Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis and mechanical characterization of natural fibre polymer matrix laminated hybrid composites reinforced with glass-fibre and flax-fibre synthesized by hand-lay-up techniques

In the current study, natural fibre polymer laminated hybrid composites were synthesized using flax and glass fibres. The hand-lay-up fabrication route was used to develop the natural fibre polymer matrix composites (NFPMCs). The final structures of hybrid laminated composite have excellent chemical resistance, better mechanical properties, low density, and low cost. Such composites structures are suitable for the construction of automobile and aircraft bodies due to their remarkable weight to strength properties and ratio. Natural fibre composites (NFCs) with polymer matrix are an advanced and fascinating green option for materials used in the construction of automobiles and aircraft. The glass fibre and flax fibre reinforced laminate offer exceptional strength with reduced weight because structural components require light materials. The tensile and compression tests were carried out on a Hounsfield tensometer, and strain gauges were used to measure the extension and contractions. Additionally, impact strength was tested with an impact testing machine using a method created by Izod and Charpy, and flexural strength was calculated using an Instron universal testing machine. Finally, in the case of the Charpy test, the impact strength showed 54kJ/m2 along the fibre direction, which is 3.4 times higher than the transverse fibre direction. Similarly, in the case of the Izod test, the impact strength along fibre direction is 17kJ/m2, which is three times higher than the transverse fibre direction. Similarly, the tensile and flexural properties of fabricated hybrid composites are remarkably improved.
Skeleton Embedded Views for Non-Rigid 3D Shape Classification and Retrieval

With the application of deep learning in 3D recognition, view-based methods show excellent performance in non-rigid 3D model classification and retrieval. However, view-based methods often have problems such as occlusion and lack of spatial pose information which are caused by different viewpoints. Aiming at this problem, we propose a classification and retrieval method for non-rigid 3D model based on skeleton feature embedded view. Firstly, this paper uses virtual cameras to obtain multi-views of non-rigid 3D models from different viewpoints, and then extracts the skeleton features of the views by using the method of topology thinning, and embeds the skeleton features into the original views. Secondly, the network structure of ResNet50 is improved and used as the backbone network to build a multi-branch convolutional neural network (MBCNN); Finally, the classification and retrieval of non-rigid 3D models are realized by MBCNN. Experiments on the non-rigid 3D model datasets SHREC10, SHREC11 and SHREC15 demonstrate the effectiveness of the proposed algorithm. The classification accuracy rates for the above three datasets are 100%, 100%, and 99.6% respectively. The results show that our method is much better than the existing non-rigid 3D shape retrieval methods.
Indoor PDR Method Based on Foot-Mounted Low-Cost IMMU

Indoor Pedestrian Dead Reckoning (PDR) based on Inertial and Magnetic Measurement Unit (IMMU) can accurately provide the position of pedestrians, and gradually becomes popular research on indoor positioning. In this paper, a novel PDR algorithm based on low-cost IMMU is proposed, which implements PDR from four steps: step detection, gait detection, step size estimation and attitude solution. According to the pitch angle, it is judged whether a new step is generated, and gait detection algorithm based on the standard deviation of the acceleration modulus and the angular velocity threshold + the angular velocity standard deviation threshold is proposed, which is the basis of step length estimation and attitude solution. The performance of the algorithm is verified through indoor experiments. The results show that the average distance error in the indoor environment was 1.32% and the average end-to-end error was 1.21%. Therefore, this paper based on low-cost IMMU indoor PDR has great application value.
Machine Learning Approach for Estimation of High Acquity ECG Monitoring System

Electrocardiogram (ECGs) are simple, painless tests that are used to measure the electrical activity of the heart. Various cardiovascular conditions are early diagnosed using this test. It is also useful for detecting heart-related disorders such as arrhythmia, and heart valve complications. In this paper, a threelead monitoring system is designed using Arduino UNO and an ECG sensor AD8232. The ECG signals of a patient are collected by the AD8232 ECG sensor. Then acquired signals are further processed by an Arduino Uno, which digitizes the collected data and displays the continuous ECG on an OLED screen to make the system portable. These OLEDs are used to obtain, process, and display the amplitudes and intervals of some critical components. We approach a method to estimate P, QRS Complex, and T amplitudes as well as time durations (PR, RR, QRS, and QT). It is used to diagnose the heart condition, whether it functions normal or abnormal. In order to classify the data samples, supervised machine learning algorithms namely, Support Vector Machines(SVM) and Logistic Regression are applied, resulting in an accuracy of 92.5 % and 87.5 %, respectively.
Performance Analysis of High-Efficiency WPT for Communication Technologies

Wireless Power Transfer (WPT) is a technology which is become an important topic nowadays due to many advantages that have, from the ease of use, safe, reliability, no need wires, and so on, and many scientists are trying to develop this technology to be used for more new smartphone devices, also WPT is considered one of green technology. In this research paper, a wireless power transfer system for the mobile battery charger had been designed and discussed using Mat lab program to get a 10 Watt to charge a mobile device with acceptable distance and efficiency. There are three methods for WPT includes electromagnetic induction (EI), magnetic resonance coupling (MRC), and radio waves (RW) which are categorized depending on the distance that sends the power. Magnetic resonance coupling is the method that has been designed is used for short and medium distances. In the result, the effect of distance system performance has been discussed.
Experimental Evaluation of Insider Threat Detection Methods Based on Temporal Representation

At present, insider threat detection faces many challenges, such as imbalanced data sets, the complexity of malicious behavior, high data dimension, and difficulty for manual analysis to keep up with the data growth rate. An experimental evaluation of insider threat detection methods is carried in this paper. Firstly, the open data set is divided by day and week. Then, various temporal representations including percentile, mean difference, median difference, and concatenation of instances representations, are used to extract potential information. Four unsupervised machine learning algorithms and a supervised algorithm are comprehensively compared to explore the best detection schemes under different temporal representations. To comprehensively evaluate the detection performance of the algorithms, four commonly used performance metrics (i.e. Detection rate (DR), Precision (PR), F1-score (F1), Area Under the Curve (AUC)) are introduced. The experimental results show that the random forest algorithm can achieve the best detection results using day granularity data combined with median differential representation-meddiff30, and the DR, PR, F1, and AUC are 75.43%, 99.59%, 85.84%, and 87.71 % respectively. For the four unsupervised detection algorithms, under the 20% investigation budget, the autoencoder combined with percentile representation-percentile30 in day granularity achieved DR of 95.83%, and AUC of 94.93%, with higher PR and F1 values under the same conditions. The local outlier factor algorithm combined with concatenation representation-c3 under week granularity achieved the highest DR, but the scheme of the autoencoder combined with percentile30 still achieved the highest PR, F1, and AUC.
IRS-Assisted Secure OFDMA With Untrusted Users

Intelligent reflecting surfaces (IRSs) have recently started gaining research attention for improving the performance of next generation wireless communication systems. Considering the potential of physical layer security, in providing secure communication, this paper investigates an IRS assisted Orthogonal Frequency-Division Multiple Access (OFDMA) based downlink secure communication system in the presence of untrusted users. More specifically, we consider the joint optimization of subcarrier allocation, power allocation, and IRS reflection coefficients, and present a low complexity solution. After obtaining optimal subcarrier allocation, the problem is decoupled into two individual optimization problems namely, power allocation for a known reflection coefficients, and reflection coefficients optimization for a known power allocation, which are solved in closed form. The joint optimization is achieved through alternating optimization using these individual optimizations. The proposed scheme is shown to outperform the benchmarks through extensive numerical results, which further highlight design insights for IRS-assisted OFDMA based secure communication system.
A New Density Clustering Method based on Dynamic Local Density

Density-based clustering is an important research direction of data mining because density-based clustering algorithms can find clusters with arbitrary shape and are robust to noises. Local density estimation is widely used in the task of clustering and outlier detection. Traditionally, distance-based and statistic-based local density estimation methods are usually adopted for data mining. However, the effectiveness and stability of local density estimation models for clustering are still to be improved. In this paper, we propose a novel density clustering method based on dynamic local density estimation. First, we show Poisson distribution to fit the distribution of reverse $k$ nearest neighbor counts and describe the model of dynamic local density estimation. Second, we construct a cluster order based on dynamic local density. Finally, decision graph is developed for density clustering and the identified break points partition the cluster order into clusters. Experiment results show that the new dynamic local density estimation model is effective and can help improve the performance of density clustering.
Fluid Speed Detector using Impulse Detection with Two Load Cells and NodeMCU ESP8266

Fluid flow sensors or flow meters have been made and used as a measuring instrument for gas or fluid flow. However, most flow meters have moving mechanical parts so that it can cause slippage as in flow sensors that use gear used to measure waste flow accompanied by residue in the form of solids, so this will cause the flow to become clogged when it enters the gear, from flow sensors. For this problem, it is necessary to make a flow meter sensor that can measure the flow of fluid types accompanied by solid residues. The method for measuring fluid flow can use the concept of kinematics where the fluid rate can be known from the length of the path taken by the fluid to the fluid travel time on a predetermined path. The implementation to determine the fluid travel time on a predetermined path is to use two load cell sensors which are placed at the starting point of the track and the end of the track at a certain distance. The load cell can detect the movement of the fluid hitting a thin rod attached to the load cell so that the detected quantity is mass. Impulse detection to determine fluid velocity is obtained from the time interval when there is a change in mass from the first load cell to the second load cell at a predetermined distance. The test was carried out on a PVC pipe with a diameter of 4 inches which is positioned horizontally, the distance between the two load cells in a horizontal PVC pipe position is 7 cm. Calibration of the tool using the IR Sensor TCRT5000 with the help of a white object, so that the test results obtained the detected fluid flow velocity is from 0.2 m/s to 3 m/s. The average error of the flow rate measurement between the load cell and the TCRT-5000 is 1.95 percent.
Two-section InGaN multiple-quantum-well laser diode with integrated electroabsorption modulator

Q-switching is demonstrated in a two-section InGaN multiple-quantum-well (MQW) laser diode consisting of an electroabsorption modulator and amplifier (gain) section. The modulator and gain sections are optically coupled and share the same InGaN MQW active region, but they are electrically separated by a narrow dry-etched trench. Applying a reverse bias voltage to the modulator section controls the absorption in the modulator portion of the device by compensating the piezoelectric field in the InGaN quantum wells. Changes in the absorption coefficient as large as 5000 cm−1 were realized with a moderate reverse bias of 7.2 V. By forward biasing, the amplifier section at a constant current of 225 mA and by controlling the reverse bias modulator voltage, the output power of the two-section laser diode could be switched between <0.5 mW (off state) and more than 3 mW (on state) with a laser emission wavelength near 401 nm.
Radiating electron in a magnetic field

The paper considers the application of Dirac's classical theory of radiating electrons to examine the motion of an electron in a uniform magnetic field. The non-relativistic equations of motion are solved exactly. It is shown that for the particular case when the motion is confined to a plane, the physical motion is such that the electron describes an equiangular spiral with velocity which steadily decreases as exp {negative constant × t}; and in the non-physical motion the electron spirals outwards with velocity which steadily increases as exp {positive constant × t}, and ultimately the electron escapes to infinity. The relativistic equations of motion are solved approximately as a series in ascending powers of the field. It is shown that in the physical motion the velocity begins to decrease, and hence after a time the motion is given correctly by the non-relativistic solution. I take this opportunity to express my deepest gratitude to Prof. Dirac, who suggested this problem, for his patient guidance and supervision.
A Comparative Hybrid Optimisation Analysis of Load Frequency Control in a Single Area Power System Using Metaheuristic Algorithms and Linear Quadratic Regulator

Load frequency control (LFC) in a power system plays an important role to ensure system frequency and the desired demand or output power in an interconnected network is regulated and able to control the change when it is subjected to load disturbance. The research to date has tended to focus on a proportional-integral-derivative (PID) controller to optimise the performance of the network rather than looking into hybrid optimisation techniques. In this paper, three metaheuristic algorithms namely the particle swarm optimisation (PSO), the artificial bee colony optimisation (ABC), and the grey wolf optimisation (GWO) are employed to optimise the selection of the linear quadratic regulator (LQR). These three methods are then evaluated and the outcomes suggest that PSO+LQR and GWO+LQR provide a significant performance improvement of the LFC in terms of the steady-state frequency deviation, the settling time, and the rise time.
A Simple Efficient Policy to Reduce Broadcast Redundancy in Wired Networks

Broadcasting plays an important role in computer networks and is the base of link-state routing protocols. Flooding is the simplest approach to implement network-wide broadcasting, but it suffers from high overhead. Flooding-based variants like multi-point relay (MPR) and tree-based flooding can reduce the broadcast redundancy efficiently, with the demand of maintaining specific routing structure in a network. In this paper, we propose a simple and efficient policy to reduce the broadcast redundancy in flooding-based approaches. The policy eliminates the unnecessary forwarding operations to common neighbors. While the flooding approach has the communication complexity of $\boldsymbol{O(m)}$ in wired networks, the policy can reduce the complexity to $\boldsymbol{O(m-t^{*}n)}$, where $\boldsymbol{m}$ is the link number and $\boldsymbol{n}$ is the node number of a network, and $\boldsymbol{t}$ is the average common neighbor degree which is a newly-proposed metric in this paper. Simulation results on different networks have verified the effectiveness of the proposed policy which reduces the broadcast redundancy of all flooding-based approaches. The application of the policy to link-state routing protocols is also discussed.
A Taxonomy for Resource Management in Edge Computing, Applications and Future Realms

Mobile Edge computing (MEC) is a computing paradigm which extends the cloud computing services (i.e., storage and computation resources) at the edge of network or closer to the end devices/users to deal with the conventional limitations of a cloud (e.g., high latency) for latency-stringent applications. But it is constrained with computing resources, hence efficient resource management is of exigence in MEC. This article presents a distinctive taxonomy of resource management by considering the latest and state-of-the-art techniques and algorithms in the MEC. The preferred literature from the existing research works is classified for resource management in MEC in terms of resource type, objective, resource location, and resource usage. Moreover, applications related to the MEC platform are also elaborated. Further research challenges and potential future directions are explored for viable investigation and research.
Usability Evaluation of Academic Information System Using the WEBUSE Method: A Study on University of Trunojoyo Madura Web Portal

A website is a platform to convey information, one of them is the academic information system on higher education (SIAKAD). It is used by lecturers and students. However, the users often need help to understand the function of the features or commands available in it. Consequently, the users require a longer time to access information and provide feedback. Therefore, this study was conducted to evaluate and determine the level of usability of the system and give some recommendations for improvement. The result of the questionnaire from users reflects user satisfaction. The level of usability was determined based on the respondents’ answers. A case study was conducted on the website siakad.trunojoyo. ac.id. According to the findings of this study, ten attributes have a moderate level of usability, whereas the other fourteen attributes have an excellent level. The testing on each attribute has the highest and lowest values. For example, the attribute with the lowest value is the user interface (UI) design on the web (0.508), and the system which does not contain too much advertising is the highest value (0.737). All in all, the highest level of usability is the website does not have too many advertisements. On the other hand, the lowest level is the attribute of the user interface (UI) design. Finally, the primary issue is content updates are frequently delayed.
Dielectric enhancement in polymer-nanoparticle composites through interphase polarizability

Dielectric measurements on polyimide-oxide nanoparticle composite thin films show a composite dielectric constant (ecomposite) that increased monotonically with increasing oxide content well above the value predicted by Maxwell’s rule for dielectric mixtures below the percolation threshold. Above certain volume fractions, the measured ecomposite values were found to exceed the corresponding nanoparticle e such that epolymer<eparticle<ecomposite contrasted to conventional composites where epolymer<ecomposite<eparticle. The ecomposite was independent of frequency to 10MHz with dielectric loss of <0.005 throughout this range, indicating that the observed enhancement in e does not originate from space-charge related contributions and hence should be due to dipolar contributions. The observed e enhancement (ecomposite−eMaxwell) showed a correlation with the total surface area of the nanoparticles. The dielectric model of Vo and Shi [Microelectron. J. 33, 409 (2002), and references therein] showed that the enha.
IDENTIFICATION FOR UNIVARIATE TIME SERIES MODELS WITH HETEROSKEDASTIC ERRORS

ABSTRACT The SCAN (smallest canonical autocorrelation) method of Tsay and Tiao [17] is modified to become robust to heteroskedastic data with improved Bartlett's formula for the variance estimation of autocorrelations provided by Berlinet and Francq [3]. Data with ARCH[1] variances are typically emphasized in our simulation studies. Our simulation results show that the original SCAN method suffers from severe size distortion for heteroskedastic data, and the distortion is getting larger as sample size is. On the contrary, the modified SCAN method not only has appropriate empirical size but also empirical power for both homoskedastic and heteroskedastic data. Therefore, our modified SCAN method is suggested for identification of univariate time series models with heteroskedastic errors.
Applied Stochastic Fractal Search Algorithm to solve Economic Emission Dispatch Problems

Nowadays using of energy sources to produce electricity has many changes in increasing the proportion of using renewable energy. However, with the stability in generating capacity, fossil energy resources still play an important role in the power system. Therefore, solving the economic dispatch problem in terms of ensuring environmental factors is always a difficult task. This paper contributes one of the new effective method for solving the environmental economic dispatch problems- stochastic fractal search algorithm (SFSA). The problems are solved in three optimization case: fuel cost, emission, and economic emission with the constraints on load balance, power generation limits, and fuel. The SFSA will be applied to electrical systems include: 5-unit; 10-unit with power retaining in three weeks and IEEE 30 bus with wind turbines. These results have been compared with other algorithms in the same power system to demonstrate the effectiveness of SFSA.
Unsourced Random Access for Distributed State Monitoring in Internet of Things

Distributed state monitoring is mission-critical in Internet of Things (IoT), in which multiple sensors independently observe the system outputs at different locations and then send them to the fusion center to estimate the global system state vector. As is typical in a linear system, the observation of each sensor is only determined by a subset of components of the global state vector, while the fusion center only cares about the state information embedded in that observation rather than which sensor sends it out. This inspires us that the whole transmission and estimation process can be unsourced, i.e., during which the identities of the transmitter are not needed. Based on this finding, in this paper, we propose a new and efficient approach to accomplish the distributed state monitoring under an unsourced random access(URA) framework. Specifically, all active IoT sensors send their observations to the fusion center using a URA protocol, then the fusion center estimates the global system state and broadcasts a reliability indicator of the estimated state, based on which each sensor decides its activation probability in the next time slot. The procedure stops until all the estimates of the state components are reliable enough. Simulation results verify the excellent performance of the proposed scheme in terms of estimation error and convergence rate.