bluuebunny/crossref_metadata_2025 huggingface dataset:
Numerical Investigation of the Flow Regime in the Vanes and the Torsional Self-Excited Vibration of Guide Vane in the Pump Mode of a Reversible Pump-Turbine
Authors: Liang Quanwei , Kang Wenzhe , Zhou Lingjiu , Wang Zhengwei
Published: November 2022

Guide vanes (GVs) are installed between the runner and the stay vanes for flow guidance and discharge regulation in reversible pump-turbines. The unstable torsional self-excited vibration of the guide vane (GV) may occur when running at small guide vane opening angles during the transient operations involving pump flow. In addition, the double-stage radial vanes may induce complex flow in the vanes and influence the stability of torsional self-excited vibration of the guide vane. In this study, numerical simulations were conducted at small guide vane opening angles in pump mode for two different guide vanes based on the three-dimensional computational fluid dynamics (CFD) method. The flow regime with a deflection was formed on the trailing edge with a circle in the vaneless region that rotated reversely against the runner rotation when operating at smaller guide vane opening angles for both of the two guide vanes. Based on this, the coupling simulations based on the CFD method with a single-degree-of-freedom (1DOF) oscillator were carried out under these operating conditions. Two flow types were formed at small opening angles when adopting different inlet boundary conditions. The results showed the flow regime with a deflection on the trailing edge may aggravate the instability of torsional vibration when applied as an initial flow field. Moreover, the vibration instability of the torsional self-excitation for two guide vanes was analyzed, showing that modifying the profile of guide vane airfoil is an efficient and reliable approach for weakening the torsional vibration instability.
Improving the Frequency Response of Hybrid Microgrid under Renewable Sources’ Uncertainties Using a Robust LFC-Based African Vulture Optimization Algorithm
Authors: Hossam-Eldin Ahmed , Mostafa Hamada , Kotb Hossam , AboRas Kareem M. , Selim Ali , Kamel Salah
Published: November 2022

Power systems have recently faced significant challenges due to the increased penetration of renewable energy sources (RES) such as frequency deviation due to fluctuations, unpredictable nature, and uncertainty of this RES. In this paper, a cascaded controller called (1+PD)-PID is proposed to reduce the influence of RES uncertainties on the system and to maintain the system’s reliability during fluctuations. The proposed controller is a combination of (1+PD) and PID controllers in order. The output signal of the (1+PD) controller along with the frequency deviation and the power difference between adjacent areas are used as inputs to the PID controller to create the load reference signal. The parameters of the suggested controller are optimally tuned using the African Vulture Optimization Algorithm (AVOA) to ensure the best performance of the controller. A two-area interconnected system with non-reheat thermal power units combined with RES such as solar and wind energy is modeled using MATLAB/Simulink to evaluate the system response. The controller effectiveness is verified by subjecting the studied system to various types of fluctuations such as step load disturbance, variable load perturbation and RES penetration. The obtained simulation results prove that the proposed (1+PD)-PID controller in integration with AVOA offers a significant improvement in the system performance specifications. Moreover, the proposed AVOA-based (1+PD)-PID controller has proven its superiority over other comparable controllers having the least fitness function of 6.01 × 10−5.
Electrochemical Studies of the Interaction of Phospholipid Nanoparticles with dsDNA
Authors: Agafonova Lyubov , Tikhonova Elena , Sanzhakov Maxim , Kostryukova Lyubov , Shumyantseva Victoria
Published: November 2022

The effect of phospholipid nanoparticles with different contents of phosphatidylcholine (PhNP80 and PhNP100) on dsDNA was studied by means of the electrochemical method. Changes in the electrochemical behavior of heterocyclic bases guanine, adenine and thymine in the range of potentials of 0.2–1.2 V in the presence of PhNPs were used for the assessment of the binding mechanism of the ligand–DNA interaction. Comparative analysis of the effect of PhNPs with different contents of phosphatidylcholine showed a more pronounced effect on the dsDNA of the PhNP100 nanosystem. From the obtained experimental data on the decrease in the amplitude of the nucleobases’ electrochemical oxidation currents, the electrochemical coefficient of the toxic effect was calculated as the ratio of the electrooxidation currents of dsDNA and dsDNA in the presence of phospholipid nanoparticles. PhNP80/100 (up to 11.4 mg/mL) does not influence dsDNA, PhNP80/100 (14.3–28.5 mg/mL) has a moderate toxic effect on dsDNA, PhNP80/100 at concentrations above 28.5 mg/mL already have a toxic effect, significantly reducing the maximum amplitude of the heterocyclic bases’ electrochemical oxidation current. Peak potentials of electrochemical oxidation of nucleobases did not shift in the presence of PhNP80 and PhNP100 (in the concentration range of 2.3–42.2 mg/mL), which could be possible through a groove-binding mode of phospholipid nanoparticle interaction with dsDNA.
YPD-SLAM: A Real-Time VSLAM System for Handling Dynamic Indoor Environments
Authors: Wang Yi , Bu Haoyu , Zhang Xiaolong , Cheng Jia
Published: November 2022

Aiming at the problem that Simultaneous localization and mapping (SLAM) is greatly disturbed by many dynamic elements in the actual environment, this paper proposes a real-time Visual SLAM (VSLAM) algorithm to deal with a dynamic indoor environment. Firstly, a lightweight YoloFastestV2 deep learning model combined with NCNN and Mobile Neural Network (MNN) inference frameworks is used to obtain preliminary semantic information of images. The dynamic feature points are removed according to epipolar constraint and dynamic properties of objects between consecutive frames. Since reducing the number of feature points after rejection affects the pose estimation, this paper innovatively combines Cylinder and Plane Extraction (CAPE) planar detection. We generate planes from depth maps and then introduce planar and in-plane point constraints into the nonlinear optimization of SLAM. Finally, the algorithm is tested on the publicly available TUM (RGB-D) dataset, and the average improvement in localization accuracy over ORB-SLAM2, DS-SLAM, and RDMO-SLAM is about 91.95%, 27.21%, and 30.30% under dynamic sequences, respectively. The single-frame tracking time of the whole system is only 42.68 ms, which is 44.1%, being 14.6–34.33% higher than DS-SLAM, RDMO-SLAM, and RDS-SLAM respectively. The system that we proposed significantly increases processing speed, performs better in real-time, and is easily deployed on various platforms.
A Multi-AUV Maritime Target Search Method for Moving and Invisible Objects Based on Multi-Agent Deep Reinforcement Learning
Authors: Wang Guangcheng , Wei Fenglin , Jiang Yu , Zhao Minghao , Wang Kai , Qi Hong
Published: November 2022

Target search for moving and invisible objects has always been considered a challenge, as the floating objects drift with the flows. This study focuses on target search by multiple autonomous underwater vehicles (AUV) and investigates a multi-agent target search method (MATSMI) for moving and invisible objects. In the MATSMI algorithm, based on the multi-agent deep deterministic policy gradient (MADDPG) method, we add spatial and temporal information to the reinforcement learning state and set up specialized rewards in conjunction with a maritime target search scenario. Additionally, we construct a simulation environment to simulate a multi-AUV search for the floating object. The simulation results show that the MATSMI method has about 20% higher search success rate and about 70 steps shorter search time than the traditional search method. In addition, the MATSMI method converges faster than the MADDPG method. This paper provides a novel and effective method for solving the maritime target search problem.
A Hybrid Spider Monkey and Hierarchical Particle Swarm Optimization Approach for Intrusion Detection on Internet of Things
Authors: Ethala Sandhya , Kumarappan Annapurani
Published: November 2022

The Internet of Things (IoT) network integrates physical objects such as sensors, networks, and electronics with software to collect and exchange data. Physical objects with a unique IP address communicate with external entities over the internet to exchange data in the network. Due to a lack of security measures, these network entities are vulnerable to severe attacks. To address this, an efficient security mechanism for dealing with the threat and detecting attacks is necessary. The proposed hybrid optimization approach combines Spider Monkey Optimization (SMO) and Hierarchical Particle Swarm Optimization (HPSO) to handle the huge amount of intrusion data classification problems and improve detection accuracy by minimizing false alarm rates. After finding the best optimum values, the Random Forest Classifier (RFC) was used to classify attacks from the NSL-KDD and UNSW-NB 15 datasets. The SVM model obtained accuracy of 91.82%, DT of 98.99%, and RFC of 99.13%, and the proposed model obtained 99.175% for the NSL-KDD dataset. Similarly, SVM obtained accuracy of 85.88%, DT of 88.87%, RFC of 91.65%, and the proposed model obtained 99.18% for the UNSW NB-15 dataset. The proposed model achieved accuracy of 99.175% for the NSL-KDD dataset which is higher than the state-of-the-art techniques such as DNN of 97.72% and Ensemble Learning at 85.2%.
Design and Implementation of a Cloud PACS Architecture
Authors: Kawa Jacek , Pyciński Bartłomiej , Smoliński Michał , Bożek Paweł , Kwasecki Marek , Pietrzyk Bartosz , Szymański Dariusz
Published: November 2022

The limitations of the classic PACS (picture archiving and communication system), such as the backward-compatible DICOM network architecture and poor security and maintenance, are well-known. They are challenged by various existing solutions employing cloud-related patterns and services. However, a full-scale cloud-native PACS has not yet been demonstrated. The paper introduces a vendor-neutral cloud PACS architecture. It is divided into two main components: a cloud platform and an access device. The cloud platform is responsible for nearline (long-term) image archive, data flow, and backend management. It operates in multi-tenant mode. The access device is responsible for the local DICOM (Digital Imaging and Communications in Medicine) interface and serves as a gateway to cloud services. The cloud PACS was first implemented in an Amazon Web Services environment. It employs a number of general-purpose services designed or adapted for a cloud environment, including Kafka, OpenSearch, and Memcached. Custom services, such as a central PACS node, queue manager, or flow worker, also developed as cloud microservices, bring DICOM support, external integration, and a management layer. The PACS was verified using image traffic from, among others, computed tomography (CT), magnetic resonance (MR), and computed radiography (CR) modalities. During the test, the system was reliably storing and accessing image data. In following tests, scaling behavior differences between the monolithic Dcm4chee server and the proposed solution are shown. The growing number of parallel connections did not influence the monolithic server’s overall throughput, whereas the performance of cloud PACS noticeably increased. In the final test, different retrieval patterns were evaluated to assess performance under different scenarios. The current production environment stores over 450 TB of image data and handles over 4000 DICOM nodes.
Compact Camera Fluorescence Detector for Parallel-Light Lens-Based Real-Time PCR System
Authors: Koo Seul-Bit-Na , Kim Yu-Seop , Park Chan-Young , Lee Deuk-Ju
Published: November 2022

The polymerase chain reaction is an important technique in biological research. However, it is time consuming and has a number of disadvantages. Therefore, real-time PCR technology that can be used in real-time monitoring has emerged, and many studies are being conducted regarding its use. Real-time PCR requires many optical components and imaging devices such as expensive, high-performance cameras. Therefore, its cost and assembly process are limitations to its use. Currently, due to the development of smart camera devices, small, inexpensive cameras and various lenses are being developed. In this paper, we present a Compact Camera Fluorescence Detector for use in parallel-light lens-based real-time PCR devices. The proposed system has a simple optical structure, the system cost can be reduced, and the size can be miniaturized. This system only incorporates Fresnel lenses without additional optics in order for the same field of view to be achieved for 25 tubes. In the center of the Fresnel lens, one LED and a complementary metal-oxide semiconductor camera were placed in directions that were as similar as possible. In addition, to achieve the accurate analysis of the results, image processing was used to correct them. As a result of an experiment using a reference fluorescent substance and double-distilled water, it was confirmed that stable fluorescence detection was possible.
Adverse Weather Target Detection Algorithm Based on Adaptive Color Levels and Improved YOLOv5
Authors: Yao Jiale , Fan Xiangsuo , Li Bing , Qin Wenlin
Published: November 2022

With the continuous development of artificial intelligence and computer vision technology, autonomous vehicles have developed rapidly. Although self-driving vehicles have achieved good results in normal environments, driving in adverse weather can still pose a challenge to driving safety. To improve the detection ability of self-driving vehicles in harsh environments, we first construct a new color levels offset compensation model to perform adaptive color levels correction on images, which can effectively improve the clarity of targets in adverse weather and facilitate the detection and recognition of targets. Then, we compare several common one-stage target detection algorithms and improve on the best-performing YOLOv5 algorithm. We optimize the parameters of the Backbone of the YOLOv5 algorithm by increasing the number of model parameters and incorporating the Transformer and CBAM into the YOLOv5 algorithm. At the same time, we use the loss function of EIOU to replace the loss function of the original CIOU. Finally, through the ablation experiment comparison, the improved algorithm improves the detection rate of the targets, with the mAP reaching 94.7% and the FPS being 199.86.
COVIDX-LwNet: A Lightweight Network Ensemble Model for the Detection of COVID-19 Based on Chest X-ray Images
Authors: Wang Wei , Liu Shuxian , Xu Huan , Deng Le
Published: November 2022

Recently, the COVID-19 pandemic coronavirus has put a lot of pressure on health systems around the world. One of the most common ways to detect COVID-19 is to use chest X-ray images, which have the advantage of being cheap and fast. However, in the early days of the COVID-19 outbreak, most studies applied pretrained convolutional neural network (CNN) models, and the features produced by the last convolutional layer were directly passed into the classification head. In this study, the proposed ensemble model consists of three lightweight networks, Xception, MobileNetV2 and NasNetMobile as three original feature extractors, and then three base classifiers are obtained by adding the coordinated attention module, LSTM and a new classification head to the original feature extractors. The classification results from the three base classifiers are then fused by a confidence fusion method. Three publicly available chest X-ray datasets for COVID-19 testing were considered, with ternary (COVID-19, normal and other pneumonia) and quaternary (COVID-19, normal) analyses performed on the first two datasets, bacterial pneumonia and viral pneumonia classification, and achieved high accuracy rates of 95.56% and 91.20%, respectively. The third dataset was used to compare the performance of the model compared to other models and the generalization ability on different datasets. We performed a thorough ablation study on the first dataset to understand the impact of each proposed component. Finally, we also performed visualizations. These saliency maps not only explain key prediction decisions of the model, but also help radiologists locate areas of infection. Through extensive experiments, it was finally found that the results obtained by the proposed method are comparable to the state-of-the-art methods.
Characterization of Damage Progress in the Defective Grouted Sleeve Connection Using Combined Acoustic Emission and Ultrasonics
Authors: Zhang Lu , Fang Zhenmin , Tang Yongze , Li Hongyu , Liu Qizhou
Published: November 2022

The grouted sleeve connection is one of the most widely used connections for prefabricated buildings (PBs). Usually, its quality can have a significant impact on the safety of the whole PB, especially for the internal flaws that form during sleeve grouting. It is directly related to the mechanical performance and failure behavior of the grouted sleeve. Therefore, it is essential to understand the damage progression of the defective grouted sleeve connection. However, destructive testing is the mainstream measure to evaluate the grout sleeves, which is not applicable for in situ inspection. Therefore, this paper proposes a combined acoustic emission (AE) and ultrasonic testing (UT) method to characterize the damage progress of a grouted sleeve with different degrees of internal flaws under tensile loading. The UT was conducted before loading to evaluate the internal flaws. Additionally, the AE was used as the processing monitoring technique during the tensile testing. Two damage modes were identified: (i) brittle mode associated with the rebar pullout; (ii) ductile mode associated with the rapture of the rebar. The UT energy ratio was selected as the most sensitive feature to the internal flaws, both numerically and experimentally. The AE signatures of different damage phases and different damage modes were determined and characterized. For the brittle and ductile damage modes, two and three phases appeared in the AE activities, respectively. The proposed combined AE and UT method can provide a reliable and convenient nondestructive evaluation of grouted sleeves with internal flaws. Moreover, it can also characterize the damage progress of the grouted sleeve connections in real-time.
Learning-Based Image Damage Area Detection for Old Photo Recovery
Authors: Kuo Tien-Ying , Wei Yu-Jen , Su Po-Chyi , Lin Tzu-Hao
Published: November 2022

Most methods for repairing damaged old photos are manual or semi-automatic. With these methods, the damaged region must first be manually marked so that it can be repaired later either by hand or by an algorithm. However, damage marking is a time-consuming and labor-intensive process. Although there are a few fully automatic repair methods, they are in the style of end-to-end repairing, which means they provide no control over damaged area detection, potentially destroying or being unable to completely preserve valuable historical photos to the full degree. Therefore, this paper proposes a deep learning-based architecture for automatically detecting damaged areas of old photos. We designed a damage detection model to automatically and correctly mark damaged areas in photos, and this damage can be subsequently repaired using any existing inpainting methods. Our experimental results show that our proposed damage detection model can detect complex damaged areas in old photos automatically and effectively. The damage marking time is substantially reduced to less than 0.01 s per photo to speed up old photo recovery processing.
The Number and Structure of Muscle Synergies Depend on the Number of Recorded Muscles: A Pilot Simulation Study with OpenSim
Authors: Brambilla Cristina , Scano Alessandro
Published: November 2022

The muscle synergy approach is used to evaluate motor control and to quantitatively determine the number and structure of the modules underlying movement. In experimental studies regarding the upper limb, typically 8 to 16 EMG probes are used depending on the application, although the number of muscles involved in motor generation is higher. Therefore, the number of motor modules may be underestimated and the structure altered with the standard spatial synergy model based on the non-negative matrix factorization (NMF). In this study, we compared the number and structure of muscle synergies when considering 12 muscles (an “average” condition that represents previous studies) and 32 muscles of the upper limb, also including multiple muscle heads and deep muscles. First, we estimated the muscle activations with an upper-limb model in OpenSim using data from multi-directional reaching movements acquired in experimental sessions; then, spatial synergies were extracted from EMG activations from 12 muscles and from 32 muscles and their structures were compared. Finally, we compared muscle synergies obtained from OpenSim and from real experimental EMG signals to assess the reliability of the results. Interestingly, we found that on average, an additional synergy is needed to reconstruct the same R2 level with 32 muscles with respect to 12 muscles; synergies have a very similar structure, although muscles with comparable physiological functions were added to the synergies extracted with 12 muscles. The additional synergies, instead, captured patterns that could not be identified with only 12 muscles. We concluded that current studies may slightly underestimate the number of controlled synergies, even though the main structure of synergies is not modified when adding more muscles. We also show that EMG activations estimated with OpenSim are in partial (but not complete) agreement with experimental recordings. These findings may have significative implications for motor control and clinical studies.
Collection of a Continuous Long-Term Dataset for the Evaluation of Wi-Fi-Fingerprinting-Based Indoor Positioning Systems
Authors: Silva Ivo , Pendão Cristiano , Moreira Adriano
Published: November 2022

Indoor positioning and navigation have been attracting interest from the research community for quite some time. Nowadays, new fields, such as the Internet of Things, Industry 4.0, and augmented reality, are increasing the demand for indoor positioning solutions capable of delivering specific positioning performances not only in simulation but also in the real world; hence, validation in real-world environments is essential. However, collecting real-world data is a time-consuming and costly endeavor, and many research teams lack the resources to perform experiments across different environments, which are required for high-quality validation. Publicly available datasets are a solution that provides the necessary resources to perform this type of validation and to promote research work reproducibility. Unfortunately, for different reasons, and despite some initiatives promoting data sharing, the number and diversity of datasets available are still very limited. In this paper, we introduce and describe a new public dataset which has the unique characteristic of being collected over a long period (2+ years), and it can be used for different Wi-Fi-based positioning studies. In addition, we also describe the solution (Wireless Sensor Network (WSN) + mobile unit) developed to collect this dataset, allowing researchers to replicate the method and collect similar datasets in other spaces.
Characterization of the GNSS RFI Threat to DFMC GBAS Signal Bands
Authors: Sokolova Nadezda , Morrison Aiden , Diez Anja
Published: November 2022

This article presents analysis results from a long-term multi-site Global Navigation Satellite System (GNSS) Radio Frequency Interference (RFI) monitoring campaign in the context of Ground Based Augmentation System (GBAS) Dual Frequency Multi Constellation (DFMC) concept operation. GBAS resilience against unintentional RFI is an important area for investigation as the ground station receivers often must operate adjacent to high-traffic roads where chances of being affected by RFI are high. To be able to develop algorithms and reaction strategies necessary to ensure continuity and availability of service, knowledge of interference signal characteristics and frequency band/bands affected, as well as relative occurrence rates between the considered frequencies and frequency combinations, is necessary. The analysis presented in the article covers the prevalence and properties of the RFI events observed on the GPSs L1 and L5 and the Galileo E1 and E5a frequency bands that are considered by the on-going DFMC GBAS concept development initiatives. Due to being spectrally adjacent, the observed event analysis is also carried out for the Galileo E5b and GLONASS G1 frequency bands. The article also addresses the issue of spectral occupancy distribution of the observed events and presents new interesting RFI event types captured during the considered monitoring period.
Kinematics Calibration and Validation Approach Using Indoor Positioning System for an Omnidirectional Mobile Robot
Authors: Popovici Alexandru-Tudor , Dosoftei Constantin-Catalin , Budaciu Cristina
Published: November 2022

Monitoring and tracking issues related to autonomous mobile robots are currently intensively debated in order to ensure a more fluent functionality in supply chain management. The interest arises from both theoretical and practical concerns about providing accurate information about the current and past position of systems involved in the logistics chain, based on specialized sensors and Global Positioning System (GPS). The localization demands are more challenging as the need to monitor the autonomous robot’s ongoing activities is more stringent indoors and benefit from accurate motion response, which requires calibration. This practical research study proposes an extended calibration approach for improving Omnidirectional Mobile Robot (OMR) motion response in the context of mechanical build imperfections (misalignment). A precise indoor positioning system is required to obtain accurate data for calculating the calibration parameters and validating the implementation response. An ultrasound-based commercial solution was considered for tracking the OMR, but the practical observed errors of the readily available position solutions requires special processing of the raw acquired measurements. The approach uses a multilateration technique based on the point-to-point distances measured between the mobile ultrasound beacon and a current subset of fixed (reference) beacons, in order to obtain an improved position estimation characterized by a confidence coefficient. Therefore, the proposed method managed to reduce the motion error by up to seven-times. Reference trajectories were generated, and robot motion response accuracy was evaluated using a Robot Operating System (ROS) node developed in Matlab-Simulink that was wireless interconnected with the other ROS nodes hosted on the robot navigation controller.
Controlled Electromagnetic Field Based Safety System for Handheld Circular Saw
Authors: Teixidó Pedro , Hinojo-Montero José M. , Gómez-Galán Juan Antonio , Muñoz-Chavero Fernando , Sánchez-Rodríguez Trinidad , Aponte Juan
Published: November 2022

This paper presents the design of a safety system based on controlled electromagnetic field (CEMF) sensing technology to prevent accidents caused by power tools, especially related to handheld circular saws. The safety system creates an invisible protection bubble of electromagnetic field around the cutting edge. The system can provide early warning or critical warning when a person penetrates the safety bubble. This paper covers how the CEMF technology has been adapted to add value within this application where it needs to coexist with a difficult environment of metallic parts turning thousands of times per minute, strong vibrations, and different ranges of materials to be processed. The proposed contactless solution successfully detects the user, providing enough time for the power tool to totally stop its movement before touching and harming the user. This key property has required a careful optimization of the electromagnetic field generation, the design of a shield circuitry capable of operating properly in a large metal device, and the development of a multi-frame algorithm to address the stringent requirements related to the ability of the system to react to both very fast and very slow events. The feasibility of the system has been validated by a virtual testbench.
Attempts to Attract Eyesight in E-Commerce May Have Negative Effects
Authors: Sulikowski Piotr , Ryczko Konrad , Bąk Iwona , Yoo Soojeong , Zdziebko Tomasz
Published: November 2022

E-commerce shop owners often want to attract user attention to a specific product to enhance the chances of sales, to cross-sell, or up-sell. The way of presenting a recommended item is as important as the recommendation algorithms are to gain that attention. In this study, we examined the following types of highlights: background, shadow, animation, and border, as well as the position of the item in a 5 × 2 grid in a furniture online store, and their relationships with user fixations and user interest. We wanted to verify the effects highlighting had on attracting user attention. Various levels of intensity were considered for each highlight: low, medium, and strong. Methods used for data collection were both implicit and explicit: eye tracking, tracking cart’s contents, and a supplementary survey. Experimental results showed that a low-intensity background highlight should be the first-choice solution to best attract user attention in the presented shopping scenario, resulting in the best fixation times and most users’ selections. However, in the case of the highest-intensity animations, highlighting seemed to have negative effects; despite successful attempts to attract eyesight and a long fixation time, users did not add the highlighted products to cart.
Bayesian Aggregation Improves Traditional Single-Image Crop Classification Approaches
Authors: Matvienko Ivan , Gasanov Mikhail , Petrovskaia Anna , Kuznetsov Maxim , Jana Raghavendra , Pukalchik Maria , Oseledets Ivan
Published: November 2022

Accurate information about growing crops allows for regulating the internal stocks of agricultural products and drawing strategies for negotiating agricultural commodities on financial markets. Machine learning methods are widely implemented for crop type recognition and classification based on satellite images. However, field classification is complicated by class imbalance and aggregation of pixel-wise into field-wise forecasting. We propose here a Bayesian methodology for the aggregation of classification results. We report the comparison of class balancing techniques. We also report the comparison of classical machine learning methods and the U-Net convolutional neural network for classifying crops using a single satellite image. The best result for single-satellite-image crop classification was achieved with an overall accuracy of 77.4% and a Macro F1-score of 0.66. Bayesian aggregation for field-wise classification improved the result obtained using majority voting aggregation by 1.5%. We demonstrate here that the Bayesian aggregation approach outperforms the majority voting and averaging strategy in overall accuracy for the single-image crop classification task.
Novel Modification of the Collective Dynamic Routing Method for Sensors’ Communication in Wi-Fi Public Networks
Authors: Kozlov Sergey , Spirina Elena , Ashaev Ivan , Bukharina Anna , Gaysin Artur
Published: November 2022

The widespread use of the Internet of Things makes it relevant to use public IP networks for simultaneous access by both users and wireless sensors. To achieve this, a significant reduction in the subscriber devices’ energy consumption is required. This paper analyzes the application features of the collective dynamic routing method both with and without the use of a robust method for estimating the channel data rate for sensors’ communication in wireless public networks. Based on the analysis, a novel modification of the collective dynamic routing method has been developed that reduces the sensors’ energy consumption while keeping a high data rate and short delivery time for users. An analysis of the network load, the total data transfer rate over the network, and the parameters affecting the sensors’ energy consumption was carried out for a segment of a seamless IEEE 802.11ax network. The simulation demonstrated a high efficiency of using a novel modification of the collective dynamic routing method for access to users and wireless sensors.
Joint Power Control and Phase Shift Design for Future PD-NOMA IRS-Assisted Drone Communications under Imperfect SIC Decoding
Authors: Aziz Saddam , Irshad Muhammad , Afef Kallekh , Mohamed Heba G. , Alotaibi Najm , Tarmissi Khaled , Alnfiai Mrim M. , Hamza Manar Ahmed
Published: November 2022

Intelligent reflecting surfaces (IRS) and power-domain non-orthogonal multiple access (PD-NOMA) have recently gained significant attention for enhancing the performance of next-generation wireless communications networks. More specifically, IRS can smartly reconfigure the incident signal of the source towards the destination node, extending the wireless coverage and improving the channel capacity without consuming additional energy. On the other side, PD-NOMA can enhance the number of devices in the network without using extra spectrum resources. This paper proposes a new optimization framework for IRS-enhanced NOMA communications where multiple drones transmit data to the ground Internet of Things (IoT) devices under successive interference cancellation errors. In particular, the power budget of each drone, PD-NOMA power allocation of IoT devices, and the phase shift matrix of IRS are simultaneously optimized to enhance the total spectral efficiency of the system. Given the system model and optimization setup, the formulated problem is coupled with three variables, making it very complex and non-convex. Thus, this work first transforms and decouples the problem into subproblems and then obtains the efficient solution in two steps. In the first step, the closed-form solutions for the power budget and PD-NOMA power allocation subproblem at each drone are obtained through Karush–Kuhn–Tucker (KKT) conditions. In the second step, the subproblem of efficient phase shift design for each IRS is solved using successive convex approximation and DC programming. Numerical results demonstrate the performance of the proposed optimization scheme in comparison to the benchmark schemes.
Five-Direction Occlusion Filling with Five Layer Parallel Two-Stage Pipeline for Stereo Matching with Sub-Pixel Disparity Map Estimation
Authors: Ma Yunhao , Fang Xiwei , Guan Xinyu , Li Ke , Chen Lei , An Fengwei
Published: November 2022

Binocular stereoscopic matching is an essential method in computer vision, imitating human binocular technology to obtain distance information. Among plentiful stereo matching algorithms, Semi-Global Matching (SGM) is recognized as one of the most popular vision algorithms due to its relatively low power consumption and high accuracy, resulting in many excellent SGM-based hardware accelerators. However, vision algorithms, including SGM, are still somewhat inaccurate in actual long-range applications. Therefore, this paper proposes a disparity improvement strategy based on subpixel interpolation and disparity optimization post-processing using an area optimization strategy, hardware-friendly divider, split look-up table, and the clock alignment multi-directional disparity occlusion filling, and depth acquisition based on floating-point operations. The hardware architecture based on optimization algorithms is on the Stratix-IV platform. It consumes about 5.6 K LUTs, 12.8 K registers, and 2.5 M bits of on-chip memory. Meanwhile, the non-occlusion error rate of only 4.61% is about 1% better than the state-of-the-art works in the KITTI2015 dataset. The maximum working frequency can reach up to 98.28 MHz for the 640 × 480 resolution video and 128 disparity range with the power dissipation of 1.459 W and 320 frames per second processing speed.
Flow-Based Chemiluminescence Microarrays as Screening Platform for Affinity Binders to Capture and Elute Bacteria
Authors: Neumair Julia , Elsner Martin , Seidel Michael
Published: November 2022

Affinity describes the non-covalent but selective interaction between an affinity binder (e.g., proteins, antibiotics, or antibodies) and its counterpart (e.g., bacteria). These affinity binders can serve to detect bacteria and respond to the need for selective concentration via affinity chromatography for trace analysis. By changing the pH value or salt and protein contents, affinity bindings can be reversed, and bacteria can be recovered for characterisation. Analytical microarrays use multiple affinity binders immobilised on the surface in a distinct pattern, which immensely reduces screening time for the discovery of superior binding motifs. Here, flow-based microarray systems can inform not only about binding, but also about desorption. In this work, we pioneer a screening assay for affinity binders against both gram-positive and negative bacteria based on an automated flow-based chemiluminescence (CL) microarray. Biotinylation of model organisms E. coli and E. faecalis enabled labelling with horseradish-peroxidase-coupled streptavidin, and detection with CL. Polymyxin B, an antibiotic against gram-negative bacteria, was found to bind both E. coli and E. faecalis. Simultaneous screening for desorption methods unexpectedly revealed methyl alpha-D-mannopyranoside as a promising buffer for desorption from Polymyxin B. This proof-of-principle study shows that our new platform greatly facilitates the screening of new affinity binders against bacteria, with promise for future automation.
Sprint Acceleration Mechanical Outputs Derived from Position– or Velocity–Time Data: A Multi-System Comparison Study
Authors: Fornasier-Santos Charly , Arnould Axelle , Jusseaume Jérémy , Millot Benjamin , Guilhem Gaël , Couturier Antoine , Samozino Pierre , Slawinski Jean , Morin Jean-Benoît
Published: November 2022

To directly compare five commonly used on-field systems (motorized linear encoder, laser, radar, global positioning system, and timing gates) during sprint acceleration to (i) measure velocity–time data, (ii) compute the main associated force–velocity variables, and (iii) assess their respective inter-trial reliability. Eighteen participants performed three 40 m sprints, during which five systems were used to simultaneously and separately record the body center of the mass horizontal position or velocity over time. Horizontal force–velocity mechanical outputs for the two best trials were computed following an inverse dynamic model and based on an exponential fitting of the position- or velocity-time data. Between the five systems, the maximal running velocity was close (7.99 to 8.04 m.s−1), while the time constant showed larger differences (1.18 to 1.29 s). Concurrent validity results overall showed a relative systematic error of 0.86 to 2.28% for maximum and theoretically maximal velocity variables and 4.78 to 12.9% for early acceleration variables. The inter-trial reliability showed low coefficients of variation (all <5.74%), and was very close between all of the systems. All of the systems tested here can be considered relevant to measure the maximal velocity and compute the force–velocity mechanical outputs. Practitioners are advised to interpret the data obtained with either of these systems in light of these results.
Multi-Currency Integrated Serial Number Recognition Model of Images Acquired by Banknote Counters
Authors: Jang Woohyuk , Lee Chaewon , Jeong Dae Sik , Lee Kunyoung , Lee Eui Chul
Published: November 2022

The objective of this study was to establish an automated system for the recognition of banknote serial numbers by developing a deep learning (DL)-based optical character recognition framework. An integrated serial number recognition model for the banknotes of four countries (South Korea (KRW), the United States (USD), India (INR), and Japan (JPY)) was developed. One-channel image data obtained from banknote counters were used in this study. The dataset used for the multi-currency integrated serial number recognition contains about 150,000 images. The class imbalance problem and model accuracy were improved through data augmentation based on geometric transforms that consider the range of errors that occur when a bill is inserted into the counter. In addition, by fine-tuning the recognition network, it was confirmed that the performance was improved when the serial numbers of the banknotes of four countries were recognized instead of the serial number of a banknote from each country from a single-currency dataset, and the generalization performance was improved by training the model to recognize the diverse serial numbers of multiple currencies. Therefore, the proposed method shows that real-time processing of less than 30 ms per image and character recognition with 99.99% accuracy are possible, even though there is a tradeoff between inference speed and serial number recognition accuracy when data augmentation based on the characteristics of banknote counters and a 1-stage object detector for banknote serial number recognition is used.
Extraction, Separation and Purification of Bioactive Anticancer Components from Peganum harmala against Six Cancer Cell Lines Using Spectroscopic Techniques
Authors: Sadaf Huma Mehreen , Bibi Yamin , Ayoubi Samha Al , Safdar Naila , Sher Ahmad , Habib Darima , Nisa Sobia , Malik Khafsa , Kumar Sunjeet , Ahmed Waseem , Qayyum Abdul
Published: November 2022

Conventional cancer treatments normally involve chemotherapy or a combination of radio- and chemotherapy. However, the adverse effects of synthetic medicines encouraged the exploration of novel therapeutic medications of a bio-friendly nature. In an effort to explore anticancer compounds from natural resources, crude extract of Peganum harmala (seeds) was fractionated on the basis of polarity, and the fractions were further tested for anticancer activity. Brine shrimp lethality assays and potato disc antitumor assays were used to test each fraction for cytotoxic and antitumor potential. The ethyl acetate fraction was found to be most potent, with LC50 and IC50 values of 34.25 µg/mL and 38.58 µg/mL, respectively. Further activity-guided fractionation led to the isolation of the bioactive compound PH-HM-10 which was identified and characterized by Mass Spectroscopy (MS), Infrared Spectroscopy (IR), Proton Nuclear Magnetic Resonance Spectroscopy (1HNMR), Carbon Nuclear Magnetic Resonance Spectroscopy (13CNMR) and Heteronuclear Single Quantum Correlation (HSQC). Anticancer aspects in the isolated compound were determined against six human cancer cell lines with a maximum anticancer effect (IC50 = 36.99 µg/mL) against the tested human myeloid leukemia (HL-60) cell line, followed by the human lung adenocarcinoma epithelial cell line (A549) and the breast cancer cell line (MCF-7) with an IC50 of 63.5 µg/mL and 85.9 µg/mL, respectively). The findings of the current study suggest that the isolated compound (Pegaharmine E) is significantly active against the tested cancer cell lines and can be further investigated to develop future novel anticancer chemotherapeutic agents.
Modelling the Trust Value for Human Agents Based on Real-Time Human States in Human-Autonomous Teaming Systems
Authors: Lin Chin-Teng , Fan Hsiu-Yu , Chang Yu-Cheng , Ou Liang , Liu Jia , Wang Yu-Kai , Jung Tzyy-Ping
Published: November 2022

The modelling of trust values on agents is broadly considered fundamental for decision-making in human-autonomous teaming (HAT) systems. Compared to the evaluation of trust values for robotic agents, estimating human trust is more challenging due to trust miscalibration issues, including undertrust and overtrust problems. From a subjective perception, human trust could be altered along with dynamic human cognitive states, which makes trust values hard to calibrate properly. Thus, in an attempt to capture the dynamics of human trust, the present study evaluated the dynamic nature of trust for human agents through real-time multievidence measures, including human states of attention, stress and perception abilities. The proposed multievidence human trust model applied an adaptive fusion method based on fuzzy reinforcement learning to fuse multievidence from eye trackers, heart rate monitors and human awareness. In addition, fuzzy reinforcement learning was applied to generate rewards via a fuzzy logic inference process that has tolerance for uncertainty in human physiological signals. The results of robot simulation suggest that the proposed trust model can generate reliable human trust values based on real-time cognitive states in the process of ongoing tasks. Moreover, the human-autonomous team with the proposed trust model improved the system efficiency by over 50% compared to the team with only autonomous agents. These results may demonstrate that the proposed model could provide insight into the real-time adaptation of HAT systems based on human states and, thus, might help develop new ways to enhance future HAT systems better.
Afterglow Light Curves from Off-Axis GRB Jets in Stratified Circumburst Medium
Authors: Zhao Xiao-Hong , Cheng Kang-Fa
Published: November 2022

We study the gamma-ray burst (GRB) afterglow light curves produced by an off-axis jet in a stratified circumburst medium and summarize the temporal indices of the coasting phase, the deceleration phase, the Newtonian phase, and the deep Newtonian phase for various viewing angles and power-law indices of medium density. Generally, the afterglow light curves of off-axis GRBs in the homogeneous interstellar medium have a steep rise arising due to jet deceleration. In the stratified medium, the flux rises is more shallow but peaks earlier for the same viewing angle due to faster deceleration of the jet running into the denser stratified medium, compared with the case of the interstellar medium (ISM). Observations of off-axis bursts will possibly increase over the coming years due to the arrival of the multi-messenger era and the forthcoming surveys in multiple bands. The temporal indices of off-axis afterglows derived in the paper will provide a reference for comparison with the observations and can diagnose the circumburst environment. The numerical code calculating the afterglow light curve also can be used to fit the multi-wavelength light curves.