bluuebunny/crossref_metadata_2025_split huggingface dataset:
IoT Based 3-Dimensional GPS Tracking System
Authors: Patel Shreyans , Rathod Ghansyam 
Published: September 2021

Abstract
This study solves the problem of identifying the exact location of the device in multi-story buildings by displaying the latitude, longitude and even the altitude data of the device with the use of a simple prototype. By simple mapping of buildings, this prototype can be programmed to give altitude output as the floor of the building on which the device is located. This paper addresses some opportunities brought by smart tracking methods. The prototype aims to locate the device and send its geolocation coordinates via email to the user so that they can recover their precious device or use the system for tracking applications. This study is based on the Internet of Things (IoT) so that the prototype can be used for locating any Android or iOS device connected to the internet with a GPS sensor available, directly through communication between the device and the controller through a Web Server or Cloud. In addition to the services of retrieving the device location that is already provided by many companies, the innovation done is that the prototype can also detect the altitude at which the object is located. This makes it very easy to find in multi-story buildings where just latitude and longitude data is not enough to find it quickly.
Designing Of Uwb Bandpass Filter By Cascading Highpass And Lowpass Filter
Authors: Vats Mudita , Rai Mayank , Choudhary Shilpa , Sachan Smriti , Sharma Mukul 
Published: September 2021

Abstract
A Ultra WideBand(UWB) bnadpass filter(BPF)is introduced in this paper. According to the paper, we developed good insertion loss and return loss of bandpass filter by cascade the highpass and lowpass filter intead of using simple bnadpass filter. This research upgrade the outcome and prototype of the bandpass filter for wireless communicaton. In this paper butterworth technique is used in the designing of the bandpass filter. In this paper we designed Ultra-wideband bandpass filter and compare result with the simple bandpass filter also improved the various parameters. Simulations have been performed by using Microwave office 2002.
Non Zero Spin Boundary Conditional Vector of porous sphere flow for fixed micropolar fluids
Authors: Sudesh Kumar Garg , Mishra Rashmi , Singh Manvinder 
Published: September 2021

Abstract
This paper presents the non zero spin boundary conditional vector of porous sphere flow for fixed micropolar fluids using brinkman and stokes equation. The fluid flows in and out words of the porous sphere for formulating the stream functions. The evaluation and knowledge of the flow of disparity material parameters are considered for validating and getting the results by using previous case. This present research paper proposes to examine the analytical study of porous sphere flow fixed in micropolar fluid with non zero boundary conditional vector. The brinkman and stokes equation are utilized for flowing in and out wards of the porous sphere and also in the formulations of stream function. The flow is knowledgeable by porous sphere is to evaluate and its disparity with material parameter is considered. Results are validating by means of previous known cases.
Bidirectional Visitor Counter using Microcontroller with Sanitization and Thermal Screening
Authors: Verma Khushal , Chauhan Savita , Singh Jay , Kumar Nagendra 
Published: September 2021

Abstract
In the modern world there is continual requirement for automated devices. The COVID-19 pandemic is the expounding global health emergency of our time and the greatest challenge we have faced in many years. Authors have to move on by implementing strong protective measures to reduce the spread of COVID-19. This model is designed and presented in order to count the visitors along with their sanitization and thermal screening. This system can be used in any auditorium, halls, schools, offices, malls, etc. Authors have designed a model which can keep a track on number of people entering and exiting from the hall. It sanitizes the visitors entering and exiting from the hall, and check the temperature of each visitor entering in the hall. If the temperature is not according to COVID – 19 standards then a buzzer will buzz indicating that visitor cannot enter in hall. Depending upon the sensors interruption the system recognize entry and exit of visitor.
Persuasive Factors and Weakness for Security Vulnerabilities in BIG IOT Data in Healthcare Solution
Authors: Gaur Madhu Sharma , Kumar Sanjeev , Gaur Navneet K , Sharma Prem Sagar 
Published: September 2021

Abstract
In the present scenario, public health is a global challenge. In view of COVID-19 pandemic, interventions of emerging technologies hasbeen highly increased and post-pandemica big technological shift is expected for providing information and communication Technology-enabled solutions to healthcare as well a meeting other social challenges. Internet of Things or IoT and Big data are the technologies prominently being used in healthcare applications. In smart city visualization to provide ubiquitous computing environment, urge of smart, small but powerful sensor devices or IoT technology-enabled healthcare solutions deployments done over open networked infrastructure and underlying architecture. Such highly dynamic and heterogeneous environment with rapid digital transformation enforcing trusted security resource-restrictions and performance implication. In this paper, firstly, we explore the existing security, privacy and authentication weakness in reference to IoT or IoMT and big data enabled healthcare applications. Secondly, scaling the low to high security risks done based on the major weaknesses. In this work primarily we focus on most challenging attacks like Denial of Services (DoS), Man in the middle and dynamic intrusions. In winding-up machine learning based intelligent adaptive approach proposed for underlying deficiencies and insufficiencies in IoT enabled Healthcare application security. The key driving forces for the imprecision of trust and security with emerging Big IoT also presented as future scope.
Sparsity Augmented Restoration of Underwater Images using Compressive Sensing
Authors: Mathias Ajisha , Dhanalakshmi Samiappan , Kumar R 
Published: September 2021

Abstract
The study of underwater environments is a difficult task because both laws of reflection and refraction are influenced in the visualization of underwater medium. Other variables, such as aquatic flora and fauna, also have a major effect on the lighting conditions. While optical processing of such images is regarded, degradation can take place due to various environmental and capturing device defects. Also, due to over hitting of samples, the algorithm based on traditional Nyquist algorithm can prove computationally sensitive and produce lower accuracy. Compressive Sensing (CS) provides an alternative approach to overcome the over-hitting problem. The efficiency of underwater algorithms can be improved by incoherent sampling combined with sparsity. The proposed model is applied to the underwater blurred image and the algorithm is evaluated for restoration using directional gradient priors. The contribution of the proposed algorithm can be characterized as: a) Incoherent sampling reduces the problem of hitting (b) Enhancement of sparsity coefficients for restoration algorithms. The proposed contribution to the application of underwater imaging is mainly because there are less random samples in the compressive sensing that produce better sparsity prior to the initial stage, which makes the solution to the ill-posed problem highly convergent with high accuracy. Therefore, we propose a new Temporal Sparse Bayesian Learning (TSBL) using compressive sensing that leads to higher resolution image enhancement in underwater conditions.
Analytical Modeling of Triple-Material Trigate Junctionless Tunnel Field Effect Transistor
Authors: Maria Jossy A , Vigneswaran T , Malarvizhi S , Nagarajan K K 
Published: September 2021

Abstract
An analytical two-dimensional (2D) model is developed to obtain expressions for various parameters of triple-material trigate junctionless tunnel field-effect transistors (TMTG-JTFETs). The 2D Poisson’s equation uses the superposition approximation method to calculate the sum of potentials arising from different point charges. The electric field distribution in the channel is obtained from the gradient of the electric potential. The drain current of the device is calculated using the Kane’s model in the presence of an electric field and a band structure. The TMTG-JTFET, with a channel length of 40nm, exhibits significant electrical characteristics with very low off-current (Ioff) and high on-current compared to a dual-material double-gate TFET. To validate the model, analytical results are confirmed via comparison with technology computer-aided design simulation results.
A Coarse-to-Fine Object Tracking Based on Attention Mechanism
Authors: Wen Haixi , Chen Xiaoming , Zhou Li 
Published: September 2021

Abstract
In the visual object tracking task, although the convolutional neural network has excellent performance, it is difficult to learn the global and long-range semantic information interaction due to the inherent locality of convolution. Simultaneously, the existing template updating methods focus on the target position of the next frame, which will integrate more irrelevant background and cause tracking instability. Therefore, to address this issue, inspired by Transformer, in this paper, we propose a coarse-to-fine tracking method based on the attention mechanism (CTFT). First, employing swin transformer block instead of the original convolutional network backbone to achieve self-attention from local to global. Second, using attention to effectively combine template and search region features in the part of feature fusion, and explore the possibility of using attention mechanism as the backbone in object tracking for the first time. Finally, a tracking strategy from coarse to fine is proposed. In the offline coarse tracking stage, the initial estimation of the target object is generated to obtain the coarse regress bounding box of the target. In the online fine tracking stage, we make use of the coarse regress bounding box to expand the corresponding object frame, and use the segmentation to get the fine regress bounding box of the object. Experiments on challenging benchmarks including GOT-10k, LaSOT, VOT2018, VOT2019, UAV123 and OTB-100 demonstrate that the proposed CTFT outperforms many state-of-the-art trackers and achieves leading performance.
Evaluate the viscosity of small aquaculture water by computer vision technology
Authors: Ma Yinchi , Wei Wei 
Published: September 2021

Abstract
The royal goldfish has high ornamental value and is usually cultured in small aquaculture water, which requires high water quality. In the industry, water viscosity is usually used to measure the quality of water quality. The fresh and suitable water is conducive to the survival and reproduction of the royal goldfish, while the water with excessive viscosity is easy to cause the royal goldfish to get sick or even die. In this paper, the computer vision technology is used to track and detect the bubble group on the surface of the royal goldfish small aquaculture water in real-time, and the time change fitting model of the bubble group covering the water surface area is established, which can directly reflect the change of the bubble burst rate of the small aquaculture water. Based on this, the viscosity level of the small aquaculture water is quantitatively evaluated. The experimental results show that the coefficient a in the fitting model is highly correlated with the viscosity level. This method can evaluate the viscosity of small aquaculture water in real-time and automatically, and send early warning information to aquaculture technicians quickly, which is of great significance to ensure the health and safety of royal goldfish culture.
Cross-Domain Sequential Recommendation Based on Self-Attention and Transfer Learning
Authors: Liu Dantong , Zhu Cui 
Published: September 2021

Abstract
Cross-domain recommendation provides effective solutions to the problems of cold start and data sparsity by transferring information from one or more source domains to another target domain. However, people’s interactive behaviour usually occurs in a sequence, but most cross-domain recommendation models often ignore the sequential information. At the same time, most sequential recommendation models only consider the case of single-domain. In this paper, we propose a new cross-domain sequential recommendation model (SATLR) based on self-attention and transfer learning, which make recommendations based on user preferences and sequential dependencies learn from cross-domain. The experiment results demonstrate that on the three groups of datasets from amazon, our model is better than other recommendation models.
An improved algorithm for disparity estimation of SGM stereo matching based on edge detection
Authors: Zhang Jian , Huang Jing 
Published: September 2021

Abstract
In the fields of depth measurement and computer vision, generating sufficiently accurate disparity maps through various stereo matching algorithms is an important foundation. The SGM semi-global matching algorithm is one of the best stereo matching algorithms, But its filling accuracy is poor in the invalid parallax area. Therefore, on the original basis, an improved algorithm of SGM stereo matching disparity filling based on edge detection is proposed. The experiment uses a standard data set to verify the algorithm. The results show that compared with the original SGM stereo matching algorithm, the paper algorithm can achieve better disparity estimation results in the invalid disparity area.
An Optimized Positioning Algorithm Based on Improved Gaussian Filtering
Authors: Chen Min , Zhang Dongmei , Zhao Yue , Wu Taojiang 
Published: September 2021

Abstract
WiFi offers simple, convenient, ubiquitous, and economic solutions for indoor positioning services, by matching a pre-established WiFi’s RSSI fingerprint database to a mobile terminal’s received RSSI values. A setback of this fingerprint matching method is its low precision, only miserably on order of meters, due to signal impairment by indoor complicated environment. To circumvent this, we revise the traditional weighted K-neighborhood algorithm by incorporating a Bayesian probability optimization. The proposed combination of Bayesian with weighted K-neighborhood algorithm improves the accuracy and reduces the average running time. Computer simulation shows that proposed Bayesian probabilistic optimization algorithm improves the positioning accuracy from 34% to 46%, with an average of about 14.86%, and the computation stability is also enhanced.
Speech biohashing authentication based on power spectrum
Authors: Huang Yibo , Chen Tengfei , Pu Xiangrong , Zhang Qiuyu 
Published: September 2021

Abstract
In order to solve hashing sequences leakage in the existing speech authentication algorithms, this paper proposed a speech biohashing authentication based on power spectrum. Fristly, the algorithm pre-processed the original speech, then used the improved covariance method to extract the power spectrum feature of the speech, and generated a one-dimensional feature vector from the feature matrix. A pseudo-random matrix was generated by using Sinc function transform, the feature vector and pseudo-random matrix are projected and mapped to construct the biometric template. Finally, binarization process was used to generate hash index, and upload it to the cloud. The experimental results show that the algorithm has good discrimination, robustness and authentication performance. At the same time, the biometric template has good security and ensures the irreversibility of the hashing sequence.
Computational simulation in Molecular Recognition and Spectroscopic Characteristics of a Probe for Zn2+, Cu2+, Hg2+, Fe3+
Authors: Wang Xiaofeng , Lan Wenbo , He Liping , Tan Bin , Meng Yanbin , Lu Guowen , He Junshan , Li Manhua , Chen Aoling , Shi Yurong 
Published: September 2021

Abstract
Using computer technology and density functional theory methods, the binding and recognition ability of quinoline derivative probes to Zn2+, Cu2+, Hg2+ and Fe3+ ions were studied. And the UV-Vis absorption and fluorescence spectra before and after the complexes formed by the probe and Zn2+, Cu2+, Hg2+ and Fe3+ were compared and analyzed. Finally, the energy gap of the frontier molecular orbital energy of each complex was explored and compared. The results of the study show that the probe has obvious differences in coordination recognition ability with Zn2+, Cu2+, Hg2+ and Fe3+. The binding energy of the probe to Fe3+ is much greater than that of other ions. Moreover, the complexes formed by the probe and the metal ion have significant differences in the ultraviolet-visible absorption spectrum and the fluorescence spectrum. These will provide an important reference for the probe molecule’s identification of different metal ions and the qualitative and quantitative detection and analysis of metal ions.
Design and implementation of a general SQL parser
Authors: Wu Kehe , Di Dan , Li Wei , Cui Wenchao 
Published: September 2021

Abstract
With the rapid development of computer network, the information security of database becomes more and more important. In database security protection, SQL statements need to be parsed and reconstructed. However, the database audit products on the market lack accurate analysis of SQL statements. Therefore, this paper constructs an SQL parser from four aspects: lexical analysis, syntax analysis, SQL spanning tree optimization and error detection.
Complex-valued U-Net for PolSAR Image Semantic Segmentation
Authors: Liu Ao , Yu Lingjuan , Zeng Zhaoxin , Xie Xiaochun , Guo Yuting , Shao Qiqi 
Published: September 2021

Abstract
As an image semantic segmentation network, U-Net has the advantage of simple structure which is suitable for semantic segmentation of PolSAR images with small datasets. However, the original U-Net is a real-valued (RV) network, whose input must be RV. If it is directly used in the segmentation of PolSAR image, the complex-valued (CV) input must be converted into RV, which results in the loss of information. In this paper, a CV U-Net, which is mathematically strict, is proposed for semantic segmentation of PolSAR images. Considering that the PolSAR dataset is small, the structure and parameters of CV U-Net are furtherly simplified based on the original U-Net to prevent overfitting. Experimental results on the Flevoland dataset show that the proposed CV U-Net has better segmentation performance than the original RV U-Net and some other semantic segmentation networks.
Research Progress of Speech Emotion Recognition Based on Discrete Emotion Model
Authors: Yang Chunfeng , Lu Jiajia , Wu Qiang , Chen Huiyu 
Published: September 2021

Abstract
Speech emotion recognition is one of the important technologies of human-computer interaction, and neural networks have made great contributions in it. In this survey, the commonly used discrete emotion databases and speech emotion feature parameters are firstly introduced. Then the feature extraction methods and emotion recognition models in recent three years’ research in China are summarized, and the former one can be divided into two directions, selecting the feature subset from the existing features and using neural network to extract new features. Finally, the challenges encountered in speech emotion recognition are described, and the future research and development of speech emotion recognition based on discrete emotion models are prospected.
Signal acquisition system for broken wire of elevator wire rope based on TMR sensor
Authors: Dai Liyong , Zhao Mingfu , Tang Bin , Yang Ninghui , Xiao Yu , Wu Taojiang 
Published: September 2021

Abstract
Aiming at the problems of low detection accuracy, large size and quality of the detection equipment, and difficulty in online monitoring in the magnetic flux leakage detection of elevator wire ropes, a broken wire signal acquisition system was built based on the tunnel magnetoresistance sensor TMR2703, and a dual-loop excitation device was designed and simulated to verify. The dipole model determines the detection direction of the sensor, acquires the magnetic flux leakage signal with the signal preprocessing circuit and the differential detection ring, develops the host computer in LabVIEW, displays the broken wire signal waveform in real time, and stores the collected data in the form of TDMS files, completed the feature extraction of broken wire signals. Experiments show that the system can collect the magnetic flux leakage signal of the wire rope in real time, and can accurately extract the characteristic value of the magnetic flux leakage signal with different numbers of broken wires. The peak value, valley value, peak-to-peak value and signal wave width of the signal have an increase with the number of broken wires. The law of increase can be used to judge the damage degree of the wire rope according to the detection waveform.
Action recognition based on element-level fine-grained multi-modal fusion
Authors: Peng Guozheng , Han Lixin , Yang Jiaxue 
Published: September 2021

Abstract
Traditional action recognition algorithms often only pay attention to video RGB features or optical flow features. These methods do not make good use of the audio information in the video. Based on RGB and optical flow characteristics, this paper introduces the processing of audio information, and classifies videos based on element-level fine-grained multi-modal fusion. Through experimental comparison, the accuracy of the multi-modal fusion algorithm proposed in this paper is improved by 7.38% on the HMDB51 dataset and 3.18% on the UCF101 dataset compared to the simple modal splicing. At the same time, it is proved that the introduction of audio modes can effectively improve the performance of the model.
Feature Fusion Network for Low-Light Image Enhancement
Authors: Yu Yabin 
Published: September 2021

Abstract
With the popularity of electronic cameras, we can get pictures under various lighting conditions in our daily life. The images which obtained in low illumination environment will lose contrast, colour, and content details which will bring obstacles for understanding the image. To tackle this problem, a method is proposed in this paper to tackle low-light image enhancement. This method fuses the high level and the low level feature, uses an attention module to model the context information and selects the critical feature to supplemented the high level feature to reconstruct the enhanced image. A number of experiments demonstrate that our method can get images with improved and reduced noise.
An unsupervised prediction method for radar transmitter degradation fault based on isolation forest
Authors: Zhai Yuting , Shao Xiaofang , Li Jiasen , Fang Shaojun 
Published: September 2021

Abstract
The transmitter is prone to premature degradation and failure for the frequent work and the harsh working environment of the marine radars. The existing method of degradation fault based on data-driven require a large number of negative samples or need to set failure threshold. To solve the above problems, an unsupervised prediction method for radar transmitter degradation fault based on isolation forest is proposed. The proposed unsupervised prediction method can predict the degradation fault of radar transmitter when there are no fault samples and the fault threshold is not reached.