Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Aortic Valve from Echocardiography in Real-Time Using Convolutional Neural Network

This study proposes the development of Convolutional Neural Network (CNN) for an automatic detection system using various deep learning methods for echocardiography. We tested the performance of the CNN by measuring accuracy and the effect on video frame rate to best represent the application of CNN in an actual patient echocardiography examination. The study focuses on the system detecting the aortic valve of the heart as it is clinically significant. Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) architectures with various feature extractors were trained on echocardiography images from 23 patients. Afterward, the detection models were tested on 5 echocardiography videos. The results showed that the Faster R-CNN Inception v2 attained the best accuracy (0.949) and F1 score (0.950). The second-best performer was SSD Inception v2 with 0.865 accuracy and 0.844 F1 score. In terms of prediction speed, SSD architectures were relatively faster and achieved mean frame rate of 34.22 frames-per-second (fps) and 27.66fps for MobileNet and Inception v2 feature extractor respectively. However, the frame rate performance loss for SSD Inception v2 was 49.71% compared to the original 55fps echocardiography video. The findings in this study facilitate a foundation in utilizing convolutional neural network to the echocardiography field.
Slopping detection system for LD converters using sound signal digital and image processing

The steel production is divided into a series of steps. One of these steps is the transformation of pig iron into steel and the most widely used method in the world for this is through blowing of oxygen using the so-called LD converters. The reactions that provide such a transformation produce a foam inside the converter. Although it is fundamental to the process, the foam can overflow the top of the converter, an event called slopping, causing serious problems such as loss of efficiency of the process, damage to the equipment, risk to employees and environmental pollution. The detection and control of such event is crucial. The current work presents a method for slopping detection using sound and image processing, based on previous works. Sound and video data from 67 heats were collected at a steel mill and the results of the detection system were obtained.
Graph-Based Static 3D Point Clouds Geometry Coding

Recently, 3D visual representation models such as light fields and point clouds are becoming popular due to their capability to represent the real world in a more complete and immersive way, paving the road for new and more advanced visual experiences. The point cloud representation model is able to efficiently represent the surface of objects/scenes by means of a set of 3D points and associated attributes and is increasingly being used from autonomous cars to augmented reality. Emerging imaging sensors have made it easier to perform richer and denser point cloud acquisitions, notably with millions of points, making it impossible to store and transmit these very high amounts of data without appropriate coding. This bottleneck has raised the need for efficient point cloud coding solutions in order to offer more immersive visual experiences and better quality of experience to the users. In this context, this paper proposes an efficient lossy coding solution for the geometry of static point clouds. The proposed coding solution uses an octree-based approach for a base layer and a graph-based transform approach for the enhancement layer where an Inter-layer residual is coded. The performance assessment shows very significant compression gains regarding the state-of-the-art, especially for the most relevant lower and medium rates.
An Automatic Object Detection and Location System applying Faster R-CNN

A Great effort has been done to solve the problem of object detection and recognition in computer vision. This paper describes the results of experiments on detection and recognition of objects in images by using the Faster Region Convolutional Neural Networks (Faster R-CNN) technique. The experiments were performed using a calibrated vision system, a set of 150 images containing a total of 4 different objects. The obtained accuracy for the detection and recognition task was satisfactory, varying from 90.13% to 100%. With these results, we can use the proposed method to estimate the joint configuration of a manipulator and allow it to grasp a desired object.
Lightweight Spatial Pyramid Convolutional Neural Network for Traffic Sign Classification

In this paper, we proposed a lightweight spatial pyramid convolutional neural network (SP-CNN) classifier for image-based traffic sign classification. The lightweight SP-CNN classifier is formed based on ResNet (residual network) CNN architecture which originally used for CIFAR10 image classification problems. Our proposed classifier consists of five parallel convolutional networks and each network processes a cropped region using spatial pyramid configuration. For smoother transitions between the regions cropped in the level 1 of spatial pyramid configuration, we overlap the level 1 of spatial pyramid regions configuration for around 12.5% on each axis. The proposed classifier trained by fine-tuning the CIFAR10 weights with NAG (Nesterov Accelerated Gradient) training algorithm. Experiments on GTSRB (German Traffic Sign Recognition Benchmark) dataset show that our lightweight SP-CNN version produces an accuracy of 99.70% and an execution time of 60 ms. The proposed classifier produces a very competitive accuracy compared with other methods but with less number of parameters.
Experimental Analysis of Photovoltaic Modeling for Partially Shading Conditions

Predicting the behavior of I-V and P-V curves for photovoltaic (PV) generation is possible through mathematical models for photovoltaic cells. However, current models are made for uniform conditions, making it difficult to analyze in case of non-uniform conditions such as partially shading conditions (PSC). At this point, the present work applied the Villalva's model in non-uniform conditions, transformed by the curve superposition technique for different scenarios, and later, reproduces the scenarios experimentally. The experimental scenarios used a photovoltaic system with certain black objects were positioned on the PV modules. In this way, it was possible to compare the transformed mathematical model for non-uniform conditions with real curves of a photovoltaic system. As results, the I-V and P-V curves generated by the model were closer to the experimental tests and the values of absolute and root mean square errors were acceptable. Thus, it has been proven experimentally that with the transformation by the curve superposition technique the photovoltaic modeling can be used for PV studies under PSC.
Modeling and Control of a Converter Based AC Subsea Power Cable Emulator

In this paper, a power converter based AC subsea power cable emulator for Power Hardware-in-the-Loop applications is introduced. Lumped parameters cable models are compared to Bergeron travelling waves model response and a three-phase coupled T model is derived in discrete time domain. A three-phase back-to-back PWM voltage source converter is modeled and its control system designed in order to emulate the developed cable model. Finally, simulation results verify the modeling and control schemes in transient and steady state situations.
A spin orbit torque based true random number generator with real-time optimization

True random number generators (TRNG) is desired by various applications such as data encryption and statistical sampling. Due to the stochastic switching behavior, spin transfer torque magnetic tunnel junction (STT-MTJ) is used for the design of TRNG. However, it requires a high-complexity circuit to precisely control the duration and amplitude of the applied current. In this work, we propose a spin orbit torque MTJ based TRNG (SOT-TRNG) with a correction circuit. The controlling of the current is simplified, and the optimization modules are easy to be implemented. The transient simulation and the National Institute of Standards and technology (NIST) tests results demonstrate that the proposed TRNG can generate true random numbers with high speed meanwhile tolerate process, voltage, and temperature (PVT) variations. Furthermore, the comparison results show that the SOT-TRNG has a wider operational margin of current and stronger robustness.
A Simple Wideband Magneto-Electric Dipole Antenna

By cutting a vertical slot on the quarter-wavelength patch antenna of the magneto-electric dipole antenna and changing the dimensions of the half-wavelength electric dipole antennas, a simple wideband magneto-electric dipole antenna with a defected ground structure is demonstrated. Experimentally, a prototype is fabricated and measured. The proposed antenna achieves a wide impedance bandwidth of 91.3% covering 1.38GHz to 3.7 GHz. Over this band, the measured antenna gain is ranged from 6 dBi to 7.8 dBi. This design has a low cross-polarization level and low backlobe radiation level.
Meta-Dome Structure for Simultaneously Absorbing Radar and Protecting Environment

In this study, we proposed a meta-dome structure for simultaneously absorbing radar and protecting environment. To protect a metamaterial absorber, we used a FR-4 dielectric material on the metamaterial absorber. Furthermore, we proposed not only protecting structure for the metamaterial absorber but also broadband absorbing radar signal application. The proposed meta-dome has an absorption frequency band from 4.6 to 12 GHz. In addition, the proposed meta-dome has advantage of polarization insensitivity for practical applications. Experimentally, we achieved in the absorption frequency from 5 to 14 GHz for all polarization angle under normal incidence. The proposed meta-dome is demonstrated by full-wave simulation and measurement.
A Conceptual Scheme for Ransomware Background Knowledge Construction

Various methods have been implemented to detect and mitigate malware. Ransomware is one of the rising malware which getting attention from world due to its impact of attack in the cyber space. Detection of potential features of Malware using traditional approach and usage of text mining is nothing new. However, identifying the Ransomware related entity from external sources and unstructured textual data like forum is new exposure towards the application of text mining in malware domain. Therefore, in this paper, a conceptual scheme is proposed to construct a Background Knowledge of Ransomware which necessary to improve the accuracy of NER when classifying the Ransomware related entity from unstructured data like online forum. From this work, the analysis related to malware also could be understood by people who have no or less expertise in Malware domain since it uses the casual text representation that are obtain from user-generated content made publicly.
Optimized Design of Broadband Radar Absorbent Material

Radar absorbent material (RAM) is widely used such as interference prevention between devices using radio waves and the wall of anechoic chamber. They have excellent characteristics depending on the applications, such as broadband characteristics, oblique incidence characteristics and both polarization characteristics. In recent years, various optimization methods have been used in RAM design studies. In this paper, we study the optimized design of two flat layers RAM with broadband characteristics by using Non-dominated sorting genetic algorithm-II (NSGA-II). The index of radio wave absorption amount is 20 dB corresponding to the absorption rate of 99 %, and the objective of this study is to obtain broadband characteristics satisfying the index. As a result, it is confirmed that broadband characteristics with relative bandwidth of 158 % can be obtained from 1.78 GHz with respect to 20 dB. In addition, two flat layers RAM with the maximum relative bandwidth is possible to be designed for the required minimum frequency at 1.78 GHz or higher by using the proposed optimized design method.
A Robust Volt/Var Control Via Multi-Objective optimization

Reducing losses in distribution systems is one of the main goals of VolUVar control that has been focused by researchers in decades. Despite the financial benefits, this approach can lead to poor robustness of the system operation if the objective is just achieving minimal losses. In order to ensure power quality supply, even for unforeseen scenario changes, and to reduce power losses, this paper proposes a multi-objective formulation for the VolUVar control. A robustness index is proposed and considered as one of the objective functions. Trade-off solutions, between loss reduction and robustness gain, are searched by a multi-objective particle swarm optimization (MOPSO) algorithm. Tests, carried out in the IEEE 123-bus feeder, showed that the method enhances the robustness of system operation, upholding power quality for scenario changes – variations in loading and distribution generation – and, nevertheless, preserving near-optimal loss minimization. The results encourage on-line applications in the smart grid context.
Cluster Formation Strategy as Ancillary Service of a Battery Storage System in a Secondary Distribution System

This paper presents a cluster formation strategy to minimize the computational efforts developed by the Distribution System Operator (DSO) in optimizing a distribution system. The DSO optimizes the distribution system aiming an operation at minimum distribution power losses. The cluster makes the DSO to “see” a reduced number of Buses compared to the real system. As result, the DSO uses a reduced number of data on its optimization algorithm. The Battery Storage System (BSS) creates the cluster by enveloping candidate Buses according to their load power consumption and the power loss in the feeder caused by the loads connected to such Bus. The cluster is a virtual conception. Neither physical changes nor reconfiguration through switch-breakers are required. Details describing the BSS and how the BSS creates the cluster are carefully explained. A case study is conducted in order to show the efficacy of the proposed strategy.
FE Switching in SrRuO3/NiFe2O4/BaTiO3/LSMO Heterostructures on SrTiO3Substrates

This work reports the successful fabrication of an all-oxide heteroepitaxial structure with a bilayer tunnel junction consisting of ferroelectric and ferrimagnetic layers. Each layer of the structure exhibits high crystallinity and well-defined interfaces. The transport properties of the junction are found to be well described by the characteristics of an asymmetric ferroelectric tunnel junction.
Spatial Correlated Data Monitoring in Semiconductor Manufacturing Using Gaussian Process Model

In semiconductor manufacturing, various wafer tests are conducted in each stage. The analysis and monitoring of collected wafer testing data plays an important role in identifying potential problems and improving process yield. There exists three variation sources: 1) lot-to-lot variation; 2) wafer-to-wafer variation; and 3) site-to-site variation, which means the measurements cannot be considered independently. However, most existing control charts for monitoring wafer quality are based on the assumption that data are independently and identically distributed. To deal with the variations, we propose a mixed-effects model incorporating a Gaussian process to account for the variations. Based on the model, two control charts are implemented to detect anomalies of the measurements which can monitor the changes of the variations and the quality of products, respectively. Simulation studies and results from real applications show that this model and control scheme is effective in estimating and monitoring the variation sources in the manufacturing process.
A Current Sensitivity Model for Power System Stability Studies

Mathematical models have an important role in the study, analysis and planning of electric power systems operation. Suitable models produce more reliable data regarding the real behavior of the system. The main objective of this article is to present a model for the study of power system small signal stability, the Current Sensitivity Model (CSM). The CSM is composed by sets of differential and algebraic equations. The differential equations are used to represent the dynamics of power systems equipment, such as generators and controllers. The algebraic equations represents the current nodal balance in all systems buses, where the current balance is the fundamental concept of the model. Simulations are performed in three test systems to evaluate CSM. In each of these simulations data obtained under CSM is compared with data obtained under Heffron and Phillips Model (HPM). The comparison allows to establish that for systems composed of a single machine connected to an infinite bus through a transmission line there are no differences between the models. For systems composed of more than one machine, defined as multimachine systems, numerical differences between CSM and HPM arises. Those differences are a result of the different concepts used to formulate the models, with the structure preservation being one of the key points, and one of the advantages of CSM over HPM.
High Performance Computing (HPC) Implementation: A Survey

To maintain excellence in global hyper-competitive economies in the upcoming decades, manufacturers must improve the design, development, and distribution of subsequent product generations, production technologies, equipment, and processes. High-performance computing (HPC) solves the application of significant challenges using computer modeling, simulation, and analytical technology. By using HPC technology, products design will be faster, making and testing prototypes will save time, the production process can be simplified, the cost of innovation will decrease, and high-value innovation will be quickly developed. This study uses the PRISMA method, based on predetermined keywords and analyzes the algorithms, frameworks and programming languages and the processes that are most commonly used to optimize the Graphics Processing Unit (GPU). The results of this study can be a reference for future research on the implementation of High-Performance Computing (HPC) Application by combine modeling, simulation, and analysis.
Radio Frequency Exposure Compliance of Multiple Antennas for Cellular Equipment Based on Semidefinite Relaxation

Human exposure to radio frequency (RF) electromagnetic fields should not exceed the exposure limits set by the relevant regulatory guidelines. In this paper, we provide approaches to determine upper bounds of maximum exposure from multiple antennas. When the total transmitted power is fixed, the maximum exposure can be determined by an eigenvalue decomposition. If an individual antenna has additional power constraint, the exposure maximization problem can be relaxed to a semidefinite program. Two case studies are given to demonstrate the approaches to the maximum exposure of cellular equipment using multiple antennas. The methods and the obtained results provide valuable insights into RF compliance procedures of cellular equipment.
Microstrip Patch Array Antenna Using a Parallel and Series Combination Feed Network

In this paper, we describe $\mathrm {a}4 \times 4$ microstrip patch array antenna with a high gain and low side lobe level (SLL) using a parallel and series combination feed network with different power distribution ratios. Several quarter-wavelength impedance transformers are connected, and the power distribution ratio is controlled by adjusting the characteristic impedance of the feed network. The optimized $4 \times 4$ microstrip patch array antenna has a $\vert \mathrm {S} _{\mathbf {11}} \vert \lt {(! {-}! )}10$ dB impedance bandwidth of 220 MHz which is a fractional bandwidth of 2.1%. At 12.5 GHz, the antenna has a gain of 18.2 dBi and SLLs of–28.0 and–26.5 dB in the x-z and y-z planes, respectively. The size of the array antenna is 100 mm $\times 100$ mm $\times 0.7874$ mm (3.5$\lambda \mathbf {o} { \times 3.5} \lambda \mathbf {o} { \times 0.028} \lambda \mathbf {o}$at 12.5 GHz).
Simplified Model and Control Strategy of a High Voltage Gain Interleaved DC-DC Boost Converter

This paper presents a methodology for modeling and control designing of a non-isolated Interleaved DC-DC Boost Converter based on the Three-State Switching Cell (3SSC). This topology is suitable for applications that requires a high input-to-output voltage ratio conversion. The plant modeling is done so as to achieve a Boost converter equivalent model that closely matches the phases dynamic behavior of the original converter. Control strategy adopted is the averaged current mode control with four inner current loops, one for each phase of the MIPTs (Multi-Interphase Transformers), and an outer voltage loop. Simulation results are presented in order to validate the proposed model and control strategy.
A pilot study: Neurophysiological Study on the Effect of Chronic Ankle Pain Intervene with Video Assisted Mindful Deep Breathing

Chronic ankle pain are reasonably common among sports injuries that leads to chronic ankle instability (CAI). When ankle gives way, pain and recurrent sprains are the characteristics of CAI reported among repeated ankle injuries. This situation may bring down life's quality that possibly leads to neurophysiological changes due to depression, anxiety and stress. Mindfulness for pain management has been popular in recent decades. Here, we aim to employ a new and standardization on the tool for the recruited participants to do the mindful deep breathing. Therefore, a validated method video assisted mindful deep breathing (VAMDB) can improve on conflict monitoring, to modulate brain network dynamics underlying in the pain waves experience in this study. Characterization on brainwaves changes with electroencephalogram (EEG) study for the pre- and post-intervention on the neurophysiological response of brain among collegiate athletes are reported here. A total of 12 participants aged between 18 and 25 years were recruited. 8 participants with chronic ankle pain from INTI physiotherapy centre were grouped into intervention (INT) n=4, and nonintervention (NINT) n=4 respectively. Another 4 participants without pain and intervention was assigned as control group (CONT). The data was processed with MATLAB and analysed by ANCOVA within the group (INT, NINTand CONT). The pre- and post-treatment effect was significant within the subject factor. Delta, theta, alpha, beta and gamma bandwidths in occipital region shows significant effect on VAMDB between pre and post intervention. This pilot study is the first to explore the 3-min short duration VADMB among ankle pain participants.
Fabrication of micro-thermoelectric cooler for the thermal management of photonic devices

The work demonstrates and discusses the fabrication of micro thermoelectric cooler $(\mu$ -TEC) for thermal management of photonic devices. The device is fabricated using cost-effective electrodeposition technique on silicon wafer with 210 pairs of the electrodeposited p-type BiSbTe and n-type BiTe pillars. The complete device is fabricated using flip-chip bonding technique. We discuss the challenges in device fabrication and solutions employed to successfully construct the device.