Miracle-dz/UniFAR-FTUs huggingface dataset:
Safeguarding Non-Best User Association Aided 5G K-Tier HetNets Using Physical Layer Security

This paper explores the potential of physical layer security for K- tier heterogeneous networks (HetNets) with nonbest user association (UA) scheme. By modeling the spatial positions of network elements in each tier as homogeneous Poisson point processes (PPPs), we first present the non-best UA probability of a typical user equipment associating with the m-th average biased received power (ABRP) with passive eavesdroppers, then the expression of total secrecy probability for the K-tier HetNets is derived with stochastic geometry. Both the analytical and numerical results show that the implementation of the non-best UA scheme can significantly improve the secrecy probability, which indicates that non-best UA could be a promising solution for safeguarding K-tier HetNets. Moreover, we show that the secrecy probability with non-best UA scheme is not always decreasing over the transmission power of the base stations, which is quite different from the best ones.
Simulation of a Standalone Microgrid with Hybrid Energy Sources

Hybrid energy sources are gathering attention due to various reasons such as environmental concerns of greenhouse gas emissions, increasing demand, fossil fuels depletion, and energy efficiency. Microgrids and their control are key elements for integrating renewable and distributed energy resources. Considering that, a microgrid formed by photovoltaic panels, battery energy storage and diesel generator is analyzed in this paper. The control technique of the microgrid, switching between the available sources, and maintaining the parameters of the grid are discussed in detail. The transition of sources between various modes, the causes leading to these transitions and effects are also investigated. Corresponding models are built and simulated in MATLAB/Simulink environment, and the behavior of the microgrid is observed. The main objective of thus formed microgrid is to utilize the renewable source to the maximum, while providing uninterrupted power supply to the critical loads. The diesel generator is employed only when both photovoltaic panels and battery are unable to meet the critical load demand.
Effects of the Mosaic Pixel Structure on Display Images.

The pixell structures in a display influence the visual characteristics differently than in conventional raster scans. Therefore, it is very important to further improve the design of the mosaic dot pixel structure. This paper proposes a new model that analyzes the relation ship between pixel structures and visual disturbances. In this model, the power spectral density was investigated in terms of the relationship between the modulation transfer function of the pixel structure display and human visual characteristics. Visual disturbances were estimated from the spectral density and total noise levels. Using these estimations, the effects of pixel aperture and pitch , and color dot pixel structure can be evaluated.
Energy Efficiency Maximization of AF Relaying SWIPT Systems with Energy Recycling

This paper studies a wireless-powered amplify-and- forward (AF) relaying system, where the relay harvests energy from the source or itself using an antenna switching protocol. At the first time slot, each antenna of the relay either receives information or harvests energy from the source and, then, a subset of antennas is chosen to forward information while the remaining antennas continue to harvest energy at the second time slot, which enables energy recycling. To maximize the energy efficiency of the relaying system, a mixed- integer non- linear fractional-combinatorial problem is formulated, which is however mathematically intractable. Accordingly, a suboptimal low-complexity iterative algorithm is developed, including joint power allocations at the source and relay and antenna switching at the relay. In particular, the joint power allocations is first performed by the BCD and Dinkelbach algorithms for a given antenna combination in the inner iteration while a low-complexity greedy algorithm is adopted to select the optimal antenna subset in the outer iteration. Simulation results demonstrate that energy recycling benefits higher energy efficiency.
Outlier Detection Based on Majority Voting: A Case Study on Real Estate Prices

Outliers in the data are very common for various fields. So filtering the data is prominent both for computing the desired result for a data set correctly or noticing unusual behaviours. In this case study, outlier detection is used to detect false ads, which are placed in the wrong category or have the wrong values, in a real estate sale website. To accomplish this, two websites are crawled, and the real estates with the unexpectedly low or high price per meter-square value are considered as the outlier candidates. To detect outliers, five outlier detection algorithms are run separately and majority voting is used to determine the absolute result, the average price per meter-square in the location. Evaluating the results of algorithms by majority voting, enabled to tolerate deficiencies of an algorithm by others automatically with some other benefits as well.
360 Degree Profiling and Social Linkage Analysis of Persons of Interest

Scourge of Terror Incidents is increasing because of advent of newer methodologies, weapons and techniques. National Security Agencies control these insurgent terror incidents by maintaining records of Persons of Interests for their tracking. As the person's data can come from multiple sources and in various formats, therefore analysing it with traditional methods becomes major challenge. Also viewing linkages between different entities becomes cumbersome. This paper presents a Machine learning and Computer Vision driven solution, which will help Security Agencies to combat terrorism. This solution will not only build 360 degree Person's profile but also show his linkages with various Incidents of Interest and Entities of Interest. The solution proposed is efficient in terms of performance and speed because it leverages the power of distributed computing and multithreading. So, we are building a Big Data & AI based distributed high performance system which will characterize the 4 V's of Big Data: Volume, Velocity, Variety and Veracity.
Exploring the Influencing Factors of the Diffusion of Electric Vehicles Sharing Service in Shanghai Using Fuzzy Cognitive Maps

With the increasing of environmental pollution, traffic jams and parking problems, people have begun to look for low-carbon travel ways. At the same time, the development of information technologies such as mobile internet, third-party payment, and GPS positioning, has provided a foundation for the emergence of travel sharing services. As a new industry, the diffusion of travel sharing services in cities is the focus of enterprises and government. This article takes the electric vehicle sharing service as an example to carry out the study. Starting from government, enterprises and the users, we collect and analyze the factors that affect the diffusion of the electric vehicle sharing service. Fuzzy cognitive map is a technique for analyzing complex decision-making problems and we use it to identify the key factors affecting the diffusion of electric vehicle sharing service in this study. Simulations of the diffusion is based on the current state of electric vehicle sharing service in Shanghai. The results show that different stakeholder groups have different views and identify different factors as driving factors for the diffusion of electric vehicle sharing services. The simulations have indicated that the model developed based on fuzzy cognitive maps is more sensitive to political factors.
Prediction of Upcoming Conferences Ranking in Bangladesh based on Analytic Network Process and Machine Learning

The main objective of an academic conference is the participant's scope of submitting research papers. Different types of academic institutions and organizations arrange many academic conferences annually. It is virtually impossible for academics to attend all the conferences. Consequently, selection of the best conference is of utmost importance for academics. Selection of best conference paper was dependent on significant factors such as: conference location, registration fee, conference language, subject of the conference and submission deadline. In this research paper, Analytic Network Process (ANP) was used to accurately rank ten significant academic conferences based on the specific criteria mentioned above. Machine Learning (ML) algorithm have been used to rank upcoming conferences. The upcoming conferences will be considered based on the condition that the machine learning scores should be more than 80%. Multiple linear regression machine learning algorithm was used to find out the best upcoming academic conference.
Design Studies of Magnetron Injection Gun for V and W Band Gyrotrons

In this paper, design studies of electron guns supporting second harmonic operation of V and W band gyrotrons for plasma diagnostics and defence (active denial systems) applications are presented. The in house code Gyrotron Design Suite Second Harmonic Version 2018 (GDS2H-V. 2018) is used for the initial design studies of the magnetron injection guns along with the magnetic guidance system. The particle trajectory code EGUN is used for the parametric optimization and simulation of the designed electron guns.
Sentiment Analysis Using Lexicon Based Approach

With increasing easy access to internet there is an emergence of e-commerce and social media portals. There is a huge surge in the production of the human sentiments in form of customer reviews and feedback on these platforms. As per a survey approximately 2,500,000 Terabytes of data is created every day and 90% of the data which exist today has been created in the past 2 years only. This huge amount of data created is called big data. The main problem associated with this huge data is that it is in unstructured form. So, to gain information, first we must process it using various methods. This paper proposes a Human sentiment analysis model (HSAM), which can perform sentiment analysis on any given data set.
Data Loss Prevention and Data Protection in Cloud Environments Based on Authentication Tokens

Data leek issues represents a very high or critical importance within the cloud developers. Given the tremendous growth of cloud computing in the past 3 years the authors of this paper believe that an analysis of the implementation of modern security solutions for data leaks and data protection is of the utmost importance. Authentication tokens, provided by several different cloud providers and third parties, are one of the most common and useful tools that developers have in order to secure their applications. The goal of this paper is to analyze how these tokens are currently used and to provide a solution in order to improve their implementation by reviewing their compatibility with novel security concepts or challenges such as data tagging, hybrid data security algorithms, microservices deployment using Docker or serverless applications
A 2.4-mm2 130-mW MMSE-Nonbinary LDPC Iterative Detector Decoder for 4 $\times$ 4 256-QAM MIMO in 65-nm CMOS

Iterative detection and decoding (IDD) employs a soft-in soft-out (SISO) detector and an SISO forward error correction (FEC) decoder in an iterative loop to improve the receiver performance in multiple-input multiple-output (MIMO) wireless communications. This paper describes a 256-QAM 4 $\times $ 4 prototype IDD design made up of a minimum mean square error (MMSE) detector and a nonbinary low-density parity-check (NBLDPC) decoder with the symbol size of the NBLDPC code matched to the modulation to enhance performance. By directly translating between nonbinary symbols and constellation points, the detector–decoder interface is simplified. We present a Gb/s MMSE detector using a shortened tandem scheduling, a low-latency dual-lookup reciprocal unit, an optimized interleaved microarchitecture, and a Gb/s NBLDPC decoder with efficient internal skipping paths and memory allocation. The designs were demonstrated in a 0.7-mm2 1.38-Gb/s MMSE detector and a 1.7-mm2 1.02-Gb/s-NBLDPC decoder that are integrated in a 65-nm CMOS test chip. The chip is measured to achieve 19.2 pJ/b in detection and 20.1 pJ/b/iteration in decoding.
Low Density Signature Based Packet Access with Phase Only Adaptive Precoding

This paper proposes a low density signature-based packet access with phase only adaptive precoding. The proposed access allows multiple user terminals to send their packets simultaneously though only one antenna is put on terminals and on an access point, in order to increase network throughput. The proposed phase only adaptive precoding improves the transmission performance by rotating the transmission signal phase. This paper proposes a criterion that defines the optimum rotation angle, and actual two techniques to estimate the angle. When 6 packets are transmitted in 4 time slots, the proposed adaptive precoding attains about a gain of 4dB at the BER of 10^{-4} in a single input single output Rician fading channel, i.e., overloading ratio is 1.5.
Joint IWMMSE-Based Channel Estimation and Finsler-Manifold-Based Codebook for the Design of V2X FDD Massive MIMO Systems

With the rapid development of V2X communications, how to guarantee per-vehicle rate and robustness of V2X communications has become important issue for the intelligent transportation systems. When there is distortion in CSI exchange under a real (e.g., noisy) environment, the performance of the precoder feedback method will seriously degrade due to transmission latency and quantization error. In this work, we propose Finsler manifolds codebook feedback scheme for V2X massive MIMO systems, where the vehicles exchange their CSI information via V2V communications, estimate the direction from a propagating wave in the precoder of antenna transceivers, and transmit their CSIs back to the Roadside Unit (RSU). That is, with the minimizing the iterative weighted minimum mean squared estimation (IWMMSE) of the received signals, we could cope with the optimization problem of the precoder feedback scheme on Finsler manifolds for maximizing the received signal power. Moreover, we are able to properly manage the optimal precoder bit allocation in mmWave massive MIMO systems. Simulation results show that, with the IWMMSE-based estimation, our Finsler manifolds codebook feedback scheme outperforms existing approaches in terms of per vehicle rate as well as the interference mitigation, i.e., our massive MIMO system could obtain higher adaptability and stability for V2X communications.
Evaluating Indoor Localization Performance on an IEEE 802.11ac Explicit-Feedback-Based CSI Learning System

There is a demand for device-free user location estimation with high accuracy in order to realize various indoor applications. This paper proposes an IEEE 802.11ac explicit feedback-based channel state information (CSI) learning system which can be used for device-free user location estimation. The proposed CSI learning system captures CSI feedback from off-the-shelf Wi-Fi devices and extracts 624 features from a CSI feedback frame defined in IEEE 802.11ac. We evaluated the proposed system using location estimation with six patterns: different combinations of device-free user movement and access point antenna orientation. The evaluation results show that the machine learning based localization achieves approximately 96$\%$ accuracy for seven positions of the user, and the divergence of CSI improves localization performance.
Comprehensive and Comparative Study of Cloud Simulators

Computing and storage capacity are increasingly growing in cloud computing and hence, developers and programmers are deploying their files and applications on cloud. However, it is complex and difficult to deploy a real cloud and is costly as well. Moreover, resource utilization is not much efficient in real cloud. Hence, cloud simulation is considered as a fruitful and effective alternative. In this paper, we provide a complete study and analysis on a variety of cloud simulation tools. Moreover, we present parametric evaluation of cloud simulation tools on the basis of our identified parameters. In addition, we provide challenges and issues encountered by implementers and users of cloud computing during simulation of cloud. Our survey gives thorough overview of various cloud simulation tools, helping researchers and developers in selecting the most appropriate cloud simulation tool. Furthermore, it can be used as a catalog of available cloud simulators to come up with new design.
K-Means MU-MIMO User Clustering for Optimized Precoding Performance

Multi-User (MU) Multiple-Input-Multiple-Output (MIMO) systems have been extensively investigated over the last few years from both theoretical and practical perspectives. Linear Precoding (LP) schemes for MU-MIMO are already used in Long Term Evolution (LTE) for their low complexity, however, they do not work well for users with strongly correlated channels. Finding the optimum set of co-scheduled users, provided their channels are separated enough, could require an exhaustive search, and thus may not be affordable for practical systems. The purpose of this paper is to present a new semi-orthogonal users selection algorithm based on the statistical K-means clustering and to assess its performance in MU-MIMO systems.
Facial Action Units for Training Convolutional Neural Networks

This paper deals with the problem of training convolutional neural networks (CNNs) with facial action units (AUs). In particular, we focus on the imbalance problem of the training datasets for facial emotion classification. Since training a CNN with an imbalanced dataset tends to yield a learning bias toward the major classes and eventually leads to deterioration in the classification accuracy, it is required to increase the number of training images for the minority classes to have evenly distributed training images over all classes. However, it is difficult to find the images with a similar facial emotion for the oversampling. In this paper, we propose to use the AU features to retrieve an image with a similar emotion. The query selection from the minority class and the AU-based retrieval processes repeat until the numbers of training data over all classes are balanced. Also, to improve the classification accuracy, the AU features are fused with the CNN features to train a support vector machine (SVM) for final classification. The experiments have been conducted on three imbalanced facial image datasets, RAF-DB, FER2013, and ExpW. The results demonstrate that the CNNs trained with the AU features improve the classification accuracy by 3%–4%.
A Modified Venturini PWM scheme for Matrix converters

Matrix converters have been deserved much attention in industry due to lack of DC-link capacitors over the conventional VSIs. Since from the generation of this converter concept its complexity incorporates the development of proper modulation techniques, control strategy and optimized converter resources. This paper presents a new modulation technique to enhance the amplitude of the output voltage by changing the offset duty ratio with in build input power factor correction. Paper presents the optimized filter parameter of modified Venturini method over the classical modulation technique.
Review of Fuzzy Soft Classification with Contextual Information

As real world changes very rapidly, it is creating a problem of mixed pixels and the pixels belonging to one class are moving to the other class according to their spatial resolution. Therefore, uncertainty increases at the boundary locations. Fuzzy C-mean, Possibilistic C-mean, and Noise clustering soft classification techniques used to classify mixed pixels on the basis of spectral information. There are some models as like Smoothness Prior and Discontinuity Adaptive prior based on spatial information, are available to classify the pixels with minimum uncertainty. Spatial information of pixel gives the contextual information of pixel, which is based on the neighborhood pixel. With the help of neighboring pixels, Smoothness prior smoothen the pixels and preserve the edges. Whereas Discontinuity Adaptive Prior smoothen the pixels and overcome the problem of over smoothness. In this paper, we are going to review the Fuzzy C mean, Possibilistic C mean, and Noise Clustering with Smoothness Prior and Discontinuity Adaptive Prior.
Unimodular Sequence Design with Good Local Auto- and Cross-Ambiguity Function for MSPSR System

This paper focuses on designing a set of dedicated sequences for Multi-Static Primary Surveillance Radar (MSPSR) systems. An efficient algorithm to generate a set of unimodular sequences with good local auto-ambiguity functions (AFs) and cross-AFs over speciﬁc Doppler bins and delay bins of interest is proposed. The performance of the proposed algorithm is evaluated in comparison with a prior art called the energy gradient method via numerical simulation. The numerical results show that the newly generated sequences possess lower maximum sidelobe in the area of interest.
A Survey on Stock Market Prediction

It is complicated to estimate the stock market where relations between input and output are random in nature. Predicting the cost of the share market is the most complex job of the financial time series. Forecasting of the stock should be possible by utilizing the present and past information available on the market. The execution measurements that should be achieved if there should be an occurrence of the stock forecast are exactness, adaptability and less time utilization. There are numerous sorts of research done as such far with the end goal to predict the stock market to complete the characterized measurements. Several techniques have been accessible in data mining for forecasting the stock market, for example, Fuzzy systems, Artificial Neural Network (ANN), if-then-else rules, Bayesian algorithm et cetera. In this paper, the different strategies are available and used for forecasting the stock markets are talked about. This review knows which method is the finest to use for predicting the stock market. The most important application of market share is to predict market trends. Likewise, the market shows the performance of the future, which constantly encourages financial experts to understand when and what shares can be purchased to improve their risk. For this reason, a large number of research has been prepared so far to analyze the stock market by means of mining of the data.
Joint Data and Pilot Power Control Algorithm for Weighted Sum SE in Uplink Single-Cell MU-Massive MIMO Systems

This paper consider the joint data and pilot power control problem in uplink single-cell multi-user massive multiple-input-multiple-output (MU-Massive- MIMO) systems. Instead of using equal data and pilot power allocation among each user equipment (UE), in this paper, we formulate the power control as an optimization problem for weighted sum spectral efficiency (SE) maximization objective subject to per-user pilot and data power budget in the uplink. To this end, we derive a lower bound expression on the average uplink SINR with maximum ratio combination (MRC) receiver under imperfect channel knowledge. Since the optimization of weighted sum SE has been proved to be a NP-hard problem, we propose an efficient algorithm to obtain an approximated optimal solution with polynomial complexity, by joint iterative optimization over data power and pilot power for each UE. The convergence of the proposed algorithm is guaranteed and discussed. Numerical results demonstrate the optimality of the proposed algorithm and show notable SE performance gains over suboptimal algorithms in previous work.