bluuebunny/crossref_metadata_2025 huggingface dataset:
External Tropospheric Corrections by Using Kriging Interpolation for Improving PPP-RTK Positioning Solutions
Authors: Song Chuanfeng , Ma Hongyang , Zhu Huizhong , Wu Bo , Shen Nan
Published: August 2022

With the availability of satellite carrier-phase delay corrections provided by a reference network or the International GNSS Service (IGS), the integer ambiguity resolution for a single receiver can be successfully achieved, which is the so-called PPP-RTK concept. Although PPP-RTK can significantly shorten the convergence time, it is still worthwhile to further investigate fast and high-precision GNSS parameter estimation to improve efficiency and productivity. In order to fully exploit the potential of GNSS for positioning applications, we herein introduce external troposphere corrections as constrained pseudo observables to the undifferenced and uncombined PPP-RTK model. Since the uncertainties of the corrections are considered in the data processing, the PPP-RTK model with the weighted tropospheric corrections is referred to as the tropospheric-weighted model. Kriging interpolation is applied to generate the tropospheric corrections, as well as the corresponding variances. The quality of the tropospheric-weighted model is assessed by the positioning Root Mean Square (RMS) errors and the convergence time to reach a 10 cm accuracy. The 90% 3D convergence time of the kinematic positioning mode of the tropospheric-weighted model is 43.5 min with the ambiguity-float solution and 21.5 min with the ambiguity-fixed solution, which are shortened by 4.5 min and 5.5 min as compared to those of the standard PPP-RTK model, respectively. As for the static positioning mode, the 90% 3D convergence time of the tropospheric-weighted model for the ambiguity-float and -fixed solutions is 25.5 min and 15 min, while the 3D convergence time is 31.5 min and 18.5 min for the standard PPP-RTK model, respectively. The results also show that the tropospheric-weighted model can still work well in a 5 cm convergence threshold.
LAI-Based Phenological Changes and Climate Sensitivity Analysis in the Three-River Headwaters Region
Authors: Dai Xiaoai , Fan Wenjie , Shan Yunfeng , Gao Yu , Liu Chao , Nie Ruihua , Zhang Donghui , Li Weile , Zhang Lifu , Sun Xuejian , Liu Tiegang , Yang Zhengli , Fu Xiao , Ma Lei , Liang Shuneng , Wang Youlin , Lu Heng
Published: August 2022

Global climate changes have a great impact on terrestrial ecosystems. Vegetation is an important component of ecosystems, and the impact of climate changes on ecosystems can be determined by studying vegetation phenology. Vegetation phenology refers to the phenomenon of periodic changes in plants, such as germination, flowering and defoliation, with the seasonal change of climate during the annual growth cycle, and it is considered to be one of the most efficient indicators to monitor climate changes. This study collected the global land surface satellite leaf area index (GLASS LAI) products, meteorological data sets and other auxiliary data in the Three-River headwaters region from 2001 to 2018; rebuilt the vegetation LAI annual growth curve by using the asymmetric Gaussian (A-G) fitting method and extracted the three vegetation phenological data (including Start of Growing Season (SOS), End of Growing Season (EOS) and Length of Growing Season (LOS)) by the maximum slope method. In addition, it also integrated Sen’s trend analysis method and the Mann-Kendall test method to explore the temporal and spatial variation trends of vegetation phenology and explored the relationship between vegetation phenology and meteorological factors through a partial correlation analysis and multiple linear regression models. The results of this study showed that: (1) the SOS of vegetation in the Three-River headwaters region is concentrated between the beginning and the end of May, with an interannual change rate of −0.14 d/a. The EOS of vegetation is concentrated between the beginning and the middle of October, with an interannual change rate of 0.02 d/a. The LOS of vegetation is concentrated between 4 and 5 months, with an interannual change rate of 0.21 d/a. (2) Through the comparison and verification with the vegetation phenological data observed at the stations, it was found that the precision of the vegetation phonology extracted by the A-G method and the maximum slope method based on GLASS LAI data is higher (MAE is 7.6 d, RMSE is 8.4 d) and slightly better than the vegetation phenological data (MAE is 9.9 d, RMSE is 10.9 d) extracted based on the moderate resolution imaging spectroradiometer normalized difference vegetation index (MODIS NDVI) product. (3) The correlation between the SOS of vegetation and the average temperature in March–May is the strongest. The SOS of vegetation is advanced by 1.97 days for every 1 °C increase in the average temperature in March–May; the correlation between the EOS of vegetation and the cumulative sunshine duration in August–October is the strongest. The EOS of vegetation is advanced by 0.07 days for every 10-h increase in the cumulative sunshine duration in August–October.
Response Relationship between the Regional Thermal Environment and Urban Forms during Rapid Urbanization (2000–2010–2020): A Case Study of Three Urban Agglomerations in China
Authors: Zhang Ninghui , Ye Haipeng , Wang Miao , Li Zehong , Li Shifeng , Li Yu
Published: August 2022

Urban agglomerations are currently facing regional thermal environment deterioration. However, the relationship between thermal environment changes in urban agglomerations in response to urban expansion and the underlying urban morphology-driven mechanisms is not clear. This study utilized data from the three largest urban agglomerations in China for 2000, 2010, and 2020 to explore the response of regional heat island changes to urban morphological variations induced by urban expansion through the quantification of urban landscape form, correlation analysis, and relative importance analysis. The results indicate that the distribution of heat source and built-up areas in urban agglomerations has clear spatial and temporal consistency. Moreover, a high regional heat island intensity (RHII) cluster was shown in a “strip-like” form in Beijing–Tianjin–Hebei and the Yangtze River Delta, while the Pearl River Delta, with the most rapid expansion and contiguity of heat source areas, showed a “ring-like” form. RHII was positively correlated with the area of urban clusters and the proportion of built-up areas. However, configuration metrics, such as patch aggregation, also positively affected RHII. Thus, different landscape structures with the same impervious surface area percentage resulted in different RHII values. The relative importance of urban form metrics varied in different urbanization stages; the impervious layer rate was dominant for low and high urban intensity levels, while the shape complexity of urban patches primarily mitigated the thermal environment at the medium urban development level. These results revealed the response relationship between the regional thermal environment and urban morphology, providing insights into how we can improve the regional thermal environment through targeted strategies for optimizing urban form patterns for areas at different urbanization stages.
Acoustic Resonance Testing of Small Data on Sintered Cogwheels
Authors: Ju Yong Chul , Kraljevski Ivan , Neunübel Heiko , Tschöpe Constanze , Wolff Matthias
Published: August 2022

Based on the fact that cogwheels are indispensable parts in manufacturing, we present the acoustic resonance testing (ART) of small data on sintered cogwheels for quality control in the context of non-destructive testing (NDT). Considering the lack of extensive studies on cogwheel data by means of ART in combination with machine learning (ML), we utilize time-frequency domain feature analysis and apply ML algorithms to the obtained feature sets in order to detect damaged samples in two ways: one-class and binary classification. In each case, despite small data, our approach delivers robust performance: All damaged test samples reflecting real-world scenarios are recognized in two one-class classifiers (also called detectors), and one intact test sample is misclassified in binary ones. This shows the usefulness of ML and time-frequency domain feature analysis in ART on a sintered cogwheel dataset.
Smart Brace for Static and Dynamic Knee Laxity Measurement
Authors: Bellitti Paolo , Borghetti Michela , Lopomo Nicola Francesco , Sardini Emilio , Serpelloni Mauro
Published: August 2022

Every year in Europe more than 500 thousand injuries that involve the anterior cruciate ligament (ACL) are diagnosed. The ACL is one of the main restraints within the human knee, focused on stabilizing the joint and controlling the relative movement between the tibia and femur under mechanical stress (i.e., laxity). Ligament laxity measurement is clinically valuable for diagnosing ACL injury and comparing possible outcomes of surgical procedures. In general, knee laxity assessment is manually performed and provides information to clinicians which is mainly subjective. Only recently quantitative assessment of knee laxity through instrumental approaches has been introduced and become a fundamental asset in clinical practice. However, the current solutions provide only partial information about either static or dynamic laxity. To support a multiparametric approach using a single device, an innovative smart knee brace for knee laxity evaluation was developed. Equipped with stretchable strain sensors and inertial measurement units (IMUs), the wearable system was designed to provide quantitative information concerning the drawer, Lachman, and pivot shift tests. We specifically characterized IMUs by using a reference sensor. Applying the Bland–Altman method, the limit of agreement was found to be less than 0.06 m/s2 for the accelerometer, 0.06 rad/s for the gyroscope and 0.08 μT for the magnetometer. By using an appropriate characterizing setup, the average gauge factor of the three strain sensors was 2.169. Finally, we realized a pilot study to compare the outcomes with a marker-based optoelectronic stereophotogrammetric system to verify the validity of the designed system. The preliminary findings for the capability of the system to discriminate possible ACL lesions are encouraging; in fact, the smart brace could be an effective support for an objective and quantitative diagnosis of ACL tear by supporting the simultaneous assessment of both rotational and translational laxity. To obtain reliable information about the real effectiveness of the system, further clinical validation is necessary.
SF-YOLOv5: A Lightweight Small Object Detection Algorithm Based on Improved Feature Fusion Mode
Authors: Liu Haiying , Sun Fengqian , Gu Jason , Deng Lixia
Published: August 2022

In the research of computer vision, a very challenging problem is the detection of small objects. The existing detection algorithms often focus on detecting full-scale objects, without making proprietary optimization for detecting small-size objects. For small objects dense scenes, not only the accuracy is low, but also there is a certain waste of computing resources. An improved detection algorithm was proposed for small objects based on YOLOv5. By reasonably clipping the feature map output of the large object detection layer, the computing resources required by the model were significantly reduced and the model becomes more lightweight. An improved feature fusion method (PB-FPN) for small object detection based on PANet and BiFPN was proposed, which effectively increased the detection ability for small object of the algorithm. By introducing the spatial pyramid pooling (SPP) in the backbone network into the feature fusion network and connecting with the model prediction head, the performance of the algorithm was effectively enhanced. The experiments demonstrated that the improved algorithm has very good results in detection accuracy and real-time ability. Compared with the classical YOLOv5, the mAP@0.5 and mAP@0.5:0.95 of SF-YOLOv5 were increased by 1.6% and 0.8%, respectively, the number of parameters of the network were reduced by 68.2%, computational resources (FLOPs) were reduced by 12.7%, and the inferring time of the mode was reduced by 6.9%.
Development of a Low-Power IoMT Portable Pillbox for Medication Adherence Improvement and Remote Treatment Adjustment
Authors: Karagiannis Dimitrios , Mitsis Konstantinos , Nikita Konstantina S.
Published: August 2022

Patients usually deviate from prescribed medication schedules and show reduced adherence. Even when the adherence is sufficient, there are conditions where the medication schedule should be modified. Crucial drug–drug, food–drug, and supplement–drug interactions can lead to treatment failure. We present the development of an internet of medical things (IoMT) platform to improve medication adherence and enable remote treatment modifications. Based on photos of food and supplements provided by the patient, using a camera integrated to a portable 3D-printed low-power pillbox, dangerous interactions with treatment medicines can be detected and prevented. We compare the medication adherence of 14 participants following a complex medication schedule using a functional prototype that automatically receives remote adjustments, to a dummy pillbox where the adjustments are sent with text messages. The system usability scale (SUS) score was 86.79, which denotes excellent user acceptance. Total errors (wrong/no pill) between the functional prototype and the dummy pillbox did not demonstrate any statistically significant difference (p = 0.57), but the total delay of the intake time was higher (p = 0.03) during dummy pillbox use. Thus, the proposed low-cost IoMT pillbox improves medication adherence even with a complex regimen while supporting remote dose adjustment.
Assessment of a Therapeutic X-ray Radiation Dose Measurement System Based on a Flexible Copper Indium Gallium Selenide Solar Cell
Authors: Shin Dong-Seok , Kim Tae-Ho , Rah Jeong-Eun , Kim Dohyeon , Yang Hye Jeong , Lee Se Byeong , Lim Young Kyung , Jeong Jonghwi , Kim Haksoo , Shin Dongho , Son Jaeman
Published: August 2022

Several detectors have been developed to measure radiation doses during radiotherapy. However, most detectors are not flexible. Consequently, the airgaps between the patient surface and detector could reduce the measurement accuracy. Thus, this study proposes a dose measurement system based on a flexible copper indium gallium selenide (CIGS) solar cell. Our system comprises a customized CIGS solar cell (with a size 10 × 10 cm2 and thickness 0.33 mm), voltage amplifier, data acquisition module, and laptop with in-house software. In the study, the dosimetric characteristics, such as dose linearity, dose rate independence, energy independence, and field size output, of the dose measurement system in therapeutic X-ray radiation were quantified. For dose linearity, the slope of the linear fitted curve and the R-square value were 1.00 and 0.9999, respectively. The differences in the measured signals according to changes in the dose rates and photon energies were <2% and <3%, respectively. The field size output measured using our system exhibited a substantial increase as the field size increased, contrary to that measured using the ion chamber/film. Our findings demonstrate that our system has good dosimetric characteristics as a flexible in vivo dosimeter. Furthermore, the size and shape of the solar cell can be easily customized, which is an advantage over other flexible dosimeters based on an a-Si solar cell.
Development of an Early Embryo Detection Methodology for Quail Eggs Using a Thermal Micro Camera and the YOLO Deep Learning Algorithm
Authors: Nakaguchi Victor Massaki , Ahamed Tofael
Published: August 2022

Poultry production utilizes many available technologies in terms of farm-industry automation and sanitary control. However, there is a lack of robust techniques and affordable equipment for avian embryo detection and sexual segregation at the early stages. In this work, we aimed to evaluate the potential use of thermal micro cameras for detecting embryos in quail eggs via thermal images during the first 168 h (7 days) of incubation. We propose a methodology to collect data during the incubation period. Additionally, to support the visual analysis, YOLO deep learning object detection algorithms were applied to detect unfertilized eggs; the results showed its potential to distinguish fertilized eggs from unfertilized eggs during the incubation period, after filtering radiometric images. We compared YOLOv4, YOLOv5 and SSD-MobileNet V2 trained models. The mAP@0.50 of the YOLOv4, YOLOv5 and SSD-MobileNet V2 was 98.62%, 99.5% and 91.8%, respectively. We also compared three testing datasets for different intervals of rotation of eggs, as our hypothesis was that fewer turning periods could improve the visualization of fertilized egg features, and applied three treatments: 1.5 h, 6 h, and 12 h. The results showed that turning eggs in different periods did not exhibit a linear relation, as the F1 Score for YOLOv4 of detection for the 12 h period was 0.569, that for the 6 h period was 0.404 and that for the 1.5 h period was 0.384. YOLOv5 F1 Scores for 12 h, 6 h and 1.5 h were 1, 0.545 and 0.386, respectively. SSD-MobileNet V2 performed F1 scores of 0.60 for 12 h, 0.22 for 6 h and 0 for 1.5 h turning periods.
In-Field Automatic Identification of Pomegranates Using a Farmer Robot
Authors: Devanna Rosa Pia , Milella Annalisa , Marani Roberto , Garofalo Simone Pietro , Vivaldi Gaetano Alessandro , Pascuzzi Simone , Galati Rocco , Reina Giulio
Published: August 2022

Ground vehicles equipped with vision-based perception systems can provide a rich source of information for precision agriculture tasks in orchards, including fruit detection and counting, phenotyping, plant growth and health monitoring. This paper presents a semi-supervised deep learning framework for automatic pomegranate detection using a farmer robot equipped with a consumer-grade camera. In contrast to standard deep-learning methods that require time-consuming and labor-intensive image labeling, the proposed system relies on a novel multi-stage transfer learning approach, whereby a pre-trained network is fine-tuned for the target task using images of fruits in controlled conditions, and then it is progressively extended to more complex scenarios towards accurate and efficient segmentation of field images. Results of experimental tests, performed in a commercial pomegranate orchard in southern Italy, are presented using the DeepLabv3+ (Resnet18) architecture, and they are compared with those that were obtained based on conventional manual image annotation. The proposed framework allows for accurate segmentation results, achieving an F1-score of 86.42% and IoU of 97.94%, while relieving the burden of manual labeling.
Hybrid SFNet Model for Bone Fracture Detection and Classification Using ML/DL
Authors: Yadav Dhirendra Prasad , Sharma Ashish , Athithan Senthil , Bhola Abhishek , Sharma Bhisham , Dhaou Imed Ben
Published: August 2022

An expert performs bone fracture diagnosis using an X-ray image manually, which is a time-consuming process. The development of machine learning (ML), as well as deep learning (DL), has set a new path in medical image diagnosis. In this study, we proposed a novel multi-scale feature fusion of a convolution neural network (CNN) and an improved canny edge algorithm that segregate fracture and healthy bone image. The hybrid scale fracture network (SFNet) is a novel two-scale sequential DL model. This model is highly efficient for bone fracture diagnosis and takes less computation time compared to other state-of-the-art deep CNN models. The innovation behind this research is that it works with an improved canny edge algorithm to obtain edges in the images that localize the fracture region. After that, grey images and their corresponding canny edge images are fed to the proposed hybrid SFNet for training and evaluation. Furthermore, the performance is also compared with the state-of-the-art deep CNN models on a bone image dataset. Our results showed that SFNet with canny (SFNet + canny) achieved the highest accuracy, F1-score and recall of 99.12%, 99% and 100%, respectively, for bone fracture diagnosis. It showed that using a canny edge algorithm improves the performance of CNN.
Non-Contact Shear Wave Generation and Detection Using High Frequency Air-Coupled Focused Transducer and Fiber Optic Based Sagnac Interferometer for Mechanical Characterization
Authors: Chen Gui , Xia Jinjun
Published: August 2022

In shear wave-based material mechanical characterization, the transmit/receiver transducer is generally in contact with the material through a coupling medium. In many applications, especially in biological tissue-related characterization, the application of the coupling medium and the contact method are not ideal, sometimes even unacceptable, due to contamination or stress response concerns. To avoid contact, we developed a 1 MHz air-coupled focused PZT transducer as a moderate pressure generator that could induce a shear wave in soft material and a fiber optic-based Sagnac system for the detection of the propagating shear wave. A calibration indicated that the fabricated air-coupled focused PZT transducer could generate pressure above 1 KPa within its focal range. This pressure is three to five times as much as the pressure generated by a 1 MHz air-coupled transducer currently available on the market. The integrated system was demonstrated through shear wave generation by the fabricated air-coupled PZT transducer and shear wave detection by the fiber optic Sagnac system in a nylon membrane. The results demonstrated the capability of the integrated system in non-contact material mechanical characterization, such as in material modulus measurement.
A Robotic Experimental Setup with a Stewart Platform to Emulate Underwater Vehicle-Manipulator Systems
Authors: Cetin Kamil , Tugal Harun , Petillot Yvan , Dunnigan Matthew , Newbrook Leonard , Erden Mustafa Suphi
Published: August 2022

This study presents an experimental robotic setup with a Stewart platform and a robot manipulator to emulate an underwater vehicle–manipulator system (UVMS). This hardware-based emulator setup consists of a KUKA IIWA14 robotic manipulator mounted on a parallel manipulator, known as Stewart Platform, and a force/torque sensor attached to the end-effector of the robotic arm interacting with a pipe. In this setup, we use realistic underwater vehicle movements either communicated to a system in real-time through 4G routers or recorded in advance in a water tank environment. In addition, we simulate both the water current impact on vehicle movement and dynamic coupling effects between the vehicle and manipulator in a Gazebo-based software simulator and transfer these to the physical robotic experimental setup. Such a complete setup is useful to study the control techniques to be applied on the underwater robotic systems in a dry lab environment and allows us to carry out fast and numerous experiments, circumventing the difficulties with performing similar experiments and data collection with actual underwater vehicles in water tanks. Exemplary controller development studies are carried out for contact management of the UVMS using the experimental setup.
Real-Time Short-Term Pedestrian Trajectory Prediction Based on Gait Biomechanics
Authors: González Leticia , López Antonio M. , Álvarez Juan C. , Álvarez Diego
Published: August 2022

The short-term prediction of a person’s trajectory during normal walking becomes necessary in many environments shared by humans and robots. Physics-based approaches based on Newton’s laws of motion seem best suited for short-term predictions, but the intrinsic properties of human walking conflict with the foundations of the basic kinematical models compromising their performance. In this paper, we propose a short-time prediction method based on gait biomechanics for real-time applications. This method relays on a single biomechanical variable, and it has a low computational burden, turning it into a feasible solution to implement in low-cost portable devices. We evaluate its performance from an experimental benchmark where several subjects walked steadily over straight and curved paths. With this approach, the results indicate a performance good enough to be applicable to a wide range of human–robot interaction applications.
Wrist Photoplethysmography Signal Quality Assessment for Reliable Heart Rate Estimate and Morphological Analysis
Authors: Moscato Serena , Lo Giudice Stella , Massaro Giulia , Chiari Lorenzo
Published: August 2022

Photoplethysmographic (PPG) signals are mainly employed for heart rate estimation but are also fascinating candidates in the search for cardiovascular biomarkers. However, their high susceptibility to motion artifacts can lower their morphological quality and, hence, affect the reliability of the extracted information. Low reliability is particularly relevant when signals are recorded in a real-world context, during daily life activities. We aim to develop two classifiers to identify PPG pulses suitable for heart rate estimation (Basic-quality classifier) and morphological analysis (High-quality classifier). We collected wrist PPG data from 31 participants over a 24 h period. We defined four activity ranges based on accelerometer data and randomly selected an equal number of PPG pulses from each range to train and test the classifiers. Independent raters labeled the pulses into three quality levels. Nineteen features, including nine novel features, were extracted from PPG pulses and accelerometer signals. We conducted ten-fold cross-validation on the training set (70%) to optimize hyperparameters of five machine learning algorithms and a neural network, and the remaining 30% was used to test the algorithms. Performances were evaluated using the full features and a reduced set, obtained downstream of feature selection methods. Best performances for both Basic- and High-quality classifiers were achieved using a Support Vector Machine (Acc: 0.96 and 0.97, respectively). Both classifiers outperformed comparable state-of-the-art classifiers. Implementing automatic signal quality assessment methods is essential to improve the reliability of PPG parameters and broaden their applicability in a real-world context.
A Primer on the Factories of the Future
Authors: Anumbe Noble , Saidy Clint , Harik Ramy
Published: August 2022

In a dynamic and rapidly changing world, customers’ often conflicting demands have continued to evolve, outstripping the ability of the traditional factory to address modern-day production challenges. To fix these challenges, several manufacturing paradigms have been proposed. Some of these have monikers such as the smart factory, intelligent factory, digital factory, and cloud-based factory. Due to a lack of consensus on general nomenclature, the term Factory of the Future (or Future Factory) has been used in this paper as a collective euphemism for these paradigms. The Factory of the Future constitutes a creative convergence of multiple technologies, techniques, and capabilities that represent a significant change in current production capabilities, models, and practices. Using the semi-narrative research methodology in concert with the snowballing approach, the authors reviewed the open literature to understand the organizing principles behind the most common smart manufacturing paradigms with a view to developing a creative reference that articulates their shared characteristics and features under a collective lingua franca, viz., Factory of the Future. Serving as a review article and a reference monograph, the paper details the meanings, characteristics, technological framework, and applications of the modern factory and its various connotations. Amongst other objectives, it characterizes the next-generation factory and provides an overview of reference architectures/models that guide their structured development and deployment. Three advanced communication technologies capable of advancing the goals of the Factory of the Future and rapidly scaling advancements in the field are discussed. It was established that next-generation factories would be data rich environments. The realization of their ultimate value would depend on the ability of stakeholders to develop the appropriate infrastructure to extract, store, and process data to support decision making and process optimization.
Infrared Sensation-Based Salient Targets Enhancement Methods in Low-Visibility Scenes
Authors: Tan Hongjun , Ou Dongxiu , Zhang Lei , Shen Guochen , Li Xinghua , Ji Yuqing
Published: August 2022

Thermal imaging is an important technology in low-visibility environments, and due to the blurred edges and low contrast of infrared images, enhancement processing is of vital importance. However, to some extent, the existing enhancement algorithms based on pixel-level information ignore the salient feature of targets, the temperature which effectively separates the targets by their color. Therefore, based on the temperature and pixel features of infrared images, first, a threshold denoising model based on wavelet transformation with bilateral filtering (WTBF) was proposed. Second, our group proposed a salient components enhancement method based on a multi-scale retinex algorithm combined with frequency-tuned salient region extraction (MSRFT). Third, the image contrast and noise distribution were improved by using salient features of orientation, color, and illuminance of night or snow targets. Finally, the accuracy of the bounding box of enhanced images was tested by the pre-trained and improved object detector. The results show that the improved method can reach an accuracy of 90% of snow targets, and the average precision of car and people categories improved in four low-visibility scenes, which demonstrates the high accuracy and adaptability of the proposed methods of great significance for target detection, trajectory tracking, and danger warning of automobile driving.
Key Challenges and Emerging Technologies in Industrial IoT Architectures: A Review
Authors: Mirani Akseer Ali , Velasco-Hernandez Gustavo , Awasthi Anshul , Walsh Joseph
Published: August 2022

The Industrial Internet of Things (IIoT) is bringing evolution with remote monitoring, intelligent analytics, and control of industrial processes. However, as the industrial world is currently in its initial stage of adopting full-stack development solutions with IIoT, there is a need to address the arising challenges. In this regard, researchers have proposed IIoT architectures based on different architectural layers and emerging technologies for the end-to-end integration of IIoT systems. In this paper, we review and compare three widely accepted IIoT reference architectures and present a state-of-the-art review of conceptual and experimental IIoT architectures from the literature. We identified scalability, interoperability, security, privacy, reliability, and low latency as the main IIoT architectural requirements and detailed how the current architectures address these challenges by using emerging technologies such as edge/fog computing, blockchain, SDN, 5G, Machine Learning, and Wireless Sensor Networks (WSN). Finally, we discuss the relation between the current challenges and emergent technologies and present some opportunities and directions for future research work.
A Case Study on Vestibular Sensations in Driving Simulators
Authors: Riera Jose V. , Casas Sergio , Alonso Francisco , Fernández Marcos
Published: August 2022

Motion platforms have been used in simulators of all types for several decades. Since it is impossible to reproduce the accelerations of a vehicle without limitations through a physically limited system (platform), it is common to use washout filters and motion cueing algorithms (MCA) to select which accelerations are reproduced and which are not. Despite the time that has passed since their development, most of these algorithms still use the classical washout algorithm. In the use of these MCAs, there is always information that is lost and, if that information is important for the purpose of the simulator (the training simulators), the result obtained by the users of that simulator will not be satisfactory. This paper shows a case study where a BMW 325Xi AUT fitted with a sensor, recorded the accelerations produced in all degrees of freedom (DOF) during several runs, and data have been introduced in mathematical simulation software (washout + kinematics + actuator simulation) of a 6DOF motion platform. The input to the system has been qualitatively compared with the output, observing that most of the simulation adequately reflects the input to the system. Still, there are three events where the accelerations are lost. These events are considered by experts to be of vital importance for the outcome of a learning process in the simulator to be adequate.
Semi-Supervised Domain Adaptation for Multi-Label Classification on Nonintrusive Load Monitoring
Authors: Hur Cheong-Hwan , Lee Han-Eum , Kim Young-Joo , Kang Sang-Gil
Published: August 2022

Nonintrusive load monitoring (NILM) is a technology that analyzes the load consumption and usage of an appliance from the total load. NILM is becoming increasingly important because residential and commercial power consumption account for about 60% of global energy consumption. Deep neural network-based NILM studies have increased rapidly as hardware computation costs have decreased. A significant amount of labeled data is required to train deep neural networks. However, installing smart meters on each appliance of all households for data collection requires the cost of geometric series. Therefore, it is urgent to detect whether the appliance is used from the total load without installing a separate smart meter. In other words, domain adaptation research, which can interpret the huge complexity of data and generalize information from various environments, has become a major challenge for NILM. In this research, we optimize domain adaptation by employing techniques such as robust knowledge distillation based on teacher–student structure, reduced complexity of feature distribution based on gkMMD, TCN-based feature extraction, and pseudo-labeling-based domain stabilization. In the experiments, we down-sample the UK-DALE and REDD datasets as in the real environment, and then verify the proposed model in various cases and discuss the results.
Completion Time Minimization for UAV-UGV-Enabled Data Collection
Authors: Li Zhijian , Zhao Wendong , Liu Cuntao
Published: August 2022

In unmanned aerial vehicle (UAV)-enabled data collection systems, situations where sensor nodes (SNs) cannot upload their data successfully to the UAV may exist, due to factors such as SNs’ insufficient energy and the UAV’s minimum flight altitude. In this paper, an unmanned ground vehicle (UGV)-UAV-enabled data collection system is studied, where data collection missions are conducted by a UAV and a UGV cooperatively. Two cooperative strategies are proposed, i.e., collaboration without information interaction, and collaboration with information interaction. In the first strategy, the UGV collects data from remote SNs (i.e., the SNs that cannot upload data to the UAV) as well as some normal SNs (i.e., the SNs that can upload data to the UAV), while the UAV only collects data from some normal SNs. Then, they carry the data back to the data center (DC) without interacting with each other. In the second strategy, the UGV only collects data from remote SNs, while transmitting the collected data to the UAV at a data interaction point, then the data are carried back to the DC by the UAV. There are mobile data collection nodes on the ground and in the air, and the task is to find trajectories to minimize the data collection time in the data center. A collaborative strategy selection algorithm, combining a multi-stage-based SN association and UAV-UGV path optimization algorithm, is proposed to solve the problem effectively, where techniques including convex optimization and genetic algorithm are adopted. The simulation result shows that the proposed scheme reduces the mission completion time by 36% compared with the benchmark scheme.
Heterogeneous Transfer Learning for Wi-Fi Indoor Positioning Based Hybrid Feature Selection
Authors: Gidey Hailu Tesfay , Guo Xiansheng , Li Lin , Zhang Yukun
Published: August 2022

This paper presents the application of heterogeneous transfer learning (HetTL) methods which consider hybrid feature selection to reduce the training calibration effort and the noise generated by fingerprint duplicates obtained from multiple Wi-Fi access points. The Cramer–Rao Lower Bound analysis (CRLB) was also applied to evaluate and estimate a lower limit for the variance of a parameter estimator used to analyze positioning performance. We developed two novel algorithms for feature selection in fingerprint-based indoor positioning problems (IPP) to enhance positioning performance in the target domain with the HetTL. The algorithms comprised two scenarios: (i) a principal component analysis-based approach (PCA-based) and (ii) a hybrid approach that takes both PCA and correlation effect analysis into account (hybrid scenario). Accordingly, a new feature vector was constructed by retaining only the most significant predictors, and the most efficient feature dimensions were also determined by using a hybrid-based approach. Experimental results showed that the hybrid-based proposed algorithm has the minimum mean absolute error. The CRLB analysis also showed that the number of Wi-Fi access points could affect the lower bound location estimation error; however, identifying the most significant predictors is an effective approach to improve positioning performance.
Optical Design of a Novel Wide-Field-of-View Space-Based Spectrometer for Climate Monitoring
Authors: Schifano Luca , Berghmans Francis , Dewitte Steven , Smeesters Lien
Published: August 2022

We report on a near-infrared imaging spectrometer for sensing the three most prominent greenhouse gases in the atmosphere (water vapor, carbon dioxide and methane). The optical design of the spectrometer involves freeform optics, which enables achieving exceptional performance and allows progressing well beyond the state-of-the-art in terms of compactness, field-of-view, and spatial resolution. The spectrometer is intended to be launched on a small satellite orbiting at 700 km and observing the Earth with a wide field-of-view of 120° and a spatial resolution of 2.6 km at nadir. The satellite will ultimately allow for improved climate change monitoring.
Reliable Data Collection Methodology for Face Recognition in Preschool Children
Authors: Won Hye-min , Lee Hyeogjin , Song Gyuwon , Kim Yeonghun , Kwak Nojun
Published: August 2022

Most face datasets target adults who can make their own decisions. In the case of children, consent from parents or guardians is necessary to collect biometric information, thus making it very difficult. As a result, the amount of data on children is quite small and inevitably private. In this work, we built a database by collecting face data of 74 children aged 2–7 years in daycare facilities. In addition, we conducted an experiment to determine the best location to perform face recognition on children by installing cameras in various locations. This study presents the points and methods to be considered to build a children’s face dataset and also studies the optimal camera installation setups for the face recognition of children.
Periodicity Intensity of the 24 h Circadian Rhythm in Newborn Calves Show Indicators of Herd Welfare
Authors: Rhodes Victoria , Maguire Maureen , Shetty Meghana , McAloon Conor , Smeaton Alan F.
Published: August 2022

Circadian rhythms are a process of the sleep-wake cycle that regulates the physical, mental and behavioural changes in all living beings with a period of roughly 24 h. Wearable accelerometers are typically used in livestock applications to record animal movement from which we can estimate the activity type. Here, we use the overall movement recorded by accelerometers worn on the necks of newborn calves for a period of 8 weeks. From the movement data, we calculate 24 h periodicity intensities corresponding to circadian rhythms, from a 7-day window that slides through up to 8-weeks of data logging. The strength or intensity of the 24 h periodicity is computed at intervals as the calves become older, which is an indicator of individual calf welfare. We observe that the intensities of these 24 h periodicities for individual calves, derived from movement data, increase and decrease synchronously in a herd of 19 calves. Our results show that external factors affecting the welfare of the herd can be observed by processing and visualising movement data in this way and our method reveals insights that are not observable from movement data alone.
Equivalent Calibration Method Based on a Blackbody Baffle Substitution for a Large External Surface-Source Blackbody
Authors: Pang Xinyu , Yu Yi , Li Zhou , Sun Zhiyuan , Li Chun , Yang Guoqing
Published: August 2022

Highly accurate measurements of infrared systems cannot be achieved without precise radiometric calibrations. In order to correctly interpret and process infrared images and monitor the performance of infrared cameras, their radiometric calibration is also required periodically. In this paper, an equivalent calibration method is proposed based on an internal blackbody baffle. It is used for the replacement of a large surface-source blackbody covering the aperture for the field calibration of large-aperture equipment. Subsequently, the expressions of the equivalent calibration conversion function (ECCF) are derived based on the grayscale response of the camera and the calibration models of the two methods, and experimental measurements and fits are performed using a cooled mid-wave infrared camera. The results show that the measured functional form is consistent with the physical meaning. Moreover, in the target imaging experiments, the results of the inversion using the equivalent calibration conversion function and the results of the direct calibration of the external blackbody are largely consistent with the average error of 0.198% between the two, and the maximum error is within 1%. The maximum error between the inversion result of radiation brightness and the actual value of the target is 6.29%, and the accuracy fully meets the radiometric measurement requirements.
Entropy-Aware Model Initialization for Effective Exploration in Deep Reinforcement Learning
Authors: Jang Sooyoung , Kim Hyung-Il
Published: August 2022

Effective exploration is one of the critical factors affecting performance in deep reinforcement learning. Agents acquire data to learn the optimal policy through exploration, and if it is not guaranteed, the data quality deteriorates, which leads to performance degradation. This study investigates the effect of initial entropy, which significantly influences exploration, especially in the early learning stage. The results of this study on tasks with discrete action space show that (1) low initial entropy increases the probability of learning failure, (2) the distributions of initial entropy for various tasks are biased towards low values that inhibit exploration, and (3) the initial entropy for discrete action space varies with both the initial weight and task, making it hard to control. We then devise a simple yet powerful learning strategy to deal with these limitations, namely, entropy-aware model initialization. The proposed algorithm aims to provide a model with high initial entropy to a deep reinforcement learning algorithm for effective exploration. Our experiments showed that the devised learning strategy significantly reduces learning failures and enhances performance, stability, and learning speed.
The Role of Dual Bronchodilation and the Conscientious Use of Inhaled Corticosteroid in COPD
Authors: Figueiredo Ricardo G. , Araujo Lucas F. , de Bessa Junior José
Published: August 2022

Chronic obstructive pulmonary disease (COPD) is a global public health burden that stands out as a leading cause of mortality around the world, especially in less developed countries. COPD treatment should be centered on the individual necessities of each patient, integrating pharmacological therapy, immunization, nutritional support, pulmonary rehabilitation and control of comorbidities. Combining a long-acting antimuscarinic (LAMA) with a long-acting beta2-agonist (LABA) bronchodilator has proven to be more effective than monotherapy with LABA or LAMA, in terms of the functional improvement of forced expiratory volume (FEV1), symptoms and life quality and may be helpful in patients with progressive dyspnea. The eosinophil blood count is a simple biomarker that could guide personalized treatment strategies by identifying patients with greater clinical benefits of inhaled corticosteroid (ICS) treatment. However, one critical concern is the increased risk of pneumonia in individuals treated with ICS.
Imagining the Post-COVID-19 Polity: Narratives of Possible Futures
Authors: McAuley James White , Nesbitt-Larking Paul W.
Published: August 2022

The COVID-19 crisis is arguably the most important development of the 21st century so far and takes its place alongside the great eruptions of the past century. As with any crisis, the current pandemic has stimulated visions and proposals for post-COVID-19 societies. Our focus is on narratives—both predictive and prescriptive—that envisage post-COVID-19 political societies. Combining narrative analysis with thematic analysis, we argue that societal changes conditioned by the pandemic have accelerated a turn toward five inter-related developments: A renaissance in rationality and evidence-based science; a return to social equality and equity, including wage equity and guaranteed incomes; a reimagining of the interventionist state in response to crises in the economy, society, the welfare state, and social order; a reorientation to the local and communitarian, with reference in particular to solidaristic mutual aid, community animation, local sourcing, and craft production; and the reinvention of democracy through deep participation and deliberative dialogical decision making. The empirical focus of our work is an analysis of predominantly legacy media content from the Canadian Periodicals Index related to life after the pandemic and post-COVID-19 society.
Pre-Harvest Application of Multi-Walled Carbon Nanotubes Improves the Antioxidant Capacity of ‘Flame Seedless’ Grapes during Storage
Authors: Sha Riye , Zhu Shuhua , Wu Linnan , Li Xujiao , Zhang Huanhuan , Yao Dongdong , Lv Qi , Wang Fangxia , Zhao Fengyun , Li Pengcheng , Yu Kun
Published: August 2022

As a widely distributed fruit, grapes are susceptible to oxidative damage during storage and transportation, resulting in declining quality and commodity value. This study aimed to investigate the effects of preharvest application of different concentrations of multi-walled carbon nanotubes (MWCNTs) on the postharvest quality of ‘Flame Seedless’ grapes. The results showed that low-concentration (25 and 50 mg L−1) MWCNTs treatments maintained the comprehensive quality index, firmness, soluble sugar, titratable acid, pH value, and ascorbic acid (AsA) content of grapes. MWCNTs at 25 and 50 mg L−1 increased the activities of peroxidase (POD), catalase (CAT), superoxide dismutase (SOD), and ascorbic acid (APX). Furthermore, MWCNTs reduced the malondialdehyde (MDA) content and decreased the accumulation of excessive reactive oxygen species (ROS) in grape peel and pulp tissues. In addition, transmission electron microscopy (TEM) images demonstrated that MWCNTs were absorbed by parenchymal cells in the grape peel and pulp through the epidermal cell layer. MWCNTs with a specific concentration can be used as a new inducer for the biosynthesis of antioxidants to reduce oxidative damage in grapes during storage.