bluuebunny/crossref_metadata_2025 huggingface dataset:
Archaeological Application of Airborne LiDAR with Object-Based Vegetation Classification and Visualization Techniques at the Lowland Maya Site of Ceibal, Guatemala
Authors: Inomata Takeshi , Pinzón Flory , Ranchos José Luis , Haraguchi Tsuyoshi , Nasu Hiroo , Fernandez-Diaz Juan Carlos , Aoyama Kazuo , Yonenobu Hitoshi
Published: June 2017

The successful analysis of LiDAR data for archaeological research requires an evaluation of effects of different vegetation types and the use of adequate visualization techniques for the identification of archaeological features. The Ceibal-Petexbatun Archaeological Project conducted a LiDAR survey of an area of 20 × 20 km around the Maya site of Ceibal, Guatemala, which comprises diverse vegetation classes, including rainforest, secondary vegetation, agricultural fields, and pastures. We developed a classification of vegetation through object-based image analysis (OBIA), primarily using LiDAR-derived datasets, and evaluated various visualization techniques of LiDAR data. We then compared probable archaeological features identified in the LiDAR data with the archaeological map produced by Harvard University in the 1960s and conducted ground-truthing in sample areas. This study demonstrates the effectiveness of the OBIA approach to vegetation classification in archaeological applications, and suggests that the Red Relief Image Map (RRIM) aids the efficient identification of subtle archaeological features. LiDAR functioned reasonably well for the thick rainforest in this high precipitation region, but the densest parts of foliage appear to create patches with no or few ground points, which make the identification of small structures problematic.
Mapping Torreya grandis Spatial Distribution Using High Spatial Resolution Satellite Imagery with the Expert Rules-Based Approach
Authors: Wang Yajie , Lu Dengsheng
Published: June 2017

Rapid expansion of Torreya forests in the mountainous region in Zhejiang Province in the past three decades has produced many environmental problems such as soil erosion and poor water quality, requiring an update of its spatial distribution in a timely way. However, to date there are no suitable approaches available for mapping Torreya forest distribution, especially the new Torreya plantations, due to the complex landscapes. This research used high spatial resolution Chinese Gaofen (GF-1) and Ziyuan (ZY-3) satellite images and digital elevation model (DEM) data to extract old Torreya forests and new Torreya plantations using a newly proposed expert rules-based approach. Different variables such as spectral bands, vegetation indices, textural images, and DEM-derived variables were examined, and separability analyses of different land covers were explored. An expert rules-based approach was developed for the extraction of old Torreya forests and new Torreya plantations. The accuracy assessment using field survey data and Google Earth images indicates that this newly-proposed approach can effectively distinguish both old Torreya forests and new Torreya plantations from other land covers with producer’s accuracies of 84% and 92%, and user’s accuracies of 77% and 85%, respectively, much better classification accuracies than the maximum likelihood classifier. This new approach may be used for other study area for extracting Torreya forest distribution. This research provides valuable data sources for better managing existing Torreya forests and planning potential Torreya expansions in this region in the near future.
Detection of Oil near Shorelines during the Deepwater Horizon Oil Spill Using Synthetic Aperture Radar (SAR)
Authors: Garcia-Pineda Oscar , Holmes Jamie , Rissing Matt , Jones Russell , Wobus Cameron , Svejkovsky Jan , Hess Mark
Published: June 2017

During any marine oil spill, floating oil slicks that reach shorelines threaten a wide array of coastal habitats. To assess the presence of oil near shorelines during the Deepwater Horizon (DWH) oil spill, we scanned the library of Synthetic Aperture Radar (SAR) imagery collected during the event to determine which images intersected shorelines and appeared to contain oil. In total, 715 SAR images taken during the DWH spill were analyzed and processed, with 188 of the images clearly showing oil. Of these, 156 SAR images showed oil within 10 km of the shoreline with appropriate weather conditions for the detection of oil on SAR data. We found detectable oil in SAR images within 10 km of the shoreline from west Louisiana to west Florida, including near beaches, marshes, and islands. The high number of SAR images collected in Barataria Bay, Louisiana in 2010 allowed for the creation of a nearshore oiling persistence map. This analysis shows that, in some areas inside Barataria Bay, floating oil was detected on as many as 29 different days in 2010. The nearshore areas with persistent floating oil corresponded well with areas where ground survey crews discovered heavy shoreline oiling. We conclude that satellite-based SAR imagery can detect oil slicks near shorelines, even in sheltered areas. These data can help assess potential shoreline oil exposure without requiring boats or aircraft. This method can be particularly helpful when shoreline assessment crews are hampered by difficult access or, in the case of DWH, a particularly large spatial and temporal spill extent.
A Review of Activity Trackers for Senior Citizens: Research Perspectives, Commercial Landscape and the Role of the Insurance Industry
Authors: Tedesco Salvatore , Barton John , O’Flynn Brendan
Published: June 2017

The objective assessment of physical activity levels through wearable inertial-based motion detectors for the automatic, continuous and long-term monitoring of people in free-living environments is a well-known research area in the literature. However, their application to older adults can present particular constraints. This paper reviews the adoption of wearable devices in senior citizens by describing various researches for monitoring physical activity indicators, such as energy expenditure, posture transitions, activity classification, fall detection and prediction, gait and balance analysis, also by adopting consumer-grade fitness trackers with the associated limitations regarding acceptability. This review also describes and compares existing commercial products encompassing activity trackers tailored for older adults, thus providing a comprehensive outlook of the status of commercially available motion tracking systems. Finally, the impact of wearable devices on life and health insurance companies, with a description of the potential benefits for the industry and the wearables market, was analyzed as an example of the potential emerging market drivers for such technology in the future.
Air Flow Detection in Crude Oil by Infrared Light
Authors: Dutra Guilherme , Martelli Cicero , Da Silva Marco , Patyk Rodolfo , Morales Rigoberto
Published: June 2017

In this paper, we used infrared light in the range of 8–12 μm to develop and test an optical imaging system to detect air bubbles flowing in oil. The system basically comprises a broadband light source and a 31 × 32 thermopile array to generate images. To analyze the effects related to light absorption, reflection, and refraction on air-oil boundaries, a numerical model was developed and the predominance of the refraction instead of the absorption in bubbles with diameters below a certain critical value was observed. The IR region of the electromagnetic spectrum has both optical and thermic behavior. To understand the limits of each effect on the oil flow imaging, a study of the influence of temperature variation on the petroleum optical detection was performed. The developed optical imaging system allowed the detection of air flow in static oil and in oil-air two-phase flow. With the presented system, it was possible to achieve images through up to 12 mm of oil volumes, but this may be enhanced by the use of optimized IR sources and detectors.
Resonance-Based Sparse Signal Decomposition and its Application in Mechanical Fault Diagnosis: A Review
Authors: Huang Wentao , Sun Hongjian , Wang Weijie
Published: June 2017

Mechanical equipment is the heart of industry. For this reason, mechanical fault diagnosis has drawn considerable attention. In terms of the rich information hidden in fault vibration signals, the processing and analysis techniques of vibration signals have become a crucial research issue in the field of mechanical fault diagnosis. Based on the theory of sparse decomposition, Selesnick proposed a novel nonlinear signal processing method: resonance-based sparse signal decomposition (RSSD). Since being put forward, RSSD has become widely recognized, and many RSSD-based methods have been developed to guide mechanical fault diagnosis. This paper attempts to summarize and review the theoretical developments and application advances of RSSD in mechanical fault diagnosis, and to provide a more comprehensive reference for those interested in RSSD and mechanical fault diagnosis. Followed by a brief introduction of RSSD’s theoretical foundation, based on different optimization directions, applications of RSSD in mechanical fault diagnosis are categorized into five aspects: original RSSD, parameter optimized RSSD, subband optimized RSSD, integrated optimized RSSD, and RSSD combined with other methods. On this basis, outstanding issues in current RSSD study are also pointed out, as well as corresponding instructional solutions. We hope this review will provide an insightful reference for researchers and readers who are interested in RSSD and mechanical fault diagnosis.
High Dynamic Range Spectral Imaging Pipeline For Multispectral Filter Array Cameras
Authors: Lapray Pierre-Jean , Thomas Jean-Baptiste , Gouton Pierre
Published: June 2017

Spectral filter arrays imaging exhibits a strong similarity with color filter arrays. This permits us to embed this technology in practical vision systems with little adaptation of the existing solutions. In this communication, we define an imaging pipeline that permits high dynamic range (HDR)-spectral imaging, which is extended from color filter arrays. We propose an implementation of this pipeline on a prototype sensor and evaluate the quality of our implementation results on real data with objective metrics and visual examples. We demonstrate that we reduce noise, and, in particular we solve the problem of noise generated by the lack of energy balance. Data are provided to the community in an image database for further research.
Optimal Design of an Hourglass in-Fiber Air Fabry-Perot Microcavity—Towards Spectral Characteristics and Strain Sensing Technology
Authors: Wang Qi , Yan Dongchao , Cui Binbin , Guo Zixuan
Published: June 2017

An hourglass in-fiber air microcavity Fabry-Perot interferometer is proposed in this paper, and its second reflecting surface of in-fiber microcavity is designed to be a concave reflector with the best curvature radius in order to improve the spectral characteristics. Experimental results proved that the extinction ratio of Fabry-Perot interferometer with cavity length of 60 μm and concave reflector radius of 60 μm is higher than for a rectangular Fabry-Perot interferometer with cavity length of 60 μm (14 dB: 11 dB). Theory and numerical simulation results show that the strain sensitivity of sensor can be improved by reducing the microcavity wall thickness and microcavity diameter, and when the in-fiber microcavity length is 40 μm, the microcavity wall thickness is 10 μm, the microcavity diameter is 20 μm, and the curvature radius of reflective surface II is 50 μm, the interference fringe contrast of is greater than 0.97, an Axial-pull sensitivity of 20.46 nm/N and resolution of 1 mN can be achieved in the range of 0–1 N axial tension. The results show that the performance of hourglass in-fiber microcavity interferometer is far superior to that of the traditional Fabry-Perot interferometer.
A Novel Real-Time Path Servo Control of a Hardware-in-the-Loop for a Large-Stroke Asymmetric Rod-Less Pneumatic System under Variable Loads
Authors: Lin Hao-Ting
Published: June 2017

This project aims to develop a novel large stroke asymmetric pneumatic servo system of a hardware-in-the-loop for path tracking control under variable loads based on the MATLAB Simulink real-time system. High pressure compressed air provided by the air compressor is utilized for the pneumatic proportional servo valve to drive the large stroke asymmetric rod-less pneumatic actuator. Due to the pressure differences between two chambers, the pneumatic actuator will operate. The highly nonlinear mathematical models of the large stroke asymmetric pneumatic system were analyzed and developed. The functional approximation technique based on the sliding mode controller (FASC) is developed as a controller to solve the uncertain time-varying nonlinear system. The MATLAB Simulink real-time system was a main control unit of a hardware-in-the-loop system proposed to establish driver blocks for analog and digital I/O, a linear encoder, a CPU and a large stroke asymmetric pneumatic rod-less system. By the position sensor, the position signals of the cylinder will be measured immediately. The measured signals will be viewed as the feedback signals of the pneumatic servo system for the study of real-time positioning control and path tracking control. Finally, real-time control of a large stroke asymmetric pneumatic servo system with measuring system, a large stroke asymmetric pneumatic servo system, data acquisition system and the control strategy software will be implemented. Thus, upgrading the high position precision and the trajectory tracking performance of the large stroke asymmetric pneumatic servo system will be realized to promote the high position precision and path tracking capability. Experimental results show that fifth order paths in various strokes and the sine wave path are successfully implemented in the test rig. Also, results of variable loads under the different angle were implemented experimentally.
Design and Performance of a Metal-Shielded Piezoelectric Sensor
Authors: Sáenz de Inestrillas Álvaro , Camarena Francisco , Bou Cabo Manuel , Barreiro Julián , Reig Antonio
Published: June 2017

In certain circumstances when acoustic measurements are required in the presence of explosive atmospheres the sensor must be placed inside a Faraday Cage. Piezoelectric active materials are suitable for this purpose as they do not need an electrical power supply, although the metal shielding can considerably reduce sensor sensitivity, which is already low at the acoustic frequency range (<20 kHz). This paper describes a metal-shielded piezoelectric sensor designed to work in the range of frequencies between 1 and 2 kHz and in these environmental conditions. The main idea was to add a thin material layer to the front face of the piezoelectric ceramic in order to force the system to vibrate in flexure mode at low frequencies. The resonant frequency and sensitivity of the system was studied as a function of the radius, thickness, and material of the thin layer. The study includes a comparison of theoretical model, FEM simulation, and real data measured using three aluminum and three steel prototypes of different sizes.
Magnetic Lateral Flow Strip for the Detection of Cocaine in Urine by Naked Eyes and Smart Phone Camera
Authors: Wu Jing , Dong Mingling , Zhang Cheng , Wang Yu , Xie Mengxia , Chen Yiping
Published: June 2017

Magnetic lateral flow strip (MLFS) based on magnetic bead (MB) and smart phone camera has been developed for quantitative detection of cocaine (CC) in urine samples. CC and CC-bovine serum albumin (CC-BSA) could competitively react with MB-antibody (MB-Ab) of CC on the surface of test line of MLFS. The color of MB-Ab conjugate on the test line relates to the concentration of target in the competition immunoassay format, which can be used as a visual signal. Furthermore, the color density of the MB-Ab conjugate can be transferred into digital signal (gray value) by a smart phone, which can be used as a quantitative signal. The linear detection range for CC is 5–500 ng/mL and the relative standard deviations are under 10%. The visual limit of detection was 5 ng/mL and the whole analysis time was within 10 min. The MLFS has been successfully employed for the detection of CC in urine samples without sample pre-treatment and the result is also agreed to that of enzyme-linked immunosorbent assay (ELISA). With the popularization of smart phone cameras, the MLFS has large potential in the detection of drug residues in virtue of its stability, speediness, and low-cost.
Adaptive Temporal Matched Filtering for Noise Suppression in Fiber Optic Distributed Acoustic Sensing
Authors: Ölçer İbrahim , Öncü Ahmet
Published: June 2017

Distributed vibration sensing based on phase-sensitive optical time domain reflectometry ( ϕ -OTDR) is being widely used in several applications. However, one of the main challenges in coherent detection-based ϕ -OTDR systems is the fading noise, which impacts the detection performance. In addition, typical signal averaging and differentiating techniques are not suitable for detecting high frequency events. This paper presents a new approach for reducing the effect of fading noise in fiber optic distributed acoustic vibration sensing systems without any impact on the frequency response of the detection system. The method is based on temporal adaptive processing of ϕ -OTDR signals. The fundamental theory underlying the algorithm, which is based on signal-to-noise ratio (SNR) maximization, is presented, and the efficacy of our algorithm is demonstrated with laboratory experiments and field tests. With the proposed digital processing technique, the results show that more than 10 dB of SNR values can be achieved without any reduction in the system bandwidth and without using additional optical amplifier stages in the hardware. We believe that our proposed adaptive processing approach can be effectively used to develop fiber optic-based distributed acoustic vibration sensing systems.
Proof of Concept of Home IoT Connected Vehicles
Authors: Kim Younsun , Oh Hyunggoy , Kang Sungho
Published: June 2017

The way in which we interact with our cars is changing, driven by the increased use of mobile devices, cloud-based services, and advanced automotive technology. In particular, the requirements and market demand for the Internet of Things (IoT) device-connected vehicles will continuously increase. In addition, the advances in cloud computing and IoT have provided a promising opportunity for developing vehicular software and services in the automotive domain. In this paper, we introduce the concept of a home IoT connected vehicle with a voice-based virtual personal assistant comprised of a vehicle agent and a home agent. The proposed concept is evaluated by implementing a smartphone linked with home IoT devices that are connected to an infotainment system for the vehicle, a smartphone-based natural language interface input device, and cloud-based home IoT devices for the home. The home-to-vehicle connected service scenarios that aim to reduce the inconvenience due to simple and repetitive tasks by improving the urban mobility efficiency in IoT environments are substantiated by analyzing real vehicle testing and lifestyle research. Remarkable benefits are derived by making repetitive routine tasks one task that is executed by a command and by executing essential tasks automatically, without any request. However, it should be used with authorized permission, applied without any error at the right time, and applied under limited conditions to sense the habitants’ intention correctly and to gain the required trust regarding the remote execution of tasks.
Multi-Axis Force Sensor for Human–Robot Interaction Sensing in a Rehabilitation Robotic Device
Authors: Grosu Victor , Grosu Svetlana , Vanderborght Bram , Lefeber Dirk , Rodriguez-Guerrero Carlos
Published: June 2017

Human–robot interaction sensing is a compulsory feature in modern robotic systems where direct contact or close collaboration is desired. Rehabilitation and assistive robotics are fields where interaction forces are required for both safety and increased control performance of the device with a more comfortable experience for the user. In order to provide an efficient interaction feedback between the user and rehabilitation device, high performance sensing units are demanded. This work introduces a novel design of a multi-axis force sensor dedicated for measuring pelvis interaction forces in a rehabilitation exoskeleton device. The sensor is conceived such that it has different sensitivity characteristics for the three axes of interest having also movable parts in order to allow free rotations and limit crosstalk errors. Integrated sensor electronics make it easy to acquire and process data for a real-time distributed system architecture. Two of the developed sensors are integrated and tested in a complex gait rehabilitation device for safe and compliant control.
Remote Sensing Image Change Detection Based on NSCT-HMT Model and Its Application
Authors: Chen Pengyun , Zhang Yichen , Jia Zhenhong , Yang Jie , Kasabov Nikola
Published: June 2017

Traditional image change detection based on a non-subsampled contourlet transform always ignores the neighborhood information’s relationship to the non-subsampled contourlet coefficients, and the detection results are susceptible to noise interference. To address these disadvantages, we propose a denoising method based on the non-subsampled contourlet transform domain that uses the Hidden Markov Tree model (NSCT-HMT) for change detection of remote sensing images. First, the ENVI software is used to calibrate the original remote sensing images. After that, the mean-ratio operation is adopted to obtain the difference image that will be denoised by the NSCT-HMT model. Then, using the Fuzzy Local Information C-means (FLICM) algorithm, the difference image is divided into the change area and unchanged area. The proposed algorithm is applied to a real remote sensing data set. The application results show that the proposed algorithm can effectively suppress clutter noise, and retain more detailed information from the original images. The proposed algorithm has higher detection accuracy than the Markov Random Field-Fuzzy C-means (MRF-FCM), the non-subsampled contourlet transform-Fuzzy C-means clustering (NSCT-FCM), the pointwise approach and graph theory (PA-GT), and the Principal Component Analysis-Nonlocal Means (PCA-NLM) denosing algorithm. Finally, the five algorithms are used to detect the southern boundary of the Gurbantunggut Desert in Xinjiang Uygur Autonomous Region of China, and the results show that the proposed algorithm has the best effect on real remote sensing image change detection.
Ultrasensitive Magnetic Nanoparticle Detector for Biosensor Applications
Authors: Liang Yu-Chi , Chang Long , Qiu Wenlan , Kolhatkar Arati , Vu Binh , Kourentzi Katerina , Lee T. , Zu Youli , Willson Richard , Litvinov Dmitri
Published: June 2017

Ta/Ru/Co/Ru/Co/Cu/Co/Ni80Fe20/Ta spin-valve giant magnetoresistive (GMR) multilayers were deposited using UHV magnetron sputtering and optimized to achieve a 13% GMR ratio before patterning. The GMR multilayer was patterned into 12 sensor arrays using a combination of e-beam and optical lithographies. Arrays were constructed with 400 nm × 400 nm and 400 nm × 200 nm sensors for the detection of reporter nanoparticles. Nanoparticle detection was based on measuring the shift in high-to-low resistance switching field of the GMR sensors in the presence of magnetic particle(s). Due to shape anisotropy and the corresponding demag field, the resistance state switching fields were significantly larger and the switching field distribution significantly broader in the 400 nm × 200 nm sensors as compared to the 400 nm × 400 nm sensors. Thus, sensor arrays with 400 nm × 400 nm dimensions were used for the demonstration of particle detection. Detection of a single 225 nm Fe3O4 magnetic nanoparticle and a small number (~10) of 100 nm nanoparticles was demonstrated. With appropriate functionalization for biomolecular recognition, submicron GMR sensor arrays can serve as the basis of ultrasensitive chemical and biological sensors.
Convolutional Neural Network-Based Finger-Vein Recognition Using NIR Image Sensors
Authors: Hong Hyung , Lee Min , Park Kang
Published: June 2017

Conventional finger-vein recognition systems perform recognition based on the finger-vein lines extracted from the input images or image enhancement, and texture feature extraction from the finger-vein images. In these cases, however, the inaccurate detection of finger-vein lines lowers the recognition accuracy. In the case of texture feature extraction, the developer must experimentally decide on a form of the optimal filter for extraction considering the characteristics of the image database. To address this problem, this research proposes a finger-vein recognition method that is robust to various database types and environmental changes based on the convolutional neural network (CNN). In the experiments using the two finger-vein databases constructed in this research and the SDUMLA-HMT finger-vein database, which is an open database, the method proposed in this research showed a better performance compared to the conventional methods.
Comparison and Evaluation of Annual NDVI Time Series in China Derived from the NOAA AVHRR LTDR and Terra MODIS MOD13C1 Products
Authors: Guo Xiaoyi , Zhang Hongyan , Wu Zhengfang , Zhao Jianjun , Zhang Zhengxiang
Published: June 2017

Time series of Normalized Difference Vegetation Index (NDVI) derived from multiple satellite sensors are crucial data to study vegetation dynamics. The Land Long Term Data Record Version 4 (LTDR V4) NDVI dataset was recently released at a 0.05 × 0.05° spatial resolution and daily temporal resolution. In this study, annual NDVI time series that are composited by the LTDR V4 and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13C1) are compared and evaluated for the period from 2001 to 2014 in China. The spatial patterns of the NDVI generally match between the LTDR V4 and MOD13C1 datasets. The transitional zone between high and low NDVI values generally matches the boundary of semi-arid and sub-humid regions. A significant and high coefficient of determination is found between the two datasets according to a pixel-based correlation analysis. The spatially averaged NDVI of LTDR V4 is characterized by a much weaker positive regression slope relative to that of the spatially averaged NDVI of the MOD13C1 dataset because of changes in NOAA AVHRR sensors between 2005 and 2006. The measured NDVI values of LTDR V4 were always higher than that of MOD13C1 in western China due to the relatively lower atmospheric water vapor content in western China, and opposite observation appeared in eastern China. In total, 18.54% of the LTDR V4 NDVI pixels exhibit significant trends, whereas 35.79% of the MOD13C1 NDVI pixels show significant trends. Good agreement is observed between the significant trends of the two datasets in the Northeast Plain, Bohai Economic Rim, Loess Plateau, and Yangtze River Delta. By contrast, the datasets contrasted in northwestern desert regions and southern China. A trend analysis of the regression slope values according to the vegetation type shows good agreement between the LTDR V4 and MOD13C1 datasets. This study demonstrates the spatial and temporal consistencies and discrepancies between the AVHRR LTDR and MODIS MOD13C1 NDVI products in China, which could provide useful information for the choice of NDVI products in subsequent studies of vegetation dynamics.
Study on an Indoor Positioning System for Harsh Environments Based on Wi-Fi and Bluetooth Low Energy
Authors: De Blasio Gabriel , Quesada-Arencibia Alexis , García Carmelo , Molina-Gil Jezabel , Caballero-Gil Cándido
Published: June 2017

This paper presents a study of positioning system that provides advanced information services based on Wi-Fi and Bluetooth Low Energy (BLE) technologies. It uses Wi-Fi for rough positioning and BLE for fine positioning. It is designed for use in public transportation system stations and terminals where the conditions are “hostile” or unfavourable due to signal noise produced by the continuous movement of passengers and buses, data collection conducted in the constant presence thereof, multipath fading, non-line of sight (NLOS) conditions, the fact that part of the wireless communication infrastructure has already been deployed and positioned in a way that may not be optimal for positioning purposes, variable humidity conditions, etc. The ultimate goal is to provide a service that may be used to assist people with special needs. We present experimental results based on scene analysis; the main distance metric used was the Euclidean distance but the Mahalanobis distance was also used in one case. The algorithm employed to compare fingerprints was the weighted k-nearest neighbor one. For Wi-Fi, with only three visible access points, accuracy ranged from 3.94 to 4.82 m, and precision from 5.21 to 7.0 m 90% of the time. With respect to BLE, with a low beacon density (1 beacon per 45.7 m2), accuracy ranged from 1.47 to 2.15 m, and precision from 1.81 to 3.58 m 90% of the time. Taking into account the fact that this system is designed to work in real situations in a scenario with high environmental fluctuations, and comparing the results with others obtained in laboratory scenarios, our results are promising and demonstrate that the system would be able to position users with these reasonable values of accuracy and precision.
Two-Dimensional DOA Estimation for Coherently Distributed Sources with Symmetric Properties in Crossed Arrays
Authors: Dai Zhengliang , Cui Weijia , Ba Bin , Wang Daming , Sun Youming
Published: June 2017

In this paper, a novel algorithm is proposed for the two-dimensional (2D) central direction-of-arrival (DOA) estimation of coherently distributed (CD) sources. Specifically, we focus on a centro-symmetric crossed array consisting of two uniform linear arrays (ULAs). Unlike the conventional low-complexity methods using the one-order Taylor series approximation to obtain the approximate rotational invariance relation, we first prove the symmetric property of angular signal distributed weight vectors of the CD source for an arbitrary centrosymmetric array, and then use this property to establish two generalized rotational invariance relations inside the array manifolds in the two ULAs. Making use of such relations, the central elevation and azimuth DOAs are obtained by employing a polynomial-root-based search-free approach, respectively. Finally, simple parameter matching is accomplished by searching for the minimums of the cost function of the estimated 2D angular parameters. When compared with the existing low-complexity methods, the proposed algorithm can greatly improve estimation accuracy without significant increment in computation complexity. Moreover, it performs independently of the deterministic angular distributed function. Simulation results are presented to illustrate the performance of the proposed algorithm.
Node Scheduling Strategies for Achieving Full-View Area Coverage in Camera Sensor Networks
Authors: Wu Peng-Fei , Xiao Fu , Sha Chao , Huang Hai-Ping , Wang Ru-Chuan , Xiong Nai-Xue
Published: June 2017

Unlike conventional scalar sensors, camera sensors at different positions can capture a variety of views of an object. Based on this intrinsic property, a novel model called full-view coverage was proposed. We study the problem that how to select the minimum number of sensors to guarantee the full-view coverage for the given region of interest (ROI). To tackle this issue, we derive the constraint condition of the sensor positions for full-view neighborhood coverage with the minimum number of nodes around the point. Next, we prove that the full-view area coverage can be approximately guaranteed, as long as the regular hexagons decided by the virtual grid are seamlessly stitched. Then we present two solutions for camera sensor networks in two different deployment strategies. By computing the theoretically optimal length of the virtual grids, we put forward the deployment pattern algorithm (DPA) in the deterministic implementation. To reduce the redundancy in random deployment, we come up with a local neighboring-optimal selection algorithm (LNSA) for achieving the full-view coverage. Finally, extensive simulation results show the feasibility of our proposed solutions.
A High-Speed Target-Free Vision-Based Sensor for Bus Rapid Transit Viaduct Vibration Measurements Using CMT and ORB Algorithms
Authors: Hu Qijun , He Songsheng , Wang Shilong , Liu Yugang , Zhang Zutao , He Leping , Wang Fubin , Cai Qijie , Shi Rendan , Yang Yuan
Published: June 2017

Bus Rapid Transit (BRT) has become an increasing source of concern for public transportation of modern cities. Traditional contact sensing techniques during the process of health monitoring of BRT viaducts cannot overcome the deficiency that the normal free-flow of traffic would be blocked. Advances in computer vision technology provide a new line of thought for solving this problem. In this study, a high-speed target-free vision-based sensor is proposed to measure the vibration of structures without interrupting traffic. An improved keypoints matching algorithm based on consensus-based matching and tracking (CMT) object tracking algorithm is adopted and further developed together with oriented brief (ORB) keypoints detection algorithm for practicable and effective tracking of objects. Moreover, by synthesizing the existing scaling factor calculation methods, more rational approaches to reducing errors are implemented. The performance of the vision-based sensor is evaluated through a series of laboratory tests. Experimental tests with different target types, frequencies, amplitudes and motion patterns are conducted. The performance of the method is satisfactory, which indicates that the vision sensor can extract accurate structure vibration signals by tracking either artificial or natural targets. Field tests further demonstrate that the vision sensor is both practicable and reliable.
Sustainability from the Occurrence of Critical Dynamic Power System Blackout Determined by Using the Stochastic Event Tree Technique
Authors: Othman Muhammad , Salim Nur , Musirin Ismail
Published: June 2017

With the advent of advanced technology in smart grid, the implementation of renewable energy in a stressed and complicated power system operation, aggravated by a competitive electricity market and critical system contingencies, this will inflict higher probabilities of the occurrence of a severe dynamic power system blackout. This paper presents the proposed stochastic event tree technique used to assess the sustainability against the occurrence of dynamic power system blackout emanating from implication of critical system contingencies such as the rapid increase in total loading condition and sensitive initial transmission line tripping. An extensive analysis of dynamic power system blackout has been carried out in a case study of the following power systems: IEEE RTS-79 and IEEE RTS-96. The findings have shown that the total loading conditions and sensitive transmission lines need to be given full attention by the utility to prevent the occurrence of dynamic power system blackout.
Brazilian Solidarity Economy Rural Organizations: Validating Performance Evaluation Criteria Based on Decision Makers’ Perception
Authors: Telles Leomara Battisti , Macedo Luciano Medina , Bittencourt Juliana Vitória Messias
Published: June 2017

The majority of the world’s current economic models are destructive and unsustainable. Little progress has been made in recent years to change this, despite significant discussion surrounding the theme. In this context, the solidarity economy presents itself as an alternative that seeks environmental and economic sustainability, as well as social promotion. In Brazil, Solidarity Economy Rural Organizations (SEROs) began in the 1970s. As with organizations from other sectors, they should be evaluated in relation to their ability to achieve social and solidarity objectives. Thus, this study aims to present a theoretical model of performance evaluation indicators for Solidarity Economy Rural Organizations in Southern Brazil based on the perceptions of the organizations’ decision makers. SERO representatives interviewed were asked to rank 36 criteria commonly used in performance evaluations, which were grouped into six dimensions: (1) legal documents and standards; (2) valuing of human work; (3) technology and economy; (4) acknowledgment of women; (5) preservation of and respect for nature; and (6) cooperation and solidarity. The results show that SERO representatives consider that performance evaluations should adhere to the Brazilian Declaration of Solidarity Economy Principles. Additionally, we identified a greater concern with criteria correlated to technical and legal aspects than criteria related to solidarity and humanitarianism. These results are relevant for the maintenance of rural solidarity economy organization as they provide a base for developing processes and tools to be used in SERO performance evaluations; such processes are necessary to maintain sustainable development in low-income economies, and to enable solidarity organizations to reach their objectives.
Observing Trip Chain Characteristics of Round-Trip Carsharing Users in China: A Case Study Based on GPS Data in Hangzhou City
Authors: Hui Ying , Ding Mengtao , Zheng Kun , Lou Dong
Published: June 2017

Carsharing as a means to provide individuals with access to automobiles to complete a personal trip has grown significantly in recent years in China. However, there are few case studies based on operational data to show the role carsharing systems play in citizens’ daily trips. In this study, vehicle GPS data of a round-trip carsharing system in Hangzhou, China was used to describe the trip chain characteristics of users. For clearer delineation of carshare usage, the car use time length of all observations chosen in the study was within 24 h or less. Through data preprocessing, a large pool (26,085) of valid behavior samples was obtained, and several trip chaining attributes were selected to describe the characteristics. The pool of observations was then classified into five clusters, with each cluster having significant differences in one or two trip chain characteristics. The cluster results reflected that different use patterns exist. By a comparative analysis with trip survey data in Hangzhou, differences in trip chain characteristics exist between carsharing and private cars, but in some cases, shared vehicles can be a substitute for private cars to satisfy motorized travel. The proposed method could facilitate companies in formulating a flexible pricing strategy and determining target customers. In addition, traffic administration agencies could have a deeper understanding of the position and function of various carsharing modes in an urban transportation system.
Effect of the Changeable Organic Semi-Transparent Solar Cell Window on Building Energy Efficiency and User Comfort
Authors: Tak Sehyun , Woo Soomin , Park Jiyoung , Park Sungjin
Published: June 2017

Building-integrated photovoltaics (BIPV) are one of the most important sustainability technologies for building energy, and the semi-transparent solar cell is one of the most promising photovoltaic systems for building integration because it can generate electricity and is transparent with a range of beneficial optical properties. On the other hand, the utilization of semi-transparent solar cells for a building facade is limited, as the optimal transparency and power conversion efficiency (PCE %) of the solar cell vary according to the purpose of the space, facing orientation, and number of occupants. This study designed a changeable organic semi-transparent solar cell window (COSW), in which the transparency can be altered by adjusting its temperature and solvent vapor pressure. A simulation test with the proposed COSW was conducted to examine the effects of the proposed window on energy consumption, electricity production, and occupant comfort. The results show that the proposed window has a huge potential for energy conservation and occupant comfort. Compared to the double-glazed Low-E windows, the proposed window reduces the energy consumption by approximately 14.80 kW/m2 (53.29 MJ/m2), 11.51 kW/m2 (41.45 MJ/m2), and 15.02 kW/m2 (54.07 MJ/m2), for the south-facing, east-facing, and west-facing facades, respectively, and increases user satisfaction, particularly in spring and autumn.
Property Rights and the Soybean Revolution: Shaping How China and Brazil Are Telecoupled
Authors: Torres Sara , Moran Emilio , Silva Ramon
Published: June 2017

China currently has the largest population in the world and is currently experiencing rapid economic and urban growth, becoming the world’s number one pork and poultry consumer. In order to meet this growing demand for meat, China has increased its demand for soybeans to produce chicken and pork. It has imported soybeans from the United States, Brazil, and Argentina, while keeping its soybean production for direct human consumption stable at home. Brazil has become the largest soybean exporter to China, and, in response specifically to Chinese demand, has become the second largest producer of soybeans in the world. This has changed land use in Brazil, particularly in its central plateau. In this paper, we indicate how these two countries, telecoupled by trade in soybeans, are depending on each other as they try to balance environmental and economic objectives. Brazil, as a sending system, has created pressures on its natural ecosystems, which have led to losses particularly in the Cerrado biome and its ecotones in the Amazon’s tropical moist forest biome. China, as a receiving system, has created a land asset important to regenerating its lost natural systems (e.g., forest cover areas). Both countries have different property rights regimes, which have created distinct circumstances in which they are to protect or regenerate their natural ecosystems. Throughout this paper, we analyze how both countries have dealt with the lure offered by the soybean commodity trade.
Continuity and Change: Dealing with Political Volatility to Advance Climate Change Mitigation Strategies—Examples from the Transport Sector
Authors: Lah Oliver
Published: June 2017

As the recent withdrawal of the United States from the Paris Agreement has shown, political volatility directly affects climate change mitigation policies, in particular in sectors, such as transport associated with long-term investments by individuals (vehicles) and by local and national governments (urban form and transport infrastructure and services). There is a large potential for cost-effective solutions to reduce greenhouse gas emissions and to improve the sustainability of the transport sector that is yet unexploited. Considering the cost-effectiveness and the potential for co-benefits, it is hard to understand why efficiency gains and CO2 emission reductions in the transport sector are still lagging behind this potential. Particularly interesting is the fact that there is substantial difference among countries with relatively similar economic performances in the development of their transport CO2 emissions over the past thirty years despite the fact that these countries had relatively similar access to efficient technologies and vehicles. This study aims to explore some well-established political science theories on the particular example of climate change mitigation in the transport sector in order to identify some of the factors that could help explain the variations in success of policies and strategies in this sector. The analysis suggests that institutional arrangements that contribute to consensus building in the political process provide a high level of political and policy stability which is vital to long-term changes in energy end-use sectors that rely on long-term investments. However, there is no direct correlation between institutional structures, e.g., corporatism and success in reducing greenhouse gas emissions in the transport sector. Environmental objectives need to be built into the consensus-based policy structure before actual policy progress can be observed. This usually takes longer in consensus democracies than in politically more agile majoritarian policy environments, but the policy stability that builds on corporatist institutional structures is likely to experience changes over a longer-term, in this case to a shift towards low-carbon transport that endures.
Dust Accumulation on the Surface of Photovoltaic Panels: Introducing the Photovoltaic Soiling Index (PVSI)
Authors: Menoufi Karim
Published: June 2017

This article presents an empirical review of research concerning the impact of dust accumulation on the performance of photovoltaic (PV) panels. After examining the articles published in international scientific journals, many differences between the studies were found within the context of the PV technologies used, the contribution to this type of study from different countries, and the variety in the representation of the results where each study has its unique parameters, testing equipment, and relevant standards. Due to those variations and differences, it has been found that it is very difficult and impractical to compare between results of the corresponding studies. Hence, in addition to the valuable reviews found in literature, this article demonstrates another new perspective that highlights the gaps in the studies related to the impact of dust accumulation on PV panels. The conclusions of this study are considered to be the seed for establishing a new initiative—The Photovoltaic Soiling Index (PVSI)—which would be an indicator for the performance of PV panels under exposure to dust at the Standard Test Conditions (STC), as well as at other operating conditions in different locations around the globe.
Strategies for Promoting Green Building Technologies Adoption in the Construction Industry—An International Study
Authors: Chan Albert Ping Chuen , Darko Amos , Ameyaw Ernest Effah
Published: June 2017

Because green building technologies (GBTs) adoption is a promising way of ameliorating the sustainability performance of buildings, GBTs are receiving increased interest in the global construction community. The barriers to the adoption of GBTs, such as higher cost and lack of awareness, further indicate that proper strategies need to be devised for promoting the wider adoption of GBTs in buildings development. However, there exist limited empirical studies identifying the strategies for promoting GBTs adoption in the construction industry. This study aims to identify the strategies that are important for promoting GBTs adoption in construction. After a comprehensive literature review to identify strategies for the promotion of GBTs adoption, empirical data were gleaned through a questionnaire survey with 104 green building experts around the world. The analysis results validated the importance of all of the 12 promotion strategies used for the study. Green building experts from different countries and with different backgrounds had significant agreement on the relative importance ranking of the promotion strategies. Furthermore, “financial and further market-based incentives for GBTs adopters”, “availability of better information on cost and benefits of GBTs”, “mandatory governmental policies and regulations”, and “green rating and labeling” were identified as the top four important promotion strategies. The research findings provide a valuable reference to assist practitioners and policy makers in developing practical strategies for promoting GBTs adoption to eventually achieve the sustainable development of buildings. From the perspective of international experts, this study adds to the green building literature by offering empirical evidence of the important strategies for promoting GBTs adoption in the construction industry. Future research will investigate the interrelationships among the promotion strategies and their impacts on the GBTs adoption process.