bluuebunny/crossref_metadata_2025 huggingface dataset:
Real-Time Bottom Tracking Using Side Scan Sonar Data Through One-Dimensional Convolutional Neural Networks
Authors: Yan Jun , Meng Junxia , Zhao Jianhu
Published: December 2019

As one of the most commonly used acoustic systems in seabed surveys, the altitude of the side scan sonar from the seafloor is always difficult to determine, especially when raw signal levels and gain information are unavailable. The inaccurate sonar altitudes would limit the applications of sonar image geocoding, target detection, and sediment classification. The sonar altitude can be obtained by using bottom tracking methods, but traditional methods often require manual thresholds or complex post-processing procedures, which cannot ensure accurate and real-time bottom tracking. In this paper, a real-time bottom tracking method of side scan data is proposed based on a one-dimensional convolution neural network. First, according to the characteristics of side scan backscatter strength sequences, positive (bottom sequences) and negative (water column and seabed sequences) samples are extracted to establish the sample sets. Second, a one-dimensional convolution neural network is carefully designed and trained by using the sample set to recognize the bottom sequences. Third, a complete processing procedure of the real-time bottom tracking method is established by traversing each side scan ping data and recognizing the bottom sequences. The auxiliary methods for improving real-time performance and sample data augmentation are also explained in detail. The proposed method is implemented on the measured side scan data from the marine area in Meizhou Bay. The trained network model achieves a 100% recognition of the initial sample set as well as 100% bottom tracking accuracy of the training survey line. The average bottom tracking accuracy of the testing survey lines excluding missed pings reaches 99.2%. By comparison with multi-beam bathymetric data and the statistical analysis of real-time performance, the experimental results prove the validity and accuracy of the proposed real-time bottom tracking method.
Structure-Adaptive Clutter Suppression for Infrared Small Target Detection: Chain-Growth Filtering
Authors: Huang Suqi , Liu Yuhan , He Yanmin , Zhang Tianfang , Peng Zhenming
Published: December 2019

Robust detection of infrared small target is an important and challenging task in many photoelectric detection systems. Using the difference of a specific feature between the target and the background, various detection methods were proposed in recent decades. However, most methods extract the feature in a region with fixed shape, especially in a rectangular region, which causes a problem: when faced with complex-shape clutters, the rectangular region involves the pixels inside and outside the clutters, and the significant grey-level difference among these pixels leads to a relatively large feature in the clutter area, interfering with the target detection. In this paper, we propose a structure-adaptive clutter suppression method, called chain-growth filtering, for robust infrared small target detection. The well-designed filtering model can adjust its shape to fit various clutter structures such as lines, curves and irregular edges, and thus has a more robust clutter suppression capability than the fixed-shape feature extraction strategy. In addition, the proposed method achieves a considerable anti-noise ability by employing guided filter as a preprocessing approach and enjoys the capability of multi-scale target detection without complex parameter tuning. In the experiment, we evaluate the performance of the detection method through 12 typical infrared scenes which contain different types of clutters. Compared with seven state-of-the-art methods, the proposed method shows the superior clutter-suppression effects for various types of clutters and the excellent detection performance for various scenes.
Incorporation of Unmanned Aerial Vehicle (UAV) Point Cloud Products into Remote Sensing Evapotranspiration Models
Authors: Aboutalebi Mahyar , Torres-Rua Alfonso F. , McKee Mac , Kustas William P. , Nieto Hector , Alsina Maria Mar , White Alex , Prueger John H. , McKee Lynn , Alfieri Joseph , Hipps Lawrence , Coopmans Calvin , Dokoozlian Nick
Published: December 2019

In recent years, the deployment of satellites and unmanned aerial vehicles (UAVs) has led to production of enormous amounts of data and to novel data processing and analysis techniques for monitoring crop conditions. One overlooked data source amid these efforts, however, is incorporation of 3D information derived from multi-spectral imagery and photogrammetry algorithms into crop monitoring algorithms. Few studies and algorithms have taken advantage of 3D UAV information in monitoring and assessment of plant conditions. In this study, different aspects of UAV point cloud information for enhancing remote sensing evapotranspiration (ET) models, particularly the Two-Source Energy Balance Model (TSEB), over a commercial vineyard located in California are presented. Toward this end, an innovative algorithm called Vegetation Structural-Spectral Information eXtraction Algorithm (VSSIXA) has been developed. This algorithm is able to accurately estimate height, volume, surface area, and projected surface area of the plant canopy solely based on point cloud information. In addition to biomass information, it can add multi-spectral UAV information to point clouds and provide spectral-structural canopy properties. The biomass information is used to assess its relationship with in situ Leaf Area Index (LAI), which is a crucial input for ET models. In addition, instead of using nominal field values of plant parameters, spatial information of fractional cover, canopy height, and canopy width are input to the TSEB model. Therefore, the two main objectives for incorporating point cloud information into remote sensing ET models for this study are to (1) evaluate the possible improvement in the estimation of LAI and biomass parameters from point cloud information in order to create robust LAI maps at the model resolution and (2) assess the sensitivity of the TSEB model to using average/nominal values versus spatially-distributed canopy fractional cover, height, and width information derived from point cloud data. The proposed algorithm is tested on imagery from the Utah State University AggieAir sUAS Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) collected since 2014 over multiple vineyards located in California. The results indicate a robust relationship between in situ LAI measurements and estimated biomass parameters from the point cloud data, and improvement in the agreement between TSEB model output of ET with tower measurements when employing LAI and spatially-distributed canopy structure parameters derived from the point cloud data.
Remote Sensing for Optimal Estimation of Water Temperature Dynamics in Shallow Tidal Environments
Authors: Pivato Mattia , Carniello Luca , Viero Daniele Pietro , Soranzo Chiara , Defina Andrea , Silvestri Sonia
Published: December 2019

Given the increasing anthropogenic pressures on lagoons, estuaries, and lakes and considering the highly dynamic behavior of these systems, methods for the continuous and spatially distributed retrieval of water quality are becoming vital for their correct monitoring and management. Water temperature is certainly one of the most important drivers that influence the overall state of coastal systems. Traditionally, lake, estuarine, and lagoon temperatures are observed through point measurements carried out during field campaigns or through a network of sensors. However, sporadic measuring campaigns or probe networks rarely attain a density sufficient for process understanding, model development/validation, or integrated assessment. Here, we develop and apply an integrated approach for water temperature monitoring in a shallow lagoon which incorporates satellite and in-situ data into a mathematical model. Specifically, we use remote sensing information to constrain large-scale patterns of water temperature and high-frequency in situ observations to provide proper time constraints. A coupled hydrodynamic circulation-heat transport model is then used to propagate the state of the system forward in time between subsequent remote sensing observations. Exploiting the satellite data high spatial resolution and the in situ measurements high temporal resolution, the model may act a physical interpolator filling the gap intrinsically characterizing the two monitoring techniques.
Mapping Cynodon Dactylon Infesting Cover Crops with an Automatic Decision Tree-OBIA Procedure and UAV Imagery for Precision Viticulture
Authors: de Castro Ana I. , Peña José M. , Torres-Sánchez Jorge , Jiménez-Brenes Francisco M. , Valencia-Gredilla Francisco , Recasens Jordi , López-Granados Francisca
Published: December 2019

The establishment and management of cover crops are common practices widely used in irrigated viticulture around the world, as they bring great benefits not only to protect and improve the soil, but also to control vine vigor and improve the yield quality, among others. However, these benefits are often reduced when cover crops are infested by Cynodon dactylon (bermudagrass), which impacts crop production due to its competition for water and nutrients and causes important economic losses for the winegrowers. Therefore, the discrimination of Cynodon dactylon in cover crops would enable site-specific control to be applied and thus drastically mitigate damage to the vineyard. In this context, this research proposes a novel, automatic and robust image analysis algorithm for the quick and accurate mapping of Cynodon dactylon growing in vineyard cover crops. The algorithm was developed using aerial images taken with an Unmanned Aerial Vehicle (UAV) and combined decision tree (DT) and object-based image analysis (OBIA) approaches. The relevance of this work consisted in dealing with the constraint caused by the spectral similarity of these complex scenarios formed by vines, cover crops, Cynodon dactylon, and bare soil. The incorporation of height information from the Digital Surface Model and several features selected by machine learning tools in the DT-OBIA algorithm solved this spectral similarity limitation and allowed the precise design of Cynodon dactylon maps. Another contribution of this work is the short time needed to apply the full process from UAV flights to image analysis, which can enable useful maps to be created on demand (within two days of the farmer´s request) and is thus timely for controlling Cynodon dactylon in the herbicide application window. Therefore, this combination of UAV imagery and a DT-OBIA algorithm would allow winegrowers to apply site-specific control of Cynodon dactylon and maintain cover crop-based management systems and their consequent benefits in the vineyards, and also comply with the European legal framework for the sustainable use of agricultural inputs and implementation of integrated crop management.
An Experimental Evaluation of the Feasibility of Inferring Concentrations of a Visible Tracer Dye from Remotely Sensed Data in Turbid Rivers
Authors: Legleiter Carl J. , Manley Paul V. , Erwin Susannah O. , Bulliner Edward A.
Published: December 2019

The movement of contaminants and biota within river channels is influenced by the flow field via various processes of dispersion. Understanding and modeling of these processes thus can facilitate applications ranging from the prediction of travel times for spills of toxic materials to the simulation of larval drift for endangered species of fish. A common means of examining dispersion in rivers involves conducting tracer experiments with a visible tracer dye. Whereas conventional in situ instruments can only measure variations in dye concentration over time at specific, fixed locations, remote sensing could provide more detailed, spatially-distributed information for characterizing dispersion patterns and validating two-dimensional numerical models. Although previous studies have demonstrated the potential to infer dye concentrations from remotely sensed data in clear-flowing streams, whether this approach can be applied to more turbid rivers remains an open question. To evaluate the feasibility of mapping spatial patterns of dispersion in streams with greater turbidity, we conducted an experiment that involved manipulating dye concentration and turbidity within a pair of tanks while acquiring field spectra and hyperspectral and RGB (red, green, blue) images from a small Unoccupied Aircraft System (sUAS). Applying an optimal band ratio analysis (OBRA) algorithm to these data sets indicated strong relationships between spatially averaged reflectance (i.e., water color) and Rhodamine WT dye concentration across four different turbidity levels from 40–60 NTU. Moreover, we obtained high correlations between spectrally based quantities (i.e., band ratios) and dye concentration for the original, essentially continuous field spectra; field spectra resampled to the bands of a five-band imaging system and an RGB camera; and both hyperspectral and RGB images acquired from an sUAS during the experiment. The results of this study thus confirmed the potential to map dispersion patterns of tracer dye via remote sensing and suggested that this empirical approach can be extended to more turbid rivers than those examined previously.
Delineation of Agricultural Field Boundaries from Sentinel-2 Images Using a Novel Super-Resolution Contour Detector Based on Fully Convolutional Networks
Authors: Masoud Khairiya Mudrik , Persello Claudio , Tolpekin Valentyn A.
Published: December 2019

Boundaries of agricultural fields are important features necessary for defining the location, shape, and spatial extent of agricultural units. They are commonly used to summarize production statistics at the field level. In this study, we investigate the delineation of agricultural field boundaries (AFB) from Sentinel-2 satellite images acquired over the Flevoland province, the Netherlands, using a deep learning technique based on fully convolutional networks (FCNs). We designed a multiple dilation fully convolutional network (MD-FCN) for AFB detection from Sentinel-2 images at 10 m resolution. Furthermore, we developed a novel super-resolution semantic contour detection network (named SRC-Net) using a transposed convolutional layer in the FCN architecture to enhance the spatial resolution of the AFB output from 10 m to 5 m resolution. The SRC-Net also improves the AFB maps at 5 m resolution by exploiting the spatial-contextual information in the label space. The results of the proposed SRC-Net outperform alternative upsampling techniques and are only slightly inferior to the results of the MD-FCN for AFB detection from RapidEye images acquired at 5 m resolution.
Low Overlapping Point Cloud Registration Using Line Features Detection
Authors: Prokop Miloš , Shaikh Salman Ahmed , Kim Kyoung-Sook
Published: December 2019

Modern robotic exploratory strategies assume multi-agent cooperation that raises a need for an effective exchange of acquired scans of the environment with the absence of a reliable global positioning system. In such situations, agents compare the scans of the outside world to determine if they overlap in some region, and if they do so, they determine the right matching between them. The process of matching multiple point-cloud scans is called point-cloud registration. Using the existing point-cloud registration approaches, a good match between any two-point-clouds is achieved if and only if there exists a large overlap between them, however, this limits the advantage of using multiple robots, for instance, for time-effective 3D mapping. Hence, a point-cloud registration approach is highly desirable if it can work with low overlapping scans. This work proposes a novel solution for the point-cloud registration problem with a very low overlapping area between the two scans. In doing so, no initial relative positions of the point-clouds are assumed. Most of the state-of-the-art point-cloud registration approaches iteratively match keypoints in the scans, which is computationally expensive. In contrast to the traditional approaches, a more efficient line-features-based point-cloud registration approach is proposed in this work. This approach, besides reducing the computational cost, avoids the problem of high false-positive rate of existing keypoint detection algorithms, which becomes especially significant in low overlapping point-cloud registration. The effectiveness of the proposed approach is demonstrated with the help of experiments.
Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges
Authors: Yao Xiaochuang , Li Guoqing , Xia Junshi , Ben Jin , Cao Qianqian , Zhao Long , Ma Yue , Zhang Lianchong , Zhu Dehai
Published: December 2019

In the era of big data, the explosive growth of Earth observation data and the rapid advancement in cloud computing technology make the global-oriented spatiotemporal data simulation possible. These dual developments also provide advantageous conditions for discrete global grid systems (DGGS). DGGS are designed to portray real-world phenomena by providing a spatiotemporal unified framework on a standard discrete geospatial data structure and theoretical support to address the challenges from big data storage, processing, and analysis to visualization and data sharing. In this paper, the trinity of big Earth observation data (BEOD), cloud computing, and DGGS is proposed, and based on this trinity theory, we explore the opportunities and challenges to handle BEOD from two aspects, namely, information technology and unified data framework. Our focus is on how cloud computing and DGGS can provide an excellent solution to enable big Earth observation data. Firstly, we describe the current status and data characteristics of Earth observation data, which indicate the arrival of the era of big data in the Earth observation domain. Subsequently, we review the cloud computing technology and DGGS framework, especially the works and contributions made in the field of BEOD, including spatial cloud computing, mainstream big data platform, DGGS standards, data models, and applications. From the aforementioned views of the general introduction, the research opportunities and challenges are enumerated and discussed, including EO data management, data fusion, and grid encoding, which are concerned with analysis models and processing performance of big Earth observation data with discrete global grid systems in the cloud environment.
Optimal Spectral Wavelengths for Discriminating Orchard Species Using Multivariate Statistical Techniques
Authors: Abbasi Mozhgan , Verrelst Jochem , Mirzaei Mohsen , Marofi Safar , Riyahi Bakhtiari Hamid Reza
Published: December 2019

Sustainable management of orchard fields requires detailed information about the tree types, which is a main component of precision agriculture programs. To this end, hyperspectral imagery can play a major role in orchard tree species mapping. Efficient use of hyperspectral data in combination with field measurements requires the development of optimized band selection strategies to separate tree species. In this study, field spectroscopy (350 to 2500 nm) was performed through scanning 165 spectral leaf samples of dominant orchard tree species (almond, walnut, and grape) in Chaharmahal va Bakhtiyari province, Iran. Two multivariable methods were employed to identify the optimum wavelengths: the first includes three-step approach ANOVA, random forest classifier (RFC) and principal component analysis (PCA), and the second employs partial least squares (PLS). For both methods we determined whether tree species can be spectrally separated using discriminant analysis (DA) and then the optimal wavelengths were identified for this purpose. Results indicate that all species express distinct spectral behaviors at the beginning of the visible range (from 350 to 439 nm), the red edge and the near infrared wavelengths (from 701 to 1405 nm). The ANOVA test was able to reduce primary wavelengths (2151) to 792, which had a significant difference (99% confidence level), then the RFC further reduced the wavelengths to 118. By removing the overlapping wavelengths, the PCA represented five components (99.87% of variance) which extracted optimal wavelengths were: 363, 423, 721, 1064, and 1388 nm. The optimal wavelengths for the species discrimination using the best PLS-DA model (100% accuracy) were at 397, 515, 647, 1386, and 1919 nm.
A Systematic Study on Transit Time and Its Impact on Accuracy of Concentration Measured by Microfluidic Devices
Authors: Zhang Yushan , Guo Tianyi , Xu Changqing
Published: December 2019

Gating or threshold selection is very important in analyzing data from a microflow cytometer, which is especially critical in analyzing weak signals from particles/cells with small sizes. It has been reported that using the amplitude gating alone may result in false positive events in analyzing data with a poor signal-to-noise ratio. Transit time (τ) can be set as a gating threshold along with side-scattered light or fluorescent light signals in the detection of particles/cells using a microflow cytometer. In this study, transit time of microspheres was studied systematically when the microspheres passed through a laser beam in a microflow cytometer and side-scattered light was detected. A clear linear relationship between the inverse of the average transit time and total flow rate was found. Transit time was used as another gate (other than the amplitude of side-scattering signals) to distinguish real scattering signals from noise. It was shown that the relative difference of the measured microsphere concentration can be reduced significantly from the range of 3.43%–8.77% to the range of 8.42%–111.76% by employing both amplitude and transit time as gates in analysis of collected scattering data. By using optimized transit time and amplitude gate thresholds, a good correlation with the traditional hemocytometer-based particle counting was achieved (R2 > 0.94). The obtained results suggest that the transit time could be used as another gate together with the amplitude gate to improve measurement accuracy of particle/cell concentration for microfluidic devices.
A Distributed Testbed for 5G Scenarios: An Experimental Study
Authors: Chamran Mohammad Kazem , Yau Kok-Lim Alvin , Noor Rafidah M. D. , Wong Richard
Published: December 2019

This paper demonstrates the use of Universal Software Radio Peripheral (USRP), together with Raspberry Pi3 B+ (RP3) as the brain (or the decision making engine), to develop a distributed wireless network in which nodes can communicate with other nodes independently and make decision autonomously. In other words, each USRP node (i.e., sensor) is embedded with separate processing units (i.e., RP3), which has not been investigated in the literature, so that each node can make independent decisions in a distributed manner. The proposed testbed in this paper is compared with the traditional distributed testbed, which has been widely used in the literature. In the traditional distributed testbed, there is a single processing unit (i.e., a personal computer) that makes decisions in a centralized manner, and each node (i.e., USRP) is connected to the processing unit via a switch. The single processing unit exchanges control messages with nodes via the switch, while the nodes exchange data packets among themselves using a wireless medium in a distributed manner. The main disadvantage of the traditional testbed is that, despite the network being distributed in nature, decisions are made in a centralized manner. Hence, the response delay of the control message exchange is always neglected. The use of such testbed is mainly due to the limited hardware and monetary cost to acquire a separate processing unit for each node. The experiment in our testbed has shown the increase of end-to-end delay and decrease of packet delivery ratio due to software and hardware delays. The observed multihop transmission is performed using device-to-device (D2D) communication, which has been enabled in 5G. Therefore, nodes can either communicate with other nodes via: (a) a direct communication with the base station at the macrocell, which helps to improve network performance; or (b) D2D that improve spectrum efficiency, whereby traffic is offloaded from macrocell to small cells. Our testbed is the first of its kind in this scale, and it uses RP3 as the distributed decision-making engine incorporated into the USRP/GNU radio platform. This work provides an insight to the development of a 5G network.
A Data Clustering Algorithm for Detecting Selective Forwarding Attack in Cluster-Based Wireless Sensor Networks
Authors: Fu Hao , Liu Yinghong , Dong Zhe , Wu Yuanming
Published: December 2019

In cluster-based wireless sensor networks, cluster heads (CHs) gather and fuse data packets from sensor nodes; then, they forward fused packets to the sink node (SN). This helps wireless sensor networks balance energy effectively and efficiently to prolong their lifetime. However, cluster-based WSNs are vulnerable to selective forwarding attacks. Compromised CHs would become malicious and launch selective forwarding attacks in which they drop part of or all the packets from other nodes. In this paper, a data clustering algorithm (DCA) for detecting a selective forwarding attack (DCA-SF) is proposed. It can capture and isolate malicious CHs that have launched selective forwarding attacks by clustering their cumulative forwarding rates (CFRs). The DCA-SF algorithm has been strengthened by changing the DCA parameters (Eps, Minpts) adaptively. The simulation results show that the DCA-SF has a low missed detection rate of 1.04% and a false detection rate of 0.42% respectively with low energy consumption.
Towards Improved Field Application of Using Distributed Temperature Sensing for Soil Moisture Estimation: A Laboratory Experiment
Authors: Apperl Benjamin , Bernhardt Matthias , Schulz Karsten
Published: December 2019

The “dual probe heat pulse” (DPHP) method using actively heated fiber optic (AHFO) cables combined with distributed temperate sensing (DTS) technology has been developed for monitoring thermal properties and soil water content at the field scale. Field scale application, however, requires the use of robust and thicker fiber optic cables, corroborating the assumption of an infinite thin heat source in the evaluation process. We therefore included a semi-analytical solution of the heat transport equation into the evaluation procedure in order to consider the finite thermal properties of the heating cable without a calibration procedure to estimate effective thermal properties of the soil. To test this new evaluation procedure, we conducted a laboratory experiment and tested different heating scenarios to infer soil moisture from volumetric heat capacity. Estimates were made by analyzing the shift of the temperature amplitude at the sensing cable and the characteristics of the response heating curve. The results were compared with results from the calibrated infinite line source solution and in situ water content point measurements and showed a good approximation of thermal properties for strong and short heat pulses. Volumetric water content estimates are similarly accurate to the results of the calibrated infinite line source solution. Problems arose with the cable spacing and the resettlement process after burying the cable.
Extending the Battery Life of the ZigBee Routers and Coordinator by Modifying Their Mode of Operation
Authors: Marrero Domingo , Suárez Alvaro , Macías Elsa , Mena Vicente
Published: December 2019

Wireless sensor networks proliferate more and more in all social scopes and sectors. Such networks are implemented in smart homes, smart cities, security systems, medical resources, agriculture, automotive industry, etc. Communication devices and sensors of such networks are powered with batteries: the enlarging of battery life is a hot research topic. We focus on wireless sensor networks based on ZigBee technology. While sleep standard operation mode is defined for end devices, it is not the case for the rest of devices (routers and Coordinator), which usually always remain in active mode. We designed a formal optimization model for maximizing the enlarging of the battery life of routers and Coordinator, allowing us to delimit practical successful conditions. It was successfully tested with a standard ZigBee datasheet comprising technical data for sensors, routers, and coordinators. It was tested in a practical wireless sensor network assembly with XBee S2C devices. We derived, from the previous model, a novel but simple protocol of communication among routers and coordinators. It was tested in different use cases. We showed that when end devices generate traffic at regular intervals, the enlarging of the battery life of routers and Coordinator was possible only under certain use cases.
Development of a Multi-Array Pressure Sensor Module for Radial Artery Pulse Wave Measurement
Authors: Roh Donggeun , Han Sangjin , Park Junyung , Shin Hangsik
Published: December 2019

This study proposes a new structure for a pressure sensor module that can reduce errors caused by measurement position and direction in noninvasive radial artery pulse wave measurement, which is used for physiological monitoring. We have proposed a structure for a multi-array pressure sensor with a hexagonal arrangement and polydimethylsiloxane that easily fits to the structure of the radial artery, and evaluated the characteristics and pulse wave measurement of the developed sensor by finite element method simulation, a push–pull gauge test, and an actual pulse wave measurement experiment. The developed sensor has a measuring area of 17.6 × 17.6 mm2 and a modular structure with the analog front end embedded on the printed circuit board. The finite element method simulation shows that the developed sensor responds linearly to external pressure. According to the push–pull gauge test results for each channel, there were differences between the channels caused by the unit sensor characteristics and fabrication process. However, the correction formula can minimize the differences and ensure the linearity, and root-mean-squared error is 0.267 kPa in calibrated output. Although additional experiments and considerations on inter-individual differences are required, the results suggested that the proposed multiarray sensor could be used as a radial arterial pulse wave sensor.
Routing Schemes in FANETs: A Survey
Authors: Khan Muhammad Fahad , Yau Kok-Lim Alvin , Noor Rafidah Md , Imran Muhammad Ali
Published: December 2019

Flying ad hoc network (FANET) is a self-organizing wireless network that enables inexpensive, flexible, and easy-to-deploy flying nodes, such as unmanned aerial vehicles (UAVs), to communicate among themselves in the absence of fixed network infrastructure. FANET is one of the emerging networks that has an extensive range of next-generation applications. Hence, FANET plays a significant role in achieving application-based goals. Routing enables the flying nodes to collaborate and coordinate among themselves and to establish routes to radio access infrastructure, particularly FANET base station (BS). With a longer route lifetime, the effects of link disconnections and network partitions reduce. Routing must cater to two main characteristics of FANETs that reduce the route lifetime. Firstly, the collaboration nature requires the flying nodes to exchange messages and to coordinate among themselves, causing high energy consumption. Secondly, the mobility pattern of the flying nodes is highly dynamic in a three-dimensional space and they may be spaced far apart, causing link disconnection. In this paper, we present a comprehensive survey of the limited research work of routing schemes in FANETs. Different aspects, including objectives, challenges, routing metrics, characteristics, and performance measures, are covered. Furthermore, we present open issues.
A 28 nm Bulk-CMOS Analog Front-End for High-Rate ATLAS Muon Drift-Tube Detectors
Authors: Pipino Alessandra , Resta Federica , Mangiagalli Luca , De Matteis Marcello , Kroha Hubert , Richter Robert , Kortner Oliver , Baschirotto Andrea
Published: December 2019

This paper presents the design and experimental characterization of a 28 nm Complementary Metal Oxide Semiconductor (CMOS) Analog Front-End (AFE) for fast-tracking small-diameter Muon Drift-Tube (sMDT) detectors. The device exploits an innovative analog signal processing that allows a strong increase in the detection rate of events and significantly reduces the impact of fake/pile-up events, which often corrupt incident radiation energy events. The proposed device converts the input charge coming from incident radiations into voltage by a dedicated Charge-Sensitive Preamplifier (CSPreamp). Therefore, the fast-tracking concept relies on sampling the slope of the CSPreamp output voltage and using it for detecting both the incident event arrival instant and the amount of charge that has been effectively read out by MDT detectors. This avoids the long processing times intrinsically needed for baseline recovery transient, during which the detected signal can be severely corrupted by additional and unwanted extra-events, resulting in extra-charge (and thus in CSP output voltage extra-transient) during the signal roll-off. The proposed analog channel operates with a 5–100 fC input charge range and has a maximum dead-time of 200 ns (against the 545 ns of the state-of-the-art). It occupies 0.03 mm2 and consumes 1.9 mW from 1 V of supply voltage.
Preparation and NH3 Gas-Sensing Properties of Double-Shelled Hollow ZnTiO3 Microrods
Authors: Su Pi-Guey , Liu Xiang-Hong
Published: December 2019

A novel double-shelled hollow (DSH) structure of ZnTiO3 microrods was prepared by self-templating route with the assistance of poly(diallyldimethylammonium chloride) (PDDA) in an ethylene glycol (EG) solution, which was followed by calcining. Moreover, the NH3 gas-sensing properties of the DSH ZnTiO3 microrods were studied at room temperature. The morphology and composition of DSH ZnTiO3 microrods films were analyzed using scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X-ray diffractometry (XRD). The formation process of double-shelled hollow microrods was discussed in detail. The comparative gas-sensing results revealed that the DSH ZnTiO3 microrods had a higher response to NH3 gas at room temperature than those of the TiO2 solid microrods and DSH ZnTiO3 microrods did in the dark. More importantly, the DSH ZnTiO3 microrods exhibited a strong response to low concentrations of NH3 gas at room temperature.
Estimating the Instantaneous Screw Axis and the Screw Axis Invariant Descriptor of Motion by Means of Inertial Sensors: An Experimental Study with a Mechanical Hinge Joint and Comparison to the Optoelectronic System
Authors: Ancillao Andrea , Vochten Maxim , Aertbeliën Erwin , Decré Wilm , De Schutter Joris
Published: December 2019

The motion of a rigid body can be represented by the instantaneous screw axis (ISA, also known as the helical axis). Recently, an invariant representation of motion based on the ISA, namely, the screw axis invariant descriptor (SAID), was proposed in the literature. The SAID consists of six scalar features that are independent from the coordinate system chosen to represent the motion. This method proved its usefulness in robotics; however, a high sensitivity to noise was observed. This paper aims to explore the performance of inertial sensors for the estimation of the ISA and the SAID for a simple experimental setup based on a hinge joint. The free swing motion of the mechanical hinge was concurrently recorded by a marker-based optoelectronic system (OS) and two magnetic inertial measurement units (MIMUs). The ISA estimated by the MIMU was more precise, while the OS was more accurate. The mean angular error was ≈2.2° for the OS and was ≈4.4° for the MIMU, while the mean standard deviation was ≈2.3° for the OS and was ≈0.2° for the MIMU. The SAID features based on angular velocity were better estimated by the MIMU, while the features based on translational velocity were better estimated by the OS. Therefore, a combination of both measurements systems is recommended to accurately estimate the complete SAID.
Cancer-Specific hNQO1-Responsive Biocompatible Naphthalimides Providing a Rapid Fluorescent Turn-On with an Enhanced Enzyme Affinity
Authors: Park Sun Young , Jung Eugeine , Kim Jong Seung , Chi Sung-Gil , Lee Min Hee
Published: December 2019

Human NAD(P)H:quinone oxidoreductase 1 (hNQO1) is overexpressed in cancer cells and associated with the drug resistance factor of cancer. The objective of this work is the development of fluorescent probes for the efficient detection of hNQO1 activity in cancer cells, which can be employed for the cancer diagnosis and therapeutic agent development. Herein, we report naphthalimide-based fluorescent probes 1 and 2 that can detect hNQO1. For hNQO1 activity, the probes showed a significant fluorescence increase at 540 nm. In addition, probe 1, the naphthalimide containing a triphenylphosphonium salt, showed an enhanced enzyme efficiency and rapid detection under a physiological condition. The detection ability of probe 1 was superior to that of other previously reported probes. Moreover, probe 1 was less cytotoxic during the cancer cell imaging and readily provided a strong fluorescence in hNQO1-overexpressed cancer cells (A549). We proposed that probe 1 can be used to detect hNQO1 expression in live cells and it will be applied to develop the diagnosis and customized treatment of hNQO1-related disease.
A Real-Time Automatic Plate Recognition System Based on Optical Character Recognition and Wireless Sensor Networks for ITS
Authors: Dalarmelina Nicole do Vale , Teixeira Marcio Andrey , Meneguette Rodolfo I.
Published: December 2019

Automatic License Plate Recognition has been a recurrent research topic due to the increasing number of cameras available in cities, where most of them, if not all, are connected to the Internet. The video traffic generated by the cameras can be analyzed to provide useful insights for the transportation segment. This paper presents the development of an intelligent vehicle identification system based on optical character recognition (OCR) method to be used on intelligent transportation systems. The proposed system makes use of an intelligent parking system named Smart Parking Service (SPANS), which is used to manage public or private spaces. Using computer vision techniques, the SPANS system is used to detect if the parking slots are available or not. The proposed system makes use of SPANS framework to capture images of the parking spaces and identifies the license plate number of the vehicles that are moving around the parking as well as parked in the parking slots. The recognition of the license plate is made in real-time, and the performance of the proposed system is evaluated in real-time.
Salt Priming Protects Photosynthetic Electron Transport against Low-Temperature-Induced Damage in Wheat
Authors: Li Hui , Li Huawei , Lv Yanjie , Wang Yongjun , Wang Zongshuai , Xin Caiyun , Liu Shengqun , Zhu Xiancan , Song Fengbin , Li Xiangnan
Published: December 2019

Low temperature limits the photochemical efficiency of photosystems in wheat plants. To test the effect of salt priming on the photosynthetic electron transport in wheat under low temperature, the germinating seeds of a winter wheat cv. Jimai44 were primed with varying concentrations of NaCl solutions (0, 10, 30, and 50 mM NaCl, indicated by S0, S10, S30, and S50, respectively) for 6 d, and after 11 d of recovery, the seedlings were subsequently exposed to 24-h low-temperature stress (2 °C). Under low temperature, the S30 plants possessed the highest absorption flux per reaction center and higher density of reaction center per cross-section among the treatments. In addition, S30 plants had higher trapped energy flux for reducing QA and fraction of QA-reducing reaction centers and non-QB reducing center than the non-primed plants under low temperature, indicating that S30 plants could maintain the energy balance of photosystems and a relatively higher maximum quantum efficiency of photosystem II under low temperature. In addition, the low temperature-induced MDA accumulation and cell death were alleviated by salt priming in S30 plants. It was suggested that salt priming with an optimal concentration of NaCl solution (30 mM) during seed germination enhanced the photochemical efficiency of photosystems in wheat seedlings, which could be a potential approach to improve cold tolerance in wheat at an early stage.
Cluster-Based Control Plane Messages Management in Software-Defined Flying Ad-Hoc Network
Authors: Cumino Pedro , Maciel Kaled , Tavares Thaís , Oliveira Helder , Rosário Denis , Cerqueira Eduardo
Published: December 2019

Collaboration between multiple Unmanned Aerial Vehicles (UAVs) to establish a Flying Ad-hoc Network (FANET) is a growing trend since future applications claim for more autonomous and rapidly deployable systems. In this context, Software-Defined Networking FANET (SDN-FANET ) separates the control and data plane and provides network programmability, which considers a centralized controller to perform all FANET control functions based on global UAV context information, such as UAV positions, movement trajectories, residual energy, and others. However, control message dissemination in an SDN-FANET with low overhead and high performance is not a trivial task due to FANET particular characteristics, i.e., high mobility, failures in UAV to UAV communication, and short communication range. With this in mind, it is essential to predict UAV information for control message dissemination as well as consider hierarchical network architecture, reducing bandwidth consumption and signaling overhead. In this article, we present a Cluster-bAsed control Plane messages management in sOftware-defined flying ad-hoc NEtwork, called CAPONE. Based on UAV contextual information, the controller can predict UAV information without control message transmission. In addition, CAPONE divides the FANET into groups by computing the number of clusters using the Gap statistics method, which is input for a Fuzzy C-means method to determine the group leader and members. In this way, CAPONE reduces the bandwidth consumption and signaling overhead, while guaranteeing the control message delivering in FANET scenarios. Extensive simulations are used to show the gains of the CAPONE in terms of Packet Delivery Ratio, overhead, and energy compared to existing SDN-FANET architectures.
Kinematic Modeling of a Combined System of Multiple Mecanum-Wheeled Robots with Velocity Compensation
Authors: Li Yunwang , Ge Shirong , Dai Sumei , Zhao Lala , Yan Xucong , Zheng Yuwei , Shi Yong
Published: December 2019

In industry, combination configurations composed of multiple Mecanum-wheeled mobile robots are adopted to transport large-scale objects. In this paper, a kinematic model with velocity compensation of the combined mobile system is created, aimed to provide a theoretical kinematic basis for accurate motion control. Motion simulations of a single four-Mecanum-wheeled virtual robot prototype on RecurDyn and motion tests of a robot physical prototype are carried out, and the motions of a variety of combined mobile configurations are also simulated. Motion simulation and test results prove that the kinematic models of single- and multiple-robot combination systems are correct, and the inverse kinematic correction model with velocity compensation matrix is feasible. Through simulations or experiments, the velocity compensation coefficients of the robots can be measured and the velocity compensation matrix can be created. This modified inverse kinematic model can effectively reduce the errors of robot motion caused by wheel slippage and improve the motion accuracy of the mobile robot system.
Architecting and Deploying IoT Smart Applications: A Performance–Oriented Approach
Authors: Zyrianoff Ivan , Heideker Alexandre , Silva Dener , Kleinschmidt João , Soininen Juha-Pekka , Salmon Cinotti Tullio , Kamienski Carlos
Published: December 2019

Layered internet of things (IoT) architectures have been proposed over the last years as they facilitate understanding the roles of different networking, hardware, and software components of smart applications. These are inherently distributed, spanning from devices installed in the field up to a cloud datacenter and further to a user smartphone, passing by intermediary stages at different levels of fog computing infrastructure. However, IoT architectures provide almost no hints on where components should be deployed. IoT Software Platforms derived from the layered architectures are expected to adapt to scenarios with different characteristics, requirements, and constraints from stakeholders and applications. In such a complex environment, a one-size-fits-all approach does not adapt well to varying demands and may hinder the adoption of IoT Smart Applications. In this paper, we propose a 5-layer IoT Architecture and a 5-stage IoT Computing Continuum, as well as provide insights on the mapping of software components of the former into physical locations of the latter. Also, we conduct a performance analysis study with six configurations where components are deployed into different stages. Our results show that different deployment configurations of layered components into staged locations generate bottlenecks that affect system performance and scalability. Based on that, policies for static deployment and dynamic migration of layered components into staged locations can be identified.
Characterisation of Morphic Sensors for Body Volume and Shape Applications
Authors: El Arja Sami , Jayarathna Titus , Naik Ganesh , Breen Paul , Gargiulo Gaetano
Published: December 2019

Stretchable conductive materials are originally conceived as radio frequency (RF) and electromagnetic interference (EMI) shielding materials, and, under stretch, they generally function as distributed strain-gauges. These commercially available conductive elastomers have found their space in low power health monitoring systems, for example, to monitor respiratory and cardiac functions. Conductive elastomers do not behave linearly due to material constraints; hence, when used as a sensor, a full characterisation to identify ideal operating ranges are required. In this paper, we studied how the continuous stretch cycles affected the material electrical and physical properties in different embodiment impressed by bodily volume change. We simulated the stretch associated with breathing using a bespoke stress rig to ensure reproducibility of results. The stretch rig is capable of providing constant sinusoidal waves in the physiological ranges of extension and frequency. The material performances is evaluated assessing the total harmonic distortion (THD), signal-to-noise ratio (SNR), correlation coefficient, peak to peak (P-P) amplitude, accuracy, repeatability, hysteresis, delay, and washability. The results showed that, among the three controlled variables, stretch length, stretch frequency and fabric width, the most significant factor to the signal quality is the stretch length. The ideal working region is within 2% of the original length. The material cut in strips of >3 mm show more reliable to handle a variety of stretch parameter without losing its internal characteristics and electrical properties.
Sinusitis to Relaunch
Authors: Vazquez Franck
Published: December 2019

Since its inception in 2016 Sinusitis has published 25 articles on the topic of rhinitis, rhinosinusitis, nasal polyps, surgery in diseases of the upper airways, and new techniques and methods to investigate and treat upper airways disease [...]