Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of RF Harvester with Different Matching Circuits

In this paper, an RF harvester along with a power conditioning unit is presented. The proposed solution is tuned to work at 2.42 GHz. RF harvested front-end is simulated, and its supporting back-end power conditioning unit is discussed in detail. The variation in the harvester's Gain, Directivity and Efficiency at different frequencies is analyzed. Different power conditioning units are proposed, and the best one for the proposed RF harvester is discussed in detail. The proposed low-cost solution is highly suitable for far-field RF power harvesting, especially for small portable gadgets.
Development and Analysis of a Multimaterial FDM 3D Printed Capacitive Accelerometer

Nowadays quick fabrication of mechanical spare and replacement has become more and more necessary. This is the main reason why rapid prototyping and additive manufacturing has spread in several fields, including sensor manufacturing. In this work, we propose a Fused-Deposition-Fabricated Electro-Mechanical System (FDFEMS) accelerometer sensor. As the name suggests, it was implemented with Fused Deposition Modelling (FDM) additive manufacturing and material switching capabilities of the E3D ToolChanger motion platform. Fabrication process is based on insulating plastic to implement the mechanical structure of the device, conducting plastic to manufacture the actual differential capacitance sensing element and soluble material for sacrificial scaffolding. Together with the accelerometer, we also propose a novel fully differential interface circuit for differential capacitive sensors, based on the autobalanced bridge theory where capacitive sensor variations are balanced through voltage-controlled impedances placed in the bridge branches. Measurements from the sensor system were compared to theoretical values, showing good agreement. Furthermore, these measurements were repeated with different sensors and different test masses, to determine variations of the parameters evaluated. Specifically, measured sensitivity, damping factor and quality factor, with a fill factor of 100%, have resulted equal to 0.201 V/g, 0.055, and 9.042, respectively. It was then observed that the decrease of the proof mass fill factor led to a higher sensor resonant frequency, hence a wider flat band region in the frequency response, with decreased sensitivity and resolution. Mainly, this paper aims to evaluate and discuss the feasibility of the 3D printing Fused Deposition Modelling technique for manufacturing accelerometer sensors and classify sensor parameters as a function of its internal density, that is an easily selectable parameter for this kind of modelling.
Multiple vandermonde decomposition based channel estimation for MmWave MIMO high-mobility communication in 5G and beyond

Millimeter wave (mmWave) massive massive multiple input multiple output (MIMO) technique has been regarded as the viable solution for vehicular communications in 5G and beyond. To achieve the substantial increase in date rates, it is important to take an effective channel state information (CSI). However, existing channel estimation strategies are unavailable since the users high-mobility. To solve above issues, in this paper, inspired by a specific antenna structure, we propose a novel approach for fast time-varying channel estimation. Specifically, by considering the vehicle scenario with high-mobility, a corresponding mathematical model is firstly established. Then, based on the special structural of the sparse array, the switch network is used to replace the convention phase shifter of mmWave hybrid system, which can effectively reduce the number of radio-frequency (RF) chains and antennas. Furthermore, by solving the semidefinite programming (SDP) duality problem, the Doppler frequency and path parameters are effectively estimated. Simulation results are shown that the computational complexity and estimation accuracy of the proposed algorithm is superior than that of the traditional schemes.
3D FEM Model of a Hybrid Stepper Using Scalar-Vector Potential Formulations

The paper presents an analysis of a few 3D Finite Element Method (FEM) models based on different magnetic scalar and vector potential combinations, of a bi-phase hybrid stepper motor (HSM). The computing time is compared against a reference solution determined using only the magnetic vector potential. The model accuracy is estimated by comparing the HSM holding torque characteristics computed for each model. As HSM 3D FEM models generate matrix systems of equations with millions of unknowns, reducing the computational time and computer memory requirement are essential, hence the practical relevance of this study.
Temperature dependent dual hydrogen sensor response of Pd nanoparticle decorated Al doped ZnO surfaces

Sputter deposited Al doped ZnO (AZO) thin films exhibit a dual hydrogen sensing response in the temperature range 40 °C–150 °C after surface modifications with palladium nanoparticles. The unmodified AZO films showed no response in hydrogen in the temperature range 40 °C–150 °C. The operational temperature windows on the low and high temperature sides have been estimated by isolating the semiconductor-to-metal transition temperature zone of the sensor device. The gas response pattern was modeled by considering various adsorption isotherms, which revealed the dominance of heterogeneous adsorption characteristics. The Arrhenius adsorption barrier showed dual variation with change in hydrogen gas concentration on either side of the semiconductor-to-metal transition. A detailed analysis of the hydrogen gas response pattern by considering the changes in nano palladium due to hydrogen adsorption, and semiconductor-to-metal transition of nanocrystalline Al doped ZnO layer due to temperature, along with material characterization studies by glancing incidence X-ray diffraction, atomic force microscopy, and transmission electron microscopy, are presented.
A Research on the Measurement of Mirror Deflection Angle with Laser

A method for measuring deflection angle of mirror with laser is introduced.It is suitable for the measurement in assembling of an optical system.The centroid of imaging spot is extracted with CCD subdivision technique,thus the measuring accuracy of the deflection angle can be improved.Within the measuring range of 1.25°,a measuring accuracy of ±3″ can be achieved.
Application of Neural Network Principal Components to Climate Data

Abstract Principal component analysis (PCA) is one of the most widely used methods in the examination of climate data. However, PCA of a dataset is handicapped if the data size is large. PCA of a large dataset would require huge computer resources in terms of memory and CPU time. Neural network principal component analysis (NNPCA), which has been used mainly in the signal-processing field, can be a useful tool in the analysis of large climate datasets. NNPCA requirements of computer memory and CPU time are far less than what are needed by conventional methods of PCA, such as singular value decomposition of the data matrix. In this paper, an NNPCA application to climate data is introduced. NNPCA is applied to reanalysis data of monthly and daily global maps of the 850-hPa geopotential height from the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis data. These data, covering the period from 1948 to 2003, are composed of 648 monthly maps and 20 117 daily ones.
Application of Compressed Sensing to Radar Signals

This paper presents the application of compressed sensing techniques on radar echo signals. Reconstruction algorithms, specifically Orthogonal Matching Pursuit (OMP), Compressive Sampling Matching Pursuit (CoSAMP), and Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) are used without dictionary learning in this paper. The abovementioned reconstruction algorithms were tested for two Compression Ratio values. To verify the results of all the reconstruction algorithms, the modified Cell-Averaging Constant False Alarm Rate (CA-CFAR) was applied to both, the original radar data, and the reconstructed radar data. For the simulation purpose, synthetic radar signals with different Signal-to-Noise Ratio (SNR) values were used. The best results were achieved using the FISTA algorithm.
Teaching Practices Impact on the Level of Contentedness for Electrical Engineering Students. A Longitudinal Study

Results of a longitudinal research carried out within the Faculty of Electrical Engineering (University POLITEHNICA of Bucharest) to identify the dynamics of student preferences regarding the teaching-learning-assessment process are presented in this paper. The research was carried out throughout one full generation (four academic years) of students. The results showed that the academic maturity of the students (defined as the transition to a higher academic year) majorly impacts only the students' preferences regarding some aspects like the way of conducting the laboratory and project applications, the subject’s final evaluation procedure, the fining of academic deception and the mandatory evaluation of professors' activity by students. The studied generation (2016-2019) is the last one before the COVID-19 pandemic, before the paradigm shifts through the sudden transition to fully online activities highlighting the relevance of this research.
Multitrack Symbolic Music Composition with LSTM-GAN

Music generation is becoming a heated subject in the field of machine learning. Particularly, in the direction of multitrack, sequential, and symbolic music composition, methods based on generative adversarial networks (GAN) such as MuseGAN and BinaryMuseGAN have shown outstanding performance and have been adopted by many researchers. However, there are still issues in musicality and temporal correlation that need to be addressed. In this paper, an LSTM-based GAN is proposed in order to capture more temporal information from real-world music samples. One bar is seen as the smallest unit and fed into the LSTM network in the discriminator. Additionally, a processor is added between the generator and the discriminator to extract important information from the data. This LSTM-GAN scheme helps the discriminator find important differentiators more quickly and hence improves the ability of the generator to fabricate authentic data. The proposed method is validated via experiments, where the generated data shows similarities with the authentic ones.
Dry and Moist Convection Forced by an Urban Heat Island

Abstract This study numerically investigates dry and moist convection forced by an urban heat island using a two-dimensional, nonhydrostatic, compressible model with explicit cloud microphysical processes (Advanced Regional Prediction System). The urban heat island is represented by specified heating. Extensive numerical experiments with various heating amplitudes, representing the intensity of the urban heat island, uniform basic-state wind speeds, and basic-state relative humidities, are performed to examine their roles in characterizing urban-induced convection. Two flow regimes can be identified in dry simulations. One regime is characterized only by stationary gravity waves near the heating region and is revealed when the urban heat island intensity is very weak. The other regime is characterized both by stationary gravity waves near the heating region and by a downwind updraft cell that moves in the downstream direction. The intensity of the downwind updraft cell increases as the heat island intensi.
A Review of International Research on Artificial Intelligence in Teachers' Teaching

According to various international reports, artificial intelligence in education is currently an emerging field in educational technology. In this paper, we analyzed 610 articles included in web of science in this field from 2002-2022 using Citespace software, and the results show that the number of articles issued is on a growing trend and the current research heat is rising; keyword analysis shows that the hot spots of research are focused on the transformation of teachers' roles in the era of AI, educational applications of AI tools, AI-driven teachers From the frontier evolution, the future international research on the application of AI to teachers' teaching will develop in the direction of practical knowledge and multidisciplinary integration, and will pay more attention to the role of AI in shaping teachers' personal teaching ability. The study has implications for the selection of topics and frontier identification for future research.
Numerical Simulation and Experimental Validation of a Magnetic Gearbox Amplifier

The paper presents a magnetic gearbox amplifier used for Flywheel Storage Systems (FESS) applications. This paper describes the design, functionality, and numerical simulation of such a magnetic gearbox amplifier. Also in the paper is presented an experimental model manufactured with a 3D printer using FDM technology, which is assembled and tested on a stand. The results of the numerical simulation are compared and validated by the experimental testing of the magnetic gearbox amplifier.
MIMO Antenna with Low Mutual Coupling and High Gain for n257-Band 5G mm-Wave

In this communication, a 2-port printed MIMO antenna of a dimension of 12× 8.5 mm2 with high isolation and high gain is reported for 28-GHz (n257-band) millimeter-wave applications. The conducting patch is placed on high-frequency laminates of duroid 5880 substrate (εr = 2.2, tanδ = 0.009, thickness = 0.8 mm). The port orientation investigation is also carried out to attain maximum isolation between the antenna elements. Diversity performance parameters (ECC, DG, TARC, and CCL) are investigated and calculated to justify the MIMO identity of the proposed model. The simulated findings are verified by the measured results of the fabricated archetype. All simulated/measured results are within the permissible limit and in good coherence.
Research on the Interaction Design of Online Learning Platforms for University Students Based on a User’s Mental Model

Based on a user's mental model, the interaction (HCI) design strategy of online learning platforms for university students is explored to provide a theoretical basis for designing and developing online learning platforms. Through the theoretical overview, this study analyses the significance of the involvement of mental models in the interaction design of online learning platforms. A user's mental model for the interaction design of online learning platforms for university students is integrated. Through the quantitative and qualitative analysis of the questionnaire, the current problems and needs of university students in the interactive experience of online learning platforms are summarized. Combining a user's mental model and the questionnaire data, this study proposes an interaction design strategy for university students' online learning platform based on the mental model from three dimensions: the visual, the behavioral, and the reflective dimensions. It is hoped that it will meet the learning needs of university students in the online learning platform， and optimize their online learning experience.
Semiclassical scattering by the Coulomb potential

Nous examinerons l'evolution quantique generee par l'hamiltonien -n 2 /2 Δ + V, ou V est un potentiel de Coulomb. Pour une certaine classe de fonctions d'ondes avec une densite de probabilite gaussienne, nous construirons une solution approximative semi-classique dependante du temps. Nous montrerons ainsi que les operateurs d'ondes modifes peuvent etre approches a l'ordre dominant en ∞ grâce a l'utilisation des transformations classiques de Moller correspondantes.
Construction of a Generator for Power Frequency Magnetic Field Immunity Test

The power frequency magnetic field immunity test is a common test applied to a large variety of devices that must be checked for electromagnetic compatibility. Implementing this test is not difficult since it involves working with frequencies at which the generated magnetic field can be considered quasi-stationary. Considering the high price level of devices intended for use in electromagnetic compatibility laboratories, it may be worth contemplating assembling the equipment for this test on one’s own. This paper includes a discussion on the requirements given by the relevant standards and their implications for the design of the test signal generator. Additionally, a complete design process for such equipment is provided, along with a discussion on the possibility of further improvements. The design of the test coil is adapted to allow its excitation from a powerful low-frequency amplifier, which opens up possibilities for the use of more advanced drivers. However, the test coil can also be powered using a normal mains transformer, simplifying the design of the entire device
A Comparison of Low Complexity CNN Models and Data Enhancement for Efficient Android Implementation of Leaves Recognition

Classification of flora data is a high complexity problem due to the similarity of plants. Identification and determination of plant species is a task that most of the general public is unable to do and it can be a challenge even for the expertise of qualified individuals. This paper studies the possibility of using lightweight convolutional neural networks on resources constrained devices or platforms to accurately classify leaves of plants. Several low complexity convolutional neural networks are considered for the problem of leaves recognition in terms of accuracy, precision, latency and complexity. A novel scheme for enhancement and augmentation of the leaf’s images is proposed and demonstrated as capable to improve performance. MobileNet, L-CNN and NL-CNN models with Android implementations in Tensorflow Lite1 are considered, demonstrating the capability to build a portable intelligent instrument capable to identify plants by their leaves with an accuracy of 95.3%.
Variations in Tropical Sea Surface Temperature and Surface Wind Fields Associated with the Southern Oscillation/El Niño

Abstract Surface marine observations, satellite data, and station observations of surface pressure and precipitation are used to describe the evolution of sea surface temperature (SST) anomalies, surface wind fields, and precipitation anomaly patterns during major warm episodes in the eastern and central tropical Pacific. The sequence of events is described in terms of composite SST and wind fields (30°N–30°S) for six warm episodes since 1949, and time series and cross-spectral analyses of mean monthly data along six shipping lanes which cross the equator between the South American coast and 170°W. During the months preceding a warm episode, the equatorial easterlies are stronger than normal west of the dateline. This and other coherent and strongly developed anomaly patterns over the western equatorial Pacific and South Pacific are associated with a South Pacific Convergence Zone (SPCZ) located southwest of its normal position. During October-November prior to El Nino, the equatorial easterly anomalies i.
Turbulent flow simulation using LES with dynamical mixed one‐equation subgrid models in complex geometries

An innovative computational model is presented for the large eddy simulation of multi‐dimensional unsteady turbulent flow problems in complex geometries. The main objectives of this research are (i) to better understand the structure of turbulent flows in complex geometry and (ii) to investigate the 3D characteristics of such complex fluid flow. The filtered Navier–Stokes equations are used to simulate large scales of the turbulence, while the energy transfer from large scales to subgrid‐scales (SGS) is simulated using dynamical mixed one‐equation subgrid models. In the proposed SGS model, the SGS kinetic energy, ksgs, is used for scaling the velocity for the eddy‐viscosity part of the model. The proposed SGS model contains not only some information on the small scales as described in traditional Smagorinsky model or Germano dynamical model but also includes additional scale‐similarity as that in the models of Ghosal et al. (J. Comput. Phys. 1995; 118:24–37) or Davidson (11th International Symposium on Turbulent Shear Flow, Grenoble, vol. 3, 1997; 26.1–26.6). The Navier–Stokes equations and the derived ksgs equation are solved using implicit finite‐volume method. The models have been applied to simulate the 3D flows over a backward‐facing step and in a strong 3D skew runner blade passage of a Francis hydro turbine, respectively. Good agreement between simulated results and experimental results as well as other numerical results was obtained. Copyright © 2009 John Wiley & Sons, Ltd.
Design of One-Class Classifier Using Hyper-Rectangles

Recently, the importance of one-class classification problem is more increasing. However, most of existing algorithms have the limitation on providing the information that effects on the prediction of the target value. Motivated by this remark, in this paper, we suggest an efficient one-class classifier using hyper-rectangles (H-RTGLs) that can be produced from intervals including observations. Specifically, we generate intervals for each feature and integrate them. For generating intervals, we consider two approaches : (i) interval merging and (ii) clustering. We evaluate the performance of the suggested methods by computing classification accuracy using area under the roc curve and compare them with other one-class classification algorithms using four datasets from UCI repository. Since H-RTGLs constructed for a given data set enable classification factors to be visible, we can discern which features effect on the classification result and extract patterns that a data set originally has.
Physiologically Based Pharmacokinetic Modeling to Characterize the Effect of Molecular Charge on Whole-Body Disposition of Monoclonal Antibodies

Motivated by a series of work demonstrating the effect of molecular charge on antibody pharmacokinetics (PK), physiological-based pharmacokinetic (PBPK) models are emerging that relate in silico calculated charge or in vitro measures of polyspecificity to antibody PK parameters. However, only plasma data has been used for model development in these studies, leading to unvalidated assumptions. Here, we present an extended platform PBPK model for antibodies that incorporate charge-dependent endothelial cell pinocytosis rate and nonspecific off-target binding in the interstitial space and on circulating blood cells, to simultaneously characterize whole-body disposition of three antibody charge variants. Predictive potential of various charge metrics was also explored, and the difference between positive charge patches and negative charge patches (i.e., PPC-PNC) was used as the charge parameter to establish quantitative relationships with nonspecific binding affinities and endothelial cell uptake rate. Whole-body disposition of these charge variants was captured well by the model, with less than 2-fold predictive error in area under the curve of most plasma and tissue PK data. The model also predicted that with greater positive charge, nonspecific binding was more substantial, and pinocytosis rate increased especially in brain, heart, kidney, liver, lung, and spleen, but remained unchanged in adipose, bone, muscle, and skin. The presented PBPK model contributes to our understanding of the mechanisms governing the disposition of charged antibodies and can be used as a platform to guide charge engineering based on desired plasma and tissue exposures. Graphical Abstract