Miracle-dz/UniFAR-FTUs huggingface dataset:
A Two-Tier Deep Neural Network Detector for Two-User Rate-Splitting Multiple Access Systems

In this paper, we propose a two-tier, deep neural network (DNN)-based detector for a two-user, multi-antenna-equipped rate-splitting multiple access (RSMA) uplink system. The proposed DNN model primarily consists of a long short-term memory architecture, with one network that decodes the common streams and another that decodes the private streams, in an orthogonal frequency division multiplexing (OFDM) setting. The DNN-based detector is designed to decode RSMA messages from the superimposed signal at the base station (BS) in one go, and overcomes the disadvantages of a successive interference cancellation-based receiver. Through Monte Carlo simulations, we show that the proposed detector nearly attains the performance of the optimal maximum likelihood detector for various modulation schemes on common and private streams with OFDM. Further, we study the effect of number of transmit antennas and the DNN hyperparameters on the performance of the proposed detector, in terms of the symbol error rate.
Concerning singular transformations _{} of surfaces applicable to quadrics

The theory of transformations Bk of deforms by flexure of quadricst may be regarded as a later development of the concepts introduced by Sophus Lie in his researches on the transformations of surfaces of constant curvature. The attempt to make a possible extension of the same principles to researches of another class of applicable surfaces leads us naturally to propose a problem of arranging plane elements, orfacettes, in space-which for the sake of clearness we will explain in detail.t A facette consists of a plane and a point of it called the center. We think of a surface S as the totality of its ooI2 facettes f, the planes of the facettes being tangent to S at. their respective centers. We consider associated with each facette f a simple infinity of facettes f', in accordance with any continuous law whatever. We imagine also that in every deformation of S, each facette f and the oo associated facettes f' are carried along as an invariable system. In each configuration of S the associated facettes f' form a triply infinite system, and in general they cannot be arranged into a series of oo1 surfaces S', each consisting of o2 facettesf'. The problem in view consists precisely in determining all the cases for which the above circumstance is true in all deformations of S. Of the general problem thus stated it is easy to indicate an infinity of particular solutions amongst which are immediately evident those in which each of the ool surfaces S' remains constituted always of the same oo2 facettes f'. But, in view of the eventual applications to problems of deformation, it is opportune to limit the problem much more, and to suppose that everv facette f and each of its associated facettes f' has the center of one in the plane of the other. Thus the surface S and each of its transforms S' are always the focal surfaces of the rectilinear congruence formed by the joins of corresponding points.
A Survey: Faults, Fault-tolerance & Fault Detection Techniques in WSN

Present modern world become more automated, forecast-able and accurate by using wireless sensor network. To make a WSN more undependable and reliable, researchers have to make advance research on each and every domain of WSN, mainly on fault tolerant techniques. This paper thoroughly investigate the mechanism to achieve a fault tolerant network. The prime focus of this paper is to make understanding about faults, failures and its classification and detection in the network and explain the techniques to detection of fault, prevention of fault and recovery of the fault in the sensor network, these are the very crucial parameters of a fault tolerant network.
Analysis and Classification of Electroglottography Signals for the Detection of Speech Disorders

This paper discusses about an automated system for classifying the pathological electroglottography (EGG). There are four major steps involved in the proposed technique: preprocessing, feature extraction, feature selection and then classification. For this study, we exported the signals from the Saarbrucken Voice Database (SVD) dataset, which includes both the pathological and normal EGG of the vowels /a/, /i/, and /u/ at normal pitch. The various features such as, spectral, cepstral, temporal and statistical features of pathological and normal EGG signals are extracted, and the most pertinent features have been chosen by comparing the feature score using the criteria of minimum redundancy maximum relevance (MRMR). The performance of selected features have been evaluated by well known classifiers, Ensemble learner, Support vector machine, K-nearest neighbour, Multi-layer perceptron. It is observed that the selected features perform best on ensemble learner classifier, with an accuracy of 93.15%, for the classification of normal and abnormal EGG signals.
Detent Force Reduction Design for the C-Core Single-Phase Permanent Magnet Linear Oscillation Actuator

Single-phase permanent magnet linear oscillation actuators (SP-PMLOAs) have been paid extensive attention in the field of compressors and industry applications. However, the detent force of SP-PMLOA limits the effective stroke and affects the control accuracy of the system. The purpose of this paper is to present a novel C-core SP-PMLOA with double stators arc-teeth and auxiliary slots to reduce detent force. Firstly, the basic structure and dynamic equation of the traditional linear oscillation actuator are introduced. Then, considering the magnetic flux leakage, the detent force formulation is derived by the flux tube method, and a novel structure is presented to reduce detent force and increase the effective stroke by the parametric scan. In addition, the traditional and presented structures of C-core SP-PMLOA are analyzed by 2-D finite element analysis (FEA) mothed. Finally, a prototype is built and tested to verify the results of FEA and calculations. The experimental results show that the presented structure can significantly reduce the detent force and increase the effective stroke.
Deadbeat Voltage Control for a Grid-Forming Power Converter With LCL Filter

Grid-forming power converters are controlled as voltage sources to regulate the grid voltage and frequency. These converters can increase power system strength if they impose a voltage waveform resilient to grid transients. For this reason, in this paper, we propose a deadbeat control strategy of the capacitor voltage for high power converters with LCL filter. To damp the LCL resonant poles, an active damping strategy is developed, based on a modification of the deadbeat control law. With this purpose, a notch filter is applied to the electrical variables allowing to emulate different damping resistances for the fundamental component and the harmonics. As a result, the active damping does not introduce tracking errors of the fundamental frequency component, while it provides damping to the filter resonance. The proposed strategy does not require knowledge of the grid impedance, an interesting feature in grid-connected power converters because the grid impedance is generally unknown. Experimental results validate the proposed strategy.
Cognitive Radio Technique for Blockchain-based Vehicles with IoT

In the In the Internet of Vehicles (IoV), intelligent decision-making is performed by objects with cognition. by comprehending both the social and physical worlds, Cognition Radio Network (CRN) is seen as a workable option. The existence of dangerous devices (MD) further impairs network performance, although For services to be improved and safety metrics to be driven, spectrum availability and information exchange among vehicles are crucial. Recently, a blockchain approach has been added to CRN-based IoV to stop data modification from these Entities and enable the cars to monitor both legal and illegitimate network activity. In this paper, we provide security to IoV throughout spectrum sensors and details transfer using CRN by sensing the paths through with a decision-making technique called Method for Order Personal taste by Similarity to a Solution (topsis (TOPSIS), a technique that inspires the confidence of its Behavioral Users (CU) by analysing certain predefined attributes. The network also maintains a blockchain to track every action involving information that has been saved. Using different frequency sensing and security settings in comparison to an IoV baseline solution, the suggested method is thoroughly evaluated against a number of security criteria. Numerous simulations indicate that when compared to the baseline system, our suggested approach is around 70% more effective at identifying malicious nodes and DoS threats.
Deep learning for the detection of fraudulent credit card activity

As the world is forwarding to new technology’s innovation and research. for one keeping their privacy defended is the most rock-hard task in the current scenario’s privacy breach is common in which offensive and unauthorized access by a third party is committed in order to steal the confidential information which termed in cyber security attack as spyware. such of the massive and worldwide problems can be tackled with the help of deep learning this research paper will demonstrate in modelling of data set using deep learning. This study intends to distinguish between legitimate and fraudulent financial dealings by employing a variety of deep learning techniques, including the convolutional neural network (CNN) and the long short-term memory (LSTM), both of which are utilised to make accurate predictions regarding financial dealings. As far as we will analyze and pre-process the data set and compare both CNN and LSTM with each other in order to find the optimal solution.
Frequency-Domain Analysis and Design of Thomson-Coil Actuators

Thomson coil actuators consist of an electric coil repelling a metallic disk. They are considered the fastest electromagnetic actuators for short displacements and for this reason are favored for DC circuit breakers. Although the basic physics are understood, the actuator's minimal components are very interdependent, making analysis difficult. Actuator design so far has thus been limited to trial and error based on finite-element models. In this paper, a novel approach is presented based on the electromagnetic frequency response of the actuator. This method is shown to be an intriguing analysis tool and various actuator design directions are presented. Experimental results provide a confirmation of the analysis.
Compensation Voltage Injection Based Neutral Point Voltage Fluctuation Suppression for NPC Converter

Three-level neutral point clamped (NPC) inverter is widely used in new energy power generation, motor drive and many other occasions. However, the neutral point voltage imbalance of NPC inverter will affect the output of the inverter system, and the control of neutral point voltage has become a research hotspot. In this paper, a compensation voltage injection based neutral point voltage balancing method is proposed, which controls the neutral point current in the switching cycle to realize neutral point voltage self-balance. MATLAB/Simulink simulation model is established to verify the effectiveness of the method, and the simulation results show that the low-frequency fluctuation of the neutral point voltage caused by the modulation wave is eliminated by injecting compensation voltage, the magnitude of high-frequency fluctuation is less than 5% of the low-frequency fluctuation.
Real-time image restoration for digital multifocusing in a multiple color- filter aperture camera

A multiple color-filter aperture (MCA) can provide a single camera with depth information and multifocusing. However, the original version of the MCA system exhibits inherent limitations such as manual, empirical tuning parameters for the color channel registration and fusion (CRF) process. Furthermore, a CRF output image still contains undesired out-of-focus blur because of the finite-sized apertures and the lateral displacement of each color-filter aperture, which results in low exposure, color mixing, deviation of color convergence, and divergence of light rays. For overcoming these problems, we present a real-time image processing solution for digital multifocusing in a MCA system.
H9 and H10 Transformer-Less Solar Photovoltaic Inverters for Leakage Current Suppression and Harmonic Current Reduction

Now-a-days, transformer-less inverters integrating renewable energy resources as solar photovoltaic systems are commonly employed in many grid-connected distributed energy systems in both industrial and residential settings. Transformer-less photovoltaic (TLPV) inverters have attracted increased attention due to their unique advantages, such as increased efficiency, low cost, and ease of installation. Converting fluctuating solar dc power to consistent ac power has been a difficulty in the recent decade to avoid security risks like ground fault currents and leakage currents. Leakage current minimization is one of the most crucial factors in the TLPV inverters. Different TLPV inverter topologies have been presented in the past, with leakage current being reduced by galvanic isolation on the dc or ac side of the inverter, common mode voltage (CMV) clamping, and modulation techniques. This paper proposed two novel single-phase grid-tied TLPV inverter topologies with control blocks, which can handle a wide variety of grid voltages. The proposed TLPV inverters offer very small leakage current, reduced total harmonic distortion (THD), and improved power quality. Based on the number of semiconductor devices, the proposed inverters are termed as H9 and H10 inverters. The switches are controlled using the unipolar sinusoidal pulse width modulation, which results in constant common mode voltage and reduction in leakage currents to 11.09 and 11.73 mA for the proposed H9 and H10 inverters, respectively. The proposed H9 and H10 inverters minimize the THDs to 1.62 and 2.09%, respectively. Besides, the proposed H9 and H10 inverters exhibit 98.19 and 98.12% European efficiency. The simulations are carried out on MATLAB/Simulink and the results are experimentally validated using downscale laboratory test platforms
A Novel Embedding Architecture and Score Level Fusion Scheme for Occluded Image Acquisition in Ear Biometrics System

Significant progress in the field of ear-based biometrics has been made in recent studies. But methods to deal with degradation caused by hair occlusions during ear image acquisition have not been addressed yet. Use of occluded ear images from both sides can give better or comparable results to that of clean image acquisition from a single side. To test this, in this work we introduce a novel embedding generation network using conventional CNNs along with parallel paths of Learnable Scattering Wavelet Networks. We have shown results of experiments for augmented training of proposed embedding network and score level fusion of both side-ear images to mitigate the effects of hair occlusions in verification and identification tasks. We also show extensive simulation results over closed and open testing sets to analyse the one-shot learning capabilities of our proposed embedding network. The reported performance metrics show the improvement achieved by using our proposed embedding network and fusing both sides of occluded ear images.
Short-Term Load Forecasting using Conditionally Restricted Boltzman Machine Optimized by Modified Grasshopper Optimization Algorithm

This paper proposes a novel hybrid model by integrating data pre-processing, feature extraction (FX), and forecasting & optimization modules to improve the accuracy and convergence rate. Where, the radial basis kernel-principal component analysis (RB-KPCA) based FX algorithm eliminates the redundant and irrelevant features to ensure the high computational efficiency. While, the modified grasshopper optimization algorithm (mGOA) optimizes the appropriate control parameters of the conditionally restricted Boltzmann machine (CRBM) to avoid the solution trapping into the local optimum and returns the results with improved accuracy. Furthermore, since the accuracy and convergence rate are two contradictory objectives, which are difficult to achieve, simultaneously. However, the the proposed FX-CRBM-mGOA forecasting model is designed in such a way to simultaneously achieve these relatively independent objectives. To evaluate the performance in terms of MAPE, accuracy and convergence rate, the proposed model is implemented on the publically available dataset of the Dayton power grid, USA. The results show that the MAPE of the proposed model is 0.4525%, the information-based artificial neural network (MI-ANN) is 2.4202%, long short-term memory (LSTM) is 3.231%, ANN-based accurate and fast converging (AFC-ANN) is 2.452%, and Bi-Level is 2.123%. The devised framework outperforms MI-ANN by 3.2%, Bi-level by 2.3%, and AFC-ANN by 3.1% in terms of forecast accuracy. Furthermore, the average execution time of the proposed model is 49s, the AFC-ANN model is 69s, the Bi-level model is 52s, MI-ANN model is 78s, and LSTM model is 54s.
Design of Optimized low-power GPS-Yagi Antenna using Machine Learning techniques

The time-consuming process of developing analytical models can be accelerated with the help of machine learning, which is a technique for data processing. Using machine learning, antenna designers can quickly and intelligently optimize their physical antenna designs. This is achieved by developing trained models of the designers’ designs. Consequently, antenna designers can create more efficient antennas. Due to this, antenna designers can continue developing new designs despite the increasing complexity of antennas. When discussing this specific type of antenna, “Yagi” is frequently used as an abbreviation for “Yagi-Uda” the full name of the antenna. The length of the “driven dipole” may be equal to or shorter than the length of the “directors. ” The ability to rapidly execute a diverse set of optimization algorithms and objectives, made possible by the trained models, makes it easier to conduct rapid comparisons and a diverse set of studies (including stochastic analysis for tolerance studies, etc.). While the device was operating at a frequency of ten gigahertz, the concept of two parasitic directors was conceived, and these parasitic directors were designed and optimized to improve the device’s directivity further. This antenna offers numerous advantages, one of which is its simplicity of manufacture. This antenna can be manufactured relatively easily due to its diminutive size and uncomplicated overall layout. The stochastic global search and optimization method known as simulated annealing (SA) is extraordinarily effective. This method is implemented instead of more conventional ones to achieve optimal element spacing.
An Additive–Multiplicative Restricted Mean Residual Life Model

The mean residual life measures the expected remaining life of a subject who has survived up to a particular time. When survival time distribution is highly skewed or heavy tailed, the restricted mean residual life must be considered. In this paper, we propose an additive–multiplicative restricted mean residual life model to study the association between the restricted mean residual life function and potential regression covariates in the presence of right censoring. This model extends the proportional mean residual life model using an additive model as its covariate dependent baseline. For the suggested model, some covariate effects are allowed to be time‐varying. To estimate the model parameters, martingale estimating equations are developed, and the large sample properties of the resulting estimators are established. In addition, to assess the adequacy of the model, we investigate a goodness of fit test that is asymptotically justified. The proposed methodology is evaluated via simulation studies and further applied to a kidney cancer data set collected from a clinical trial.
Neuroendocrine and Neurochemical Processes in Depression

Neuroendocrine and neurochemical theories of depression continued to be of importance in understanding pathophysiology and suggesting new kinds of pharmacological intervention. Monoamine theories still dominate the neurochemistry of depression and results from monoamine depletion studies suggest that in certain circumstances lowered activity of serotonin and noradrenaline pathways can indeed lead to clinical depressive symptomatology. More recent developments have implicated changes in the amino acid neurotransmitters, GABA and glutamate, in depressed patients; the ability of the NMDA receptor antagonist, ketamine, rapidly to relieve depressive symptomatology has been a spur to much basic research on the cellular mechanism of glutamatergic antidepressant action. The link between inflammation and depression has led to new kinds of immunological investigations in depressed patients and the possibility of targeted anti-inflammatory treatments. Finally HPA axis abnormalities remain a focus of interest, particularly from the point of view of the many medical co-morbidities which frequently complicate chronic depressive disorders.
Emotion Detection using Natural Language Processing

Online reviews of products, travel destinations, or movies are frequently given as sure opinions. As a result, sentiment analysis and opinion gathering become routine tasks. Consequently, the demand for automated text sentiment analysis has increased. Instead of reading reviews, it is useful to obtain opinions and sentiments regarding a particular subject. The Support Vector Machine, Naive Bayes, Decision Tree, and K-Nearest Neighbor are machine learning approaches, as well as the deep neural network, a Recurrent Neural Network and Long Short-Term Memory (LSTM), are the two main methodologies of sentiment analysis employed in this paper. Additionally, in this work applications of two techniques using three Twitter datasets are done: Netflix, Flipkart, and Tourism. Additionally, visual comparison of several strategies is also shown, and the Recurrent Neural Network experiment results are displayed.
Discrete Time Distributed Secondary Control of AC Microgrid: A Digital Control Approach

Distributed control of microgrids are becoming popular due to their scalability and reliability. Most distributed controllers designed for the microgrids are in the continuous time domain and subsequently implemented on digital controllers, which work in discrete time. Due to sampling time concerns, continuous time-domain control gains may not function well for digital controllers. This paper presents a discrete-time distributed secondary control to achieve frequency restoration and average voltage control in an ac microgrid. The proposed distributed frequency control ensures frequency restoration while maintaining proportionate active power sharing. The distributed voltage and reactive power control guarantee the average microgrid voltage is restored to the reference value and achieves proportionate reactive power sharing. Simulation in MATLAB/Simulink to verify the effectiveness of the proposed control scheme. The proposed controller is implemented on a digital FPGA controller and tested the experimental setup with different loads, and the results are presented.
THE METRIC THEORY OF MIXED TYPE LINEAR FORMS

In this paper the metric theory of Diophantine approximation of linear forms that are of mixed type is investigated. Khintchine–Groshev theorems are established together with the Hausdorff measure generalizations. The latter includes the original dimension results obtained in [H. Dickinson, The Hausdorff dimension of sets arising in metric Diophantine approximation, Acta Arith.68(2) (1994) 133–140] as special cases.