Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloud-Based FPGA Custom Computing Machines for Streaming Applications

A novel platform for launching and using field-programmable gate arrays (FPFA) custom computing machines (CCMs) in clouds and data centers is proposed. Based on a developed FPGA virtualization scheme, it allows users to create independent computing services on network-attached standalone FPGAs. The interface of the virtual FPGA (vFPGA)-based CCM is automatically generated by a virtualization layer and based on the user’s specifications. An FPGA hypervisor has been developed that can be easily integrated with any cloud management tool. It allows the users to launch/use/tear down vFPGA-based CCMs in a similar manner to conventional virtual machines (VMs). A complete prototype of the proposed platform has been realized and tested with a streamed image processing application. Its performance was 3–4<inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> and ~1.4–2.4<inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> times better than an SW implementation on a VM and a powerful server, respectively. Compared with other platforms for FPGA attachment to a cloud or datacenter, the proposed platform has relatively low overhead in terms of FPGA resources while providing the highest level of abstraction and virtualization.
Metal Gate Work Function Engineering: Sub-Nano Regime Double Gate MOSFETs

The effect of metal gate (MG) work function (WF) on MOSFETs having double gate has been researched for the next generation logic applications. GaN-based Double-Gate MOSFETs, by tuning the value of work functions (WF) are investigated by simulation to establish the transport characteristics to mitigate SCEs (short channel effects). The metal gate (MG) work functions (WF) is varied between 3.5 eV and 4.8 eV and the device performance is evaluated. The reduction of SCEs is observed for multiple metal gate (MG) work functions (WF). The simulation results apprise that by tuning the work function of GaN-based double-gate MOSFETs, the threshold voltage tuning can be possible. The reduction of short channel effects (SCEs) can be possible by fixing the congenial value of the threshold voltage (VT). The simulations have been performed for GaN-based DG-MOSFETs considering 9.7 nm (ITRS- Year: 2021) length of the gate (LG). The device turn on and turn off voltage (Gate-Source) is respectively, VGS =1 V and V GS= 0 V. The effective oxide thickness (EOT) is considered as 0.56 nm for a gate length of 9.7 nm. In the device simulation focus has been set on VTH, ION, IOFF, DIBL and SS, this metrics has been done for multiple metal gate work functions. The value of drain induced barrier lowering (DIBL) and subthreshold leakage current is reduced and threshold voltage (VT) is increased with tuning the metal gate work functions. The proposed GaN-based DG-MOSFETs suggests that the reduction of short channel effects (SCEs) can be possible by tuning the MG-WF (metal gate-work function).
High-Voltage p-GaN HEMTs With OFF-State Blocking Capability After Gate Breakdown

In this letter, we report high-performance p-GaN HEMTs on Si with robust gate operation. For the first time, the preserved OFF-state drain blocking capability has been demonstrated in p-GaN HEMTs after forward gate breakdown. Benefitting from the reduced metal/p-GaN contact region, the gate breakdown was limited to take place only at the metal/p-GaN junction, with the p-GaN/AlGaN/GaN junction intact. The device shows state-of-the-art characteristics with a large threshold voltage of 1.75 V at <inline-formula> <tex-math notation="LaTeX">${I}_{\text {D}}$ </tex-math></inline-formula> of <inline-formula> <tex-math notation="LaTeX">$100~\mu \text{A}$ </tex-math></inline-formula>/mm (2.3 V by linear extrapolation), a high maximum drain current of 610 mA/mm at <inline-formula> <tex-math notation="LaTeX">${V}_{\text {GS}}$ </tex-math></inline-formula> of 8 V, a low specific ON-resistance of 1.8 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega \cdot \text {cm}^{{2}}$ </tex-math></inline-formula>, and a high breakdown voltage of 1100 V defined at <inline-formula> <tex-math notation="LaTeX">${I} _{\text {D}}$ </tex-math></inline-formula> of <inline-formula> <tex-math notation="LaTeX">$1~\mu \text{A}$ </tex-math></inline-formula>/mm with grounded substrate.
Expeditious Estimation of Angle-of-Arrival for Hybrid Butler Matrix Arrays

Arrays of Butler matrices provide a promising front-end design for massive MIMO transceivers with low cost and low complexity. However, this advanced design does not necessarily translate to effective applications, unless the angle-of-arrival (AoA) of signals avails to the Butler matrices. This paper presents an efficient approach to the unprecedented AoA estimation for the arrays of Butler matrices. Specifically, we design a new beam synthesis method to recursively narrow down and increasingly focus on the angular region of interest, and hence achieving robust estimation of the phase offset between Butler matrices. With the phase offset canceled in the received signals, we are able to identify the set of critical Butler beams with the dominating effect on the AoA estimation, and estimate the AoA accordingly with minimum signaling. The mean squared error of the proposed estimation is analyzed in the presence of non-negligible noises, with closed-form lower bounds derived. Validated by simulations, the proposed algorithm is able to indistinguishably approach the lower bounds, and significantly outperforms the state-of-the-art developed for discrete antenna arrays by orders of magnitude in terms of accuracy, especially in low signal-to-noise regimes.
1.2 Pb/s Throughput Transmission Using a 160 $\mu$m Cladding, 4-Core, 3-Mode Fiber

This paper extends a recent demonstration of 1.2 Pb/s transmission using a few-mode multi-core fiber with a cladding diameter of 160 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>m. The fiber was a 3.37 km, 4-core, 3-mode few-mode multi-core fiber with spatial multiplexing and demultiplexing performed with especially designed mode-selective couplers. We transmitted 368 wavelength division multiplexed spatial super channels across the <italic>C</italic> and <italic>L</italic> bands. We selected polarization-division-multiplexed-256-quadrature amplitude modulation format to maximize the generalized mutual information of the transmitted signals, which were decoded using punctured soft-decision forward error correction with a code rate granularity of 0.01. The maximum crosstalk of the system was <inline-formula><tex-math notation="LaTeX">$-$</tex-math></inline-formula>31 dB between LP<inline-formula><tex-math notation="LaTeX">$_{11}$</tex-math></inline-formula> modes of adjacent cores, leading to a Q-factor penalty of approximately 0.5 dB. We estimated the system mode-dependent loss values ranging from 3 dB to 5 dB across <italic>C</italic> and <italic>L</italic> bands with negligible wavelength dependence.
Visualization of Location-Referenced Web Textual Information Based on Map Mashups

Space is the most fundamental organizing dimension for information that forms the basic spatial understanding around which all other temporal and semantic details are situated. Various types of web information are present in our daily lives, and spatial content can facilitate the understanding of that information. Hence, many studies have been conducted to extract the implicit spatial location from different web resources, and they have mainly investigated web textual information. However, the existing studies mainly focus on the extraction or simple presentation of the location of the information, while further exploration and visualization of the information through the comprehensive consideration of its spatial, temporal, and semantic dimensions have rarely been performed. Thus, this paper proposes a novel modeling and visualization framework for location-referenced web textual information. The framework applies an information model to structurally extract and resolve spatial content along with the temporal and semantic elements from the location-referenced information items. Based on the constructed information model, the information items are hierarchically clustered and visualized by combining map mashups with cartographic processes and methods. This framework enables users to interactively index and browses individual information items. Furthermore, the association relationships involved in the information set are explored in our work. With knowledge graphs that are automatically generated based on the information model, a high-level understanding of the information set can be obtained by users.
Quench Safety Simulation and Verification of Superconducting Coils for the SC200 Superconducting Cyclotron

The SC200 superconducting cyclotron for hadron therapy is under construction based on an agreement of the Institute of Plasma Physics Chinese Academy of Sciences and the Joint Institute for Nuclear Research in Russia. The cyclotron will provide a 200-MeV proton beam with maximum current of 400 nA. Thanks to the superconducting magnet, the cyclotron is very compact and light. The yoke weight is about 35 tons. The Hefei team took the responsibility to develop this laser-triggered switching magnet in the past two years. Early this year, the magnet was tested with the iron system. The operating current is 140 A. The magnetic energy is about 4 MJ. In order to decrease the quench risk of the switched capacitor coil, an FEM quench model that can perform quench dynamics simulations of the main magnet system is developed. A quench test is performed to verify the safety of the coil. The hotspot temperature is about 60 K, the max voltage is less than 800 V, and the energy extraction time is about 15 s. The magnet system is, therefore, safe during a quench.
Polarized Topic Modeling for User Characteristics in Online Discussion Community

Topic modeling methods, such as latent Dirichlet allocation (LDA), are successfully applied in a number of computational linguistics applications. This paper presents a new approach to topic modeling within a new domain other than linguistic analysis. We present a pilot study where an LDA model is applied to an online community rather than the textual contents they produced using the idea that a user in an article is analogous to a word in a document within the context of the LDA model. We also propose a method for determining polarity using positive (+) and negative (−) signs regarding topics. As a result, each user has a topic score whose absolute value is equal to the topic distribution learned from topic modeling, and its sign indicates the polarity on that specific subject. We demonstrate the effectiveness of our proposed approach with experimental results, which provide opportunities to apply the LDA model to targets other than lexical elements.
Interference Management for Cellular-Connected UAVs: A Deep Reinforcement Learning Approach

In this paper, an interference-aware path planning scheme for a network of cellular-connected unmanned aerial vehicles (UAVs) is proposed. In particular, each UAV aims at achieving a tradeoff between maximizing energy efficiency and minimizing both wireless latency and the interference caused on the ground network along its path. The problem is cast as a dynamic game among UAVs. To solve this game, a deep reinforcement learning algorithm, based on echo state network (ESN) cells, is proposed. The introduced deep ESN architecture is trained to allow each UAV to map each observation of the network state to an action, with the goal of minimizing a sequence of time-dependent utility functions. Each UAV uses the ESN to learn its optimal path, transmission power, and cell association vector at different locations along its path. The proposed algorithm is shown to reach a subgame perfect Nash equilibrium upon convergence. Moreover, an upper bound and a lower bound for the altitude of the UAVs are derived thus reducing the computational complexity of the proposed algorithm. The simulation results show that the proposed scheme achieves better wireless latency per UAV and rate per ground user (UE) while requiring a number of steps that are comparable to a heuristic baseline that considers moving via the shortest distance toward the corresponding destinations. The results also show that the optimal altitude of the UAVs varies based on the ground network density and the UE data rate requirements and plays a vital role in minimizing the interference level on the ground UEs as well as the wireless transmission delay of the UAV.
Wideband Filtering Phase Shifter Using Transversal Signal-Interference Techniques

A wideband filtering phase shifter is presented in this letter using transversal signal-interference techniques. The proposed structure creates two signal-propagation paths by introducing extra coupling between two short-ended stubs. Cascaded coupled-line sections and parallel short-ended stubs can generate a constant passband and multiple transmission zeros (TZs) in the stopband and meanwhile provide the arbitrary value of phase shift within the filtering band range. The positions of TZs and phase shift range can be easily controlled. Explicit relations between the objective filtering band and the related parameters are given, and cases with different in-band phase shift values are studied. To validate the design, a prototype is built, simulated, and tested using microstrip lines. The experimental results agree with the predicted ones, demonstrating that the device can realize any given in-band phase shift with a wideband filtering response.
Automated Prediction of Heart Disease Patients using Sparse Discriminant Analysis

Linear Discriminant Analysis (LDA) is an easy and efficient method for pattern classification, while it is also broadly utilized for the initial discovery of diseases using Electronic Health Records (EHR) data. Nonetheless, the performance of LDA for EHR data classification is recurrently influenced by two major factors: poor evaluation of LDA parameters (e.g., covariance matrix), and “linear inseparability” of the EHR data for classification. In this paper, we propose a novel classifier SDA -Sparse Discriminant Analysis method for heart disease detection. The time complexity will be reduced in this algorithm by optimal scoring analysis of LDA and will be comprehensive to execute sparse discrimination through the combination of Gaussians if limits between classes are nonlinear or if subgroups are available inside every class. On the whole, compared to previous techniques, our proposed technique is more appropriate for the diagnosis of heart disease patients with higher accuracy.
An Algorithm for l-diversity Clustering of a Point-Set

Clustering a set of data means to divide the data into some disjoint groups on the basis of some properties of the entries. Given a set of n points plotted in a metric space where each of them have a color, an l-diversity clustering algorithm clusters the points into a set of disjoint clusters such that each cluster has $l$ points and all points in a cluster have distinct colors. To produce clusters with better qualities, we have proposed an l-diversity clustering algorithm which minimizes the length of the maximum radius of all the clusters, the sum of radii of all clusters and the amount of data loss. We have compared the outputs of our algorithm with that of the previously best known l-diversity clustering algorithm for the same input data sets. For each input data set, our algorithm produced better clustering by minimizing the maximum radius, the sum of radii and the data loss.
ONGOING POLICY REFORM IN THAILAND’S INITIAL TEACHER EDUCATION CURRICULUM: INCOMPLETE POLICY BORROW

This article reports on a review of Initial Teacher Education in Thailand’s Rajabhat universities conducted in 2016/17 and the subsequent national initial teacher education curriculum reform drawing on the review’s findings and recommendations. The research was conducted in three interconnected phases. The first included a review of secondary data made available by the sample Rajabhat universities (n=5) and the Thai Ministry of Education. Phase two included a period of fieldwork in Thailand during which the research team collected data from officers of the Ministry of Education (n=6), university senior managers (n=38), initial teacher education course leaders and academic staff (n=54) and student teachers’ (n~125). During the final phase of the research the research team liaised with a series of Thai stakeholders (e.g. the Teachers’ Council of Thailand) to confirm maters of accuracy and disentangle local custom and practice from national policy. A key recommendation of the research was to consider reducing the length of the undergraduate route into teaching and ensure trainee teachers spent time in school in each on the four years of their course. Since the report policy changes have been implemented across Thailand’s initial teacher education landscape including the recommended reduction in initial teacher education course length from five to four years in March 2019.
Functionally Graded Virtual Robogami: A Geometry Approach

Functional gradient takes a specific position in various fields of research. Functionally graded materials (FGM) are highly respected regarding their nonconstant, FG certain property. A virtual geometrical model of a heaven-and-hell-origami-like object is known from previous research. Its parts exhibit a FG shape, and, originally, they are not planar elements. An objective of this paper is to present a given origami-like object from two aspects: a) reconfigurability of the object and b) modular origami-like structure, mori, created by composing several mentioned origami-like objects. All activities are done by using parametric equations of the relevant components as well as via those linear transformations, which conserve shape and size of elements and in such a way they correspond to robotic motions, known at the current level of knowledge. The findings based on geometrical “wings” and “shackles” - possibilities and limitations - are stated. Potential applications, educational use as well as Easter egg (“secret” feature) are sketched.
Video Shot Boundary Detection for Low Complexity HEVC Encoders

This paper proposes a new preprocessing phase in High Efficiency Video Coding of high resolution video: a module for shot boundary detection and a complementary decision of ad-hoc intra frame encoding. The data processing involves only the chroma subsampled components, thus delivering faster results with less computations. Experiments using the HM (HEVC test Model) reference encoder from ITV show the method's efficiency and increasing detection accuracy, while conveniently introducing infrequently intra coded frames that improve the SNR (Signal to Noise Ratio) quality measure for the following inter-predicted frames.
User Selection and Power Minimization in Full-Duplex Cloud Radio Access Networks

In this paper, we investigate the network power consumption (NPC) minimization in full-duplex cloud radio access networks (C-RANs) with quality-of-service requirements. We jointly optimize the beamforming vectors, the users’ transmit power, the fronthaul compression ratio, and the set of transmitting remote radio heads (RRHs). Users’ requirements of signal-to-interference-and-noise ratio, per-RRH and user power constraints, and fronthaul capacity constraints are considered. Due to these conflicting constraints, the optimization problem may be infeasible. Thus, we solve this problem in two steps. In Step I, a minimum-mean-square-error-based user selection algorithm is proposed to find the largest subset of feasible users. In Step II, an algorithm based on the reweighted $l_1$-norm minimization method is proposed to solve the NPC problem with the users selected in Step I. The solutions obtained by the proposed algorithms are proved to monotonically converge and satisfy the Karush–Kuhn–Tucker conditions. Moreover, our proposed algorithms are applicable to both full-duplex and half-duplex C-RANs. We comprehensively compare these two types of C-RANs and propose some valuable insights for system design.
Modeling and Control of Underwater Mine Explorer Robot UX-1

This paper presents the design and experimental assessment of the control system for the UX-1 robot, a novel spherical underwater vehicle for flooded mine tunnel exploration. Propulsion and maneuvering are based on an innovative manifold system. First, the overall design concepts of the robot are presented. Then, a theoretical six degree-of-freedom (DOF) dynamic model of the system is derived. Based on the dynamic model, two control systems have been developed and tested, one based on the principle of nonlinear state feedback linearization and another based on a finite horizon linear quadratic regulator (LQR). A series of experimental tests have been carried out in a controlled environment to experimentally identify the complex parameters of the dynamic model. Furthermore, the two proposed controllers have been tested in underwater path tracking experiments designed to simulate navigation in mine tunnel environments. The experimental results demonstrated the effectiveness of both the proposed controllers and showed that the state feedback linearization controller outperforms the finite horizon LQR controller in terms of robustness and response time, while the LQR appears to be superior in terms of fall time.
Joint Optimization of Dimension Assignment and Compression in Distributed Estimation Fusion

This paper studies linear distributed estimation of an unknown random parameter vector in a bandwidth-constrained multisensor network. To meet the bandwidth limitations, each sensor converts its observation into a low-dimensional datum via a suitable linear transformation. Then, the fusion center estimates the parameter vector by linearly combining all the received low-dimensional data, aiming at minimizing the estimation mean square error. The main purpose of this paper is to jointly determine the compression dimension of each sensor (referred to as dimension assignment) and design the corresponding compression matrix when the total compression dimensions is limited. Such a joint design problem can be formulated as a rank-constrained optimization problem and it is shown to be NP-hard for the first time. In addition, successive quadratic upper-bound minimization (SQUM), SQUM-block coordinate descent (SQUM-BCD) and nuclear norm regularization (NNR) methods are developed to solve it approximately. Furthermore, we show that any accumulation point of the sequence generated by the SQUM method satisfies the Karush-Kuhn-Tucker conditions of the rank-constrained optimization problem, and the Phase II algorithm of the SQUM-BCD and NNR methods (both are two-phase algorithms and have the same Phase II algorithm) guarantees convergence at least to a stationary point. Numerical experiments illustrate the advantages of the proposed methods compared with the existing method.
A Text Classifier Using Weighted Average Word Embedding

In this paper, we propose a new technique for text representation by generating a sentence vector using a weighted average of words representation where Naive Bayes log count ratio is used as the weight of each word. The quality of this representation is measured in a text classification task using FastText and Word2Vec models. Results show accuracy improvement over unweighted average techniques using the same models. Also, we compare our results to other traditional text representation and classification techniques such as Term Frequency-Inverse Document Frequency (TF-IDF) and Naive Bayes Support Vector Machine (NBSVM).