bluuebunny/crossref_metadata_2025 huggingface dataset:
Optical Sideband Injection Locking Using Waveguide Based External Cavity Semiconductor Lasers for Narrow-Line, Tunable Microwave Generation
Authors: Khan Md. Rezaul Hoque , Hoque Md. Ashraful
Published: July 2019

The generation by optical injection locking of spectrally unadulterated microwave signals using waveguide based external cavity semiconductor lasers (WECSL) is demonstrated. A tunable frequency of 2–11 GHz, limited by the modulator’s bandwidth and the photodetector (PD), was created as proof-of-experiment by the injection locking of the two WESCLs. A single sideband (SSB) phase noise of −75 dBc/Hz from the generated carrier at 10 kHz offset and a phase noise variance at an optimum injection ratio region was 0.03 rad2, corresponding to 1.7°, were observed. The main feature of this approach is the consolidation of the upsides of microwave generation at low phase noise with a broad tuning range and the capacity of hybrid photonic integration. In addition, the injection locking characteristics were used to determine the Q factor of the complicated optical cavities with unknown inner losses.
Separation of Protein-Binding Anthraquinones from Semen Cassiae Using Two-Stage Foam Fractionation
Authors: Ding Linlin , Wang Yanyan , Yue Chuanjun , Wu Zeying , Sun Ye , Wang Min , Li Rui
Published: July 2019

Anthraquinones are compounds of high medicinal value in many plants. Based on their good protein binding affinity, foam fractionation was attempted to separate them using proteins in the aqueous extract of Semen Cassiae as collectors. Firstly, the interaction between anthraquinones and Semen Cassiae proteins has been analyzed by the Stem–Volmer equation with physcion as a standard. The results show that physcion had good interaction with the proteins via hydrophobic forces. More importantly, the proteins effectively assisted the foam fractionation of several anthraquinones including aurantio-obtusifolin, aloe-emodin, rhein, emodin, chrysophanol, and physcion. On this basis, a two-stage foam fractionation technology was developed for process intensification using a foam fractionation with vertical sieve trays (VSTs). VSTs, initial feed concentration of total anthraquinones, temperature, volumetric air flow rate and pore diameter of gas distributor had significant effects on enrichment ratio and recovery yield of anthraquinones. Under suitable conditions, the enrichment ratio of total anthraquinones reached 47.0 ± 4.5 with a concentration of 939 ± 94 mg/L in the foamate while their total recovery percentage reached more than 47.7%. In addition, foam fractionation also increased the purity and hydroxyl radical scavenging activity of total anthraquinones. The results had significant implications for the separation of anthraquinones from Semen Cassiae.
Sustainable Green Procedure for Extraction of Hesperidin from Selected Croatian Mandarin Peels
Authors: Jokić Stela , Šafranko Silvija , Jakovljević Martina , Cikoš Ana-Marija , Kajić Nikolina , Kolarević Filip , Babić Jurislav , Molnar Maja
Published: July 2019

The peels of Citrus reticulata Blanco mandarin cultivars of different Croatian varieties (Zorica rana, Chahara, Okitsu, Kuno) were extracted using 15 different choline chloride-based deep eutectic solvents (DESs) at 50 °C for 30 min and with 20% water addition. The extracts were analyzed by high performance liquid chromatography with diode array detection (HPLC-DAD) to determine the most suitable DES for the extraction of hesperidin in the samples. The screening results indicated that choline chloride: acetamide (1:2) provided the most efficient hesperidin extraction (112.14 mg/g of plant), while choline chloride:citric acid (1:1) solvent showed the lowest hesperidin yield (1.44 mg/g of plant). The Box–Behnken design was employed to optimize extraction parameters for each variety of mandarin peel, including extraction time, temperature and water content on hesperidin extraction. The results indicated that hesperidin content in mandarin peels was completely variety-dependent. Being a novel and efficient green media for hesperidin extraction, deep eutectic solvents could also serve as promising solvent systems for the production of extracts rich in bioactive compounds.
Treasure Hunt—Roman Inquisition and Magical Practices Ad Inveniendos Thesauros in Southern Tuscany
Authors: Tedesco Vincenzo
Published: July 2019

Resorting to the supernatural to find something lost is a practice that can be observed over a very large range of times and places. With the affirmation of Christianity, these kinds of habits and beliefs were considered superstitious by the Church. During the early modern era, the institution appointed to control the integrity of the faithful in the Italian peninsula was the Supreme Sacred Congregation of the Roman and Universal Inquisition, which had a significant number of local tribunals spread over the territory. This essay aims to study the diffusion of the practice of finding treasures by using magical items and rituals in the area under the jurisdiction of the Sienese tribunal of the Holy Office (approximately the entire southern Tuscany), whose trial sources are preserved in the Archive of the Congregation for the Doctrine of the Faith (Vatican City). The research, based on around seventy individual cases, shows an interesting belief from a historical–anthropological point of view, namely: although in most cases people were looking for everyday objects that they had lost, sometimes, they used the same rituals to search for ancient treasures that they heard were buried or hidden in a particular place (church, field, or cellar), with the presence of guardians like spirits or demons, that had to be driven away with a prayer or an exorcism before taking possession of the treasure.
Low-Power and Low-Cost Environmental IoT Electronic Nose Using Initial Action Period Measurements
Authors: García-Orellana Carlos J. , Macías-Macías Miguel , González-Velasco Horacio M. , García-Manso Antonio , Gallardo-Caballero Ramón
Published: July 2019

In this work, we present a complete hardware development and current consumption study of a portable electronic nose designed for the Internet-of-Things (IoT). Thanks to the technique of measuring in the initial action period, it can be reliably powered with a moderate-sized battery. The system is built around the well-known SoC (System on Chip) ESP8266EX, using low-cost electronics and standard sensors from Figaro’s TGS26xx series. This SoC, in addition to a powerful microcontroller, provides Wi-Fi connectivity, making it very suitable for IoT applications. The system also includes a precision analog-to-digital converter for the measurements and a charging module for the lithium battery. During its operation, the designed software takes measurements periodically, and keeps the microcontroller in deep-sleep state most of the time, storing several measurements before uploading them to the cloud. In the experiments and tests carried out, we have focused our work on the measurement and optimization of current consumption, with the aim of extending the battery life. The results show that taking measurements every 4 min and uploading data every five measurements, the battery of 750 mAh needs to be charged approximately once a month. Despite the fact that we have used a specific model of gas sensor, this methodology is quite generic and could be extended to other sensors with lower consumption, increasing very significantly the duration of the battery.
On the Performance of Random Cognitive mmWave Sensor Networks
Authors: Song Yi , Yang Weiwei , Xiang Zhongwu , Wang Biao , Cai Yueming
Published: July 2019

This paper investigates the secrecy performance of a cognitive millimeter wave (mmWave) wiretap sensor network, where the secondary transmitter (SU-Tx) intends to communicate with a secondary sensor node under the interference temperature constraint of the primary sensor node. We consider that the random-location eavesdroppers may reside in the signal beam of the secondary network, so that confidential information can still be intercepted. Also, the interference to the primary network is one of the critical issues when the signal beam of the secondary network is aligned with the primary sensor node. Key features of mmWave networks, such as large number of antennas, variable propagation law and sensitivity to blockages, are taken into consideration. Moreover, an eavesdropper-exclusion sector guard zone around SU-Tx is introduced to improve the secrecy performance of the secondary network. By using stochastic geometry, closed-form expression for secrecy throughput (ST) achieved by the secondary sensor node is obtained to investigate secrecy performance. We also carry out the asymptotic analysis to facilitate the performance evaluation in the high transmit power region. Numerical results demonstrate that the interference temperature constraint of the primary sensor node enables us to balance secrecy performance of the secondary network, and provides interesting insights into how the system performance of the secondary network that is influenced by various system parameters: eavesdropper density, antenna gain and sector guard zone radius. Furthermore, blockages are beneficial to improve ST of the secondary sensor node under certain conditions.
Development of an Automatic Polarization Raman LiDAR for Aerosol Monitoring over Complex Terrain
Authors: Wang Longlong , Stanič Samo , Eichinger William , Song Xiaoquan , Zavrtanik Marko
Published: July 2019

High temporal and spatial resolution profiling of aerosol properties is required to study air pollution sources, aerosol transport, and features of atmospheric structures over complex terrain. A polarization Raman LiDAR with remote operation capability was developed for this purpose and deployed in the Vipava Valley, Slovenia, a location in the Alpine region where high concentrations of aerosols originating from a number of different local and remote sources were found. The system employs two high-power Nd:YAG pulsed lasers at 355 nm and 1064 nm as transmitters and provides the capability to extract the extinction coefficient, backscatter coefficients, depolarization ratio, Ångström exponent, and LiDAR ratio profiles. Automatized remote operation in an indoor environment provides a high duty cycle in all weather conditions. In addition to the detailed description of the device, an assessment of its potential and the retrieval uncertainties of the measured quantities is discussed. System optimization and performance studies include calibration of the depolarization ratio, merging of near-range (analog) and far-range (photon counting) data, determination of overlap functions, and validation of the retrieved observables with radiosonde data. Two cases for assessing LiDAR performance under specific weather conditions (during rain and in the presence of mineral dust) are also presented.
Erratum: Wang, Y. et al., A Privacy Preserving Scheme for Nearest Neighbor Query. Sensors 2018, 18, 2440
Authors: Wang Yuhang , Tian Zhihong , Zhang Hongli , Su Shen , Shi Wei
Published: July 2019

The authors wish to make the following erratum to this paper [...]
IoTDS: A One-Class Classification Approach to Detect Botnets in Internet of Things Devices
Authors: Bezerra Vitor Hugo , da Costa Victor Guilherme Turrisi , Barbon Junior Sylvio , Miani Rodrigo Sanches , Zarpelão Bruno Bogaz
Published: July 2019

Internet of Things (IoT) devices have become increasingly widespread. Despite their potential of improving multiple application domains, these devices have poor security, which can be explored by attackers to build large-scale botnets. In this work, we propose a host-based approach to detect botnets in IoT devices, named IoTDS (Internet of Things Detection System). It relies on one-class classifiers, which model only the legitimate device behaviour for further detection of deviations, avoiding the manual labelling process. The proposed solution is underpinned by a novel agent-manager architecture based on HTTPS, which prevents the IoT device from being overloaded by the training activities. To analyse the device’s behaviour, the approach extracts features from the device’s CPU utilisation and temperature, memory consumption, and number of running tasks, meaning that it does not make use of network traffic data. To test our approach, we used an experimental IoT setup containing a device compromised by bot malware. Multiple scenarios were made, including three different IoT device profiles and seven botnets. Four one-class algorithms (Elliptic Envelope, Isolation Forest, Local Outlier Factor, and One-class Support Vector Machine) were evaluated. The results show the proposed system has a good predictive performance for different botnets, achieving a mean F1-score of 94% for the best performing algorithm, the Local Outlier Factor. The system also presented a low impact on the device’s energy consumption, and CPU and memory utilisation.
Differential Microfluidic Sensors Based on Dumbbell-Shaped Defect Ground Structures in Microstrip Technology: Analysis, Optimization, and Applications
Authors: Vélez Paris , Muñoz-Enano Jonathan , Gil Marta , Mata-Contreras Javier , Martín Ferran
Published: July 2019

A microstrip defect ground structure (DGS) based on a pair of dumbbell-shaped slots is used for sensing. The device is a differential sensor consisting of a pair of mirrored lines loaded with a dumbbell-shaped DGS, and the output variable is the cross-mode transmission coefficient. Such a variable is very sensitive to asymmetries in the line pair, e.g., caused by an asymmetric dielectric load in the dumbbell-shaped DGSs. Therefore, the sensor is of special interest for the dielectric characterization of solids and liquids, or for the measurement of variables related to complex permittivity changes. It is shown in this work that by adding fluidic channels on top of the dumbbell-shaped DGSs, the device is useful for liquid characterization, particularly for the measurement of solute concentration in very diluted solutions. A sensitivity analysis useful for sensor design is carried out in this paper.
Differential Run-Length Encryption in Sensor Networks
Authors: Chianphatthanakit Chiratheep , Boonsongsrikul Anuparp , Suppharangsan Somjet
Published: July 2019

Energy is a main concern in the design and deployment of Wireless Sensor Networks because sensor nodes are constrained by limitations of battery, memory, and a processing unit. A number of techniques have been presented to solve this power problem. Among the proposed solutions, the data compression scheme is one that can be used to reduce the volume of data for transmission. This article presents a data compression algorithm called Differential Run Length Encryption (D-RLE) consisting of three steps. First, reading values are divided into groups by using a threshold of Chauvenet’s criterion. Second, each group is subdivided into subgroups whose consecutive member values are determined by a subtraction scheme under a K-RLE based threshold. Third, the member values are then encoded to binary based on our ad hoc scheme to compress the data. The experimental results show that the data rate savings by D-RLE can be up to 90% and energy usage can be saved more than 90% compared to data transmission without compression.
Spatio-Temporal Synchronization of Cross Section Based Sensors for High Precision Microscopic Traffic Data Reconstruction
Authors: Fazekas Adrian , Oeser Markus
Published: July 2019

The next generation of Intelligent Transportation Systems (ITS) will strongly rely on a high level of detail and coverage in traffic data acquisition. Beyond aggregated traffic parameters like the flux, mean speed, and density used in macroscopic traffic analysis, a continuous location estimation of individual vehicles on a microscopic scale will be required. On the infrastructure side, several sensor techniques exist today that are able to record the data of individual vehicles at a cross-section, such as static radar detectors, laser scanners, or computer vision systems. In order to record the position data of individual vehicles over longer sections, the use of multiple sensors along the road with suitable synchronization and data fusion methods could be adopted. This paper presents appropriate methods considering realistic scale and accuracy conditions of the original data acquisition. Datasets consisting of a timestamp and a speed for each individual vehicle are used as input data. As a first step, a closed formulation for a sensor offset estimation algorithm with simultaneous vehicle registration is presented. Based on this initial step, the datasets are fused to reconstruct microscopic traffic data using quintic Beziér curves. With the derived trajectories, the dependency of the results on the accuracy of the individual sensors is thoroughly investigated. This method enhances the usability of common cross-section-based sensors by enabling the deriving of non-linear vehicle trajectories without the necessity of precise prior synchronization.
Citrus Pests and Diseases Recognition Model Using Weakly Dense Connected Convolution Network
Authors: Xing Shuli , Lee Marely , Lee Keun-kwang
Published: July 2019

Pests and diseases can cause severe damage to citrus fruits. Farmers used to rely on experienced experts to recognize them, which is a time consuming and costly process. With the popularity of image sensors and the development of computer vision technology, using convolutional neural network (CNN) models to identify pests and diseases has become a recent trend in the field of agriculture. However, many researchers refer to pre-trained models of ImageNet to execute different recognition tasks without considering their own dataset scale, resulting in a waste of computational resources. In this paper, a simple but effective CNN model was developed based on our image dataset. The proposed network was designed from the aspect of parameter efficiency. To achieve this goal, the complexity of cross-channel operation was increased and the frequency of feature reuse was adapted to network depth. Experiment results showed that Weakly DenseNet-16 got the highest classification accuracy with fewer parameters. Because this network is lightweight, it can be used in mobile devices.
A Combined Offline and Online Algorithm for Real-Time and Long-Term Classification of Sheep Behaviour: Novel Approach for Precision Livestock Farming
Authors: Vázquez-Diosdado Jorge A. , Paul Veronica , Ellis Keith A , Coates David , Loomba Radhika , Kaler Jasmeet
Published: July 2019

Real-time and long-term behavioural monitoring systems in precision livestock farming have huge potential to improve welfare and productivity for the better health of farm animals. However, some of the biggest challenges for long-term monitoring systems relate to “concept drift”, which occurs when systems are presented with challenging new or changing conditions, and/or in scenarios where training data is not accurately reflective of live sensed data. This study presents a combined offline algorithm and online learning algorithm which deals with concept drift and is deemed by the authors as a useful mechanism for long-term in-the-field monitoring systems. The proposed algorithm classifies three relevant sheep behaviours using information from an embedded edge device that includes tri-axial accelerometer and tri-axial gyroscope sensors. The proposed approach is for the first time reported in precision livestock behavior monitoring and demonstrates improvement in classifying relevant behaviour in sheep, in real-time, under dynamically changing conditions.
DEM Generation from Fixed-Wing UAV Imaging and LiDAR-Derived Ground Control Points for Flood Estimations
Authors: Escobar Villanueva Jairo R. , Iglesias Martínez Luis , Pérez Montiel Jhonny I.
Published: July 2019

Geospatial products, such as digital elevation models (DEMs), are important topographic tools for tackling local flood studies. This study investigates the contribution of LiDAR elevation data in DEM generation based on fixed-wing unmanned aerial vehicle (UAV) imaging for flood applications. More specifically, it assesses the accuracy of UAV-derived DEMs using the proposed LiDAR-derived control point (LCP) method in a Structure-from-Motion photogrammetry processing. Also, the flood estimates (volume and area) of the UAV terrain products are compared with a LiDAR-based reference. The applied LCP-georeferencing method achieves an accuracy comparable with other studies. In addition, it has the advantage of using semi-automatic terrain data classification and is readily applicable in flood studies. Lastly, it proves the complementarity between LiDAR and UAV photogrammetry at the local level.
Adaptive Noise Reduction for Sound Event Detection Using Subband-Weighted NMF
Authors: Zhou Qing , Feng Zuren , Benetos Emmanouil
Published: July 2019

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). First, a scheme for noise dictionary learning from the input noisy signal is employed by the technique of robust NMF, which supports adaptation to noise variations. The estimated noise dictionary is used to develop a supervised source separation framework in combination with a pre-trained event dictionary. Second, to improve the separation quality, we extend the basic NMF model to a weighted form, with the aim of varying the relative importance of the different components when separating a target sound event from noise. With properly designed weights, the separation process is forced to rely more on those dominant event components, whereas the noise gets greatly suppressed. The proposed method is evaluated on a dataset of the rare sound event detection task of the DCASE 2017 challenge, and achieves comparable results to the top-ranking system based on convolutional recurrent neural networks (CRNNs). The proposed weighted NMF method shows an excellent noise reduction ability, and achieves an improvement of an F-score by 5%, compared to the unweighted approach.
Physical Unclonable Functions in the Internet of Things: State of the Art and Open Challenges
Authors: Babaei Armin , Schiele Gregor
Published: July 2019

Attacks on Internet of Things (IoT) devices are on the rise. Physical Unclonable Functions (PUFs) are proposed as a robust and lightweight solution to secure IoT devices. The main advantage of a PUF compared to the current classical cryptographic solutions is its compatibility with IoT devices with limited computational resources. In this paper, we investigate the maturity of this technology and the challenges toward PUF utilization in IoT that still need to be addressed.
Application of ECIS to Assess FCCP-Induced Changes of MSC Micromotion and Wound Healing Migration
Authors: Chiu Sheng-Po , Lee Yu-Wei , Wu Ling-Yi , Tung Tse-Hua , Gomez Sofia , Lo Chun-Min , Wang Jia-Yi
Published: July 2019

Electric cell-substrate impedance sensing (ECIS) is an emerging technique for sensitively monitoring morphological changes of adherent cells in tissue culture. In this study, human mesenchymal stem cells (hMSCs) were exposed to different concentrations of carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone (FCCP) for 20 h and their subsequent concentration-dependent responses in micromotion and wound healing migration were measured by ECIS. FCCP disrupts ATP synthesis and results in a decrease in cell migration rates. To detect the change of cell micromotion in response to FCCP challenge, time-series resistances of cell-covered electrodes were monitored and the values of variance were calculated to verify the difference. While Seahorse XF-24 extracellular flux analyzer can detect the effect of FCCP at 3 μM concentration, the variance calculation of the time-series resistances measured at 4 kHz can detect the effect of FCCP at concentrations as low as 1 μM. For wound healing migration, the recovery resistance curves were fitted by sigmoid curve and the hill slope showed a concentration-dependent decline from 0.3 μM to 3 μM, indicating a decrease in cell migration rate. Moreover, dose dependent incline of the inflection points from 0.3 μM to 3 μM FCCP implied the increase of the half time for wound recovery migration. Together, our results demonstrate that partial uncoupling of mitochondrial oxidative phosphorylation reduces micromotion and wound healing migration of hMSCs. The ECIS method used in this study offers a simple and sensitive approach to investigate stem cell migration and its regulation by mitochondrial dynamics.
Extracting Diameter at Breast Height with a Handheld Mobile LiDAR System in an Outdoor Environment
Authors: Zhou Sanzhang , Kang Feng , Li Wenbin , Kan Jiangming , Zheng Yongjun , He Guojian
Published: July 2019

Mobile laser scanning (MLS) is widely used in the mapping of forest environments. It has become important for extracting the parameters of forest trees using the generated environmental map. In this study, a three-dimensional point cloud map of a forest area was generated by using the Velodyne VLP-16 LiDAR system, so as to extract the diameter at breast height (DBH) of individual trees. The Velodyne VLP-16 LiDAR system and inertial measurement units (IMU) were used to construct a mobile measurement platform for generating 3D point cloud maps for forest areas. The 3D point cloud map in the forest area was processed offline, and the ground point cloud was removed by the random sample consensus (RANSAC) algorithm. The trees in the experimental area were segmented by the European clustering algorithm, and the DBH component of the tree point cloud was extracted and projected onto a 2D plane, fitting the DBH of the trees using the RANSAC algorithm in the plane. A three-dimensional point cloud map of 71 trees was generated in the experimental area, and estimated the DBH. The mean and variance of the absolute error were 0.43 cm and 0.50, respectively. The relative error of the whole was 2.27%, the corresponding variance was 15.09, and the root mean square error (RMSE) was 0.70 cm. The experimental results were good and met the requirements of forestry mapping, and the application value and significance were presented.
Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview
Authors: Sousa Lima Wesllen , Souto Eduardo , El-Khatib Khalil , Jalali Roozbeh , Gama Joao
Published: July 2019

The ubiquity of smartphones and the growth of computing resources, such as connectivity, processing, portability, and power of sensing, have greatly changed people’s lives. Today, many smartphones contain a variety of powerful sensors, including motion, location, network, and direction sensors. Motion or inertial sensors (e.g., accelerometer), specifically, have been widely used to recognize users’ physical activities. This has opened doors for many different and interesting applications in several areas, such as health and transportation. In this perspective, this work provides a comprehensive, state of the art review of the current situation of human activity recognition (HAR) solutions in the context of inertial sensors in smartphones. This article begins by discussing the concepts of human activities along with the complete historical events, focused on smartphones, which shows the evolution of the area in the last two decades. Next, we present a detailed description of the HAR methodology, focusing on the presentation of the steps of HAR solutions in the context of inertial sensors. For each step, we cite the main references that use the best implementation practices suggested by the scientific community. Finally, we present the main results about HAR solutions from the perspective of the inertial sensors embedded in smartphones.
The Effect of the Color Filter Array Layout Choice on State-of-the-Art Demosaicing
Authors: Stojkovic Ana , Shopovska Ivana , Luong Hiep , Aelterman Jan , Jovanov Ljubomir , Philips Wilfried
Published: July 2019

Interpolation from a Color Filter Array (CFA) is the most common method for obtaining full color image data. Its success relies on the smart combination of a CFA and a demosaicing algorithm. Demosaicing on the one hand has been extensively studied. Algorithmic development in the past 20 years ranges from simple linear interpolation to modern neural-network-based (NN) approaches that encode the prior knowledge of millions of training images to fill in missing data in an inconspicious way. CFA design, on the other hand, is less well studied, although still recognized to strongly impact demosaicing performance. This is because demosaicing algorithms are typically limited to one particular CFA pattern, impeding straightforward CFA comparison. This is starting to change with newer classes of demosaicing that may be considered generic or CFA-agnostic. In this study, by comparing performance of two state-of-the-art generic algorithms, we evaluate the potential of modern CFA-demosaicing. We test the hypothesis that, with the increasing power of NN-based demosaicing, the influence of optimal CFA design on system performance decreases. This hypothesis is supported with the experimental results. Such a finding would herald the possibility of relaxing CFA requirements, providing more freedom in the CFA design choice and producing high-quality cameras.
Detecting Anomalies of Satellite Power Subsystem via Stage-Training Denoising Autoencoders
Authors: Jin Weihua , Sun Bo , Li Zhidong , Zhang Shijie , Chen Zhonggui
Published: July 2019

Satellite telemetry data contains satellite status information, and ground-monitoring personnel need to promptly detect satellite anomalies from these data. This paper takes the satellite power subsystem as an example and presents a reliable anomaly detection method. Due to the lack of abnormal data, the autoencoder is a powerful method for unsupervised anomaly detection. This study proposes a novel stage-training denoising autoencoder (ST-DAE) that trains the features, in stages. This novel method has better reconstruction capabilities in comparison to common autoencoders, sparse autoencoders, and denoising autoencoders. Meanwhile, a cluster-based anomaly threshold determination method is proposed. In this study, specific methods were designed to evaluate the autoencoder performance in three perspectives. Experiments were carried out on real satellite telemetry data, and the results showed that the proposed ST-DAE generally outperformed the autoencoders, in comparison.
Moving Object Detection Based on Optical Flow Estimation and a Gaussian Mixture Model for Advanced Driver Assistance Systems
Authors: Cho Jaechan , Jung Yongchul , Kim Dong-Sun , Lee Seongjoo , Jung Yunho
Published: July 2019

Most approaches for moving object detection (MOD) based on computer vision are limited to stationary camera environments. In advanced driver assistance systems (ADAS), however, ego-motion is added to image frames owing to the use of a moving camera. This results in mixed motion in the image frames and makes it difficult to classify target objects and background. In this paper, we propose an efficient MOD algorithm that can cope with moving camera environments. In addition, we present a hardware design and implementation results for the real-time processing of the proposed algorithm. The proposed moving object detector was designed using hardware description language (HDL) and its real-time performance was evaluated using an FPGA based test system. Experimental results demonstrate that our design achieves better detection performance than existing MOD systems. The proposed moving object detector was implemented with 13.2K logic slices, 104 DSP48s, and 163 BRAM and can support real-time processing of 30 fps at an operating frequency of 200 MHz.
Experimental Validation of Gaussian Process-Based Air-to-Ground Communication Quality Prediction in Urban Environments
Authors: Ladosz Pawel , Kim Jongyun , Oh Hyondong , Chen Wen-Hua
Published: July 2019

This paper presents a detailed experimental assessment of Gaussian Process (GP) regression for air-to-ground communication channel prediction for relay missions in urban environment. Considering restrictions from outdoor urban flight experiments, a way to simulate complex urban environments at an indoor room scale is introduced. Since water significantly absorbs wireless communication signal, water containers are utilized to replace buildings in a real-world city. To evaluate the performance of the GP-based channel prediction approach, several indoor experiments in an artificial urban environment were conducted. The performance of the GP-based and empirical model-based prediction methods for a relay mission was evaluated by measuring and comparing the communication signal strength at the optimal relay position obtained from each method. The GP-based prediction approach shows an advantage over the model-based one as it provides a reasonable performance without a need for a priori information of the environment (e.g., 3D map of the city and communication model parameters) in dynamic urban environments.
Wireless, Portable Fiber Bragg Grating Interrogation System Employing Optical Edge Filter
Authors: Ogawa Ken , Koyama Shouhei , Haseda Yuuki , Fujita Keiichi , Ishizawa Hiroaki , Fujimoto Keisaku
Published: July 2019

A small-size, high-precision fiber Bragg grating interrogator was developed for continuous plethysmograph monitoring. The interrogator employs optical edge filters, which were integrated with a broad-band light source and photodetector to demodulate the Bragg wavelength shift. An amplifier circuit was designed to effectively amplify the plethysmograph signal, obtained as a small vibration of optical power on the large offset. The standard deviation of the measured Bragg wavelength was about 0.1 pm. The developed edge filter module and amplifier circuit were encased with a single-board computer and communicated with a laptop computer via Wi-Fi. As a result, the plethysmograph was clearly obtained remotely, indicating the possibility of continuous vital sign measurement.
SemanticDepth: Fusing Semantic Segmentation and Monocular Depth Estimation for Enabling Autonomous Driving in Roads without Lane Lines
Authors: Palafox Pablo R. , Betz Johannes , Nobis Felix , Riedl Konstantin , Lienkamp Markus
Published: July 2019

Typically, lane departure warning systems rely on lane lines being present on the road.However, in many scenarios, e.g., secondary roads or some streets in cities, lane lines are eithernot present or not sufficiently well signaled. In this work, we present a vision-based method tolocate a vehicle within the road when no lane lines are present using only RGB images as input.To this end, we propose to fuse together the outputs of a semantic segmentation and a monoculardepth estimation architecture to reconstruct locally a semantic 3D point cloud of the viewed scene.We only retain points belonging to the road and, additionally, to any kind of fences or walls thatmight be present right at the sides of the road. We then compute the width of the road at a certainpoint on the planned trajectory and, additionally, what we denote as the fence-to-fence distance.Our system is suited to any kind of motoring scenario and is especially useful when lane lines arenot present on the road or do not signal the path correctly. The additional fence-to-fence distancecomputation is complementary to the road’s width estimation. We quantitatively test our methodon a set of images featuring streets of the city of Munich that contain a road-fence structure, so asto compare our two proposed variants, namely the road’s width and the fence-to-fence distancecomputation. In addition, we also validate our system qualitatively on the Stuttgart sequence of thepublicly available Cityscapes dataset, where no fences or walls are present at the sides of the road,thus demonstrating that our system can be deployed in a standard city-like environment. For thebenefit of the community, we make our software open source.
Use of Computing Devices as Sensors to Measure Their Impact on Primary and Secondary Students’ Performance
Authors: Fernández-Soriano Francisco Luis , López Belén , Martínez-España Raquel , Muñoz Andrés , Cantabella Magdalena
Published: July 2019

The constant innovation in new technologies and the increase in the use of computing devices in different areas of the society have contributed to a digital transformation in almost every sector. This digital transformation has also reached the world of education, making it possible for members of the educational community to adopt Learning Management Systems (LMS), where the digital contents replacing the traditional textbooks are exploited and managed. This article aims to study the relationship between the type of computing device from which students access the LMS and how affects their performance. To achieve this, the LMS accesses of students in a school comprising from elementary to bachelor’s degree stages have been monitored by means of different computing devices acting as sensors to gather data such as the type of device and operating system used by the students.The main conclusion is that students who access the LMS improve significantly their performance and that the type of device and the operating system has an influence in the number of passed subjects. Moreover, a predictive model has been generated to predict the number of passed subjects according to these factors, showing promising results.
On Generalized Slash Distributions: Representation by Hypergeometric Functions
Authors: Zörnig Peter
Published: July 2019

The popular concept of slash distribution is generalized by considering the quotient Z = X/Y of independent random variables X and Y, where X is any continuous random variable and Y has a general beta distribution. The density of Z can usually be expressed by means of generalized hypergeometric functions. We study the distribution of Z for various parent distributions of X and indicate a possible application in finance.
Sustainable Supply Chain Management in the Automotive Industry: A Process-Oriented Review
Authors: Masoumi S. Maryam , Kazemi Nima , Abdul-Rashid Salwa Hanim
Published: July 2019

The holistic shift from traditional supply chain to sustainable supply chain has been practiced in different industries for many years. The automotive industry, as one of the largest and most influential industries in the world, could have a substantial effect on the movement toward a sustainable society. Despite the growing body of literature in the field of sustainable supply chain management, there is no review article that comprehensively synthesizes the state-of-the-art research in the automotive industry. To cover this gap, this paper reviews the sustainable supply chain management literature in the automotive industry published between 1995 and 2017. A systematic review and content analysis were conducted to collect the studies and analyze their content. The content analysis was structured based upon a set of key business processes following the Integration Definition Function (IDEF0) method, which is a structured approach of analyzing business processes. The study provides a practical guideline for designing a sustainable automotive supply chain and culminates with the outlined research gaps and recommendations for future research.
The Impact of Traffic Crashes on Urban Network Traffic Flow
Authors: Zeng Junwei , Qian Yongsheng , Wang Bingbing , Wang Tingjuan , Wei Xuting
Published: July 2019

This paper aims to investigate the impact of occasional traffic crashes on the urban traffic network flow. Toward this purpose, an extended model of coupled Nagel–Schreckenberg (NaSch) and Biham–Middleton–Levine (BML) models is presented. This extended model not only improves the initial conditions of the coupled models, but also gives the definition of traffic crashes and their spatial/time distribution. Further, we simulated the impact of the number of traffic crashes, their time distribution, and their spatial distribution on urban network traffic flow. This research contributes to the comprehensive understanding of the operational state of urban network traffic flow after traffic crashes, towards mastering the causes and propagation rules of traffic congestion. This work also a theoretical guidance value for the optimization of urban traffic network flow and the prevention and release of traffic crashes.
Identifying Factors Affecting the Quality of Teaching in Basic Science Education: Physics, Biological Sciences, Mathematics, and Chemistry
Authors: Cho Joonmo , Baek Wonyoung
Published: July 2019

Basic science education provides the most fundamental knowledge for preparing students to pursue departmental major courses. Considering that basic science courses are laboratory classes conducted alongside theory classes, the factors affecting instructor–student communication and feedback can vary between theory and laboratory classes. We applied the ordinary least squares model to the refined data of basic science courses. We drew on variables reflecting instructor–student interaction such as class size, type of subject, and instructor characteristics to analyze the factors affecting student satisfaction with theory and laboratory classes. The analysis results indicated that the educational environment of a large-sized class could be improved by subdividing it into smaller groups to facilitate feedback. The use of online platforms to supplement offline courses provides an additional mechanism for the exchange of feedback and positively affects student satisfaction. We also confirmed empirically that the instructor–student communication which takes place during laboratory work, in contrast to the one-sided conveyance of course materials by the instructor in lectures, was a crucial factor in the quality of education. These results are linked to the demand for knowledge in engineering education, the student’s educational performance, and the labor market performance needed to establish a sustainable system in engineering education.
Global Research on Carbon Emissions: A Scientometric Review
Authors: Udara Willhelm Abeydeera Lebunu Hewage , Wadu Mesthrige Jayantha , Samarasinghalage Tharushi Imalka
Published: July 2019

Greenhouse gases such as sulfur dioxide, nitrogen dioxide, and carbon dioxide have been recognized as the prime cause of global climate change, which has received significant global attention. Among these gases, carbon dioxide is considered as the prominent gas which motivated researchers to explore carbon reduction and mitigation strategies. Research work on this domain expands from carbon emission reporting to identifying and implementing carbon mitigation and reduction strategies. A comprehensive study to map global research on carbon emissions is, however, not available. Therefore, based on a scientometric analysis method, this study reviewed the global literature on carbon emissions. A total of 2945 bibliographic records, from 1981 to 2019, were extracted from the Web of Science core collection database and analyzed using techniques such as co-author and co-citation analysis. Findings revealed an increasing trend of publications in the carbon emission research domain, which has been more visible in the past few years, especially during 2016–2018. The most significant contribution to the domain was reported from China, the United States, and England. While most prolific authors and institutions of the domain were from China, authors and institutions from the United States reported the best connection links. It was revealed that evaluating greenhouse gas emissions and estimating the carbon footprint was popular among the researchers. Moreover, climate change and environmental effects of carbon emissions were also significant points of concern in carbon emission research. The key findings of this study will be beneficial for the policymakers, academics, and institutions to determine the future research directions as well as to identify with whom they can consult to assist in developing carbon emission control policies and future carbon reduction targets.
Individual-Specific Classification of Mental Workload Levels Via an Ensemble Heterogeneous Extreme Learning Machine for EEG Modeling
Authors: Tao Jiadong , Yin Zhong , Liu Lei , Tian Ying , Sun Zhanquan , Zhang Jianhua
Published: July 2019

In a human–machine cooperation system, assessing the mental workload (MW) of the human operator is quite crucial to maintaining safe operation conditions. Among various MW indicators, electroencephalography (EEG) signals are particularly attractive because of their high temporal resolution and sensitivity to the occupation of working memory. However, the individual difference of the EEG feature distribution may impair the machine-learning based MW classifier. In this paper, we employed a fast-training neural network, extreme learning machine (ELM), as the basis to build an individual-specific classifier ensemble to recognize binary MW. To improve the diversity of the classification committee, heterogeneous member classifiers were adopted by fusing multiple ELMs and Bayesian models. Specifically, a deep network structure was applied in each weak model aiming at finding informative EEG feature representations. The structure of hyper-parameters of the proposed heterogeneous ensemble ELM (HE-ELM) was then identified and then its performance was compared against several competitive MW classifiers. We found that the HE-ELM model was superior for improving the individual-specific accuracy of MW assessments.
MODERN TECHNOLOGIES OF MATERIALS FOR PROTECTION THE WORKING SURFACES OF HYDROCLONE AGAINST WEAR
Authors: Фадин Ю.М. , Fadin Yuriy , Хахалев П. , Hahalev Pavel , Трапезникова Е. , Trapeznikova E.
Published: July 2019

The article considers the process of portland cement production by wet process. Portland cement is a hydraulic binder that is produced by cement clinker, gypsum and additives. Calcium silicates are commonly used as additives. Portland cement production consists of following processes: crushing of clinker and gypsum stone; preparation of mineral additives (crushing, drying); grinding of clinker with active mineral additives and gypsum; storage, packaging and shipping the cement to consumer. Since grinding is carried out in a closed cycle, it is necessary to select the classifying equipment. For this purpose, an analysis is made, which reveals that the hydrocyclone is the most multi-purpose and less expensive in operation of classifying equipment. The study of the hydrocyclone design demonstrates a significant drawback in the form of increased wear of the hydrocyclone working surfaces, which has an impact on durability and work efficiency. The degree of wear the hydrocyclone surfaces depends on the nature of the pulp impact and the mechanical characteristics of material of the hydrocyclone working surface. This article is devoted to the study of the most popular and frequently used wear-resistant materials to protect work surfaces from wear.