Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Analysis of NIRS-EEG Motor Imagery Data Using Features from Spatial, Spectral and Temporal Domain

Brain-computer interface (BCI) systems, which provide users with an additional channel to communicate with external devices, have been mainly developed using electroencephalography (EEG) and near-infrared spectroscopy (NIRS). To complement each modality's pros and cons, various hybrid NIRS-EEG studies have been investigated. However, most studies focused on enhancing the classification accuracy rather than analyzing the characteristics of used features. This study aimed to investigate whether EEG features from spatial, temporal, and spectral domains would exhibit the diverse efficacy in hybrid NIRS-EEG BCI. Open access NIRS and EEG recordings of left/ right hand gripping imagery from twenty-nine healthy subjects were utilized. Common spatial patterns (CSP), time domain parameters (TDP), and power spectral density (PSD) were separately employed with NIRS to evaluate the discrimination performance. Within dataset, NIRS with CSP showed the highest classification accuracy with linear support vector machine (LSVM) classifier (mean accuracy, 71.4%). For kernel SVM (KSVM) classifiers, mean accuracy of NIRS with TDP features was lower than accuracy of only NIRS features (mean accuracy, NIRS: 53.1% and NIRS with TDP: 50.5%). The findings suggested that binary motor imagery tasks, which involve localized brain activation, could be enhanced by applying features including rich spatial information.
Velocity Control for Ripple Reduction in Permanent Magnet Synchronous Motors With Low Performance Current Sensing

In this study, a velocity control method is proposed for minimizing velocity ripples in permanent-magnet synchronous motors with low-performance current sensing. The proposed method involves the use of desired currents, peak estimation, and a proposed tracking controller. The desired currents are used to achieve velocity control with only the current sensing. Peak estimation is performed to estimate the peak of the measured distorted current that occurs due to low-performance sensing. The proposed tracking controller is developed based on a proportional-integral controller using the estimated peak obtained from the measured currents. The proposed method aids in reducing velocity ripples and improving velocity tracking performance of permanent-magnet synchronous motors. The primary contribution of this method is the improvement of velocity-control performance with low-performance current sensing. The stability of the closed loop is proven by employing the input-to-state stable property. To verify the effectiveness of the proposed method, we performed simulations and experiments with different three cases.
Low Complexity Equalization Method for ZP-OFDM under Highly Mobile Environments

Many Interferences from multipath fading channels in the mobile channels, there are many types of research trying to compensate for such interferences. The Zero-padded OFDM (ZP-OFDM) is a popular method that has much attention as an efficient technique to improve the performances. In the high-speed mobile channel, the Doppler frequency destroyed orthogonality among subcarriers and inter-carrier interference (ICI), It would degrade the bit error rate (BER) performance. We proposed both equalization (TDE) method and CIR estimation with less computation complexity for the ZP-OFDM signal that can solve this problem. The salient feature of our proposed method is using a training sequence (TS) instead of using Zero-padded which enables for estimation of CIR. And we show that the proposed method shows the less computational complexity when compared with the conventional frequency-domain equalization, the proposed TDE method employs an iterative solution instead of using the direct inverse matrix method. We validate the proposed methods presented by computer simulations under highly mobile environments. The simulation results show a comparison of the effectiveness of the proposed TDE method and conventional TDE methods.
A Novel Marine Oil Spillage Identification Scheme Based on Convolution Neural Network Feature Extraction From Fully Polarimetric SAR Imagery

Marine oil spill pollution has caused serious impacts on marine ecological environments, ecological resources and marine economy. Synthetic Aperture Radar (SAR), especially polarmetric SAR (PolSAR), has been proven to be a powerful and efficient tool for marine oil spill detection. In general, traditional oil spill detection methods mainly rely on artificially-extracted polarization characteristics, and the detection accuracy is limited by the quality of feature extraction. Recently proposed Convolutional neural network (CNN) is capable of mining spatial feature from large data set automatically. Inspired by these, in this paper we proposed a novel oil spill identification method based on multi-layer deep feature extraction by CNN. Firstly, PolSAR data are converted into a 9-channel data block to feed the CNN. Then, a 5-layer CNN architecture is built to extract two high-level features from the original data automatically. The features are fused after dimension reduction via principal component analysis (PCA). Finally, support vector machine method with radial basis function kernel (RBF-SVM) is utilized for classification. Three sets of RADARSAT-2 fully polarimetric SAR data were used in this study to validate the proposed method. The obtained results reveal that the proposed method provides competitive results in overall classification accuracy and kappa coefficient. Moreover, this method can improve the accuracy of oil spill detection, reduce the false alarm rate, and effectively distinguish an oil spill from a biogenic slick.
Optimal Capacitor Allocation in Sub-Transmission Networks to Mitigate Overloading Considering Harmonic Resonance

In conventional electricity grids, although power plants can support the active power of loads, transmission and sub-transmission networks deal with overloading situations in the transformers caused by reactive power of loads. Hence, local reactive power compensation of loads by capacitor allocation is an important solution. In this paper, the genetic algorithm (GA) is employed for the optimal capacitor allocation then the transformer tap changing is used to regulate the voltage profile in the buses. The tap regulation can eliminate the over-voltage, which is produced by capacitors installation. Eventually, harmonic analysis is applied to prevent harmonic resonance in capacitor banks. For validating the efficiency of the proposed method, it is examined in the typical transmission and subtransmission networks. The simulations are done in DIgSILENT software using DIgSILENT Programming Language (DPL) that is interfaced with MATLAB software.
A Context-Aware Map Matching Method Based on Supported Degree

Map matching technology is indispensable in the tide of building smart cities. The difficulty degree of matching depends on the matching context (road network density and GPS point quality). However, most existing map matching algorithms use same strategies under different matching contexts, which are hard to balance accuracy and efficiency. Therefore, we propose a new method of map matching, which includes two matching phases: projection matching (Strategy 1) for every GPS points and connectivity matching (Strategy 2) for portions without credible results from the first phase. Thereinto, supported degree is employed to judge the credibility of the projection matching result, which reflect the difficulty degree of matching in each region. In the connectivity matching phase, for matching complex portions, tree structure is creatively adopted, which can represent the connectivity between roads. Besides, we present novel tricks to increase the efficiency, such as considering road segment as the basic element of map matching and simplifying connectivity tree based on limiting-velocity. Finally, to evaluate the performance of this new method, we have compared it with conventional algorithms on the same dataset, which consists of 480,973 GPS points. The proportion of the error road segment length in total trajectory length is used as the criterion to estimate the matching accuracy of the algorithm. When the sampling period is 10s, the algorithm can improve the matching accuracy rate to over 95%. Meanwhile, the running efficiency of this algorithm is obviously better than other algorithms, in sampling period less than 100s.
System and application performance of function placement strategies for virtualized mobile fronthaul/backhaul networks

In this paper, we focused on the function placement problem of mobile cellular networks on the premise of integrated control of the fronthaul and the backhaul network. We construct the analytical model of the problem to derive the end-to-end delay and packet loss rate for application traffic, and the power consumption of the whole system. Through numerical evaluations, we present that by properly placing the network functions of the fronthaul and the backhaul networks, it is possible to reduce the end-to-end delay time of application traffic significantly, while the power consumption and packet loss rate remain almost unchanged.
Navigation Support Method Taking into Account Urban Canyon Limitations for Visually Impaired People

The primary purpose of this work is to create an application to help navigate for people with visual impairments in using urban transport. The relevance of the topic was established by analyzing the transport accessibility for people with visual impairments. As a result, a list of user requirements was determined. Analysis of the market and available analogs revealed the main advantages and disadvantages of existing software systems. The basis of the architectural decision is the principle of responsibility distribution. The software was created using the following technologies: Google Cloud Service, Google Speech-To-Text, Google Geocode API, Google Directions API, Google Maps API, Xamarin, Firebase, ASP.NET MVC.
The Large Area Picosecond Photodetector (LAPPD) 8" MCP-PMT: Recent Results

The Large Area Picosecond PhotoDetector (LAPPD) is a 200 mm square microchannel plate photomultiplier (MCP-PMT). It is a large area, position-sensitive, high speed, high gain device. It is suitable for detection of light such as Cherenkov or scintillation light. The large area is well-matched to large detection volumes, or imaging applications where tiling of small detectors is undesirable because of cost or lost sensitive area. Time resolution has been measured as a function of both MCP and photocathode voltage. A rapid improvement was observed as the photocathode voltage was raised to 50 volts. At higher voltages, the time resolution continued to improve, but at a diminishing rate. A similar improvement occurred with an increase in MCP voltage, up to a point beyond which there was no further benefit. This point corresponded to a gain of 3.2x106. Gain has been measured as a function of light pulse repetition rate. As the laser trigger rate was increased, the LAPPD gain fell to approximately half of the initial value at repetition rates of 12 to 38 kHz/mm2. These gain measurements were made in the mid 106 gain range. Gain recovery time has also been measured. The LAPPD was triggered with a laser repeatedly at a high rate, then allowed to recover before the next laser pulse. The gain recovered to its initial value with a 15 mS recovery time, and half the initial value with a 5 mS recovery time.
Sentiment Analysis of User Responses of Tourist Services

In the research the method of tonality analysis was used to find thoughts in the text and determine their properties. The tonality is considered the position of the author on the topic (usually "positive" or "negative") Content analysis of user’s responses using corps managers has been applied. This gives grounds to claim that based on the proposed technology it is possible to track the users responses of tourist services and to formulate recommendations for tourism firms for improvement of their activity. The application of sentiment analysis is aimed to improve the tourism industry.
Equivalent Complex Valued Deep Semantic Segmentation Network For SAR Images

The particularity and complexity of microwave scattering mechanism bring great challenges to target interpretation based on SAR image. In recent years, deep learning method has been applied to SAR interpretation with good results. However, because of the special characteristics of SAR image, the deep learning networks should be adapted so as to get better results. This paper makes an extension to the deep semantic segmentation network to handle the complex valued SAR images. In complex valued domain, the network effectively utilizes phase information of SAR data and provides an advantage for efficient image interpretation. An experiment of landcover classification in polarimetric SAR data is carried out, which verifies the effectiveness of the proposed network.
Optimal Scheduling of Air Conditioning Loads by Aggregator under Dynamic Price

The key to the response of the air conditioning load demand is to deal with the relationship between power and indoor temperature. In this paper, an optimal scheduling strategy for an aggregator to control residential air conditionings under dynamic electricity price is proposed. Firstly, an aggregated model of a group air conditioning load model is introduced based on a single air conditioning load model. And an optimization model for aggregator considering the users’ temperature comfort is established. Then, according to the aggregated model, the power and temperature of air conditionings are decoupled, which are easier to be solved in the demand response. In these decoupled problems, the indoor temperature is the only control variable. The control strategy of air conditionings is obtained by resolving the model. This strategy can be used by the aggregator to adjust the air conditioning temperature according to the dynamic electricity price so as to minimize the electricity consumption cost on the premise of guaranteeing the users’ comfort. Finally, the accuracy of the method is verified by an example.
Application Prospects of Compressed Sensing in Ubiquitous Internet of Things in Electricity

The application of Compressed Sensing (CS) in smart grids has become a trend. With the proposal of Ubiquitous Internet of Things in Electricity (UIoTE), CS will have a broader application prospect in this field. However, the research of CS in UIoTE has not yet been carried out while many key technologies in its application need to be explored, which should be based on application research of CS in smart grid. To this end, this paper studies the future application prospects and its methods of CS in UIoTE. Firstly, the application necessity of CS in UIoTE is analyzed in detail, and then, the research advances of CS in smart grid is summarized. On this basis, this paper focuses on the application of CS in UIoTE, proposing the application fields and research directions of CS in UIoTE as well as the application model of CS for information collection in UIoTE. Finally, this paper looks forward to the future design ideas of the three elements of CS in UIoTE. The work done in this paper aims to provide reference and guidance for research on CS in UIoTE.
DrowsyDet: A Mobile Application for Real-time Driver Drowsiness Detection

The number of accidents during driving is increasing day by day and drowsy driving has been implicated as a causal factor of road accidents. Research on driver drowsiness detection is important to improve road traffic safety. However, most of the conventional methods are intrusive since they often require expensive sensors. Moreover, their performances are rather limited since only single features and shallow classifiers are adopted. In this paper, we propose DrowsyDet, a real-time mobile application to detect driver drowsiness. DrowsyDet consists of the following steps. Firstly, the facial region and landmarks are extracted with a face detection model and landmark model. Secondly, three CNN (Convolutional Neural Networks) models are built to classify facial drowsiness state, eyes state, and mouth state respectively. Finally, the drivers' state (i.e. normal, yawn, and sleep) is obtained based on all the models' results. All the models are lightweight CNN models that can be deployed on a mobile phone. Compared with existing methods, DrowsyDet is non-intrusive since it only requires a mobile phone without the Internet. We also build a large driver drowsiness dataset to test our approach, which includes different genders, ages, head poses, and illuminations. Experimental results demonstrate that DrownsyDet can reach a significantly high average accuracy of 97.8%.
GeminiNet: Combine Fully Convolution Network With Structure of Receptive Fields for Object Detection

Pneumonia is a relatively common disease that will endanger the lives of patients if left untreated. End-to-end detection of pneumonia using neural networks will be helpful for reducing related workforce. CNN’s processing of images shows remarkable performance, naturally, the use of CNN based methods for assisted reading will be a trend in modern medicine. The property of current detection algorithms is not yet satisfactory, so further research is extremely needed. In this article, we design GeminiNet to identify and localize the pneumonia in Chest X-ray (CXR) images. It uses a popular fully convolution architecture with computation shared on the entire image, combining RoI Align and PSRoI Pooling to capture global and local information and output. Our approach introduces DetNet59, a network designed specifically for detection to capture deep features. In the sixth stage of DetNet59, the structure of the retina-like convolutional layers is added to replace the fully connected layer. This structure uses the dilated convolution to extend the reconstructive field, and the convolution kernels of three different scales are used for parallel calculation to collect rich feature information. GeminiNet is validated on the RSNA dataset. We augment dataset by flipping on horizontal and vertical for the small amount of data. At IoU (Intersection over Union) =0.5, AP reached 0.4575, 0.078 higher than ResNet50, and reached 0.7758 on the AUC indicator. GeminiNet achieves 8fps in detection speed, which is better than the 7fps of the popular Faster R-CNN architecture.
Sequence Alignment using PSoC

Today's computational genomics performs a very large scale database search of genomic data. The difficulty lies in extracting information from these databases to obtain optimum alignments. The alignment score is a critical parameter to find the similarities between two DNA sequences. The higher score implies high ratio of similarity. In the current paper global alignment based on Needleman-Wunsch Algorithm and local alignment based on Smith-Waterman Algorithm are implemented on PSOC4 with a core comprising of ARM Cortex-MO and the alignment score is calculated. The paper presents implementation and comparison of both algorithms based on different parameters like the execution time.
Power Load Prediction Based On An Improved Clock-Work RNN

Through accurate power load prediction, the smart grid can provide more efficient, reliable and environmentally friendly power services than the traditional power grid. In recent years, Recurrent Neural Networks (RNNs) have received more and more attention in power load prediction because traditional machine learning models cannot capture the time dependencies that often exist in power load data. However, due to the self-connection of the hidden layer, the vanishing gradient problem is very easy to occur with the increasing depth of the simple RNN, which leads to the decline of the prediction accuracy. In order to solve this problem, this paper adopts the recently developed RNN architecture called Clock-Work RNN (CW-RNN) on the task of one day ahead prediction. The research focuses on the construction strategy of recurrent connection matrix related to the hidden layer in CW-RNN. An improved CW-RNN named CW-RNN-SCR adopts the strategy of connecting adjacent modules into a simply closed ring, which not only improves the prediction accuracy but also reduces the number of parameters required for training. Experimental results show that the improved CW-RNN can achieve better performance than the traditional RNN architectures.
Data Mining Based Prediction of Malnutrition in Afghan Children

Proper nutrition is an essential component for the survival, growth, and development of children in society. Malnutrition is a global problem in today's life. The primary target of this phenomenon is children under five years of age and mostly for developing countries. In this study, a data mining approach is proposed to predict the malnutrition status of children under five years of age in Afghanistan. Random Forest, PART rule induction, and Naïve Bayes classifiers were applied to Afghanistan Nutrition SMART Survey data. The results were compared with Logistic Regression statistical method. Random Forest and PART rule induction classifiers performed well with the highest accuracy for stunted, underweight, wasted, and nutritional oedema statuses with and without transformed attributes. This study defines how data mining classification techniques can classify malnutrition status for children under five years of age. Generally, our method was the one that obtained the most robust results to predict the malnutrition status based on clinical sign and anthropometric parameters of preschool-age Afghan children.
An Evaluation Method of Intelligent Wind Power Operation and Maintenance Based on Cyber-Physical-Social Systems

With the increasing complexity of industrial system, the development of Cyber-Physical-Social Systems (CPSS) based on Cyber-Physical-Systems (CPS) has become a trend. In this paper, the basic framework of wind power operation and maintenance CPSS is constructed, and an intelligent evaluation method of operation and maintenance engineers based on Bayesian psychometric model is proposed. Taking the rotor cognitive ability of wind turbine as an example, the capability model is constructed. Then, the evidence variables of appropriate granularity, which are involved in the practical task of rotor winding insulation fault diagnosis, are extracted to design the evidence model. According to the Item Response Theory (IRT), the relationship between the potential variables in the model is estimated, and the Graded Response Model (GRM) and the Effective Theta method are used to adjust the appropriate parameters. Finally, the evaluation results are obtained by simulating the model with Winbugs. The method is used to test the operation and maintenance engineers of a wind power company, and the results show that the proposed method is feasible and it can effectively improve the operation and maintenance efficiency of wind power company.
Analysing the Changing Brain: Immediate Brain Plasticity After One Hour of BCI

In this brief contribution I will discuss recent directions of our research where nonlinear learning methods are employed for analysing multimodal brain data, both in the context of BCI and beyond - essentially summarizing some steps taken by the BBCI team and co-workers. Clearly, unavoidably and intentionally this abstract will have a high overlap to prior own contributions and touch upon ongoing unpublished respectively pre-pulished work, nevertheless extensively providing pointers to various research directions.