Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Low-Ranks plus Sparsity based Tensor Reconstruction for Dynamic MRI

Dynamic magnetic resonance imaging (DMRI) sequence can be represented as the sum of a low-rank component and a sparse tensor component. To exploit the low rank structure in multi-way data, the current works use either the Tucker rank or the CANDECOMP/PARAFAC (CP) rank for the low rank tensor component. In fact, these two kinds of tensor ranks represent different structures in high-dimensional data. In this paper, We propose a multiple low ranks plus sparsity based tensor reconstruction method for DMRI. The simultaneous minimization of both CP and Tucker ranks can better exploit multi-dimensional coherence in the low rank component of DMRI data, and the sparse component is regularized by the tensor total variation minimization. The reconstruction optimization model can be divided into two sub-problems to iteratively calculate the low rank and sparse components. For the sub-problem about low rank tensor component, the rank-one tensor updating and sum of nuclear norm minimization methods are used to solve it. To obtain the sparse tensor component, the primal dual method is used. We compare the proposed method with four state-of-the-art ones, and experimental results show that the proposed method can achieve better reconstruction quality than state-of-the-art ones.
Power Allocation Optimization in MC-NOMA Systems for Maximizing Weighted Sum-Rate

In this paper, we investigate a power allocation scheme in downlink multi-carrier non-orthogonal multiple access (MC-NOMA) systems for maximizing weighted sum-rate. Taking user-priority into account, we add the weighting factors into the formulated optimization problem. Firstly, we give sufficient and necessary concavity conditions that should be satisfied by the proposed weighted sum-rate maximization problem. Then, for more generally non-concave case, we use a first order approximation to convert the optimization problem and provide an iterative power allocation algorithm to find the globally optimal solution. Simulation results show that our proposed algorithm is superior to orthogonal frequency division multiple access (OFDMA) scheme and average power allocation scheme.
Comparative Analysis of the Resource Blocks Allocation Balancing Model in the LTE Downlink Using RAT 1 with Existing Solutions

Comparative analysis of the balancing model of resource blocks allocation in the LTE downlink using RAT 1 with existing algorithms is done. The proposed model allow to considerably decrease the computational complexity of finding final solutions on resource block allocation among user equipments in the LTE network due to linear nature of the obtained optimization problem of mixed integer programming. Comparative analysis has shown the effectiveness of the balancing model, especially in a complex signal-to-noise conditions, the presence of heterogeneous requirements for the type and level of the service quality. It is shown that the proposed time-frequency resource management model has provided the highest efficiency under conditions of QoS requirements of user requests, comparable to or exceeding the bandwidth of the LTE downlink.
A Permanent Magnet Linear Generator for the Enhancement of the Reliability of a Wave Energy Conversion System

In this paper, a linear generator for a highly reliable wave energy conversion system is designed and tested. In order to store energy, the system is able to produce hydrogen. The wave energy conversion system consists of an electrical linear generator, a power conversion system, and a sea-water electrolyzer. A small-scale prototype of the system is designed and built. The design is oriented to the enhancement of the system robustness and reliability and a failure mode and effects and criticality analysis are used. In order to guarantee an easy extension of the power capability of the marine plant, a modular architecture of the system is adopted. The design strategy is described. The robustness and reliability of the proposed solution are discussed. Simulation and experimental results on the prototype are shown.
Three clustering optimization algorithms based on dictionary learning

This paper proposes $l_{2}$ norm, $l_{1}$ norm and $\iota _{\infty }$ norm of clustering optimization algorithms based on dictionary learning. By solving an optimization problem to assign each feature to a cluster and solving another optimization problem to re-calculating the vectors representing the clusters, each algorithm keeps iterating until it converges. Computer simulation experiments show that the three algorithms have good clustering results and the convergence is confirmed. The runtime of l2 norm clustering optimization algorithm is much faster than h norm and $ l\infty $ norm clustering optimization algorithms.
SPWM-Based Direct Digital Control With Average-Voltage Model and D–Σ Process for Paralleled 3φ3W Grid-Connected Converters to Reduce Circulating Currents

This paper presents a sinusoidal pulsewidth modulation (SPWM) based direct digital control with average-voltage model (AVM) and division–summation process for paralleled three-phase three-wire (3φ3W) grid-connected converters to reduce circulating currents. With the direct digital control, plant of each single-phase converter can be derived directly, and a controller is then designed to cover the variation effects of filter inductance and grid voltage. Based on SPWM and AVM over a switching cycle, each 3φ3W converter can be decomposed into three single-phase converters and each converter is controlled individually to track its sinusoidal reference current tightly, reducing circulating currents significantly. Equal current sharing is therefore also achieved. The control laws for achieving grid connection are derived in detail. In the design and implementation, the inductances corresponding to various inductor currents are measured offline and stored in the controller for scheduling loop gain cycle by cycle. Experimental and simulated results from a three-converter system confirm the analysis and discussion of the proposed control approaches.
Critical brain dynamics in the EEG resting state

Numerous investigations have evidenced that human brain network may be operated in a critical state. Here we investigated whether the resting brain with eyes-close state was more likely a critical state than eyes-open state by using EEG recording technique and subsequent frequency domain analysis. EEGs of 26 healthy subjects (mean age ± SD 21.54 ± 3.70$) during eyes-close and eyes-open state were collected. Then we analyzed the power law distribution of cascades size and duration and calculated the branch parameter of the EEG data in frequency domain. We found the critical exponent of cascade size and cascade duration of eyes-close state were consistently lower than that in the eyes-open state for different time-windows, and the branch parameter showed consistent results. The critical exponents of the distributed avalanche sizes and durations during eyes-close state were observed around -1.5 and -2 respectively for a certain time window, correspondingly, the branch parameter was approached 1. This indicated that the brain activity dynamic of eyes-close state was more likely around criticality, while eyes-open brain dynamics was inclined to a supercritical state.
An Efficient Virtual Nodes-based APIT Localization Algorithm with Low Computational Cost

In many practical applications of wireless sensor networks, node localization is an important supporting technology. APIT algorithm is widely used due to its high localization accuracy. However, the edge effect of this algorithm limits its localization performance. Among all improved algorithms, virtual nodes-based APIT localization algorithm (VN-APIT) is robust and has a low average localization error. This paper proposes a more efficient localization algorithm with low computational cost (EVN-APIT) based on VN-APIT. By roughly estimating the approximate position of the node based on the received signal strength and then deploying corresponding virtual nodes in the network, this algorithm can reduce about one-third of the calculations in theory. Simulation results have also proved that compared with VN-APIT, the proposed algorithm can achieve the almost same localization accuracy at the expense of two thirds of the calculation.
A fast blind source separation algorithm for binaural hearing aids based on frequency bin selection

In binaural signal processing, due to the large distance between two ears, spatial aliasing occurs at the high frequencies. Therefore, the fast algorithms based on frequency bin selection usually limit the frequency range to below 1kHz to avoid dealing with the aliasing problem. In this paper, a fast Blind Source Separation (BSS) algorithm based on frequency bin selection without limiting the range of frequency bin selection was proposed. Efficient method was used to estimate the propagation model parameters to solve the inaccurate delay problem and permutation ambiguity. Besides, we used outlier detection method to further remove frequency bins with poor separation performance after the first selection, which ensures the accuracy of the normalized attenuation and delay matrices. Simulation results show that the proposed algorithm reduces computational complexity and improves separation performance compared with limited range frequency bin selection BSS.
A Convoluted Structure for Miniaturized Dual-Bandstop Frequency Selective Surface

A miniaturized dual-bandstop frequency selective surface (FSS) using convoluted structures is proposed in this letter. The working central frequencies of the proposed FSS are 2.66 and 5 GHz with −3 dB bandwidths of 1.5 and 1.2 GHz, respectively. The side length of the unit cell is 6.88 mm, which is about 5.9% of the wavelength at the first working frequency. The equivalent circuit and current distributions are introduced to explain the FSS frequency performance. The effects of the geometrical parameters on the frequency response of the FSS are studied. The simulated results show that the ratio of the frequencies of the two working transmission zeros can be tuned over a wide range. A prototype of the proposed FSS is fabricated and measured. The simulation and measured results show that the working frequencies, the bandwidths of the passband, and two stopbands of the proposed FSS perform excellent stabilities under different polarizations and incidence angles. This FSS is a good candidate to isolate the wireless networks.
A Differentially Fed Dual-Polarized Slot Antenna With High Isolation and Low Profile for Base Station Application

In this letter, a low-profile differentially fed dual-polarized slot antenna is proposed for base station application. Its radiator is an octagon patch etched with two normal H-shaped slots. The folded feeding line is introduced to accommodate the differentially fed scheme and tune impedance matching. Both high isolation and stable radiation patterns are realized by using the differential feeding technique. Measured results demonstrate that the impedance bandwidths (VSWR < 1.5) of the two polarizations are 19.3% (3.14–3.81 GHz) and 20.3% (3.10–3.80 GHz), respectively. The proposed antenna has a high isolation of larger than 43 dB over the entire operation band. The measured gain is higher than 8.1 dBi within the operating frequency.
Two-Time-Scale Redesign for Antilock Braking Systems of Ground Vehicles

An antilock braking system (ABS) is one of the most effective active safety control systems for ground vehicles, since it can keep the rotational wheel from locking and, consequently, guarantee the braking safety and handling stability. There have been a variety of ABS control schemes proposed by many researchers. However, most of the results employ sundry tire–road friction models, the alleged <inline-formula><tex-math notation="LaTeX">$\mu\, \text{--}\,\lambda$</tex-math></inline-formula> curves (<inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> is the tire–road friction coefficient, while <inline-formula><tex-math notation="LaTeX">$\lambda$</tex-math></inline-formula> is the tire slip ratio, which is mathematically defined as <inline-formula><tex-math notation="LaTeX">$\lambda =({v-\omega r})/{v}$</tex-math></inline-formula>), making the ABS controller extremely complicated for the highly nonlinear characteristics of the <inline-formula><tex-math notation="LaTeX">$\mu\, \text{--}\,\lambda$</tex-math></inline-formula> relationship. Furthermore, the <italic>a priori</italic> knowledge of road conditions for these ABS controllers restricts their practicability. To circumvent these problems, a two-time-scale ABS control scheme is proposed in this paper, without considering the intricate <inline-formula><tex-math notation="LaTeX">$\mu\, \text{--}\,\lambda$</tex-math></inline-formula> relationship, making the <italic>a priori</italic> knowledge of the road condition no longer a prerequisite; thus, the designed ABS controller is rather simple. In addition, a modified fast-time-scale estimator is involved to estimate the road condition, which is significant in vehicle active dynamics control. The effectiveness of the proposed ABS controller is verified via numerical simulations and CarSim–MATLAB cosimulations.
Emotion Recognition Research Based on Integration of Facial Expression and Voice

Emotion recognition is becoming more and more important with the growing interests on intelligent and natural human-machine interaction. This paper presents a research on emotion recognition based on the combination of facial expression and human voices. The geometry shape of facial organs, represented by cubic spline coefficients, as well as the facial texture, represented by Histogram of Gradients (HOG), are combined together to describe the emotion shown on human face. A classifier for facial expression-based emotion recognition is trained using Support Vector Machine (SVM)with the image data provided by CHEAVD 2.0. Meanwhile, another voice-based emotion classifier is trained also using SVM with acoustic features extracted from the voices accompanying the video. The recognition results are then combined at decision-level using Bayesian rule. This approach can achieve an accuracy of 38.41 % with the test data of CHEAVD 2.0. More details of the testing result are shown in the paper. In the end, pros and constraints of our approach are discussed.
Message Passing Algorithm for GFDM-IM Detection

The generalized frequency division multiplexing with index modulation (GFDM-IM) is a recently developed multi-carrier technique, which has the signal feature that only part of subcarriers are activated. In this paper, the message passing (MP)-based signal detection of GFDM-IM is studied, and two MP detectors are presented. In the first MP detector, MP algorithm is performed directly in the factor graph constructed by the product of GFDM modulation matrix and channel matrix. In the second MP detector, the received signal is first frequency-domain equalized, and then MP algorithm is performed based on a sparse factor graph by utilizing the structured sparsity of the modulation matrix. In both MP detectors, an additional pattern node is introduced to leverage the relation in the variable nodes belonging to the same IM block introduced by activation pattern constraint. Simulation results show that, the proposed MP detectors show some superiority over conventional linear detectors in terms of error performance and/or complexity.
The Fast Solver for Calculating the Scattered Fields From the Multiscale Scatterers

In this paper, the efficient methods for calculating the scattered fields from the electrically large scatterers and multiscale scatterers are discussed. The method of moment physical optics (MoM-PO) method is introduced to obtain the scattered fields from the multiscale scatterers. Due to the highly oscillatory behavior of the PO integrals, the numerical contour deformation technique is proposed to calculate the PO scattered fields. The z buffer acceleration technique based on General Public Utilities is adopted to distinguish between the lit and shadow regions of the surfaces. Numerical examples from the electrically large and multiscale scatterers are provided to benchmark the efficiency of the proposed method.
PARAMETERIZATION AND CALIBRATION OF MICRO-SIMULATION MODEL FOR CERVICAL CANCER AND HIV IN ZAMBIA

Zambia has one of the highest rates of cervical cancer in the world. To help policy makers make future intervention decisions regarding cervical cancer, we created a micro-simulation model to simulate cervical cancer and HIV in Zambia. Model calibration faced two major challenges: (1) Much of the available input data was on women in the United States, which do not allow calibration to align to the age-specific targets from Zambia; (2) Significant computational resources were not always available. We addressed issue one by creating age-specific calibration parameters to help better match specific targets. Issue two was addressed by using predictive models before conducting calibration simulations to discard parameter sets that are likely to produce poor results. This paper will demonstrate these two modeling strategies and show the dramatic effect they had on our ability to accurately calibrate to model targets.
A Large Range of Motion 3D MEMS Scanner With Five Degrees of Freedom

Here, we discuss a novel, mixed mode 3D XYZ scanner built within a single foundry process. The device has a large range of motion in X, Y, and Z (<inline-formula> <tex-math notation="LaTeX">$14.1~\mu \text{m}$ </tex-math></inline-formula> in X and Y and <inline-formula> <tex-math notation="LaTeX">$97.9~\mu \text{m}$ </tex-math></inline-formula> in Z) and can also rotate about two axes (7.4°), making it a 5 degree of freedom scanner. Vertical actuation can be accomplished with both thermal actuators, which have a larger range of motion, and capacitive actuators, which are faster, responding fully up to 3.2 kHz. Although useful for many applications, including scanning probe microscopy, micrometer scale optical microscopy, and manipulation of biological objects, the device was designed to be a 3D scanner for spray-painting atoms upon a surface with nanoscale precision and resolution for nanofabrication. Demonstrating the ability to combine the device with other complicated MEMS systems, it is integrated with an XY scanner designed to serve as a shutter of atomic flux. The full system has 7 degrees of freedom and 12 actuation motors, and because it is built in a low cost commercial foundry with a robust and stable process, it is easy and inexpensive to fabricate multiple copies or integrate into other complicated systems, making a system of systems. [2018-0213]
AGENT-BASED SELF-ORGANIZATION VERSUS CENTRAL PRODUCTION PLANNING

Industry 4.0, a part of the German high tech strategy, prefers self-organization in production over central production planning for the sake of greater flexibility, faster response to disruptions and to deviations, and less effort. Current planning systems usually plan centrally. We developed a universal self-organizing production and empirically compared its performance to a centrally planned production. The self-organizing production is based upon agents. For better comprehensibility, we additionally implemented central planning. The results of self-organization in production are promising in relation to central planning; especially when disruptions and deviations occur.
Video Tracking Based on Template Matching and Particle Filter

In recent years, object tracking is still a challenging problem although many approaches have been successfully proposed. We propose a video tracking based on template matching and particle filter, for solving some issues in object tracking. The proposed method includes template matching and particles weighting. The object can be successfully tracked by template matching, except for some challenging sequences. To compensate for template matching, we exploit particle filter with Speeded Up Robust Features (SURF) to repair the failed tracking. Experimental results of the effectiveness and robustness are demonstrated, and the comparative performance is also shown.
A Multiuser Collaborative Strategy for MI-BCI System

Brain-computer Interface (BCI) provides a direct communication pathway for the brain and the outward environment. Specifically, motor imagery-based BCIs (MI-BCIs) has the advantage of actively outputting instructions without any external stimuli. Although this paradigm has been investigated for many years, individual MI-BCI is still of low performance. To this end, a collaborative strategy was proposed for MI-BCI system in this study. A20-channel EEG was adopted to inspect the classification performances of collaborative and individual MI-BCI. For 8 healthy subjects, four different motor imagery mental tasks (both hands, feet, left hand and right hand) were tested. Experimental results showed that, compared with that of individual system, the performance of MI-BCI with multiuser collaborative strategy could be improved by 16.5%. The proposed collaborative strategy could provide an available approach for BCI modulation and neural feedback research.
Epileptic Seizure Detection Using Deep Convolutional Network

In this paper, a patient specific seizure detection system using channel-restricted convolutional neural network(CR-CNN) with deep structure is represented. The binary patterns of brainwave activity reflected on ictal and interictal EEG are auto-memorized based on back-propagation mechanism. It is well trained using massive historical scalp EEG data of 23 pediatric patients with epilepsy from CHB-MIT database. Experimental results demonstrate that the proposed detector achieves the state of the art performance. The average false alarms rate reaches 0.12 per hour and only one out of the 167 seizures is missed.
Methods for Extracting the Descriptions of Sci-Tech Effects and Morphological Features of Technical Systems from Patents

For the designing of the new technical systems are used the databases of chemical, physical and technical (sci-tech) effects and morphological features (technical functions, structural elements of the technical systems). Authors developed the method for extraction the chemical effect descriptions and technical functions from the patents of USPTO and RosPatent databases.
Design of Recursive Filters Using Frequency-Response Masking Technique

This paper presents a new method for designing IIR filters using frequency-response masking technique. In the proposed method, bandedge shaping filters and masking filters are elliptic filters which are implemented as a parallel connection of two allpass filters. An additional allpass filter is cascaded with one of the masking filters to compensate the phase difference of two masking filters in the transition bands of the bandedge shaping filters within the passbands of the masking filters. Two design examples are presented to demonstrate the effectiveness of our method.