Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis and characterization of PVDF/clay/PZT nanocomposites with enhanced electroactive β-phase

in this study, hybrid nanocomposites of poly vinylidene fluoride (PVDF) matrix were synthesized to maximize the electroactive β-phase. PVDF/clay, PVDF/clay/PZT (lead zirconate titanate) and PVDF/PZT composites series with PZT loading of (0–40 %) were fabricated using melt casting technique. PVDF/clay/PZT composites parameters were optimized as a function of micro sized PZT filler concentrations and montmorillonite clay incorporated in the PVDF matrix with different surface modifications. As the reported literature on PVDF/PZT composites for enhancement of β-phase is rare, thus a possible spectrum of PVDF/PZT formulations containing different weight percentages of PZT powder (0, 10, 20, 30 and 40 wt. %) were developed and studied. All the composites were made by melt mixing and casted into flexible and homogeneous thin films. Presence of electroactive β-phase was confirmed by different characterization techniques including X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), Thermogravimetric analysis (TGA) and Differential scanning calorimetry (DSC). The gradual but noteworthy increment in polar β-phase was observed by fixing the composition of PVDF and nanoclay but varying the concentration of PZT. Further, it was observed that the change of organic surface modifications in montmorillonite clay not only improved the dispersion of filler in PVDF matrix but also maximized the electroactive β-phase.
RGB-D Images for Objects Recognition using 3D Point Clouds and RANSAC Plane Fitting

in this paper, we highlighted object localization and recognition using RGB-D images that is top of RGB scenarios and provide semantically richer pixel-level support aps for individual object. Indeed, depth information levels with disparity-range of various objects in an image are used to extract objects of interest. Using proposed methodology, we extract point clouds from a depth image to proper plane fitting using Random Sample Consensus (RANSAC). RANSAC is challenging to handle the contour with thin edges. After local segmentation, we extracts various features like HOG and shape cues values to explore spatial properties of each object class. For object classification, we applied two well-known classifiers i.e., random forest (RF) and linear SVM. In the experimental evaluation, we achieved a gain of 16% relative improvement over current state-of-the-art methods. The proposed architecture can be used in autonomous cars, traffic monitoring and sports scenes.
Instructional Use of Virtual Reality in E-Learning Environments

In the education method, the learning area is the critical field, and the students are the kernel in this learning. Students face issues with understanding due to the quality, necessity of abstract think and ideas. Instructional centres around the worlds have begun to introduced powerful new technology. Based tools that facilitate meet the need of the numerous student's population. Before the many previous ages, the virtual reality (VR) has affected since being the scope of play to professional developments. It plays an essential role in teaching method, provide a tremendous and interest approach to getting info. E-learning may be a brand new teach model today. This report discovers the emergence of utilizing Artificial Intelligence (AI) such as virtual reality (VR) in students learning of the higher education sector. This study summarizes the impact of these new technologies. It gives a general view of virtual reality applications that used primarily in the education sector. This paper study some previous research on virtual reality in education. And it aims to blend the survey of virtual reality, school-aged students besides teaching. A paper showed employing a combination of keywords, with modifiers to produce an initial set of relevant documents. The results revealed factors related to the most widely used virtual reality and virtual reality applications in education—giving college students the chance to experience content in ways by no means before possible.
Classification of the Severity Level for Lower Limb Joint Injuries

This project is to study the classification of the severity level of lower limb joint injuries. Lower limb joint injuries can be defined as the limitations of hip, knee and ankle joint Range of Motion (ROM) during body movement. The medical measurement system, such as Electrogoniometer (EGM), Goniometer (GM) and Visual Tracking System (VTS) are commonly used to measure ROM in order to identify the severity level of lower limb joint injuries, which categorised into normal, mild, moderate and severe. There are 20 healthy subjects (10 male and 10 female) and 52 lower limb injury subjects are used in this study. In this project, the classification approach is introduced using the statistical analysis method for feature extraction. Thus, based from the statistical analysis results, it was discovered that mean, variance, standard deviation, standard error of mean, f-value, p-value, t-value and confidence interval for upper and lower boundary are the nine significant features. These extracted features are then classified using Artificial Neural Networks Levenberg-Marquardt (ANN-LM), Artificial Neural Networks Scaled Conjugate Gradient (ANN-SCG) and k-Nearest Neighbor (k-NN). From results attained, EGM is concluded as the best system for measurement of ROM and has proved that ANN-SCG has the highest classification accuracy of 94%, 95% and 90% for hip, knee and ankle compared to ANN-LM with accuracy of 68.8%, 85% and 82.5% and k-NN with accuracy of 89%, 83% and 87.64%.
Preserving interaction design principles while implementing Material Design Guidelines

The paper presents recommendations for a responsive user interface (UI) implementation. Google guidelines for UI implementation - Material Design, are briefly presented and compared against the Nielsen’s design principles. We have identified limitations in preserving interaction design principles while implementing Material Design Guidelines. With objective to achieve flexible and responsive layouts respective improvement recommendations are discussed. Sample design case is presented, illustrated by screens before and after the implementation of the recommendations on Android devices with different resolutions.
Monitoring Driver’s Drowsiness Status at Night Based on Computer Vision

Drivers drowsiness and fatigue decreases the vehicle management skills of a driver. The operator driving vehicle in night has become a significant downside today. Driver in a drowsiness state is the one among the important reason of increasing amount of road accidents and death. Hence the drowsiness detection of driver is considering as most active research field. Many ways are created recently to detect the drowsiness of driver. Existing methods can be classified in three categories based on physiological measures, performance measures of vehicles and ocular measures. Few ways are intrusive and distract the driver from comfortable driving. Some of the methods need expensive sensors for information handling. Therefore, a low cost, real time system to detect the driver’s drowsiness is developed in this paper. In this proposed system, real time video of driver records using a digital camera. Using some image processing techniques, face of the driver is detected in each frame of video. Facial landmarks points on the driver’s face is localized using one shape predictor and calculating eye aspect ratio, mouth opening ratio, yawning frequency subsequently. Drowsiness is detected based on the values of these parameters. Adaptive thresholding method is used to set the thresholds. Machine learning algorithms were also implemented in an offline manner. Proposed system tested on the Face Dataset and also tested in real-time. The experimental results shows that the system is accurate and robust.
Analysis of High Speed Hybrid Full Adder

Multiplier plays an important role in the design of FIR filters in digital signal processors (DSP). In VLSI the multipliers performance speed affects the overall speed of the system. Moreover, multiplication process uses execution time in most of the DSP devices. Hence, high speed is required in multiplier. This paper presents the analysis of a high speed new radix-4 multiplier using Shannon adder. The proposed hybrid adder is implemented in order to achieve higher reduction of power in the high speed parallel radix-4 multiplier. Hence this modified radix-4 multiplier is a suitable candidate for obtaining less propagation delay, low power consumption and better power delay product. The circuit simulations are done using Synopsys H-Spice software. The obtained simulation results exhibit that the proposed structure performance is better in terms of Propagation delay, low power consumption and Power delay product when compared with the advanced technology in CMOS.
Emotion Recognition from German Speech Using Feature Selection Based Decision Support System

The speech signal plays a vital role in human communication. From last very years emotion characterization from speech has gained popularity in domain of signal and speech processing. In this article we propose a decision support system for emotion recognition from German speech signal database. A set of 535 speech signals with 7 emotion labels are investigated using prominent feature descriptors viz, Mel frequency coefficients, signal energy, pitch , zero crossing rate and formant feature numeric’s to generate a set of 37 features for each speech signal respectively. Decision support system is modelled in feature space using Support Vector Machine and resultant classification accuracy is further enhanced using Information Theory based feature selection measures to achieve the highest test classification accuracy of 93 percent respectively.
Student’s Feedback by emotion and speech recognition through Deep Learning

Student’s learning is very important part and definitely it is require that student’s attentiveness during learning is very much required. This is possible only when a teacher teaching in the class identifies. But, Time changed many things; COVID19 takes an educational system to a new turn. During online class interaction of teachers and students has became very weak in comparison with face to face learning. The only way to understand student’s attention in class is by their speech and facial expressions emotion’s recognition. Success may be achieved through driving the learner’s attention during online class. In this paper, an innovative approach for video/ image analysis for real time facial expressions is developed, so as to recognize the quality of understanding and active involvement of the attendees in learning and teaching process. This study will help to improve the quality of services in various teaching, trainings, mentoring and consultation which improve and act as a solution to normal feedback procedure of pen and paper method. This system will help both teachers and learners to improve their way of teaching and learning through calculated Concentration Index.
Power Decoupling Control for Single-Phase Grid-Tied PEMFC Systems With Virtual-Vector-Based MPC

The fuel cell grid-tied power generation system usually includes a dc-dc converter and a dc-ac inverter. In a single-phase system, inherent low-order current pulsations are introduced into the system, which can have harmful effects on the fuel cell stack. For example, reducing the output voltage and output efficiency, a reduction in service life, and even accelerates the degradation rate of the membrane electrode of a proton exchange membrane fuel cell (PEMFC). In addition, dc/ac coupling power can cause distortion in the dc input current and ac grid current. To eliminate the input ripple and ensure high ac power quality on the grid side, this paper proposes a novel power decoupling control for single-phase grid-tied PEMFC systems, which uses an improved model predictive control (MPC) algorithm. With the help of the virtual vector methods, which are realized by a two-stage optimization method, excellent tracking effect and robustness can be ensured. Simulations and experimental results show that the proposed algorithm can not only completely eliminate the input current ripple and reduce the total harmonic distortion (THD) of ac current on the grid side, but also improve the transient performance of the system.
Effective Prediction of Heart Disease Using Data Mining and Machine Learning: A Review

Currently, health diseases are expanding gradually. Specifically, cardiovascular diseases are basic nowadays. Cardiovascular disease is the crucial reason of mortality today, mostly in developing countries. Early recognition of cardiovascular infections and continual management of clinicians can decrease the death rate. But it is impossible to check patients consistently in all cases precisely since it requires more time and mastery. The healthcare areas have an immense measure of information. Notwithstanding, a large portion of it is not adequately utilized. The put away information can be valuable for wellspring of anticipating the event of future sickness. Productive instruments to extricate information with the help of data sets for scientific discovery of sickness or different reasons are very little pervasive. There are several information mining and machine learning techniques available to extract important information from the dataset through the different heart attributes of patient. The objective of this paper is to sum up the new examination along relative outcomes on coronary illness expectation furthermore construct scientific ends by using techniques of data mining and classification using machine learning.
A Comprehensive Survey on Communication Technologies for a Grid Connected Microgrid System

Phasor Measurement Unit (PMU) plays an important role in a Wide Area Management System (WAMS) for measuring the synchronized and accurate data such as frequency, current and voltage phasor of any system. However, the data quality and the communication techniques are the main concern for synchrophasor application. The communication of the synchrophasor data collected from Phasor Measurement Units (PMUs) and the Phasor Data Concentrator (PDC) is called Synchrophasor Communication System (SPCS). In this paper, a comprehensive survey has been done on communication technologies which are used in a grid connected microgrid system, communication standards and applications.
Implementation of Approximate Computing Circuits for Error Tolerant Image Processing Applications

A compressor is utilized to diminish the incomplete creation stages in multiplier and it brings about improving the multiplier execution. To improve the presentation further, inexact 4:2 compressors and 5;2 compressors have been proposed. These 4:2 and 5:2 inexact compressors have been broke down utilizing $8 \times 8$ Dadda multipliers. The plan measurements of proposed estimated 4:2 and 5:2 compressors and estimated $8 \times 8$ Dadda multipliers are combined in Rhythm RTL compiler, and contrasts plan measurements and three extraordinary innovation hubs. The blunder measurements, for example, ED, ER, PR, MED, and NED of inexact 4:2, 5:2 compressors and rough Dadda multipliers are processed. The use of the proposed estimated $8 \times 8$ Dadda multipliers to picture handling has been outlined. A picture honing approach has been decided to break down the picture quality utilizing estimated Dadda multipliers. The equipment execution of edge recognition utilizing Sobel administrator has been examined by applying the appropriate rough computational circuits. Consequently above planned inexact circuits can be utilized contingent upon explicit executions. Rough figuring circuits performs best advancement on huge measures of figured information delivered on the planet utilizing future scaled CMOS and post-silicon advances where precision is definitely not a significant limitation.
Recommender System for Postpartum Depression Monitoring based on Sentiment Analysis

Emotions influence all aspects of human behavior. All of these aspects shape people's lives, directly impacting their ways of life. Some diseases are directly linked to emotions. Among them, depression is one of the diseases with the greatest impact on society. Hence, faced with this problem, the objective of this study is to present a context-aware solution based on text mining for gestational depression prevention. This system uses text mining to analyze documents filled from pregnant women in order to identify their feelings through natural language processing techniques and probabilistic algorithms. As a case study, the analyzed texts were obtained from forms answered by pregnant women. The model performance is evaluated using metrics associated with the confusion matrix. The results show that the proposed model has achieved a reliable performance in all metrics, mainly when classifying new cases. Thus, the results obtained by the model can be used as support to health professionals in monitoring high-risk pregnancies.
FOPID Control with Parameter Optimization for Hydrostatically-Actuated Autonomous Excavators

This article presents our recent work towards development of a controller for the new class of modernized excavators. There is an increasing demand to develop autonomous earth-moving machines that are: more efficient than the conventional ones and capable of operating with minimal supervision by operators. A fractional-order pro-portional-integral-derivative (FOPID) control is applied to a newly designed electro-hydrostatic actuator (EHA) circuit for these machines. We further employ the Oustaloup recursive method for which the parameters of the controller are chosen based on the modified Nelder-Mead optimization algorithm. Our experimental results indicate that the proposed controller is capable of controlling the speed of the link with a negligible error. This work paves the road for development of autonomous, energy efficient, off-road mobile machines.
A Deep Neural Network based efficient Software-Hardware Co-design Architecture for Sparsity Exploration

The presence of many zero values -- is a pervasive property of modern deep neural networks, as it is inherently induced by state-of-the-art algorithmic optimizations. Recent efforts in hardware design for acceleration of neural networks have targeted the structure of computation of these workloads. However, when run on these value-agnostic accelerators, value sparsity is not exploited to provide performance or efficiency benefits, and instead results in wasted computation. This paper presents the architectural optimizations that efficiently leverage value sparsity in network weights in order to achieve significant performance benefits, with minimal hardware overhead. The culmination of this work is a hardware front-end (data fetching and staging unit) which, when paired with our novel, co-designed software scheduling algorithm, achieves more than $\mathrm{a}2 \times$ speedup on average for the networks studied, with just an 8.2% overhead in compute area.
COVID-19 mRNA Vaccine Degradation Prediction using Regularized LSTM Model

Due to the advantages of mRNA vaccines such as potency, safety, and production feasibility, recent researches in vaccinology has seen strong focus in mRNA vaccines. As leading researches involving COVID-19 mRNA vaccine candidates are being carried out, the challenge of overcoming the stability tradeoff of mRNA vaccines stand between the production and effective mass distribution stages. With the help of the OpenVaccine RNA database with degradation rate measurements provided by Stanford researchers, we developed an artificial recurrent neural network model to help bioinformatics researchers identify whether and where mRNAs might be unstable and prone to degrade under certain incubation measures. For this purpose, we’ve prepared a regularized LSTM model which minimizes mean columnwise root mean squared error for several degradation rates. We’ve found that recurrent algorithms perform better than tree-based algorithms.
Computational Polarimetric Imaging Using Two-Dimensional Dynamic Metasurface Apertures

In this paper, we present a two-dimensional dynamic metasurface aperture to perform computational polarimetric microwave imaging for the first time. First, a novel tunable dual-polarized metamaterial radiator element integrated with two PIN diodes is designed to radiate and capture crossand co-polarized field components. The diodes placed in orthogonal positions are simultaneously switched on or off to configure the transmit and receive polarization states. In diode on state, the metamaterial elements are in off state and the element radiated power is low whereas in diode off state, the metamaterial elements are in on state and the element radiated power is high. By sparsely reconfiguring and random assignment of the developed metamaterial elements across the array aperture, dynamic modulation of the radiated fields is achieved. Using this principle, we synthesize polarimetric, spatio-temporally incoherent wave-chaotic modes that facilitate polarimetric computational imaging. Leveraging the novelty of the dualpolarized dynamic characteristics of the wave-chaotic radiation, polarimetric imaging is computed in the near-field region at K-band frequencies and the polarimetric responses of specific targets are retrieved. The approach is verified by electromagnetic full-wave simulations, and imaging a T-shaped object consisting of two orthogonal metal strips, it is demonstrated that the target characteristics from a set of backscatter measurements compressed by the developed dynamic metasurface antenna
Essential Preliminary Processing methods of Hyper spectral images of crops

Hyper spectral images assimilate spatial and spectral details together. They can provide useful information easily and non-destructively about both external physical and internal chemical characteristics of various crops. The collected high-quality hyper spectral images still contain a huge amount of useless material, such as incompatible illumination, background specula reflection, and awful pixels that need to be removed, despite rapid improvements in acquisition techniques and latest tools. For almost any hyper spectral image, pre-processing is necessary to promote pure images or pixels, or to scale back negative effects on the next study of detection, classification, and prediction. Before further image analysing technique, this manuscript will list some potential solutions to deal with the problems listed above. For further processing of data, acquired clean images or pure signals may be used. Finally, hyper spectral image post-processing may be done to improve the image classification outcome or to produce distribution maps or chemical images to display non-identical sample spatial component concentration distributions.
Long Short Term Memory Model Based Position-Stiffness Control of Antagonistically Driven Twisted-Coiled Polymer Actuators Using Model Predictive Control

Twisted-coiled polymer actuators (TCA) have many interesting properties that show potentials for making high performance bionic devices. This letter presents a long short term memory (LSTM) model to predict the behavior of an antagonistic joint driven by hybrid TCA bundles made from Spandex and nylon fibers. By using automatic differentiation, which can be done with PyTorch, a linearized discrete state space model can be formulated and then be utilized in model predictive control (MPC). The experimental results show that by combining LSTM and MPC in modeling and control of the TCA, both high prediction performance and control performance can be achieved. It is verified that using the LSTM model, the joint angle and actuator temperatures can be predicted accurately with an avarage steady state error less than 0.1 deg and 0.2 Cel. deg. , respectively. The MPC control results show the controller's ability to reach the desired joint angle with an avarage steady state error of 0.21 degrees in set-point regulation and track a sinusoidal waveform at composite frequencies of 0.1 Hz to 0.15 Hz with the steady-state error less than 0.38 degrees while changing joint stiffness.