bluuebunny/crossref_metadata_2025 huggingface dataset:
Winter Wheat Phenology Variation and Its Response to Climate Change in Shandong Province, China
Authors: Zhao Yijing , Wang Xiaoli , Guo Yu , Hou Xiyong , Dong Lijie
Published: September 2022

Crop phenology is considered to be an important indicator reflecting the biophysical and physiological processes of crops facing climate change. Therefore, quantifying crop phenology change and its relationship with climate variables is of great significance for developing agricultural management and adaptation strategies to cope with global warming. Based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) product, winter wheat green-up date, heading date, jointing date, and maturity date were first retrieved by Savitzky–Golay (S-G) filtering and threshold methods and then the variation of winter wheat phenology and its correlation with mean (Tmean), minimum (Tmin), and maximum (Tmax) temperature and precipitation (Pre) during 2003–2019 were comprehensively analyzed in Shandong Province, China. Results showed that green-up date, jointing date, heading date, and maturity date generally ranged from 50–70 DOY, 75–95 DOY, 100–120 DOY, and 130–150 DOY. Winter wheat phenology presented a spatial pattern of the South earlier than the North and the inland earlier than the coastal regions. For every 1° increase in latitude/longitude, green-up date, jointing date, heading date, and maturity date were respectively delayed by 3.93 days/0.43 days, 2.31 days/1.19 days, 2.80 days/1.14 days, and 2.12 days/1.09 days. Green-up date and jointing date were both advanced in the West and delayed in the Eastern coastal areas and the South, and heading date and maturity date respectively showed a widespread advance and a delayed tendency in Shandong Province, however, the trend of winter wheat phenological changes was generally insignificant. In addition, green-up date, jointing date, and heading date generally presented a significant negative correlation with mean/minimum temperature, while maturity date was positively associated with the current month maximum temperature, notably in the West of Shandong Province. Regarding precipitation, a generally insignificant relationship with winter wheat phenology was detected. Results in this study are anticipated to provide insight into the impact of climate change on winter wheat phenology and to supply reference for the agricultural production and field management of winter wheat in Shandong Province, China.
A New Approach to Satellite-Derived Bathymetry: An Exercise in Seabed 2030 Coastal Surveys
Authors: Louvart Pierre , Cook Harry , Smithers Chloe , Laporte Jean
Published: September 2022

ARGANS has years of experience in analysing the factors limiting light transmission in coastal environments around the world. This has led its Satellite-Derived Bathymetry (SDB) team to the conclusion that current satellite instruments and their resolution are unable to provide the absolute precision required for safe navigation according to International Hydrographic Bureau (IHO) standards, except in the clearest of waters characterised by sufficiently well-defined environmental parameters. This limitation is caused by the variability itself of the parameters in the radiative transfer equation (RTE), which is too high to provide results within the strict accuracy ranges of IHO S.44, as shown by scatter plots characterised time and again by large biases and standard deviations of several metres. The radiative transfer equation and Hydrolight simulations are not at fault, but their results must be interpreted with extreme caution if the level of accuracy required for safe navigation over large areas is to be guaranteed. Therefore, ARGANS has developed an innovative, alternative method. This is based on the classification of the full range of images pixels allowing for homogeneous sub-areas to be determined and linked together by artificial intelligence and statistical clustering of similar parameters. Thanks to the sponsorship of the European Space Agency and Seabed 2030, ARGANS has been able to test its Water Column Parameter Estimator (WCPE), first in the challenging waters of Madagascar updating over 1000 km of lead line exploratory surveys, then in the South Pacific coral environment and then in the turbid coastal water of Qatar. The parameters determined by WCPE can be extrapolated to unknown regions of similar environment and propagated from one place to the next, using a chain method somewhat inspired by photogrammetric techniques of old. Further progress and automation can be expected from an improved control of sediment plumes that can obscure or distort all optical methods, whether satellite or LIDAR.
A New Method to Evaluate Gold Mineralisation-Potential Mapping Using Deep Learning and an Explainable Artificial Intelligence (XAI) Model
Authors: Pradhan Biswajeet , Jena Ratiranjan , Talukdar Debojit , Mohanty Manoranjan , Sahu Bijay Kumar , Raul Ashish Kumar , Abdul Maulud Khairul Nizam
Published: September 2022

Geoscientists have extensively used machine learning for geological mapping and exploring the mineral prospect of a province. However, the interpretation of results becomes challenging due to the complexity of machine learning models. This study uses a convolutional neural network (CNN) and Shapley additive explanation (SHAP) to estimate potential locations for gold mineralisation in Rengali Province, a tectonised mosaic of volcano-sedimentary sequences juxtaposed at the interface of the Archaean cratonic segment in the north and the Proterozoic granulite provinces of the Eastern Ghats Belt in Eastern India. The objective is to integrate multi-thematic data involving geological, geophysical, mineralogical and geochemical surveys on a 1:50 K scale with the aim of prognosticating gold mineralisation. The available data utilised during the integration include aero-geophysical (aeromagnetic and aerospectrometric), geochemical (national geochemical mapping), ground geophysical (gravity), satellite gravity, remote sensing (multispectral) and National Geomorphology and Lineament Project structural lineament maps obtained from the Geological Survey of India Database. The CNN model has an overall accuracy of 90%. The SHAP values demonstrate that the major contributing factors are, in sequential order, antimony, clay, lead, arsenic content and a magnetic anomaly in CNN modelling. Geochemical pathfinders, including geophysical factors, have high importance, followed by the shear zones in mineralisation mapping. According to the results, the central parts of the study area, including the river valley, have higher gold prospects than the surrounding areas. Gold mineralisation is possibly associated with intermediate metavolcanics along the shear zone, which is later intruded by quartz veins in the northern part of the Rengali Province. This work intends to model known occurrences with respect to multiple themes so that the results can be replicated in surrounding areas.
On the Use of Assistive Technology during the COVID-19 Outbreak: Results and Lessons Learned from Pilot Studies
Authors: Fiorini Laura , Rovini Erika , Russo Sergio , Toccafondi Lara , D’Onofrio Grazia , Cornacchia Loizzo Federica Gabriella , Bonaccorsi Manuele , Giuliani Francesco , Vignani Gianna , Sancarlo Daniele , Greco Antonio , Cavallo Filippo
Published: September 2022

As a consequence of the COVID-19 emergency, frail citizens felt isolated because of social isolation, suspended and/or strongly reduced home assistance, and limited access to hospitals. In this sense, assistive technology could play a pivotal role in empowering frail older adults reducing their isolation, as well as in reinforcing the work of formal caregivers and professionals. In this context, the goal of this paper is to present four pilot studies—conducted from March 2020 to April 2021—to promptly react to COVID-19 by providing assistive technology solutions, aiming to (1) guarantee high-quality service to older adults in-home or in residential facility contexts, (2) promote social inclusion, and (3) reduce the virus transmission. In particular, four services, namely, telepresence service, remote monitoring service, virtual visit, and environmental disinfection, were designed, implemented, and tested in real environments involving 85 end-users to assess the user experience and/or preliminary assess the technical feasibility. The results underlined that all the proposed services were generally accepted by older adults and professionals. Additionally, the results remarked that the use of telepresence robots in private homes and residential facilities increased enjoyment reducing anxiety, whereas the monitoring service supported the clinicians in monitoring the discharged COVID-19 patients. It is also worth mentioning that two new services/products were developed to disinfect the environment and to allow virtual visits within the framework of a hospital information system. The virtual visits service offered the opportunity to expand the portfolio of hospital services. The main barriers were found in education, technology interoperability, and ethical/legal/privacy compliance. It is also worth mentioning the key role played by an appropriate design and customer needs analysis since not all assistive devices were designed for older persons.
Stochastic Recognition of Human Physical Activities via Augmented Feature Descriptors and Random Forest Model
Authors: Tahir Sheikh Badar ud din , Dogar Abdul Basit , Fatima Rubia , Yasin Affan , Shafiq Muhammad , Khan Javed Ali , Assam Muhammad , Mohamed Abdullah , Attia El-Awady
Published: September 2022

Human physical activity recognition from inertial sensors is shown to be a successful approach for monitoring elderly individuals and children in indoor and outdoor environments. As a result, researchers have shown significant interest in developing state-of-the-art machine learning methods capable of utilizing inertial sensor data and providing key decision support in different scenarios. This paper analyzes data-driven techniques for recognizing human daily living activities. Therefore, to improve the recognition and classification of human physical activities (for example, walking, drinking, and running), we introduced a model that integrates data preprocessing methods (such as denoising) along with major domain features (such as time, frequency, wavelet, and time–frequency features). Following that, stochastic gradient descent (SGD) is used to improve the performance of the extracted features. The selected features are catered to the random forest classifier to detect and monitor human physical activities. Additionally, the proposed HPAR system was evaluated on five benchmark datasets, namely the IM-WSHA, PAMAP-2, UCI HAR, MobiAct, and MOTIONSENSE databases. The experimental results show that the HPAR system outperformed the present state-of-the-art methods with recognition rates of 90.18%, 91.25%, 91.83%, 90.46%, and 92.16% from the IM-WSHA, PAMAP-2, UCI HAR, MobiAct, and MOTIONSENSE datasets, respectively. The proposed HPAR model has potential applications in healthcare, gaming, smart homes, security, and surveillance.
Appliance-Level Anomaly Detection by Using Control Charts and Artificial Neural Networks with Power Profiles
Authors: Apaydin-Özkan Hanife
Published: September 2022

Nowadays, the development of the Internet of Things (IoT) concept has increased the interest in some technologies, one of which is the detection of anomalies in home appliances before they occur. In this work, in order to contribute to the works that use appliance power profiles for anomaly detection, a novel Appliance Monitoring and Anomaly Detection System (AM-ADS) is presented. AM-ADS consists of a main controller, a database, IoT-based communication units, home appliances, and power measurement units (smart plugs or special measurement equipments) mounted on appliances. In AM-ADS, a new Control Chart (CC) based method, for the cases that a limited number of historical power profiles are available; and a new Artificial Neural Network (ANN) based method, for the cases that a sufficient number of historical power profiles of each anomaly free and anomalous situations are available, are used according to the developed rule-based AM-ADS procedure to maximize the advantages and to eliminate the disadvantages of these methods as much as possible. According to the AM-ADS procedure, power consumptions of appliances, which provide meaningful information about the health of appliances, are measured during their operations and the corresponding power profiles are created. Active power, power factor, and operation duration features of power profiles are considered as decisive control parameters and different characteristics of these parameters are used as inputs for CC and ANN-based methods. The efficiency and performance of AM-ADS are validated by application case studies, where the ability to detect anomalies varies between 94.56% and 99.03% when a limited number of historical data is available; and the ability to detect and classify anomalies varies between 96.34% and 99.45% when a sufficient number of historical data is available.
Digital Pregnancy Test Powered by an Air-Breathing Paper-Based Microfluidic Fuel Cell Stack Using Human Urine as Fuel
Authors: Vera-Estrada Irma Lucia , Olivares-Ramírez Juan Manuel , Rodríguez-Reséndiz Juvenal , Dector Andrés , Mendiola-Santibañez Jorge Domingo , Amaya-Cruz Diana María , Sosa-Domínguez Adrían , Ortega-Díaz David , Dector Diana , Ovando-Medina Victor Manuel , Antonio-Carmona Iveth Dalila
Published: September 2022

The direct integration of paper-based microfluidic fuel cells (μFC’s) toward creating autonomous lateral flow assays has attracted attention. Here, we show that an air-breathing paper-based μFC could be used as a power supply in pregnancy tests by oxidizing the human urine used for the diagnosis. We present an air-breathing paper-based μFC connected to a pregnancy test, and for the first time, as far as we know, it is powered by human urine without needing any external electrolyte. It uses TiO2-Ni as anode and Pt/C as cathode; the performance shows a maximum value of voltage and current and power densities of ∼0.96 V, 1.00 mA cm−2, and 0.23 mW cm−2, respectively. Furthermore, we present a simple design of a paper-based μFC’s stack powered with urine that shows a maximum voltage and maximum current and power densities of ∼1.89 V, 2.77 mA cm−2 and 1.38 mW cm−2, respectively, which powers the display of a pregnancy test allowing to see the analysis results.
Fusion of Different Image Sources for Improved Monitoring of Agricultural Plots
Authors: Moltó Enrique
Published: September 2022

In the Valencian Community, the applications of precision agriculture in multiannual woody crops with high added value (fruit trees, olive trees, almond trees, vineyards, etc.) are of priority interest. In these plots, canopies do not fully cover the soil and the planting frames are incompatible with the Resolution of Sentinel 2. The present work proposes a procedure for the fusion of images with different temporal and spatial resolutions and with different degrees of spectral quality. It uses images from the Sentinel 2 mission (low resolution, high spectral quality, high temporal resolution), orthophotos (high resolution, low temporal resolution) and images obtained with drones (very high spatial resolution, low temporal resolution). The procedure is applied to generate time series of synthetic RGI images (red, green, infrared) with the same high resolution of orthophotos and drone images, in which gray levels are reassigned from the combination of their own RGI bands and the values of the B3, B4 and B8 bands of Sentinel 2. Two practical examples of application are also described. The first shows the NDVI images that can be generated after the process of merging two RGI Sentinel 2 images obtained on two specific dates. It is observed how, after the merging, different NDVI values can be assigned to the soil and vegetation, which allows them to be distinguished (contrary to the original Sentinel 2 images). The second example shows how graphs can be generated to describe the evolution throughout the vegetative cycle of the estimated values of three spectral indices (NDVI, GNDVI, GCI) on a point in the image corresponding to soil and on another assigned to vegetation. The robustness of the proposed algorithm has been validated by using image similarity metrics.
Images Enhancement of Ancient Mural Painting of Bey’s Palace Constantine, Algeria and Lacuna Extraction Using Mahalanobis Distance Classification Approach
Authors: Nasri Adel , Huang Xianfeng
Published: September 2022

As a result of human activity and environmental changes, several types of damages may occur to ancient mural paintings; indeed, lacunae, which refer to the area of paint layer loss, are the most prevalent kind. The presence of lacuna is an essential sign of the progress of mural painting deterioration. Most studies have focused on detecting and removing cracks from old paintings. However, lacuna extraction has not received the necessary consideration and is not well-explored. Furthermore, most recent studies have focused on using deep learning for mural protection and restoration, but deep learning requires a large amount of data and computational resources which is not always available in heritage institutions. In this paper, we present an efficient method to automatically extract lacunae and map deterioration from RGB images of ancient mural paintings of Bey’s Palace in Algeria. Firstly, a preprocessing was applied using Dark Channel Prior (DCP) to enhance the quality and improve visibility of the murals. Secondly, a determination of the training sample and pixel’s grouping was assigned to their closest sample based on Mahalanobis Distance (MD) by calculating both the mean and variance of the classes in three bands (R, G, and B), in addition to the covariance matrix of all the classes to achieve lacuna extraction of the murals. Finally, the accuracy of extraction was calculated. The experimental results showed that the proposed method can achieve a conspicuously high accuracy of 94.33% in extracting lacunae from ancient mural paintings, thus supporting the work of a specialist in heritage institutions in terms of the time- and cost-consuming documentation process.
Weak Fault Feature Extraction Method Based on Improved Stochastic Resonance
Authors: Yang Zhen , Li Zhiqian , Zhou Fengxing , Ma Yajie , Yan Baokang
Published: September 2022

Aiming at the problems of early weak fault feature extraction of bearings in rotating machinery, an improved stochastic resonance (SR) is proposed combined with the advantage of SR to enhance weak characteristic signals with noise energy. Firstly, according to the characteristics of the large parameters of the actual fault signal, the amplitude transform coefficient and frequency transform coefficient are introduced to convert the large parameter signal into small parameter signal which can be processed by SR, and the relationship of second-order parameters are introduced. Secondly, a comprehensive evaluation index (CEI) consisted of power spectrum kurtosis, correlation coefficient, structural similarity, root mean square error, and approximate entropy, is constructed through BP neural network. Moreover, this CEI is adopted as fitness function to search the optimal damping coefficient and amplitude transform coefficient with adaptive weight particle swarm optimization (PSO). Finally, according to the improved optimal SR system, the weak fault feature can be extracted. The simulation and experiment verify the effectiveness of the proposed method compared with traditional second-order general scale transform adaptive SR.
An Ultrasonic Tomography System for the Inspection of Columns in Architectural Heritage
Authors: Aparicio Secanellas Sofía , Liébana Gallego Juan Carlos , Anaya Catalán Guillermo , Martín Navarro Rodrigo , Ortega Heras Javier , García Izquierdo Miguel Ángel , González Hernández Margarita , Anaya Velayos José Javier
Published: September 2022

A new versatile and geometrically reconfigurable ultrasonic tomography system (UTS) has been designed to inspect and obtain information about the internal structure and inner damage of columns in heritage buildings. This nondestructive system is considered innovative because it aims to overcome common limitations of existing systems. Tomographic inspections are typically carried out manually and are thus limited to small portions of construction elements. The proposed UTS allows the automatization of the inspection and the generation of numerous tomographic slices along the height of the column. It is valid for multiple types of columns and materials. In the present work, the system was tested on two limestone columns of the north façade of the Convent of Carmo in Lisbon, Portugal. The UTS is composed of a mechanical and an electronic system. The mechanical system consists of four linear motion subsystems mounted in a square setup. A transducer is placed on each of the axes, acting as emitter or receiver of the ultrasonic signals. The mechanical system also includes a guide system to adapt the inspections to the complex geometry of the columns. The electronic system allows the control and the synchronization of the movements and the emission/reception configuration of the four ultrasonic transducers.
Acoustic Limescale Layer and Temperature Measurement in Ultrasonic Flow Meters
Authors: Landskron Johannes , Dötzer Florian , Benkert Andreas , Mayle Michael , Drese Klaus Stefan
Published: September 2022

Guided acoustic waves are commonly used in domestic water meters to measure the flow rate. The accuracy of this measurement method is affected by factors such as variations in temperature and limescale deposition inside of the pipe. In this work, a new approach using signals from different sound propagation paths is used to determine these quantities and allow for subsequent compensation. This method evaluates the different propagation times of guided Lamb waves in flow measurement applications. A finite element method-based model is used to identify the calibration curves for the device under test. The simulated dependencies on temperature and layer thickness are validated by experimental data. Finally, a test on simulated data with varying temperatures and limescale depositions proves that this method can be used to separate both effects. Based on these values, a flow measurement correction scheme can be derived that provides an improved resolution of guided acoustic wave-based flow meters.
A Weighted Decision-Level Fusion Architecture for Ballistic Target Classification in Midcourse Phase
Authors: Wei Nannan , Zhang Limin , Zhang Xinggan
Published: September 2022

The recognition of warheads in the target cloud of the ballistic midcourse phase remains a challenging issue for missile defense systems. Considering factors such as the differing dimensions of the features between sensors and the different recognition credibility of each sensor, this paper proposes a weighted decision-level fusion architecture to take advantage of data from multiple radar sensors, and an online feature reliability evaluation method is also used to comprehensively generate sensor weight coefficients. The weighted decision-level fusion method can overcome the deficiency of a single sensor and enhance the recognition rate for warheads in the midcourse phase by considering the changes in the reliability of the sensor’s performance caused by the influence of the environment, location, and other factors during observation. Based on the simulation dataset, the experiment was carried out with multiple sensors and multiple bandwidths, and the results showed that the proposed model could work well with various classifiers involving traditional learning algorithms and ensemble learning algorithms.
Specular Reflection Suppression through the Adjustment of Linear Polarization for Tumor Diagnosis Using Fluorescein Sodium
Authors: Lee Sangyun , Yoon Kicheol , Kim Jungmin , Kim Kwang Gi
Published: September 2022

In tumor surgery, the edges of the tumor can be visually observed using a fluorescent contrast agent and a fluorescent imaging device. By distinguishing it from normal tissues and blood vessels, it is possible to objectively judge the extent of resection while visually observing it during surgery, and it guarantees safe tumor resection based on more information. However, the main problem of such an imaging device is the specular reflection phenomenon. If specular reflection overlaps with important lesion locations, they are a major factor leading to diagnostic errors. Here, we propose a method to reduce specular reflection that occurs during tumor diagnosis using a linear polarization filter and fluorescent contrast agent. To confirm the effect of removing specular reflection, a self-made fluorescein sodium vial phantom was used, and the reliability of the results was increased using a large animal (pig) test. As a result of the experiment, it was possible to obtain an image in which specular reflection was removed by controlling the rotation angle of the filter by 90° and 270°, and the same results were confirmed in the phantom experiment and the animal experiment.
HAT4RD: Hierarchical Adversarial Training for Rumor Detection in Social Media
Authors: Ni Shiwen , Li Jiawen , Kao Hung-Yu
Published: September 2022

With the development of social media, social communication has changed. While this facilitates people’s communication and access to information, it also provides an ideal platform for spreading rumors. In normal or critical situations, rumors can affect people’s judgment and even endanger social security. However, natural language is high-dimensional and sparse, and the same rumor may be expressed in hundreds of ways on social media. As such, the robustness and generalization of the current rumor detection model are in question. We proposed a novel hierarchical adversarial training method for rumor detection (HAT4RD) on social media. Specifically, HAT4RD is based on gradient ascent by adding adversarial perturbations to the embedding layers of post-level and event-level modules to deceive the detector. At the same time, the detector uses stochastic gradient descent to minimize the adversarial risk to learn a more robust model. In this way, the post-level and event-level sample spaces are enhanced, and we verified the robustness of our model under a variety of adversarial attacks. Moreover, visual experiments indicate that the proposed model drifts into an area with a flat loss landscape, thereby, leading to better generalization. We evaluate our proposed method on three public rumor datasets from two commonly used social platforms (Twitter and Weibo). Our experimental results demonstrate that our model achieved better results compared with the state-of-the-art methods.
Shape–Texture Debiased Training for Robust Template Matching
Authors: Gao Bo , Spratling Michael W.
Published: September 2022

Finding a template in a search image is an important task underlying many computer vision applications. This is typically solved by calculating a similarity map using features extracted from the separate images. Recent approaches perform template matching in a deep feature space, produced by a convolutional neural network (CNN), which is found to provide more tolerance to changes in appearance. Inspired by these findings, in this article we investigate whether enhancing the CNN’s encoding of shape information can produce more distinguishable features that improve the performance of template matching. By comparing features from the same CNN trained using different shape–texture training methods, we determined a feature space which improves the performance of most template matching algorithms. When combining the proposed method with the Divisive Input Modulation (DIM) template matching algorithm, its performance is greatly improved, and the resulting method produces state-of-the-art results on a standard benchmark. To confirm these results, we create a new benchmark and show that the proposed method outperforms existing techniques on this new dataset.
Robust Template Matching Using Multiple-Layered Absent Color Indexing
Authors: Wei Guodong , Tian Ying , Kaneko Shun’ichi , Jiang Zhengang
Published: September 2022

Color is an essential feature in histogram-based matching. This can be extracted as statistical data during the comparison process. Although the applicability of color features in histogram-based techniques has been proven, position information is lacking during the matching process. We present a conceptually simple and effective method called multiple-layered absent color indexing (ABC-ML) for template matching. Apparent and absent color histograms are obtained from the original color histogram, where the absent colors belong to low-frequency or vacant bins. To determine the color range of compared images, we propose a total color space (TCS) that can determine the operating range of the histogram bins. Furthermore, we invert the absent colors to obtain the properties of these colors using threshold hT. Then, we compute the similarity using the intersection. A multiple-layered structure is proposed against the shift issue in histogram-based approaches. Each layer is constructed using the isotonic principle. Thus, absent color indexing and multiple-layered structure are combined to solve the precision problem. Our experiments on real-world images and open data demonstrated that they have produced state-of-the-art results. Moreover, they retained the histogram merits of robustness in cases of deformation and scaling.
Modeling Threats to AI-ML Systems Using STRIDE
Authors: Mauri Lara , Damiani Ernesto
Published: September 2022

The application of emerging technologies, such as Artificial Intelligence (AI), entails risks that need to be addressed to ensure secure and trustworthy socio-technical infrastructures. Machine Learning (ML), the most developed subfield of AI, allows for improved decision-making processes. However, ML models exhibit specific vulnerabilities that conventional IT systems are not subject to. As systems incorporating ML components become increasingly pervasive, the need to provide security practitioners with threat modeling tailored to the specific AI-ML pipeline is of paramount importance. Currently, there exist no well-established approach accounting for the entire ML life-cycle in the identification and analysis of threats targeting ML techniques. In this paper, we propose an asset-centered methodology—STRIDE-AI—for assessing the security of AI-ML-based systems. We discuss how to apply the FMEA process to identify how assets generated and used at different stages of the ML life-cycle may fail. By adapting Microsoft’s STRIDE approach to the AI-ML domain, we map potential ML failure modes to threats and security properties these threats may endanger. The proposed methodology can assist ML practitioners in choosing the most effective security controls to protect ML assets. We illustrate STRIDE-AI with the help of a real-world use case selected from the TOREADOR H2020 project.
Automatic Segmentation of Standing Trees from Forest Images Based on Deep Learning
Authors: Shi Lijuan , Wang Guoying , Mo Lufeng , Yi Xiaomei , Wu Xiaoping , Wu Peng
Published: September 2022

Semantic segmentation of standing trees is important to obtain factors of standing trees from images automatically and effectively. Aiming at the accurate segmentation of multiple standing trees in complex backgrounds, some traditional methods have shortcomings such as low segmentation accuracy and manual intervention. To achieve accurate segmentation of standing tree images effectively, SEMD, a lightweight network segmentation model based on deep learning, is proposed in this article. DeepLabV3+ is chosen as the base framework to perform multi-scale fusion of the convolutional features of the standing trees in images, so as to reduce the loss of image edge details during the standing tree segmentation and reduce the loss of feature information. MobileNet, a lightweight network, is integrated into the backbone network to reduce the computational complexity. Furthermore, SENet, an attention mechanism, is added to obtain the feature information efficiently and suppress the generation of useless feature information. The extensive experimental results show that using the SEMD model the MIoU of the semantic segmentation of standing tree images of different varieties and categories under simple and complex backgrounds reaches 91.78% and 86.90%, respectively. The lightweight network segmentation model SEMD based on deep learning proposed in this paper can solve the problem of multiple standing trees segmentation with high accuracy.
Aurora Image Classification with Deep Metric Learning
Authors: Endo Takeru , Matsumoto Mitsuharu
Published: September 2022

In recent years, neural networks have been increasingly used for classifying aurora images. In particular, convolutional neural networks have been actively studied. However, there are not many studies on the application of deep learning techniques that take into account the characteristics of aurora images. Therefore, in this study, we propose the use of deep metric learning as a suitable method for aurora image classification. Deep metric learning is one of the deep learning techniques. It was developed to distinguish human faces. Identifying human faces is a more difficult task than standard classification tasks because this task is characterized by a small number of sample images for each class and poor feature variation between classes. We thought that the face identification task is similar to aurora image classification in that the number of labeled images is relatively small and the feature differences between classes are small. Therefore, we studied the application of deep metric learning to aurora image classification. As a result, our experiments showed that deep metric learning improves the accuracy of aurora image classification by nearly 10% compared to previous studies.
Optimal Implementation Parameters of a Nonlinear Electrical Impedance Tomography Method Using the Complete Electrode Model
Authors: Park Jeongwoo , Kang Jun Won , Choi Eunsoo
Published: September 2022

This study discusses a nonlinear electrical impedance tomography (EIT) technique under different analysis conditions to propose its optimal implementation parameters. The forward problem for calculating electric potential is defined by the complete electrode model. The inverse problem for reconstructing the target electrical conductivity profile is presented based on a partial-differential-equation-constrained optimization approach. The electrical conductivity profile is iteratively updated by solving the Karush–Kuhn–Tucker optimality conditions and using the conjugate gradient method with an inexact line search. Various analysis conditions such as regularization scheme, number of electrodes, current input patterns, and electrode arrangement were set differently, and the corresponding results were compared. It was found from this study that the proposed EIT method yielded appropriate inversion results with various parameter settings, and the optimal implementation parameters of the EIT method are presented. This study is expected to expand the utility and applicability of EIT for the non-destructive evaluation of structures.
Comparison of Named Data Networking Mobility Methodology in a Merged Cloud Internet of Things and Artificial Intelligence Environment
Authors: Azamuddin Wan Muhd Hazwan , Aman Azana Hafizah Mohd , Hassan Rosilah , Mansor Norhisham
Published: September 2022

In-network caching has evolved into a new paradigm, paving the way for the creation of Named Data Networking (NDN). Rather than simply being typical Internet technology, NDN serves a range of functions, with a focus on consumer-driven network architecture. The NDN design has been proposed as a method for replacing Internet Protocol (IP) addresses with identified content. This study adds to current research on NDN, artificial intelligence (AI), cloud computing, and the Internet of Things (IoT). The core contribution of this paper is the merging of cloud IoT (C-IoT) and NDN-AI-IoT. To be precise, this study provides possible methodological and parameter explanations of the technologies via three methods: KITE, a producer mobility support scheme (PMSS), and hybrid network mobility (hybrid NeMO). KITE uses the indirection method to transmit content using simple NDN communication; the PMSS improves producer operation by reducing handover latency; and hybrid NeMO provides a binding information table to replace the base function of forwarding information. This study also describes mathematical equations for signaling cost and handover latency. Using the network simulator ndnSIM NS-3, this study highlights producer mobility operation. Mathematical equations for each methodology are developed based on the mobility scenario to measure handover latency and signaling cost. The results show that the efficiency of signaling cost for hybrid NeMO is approximately 4% better than that of KITE and the PMSS, while the handover latency for hybrid NeMO is 46% lower than that of KITE and approximately 60% lower than that of the PMSS.
A Multimodal Sensing Device for Simultaneous Measurement of Dissolved Oxygen and Hydrogen Ions by Monolithic Integration of FET-Based Sensors
Authors: Noda Toshihiko , Loo Sylvia Mei Lin , Noda Yoshiko , Akai Daisuke , Hizawa Takeshi , Choi Yong-Joon , Takahashi Kazuhiro , Sawada Kazuaki
Published: September 2022

We examined the possibility of measuring dissolved oxygen by using a potentiometric solid-state semiconductor sensor. Thin films of tin (IV) oxide (SnO2) are widely used in oxygen gas sensors. However, their ability to detect dissolved oxygen (DO) in solutions is still unknown. In this paper, we present a method for investigating the dissolved oxygen-sensing properties of SnO2 thin films in solutions by fabricating a SnO2-gate field-effect transistor (FET). A similarly structured hydrogen ion-sensitive silicon nitride (Si3N4)-gate FET was fabricated using the same method. The transfer characteristics and sensitivities were experimentally obtained and compared. The transfer characteristics of the FET show a shift in threshold voltage in response to a decrease in DO concentration. The SnO2-gate FET exhibited a sensitivity of 4 mV/ppm, whereas the Si3N4-gate FET showed no response to DO. Although the SnO2-gate FET responds to pH changes in the solution, this sensitivity issue can be eliminated by using a Si3N4-gate FET, which is capable of selectively sensing hydrogen ions without DO sensitivity. The experimental results indicate the promising properties of SnO2 thin films for multimodal sensing applications.
Application of Teager–Kaiser Energy Operator in the Early Fault Diagnosis of Rolling Bearings
Authors: Shi Xiangfu , Zhang Zhen , Xia Zhiling , Li Binhua , Gu Xin , Shi Tingna
Published: September 2022

Rolling bearings are key components that support the rotation of motor shafts, operating with a quite high failure rate among all the motor components. Early bearing fault diagnosis has great significance to the operation security of motors. The main contribution of this paper is to illustrate Gaussian white noise in bearing vibration signals seriously masks the weak fault characteristics in the diagnosis based on the Teager–Kaiser energy operator envelope, and to propose improved TKEO taking both accuracy and calculation speed into account. Improved TKEO can attenuate noise in consideration of computational efficiency while preserving information about the possible fault. The proposed method can be characterized as follows: a series of band-pass filters were set up to extract several component signals from the original vibration signals; then a denoised target signal including fault information was reconstructed by weighted summation of these component signals; finally, the Fourier spectrum of TKEO energy of the resulting target signal was used for bearing fault diagnosis. The improved TKEO was applied to a vibration signal dataset of run-to-failure rolling bearings and compared with two advanced diagnosis methods. The experimental results verify the effectiveness and superiority of the proposed method in early bearing fault detection.
In Situ Measurements of Plankton Biorhythms Using Submersible Holographic Camera
Authors: Dyomin Victor , Davydova Alexandra , Kirillov Nikolay , Morgalev Sergey , Naumova Elena , Olshukov Alexey , Polovtsev Igor
Published: September 2022

The paper presents a diagnostic complex for plankton studies using the miniDHC (digital holographic camera). Its capabilities to study the rhythmic processes in plankton ecosystems were demonstrated using the natural testing in Lake Baikal in summer. The results of in situ measurements of plankton to detect the synchronization of collective biological rhythms with medium parameters are presented and interpreted. The most significant rhythms in terms of the correlation of their parameters with medium factors are identified. The study shows that the correlation with water temperature at the mooring site has the greatest significance and reliability. The results are verified with biodiversity data obtained by the traditional mesh method. The experience and results of the study can be used for the construction of a stationary station to monitor the ecological state of the water area through the digitalization of plankton behavior.
Volatile Organic Compound Monitoring during Extreme Wildfires: Assessing the Potential of Sensors Based on LbL and Sputtering Films
Authors: Magro Cátia , Gonçalves Oriana C. , Morais Marcelo , Ribeiro Paulo A. , Sério Susana , Vieira Pedro , Raposo Maria
Published: September 2022

A new theory suggests that flammable gases generated by heated vegetation, in particular the volatile organic compounds (VOC) common to Mediterranean plants, may, under certain topographic and wind conditions, accumulate in locations where, after the arrival of the ignition source, they rapidly burst into flames as explosions. Hence, there is a need for the development of a system that can monitor the development of these compounds. In this work, a sensor’s array is proposed as a method for monitoring the amount of eucalyptol and α-pinene, the major VOC compounds of the Eucalyptus and Pine trees. The detection of the target compounds was assessed using the impedance spectroscopy response of thin films. Combinations of layers of polyelectrolytes, such as poly(allylamine hydrochloride) (PAH), polyethyleneimine (PEI), poly(sodium 4-sytrenesulfonate) (PSS) graphene oxide (GO), and non/functionalized multiwall nanotubes (MWCNT-COOH or MWCNT), namely, PAH/GO, PEI/PSS, PEI/GO, PAH/MWCNT, PAH/MWCNT-COOH, films, and TiO2 and ZnO sputtered films, were deposited onto ceramic supports coated with gold interdigitated electrodes. The results showed that concentrations of the target VOCs, within the range of 68 to 999 ppmv, can be easily distinguished by analyzing the impedance spectra, particularly in the case of the ZnO- and PAH/GO-film-based sensors, which showed the best results in the detection of the target compounds. Through principal component analysis (PCA), the best set of features attained for the ZnO and PAH/GO based sensor devices revealed a linear trend of the PCA’s first principal component with the concentration within the range 109 and 807 ppmv. Thus, the values of sensitivity to eucalyptol and α-pinene concentrations, which were (2.2 ± 0.3) × 10−4 and (5.0 ± 0.7) × 10−5 per decade, respectively, as well as resolutions of 118 and 136 ppbv, respectively, were identified.
GAPSO-Optimized Fuzzy PID Controller for Electric-Driven Seeding
Authors: Wang Song , Zhao Bin , Yi Shujuan , Zhou Zheng , Zhao Xue
Published: September 2022

To improve the seeding motor control performance of electric-driven seeding (EDS), a genetic particle swarm optimization (GAPSO)-optimized fuzzy PID control strategy for electric-driven seeding was designed. Since the parameters of the fuzzy controller were difficult to determine, two quantization factors were applied to the input of the fuzzy controller, and three scaling factors were introduced into the output of fuzzy controller. Genetic algorithm (GA) and particle swarm optimization (PSO) were combined into GAPSO by a genetic screening method. GAPSO was introduced to optimize the initial values of the two quantization factors, three scaling factors, and three characteristic functions before updating. The simulation results showed that the maximum overshoot of the GAPSO-based fuzzy PID controller system was 0.071%, settling time was 0.408 s, and steady-state error was 3.0693 × 10−5, which indicated the excellent control performance of the proposed strategy. Results of the field experiment showed that the EDS had better performance than the ground wheel chain sprocket seeding (GCSS). With a seeder operating speed of 6km/h, the average qualified index (Iq) was 95.83%, the average multiple index (Imult) was 1.11%, the average missing index (Imiss) was 3.23%, and the average precision index (Ip) was 14.64%. The research results provide a reference for the parameter tuning mode of the fuzzy PID controller for EDS.
In-Vehicle Communication Cyber Security: Challenges and Solutions
Authors: Rathore Rajkumar Singh , Hewage Chaminda , Kaiwartya Omprakash , Lloret Jaime
Published: September 2022

In-vehicle communication has become an integral part of today’s driving environment considering the growing add-ons of sensor-centric communication and computing devices inside a vehicle for a range of purposes including vehicle monitoring, physical wiring reduction, and driving efficiency. However, related literature on cyber security for in-vehicle communication systems is still lacking potential dedicated solutions for in-vehicle cyber risks. Existing solutions are mainly relying on protocol-specific security techniques and lacking an overall security framework for in-vehicle communication. In this context, this paper critically explores the literature on cyber security for in-vehicle communication focusing on technical architecture, methodologies, challenges, and possible solutions. In-vehicle communication network architecture is presented considering key components, interfaces, and related technologies. The protocols for in-vehicle communication have been classified based on their characteristics, and usage type. Security solutions for in-vehicle communication have been critically reviewed considering machine learning, cryptography, and port-centric techniques. A multi-layer secure framework is also developed as a protocol and use case-independent in-vehicle communication solution. Finally, open challenges and future dimensions of research for in-vehicle communication cyber security are highlighted as observations and recommendations.
Autonomous Reading of Gauges in Unstructured Environments
Authors: Milana Edoardo , Ramírez-Agudelo Oscar Hernán , Estevam Schmiedt Jacob
Published: September 2022

This paper introduces GAUREAD, an end-to-end computer vision system that is able to autonomously read analogic gauges with circular shapes and linear scales in unstructured environments. Existing gauge reading software still relies on some manual entry, like the gauge location and the gauge scale, or they are able to work just with a frontal view. On the contrary, GAUREAD comprises all the necessary steps to make the measurement unconstrained from previous information, including gauge detection from scene, perspective rectification and scale reconstruction. Our algorithm achieves a speed of 800 milliseconds per reading on the NVIDIA Jetson Nano 4 GB. Experimental tests show that GAUREAD can provide a measurement with an error within 3% for perspective angles below 20° and within 9% up to 50°. The system is foreseen to be implemented on mobile robotics to automatise not only safety routines, but also critical security operations.