Miracle-dz/UniFAR-FTUs huggingface dataset:
Realization of Resonance Ratio Control Focusing on Duality of Torque and Velocity for Two-Inertia System with Environment

Conventionally, the two-inertia system with environment has two resonant frequencies and one anti-resonant frequency. In the case of using resonance ratio control (RRC) for a two-inertia system with environment, it is difficult to determine the resonance ratio. Thus, this paper focuses on the duality between torque and velocity, and a two-spring resonant system is constructed based on motor-side velocity control. In addition, RRC based on motor-side velocity control and load-side velocity observer (LVOB) is proposed, and the duality between the two-inertia resonant system and two-spring resonant system is demonstrated. Finally, load-side torque control based on RRC is confirmed, and that the RRC is the same as conventional RRC. Compared with the RRC of two-inertia system and RRC of two-spring system, a duality is demonstrated, and the effectiveness of the proposed control method is verified through numerical simulation and experiment.
2L-2L Converter: Switched Inductor Based High Voltage Step-up Converter for Fuel Cell Vehicular Applications

In this article, a novel 2L-2L power converter is articulated for vehicular application. Due to low output voltage generated from fuel cells; series/parallel arrangement is not a key to attain high voltage/current. As a result, to attain high voltage at the output, high gain DC to DC power converters play a cardinal job. A novel 2L-2L converter is proposed by replacing inductors of L-L converter by switched inductors to enhance the voltage conversion capability. The CCM and DCM operations of the 2L-2L converter with its mathematical analysis are described in detail. Moreover, the proposed 2L-2L converter is compared with similar converters to spotlight the key features. The result obtained through simulation work is presented which validate the performance and mathematical analysis of 2L-2L converter.
Average Fade Duration of Triple Nakagami-m Random Process and Application in Wireless Relay Communication System

In this paper, the average fade duration (AFD) of the wireless relay communication system with three sections in the presence of Nakagami-m fading is determined and presented graphically. AFD is used to show how long a user is in continuous outage, below a specified level, after crossing that level in a downward direction. This result is useful in information theory, for example for coding design.
Wideband Magnetoelectric Dipole Antenna with Stable Wide E-Plane Beamwidth

This paper presents a wideband magnetoelectric dipole antenna for wide beamwidth applications. Placed on aluminum plate and fed by a L-shaped feeding line, the proposed antenna can provide a wide bandwidth with satisfactory radiation properties. The two arms of antenna are bent to substantially increase the beamwidth. It is found that the antenna can deliver a measured impedance bandwidth from 2.0 to 2.8GHz for $\mathbf{SWR}< 2$. Measured results show that the half-power beamwidth of the proposed antenna is $174^{\circ}\pm 4.6^{\circ}$ in the E-plane and $96^{\circ}\pm 4.4^{\circ}$ in the H-plane. Gain is $4.6\mathbf{dBi}\pm 0.44\mathbf{dBi}$ at the frequency band of 2.0-2.8GHz (33.3%). Due to its simple configuration, wide bandwidth and stable wide beamwidth, it is promising for applications in base stations and wideband wide-angle-scanning phased arrays.
A Li-Ion Battery Charger for Bio-Medical Applications in 180nm CMOS Technology

This paper presents a Li-Ion battery charger for bio-medical applications, which controlled the three-mode of the charging process trickle current, constant current and constant voltage, with a maximum current of 198mA and charging voltage of 4.2V at the power supply of 4.9V using TSMC 180nm CMOS technology.
The Design of Soft Base Station Based on Docker

Today's mobile network is increasingly developing toward virtualization. Through Network Function Virtualization (NFV) [1], we can flexibly deploy BS to virtual machines. Some signal processing modules implemented by software have extremely high complexity and real-time requirements, such as LTE turbo decoder [2], FFT. General-purpose computer is difficult to satisfy these requirements, the virtual machine is even worse. To solve above challenges, in this paper, we propose to design the LTE soft base stations(SBS) in Docker [3], utilize a range of hardware accelerators (HA) to speed up signal processing and design an efficient HA framework for multiple SBSs sharing the HA resources simultaneously. In our design, the real computer has multiple FPGA accelerators with different functions, which are accessed by a unified Linux driver. Each SBS runs in a Docker container and has a virtual acceleration memory. They can send processing requests and get responses to communicate with the host OS by reading and writing to the memory. In host OS, we design an efficient scheduler to distribute processing task to HA. In this paper, we use LTE turbo decoder as an example of a hardware accelerator and evaluate the performance by running multiple 10M bandwidth LTE SBS.
Directivities and Thresholds of Laser Modes of Triangular Active Microcavities with Piercing Holes

Directivities, spectra, and thresholds of laser modes of an equilateral triangular active microcavity with a piercing hole are calculated numerically solving the Lasing Eigenvalue Problem on the basis of the set of Muller boundary integral equations. An effective Nyström approximation is used. The computational experiments demonstrate that a hole located at certain place and having suitable radius can lead to a notable growth of the directivity of the lasing emission with the conservation of the low thresholds.
Analysis of the use of M-Learning Environments in Higher Education from the Perspective of Students from the Academic Department of Computer Systems at the Autonomous University of Baja California Sur, Mexico

This paper presents the study's results that was applied to a student's group from the Academic Department of Computational Systems (DASC) from the Autonomous University of Baja California Sur (UABCS) with the objective of analyzing from the perspective of the students the use of m-Learning environments in higher education. Currently there is a fairly widespread tendency to investigate the use of mobile devices in education, since this type of device makes learning more flexible due to the possibility of use anywhere and anytime. There are numerous projects that refer to the use of m-Learning and its incorporation in educational processes.
Indoor Localization using Wireless Fidelity (WiFi) and Bluetooth Low Energy (BLE) signals

In this paper, we exploit the wireless signals of the two most common wireless standard used in indoor environments i.e. Wireless Fidelity (WiFi) and Bluetooth Low Energy (BLE) for localization application. We demonstrate that by inferring wireless signals, we can keep a track of localization that can provide key performance indicators in maintaining a healthy and active life of elderlies. As WiFi and BLE operate at multiple transmission power levels, the localization accuracy can vary. In this regard, we investigate the feasibility of the WiFi and BLE wireless signals in tracking the location of an elderly in a cluttered home environment. We use fingerprinting localization algorithm for location estimation. We first demonstrate that both wireless standards can be used for indoor localization. We further present the results for localization estimated from multiple transmission power levels for WiFi and BLE. The results show WiFi signals to be more stable compared to BLE signals. However, despite operating at much low transmission power levels BLE provides comparable results that are sufficient for locating elderlies in an indoor environment.
Ranking the Future Influence of Scientific Literatures

The future influence of scientific literatures plays an important role under many decision-making circumstances like choosing which recently published papers to follow or identifying rising stars from different scientific research domains. But the large number of scientific literatures published every year disables researchers to manually evaluate the quality and potential of all the literatures. Therefore, the problem of automatically ranking the future influence of scientific literatures has drawn much interest. Previous works have typically addressed this problem by incorporating heuristically designed time-aware functions into traditional graph-based ranking methods. These functions were developed by manually analyzing the target datasets and aimed to model the underlying dynamic nature. In contrast, there is no generic method which can adaptively capture the dynamic nature of different datasets without manual intervention. In this paper, we focus on addressing the issue of ranking the future influence of scientific literatures. Our main contribution is a generic and effective method which adaptively learns the underlying dynamic nature of different scientific literature datasets and applies the learned knowledge to ranking. This method creatively transforms the raw problem into a learning to rank problem with the help of HSHMRR, a new mutual reinforcement ranking framework used to precisely measure the importance of different types of scientific entities (papers, researchers, venues and institutions) in different time periods. Compared with previous works, our proposed method can be directly applied on different datasets with different target time periods and different definition of the future influence. Experiment results on three datasets extracted from Microsoft Academic Graph confirm the effectiveness of our proposed method, which outperforms the state-of-the-art methods by at most 29% in terms of Spearman's rank correlation coefficient.
Harmonic Minimization in Modified Multilevel Inverter For Hybrid Renewable Energy Sources

Renewable energy sources play a significant role for uninterrupted power supply in remote places. The paper presents the concept of a micro-grid fed from renewable energy sources for both PV/wind with maximum power point tracking and cascaded H bridge multilevel inverter system. The entire system is grid connected. A modified design is carried out for 13–level multilevel inverter topology where it works at a high switching frequency to reduce the size and losses. An LCL filter and a STATCOM are connected for smoothing of the grid line voltage. The multilevel inverter gating signals are driven using Artificial Neural Network technique for performance improvement. The objective here is to calculate the total harmonic distortion of modified cascaded H bridge multilevel inverter to improve the power quality of grid connected systems. The entire model is implemented with the help of Simulink®MATLAB. The simulation results are analyzed, presented and compared.
A Robust and Simple Phase-Locked Loop for Unbalanced Power Grid Applications

This paper proposes an improved phase-locked loop (PLL) for estimation of frequency and phasors of three-phase voltage and current signals in power systems. It works for balanced and unbalanced inputs amd offers highly robust performance against wide range of input signal attitudes including its amplitude, and angle. Therefore, it can be reliably applied to different signals in power systems such as fault currents and short-circuit voltages. The parameters of the proposed system are conveniently reduced to the selection of two damping ratios based on developing a linear mathematical model. The simulation results confirm that the system is capable of tracking wide range of changes in frequency, amplitude and phase angle with excellent robustness and short transient time. The proposed system not only reduces the computations compared to its counterparts, but also improves a wide range of applications including grid-synchronization of distributed generation units, power quality monitoring, and measurement of synchrophasors.
Incremental Learning of Single-stage Detectors with Mining Memory Neurons

Object detection has made significant progress in recent years. However, there are still open questions regarding the incremental learning of object detectors. When we additionally detect objects of new classes on the trained model without the original training data, they suffer from “catastrophic forgetting” completely or abruptly forgetting previously learned classes upon learning new classes. Humans typically do not show catastrophic forgetting. Unlike prior work that uses knowledge distillation losses to maintain accuracy on original classes, this paper presents a simple and effective method for adding new class to the original deep object detectors while avoiding catastrophic forgetting. Our method is based on the framework of single-stage object detection. The core of our proposed solution is mining memory neurons in original model, then the rest neurons are updated and employed to detect new class. This strategy ensure minimal drop in performance of original classes. We conduct extensive experiments on the PASCAL VOC 2007 dataset, along with a detailed empirical analysis of our approach, and achieve much better performance against catastrophic forgetting than prior incremental object detectors.
Wear Reduction Control Method in a Blade Pitch System of Wind Turbines

Mechanical transformation of speed is needed in a lot of industrial applications but it is attended by backlash. Gear parts without closed linkage can accelerate and crash to another. So with the backlash comes the wear. There are many technical solutions and ideas to reduce the backlash by mechanical and electrical components and adaptions, but in most cases with the goal to increase the precision. In some applications, like the described pitch drive, the precision is not the problem and the increasing costs for backlash elimination are not to justified because the reduction of wear is more important. Further the extensive adaption has influence on functional safety and maintenance. This paper gives a short overview of mechanisms that reduce the wear in a given application without mechanical adaptions, only by adding a software module. In the first step backlash is detected and in dependence on its reason in a second step a reaction is induced by the drive to spare the mechanics.
Epilepsy EEG Classification and Visualization using Linear Discriminate Analysis

Electroencephalography (EEG) is an important tool in epilepsy diagnosis practice. Many computer-based EEG analysis methods have been presented in the literature. Most of them addressed automatic classification of EEG recorded from epilepsy patients. In our previous work, a novel visualization tool for epilepsy EEG was introduced to investigate the focus based on Ensemble Empirical Mode Decomposition (EEMD) and Linear Discriminate Analysis (LDA). As well, two contours were rendered to highlight the focus of spike and seizure EEG respectively. However, it segmented EEG records into 2-second epochs and classified them independently. Some seizure epochs were falsely classified into normal. In this study, we consider EEG records continuously. The 8-second EEG in front of the current epoch is also taken into consideration. More features are extracted. Instead of using a greedy search for important features, as done in the previous work, a genetic algorithm with enforced reduction is adopted. The EEG used in this study was collected from six subjects (two normal and four with epilepsy) in National Taiwan University Hospital (NTHU). The classification accuracy is significantly enhanced. Furthermore, contours for the spike and seizure are merged into one. It is much easier for clinicians and nurses to locate the seizure focus.
Hammerstein Adaptive Filter Based Control Technique for Optimum Operation of a Grid Interfaced PV System

This work presents a hammerstein adaptive filter based technique for power quality improvement of a three phase grid tied two stage solar photovoltaic system. This system provides the PV power to connected load and surplus energy to the utility. The key objective of this work, is to extract positive sequence components (PSCs) from distorted grid voltages for estimation of unit templates and proposed control extracts load current fundamental component. A feed-forward term for PV source is also introduced for enhanced dynamic response of the system. This system is examined for the conditions of unbalanced load, voltage distortions, voltage swell/sag and variable PV irradiation to prove the efficacy of the control algorithm. The system performance is acceptable and grid currents THD’s are in limits, as suggested by the IEEE-519 standard.
A Robust and Structurally Simple Controller for Inverter Applications

This paper presents a robust servomechanism controller for operation of converter-based distributed energy resource (DER) units. The aim is to maintain the terminal voltage at a desired reference value and to ensure strong robustness versus uncertainties and load changes. The control design is based on the theories of robust servomechanism problem (RSP) and the linear quadratic tracker (LQT) and has a multi-input multi-output (MIMO) approach. Despite its simple structure, the proposed controller is characterized by fast transient response, low coupling among output channels and zero steady-state error. The performance of the proposed controller is evaluated through the extensive computer simulations using electromagnetic transient program (EMTP).
A Color Image Encryption Scheme Using Inverse Fractal Interpolation Function

A color image encryption scheme based on inverse fractal interpolation function has been proposed. The inverse fractal interpolation function owns good chaotic natures and therefore can be applied to generate pseudo-random sequences to scramble the plain image pixel positions and then diffuse the scrambled image to get ideal cipher-image. The performance and security analysis of the proposed image encryption scheme have been carried out, including key sensitivity analysis, histogram analysis, correlation analysis, entropy analysis, and differential attack analysis, etc. Results show that the proposed image encryption scheme is secure enough to be adopted for the secure image communication applications.
Control of a Multiphase Machine Fed by Multilevel Inverter Based on Sliding Mode Controller

The study presented is about the control of an electrical drive system based on the association of multiphase machines and multilevel inverters in order to obtain a power segmentation configuration, which regroups the advantages of the machine and inverter topologies. Thus, the dual star induction machine (DSIM) is taken as an example of multiphase machine fed by two multilevel inverters. The study aims to compare the performances and robustness of two kinds of controllers applied to the drive system: the conventional linear PI controller and the non-linear sliding mode controller (SMC). Simulation results show that the SMC exhibited good performances and better rejection of perturbations with lower oscillations for all the operating cases and different voltage levels. The FFT spectrum of the output currents confirms an improvement of the THD due to the combination of multilevel inverter and the SMC.
Speech Enhancement Based on Energy-Focused NMF

In this paper, we propose a novel loss function of Non-Negative Matrix Factorization (NMF) based on focusing energy on the main part. Based on this loss function, a two-steps model and a new Energy-Focused Non-Negative Matrix Factorization (EF-NMF) model are derived for speech enhancement. We use the statistical distribution to explain the energy-focused meaning and get the two different EF-NMF models inspired by the greedy algorithm. Also, IMCRA is combined to estimate the speech energy in the speech energy-focused elements. Finally, we use the IMCRA combined with EF-NMF to drive linear minimum mean square error (LMMSE) estimator. Comparing with the classical NMF that respectively trains the noise and speech basis vectors, the EF-NMF can focus more speech energy on the speech activation matrix when it decomposes noisy speech signal. The experimental results show that the EF-NMF gives better quality and intelligibility than the traditional training method of NMF, and better speech quality is gained by estimating speech elements of the matrix with IMCRA.
Global Software Development and Capability Maturity Model Integration: A Systematic Literature Review

Global Software Development (GSD) offers many benefits. On the other hand, it also has several challenges such as its impact on software quality. The implementation of software processes improvement, such as the Capability Maturity Model Integration (CMMI), can improve quality of software products. However, CMMI as a process improvement model has been rarely discussed in GSD context. To fill this gap a systematic literature study was conducted by using Kitchenham's method. It investigated GSD research from several databases between 2013–2018. At the end there are 12 selected papers. The findings indicate that CMMI is essential for the success of GSD projects. However, it requires multiple customizations and combinations with other standards. Concept Process Areas (PAs) and maturity level from CMMI are used in new standards Software Process Improvement (SPI) with different approaches. The results of this study are potential as a reference to develop customized CMMI to increase success rate in GSD projects.
Assessment of Electrocardiogram Quality Using Lossless Compression Technique for Heart Rate Variability Analysis

A simple method of assessment of electrocardiogram (ECG) quality based on lossless data compression is proposed. It is intended for a quick preliminary assessment of ECG in terms of further analysis of heart rate variability (HRV) based on a sequence of R-peaks. The method does not require a priori information about the type, nature and intensity of noise. The only output parameter of pre-processing step is the ECG compression ratio. Thresholds of compression ratio are determined, above which the results of further analysis of HRV are the same as for the corresponding ECG not distorted by noise. For a sampling frequency of 250 Hz, the threshold is equal to 1.4, for a frequency of 2000 Hz, it is 4.0. The results are shown for both synthetic ECG and ECG received using a personal screening diagnostic device.
A 68/36ppm/○C Tc 32.768Khz-To-1Mhz Rc-Based Oscillator With 72/6Pj Start-Up Energy

This paper describes a 32.768kHz-to-1MHz RC-based oscillator (RCO) suitable for high- and low-duty-cycle sleep-mode timers in emerging sensor node applications. In contrast to crystal oscillators (XO) based frequency references which employ long and energy intensive restarts, the proposed RCO achieves a 1000X reduction in restarting time. Measurement shows that the implemented RCO achieves a temperature stability of 68.5ppm/○C @32.768kHz and 37.5ppm/○C @1MHz. A proposed autonomous digital scheme uses coarse and fine tuning to compensate for process and temperature variations. Measurements report an energy efficiency of 1.46nW/kHz@1MHz occupying an area of 0.055mm2 in a pure digital 180nm CMOS process node.