Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Compact, Broadband, High Gain Millimeter-Wave Antenna for 5G Beam Steering Applications

The millimeter-wave (mmWave) antennas for smartphones are one of the key components to complete the transition to 5G mobile networks. Although research and development of mmWave 5G antennas for cellular handsets are currently at the center of a significant research effort in both academia and telecommunication industry, a systematic antenna design approved by wireless community has not been proposed yet. With this communication, we propose a novel, high gain, wide band and compact mmWave 5G antenna, namely clover antenna for cellular handsets. The presented antenna has clover like conductor profile whose parameters can be adjusted to obtain high gain or wide band. The designed antennas are simulated with a widely used full-wave analysis tool. Numerical results of the mmWave antenna are confirmed successfully by the experimental results in <inline-formula><tex-math notation="LaTeX">${{\text{24}}}$</tex-math></inline-formula>–<inline-formula><tex-math notation="LaTeX">${\text{28}}$</tex-math></inline-formula> GHz band. The antenna achieves measured peak gain of <inline-formula><tex-math notation="LaTeX">${\text{ 7.8}}$</tex-math></inline-formula>–<inline-formula><tex-math notation="LaTeX">${\text{9}}$</tex-math></inline-formula> dBi in the band. Besides, with a <inline-formula><tex-math notation="LaTeX">${\text{16}}$</tex-math></inline-formula>-element clover antenna array, the beam steering capability of the antenna is demonstrated. Beam steering between <inline-formula><tex-math notation="LaTeX">${{ \pm \text{45}^\circ }}$</tex-math></inline-formula> is achieved with low side lobe levels. Practical design considerations for the integration of the arrays in handset to obtain full-coverage in horizontal plane are investigated. The calculated spatial peak power density values of the proposed array on the outer surface of a head phantom are demonstrated for different scan angles.
Brain–Computer Interface Software: A Review and Discussion

Software is a critical component of brain–computer interfaces (BCIs). While BCI hardware enables the retrieval of brain signals, BCI software is required to analyze these signals, produce output, and provide feedback. Users from multiple research areas have adopted BCI software platforms to investigate various concepts. Recently, interest in web-based BCI software has also emerged. The system design and control signal techniques of state-of-the-art BCI software platforms have been previously investigated. However, there is limited literature discussing user adoption of BCI software platforms. Additionally, there is a lack of work discussing the recent emergence of web tools relevant to BCI applications. This article aims to address these gaps by presenting a bibliometric review of the state-of-the-art BCI software. Furthermore, we discuss web-based BCIs and present tools that may be used to develop future web-based BCI applications.
Deep Learning for Opinion Mining: A Systematic Survey

The hype of sharing the thoughts, plans, opinions, sentiments, reviews what-so-ever comes in our mind on Social networking sites are increasing day by day and is at an all-time high. The data collected and made from this is enormous, and using them for analyzing, reviewing and as a guiding light by the calculative minds shows the advent of this era and the tremendous growth of technology. It also helps the industries, organizations in various forms. One of them is to gather the thoughts of the people globally, which is termed as opinion mining or sentimental analysis. This helps them to understand the market demand and help make the things consumers want and to separate the goods that are undesired or to fix a specific issue. It enables growth at a much higher pace. This study focuses on the literature done in the years 2015–2019 and will help the researchers and scholars to analyze the recent works in the field of Opinion mining.
The Design and Implementation of Trade Finance Application based on Hyperledger Fabric Permissioned Blockchain Platform

Blockchain has grown beyond cryptocurrency. It has found a sweet spot in applications that required increased trust and transparency among multi-party transactions. This paper describes our experience in the design, implementation and architecture of blockchain-based trade finance application. The implementation is based on permissioned blockchain Hyperledger Fabric. Recently, the number of projects embarked on blockchain application have grown significantly over the year. However, the current level of understanding of blockchain application is insufficient and the architectural aspects of the system has remained largely unexplored. This paper attempts to solve this problem. It applies the concept of software connectors as a medium to explore fundamental building blocks of software interaction and how they are composed into a more complex interaction
Q-Adaptive Extended Kalman Filter for Nonlinear Systems with Unknown Noise Statistics

A ‘Q’-Adaptive Extended Kalman Filter (QAEKF) has been characterized in this work for nonlinear state estimation when process noise covariance (Q) is unknown. Innovation based Direct Covariance Matching technique has been used here for adaptation. An important pragmatic regarding the initialization of Q matrix to start the adaptation process is obtained from this detailed study. Tradeoff of sliding window size, an important tuning parameter required for adaptation, has also been investigated. Nonlinear LEO satellite planar model is considered here to validate the algorithm. Simulation results clearly demonstrate the improved performance of Q-AEKF over traditional non-adaptive Extended Kalman Filter in terms of root mean square error, transient error peak and steady state error.
An Improved DTC Technique for Three-Level NPC VSI Fed Five-Phase Induction Motor Drive to Eliminate Common Mode Voltage

This paper proposes a virtual vector (VV) based common mode voltage (CMV) elimination method for direct torque control (DTC) of three-level neutral point clamped voltage source inverter (THL-NPC VSI) fed five-phase induction motor (FPIM) drive. Consequently, the proposed method will improve the leakage current in bearings and distortion in the stator currents. This is achieved by selecting 51 out of 243 voltage vectors, which will give zero CMV. Out of 51 voltage vectors, only 31 vectors are used to form as VVs to develop look-up table for DTC operation. The collative results are provided to show the superiority of the proposed method over the existed approach. The simulation and OPAL-RT based hardware in loop (HIL) results are provided to validate the proposed method in both steady-state and dynamic conditions.
Fast Hyperspectral Subspace identification using Eigenvalue based energy thresholding

Blind hyperspectral unmixing comprises three subproblems. First is subspace identification that is finding the number of pure endmembers present. Second endmember extraction and the third is abundance estimation. There are many approaches for endmember extraction and abundance estimation in literature. But out of that many approaches requires prior knowledge of the number of endmembers. We are proposing a new approach EET (Eigenvalue based Energy Thresholding) for hyperspectral subspace identification. This proposed approach is best suitable for real-time application as computation time is low compared to other approaches. We have compared our algorithm results with other state-of-art algorithms on real and synthetic datasets.
Three-Dimensional Quantification of Intercropping Crops in Field by ground and aerial photography

High-throughput phenotyping of plant three-dimensional (3D) architecture is critical for determining plant phenotypic characteristics. The acquisition of 3D architecture of plant phenotypic traits based on multi-view photographing has been widely applied in greenhouse research. Growth process of the plants can be dynamically monitored. However, the application of this method in the field is more difficult and less due to the complex environment. In this study, maize/soybean intercropping plant populations in the field were selected as the research objects. We combined ground and aerial photography to obtain the image sequences. at the stage of seedling, jointing, tasseling and grain filling. The targeted plants were photographed with fixed point from multi-view hemispherical directions on ground photography before tasseling stage. Then, Unmanned Aerial Vehicle was used to take photos in the way of concentric circles with different radius. We preprocessed the image sequences by Support Vector Machine (SVM) method, and pixel information only containing targeted plants were achieved. We evaluated the accuracy of calculated individual height, blade length and maximum width with the measured data. Image sensitivity analysis was also done at 25 and 79 days after emergence by reducing the image numbers. Canopy coverage and plant height were compared between different scenarios. The results showed that there was a good agreement between measured and calculated plant height, blade length and blade maximum width with R2>0.90. Then the dynamic changes of plant height, crown surface and organ growth were extracted based on reconstructed 3D architecture. Sensitivity analysis showed that at the early growth stage, 50 images are enough for 3D reconstruction of the plant. However, all 300 images need to be included at the late growth stage of plants. The results can provide a basis for high-throughput phenotypic analysis related to genotypes and help to evaluate the plant architecture and canopy radiation interception.
Class Centroid Based Convolutional Neural Network for Skin Cancer Detection

Detecting skin cancer from dermoscopy image is oneamong applications where deep neural network has been the state of the art. In contrast to previous studies which addressed lesion segmentation or ensemble learning, the proposed study aims to evaluate a network training method which minimizes the distance between instance and a pre-defined class centroid via regression. A fine-tuned classification network and a non-finetuned network were also evaluated for comparison. The results demonstrated that the proposed strategy performed comparably equal to the classification network which optimized with cross entropy loss function. Moreover, principal component analysis was also performed to better understand the characteristics of the generated features. The visualization of principal components indicates that regression network generated different pattern compared to the classification network.
Network-based quantitative frameworks to identify pleotropic factors that influence for cardiomyopathy progression

This paper presents network-based quantitative frameworks to study the complex relationship of cardiomyopathy (CMP) and risk factors that influence CMP progression in order to identify new CMP biomarkers. We analyzed gene expression microarray data from CMP affected and unaffected (control) tissues, and data from individuals with high body fat, high fat diet and type-II diabetes. We examined differentially expressed genes (DEGs) for each dataset and compared CMP with each factor pairwise to identify common DEG overlaps. In our analysis, 2589 DEGs are identified for CMP of which 1283 genes are over expressed and 1306 genes are under expressed. Protein-protein interaction (PPI) network found 10 core genes, namely SMARCA4, NCOR2 and histone genes HIST1H4K, HIST1H4I, HIST2H4B, HIST1H4H, HIST2H4A, HIST4H4, HIST1H4F and HIST1HL. Ontological and pathway analysis with this information identified significant pathways for CMP progression. The findings were validated using dbGaP (gene SNP-disease linkage) and OMIM databases for gold-standard benchmarking of their significance in disease progression. Thus, our network-based method identified a number of factors, notably histones, that may be pleiotropic influencing factors of the CMP.
A Probabilistic Model for the Deployment of Human-Enabled Edge Computing in Massive Sensing Scenarios

Human-enabled edge computing (HEC) is a recent smart city technology designed to combine the advantages of massive mobile crowdsensing (MCS) techniques with the potential of multiaccess edge computing (MEC). In this context, the architectural hierarchy of the network shifts the management of sensing information close to terminal nodes through the use of intermediate entities (edges) bridging the direct Cloud-Device communication channel. Recent proposals suggest the implementation of those edges, not only employing fixed MEC nodes, but also opportunistically using as edge nodes mobile devices selected among the terminal ones. However, inappropriate selection techniques may lead to an overestimation or an underestimation of the number of nodes to be used in such a layer. In this article, we propose a probabilistic model for the estimation of the number of mobile nodes to be selected as substitutes of fixed ones. The effectiveness of our model is verified with tests performed on real-world mobility traces.
Massive Beam-Division Multiple Access for B5G Cellular Internet of Things

In this article, we investigate the issue of massive access in a beyond fifth-generation (B5G) cellular Internet of Things (IoT) network. To reduce the overhead of channel state information acquisition and the complexity of transceiver design, an integrated framework of massive beam-division multiple access (BDMA) is proposed according to the characteristics of beamspace propagation. Then, we analyze the performance of the proposed massive BDMA scheme and derive a closed-form expression for the weighted sum rate in terms of channel conditions and system parameters. To improve the overall performance, we propose a massive access algorithm by jointly optimizing transmit power and receive vector. It is found that the optimal receive vector for each IoT device is the base beam corresponding to the arrival of angle of the IoT device’s signal in the beamspace, which significantly simplifies the design of the receiver. Considering the constraint of radio-frequency (RF) chains in practical networks, we propose a clustering-based massive access algorithm, which allocates an RF chain for a cluster. Finally, extensive simulation results confirm that the proposed algorithms can provide small overhead and low complexity massive access schemes for B5G cellular IoT.
Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization

In this paper, we study unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) with the objective to optimize computation offloading with minimum UAV energy consumption. In the considered scenario, a UAV plays the role of an aerial cloudlet to collect and process the computation tasks offloaded by ground users. Given the service requirements of users, we aim to maximize UAV energy efficiency by jointly optimizing the UAV trajectory, the user transmit power, and computation load allocation. The resulting optimization problem corresponds to nonconvex fractional programming, and the Dinkelbach algorithm and the successive convex approximation (SCA) technique are adopted to solve it. Furthermore, we decompose the problem into multiple subproblems for distributed and parallel problem solving. To cope with the case when the knowledge of user mobility is limited, we adopt a spatial distribution estimation technique to predict the location of ground users so that the proposed approach can still be applied. Simulation results demonstrate the effectiveness of the proposed approach for maximizing the energy efficiency of UAV.
A Comparative Assessment of three Control Algorithms for Grid Connected PV System with Distorted Grid Conditions

This paper presents comparison of three different control algorithms for three phase 10kW single stage grid connected Photovoltaic system. These control algorithms are Improved Linear Sinusoidal Tracer(ILST), Damped Second Order Generalized Integrator(DSOGI) and Lattice Wave Digital Filter(LWDF). The outcome of the comparative assessment of their effectiveness in accomplishing following control functions e.g. (i) extraction of fundamental component of load current, (ii) maximum power extraction (iii) harmonics mitigation and (iv) injecting active power to the grid is presented in this paper. The assessment is performed under sudden load change and change in irradiance conditions. A 10kW GCPV system with distorted grid is simulated using MATLAB/Simulink 2017a. From the above assessment, it is found that THD of the grid currents yielded with all the three control algorithms are within the limit of IEEE-1547 and IEEE-519 standards. It is found that amongst the three controllers, LWDF scheme exhibits superior performance.
Optimal step length for the Newton method near the minimum of a self-concordant function

The quadratic convergence region of the exact Newton method around the minimum of a self-concordant function makes up a fraction of the Dikin ellipsoid. Outside of this region, the Newton method has to be damped in order to ensure convergence. However, the available estimates of both the size of the convergence region and the step length to be used outside of it are based on conservative relations between the Hessians at different points and are hence sub-optimal. In this contribution we use methods of optimal control theory to compute the optimal step length of the Newton method on the class of self-concordant functions, as a function of the Newton decrement. With this step length quadratic convergence can be achieved on the whole Dikin ellipsoid. The exact bounds are expressed in terms of solutions of ordinary differential equations which cannot be integrated explicitly. As an application, the neighbourhood of the central path in which the iterates of path-following methods for conic programming are required to stay can be enlarged, enabling faster progress along the central path during each iteration and hence fewer iterations to achieve a given accuracy.
A Review on Prediction of Collective Behavior

— The social media provide platform to connect peoples with each other and they can share their views, idea, thoughts. Now a day lot of data are generated by social media. The heterogeneous peoples are like to connect with each other through the social sites such as Facebook, Twitter, Flickr, and YouTube etc, to classify this large data is also a challenging task. This paper is to study of predict collective behavior of individuals on the social network. For classification of large network into specific communities by consider pattern of social behaviour. The k-means variant edge-centric clustering algorithm is used for finding the communities of people. To classify the large network we are going to use the edge-centric clustering technique to extract the sparse social dimensions from network. The social dimension based approach has been shows beneficial for the study. With this approach we can handle millions of user data to demonstrate behavioural approach of user in social sites for marketing purpose.
On the growth rate of plasmoid chains during nonlinear viscoresistive evolution of the tilt instability

We investigate by means of two-dimensional incompressible magnetohydrodynamic (MHD) numerical simulations, the onset phase of the fast collisional magnetic reconnection regime that is supported by the formation of plasmoid chains when the Lundquist number S exceeds a critical value. The present study extends previous results obtained at magnetic Prandtl number Pm = 1 (Baty 2020) to a range of different Pm values. We use FINMHD code where a set of reduced visco-resistive MHD equations is employed to form two quasi-singular current layers as a consequence of the tilt instability. The results reinforce the conclusion that, a phase of sudden super-Alfvenic growth (when Pm is not too high) of plasmoid chains is obtained, following a previous quiescent phase during current sheet formation on a slower Alfvenic time scale. We compare our results with predictions from the general theory of the plasmoid instability. We also discuss the importance of this onset phase to reach the ensuing stochastic time-dependent reconnection regime, where a fast time-averaged rate independent of S is obtained. Finally, we briefly discuss the relevance of our results to explain the flaring activity in solar corona and internal disruptions in tokamaks.
Student Performance Assessment and Prediction System using Machine Learning

Student Performance Analysis System is an emerging field and is very crucial to schools and universities in helping their students and professors. Most of the pre-existing methods are based only on past academic performance of students. This paper aims to develop models which can predict the student's performance and grades while keeping in mind other equally essential personality factors like interests, attributes and opinions (IAO variables) which affect their lifestyle. It uses various machine learning and deep learning techniques to predict the performance of students, and basic exploratory data analysis to derive various correlations of student's performance with psychographic attributes.
Integrated Resource Management for Terrestrial-Satellite Systems

As data traffic in terrestrial-satellite systems surges, the integration of power allocation for caching, computing, and communication (3C) has attracted much research attention. However, previous works on 3C power allocation in terrestrial-satellite systems mostly focus on maximizing the overall system throughput. In this paper, we aim to guarantee both throughput fairness and data security in terrestrial-satellite systems. Specifically, we first divide the system implementation into three steps, i.e., data accumulation, blockchain computing, and wireless transmission. Then, we model and analyze the delay and power consumption in each step by proposing several theorems and lemmas regarding 3C power allocation. Based on the theorems and lemmas, we further formulate the problem of 3C power allocation as a Nash bargaining game and construct an optimization model for the game. Last, we solve the optimization problem using dual decomposition and obtain the optimal period of the satellite serving the ground stations as well as the optimal 3C power allocation solution. The optimal solution can provide guidelines for parameter configuration in terrestrial-satellite systems. The performance of the proposed terrestrial-satellite architecture is verified by extensive simulations.
Selecting Palm Oil Cultivation Land using ARAS Method

The importance of selecting alternative land used for palm oil cultivation requires some priority assessment of the land, therefore the need to implement a decision support system in choosing which land to plant palm oil in the future. There is no system for selecting land priority to plant palm oil purpose of this study is to develop a system for selecting palm oil cultivation land by implementing a decision support system using the Additive Ratio Assessment (ARAS) method in analyzing each alternative land criteria. The results showed that the alternative lands ranking that had the highest value AL3, which was Mendik Makmur, had the highest K value of 0.869, which placed the land Mendik Makmur in the first rank based on ARAS method calculations. The resulting accuracy testing ARAS method by comparing the ranking results with other methods against the results of expert assessments get an accuracy score of 77%, with 7 alternative ranking positions are the same as the results of the assessment of experts from a total of 9 alternatives.