bluuebunny/crossref_metadata_2025 huggingface dataset:
Multiphoton Resonance in Attosecond Transient Absorption
Authors: Xue Jinxing , Wang Meng , Zhou Cangtao , Ruan Shuangchen
Published: April 2022

We present the theory and simulation of attosecond transient absorption in helium atoms under the single-active-electron approximation. This study investigates the attosecond dynamics of intrinsic atomic states that interact with a field comprising vacuum ultraviolet (VUV) and extreme ultraviolet (XUV) fields. The absorption spectrum of the helium atom is obtained from the response function, which is constructed by numerically solving the three-dimensional time-dependent Schrödinger equation. We observe a fine structure near the intrinsic atomic level, which is modulated with a 0.2 fs period. Based on high-order time-dependent perturbation theory, the frequency-dependent phase of the dipole response induced by the VUV and XUV fields is analytically obtained, and the fine structure is well explained by the phase difference. In addition, the absorption fringes are dependent on the chirp of the VUV field. This study investigates the features of the attosecond transient absorption in the VUV region, which may have valuable applications in the study of ultrafast phenomena in atoms, molecules, and solids.
Multi-Wavelength Terahertz Parametric Generator Using a Seed Laser Based on Four-Wave Mixing
Authors: Mine Sota , Kawase Kodo , Murate Kosuke
Published: April 2022

In this study, we developed a multi-wavelength terahertz-wave parametric generator that operates with only one injection seeding laser. Tunable lasers used as an injection seeder must be single-frequency oscillators, and conventional multi-wavelength terahertz-wave parametric generator requires basically the same number of lasers as the number of wavelengths. In order to solve this problem, we developed a new external cavity semiconductor laser that incorporates a DMD in its wavelength-selective mechanism. In this process, stable multi-wavelength oscillation from a single laser was made possible by efficiently causing four-wave mixing. This seed laser can be applied to practical real-time terahertz spectroscopy by arbitrarily switching the desired wavelength to be generated and the interval between multiple wavelengths.
Photoprotection Is Achieved by Photorespiration and Modification of the Leaf Incident Light, and Their Extent Is Modulated by the Stomatal Sensitivity to Water Deficit in Grapevines
Authors: Villalobos-González Luis , Alarcón Nicolás , Bastías Roberto , Pérez Cristobal , Sanz René , Peña-Neira Álvaro , Pastenes Claudio
Published: April 2022

Absorbed energy in excess of that used by photosynthesis induces photoinhibition, which is common in water deficit conditions, resulting in reductions in stomatal conductance. In grapevines, controlled water deficit is a common field practice, but little is known about the impact of a given water shortage on the energy transduction processes at the leaf level in relation to contrasting stomatal sensitivities to drought. Here, we assessed the effect of a nearly similar water deficit condition on four grapevine varieties: Cabernet Sauvignon (CS) and Sauvignon Blanc (SB), which are stomatal sensitive, and Chardonnay (CH) and Carménère (CM), which are less stomatal sensitive, grown in 20 L pots outdoors. Plants were maintained to nearly 94% of field capacity (WW) and 83% field capacity (WD). We have assessed plant water status, photosynthesis (AN), photorespiration, AN vs. PAR, ACi curves, photochemical (qP) and non-photochemical (qN) fluorescence quenching vs. PAR, the photoprotective effectiveness of NPQ (qPd) and light interception by leaves. Photorespiration is important under WD, but to a different extent between varieties. This is related to stomatal sensitivity, maintaining a safe proportion of PSII reaction centres in an open state. Additionally, the capacity for carboxylation is affected by WD, but to a greater extent in more sensitive varieties. As for qN, in WD it saturates at 750 μmol PAR m−2s−1, irrespective of the variety, which coincides with PAR, from which qN photoprotective effectiveness declines, and qP is reduced to risky thresholds. Additionally, that same PAR intensity is intercepted by WD leaves from highly stomatal-sensitive varieties, likely due to a modification of the leaf angle in those plants. Pigments associated with qN, as well as chlorophylls, do not seem to be a relevant physiological target for acclimation.
Novel Plant Breeding Techniques Shake Hands with Cereals to Increase Production
Authors: Haroon Muhammad , Wang Xiukang , Afzal Rabail , Zafar Muhammad Mubashar , Idrees Fahad , Batool Maria , Khan Abdul Saboor , Imran Muhammad
Published: April 2022

Cereals are the main source of human food on our planet. The ever-increasing food demand, continuously changing environment, and diseases of cereal crops have made adequate production a challenging task for feeding the ever-increasing population. Plant breeders are striving their hardest to increase production by manipulating conventional breeding methods based on the biology of plants, either self-pollinating or cross-pollinating. However, traditional approaches take a decade, space, and inputs in order to make crosses and release improved varieties. Recent advancements in genome editing tools (GETs) have increased the possibility of precise and rapid genome editing. New GETs such as CRISPR/Cas9, CRISPR/Cpf1, prime editing, base editing, dCas9 epigenetic modification, and several other transgene-free genome editing approaches are available to fill the lacuna of selection cycles and limited genetic diversity. Over the last few years, these technologies have led to revolutionary developments and researchers have quickly attained remarkable achievements. However, GETs are associated with various bottlenecks that prevent the scaling development of new varieties that can be dealt with by integrating the GETs with the improved conventional breeding methods such as speed breeding, which would take plant breeding to the next level. In this review, we have summarized all these traditional, molecular, and integrated approaches to speed up the breeding procedure of cereals.
Identification of Potential Pathways of Morella cerifera Seedlings in Response to Alkali Stress via Transcriptomic Analysis
Authors: Jiao Yun , Xie Rang-Jin , Jia Hui-Min
Published: April 2022

Alkali stress, a type of abiotic stress, severely inhibits plant growth. Only a few studies have investigated the mechanism underlying the transcriptional-level response of Morella cerifera to saline-alkali stress. Based on RNA-seq technology, gene expression differences in the fibrous roots of M. cerifera seedlings exposed to low- and high-concentration alkali stress (LAS and HAS, respectively) were investigated, and the corresponding 1312 and 1532 alkali stress-responsive genes were identified, respectively. According to gene set enrichment analysis, 65 gene sets were significantly enriched. Of these, 24 gene sets were shared by both treatment groups. LAS and HAS treatment groups exhibited 9 (all downregulated) and 32 (23 downregulated) unique gene sets, respectively. The differential gene sets mainly included those involved in trehalose biosynthesis and metabolism, phospholipid translocation, and lignin catabolism. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that M. cerifera seedlings were specifically enriched in stilbenoid, diarylheptanoid, and gingerol biosynthesis; phenylalanine, tyrosine, and tryptophan biosynthesis; and sesquiterpenoid and triterpenoid biosynthesis. Moreover, the related genes involved in hormone signaling pathways and transcription factors were determined through a localization analysis of core abiotic stress pathways. These genes and their molecular mechanisms will be the focus of future research.
Renewable Energy-Driven Desalination: New Trends and Future Prospects of Small Capacity Systems
Authors: García-Rodríguez Lourdes , Delgado-Torres Agustín M.
Published: April 2022

New trends and future prospects for small capacity systems of Renewable Energy-driven DESalination (REDES) are reviewed and assessed in this paper over a nominal desalination capacity range of 3–1000 m3/d. A thorough literature review is reported in order to evaluate current research and developing activities. Outstanding commercial prospects in the near future are identified for two off-grid REDES technologies under development. First, wave energy converters with direct coupling to seawater desalination. Second, solar micro gas turbines with biofuel backup coupled to reverse osmosis (RO) desalination and/or zero liquid discharge water treatment. These systems, as well as mature REDES plants (namely PV/RO and wind turbines/RO), will benefit from forthcoming advances in energy efficiency in the RO process itself. The Closed Circuit RO desalination (CCROTM) concept may be a key configuration for enhancing RE-driven RO desalination. Additionally, opportunities for innovation in seawater RO desalination with variable power consumption are highlighted. On the other hand, our conclusions highlight opportunities for developing novel portable REDES systems based on solar membrane distillation with a portable linear Fresnel concentrator manufactured by SOLATOM. Additionally, the concept of portable systems could foster the commercial development of microbial desalination cells combined with solar PV energy and RO powered by tidal currents.
Innovative Food Packaging, Food Quality and Safety, and Consumer Perspectives
Authors: Yan Mary R. , Hsieh Sally , Ricacho Norberto
Published: April 2022

Packaging is an integral part of the food industry associated with food quality and safety including food shelf life, and communications from the marketing perspective. Traditional food packaging provides the protection of food from damage and storage of food products until being consumed. Packaging also presents branding and nutritional information and promotes marketing. Over the past decades, plastic films were employed as a barrier to keep food stuffs safe from heat, moisture, microorganisms, dust, and dirt particles. Recent advancements have incorporated additional functionalities in barrier films to enhance the shelf life of food, such as active packaging and intelligent packaging. In addition, consumer perception has influences on packaging materials and designs. The current trend of consumers pursuing environmental-friendly packaging is increased. With the progress of applied technologies in the food sector, sustainable packaging has been emerging in response to consumer preferences and environmental obligations. This paper reviews the importance of food packaging in relation to food quality and safety; the development and applications of advanced smart, active, and intelligent packaging systems, and the properties of an oxygen barrier. The advantages and disadvantages of these packaging are discussed. Consumer perceptions regarding environmental-friendly packaging that could be applied in the food industry are also discussed.
Choreographing the Dance of Dissent: Roman Catholic Womenpriests’ Claims to Authority
Authors: Gringeri Christina , Himonas Alexandra
Published: April 2022

In June 2002, seven Roman Catholic women were ordained to the priesthood by two bishops on a boat floating on the Danube River in the presence of hundreds of spectators. Their ordinations broke with two millennia of Catholic tradition prohibiting women priests and started a global movement. Post-ordination, the women priests were excommunicated by the Vatican, yet they still identify as Catholic priests. We explored the central tension of being insiders/outsiders by examining womenpriests’ claims to authority. The thematic analysis of interviews with over one hundred womenpriests and bishops led us to note an emerging theme in their language regarding claims to authority. In this analysis, we asked: how do womenpriests claim authority in ways that differentiate them from Roman Catholic Church authority as well as in ways that clearly position them within the tradition? We found four main strategies by which they make these claims: reclaiming the history and tradition of the early church; claiming the role of the magisterium; embodying authority; and community-based sources of power and authority. We concluded that the womenpriests are engaged in a delicate choreography of the dance of dissent, positioning themselves as the change some people may want to see in Catholicism while remaining true to Catholic roots and rituals.
Variable Selection Algorithm for a Mixture of Poisson Regression for Handling Overdispersion in Claims Frequency Modeling Using Telematics Car Driving Data
Authors: Chan Jennifer S. K. , Choy S. T. Boris , Makov Udi , Shamir Ariel , Shapovalov Vered
Published: April 2022

In automobile insurance, it is common to adopt a Poisson regression model to predict the number of claims as part of the actuarial pricing process. The Poisson assumption can rarely be justified, often due to overdispersion, and alternative modeling is often considered, typically zero-inflated models, which are special cases of finite mixture distributions. Finite mixture regression modeling of telematics data is challenging to implement since the huge number of covariates computationally prohibits the essential variable selection needed to attain a model with desirable predictive power devoid of overfitting. This paper aims at devising an algorithm that can carry the task of variable selection in the presence of a large number of covariates. This is achieved by generating sub-samples of the data corresponding to each component of the Poisson mixture, and wherein variable selection is applied following the enhancement of the Poisson assumption by means of controlling the number of zero claims. The resulting algorithm is assessed by measuring the out-of-sample AUC (Area Under the Curve), a Machine Learning tool for quantifying predictive power. Finally, the application of the algorithm is demonstrated by using data of claim history and telematics data describing driving behavior. It transpires that unlike alternative algorithms related to Poisson regression, the proposed algorithm is both implementable and enjoys an improved AUC (0.71). The proposed algorithm allows more accurate pricing in an era where telematics data is used for automobile insurance.
Assessment of Land Surface Temperature Estimates from Landsat 8-TIRS in A High-Contrast Semiarid Agroecosystem. Algorithms Intercomparison
Authors: Galve Joan M. , Sánchez Juan M. , García-Santos Vicente , González-Piqueras José , Calera Alfonso , Villodre Julio
Published: April 2022

Monitoring Land Surface Temperature (LST) from Landsat satellites has been shown to be effective in the estimation of crop water needs and modeling water use efficiency. Accurate LST estimation becomes critical in semiarid areas under water scarcity scenarios. This work shows the assessment of some well-known Single-Channel (SC) and Split-Window (SW) algorithms, adapted to Landsat 8/TIRS, under the conditions of a high-contrast semiarid agroecosystem. The recently released Landsat 8 Level-2 LST product (L8_ST) has also been included in the performance analysis. Ground measurements of surface temperature were taken for the evaluation during the summers of 2018–2019 in the cropland area of the Barrax test site, Spain. A dataset of 44 ground samples and 11 different L8/TIRS dates/scenes was gathered, covering a variety of crop fields and surface conditions. In addition, a simplified Single Band Atmospheric Correction (L-SBAC) was introduced based on a linearization of the atmospheric correction parameters with the water vapor content (w) and a redefinition of the emissivity threshold for the emissivity correction in the study site. The best results show differences within ±4.0 K for temperatures ranging 300–325 K. Statistics for the L-SBAC result in a RMSE of ±1.8 K with negligible systematic deviation. Similar results were obtained for the other SC and SW algorithms tested, whereas an overestimation of 1.0 K was observed for the L8_ST product because of inappropriate assignment of emissivity values. These results show the potential of the proposed linearization approach and set the uncertainty for LST estimates in high-contrast semiarid agroecosystems.
Depth Inversion from Wave Frequencies in Temporally Augmented Satellite Video
Authors: Gawehn Matthijs , Almar Rafael , Bergsma Erwin W. J. , de Vries Sierd , Aarninkhof Stefan
Published: April 2022

Optical satellite images of the nearshore water surface offer the possibility to invert water depths and thereby constitute the underlying bathymetry. Depth inversion techniques based on surface wave patterns can handle clear and turbid waters in a variety of global coastal environments. Common depth inversion algorithms require video from shore-based camera stations, UAVs or Xband-radars with a typical duration of minutes and at framerates of 1–2 fps to find relevant wave frequencies. These requirements are often not met by satellite imagery. In this paper, satellite imagery is augmented from a sequence of 12 images of Capbreton, France, collected over a period of ∼1.5 min at a framerate of 1/8 fps by the Pleiades satellite, to a pseudo-video with a framerate of 1 fps. For this purpose, a recently developed method is used, which considers spatial pathways of propagating waves for temporal video reconstruction. The augmented video is subsequently processed with a frequency-based depth inversion algorithm that works largely unsupervised and is openly available. The resulting depth estimates approximate ground truth with an overall depth bias of −0.9 m and an interquartile range of depth errors of 5.1 m. The acquired accuracy is sufficiently high to correctly predict wave heights over the shoreface with a numerical wave model and to find hotspots where wave refraction leads to focusing of wave energy that has potential implications for coastal hazard assessments. A more detailed depth inversion analysis of the nearshore region furthermore demonstrates the possibility to detect sandbars. The combination of image augmentation with a frequency-based depth inversion method shows potential for broad application to temporally sparse satellite imagery and thereby aids in the effort towards globally available coastal bathymetry data.
SOLS: An Open-Source Spaceborne Oceanic Lidar Simulator
Authors: Zhang Zhenhua , Chen Peng , Mao Zhihua
Published: April 2022

In recent years, oceanic lidar has seen a wide range of oceanic applications, such as optical profiling and detecting bathymetry. Furthermore, spaceborne lidars, CALIOP and ICESat-2, designed for atmospheric and ice science applications, have been used for ocean backscattering retrievals, but, until now, there has been no spaceborne lidar specifically designed for ocean detection. There is a demand for an effective lidar simulator to study the detection potential capability of spaceborne oceanic lidar. In this study, an open-source spaceborne oceanic lidar simulator named SOLS was developed, which is available freely. Moreover, the maximum detectable depth and corresponding optimal wavelength for spaceborne lidar were analyzed at a global scale by using SOLS. The factors controlling detection limits of a spaceborne ocean profiling lidar in different cases were discussed. Then, the maximum detectable depths with different relative measurement errors and the influence of solar background radiance were estimated. Subsequently, the effects of laser and detector parameters on maximum detectable depths were studied. The relationship between the lidar detectable depth and the ocean mixed layer depth was also discussed. Preliminary results show that the maximum detectable depth could reach deeper than 120 m in the oligotrophic sea at low latitudes. We found that 490 nm is the optimal wavelength for most of the open seawater. For coastal water, 532 nm is a more suitable choice considering both the technical maturity and geophysical parameters. If possible, a lidar equipped with 440 nm could achieve the greatest depth in oligotrophic seawater in subtropical gyres north and south of the equator. The upper mixed layer vertical structure in most of the global open ocean is within the lidar maximum detectable depth. These results show that SOLS can help the design of future spaceborne oceanic lidar systems a lot.
Exploiting Graph and Geodesic Distance Constraint for Deep Learning-Based Visual Odometry
Authors: Fang Xu , Li Qing , Li Qingquan , Ding Kai , Zhu Jiasong
Published: April 2022

Visual odometry is the task of estimating the trajectory of the moving agents from consecutive images. It is a hot research topic both in robotic and computer vision communities and facilitates many applications, such as autonomous driving and virtual reality. The conventional odometry methods predict the trajectory by utilizing the multiple view geometry between consecutive overlapping images. However, these methods need to be carefully designed and fine-tuned to work well in different environments. Deep learning has been explored to alleviate the challenge by directly predicting the relative pose from the paired images. Deep learning-based methods usually focus on the consecutive images that are feasible to propagate the error over time. In this paper, graph loss and geodesic rotation loss are proposed to enhance deep learning-based visual odometry methods based on graph constraints and geodesic distance, respectively. The graph loss not only considers the relative pose loss of consecutive images, but also the relative pose of non-consecutive images. The relative pose of non-consecutive images is not directly predicted but computed from the relative pose of consecutive ones. The geodesic rotation loss is constructed by the geodesic distance and the model regresses a Lie algebra so(3) (3D vector). This allows a robust and stable convergence. To increase the efficiency, a random strategy is adopted to select the edges of the graph instead of using all of the edges. This strategy provides additional regularization for training the networks. Extensive experiments are conducted on visual odometry benchmarks, and the obtained results demonstrate that the proposed method has comparable performance to other supervised learning-based methods, as well as monocular camera-based methods. The source code and the weight are made publicly available.
Determination of Traffic Lane in Tunnel and Positioning of Autonomous Vehicles Using Chromaticity of LED Lights
Authors: Woo Joo , Jo So-Hyeon , Byun Gi-Sig , Kim Sun-Young , Jee Seok-Geun , Seong Ju-Hyeon , Jeong Yeon-Man , Jeong Jae-Hoon
Published: April 2022

Currently, the location recognition and positioning system are the essential parts of unmanned vehicles. Among them, location estimation under GPS-denied environments is currently being studied using IMU, Wi-Fi, and VLC, but there are problems such as cumulative errors, hardware complexity, and precision positioning. To address this problem with the current positioning system, the present study proposed a lane positioning technique by analyzing the chromaticity coordinates, judging from the color temperature of LED lights in tunnels. The tunnel environment was built using LEDs with three color temperatures, and to solve nonlinear problems such as lane positioning from chromaticity analysis, a single input single output fuzzy algorithm was developed to estimate the position of an object on lanes using chromaticity values of signals measured by RGB sensors. The RGB value measured by the sensor removes the disturbance through the pre-processing filter, accepts only the tunnel LED information, and estimates where it is located on the x-distance indicating the lane position through a fuzzy algorithm. Finally, the performance of the fuzzy algorithm was evaluated through experiments, and the accuracy was shown with an average error of less than 4.86%.
Sensor Fault Diagnostics Using Physics-Informed Transfer Learning Framework
Authors: Guc Furkan , Chen Yangquan
Published: April 2022

The field of smart health monitoring, intelligent fault detection and diagnosis is expanding dramatically in order to maintain successful operation in many engineering applications. Considering possible fault scenarios that can occur in a system, indicating the type of fault in a sensor is one of the most important and challenging problems in the area of intelligent sensor fault diagnostics. Within this frame of reference, we extended the physics-informed transfer learning framework, first presented previously for a fault cause assignment, to the level of sensor fault diagnostics for a range of different fault scenarios. Hence, the framework is utilized to perform intelligent sensor fault diagnostics for the first time. The underlying dynamics of the reference system are extracted using a completely data-driven methodology and dynamic mode decomposition with control (DMDc) in order to generate time-frequency illustrations of each sample with continuous wavelet transform (CWT). Then, sensor fault diagnostics for bias, drift over time, sine disturbance and increased noise sensor fault scenarios are achieved using the idea of transfer learning with a pre-trained image classification algorithm. The classification results yields a good performance on sensor fault diagnostics with 91.5% training and 84.7% test accuracy along with a fair robustness level with a set of reference benchmark system parameters.
Derivative-Free Power Flow Solution for Bipolar DC Networks with Multiple Constant Power Terminals
Authors: Medina-Quesada Ángeles , Montoya Oscar Danilo , Hernández Jesus C.
Published: April 2022

This paper analyzes the power flow solution in bipolar direct current networks with radial structures considering multiple monopolar and bipolar constant power loads. The electrical configuration of the bipolar DC grid considers that the reference pole is non-grounded along the feeder, which produces important neutral currents and voltage imbalances along the DC grid. The power flow problem is formulated through the triangular-based representation of the grid topology, which generates a recursive formulation that allows determining the voltage values in the demand nodes through an iterative procedure. The linear convergence of the triangular-based power flow method is tested through multiple load variations with respect to the nominal grid operative condition. Numerical results in the 21- and the 85-bus grids reveal the relevant variations in the voltage profiles and total grid power losses when the neutral cable is solidly grounded or not.
Experimental Validation of High Precision Web Handling for a Two-Actuator-Based Roll-to-Roll System
Authors: Kim Jaeyoung , Kim Kyungrok , Kim Hyunchang , Park Pyoungwon , Lee Seonghyun , Lee Taikmin , Kang Dongwoo
Published: April 2022

In this paper, experimental validation of high precision web handling for a two-actuator-based roll-to-roll (R2R) system is presented. To achieve this, the tension control loop is utilized to regulate the tension in the unwinder module, and the velocity loop is utilized to regulate the web speed in the rewinder module owing to the limitation of the number of actuators. Moreover, the radius estimation algorithm is applied to achieve an accurate web speed and the control sequence of the web handling in the longitudinal axis is developed to manipulate the web handling for convenience. Having these, the tension control performances are validated within ±0.79, ±1.32 and ±1.58 percent tension tracking error and 1.6, 1.53 and 1.33 percent web speed error at the speeds of 0.1 m/s, 0.2 m/s, and 0.3 m/s, respectively. The tension control performance is verified within ±0.3 N tracking error in the changes of the reference tension profile at 0.1 m/s web speed. Lastly, the air floating roller is used to minimize the friction terms and the inertia of the idle roller in the tension zone so that tension control performance can be better achieved during web transportation.
Radiation Hardness Study of Single-Photon Avalanche Diode for Space and High Energy Physics Applications
Authors: Wu Ming-Lo , Ripiccini Emanuele , Kizilkan Ekin , Gramuglia Francesco , Keshavarzian Pouyan , Fenoglio Carlo Alberto , Morimoto Kazuhiro , Charbon Edoardo
Published: April 2022

The radiation hardness of 180 nm complementary metal–oxide–semiconductor (CMOS) and 55 nm bipolar–CMOS–double-diffused MOS single-photon avalanche diodes (SPADs) is studied using 10 MeV and 100 MeV protons up to a displacement damage dose of 1 PeV/g. It is found that the dark count rate (DCR) levels are dependent on the number and the type of defects created. A new stepwise increase in the DCR is presented. Afterpulsing was found to be a significant contributor to the observed DCR increase. A new model for DCR increase prediction is proposed considering afterpulsing. Most of the samples under test retain reasonable DCR levels after irradiation, showing high tolerance to ionizing and displacement damage caused by protons. Following irradiation, self-healing was observed at room temperature. Furthermore, high-temperature annealing shows potential for accelerating recovery. Overall, the results show the suitability of SPADs as optical detectors for long-term space missions or as detectors for high-energy particles.
Object-Level Visual-Text Correlation Graph Hashing for Unsupervised Cross-Modal Retrieval
Authors: Shi Ge , Li Feng , Wu Lifang , Chen Yukun
Published: April 2022

The core of cross-modal hashing methods is to map high dimensional features into binary hash codes, which can then efficiently utilize the Hamming distance metric to enhance retrieval efficiency. Recent development emphasizes the advantages of the unsupervised cross-modal hashing technique, since it only relies on relevant information of the paired data, making it more applicable to real-world applications. However, two problems, that is intro-modality correlation and inter-modality correlation, still have not been fully considered. Intra-modality correlation describes the complex overall concept of a single modality and provides semantic relevance for retrieval tasks, while inter-modality correction refers to the relationship between different modalities. From our observation and hypothesis, the dependency relationship within the modality and between different modalities can be constructed at the object level, which can further improve cross-modal hashing retrieval accuracy. To this end, we propose a Visual-textful Correlation Graph Hashing (OVCGH) approach to mine the fine-grained object-level similarity in cross-modal data while suppressing noise interference. Specifically, a novel intra-modality correlation graph is designed to learn graph-level representations of different modalities, obtaining the dependency relationship of the image region to image region and the tag to tag in an unsupervised manner. Then, we design a visual-text dependency building module that can capture correlation semantic information between different modalities by modeling the dependency relationship between image object region and text tag. Extensive experiments on two widely used datasets verify the effectiveness of our proposed approach.
Comprehensive Generation of Historical Construction CAD Models from Data Provided by a Wearable Mobile Mapping System: A Case Study of the Church of Adanero (Ávila, Spain)
Authors: Rodríguez-Martín Manuel , Sánchez-Aparicio Luis Javier , Maté-González Miguel Ángel , Muñoz-Nieto Ángel Luis , Gonzalez-Aguilera Diego
Published: April 2022

This paper presents the results of a complex three-dimensional reconstruction of the church of Nuestra Señora de la Asunción (Ávila, Spain) as an example of a successful process of verticalization from point clouds to a comprehensive computer-aided design (CAD) model. The reconstruction was carried out using the novel and advanced wearable mobile mapping system ZEB-REVO in combination with a lifting pole, in order to cover the whole geometry of the temple and, also, to model the different constructive elements. To this end, a set of good practices was followed, which allowed for passing from reality to the CAD model, such as the use of closed loops or even the use of different parametric and non-parametric strategies to capture the real geometry of the elements. As a result, this paper outlines the main guidelines for passing from point clouds to comprehensive CAD models, the former being useful for the application of smart preventive conservation processes, heritage building information models or even advanced numerical simulations.
Adaptive Ascent Control of a Collaborative Object Transportation System Using Two Quadrotors
Authors: Pokorný Miroslav , Nowaková Jana , Dočekal Tomáš
Published: April 2022

The paper focuses on the issue of collaborative control of a two quadrotor (Unmanned Aerial Vehicle QDR) system. In particular, two quadrotors perform the task of horizontally transporting a long payload along a predefined trajectory. A leader–follower method is used to synchronize the motion of both QDRs. Conventional PD controllers drive the motion of the leader QDR-L to follow a predefined trajectory. To control a follower QDR-F drive, in the case of indoor applications, a Position Feedback Controller approach (PFC) can be used. To control the QDR-F, the PFC system uses the position information of QDR-L and the required accurate tracking cameras. In our solution, outdoor applications are considered, and usage of the Global Positioning System (GPS) is needed. However, GPS errors can adversely affect the system’s stability. The Force Feedback Controller approach (FFC) is therefore implemented to control the QDR-F motion. The FFC system assumes a rigid gripping of payload by both QDRs. The QDR-F collaborative motion is controlled using the feedback contact forces and torques acting on it due to the motion of the QDR-L. For FFC implementation, the principle of admittance control is used. The admittance controller simulates a virtual “mass-spring-damper” system and drives the motion of the QDR-F according to the contact forces. With the FFC control scheme, the follower QDR-F can be controlled without using the QDR-L positional feedback and the GPS. The contribution to the quality of payload transportation is the novelty of the article. In practice, one of the requirements may be to maintain the horizontal position of the payload. In this paper, an original solution is presented to minimize the horizontal position difference of both QDRs. A new procedure of the transfer admittance controller adaptation according to the mass of the transported payload is designed. The adaptive admittance FFC system is implemented in a Matlab-Simulink environment. The effectiveness of its trajectory tracking and horizontal stabilization functions for variations of the payload mass are demonstrated by numerical calculations.
Radar Target Tracking for Unmanned Surface Vehicle Based on Square Root Sage–Husa Adaptive Robust Kalman Filter
Authors: Qiao Shuanghu , Fan Yunsheng , Wang Guofeng , Mu Dongdong , He Zhiping
Published: April 2022

Dynamic information such as the position and velocity of the target detected by marine radar is frequently susceptible to external measurement white noise generated by the oscillations of an unmanned surface vehicle (USV) and target. Although the Sage–Husa adaptive Kalman filter (SHAKF) has been applied to the target tracking field, the precision and stability of SHAKF remain to be improved. In this paper, a square root Sage–Husa adaptive robust Kalman filter (SR-SHARKF) algorithm together with the constant jerk model is proposed, which can not only solve the problem of filtering divergence triggered by numerical rounding errors, inaccurate system mathematics, and noise statistical models, but also improve the filtering accuracy. First, a novel square root decomposition method is proposed in the SR-SHARKF algorithm for decomposing the covariance matrix of SHAKF to assure its non-negative definiteness. After that, a three-segment approach is adopted to balance the observed and predicted states by evaluating the adaptive scale factor. Finally, the unbiased and the biased noise estimators are integrated while the interval scope of the measurement noise is constrained to jointly evaluate the measurement and observation noise for better adaptability and reliability. Simulation and experimental results demonstrate the effectiveness of the proposed algorithm in eliminating white noise triggered by the USV and target oscillations.
Feelings from the Heart Part II: Simulation and Validation of Static and Dynamic HRV Decrease-Trigger Algorithms to Detect Stress in Firefighters
Authors: Rominger Christian , Schwerdtfeger Andreas R.
Published: April 2022

Several mobile devices have multiple sensors on board and interact with smartphones. This allows for a complex online evaluation of physiological data, important for interactive psychophysiological assessments, which targets the triggering of psychological states based on physiological data such as heart rate variability (HRV). However, algorithms designed to trigger meaningful physiological processes are rare. One exception is the concept of additional HRV reduction (AddHRVr), which aims to control for metabolic-related changes in cardiac activity. In this study we present an approach, based on data of a previous study, which allows algorithm settings to be derived that could be used to automatically trigger the assessment of psychosocial states by online-analysis of transient HRV changes in a sample of 38 firefighters. Settings of a static and a dynamic AddHRVr algorithm were systematically manipulated and quantified by binary triggers. These triggers were subjected to multilevel models predicting increases of objective stress during a period of 24 h. Effect estimates (i.e., odds) and bootstrap power simulations were calculated to inform about the most robust algorithm settings. This study delivers evidence that a dynamic AddHRVr algorithm can trigger transitions of stress, which should be further validated in future interactive psychophysiological assessments.
A Robust Fire Detection Model via Convolution Neural Networks for Intelligent Robot Vision Sensing
Authors: An Qing , Chen Xijiang , Zhang Junqian , Shi Ruizhe , Yang Yuanjun , Huang Wei
Published: April 2022

Accurate fire identification can help to control fires. Traditional fire detection methods are mainly based on temperature or smoke detectors. These detectors are susceptible to damage or interference from the outside environment. Meanwhile, most of the current deep learning methods are less discriminative with respect to dynamic fire and have lower detection precision when a fire changes. Therefore, we propose a dynamic convolution YOLOv5 fire detection method using a video sequence. Our method first uses the K-mean++ algorithm to optimize anchor box clustering; this significantly reduces the rate of classification error. Then, the dynamic convolution is introduced into the convolution layer of YOLOv5. Finally, pruning of the network heads of YOLOv5’s neck and head is carried out to improve the detection speed. Experimental results verify that the proposed dynamic convolution YOLOv5 fire detection method demonstrates better performance than the YOLOv5 method in recall, precision and F1-score. In particular, compared with three other deep learning methods, the precision of the proposed algorithm is improved by 13.7%, 10.8% and 6.1%, respectively, while the F1-score is improved by 15.8%, 12% and 3.8%, respectively. The method described in this paper is applicable not only to short-range indoor fire identification but also to long-range outdoor fire detection.
An Enhanced Hybrid Visual–Inertial Odometry System for Indoor Mobile Robot
Authors: Liu Yanjie , Zhao Changsen , Ren Meixuan
Published: April 2022

As mobile robots are being widely used, accurate localization of the robot counts for the system. Compared with position systems with a single sensor, multi-sensor fusion systems provide better performance and increase the accuracy and robustness. At present, camera and IMU (Inertial Measurement Unit) fusion positioning is extensively studied and many representative Visual–Inertial Odometry (VIO) systems have been produced. Multi-State Constraint Kalman Filter (MSCKF), one of the tightly coupled filtering methods, is characterized by high accuracy and low computational load among typical VIO methods. In the general framework, IMU information is not used after predicting the state and covariance propagation. In this article, we proposed a framework which introduce IMU pre-integration result into MSCKF framework as observation information to improve the system positioning accuracy. Additionally, the system uses the Helmert variance component estimation (HVCE) method to adjust the weight between feature points and pre-integration to further improve the positioning accuracy. Similarly, this article uses the wheel odometer information of the mobile robot to perform zero speed detection, zero-speed update, and pre-integration update to enhance the positioning accuracy of the system. Finally, after experiments carried out in Gazebo simulation environment, public dataset and real scenarios, it is proved that the proposed algorithm has better accuracy results while ensuring real-time performance than existing mainstream algorithms.
Analysis of the Challenges of Artificial Intelligence of Things (AIoT) for the Smart Supply Chain (Case Study: FMCG Industries)
Authors: Nozari Hamed , Szmelter-Jarosz Agnieszka , Ghahremani-Nahr Javid
Published: April 2022

In today’s competitive world, supply chain management is one of the fundamental issues facing businesses that affects all an organization’s activities to produce products and provide services needed by customers. The technological revolution in supply chain logistics is experiencing a significant wave of new innovations and challenges. Despite the current fast digital technologies, customers expect the ordering and delivery process to be faster, and as a result, this has made it easier and more efficient for organizations looking to implement new technologies. “Artificial Intelligence of Things (AIoT)”, which means using the Internet of Things to perform intelligent tasks with the help of artificial intelligence integration, is one of these expected innovations that can turn a complex supply chain into an integrated process. AIoT innovations such as data sensors and RFID (radio detection technology), with the power of artificial intelligence analysis, provide information to implement features such as tracking and instant alerts to improve decision making. Such data can become vital information to help improve operations and tasks. However, the same evolving technology with the presence of the Internet and the huge amount of data can pose many challenges for the supply chain and the factors involved. In this study, by conducting a literature review and interviewing experts active in FMCG industries as an available case study, the most important challenges facing the AIoT-powered supply chain were extracted. By examining these challenges using nonlinear quantitative analysis, the importance of these challenges was examined and their causal relationships were identified. The results showed that cybersecurity and a lack of proper infrastructure are the most important challenges facing the AIoT-based supply chain.
Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan
Authors: Zaitouny Ayham , Fragkou Athanasios D. , Stemler Thomas , Walker David M. , Sun Yuchao , Karakasidis Theodoros , Nathanail Eftihia , Small Michael
Published: April 2022

Non-recurrent congestion disrupts normal traffic operations and lowers travel time (TT) reliability, which leads to many negative consequences such as difficulties in trip planning, missed appointments, loss in productivity, and driver frustration. Traffic incidents are one of the six causes of non-recurrent congestion. Early and accurate detection helps reduce incident duration, but it remains a challenge due to the limitation of current sensor technologies. In this paper, we employ a recurrence-based technique, the Quadrant Scan, to analyse time series traffic volume data for incident detection. The data is recorded by multiple sensors along a section of urban highway. The results show that the proposed method can detect incidents better by integrating data from the multiple sensors in each direction, compared to using them individually. It can also distinguish non-recurrent traffic congestion caused by incidents from recurrent congestion. The results show that the Quadrant Scan is a promising algorithm for real-time traffic incident detection with a short delay. It could also be extended to other non-recurrent congestion types.
Highly Sensitive Carbon Monoxide Sensor Element with Wide-Range Humidity Resistance by Loading Pd Nanoparticles on SnO2 Surface
Authors: Suematsu Koichi , Uchiyama Akihito , Watanabe Ken , Shimanoe Kengo
Published: April 2022

To develop a highly sensitive carbon monoxide (CO) sensor with a wide range of humidity resistance, we focused on the Pd loading method on SnO2 nanoparticles and the thickness of the sensing layer. The Pd nanoparticles were loaded on the SnO2 surface using the surface immobilization method (SI-Pd/SnO2) and the colloidal protection method (CP-Pd/SnO2). The XPS analysis indicated that the Pd nanoparticles were a composite of PdO and Pd, regardless of the loading method. According to the evaluation of the electrical properties at 350 °C, the CO response in a humid atmosphere and the resistance toward humidity change using CP-Pd/SnO2 were higher than those using SI-Pd/SnO2, even though the Pd loading amount of SI-Pd/SnO2 was slightly larger than that of CP-Pd/SnO2. In addition, Pd/SnO2 prepared via the CP method with a thinner sensing layer showed a higher sensor response and greater stability to humidity changes at 300 °C, even though the humidity change influenced the CO response at 250 and 350 °C. Thus, the overall design of the surface Pd, including size, dispersity, and oxidation state, and the sensor fabrication, that is, the thickness of the sensing layer, offer a high-performance semiconductor-type CO gas sensor with a wide range of humidity resistance.