])/EVOH films demonstrated excellent antimicrobial efficacy against E. coli and Alternaria alternata in in vitro micro-atmosphere assays and against A. alternata and Rhizopus in cherry tomatoes, used as the food model. The results presented here suggest that sensitive essential oils, such as carvacrol, can be incorporated into plastic polymers constructed of tailored multiple layers, without losing their antimicrobial efficacy.
Synthesis of μ-ABC Tricyclic Miktoarm Star Polymer via Intramolecular Click Cyclization
Authors: Shingu Tomoki , Yamamoto Takuya , Tajima Kenji , Isono Takuya , Satoh Toshifumi
Published: August 2018

Cyclic polymers exhibit unique physical and chemical properties because of the restricted chain mobility and absence of chain ends. Although many types of homopolymers and diblock copolymers possessing cyclic architectures have been synthesized to date, there are relatively few reports of cyclic triblock terpolymers because of their synthetic difficulties. In this study, a novel synthetic approach for μ-ABC tricyclic miktoarm star polymers involving t-Bu-P4-catalyzed ring-opening polymerization (ROP) of glycidyl ethers and intramolecular copper-catalyzed azido-alkyne cycloaddition (CuAAC) was developed. First, the t-Bu-P4-catalyzed ROP of decyl glycidyl ether, dec-9-enyl glycidyl ether, and 2-(2-(2-methoxyethoxy) ethoxy) ethyl glycidyl ether with the aid of functional initiators and terminators was employed for the preparation of a clickable linear triblock terpolymer precursor possessing three azido and three ethynyl groups at the selected positions. Next, the intramolecular CuAAC of the linear precursor successfully produced the well-defined tricyclic triblock terpolymer with narrow dispersity in a reasonable yield. The present strategy is useful for synthesizing model polymers for studying the topological effects on the triblock terpolymer self-assembly.
Bacteriostatic Activity of LLDPE Nanocomposite Embedded with Sol–Gel Synthesized TiO2/ZnO Coupled Oxides at Various Ratios
Authors: Saharudin Khairul Arifah , Sreekantan Srimala , Basiron Norfatehah , Khor Yong Ling , Harun Nor Hazliana , S. M. N. Mydin Rabiatul Basria , Md Akil Hazizan , Seeni Azman , Vignesh Kumaravel
Published: August 2018

Metal oxide-polymer nanocomposite has been proven to have selective bactericidal effects against the main and common pathogens (Gram-positive Staphylococcus aureus (S. aureus) and Gram-negative Escherichia coli (E. coli)) that can cause harmful infectious diseases. As such, this study looked into the prospect of using TiO2/ZnO with linear low-density polyethylene (LLDPE) to inactivate S. aureus and E. coli. The physical, structural, chemical, mechanical, and antibacterial properties of the nanocomposite were investigated in detail in this paper. The production of reactive species, such as hydroxyl radicals (•OH), holes (h+), superoxide anion radicals (O2•¯), and zinc ion (Zn2+), released from the nanocomposite were quantified to elucidate the underlying antibacterial mechanisms. LLDPE/25T75Z with TiO2/ZnO (1:3) nanocomposite displayed the best performance that inactivated S. aureus and E. coli by 95% and 100%, respectively. The dominant reactive active species and the zinc ion release toward the superior antibacterial effect of nanocomposite are discussed. This work does not only offer depiction of the effective element required for antimicrobial biomedical appliances, but also the essential structural characteristics to enhance water uptake to expedite photocatalytic activity of LLDPE/metal oxide nanocomposite for long term application.
Hybrid Effect of Twisted Steel and Polyethylene Fibers on the Tensile Performance of Ultra-High-Performance Cementitious Composites
Authors: Kim Min-Jae , Kim Soonho , Yoo Doo-Yeol
Published: August 2018

The hybrid effect of twisted steel (T) fibers with an aspect ratio of 100 and polyethylene (PE) fibers with four different aspect ratios of 400, 600, 900, and 1200 on the mechanical performance of ultra-high-performance cementitious composite (UHPCC) was investigated. This involved a total of 17 different sample types at an identical fiber volume fraction of 2% being made and subjected to compressive and tensile loads. Samples were made by replacing 0.5%, 1.0%, 1.5%, and 2.0% of T fibers with four different types of PE fibers. In addition, the pullout behaviors of fibers at cracked sections and the cracking behaviors of specimens were evaluated in order to determine the effect of the pullout mechanism of each fiber on the overall tensile performance. Test results indicate that the compressive strength decreased in proportion to the amount of PE fibers, regardless of their aspect ratio. The fiber hybridization had a great synergetic effect, successfully improving the tensile strength and strain capacity of UHPCCs; this effect was dependent on the aspect ratio of the PE fibers. Finally, the cracking behaviors were determined to be more related to the fiber type and pullout mechanisms than the tensile strength or strain capacity of UHPCCs.
Tough and Self-Healable Nanocomposite Hydrogels for Repeatable Water Treatment
Authors: Yu Kunhao , Wang Di , Wang Qiming
Published: August 2018

Nanomaterials with ultrahigh specific surface areas are promising adsorbents for water-pollutants such as dyes and heavy metal ions. However, an ongoing challenge is that the dispersed nanomaterials can easily flow into the water stream and induce secondary pollution. To address this challenge, we employed nanomaterials to bridge hydrogel networks to form a nanocomposite hydrogel as an alternative water-pollutant adsorbent. While most of the existing hydrogels that are used to treat wastewater are weak and non-healable, we present a tough TiO2 nanocomposite hydrogel that can be activated by ultraviolet (UV) light to demonstrate highly efficient self-healing, heavy metal adsorption, and repeatable dye degradation. The high toughness of the nanocomposite hydrogel is induced by the sequential detachment of polymer chains from the nanoparticle crosslinkers to dissipate the stored strain energy within the polymer network. The self-healing behavior is enabled by the UV-assisted rebinding of the reversible bonds between the polymer chains and nanoparticle surfaces. Also, the UV-induced free radicals on the TiO2 nanoparticle can facilitate the binding of heavy metal ions and repeated degradation of dye molecules. We expect this self-healable, photo-responsive, tough hydrogel to open various avenues for resilient and reusable wastewater treatment materials.
Input Shaping Predictive Functional Control for Different Types of Challenging Dynamics Processes
Authors: Abdullah Muhammad , Rossiter John Anthony
Published: August 2018

Predictive functional control (PFC) is a fast and effective controller that is widely used for processes with simple dynamics. This paper proposes some techniques for improving its reliability when applied to systems with more challenging dynamics, such as those with open-loop unstable poles, oscillatory modes, or integrating modes. One historical proposal considered is to eliminate or cancel the undesirable poles via input shaping of the predictions, but this approach is shown to sometimes result in relatively poor performance. Consequently, this paper proposes to shape these poles, rather than cancelling them, to further enhance the tuning, feasibility, and stability properties of PFC. The proposed modification is analysed and evaluated on several numerical examples and also a hardware application.
A Coupled Thermal-Hydraulic-Mechanical Nonlinear Model for Fault Water Inrush
Authors: Liu Weitao , Zhao Jiyuan , Nie Ruiai , Liu Yuben , Du Yanhui
Published: August 2018

A coupled thermal-nonlinear hydraulic-mechanical (THM) model for fault water inrush was carried out in this paper to study the water-rock-temperature interactions and predict the fault water inrush. First, the governing equations of the coupled THM model were established by coupling the particle transport equation, nonlinear flow equation, mechanical equation, and the heat transfer equation. Second, by setting different boundary conditions, the mechanical model, nonlinear hydraulic-mechanical (HM) coupling model, and the thermal-nonlinear hydraulic-mechanical (THM) coupling model were established, respectively. Finally, a numerical simulation of these models was established by using COMSOL Multiphysics. Results indicate that the nonlinear water flow equation could describe the nonlinear water flow process in the fractured zone of the fault. The mining stress and the water velocity had a great influence on the temperature of the fault zone. The temperature change of the fault zone can reflect the change of the seepage field in the fault and confined aquifer. This coupled THM model can provide a numerical simulation method to describe the coupled process of complex geological systems, which can be used to predict the fault water inrush induced by coal mining activities.
TB Presenting as Recurrent Pneumonia in a HIV-Infected Infant in Central Viet Nam
Authors: Nguyen Phuong , Nguyen Son , Nguyen Thinh , Marais Ben
Published: August 2018

We report on a six-month-old infant admitted to our intensive care unit (ICU) with recurrent severe pneumonia. The mother was infected with human immunodeficiency virus (HIV)-infected, but initially failed to disclose this to doctors. Neither did she report the grandmother of the child’s chronic coughing, likely due to tuberculosis (TB). The infant was diagnosed with X-pert MTB/RIF® confirmed TB and tested positive for HIV infection. Once a correct diagnosis was established, the child demonstrated good recovery with appropriate TB and antiretroviral treatment (ART). The case demonstrates the importance of including TB in the differential diagnosis for young children not responding to first-line pneumonia treatment, especially in TB endemic areas. Taking a meticulous TB and HIV exposure history, with careful consideration of potential social stigma, is essential. It also demonstrates how the inaccessibility of HIV results and the absence of a continuous patient record may jeopardize patient care.
Airborne Remote Sensing of the Upper Ocean Turbulence during CASPER-East
Authors: Savelyev Ivan , Miller William , Sletten Mark , Smith Geoffrey , Savidge Dana , Frick Glendon , Menk Steven , Moore Trent , De Paolo Tony , Terrill Eric , Wang Qing , Shearman Robert
Published: August 2018

This study takes on the challenge of resolving upper ocean surface currents with a suite of airborne remote sensing methodologies, simultaneously imaging the ocean surface in visible, infrared, and microwave bands. A series of flights were conducted over an air-sea interaction supersite established 63 km offshore by a large multi-platform CASPER-East experiment. The supersite was equipped with a range of in situ instruments resolving air-sea interface and underwater properties, of which a bottom-mounted acoustic Doppler current profiler was used extensively in this paper for the purposes of airborne current retrieval validation and interpretation. A series of water-tracing dye releases took place in coordination with aircraft overpasses, enabling dye plume velocimetry over 100 m to 10 km spatial scales. Similar scales were resolved by a Multichannel Synthetic Aperture Radar, which resolved a swath of instantaneous surface velocities (wave and current) with 10 m resolution and 5 cm/s accuracy. Details of the skin temperature variability imprinted by the upper ocean turbulence were revealed in 1–14,000 m range of spatial scales by a mid-wave infrared camera. Combined, these methodologies provide a unique insight into the complex spatial structure of the upper ocean turbulence on a previously under-resolved range of spatial scales from meters to kilometers. However, much attention in this paper is dedicated to quantifying and understanding uncertainties and ambiguities associated with these remote sensing methodologies, especially regarding the smallest resolvable turbulent scales and reference depths of retrieved currents.
Volunteered Geographic Information for Disaster Risk Reduction—The Missing Maps Approach and Its Potential within the Red Cross and Red Crescent Movement
Authors: Scholz Stefan , Knight Paul , Eckle Melanie , Marx Sabrina , Zipf Alexander
Published: August 2018

For the last few years, the increasing need for humanitarian support has led to increasing demand and responsibilities for the international humanitarian system. This trend raises questions regarding the use of alternative and complementary data sources and potential additional actors and communities that could be involved in support efforts and cover some of the tasks of humanitarian organizations. The article provides an overview of the Red Cross and Red Crescent movement, their practices and activities as well as current needs and challenges. The article illustrates the potential of OpenStreetMap and digital volunteers for humanitarian activities, with a particular focus on disaster risk reduction in the scope of the Missing Maps project. The background and objective of the collaborative project as well as its potential and impact for the Red Cross and Red Crescent movement are elucidated. The conclusion and outlook section presents future plans and visions to make further use of the potential of the Missing Maps approach in additional sectors and contexts.
Troposphere Water Vapour Tomography: A Horizontal Parameterised Approach
Authors: Zhao Qingzhi , Yao Yibin , Yao Wanqiang
Published: August 2018

Global Navigation Satellite System (GNSS) troposphere tomography has become one of the most cost-effective means to obtain three-dimensional (3-d) image of the tropospheric water vapour field. Traditional methods divide the tomography area into a number of 3-d voxels and assume that the water vapour density at any voxel is a constant during the given period. However, such behaviour breaks the spatial continuity of water vapour density in a horizontal direction and the number of unknown parameters needing to be estimated is very large. This is the focus of the paper, which tries to reconstruct the water vapor field using the tomographic technique without imposing empirical horizontal and vertical constraints. The proposed approach introduces the layered functional model in each layer vertically and only an a priori constraint is imposed for the water vapor information at the location of the radiosonde station. The elevation angle mask of 30° is determined according to the distribution of intersections between the satellite rays and different layers, which avoids the impact of ray bending and the error in slant water vapor (SWV) at low elevation angles on the tomographic result. Additionally, an optimal weighting strategy is applied to the established tomographic model to obtain a reasonable result. The tomographic experiment is performed using Global Positioning System (GPS) data of 12 receivers derived from the Satellite Positioning Reference Station Network (SatRef) in Hong Kong. The quality of the established tomographic model is validated under different weather conditions and compared with the conventional tomography method using 31-day data, respectively. The numerical result shows that the proposed method is applicable and superior to the traditional one. Comparisons of integrated water vapour (IWV) of the proposed method with that derived from radiosonde and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim data show that the root mean square (RMS)/Bias of their differences are 3.2/−0.8 mm and 3.3/−1.7 mm, respectively, while the values of traditional method are 5.1/−3.9 mm and 6.3/−5.9 mm, respectively. Furthermore, the water vapour density profiles are also compared with radiosonde and ECMWF data, and the values of RMS/Bias error for the proposed method are 0.88/0.06 g/m3 and 0.92/−0.08 g/m3, respectively, while the values of the traditional method are 1.33/0.38 g/m3 and 1.59/0.40 g/m3, respectively.
Ocean Wave Measurement Using Short-Range K-Band Narrow Beam Continuous Wave Radar
Authors: Cui Jian , Bachmayer Ralf , DeYoung Brad , Huang Weimin
Published: August 2018

We describe a technique to measure ocean wave period, height and direction. The technique is based on the characteristics of transmission and backscattering of short-range K-band narrow beam continuous wave radar at the sea surface. The short-range K-band radar transmits and receives continuous signals close to the sea surface at a low-grazing angle. By sensing the motions of a dominant facet at the sea surface that strongly scatters signals back and is located directly in front of the radar, the wave orbital velocity can be measured from the Doppler shift of the received radar signal. The period, height and direction of ocean wave are determined from the relationships among wave orbital velocity, ocean wave characteristics and the Doppler shift. Numerical simulations were performed to validate that the dominant facet exists and ocean waves are measured by sensing its motion. Validation experiments were conducted in a wave tank to verify the feasibility of the proposed ocean wave measurement method. The results of simulations and experiments demonstrate the effectiveness of the short-range K-band narrow beam continuous wave radar for the measurement of ocean waves.
Unsupervised Deep Feature Learning for Remote Sensing Image Retrieval
Authors: Tang Xu , Zhang Xiangrong , Liu Fang , Jiao Licheng
Published: August 2018

Due to the specific characteristics and complicated contents of remote sensing (RS) images, remote sensing image retrieval (RSIR) is always an open and tough research topic in the RS community. There are two basic blocks in RSIR, including feature learning and similarity matching. In this paper, we focus on developing an effective feature learning method for RSIR. With the help of the deep learning technique, the proposed feature learning method is designed under the bag-of-words (BOW) paradigm. Thus, we name the obtained feature deep BOW (DBOW). The learning process consists of two parts, including image descriptor learning and feature construction. First, to explore the complex contents within the RS image, we extract the image descriptor in the image patch level rather than the whole image. In addition, instead of using the handcrafted feature to describe the patches, we propose the deep convolutional auto-encoder (DCAE) model to deeply learn the discriminative descriptor for the RS image. Second, the k-means algorithm is selected to generate the codebook using the obtained deep descriptors. Then, the final histogrammic DBOW features are acquired by counting the frequency of the single code word. When we get the DBOW features from the RS images, the similarities between RS images are measured using L1-norm distance. Then, the retrieval results can be acquired according to the similarity order. The encouraging experimental results counted on four public RS image archives demonstrate that our DBOW feature is effective for the RSIR task. Compared with the existing RS image features, our DBOW can achieve improved behavior on RSIR.
The Locking Depth of the Cholame Section of the San Andreas Fault from ERS2-Envisat InSAR
Authors: Bacques Guillaume , De Michele Marcello , Raucoules Daniel , Aochi Hideo
Published: August 2018

The Cholame section of the San Andreas Fault (SAF), which has been considered locked since 1857, has been little studied using geodetic methods. In this study, we propose to use Interferometric Synthetic Aperture Radar (InSAR) to contribute to the improvement of the knowledge of this section of the SAF. In particular, the objective of this work is to provide a description of the transition between the Parkfield and Cholame-Carrizo segments further southeast by producing an estimate of the locking depth of the Cholame segment by combining ERS2 (European Remote Sensing) and Envisat Advanced SAR (ASAR) satellites data. Our results indicate that the locking depth between the Parkfield and the Cholame-Carrizo segments deepens to the southeast. We then use these results as a hint to refine the tectonic loading on this section of the SAF.
A Piezoresistive Sensor to Measure Muscle Contraction and Mechanomyography
Authors: Esposito Daniele , Andreozzi Emilio , Fratini Antonio , Gargiulo Gaetano , Savino Sergio , Niola Vincenzo , Bifulco Paolo
Published: August 2018

Measurement of muscle contraction is mainly achieved through electromyography (EMG) and is an area of interest for many biomedical applications, including prosthesis control and human machine interface. However, EMG has some drawbacks, and there are also alternative methods for measuring muscle activity, such as by monitoring the mechanical variations that occur during contraction. In this study, a new, simple, non-invasive sensor based on a force-sensitive resistor (FSR) which is able to measure muscle contraction is presented. The sensor, applied on the skin through a rigid dome, senses the mechanical force exerted by the underlying contracting muscles. Although FSR creep causes output drift, it was found that appropriate FSR conditioning reduces the drift by fixing the voltage across the FSR and provides voltage output proportional to force. In addition to the larger contraction signal, the sensor was able to detect the mechanomyogram (MMG), i.e., the little vibrations which occur during muscle contraction. The frequency response of the FSR sensor was found to be large enough to correctly measure the MMG. Simultaneous recordings from flexor carpi ulnaris showed a high correlation (Pearson’s r > 0.9) between the FSR output and the EMG linear envelope. Preliminary validation tests on healthy subjects showed the ability of the FSR sensor, used instead of the EMG, to proportionally control a hand prosthesis, achieving comparable performances.
Multiparametric Monitoring in Equatorian Tomato Greenhouses (II): Energy Consumption Dynamics
Authors: Erazo-Rodas Mayra , Sandoval-Moreno Mary , Muñoz-Romero Sergio , Huerta Mónica , Rivas-Lalaleo David , Rojo-Álvarez José Luis
Published: August 2018

Tomato greenhouses are a crucial element in the Equadorian economy. Wireless sensor networks (WSNs) have received much attention in recent years in specialized applications such as precision farming. The energy consumption in WSNs is relevant nowadays for their adequate operation, and attention is being paid to analyzing the affecting factors, energy optimization techniques working on the network hardware or software, and characterizing the consumption in the nodes (especially in the ZigBee standard). However, limited information exists on the analysis of the consumption dynamics in each node, across different network technologies and communication topologies, or on the incidence of data transmission speed. The present study aims to provide a detailed analysis of the dynamics of the energy consumption for tomato greenhouse monitoring in Ecuador, in three types of WSNs, namely, ZigBee with star topology, ZigBee with mesh topology (referred to here as DigiMesh), and WiFi with access point topology. The networks were installed and maintained in operation with a line of sight between nodes and a 2-m length, whereas the energy consumption measurements of each node were acquired and stored in the laboratory. Each experiment was repeated ten times, and consumption measurements were taken every ten milliseconds at a rate of fifty thousand samples for each realization. The dynamics were scrutinized by analyzing the recorded time series using stochastic-process analysis methods, including amplitude probability functions and temporal autocorrelation, as well as bootstrap resampling techniques and representations of various embodiments with the so-called M-mode plots. Our results show that the energy consumption of each network strongly depends on the type of sensors installed in the nodes and on the network topology. Specifically, the CO2 sensor has the highest power consumption because its chemical composition requires preheating to start logging measurements. The ZigBee network is more efficient in energy saving independently of the transmission rate, since the communication modules have lower average consumption in data transmission, in contrast to the DigiMesh network, whose consumption is high due to its topology. Results also show that the average energy consumption in WiFi networks is the highest, given that the coordinator node is a Meshlium™ router with larger energy demand. The transmission duration in the ZigBee network is lower than in the other two networks. In conclusion, the ZigBee network with star topology is the most energy-suitable one when designing wireless monitoring systems in greenhouses. The proposed methodology for consumption dynamics analysis in tomato greenhouse WSNs can be applied to other scenarios where the practical choice of an energy-efficient network is necessary due to energy constrains in the sensor and coordinator nodes.
Methods of Population Spatialization Based on the Classification Information of Buildings from China’s First National Geoinformation Survey in Urban Area: A Case Study of Wuchang District, Wuhan City, China
Authors: Li Linze , Li Jiansong , Jiang Zilong , Zhao Lingli , Zhao Pengcheng
Published: August 2018

Most of the currently mature methods that are used globally for population spatialization are researched on a single level, and are dependent on the spatial relationship between population and land covers (city, road, water area, etc.), resulting in difficulties in data acquisition and an inability to identify precise features on the different levels. This paper proposes a multi-level population spatialization method on the different administrative levels with the support of China’s first national geoinformation survey, and then considers several approaches to verify the results of the multi-level method. This paper aims to establish a multi-level population spatialization method that is suitable for the administrative division of districts and streets. It is assumed that the same residential house has the same population density on the district level. Based on this assumption, the least squares regression model is used to obtain the optimized prediction model and accurate population space prediction results by dynamically segmenting and aggregating house categories.In addition, it is assumed that the distribution of the population is relatively regular in communities that are spatially close to each other, and that the population densities on the street level are similar, so the average population density is assessed by optimizing the community and surrounding residential houses on the street level. Finally, the scientificalness and rationality of the proposed method is proved by spatial autocorrelation analysis, overlay analysis, cross-validation analysis and accuracy assessment methods.
Vehicle Counting Based on Vehicle Detection and Tracking from Aerial Videos
Authors: Xiang Xuezhi , Zhai Mingliang , Lv Ning , El Saddik Abdulmotaleb
Published: August 2018

Vehicle counting from an unmanned aerial vehicle (UAV) is becoming a popular research topic in traffic monitoring. Camera mounted on UAV can be regarded as a visual sensor for collecting aerial videos. Compared with traditional sensors, the UAV can be flexibly deployed to the areas that need to be monitored and can provide a larger perspective. In this paper, a novel framework for vehicle counting based on aerial videos is proposed. In our framework, the moving-object detector can handle the following two situations: static background and moving background. For static background, a pixel-level video foreground detector is given to detect vehicles, which can update background model continuously. For moving background, image-registration is employed to estimate the camera motion, which allows the vehicles to be detected in a reference coordinate system. In addition, to overcome the change of scale and shape of vehicle in images, we employ an online-learning tracker which can update the samples used for training. Finally, we design a multi-object management module which can efficiently analyze and validate the status of the tracked vehicles with multi-threading technique. Our method was tested on aerial videos of real highway scenes that contain fixed-background and moving-background. The experimental results show that the proposed method can achieve more than 90% and 85% accuracy of vehicle counting in fixed-background videos and moving-background videos respectively.
Self-Calibration Algorithm for a Pressure Sensor with a Real-Time Approach Based on an Artificial Neural Network
Authors: Almassri Ahmed M. M. , Wan Hasan Wan Zuha , Ahmad Siti Anom , Shafie Suhaidi , Wada Chikamune , Horio Keiichi
Published: August 2018

This paper presents a novel approach to predicting self-calibration in a pressure sensor using a proposed Levenberg Marquardt Back Propagation Artificial Neural Network (LMBP-ANN) model. The self-calibration algorithm should be able to fix major problems in the pressure sensor such as hysteresis, variation in gain and lack of linearity with high accuracy. The traditional calibration process for this kind of sensor is a time-consuming task because it is usually done through manual and repetitive identification. Furthermore, a traditional computational method is inadequate for solving the problem since it is extremely difficult to resolve the mathematical formula among multiple confounding pressure variables. Accordingly, this paper describes a new self-calibration methodology for nonlinear pressure sensors based on an LMBP-ANN model. The proposed method was achieved using a collected dataset from pressure sensors in real time. The load cell will be used as a reference for measuring the applied force. The proposed method was validated by comparing the output pressure of the trained network with the experimental target pressure (reference). This paper also shows that the proposed model exhibited a remarkable performance than traditional methods with a max mean square error of 0.17325 and an R-value over 0.99 for the total response of training, testing and validation. To verify the proposed model’s capability to build a self-calibration algorithm, the model was tested using an untrained input data set. As a result, the proposed LMBP-ANN model for self-calibration purposes is able to successfully predict the desired pressure over time, even the uncertain behaviour of the pressure sensors due to its material creep. This means that the proposed model overcomes the problems of hysteresis, variation in gain and lack of linearity over time. In return, this can be used to enhance the durability of the grasping mechanism, leading to a more robust and secure grasp for paralyzed hands. Furthermore, the exposed analysis approach in this paper can be a useful methodology for the user to evaluate the performance of any measurement system in a real-time environment.
Joint Center Estimation Using Single-Frame Optimization: Part 2: Experimentation
Authors: Frick Eric , Rahmatalla Salam
Published: August 2018

Human motion capture is driven by joint center location estimates, and error in their estimation can be compounded by subsequent kinematic calculations. Soft tissue artifact (STA), the motion of tissue relative to the underlying bones, is a primary cause of error in joint center calculations. A method for mitigating the effects of STA, single-frame optimization (SFO), was introduced and numerically verified in Part 1 of this work, and the purpose of this article (Part 2) is to experimentally compare the results of SFO with a marker-based solution. The experimentation herein employed a single-degree-of-freedom pendulum to simulate human joint motion, and the effects of STA were simulated by affixing the inertial measurement unit to the pendulum indirectly through raw, vacuum-sealed meat. The inertial sensor was outfitted with an optical marker adapter so that its location could be optically determined by a camera-based motion-capture system. During the motion, inertial effects and non-rigid attachment of the inertial sensor caused the simulated STA to manifest via unrestricted motion (six degrees of freedom) relative to the rigid pendulum. The redundant inertial and optical instrumentation allowed a time-varying joint center solution to be determined both by optical markers and by SFO, allowing for comparison. The experimental results suggest that SFO can achieve accuracy comparable to that of state-of-the-art joint center determination methods that use optical skin markers (root mean square error of 7.87–37.86 mm), and that the time variances of the SFO solutions are correlated (r =  0.58–0.99) with the true, time-varying joint center solutions. This suggests that SFO could potentially help to fill a gap in the existing literature by improving the characterization and mitigation of STA in human motion capture.
Indirect Measurement of Ground Reaction Forces and Moments by Means of Wearable Inertial Sensors: A Systematic Review
Authors: Ancillao Andrea , Tedesco Salvatore , Barton John , O’Flynn Brendan
Published: August 2018

In the last few years, estimating ground reaction forces by means of wearable sensors has come to be a challenging research topic paving the way to kinetic analysis and sport performance testing outside of labs. One possible approach involves estimating the ground reaction forces from kinematic data obtained by inertial measurement units (IMUs) worn by the subject. As estimating kinetic quantities from kinematic data is not an easy task, several models and protocols have been developed over the years. Non-wearable sensors, such as optoelectronic systems along with force platforms, remain the most accurate systems to record motion. In this review, we identified, selected and categorized the methodologies for estimating the ground reaction forces from IMUs as proposed across the years. Scopus, Google Scholar, IEEE Xplore, and PubMed databases were interrogated on the topic of Ground Reaction Forces estimation based on kinematic data obtained by IMUs. The identified papers were classified according to the methodology proposed: (i) methods based on direct modelling; (ii) methods based on machine learning. The methods based on direct modelling were further classified according to the task studied (walking, running, jumping, etc.). Finally, we comparatively examined the methods in order to identify the most reliable approaches for the implementation of a ground reaction force estimator based on IMU data.
DIFTOS: A Distributed Infrastructure-Free Traffic Optimization System Based on Vehicular Ad Hoc Networks for Urban Environments
Authors: Zhang Weidong , Aung Nyothiri , Dhelim Sahraoui , Ai Yibo
Published: August 2018

Aiming to alleviate traffic congestion, many congestion avoidance and traffic optimization systems have been proposed recently. However, most of them suffer from three main problems. Firstly scalability: they rely on a centralized server, which has to perform intensive communication and computational tasks. Secondly unpredictability: they use smartphones and other sensors to detect the congested roads and warn upcoming vehicles accordingly. In other words, they are used to solve the problem rather than avoiding it. Lastly, infrastructure dependency: they assume the presence of pre-installed infrastructures such as roadside unit (RSU) or cellular 3G/4G networks. Motivated by the above-mentioned reasons, in this paper, we proposed a fully distributed and infrastructure-less congestion avoidance and traffic optimization system for VANET (Vehicular Ad-hoc Networks) in urban environments named DIFTOS (Distributed Infrastructure-Free Traffic Optimization System), in which the city map is divided into a hierarchy of servers. The vehicles that are located in the busy road intersections play the role of servers, thus DIFTOS does not rely on any centralized server and does not need internet connectivity or RSU or any kind of infrastructure. As far as we know, in the literature of congestion avoidance using VANET, DIFTOS is the first completely infrastructure-free congestion avoidance system. The effectiveness and scalability of DIFTOS have been proved by simulation under different traffic conditions.
Towards a Meaningful 3D Map Using a 3D Lidar and a Camera
Authors: Jeong Jongmin , Yoon Tae Sung , Park Jin Bae
Published: August 2018

Semantic 3D maps are required for various applications including robot navigation and surveying, and their importance has significantly increased. Generally, existing studies on semantic mapping were camera-based approaches that could not be operated in large-scale environments owing to their computational burden. Recently, a method of combining a 3D Lidar with a camera was introduced to address this problem, and a 3D Lidar and a camera were also utilized for semantic 3D mapping. In this study, our algorithm consists of semantic mapping and map refinement. In the semantic mapping, a GPS and an IMU are integrated to estimate the odometry of the system, and subsequently, the point clouds measured from a 3D Lidar are registered by using this information. Furthermore, we use the latest CNN-based semantic segmentation to obtain semantic information on the surrounding environment. To integrate the point cloud with semantic information, we developed incremental semantic labeling including coordinate alignment, error minimization, and semantic information fusion. Additionally, to improve the quality of the generated semantic map, the map refinement is processed in a batch. It enhances the spatial distribution of labels and removes traces produced by moving vehicles effectively. We conduct experiments on challenging sequences to demonstrate that our algorithm outperforms state-of-the-art methods in terms of accuracy and intersection over union.
Modeling and Control of a Micro AUV: Objects Follower Approach
Authors: Monroy-Anieva Jesus Arturo , Rouviere Cyril , Campos-Mercado Eduardo , Salgado-Jimenez Tomas , Garcia-Valdovinos Luis Govinda
Published: August 2018

This work describes the modeling, control and development of a low cost Micro Autonomous Underwater Vehicle (μ-AUV), named AR2D2. The main objective of this work is to make the vehicle to detect and follow an object with defined color by means of the readings of a depth sensor and the information provided by an artificial vision system. A nonlinear PD (Proportional-Derivative) controller is implemented on the vehicle in order to stabilize the heave and surge movements. A formal stability proof of the closed-loop system using Lyapunov’s theory is given. Furthermore, the performance of the μ-AUV is validated through numerical simulations in MatLab and real-time experiments.
Blockchain and IoT Integration: A Systematic Survey
Authors: Panarello Alfonso , Tapas Nachiket , Merlino Giovanni , Longo Francesco , Puliafito Antonio
Published: August 2018

The Internet of Things (IoT) refers to the interconnection of smart devices to collect data and make intelligent decisions. However, a lack of intrinsic security measures makes IoT vulnerable to privacy and security threats. With its “security by design,” Blockchain (BC) can help in addressing major security requirements in IoT. BC capabilities like immutability, transparency, auditability, data encryption and operational resilience can help solve most architectural shortcomings of IoT. This article presents a comprehensive survey on BC and IoT integration. The objective of this paper is to analyze the current research trends on the usage of BC-related approaches and technologies in an IoT context. This paper presents the following novelties, with respect to related work: (i) it covers different application domains, organizing the available literature according to this categorization, (ii) it introduces two usage patterns, i.e., device manipulation and data management (open marketplace solution), and (iii) it reports on the development level of some of the presented solutions. We also analyze the main challenges faced by the research community in the smooth integration of BC and IoT, and point out the main open issues and future research directions. Last but not least, we also present a survey about novel uses of BC in the machine economy.
An Outlook on Physical and Virtual Sensors for a Socially Interactive Internet
Authors: Armando Ngombo , Rodrigues André , Pereira Vasco , Sá Silva Jorge , Boavida Fernando
Published: August 2018

The Internet keeps changing at a rapid pace, driven mainly by the emerging concepts and applications that make it aware of the physical world and responsive to user context. The Internet of Things (IoT) concept is quickly giving way to more advanced and highly interactive environments that go well beyond the mere sensing of the physical world. Today, in addition to traditional electronic devices, IoT sensing/actuating includes both software and human-based entities. This paper provides an outlook on the future of sensing/actuating approaches on the Internet at large, which we see increasingly related to all kinds of socially interactive technologies. With these objectives in mind, we propose a taxonomy to deal with the heterogeneity of sensing/actuating approaches in IoT. We also analyse the state-of-the-art of Social Sensing. Finally, we identify open issues and associated research opportunities, the main ones being the integration of all sensing approaches, the combination of social sciences, engineering, and computing as enablers of context-aware, cognitive applications and, last but not least, the unified management of large sets of very heterogeneous sensors/actuators.
The Shared Bicycle and Its Network—Internet of Shared Bicycle (IoSB): A Review and Survey
Authors: Shen Shu , Wei Zhao-Qing , Sun Li-Juan , Su Yang-Qing , Wang Ru-Chuan , Jiang Han-Ming
Published: August 2018

With the expansion of Intelligent Transport Systems (ITS) in smart cities, the shared bicycle has developed quickly as a new green public transportation mode, and is changing the travel habits of citizens heavily across the world, especially in China. The purpose of the current paper is to provide an inclusive review and survey on shared bicycle besides its benefits, history, brands and comparisons. In addition, it proposes the concept of the Internet of Shared Bicycle (IoSB) for the first time, as far as we know, to find a feasible solution for those technical problems of the shared bicycle. The possible architecture of IoSB in our opinion is presented, as well as most of key IoT technologies, and their capabilities to merge into and apply to the different parts of IoSB are introduced. Meanwhile, some challenges and barriers to IoSB’s implementation are expressed thoroughly too. As far as the advice for overcoming those barriers be concerned, the IoSB’s potential aspects and applications in smart city with respect to technology development in the future provide another valuable further discussion in this paper.
An Inexpensive and Easy to Use Cervical Range of Motion Measurement Solution Using Inertial Sensors
Authors: Raya Rafael , Garcia-Carmona Rodrigo , Sanchez Cristina , Urendes Eloy , Ramirez Oscar , Martin Alvaro , Otero Abraham
Published: August 2018

Neck injuries and the related pain have a high prevalence and represent an important health problem. To properly diagnose and treat them, practitioners need an accurate system for measuring Cervical Range Of Motion (CROM). This article describes the development and validation of an inexpensive, small (4 cm × 4 cm × 8 cm), light (< 200 g) and easy to use solution for measuring CROM using wearable inertial sensors. The proposed solution has been designed with the clinical practice in mind, after consulting with practitioners. It is composed of: (a) two wearable wireless MEMS-based inertial devices, (b) a recording and report generation software application and (c) a measurement protocol for assessing CROM. The solution provides accurate (none of our results is outside the ROM ranges when compared with previously published results based on an optical tracking device) and reliable measurements (ICC = 0.93 for interrater reliability when compared with an optical tracking device and ICC > 0.90 for test-retest reliability), surpassing the popular CROM instrument’s capabilities and precision. It also fulfills the needs for clinical practice attending to effectiveness, efficiency (4 min from setup to final report) and user’s satisfaction (as reported by practitioners). The solution has been certified for mass-production and use in medical environments.
Electronic Tongue for Brand Uniformity Control: A Case Study of Apulian Red Wines Recognition and Defects Evaluation †
Authors: Lvova Larisa , Yaroshenko Irina , Kirsanov Dmitry , Di Natale Corrado , Paolesse Roberto , Legin Andrey
Published: August 2018

The potentiometric electronic tongue system has been tested as a potential analytical tool for brand uniformity control of monoculture Apulian red wines (Primitivo and Negroamaro). The sensor array was composed of eight porphyrin coatings obtained by electrochemical polymerization process and was employed for both wines discrimination and quantitative detection of wine defect compounds: “off-odour” 3-(methylthio)-propanol; isoamyl alcohol fusel oil; benzaldehyde (marker of the yeast activity) and acetic acid (marker of vinegar formation). PLS-DA applied to Electronic tongue output data has permitted a correct discrimination of more than 70% of analysed wines in respect to the original brand affiliation. Satisfactory PLS1 predictions were obtained in real wine samples; with R2 = 0.989 for isoamyl alcohol and R2 = 0.732 for acetic acid. Moreover; the possibility to distinguish wine samples on the base of permitted levels of fault compounds content was shown.
Physical Extraction and Feature Fusion for Multi-Mode Signals in a Measurement System for Patients in Rehabilitation Exoskeleton
Authors: Yang Canjun , Wei Qianxiao , Wu Xin , Ma Zhangyi , Chen Qiaoling , Wang Xin , Wang Hansong , Fan Wu
Published: August 2018

Measurement system of exoskeleton robots can reflect the state of the patient. In this study, we combined an inertial measurement unit and a visual measurement unit to obtain a repeatable fusion measurement system to compensate for the deficiencies of the single data acquisition mode used by exoskeletons. Inertial measurement unit is comprised four distributed angle sensors. Triaxial acceleration and angular velocity information were transmitted to an upper computer by Bluetooth. The data sent to the control center were processed by a Kalman filter to eliminate any noise. Visual measurement unit uses camera to acquire real time images and related data information. The two data acquisition methods were fused and have its weight. Comparisons of the fusion results with individual measurement results demonstrated that the data fusion method could effectively improve the accuracy of system. It provides a set of accurate real-time measurements for patients in rehabilitation exoskeleton and data support for effective control of exoskeleton robot.