Miracle-dz/UniFAR-FTUs huggingface dataset:
Utilizing Subject-Specific Discriminative EEG Features for Classification of Motor Imagery Directions

Electroencephalogram (EEG)-based BrainComputer Interface (BCI) technology needs efficient algorithms to find distinct EEG patterns/features to realize applications with distinct high-dimensional control signals. This paper proposes a novel feature extraction methodology for separating EEG patterns associated right hand motor imagery performed towards left and right directions. The most discriminative subject-specific feature set is chosen based on Fisher’s ratio of absolute phase values of EEG in 6 low frequency sub bands. Using this, the proposed BCI system is capable of providing better classification results than state-ofthe-art methodology with fixed channels, fusing absolute phase and spatial features from selected subject-specific discriminative channels. Experimental analysis shows that though parietal lobe is vital in providing distinguishable features, the channel set that provide maximum accuracy, is highly subject-specific. Hence, subject-specific BCI that can decode finer parameters of imagined movement are feasible and further research to understand the activations elicited in parietal lobe can contribute towards robust BCI systems.
Recognition Of Atypical Behavior In Autism Diagnosis From Video Using Pose Estimation Over Time

Autism spectrum disorder (ASD), similar to many other developmental or behavioral conditions, is difficult to be precisely diagnosed. This difficulty increases when the subjects are young children due to the huge overlap between the ASD symptoms and typical behaviors of young children. Therefore, it is important to develop reliable methods that could help distinguish ASD from normal behaviors of children. In this paper, we implemented a computer vision based automatic ASD prediction approach to detect autistic characteristics in a video dataset recorded from a mix of children with and without ASD. Our target dataset contains 555 videos, out of which 8349 episodes (each approximately 10 seconds) are derived. Each episode is labeled as an atypical or typical behavior by medical experts. We first estimate children pose in each video frame by re-training a state-of-the-art human pose estimator on our manually annotated children pose dataset. Particle filter interpolation is then applied on the output of the pose estimator to predict the locations of missing body keypoints. For each episode, we calculate the children motion pattern defined as the trajectory of their keypoints over time by temporally encoding the estimated pose maps. Finally, a binary classification network is trained on the pose motion representations to discriminate between typical and atypical behaviors. We were able to achieve a classification accuracy of 72.4% (precision =0.72 and recall =0.92) on our test dataset.
A Simplified and Fast Dehazing Processing Suitable for Embedded Systems

The main purpose of image dehazing is to remove undesirable degradation caused by fog and mist to improve the visual quality of an image and provide benefits to postprocessing. In particular, image dehazing systems applied to actual applications such as video surveillance and in-vehicle cameras are required to realize a balanced performance for video streams between dehazing quality and processing speed in low-cost portable embedded devices. To cope with this requirement, in this study, we propose a simple and fast dehazing algorithm which provides high-quality dehazed images and a practical processing speed. Concretely, the proposed method utilizes a simplified transmittance estimation technique with non-linear processing focusing on an optimization of its hardware implementation on a low-cost embedded target board with a graphics processing unit (GPU). Through a series of comparative experiments with typical simplified dehazing methods, we verify the effectiveness of the proposed method on dehazing quality and processing speed.
Research on Data Storage System of E-Commerce Purchasing Based on Big Data Technology

The storage of e-commerce procurement data is randomly distributed, which is interfered by the characteristic quantity of neighborhood distribution state, resulting in insufficient query performance. In order to improve the automatic location and storage ability of e-commerce procurement data, an automatic location and storage method of e-commerce procurement data based on big data technology is proposed. The distributed adaptive storage structure model of e-commerce procurement data is constructed, and the multivariate feature mining of e-commerce procurement data is carried out by using the method of regional grid computing, and the high dimension characteristic quantity of e-commerce procurement data is extracted. Combined with phase space reconstruction method, the storage space structure of e-commerce procurement data is reorganized, and big data mining and spectral feature extraction technology are used to realize the knowledge connection of e-commerce procurement data, and the automatic location storage optimization is realized. The simulation results show that the load of automatic location storage of e-commerce procurement data is large, the storage capacity is improved, and the real-time access ability of e-commerce procurement data is better.
The Repository of Web Document Summarization using Social Information

Summarization using social information is a task which extracts summary sentences and relevant user posts of a Web document by integrating its relevant social information. Prior studies introduced several strong models for this task; however, there are gaps from papers to the reproduction of such models. This paper leverages the gaps by investigating summa-rization algorithms to facilitate next studies. The investigation was conducted by implementing traditional and state-of-the-art methods, from unsupervised to supervised learning fashion. We used three datasets in English and Vietnamese to confirm the efficiency of the methods. Experimental results indicate that sophisticated models obtain improvements of ROUGE-scores compared to the basic ones, which do not use social information. However, in some cases, simple methods comparably perform state-of-the-art methods, suggesting that the performance of summarization methods can be still improved.
Active Power Decoupling Design of AC-DC Converter for On-board Chargers

Single-phase converters are widely used in On-board Chargers (OBC) of electric vehicles, however, the second-order ripple power of single-phase converter causes second-order ripple voltages on the dc bus. Thus, an active power decoupling method based on single-phase converter is proposed in this paper. The control method, taking single-phase PWM rectifier as the basic converter and adopting buck-boost power decoupling circuit, introduces second-order ripple of DC bus voltage into power decoupling circuit, which acts as compensation of the phase deviation between the command value and the actual value of the second-order ripple current. Therefore, the estimation of the second-order ripple current is more accurate, the power decoupling circuit absorbs the second-order ripple power behind the H bridge more completely, and the DC bus voltage ripple is effectively suppressed. Finally, experiment results of the single-phase PWM rectifier has been given to verify the validity of the proposed method.
Low-Power, High-Speed Time-Based Subranging ADCs

Time-based analog-to-digital converters (ADCs) are gaining attention as a key technology to solve challenges concerning CMOS scaling. In this paper, our latest research results of low-power, high-speed time-based subranging ADCs are presented. A time-based ADC combining a flash ADC and Vernier time-to-digital converter (TDC) was proposed to achieve a high-speed operation. An 8-bit test chip fabricated with 65-nm CMOS technology demonstrated a high sampling frequency of 900 MHz and a low power consumption of 3.5 mW. A fully-time-based subranging ADC was also proposed to reduce power consumption. Two time-based ADCs were used for both coarse and fine ADCs. An 8-bit test chip fabricated with 65-nm CMOS technology demonstrated a high sampling frequency of 950 MHz and a low power consumption of 2.3 mW.
Face Recognition Using LBP on an Image Transformation Based on Complex Network Degrees

Automated visual face recognition involves acquiring descriptive features from the image. Local Binary Patterns (LBP) is a powerful method to that end, capably characterizing local features. An crucial limitation of LBP, however, is that the feature vector's size becomes unmanageable when the method employed on even moderately large regions. In order to describe larger scale features, this work proposes a descriptor based on applying the LBP histogram applied to an image transformation based on node degree data derived from a complex network representation of the original image. The complex network generation heuristic and parameters are discussed. The complex network representation is shown to be able to condense larger scale image patterns into a local value that can be handled by LBP. LBP applied to this image transformation yields results that outperform LBP. We validate our proposed approach by applying our method to a face recognition task using three challenging databases. Results demonstrate that, for a large enough complex network generation radius, our method consistently outperforms LBP, while using a feature vector of the same size.
Phase-Based Tasks Scheduling in Data Centers Powered Exclusively by Renewable Energy

Data centers are considered nowadays as the factories of the digital age, being currently responsible for consuming more energy than the entire United Kingdom. On the other side, the global total capacity of renewable power increases continuously. The combination of these two factors calls for new approaches in designing data centers powered only by renewable energy sources. Our work focuses on task scheduling optimization under a power envelope, and on the way to handle power starvation, i.e. when the available power does not provide sufficient resources to execute a given workload. To do so we utilize the concept of task degradation through cross-correlation to find where to place the tasks in order to reduce the data center profit degradation. The results show that our algorithm could obtain more than 34% increase in profit when compared to algorithms from the literature, while fulfilling the power profile and resources constraints
Design of a Semi-Supervised Learning Strategy based on Convolutional Neural Network for Vehicle Maneuver Classification

Among state-of-the-art vehicle maneuver classification algorithms, Hidden Markov Models are commonly applied for predicting maneuver probability. To generate a model, a sufficient number of labeled samples is necessary for training. However, annotations that contain information such as class and bounding box are not always available. Manually labeling data is tedious, inaccurate, and time consuming, especially when the dataset is extremely large. Besides, there exists lots of redundant data that negatively influences model training. In this paper, we explore the possibility of using only a few manually labeled samples to train a Convolutional Neural Network (CNN) model for vehicle maneuver classification with relatively high accuracy. We define three maneuver classification groups: motion, velocity, and turning. Each group has subclasses, reflecting different aspects of the vehicle movement. Based on the defined maneuver classes, we design a simple CNN model to distinguish vehicle maneuvers. We also propose a learning strategy that requires only a few samples for training, while maintaining high recognition precision. Comprehensive experiments were performed to demonstrate the capability of our model and performance of our training strategy. In result, our model achieves an overall of 93.48% precision for maneuver recognition with only 3.5% Controller Area Network (CAN) bus data for training.
Soil model establishment of UHVDC grounding electrode under complex geological conditions

In recent years, with the large-scale development of long-distance, large-capacity DC projects, the degree and extent of the impact of the ground electrode on the surrounding power facilities cannot be ignored. If it cannot be accurately evaluated during the design phase, it will be detrimental to the safe operation of the power grid and bring additional investment. The accurate soil model is a prerequisite for analyzing the Earth Surface Potential (ESP) distribution and evaluating the effects of DC bias. However, the existing soil model calculation results have large errors, which make the evaluation of the DC bias effect inaccurate. To this end, this paper proposes a non-axisymmetric horizontal-vertical two-way layered composite soil model. Based on the three-dimensional resistivity data, the polar soil is dispersed into several small soil blocks, and the surface potential distribution is calculated by the finite element method. At the same time, in order to solve the problem of the computational increase of the three-dimensional model, the model is simplified according to the calculation principle, so the calculation memory is reduced and the calculation speed is improved while ensuring that the calculation accuracy is not affected.
User's Area (Zone) Estimation Method using BLE Beacons for IoT Service in Subway Station Environment

A novel location estimation method using IoT (Internet of Things) sensors to detect classified service areas in subway station is presented in this paper. By using RSSI (Received Signal Strength Indicator) values of BLE (Bluetooth Low Energy) Beacons, virtual areas are divided electromagnetically according to specific service facilities for IoT service. The virtual service area is called Zone. MLP (Multi-Layer Perceptron) algorithm applies to recognize the assigned Zone with the received RSSIs from multiple IoT sensors constructed in a real subway station. For verification of the proposed method, an experimental test is carried out in a real subway station environment. For recognition of 2 divided Zones, 10 sensors are used with their different transmitting power levels. From the test results, it is noticed that zone recognition accuracy obtains 78.1% experimentally for a real subway station environment.
A Coordination Control Strategy for An Isolated Bidirectional AC/DC Matrix Converter

In order to achieve good input and output performances, a coordination control strategy for an isolated bidirectional AC/DC matrix converter is proposed in this paper. In this strategy, the control principle of Dual Active Bridge (DAB) is introduced to coordinate the front 3-1 matrix converter stage and the back full-bridge inverter stage. A nonlinear PWM method is employed for the front stage, and the partition phase-shifting control is adopted for the back stage. The simulation results show that sinusoidal grid currents with almost same phase to the grid voltages are realized, and output voltage and current with small ripples in the battery side are achieved, which demonstrate the validity and effectiveness of the proposed strategy.
Graph-based Indexing Method for Searching in RDF Data

RDF is a generic graph-based data model of Semantic Web, and SPARQL is a query language for accessing the RDF data. With the increasing size of RDF data, answering complex SPARQL queries is expensive because multiple self-joins are needed to process. In this work, we consider an indexing and searching approach based on the graph structure of RDF data to reduce the number of join operations. It can speed up the queries’ performance and support chain and star shaped SPARQL query. Chain and star shaped subgraphs are extracted from the RDF data graph by considering the structure of edges around each vertex. The subgraphs obtained are stored as the index, named as CS-index. To execute a query, the query is firstly decomposed into query subgraphs based on the common join variable of its all triple patterns. And the query results are retrieved by searching the query subgraphs in CS-index, not in the whole data graph. The proposed index structure and searching approach tend to speed up the query response time by reducing the number of joins. We conduct a performance study on LUBM data set and see that our method outperforms the contest by a few orders of magnitude.
Optimization of Asymmetrical Coil Structures for Magnetic Coupled Wireless Power Transfer Systems

Transmitter coil (TX) and receiver coil (RX) are symmetric which is not appropriate for practical mobile electronics or electric vehicle wireless charging scenarios. For the regular planar-spiral TX, two novel reconfigurable receiver coils are proposed and optimized in this paper. One is composed by helix coil and several additional coils, the other one is composed by reconstructed helix coil wound by loops with gradually increasing radius. The optimal radius of the RX is determined depending on the specific transmission distance and TX outer-radius. By disposing additional coils or extra coils to the RX, the total magnetic flux of the RX can be controlled to keep the received power stable under vertical misalignment.
Efficiency Comparison of PAM-PWM Inverter with Partially Boost Converter for Motor Drive System

As a motor drive system for PMSM, there is a system that consists of a boost converter with bidirectional power conversion capability and a three-phase inverter. The efficiency of such a motor drive system is required to be high. Therefore, the authors investigated a system in which a PAM-PWM inverter control scheme is applied to boost converter installing with a bypass switch as a motor drive system. In addition, the previous study reported the basic characteristics of the proposed system where the load resistor was connected. This paper compares the operation waveforms and efficiencies of the conventional and proposed systems when the inverter in the system connects to an induction motor coupled with a DC generator as the load under several conditions. As a result, it is clarified that the efficiency of the proposed method is higher than that of the conventional method.
A computational model for anti-cancer drug sensitivity prediction

Various methods have been developed to build models for predicting drug response in cancer treatment based on patient data through machine learning algorithms. Drug prediction models can offer better patient data classification, optimising sensitivity identification in cancer therapy for suitable drugs. In this paper, a computational model based on Deep Neural Networks has been designed for prediction of anti-cancer drug response based on genetic expression data using publicly available drug profiling datasets from Cancer Cell Line Encyclopedia (CCLE). The model consists of several parts, including continuous drug response prediction, discretization and a drug sensitivity result output. Regularization and compression of neuron connections is also implemented to make the model compact and efficient, outperforming other widely used algorithms, such as elastic net (EN), random forest (RF), support vector regression (SVR) and simple artificial neural network (ANN) in sensitivity analysis and predictive accuracy.
Low Power Antenna Design for Free Space Optical Communications inside the Ariane 5 VEB

In this paper the prototyping of a low power optical antenna is presented. This antenna is designed so that several communication protocols can be implemented and tested. A transceiver is designed and constructed using discrete commercial off-the-shelf electronic devices. Some theoretical background, simulations and practical tests are proposed and the results are finally summarized.
A Simple Ripple Voltage Compensation Method in Hybrid AC-DC Microgrids

In hybrid AC-DC microgrids (HMGs), AC and DC microgrids (MGs) are linked by interlinking converter (IC). In HMGs, the operated single-phase inverter with variable frequencies (SPI-VF) makes DC bus voltage ripple. This paper proposes an IC control strategy that can mitigate ripple voltage at different frequencies. In the proposed IC control scheme, to save system cost, only DC bus voltage is measured instead of SPI-VF current/voltage. Moreover, DC bus voltage is constantly kept despite of the wide load power variation. Simulation and experimental results are carried out to verify the control scheme performance.
Barriers to Screening for Domestic Violence Among Public Health Nurses : A Descriptive Study

Methods: A quantitative, descriptive survey was distributed to public health nurses stationed in three regional public health offices in a large, urban county in Northern California. Thirty-two nurses responded to the survey. Pender’s Health Promotion Model was utilized as a theoretical framework to test and improve nurses’ screening skills by identifying perceived barriers to action and by assessing situational influences.
Design and Comparative Analysis of Consequent Pole Rotor Configurations in PM Vernier Motors for In-wheel Drive Application

In this paper, the interior consequent pole permanent magnet vernier motors (PMVM) with V-shaped PMVM (V-PMVM) and U-shaped PMVM (U-PMVM) are investigated and analyzed for in-wheel drive applications. Firstly, the torque comparison between the proposed PMVM and conventional PM motors is carried out through theoretical analysis. Then, the design parameters of V-PMVM and U-PMVM are optimized by sensitivity analysis to suppress the torque ripple and improve output torque capability. In order to fairly compare the performance, V-PMVM and U-PMVM of 20-pole-12-slot with same overall dimensions are selected for further analysis by finite-element analysis (FEA). Meanwhile, the V-PMVM of 40-pole-24-slot is also optimized for the comparisons, aiming at exploring the performance of different pole-slot combinations. Finally, the theoretical analysis results are validated by the FEA.
Development of Bidirectional AC-DC Converter for Energy Storage Systems

In this paper, we deals with the design problems of bidirectional AC-DC converters for charge/ discharge control and grid connection of energy storage system. The bidirectional DC-DC converter will be designed and implemented as a noninverting buck-boost type topology. The buck mode will be operated in the charge mode and the boost mode will also be operated in the discharge mode. In charge mode, the AC-DC converter will be controlled to operate as a bridge diode rectifier utilizing the reverse diode of the bi-directional switch. In the discharge mode, the six switches of the DC-AC converter will be controlled by the SPWM method.
Detecting Eccentricity and Demagnetization Fault of Permanent Magnet Synchronous Generators in Transient State

Eccentricity and demagnetization fault of a four-pole 1.5 kW surface mounted permanent-magnet synchronous-generator (PMSG) were modelled by using time-discretised finite element analysis (FEA). Both fault types are caused by magnetic asymmetry in the generator. The faulty behaviour of a PMSG under transient operating condition is studied with FEA. Two search coils were wound around stator teeth on opposite sides of the rotor. The induced voltage from these coils will be equal in healthy case. A fault is detected when the induced voltages are non-identical. The simulation results revealed that the envelope of the induced search coil voltage had sinusoids during dynamic eccentricity and demagnetization. Finally, a novel fault scheme is proposed to detect the mentioned faults during transient state.