bluuebunny/crossref_metadata_2025 huggingface dataset:
Interfacial Thermal Conductivity and Its Anisotropy
Authors: Wang Xiaoyu , Jameson Cynthia J. , Murad Sohail
Published: December 2019

There is a significant effort in miniaturizing nanodevices, such as semi-conductors, currently underway. However, a major challenge that is a significant bottleneck is dissipating heat generated in these energy-intensive nanodevices. In addition to being a serious operational concern (high temperatures can interfere with their efficient operation), it is a serious safety concern, as has been documented in recent reports of explosions resulting from many such overheated devices. A significant barrier to heat dissipation is the interfacial films present in these nanodevices. These interfacial films generally are not an issue in macro-devices. The research presented in this paper was an attempt to understand these interfacial resistances at the molecular level, and present possibilities for enhancing the heat dissipation rates in interfaces. We demonstrated that the thermal resistances of these interfaces were strongly anisotropic; i.e., the resistance parallel to the interface was significantly smaller than the resistance perpendicular to the interface. While the latter is well-known—usually referred to as Kapitza resistance—the anisotropy and the parallel component have previously been investigated only for solid-solid interfaces. We used molecular dynamics simulations to investigate the density profiles at the interface as a function of temperature and temperature gradient, to reveal the underlying physics of the anisotropy of thermal conductivity at solid-liquid, liquid-liquid, and solid-solid interfaces.
Spatial and Temporal Variations in the Extent and Thickness of Arctic Landfast Ice
Authors: Li Zixuan , Zhao Jiechen , Su Jie , Li Chunhua , Cheng Bin , Hui Fengming , Yang Qinghua , Shi Lijuan
Published: December 2019

Analyses of landfast ice in Arctic coastal areas provide a comprehensive understanding of the variations in Arctic sea ice and generate data for studies on the utilization of the Arctic passages. Based on our analysis, Arctic landfast ice mainly appears in January–June and is distributed within the narrow straits of the Canadian Archipelago (nearly 40%), the coastal areas of the East Siberian Sea, the Laptev Sea, and the Kara Sea. From 1976–2018, the landfast ice extent gradually decreased at an average rate of −1.1 ± 0.5 × 104 km2/yr (10.5% per decade), while the rate of decrease for entire Arctic sea ice was −6.0 ± 2.4 × 104 km2/yr (5.2% per decade). The annual maximum extent reached 2.3 × 106 km2 in the early 1980s, and by 2018, the maximum extent decreased by 0.6 × 106 km2, which is an area approximately equivalent the Laptev Sea. The mean duration of Arctic landfast ice is 44 weeks, which has gradually been reduced at a rate of −0.06 ± 0.03 weeks/yr. Regional landfast ice extent decreases in 16 of the 17 subregions except for the Bering Sea, making it the only subregion where both the extent and duration increases. The maximum mean landfast ice thickness appears in the northern Canadian Archipelago (>2.5 m), with the highest increasing trend (0.1 m/yr). In the Northeast Passage, the mean landfast ice thickness is 1.57 m, with a slight decreasing trend of −1.2 cm/yr, which is smaller than that for entire Arctic sea ice (−5.1 cm/yr). The smaller decreasing trend in the landfast ice extent and thickness suggests that the well-known Arctic sea ice decline largely occurred in the pack ice zone, while the larger relative extent loss indicates a faster ice free future in the landfast ice zone.
EMMCNN: An ETPS-Based Multi-Scale and Multi-Feature Method Using CNN for High Spatial Resolution Image Land-Cover Classification
Authors: Zhang Shuyu , Li Chuanrong , Qiu Shi , Gao Caixia , Zhang Feng , Du Zhenhong , Liu Renyi
Published: December 2019

Land-cover information is significant for land-use planning, urban management, and environment monitoring. This paper presented a novel extended topology-preserving segmentation (ETPS)-based multi-scale and multi-feature method using the convolutional neural network (EMMCNN) for high spatial resolution (HSR) image land-cover classification. The EMMCNN first segmented the images into superpixels using the ETPS algorithm with false-color composition and enhancement and built parallel convolutional neural networks (CNNs) with dense connections for superpixel multi-scale deep feature learning. Then, the multi-resolution segmentation (MRS) object hand-delineated features were extracted and mapped to superpixels for complementary multi-segmentation and multi-type representation. Finally, a hybrid network was designed to consist of 1-dimension CNN and multi-layer perception (MLP) with channel-wise stacking and attention-based weighting for adaptive feature fusion and comprehensive classification. Experimental results on four real HSR GaoFen-2 datasets demonstrated the superiority of the proposed EMMCNN over several well-known classification methods in terms of accuracy and consistency, with overall accuracy averagely improved by 1.74% to 19.35% for testing images and 1.06% to 8.78% for validating images. It was found that the solution combining an appropriate number of larger scales and multi-type features is recommended for better performance. Efficient superpixel segmentation, networks with strong learning ability, optimized multi-scale and multi-feature solution, and adaptive attention-based feature fusion were key points for improving HSR image land-cover classification in this study.
Estimation and Analysis of BDS-3 Differential Code Biases from MGEX Observations
Authors: Wang Qisheng , Jin Shuanggen , Yuan Liangliang , Hu Youjian , Chen Jie , Guo Jiabin
Published: December 2019

The third generation of China’s BeiDou Navigation Satellite System (BDS-3) began to provide global services on 27 December, 2018. Differential code bias (DCB) is one of the errors in precise BDS positioning and ionospheric modeling, but the impacts on BDS-2 satellites and receiver DCB are unknown after joining BDS-3 observations. In this paper, the BDS-3 DCBs are estimated and analyzed using the Multi-Global Navigation Satellite System (GNSS) Experiment (MGEX) observations during the period of day of year (DOY) 002–031, 2019. The results indicate that the estimated BDS-3 DCBs have a good agreement with the products provided by the Chinese Academy of Sciences (CAS) and Deutsche Zentrum für Luft- und Raumfahrt (DLR). The differences between our results and the other two products are within ±0.2 ns, with Standard Deviations (STDs) of mostly less than 0.2 ns. Furthermore, the effects on satellite and receiver DCB after adding BDS-3 observations are analyzed by BDS-2 + BDS-3 and BDS-2-only solutions. For BDS-2 satellite DCB, the values of effect are close to 0, and the effect on stability of DCB is very small. In terms of receiver DCB, the value of effect on each station is related to the receiver type, but their mean value is also close to 0, and the stability of receiver DCB is better when BDS-3 observations are added. Therefore, there is no evident systematic bias between BDS-2 and BDS-2 + BDS-3 DCB.
Overview of the New Version 3 NASA Micro-Pulse Lidar Network (MPLNET) Automatic Precipitation Detection Algorithm
Authors: Lolli Simone , Vivone Gemine , Lewis Jasper R. , Sicard Michaël , Welton Ellsworth J. , Campbell James R. , Comerón Adolfo , D’Adderio Leo Pio , Tokay Ali , Giunta Aldo , Pappalardo Gelsomina
Published: December 2019

Precipitation modifies atmospheric column thermodynamics through the process of evaporation and serves as a proxy for latent heat modulation. For this reason, a correct precipitation parameterization (especially for low-intensity precipitation) within global scale models is crucial. In addition to improving our modeling of the hydrological cycle, this will reduce the associated uncertainty of global climate models in correctly forecasting future scenarios, and will enable the application of mitigation strategies. In this manuscript we present a proof of concept algorithm to automatically detect precipitation from lidar measurements obtained from the National Aeronautics and Space Administration Micropulse lidar network (MPLNET). The algorithm, once tested and validated against other remote sensing instruments, will be operationally implemented into the network to deliver a near real time (latency <1.5 h) rain masking variable that will be publicly available on MPLNET website as part of the new Version 3 data products. The methodology, based on an image processing technique, detects only light precipitation events (defined by intensity and duration) such as light rain, drizzle, and virga. During heavy rain events, the lidar signal is completely extinguished after a few meters in the precipitation or it is unusable because of water accumulated on the receiver optics. Results from the algorithm, in addition to filling a gap in light rain, drizzle, and virga detection by radars, are of particular interest for the scientific community as they help to fully characterize the aerosol cycle, from emission to deposition, as precipitation is a crucial meteorological phenomenon accelerating atmospheric aerosol removal through the scavenging effect. Algorithm results will also help the understanding of long term aerosol–cloud interactions, exploiting the multi-year database from several MPLNET permanent observational sites across the globe. The algorithm is also applicable to other lidar and/or ceilometer network infrastructures in the framework of the Global Aerosol Watch (GAW) aerosol lidar observation network (GALION).
Bayesian Harmonic Modelling of Sparse and Irregular Satellite Remote Sensing Time Series of Vegetation Indexes: A Story of Clouds and Fires
Authors: Vicario Saverio , Adamo Maria , Alcaraz-Segura Domingo , Tarantino Cristina
Published: December 2019

Vegetation index time series from Landsat and Sentinel-2 have great potential for following the dynamics of ecosystems and are the key to develop essential variables in the realm of biodiversity. Unfortunately, the removal of pixels covered mainly by clouds reduces the temporal resolution, producing irregularity in time series of satellite images. We propose a Bayesian approach based on a harmonic model, fitted on an annual base. To deal with data sparsity, we introduce hierarchical prior distribution that integrate information across the years. From the model, the mean and standard deviation of yearly Ecosystem Functional Attributes (i.e., mean, standard deviation, and peak’s day) plus the inter-year standard deviation are calculated. Accuracy is evaluated with a simulation that uses real cloud patterns found in the Peneda-Gêres National Park, Portugal. Sensitivity to the model’s abrupt change is evaluated against a record of multiple forest fires in the Bosco Difesa Grande Regional Park in Italy and in comparison with the BFAST software output. We evaluated the sensitivity in dealing with mixed patch of land cover by comparing yearly statistics from Landsat at 30m resolution, with a 2m resolution land cover of Murgia Alta National Park (Italy) using FAO Land Cover Classification System 2.
A Finger Grip Force Sensor with an Open-Pad Structure for Glove-Type Assistive Devices
Authors: Park Junghoon , Heo Pilwon , Kim Jung , Na Youngjin
Published: December 2019

This paper presents a fingertip grip force sensor based on custom capacitive sensors for glove-type assistive devices with an open-pad structure. The design of the sensor allows using human tactile sensations during grasping and manipulating an object. The proposed sensor can be attached on both sides of the fingertip and measure the force caused by the expansion of the fingertip tissue when a grasping force is applied to the fingertip. The number of measurable degrees of freedom (DoFs) are the two DoFs (flexion and adduction) for the thumb and one DoF (flexion) for the index and middle fingers. The proposed sensor allows the combination with a glove-type assistive device to measure the fingertip force. Calibration was performed for each finger joint angle because the variations in the expansion of the fingertip tissue depend on the joint angles. The root mean square error (RMSE) for fingertip force estimation ranged from 3.75% to 9.71% after calibration, regardless of the finger joint angles or finger posture.
Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data
Authors: Huang Min , Liu Zhen
Published: December 2019

Vibration sensing data is an important resource for mechanical fault prediction, which is widely used in the industrial sector. Artificial neural networks (ANNs) are important tools for classifying vibration sensing data. However, their basic structures and hyperparameters must be manually adjusted, which results in the prediction accuracy easily falling into the local optimum. For data with high levels of uncertainty, it is difficult for an ANN to obtain correct prediction results. Therefore, we propose a multifeature fusion model based on Dempster-Shafer evidence theory combined with a particle swarm optimization algorithm and artificial neural network (PSO-ANN). The model first used the particle swarm optimization algorithm to optimize the structure and hyperparameters of the ANN, thereby improving its prediction accuracy. Then, the prediction error data of the multifeature fusion using a PSO-ANN is repredicted using multiple PSO-ANNs with different single feature training to obtain new prediction results. Finally, the Dempster-Shafer evidence theory was applied to the decision-level fusion of the new prediction results preprocessed with prediction accuracy and belief entropy, thus improving the model’s ability to process uncertain data. The experimental results indicated that compared to the K-nearest neighbor method, support vector machine, and long short-term memory neural networks, the proposed model can effectively improve the accuracy of fault prediction.
Three-Dimensional Micro-Motion Feature Extraction of the Vibrating Target Based on Multi-Channel Radar in the Terahertz Band
Authors: Tang Bin , Yang Qi , Zhang Ye , Deng Bin , Wang Hongqiang
Published: December 2019

Vibration induces a micro-Doppler effect, which contains crucial motion information and features of the micro-motion target. However, the traditional radar can only acquire micro-motion information in the radial direction. The multi-channel radar is capable of acquiring actual micro-motion information in a three-dimensional space, including amplitude, frequency, and three-dimensional direction of vibration. Moreover, the high-accuracy parameter estimation can be achieved in the terahertz band. In this paper, a method for three-dimensional micro-motion feature exaction and parameter estimation is proposed, including a phase-derived-range algorithm, interference, and auto-correlation. Meanwhile, to prove feasibility of the method, the results of simulations and experiment based on a 0.22 THz multi-channel radar system are presented after theoretical analyses.
Human Motion Recognition of Knitted Flexible Sensor in Walking Cycle
Authors: Li Yutian , Miao Xuhong , Niu Li , Jiang Gaoming , Ma Pibo
Published: December 2019

Knitted fabric sensors have been widely used as strain sensors in the sports health field and its large strain performance and structure are suitable for human body movements. When a knitted structure is worn, different human body movements are reflected through the large strain deformation of fabric structure and consequently change the electrical signal. Here, the mechanical and electrical properties of highly elastic knitted sweatpants were tested under large strain. This sensor has good sensitivity and stability during movement. Compared with traditional motion monitoring, this technique divides the walking cycle into two stages, namely, stance and swing phases, which can be further subdivided into six stages. The corresponding resistance characteristic values can accurately distinguish the gait cycle. Analysis on hysteresis and repeatability revealed that the sensor exhibits a constant electrical performance. Four kinds of motion postures were predicted and judged by comparing the resistance characteristic range value, peak value calculation function and time axis. The measured sensor outputs were transferred to a computer via 4.0 Bluetooth. Matlab language was used to detect the status through a rule-based algorithm and the sensor outputs.
Gait Asymmetry Post-Stroke: Determining Valid and Reliable Methods Using a Single Accelerometer Located on the Trunk
Authors: Buckley Christopher , Micó-Amigo M. Encarna , Dunne-Willows Michael , Godfrey Alan , Hickey Aodhán , Lord Sue , Rochester Lynn , Del Din Silvia , Moore Sarah A.
Published: December 2019

Asymmetry is a cardinal symptom of gait post-stroke that is targeted during rehabilitation. Technological developments have allowed accelerometers to be a feasible tool to provide digital gait variables. Many acceleration-derived variables are proposed to measure gait asymmetry. Despite a need for accurate calculation, no consensus exists for what is the most valid and reliable variable. Using an instrumented walkway (GaitRite) as the reference standard, this study compared the validity and reliability of multiple acceleration-derived asymmetry variables. Twenty-five post-stroke participants performed repeated walks over GaitRite whilst wearing a tri-axial accelerometer (Axivity AX3) on their lower back, on two occasions, one week apart. Harmonic ratio, autocorrelation, gait symmetry index, phase plots, acceleration, and jerk root mean square were calculated from the acceleration signals. Test–retest reliability was calculated, and concurrent validity was estimated by comparison with GaitRite. The strongest concurrent validity was obtained from step regularity from the vertical signal, which also recorded excellent test–retest reliability (Spearman’s rank correlation coefficients (rho) = 0.87 and Intraclass correlation coefficient (ICC21) = 0.98, respectively). Future research should test the responsiveness of this and other step asymmetry variables to quantify change during recovery and the effect of rehabilitative interventions for consideration as digital biomarkers to quantify gait asymmetry.
Detecting Debonding between Steel Beam and Reinforcing CFRP Plate Using Active Sensing with Removable PZT-Based Transducers
Authors: Jiang Jian , Jiang Jinwei , Deng Xiaowei , Deng Zifeng
Published: December 2019

Carbon fiber reinforced polymer (CFRP) plates are widely used to retrofit or reinforce steel structures, and the debonding damage between the steel structure and the CFRP plate is a typical failure in strengthening steel structures. This paper proposes a new approach to detecting debonding between a steel beam and a reinforcing CFRP plate by using removable lead zirconate titanate (PZT)-based transducers and active sensing. The removable PZT-based transducers are used to implement the active sensing approach, in which one transducer, as an actuator, is used to generate stress wave, and another transducer, as a sensor, is used to detect the stress wave that propagates across the bonding between the steel beam and the reinforcing CFRP plate. The bonding condition significantly influences the received sensor signal, and a wavelet-packet-based energy index (WPEI) is used to quantify the energy of the received signal to evaluate the severity of debonding between the steel beam and the reinforcing CFRP plate. To validate the proposed approach, experimental studies were performed, and two removable PZT-based transducers were designed and fabricated to detect the debonding between a steel beam and the reinforcing CRFP plate. The experimental results demonstrate the feasibility of the proposed method in detecting the debonding between a steel beam and the reinforcing CFRP plate using removable PZT-based transducers.
Reinforcement Learning (RL)-Based Energy Efficient Resource Allocation for Energy Harvesting-Powered Wireless Body Area Network
Authors: Xu Yi-Han , Xie Jing-Wei , Zhang Yang-Gang , Hua Min , Zhou Wen
Published: December 2019

Wireless body area networks (WBANs) have attracted great attention from both industry and academia as a promising technology for continuous monitoring of physiological signals of the human body. As the sensors in WBANs are typically battery-driven and inconvenient to recharge, an energy efficient resource allocation scheme is essential to prolong the lifetime of the networks, while guaranteeing the rigid requirements of quality of service (QoS) of the WBANs in nature. As a possible alternative solution to address the energy efficiency problem, energy harvesting (EH) technology with the capability of harvesting energy from ambient sources can potentially reduce the dependence on the battery supply. Consequently, in this paper, we investigate the resource allocation problem for EH-powered WBANs (EH-WBANs). Our goal is to maximize the energy efficiency of the EH-WBANs with the joint consideration of transmission mode, relay selection, allocated time slot, transmission power, and the energy constraint of each sensor. In view of the characteristic of the EH-WBANs, we formulate the energy efficiency problem as a discrete-time and finite-state Markov decision process (DFMDP), in which allocation strategy decisions are made by a hub that does not have complete and global network information. Owing to the complexity of the problem, we propose a modified Q-learning (QL) algorithm to obtain the optimal allocation strategy. The numerical results validate the effectiveness of the proposed scheme as well as the low computation complexity of the proposed modified Q-learning (QL) algorithm.
Application of Graphene-Based Materials for Detection of Nitrate and Nitrite in Water—A Review
Authors: Li Daoliang , Wang Tan , Li Zhen , Xu Xianbao , Wang Cong , Duan Yanqing
Published: December 2019

Nitrite and nitrate are widely found in various water environments but the potential toxicity of nitrite and nitrate poses a great threat to human health. Recently, many methods have been developed to detect nitrate and nitrite in water. One of them is to use graphene-based materials. Graphene is a two-dimensional carbon nano-material with sp2 hybrid orbital, which has a large surface area and excellent conductivity and electron transfer ability. It is widely used for modifying electrodes for electrochemical sensors. Graphene based electrochemical sensors have the advantages of being low cost, effective and efficient for nitrite and nitrate detection. This paper reviews the application of graphene-based nanomaterials for electrochemical detection of nitrate and nitrite in water. The properties and advantages of the electrodes were modified by graphene, graphene oxide and reduced graphene oxide nanocomposite in the development of nitrite sensors are discussed in detail. Based on the review, the paper summarizes the working conditions and performance of different sensors, including working potential, pH, detection range, detection limit, sensitivity, reproducibility, repeatability and long-term stability. Furthermore, the challenges and suggestions for future research on the application of graphene-based nanocomposite electrochemical sensors for nitrite detection are also highlighted.
Unmanned Aerial Vehicle-Borne Sensor System for Atmosphere-Particulate-Matter Measurements: Design and Experiments
Authors: Wang Tonghua , Han Wenting , Zhang Mengfei , Yao Xiaomin , Zhang Liyuan , Peng Xingshuo , Li Chaoqun , Dan Xvjia
Published: December 2019

An unmanned aerial vehicle (UAV) particulate-matter (PM) monitoring system was developed that can perform three-dimensional stereoscopic observation of PM2.5 and PM10 in the atmosphere. The UAV monitoring system was mainly integrated by modules of data acquisition and processing, wireless data transmission, and global positioning system (GPS). Particularly, in this study, a ground measurement-control subsystem was added that can display and store collected data in real time and set up measurement scenarios, data-storage modes, and system sampling frequency as needed. The UAV PM monitoring system was calibrated via comparison with a national air-quality monitoring station; the data of both systems were highly correlated. Since rotation of the UAV propeller affects measured PM concentration, this study specifically tested this effect by setting up another identical monitoring system fixed at a tower as reference. The UAV systems worked simultaneously to collect data for comparison. A correction method for the propeller disturbance was proposed. Averaged relative errors for the PM2.5 and PM10 concentrations measured by the two systems were 6.2% and 6.6%, respectively, implying that the UAV system could be used for monitoring PM in an atmosphere environment.
Electroencephalogram Profiles for Emotion Identification over the Brain Regions Using Spectral, Entropy and Temporal Biomarkers
Authors: Al-Qazzaz Noor Kamal , Sabir Mohannad K. , Ali Sawal Hamid Bin Mohd , Ahmad Siti Anom , Grammer Karl
Published: December 2019

Identifying emotions has become essential for comprehending varied human behavior during our daily lives. The electroencephalogram (EEG) has been adopted for eliciting information in terms of waveform distribution over the scalp. The rationale behind this work is twofold. First, it aims to propose spectral, entropy and temporal biomarkers for emotion identification. Second, it aims to integrate the spectral, entropy and temporal biomarkers as a means of developing spectro-spatial ( S S ) , entropy-spatial ( E S ) and temporo-spatial ( T S ) emotional profiles over the brain regions. The EEGs of 40 healthy volunteer students from the University of Vienna were recorded while they viewed seven brief emotional video clips. Features using spectral analysis, entropy method and temporal feature were computed. Three stages of two-way analysis of variance (ANOVA) were undertaken so as to identify the emotional biomarkers and Pearson’s correlations were employed to determine the optimal explanatory profiles for emotional detection. The results evidence that the combination of applied spectral, entropy and temporal sets of features may provide and convey reliable biomarkers for identifying S S , E S and T S profiles relating to different emotional states over the brain areas. EEG biomarkers and profiles enable more comprehensive insights into various human behavior effects as an intervention on the brain.
TiO2-x/TiO2-Structure Based ‘Self-Heated’ Sensor for the Determination of Some Reducing Gases
Authors: Ramanavicius Simonas , Tereshchenko Alla , Karpicz Renata , Ratautaite Vilma , Bubniene Urte , Maneikis Audrius , Jagminas Arunas , Ramanavicius Arunas
Published: December 2019

In this research we report the gas-sensing properties of TiO2-x/TiO2-based hetero-structure, which was ‘self-heated’ by current that at constant potential passed through the structure. Amperometric measurements were applied for the evaluation of sensor response towards ethanol, methanol, n-propanol and acetone gases/vapours. The sensitivity towards these gases was based on electrical resistance changes, which were determined by amperometric measurements of current at fixed voltage applied between Pt-based contacts/electrodes deposited on the TiO2-x/TiO2-based layer. X-ray diffraction (XRD) analysis revealed the formation of TiO2-x/TiO2-based hetero-structure, which is mainly based on Ti3O5/TiO2 formed during the hydro-thermal oxidation-based sensing-layer preparation process. Additionally, photoluminescence and time-resolved photoluminescence decay kinetics-based signals of this sensing structure revealed the presence of TiO2 mainly in the anatase phase in the TiO2-x/TiO2-based hetero-structure, which was formed at 400 °C annealing temperature. The evaluation of TiO2-x/TiO2-based gas-sensing layer was performed at several different temperatures (25 °C, 72 °C, 150 °C, 180 °C) and at these temperatures different sensitivity to the aforementioned gaseous materials was determined.
Is a Wearable Sensor-Based Characterisation of Gait Robust Enough to Overcome Differences Between Measurement Protocols? A Multi-Centric Pragmatic Study in Patients with Multiple Sclerosis
Authors: Angelini Lorenza , Carpinella Ilaria , Cattaneo Davide , Ferrarin Maurizio , Gervasoni Elisa , Sharrack Basil , Paling David , Nair Krishnan Padmakumari Sivaraman , Mazzà Claudia
Published: December 2019

Inertial measurement units (IMUs) allow accurate quantification of gait impairment of people with multiple sclerosis (pwMS). Nonetheless, it is not clear how IMU-based metrics might be influenced by pragmatic aspects associated with clinical translation of this approach, such as data collection settings and gait protocols. In this study, we hypothesised that these aspects do not significantly alter those characteristics of gait that are more related to quality and energetic efficiency and are quantifiable via acceleration related metrics, such as intensity, smoothness, stability, symmetry, and regularity. To test this hypothesis, we compared 33 IMU-based metrics extracted from data, retrospectively collected by two independent centres on two matched cohorts of pwMS. As a worst-case scenario, a walking test was performed in the two centres at a different speed along corridors of different lengths, using different IMU systems, which were also positioned differently. The results showed that the majority of the temporal metrics (9 out of 12) exhibited significant between-centre differences. Conversely, the between-centre differences in the gait quality metrics were small and comparable to those associated with a test-retest analysis under equivalent conditions. Therefore, the gait quality metrics are promising candidates for reliable multi-centric studies aiming at assessing rehabilitation interventions within a routine clinical context.
Sparsity-Aware Noise Subspace Fitting for DOA Estimation
Authors: Zheng Chundi , Chen Huihui , Wang Aiguo
Published: December 2019

We propose a sparsity-aware noise subspace fitting (SANSF) algorithm for direction-of-arrival (DOA) estimation using an array of sensors. The proposed SANSF algorithm is developed from the optimally weighted noise subspace fitting criterion. Our formulation leads to a convex linearly constrained quadratic programming (LCQP) problem that enjoys global convergence without the need of accurate initialization and can be easily solved by existing LCQP solvers. Combining the weighted quadratic objective function, the ℓ 1 norm, and the non-negative constraints, the proposed SANSF algorithm can enhance the sparsity of the solution. Numerical results based on simulations, using real measured ultrasonic, and radar data, show that, compared to existing sparsity-aware methods, the proposed SANSF can provide enhanced resolution with a lower computational burden.
Study on the Classification Performance of Underwater Sonar Image Classification Based on Convolutional Neural Networks for Detecting a Submerged Human Body
Authors: Nguyen Huu-Thu , Lee Eon-Ho , Lee Sejin
Published: December 2019

Auto-detecting a submerged human body underwater is very challenging with the absolute necessity to a diver or a submersible. For the vision sensor, the water turbidity and limited light condition make it difficult to take clear images. For this reason, sonar sensors are mainly utilized in water. However, even though a sonar sensor can give a plausible underwater image within this limitation, the sonar image’s quality varies greatly depending on the background of the target. The readability of the sonar image is very different according to the target distance from the underwater floor or the incidence angle of the sonar sensor to the floor. The target background must be very considerable because it causes scattered and polarization noise in the sonar image. To successfully classify the sonar image with these noises, we adopted a Convolutional Neural Network (CNN) such as AlexNet and GoogleNet. In preparing the training data for this model, the data augmentation on scattering and polarization were implemented to improve the classification accuracy from the original sonar image. It could be practical to classify sonar images undersea even by training sonar images only from the simple testbed experiments. Experimental validation was performed using three different datasets of underwater sonar images from a submerged body of a dummy, resulting in a final average classification accuracy of 91.6% using GoogleNet.
Characteristics of Highly Sensitive Hydrogen Sensor Based on Pt-WO3/Si Microring Resonator
Authors: Matsuura Sosuke , Yamasaku Naoki , Nishijima Yoshiaki , Okazaki Shinji , Arakawa Taro
Published: December 2019

Hydrogen gas has attracted attention as a new energy carrier, and simple but highly sensitive hydrogen sensors are required. We fabricated an optical hydrogen sensor based on a silicon microring resonator (MRR) with tungsten oxide (WO3) using a complementary metal-oxide-semiconductor (CMOS)-compatible process for the MRR and a sol-gel method for the WO3 layer and investigated its sensing characteristics at device temperatures of 5, 20, and 30 °C. At each temperature, a hydrogen concentration of as low as 0.1 vol% was successfully detected. The gas sensitivity increased with decreasing temperature. The dependence of the sensitivity on the device temperature can be attributed to the thickness of tungsten bronze (HxWO3) formed by WO3 during exposure to hydrogen gas. In addition, a hydrogen gas sensor based on a silicon-MRR-enhanced Mach–Zehnder interferometer (MRR-MZI) is proposed and its significantly high sensing ability using improved changes in the transmittance of light is theoretically discussed.
Underwater Target Tracking Using Forward-Looking Sonar for Autonomous Underwater Vehicles
Authors: Zhang Tiedong , Liu Shuwei , He Xiao , Huang Hai , Hao Kangda
Published: December 2019

In the scenario where autonomous underwater vehicles (AUVs) carry out tasks, it is necessary to reliably estimate underwater-moving-target positioning. While cameras often give low-precision visibility in a limited field of view, the forward-looking sonar is still an attractive method for underwater sensing, which is especially effective for long-range tracking. This paper describes an online processing framework based on forward-looking-sonar (FLS) images, and presents a novel tracking approach based on a Gaussian particle filter (GPF) to resolve persistent multiple-target tracking in cluttered environments. First, the character of acoustic-vision images is considered, and methods of median filtering and region-growing segmentation were modified to improve image-processing results. Second, a generalized regression neural network was adopted to evaluate multiple features of target regions, and a representation of feature subsets was created to improve tracking performance. Thus, an adaptive fusion strategy is introduced to integrate feature cues into the observation model, and the complete procedure of underwater target tracking based on GPF is displayed. Results obtained on a real acoustic-vision AUV platform during sea trials are shown and discussed. These showed that the proposed method is feasible and effective in tracking targets in complex underwater environments.
Metabolic Syndrome—An Emerging Constellation of Risk Factors: Electrochemical Detection Strategies
Authors: Sasya Madhurantakam , Devi K. S. Shalini , Babu Jayanth K. , Balaguru Rayappan John Bosco , Krishnan Uma Maheswari
Published: December 2019

Metabolic syndrome is a condition that results from dysfunction of different metabolic pathways leading to increased risk of disorders such as hyperglycemia, atherosclerosis, cardiovascular diseases, cancer, neurodegenerative disorders etc. As this condition cannot be diagnosed based on a single marker, multiple markers need to be detected and quantified to assess the risk facing an individual of metabolic syndrome. In this context, chemical- and bio-sensors capable of detecting multiple analytes may provide an appropriate diagnostic strategy. Research in this field has resulted in the evolution of sensors from the first generation to a fourth generation of ‘smart’ sensors. A shift in the sensing paradigm involving the sensing element and transduction strategy has also resulted in remarkable advancements in biomedical diagnostics particularly in terms of higher sensitivity and selectivity towards analyte molecule and rapid response time. This review encapsulates the significant advancements reported so far in the field of sensors developed for biomarkers of metabolic syndrome.
Experimental Evaluation of Advertisement-Based Bluetooth Low Energy Communication
Authors: Nikodem Maciej , Bawiec Marek
Published: December 2019

This paper addresses the efficiency of Bluetooth Low Energy (BLE) communication in a network composed of a large number of tags that transmit information to a single hub using advertisement mode. Theoretical results show that the use of advertisements enables hundreds and thousands of BLE devices to coexist in the same area and at the same time effectively transmit messages. Together with other properties (low power consumption, medium communication range, capability to detect a signal’s angle-of-arrival, etc.), this makes BLE a competing technology for the Internet of Things (IoT) applications. However, as the number of communicating devices increases, the advertisement collision intensifies and the communication performance of BLE drops. This phenomena was so far analyzed theoretically, in simulations and in small-scale experiments, but large-scale experiments are not presented in the literature. This paper complements previous results and presents an experimental evaluation of a real IoT-use case, which is the deployment of over 200 tags communicating using advertisements. We evaluate the impact of the number of advertisements on the effective data reception rate and throughput. Despite the advertisement collision rate in our experiment varying between 0.22 and 0.33, we show that BLE, thanks to the multiple transmission of advertisements, can still ensure acceptable data reception rates and fulfill the requirements of a wide range of IoT applications.
Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects
Authors: Angelopoulos Angelos , Michailidis Emmanouel T. , Nomikos Nikolaos , Trakadas Panagiotis , Hatziefremidis Antonis , Voliotis Stamatis , Zahariadis Theodore
Published: December 2019

The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research.
An RSSI-Based Low-Power Vehicle-Approach Detection Technique to Alert a Pedestrian
Authors: Watanabe Yoshito , Shoji Yozo
Published: December 2019

Information about an approaching vehicle is helpful for pedestrians to avoid traffic accidents while most of the past studies related to collision avoidance systems have focused on alerting drivers and controlling vehicles. This paper proposes a technique to detect an approaching vehicle aiming at alerting a pedestrian by observing the variation of the received signal strength indicator (RSSI) of the repeatedly radiated beacons from a vehicle, called the alert beacons. A linear regression algorithm is first applied to RSSI samples. The decision about whether a vehicle is approaching or not is made by the Student’s t-test for the linear regression coefficient. A passive method, where the pedestrian’s device behaves only as a receiver, is first described. The neighbor-discovery-based (ND-based) method, in which the pedestrian’s device repeatedly broadcasts advertising beacons and the moving vehicle in the vicinity returns the alert beacon when it receives the advertising beacon, is then proposed to improve the detection performance as well as reduce the device’s energy consumption. The theoretical detection error rate under Rayleigh fading is derived. It is revealed that the proposed ND-based method achieves a lower detection error rate when compared with the passive method under the same delay.
Magnetic Tunnel Junction Applications
Authors: Maciel Nilson , Marques Elaine , Naviner Lírida , Zhou Yongliang , Cai Hao
Published: December 2019

Spin-based devices can reduce energy leakage and thus increase energy efficiency. They have been seen as an approach to overcoming the constraints of CMOS downscaling, specifically, the Magnetic Tunnel Junction (MTJ) which has been the focus of much research in recent years. Its nonvolatility, scalability and low power consumption are highly attractive when applied in several components. This paper aims at providing a survey of a selection of MTJ applications such as memory and analog to digital converter, among others.
Wireless Acoustic Sensor Nodes for Noise Monitoring in the City of Linares (Jaén)
Authors: Fernandez-Prieto Jose-Angel , Cañada-Bago Joaquín , Gadeo-Martos Manuel-Angel
Published: December 2019

Noise pollution is a problem that affects millions of people worldwide. Over the last few years, many researchers have devoted their attention to the design of wireless acoustic sensor networks (WASNs) to monitor the real data of continuous and precise noise levels and to create noise maps in real time and space. Although WASNs are becoming a reality in smart cities, some research studies argue that very few projects have been deployed around the world, with most of them deployed as pilots for only days or weeks, with a small number of nodes. In this paper, we describe the design and implementation of a complete system for a WASN deployed in the city of Linares (Jaén), Spain, which has been running continuously for ten months. The complete system covers the network topology design, hardware and software of the sensor nodes, protocols, and a private cloud web server platform. As a result, the information provided by the system for each location where the sensor nodes are deployed is as follows: LAeq for a given period of time; noise indicators Lden, Lday, Levening, and Lnight; percentile noise levels (LA01T, LA10T, LA50T, LA90T, and LA99T); a temporal evolution representation of noise levels; and the predominant frequency of the noise. Some comparisons have been made between the noise indicators calculated by the sensor nodes and those from a commercial sound level meter. The results suggest that the proposed system is perfectly suitable for use as a starting point to obtain accurate maps of the noise levels in smart cities.
DeepLocate: Smartphone Based Indoor Localization with a Deep Neural Network Ensemble Classifier
Authors: Ashraf Imran , Hur Soojung , Park Sangjoon , Park Yongwan
Published: December 2019

A quickly growing location-based services area has led to increased demand for indoor positioning and localization. Undoubtedly, Wi-Fi fingerprint-based localization is one of the promising indoor localization techniques, yet the variation of received signal strength is a major problem for accurate localization. Magnetic field-based localization has emerged as a new player and proved a potential indoor localization technology. However, one of its major limitations is degradation in localization accuracy when various smartphones are used. The localization performance is different from various smartphones even with the same localization technique. This research leverages the use of a deep neural network-based ensemble classifier to perform indoor localization with heterogeneous devices. The chief aim is to devise an approach that can achieve a similar localization accuracy using various smartphones. Features extracted from magnetic data of Galaxy S8 are fed into neural networks (NNs) for training. The experiments are performed with Galaxy S8, LG G6, LG G7, and Galaxy A8 smartphones to investigate the impact of device dependence on localization accuracy. Results demonstrate that NNs can play a significant role in mitigating the impact of device heterogeneity and increasing indoor localization accuracy. The proposed approach is able to achieve a localization accuracy of 2.64 m at 50% on four different devices. The mean error is 2.23 m, 2.52 m, 2.59 m, and 2.78 m for Galaxy S8, LG G6, LG G7, and Galaxy A8, respectively. Experiments on a publicly available magnetic dataset of Sony Xperia M2 using the proposed approach show a mean error of 2.84 m with a standard deviation of 2.24 m, while the error at 50% is 2.33 m. Furthermore, the impact of devices on various attitudes on the localization accuracy is investigated.
An Integrated Method for Coding Trees, Measuring Tree Diameter, and Estimating Tree Positions
Authors: Sun Linhao , Fang Luming , Weng Yuhui , Zheng Siqing
Published: December 2019

Accurately measuring tree diameter at breast height (DBH) and estimating tree positions in a sample plot are important in tree mensuration. The main aims of this paper include (1) developing a new, integrated device that can identify trees using the quick response (QR) code technique to record tree identifications, measure DBH, and estimate tree positions concurrently; (2) designing an innovative algorithm to measure DBH using only two angle sensors, which is simple and can reduce the impact of eccentric stems on DBH measures; and (3) designing an algorithm to estimate the position of the tree by combining ultra-wide band (UWB) technology and altitude sensors, which is based on the received signal strength indication (RSSI) algorithm and quadrilateral localization algorithm. This novel device was applied to measure ten 10 × 10 m square plots of diversified environments and various tree species to test its accuracy. Before measuring a plot, a coded sticker was fixed at a height of 1.3 m on each individual tree stem, and four UWB module anchors were set up at the four corners of the plot. All individual trees’ DBHs and positions within the plot were then measured. Tree DBH, measured using a tree caliper, and the values of tree positions, measured using tape, angle ruler, and inclinometer, were used as the respective reference values for comparison. Across the plots, the decode rate of QR codes was 100%, with an average response time less than two seconds. The DBH values had a bias of 1.89 mm (1.88% in relative terms) and a root mean square error (RMSE) of 5.38 mm (4.53% in relative terms). The tree positions were accurately estimated; the biases on the x-axis and the y-axis of the tree position were −8.55–14.88 cm and −12.07–24.49 cm, respectively, and the corresponding RMSEs were 12.94–33.96 cm and 17.78–28.43 cm. The average error between the estimated and reference distances was 30.06 cm, with a standard deviation of 13.53 cm. The device is cheap and friendly to use in addition to its high accuracy. Although further studies are needed, our method provides a great alternative to conventional tools for improving the efficiency and accuracy of tree mensuration.