bluuebunny/crossref_metadata_2025 huggingface dataset:
Using Aerial Thermal Imagery to Evaluate Water Status in Vitis vinifera cv. Loureiro
Authors: Araújo-Paredes Cláudio , Portela Fernando , Mendes Susana , Valín M. Isabel
Published: October 2022

The crop water stress index (CWSI) is a widely used analytical tool based on portable thermography. This method can be useful in replacing the traditional stem water potential method obtained with a Scholander chamber (PMS Model 600) because the latter is not feasible for large-scale studies due to the time involved and the fact that it is invasive and can cause damage to the plant. The present work had three objectives: (i) to understand if CWSI estimated using an aerial sensor can estimate the water status of the plant; (ii) to compare CWSI from aerial-thermographic and portable thermal cameras with stem water potential; (iii) to estimate the capacity of an unmanned aerial vehicle (UAV) to calculate and spatialize CWSI. Monitoring of CWSI (CWSIP) using a portable device was performed directly in the canopy, by measuring reference temperatures (Tdry, Twet, and canopy temperature (Tc)). Aerial CWSI calculation was performed using two models: (i) a simplified CWSI model (CWSIS), where the Tdry and Twet were estimated as the average of 1% of the extreme temperature, and (ii) an air temperature model (CWSITair) where air temperatures (Tair + 7 °C) were recorded as Tdry and in the Twet, considering the average of the lowest 33% of histogram values. In these two models, the Tc value corresponded to the temperature value in each pixel of the aerial thermal image. The results show that it was possible to estimate CWSI by calculating canopy temperatures and spatializing CWSI using aerial thermography. Of the two models, it was found that for CWSITair, CWSIS (R2 = 0.55) evaluated crop water stress better than stem water potential. The CWSIS had good correlation compared with the portable sensor (R2 = 0.58), and its application in field measurements is possible.
Performance Improvement of a Vehicle Equipped with Active Aerodynamic Surfaces Using Anti-Jerk Preview Control Strategy
Authors: Ahmad Ejaz , Youn Iljoong
Published: October 2022

This paper presents a formulation of a preview optimal control strategy for a half-car model equipped with active aerodynamic surfaces. The designed control strategy consists of two parts: a feed-forward controller to deal with the future road disturbances and a feedback controller to deal with tracking error. An anti-jerk functionality is employed in the design of preview control strategy that can reliably reduce the jerk of control inputs to improve the performance of active aerodynamic surfaces and reduce vehicle body jerk to enhance the ride comfort without degrading road holding capability. The proposed control scheme determines proactive control action against oncoming potential road disturbances to mitigate the effect of deterministically known road disturbances. The performance of proposed anti-jerk optimal control strategy is compared with that of optimal control without considering jerk. Simulation results considering frequency and time domain characteristics are carried out using MATLAB to demonstrate the effectiveness of the proposed scheme. The frequency domain characteristics are discussed only for the roll inputs, while time domain characteristics are discussed for the corresponding ground velocity inputs of bump and asphalt road, respectively. The results show that using anti-jerk optimal preview control strategy improves the performance of vehicle dynamics by reducing jerk of aerodynamic surfaces and vehicle body jerk simultaneously.
Electrical Disturbances in Terms of Methods to Reduce False Activation of Aerial Fire Protection Systems
Authors: Żyluk Andrzej , Zieja Mariusz , Szelmanowski Andrzej , Tomaszewska Justyna , Perlińska Magdalena , Głyda Krzysztof
Published: October 2022

The paper presents an analysis of false triggers of fire protection systems installed on aircraft. They not only cause task interruption but also have a direct impact on flight safety, forcing the crew to land in a risky area. Simulation models of electronic actuators were developed to determine the conditions under which false alarms occur. Testing of the simulation models was carried out in the computational package Matlab-Simulink and Circum-Maker for different electrical disturbance generation conditions. The simulation of overvoltage, voltage drops and voltage decays in the on-board electrical network supplying the fire protection system, occurring during the start-up of aircraft engines and during the switching on and off of on-board high-power devices, was studied. The conducted studies have practical applications since the simulation results are an important element for planning experimental tests of the SSP-FK-BI executive blocks under electrical disturbance conditions. Based on the simulation and experimental studies, the conditions causing false tripping of the fire protection system and the parameters for selected disturbance factors have been determined.
Altitude Optimization and Task Allocation of UAV-Assisted MEC Communication System
Authors: Huang Shuqi , Zhang Jun , Wu Yi
Published: October 2022

Unmanned aerial vehicles (UAVs) are widely used in wireless communication systems due to their flexible mobility and high maneuverability. The combination of UAVs and mobile edge computing (MEC) is regarded as a promising technology to provide high-quality computing services for latency-sensitive applications. In this paper, a novel UAV-assisted MEC uplink maritime communication system is proposed, where an MEC server is equipped on UAV to provide flexible assistance to maritime user. In particular, the task of user can be divided into two parts: one portion is offloaded to UAV and the remaining portion is offloaded to onshore base station for computing. We formulate an optimization problem to minimize the total system latency by designing the optimal flying altitude of UAV and the optimal task allocation ratio. We derive a semi closed-form expression of the optimal flying altitude of UAV and a closed-form expression of the optimal task allocation ratio. Simulation results demonstrate the precision of the theoretical analyses and show some interesting insights.
Soil Moisture Sensor Information Enhanced by Statistical Methods in a Reclaimed Water Irrigation Framework
Authors: Giorgio Anthony , Del Buono Nicoletta , Berardi Marco , Vurro Michele , Vivaldi Gaetano Alessandro
Published: October 2022

Time series modeling and forecasting play important roles in many practical fields. A good understanding of soil water content and salinity variability and the proper prediction of variations in these variables in response to changes in climate conditions are essential to properly plan water resources and appropriately manage irrigation and fertilization tasks. This paper provides a 48-h forecast of soil water content and salinity in the peculiar context of irrigation with reclaimed water in semi-arid environments. The forecasting was performed based on (i) soil water content and salinity data from 50 cm beneath the soil surface with a time resolution of 15 min, (ii) hourly atmospheric data and (iii) daily irrigation amounts. Exploratory data analysis and data pre-processing phases were performed and then statistical models were constructed for time series forecasting based on the set of available data. The obtained prediction models showed good forecasting accuracy and good interpretability of the results.
Efficient Top-K Identical Frequent Itemsets Mining without Support Threshold Parameter from Transactional Datasets Produced by IoT-Based Smart Shopping Carts
Authors: Rehman Saif Ur , Alnazzawi Noha , Ashraf Jawad , Iqbal Javed , Khan Shafiullah
Published: October 2022

Internet of Things (IoT)-backed smart shopping carts are generating an extensive amount of data in shopping markets around the world. This data can be cleaned and utilized for setting business goals and strategies. Artificial intelligence (AI) methods are used to efficiently extract meaningful patterns or insights from such huge amounts of data or big data. One such technique is Association Rule Mining (ARM) which is used to extract strategic information from the data. The crucial step in ARM is Frequent Itemsets Mining (FIM) followed by association rule generation. The FIM process starts by tuning the support threshold parameter from the user to produce the number of required frequent patterns. To perform the FIM process, the user applies hit and trial methods to rerun the aforesaid routine in order to receive the required number of patterns. The research community has shifted its focus towards the development of top-K most frequent patterns not using the support threshold parameter tuned by the user. Top-K most frequent patterns mining is considered a harder task than user-tuned support-threshold-based FIM. One of the reasons why top-K most frequent patterns mining techniques are computationally intensive is the fact that they produce a large number of candidate itemsets. These methods also do not use any explicit pruning mechanism apart from the internally auto-maintained support threshold parameter. Therefore, we propose an efficient TKIFIs Miner algorithm that uses depth-first search strategy for top-K identical frequent patterns mining. The TKIFIs Miner uses specialized one- and two-itemsets-based pruning techniques for topmost patterns mining. Comparative analysis is performed on special benchmark datasets, for example, Retail with 16,469 items, T40I10D100K and T10I4D100K with 1000 items each, etc. The evaluation results have proven that the TKIFIs Miner is at the top of the line, compared to recently available topmost patterns mining methods not using the support threshold parameter.
Dingo Optimization Based Cluster Based Routing in Internet of Things
Authors: Aravind Kalavagunta , Maddikunta Praveen Kumar Reddy
Published: October 2022

The Wireless Sensor Network (WSN) is a collection of distinct, geographically distributed, Internet-connected sensors, which is capable of processing, analyzing, storing, and exchanging collected information. However, the Internet of Things (IoT) devices in the network are equipped with limited resources and minimal computing capability, resulting in energy conservation problems. Although clustering is an efficient method for energy saving in network nodes, the existing clustering algorithms are not effective due to the short lifespan of a network, an unbalanced load among the network nodes, and increased end-to-end delays. Hence, this paper proposes a novel cluster-based approach for IoT using a Self-Adaptive Dingo Optimizer with Brownian Motion (SDO-BM) technique to choose the optimal cluster head (CH) considering the various constraints such as energy, distance, delay, overhead, trust, Quality of Service (QoS), and security (high risk, low risk, and medium risk). If the chosen optimal CH is defective, then fault tolerance and energy hole mitigation techniques are used to stabilize the network. Eventually, analysis is done to ensure the progression of the SADO-BM model. The proposed model provides optimal results compared to existing models.
A Metamaterial Inspired AMC Backed Dual Band Antenna for ISM and RFID Applications
Authors: Najumunnisa Md. , Sastry Ambadapudi Srinivasa Chandrasekhara , Madhav Boddapati Taraka Phani , Das Sudipta , Hussain Niamat , Ali Syed Samser , Aslam Muhammad
Published: October 2022

This work presents the design and fabrication of a metamaterial-based stimulated dual band antenna on FR4 material (dielectric constant 4.3) to operate in Industrial, Scientific and Medical (ISM) and Radio-frequency Identification (RFID) applications. The antenna model had an overall dimension of 70 × 31 × 1.6 mm3 with etched T-slots and L-slots for dual band resonance. The main objective of this work was to enhance the gain performance characteristic at the selected dual band frequencies of 0.915 GHz and 2.45 GHz. Initially, it achieved a narrow bandwidth of 0.018 GHz with a gain of 1.53 dBi at a lower frequency, and 0.13 GHz of bandwidth featuring 4.49 dBi of gain at a higher frequency. The antenna provided an impedance bandwidth of 2% (0.905–0.923 GHz) and 5% (2.382–2.516 GHz) at two resonating frequencies. The antenna was integrated with a designed novel AMC structure to enhance the gain in CST Microwave Studio software with the finite integration method. The characteristic features of the AMC unit cell were observed at 0.915 GHz and 2.45 GHz frequencies and after antenna integration, the final prototype achieved a gain of 2.87 dBi at 0.915 GHz and 6.8 dBi at 2.45 GHz frequencies.
ST-DeepGait: A Spatiotemporal Deep Learning Model for Human Gait Recognition
Authors: Konz Latisha , Hill Andrew , Banaei-Kashani Farnoush
Published: October 2022

Human gait analysis presents an opportunity to study complex spatiotemporal data transpiring as co-movement patterns of multiple moving objects (i.e., human joints). Such patterns are acknowledged as movement signatures specific to an individual, offering the possibility to identify each individual based on unique gait patterns. We present a spatiotemporal deep learning model, dubbed ST-DeepGait, to featurize spatiotemporal co-movement patterns of human joints, and accordingly classify such patterns to enable human gait recognition. To this end, the ST-DeepGait model architecture is designed according to the spatiotemporal human skeletal graph in order to impose learning the salient local spatial dynamics of gait as they occur over time. Moreover, we employ a multi-layer RNN architecture to induce a sequential notion of gait cycles in the model. Our experimental results show that ST-DeepGait can achieve recognition accuracy rates over 90%. Furthermore, we qualitatively evaluate the model with the class embeddings to show interpretable separability of the features in geometric latent space. Finally, to evaluate the generalizability of our proposed model, we perform a zero-shot detection on 10 classes of data completely unseen during training and achieve a recognition accuracy rate of 88% overall. With this paper, we also contribute our gait dataset captured with an RGB-D sensor containing approximately 30 video samples of each subject for 100 subjects totaling 3087 samples. While we use human gait analysis as a motivating application to evaluate ST-DeepGait, we believe that this model can be simply adopted and adapted to study co-movement patterns of multiple moving objects in other applications such as in sports analytics and traffic pattern analysis.
Brain Age Prediction: A Comparison between Machine Learning Models Using Brain Morphometric Data
Authors: Han Juhyuk , Kim Seo Yeong , Lee Junhyeok , Lee Won Hee
Published: October 2022

Brain structural morphology varies over the aging trajectory, and the prediction of a person’s age using brain morphological features can help the detection of an abnormal aging process. Neuroimaging-based brain age is widely used to quantify an individual’s brain health as deviation from a normative brain aging trajectory. Machine learning approaches are expanding the potential for accurate brain age prediction but are challenging due to the great variety of machine learning algorithms. Here, we aimed to compare the performance of the machine learning models used to estimate brain age using brain morphological measures derived from structural magnetic resonance imaging scans. We evaluated 27 machine learning models, applied to three independent datasets from the Human Connectome Project (HCP, n = 1113, age range 22–37), the Cambridge Centre for Ageing and Neuroscience (Cam-CAN, n = 601, age range 18–88), and the Information eXtraction from Images (IXI, n = 567, age range 19–86). Performance was assessed within each sample using cross-validation and an unseen test set. The models achieved mean absolute errors of 2.75–3.12, 7.08–10.50, and 8.04–9.86 years, as well as Pearson’s correlation coefficients of 0.11–0.42, 0.64–0.85, and 0.63–0.79 between predicted brain age and chronological age for the HCP, Cam-CAN, and IXI samples, respectively. We found a substantial difference in performance between models trained on the same data type, indicating that the choice of model yields considerable variation in brain-predicted age. Furthermore, in three datasets, regularized linear regression algorithms achieved similar performance to nonlinear and ensemble algorithms. Our results suggest that regularized linear algorithms are as effective as nonlinear and ensemble algorithms for brain age prediction, while significantly reducing computational costs. Our findings can serve as a starting point and quantitative reference for future efforts at improving brain age prediction using machine learning models applied to brain morphometric data.
Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN
Authors: Khan Sajjad Ahmad , Lim Huhnkuk
Published: October 2022

Vehicular Named Data Networking (VNDN) is one of the potential and future networking architectures that allow Connected and Autonomous Vehicles (CAV) to exchange data by simply disseminating the content over the network. VNDN only supports a pull-based data forwarding model, where the content information is forwarded upon request. However, in critical situations, it is essential to design a push-based data forwarding model in order to broadcast the critical data packets without any requests. One of the challenges of push-based data forwarding in VNDN is the broadcasting effect, which occurs when every vehicle broadcasts critical information over the network. For instance, in emergency situations such as accidents, road hazards, and bad weather conditions, the producer generates a critical data packet and broadcasts it to all the nearby vehicles. Subsequently, all vehicles broadcast the same critical data packet to each other, which leads to a broadcast storm on the network. Therefore, this paper proposes a Fuzzy Logic-based Push Data Forwarding (FLPDF) scheme to mitigate the broadcast storm effect. The novelty of this paper is the suggestion and application of a fuzzy logic approach to mitigate the critical data broadcast storm effect in VNDN. In the proposed scheme, vehicles are grouped into clusters using the K-means clustering algorithm, and then Cluster Heads (CHs) are selected using a fuzzy logic approach. A CH is uniquely responsible for broadcasting the critical data packets to all other vehicles in a cluster. A Gateway (GW) has the role of forwarding the critical data packets to the nearest clusters via their GWs. The simulation results show that the proposed scheme outperforms the naive method in terms of transmitted data packets and efficiency. The proposed scheme generates five times fewer data packets and achieves six times higher efficiency than the naive scheme.
Multi-Center Evaluation of Gel-Based and Dry Multipin EEG Caps
Authors: Ng Chuen Rue , Fiedler Patrique , Kuhlmann Levin , Liley David , Vasconcelos Beatriz , Fonseca Carlos , Tamburro Gabriella , Comani Silvia , Lui Troby Ka-Yan , Tse Chun-Yu , Warsito Indhika Fauzhan , Supriyanto Eko , Haueisen Jens
Published: October 2022

Dry electrodes for electroencephalography (EEG) allow new fields of application, including telemedicine, mobile EEG, emergency EEG, and long-term repetitive measurements for research, neurofeedback, or brain–computer interfaces. Different dry electrode technologies have been proposed and validated in comparison to conventional gel-based electrodes. Most previous studies have been performed at a single center and by single operators. We conducted a multi-center and multi-operator study validating multipin dry electrodes to study the reproducibility and generalizability of their performance in different environments and for different operators. Moreover, we aimed to study the interrelation of operator experience, preparation time, and wearing comfort on the EEG signal quality. EEG acquisitions using dry and gel-based EEG caps were carried out in 6 different countries with 115 volunteers, recording electrode-skin impedances, resting state EEG and evoked activity. The dry cap showed average channel reliability of 81% but higher average impedances than the gel-based cap. However, the dry EEG caps required 62% less preparation time. No statistical differences were observed between the gel-based and dry EEG signal characteristics in all signal metrics. We conclude that the performance of the dry multipin electrodes is highly reproducible, whereas the primary influences on channel reliability and signal quality are operator skill and experience.
Measurement of Cable Force through a Fiber Bragg Grating-Type Thin Rod Vibration Sensor and Its Application
Authors: Zhu Wanxu , Teng Wei , Liu Fengrong , Wu Dongwen , Wu Yujiao
Published: October 2022

The key to evaluating the health status of cable-stayed bridges lies in the accuracy of cable force measurement. When measuring the cable force using the conventional frequency method, the clearance between the bracing cable and the protective tube is typically disregarded. Moreover, due to their large size, existing vibration sensors are difficult to install into protective tubes for steel strand-type bracing cables to measure the cable force. To address the above difficulties, a type of thin rod vibration sensor only 5 mm in diameter was designed based on the high sensitivity of Fiber Bragg grating (FBG), and high-throughput data processing software for engineering calculation (EC) was self-developed. Then, the recognition principle of the thin rod vibration sensor was theoretically analyzed and a step-by-step tension test was carried out. The results demonstrated that the relative error of the cable force measured by the thin rod vibration sensor within 12.865 Hz was less than 5% and the sensitivity reached 28.7 pm/Hz, indicating its high measurement precision. Upon subsequent application of the thin rod vibration sensor to a monitoring test in the field, the relative error of the fundamental frequency between artificial and natural excitations was less than 4%. In addition, the error relative to both the theoretical frequency and the third-party sampling frequency was less than 5%, further verifying the accuracy and applicability for monitoring the cable force of bridges under natural excitation. Compared with the traditional cantilever FBG sensor, the improved sensor with supporting data processing software has the advantages of small cross-section, high reliability, and good sensitivity. The research results can provide a reference for the subsequent accurate measurement of cable force and the development of a supporting sensor data processing system.
VBlock: A Blockchain-Based Tamper-Proofing Data Protection Model for Internet of Vehicle Networks
Authors: Sey Collins , Lei Hang , Qian Weizhong , Li Xiaoyu , Fiasam Linda Delali , Kodjiku Seth Larweh , Adjei-Mensah Isaac , Agyemang Isaac Osei
Published: October 2022

The rapid advancement of the Internet of Vehicles (IoV) has led to a massive growth in data received from IoV networks. The cloud storage has been a timely service that provides a vast range of data storage for IoV networks. However, existing data storage and access models used to manage and protect data in IoV networks have proven to be insufficient. They are centralized and usually accompanied by a lack of trust, transparency, security, immutability, and provenance. In this paper, we propose VBlock, a blockchain-based system that addresses the issues of illegal modification of outsourced vehicular data for smart city management and improvement. We introduce a novel collusion-resistant model for outsourcing data to cloud storage that ensures the network remains tamper-proof, has good data provenance and auditing, and solves the centralized problems prone to the single point of failure. We introduced a key revocation mechanism to secure the network from malicious nodes. We formally define the system model of VBlock in the setting of a consortium blockchain. Our simulation results and security analysis show that the proposed model provides a strong security guarantee with high efficiency and is practicable in the IoV environment.
Personalized Deep Bi-LSTM RNN Based Model for Pain Intensity Classification Using EDA Signal
Authors: Pouromran Fatemeh , Lin Yingzi , Kamarthi Sagar
Published: October 2022

Automatic pain intensity assessment from physiological signals has become an appealing approach, but it remains a largely unexplored research topic. Most studies have used machine learning approaches built on carefully designed features based on the domain knowledge available in the literature on the time series of physiological signals. However, a deep learning framework can automate the feature engineering step, enabling the model to directly deal with the raw input signals for real-time pain monitoring. We investigated a personalized Bidirectional Long short-term memory Recurrent Neural Networks (BiLSTM RNN), and an ensemble of BiLSTM RNN and Extreme Gradient Boosting Decision Trees (XGB) for four-category pain intensity classification. We recorded Electrodermal Activity (EDA) signals from 29 subjects during the cold pressor test. We decomposed EDA signals into tonic and phasic components and augmented them to original signals. The BiLSTM-XGB model outperformed the BiLSTM classification performance and achieved an average F1-score of 0.81 and an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.93 over four pain states: no pain, low pain, medium pain, and high pain. We also explored a concatenation of the deep-learning feature representations and a set of fourteen knowledge-based features extracted from EDA signals. The XGB model trained on this fused feature set showed better performance than when it was trained on component feature sets individually. This study showed that deep learning could let us go beyond expert knowledge and benefit from the generated deep representations of physiological signals for pain assessment.
Determining the Stir-Frying Degree of Gardeniae Fructus Praeparatus Based on Deep Learning and Transfer Learning
Authors: Zhang Yuzhen , Wang Chongyang , Wang Yun , Cheng Pengle
Published: October 2022

Gardeniae Fructus (GF) is one of the most widely used traditional Chinese medicines (TCMs). Its processed product, Gardeniae Fructus Praeparatus (GFP), is often used as medicine; hence, there is an urgent need to determine the stir-frying degree of GFP. In this paper, we propose a deep learning method based on transfer learning to determine the stir-frying degree of GFP. We collected images of GFP samples with different stir-frying degrees and constructed a dataset containing 9224 images. Five neural networks were trained, including VGG16, GoogLeNet, Resnet34, MobileNetV2, and MobileNetV3. While the model weights from ImageNet were used as initial parameters of the network, fine-tuning was used for four neural networks other than MobileNetV3. In the training of MobileNetV3, both feature transfer and fine-tuning were adopted. The accuracy of all five models reached more than 95.82% in the test dataset, among which MobileNetV3 performed the best with an accuracy of 98.77%. In addition, the results also showed that fine-tuning was better than feature transfer in the training of MobileNetV3. Therefore, we conclude that deep learning can effectively recognize the stir-frying degree of GFP.
Bearing Fault Diagnosis Based on Randomized Fisher Discriminant Analysis
Authors: Ye Hejun , Wu Ping , Huo Yifei , Wang Xuemei , He Yuchen , Zhang Xujie , Gao Jinfeng
Published: October 2022

In this paper, a novel randomized Fisher discriminant analysis (RFDA) based bearing fault diagnosis method is proposed. First, several representative time-domain features are extracted from the raw vibration signals. Second, linear Fisher discriminant analysis (FDA) is extended to nonlinear FDA named RFDA by introducing the random feature map to deal with the non-linearity issue. Specifically, the extracted time-domain features data are mapped onto a high-dimensional space using the random feature map function rather than kernel functions. Third, the time-domain features are fed into the built RFDA model to extract the discriminant features for diagnosis. Moreover, a Bayesian inference is employed to identify the class of the collected vibration signals to diagnose the bearing status. The proposed method uses random Fourier features to approximate the kernel matrix in the kernel Fisher discriminant analysis. Through employing randomized Fisher discriminant analysis, the nonlinearity issue is dealt with, and the computational burden is remarkably reduced compared to the kernel Fisher discriminant analysis (KFDA). To illustrate the superior performance of the proposed RFDA-based bearing fault diagnosis method, comparative experiments are conducted on two widely used datasets, the Case Western Reserve University (CWRU) bearing dataset and the Paderborn University (PU) bearing dataset. For the CWRU dataset, the computation time of RFDA is much shorter than KFDA, while the accuracy rate reaches the same level of KFDA. For the PU dataset, the accuracy rate of RFDA is slightly higher than KFDA, and the computation time is only 44.14% of KFDA.
Multi-Agent Systems for Resource Allocation and Scheduling in a Smart Grid
Authors: Binyamin Sami Saeed , Ben Slama Sami
Published: October 2022

Multi-Agent Systems (MAS) have been seen as an attractive area of research for civil engineering professionals to subdivide complex issues. Based on the assignment’s history, nearby agents, and objective, the agent intended to take the appropriate action to complete the task. MAS models complex systems, smart grids, and computer networks. MAS has problems with agent coordination, security, and work distribution despite its use. This paper reviews MAS definitions, attributes, applications, issues, and communications. For this reason, MASs have drawn interest from computer science and civil engineering experts to solve complex difficulties by subdividing them into smaller assignments. Agents have individual responsibilities. Each agent selects the best action based on its activity history, interactions with neighbors, and purpose. MAS uses the modeling of complex systems, smart grids, and computer networks. Despite their extensive use, MAS still confronts agent coordination, security, and work distribution challenges. This study examines MAS’s definitions, characteristics, applications, issues, communications, and evaluation, as well as the classification of MAS applications and difficulties, plus research references. This paper should be a helpful resource for MAS researchers and practitioners. MAS in controlling smart grids, including energy management, energy marketing, pricing, energy scheduling, reliability, network security, fault handling capability, agent-to-agent communication, SG-electrical cars, SG-building energy systems, and soft grids, have been examined. More than 100 MAS-based smart grid control publications have been reviewed, categorized, and compiled.
Design and 3D FEM Analysis of a Flexible Piezoelectric Micromechanical Ultrasonic Transducer Based on Sc-Doped AlN Film
Authors: Ren Qinghua , Chen Junhong , Liu Xin , Zhang Songsong , Gu Yuandong
Published: October 2022

In this paper, a flexible piezoelectric micromachined ultrasonic transducer (PMUT) based on Scandium (Sc)-doped Aluminum Nitride (AlN) film was designed and modeled by the three-dimensional finite element method (3D-FEM). The resonant frequency of 218.1 kHz was reported. It was noticeable that a high effective electromechanical coupling coefficient (k2eff) of 1.45% was obtained when a combination of a flexible PI and a thin Si layer was used as the PMUT supporting structure layer. Compared with a pure Si supporting layer counterpart, the coupling coefficient had been improved by 110.68%. Additionally, the increase of Sc doping concentration in AlN film further enhanced the device electromechanical coupling coefficient and resulted in an improvement for transmitting/receiving sensitivity of the proposed flexible PMUT. When the doping concentration of Sc reached 30%, the emission sensitivity was as large as 1.721 μm/V, which was 2.86 times greater than that of conventional AlN film-based PMUT. The receiving sensitivity was found to be 2.11 V/KPa, which was as high as 1.23 times the performance of an undoped device. Furthermore, the bending simulation result showed that the proposed flexible PMUT device can maintain a good mechanical stability when the bending radius is greater than 1.5 mm. The simulation of sound field characteristics demonstrated that the flexible PMUT based on AlScN could receive stable sound pressure signals under the bending radius of 1.5 cm.
Bias Stability Investigation of a Triaxial Navigation-Compatible Accelerometer with an Electrostatic Spring
Authors: Chen Decong , Bai Yanzheng , Wang Chengrui , Wu Shuchao , Xiao Chunyu , Yu Jianbo , Zhou Zebing
Published: October 2022

The bias stability performance of accelerometers is essential for an inertial navigation system. The traditional pendulous accelerometer usually has a flexible connection structure, which could limit the long-term bias stability. Here, based on the main technologies employed in previous space missions of our group, we developed a terrestrial triaxial navigation-compatible accelerometer. Because there is no mechanical connection between the inertial test mass and the frame, the bias performance relies on the stability of the equivalent electrostatic spring, where further sources are analyzed to get the optimal electrostatic force scheme. To investigate the bias stability under different ranges, the vertical and horizontal measurement ranges are designed at 5 g and ±10 mg, respectively. A low-noise high-voltage levitation scheme is adopted to extend the vertical measurement range from sub-mg to more than earth’s 1-g gravity. Finally, the experimental validation results show that the 24-h bias stability of vertical and two horizontal directions come to 13.8 μg, 0.84 μg, and 0.77 μg, respectively.
Design and Implementation of a Wireless Recorder System for Seismic Noise Array Measurements
Authors: Jornet-Monteverde Julio Antonio , Galiana-Merino Juan José , Soler-Llorens Juan Luis
Published: October 2022

In this work, a wireless data acquisition system for seismic noise array measurements is presented. The developed system is composed of a series of nodes and a central server arranged in a point-to-multipoint topology. The nodes consist of a CC3200 microcontroller, an analog-to-digital converter, and a low-noise conditioning circuit designed specifically to register seismic noise, and which is connected to the seismic sensor. As a server, a Raspberry Pi 4B has been used that will receive the samples from the nodes via Wi-Fi and will save them in files. It also incorporates a Web interface developed with JavaScript node.js technology that allows to configure the number of nodes as well as different options, to start and stop the records, and to view in real time the different signals received from the nodes. The system can be deployed anywhere since each of the nodes use independent batteries as a power supply. In addition, it is possible to operate the system remotely if internet connectivity is available. The prototype has been tested in four different locations in the Alicante province (southeast Spain), demonstrating its suitability for seismic noise array measurements.
Optical and Mechanical Properties of Layered Infrared Interference Filters
Authors: Bembenek Michał , Makoviichuk Mykola , Shatskyi Ivan , Ropyak Liubomyr , Pritula Igor , Gryn Leonid , Belyakovskyi Volodymyr
Published: October 2022

The design and manufacturing technology of interference-absorbing short-wave filters based on a layered composition of Si–SiO on a sapphire substrate of various shapes was developed. A transition layer of SiO was applied to the surface of the substrate, alternating with layers of Si–SiO with an odd number of quarter-wave layers of materials with high (Si) and low refractive indices (SiO), and the application of an outer layer of SiO as an appropriate control of the materials’ thickness. The optical properties of the infrared light filter were studied. It was established that the created design of the light filter provides the minimum light transmission in the visible region of the spectrum from 0.38 to 0.78 µm and the maximum in the near infrared region from 1.25 to 5 µm and has stable optical indicators. A method for studying the stress–strain state and strength of a multilayer coating of a light filter under the action of a local arbitrarily oriented load was developed. For simplicity in the analysis and for obtaining results in the analytical form, the one-dimensional model of the configuration “multilayer covering—firm substrate” constructed earlier by authors was used. From a mechanical point of view, the upper protective layer of the multilayer coating was modeled by a flexible plate, and the inner operational composite N-layer was subjected to Winkler’s hypothesis about the proportionality of stresses and elastic displacements.
A New Method for Image Protection Using Periodic Haar Piecewise-Linear Transform and Watermarking Technique
Authors: Dziech Andrzej , Bogacki Piotr , Derkacz Jan
Published: October 2022

The paper presents a novel data-embedding method based on the Periodic Haar Piecewise-Linear (PHL) transform. The theoretical background behind the PHL transform concept is introduced. The proposed watermarking method assumes embedding hidden information in the PHL transform domain using the luminance channel of the original image. The watermark is embedded by modifying the coefficients with relatively low values. The proposed method was verified based on the measurement of the visual quality of an image with a watermark with respect to the length of the embedded information. In addition, the bit error rate (BER) is also considered for different sizes of a watermark. Furthermore, a method for the detection of image manipulation is presented. The elaborated technique seems to be suitable for applications in digital signal and image processing where high imperceptibility and low BER are required, and information security is of high importance. In particular, this method can be applied in systems where the sensitive data is transmitted or stored and needs to be protected appropriately (e.g., in medical image processing).
AI-Based Support System for Monitoring the Quality of a Product within Industry 4.0 Paradigm
Authors: Rojek Izabela , Dostatni Ewa , Kopowski Jakub , Macko Marek , Mikołajewski Dariusz
Published: October 2022

Three-dimensional (3D) printing, also known as additive manufacturing (AM), has already shown its potential in the fourth technological revolution (Industry 4.0), demonstrating remarkable applications in manufacturing, including of medical devices. The aim of this publication is to present the novel concept of support by artificial intelligence (AI) for quality control of AM of medical devices made of polymeric materials, based on the example of our own elbow exoskeleton. The methodology of the above-mentioned inspection process differs depending on the intended application of 3D printing as well as 3D scanning or reverse engineering. The use of artificial intelligence increases the versatility of this process, allowing it to be adapted to specific needs. This brings not only innovative scientific and technological solutions, but also a significant economic and social impact through faster operation, greater efficiency, and cost savings. The article also indicates the limitations and directions for the further development of the proposed solution.
A Study of the Lossy Mode Resonances during the Synthesis Process of Zinc Telluride Films
Authors: Kuznetsov Petr I. , Sudas Dmitriy P. , Savelyev Evgeny A.
Published: October 2022

Films of zinc telluride (ZnTe) were deposited on the surface of a chemically thinned section of an optical fiber by metalorganic chemical vapor deposition. The boundary values of temperatures and the concentration ratios of the initial tellurium and zinc precursors at which the synthesis of ZnTe coatings is possible are determined. The influence of the position of the thinned part of the optical fiber in the reactor on the growth rate of films on the side surface of the fiber was studied, on the basis of which, the parameters of the deposition zone were determined. By placing a section of an optical fiber with an etched cladding in the center of this zone, sensitive elements for refractometers were created. The principle of their operation is based on the dependence of the spectral position of the lossy mode resonance (LMR) maximum on the refractive index (RI) of the external medium. It has been found that even thin films deposited on a light guide in a continuous process have cracks. It is shown that the interruption of the deposition process makes it possible to avoid the appearance of defects in the zinc telluride layers even with the repeated deposition of the sensor. The sensitivity of the spectral position of the LMR to changes in the RI of aqueous sodium chloride solutions in the range from 1.33 to 1.35 for the first transverse electric and transverse magnetic LMRs was 6656 and 6240 nm per refractive index unit, respectively.
SDHAR-HOME: A Sensor Dataset for Human Activity Recognition at Home
Authors: Ramos Raúl Gómez , Domingo Jaime Duque , Zalama Eduardo , Gómez-García-Bermejo Jaime , López Joaquín
Published: October 2022

Nowadays, one of the most important objectives in health research is the improvement of the living conditions and well-being of the elderly, especially those who live alone. These people may experience undesired or dangerous situations in their daily life at home due to physical, sensorial or cognitive limitations, such as forgetting their medication or wrong eating habits. This work focuses on the development of a database in a home, through non-intrusive technology, where several users are residing by combining: a set of non-intrusive sensors which captures events that occur in the house, a positioning system through triangulation using beacons and a system for monitoring the user’s state through activity wristbands. Two months of uninterrupted measurements were obtained on the daily habits of 2 people who live with a pet and receive sporadic visits, in which 18 different types of activities were labelled. In order to validate the data, a system for the real-time recognition of the activities carried out by these residents was developed using different current Deep Learning (DL) techniques based on neural networks, such as Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM) or Gated Recurrent Unit networks (GRU). A personalised prediction model was developed for each user, resulting in hit rates ranging from 88.29% to 90.91%. Finally, a data sharing algorithm has been developed to improve the generalisability of the model and to avoid overtraining the neural network.
Damage Detection for Rotating Blades Using Digital Image Correlation with an AC-SURF Matching Algorithm
Authors: Gu Jiawei , Liu Gang , Li Mengzhu
Published: October 2022

The motion information of blades is a key reflection of the operation state of an entire wind turbine unit. However, the special structure and operation characteristics of rotating blades have become critical obstacles for existing contact vibration monitoring technologies. Digital image correlation performs powerfully in non-contact, full-field measurements, and has increasingly become a popular method for solving the problem of rotating blade monitoring. Aiming at the problem of large-scale rotation matching for blades, this paper proposes a modified speeded-up robust features (SURF)-enhanced digital image correlation algorithm to extract the full-field deformation of blades. Combining an angle compensation (AC) strategy, the AC-SURF algorithm is developed to estimate the rotation angle. Then, an iterative process is presented to calculate the accurate rotation displacement. Subsequently, with reference to the initial state of rotation, the relative strain distribution caused by flaws is determined. Finally, the sensitivity of the strain is validated by comparing the three damage indicators including unbalanced rotational displacement, frequency change, and surface strain field. The performance of the proposed algorithm is verified by laboratory tests of blade damage detection and wind turbine model deformation monitoring. The study demonstrated that the proposed method provides an effective and robust solution for the operation status monitoring and damage detection of wind turbine blades. Furthermore, the strain-based damage detection algorithm is more advantageous in identifying cracks on rotating blades than one based on fluctuated displacement or frequency change.
Brain Age Prediction/Classification through Recurrent Deep Learning with Electroencephalogram Recordings of Seizure Subjects
Authors: Jusseaume Kameron , Valova Iren
Published: October 2022

With modern population growth and an increase in the average lifespan, more patients are becoming afflicted with neurodegenerative diseases such as dementia and Alzheimer’s. Patients with a history of epilepsy, drug abuse, and mental health disorders such as depression have a larger risk of developing Alzheimer’s and other neurodegenerative diseases later in life. Utilizing recordings of patients’ brain waves obtained from the Temple University abnormal electroencephalogram (EEG) corpus, deep leaning long short-term memory neural networks are utilized to classify and predict patients’ brain ages. The proposed deep learning neural network model structure and brain wave-processing methodology leads to an accuracy of 90% in patients’ brain age classification across six age groups, with a mean absolute error value of 7 years for the brain age regression analysis. The achieved results demonstrate that the use of raw patient-sourced brain wave information leads to higher performance metrics than methods utilizing other brain wave-preprocessing methods and outperforms other deep learning models such as convolutional neural networks.
The Use of Audio Signals for Detecting COVID-19: A Systematic Review
Authors: Aleixandre José Gómez , Elgendi Mohamed , Menon Carlo
Published: October 2022

A systematic review on the topic of automatic detection of COVID-19 using audio signals was performed. A total of 48 papers were obtained after screening 659 records identified in the PubMed, IEEE Xplore, Embase, and Google Scholar databases. The reviewed studies employ a mixture of open-access and self-collected datasets. Because COVID-19 has only recently been investigated, there is a limited amount of available data. Most of the data are crowdsourced, which motivated a detailed study of the various pre-processing techniques used by the reviewed studies. Although 13 of the 48 identified papers show promising results, several have been performed with small-scale datasets (<200). Among those papers, convolutional neural networks and support vector machine algorithms were the best-performing methods. The analysis of the extracted features showed that Mel-frequency cepstral coefficients and zero-crossing rate continue to be the most popular choices. Less common alternatives, such as non-linear features, have also been proven to be effective. The reported values for sensitivity range from 65.0% to 99.8% and those for accuracy from 59.0% to 99.8%.