bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessment of Multisource Remote Sensing Image Fusion by several dissimilarity Methods
Authors: Li Feiyan 
Published: September 2021

Abstract
Recently, advancements in remote sensing technology have made it easier to obtain various temporal and spatial resolution satellite data. Remote sensing techniques can be a useful tool to detect vegetation and soil conditions, monitor crop diseases and natural disaster prevention, etc. Although the same scene taken by different sensors belong to the same ground object, the information that they offered are redundant, complementary and collaborative due to the spatial, spectral and temporal resolution are different. The method of image fusion can integrate an image with rich details and valuable information from multi-source remote sensing images, which aim to obtain more comprehensive and precise observations of the ground object. By using aspects from multi-source image fusion, this review presents the current status and future trends in remote sensing image fusion. First, different image properties and their applications are presented for remote sensing datasets at home and abroad. Second, a general summary and inductive analysis of the challenging difficulty of different types of multisource image fusion methods is conducted. Third, experiments are tested on eight different methodological approaches, and experimental results demonstrate that GSA method is the best alternative in terms of obtaining high spatial resolution and retaining the spectral information.
A Fast Instantaneous Frequency Estimation for Underwater Acoustic Target Feature Extraction
Authors: Ma Yanxin , Zhang Yifan , Zhu Jiahua , Xu Ke , Cai Yujin 
Published: September 2021

Abstract
Traditional auditory features merely present the amplitude characteristics of target signals in frequency domain. Such features are susceptible to environmental noise, resulting in significant degradation of recognition stability. Inspired by instantaneous information applied in speech signal processing field, this paper proposed a feature extraction method using sub-based instantaneous frequency. A fast instantaneous frequency information extraction algorithm is proposed with the normalized Gammatone filterbanks. Experiments confirm that the proposed feature extraction method can effectively maintain the recognition accuracy under low SNR conditions while reduce the computation cost.
Two new methods for facial expression recognition using Convolutional Neural Networks
Authors: Wang Jinfeng , Wang Xuegang 
Published: September 2021

Abstract
In this research, we propose two novel methods for facial expression recognition to improve the accuracy of recognition. The first of our novel approach is to add the Batch Normalization (BN) layer to the CNN model, and the second of the novel approach is to preprocess the image before image training, such as rotating image, cropping the image and adding Gaussian noise in the picture, especially it is beneficial for unbalanced classifications. Our model consists of 3 CNN layers, 3 BN layers, three average-pooling layers, and three fully-connected layers; our model has a satisfying performance on the prediction category after adopting the two methods mentioned above. Our CNN model is trained and tested with Kaggle facial expression recognition challenge databases. The implemented system can automatically recognize seven expressions in real-time: anger, disgust, fear, happiness, neutral, sadness, and sur-prise. The experimental results demonstrate the effectiveness of our proposed approach.
Research and application of substation cable trench inspection robot communication system
Authors: Sun Tao , Ye Liangpeng , Xie Jia , Zhang Jiaqing , Fan Minghao , Fan Hao 
Published: September 2021

Abstract
The substation cable trench inspection robot is mainly used to inspect the high-voltage cables in the underground cable trench of the substation, so as to find various hidden dangers in time. However, due to serious electromagnetic interference in the cable trench and no communication network underground, the environment is more complicated. Aiming at the complex environment in the cable trench, this paper designs a WLAN-based cable trench inspection robot communication program, combining WLAN with directional antennas, and relaying through relay routing, so that the communication between the robot and the PC is divided into pipeline communication and ground communication Two parts. At the same time, the communication hardware, software, and protocol are designed based on the B/S architecture, which effectively improves the communication distance. Through field verification, the packet loss rate of the communication system proposed in the thesis is less than 0.5‰, the communication is stable, and the communication task of the robot in the cable trench of the substation can be completed well.
Speech error recognition based on broad learning
Authors: Yan Yicheng , Wang Liwen , Guo Wei 
Published: September 2021

Abstract
In recent years, with the increasing popularity of Chinese in the world, more foreigners have begun to learn Chinese. In this regard, this paper studies the phonetic errors in a single sentence, and designs a phonetic error classification model based on the broad learning network. First, the Laplace wavelet convolutional layer is designed as the first layer of the traditional CNN, and the Morlet-CNN model is used to extract the speech signal. Then, the extracted speech signal is input into broad learning network for training. Finally, the speech with different error types is tested. By comparing with the experimental results of a single speech amplitude feature and MFCC feature, the classification accuracy of the proposed model has been significantly improved, which verifies the effectiveness of the model.
Energy-Saving Technology in Finite Element Calculation
Authors: Wang Feng , Han Qianwen , Ma Yurong , Jiang Yaqiong , Li Dan , Zhou Wei 
Published: September 2021

Abstract
In the finite element calculation of three-dimensional impact mechanics, there are usually such problems as large memory consumption and more machine consumption, which leads to a great waste of power resources. Based on the analysis of the calculation process of three-dimensional penetration sliding surface, a method of improving the calculation efficiency is proposed, which is the cell search method, based on the comparison calculation and without affecting the calculation accuracy. This method only judges the main elements in the cell involved by the slave nodes in the penetration channel area, greatly reduces the calculation amount of the elements, and improves the efficiency of the program calculation. The method is applied to the programming of finite element, and the results are satisfactory. The application of cell search method has played a good role in promoting the development of energy-saving technology.
Research on online signature verification based on anomaly detection
Authors: Xia Xinghua , Wang Shuang 
Published: September 2021

Abstract
With the advancement of modern information technology, personal information security has become the focus of social attention. As a biological behavior feature, online signatures have been trained for a long time, have very personal characteristics, and are collected as a real-time sequence. In view of this characteristic, we can know that it is difficult for forgers to imitate the intrinsic and essential characteristics of the signer. In addition, a signature is a rapid writing process driven by the central nervous system and personal writing habits. Even the signer himself may write an unstable and similar forgery signature, so it is necessary to analyze the variability and stability of signatures. Therefore, this paper proposes a method of using the variance and characteristics of local features to find a stable segment of the user’s signature for authentication. In addition, we also adopt a two-stage authentication strategy that combines the above method with the traditional anomaly detection algorithm. An experiment was conducted on the open-access online signature database MCYT, and an EER of 2.63% was obtained. The experiment proved the effectiveness and robustness of the method.
Improving Non-Autoregressive Machine Translation via Autoregressive Training
Authors: Wang Shuheng , Shi Shumin , Huang Heyan , Zhang Wei 
Published: September 2021

Abstract
In recent years, non-autoregressive machine translation has attracted many researchers’ attentions. Non-autoregressive translation (NAT) achieves faster decoding speed at the cost of translation accuracy compared with autoregressive translation (AT). Since NAT and AT models have similar architecture, a natural idea is to use AT task assisting NAT task. Previous works use curriculum learning or distillation to improve the performance of NAT model. However, they are complex to follow and diffucult to be integrated into some new works. So in this paper, to make it easy, we introduce a multi-task framework to improve the performance of NAT task. Specially, we use a fully shared encoder-decoder network to train NAT task and AT task simultaneously. To evaluate the performance of our model, we conduct experiments on serval benchmask tasks, including WMT14 EN-DE, WMT16 EN-RO and IWSLT14 DE-EN. The experimental results demonstrate that our model achieves improvements but still keeps simple.
Ranking Consistency Analysis and Evaluation of Multiple Attribute Decision Making Method
Authors: Zhang Lixia , Qu Beibei , Gao Huisheng , Zhang Jianliang 
Published: September 2021

Abstract
In the actual decision-making process, different multiple attribute decision making methods used for the same decision-making problem may get different ranking results. This inconsistent ranking can easily lead to decision-making risks. This paper simulates the ranking consistency of six typical multiple attribute decision making methods, including the ranking consistency indexes, consistency probability, the overall consistency of the decision-making methods, and the influence of attribute number and attribute weight on ranking consistency. The results show that various methods have different ranking consistency indexes; the overall consistency of decision-making methods increases with the decrease of the number of attributes; under the condition of equal attribute weights, the decision-making methods have generally better ranking consistency. The research results have reference value for the selection of multiple attribute decision making methods and attribute optimization.
Analysis on Heterogeneous Computing
Authors: Song Changxu 
Published: September 2021

Abstract
In the Internet industry, with the popularization of informatization and the rapid increase in data volume, people have new requirements for storage space. At the same time, computer applications such as artificial intelligence and big data have rapidly increased demand for computing power and diversified application scenarios. Heterogeneous computing has become the focus of research. This article introduces the choice of architecture for heterogeneous computing systems and programming languages for heterogeneous computing. Some typical technologies of heterogeneous computing are illustrated, including data communication and access, task division and mapping between processors. However, this also brings difficulties. The challenges facing hybrid parallel computing, such as programming difficulties, poor portability of the algorithm, complex data access, unbalanced resource load. Studies have shown that there are many ways to improve the status quo and solve problems, including the development of a unified programming method, a good programming model and the integration of storage and computing, intelligent task allocation, as well as the development of better packaging technologies. Finally, the application prospects and broad market prospects of heterogeneous computing systems are prospected. In the next ten years, due to the various advantages of heterogeneous computing systems, innovation in more fields will be stimulated and heterogeneous computing systems will shine in the AI artificial intelligence fields such as smart self-service equipment, smart robots, and smart driving cars. Moreover, this emerging technology will bring new industries and new jobs, thereby driving economic prosperity and social development and even benefiting the entire human society.
Impact Time Control Guidance Law for Large Initial Lead Angles Based on Sliding Mode Control
Authors: Liu Shuangxi , Liu Wei , Yan Binbin , Liu Shijun , Yin Yingming 
Published: September 2021

Abstract
In view of the interception scenario where a missile intercepts a stationary target in the longitudinal plane, an impact time control guidance (ITCG) law is proposed by utilizing the sliding mode control (SMC). An amendatory approach of t

go
(time-to-go) is adopted where there is no need to take the small-angle assumption into consideration. Under the basis of SMC, the error of impact time is selected as a sliding mode variable, which can converge to be zero in finite time by using the fast power reaching law (FPRL). The guidance law performance has been conducted in one-to-one engagement with different desired impact time. Simulation results demonstrate that the proposed guidance scheme can operate effectively even with a large initial lead angle of the missile, which broadens the initial launch conditions of the missile as well as improving the application capability of the guidance law.
Beamforming against main lobe interference based on radial basis function neural network
Authors: Wang Shuai , Xiang Jianjun , Peng Fang , Li Zhijun , Li Haoyang 
Published: September 2021

Abstract
Aiming at the problem that the performance of traditional beamforming algorithm deteriorates sharply in the presence of main lobe interference, a beamforming algorithm based on radial basis function (RBF) neural network is proposed. Firstly, the minimum variance distortionless response (MVDR) is used to solve the optimal beam pattern in the presence of side lobe interference. Then, the training set of RBF neural network is constructed according to the optimal beam pattern and the direction information of main lobe interference to train the network, so that the trained RBF neural network can suppress the main lobe interference while maintaining the ability of optimal beamforming. The simulation results show that the method can overcome the limitations of traditional beamforming algorithm, suppress the main lobe interference and side lobe interference, and form the correct beam direction. At the same time, the algorithm also has good real-time performance.
Design and Implementation of an Early Warning System Based on the Risk Measurement Model
Authors: Feng Su , Li Jing , Wang Huan , Tang Qi , Liu Liqiong , Yin Fengnian 
Published: September 2021

Abstract
In recent years, there are still some defects in the risk management of China’s inspection and quarantine work, such as the omission of major factors in the conformity assessment of inbound and outbound commodities and the subjective and unscientific assignment of risk indicators, which affect the ability of risk monitoring. Therefore, this paper constructs an inspection and quarantine risk measurement model based on random forest algorithm, and develops a risk early warning system for inspection and quarantine business. Firstly, according to the target requirements, the training data set is extracted from the original rough data set, and the overall data is cleaned and modified. Secondly, the integrated machine learning model is used to select the feature values with better prediction ability. Then, the risk measurement model is constructed based on random forest algorithm and deployed in multi-model parallel mode. Finally, a complete risk early warning system is developed based on the risk prediction model. Through the operation of this system, the abilities of risk analysis and discrimination for inspection and quarantine have been greatly improved, and the comprehensiveness and accuracy of risk prevention and control have been effectively guaranteed.
Low Power Memory System Design Using Power Gated SRAM Cell
Authors: Pal Srijani , Salimath Divya S , Chandran Banusha , Anita Angeline A , Kanchana Bhaaskaran V S 
Published: September 2021

Abstract
Static Random-Access Memory (SRAM) is widely used in cache memory, microprocessors, general computing applications and electronic circuits involving ASIC, FPGA and CPLD. The most commonly used SRAM is the 6T SRAM. However, it incurs higher power consumption and degraded signal to noise margin (SNM) during write and read operations. To overcome these shortcomings, a single ended power gated 11T SRAM for low power operation is proposed. The power consumption reduction is achieved using power gating through virtual VSS (VVSS) signal and transmission gates. Due to the introduction of transmission gates, memory cells realize enhanced write margin characteristics as compared to existing technologies. The proposed cell realizes 33.33% lower power consumption and 50% improvement in read SNM as compared to existing SRAM technologies. To study the impact of technology scaling on our proposed design, the work is carried out in Cadence Virtuoso® tool using both 180nm CMOS technology and BPTM 32nm FinFET technology.
A Different Shaped Radiating Element Wide Band Multi-Band Massive MIMO Antenna for 5G/WLAN applications with Enhanced Correlation Coefficient
Authors: Sarade Shrenik Suresh , Ruikar S. D. 
Published: September 2021

Abstract
In this paper, a different shaped 8-element multiband multiple input multiple output (MIMO) massive antenna is investigated for 5G and various applications. It can be operated on various wireless communication application such as WLAN (5.15GHz to 5.30GHz and 5.73GHz to 5.85GHs) and 5G band & C-band (4 to 8GHz) for satellite communication with wideband. The 8-element Massive MIMO antenna is designed for 6GHz. This antenna is working on multiband with wideband bandwidth. The defected ground structure (DGS) systems are used for enhancement of return loss, isolation, total active reflection coefficients (TARC), correlation coefficients (CC) and envelope correlation coefficients (ECC). The rectangular shaped DGS is used in the ground plane for improvement of various parameter of antenna. This antenna provides return loss (RL) and isolation is less than -10dB.
Analysis of an algorithm used for finding Unipolar Orthogonal Codes
Authors: Vinutha J N , Trivedi Hitesh , Tarannum Suraiya , Harshavardhana M N 
Published: September 2021

Abstract
An Auto-Correlation and Cross-Correlation property of an Orthogonal Code enables the transfer of data with less interference. With more Orthogonal Codes, an Optical Fiber Communication channel can support a large number of users efficiently and reliably with high bandwidth. This paper analyses an algorithm used for finding Unipolar Orthogonal Codes using four major parameters (n, w, λa, and λc) where n is the length of a binary string, w is the weight of the string n (number of one’s in the string), λa is the Auto-Correlation, and λc is the Cross-Correlation. These four parameters jointly with the Johnson Bound (JA) condition are used in the algorithm. The paper illustrates the analysis of a possible number of Orthogonal Code Sets calculated using Orthogonal Codes generated from a software code that is based on the algorithm in consideration. The data analysis is carried out and the findings are summarized.
Symmetric Key Cryptosystem based on Sequential State Machine
Authors: Kodada Basappa B , D’Mello Demian Antony 
Published: September 2021

Abstract
Cryptography is a type of art that translate strings of plain text into strings of cipher text and is very important in sharing of secrete information in communication system. It is very essential to have cryptographic algorithms to provide high security by utilizing optimum computational resources during information sharing to achieve data confidentiality, privacy, integrity and other security services. The traditional conventional encryption algorithms (DES, AES) have high computational cost due to generating number of sub keys and bilinear concept. Hence this paper propose the sequential state machine (Mealy state Machine) based cryptosystem which is very light weight in nature and provide high security due to its weakly invertability concept with relatively smaller key size. The paper also presents the performance analysis on proposed algorithm and describes the security analysis on it.
Thermomechanical characterization of functionally stabilized nickel-titanium-copper shape memory alloy
Authors: Iyer Sanesh , Hubert Pascal 
Published: September 2021

Abstract
Shape memory alloy hybrid composites have promise in realizing the 21st century goal of morphing structures. There is considerable work to be done in the development of characterization and modeling techniques for these materials. The proposed characterization methodology adapts existing standards to include previously omitted factors required for the numerical modelling of shape memory alloys and their integration into end-use applications. A nickel-titanium-copper (NiTiCu) shape memory alloy is characterized using these methods and then numerically modelled. Samples’ mechanical behaviour is shown to stabilize after 43 cycles of mechanical loading. Thermomechanical properties measured before and after stabilization are shown to vary inconsistently by up to 72%, demonstrating the need for stabilization for accurate thermomechanical characterizations and consistency in end-use applications. Physical experiments are numerically replicated in Abaqus\Standard using the measured properties. Sufficient correlation is shown for the design of shape memory alloy hybrid composites. The result of this work is a comprehensive thermomechanical characterization approach for shape memory alloys which can be used to develop morphing SMA hybrid composite structures.
Effects of functional task exercise on everyday problem-solving ability and functional status in older adults with mild cognitive impairment—a randomised controlled trial
Authors: Law Lawla L F , Mok Vincent C T , Yau Matthew K S , Fong Kenneth N K 
Published: September 2021

AbstractObjectiveTo investigate the effect of functional task exercise on everyday problem-solving ability and functional status in older adults with mild cognitive impairment compared to single exercise or cognitive training and no treatment control.DesignA single-blind, four-arm randomised controlled trial.SettingOut-patient clinic and community centre.ParticipantsOlder adults with mild cognitive impairment aged ≥60 living in community.MethodsParticipants (N = 145) were randomised to 8-week functional task exercise (N = 34), cognitive training (N = 38), exercise training (N = 37), or wait-list control (N = 36) group. Outcomes measures: Neurobehavioral Cognitive Status Examination, Category Verbal Fluency Test, Trail Making Test, Problems in Everyday Living Test, Activities of Daily Living Questionnaire, Instrumental Activities of Daily Living Scale; Chair stand test, Berg Balance Scale, and Short Form-12 Health Survey were conducted at baseline, post-intervention and 5-months follow-up.ResultsPost-intervention results of ANCOVA revealed cognitive training improved everyday problem-solving (P = 0.012) and exercise training improved functional status (P = 0.003) compared to wait-list control. Functional task exercise group demonstrated highest improvement compared to cognitive training, exercise training and wait-list control groups in executive function (P range = 0.003–0.018); everyday problem-solving (P &lt; 0.001); functional status (P range = &lt;.001–0.002); and physical performance (P = 0.008) at post-intervention, with all remained significant at 5-month follow-up, and further significant improvement in mental well-being (P = 0.043).ConclusionsFunctional task exercise could be an effective intervention to improve everyday problem-solving ability and functional status in older adults with mild cognitive impairment. The findings support combining cognitive and exercise intervention may give additive and even synergistic effects.