bluuebunny/crossref_metadata_2025 huggingface dataset:
Object Detection Based on Global-Local Saliency Constraint in Aerial Images
Authors: Li Chengyuan , Luo Bin , Hong Hailong , Su Xin , Wang Yajun , Liu Jun , Wang Chenjie , Zhang Jing , Wei Linhai
Published: May 2020

Different from object detection in natural image, optical remote sensing object detection is a challenging task, due to the diverse meteorological conditions, complex background, varied orientations, scale variations, etc. In this paper, to address this issue, we propose a novel object detection network (the global-local saliency constraint network, GLS-Net) that can make full use of the global semantic information and achieve more accurate oriented bounding boxes. More precisely, to improve the quality of the region proposals and bounding boxes, we first propose a saliency pyramid which combines a saliency algorithm with a feature pyramid network, to reduce the impact of complex background. Based on the saliency pyramid, we then propose a global attention module branch to enhance the semantic connection between the target and the global scenario. A fast feature fusion strategy is also used to combine the local object information based on the saliency pyramid with the global semantic information optimized by the attention mechanism. Finally, we use an angle-sensitive intersection over union (IoU) method to obtain a more accurate five-parameter representation of the oriented bounding boxes. Experiments with a publicly available object detection dataset for aerial images demonstrate that the proposed GLS-Net achieves a state-of-the-art detection performance.
Using Small Unmanned Aircraft Systems for Measuring Post-Flood High-Water Marks and Streambed Elevations
Authors: Forbes Brandon T. , DeBenedetto Geoffrey P. , Dickinson Jesse E. , Bunch Claire E. , Fitzpatrick Faith A.
Published: May 2020

Floods affected approximately two billion people around the world from 1998–2017, causing over 142,000 fatalities and over 656 billion U.S. dollars in economic losses. Flood data, such as the extent of inundation and peak flood stage, are needed to define the environmental, economic, and social impacts of significant flood events. Ground-based global positioning system (GPS) surveys of post-flood high-water marks (HWMs) and topography are commonly used to define flood inundation and stage, but can be time-consuming, difficult, and expensive to conduct. Here, we demonstrate and test the use of small unmanned aircraft systems (sUAS) and close-range remote sensing techniques to collect high-accuracy flood data to define peak flood stage elevations and river cross-sections. We evaluate the elevation accuracy of the HWMs from sUAS surveys by comparison with traditional GPS surveys, which have acceptable accuracy for many post-flood assessments, at two flood sites on two small streams in the U.S. Mean elevation errors for the sUAS surveys were 0.07 m and 0.14 m for the semiarid and temperate sites, respectively; those values are similar to typical errors when measuring HWM elevations with GPS surveys. Results demonstrate that sUAS surveys of HWMs and cross-sections can be an accurate and efficient alternative to GPS surveys; we provide insights that can be used to decide whether sUAS or GPS techniques will be most efficient for post-flood surveying.
Combined Impact of Sample Size and Modeling Approaches for Predicting Stem Volume in Eucalyptus spp. Forest Plantations Using Field and LiDAR Data
Authors: Silva Vanessa Sousa da , Silva Carlos Alberto , Mohan Midhun , Cardil Adrián , Rex Franciel Eduardo , Loureiro Gabrielle Hambrecht , Almeida Danilo Roberti Alves de , Broadbent Eben North , Gorgens Eric Bastos , Dalla Corte Ana Paula , Silva Emanuel Araújo , Valbuena Rubén , Klauberg Carine
Published: May 2020

Light Detection and Ranging (LiDAR) remote sensing has been established as one of the most promising tools for large-scale forest monitoring and mapping. Continuous advances in computational techniques, such as machine learning algorithms, have been increasingly improving our capability to model forest attributes accurately and at high spatial and temporal resolution. While there have been previous studies exploring the use of LiDAR and machine learning algorithms for forest inventory modeling, as yet, no studies have demonstrated the combined impact of sample size and different modeling techniques for predicting and mapping stem total volume in industrial Eucalyptus spp. tree plantations. This study aimed to compare the combined effects of parametric and nonparametric modeling methods for estimating volume in Eucalyptus spp. tree plantation using airborne LiDAR data while varying the reference data (sample size). The modeling techniques were compared in terms of root mean square error (RMSE), bias, and R2 with 500 simulations. The best performance was verified for the ordinary least-squares (OLS) method, which was able to provide comparable results to the traditional forest inventory approaches using only 40% (n = 63; ~0.04 plots/ha) of the total field plots, followed by the random forest (RF) algorithm with identical sample size values. This study provides solutions for increasing the industry efficiency in monitoring and managing forest plantation stem volume for the paper and pulp supply chain.
Low-cost Fiberoptic Probe for Ammonia Early Detection in Fish Farms
Authors: Leal-Junior Arnaldo G. , Frizera Anselmo , Marques Carlos
Published: May 2020

Recirculating aquaculture systems (RAS) are complex systems in which there is an interaction between the fish biomass and water chemistry, where small variations in the environment can lead to major effects in the production. Ammonia is one of the key limiting factors in RAS and its early detection in small concentrations prevents fish mortality and improves the production quality. Aiming at this background, this paper presents a low-cost fiberoptic probe for the early detection of ammonia. The sensor was based on the chemical interaction between the Oxazine 170 perchlorate layer, deposited in an uncladed polymer optical fiber (POF), and the ammonia dissolved in water. In addition, a thin metallic layer (composed by gold and palladium) was deposited in the fiber end facet and acted as a reflector for the optical signals, enabling the use of the proposed sensor in reflection mode. Different configurations of the sensor were tested, where the effects of polydimethylsiloxane (PDMS) protective layer, thermal treatments, and the use on reflection or transmission modes were compared in the assessment of ammonia concentrations in the range of 100 ppb to 900 ppb. Results showed a better performance (as a function of the sensor sensitivity and linearity) of the sensor with the annealing thermal treatment and without the PDMS layer. Then, the proposed fiberoptic probe was applied on the ammonia detection in high-salinity water, where ammonia concentrations as low as 100 ppb were detected.
Automated Procurement of Training Data for Machine Learning Algorithm on Ship Detection Using AIS Information
Authors: Song Juyoung , Kim Duk-jin , Kang Ki-mook
Published: May 2020

Development of convolutional neural network (CNN) optimized for object detection, led to significant developments in ship detection. Although training data critically affect the performance of the CNN-based training model, previous studies focused mostly on enhancing the architecture of the training model. This study developed a sophisticated and automatic methodology to generate verified and robust training data by employing synthetic aperture radar (SAR) images and automatic identification system (AIS) data. The extraction of training data initiated from interpolating the discretely received AIS positions to the exact position of the ship at the time of image acquisition. The interpolation was conducted by applying a Kalman filter, followed by compensating the Doppler frequency shift. The bounding box for the ship was constructed tightly considering the installation of the AIS equipment and the exact size of the ship. From 18 Sentinel-1 SAR images using a completely automated procedure, 7489 training data were obtained, compared with a different set of training data from visual interpretation. The ship detection model trained using the automatic training data obtained 0.7713 of overall detection performance from 3 Sentinel-1 SAR images, which exceeded that of manual training data, evading the artificial structures of harbors and azimuth ambiguity ghost signals from detection.
Knowledge-Based Classification of Grassland Ecosystem Based on Multi-Temporal WorldView-2 Data and FAO-LCCS Taxonomy
Authors: Adamo Maria , Tomaselli Valeria , Tarantino Cristina , Vicario Saverio , Veronico Giuseppe , Lucas Richard , Blonda Palma
Published: May 2020

Grassland ecosystems can provide a variety of services for humans, such as carbon storage, food production, crop pollination and pest regulation. However, grasslands are today one of the most endangered ecosystems due to land use change, agricultural intensification, land abandonment as well as climate change. The present study explores the performance of a knowledge-driven GEOgraphic-Object—based Image Analysis (GEOBIA) learning scheme to classify Very High Resolution (VHR) images for natural grassland ecosystem mapping. The classification was applied to a Natura 2000 protected area in Southern Italy. The Food and Agricultural Organization Land Cover Classification System (FAO-LCCS) hierarchical scheme was instantiated in the learning phase of the algorithm. Four multi-temporal WorldView-2 (WV-2) images were classified by combining plant phenology and agricultural practices rules with prior-image spectral knowledge. Drawing on this knowledge, spectral bands and entropy features from one single date (Post Peak of Biomass) were firstly used for multiple-scale image segmentation into Small Objects (SO) and Large Objects (LO). Thereafter, SO were labelled by considering spectral and context-sensitive features from the whole multi-seasonal data set available together with ancillary data. Lastly, the labelled SO were overlaid to LO segments and, in turn, the latter were labelled by adopting FAO-LCCS criteria about the SOs presence dominance in each LO. Ground reference samples were used only for validating the SO and LO output maps. The knowledge driven GEOBIA classifier for SO classification obtained an OA value of 97.35% with an error of 0.04. For LO classification the value was 75.09% with an error of 0.70. At SO scale, grasslands ecosystem was classified with 92.6%, 99.9% and 96.1% of User’s, Producer’s Accuracy and F1-score, respectively. The findings reported indicate that the knowledge-driven approach not only can be applied for (semi)natural grasslands ecosystem mapping in vast and not accessible areas but can also reduce the costs of ground truth data acquisition. The approach used may provide different level of details (small and large objects in the scene) but also indicates how to design and validate local conservation policies.
Evaluation of the Sensitivity of SMOS L-VOD to Forest Above-Ground Biomass at Global Scale
Authors: Mialon Arnaud , Rodríguez-Fernández Nemesio J. , Santoro Maurizio , Saatchi Sassan , Mermoz Stéphane , Bousquet Emma , Kerr Yann H.
Published: May 2020

The present study evaluates the L band Vegetation Optical Depth (L-VOD) derived from the Soil Moisture and Ocean Salinity (SMOS) satellite to monitor Above Ground Biomass (AGB) at a global scale. Although SMOS L-VOD has been shown to be a good proxy for AGB in Africa and Tropics, little is known about this relationship at large scale. In this study, we further examine this relationship at a global scale using the latest AGB maps from Saatchi et al. and GlobBiomass computed using data acquired during the SMOS period. We show that at a global scale the L-VOD from SMOS is well-correlated with the AGB estimates from Saatchi et al. and GlobBiomass with the Pearson’s correlation coefficients (R) of 0.91 and 0.94 respectively. Although AGB estimates in Africa and the Tropics are well-captured by SMOS L-VOD (R > 0.9), the relationship is less straightforward for the dense forests over the northern latitudes (R = 0.32 and 0.69 with Saatchi et al. and GlobBiomass respectively). This paper gives strong evidence in support of the sensitivity of SMOS L-VOD to AGB estimates at a globale scale, providing an interesting alternative and complement to exisiting sensors for monitoring biomass evolution. These findings can further facilitate research on biomass now that SMOS is providing more than 10 years of data.
Integration of TanDEM-X and SRTM DEMs and Spectral Imagery to Improve the Large-Scale Detection of Opencast Mining Areas
Authors: Wu Qianhan , Song Chunqiao , Liu Kai , Ke Linghong
Published: May 2020

Land use and land cover (LULC) is a key variable of the Earth’s system and has become an important indicator to evaluate the impact of human activities on the Earth’s ecosystems. With the increasing demand of mine resources, widespread opencast mining has led to significant changes in LULC and caused substantial damage to the environment. An efficient approach of detecting mining activities at large scales is of critical importance in mitigating their potential impacts on downstream settlements and in assessing LULC characteristics. In this study, we present a novel approach for enabling large-scale automatic detection of opencast mining areas by integrating multitemporal digital elevation models (DEMs, including the SRTM DEM and the recently released TanDEM-X DEM) and multispectral imagery in object-based image analysis and random forest (RF) algorithms. A sequence of data preparation, image segmentation, threshold analysis, calculation of metrics, and influence factor regulation was developed and tested on the Landsat 8 sample dataset in Inner Mongolia in China, which is a mineral-rich area. Aside from spectral metrics, such as brightness and reflectance value, introduced topographical features enhanced the modeling and classification significantly, and the overall performance is greatly influenced by feature selection (the out-of-bag error rate in the RF algorithm is 7.54% for the integrated DEM method in comparison with 12.70% for the only-optical images method). The integrated use of spectral imagery and multitemporal DEMs reveals that the identified mining area is about 1100 km2 in the study area and period, and the topographic changes of opencast mining in terms of elevation difference is between −258 and 162 m. The results show that the method can map the locations and extents of mining areas automatically from spectral and DEM data and can potentially be applied to larger areas.
SI Traceable Solar Spectral Irradiance Measurement Based on a Quantum Benchmark: A Prototype Design
Authors: Zheng Xiaobing , Xia Maopeng , Zhai Wenchao , Hu Youbo , Li Jianjun , Yuan Yinlin , Pang Weiwei
Published: May 2020

We propose a space benchmark sensor with onboard SI (Système International) traceability by means of quantum optical radiometry. Correlated photon pairs generated by spontaneous parametric down-conversion (SPDC) in nonlinear crystals are used to calibrate the absolute responsivity of a solar observing radiometer. The calibration is systematic, insensitive to degradation and independent of external radiometric standards. Solar spectral irradiance at 380–2500 nm is traceable to the photon rate and Planck’s constant with an expected uncertainty of about 0.35%. The principle of SPDC calibration and a prototype design of the solar radiometer are introduced. The uncertainty budget is analyzed in consideration of errors arising from calibration and observation modes.
3D Point Cloud and BIM-Based Reconstruction for Evaluation of Project by As-Planned and As-Built
Authors: Kim Seungho , Kim Sangyong , Lee Dong-Eun
Published: May 2020

Progress management of a construction project can detect changes early by visualizing the progress of the project, as it is important to be capable of predicting the success or failure of future project objectives. However, to perform reliable progress management tasks, accurate measurement data is required. In this study, the basic principle of the evaluation of project progress was performed through the 3D point cloud and the 4D attributes of BIM. The evaluation of project progress proposed in this study was based on as-built data to assess the progress of the project site. The specific improvements via the proposed process for this study in the construction project-progress control area were as follows: (1) visualization of construction project progress, (2) calculation of project as-built quantity, and (3) evaluation of a project’s progress. This study improved the efficiency and productivity in the management of a construction project through detection of the progress process. It provided easy monitoring of the overall project status, such as productivity analysis, progress rate and quality verifications, and easy identification of the problems created and foreseeable engineering tasks.
A Guideline for Effectively Synthesizing and Characterizing Magnetic Nanoparticles for Advancing Nanobiotechnology: A Review
Authors: Zamani Kouhpanji Mohammad Reza , Stadler Bethanie J. H.
Published: May 2020

The remarkable multimodal functionalities of magnetic nanoparticles, conferred by their size and morphology, are very important in resolving challenges slowing the progression of nanobiotechnology. The rapid and revolutionary expansion of magnetic nanoparticles in nanobiotechnology, especially in nanomedicine and therapeutics, demands an overview of the current state of the art for synthesizing and characterizing magnetic nanoparticles. In this review, we explain the synthesis routes for tailoring the size, morphology, composition, and magnetic properties of the magnetic nanoparticles. The pros and cons of the most popularly used characterization techniques for determining the aforementioned parameters, with particular focus on nanomedicine and biosensing applications, are discussed. Moreover, we provide numerous biomedical applications and highlight their challenges and requirements that must be met using the magnetic nanoparticles to achieve the most effective outcomes. Finally, we conclude this review by providing an insight towards resolving the persisting challenges and the future directions. This review should be an excellent source of information for beginners in this field who are looking for a groundbreaking start but they have been overwhelmed by the volume of literature.
Improved Pixel-Level Pavement-Defect Segmentation Using a Deep Autoencoder
Authors: Augustauskas Rytis , Lipnickas Arūnas
Published: May 2020

Convolutional neural networks perform impressively in complicated computer-vision image-segmentation tasks. Vision-based systems surpass humans in speed and accuracy in quality inspection tasks. Moreover, the maintenance of big infrastructures, such as roads, bridges, or buildings, is tedious and time-demanding work. In this research, we addressed pavement-quality evaluation by pixelwise defect segmentation using a U-Net deep autoencoder. Additionally, to the original neural network architecture, we utilized residual connections, atrous spatial pyramid pooling with parallel and “Waterfall” connections, and attention gates to perform better defect extraction. The proposed neural network configurations showed a segmentation performance improvement over U-Net with no significant computational overhead. Statistical and visual performance evaluation was taken into consideration for the model comparison. Experiments were conducted on CrackForest, Crack500, GAPs384, and mixed datasets.
Latitude Determination and Error Analysis for Stationary SINS in Unknow-Position Condition
Authors: Wang Suier , Yang Gongliu , Chen Wei , Wang Lifen
Published: May 2020

The initial geographic latitude information is the key to the self-alignment of the strapdown inertial navigation system (SINS), but how to determine the latitude when the latitude cannot be obtained directly or in a short time? The latitude determination (LD) methods are introduced, including magnitude method, geometric method, and analytical methods 1 and 2, to solve this situation only by the output of the SINS itself. Simulation and experimental test results validate the efficiency of these LD methods. In order to improve the accuracy of the LD, the error of the LD method is derived through comparative analysis. Based on the relationship between LD error and inertial measurement unit (IMU) bias. Partial bias estimation method is introduced and executed during latitude determination. After compensating the estimated IMU bias, the accuracy of the LD will be further improved. Latitude errors are also affected by the latitude where SINS is located. Comprehensive simulation and experimental tests verify the effectiveness of the method. The IMU determined latitude can not only be used to achieve the self-alignment of the SINS, but also to correct the navigation latitude of the long-term SINS, thereby improving the autonomy and positioning accuracy of the navigation system.
Heat Transfer and Temperature Characteristics of a Working Digital Camera
Authors: Zhou Shichao , Zhu Haibin , Ma Qinwei , Ma Shaopeng
Published: May 2020

Digital cameras represented by industrial cameras are widely used as image acquisition sensors in the field of image-based mechanics measurement, and their thermal effect inevitably induces thermal-induced errors of the mechanics measurement. To deeply understand the errors, the research for digital camera’s thermal effect is necessary. This study systematically investigated the heat transfer processes and temperature characteristics of a working digital camera. Concretely, based on the temperature distribution of a typical working digital camera, the heat transfer of the working digital camera was investigated, and a model describing the temperature variation and distribution was presented and verified experimentally. With this model, the thermal equilibrium time and thermal equilibrium temperature of the camera system were calculated. Then, the influences of thermal parameters of digital camera and environmental temperature on the temperature characteristics of working digital camera were simulated and experimentally investigated. The theory analysis and experimental results demonstrate that the presented model can accurately describe the temperature characteristics and further calculate the thermal equilibrium state of working digital camera, all of which contribute to guiding mechanics measurement and thermal design based on such camera sensors.
Crack Detection Zones: Computation and Validation
Authors: Pfingstl Simon , Steiner Martin , Tusch Olaf , Zimmermann Markus
Published: May 2020

During the development of aerospace structures, typically many fatigue tests are conducted. During these tests, much effort is put into inspections in order to detect the onset of failure before complete failure. Strain sensor data may be used to reduce inspection effort. For this, a sufficient number of sensors need to be positioned appropriately to collect the relevant data. In order to minimize cost and effort associated with sensor positioning, the method proposed here aims at minimizing the number of necessary strain sensors while positioning them such that fatigue-induced damage can still be detected before complete failure. A suitable detection criterion is established as the relative change of strain amplitudes under cyclic loading. Then, the space of all possible crack lengths is explored. The regions where the detection criterion is satisfied before complete failure occurs are assembled into so-called detection zones. One sensor in this zone is sufficient to detect criticality. The applicability of the approach is demonstrated on a representative airplane structure that resembles a lower wing section. The method shows that four fatigue critical spots can be monitored using only one strain sensor in a non-intuitive position. Furthermore, we discuss two different strain measures for crack detection. The results of this paper can be used for reliable structural health monitoring using a minimum number of sensors.
Smart Electronic Eyedrop Bottle for Unobtrusive Monitoring of Glaucoma Medication Adherence
Authors: Aguilar-Rivera Marcelo , Erudaitius Dieanira T. , Wu Vincent M. , Tantiongloc Justin C. , Kang Dae Y. , Coleman Todd P. , Baxter Sally L. , Weinreb Robert N.
Published: May 2020

Glaucoma, the leading cause of irreversible blindness, affects >70 million people worldwide. Lowering intraocular pressure via topical administration of eye drops is the most common first-line therapy for glaucoma. This treatment paradigm has notoriously high non-adherence rates: ranging from 30% to 80%. The advent of smart phone enabled technologies creates promise for improving eyedrop adherence. However, previous eyedrop electronic monitoring solutions had awkward medication bottle adjuncts and crude software for monitoring the administration of a drop that adversely affected their ability to foster sustainable improvements in adherence. The current work begins to address this unmet need for wireless technology by creating a “smart drop” bottle. This medication bottle is instrumented with sensing electronics that enable detection of each eyedrop administered while maintaining the shape and size of the bottle. This is achieved by a thin electronic force sensor wrapped around the bottle and underneath the label, interfaced with a thin electronic circuit underneath the bottle that allows for detection and wireless transmission to a smart-phone application. We demonstrate 100% success rate of wireless communication over 75 feet with <1% false positive and false negative rates of single drop deliveries, thus providing a viable solution for eyedrop monitoring for glaucoma patients.
Deep Learning-Based Human Activity Real-Time Recognition for Pedestrian Navigation
Authors: Ye Junhua , Li Xin , Zhang Xiangdong , Zhang Qin , Chen Wu
Published: May 2020

Several pedestrian navigation solutions have been proposed to date, and most of them are based on smartphones. Real-time recognition of pedestrian mode and smartphone posture is a key issue in navigation. Traditional ML (Machine Learning) classification methods have drawbacks, such as insufficient recognition accuracy and poor timing. This paper presents a real-time recognition scheme for comprehensive human activities, and this scheme combines deep learning algorithms and MEMS (Micro-Electro-Mechanical System) sensors’ measurements. In this study, we performed four main experiments, namely pedestrian motion mode recognition, smartphone posture recognition, real-time comprehensive pedestrian activity recognition, and pedestrian navigation. In the procedure of recognition, we designed and trained deep learning models using LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) networks based on Tensorflow framework. The accuracy of traditional ML classification methods was also used for comparison. Test results show that the accuracy of motion mode recognition was improved from 89.9 % , which was the highest accuracy and obtained by SVM (Support Vector Machine), to 90.74 % (LSTM) and 91.92 % (CNN); the accuracy of smartphone posture recognition was improved from 81.60 % , which is the highest accuracy and obtained by NN (Neural Network), to 93.69 % (LSTM) and 95.55 % (CNN). We give a model transformation procedure based on the trained CNN network model, and then obtain the converted . t f l i t e model, which can be run in Android devices for real-time recognition. Real-time recognition experiments were performed in multiple scenes, a recognition model trained by the CNN network was deployed in a Huawei Mate20 smartphone, and the five most used pedestrian activities were designed and verified. The overall accuracy was up to 89.39 % . Overall, the improvement of recognition capability based on deep learning algorithms was significant. Therefore, the solution was helpful to recognize comprehensive pedestrian activities during navigation. On the basis of the trained model, a navigation test was performed; mean bias was reduced by more than 1.1 m. Accordingly, the positioning accuracy was improved obviously, which is meaningful to apply DL in the area of pedestrian navigation to make improvements.
Person Re-ID by Fusion of Video Silhouettes and Wearable Signals for Home Monitoring Applications
Authors: Masullo Alessandro , Burghardt Tilo , Damen Dima , Perrett Toby , Mirmehdi Majid
Published: May 2020

The use of visual sensors for monitoring people in their living environments is critical in processing more accurate health measurements, but their use is undermined by the issue of privacy. Silhouettes, generated from RGB video, can help towards alleviating the issue of privacy to some considerable degree. However, the use of silhouettes would make it rather complex to discriminate between different subjects, preventing a subject-tailored analysis of the data within a free-living, multi-occupancy home. This limitation can be overcome with a strategic fusion of sensors that involves wearable accelerometer devices, which can be used in conjunction with the silhouette video data, to match video clips to a specific patient being monitored. The proposed method simultaneously solves the problem of Person ReID using silhouettes and enables home monitoring systems to employ sensor fusion techniques for data analysis. We develop a multimodal deep-learning detection framework that maps short video clips and accelerations into a latent space where the Euclidean distance can be measured to match video and acceleration streams. We train our method on the SPHERE Calorie Dataset, for which we show an average area under the ROC curve of 76.3% and an assignment accuracy of 77.4%. In addition, we propose a novel triplet loss for which we demonstrate improving performances and convergence speed.
A Survey of Lost-in-Space Star Identification Algorithms Since 2009
Authors: Rijlaarsdam David , Yous Hamza , Byrne Jonathan , Oddenino Davide , Furano Gianluca , Moloney David
Published: May 2020

The lost-in-space star identification algorithm is able to identify stars without a priori attitude information and is arguably the most critical component of a star sensor system. In this paper, the 2009 survey by Spratling and Mortari is extended and recent lost-in-space star identification algorithms are surveyed. The covered literature is a qualitative representation of the current research in the field. A taxonomy of these algorithms based on their feature extraction method is defined. Furthermore, we show that in current literature the comparison of these algorithms can produce inconsistent conclusions. In order to mitigate these inconsistencies, this paper lists the considerations related to the relative performance evaluation of these algorithms using simulation.
Ocean Surface Drifting Buoy System Based on UAV-Enabled Wireless Powered Relay Network
Authors: Chen Haole , Yin Feng , Huang Wei , Liu Mingliu , Li Deshi
Published: May 2020

We design an ocean surface drifting buoy system based on an unmanned aerial vehicle (UAV)-enabled wireless powered relay network in which the UAV acts as mobile hybrid access point that broadcasts energy to all buoys in the downlink and forwards information from the buoys to a ship signal tower (ST) in the uplink. In order to maximize the resource allocation efficiency of the system, due to the different initial energy reserve of the buoys, a novel communication mode selection strategy is proposed. In the direct transmission mode (DT mode), an energy-sufficient buoy transmits information directly to the ST, and in the relay transmission mode (RT mode), an energy-insufficient buoy relays information to the ST through the UAV. By applying the block coordinate descent and successive convex optimization, a joint UAV trajectory and resource allocation algorithm is proposed to maximize the minimum throughput of the buoys to work in the RT mode. Simulation results show that the proposed algorithm can significantly improve the minimum throughput of the ocean surface drifting buoys.
Accurate Non-Disturbance Population Survey Method of Nesting Colonies in the Reedbed with Georeferenced Aerial Imagery
Authors: Bakó Gábor , Molnár Zsolt , Szilágyi Zsófia , Biró Csaba , Morvai Edina , Ábrám Örs , Molnár András
Published: May 2020

High altitude aerial surveys have the potential to improve disturbance-free data collection in wildlife research, but previously, bird species were not recognizable in high-altitude orthophotos. This method of aerial surveying is effective and can be repeated frequently due to its low cost; it also has the additional advantage of being able to monitor the status of protected areas. In the case of waterbirds, due to the low vegetation coverage, aerial remote sensing is an exceptionally effective technique for surveying populations and detecting nests. Aerial surveys made at low altitudes can cause serious stress for birds. The method we developed and employed is unlikely to be detected by either ground-based or nesting birds but is far more reliable compared to the low-resolution imaging methods and to the evaluation of non-georeferenced photo series. The modern sensors and photogrammetric procedures enable the use of the present method worldwide; furthermore, the large-scale ortho image-derived information has become obtainable more frequently. Direct georeferencing makes the field geodetic survey unnecessary. Orthophotos with a 0.7 cm spatial resolution allow us to reliably identify even the individuals of smaller species, and by the use of oblique images, they can be tracked from two or four different directions.
A Prototype Microwave System for 3D Brain Stroke Imaging
Authors: Tobon Vasquez Jorge A. , Scapaticci Rosa , Turvani Giovanna , Bellizzi Gennaro , Rodriguez-Duarte David O. , Joachimowicz Nadine , Duchêne Bernard , Tedeschi Enrico , Casu Mario R. , Crocco Lorenzo , Vipiana Francesca
Published: May 2020

This work focuses on brain stroke imaging via microwave technology. In particular, the open issue of monitoring patients after stroke onset is addressed here in order to provide clinicians with a tool to control the effectiveness of administered therapies during the follow-up period. In this paper, a novel prototype is presented and characterized. The device is based on a low-complexity architecture which makes use of a minimum number of properly positioned and designed antennas placed on a helmet. It exploits a differential imaging approach and provides 3D images of the stroke. Preliminary experiments involving a 3D phantom filled with brain tissue-mimicking liquid confirm the potential of the technology in imaging a spherical target mimicking a stroke of a radius equal to 1.25 cm.
Conductivity Classification of Non-Magnetic Tilting Metals by Eddy Current Sensors
Authors: Du Yue , Zhang Zhijie , Yin Wuliang , Zhu Shuang , Chen Ziqi , Xu Hanyang
Published: May 2020

Metallic waste classification benefits the environment, resource reuse and industrial economy. This paper provides a fast, non-contact and convenient method based on eddy current to classify metals. The characteristic phase to characterize different conductivity is introduced and extracted from mutual inductance in the form of amplitude and phase. This characteristic phase could offer great separation for non-tilting metals. Although it is hard to classify tilting metals by only using the characteristic phase, we propose the technique of phase compensation utilizing photoelectric sensors to obtain the rectified phase corresponding to the non-tilting situation. Finally, we construct a classification algorithm involving phase compensation. By conducting a test, a

95
%

classification rate is achieved.
Affective Latent Representation of Acoustic and Lexical Features for Emotion Recognition
Authors: Kim Eesung , Song Hyungchan , Shin Jong Won
Published: May 2020

In this paper, we propose a novel emotion recognition method based on the underlying emotional characteristics extracted from a conditional adversarial auto-encoder (CAAE), in which both acoustic and lexical features are used as inputs. The acoustic features are generated by calculating statistical functionals of low-level descriptors and by a deep neural network (DNN). These acoustic features are concatenated with three types of lexical features extracted from the text, which are a sparse representation, a distributed representation, and an affective lexicon-based dimensions. Two-dimensional latent representations similar to vectors in the valence-arousal space are obtained by a CAAE, which can be directly mapped into the emotional classes without the need for a sophisticated classifier. In contrast to the previous attempt to a CAAE using only acoustic features, the proposed approach could enhance the performance of the emotion recognition because combined acoustic and lexical features provide enough discriminant power. Experimental results on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) corpus showed that our method outperformed the previously reported best results on the same corpus, achieving 76.72% in the unweighted average recall.
Measurement of Light Absorbing Aerosols with Folded-Jamin Photothermal Interferometry
Authors: Lee Jeonghoon , Moosmüller Hans
Published: May 2020

In this study, a photothermal interferometer was developed, based on a folded-Jamin polarization instrument with refractive-index sensitive configuration, in order to characterize light-absorbing aerosols. The feasibility of our interferometric technique was demonstrated by performing photothermal spectroscopy characterizing spark-generated black carbon particles with atmospherically relevant concentrations and atmospheric aerosols in a metropolitan area. The sensitivity of this interferometric system for both laboratory-generated aerosols and atmospheric aerosols was ~ 1 (μg/m3)/μV, which is sufficient for the monitoring of black carbon aerosol in urban areas.
Ultrasonic Touch Sensing System Based on Lamb Waves and Convolutional Neural Network
Authors: Chang Cheng-Shen , Lee Yung-Chun
Published: May 2020

A tactile position sensing system based on the sensing of acoustic waves and analyzing with artificial intelligence is proposed. The system comprises a thin steel plate with multiple piezoelectric transducers attached to the underside, to excite and detect Lamb waves (or plate waves). A data acquisition and control system synchronizes the wave excitation and detection and records the transducer signals. When the steel plate is touched by a finger, the waveform signals are perturbed by wave absorption and diffraction effects, and the corresponding changes in the output signal waveforms are sent to a convolutional neural network (CNN) model to predict the x- and y-coordinates of the finger contact position on the sensing surface. The CNN model is trained by using the experimental waveform data collected using an artificial finger carried by a three-axis motorized stage. The trained model is then used in a series of tactile sensing experiments performed using a human finger. The experimental results show that the proposed touch sensing system has an accuracy of more than 95%, a spatial resolution of 1 × 1 cm2, and a response time of 60 ms.
Large-Scale Crowd Analysis through the Use of Passive Radio Sensing Networks
Authors: Denis Stijn , Bellekens Ben , Kaya Abdil , Berkvens Rafael , Weyn Maarten
Published: May 2020

The creation of an automatic crowd estimation system capable of providing reliable, real-time estimates of human crowd sizes would be an invaluable tool for organizers of large-scale events, particularly so in the context of safety management. We describe a set of experiments in which we installed a passive Radio Frequency (RF) sensor network in different environments containing thousands of human individuals and discuss the accuracy with which the resulting measurements can be used to estimate the sizes of these crowds. Depending on the selected training approach, a median crowd estimation error of 184 people could be obtained for a large scale environment which contained 3227 people at its peak. Additionally, we look into the potential benefits of dividing one of our experimental environments into multiple subregions and open up a potentially interesting new topic of research regarding the estimation of crowd flows. Finally, we investigate the combination of our measurements with another sources of crowd-related data: sales data from drink stands within the environment. In doing so, we aim to integrate the concept of an automatic RF-based crowd estimation system into the broader domain of crowd analysis.
Tourist-Perceived Quality and Loyalty Intentions towards Rural Tourism in China
Authors: Chi Xiaoting , Lee Seul Ki , Ahn Young-joo , Kiatkawsin Kiattipoom
Published: May 2020

The growing interest in ruralism among Chinese people has been observed as China’s population in urban areas has exceeded its rural population. Rural tourism has become one of the leading tourism sectors in China, in large part because many of China’s popular tourist attractions are surrounded by rural communities. This study identified 12 dimensions of tourist-perceived quality. In addition, perceived value and satisfaction were used as mediators to explain the relationships between perceived quality and three dimensions of behavioral intentions (i.e., revisitation intention, positive word-of-mouth, and willingness to pay for special rural products). Furthermore, the moderating impact of urban versus rural residence was tested. A total sample of 495 was used for data analysis. Four tourist-perceived quality dimensions (i.e., tourism infrastructure and transportation, hospitality and learning, handicrafts and culture, and rural environment) were found. The results of this study demonstrated the empirical evidence of the relationship between perceived quality, perceived value, satisfaction, and behavioral intentions. Finally, the results showed a moderating effect. The findings of this study can contribute to increasing various behavioral intentions and sustainable rural tourism in China.
Small-Scale Variation in Nitrogen Use Efficiency Parameters in Winter Wheat as Affected by N Fertilization and Tillage Intensity
Authors: Hausherr Lüder Ruth-Maria , Qin Ruijun , Richner Walter , Stamp Peter , Streit Bernhard , Herrera Juan Manuel , Noulas Christos
Published: May 2020

Limited information exists on how tillage and nitrogen (N) fertilization affects small-scale variation in nitrogen use efficiency (NUE) and crop performance. In a two-year field study under temperate conditions, we investigated how tillage (NT, no-tillage; CT, conventional tillage) and N fertilization affected the small-scale variation in NUE and winter wheat performance (grain yield, Gw; grain protein concentration, GPC). A randomized complete block design with three replications was used. Within each tillage plot (12 × 35 m2), N rates (0, 50, 100, 150, 200, 250 kg N ha−1) were completely randomized within each of four groups of microplots (1.5 × 1.5 m2). Early-season soil mineral N (Nmin) was also monitored in both years. At rates < 150 kg N ha−1, NT was not competitive with CT in terms of Gw and NUE. Gw and aboveground plant N were not correlated with Nmin prior to application of N fertilizer. NT usually led to larger spatial heterogeneity of Nmin, Gw, and NUE. The small-scale variability of Gw, GPC, NUE, and N supply decreased with increasing N fertilization rates under both tillage systems. Significant increases in Gw and GPC were observed with increasing N rates, whereas NUE decreased slightly with increasing N rates in both NT and CT. The overall moderate spatial variation in Nmin, Gw, and NUE did not justify site-specific N fertilization in these small fields, with the exception of the stony within-plot positions, which were not responsive to rates of N > 50 kg N ha−1.
Criteria and Decision Support for A Sustainable Choice of Alternative Marine Fuels
Authors: Andersson Karin , Brynolf Selma , Hansson Julia , Grahn Maria
Published: May 2020

To reach the International Maritime Organization, IMO, vision of a 50% greenhouse gas (GHG) emission reduction by 2050, there is a need for action. Good decision support is needed for decisions on fuel and energy conversion systems due to the complexity. This paper aims to get an overview of the criteria types included in present assessments of future marine fuels, to evaluate these and to highlight the most important criteria. This is done using a literature review of selected scientific articles and reports and the authors’ own insights from assessing marine fuels. There are different views regarding the goal of fuel change, what fuel names to use as well as regarding the criteria to assess, which therefore vary in the literature. Quite a few articles and reports include a comparison of several alternative fuels. To promote a transition to fuels with significant GHG reduction potential, it is crucial to apply a life cycle perspective and to assess fuel options in a multicriteria perspective. The recommended minimum set of criteria to consider when evaluating future marine fuels differ somewhat between fuels that can be used in existing ships and fuels that can be used in new types of propulsion systems.
A DSGE-VAR Analysis for Tourism Development and Sustainable Economic Growth
Authors: Alaminos David , León-Gómez Ana , Sánchez-Serrano José Ramón
Published: May 2020

This paper aims to provide a better basis for understanding the transmission connection between tourism development and sustainable economic growth in the empirical scenario of International countries. In this way, we have applied the dynamic stochastic general equilibrium (DSGE) model in different countries in order to check the power of generalization of this framework to study the tourism development. Also, we extend this model to obtain the long-term effects of tourism development with confidence intervals. The influence of tourism development on sustainable economic growth is proved by our results and show the indirect consequences between tourist activity and other industries produced through the external effects of investment and human capital and public sector. Our study confirms that the DSGE technique can be a generalized model for the analysis of tourism development and, especially, can improve previous precision results with the DSGE-VAR model, where vector autoregression (VAR) is introduced in the DSGE model. The simulation results reveal even more than when the productivity of the economy in general enhances, as the current tourist demand increases in greater proportion than more than the national tourism demand. For its part, the consumption of domestic tourism rises more than the consumption of inbound tourism if the productivity of the tourism production enhances, but non-tourism prices decrease at a slower rate and tourism investment needs a longer time to recover to what is established.