Miracle-dz/UniFAR-FTUs huggingface dataset:
A 3.3-mW 25.2-to-29.4-GHz Current-Reuse VCO Using a Single-Turn Multi-Tap Inductor and Differential-Only Switched-Capacitor Arrays With a 187.6-dBc/Hz FOM

A millimeter-wave current-reuse voltage-controlled oscillator (VCO) features a single-turn multi-tap inductor and two separate differential-only switched-capacitor arrays to improve the power efficiency and phase noise (PN). Specifically, a single-branch complementary VCO topology, in conjunction with a multi-resonant Resistor-Inductor-Capacitor-Mutual inductance (RLCM) tank, allows sharing the bias current and reshaping the impulse-sensitivity-function. The latter is based on an area- efficient RLCM tank to concurrently generate two high quality- factor differential-mode resonances at the fundamental and 2nd- harmonic oscillation frequencies. Fabricated in 65-nm CMOS technology, our VCO at 27.7 GHz shows a PN of −109.91-dBc/Hz at 1-MHz offset (after on-chip divider-by-2), while consuming just 3.3 mW at a 1.1-V supply. It corresponds to a Figure-of-Merit (FOM) of 187.6 dBc/Hz. The frequency tuning range is 15.3% (25.2 to 29.4 GHz) and the core area is 0.116 mm2.
Ringing Suppression of SiC MOSFET Using a Strongly Coupled External Resonator Through Analogy With Passive PT-Symmetry

Suppressing parasitic ringing, unwanted oscillation caused by switching, sufficiently and reliably, is of great importance in wide bandgap semiconductor devices. RC-circuits inserted in parallel with switching devices have been widely used to suppress such parasitic ringing in power electronics circuits due to simple configurations. However, short-circuit current would flow through power lines if capacitors are broken. In this article, we show that parasitic ringing can be damped with an external RLC resonator implemented in parallel to a bus bar. The configuration enables the strong magnetic coupling, which results in high ringing suppression effect. Moreover, the external resonator is designed through analogy with passive parity-time (PT) symmetry. In the range of unbroken phase, ringing energy is distributed effectively to the external resonator, and the damping rate is increased by increasing the loss of the external resonator. Our results represent a significant step toward practical applications of passive PT-symmetry.
A Unified Online Deep Learning Prediction Model for Small Signal and Transient Stability

This paper proposes a novel unified prediction approach for both small-signal and transient rotor angle stability as opposed to other studies that have only addressed transient rotor angle stability. Deep learning techniques are employed in this paper to train an online prediction model for rotor angle stability (RAS) using the voltage phasor measurements which are collected across the entire system. As a result, the trained model provides a fast yet accurate prediction of the transient stability status when a power system is subjected to a disturbance. Also, if the system is transiently stable, the prediction model updates the power system operator concerning the damping of low-frequency local and inter-area modes of oscillations. Therefore, the presented approach provides information concerning the transient stability and oscillatory dynamic response of the system such that proper control actions are taken. To achieve these objectives, advanced deep learning techniques are employed to train the online prediction model using a dataset which is generated through extensive time domain simulations for wide range of operating conditions. A convolutional neural network (CNN) transient stability classifier is trained to operate on the transient response of the phasor voltages across the entire system and provide a binary stability label. In tandem, a long-short term memory (LSTM) network is trained to learn the oscillatory response of a predicted stable system to capture the step-by-step dynamic evolution of the critical poorly damped low-frequency oscillations. The superior performance of the proposed model is tested using the New-England 10-machine, 39-bus, IEEE 16-machine, 68-bus, 5-area and IEEE 50-machine, 145-bus test systems and is verified with time domain simulation.
A Hypothesis Testing for Large Weighted Networks With Applications to Functional Neuroimaging Data

Neuroimaging techniques have been routinely applied in various studies in neuroscience, which contribute to providing novel insights into brain functions. One of the most important and challenging questions related to data collected from such studies is hypothesis testing for the differences between two samples of networks of brain regions. This is due to the fact that networks constructed from neuroimaging studies, which can be weighted and large, are very complex. Focusing on this problem, a novel hypothesis testing procedure is proposed under a general framework for large weighted networks. The asymptotic null distribution is derived and the power guarantee is also provided theoretically. Simulation experiments and practical application to the real brain networks are carried out to demonstrate the effectiveness of the proposed testing method.
Joint User Pairing, Subchannel Assignment and Power Allocation in Cooperative Non-Orthogonal Multiple Access Networks

This paper addresses the problem of user pairing, subchannel assignment and power allocation in a downlink cooperative non-orthogonal multiple access (NOMA) network. A joint user pairing and subchannel assignment algorithm is proposed that pairs a user having strong channel conditions with a user having weak channel conditions and assigns them a subchannel simultaneously. Each strong user acts as a relay for the weak user. A Stackelberg game (SG) is employed and a closed-form solution is given to allocate power among the users by the base station (BS) considering the mutual benefits of each user and the BS. Simulation results prove the effectiveness of the proposed scheme in terms of average sum rate of users as compared to that of the cooperative orthogonal multiple access (OMA) scheme and existing NOMA schemes.
Evaluation of optical motion capture system performance in humanrobot collaborative cells

This article describes a new methodology for the metrological evaluation of a human-robot collaborative environment based on optical motion capture (OMC) systems. By taking advantage of the existing industrial robot in the production cell, the workspace calibration procedure can be automatized, reducing the need of human intervention. The method is inspired on the ASTM E3064 test guide, and the results presented show that the metrological characteristics so obtained are compatible and comparable in quality to the ones with the manual procedure.
System-Level Investigation of Multi-MW Direct-Drive Wind Power PM Vernier Generators

Surface mounted permanent magnet Vernier (SPM-V) machines are known for their high torque density but relatively poor power factor compared to conventional SPM machines. The high torque density feature of the SPM-V machines is desirable for direct-drive offshore wind power applications as it leads to reduced generator size, mass and cost. However, their poor power factor can negatively affect the converter cost and efficiency. This paper compares the system-level performance, including generator active and structural components and converter, between the SPM-V and the conventional SPM generator systems. Four different power ratings, i.e. 0.5MW, 3MW, 5MW and 10MW, have been considered to study the trend of system-level performance with increasing power rating. The study shows that the SPM-V generators can be lighter and cheaper than their conventional SPM counterparts. However, after the consideration of converter cost and efficiency, the conventional SPM generator exhibited slightly better overall performance. Nonetheless, with the development of novel Vernier topologies and reduction in converter costs in the future due to emerging technologies, the Vernier generators can still be competitive for direct-drive offshore wind power applications.
Dual Phase LLC Resonant Converter With Variable Frequency Zero Circulating Current Phase-Shift Modulation for Wide Input Voltage Range Applications

This article proposes a novel dual phase LLC resonant converter with an improved variable frequency-based zero circulating current phase-shift modulation scheme for wide input voltage applications. The presented topology employs a six-switch dual active bridge with two output ports reducing the voltage stress across upper and lower switches to half of the input voltage. The proposed modulation scheme is a three-variable control that enables the converter to operate with increased voltage gain range under all input voltage and loading conditions. Variable frequency control leads to the soft switching operation of the switches connected to the primary side for all load ranges. The phase-shift control is used as power transfer control along with maintaining a constant output voltage under changing input voltage. Moreover, the duty cycle calculation based on load conditions helps in eliminating the reverse power flow especially under light-load conditions. To verify the proposed converter and modulation scheme, a 1.5-kW prototype with 210–400-V input and 80-V output is built and tested with a peak load efficiency of 96.7%.
Symmetry Reduction in Optimal Control of Multiagent Systems on Lie Groups

We study the reduction of degrees of freedom for the equations that determine necessary optimality conditions for extrema in an optimal control problem for a multiagent system by exploiting the physical symmetries of agents, where the kinematics of each agent is given by a left-invariant control system. Reduced optimality conditions are obtained using techniques from variational calculus and Lagrangian mechanics. A Hamiltonian formalism is also studied, where the problem is explored through an application of Pontryagin's maximum principle for left-invariant systems, and the optimality conditions are obtained as integral curves of a reduced Hamiltonian vector field. We apply the results to an energy-minimum control problem for multiple unicycles.
A New Language Independent Strategy for Clickbait Detection

Clickbait is a bad habit of today’s web publishers, which resort to such a technique in order to deceive web visitors and increase publishers’ page views and advertising revenue. Clickbait incidence is also an indicator for fake news and so, clickbait detection represents a mean in the fight against spreading false information. Recently, both the research community and the big actors on the WWW scene such as social networks and search engines, turn their attention towards this negative phenomenon that is more and more present in our everyday browsing experience. The detection techniques are usually based on intelligent classifiers, features selection being also of great importance. This paper aims to bring its own contributions in clickbait analysis and detection by presenting a new language independent strategy for clickbait detection that considers only general features that are non language specific. This approach is justified by the need for a higher level of abstractization in the clickbait detection, allowing its usability for articles written in different languages. A complex experiment on a real sample dataset was conducted and the obtained results are compared with the most relevant previous work results.
Review Study of Interpretation Methods for Future Interpretable Machine Learning

In recent years, black-box models have developed rapidly because of their high accuracy. Balancing the interpretability and accuracy is increasingly important. The lack of interpretability severely limits the application of the model in academia and industry. Despite the various interpretable machine learning methods, the perspective and meaning of the interpretation are also different. We provide a review of the current interpretable methods and divide them based on the model being applied. We divide them into two categories: interpretable methods with the self-explanatory model and interpretable methods with external co-explanation. And the interpretable methods with external co-explanation are further divided into subbranch methods based on instances, SHAP, knowledge graph, deep learning, and clustering model. The classification aims to help us understand the model characteristics applied in the interpretable method better. This survey makes the researcher find a suitable model to solve interpretability problems more easily. And the comparison experiments contribute to discovering complementary features from different methods. At the same time, we explore the future challenges and trends of interpretable machine learning to promote the development of interpretable machine learning.
Moments of Signals over Wireless Relay Fading Environment with Line-of-Sight

In this work, the product of three arbitrary, independent and not necessarily identically distributed (i.n.i.d) Rician random variables (RVs) will be considered. Probability density function (PDF) and moments of product of three Rician RVs will be calculated. PDF can be used for evaluation the bit error probability of wireless relay communication system operating over Rician multipath fading channel. Further, amount of fading of product of three Rician RVs will be derived. The proposed mathematical analysis will be complemented by a few numerical results. Based on them, the impact of Rician factor and signal powers on performance quantities will be analyzed. An application of these results for the wireless relay communications community will be described.
University Students’ Perception of Online Learning in Covid-19 Pandemic : A Case Study in a Translation Course

pandemic changed the pattern of our higher education. All conventional face-to-face classes turn into online classes. This makes it difficult for practice-based courses to organize quality education, because these courses rely on laboratory facilities as a place to increase student competency. One of the practical courses in Dian Nuswantoro University S1 English Study Program is a translation course. This subject is often regarded as a vocational subject in the language field. Therefore, this article aims to find out the teaching procedures for translation courses during the Covid-19 pandemic and assess students' perceptions of the teaching of these courses. Using a qualitative paradigm, the authors used field observations and questionnaires to describe the teaching procedures for the translation course and determine students' perceptions of the learning process in the course. From documented field observations, the lecturer of the course uses Google Classroom's online learning tools to manage theoretical assignments, Google Meet for material discussion, and OmegaT for translation practice. Based on the teaching method of the lecturer, 80% of respondents stated that they did not like the online lecture mode because of the difficulties they experienced. Interestingly, the results of the questionnaire showed a positive perception of 90% on the use of OmegaT and Google Classroom. This can be a positive improvement for online lectures in this study program in the future.
QoS based Deep Reinforcement Learning for V2X Resource Allocation

The 3rd generation partnership project (3GPP) standard has introduced vehicle to everything (V2X) communi-cation in Long Term Evolution (LTE) to pave the way for future intelligent transport solutions. V2X communication envisions to support a diverse range of use cases for e.g. cooperative collision avoidance, infotainment with stringent quality of service (QoS) requirements. The QoS requirements range from ultra-reliable low latency to high data rates depending on the supported application. This paper presents a QoS aware decentralized resource allocation for V2X communication based on a deep reinforcement learning (DRL) framework. The proposed scheme incorporates the independent QoS parameter, i.e. priority associated to each V2X message, that reflects the latency required in both user equipment (UE) and the base station. The goal of the approach is to maximize the throughput of all vehicle to infrastructure (V2I) links while meeting the latency constraints of vehicle to vehicle (V2V) links associated to the respective priority. A performance evaluation of the algorithm is conducted based on system level simulations for both urban and highway scenarios. The results show that incorporating the QoS parameter (i.e. priority) pertaining to the type of service supported is crucial in order to meet the latency requirements of the mission critical V2X applications.
Joint Task Offloading and Resource Management in NOMA-Based MEC Systems: A Swarm Intelligence Approach

In this paper, we study the issue of computation offloading in non-orthogonal multiple access (NOMA)-based multi-access edge computing (MEC) systems. A joint optimization problem of offloading decision, subchannel assignment, transmit power, and computing resource allocation is investigated to improve system performance in terms of both completion time and energy consumption. The formulated problem is a mixed-integer non-linear programming one, it is therefore hard to solve. To make the problem tractable, we first decompose the problem into subproblems of computing resource allocation (CRA), transmit power control (TPC), and subchannel assignment (SA). Then, we address the CRA subproblem by a convex optimization technique. For the remaining two subproblems TPC and SA, we propose to use a gradient-free swarm intelligence approach, namely whale optimization algorithm, to provide a very general but efficient solution. Computer simulations are performed to show the convergence of the proposed algorithm, also its better performance in comparison with conventional schemes.
2-DOF Robust $ H_{\infty }$ Control for Permanent Magnet Synchronous Motor With Disturbance Observer

This article presents a two-degree-of-freedom (2-DOF) <inline-formula><tex-math notation="LaTeX">$ H_{\infty }$</tex-math></inline-formula> speed control structure for permanent magnet synchronous motors (PMSMs). This structure consists of three parts: an <inline-formula><tex-math notation="LaTeX">$ H_{\infty }$</tex-math></inline-formula> disturbance observer (DOB), an <inline-formula><tex-math notation="LaTeX">$ H_{\infty }$</tex-math></inline-formula> feedback controller, and an <inline-formula><tex-math notation="LaTeX">$ H_{\infty }$</tex-math></inline-formula> feedforward controller. First, the <inline-formula><tex-math notation="LaTeX">$ H_{\infty }$</tex-math></inline-formula> problem, and the feedback controller design method for PMSM are presented. The stability theory, and the solution are introduced. Second, the disturbance observer is designed as an <inline-formula><tex-math notation="LaTeX">$ H_{\infty }$</tex-math></inline-formula> disturbance tracking problem to estimate the external load torque, and increase the robustness. The speed controller is redesigned, and regards the estimated torque as one of the input. Finally, based on the 2-DOF structure, an <inline-formula><tex-math notation="LaTeX">$ H_{\infty }$</tex-math></inline-formula> feedforward controller is designed. The generalized plant takes the whole close-loop model into account. The proposed method has a fast dynamic, and strong robustness against the external disturbance, and parameter variation. Several simulations, and experiments of conventional proportional integral controller, standard <inline-formula><tex-math notation="LaTeX">$ H_{\infty }$</tex-math></inline-formula> speed controller, and proposed method are operated in a PMSM system, and compared to validate the accuracy of the disturbance observation, and the effectiveness of the proposed method.
Coverage Probability of Cell-Free Massive MIMO Systems

Despite that ongoing and future networks become denser and increasingly irregular, prior works in the area of cell-free (CF) massive multiple-input-multiple-output (mMIMO) systems relied on the strong assumption of uniformly distributed access points (APs). Actually, this randomness was accounted for only during the simulation and not in the analysis. Consequently, the direction of this paper is towards the application and investigation of a more realistic model for the spatial randomness of the APs in terms of a Poisson point process (PPP). Specifically, we derive the downlink coverage probability (CP) of CF mMIMO systems by means of stochastic geometry and deterministic equivalent tools. Notably, it is the only work having derived the CP for CF mMIMO systems. Among the results, we highlight the outperformance of CF mMIMO systems against small cells (SCs), which increases with the density of the APs due to channel hardening, favorable propagation, and interference suppression. Moreover, we observe the saturation of the CP at high AP density.
High-Resolution Ultrasound Sensing for Robotics Using Dense Microphone Arrays

State-of-the-art autonomous vehicles use all kinds of sensors based on light, such as a camera or LIDAR (Laser Imaging Detection And Ranging). These sensors tend to fail when exposed to airborne particles. Ultrasonic sensors have the ability to work in these environments since they have longer wavelengths and are based on acoustics, making them able to pass through the mentioned distortions. However, they have a lower angular resolution compared to their optical counterparts. In this article a 3D in-air sonar sensor is simulated, consisting of a Uniform Rectangular Array similar to the newly developed micro Real Time Imaging Sonar (<inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>RTIS) by CoSys-Lab. Different direction of arrival techniques will be compared for an 8 by 8 uniform rectangular microphone array in a simulation environment to investigate the influence of different parameters in a completely controlled environment. We will investigate the influence of the signal-to-noise ratio and number of snapshots to the angular and spatial resolution in the direction parallel and perpendicular to the direction of the emitted signal, respectively called the angular and range resolution. We will compare these results with real-life imaging results of the <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>RTIS. The results presented in this article show that, despite the fact that in-air sonar applications are limited to only one snapshot, more advanced algorithms than Delay-And-Sum beamforming are viable options, which is confirmed with the real-life data captured by the <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>RTIS.
Simplified and Reliable Wireless Data Transmission in Ultra Dense Networks

One of the challenges identified for future wireless networks beyond 5G is to achieve extremely low energy consumption while keeping required level of quality-of-service, expressed in terms of reliability and system throughout. Inspired by the functioning of the human brain and nervous system, in this paper we consider extremely dense wireless network, where intentionally the distance between the nodes is reduced in such a way that as simple as possible transmission schemes can be applied, i.e., with simple modulations, lack of error-coding and no retransmissions. Assuming such simplified data transmission scheme, we investigate the reliability and availability of such dense wireless network, considering that errors may appear both during wireless data propagation and due to malfunctioning of the nodes.