Miracle-dz/UniFAR-FTUs huggingface dataset:
The Design and Evaluation of Interactive Video-Based Flipped Classroom on Mathematics Learning

In recent years, flipped learning has received an increasing emphasis; it stresses that students can memorize and understand the knowledge through the instructional video recorded by teachers before the class, and hence more time is available for students to practice, apply knowledge and discuss in class. However, watching instructional video is one way behavior, there is no interaction between learner and learning system, so learner may lost his/her attention after a period of time. Therefore, in this study, an interactive video lectures was proposed for developing a new flipped learning system. To examine the effectiveness of the proposed approach, an experiment was conducted in a math learning activity in an elementary school. The participants were two classes of 49 fifth graders. One classes was assigned to the experimental group, while the other was the control group. The students in the experimental group used the flipped learning mode with interactive video lectures, while those in the control group learned with the conventional flipped learning mode. The results show that students who learned with the flipped learning mode with interactive video lectures performed better in comprehension and retention of mathematics than the control group.
Vector Control of PMSM in Circulation Pump Application and an Adaptive Approach for EEI Optimization

In this study, the vector control application of a circulation pump with a wet rotor permanent magnet synchronous motor (PMSM) and the control algorithm developed to reduce the energy efficiency index (EEI) of the pump group is presented. In order to meet the needs of the market, vector control was used in the control of the motor. In addition, system parameters such as flow and head can be estimated by means of axis currents. Proposed control method in this study was named as semi-analytical because at the beginning of the analysis experimental data were required. The basis of the control algorithm is based on the creation of a speed lookup table with different flow rate references for the PMSM to optimize the EEI value. Proposed optimization method was analyzed in the Mathcad and compared with the test results. The suggested control method was applied on a wet rotor circulation pump with an EEI of 0.23 and EEI was reduced to 0.20. With this vector control based optimization method developed, a feasible solution is presented to manufacturers who cannot meet EEI standard despite using high efficiency PMSM and impeller.
Test of the First Full-Length Prototype of the HL-LHC D2 Orbit Corrector Based on Canted Cosine Theta Technology

In the context of CERN's high-luminosity upgrade project (HL-LHC) for the Large Hadron Collider (LHC), a new double aperture beam orbit corrector magnets will be installed near the recombination dipole (D2). These 2.2 m long NbTi dipoles are built with the canted cosine theta (CCT) technique. The two independently powered apertures are oriented such that their field vectors are perpendicular to each other and to the direction of the beams. A full-length double aperture prototype was built and tested at CERN in the SM18 test facility. Here we present the results of powering tests at 1.9 and 4.5 K: training of each aperture, magnetic field quality and cross-talk effects, quench detection system effectiveness, quench protection performance and quench-back with several energy extraction systems.
Comparative Study of Name Matching Algorithms

Names are the basic criterion of identification for differentiating any people or objects. Names are essential for society which uses ID systems to identify individuals. Since, the world is behind data digitization, names are of major importance in technological world too. Name matching has a pivotal contribution in information retrieval or deductive systems to interact with names or to discover names that allude to indistinguishable objects, people or organizations. Name matching is a significant element in many applications that involves data mining and linking. This methodology of name matching experiences various complexities, like matching information from various dialects or information, composed by individuals from different societies. Any application which deals with names are subjected to errors which are not avoidable, therefore considering the significance of name matching; numerous algorithmic rules have been written to address these aspects. These rules are framed considering the name variations which were generated as a result of spelling, design or phonetic alterations. This paper describes the types of name variations and the basic description of different name matching algorithms under Phonetics, Pattern and Dictionary. It also depicts the issues faced in name matching to improve data accuracy by implementing Soundex, Double Metaphone and Levenshtein on sample data.
Physical Layer Multicasting in Massive MIMO Systems With Statistical CSIT

We present a transmit design for physical layer multicasting in single-cell massive multiple-input-multiple-output (MIMO) systems using statistical channel state information at the base station (BS). We first establish the relationship between the transmit design problems under the quality of service (QoS) and max-min fair (MMF) criteria. According to the relationship between the QoS and MMF problems, we only focus on the transmit covariance optimizations under the latter criterion. We show that when the uniform linear array is equipped at the BS, the eigenvectors of the optimal transmit covariance are given by columns of the discrete Fourier transform matrix, which reveals the optimality of the beam domain transmission in massive MIMO multicasting. A dual algorithm together with stochastic programming is proposed to specify the eigenvalues of the transmit covariance. To reduce the complexity involved in stochastic programming, we further formulate two simplified MMF problems by using the deterministic equivalents and upper bound to approximate the ergodic achievable rate, respectively. We show that the optimality of the beam domain transmission still holds for the two simplified MMF problems. Then we present two low-complexity transmit designs. Simulation results demonstrate the performances of the proposed algorithms.
Flexible-joint Impedance Control Based on Backstepping Method

In this paper, an impedance control method based on backstepping approach is proposed aiming at the problems of compliance control when the flexible joint contacts with the environment. The stability of the controller is proved based on Lyapunov stability theory. The controller design combines stability of a Lyapunov function and the desired dynamic of impedance model. The experiment results show the effectiveness and feasibility of the proposed control approach.
An 8-bit Bit-Slice TEA-Cryptographic Accelerator for 64-bit RSFQ Secure Coprocessors

An 8-bit bit-slice TEA-cryptographic accelerator for 64-bit RSFQ secure coprocessors is proposed. The accelerator is based on Tiny Encryption Algorithm (TEA) and mainly consists of bit-slice adders and bit-slice shifters. Synchronous concurrent-flow clocking is used to design a fully pipelined RSFQ logic design. For verifying the algorithm and the logic design, the RSFQ logic circuits of the proposed accelerator have been simulated with a target operating frequency of 50 GHz. It consists of 21 stages. The throughput is 7.672 × 107 64-bit TEA encryptions per second.
A Runtime Power-Aware Phase Predictor for CGRAs

As computation migrates to the edge, energy efficiency is becoming even more critical in the design of electronic devices. Therefore, new applications for the Internet of Things pose a challenge for designers to find the optimal balance between performance and energy consumption. While Coarse Grained Reconfigurable Architectures (CGRA) have been an alternative to provide the required acceleration, they are far from being the most energy-efficient, since their functional units may be underutilized by some applications. In this work, we introduce a novel approach to handle these CGRA resources, which leverages the variable usage of functional units due to the application program phases. By using our new approach and performing power-gating of under-utilized parts of the CGRA, we demonstrate that it is possible to achieve reductions of up to 52.3% in power dissipation and 75% in energy consumption.
Decision-Tree Based Pixel Classification for Real-time Citrus Segmentation on FPGA

According to Food and Agriculture Organization, Mexico is one of the top five citrus producers in the world. In order to achieve the required quality control to export their products, citrus producers require sorting machines able to classify millions of fruits according to certain characteristics, as their size and color. Computer vision provides image processing tools, as image segmentation, that could be used as first stage in a classification process. Fruit classification must be fast in order to be able to process as much fruits per second as possible. In this paper, an FPGA architecture for image segmentation of orange images based on decision-tree models is proposed. A decision-tree model is proposed as an alternative to global thresholding and adaptive thresholding algorithms. It was observed that in this scenario, global thresholding fails due to the noise produced by the fast moving fruits in a classification line, and adaptive thresholding algorithms are not suitable for real-time applications, because of their high requirements in computing power and memory. A decision-tree model requires less hardware compared to both algorithms. The proposed model can achieve real-time segmentation because it is based on pixel serialization, and not on pixel neighborhood processing. The proposed architecture was implemented in a Spartan-6 FPGA. It runs at 60 fps and attains an accuracy of 97.1% of correct segmented pixels, compared to an offline manual segmentation of the frames.
Shape Recovery of Polyp Using Blood Vessel Detection and Matching Estimation by U-Net

In the medical diagnosis, endoscopic polyps are examined and discrimination between benign and malignant is judged via the visual observation by a medical doctor. Estimating size of the polyp is important because the larger polyp tends to become malignant if the size is more than 1 cm since it is not easy to estimate the size of the polyp except medical doctors who have several years of experience. This paper proposes reliable correspondence between two images with a slight movment of endoscope using ORB features estimating the absolute size of polyp using blood vessel information in the endoscopic images. The effectiveness of the proposed approach is confirmed via experiments.
Performance Analysis Of Decoupling DC-Link Capacitors For A SiC-MOSFET-Inverter Module

This paper investigates the voltage stabilization and thus the switching performance in relation to the placement of an auxiliary DC-Link “decoupling” capacitor. Additionally, the transient switching behavior of a SiC-MOSFET-module and its overall system loss are presented. This paper compares two approaches: a SiC-MOSFET-module with an embedded decoupling capacitor and the utilization of a decoupling capacitor placed directly at the input pins of the SiC-module. The decoupling capacitor divides the main current commutation loop into two new LC-circuits. The placement of the capacitor is strongly influenced by the inductance ratio between the new LC-circuits as well as the physical size limits of the capacitor and therefore the nominal capacitance itself. The stray inductance and the capacitance of each LC-circuit have a direct influence on the overshoot voltage and the system losses. Based on a 1200V and 600A SiC-module inverter setup and its SPICE model, the influence of both approaches will be evaluated in theory and validated with measurements.
Predict the Next Location From Trajectory Based on Spatiotemporal Sequence

The achievement of wireless communication technology of mobile devices has been witnessed, which produces a large number of trajectory data of mobile users. Processing and analyzing these trajectories could obtain users' movement patterns and behavior rules, leading to provide better location-based services such as point-of-interest recommendation and location prediction. However, enormous volumes of GPS trajectory with high frequency will pose challenges in storage, transmission and computation. Recently, with the rise of social networking sites, more and more users tend to share their geographic locations in real time, thus forming check-in sequences. Hence, this paper proposes a model called SSTLP for predicting next location from trajectory based on spatiotemporal sequence. Firstly, construct location transition probability model by capturing the change of locations in historical trajectory. Secondly, compute distance possibilities of locations by the combination of normal distribution and cosine similarity and then the next location could be figured out. Experiments on real-world data set demonstrate that the proposed model outperforms traditional prediction algorithms.
A Complete Solution to the Stability of Discrete-time Scalar Systems with A Single Delay

The stability of discrete-time scalar systems with a single delay is studied. A complete characterization of the stability set of delays is presented. Results show that discrete-time systems distinguish from continuous-time systems in the following aspect: the stability of the system at some value of delay d can not ensure the stability at delay of less than d. In addition, a necessary and sufficient condition for the systems admitting a delay margin is obtained and this delay margin is given analytically.
Air quality data analysis and forecasting platform based on big data

Nowadays, with the continuous development of big data technology, various industries use big data technology to process and mine massive data, and realize the value of data efficiently. In terms of air quality data processing, big data technology can also play a certain advantage. The platform is based on big data technology to design an air quality data analysis and prediction platform including data layer, business layer, interaction layer and visualization platform. Data is cleaned, calibrated, and stored in the data layer to ensure data consistency, integrity, and security. The air quality data is analyzed and predicted at the business layer. The interaction layer includes the functions of algorithm management, data query, and the data visualization platform provides intuitive information display. This design is a significant application for fully exploiting environmental data information. It has powerful data processing functions and scalability, which is a reliable data analysis and prediction platform.
A Survey On Location Recommendation Based On PSI Model &Trajectory Mining

The enormous growth in the web and modern mobile communication devices with integrated position sensors has increased the popularity of location-based services. Traffic planning and control, weather forecasting, homeland security, and travel proposal frameworks are the fields of utilization where precise location prediction is vital. Numerous investigates, and travel recommendation systems are the fields of applications where accurate location prediction is necessary. Many types of research have been conducted in the area of location prediction and various algorithms and models for predicting a user's next location. Most of the existing algorithms forecast a user's next location by learning that user's past was moving patterns. The primary challenge in the field of location prediction is the random behavior of individual walking patterns. Another dominant area forecast model is proposed which thinks about both personal inclinations and social cooperation's (PSI) at a gathering level to ease the impact of irregularity and it improves the area expectation execution. PSI demonstrate both association rule mining and pair-wise edge regression. The regular spots of interest and hot spots of interest are minded from the client directions. To describe exterior social communications, a related gathering is identified, and a diagram of the group was moving examples is then extracted dependent on association rule mining. At last, the following area is anticipated by learning the person's common interest and gathering moving patterns through a pair of wise ridge regression. As opposed to the conventional methodologies, the preferred fundamental standpoint of executed calculation is its effectiveness to display the human development by teaching people interior moving inclinations and gathering level outside social cooperation's. Expanding upon gathering level example mining, PSI(Preference and Social Interaction) gives an increasingly vigorous expectation demonstrate by learning both individual and gathering pattern data at the same time, mitigating the irregularity of area forecast from individual chronicled direction information as it were. The area-based administrations with private client positions may raise genuine security concerns if these positions are not protected adequately. Thus, it is obligatory to incorporate area protection ideas to guarantee the client's acknowledgment of area-based administrations.
Real-time 3D Grid Map Building for Autonomous Driving in Dynamic Environment

For autonomous driving, grid map is a popular tool to represent the environment, which can be used for obstacles crossing. To preserve more original information obtained from the LIDAR and represent the vertical structures in the three dimensional (3D) environment, this paper presents a novel method to generate 3D occupancy grid map by integrating multi-frame point cloud. Firstly, we use the information of the Inertial Measurement Unit (IMU) to correct the distortion of the point cloud after a delicate LIDAR/IMU hand-eye calibration procedure. Secondly, the method based on the Gaussian process regression is utilized to complete the ground segmentation and the occupancy grid sub-map could be obtained. Thirdly, we adopt scan registration method to calculate the accurate pose transformation between different frames of point cloud. Through the pose transformation, a series of sub-maps could be fused together. Finally, we avoid the influence of dynamic objects by adopting the 3D ray casting algorithm. Extensive experiments are performed, which proves our approach could consistently produce accurate and stable 3D grid maps in real-time.
Subway 3D Bound Limitation Visualization Method Based on Stereo Vision

During subway driving, foreign objetcs such as rolling stones, pedestrians may invade subway driving safety bound, which brings safety hazards to subway normal running. One potential method to solve this problem is foreign body intrusion detection technology with 3D bound limitation visualization module. In the paper, we utilize deep learning and stereo vision technology to extract subway track from binocular camera videos, calculate 3D coordinates of subway track and the vertex coordinates of subway safty boundary section at each horizontal position. By connecting these vertices along with subway track’s direction, the bounding section can be obtained. Finally, multiple sections are combined to form a complete safety bound limitation. The proposed method releases a more intuitive and accurate intrusion determination implementation and provides a potential safty solution for subway assisting driving.
Dual-Polarized Magneto-Electric Dipole Antenna and MIMO Array for 5G Millimeter-Wave applications

A dual polarized substrate-based magneto-electric dipole antenna is proposed. Two orthogonal and suspended L-shaped probes are employed to feed the antenna. The simulated impedance bandwidth of the antenna is 17% (25–29.8GHz) with an isolation of over 25dB and good radiation characteristics. A $3\times 6$ multi-input-multi-output (MIMO) array which can generate two-dimension (2D) multi-beams with dual polarization is designed by using the radiation element. The MIMO array is in triangular grid and can be divided into 3 groups of row subarrays or 3 groups of column subarrays. Beams at −30°, 0°, and 30°with −10dB side-lobe level are formed digitally at both azimuth and elevation planes with the 3 groups of subarrays. The proposed MIMO array with spatial diversity and polarization diversity is suitable for 28GHz millimeter-wave applications of the 5G mobile communications.
Dual-Beam Leaky-Wave Antenna Array With Capability of Fixed-Frequency Beam Switching

A novel fixed-frequency beam switchable dual-beam antenna array is presented, which consists five dual-beam leaky-wave antenna (LWA) elements designed on substrate integrated waveguide (SIW) and integrated together on a common substrate. The LWAs work on fundamental wave and −1th spatial harmonic wave simultaneously and excite two symmetrical radiation beams in forward and backward direction respectively. In order to realize beam switching, three voltage-driven radio frequency chip switches (CSW-RFs) are used and integrated behind the antenna aperture. Since the periodic slot in LWA causes open stopband at the operating frequency, so the staggered radius amplification of local vias in the via walls of SIW is performed to suppress the open stopband. In addition, electromagnetic band-gap (EBG) structures, capable of reducing coupling between neighboring antenna elements, are embedded in between LWAs. The proposed dual-beam antenna array with beam switching capability between ±35, ±45, ±55, ±65 and ±75 degrees at 28 GHz are simulated, fabricated and measured. Both analysis and measurement results show that the proposed antenna array exhibits an excellent performance, which can be used in fixed-frequency beam-switching millimeter wave communication.
Polynomial Augmented Extended Kalman Filter to Estimate the State of Charge of Lithium-Ion Batteries

In battery-electric vehicles, an accurate knowledge of the current state of charge (SOC) of the battery is crucial for safe and efficient operation. Offset-free SOC estimation for Lithium-ion batteries (LIB) during operation still is a challenging problem, due to the uncertain output nonlinearity present in battery models. In battery management systems employed in such vehicles, the age- and cycle-dependent relationship between the open circuit voltage (OCV) and the state of charge of each cell (SOC) can be kept track of using lookup tables. Between the scattered data points, linear interpolation is often used. Another common approach is to switch between models, each with a different piecewise polynomial fit of low order. In this contribution, a single polynomial fit of a higher order with state-dependent coefficients for the whole SOC-OCV range is proposed, so as to avoid switching and facilitate usage of Taylor-based linearization algorithms like in the extended Kalman filter. While the polynomial order is high, the number of parameters is only three; the parameters are estimated and updated by the Kalman filter itself. This augmentation of the observer's state vector with said polynomial parameters is inspired by the idea of an increased “stochastization,” introducing redundancy that aids to achieve a more accurate SOC estimation. In this sense, the algorithm can be described as a model-adaptive extended Kalman estimator. Two variants of a polynomial EKF are investigated: polynomial EKF with output and with state nonlinearity models. Their performances are compared in real experiments using a dedicated test bench based on a Samsung ICR18650-26F cell, demonstrating the effectiveness of the algorithms. The variant that includes the terminal voltage in the state vector, i.e. the state nonlinearity variant of the EKF, shows better result, also compared to the existing literature.