Miracle-dz/UniFAR-FTUs huggingface dataset:
Barriers to access to education for migrant children

Wilfred Lunga (Human Sciences Research Council (HSRC)) , Faton Bislimi (Kosovo Public Policy Center & RIT Kosovo (AUK), Kosovo) , Fawwaz A. Momani (Refugees, Displaced Persons, and Forced Migration Studies Centre) , Ismael Nouns (Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH) , Konosoang Sobane (Human Sciences Research Council (HSRC)) July 24, 2018 | Last updated: December 10, 2020 Tags: Displacement and migration , Education and skills
Classification of Underwater Acoustic Target Using Auditory Spectrum Feature and SVDD Ensemble

Underwater Acoustic Target Classification (UATC) is an important field in underwater acoustic signal processing. Developing a robust, efficient and high-performance classifier is challenging due to the complex ocean environment, to the great variety of underwater acoustic target classes, and to the ambiguity that arises because of similar classes. Furthermore, in the case of only one class target data is available, the classification model has to be learned from one class samples. In this paper, we proposed a hybrid method combining auditory spectrum and support vector data description ensemble for One Class Underwater Acoustic Target Classification (OCUTAC). The proposed new method consists of 3 stages: (1) Feature extraction method which is based on auditory spectrum for underwater acoustic signal processing. (2) Auditory Spectrum Features extracted from different training datasets which obtained via bagging method are used to construct different individual SVDD classifiers. (3) Individual SVDD classifiers are combined into a single module using a majority voting approach to make a final decision. Experimental results with real data show that the proposed hybrid method is effective for One Class Underwater Acoustic Target Classification.
A Characterization and Comparison of Spatial-Temporal Applications and Internet Big Data Benchmarks

Urban traffic data analysis platform is an important infrastructure to a modern city. As the spatial-temporal data produced in traffic transportation systems explosively growth, the operators in traffic field are trying to adopt the new emerging big data solutions born in the internet area. However, it is hard to find a high cost/performance solution to build this platform because diverse combinations of hardware and software configuration. Currently, the operators selecting solutions depend on simple evaluation results based on internet benchmarks such as terasort. Two issues including: (1) is it appropriate that evaluating a solution for spatial-temporal applications by internet benchmark; and (2) what is the characteristic of spatial-temporal application and the potential optimization measurements, have never been fully explored. We address this issue by a novel workload characterization tool, called Extensible Metric Importance Analysis (EMIA), for big data applications. The key idea is a performance model based on ensemble learning, which takes the program metrics as the input, outputs the performance metric such as execution time, and ranks these metrics as their corresponding importance. Based on EMIA, we apply principal component analysis (PCA) to program behaviors of five representative spatial-temporal applications and nine popular internet big data benchmarks. Experimental results show that spatial-temporary applications present unique characteristics and it is unreasonable to evaluate solutions for spatial-temporary applications by internet benchmarks. Moreover, we optimize spatial-temporary applications via applying measurements to the key factors identified by EMIA, achieving obviously performance improvement.
A Novel Single-Stage AC/DC Converter Used in Battery Charger Applications

In this paper, a novel single-stage AC/DC converter for battery charging applications is introduced. The proposed topology uses only two active switches to perform a Power Factor Correction (PFC) AC/DC conversion and a DC/DC conversion at the same time. The PFC AC/DC conversion maintains a DC voltage across two storage capacitors. The DC/DC converter is a half-bridge DC/DC topology which regulates the output voltage for the load and provides galvanic isolation. One of the important advantages of the proposed single-stage AC/DC coverer is that it inherently provides Zero-Voltage-Switching (ZVS) in DCM. Moreover, it can achieve high PFC while having low output current ripple, which is essential for Lithium Ion (Li-Ion) batteries. Simulation and experimental results are provided to show the superior performance of the converter in achieving ZVS, PFC, and low output current ripple.
Plankton Image Classification via Multi-Class Imbalanced Learning

The plankton is an incredibly large group of organisms. As a fatal component in the ocean, their distribution and richness can impact on the material cycle of the water ecosystem and the balance of ecological system. Thus it's a valuable work to detect and identify plankton. We will be able to alert against the red tide if our equipment can automatically identify them. The main deficiency is that data in the main plankton datasets are quite imbalanced, i.e. some species have more than 100,000 images while some others have only less than 10 images in one dataset. Imbalanced learning has been a research emphasis in recent years because of the growing number of class-imbalanced classification problems. The problem is particularly problematic when it comes to plankton classification. We proposed a new algorithm EasyEnsemble.D to overcome this deficiency. EasyEnsemble.D samples subsets from majority classes and classify them according to different weight. Then output the combination of each weak learner. The results show that as for ZooScan dataset, EasyEnsemble.D has higher F-measure and G-mean values than other imbalanced learning methods. It can be used in both dichotomy problems and multiple problems.
Pseudo-Partial-Power Converter without High Frequency Transformer for Electric Vehicle Fast Charging Stations

This paper presents a new partial power converter (PPC) for the DC-DC stage of electric vehicle (EV) fast charging stations. The proposed converter handles only a fraction of the total power delivered from the grid to the battery, increasing the overall system efficiency and power density, while potentially reducing the cost of the charger. The proposed topology is based on a switched capacitor between the AC terminals of an H-bridge converter, and does not require high frequency isolation transformers to provide a controllable voltage source between the DC-link and the battery. The proposed concept can be implemented using interleaved power cells, which can improve the power quality, reduce inductor size, and enable scalability for higher power rating chargers. The operating principle, partial operation analysis, control scheme, and simulation results are presented to validate the proposed concept.
Novel tools for the surveillance and control of dengue: findings by the DengueTools research consortium

ABSTRACT Background: Dengue fever persists as a major global disease burden, and may increase as a consequence of climate change. Along with other measures, research actions to improve diagnosis, surveillance, prevention, and predictive models are highly relevant. The European Commission funded the DengueTools consortium to lead a major initiative in these areas, and this review synthesises the outputs and findings of this work conducted from 2011 to 2016. Research areas: DengueTools organised its work into three research areas, namely [1] Early warning and surveillance systems; [2] Strategies to prevent dengue in children; and [3] Predictive models for the global spread of dengue. Research area 1 focused on case-studies undertaken in Sri Lanka, including developing laboratory-based sentinel surveillance, evaluating economic impact, identifying drivers of transmission intensity, evaluating outbreak prediction capacity and developing diagnostic capacity. Research area 2 addressed preventing dengue transmission in school children, with case-studies undertaken in Thailand. Insecticide-treated school uniforms represented an intriguing potential approach, with some encouraging results, but which were overshadowed by a lack of persistence of insecticide on the uniforms with repeated washing. Research area 3 evaluated potential global spread of dengue, particularly into dengue-naïve areas such as Europe. The role of international travel, changing boundaries of vectors, developing models of vectorial capacity under different climate change scenarios and strategies for vector control in outbreaks was all evaluated. Concluding remarks: DengueTools was able to make significant advances in methods for understanding and controlling dengue transmission in a range of settings. These will have implications for public health agendas to counteract dengue, including vaccination programmes. Outlook: Towards the end of the DengueTools project, Zika virus emerged as an unexpected epidemic in the central and southern America. Given the similarities between the dengue and Zika viruses, with vectors in common, some of the DengueTools thinking translated readily into the Zika situation.
Magnet Temperature Estimation in Permanent Magnet Synchronous Machines Using the High Frequency Inductance

Permanent magnet synchronous machines (PMSMs) torque production capability depends on the permanent magnets (PMs) magnetization state, which can be affected by PMs temperature and of the current flowing throughout the stator windings; knowledge of the PMs temperature can be therefore of great importance both for control and monitoring purposes. PMs temperature can be measured or estimated; PM temperature measurement is not easy and is not normally implemented in commercial drives. PM temperature estimation methods can be divided into thermal models based, BEMF based and signal injection based methods. Existing HF signal injection methods estimate the PM temperature from the measured stator HF resistance. Unfortunately the resistance is also affected by magnetoresistive effect, which can limit the accuracy of the estimates. This paper proposes the use of the stator $\boldsymbol{d}$-axis HF inductance for PM temperature estimation. This makes temperature estimation insensitive to magnetoresistive effect. In addition, it allows the use of higher frequencies, reducing the adverse impact of the injected signal on machine performance.11This work was supported in part by the Research, Technological Development and Innovation Programs of the Spanish Ministry Economy and Competitiveness, under grant MINECO-17-ENE2016-80047-R.
A 4-Gb/s CMOS Modulator with High Isolation and Large Output Power for Q-Band Applications

This paper presents a CMOS On-Off keying (OOK) modulator with high isolation, high output power for Q-band applications. In this design, an innovative double-loop drainage technique is adopted to reduce the leakage current from the carrier to the output load. Moreover, a fully symmetrical layout is also implemented for further reducing the feed-through due to mismatch. Thus, the isolation of the circuit is effectively increased. According to the measurement results, the modulator achieves a high isolation better than 62 dB, a conversion gain of 1 dB, an $\text{OP}_{{1}\text{dB}}$ of 3 dBm, and a data rate up to 4 Gb/s. Fabricated in a standard 90-nm CMOS process, the modulator occupies a silicon area of $0.56\times 0.73\text{mm}^{2}$ and consumes a total dc power of 25 mW.
Adaptive Filter of Input Information Signal for Discharge Pulse Installation Control System

The synthesis of optimal filter, that supplies the maximal accuracy of informational coordinate estimation for control system of the discharge pulse installation, was done. The proofs of dependence between optimal value of the filter’s coefficients and the change rate of the average value and dispersion of input informational signal were given. Also the coefficients of two-section recursive adaptive filter were calculated. Application of the developed filter improves efficiency of discharge pulse installations.
Varying-Parameter RNN Activated by Finite-Time Functions for Solving Joint-Drift Problems of Redundant Robot Manipulators

Joint-drift problem may lead to task execution failure or robot damage. To solve this problem, a finite-time varying-parameter recurrent neural network (FT-VP-RNN) is proposed and investigated in this paper. First, a quadratic programming (QP) based joint-drift-free (JDF) scheme is developed, which consists of an optimization criterion and a kinematic equation at velocity layer. A feedback control is then added into the kinematic equation as the equality constraint, and a feedback-considered joint-drift-free (FC-JDF) scheme is obtained. Second, a novel FT-VP-RNN is designed to solve the FC-JDF scheme and a corresponding finite-time convergence theorem is proposed. The outstanding advantages of the proposed FT-VP-RNN are the real-time computation, exponential convergence, and the ability to eliminate the initial errors. Finally, three path-tracking simulations and comparisons are conducted to verify the effectiveness, accuracy, practicability, and safety of the proposed FT-VP-RNN for solving the joint-drift problems of redundant robot manipulators.
Realization of a Wideband Sum and Difference Network for Dual Circular Polarization

The paper analyze a new type of dual circularization wideband 8 ports beam forming network. The individual units of the array fed with the desired amplitude ratio and phase relationship form a monopulse tracing sum-mode and difference-mode lobe pattern. 90° phase shifter and 3dB hybrid with wideband have been chosen for the design of the network. In order to lower the losses and reduce the interferences, the network adopted stripline structure. It is designed to operate within the range of frequencies band 0.9GHz-2.35GHz. Finally, the network realized $\pm 45^{0}\mathrm{and}\pm 90^{0}$ phase difference for the antenna array with two simple elements. The average transmission loss within 3 dB and phase deviation of $\pm 10^{0}$.
Surface Grafted Core-Shell IPN Hydrogel Nanoparticles for Delivery of Hydrophobic Drugs

Poly (2-hydroxyethyl methacrylate) (PHEMA) polymer was synthesised by self-polymerization and grafted with interpenetrating chitosan network with aim to provide added functionality. Curcumin loaded PHEMA nanoparticles were prepared by double emulsion nanoprecipitation method and interpenetrating polymer network with chitosan were constructed by glycosidic coupling. The formulated nanoparticles were characterized for particle size, surface morphology, surface modification and encapsulation of drug. Potential anti-cancer activity was evaluated by in vitro cell toxicity and apoptosis assays on MCF-7 cell line. Experimental results indicate both PHEMA and modified Chitosan-PHEMA as effective nanoparticulate delivery system for hydrophobic anti-cancer drugs.
Design of High Speed Reluctance Synchronous Motor for Automotive Purposes

This paper deals with high speed electrical machines in automotive industrial and in common applications. The different types of electrical machines and their suitability for high speed utilization are discussed. Advantages and disadvantages of high speed reluctance synchronous machines (RSM) in comparison with other types of high speed machines are highlighted. The electromagnetic design of such a RSM is carried out on the base of known design procedures enriched by high speed approach. The stator consists of two layers, three phase winding with 12 and 24 slots will be analyzed to compare maximal, average torque and torque ripple. Calculations are carried out by means of finite element analysis (FEA). The different rotor topologies are designed, with multiple geometrical degrees of freedom. Different rotor topologies are also investigated by means of FEA to be able to compare the maximal, average torque and torque ripple. Further mechanical aspects of rotor for RSM are done. The selection of thickness of tangential and central bridges is taken into account. A mechanical stress in such designed machine is calculated versus various speed.
A Study on the Axial Leakage Magnetic Flux in a Spoke Type Permanent Magnet Synchronous Motor

A spoke type permanent magnet synchronous motor (Spoke PMSM) generates axial leakage magnetic flux due to its flux-concentrating rotor structure and therefore degrades motor performance. Although a three dimensional finite element method (3D-FEM) can be used to estimate the axial leakage magnetic flux, it requires a lot of analyzing time. In this study, a calibration coefficient is proposed to estimate the axial leakage magnetic flux using a two dimensional finite element method (2D-FEM) only instead of applying 3D-FEM. This calibration coefficient is expressed by the major geometric parameters of a motor and can be calculated using the material information of the permanent magnet and the bridge of rotor core in 2D-FEM. The validity of the proposed equation is verified by 3D-FEM and tests
A dexterity measure for robot manipulators

A dexterity measure is presented which can be used for the design evaluation of robot (redundant) manipulators. The proposed criterion is applied (in an absolute fashion) to the evaluation of the kinematic dexterity of five different designs of a HERA redundant manipulator to be used in space operations. The selection of these designs is based on augmenting the original HERA six-degree-of-freedom design with an extra joint and considering only the resultant manipulators to be capable of avoiding all possible kinds of internal singularity. The results are in agreement with the conclusions from previous subjective studies conducted for the design analysis of an industrial arm as a redundant manipulator. <<ETX>>
Missing Data Estimation in Mobile Sensing Environments

Mobile sensing techniques have been increasingly deployed in many Internet of Things-based applications because of their cost efficiency, wide coverage, and flexibility. However, these techniques are unreliable in many situations due to noise of different kinds, loss of communication, or insufficient energy. As such, datasets created from mobile sensing scenarios are likely to contain large amount of missing data, which makes further data analysis difficult, inaccurate, or even impossible. We find that the existing estimation models and techniques developed for static sensing do not work well in the mobile sensing scenarios. To address the problem, we propose a spatio-temporal method, which is specifically designed for answering queries in such applications. Experiments on a real-world, incomplete mobile sensing dataset show that the proposed method outperforms the state-of-the-art noticeably in terms of estimation errors. More importantly, the proposed model is tolerant to datasets with extremely high-missing data rates. Training with the proposed model is also efficient, which makes it suitable for deployment on computationally constrained devices and platforms that need to process massive amounts of data in real time.
Draft Genome Sequence of Lactobacillus kosoi NBRC 113063, Isolated from Kôso, a Japanese Sugar-Vegetable Fermented Beverage

Lactobacillus kosoi NBRC 113063 is a fructophilic species isolated from kôso, a Japanese sugar-vegetable fermented beverage. The draft genome sequence of Lactobacillus kosoi NBRC 113063 is useful for understanding the carbohydrate metabolism of fructophilic lactic acid bacteria. ABSTRACT Lactobacillus kosoi NBRC 113063 is a fructophilic species isolated from kôso, a Japanese sugar-vegetable fermented beverage. The draft genome sequence of Lactobacillus kosoi NBRC 113063 is useful for understanding the carbohydrate metabolism of fructophilic lactic acid bacteria.
T-Shape Resonator-Fed Duplex Antenna with Enhanced Bandwidth

A T-shape resonator-fed duplex antenna with enhanced bandwidth is proposed. A square stacked patch is introduced to achieve a dual-band antenna. The stacked patch is fed by the T-shape resonators which are consists of three 1/4 wavelength short-circuited lines in every operation band. The dual-band antenna is regarded as the last-order resonator and coupled by the filter network of two channels through a gap and a slot. Compared with the traditional stand-alone patch antenna, the proposed duplex antenna shows wider bandwidth, more stable gain in radiation band. The working principle and design process are described in this paper. Finally, a duplex filter antenna operating at S band is fabricated and measured. The isolation between the transmission channel (uplink) 2.27- $\mathbf{2.51GHz (FBW=\ 10\%)}$ and receiving channel (downlink) 2.83- $\mathbf{3.03GHz(FBW=6.9\%)}$ is over 33dB. The measured results show a good agreement with simulation results.
Active Damping of LCL Filters with All-Pass Filters Considering Grid Impedance Variations and Parameter Drifts

Active damping is a common way to stabilize the current control of LCL-filtered inverter systems. In this paper, the active damping is realized with digital filters to reshape the open-loop phase response, where no additional sensors are required. A safety region of −180°-phase crossing is first identified within a predefined range of grid impedance changes and LCL parameter variations. Once the phase of the current control loop is reshaped to the safety region, a stable control loop can be ensured. Different digital filters are selected for comparison, which shows that the allpass filter has a unit gain and adjustable phase-lag capability, being a promising solution for phase reshaping. Therefore, the allpass filter is employed to move the −180°-phase crossing into the targeted safety region. By doing so, the all-pass filter-based active damping can be separated from the current controller design, without retuning the PI parameters. Simulations are performed and the results verify that the proposed method can ensure the system stability, while maintaining fast dynamics.
A Neural Network Energy Management Controller Applied to a Hybrid Energy Storage System using Multi-Source Inverter

In this paper, a Neural Network Energy Management Controller (NN-EMC) is designed and applied to a Hybrid Energy Storage System (HESS) using the Multi-Source Inverter (MSI). Its aim is to manage the current sharing between a Li-ion battery and an Ultracapacitor by actively controlling the operating modes of the MSI. A discharge duty cycle that biases the use of one source over another is used as the control variable. To limit the battery wear and the input source power loss, an optimized solution is obtained with Dynamic Programming (DP). The NN-EMC is designed with an artificial neural network and trained with the optimized duty cycle obtained by DP. The DP/NN-EMC solution was compared to the battery-only Energy Storage System (ESS) and the HESS-MSI with 50% discharge duty cycle. Both the battery RMS current and peak battery current have been found to be reduced by 50% using the NN-EMC compared to the battery-only ESS for the New York City drive cycle.
Parallelization of a gossip algorithm for vehicle routing problems

A large body of research on the vehicle routing problem and its variants focuses on developing efficient solution procedures. Yet, not so many research articles have addressed parallelism in their proposed algorithms. Parallelized optimization algorithms can yield better solution quality in less amount of time. The main contribution of this paper is parallelization of a distributed algorithm based on the gossip protocol for vehicle routing problems VRP. The proposed algorithm can be applied to different variants of VRPs. While the resulting speed-ups are promising, the required effort for implementation of the parallelism is minimal, which makes the algorithm even more appealing.