bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification of Vibration Events in Rotating Blades Using a Fiber Optical Tip Timing Sensor
Authors: Ye Dechao , Duan Fajie , Jiang Jiajia , Niu Guangyue , Liu Zhibo , Li Fangyi
Published: March 2019

The blade tip timing (BTT) technique has been widely used in rotation machinery for non-contact blade vibration measurements. As BTT data is under-sampled, it requires complicated algorithms to reconstruct vibration parameters. Before reconstructing the vibration parameters, the right data segment should first be extracted from the massive volumes of BTT data that include noise from blade vibration events. This step requires manual intervention, is highly dependent on the skill of the operator, and has also made it difficult to automate BTT technique applications. This article proposes an included angle distribution (IAD) correlation method between adjacent revolutions to identify blade vibration events automatically in real time. All included angles of the rotor between any two adjacent blades were accurately detected by only one fiber optical tip timing sensor. Three formulas for calculating IAD correlation were then proposed to identify three types of blade vibration events: the blades’ overall vibrations, vibration of the adjacent two blades, and vibration of a specific blade. Further, the IAD correlation method was optimized in the calculating process to reduce computation load when identifying every blade’s vibration events. The presented IAD correlation method could be used for embedded, real-time, and automatic processing applications. Experimental results showed that the proposed method could identify all vibration events in rotating blades, even small events which may be wrongly identified by skillful operators.
Automated Accelerometer-Based Gait Event Detection During Multiple Running Conditions
Authors: Benson Lauren C. , Clermont Christian A. , Watari Ricky , Exley Tessa , Ferber Reed
Published: March 2019

The identification of the initial contact (IC) and toe off (TO) events are crucial components of running gait analyses. To evaluate running gait in real-world settings, robust gait event detection algorithms that are based on signals from wearable sensors are needed. In this study, algorithms for identifying gait events were developed for accelerometers that were placed on the foot and low back and validated against a gold standard force plate gait event detection method. These algorithms were automated to enable the processing of large quantities of data by accommodating variability in running patterns. An evaluation of the accuracy of the algorithms was done by comparing the magnitude and variability of the difference between the back and foot methods in different running conditions, including different speeds, foot strike patterns, and outdoor running surfaces. The results show the magnitude and variability of the back-foot difference was consistent across running conditions, suggesting that the gait event detection algorithms can be used in a variety of settings. As wearable technology allows for running gait analyses to move outside of the laboratory, the use of automated accelerometer-based gait event detection methods may be helpful in the real-time evaluation of running patterns in real world conditions.
Investigation of Electrical Stability and Sensitivity of Electric Double Layer Gated Field-Effect Transistors (FETs) for miRNA Detection
Authors: Kuo Wen-Che , Sarangadharan Indu , Pulikkathodi Anil Kumar , Chen Po-Hsuan , Wang Shin-Li , Wu Chang-Run , Wang Yu-Lin
Published: March 2019

In this research, we developed a miRNA sensor using an electrical double layer (EDL) gated field-effect transistor (FET)-based biosensor with enhanced sensitivity and stability. We conducted an in-depth investigation of the mechanisms that give rise to fluctuations in the electrical signal, affecting the stability and sensitivity of the miRNA sensor. Firstly, surface characteristics were studied by examining the metal electrodes deposited using different metal deposition techniques. The lower surface roughness of the gold electrode improved the electrical current stability. The temperature and viscosity of the sample solution were proven to affect the electrical stability, which was attributed to reducing the effect of Brownian motion. Therefore, by controlling the test conditions, such as temperature and sample viscosity, and the surface characteristics of the metal electrodes, we can enhance the stability of the sensor. Metal electrodes deposited via sputtering and e-beam evaporator yielded the lowest signal fluctuation. When ambient temperature was reduced to 3 °C, the sensor had better noise characteristics compared to room temperature testing. Higher viscosity of samples resulted in lower signal fluctuations. Lastly, surface functionalization was demonstrated to be a critical factor in enhancing the stability and sensitivity. MiRNA sensors with higher surface ratios of immobilized DNA probes performed with higher sensitivity and stability. This study reveals methods to improve the characteristics of EDL FET biosensors to facilitate practical implementation in clinical applications.
Integrated Satellite, Unmanned Aerial Vehicle (UAV) and Ground Inversion of the SPAD of Winter Wheat in the Reviving Stage
Authors: Zhang Suming , Zhao Gengxing , Lang Kun , Su Baowei , Chen Xiaona , Xi Xue , Zhang Huabin
Published: March 2019

Chlorophyll is the most important component of crop photosynthesis, and the reviving stage is an important period during the rapid growth of winter wheat. Therefore, rapid and precise monitoring of chlorophyll content in winter wheat during the reviving stage is of great significance. The satellite-UAV-ground integrated inversion method is an innovative solution. In this study, the core region of the Yellow River Delta (YRD) is used as a study area. Ground measurements data, UAV multispectral and Sentinel-2A multispectral imagery are used as data sources. First, representative plots in the Hekou District were selected as the core test area, and 140 ground sampling points were selected. Based on the measured SPAD values and UAV multispectral images, UAV-based SPAD inversion models were constructed, and the most accurate model was selected. Second, by comparing satellite and UAV imagery, a reflectance correction for satellite imagery was performed. Finally, based on the UAV-based inversion model and satellite imagery after reflectance correction, the inversion results for SPAD values in multi-scale were obtained. The results showed that green, red, red-edge and near-infrared bands were significantly correlated with SPAD values. The modeling precisions of the best inversion model are R2 = 0.926, Root Mean Squared Error (RMSE) = 0.63 and Mean Absolute Error (MAE) = 0.92, and the verification precisions are R2 = 0.934, RMSE = 0.78 and MAE = 0.87. The Sentinel-2A imagery after the reflectance correction has a pronounced inversion effect; the SPAD values in the study area were concentrated between 40 and 60, showing an increasing trend from the eastern coast to the southwest and west, with obvious spatial differences. This study synthesizes the advantages of satellite, UAV and ground methods, and the proposed satellite-UAV-ground integrated inversion method has important implications for real-time, rapid and precision SPAD values collected on multiple scales.
Deep Convolutional Neural Network for Flood Extent Mapping Using Unmanned Aerial Vehicles Data
Authors: Gebrehiwot Asmamaw , Hashemi-Beni Leila , Thompson Gary , Kordjamshidi Parisa , Langan Thomas E.
Published: March 2019

Flooding is one of the leading threats of natural disasters to human life and property, especially in densely populated urban areas. Rapid and precise extraction of the flooded areas is key to supporting emergency-response planning and providing damage assessment in both spatial and temporal measurements. Unmanned Aerial Vehicles (UAV) technology has recently been recognized as an efficient photogrammetry data acquisition platform to quickly deliver high-resolution imagery because of its cost-effectiveness, ability to fly at lower altitudes, and ability to enter a hazardous area. Different image classification methods including SVM (Support Vector Machine) have been used for flood extent mapping. In recent years, there has been a significant improvement in remote sensing image classification using Convolutional Neural Networks (CNNs). CNNs have demonstrated excellent performance on various tasks including image classification, feature extraction, and segmentation. CNNs can learn features automatically from large datasets through the organization of multi-layers of neurons and have the ability to implement nonlinear decision functions. This study investigates the potential of CNN approaches to extract flooded areas from UAV imagery. A VGG-based fully convolutional network (FCN-16s) was used in this research. The model was fine-tuned and a k-fold cross-validation was applied to estimate the performance of the model on the new UAV imagery dataset. This approach allowed FCN-16s to be trained on the datasets that contained only one hundred training samples, and resulted in a highly accurate classification. Confusion matrix was calculated to estimate the accuracy of the proposed method. The image segmentation results obtained from FCN-16s were compared from the results obtained from FCN-8s, FCN-32s and SVMs. Experimental results showed that the FCNs could extract flooded areas precisely from UAV images compared to the traditional classifiers such as SVMs. The classification accuracy achieved by FCN-16s, FCN-8s, FCN-32s, and SVM for the water class was 97.52%, 97.8%, 94.20% and 89%, respectively.
Container Migration in the Fog: A Performance Evaluation
Authors: Puliafito Carlo , Vallati Carlo , Mingozzi Enzo , Merlino Giovanni , Longo Francesco , Puliafito Antonio
Published: March 2019

The internet of things (IoT) is essential for the implementation of applications and services that require the ability to sense the surrounding environment through sensors and modify it through actuators. However, IoT devices usually have limited computing capabilities and hence are not always sufficient to directly host resource-intensive services. Fog computing, which extends and complements the cloud, can support the IoT with computing resources and services that are deployed close to where data are sensed and actions need to be performed. Virtualisation is an essential feature in the cloud as in the fog, and containers have been recently getting much popularity to encapsulate fog services. Besides, container migration among fog nodes may enable several emerging use cases in different IoT domains (e.g., smart transportation, smart industry). In this paper, we first report container migration use cases in the fog and discuss containerisation. We then provide a comprehensive overview of the state-of-the-art migration techniques for containers, i.e., cold, pre-copy, post-copy, and hybrid migrations. The main contribution of this work is the extensive performance evaluation of these techniques that we conducted over a real fog computing testbed. The obtained results shed light on container migration within fog computing environments by clarifying, in general, which migration technique might be the most appropriate under certain network and service conditions.
Unobtrusive Mattress-Based Identification of Hypertension by Integrating Classification and Association Rule Mining
Authors: Liu Fan , Zhou Xingshe , Wang Zhu , Cao Jinli , Wang Hua , Zhang Yanchun
Published: March 2019

Hypertension is one of the most common cardiovascular diseases, which will cause severe complications if not treated in a timely way. Early and accurate identification of hypertension is essential to prevent the condition from deteriorating further. As a kind of complex physiological state, hypertension is hard to characterize accurately. However, most existing hypertension identification methods usually extract features only from limited aspects such as the time-frequency domain or non-linear domain. It is difficult for them to characterize hypertension patterns comprehensively, which results in limited identification performance. Furthermore, existing methods can only determine whether the subjects suffer from hypertension, but they cannot give additional useful information about the patients’ condition. For example, their classification results cannot explain why the subjects are hypertensive, which is not conducive to further analyzing the patient’s condition. To this end, this paper proposes a novel hypertension identification method by integrating classification and association rule mining. Its core idea is to exploit the association relationship among multi-dimension features to distinguish hypertensive patients from normotensive subjects. In particular, the proposed method can not only identify hypertension accurately, but also generate a set of class association rules (CARs). The CARs are proved to be able to reflect the subject’s physiological status. Experimental results based on a real dataset indicate that the proposed method outperforms two state-of-the-art methods and three common classifiers, and achieves 84.4%, 82.5% and 85.3% in terms of accuracy, precision and recall, respectively.
A Perspective of Non-Fiber-Optical Metamaterial and Piezoelectric Material Sensing in Automated Structural Health Monitoring
Authors: Annamdas Venu Gopal Madhav , Soh Chee Kiong
Published: March 2019

Metamaterials are familiar in life sciences, but are only recently adopted in structural health monitoring (SHM). Even though they have existed for some time, they are only recently classified as smart materials suitable for civil, mechanical, and aerospace (CMA) engineering. There are still not many commercialized metamaterial designs suitable for CMA sensing applications. On the other hand, piezoelectric materials are one of the popular smart materials in use for about 25 years. Both these materials are non-fiber-optical in nature and are robust to withstand the rugged CMA engineering environment, if proper designs are adopted. However, no single smart material or SHM technique can ever address the complexities of CMA structures and a combination of such sensors along with popular fiber optical sensors should be encouraged. Furthermore, the global demand for miniaturization of SHM equipment, automation and portability is also on the rise as indicated by several global marketing strategists. Recently, Technavio analysts, a well-known market research company estimated the global SHM market to grow from the current US $ 1.48 billion to US $ 3.38 billion by 2023, at a compound annual growth rate (CAGR) of 17.93%. The market for metamaterial is expected to grow rapidly at a CAGR of more than 22% and the market for piezoelectric materials is expected to accelerate at a CAGR of over 13%. At the same time, the global automation and robotics market in the automotive industry is expected to post a CAGR of close to 8%. The fusion of such smart materials along with automation can increase the overall market enormously. Thus, this invited review paper presents a positive perspective of these non-fiber-optic sensors, especially those made of metamaterial designs. Additionally, our recent work related to near field setup, a portable meta setup, and their functionalities along with a novel piezoelectric catchment sensor are discussed.
Minimum-Throughput Maximization for Multi-UAV-Enabled Wireless-Powered Communication Networks
Authors: Wu Fahui , Yang Dingcheng , Xiao Lin , Cuthbert Laurie
Published: March 2019

This paper considers a wireless-powered communication network (WPCN) system that uses multiple unmanned aerial vehicles (UAVs). Ground users (GUs) first harvest energy from a mobile wireless energy transfer (WET) UAV then use the energy to power their information transmission to a data gatherer (DG) UAV. We aim to maximize the minimum throughput for all GUs by jointly optimizing UAV trajectories, and the resource allocation of ET UAV and GUs. Because of the non-convexity of the formulated problem, we propose an alternating optimization algorithm, applying successive convex optimization techniques to solve the problem; the UAV trajectories and resource allocation are alternately optimized in each iteration. Numerical results show the efficiency of the proposed algorithm in different scenarios.
A Three-Hierarchy Evaluation of Polarimetric Performance of GF-3, Compared with ALOS-2/PALSAR-2 and RADARSAT-2
Authors: Wang Zezhong , Jiao Jian , Zeng Qiming , Liu Junyi
Published: March 2019

GaoFen-3 (GF-3) is the first Chinese civilian multi-polarization synthetic aperture radar (SAR) satellite, launched on 10 August of 2016, and put into operation at the end of January 2017. The polarimetric SAR (PolSAR) system of GF-3 is able to provide quad-polarization (quad-pol) images in a variety of geophysical research and applications. However, this ability increases the complexity of maintaining image quality and calibration. As a result, to evaluate the quality of polarimetric data, polarimetric signatures are necessary to guarantee accuracy. Compared with some other operational space-borne PolSAR systems, such as ALOS-2/PALSAR-2 (ALOS-2) and RADARSAT-2, GF-3 has less reported calibration and image quality files, forcing users to validate the quality of polarimetric imagery of GF-3 before quantitative applications. In this study, without the validation data obtained from a calibration infrastructure, an innovative, three-hierarchy strategy was proposed to assess PolSAR data quality, in which the performance of GF-3 data was evaluated with ALOS-2 and RADARSAT-2 data as references. Experimental results suggested that: (1) PolSAR data of GF-3 satisfied backscatter reciprocity, similar with that of RADARSAT-2; (2) most of the GF-3 PolSAR images had no signs of polarimetric distortion affecting decomposition, and the system of GF-3 may have been improved around May 2017; and (3) the classification accuracy of GF-3 varied from 75.0% to 91.4% because of changing image-acquiring situations. In conclusion, the proposed three-hierarchy approach has the ability to evaluate polarimetric performance. It proved that the residual polarimetric distortion of calibrated GF-3 PolSAR data remained at an insignificant level, with reference to that of ALOS-2 and RADARSAT-2, and imposed no significant impact on the polarimetric decomposition components and classification accuracy.
Analysis of Phase-Shift Pulse Brillouin Optical Time-Domain Reflectometry
Authors: Horiguchi Tsuneo , Masui Yuki , Zan Mohd Saiful Dzulkefly
Published: March 2019

Distributed strain and temperature can be measured by using local Brillouin backscatter in optical fibers based on the strain and temperature dependence of the Brillouin frequency shift. The technique of analyzing the local Brillion backscatter in the time domain is called Brillouin optical time domain reflectometry (BOTDR). Although the best spatial resolution of classic BOTDR remains at around 1 m, some recent BOTDR techniques have attained as high as cm-scale spatial resolution. Our laboratory has proposed and demonstrated a high-spatial-resolution BOTDR called phase-shift pulse BOTDR (PSP-BOTDR), using a pair of probe pulses modulated with binary phase-shift keying. PSP-BOTDR is based on the cross-correlation of Brillouin backscatter and on the subtraction of cross-correlations obtained from the Brillouin scatterings evoked by each phase-modulated probe pulse. Although PSP-BOTDR has attained 20-cm spatial resolution, the spectral analysis method of PSP-BOTDR has not been discussed in detail. This article gives in-depth analysis of the Brillouin backscatter and the correlations of the backscatters of the PSP-BOTDR. Based on the analysis, we propose new spectral analysis methods for PSP-BOTDR. The analysis and experiments show that the proposed methods give better frequency resolution than before.
Study of Flow Rate Measurements Derived from Temperature Profiles of an Emulated Well by a Laboratory Prototype
Authors: Silva Werbet L. A. , Lima Verivan S. , Fonseca Diego A. M. , Salazar Andrés O. , Maitelli Carla W. S. P. , Echaiz E. German A.
Published: March 2019

The objective of this work is the study of the procedure for flow rate measurement derived from temperature profiles. This method is deemed appropriate because of the inability to mount conventional flow meters in multiple-zone oil wells. In this work, a reduced-scale prototype well with horizontal geometry was developed to study and validate a method of flow profiling by temperature measurements in the well column based on a heat transfer mathematical model studied by Ramey in 1962. Flow sensors were installed at key points to provide validation data for the flow measurements derived from the temperature. The plant was automated and all the tests were managed from a workstation. It was possible to test different situations to provide a variability of evaluation scenarios. The initial experiments used injected fluid flow rates of 15 and 18 L/min in the well inlet. The results of the calculated flow values in different work conditions were compared with a relatively low error reference meter.
Enhancing Output Power of a Cantilever-Based Flapping Airflow Energy Harvester Using External Mechanical Interventions
Authors: Wang Liuqing , Zhu Dibin
Published: March 2019

This paper presents a flapping airflow energy harvester based on oscillations of a horizontal cantilever beam facing the direction of airflow. A wing is attached to the free end of a cantilever beam and a bluff body is placed in front of the wing from where vortex falls off, producing vortices under the wing and driving it to oscillate. An electromagnetic transducer is integrated to convert the flow induced vibration into electrical energy. This flapping energy harvester, however, may stop oscillating or vibrate in the second mode under high electrical damping, and thus may be unable to achieve its optimum performance. Simple yet effective mechanical interventions can be applied to the harvester to enhance its power output, i.e., to increase flow velocity and to apply external magnetic interaction. The effect of airflow velocities on output power was investigated experimentally and the results show that the energy harvester scavenges more power in airflow at higher Reynolds numbers (higher flow velocity at R e < 24,000). The external magnetic excitation is achieved though placing one magnet to the wing and another one above the wing to induce a repelling force, aiding the beam to oscillate in high electrical damping. Experimental results show that the power output can be enhanced by 30% when the magnet interaction is properly integrated.
Low-Cost Interrogation System for Long-Period Fiber Gratings Applied to Remote Sensing
Authors: dos Santos P.S.S. , Jorge P.A.S. , de Almeida José M.M.M. , Coelho L.
Published: March 2019

We present a portable and low-cost system for interrogation of long-period fiber gratings (LPFGs) costing around a 30th of the price of a typical setup using an optical spectrum analyzer and a broadband light source. The unit is capable of performing real-time monitoring or as a stand-alone data-logger. The proposed technique uses three thermally modulated fiber-coupled laser diodes, sweeping a few nanometers around their central wavelength. The light signal is then modulated by the LPFG and its intensity is acquired by a single photo-detector. Through curve-fitting algorithms the sensor transmission spectrum is reconstructed. Testing and validation were accomplished by inducing variations in the spectral features of an LPFG through changes either in external air temperature from 22 to 425 °C or in refractive index (RI) of the surrounding medium from 1.3000 to 1.4240. A dynamic resolution between 3.5 and 1.9 °C was achieved, in temperatures from 125 to 325 °C. In RI measurements, maximum wavelength and optical power deviations of 2.75 nm and 2.86 dB, respectively, were obtained in the range from 1530 to 1570 nm. The worse RI resolution obtained was 3.47 × 10 − 3 . The interrogation platform was then applied in the detection of iron corrosion, expressing wavelength peak values within 1.12 nm from the real value in the region between 1530 and 1570 nm.
A Novel Embeddable Tubular Piezoceramics-Based Smart Aggregate for Damage Detection in Two-Dimensional Concrete Structures
Authors: Gao Weihang , Li Hongnan , Ho Siu Chun Michael
Published: March 2019

Due to their multiple advantages, piezoceramic materials have been widely used in structural health monitoring (SHM). Piezoceramic patch-based smart aggregate (SA) and spherical piezoceramic-based smart aggregate (SSA) have been developed for damage detection of concrete structures. However, the stress waves generated by these two types of transducers are limited by their geometry and are unsuitable for use in two-dimensional concrete structures (e.g., shear walls, floors and cement concrete pavements). In this paper, a novel embeddable tubular smart aggregate (TSA) based on a piezoceramic tube was designed, fabricated and tested for use in two-dimensional (2D) structures. Due to its special geometry, radially uniform stress waves can be generated, and thus the TSA is suitable for damage detection in planar structures. The suitability of the transducer for use in structural health monitoring was investigated by characterizing the ability of the transducer to transmit and measure stress waves. Three experiments, including impedance analysis, time of arrival analysis and sweep frequency analysis, were conducted to test the proposed TSA. The experimental results show that the proposed TSA is suitable for monitoring the health condition of two-dimensional concrete structures.
A New Water Level Measurement Method Combining Infrared Sensors and Floats for Applications on Laboratory Scale Channel under Unsteady Flow Regime
Authors: Majdalani Samer , Chazarin Jean-Philippe , Moussa Roger
Published: March 2019

In this paper, we studied water transport under an unsteady flow regime in an experimental channel (4 m in length; 3 cm in width). Our experiments implicated some measuring requirements, specifically, a water level (WL) detection technique that is able to measure WL in a range of 2 cm with a precision of 1 mm. The existing WL detection techniques could not meet our measurement requirements. Therefore, we propose a new measurement method that combines two approaches: An “old” water contact technique (float) with a “new” remote non-contact technique (infrared sensor). We used an extruded polystyrene (XPS Foam) that needed some adequate treatment before using it as float in experimental measurements. The combination of IR-sensors with treated float foam lead to a sensitive measurement method that is able to detect flat and sharp flow signals, as well as highly dynamic variations of water surface level. Based on the experimental measurements of WL and outflow at the channel output, we deduced a loop rating curve that is suitable with a power law adjustment. The new measurement method could be extended to larger scale applications like rivers and more complicated cross section geometry of irregular shape.
Soil Moisture Retrieval by Integrating TASI-600 Airborne Thermal Data, WorldView 2 Satellite Data and Field Measurements: Petacciato Case Study
Authors: Palombo Angelo , Pascucci Simone , Loperte Antonio , Lettino Antonio , Castaldi Fabio , Muolo Maria Rita , Santini Federico
Published: March 2019

Soil moisture (SM) plays a fundamental role in the terrestrial water cycle and in agriculture, with key applications such as the monitoring of crop growing and hydrogeological management. In this study, a calibration procedure was applied to estimate SM based on the integration of in situ and airborne thermal remote sensing data. To this aim, on April 2018, two airborne campaigns were carried out with the TASI-600 multispectral thermal sensor on the Petacciato (Molise, Italy) area. Simultaneously, soil samples were collected in different agricultural fields of the study area to determine their moisture content and the granulometric composition. A WorldView 2 high-resolution visible-near infrared (VNIR) multispectral satellite image was acquired to calculate the albedo of the study area to be used together with the TASI images for the estimation of the apparent thermal inertia (ATI). Results show a good correlation (R2 = 0.62) between the estimated ATI and the SM of the soil samples measured in the laboratory. The proposed methodology has allowed us to obtain a SM map for bare and scarcely vegetated soils in a wide agricultural area in Italy which concerns cyclical hydrogeological instability phenomena.
Comparison of Selected Machine Learning Algorithms for Industrial Electrical Tomography
Authors: Rymarczyk Tomasz , Kłosowski Grzegorz , Kozłowski Edward , Tchórzewski Paweł
Published: March 2019

The main goal of this work was to compare the selected machine learning methods with the classic deterministic method in the industrial field of electrical impedance tomography. The research focused on the development and comparison of algorithms and models for the analysis and reconstruction of data using electrical tomography. The novelty was the use of original machine learning algorithms. Their characteristic feature is the use of many separately trained subsystems, each of which generates a single pixel of the output image. Artificial Neural Network (ANN), LARS and Elastic net methods were used to solve the inverse problem. These algorithms have been modified by a corresponding increase in equations (multiply) for electrical impedance tomography using the finite element method grid. The Gauss-Newton method was used as a reference to machine learning methods. The algorithms were trained using learning data obtained through computer simulation based on real models. The results of the experiments showed that in the considered cases the best quality of reconstructions was achieved by ANN. At the same time, ANN was the slowest in terms of both the training process and the speed of image generation. Other machine learning methods were comparable with the deterministic Gauss-Newton method and with each other.
Biosensors for Epilepsy Management: State-of-Art and Future Aspects
Authors: Tiwari Shivani , Sharma Varsha , Mujawar Mubarak , Mishra Yogendra Kumar , Kaushik Ajeet , Ghosal Anujit
Published: March 2019

Epilepsy is a serious neurological disorder which affects every aspect of patients’ life, including added socio-economic burden. Unfortunately, only a few suppressive medicines are available, and a complete cure for the disease has not been found yet. Excluding the effectiveness of available therapies, the timely detection and monitoring of epilepsy are of utmost priority for early remediation and prevention. Inability to detect underlying epileptic signatures at early stage causes serious damage to the central nervous system (CNS) and irreversible detrimental variations in the organ system. Therefore, development of a multi-task solving novel smart biosensing systems is urgently required. The present review highlights advancements in state-of-art biosensing technology investigated for epilepsy diseases diagnostics and progression monitoring or both together. State of art epilepsy biosensors are composed of nano-enabled smart sensing platform integrated with micro/electronics and display. These diagnostics systems provide bio-information needed to understand disease progression and therapy optimization timely. The associated challenges related to the development of an efficient epilepsy biosensor and vision considering future prospects are also discussed in this report. This review will serve as a guide platform to scholars for understanding and planning of future research aiming to develop a smart bio-sensing system to detect and monitor epilepsy for point-of-care (PoC) applications.
A 100-km BOFDA Assisted by First-Order Bi-Directional Raman Amplification
Authors: Kapa Thomas , Schreier Andy , Krebber Katerina
Published: March 2019

We present, to our knowledge for the first time, a 100-km Brillouin Optical Frequency-Domain Analysis (BOFDA) employing a 200-km fiber loop. Compared to our previous publication, enhanced sensor length, sensor accuracy and spatial resolution are presented. The performance improvements are achieved by applying distributed Raman amplification (DRA) and a digital high-pass filter. We report on temperature measurements over sensing distances of 75 km and 100 km both with a 12.5-m spatial resolution. Temperature changes of 5 ° C have been measured along 75 km sensing fiber. A temperature change of 30 ° C has been detected at 99.5 km.
Evaluation on Context Recognition Using Temperature Sensors in the Nostrils
Authors: Kodama Ryosuke , Terada Tsutomu , Tsukamoto Masahiko
Published: March 2019

We can benefit from various services with context recognition using wearable sensors. In this study, we focus on the contexts acquired from sensor data in the nostrils. Nostrils can provide various contexts on breathing, nasal congestion, and higher level contexts including psychological and health states. In this paper, we propose a context recognition method using the information in the nostril. We develop a system to acquire the temperature in the nostrils using small temperature sensors connected to glasses. As a result of the evaluations, the proposed system can detect breathing correctly, workload at an accuracy of 96.4%, six behaviors at an accuracy of 54%, and eight behaviors in daily life at an accuracy of 86%. Moreover, the proposed system can detect nasal congestion, therefore, it can log nasal cycles that are considered to have a relationship with the autonomic nerves and/or biological states.
SEVIRI Hyper-Fast Forward Model with Application to Emissivity Retrieval
Authors: Masiello Guido , Serio Carmine , Venafra Sara , Poutier Laurent , Göttsche Frank-M.
Published: March 2019

Timely processing of observations from multi-spectral imagers, such as SEVIRI (Spinning Enhanced Visible and Infrared Imager), largely depends on fast radiative transfer calculations. This paper mostly concerns the development and implementation of a new forward model for SEVIRI to be applied to real time processing of infrared radiances. The new radiative transfer model improves computational time by a factor of ≈7 compared to the previous versions and makes it possible to process SEVIRI data at nearly real time. The new forward model has been applied for the retrieval of surface parameters. Although the scheme can be applied for the simultaneous retrieval of temperature and emissivity, the paper mostly focuses on emissivity. The inverse scheme relies on a Kalman filter approach, which allows us to exploit a sequential processing of SEVIRI observations. Based on the new forward model, the paper also presents a validation retrieval performed with in situ observations acquired during a field experiment carried out in 2017 at Gobabeb (Namib desert) validation station. Furthermore, a comparison with IASI (Infrared Atmospheric Sounder Interferometer) emissivity retrievals has been performed as well. It has been found that the retrieved emissivities are in good agreement with each other and with in situ observations, i.e., average differences are generally well below 0.01.
Robust Wireless Sensor and Actuator Networks for Networked Control Systems
Authors: Park Bongsang , Nah Junghyo , Choi Jang-Young , Yoon Ick-Jae , Park Pangun
Published: March 2019

The stability guarantee of wireless networked control systems is still challenging due to the complex interaction among the layers and the vulnerability to network faults, such as link and node failures. In this paper, we propose a robust wireless sensor and actuator network (R-WSAN) to maintain the control stability of multiple plants over the spatial-temporal changes of wireless networks. The proposed joint design protocol combines the distributed controller of control systems and the clustering, resource scheduling, and control task sharing scheme of wireless networks over a hierarchical cluster-based network. In particular, R-WSAN decouples the tasks from the inherently unreliable nodes and allows control tasks to share between nodes of wireless networks. Our simulations demonstrate that R-WSAN provides the enhanced resilience to the network faults for sensing and actuation without significantly disrupting the control performance.
Front-Inner Lens for High Sensitivity of CMOS Image Sensors
Authors: Seok Godeun , Kim Yunkyung
Published: March 2019

Due to the continuing improvements in camera technology, a high-resolution CMOS image sensor is required. However, a high-resolution camera requires that the pixel pitch is smaller than 1.0 μm in the limited sensor area. Accordingly, the optical performance of the pixel deteriorates with the aspect ratio. If the pixel depth is shallow, the aspect ratio is enhanced. Also, optical performance can improve if the sensitivity in the long wavelengths is guaranteed. In this current work, we propose a front-inner lens structure that enhances the sensitivity to the small pixel size and the shallow pixel depth. The front-inner lens was located on the front side of the backside illuminated pixel for enhancement of the absorption. The proposed structures in the 1.0 μm pixel pitch were investigated with 3D optical simulation. The pixel depths were 3.0, 2.0, and 1.0 μm. The materials of the front-inner lens were varied, including air and magnesium fluoride (MgF2). For analysis of the sensitivity enhancement, we compared the typical pixel with the suggested pixel and confirmed that the absorption rate of the suggested pixel was improved by a maximum of 7.27%, 10.47%, and 29.28% for 3.0, 2.0, and 1.0 μm pixel depths, respectively.
Research on a Novel Fabry–Perot Interferometer Model Based on the Ultra-Small Gradient-Index Fiber Probe
Authors: Wang Chi , Sun Jianmei , Yang Chenye , Kuang Bin , Fang Dong , Asundi Anand
Published: March 2019

A novel Fabry–Perot (F–P) interferometer model based on the ultra-small gradient-index (GRIN) fiber probe is investigated. The signal arm of the F–P interferometer is organically combined with the ultra-small GRIN fiber probe to establish the theoretical model of the novel F–P interferometer. An interferometer experimental system for vibration measurements was built to measure the performance of the novel F–P interferometer system. The experimental results show that under the given conditions, the output voltage of the novel interferometer is 3.9 V at the working distance of 0.506 mm, which is significantly higher than the output voltage 0.48 V of the single-mode fiber (SMF) F–P interferometer at this position. In the range of 0.1–2 mm cavity length, the novel interferometer has a higher output voltage than an SMF F–P interferometer. Therefore, the novel F–P interferometer is available for further study of the precise measurement of micro vibrations and displacements in narrow spaces.
Chemical, Physical, and Hydraulic Properties as Affected by One Year of Miscanthus Biochar Interaction with Sandy and Loamy Tropical Soils
Authors: de Jesus Duarte Sara , Glaser Bruno , Paiva de Lima Renato , Pelegrino Cerri Carlos Eduardo
Published: March 2019

Biochar application has improved soil properties contributing to crop growth. This study evaluates the effect of biochar amount on soil physical, chemical and hydraulic properties in sandy (SD) and clay loam (CL) soils under tropical conditions. An incubation experiment was installed under laboratory conditions with eight treatments (control, two kinds of soils, SD and CL, and three biochar doses (6.25, 12.5, and 25 Mg ha−1). Analyses of soil water retention, bulk density (BD), total porosity (TP), pores size, total carbon (TC), and N were performed after one year. The BD slightly decreased by 0.035 and 0.062 Mg m−3 and TP increased by 1.87 and 2.31% in CL and SD soil respectively, upon 6.25 to 25 Mg ha−1 biochar application. TC increased in CL and SD by 6.5 and 4.2 kg kg−1, respectively, compared to control. The total nitrogen content increased upon biochar addition in CL soil than in SD soil. We found a positive effect of biochar on water availability, microporosity, and a small effect on water retention, especially for CL soil at high biochar application, but this influence did not occur for SD, possibly due to the short time of interaction.
The Effects of a Physically Active Lifestyle on the Health of Former Professional Football Players
Authors: Melekoğlu Tuba , Sezgin Erdi , Işın Ali , Türk Ayşen
Published: March 2019

The purpose of this investigation was to determine if a physically active lifestyle affects the health of former football players. Sixty former professional football players aged 40–50 years and who ended their sports career at least ten years ago were recruited for the study and grouped into two groups based on their physical activity habits after their retirement. Health and lifestyle characteristics were collected through a questionnaire to obtain information about recreational physical activity levels, diseases, family medical history, smoking, alcohol intake and dietary habits. Furthermore, lung functions, blood parameters and cardiovascular health were evaluated. Our results showed that body weight and body fat percentage were significantly higher in retired footballers who had a sedentary lifestyle compared to those who were physically active. The absolute and predicted values for forced expiratory volume in one-second values were higher in the active group. Twelve retired athletes were found to have intraventricular conduction delay. The findings suggest that former footballers who have higher levels of physical activity have advanced body composition, respiratory functions and serum lipids compared to former footballers with less active lifestyles. It is recommended that former elite athletes should maintain physically active lifestyles to sustain their health and reduce the risk of disease and disability in the later years of life.
Smart DAG Tasks Scheduling between Trusted and Untrusted Entities Using the MCTS Method
Authors: Cheng Yuxia , Wu Zhiwei , Liu Kui , Wu Qing , Wang Yu
Published: March 2019

Task scheduling is critical for improving system performance in the distributed heterogeneous computing environment. The Directed Acyclic Graph (DAG) tasks scheduling problem is NP-complete and it is hard to find an optimal schedule. Due to its key importance, the DAG tasks scheduling problem has been extensively studied in the literature. However, many previously proposed traditional heuristic algorithms are usually based on greedy methods and also lack the consideration of scheduling tasks between trusted and untrusted entities, which makes the problem more complicated, but there still exists a large optimization space to be explored. In this paper, we propose a trust-aware adaptive DAG tasks scheduling algorithm using the reinforcement learning and Monte Carlo Tree Search (MCTS) methods. The scheduling problem is defined using the reinforcement learning model. Efficient scheduling state space, action space and reward function are designed to train the policy gradient-based REINFORCE agent. The MCTS method is proposed to determine actual scheduling policies when DAG tasks are simultaneously executed in trusted and untrusted entities. Leveraging the algorithm’s capability of exploring long term reward, the proposed algorithm could achieve good scheduling policies while guaranteeing trusted tasks scheduled within trusted entities. Experimental results showed the effectiveness of the proposed algorithm compared with the classic HEFT/CPOP algorithms.
Nonproliferation and Security Implications of the Evolving Civil Nuclear Export Market
Authors: Nguyen Viet Phuong , Yim Man-Sung
Published: March 2019

In recent decades, the nuclear export market has observed a marked shift of demand from traditional customers in the Western world to Asia. The lack of projects in the United States, the delay in the French construction of advanced reactors, and the Fukushima accident in Japan have also led to the declining export capabilities of their companies. In contrast, Russia has gained numerous contracts, and China will likely become another major exporter. In this paper, the evolution of the market was examined from both the supply and demand sides with issues including the more concentrated and uncertain market, the lack of full participation by emerging suppliers to the nonproliferation regime, and the lesser governance capabilities of the newcomers. Addressing these issues, a range of policy suggestions was made, including the reinforcement of market shares of Western suppliers, the encouragement of newcomers to adhere to international norms, and a better safeguards contribution scheme.
Distribution Characteristics and Pollution Assessment of Soil Heavy Metals under Different Land-Use Types in Xuzhou City, China
Authors: Fang Aman , Dong Jihong , An Yingli
Published: March 2019

Xuzhou, as a mining city in China, has been experiencing 130 years of coal mining and processing. To explore the spatial distribution characteristics and pollution status of soil heavy metals (Cr, Cd, As, Hg, Zn, and Pb) under different land-use types, a total of 2697 topsoil samples were collected in all of the areas (except for water) of Xuzhou in 2016. Overall, the mean concentrations of Cr (70.266 mg/kg), Cd (0.141 mg/kg), As (10.375 mg/kg), Hg (0.036 mg/kg), Zn (64.788 mg/kg), and Pb (24.84 mg/kg) in Xuzhou soils were lower than the environmental quality standard for soils (GB15618-1995). However, the mean concentrations of Cr, Hg, and Pb exceeded their corresponding background values, with the mean concentration of Hg being almost three times its background value. For different land-use types, the highest mean concentration of Cr was concentrated in grassland soils. The mean concentrations of Cd, As, Zn, and Pb in mining area soils were higher than those in the other soils. The mean concentration of Hg was the highest in the built-up area soils. Based on the potential ecological risk assessment, the forestland, garden land, grassland, and others were at low and moderate risk levels, the farmland and mining area were at low, moderate, and high risk levels, and the built-up area was at various risk levels in Xuzhou. There was a significant positive correlation between Cr, Pb, and Hg concentrations and the corresponding organic carbon contents in the farmland, built-up area, garden land, forestland, and other soils ( p < 0.01 ). A high degree of correlation was found between Cr and Hg concentrations, as well as organic carbon contents in grassland soils, with values of p < 0.05 and p < 0.01 , respectively. An obvious correlation could be seen between Hg concentrations and organic carbon contents in mining area soils ( p < 0.01 ).