bluuebunny/crossref_metadata_2025 huggingface dataset:
Salp Swarm Optimization Algorithm-Based Controller for Dynamic Response and Power Quality Enhancement of an Islanded Microgrid
Authors: Jumani Touqeer , Mustafa Mohd. , Md. Rasid Madihah , Anjum Waqas , Ayub Sara
Published: November 2019

The islanded mode of the microgrid (MG) operation faces more power quality challenges as compared to grid-tied mode. Unlike the grid-tied MG operation, where the voltage magnitude and frequency of the power system are regulated by the utility grid, islanded mode does not share any connection with the utility grid. Hence, a proper control architecture of islanded MG is essential to control the voltage and frequency, including the power quality and optimal transient response during different operating conditions. Therefore, this study proposes an intelligent and robust controller for islanded MG, which can accomplish the above-mentioned tasks with the optimal transient response and power quality. The proposed controller utilizes the droop control in addition to the back to back proportional plus integral (PI) regulator-based voltage and current controllers in order to accomplish the mentioned control objectives efficiently. Furthermore, the intelligence of the one of the most modern soft computational optimization algorithms called salp swarm optimization algorithm (SSA) is utilized to select the best combination of the PI gains (kp and ki) and dc side capacitance (C), which in turn ensures optimal transient response during the distributed generator (DG) insertion and load change conditions. Finally, to evaluate the effectiveness of the proposed control approach, its outcomes are compared with that of the previous approaches used in recent literature on basis of transient response measures, quality of solution and power quality. The results prove the superiority of the proposed control scheme over that of the particle swarm optimization (PSO) and grasshopper optimization algorithm (GOA) based MG controllers for the same operating conditions and system configuration.
Inhibition of Key Enzymes Linked to Obesity and Cytotoxic Activities of Whole Plant Extracts of Vernonia mesplilfolia Less
Authors: Unuofin Jeremiah Oshiomame , Otunola Gloria Aderonke , Afolayan Anthony Jide
Published: November 2019

The whole plant of Vernonia mespilifolia is widely used as a traditional remedy for obesity in South Africa. The aim of this study was to investigate the anti-obesity and cytotoxic effects of Vernonia mespilifolia extracts in vitro. The α-amylase, α-glucosidase, and lipase inhibitory activities of aqueous and ethanol extracts of Vernonia mespilifolia were investigated, while the cytotoxic effects of these extracts were analyzed using Hoechst 33342 and propidium iodide (PI) dual staining on a human cervical HeLa cell line. The results showed that the LC50 (the concentration of a material will kill 50% of test organisms) values of aqueous and ethanol extracts of Vernonia mespilifolia were >200 and 149 µg/mL, respectively, to HeLa cells. Additionally, the ethanol extract exhibited the strongest inhibitory effect on the pancreatic lipase (Half-maximal inhibitory concentration (IC50) = 331.16 µg/mL) and on α-amylase (IC50 = 781.72 µg/mL), while the aqueous extract has the strongest α-glucosidase (IC50 = 450.88 µg/mL). Our results suggest that Vernonia mespilifolia’s acclaimed anti-obesity effects could be ascribed to its ability to inhibit both carbohydrate and fat digesting enzymes.
Wind Power Short-Term Forecasting Hybrid Model Based on CEEMD-SE Method
Authors: Wang Keke , Niu Dongxiao , Sun Lijie , Zhen Hao , Liu Jian , De Gejirifu , Xu Xiaomin
Published: November 2019

Accurately predicting wind power is crucial for the large-scale grid-connected of wind power and the increase of wind power absorption proportion. To improve the forecasting accuracy of wind power, a hybrid forecasting model using data preprocessing strategy and improved extreme learning machine with kernel (KELM) is proposed, which mainly includes the following stages. Firstly, the Pearson correlation coefficient is calculated to determine the correlation degree between multiple factors of wind power to reduce data redundancy. Then, the complementary ensemble empirical mode decomposition (CEEMD) method is adopted to decompose the wind power time series to decrease the non-stationarity, the sample entropy (SE) theory is used to classify and reconstruct the subsequences to reduce the complexity of computation. Finally, the KELM optimized by harmony search (HS) algorithm is utilized to forecast each subsequence, and after integration processing, the forecasting results are obtained. The CEEMD-SE-HS-KELM forecasting model constructed in this paper is used in the short-term wind power forecasting of a Chinese wind farm, and the RMSE and MAE are as 2.16 and 0.39 respectively, which is better than EMD-SE-HS-KELM, HS-KELM, KELM and extreme learning machine (ELM) model. According to the experimental results, the hybrid method has higher forecasting accuracy for short-term wind power forecasting.
Effects of Processing Conditions on the Simultaneous Extraction and Distribution of Oil and Protein from Almond Flour
Authors: de Almeida Neiva M. , F. G. Dias Fernanda , Rodrigues Maria I. , L. N. de Moura Bell Juliana M.
Published: November 2019

The enzyme-assisted aqueous extraction process (EAEP) is an environmentally friendly strategy that simultaneously extracts oil and protein from several food matrices. The aim of this study was to investigate the effects of pH (6.5–9.5), temperature (45–55 °C), solids-to-liquid ratio (SLR) (1:12–1:8), and amount of enzyme (0.5–1.0%) on the extraction and separation of oil and protein from almond flour using a fractional factorial design. Oil and protein extraction yields from 61 to 75% and 64 to 79% were achieved, respectively. Experimental conditions resulting in higher extractability were subsequently replicated for validation of the observed effects. Oil and protein extraction yields of 75 and 72% were achieved under optimized extraction conditions (pH 9.0, 50 °C, 1:10 SLR, 0.5% (w/w) of enzyme, 60 min). Although the use of enzyme during the extraction did not lead to significant increase in extraction yields, it did impact the extracted protein functionality. The use of enzyme and alkaline pH (9.0) during the extraction resulted in the production of more soluble peptides at low pH (5.0), highlighting possible uses of the EAEP skim protein in food applications involving acidic pH. The implications of the use of enzyme during the extraction regarding the de-emulsification of the EAEP cream warrant further investigation.
Silencing and Oblivion of Psychological Trauma, Its Unconscious Aspects, and Their Impact on the Inflation of Vajrayāna. An Analysis of Cross-Group Dynamics and Recent Developments in Buddhist Groups Based on Qualitative Data
Authors: Anders Anne Iris Miriam
Published: November 2019

The commercialization of Buddhist philosophy has led to decontextualization and indoctrinating issues across groups, as well as abuse and trauma in that context. Methodologically, from an interdisciplinary approach, based on the current situation in international Buddhist groups and citations of victims from the ongoing research, the psychological mechanisms of rationalizing and silencing trauma were analyzed. The results show how supposedly Buddhist terminology and concepts are used to rationalize and justify economic, psychological and physical abuse. This is discussed against the background of psychological mechanisms of silencing trauma and the impact of ignoring the unconscious in that particular context. Inadequate consideration regarding the teacher–student relationship, combined with an unreflective use of Tibetan honorary titles and distorted conceptualizations of methods, such as the constant merging prescribed in so-called 'guru yoga', resulted in giving up self-responsibility and enhanced dependency. These new concepts, commercialized as 'karma purification' and 'pure view', have served to rationalize and conceal abuse, as well as to isolate the victims. Therefore, we are facing societal challenges, in terms of providing health and economic care to the victims and implementing preventive measures. This use of language also impacts on scientific discourse and Vajrayāna itself, and will affect many future generations.
Training for the “Unknown and Unknowable”: CrossFit and Evangelical Temporality
Authors: Musselman Cody
Published: November 2019

This article looks at the relationship between the U.S. military and CrossFit, a functional fitness training method and sport, and focuses on how their affinities coalesce around the idea of preparedness. CrossFit makes a sport and spectacle out of preparing for the “unknown and unknowable” challenges of life. This approach to life and fitness is attractive to service members, first responders, and average citizens alike who live in an age of constant anticipation, awaiting unknown threats. This article draws from fieldwork observations, interviews, CrossFit videos and articles, social media posts, and discussion board threads to argue that CrossFit, with its emphasis on preparedness, exhibits an evangelical temporality that is particularly symbiotic with American militarism. This article introduces two new terms, “evangelical temporality” and “generic evangelicalism,” to discuss a disposition towards time marked by a sense of expectation; by the anticipation of rupture and change that necessitates a state of constant preparedness; and by a firm conviction that time is running out. In three acts, this article explores how CrossFit, as a militaristic sport and a lifestyle centered on preparedness, benefits from and adds to the prevailing sense of uncertainty, expectation, and preparation that characterizes evangelical temporality in America.
“Hmm, Did You Hear What I Just Said?”: Development of a Re-Engagement System for Socially Interactive Robots
Authors: Cao Hoang-Long , Torrico Moron Paola Cecilia , Esteban Pablo G. , De Beir Albert , Bagheri Elahe , Lefeber Dirk , Vanderborght Bram
Published: November 2019

Maintaining engagement is challenging in human–human interaction. When disengagements happen, people try to adapt their behavior with an expectation that engagement will be regained. In human–robot interaction, although socially interactive robots are engaging, people can easily drop engagement while interacting with robots. This paper proposes a multi-layer re-engagement system that applies different strategies through human-like verbal and non-verbal behaviors to regain user engagement, taking into account the user’s attention level and affective states. We conducted a usability test in a robot storytelling scenario to demonstrate technical operation of the system as well as to investigate how people react when interacting with a robot with re-engagement ability. Our usability test results reveal that the system has the potential to maintain a user’s engagement. Our selected users gave positive comments, through open-ended questions, to the robot with this ability. They also rated the robot with the re-engagement ability higher on several dimensions, i.e., animacy, likability, and perceived intelligence.
Extended D-TomoSAR Displacement Monitoring for Nanjing (China) City Built Structure Using High-Resolution TerraSAR/TanDEM-X and Cosmo SkyMed SAR Data
Authors: Chen Fulong , Zhou Wei , Chen Caifen , Ma Peifeng
Published: November 2019

The availability of high-resolution spaceborne synthetic aperture radar (SAR) data coupled with the ongoing refinement of tomographic SAR (TomoSAR) technology has made use of radar data feasible for preventive monitoring and assessment of built structures. In this study, we first applied extended differential TomoSAR (D-TomoSAR) to a set of 26 scenes of TerraSAR/TanDEM-X (TSX/TDX) (2013–2015) and 32 scenes of Cosmo-SkyMed (CSK) (2015–2017) images to estimate motions of skyscrapers, bridges and historical monuments in Nanjing City, China. The calculation and isolation of unknown parameters in the D-TomoSAR model, including linear velocity, thermal dynamics and structural heights, helped to estimate millimetric statistics of motion time series. Then, aforementioned two SAR datasets were tentatively tested using amplitude dispersion and phase stability indicators, highlighting the performance and sensitivity of X-band SAR in structural displacement monitoring. Experimental results demonstrated that motion indexes, e.g., heterogeneities of thermal amplitudes and spatiotemporal displacements, were useful to evaluate the conditions of built structures being monitored, in particular when their structural topology were visible owing to the enhanced density of persistent scatterer (PS) measurements. This study implies the value of high-resolution D-TomoSAR tools in the preventive monitoring and health diagnosis of built structures elsewhere over the world.
Mapping Succession in Non-Forest Habitats by Means of Remote Sensing: Is the Data Acquisition Time Critical for Species Discrimination?
Authors: Osińska-Skotak Katarzyna , Radecka Aleksandra , Piórkowski Hubert , Michalska-Hejduk Dorota , Kopeć Dominik , Tokarska-Guzik Barbara , Ostrowski Wojciech , Kania Adam , Niedzielko Jan
Published: November 2019

The process of secondary succession is one of the most significant threats to non-forest (natural and semi-natural open) Natura 2000 habitats in Poland; shrub and tree encroachment taking place on abandoned, low productive agricultural areas, historically used as pastures or meadows, leads to changes to the composition of species and biodiversity loss, and results in landscape transformations. There is a perceived need to create a methodology for the monitoring of vegetation succession by airborne remote sensing, both from quantitative (area, volume) and qualitative (plant species) perspectives. This is likely to become a very important issue for the effective protection of natural and semi-natural habitats and to advance conservation planning. A key variable to be established when implementing a qualitative approach is the remote sensing data acquisition date, which determines the developmental stage of trees and shrubs forming the succession process. It is essential to choose the optimal date on which the spectral and geometrical characteristics of the species are as different from each other as possible. As part of the research presented here, we compare classifications based on remote sensing data acquired during three different parts of the growing season (spring, summer and autumn) for five study areas. The remote sensing data used include high-resolution hyperspectral imagery and LiDAR (Light Detection and Ranging) data acquired simultaneously from a common aerial platform. Classifications are done using the random forest algorithm, and the set of features to be classified is determined by a recursive feature elimination procedure. The results show that the time of remote sensing data acquisition influences the possibility of differentiating succession species. This was demonstrated by significant differences in the spatial extent of species, which ranged from 33.2% to 56.2% when comparing pairs of maps, and differences in classification accuracies, which when expressed in values of Cohen’s Kappa reached ~0.2. For most of the analysed species, the spring and autumn dates turned out to be slightly more favourable than the summer one. However, the final recommendation for the data acquisition time should take into consideration the phenological cycle of deciduous species present within the research area and the abiotic conditions.
Mapping and Monitoring Fractional Woody Vegetation Cover in the Arid Savannas of Namibia Using LiDAR Training Data, Machine Learning, and ALOS PALSAR Data
Authors: Wessels Konrad , Mathieu Renaud , Knox Nichola , Main Russell , Naidoo Laven , Steenkamp Karen
Published: November 2019

Namibia is a very arid country, which has experienced significant bush encroachment and associated decreased livestock productivity. Therefore, it is essential to monitor bush encroachment and widespread debushing activities, including selective bush thinning and complete bush clearing. The aim of study was to develop a system to map and monitor fractional woody cover (FWC) at national scales (50 m and 75 m resolution) using Synthetic Aperture Radar (SAR) satellite data (Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) global mosaics, 2009, 2010, 2015, 2016) and ancillary variables (mean annual precipitation—MAP, elevation), with machine learning models that were trained with diverse airborne Light Detection and Ranging (LiDAR) data sets (244,032 ha, 2008–2014). When only the SAR variables were used, an average R2 of 0.65 (RSME = 0.16) was attained. Adding either elevation or MAP, or both ancillary variables, increased the mean R2 to 0.75 (RSME = 0.13), and 0.79 (RSME = 0.12). The inclusion of MAP addressed the overestimation of FWC in very arid areas, but resulted in anomalies in the form of sharp gradients in FWC along a MAP contour which were most likely caused by to the geographic distribution of the LiDAR training data. Additional targeted LiDAR acquisitions could address this issue. This was the first attempt to produce SAR-derived FWC maps for Namibia and the maps contain substantially more detailed spatial information on woody vegetation structure than existing national maps. During the seven-year study period the Shrubland–Woodland Mosaic was the only vegetation structural class that exhibited a regional net gain in FWC of more than 0.2 across 9% (11,906 km2) of its area that may potentially be attributed to bush encroachment. FWC change maps provided regional insights and detailed local patterns related to debushing and regrowth that can inform national rangeland policies and debushing programs.
Fast Super-Resolution of 20 m Sentinel-2 Bands Using Convolutional Neural Networks
Authors: Gargiulo Massimiliano , Mazza Antonio , Gaetano Raffaele , Ruello Giuseppe , Scarpa Giuseppe
Published: November 2019

Images provided by the ESA Sentinel-2 mission are rapidly becoming the main source of information for the entire remote sensing community, thanks to their unprecedented combination of spatial, spectral and temporal resolution, as well as their associated open access policy. Due to a sensor design trade-off, images are acquired (and delivered) at different spatial resolutions (10, 20 and 60 m) according to specific sets of wavelengths, with only the four visible and near infrared bands provided at the highest resolution (10 m). Although this is not a limiting factor in general, many applications seem to emerge in which the resolution enhancement of 20 m bands may be beneficial, motivating the development of specific super-resolution methods. In this work, we propose to leverage Convolutional Neural Networks (CNNs) to provide a fast, upscalable method for the single-sensor fusion of Sentinel-2 (S2) data, whose aim is to provide a 10 m super-resolution of the original 20 m bands. Experimental results demonstrate that the proposed solution can achieve better performance with respect to most of the state-of-the-art methods, including other deep learning based ones with a considerable saving of computational burden.
Self-Powered Speed Sensor for Turbodrills Based on Triboelectric Nanogenerator
Authors: Wu Chuan , Fan Chenxing , Wen Guojun
Published: November 2019

Turbodrills play an important role in underground energy mining. The downhole rotational speed of turbodrills is one of the key parameters for controlling the drilling technology. Therefore, it is necessary to measure the rotational speed of the turbodrills in real time. However, there is no dedicated speed sensor for the working environment of turbodrills at present. Therefore, based on the working principle of triboelectric nanogenerator (TENG), a self-powered speed sensor which can measure the speed of the turbodrills is proposed in this study. Besides, since the sensor is self-powered, it can operate without power supply. According to the laboratory test results, the measurement error of the sensor is less than 5%. In addition, the self-powered performance of the sensor was also explored in this study. The test shows that the maximum generating voltage of the sensor is about 27 V, the maximum current is about 7 μA, the maximum power is about 2 × 10−4 W, and the generated electricity can supply power for ten LED (light-emitting diode), which not only meets the power supply requirements of the sensor itself, but also makes it possible to further power other underground instruments.
Development of Land-Use/Land-Cover Maps Using Landsat-8 and MODIS Data, and Their Integration for Hydro-Ecological Applications
Authors: Afrin Sadia , Gupta Anil , Farjad Babak , Ahmed M. , Achari Gopal , Hassan Quazi K.
Published: November 2019

The Athabasca River watershed plays a dominant role in both the economy and the environment in Alberta, Canada. Natural and anthropogenic factors rapidly changed the landscape of the watershed in recent decades. The dynamic of such changes in the landscape characteristics of the watershed calls for a comprehensive and up-to-date land-use and land-cover (LULC) map, which could serve different user-groups and purposes. The aim of the study herein was to delineate a 2016 LULC map of the Athabasca River watershed using Landsat-8 Operational Land Imager (OLI) images, Moderate Resolution Imaging Spectroradiometer (MODIS)-derived enhanced vegetation index (EVI) images, and other ancillary data. In order to achieve this, firstly, a preliminary LULC map was developed through applying the iterative self-organizing data analysis (ISODATA) clustering technique on 24 scenes of Landsat-8 OLI. Secondly, a Terra MODIS-derived 250-m 16-day composite of 30 EVI images over the growing season was employed to enhance the vegetation classes. Thirdly, several geospatial ancillary datasets were used in the post-classification improvement processes to generate a final 2016 LULC map of the study area, exhibiting 14 LULC classes. Fourthly, an accuracy assessment was carried out to ensure the reliability of the generated final LULC classes. The results, with an overall accuracy and Cohen’s kappa of 74.95% and 68.34%, respectively, showed that coniferous forest (47.30%), deciduous forest (16.76%), mixed forest (6.65%), agriculture (6.37%), water (6.10%), and developed land (3.78%) were the major LULC classes of the watershed. Fifthly, to support the data needs of scientists across various disciplines, data fusion techniques into the LULC map were performed using the Alberta merged wetland inventory 2017 data. The results generated two useful maps applicable for hydro-ecological applications. Such maps depicted two specific categories including different types of burned (approximately 6%) and wetland (approximately 30%) classes. In fact, these maps could serve as important decision support tools for policy-makers and local regulatory authorities in the sustainable management of the Athabasca River watershed.
Full Digital Processing System of Photoelectric Encoder
Authors: Zhao Changhai , Wan Qiuhua , Liang Lihui , Sun Ying
Published: November 2019

A photoelectric signal, output by a photoelectric receiver, may detrimentally change after the photoelectric encoder is used for a period of time or when the environment changes; this will directly affect the accuracy of the encoder and lead to fatal errors in the encoder. To maintain its high accuracy, we propose an encoder that can work in a variety of environments and that adopts full digital processing. A signal current that travels from the receiver of a photoelectric encoder is converted into a voltage signal via current limiting resistance. All signals are directly processed in the data processor component of the system. The encoder converts all the signals into its normalized counterpart. Then, the angle of the encoder is calculated using the normalized value. The calculated encoder angle compensates for any error. The final encoder angle is obtained, and the encoder angle is output accordingly. Experiments show that this method can greatly reduce the encoder’s volume. This method also reduces the encoder error from 167 arcseconds to 53 arcseconds. The encoder can still maintain a high accuracy during environmental changes, especially in harsh environments where there are higher accuracy requirements.
An Eco-Friendly Disposable Plasmonic Sensor Based on Bacterial Cellulose and Gold
Authors: Cennamo Nunzio , Trigona Carlo , Graziani Salvatore , Zeni Luigi , Arcadio Francesco , Di Pasquale Giovanna , Pollicino Antonino
Published: November 2019

In several application fields, plasmonic sensor platforms combined with bio-receptors are intensively used to obtain biosensors. Most of these commercial devices are based on a disposable chip. Usually a gold chip, functionalized with a specific bio-receptor, inside a costly sensor system, is used. In this work, we propose a low-cost and small-size sensor system, used for monitoring a disposable plasmonic chip, based on an innovative optical waveguide made of bacterial cellulose (BC). In particular, we have sputtered gold on the green slab waveguide that is able to excite localized surface plasmon resonance (LSPR). Experimental results are presented on the capabilities of using the BC-based composite as an eco-friendly plasmonic sensor platform, which could be exploited for realizing disposable biosensors. The sensor has been used with optical fibers and simple equipment. More specifically, the fibers connect the green disposable LSPR sensor with a light source and with a spectrometer. The novel plasmonic sensing approach has been tested using two different optical waveguide configurations of BC, with and without ions inside BC.
Temperature Sensor Assisted Lifetime Enhancement of Satellite Embedded Systems via Multi-Core Task Mapping and DVFS
Authors: Kim Beomsik , Yang Hoeseok
Published: November 2019

Recently, thanks to the miniaturization and high performance of commercial-off-the-shelf (COTS) computer systems, small satellites get popular. However, due to the very expensive launching cost, it is critical to reduce the physical size and weight of the satellite systems such as cube satellites (CubeSats), making it infeasible to install high capacity batteries or solar panels. Thus, the low-power design is one of the most critical issues in the design of such systems. In addition, as satellites make a periodic revolution around the Earth in a vacuum, their operating temperature varies greatly. For instance, in a low earth orbit (LEO) CubeSats, the temperatures vary from 30 to −30 degrees Celsius, resulting in a big thermal cycle (TC) in the electronic parts that is known to be one of the most critical reliability threats. Moreover, such LEO CubeSats are not fully protected by active thermal control and thermal insulation due to the cost, volume, and weight problems. In this paper, we propose to utilize temperature sensors to maximize the lifetime reliability of the LEO satellite systems via multi-core mapping and dynamic voltage and frequency scaling (DVFS) under power constraint. As conventional reliability enhancement techniques primarily focus on reducing the temperature, it may cause enlarged TCs, making them even less reliable. On the contrary, we try to maintain the TC optimal in terms of reliability with respect to the given power constraint. Experimental evaluation shows that the proposed technique improves the expected lifetime of the satellite embedded systems by up to 8.03 times in the simulation of Nvidia’s Jetson TK1.
Flexible UHF RFID Tag for Blood Tubes Monitoring
Authors: El Khamlichi Mohamed , Alvarez Melcon Alejandro , El Mrabet Otman , Ennasar Mohammed Ali , Hinojosa Juan
Published: November 2019

Low-cost and flexible radio frequency identification (RFID) tag for automatic identification, tracking, and monitoring of blood products is in great demand by the healthcare industry. A robust performance to meet security and traceability requirements in the different blood sample collection and analysis centers is also required. In this paper, a novel low-cost and flexible passive RFID tag is presented for blood sample collection tubes. The tag antenna is based on two compact symmetrical capacitive structures and works at the ultra-high frequency (UHF) European band (865 MHz–868 MHz). The tag antenna is designed considering the whole dielectric parameters such as the blood, substrate and tube. In this way, it operates efficiently in the presence of blood, which has high dielectric permittivity and loss. Measurement results of the proposed tag have confirmed simulation results. The measured performance of the tag shows good matching in the desired frequency band, leading to reading ranges up to 2.2 m, which is 4.4 times higher than typical commercial tags. The potential of this tag as a sensor to monitor the amount of blood contained in clinic tubes is also demonstrated. It is expected that the proposed tag can be useful and effective in future RFID systems to introduce security and traceability in different blood sample collection and analysis centers.
Real-Time Visual Tracking with Variational Structure Attention Network
Authors: Kim Yeongbin , Shin Joongchol , Park Hasil , Paik Joonki
Published: November 2019

Online training framework based on discriminative correlation filters for visual tracking has recently shown significant improvement in both accuracy and speed. However, correlation filter-base discriminative approaches have a common problem of tracking performance degradation when the local structure of a target is distorted by the boundary effect problem. The shape distortion of the target is mainly caused by the circulant structure in the Fourier domain processing, and it makes the correlation filter learn distorted training samples. In this paper, we present a structure–attention network to preserve the target structure from the structure distortion caused by the boundary effect. More specifically, we adopt a variational auto-encoder as a structure–attention network to make various and representative target structures. We also proposed two denoising criteria using a novel reconstruction loss for variational auto-encoding framework to capture more robust structures even under the boundary condition. Through the proposed structure–attention framework, discriminative correlation filters can learn robust structure information of targets during online training with an enhanced discriminating performance and adaptability. Experimental results on major visual tracking benchmark datasets show that the proposed method produces a better or comparable performance compared with the state-of-the-art tracking methods with a real-time processing speed of more than 80 frames per second.
Microservice Security Agent Based On API Gateway in Edge Computing
Authors: Xu Rongxu , Jin Wenquan , Kim Dohyeun
Published: November 2019

Internet of Things (IoT) devices are embedded with software, electronics, and sensors, and feature connectivity with constrained resources. They require the edge computing paradigm, with modular characteristics relying on microservices, to provide an extensible and lightweight computing framework at the edge of the network. Edge computing can relieve the burden of centralized cloud computing by performing certain operations, such as data storage and task computation, at the edge of the network. Despite the benefits of edge computing, it can lead to many challenges in terms of security and privacy issues. Thus, services that protect privacy and secure data are essential functions in edge computing. For example, the end user’s ownership and privacy information and control are separated, which can easily lead to data leakage, unauthorized data manipulation, and other data security concerns. Thus, the confidentiality and integrity of the data cannot be guaranteed and, so, more secure authentication and access mechanisms are required to ensure that the microservices are exposed only to authorized users. In this paper, we propose a microservice security agent to integrate the edge computing platform with the API gateway technology for presenting a secure authentication mechanism. The aim of this platform is to afford edge computing clients a practical application which provides user authentication and allows JSON Web Token (JWT)-based secure access to the services of edge computing. To integrate the edge computing platform with the API gateway, we implement a microservice security agent based on the open-source Kong in the EdgeX Foundry framework. Also to provide an easy-to-use approach with Kong, we implement REST APIs for generating new consumers, registering services, configuring access controls. Finally, the usability of the proposed approach is demonstrated by evaluating the round trip time (RTT). The results demonstrate the efficiency of the system and its suitability for real-world applications.
Hg2+ Optical Fiber Sensor Based on LSPR Generated by Gold Nanoparticles Embedded in LBL Nano-Assembled Coatings
Authors: Martínez-Hernández María Elena , Goicoechea Javier , Arregui Francisco J.
Published: November 2019

Mercury is an important contaminant since it is accumulated in the body of living beings, and very small concentrations are very dangerous in the long term. This paper reports the fabrication of a highly sensitive fiber optic sensor using the layer-by-layer nano-assembly technique with gold nanoparticles (AuNPs). The gold nanoparticles were obtained via a water-based synthesis route that use poly acrylic acid (PAA) as stabilizing agent, in the presence of a borane dimethylamine complex (DMAB) as reducing agent, giving PAA-capped AuNPs. The sensing mechanism is based on the alteration of the Localized Surface Plasmon Resonances (LSPR) generated by AuNPs thanks to the strong chemical affinity of metallic mercury towards gold, which lead to amalgam alloys.
Classification of Low Frequency Signals Emitted by Power Transformers Using Sensors and Machine Learning Methods
Authors: Jancarczyk Daniel , Bernaś Marcin , Boczar Tomasz
Published: November 2019

This paper proposes a method of automatically detecting and classifying low frequency noise generated by power transformers using sensors and dedicated machine learning algorithms. The method applies the frequency spectra of sound pressure levels generated during operation by transformers in a real environment. The spectra frequency interval and its resolution are automatically optimized for the selected machine learning algorithm. Various machine learning algorithms, optimization techniques, and transformer types were researched: two indoor type transformers from Schneider Electric and two overhead type transformers manufactured by ABB. As a result, a method was proposed that provides a way in which inspections of working transformers (from background) and their type can be performed with an accuracy of over 97%, based on the generated low-frequency noise. The application of the proposed preprocessing stage increased the accuracy of this method by 10%. Additionally, machine learning algorithms were selected which offer robust solutions (with the highest accuracy) for noise classification.
A Revisiting Method Using a Covariance Traveling Salesman Problem Algorithm for Landmark-Based Simultaneous Localization and Mapping
Authors: Ryu Hyejeong
Published: November 2019

This paper presents an efficient revisiting algorithm for landmark-based simultaneous localization and mapping (SLAM). To reduce SLAM uncertainty in terms of a robot’s pose and landmark positions, the method autonomously evaluates valuable landmarks for the data associations in the SLAM algorithm and selects positions to revisit by considering both landmark visibility and sensor measurement uncertainty. The optimal path among the selected positions is obtained by applying the traveling salesman problem (TSP) algorithm. To plan a path that reduces overall uncertainty, the cost matrix associated with the change in covariance between all selected positions of all pairs is applied for the TSP algorithm. From simulations, it is verified that the proposed method efficiently reduces and maintains SLAM uncertainty at the low level compared to the backtracking method.
Combination of an Axicon Fiber Tip and a Camera Device into a Sensitive Refractive Index Sensor
Authors: Tai Yi-Hsin , Tsai Po-Cheng , Ho Ya-Lun , Delaunay Jean-Jacques , Wei Pei-Kuen
Published: November 2019

An axicon fiber tip combined with a camera device is developed to sensitively detect refractive indexes in solutions. The transparent axicon tips were made by etching optical fibers through a wet end-etching method at room temperature. When the axicon fiber tip was immersed in various refractive index media, the angular spectrum of the emitted light from the axicon fiber tip was changed. Using a low numerical aperture lens to collect the directly transmitted light, a high intensity sensitivity was achieved when the tip cone angle was about 35 to 40 degrees. We combined the axicon fiber tip with a laser diode and a smartphone into a portable refractometer. The front camera of the smartphone was used to collect the light emitted from the axicon fiber tip. By analyzing the selected area of the captured images, the refractive index can be distinguished for various solutions. The refractive index sensitivity was up to 56,000%/RIU, and the detection limit was 1.79 × 10−5 RIU. By measuring the refractive index change via the axicon fiber tip, the concentration of different mediums can be sensitively detected. The detection limits of the measurement for sucrose solutions, saline solutions, and diluted wine were 8.86 × 10−3 °Bx, 0.12‰, and 0.35%, respectively.
Sensor Technologies for Caring People with Disabilities
Authors: García-Peñalvo Francisco José , Franco-Martín Manuel
Published: November 2019

Today, the population uses technology for every daily activity involving business, education, communication, entertainment, etc. Technology may also help us to take care of people who suffer some kind of disability. Complex technological ecosystems with pervasive and intelligent capabilities get along with us, facilitating the vigilance of those who need special attention or assisted living cares due to their health limitations. The advances in sensor research have enriched the powerful of these ecosystems to achieve more sophisticated monitoring and alarm systems, also taking into account the balance between the level of assistance and the people’s privacy. The Special Issue on “Sensor Technologies for Caring People with Disabilities” aims to present recent developments on sensor technologies for caring people with disabilities, focusing on the different configurations that can be used and novel applications in the field.
Review of Electrochemical DNA Biosensors for Detecting Food Borne Pathogens
Authors: Wu Qiaoyun , Zhang Yunzhe , Yang Qian , Yuan Ning , Zhang Wei
Published: November 2019

The vital importance of rapid and accurate detection of food borne pathogens has driven the development of biosensor to prevent food borne illness outbreaks. Electrochemical DNA biosensors offer such merits as rapid response, high sensitivity, low cost, and ease of use. This review covers the following three aspects: food borne pathogens and conventional detection methods, the design and fabrication of electrochemical DNA biosensors and several techniques for improving sensitivity of biosensors. We highlight the main bioreceptors and immobilizing methods on sensing interface, electrochemical techniques, electrochemical indicators, nanotechnology, and nucleic acid-based amplification. Finally, in view of the existing shortcomings of electrochemical DNA biosensors in the field of food borne pathogen detection, we also predict and prospect future research focuses from the following five aspects: specific bioreceptors (improving specificity), nanomaterials (enhancing sensitivity), microfluidic chip technology (realizing automate operation), paper-based biosensors (reducing detection cost), and smartphones or other mobile devices (simplifying signal reading devices).
Relationship between Cyclic and Non-Cyclic Force-Velocity Characteristics in BMX Cyclists
Authors: Gross Micah , Gross Thomy
Published: November 2019

Especially for bicycle motocross (BMX) cyclists, transfer of muscular force-velocity (Fv) characteristics between common strength training exercises and cycling is important. This study investigated the relationship between Fv characteristics in a common training exercise (squat jumps) and a sport-specific task (cycling) in high-level BMX racers by exploring the degree to which Fv and torque–cadence (Tc) characteristics correspond. Twelve BMX racers performed an Fv (multiple loaded squat jump) and two Tc tests (ramp starts and flat-ground sprints). Results revealed very large correlations between F 0 and T o r 0   s t a r t (r = 0.77) and between P m a x   j u m p and P m a x   s t a r t (r = 0.85). On the other hand, the relationships between v 0 and C a d 0   s t a r t (r = –0.25) and between S F v and S T c   s t a r t (r = –0.14) were small and negative. Similar results were observed for sprints. Based on dichotomous classifications (greater or less than group median), several discrepancies occurred, particularly for the profile slopes and high-speed variables. Thus, we recommend performing both jump-based and cycling-specific F v testing. Of additional note, T c characteristics on flat ground were similar to, but slightly different from those on the start ramp. Therefore, where possible, Tc tests should be carried out on a ramp.
Duty Factor Is a Viable Measure to Classify Spontaneous Running Forms
Authors: Patoz Aurélien , Gindre Cyrille , Thouvenot Adrien , Mourot Laurent , Hébert-Losier Kim , Lussiana Thibault
Published: November 2019

Runners were classified using two different methods based on their spontaneous running form: (1) subjectively using the V®score from the Volodalen® scale, leading to terrestrial and aerial groups; and (2) objectively using the duty factor (DF), leading to high (DFhigh) and low (DFlow) DF groups. This study aimed to compare these two classification schemes. Eighty-nine runners were divided in two groups using the V®score (VOL groups) and were also ranked according to their DF. They ran on a treadmill at 12 km·h−1 with simultaneous recording of running kinematics, using a three-dimensional motion capture system. DF was computed from data as the ratio of ground contact time to stride time. The agreement (95% confidence interval) between VOL and DF groups was 79.8% (69.9%, 87.6%), with relatively high sensitivity (81.6% (68.0%, 91.2%)) and specificity (77.5% (61.6%, 89.2%)). Our results suggest that the DF and V®score reflect similar constructs and lead to similar subgroupings of spontaneous running form (aerial runners if DF < 27.6% and terrestrial runners if DF > 28.8% at 12 km·h−1). These results suggest that DF could be a useful objective measure to monitor real-time changes in spontaneous running form using wearable technology. As a forward-looking statement, spontaneous changes in running form during racing or training could assist in identifying fatigue or changes in environmental conditions, allowing for a better understanding of runners.
A Comparison of Two Commercial Swim Bench Ergometers in Determining Maximal Aerobic Power and Correlation to a Paddle Test in a Recreational Surfing Cohort
Authors: Furness James , Bertacchini Linley , Hicklen Lisa , Monaghan Dane , Canetti Elisa , Climstein Mike
Published: November 2019

The recent addition of surfing to the Tokyo 2020 Olympic Games has fueled a surge in commercial and research interest in understanding the physiological demands of the sport. However, studies specific to maximal aerobic testing of surfers are scarce. Therefore, the primary aim of this study was to compare two commercially available swim bench (SWB) ergometers in the determination of maximal aerobic capacity in recreational surfers. A secondary aim was to correlate (independent of one another) the two ergometer findings of VO2peak to the time taken to complete a water-based 400-m paddle test. This cross-sectional study consisted of 17 recreational surfers aged between 18–58 years. Participants were randomized to either the SwimFast ergometer or VASA ergometer and tested for maximal aerobic capacity, followed by a 400-m paddle test. There were no significant differences between the two SWB ergometers in the determination of relative VO2peak (mean difference 0.33 mL/kg/min; 95% CI −1.24–1.90; p = 0.66). Correlations between VO2peak obtained from maximal paddling effort on the SwimFast and the VASA and the 400-m paddle test (total time (s)) showed a negative significant correlation r = −0.819, p = 0.024; r = −0.818, p = 0.024, respectively. Results suggest that either ergometer (SwimFast or VASA) can be used to determine peak aerobic capacity within a recreational surfing cohort. The significant correlation of the two SWB ergometers and the 400-m paddle test suggest that the 400-m paddle test may be a suitable field-based method of determining aerobic capability. Collectively, these preliminary findings provide initial evidence for similarities in VO2peak on two commercial ergometers and their correlations with a field-based test. However, further research is needed with a larger sample size and inclusive of competitive surfers to provide robust findings which can be generalized to the surfing population.
Communities’ Livelihood Vulnerability to Climate Variability in Ethiopia
Authors: Abeje Misganaw Teshager , Tsunekawa Atsushi , Haregeweyn Nigussie , Nigussie Zerihun , Adgo Enyew , Ayalew Zemen , Tsubo Mitsuru , Elias Asres , Berihun Daregot , Quandt Amy , Berihun Mulatu Liyew , Masunaga Tsugiyuki
Published: November 2019

Ethiopia has experienced more than 10 major drought episodes since the 1970s. Evidence has shown that climate change exacerbates the situation and presents a daunting challenge to predominantly rain-fed agricultural livelihoods. The aim of this study was to analyze the extent and sources of smallholder famers’ livelihood vulnerability to climate change/variability in the Upper Blue Nile basin. We conducted a household survey (n = 391) across three distinct agroecological communities and a formative composite index of livelihood vulnerability (LVI) was constructed. The Mann–Kendall test and the standard precipitation index (SPI) were employed to analyze trends of rainfall, temperature, and drought prevalence for the period from 1982 to 2016. The communities across watersheds showed a relative difference in the overall livelihood vulnerability index. Aba Gerima (midland) was found to be more vulnerable, with a score of 0.37, while Guder (highland) had a relatively lower LVI with a 0.34 index score. Given similar exposure to climate variability and drought episodes, communities’ livelihood vulnerability was mainly attributed to their low adaptive capacity and higher sensitivity indicators. Adaptive capacity was largely constrained by a lack of participation in community-based organizations and a lack of income diversification. This study will have practical implications for policy development in heterogeneous agroecological regions for sustainable livelihood development and climate change adaptation programs.