Miracle-dz/UniFAR-FTUs huggingface dataset:
SGX-UAM: A Secure Unified Access Management Scheme With One Time Passwords via Intel SGX

With the convergence of fixed and mobile networks, heterogeneous networks are becoming ubiquitous. Internet giants are seeing the plight of identity authentication. To address this issue, unified access management (UAM) was conceived. This paper provides a novel unified access management scheme, named SGX-UAM, with one-time passwords (OTPs) based on Intel software guard extensions (SGX). SGX-UAM outperforms generic UAM for providing resistance to most client attacks, man-in-the-middle (MITM) attacks, phishing attacks, most replay attacks and most denial of service (DoS) attacks to which generic UAM implementaions are vulnerable. Specifically, client attacks are prevented by ensuring input security and memory security, where the former is achieved through shuffle mapping and “periodic hooking” strategy, the latter is mainly guaranteed by Intel SGX; MITM attacks are prevented by transferring ciphertext rather than plaintext; phishing attacks are avoided by authorization control; replay attacks cannot succeed because we adopts OTPs, which contain time-related dynamic factors that expire in a few seconds; as for DoS attack, we blunted its edge by blocking-invocation for identical user connection. SGX-UAM also differs from generic UAM in that it relieves the security concerns of sevice providers (SPs) and protects users’ privacy at little cost of performance. An exceptional value of SGX-UAM is that it brings a lightweight OTP solution that eliminates the need of additional hardware devices, thus reducing the costs. The experimental results show that SGX-UAM consumes almost the same time with OpenID and OAuth2.0 for one login request and performs steadily when handling sequential login requests. Furthermore, the resource usage for SGX-UAM is acceptable.
Power Profile Measurement and System Design Analysis for a Wearable Photovoltaic Application

As wearable devices are gaining popularity for applications, like fitness trackers and health monitoring devices, an important aspect is how to most effectively power them. Solar photovoltaic (PV) power has great potential for powering wearable devices, but faces some challenges. The PV panels used in wearable applications will face different angles and capture different amounts of sunlight, which is challenging to model. To determine a realistic power profile for PV wearables, the power generated from each PV panel is measured for a variety of usage scenarios. A measurement device was developed that can simultaneously measure and log data of four identical PV panels used in a wearable bag application under realistic conditions. After data was collected, a modeling and fitting procedure was used to calculate the irradiance level on each PV panel. The resulting model was used to determine the PV profiles over time for the maximum power point (MPP) voltage, current, and power. These realistic profiles were then used to properly design, simulate, and compare three different converter architectures that transfer power from the wearable PV panels to the load.
Asymmetric Millimeter-Wave Spectrum in Laser Mode Partition Noise Generated by Fiber Transmission

In this article, we report on the asymmetry of intensity noise spectrum at millimeter-wave (mm-wave) heterodyne carriers. Passively mode-locked laser diode (PMLLD) is employed for a low phase noise mm-wave photonic generation using direct detection. This optical source produces an excess intensity noise known by laser mode partition noise (LMPN) in which its spectrum usually appears as symmetric double side-bands around the beat-note. The propagation over a dispersive medium such as optical fiber can modify the spectral behavior and profile of the LMPN. This article explores the impact of fiber transmission on the distortions of the LMPN in optical and electrical spectrum. Experimental results reveal, for the first time, that the LMPN symmetry is converted to an asymmetric deformation on the generated beating carrier. The mathematical model and numerical simulation are presented to give hints for analyzing this phenomenon. The findings of the simulations and measurements demonstrate the main role of second- and third-order fiber dispersion on the LMPN profile.
Using Redundant and Disjoint Time-Variant Soft Robotic Sensors for Accurate Static State Estimation

Soft robotic sensors have been limited in their applications due to their highly nonlinear time variant behavior. Current studies are either looking into techniques to improve the mechano-electrical properties of these sensors or into modelling algorithms that account for the history of each sensor. Here, we present a method for combining multi-material soft strain sensors to obtain equivalent higher quality sensors; better than each of the individual strain sensors. The core idea behind this work is to use a combination of redundant and disjoint strain sensors to compensate for the time-variant hidden states of a soft-bodied system, to finally obtain the true strain state in a static manner using a learning-based approach. We provide methods to develop these variable sensors and metrics to estimate their dissimilarity and efficacy of each sensor combinations, which can double down as a benchmarking tool for soft robotic sensors. The proposed approach is experimentally validated on a pneumatic actuator with embedded soft strain sensors. Our results show that static data from a combination of nonlinear time variant strain sensors is sufficient to accurately estimate the strain state of a system.
A High Efficiency Phase-Shift Full-Bridge Converter With Improved Clamping Circuit to Eliminate Oscillation for EV Battery Charger

In this paper, the phase-shift full-bridge converter with a novel clamping circuit is proposed for an electric vehicle charger. The proposed clamping circuit consists of two capacitors and two diodes, and it can solve the various problem in converter operation including voltage oscillation elimination of rectifier diodes. Thus, the proposed converter can reduce the conduction loss by the low forward voltage drop of the Si diode. Besides, it can utilize more wide voltage gain by reduced the duty-cycle loss and secure enough ZVS energy in constant current mode. Therefore, the proposed converter can achieve high efficiency by reduced conduction losses. The feasibility of the proposed convert has been verified with a 3.3 kW EV battery charger prototype under 385 V input and 270 - 420 V/7.85 A output conditions.
Deploying SDN Control in Internet of UAVs: Q-Learning-Based Edge Scheduling

Nowadays, wilderness monitoring provides massive data output for supporting agricultural production, environmental protection, and disaster monitoring. However, smart upgrading alone for these wireless nodes cannot meet the softwarized network needs today, relating to the explosion of multi-dimensional data and multi-species equipment. In this article, we conduct a comprehensive solution for the UAV based data collection strategy in an “air-to-ground” intelligent softwarized collection system. The innovation in this article is that after using the IoT nodes to complete the data collection process through the proposed bandwidth-weighted traffic pushing optimization (BWPTO) algorithm, the system infers the future changes according to the current network state using a deep Q-learning (DQL) network. Then, by developing the proposed AIIPO (Air-to-Ground Intelligent Information Pushing Optimization) algorithm, the entire network can “forward-looking” the uploaded information to potentially idle nodes in the future, thus achieve the optimized system performance. Through the final mathematical experiments, we prove the optimality of our proposed routing algorithm and forwarding strategy, which are more applicable in the dynamic “air-to-ground” distributed data collection system than other benchmark solutions.
Misalignment Detection of Rotor System Based on Adaptive Input-output Model Identification of Motor Speed

Traditionally, the misalignment detection of rotor system is based on vibration signal analysis. However, the vibration-based method is high-cost and limited by the installation space due to the need of vibration sensors. To solve the problems, this paper employs the speed signal which has already been collected for motor control to detect the misalignment. Furthermore, fault features, the speed ripples related to rotation frequency fr and its second-harmonic 2fr, are modeled using input-output identification method. Compared to Fast Fourier Transform, the amount of data needed is greatly reduced. The effectiveness of the proposed method is validated with the experimental platform.
A Fuzzy Sliding Mode Controller for Reducing Torques Applied to a Rehabilitation Robot

Exoskeletons are the most efficient devices used to help children, suffering from several diseases, to walk compared to the use of wheelchairs. In rehabilitation, relearning to walk is very important. In fact, repetitive tasks improve human locomotory performances. This paper aims to deal with the control problem of a two degrees of freedom lower limb exoskeleton. During the control process, high torques are considered as a major constraint. Thus, a fuzzy sliding mode controller is implemented. Moreover, stability analysis are presented using Lyapunov theory.
Adaptive Control of Soft Robots Based on an Enhanced 3D Augmented Rigid Robot Matching

Despite having proven successful in generating precise motions under dynamic conditions in highly deformable soft-bodied robots, model based techniques are also prone to robustness issues connected to the intrinsic uncertain nature of the dynamics of these systems. This letter aims at tackling this challenge, by extending the augmented rigid robot formulation to a stable representation of three dimensional motions of soft robots, under Piecewise Constant Curvature hypothesis. In turn, the equivalence between soft-bodied and rigid robots permits to derive effective adaptive controllers for soft-bodied robots, achieving perfect posture regulation under considerable errors in the knowledge of system parameters. The effectiveness of the proposed control design is demonstrated through extensive simulations.
Turbulence Properties of Solid–Liquid Flow in the Near-Wall Region of a Stirred Tank

The experiment was performed in a flat-bottomed cubic tank and equipped with a down-pumping 3-blade CBY propeller, investigating solid–liquid two-phase flow in the near-wall region by using 2D-PIV technique. Flow fields with ten different volumetric particle concentrations were investigated, including the single-phase flow. Because measurement is taken in the plane which is 2 mm from the wall, and root-mean-square velocity in the radial dimension could be omitted compared to the other two dimensions, flow field is considered to be two-dimensional. The mean velocity, turbulent kinetic energy and turbulent dissipation rate and their relationship with particle concentration were obtained and examined in this work. The increasing particle concentration decreases the flow mean velocity but increases the turbulent dissipation rate, while the turbulent kinetic energy (TKE) presents a complicated trend. When the particle concentration is below 0.9%, as in most previous studies did, TKE is attenuated; when the concentration grows up to 4%, TKE increases as well.
Temporal Series Crop Classification Study in Rural China Based on Sentinel-1 SAR Data

Crop classification is one of the focused topics in remote sensing study nowadays. Optical imageries, although providing much information, are often contaminated with cloud or other weather effects while SAR imageries are more resilient to those. Temporal series data are often used to improve classification accuracy, especially in crop classification. This article investigates the usage of temporal series SAR imagery on crop classification in vast rural areas of China. The selected area of interest has a complicated, heavily mixed agriculture as well as lots of nonagricultural landcovers. Total ten classes are considered, six of them being crop types. A pixel-based classifier using subspace kth nearest neighbor (KNN) algorithm is applied to open source Sentinel-1 polarized SAR data. Discussion includes analyzing temporal features of the SAR observation, time domain smoothing, feature engineering, different classification algorithms, and selection of temporal series. The study results in an overall accuracy of 98.2% for the ten classes in fivefold cross validation, indicating a propitious application for agricultural monitoring using SAR data.
Towards Lightweight Magnetic Components for Converters with Wide-bandgap Devices

Translating the higher switching speeds of new wide-bandgap devices into higher performance power conversion systems is often limited by the difficulties of realizing compact and low-loss magnetic components. This paper discusses some of the main technical challenges that limit magnetic component performance across a range of high-frequency converter applications including on-board DC-DC converters and wireless charging systems. Practical examples of innovative solutions to these challenges are presented, including core-less substrate- embedded resonant inductors for 13.56 MHz class-E inverters, high current (350 A) DC inductors with integrated thermal management, which demonstrate a 52% weight reduction, and a light-weight 400 Hz AC filter inductor with a 2.4 T core.
A Neighborhood Deep Neural Network Model using Sliding Window for Stock Price Prediction

Stock price prediction plays a crucial role in building a trading strategy for investors. The successful forecasting of stocks’ future price will help the investors to increase their profit. However, it is difficult to predict exactly the trend of the stock market due to the complex relationship between stock prices and external factors such as news, global economy, public sentiments, and other sensitive financial information. Hence, historical prices are the main investigation data for researchers since collecting external factors is still a challenge. In this paper, we propose a novel method using the state-of-the-art deep neural network for time series prediction (long short-term memory - LSTM) along with deep concern in historical data of stocks and their nearest neighbors in terms of similarity. Experimental results on four active stocks in the United State and three stocks in the Vietnam stock market show that our model outperforms the competitive methods (vanilla LSTM, convolutional neural network - CNN, random forest, and linear regression).
A Formative Assessment Framework Using Game-Quiz Educational Approach

The practice of using formative assessments in facilitating student learning is well represented in literature. However, using educational games as a tool to implement the formative assessment in a class setting is a complex task. This paper is to present a developed framework for implementing formative assessment using Game-quiz educational approach for undergraduate students to enhance the learning outcomes and the learners’ attitudes towards active engagement with the education process. The primary objective of this framework is to identify the weaknesses of the students’ learning process, so educators can adopt the most appropriate teaching approaches to improve the students’ learning outcomes.
EE-SE Trade-off Optimization for Downlink NOMA Systems

As a potential multiple access scheme for future radio access, Non Orthogonal Multiple Access (NOMA) has been considered as an emerging radio access technique for the fifth-generation (5G) wireless communications. Green Radio (GR), which focuses on Energy Efficiency (EE), is becoming an inevitable trend for future wireless network design. Recent works have concentrated on the relationship between EE and SE and provided good thoughts into the EE-SE trade-off. In this paper, we study the EE-SE trade-off problem for NOMA systems in downlink. The trade-off problem is formulated as a nonconvex optimization topic in which the main idea is to maximize the EE by imposing a satisfying minimum SE The Quality of Service (QoS) requirement for each user is also introduced to ensure optimality. For this, an optimal power allocation is proposed. Through simulations, we demonstrate the proposed schemes efficiency in achieving a flexible trade-off between EE and SE.
Performance Analysis of Robust Reference Current Tracking Controllers for Single Phase LCL-type Grid Connected Converter through Active Damping Approach

The LCL filter plays a major role to attenuate the switching frequency harmonics due to the power electronic converter. Furthermore, LCL Filters are naturally resonant which affect the stability of the system. To tackle the resonance problem, generally passive damping methods are a simple and straight forward solution but it effects the efficiency due to the insertion of resistance in LCL components. It is important to explore the alternatives for the passive damping methods to improve the overall system efficiency. This paper presents, the design of reference current tracking controller based on the LCL filter Capacitor feedback current using active damping approach. Active damping technique used in the proposed controller helps to suppress the resonant peak and injects high quality power into the grid. Further, the impact of LCL filter parameters on robustness of the proposed controllers is studied. Finally, performance of the proposed resonant controller is compared with the existing PI controller through MATLAB simulations.
A New Control Method for Current-Source Inverter fed Motor Drive System Without Additional DC-link Current Regulator

With the rapid development of wide band-gap silicon carbide (SiC) devices, current-source-inverter (CSI) can minimize the bulky and heavy dc choke and has attracted more attentions for its advantages including output friendly waveforms, short circuit current tolerance ability and four-quant operation. Furthermore, CSI has an inherent ability to boost the output voltage which is suitable for motors to increase the speed without additional flux weakening control strategies. However, when the CSI works in the boost operation mode, the dc choke current will be uncontrolled without an active front end. In this paper, a new control method for CSI working in boost mode is proposed. With this method, the dc choke current and rotor speed can be controlled simultaneously. Both simulations and experimental results verify the effectiveness of the proposed method.
Fuzzy-Self-Tuning PI Speed Regulator for DTC of Three-Level Inverter fed IM

This paper reveals a design method to enhance the speed profile of an induction motor (IM) drive fed by three level voltage source inverter (VSI) under direct torque control (DTC). Given that the conventional proportional integral (PI) controller cannot handle the abrupt load variation, we suggest inserting an accurate fuzzy self-tuning PI speed regulator to overcome this limitation. This latter is better than the fixed gains PI controller in terms of robustness against load disturbance. The holistic approach of DTC control scheme combined with the proposed adjustable speed PI controller gains using a fuzzy logic regulator is elaborated and developed. Simulation results verifying the feasibility and effectiveness of the propounded technique is carried out through MATLAB/Simulink software.
One-pot synthesis of stable phosphonium ylides using 2-mercaptopyrimidine derivatives

The reaction of dialkyl acetylenedicarboxylates with 2-mercaptopyrimidine and 2-mercapto-4,6-dimethylpyrimidine in the presence of triphenylphosphine leads to stable phosphorus ylides in excellent yields. These stable ylides exist in solution as a mixture of two geometrical isomers as a result of restricted rotation around the carbon-carbon partial double bond resulting from conjugation of the ylide moiety with the adjacent carbon group.
Analysis and exploitation of Geospatial Big Data: State of art

Over the past decade, big data with a spatial component "GEOSPATIAL BIG DATA" (GSBD) has become a focus of global interest, attracting increasing attention from universities, industry, governments and other organizations. The possibility of managing and processing GSBD to help decision-making therefore appears to be an important scientific and societal issue. This state of the art successively explores the concepts of big data, of spatial component and presents some main existing solutions for the management and processing of GSBD.
Dynamic Deployment of Virtualized Network Function - From Concept to Realization

Evolving technologies over the last decade have to a great extend enabled us to meet communication requirements of the envisioned application domains of smart cities. Software Defined Networking (SDN), Network Function Virtualization (NFV), and cloud computing are some of the prominent enabling technologies in this regard. This paper demonstrates the interplay of these enabling technologies and contributes with a service orchestrator. The major focus of paper remains on detailing the technical aspects of the enabling technologies and their potential interplay for different application domains. We also discuss the need for dynamic deployment of vNFs in future mobile networks. In this connection, we compose a use case and implement it on an extensively developed validation framework.
Mitigating TCP Protocol Misuse With Programmable Data Planes

This article proposes a new approach for detecting and mitigating the impact of misbehaving TCP end-hosts, specifically the Optimistic ACK attack, and Explicit Congestion Notification (ECN) abuse. In contrast to the state-of-the-art, we show that it is possible to mitigate such misbehavior leveraging emerging programmable data planes while not requiring any end-host or protocol modifications. A key challenge in doing so is to implement expressive, complex and stateful functions in the data plane within its restricted programming model. In this regard, we propose a security monitoring function that uses Extended Finite State Machine (EFSM) abstraction for monitoring stateful protocols in the data plane. We also design a mechanism for mapping a protocol’s EFSM to programmable data plane primitives. Our evaluation results demonstrate that our approach can fully or partially restore the throughput loss caused by misbehaving end-hosts that manipulate TCP congestion control through misinformation.
Towards Decomposing Monolithic Applications into Microservices

Continuously changing the existing software systems results in large and monolith software solutions making them difficult to maintain. As maintenance and development of monolithic software systems is a difficult task, there is a need for decomposing these monolithic systems into smaller subsystems, components and services, i.e., microservices. Service-oriented architectures yield more maintenance and less complexity in developing large-scale software applications. Thus, this paper focuses on decomposing monolithic software systems into microservices in order to maintain them with less development effort. Moreover, it addresses to the problem of architectural refactoring and improvement of software systems during architectural migration.
Robust Adaptive Sliding Mode Control of Nonlinear Systems Using Neural Network

Many higher order complex nonlinear systems with unknown parameters cannot be expressed in the so called linear-in-the-unknown-parameters form. This brings a huge obstacle to the application of adaptive control to estimate the unknown parameters. Instead of estimating each of the unknown parameters of a function, a feed-forward neural network (NN) can approximate the whole function due to its universal approximation property. In this study, an adaptive neural network integral fast terminal sliding mode controller (NN-IFTSMC) has been proposed for a general n-th order nonlinear systems with parametric uncertainties and external disturbances. The closed loop system has been proven to be bounded near the origin using Lyapunov criterion. The designed control scheme was applied to a quadrotor (UAV) and an excellent trajectories' tracking were obtained.