bluuebunny/crossref_metadata_2025 huggingface dataset:
Pesticide Aptasensors—State of the Art and Perspectives
Authors: Phopin Kamonrat , Tantimongcolwat Tanawut
Published: November 2020

Contamination by pesticides in the food chain and the environment is a worldwide problem that needs to be actively monitored to ensure safety. Unfortunately, standard pesticide analysis based on mass spectrometry takes a lot of time, money and effort. Thus, simple, reliable, cost-effective and field applicable methods for pesticide detection have been actively developed. One of the most promising technologies is an aptamer-based biosensor or so-called aptasensor. It utilizes aptamers, short single-stranded DNAs or RNAs, as pesticide recognition elements to integrate with various innovative biosensing technologies for specific and sensitive detection of pesticide residues. Several platforms for aptasensors have been dynamically established, such as colorimetry, fluorometry, electrochemistry, electrochemiluminescence (ECL) and so forth. Each platform has both advantages and disadvantages depending on the purpose of use and readiness of technology. For example, colorimetric-based aptasensors are more affordable than others because of the simplicity of fabrication and resource requirements. Electrochemical-based aptasensors have mainly shown better sensitivity than others with exceedingly low detection limits. This paper critically reviews the progression of pesticide aptasensors throughout the development process, including the selection, characterization and modification of aptamers, the conceptual frameworks of integrating aptamers and biosensors, the ASSURED (affordable, sensitive, specific, user-friendly, rapid and robust, equipment-free and deliverable to end users) criteria of different platforms and the future outlook.
AC/DC Current Sensor for Rotating Applications
Authors: Pardo-Vicente Miguel Angel , Platero Carlos A. , Sánchez-Fernández José Ángel , Blázquez Francisco
Published: November 2020

There are several techniques for current measurement. Most of them are capable of measuring both alternating and direct current (AC/DC) components. However, they have severe drawbacks for rotating applications (large size, sensitivity to external fields, and low signal amplitude). In addition to these weaknesses, measured signals should be transmitted to a stationary part. In order to contribute solving these difficulties, this paper presents a sensor that can measure AC/DC simultaneously based on the electromagnetic coupling of two coils. To this aim, the measured waveform is analysed. In this paper, the design of such a sensor is presented. This design is validated through computer simulations and a prototype is built. The performance of this sensor prototype is analysed through experimental tests.
A Method to Increase the Frequency Stability of a TCXO by Compensating Thermal Hysteresis
Authors: Wang Zhaoyang , Wu Jie
Published: November 2020

Due to the rapid growth of electronic information technology, the need for the higher stability of crystal oscillators has increased. The temperature-compensated X’tal (crystal) oscillator (TCXO), a type of crystal oscillator with high frequency stability, has been widely used in communications, sensor networks, automotive electronics, industrial control, measuring devices, and other equipment. The traditional TCXO only performs frequency compensation based on the current temperature, without considering the error caused by thermal hysteresis. As the frequency stability of the TCXO improves, the thermal hysteresis of the crystal oscillator has a negligible influence on the frequency stability of the crystal oscillator. This study measured different compensation tables for hysteresis curves at different temperatures and used a microprocessor to store the historical information of crystal temperature changes. Furthermore, corresponding algorithms were designed to select the correct values, according to the temperature change history, to compensate for the thermal hysteresis of the crystal oscillator error. Experiments show that this method can reduce the hysteresis error of the crystal oscillator from 700 to 150 ppb (−40 to 80 °C).
UAPF: A UWB Aided Particle Filter Localization For Scenarios with Few Features
Authors: Wang Yang , Zhang Weimin , Li Fangxing , Shi Yongliang , Nie Fuyu , Huang Qiang
Published: November 2020

Lidar-based localization doesn’t have high accuracy in open scenarios with few features, and behaves poorly in robot kidnap recovery. To address this problem, an improved Particle Filter localization is proposed who could achieve robust robot kidnap detection and pose error compensation. UAPF adaptively updates the covariance by Jacobian from Ultra-wide Band information instead of predetermined parameters, and determines whether robot kidnap occurs by a novel criterion called KNP (Kidnap Probability). Besides, pose fusion of ranging-based localization and PF-based localization is conducted to decrease the uncertainty. To achieve more accurate ranging-based localization, linear regression of ranging data adopts values of maximum probability rather than average distances. Experiments show UAPF can achieve robot kidnap recovery in less than 2 s and position error is less than 0.1 m in a hall of 40 by 15 m, when the currently prevalent lidar-based localization costs more than 90 s and converges to wrong position.
Tunnel Deformation Inspection via Global Spatial Axis Extraction from 3D Raw Point Cloud
Authors: Yi Cheng , Lu Dening , Xie Qian , Xu Jinxuan , Wang Jun
Published: November 2020

Global inspection of large-scale tunnels is a fundamental yet challenging task to ensure the structural stability of tunnels and driving safety. Advanced LiDAR scanners, which sample tunnels into 3D point clouds, are making their debut in the Tunnel Deformation Inspection (TDI). However, the acquired raw point clouds inevitably possess noticeable occlusions, missing areas, and noise/outliers. Considering the tunnel as a geometrical sweeping feature, we propose an effective tunnel deformation inspection algorithm by extracting the global spatial axis from the poor-quality raw point cloud. Essentially, we convert tunnel axis extraction into an iterative fitting optimization problem. Specifically, given the scanned raw point cloud of a tunnel, the initial design axis is sampled to generate a series of normal planes within the corresponding Frenet frame, followed by intersecting those planes with the tunnel point cloud to yield a sequence of cross sections. By fitting cross sections with circles, the fitted circle centers are approximated with a B-Spline curve, which is considered as an updated axis. The procedure of “circle fitting and B-SPline approximation” repeats iteratively until convergency, that is, the distance of each fitted circle center to the current axis is smaller than a given threshold. By this means, the spatial axis of the tunnel can be accurately obtained. Subsequently, according to the practical mechanism of tunnel deformation, we design a segmentation approach to partition cross sections into meaningful pieces, based on which various inspection parameters can be automatically computed regarding to tunnel deformation. A variety of practical experiments have demonstrated the feasibility and effectiveness of our inspection method.
An Electrochemical Sensor Based on Chalcogenide Molybdenum Disulfide-Gold-Silver Nanocomposite for Detection of Hydrogen Peroxide Released by Cancer Cells
Authors: Hu Jinchun , Zhang Congcong , Li Xue , Du Xin
Published: November 2020

Hydrogen peroxide (H2O2) as a crucial signal molecule plays a vital part in the growth and development of various cells under normal physiological conditions. The development of H2O2 sensors has received great research interest because of the importance of H2O2 in biological systems and its practical applications in other fields. In this study, a H2O2 electrochemical sensor was constructed based on chalcogenide molybdenum disulfide–gold–silver nanocomposite (MoS2-Au-Ag). Transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS) and energy dispersive spectroscopy (EDS) were utilized to characterize the nanocomposites, and the electrochemical performances of the obtained sensor were assessed by two electrochemical detection methods: cyclic voltammetry and chronoamperometry. The results showed that the MoS2-Au-Ag-modified glassy carbon electrode (GCE) has higher sensitivity (405.24 µA mM−1 cm−2), wider linear detection range (0.05–20 mM) and satisfactory repeatability and stability. Moreover, the prepared sensor was able to detect the H2O2 discharge from living tumor cells. Therefore, this study offers a platform for the early diagnosis of cancer and other applications in the fields of biology and biomedicine.
Analysis of Electric Motor Magnetic Core Loss under Axial Mechanical Stress
Authors: Kumar L. Ashok , Raj Bagianathan Madhan , Vijayakumar Varadarajan , Indragandhi Vairavasundaram , Subramaniyaswamy Vairavasundaram , Karimi Hamid. R. , Veluvolu Kalyana C.
Published: November 2020

The electrical machine core is subjected to mechanical stresses during manufacturing processes. These stresses include radial, circumferential and axial components that may have significant influence on the magnetic properties and it further leads to increase in iron loss and permeability in the stator core. In this research work, analysis of magnetic core iron loss under axial mechanical stress is investigated. The magnetic core is designed with Magnetic Flux Density (MF) ranging from 1.0 T to 1.5 T with estimated dimensions under various input voltages from 5 V to 85 V. Iron losses are predicted by the axial pressure created manually wherever required and is further applied to the designed magnetic core in the range of 5 MPa to 50 MPa. Finite element analysis is employed to estimate the magnetic core parameters and the magnetic core dimensions. A ring core is designed with the selected dimensions for the experimental evaluation. The analysis of iron loss at 50 Hz frequency for non-oriented electrical steel of M400-50A is tested experimentally using the Epstein frame test and force-fit setup test. Experimental evaluation concludes that the magnetic core saturates when it reaches its knee point of the B-H curve of the chosen material and also reveals that the axial pressure has a high impact on the magnetic properties of the material.
A Review of Low-Cost Particulate Matter Sensors from the Developers’ Perspectives
Authors: Alfano Brigida , Barretta Luigi , Del Giudice Antonio , De Vito Saverio , Di Francia Girolamo , Esposito Elena , Formisano Fabrizio , Massera Ettore , Miglietta Maria Lucia , Polichetti Tiziana
Published: November 2020

The concerns related to particulate matter’s health effects alongside the increasing demands from citizens for more participatory, timely, and diffused air quality monitoring actions have resulted in increasing scientific and industrial interest in low-cost particulate matter sensors (LCPMS). In the present paper, we discuss 50 LCPMS models, a number that is particularly meaningful when compared to the much smaller number of models described in other recent reviews on the same topic. After illustrating the basic definitions related to particulate matter (PM) and its measurements according to international regulations, the device’s operating principle is presented, focusing on a discussion of the several characterization methodologies proposed by various research groups, both in the lab and in the field, along with their possible limitations. We present an extensive review of the LCPMS currently available on the market, their electronic characteristics, and their applications in published literature and from specific tests. Most of the reviewed LCPMS can accurately monitor PM changes in the environment and exhibit good performances with accuracy that, in some conditions, can reach R2 values up to 0.99. However, such results strongly depend on whether the device is calibrated or not (using a reference method) in the operative environment; if not, R2 values lower than 0.5 are observed.
Measuring Smoothness as a Factor for Efficient and Socially Accepted Robot Motion
Authors: Guillén Ruiz Silvia , Calderita Luis V. , Hidalgo-Paniagua Alejandro , Bandera Rubio Juan P.
Published: November 2020

Social robots, designed to interact and assist people in social daily life scenarios, require adequate path planning algorithms to navigate autonomously through these environments. These algorithms have not only to find feasible paths but also to consider other requirements, such as optimizing energy consumption or making the robot behave in a socially accepted way. Path planning can be tuned according to a set of factors, being the most common path length, safety, and smoothness. This last factor may have a strong relation with energy consumption and social acceptability of produced motion, but this possible relation has never been deeply studied. The current paper focuses on performing a double analysis through two experiments. One of them analyzes energy consumption in a real robot for trajectories that use different smoothness factors. The other analyzes social acceptance for different smoothness factors by presenting different simulated situations to different people and collecting their impressions. The results of these experiments show that, in general terms, smoother paths decrease energy consumption and increase acceptability, as far as other key factors, such as distance to people, are fulfilled.
GhoMR: Multi-Receptive Lightweight Residual Modules for Hyperspectral Classification
Authors: Das Arijit , Saha Indrajit , Scherer Rafał
Published: November 2020

In recent years, hyperspectral images (HSIs) have attained considerable attention in computer vision (CV) due to their wide utility in remote sensing. Unlike images with three or lesser channels, HSIs have a large number of spectral bands. Recent works demonstrate the use of modern deep learning based CV techniques like convolutional neural networks (CNNs) for analyzing HSI. CNNs have receptive fields (RFs) fueled by learnable weights, which are trained to extract useful features from images. In this work, a novel multi-receptive CNN module called GhoMR is proposed for HSI classification. GhoMR utilizes blocks containing several RFs, extracting features in a residual fashion. Each RF extracts features which are used by other RFs to extract more complex features in a hierarchical manner. However, the higher the number of RFs, the greater the associated weights, thus heavier is the network. Most complex architectures suffer from this shortcoming. To tackle this, the recently found Ghost module is used as the basic building unit. Ghost modules address the feature redundancy in CNNs by extracting only limited features and performing cheap transformations on them, thus reducing the overall parameters in the network. To test the discriminative potential of GhoMR, a simple network called GhoMR-Net is constructed using GhoMR modules, and experiments are performed on three public HSI data sets—Indian Pines, University of Pavia, and Salinas Scene. The classification performance is measured using three metrics—overall accuracy (OA), Kappa coefficient (Kappa), and average accuracy (AA). Comparisons with ten state-of-the-art architectures are shown to demonstrate the effectiveness of the method further. Although lightweight, the proposed GhoMR-Net provides comparable or better performance than other networks. The PyTorch code for this study is made available at the iamarijit/GhoMR GitHub repository.
Optimization and Experiment of a Novel Compliant Focusing Mechanism for Space Remote Sensor
Authors: Li Yan , Ge Wenjie , Zhang Xu , Tong Xinxing
Published: November 2020

The change of an external environment leads to the defocusing phenomenon of the space optical remote sensor. The performance of the focusing mechanism is related to the image quality of the remote sensor. It was optimized for a novel focusing mechanism comprised of a flexural hinge lever-type amplifier and several piezoelectric ceramics to improve the performance on high loads and large stroke in this research. It has advantages of a lightweight, simple structure and high reliability compared with the traditional focusing mechanism. The input displacement from the piezoelectric actuators was amplified by a two-stage flexure hinge lever-type mechanism. Dimensional parameters of the flexural hinges were considered as design variables. Based on the optimization ideology, reasonable compliance and dimension parameters of the flexural hinges were analyzed for the focusing mechanism. Simulation and experiments of deformation were conducted to validate the correctness of design optimization. The results show that the focusing mechanism designed by the proposed method has the capabilities of an amplification ratio of 100 times and a loading carrying capacity of 2 kg. This work provides a novel strategy to design an excellent focusing mechanism with lightweight, high loads and large stroke. Moreover, it is believed that this approach can be extended to other complex sensors.
Ultra-Sensitive Isopropanol Biochemical Gas Sensor (Bio-Sniffer) for Monitoring of Human Volatiles
Authors: Chien Po-Jen , Suzuki Takuma , Ye Ming , Toma Koji , Arakawa Takahiro , Iwasaki Yasuhiko , Mitsubayashi Kohji
Published: November 2020

Our groups have previously developed a biochemical gas sensor to measure isopropanol (IPA) in exhaled air and have applied it for breath IPA investigation in healthy subjects and diabetes patients. In this study, the original bio-sniffer was modified with a series of components that improved the limit of detection (LOD). First, the modified IPA bio-sniffer used a C8855-type photomultiplier tube (PMT) that performed well in the photon sensitivity at the peak wavelength of nicotinamide adenine dinucleotide (NADH) fluorescence. Second, the multi-core bifurcated optical fiber, which incorporated 36 fibers to replace the previous dual-core type, enhanced the fluorescence collection. Third, the optical fiber probe was reinforced for greater width, and the flow-cell was redesigned to increase the area of the enzyme-immobilized membrane in contact with the air sample. These modifications lowered the detection limit to 0.5 ppb, a significant increase over the previous 1.0 ppb. Moreover, the modified bio-sniffer successfully analyzed the IPA concentration in exhaled air from a volunteer, which confirmed its capability for real-world sample detection. The modified bio-sniffer is more applicable to breath measurement and the detection of other extremely-low-concentration samples.
Broadband Time-Resolved Absorption and Dispersion Spectroscopy of Methane and Ethane in a Plasma Using a Mid-Infrared Dual-Comb Spectrometer
Authors: Abbas Muhammad Ali , Dijk Luuk van , Jahromi Khalil Eslami , Nematollahi Mohammadreza , Harren Frans J. M. , Khodabakhsh Amir
Published: November 2020

Conventional mechanical Fourier Transform Spectrometers (FTS) can simultaneously measure absorption and dispersion spectra of gas-phase samples. However, they usually need very long measurement times to achieve time-resolved spectra with a good spectral and temporal resolution. Here, we present a mid-infrared dual-comb-based FTS in an asymmetric configuration, providing broadband absorption and dispersion spectra with a spectral resolution of 5 GHz (0.18 nm at a wavelength of 3333 nm), a temporal resolution of 20 μs, a total wavelength coverage over 300 cm−1 and a total measurement time of ~70 s. We used the dual-comb spectrometer to monitor the reaction dynamics of methane and ethane in an electrical plasma discharge. We observed ethane/methane formation as a recombination reaction of hydrocarbon radicals in the discharge in various static and dynamic conditions. The results demonstrate a new analytical approach for measuring fast molecular absorption and dispersion changes and monitoring the fast dynamics of chemical reactions over a broad wavelength range, which can be interesting for chemical kinetic research, particularly for the combustion and plasma analysis community.
Galileo Broadcast Ephemeris and Clock Errors Analysis: 1 January 2017 to 31 July 2020
Authors: Alonso María Teresa , Sanz Jaume , Juan José Miguel , Rovira García Adrià , Casado Guillermo González
Published: November 2020

A preliminary analysis of Galileo F/NAV broadcast Clock and Ephemeris is performed in this paper with 43 months of data. Using consolidated Galileo Receiver Independent Exchange (RINEX) navigation files, automated navigation data monitoring is applied from 1 January 2017 to 31 July 2020 to detect and verify potential faults in the satellite broadcast navigation data. Based on these observation results, the Galileo Signal-in-Space is assessed, and the probability of satellite failure is estimated. The Galileo nominal ranging accuracy is also characterized. Results for GPS satellites are included in the paper to compare Galileo performances with a consolidated constellation. Although this study is limited by the short observation period available, the analysis over the last three-year window shows promising results with Psat= 3.2 × 10−6/sat, which is below the value of 1 × 10−5 stated by the Galileo commitments.
Multi-User Precoder Designs for RGB Visible Light Communication Systems
Authors: Viñals Roser , Muñoz Olga , Agustín Adrián , Vidal Josep
Published: November 2020

In this paper, we design linear precoders for the downlink of a visible light communication (VLC) system that simultaneously serves multiple users. Instead of using phosphor-coated white light-emitting diodes (PWLEDs), we focus on Red-Green-Blue light-emitting diodes (RGB-LEDs) that allow modulating three separate data streams on the three primary colors of the RGB-LEDs. For this system, we design a zero-forcing (ZF) precoder that maximizes the weighted sum rate for a multilevel pulse amplitude modulation (M-PAM). The precoding design in RGB-based systems presents some challenges due to the system constraints, such as the limited power, the non-negative amplitude constraints per light-emitting diode (LED), and the need to guarantee white light emission while transmitting with RGB-LEDs. For comparison purposes, we also consider the ZF design for a PWLED-based system and evaluate the performance of both a PWLED- and an RGB-based system.
Breast Tumor Classification in Ultrasound Images Using Combined Deep and Handcrafted Features
Authors: Daoud Mohammad I. , Abdel-Rahman Samir , Bdair Tariq M. , Al-Najar Mahasen S. , Al-Hawari Feras H. , Alazrai Rami
Published: November 2020

This study aims to enable effective breast ultrasound image classification by combining deep features with conventional handcrafted features to classify the tumors. In particular, the deep features are extracted from a pre-trained convolutional neural network model, namely the VGG19 model, at six different extraction levels. The deep features extracted at each level are analyzed using a features selection algorithm to identify the deep feature combination that achieves the highest classification performance. Furthermore, the extracted deep features are combined with handcrafted texture and morphological features and processed using features selection to investigate the possibility of improving the classification performance. The cross-validation analysis, which is performed using 380 breast ultrasound images, shows that the best combination of deep features is obtained using a feature set, denoted by CONV features that include convolution features extracted from all convolution blocks of the VGG19 model. In particular, the CONV features achieved mean accuracy, sensitivity, and specificity values of 94.2%, 93.3%, and 94.9%, respectively. The analysis also shows that the performance of the CONV features degrades substantially when the features selection algorithm is not applied. The classification performance of the CONV features is improved by combining these features with handcrafted morphological features to achieve mean accuracy, sensitivity, and specificity values of 96.1%, 95.7%, and 96.3%, respectively. Furthermore, the cross-validation analysis demonstrates that the CONV features and the combined CONV and morphological features outperform the handcrafted texture and morphological features as well as the fine-tuned VGG19 model. The generalization performance of the CONV features and the combined CONV and morphological features is demonstrated by performing the training using the 380 breast ultrasound images and the testing using another dataset that includes 163 images. The results suggest that the combined CONV and morphological features can achieve effective breast ultrasound image classifications that increase the capability of detecting malignant tumors and reduce the potential of misclassifying benign tumors.
Estimating System State through Similarity Analysis of Signal Patterns
Authors: Namgung Kichang , Yoon Hyunsik , Baek Sujeong , Kim Duck Young
Published: November 2020

State prediction is not straightforward, particularly for complex systems that cannot provide sufficient amounts of training data. In particular, it is usually difficult to analyze some signal patterns for state prediction if they were observed in both normal and fault-states with a similar frequency or if they were rarely observed in any system state. In order to estimate the system status with imbalanced state data characterized insufficient fault occurrences, this paper proposes a state prediction method that employs discrete state vectors (DSVs) for pattern extraction and then applies a naïve Bayes classifier and Brier scores to interpolate untrained pattern information by using the trained ones probabilistically. Each Brier score is transformed into a more intuitive one, termed state prediction power (SPP). The SPP values represent the reliability of the system state prediction. A state prediction power map, which visualizes the DSVs and corresponding SPP values, is provided a more intuitive way of state prediction analysis. A case study using a car engine fault simulator was conducted to generate artificial engine knocking. The proposed method was evaluated using holdout cross-validation, defining specificity and sensitivity as indicators to represent state prediction success rates for no-fault and fault states, respectively. The results show that specificity and sensitivity are very high (equal to 1) for high limit values of SPP, but drop off dramatically for lower limit values.
New Arc Stability Index for Industrial AC Three-Phase Electric Arc Furnaces Based on Acoustic Signals
Authors: Guerra-Serrano Juan , Sánchez-Roca Angel , González-Yero Guillermo , Sánchez-Orozco Mario C. , Pérez de la Parte Mercedes , Jiménez Macías Emilio , Blanco-Fernández Julio
Published: November 2020

This research proposes a new index to evaluate the stability of the melting process, in three-phase electric arc furnaces (EAFs), based on the acoustic signals generated during the different stages of the casting. The proposed stability index is obtained by characterizing the time and frequency domain of the acoustic signals. During EAF monitoring, acoustic signals were acquired using a microphone coupled to an NI USB-9234 acquisition system. To validate the results, the voltage and current signals were measured with the aid of a Circutor AR6 power analyzer for three-phase electrical networks. The results showed that the acoustic signal energy in the frequency range of 1 to 12 kHz can be used as an indicator of the process stability in the EAF. Finally, the validity of the proposed stability index is evaluated from the process characterization using the harmonic distortion analysis methods and the dynamic U-I characteristics of the arc voltage and current signals. The results obtained demonstrated the effectiveness of the proposal and constitute a starting point for advances in the automatic control of the process in the EAF, from the acoustic signals.
iTrust—A Trustworthy and Efficient Mapping Scheme in Elliptic Curve Cryptography
Authors: Almajed Hisham , Almogren Ahmad , Alabdulkareem Mohammed
Published: November 2020

Recently, many platforms have outsourced tasks to numerous smartphone devices known as Mobile Crowd-sourcing System (MCS). The data is collected and transferred to the platform for further analysis and processing. These data needs to maintain confidentiality while moving from smartphones to the platform. Moreover, the limitations of computation resources in smartphones need to be addressed to balance the confidentiality of the data and the capabilities of the devices. For this reason, elliptic curve cryptography (ECC) is accepted, widespread, and suitable for use in limited resources environments such as smartphone devices. ECC reduces energy consumption and maximizes devices’ efficiency by using small crypto keys with the same strength of the required cryptography of other cryptosystems. Thus, ECC is the preferred approach for many environments, including the MCS, Internet of Things (IoT) and wireless sensor networks (WSNs). Many implementations of ECC increase the process of encryption and/or increase the space overhead by, for instance, incorrectly mapping points to EC with extra padding bits. Moreover, the wrong mapping method used in ECC results in increasing the computation efforts. This study provides comprehensive details about the mapping techniques used in the ECC mapping phase, and presents performance results about widely used elliptic curves. In addition, it suggests an optimal enhanced mapping method and size of padding bit to secure communications that guarantee the successful mapping of points to EC and reduce the size of padding bits.
An Improved Unscented Particle Filter Approach for Multi-Sensor Fusion Target Tracking
Authors: Luo Junhai , Wang Zhiyan , Chen Yanping , Wu Man , Yang Yang
Published: November 2020

In this paper, a new approach of multi-sensor fusion algorithm based on the improved unscented particle filter (IUPF) and a new multi-sensor distributed fusion model are proposed. Additionally, we employ a novel multi-target tracking algorithm that combines the joint probabilistic data association (JPDA) algorithm and the IUPF algorithm. To improve the real-time performance of the UPF algorithm for the maneuvering target, minimum skew simplex unscented transform combined with a scaled unscented transform is utilized, which significantly reduces the calculation of UPF sample selection. Moreover, a self-adaptive gain modification coefficient is defined to solve the low accuracy problem caused by the sigma point reduction, and the problem of particle degradation is solved by modifying the weights calculation method. In addition, a new multi-sensor fusion model is proposed, which better integrates radar and infrared sensors. Simulation results show that IUPF effectively improves real-time performance while ensuring the tracking accuracy compared with other algorithms. Besides, compared with the traditional distributed fusion architecture, the proposed new architecture makes better use of the advantages of radar and an infrared sensor and improves the tracking accuracy.
Rotate Vector (RV) Reducer Fault Detection and Diagnosis System: Towards Component Level Prognostics and Health Management (PHM)
Authors: Rohan Ali , Raouf Izaz , Kim Heung Soo
Published: November 2020

In prognostics and health management (PHM), the majority of fault detection and diagnosis is performed by adopting segregated methodology, where electrical faults are detected using motor current signature analysis (MCSA), while mechanical faults are detected using vibration, acoustic emission, or ferrography analysis. This leads to more complicated methods for overall fault detection and diagnosis. Additionally, the involvement of several types of data makes system management difficult, thus increasing computational cost in real-time. Aiming to resolve that, this work proposes the use of the embedded electrical current signals of the control unit (MCSA) as an approach to detect and diagnose mechanical faults. The proposed fault detection and diagnosis method use the discrete wavelet transform (DWT) to analyze the electric motor current signals in the time-frequency domain. The technique decomposes current signals into wavelets, and extracts distinguishing features to perform machine learning (ML) based classification. To achieve an acceptable level of classification accuracy for ML-based classifiers, this work extends to presenting a methodology to extract, select, and infuse several types of features from the decomposed wavelets of the original current signals, based on wavelet characteristics and statistical analysis. The mechanical faults under study are related to the rotate vector (RV) reducer mechanically coupled to electric motors of the industrial robot Hyundai Robot YS080 developed by Hyundai Robotics Co. The proposed approach was implemented in real-time and showed satisfying results in fault detection and diagnosis for the RV reducer, with a classification accuracy of 96.7%.
An Efficient Correlation-Based Cache Retrieval Scheme at the Edge for Internet of Things
Authors: Dinh Ngoc-Thanh , Kim Young-Han
Published: November 2020

Existing caching mechanisms considers content objects individually without considering the semantic correlation among content objects. We argue that this approach can be inefficient in Internet of Things due to the highly redundant nature of IoT device deployments and the data accuracy tolerance of IoT applications. In many IoT applications, an approximate answer is acceptable. Therefore, a cache of an information object having a high semantic correlation with the requested information object can be used instead of a cache of the exact requested information object. In this case, caching both of the information objects can be inefficient and redundant. This paper proposes a caching retrieval scheme which considers the semantic information correlation of information objects of nodes for cache retrieval. We illustrate the benefits of considering the semantic information correlation in caching by studying IoT data caching at the edge. Our experiments and analysis show that semantic correlated caching can significantly improve the efficiency, cache hit, and reduce the resource consumption of IoT devices.
Subcarrier Frequency-Modulated Continuous-Wave Radar in the Terahertz Range Based on a Resonant-Tunneling-Diode Oscillator
Authors: Dobroiu Adrian , Shirakawa Yusuke , Suzuki Safumi , Asada Masahiro , Ito Hiroshi
Published: November 2020

We introduce a new principle for distance measurement in the terahertz-wave range using a resonant-tunneling-diode (RTD) oscillator as a source at 511 GHz and relying on the frequency-modulated continuous-wave (FMCW) radar technique. Unlike the usual FMCW radar, where the sawtooth frequency modulation is applied to the carrier, we propose applying it to a subcarrier obtained by amplitude modulation; this is advantageous when the source cannot be controlled precisely in oscillation frequency, but can easily be modulated in amplitude, as is the case of the RTD oscillator. The detailed principle and a series of proof-of-concept experimental results are presented.
Seismic Data Augmentation Based on Conditional Generative Adversarial Networks
Authors: Li Yuanming , Ku Bonhwa , Zhang Shou , Ahn Jae-Kwang , Ko Hanseok
Published: November 2020

Realistic synthetic data can be useful for data augmentation when training deep learning models to improve seismological detection and classification performance. In recent years, various deep learning techniques have been successfully applied in modern seismology. Due to the performance of deep learning depends on a sufficient volume of data, the data augmentation technique as a data-space solution is widely utilized. In this paper, we propose a Generative Adversarial Networks (GANs) based model that uses conditional knowledge to generate high-quality seismic waveforms. Unlike the existing method of generating samples directly from noise, the proposed method generates synthetic samples based on the statistical characteristics of real seismic waveforms in embedding space. Moreover, a content loss is added to relate high-level features extracted by a pre-trained model to the objective function to enhance the quality of the synthetic data. The classification accuracy is increased from 96.84% to 97.92% after mixing a certain amount of synthetic seismic waveforms, and results of the quality of seismic characteristics derived from the representative experiment show that the proposed model provides an effective structure for generating high-quality synthetic seismic waveforms. Thus, the proposed model is experimentally validated as a promising approach to realistic high-quality seismic waveform data augmentation.
Satellite Laser Ranging for Retrieval of the Local Values of the Love h2 and Shida l2 Numbers for the Australian ILRS Stations
Authors: Jagoda Marcin , Rutkowska Miłosława , Lejba Paweł , Katzer Jacek , Obuchovski Romuald , Šlikas Dominykas
Published: November 2020

This paper deals with the analysis of local Love and Shida numbers (parameters h2 and l2) values of the Australian Yarragadee and Mount Stromlo satellite laser ranging (SLR) stations. The research was conducted based on data from the Medium Earth Orbit (MEO) satellites, LAGEOS-1 and LAGEOS-2, and Low Earth Orbit (LEO) satellites, STELLA and STARLETTE. Data from a 60-month time interval, from 01.01.2014 to 01.01.2019, was used. In the first research stage, the Love and Shida numbers values were determined separately from observations of each satellite; the obtained values of h2, l2 exhibit a high degree of compliance, and the differences do not exceed formal error values. At this stage, we found that it was not possible to determine l2 from the data of STELLA and STARLETTE. In the second research stage, we combined the satellite observations of MEO (LAGEOS-1+LAGEOS-2) and LEO (STELLA+STARLETTE) and redefined the h2, l2 parameters. The final values were adopted, and further analyses were made based on the values obtained from the combined observations. For the Yarragadee station, local h2 = 0.5756 ± 0.0005 and l2 = 0.0751 ± 0.0002 values were obtained from LAGEOS-1 + LAGEOS-2 and h2 = 0.5742 ± 0.0015 were obtained from STELLA+STARLETTE data. For the Mount Stromlo station, we obtained the local h2 = 0.5601 ± 0.0006 and l2 = 0.0637 ± 0.0003 values from LAGEOS-1+LAGEOS-2 and h2 = 0.5618 ± 0.0017 from STELLA + STARLETTE. We found discrepancies between the local parameters determined for the Yarragadee and Mount Stromlo stations and the commonly used values of the h2, l2 parameters averaged for the whole Earth (so-called global nominal parameters). The sequential equalization method was used for the analysis, which allowed to determine the minimum time interval necessary to obtain stable h2, l2 values. It turned out to be about 50 months. Additionally, we investigated the impact of the use of local values of the Love/Shida numbers on the determination of the Yarragadee and Mount Stromlo station coordinates. We proposed to determine the stations (X, Y, Z) coordinates in International Terrestrial Reference Frame 2014 (ITRF2014) in two computational versions: using global nominal h2, l2 values and local h2, l2 values calculated during this research. We found that the use of the local values of the h2, l2 parameters in the process of determining the stations coordinates influences the result.
Interference Torque of a Gas-Dynamic Bearing Gyroscope Subject to a Uniform Change of the Specific Force and the Carrier Angular Velocity
Authors: Li Yan , Zhang Desheng , Duan Fuhai
Published: November 2020

The work is devoted to an analysis of interference torque of a gas-dynamic bearing gyroscope, while a condition with uniformly changed specific force and carrier angular velocity are taken into account. A five-degrees-of-freedom (5-DOF) model is established considering the translation and tilt of the rotor, which solves dynamic rotor equations and the Reynolds equation simultaneously. The model makes it possible to obtain the rotor trajectory under time-transient specific force and carrier angular velocity. The interference torque of the gyroscope is analyzed based on the rotor trajectory. Results indicate that the gas-dynamic bearings show a significant hysteresis effect with a perturbation of bearing force or bearing moment, which indicates the necessity of transient research. Interference torque is large when the carrier angular velocity starts to change or stops to change, and when the specific force stops to change. When the specific force change rate is less than 8.4 km/s3 with no change of the carrier angular velocity, the condition could be simplified as a steady state, which is consistent with the previous study.
Modified Split Ring Resonators Sensor for Accurate Complex Permittivity Measurements of Solid Dielectrics
Authors: al-Behadili Amer Abbood , Mocanu Iulia Andreea , Codreanu Norocel , Pantazica Mihaela
Published: November 2020

In this paper, a sensor using modified Split Ring Resonators (SRRs) is designed, simulated, fabricated, and used for advanced investigation and precise measurements of the real part and imaginary part solid dielectrics’ permittivity. Adding vertical strips tightly coupled to the outer ring of the SRR leads to the appearance of two resonant frequencies at 1.24 GHz and 2.08 GHz. This modified geometry also assures an improved sensitivity. Using the full wave electromagnetic solver, both the unloaded and loaded sensors are investigated. The numerical simulations are used to develop a mathematical model based on a curve fitting tool for both resonant frequencies, allowing to obtain analytical relations for real and imaginary parts of permittivity as a function of the sample’s thickness and quality factor. The sensor is designed and fabricated on 1.6 mm thick FR-4 substrate. The measurements of different samples, such as transparent glass, acrylic glass, plexiglass, and Teflon, confirm that the modified SRR sensor is easy to implement and gives accurate results for all cases, with measurement errors smaller than 4.5%. In addition, the measurements highlight the importance of the second resonant frequency in the cases in which numerical limitations do not allow the usage of the first resonant frequency (1 mm thick sample).
Multimodal Data Fusion in Learning Analytics: A Systematic Review
Authors: Mu Su , Cui Meng , Huang Xiaodi
Published: November 2020

Multimodal learning analytics (MMLA), which has become increasingly popular, can help provide an accurate understanding of learning processes. However, it is still unclear how multimodal data is integrated into MMLA. By following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper systematically surveys 346 articles on MMLA published during the past three years. For this purpose, we first present a conceptual model for reviewing these articles from three dimensions: data types, learning indicators, and data fusion. Based on this model, we then answer the following questions: 1. What types of data and learning indicators are used in MMLA, together with their relationships; and 2. What are the classifications of the data fusion methods in MMLA. Finally, we point out the key stages in data fusion and the future research direction in MMLA. Our main findings from this review are (a) The data in MMLA are classified into digital data, physical data, physiological data, psychometric data, and environment data; (b) The learning indicators are behavior, cognition, emotion, collaboration, and engagement; (c) The relationships between multimodal data and learning indicators are one-to-one, one-to-any, and many-to-one. The complex relationships between multimodal data and learning indicators are the key for data fusion; (d) The main data fusion methods in MMLA are many-to-one, many-to-many and multiple validations among multimodal data; and (e) Multimodal data fusion can be characterized by the multimodality of data, multi-dimension of indicators, and diversity of methods.
A Novel Mixed Methods Approach to Synthesize EDA Data with Behavioral Data to Gain Educational Insight
Authors: Reid Clodagh , Keighrey Conor , Murray Niall , Dunbar Rónán , Buckley Jeffrey
Published: November 2020

Whilst investigating student performance in design and arithmetic tasks, as well as during exams, electrodermal activity (EDA)-based sensors have been used in attempts to understand cognitive function and cognitive load. Limitations in the employed approaches include lack of capacity to mark events in the data, and to explain other variables relating to performance outcomes. This paper aims to address these limitations, and to support the utility of wearable EDA sensor technology in educational research settings. These aims are achieved through use of a bespoke time mapping software which identifies key events during task performance and by taking a novel approach to synthesizing EDA data from a qualitative behavioral perspective. A convergent mixed method design is presented whereby the associated implementation follows a two-phase approach. The first phase involves the collection of the required EDA and behavioral data. Phase two outlines a mixed method analysis with two approaches of synthesizing the EDA data with behavioral analyses. There is an optional third phase, which would involve the sequential collection of any additional data to support contextualizing or interpreting the EDA and behavioral data. The inclusion of this phase would turn the method into a complex sequential mixed method design. Through application of the convergent or complex sequential mixed method, valuable insight can be gained into the complexities of individual learning experiences and support clearer inferences being made on the factors relating to performance. These inferences can be used to inform task design and contribute to the improvement of the teaching and learning experience.
VODCA: Verification of Diagnosis Using CAM-Based Approach for Explainable Process Monitoring
Authors: Oh Cheolhwan , Jeong Jongpil
Published: November 2020

Process monitoring at industrial sites contributes to system stability by detecting and diagnosing unexpected changes in a system. Today, as the infrastructure of industrial sites is advancing because of the development of communication technology, vast amounts of data are generated, and the importance of a way to effectively monitor such data in order to diagnose a system is increasing daily. Because a method based on a deep neural network can effectively extract information from a large amount of data, methods have been proposed to monitor processes using such networks to detect system faults and abnormalities. Neural-network-based process monitoring is effective in detecting faults, but has difficulty in diagnosing because of the limitations of the black-box model. Therefore, in this paper we propose a process-monitoring framework that can detect and diagnose faults. The proposed method uses a class activation map that results from diagnosis of faults and abnormalities, and verifies the diagnosis by post-processing the class activation map. This improves the detection of faults and abnormalities and generates a class activation map that provides a more verified diagnosis to the end user. In order to evaluate the performance of the proposed method, we did a simulation using publicly available industrial motor datasets. In addition, after establishing a system that can apply the proposed method to actual manufacturing companies that produce sapphire nozzles, we carried out a case study on whether fault detection and diagnosis were possible.