Miracle-dz/UniFAR-FTUs huggingface dataset:
Constructing Hierarchical Spatiotemporal Information for Action Recognition

Video action recognition is widely applied in video indexing, intelligent surveillance, multimedia understanding, and other fields. Recently, it was greatly improved by incorporating the convolutional neural network (ConvNet). The features of shadow layers in ConvNet tend to model the apparent and motion of actions, and the features of deep layers tend to represent actions. In this paper, we propose to construct hierarchical information by combining the spatiotemporal features of shadow and deep layers in 3D ConvNet for action recognition. Specifically, we use Res3D to extract spatiotemporal information from different types of layers, and transfer the knowledge learned from RGB to optical flow field. We also propose a Parallel Pair Discriminant Correlation Analysis (PPDCA) to fuse the multiple layers' spatiotemporal information into a compact hierarchal action representation. The experimental results show that there is a good balance between accuracy and dimension in our proposed hierarchical spatiotemporal information, and our method not only outperforms the single layer Res3D methods but also achieves recognition performance comparable to that of state-of-the-art methods.
Neuroadaptive Cooperative Control Without Velocity Measurement for Multiple Humanoid Robots Under Full-State Constraints

This paper studies the cooperative control problem of multiple humanoid robots handling a common payload in the presence of position and velocity constraints, unmeasurable velocity, as well as nonparametric uncertainties. By using a state observer to estimate the unmeasured velocity, a neuroadaptive output-feedback control scheme is developed, which by blending an error transformation with barrier Lyapunov function ensures that the full-state tracking error converges to a prescribed compact set around origin within a given finite time at a preassignable convergence rate. Furthermore, it is shown that all the signals in the closed-loop system are ultimately semiglobally uniformly bounded. Simulation results are verified to show the effectiveness and benefits of the proposed scheme.
Performance Evaluation on Polar Codes in High Mobility Scenarios

As a critical scenario in 5G wireless system, high speed railway (HSR) is deployed widely around the world. However, implementing 5G in HSR scenarios remains a challenge as high speed makes the channel doubly selective. Nevertheless, polar codes, as the first provably capacity-achieving codes with low encoding and decoding complexity appear to be a promising coding strategy to tackle this problem. To the best of our knowledge, although polar codes have been studied extensively in theoretical issues, the performance of polar codes in high mobility scenarios, i.e., HSR, has not been investigated before. To give guidance on practical application of polar codes in HSR scenario, we model the channel with doubly selective feature based on 3GPP standard, and implement polar codes in such channel to get the performance evaluation compared with polar codes in AWGN channel. Numerical results show that polar codes with short block-length and large list size are preferable in high speed mobility scenarios.
Extrinsic Calibration of 2D Laser Rangefinders Using Indoor Geometric Constraints

Laser rangefinders are widely used in navigation, mapping and SLAM systems, while the extrinsic calibration is the key factor of their performance. An extrinsic calibration method of multiple 2D laser rangefinders using indoor geometric constraints is proposed. Taking one common indoor corridor as the calibration environment, the co-planarity constraint and the orthogonality constraint are used to convert the calibration problem to a nonlinear optimization problem, which is solved by Levenberg-Marquardt. A co-planarity assessment method based on the volume of tetrahedron consisted by line pairs is proposed to improve the stability of finding correct corridor observations. Experimental results are presented to validate the method.
Genome Sequences of Three Isolates of Fusarium verticillioides

Fusarium verticillioides is an important pathogen of maize worldwide. Here, three Australian isolates of F. verticillioides, originally obtained from maize or sorghum, were sequenced using Illumina technology to expand the available genomic resources for this important pathogen. ABSTRACT Fusarium verticillioides is an important pathogen of maize worldwide. Here, three Australian isolates of F. verticillioides, originally obtained from maize or sorghum, were sequenced using Illumina technology to expand the available genomic resources for this important pathogen.
Analysis and Design of a Quasi-Proportional-Resonant Based Voltage Balancing Control for Grid-Connected Nested Neutral Point Clamped Converter

This paper presents the analysis and design for a quasi-proportional-resonant (quasi-PR) controller used in a voltage balancing control technique for the flying capacitors of a grid-connected five-level nested neutral point clamped (NNPC) converter. This voltage balancing control technique entails using the quasi-PR controller to obtain the deviation of the flying capacitor voltage from its rated value and using the output signal from the controller to adjust the duty cycle of the switching power semiconductor devices of the converter. The parameters of the quasi-proportional-resonant controller are obtained by the derived mathematical formulae presented in this paper. The design of the quasi-PR controller is analysed using a bode-plot with the aid of MATLAB/Simulink. In addition, a three phase prototype of the grid-connected five-level NNPC has been developed to verify the theoretical and simulation analysis.
A Survey on Audio Content-Based Classification

With the increased usage of mobile electronic devices, social media platforms, and electronic based applications in everyday life, the upload and usage of multimedia clips is exponentially increasing every year. There are many research initiatives tackling the challenge of multimodal video classification and it has been found that audio-based classification is less computationally expensive and just as effective, in many cases. However, research targeted towards acoustic-based detection is still in its initial stages. Audio content-based classification pertains to several domains: music and speech signal processing, which are relatively popular research interests, and event, genre and scene-based classification which are still areas that need a lot of development. There is also a problem with the difficulty of assessing performances of different systems with a unified audio dataset, due to the lack of development in this field. The objective of this study is to present information about audio processing techniques and studies conducted under several classification tracks; namely acoustic-based event, genre, and scene detection, as well as combination-based classification works.
Design of Compact Dual-Mode Dual-Band Microstrip Bandpass Filter Using Stepped-Impedance Resonator for Wireless Communication Applications

A compact dual-mode dual-band microstrip bandpass filter (BPF) based on stepped impedance resonator (SIR) for wireless communication applications is introduced in this paper. By choosing a proper impedance ratio, a BPF operating at 2.4 and 5.2 GHz is designed and analyzed. By adjusting the perturbation size, two degenerate modes are excited and coupling within the first passband. Two transmission zeros which further improve the performance of the BPF locate on both sides of the first passband. To verify the proposed concept, a dual-band BPF example is designed, fabricated and measured. The center frequencies (CFs) of the two passbands are 2.4 GHz and 5.2 GHz, respectively. The measured insertion losses (ILs) in the two passbands are less than 1.3 dB and 1.72 dB, and their associated return losses (RLs) are greater than -27.8 dB and 20.3 dB, respectively. The area of fabricated filter is about $16.3 \times 16.3$ mm2.
ZetaHunter, a Reproducible Taxonomic Classification Tool for Tracking the Ecology of the Zetaproteobacteria and Other Poorly Resolved Taxa

Like many taxa, the Zetaproteobacteria lack well-defined taxonomic divisions, making it difficult to compare them between studies. We designed ZetaHunter to reproducibly assign 16S rRNA gene sequences to previously described operational taxonomic units (OTUs) based on a curated database. ABSTRACT Like many taxa, the Zetaproteobacteria lack well-defined taxonomic divisions, making it difficult to compare them between studies. We designed ZetaHunter to reproducibly assign 16S rRNA gene sequences to previously described operational taxonomic units (OTUs) based on a curated database. While ZetaHunter can use any given database, we included a curated classification of publicly available Zetaproteobacteria.
Redox-responsive hyaluronic acid-functionalized graphene oxide nanosheets for targeted delivery of water-insoluble cancer drugs

Background Gefitinib (Gef), an important epidermal growth factor receptor (EGFR), is used to treat lung cancer, but low water solubility and poor bioavailability severely limit its application in cancer therapy. Methods In this study, nanographene oxide (NGO) was decorated with hyaluronic acid (HA) by a linker cystamine dihydrochloride containing disulfide bonds (-SS-), followed by the incorporation of gefitinib, thus, constructing a HA-functionalized GO-based gefitinib delivery system (NGO-SS-HA-Gef). Subsequently, studies of biological experiments in vitro and in vivo were performed to investigate the therapeutic effect of the system in lung cancer. Results The HA-grafted GO nanosheets possessed enhanced physiological stability, admirable biocompatibility, and no obvious side effects in mice and could act as a nanocarrier for the delivery of gefitinib to tumor. Cellular uptake and intracellular cargo release assays showed that the uptake of NGO-SS-HA by A549 cells was facilitated via CD44 receptor-mediated endocytosis, and that more drug was released from NGO-SS-HA in the presence of GSH than in the absence of GSH. The target-specific binding of NGO-SS-HA to cancer cells with redox-responsive cargo release significantly enhanced the abilities of gefitinib-loaded GO nanosheets to induce cell apoptosis, suppress cell proliferation, and inhibit tumor growth in lung cancer cell-bearing mice. Conclusion The results demonstrated the potential utility of NGO-SS-HA-Gef for therapeutic applications in the treatment of lung cancer.
Design of a Novel Multiband Miniaturized Cylindrical Conformal Microstrip Antenna

Conformal microstrip antenna is an important study field in microwave and communications. In this paper, a novel design of miniaturized cylindrical conformal microstrip antenna (MCCMA) for multi-band DCS/Bluetooth/WLAN applications is proposed. It consists of a radiating element with a hexagon slot and several rectangular slots, a FR4 substrate, a coax feeding and GND. The miniaturization characteristic is achieved by using the rectangular slot to bend the path of the current. Simulation and analysis show that the antenna has a multi-band characteristic and can achieve a reduction of the size. A radius value of the antenna is $0.13\lambda_{0}$ with respect to the lowest operating frequency centered at 1.8 GHz. It can cover the 1.8 GHz DCS, 2.45GHz/5.8GHz WLAN and 2.45GHz Bluetooth bands on HFSS Software. The simulated results show that the antenna has good radiation patterns across the operations which can be applied in present wireless communication system.
Draft Genome Sequences of Two Multidrug-Resistant Salmonella enterica Serovar Typhimurium Clinical Isolates from Uruguay

Multidrug-resistant Salmonella enterica isolates are an increasing problem worldwide; nevertheless, the mechanisms responsible for such resistance are rarely well defined. Multidrug-resistant S. enterica serovar Typhimurium isolates ST3224 and ST827 were collected from two patients. ABSTRACT Multidrug-resistant Salmonella enterica isolates are an increasing problem worldwide; nevertheless, the mechanisms responsible for such resistance are rarely well defined. Multidrug-resistant S. enterica serovar Typhimurium isolates ST3224 and ST827 were collected from two patients. The characteristics of both genomes and antimicrobial resistance genes were determined using next-generation sequencing.
A Highly Reproducible Fabrication Process for Large-Area Plasmonic Filters for Optical Applications

Plasmonic filters are advantageous for fabricating the state-of-the-art color filters without any coloring resist materials. In this paper, a filter design is introduced for the optical structure of plasmonic filters and their fabrication, which allows highly reproducible nanopatterning on large areas. The optical structure of these plasmonic filters consists of 2-D hole arrays in a thin metal film. Although laser interference lithography technology allows a high accuracy of regularity across the whole fabrication region, the exposure light is easily disturbed by vibrations or dust in the air. The suggested process enhances the accuracy and reproducibility of nanopatterns, which also exhibit a high performance. This paper will help realize plasmonic filters in conjunction with industry.
A Study of Spectrum Sensing Using an Experimental Platform for Cognitive Radios

Cognitive radio is a technology in which intelligent radios can change their parameters through software. The technology makes it possible for opportunistic users to occupy frequency bands allocated to licensed users that are not being used by these users for a certain period of time. A technique known as spectrum sensing can be employed in the search for these “free” frequency bands in a network. Therefore, spectrum sensing is one of the most essential characteristics of a cognitive radio to ensure that opportunist users do not interfere with licensed users. A number of theoretical studies have addressed spectrum sensing. In this work, a platform known as Universal Software Radio Peripheral (USRP) is used to experimentally analyze the process of sensing the spectrum in actual scenarios, exploring its advantages and limitations.
A Multi-Chain Model for CBDC

Recently digital currency has received significant attention, and among many topics within digital currency, CBDC (Central Bank issued Digital Currency) has been a hot topic as it will affect national currency systems. RSCoin is the first CBDC model sponsored by Bank of England, and it uses the UTXO model from Bitcoin. This paper proposes a new CBDC model Panda model that can store account balance like current banking systems, but use efficient consensus protocols to ensure that relevant parties have consistent views of transactions and accounts. The Panda model is scalable as it can include as many financial institutions or individuals as possible in the system with increasing or decreasing workloads. The model has been simulated in the TaiShan Sandbox in Qingdao.
Spectral Sidelobe Suppression of Nonuniform Pulse Repetition Frequency Waveforms in Strong Clutter

Standard spectral analysis of nonuniform sampled signals generates undesirable sidelobe levels and cannot be controlled via a window function as in the case of uniform sampled signals. We analyse two periodogram based techniques and four interpolation based techniques, and find that the iterative adaptive periodogram and the weighted bandlimited interpolation techniques are capable of achieving desired sidelobe levels. The weighted bandlimited interpolation is computationally less demanding and generates a spectrum similar to the uniform sampled signals, hence has advantages over the other techniques considered.
Systematic Mapping Study of Literature on Educational Data Mining to Determine Factors That Affect School Performance

This document presents the results of conducting a Systematic Mapping Study (SMS) on the mining of educational data to determine factors that affect school performance in the higher education system. As a result of this analysis, 20 primary studies were obtained, where it is observed how data mining is applied to predict school performance and thus decrease the dropout rates of students. Through this work a more integral perspective is offered from the process of information capture and automatic consolidation until proposing a prediction model based on the analysis and use of data mining techniques, contributing with new analytical approaches to the current studies, taking consider the characteristics of educational management. In conclusion, the studies consider that, by using educational data mining applied to large volumes of data existing in higher education institutions, it would improve decision making to guarantee a high-quality education, highlighting the performance problems from the beginning and proposing corrective actions. In addition, these studies show data mining techniques used, such as logistic regression, decision trees, random forests, Naive Bayes to explore data from educational environments.
Interactive Bulk Synchronous Parallel Functional Programming in a Browser

This paper presents the design and implementation of a sequential simulator for the parallel functional language BSML based on the parallel computation model Bulk Synchronous Parallelism (BSP). This simulator is implemented in sequential, runs in any browser, and provides a graphical representation of the parallel executions. Due to the pure functional nature of BSML, the results obtained by this simulator are the same than the results that could be obtained by executing the program in parallel.
A Graphene-Based Tunable THz Metamaterial Absorber

A graphene-based tunable THz metamaterial absorber is presented in this paper. The proposed absorber is a periodical structure with the unit cell composed of four square gold patches on single-layered graphene, which are placed on a dielectric spacer supported by a gold bottom layer. Simulation results indicate that the absorption frequency can be tuned by varying the chemical potential of graphene through controlling of the electrostatic gating or the geometry size of the gold patch. By introducing more gold patches with different size into the unit cell, we can obviously extend the absorption bandwidth to achieve a broadband absorber.
Modulation Methods for 3L-NPC Converter Power Loss Management in STATCOM Application

The uneven power loss among the semiconductor devices is one critical issue of the three-level neutral-point clamped (3L-NPC) converter, which may limit the system power capacity and degrade the reliability. Besides, different operating conditions will result in different loss distribution profiles. Among the different operating conditions, the case with low power factor and high modulation usage, which is common in the static synchronous compensator (STATCOM) application, has not been paid much attention in the literature. In this paper, the power loss distribution under this condition is theoretically analysed. And a novel carrier-based modulation technique is proposed to achieve power loss redistribution. The loss distribution under the proposed modulation technique is more even, and the loss of the most stressed device can be reduced. Besides, an active neutral-point (NP) voltage balancing control strategy is also proposed based on the proposed modulation scheme. The power loss redistribution and NP voltage control performance are both verified through simulation.
Accelerating a Secure Programmable Edge Network System for Smart Classroom

Internet-of-Things (IoT) applications have been challenged by vast emerging security threats. The increasing number of connected devices on an IoT network may encounter potential attacks while relaying messages over the public Internet. Security methods in traditional wireless networks are often specific to single architectures and are therefore ineffective in solving security issues that may surface on hybrid IoT networks. In this paper, we propose a software defined end-edge-cloud network architecture for smart IoT applications. We apply a deep-learning (DL) based intrusion detection system (IDS) to secure this architecture. We implement this system based on the Caffe framework and train several popular convolutional neural networks (CNNs) including LeNet, AlexNet, and ResNet-50. We evaluate the performance of this system in terms of accuracy, precision, recall, and F_1 score. We then investigate the FPGA-based acceleration technique to reduce the training and runtime of CNNs. We implement the FPGA acceleration based on a Xilinx KU115 board which achieves efficient performance per watt while training and deploying the IDS. For accelerating CNNs on the FPGA, we investigate a Winograd convolution engine in the Xilinx SDAccel development environment which offers automation schemes for accelerating computations. Our preliminary study may provide some insights into the experimental support for advancing IoT research and development for securing various smart applications.
Design and Performance Evaluation of the Modular Multilevel Converter (MMC)-based Grid-tied PV-Battery Conversion System

During recent decades, grid-connected photovoltaic (PV) system has been widely investigated. Due to intermittent PV generation, battery energy storage system (BESS) is commonly coordinated with grid-tied PV plant to improve grid-integration performance. Traditionally, the grid-tied PV system integrated with BESS is based on two-level inverters and low interface voltage, which requires bulky line-frequency transformers and passive filters for medium-voltage grid integration. To reduce volume and weight, in this paper, a modular transformerless hybrid PV-BESS conversion system based on modular multilevel converter (MMC) is proposed for medium-voltage grid integration, which integrates distributed PV and battery modules into submodules (SMs). The proposed MMC-based PV-BESS system is designed for a 4.16 kV/1 MVA medium-voltage grid based on various semiconductor devices and passive components. Its functionality, efficiency, and volume of the proposed PV-BESS system are evaluated and compared with the traditional two-level PV-BESS system. The study results show that the proposed solution can significantly reduce overall system volume while keeping high efficiency and providing ac and dc grid interconnection.
Planar Nonlinear Coupled Inductors for Improving Light Load Efficiency of DC/DC Converters

This paper presents a planar nonlinear coupled inductor for improving light load efficiency of DC/DC converters. The nonlinear coupled inductor features higher self-inductance value and lower coupling coefficient at light load. Three layers, formed by magnetic materials with two kinds of permeability, are used to make the nonlinear coupled inductor. The nonlinearity is controlled by the permeability and thickness of the high-permeability center plate. Finite element analysis (FEA) is employed to design the nonlinear coupled inductor by sweeping the conductor width and middle layer thickness. The coupled inductor structure as well as its influence on the inductance value is also analyzed in this paper. Two prototypes with two permeability distribution are made to test the performances and they achieve high light load efficiency. Compared with the conventional constant coupling coefficient inductor, the nonlinear coupled inductor has higher efficiency at light load.