Miracle-dz/UniFAR-FTUs huggingface dataset:
System Architecture based on IoT for Smart Campus Parking Lots

This work aims to design a system architecture of internet-of-things (IoT) for managing electric vehicles charging in a university campus. The electric vehicle management system is composed of electric vehicles, charging stations, parking lot local controllers and a university central controller. The proposed architecture consists of three layers: a power system layer, a communication network layer and an application layer. The electric vehicle system components, data traffic and communication requirements that should be taken into account are defined in order to implement the smart campus parking lots. The performance analysis and practical feasibility of implementing IoT-based architecture have been investigated for a smart parking lot in Chonbuk National University Campus, South Korea.
Sentiment Classification and Prediction of Job Interview Performance

Attracting and hiring talented employees is a challenge for companies. The job interview process is a very critical step for both employer and candidate. Having a smooth hiring process in a company will increase future employees' satisfaction. Candidates tend to share their feedback and experience of interviews and company's hiring process with others. Having a negative experience can affect its brand image and reputation as an employer. This will make it hard to attract talented employees. In this research, machine learning and neural network models, such as support vector machines, logistic regression, Naïve Bayes, and long short–term memory (LSTM), were trained to predict the candidates' sentiments after a job interview. Each model was trained using several data representations and weighting approaches, such as term binary, term frequency, and term frequency–inverse document frequency (TF–IDF). As a result, training logistic regression with TF–IDF and unigram word representation achieved an F1-measure of 0.814.
Surface-EMG based Wrist Kinematics Estimation using Convolutional Neural Network

In the past decades, classical machine learning (ML) methods have been widely investigated in wrist kinematics estimation for the control of prosthetic hands. Currently deeper structures have shown great potential to further improve prediction accuracy. In this paper we present a single stream convolutional neural network (CNN) for mapping surface electromyography (sEMG) to wrist angles within three degrees-of-freedom (DOFs). Two types of two dimensional (2D) sEMG images are constructed in time domain and spectrum as CNN inputs, respectively. Six typical linear and nonlinear ML models are implemented for comparison, where four efficient time-spatial hand-crafted features are extracted to represent feature engineering. Experiment results with four able-bodied participants illustrate that CNN with 2D spectrum sEMG images can achieve highest accuracy in most testing sessions. In other sessions, it is still competitive to the most promising ML techniques. The core strength of deep learning (DL), i.e. feature learning via deep structures and efficient algorithms, is verified to be more powerful than classical feature engineering, particularly in smaller datasets.
A Microstrip line Reflection-Type Phase Shifter for 60 GHz Phased Array

A microstrip line reflection-type phase shifter is presented in this paper for 60 GHz mmWave phased array system. Based on the characteristic of varactor diode, an impedance-transforming quadrature coupler and a novel parallel resonant network are designed to extend the phase tuning range. To the best of our knowledge, it is the first time a uniplanar microstrip line phase shifter is implemented at 60 GHz to achieves 93.3° phase tuning range. The analytical equations are derived for the design theory, and EM simulation and measurement are conducted to prove the proposed concept.
Air Quality Arduino Based Monitoring System

Air quality is a very important health factor. However, there are situations where humans are exposed to poisonous air that has a high concentration of Volatile Organic compounds (VOC), especially indoor environments such as car cabins and offices. It is important for a better health to breathe clean air that has low concentrations of VOC. This paper presents a design for a system that aims to notify the residents of VOC's concentration level in both indoor and outdoor environments. The system is Arduino-based, it will monitor and detect total volatile organic compounds (TVOC) and then inform the user via wireless communication system of its levels to take actions.
Injection Locked Oscillator Theory for Frequency Selective Limiters

Ferrite frequency selective limiters (FSL) can be realized as a transmission line coupled to a magnetized ferrite medium. Above a critical threshold power level, spin wave modes near one half the applied frequency grow exponentially and couple strongly to thermal phonons, resulting in an increase in RF power absorption at frequencies near that of the high level signal. The measured absorption is asymmetrical around the strong signal. Treating the FSL as a manifold of injection locked oscillators provides a possible explanation for this asymmetry.
Price-based Power Allocation for Multi-UAV Enabled Wireless Networks

In this paper, we study the problem of downlink power allocation in the multi-UAV enabled wireless networks, where the unmanned aerial vehicles (UAVs) act as the aerial base stations (BSs) to serve a group of users on the ground. To solve the problem, we propose a price-based optimal power allocation scheme, and model the interaction between the UAVs and the ground users as a Stackelberg game. As the leaders of the game, the UAVs choose the optimal power price to maximize their own revenue. Each ground user of the networks selects optimal power strategy to maximize its own utility. In order to solve the equilibrium program with equilibrium constraints (EPEC) problem of the Stackelberg game, we investigate the lower equilibrium of the ground users, and propose a distributed iterative algorithm to obtain the equilibrium of the lower game. Simulation results show that through the price-based optimal power allocation scheme, the capacity of the BSs and the rates of users are improved.
Brain Tumor Segmentation Using Multi-Cascaded Convolutional Neural Networks and Conditional Random Field

Accurate segmentation of brain tumor is an indispensable component for cancer diagnosis and treatment. In this paper, we propose a novel brain tumor segmentation method based on multi-cascaded convolutional neural network (MCCNN) and fully connected conditional random fields (CRFs). The segmentation process mainly includes the following two steps. First, we design a multi-cascaded network architecture by combining the intermediate results of several connected components to take the local dependencies of labels into account and make use of multi-scale features for the coarse segmentation. Second, we apply CRFs to consider the spatial contextual information and eliminate some spurious outputs for the fine segmentation. In addition, we use image patches obtained from axial, coronal, and sagittal views to respectively train three segmentation models, and then combine them to obtain the final segmentation result. The validity of the proposed method is evaluated on three publicly available databases. The experimental results show that our method achieves competitive performance compared with the state-of-the-art approaches.
Design Principle for Linear Electrical Machines to Minimize Power Loss in Periodic Motions

This paper describes a novel approach to the design of linear machines that are not run at a steady state operating point. The focus lies on minimizing the power loss to achieve highest possible efficiency. First analytical considerations reveal the potential of designing electrical machines for periodic, accelerated motion. It shows the influence of the machine size on the power losses due to the dependence of machine force as well as moving mass on this parameter. Based on these findings, a simulation model was created with which a linear machine can be optimized for a specific motion and load cycle. The simulation consist of a stationary and a time dependent finite element analysis of the linear machine in conjunction with an operational model that includes a given movement cycle. It was used to design a flat type linear machine for a free piston linear generator. The simulation results validate the analytical calculations and illustrate the differences to conventional design approaches for linear machines.
Analytical Study of Unipolar Junction Transistor as a Novel Dual Material Double Gate MOSFET to Suppress Short-Channel Effect

We propose a new 2D analytical modeling of dual material double gate MOSFET which worked as unipolar junction transistor. A region of $\mathrm{N}+$ on the source terminal and a P-region on the drain and gate terminals are used to construct the structure of unipolar junction as a novel dual material dual gate MOSFET by forming an unipolar junction at the drain side. Here we observed different variation such as surface potential of the structure, distribution of electric field, threshold voltage, DIBL, subthreshold swings. These observations are analytically modeled to solve 2D Poisson's equation by applying parabolic approximation method. A comparative study for conventional double gate MOSFET (CDG), Unipolar junction single material double gate MOSFET (UJ-SMG) and unipolar junction dual material double gate MOSFET (UJ-DMDG) structures has been observed. Results reveal that UJ-DMDG MOSFET structure provides better result to suppress the short channel effect as compared to CDG and UJ-SMG MOSFET.
Impedance Control of Series Elastic Actuators in Exoskeleton Using Recurrent Neural Network*

Exoskeleton robot is a system with human-machine interaction and could help human to carry heavy load. Series elastic actuators (SEAs) in force-controlled robots can achieve compliant interactions with humans. In order to get flexible position and force control, this paper introduced impedance control into exoskeleton robot and use a recurrent neural network to improve the adaptive ability of the system. Finally, simulation results of force tracking and trajectory tracking are presented. By setting virtual impedance parameters, this controller can achieve stable track tracking and force tracking. Recurrent neural network can improve the performance of adaptability and robustness.
Sub-Threshold Drain Current Model of Shell-Core Architecture Double Gate JunctionLess Transistor

Sub-threshold model for advanced shell doped Double Gate Junctionless transistor has been presented in this work. Electrical parameters such as potential, threshold voltage Vth, leakage current Ioff, sub-threshold slopes SS and Drain Induced Barrier Lowering DIBL are evaluated analytically and compared with the results extracted from ATLAS TCAD software. Different configurations of shell doping have been used in this work such as: high-low-high, low-high-low, low-low-high and uniform. Obtained results shows that high-low-high doping profile of DG-JL transistor suppresses the leakage current more efficiently and also provide good sub-threshold slope and DIBL compared to uniform and other doping profiles. In shell doped DG-JL transistor, additional tuning parameter is present (i.e. the thickness of individual doping layer)which further helps in optimizing the device design for sub-20nm circuits' applications.
3D Modelling of Human Hand with Motion Constraints

This paper describes the gestures and features of human hand motion, by considering the natural motion constraints of a hand. There are many restrictions that cannot be represented straight forwardly. Our aim is to reduce motion constraints by proposing some learning approaches to analyse and collect the dataset of motion behaviour of hand. The learning model involves application of Heat Map technology, along with usage of machine learning algorithms. We will implement some data mining algorithms to improve the accuracy of results and reduce redundancy in the testing data of hands space. This will result in better forecasting and prediction capability. Also we are using a microprocessor chip which will use sonar/radar for capturing ultrasonic waves, generated due to movement done by hands.
Virtual Impedance Design for Power Quality and Harmonic Sharing Improvement in Microgrids

One of the main challenges of decentrally operated islanded microgrids has been proper harmonic sharing for parallel connected inverters. This is largely affected by differences in the feeder impedances of the inverters. Virtual impedances are able to improve the harmonic current sharing, at the cost of deteriorating the power quality at the outputs of the inverters. This paper proposes a method for minimally setting the virtual impedances based on an optimization algorithm and estimation of the feeder impedances. The scheme ensures proper sharing between the units, while the power quality at the inverter terminals are minimally affected. The scheme is verified by numerically simulating a test microgrid.
Adaptive Edge Detection Algorithm Based on Grey Entropy Theory and Textural Features

The traditional edge detection method is altogether inaccurate, nonadaptive, and particularly ineffective on noisy images. This paper proposes a novel edge detection algorithm based on gray entropy theory and local texture features. In the $3\times 3$ neighborhood window, 28 comparison sequences are constructed according to local texture features. The reference sequence is composed of the median of all elements in the $3\times 3$ neighborhood window. A total of 28 gray relation degrees as obtained by gray relation analysis between the 28 comparison sequences and reference sequences, as well as 28 gray relation degrees, are analyzed by gray entropy theory to initially filter the image. Gray entropy analysis is then performed on the comparison sequences composed of 28 texture features and reference sequences composed of the central pixel points of the filtered image to determine the maximum gray entropy difference. A comparative threshold adaptive acquisition method is designed to separate gray entropy difference sequence elements and identify all edge points accordingly. The simulation results show that the proposed algorithm effectively achieves adaptive edge detection and has strong anti-noise capability. The results of this study may provide a workable reference for edge information detection in the field of artificial intelligence (e.g., image recognition, pattern recognition applications).
Fiducial ECG-Based Biometry: Comparison of Classifiers and Dimensionality Reduction Methods

Biometry is becoming increasingly important in order to identify or authenticate individuals. Since the seminar work of Biel et at. in 1999 and 2001, the feasibility of using the electrocardiogram (ECG) for biometric recognition has been considered by several authors. Both fiducial methods, which are based on using fiducial points related to the detected QRS complexes, and non-fiducial methods, which do not require the extraction of the QRS complexes from the signals, have been considered. However, the feasibility of ECG-based biometry is still unclear, as the results from different studies are difficult to compare. In this paper, we concentrate on fiducial methods, comparing the performance of several classifiers and dimensionality reduction techniques on a publicly available dataset. Our results show that ECG-based biometry is indeed a feasible alternative to other widely used biometric traits, since an accuracy above 99.95% can be attained with the appropriate choice of the dimensionality reduction method and classifier.
Biomechanical simulation analysis of human lower limbs assisted by exoskeleton

Aiming at the problem that the current iterative period of exoskeleton robot design is long and it takes a lot of time and cost. Using the OpenSim simulation method, a musculoskeletal biomechanical model with auxiliary devices was constructed, and the coupling simulation between the human lower limbs and the rehabilitation robot was realized. The muscle strength and metabolism curve of the exoskeleton lower limbs were obtained, and the stiffness of the exoskeleton was analyzed. The results showed that after the exoskeleton was added, the metabolic cost of the lower limbs of the human body was reduced by 6.2%, and the stiffness should be 275 N / m. This research can help designers to quickly verify the hypothesis, which will provide a basis for the design and development of exoskeleton robots, and has important theoretical research significance for its mechanical structure and control design.
The VR Simulation of Hidrological Data

Urban plan “Extension of the Zemun quay” was made from the need for access to the population of the Danube coast. The plan envisages the construction of a promenade and facilities of various contents in a zone of quay which is not defended from high water level. The visualization of the plan was made for the optimal level of the Danube River water level in immersive virtual reality (VR). As a variety of construction is planned at this location, simulation of the extreme hydrological data of the Danube River was also performed. The aim of visualization and simulation of this specific location and the impact of natural factors on the objects is to encourage and assist decision-makers in building investors. The direct experience gained in VR will enable investors to determine which type of facility they will invest in. This paper will show basic research activities performed for the purpose of simulating the consequences of the extremely high water levels of the Danube.
Accurate Frequency Estimation of a Real Sinusoid by Three New Interpolators

Interpolators are widely employed in the frequency estimation of sinusoidal signals. However, for a real-valued sinusoidal signal, the existing interpolators do not consider the negative frequency component, which leads to a frequency estimation error floor. To solve this problem and further improve the estimation accuracy, three new interpolators are proposed in this paper. These three new interpolators consist of two initial interpolators and one fine interpolator. In the proposed algorithm, the initial interpolators consider both the positive and negative frequency components and are utilized first to eliminate this error floor and obtain an initial frequency estimate. Then, the fine interpolator for the frequency estimation of a complex sinusoid is exploited to improve the accuracy of the initial frequency estimate. The theoretical analysis demonstrates that the frequency estimation mean square error of the proposed algorithm is almost equal to the Cramer–Rao lower bound. Compared with the existing time-domain analysis algorithms, the proposed algorithm has better estimation performance, especially in the case of a low signal-to-noise ratio. Compared with the existing frequency-domain analysis algorithms, the proposed algorithm has lower computational complexity and a wider valid estimation range.
Quadrifilar helical antenna with integrated compact feed for TTC application in LEO satellites

This paper presents design and development of Quadrifilar Helical Antenna(QHA) with integrated compact feed for Telemetry and Telecommand(TTC) application in LEO satellites with pattern/path loss compensation. A half height quadrifilar structure is fabricated on 20mil RT-Duriod substrate along with feed. All helix arms are excited with a microstrip line in progressive phase shift of 90 degree each to get the required circular polarization. Being single layer, the construction is simple and light weight. It is also rugged to withstand temperature, vibration and shocks. The simulation results of the antenna agree well with the measured results. The antenna ReturnLoss(RL) and Axial Ratio(AR) performance is good from 2.0 to 2.3GHz. It is proposed to use in LEO satellites.
2.4 GHz Circularly polarized Microstrip Patch Antenna with Diagonal Structural Symmetry

In this paper, a single feed circularly polarized microstrip patch antenna has been presented. The proposed antenna geometry is a square shaped patch with all four corners are etched and exhibits diagonal symmetry. Antenna parameters are obtained using the electromagnetic simulation tool. The dependency of bandwidth, and radiation efficiency with respect to substrate thickness has been studied. The antenna parameters of the proposed structure are compared with the conventional antenna structure.
Odometry Estimation via CNN using Sparse LiDAR Data

3D depth sensors such as LiDAR have proven to be very useful in recognizing the surrounding environment for the past decade, but the methods using these 3D data directly is not very much. Especially, there is few methods exist to use deep learning because of the sparseness characteristic of the LiDAR data. This paper presents the odometry estimation of sparse LiDAR(3D laser scanning) data using deep learning. We first voxelize the given consecutive LiDAR scans and concatenate the voxelized data to make input tensor. Then we train the 3D convolutional neural networks with the tensor to calculate 6DoF pose between the LiDAR scans. The proposed method can replace position information of the wheel encoders of GPS data when the data is absenting.
Indoor Visible Light Positioning Using Spring-Relaxation Technique in Real-World Setting

GPS has limitations in indoor applications. Consequently, other indoor localization techniques and systems are active areas of research. Visible Light Positioning (VLP) is a promising option, especially given the growing popularity of LED-based lighting and the expected adoption of the forthcoming Visible Light Communication (VLC). This paper reports a novel VLP technique. The developed technique uses received signal strength for ranging. It is followed by the iterative estimation of a location using spring relaxation. The performance of the proposed technique was experimentally evaluated in indoor settings and benchmarked against the lateration- and fingerprint-based localization approaches in multiple scenarios. The obtained results demonstrate that the proposed VLP approach offers an opportunity to outperform the existing techniques in terms of localization accuracy and precision.
Fuelcell power supply system – modelling and optimization

The paper presents an analytical model for sizing supercapacitors as part of a hybrid power supply. The load is assumed to be modeled by its power over time requirements. Approximating the theoretical curve as combinations of constant and linearly increasing power segments equations are obtained for the supercapacitor current as a function of its initial and final voltage. This allows for sizing the supercapacitor for wide variety of application. The model is verified by means of simulation and experimental results.
Impedance Compensation of the Welding Area of the RF Connector and Microstrip Line

The transmission loss of the signal in the radio frequency circuit has gained much more attention. In this paper, the loss of the impedance discontinuity caused by the solder joint was mainly investigated and two compensation schemes were proposed. Then, the SMP RF connector and microstrip line models were established in 3D electromagnetic field simulation software, and the S parameters and time domain reflectometry (TDR) before and after compensation were compared. Finally, the S parameters of SMP RF connector and microstrip line before and after the compensation were measured. Both the simulation and the measurement show that the signal integrity and transmission performance of compensated RF connector and microstrip line has been significantly improved.