bluuebunny/crossref_metadata_2025 huggingface dataset:
Revisions to the Australian tropical cyclone best track database
Authors: Courtney Joseph B. , Foley Gary R. , van Burgel Johannes L. , Trewin Blair , Burton Andrew D. , Callaghan Jeffrey , Davidson Noel E.
Published: September 2021

The Australian tropical cyclone (TC) best track database (BT) maintained by the Bureau of Meteorology has records since 1909 of varying quality and completeness. Since 2005 a series of efforts to improve the database have included: removing internal inconsistencies, adding fixes, and identifying errors using comparisons with other datasets; upgrading intensity information since 1973 including adding maximum winds (Vm) prior to 1984–85, rederiving Dvorak Current Intensity numbers from archived material and accounting for different wind–pressure relationships used; a partial reanalysis of satellite imagery including microwave imagery using the HURSAT dataset since 1987; and considering an objective intensity dataset. The BT homogeneity is reviewed in the context of improvements in satellite technology, observational coverage, scientific developments, BT procedures and the subjective variation between analysts across time and offices. The scale of these variances is greatest in the early stages prior to 1981 in the absence of geostationary satellite imagery until 1978, satellite calibration issues from 1978–80 and prior to the introduction of the enhanced infra-red Dvorak technique in 1981. The current era since 2003 is considered to be the most accurate, comprehensive and homogeneous corresponding to the expansion of the TC database to include the current suite of fields; the application of microwave and scatterometry imagery; greater standardisation of BT practices and slight changes in the application of the Dvorak technique. These improvements have generated a more consistent dataset that could be used for weather and climate research and other TC-related work.
Attribution of river water-quality trends to agricultural land use and climate variability in New Zealand
Authors: Snelder T. H. , Fraser C. , Larned S. T. , Monaghan R. , De Malmanche S. , Whitehead A. L.
Published: September 2021

Trends at 1051 river monitoring sites across New Zealand incrementing annually for time windows of 10 and 20 years over the 28-year period ending 2017 were assessed from regular observations of six water quality variables. Between-site variation in trend strength and direction was modelled as a function of an indicator based on the Southern Oscillation Index (SOI) and the mean of and changes to catchment: (1) stocking intensity associated with pastoral livestock; and (2) area associated with plantation forest. The SOI indicator made consistent contributions to the models for the 10-year windows, but the land use indicators did not, indicating that land use signals were generally swamped by the effects of climate variability at this timescale. Some land use indicators made consistent and certain contributions to the models for the 20-year time windows. Depending on the water quality variable, some land use indicators were associated with both water quality improvement and degradation. The relationships were generally consistent with plausible explanations including changes in land use, land use intensity and land management practices. Robust attribution of water quality changes to changes to specific agricultural land uses will enable the development of precise and effective policies to achieve water quality improvement.
Topological spin/structure couplings in layered chiral magnet Cr
1/3
TaS
2
: The discovery of spiral magnetic superstructure
Authors: Du Kai , Huang Fei-Ting , Kim Jaewook , Lim Seong Joon , Gamage Kasun , Yang Junjie , Mostovoy Maxim , Garlow Joseph , Han Myung-Geun , Zhu Yimei , Cheong Sang-Wook
Published: September 2021

Significance

Magnets in which chirality of crystal lattice gives rise to noncollinear spin orders are ideal playgrounds for studies of novel physical phenomena originating from the nontrivial topology of magnetic textures. Most studies have been focused on homochiral materials, while topological magnetic states of heterochiral magnets have rarely been explored. Here, we report a layered chiral helimagnet with highly tunable topological heterochiral states. Tuning the chiral domain density, we have observed a topological magnetic texture that has the form of a spiral unfolding in the radial direction. In weak in-plane fields, this spiral magnetic superstructure transforms into a nonspiral array of concentric rings. Our results open a horizon for studies of tunable topological magnetic textures.
Topological Dirac surface states in ternary compounds GeBi2Te4, SnBi2Te4 and Sn0.571Bi2.286Se4
*
Authors: Li Yunlong , Huang Chaozhi , Wang Guohua , Hu Jiayuan , Duan Shaofeng , Xu Chenhang , Lu Qi , Jing Qiang , Zhang Wentao , Qian Dong
Published: September 2021

Using high-resolution angle-resolved and time-resolved photoemission spectroscopy, we have studied the low-energy band structures in occupied and unoccupied states of three ternary compounds GeBi2Te4, SnBi2Te4 and Sn0.571Bi2.286Se4 near the Fermi level. In previously confirmed topological insulator GeBi2Te4 compounds, we confirmed the existence of the Dirac surface state and found that the bulk energy gap is much larger than that in the first-principles calculations. In SnBi2Te4 compounds, the Dirac surface state was observed, consistent with the first-principles calculations, indicating that it is a topological insulator. The experimental detected bulk gap is a little bit larger than that in calculations. In Sn0.571Bi2.286Se4 compounds, our measurements suggest that this nonstoichiometric compound is a topological insulator although the stoichiometric SnBi2Se4 compound was proposed to be topological trivial.
Research on Acoustic Emission Signal Denoising Based on Autoencoder
Authors: Zhou Jun , Yang mei , Yin Yue
Published: September 2021

Acoustic emission signal denoising is the premise of acoustic emission signal identification. The traditional filter and wavelet analysis have the problems of relying on prior information and poor adaptability in acoustic emission signal denoising. Therefore, a noise denoising model of acoustic emission signal based on noise reduction autoencoder is proposed. By unsupervised learning training, the noise reduction autoencoder has more stable invariant characteristics, so that the error between the reconstructed signal and the original signal converges to a minimum, thus achieving the purpose of denoising. The denoising experiments are carried out on the basis of processing 3000 corrosive acoustic emission signal samples. The experimental results show that the model has better denoising effect when the number of hidden layer neurons is 400. The proposed DAE model has better performance and robustness than Donoho wavelet threshold denoising method.
Automatic bridge crack detection device based on quadrotor UAV
Authors: Song Xiangbo
Published: September 2021

For the bridge located in deep valley, with high tower and long span structure, the traditional artificial crack detection can not meet the needs of today. In order to solve this problem, a new bridge crack detection equipment is designed based on quadrocopter unmanned aerial vehicle. According to this equipment, the developed system contains: reconnaissance unmanned aerial vehicle system, acquisition and transmission system, crack positioning system, and graphics operations system. Reconnaissance UAV system is improved based on quadrocoper UAV to meet the requirements of flight power, flight control, flight safety and signal stability. In the integrated transmission system, the designed UAV equipped with Hawkeye 5S motion camera, and 4G communication is used to complete the real-time image transmission; The crack location system uses BDS / GPS dual-mode combined single-point positioning module to realize the accurate location of bridge surface cracks; Graphics operations system, based on C++ and python language, designed a superpotent actual-time video streaming graphics operations method, and used median filter, image grayscale, histogram equalization, threshold segmentation and other methods to process the crack image. The designed bridge crack detection equipment can effectively solve the problem of low crack detection efficiency, and realize the intelligence and automation of crack identification. It has practical engineering application value and promotes the development of bridge crack detection technology.
A methodology for removing biofouling of the hull based on ultrasonic guided waves
Authors: Han Cong , Qu Zhigang
Published: September 2021

Marine biofouling is considered as the undesired growth and accumulation of biological organisms on the surface of materials submerged in seawater. Marine biofouling could increase the resistance and fuel consumption of ships. In this paper, a novel method for removing biofouling on ship hull based on cavitation effect and ultrasonic guided waves (UGWs) is proposed, which is eco-friendly and could remove biofouling online. The simulation model is established by finite element method to study the sound pressure distribution on the steel plat. The biofouling removal experiment is designed, which reveals that it is feasible to remove biofouling efficiently with UGWs.
Research on Wavelet Transform Technology and Image Processing Performance Based on Matlab
Authors: Xie Wendi , Zhao Yurong , Wang Yang
Published: September 2021

This paper first introduces the basic concept, principle and application of wavelet transform. Then, the performance characteristics of Haar wavelet, dbN wavelet and symN wavelet, two different threshold shrinkage algorithms Visushrink and Bayesshrink, and two common threshold functions are described in detail. Finally, Matlab software programming is used to realize the simulation of the original image based on different wavelet bases in the influence of Gaussian noise, Poisson noise and salt and pepper noise, different threshold acquisition algorithm, different threshold function, and compare their peak signal to noise ratio (PSNR) value to complete the Image processing performance analysis.
Analysis on the Applicability of High-resolution Remote Sensing Images for Highway Construction
Authors: Li Sitao , Wang Zhaoming , Shao Shegang , Fang Liuyang , Wang Dan , Liu Zhiqiang
Published: September 2021

This paper first analyzes and determines the scope and contents of highway engineering environmental supervision from the aspects of highway engineering composition, construction stage division, impact environmental factors and their characteristics, environmental protection requirements, etc., combines with the characteristics of high-resolution remote sensing images, proposes the construction process and supervision precision requirements for the highway engineering in which environment protection control can be carried out by using satellite remote-sensing images and UAV (unmanned aerial vehicle) remote-sensing images. Through the environmental investigation experiment of high-resolution satellite remote sensing images commonly used in typical road sections, the satellite remote-sensing images meeting the highway engineering environmental supervision requirements are selected through comparative analysis from the perspectives of spatial resolution, revisit cycle, spectral waveband characteristics, environmental supervision precision requirements and image purchase cost rationality.
Infrared image detection of insulators based on Centernet model
Authors: Li Xiaogang , Ku Yongheng , Cui Yaohui , Tian Zhansheng , Sun Wukui
Published: September 2021

The infrared image can reflect the temperature information inside the insulator, which is helpful to realize the follow-up fault diagnosis. However, insulators are usually located in remote areas, and the complex background will affect the accuracy of the algorithm model. Therefore, we established an infrared dataset of insulators. We introduced the Centernet network to infrared detection of insulators, and conducted training and testing on the data set. The results show that our proposed model has a significant improvement of 4% to 5% compared with several mainstream detection models. This can meet the needs of insulator fault detection.
Feature Relation based Graph Convolution for 3D Point Cloud Analysis
Authors: Wang Yang , Xiao Shunping
Published: September 2021

3D point cloud recognition is still a challenge task since the shape implied in irregular points is difficult to capture. Standard convolution is inherently limited for these tasks due to its isotropy about features. In this paper, a novel graph convolutional network is introduced for point cloud classification and segmentation task. The proposed convolution adds an additional layer on the basis of relation-shape convolutional neural network, which can obtain more information and make the representation of point cloud more robust. At the same time, a feature relation method is proposed instead of the coordinate relation used in relation-shape convolutional neural network. The experimental results on challenging classification and segmentation datasets shows that the proposed method can learn discriminating features for recognition and semantic segmentation.
A CA-GCI Based Consensus Fusion Approach for Distributed Sensors with Different Fields of View
Authors: Ma Feng , Lu Huanzhang , Zhang Luping , Shen Xinglin
Published: September 2021

This paper is concerned with the distributed fusion problem of sensors with different fields of view (FoVs). The recently derived CA-GCI method provides solution to the fusion problem of sensors with different Fovs. However, it does not mention about the way that how to share information among more than 3 sensors. Consensus method is suitable for information sharing in distributed network, but it always based on the assumption that the sensors all have the same FoV. Thus the existing method cannot be applied directly. Based on the combination of the consensus method and the newly derived CA-GCI method, this paper proposes a consensus fusion approach for the sensors with different FoVs in a distributed network. Furthermore, this paper derives the minimum iteration steps required to ensure the network consensus is |풩| – 1, where |풩| represents the number of nodes in the networks. Simulation results demonstrate the effectiveness of the proposed approach.
Yolov5-based channel pruning is used for real-time detection of construction workers’ safety helmets and anti-slip shoes in informationalized construction sites
Authors: Shen Xuehui , Li Baojiang , Wang Xichao , Chen Guochu , Lu Junquan , Lin Bin , Zheng Jianming
Published: September 2021

In view of the problems that the application of target detection model in edge equipment occupied too much memory and the operation was slow, a lightweight model based on YoloV5 is proposed to detect the wearing condition of safety helmet and anti-slip shoes. The channel prunning is carried out on the basis of the original model, and the channel with low weight is deleted to reduce the parameters of the model and improve the detection speed. The experimental results show that the recognition accuracy of the network after channel pruning is 93.2%, which is basically the same as that before channel pruning. Meanwhile, the number of parameters is reduced by 63.9%, and the detection speed is 123.45fps. It provides technical support for the development of embedded detection equipment.
Kernel Function Based Mean Matrix Estimation and Its Application to Radar Target Detection
Authors: Cao Xingwei , Cheng Yongqiang , Wu Hao , Yang Zheng , Jing He , Wang Hongqiang
Published: September 2021

The estimation of mean matrix for a set of Hermitian positive definite (HPD) matrices is of fundamental importance to radar target CFAR detection, which can be considered based on a geometric framework. However, false targets can cause large errors in the estimation of the mean clutter matrix. In this paper, we consider the nonlinearity of kernel function and the mean matrix estimation method based on kernel function is designed. In order to reduce the influence of false targets in the estimation of mean matrix, we apply the kernel function to the nonlinear adjustment of the geometric distance. Then, the kernel function based constant false alarm rate (CFAR) detector is designed. Finally, numerical experiments based on simulated clutter data and measured sea clutter data are performed. We come to a conclusion that the method of matrix mean value estimation based on kernel function can restrain the false target in the reference cells effectively.
RETRACTED: Design strategy research on active interactive system for household appliances based on artificial intelligence
Authors: Dai Yiheng
Published: September 2021

In the era of artificial intelligence, active interaction has become an emerging design and development trend, bringing many opportunities and possibilities to the system innovation and experience upgrade of intelligent household appliances. This research firstly analyzed and sorted out documents in related fields such as human-agent active interaction, interaction design in intelligent household appliances and user-centered interaction design, combined with active interaction cases at home and abroad as well as other smart life fields. According to the system process and operating mode, the interaction stages of “perception-display-control-understanding” were divided. And the connotation, performance, valve and opportunity points of design intervention and opportunity points of active interaction in each stage were further analyzed. Finally, this research constructed a smart refrigerator active interaction strategy from the user’s perspective, from the four levels of user needs that are knowable, visible, controllable, and predictable, which could realize the trust relationship of man-machine collaboration and an orderly and active interaction system.
Research on character Action Control Method Based on Multi Phase-Functioned Neural Network and State Machine
Authors: Wang Zhe , Lei Xin , Li Jing
Published: September 2021

Aiming at the situation that the phase-functioned neural network can’t produce natural action or interact with the world poorly in some specific scenes, this paper proposes to divide different states according to the phase information of characters, and train several independent networks with different weights to predict the next frame state of characters. The experimental results show that, while the complexity of the model is reduced, the action generated by this method is more natural and fluent, which has good application value.
Wavelet Analysis-Based Simulation Study Of Small-Current Ground Fault System Routing
Authors: Cai Rui , Zhao Yuchen , Wang Qian , Liu Haorui
Published: September 2021

Small-current grounding is commonly used in medium-voltage distribution networks in China, including ungrounded neutral points, grounding via arc extinguishing coil and resistance grounding. In this paper, the power system modeling simulation of the grounding system via arc extinguishing coil and the transient characteristics when single-phase grounding occurs are studied. The principles and methods for selecting the wavelet basis function in transient component line selection are given. Wavelet analysis of the line current is carried out. It is verified by simulation that the wavelet analysis method in the small current ground fault selection can compare the difference of different lines.
A Joint Positioning Algorithm of TDOA and TOF Based on Ultra-wideband
Authors: Liu Chang , Bai Fengshan , Wu Chunsheng
Published: September 2021

Ultra-wideband (UWB) positioning is a mainstream positioning technology in recent years, and the main positioning algorithms used in UWB based positioning systems are TDOA and TOA. Aiming at the problem of unstable positioning accuracy of TDOA algorithm in traditional UWB positioning system, this paper proposes a joint positioning algorithm of TDOA and TOF based on UWB. The algorithm needs only one time of TOF ranging and combined with the TDOA algorithm, the distances between all base stations and a single tag in the positioning area can be obtained. Compared with the single TDOA algorithm, the algorithm has stable measurement accuracy and can achieve the same positioning accuracy whether the tag is inside or outside the triangle formed by the base station. The simulation results show that the average positioning error of the joint positioning algorithm of TDOA and TOF is less than 10 cm. The algorithm is effective and reliable, and meets the positioning requirements.
Research on the Application of the IPv6 Network Protocol
Authors: Xu Guan
Published: September 2021

With the rapid development of computer network technology, network applications have been very popular in people’s lives and work. The computer network technology includes many aspects, mainly including the communication technology and the computer technology. Although the network application has been relatively impeccable now, although people can contact anyone anywhere at any time through the network, it is still not perfect enough, and there are still great bottlenecks in the network technology. The network protocol plays an important role in the network technology. At present, the IPv4 protocol is more widely used in the Internet than others. Although the IPv6 protocol also has some applications, it is used less widely. For this reason, it is very necessary to research the application of the IPv6 network protocol. This paper mainly studies the main technical performance of the IPv6 network protocol, and introduces its application with specific examples. This paper analyzes the tunnel technology, network address translator technology and dual-stack technology of IPv6. And this paper introduces the basic configuration of the IPv6 address and of the static routing according to a specific type of router.
A sorting method of node based on Eigenvector and Closeness centrality
Authors: Zhu Jingcheng , Wang Lunwen , Wu Tao
Published: September 2021

Accurately identifying influential nodes in a complex network is of great significance to information dissemination. At present, researchers have put forward methods such as Degree centrality, Betweenness centrality, Closeness centrality and Eigenvector centrality, but these methods have certain limitations. There are many factors that affect the results of the node sorting, such as the number of neighbor nodes, node location information, etc. If multiple factors are combined, the proposed method can show more characteristics. First, closeness centrality of neighboring nodes is accumulated to reflect the influence of location information, and then integrated with Eigenvector centrality, the ECCN centrality is proposed to identify the network Influential nodes, this method integrates the path length and the number and quality of neighbor nodes. The SIR propagation model is used to simulate the influence of nodes on multiple real networks, and all centralities are compared with the results of the SIR model. Experimental analysis shows that the proposed method can more accurately identify influential nodes than other methods.
Hardware Circuit Design and Implementation of Digital IF Receiver
Authors: Wang Rui
Published: September 2021

At present, most of the digital IF receivers have bottlenecks in the acquisition and analysis of ultra-high frequency bandwidth signals. At the same time, they have the disadvantages of low resolution and low input bandwidth, which cannot meet the needs of ultra-high frequency signal measurement. In this paper, a high-resolution, high-bandwidth digital IF receiver hardware system is designed based on ADC + FPGA architecture. The front-end analog signal is discretized by two high-speed ADC chips and sent to FPGA to decode the sampling data, and the FFT IP core is called to calculate the power spectrum of the signal. The spectrum data are sent to the host computer through RS485 module and Ethernet chip to display the spectrum image of the original signal in real time, so as to analyze the temperature profile of the target atmosphere.
Application of Artificial Intelligence Voice Technology in Radio and Television Media
Authors: Hu Meihui , Xiang Zhiwei , Li Kai
Published: September 2021

With the application of artificial intelligence in various fields, more and more people see the contribution of artificial intelligence technology to the development of the industry, and put more energy in the research of artificial intelligence language, and strive to provide better technical support for the development of more industries. In radio and television media, artificial intelligence voice technology can play a very important value, it can effectively improve the efficiency and quality of traditional audio work, optimize the singing system, broadcasting system and retrieval system, so as to provide better service quality for the masses. This paper discusses the application and development of artificial intelligence technology based on the fusion media environment, analyzes the application and development of artificial intelligence technology in promoting production efficiency, intelligent robot writing, intelligent face recognition, intelligent speech semantic recognition, intelligent OCR recognition, automatic broadcast and other aspects. These AI applications provide efficient and secure support for services, greatly improving service efficiency.
Physics-informed neural network for solving coupled Korteweg-de Vries equations
Authors: Xiao Chaohao , Zhu Xiaoqian , Yin Fukang , Cao Xiaoqun
Published: September 2021

The studies of coupled partial differential equations are focus of engineering and applied mathematics. Although traditional numerical methods have been widely used, researchers are still looking for new methods to solve coupled partial differential equations. In this paper, physical information neural network (PINN) is introduced to solve one-dimensional coupled Korteweg-de Vries (cKdV) equations. Compared with the traditional neural network, the innovation of PINN is to embed the physical constraints of the equations into the network loss function. Moreover, within the acceptable relative error range, the solution can take a longer single time step than the presently available. The results revealed that PINN can solve the cKdV equations with reasonable errors only by training a small amount of data.
An improved sine cosine algorithm for assignment problem
Authors: Ye Dan , Liu Yu , Zhang Shan , Yu Fei , Meng Hongyu , Li Bo , Shang Erzhen
Published: September 2021

The assignment problem is a NP-hard combinatorial optimization problem, where assignees are being assigned to perform tasks. This paper presents an improved sine and cosine algorithm (ISCA) to solve this problem. Nonlinear decreasing inertia weight, chaotic map and greedy strategy are added to the original sine cosine algorithm (SCA) to enhance the ability of focusing on optimal and avoiding local optima. Simulation results show that the proposed algorithm can get more competitive solutions when compared with differential evolution (DE), particle swarm optimization (PSO) and original SCA.
K-means clustering algorithm based on bee colony strategy
Authors: Zhang Zhenrong , Lan Jiayi , Zhang Zhenrong
Published: September 2021

The traditional swarm intelligence optimization K-means algorithm has some problems, such as poor global search ability and blind area selection of initial center points, which leads to the reduction of clustering availability. In order to avoid the above limitations, this paper proposed IABC k-means algorithm. Firstly, the employed bees stage in the traditional artificial bee colony ABC algorithm uses the current colony optimal solution information to guide its optimization search. Secondly, this paper wanted to solve the search ability of the employed bees and expand the information sharing range between various individuals, a random guidance mechanism is proposed in the onlookers bees stage. Finally, chaotic sequence is introduced in the scout bee stage to accelerate the convergence speed of the algorithm. IABC algorithm is proposed and applied to K-means clustering algorithm to improve the poor global search ability of K-means algorithm and the random selection of initial center points. Experiments show that the IABC and IABCK-means proposed in this paper effectively improves the clustering availability.
Evaluation method of degradation index based on AdaBoost regression
Authors: Li Pan , Shen Zhongjie , Zhang Xingwu
Published: September 2021

In the field of bearing monitoring, it has always been a difficult problem in the industry to judge the performance of the degradation index. A degradation index evaluation method is proposed based on AdaBoost regression. In this method, AdaBoost regression method is used for the bearing degradation index correlation evaluation criteria. The comprehensive weighted evaluation of degradation characteristics was obtained by the combination with robustness and monotonicity. Features were optimized according to the evaluation score. The analysis of accelerated life experimental data shows that the degradation feature evaluation method based on AdaBoost regression can effectively select the degradation features with good characterization.
Practical Thinking on Neural Network Phishing Website Detection Research Based on Decision Tree and Optimal Feature Selection
Authors: Zhang Qi
Published: September 2021

This article analyzes the detection methods of neural network phishing websites. The research content of this paper includes naive Bayes method, decision tree method, support vector machine method, neural network technology. The author combines the key points of phishing website detection based on decision tree and optimal feature selection to study such as URL feature and HTML feature analysis, website application feature analysis, K-Medoids cluster analysis, feature set screening. The author uses simulation experiments to complete the website performance check. The purpose of this article is to optimize the performance of phishing website detection and improve the security of the website’s operating environment.
Electroencephalogram signal characterization of tinnitus patients based on sample entropy algorithm and wavelet transform
Authors: Mai Jianbiao , Wang Xinzui , Li Zhaobo , Jia Haiyin , Fu Hui
Published: September 2021

Tinnitus is a disembodied, abnormal sound hallucination in the ear or skull, such as buzzing or hissing, in the absence of an external sound source. Tinnitus is a subjective sensation with no objective observable signs, and its causes are extremely complex. This paper explores the differences in sample entropy of EEG signals between tinnitus patients and normal subjects at different electrodes, using the non-parametric test Kruskal-Wallis test to find areas where there are significant differences between the different lateral tinnitus groups and the control group. Thirty tinnitus patients and 10 healthy controls were used to participate in the scalp EEG signal acquisition. The wavelet transform was first chosen to obtain the activity of each frequency band of the EEG, and then the electrophysiological differences between the two experimental groups were investigated by comparing the sample entropy of the EEG of the tinnitus patients with that of the healthy controls. The results reflect significant differences (p<0.05) in tinnitus patients at FT7, T7, C5, C6, TP7 and CP5 electrodes, mainly in the Delta band. These results compare the abnormalities of sample entropy in the resting state of patients with tinnitus on different sides of the ear with those of controls from an electroneuro-physiological perspective, and are expected to be used as a potential characteristic indicator to distinguish normal people from tinnitus patients for the auxiliary diagnosis of tinnitus and to provide physicians with an aid in diagnosing depressed patients.
Simulation of Different Third Harmonic Injected PWM Strategies for 5-Level Diode-Clamped Multilevel Inverter
Authors: Malladi Lakshmi Swarupa , Chapala Shravani , Veluri Sarada
Published: September 2021

Multilevel Inverters (MLI) have widespread use in industrial drive applications, HVDC transmission with Uninterruptable Power Supply systems. They also have applications in the stand-alone and the grid connected renewable energy systems. Multi-level Inverters also find optimum application in Stand-alone and Grid-connected Systems. Majority of losses i.e. switching losses are considered with constrained device rating, the traditional two-level inverters face limits especially when operating at high frequency. The solution which is optimum and appropriate by many power quality researchers is the use of MLIs. Among various MLI topologies the Diode-Clamped Multilevel Inverter (DCMLI) give a promising solution as they can give better voltage performance for different switching frequencies and an improved total harmonic distortion (THD) profile without the use of filter. To meet the voltage and frequency requirement various modulation techniques are applied to MLIs. The additional switching states created by these topologies provide greater flexibility to control more circuit breakers. This paper presents the comparison of a 5-level DCMLIs with several modulation schemes. The simulation results are discussed in detail and the comparison table is presented in terms of Output voltage and THD.
High Performance Wallace Tree Multiplier Using Majority Gate Based Adders
Authors: Mummudi Murasu M , Sujith Sanjana , Anita Angeline A , Sasi Priya P. , Kanchana Bhaaskaran V S
Published: September 2021

An area efficient high performance Wallace Tree Multiplier using Majority Gate based Adders is presented in this paper. The proposed Wallace tree multiplier is designed using 9T majority function based full adder. Design is implemented in Cadence Virtuoso® using a 180nm technology library. The design and analysis of the proposed design offers reduced delay of 33.12% while compared to the conventional wallace tree multiplier design using 16T full adder. The design also offers reduced transistor count of 208 which is minimal compared to that of the conventional design.
Power-efficient Strategies for Sensing in Autonomous Mobile Robots, a critical requirement of I4.0 standard
Authors: Bali Jyoti , Arpitha H , Anushree N , Giriyapur Arunkumar
Published: September 2021

In a production environment, there are several challenges in meeting the Industry 4.0 (I4.0) standard requirements. Energy efficiency is an essential area of focus. In the production setup, the critical and real-time control systems need to be very efficient while implementing functions, namely, accurate sensing, fast processing and precise actuation. Automated Guided vehicles (AGVs) and Automated Guided Vehicles are an integral part of modern and intelligent manufacturing systems. Power consumption in such systems is directly proportional to the performance level achieved. However, there is a need to evolve strategies to reduce power consumption and attain optimal performance. Field Programmable Gate Array(FPGA) based controller solutions can provide competent performance at optimized power consumption. The proposed work discusses the requirements of I4.0 concerning energy efficiency infrastructures for the intelligent manufacturing setup. The need to develop efficient subsystems for sensing, decision-making and actuation based on FPGA is stressed. Thus the focus is on the FPGA based power-efficient models used for sensor fusion technique in Autonomous Mobile Robots. The fundamentals of sensor fusion technique and the need to fuse sensor data for improved decision making and actuation are emphasized.
Mathematical model and analysis of PV Converter - Inverter System
Authors: Pushpa K R , Geetha R S
Published: September 2021

Photo Voltaic (PV) system is commonly preferred renewable energy source for most of the residential, commercial as well as for utility applications. Photo Voltaic systems are extensively used for standalone and grid tied applications. To utilize the PV generated power to the best possible extent, control and conversion of power in PV systems plays an important role. In this paper, mathematical model of the complete PV system that comprises of solar panel, DC-DC Boost converter and a H bridge inverter is developed. The developed model is validated for constant load condition at varying irradiation levels and at fixed duty cycle. The performance analysis of the PV system in terms of efficiency is carried out for 200W resistive load.
RETRACTED: A Novel approach in Hybrid Median Filtering for Denoising Medical images
Authors: Seetharaman R. , Tharun M. , Anandan K.
Published: September 2021

Image denoising is a key pre-processing step in medical image analysis. At current, deep learning-based models have shown a great promise, which outperformed many conventional methods over the past three decades. Speckle noise removal is a major issue in preserving all the delicate details and the edges in ultrasound image processing as it degrades the visual evaluation of ultrasound images. The multiplicative behavior of speckle-noise is converted into additive by using log transform as the additive noise removal is easy as compared to multiplicative noise. An innovative approach for denoising highly distorted images affected by speckle noise is proposed. This paper presents a result of significant work in image denoising and exploring several thresholding methods of denoising images such as SureShrink, VisuShrink and BayesShrink. The results of different approaches of wavelet-based image denoising methods are tabulated to find the best method. The main aim is to show the result of wavelet coefficients on a new basis, so that the noise can be minimized or removed from the data.
Fruit Classification Comparison Based on CNN and YOLO
Authors: Raj Riyanshu , Nagaraj S S , Ritesh Saurav , Thushar T A , Aparanji V M
Published: September 2021

Today, agriculture has become more of science than art. The demand for fresh produce is growing, while availability of field workers is diminishing. Also, Farmers keep investing in land, irrigation, and labor, and much of the fruit is left hanging on the trees because there are not enough people to pick it when it is ripe. Automation in the agricultural field using modern information and Communication Technology can increase the efficiency, reliability, and precision; and reduces the need of human intervention. Thus, to meet solution to this problem is by reducing the costs for farmers and increasing the yield of crops which can be achieved by classifying of fruit by using modern technique in the field of Deep Neural Networking that involves CNN, YOLO etc. which provides a helping hand to farmers to optimize their farming patterns and boost yields by allowing them to grow taller fruit trees that can be harvested by classifying based on the condition of fruit and saving significant labour cost. In conclusion, the proposed methods are highly acceptable and recognizing the necessity, and unresolved classifications.
Power Allocation Scheme for Non-Orthogonal Multiple Access System
Authors: Seetharaman R. , Tharun M. , Gayathri S. , Anandan K.
Published: September 2021

Since there exists a principal power-defer tradeoff in the NOMA framework, power proficiency additionally becomes basic for Ultra-Reliable Low Latency Communication (URLLC), particularly where countless gadgets are battery-fueled. Joining these contemplations, we reproduce Dynamic Power Allocation (DPA) for power area non-symmetrical different access (PD-NOMA) with client versatility. The exhibition of the DPA is contrasted and Static Power Allocation under client versatility. The characterization of clients in (NOMA) depends on power, while it depends on schedule, recurrence and code in Orthogonal Multiple Access (OMA). Since there exists an essential force defer tradeoff in the NOMA framework, power effectiveness likewise becomes basic for Ultra-Reliable Low Latency Communication (URLLC), particularly where countless gadgets are battery-controlled. Consolidating these contemplations, we reenact Dynamic Power Allocation (DPA) for power area non-symmetrical numerous entrance (PD-NOMA) with client portability. Force effectiveness becomes basic particularly for little and cumbersome battery-controlled IoT gadgets. Further, adaptability is additionally critical to speak with different machine-type gadgets just as human clients while meeting an assortment of nature of administration (QoS) prerequisites. The presentation of the DPA is contrasted and Static Power Allocation under client portability.
Design and preliminary test of a 105/140 GHz dual-frequency MW-level gyrotron
Authors: HU Linlin , SUN Dimin , HUANG Qili , ZHUO Tingting , MA Guowu , JIANG Yi , GONG Shenggang , ZENG Zaojin , GUO Zixing , DU Chaohai , LI Fanhong , CHEN Hongbin , MENG Fanbao , MA Hongge
Published: September 2021

A dual-frequency (105/140 GHz) MW-level continuous-wave gyrotron was developed for fusion application at Institute of Applied Electronics, China Academy of Engineering Physics. This gyrotron employs a cylindrical cavity working in the TE18,7 mode at 105 GHz and the TE24,9 mode at 140 GHz. A triode magnetron injection gun and a built-in quasi-optical mode converter were designed to operate at these two frequencies. For the proof-test phase, the gyrotron was equipped with a single-disk boron nitride window to achieve radio frequency output with a power of ∼500 kW for a short-pulse duration. In the preliminary short-pulse proof-test in the first quarter of 2021, the dual-frequency gyrotron achieved output powers of 300 kW at 105 GHz and 540 kW at 140 GHz, respectively, under 5 Hz 1 ms continuous pulse-burst operations. Power upgrade and pulse-width extension were hampered by the limitation of the high-voltage power supply and output window. This gyrotron design was preliminarily validated.
Do home adaptation interventions help to reduce emergency fall admissions? A national longitudinal data-linkage study of 657,536 older adults living in Wales (UK) between 2010 and 2017
Authors: Hollinghurst Joe , Daniels Helen , Fry Richard , Akbari Ashley , Rodgers Sarah , Watkins Alan , Hillcoat-Nallétamby Sarah , Williams Neil , Nikolova Silviya , Meads David , Clegg Andy
Published: September 2021

Background

falls are common in older people, but evidence for the effectiveness of preventative home adaptations is limited.

Aim

determine whether a national home adaptation service, Care&Repair Cymru (C&RC), identified individuals at risk of falls occurring at home and reduced the likelihood of falls.

Study Design

retrospective longitudinal controlled non-randomised intervention cohort study.

Setting

our cohort consisted of 657,536 individuals aged 60+ living in Wales (UK) between 1 January 2010 and 31 December 2017. About 123,729 individuals received a home adaptation service.

Methods

we created a dataset with up to 41 quarterly observations per person. For each quarter, we observed if a fall occurred at home that resulted in either an emergency department or an emergency hospital admission. We analysed the data using multilevel logistic regression.

Results

compared to the control group, C&RC clients had higher odds of falling, with an odds ratio (OR [95% confidence interval]) of 1.93 [1.87, 2.00]. Falls odds was higher for females (1.44 [1.42, 1.46]), older age (1.07 [1.07, 1.07]), increased frailty (mild 1.57 [1.55, 1.60], moderate 2.31 [2.26, 2.35], severe 3.05 [2.96, 3.13]), and deprivation (most deprived compared to least: 1.16 [1.13, 1.19]). Client fall odds decreased post-intervention; OR 0.97 [0.96, 0.97] per quarter. Regional variation existed for falls (5.8%), with most variation at the individual level (31.3%).

Conclusions

C&RC identified people more likely to have an emergency fall admission occurring at home, and their service reduced the odds of falling post-intervention. Service provisioning should meet the needs of an individual and need varies by personal and regional circumstance.
Making progress: but a way to go—the age and ageing care-home collection
Authors: Gordon Adam L , Bennett Chloe , Goodman Claire , Achterberg Wilco P
Published: September 2021

Care homes enable people with advanced physical and cognitive impairment to live well with 24-h support from staff. They are a feature of care systems in most countries. They have proved pivotal to the coronavirus disease 2019 (COVID-19) response.

We searched Age and Ageing for care-home articles published since 2015. From these we collated 42 into the Age and Ageing care-home collection. This collection draws together important papers that show how Age and Ageing is helping to shape and grow care-home research.

The  collection outlines the technical issues that researchers face by grouping together important feasibility trials conducted in the sector. It looks at the challenges of measuring quality of life and working with routine data in care homes. It brings together observational studies considering loneliness, functional dependency, stroke outcomes, prescribing and acute deterioration. Health services research in care homes is represented by two studies that demonstrate realist evaluation as a way to make sense of service innovations.

Papers are included that consider: non-pharmacological strategies for residents with dementia, end-of-life care, sexuality and intimacy and the care-home workforce. Given the importance of the COVID-19 pandemic in care homes, all of the care home COVID-19 papers published in Age and Ageing to date are included.

Finally, a group of papers that present innovative approaches to research in care homes, each of which give voice to residents and/or staff, are collated and presented as a way of moving towards a more resident and care home centred research agenda.
An Evaluation of Cardiac Health in the Spontaneously Hypertensive Rat Colony: Implications of Evolutionary Driven Increases in Concentric Hypertrophy
Authors: Holjak Emma J B , Savinova Iryna , Nelson Victoria L , Ogilvie Leslie M , Ng Anabelle M , Edgett Brittany A , Platt Mathew J , Brunt Keith R , Ask Kjetil , Simpson Jeremy A
Published: September 2021

Background

The Spontaneously Hypertensive Rat (SHR) Colony was established in 1963 and is the most commonly used rodent model for studying heart failure (HF). Ideally, animal models should recapitulate the clinical disease as closely as possible. Any drift in a genetic model may create a new model that no longer adequately represents the human pathology. Further, instability overtime may lead to conflicting data between laboratories and/or irreproducible results. While systolic blood pressure (SBP) is closely monitored during inbreeding, the sequelae of HF (e.g., cardiac hypertrophy) are not. Thus, the object of this review was to investigate whether the hypertension-induced sequelae of HF in the SHR have remained stable after decades of inbreeding.

Methods

A systematic review was performed to evaluate indices of cardiovascular health in the SHR over the past 60 years. For post hoc statistical analyses, studies were separated into 2 cohorts: Initial (mid to late 1900s) and Current (early 2000s to present) Colony SHRs. Wistar-Kyoto rats (WKY) were used as controls.

Results

SBP was consistent between Initial and Current Colony SHRs. However, Current Colony SHRs presented with increased concentric hypertrophy (i.e., elevated heart weight and posterior wall thickness) while cardiac output remained consistent. Since these changes were not observed in the WKY controls, cardiac-derived changes in Current Colony SHRs were unlikely due to differences in environmental conditions.

Conclusions

Together, these data firmly establish a cardiac-based phenotypic shift in the SHR model and provide important insights into the beneficial function of concentric hypertrophy in hypertension-induced HF.