bluuebunny/crossref_metadata_2025 huggingface dataset:
The Impact of Spatiotemporal Changes in Land Development (1984–2019) on the Increase in the Runoff Coefficient in Erbil, Kurdistan Region of Iraq
Authors: Mustafa Andam , Szydłowski Michał
Published: April 2020

Nowadays, geospatial techniques are a popular approach for estimating urban flash floods by considering spatiotemporal changes in urban development. In this study, we investigated the impact of Land Use/Land Cover (LULC) changes on the hydrological response of the Erbil basin in the Kurdistan Region of Iraq (KRI). In the studied area, the LULC changes were calculated for 1984, 1994, 2004, 2014 and 2019 using the Digital Elevation Model (DEM) and satellite images. The analysis of LULC changes showed that the change between 1984 and 2004 was slower than that between 2004 and 2019. The LULC analysis revealed a 444.4% growth in built-up areas, with a 60.4% decrease in agricultural land between 1984 and 2019. The influence of LULC on urban floods caused by different urbanization scenarios was ascertained using the HEC-GeoHMS and HEC-HMS models. Over 35 years, there was a 15% increase in the peak discharge of outflow, from 392.2 m3/s in 1984 to 450 m3/s in 2014, as well as the runoff volume for a precipitation probability distribution of 10%, which increased from 27.4 mm in 1984 to 30.9 mm in 2014. Overall, the probability of flash floods increased in the center of the city due to the large expansion of built-up areas.
Detecting Change in Forest Structure with Simulated GEDI Lidar Waveforms: A Case Study of the Hemlock Woolly Adelgid (HWA; Adelges tsugae) Infestation
Authors: Boucher Peter , Hancock Steven , Orwig David , Duncanson Laura , Armston John , Tang Hao , Krause Keith , Cook Bruce , Paynter Ian , Li Zhan , Elmes Arthur , Schaaf Crystal
Published: April 2020

The hemlock woolly adelgid (HWA; Adelges tsugae) is an invasive insect infestation that is spreading into the forests of the northeastern United States, driven by the warmer winter temperatures associated with climate change. The initial stages of this disturbance are difficult to detect with passive optical remote sensing, since the insect often causes its host species, eastern hemlock trees (Tsuga canadensis), to defoliate in the midstory and understory before showing impacts in the overstory. New active remote sensing technologies—such as the recently launched NASA Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar—can address this limitation by penetrating canopy gaps and recording lower canopy structural changes. This study explores new opportunities for monitoring the HWA infestation with airborne lidar scanning (ALS) and GEDI spaceborne lidar data. GEDI waveforms were simulated using airborne lidar datasets from an HWA-infested forest plot at the Harvard Forest ForestGEO site in central Massachusetts. Two airborne lidar instruments, the NASA G-LiHT and the NEON AOP, overflew the site in 2012 and 2016. GEDI waveforms were simulated from each airborne lidar dataset, and the change in waveform metrics from 2012 to 2016 was compared to field-derived hemlock mortality at the ForestGEO site. Hemlock plots were shown to be undergoing dynamic changes as a result of the HWA infestation, losing substantial plant area in the middle canopy, while still growing in the upper canopy. Changes in midstory plant area (PAI 11–12 m above ground) and overall canopy permeability (indicated by RH10) accounted for 60% of the variation in hemlock mortality in a logistic regression model. The robustness of these structure-condition relationships held even when simulated waveforms were treated as real GEDI data with added noise and sparse spatial coverage. These results show promise for future disturbance monitoring studies with ALS and GEDI lidar data.
Landslide Mapping and Monitoring Using Persistent Scatterer Interferometry (PSI) Technique in the French Alps
Authors: Aslan Gokhan , Foumelis Michael , Raucoules Daniel , De Michele Marcello , Bernardie Severine , Cakir Ziyadin
Published: April 2020

Continuous geodetic measurements in landslide prone regions are necessary to avoid disasters and better understand the spatiotemporal and kinematic evolution of landslides. The detection and characterization of landslides in high alpine environments remains a challenge associated with difficult accessibility, extensive coverage, limitations of available techniques, and the complex nature of landslide process. Recent studies using space-based observations and especially Persistent Scatterer Interferometry (PSI) techniques with the integration of in-situ monitoring instrumentation are providing vital information for an actual landslide monitoring. In the present study, the Stanford Method for Persistent Scatterers InSAR package (StaMPS) is employed to process the series of Sentinel 1-A and 1-B Synthetic Aperture Radar (SAR) images acquired between 2015 and 2019 along ascending and descending orbits for the selected area in the French Alps. We applied the proposed approach, based on extraction of Active Deformation Areas (ADA), to automatically detect and assess the state of activity and the intensity of the suspected slow-moving landslides in the study area. We illustrated the potential of Sentinel-1 data with the aim of detecting regions of relatively low motion rates that be can attributed to activate landslide and updated pre-existing national landslide inventory maps on a regional scale in terms of slow moving landslides. Our results are compared to pre-existing landslide inventories. More than 100 unknown slow-moving landslides, their spatial pattern, deformation rate, state of activity, as well as orientation are successfully identified over an area of 4000 km2 located in the French Alps. We also address the current limitations due the nature of PSI and geometric characteristic of InSAR data for measuring slope movements in mountainous environments like Alps.
Assessment of Urban Dynamics to Understand Spatiotemporal Differentiation at Various Scales Using Remote Sensing and Geospatial Tools
Authors: Anees Mangalasseril Mohammad , Mann Deepika , Sharma Mani , Banzhaf Ellen , Joshi Pawan K
Published: April 2020

Analysis of urban dynamics is a pivotal step towards understanding landscape changes and developing scientifically sound urban management strategies. Delineating the patterns and processes shaping the evolution of urban regions is an essential part of this step. Utilizing remote-sensing techniques and Geographic Information System (GIS) tools, we performed an integrated analysis on urban expansion in Srinagar city and surrounding areas from 1999 to 2017 at multiple scales in order to assist urban planning initiatives. To capture various spatial indicators of expansion, we analysed (i) land use/land cover (LULC) changes, (ii) rate and intensity of changes to built-up areas, (iii) spatial differentiation in landscape metrics (at 500, 1000 and 2000 m cell-size), and (iv) growth type of the urban expansion. Global Moran’s I statistics and local indicators of spatial association (LISA) were also employed to identify hotspots of change in landscape structure. Our methodology utilizes a range of geovisualization tools which are capable of appropriately addressing various elements required for strategic planning in growing cities. The results highlight aggregation and homogenization of the urban core as well as irregularity and fragmentation in its periphery. A combination of spatial metrics and growth type analysis supports the supposition that there is a continuum in the diffusion-coalescence process. This allows us to extend our understanding of urban growth theory and to report deviations from accepted stages of growth. As our results show, each dominating growth phase of the city—both diffusion (1999) and coalescence (2009 and 2017)—is interspersed with features from the other type. An improved understanding of spatial differentiation and the identification of hotspots can serve to make urban planning more tailored to such local conditions. An important insight derived from the results is the applicability of remote-sensing data in urban planning measures and the usefulness of freely available medium resolution data in gaining a comprehensive understanding of the evolution of cities.
Vibration Control of Scanning Electron Microscopes with Experimental Approaches for Performance Enhancement
Authors: Shin Yun-Ho , Moon Seok-Jun , Kim Yong-Ju , Oh Ki-Yong
Published: April 2020

A vibration isolator embedded in precision equipment, such as a scanning electron microscope (SEM), wafer inspection equipment, and nanoimprint lithography equipment, play a critical role in achieving the maximum performance of the equipment during the fabrication of nano/micro-electro-mechanical systems. In this study, the factors that degrade the performance of SEM equipment with isolation devices are classified and discussed, and improvement measures are proposed from the viewpoints of the measured image patterns and vibrations in comparison with the relevant vibration criteria. In particular, this study quantifies the image patterns measured using SEMs, and the results are discussed along with the measured vibration. A guide for the selection of mounting equipment is presented by performing vibration analysis on the lower mount of the dual elastic mount configuration applied to the SEM, as well as the image patterns analyzed with that configuration. In addition, design modifications for the mount and its arrangement are suggested based on impact tests and numerical simulations.
Multi-View Visual Question Answering with Active Viewpoint Selection
Authors: Qiu Yue , Satoh Yutaka , Suzuki Ryota , Iwata Kenji , Kataoka Hirokatsu
Published: April 2020

This paper proposes a framework that allows the observation of a scene iteratively to answer a given question about the scene. Conventional visual question answering (VQA) methods are designed to answer given questions based on single-view images. However, in real-world applications, such as human–robot interaction (HRI), in which camera angles and occluded scenes must be considered, answering questions based on single-view images might be difficult. Since HRI applications make it possible to observe a scene from multiple viewpoints, it is reasonable to discuss the VQA task in multi-view settings. In addition, because it is usually challenging to observe a scene from arbitrary viewpoints, we designed a framework that allows the observation of a scene actively until the necessary scene information to answer a given question is obtained. The proposed framework achieves comparable performance to a state-of-the-art method in question answering and simultaneously decreases the number of required observation viewpoints by a significant margin. Additionally, we found our framework plausibly learned to choose better viewpoints for answering questions, lowering the required number of camera movements. Moreover, we built a multi-view VQA dataset based on real images. The proposed framework shows high accuracy (94.01%) for the unseen real image dataset.
Modeling and Characterization of Scaling Factor of Flexible Spiral Coils for Wirelessly Powered Wearable Sensors
Authors: Biswas Dipon K. , Sinclair Melissa , Le Tien , Pullano Salvatore Andrea , Fiorillo Antonino S. , Mahbub Ifana
Published: April 2020

Wearable sensors are a topic of interest in medical healthcare monitoring due to their compact size and portability. However, providing power to the wearable sensors for continuous health monitoring applications is a great challenge. As the batteries are bulky and require frequent charging, the integration of the wireless power transfer (WPT) module into wearable and implantable sensors is a popular alternative. The flexible sensors benefit by being wirelessly powered, as it not only expands an individual’s range of motion, but also reduces the overall size and the energy needs. This paper presents the design, modeling, and experimental characterization of flexible square-shaped spiral coils with different scaling factors for WPT systems. The effects of coil scaling factor on inductance, capacitance, resistance, and the quality factor (Q-factor) are modeled, simulated, and experimentally validated for the case of flexible planar coils. The proposed analytical modeling is helpful to estimate the coil parameters without using the time-consuming Finite Element Method (FEM) simulation. The analytical modeling is presented in terms of the scaling factor to find the best-optimized coil dimensions with the maximum Q-factor. This paper also presents the effect of skin contact with the flexible coil in terms of the power transfer efficiency (PTE) to validate the suitability as a wearable sensor. The measurement results at 405 MHz show that when in contact with the skin, the 20 mm× 20 mm receiver (RX) coil achieves a 42% efficiency through the air media for a 10 mm distance between the transmitter (TX) and RX coils.
Ultrasound Standing Wave-Based Cell-to-liquid Separation for Measuring Viscosity and Aggregation of Blood Sample
Authors: Kim Gwangho , Jeong Sanghwa , Kang Yang Jun
Published: April 2020

When quantifying mechanical properties of blood samples flowing in closed fluidic circuits, blood samples are collected at specific intervals. Centrifugal separation is considered as a required procedure for preparing blood samples. However, the use of centrifuge is associated with several issues, including the potential for red blood cell (RBC) lysis, clotting activation, and RBC adhesions in the tube. In this study, an ultrasonic transducer is employed to separate RBCs or diluent from blood sample. The ultrasonic radiation force is much smaller than the centrifugal force acting in centrifuge, it can avoid critical issues occurring under centrifuge. Then, the RBC aggregation and blood viscosity of the blood sample are obtained using the microfluidic technique. According to the numerical results, ultrasonic transducers exhibited a maximum quality factor at an excitation frequency of 2.1 MHz. Periodic pattern of acoustic pressure fields were visualized experimentally as a column mode. The half wavelength obtained was as 0.5 λ = 0.378 ± 0.07 mm. The experimental results agreed with the analytical estimation sufficiently. An acoustic power of 2 W was selected carefully for separating RBCs or diluent from various blood samples (i.e., Hct = 20% ~ 50%; diluent: plasma, 1x phosphate-buffered saline (PBS), and dextran solution). The present method was employed to separate fixed blood samples which tended to stack inside the tube while using the centrifuge. Fixed RBCs were collected easily with an ultrasonic transducer. After various fixed blood samples with different base solutions (i.e., glutaraldehyde solution, 1x PBS, and dextran solution) were prepared using the present method, RBC aggregation and the viscosity of the blood sample are successfully obtained. In the near future, the present method will be integrated into ex vivo or in vitro fluidic circuit for measuring multiple mechanical properties of blood samples for a certain longer period.
Orbit Angular Momentum MIMO with Mode Selection for UAV-Assisted A2G Networks
Authors: Hu Tao , Wang Yang , Ma Bo , Zhang Jie
Published: April 2020

As an emerging solution for line-of-sight (LOS) wireless communications, in air-to-ground (A2G) channels, the unmanned aerial vehicle (UAV), and allowing the dynamic and flexible network deployments enables the supplement or/and replacement of the terrestrial base stations (BSs). However, in conventional multiple-input-multiple-output (MIMO) systems, high-speed communications are significantly limited by channel crosstalks and spectrum scarcities. An orbit angular momentum (OAM) wireless network, allowing co-existence of multiple physical channels within the same frequency band, offers new degrees of freedom to address this dilemma. In this paper, we investigate the UAV-based A2G radio vortex wireless networks and study its channel model. Then we propose a branch and bound search-based mode selection (BBS-MS) scheme, which uses the spatial distribution characteristics of vortex beams to optimize the spectrum efficiency (SE). Theoretical derivations and numerical results demonstrate that our developed BBS-MS scheme can obtain the optimal performance, which outperforms conventional OAM-based MIMO systems. Also, it possesses a lower complexity compared with exhaustive searches.
In-Situ LED-Based Observation of Snow Surface and Depth Transects
Authors: Barnes Celeste , Hopkinson Chris , Porter Thomas , Xi Zhouxin
Published: April 2020

As part of a new snowpack monitoring framework, this study evaluated the feasibility of using an LED LIDAR (Leddar) time of flight sensor for snowpack depth measurement. The Leddar sensor has two additional features over simple sonic ranging sensors: (i) the return signal is divided into 16 segments across a 48° field of view, each recording individual distance-to-target (DTT) measurements; (ii) an index of reflectance or intensity signal is recorded for each segment. These two features provide information describing snowpack morphology and surface condition. The accuracy of Leddar sensor DTT measurements for snow depth monitoring was found to be < 20 mm, which was better than the 50 mm quoted by the manufacturer, and the precision was < 5 mm. Leddar and independent sonic ranger snow depth measurement showed strong linear agreement (r2 = 0.98). There was also a strong linear relationship (r2 = 0.98) between Leddar and manual field snow depth measurements. The intensity signal response was found to correlate with snow surface albedo and inversely with air temperature (r = 0.77 and −0.77, respectively).
A Novel Loop Closure Detection Approach Using Simplified Structure for Low-Cost LiDAR
Authors: Ye Qin , Shi Pengcheng , Xu Kunyuan , Gui Popo , Zhang Shaoming
Published: April 2020

Reducing the cumulative error is a crucial task in simultaneous localization and mapping (SLAM). Usually, Loop Closure Detection (LCD) is exploited to accomplish this work for SLAM and robot navigation. With a fast and accurate loop detection, it can significantly improve global localization stability and reduce mapping errors. However, the LCD task based on point cloud still has some problems, such as over-reliance on high-resolution sensors, and poor detection efficiency and accuracy. Therefore, in this paper, we propose a novel and fast global LCD method using a low-cost 16 beam Lidar based on “Simplified Structure”. Firstly, we extract the “Simplified Structure” from the indoor point cloud, classify them into two levels, and manage the “Simplified Structure” hierarchically according to its structure salience. The “Simplified Structure” has simple feature geometry and can be exploited to capture the indoor stable structures. Secondly, we analyze the point cloud registration suitability with a pre-match, and present a hierarchical matching strategy with multiple geometric constraints in Euclidean Space to match two scans. Finally, we construct a multi-state loop evaluation model for a multi-level structure to determine whether the two candidate scans are a loop. In fact, our method also provides a transformation for point cloud registration with “Simplified Structure” when a loop is detected successfully. Experiments are carried out on three types of indoor environment. A 16 beam Lidar is used to collect data. The experimental results demonstrate that our method can detect global loop closures efficiently and accurately. The average global LCD precision, accuracy and negative are approximately 0.90, 0.96, and 0.97, respectively.
MIMU/Odometer Fusion with State Constraints for Vehicle Positioning during BeiDou Signal Outage: Testing and Results
Authors: Zhu Kai , Guo Xuan , Jiang Changhui , Xue Yujingyang , Li Yuanjun , Han Lin , Chen Yuwei
Published: April 2020

With the rapid development of autonomous vehicles, the demand for reliable positioning results is urgent. Currently, the ground vehicles heavily depend on the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) providing reliable and continuous navigation solutions. In dense urban areas, especially narrow streets with tall buildings, the GNSS signals are possibly blocked by the surrounding tall buildings, and under this condition, the geometry distribution of the in-view satellites is very poor, and the None-Line-Of-Sight (NLOS) and Multipath (MP) heavily affects the positioning accuracy. Further, the INS positioning errors will quickly diverge over time without the GNSS correction. Aiming at improving the position accuracy under signal challenging environment, in this paper, we developed an MIMU(Micro Inertial Measurement Unit)/Odometer integration system with vehicle state constraints (MO-C) for improving the vehicle positioning accuracy without GNSS. MIMU/Odometer integration model and the constrained measurements are given in detail. Several field tests were carried out for evaluating and assessing the MO-C system. The experiments were divided into two parts, firstly, field testing with data post-processing and real-time processing was carried out for fully assessing the performance of the MO-C system. Secondly, the MO-C was implemented in the BeiDou Satellite Navigation System (BDS)/integrated navigation system (INS) for evaluating the MO-C performance during the BDS signal outage. The MIMU standalone positioning accuracy was compared with that from the MIMU/Odometer integration (MO), MO-C and MIMU with constraints (M-C) for assessing the Odometer, and the influence of the constraint on the positioning errors reduction. The results showed that the latitude and longitude errors could be suppressed with Odometer assisting, and the height errors were suppressed while the state constraints were included.
UAV Swarm Mission Planning in Dynamic Environment Using Consensus-Based Bundle Algorithm
Authors: Zhang Yaozhong , Feng Wencheng , Shi Guoqing , Jiang Frank , Chowdhury Morshed , Ling Sai Ho
Published: April 2020

To solve the real-time complex mission-planning problem for Multiple heterogeneous Unmanned Aerial Vehicles (UAVs) in the dynamic environments, this paper addresses a new approach by effectively adapting the Consensus-Based Bundle Algorithms (CBBA) under the constraints of task timing, limited UAV resources, diverse types of tasks, dynamic addition of tasks, and real-time requirements. We introduce the dynamic task generation mechanism, which satisfied the task timing constraints. The tasks that require the cooperation of multiple UAVs are simplified into multiple sub-tasks to perform by a single UAV independently. We also introduce the asynchronous task allocation mechanism. This mechanism reduces the computational complexity of the algorithm and the communication time between UAVs. The partial task redistribution mechanism has been adopted for achieving the dynamic task allocation. The real-time performance of the algorithm is assured on the premise of optimal results. The feasibility and real-time performance of the algorithm are validated by conducting dynamic simulation experiments.
Laser Spot Center Location Method for Chinese Spaceborne GF-7 Footprint Camera
Authors: Ren Chaofeng , Xie Junfeng , Zhi Xiaodong , Yang Yun , Yang Shuai
Published: April 2020

The Gaofen-7 (GF-7) satellite is equipped with two area array sensor footprint cameras to capture the laser altimeter spot. In order to establish a direct correspondence between the laser data and the stereo image data, a new method is proposed to fit the center of the spot using the brightness difference between the spot image and the footprint image. First, the geometric registration between the spot image and the footprint image is completed based on feature matching or template matching. Then, the brightness values between the two images are extracted from the corresponding image position to form a measurement, and the least squares adjustment method is used to calculate the parameters of the brightness conversion model between the spot image and the footprint image. Finally, according to the registration relationship, the center of the identified spots is respectively positioned in the footprint images, so that the laser spots are accurately identified in the along-track stereo footprint images. The experimental results show that the spot error of this method is less than 0.7 pixel, which has higher reliability and stability, and can be used for a GF-7 satellite footprint camera.
Counter a Drone in a Complex Neighborhood Area by Deep Reinforcement Learning
Authors: Çetin Ender , Barrado Cristina , Pastor Enric
Published: April 2020

Counter-drone technology by using artificial intelligence (AI) is an emerging technology and it is rapidly developing. Considering the recent advances in AI, counter-drone systems with AI can be very accurate and efficient to fight against drones. The time required to engage with the target can be less than other methods based on human intervention, such as bringing down a malicious drone by a machine-gun. Also, AI can identify and classify the target with a high precision in order to prevent a false interdiction with the targeted object. We believe that counter-drone technology with AI will bring important advantages to the threats coming from some drones and will help the skies to become safer and more secure. In this study, a deep reinforcement learning (DRL) architecture is proposed to counter a drone with another drone, the learning drone, which will autonomously avoid all kind of obstacles inside a suburban neighborhood environment. The environment in a simulator that has stationary obstacles such as trees, cables, parked cars, and houses. In addition, another non-malicious third drone, acting as moving obstacle inside the environment was also included. In this way, the learning drone is trained to detect stationary and moving obstacles, and to counter and catch the target drone without crashing with any other obstacle inside the neighborhood. The learning drone has a front camera and it can capture continuously depth images. Every depth image is part of the state used in DRL architecture. There are also scalar state parameters such as velocities, distances to the target, distances to some defined geofences and track, and elevation angles. The state image and scalars are processed by a neural network that joints the two state parts into a unique flow. Moreover, transfer learning is tested by using the weights of the first full-trained model. With transfer learning, one of the best jump-starts achieved higher mean rewards (close to 35 more) at the beginning of training. Transfer learning also shows that the number of crashes during training can be reduced, with a total number of crashed episodes reduced by 65%, when all ground obstacles are included.
Hardware/Software Co-Design of Fractal Features Based Fall Detection System
Authors: Tahir Ahsen , Morison Gordon , Skelton Dawn A. , Gibson Ryan M.
Published: April 2020

Falls are a leading cause of death in older adults and result in high levels of mortality, morbidity and immobility. Fall Detection Systems (FDS) are imperative for timely medical aid and have been known to reduce death rate by 80%. We propose a novel wearable sensor FDS which exploits fractal dynamics of fall accelerometer signals. Fractal dynamics can be used as an irregularity measure of signals and our work shows that it is a key discriminant for classification of falls from other activities of life. We design, implement and evaluate a hardware feature accelerator for computation of fractal features through multi-level wavelet transform on a reconfigurable embedded System on Chip, Zynq device for evaluating wearable accelerometer sensors. The proposed FDS utilises a hardware/software co-design approach with hardware accelerator for fractal features and software implementation of Linear Discriminant Analysis on an embedded ARM core for high accuracy and energy efficiency. The proposed system achieves 99.38% fall detection accuracy, 7.3× speed-up and 6.53× improvements in power consumption, compared to the software only execution with an overall performance per Watt advantage of 47.6×, while consuming low reconfigurable resources at 28.67%.
Characterizing Human Box-Lifting Behavior Using Wearable Inertial Motion Sensors
Authors: Hlucny Steven D. , Novak Domen
Published: April 2020

Although several studies have used wearable sensors to analyze human lifting, this has generally only been done in a limited manner. In this proof-of-concept study, we investigate multiple aspects of offline lift characterization using wearable inertial measurement sensors: detecting the start and end of the lift and classifying the vertical movement of the object, the posture used, the weight of the object, and the asymmetry involved. In addition, the lift duration, horizontal distance from the lifter to the object, the vertical displacement of the object, and the asymmetric angle are computed as lift parameters. Twenty-four healthy participants performed two repetitions of 30 different main lifts each while wearing a commercial inertial measurement system. The data from these trials were used to develop, train, and evaluate the lift characterization algorithms presented. The lift detection algorithm had a start time error of 0.10 s ± 0.21 s and an end time error of 0.36 s ± 0.27 s across all 1489 lift trials with no missed lifts. For posture, asymmetry, vertical movement, and weight, our classifiers achieved accuracies of 96.8%, 98.3%, 97.3%, and 64.2%, respectively, for automatically detected lifts. The vertical height and displacement estimates were, on average, within 25 cm of the reference values. The horizontal distances measured for some lifts were quite different than expected (up to 14.5 cm), but were very consistent. Estimated asymmetry angles were similarly precise. In the future, these proof-of-concept offline algorithms can be expanded and improved to work in real-time. This would enable their use in applications such as real-time health monitoring and feedback for assistive devices.
Groupwise Image Alignment via Self Quotient Images
Authors: Lamprinou Nefeli , Nikolikos Nikolaos , Psarakis Emmanouil Z.
Published: April 2020

Compared with pairwise registration, the groupwise one is capable of handling a large-scale population of images simultaneously in an unbiased way. In this work we improve upon the state-of-the-art pixel-level, Least-Squares (LS)-based groupwise image registration methods. Specifically, the registration technique is properly adapted by the use of Self Quotient Images (SQI) in order to become capable for solving the groupwise registration of photometrically distorted, partially occluded as well as unimodal and multimodal images. Moreover, the proposed groupwise technique is linear to the cardinality of the image set and thus it can be used for the successful solution of the problem on large image sets with low complexity. From the application of the proposed technique on a series of experiments for the groupwise registration of photometrically and geometrically distorted, partially occluded faces as well as unimodal and multimodal magnetic resonance image sets and its comparison with the Lucas–Kanade Entropy (LKE) algorithm, the obtained results look very promising, in terms of alignment quality, using as figures of merit the mean Peak Signal to Noise Ratio ( m P S N R ) and mean Structural Similarity ( m S S I M ), and computational cost.
Incorporating Noise Robustness in Speech Command Recognition by Noise Augmentation of Training Data
Authors: Pervaiz Ayesha , Hussain Fawad , Israr Huma , Tahir Muhammad Ali , Raja Fawad Riasat , Baloch Naveed Khan , Ishmanov Farruh , Zikria Yousaf Bin
Published: April 2020

The advent of new devices, technology, machine learning techniques, and the availability of free large speech corpora results in rapid and accurate speech recognition. In the last two decades, extensive research has been initiated by researchers and different organizations to experiment with new techniques and their applications in speech processing systems. There are several speech command based applications in the area of robotics, IoT, ubiquitous computing, and different human-computer interfaces. Various researchers have worked on enhancing the efficiency of speech command based systems and used the speech command dataset. However, none of them catered to noise in the same. Noise is one of the major challenges in any speech recognition system, as real-time noise is a very versatile and unavoidable factor that affects the performance of speech recognition systems, particularly those that have not learned the noise efficiently. We thoroughly analyse the latest trends in speech recognition and evaluate the speech command dataset on different machine learning based and deep learning based techniques. A novel technique is proposed for noise robustness by augmenting noise in training data. Our proposed technique is tested on clean and noisy data along with locally generated data and achieves much better results than existing state-of-the-art techniques, thus setting a new benchmark.
A Smart Rig for Calibration of Gas Sensor Nodes
Authors: Benammar Mohieddine A. , Ahmad Sabbir H. M. , Abdaoui Abderrazak , Tariq Hasan , Touati Farid , Al-Hitmi Mohammed , Crescini Damiano
Published: April 2020

Electrochemical gas sensors require regular maintenance to check and secure proper functioning. Standard procedures usually involve testing and recalibration of the sensors, for which working environments are needed. Periodic calibration is therefore necessary to ensure reliable and accurate measurements. This paper proposes a dedicated smart calibration rig with a set of novel features enabling simultaneous calibration of multiple sensors. The proposed calibration rig system comprises a gas mixing system, temperature control system, a test chamber, and a process-control PC that controls all calibration phases. The calibration process is automated by a LabVIEW-based platform that controls the calibration environment for the sensor nodes, logs sensor data, and best fit equation based on interpolation for every sensor on the node and uploads it to the sensor node for next deployments. The communication between the PC and the sensor nodes is performed using the same IEEE 802.15.4 (ZigBee) protocol that the nodes also use in field deployment for air quality measurement. The results presented demonstrate the effectiveness of the sensors calibration rig.
Automatic Anomaly Detection on In-Production Manufacturing Machines Using Statistical Learning Methods
Authors: Pittino Federico , Puggl Michael , Moldaschl Thomas , Hirschl Christina
Published: April 2020

Anomaly detection is becoming increasingly important to enhance reliability and resiliency in the Industry 4.0 framework. In this work, we investigate different methods for anomaly detection on in-production manufacturing machines taking into account their variability, both in operation and in wear conditions. We demonstrate how the nature of the available data, featuring any anomaly or not, is of importance for the algorithmic choice, discussing both statistical machine learning methods and control charts. We finally develop methods for automatic anomaly detection, which obtain a recall close to one on our data. Our developed methods are designed not to rely on a continuous recalibration and hand-tuning by the machine user, thereby allowing their deployment in an in-production environment robustly and efficiently.
Rotating Focused Field Eddy-Current Sensing for Arbitrary Orientation Defects Detection in Carbon Steel
Authors: Xu Zhiyuan , Wang Xiang , Deng Yiming
Published: April 2020

This paper presents a rotating focused field eddy-current (EC) sensing technique, which leverages the advantages of magnetic field focusing and rotating magnetic field, for arbitrary orientation defects detection. The sensor consists of four identical excitation coils orthogonally arranged in an upside-down pyramid configuration and a giant magneto-resistive (GMR) detection element. The four coils are connected to form two figure-8-shaped focusing sub-probes, which are fed by two identical harmonic currents with 90 degrees phase difference. A finite element model-based study of arbitrary orientation defects detection was performed to understand the probe operational characteristics and optimize its design parameters. Probe prototyping and experimental validation were also carried out on a carbon steel plate specimen with four prefabricated surface-breaking defects. In-situ spot inspection with the probe rotating above the defect and a manual line-scan inspection were both conducted. Results showed that the probe has the capability of detecting defects with any orientations while maintaining the same sensitivity and the defect depth can be quantitatively evaluated by using the signal amplitude. Compared with the existing rotating field probes, the presented probe does not require additional excitation adjustment or data fusion. Meanwhile, due to its focusing effect, it can generate a strong rotating magnetic field at the defect location with a weak background noise, thus yielding superior signal-to-noise ratio.
Antimalarial Drugs in Ghana: A Case Study on Personal Preferences
Authors: Yeboah Prince , Forkuo Arnold Donkor , Amponsah Obed Kwabena Offe , Adomako Nana Ofori , Abdin Ahmad Yaman , Nasim Muhammad Jawad , Werner Pitsch , Panyin Anto Berko , Emrich Eike , Jacob Claus
Published: April 2020

Malaria is a serious infection affecting millions of people in Africa. Our study investigated the personal preferences and applications of antimalarial medicines in Ghana. Based on over 1000 questionnaires distributed in Ghana from January to May 2019, we noticed that although Western medications to fight this disease are widely available, most patients in Ghana prefer treatment with locally produced herbal remedies. This preference appears to be due to a combination of traditional venues for obtaining medicines “on the street” rather than in licensed pharmacies, trust in local and “green” products, extensive advertisement of such local products, and an inherent distrust of imported and synthetic or unnatural medicines. Going local and natural is a trend also observed in other countries across the globe, and adds to the acceptance or rejection of drugs regardless of their activity or toxicity. In fact, adverse side effects associated with herbal remedies, such as general weakness and swollen, sore mouth, do not seem to deter the respondents of this study in Ghana. We propose a combination of (a) increasing public awareness of the benefits of modern medicine and (b) an improvement and control of the quality of herbal remedies to raise the standard of malaria treatment in countries such as Ghana.
From Coffee to Biodiesel—Deep Eutectic Solvents for Feedstock and Biodiesel Purification
Authors: Sander Aleksandra , Petračić Ana , Parlov Vuković Jelena , Husinec Lana
Published: April 2020

Over three billion cups of coffee are consumed daily, making waste coffee grounds readily available throughout the world. Containing approximately 10–15 wt% of oil, they have great potential for biodiesel production. The goal of this work was to produce high quality biodiesel from waste coffee grounds. One fresh and four different types of waste coffee grounds were collected. Oil was extracted by the Soxhlet method with n-hexane and then purified via extractive deacidification with a potassium carbonate-based deep eutectic solvent. Biodiesels were synthesized by means of alkali catalyzed transesterification at different catalyst:methanol:oil mass ratios and reaction times. Impurities present in crude biodiesels were extracted with a choline chloride-based deep eutectic solvent. All batch extraction experiments were performed at room conditions in a small scale extractor. Optimal conditions for synthesis and purification were defined in order to assure high quality of the produced biodiesel. Additionally, continuous column extraction with the choline chloride-based solvent was tested as a purification method for crude biodiesel. Stabilization time and optimal biodiesel to solvent mass ratio were determined. The potassium carbonate-based solvent efficiently reduced the total acid number of the feedstock (deacidification efficiency ranged from 86.18 to 94.15%), while the one based on choline chloride removed free glycerol and glycerides from crude biodiesels. After continuous purification, the purified biodiesel was of excellent quality with glycerol and glyceride contents below the EN 14214 limit.
Characterization of Turmeric and Curry Samples by Liquid Chromatography with Spectroscopic Detection Based on Polyphenolic and Curcuminoid Contents
Authors: Vidal-Casanella Oscar , Nuñez Nerea , Sentellas Sonia , Núñez Oscar , Saurina Javier
Published: April 2020

This paper deals with the characterization of turmeric and related products using the compositional fingerprints of curcuminoids (e.g., curcumin, demethoxycurcumin and bisdemethoxycurcumin) and other phenolic compounds (e.g., hydroxybenzoic and hydroxycinnamic acids and flavonoids) as the source of analytical information. Under this approach, the quantitative determination of analytes becomes unnecessary and even data from unknown components can be advantageously exploited for sample exploration and authentication. The methodology relied on sample extraction with hydro-organic solvents to recover the components of interest and further analysis of the corresponding extracts by liquid chromatography with diode array detection (HPLC-DAD). Extraction conditions were optimized focusing on the independent recovery of curcuminoids and polyphenols. Two different HPLC methods under reversed-phase mode were used to generate the chromatographic fingerprints at 420 and 280 nm for the specific monitoring of curcuminoids and polyphenols, respectively. Both extraction and separation steps were optimized under experimental design approaches to achieve the richest compositional fingerprints in terms of variety of components. The resulting data was subsequently treated chemometrically by principal component analysis (PCA) and related classification methods to achieve a better overall description of samples. Polyphenolic fingerprints were appropriate to discriminate among turmeric and mixed spices, while curcuminoid fingerprints could be useful to distinguish turmeric varieties.
Reliability of Change of Direction and Agility Assessments in Youth Soccer Players
Authors: Dugdale James H. , Sanders Dajo , Hunter Angus M.
Published: April 2020

Considering the vast physical and neural developments experienced throughout adolescence, the reliability of physical performance may vary in youth populations. This study aimed to examine the reliability of change of direction (COD) and agility tests in youth soccer players. Altogether, 86 youth soccer players, aged 13.6 ± 2.0 years, volunteered to participate. Data were collected from a modified 505 COD test (m505COD) and the Y-sprint drill in both pre-planned (Y-SprintPRE) and reactive (Y-SprintREACT) conditions during 2 sessions, 7 days apart. Anthropometric data including body mass, standing stature, and sitting height were also collected. COD and agility tests demonstrated good reliability (ICC = 0.81–0.91; CV = 1.2–2.0; d = 0.00–0.31; p < 0.01) for our entire sample. However, we observed a small negative relationship between age and intersession differences for the Y-SprintPRE (r = −0.28; p = 0.04), and moderate negative relationships between both age (r = −0.41; p < 0.01), and maturity offset (r = −0.39; p < 0.01) for the Y-SprintREACT. Although the COD and agility tests adopted within this study possess good intersession reliability, we observed greater intersession differences for younger and less mature individuals. We suggest that while COD and agility tests may provide meaningful objective data for monitoring the development of youth soccer players, these tests should be used with caution when evaluating younger, more immature athletes.
Nitrous Oxide Emissions and Methane Uptake from Organic and Conventionally Managed Arable Crop Rotations on Farms in Northwest Germany
Authors: Biernat Lars , Taube Friedhelm , Loges Ralf , Kluß Christof , Reinsch Thorsten
Published: April 2020

Land-use extensification by shifting from conventional to organic arable farming is often discussed as a measure for reducing greenhouse gas (GHG) emissions from agricultural land. Doubts about the benefits arise when emissions are calculated per product unit, particularly where high yields are possible under conventional management. Among the non-CO2 GHG emissions, nitrous oxide (N2O) is the main contributor from arable land and is controlled by soil type, environmental conditions and management. In order to investigate how land-use change from conventional to organic farming would perform under highly productive site conditions in northwest Germany, and how this would affect the important greenhouse gases N2O and methane (CH4), an on-farm field research was conducted over two experimental years. Two site-specific organic crop rotations, (i) with 25% legumes (grass + clover - winter wheat – winter rye – oats) and (ii) with 40% legumes (grass + clover – winter wheat – winter rye – spring field peas – winter rye), were compared with (iii) a conventional arable rotation (winter oilseed rape – winter wheat – winter wheat – sugar beet – winter wheat) and two reference systems, (iv) extensive grassland and (v) a beech forest), which were chosen as the baseline. The results showed that organic farming had lower N2O emissions of 0.7 N2O–N ha−1 year−1 than the conventional rotation, with 2.1 kg N2O–N ha−1 year−1 (p < 0.05), but higher emissions than the extensive grassland (0.3 kg N2O ha−1 year−1) and beech forest (0.4 kg N2O ha−1 year−1). CH4 emissions were a negligible part of total GHG emissions (as CO2 equivalents) in the two arable systems, and considerable uptake of CH4 from the forest soils showed this was a GHG sink in the first experimental year. Organic systems produced up to 40% lower crop yields, but the emissions per product unit in rotation (iii) was not superior to (ii) during the two experimental years. Thus, arable organic farming showed the ability to produce agricultural commodities with low N2O emissions per unit area, and no differences in product-related emissions compared with conventional farming. Conventional and organic systems both showed potential for further mitigation of N2O emissions by controlling the field level nitrogen surplus to a minimum, and by the optimized timing of the removal of the grass–clover ley phase.
Comprehensive Spatio-Temporal Analysis of Travel Climate Comfort Degree and Rainstorm-Flood Disaster Risk in the China–Russia Border Region
Authors: Zhou Yezhi , Wang Juanle , Grigorieva Elena , Egidarev Eugene , Zhang Wenxuan
Published: April 2020

Infrastructure and tourism is gradually increasing along the China–Russia border with the development of the China–Mongolia–Russia economic corridor. Facing the issues of thermal comfort and rainstorm-flood risk in the neighborhood area between China and Russia, we constructed homologous evaluation models to analyze spatial regularity and internal variations of their effect. Among the results, approximately 55% of the area was classified into the categories of “comfort” and “high comfort” in summer. Oppositely, the situation of most areas in winter corresponds to physical discomfort. On the other hand, the high-risk area of rainstorm-flood in spring and summer is principally located in the northern and southern regions, respectively, while this is further expanded in autumn. After that, the risk level turns to medium and low. Subsequently, a comprehensive assessment coordinate system of the two results was constructed to identify the distribution pattern of a seasonal suitable area for traveling in binary ways. The evaluation shows that Great Khingan Range in the north-western Heilongjiang province is the preferable place among most of seasons, especially in summer. While on the Russian side, the corresponding area is mainly spread over its southern coastal cities. The study is expected to provide recommendations for reasonable year-round travel time, space selection, and risk decision support for millions of people traveling between China and Russia.