Miracle-dz/UniFAR-FTUs huggingface dataset:
SiC tools for reliability study

Understanding the failure mechanisms is essential to ensure reliability for a new technology of semiconductors. Amongst various existing tools dedicated to silicon-based devices, there is no consensual method for silicon carbide (SiC) devices. This semiconductor offers very interesting properties for power electronics in comparison with Si, but these failures are different and need to be studied. This paper will compare different methods applied to failures, focusing on lock-in thermography and micro-Raman analysis. Three devices have been evaluated, a vertical diode, a lateral diode and a MESFET.
Scientific mapping of 35 years of human factors research in occupational human-robot-interaction

The field of human-robot-interaction is undergoing a constant change due to ongoing technological innovations. This paper addresses the research question, whether human factors concepts related to occupational human-robot-interaction show a similar development. First a search algorithm based literature search was done in different databases. We then used a combined approach of text mining technique as well as a rater based judgment in order to map the identified literature according to human factors concepts.
EmuStream—An End-to-End Platform for Streaming Video Performance Measurement

Cord-cutting has spread like wildfire as streaming video become commonplace in the Internet. This motivated intensive research in adaptive video streaming to improve its quality-of-experience (QoE) in the presence of network quality variations. Much of the existing research either employed dummy video contents or open source videos for QoE evaluation which may not capture the full spectrum of characteristics of real-world contents. This work fills this gap by developing a novel EmuStream platform based on real-world contents to enable realistic experiments and evaluation of any adaptive streaming algorithm in any streaming platform. First, EmuStream offers the largest (700+ titles) publicly available video bitrate trace dataset derived from real-world video contents. Second, we developed a mathematical model based on the dataset which can generate bitrate trace data for arbitrary target bitrates without actual video encoding. Third, we developed a novel Virtual Video Generator (VVG) which can generate h.264/h.265-compliant virtual videos that share the same frame/segment sizes as the source videos to enable experiments mimicking the streaming of commercial video contents. Last but not least, we developed a novel Streaming Performance Meter (SPM) that can measure detailed frame-by-frame playback performance from a video recording of the streaming playback of a virtual video. By decoding the virtual video’s specially-coded frames, SPM can determine the playback timing and bitrate selected for each frame for use in computing any desired QoE metric. This paper presents the EmuStream platform and validates the trace data estimation model and VVG/SPM tools via controlled experiments.
Design of Smart Footwear Information System for Measuring Gait Parameters

In this study we design an information system of smart footwear intended to measure gait parameters such as distance, walking speed, walking phase in the gait cycle, and walking symmetry. The footwear includes pressure sensors attached under the five landmarks of each foot sole namely big toe, 1st, 3rd, and 5th metatarsals, and calcaneus. The acquired electrical signals then transmitted to a computer or smartphone for signal processing, data analysis, and information display. The information system is designed according to the design guidelines, such as sufficient contrast and easy to read. The information display consists of user information, step mileages, and walking pressure and symmetry. In a further study for segregation and parsing of the acquired walking data, the involvement of healthcare professionals is necessary so that we would be able to categorize the numerical data to the associated health condition.
Miniaturized High-Performance Cube-Shaped Filter Made Up of Quasi-TM010 Mode Dielectric-Loaded Cavities

In this paper, a compact quasi-TM<sub>010</sub> mode dielectric-loaded bandpass filter with 8th-order pseudoelliptic filtering function and symmetric positioning of 4 transmission zeros is analyzed and designed. The resonant frequency and field components of quasi-TM<sub>010</sub> mode of a cylindrical dielectric resonator (DR) were first calculated and simulated. Compared to the pure TM<sub>010</sub> mode, the unloaded Q factor of quasi-TM<sub>010</sub> mode can increase by 3000 to more than 8000 and then the dimensions of the single DR at 2.595GHz are <inline-formula> <tex-math notation="LaTeX">$\pi \times 34^{2}\times 20$ </tex-math></inline-formula> mm<sup>3</sup> (using <inline-formula> <tex-math notation="LaTeX">$\varepsilon _{\mathrm {r}}= 45$ </tex-math></inline-formula>). The magnetic coupling and electric coupling between dielectric-loaded cavities are studied and the dielectric window is innovatively proposed to generate electric coupling and to avoid the use of metallic probes that are cumbersome to assemble. In final, a quasi-TM<sub>010</sub> mode double-layer filter with two pairs of symmetric transmission zeros is designed according to the cubic-like topology and fabricated and tested which exhibits close agreements between the simulated and measured results demonstrating the advantages of low insertion loss, very close-to-band rejections and compact configuration. The proposed filter has great potential for development in 5G mobile communication systems and microwave radar communication systems.
Composite Right/Left-Handed Leaky-Wave Antennas for Wide-Angle Beam Scanning With Flexibly Chosen Frequency Range

A composite right/left-handed (CRLH) leaky-wave antenna (LWA) can effectively scan the radiation beam from backward-to-forward direction. However, in most cases, a large range of frequency sweep is required to achieve a wide-angle beam scan, which could limit their applications. An in-depth study is conducted on an equivalent circuit model for a CRLH LWA unit cell to find the controlling parameters on the frequency sweeping range. A systematic design guideline is given for a CRLH LWA for a wide-angle beam scan in a flexibly chosen frequency range. It is shown that beam scanning by sweeping frequency in a target range can be achieved by systematically designing the unit cell parameters. To verify our approach, a novel CRLH unit cell is developed and used to design an LWA for a wide-angle beam scan in a narrow frequency range. Finally, the concept is validated through realization of the antenna and its measurement. The measured results show that the antenna prototype can scan its beam from −56° to +51° when frequency sweeps from 5.1 to 6.11 GHz (i.e., 18.02% of fractional bandwidth).
Control of a Double Pendulum Crane System Using PSO-Tuned LQR

A Gantry crane system is commonly used for point to point transportation as well as lowering and lifting of payload. The use of cables for hosting of payload can lead to natural swaying which makes it difficult to perform alignment, fine positioning as well as detrimental to safe and efficient operations. This paper presents a particle swarm optimization (PSO) based linear quadratic regulator (LQR) controller; for position and sway control of a double pendulum crane system. The major problem of designing an LQR controller is that, the Q and R parameter are obtained by trial and error which is quiet laborious. This paper optimized Q and R parameters by using PSO algorithm to obtain the best or optimal results. Simulation studies were carried out on the double pendulum nonlinear crane model which is created in MATLAB/Simulink environment. The simulation results show the effectiveness of the proposed controller in terms of reducing the trolley position percentage overshoot, hook oscillation and payload oscillation by 65%, 89.8% and 88.6% respectively.
Online makespan minimization in MapReduce-like systems with unsplit map tasks

This paper studies an online over-list model of the makespan minimization in MapReduce-like systems with unsplit map tasks. In this paper, we assume that each job has k map tasks. We study two cases: the first is that each job has only one reduce task which cannot be divided; the second is that each job has one reduce task which can be divided into a series of reduce task parts based on its input. For the first case, we propose a $\left(2-\frac{1}{h}\right)$-competitive algorithm, and prove the algorithm is optimal, where h is the number of machines. For the second case, we prove that any algorithm has a competitive ratio at least $2- \frac{1}{\beta h}$, where $\beta$ is the minimax ratio between the processing time of those reduce task parts from reduce task RJ which are assigned in machine i and the processing time of the reduce task RJ for arbitrary i and $j, \beta\leq \frac{\lceil\frac{k}{2}\rceil}{k-1}$. We then present an optimal algorithm.
Energy Efficiency Characterization in Heterogeneous IoT System With UAV Swarms Based on Wireless Power Transfer

An unmanned aerial vehicle (UAV) swarm together with a large-scale heterogeneous Internet of Things (IoT) network consisting of macrocells and energy-constrained IoT transmitters (IoT-Ts) is investigated. The UAVs are utilized as flying robot swarms that intelligently transfer energy to the energy-constrained IoT-Ts on the ground. Each IoT-T has an associated IoT device (IoT-D) that is placed at a fixed distance in a random direction. The transmission probability of the energy-constrained IoT-Ts is derived by considering one-slot charging and two-slot charging according to three dimensional (3D) locations, respectively. The coverage probability of each type of IoT-D is investigated. The energy efficiency is derived by considering the transmission power of the active IoT-Ts and the effect of the association biasing factor, and the energy efficiency is also maximized by deploying the optimal density of IoT-Ts. Simulation results are examined to validate the accuracy of our theoretical analysis. Results illustrate the insightful effects of the network parameters, and the helpful guidelines for practical UAV swarms and IoT system design.
A Dynamic Condition-Based Maintenance Model Using Inverse Gaussian Process

Condition-based maintenance has been widely used in the maintenance strategy of equipment or systems. Aiming at the maintenance decision-making of the equipment with dynamic degradation characteristics, a dynamic condition-based maintenance model is proposed based on Inverse Gaussian process in this paper. Firstly, an Inverse Gaussian process with stochastic parameter is proposed to describe the change of the equipment degradation characteristics during operation, and the important stochastic properties related to condition-based maintenance is deduced. Secondly, the dynamic maintenance threshold function is proposed, and its different values at different degradation stages can reduce the early failure risk of the equipment while ensuring a lower expected cost ratio. On this basis, a dynamic maintenance decision-making model with multi-objectives is established. A numerical example is illustrated to verify the correctness and practicability of the proposed method, and the sensitivity analysis results of the related parameters prove the necessity of considering the dynamic degradation characteristics of equipment. The comparison result proves that the method proposed in this paper can obtain better safety and economy of maintenance than the method with fixed thresholds.
Automatic generation of material laws based on rheological models using a genetic algorithm

Developing material laws based on rheological models is an established method in the field of material modelling. Here, the identification of an appropriate rheological model fitting a given set of experimental data is often a challenging task. This contribution presents an automated method for searching such rheological models. To this end, a tree representation for rheological models is defined first. Then, a generic material law, that can implement any rheological model given by a model tree, is introduced. Based on this, a genetic algorithm is used to generate rheological models, which are subsequently evaluated by fitting to measurements. This methods allows to search for a global optimum in the space of material models. Successful applications to both synthetic and actual experimental data are demonstrated.
Principal component analysis of the magnetic transition in the three-dimensional Fermi-Hubbard model

Machine learning techniques have been widely used in the study of strongly correlated systems in recent years. Here, we review some applications to classical and quantum many-body systems and present results from an unsupervised machine learning technique, the principal component analysis, employed to identify the finite-temperature phase transition of the three-dimensional Fermi-Hubbard model to the antiferromagnetically ordered state. We find that this linear method can capture the phase transition as well as other more complicated and nonlinear counterparts.
A cosine approximation to the skew normal distribution

We propose a new approximation to the skew normal distribution, a cosine approximation (CASN). This distribution is in a closed form and easy to use. CASN is especially useful in statistical inference as it approximates the tail probabilities with very small absolute errors. Graphical and numerical comparisons are conducted to compare the probability density functions of skew normal and the CASN . Mathematics Subject Classification: 62E17
Integrated Longitudinal and Lateral Control System Design and Case Study on an Electric Vehicle

This paper presents the design of an integrated longitudinal and lateral controller for autonomous vehicle and field tests with an electric vehicle. First, the longitudinal design was studied which includes the spacing policy as the upper level controller and throttle and brake control as the lower level controller. A safety spacing policy was proposed considering both the vehicle states and the vehicle capability. A coordinated throttle and brake controller was also designed to ensure the vehicle pursuing the desired acceleration. Second, a multimodel lateral controller was proposed which can perform the lane tracking and lane changing manoeuvres. Then, an integrated control structure was proposed to manage both the longitudinal and lateral controller. Finally, simulation and visualization works were carried out to validate the proposed solutions. An electric vehicle experiment platform was also built, and field tests showed encouraging results.
PENGARUH 2-iP, BA, 2,4-D, DAN TDZ PADA EMBRIOGENESIS SOMATIK IN VITRO KOPI ROBUSTA UNGGUL LAMPUNG

Pengaruh 2-iP, BA, 2,4-D, dan TDZ pada Embriogenesis Somatik In Vitro Kopi Robusta Unggul Lampung. Embriogenesis somatik in vitro kopi Robusta terdiri dari empat tahap: induksi kalus primer, induksi kalusembriogenik, regenerasi embrio somatik, dan regenerasi planlet. Penelitian bertujuan mempelajari pengaruh zat pengatur tumbuh (ZPT) terhadap pembentukan kalus primer kopi Robusta unggul Lampung klon Komari. Eksplan potongan daun ditanam pada media induksi kalus primer dengan penambahan (mg/l): BA 1; 2-iP 1; 2-iP 1 + 2,4-D 0,5 ;2-iP 1 + 2,4-D 1 ;TDZ 1 + 2,4-D 0,5; dan TDZ 1 + 2,4-D 1 sebagai perlakuan. Percobaan menggunakan rancangan acak lengkap dengan tiga ulangan, 3 botol kultur per ulangan, 5 eksplan per botol. Hasil percobaan menunjukkan, pada 4 minggu setelah tanam, semua perlakuan dapat menginduksi kalus primer 100%, namun waktu terbentuknya kalus pertama kali dan bobot segar kalus primer berbeda-beda. Kalus primer paling cepat terbentuk pada perlakuan 2-iP (14,7 hari setelah tanam), sedangkan paling lambat (16,0-16,3) pada perlakuan TDZ + 0,5-1,0 mg/L 2,4-D dan BA 1 mg/l. Bobot segar kalus primer tertinggi didapat pada perlakuan 2-iP + 2,4-D (96-110 mg/eksplan) dan terkecil pada perlakuan 2-iP atau BA saja (24-18 mg/eksplan). Kalus primer berkembang menjadi kalus embriogenik waktu dipindah ke media induksi kalus embriogenik. Subkultur kalus embriogenik ke media regenerasi menghasilkan embrio somatik.
First-principles calculations of magnetic properties of germanene under strain

Abstract Magnetic properties of germanene with five different nonmetal atoms have been analyzed and discussed by the DFT calculations. Magnetic moments could be induced by C, N, and O adatoms in the germanene. We further study the effect of strain on the magnetism in O-adsorbed germanene, and a tunable magnetism shows. The analysis of the PDOS shows that the p–p hybridization between O and Ge atoms is responsible for such magnetic behavior. Our results indicate that the magnetism induced by the O adatom is switchable depending on the strength of strain, which might show some potential applications in the nanoscale devices.
Spatially varying coefficient models: Theory and methods

This thesis contains three papers focusing on estimation and inference in the spatial data. In the first paper (Chapter 2), we study the estimation and inference in spatial varying coefficient models for data distributed over complex domains. We use bivariate splines over triangulations to represent the coefficient functions. The estimators of the coefficient functions are consistent, and rates of convergence of the proposed estimators are established. In the second paper (Chapter 3), we extend the idea from the first project and consider a class of flexible partially linear spatially varying coefficient autoregressive models. Under some regularity conditions, the estimated constant coefficients are asymptotically normally distributed, and the estimated varying coefficients are consistent and possess the optimal convergence rate. We further develop an efficient algorithm to calculate the distance between neighbors over complex domains. In addition, we propose a model selection approach to identify explanatory variables with constant and varying effects. The proposed method is much more computationally efficient than the local smoothing method such as the geographically weighted regression, and thus capable of handling large scale of spatial data. The performance of the proposed estimation and model identification methods are evaluated by two simulation examples and the Sydney real estate dataseet. Note that selecting the smoothing parameter and triangulation mesh is a critical part of implementing the bivariate penalized spline fitting in the first paper and second paper. In the third paper (Chapter 4), we propose using the generalized cross-validation to show how to select the roughness penalty parameter and the triangulation jointly. We demonstrate the effectiveness of the proposed method via extensive numerical studies. We extend our method to spatially varying coefficient models, a generalization of linear regression models for exploring non-stationarity of a regression relationship in spatial data analysis.
A Physical Activity Intervention in a Community Alcohol Service

People recovering from alcohol-related or drug-related problems are often in poor physical condition and have an extremely sedentary lifestyle. This article describes a study in which an occupational therapist in a community alcohol service implemented a physical activity intervention. The aim of the intervention was for clients with substance misuse problems to increase independent physical activities, to engage more in community-based activities, to increase their awareness of the benefits of exercise and to perceive benefits in their health following the programme. Seven people attended a 6-week programme consisting of nine sessions. Each session included an exercise class and group-based physical activity counselling. Case studies for five of the participants are presented. It was shown that it is possible for an intervention combining physical activity counselling with supervised and independent exercise to have an impact on the lives of a few individuals with substance misuse problems. Four of the five participants made substantial gains in all the target areas relating to independence, integration, education and health. It was also shown that it is feasible for an occupational therapist to play a pivotal role in promoting fitness-oriented activities for those with substance misuse problems. Controlled studies are required with larger samples, longer follow-up periods and the comparison of different intervention elements.
Products of Variables in Structural Equation Models: Multiplicative Reticular Action Models

A general method is introduced in which variables that are products of other variables in the context of a structural equation model (SEM) can be decomposed into the sources of variance due to the multiplicands. The result is a new category of SEM which we call a Multiplicative Reticular Action Model (XRAM). XRAM can include interactions between latent variables, multilevel random coefficients, latent variable moderators, and novel constructs such as factors of paths and twin genetic decomposition of multilevel random coefficients. The method relies on an assumption that all variance sources in a model can be decomposed into linear combinations of independent normal standardized variables. Although the distribution of a variable that is an outcome of multiplication between other variables is not normal, the assumption is that it can be decomposed into sources that are normal if one takes into account the non-normality induced by the multiplication. The method is applied to an example to show how in a special case it is equivalent to known unbiased and efficient estimators in the statistical literature. Two simulations are presented that demonstrate the precision of the approximation and implement the method to estimate parameters in a multilevel autoregressive framework.
Combining Multilevel Features for Remote Sensing Image Scene Classification With Attention Model

Remote sensing (RS) image scene classification is a challenging task due to its intraclass variety and the interclass similarity. Recently, many convolutional neural network (CNN)-based methods explore the network to handle this task. However, RS images usually have confusing background in addition to the relevant objects, and features only derived from the whole RS images cannot achieve satisfying results. To solve the problem, this letter proposed a method of utilizing the attention network to localize multiscale discriminative regions of the RS scene images and combining features learned from the localized regions by a classification network. Specifically, the classification network is composed of three subnetworks, which are trained by certain scaled regions separately. To learn more discriminative feature representations, feature fusion module is introduced to fuse the features of the three subnetworks in a more effective way. Experiments conducted on the AID and NWPU-RESISC45 data sets evaluate the effectiveness of the proposed method.