Miracle-dz/UniFAR-FTUs huggingface dataset:
Communication-Less Receiver-Side Resonant Frequency Tuning for Magnetically Coupled Wireless Power Transfer Systems

Compensating for deviations in the resonant frequency is crucial in magnetic resonance coupling wireless power transfer (WPT) systems. Thus, this study proposes a communication-less receiver-side resonant frequency-tuning scheme that compensates for the reactance in the receiver without communicating with the transmitter. The proposed scheme comprises an inductor-capacitor-capacitor compensation topology at the transmitter and a half-bridge circuit at the receiver, whose operating phase is set to be orthogonal to the receiver current. Resonant frequency tuning can be achieved by adjusting the DC voltage applied to the half-bridge circuit to maximize the power received at the load. The reactance compensation ability of the proposed scheme is analyzed through experiments on a 200 kHz WPT system. When the secondary capacitance deviated from −20% to +20%, the efficiency degradation was maintained within 6.7% with the proposed scheme, whereas the efficiency degraded by up to 33.3% without compensation.
Implementation of Medical Insurance Price Prediction System using Regression Algorithms

Medical expenses are increasing day by day due to unexpected epidemic diseases. People have awareness about the medical insurance and claiming processes which is helping in critical situations. Predicting the medical insurance cost is very important work in health care sectors. Many researcheis applied different machine learning algoiithms to predict the insurance premium in Kaggle data set with seven attributes such as age, sex, bmi, children, smoker, region and charges. But these features only not suitable for prediction. In this paper, the dataset with 24 features including all relevant attributes needed for prediction of insurance cost was used. The implementation done using regression algorithms such as Linear Regression, Decision Tree Regression, Lasso Regression, Ridge Regression, Random Forest Regression, ElasticNet Regression, Support Vector Regression, K Nearest Neighbor Regression and Neural Network Regression in R Programming. There were seven metiices applied to measure the performance of the model namely Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), RSquared (R2), Adjusted R-Squared (Adj. $\mathrm{R}^{2}$), and Explained Variance Score (EVS). Random Forest Regression outperformed with 0.9533 as RS quared value. This prediction system will reduce the manual work and give the prediction accurately in medical field.
Analytical saturated domain orientation textures and electromechanical properties of ferroelectric ceramics due to electric/mechanical poling

Saturated domain orientation textures of three types of pseudocubic (tetragonal, rhombohedral, and orthorhombic) ferroelectric ceramics after complete electric and uniaxial tension (compression) poling is studied analytically in this paper. A one-dimensional orientation distribution function (ODF) of the domain polar vectors is explicitly derived from the uniform inverse pole figures of the poling field axes on a stereographic projection with respect to the fixed crystallite coordinates. The analytical ODF is used to obtain the analytical solutions of saturated polarization and strain after electric/mechanical poling. Based on the closed form solution of the saturated domain orientation textures, the resultant intrinsic electromechanical properties of ferroelectric ceramics, which depend only on the ODF and properties of the corresponding single crystals, are obtained. The results show how the macroscopic symmetries of ferroelectric crystals change from 4mm (tetragonal), 3m (rhombohedral), and mm2 (orthor.
An Evaluation of Modeling Options for Cloud-native Application Architectures to Enable Quality Investigations

Cloud-native as a style to build web applications embraces the potential of cloud computing. A possibility to evaluate software architectures of cloud-native applications according to quality aspects would be beneficial to developers, but is challenging due to the thematic breadth of cloud-native ranging from design to operation of applications. This work therefore evaluates existing architecture description languages according to their suitability for representing those architectural aspects specific to the cloud-native style. The evaluation is driven by a recently proposed quality model for which a suitable architectural modeling option is needed. We find that several existing languages are suitable as a basis for implementing an extension specific to the quality model. We choose TOSCA as an established standard to implement such an extension with corresponding tooling support.
Multi-Level Fine-Tuned Transformer for Gait Recognition

Gait Recognition aims to identify individuals by their walking patterns. Abundant spatial and temporal characteristics are contained in human walking for indicating unique personal identity. There have been a large number of existing gait recognition approaches achieving good performance via the analysis of spatial and temporal characteristics. However, most of them directly concatenate the separately extracted temporal features and spatial ones for gait recognition, rarely considering the overall spatiotemporal structure and the inner connection between them. Therefore, we establish a Multi-Level Fine-tuned Transformer trained on sequential gait data, to capture and analyze the overall spatiotemporal information for gait recognition. Specifically, we first design a point-level spatial feature extractor for spatial feature extraction of frames of the gait sequence, based on typical Transformer architecture with positional encoding and multi-head self-attention mechanism. Subsequently, a frame-level temporal feature extractor is employed for the overall spatiotemporal features by learning dynamic information of the extracted spatial features from frames. Moreover, fine-tuned strategy is followed in the process of model training, to profoundly explore intra-frame spatial information, as well as global spatiotemporal features of the whole walking sequence. Extensive experiments conducted on three public datasets (normal and abnormal gait) show promising classification results, compared with several state-of-the-art techniques.
Optimization of Efficiency using Loss minimization in Quadratic Boost Converter Multilevel Inverter fed Induction Motor System

Loss minimization of a Quadratic Boost Converter (QBC) for Multilevel inverter (MLI) fed Induction Motor (IM) is intended so that the efficiency can be optimized. QBC with MLI is placed between the DC-source & the induction motor. This work proposes QBC-between the DC source and inverter. The DC output is boosted by QBC and fed to the inverter. The inverter output activates the induction motor. This work deals with loss minimization of closed loop Quadratic boost converter multilevel inverter (QBCMLI) system using Proportional Integral (PI), Fractional Order PID (FOPID) and Proportional Resonant (PR) controllers. The objective of the proposed system is to maximize the efficiency of the induction motor drive modules incorporating PI, FOPID and PR controllers. The investigations are done in closed loop systems. Voltage mode PRC algorithm is proposed to optimize the performance of QBCMLIIM system. The outcome illustrates minimization of losses by utilizing PR controlled QBCMLIIM system.
SSVEP-Based Brain-Computer Interface System for Managing Disabilities

Brain-Computer Interface will recognize patterns in mental activity using the computer's algorithm in controlling a third-party device. Ideally, BDI will measure the activity of the brain to command and control messages. Electroencephalogram (EEG) is a commonly used approach in BCI based on its non-invasive and convenient use. As a result, more and more BCIs have been designed for persons with disabilities who suffer from spinal cord injury helping to rehabilitate and live. BCI includes feature extraction methods, the signal processing. Later, the different hybrid BCI combined methods and review the synchronous/asynchronous BCI designs.
Smart LPG Gas Leakage Detection and Monitoring System

Most of the time, LPG gas is used in our homes, and its use has become a basic necessity for everyone. In the past, gas leaks have been seen, and even now, which has resulted in numerous accidents, it is dangerous to inhale where there is a risk of losing our lives, and if its level increases, it may explode. To avoid such losses, gas leakage has to be detected, and necessary measures have to be taken to avoid it. Continuous monitoring of the atmosphere is an important component in detecting gas leaks and can only be done artificially. Means Our work proposes a system that helps detect gas leakages in houses and industries and tries to minimize the circumstances that lead to gas leakage. It is also important to monitor the pressure in the massive gas containers storing toxic gases to avoid bursting, which leads to a sudden gas leakage. The system is a cloud-based Internet of Things application that receives sensor information and reacts accordingly. There are different proposed solutions to this problem, but none of them became successful. This IOT project is made to identify leaks and alert people, helping to decrease gas leakage incidents. The gas leak from the cylinder is detected using the gas sensor MQ2. Alcohol, smoke, propane, H2, LPG, CH4, and CO are all detectable by the MQ-2. It is linked to the NodeMCU (ESP8266), which is set up to send the message through the cloud directly to the user's smartphone. For quick feedback, a buzzer is also attached to the circuit. alarm to the residents of the house, allowing us to turn off the cylinder and save it from exploding.
Analysis of Emotion Detection of Images using Sentiment Analysis and Machine Learning Algorithm

During communication, people are capable of producing hundreds of expressions that differ in complexity, intensity, and meaning. The main factor in a person’s life is their emotions. Even though these pictures are so diverse from one another, they all express the same emotion–-"love. " This study investigates the possibility to anticipate an image’s underlying emotion using deep learning. However, there are times when it is difficult to comprehend the emotions that a person must express. In addition to being developed and managed for security concerns,observation cameras are widely used in the media industry for player articulation. The method used to capture a person’s exact emotion on camera. For the purpose of recognizing emotions, machine learning techniques, CNN algorithms, and deep learning models are also used.
Model for Predicting Prospective Big-Mart Sales Based on Grid Search Optimization (GSO)

Predicting sales before actual sales are critical for any retailing company, to sustain a thriving business, such as Big Mart or Mall Statistical models, for example, are examples of traditional forecasting models that are frequently employed as a strategy for forecasting future sales; however, these techniques take significantly longer to estimate sales and are incapable of dealing with non-linear data. As a result, both nonlinear and linear data are dealt with using Machine Learning (ML) methodologies. ML techniques can also be used to process vast amounts of data efficiently, such as the Big Mart dataset, which has a vast amount of client data and data item attributes. A store is looking for a model that can forecast precise sales so that may anticipate consumer demand and update sale stocks ahead of time. In this study, we offer a prediction model for estimating a company's sales, such as Big. We provide a Grid Search Optimization (GSO) approach in this research for optimizing parameters and selecting the optimal tuning hyperparameters for projecting future sales of a retailing firm like Big Mart, and we discovered that our model beats others.
Intelligent Controller for Flyback Converter with 31-Level Inverter for Grid-Connected Hybrid System

Rapid exhaustion of fossil fuel energy systems, emission of carbon, and climate change issues has been paid more attention in the recent scenario. The development of power electronic devices, the growth of industries and population increases the need for green energy. The Hybrid renewable energy of wind and PV system are the better choice due to the benefits of the reliability of power based on the weather condition, an extension of the grid system, and remote electrification. In this research, the isolated grid integrated multilevel inverter for hybrid (Solar and wind) battery-based fly back converter application is proposed with optimal controlling techniques. The primary objective of this research is to design and develop the fly back converter with an effective controller to obtain the higher power output during the operation of continuous and discontinuous conduction mode respectively. Even though, the researchers are mostly implemented with Cascaded H-Bridge (CHB) inverter-based grid-tied system. CHB are increasing the harmonics, the requirement of the filter circuit and overall efficiency. To solve these issues thirty-one level inverter is proposed by considering intelligent techniques as a secondary objective. The modeling and controlling techniques of the discussing system are done by MATLAB tool. The prototype setup provides the experimental feasibility of the developed hybrid PV-wind connected fly back converter with a thirty-one-level inverter-based grid-tied system by considering the reliability of intelligent controller system.
A Miniaturized Planar Multibeam Antenna for Millimeter-Wave Vehicular Communication

Existing millimeter-wave beamforming network-based multibeam antennas usually suffer from complex configuration and bulky size, which limit their applications on a vehicle. To address these issues, in this paper, a symmetrical series-fed array topology is proposed for eliminating the conventional beamforming network. The topology is constructed by applying two unequal main transmission lines (TLs) to symmetrically feed a linear antenna array in series. To effectively suppress the mutual coupling between two TLs, an antenna element exhibiting co-polarized in-band full-duplex performance is implemented. When the four terminals are excited, respectively, four groups of excitation signals with different phase differences will be delivered to antennas to implement the desired beam steering characteristics. For validation, an antenna array operating at 28 GHz is designed, fabricated, and measured. Thanks to the simplicity of topology, the overall size of the proposed multibeam antenna is only 5. 57λ2 with a radiation efficiency of 85.6%. Meanwhile, a comparable performance is obtained when compared to a conventional beamforming network-based multibeam antenna. All of these results demonstrate the potential of the proposed topology in miniaturized millimeter-wave vehicular communication.
Power Generation of Wind-PV-Battery based Hybrid Energy System for Standalone AC Microgrid Applications

This article describes the power generation of wind,PV, and,battery-based hybrid energy systems for standalone AC microgrid applications. There are many results for resolving issues with the supply of electrical power, particularly in rural places where electrical networks are difficult to access. The usage of networks that are not linked to electrical systems allows for the provision of electricity to remote places, which is one way for determining this issue. They are denoted to as standalone microgrid systems. The standalone microgrid has its sources of electricity, extension (or)addition with an energy storage system. They are utilized where power transmission and distribution from a major centralized energy source is too far and costly to operate. In this article, a standalone AC microgrid scheme with a hybrid power system comprised of wind, photovoltaic, and batteries are designed and managed.
An Investigation on Distributed Denial of Service Attack in Edge Computing

The data-driven industry has benefited greatly from the edge computing platform. The general architecture places data storage and computation close to the source of the data. Recent industrial applications and machine learning environments necessitate fast data transmission to a centralized data center for further processing. As a result, clients expect low latency when collecting and processing the data. Data processing can utilize edge computing in three different ways. Bots, distributed denial of service attacks, artificial intelligence and machine learning are all examples of DDoS attacks. The goal of DDoS is to impede the availability of network-connected systems. They generate excessive traffic to the targeted servers will deplete the computational and communication resources. Edge computing provides substantial assistance to lightweight devices to complete a complex task. According to the statistics, the attacks are distiibuted denial of service, malware, side channel attacks, authorization and authentication attacks. This research work examines the Distributed denial of service attacks on cloud and fog computing, which are similar to edge computing. They offer what appear to be effective solutions to the threat. This study also discusses about the current research challenges and future directions in this field.
Short Analysis of Machine Learning and Deep Learning Techniques used for Glaucoma Detection

Glaucoma is a neurodegenerative and chronic ocular disease derived from optic neuropathy and caused by visual disturbances regarding degeneration of optic nerve fiber and cupping in the optic disc. Recently, glaucoma detection and monitoring need precise eye evaluation and additional testing. Owing to the advance of artificial intelligence, deep learning methods require to process the availability of huge-scale datasets and recent progressions in computing technology. Moreover, these deep learning methods produce higher performance in pattern recognition and image classification tasks. The clinical studies are hugely derived based on the auxiliary imaging tests, where the utilization of deep learning and machine learning techniques helps analyze the huge range of medical data attained from the imaging tests particularly for glaucoma to evaluate the macula and optic nerve. Hence, this work aims to make an analysis of deep learning and machine learning algorithms used for detecting glaucoma diseases by considering the recently developed works. This review includes the algorithmic classification, datasets used, performance measures,and also the tools used for their implementation. In addition, the chronological review was also added. Finally, the current research gaps are discussed to understand the complexity of findingsuitable solutions for glaucoma detection in practical applications.
Sentiment Analysis on Drug Reviews Using Diverse Classification Techniques

Free text reviews are abundantly distributed over the internet among the standard population of the world. Although online doctors were consulted during the pandemic, many drugs are taken without prescription by ordinary people. Reviews are distributed about the medicines through online feeds, posts and blogs, which are merely helpful in identifying the side effects and their adverse reactions in combination with another drug or standalone adverse reactions. Spelling mistakes, jargon and short names are normalized throughout the medical terminologies using Natural language processing (NLP) and RxNorm from the reviews extracted from these sources. The concept of Bidirectional Encoder Representations from Transformers (BERT) is used to mine the association rules for the drug interactions when used in conjugation with another drug, and the idea of Adaptive Fuzzy logic neural networks is used to validate the results of association rules derived from the BERT. Inconsistencies identified by the regulations generated were rejected efficiently by the Support vector machines (SVM), Radial Basis Function (RBF)and the system shows 60.11 percentage of accuracy with 10 cross-fold validation, whereas the results are equivalent to the accuracy obtained using SVM with RBF kernel. The data set is reduced from its dimension using Linear discriminant analysis (LDA) by applying the mining process and BERT on two drugs, namely “Azithromycin” in conjugation with Buprenorphine has Type ‘A’ side effects, and Prozac in conjugation with Metronidazole shows different side effects. Deplin and Zoloft give very positive symptoms and are independent of dosage interactions.
Design and Simulation of Five Level Buck Boost Converter for Grid Connected Hybrid Power System using Matlab/Simulink

This research describes a revolutionary voltage-lift switched inductor (VLSI) cell-based high step-up DC-DC multilevel buck-boost converter. Cascade of traditional DC-DC converters is not a workable solution to reach high conversion ratio. In this study, a VLSI cell is used to improve the multilevel buck-boost converter’s boost capabilities. The suggested DC-DC multilevel topology’s key benefit is that it achieves large conversion ratios without the use of coupled inductors, transformers, or high duty cycles. The 200W-rated buck-boost multilevel converter that is being suggested has three stages and a 220V output voltage. The duty cycle is 70%, the switching frequency is 50 kHz, and the input supply voltage is 12 V.
A Review Paper on Real-Time Video Analysis in Dense Environment for Surveillance System

Dense environmental conditions such as snow, fog, lightning, heavy rain, and darkness drastically lower the quality of outdoor surveillance videos. The primary functions of video surveillance systems in crowded environments have received significant attention, particularly in detection, categorization, and event or object recognition. The methods and algorithms for real-time video analysis in various weather conditions have also significantly advanced with the advancement of technology. Examples include background extraction, the see-through algorithm, deep learning models, CNN for nighttime intrusions, the System for high-quality underwater Monitoring using optical-wireless video surveillance, the low-visibility enhancement network (LVENet), edge computing, and many others. Using various elements of these methodologies, the current research increased monitoring performance and avoided potential human failures. In-depth information about these video surveillance methods, systems, and supporting details is provided in this study. An overview of employed construction and architectural styles is given, and the critical assessments of these systems are then covered. Current surveillance systems and various methods for achieving accuracy in real-time video analysis in adverse weather circumstances are contrasted in terms of their features, benefits, and challenges, which are discussed in this paper, to provide a complete image and a broad view of the System. Future trends are also highlighted, pointing to the study that will be conducted in the future.
MusicDress: A Heterogeneous Dataset for Comparing Music Recommender Systems

To compare different types of music recommender systems, datasets are necessary that offer a combination of diverse features. We propose MusicDress, a novel dataset covering four different elements of music: timbre, rhythm, melody, and harmony. The dataset extends to lyrics and user data by linking to publicly available data sources. It comprises features of 2,136 individual songs and enables the comparison of hybrid recommender systems that combine content-based, context-based, and collaborative filtering approaches.