Miracle-dz/UniFAR-FTUs huggingface dataset:
High Performance Decoupling Current Control by Linear Extended State Observer for Three-Phase Grid-Connected Inverter With an LCL Filter

Compared with traditional single L-type filters, LCL filters for three-phase grid-connected inverters can suppress the switching voltage and current ripple better, thus reducing harmonic pollution to the power grid. However, the increase in system order also makes it difficult to control the system. To solve the problems of complex model and severe coupling of LCL filter in the d-q coordinate system, the decoupling control of current inner loop by the linear extended state observer (LESO) was proposed based on the analysis of mathematical model of grid-connected inverter with an LCL filter. Moreover, given the relatively low speed of system response in traditional active disturbance rejection control (ADRC), the tracking differentiator (TD) was removed, and the nonlinear control law was replaced with the PI controller. Compared with the conventional feedforward decoupling method, the reasonably designed LESO used in this work can not only realize decoupling control of the original system to improve its performance, but also save the cost of sensor and achieve a better decoupling effect. The effectiveness of the proposed current control strategy was verified by experimental results.
Predicting Collaboration Relationships on Social Network

Scientific papers are mostly performed by collaboration between research groups. Researchers work in various subjects and in several research areas. Therefore, identifying a strong research group is a quite complex task. To cope with this complex task, we propose a network-based method that predicts the best appropriate collaborations for researchers in a co-authorship network. In this paper, we have constructed a weighted co-authorship network by using a real database of scientific papers. Almost all of the methods in this area use the unweighted network model. But, weighted networks are particularly important when representing the network-based properties. The constructed network consists of nodes and links which represent respectively authors and their collaborations. In these networks, if two researchers are the common authors in the same paper, they are considered connected. Typically, the link weights represent the number of papers that two authors have made collaboratively. We also use local LCP-based algorithms to calculate the betweenness and closeness of the authors in this paper. The experimental results demonstrate the success of the proposed method.
Characters Verification via Siamese Convolutional Neural Network

In the printing and carving industries, it is necessary to check whether printed outputs or carved wares are missing or etched through comparing the drawings. Traditional approaches and identification methods can’t be used for this application where the number of character categories are not determined, and where the character may be unique designed by manufacturer. Driven by the one-to-one matching pattern, we propose an end-to-end dual input network for automatic comparison, which uses convolutional neural network to extract features from the scanned images which collected from printed matters. Then, we convert the corresponding drawing to the vector of the same dimension to calculate distance and the match/mismatch result. Experiments show that our method can effectively solve the problem of character comparison with many types, and at the same time propose an automated comparing program for the industrial imprinting process.
Vision-Based Road-Following Using Results of Semantic Segmentation for Autonomous Navigation

Recent research into autonomous navigation of a robot have used accurate and dense three-dimensional sensors such as 3DLiDAR and RADAR for map building and localization. However, humans move from a given position to their destination without accurate metric maps in urban scenes: a topological map including only landmarks and their connections enables navigation. Our study endeavors to develop a visual navigation scheme based on a topological map, similar to the scheme used by humans, and this paper proposes a unique road-following scheme using a combination of image processing schemes with the results of semantic segmentation. The proposed scheme identifies a target point toward which the robot moves. To confirm the feasibility of the proposed scheme, a moving experiment on a 500-meter-long course in our university campus, where several people were moving back and forth, was conducted with a robot named ‘Emu’ using only three webcams as external sensors. The experimental results demonstrated that the robot controlled by the proposed scheme could navigate the course adequately.
Cloud Based IoE Connectivity Engines for The Next Generation Networks: Challenges and Architectural Overview

Emerging 5G and beyond networks have been proposed to provide communication of anywhere, anytime, and any type for massive machine type objects and Internet of Things (IoT) that can be broadly called as Internet of Everything (IoE). With such next generation networks, creating, processing, storing, and sharing of large amount of data will take place at everywhere in every time. The problem is how to manage this life cycle of data, its critical timing and data traffic accurately and efficiently. This paper discusses issues and challenges with Cloud gateways with a focus on Cloud Based IoE Globe architecture for next generation networks, thorough which a large amount of data is assumed to be carried in the network. Cloud gateway that forms the middleware layer in IoE architecture is one of the most effective solutions for the issues identified in this paper. Cloud Gateway Engines are the cornerstone of its architecture. This paper also proposes an architecture for connectivity engines in IoE, which is called as Topologically Connected Gateway Engine (TCGE) architecture. Requirements and functionalities of proposed TCGE architecture are discussed.
Open Government Data: Analysing Benefits and Challenges

Open Government Data enables stakeholders to monitor and participate in governance processes by accessing governance information and decision-making areas while providing transparency, accountability, cooperation, participation, new job opportunities that provide public benefits. However, many open government data initiatives either fail or never start because of the challenges it has. After conducting the systematic literature review, it was determined that there is a lack of study covering analyzing the benefits and challenges of open government data in a holistic perspective and providing a road map to overcome these difficulties for our country. In order to satisfy this gap, this study aims to define the benefits and challenges of open government data from a holistic socio-technical perspective and derive a roadmap including guidelines to overcome these difficulties for the benefit of organizations aiming to transition to open government data.
A Method to Visualization Data Collection by Using Gamification

Using game design elements in non-game contexts is an established concept around the world. Gamification is all about improving actual user engagement with the system, making users contribute more time and resources for data collection. This study reviews an overview of gamification and user motivations for playing games, and the researchers discuss basic game design elements. The game mechanics are included points, levels, leaderboards, badges and challenges. Gamification can be applied to behavioral data collection processes in terms of visualization. The findings show the reliability of a questionnaire that is designed to measure the investment risk. The visualization questionnaire can represent the behavioral data that will be applied to gamification design.
An improved fuzzy c-means clustering algorithm with guided filter for Image Segmentation

Fuzzy c-means clustering with guided filter (FCM+GF) is an effective method for noisy image segmentation. However, the parameter ε of guided filter in the FCM+GF is set to a fixed value, which weakens the ability of the FCM+GF to partition images with different noise rates. In this paper, an improved fuzzy c-means with guided filter method (FCM+GF_I) is proposed. In our method, a new influence factor ρ is defined to adjust the guidance image. By adjusting the value of ρ, the proposed FCM+GF_I method achieves good performance on different noisy images. Experiments on Brain MR images show the superiority and efficiency of our method.
Sustainable Member Motivation System Proposal for NGOs: NGO-TR

Non-governmental organizations (NGO) depend on their members to survive. They need to reach the potential volunteers. They also aspire to keep track of their members’ effectiveness and to motivate those members to be more active. The volunteers need a transparent system, where their privacy is preserved. Our meetings with several NGOs show that there is a solid need for a transparent, effective and trustworthy system, where NGOs can track their members’ effectiveness, create enthusiasm and even collaborate with the volunteers from other NGOs. A system, where gamification principles are used for member motivation and the reputation of any member is calculated by his/her activities, is proposed. The blockchain technology will be used for the gamification process and the decentralized digital identity management. The decentralized digital identity management approach will help in providing the volunteers to gain full control over their identities and ensure the privacy. A design approach for the proposed system, the expected requirements and some possible use case scenarios have been presented. The prototype is implemented via Hyperledger Fabric and Hyperledger Indy software platforms, as a proof of concept. Chaincodes are to be implemented for the system to work autonomously.
A Low-Noise High Input Impedance Analog Front-End Design for Neural Recording Implant

This paper presents a high input impedance, low-noise, and power-efficient analog front-end (AFE) for neural recording implant. A low-noise, low-power input buffer is used as the input block of the AFE to achieve a high input impedance (over $700\ \mathrm{G}\Omega$ @ 0.6 Hz). The buffer is followed by a capacitively-coupled instrumentation amplifier (CCIA) in the second stage to improve the Common-Mode Rejection Ratio (CMRR) and the input-referred noise of the design. A new chopper stabilization control technique is proposed and used in the CCIA stage to reduce the charge injection and clock feedthrough and consequently the high-frequency ripple of the AFE output signal. A programmable gain amplifier (PGA) is designed as the third stage to adjust the overall gain of the AFE. Benefiting from PGA, the AFE is able to adapt its gain with both action potential and local field potential signals. The proposed AFE is designed and simulated using standard TSMC 180nm CMOS process and operates in a wide frequency band of 0.6 Hz to 1000 Hz with low input-referred noise of $2.4\ \mu\text{Vrms}$ and a CMRR over 100 dB at 0.6 Hz. The total power consumption is lower than $5.5\ \mu \mathrm{W}$ per channel. By applying the proposed chopper stabilization control technique, the amplitude of high-frequency output ripple is reduced by 45% compared to the conventional switched control.
An Auto-carrier Loading Problem with Inter-set Costs in Urban Outbound Logistics of Automobiles

This paper studies a loading problem arising in the urban outbound logistics of automobiles. The problem is to assign a given set of orders of finished cars to a given set of heterogeneous auto-carriers such that the cars can be loaded and delivered from a depot to multiple destinations. A value is given to each order to represent its urgency level, and an interset cost is defined among the destinations to estimate the transportation cost. The objective is to maximize the weighted total value of the assigned orders minus the total inter-set cost subject to a full-load constraint and a special loading structure. An integer programming model and a column generation based heuristic algorithm are proposed. In addition, parallel programing is tested in the computational experiments. Computational results show that our algorithm can generate near-optimality solutions for practically-sized problems efficiently, and outperforms the integer programing model and a rule-based method applied in practice.
Hierarchical Model for Multimedia Content Classification

An automatic content classification technique is an essential tool in multimedia applications. Present research for audio-based classifiers look at short- and long-term analysis of signals, using both temporal and spectral features as well as using auditory models. In this paper, we present a hierarchical scheme that maps familiar single-type streaming content sources (e.g., either music, movie, or voice) to processes in a JavaScript Object Notation (JSON) configuration file. For streaming sources containing mixed-type of content (e.g., movies and music) we employ a machine learning (ML) model to classify between the movie, music, and voice-based on metadata. Towards this end, statistical models of the various metadata are created since a large metadata dataset is not available. Subsequently, synthetic metadata are generated from these statistical models, and the synthetic metadata is input to the ML classifier as feature vectors. We demonstrate high discriminating capabilities (accuracy ≈ 90%), with very low latency (viz., ≈ on an average 7 ms), using real metadata from real-world content such as from YouTubeTM, Blu-rayTM, and user-generated content (UGC). The combined hierarchical system (comprising JSON file and ML-model) reliably identifies the content type with an accuracy greater than 90%.
Automatic Sleep Stage Classification by a Density - Distance- Based K - means Clustering Algorithm with Amendments

Sleep staging has important significance for the monitoring, prevention and treatment of sleep disorders. In this study, the clustering algorithm of K-means is investigated in order to realize the automatic sleep stage classification. Unlike the traditional K-means clustering algorithm, density-distance-based processing procedures were presented to determine the cluster centers. Additionally, the clustering results were amended based on the regularity of sleep stages during one's overnight sleep process. The presented algorithm was tested and analyzed in detail with six subjects from the Sleep-EDF database. The averaged accuracy is about 74%. Comparing with the traditional K-means clustering algorithm, the developed algorithm achieved more accurate and reasonable sleep staging results which can be an assistant tool for clinical inspection.
On-Chip Power Stage and Gate Driver for Fast Switching Applications

The demand for low-cost integrated circuits for automotive applications is increasing, while their cost must remain low to keep the product competitive. In this scenario, a high-speed monolithic integrated circuit to control a load current is here proposed. It is composed by a level shifter and a gate driver for a high power switch in a DC-DC Buck converter to be realized in a low cost technology. For chip area reduction, an n-channel vertical power transistor is chosen as power transistor. High switching frequency (up to 1MHz) can be used to reduce external components size. The device is optimized to achieve 94.4% efficiency. Implementation concept, schematic and layout will be shown.
Capacitive Fingerprint Sensor for Contactless Payment Cards

This paper examines requirements and solutions for a low-power, low-noise, and low-cost capacitive fingerprint sensor specifically for contactless payment cards. A first prototype chip realized in a $1.4\ \mu\mathrm{m}$ CMOS process comprises 96 x 96 sensing elements at a resolution of 254 dpi, has a die area of 9.8 x 10.6 mm2 and consumes 1.9mA at 2.5V. The single-sense-amplifier design avoids a bezel and has an embedded ESD-protection down to pixel cell level. A new oversampling method is proposed that enhances the sensitivity and effectively attenuates sampling errors caused by quantization and settling without compromising the sample-rate.
The Application of SPOC-Based Deep Learning Model in Psychological Health Education of College Students in Post-MOOC Era

This paper introduces the rise, development and popularization of MOOC network education mode to analyze the practical application problems of this education mode in the post-MOOC era, and introduces a deep learning model based on SPOC to explore its application in psychological health education of college students. The course adopts the encrypted form. Students use the password provided by the teacher to enter the MOOC platform to watch the learning video before class, discuss with the classmates in the class, participate in the practical operation, and the teacher gives on-the-spot guidance. The class assessment will take into account online and offline performance. This model aims to optimize the mental health education curriculum of college students by taking advantage of MOOC's rich learning resources and combining the interaction and timeliness of traditional classrooms.
Integration of a Virtual Protheses with a Multichannel Tactile Feedback Interface

Tactile feedback is a prime element for improving the amputee ability to interact with different objects. Providing effective sensory feedback to the prostheses is still an unresolved issue. Different approaches had been used to enable bi-directional communication between the prosthesis and the user; however, none could fully replicate the human hand natural sensation. In this regards, we used a 3D virtual hand simulator to communicate the tactile information resulting from hand manipulation. A MATLAB based graphical interface has been developed to control the virtual prosthesis and map tactile sensory data to a multichannel electrotactile stimulation system. Moreover, we presented a spatially coded hand map in accordance with the custom designed electrode array.
PAPR Reduction in MIMO (2×2)-FBMC-OQAM Systems Using Attenuating QAM Symbols

In this paper, a scheme named Attenuating Quadrature Amplitude Modulation Symbols (AQAMS) with Multi-input Multi-output (MIMO) system is proposed to reduce the Peak-to- Average Power Ratio (PAPR) of the Filter Bank Multi Carrier (FBMC) system adopting Offset Quadrature Amplitude Modulation (FBMC-OQAM) structures. A remarkable strategy has been followed mainly relying on multiplying every Quadrature Amplitude Modulation (QAM) by real decreasing magnitudes, then the transmitted signal becomes closer to a constant cover i.e. reduced in PAPR features. Simulation findings following the AQAMS technique illustrate concrete progress regarding PAPR reduction.
Prediction of SFRSCC's T50 Performance Using Regression Tree Algorithm

Concrete is an important building material which has been in human life for more than a century and is used continuously. Despite its age, many properties are still not known. There are different types of concrete according to the materials in the mixture. One of these types of concrete, called special concrete, is steel fiber reinforced self-compacting concrete (SFRSCC). There are different criteria for measuring the performance of concrete. Since the properties are not known exactly, different experiments are carried out in order to provide the characteristics of the requested concrete. In this study, the model was designed by using regression tree method to predict the performance of T50 of SFRSCC. In the designed model using regression tree algorithm, R-squared value was calculated as 0.7341, root mean square error (RMSE) value was calculated as 1.2636, mean absolute error (MAE) value was calculated as 0.8617.
H-Shaped Stacked UWB Dielectric Resonator Antenna With Dual Band-Notched Characteristics for WLAN/ITU Bands

In this article, a novel H-shaped stacked ultra wideband (UWB) Dielectric Resonator Antenna (DRA) With Dual Band-Notched Characteristics for WLAN/ITU Bands is presented and studied. The proposed DRA structure consists of H-shaped stacked dielectric resonator fed by a beveled patch, adhered to the back side surface of the stacked resonators and connected to the microstrip line, partial ground plane. To realize dual band-notched characteristics of WLAN (5.1–5.9 GHz) and ITU8.0 (8.025–8.4 GHz), a C-shaped slot is embedding on the beveled patch and U-shaped band stop filter is placed near the microstrip line. A comprehensive parametric study is carried out using HFSS software to achieve the optimum antenna performance and optimize the bandwidth of the proposed antenna. From the simulation results, it is found that the proposed antenna structure operates over a frequency range of 3.59 GHz to 12.23 GHz with a fractional bandwidth of 109.23%, with two band rejections in the frequency bands of 5.20–5.68 GHz and 8.10–8.43 GHz, which Indicating that the antenna is a good candidate for UWB applications, and having better gain and radiation characteristics. The center frequencies and bandwidths of the two notched bands can be adjusted by altering the parameters of the two slots.
Performance Evaluation of V2X Communications and Services in Cellular Network with a Realistic Simulation Environment

V2X communications is one key feature in the 5G standards that enables the vehicles get connected and access the cellular services anywhere anytime. It is essential to build a realistic simulation environment for large scale demonstration of the system under test including varying number of vehicles, application/network services, road network infrastructure and new solution approaches/methods proposed to enhance or make improvements on the system performance. This paper describes the simulation environment developed for this purpose and also evaluates the V2X communication and the services provided by the cellular network. The results provide insights on the system performance and the follow-up studies to improve the performance.
Extended Battery Life in Smart Watch using Photovoltaic with Quantum Dot and Conversion System

Low output power from harvesting sources, in ambient conditions constraints its application as a sustainable power source to mobile electronics in extending battery life. In this work, a novel ultra-low power, energy conversion system that harvests and adds energies from a novel solar harvester to enhance battery life of a smart watch in ambient operating conditions has been proposed. Eventually, an ultra-low power circuit has been designed that can capture the power inputs from a photovoltaic sources using Quantum Dot (QD) in room configuration, add them optimally and finally charge the battery. As a proof of concept the solar panels are added to the straps of the smart watch to retain the flexibility of the straps and the conversion circuit board added to the back panel of the watch. Experimental results reported shows that in practical operating light conditions there is about 1.5 times increase in operating time of a smart watch full load conditions.
Textile-Based Batteryless Moisture Sensor

In this letter, we establish a new type of passive ultrahigh-frequency (UHF) radio frequency identification (RFID) technology-based sensor with a referenced readout for moisture. We introduce the sensor system, which consists of a sensor tag and a reference tag. In a highly moist environment, the sensor tag, which is fabricated by embroidery and three-dimensional (3-D) printing, permanently changes its shape from flat to curved, which then influences its wireless performance. By recording the backscattered powers of the sensor tag and the unaffected reference tag, the presence of moisture could be detected. Based on our preliminary results, when the mean value of ΔP% was higher than 0.8, the sensor system indicated exposure to a high-moisture environment. Our moisture sensing system can provide cost-effective zero-energy monitoring of moisture exposure for versatile application fields.