Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Resource Allocation for Multi-User Scalable Video Streaming in Satellite Communication Networks

The problem of adaptive multi-user scalable video streaming transmission in satellite communication networks has attracted much attention from researchers. Despite the stochastic nature of channel quality, we employ the Scalable Video Coding (SVC) technology to select the optimal number of video layers for users dynamically. And we study an optimization problem to maximize the Quality of Experience (QoE) of users when trying to avoid the playback interruption, while satisfying the battery energy, the time resource and the video rate constraints. By introducing the Lyapunov optimization theory, we decompose the optimization problem into three independent subproblems for energy allocation, time utilization management and video layer selection. To solve these subproblems, a joint optimizing strategy for multi-user SVC video streaming transmission is proposed. Numerical simulations are performed by using the actual H.264 SVC video streaming. The simulation results show that the proposed algorithm can reduce the playback interruption rate and improve the QoE of users significantly compared with existing algorithm.
Achieving a Blockchain-based Privacy-preserving Quality-aware Knowledge Marketplace in Crowdsensing

It is increasingly popular to utilize the wisdom of the crowd for knowledge discovery and monetization. Most of the existing knowledge marketplaces in crowdsensing are implemented by a third-party platform, which may compromise users' rights and be vulnerable to incurring attacks in practice. To eliminate the untrustworthy behaviors of the third party and improve tolerance for the attacks, some blockchain-based knowledge marketplaces in crowdsensing have been proposed. However, the existing blockchain-based knowledge marketplaces fail to simultaneously guarantee privacy (i.e., data privacy and task privacy) and quality awareness. In this paper, we design a blockchain-based privacy-preserving quality-aware knowledge marketplace (PQKM) based on truth discovery, secure K-nearest neighbor computation, matrix decomposition, and data perturbation. PQKM privately calculates users' data quality and automatically rewards users based on their data quality. Detailed security analysis demonstrates that PQKM can preserve data privacy and task privacy during knowledge discovery and monetization. Extensive experiments are conducted on the open real-world dataset to show that PQKM has acceptable efficiency and affordable performance.
PV-Fed Transformer-Less Five-Level Grid-Tied Inverter

This paper suggests a Transformer-Less Switched-Capacitor based 5-Level Grid-Tied Inverter ($TLSC5LGTI$) with double boosting capability and self-balanced capacitors. The presence of Common-Ground-Point (CGP) has removed the leakage-current, which makes the suggested converter appropriate for Photovoltaic (PV) applications. The suggested structure employs Peak Current Control (PCC) technique to generate the switching pulses, and control the active/reactive power flow by properly controlling the injected current into the grid. The 5-level waveform leads to less Total Harmonic Distortion (THD), and compact filter size. The correct function of suggested TLSC5LGTI has been affirmed by simulation and experimental analysis.
IPL Data Analysis and Visualization for Team Selection and Profit Strategy

Day by day, the role of data science and machine learning in cricket is increasing due to the large amount of data generated from a single player on a whole line. The field of data science is the intensive study of data to extract insights and knowledge from the data and apply the acquired knowledge and actionable insights. These available data and statistics are used to predict things like the team's first-innings score and the probability of winning the second team, etc. This study works on Indian Premier League (IPL) Data Analysis 2022 and Data Visualization for the duration (2008–2020) using Python. This application modules are followed by preprocessing, data analysis, and visualization, and finally create a model that predicts the team's overall score and the probability of winning. When building models, python algorithms such as Numpy is used for Scientific Computing, Pandas used to perform Data Analysis, and finally Matplotlib and Seaborn to enable Data Visualization. Finally, this paper helps the trainers and owners of the team during the auction to select an emerging player to win the matches in the entire season and to win the trophy and to get profit to the owners by the profit strategy done in this analysis report.
Analysis of Three-Phase to Five-Phase System Under Unbalance condition

Multi-phase (more than three phase) is being increasingly used in many applications. Due to higher phase numbers, there is more possibility for a multi-phase system to have unbalancing between different phase voltages in comparison to a three-phase system. Performance and behaviour of a multi-phase system under unbalance condition is of much importance. Conversion of an unbalanced system to a balanced system was proposed by C. L. Fortescue and balanced components was given the name ‘‘symmetrical components In this paper three-to-five phase transformation has been analysed under various unbalanced conditions using simulation and experimental analysis. Symmetrical components transformation of a five-phase system is presented and determination of active and reactive power with the help of symmetrical components is developed. Furthermore, sequence circuit of three-to-five phase transformation is developed to model various operating conditions. With this modelling, the degree and nature of different unbalances and their effects on the system can be predicted in advance. Both simulation and laboratory tests results for a three-to-five-phase transformation is presented. It has been concluded that experimental results are well in line with the analytical results.
Classification of the Stress Levels Based on Heartbeat Evoked Potentials – a Pilot Study

It is known that emotion affects the brain signals (e.g., EEG) and heart rate variability (HRV). However, few studies have explored how the heartbeat-evoked potentials (HEPs) may relate to stress, a type of emotion. Here we show that the features extracted from the HEPs can be used to classify the low and high stress levels induced using a mental arithmetic (MA) paradigm. The HEPs were derived from 22 channels of scalp EEGs and one channel of ECGs simultaneously recorded when the subjects were solving the simple or complex MA problems. The feature matrix was constructed from the HEP epochs differing significantly between the two types of MA problems. Among three machine learning models, the best average classification accuracy reaches 99.89%.
Joint Learning of Similarity Graph and Low-rank Tensor for Multi-view Clustering

Multi-view clustering methods have drawn more and more interest in recent years with the increase of unlabeled multi-view data. In order to make full use of the consistent and complementary information of multi-view data, and reduce the influence of noise and outlier, a similarity graph and low-rank tensor joint learning method (SGLTL) is proposed. SGLTL first obtains the similarity matrix of each view through the least-square regression method to pursue the low-order correlation. Then, the similarity matrices are integrated into a tensor with low-rank constraints to capture the high-order correlation. Finally, the similarity matrices and low-rank tensor are jointly learned to fully exploit the multi-view information and effectively remove noise and outliers. Experimental results on different benchmark datasets demonstrate the effectiveness of the proposed method.
Comparison of Conventional, Modern and Intelligent Control Techniques on UAV Control

There has been substantial advancement over the last decade in the field of unmanned aerial vehicles (UAVs) development because of their surprising capabilities and numerous applications in the robotics field which includes disaster assistance, search and rescue operations, data, and image acquisition of affected areas, and many more. This study’s primary aim is to give an overview of the UAV quadrotor system. In this paper, the detailed modeling, effects, drawbacks, and benefits of different control algorithms have been identified based on results and observations made by various researchers. The research paper provides an easy comparison of different applied control algorithms.
Leveraging Logistic Regression in Development of a Parkinson’s Disease (PD) Kinematics-based Diagnostic Framework

Parkinson’s Disease (PD) is a neurodegenerative movement disorder caused by reduced dopamine, dopaminergic neurons, and nerve cells in the substantia nigra. Diagnosis of PD today largely clinical and relies on the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS, also referred to as UPDRS), wherein clinicians observe gait patterns in patients and provide a subjective score ranging from 0-4 based on the ability of the patient to ambulate with out without aid. Diagnosis of PD is also complicated by 1) a lack of distinguishability from other gait and neurodegenerative disorders, including essential tremor (ET) and 2) increasing ambiguity of PD symptoms including gait deterioration and falls further from the onset of the condition. However, early PD diagnosis is imperative for patients to respond appropriately to intervention and relieve motor symptoms such as tremor and rigidity. This study aims to develop a gait-based diagnostic framework for PD using data collected at the University of Minnesota from healthy controls and PD patients over 3 years. The model was developed using R’s ggplot for feature selection and generalized linear model library (GLM) for logistic regression. The final metrics, including accuracy, standard error, and p-values, displayed the value in applying gait variables, such as cadence, in diagnostic models of PD. Such findings may offer potential future directives for the classification of PD from other tremor-related and neurodegenerative disorders such as multiple sclerosis, as well as exhaustive diagnosis of stages of PD based on the presence of motor symptoms, applying the H&Y scale.
Sensitivity results in stochastic optimal control: A Lagrangian perspective

In this work we provide a first order sensitivity analysis of some parameterized stochastic optimal control problems. The parameters and their perturbations can be given by random processes and affect the state dynamics. We begin by proving a one-to-one correspondence between the adjoint states appearing in a weak form of the stochastic Pontryagin principle and the Lagrange multipliers associated to the state equation when the stochastic optimal control problem is seen as an abstract optimization problem on a suitable Hilbert space. In a first place, we use this result and classical arguments in convex analysis, to study the differentiability of the value function for convex problems submitted to linear perturbations of the dynamics. Then, for the linear quadratic and the mean variance problems, our analysis provides the stability of the optimizers and the C 1 -differentiability of the value function, as well as explicit expressions for the derivatives, even when the data perturbation is not convex in the sense of [R.T. Rockafellar, Conjugate duality and optimization. Society for Industrial and Applied Mathematics, Philadelphia, Pa. (1974).
Control and Mitigation of Harmonics and Power Filtering in Photovoltaic Grid Tied System Using MFI

Photo-voltaic based power generation distribution system uses power electronics converters for grid connected interfacing. In this study, a multifunctional (MFIs) grid-tie inverter with harmonics current mitigation/compensation techniques have been proposed as well as examined for the Grid tied (On-grid) Photovoltaics networks. This concept is subjected to modification and improvement of this proposed PV inverter-based control techniques with also modifications in active power filtering control algorithm. Grid -tie Photo-Voltaic inverter have same analysis and control techniques like active power filtering schemes. This study presents, conventional PLL algorithms are implemented to calculate the angular positions of the synchronous references in the system. Generated power from photovoltaic system is accounted and supplied to the control-loop. The evaluated current at the load side are progressed to achieve harmonic currents component. Then, the resultant signals are utilized to drives the grid-tied multifunctional inverter.
Performance Comparison of Different AGNR RTD

The graphene nanoribbon-based resonant-tunneling diodes (RTDs) have lately grabbed the interest of numerous scientists. In this paper, we have investigated the performance of double and triple-barrier hetero-structure armchair graphene nanoribbon (AGNR) RTD and made a comparison between them. The investigation shows that triple barrier hetero-structure (TBHS) RTD has superior characteristics in terms of peak-to-valley current ratio (PVR) and power dissipated at the valley current. In addition, we compared the performance of the double barrier hetero-structure (DBHS) with the electrostatically doped (ED) AGNR RTD. We discover that the hetero-structure RTD has a superior peak-to-valley current ratio and valley current power dissipation characteristics. Our article also examines the effect of a single vacancy defect in the barrier and well regions of a double barrier hetero-structure (DBHS) AGNR RTD. Introducing a single vacancy defect in the well region decreases the PVR and increases the power dissipated at the valley current. Introducing a single vacancy defect in the barrier area, on the other hand, enhances the PVR and lowers the power wasted by the valley current.
A Natural Language Processing System for Truth Detection and Text Summarization

In the futuristic world, differentiating between accurate information and false information would be challenging. To identify the fake news a truth detection system will be extremely useful. Different artificial intelligence models were compared, and the highest performing algorithm was chosen. This model was subsequently updated using a larger data-set in order to achieve better results in more real-world applications. The most essential assumptions are that the user will only utilize the subject field in which the model will work. Earlier works have implemented the process of fake news and truth detection separately from the process of summarizing the said news/information. No method was available for streamlining the process to detect whether given information was true or not and to then summarizes the given information automatically. This study has compared and contrasted the differences in accuracy and overall performance of each algorithm for fake news detection and summarization of text. Using smaller dataset, LSTM cells outperformed all other models with an accuracy of 99%. For the larger dataset, the modified LSTM has worked really well with 98.6 percent accuracy.
A Multimode Control Scheme for Output Regulation and Voltage Balancing in a Stacked-Switch Resonant Converter With Extended High Efficiency for Wide Gain Range Applications

In this article, a new hybrid comprehensive multimode control system is proposed for a stacked-switch bidirectional CLLC resonant converter for wide voltage gain applications. By configuring the presented leg with different switching patterns, the presented leg in the converter can operate in different modes: full-wave (FW), half-wave (HW), or voltage-doubler (VD) through the converter’s built-in circuit redundancy. The proposed hybrid control scheme consists of different control modes: variable frequency modulation, pulse-width modulation (PWM), and asymmetric PWM (APWM) control. Correspondingly, four voltage gain curves are derived from the proposed converter. With the proposed approach, the converter is able to achieve constant voltage regulation while constraining the range of switching frequency, allowing very high efficiencies to be realized for a wide range of input voltages. Soft-switching is achieved for all the semiconductor devices for the entire operating range. The steady-state and dynamic performances of the proposed hybrid comprehensive control system are verified through a proof-of-concept 400-V $\sim ~1$ -kV/ 700-V, 1-kW laboratory prototype. Results confirmed that the efficiency is maintained between 95.03% and 96.97% throughout the specified input voltage range.
Interval Optimization Technique Based Multi-Objective Scheduling of Electric Vehicles

Increasing penetration of electric vehicles (EVs) mandates the adoption of scheduling strategies to mitigate adverse effects of their charging load on distribution system. EVs scheduling is a multi-objective problem where the objectives fall in the interest of either EV users or distribution system operator (DSO). Therefore, this paper aims to deal with multi-objective scheduling of EVs to couple the interest of both the entities. To consider the interest of EV users, an economic objective of minimizing the charging cost is considered whereas to address the interest of DSO a technical objective of load levelling is considered. A multi-objective grey wolf optimization (MOGWO) is used to handle the conflicting objectives followed by technique for order preference by similarity to ideal solution (TOPSIS) based approach for obtaining trade-off solution. In addition, uncertainty in forecasted load demand and electricity price is handled using interval optimization. Results show that EVs scheduling with interval optimization is more robust as compared to optimistic and pessimistic cases.
A New Deep Learning Method for Accurate Cardiac Heart Failure Prediction from RR Interval Measurements

cardiovascular diseases are the major cause of death worldwide. Early detection of heart failure will assist patients and medical professionals in taking better precautions to reduce risks. The objective of this study is to find a technique that can reliably forecast the risk of cardiovascular illnesses. With the help of the training data we offer, deep learning algorithms like Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) make these predictions. Prediction accuracy will be reduced by a lack of medical data. As a part of our study, we examined DNN architectures to forecast cardiac failure. Over the training data, existing deep learning methods were employed. A new deep learning method that can predict heart failure using RR interval measurements is developed by comparing the accuracy performance of the proposed and existing models. The Physiobank NSR-RR and CHF-RR databases were used to compile the findings. The new model, which was based on experimental findings using these two free RR interval databases, attained a 94% accuracy rate compared to the existing model's 93.1% accuracy rate.
A High-Voltage Gain DC/DC Resonant SC Converter for High-Power and Wide Input Voltage and Load Variation Ranges Applications

In this paper, a novel high step-up dc-dc resonant switched-capacitor converter (R-SCC) is introduced, which can be used for high-power, high-frequency, and wide output load and input voltage variation ranges applications. Due to symmetric structure of the proposed converter, low output voltage ripple and low input current ripple values are achieved. Therefore, the size of input and output filters are very small. Also, simple control method and gate-drive circuits can be applied, in practice, and soft-switching operation is realized for all diodes and switches of the structure. The proposed converter can properly operate at above and below its resonant frequency. Also, its output voltage can be regulated by different control methods such as phase shifted, variable switching frequency, and PWM approaches, properly. Low switching frequency variation guarantees output voltage regulation, even when its input voltage and output load change in wide ranges. To confirm the proposed converter features, the simulation results are presented to adjust a 400 V output voltage for wide input voltage (60-120 V) and output power (100-1600 W) variation ranges. Also, switching frequency changes from 319 kHz up to 415 kHz is obligatory.
Fast Classification of Alzheimer's Disease Based on Improved Convolutional Neural Network

The purpose of this paper is to apply an improved convolutional neural network to classify magnetic resonance images and predict the degree of Alzheimer's disease. Currently, tens of millions of people worldwide suffer from Alzheimer's disease, which greatly increases the cost to society. Early detection facilitates prompt treatment. Traditional diagnosis includes neuropsychological tests, hematological tests and neuroimaging tests. Due to the complexity, subjectivity, instability and other factors of manual inspection, the diagnosis may be wrong. Therefore, it is necessary to develop an objective, stable and automatic auxiliary diagnosis method. Since magnetic resonance images are unique and easy to obtain, they are very helpful for diagnosis. Therefore, this paper proposes a magnetic resonance image detection technology based on computer vision. Convolutional neural network has an outstanding performance in the field of image recognition and classification. Based on the improved convolutional neural network, the final training accuracy reaches 100%, and the test accuracy exceeds 99.7%, which can meet the needs of auxiliary diagnosis.
A Rehabilitation Training System for Hand-function Exoskeletons Based on the Cloud Platform

The rehabilitation effect was quantitatively evaluated and fed back in time in the rehabilitation training for stroke patients with limb movement disorders. The rehabilitation training system for hand-function exoskeletons based on the cloud platform promoted the efficient allocation of medical resources by breaking the Spatio-temporal limitations to a large extent. Hand-function rehabilitation training was integrated into games by combining the new somatosensory device LeapMotion with the Unity3D game engine. The rehabilitation training of patients distributed in different regions was tracked by the cloud platform technology based on the SSM and Shiro framework through the Internet. It provided timely feedback on the evaluation of the rehabilitation effect based on quantitative data. This system is of great significance in improving the rehabilitation training quality of hand-function patients, promoting the early recovery of patients, and reducing the burden on the family and society.
The standard fourth moment coefficient of kurtosis and its influence function: an early intuition by L. Faleschini

Riassunto: Che cosa misura la curtosi? Quali trasformazioni permettono di conseguire aumenti o riduzioni della curtosi? In un pionieristico lavoro del 1948, L. Faleschini rispondeva a questi interrogativi calcolando la derivata parziale del coefficiente di curtosi 2 rispetto ad una frequenza puntuale assoluta. Oltre a rivalutare la portata della “derivata di Faleschini”, il presente lavoro ne esplicita un’inattesa identita formale con il concetto di curva di influenza, coniato da Hampel nel 1968 ed applicato per la prima volta a 2 da Ruppert soltanto nel 1987.