Miracle-dz/UniFAR-FTUs huggingface dataset:
Idle-State Detection in Multi-user Motor Imagery Brain Computer Interface with Cross-Brain CSP and Hyper-Brain-Network

Motor imagery (MI) is a kind of spontaneous controlled brain computer interface (BCI) paradigm, which is more likely to the concept of 'mind control'. The idle state detection is an important problem to construct a robust MI-BCI system since it needs to tell whether the subject is in MI task and the idle state contains much diverse cases. Herein, EEG-based multi-user BCI refers to two or more subjects engage in a coordinate task while their EEG are simultaneously recorded. The objective of this paper is to explore how the multi-user MI-BCI performance in idle detection based on CSP (common spatial pattern) and brain-network features. We proposed several strategies for cross-brain feature fusion. Results show that 1) Through CSP features, the classification accuracy of cross-brain outperforms the single brain CSP feature across different strategies. 2) Through brain-network features, the classification accuracy of concatenated with the paired subjects outperforms the single brain-network, while the inter-brain-network is lower than single subject 3) alpha frequency band shows better performance than other bands. Multi-user MI-BCI would be a potential way to improve the idle state detection accuracy.
A Novel Application of Intelligent Algorithms in Fault Detection of Rudder System

The rudder system is extensively used in aerospace, ships, missiles and other safety demanding areas. Therefore, it is paramount to ensure that the performance of the system is optimal. Rudder system testing equipment is a special tool used to diagnose its failure. Traditional ones can only artificially analyze the massive and complex tested data. Due to the low-test degree of automation, the performance of such testing tools is limited. Aiming to address this shortcoming, we developed a new rudder system testing equipment with four independent loading platforms and intelligent data analysis systems. It sufficiently shortens the installation and commission time of pneumatic actuators and the processing time of the testing data which largely improves its performance and accuracy. Given the imbalanced nature of the data an adaptive sampling algorithm considering informative instances (ASCIN) leveraging the Support Vector Machine (SVM) is proposed to process the originally collected data. The optimal parameters in SVM and ASCIN are searched by Whale Optimization Algorithm (WOA). Experiments are designed to assess the performance of ASCIN in comparison with existing approaches in the area of imbalanced data learning. The results show that the algorithm developed in this study has higher performance relative to traditional approaches. The application of these intelligent algorithms in fault detection and location of rudder system overcomes the limitation of traditional testing equipment and provides a new concept for future research into more intelligent one.
A Novel Cascaded Multilevel Inverter Using Mcpwm with Minimum Switches

Multilevel inverter present attractive advantages in high power applications. A multilevel inverter not only achieves high power ratings, but also enables the use of renewable energy sources. The proposed method is 15-level multilevel inverter which has number of modules are connected in series. Each module has one switch and one parallel connected diode. Only one switch operates at a time preventing short circuit. This method involves less number of switches associated with more number of voltage levels. The stages with higher DC link have more advantages like low commutation, reduced switching losses, increased efficiency and low input stages with more number of output levels. In this paper, a bypass diode technique is introduced to the conventional cascade multilevel inverter topology which reduces the number of controlled switches used in the system. It dramatically reduces the switches for high number of levels that reduces the switching losses; cost and low order harmonics and thus effectively reduces Total Harmonic Distortion (THD).
Analysis and Reduction of Conducted Interference of a Full SiC Inverter

In spite of its low loss and high efficiency over Si device, SiC device will lead to more severe electromagnetic interference (EMI) when utilized in electric vehicles (EV). In this paper, the effects of supply voltage and load current on the conduction interference level are analyzed. and an EMI filter is designed to reduce the conducted interference of a full SiC inverter to conform CISPR 25 Class 3.
Evaluation of Multi-Point Dynamic Pressure Reproduction Using Microphone-Based Tactile Sensor Array

An airborne ultrasound tactile display (AUTD) is a non-contact tactile display that can reproduce arbitrary pressure distribution, but research on the fidelity of its tactile feel is still insufficient. This study aims at the development of tactile reproduction system by AUTD, and we designed a tactile sensor composed of 4×4 ch microphone array in this paper. In addition, the basic performance as a tactile reproduction system was evaluated by reproducing the pressure by AUTD based on the simulated pressure distribution recorded by the sensor.
Angle Compensation-Based Voltage Redistribution for Induction Motor Drives in the Field-Weakening Windup Region

Due to the limited inverter output voltage, the current regulator of induction motor (IM) drives is prone to saturating during the transient process of field-weakening region. This windup problem seriously deteriorates the dynamic performance of IM field-weakening control. To address this problem, this paper proposes an angle compensation-based voltage redistribution method. The proposed method can rotate the voltage command output from the current regulator to increase the d-axis voltage margin. Compared with the conventional field-weakening control, the proposed method can effectively avoid the fluctuant current and achieve smoother performance in the field-weakening windup region. The experimental results from a 3.7kW IM drive system verify the effectiveness of the proposed algorithm.
Electrically Excited Synchronous Machine with Additional Permanent Magnets for Traction Application

This paper presents a novel hybrid permanent magnet and electrically excited synchronous machine in a realistic scenario for traction application. This concept is well known since it combines the advantages of interior permanent magnet synchronous machines and electrically excited synchronous machines in base load and flux weakening regions. With the help of magnets, the efficiency in whole base load region is increased and an improvement in WLTP cycle efficiency of 0.6 % can be achieved.
A Deep Learning Model for Dimensional ValenceArousal Intensity Prediction in Stock Market

This paper proposes a dimensional valence-arousal method to define sentiment status in the stock market. In the past, many kinds of research have focused on the valence sentiment on stock messages because it represents the stock trend such as upward and downward. In this case, if the stock price jumps or collapses (positive/negative trend) in the short term, the investor will necessarily need to immediately trade at this moment, but some case is not. Therefore, the valence-arousal method can be used to define the trend intensity and trading intensity for a stock message of the stock market. In order to obtain a powerful prediction model to learn the intensity of trend and trading of a stock message that we propose a keyword-based attention network into Hierarchical Attention Networks (HAN), namely HKAN model, to learn the relation between dimensional sentiments (trend and trading) and stock messages. The experimental results show that our proposed HKAN model for stock VA prediction has outperformed other baseline models such as HAN and Hierarchical Hybrid Attention Networks.
Degenerative findings on MRI of the lumbar spine : prevalence, environmental determinants and association with low back symptoms

Earlier studies on lumbar degenerative imaging findings in magnetic resonance imaging (MRI) have been done mainly in adult populations and the associations of degenerative findings with low back pain (LBP) are controversial. Only a few studies have involved adolescents and young adults. Heritability has been acknowledged as the only explicit risk factor of disc degeneration (DD). This study investigated the prevalence and environmental determinant of lumbar degenerative findings in MRI and their association with low back symptoms among young adults. The data were based on two physical assessments, three questionnaires and one lumbar MRI that were executed on members of the Oulu Back Study (n=558), a subsample of Northern Finland Birth Cohort 1986, between 16 and 21 years of age. Prevalences of lumbar DD (54%), bulging (25%), protrusion (18%) and Schmorl’s node (17%) were high, whereas other degenerative findings were rare among young adults. Males had higher prevalence of DD and Schmorl’s nodes than females. DD and herniations were associated with low back symptoms. On the other hand, symptoms were present among subjects without DD or other abnormal findings on MRI. Of the environmental determinants, high body mass index and MRI-based obesity measurements of visceral adiposity were associated with lumbar DD among males. Waist circumference and smoking showed a comparable association with DD among males, but the level of physical activity was not associated with DD in either gender. Low back symptoms are more common among young adults with a higher degree of DD or presence of disc herniation. Smoking and overweight are associated with lumbar DD among young male adults.
Hardware Implementation of a Classifier Trained with Informed-Filters Using Only Color Features

We are constructing a novel sensor networking system for real-time sensing human vital signs during exercise in real time using image-assisted routing. Image-assisted routing requires accurate localization of humans wearing sensor nodes using aerial images captured from cameras mounted on UAVs. To detect humans in aerial images in real time, we propose a hardware implementation of a classifier constructed with informed-filters using only color features that shows a higher accuracy than deep learning for sports scenes. Experimental results showed that the detection accuracy of the classifier did not become worse when we only applied fixed-point operations. The proposed hardware also requires a few computation resources in an FPGA though a large amount of memory was required. The processing time for an image whose size was $2560 \times 1352$ was about 36ms, demonstrating that our hardware can achieve very fast computation on an FPGA.
Multi-objective shape optimization of Permanent Magnet Synchronous Motor based on Kriging surrogate model and design domain reduction

Permanent Magnet Synchronous Motor (PMSM) is a nonlinear, multi-physics coupled system that makes it difficult to build an accurate mathematical model to optimize design parameters. Traditional design for PMSM always relies on the experience of engineers. Although some works have been done for the size optimization of motors, the performances of PMSM still need to be improved. In this paper, a multi-objective shape optimization method is proposed for the optimal design of PMSMs. In the optimization model, the shape of slot and the size of permanent magnets are considered as design variables. The objective is to minimize the torque ripple and the loss under the constraint of average torque of motors. To obtain the accurate global solution, the Kriging surrogate model algorithm with an effective design domain reduction is used. Several novel designs that can obviously reduce the torque ripple and loss of PMSM are obtained by using the proposed method. The optimization results also indicate that using the proposed shape optimization algorithm is more effective in the optimal performance design of PMSM than using size optimization methods.
Fake News Detection Using One-Class Classification

Fake news have attracted attention of general public because of the influence they can exert on important activities of society, such as elections. Efforts have been made to detect them, but usually they rely on human labour fact-checking, what can be costly and time consuming. Computational approaches have typically relied on supervised learning models, in which a model is trained based on fake and true news samples. Such approach allows a large amount of news to be classified in a short time, but it demands datasets labelled with positive and negative instances. Our work proposes to detect fake news by training a model with only fake samples in the training dataset, through One-Class Classification (OCC). We compare a novel algorithm, called DCDistanceOCC, to others published in literature, and got similar, or even better, results. The case study is the Brazilian politics scenario starting at the 2018 general elections on Twitter and WhatsApp. These two platforms were a fertile ground to fake news proliferation. We also evaluated the models over another available dataset from literature. To the best of our knowledge, this is the first paper to identify fake news using an OCC approach and also the first one to provide Portuguese-based WhatsApp and Twitter datasets with fake news.
CLASP: a 999-style Priority Lanes Framework for 5G-era Critical Data Services

Society is increasingly reliant on digital services for its proper functioning. Yet, going into the 5G era, the prevailing paradigm for data treats all traffic as equal regardless of how critical they are to the proper functioning of society. We argue that this is a suboptimal scenario and that services such as driverless cars and road/rail traffic updates are too important for society to be treated the same way as entertainment services. Our contribution in this paper is to propose the CLASP (Critical, Localized, Authorized, Specific, Perishable) framework to guide regulators and policymakers in deciding and managing 999-style priority lanes for critical data services during atypical scenarios in the 5G era. Our evaluation shows that reserving a 100kbps ‘lane’ for CLASP-prioritised traffic for all users does not lead to an overall statistically significant deterioration in atypical scenarios.
Comparative Study of Fingerprint Database Indexing Methods

Nowadays, there are large country-sized fingerprint databases for identification, border access controls and also for Visa issuance procedures around the world. Fingerprint indexing techniques aim to speed up the research process in automatic fingerprint identification systems. Therefore, several preselection, classification and indexing techniques have been proposed in the literature. Even if, the proposed systems have been evaluated with different experimental protocols, it is difficult to assess their relative performance. The main objective of this paper is to provide a comparative study of fingerprint indexing methods using a common experimental protocol. Four fingerprint indexing methods, using naive, cascade, matcher and Minutiae Cylinder Code (MCC) approaches are evaluated on FVC databases from the Fingerprint Verification Competition (FVC) using the Cumulative Matches Curve (CMC) and the computing time required. Our study shows that MCC gives the best compromise between identification accuracy and computation time.
An efficient modular security plane AM-SecP for hybrid distributed SDN

Software defined networks (SDNs) represent new centralized network architecture that facilitates the deployment of services, applications and policies from the upper layers, relatively the management and control planes to the lower layers the data plane and the end user layer. SDNs give several advantages in terms of agility and flexibility, especially for mobile operators and for internet service providers. However, the implementation of these types of networks faces several technical challenges and security issues. In this paper we will focus on SDN’s security issues and we will propose the implementation of a centralized security layer named AM-SecP. The proposed layer is linked vertically to all SDN layers which ease packets inspections and detecting intrusions. The purpose of this architecture is to stop and to detect malware infections, we do this by denying services and tunneling attacks without encumbering the networks by expensive operations and high calculation cost. The implementation of the proposed framework will be also made to demonstrate his feasibility and robustness.
MSGCNN: Multi-scale Graph Convolutional Neural Network for Point Cloud Segmentation

Point cloud has emerged as a scalable and flexible geometric representation for 3D data. Graph convolutional neural networks (GCNNs) have shown superior performance and robustness in point cloud processing with structure-awareness and permutation invariance. However, naive graph convolution networks are limited in point cloud segmentation tasks especially in the border areas of multiple segmentation instances due to the lack of multi-scale feature extraction ability. In this paper, we propose a novel multi-scale graph convolutional neural network (MSGCNN) to allow multi-scale feature learning for fine-grained point cloud segmentation. The proposed geometrical interpretable multi-scale point cloud processing framework is able to considerately enlarge the graph filters receptive fields and exploit discriminative multi-scale structure-aware point features for the superior segmentation performance against naive graph convolution networks especially in border area. Experimental results for part segmentation task on ShapeNet datasets show that MSGCNN achieves competitive performance with state-of-the-arts. In comparison to naive graph convolution networks, MSGCNN is shown to obtain better visual quality in the border area. We further validate that our model is robust to data point missing and noise perturbation with the learned multi-scale structure-aware point features.
Detection Defect in Printed Circuit Boards using Unsupervised Feature Extraction Upon Transfer Learning

Automatic optical inspection for manufacturing traditionally was based on computer vision. However, there are emerging attempts to do it using deep learning approach. Deep convolutional neural network allows to learn semantic image features which could be used for defect detection in products. In contrast to the existing approaches where supervised or semi-supervised training is done on thousands of images of defects, we investigate whether unsupervised deep learning model for defect detection could be trained with orders of magnitude smaller amount of representative defect-free samples (tenths rather than thousands). This research is motivated by the fact that collection of large amounts of defective samples is difficult and expensive. Our model undergoes only one-class training and aims to extract distinctive semantic features from the normal samples in an unsupervised manner. We propose a variant of transfer learning, that consists of combination of unsupervised learning used upon VGG16 with pre-trained on ImageNet weight coefficients. To demonstrate a defect detection, we used a set of Printed Circuit Boards (PCBs) with different types of defects - scratch, missing washer/extra hole, abrasion, broken PCB edge. The trained model allows us to make clusters of normal internal representations of features of PCB in high-dimensional feature space, and to localize defective patches in PCB image based on distance from normal clusters. Initial results show that more than 90% of defects were detected.
A Decoupled Control Scheme of Four-Port Solid State Transformer

In this study, a four-port solid-state transformer (SST) with decoupled control scheme to control the power flow and the output voltage is proposed. The proposed decoupled control scheme controls all of the four ports’ powers independently. In addition, the design of the four-port transformer including core material selection and winding placement is investigated. The designed transformer is modeled in ANSYS-Maxwell and also co-simulated with ANSYS-Simplorer. The operating frequency of the system is designed for 100 kHz; therefore, a very compact size is obtained for the entire multi-port converter. The performance of the proposed system is validated throughout MATLAB/Simulink simulation and experimental studies carried out for a 10kW/port SST prototype. The obtained results show that the four-port SST provides an interface for four-different power supplies or loads. It is seen that the proposed decoupled control scheme can control the output voltage at the desired value and track the reference power signals for each port. It provides as well a good steady state and dynamic performance.
Correlation of Heidelberg´s retinal tomograph and blue-on-yellow perimetry in glaucoma-suspected patients

OBJECTIVE: To assess the correlation between the results achieved with the Heidelberg retinal tomography and the blue-on-yellow perimetry in glaucomasuspected patients. METHODS: A prospective longitudinal and descriptive study was conducted on 30 glaucoma-suspected patients, from November 2006 to April 2007. Three topographic tests and three perimetric tests were performed on these patients in a 6-month period at 2month intervals. RESULTS: Over 40 years of age prevailed (56,67 %). Topographic findings indicative of glaucoma were observed in 63,3 % of the sample and the most frequent were excavation higher than 0,6 in the vertical axis and the reduction of the neuroretinal ring (56,67 % in both cases). Perimetric changes appeared in 43,3 % in all the studied cases and the most common changes were increase of the blind spot and the general loss of sensitivity (10 %) in the first study, and also in the second study (13,33 %); the third study similarly yielded a general loss of sensitivity in 43,33 % of patients. CONCLUSIONS: Topographic changes occurred before the perimetric changes in a high number of cases.
A New Design of Filtering Power Dividers With Arbitrary Constant Phase Difference, Impedance Transformation, and Good Isolation

This paper proposes a new class of filtering power dividers integrated with phase shifter and impedance transformer simultaneously for the first time. In contrast to the conventional approaches, the proposed filtering power dividers consist of two asymmetrical generalized bandpass networks, that can achieve arbitrary constant phase difference and good isolation at two output ports. On the one hand, the magnitude and phase properties of two generalized <inline-formula> <tex-math notation="LaTeX">$n^{\mathrm {th}}$ </tex-math></inline-formula>-order bandpass networks are systematically studied and fully considered to realize the prescribed power division, filtering function, constant phase difference, and impedance transformation in the co-design. On the other hand, in order to obtain good isolation, the resonator’s type and impedance of these two asymmetrical networks are carefully considered and closed-form formula of isolation resistor is also deduced. Theoretical results show that the proposed topology can achieve perfect isolation over entire passband for the arbitrary phase difference. Meanwhile, the synthesis procedures are presented for a quick design process. Finally, two porotypes (90° power divider and 120° power divider) with specified different operation bandwidth (15% and 25%), filter order (3rd and 6th), and terminated port impedances (<inline-formula> <tex-math notation="LaTeX">$50\Omega $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$100\Omega$ </tex-math></inline-formula>) are synthesized, fabricated, and tested to verify the proposed design method and predicted frequency responses. It firstly integrates the power divider, filter, phase shifter, and impedance transformer into single component and has several additional advantages, such as low cost, simple geometry, small phase deviation and amplitude imbalance, and easiness for design and fabrication.