bluuebunny/crossref_metadata_2025 huggingface dataset:
Is an NWP-Based Nowcasting System Suitable for Aviation Operations?
Authors: Mazzarella Vincenzo , Milelli Massimo , Lagasio Martina , Federico Stefano , Torcasio Rosa Claudia , Biondi Riccardo , Realini Eugenio , Llasat Maria Carmen , Rigo Tomeu , Esbrí Laura , Kerschbaum Markus , Temme Marco-Michael , Gluchshenko Olga , Parodi Antonio
Published: September 2022

The growth of air transport demand expected over the next decades, along with the increasing frequency and intensity of extreme weather events, such as heavy rainfalls and severe storms due to climate change, will pose a tough challenge for air traffic management systems, with implications for flight safety, delays and passengers. In this context, the Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM (SINOPTICA) project has a dual aim, first to investigate if very short-range high-resolution weather forecast, including data assimilation, can improve the predictive capability of these events, and then to understand if such forecasts can be suitable for air traffic management purposes. The intense squall line that affected Malpensa, the major airport by passenger traffic in northern Italy, on 11 May 2019 is selected as a benchmark. Several numerical experiments are performed with a Weather Research and Forecasting (WRF) model using two assimilation techniques, 3D-Var in WRF Data Assimilation (WRFDA) system and a nudging scheme for lightning, in order to improve the forecast accuracy and to evaluate the impact of assimilated different datasets. To evaluate the numerical simulations performance, three different verification approaches, object-based, fuzzy and qualitative, are used. The results suggest that the assimilation of lightning data plays a key role in triggering the convective cells, improving both location and timing. Moreover, the numerical weather prediction (NWP)-based nowcasting system is able to produce reliable forecasts at high spatial and temporal resolution. The timing was found to be suitable for helping Air Traffic Management (ATM) operators to compute alternative landing trajectories.
Cross-Comparison between Sun-Synchronized and Geostationary Satellite-Derived Land Surface Temperature: A Case Study in Hong Kong
Authors: Adeniran Ibrahim Ademola , Zhu Rui , Yang Jinxin , Zhu Xiaolin , Wong Man Sing
Published: September 2022

Harmonization of satellite imagery provides a good opportunity for studying land surface temperature (LST) as well as the urban heat island effect. However, it is challenging to use the harmonized data for the study of LST due to the systematic bias between the LSTs from different satellites, which is highly influenced by sensor differences and the compatibility of LST retrieval algorithms. To fill this research gap, this study proposes the comparison of different LST images retrieved from various satellites that focus on Hong Kong, China, by applying diverse retrieval algorithms. LST images generated from Landsat-8 using the mono-window algorithm (MWAL8) and split-window algorithm (SWAL8) would be compared with the LST estimations from Sentinel-3 SLSTR and Himawari-8 using the split-window algorithm (SWAS3 and SWAH8). Intercomparison will also be performed through segregated groups of different land use classes both during the daytime and nighttime. Results indicate that there is a significant difference among the quantitative distribution of the LST data generated from these three satellites, with average bias of up to −1.80 K when SWAH8 was compared with MWAL8, despite having similar spatial patterns of the LST images. The findings also suggest that retrieval algorithms and the dominant land use class in the study area would affect the accuracy of image-fusion techniques. The results from the day and nighttime comparisons revealed that there is a significant difference between day and nighttime LSTs, with nighttime LSTs from different satellite sensors more consistent than the daytime LSTs. This emphasizes the need to incorporate as much night-time LST data as available when predicting or optimizing fine-scale LSTs in the nighttime, so as to minimize the bias. The framework designed by this study will serve as a guideline towards efficient spatial optimization and harmonized use of LSTs when utilizing different satellite images associated with an array of land covers and at different times of the day.
Bistatic Radar Scattering from Non-Gaussian Height Distributed Rough Surfaces
Authors: Yang Ying , Chen Kun-Shan , Wang Suyun
Published: September 2022

In modeling a rough surface, it is common to assume a Gaussian height distribution. This hypothesis cannot describe an eventual asymmetry between crests and troughs of natural surfaces. We analyzed the bistatic scattering from a rough surface with non-Gaussian height distributions using the Kirchhoff scattering theory. Two extreme cases of Gamma-distributed surfaces were compared in particular: exponential and Gaussian distributions. The bistatic angular dependence was examined under various root mean square (RMS) heights and power spectrum densities. Contribution sources to the coherent and incoherent scattering components were singled out relating to the surface height distribution. For an exponential height surface, the coherent scattering strengthens even as the surface becomes rough. The non-Gaussian effect on the incoherent scattering is connected with surface power spectrum density. The height distribution impacts differ in the different regions of the bistatic scattering plane and thus complicate the differentiation of the scattering patterns due to height distribution.
Adaptive Support-Driven Sparse Recovery STAP Method with Subspace Penalty
Authors: Wang Degen , Wang Tong , Cui Weichen , Liu Cheng
Published: September 2022

Detecting a moving target is an attractive topic in many fields, such as remote sensing. Space-time adaptive processing (STAP) plays a key role in detecting moving targets in strong clutter backgrounds for airborne early warning radar systems. However, STAP suffers serious clutter suppression performance loss when the number of training samples is insufficient due to the inhomogeneous clutter environment. In this article, an efficient sparse recovery STAP algorithm is proposed. First, inspired by the relationship between multiple sparse Bayesian learning (M-SBL) and subspace-based hybrid greedy algorithms, a new optimization objective function based on a subspace penalty is established. Second, the closed-form solution of each minimization step is obtained through the alternating minimization algorithm, which can guarantee the convergence of the algorithm. Finally, a restart strategy is used to adaptively update the support, which reduces the computational complexity. Simulation results show that the proposed algorithm has excellent performance in clutter suppression, convergence speed and running time with insufficient training samples.
Radar Emitter Recognition Based on Parameter Set Clustering and Classification
Authors: Xu Tao , Yuan Shuo , Liu Zhangmeng , Guo Fucheng
Published: September 2022

An important task in the Electronic Support Measures (ESM) field is analyzing and recognizing radar signals. Feature extraction is one of the primary key elements of radar emitter recognition algorithms. Current research mainly finds statistical features such as the mean and variance of parameters from pluses as the input features of the classifier. However, data noise in intercepted pulse signals greatly interferes with the accuracy of the extracted statistical features and seriously affects the recognition rate of radar emitters. In this paper, we proposed a method of radar emitter recognition. We first clustered parameter sets to establish a set of frequent items and their corresponding clustering centers. Next, we concatenated the clustering centers of each frequent item into a feature vector associated with the data volume dimensions. Then, we built a decision tree classification model based on the feature vector, and finally we used the learned model for the recognition of unknown radar pulse trains. The simulation results show that the proposed method has better robustness when applied to a variety of data volumes and data noise scenarios compared with long short-term memory (LSTM) and support vector machine (SVM) methods.
Debris-Flow Susceptibility Assessment in China: A Comparison between Traditional Statistical and Machine Learning Methods
Authors: Huang Han , Wang Yongsheng , Li Yamei , Zhou Yang , Zeng Zhaoqi
Published: September 2022

Debris flows, triggered by dual interferences extrinsically and intrinsically, have been widespread in China. The debris-flow susceptibility (DFS) assessment is acknowledged as the benchmark for the mitigation and prevention of debris flow risks, but DFS assessments at the national level are lacking. The role of human activities in the DFS assessment has always been overlooked. On the basis of a detailed inventory of debris-flow sites and a large set of environmental and human-related characteristics, this research presents the comparative performance of the well-known information value (IV), logistic regression (LR) and random forest (RF) models for DFS assessments in China. Twelve causative factors, namely, elevation, slope, aspect, rainfall, the normalized difference vegetation index (NDVI), land use, landform, geology, distance to faults, density of villages, distance to rivers and distance to roads, were considered. Debris-flow susceptibility maps were then generated after the nonlinear relationship between the debris-flow occurrence and the causative factors was captured. Finally, the predictive performance of the three maps was evaluated through receiver operating characteristic (ROC) curves, and the validation results showed that areas under the ROC curves were 81.98%, 79.96% and 97.38% for the IV, LR and RF models, respectively, indicating that the RF model outperformed the other two traditional statistical methods. The importance ranking of the RF model also revealed that distance to roads, slope and rainfall dominated the spatial distribution of debris flows. This is the first experiment to compare between the traditional statistical and machine learning methods in DFS studies for the whole of China. Our results could provide some empirical support for China’s policymakers and local practitioners in their efforts to enable residents to be less vulnerable to disasters.
Estimate the Earliest Phenophase for Garlic Mapping Using Time Series Landsat 8/9 Images
Authors: Guo Yan , Xia Haoming , Zhao Xiaoyang , Qiao Longxin , Qin Yaochen
Published: September 2022

Garlic is the major economic crop in China. Timely and accurate identification and mapping of garlic are significant for garlic yield prediction and garlic market management. Previous studies on garlic mapping were mainly based on all observations of the entire growing season, so the resulting maps have a hysteresis. Here, we determined the optimal identification strategy and the earliest identifiable phenophase for garlic based on all available Landsat 8/9 time series imagery in Google Earth Engine. Specifically, we evaluated the performance of different vegetation indices for each phenophase to determine the optimal classification metrics for garlic. Secondly, we identified garlic using random forest algorithm and classification metrics of different time series lengths. Finally, we determined the earliest identifiable phenophase of garlic and generated an early-season garlic distribution map. Garlic could be identified as early as March (bud differentiation period) with an F1 of 0.91. Our study demonstrates the differences in the performance of vegetation indices at different phenophases, and these differences provide a new idea for mapping crops. The generated early-season garlic distribution map provides timely data support for various stakeholders.
Quantitative Evaluation of Environmental Loading Products and Thermal Expansion Effect for Correcting GNSS Vertical Coordinate Time Series in Taiwan
Authors: Liu Bin , Ma Xiaojun , Xing Xuemin , Tan Jianbo , Peng Wei , Zhang Liqun
Published: September 2022

We explored the driving factors of nonlinear signals in vertical coordinate sequences of stations in a Taiwan global navigation satellite system (GNSS) network, including atmospheric loading (ATML), hydrological loading (HYDL), and non-tidal ocean loading (NTOL) effects. At the same time, we used the finite element analysis software MIDAS to quantify the vertical displacements of different types of monuments due to the thermal expansion effect, including deep drilled braced (DDB) and short drilled braced (SDB). By quantitatively comparing the correction results of GNSS time series with different single mass loading models, we found that there was little difference in the correction of different environmental loading products. We compared different combinations of each loading product to correct the GNSS time series, and finally selected the best combination suitable for Taiwan GNSS network, that is, ATML (GFZ_ECMWF IB) + HYDL (IMLS_MERRA2) + NTOL (IMLS_MPIOM06). We found that the spatial and temporal models of ATML and NTOL are very similar, with non-tidal atmospheric loading and non-tidal ocean loading working together, a pattern that may be related to tropical cyclones. Both models also showed good correction effect on GNSS stations in the western plain of Taiwan, but with limited correction effect in the eastern part of Taiwan. This may be due to the influence of the subtropical monsoon climate in Taiwan and the barrier of the central mountain range, resulting in obvious differences between eastern and western Taiwan. The hydrological loading was found to act in the opposite way to the thermal expansion effect in the temporal domain, indicating that some displacements in hydrological loading may cancel out displacements caused by the thermal expansion effect. This aspect of displacement is not included in the hydrological loading model but should be considered when accurately estimating the temporal and spatial variation of water storage capacity in Taiwan using GNSS observed displacements.
Occupational Stress Monitoring Using Biomarkers and Smartwatches: A Systematic Review
Authors: Morales Analúcia , Barbosa Maria , Morás Laura , Cazella Silvio César , Sgobbi Lívia F. , Sene Iwens , Marques Gonçalo
Published: September 2022

This article presents a systematic review of the literature concerning scientific publications on wrist wearables that can help to identify stress levels. The study is part of a research project aimed at modeling a stress surveillance system and providing coping recommendations. The investigation followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. In total, 38 articles were selected for full reading, and 10 articles were selected owing to their alignment with the study proposal. The types of technologies used in the research stand out amongst our main results after analyzing the articles. It is noteworthy that stress assessments are still based on standardized questionnaires, completed by the participants. The main biomarkers collected by the devices used in the selected works included: heart rate variation, cortisol analysis, skin conductance, body temperature, and blood volume at the wrist. This study concludes that developing a wrist wearable for stress identification using physiological and chemical sensors is challenging but possible and applicable.
Detection of Hydrogen Peroxide in Liquid and Vapors Using Titanium(IV)-Based Test Strips and Low-Cost Hardware
Authors: Hossain Rayhan , Dickinson Jimmy J. , Apblett Allen , Materer Nicholas F.
Published: September 2022

Titanium(IV) solutions are known to detect hydrogen peroxide in solutions by a colorimetric method. Xplosafe’s XploSens PS commercial titanium(IV)-based peroxide detection test strips are used to detect hydrogen peroxide in liquids. The use of these test strips as gas-phase detectors for peroxides was tested using low-cost hardware. The exposure of these strips to hydrogen peroxide liquid or gas leads to the development of an intense yellow color. For liquids, a digital single-lens reflex camera was used to quantify the color change using standardized solutions containing between 50 and 500 ppm peroxide by mass. Analysis of the images with color separation can provide a more quantitative determination than visual comparison to a color chart. For hydrogen peroxide gas, an inexpensive web camera and a tungsten lamp were used to measure the reflected light intensity as a function of exposure from a test strip held in a custom cell. First-order behavior in the color change with time was observed during the exposure to peroxide vapor over a range of peroxide concentrations from 2 and 30 ppm by volume. For a 1-min measurement, the gas-phase detection limit is estimated to be 1 ppm. A 0.01 ppm detection limit can be obtained with a 1-h exposure time. Titanium(IV)-based peroxide detection test strips are sensitive enough to work as a gas-phase hydrogen peroxide detector.
Numerical Approach to Facial Palsy Using a Novel Registration Method with 3D Facial Landmark
Authors: Kim Junsik , Jeong Hyungwha , Cho Jeongmok , Pak Changsik , Oh Tae Suk , Hong Joon Pio , Kwon Soonchul , Yoo Jisang
Published: September 2022

Treatment of facial palsy is essential because neglecting this disorder can lead to serious sequelae and further damage. For an objective evaluation and consistent rehabilitation training program of facial palsy patients, a clinician’s evaluation must be simultaneously performed alongside quantitative evaluation. Recent research has evaluated facial palsy using 68 facial landmarks as features. However, facial palsy has numerous features, whereas existing studies use relatively few landmarks; moreover, they do not confirm the degree of improvement in the patient. In addition, as the face of a normal person is not perfectly symmetrical, it must be compared with previous images taken at a different time. Therefore, we introduce three methods to numerically approach measuring the degree of facial palsy after extracting 478 3D facial landmarks from 2D RGB images taken at different times. The proposed numerical approach performs registration to compare the same facial palsy patients at different times. We scale landmarks by performing scale matching before global registration. After scale matching, coarse registration is performed with global registration. Point-to-plane ICP is performed using the transformation matrix obtained from global registration as the initial matrix. After registration, the distance symmetry, angular symmetry, and amount of landmark movement are calculated for the left and right sides of the face. The degree of facial palsy at a certain point in time can be approached numerically and can be compared with the degree of palsy at other times. For the same facial expressions, the degree of facial palsy at different times can be measured through distance and angle symmetry. For different facial expressions, the simultaneous degree of facial palsy in the left and right sides can be compared through the amount of landmark movement. Through experiments, the proposed method was tested using the facial palsy patient database at different times. The experiments involved clinicians and confirmed that using the proposed numerical approach can help assess the progression of facial palsy.
Autonomous Rear Parking via Rapidly Exploring Random-Tree-Based Reinforcement Learning
Authors: Shahi Saugat , Lee Heoncheol
Published: September 2022

This study addresses the problem of autonomous rear parking (ARP) for car-like nonholonomic vehicles. ARP includes path planning to generate an efficient collision-free path from the start point to the target parking slot and path following to produce control inputs to stably follow the generated path. This paper proposes an efficient ARP method that consists of the following five components: (1) OpenAI Gym environment for training the reinforcement learning agent, (2) path planning based on rapidly exploring random trees, (3) path following based on model predictive control, (4) reinforcement learning based on the Markov decision process, and (5) travel length estimation between the start and the goal points. The evaluation results in OpenAI Gym show that the proposed ARP method can successfully be used by minimizing the difference between the reference points and trajectories produced by the proposed method.
LPV Control and Virtual-Sensor-Based Fault Tolerant Strategies for a Three-Axis Gimbal System
Authors: Medero Ariel , Puig Vicenç
Published: September 2022

This paper deals with the LPV control of a three-axis gimbal including fault-tolerant capabilities. First, the derivation of an analytical model for the considered system based on the robotics Serial-Link (SL) theory is derived. Then, a series of simplifications that allow obtaining a quasi-LPV model for the considered gimbal is proposed. Gain scheduling LPV controllers with PID structure are designed using pole placement by means of linear matrix inequalities (LMIs). Moreover, exploiting the sensor redundancy available in the gimbal, a virtual-sensor-based fault tolerant control (FTC) strategy is proposed. This virtual sensor uses a Recursive Least Square (RLS) estimation algorithm and an LPV observer for fault detection and estimation. Finally, the proposed LPV control scheme including the virtual sensor strategy is tested in simulation in several scenarios.
Open and Cost-Effective Digital Ecosystem for Lake Water Quality Monitoring
Authors: Strigaro Daniele , Cannata Massimiliano , Lepori Fabio , Capelli Camilla , Lami Andrea , Manca Dario , Seno Silvio
Published: September 2022

In some sectors of the water resources management, the digital revolution process is slowed by some blocking factors such as costs, lack of digital expertise, resistance to change, etc. In addition, in the era of Big Data, many are the sources of information available in this field, but they are often not fully integrated. The adoption of different proprietary solutions to sense, collect and manage data is one of the main problems that hampers the availability of a fully integrated system. In this context, the aim of the project is to verify if a fully open, cost-effective and replicable digital ecosystem for lake monitoring can fill this gap and help the digitalization process using cloud based technology and an Automatic High-Frequency Monitoring System (AHFM) built using open hardware and software components. Once developed, the system is tested and validated in a real case scenario by integrating the historical databases and by checking the performance of the AHFM system. The solution applied the edge computing paradigm in order to move some computational work from server to the edge and fully exploiting the potential offered by low power consuming devices.
Electrodeless Heart and Respiratory Rate Estimation during Sleep Using a Single Fabric Band and Event-Based Edge Processing
Authors: Jayarathna Titus , Gargiulo Gaetano D. , Lui Gough Y. , Breen Paul P.
Published: September 2022

Heart rate (HR) and respiratory rate (RR) are two vital parameters of the body medically used for diagnosing short/long-term illness. Out-of-the-body, non-skin-contact HR/RR measurement remains a challenge due to imprecise readings. “Invisible” wearables integrated into day-to-day garments have the potential to produce precise readings with a comfortable user experience. Sleep studies and patient monitoring benefit from “Invisibles” due to longer wearability without significant discomfort. This paper suggests a novel method to reduce the footprint of sleep monitoring devices. We use a single silver-coated nylon fabric band integrated into a substrate of a standard cotton/nylon garment as a resistive elastomer sensor to measure air and blood volume change across the chest. We introduce a novel event-based architecture to process data at the edge device and describe two algorithms to calculate real-time HR/RR on ARM Cortex-M3 and Cortex-M4F microcontrollers. RR estimations show a sensitivity of 99.03% and a precision of 99.03% for identifying individual respiratory peaks. The two algorithms used for HR calculation show a mean absolute error of 0.81 ± 0.97 and 0.86±0.61 beats/min compared with a gold standard ECG-based HR. The event-based algorithm converts the respiratory/pulse waveform into instantaneous events, therefore reducing the data size by 40–140 times and requiring 33% less power to process and transfer data. Furthermore, we show that events hold enough information to reconstruct the original waveform, retaining pulse and respiratory activity. We suggest fabric sensors and event-based algorithms would drastically reduce the device footprint and increase the performance for HR/RR estimations during sleep studies, providing a better user experience.
An Efficient Distributed Approach for Cooperative Spectrum Sensing in Varying Interests Cognitive Radio Networks
Authors: Trigka Maria , Dritsas Elias
Published: September 2022

The rapid growth in wireless communications, coupled with insufficient utilization of the spectrum, led to the development of new wireless services and the promising technology of cognitive radio (CR) networks, which facilitate periodic access to the unoccupied spectrum bands and thus increases spectral efficiency. A fundamental task in CR networks is spectrum sensing, through which unauthorized secondary users (SUs) detect unoccupied bands in the spectrum. To achieve this, an accurate estimate of the power spectrum is necessary. From this perspective, and given that many other factors can affect individual detection, such as pathloss and receiver uncertainty, we aim to improve its estimate by exploiting the spatial diversity in the SUs’ observations. Spectrum sensing is treated as a parameters estimation problem, assuming that the parameters’ vector of each SU consists of some global and partially common parameters. To exploit this modeling, distributed and cooperative spectrum sensing is the subject of interest in this study. Diffusion techniques, and especially the Adapt-Then-Combine (ATC) method will be exploited, where each SU cooperates with a group of nodes in its neighborhood that share the same parameters of interest. We consider a network of three static PUs with overlapping power spectrums, and thus, frequency bands. The performance of the employed method will be evaluated under two scenarios: (i) when the PUs spectrum varies, since some frequency bands are not yet utilized, and (ii) when the frequency bands of the PUs are fixed, but there is a mobile SU in the network, changing regions and parameters of interest. Experimental results and performance analysis reveal the ATC algorithm robustness and efficiency.
Workshop Safety Helmet Wearing Detection Model Based on SCM-YOLO
Authors: Zhang Bin , Sun Chuan-Feng , Fang Shu-Qi , Zhao Ye-Hai , Su Song
Published: September 2022

In order to overcome the problems of object detection in complex scenes based on the YOLOv4-tiny algorithm, such as insufficient feature extraction, low accuracy, and low recall rate, an improved YOLOv4-tiny safety helmet-wearing detection algorithm SCM-YOLO is proposed. Firstly, the Spatial Pyramid Pooling (SPP) structure is added after the backbone network of the YOLOv4-tiny model to improve its adaptability of different scale features and increase its effective features extraction capability. Secondly, Convolutional Block Attention Module (CBAM), Mish activation function, K-Means++ clustering algorithm, label smoothing, and Mosaic data enhancement are introduced to improve the detection accuracy of small objects while ensuring the detection speed. After a large number of experiments, the proposed SCM-YOLO algorithm achieves a mAP of 93.19%, which is 4.76% higher than the YOLOv4-tiny algorithm. Its inference speed reaches 22.9FPS (GeForce GTX 1050Ti), which meets the needs of the real-time and accurate detection of safety helmets in complex scenes.
Localization of Cracks in Concrete Structures Using an Unmanned Aerial Vehicle
Authors: Woo Hyun-Jung , Seo Dong-Min , Kim Min-Seok , Park Min-San , Hong Won-Hwa , Baek Seung-Chan
Published: September 2022

Active research on crack detection technology for structures based on unmanned aerial vehicles (UAVs) has attracted considerable attention. Most of the existing research on localization of cracks using UAVs mounted the Global Positioning System (GPS)/Inertial Measurement Unit (IMU) on the UAVs to obtain location information. When such absolute position information is used, several studies confirmed that positioning errors of the UAVs were reflected and were in the order of a few meters. To address these limitations, in this study, without using the absolute position information, localization of cracks was defined using relative position between objects in UAV-captured images to significantly reduce the error level. Through aerial photography, a total of 97 images were acquired. Using the point cloud technique, image stitching, and homography matrix algorithm, 5 cracks and 3 reference objects were defined. Importantly, the comparative analysis of estimated relative position values and ground truth values through field measurement revealed that errors in the range 24–84 mm and 8–48 mm were obtained on the x- and y-directions, respectively. Also, RMSE errors of 37.95–91.24 mm were confirmed. In the future, the proposed methodology can be utilized for supplementing and improving the conventional methods for visual inspection of infrastructures and facilities.
Transportation Mode Detection Using Temporal Convolutional Networks Based on Sensors Integrated into Smartphones
Authors: Wang Pu , Jiang Yongguo
Published: September 2022

In recent years, with the development of science and technology, people have more and more choices for daily travel. However, assisting with various mobile intelligent services by transportation mode detection has become more urgent for the refinement of human activity identification. Although much work has been done on transportation mode detection, accurate and reliable transportation mode detection remains challenging. In this paper, we propose a novel transportation mode detection algorithm, namely T2Trans, based on a temporal convolutional network (i.e., TCN), which employs multiple lightweight sensors integrated into a phone. The feature representation learning of multiple preprocessed sensor data using temporal convolutional networks can improve transportation mode detection accuracy and enhance learning efficiency. Extensive experimental results demonstrated that our algorithm attains a macro F1-score of 86.42% on the real-world SHL dataset and 88.37% on the HTC dataset, with an average accuracy of 86.37% on the SHL dataset and 89.13% on the HTC dataset. Our model can better identify eight transportation modes, including stationary, walking, running, cycling, car, bus, subway, and train, with better transportation mode detection accuracy, and outperform other benchmark algorithms.
Air-Coupled Ultrasound Sealing Integrity Inspection Using Leaky Lamb Waves in a Simplified Model of a Lithium-Ion Pouch Battery: Feasibility Study
Authors: Cho Hyunwoo , Kil Eunwoo , Jang Jihun , Kang Jinbum , Song Ilseob , Yoo Yangmo
Published: September 2022

Inspecting the sealing integrity of lead tabs is an important means of ensuring the reliability and safety of pouch-type lithium-ion (Li-ion) batteries with a thin multi-layered aluminum (Al) laminated film. This paper presents a new air-coupled ultrasonic non-destructive testing (NDT) inspection method based on leaky Lamb wave transmission; and reception for evaluating the sealing integrity between the lead tab and the Al pouch film. The proposed method uses the critical incidence angle between the air and the layer with the fastest Lamb wave velocity to maximize the signal-to-noise ratio in the through-transmission mode. To determine the critical incidence angle, phantom experiments with two test pieces (i.e., an Al tab and an Al tab sealed with an Al pouch film) are conducted. In addition, 2D scans are performed at various incidence angles for an inhouse pouch-type Li-ion battery with a 1-mm-wide foreign material inserted as a defect. At the critical incidence angle (i.e., 22°), the proposed air-coupled ultrasonic NDT method in through-transmission mode successfully identifies the shape and location of the defect through c-scan image reconstruction. These preliminary results indicate that the proposed air-coupled ultrasonic NDT method with leaky Lamb waves can be used to inspect the sealing integrity of Li-ion pouch batteries in dry test conditions.
Application of Meta-Heuristics in 5G Network Slicing: A Systematic Review of the Literature
Authors: Gomes Rayner , Vieira Dario , de Castro Miguel Franklin
Published: September 2022

Network slicing is a vital component of the 5G system to support diverse network scenarios, creating virtual networks (slices) by mapping virtual network requests to real networks. The mapping is an arduous computing process, mathematically studied and known as the Virtual Network Embedding (VNE) problem, and its complexity is NP-Hard. The mapping process is oriented to respect the QoS demands from the virtual network requests and the available resources in the physical-substrate infrastructure. Meta-heuristic approaches are a suitable way to solve the VNE problems because of their capacity to escape from the local optimum and adapt the solution search to complex networks; these abilities are essential in 5G networks scenarios. This article presents a systematic review of meta-heuristics organized by application, development and problem-solving approaches to VNE. It also provides the standard parameters to model the infrastructure and virtual network requests to simulate network slicing as a service. Finally, our work proposes some future research based on the discovered gaps.
BIOS-Based Server Intelligent Optimization
Authors: Qi Xianxian , Yang Jianfeng , Zhang Yiyang , Xiao Baonan
Published: September 2022

Servers are the infrastructure of enterprise applications, and improving server performance under fixed hardware resources is an important issue. Conducting performance tuning at the application layer is common, but it is not systematic and requires prior knowledge of the running application. Some works performed tuning by dynamically adjusting the hardware prefetching configuration with a predictive model. Similarly, we design a BIOS (Basic Input/Output System)-based dynamic tuning framework for a Taishan 2280 server, including dynamic identification and static optimization. We simulate five workload scenarios (CPU-instance, etc.) with benchmark tools and perform scenario recognition dynamically with performance monitor counters (PMCs). The adjustable configurations provided by Kunpeng processing reach 2N(N>100). Therefore, we propose a joint BIOS optimization algorithm using a deep Q-network. Configuration optimization is modeled as a Markov decision process starting from a feasible solution and optimizing gradually. To improve the continuous optimization capabilities, the neighborhood search method of state machine control is added. To assess its performance, we compare our algorithm with the genetic algorithm and particle swarm optimization. Our algorithm shows that it can also improve performance up to 1.10× compared to experience configuration and perform better in reducing the probability of server downtime. The dynamic tuning framework in this paper is extensible, can be trained to adapt to different scenarios, and is more suitable for servers with many adjustable configurations. Compared with the heuristic intelligent search algorithm, the proposed joint BIOS optimization algorithm can generate fewer infeasible solutions and is not easily disturbed by initialization.
A Spatial-Motion-Segmentation Algorithm by Fusing EDPA and Motion Compensation
Authors: Liu Xinghua , Zhao Yunan , Yang Lei , Ge Shuzhi Sam
Published: September 2022

Motion segmentation is one of the fundamental steps for detection, tracking, and recognition, and it can separate moving objects from the background. In this paper, we propose a spatial-motion-segmentation algorithm by fusing the events-dimensionality-preprocessing algorithm (EDPA) and the volume of warped events (VWE). The EDPA consists of depth estimation, linear interpolation, and coordinate normalization to obtain an extra dimension (Z) of events. The VWE is conducted by accumulating the warped events (i.e., motion compensation), and the iterative-clustering algorithm is introduced to maximize the contrast (i.e., variance) in the VWE. We established our datasets by utilizing the event-camera simulator (ESIM), which can simulate high-frame-rate videos that are decomposed into frames to generate a large amount of reliable events data. Exterior and interior scenes were segmented in the first part of the experiments. We present the sparrow search algorithm-based gradient ascent (SSA-Gradient Ascent). The SSA-Gradient Ascent, gradient ascent, and particle swarm optimization (PSO) were evaluated in the second part. In Motion Flow 1, the SSA-Gradient Ascent was 0.402% higher than the basic variance value, and 52.941% faster than the basic convergence rate. In Motion Flow 2, the SSA-Gradient Ascent still performed better than the others. The experimental results validate the feasibility of the proposed algorithm.
Cooperative Search Method for Multiple UAVs Based on Deep Reinforcement Learning
Authors: Gao Mingsheng , Zhang Xiaoxuan
Published: September 2022

In this paper, a cooperative search method for multiple UAVs is proposed to solve the problem of low efficiency of multi-UAV task execution by using a cooperative game with incomplete information. To improve search efficiency, CBBA (Consensus-Based Bundle Algorithm) is applied to designate the tasks area for each UAV. Then, Independent Deep Reinforcement Learning (IDRL) is used to solve Nash equilibrium to improve UAVs’ collaborations. The proposed reward function is smartly developed to guide UAVs to fly along the path with higher reward value while avoiding the collisions between UAVs during flights. Finally, extensive experiments are carried out to compare our proposed method with other algorithms. Simulation results show that the proposed method can obtain more rewards in the same period of time as other algorithms.
LoRa Network-Based System for Monitoring the Agricultural Sector in Andean Areas: Case Study Ecuador
Authors: Rivera Guzmán Edgar Fabián , Mañay Chochos Edison David , Chiliquinga Malliquinga Mauricio Danilo , Baldeón Egas Paúl Francisco , Toasa Guachi Renato Mauricio
Published: September 2022

This article focuses on the development of a system based on the long-range network (LoRa), which is used for monitoring the agricultural sector and is implemented in areas of the Andean region of Ecuador. The LoRa network is applied for the analysis of climatic parameters by monitoring temperature, relative humidity, soil moisture and ultraviolet radiation. It consists of two transmitter nodes and one receiver node, a LoRa Gateway with two communication channels for data reception and one for data transmission, and an IoT server. In addition, a graphical user interface has been developed in Thinger.io to monitor the crops and remotely control the actuators. The research conducted contains useful information for the deployment of a LoRa network in agricultural crops located in mountainous areas above 2910 m.a.s.l., where there are terrains with irregular orography, reaching a coverage of 50 hectares and a range distance of 875 m to the farthest point in the community of Chirinche Bajo, Ecuador. An average RSSI of the radio link of −122 dBm was obtained in areas with a 15% slope and 130 m difference in height according to the Gateway, where the presence of vegetation, eucalyptus trees and no line-of-sight generated interference to the radio signal. The success rate of PDR packet delivery with an SF of nine, had a better performance, with values of no less than 76% and 92% in uplink and downlink respectively. Finally, the technological gap is reduced, since the network reaches places where traditional technologies do not exist, allowing farmers to make timely decisions in the production process in the face of adverse weather events.
Smart-Home Concept for Remote Monitoring of Instrumental Activities of Daily Living (IADL) in Older Adults with Cognitive Impairment: A Proof of Concept and Feasibility Study
Authors: Lee Myeounggon , Mishra Ram Kinker , Momin Anmol , El-Refaei Nesreen , Bagheri Amir Behzad , York Michele K. , Kunik Mark E. , Derhammer Marc , Fatehi Borna , Lim James , Cole Rylee , Barchard Gregory , Vaziri Ashkan , Najafi Bijan
Published: September 2022

Assessment of instrumental activities of daily living (IADL) is essential for the diagnosis and staging of dementia. However, current IADL assessments are subjective and cannot be administered remotely. We proposed a smart-home design, called IADLSys, for remote monitoring of IADL. IADLSys consists of three major components: (1) wireless physical tags (pTAG) attached to objects of interest, (2) a pendant–sensor to monitor physical activities and detect interaction with pTAGs, and (3) an interactive tablet as a gateway to transfer data to a secured cloud. Four studies, including an exploratory clinical study with five older adults with clinically confirmed cognitive impairment, who used IADLSys for 24 h/7 days, were performed to confirm IADLSys feasibility, acceptability, adherence, and validity of detecting IADLs of interest and physical activity. Exploratory tests in two cases with severe and mild cognitive impairment, respectively, revealed that a case with severe cognitive impairment either overestimated or underestimated the frequency of performed IADLs, whereas self-reporting and objective IADL were comparable for the case with mild cognitive impairment. This feasibility and acceptability study may pave the way to implement the smart-home concept to remotely monitor IADL, which in turn may assist in providing personalized support to people with cognitive impairment, while tracking the decline in both physical and cognitive function.
Visual Cues for Turning in Parkinson’s Disease
Authors: Das Julia , Vitorio Rodrigo , Butterfield Allissa , Morris Rosie , Graham Lisa , Barry Gill , McDonald Claire , Walker Richard , Mancini Martina , Stuart Samuel
Published: September 2022

Turning is a common impairment of mobility in people with Parkinson’s disease (PD), which increases freezing of gait (FoG) episodes and has implications for falls risk. Visual cues have been shown to improve general gait characteristics in PD. However, the effects of visual cues on turning deficits in PD remains unclear. We aimed to (i) compare the response of turning performance while walking (180° and 360° turns) to visual cues in people with PD with and without FoG; and (ii) examine the relationship between FoG severity and response to visual cues during turning. This exploratory interventional study measured turning while walking in 43 participants with PD (22 with self-reported FoG) and 20 controls using an inertial sensor placed at the fifth lumbar vertebrae region. Participants walked straight and performed 180° and 360° turns midway through a 10 m walk, which was done with and without visual cues (starred pattern). The turn duration and velocity response to visual cues were assessed using linear mixed effects models. People with FoG turned slower and longer than people with PD without FoG and controls (group effect: p < 0.001). Visual cues reduced the velocity of turning 180° across all groups and reduced the velocity of turning 360° in people with PD without FoG and controls. FoG severity was not significantly associated with response to visual cues during turning. Findings suggest that visual cueing can modify turning during walking in PD, with response influenced by FoG status and turn amplitude. Slower turning in response to visual cueing may indicate a more cautious and/or attention-driven turning pattern. This study contributes to our understanding of the influence that cues can have on turning performance in PD, particularly in freezers, and will aid in their therapeutic application.
Automated Fluid Intake Detection Using RGB Videos
Authors: Cohen Rachel , Fernie Geoff , Roshan Fekr Atena
Published: September 2022

Dehydration is a common, serious issue among older adults. It is important to drink fluid to prevent dehydration and the complications that come with it. As many older adults forget to drink regularly, there is a need for an automated approach, tracking intake throughout the day with limited user interaction. The current literature has used vision-based approaches with deep learning models to detect drink events; however, most use static frames (2D networks) in a lab-based setting, only performing eating and drinking. This study proposes a 3D convolutional neural network using video segments to detect drinking events. In this preliminary study, we collected data from 9 participants in a home simulated environment performing daily activities as well as eating and drinking from various containers to create a robust environment and dataset. Using state-of-the-art deep learning models, we trained our CNN using both static images and video segments to compare the results. The 3D model attained higher performance (compared to 2D CNN) with F1 scores of 93.7% and 84.2% using 10-fold and leave-one-subject-out cross-validations, respectively.
A Semantic Data-Based Distributed Computing Framework to Accelerate Digital Twin Services for Large-Scale Disasters
Authors: Kwon Jin-Woo , Yun Seong-Jin , Kim Won-Tae
Published: September 2022

As natural disasters become extensive, due to various environmental problems, such as the global warming, it is difficult for the disaster management systems to rapidly provide disaster prediction services, due to complex natural phenomena. Digital twins can effectively provide the services using high-fidelity disaster models and real-time observational data with distributed computing schemes. However, the previous schemes take little account of the correlations between environmental data of disasters, such as landscapes and weather. This causes inaccurate computing load predictions resulting in unbalanced load partitioning, which increases the prediction service times of the disaster management agencies. In this paper, we propose a novel distributed computing framework to accelerate the prediction services through semantic analyses of correlations between the environmental data. The framework combines the data into disaster semantic data to represent the initial disaster states, such as the sizes of wildfire burn scars and fuel models. With the semantic data, the framework predicts computing loads using the convolutional neural network-based algorithm, partitions the simulation model into balanced sub-models, and allocates the sub-models into distributed computing nodes. As a result, the proposal shows up to 38.5% of the prediction time decreases, compared to the previous schemes.