Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Pre-charge Circuit Using Virtual Miller Capacitor in a Single Phase Rectifier

In all the grid connected converters, irrespective of the front end topology (whether it is a diode rectifier, a bridgeless active power factor correction rectifier, or an active front end rectifier (AFE)), there is always a DC bus electrolytic capacitor with high value after the front end, for energy storage and line frequency ripple mitigation requirements. During start-up, there is a pre-charge circuit associated with this capacitor to limit inrush current. The existing pre-charge circuits usually include charge resistor and mechanical contactor, are often related to a high component volume and also high surge current at beginning. To address that, this paper proposes a novel precharge circuit including only solid state device. This circuit utilize a “virtual” miller capacitor along with a MOSFET to control the inrush/charge current flowing through the bus capacitor, although the input is AC, the discontinuous charge current is controlled within a constant value profile. The major benefits of proposed method include much reduced volume, lowest peak current stress, and extended life time/reliability.
Sequencing and Analysis of the Genome of the Filamentous Fungus Penicillium verrucosum BFE808, Which Produces the Mycotoxins Citrinin and Ochratoxin

Penicillium verrucosum is a filamentous ascomycete that occurs worldwide. Various cereals and the products thereof are the main habitats of this fungal species, where it produces the mycotoxins ochratoxin and citrinin. ABSTRACT Penicillium verrucosum is a filamentous ascomycete that occurs worldwide. Various cereals and the products thereof are the main habitats of this fungal species, where it produces the mycotoxins ochratoxin and citrinin. Here, we report the first draft genome sequence of P. verrucosum strain BFE808, isolated from wheat kernels.
Coupled Online Robust Learning of Observation and Dictionary for Adaptive Analog-to-Information Conversion

This letter proposes a coupled compressed samples online robust learning (<inline-formula><tex-math notation="LaTeX">$\text{C}^2\text{ORL}$</tex-math></inline-formula>) algorithm for constructing an adaptive analog-to-information conversion (AIC) structure. The <inline-formula><tex-math notation="LaTeX">$\text{C}^2\text{ORL}$</tex-math></inline-formula> optimizes both the dictionary and the observation matrix in each mini-batch learning process. The compressed samples are used for learning to resolve the issue that both the analog received signals and the unstable reconstructed signals affected by reconstruction error are not suitable for direct training. Furthermore, the online mechanism and the robust objective functions are adopted in order to make the learning algorithm better matched for the requirements of the analog signal compressed sensing. Based on the <inline-formula><tex-math notation="LaTeX">$\text{C}^2\text{ORL}$</tex-math></inline-formula>, we construct an adaptive AIC structure, which can considerably improve the performance in the sense of minimizing the reconstruction error. Also, the structure is more tolerant to the outliers and the noise in the receive process and has denoising ability to improve the signal-to-noise ratio under the noise background. The mathematical expression shows that the proposed structure clearly reflects the optimization brought by the coupled learning algorithm. Finally, the experiments demonstrate that the proposed scheme considerably outperforms any existing approaches.
High speed, Low Resource and Non-volatile MTJ-CMOS Hybrid Logical Circuits

Magnetic tunnel junction (MTJ) device, a nonvolatile spintronics device, draws wide attention for its low current drive, non-volatility, small size, infinite erasure, and low standby power. It is widely used in magnetic random-access memory (MRAM) or embedded in CMOS logical circuits. In this paper, a novel high speed, low resource and non-volatile MTJ-CMOS hybrid logical circuit structure is proposed, which consists of Multi-Voltage controlled Multi-Write (MVMW) write circuit and High-Speed Sense Amplifier (HSSA) circuit. The MVMW write circuit enhances the write speed by shortening the critical path while achieving two independent write controls of the MTJs. Also, a voltage comparator sense amplifier is employed to increase the read speed (about 110ps). By using a dynamic compact MTJ model and 55 nm CMOS design kit, we designed a circuit architecture which can be used to form all kinds of logical computations. An AND/NAND logical is used to demonstrate functionality of the architecture and compared it with a traditional MTJ-CMOS hybrid circuit. The proposed non-volatile MTJ-CMOS hybrid circuit uses only 62.5% of resources, achieving a 4.37X write speed and a 2.64X read speed. The overall average speed increases 3.12 times with similar power consumption. Moreover, it is compatible for asynchronous circuits design.
Posture Recognition and Adaptive Step Detection Based on Hand-held Terminal

A postures recognition and adaptive step detection algorithm based on low-cost microelectromechanical system (MEMS) sensors are proposed in this paper. The traditional Pedestrian Dead Reckoning (PDR) algorithm relies on the fixed sensor orientation, is unable to adapt to the high degree of freedom of hand-held terminal in navigation. The recognition method can recognize the postures of smartphone, such as texting, phoning, swinging and placed in the pocket, the algorithm consists of posture definition, feature extraction and class determination. An adaptive step detection algorithm is also proposed according to the detected device posture, the algorithm includes median threshold crossing detection, effective peak detection and adaptive step interval detection. The experiment results show that the proposed algorithm is able to identify the device postures effectively, and detect the pedestrian steps accurately. The accuracy of recognition can be above 95%, the accuracy of step detection can be above 97%. The research can provide important foundation for pedestrian dead reckoning based on hand-held terminals, and also be used in health management, behavior surveillance and other fields.
MSAT: A Novel Multipactor Simulation and Analysis Tool

Mulitipactor, caused by the interactions during electrons, electromagnetic fields and device materials in microwave components under vacuum, leads to the un-repairable damage to the on-orbit satellites and is still a bottle-neck problem for the high-reliability performance requirement. A novel multipactor simulation and analysis tool, which combines the full wave electromagnetic algorithm with the particle-in-cell method, is proposed for the visualization of the sophisticated physical evolution process and the prediction of the breakdown threshold of multipactor in practical EM systems. Based on the accurate description of Secondary Electron Emission(SEE) phenomenon through experimental measurements and numerical fitting, the electron multipaction principles and basic theories have been deeply studied. The numerical calculation and accurate threshold analysis of multipactor in practical components were realized. Experimental results match well with simulation results and the simulation error was less than 1.5dB, which lays the technology foundation for the study on the dielectric multipaction principle and numerical simulation.
Automatic recognition of image details using stereovision and 2D algorithms

This paper presents a utilization of the stereovision in the automotive applications and video monitoring. Two video channels of stereovision system offer a new quality of recognition of image details. Even with popular object recognition algorithms the stereovision significantly improves the analysis plausibility. Preliminary experiments with the specially prepared 3D video test database show improvement of the recognition effectiveness by 10-30% in comparison to the single video channel.
Hemoglobin targets for the anemia in patients with dialysis-dependent chronic kidney disease: a meta-analysis of randomized, controlled trials

Abstract Background: Anemia is extremely common among dialysis patients and underlies some of the symptoms associated with reduced kidney function, including fatigue, depression, reduced exercise tolerance, and dyspnea. Objectives: A clearer cognition of the prognosistic impact of hemoglobin (Hb) or hematocrit (Hct) target for the outcomes of dialysis patients is urgent. This article aims to establish the suitable hemoglobin in order to provide clinical guidance. Methods: MEDLINE, EmBase, the Cochrane Library and other databases were searched with both MeSH terms and keywords to gather randomized controlled trials that assessed all-cause mortality, cardiovascular events, fistula thrombosis, infectious diseases and transfusion among dialysis-dependent patients using erythropoiesis-stimulating agents. The meta-analysis was accomplished via Revman 5.3 version. Findings: Totally, nine eligible studies were included, with study subjects involving 3228 patients. There was a significantly higher risk of fistula thrombosis without heterogeneity (RR 1.34, 95% CI 1.15–1.55; p < 0.05) in the higher Hb target group than in the lower Hb target group in the fixed effects model. However, no significant difference was found in all-cause mortality in the fixed effects model (RR 1.09, 95% CI 0.93–1.27; p = 0.30), cardiovascular events (RR 0.77, 95% CI 0.31–1.92; p = 0.58), infectious diseases (RR 0.69, 95% CI 0.24–1.96; p = 0.49) and transfusion (RR 0.92, 95% CI 0.42–1.99; p = 0.82) in the random effects model between the higher Hb target group and the lower Hb target group. Discussion: The results favor lower Hb target. To target lower Hb target when treating dialysis patients with anemia may decrease the risk of fistula thrombosis without increasing the risk of death, cardiovascular events, infectious diseases and transfusion.
Real-time Implementation of Nonlinear Model Predictive Control for Mechatronic Systems Using a Hybrid Model

Nonlinear Model Predictive Control (NMPC) is an aspiring control method for the implementation of advanced controller behavior. The present work shows the symbolic math implementation of a mechatronic system model containing aerodynamic nonlinearities modeled by Feedforward Neural Networks. Gradients for the optimization are obtained efficiently by exploiting the feedforward property of the Neural Networks and symbolic computation. Current research on the implementation of damage metrics into the cost function is stated briefly. In order to achieve real-time capability, the method Real-time Iteration is used.
Visual Indoor Positioning Method Using Image Database

With the increasing significance and usage of ordinary location-based services, a number of technologies have been invented for indoor positioning systems over the years. Some of them, however, suffer from limited precision and high expenses. In this paper, we propose a simple visual indoor positioning method using images captured by handheld cameras, in which case, there is no need for infrastructure components. Taking advantage of Bag-of-Visual-Words model, our proposed method consists of two stages. By using image processing algorithms i.e. feature extraction, description and clustering, an image database and its visual vocabulary are prepared in the first stage, along with the corresponding indoor position information. Then, a query image is matched with the image database from stage one to get the most several similar ones, whose position will be taken to get the location estimation by the method of voting and re-classification. We conducted some experiments to demonstrate the feasibility of the visual indoor positioning method. According to the simulation results, 90\% of the positioning errors are within 1.2 meters and the average positioning time is no more than 2 seconds, which turns out to achieve a balance between speed and accuracy.
Application of recurrent U-net architecture to speech enhancement

In this paper a recurrent U-net neural architecture is proposed to speech enhancement. The mentioned neural network architecture is trained to provide a mapping between a spectrogram of a noisy speech and both spectrograms of isolated speech and noise. Some key design choices are being evaluated in experiments and discussed, including: number of levels of the U-net, presence/absence of recurrent layers, presence/absence of max pooling layers as well and upsampling algorithm used in decoder part of the network.
Characteristic Investigation of Ionospheric Clutter of a Coastal High-Frequency Radar

High frequency (HF) radar can overcome the limits caused by the curvature of the earth when its electromagnetic wave operated over high conductivity sea water. The HF radar spectrum is critical for large-area current mapping because of mechanism of Bragg resonance. Taiwan is located in an equatorial ionization anomaly area, in which the ionosphere causes strong interference on HF radar bands that may limit system performance. The unwanted radar echoes, called ionospheric clutter, are of various types with time-variable and range-specific properties, which require different suppression techniques. Additionally, these radar echoes could yield ionospheric information as a by-product of an HF radar system function. It is therefore critical to understand the regional characteristics of these ionospheric clutter phenomena. In this study, we first identify the ionospheric clutter boundaries for the Doppler spectrum range. Then, the distribution pattern of the spectra for identified areas was analyzed to determine the characteristics of ionospheric clutter and the extent to which the ionospheric clutter affects the system. The identified echoes at these ranges are presumed mainly to come from the E-layer, which may influence the system performance in those areas. The results show there exist a similarity of distribution in spectral density and DOAs which ionospheric clutter occurs. Hence, through aforementioned analysis can help distinguish the regions in which ionospheric clutter occurs and provide an opportunity for noise suppression strategies.
Voltage Balancing Control for Series Connected MOSFETs Based on Time Delay Adjustment Under Start-Up and Steady-State Operations

Recently, with research and development of SiC power devices, 1.2-V SiC MOSFETs have become commercially available. Over 3.3-kV SiC-MOSFETs have also been developed; however, it is difficult to use those devices without any special applications. When the medium-voltage converter circuit is designed using commercial SiC-MOSFETs, the 1.2-kV SiC-MOSFETs have to be connected in series. However, the voltage sharing under turn-off operations may not be balanced because each characteristic of the MOSFET is different, and the propagation time delay of the gate drive circuit is also different. This paper proposes a voltage balancing control strategy for series-connected SiC MOSFETs under steady state and start-up conditions using digital control circuits. To compensate for voltage unbalance conditions, feedback control of each drain-source voltage of the MOSFET under the OFF-state is proposed. The control circuit is based on a digital circuit, where controlling the voltage balancing under any conditions can be achieved. Experimental results using a buck chopper circuit utilizing SiC MOSFETs rated at 1.2 kV are also presented.
A fast initialization method of Visual-Inertial Odometry based on monocular camera

In recent years, due to the low cost and high precision characteristic of Visual-Inertial Odometry (VIO), VIO has become a hot research issue. However, the common visual and inertial joint positioning takes a lot of time for joint initialization, so improving the speed of the joint initialization is of great significance in real-time operate. This paper will propose a rapid method of joint initialization parameters. Compared with the traditional initialization method, the initialization method mentioned in this paper has a faster initialization speed with little effect on positioning accuracy. The scheme in the paper uses EuRoc dataset under monocular visual and IMU tight coupling system. The experimental results show that the initialization time can be controlled within 10 seconds using the method proposed in this paper.
Optical Coupling of 3D Silicon Micromirrors

Free space optical coupling is considered the important feature which affects on the performance of the optical systems. Where coupling elements such as lenses and mirrors are aided in overcome the divergence of the Gaussian beams and to achieve phase matching. Here we concentrate on the studying of the free space coupling of 3D (spherical) mirror based on the propagation of a Gaussian beam. The design and fabrication of 3D mirror in the Optical microelectromechanical systems (MEMS) are studied. The coupling efficiency is a very important factor in the design of any MEMS circuit. High coupling efficiency is obtained by assembling optical parts which increase the integration effort. In this work, we report a high coupling efficiency, monolithically integrated 3D silicon micromirror that is capable of influencing on the Gaussian beams which propagate in the plane of the silicon substrate. A micro machining method is presented for fabricating the 3D micromirror.
Geosynchronous Sar Moving Target Detection and Motion Parameters Estimation Using the Non Uniform Cubic Phase Function Algorithm

This paper addresses the issue of moving target detection and velocity estimation in the Geosynchronous Synthetic Aperture Radar (GEO SAR) scenario, this problem has not been handled yet despite the promising potential of GEO SAR systems in the global monitoring domain. New expressions for the Doppler centroid and the Doppler rate that take into account the specific features of a geosynchronous orbit are developed. Moreover, a new Ground Moving Target Indication (GMTI) technique was proposed, it makes use of the aforementioned expressions combined with the use of the Non Uniform Cubic Phase Function (NUCPF) algorithm to estimate the moving target motion parameters. Simulation results are carried out to demonstrate the accuracy of the developed expressions and the effectiveness of the proposed GEO SAR GMTI technique.
Energy-Aware Distribution of Data Fragments in Unattended Wireless Sensor Networks

In this paper, we study and analyze data fragment placement techniques to exploit their problems and design an energy-aware fragment placement method in Unattended Wireless Sensor Networks (UWSN). In UWSN, the survivability of data is of utmost importance because sensed data cannot be immediately transmitted and must reside in the storage of sensor nodes for extended periods of time. The data, which have delay-tolerant characteristics, can be exploited by attackers that access the storage to erase or steal the data. Even though data fragmentation methods have been designed to solve this problem, these works do not consider a critical issue of where to place data fragments in the sensor network. In this work, we show that basic existing methods of data fragment placement have some problems, especially in terms of energy efficiency and data survival probability. Based on our analysis, we propose a lightweight method that allows us to efficiently select sensor nodes that are used to safely distribute and place data fragments. We show through simulation and preliminary testbed analysis that our proposed method can improve the overall performance of data fragmentation.
A Novel Partitioned-primary Hybrid-excited Flux-switching Linear Machine with Dual-PM

A novel partitioned-primary hybrid-excited flux-switching linear machine with dual-PM (DP-PPHEFSLM) is proposed and investigated in this paper, which possesses many merits, i.e., high thrust density, improved space utilization, flexible flux control capability, robust secondary, etc. Unlike conventional single-primary hybrid-excited topologies, this proposed topology separates armature windings and field windings into two different parts of the partitioned-primary, with each part placed with sandwiched or buried PMs. It solves the problem of crowded primary in conventional structures due to greatly increased armature and field slot areas. Besides, the thrust density is further increased and the secondary normal force can be reduced by skillfully employing dual-PM structure. Firstly, the topology, operation principle and viable slot/pole combinations of the proposed machines are elaborately addressed. Based on 2-D finite-element method (FEM), the proposed machines are globally optimized using genetic algorithm. Then, the electromagnetic performances are further investigated. By these 2-D FEM predicted results, the proposed DP-PPHEFSLMs are manifested having not only superior thrust density, but also satisfactory flux-adjusting capability.
A Representation Method and Optimization of Network Flow Based on Coalgebra

An algebraic system of the flow is established based on the non-deterministic transition system with output. In the system, status transition is regarded as transition of network flow, while the output is regarded as observations of flow. By using status equivalence, bisimulation between two flow systems is proposed to optimize network flows. Finally, final systems are constructed to study the properties of network flow.
Human Detection in Intelligent Video Surveillance: A Review

With the rapid development of networked video surveillance systems, human detection is essential. These tasks are not only inherently challenging due to changing human appearance, but also have enormous potentials for a wide range of practical applications, such as security and surveillance. This review paper extensively surveys the current progress made toward human detection in intelligent video surveillance. The algorithms presented in this paper are classified as either human detection without classifier training or human detection with classifier training. In the core techniques of human detection without classifier training, three critical processing stages are discussed including background subtraction, Gaussian mixture model (GMM) and skin color model. In the core techniques of human detection with classifier training, two main types are mentioned including holistic human detector, and part-based human detector. Our survey aims to address existing problems, challenges and future research directions based on the analyses of the current progress made toward human detection techniques in computer vision.
Estimation of VO2 max based on 20 m shuttle run test using statistical learning methods: An example of male physical education students

This study shows selected statistical learning models for prediction VO2max based on 20 m shuttle run and selected somatic parameters. The research were conducted on 80 male physical education students (age: 21.4±1.6). Predictive models were determined from 65 patterns and tested using 15 patterns. The 9 variables were used to build the models, including one dependent variable and 8 independent variables. The prediction methods used were as follows: ordinary least square regression, Ridge regression, Lasso regression, multilayer perceptron and network with radial basis functions. The research show that the model with the least error of prediction is the network with radial basis functions generating the RMSECV = 4.04 error. The best method for new data generate an error on the level of 3.04 compared to the real values.