bluuebunny/crossref_metadata_2025 huggingface dataset:
Hazard Implications of the 2016 Mw 5.0 Cushing, OK Earthquake from a Joint Analysis of Damage and InSAR Data
Authors: Barba-Sevilla Magali , Baird Bridger W. , Liel Abbie B. , Tiampo Kristy F.
Published: October 2018

The Cushing Hub in Oklahoma, one of the largest oil storage facilities in the world, is federally designated as critical national infrastructure. In 2014, the formerly aseismic city of Cushing experienced a Mw 4.0 and 4.3 induced earthquake sequence due to wastewater injection. Since then, an M4+ earthquake sequence has occurred annually (October 2014, September 2015, November 2016). Thus far, damage to critical infrastructure has been minimal; however, a larger earthquake could pose significant risk to the Cushing Hub. In addition to inducing earthquakes, wastewater injection also threatens the Cushing Hub through gradual surface uplift. To characterize the impact of wastewater injection on critical infrastructure, we use Differential Interferometric Synthetic Aperture Radar (DInSAR), a satellite radar technique, to observe ground surface displacement in Cushing before and during the induced Mw 5.0 event. Here, we process interferograms of Single Look Complex (SLC) radar data from the European Space Agency (ESA) Sentinel-1A satellite. The preearthquake interferograms are used to create a time series of cumulative surface displacement, while the coseismic interferograms are used to invert for earthquake source characteristics. The time series of surface displacement reveals 4–5.5 cm of uplift across Cushing over 17 months. The coseismic interferogram inversion suggests that the 2016 Mw 5.0 earthquake is shallower than estimated from seismic inversions alone. This shallower source depth should be taken into account in future hazard assessments for regional infrastructure. In addition, monitoring of surface deformation near wastewater injection wells can be used to characterize the subsurface dynamics and implement measures to mitigate damage to critical installations.
Establishment and Evaluation of a New Meteorological Observation-Based Grid Model for Estimating Zenith Wet Delay in Ground-Based Global Navigation Satellite System (GNSS)
Authors: Yao Yibin , Sun Zhangyu , Xu Chaoqian
Published: October 2018

With the availability to high-accuracy a priori zenith wet delay (ZWD) data, the positioning efficiency of the precise point positioning (PPP) processing can be effectively improved, including accelerating the convergence time and improving the positioning precision, in ground-based Global Navigation Satellite System (GNSS) technology. Considering the limitations existing in the state-of-the-art ZWD models, this paper established and evaluated a new in-situ meteorological observation-based grid model for estimating ZWD named GridZWD using the radiosonde data and the European Centre for Medium-Range Weather Forecasts (ECWMF) data. The results show that ZWD has a strong correlation with the meteorological parameter water vapor pressure in continental and high-latitude regions. The root of mean square error (RMS) of 24.6 mm and 36.0 mm are achievable by the GridZWD model when evaluated with the ECWMF data and the radiosonde data, respectively. An accuracy improvement of approximately 10%~30% compared with the state-of-the-art models (e.g., the Saastamoinen, Hopfield and GPT2w models) can be found for the new built model.
An Extension of Phase Correlation-Based Image Registration to Estimate Similarity Transform Using Multiple Polar Fourier Transform
Authors: Dong Yunyun , Jiao Weili , Long Tengfei , He Guojin , Gong Chengjuan
Published: October 2018

Image registration is a core technology of many different image processing areas and is widely used in the remote sensing community. The accuracy of image registration largely determines the effect of subsequent applications. In recent years, phase correlation-based image registration has drawn much attention because of its high accuracy and efficiency as well as its robustness to gray difference and even slight changes in content. Many researchers have reported that the phase correlation method can acquire a sub-pixel accuracy of 1 / 10 or even 1 / 100 . However, its performance is acquired only in the case of translation, which limits the scope of the application of the method. However, there are few reports on the estimation of scales and angles based on the phase correlation method. To take advantage of the high accuracy property and other merits of phase correlation-based image registration and extend it to estimate the similarity transform, we proposed a novel algorithm, the Multilayer Polar Fourier Transform (MPFT), which uses a fast and accurate polar Fourier transform with different scaling factors to calculate the log-polar Fourier transform. The structure of the polar grids of MPFT is more similar to the one of the log-polar grid. In particular, for rotation estimation only, the polar grid of MPFT is the calculation grid. To validate its effectiveness and high accuracy in estimating angles and scales, both qualitative and quantitative experiments were carried out. The quantitative experiments included a numerical simulation as well as synthetic and real data experiments. The experimental results showed that the proposed method, MPFT, performs better than the existing phase correlation-based similarity transform estimation methods, the Pseudo-polar Fourier Transform (PPFT) and the Multilayer Fractional Fourier Transform method (MLFFT), and the classical feature-based registration method, Scale-Invariant Feature Transform (SIFT), and its variant, ms-SIFT.
Towards Estimating Land Evaporation at Field Scales Using GLEAM
Authors: Martens Brecht , De Jeu Richard A. M. , Verhoest Niko E. C. , Schuurmans Hanneke , Kleijer Jonne , Miralles Diego G.
Published: October 2018

The evaporation of water from land into the atmosphere is a key component of the hydrological cycle. Accurate estimates of this flux are essential for proper water management and irrigation scheduling. However, continuous and qualitative information on land evaporation is currently not available at the required spatio-temporal scales for agricultural applications and regional-scale water management. Here, we apply the Global Land Evaporation Amsterdam Model (GLEAM) at 100 m spatial resolution and daily time steps to provide estimates of land evaporation over The Netherlands, Flanders, and western Germany for the period 2013–2017. By making extensive use of microwave-based geophysical observations, we are able to provide data under all weather conditions. The soil moisture estimates from GLEAM at high resolution compare well with in situ measurements of surface soil moisture, resulting in a median temporal correlation coefficient of 0.76 across 29 sites. Estimates of terrestrial evaporation are also evaluated using in situ eddy-covariance measurements from five sites, and compared to estimates from the coarse-scale GLEAM v3.2b, land evaporation from the Satellite Application Facility on Land Surface Analysis (LSA-SAF), and reference grass evaporation based on Makkink’s equation. All datasets compare similarly with in situ measurements and differences in the temporal statistics are small, with correlation coefficients against in situ data ranging from 0.65 to 0.95, depending on the site. Evaporation estimates from GLEAM-HR are typically bounded by the high values of the Makkink evaporation and the low values from LSA-SAF. While GLEAM-HR and LSA-SAF show the highest spatial detail, their geographical patterns diverge strongly due to differences in model assumptions, model parameterizations, and forcing data. The separate consideration of rainfall interception loss by tall vegetation in GLEAM-HR is a key cause of this divergence: while LSA-SAF reports maximum annual evaporation volumes in the Green Heart of The Netherlands, an area dominated by shrubs and grasses, GLEAM-HR shows its maximum in the national parks of the Veluwe and Heuvelrug, both densely-forested regions where rainfall interception loss is a dominant process. The pioneering dataset presented here is unique in that it provides observational-based estimates at high resolution under all weather conditions, and represents a viable alternative to traditional visible and infrared models to retrieve evaporation at field scales.
Ground and Multi-Class Classification of Airborne Laser Scanner Point Clouds Using Fully Convolutional Networks
Authors: Rizaldy Aldino , Persello Claudio , Gevaert Caroline , Oude Elberink Sander , Vosselman George
Published: October 2018

Various classification methods have been developed to extract meaningful information from Airborne Laser Scanner (ALS) point clouds. However, the accuracy and the computational efficiency of the existing methods need to be improved, especially for the analysis of large datasets (e.g., at regional or national levels). In this paper, we present a novel deep learning approach to ground classification for Digital Terrain Model (DTM) extraction as well as for multi-class land-cover classification, delivering highly accurate classification results in a computationally efficient manner. Considering the top–down acquisition angle of ALS data, the point cloud is initially projected on the horizontal plane and converted into a multi-dimensional image. Then, classification techniques based on Fully Convolutional Networks (FCN) with dilated kernels are designed to perform pixel-wise image classification. Finally, labels are transferred from pixels to the original ALS points. We also designed a Multi-Scale FCN (MS-FCN) architecture to minimize the loss of information during the point-to-image conversion. In the ground classification experiment, we compared our method to a Convolutional Neural Network (CNN)-based method and LAStools software. We obtained a lower total error on both the International Society for Photogrammetry and Remote Sensing (ISPRS) filter test benchmark dataset and AHN-3 dataset in the Netherlands. In the multi-class classification experiment, our method resulted in higher precision and recall values compared to the traditional machine learning technique using Random Forest (RF); it accurately detected small buildings. The FCN achieved precision and recall values of 0.93 and 0.94 when RF obtained 0.91 and 0.92, respectively. Moreover, our strategy significantly improved the computational efficiency of state-of-the-art CNN-based methods, reducing the point-to-image conversion time from 47 h to 36 min in our experiments on the ISPRS filter test dataset. Misclassification errors remained in situations that were not included in the training dataset, such as large buildings and bridges, or contained noisy measurements.
Analysis and Design of a CMOS Ultra-High-Speed Burst Mode Imager with In-Situ Storage Topology Featuring In-Pixel CDS Amplification
Authors: Wu Linkun , San Segundo Bello David , Coppejans Philippe , Craninckx Jan , Süss Andreas , Rosmeulen Maarten , Wambacq Piet , Borremans Jonathan
Published: October 2018

This paper presents an in-situ storage topology for ultra-high-speed burst mode imagers, enabling low noise operation while keeping a high frame depth. The proposed pixel architecture contains a 4T pinned photodiode, a correlated double sampling (CDS) amplification stage, and an in-situ memory bank. Focusing on the sampling noise, the system level trade-off of the proposed pixel architecture is discussed, showing its advantages on the noise, power, and scaling capability. Integrated with an AC coupling CDS stage, the amplification is obtained by exploiting the strong capacitance to the voltage relation of a single NMOS transistor. A comprehensive noise model is developed for optimizing the trade-off between the area and noise. As a proof-of-concept, a prototype imager with a 30 µm pixel pitch was fabricated in a CMOS 130 nm technology. A 108-cell memory bank is implemented allowing dense layout and parallel readout. Two types of CDS amplification stages were investigated. Despite the limited memory capacitance of 10 fF/cell, the photon transfer curves of both pixel types were measured over different operation speeds up to 20 Mfps showing a noise performance of 8.4 e−.
Quantification of DNA by a Thermal-Durable Biosensor Modified with Conductive Poly(3,4-ethylenedioxythiophene)
Authors: Gu Yesong , Tseng Po-Yuan , Bi Xiang , Yang Jason H. C.
Published: October 2018

The general clinical procedure for viral DNA detection or gene mutation diagnosis following polymerase chain reaction (PCR) often involves gel electrophoresis and DNA sequencing, which is usually time-consuming. In this study, we have proposed a facile strategy to construct a DNA biosensor, in which the platinum electrode was modified with a dual-film of electrochemically synthesized poly(3,4-ethylenedioxythiophene) (PEDOT) resulting in immobilized gold nanoparticles, with the gold nanoparticles easily immobilized in a uniform distribution. The DNA probe labeled with a SH group was then assembled to the fabricated electrode and employed to capture the target DNA based on the complementary sequence. The hybridization efficiency was evaluated with differential pulse voltammetry (DPV) in the presence of daunorubicin hydrochloride. Our results demonstrated that the peak current in DPV exhibited a linear correlation the concentration of target DNA that was complementary to the probe DNA. Moreover, the electrode could be reused by heating denaturation and re-hybridization, which only brought slight signal decay. In addition, the addition of the oxidized form of nicotinamide adenine dinucleotide (NAD+) could dramatically enhance the sensitivity by more than 5.45-fold, and the limit-of-detection reached about 100 pM.
Temperature Compensation Method for Digital Cameras in 2D and 3D Measurement Applications
Authors: Adamczyk Marcin , Liberadzki Paweł , Sitnik Robert
Published: October 2018

This paper presents the results of several studies concerning the effect of temperature on digital cameras. Experiments were performed using three different camera models. The presented results conclusively demonstrate that the typical camera design does not adequately take into account the effect of temperature variation on the device’s performance. In this regard, a modified camera design is proposed that exhibits a highly predictable behavior under varying ambient temperature and facilitates thermal compensation. A novel temperature compensation method is also proposed. This compensation model can be applied in almost every existing camera application, as it is compatible with every camera calibration model. A two-dimensional (2D) and three-dimensional (3D) application of the proposed compensation model is also described. The results of the application of the proposed compensation approach are presented herein.
Survey of Demosaicking Methods for Polarization Filter Array Images
Authors: Mihoubi Sofiane , Lapray Pierre-Jean , Bigué Laurent
Published: October 2018

Snapshot polarization imaging has gained interest in the last few decades. Recent research and technology achievements defined the polarization Filter Array (PFA). It is dedicated to division-of-focal plane polarimeters, which permits to analyze the direction of light electric field oscillation. Its filters form a mosaicked pattern, in which each pixel only senses a fraction of the total polarization states, so the other missing polarization states have to be interpolated. As for Color or Spectral Filter Arrays (CFA or SFA), several dedicated demosaicking methods exist in the PFA literature. Such methods are mainly based on spatial correlation disregarding inter-channel correlation. We show that polarization channels are strongly correlated in images. We therefore propose to extend some demosaicking methods from CFA/SFA to PFA, and compare them with those that are PFA-oriented. Objective and subjective analysis show that the pseudo panchromatic image difference method provides the best results and can be used as benchmark for PFA demosaicking.
Direction-of-Arrival Estimation for Circulating Space-Time Coding Arrays: From Beamspace MUSIC to Spatial Smoothing in the Transform Domain
Authors: Wang Huake , Liao Guisheng , Xu Jingwei , Zhu Shengqi , Zeng Cao
Published: October 2018

As a special type of coherent collocated Multiple-Input Multiple-Output (MIMO) radar, a circulating space-time coding array (CSTCA) transmits an identical waveform with a tiny time shift. It provides a simple way to achieve a full angular coverage with a stable gain and a low sidelobe level (SLL) in the range domain. In this paper, we address the problem of direction-of-arrival (DOA) estimation in CSTCA. Firstly, we design a novel two-dimensional space-time matched filter on receiver. It jointly performs equivalent transmit beamforming in the angle domain and waveform matching in the fast time domain. Multi-beams can be formed to acquire controllable transmit freedoms. Then, we propose a beamspace multiple signal classification (MUSIC) algorithm applicable in case of small training samples. Next, since targets at the same range cell show characteristics of coherence, we devise a transformation matrix to restore the rotational invariance property (RIP) of the receive array. Afterwards, we perform spatial smoothing in element domain based on the transformation. In addition, the closed-form expression of Cramer-Rao lower bound (CRLB) for angle estimation is derived. Theoretical performance analysis and numerical simulations are presented to demonstrate the effectiveness of proposed approaches.
New Approach of High Sensitivity Techniques Using Collective Detection Method with Multiple GNSS Receivers
Authors: Andrianarison Maherizo , Landry René
Published: October 2018

The Collective Detection (CD) technique is a promising approach to meet the requirements for signal acquisition in GNSS-harsh environments. The CD approach has been proposed because of its potential to operate as both a direct positioning method and a high-sensitivity acquisition method. This paper is dedicated to the development of a new CD architecture for processing satellite signals in challenging environments. It proposes the best signal acquisition method used according to the reception conditions of the different receivers that can assist the user in difficulty. Knowing that the CD approach is beneficial in the case where the maximum of satellite signals can be combined, the proposed approach consists in choosing the best receiver(s) from several connected receivers to serve as a reference station, as smart cooperative navigation concept. New metrics of the CD with optimal weighting of visible satellites are exploited. Analysis of optimization method in order to use better satellites according to some defined parameters (elevation, C / N 0 , and GDOP) were carried out. Real GPS L1 C/A signals are exploited to analyze the efficiency of the proposed approach. A comparison of the results through the accumulation of some good satellites among all visible satellites have shown the effectiveness of this method.
Sloshing Measurements inside a Liquid Hydrogen Tank with External-Heating-Type MgB2 Level Sensors during Marine Transportation by the Training Ship Fukae-Maru
Authors: Maekawa Kazuma , Takeda Minoru , Miyake Yuuki , Kumakura Hiroaki
Published: October 2018

Recently, a project was initiated in Japan to transport a large amount of liquid hydrogen (LH2) from Australia to Japan by sea. It is important to understand the sloshing and boil-off that are likely to occur inside an LH2 tank during marine transportation by ship, but such characteristics are yet to be experimentally clarified. To do so, we combined the liquid level detected by five 500 mm long external-heating-type magnesium diboride (MgB2) level sensors with synchronous measurements of temperature, pressure, ship motion, and acceleration during a zigzag maneuver. During this zigzag maneuver, the pressure of gaseous hydrogen (GH2) in the small LH2 tank increased to roughly 0.67 MPaG/h, and the temperature of the GH2 in the small LH2 tank increased at the position of gaseous hydrogen at roughly 1.0 K/min when the maximum rolling angle was 5°; the average rolling and liquid-oscillation periods were 114 and 118 s, respectively, as detected by the MgB2 level sensors, which therefore detected a long-period LH2 wave due to the ship’s motion.
A Lightweight Anonymous Client–Server Authentication Scheme for the Internet of Things Scenario: LAuth
Authors: Chen Yuwen , Martínez José-Fernán , Castillejo Pedro , López Lourdes
Published: October 2018

The Internet of Things (IoT) connects different kinds of devices into a network, and enables two-way communication between devices. A large amount of data are collected by these devices and transmitted in this network, it is necessary to ensure secure communications between these devices, to make it impossible for an adversary to undermine this communication. To ensure secure communication, many authentication protocols have been proposed, in this study, a fully anonymous authentication scheme for the Internet of things scenario has been proposed, it enables the remote client to anonymously connect to the server and being serviced by the server. The proposed scheme has been verified by AVISPA and BAN Logic, and the result shows that it is safe. Besides, the simulation shows that the proposed scheme is more efficient in computation cost and communication cost.
Differentially Private and Skew-Aware Spatial Decompositions for Mobile Crowdsensing
Authors: Kim Jong Seon , Chung Yon Dohn , Kim Jong Wook
Published: October 2018

Mobile Crowdsensing (MCS) is a paradigm for collecting large-scale sensor data by leveraging mobile devices equipped with small and low-powered sensors. MCS has recently received considerable attention from diverse fields, because it can reduce the cost incurred in the process of collecting a large amount of sensor data. However, in the task assignment process in MCS, to allocate the requested tasks efficiently, the workers need to send their specific location to the requester, which can raise serious location privacy issues. In this paper, we focus on the methods for publishing differentially a private spatial histogram to guarantee the location privacy of the workers. The private spatial histogram is a sanitized spatial index where each node represents the sub-regions and contains the noisy counts of the objects in each sub-region. With the sanitized spatial histograms, it is possible to estimate approximately the number of workers in the arbitrary area, while preserving their location privacy. However, the existing methods have given little concern to the domain size of the input dataset, leading to the low estimation accuracy. This paper proposes a partitioning technique SAGA (Skew-Aware Grid pArtitioning) based on the hotspots, which is more appropriate to adjust the domain size of the dataset. Further, to optimize the overall errors, we lay a uniform grid in each hotspot. Experimental results on four real-world datasets show that our method provides an enhanced query accuracy compared to the existing methods.
Data Collection Based on Opportunistic Node Connections in Wireless Sensor Networks
Authors: Yang Guisong , Peng Zhiwei , He Xingyu
Published: October 2018

The working–sleeping cycle strategy used for sensor nodes with limited power supply in wireless sensor networks can effectively save their energy, but also causes opportunistic node connections due to the intermittent communication mode, which can affect the reliability of data transmission. To address this problem, a data collection scheme based on opportunistic node connections is proposed to achieve efficient data collection in a network with a mobile sink. In this scheme, the mobile sink first broadcasts a tag message to start a data collection period, and all nodes that receive this message will use the probe message to forward their own source information to the mobile sink. On receiving these probe messages, the mobile sink then constructs an opportunistic connection random graph by analyzing the source information included in them, and calculates the optimal path from itself to each node in this random graph, therefore a spanning tree could be generated with the mobile sink play as the root node, finally, it broadcasts this spanning tree so that each node could obtain an optimal path from itself to the mobile sink to forward the sensing data. In addition, a routing protocol that adapts to different nodes operating statuses is proposed to improve the reliability of data transmission. Simulation results show that the proposed scheme works better concerning the packet delivery rate, energy consumption and network lifetime.
Optical Fiber Displacement Sensor Based on Microwave Photonics Interferometry
Authors: Dong Hao , Liu Shicheng , Yang Liming , Peng Jiangbo , Cheng Keming
Published: October 2018

An optical fiber displacement sensor based on the microwave photonics interferometric (MWPI) method is proposed and experimented, which provides an ideal solution for large range displacement measurement with high resolution. The sensor used a Michelson microwave photonics interferometer to sense the displacement with one sensing arm and a length-adjusted reference arm. The displacement variation would change the period of the microwave response function of the interferometer. According to the principle that the phase difference in one free spectral range (FSR) of the microwave response function is 360°, the displacement can be retrieved by the microwave response function by means of a vector network analyzer (VNA). A programmable path-switching true time delay line was used in the reference arm to decrease the microwave bandwidth. The measurement results show that the displacement sensing range is larger than 3 m and the measurement resolution is 31 μm. Finally, the measurement stability is tested, and the factors affecting the measurement resolution of this method and the main source of errors are investigated in detail.
A Novel Hybrid Swarm Optimized Multilayer Neural Network for Spatial Prediction of Flash Floods in Tropical Areas Using Sentinel-1 SAR Imagery and Geospatial Data
Authors: Ngo Phuong-Thao Thi , Hoang Nhat-Duc , Pradhan Biswajeet , Nguyen Quang Khanh , Tran Xuan Truong , Nguyen Quang Minh , Nguyen Viet Nghia , Samui Pijush , Tien Bui Dieu
Published: October 2018

Flash floods are widely recognized as one of the most devastating natural hazards in the world, therefore prediction of flash flood-prone areas is crucial for public safety and emergency management. This research proposes a new methodology for spatial prediction of flash floods based on Sentinel-1 SAR imagery and a new hybrid machine learning technique. The SAR imagery is used to detect flash flood inundation areas, whereas the new machine learning technique, which is a hybrid of the firefly algorithm (FA), Levenberg–Marquardt (LM) backpropagation, and an artificial neural network (named as FA-LM-ANN), was used to construct the prediction model. The Bac Ha Bao Yen (BHBY) area in the northwestern region of Vietnam was used as a case study. Accordingly, a Geographical Information System (GIS) database was constructed using 12 input variables (elevation, slope, aspect, curvature, topographic wetness index, stream power index, toposhade, stream density, rainfall, normalized difference vegetation index, soil type, and lithology) and subsequently the output of flood inundation areas was mapped. Using the database and FA-LM-ANN, the flash flood model was trained and verified. The model performance was validated via various performance metrics including the classification accuracy rate, the area under the curve, precision, and recall. Then, the flash flood model that produced the highest performance was compared with benchmarks, indicating that the combination of FA and LM backpropagation is proven to be very effective and the proposed FA-LM-ANN is a new and useful tool for predicting flash flood susceptibility.
Validation of Instantaneous Respiratory Rate Using Reflectance PPG from Different Body Positions
Authors: Jarchi Delaram , Salvi Dario , Tarassenko Lionel , Clifton David A.
Published: October 2018

Respiratory rate (RR) is a key parameter used in healthcare for monitoring and predicting patient deterioration. However, continuous and automatic estimation of this parameter from wearable sensors is still a challenging task. Various methods have been proposed to estimate RR from wearable sensors using windowed segments of the data; e.g., often using a minimum of 32 s. Little research has been reported in the literature concerning the instantaneous detection of respiratory rate from such sources. In this paper, we develop and evaluate a method to estimate instantaneous respiratory rate (IRR) from body-worn reflectance photoplethysmography (PPG) sensors. The proposed method relies on a nonlinear time-frequency representation, termed the wavelet synchrosqueezed transform (WSST). We apply the latter to derived modulations of the PPG that arise from the act of breathing.We validate the proposed algorithm using (i) a custom device with a PPG probe placed on various body positions and (ii) a commercial wrist-worn device (WaveletHealth Inc., Mountain View, CA, USA). Comparator reference data were obtained via a thermocouple placed under the nostrils, providing ground-truth information concerning respiration cycles. Tracking instantaneous frequencies was performed in the joint time-frequency spectrum of the (4 Hz re-sampled) respiratory-induced modulation using the WSST, from data obtained from 10 healthy subjects. The estimated instantaneous respiratory rates have shown to be highly correlated with breath-by-breath variations derived from the reference signals. The proposed method produced more accurate results compared to averaged RR obtained using 32 s windows investigated with overlap between successive windows of (i) zero and (ii) 28 s. For a set of five healthy subjects, the averaged similarity between reference RR and instantaneous RR, given by the longest common subsequence (LCSS) algorithm, was calculated as 0.69; this compares with averaged similarity of 0.49 using 32 s windows with 28 s overlap between successive windows. The results provide insight into estimation of IRR and show that upper body positions produced PPG signals from which a better respiration signal was extracted than for other body locations.
Stereo Camera Head-Eye Calibration Based on Minimum Variance Approach Using Surface Normal Vectors
Authors: Lee Joong-Jae , Jeong Mun-Ho
Published: October 2018

This paper presents a stereo camera-based head-eye calibration method that aims to find the globally optimal transformation between a robot’s head and its eye. This method is highly intuitive and simple, so it can be used in a vision system for humanoid robots without any complex procedures. To achieve this, we introduce an extended minimum variance approach for head-eye calibration using surface normal vectors instead of 3D point sets. The presented method considers both positional and orientational error variances between visual measurements and kinematic data in head-eye calibration. Experiments using both synthetic and real data show the accuracy and efficiency of the proposed method.
Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures
Authors: Bennati Stefano , Dusparic Ivana , Shinde Rhythima , Jonker Catholijn M.
Published: October 2018

Provision of smart city services often relies on users contribution, e.g., of data, which can be costly for the users in terms of privacy. Privacy risks, as well as unfair distribution of benefits to the users, should be minimized as they undermine user participation, which is crucial for the success of smart city applications. This paper investigates privacy, fairness, and social welfare in smart city applications by means of computer simulations grounded on real-world data, i.e., smart meter readings and participatory sensing. We generalize the use of public good theory as a model for resource management in smart city applications, by proposing a design principle that is applicable across application scenarios, where provision of a service depends on user contributions. We verify its applicability by showing its implementation in two scenarios: smart grid and traffic congestion information system. Following this design principle, we evaluate different classes of algorithms for resource management, with respect to human-centered measures, i.e., privacy, fairness and social welfare, and identify algorithm-specific trade-offs that are scenario independent. These results could be of interest to smart city application designers to choose a suitable algorithm given a scenario-specific set of requirements, and to users to choose a service based on an algorithm that matches their privacy preferences.
A Novel PARAFAC Model for Processing the Nested Vector-Sensor Array
Authors: Rao Wei , Li Dan , Zhang Jian Qiu
Published: October 2018

In this paper, a novel parallel factor (PARAFAC) model for processing the nested vector-sensor array is proposed. It is first shown that a nested vector-sensor array can be divided into multiple nested scalar-sensor subarrays. By means of the autocorrelation matrices of the measurements of these subarrays and the cross-correlation matrices among them, it is then demonstrated that these subarrays can be transformed into virtual scalar-sensor uniform linear arrays (ULAs). When the measurement matrices of these scalar-sensor ULAs are combined to form a third-order tensor, a novel PARAFAC model is obtained, which corresponds to a longer vector-sensor ULA and includes all of the measurements of the difference co-array constructed from the original nested vector-sensor array. Analyses show that the proposed PARAFAC model can fully use all of the measurements of the difference co-array, instead of its partial measurements as the reported models do in literature. It implies that all of the measurements of the difference co-array can be fully exploited to do the 2-D direction of arrival (DOA) and polarization parameter estimation effectively by a PARAFAC decomposition method so that both the better estimation performance and slightly improved identifiability are achieved. Simulation results confirm the efficiency of the proposed model.
Effects and Constraints of Optical Filtering on Ambient Light Suppression in LED-Based Underwater Communications
Authors: Sticklus Jan , Hieronymi Martin , Hoeher Peter Adam
Published: October 2018

Optical communication promises to be a high-rate supplement for acoustic communication in short-range underwater applications. In the photic zone of oceanic and coastal waters, underwater optical communication systems are exposed by remaining sunlight. This ambient light generates additional noise in photodetectors, thus degrading system performance. This effect can be diminished by the use of optical filters. This paper investigates light field characteristics of different water types and potential interactions with optical underwater communication. A colored glass and different thin film bandpass filters are examined as filter/detector combinations under varying light and water conditions, and their physical constraints are depicted. This is underlined by various spectral measurements as well as optical signal-to-noise ratio calculations. The importance of matching the characteristics of the light emitting diode (LED) light source, the photodetector, and the filter on the ambient conditions using wider angle of incidents is emphasized.
3D Imaging Based on Depth Measurement Technologies
Authors: Chen Ni , Zuo Chao , Lam Edmund , Lee Byoungho
Published: October 2018

Three-dimensional (3D) imaging has attracted more and more interest because of its widespread applications, especially in information and life science. These techniques can be broadly divided into two types: ray-based and wavefront-based 3D imaging. Issues such as imaging quality and system complexity of these techniques limit the applications significantly, and therefore many investigations have focused on 3D imaging from depth measurements. This paper presents an overview of 3D imaging from depth measurements, and provides a summary of the connection between the ray-based and wavefront-based 3D imaging techniques.
Application of Self-Organizing Maps to the Analysis of Ignitable Liquid and Substrate Pyrolysis Samples
Authors: Thurn Nicholas , Williams Mary R. , Sigman Michael E.
Published: October 2018

Classification of un-weathered ignitable liquids is a problem that is currently addressed by visual pattern recognition under the guidelines of Standard Test Method for Ignitable Liquid Residues in Extracts from Fire Debris Samples by Gas Chromatography-Mass Spectrometry, ASTM E1618-14. This standard method does not separately address the identification of substrate pyrolysis patterns. This report details the use of a Kohonen self-organizing map coupled with extracted ion spectra to organize ignitable liquids and substrate pyrolysis samples on a two-dimensional map with groupings that correspond to the ASTM-classifications and separate the substrate pyrolysis samples from the ignitable liquids. The component planes give important information regarding the ions from the extracted ion spectra that contribute to the different classes. Some additional insight is gained into grouping of substrate pyrolysis samples based on the nature of the unburned material as a wood or non-wood material. Further subclassification was not apparent from the self-organizing maps (SOM) results.
Customized Education as a Framework for Strengthening Collaboration between Higher Education Institutions and Regional Actors in Sustainable Development—Lessons from Albania and Kosovo
Authors: Meyer Jonas , Pillei Michael , Zimmermann Friedrich , Stöglehner Gernot
Published: October 2018

Higher education institutions (HEIs) in Albania and Kosovo have invested considerable efforts to tackle present regional sustainability challenges on a research-based and strategic level. This happened through their traditional role as a provider of research and education. Additional activities fostering HEIs as a driver for sustainable regional development and innovation have been initiated as part of their third mission. However, customized education approaches were missing in order to fulfill specific regional demands of different research and learning projects in collaboration with regional actors. This paper explores the recently finished project “Connecting Science-Society Collaboration for Sustainability Innovation—ConSus” which focuses on the efforts of HEIs in Albania and Kosovo to tackle present regional sustainability challenges in both countries. An analytical framework regarding collaborative learning opportunities towards sustainable regional development and innovation has been drafted based on five key elements (capacities of HEIs in regional development, planning, learning and implementation theory, and action research). This framework has been applied to analyze the learning activities of the ConSus project. As a main finding, customized education approaches in the contexts of sustainable development (SD) and collaboration of scientific and regional actors are important elements to cope comprehensively with real-world problems.
Offshore Wind Farms as Potential Locations for Flat Oyster (Ostrea edulis) Restoration in the Dutch North Sea
Authors: Kamermans Pauline , Walles Brenda , Kraan Marloes , Van Duren Luca A. , Kleissen Frank , Van der Have Tom M. , Smaal Aad C. , Poelman Marnix
Published: October 2018

The “Dutch Energy Agreement” motivates governments and industries to invest in renewable energy sources, of which offshore wind energy is one of the solutions to meet the agreed target of 16% of the total energy budget from renewable resources by 2023. An option for the multi-use of wind farms is nature-inclusive building, in which the design and construction of wind farms make use of the potential for co-design with oyster bed restoration. This can support the government’s ambitions, for the Dutch North Sea, to achieve biodiversity goals, restore ecosystem functions, and enhance ecosystem services, including future seafood production. For the recovery of flat oyster (Ostrea edulis) beds, knowledge is required about the conditions under which active restoration of this species in the North Sea can be successfully implemented. This paper gives a framework and presents results to determine suitability of wind farms for flat oyster restoration, and provides recommendations for pilot studies. Our analysis showed that a number of wind farms in the Dutch section of the North Sea are suitable locations for development of flat oyster beds. Combining oyster restoration and oyster culture, as a protein source, is a viable option worth investigating.
Does Adding Local Tree Elements into Dwellings Enhance Individuals’ Homesickness? Scenario-Visualisation for Developing Sustainable Rural Landscapes
Authors: Huang Shuping , Konijnendijk van den Bosch Cecil , Fu Weicong , Qi Jinda , Chen Ziru , Zhu Zhipeng , Dong Jianwen
Published: October 2018

Rural residential settings are important elements of livable and sustainable rural areas across the world. Enhancing people’s attachment to these landscapes through fostering feelings of homesickness could help in the pursuit of better rural residential settings. We studied homesickness, an emotion found to be associated with higher place attachment and quality of life, related to rural landscapes in southeast China, looking specifically at the presence and configuration of rural dwellings and trees. We used Photoshop to manipulate different configurations of typical rural dwellings and trees, and three series with twelve types of landscape scenes were generated. We looked at the following six emotional factors linked to homesickness: naturalness; regional culture; identity; psychology; experience; and landscape aesthetics. The analytic hierarchy process (AHP) and semantic differential (SD) methods were used to evaluate the level in which the landscape evoked feelings of homesickness amongst study participants, i.e., a group of university students from different disciplines. Results show that the homesickness emotional response level was higher in most of the simulated landscapes, as compared to the original landscape, and that response levels differed significantly between the three types of visualized landscape configurations. The emotional response level showed differences for manipulated landscape scenes with twelve different trees added to dwellings. Through cluster analysis of the results, we divided trees into three grades of emotional response for each dwelling type. Adding trees thus was found to change the emotional response to the landscape, and different tree configurations with different types of dwellings results in different responses. The study shows that careful design of the rural landscape can help build stronger emotional relations of humans with their local environment, which is a key ingredient for sustainable countryside living.
Sustainable Supply Chain and Innovation: A Review of the Recent Literature
Authors: Tebaldi Letizia , Bigliardi Barbara , Bottani Eleonora
Published: October 2018

Increasingly, the effects of supply chains on environmental issues have been a focal point in discussions involving stakeholders, authorities, and customers. The aim of this paper is to inspect how innovation and sustainability are integrated into the wider context of the supply chains. To this end, a systematic literature review was carried out with a particular focus on papers published in recent years (2015–2017) so as to continue from a previous review on the sustainable supply chain innovation topic covering the time span of 1996–2014. The descriptive aspects of the published papers are firstly examined, i.e., the year of publication, journal, research methodology, industry field and country of the study, followed by the thematic ones, i.e., key themes of the innovation process, the types of innovation found, their newness, dimensions, and main theories that emerged. The analysis shows an upward trend of literature in numerical terms and a wider spread of sustainable innovations. Additionally, the research highlights further areas of research that are deserving of attention. These areas include analyses of specific industrial sectors which have been less covered in the published literature; research activities in the less developed countries; more attention on the social dimension of sustainability; a more general contribution from some nations that turned out to be less productive or even inactive on the sustainable supply chain innovation topic. Finally, a framework is developed which could constitute the basis for further developments and research on this issue.
Effects of Industrial Operations on Socio-Environmental and Public Health Degradation: Evidence from a Least Developing Country (LDC)
Authors: Hoque Asadul , Mohiuddin Muhammad , Su Zhan
Published: October 2018

Socio-ecological consequences emanating from inadequate compliance of environmental standards by the business firms’ operations in institutionally weak developing countries must be included in the research on organizations and their relationship with the natural environment. Business firms should be held accountable for the socio-ecological degradation generated from their unsustainable business operations. To improve our understanding of the environmental degradation created by polluting manufacturing firms in developing countries, we have adopted an exploratory qualitative research approach. Results of this study indicate that polluting industries’ (e.g., tannery, pulp & paper, fertilizer, textile, and cement) unsustainable practices have enormous impact on human health and the natural environment, resulting in enormous socio-ecological problems that ultimately create huge social costs in countries such as Bangladesh. Corporate environmental responsiveness is largely nonexistent in the polluting industries in Bangladesh.
Modeling the Factors Influencing the Activity Spaces of Bikeshare around Metro Stations: A Spatial Regression Model
Authors: Ma Xinwei , Ji Yanjie , Jin Yuchuan , Wang Jianbiao , He Mingjia
Published: October 2018

Metro-bikeshare integration is considered a green and efficient travel model. To better understand bikeshare as a feeder mode to the metro, this study explored the factors that influence the activity spaces of bikeshare around metro stations. First, metro-bikeshare transfer trips were recognized by matching bikeshare smartcard data and metro smartcard data. Then, standard deviation ellipse (SDE) was used for the calculation of the metro-bikeshare activity spaces. Moreover, an ordinary least squares (OLS) regression and a spatial error model (SEM) were established to reveal the effects of social-demographic, travel-related, and built environment factors on the activity spaces of bikeshare around metro stations, and the SEM outperformed OLS significantly in terms of model fit. Results show that the average metro-bikeshare activity space on weekdays is larger than that on weekends. The proportion of local residents promotes the increase in activity space on weekends, while a high density of road and metro impedes the activity space on weekdays. Additionally, with increased job density, the activity space becomes smaller significantly throughout the week. Also, both on weekdays and weekends, the closer to the central business district (CBD), the smaller the activity space. This study can offer meaningful guidance to policymakers and city planners aiming to make the bikeshare distribution more reasonable.