Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of a dual band-notched UWB antenna and improvement of the 5.5 GHz WLAN notched characteristic

ABSTRACT A novel dual band-notched ultra-wideband (UWB) planar antenna with improved characteristic in 5.5 GHz WLAN notched band is presented. Two kinds of L-shaped slots are inserted in the radiator in order to obtain the dual notched characteristics. A separated strip is added in the middle of the U-shaped radiator for further improving the performance. The antenna has a measured impedance bandwidth of 3.1–11.0 GHz with VSWR < 2, while it has dual notched bands of 3.4–4.0 GHz and 4.9–6.0 GHz. Besides, the equivalent circuit model, transmission line model and transfer function are also studied to investigate the band-notched performance of the proposed antenna.
Asymptotiques dans des modèles de boules aléatoires poissoniennes et non-poissoniennes

Dans cette these, on etudie le comportement asymptotique de modeles de boules aleatoires engendrees selon differents processus ponctuels, apres leur avoir applique un changement d’echelle qui peut etre vu comme un dezoom. Des theoremes limites existent pour des processus de Poisson et on generalise ces resultats en considerant tout d’abord des boules engendrees par des processus determinantaux, qui induisent de la repulsion entre les points. Cela permet de modeliser de nombreux phenomenes, comme par exemple la repartition des arbres dans une foret. On s’interesse ensuite a un cas particulier des processus de Cox, les processus shot-noise, qui presentent des amas de points, modelisant notamment la presence de corpuscules dans des nano-composites.
INTRODUCTION TO THE SPECIAL ISSUE ON LEARNING, OPTIMIZATION, AND THEORY OF G-NETWORKS

Abstract We introduce the special issue on “Learning, Optimization, and the Theory of G-Networks” of the journal Probability in the Engineering and Informational Sciences that appears in 2019. We first outline some of the applications and developments of G-Networks which motivate the ongoing interest for this area, including some areas which could not be covered in this special issue. We then briefly discuss the contributions presented in the ten papers that are published in this special issue in the context of related work.
Forecasting Kebutuhan Obat Menggunakan Metode Pola Konsumsi, Pola Mordibitas dan Winter’s Exponential Smoothing di RS Paru Dr.H.A Rotinsulu Bandung

Abstrak — Perencanaan obat yang mendekati kebutuhan merupakan kunci keberhasilan layanan pasien di RS. Pola pengunaan obat yang fluktuatif dari waktu ke waktu membuat manajemen sulit dalam membuat perencanaan yang akurat. oleh karena itu diperlukan semacam pemodelan peramalan (forecasting) guna memprediksi kebutuhan obat di masa yang akan dating. Penelitian ini mengimplementasikan metode pola konsumsi, pola mordibitas dan Winter's Exponential Smoothing pada perencanaan kebutuhan obat di RS Paru Dr.H. A Rotinsulu. Data obat yang dipergunakan diklasifikasikan terlebih dahulu menggunakan metode ABC dan data historical yang digunakan yaitu pada tahun 2014, 2015 dan 2016. Diharapkan dengan implementasi forecasting kebutuhan obat tersebut dapat mengoptimalkan perencanaan kebutuhan obat sesuai dengan kebutuhan dan kondisi real di lapangan
Monkey search algorithm for ECE components partitioning

The paper considers one of the important design problems – a partitioning of electronic computer equipment (ECE) components (blocks). It belongs to the NP-hard class of problems and has a combinatorial and logic nature. In the paper, a partitioning problem formulation can be found as a partition of graph into parts. To solve the given problem, the authors suggest using a bioinspired approach based on a monkey search algorithm. Based on the developed software, computational experiments were carried out that show the algorithm efficiency, as well as its recommended settings for obtaining more effective solutions in comparison with a genetic algorithm.
Analytic approximation and differentiability of joint chance constraints

ABSTRACT An inner–outer approximation approach was recently developed to solve single chance constrained optimization (SCCOPT) problems. In this paper, we extend this approach to address joint chance constrained optimization (JCCOPT) problems. Using an inner–outer approximation, two smooth parametric optimization problems are defined whose feasible sets converge to the feasible set of JCCOPT from inside and outside, respectively. Any optimal solution of the inner approximation problem is a priori feasible to the JCCOPT. As the approximation parameter tends to zero, a subsequence of the solutions of the inner and outer problems, respectively, converge asymptotically to an optimal solution of the JCCOPT. As a main result, the continuous differentiability of the probability function of a joint chance constraint is obtained by examining the uniform convergence of the gradients of the parametric approximations.
Short-text learning in social media: a review

Abstract Social networks occupy a ubiquitous and pervasive place in the life of their users. The substantial amount of content generated and shared by social networking users offers new research opportunities across a wide variety of disciplines, including media and communication studies, linguistics, sociology, psychology, information and computer sciences, or education. This situation, in combination with the continuous growth of social media data, creates an imperative need for content organisation. Thus, large-scale text learning tasks in social environments arise as one of the most relevant problems in machine learning and data mining. Interestingly, social media data pose several challenges due to its sparse, high-dimensional and large-volume characteristics. This survey reviews the field of social media data learning, focusing on classification and clustering techniques, as they are two of the most frequent learning tasks. It reviews not only new techniques that have been developed to tackle the new challenges posed by short-texts, but also how traditional techniques can be adapted to overcome such challenges. Then, open issues and research opportunities for social media data learning are discussed.
A Robust Walking Controller Based on Online Optimization of Ankle, Hip, and Stepping Strategies

In this paper, we propose a biped walking controller that optimized three push recovery strategies: the ankle, hip, and stepping strategies. We suggested formulations that related the effects of each strategy to the stability of walking based on the linear inverted pendulum with flywheel model. With these relations, we could set up an optimization problem that integrates all the strategies, including step time change. These strategies are not applied hierarchically, but applied according to each weighting factor. Various combinations of weighting factors can be used to determine how the robot should respond to an external push. The optimization problem derived here includes many nonlinear components, but it has been linearized though some assumptions and it can be applied to a robot in real time. Our method is designed to be robust to modeling errors or weak perturbations, by exploiting the advantages of the foot. Hence, it is very practical to apply this algorithm to a real robot. The effectiveness of the walking controller has been verified through abstracted model simulation, full dynamics simulation, and a practical robot experiments.
Image Small Target Detection based on Deep Learning with SNR Controlled Sample Generation

: A small target detection method based on deep learning is proposed. First, random background parts are sampled from some cloud-sky images. Then, random generated target spots are added to the backgrounds with controlled signal to background noise ratio (SNR) to generate target samples. Then training and testing results show that the performance of deep nets is superior to tradition small target detection techniques and the selection of sampling SNRhas an important effect on nets training performances. SNR = 1 is a good selection for deep nets training, not onlyfor small target detection,but also for other applications.
Technical Efficiency of Smallholder Wheat Producers in Simada District of Amhara region, Ethiopia

Wheat (Triticum aestivum) is one of the major staple cereal crops in Ethiopia. High productivity and efficiency in its production is critical to improve food security, reduce the level of poverty and achieve agricultural growth. Therefore, the objective of study was to measure the level of technical efficiency of wheat producers and to identify factors that contribute for efficiency differences among smallholder farmers in the study area. The study was based on cross-sectional data collected from 154 wheat producer farmers by multi- stag sampling technique during 2017/18 production season. Technical efficiency of sampled farmer in wheat production was estimated by Cobb-Douglas functional form in stochastic frontier model (SFM) with single stage estimation method. The estimated SFM indicated that land, labor and seed were significantly and positively influence wheat production at1 percent of significant level. However, urea was negatively influence wheat production at 10 percent of significant level. Production function in wheat production existed in increasing return to scale (1.22). The estimated gamma (γ) parameter was about 0.29. It indicated that the relative deviations of actual output from the frontier output due to inefficiency. The estimated mean level of TE of wheat producers was 0.79. From specific socioeconomic and institutional factors hypothesized that influence technical efficiency of farmers in wheat production, credit access and frequency of extension contact were significantly and positively influence the level of technical efficiency in wheat production at 10 percent of significance level. Education level was significantly and negatively influences the level of technical efficiency in wheat production at10 percent of significance level. Raw sowing and frequency of plowing were significantly and negatively influence the level of technical efficiency in wheat production at 5and1 percent of significance level, respectively. Therefore, the study suggested the above mentioned significant variables have important for policy implications in that development programs may give fruitful attention so as to mitigate the existing level of inefficiency of farmers in the production of wheat in study area. Keywords: Technical Efficiency, Stochastic Frontier Model, Single Stage Estimation, Ethiopia DOI : 10.7176/DCS/10-1-01 Publication date: January 31 st 2020
Academic achievement by graduates from for-profit and nonprofit institutions: Evidence from CPA exam performance

ABSTRACT This study shows that graduates from nonprofit educational institutions outperform graduates from for-profit institutions on the four sections of the certified public accountant (CPA) exam. Specifically, it (1) documents univariate differences in CPA exam scores, score distributions, pass rates, and time to complete the CPA exam; (2) investigates the differences in a multivariate context; and (3) examines the robustness of the results to various levels of control for student differences.
Face Recognition using a Newly Developed Linear Subspace Learning Method

Face recognition is considered a specific physiological biometric in order to identify an individual according to the physical features of the human face. Much research has been conducted in such areas, but still more accurate processes are required for biometric facial recognition. This article presents a novel linear subspace learning method for face recognition which not only can take advantage of principle components analysis (PCA) as a successful feature extraction, but also can apply nearest local centroid mean vector (LMKNCN) as an effective classifier to improve the classification performance. The main goal of this particular scheme is to handle two common existing issues in recognition techniques: named sensitivity to the training sample size and negative effects of outliers. Moreover, to illustrate the performance of proposed developed PCA, we compare it with the latest dimensionality reduction techniques such as traditional PCA and KPCA on publicly available face dataset. Experimental results illustrate that our newly developed method has significantly achieved better performance over the same face database compared with the former KNN-based algorithms.
Output feedback controller for polytopic systems exploiting the direct searching of the design space

This paper deals with the problem of designing a robust static output feedback controller for polytopic systems. The current research that tackled this problem is mainly based on LMI method, which is conservative by nature. In this paper, a novel approach is proposed, which considers the design space of the controller parameters and iteratively partitions the space to small simplexes. Then, by assessing the stability in each simplex, the solution space for design parameters is directly determined. It has been theoretically proved that, if there exists a feasible solution in the design space, the algorithm can find it. To validate the result of the proposed approach, comparative simulation examples are given to illustrate the performance of the design methodology as compared to those of previous approaches.
Estimation de directions d'arrivée de signaux DVB-T en réception mobile

Ce papier, associe au projet CAVITE (CAracterisation Vectorielle des canaux de propagation pour la transmission d'Image de TElevision numerique terrestre avec des mobiles), presente une methode pour estimer les directions d'arrivee de signaux DVB-T (Digital Video Broadcasting on Terrestrial network) dans une configuration de reception mobile ainsi que les resultats des sondages de canal realises. Le sondeur passif est decrit, aussi bien d'un point de vue materiel que logiciel avec les traitements d'antennes et les outils de detection de direction d'arrivee. Les resultats de sondage dans quatre types d'environnement sont presentes : sur autoroute, en campagne, en ville (faible densite) et sur Paris (forte densite).
A Characterization of Flat Pseudo-Riemannian Manifolds

This thesis is concerned with the curvature of pseudo-Riemannian manifolds. A pseudoRiemannian manifold is called flat when it can be covered by charts that intertwine the pseudometrics of the manifold and psuedo-Euclidian space. In general, the curvature of a manifold is described by an operator κ∇, called the Riemann curvature. In the main theorem of this thesis, we will prove that a pseudo-Riemannian manifold is flat if and only if κ∇ vanishes identically.
Magnetic Resonance Wireless Power Transfer Over 10 m With Multiple Coils Immersed in Seawater

An autonomous underwater vehicle (AUV) is used for exploring marine resources and monitoring infrastructure facilities. Because an AUV is powered by batteries, it needs to be lifted out of the water to have them replaced or recharged. Thus, wirelessly feeding power to an AUV under the sea would improve the efficiency of marine activities. However, it is difficult to wirelessly power an object under the sea because seawater has high conductivity. We examine the electromagnetic field in lossy materials such as a conductor, a coil cover, and seawater. We design a wireless magnetic resonance-based power transfer system with multiple coils using bandpass filter (BPF) theory. The coil structure is sealed so that water cannot enter it. We perform experiments on this system completely immersed in seawater and measure the coil coupling coefficient, efficiency, and wattage. The results confirm wireless power transfer exceeding a distance of 10 m in an undersea setting. We verify the transmission efficiency by changing the receiving-coil position on the central axis of the transfer coils. Furthermore, we confirm a dependence on transfer on the order of 1 to 100 W.
Probability-based 3D k-space sorting for motion robust 4D-MRI.

Background Current 4D-MRI techniques are prone to breathing-variation-induced motion artifacts. This study developed a novel method for motion-robust multi-cycle 4D-MRI using probability-based multi-cycle sorting to overcome this deficiency. Methods The main cycles were first extracted from the breathing signal. 3D k-space data were then sorted using a result-driven method for each main cycle. The new method was tested on a 4D-extended cardiac-torso (XCAT) phantom with a patient and an artificially generated breathing curve. For comparison, the k-space data were sorted using conventional phase sorting to generate single-cycle 4D-MRI images. Signal-to-noise ratio (SNR) of tumor and liver, tumor volume consistency, and average intensity projection (AIP) accuracy were compared between the two methods. The original phantom images were used as references for the evaluation. Results The new method showed improved tumor-to-liver SNR and tumor volume consistency as compared to 3D k-space phase sorting in both the simulated artificial and real patient breathing signals. For the artificial breathing cycles, the average tumor-to-liver SNR and standard deviation (SD) of tumor volume were 2.53 and 3.80% for cycle 1, 2.24 and 6.16% for cycle 2 of probability-based sorting as compared to 1.47 and 21.83% obtained using the phase sorting method; for the patient breathing curve, values of 1.99 and 2.71%, 1.97 and 3.29%, 1.88 and 4.16% were observed for cycle 1, cycle 2 and cycle 3 of probability-based sorting, versus 1.44 and 7.20% for phase sorting method. Furthermore, the AIP accuracy was improved in the probability-based sorting approach when compared to phase sorting, with the average intensity difference per voxel reduced from 0.39 to 0.15 for the artificial curve, and from 0.46 to 0.21 for the patient curve. Conclusions We demonstrated the feasibility of probability-based 3D k-space sorting for motion-robust multi-cycle 4D-MRI reconstruction with breathing variation induced motion artifact reduction compared with conventional 2D image sorting and 3D phase sorting methods. This new technique can potentially improve the accuracy of radiation treatment guidance for mobile targets.
Economies-of-Scale in Many-Server Queueing Systems: Tutorial and Partial Review of the QED Halfin-Whitt Heavy-Traffic Regime

Multiserver queueing systems describe situations in which users require service from multiple parallel servers. Examples include check-in lines at airports, waiting rooms in hospitals, queues in contact centers, data buffers in wireless networks, and delayed service in cloud data centers. These are all situations with jobs (clients, patients, tasks) and servers (agents, beds, processors) that have large capacity levels, ranging from the order of tens (checkouts) to thousands (processors). This survey investigates how to design such systems to exploit resource pooling and economies-of-scale. In particular, we review the mathematics behind the quality- and efficiency-driven (QED) regime, which lets the system operate close to full utilization, while the number of servers grows simultaneously large and delays remain manageable. Aimed at a broad audience, we describe in detail the mathematical concepts for the basic Markovian many-server system, and we provide only sketches or references for more advanced settings related to, e.g., load balancing, overdispersion, parameter uncertainty, general service requirements, and queueing networks. While serving as a partial survey of a massive body of work, the tutorial is not meant to be exhaustive.
Tuning the doping level of graphene in the vicinity of the Van Hove singularity via ytterbium intercalation

In heavily $n$-doped graphene, when pushing the Fermi level to the vicinity of the Van Hove singularity, exotic electronic ground states are expected to occur driven by many-body interactions. These competing phases, such as chiral superconductivity, charge, or spin density waves, find their stability based on the amount of doping induced in the graphene layer. In this work we present a method for effectively tuning the doping level of graphene near the Van Hove singularity. Epitaxially grown buffer layer graphene on SiC(0001) is decoupled from the SiC substrate and strongly $n$ doped up to its Van Hove singularity via ytterbium intercalation. Upon annealing the Yb/graphene system at increasing temperatures, a topological transition at the Fermi level and a continuous upshift of the Dirac point are observed, indicating a decrease in charge carrier density. The intercalated Yb atoms adopt an ordered pattern while their concentration is reduced with annealing temperature. These variations significantly affect the charge transfer to the graphene layer and allow the systematic control of the doping level in the vicinity of the Van Hove singularity via one single experimental parameter. As such, the Yb intercalation technique can provide a reliable and convenient way of accessing different ordered ground states predicted in highly doped graphene.
The Italian Domination Numbers of Generalized Petersen Graphs P(n,3)

An Italian dominating function of G is a function f : V ( G ) → { 0 , 1 , 2 } , for every vertex v such that f ( v ) = 0 , it holds that ∑ u ∈ N ( v ) f ( u ) ≥ 2 . The Italian domination number γ I ( G ) is the minimum weight of an Italian dominating function on G. In this paper, we determine the exact values of the Italian domination numbers of P ( n , 3 ) .
Predicting student success by modeling student interaction in asynchronous online courses

Abstract Early-warning intervention for students at risk of failing their online courses is increasingly important for higher education institutions. Students who show high levels of engagement appear less likely to be at risk of failing, and how engaged a student is in their online experience can be characterized as factors contributing to their social presence. Social presence begins with teacher-student and student-student interaction in online courses. Fortunately, student interaction data can be gleaned from learning management systems, used to model and predict at-risk students at an early stage. This research addresses an existing model for predicting at-risk students to test a previous hypothesis that a holiday effect is a contributor for failure. A new analysis then presents an alternative approach, one that tests the frequency of student interaction rather than amount of interaction as a preferable indicator.
General Mathematical Solution for Selective Harmonic Elimination

This article proposes a general mathematical solution for a selective harmonic elimination (SHE) technique. The proposed procedure is valid for both symmetrical and unsymmetrical multilevel inverter (MLI) inverter configurations with a wide range of modulation indexes (mi). The proposed procedure eliminates the targeted number of harmonics “<inline-formula> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula>” for switching angles <inline-formula> <tex-math notation="LaTeX">$\text {NS}= 2^{l}$ </tex-math></inline-formula> regardless to the MLI output waveform levels. The proposed procedure receives the values and ratios of the dc sources, and then, it calculates the NS values to generate a suitable set of harmonic equations. In this proposed algorithm, the nonlinear transcendental harmonic equations were transformed into a linear system of equations (<inline-formula> <tex-math notation="LaTeX">$A X=B$ </tex-math></inline-formula>); the resulting equations can be solved online easily by formulating matrix A and matrix B and then multiplying the inversion of matrix A by matrix B. It has been found that only the last element in matrix B needs to be updated for each change in the modulation index or in the ac output fundamental component, resulting in reduced computation time. The proposed technique generates the switching angles <inline-formula> <tex-math notation="LaTeX">$\alpha _{1}, \alpha _{2}, \ldots, \alpha _{k}$ </tex-math></inline-formula> based on a preset objective functions. The simulation and experimental results in some selected cases are provided to validate the proposed algorithms.