Miracle-dz/UniFAR-FTUs huggingface dataset:
An Automated System for Diagnosis of Sleep Apnea Syndrome Using Single-Channel EEG Signal

Today, a large number of people all around the world are affected or threatened by different kinds of sleep disorders, especially sleep apnea syndrome (SAS). Since EEG activities during sleep exhibit physiological and pathological situation of the brain, analyzing the oscillations during various sleep stages are of fundamental importance for the assessment of sleep disorders. On the other hand, recent developments in the fields of signal processing and pattern recognition have prompted sleep researchers to employ accurate and reliable computerized systems in diagnosis processes. The purpose of this paper was to develop a novel automated methodology for diagnosing apneic patients from healthy subjects using single-channel EEG signal. To this end, it was concentrated on the rapid eye movement (REM) sleep. A comprehensive time-domain based feature extraction scheme was proposed for extracting features from various EEG frequency bands of REM sleep. Uninformative features were eliminated by minimal redundancy maximal relevance (mRMR) feature selection algorithm. An ensemble learning method namely random undersampling boosting (RUSBoost) was then utilized for the purpose of classification. The effectiveness and generalizability of the proposed approach were evaluated using k-fold cross-validation technique. The experimental results demonstrate that the proposed scheme can be used as an efficient method for automated SAS screening with regard to promising classification performance in terms of accuracy, sensitivity, and specificity.
Comprehensive Overview of Low Voltage Ride Through Methods of Grid Integrated Wind Generator

The wind power generation is a rapidly growing grid integrated renewable energy (RE) technology with an installed capacity of 539.291 GW. The capability of the wind energy conversion system (WECS) to remain integrated into the utility network in the case of low voltage events is called low-voltage ride-through (LVRT) capability. This paper offers a comprehensive overview of improvement techniques of the LVRT capability in WECS to increase the wind energy penetration level in the utility grid. Exhibited portrait manifests a broad spectrum of 1) wind turbines, 2) electrical generators used for wind power applications, 3) international grid codes applicable for grid integration of WECS, 4) LVRT fundamentals in WECS, 5) wind turbines LVRT methods by doubly fed induction generator (DFIG), 6) wind turbines LVRT methods by permanent magnet synchronous generators (PMSG), and 7) LVRT methods of wind turbines using squirrel cage induction generator (SCIG). This ready-reckoner paper critically reviews and classifies more than 190 research papers on LVRT issues, practices, and available technologies for grid integration in wind energy systems, and it aims to be a quick reference for the researchers, designers, manufacturers, and engineers working in the same field.
An Anonymization Service for Software-Defined Networks

This paper presents a service to make the anonymization in SDN to guarantee the availability of the services in the network through the concealment of the stations. Because of this it was developed the anonymizer BomIP using C Language and Libpcap Library, then it was included as a service on RunOS OpenFlow Controller. To validate this service it were made three cases studies on a simulated environment of a Denial of Service attack. The results shows that BomIP has a running time 65% better than others anonymizers, besides BomIP guarantees that all packets can be tracked and a mitigation of 80% from the attacks trials, supporting the services provides by the newtork to continue running.
A Wideband Inductorless LNA Employing Active Complementary Current-Reuse Balun

This paper presents a new fully-integrated CMOS single-to-differential low-noise amplifier (LNA) topology for cognitive radios. Operating from 50 MHz to 5 GHz, the proposed wideband LNA uses two current-reuse shunt-feedback (SFB) amplifiers as the core circuit and an inverter amplifier as the active balun. The transconductance of the amplifiers is boosted by utilizing both nMOS and pMOS transistors in complementary structures. Designed in TSMC 0.18 µ m CMOS technology, the LNA achieves a voltage gain of 25.3 dB and a noise figure of 3.6 to 5.0 dB, with a power dissipation of 16.7 mW. The complementary characteristics leads to partial second-order distortion cancellation. The achieved IIP3 and IIP2 are from 0 to 9.5 dBm and > 31 dBm, respectively. The proposed inductorless balun-LNA is post-layout simulated and it occupies only 0.0094 mm2.
Design and Analysis of Mutually Coupled SRMs for Low Torque Ripple Applications Using Standard Voltage Source Inverters

Mutually coupled switched reluctance motors (MC-SRMs) are alternatives in the family of reluctance motors that can overcome switched reluctance motor's (SRM) system level integration and control challenges by utilizing standard three-phase voltage source inverters (VSIs). The objective of this work is to investigate MCSRM torque ripple minimization from the machine design aspects in order to reduce the burden on current controllers. In this work, two different topologies of 12/8 MCSRMs, full pitched and concentrated wound, are designed and analyzed for high-performance applications with stringent torque ripple requirements, such as the electric power steering (EPS). The design targets and constraints, initial sizing process, along with the design guidelines are illustrated. Optimization analysis is performed based on Genetic Algorithm (GA) with the objective of minimal torque ripple design. The torque ripple is limited to 17% without any current profiling or torque sharing functions. Design guidelines are discussed to select turn numbers for low voltage and low ripple applications. The considered topologies are then compared based on torque ripple, torque density, efficiency, etc. Designed models are analyzed with finite element (FE) simulation tools and the parameters affecting the torque ripple for both the topologies are discussed.
Comparison of Direct Digitization Current Sensing Circuits for EIS

In this work, a qualitative comparison between state of the art current recorders for biomedical applications is presented. Prior work showed that direct digitizing frontends outweight their analog counterparts. This work performs a comparison between the direct digitization frontends, namely time-based and current-input CT∑ΔM frontends. It is shown that time-based converters - due to usage of current conveyors to generate a low impedance node - suffer from inferior linearity. Also the trade-off between SNR versus conversion time limits their achievable SNR and the maximum input signal frequency. On the other hand, with the usage of a FIR-DAC within a current-input CT∑ΔM, the requirements of the critical first amplifier are highly relaxed. In addition, the FIR-DAC reduces the influence of clock jitter and allows the current-input CT∑ΔM to achieve a higher SNR and linearity compared to the time-based converters.
Research on Security Protection Technology System of Power internet of things

With the rapid development of Internet of Things applications, the power Internet of Things technologies and applications covering the various production links of the power grid "transmission, transmission, transformation, distribution and use" are becoming more and more popular, and the terminal, network and application security risks brought by them are receiving more and more attention. Combined with the architecture and risk of power Internet of Things, this paper first proposes the overall security protection technology system and strategy for power Internet of Things; then analyzes terminal identity authentication and authority control, edge area autonomy and data transmission protection, and application layer cloud fog security management. And the whole process real-time security monitoring; Finally, through the analysis of security risks and protection, the technical difficulties and directions for the security protection of the Internet of Things are proposed.
Automatic User Categorization Through Large Transaction Data

Nowadays, transaction data of users on the network platform has become important raw materials to analyze their purchase behaviors and consumption patterns. In fields such as advertising and marketing, it is important to identify certain groups of users to target. Therefore, an automatic user categorization method is meaningful. In this paper, we introduce a user categorization method on e-commerce transaction data based on the products that users are interested in. The categorization method introduces a novel distance metric for two users and an efficient clustering algorithm based on it. We conduct our experiments on the yelp website, and the dataset with 1.2 million users are tested. Compared with two commonly used clustering algorithms, the proposed method shows the best efficiency, which is suitable for large data sets. Furthermore, the clusters of users obtained are summarized by keywords which are extracted from their review comments.
Capsule-Based Persian/Arabic Robust Handwritten Digit Recognition Using EM Routing

In this paper, the problem of handwritten digit recognition has been addressed. However, the underlying language is Persian/Arabic, and the system with which this task is a capsule network (CapsNet) which has recently emerged as a more advanced architecture than its ancestor, namely CNN (Convolutional Neural Network). The training of the architecture is performed using Hoda dataset, which has been provided for Persian/Arabic handwritten digits. The output of the system, clearly outperforms the results achieved by its ancestors, as well as other previously presented recognition algorithms.
Digital Background Calibration for Binary DACs in Continuous Time Delta Sigma Modulators

$A$ digital background calibration technique was proposed to estimate and correct the mismatch error related to the elements of a multi-bit binary DAC applied in a continuous time delta sigma modulator (CTDSM). Due to the large mismatch error in binary-weighted DACs, the use of these in delta-sigma modulators rarely happens. In order to make it possible to use of binary DAC a digital technique is presented. This study aimed to find the error associated with each binary DAC element using the correlation technique between a random signal and a modulator output. Results of simulation on a third-order six-bit delta sigma demonstrated that this method can estimate the error value of each element with high precision and raise the modulator accuracy up to an ideal value, independent of the OSR value. Compared to a six-bit unary DAC calibrated in the same way in which the coefficient of error of 26–1 elements should be calculated, this number was reduced to 6 coefficients in a binary DAC and thus, the time and circuit necessary for calibration significantly decreased.
Classification of the Air Quality Level based on Analysis of the Sky Images

Air pollution is one of the world's most substantial environmental health risks, and efforts are being made to reduce its adverse impacts on human health. The prime step in preventing the threats posed by air pollution is devising tools for reporting the air quality to the public by which they are able to make proper decisions to reduce the exposure to the pollution. In this paper, we present an image-processing-based method for classification of the air quality level (AQL) by leveraging on image analytics and computer vision techniques. By analyzing an image taken of the sky and extracting useful features from it, the proposed method can accurately classify the AQL and is suitable to be embedded into portable devices such as smartphones to inform the users of the air pollution exposure status. A dataset of 3422 images, called ImSkySet, was built and used to extract features for training the classifier. We have thoroughly evaluated the performance of the system and reported the experimental results which show an average accuracy of approximately 82.02%. Our results provide a promising case for the benefits of utilizing image processing approaches to monitor the air quality for the purpose of raising public awareness.
Multivariate Mixed EWMA-CUSUM Control Chart for Monitoring the Process Variance-Covariance Matrix

The dispersion control charts monitor the variability of a process that may increase or decrease. An increase in dispersion parameter implies deterioration in the process for an assignable cause, while a decrease in dispersion indicates an improvement in the process. Multivariate variability control charts are used to monitor the shifts in the process variance-covariance matrix. Although multivariate EWMA and CUSUM dispersion control charts are designed to detect the small amount of change in the covariance matrix but to gain more efficiency, we have developed a Mixed Multivariate EWMA-CUSUM (MMECD) chart. The proposed MMECD chart is compared with its existing counterparts by using some important performance run length-based properties such as ARL, SDRL, EQL, SEQL, and different quantile of run length distribution. A real application related to carbon fiber tubing process is presented for practical considerations.
Module Connection Topologies and Interleaving Strategies for Integrated Modular Motor Drives

The search towards increasingly high power density in electric drives led to the integration of the power electronics in the housing of the electrical machine. To obtain such a socalled integrated modular motor drive, the machine is split into several modules, with their own dedicated converter and control signals. The individual control of the modules allows to eliminate unwanted current harmonics by means of multiphase control. However, the integration of the power electronics in the machine housing imposes a stringent volume constraint on the DC link capacitor. In order to be able to downsize the DC link capacitor, without compromising the additional degrees of freedom that allow to compensate current harmonics, a new interleaving strategy is proposed in this article. Simulations and experimental results for a 4 kW test setup show that multiphase control can be combined with interleaving, resulting in both a reduction in stator current harmonic distortion and DC link current ripple. Fifteen phase- instead of three phase control reduces the stator current harmonic distortion for the considered drive by a factor 1.74, but increases the DC link current ripple with a factor 1.15. If combined with interleaving, the reduction in stator current harmonic distortion remains a factor 1.69, but the difference in DC link current ripple decreases to a factor 1.01. The proposed fifteen phase interleaving strategy leads to a real-life reduction of more than 50% in the DC link current ripple.
Research on Tracking Algorithm of Beidou B1C Signal

This article researches the structure and the tracking algorithm of the Beidou B1C signal. Compared with the traditional PSK modulation, BOC modulation better solves the problems of frequency band crowding and low tracking accuracy. The Beidou III system added the B1C signal using QMBOC modulation in December 2017. For the tracking algorithm of B1C signal, this paper researches the single-component BOC(1,1) signal tracking algorithm based on delay-locked loop structure and ASPeCT algorithm using pseudo-correlation function. The simulation proves that ASPeCT algorithm can attenuate the MBOC signal secondary peak and improve the tracking accuracy.
Research on Edge Detection Operator of a Convolutional Neural Network

With the rapid development of image technology, edge detection technology is becoming more and more diverse. In order to fully improve the edge detection effect, it is necessary to innovate in the detection algorithm. In recent years, deep learning has become an emerging research direction in the field of machine learning, and convolutional neural network[1] (CNN) is a deep learning model that has been widely studied and applied. Since CNN can automatically detect the multi-directionality of image features, it is widely used in image classification and recognition. The traditional edge detection operators are based on gradients, which can only be detected from several fixed directions, resulting in the loss of edge information in other directions. For the above, edge detection algorithm is proposed based on CNN in this paper. The algorithm is as follows: Firstly, because the convolutional network can automatically extract image features. In this paper, CNN is constructed under the framework of tensorflow to obtain the edge detection operator after training. The prepared data set is input into the network for training, and the training result is saved on matlab for verification. In addition, two comparison experiments were carried out in the experiment. The first set of experiments uses six different sets of optimizers to train the network and generate corresponding edge detection operators. They are used to perform edge processing on the image of the test set. By comparing the result of processing, the optimizer suitable for this paper was selected as Adam Optimizer and Gradient Descent Optimizer. The second set of experiments was performed by comparing the edge detection operator generated by Adam Optimizer with the classical operator. The experimental results show that the edge detection operator based on convolutional neural network detects more abundant edge information than the classical operator, and also achieves higher effect on the edge detection of the target.
PSA: An Architecture for Proactively Securing Protocol-Oblivious SDN Networks

Up to now, Software-defined network (SDN) has been developing for many years and various controller implementations have appeared. Most of these controllers contain the normal business logic as well as security defense function. This makes the business logic on the controller tightly coupled with the security function, which increases the burden of the controller and is not conducive to the evolution of the controller. To address this problem, we propose a proactive security framework PSA, which decouples the business logic and security function of the controller, and deploys the security function in the proactive security layer which lies between the data plane and the control plane, so as to provide a unified security defense framework for different controller implementations. Based on PSA, we design a security defense application for the data-to-control plane saturation attack, which overloads the infrastructure of SDN networks. We evaluate the prototype implementation of PSA in the software environments. The results show that PSA is effective with adding only minor overhead into the entire SDN infrastructure.
An HTS Machine Concept with a Passive Rotor

Electrical motors or generators normally incorporate copper windings on either the rotor or stator or both components. Copper windings, as well known, have significant resistive. On the other hand, superconductors, with their near zero resistance, have negligible losses under large current flows. Researchers have voiced concern that the main challenge associated with superconducting machines is the necessary cryogenic cooling system and the need to couple them to the rotating rotor structure. This paper describes a novel architecture for superconducting motors or generators. This paper approaches the problem from a new perspective that eliminates the need for rotating the cryocoolers by keeping the field windings stationary and yet generating an emf which is more effective than a comparable conventional copper machine. MgB2 or 2nd generation superconductors are proposed for both the DC excitation and AC armature coils. Thus the proposed machine becomes fully superconducting. The main benefit of the proposed fully superconducting machine will have high torque at a compact size which is expected to be useful at offshore wind power generators where weight and size is the biggest concern due to transportation and installation limits. The challenges associated with this particular topology are also discussed and possible solutions proposed. A comparison of the torque with a comparable copper machine provides encouraging results which warrant further pursuit of this topology.
Architecture of Vehicle Trajectories Extraction With Roadside LiDAR Serving Connected Vehicles

This paper developed a data processing procedure for detection and tracking of multi-lane multi-vehicle trajectories with a roadside Light Detection and Ranging (LiDAR) sensor. Different from the existing perception methods for the autonomous vehicle system, this procedure was explicitly developed to extract trajectories from a roadside LiDAR sensor. The proposed procedure includes five main steps: region of interest (ROI) selection, ground surface filtering, point clustering, vehicle/non-vehicle classification, and geometrical vehicle tracking. The case study showed that the trajectories of vehicles can be generated with the proposed method. This paper is the start of the new-generation connected infrastructures serving connected/autonomous vehicles with the roadside LiDAR sensors. It will accelerate the deployment of connected-vehicle technologies to improve traffic safety, mobility, and fuel efficiency.
Periodic Pattern Detection Algorithms for Personal Trajectory Data Based on Spatiotemporal Multi-Granularity

Identifying periodic patterns in individuals’ trajectories is the basis of location awareness and personalized location services. It can help us understand personal behaviors. However, fuzziness and uncertainty of trajectory data, as well as noise and period distortion, make it difficult to recognize periodic patterns. In addition, the period lengths are usually unknown, and patterns have multiple granularities. Most of the existing mining algorithms focus on the discovery of patterns with different period lengths at a specific spatial scale, and few algorithms identify periodic patterns from the perspective of spatiotemporal multi-granularity. Based on the existing studies, we propose a framework for identifying periodic patterns with different spatiotemporal granularities from personal trajectory data. First, a sequence of trajectory points is transformed into a time series of locations by using trajectory abstraction. Then, a multi-granularity behavior model is defined from spatial and temporal information. Finally, the single behavior periodic patterns can be discovered without knowing the period length by using a novel algorithm. Based on the association rules between locations, we can determine the periodic patterns of multiple behaviors from single behavior patterns. To evaluate the accuracy and efficiency of the algorithms, an artificial trajectory sequence and a real-world trajectory dataset called GeoLife are used in comparative experiments. The experimental results show that the proposed algorithm has higher accuracy on the promise of efficiency.
A Concept Proposal on Modeling of Security Fatigue Level

The threat of cybercrimes has recently increased as the Internet has becomes more of a necessity. Therefore, the importance of information security is also increasing. In addition to information-security technology, determining an information-security policy is important. For example, in the information security management system, the three elements of confidentiality, availability, and integrity are maintained with sufficient balance, enabling continuous improvement. However, a strict information-security policy may decrease security consciousness of the employee of a field. A report of the National Institute of Standards and Technology also defines the fatigue produced by carrying out computer-safety measures as "Security Fatigue". In this paper, we propose a model for visualizing the security-fatigue level. We evaluated the model and found that it contributes to a safe and secure ICT infrastructure.