Miracle-dz/UniFAR-FTUs huggingface dataset:
Vehicle to Grid Implementation Considering Electric Vehical Battery Health Constraints

This research article shows a complete working system for V2G and G2V applications. Battery energy storage system (BESS), are essential part of a grid system. Solar and wind power DGs utilize batteries in transients as well as for storage applications. So EVs (Electric vehicles) are a good option as a portable BESS. Because of the fact that, EVs are penetrating in the national grids at distribution generation (DG) level, it is very important from many viewpoints, that the battery of the EV should be efficient in its performance and should have a large span of life so as to make the system work efficiently. In this study, a complete model of a lithium ion battery of an EV along with the GRID TIED INVERTER to deliver power to the grid has been worked out and an understanding about the key regions of battery operation of the have been monitored. The generated sine wave has a very low Total Harmonic Distortion which ensures better performance of the set-up.
A Basic Concept of Image Classification for Covid-19 Patients Using Chest CT Scan and Convolutional Neural Network

On March 12, 2020 WHO announced the status of a global pandemic related to the increasing Covid-19 cases. The outbreak has hit around 188 Countries. Healthcare professionals have repeatedly performed laboratory tests to get the right results to patients, such as, check the chest CT images of the patient's lungs. This is an essential role in clinical treatment and teaching task. In this paper, we tried to classify chest CT image of Covid-19 patient. CNN produce spatial characteristic from images so it very expeditious way for image classification problem. Three techniques are evaluated through experiments. The results of the experiments show the test set has 1119 Covid-19 chest CT images and 446 normal chest CT images. The experiment results represent that our offer model delivered the highest accuracy score of 97.57% among the other models, Inception ResNet-V2 and Inception-V3.
Task Offloading and Trajectory Control for UAV-Assisted Mobile Edge Computing Using Deep Reinforcement Learning

Mobile Edge Computing (MEC) has been widely employed to support various Internet of Things (IoT) and mobile applications. By leveraging the advantages of easily deployed and flexibility of Unmanned Aerial Vehicle (UAV), one of MEC primary functions is employing UAVs equipped with MEC servers to provide computation supports for the offloaded tasks by mobile users in temporally hotspot areas or some emergent scenarios, such as sports game areas or destroyed by natural disaster areas. Despite the numerous advantages of UAV carried with a MEC server, it is restricted by its limited computation resources and sensitive energy consumption. Moreover, due to the complexity of UAV-assisted MEC system, its computational resource optimization and energy consumption optimization cannot be achieved well in traditional optimization methods. Furthermore, the computational cost of the MEC system optimization is often exponentially growing with the increase of the MEC servers and mobile users. Therefore, it is considerably challenging to control the UAV positions and schedule the task offloading ratio. In this paper, we proposed a novel Deep Reinforcement Learning (DRL) method to optimize UAV trajectory controlling and users’ offloaded task ratio scheduling and improve the performance of the UAV-assisted MEC system. We maximized the system stability and minimized the energy consumption and computation latency of UAV-assisted the MEC system. The simulation results show that the proposed method outperforms existing work and has better scalability.
Classification of BOLD FMRI Signals using Wavelet Transform and Transfer Learning for Detection of Autism Spectrum Disorder

The World Health Organization (WHO) has reported a continuous rise in the prevalence of autism worldwide, in which 1 in 160 children in the world has ASD. The problem in ASD treatment has no definite cure, and one possible option is to control the disorder's progress. Several attempts to use resting-state functional magnetic resonance imaging (fMRI) as an assisting tool combined with a classifier have been reported. Still, researchers barely reach an accuracy of 70% for replicated models with independent datasets. Most of the ASD studies have used functional connectivity and structural measurements and ignored the temporal dynamics features of fMRI data analysis. The purpose of this study is to present several deep learning models to diagnose ASD based on temporal dynamic features of fMRI data and improve the classification results on a sample of data. The sample size is 82 subjects (41 ASD and 41 normal cases) collected from three different sites of Autism Brain Imaging Data Exchange (ABIDE). The default mode network (DMN) regions are selected for blood-oxygen-level-dependent (BOLD) signals extraction. The extracted BOLD signals' time-frequency components are converted to scalogram images and used as input for pre-trained convolutional neural networks for feature extraction such as Googlenet, DenseNet201, Resnet18, and Resnet101. The extracted features are trained using two classifiers: support vector machine (SVM) and K-nearest neighbors (KNN). Finally, the performance of each model is evaluated based on accuracy, sensitivity, and specificity metrics. The best results obtained from the KNN classifier with DenseNet201 as a pre-trained model are accuracy 85.9%, sensitivity 79.3%, specificity 92.6%. Compared with previous studies, it is concluded that the proposed model can be considered as an efficient tool for the diagnosis of ASD. From another perspective, the proposed method can be applied to analyzing rs-fMRI data related to brain disorders.
A Study on Vehicle Detection through Aerial Images: Various Challenges, Issues and Applications

Nowadays vehicle detection and counting at the border of countries, as well as states/cities, has become popular through aerial images because of security concerns. It will play a vital role to reduce the various crimes i.e. (children kidnapping, drug/alcohol smuggling, traffic misconduct, weapons smuggling, sexual misconduct and mission of country-related crime, etc.) at the border of the cities as well as countries. Vehicle detection and counting have various other applications like traffic management, parking allotment, tracking the rescue vehicle in hill areas, digital watermarking, vehicle tracking at the toll plaza and urban planning, etc. However, vehicle detection and counting task are very challenging and difficult because of the complex background, the small size of the vehicle, other similar visual appearance objects, distance, etc. Till now, traditional methodology introduced several robust algorithms which has limitations while extracting the features from aerial images. Recently, deep learning-based algorithms introduced and the outcomes of these algorithms are robust for such kind of applications in the area of computer vision. But accuracy of these algorithms is not optimized in aerial images because the deep learning algorithm required a huge amount of data to train the machine and the size of the object in aerial images is also too small. All these factors affecting the efficiency of the real-time device. This paper provides a brief description of traditional algorithms as well as machine learning and deep learning concepts to identifying the object through aerial images. The study has shown the comprehensive analysis of benchmark datasets and their parameters and corresponding challenges used by researchers and scientists in the area of object detection/tracking through aerial images
Predicting Periodic Energy Saving Pattern of Continuous IoT Based Transmission Data Using Machine Learning Model

The emerging applications of the Internet of Things (IoT) in various sectors generate a gigantic amount of continuous time-series data. As IoT based sensors nodes are very energy-constrained devices, continuous transmission of huge amounts of sensor data from IoT nodes is challenging but inevitable. It requires massive energy consumption. In this paper, we present an energy-saving pattern by predicting the periodic sensor data after analyzing the continuous transmission data from IoT nodes (at the server beforehand). Our system consists of an IoT based sensor network and a data processing unit. In the sensor network, two types of sensor data, such as temperature and humidity, are collected from four different nodes and sent to the processing unit (integrated on Raspberry Pi). In the processing unit, we worked with two machine learning models-Autoregressive Integrated Moving Average (ARIMA) and Long Short Term Memory (LSTM), which are applied separately on the data of four nodes to make a prediction of future values. A comparative analysis of two models is done in terms of different evaluation metrics where the accuracy of LSTM outperforms ARIMA. Finally, it is shown that with the prediction accuracy of both models, the efficient energy-saving pattern is a chieved by effectively reducing the continuous transmission of data.
UML Profile for Business Process Modeling Notation

In an enterprise, Information Systems (IS) are developed to support the Business Processes (BPs). A business process supporting information system is an important asset to define customer's needs and consist of set of activities to produce result of value to the customers. Business process modeling is often confused with software modeling. Business processes modeling carried out using Event-driven Process Chain (EPC), Business Process Execution Language (BPEL) or Business Process Modeling Notation (BPMN) etc. facilitates the understanding, analyzing and improving business processes but on the other hand, their models are too coarse to be used by software engineers. The advent of Unified Modeling Languages (UML) standardized by Object Management Group (OMG) has opened the door for business process modeling. In this paper, a UML profile is proposed to ease business process modeling i.e. BPMN so that software engineers can use business process models in familiar notations. As a result, UML model will be close to software engineer without being forced to master their peculiarities for understanding BPMN or to relate it with software requirements. A real-world insurance claim process case study is modeled using UMLPBPMN to show its applicability. Furthermore, obtained UML model can be verified using any formal language such as Petri-nets, timed automata etc. by applying model transformation.
A Hysteresis Space Vector PWM for PV Tied Z-Source NPC-MLI With DC-Link Neutral Point Balancing

The Photo-voltaic (PV) tied Z-source Neutral-point clamped multilevel inverter (Z-NPC-MLI) is used in solar grid connected applications due to its single stage conversion and better performance. Though the Z source inverters adaptation is accepted in grid connected technology, the need for suitable controller and PWM scheme are necessary to meet out the performance such as shoot through switching, neutral point balancing, and harmonic reduction. The space vector pulse width modulation (SVPWM) strategy is a prominent modulation technique for Z-source NPC-MLIs due to the flexibility to select the appropriate voltage vector. Previous publications have shown the control of a Z-source MLI using the SVPWM with and without modification of shoot through switching. However, the current controller (CC) based SVPWM is not matured, which is the most essential consideration for the grid connected inverter to provide neutral point balancing, shoot through control for low harmonic distortion and a high quality current. With all these aims, this paper presents a PV tied Z-NPC-MLI grid connected system with a unique hysteresis current control SVPWM (HSVM) strategy with neutral point (NP) balancing control and direct current control in the inverter input side. Also, the proposed HSVM is assuring the grid connection with high quality voltage and current waveforms. This CC based SVPWM for Z-NPC MLI has been validated through simulation and FPGA based experimental investigations. The results are confirmed the feasibility and reliability of the proposed HSVM for the PV tie grid connected Z- Source NPC-MLI.
A Low-cost Photopaper Based Wideband Wearable Antenna for WBAN Applications

A compact, flexible and low-cost antenna on a commercially available substrate is anticipated in this study for wearable applications of Medical Body Area Network (MBAN) band (2.36 – 2.40 GHz) and Industrial, Scientific and Medical (ISM) band (2.40 – 2.48). The proposed antenna fabricated on flexible photopaper based substrate with 0.27 mm thickness. The antenna is a combination of trapezoidal shape radiating patch with a partial ground plane of $28\ \text{mm} \times 13\ \text{mm}$. The overall size of the proposed design is $28 \times 40\ \text{mm}^{2}$, with 1 mm wide input microstrip feedline. The results of free space analysis reveal that designed antenna yields 1.2 GHz impedance bandwidth, a maximum gain of 1.61 dBi and radiation efficiency of 86% at a centre frequency of 2.45 GHz. The structural deformation evaluated numerically along both x and y axis at three different curvature radii, which show minor frequency shift with an omnidirectional radiation gain patterns. For the on-body evaluation of Specific Absorption Rate (SAR), antenna is tested numerically on the four layered human body model with three different separation levels between antenna and the body model, along with experimental performance evaluation on the 24 years old male volunteer. Both numerical and experimental results of on-body analysis proved that the proposed design has a good robustness to both on-body loading and bending analysis. Thus making our proposed antenna a good candidate for the on-body communication applications.
A Research Survey on State of the art Heart Disease Prediction Systems

Disease prediction systems are the better alternatives, to avoid the human errors in disease diagnosis and also assist in disease prevention with early detections. Highdemand in preventing the rapidly increasing heart disease death tolls expanded the horizons of the former research scholars for introducing the intelligent heart disease prediction systems. Prediction of heart disease from patient’s health record attributes is, a proven multi-dimensional decision-making system, which merely depends on mining attribute correlations too. Patient Health Records (PHR’s) with structured categorical data and unstructured text/image data are the major input resources for heart disease prediction. Heart disease dataset preparation, prediction system’s process flow design, process execution and results evaluation are the most common life cycle modules of any heart disease prediction system. Although many former research were introduced various heart disease prediction models, but they are still suffering from some common set of problems. Input dataset attributes modeling, attribute risk factor calculation, correlations mining; threshold determination and achieving the high accuracy in disease prediction are the major limitations of the existing heart disease prediction systems. As part of my research on designing intelligent heart disease prediction models, several research papers are analyzed and narrated that knowledge in a proper manner with detailed description. The main objective of this study is to represent the current scenario of heart disease prediction systems and their associated modules in brief.
A critical analysis of small but heavy-lift multirotor and their flight controllers

Multirotor unmanned aerial vehicles (UAVs) have gone from a single rotor to multiple rotors. Multirotor unmanned aerial vehicles (UAVs) are now used for surveillance and rescue operations. These are used for surveillance over disastrous areas and to perform necessary rescue action. The capacity of multirotor UAVs to carry heavy payload is less with small and limited propellers. Therefore, the number of propellers have been increased, and ultimately the size of multirotor UAV has also been increased. The rotor arrangement is fundamental in the thrust of multirotor UAVs. The opensource software has been developed to integrate with the flight controller hardware. The current focus of research is to reduce the size of multirotor UAVs and increase the payload capacity of multirotor UAVs. The thrust has been reduced in case of overlapping rotor configuration, such as in coaxial and half propeller overlap configuration. These configurations are adopted to reduce the size of multirotor UAV. In this study, different multirotor UAV configurations have been studied for selection of best configuration for small but heavy-lift multirotor. The overlapping configurations have been found to increase the payload and reduce the size of the multirotor. The thrust has been increased to about 90% in the case of some propeller overlap configuration with a rotor plane angle. For coaxial configuration, the rotor thrust was about 76% of the thrust of the independent rotor. The thrust generated by the flat configuration is more than the coaxial and less propeller overlap configuration. The flight controllers are responsible for the stability and control of UAVs. Many flight controllers are working on the latest technology and can be programmed for various features. The flight controllers also contribute to increasing stability. Mostly open-source software is available to integrate with the flight controller hardware. The ARM-based flight controllers like Pixhawk flight controllers are the best to control the multirotor. These flight controllers can be programmed quickly and can be installed on the multirotor.
Twitter Spam Detection Using Natural Language Processing by Encoder Decoder Model

With the increasing demand of social life in today’s world one famous platform known to be as Twitter plays important role for every citizen to connect socially, whether in the form of tweeting a tweet for other person or exploring various fields in the running world. But this platform, now a days gets infected by some spammers, with the intention of increasing traffic in their spam web sites they connect their URL with the informative tweet where there is no relation between the content present in URL and the tweet message, these are called spammed tweets. This paper provides a unique approach to detect whether the tweet mentioned by the user is spam or not spam using the encoder decoder technique used with vectorizer converter on the mentioned tweets and on its linked URL, which leads to the prediction of similarity score between them.
Semantic Service Clustering With Lightweight BERT-Based Service Embedding Using Invocation Sequences

Service clustering is an efficient method for facilitating service discovery and composition. Traditional approaches based on the self-description documents for services usually utilize service signatures. In Web service composition, service clustering can also be performed by the invocation relationship between services. Therefore, based on the successful development of several embedding techniques for words in several contexts, a novel deep learning-based service embedding using invocation sequences is devised for service clustering. Moreover, many microservices are being created because of the rapid development of the Internet of Things (IoT), and edge, and fog computing. Following these developments, Web service composition based on these environments has emerged in abundance. More efficient lightweight approaches to analyze large numbers of services are necessary for service clustering. Consequently, a lightweight deep learning-based approach for the semantic clustering of service composition is presented to address these requirements. In this paper, we first propose the concept of service embedding to capture semantic information from invocation sequences. Second, we suggest using state-of-the-art neural language sequence models for service embedding and develop a corresponding lightweight Bidirectional Encoder Representations of Transformers (BERT)-based model. Next, combined with K-means clustering, the semantic clustering of service composition is evaluated. Finally, the experimental results show that the clustering process can be effectively performed by the lightweight BERT-based model.
A Brain-Switch based on change in SSVEP Magnitude

A brain-switch is a two-command Brain-Computer Interface (BCI) that can be used to detect control and no-control intentions of a BCI operator. This paper presents a brain-switch that works on the principle of detecting change in the Steady-State Visually Evoked Potential (SSVEP) magnitude. The SSVEP magnitude was modulated using eyeaccommodation or focusing mechanism. This switch works on the principle that when a person keeps his/her focus stable on an SSVEP stimulus or at a fixation target (kept between the stimulus and the subject), the change/variation in the SSVEP magnitude is lesser as compared to the change that is observed when the subject shifts the focus between the stimulus and the fixation target. Detecting this change, the switch was said to be operated (in control state) when a subject shifted his focus away from the stimulus or towards the stimulus. This switch could achieve an offline accuracy of 97.8 %, True Positive Rate (TPR) of 93.3 % and False Positive Rate (FPR) of 0 % for a subject.
A Deep Transfer Learning Approach to Diagnose Covid-19 using X-ray Images

The Covid-19 disease which was caused by novel coronavirus (SARS-CoV-2) has already become a great threat for humans beings. The virus is spreading rapidly around the world. Therefore, we crucially need quick diagnostic tests to identify affected patients and to minimize the spread of the virus. With the advancements of Machine Learning, the detection of Covid-19 in the early stage would facilitate taking precautions as early as possible. However, because of the lack of data-sets, especially chest X-ray images of Covid-19 affected patients, it has become challenging to detect this disease. In this paper, a deep transfer learning-based pre-trained model is named VGG16 along with adapt histogram equalization has been developed to diagnose Covid-19 by using X-ray images. An image processing technique named adaptive histogram equalization has been used to generate more images by using the existing data set. It can be observed that VGG-16 provides the highest accuracy which is 98.75% in comparison to two other pre-trained models such as VGG-19 and Mobilnenet-V2(97% accuracy for VGG-19, 92.65% accuracy for Mobilenet-V2).
Lyapunov Based Trajectory Tracking Dynamic Control for a QBOT-2

This paper presents the nonlinear kinematic and dynamic control for a QBOT2, a wheeled mobile robot (WMR). The work presents the design and simulation of a trajectory tracking controller for the system. The kinematic controller is a Lyapunov based nonlinear feedback control. The method of Backstepping is used to design the dynamic controller. The controllers are implemented on the system hardware using QUARC, a MATLAB-Simulink based software.