Miracle-dz/UniFAR-FTUs huggingface dataset:
Text Mining Techniques to Capture Facts for Cloud Computing Adoption and Big Data Processing

Digital libraries, journals and conference proceedings repositories are a great source of information. These sources are very useful for the purpose of research and development. This paper presents an overview of text mining and its application towards information extraction from literature. In this study, we used word cloud, term frequency analysis, similarity analysis, cluster analysis, and topic modeling to extract information from multi-domain research articles. Cloud computing and big data are new emerging trends. So it is important to extract useful patterns and knowledge from published articles in these domains and discover the relationship between them. Therefore, a total of two hundred research articles published from 2010 to 2018 in these domains, were selected. The source of these articles is high impact factor journals from reputed publishers namely IEEE, Springer, Wiley, Elsevier, and ACM. It is a cross-domain analysis in cloud computing and big data domains to find the latest trends, related topics, tools, terms, and author affiliation from extracted data. This study identifies the ten major areas of big data using cloud computing, fourteen factors towards cloud adoption, and hurdles in adoption. Moreover finding shows that IEEE has more sources for subject cloud computing application towards big data, then comes Springer, Wiley, and Elsevier. Furthermore, it has been observed in the analysis that the number of articles in these domains increased from 2013 onward.
Sub-Pixel Mapping with Multiple Shifted Hyperspectral Images Based on Multiobjective Evolutionary Algorithm

Sub-pixel mapping (SPM) can interpret the sub-pixel spatial distribution of land-cover classes in hyperspectral image, which is an ill-posed problem due to the inadequate information of a single image. Auxiliary information provided by multiple shifted (MS) images can make SPM problem well-posed and improve mapping accuracy. The maximum a posteriori (MAP) technique can incorporate the auxiliary information of MS images, but it introduces a fixed weight parameter to fuse the auxiliary information and spatial prior information, heavily influencing the mapping result. This paper proposed a novel SPM method to model the auxiliary information and spatial prior information into two objective functions, which can be simultaneously optimized by the devised multiobjective evolutionary algorithm. Therefore, there is no need of weight parameter, and the two objective functions can be intelligently integrated during the evolution. Experimental results and parameter analysis have indicated the superiority of the proposed method.
Four-Wire Three-Phase Active Power Filter With Fuzzy Controller For Compensation Of Distortion of Currents Unbalance in the Conditions of Fluctuated Load

The methods of control for active power filters are based on modern understanding the power’s components. Theoretical insights concerning the definition of inactive components of power, among which pq-and pqr-theory of instantaneous power stand out, are used to determine the specified current of the active power filter. At the same time, a significant indicator of the quality of the device in general is the process of adjustment of current. On the basis of the analysis of known works, it was figured out that the parameters of the compensator’s closed-loop current controller shall be defined by the load mode which very often is changeable. Thus the need for adjusting the parameters of the controller arises. The results of previous studies have shown that special based on fuzzy logic approach can be used to adjust the coefficients of the controller. The controller, which is designed using fuzzy logic for correction integral and differential terms, was proposed. The model of a four-wire three-phase power active filter with a fuzzy controller for compensation of distortions of currents unbalances under variable load mode was developed using the graphical programming entwinement. During the study, it was found that the current and power unbalance does not exceed 1% for the calculated mode. Conclusions of research has shown that the current and power imbalance does not exceed 1% for the calculated mode.
Cotton Leaf Area Index Estimation Using Unmanned Aerial Vehicle Multi-Spectral Images

The objective of this study is to estimating cotton leaf area index (LAI) using multi-spectral images acquired by Unmanned Aerial Vehicle (UAV). For this purpose, cotton nitrogen and irrigation experiment was conducted with four N treatments and four irrigation treatments in the suburbs of Shihezi city, Xinjiang province. A five bands multi-spectral senor was carried on a four rotors UAV and used to acquire images. Meantime, field campaigns were conducted to measure LAI in each plot. Based on above data, commonly used spectral indices were selected and used to design LAI estimation model. During this process, three quarters of samples were used to make model, and the reminders were used to validation. The results showed images acquired by UAV can be used to monitor cotton LAI. Among the spectral indices, Ratio Vegetation Index (RVI) is the best index for LAI estimation, with R2 value of 0.70 and RMSE value of 0.59 during calibration, and R2 value of 0.65 and RMSE value of 0.62 during validation.
The Design of File Management System Based on Website and Qr

The file management of universities has always been a headache nowadays. At present, universities have a variety of public and personal documents or files to manage. The management method still maintains manual physical management, which is time-consuming and laborious, and may even cause serious problems such as "lost file". In order to manage files in a more standardized and efficient way, this design which involves a webpage connected with automation control access system optimizes the archive management of various files. The operation with its linking function allows teachers to preview and edit the retrievable items and the website administer to manage the accounts. The application of webpage can avoid problems such as cumbersome installations and running environment of APPs in mobile phones, and achieves the function of mobile terminals with Quick Look and Quick View, making the files stored at the fixed point and accessible at any time. For the front-end part of the webpage, it draws on a lot of excellent foreign website designs, and applies a flatter icon design and a more fluid transition effect. Connected with the widely-used ASP.Net backend framework, the filing cabinet enjoys a more comfortable and smooth operation experience in addition to its function of efficient filing. Furthermore, it gives the chance to teachers and students to experience the charm of TUTE on the homepage and to get an overview of the university's profile and the latest news. This design is a combination of practicality and aesthetics.
The Impact of Loads Aggregation and Correlation in Distribution State Estimation

Distribution networks have experienced significant changes in the last two decades, and have moved from passive networks to active ones. These critical changes require careful control and monitoring, involved with measurement devices and communication platforms. However, in distribution networks with a very large number of customer loads, it is not cost-effective to consider extensive measurement devices. As a result, distribution state estimation (DSE) methods can be used to estimate the states of the unmeasured nodes, where pseudo measured data is used for unmeasured nodes to make DSE method observable. However, pseudo data brings high uncertainty to DSE algorithm, which decreases the accuracy of the estimated states. To increase the accuracy in pseudo data, conditional multivariate complex Gaussian distribution (CMCGD) is employed in this study. CMCGD method increases the accuracy of pseudo data in presence of high correlation between the sets of aggregated load data. As a result, loads aggregation is considered in this study to decrease uncertainty and increase the correlation in pseudo data. Although loads aggregation increases the correlation, it decreases the size of the impedance matrix by reducing the number of branches. Ignoring the cable impedances in distribution networks, will decrease the accuracy of the estimated states, even with a highly correlated set of pseudo data. In this paper, we aim to show that each distribution network with its own cable impedances requires a balance between loads aggregation and the reduction in the size of the impedance matrix. An investigation is provided on a typical distribution network to show how to achieve a minimum estimation error, when both factors of loads aggregation and size of the impedance matrix are considered.
Target Recognition in Sar Image Via Sparse Representation in Transformed Domain

To solve target recognition under extended environments, this paper proposes sparse representation in the transformed domain. Since the signal energy in the frequency domain is mainly concentrated on a small portion of low frequencies, this part of spectrum therefore carry the vital information that distinguishes a class of target from the other. We intend to define a frequency descriptor by the bag of low frequencies. The defined descriptor is used to build sparse signal modeling. The frequency descriptors of the training are concatenated to form an over-complete dictionary. It is used to encode the counterpart of query as a linear combination of themselves. Sparsity has been harnessed to generate the optimal representation, from which the inference can be reached.
Hyperspectral Remote Sensing Image Band Selection Via Multi-Objective Sine Cosine Algorithm

For hyper-spectral image band selection, there are two main key concerns, which are the curial information preservation and the redundancy information reduction. Since the two objectives are contradictory, the single-objective based band selection methods are usually unsatisfactory, thus, a superior approach which can obtain a trade-off between them is needed. In order to address this problem, an evolutionary computation method called sine cosine algorithm which has the capabilities of the global exploration and the local exploitation is applied, and its multi-objective discrete version is designed for multi-objective band selection. In addition, in this paper, two novel measures are utilized for meeting the requirements. The effectiveness of the proposed method is confirmed by the experimental results obtained with two real hyper-spectral images.
Measuring the complexity of directed graphs: A polynomial-based approach

In this paper, we define novel graph measures for directed networks. The measures are based on graph polynomials utilizing the out- and in-degrees of directed graphs. Based on these polynomial, we define another polynomial and use their positive zeros as graph measures. The measures have meaningful properties that we investigate based on analytical and numerical results. As the computational complexity to compute the measures is polynomial, our approach is efficient and can be applied to large networks. We emphasize that our approach clearly complements the literature in this field as, to the best of our knowledge, existing complexity measures for directed graphs have never been applied on a large scale.
Rigorous vectorial coupled-mode theory for the isotropic waveguide with anisotropic disturbance

We present a rigorous vectorial coupled-mode theory (VCMT) for the isotropic waveguide structures with anisotropic disturbance by Maxwell equations and a modified dielectric tensor. The coupling coefficients are related to every element of a disturbed dielectric tensor. In the special case of isotropic disturbance and orthogonal mode coupling, our formulas can be reduced to the previous version of VCMT, in which the coupling coefficient does not include the polarization coupling term appearing in an earlier scalar coupled-mode theory with vector correction (SCMT-VC). The origin of the fundamental error found in the SCMT-VC is analyzed. Finally, we apply our theory to a special coupling between x- and y-polarized modes in a single-mode fiber.
Texture and Shape Features for Grass Weed Classification Using Hyperspectral Remote Sensing Images

Automatic grass weed detection and classification deals with large intraclass challenges since they are similar to grass in shape, sizes and colors. Grass weed detection and mapping is critical for site-specific weed control to reduce the cost of labor and impact of herbicides. In this paper, we investigate different shape and texture based feature extraction methods to discriminate three different grass weed categories using hyperspectral images. Feature extraction methods including Gabor features, Histogram of Oriented Gradients (HoG), and Local Binary Pattern (LBP) are evaluated in this paper. The experimental results indicate that the overall accuracy of the grass weed classification using the propose combination of texture and shape features generated from LBP and HoG is higher as compared to using shape and texture features separately.
Autophagy and Nutrients Management in Plants

Nutrient recycling and mobilization from organ to organ all along the plant lifespan is essential for plant survival under changing environments. Nutrient remobilization to the seeds is also essential for good seed production. In this review, we summarize the recent advances made to understand how plants manage nutrient remobilization from senescing organs to sink tissues and what is the contribution of autophagy in this process. Plant engineering manipulating autophagy for better yield and plant tolerance to stresses will be presented.
An Open-Loop Starting Procedure for Sensorless Control of Synchronous Reluctance Motors

This paper presents the development and test of an open-loop start-up procedure for synchronous reluctance motor drives, designed to match the polynomial torque-speed profile of a pump application. The integration of this procedure with a flux-observer allows for relatively easy sensorless operation in those applications that do not require steady-state operation at low or null speed. In particular, the paper focuses on the design of the open-loop control references in order to achieve the maximum acceleration performance and the concerns for a smooth and safe switching towards the closed-loop observer. Real-time simulation and experimental results are presented to prove for the effectiveness of the method.
The Exploitation of the Non Local Paradigm for SAR 3d Reconstruction

In the last decades, several approaches for solving the Phase Unwrapping (PhU) problem using multi-channel Interferometric Synthetic Aperture Radar (InSAR) data have been developed. Many of the proposed approaches are based on statistical estimation theory, both classical and Bayesian. In particular, the statistical approaches based on the use of the whole complex multi-channel dataset have turned to be effective. The latter are based on the exploitation of the covariance matrix, which contains the parameters of interest. In this paper, the added value of the Non Local (NL) paradigm within the InSAR multi-channel PhU framework is investigated. The analysis of the impact of NL technique is performed using multi-channel realistic simulated data and X-band data.
Estimation of Forest Structure with the Vegetation Structure Perpendicular Index (VSPI) for Dynamic Fire Spread Simulations

Spatial fire spread models simulate the progression of a wildfire across the land under given meteorological conditions. As such, they can improve the fire-fighting real-time response and the rural planning of fire prone areas. However, fire-spread models require high-resolution information of vegetation, which is often difficult to acquire, as spatially variant growth, but also past fire occurrences impact on the spatial variability of the vegetation. Hence, forests often consist of a patchwork of vegetation in different growth stages, which is impossible to consistently map from the ground. In order to address this problem, the Vegetation Structure Perpendicular Index (VSPI) is introduced, here, which is a spatially and temporally continuous proxy for fuel structure derived from Landsat data. Forest age maps are derived by fitting post-fire VSPI time series to an exponential decay curve. The forest age maps derived from VSPI are then used as input into a rate-of-spread model to predict the fire spread of the 2003 Mt Cooke wildfire in Western Australia, as a proof-of-concept.
Perturbed Triangular Patch Antenna with DGS for K Band Application

A design and performance analysis of perturbed triangular patch antenna (PTPA)with modified ring shaped defects on the ground plane have been presented in this paper. Defective Ground Structure (DGS) has been implemented to improve the antenna parameters. HFSS software has been used to perform the simulation of the proposed antenna. By implementing the DGS, the antenna gain is enhanced from 4.55 dB to 7.2 dB, the directivity is increased from 6.82 dB to 7.42 dB, and the return loss is reduced from -10.67 dB to -32.36 dB with a resonating frequency of 18.4 GHz and with a VSWR of 1.13.
Effect of White Noise on Phase Estimation by Frequency Shifting and Filtering Algorithm

The phase estimation accuracy level of voltage and current signals of the power network is affected by the inevitable noise. In this paper, the effect of the white noise on phase estimation provided by frequency shifting and filtering (FSF) is analyzed. With the help of the Equivalent Weighting Filter's merits, viz., Equivalent Noise BandWidth and Overlap Correlation, the FSF-based phase variance expression is derived theoretically by considering the overlap of two processed samples. The effectiveness of the proposed phase variance expression is verified through computer simulations.
Harmony Search Algorithm Based Power Quality Improvement in Multilevel Inverter

Cascaded Multilevel Inverters with isolate DC source have proven to be extremely beneficial in medium voltage and high-power applications. And DC normally has variations, which disturbs power quality of any system. The idea of this paper is to propose a Harmony Search method for deciding optimal switching angles for firing the switches of multilevel inverter to operate under all operating conditions. The HS Algorithm aims to lower the voltage deviation of fundamental component from desired value and diminish the harmonics. Along with THD minimization, the algorithm also focuses on decreasing even order harmonics and DC component to negligible. The proposed technique is tested with equal, unequal, equal and time-varying supply with non-linear and dynamic load. Analysis proves that the algorithm efficiently calculates the switching angles under all operating conditions. And more essentially, simulated output voltage and current equals calculated theoretical values, which mean the power quality is enhanced. For validation, the results are compared with the Sinusoidal Pulse Width Modulation (SPWM) results.
A Robust Multiscale Dictionary Learning Algorithm for Sar Object Recognition

In this paper, a novel robust multiscale dictionary learning algorithm is proposed for SAR object recognition. By extracting an SAR image’s multiscale features and introducing them into the objective function, there are several reconstruction error terms. In addition, each reconstruction error term is calculated according to the sum of the absolute values of matrix entries instead of the Frobenius norm, and it is more robust against noise. An alternating minimization strategy is proposed to optimize the objective function. Experiments on the MSTAR dataset show that the proposed scheme outperforms some representative dictionary learning methods in terms of recognition performance and robustness against noise, especially under small training set size condition.
Subject-Independent Brain–Computer Interfaces Based on Deep Convolutional Neural Networks

For a brain–computer interface (BCI) system, a calibration procedure is required for each individual user before he/she can use the BCI. This procedure requires approximately 20–30 min to collect enough data to build a reliable decoder. It is, therefore, an interesting topic to build a calibration-free, or subject-independent, BCI. In this article, we construct a large motor imagery (MI)-based electroencephalography (EEG) database and propose a subject-independent framework based on deep convolutional neural networks (CNNs). The database is composed of 54 subjects performing the left- and right-hand MI on two different days, resulting in 21 600 trials for the MI task. In our framework, we formulated the discriminative feature representation as a combination of the spectral–spatial input embedding the diversity of the EEG signals, as well as a feature representation learned from the CNN through a fusion technique that integrates a variety of discriminative brain signal patterns. To generate spectral–spatial inputs, we first consider the discriminative frequency bands in an information-theoretic observation model that measures the power of the features in two classes. From discriminative frequency bands, spectral–spatial inputs that include the unique characteristics of brain signal patterns are generated and then transformed into a covariance matrix as the input to the CNN. In the process of feature representations, spectral–spatial inputs are individually trained through the CNN and then combined by a concatenation fusion technique. In this article, we demonstrate that the classification accuracy of our subject-independent (or calibration-free) model outperforms that of subject-dependent models using various methods [common spatial pattern (CSP), common spatiospectral pattern (CSSP), filter bank CSP (FBCSP), and Bayesian spatio-spectral filter optimization (BSSFO)].
Robust Low-Rank Change Detection for Sar Image Time Series

This paper considers the problem of detecting changes in multivariate Synthetic Aperture Radar image time series. Classical methodologies based on covariance matrix analysis are usually built upon the Gaussian assumption, as well as an unstructured signal model. Both of these hypotheses may be inaccurate for high-dimension/resolution images, where the noise can be heterogeneous (non-Gaussian) and where all channels are not always informative (low-rank structure). In this paper, we tackle these two issues by proposing a new detector assuming a robust low-rank model. Analysis of the proposed method on a UAVSAR dataset shows promising results.
The Current and Retrospective Intentional Nature Exposure Scales: Development and Factorial Validity

Both nature exposure and green exercise (GE) can improve health. However, there are no scales examining frequency of engagement; or that consider interaction with nature. There are also no scales assessing these variables during childhood. The aim of this study was to develop a modified (NES-II) and retrospective (RNES-II) version of the Nature Exposure Scale to incorporate GE and to examine their factor structure and reliability. Exploratory factor analysis (EFA) explored the factor structure of the scales; followed by confirmatory factor analysis to confirm the model fit. Fit indices for the one factor five item NES-II and RNES-II models identified by EFA were poor. Use of modification indices resulted in a good model fit; NES-II: χ(5, n = 385) = 2.638; χnormed = 0.879; CFI= 1.000; RMSEA < 0.001 with 90%CI = 0.000–0.082; SRMR = 0.009; AIC = 36.638. RNES-II: χ(2, n = 385) = 7.149; χnormed = 3.574; CFI = 0.995; RMSEA = 0.082 with 90%CI = 0.023–0.151; SRMR = 0.015; AIC = 43.149. Both models demonstrated very good reliability (α = 0.84; 89 respectively). These findings indicate that the scales can be used to assess current and retrospective nature exposure. However, due to the removal of item one, the authors recommend that the scales be named the ‘intentional nature exposure scale’ and ‘retrospective intentional nature exposure scale’.