Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Predictive Torque Control for Multi-Phase Matrix-Converter

Direct Matrix Converter (DMC) is superior topology for AC/AC power transformation. Its superiority comes from its inherent advantages such as: small size, no electrolytic capacitors, and straight conversion ability. Multiphase machines, would be the future trend in high power applications due to their features; better torque concentration, less torque pulsations, and superior fault tolerance. This paper introduces the usage of five-phase Induction Motor (IM). For that purpose, a three-to-five DMC is required to drive the five-phase induction motor without affecting the existing three-phase system. An improved Model-Predictive Torque Control (MPTC) is presented in this paper to regulate the toque and the speed of the five-phase induction motor. a 15 kW five-phase IM is simulated using MATLAB platform. The functionality of the closed loop system confirms the strength and robustness of the anticipated technique.
Optimization Design of Flat-Top Pulsed Magnet for an 800-GHz Second Harmonic Gyrotron

This article proposes a technique of flat-top magnetic field for the goal of high-frequency gyrotron with a pulse duration of several hundred milliseconds. A 14.8-T flat-top pulsed magnet is designed for an 800-GHz second harmonic gyrotron. The magnet is energized by a battery bank with a bypass circuit to achieve a 620-ms pulse duration. To fulfill the requirements of the gyrotron and magnet power supply, the genetic algorithm NSGA-II has been used in the multiobjective optimization of the field distribution and power dissipation of the magnet. The Helix magnet with the layered reinforcement technique is introduced to address the problems of stress and temperature rise. The proposed scheme in this article can be easily extended to most pulsed magnets for gyrotrons.
Prokaryotic Expression and Purification of LscW Protein of Lawsonia intracellularis for biomedical engineering

In order to study the LscW of Lawsonia intracellularis, the prokaryotic expression vector of LscW protein was constructed. Two primers were designed in this experiment. The 1197 bp lscW gene was amplified from L. intracellularis B3903 strain by PCR and inserted into pCold expression vector to construct recombinant plasmid pCold/lscW, which was transformed into BL21 competent cells. The positive clones were induced by IPTG, purified by protein purification column, and confirmed by SDS-PAGE gel electrophoresis. The LscW was expressed in the supernatant and purified to obtain a single target protein band.
24GHz Zero-Bias Asymmetrical Spacer Layer Tunnel Diode Detectors

This work reports on a novel temperature-insensitive zero-bias GaAs/AlAs Asymmetric Spacer Layer Tunnel diode (ASPAT) detector with a curvature coefficient is 18V−1. The detector was fabricated and experimentally measured in the frequency band (15 to 35) GHz at various input powers. The maximum measured sensitivity is 1300 V/W at 24GHz for a −27dBm incident RF power. The minimum calculated noise equivalent power is $\sim 20\text{pW}/\sqrt{Hz}$. The detector shows good performances, comparable to reported detectors based on Schottky or backward diodes. The results reported here validate the physical model developed which can be used to design and fabricate low cost, low power, temperature insensitive high-frequency tunnel diode detector for a range of applications.
A CMOS-MEMS Thermal-Piezoresistive Oscillator for Mass Sensing Applications

We present a thermally driven piezoresistively sensed resonator primarily consisting of backend of line (BEOL) CMOS material for mass sensing applications. A detailed analysis of the operation mechanism has been performed to develop a mathematical and an electrical equivalent model. The thermal-piezoresistive resonator (TPR) is composed predominantly of low loss materials like silicon dioxide (SiO<sub>2</sub>) and polysilicon for drive and sense. A differential configuration with drive and sense isolation was employed to significantly reduce the feedthrough level. Theoretical and simulated values are validated with experimental data. The proposed design has a resonance frequency of 5.13 MHz with a transconductance (<inline-formula> <tex-math notation="LaTeX">${g}_{m}$ </tex-math></inline-formula>) of 7.8 <inline-formula> <tex-math notation="LaTeX">$\mu \text{S}$ </tex-math></inline-formula> in the vacuum (<10<sup>−3</sup> torr) for 1.2-mW bias while <inline-formula> <tex-math notation="LaTeX">${g}_{m}$ </tex-math></inline-formula> of <inline-formula> <tex-math notation="LaTeX">${5}~\mu \text{S}$ </tex-math></inline-formula> in the atmospheric pressure for 2.7-mW bias power at 5.09 MHz. One of the highest reported quality factors (<inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>) of 2600 for CMOS-MEMS was achieved in air, while the same in the vacuum was >10 000. The thermal-piezoresistive oscillator (TPO) has Allan Deviation of 80 ppb and 20 ppm in ambient pressure using a lock-in amplifier with a phase-locked loop (PLL) and interface circuit using commercial amplifiers on printed circuit board (PCB), respectively. Using silver nanoparticles, the mass sensitivity of 24.96 kHz/ng was measured. The extracted mass resolution in air was 16.3 fg, thus having great potential to serve as an aerosol sensor.
Track Based UAV Classification Using Surveillance Radars

With their rapid technological developments, Unmanned Aerial Vehicles (UAVs), and micro UAVs have become a significant threat for homeland security and air defense systems. In air defense applications, classification of UAVs and discrimination of UAV tracks from non-UAV ones have become necessary. The classification of UAVs in the presence of heavy spatio-temporally varying radar clutter and interference across rural and urban areas, or under different weather conditions such as rain or snow is particularly challenging. In this paper, a classification method for UAV vs. non-UAV target discrimination, and micro-UAV classification using kinematic and Radio Frequency (RF) characteristics is proposed. For three different classes, namely, micro UAV-1, micro UAV-2 and non-UAV, various classifier architectures are implemented and the results based on real radar data are presented.
Data-Centric Scheduling for Minimizing Queue Length in Wireless Machine-to-Machine Networks

In this paper, we consider the problem of minimizing the queue length of wireless sensors involved in data gathering. Unlike conventional approaches, we focus on the received data quality at the collector for optimizing scheduling design. While meeting the minimum data fidelity requirement from the application, we leverage correlation among gathered data to allow proactive data dropping before queue is full and reduce radio resource usage. We first formulate the problem and then transform the time-average resource allocation problem into a scheduling problem based on the Lyapunov optimization framework. To solve the problem, we investigate two heuristic algorithms called allocation-first and drop-first algorithms in addition to the optimal algorithm. Evaluation results show that data-centric algorithms can effectively reduce resource usage and support more sensors compared to the conventional scheduling algorithm while meeting the requirement on data quality.
Motion Planning With Pose Constraints Based on Direct Projection for Anthropomorphic Manipulators

This paper presents an efficient planning algorithm, called Direct-Projection Rapidly-exploring Random Tree (DP-RRT), to address the motion planning with end-effector pose constraints for anthropomorphic manipulators. The key of this planning problem is to find constraint-satisfying configurations on the constraint manifolds and connect them to generate a collision-free and smooth path. In the previous works, the configurations that satisfy pose constraints are generally calculated by the numerical iteration methods based on Jacobian projection techniques. However, such approaches have many technical challenges, such as joint limits and singularity, many numerical iterations and much computing time. In this work, we propose a Direct Projection method based on the analytic inverse kinematics (IK) that can directly project configurations onto the constraint manifolds instead of the numerical iteration methods. The proposed DP-RRT algorithm combines the Direct Projection method with the Rapidly-exploring Random Tree (RRT) algorithm, where the RRT algorithm is employed to explore the ambient space by growing tree branches, and the Direct Projection method is used to project the tree branches onto the constraint manifolds for constructing a constraint-satisfying path. As the analytic IK solver is used to calculate the constraint-satisfying configurations, the DP-RRT algorithm is characterized by high efficiency and no numerical iteration. Besides, avoiding joint limits and singularity, as well as the smoothness of the end-effector and the joints trajectory are also considered. The effectiveness of the proposed algorithm is demonstrated on the Willow Garage’s PR2 simulation platform in a wide range of pose-constrained cases.
Analysis of common-mode voltages in flexible DC system with single-pole grounded fault

In some flexible DC transmission systems with certain grounding modes, single-pole grounded faults won't cause large fault current. If the system keeps operating and clears the fault in time, the power supply reliability of the system will be greatly improved. SVPWM is widely used in the converter. However, the SVPWM will generate third harmonic voltage in the system. Firstly, the topology of multi-terminal flexible DC transmission system including structures and grounding modes is discussed. Subsequently, a common-mode equivalent circuit mode for analyzing the influence of third harmonic voltage is established. Thirdly, the formula for calculation of the third harmonic voltage and current is derived. The fault characteristics are also analyzed and a diagnostic method of fault location is proposed. Finally, the simulation experiments are made in PSCAD/EMTDC. The simulation results show that the proposed protection strategies can accurately locate the fault line after pole-to-ground fault occurs.
The Effects of Normalisation Methods on Speech Emotion Recognition

Speech emotion recognition systems require features to be extracted from the speech signal. These features include Time, Frequency, and Cepstral-domain features. To normalise features, it is a challenging task to select an appropriate normalisation algorithm since the algorithm may impact classification accuracy. This paper presents the effects of different normalisation methods applied to speech features for speech emotion recognition. Speech features are extracted from the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset and normalised before training machine and deep learning algorithms such as Logistic Regression, Support Vector Machine, Multilayer Perceptron, Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM). The CNNs and LSTMs obtained 72% for both accuracy and F1score outperforming standard machine learning algorithms. Feature normalisation improved both accuracy and F1score by more than 14% using CNN and LSTM.
A Distributed Multi-Agent Dynamic Area Coverage Algorithm Based on Reinforcement Learning

Dynamic area coverage is widely used in military and civil fields. Improving coverage efficiency is an important research direction for multi-agent dynamic area coverage. In this paper, we focus on the non-optimal coverage problem of free dynamic area coverage algorithms. We propose a distributed dynamic area coverage algorithm based on reinforcement learning and a <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>-information map. The <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>-information map can transform the continuous dynamic coverage process into a discrete <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula> point traversal process, while ensuring no-hole coverage. When agent communication covers the whole target area, agents can obtain the global optimal coverage strategy by learning the whole dynamic coverage process. In the event that communication does not cover the whole target area, agents can obtain a local optimal coverage strategy; in addition, agents can use the proposed algorithm to obtain a global optimal coverage path through off-line planning. Simulation results demonstrate that the required time for area coverage with the proposed algorithm is close to the optimal value, and the performance of the proposed algorithm is significantly better than the distributed anti-flocking Algorithms for dynamic area coverage.
A Soft Logical Interface PMIPv6-Based Handover Mechanism in VANETs

Due to the high mobility and dynamic topology changes of vehicular Ad-hoc Networks (VANETs) which generate scattered networks, the network's performance has become a critical issue for connected and autonomous vehicles deployment. In order to address this issue, the Heterogeneous Vehicular Networks (HVN) paradigm was designed and it consists in a hybridation of the vehicular network (IEEE 802.11p) and the cellular networks (3G/LTE/4G). This makes the vertical handover to be one of the key technologies for the Internet of Vehicles (IoV) deployment. In this paper, we proposed a vertical handover mechanism (PMIP-MIVH) which uses a logical interface and a Distributed PMIPv6 scheme in order to improve the handover performance. Numerical analysis shows that PMIP-MIVH performs well in terms of handover connection times, handover latency and session continuity, specifically in HVN.
Combined reliability and risks of the componentsbased maintenance framework as an asset management stage for power distribution utilities application

Generally, the conventional approach to time-based maintenance leads to a misallocation of resources and decision making, because it uses unrealistic statistical rules and assumptions, which do not conform to the behavior of real power systems. The aging of the assets, meeting the standards of reliability and quality of service, has become a major concern and challenge for worldwide distribution companies. Meeting these requirements implies incorporating new techniques and philosophies that allow improving the efficiency in the use of maintenance resources. This article presents a maintenance management framework applied to electricity distribution companies, which combines strategies based on the reliability (RCM) and risk (RBM) of the components associated with the distribution networks. Combining these strategies, allows to determine an optimal maintenance plan and as a result, it is possible to achieve the proposed objectives of the electricity distribution company, which are, to improve the reliability indexes of the system and the allocation of the resources available for maintenance. To validate and analyze the practical aspects, the proposed framework is applied in a part of a distribution network of Empresa Eléctrica Quito S.A. , although it has been successfully tested in an electrical distribution network, this approach is applicable in the power industry.
An enhanced Density-based spatial separation strategy of 3D point clouds

In this paper, we propose an enhanced density-based spatial separation strategy to solve the difficulties of adjacent cluster segmentation and low noise (e.g., background noise and outlier) robustness. Towards this goal, we designed algorithms that fused the clustering mechanism of density-based clustering and the Delaunay triangulation. The proposed strategy applied a new approach of interpreting the segmented triangle as a graph node and then clustering based on the density and distribution characteristics of adjacent nodes. Experiments using real-world 3-D point clouds (i.e. a KITTI object detection benchmark) showed the superiority of the proposed spatial separation strategy.
Mechanical Performance of the First Two Prototype 4.5 m Long Nb3Sn Low-β Quadrupole Magnets for the Hi-Lumi LHC Upgrade

The U.S. High-Luminosity LHC Accelerator Upgrade Project (HL-LHC AUP) team is collaborating with CERN in the design and fabrication of the first 4.5 m long MQXFA magnets, a 150 mm aperture high-field Nb3Sn quadrupole magnet that uses the aluminum shell-based bladder-and-key technology. The first two prototype magnets, MQXFAP1 and MQXFAP2, were assembled and tested while the first pre-series structure (MQXFA03) was in fabrication. This paper summarizes the mechanical performance of these prototype structures based on the comparison of measured strain gauge data with finite element model analyses from all load steps to powering. The MQXFAP1 magnet almost reached ultimate current before a short to ground was detected and the test was stopped. The MQXFAP2 magnet experienced a low training performance due to a fractured aluminum shell. MQXFAP1b was rebuilt with a new replacement coil, but an old coil limited the magnet from achieving the ultimate current. The mitigations and analyses of these prototype magnets are discussed in the context of the transition to pre-series production.
Power Quality Event Classification Using Long Short-Term Memory Networks

Due to the increased frequency of power quality events and complexity of modern electric grids, there is a growing need to classify such events. In this paper, a novel approach to the above problem has been explored, wherein Long Short-Term Memory networks have been employed to fulfil the power quality event classification task. Given the sheer size of the input dataset, feature extraction was carried out by deriving important statistical features from the data. The Long Short-Term Memory model used was then trained and tested on these extracted features. Following this, the model performance has been evaluated, wherein the model was shown to perform remarkably well.
A Novel Cloudlet-Dwell-Time Estimation Method for Assisting Vehicular Edge Computing Applications

Recently, to improve the efficiency and safety of the transportation system that is severely affected by the increasing traffic demand, the Internet-of-Vehicles (IoVs)/Vehicular Networks (VNets) have received more and more attention because it can effectively improve the ability of the participants in the transportation system to perceive the traffic environment around them through Vehicle-to-everything (V2X) technique. Moreover, V2X also makes it possible to share computing and storage power between vehicles, which further promotes the development of vehicular edge computing (VEC). However, due to the highly dynamic nature of the VNet's topology, based on the instant traffic flow condition, how to determine whether the vehicles on a given road can form a relatively stable cloudlet with certain computing or data storage capabilities to support certain VEC applications becomes a crucial task that needs to be solved. Therefore, in this paper, we proposed a Cloudlet-Dwell-time (CDT) estimation method to theoretically derive some essential parameters for implementing VEC applications, i.e., the vehicular cloudlet existence probability and its corresponding dwell-time. We further demonstrate the results of the proposed work based on traffic flow data chosen from the England Highways data set.
High Speed Synchronization Using Modified Pausible Clocking Method

Synchronizer proposed in the literature for transferring the data from asynchronous to synchronous blocks achieves zero probability of metastability using the pausible or stretchable clocking scheme. But their highest data transfer rate is limited by the delay in generating the consumed signal. To overcome this problem, a scheme which makes the maximum data transfer rate of these synchronizers to be independent of this delay is proposed in this paper. The efficacy of the proposed scheme is evaluated by implementing a synchronizer for transferring data from a 4 bit asynchronously pipelined ripple carry adder to 4-Data Flip flops in 90nm UMC process technology and studying the functionality through post-layout simulation. The proposed scheme results in a 75% higher data transfer rate and a 42% lower power-delay product (PDP) compared to the synchronizer using the conventional pausible clocking scheme. Power consumed and area overhead for the proposed scheme are also lower compared to synchronizers using the pausible bisynchronous FIFO and Brute force bisynchronous FIFO based technique. The proposed scheme is well suited for applications where the metastability failures are not acceptable.
Design and Implementation of an Internet of Things based Prototype for Smart Home Automation System

The concept of automation coupled with advancement in technology and Android based smart phones have resulted in refined and smart lifestyles. When automation as a concept gets implemented via internet of things (IoT) which is concerned with accumulation /aggregation of connectivity, it gives rise to a smarter home network. In this paper, an IoT based prototype of a smart home automation is designed and implemented. The proposed prototype smart home system along with its Android App and ATmega16 as its main controller is successfully implemented and the results of the implementation are presented.
Visual Analysis of Deep Neural Networks for Device-Free Wireless Localization

Device-free indoor localization is a key enabling technology for many Internet of Things (IoT) applications. Deep neural network (DNN)-based location estimators achieve high-precision localization performance by automatically learning discriminative features from noisy wireless signals without much human intervention. However, the inner workings of DNN are not transparent and not adequately understood especially in wireless localization applications. In this paper, we conduct visual analyses of DNN-based location estimators trained with Wi-Fi channel state information (CSI) fingerprints in a real-world experiment. We address such questions as 1) how well has the DNN learned and been trained, and 2) what critical features has the DNN learned to distinguish different classes, via visualization techniques. The results provide plausible explanations and allow for a better understanding of the mechanism of DNN-based wireless indoor localization.
5G Ultra-Reliable Low-Latency Communication for Factory Automation at Millimetre Wave Bands

Utilizing the abundant bandwidth in millimeter wave frequency bands is an interesting option to enable wireless communication services with challenging latency and reliability requirements for factory automation. Nevertheless, millimeter wave bands have less favorable propagation properties compared to lower bands, which impacts the coverage negatively. In this paper, we show that millimeter wave bands can fulfill extreme latency and reliability requirements with much higher system capacity than mid-band frequencies by means of beamforming, large antenna arrays, and network densification. This makes millimeter wave bands suitable for providing ultra-reliable low-latency communication services for factory automation. However, we find that small beam misalignments due to channel state information error can degrade the system capacity substantially.