bluuebunny/crossref_metadata_2025 huggingface dataset:
Data‐driven urban traffic model‐free adaptive iterative learning control with traffic data dropout compensation
Authors: Li Dai , Hou Zhongsheng
Published: April 2021

In this paper, to fully utilize the urban traffic flow characteristics of similarity and repeatability without using a mathematical traffic model, a data‐driven urban traffic control strategy based on model‐free adaptive iterative learning control (MFAILC) scheme is put forward. Firstly, by dynamically linearizing the urban traffic dynamics along the iteration axis, the traffic network system is transformed into a MFAILC data model with the help of repetitive pattern of urban traffic flow. Then, the traffic controller is designed based on the derived MFAILC data model only using the I/O data of the traffic network. Finally, a traffic data compensation method is proposed to deal with data dropout problem. Simulation study verifies the feasibility and effectiveness of the proposed control method.
Composite control for trajectory tracking of wheeled mobile robots with NLESO and NTSMC
Authors: Wang Haoyu , Zuo Zhiqiang , Wang Yijing , Yang Hongjiu
Published: April 2021

This paper proposes a control strategy integrating the non‐linear extended state observer (NLESO) and the non‐singular terminal sliding mode control (NTSMC) for the trajectory tracking of wheeled mobile robots subject to bounded disturbances. A new transformation method of chained model in terms of Lie derivative is presented to simplify the controller design. A specific NLESO combining linear term and non‐linear term is designed to estimate the disturbances with a faster convergence performance. A scheme for determining the gain range of NLESO is explicitly given to facilitate the tuning of experimental parameters. Meanwhile, the NTSMC achieves finite time convergence of the tracking error system and the chattering phenomenon in NTSMC is dramatically alleviated with the compensation from NLESO. The experimental results validate the strong robustness and good performance of the proposed control strategy.
Noise radar range doppler imaging via 2D generalized smoothed‐l0
Authors: Lu Xingyu , Gu Hong , Su Weimin
Published: April 2021

This paper presents an algorithm to enhance the range‐Doppler imaging performance for noise radar. Traditional matched filtering based method suffers from high range and Doppler sidelobes, which makes the weak targets overwhelmed by the sidelobes of strong targets or clutters in the range‐Doppler map. Sparse recovery based methods have been widely used to suppress such sidelobes, but most of them assume a repetitive transmit waveform, which cannot be applied to noise radar applications. In this letter, the range‐Doppler imaging problem for noise radar is formulated as a sparse recovery model and solved by a 2D generalized smoothed‐l0 algorithm. The proposed method can deal with the random waveforms varying among different pulses in noise radar. The robustness of the proposed method in strong noise and clutters is validated by simulation results.
Blind source separation‐based IVA‐Xception model for bird sound recognition in complex acoustic environments
Authors: Dai Yusheng , Yang Jin , Dong Yiwei , Zou Haipeng , Hu Mingzhi , Wang Bin
Published: April 2021

Identification of bird species from audio recordings has been a major area of interest within the field of ecological surveillance and biodiversity conservation. Previous studies have successfully identified bird species from given recordings. However, most of these studies are only adaptive to low‐noise acoustic environments and the cases where each recording contains only one bird's sound simultaneously. In reality, bird audios recorded in the wild often contain overlapping signals, such as bird dawn chorus, which makes audio feature extraction and accurate classification extremely difficult. This study is the first to focus on applying a blind source separation method to identify all foreground bird species contained in overlapping vocalization recordings. The proposed IVA‐Xception model is based on independent vector analysis and convolutional neural network. Experiments on 2020 Bird Sound Recognition in Complex Acoustic Environments competition (BirdCLEF2020) dataset show that this model could achieve a higher macro F1‐score and average accuracy compared with state‐of‐the‐art methods.
Novel 900 nm diode lasers with epitaxially stacked multiple active regions and tunnel junctions
Authors: Wenzel Hans , Maaßdorf Andre , Zink Christof , Martin Dominik , Weyers Markus , Knigge Andrea
Published: April 2021

A multi‐active‐region bipolar‐cascade edge‐emitting laser emitting at nearly 900 nm is presented. The three active regions and two tunnel junctions located in a single waveguide core share the same third‐order vertical mode. A slope efficiency of 3.6 W/A was measured with a threshold current density of 230 A/cm2. The epitaxial layer stack developed features with very low internal optical losses of 0.7 cm−1. The voltage extrapolated to vanishing current is only 0.3 V larger than 3 times the voltage of 1.4 V originating from the photon energy.
Low‐complexity BER computation for coherent detection of orthogonal signals
Authors: Rugini L. , Baruffa G.
Published: April 2021

The bit error rate (BER) computation for coherently detected orthogonal signals in additive white Gaussian noise requires numerical integration, which can be cumbersome in low‐complexity devices. Here, a BER computation approach that circumvents the need for repeated numerical integration is proposed. First, a low‐complexity BER approximation formula is selected, with unknown parameters to be determined. Second, this letter determines the unknown parameters by fitting the BER approximation to a few BER values known in advance. The BER comparison with the exact results shows that the accuracy of the proposed low‐complexity approach is satisfactory for several constellation sizes of interest. This enables the possibility of BER self‐computation in low‐complexity devices that use orthogonal signals, like those used in the Internet of Things (IoT).
A 7.5 mW –43 dB LO leakage source‐driven wideband CMOS millimeter‐wave mixer
Authors: Lin Jiafu , Boon Chirn Chye , Berenguer Roc , Liu Gui
Published: April 2021

This paper presents a millimeter‐wave (mm‐wave) V‐band mixer. The source‐driven transformer‐coupled up‐converter topology can alleviate the local oscillator (LO) leakage problem. The proposed V‐band up‐converter has been implemented on 65 nm CMOS technology. The experimental results show that the LO leakage level can be reduced to –43.1 dB. The up‐converter occupies 0.11 mm2 in area excluding signal pads.
Modelling of the packet delivery rate in an actual LoRaWAN network
Authors: Abdelghany Ahmed , Uguen Bernard , Moy Christophe , Lemur Dominique
Published: April 2021

The Internet of things (IoT) paradigm is paving the way to ensure connectivity and monitoring within different market fields. Thus, it is critical to understand the packet transmission performance in Low Power Wide Area Network (LPWAN), especially LoRaWAN. Packet delivery rate (PDR) is considered as a dominant factor of the network performance and its dependency on the signal power. However, previous works do not model the PDR as a function of the received signal power. An in‐depth investigation of the PDR is done by performing an outdoor measurement campaign in the area of the Campus Beaulieu in Rennes. For each received LoRa packet, the effective signal power (ESP) values are obtained as well as their high influence on PDR is proven. With the given results, modelling the PDR using an ESP‐parameterized beta distribution function is proposed. Feasibility of the proposed model is assured by simulating PDR against ESP, hence, the simulated PDR values follow the distribution of the measured ones well. This PDR model gives important guidelines for future LoRaWAN network regulation and optimization.
Reweighting neural network examples for robust object detection at sea
Authors: Becktor J. , Boukas E. , Blanke M. , Nalpantidis L.
Published: April 2021

Deep neural networks have had profound significance in addressing visual object detection and classification tasks. However, though with the caveat of needing large amounts of annotated training data. Furthermore, the possibility of neural networks overfitting to the biases and faults included in their respective datasets. In this work, methods for achieving robust neural networks, able to tolerate untrusted and possibly erroneous training data, are explored. The proposed method is shown to improve performance and help neural networks learn from untrusted data, provided a thoroughly annotated subset.
A novel frequency response measurement method for wideband ADCs' system
Authors: Zhou Yi , Ye Peng , Yang Kuojun , Zhang Yijiao
Published: April 2021

The parallel analogue to digital converter (ADC) structure with mixers such as digital bandwidth‐interleaving ADC (DBI‐ADC) is a practical structure for realising the wideband data acquisition system. The system calibration relies on accurate system frequency response measurement. However, existing measurement methods require complex synchronous circuits or are susceptible to noise. This paper proposes a new frequency response measurement method, which can be realised by a simple circuit without any synchronisation measures. At the same time, frequency response measurement can ensure sufficient accuracy. Simulation and experiment results show that the method is effective and feasible.
Segment attention‐guided part‐aligned network for person re‐identification
Authors: Wang Wen , Liu Yongwen , An Gaoyun
Published: April 2021

Part misalignment of the human body caused by complex variations in viewpoint and pose poses a fundamental challenge to person re‐identification. This letter examines Res2Net as the backbone network to extract multi‐scale appearance features. At the same time, it uses the human parsing model to extract part features, which can be used as an attention stream to guide part features re‐calibration from the spatial dimension. Additionally, in order to ensure the diversity of features, SAG‐PAN effectively integrates the global appearance features of person image with part fine‐grained features. The experimental results on the Market‐1501, DukeMTMC‐reID and CUHK03 datasets show that the proposed SAG‐PAN achieved superior performance against the existing state‐of‐the‐art methods.
Face illumination processing via dense feature maps and multiple receptive fields
Authors: Ling Shenggui , Fu Keren , Lin Ye , You Di , Cheng Peng
Published: April 2021

Recently, illumination processing of facial image based on generative adversarial networks has made favourable progress. However, the image quality is not so satisfactory and the recognition accuracy is low when the face image under extreme illumination conditions. For these reasons, an elaborately‐designed architecture based on convolutional neural network and generative adversarial networks for processing face illumination is presented. A novel dense feature maps loss that computes loss by using the varisized feature maps extracted from different convolutional layers of pre‐trained feature network is put forward. Moreover, multiple‐receptive‐fields‐based generator that uses multiple encoders during encoding phase is also proposed, and these encoders have the same structure with different kernel size. A variety of experimental results demonstrate that the method is superior to the state‐of‐the‐art methods under various illumination challenges. Code will be available soon at https://github.com/ling20cn/IP‐GAN
Simultaneous regression‐based spatial coverage estimation and object detection with deep learning
Authors: Andersen Rasmus Eckholdt , Nalpantidis Lazaros , Ravn Ole , Boukas Evangelos
Published: April 2021

Object detection has been in the focus of researchers within varying applications propelled by the recent advances in deep learning and neural networks. Many applications require both detection of class instances as well as a quantification of the spatial coverage of the class instances. While the performance of deep learning approaches for these tasks has been extensively studied there has not been much effort into creating a unified network structure to achieve both goals. The purpose of this paper is to present a regressor to the faster R‐CNN architecture that can help quantify the spatial coverage estimation of some detected object. The goal of the regressor is to provide a reproducible result of the spatial coverage. To demonstrate the developed architecture, an example use‐case of land cover estimation is used. The experiments conducted in this paper show that the network does not sacrifice object detection accuracy, and indicate that the network is able to estimate the spatial coverage of six different types of land.
Reliability evaluation of regional energy Internet considering electricity–gas coupling and coordination between energy stations
Authors: Liu Hong , Zhao Yue , Ge Shaoyun , Zhang Peng , Liu Wei , Qi Xiaoguang , Hu Ping
Published: April 2021

In view of the issues that the current comprehensive energy supply reliability evaluation mainly focuses on the distributed energy system and rarely involves the mutual coupling of energy supply networks and the coordination of energy stations in the regional energy Internet, an evaluation method of energy supply reliability considering electricity–gas coupling and coordination between energy stations was proposed. First, the framework of regional energy Internet (REI) is established, the energy flow relationship among the main parts in the REI and the influence of electricity–gas coupling on reliability are analysed; second, considering building virtual energy storage and demand response, the rigid and flexible reliability evaluation indexes are proposed from the energy and time levels; third, the operation strategies of normal, failure and repair periods of each energy station are put forward, and the optimal load reduction model of REI aiming at the minimum total rigid energy shortage is established. Moreover, the method and process of energy supply reliability evaluation combining fault mode effect analysis (FMEA) and Monte Carlo simulation are proposed. Finally, through a practical example, the impact of demand response, multiple energy storage resources, electricity–gas coupling and inter‐station coordination on reliability is analysed.
A new scheme for fault location of three‐terminal parallel transmission lines without transposer
Authors: Chen Gonggui , Cai Daiying , Long Hongyu , Long Yi , Zeng Xianjun , Zhou Ping , Kang Peng
Published: April 2021

This paper proposes a new scheme for locating the faults of three‐terminal double‐circuit transmission line without transposer in the phase domain that is not affected by type of fault. Synchronous voltage and current were measured by phasor measurement units (PMUs) installed at three busbars. Considering the coupling effect of parallel lines, the distribution line model was used to obtain more accurate results and third‐order Taylor series expansion to decouple distributed line model. The phase and branch of all faults on the transmission lines were effectively identified, and various types of faults were accurately located including normal shunt fault, evolving fault and cross‐country fault. The proposed algorithm was tested under different fault locations, resistances, initial angles and types (including cross‐country faults and evolving faults). In addition, the effects of different sampling rates, measurement errors, earth resistivities and transmission line parameter errors were also considered. The simulation calculation was performed in MATLAB software and three‐terminal transmission lines were simulated in PSCAD/EMTP software. The results indicate that maximum estimated error was less than 1.8631%.
Ensuring data integrity of healthcare information in the era of digital health
Authors: Zarour Mohammad , Alenezi Mamdouh , Ansari Md Tarique Jamal , Pandey Abhishek Kumar , Ahmad Masood , Agrawal Alka , Kumar Rajeev , Khan Raees Ahmad
Published: April 2021

Data integrity continues to be a persistent problem in the current healthcare sector. It ensures that the data is correct and has not even in any manner been improperly changed. Incorrect data might become significant health threats for patients and a big responsibility for clinicians, resulting in problems such as scam, misconduct, inadequate treatment and data theft. This sort of endangering scenario causes tremendous difficulty in handling healthcare data. This research intends to describe the threat plot of data integrity in healthcare through numerous attack statistics from around the world and Saudi Arabia and identify the criticality in Saudi Arabia in particular. A literature review by descriptive analysis, unit analysis and rating analysis to achieve the planned systematic literature review goal is outlined. The outcome of ranking analysis using a fuzzy analytical hierarchy process methodology offers a route for Saudi Arabian researchers to promote medical records or data security in Arabic healthcare. It is suggested that blockchain is the most prioritized method for regular use and adaptation across Saudi Arabia in all data integrity management techniques. To address the challenges of data integrity and future path, the authors critically examine the challenges posed by data integrity in the healthcare sector.
A robust photo‐based PM2.5$_{2.5}$ monitoring method by combining linear and non‐linear learning
Authors: Xia Zhifang
Published: April 2021

Good health is pursued by people all over the world. However, the continual industrialisation has led to more and more atmospheric contamination, and PM has caused serious harm to our life safety and living environment. Without increasing the cost of sustainable industrial production, more and more attention has been paid to the related researches on improving PM monitoring, prevention and control level. Therefore, it is extremely urgent to establish a robust PM monitoring model that can adapt to a variety of scenarios, not only in local places like campuses but also in wide area like city. Existing work has proven that PM monitoring can be achieved by means of photos. But experiments show that the stated‐of‐the‐art methods are far from ideal for PM monitoring when the author tested the performance in two public datasets. To solve the aforesaid issue, this paper ulteriorly proposes a novel photo‐based PM monitoring model, which fuses the results of existing methods by firstly using the weighted average based on the least absolute shrinkage and selection operator regression for learning the basic linear component, secondly using the support vector regression for learning the non‐linear residual component, and finally incorporating the above two outputs to infer the final PM concentration. The main contributions and innovations of this paper are embodied in: (1) the innovative use of image quality assessment model to extract 9 features for PM monitoring, (2) separately extract macro information and micro information from PM2.5 pictures, (3) two newly‐established large‐scaled datasets are applied to verify the effectiveness and robustness of the proposed PM monitoring model. Experiments show that on the latest PM datasets (local and wide), the proposed model has achieved high performance and demonstrated strong robustness.
Research on fatigue detection based on visual features
Authors: Zhao Guangzhe , He Yanqing , Yang Hanting , Tao Yong
Published: April 2021

The high incidence of traffic accidents brings immeasurable losses to life. In order to avoid such crises, researchers and automakers have used many methods to solve this problem. Among them, technology based on visual features is widely used in driver fatigue detection. As fatigue detection plays a vital role in the driving process, the high accuracy of fatigue monitoring is very important. This paper focuses on the method based on convolutional neural network to detect driver fatigue. First, in the face detection part, the Single‐Shot Multi‐Box Detector algorithm is used to improve the speed and accuracy of face detection to extract the eye and mouth regions; second, the VGG16 network is used to learn fatigue features, which is performed on the NTHU‐Drowsy Driver Detection (NTHU‐DDD) data set and the other two modified data sets Training test. The main result of this work is that the accuracy of fatigue monitoring is higher than other methods including the original method, with an accuracy rate of over 90%. And it has better generalization ability than the multi‐physical feature fusion detection method. At the same time, we propose the fatigue detection method based on convolutional neural network to improve the advanced driver assistance system (ADAS) to make it more robust and reliable decision making.
A novel image quality assessment method and coefficient of quality for digital solutions of colour blindness
Authors: S Meenakshi , Singla Anshu
Published: April 2021

Eyesight is one of the primary senses that human beings have. Reports show that colour blindness, a form of colour vision deficiency (CVD), affects about 8% of the male population and 0.5% of female population. The Assistive Technology Act of 2004 lays focus on technologies that help individuals with disabilities and deficiencies. With the rapid advancement in technologies, several assistive solutions are available for visually impaired or CVD patients. Such solutions involve simulation and compensation of conflicting colours to help the colour blind in the visual perception of colours. Given the increased usage of the web, post the pandemic, these solutions improve the quality of life for the colour blind. Defining the image quality assessment criteria for such digital solutions becomes imperative. The study proposes a novel method for image quality assessment of digital solutions aimed at assisting the colour blind users. The proposed coefficient of quality (CQ) would be useful to rank colour compensation and recolouring algorithms. Experiments were conducted with a novel questionnaire set designed for this quality measurement. The results affirm the efficiency of the assessment method proposed. This will also provide objective feedback to the researchers and experts in this area to improve their solutions for CVD patients.
An efficient way to use MS‐CLEAN associated with Shannon's entropy
Authors: Campos Ramon G. , Salles Evandro O. T.
Published: April 2021

This article covers deconvolution methods in the context of radio astronomical images. A new formulation is proposed to deal with negative brightness, deconvoluting separately the positive and negative brightness of the sky. The positive brightness is physically possible, but negative brightness is a degradation product. At the same time, the paper presents Shannon's entropy's behaviour in the context of the Multi‐Scale CLEAN (MS‐CLEAN) algorithm, defining the measured brightness as information in the scope of Shannon's entropy. The knowledge acquired is used in an example of information monitoring at scales, which automatically reduces the search space of MS‐CLEAN, and reduces the computational cost. The proposed algorithm, called Relevant Component Multi‐Scale CLEAN (RC‐CLEAN), can be up to 4 times faster than the classic MS‐CLEAN without prejudice to the identification of structures and noise reduction. Here, Structural Similarity Index (SSIM) and Peak Signal to Noise Ratio (PSNR) used to quantify the results, respectively, showed the same quality for the SSIM and gains of up to 11 dB for the PSNR. RC‐CLEAN also shows a result similar to that obtained by the standard software of large astronomical laboratories using real data.
Graph wavelet transform for image texture classification
Authors: Qiao Yu‐Long , Zhao Yue , Song Chun‐Yan , Zhang Kai‐Ge , Xiang Xue‐Zhi
Published: April 2021

Graph is a data structure that can represent complex relationships among data. Graph signal processing, unlike traditional signal processing, explicitly considers the structure and relationship among the signal samples. Graph wavelet transform can provide a multiscale analysis for the graph signal. It is well known that texture is a region property in an image, which is characterized with the intensity and relationship among pixels. In this context of the graph signal processing framework, an image texture can be considered as the signal on the graph. Therefore, a texture classification method based on graph wavelet transform is proposed. Specifically, image textures are decomposed into multiscale components by using two‐channel graph wavelet filter banks. Then the local singular value decomposition is applied to each subband. In order to improve the noise‐resistant ability, the maximum, mean and median values of the local singular values of graph‐wavelet transformation coefficients are extracted. Finally, the Weibull distributions are used to model those extracted values to describe the image textures. The experiments on the benchmark texture datasets are conducted to demonstrate the effectiveness of the proposed method.
Shadow detection from images using fuzzy logic and PCPerturNet
Authors: Chaki Jyotismita
Published: April 2021

Shadow detection is a challenging and essential task for interpreting the scene. Regardless of encouraging findings from current Deep Learning (DL) approaches used for shadow detection, the methods are also dealing with inconsistent situations where the visual representation of non‐shadow and shadow regions is equivalent. In this article, a DL based approach is introduced for image pixel‐level shadow detection. The proposed CNN‐based approach, pattern conserver convolutional neural network (PCPerturNet) profits from a new design where shadow features are defined utilizing an effective skip‐connection mapping arrangement. To make PCPerturNet robust from the change in brightness and contrast, several perturbed instances are generated by using a fuzzy‐logic based method to train the system. Also, five types of augmentations are applied to images during training to make the system robust from the change in scale, orientation and flip. PCPerturNet derives and conserves shadow patterns in manifold layers and uses those layers progressively in several units to produce the shadow mask. The output of the proposed method is tested on two freely accessible databases and one self‐created database where the accuracy rate obtained is 96.4%, 96.8%, and 89.4% which indicates that the proposed method outperforms the other shadow detection approaches used in the literature.
ILBPSDNet: Based on improved local binary pattern shallow deep convolutional neural network for character recognition
Authors: Lee Shih‐Hsiung , Yu Wei‐Fu , Yang Chu‐Sing
Published: April 2021

This paper proposes an architecture based on the improved local binary pattern (LBP) shallow deep convolution neural network, which integrates hand‐crafted feature pre‐processing and the advantage of character learning in the supervised high‐level function of CNN, in order to enhance its performance. This study introduced the information of scale space into the LBP to reduce the sensitivity to noise, and applied feature maps with two features, the maximum selection feature map (MLBP) and the first selection feature map (FLBP). The former selected the edge with the strongest intensity to reduce the influence of noise points, while the latter measured local binary features through the scale detection of an effective edge. In the network architecture design, according to the differences of input features, networks of different depths were used for learning, and the features learned by the two networks were adopted for classification. The experimental results show that, the ILBPSDNet proposed had certain recognition abilities in many character data sets, and the network parameters and computation were also reduced. Therefore, it has a significant effect in realizing the application of real‐time character recognition. Finally, compared with other latest networks, its network performance could be maintained at a certain level.
Unsupervised many‐to‐many image‐to‐image translation across multiple domains
Authors: Lin Ye , Fu Keren , Ling Shenggui , Cheng Peng
Published: April 2021

Unsupervised multi‐domain image‐to‐image translation aims to synthesize images among multiple domains without labelled data, which is more general and complicated than one‐to‐one image mapping. However, existing methods mainly focus on reducing the large costs of modelling and do not pay enough attention to the quality of generated images. In some target domains, their translation results may not be expected or even cause the model collapse. To improve the image quality, an effective many‐to‐many mapping framework for unsupervised multi‐domain image‐to‐image translation is proposed. There are two key aspects to the proposed method. The first is a many‐to‐many architecture with only one domain‐shared encoder and several domain‐specialized decoders to effectively and simultaneously translate images across multiple domains. The second is two proposed constraints extended from one‐to‐one mappings to further help improve the generation. All the evaluations demonstrate that the proposed framework is superior to existing methods and provides an effective solution for multi‐domain image‐to‐image translation.
Detail texture detection based on Yolov4‐tiny combined with attention mechanism and bicubic interpolation
Authors: Hui Tian , Xu YueLei , Jarhinbek Rasol
Published: April 2021

Aero‐engine blades crack detection is one of the important tasks in daily ground maintenance, crack is a kind of texture feature, due to the random distribution, irregular shape and vague characteristics, which is still a challenging task to realize automatic detection in working environment. A detection model based on the Yolov4‐tiny is proposed that is universal and focuses more on the characteristics of cracks, and it is implemented in embedded device. First, in order to distinguish the cracks and noises, an improved attention module is introduced into the backbone of Yolov4‐tiny to enhance the model's capability to focus on crack areas; second, in order to improve the effect of multi‐scale feature fusion, the bicubic interpolation is implemented in upsampling module; finally, in order to solve the redundant detection results of bounding‐boxes in crack areas, the optimized non‐maximum suppression method is proposed to make the detection results better corresponding to the groundTruth. The robustness of proposed detection model was demonstrated by evaluating varying lighting and noise images. The average precision on integrated datasets is 81.6%, which outperforms the original Yolov4‐tiny by an increase of 12.3%.
Investigation of numerical dispersion with time step of the FDTD methods: avoiding erroneous conclusions
Authors: Cheng Yu , Chen Guangzhi , Wang Xiang‐Hua , Yang Shunchuan
Published: April 2021

To obtain high accuracy and efficiency in the simulations, an optimum time step should be taken in finite‐difference time‐domain (FDTD) methods. The authors investigated how time steps impact on numerical dispersion of two FDTD methods including the FDTD(2,2) method and the FDTD(2,4) method. Through rigorously analytical and numerical analysis, it is found that small time steps of the FDTD methods do not always have small numerical errors. Our findings reveal that these two FDTD methods present different behaviours with respect to time steps: (1) for the FDTD(2,2) method, smaller time steps limited by the Courant‐Friedrichs‐Lewy condition increase numerical dispersion and lead to larger simulation errors and (2) for the FDTD(2,4) method, as the time step increases, numerical dispersion errors first decrease and then increase. Our findings are also comprehensively validated from one‐ to three‐dimensional cases through several numerical examples including wave propagation, resonant frequencies of cavities and a practical engineering problem.
Plane wave scattering from thin dielectric disk in free space: Generalized boundary conditions, regularizing Galerkin technique and whispering gallery mode resonances
Authors: Lucido Mario , Balaban Mykhaylo V. , Nosich Alexander I.
Published: April 2021

Considered is the plane‐wave scattering from and absorption by a thin circular dielectric disk. The analysis uses a set of the singular integral equations for the effective electric and magnetic currents, derived using the generalized boundary conditions on the disk median section. Following the recently developed analytical preconditioning procedure, these equations are discretized by the Galerkin technique with judiciously chosen expansion functions, which provide for the Fredholm second‐kind nature of the resulting matrix equations. This guarantees the code convergence and high efficiency. It is demonstrated that the developed technique delivers the most important features of thin dielectric disks–the resonances on the natural modes. In the resonances on the slab modes, the disk can be well‐transparent and the shadow is created only by its rim; in the whispering gallery mode resonances, the scattering occurs mainly in the disk plane.
The rigorous solution of the scattering problem for a finite cone embedded in a dielectric sphere surrounded by the dielectric medium
Authors: Kuryliak Dozyslav B.
Published: April 2021

Wave scattering from a finite hollow cone with perfectly conducting boundaries embedded in a dielectric sphere is considered. The structure is excited axially symmetrically by the radial electric dipole. The scattering problem is formulated in the spherical coordinate system as the boundary value problem for the Helmholtz equation. The diffracted field is given by expansion in the series of eigenfunctions. Owing to the enforcement of the conditions of continuity together with the orthogonality properties of the Legendre functions the diffraction problem is reduced to infinite system of linear algebraic equations (ISLAE) of the first kind. The usage of the analytical regularization approach transforms the ISLAE of the first kind to the second one and allows one to justify the truncation method for obtaining the numerical solution in the required class of sequences. This system is proved to be regularized by a pair of operators, which consist of the convolution type operator and the corresponding inverse one. The inverse operator is found analytically using the factorization technique. The numerical examples are presented. The static and the low‐frequency approximations as well as the transition to the limiting case when the cone degenerates into the disc are considered.
Enhancement of the correlation of the photons of thermal light
Authors: Khokhlov D.L.
Published: April 2021

The scheme of the arrangement of five 50/50 non‐polarizing beam splitters is considered. It may provide for the enhancement of the correlation of the photons of thermal light up to 75%. The correlation of the photons after the arrangement can be measured using ghost imaging, Bell test, Hong‐Ou‐Mandel interference.
Using QKD in MACsec for secure Ethernet networks
Authors: Cho Joo Yeon , Sergeev Andrew
Published: April 2021

Media access control security (MACsec) is an IEEE 802.1AE standard for secure communication on Ethernet links. MACsec ensures the confidentiality, integrity and origin authenticity of Ethernet frames. The secrecy of MACsec stems from a root key that is either configured as a pre‐shared key or derived from a mutual authentication protocol. However, both methods are not ideal because such a root key may be disclosed due to human errors or broken by quantum attacks. Here, the authors investigate the quantum key distribution (QKD) as an alternative source of trust for MACsec. QKD can be used as either a root key provider or a session key generator. The authors develop a new key exchange protocol based on QKD for Ethernet networks. Furthermore, it is verified by the experiment that QKD could be well integrated into MACsec without performance degradation.
Stockwell‐transform and random‐forest based double‐terminal fault diagnosis method for offshore wind farm transmission line
Authors: Wang Xiaodong , Gao Xing , Liu Yingming , Wang Yonghao
Published: April 2021

Due to the difficulty and time‐consumption in locating short‐distance transmission lines for deep‐sea offshore wind farm (DOWF)，this paper proposes a novel double‐terminal fault location method by using Stockwell‐transform (ST) and random‐forest (RF). After the fault type and branch are accurately determined, the accurate transmission line fault location is located. Firstly, Stockwell‐transform is employed to extract fault eigenvalues from the collected wind turbine (WT) current signals, which will reduce the sensitivity of eigenvalues to noise. And the Pearson correlation coefficient (PCC) is introduced to remove duplicate eigenvalues. Secondly, the reserved fault eigenvalues are taken as inputs to the different random‐forest to classify fault types and identify the fault branch, respectively. Finally, the double‐terminal fault location principle is established in fault negative sequence network (only ABCG uses positive sequence components). Newton‐Raphson method (NRM) is used to eliminate the influence of asynchrony data, which implies an accurate transmission line fault location for deep‐sea offshore wind farm. More than 4000 fault cases data obtained by Simulink simulation verify the feasibility and performance of the proposed method. The results show that the proposed location method has a high fault recognition rate and is immune to fault inception angle, resistance, location and noise.
Vector control strategy for motor‐generator pair drive inverters in parallel operation
Authors: Li Chenyang , Huang Yongzhang , Guan Fei , Fu Wenqi , Wu Qianyu
Published: April 2021

In view of the weak moment of inertia for power system with high penetration of renewable energy and limited transient voltage support of power electronics, a synchronous motor‐generator pair (MGP) is proposed as a possible solution for renewable energy integration to improve grid stability. In order to realize the large‐scale renewable energy integration, parallel inverters drive MGP is an ideal control scheme. Firstly, the working principle and mathematical model of MGP are introduced. Then based on a comparative analysis of control of parallel inverters and torque control of synchronous motors, the decoupling vector control method of MGP is proposed. Considering the linear relation between q‐axis current and the active power in the rotor flux‐oriented mode, the strategy of parallel drive for MGP with vector control is established. Different control loop for off‐grid and grid‐connected operation is designed, and effectiveness of this control strategy is verified by simulation. Finally, a 5 kW MGP experimental platform with dual inverters is built, the parameters are selected to test stability of the system, and power distribution and efficiency are studied. The results indicate that the parallel operation of MGP is stable under dynamic conditions, the strategy can realize the independent control of each inverter.
Theoretical investigation on an oscillating buoy WEC‐floating breakwater integrated system
Authors: Ning Dezhi , Guo Baoming , Wang Rongquan , Lin Lin
Published: April 2021

Based on the concept of cost sharing, an oscillating buoy wave energy converter‐ floating breakwater integrated system is proposed, in which the surge or pitch motion of the front‐pontoon equipped with Power Take‐off (PTO) system is allowed. In this study, the methods of eigenfunction matching and variables separation are applied to establish a theoretical model on hydrodynamic performance of the proposed integrated system. Under the condition of optimal PTO damping, the effects of the front‐pontoon width, draft and pontoon spacing on the capture width ratio, reflection coefficient and transmission coefficient are investigated, respectively. It is found that the integrated system with surging front‐pontoon performs better on the wave energy conversion than that with pitching one, especially in the high frequency region. The peaks of capture width ratio Cw are associated with the gap resonance and the increase of front‐pontoon motion induced by the ‘hydrodynamic constructive effect’.
IRCI‐free multiple subband MIMO SAR
Authors: AlShaya Mohammed , Yaghoobi Mehrdad , Mulgrew Bernard
Published: April 2021

An inter‐range cell interference (IRCI)‐free multiple subband multiple‐input multiple‐output (MIMO) synthetic aperture radar (SAR) algorithm is proposed to obtain a high‐resolution wide swath (HRWS) imaging. The received signals of the proposed configuration are formulated as multiple‐input single‐output (MISO) system identification problems using the principle of the displaced phase centre in a way that all the subband waveforms are processed simultaneously without the need to separate them at the receiver. This would maximise the bandwidth utilisation efficiency. The channel impulse response in the range dimension is identified using a frequency domain system identification‐‐based estimation algorithm, which is free from IRCI, instead of using conventional matched filters. A pulse repetition frequency lower than the Doppler bandwidth is used to obtain the HRWS imaging, and the resulted azimuth ambiguity is removed using a set of spatial filters applied on the received signals formulated as separate MISO system identification problems. Finally, both simulated and constructed raw data are used to validate the efficiency of the proposed algorithm.
Bifurcation analyses and potential landscapes of a cortex–basal ganglia–thalamus model
Authors: Yan Chenri , Liu Quansheng , Bi Yuanhong
Published: April 2021

The dynamics of cortical neuronal activity plays important roles in controlling body movement and is regulated by connection weights between neurons in a cortex–basal ganglia–thalamus (BGCT) loop. Beta‐band oscillation of cortical activity is closely associated with the movement disorder of Parkinson's disease, which is caused by an imbalance in the connection weights of direct and indirect pathways in the BGCT loop. In this study, the authors investigate how the dynamics of cortical activity are modulated by connection weights of direct and indirect pathways in the BGCT loop under low dopamine levels through bifurcation analyses and potential landscapes. The results reveal that cortical activity displays rich dynamics under different connection weights, including one, two, or three stable steady states, one or two stable limit cycles, and the coexistence of one stable limit cycle with one stable steady state or two stable ones. For a low dopamine level, cortical activity exhibits oscillation for larger connection weights of direct and indirect pathways. The stability of these stable dynamics is explored by the potential landscapes.
Weighing the Galactic disk using phase-space spirals
Authors: Widmark A. , Laporte C. , de Salas P. F.
Published: April 2021

We present a new method for inferring the gravitational potential of the Galactic disk, using the time-varying structure of a phase-space spiral in the (z, w)-plane (where z and w represent vertical position and vertical velocity). Our method of inference extracts information from the shape of the spiral and disregards the bulk density distribution that is usually used to perform dynamical mass measurements. In this manner, it is complementary to traditional methods that are based on the assumption of a steady state. Our method consists of fitting an analytical model for the phase-space spiral to data, where the spiral is seen as a perturbation of the stellar number density in the (z, w)-plane. We tested our method on one-dimensional simulations, which were initiated in a steady state and then perturbed by an external force similar to that of a passing satellite. We were able to retrieve the true gravitational potentials of the simulations with high accuracy. The gravitational potential at 400–500 parsec distances from the disk mid-plane was inferred with an error of only a few percent. This is the first paper of a series in which we plan to test and refine our method on more complex simulations, as well as apply our method to Gaia data.
Role of Transperineal Ultrasound (TPUS) in Children with Ambiguous Genitalia
Authors: Rangarajan Krithika , Jana Manisha , Wadgera Nagesh , Gupta Arun Kumar , Bajpai Minu , Kandasamy Devasenathipathy
Published: April 2021

Objectives Accurate delineation of anatomy in children with ambiguous genitalia early in life is important. This commonly involves conventional fluoroscopic genitogram (traumatic to the child) and magnetic resonance imaging (MRI) examination (involves sedation). In this study, our objectives were twofold: (1) to describe the findings on transperineal ultrasound (TPUS) in normal children and (2) to describe the findings on TPUS in children with ambiguous genitalia and correlate them with conventional genitogram.

Materials and Methods TPUS was prospectively performed in 10 children without genital ambiguity (5 girls and 5 boys). Subsequently, 15 consecutive children having disorders of sex differentiation (DSDs) with genital ambiguity underwent TPUS. The presence or absence of müllerian structures was documented. Of these patients, 14 also underwent conventional genitogram as a part of routine evaluation. The gold standard was established either by comparison with surgical findings (in patients who underwent surgery) or by comparison with a combination of findings on genitogram and transabdominal ultrasound in patients who did not undergo surgery.

Results In all normal children, lower urogenital tracts could be clearly delineated on TPUS. Out of the 15 children with ambiguous genitalia, TPUS could establish the presence/absence of müllerian structures in 14. This was concordant with findings on conventional genitogram/surgery. In one patient, müllerian structure was missed on TPUS but demonstrated on genitogram. In two children, TPUS showed the müllerian structure, which was not seen on genitogram. When both the controls and the cases were combined, TPUS had an accuracy of 95% and specificity of 100% in the detection of müllerian structures.

Conclusion TPUS is feasible and accurate in demonstration of lower urogenital tract anatomy in children with DSDs having ambiguous genitalia. It can be performed without sedation, and is suitable for use as a screening modality in children with ambiguous genitalia.
Cavanillesia platanifolia (cuipo).
Authors: Vélez-Gavilán Jeanine
Published: April 2021

A datasheet on Cavanillesia platanifolia covering, as an economically important tree, its taxonomy, importance, silviculture, distribution, biology and ecology, uses, products and pests.
Duranta erecta (golden dewdrop).
Authors: Arengo Esther
Published: April 2021

A datasheet on Duranta erecta covering, as an economically important tree, its taxonomy, importance, silviculture, distribution, biology and ecology, uses, products and pests.
Mimosa arenosa (elegant mimosa).
Authors: Rojas-Sandoval Julissa
Published: April 2021

A datasheet on Mimosa arenosa covering, as an economically important tree, its taxonomy, importance, silviculture, distribution, biology and ecology, uses, products and pests.
Corynocarpus laevigatus (karaka).
Authors: Rapson Gillian L
Published: April 2021

A datasheet on Corynocarpus laevigatus covering, as an economically important tree, its taxonomy, importance, silviculture, distribution, biology and ecology, uses, products and pests.
Investigation of solidification modes of high Manganese steels
Authors: Shen Yaozu , Liu Jianhua , Xu Hao
Published: April 2021

The solidification modes of high manganese steels were measured by experiments and comparatively calculated by different predicted methods. The DSC experiments indicate the same phase transformation sequence as the isothermal solidification experiments. It is demonstrated that the solidification modes of the Fe–0.028C–21.1Mn–5.72Al, Fe–0.28C–21.0Mn–5.80Al and Fe–0.64C–21.0Mn–5.80Al alloys are F mode, FA1 mode and FA2 mode. The change of heating/cooling rate influences the position and shape of the peaks in the DSC curves but has no effect on the solidification mode. By comparison with different calculation methods, the equilibrium model is shown to be a valuable tool to predict the solidification sequence of the high-Mn steel, from which the critical carbon range for the Fe–C–20Mn–5Al alloy is calculated to be 0.062∼0.427 wt-%.