bluuebunny/crossref_metadata_2025 huggingface dataset:
Insect Target Classes Discerned from Entomological Radar Data
Authors: Hao Zhenhua , Drake V. Alistair , Taylor John R. , Warrant Eric
Published: February 2020

Entomological radars employing the ‘ZLC’ (zenith-pointing linear-polarized narrow-angle conical scan) configuration detect individual insects flying overhead and can retrieve information about a target’s trajectory (its direction and speed), the insect’s body alignment and four parameters that characterize the target itself: its radar cross section, two shape parameters and its wingbeat frequency. Criteria have previously been developed to distinguish Australian Plague Locusts Chortoicetes terminifera, large moths, medium moths and small insects using the target-character parameters. Combinations of target characters that occur frequently, known as target ‘classes’, have also been identified previously both through qualitative analyses and more objectively with a 4D peak-finding algorithm applied to a dataset spanning a single flight season. In this study, fourteen years of radar observations from Bourke, NSW (30.0392°S, 145.952°E, 107 m above MSL) have been used to test this approach and potentially improve its utility. We found that the previous criteria for assigning targets to classes require some modification, that classes identified in the previous studies were frequently present in other years and that two additional classes could be recognized. Additionally, by incorporating air-temperature information from a meteorological model, we have shown that different classes fly in different temperature ranges. By drawing on knowledge concerning migrant species found in the regional areas around the radar site, together with morphological measurements and radar cross-section data for proxy species, we have made tentative identifications of the insect taxa likely to be contributing to each class.
Spatiotemporal Characteristics of the Association between AOD and PM over the California Central Valley
Authors: Sorek-Hamer Meytar , Franklin Meredith , Chau Khang , Garay Michael , Kalashnikova Olga
Published: February 2020

Many air pollution health effects studies rely on exposure estimates of particulate matter (PM) concentrations derived from remote sensing observations of aerosol optical depth (AOD). Simple but robust calibration models between AOD and PM are therefore important for generating reliable PM exposures. We conduct an in-depth examination of the spatial and temporal characteristics of the AOD-PM2.5 relationship by leveraging data from the Distributed Regional Aerosol Gridded Observation Networks (DRAGON) field campaign where eight NASA Aerosol Robotic Network (AERONET) sites were co-located with EPA Air Quality System (AQS) monitoring sites in California’s Central Valley from November 2012 to April 2013. With this spatiotemporally rich data we found that linear calibration models (R2 = 0.35, RMSE = 10.38 μg/m3) were significantly improved when spatial (R2 = 0.45, RMSE = 9.54 μg/m3), temporal (R2 = 0.62, RMSE = 8.30 μg/m3), and spatiotemporal (R2 = 0.65, RMSE = 7.58 μg/m3) functions were included. As a use-case we applied the best spatiotemporal model to convert space-borne MultiAngle Imaging Spectroradiometer (MISR) AOD observations to predict PM2.5 over the region (R2 = 0.60, RMSE = 8.42 μg/m3). Our results imply that simple AERONET AOD-PM2.5 calibrations are robust and can be reliably applied to space-borne AOD observations, resulting in PM2.5 prediction surfaces for use in downstream applications.
Fusion of Five Satellite-Derived Products Using Extremely Randomized Trees to Estimate Terrestrial Latent Heat Flux over Europe
Authors: Shang Ke , Yao Yunjun , Li Yufu , Yang Junming , Jia Kun , Zhang Xiaotong , Chen Xiaowei , Bei Xiangyi , Guo Xiaozheng
Published: February 2020

An accurate estimation of spatially and temporally continuous latent heat flux (LE) is essential in the assessment of surface water and energy balance. Various satellite-derived LE products have been generated to enhance the simulation of terrestrial LE, yet each individual LE product shows large discrepancies and uncertainties. Our study used Extremely Randomized Trees (ETR) to fuse five satellite-derived terrestrial LE products to reduce uncertainties from the individual products and improve terrestrial LE estimations over Europe. The validation results demonstrated that the estimation using the ETR fusion method increased the R2 of five individual LE products (ranging from 0.53 to 0.61) to 0.97 and decreased the RMSE (ranging from 26.37 to 33.17 W/m2) to 5.85 W/m2. Compared with three other machine learning fusion models, Gradient Boosting Regression Tree (GBRT), Random Forest (RF), and Gaussian Process Regression (GPR), ETR exhibited the best performance in terms of both training and validation accuracy. We also applied the ETR fusion method to implement the mapping of average annual terrestrial LE over Europe at a resolution of 0.05 ◦ in the period from 2002 to 2005. When compared with global LE products such as the Global Land Surface Satellite (GLASS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), the fusion LE using ETR exhibited a relatively small gap, which confirmed that it is reasonable and reliable for the estimation of the terrestrial LE over Europe.
Unsupervised and Generic Short-Term Anticipation of Human Body Motions
Authors: Enes Kristina , Errami Hassan , Wolter Moritz , Krake Tim , Eberhardt Bernhard , Weber Andreas , Zimmermann Jörg
Published: February 2020

Various neural network based methods are capable of anticipating human body motions from data for a short period of time. What these methods lack are the interpretability and explainability of the network and its results. We propose to use Dynamic Mode Decomposition with delays to represent and anticipate human body motions. Exploring the influence of the number of delays on the reconstruction and prediction of various motion classes, we show that the anticipation errors in our results are comparable to or even better for very short anticipation times (<0.4 s) than a recurrent neural network based method. We perceive our method as a first step towards the interpretability of the results by representing human body motions as linear combinations of previous states and delays. In addition, compared to the neural network based methods large training times are not needed. Actually, our methods do not even regress to any other motions than the one to be anticipated and hence it is of a generic nature.
Processing of Near Real Time Land Surface Temperature and Its Application in Forecasting Forest Fire Danger Conditions
Authors: Ahmed M. , Hassan Quazi , Abdollahi Masoud , Gupta Anil
Published: February 2020

Near real time (NRT) remote sensing derived land surface temperature (Ts) data has an utmost importance in various applications of natural hazards and disasters. Space-based instrument MODIS (moderate resolution imaging spectroradiometer) acquired NRT data products of Ts are made available for the users by LANCE (Land, Atmosphere Near real-time Capability) for Earth Observing System (EOS) of NASA (National Aeronautics and Space Administration) free of cost. Such Ts products are swath data with 5 min temporal increments of satellite acquisition, and the average latency is 60-125 min to be available in public domain. The swath data of Ts requires a specialized tool, i.e., HEG (HDF-EOS to GeoTIFF conversion tool) to process and make the data useful for further analysis. However, the file naming convention of the available swath data files in LANCE is not appropriate to download for an area of interest (AOI) to be processed by HEG. In this study, we developed a method/algorithm to overcome such issues in identifying the appropriate swath data files for an AOI that would be able to further processes supported by the HEG. In this case, we used Terra MODIS acquired NRT swath data of Ts, and further applied it to an existing framework of forecasting forest fires (as a case study) for the performance evaluation of our processed Ts. We were successful in selecting appropriate swath data files of Ts for our study area that was further processed by HEG, and finally were able to generate fire danger map in the existing forecasting model. Our proposed method/algorithm could be applied on any swath data product available in LANCE for any location in the world.
Affiliated Fusion Conditional Random Field for Urban UAV Image Semantic Segmentation
Authors: Kong Yingying , Zhang Bowen , Yan Biyuan , Liu Yanjuan , Leung Henry , Peng Xiangyang
Published: February 2020

Unmanned aerial vehicles (UAV) have had significant progress in the last decade, which is applied to many relevant fields because of the progress of aerial image processing and the convenience to explore areas that men cannot reach. Still, as the basis of further applications such as object tracking and terrain classification, semantic image segmentation is one of the most difficult challenges in the field of computer vision. In this paper, we propose a method for urban UAV images semantic segmentation, which utilizes the geographical information of the region of interest in the form of a digital surface model (DSM). We introduce an Affiliated Fusion Conditional Random Field (AF-CRF), which combines the information of visual pictures and DSM, and a multi-scale strategy with attention to improve the segmenting results. The experiments show that the proposed structure performs better than state-of-the-art networks in multiple metrics.
Towards Smart Gaming Olfactory Displays
Authors: Tsaramirsis Georgios , Papoutsidakis Michail , Derbali Morched , Khan Fazal Qudus , Michailidis Fotis
Published: February 2020

Olfaction can enhance the experience of music, films, computer games and virtual reality applications. However, this area is less explored than other areas such as computer graphics and audio. Most advanced olfactory displays are designed for a specific experiment, they are hard to modify and extend, expensive, and/or can deliver a very limited number of scents. Additionally, current-generation olfactory displays make no decisions on if and when a scent should be released. This paper proposes a low-cost, easy to build, powerful smart olfactory display, that can release up to 24 different aromas and allow control of the quantity of the released aroma. The display is capable of absorbing back the aroma, in an attempt to clean the air prior to releasing a new aroma. Additionally, the display includes a smart algorithm that will decide when to release certain aromas. The device controller application includes releasing scents based on a timer, text in English subtitles, or input from external software applications. This allows certain applications (such as games) to decide when to release a scent, making it ideal for gaming. The device also supports native connectivity with games developed using a game development asset, developed as part of this project. The project was evaluated by 15 subjects and it was proved to have high accuracy when the scents were released with 1.5 minutes’ delay from each other.
Noble Metal-Assisted Surface Plasmon Resonance Immunosensors
Authors: Choi Jin-Ha , Lee Jin-Ho , Son Joohyung , Choi Jeong-Woo
Published: February 2020

For the early diagnosis of several diseases, various biomarkers have been discovered and utilized through the measurement of concentrations in body fluids such as blood, urine, and saliva. The most representative analytical method for biomarker detection is an immunosensor, which exploits the specific antigen-antibody immunoreaction. Among diverse analytical methods, surface plasmon resonance (SPR)-based immunosensors are emerging as a potential detection platform due to high sensitivity, selectivity, and intuitive features. Particularly, SPR-based immunosensors could detect biomarkers without labeling of a specific detection probe, as typical immunosensors such as enzyme-linked immunosorbent assay (ELISA) use enzymes like horseradish peroxidase (HRP). In this review, SPR-based immunosensors utilizing noble metals such as Au and Ag as SPR-inducing factors for the measurement of different types of protein biomarkers, including viruses, microbes, and extracellular vesicles (EV), are briefly introduced.
A Large Detection-Range Plasmonic Sensor Based on An H-Shaped Photonic Crystal Fiber
Authors: Han Haixia , Hou Donglian , Zhao Lei , Luan Nannan , Song Li , Liu Zhaohong , Lian Yudong , Liu Jianfei , Hu Yongsheng
Published: February 2020

An H-shaped photonic crystal fiber (PCF)-based surface plasmon resonance (SPR) sensor is proposed for detecting large refractive index (RI) range which can either be higher or lower than the RI of the fiber material used. The grooves of the H-shaped PCF as the sensing channels are coated with gold film and then brought into direct contact with the analyte, which not only reduces the complexity of the fabrication but also provides reusable capacity compared with other designs. The sensing performance of the proposed sensor is investigated by using the finite element method. Numerical results show that the sensor can work normally in the large analyte RI (na) range from 1.33 to 1.49, and reach the maximum sensitivity of 25,900 nm/RIU (RI units) at the na range 1.47–1.48. Moreover, the sensor shows good stability in the tolerances of ±10% of the gold-film thickness.
A Threshold-Based Max-log-MPA Low Complexity Multiuser Detection Algorithm
Authors: Zhang Guanghua , Gu Zonglin , Zhao Qiannan , Ren Jingqiu , Lu Weidang
Published: February 2020

Sparse Code Multiple Access (SCMA) technology is a new multiple access scheme based on non-orthogonal spread spectrum technology, which was proposed by Huawei in 2014. In the algorithm application of this technology, the original Message Passing Algorithm (MPA) has slow convergence speed and high algorithm complexity. The threshold-based MPA has a high Bit Error Ratio (BER) when the threshold is low. In the Maximum logarithm Message Passing Algorithm (Max-log-MPA), the approximation method is used, which will cause some messages to be lost and the detection performance to be poor. Therefore, in order to solve the above problems, a Threshold-Based Max-log-MPA (T-Max-log-MPA) low complexity multiuser detection algorithm is proposed in this paper. The Maximum logarithm (Max-log) algorithm is combined with threshold setting, and the stability of user nodes is considered as a necessary condition for decision in the algorithm. Before message updating, the user information nodes are judged whether the necessary conditions for the stability of the user node have been met, and then the threshold is determined. Only users who meet the threshold condition and pass the necessary condition of user node stability can be decoded in advance. In the whole process, the logarithm domain MPA algorithm is used to convert an exp operation and a multiplication operation into a maximum value and addition operation. The simulation results show that the proposed algorithm can effectively reduce the computational complexity while ensuring the BER, and with the increase of signal-to-noise ratio, the effect of the Computational Complexity Reduction Ratio (CCRR) is more obvious.
State-Degradation-Oriented Fault Diagnosis for High-Speed Train Running Gears System
Authors: Cheng Chao , Wang Weijun , Luo Hao , Zhang Bangcheng , Cheng Guoli , Teng Wanxiu
Published: February 2020

As one of the critical components of high-speed trains, the running gears system directly affects the operation performance of the train. This paper proposes a state-degradation-oriented method for fault diagnosis of an actual running gears system based on the Wiener state degradation process and multi-sensor filtering. First of all, for the given measurements of the high-speed train, this paper considers the information acquisition and transfer characteristics of composite sensors, which establish a distributed topology for axle box bearing. Secondly, a distributed filtering is built based on the bilinear system model, and the gain parameters of the filter are designed to minimize the mean square error. For a better presentation of the degradation characteristics in actual operation, this paper constructs an improved nonlinear model. Finally, threshold is determined based on the Chebyshev’s inequality for a reliable fault diagnosis. Open datasets of rotating machinery bearings and the real measurements are utilized in the case studies to demonstrate the effectiveness of the proposed method. Results obtained in this paper are consistent with the actual situation, which validate the proposed methods.
Secure Communication in Cooperative SWIPT NOMA Systems with Non-Linear Energy Harvesting and Friendly Jamming
Authors: Tuan Van Phu , Hong Ic-Pyo
Published: February 2020

This paper studies the secure communication of a non-orthogonal multiple-access (NOMA) relaying system in the presence of an eavesdropper in which the NOMA communication between a source and two users is assisted by an energy-harvesting (EH) relay. The relay extracts a part of its received signal strength using a power-splitting (PS) policy then harvests energy using a non-linear EH (NLEH) circuit. A friendly jammer sends jamming signals to help secure communication. The jammer is exploited as an additional energy source. A store-and-transmit (SaT) scheme which allows the EH relay to perform energy storing and information transmitting is proposed. For performance evaluation, the closed-form expressions for three metrics, secrecy outage probability (SOP), average achievable secrecy rate (AASR) and average stored energy (ASE) are derived. These results enable studies on the effects of various system parameters, such as NOMA power-allocation factors, target secrecy rates, jammer’s location, and relay’s power levels, on the system performance.
A New Three-Dimension Deceptive Scene Generation against Single-Pass Multibaseline InSAR Based on Multiple Transponders
Authors: Ji Penghui , Dai Dahai , Xing Shiqi , Pang Bo
Published: February 2020

The interferometry synthetic aperture radar (InSAR) deceptive jamming method utilizing two synergetic transponders can generate a false three-dimension (3D) scene in a single baseline InSAR image. However, such deceptive capability could be reduced by the multibaseline InSAR system. To obtain effective deception on multibaseline InSAR, a novel deceptive scene generation method jointly employing multiple transponders is proposed. It only demands that each transponder is modulated with a complex coefficient when generating a false point. The complex modulation coefficient can be offline calculated according to the deceptive point coordinate by solving a matrix. Besides, the complex modulation coefficient can be combined with the deceptive scene template, and thus a large 3D deceptive scene is able to be created quickly in the multibaseline InSAR image by using the fast two-dimension (2D) SAR deceptive scene generation algorithm. As long as the number of transponders is not less than the number of antennas of the multibaseline InSAR system, this proposed method is effective. The effectiveness of the proposed method is validated by computer simulations.
Detection of Organophosphorous Chemical Agents with CuO-Nanorod-Modified Microcantilevers
Authors: Schlur Laurent , Agostini Pierre , Thomas Guillaume , Gerer Geoffrey , Grau Jacques , Spitzer Denis
Published: February 2020

Microcantilevers are really promising sensitive sensors despite their small surface. In order to increase this surface and consequently their sensitivity, we nanostructured them with copper oxide (CuO) nanorods. The synthesis of the nanostructure consists of the oxidation of a copper layer deposited beforehand on the surface of the sample. The oxidation is performed in an alkaline solution containing a mixture of Na(OH) and (NH4)2S2O8. The synthesis procedure was first optimized on a silicon wafer, then transferred to optical cantilever-based sensors. This transfer requires specific synthesis modifications in order to cover all the cantilever with nanorods. A masking procedure was specially developed and the copper layer deposition was also optimized. These nanostructured cantilevers were engineered in order to detect vapors of organophosphorous chemical warfare agents (CWA). The nanostructured microcantilevers were exposed to various concentration of dimethyl methylphosphonate (DMMP) which is a well-known simulant of sarin (GB). The detection measurements showed that copper oxide is able to detect DMMP via hydrogen interactions. The results showed also that the increase of the microcantilever surface with the nanostructures improves the sensors efficiency. The evolution of the detection performances of the CuO nanostructured cantilevers with the DMMP concentration was also evaluated.
A DIY Low-Cost Wireless Wind Data Acquisition System Used to Study an Arid Coastal Foredune
Authors: Domínguez-Brito Antonio C. , Cabrera-Gámez Jorge , Viera-Pérez Manuel , Rodríguez-Barrera Eduardo , Hernández-Calvento Luis
Published: February 2020

Environmental studies on coastal dune systems are faced with a considerable cost barrier due to the cost of the instrumentation and sensory equipment required for data collection. These systems play an important role in coastal areas as a protection against erosion and as providers of stability to coastal sedimentary deposits. The DIY (Do-It-Yourself) approach to data acquisition can reduce the cost of these environmental studies. In this paper, a low-cost DIY wireless wind data acquisition system is presented which reduces the cost barrier inherent to these types of studies. The system is deployed for the analysis of the foredune of Maspalomas, an arid dune field situated on the south coast of Gran Canaria (Canary Islands, Spain), for the specific purpose of studying the dynamics of a dune type (tongue dunes), which is typical of this environment. The results obtained can be of interest for the study of these coastal environments at both the local level, for the management of this particular dune field, and at the general level for other similar dune fields around the world.
Received Signal Strength-Based Indoor Localization Using Hierarchical Classification
Authors: Zhang Chenbin , Qin Ningning , Xue Yanbo , Yang Le
Published: February 2020

Commercial interests in indoor localization have been increasing in the past decade. The success of many applications relies at least partially on indoor localization that is expected to provide reliable indoor position information. Wi-Fi received signal strength (RSS)-based indoor localization techniques have attracted extensive attentions because Wi-Fi access points (APs) are widely deployed and we can obtain the Wi-Fi RSS measurements without extra hardware cost. In this paper, we propose a hierarchical classification-based method as a new solution to the indoor localization problem. Within the developed approach, we first adopt an improved K-Means clustering algorithm to divide the area of interest into several zones and they are allowed to overlap with one another to improve the generalization capability of the following indoor positioning process. To find the localization result, the K-Nearest Neighbor (KNN) algorithm and support vector machine (SVM) with the one-versus-one strategy are employed. The proposed method is implemented on a tablet, and its performance is evaluated in real-world environments. Experiment results reveal that the proposed method offers an improvement of 1.4% to 3.2% in terms of position classification accuracy and a reduction of 10% to 22% in terms of average positioning error compared with several benchmark methods.
Efficient Algorithm for Mining Non-Redundant High-Utility Association Rules
Authors: Mai Thang , Nguyen Loan T.T. , Vo Bay , Yun Unil , Hong Tzung-Pei
Published: February 2020

In business, managers may use the association information among products to define promotion and competitive strategies. The mining of high-utility association rules (HARs) from high-utility itemsets enables users to select their own weights for rules, based either on the utility or confidence values. This approach also provides more information, which can help managers to make better decisions. Some efficient methods for mining HARs have been developed in recent years. However, in some decision-support systems, users only need to mine a smallest set of HARs for efficient use. Therefore, this paper proposes a method for the efficient mining of non-redundant high-utility association rules (NR-HARs). We first build a semi-lattice of mined high-utility itemsets, and then identify closed and generator itemsets within this. Following this, an efficient algorithm is developed for generating rules from the built lattice. This new approach was verified on different types of datasets to demonstrate that it has a faster runtime and does not require more memory than existing methods. The proposed algorithm can be integrated with a variety of applications and would combine well with external systems, such as the Internet of Things (IoT) and distributed computer systems. Many companies have been applying IoT and such computing systems into their business activities, monitoring data or decision-making. The data can be sent into the system continuously through the IoT or any other information system. Selecting an appropriate and fast approach helps management to visualize customer needs as well as make more timely decisions on business strategy.
Impact of Leadership and Mobility on Consensus-Building in Sensor Networks
Authors: Norouzi Kandalan Roya , Varanasi Murali , Buckles Bill , Namuduri Kamesh
Published: February 2020

Introducing leadership and mobility is known to benefit wireless sensor networks in terms of consensus-building and collective decision-making. However, these benefits are neither analytically proven nor quantified in the literature. This paper fills this gap by investigating the mobility dynamics in wireless sensor networks analytically. The results of the analytical investigation are presented as a set of theorems and their proofs. This paper also establishes a natural synergy between the leader-follower model and its bipartite graph representation. It demonstrates the advantages of the leader-follower model for consensus-building over others in terms of improved convergence rate. It presents a strategy for choosing leaders from among the agents participating in the consensus-building process using the well-known graph-coloring solution. Then, it shows how the leader-follower model helps improve the convergence rate of consensus-building. Finally, it shows that the convergence rate of the consensus-building process can be further improved by making the leaders mobile.
Over 100 Million Frames per Second 368 Frames Global Shutter Burst CMOS Image Sensor with Pixel-wise Trench Capacitor Memory Array
Authors: Suzuki Manabu , Sugama Yuki , Kuroda Rihito , Sugawa Shigetoshi
Published: February 2020

In this paper, a prototype ultra-high speed global shutter complementary metal-oxide-semiconductor (CMOS) image sensor with pixel-wise trench capacitor memory array achieving over 100 million frames per second (fps) with up to 368 record length by burst correlated double sampling (CDS) operation is presented. Over 100 Mfps high frame rate is obtained by reduction of pixel output load by the pixel-wise memory array architecture and introduction of the burst CDS operation which minimizes the pixel driving pulse transitions. Long record length is realized by high density analog memory integration with Si trench capacitors. A maximum 125 Mfps frame rate with up to 368 record length video capturing was confirmed under room temperature without any cooling system. The photoelectric conversion characteristics of the burst CDS operation were measured and compared with those of the conventional CDS operation.
Vital Block and Vital Sign Server for ECG and Vital Sign Monitoring in a Portable u-Vital System
Authors: Bae Tae Wuk , Kwon Kee Koo , Kim Kyu Hyung
Published: February 2020

An important function in the future healthcare system involves measuring a patient’s vital signs, transmitting the measured vital signs to a smart device or a management server, analyzing it in real-time, and informing the patient or medical staff. Internet of Medical Things (IoMT) incorporates information technology (IT) into patient monitoring device (PMD) and is developing traditional measurement devices into healthcare information systems. In the study, a portable ubiquitous-Vital (u-Vital) system is developed and consists of a Vital Block (VB), a small PMD, and Vital Sign Server (VSS), which stores and manages measured vital signs. Specifically, VBs collect a patient’s electrocardiogram (ECG), blood oxygen saturation (SpO2), non-invasive blood pressure (NiBP), body temperature (BT) in real-time, and the collected vital signs are transmitted to a VSS via wireless protocols such as WiFi and Bluetooth. Additionally, an efficient R-point detection algorithm was also proposed for real-time processing and long-term ECG analysis. Experiments demonstrated the effectiveness of measurement, transmission, and analysis of vital signs in the proposed portable u-Vital system.
On-Ground Vineyard Reconstruction Using a LiDAR-Based Automated System
Authors: Moreno Hugo , Valero Constantino , Bengochea-Guevara José María , Ribeiro Ángela , Garrido-Izard Miguel , Andújar Dionisio
Published: February 2020

Crop 3D modeling allows site-specific management at different crop stages. In recent years, light detection and ranging (LiDAR) sensors have been widely used for gathering information about plant architecture to extract biophysical parameters for decision-making programs. The study reconstructed vineyard crops using light detection and ranging (LiDAR) technology. Its accuracy and performance were assessed for vineyard crop characterization using distance measurements, aiming to obtain a 3D reconstruction. A LiDAR sensor was installed on-board a mobile platform equipped with an RTK-GNSS receiver for crop 2D scanning. The LiDAR system consisted of a 2D time-of-flight sensor, a gimbal connecting the device to the structure, and an RTK-GPS to record the sensor data position. The LiDAR sensor was facing downwards installed on-board an electric platform. It scans in planes perpendicular to the travel direction. Measurements of distance between the LiDAR and the vineyards had a high spatial resolution, providing high-density 3D point clouds. The 3D point cloud was obtained containing all the points where the laser beam impacted. The fusion of LiDAR impacts and the positions of each associated to the RTK-GPS allowed the creation of the 3D structure. Although point clouds were already filtered, discarding points out of the study area, the branch volume cannot be directly calculated, since it turns into a 3D solid cluster that encloses a volume. To obtain the 3D object surface, and therefore to be able to calculate the volume enclosed by this surface, a suitable alpha shape was generated as an outline that envelops the outer points of the point cloud. The 3D scenes were obtained during the winter season when only branches were present and defoliated. The models were used to extract information related to height and branch volume. These models might be used for automatic pruning or relating this parameter to evaluate the future yield at each location. The 3D map was correlated with ground truth, which was manually determined, pruning the remaining weight. The number of scans by LiDAR influenced the relationship with the actual biomass measurements and had a significant effect on the treatments. A positive linear fit was obtained for the comparison between actual dry biomass and LiDAR volume. The influence of individual treatments was of low significance. The results showed strong correlations with actual values of biomass and volume with R2 = 0.75, and when comparing LiDAR scans with weight, the R2 rose up to 0.85. The obtained values show that this LiDAR technique is also valid for branch reconstruction with great advantages over other types of non-contact ranging sensors, regarding a high sampling resolution and high sampling rates. Even narrow branches were properly detected, which demonstrates the accuracy of the system working on difficult scenarios such as defoliated crops.
Room-Level Fall Detection Based on Ultra-Wideband (UWB) Monostatic Radar and Convolutional Long Short-Term Memory (LSTM)
Authors: Ma Liang , Liu Meng , Wang Na , Wang Lu , Yang Yang , Wang Hongjun
Published: February 2020

Timely calls for help can really make a difference for elders who suffer from falls, particularly in private locations. Considering privacy protection and convenience for the users, in this paper, we approach the problem by using impulse–radio ultra-wideband (IR-UWB) monostatic radar and propose a learning model that combines convolutional layers and convolutional long short term memory (ConvLSTM) to extract robust spatiotemporal features for fall detection. The performance of the proposed scheme was evaluated in terms of accuracy, sensitivity, and specificity. The results show that the proposed method outperforms convolutional neural network (CNN)-based methods. Of the six activities we investigated, the proposed method can achieve a sensitivity of 95% and a specificity of 92.6% at a range of 8 meters. Further tests in a heavily furnished lounge environment showed that the model can detect falls with more than 90% sensitivity, even without re-training effort. The proposed method can detect falls without exposing the identity of the users. Thus, the proposed method is ideal for room-level fall detection in privacy-prioritized scenarios.
Using Vehicle Interior Noise Classification for Monitoring Urban Rail Transit Infrastructure
Authors: Wang Yifeng , Wang Ping , Wang Qihang , Chen Zhengxing , He Qing
Published: February 2020

This study developed a multi-classification model for vehicle interior noise from the subway system, collected on smartphones. The proposed model has the potential to be used to analyze the causes of abnormal noise using statistical methods and evaluate the effect of rail maintenance work. To this end, first, we developed a multi-source data (audio, acceleration, and angle rate) collection framework via smartphone built-in sensors. Then, considering the Shannon entropy, a 1-second window was selected to segment the time-series signals. This study extracted 45 features from the time- and frequency-domains to establish the classifier. Next, we investigated the effects of balancing the training dataset with the Synthetic Minority Oversampling Technique (SMOTE). By comparing and analyzing the classification results of importance-based and mutual information-based feature selection methods, the study employed a feature set consisting of the top 10 features by importance score. Comparisons with other classifiers indicated that the proposed XGBoost-based classifier runs fast while maintaining good accuracy. Finally, case studies were provided to extend the applications of this classifier to the analysis of abnormal vehicle interior noise events and evaluate the effects of rail grinding.
Spectrum Handoff Based on DQN Predictive Decision for Hybrid Cognitive Radio Networks
Authors: Cao Kaitian , Qian Ping
Published: February 2020

Spectrum handoff is one of the key techniques in a cognitive radio system. In order to improve the agility and the reliability of spectrum handoffs as well as the system throughput in hybrid cognitive radio networks (HCRNs) combing interweave mode with underlay mode, a predictive (or proactive) spectrum handoff scheme based on a deep Q-network (DQN) for HCRNs is proposed in this paper. In the proposed spectrum handoff approach, spectrum handoff success rate is introduced into an optimal spectrum resource allocation model to ensure the reliability of spectrum handoff, and the closed-form expression for the spectrum handoff success rate is obtained based on the Poisson distribution. Furthermore, we exploit the transfer learning strategy to further improve the DQN learning process and finally achieve a priority sequence of target available channels for spectrum handoffs, which can maximize the overall HCRNs throughput while satisfying constraints on secondary users’ interference with primary user, limits on the spectrum handoff success rate, and the secondary users’ performance requirements. Simulation results show that the proposed spectrum handoff scheme outperforms the state-of-the-art spectrum handoff algorithms based on predictive decision in terms of the convergence rate, the handoff success rate and the system throughput.
Low Energy Consumption Compressed Spectrum Sensing Based on Channel Energy Reconstruction in Cognitive Radio Network
Authors: Fang Yuan , Li Lixiang , Li Yixiao , Peng Haipeng , Yang Yixian
Published: February 2020

For wireless communication networks, cognitive radio (CR) can be used to obtain the available spectrum, and wideband compressed sensing plays a vital role in cognitive radio networks (CRNs). Using compressed sensing (CS), sampling and compression of the spectrum signal can be simultaneously achieved, and the original signal can be accurately recovered from the sampling data under sub-Nyquist rate. Using a set of wideband random filters to measure the channel energy, only the recovery of the channel energy is necessary, rather than that of all the original channel signals. Based on the semi-tensor product, this paper proposes a new model to achieve the energy compression and reconstruction of spectral signals, called semi-tensor product compressed spectrum sensing (STP-CSS), which is a generalization of traditional spectrum sensing. The experimental results show that STP-CSS can flexibly generate a low-dimensional sensing matrix for energy compression and parallel reconstruction of the signal. Compared with the existing methods, STP-CSS is proved to effectively reduce the calculation complexity of sensor nodes. Hence, the proposed model markedly improves the spectrum sensing speed of network nodes and saves storage space and energy consumption.
Symbol-Level Selective Channel Estimation in Packet-Based OFDM Systems
Authors: Choi Joo-Young , Mun Cheol , Yook Jong-Gwan
Published: February 2020

Wireless access in vehicular environments to support wireless communication between vehicles has been developed to provide road safety and infotainment services. In vehicular environments where the channel changes rapidly, channel estimation is very important in improving the reliability of wireless communication. Therefore, numerous channel estimation schemes have been proposed; however, none of the schemes proposed so far can perform well over the entire signal-to-noise ratio (SNR) region. In this paper, we propose a novel channel estimation scheme that selectively uses the better scheme between two channel estimation schemes on a symbol-by-symbol basis. The results show that the proposed scheme performs symbol-by-symbol selection of the better channel estimation scheme within a packet, and thus shows excellent performance over the entire SNR region in vehicular environments in terms of the bit error rate and packet error rate.
A Highly Sensitive Impedimetric DNA Biosensor Based on Hollow Silica Microspheres for Label-Free Determination of E. coli
Authors: Yuhana Ariffin Eda , Heng Lee Yook , Tan Ling Ling , Abd Karim Nurul Huda , Hasbullah Siti Aishah
Published: February 2020

A novel label-free electrochemical DNA biosensor was constructed for the determination of Escherichia coli bacteria in environmental water samples. The aminated DNA probe was immobilized onto hollow silica microspheres (HSMs) functionalized with 3-aminopropyltriethoxysilane and deposited onto a screen-printed electrode (SPE) carbon paste with supported gold nanoparticles (AuNPs). The biosensor was optimized for higher specificity and sensitivity. The label-free E. coli DNA biosensor exhibited a dynamic linear response range of 1 × 10−10 µM to 1 × 10−5 µM (R2 = 0.982), with a limit of detection at 1.95 × 10−15 µM, without a redox mediator. The sensitivity of the developed DNA biosensor was comparable to the non-complementary and single-base mismatched DNA. The DNA biosensor demonstrated a stable response up to 21 days of storage at 4 ℃ and pH 7. The DNA biosensor response was regenerable over three successive regeneration and rehybridization cycles.
Telecoupling of the Trade of Donkey-Hides between Botswana and China: Challenges and Opportunities
Authors: Matlhola Dimpho Malebogo , Chen Ruishan
Published: February 2020

International trade has become a major threat to biodiversity in the telecoupled world. The literature on biodiversity loss mostly concentrates on wild species; however, the increasing loss of and demand for domesticated species are overlooked. Donkeys are decreasing in many countries because of economic development, urbanization, and increased demand for donkey hides in China. The donkey population in Africa has rapidly declined recently due to supplying China with donkey hides for the production of highly demanded and legalized traditional medicine, ejiao. As a result, some African countries, including Botswana, banned the export of donkey hides. Animal protection and media continuously reported threats over the extinction of donkeys and exacerbated rural livelihood in developing countries, however, limited literature has investigated the causes and effects of donkey-hide trade and its policy interventions. This paper applies the telecoupling framework and uses the data extracted from the media reports and FAO, to understand the scale and impacts of donkey-hide trade from Botswana to China. We also explore the challenges and opportunities of donkey-hide trade for Botswana, highlighting the mechanisms need to be established to regulate the donkey-hide trade, and also to harness available opportunities if the trade ban is lifted in the future.
Tourism Information Diffusion through SNSs: A Theoretical Investigation
Authors: Liu Ting , Xia Jianhong , Crowe-Delaney Lesley
Published: February 2020

Social networking sites (SNSs) are known to have a role in promoting tourism and influencing how it is marketed to consumers, but there seems to be few deep analyses of SNS’s efficacy in tourists’ decision making and destination promotion. To address this, we present Tourism Information Diffusion Ecosystem (TIDE), a novel theoretical framework to help understand this system of tourism SNS information diffusion. TIDE defines who participates in the system, what roles participants play in distributing tourist information contained within user-generated content, how content within a network is distributed, and if this user-generated information, once diffused, has been transferred into tourists’ visiting actions, and the reasons why these actions have been generated. We discovered user typologies and the powerful characteristics of this network structure to be important factors affecting visiting actions in choosing particular tourist destinations.
Ridesourcing and Travel Demand: Potential Effects of Transportation Network Companies in Bogotá
Authors: Oviedo Daniel , Granada Isabel , Perez-Jaramillo Daniel
Published: February 2020

This paper proposes a modal-shift analysis methodology based on a mix of small-scale primary data and big data sources to estimate the total amount of trips that are reallocated to transportation network companies (TNCs) services in Bogotá, Colombia. The analysis is focused on the following four modes: public transportation, private vehicles, conventional taxis, and TNC services. Based on a stated preferences survey and secondary databases of travel times and costs, the paper proposes a methodology to estimate the reallocation of travel demand once TNCs start operating. Results suggests that approximately one third of public transportation trips are potentially transferred to TNCs. Moreover, potential taxi and private vehicle–transferred trips account for almost 30% of the new TNC demand. Additionally, approximately half of the trips that are reallocated from public transport demand can be considered as complementary, while the remaining share can be considered as potential replacing trips of public transportation. The paper also estimates the potential increase in Vehicle-km travelled in each of the modes before and after substitution as a proxy to the effects of demand reallocation on sustainability, finding increases between 1.3 and 14.5 times the number of Vehicle-km depending on the mode. The paper highlights the role of open data and critical perspectives on available information to analyze potential scenarios of the introduction of disruptive technologies and their spatial, social, and economic implications.
Review of the Effects of Developments with Low Parking Requirements
Authors: Sprei Frances , Hult Cecilia , Hult Åsa , Roth Anders
Published: February 2020

Parking management and planning can be used to address several issues related to sustainable urban development. For example, parking availability affects both car ownership and usage, and parking planning can affect both land use and building costs. A tool used in several countries is minimum parking requirements (MPR) and lowering these could be a pathway to more sustainable mobility. However, the actual effects of lower MPR have not systematically been studied. In this paper we present the results of a review of sixteen developments with low MPR in Sweden, Austria, Germany, Switzerland, and the UK. Existing research and reports have been analyzed to compare these and draw conclusions on the effect of MPR on mobility patterns and mobility services. In addition, interviews were conducted with representatives from municipalities and developers. Our results indicate that the mobility patterns of individuals in the studied projects are more sustainable than in nearby projects. However, the causality of MPR and mobility is hard to establish due to the risk of self-selection and that all of the studied projects have good prerequisites for sustainable mobility practices. Many of the studied evaluations are also of poor quality with, for example, lack of appropriate control group.
Examining Vietnamese Hard-Adventure Tourists’ Visit Intention Using an Extended Model of Goal-Directed Behavior
Authors: Bui Ngoc Anh , Kiatkawsin Kiattipoom
Published: February 2020

Although adventure tourism is divided into two subgroups based on the level of risk, most previous research has not made a distinction in their research. However, due to the high risk, skills, experience, and physical condition required of participants, hard adventure tourism should be studied separately from soft tourism activities. Thus, this study investigates the antecedents influencing hard adventure tourists’ visit intention by adopting the model of goal-directed behavior. Furthermore, two new variables, social media consumption and hardy tourism knowledge, were added to improve the model’s predictive ability. Using 441 Vietnamese hard adventure tourists as research samples, the results have shown the conceptual model to be robust and effective in explaining behavioral intention. Generally, the results were consistent with previous research. However, subjective norms, negative anticipated emotion, and frequency of past behavior produced unexpected results.