bluuebunny/crossref_metadata_2025 huggingface dataset:
Atmosphere Boundary Layer Height (ABLH) Determination under Multiple-Layer Conditions Using Micro-Pulse Lidar
Authors: Dang Ruijun , Yang Yi , Li Hong , Hu Xiao-Ming , Wang Zhiting , Huang Zhongwei , Zhou Tian , Zhang Tiejun
Published: January 2019

Accurate estimation of the atmospheric boundary layer height (ABLH) is critically important and it mainly relies on the detection of the vertical profiles of atmosphere variables (temperature, humidity,’ and horizontal wind speed) or aerosols. Aerosol Lidar is a powerful remote sensing instrument frequently used to retrieve ABLH through the detection of the vertical distribution of aerosol concentration. A challenge is that cloud, residual layer (RL), and local signal structure seriously interfere with the lidar measurement of ABLH. A new objective technique presenting as giving a top limiter altitude is introduced to reduce the interference of RL and cloud layer on ABLH determination. Cloud layers are identified by looking for the rapid increase and sharp attenuation of the signal combined with the relative increase in the signal. The cloud layers whether they overlay the ABL are classified or are decoupled from the ABL are classified by analyzing the continuity of the signal below the cloud base. For cloud layer capping of the ABL, the limiter is determined to be the altitude where a positive signal gradient first occurs above the cloud upper edge. For a cloud that is decoupled from the ABL, the cloud base is considered to be the altitude limiter. For RL in the morning, the altitude limiter is the greatest positive gradient altitude below the RL top. The ABLH will be determined below the top limiter altitude using Haar wavelet (HM) and the curve fitting method (CFM). Besides, the interference of local signal noise is eliminated through consideration of the temporal continuity. While comparing the lidar-determined ABLH by HM (or CFM) and nearby radiosonde measurements of the ABLH, a reasonable concordance is found with a correlation coefficient of 0.94 (or 0.96) and 0.79 (or 0.74), presenting a mean of the relative absolute differences with respect to radiosonde measurements of 10.5% (or 12.3%) and 22.3% (or 17.2%) for cloud-free and cloudy situations, respectively. The diurnal variations in the ABLH determined from HM and CFM on four selected cases show good agreement with a mean correlation coefficient higher than 0.99 and a mean absolute bias of 0.22 km. Also, the determined diurnal ABLH are consistent with surface turbulent kinetic energy (TKE) combined with the time-height distribution of the equivalent potential temperature.
On-Orbit Geometric Calibration and Validation of Luojia 1-01 Night-Light Satellite
Authors: Zhang Guo , Wang Jingyin , Jiang Yonghua , Zhou Ping , Zhao Yanbin , Xu Yi
Published: January 2019

The Luojia 1-01 Satellite (LJ1-01) is the first professional night-light remote-sensing satellite in China, and thus, it is of pioneering significance for the development of night-light remote sensing satellites in China and the application of remote sensing in the social and economic fields. To ensure the application of night-light remote-sensing data, several studies concerning on-orbit geometric calibration and accuracy verification have been carried out for the complementary metal oxide semiconductor (CMOS) rolling shutter camera of LJ1-01 since the launch of the satellite. Owing to the lack of high-precision nightlight geometric reference at home and abroad, it is difficult to directly calibrate the nighttime light image of LJ1-01. Based on the principle of rolling shutter dynamic imaging, a rigorous geometric imaging model of the time-sharing exposure of the rolling shutter of LJ1-01 is established, and a geometric calibration method for daytime imaging calibration and compensated nighttime light data is proposed. The global public Landsat digital orthophoto image (DOM) with a 15-m resolution and 90-m Shuttle Radar Topography Mission digital elevation model (SRTM-DEM) are used as control data. The images obtained in England, Venezuela, Caracas, Damascus, and Torreon (Mexico) were selected as experimental data. The on-orbit calibration and accuracy verification of LJ1-01 were carried out. Experiments show that after on-orbit geometric calibration, the daytime calibration parameters can effectively compensate for the systematic errors of night-light images. After compensation, the positioning accuracy of night-light images without geometric control points (GCPs) is improved from nearly 20 km to less than 0.65 km. The internal accuracy of the calibrated night-light images is better than 0.3 pixels, which satisfies the requirement of subsequent applications.
High-Resolution Spaceborne, Airborne and In Situ Landslide Kinematic Measurements of the Slumgullion Landslide in Southwest Colorado
Authors: Madson Austin , Fielding Eric , Sheng Yongwei , Cavanaugh Kyle
Published: January 2019

The Slumgullion landslide, located in southwestern Colorado, has been active since the early 1700s and current data suggests that the most active portion of the slide creeps at a rate of ~1.5–2.0 cm/day. Accurate deformation measurement techniques are vital to the understanding of persistent, yet slow-moving landslides like the Slumgullion. The factors that affect slope movements at the Slumgullion are on-time scales that are well suited towards a remotely sensed approach to constrain the 12 different kinematic units that make up the persistent creeping landslide. We derive a time series of motion vectors (magnitude and direction) using subpixel offset techniques from very high resolution TerraSAR-X Staring Spotlight ascending/descending data as well as from a novel high-resolution amalgamation of airborne lidar and unmanned aerial systems (UAS) Structure from Motion (SfM) digital surface model (DSM) hillshades. Deformation rates calculated from the spaceborne and airborne datasets show high agreement (mean difference of ~0.9 mm/day), further highlighting the potential for the monitoring of ongoing mass wasting events utilizing unmanned aircraft systems (UAS) We compare pixel offset results from an 11-day synthetic aperture radar (SAR) pair acquired in July of 2016 with motion vectors from a coincident low-cost L1 only Global Navigation Satellite System (GNSS) field campaign in order to verify the remotely sensed results and to derive the accuracy of the azimuth and range offsets. We find that the average azimuth and range pixel offset accuracies utilizing the methods herein are on the order of 1/18 and 1/20 of their along-track and slant range focused ground pixel spacing values of 16.8 cm and 45.5 cm, respectively. We utilize the SAR offset time series to add a twelfth kinematic unit to the previously established set of eleven unique regions at the site of an established minislide within the main landslide itself. Lastly, we compare the calculated rates and direction from all spaceborne- and airborne-derived motion vectors for each of the established kinematic zones within the active portion of the landslide. These comparisons show an overall increased magnitude and across-track component (i.e., more westerly angles of motion) for the descending SAR data as compared to their ascending counterparts. The processing techniques and subsequent results herein provide for an improved knowledge of the Slumgullion landslide’s kinematics and this increased knowledge has implications for the advancement of measurement techniques and the understanding of globally distributed creeping landslides.
A New Approach Using AHP to Generate Landslide Susceptibility Maps in the Chen-Yu-Lan Watershed, Taiwan
Authors: Nguyen Thi To Ngan , Liu Cheng-Chien
Published: January 2019

This paper proposes a new approach of using the analytic hierarchy process (AHP), in which the AHP was combined with bivariate analysis and correlation statistics to evaluate the importance of the pairwise comparison. Instead of summarizing expert experience statistics to establish a scale, we then analyze the correlation between the properties of the related factors with the actual landslide data in the study area. In addition, correlation and dependence statistics are also used to analyze correlation coefficients of preparatory factors. The product of this research is a landslide susceptibility map (LSM) generated by five factors (slope, aspect, drainage density, lithology, and land-use) and pre-event landslides (Typhoon Kalmaegi events), and then validated by post-event landslides and new landslides occurring in during the events (Typhoon Kalmaegi and Typhoon Morakot). Validating the results by the binary classification method showed that the model has reasonable accuracy, such as 81.22% accurate interpretation for post-event landslides (Typhoon Kalmaegi), and 70.71% exact predictions for new landslides occurring during Typhoon Kalmaegi.
Long-Term Stable Online Acetylene Detection by a CEAS System with Suppression of Cavity Length Drift
Authors: He Qixin , Feng Qibo , Li Jiakun
Published: January 2019

A trace acetylene (C2H2) detection system was demonstrated using the cavity-enhanced absorption spectroscopy (CEAS) technique and a near-infrared distributed feedback (NIR-DFB) laser. A Fabry–Perot (F–P) cavity with an effective optical path length of 49.7 m was sealed and employed as a gas absorption cell. Co-axis cavity alignment geometry was adopted to acquire a larger transmitted light intensity and a higher sensitivity compared with off-axis geometry. The laser frequency was locked to the cavity fundamental mode (TEM00 mode) by using the Pound–Drever–Hall (PDH) technique continuously. By introducing a cavity length-locking loop, the drift of the cavity length was suppressed, and the stability of the system was enhanced. To demonstrate the efficacy of the system, a C2H2 absorption spectrum near 6534.36 cm−1 was acquired by tuning the laser operation temperature. Measurements of C2H2 samples with different concentrations were carried out, and a good linear relationship between C2H2 concentration and the cavity-transmitted signal voltage was observed. The measurement results showed the system could work stably for more than 2 h without major fluctuations. The Allan variance analysis results demonstrated a detection limit of 9 parts-per-billion (ppb) with an averaging time of 11 s corresponding to a minimum detectable absorption coefficient of 1.1 × 10−8 cm−1.
Mobile User Indoor-Outdoor Detection through Physical Daily Activities
Authors: Esmaeili Kelishomi Aghil , Garmabaki A.H.S. , Bahaghighat Mahdi , Dong Jianmin
Published: January 2019

An automatic, fast, and accurate switching method between Global Positioning System and indoor positioning systems is crucial to achieve current user positioning, which is essential information for a variety of services installed on smart devices, e.g., location-based services (LBS), healthcare monitoring components, and seamless indoor/outdoor navigation and localization (SNAL). In this study, we proposed an approach to accurately detect the indoor/outdoor environment according to six different daily activities of users including walk, skip, jog, stay, climbing stairs up and down. We select a number of features for each activity and then apply ensemble learning methods such as Random Forest, and AdaBoost to classify the environment types. Extensive model evaluations and feature analysis indicate that the system can achieve a high detection rate with good adaptation for environment recognition. Empirical evaluation of the proposed method has been verified on the HASC-2016 public dataset, and results show 99% accuracy to detect environment types. The proposed method relies only on the daily life activities data and does not need any external facilities such as the signal cell tower or Wi-Fi access points. This implies the applicability of the proposed method for the upper layer applications.
Hyper-Heuristic Capacitance Array Method for Multi-Metal Wear Debris Detection
Authors: Sun Yanshan , Jia Lecheng , Zeng Zhoumo
Published: January 2019

Online detection of fatigued wear debris in the lubricants of aero-engines can provide warning of engine failure during flight, thus having great economic and social benefits. In this paper, we propose a capacitance array sensor and a hyper-heuristic partial differential equation (PDE) inversion method for detecting multiple micro-scale metal debris, combined with self-adaptive cellular genetic (SA-CGA) and morphological algorithms. Firstly, different from the traditional methods, which are limited in multi-induction-Dirac-boundary-inversion, a mathematical model with non-local boundary conditions is established. Furthermore, a hyper-heuristic method based on prior knowledge is also proposed to extract the wear character. Moreover, a 12-plate array circulating sensor and corresponding detection system are designed. The experimental results were compared with the optical microscopy. The results show that under the conditions of 1~3 wear debris with diameters of between 250–900 μm, the accuracy of the proposed method is 10–38% higher than those of the traditional methods. The recognition error of the wear debris counts decreases to 0.
A Flexible Platform of Electrochemically Functionalized Carbon Nanotubes for NADH Sensors
Authors: Heras Aranzazu , Vulcano Fabio , Garoz-Ruiz Jesus , Porcelli Nicola , Terzi Fabio , Colina Alvaro , Seeber Renato , Zanardi Chiara
Published: January 2019

A flexible electrode system entirely constituted by single-walled carbon nanotubes (SWCNTs) has been proposed as the sensor platform for β-nicotinamide adenine dinucleotide (NADH) detection. The performance of the device, in terms of potential at which the electrochemical process takes place, significantly improves by electrochemical functionalization of the carbon-based material with a molecule possessing an o-hydroquinone residue, namely caffeic acid. Both the processes of SWCNT functionalization and NADH detection have been studied by combining electrochemical and spectroelectrochemical experiments, in order to achieve direct evidence of the electrode modification by the organic residues and to study the electrocatalytic activity of the resulting material in respect to functional groups present at the electrode/solution interface. Electrochemical measurements performed at the fixed potential of +0.30 V let us envision the possible use of the device as an amperometric sensor for NADH detection. Spectroelectrochemistry also demonstrates the effectiveness of the device in acting as a voltabsorptometric sensor for the detection of this same analyte by exploiting this different transduction mechanism, potentially less prone to the possible presence of interfering species.
Analysis of Model Mismatch Effects for a Model-Based Gas Source Localization Strategy Incorporating Advection Knowledge
Authors: Wiedemann Thomas , Lilienthal Achim J. , Shutin Dmitriy
Published: January 2019

In disaster scenarios, where toxic material is leaking, gas source localization is a common but also dangerous task. To reduce threats for human operators, we propose an intelligent sampling strategy that enables a multi-robot system to autonomously localize unknown gas sources based on gas concentration measurements. This paper discusses a probabilistic, model-based approach for incorporating physical process knowledge into the sampling strategy. We model the spatial and temporal dynamics of the gas dispersion with a partial differential equation that accounts for diffusion and advection effects. We consider the exact number of sources as unknown, but assume that gas sources are sparsely distributed. To incorporate the sparsity assumption we make use of sparse Bayesian learning techniques. Probabilistic modeling can account for possible model mismatch effects that otherwise can undermine the performance of deterministic methods. In the paper we evaluate the proposed gas source localization strategy in simulations using synthetic data. Compared to real-world experiments, a simulated environment provides us with ground truth data and reproducibility necessary to get a deeper insight into the proposed strategy. The investigation shows that (i) the probabilistic model can compensate imperfect modeling; (ii) the sparsity assumption significantly accelerates the source localization; and (iii) a-priori advection knowledge is of advantage for source localization, however, it is only required to have a certain level of accuracy. These findings will help in the future to parameterize the proposed algorithm in real world applications.
Procedure for Calibrating the Z-axis of a Confocal Microscope: Application for the Evaluation of Structured Surfaces
Authors: Wang Chen , Caja Jesús , Gómez Emilio , Maresca Piera
Published: January 2019

This work describes a method for the metrological characterization of structured surfaces using a confocal microscope. The proposed method is based on the calculation of texture parameters established in ISO 25178-2:2012. To ensure the traceability of these parameters, a procedure for the calibration of the Z-axis of the confocal microscope is proposed. The calculation of uncertainty associated with each parameter employs the Monte Carlo method, as well as the concept of a virtual instrument. The validity of the algorithms has been verified through the use of synthetic data provided by the National Institute of Standards and Technology (NIST) and physical standards, with minimum differences being obtained between the certified values and calculated or measured values. Finally, using the proposed method, the topography of a structured surface manufactured by laser machining is evaluated, obtaining the most used roughness parameters, as well as their measurement uncertainties and possible correlations. In general, it can be affirmed that it is possible to obtain metrologically reliable results with the proposed method.
Terahertz Spectroscopic Signatures of Microcystin Aptamer Solution Probed with a Microfluidic Chip
Authors: Zhang Mingkun , Yang Zhongbo , Tang Mingjie , Wang Deqiang , Wang Huabin , Yan Shihan , Wei Dongshan , Cui Hong-Liang
Published: January 2019

Terahertz signature detection of biological samples in aqueous solution remains a great challenge due to the strong terahertz absorption of water. Here we propose a new preparation process for fabricating a microfluidic chip and use it as an effective sensor to probe the terahertz absorption signatures of microcystin aptamer (a linear single-stranded DNA with 60 nucleotides) dissolved in TE buffer with different concentrations. The microfluidic chip made of silicon includes thousands of 2.4 μm × 2.4 μm square-cross-section channels. One repeatable terahertz absorption signature is detected and recognized around 830 GHz, fitted to a Lorentz oscillator. This signature is theorized to originate from the bending of hydrogen bonds formed between adjacent hydrated DNA bases surrounded by water molecules. Furthermore, the low-lying vibrational modes are also investigated by molecular dynamics simulations which suggest that strong resonant oscillations are highly probable in the 815–830 GHz frequency band.
Comparing UAV-Based Technologies and RGB-D Reconstruction Methods for Plant Height and Biomass Monitoring on Grass Ley
Authors: Rueda-Ayala Victor , Peña José , Höglind Mats , Bengochea-Guevara José , Andújar Dionisio
Published: January 2019

Pastures are botanically diverse and difficult to characterize. Digital modeling of pasture biomass and quality by non-destructive methods can provide highly valuable support for decision-making. This study aimed to evaluate aerial and on-ground methods to characterize grass ley fields, estimating plant height, biomass and volume, using digital grass models. Two fields were sampled, one timothy-dominant and the other ryegrass-dominant. Both sensing systems allowed estimation of biomass, volume and plant height, which were compared with ground truth, also taking into consideration basic economical aspects. To obtain ground-truth data for validation, 10 plots of 1 m2 were manually and destructively sampled on each field. The studied systems differed in data resolution, thus in estimation capability. There was a reasonably good agreement between the UAV-based, the RGB-D-based estimates and the manual height measurements on both fields. RGB-D-based estimation correlated well with ground truth of plant height ( R 2 > 0.80 ) for both fields, and with dry biomass ( R 2 = 0.88 ), only for the timothy field. RGB-D-based estimation of plant volume for ryegrass showed a high agreement ( R 2 = 0.87 ). The UAV-based system showed a weaker estimation capability for plant height and dry biomass ( R 2 < 0.6 ). UAV-systems are more affordable, easier to operate and can cover a larger surface. On-ground techniques with RGB-D cameras can produce highly detailed models, but with more variable results than UAV-based models. On-ground RGB-D data can be effectively analysed with open source software, which is a cost reduction advantage, compared with aerial image analysis. Since the resolution for agricultural operations does not need fine identification the end-details of the grass plants, the use of aerial platforms could result a better option in grasslands.
Fully Integrated Light-Sensing Stimulator Design for Subretinal Implants
Authors: Kang Hosung , Abbasi Wajahat H. , Kim Seong-Woo , Kim Jungsuk
Published: January 2019

This paper presents a fully integrated photodiode-based low-power and low-mismatch stimulator for a subretinal prosthesis. It is known that a subretinal prosthesis achieves 1600-pixel stimulators on a limited single-chip area that is implanted beneath the bipolar cell layer. However, the high-density pixels cause high power dissipation during stimulation and high fabrication costs because of special process technologies such as the complementary metal-oxide semiconductor CMOS image sensor process. In addition, the many residual charges arising from the high-density pixel stimulation have deleterious effects, such as tissue damage and electrode corrosion, on the retina tissue. In this work, we adopted a switched-capacitor current mirror technique for the single-pixel stimulator (SPStim) that enables low power consumption and low mismatch in the subretinal device. The customized P+/N-well photodiode used to sense the incident light in the SPStim also reduces the fabrication cost. The 64-pixel stimulators are fabricated in a standard 0.35-μm CMOS process along with a global digital controller, which occupies a chip area of 4.3 × 3.2 mm2 and are ex-vivo demonstrated using a dissected pig eyeball. According to measured results, the SPStim accomplishes a maximum biphasic pulse amplitude of 143 μA, which dissipates an average power of 167 μW in a stimulation period of 5 ms, and an average mismatch of 1.12 % between the cathodic and anodic pulses.
Monitoring of the Pesticide Droplet Deposition with a Novel Capacitance Sensor
Authors: Wang Pei , Yu Wei , Ou Mingxiong , Gong Chen , Jia Weidong
Published: January 2019

Rapid detection of spraying deposit can contribute to the precision application of plant protection products. In this study, a novel capacitor sensor system was implemented for measuring the spray deposit immediately after herbicide application. Herbicides with different formulations and nozzles in different mode types were included to test the impact on the capacitance of this system. The results showed that there was a linear relationship between the deposit mass and the digital voltage signals of the capacitance on the sensor surface with spray droplets. The linear models were similar for water and the spray mixtures with non-ionized herbicides usually in formulations of emulsifiable concentrates and suspension concentrates. However, the ionized herbicides in formulation of aqueous solutions presented a unique linear model. With this novel sensor, it is possible to monitor the deposit mass in real-time shortly after the pesticide application. This will contribute to the precision application of plant protection chemicals in the fields.
Piezoelectric Transducer-Based Structural Health Monitoring for Aircraft Applications
Authors: Qing Xinlin , Li Wenzhuo , Wang Yishou , Sun Hu
Published: January 2019

Structural health monitoring (SHM) is being widely evaluated by the aerospace industry as a method to improve the safety and reliability of aircraft structures and also reduce operational cost. Built-in sensor networks on an aircraft structure can provide crucial information regarding the condition, damage state and/or service environment of the structure. Among the various types of transducers used for SHM, piezoelectric materials are widely used because they can be employed as either actuators or sensors due to their piezoelectric effect and vice versa. This paper provides a brief overview of piezoelectric transducer-based SHM system technology developed for aircraft applications in the past two decades. The requirements for practical implementation and use of structural health monitoring systems in aircraft application are then introduced. State-of-the-art techniques for solving some practical issues, such as sensor network integration, scalability to large structures, reliability and effect of environmental conditions, robust damage detection and quantification are discussed. Development trend of SHM technology is also discussed.
A 5G V2X Ecosystem Providing Internet of Vehicles
Authors: Storck Carlos , Duarte-Figueiredo Fátima
Published: January 2019

The Fifth Generation (5G) cellular network can be considered the way to the ubiquitous Internet and pervasive paradigm.The Internet of Vehicles (IoV) uses the network infrastructure to allow cars to be connected to new radio technologies, and can be supported by 5G networks. In this way, the Vehicle-to-Everything (V2X) integration needs 5G connections unavoidably. This paper presents a 5G V2X ecosystem to provide IoV. The proposed ecosystem is based on the Software-Defined Networking (SDN) concept. Considering vehicles as entertainment consumer points, the network infrastructure must be huge enough to guarantee delivery and quality. For this purpose, this paper evaluates vehicular Internet-based video services traffic and Vehicle-to-Vehicle (V2V) communications in urban and rural scenarios. Simulations were performed through the Network Simulator ns-3, employing millimeter Wave (mmWave) communications. Three metrics, data transfer rate, transmission delay, and Packet Delivery Ratio (PDR), were analyzed and compared for rural and urban IoV scenarios. The results have shown satisfactory performance to the IoV communications requirements when adopting the 5G network with V2X communications.
EEG Classification of Motor Imagery Using a Novel Deep Learning Framework
Authors: Dai Mengxi , Zheng Dezhi , Na Rui , Wang Shuai , Zhang Shuailei
Published: January 2019

Successful applications of brain-computer interface (BCI) approaches to motor imagery (MI) are still limited. In this paper, we propose a classification framework for MI electroencephalogram (EEG) signals that combines a convolutional neural network (CNN) architecture with a variational autoencoder (VAE) for classification. The decoder of the VAE generates a Gaussian distribution, so it can be used to fit the Gaussian distribution of EEG signals. A new representation of input was developed by combining the time, frequency, and channel information from the EEG signal, and the CNN-VAE method was designed and optimized accordingly for this form of input. In this network, the classification of the extracted CNN features is performed via the deep network VAE. Our framework, with an average kappa value of 0.564, outperforms the best classification method in the literature for BCI Competition IV dataset 2b with a 3% improvement. Furthermore, using our own dataset, the CNN-VAE framework also yields the best performance for both three-electrode and five-electrode EEGs and achieves the best average kappa values 0.568 and 0.603, respectively. Our results show that the proposed CNN-VAE method raises performance to the current state of the art.
Deploying Acoustic Detection Algorithms on Low-Cost, Open-Source Acoustic Sensors for Environmental Monitoring
Authors: Prince Peter , Hill Andrew , Piña Covarrubias Evelyn , Doncaster Patrick , Snaddon Jake L. , Rogers Alex
Published: January 2019

Conservation researchers require low-cost access to acoustic monitoring technology. However, affordable tools are often constrained to short-term studies due to high energy consumption and limited storage. To enable long-term monitoring, energy and space efficiency must be improved on such tools. This paper describes the development and deployment of three acoustic detection algorithms that reduce the power and storage requirements of acoustic monitoring on affordable, open-source hardware. The algorithms aim to detect bat echolocation, to search for evidence of an endangered cicada species, and also to collect evidence of poaching in a protected nature reserve. The algorithms are designed to run on AudioMoth: a low-cost, open-source acoustic monitoring device, developed by the authors and widely adopted by the conservation community. Each algorithm addresses a detection task of increasing complexity, implementing extra analytical steps to account for environmental conditions such as wind, analysing samples multiple times to prevent missed events, and incorporating a hidden Markov model for sample classification in both the time and frequency domain. For each algorithm, we report on real-world deployments carried out with partner organisations and also benchmark the hidden Markov model against a convolutional neural network, a deep-learning technique commonly used for acoustics. The deployments demonstrate how acoustic detection algorithms extend the use of low-cost, open-source hardware and facilitate a new avenue for conservation researchers to perform large-scale monitoring.
Design of a New Stress Wave-Based Pulse Position Modulation (PPM) Communication System with Piezoceramic Transducers
Authors: Wu Aiping , He Sihong , Ren Yali , Wang Ning , Ho Siu Chun Michael , Song Gangbing
Published: January 2019

Stress wave-based communication has great potential for succeeding in subsea environments where many conventional methods would otherwise face excessive difficulty, and it can benefit logging well by using the drill string as a conduit for stress wave propagation. To achieve stress wave communication, a new stress wave-based pulse position modulation (PPM) communication system is designed and implemented to transmit data through pipeline structures with the help of piezoceramic transducers. This system consists of both hardware and software components. The hardware is composed of a piezoceramic transducer that can generate powerful stress waves travelling along a pipeline, upon touching, and a PPM signal generator that drives the piezoceramic transducer. Once the transducer is in contact with a pipeline surface, the generator integrated with an amplifier is utilized to excite the piezoceramic transducer with a voltage signal that is modulated to encode the information. The resulting vibrations of the transducer generates stress waves that propagate throughout the pipeline. Meanwhile, piezoceramic sensors mounted on the pipeline convert the stress waves to electric signals and the signal can be demodulated. In order to enable the encoding and decoding of information in the stress wave, a PPM-based communication protocol was integrated into the software system. A verification experiment demonstrates the functionality of the developed system for stress wave communication using piezoceramic transducers and the result shows that the data transmission speed of this new communication system can reach 67 bits per second (bps).
Energy-Efficient Fuzzy-Logic-Based Clustering Technique for Hierarchical Routing Protocols in Wireless Sensor Networks
Authors: Hamzah Abdulmughni , Shurman Mohammad , Al-Jarrah Omar , Taqieddin Eyad
Published: January 2019

In wireless sensor networks, the energy source is limited to the capacity of the sensor node’s battery. Clustering in WSN can help with reducing energy consumption because transmission energy is related to the distance between sender and receiver. In this paper, we propose a fuzzy logic model for cluster head election. The proposed model uses five descriptors to determine the opportunity for each node to become a CH. These descriptors are: residual energy, location suitability, density, compacting, and distance from the base station. We use this fuzzy logic model in proposing the Fuzzy Logic-based Energy-Efficient Clustering for WSN based on minimum separation Distance enforcement between CHs (FL-EEC/D). Furthermore, we adopt the Gini index to measure the clustering algorithms’ energy efficiency in terms of their ability to balance the distribution of energy through WSN sensor nodes. We compare the proposed technique FL-EEC/D with a fuzzy logic-based CH election approach, a k-means based clustering technique, and LEACH. Simulation results show enhancements in energy efficiency in terms of network lifetime and energy consumption balancing between sensor nodes for different network sizes and topologies. Results show an average improvement in terms of first node dead and half nodes dead.
High Level 3D Structure Extraction from a Single Image Using a CNN-Based Approach
Authors: Osuna-Coutiño J. A. de Jesús , Martinez-Carranza Jose
Published: January 2019

High-Level Structure (HLS) extraction in a set of images consists of recognizing 3D elements with useful information to the user or application. There are several approaches to HLS extraction. However, most of these approaches are based on processing two or more images captured from different camera views or on processing 3D data in the form of point clouds extracted from the camera images. In contrast and motivated by the extensive work developed for the problem of depth estimation in a single image, where parallax constraints are not required, in this work, we propose a novel methodology towards HLS extraction from a single image with promising results. For that, our method has four steps. First, we use a CNN to predict the depth for a single image. Second, we propose a region-wise analysis to refine depth estimates. Third, we introduce a graph analysis to segment the depth in semantic orientations aiming at identifying potential HLS. Finally, the depth sections are provided to a new CNN architecture that predicts HLS in the shape of cubes and rectangular parallelepipeds.
A Novel Mobile Structured Light System in Food 3D Reconstruction and Volume Estimation
Authors: Makhsous Sepehr , Mohammad Hashem M. , Schenk Jeannette M. , Mamishev Alexander V. , Kristal Alan R.
Published: January 2019

Over the past ten years, diabetes has rapidly become more prevalent in all age demographics and especially in children. Improved dietary assessment techniques are necessary for epidemiological studies that investigate the relationship between diet and disease. Current nutritional research is hindered by the low accuracy of traditional dietary intake estimation methods used for portion size assessment. This paper presents the development and validation of a novel instrumentation system for measuring accurate dietary intake for diabetic patients. This instrument uses a mobile Structured Light System (SLS), which measures the food volume and portion size of a patient’s diet in daily living conditions. The SLS allows for the accurate determination of the volume and portion size of a scanned food item. Once the volume of a food item is calculated, the nutritional content of the item can be estimated using existing nutritional databases. The system design includes a volume estimation algorithm and a hardware add-on that consists of a laser module and a diffraction lens. The experimental results demonstrate an improvement of around 40% in the accuracy of the volume or portion size measurement when compared to manual calculation. The limitations and shortcomings of the system are discussed in this manuscript.
LED Mini Lidar for Atmospheric Application
Authors: Shiina Tatsuo
Published: January 2019

The creation of a compact and easy-to-use atmospheric lidar has been the aim of researchers for a long time. Micro Pulse Lidars (MPL) and commercialized ceilometers were designed for such purposes. Laser Diodes (LD) and Diode-Pumped Solid State (DPSS) Laser technology has evolved, making lidar system more compact; however, their vulnerability to static electricity and fluctuation of electrical power prevented the growth of atmospheric lidar technology as a system suited to all kinds of users. In this study, a mini lidar with a Light Emitting Diode (LED)-based light source was designed and developed. As LED lamp modules do not need a heat sink or fan, they are resilient and can emit light for long periods with constant intensity. They also offer ease of handling for non-professionals. On the other hand, a LED lamp module has a large divergence, when compared to laser beams. A prototype LED mini lidar was thus developed, with focus on transmitting power optimization and optical design. This low-cost lidar system is not only compact, but also offers near-range measurement applications. It visualizes rapid activities of small air cells in a close range (surface atmosphere), and can verify and predict the condition of the surface atmosphere. This paper summarizes the principle, design, practical use and applications of the LED mini-lidar.
Big IFs in Productivity-Enhancing Industry 4.0
Authors: Kovacs Oliver
Published: January 2019

With the dawn of Industry 4.0, its productivity-boosting impact appears to be comfortably ensconced both in the media and in the scientific community. Still, our paper is to portend a rather dismal prognosis by outlining three big Inertia Forces (IFs) hindering the power of Industry 4.0 in reviving productivity growth in a more spectacular way. After applying a complexity view to the development of Industry 4.0 in deciphering the major IFs, the paper briefly exemplifies them by building on the case of Hungary, and it then draws some lessons for theorists and economic policy practitioners in the interest of a value-congruent development of Industry 4.0.
Coastal Aquaculture Mapping from Very High Spatial Resolution Imagery by Combining Object-Based Neighbor Features
Authors: Fu Yongyong , Deng Jinsong , Ye Ziran , Gan Muye , Wang Ke , Wu Jing , Yang Wu , Xiao Guoqiang
Published: January 2019

Coastal aquaculture plays an important role in the provision of seafood, the sustainable development of regional and global economy, and the protection of coastal ecosystems. Inappropriate planning of disordered and intensive coastal aquaculture may cause serious environmental problems and socioeconomic losses. Precise delineation and classification of different kinds of aquaculture areas are vital for coastal management. It is difficult to extract coastal aquaculture areas using the conventional spectrum, shape, or texture information. Here, we proposed an object-based method combining multi-scale segmentation and object-based neighbor features to delineate existing coastal aquaculture areas. We adopted the multi-scale segmentation to generate semantically meaningful image objects for different land cover classes, and then utilized the object-based neighbor features for classification. Our results show that the proposed approach effectively identified different types of coastal aquaculture areas, with 96% overall accuracy. It also performed much better than other conventional methods (e.g., single-scale based classification with conventional features) with higher classification accuracy. Our results also suggest that the multi-scale segmentation and neighbor features can obviously improve the classification performance for the extraction of cage culture areas and raft culture areas, respectively. Our developed approach lays a solid foundation for intelligent monitoring and management of coastal ecosystems.
Grocery Delivery or Customer Pickup—Influences on Energy Consumption and CO2 Emissions in Munich
Authors: Hardi Lukas , Wagner Ulrich
Published: January 2019

The number of supermarkets offering grocery delivery has increased in recent years. Many studies conclude that CO2 emission savings result from this concept. Since the delivery of groceries also consumes energy and produces emissions, break-even points can be calculated, where the delivery is environmentally beneficial compared to customer pickup. In this paper, influences of differing vehicle use on break-even points for savings of energy and CO2 emissions are analyzed for the case of Haidhausen Süd, a district in Munich, Germany. Internal combustion engine and electric vehicles are investigated to depict current as well as future trends. After an introduction to the methodology used, the potential to save energy and CO2 emissions related to the delivery of groceries in the chosen district of Munich are evaluated. Subsequently, influences on the break-even points are presented and discussed. As the results show, a delivery of groceries leads to energy and carbon dioxide savings in a wide range of private vehicle use for grocery shopping trips. Nevertheless, if the complete customer vehicle fleet is electrified, the use of delivery vehicles with an internal combustion engine could cause an additional environmental impact at the current modal split for shopping trips in Germany.
Insights on Urban and Periurban Adaptation Strategies Based on Stakeholders’ Perceptions on Hard and Soft Responses to Climate Change
Authors: Martinez-Juarez Pablo , Chiabai Aline , Suárez Cristina , Quiroga Sonia
Published: January 2019

Adapting to expected impacts of climate change is a task shared by multiple institutions and individuals, but much of this work falls over local and regional authorities, which has made them experts over the issue. At the same time, adaptation to climate change has been a research interest in different academic fields; while private companies provide research and development efforts on the issue. Views from perspectives may contain common ground and discrepancies, but benefits from the discussion may differ among these three sectors. This study shows the application of collaborative approaches to analyze impacts and adaptation measures at a local level. A stakeholder workshop was held in the city of Bilbao to discuss impacts of climate change and adaptation in the local context of the Basque Country. The contributions were proposed on three axes: impacts from climate change, good practices proposed or already in action, and costs and benefits derived from those strategies. Participants were asked to rank a series of measures and practices extracted from their previous inputs. These measures were analyzed after applying bootstrapping techniques, according to the perceived costs and benefits assigned to each of the grouped measures and practices. Participants estimated that groups containing green adaptation measures and those that had potentially positive impacts over climate change mitigation were the most efficient measures, as reduced costs combined with high benefits could lead to win–win adaptation strategies, while grey infrastructures were seen as providing high benefits at high costs.
Economic Assessment of Autonomous Electric Microtransit Vehicles
Authors: Ongel Aybike , Loewer Erik , Roemer Felix , Sethuraman Ganesh , Chang Fengqi , Lienkamp Markus
Published: January 2019

There is rapidly growing interest in autonomous electric vehicles due to their potential in improving safety, accessibility, and environmental outcomes. However, their market penetration rate is dependent on costs. Use of autonomous electric vehicles for shared-use mobility may improve their cost competitiveness. So far, most of the research has focused on the cost impact of autonomy on taxis and ridesourcing services. Singapore is planning for island-wide deployment of autonomous vehicles for both scheduled and on-demand services as part of their transit system in the year 2030. TUMCREATE developed an autonomous electric vehicle concept, a microtransit vehicle with 30-passenger capacity, which can complement the existing bus transit system. This study aims to determine the cost of autonomous electric microtransit vehicles and compare them to those of buses. A total cost of ownership (TCO) approach was used to compare the lifecycle costs. It was shown that although the acquisition costs of autonomous electric vehicles are higher than those of their conventional counterparts, they can reduce the TCO per passenger-km up to 75% and 60% compared to their conventional counterparts and buses, respectively.
Forecasting Models for Wind Power Using Extreme-Point Symmetric Mode Decomposition and Artificial Neural Networks
Authors: Zhou Jianguo , Xu Xiaolei , Huo Xuejing , Li Yushuo
Published: January 2019

The randomness and volatility of wind power poses a serious threat to the stability, continuity, and adjustability of the power system when it is connected to the grid. Accurate short-term wind power prediction methods have important practical value for achieving high-precision prediction of wind farm power generation and safety and economic dispatch. Therefore, this paper proposes a novel combined model to improve the accuracy of short-term wind power prediction, which involves grey correlation degree analysis, ESMD (extreme-point symmetric mode decomposition), sample entropy (SampEn) theory, and a hybrid prediction model based on three prediction algorithms. The meteorological data at different times and altitudes is firstly selected as the influencing factors of wind power. Then, the wind power sub-series obtained by the ESMD method is reconstructed into three wind power characteristic components, namely PHC (high frequency component of wind power), PMC (medium frequency component of wind power), and PLC (low frequency component of wind power). Similarly, the wind speed sub-series obtained by the ESMD method is reconstructed into three wind speed characteristic components, called SHC (high frequency component of wind speed), SMC (medium frequency component of wind speed), and SLC (low frequency component of wind speed). Subsequently, the Bat-BP model, Adaboost-ENN model, and ENN (Elman neural network), which have high forecasting accuracy, are selected to predict PHC, PMC, and PLC, respectively. Finally, the prediction results of three characteristic components are aggregated into the final prediction values of the original wind power series. To evaluate the prediction performance of the proposed combined model, 15-min wind power and meteorological data from the wind farm in China are adopted as case studies. The prediction results show that the combined model shows better performance in short-term wind power prediction compared with other models.
Profiles of Violence and Alcohol and Tobacco Use in Relation to Impulsivity: Sustainable Consumption in Adolescents
Authors: Pérez-Fuentes María del Carmen , Molero Jurado María del Mar , Barragán Martín Ana Belén , Gázquez Linares and José Jesús
Published: January 2019

The purpose of this study was to identify different adolescent profiles identified by their tobacco and alcohol use and patterns of violent behavior, as well as to analyze the extent to which such adolescents show impulsivity traits. The participants were selected by cluster random sampling. There were a total of 822 high school students in the sample, aged 13 to 18 years with a mean age of 14.84 (SD = 0.87). A cluster analysis with the following variables was conducted to form the groups: use of tobacco, use of alcohol, physical aggression, verbal aggression, anger, and hostility. A total of three groups of adolescents resulted from these six variables. A multivariate comparison demonstrated the existence of significant between-group differences, and an individual analysis of each of the dependent variables (impulsivity dimensions) showed that the relationship was statistically significant in all cases. In conclusion, the analysis of factors possibly associated with risk behavior in adolescents creates the possibility for and guides intervention in different stages of development in order to encourage sustainable consumption in adolescents.