Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Dual-Band Beam Scanning Antenna Based on slot Active Frequency-Selective Surfaces [Antenna Applications Corner]

A novel dual-band electronically beam scanning antenna based on a slot active frequency-selective surface (SAFSS) is proposed in this article. Each unit cell used in an SAFSS screen has two slots with four variable capacitors utilized to achieve high tuning ranges and dual modes. To achieve an adequate single mode, specific capacitances are used in each slot to overlap the two resonances. These dual modes in each unit cell create specific conditions for an antenna to control beams in dual bands simultaneously. An antenna prototype operating at the 1.5- and 2.45-GHz frequency bands is fabricated and measured. The results show that the fabricated antenna can scan its main beam in the dual band across the entire azimuth plane with a 45° resolution. The designed dual-band antenna is inexpensive, has easy fabrication, and is capable of sweeping beams across the full 360? angular range in dual bands simultaneously.
Dual Adaptive Nonlinear Droop Control of VSC-MTDC System for Improved Transient Stability and Provision of Primary Frequency Support

This paper presents an adaptive nonlinear droop control (ANLDC) strategy for the voltage source converter (VSC) in multi-terminal high voltage direct current (MTDC) system, which enables the converters to provide primary frequency control for connected AC grids. The presented strategy inherits the concept of an emergency control approach. In normal condition when there is no significant deviation in RoCoF and DC voltage, it will be in inactive mode. The combination of two control techniques, nonlinear frequency-droop control, and nonlinear voltage-droop control results in a dual nonlinear controller that includes the advantages of both of them at the same time. The ANLDC strategy bears the advantage of the traditional droop control that is based on local measurements and there is no need for a communication system. The simulation results indicate that the AC/DC station with the proposed dual control strategy can improve simultaneously the primary frequency response and transient stability of the connected AC grid.
Indoor Visible Light Localization Algorithm with the Optimal Optical Angle-of-Arrival Estimator

Visible light communication (VLC) has been widely used in many applications because it can support lighting and communication simultaneously. Based on VLC, many attractive localization technologies are proposed, especially angle-of-arrival (AOA) algorithm. Existing AOA systems exploit optical receivers to detect the light power, but the receivers may not be arranged as the optimal orientations to reduce the localization error. In this letter, we employ an optimal optical omnidirectional angle estimator (OOAE) as the receiver, and a novel three dimensional (3D) localization algorithm is proposed, which requires low computational cost. Furthermore, we propose two supplemented methods to reduce the positioning error when more than two white light-emitting diodes (LEDs) are exploited. Experimental results show that the proposed algorithm can achieve centimeter-level accuracy, which would provide constructive insights for the design of AOA systems.
An ERP Study on the Influence of Lyric to Song's Emotional State

Songs can express emotions and contain both lyrics and music. Early behavioral studies have shown that songs are influenced by the presence of lyrics for emotional processing. This work further examined the event-related potential (ERP) N400 responses of participants to elucidate whether lyrical and non-lyrical songs have different influences to different emotional states (happiness and sadness in this work). The song stimuli were processed and subjectively annotated with four conditions (i.e., lyrical and non-lyrical songs, and happy and sad emotional states), and the processed stimuli were presented to normal-hearing listeners in ERP experiments according to an affective priming paradigm. The song stimuli served as the prime stimuli and the affective-semantic words as the target stimuli. The experimental results showed that the non-lyrical songs evoked a larger amplitude than lyrical songs for happy emotion, and the lyrical songs evoked a larger amplitude than the non-lyrical songs for sad emotion. Results in this work suggest that the presence of the lyrics has a differential effect in happy and sad songs, and provide evidence that music and lyrics separately influence the diverse emotional processing.
Modeling of Electric Field Enhancement by Various Defects in Insulation of a High Voltage Cable

This paper presents the modeling and simulation of a defect in cable insulation, where the electric field enhancement at this local site might lead to the progressive deterioration until upcoming failure of a high voltage (HV) cable prior to its design life expectancy. The simulation results highlight the electric field enhancement of physical models by occurring various defects embedded in polymeric cable insulation layer in the cases of a protrusion, a single void and two voids using the Finite Element Analysis (FEA) technique.
Speech Enhancement Based on Nonlocal Means Denoising

This paper presents an algorithm for the enhancement of noisy speech signals by nonlocal means De noising. The algorithm is capable to process non stationary noise signals and stationary noise signals. In many previous works, most methods of speech enhancement is in the frequency domain. Unlike them, the proposed method is mainly processed noise signals in the time domain. The method is implemented in two stages: the first is modified spectral subtraction, and the second is the nonlocal means De noising. Experimental results demonstrate that the proposed method can greatly suppress background noise.
CoMAD: Context-Aware Mutual Authentication Protocol for Drone Networks

Drone technology is developing very rapidly. Flying devices accomplishing various applications are becoming an integral part of our daily life undoubtedly. Drones are characterized by extreme mobility, decent computing power, scalability, and a very short lifetime due to energy constraints. The rise of drones inevitably enabled swarms and drone networking applications. Drone networks is a path-breaking subclass of flying ad-hoc networks with unique capabilities and specific requirements. One very important challenge with swarms is the device authentication problem, in other words, proving the identity of a single or a group of drones that request to join the swarm. In this paper, we tackle this emerging problem and propose a novel context-aware mutual authentication protocol. The proposed protocol provides authentication for groups of drones and supports recovering a swarm in case of network separation. Likewise, the protocol can handle drone joins and leaves. Moreover, the protocol is not dependent on network infrastructure, secure storage, and secure channels. We tested the protocol using an automated formal security protocol verification tool, called Scyther. The tests resulted in the complete verification of the authentication and secrecy claims for arbitrary network instances and all defined use-cases. The protocol is also shown to have proven performance advantages over the existing schemes.
Simple Chipless RFID Sensor for Dielectric Sensing Using Shot-Time Matrix Pencil Method

This paper presents a simple chipless RFID sensor for dielectric sensing. The proposed chipless RFID sensor is designed based on two main parts composed of a rectangular patch and slot ring resonators. Two slot ring resonators were created within the rectangular patch in order to form two resonant frequencies. The sensor was implemented on a single-layer PCB. A short-time matrix pencil method was employed to extract the natural frequency of the sensor. The proposed sensor employed for dielectric sensing was evaluated via simulations. In simulations, the proposed sensor was mounted on the dielectric material to be determined. Simulation results have been shown that the natural frequency extracted by using the short-time matrix pencil method can be utilized to identify the dielectric permittivity of the materials.
Channel Inversion Method for Optimum Power Delivery in RF Harvesting Backscatter Systems

This work presents a method for enhanced wireless power transfer using an algorithm to calculate the optimum phases of multiple transmitting antennas in a passive UHF RFID system. The algorithm performs the calculation based on measured backscatter phase value of individual antenna port and the phase rotation caused by each port’s receiving channel. Through experimental validations, it is shown that the proposed algorithm can achieve up to 18 dB improvement in the tag RSSI using three transmitting antennas. The proposed algorithm could be used in the next generation sensor tags to optimise power delivery efficiency.
Discrimination of Decision Confidence Levels from EEG Signals

To explore the capability of utilizing electroencephalograms (EEGs) for the measurement of human decision confidence levels, this paper develops a new visual perceptual decision confidence experiment. In this experiment, a visual perceptual decision-making task is performed by 14 participants, and their EEG data are recorded. The problem of measuring decision confidence levels is considered to be a pattern classification task, and two pattern classifiers are trained with differential entropy (DE), power spectral density (PSD), differential asymmetry (DASM), rational asymmetry (RASM), and asymmetry (ASM) features extracted from multichannel EEG data. We compare the performance of these features and find that the DE feature performs better than the others for measuring levels of decision confidence. The experimental results indicate that EEG signals offer good capability for measuring human decision confidence levels. The best performance of our proposed method in measuring five levels of decision confidence reaches an accuracy of 49.14 % and F1-score of 45.07 %, and for the extreme levels of decision confidence, the recognition accuracy reaches 91.28 %, with an average F1-score of 88.92 %. Topographic maps are also used to depict the neural patterns of EEG signals, suggesting that the posterior parietal cortex and occipital cortex might be sensitive brain areas for indicating decision confidence.
16kbit 1T1R OxRAM arrays embedded in 28nm FDSOI technology demonstrating low BER, high endurance, and compatibility with core logic transistors

We present for the first time Si-doped HfO2-based OxRAM 16kbit arrays integrated in the BEOL of 28nm FDSOI CMOS, targeting low cost and low power embedded applications. Excellent LRS/HRS raw distributions are reported on 1T-1R 16kbit arrays with zero bit-fail up to 105 cycles, using core logic (GO1) selector transistors. The OxRAM compatibility with GO1 transistors paves the way to arrays with minimum bitcell area of $0.066\ \mu \mathrm{m}^{2}$ at $125\mu \mathrm{A}$ programming current. We also demonstrate direct correlation between transistor compliance current variability within the 1T-1R array and the LRS distribution. This result indicates that the intrinsic LRS variability is smaller than the measured one. SPICE Monte-Carlo simulations confirm the large compliance current dispersion measured on transistors designed at minimum gate length and show path to improve even further the LRS distribution and hence the OxRAM memory window.
Do User-Centered Designed Paradigms for BCIs improve the Modulation of EEG Signals?

The aim of this project was to compare the level of modulation of the Electroencephalographic (EEG) activity achieved by naïve users under two paradigms: traditional (or Graz) and User-Centered Design (UCD), which represents a foundation concept and methodology for the anthropocentric perspective in Brain Computer Interfaces. Both approaches made use of the same motor task: imagination of throwing out a basketball into a basket. The task was performed during online brain-computer communication. To quantify and qualify the level of modulation of the EEG signals, Wavelet entropy was applied since is a nonlinear parameter that reveals the dynamic and non-stationary nature of EEG signals, in contrast to commonly used lineal methods. Results showed that UCD approaches improve the modulation of EEG signals by providing more attractive, efficient, stimulating and novel experiences; and the average performance increased from $52.7\pm 0.028\%$ (traditional paradigm) to $60.1\pm 0.005\%$ (UCD paradigm). The performance was almost 10% higher in UCD that in traditional approaches, despite that users posed the same motor imagery skills in line with Motor Imagery Questionnaire.
Electrical stimulation of the human median nerve: A comparison between anatomical and simplified simulation models

Neuroprosthetic devices can be an innovative solution to support subjects suffering from a limb loss. The possibility of restoring tactile sensations by using sensory feedback represents a research area whose outcomes could significantly improve the quality of life of prosthesis users. One of the best techniques to restore close-to-natural and selective tactile sensations is the electrical current stimulation of nerves using neural electrodes. Their interaction with nerves is an important aspect to be studied. The aim of this work is to deepen, in the framework of computational modeling, the possibility of approximating a realistic 3D human median nerve model, based on anatomical imaging, to a simplified model using the hybrid FEM-Neuron approach. Often, a high computational simulation time is also related to the complexity of the model geometry; therefore using a geometrical simplified model can be an important aspect to be analyzed. The simplified model is built approximating inner fascicles shape to simple geometrical shapes, i.e. ellipses. The electrical current stimulation is studied in Comsol environment by using a ds-FILE electrode model implanted in the human median nerve. The results obtained from computational simulations using both anatomical and simplified models, allow concluding that the percentage activation ranges at different distances from the active site obtained by the two models are comparable.
Hardware and control co-design enabled by a state-space formulation of cascaded, interconnected PID controlled systems

In recent years, more and more attention has been paid to the simultaneous optimization of hardware and control to achieve an optimal design of complex and interacting systems. To efficiently carry out this co-design optimization, there is a need for flexible methods to apply the variable hardware and control co-design aspects while also allowing a faster system response calculation compared to traditional methods. These time savings in the response calculations are of significant importance when using iterative optimization algorithms that typically require a large number of simulations to arrive at a solution.That is why this paper proposes a general methodology to create a closed-loop state-space model consisting of an open-loop process with an observer and extensive control loop structures. These structures comprise a combination of cascaded decentralized and distributed controllers, synchronizing controllers, and feedforward controllers while taking into account reference trajectories and input disturbances. It is shown that with the proposed methodology, response calculations for a motion application are much faster compared to traditional graphical programming tools that enable to apply flexible control architecture features. This shows that the presented methodology permits the efficient co-design optimization of hardware en control aspects.
Computation Offloading Strategy for Improved Particle Swarm Optimization in Mobile Edge Computing

Mobile Edge Computing (MEC) reduces latency and energy consumption by migrating computing resources to the edge of the network. Computing offloading is one of the means to reduce latency and energy consumption in MEC. Reasonable offloading decisions can effectively reduce system cost. Aiming at the increase in system delay and energy consumption caused by the deployment of the MEC server in the 5G communication scenario, a computing offloading strategy EIPSO based on an improved particle swarm optimization (PSO) algorithm is proposed. Establish a delay, energy consumption and multiobjective optimization model, and for delay-sensitive mobile applications, the model is transformed into a delay minimization problem under energy consumption constraints, and a penalty function is added to balance delay and energy consumption. Through the proposed calculation offloading decision, the calculation task is reasonably allocated to the corresponding MEC server. The simulation results show that compared with the ALL-Local algorithm, MECR algorithm and PSAO algorithm, the total system cost of this algorithm is the smallest, and the EIPSO strategy can reduce the delay in the MEC and balance the load of the MEC server.
An Efficient NDN Routing Mechanism Design in P4 Environment

Name Data Networking (NDN) is a clean-slate network redesign that uses content names for routing and addressing. Facing the fact that TCP/IP is deeply entrenched in the current Internet architecture, NDN has made slow progress in industrial promotion. Meanwhile, new architectures represented by SDN, P4, etc., provide a flexible and programmable approach to network research. As a result, a centralized NDN routing mechanism is needed in the scenario for network integration between NDN and TCP/IP. Combining the NLSR protocol and the P4 environment, we introduce an efficient NDN routing mechanism that offers extensible NDN routing services (e.g., resources-location management and routing calculation) which can be programmed in the control plane. More precisely, the proposed mechanism allows the programmable switches to transmit NLSR packets to the control plane with the extended data plane. The NDN routing services are provided by control plane application which framework bases on resource-location mapping to achieve part of the NLSR mechanism. Experimental results show that the proposed mechanism can reduce the number of routing packets significantly, and introduce a slight overhead in the controller compared with NLSR simulation.
A Review of Knowledge-Based Defect Identification via PRPD Patterns in High Voltage Apparatus

Partial discharge (PD) as one of well-known signatures of defects in the insulation system of high voltage equipment has been studied over eight decades. It is of a great interest to power grid operators and owners to identify and distinguish local weak spots in insulation system of the key components of power system by using the obtained PD information to make timely decision thus to avoid any unexpected failures in the system. This article reviews the research progress of PD in high voltage apparatus, including motors and generators, cables, transformers and gas insulated equipment, with attempts to assess the severity of different types of PD initiating defects as well as key PD features that can be selected for classifying defects. Experimental and on-site test results for each type of discharge and defect associated are summarized and discussed. Also, development of these defects during operation of equipment caused by changes in physical and chemical parameters of defect and/or insulation, and its impact on phase-resolved PD pattern variation are thoroughly explained. It is expected that this paper can provide a comprehensive guideline for PD data analysis of available PD information extracted by conventional PD acquisition instruments for both engineers and asset managers of high voltage apparatus.
Automatic Evaluation of Fetal Heart Rate Based on Deep Learning

Fetal heart rate (FHR) monitoring has been widely applied to assess the status of fetus during pregnancy and labor in clinical practice. However the traditional way to analyze FHR highly depends on doctors’ experience, and sometimes wrong judgments can lead to unnecessary actions such as cesarean section. Thus automatic analysis of FHR in electronic fetal monitoring (EFM) through computer has been constantly tried and studied. In this work, we propose a convolutional neural network (CNN) model based on a weighted voting mechanism to divide the FHR as normal or pathological state. In the meantime, the multi-model training method based on down-sampling algorithm is used to deal with imbalanced data. In order to evaluate the effectiveness of the proposed CNN combined with the multi-model training method, we test and analyze it on an open database named CTU-UHB. The experiment results show that our method performs well and stable on this dataset.
Study of Dynamic Charging for Electric Vehicles Using Stationary Wireless Power Transfer Charging

This paper is a study of the wireless power transmission of the electric vehicle models to conserve energy without polluting the environment of public transportation optimal power management reduces charging times for the electric vehicle power supply. This paper is shows the feasibility of the principle of magnetic induction between two coils using the induction principle for the moving coils installed on the electric vehicle. The transmission coils had no movement, magnetically controlled by a half-bridge converter. The connecting magnetic field in the transmission coils to the receiver coil through the air core transformer. Therefore, the efficiency of the magnetic field intensity depends on the range of the magnetic induction transmission distance of the two coils. The findings can be used to improve the efficiency of the electric vehicles as research data. For the development of the electric vehicles in the future work.
An Investigation on Kerr Nonlinear Compensation Technology Assisted by DSP in Optical Fiber Transmission Systems

With the increase of transmission capacity and distance, the optical fiber Kerr nonlinear effect becomes obvious, limiting the performance of optical fiber communication systems. In this paper, optical fiber Kerr nonlinear compensation technology based on digital signal processing (DSP) are investigated, including phase conjugate twin wave (PCTW), digital back-propagation (DBP), radio frequency (RF) pilot assisted technology, advanced modulation format and Volterra series. The basic principles of these methods are introduced and the advantages and disadvantages of these methods are compared. Moreover, the prospects of these methods are also discussed.
Using Variational Autoencoder to augment Sparse Time series Datasets

In machine learning, data augmentation is called the process of generating synthetic samples in order to augment sparse training datasets. Reducing the error-rate of classifiers is the main motivation. In this paper, we generate synthetic training samples of time series data using a simple implementation of the Variational Autoencoder, to test whether classification performance increases when augmenting the original training sets with manifolds of generated samples. We demonstrate the effectiveness of data augmentation using the Variational Autoencoder as a generative model, by conducting experiments with different standard classifiers evaluated on nine datasets from the UCR Time Series Classification Archive. We show that our method is beneficial in most cases, as we observed an increase of accuracy and F1-Score on all datasets.
Research on the Co-movement of Bond and Stock Market

This paper establishes the AR-GARCH-T Copula model to study the Co-movement between my country's bond market and stock market. It is found that the level of linkage between the two cities in my country has extremely time-varying characteristics. At the same time, the liquidity factor of the financial market significantly affects the level of linkage between the two cities. After market liquidity becomes tighter, the correlation coefficient between the two cities will rise significantly. This research has important reference value for investment portfolio construction, financial market supervision and risk control.