Miracle-dz/UniFAR-FTUs huggingface dataset:
Single-Phase DC-AC Inverter with Transformer and Transformerless and Low Power Dissipation Filter for Photovoltaic-Based Home-Scale Electric Power System

This paper describes the effects of modulation techniques and the number of pulses on low power dissipation in a single-phase inverter and a multilevel inverter. Multilevel inverters and single-phase inverters given pulse input use PWM (Pulse Width Modulation), SPWM (Sinusoidal Pulse-Width Modulation) and PWL (Piecewise - Linear) modulation techniques, either in the form of square waves or multilevel pulses paired with the appropriate LCL filters. produce an inverter circuit that has a low THD (Total Harmonic Distortion) and high efficiency. The use of transformers is also mentioned in this paper to increase the source voltage at an output of 48 volts to around 220V-227Volt. Simulation results show that the effect of simple modulation switching techniques, such as PWM generated square waves, is simpler and more efficient if the right filter is used. The results of the simulation in PSpice show that of the switching techniques control in inverter system with fit filter can reduce the THD up to 1.81% and the efficiency is very high reaching of about 98%.
Detection of Corrosion under Coated Surface by Eddy Current Testing Method

Corrosion under insulator (CUI) is a damage of materials due to a reaction between a surface of steel and an insulation layer. The CUI can generate an impact greatly on the industry, because it can occur in equipment without warning. An inspection system was designed to detect the CUI from the outside of a steel plate using a non-destructive testing associated with the eddy current technique. This research is to develop a prototype device and software for CUI detection by using low-cost equipment. 600-1000 Hz sinusoidal signals induced by a sine wave function generator were applied to detect CUIs, and the results were demonstrated by the proposed CUI detection program. The experimental results reveal that the pick-up coil with laminated core probe can detect the CUIs effectively, and the designed sinusoidal eddy current system with the proposed CUI detection program can monitor and detect the CUIs with up to 3 mm insulation thickness.
Vision-Based Real-Time Obstacle Segmentation Algorithm for Autonomous Surface Vehicle

Among various sensors used to recognize obstacles in marine environments, vision sensors are the most basic. Vision sensors are significantly affected by the surrounding environment and cannot recognize distant objects. However, despite these drawbacks, they can detect objects that radars cannot detect in nearby regions. They can also recognize small obstacles such as boats that are not equipped with an automatic identification system (AIS) or buoys. Thus, vision sensors and radar can be used in a complementary manner. This paper proposes a vision sensor-based model, called Skip-ENet, for recognizing obstacles in real time. Compared with ENet, the amount of computation is not significantly higher. Further, Skip-ENet can segment complex marine obstacles effectively by increasing the values for the class accuracy and mean Intersection of Union (mIoU). Moreover, this model enables even low-cost embedded systems to compute 10 or more frames per second (fps). The superiority of the proposed model was verified by comparing its performance with that of the conventional segmentation models, MobileNet, ENet, and DeeplabV3+.
Reward-based Markov Chain Analysis of Slicing Flows for Inter-Cloud Virtual Resources Allocation in 5G Cellular Network *

In 5G, diverse classes of traffic (RT/NRT) are differentiated as flows of network slices, e.g., uRLLC, eMBB, mMTC, etc., traversing 5G between the end users to global cloud data centers. Clearly, for minimizing cloud access delay and maximizing cloud net profit, several mechanisms are considered in 5G Cloud era: migrating virtual cloud resources, redirecting incoming request, steering traffic via mobile edge computing (MEC), overselling virtual resources, etc. However, the first three mechanisms increase the cloud cost, but the fourth one may increase cloud reward. With general business policy of overselling, the dynamic Service License Agreements (SLAs) are required for achieving the objective. To maximize system reward while satisfying the QoS of various SLAs in 5G, an efficient cloud resource allocation is required for cloud manager. The Reward-based adaptive global Cloud Resource Management, namely RCRM, is thus proposed for 5G. Moreover, RCRM consists of some contributions: Traffic Predictions, Adaptive Cloud resource Allocation and Maximum Net Profit. RCRM analyzes the service blocking and the required number of VMs for each request by the M/M/m/m Markov chain model. Generally, since cloud providers oversell diverse types of cloud resources, the objective of maximizing net profit can be achieved. Furthermore, the cost of deploying data centers at different areas in the world is completely varied, because in different areas (or countries) the equipment deploying cost, the power cost, the building cost, and the engineering cost are definitely different. For achieving 5G network slicing in different domains, diverse-type of cloud resources among global data centers are adaptively allocated in this work. From performance evaluations, the proposed RCRM approach outperforms the others in dropping probability, SLA violation, violation penalty and net profit. Moreover, the dropping probability is analyzed and the probabilities of analysis and simulation are very close. This result can justify the correctness of the proposed Markov chain model.
A Performance Evaluation of Raspberry Pi Zero W Based Gateway Running MQTT Broker for IoT

The Internet of Things (IoT) has become widely used in recent years in a wide range of applications, such as, weather condition monitoring, transportation, smart homes, smart cities, smart farm, etc. The ecosystem of the IoT is also vast, including from sensor and hardware devices up to cloud-computing. An approach that is getting more and more attention in the IoT ecosystem is the edge-computing and one of its fundamental pieces of equipment is the edge-computing gateway (GTW), which can working as a data-processing device nearer to the things and as a bridge to the Internet, as well. The most important features for these GTWs must be robustness and efficiency and a very popular solution is to use low-cost Raspberry Pi card-size computers. Considering protocol solution, Message Queue Telemetry Transport (MQTT) communication protocol has been considered one of the most applicable to IoT because of its low-power capability. In this context, this paper describes a study about the performance evaluation of a low-power member of the Raspberry Pi family, the Raspberry Pi Zero W, working as an IoT gateway and running MQTT. The experimental results show its performance using as metrics: the processor temperature, the CPU usage level, and rate of MQTT received messages under different Quality of Services (QoS).
A Simple-Topology Compact Broadband Circularly Polarized Antenna With Unidirectional Radiation Pattern

In this letter, a geometrically simple, reflector-backed single-point-fed circularly polarized (CP) antenna with unidirectional radiation pattern is presented. The structure comprises a simple coplanar waveguide feeding circuit with an open slot etched on one side of the coplanar ground. The enhanced CP bandwidth is obtained by combining the loop mode, the slot mode, and the asymmetrical configuration of the coplanar ground planes along the feedline. All parameters are optimized at the full-wave level of description for the best impedance and axial-ratio (AR) bandwidth. The antenna footprint is only 0. 35λo × 0. 39λo at the lowest CP operating frequency of 3.1 GHz. The measurement results indicate 82% impedance bandwidth (from 3 to 7.2 GHz) and 80% AR bandwidth (from 3.1 to 7.2 GHz). A unidirectional right-hand circular polarization radiation pattern in the +z-direction is also achieved by adding a reflector. The peak realized gain of the antenna is 6.7 dBic, and the peak radiation efficiency is 98%.
A Simplified Method to Evaluate Equivalent Circuit Model and State of Charge of Li-ion Battery

This paper presents an experimental approach to find the equivalent circuit model representation of Li-ion battery. Design and implementation of an accurate battery charger require a precise battery model. The experiment is carried out in a Li-ion battery cell. The experimental results is curve fitted to find relationship between the Open Circuit Voltage (OCV) and State of Charge (SOC) forthe estimation of SOC. The Thevenin model battery parameters are estimated by the recursive least square algorithm using the experimental results. The approach is extended to build the battery model for a Li-ion battery of higher capacity.
A K-Band 3-D Printed Focal-Shifted Two-Dimensional Beam-Scanning Lens Antenna With Nonuniform Feed

A K-band (18–26.5 GHz) two-dimensional (2-D) beam scanning antenna is proposed. The hyperbolic lens is fed by an array of focal-shifted horns with nonuniform aperture. The feed at the focal point of the lens generates the boresight vertical beam, while the off-focal feeds are responsible for tilted beams. The nonuniform horn aperture helps to mitigate the gain decrement from the lens aperture tapering. It is fabricated taking full advantage of dielectric and metallic 3-D printing technologies. The lens is printed by stereolithography apparatus (SLA) in resin. The feed horn array is printed by selective laser melting (SLM). Process-wise, it features low cost, short turnaround time, and the capability to implement complex geometries versus traditional computer numerical control machining and mold casting. Simulation and measurement indicate that the antenna features desirable matching below −15 dB and mutual coupling between adjacent port below −25 dB. The gain is above 23 dBi across the K-band. Multiple beams at ±32°, ±12°, and 0° are observed on E- and H-planes. The proposed design can either function as multibeam antenna or a beam-scanning antenna together with switches. It is a capable candidate for applications such as multiple target tracking and wide region monitoring.
Fusing Multi-Sensor Measurements to Improve Heading Estimation using Kalman Gain for Indoors Smartphone Positioning Solutions

Technology progression in the last decades leads to many smartphone advancements, such as embedding variety of sensors for various measurement and applications. Positioning sensors (Accelerometer, Gyroscope, and Magnetometer) are one of the significant developments. Besides this, indoor positioning services on smartphones are the main advantage of these sensors. There are many indoor positioning applications, for instance; indoor navigation, asset tracking, controlling in warehouse, rescue operation, and entertainment applications. Nevertheless, precise position information through current positioning techniques is the main issue of these applications. The pedestrian dead reckoning (PDR) technique is one of the techniques in which the integration of onboard sensors is used for locating smartphones. Estimated distance, heading, and typical speed can be measured to determine the estimated position of the smartphone via using the PDR technique. The PDR technique offers a low positioning accuracy due to an existing unpredictable error of the embedded sensors. To solve this issue, fusing multi-sensors measurements is proposed, in this paper, to reduce the existing sensors drifts and errors. Further, the sensors' measurements with the previously estimated position are fused by using KALMAN Filter to determine the current location of the smartphone in each step of walking with higher accuracy. The obtained positioning accuracy through the proposed approach and based on trial experiments is 2 meters, which is equivalent to 10% improvement in comparison with state of the art.
Despeckle and Segmentation of Carotid Artery for Measurement of Intima-media Thickness

B-mode ultrasound images of carotid artery are used to measure the intima-media thickness (IMT), which detects the probabilities of atherosclerosis and cardiovascular diseases. Ultrasound images suffer from peculiar phenomena which creates speckle (a type of noise) on the image. The speckles present in the medical images detoriates the quality of the image. Thus the paper presents a method of removing these speckles by using local statistics mean variance (lsmv) filter. It is a preprocessing step of medical image processing. Conventional IMT was done by locating the far walls of the carotid artery. This can be changed by applying segmentation algorithm which could automatically detect the far walls and could measure the IMT. This paper approaches towards automatic edge detection method using prewitt operator. The performance analysis of the proposed method is done in case despeckling as well as segmentation method. The performance parameters show that the proposed method performs better and at the same time reduces the manual effort.
Gain Enhancement of Microstrip Antenna

Microstrip antennas are very popular in recent decades due to its small size, low cost, light weight, and easy fabrication. This antenna has many useful characteristics like thin profile, both linear and circular polarization, dual frequency operation possible, and with any shape. It has several applications in wireless communication, in telemedicine, in mobile communication. However, the types of applications of microstrip antennas are restricted by the antennas inherently low gain and narrow bandwidth. Accordingly, increasing the gain and bandwidth of the microstrip antenna has been a primary goal of research in the field. Aim of this paper is to increase gain of microstrip antenna. So, made some changes in different parameters of microstrip antenna like patch length, width or substrate size and gain and efficiency are observed.
Speed and Acceleration Control for a Two Wheel-Leg Robot Based on Distributed Dynamic Model and Whole-Body Control

The control technique for generating whole body motions on wheel-leg robot for dynamic locomotion and balance is a challenging topic. In this paper, a whole body dynamic model is built. It consisted by the torso dynamic model, the wheel-leg dynamic model and the contact force constraint between the wheels and the ground. A whole body control frame is proposed which composed by a torso motion controller, two torque solvers, a slippage predictor and an online trajectory generator. The torso controller plans the coupling wrenches between wheel-legs and torso. The torque solvers for every wheel-leg compute joint torques based on the coupling wrenches computed from the torso controller synchronously. The contact forces can be calculated by the slippage predictor based on the joint torques, then a boolean signal will be transmitted to the online trajectories generator to indicate whether the friction cone constraint will be broken. The online trajectory generator can plan reasonable acceleration trajectory with different boolean signals. The efficiency of this control frame is illustrated by several simulations in virtual simulation environment.
Cavity-Backed Broadband Circularly Polarized Cross-Dipole Antenna

This letter presents a broadband circularly polarized (CP) cross-dipole antenna. It consists of crossed dipoles, parasitic elements, a cavity-backed reflector, and a circular ring reflector. The arms of crossed dipoles are divided into two sectors by a slit. The parasitic elements are utilized to significantly enhance the CP bandwidth. A circular ring reflector is adopted to optimize the radiation pattern and improve the gain in high frequency band. The fabricated prototype of the proposed antenna with an overall size of 0. 60λ0 × 0. 60λ0 × 0. 21λ0 (λ0 is the free-space wavelength at the lowest CP operating frequency) yields a measured impedance bandwidth of 98.2% for |S11|<–10 dB (1.73–5.07 GHz) and a 3 dB axial-ratio bandwidth of 85.5% (1.94–4.84 GHz). The antenna has stable radiation patterns from 1.94 to 4.5 GHz and exhibits a right-hand circular polarization with a peak gain of approximately 10.0 dBic at 3.6 GHz.
Analysis of Terminal Voltage in Single-Phase Extended Three-Phase Transformerless PV Inverter Topologies

In this article, an insight view for the derivation of various existing three-phase transformerless PV inverter topologies which are extended from the single-phase configuration is presented. In this article, the topologies under the four categories like dc decoupled, ac decoupled, neutral point clamped (NPC) dc decoupled, and NPC ac decoupled of single-phase transformerless photovoltaic (PV) inverter configurations are considered and their extended three-phase configurations are given. Furthermore, the analysis of the terminal voltage for each three-phase PV inverter topology is also presented. The analysis given in this article is expressed using the switching function concept. The analytical waveforms obtained using the mathematical switching function equation is matching with the simulation results presented. In addition, the obtained analytical and simulation results are further supported with the experimental results from the developed lab prototype. Finally, a comparison is done for the considered three-phase transformerless inverter topologies in terms of the number of switches and diodes used, the number of switches and diodes conducting, etc.
Exploiting Mallows Distance to Quantify EEG Distribution for Personal Identification

Electroencephalogram (EEG) activity from the brain is a promising biological marker that can serve as personal identification. Despite substantial efforts, it still remains unsolved problems to quantify EEG feature distribution for brain biometrics due to the complexity of the brain. In this study, we attempt to tackle EEG-based identification challenges by exploiting a novel distribution model. The distribution dissimilarity is measured by Mallows distance, a cluster similarity sensitive distance that is robust to signal noises. Specifically, EEG signals are decomposed through several statistical feature extraction methods, autoregressive (AR) model, discrete wavelet transform (DWT), and fast Fourier transform (FFT). With the dataset obtained from the real-world application, our proposed system achieves the f-score accuracy of 96.18% and half total error rate of 2.223%, which demonstrates the feasibility and effectiveness of utilizing EEG biometrics for personal identification applications.
Numerical Assessment of the Backward Facing Step for NACA 0015 Airfoil using Computational Fluid Dynamics

The objective of this work was to investigate the lift and drag characteristics of a NACA 0015 airfoil with a backward facing step. For a free stream velocity of 30m/s, the point of separation was determined via CFD analysis for an increasing angle of attack. A backward facing step was then introduced just prior to the point of separation which extended till the trailing edge. Computational studies were conducted with the goal of enhancing the aerodynamic performance of the airfoil by inserting several backward facing steps of gradually increasing depth. The CFD analysis was conducted for 30 m/s wind speed, with steps placed just prior to the point of separation for 18° angle of attack. The results produced show that as much as a 26% reduction in drag is possible by introducing the step. But at the same time, about 12% reduction in the lift was also observed. A wind tunnel test on a NACA 0015 was also conducted to verify the results from CFD analysis. Predominantly the goal of this thesis was to numerically determine whether or not the aerodynamic performance of an airfoil can be enhanced by introducing a backward facing step on the upper surface of the airfoil.
Prototype of Pornographic Image Detection with YCbCr and Color Space (RGB) Methods of Computer Vision

Latest Internet and technology developments have brought in various changes in social interaction, specifically for the teenager to middle age people that excessively using social media. One of the negative effect of this interaction model change is the increase of wide spreading of contents that contain pornographic elements. The pornographic contents can be accessed from social media such as Facebook, Instagram, Path, Whatsapp and Youtube. The purpose of this study is to develop a system for identifying pornographic image content by designing a prototype of pornographic image detection using computer vision’s YCbCr and color space (RGB) method by filtering the algorithm of previous researchers, measuring instruments to determine the accuracy of prototypes using the Confusion Matrix algorithm. The experiments using YCbCr produced an accuracy rate of 76%, precision value 82.142%, and recall value 76.666% from 50 random datasets of pornographic positive and non-pornographic images, while the RGB method produced an accuracy rate of 43.42%, precision value 44.23%, and recall value 50.17% from 146 random dataset of pornographic positive, non-pornographic and semi-pornographic images.
Music Genre Classification of audio signals Using Particle Swarm Optimization and Stacking Ensemble

Genre classification is a process of grouping similarities, such as patterns, styles, or objectives with management data as already in the music (e.g. pop and rock). It is used along with the classification of topics. This paper will classify songs from audio signal to a hierarchy of musical genre by using feature extraction. Trimbral texture, rhythmic content and pitch content are used as the main feature sets. Feature selection is selected by using Particle Swarm Optimization (PSO) and sent selected feature to classification. The result in classification has low accuracy. Thus, using stacking ensemble method is to improve the prediction. In this paper, the purpose is to improve the prediction by using stacking ensemble method. Stacking ensemble that have the second level is base classifier and meta-classifier. In base classifier consists of 5 classification; K-Nearest Neighbors (k-NN), Decision Tree (DT), Random Forest, Support Vector Machines (SVM); and Naïve Bayes. This process is generated to build multiple classifier predictors and sent it to meta-classifier. In the process of meta-classifier will create new model to predict test data. The new model has been created from neural network which train data is the output of base classifier.
A 2.4 GHz Rectenna Based on a Solar Cell Antenna Array

Combined solar and radio frequency (RF) harvesting systems are drawing attention by offering self-powered ability to low-power devices, an essential aspect to the success of the Internet of Things. One of the main challenges of these harvesting systems is to improve rectennas efficiency by optimizing the individual energy collection and conversion capacity of each source. To overcome this issue, this letter presents a novel 2.4-GHz rectenna structure for solar and radio frequency energy harvesting applications based on a coplanar antenna array integrated with multicrystalline solar cells, whose aluminum rear terminals are used as radiators for the antenna array. The results obtained by simulations and measurements show that it is possible to collect energy from two different types of sources in a complementary manner, using the rectenna design proposed, making it possible to feed low-power devices remotely.