Miracle-dz/UniFAR-FTUs huggingface dataset:
A Switched-Capacitor Low-Pass Filter with Multiple-Channel Double-DSB OP Amplifiers

Wideband filters employing OP Amplifiers (OP Amps) are required in sensing devices. A fourth-order Switched-Capacitor Low-Pass Filter (SC LPF) with multiple-channel Double Dynamic Switching Bias Folded-Cascode OP Amps (DDSB OP Amps) consisting of $1-2 \mu \mathrm{m}$ multiple channel-length MOSFETs and short-channel sampling/CMOS switches capable of processing video signals, which enables a low gain in a passband and a high attenuation in a wide stop-band, was proposed. The DDSB OP Amp enables a high speed filtering operation while keeping a low power dissipation and low distortion. The switches with a $1.5\ \mu \mathrm{m}$ channel length were optimized to greatly reduce their feed-through. Through SPICE simulations, the DDSB SC LPF showed the practically usable attenuation performance of below −41 dB in the stop-band, nearly 9 dB lower than that in the conventional $2.5\ \mu \mathrm{m}$-channel SC LPF, without causing a marked passband gain leap. The simulation results confirmed that the DDSB SC LPF is useful for achieving a wide stop-band with a practically usable attenuation maintaining low distortion and low power consumption due to a high speed settling operation of the DDSB OP Amp and the great decrease of feed-through in the switches.
Robust Transceiver Design for Multi-Hop AF MIMO Relay Multicasting From Multiple Sources

In this article, the transceiver design optimization problem is investigated for multi-hop multicasting amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay systems, where multiple source nodes broadcast their message to multiple destination nodes via multiple serial relay nodes. Multiple antennas are installed at the sources, relays, and the destination nodes. In the transceiver design, we consider the mismatch between the true and the estimated channel state information (CSI), where the CSI mismatch follows the Gaussian-Kronecker model. A robust transceiver design algorithm is developed to jointly optimize the source, relay, and destination matrices to minimize the maximal weighted mean-squared error (WMSE) of the received message at all destination nodes. In particular, the WMSE is made statistically robust against the CSI mismatch by averaging through the distributions of the true CSI. Moreover, the WMSE decomposition is exploited to reduce the computational complexity of the transceiver optimization. Numerical simulations show a better performance of the proposed robust transceiver design against the channel mismatch.
Bot Detection Using Machine Learning Algorithms on Social Media Platforms

Using bots in social media is a significant concern for information validity and authenticity. Currently, there are several solutions for bots detection. However, the accuracy of the detection still needs improvement. The main aim of this paper is to introduce an automatic mechanism for the detection and removal of bots that exist on social media platforms. The research has the purpose of removing the non-genuine accounts, their related information, and the data which are posted by them and to make these platforms free of misleading information. Bots detection and removal will increase the authenticity of the contents presented on different social media platforms. Also, It will improve the level of privacy and authenticity of these platforms and related users. The research uses the bot detection technique based on machine learning algorithms. The components of the study are data, feature selection, and bot detection. The research performs web development and hosting on the collected data with a machine-learning algorithm to perform bot detection in social media networks. The proposed system provides a more accurate and effective system for bot detection using machine learning. The research utilizes various approaches and mechanisms that lead to the enhanced efficiency of bot detection and removals.
Reduced Current Spin-Orbit Torque Switching of a Perpendicularly Magnetized Free Layer

We demonstrate the switching of a perpendicularly magnetized free layer by spin-orbit torques. The switching is accomplished by a purely electrical, two-current pulse scheme. The first pulse realizes the selection of the cell, while the second pulse, with a lower current, completes the switching deterministically. The second current is reduced by 50% without affecting the switching performance. This current reduction results in a reduction of about 80% of the power consumption by the second pulse. Even with such a large decrease of current and power, the switching time remains robust and fast, resulting in a very efficient switching scheme.
Compressed Sensing-Based Simultaneous Recovery of Magnitude and Phase MR Images via Dual Trigonometric Sparsity

Complex-valued Magnetic Resonance Imaging (MRI) is widely used in clinical diagnosis. The magnitude images are mainly used for structure visualization, and the phase images reveal tissue properties, such as magnetic susceptibility and fluid flow information. While MRI is slow, compressed sensing (CS) can be used to reconstruct the accelerated acquisitions. Current CS-based MRI reconstruction algorithms mostly focus on magnitude image recovery, with less attention to the recovery of phase images. In this paper, we propose a novel CS algorithm to simultaneously recover the magnitude and phase MR images based on the sparsity of the trigonometric function. The CS method requires a sparse representation of the original images, and it is observed the trigonometric functions of phase images in the Wavelet domain promote sparsity. Therefore, rather than transforming the phase images directly into the Wavelet domain, we calculate the sine and cosine of the phase images whose Wavelet transforms are set as the L1-norm regularization term. The combination of the dual trigonometric functions captures a unique, faithful four-quadrant phase information, which also improves the reconstructed magnitude images through an alternating optimization procedure. Reconstructions of simulated images and in vivo images are studied, and both show the superiority of the proposed method over compared phase recovery algorithms.
Assessing Elementary School Students’ Programming Thinking Skills using Rubrics

In this study, we evaluated the abilities elementary school students can cultivate through education using programming. We focused on using programming in subject education, e.g., arithmetic and science. The effect of programming education in computer science has been reported extensively; however, it is unclear what abilities utilization of programming in may cultivate in other subjects. Therefore, in this study, we created a rubric to evaluate programming thinking skills. The rubric was applied to actual elementary school math and science classes for evaluation. As a result, in mathematics, the ability to analyze events improved, and, in science, the ability to branch improved. In this paper, we present the rubric and corresponding evaluation results.
Mechanical Fault Detection of Industrial Electrical Process by Self Organizing Map

In this paper a diagnosis approach based on Self Organizing Map is proposed for gear fault diagnosis. For this purpose, to investigate technique requires the vibration measurement which through their analysis the gear fault is detected and diagnosed. The details of the analytical method adopted and its principle are clearly presented and its application is experimentally validated using real vibration measurements. The results have an agreement with the real results.
Virtual Edge: Exploring Computation Offloading in Collaborative Vehicular Edge Computing

Vehicular edge computing (VEC) has been a new paradigm to support computation-intensive and latency-sensitive services. However, the scarcity of computational resources is still a challenge. Making efficient use of sporadic idle computational resources on smart vehicles in the vicinity to extend the resource capability of each vehicle is an important research issue. In this paper, we propose Virtual Edge, which is an efficient scheme to utilize free computational resources of multiple vehicles as a virtual server to facilitate collaborative vehicular edge computing. We design a virtual edge formation algorithm that considers both the stability of virtual edge and the computational resources available at the vehicles constituting the virtual edge. The prediction of the link duration between vehicles reduces the number of computation offloading failures caused by unexpected link disconnections. Extensive simulations with realistic vehicle movements are conducted to show the advantage of the proposed scheme over existing baselines in terms of the completion ratio of computation offloading tasks and average task execution time.
Trace-element analysis of individual synthetic and natural fluid inclusions with synchrotron radiation XRF using Monte Carlo simulations for quantification

The trace element composition of individual fluid inclusions was investigated by using synchrotron radiation X-ray fluorescence for analysis and Monte Carlo simulations for quantification. To validate the input parameters of the Monte Carlo simulation code for the applied spectrometer, area scan measurements on synthetic standard reference glasses (NIST 612 and NIST 610) were performed. Results yielded an accuracy of generally better than 20 % for all elements with Z between 37 and 92 at a standard deviation of less than 7 %. The analytical procedure was then applied to synthetic inclusions in quartz: single point analysis yielded an accuracy between 10 and 30 % with a reproducibility of ± 20 % and a repeatability of ± 7 % for the elements Cu, Rb, Sn and Cs. Ten trace elements (Mn, Fe, Cu, Zn, As, Rb, Nb, Sn, Sb, Cs) with concentrations between 2 and 10500 ppm were determined in fluid inclusions of hydrothermal quartz hosts from the Ehrenfriedersdorf Complex, Germany. Inclusions of three evolutionary stages of the Ehrenfriedersdorf Complex were studied. Vapour-rich inclusions of the pegmatite stage have low concentrations of all elements with Z > 20. Inclusions of the hydrothermal stage record progressive enrichment and differentia- tion in trace elements. Fluids of the early hydrothermal stage are enriched in Sn, Cu and As. Subsequently, they evolve to Fe-, Mn-, Zn- and Cs-rich fluids.
Research of a Novel Hybrid Shielding Structure for Inductive Power Transfer System

The performance of inductive power transfer system can be improved and the magnetic flux leakage can be reduced by adding shielding layer. But it also increases the size, weight and cost of the system. Therefore, it is necessary to optimize the structure of shielding layer to minimize it. Based on the concept of hybrid shielding layer, a new shielding layer composed of fan-shaped ferrite, nanocrystalline strip and aluminum foil is proposed in this paper. With the aim of reducing the magnetic leakage of the system and improving the utilization rate of shielding materials, the proposed shielding layer was optimized by finite element simulation. Simulation and experimental results show that the new hybrid shield proposed in this paper can not only improve the coupling coefficient of the system but also reduce the magnetic leakage of the system.
10.76 TOPS/W CNN Algorithm Circuit using Processor-In-Memory with 8T-SRAM

Implementing the CNN algorithm on-chip requires a lot of power consumption. To overcome this, research is underway to reduce power consumption by implementing certain operations of digital logic as analog circuits. The purpose of this paper is to propose a structure of new SRAM memory, and to propose CNN algorithm at the circuit level. In this study, 10.76 TOPS/W was achieved.
On the Parallel Processing for Wireless Sensors Converged Network

This paper studies the capability of multi-parallel processing to be accommodated in machine-to-machine communications. A testbed convergence between the wireless sensor network and the wireless mobile network is performed. It deals with enhancing wireless mobile network performance. This testbed is divided into three stages; combating the peak-to-average power ratio problem, observing a real data making use of an actual wireless sensors, and proposing a multi-parallel processing communications. The mentioned testbed above is done in a realistic academic environment, where selected signals from those sensors are evaluated. Thus, a design improvement is detailed in terms of the peak-to-average power ratio and the efficient use of the limited resources. In particular, the scope focuses on the mitigation of the nonlinear distortion and its effect on bit error rate. The validation process is attained based on performing an extensive simulation. It is observing some certain performance parameters such as the sensor life time, and the consumed energy. From the testbed simulation results, the proposed work shows promising results comparing to the conventional wireless sensor network. The delay reduction reaches around 85%, while the peak to average power ratio values has improved by 42.3%.
Air pollution by resuspended particulate matter: conditioning physical factors in urban areas

Objective: Identify and describe physical factors (FF) conditioning the resuspension of particulate matter (RPM) in road environments and their possible influence on urban air quality. Methodology: the literature review was conducted in ScienceDirect, Taylor & Francis, Springer and Scopus databases between the years 1990-2017. The order of importance for the FFs was established by a citation frequency index (Q). The conditioning FFs identified were as follows: dust road particle size (Q1 =3D 0.842), vehicle traffic (Q2 =3D 0.742) and traffic composition (Q2 =3D 0.630). Results: the results showed that on urban roads there were potential resuspension particles between <0.25-10 μm and its main sources of emission in order of importance were as follows: deposited road sediment (18.8%), tyre wear (2.5%), brake wear (2.5%) and pavement wear (1.30%). A linear relationship (R2 = 3D 0.531) was identified between vehicle traffic density (veh./h) and PM10 concentration (μg/m3). Regarding the traffic composition, on average, the highest emission factors for RPM were associated with heavy-duty vehicles (1016 mg/VKT) in relation to light-duty vehicles (53.4 mg/VKT) and motorcycles (3.95 mg/VKT). Finally, this review showed that Europe, Asia and North America registered the highest amount of studies on PMR (92.2%) while in Latin America these were scarce (7.80%).
A High Throughput and Flexible Rate 5G NR LDPC Encoder on a Single GPU

In order to build a high performance low-density parity-check (LDPC) communication link simulation platform, high speed LDPC encoding for information sequence is required. In this paper, a high and flexible throughput LDPC encoding implementation based on a single GPU is proposed. We discuss the parallelism of the LDPC encoding algorithm employs the core parity check bits and single diagonal parity check bits for the fifth generation new ratio. We implement the parallel LDPC encoder on CUDA platform. The experimental results show that our LDPC encoding module achieves a 38-62Gbps throughput for the rate from 1/2 to 8/9 on a single GPU. The results also demonstrate that parallel simulation tasks based on GPUs can achieve a good trade-off between performance and cost.
Comparative Study of Model Predictive Control for Modular Multilevel Converters with Separate and Decoupled Circulating Current

The model predictive control (MPC) for the modular multilevel converter (MMC) has drawn attention among researchers in recent years due to its straightforward implementation, ability to control multiple objectives, and excellent dynamics response. The output current, circulating current and arm voltage are the three key control objectives of MPC for MMC. The performance of MPC highly depends on the prediction model of these control objectives. In this paper, the separate and decoupled prediction models of circulating current are proposed for MPC. Moreover, the influence of the common-mode voltage is also analyzed. Finally, the two proposed models are validated and compared by simulation and experimental results.
Application of MA-ATI SAR for Estimating the Direction of Moving Water Surface Currents in Pi-SAR2 Images

A multiaperture along-track interferometric synthetic aperture radar (MA-ATI SAR) theory was proposed to obtain velocity vectors of moving objects. This theory utilizes two antennas to estimate two velocity components (aft- and fore-looking directions) by multi/sublook processing. The moving direction can then be calculated by the two velocity components. Combining it with the conventional range velocity estimation, the velocity vector can be estimated. In order to validate its methodology, the present study applies MA-ATI SAR to airborne polarimetric interfereometric SAR-2 (Pi-SAR2) for estimating the directions of moving water surface currents of rivers. The results were in good agreement with the true moving directions. The estimated accuracy showed the direction error of approximately 20° for the three cases out of four. One case showed the higher error, since there may be complicated flow patterns. The findings of this study have implications for the applicability of the MA-ATI method to an airborne SAR system.
Locating High-loss Links for OpenFlow Networks by Multiple Hosts to Probe Packets

We previously proposed a measurement framework for OpenFlow-based networks to promptly locate high-loss links with a small load incurred by the measurement on both the data-plane (e.g., the number of transmissions of probe packets on each link) and the control-plane (e.g., the number of accesses to switches) until locating all high-loss links. One of key components is the multicast measurement route of probe packets traversing all links in both directions. However, the previously proposed Eulerian cycle-based measurement route scheme called the backbone-and-branch tree (BBT) that uses only a single measurement host (MH) may build a too long measurement path in a large network, resulting in a low measurement accuracy and an intolerance to very high-loss, e.g., failure, links located in upstream of a measurement path. Therefore, in this paper, we newly propose an enhancement of the BBT with multiple MHs, called BBT-mMH, which can control the measurement path lengths to maintain an acceptable measurement accuracy with a small overhead on both the control-plane and data-plane. The numerical simulation demonstrates potential benefits of our proposal.
HD Map Update for Autonomous Driving With Crowdsourced Data

Current self-driving cars can perform precise localization and generate collision-free trajectories using high definition (HD) maps which provide accurate road information. Therefore, keeping HD maps up to date is important for safe autonomous driving. In general, automotive HD maps are built by the use of expensive mapping systems. In addition, a lot of manual modifications are required in many cases. The conventional HD mapping cannot be frequently carried out due to the high cost. In this letter, we used a large amount of road data collected by crowdsourcing devices. Crowdsourcing devices consist of low-cost sensors. The devices are mounted on repeatedly traveling vehicles such as buses. Although collected data shows high uncertainty and low accuracy, a large amount of data can be obtained in a short time with low expense. We present a solution that keeps HD maps up to date by using crowdsourced data. The developed solution concentrates on landmark information among crowdsourced data and HD maps. By using uncertainty information, we chose reliable observations for map updating. Observation learner algorithms were carefully designed under the consideration of differences between discrete and continuous landmarks. The triggering condition for the map update can be adjusted by the proposed update mode selection strategy. The proposed map updating scheme has been experimentally verified by the use of crowdsourced data collected from real road environments.
Evaluation of A Mesh Network based on LoRa Technology

In the last decade, the Internet of Things (IoT) technologies have brought many applications in different fields. Many communication technologies have been developed to communicate everything to each other and the Internet. Besides, the long-range power-efficient wireless technologies have emerged, such as LoRa (Long Range), which enables transmission over long distances with low energy consumption and can work within p2p, star, or mesh topologies. In this paper, we analyze a model of a mesh network based on the LoRa technology. The model considers various parameters of nodes and communication channels in the network. Based on a simulation model on OMNET++, a series of computer experiments are performed to analyze the delivery latency and packet delivery ratio (PDR) in different cases. According to the analytical results, conclusions are drawn about the possibility of using a LoRa mesh network to expand the network coverage and integrate with other short-range networks.
Real-Time Learning Analytics Dashboard for Students in Online Classes

In recent years, online classes have been increasingly conducted in various situations. However, in these classes, especially non-face-to-face and large-scale ones, it is more difficult for teachers and students to understand the status of the class during a lecture. To address this issue, we propose a real-time learning analytics dashboard that provides summarized information on teachers’ instruction and students’ learning activities during lectures. In this article, we introduce the real-time learning analytics dashboard and report its effectiveness through experiments in an online class at our university.
Living Object Grasping Using Two-Stage Graph Reinforcement Learning

Living objects are hard to grasp because they can actively dodge and struggle by writhing or deforming while or even prior to being contacted and modeling or predicting their responses to grasping is extremely difficult. This letter presents an algorithm based on reinforcement learning (RL) to attack this challenging problem. Considering the complexity of living object grasping, we divide the whole task into pre-grasp and in-hand stages and let the algorithm switch between the stages automatically. The pre-grasp stage is aimed at finding a good pose of a robot hand approaching a living object for performing a grasp. Dense reward functions are proposed for facilitating the learning of right hand actions based on the poses of both hand and object. Since an object held in hand may struggle to escape, the robot hand needs to adjust its configuration and respond correctly to the object's movement. Hence, the goal of the in-hand stage is to determine an appropriate adjustment of finger configuration in order for the robot hand to keep holding the object. At this stage, we treat the robot hand as a graph and use the graph convolutional network (GCN) to determine the hand action. We test our algorithm with both simulation and real experiments, which show its good performance in living object grasping. More results are available on our website: https://sites.google. com/view/graph-rl.