Miracle-dz/UniFAR-FTUs huggingface dataset:
Driver Drowsiness Detection Using Multi-Channel Second Order Blind Identifications

It is well known that blink, yawn, and heart rate changes give clue about a human’s mental state, such as drowsiness and fatigue. In this paper, image sequences, as the raw data, are captured from smart phones which serve as non-contact optical sensors. Video streams containing subject’s facial region are analyzed to identify the physiological sources that are mixed in each image. We then propose a method to extract blood volume pulse and eye blink and yawn signals as multiple independent sources simultaneously by multi-channel second-order blind identification (SOBI) without any other sophisticated processing, such as eye and mouth localizations. An overall decision is made by analyzing the separated source signals in parallel to determine the driver’s driving state. The robustness of the proposed method is tested under various illumination contexts and a variety of head motion modes. Experiments on 15 subjects show that the multi-channel SOBI presents a promising framework to accurately detect drowsiness by merging multi-physiological information in a less complex way.
Multiuser Detector for Uplink Grant Free NOMA Systems Based on Modified Subspace Pursuit Algorithm

Multiuser detection schemes are essential in any multiuser user access technology. Their importance cannot be over emphasized in a non-orthogonal multiple access (NOMA) system, a multiuser access technology that has been proposed for the fifth generation (5G) wireless networks. This paper proposed a multiuser detection (MUD) scheme, named the modified subspace pursuit (SP)-based MUD, for use in uplink grant free NOMA systems. In developing the scheme, temporary correlation of active user sets between adjacent time slots together with side information, a-priori knowledge of the active user support set's estimate, are exploited. The proposed MUD performs signal detection in a continuous time slots while user activities changes within a transmission frame in contrast to the traditional orthogonal matching pursuit (OMP)-based MUD that performs similar detection with the assumption that user activity remains unchanged within a whole frame. Computer simulation results show that the proposed modified SP-based MUD achieves better performance in comparison with the performances of the traditional OMP-based MUD and its variant, the computational efficient OMP-based MUD considered in this paper.
A New Method for Time Series Signal Decomposition

Because extending methods play a very important part in Empirical Mode Decomposition (EMD) given by Huang et al in 1998. One new efficient method for several time series signal decomposition based on EMD is presented in this paper. The method is tested to several time series decompositions. We have compared the results with those of N. E. Huang et al. and D. J. Huang et al. The comparison shows that this method is more feasible and more precise.
Broadband Planar SIW Cavity-Backed Slot Antennas Aided by Unbalanced Shorting Vias

Two types of substrate integrated waveguide (SIW) cavity-backed slot (CBS) antennas are proposed for bandwidth enhancement. First, a quad-resonance SIW CBS antenna is proposed using a cross-shaped slot and loading unbalanced shorting vias. Additional half-<inline-formula><tex-math notation="LaTeX">$\textrm {TE}_{120}$</tex-math></inline-formula> mode is successfully excited along with three other independent modes including half-<inline-formula><tex-math notation="LaTeX">$\textrm {TE}_{110}$</tex-math></inline-formula> and odd and even <inline-formula><tex-math notation="LaTeX">$\textrm {TE}_{210}$</tex-math></inline-formula> modes. Next, a penta-resonance SIW CBS antenna with a cross-shaped slot is proposed, in which two pairs of shorting vias are loaded. Two additional hybrid modes of half-<inline-formula><tex-math notation="LaTeX">$\textrm {TE}_{210}$</tex-math></inline-formula> and half-<inline-formula><tex-math notation="LaTeX">$\textrm {TE}_{120}$</tex-math></inline-formula> modes are introduced along with three other independent modes including half-<inline-formula><tex-math notation="LaTeX">$\textrm {TE}_{110}$</tex-math></inline-formula> and odd and even <inline-formula><tex-math notation="LaTeX">$\textrm {TE}_{310}$</tex-math></inline-formula> modes. Furthermore, the working mechanism of these two types of SIW CBS antennas is explained. Finally, prototypes of these two antennas are fabricated and measured at <italic>X</italic>-band. Their measured fractional bandwidths are <inline-formula><tex-math notation="LaTeX">$\text{20.0}\%$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\text{20.8}\%$</tex-math></inline-formula>, respectively. Simulation and measurement results agree very well, which confirms the validity of the proposed designs.
On Interference Alignment Based NOMA for Downlink Multicell Transmissions

The upcoming wireless communication systems are expected to integrate a number of nodes remarkably greater than those observed in current technologies, while offering a sensibly improved service quality for critical applications. This generates a need for innovative schemes to share the available resources among the served terminals as well as to increase the system efficiency and node fairness. Aiming to this objective, we propose a combination of non-orthogonal multiple access and interference alignment schemes applied to the downlink transmissions in a multi-cell environment. The two methods presented in this work enable an efficient reutilization of resources and the suppression of intra-and inter-cell interference in a single step during signal reception. We derive the expressions for the feasibility of our proposed solution from an analysis applied to generic system configurations. Additionally, we show numerical results that highlight the benefits of this scheme in a system setup resembling an Internet-of-things scenario.
A Real Time EEG Analysis System for the Prediction of Clinical Antidepressant Responses

Major depressive disorder (MDD) is increasingly to be recognized as a chronic, deteriorating illness with the high risk to obtain comorbidity. In order to provide clinicians with a subjective approach to decide appropriate treatments for MDD patients, a real time detection system for predicting the antidepressant responses is important. Wavelet Transform and five nonlinear methods Largest Lyapunov Exponent (LLE), Detrended Fluctuation Analysis (DFA), Fractal Dimension (FD), Correlation Dimension (CD) and Approximate Entropy (ApEn) were applied to extract the features from electroencephalography (EEG) activities in antidepressant responses. Non-parametric analysis, correlation analysis and confusion matrix were employed to evaluate the performance of classifying and decide the optimal threshold for discrimination. Moreover, the system is built to aid clinicians' in prediction of the antidepressant responses before treatments and the results can be viewed within 40 seconds.
Multiple Classifiers Global Dynamic Fusion Location System based on WiFi and Geomagnetism

The existing WiFi and geomagnetism based positioning methods using single classifier show low accuracy because they are sensitive to changing environments. In this paper, we propose a global dynamic fusion location algorithm for multiple classifiers based on WiFi and geomagnetic fingerprints. In the offline phase, we first divide a positioning environment into some grid points and construct RSS and geomagnetic fingerprints for each grid point. Then, we train multiple classifiers by using the constructed fingerprints. Second, we derive a global dynamic fusion weight training method for each grid point through the global supervised optimization learning. In the online phase, given an RSS testing sample, we select the matching weights for fusion by using K-nearest neighbor (KNN). Our proposed multiple classifiers global dynamic fusion algorithm can make full use of the intrinsic complementarity of multiple classifiers, thus effectively improving the positioning accuracy of RSS and geomagnetic fingerprints. Experimental results show that the proposed algorithm outperforms some existing methods in complex indoor environments.
RobustICA, Kurtosis- and Negentropy-Based FastICA in Maternal-Fetal ECG Separation

To separate maternal-fetal ECG is crucial for fetal ECG collection in non-invasive methods. Three ICA (Independent Component Analysis) methods, RobustICA, kurtosis-based and negentropy-based FastICA, are employed to extract fetal ECG in our contribution. Synthesized maternal-fetal ECG mixed by heart waves from the Physionet and real-world maternal-fetal ECG from the DaISy are employed to test the three algorithms. Test results show that the three ICA methods all effectively extract maternal and fetal ECG from the mixed signals. RobustICA has greater advantages in separation speed and accuracy, while its stability, indicated by larger standard deviation, is lower than the two FastICA algorithms. FastICA based on negentropy has higher accuracy and robustness than the kurtosis-based FastICA, but it is inferior in separation speed.
Research of EEG Signal Based on Permutation Entropy and Limited Penetrable Visibility Graph

Complex networks can be seen as a way of describing complex systems. Starting from the end of the twentieth Century, the theory of complex network has gradually penetrated into all fields of social science, and it has become one of the most important tools for people to solve the problem. The complex network theory is helpful in studying the interaction between different brain regions, topology structure and the dynamic information, as well as the relationship between disease and physiological function. Electroencephalogram(EEG) is an important tool for the disease diagnosis and prediction. The paper adopts Permutation Entropy(PE) and Limited Penetrable Visibility Graph(LPVG) algorithm to construct the complex networks and implement networks visualization. Using this method to research 21 normal people and 21 epilepsy EEG signal, in addition compare statistical characteristics of different brain networks. The results verify the validity of the PE and LPVG algorithm for analyzing brain functional networks and show that the properties of the different attention EEG are different. This method provides important reference for further study of the brain function network dynamics of epileptic EEG signals.
On Prediction of Image Denoising Expedience Using Neural Networks

Assessment of image visual quality is important for different stages of image processing chain. One of them is denoising that can either improve image quality or remain it the same (or even degrade). Our paper concerns the question on whether or not the use of denoising is expedient for a given image. A common model of additive white Gaussian noise (AWGN) is used. Analysis of denoising expedience is based on prediction of visual quality for acquired noisy image. A peculiarity of the proposed approach is that it employs a trained neural networks (NN) and does not require a priori known noise variance. The NN uses features extracted from spectral domain as inputs and carries out prediction of visual quality metrics for noisy image. The prediction is much faster than denoising itself and appropriately accurate. Besides, this paper evaluates the existing full-reference image quality metrics with application to image denoising using Spearman rank-order correlation coefficient (SROCC). The best SROCC values do not exceed 0.81. The image database and codes are available online at https://github.com/ViA-RiVaL/Image-Denoising-Expedience/.
A Chopper Stabilized Current-Feedback Instrumentation Amplifier for EEG Acquisition Applications

A low-noise, low-power, chopper-stabilized, current-feedback instrumentation amplifier (CFIA) for bio-potential signal acquisition applications is presented. The proposed design includes an ac-coupled chopper-stabilized CFIA to reduce 1/<inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> noise. It also has a switched-capacitor integrator to reduce the input offset and provide a high-pass filter. The proposed amplifier is designed using a Samsung 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process. The CFIA shows an rms input referred noise voltage of <inline-formula> <tex-math notation="LaTeX">$0.75~\mu \text{V}$ </tex-math></inline-formula> within a bandwidth of 0.01–100 Hz, a common-mode rejection ratio of 125 dB, and total power dissipation of <inline-formula> <tex-math notation="LaTeX">$2.3~\mu \text{W}$ </tex-math></inline-formula> at a power supply voltage of 1 V.
A PREDICTIVE SELF-CONFIGURING SIMULATOR FOR ONLINE MEDIA

This paper describes the design, implementation, and early experiences with a novel agent-based simulator of online media streams, developed under DARPA’s SocialSim Program to extract and predict trends in information dissemination on online media. A hallmark of the simulator is its self-configuring property. Instead of requiring initial set-up, the input to the simulator constitutes data traces collected from the medium to be simulated. The simulator automatically learns from the data such elements as the number of agents involved, the number of objects involved, and the rate of introduction of new agents and objects. It also develops behavior models of simulated agents and objects, and their dependencies. These models are then used to run simulations allowing for future extrapolations and "what if" analyses. Results are presented on using this system to simulate GitHub transactions. They show good performance in terms of both simulation accuracy and overhead.
DEVS-OVER-ROS (DOVER): A FRAMEWORK FOR SIMULATION-DRIVEN EMBEDDED CONTROL OF ROBOTIC SYSTEMS BASED ON MODEL CONTINUITY

Designing hybrid controllers for cyber-physical systems raises the need to interact with embedded platforms, robotic applications being a paradigmatic example. This can become a difficult, time consuming and error-prone task for non-specialists as it demands for background on low-level software/hardware interfaces often falling beyond the scope of control designers. We propose a simulation-driven methodology and tool for designing hybrid controllers based on a model continuity approach. The simulation model of a controller should evolve transparently from a desktop-based mocking up environment until its final embedded target without the need of intermediate adaptations. DEVS-over-ROS relies on the DEVS framework for robust modeling and real-time simulation of hybrid controllers, and on the ROS middleware for flexible abstraction of software/hardware interfaces for sensors and actuators. We successfully tested DoveR in a case study where a custom-made crafted robotic system is built concurrently with the design of its controller.
ADHD classification by feature space separation with sparse representation

In recent years, increasing attention has been paid to Attention Deficit Hyperactivity Disorder (ADHD) as one of the most common neurobehavioral diseases in school-age children. In this paper, sparse representation is used as an effective tool to analyze the corresponding functional brain connectivities from functional MRI data. Two diagnosis models, i.e., ADHD and healthy control models, are employed to deal with the ADHD patients and control subjects. In these models, we learn the feature spaces of ADHD and control components adaptively via dictionary learning. Moreover, we also perform an energy operation as a penalty term in the models, which forces the feature energy of subjects minimized in the wrong feature spaces. Following these models, we carefully design a framework for ADHD classification, where more sophisticated methods are adopted including synthetic minority oversampling technique and SVM. In the experiments on ADHD-200 dataset, our method can achieve a remarkable accuracy compared with several state-of-the-art classification methods.
DCH-Net: Densely Connected Highway Convolution Neural Network for Environmental Sound Classification

Environmental Sound Classification (ESC) plays a vital role in the field of machine auditory scene. Recently, the Highway Network CNN model has achieved the state-of-art results via solving the vanishing-gradient problem of much deeper CNN. However, carefully analyzing the Highway Network model shows that the Highway Network model lacks ability to maximize information flow between layers, which is essentially benefits the discriminative representation of acoustic events. Besides, the Highway Network model size is larger than 20MB for ESC task, which is still large for mobile applications. Regarding to these two issues, in this study, we propose a novel Densely Connected Highway Convolutional Network (DCH-Net) model for ESC task. Specifically, a densely highway module is developed which is able to ensure the maximum information flow between layers by connecting all layers directly with each other. Besides, to reduce the model size, a global average pooling layer is designed which replaces the traditional fully connection layers and the parameters of the model is greatly reduced. Experimental results show that our DCH-Net ESC model achieves accuracy of 69% and 90% on ESC50 and ESCIO dataset respectively, which is 2% and 10% higher than that of Highway Network based Highway networks ESC model. Meanwhile our model size is only 2MB.
Overview and Analysis of Existing Decisions of Determining the Meaning of Text Documents

The paper reviews and analyzes existing solutions for determining the meaning of text documents. Typical questions and problems of determining the meaning of text documents used in modern systems are discussed. The most used models and methods of semantic text processing are considered, as well as the classical process of text processing in semantic analysis. In the course of the analysis, it was found that when using the models considered, in practice, a partial loss of meaningful meaning of the text occurs, which in turn is not always justified, although it allows performing certain procedures of semantic analysis, although it misses the features. The most frequently used libraries of semantic analysis are analyzed in different programming languages such as Java, Rybi, C #, PHP. It should be noted that the considered libraries of semantic analysis operate in the same way, the results of their work are quite similar. Therefore, when developing, it is necessary to take into account specific goals and tasks that the developer must solve, as well as in what functional-style types of texts are analyzed. It is also expedient to perform a statistical analysis of the work of these libraries, on the same tasks and texts. It was found that it is advisable to develop a single system for solving the problem of analyzing and evaluating texts according to different criteria, for example, analyzing texts for text borrowings and borrowing ideas, as well as for determining the authorship of the text, taking into account its paraphrasing.
Convolutional Neural Networks for Image Denoising in Infocommunication Systems

Image denoising is an urgent task that arises in image processing and transmission. Images are an integral part of content transmitted in infocommunication systems. Traditional statistical image filtering algorithms are not always effective for the random nature of the noise spectrum. Image denoising by convolutional neural networks is a modern and effective approach. The article is devoted to demonstrate the possibilities of using denoising convolutional neural networks to solve one of the most difficult tasks that developers face when performing the transfer of graphical information in infocommunication systems – denoising. Unlike traditional algorithms, denoising convolutional neural networks have architecture features that allow them to perform effectively image filtering with unknown noise level. It is suggested to use denoising convolutional neural networks to generate a correction signal in the infocommunication system, which transmits a noisy image. The article proposes to use denoising convolutional neural networks to generate a correction signal in the infocommunication system, which transmits a noisy image. Such pre-trained correction elements on a large and diverse image dataset can be easily fine-tuned for special filtering tasks.
Distributed Economic Dispatch Control via Saddle Point Dynamics and Consensus Algorithms

In this brief, a distributed control algorithm is proposed to solve the economic dispatch problem. Without a central control unit, the generators work collaboratively to minimize the generation cost while balancing the supply and demand. The proposed method is based on consensus protocols and the saddle point dynamics. The consensus protocols are employed to estimate the global information in a distributed fashion, and the saddle point dynamics are leveraged to search for the optimal solution of the economic dispatch problem. By utilizing Lyapunov stability analysis, exponential stability of the optimal solution is derived if the capacity limits of the generators are not considered; with the capacity limits, practical stability of the optimal solution is obtained. No global information is needed in the proposed method, and the requirement on initial conditions of the state variables is mild. Several case studies on the IEEE 9-bus and IEEE 118-bus systems are presented to demonstrate the effectiveness of the proposed algorithms.
Fuzzified Histogram Equalization based Gamma Corrected Cosine Transformed Energy Redistribution for Image Enhancement

A non-iterative fuzzy-based approach is proposed here, for image quality enhancement by processing image in histogram-domain followed by subsequent processing in the cosine-transformed domain. This method employs gamma correction through an adaptively derived gamma value-set, which is just an association of different gamma values for individual intensity levels. For proper evaluation of highly adaptive gamma value-set, the fuzzy derived input histogram followed by the content adaptive thresholding is applied and followed by fuzzified histogram’s sub-equalization. Subsequently, its cumulative distribution inspired adaptive gamma value set is derived for interim intensity channel improvement. In addition to this, two-dimensional (2-D) discrete cosine transformation (DCT) based decomposition for gamma corrected channel in accordance with energy dependent image texture improvement framework is effectively suggested. In other words, the core approach proposal can also be understood an excellence achieved by dealing up the trade-off among fuzzified smoothing based information harvesting along with DCT based textural amplification by magnifying the lower energy coefficients by maintain the image adaptive thresholding. The experimental results derived here, effectively advocates the excellence of the proposed framework when compared in context of the state-of-the-art methodologies.
Finite-Time Convergent Control of Electrohydraulic Velocity Servo System Under Uncertain Parameter and External Load

The disturbances suppression is one common control problem in electrohydraulic systems (EHSs) since both unknown external load and hydraulic parametric uncertainty often obviously degrade the tracking performance and bias the load pressure of EHS. In this paper, a finite-time convergent controller (FTCC) is tried to use in EHS to address this problem. Different from the asymptotic convergent controller, this FTCC not only improve the dynamic and steady tracking performance of the velocity servo system but also guarantee the system state error convergent to zero in a finite time. According to the finite-time stable principle, the FTCC is derived by backstepping and fractional-type Lyapunov techniques. The effectiveness of the proposed controller is verified by comparative simulation and experimental results with the other traditional and advanced controllers.