bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimating Forest Structure from UAV-Mounted LiDAR Point Cloud Using Machine Learning
Authors: Neuville Romain , Bates Jordan Steven , Jonard François
Published: January 2021

Monitoring the structure of forest stands is of high importance for forest managers to help them in maintaining ecosystem services. For that purpose, Unmanned Aerial Vehicles (UAVs) open new prospects, especially in combination with Light Detection and Ranging (LiDAR) technology. Indeed, the shorter distance from the Earth’s surface significantly increases the point density beneath the canopy, thus offering new possibilities for the extraction of the underlying semantics. For example, tree stems can now be captured with sufficient detail, which is a gateway to accurately locating trees and directly retrieving metrics—e.g., the Diameter at Breast Height (DBH). Current practices usually require numerous site-specific parameters, which may preclude their use when applied beyond their initial application context. To overcome this shortcoming, the machine learning Hierarchical Density-Based Spatial Clustering of Application of Noise (HDBSCAN) clustering algorithm was further improved and implemented to segment tree stems. Afterwards, Principal Component Analysis (PCA) was applied to extract tree stem orientation for subsequent DBH estimation. This workflow was then validated using LiDAR point clouds collected in a temperate deciduous closed-canopy forest stand during the leaf-on and leaf-off seasons, along with multiple scanning angle ranges. The results show that the proposed methodology can correctly detect up to 82% of tree stems (with a precision of 98%) during the leaf-off season and have a Maximum Scanning Angle Range (MSAR) of 75 degrees, without having to set up any site-specific parameters for the segmentation procedure. In the future, our method could then minimize the omission and commission errors when initially detecting trees, along with assisting further tree metrics retrieval. Finally, this research shows that, under the study conditions, the point density within an approximately 1.3-meter height above the ground remains low within closed-canopy forest stands even during the leaf-off season, thus restricting the accurate estimation of the DBH. As a result, autonomous UAVs that can both fly above and under the canopy provide a clear opportunity to achieve this purpose.
Classification of Very-High-Spatial-Resolution Aerial Images Based on Multiscale Features with Limited Semantic Information
Authors: Gao Han , Guo Jinhui , Guo Peng , Chen Xiuwan
Published: January 2021

Recently, deep learning has become the most innovative trend for a variety of high-spatial-resolution remote sensing imaging applications. However, large-scale land cover classification via traditional convolutional neural networks (CNNs) with sliding windows is computationally expensive and produces coarse results. Additionally, although such supervised learning approaches have performed well, collecting and annotating datasets for every task are extremely laborious, especially for those fully supervised cases where the pixel-level ground-truth labels are dense. In this work, we propose a new object-oriented deep learning framework that leverages residual networks with different depths to learn adjacent feature representations by embedding a multibranch architecture in the deep learning pipeline. The idea is to exploit limited training data at different neighboring scales to make a tradeoff between weak semantics and strong feature representations for operational land cover mapping tasks. We draw from established geographic object-based image analysis (GEOBIA) as an auxiliary module to reduce the computational burden of spatial reasoning and optimize the classification boundaries. We evaluated the proposed approach on two subdecimeter-resolution datasets involving both urban and rural landscapes. It presented better classification accuracy (88.9%) compared to traditional object-based deep learning methods and achieves an excellent inference time (11.3 s/ha).
Comparative Analysis of the Global Forest/Non-Forest Maps Derived from SAR and Optical Sensors. Case Studies from Brazilian Amazon and Cerrado Biomes
Authors: Sano Edson E. , Rizzoli Paola , Koyama Christian N. , Watanabe Manabu , Adami Marcos , Shimabukuro Yosio E. , Bayma Gustavo , Freitas Daniel M.
Published: January 2021

Global-scale forest/non-forest (FNF) maps are of crucial importance for applications like biomass estimation and deforestation monitoring. Global FNF maps based on optical remote sensing data have been produced by the wall-to-wall satellite image analyses or sampling strategies. The German Aerospace Center (DLR) and the Japan Aerospace Exploration Agency (JAXA) also made available their global FNF maps based on synthetic aperture radar (SAR) data. This paper attempted to answer the following scientific question: how comparable are the FNF products derived from optical and SAR data? As test sites we selected the Amazon (tropical rainforest) and Cerrado (tropical savanna) biomes, the two largest Brazilian biomes. Forest estimations from 2015 derived from TanDEM-X (X band; HH polarization) and ALOS-2 (L band; HV polarization) SAR data, as well as forest cover information derived from Landsat 8 optical data were compared with each other at the municipality and image sampling levels. The optical-based forest estimations considered in this study were derived from the MapBiomas project, a Brazilian multi-institutional project to map land use and land cover (LULC) classes of an entire country based on historical time series of Landsat data. In addition to the existing forest maps, a set of 1619 Landsat 8 RGB color composites was used to generate new independent comparison data composed of circular areas with 5-km diameter, which were visually interpreted after image segmentation. The Spearman rank correlation estimated the correlation among the data sets and the paired Mann–Whitney–Wilcoxon tested the hypothesis that the data sets are statistically equal. Results showed that forest maps derived from SAR and optical satellites are statistically different regardless of biome or scale of study (municipality or image sampling), except for the Cerrado´s forest estimations derived from TanDEM-X and ALOS-2. Nevertheless, the percentage of pixels classified as forest or non-forest by both SAR sensors were 90% and 80% for the Amazon and Cerrado biome, respectively, indicating an overall good agreement.
Effects of Training Set Size on Supervised Machine-Learning Land-Cover Classification of Large-Area High-Resolution Remotely Sensed Data
Authors: Ramezan Christopher A. , Warner Timothy A. , Maxwell Aaron E. , Price Bradley S.
Published: January 2021

The size of the training data set is a major determinant of classification accuracy. Nevertheless, the collection of a large training data set for supervised classifiers can be a challenge, especially for studies covering a large area, which may be typical of many real-world applied projects. This work investigates how variations in training set size, ranging from a large sample size (n = 10,000) to a very small sample size (n = 40), affect the performance of six supervised machine-learning algorithms applied to classify large-area high-spatial-resolution (HR) (1–5 m) remotely sensed data within the context of a geographic object-based image analysis (GEOBIA) approach. GEOBIA, in which adjacent similar pixels are grouped into image-objects that form the unit of the classification, offers the potential benefit of allowing multiple additional variables, such as measures of object geometry and texture, thus increasing the dimensionality of the classification input data. The six supervised machine-learning algorithms are support vector machines (SVM), random forests (RF), k-nearest neighbors (k-NN), single-layer perceptron neural networks (NEU), learning vector quantization (LVQ), and gradient-boosted trees (GBM). RF, the algorithm with the highest overall accuracy, was notable for its negligible decrease in overall accuracy, 1.0%, when training sample size decreased from 10,000 to 315 samples. GBM provided similar overall accuracy to RF; however, the algorithm was very expensive in terms of training time and computational resources, especially with large training sets. In contrast to RF and GBM, NEU, and SVM were particularly sensitive to decreasing sample size, with NEU classifications generally producing overall accuracies that were on average slightly higher than SVM classifications for larger sample sizes, but lower than SVM for the smallest sample sizes. NEU however required a longer processing time. The k-NN classifier saw less of a drop in overall accuracy than NEU and SVM as training set size decreased; however, the overall accuracies of k-NN were typically less than RF, NEU, and SVM classifiers. LVQ generally had the lowest overall accuracy of all six methods, but was relatively insensitive to sample size, down to the smallest sample sizes. Overall, due to its relatively high accuracy with small training sample sets, and minimal variations in overall accuracy between very large and small sample sets, as well as relatively short processing time, RF was a good classifier for large-area land-cover classifications of HR remotely sensed data, especially when training data are scarce. However, as performance of different supervised classifiers varies in response to training set size, investigating multiple classification algorithms is recommended to achieve optimal accuracy for a project.
Tropical Forest and Wetland Losses and the Role of Protected Areas in Northwestern Belize, Revealed from Landsat and Machine Learning
Authors: Doyle Colin , Beach Timothy , Luzzadder-Beach Sheryl
Published: January 2021

Changes in land-use and land-cover, including both agricultural expansion and the establishment of protected areas, have altered the landscape pattern and extent of forest and wetland cover in the tropics. In Central America, land-use and land-cover change is also threatening the cultural resources of the region’s ancient Maya heritage since many ancient sites have been degraded by burning, deforestation, and plowing. In this study of Orange Walk District of northern Belize, from the 1980s to the present, we used multitemporal Landsat data with a random forest classifier to reveal trends in land-use and land-cover change and the increasing loss of forest and wetlands. We develop a random forest classifier that is time-generalized to map land-use and land-cover across the entire Landsat record, including Landsat 4, 5, 7, and 8, with a single algorithm. Including multiyear and seasonal composites was important for obtaining cloud-free coverage and distinguishing between different land-use and land-cover types. Early deforestation (1984–1987) was in small patches scattered across the landscape and likely driven by small scale agriculture such as milpa and smaller area tractor and horse-drawn plowing. The establishment of protected areas in the late 1980s and early 1990s allowed for forest regrowth in these areas, while wetland losses were high at 15%. The transition to industrial agriculture in the 2000s, however, drove a 43.6% expansion of agriculture and a 7.5% loss of forest and a 28.2% loss of wetlands during the ~15 years. Protected areas initiated in the 1980s led to a nearly 100 km2 decrease in agriculture from 1984–1987 to 1999–2001, and they became essential refugia for habitat and maintaining ecosystem services.
Muscle Co-Activation around the Knee during Different Walking Speeds in Healthy Females
Authors: Akl Abdel-Rahman , Gonçalves Pedro , Fonseca Pedro , Hassan Amr , Vilas-Boas João Paulo , Conceição Filipe
Published: January 2021

The purpose of this study was to examine the changes in co-activation around the knee joint during different walking speeds in healthy females using the co-activation index. Ten healthy females (age: 21.20 ± 7.21 years, height: 164.00 ± 4.00 cm, mass: 60.60 ± 4.99 kg) participated in this study and performed three walking speeds (slow, normal, and fast). A Qualisys 11-camera motion analysis system sampling at a frequency of 200 Hz was synchronized with a Trigno EMG Wireless system operating at a 2000 Hz sampling frequency. A significant decrease in the co-activation index of thigh muscles was observed between the slow and fast, and between the normal and fast, walking speeds during all walking phases. A non-significant difference was observed between the slow and normal walking speeds during most walking phases, except the second double support phase, during which the difference was significant. A negative relationship was found between walking speed and the co-activation index of thigh muscles in all speeds during walking phases: first double support (r = −0.3386, p < 0.001), single support (r = −0.2144, p < 0.01), second double support (r = −0.4949, p < 0.001), and Swing (r = −0.1639, p < 0.05). In conclusion, the results indicated high variability of thigh muscle co-activation in healthy females during the different walking speeds, and a decrease in the co-activation of the thigh muscles with the increase of speed.
Real-Time Multiobject Tracking Based on Multiway Concurrency
Authors: Gong Xuan , Le Zichun , Wu Yukun , Wang Hui
Published: January 2021

This paper explored a pragmatic approach to research the real-time performance of a multiway concurrent multiobject tracking (MOT) system. At present, most research has focused on the tracking of single-image sequences, but in practical applications, multiway video streams need to be processed in parallel by MOT systems. There have been few studies on the real-time performance of multiway concurrent MOT systems. In this paper, we proposed a new MOT framework to solve multiway concurrency scenario based on a tracking-by-detection (TBD) model. The new framework mainly focuses on concurrency and real-time based on limited computing and storage resources, while considering the algorithm performance. For the former, three aspects were studied: (1) Expanded width and depth of tracking-by-detection model. In terms of width, the MOT system can support the process of multiway video sequence at the same time; in terms of depth, image collectors and bounding box collectors were introduced to support batch processing. (2) Considering the real-time performance and multiway concurrency ability, we proposed one kind of real-time MOT algorithm based on directly driven detection. (3) Optimization of system level—we also utilized the inference optimization features of NVIDIA TensorRT to accelerate the deep neural network (DNN) in the tracking algorithm. To trade off the performance of the algorithm, a negative sample (false detection sample) filter was designed to ensure tracking accuracy. Meanwhile, the factors that affect the system real-time performance and concurrency were studied. The experiment results showed that our method has a good performance in processing multiple concurrent real-time video streams.
Driving Behavior Analysis of City Buses Based on Real-Time GNSS Traces and Road Information
Authors: Yang Yuan , Yan Jingjie , Guo Jing , Kuang Yujin , Yin Mingyang , Wang Shiniu , Ma Caoyuan
Published: January 2021

The driving behavior of bus drivers is related to the safety of all passengers and regulation of urban traffic. In order to analyze the relevant characteristics of speed and acceleration, accurate bus trajectories and patterns are essential for driver behavior analysis and development of effective intelligent public transportation. Exploiting real-time vehicle tracking, this paper develops a platform with vehicle-mounted terminals using differential global navigation satellite system (DGNSS) modules for driver behavior analysis. The DGNSS traces were used to derive the vehicle trajectories, which were then linked to road information to produce speed and acceleration matrices. Comprehensive field tests were undertaken on multiple bus routes in urban environments. The spatiotemporal results indicate that the platform can automatically and accurately extract the driving behavior characteristics. Furthermore, the platform’s visual function can be used to effectively monitor driving risks, such as speeding and fierce acceleration, in multiple bus routes. The details of the platform’s features are provided for intelligent transport system (ITS) design and applications.
Semantic Segmentation Leveraging Simultaneous Depth Estimation
Authors: Sun Wenbo , Gao Zhi , Cui Jinqiang , Ramesh Bharath , Zhang Bin , Li Ziyao
Published: January 2021

Semantic segmentation is one of the most widely studied problems in computer vision communities, which makes a great contribution to a variety of applications. A lot of learning-based approaches, such as Convolutional Neural Network (CNN), have made a vast contribution to this problem. While rich context information of the input images can be learned from multi-scale receptive fields by convolutions with deep layers, traditional CNNs have great difficulty in learning the geometrical relationship and distribution of objects in the RGB image due to the lack of depth information, which may lead to an inferior segmentation quality. To solve this problem, we propose a method that improves segmentation quality with depth estimation on RGB images. Specifically, we estimate depth information on RGB images via a depth estimation network, and then feed the depth map into the CNN which is able to guide the semantic segmentation. Furthermore, in order to parse the depth map and RGB images simultaneously, we construct a multi-branch encoder–decoder network and fuse the RGB and depth features step by step. Extensive experimental evaluation on four baseline networks demonstrates that our proposed method can enhance the segmentation quality considerably and obtain better performance compared to other segmentation networks.
Transformers and Generative Adversarial Networks for Liveness Detection in Multitarget Fingerprint Sensors
Authors: Sandouka Soha B. , Bazi Yakoub , Alajlan Naif
Published: January 2021

Fingerprint-based biometric systems have grown rapidly as they are used for various applications including mobile payments, international border security, and financial transactions. The widespread nature of these systems renders them vulnerable to presentation attacks. Hence, improving the generalization ability of fingerprint presentation attack detection (PAD) in cross-sensor and cross-material setting is of primary importance. In this work, we propose a solution based on a transformers and generative adversarial networks (GANs). Our aim is to reduce the distribution shift between fingerprint representations coming from multiple target sensors. In the experiments, we validate the proposed methodology on the public LivDet2015 dataset provided by the liveness detection competition. The experimental results show that the proposed architecture yields an increase in average classification accuracy from 68.52% up to 83.12% after adaptation.
A New Dynamical Method for Bearing Fault Diagnosis Based on Optimal Regulation of Resonant Behaviors in a Fluctuating-Mass-Induced Linear Oscillator
Authors: Chen Kehan , Lu Yuting , Lin Lifeng , Wang Huiqi
Published: January 2021

Stochastic resonance (SR), a typical randomness-assisted signal processing method, has been extensively studied in bearing fault diagnosis to enhance the feature of periodic signal. In this study, we cast off the basic constraint of nonlinearity, extend it to a new type of generalized SR (GSR) in linear Langevin system, and propose the fluctuating-mass induced linear oscillator (FMLO). Then, by generalized scale transformation (GST), it is improved to be more suitable for exacting high-frequency fault features. Moreover, by analyzing the system stationary response, we find that the synergy of the linear system, internal random regulation and external excitement can conduct a rich variety of non-monotonic behaviors, such as bona-fide SR, conventional SR, GSR, and stochastic inhibition (SI). Based on the numerical implementation, it is found that these behaviors play an important role in adaptively optimizing system parameters to maximally improve the performance and identification ability of weak high-frequency signal in strong background noise. Finally, the experimental data are further performed to verify the effectiveness and superiority in comparison with traditional dynamical methods. The results show that the proposed GST-FMLO system performs the best in the bearing fault diagnoses of inner race, outer race and rolling element. Particularly, by amplifying the characteristic harmonics, the low harmonics become extremely weak compared to the characteristic. Additionally, the efficiency is increased by more than 5 times, which is significantly better than the nonlinear dynamical methods, and has the great potential for online fault diagnosis.
Design and Implementation of Fast Spoken Foul Language Recognition with Different End-to-End Deep Neural Network Architectures
Authors: Ba Wazir Abdulaziz Saleh , Karim Hezerul Abdul , Abdullah Mohd Haris Lye , AlDahoul Nouar , Mansor Sarina , Fauzi Mohammad Faizal Ahmad , See John , Naim Ahmad Syazwan
Published: January 2021

Given the excessive foul language identified in audio and video files and the detrimental consequences to an individual’s character and behaviour, content censorship is crucial to filter profanities from young viewers with higher exposure to uncensored content. Although manual detection and censorship were implemented, the methods proved tedious. Inevitably, misidentifications involving foul language owing to human weariness and the low performance in human visual systems concerning long screening time occurred. As such, this paper proposed an intelligent system for foul language censorship through a mechanized and strong detection method using advanced deep Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) through Long Short-Term Memory (LSTM) cells. Data on foul language were collected, annotated, augmented, and analysed for the development and evaluation of both CNN and RNN configurations. Hence, the results indicated the feasibility of the suggested systems by reporting a high volume of curse word identifications with only 2.53% to 5.92% of False Negative Rate (FNR). The proposed system outperformed state-of-the-art pre-trained neural networks on the novel foul language dataset and proved to reduce the computational cost with minimal trainable parameters.
Towards an Alternative to Time of Flight Diffraction Using Instantaneous Phase Coherence Imaging for Characterization of Crack-Like Defects
Authors: Gauthier Baptiste , Painchaud-April Guillaume , Le Duff Alain , Bélanger Pierre
Published: January 2021

Time of flight diffraction (TOFD) is considered a reliable non-destructive testing method for the inspection of welds using a pair of single-element probes. On the other hand, ultrasonic phased array imaging has been continuously developed over the last couple of decades, and now features powerful algorithms, such as the total focusing method (TFM) and its multi-view approach to rendering detailed images of inspected parts. This article focuses on a different implementation of the TFM algorithm, relying on the coherent summation of the instantaneous signal phase. This approach presents a wide range of benefits, such as removing the need for calibration, and is highly sensitive to defect tips. This study compares the sizing and localization capabilities of the proposed method with the well-known TOFD. Both instantaneous phase algorithm and TOFD do not take advantage of the signal amplitude. Experimental tests were performed on a ¾″-thick steel sample with crack-like defects at different angles. Phase-based imaging techniques showed similar characterization capabilities as the standard TOFD method. However, the proposed method adds the benefit of generating an easy-to-interpret image that can help in localizing the defect. These results pave the way for a new characterization approach, especially in the field of automated ultrasonic testing (AUT).
How Can Wake-up Radio Reduce LoRa Downlink Latency for Energy Harvesting Sensor Nodes?
Authors: Djidi Nour El Hoda , Gautier Matthieu , Courtay Antoine , Berder Olivier , Magno Michele
Published: January 2021

LoRa is popular for internet of things applications as this communication technology offers both a long range and a low power consumption. However, LoRaWAN, the standard MAC protocol that uses LoRa as physical layer, has the bottleneck of a high downlink latency to achieve energy efficiency. To overcome this drawback we explore the use of wake-up radio combined with LoRa, and propose an adequate MAC protocol that takes profit of both these heterogeneous and complementary technologies. This protocol allows an opportunistic selection of a cluster head that forwards commands from the gateway to the nodes in the same cluster. Furthermore, to achieve self-sustainability, sensor nodes might include an energy harvesting sub-system, for instance to scavenge energy from the light, and their quality of service can be tuned, according to their available energy. To have an effective self-sustaining LoRa system, we propose a new energy manager that allows less fluctuations of the quality of service between days and nights. Latency and energy are modeled in a hybrid manner, i.e., leveraging microbenchmarks on real hardware platforms, to explore the influence of the energy harvesting conditions on the quality of service of this heterogeneous network. It is clearly demonstrated that the cooperation of nodes within a cluster drastically reduces the latency of LoRa base station commands, e.g., by almost 90% compared to traditional LoRa scheme for a 10 nodes cluster.
A Practical Experience on the Amazon Alexa Integration in Smart Offices
Authors: Bogdan Răzvan , Tatu Alin , Crisan-Vida Mihaela Marcella , Popa Mircea , Stoicu-Tivadar Lăcrămioara
Published: January 2021

Smart offices are dynamically evolving spaces meant to enhance employees’ efficiency, but also to create a healthy and proactive working environment. In a competitive business world, the challenge of providing a balance between the efficiency and wellbeing of employees may be supported with new technologies. This paper presents the work undertaken to build the architecture needed to integrate voice assistants into smart offices in order to support employees in their daily activities, like ambient control, attendance system and reporting, but also interacting with project management services used for planning, issue tracking, and reporting. Our research tries to understand what are the most accepted tasks to be performed with the help of voice assistants in a smart office environment, by analyzing the system based on task completion and sentiment analysis. For the experimental setup, different test cases were developed in order to interact with the office environment formed by specific devices, as well as with the project management tool tasks. The obtained results demonstrated that the interaction with the voice assistant is reasonable, especially for easy and moderate utterances.
On-Demand Scheduling of Command and Responses for Low-Power Multihop Wireless Networks
Authors: Park Mingyu , Paek Jeongyeup
Published: January 2021

Many IoT applications require a mechanism to disseminate commands and collect responses over a wireless network in order to control and collect data from multiple embedded devices. However, severe collisions may occur if a large number of nodes attempt to respond simultaneously and promptly, not only among the responses, but also with the dissemination of commands. This is because low-power wireless network protocols for dissemination and collection have been designed separately. Tuning the parameters of one side of the protocol has clear trade-off between reliability and latency. To address this challenge, we propose SCoRe, an on-demand scheme for joint scheduling of command and responses on multihop low-power wireless networks to improve both reliability and latency simultaneously at runtime. SCoRe gathers the amount of time required by network nodes for dissemination and collection, and allocates relative timeslots to each node recursively over multihop on-demand when (and only when) disseminating a command. While doing so, information exchange occurs only between local neighbor nodes without a need for global routing table nor time synchronization. We implement SCoRe on a low-power embedded platform, and compare with well-known dissemination and collection schemes through both simulations and testbed experiments on 30 devices. Our evaluation results show that SCoRe can improve both latency and reliability without tuning the parameters for one metric, while the legacy schemes require careful parameter selection to match only one side of SCoRe, never both.
A 13-Bit, 12-ps Resolution Vernier Time-to-Digital Converter Based on Dual Delay-Rings for SPAD Image Sensor
Authors: Huang Zunkai , Huang Jinglin , Tian Li , Wang Ning , Zhu Yongxin , Wang Hui , Feng Songlin
Published: January 2021

A three-dimensional (3D) image sensor based on Single-Photon Avalanche Diode (SPAD) requires a time-to-digital converter (TDC) with a wide dynamic range and fine resolution for precise depth calculation. In this paper, we propose a novel high-performance TDC for a SPAD image sensor. In our design, we first present a pulse-width self-restricted (PWSR) delay element that is capable of providing a steady delay to improve the time precision. Meanwhile, we employ the proposed PWSR delay element to construct a pair of 16-stages vernier delay-rings to effectively enlarge the dynamic range. Moreover, we propose a compact and fast arbiter using a fully symmetric topology to enhance the robustness of the TDC. To validate the performance of the proposed TDC, a prototype 13-bit TDC has been fabricated in the standard 0.18-µm complementary metal–oxide–semiconductor (CMOS) process. The core area is about 200 µm × 180 µm and the total power consumption is nearly 1.6 mW. The proposed TDC achieves a dynamic range of 92.1 ns and a time precision of 11.25 ps. The measured worst integral nonlinearity (INL) and differential nonlinearity (DNL) are respectively 0.65 least-significant-bit (LSB) and 0.38 LSB, and both of them are less than 1 LSB. The experimental results indicate that the proposed TDC is suitable for SPAD-based 3D imaging applications.
Model-Driven Approach for Realization of Data Collection Architectures for Cyber-Physical Systems of Systems to Lower Manual Implementation Efforts
Authors: Trunzer Emanuel , Vogel-Heuser Birgit , Chen Jan-Kristof , Kohnle Moritz
Published: January 2021

Data collection from distributed automated production systems is one of the main prerequisites to leverage information gain from data analysis in the context of Industrie 4.0, e.g., for the optimization of product quality. However, the realization of data collection architectures is associated with immense implementation efforts due to the heterogeneity of systems, protocols, and interfaces, as well as the multitude of involved disciplines in such projects. Therefore, this paper contributes with an approach for the model-driven generation of data collection architectures to significantly lower manual implementation efforts. Via model transformations, the corresponding source code is automatically generated from formalized models that can be created using a graphical domain-specific language. The automatically generated architecture features support for various established IIoT protocols. In a lab-scale evaluation and a unique generalized extrapolation study, the significant effort savings compared to manual programming could be quantified. In conclusion, the proposed approach can successfully mitigate the current scientific and industrial challenges to enable wide-scale access to industrial data.
Development of a Long Period Fiber Grating Interrogation System Using a Multimode Laser Diode
Authors: Silva Luís Henrique , Santos Paulo , Coelho Luís C. C. , Jorge Pedro , Baptista José Manuel
Published: January 2021

Optical fiber gratings have long shown their sensing capabilities. One of the main challenges, however, is the interrogation method applied, since typical systems tend to use broadband light sources with optical spectrum analyzers, laser scanning units or CCD (Charged Coupled Device) spectrometers. The following paper presents the development of an interrogation system, which explores the temperature response of a multimode laser diode, in order to interrogate long period fiber gratings. By performing a spectral sweep along one of its rejection bands, a discrete attenuation spectrum is created. Through a curve fitting technique, the original spectrum is restored. The built unit, while presenting a substantially reduced cost compared with typical interrogation systems, is capable of interrogating along a 10 nm window with measurement errors reaching minimum values as low as 0.4 nm, regarding the grating central wavelength, and 0.4 dB for its attenuation. Given its low cost and reduced dimensions, the developed system shows potential for slow-changing field applications.
WiMonitor: Continuous Long-Term Human Vitality Monitoring Using Commodity Wi-Fi Devices
Authors: Niu Xiaopeng , Li Shengjie , Zhang Yue , Liu Zhaopeng , Wu Dan , Shah Rahul C. , Tanriover Cagri , Lu Hong , Zhang Daqing
Published: January 2021

For a large population of elderly who live alone, a continuous long-term daily monitoring system is critical and imminently needed to enhance the quality of their lives. Continuous monitoring of vitality information (i.e., which area the elder is staying in, the motion state and activity intensity of an elder) is essential for elderly care. In this paper, we use existing commodity Wi-Fi devices to design and implement a long-term device-free human daily vitality system, WiMonitor. Our system can continuously capture the target’s vitality information in a multi-room home environment without compromising the privacy of the target. In a continuous 22-day experiment, WiMonitor successfully captures the human vitality information accurately. We believe our system can provide valuable long-term monitoring data for both researchers and health care personnel.
A Potential Plasmonic Biosensor Based Asymmetric Metal Ring Cavity with Extremely Narrow Linewidth and High Sensitivity
Authors: Xu Tianping , Geng Zhaoxin , Su Yue
Published: January 2021

To achieve high sensitivity and multi-mode sensing characteristics based on the plasmon effect, we explored a high-sensitivity refractive index sensor structure with narrow linewidth and high absorption characteristics based on theoretical analysis. The sensor structure is composed of periodic asymmetric ring cavity array, spacer layer and metal thin-film layer. The reflection spectrum of this structure shows six resonance modes in the wavelength range from visible to near-infrared. The sensor performance was optimized based on the change of the sensor structure parameters combining the simulation data, and the results shown that this kind of asymmetric laminated structure sensor has good sensing performance. In theory, it can be combined with microfluidic technology to achieve sensing detection of diverse test samples, multi-mode and multi-component, which has great potential in the field of biosensing.
A Proof-of-Concept Pilot for an Intervention with Pregnant Mothers Who Have Had Children Removed by the State: The ‘Early Family Drug and Alcohol Court Model’
Authors: Shaw Mike
Published: January 2021

This paper describes a ‘proof-of-concept’ pilot of the ‘Early FDAC model’. The evaluated Family Drug and Alcohol Court (FDAC) model, on which Early FDAC is based, is summarised and the rationale for introducing the pilot variation is set out. This short paper describes the learning from the pilot that set out to work with 30 families across three FDAC teams between 2015 and 2019. At the time of the pilot, there were, and remain, few other interventions in England for pregnant mothers who have already had children removed. An adaptation of the evaluated FDAC model suggested itself because of the overlap with families in public law care proceedings and emerging evidence that FDAC delivers a better experience of justice for families and professionals, better outcomes for children and families and better use of public money. Pilot families were engaged as soon as possible in the pregnancy (hence ‘Early’), and continued to receive support for up to two years. The process started in pre-proceedings with the aim of avoiding court. Where proceedings were necessary, cases were heard in an FDAC court, with provision for a post-proceedings phase. There were problems with recruitment and engagement and families had fewer ‘solvable problems’. Nevertheless, outcomes were promising, with 18 families keeping their children. This represents one-third of the referred families and almost two-thirds of the families who undertook a ‘Trial for Change’.
Genetic Factors That Could Affect Concussion Risk in Elite Rugby
Authors: Antrobus Mark R. , Brazier Jon , Stebbings Georgina K. , Day Stephen H. , Heffernan Shane M. , Kilduff Liam P. , Erskine Robert M. , Williams Alun G.
Published: January 2021

Elite rugby league and union have some of the highest reported rates of concussion (mild traumatic brain injury) in professional sport due in part to their full-contact high-velocity collision-based nature. Currently, concussions are the most commonly reported match injury during the tackle for both the ball carrier and the tackler (8–28 concussions per 1000 player match hours) and reports exist of reduced cognitive function and long-term health consequences that can end a playing career and produce continued ill health. Concussion is a complex phenotype, influenced by environmental factors and an individual’s genetic predisposition. This article reviews concussion incidence within elite rugby and addresses the biomechanics and pathophysiology of concussion and how genetic predisposition may influence incidence, severity and outcome. Associations have been reported between a variety of genetic variants and traumatic brain injury. However, little effort has been devoted to the study of genetic associations with concussion within elite rugby players. Due to a growing understanding of the molecular characteristics underpinning the pathophysiology of concussion, investigating genetic variation within elite rugby is a viable and worthy proposition. Therefore, we propose from this review that several genetic variants within or near candidate genes of interest, namely APOE, MAPT, IL6R, COMT, SLC6A4, 5-HTTLPR, DRD2, DRD4, ANKK1, BDNF and GRIN2A, warrant further study within elite rugby and other sports involving high-velocity collisions.
A Comprehensive Review on Oil Extraction and Biodiesel Production Technologies
Authors: Zulqarnain  , Ayoub Muhammad , Yusoff Mohd Hizami Mohd , Nazir Muhammad Hamza , Zahid Imtisal , Ameen Mariam , Sher Farooq , Floresyona Dita , Budi Nursanto Eduardus
Published: January 2021

Dependence on fossil fuels for meeting the growing energy demand is damaging the world’s environment. There is a dire need to look for alternative fuels that are less potent to greenhouse gas emissions. Biofuels offer several advantages with less harmful effects on the environment. Biodiesel is synthesized from the organic wastes produced extensively like edible, non-edible, microbial, and waste oils. This study reviews the feasibility of the state-of-the-art feedstocks for sustainable biodiesel synthesis such as availability, and capacity to cover a significant proportion of fossil fuels. Biodiesel synthesized from oil crops, vegetable oils, and animal fats are the potential renewable carbon-neutral substitute to petroleum fuels. This study concludes that waste oils with higher oil content including waste cooking oil, waste palm oil, and algal oil are the most favorable feedstocks. The comparison of biodiesel production and parametric analysis is done critically, which is necessary to come up with the most appropriate feedstock for biodiesel synthesis. Since the critical comparison of feedstocks along with oil extraction and biodiesel production technologies has never been done before, this will help to direct future researchers to use more sustainable feedstocks for biodiesel synthesis. This study concluded that the use of third-generation feedstocks (wastes) is the most appropriate way for sustainable biodiesel production. The use of innovative costless oil extraction technologies including supercritical and microwave-assisted transesterification method is recommended for oil extraction.
Towards Sustainable Residential Buildings in Saudi Arabia According to the Conceptual Framework of “Mostadam” Rating System and Vision 2030
Authors: Balabel Ashraf , Alwetaishi Mamdooh
Published: January 2021

Saudi Arabia vision 2030 adopts comprehensive development standards in all fields of Saudi society. To achieve such standards, many initiatives and programs have been launched to achieve sustainable goals while meeting the needs of the present without compromising the requirements of future generations. Sustainable buildings are highly important in achieving sustainable development goals due to their excellent ability to reduce energy consumption, increase building efficiency, and reduce harmful emissions of greenhouse gases. The Kingdom aims to be the largest investor in the world in the sustainable buildings sector. To this end, the “Mostadam” evaluation service was recently launched in order to assist existing and new buildings in achieving sustainable building standards in a way that is in keeping with the privacy and principles of Saudi society. The present paper presents an extensive investigation to assess the current state of sustainable buildings in Saudi cities. Moreover, the main categories of sustainable residential buildings according to the “Mostadam” rating systems are discussed, and the potential of such categories in Saudi Arabia is explored. Finally, suggestions are given for some practical strategies and future policies targeting the total transformation towards sustainable buildings and, consequently, towards sustainable cities. This will have a great impact on achieving comprehensive sustainable development in Saudi Arabia in line with vision 2030.
Evaluating the Impact of Floods on Housing Price Using a Spatial Matching Difference-In-Differences (SM-DID) Approach
Authors: Dubé Jean , AbdelHalim Maha , Devaux Nicolas
Published: January 2021

Many applications have relied on the hedonic pricing model (HPM) to measure the willingness-to-pay (WTP) for urban externalities and natural disasters. The classic HPM regresses housing price on a complete list of attributes/characteristics that include spatial or environmental amenities (or disamenities), such as floods, to retrieve the gradients of the market (marginal) WTP for such externalities. The aim of this paper is to propose an innovative methodological framework that extends the causal relations based on a spatial matching difference-in-differences (SM-DID) estimator, and which attempts to calculate the difference between sale price for similar goods within “treated” and “control” groups. To demonstrate the potential of the proposed spatial matching method, the researchers present an empirical investigation based on the case of a flood event recorded in the city of Laval (Québec, Canada) in 1998, using information on transactions occurring between 1995 and 2001. The research results show that the impact of flooding brings a negative premium on the housing price of about 20,000$ Canadian (CAN).
Intensified Interspecific Competition for Water after Afforestation with Robinia pseudoacacia into a Native Shrubland in the Taihang Mountains, Northern China
Authors: Zhu Wanrui , Li Wenhua , Shi Peili , Cao Jiansheng , Zong Ning , Geng Shoubao
Published: January 2021

Understanding how soil water source is used spatiotemporally by tree species and if native species can successfully coexist with introduced species is crucial for selecting species for afforestation. In the rocky mountainous areas of the Taihang Mountains, alien Robinia pseudoacacia L. has been widely afforested into the native shrublands dominated by Ziziphus jujuba Mill var. spinosa and Vitex negundo L. var. heterophylla to improve forest coverage and soil nutrients. However, little is known about the water relation among species, especially seasonal water use sources in different microsites. We selected the soil and plant xylem samples of two opposite microtopographic sites (ridge and valley) monthly in the growth season to analyze isotope composition. The proportions of water sources were quantified by the MixSIAR model and compared pairwise between species, microsites and seasons. We found that deep subsoil water at a depth of 40–50 cm contributed up to 50% of the total water uptake for R. pseudoacacia and Z. jujuba in the growing season, indicating that they stably used deeper soil water and had intense water competition. However, V. negundo had a more flexible water use strategy, which derived more than 50% of the total water uptake from the soil layer of 0–10 cm in the rainy season, but majorly captured soil water at a depth of 30–50 cm in the dry season. Therefore, high niche overlaps were shown in V. negundo with the other two species in the dry season, but niche segregation was seen in the rainy season. The microtopographic sites did not shift the seasonal dynamic of the water source use patterns of the three studied species, but the water use niche overlap was higher in the valley than in the ridge. Taken together, the introduced species R. pseudoacacia intensified water competition with the native semi-arbor species Z. jujuba, but it could commonly coexist with the native shrub species V. negundo. Therefore, our study on seasonal water use sources in different microsites provides insight into species interaction and site selection for R. pseudoacacia afforestation in the native shrub community in rocky mountainous areas. It is better to plant R. pseudoacacia in the shrubland in the valley so as to avoid intense water competition and control soil erosion.
Research on Knowledge Management Models at Universities Using Fuzzy Analytic Hierarchy Process (FAHP)
Authors: Pham Ngoc Thach , Do Anh Duc , Nguyen Quang Vinh , Ta Van Loi , Dao Thi Thanh Binh , Ha Dieu Linh , Hoang Xuan Truong
Published: January 2021

This study aims to investigate and evaluate factors related to the knowledge management model at universities in Hanoi, Vietnam. Based on the system literature review (SLR) approach, the study follows descriptive and inductive approach results of the document review process. Eight factors were synthesized with the fuzzy analytic hierarchy process (FAHP) to evaluate the priority order. Ten experts from seven universities participated in the survey. The results rank as follows: (1) knowledge sharing factor (this also has the highest best nonfuzzy performance (BNP) and average multiplier weight (GM)); (2) knowledge management with big data systems; (3) knowledge creation; (4) knowledge use; (5) knowledge gathering; (6) leadership; (7) knowledge rating; and (8) knowledge storage. Discussions, conclusions, limitations of the study, and suggestions for future studies are also mentioned in this study.