Miracle-dz/UniFAR-FTUs huggingface dataset:
PD-Like H∞ Control for Large-Scale Stochastic Nonlinear Systems Under Denial-of-Service Attacks

This paper focuses on the observer-based <inline-formula> <tex-math notation="LaTeX">$H_\infty $ </tex-math></inline-formula> control issue for large-scale systems under denial-of-service attacks. Due to the security vulnerability of communication networks, adversaries have the capability to hamper the system normal operation by blocking the signal exchange in both sensor-to-observer channels and the observer-to-observer channels. Under this scenario, a decentralized proportional-derivative-like controller is employed to realize the predetermined <inline-formula> <tex-math notation="LaTeX">$H_\infty $ </tex-math></inline-formula> performance while considering the improvement of dynamic response of closed-loop systems. By using the Lyapunov stability theory, a sufficient condition dependent on the attack occurring probability is established to realize the desired control performance. Furthermore, in light of the established condition, the desired controller and observer gains are formalized by resorting to the orthogonal decomposition of control matrix. Finally, the effectiveness and practicability of the control scheme are illustrated by resorting to the numerical simulation of a four area power system.
Design of High-Performance Phase-Locked Loop Using Hybrid Dual-Path Loop Architecture: an Overview (Invited Paper)

The performance requirements of phase-locked loops (PLL) for the emerging applications such 5G and IoT are continuously growing. Compared with the conventional single-path loop based PLL, including the charge pump based PLL, sub-sampling PLL and all-digital PLL, the hybrid dual-path loop based PLL architecture (HDL-PLL) can combine the advantages of all these three kinds of single-path loop based PLL to achieve better performances. This paper first gives an overview of the basics, advantages, and the recent research progress of HDL-PLL. Then, two design examples based on our recent research with their measurement results are introduced to show that the HDL-PLL architecture can effectively improve the PLL performances.
A Spectral Efficiency Enhancement for Chirp Spread Spectrum Downlink Communications

The objective of the paper is to propose a transmitter/receiver system that increases the spectral efficiency in the context of CSS-type downlink communications. For this purpose, we propose to transmit simultaneously on the same time/frequency resources signals intended for several receivers. By controlling the level of interference between the different users and the properties of quasi-orthogonality between up and down chirps, we show that each receiver can correctly demodulate the symbols intended for it.
On the Network Slicing for Enterprise Services with Hybrid SDN

Nowadays, companies strongly rely on Virtual Private Networks (VPNs) to deliver services between geographically distributed branch offices. Internet Service Providers (ISPs) must therefore offer a reliable and cost-effective connectivity solution. VPNs are commonly based on static bandwidth allocation over MPLS tunnels, which cause over-provisioning and under-utilization of network resources. Software Defined Networking (SDN) appears as a solution to provide agile enterprise networking while reducing operators cost. This paper presents the design and implementation of a Hybrid SDN-based network application to provide dynamic services. Such an architecture enables combining centralized and distributed control with traditional VPN protocols to provision services through network slices. The application performs a flexible policy-based routing, selecting the access technology according to the Quality of Service (QoS) requirements and the network conditions. The simulations executed over an VIRL emulated environment by Cisco show that the proposed network control enables the services to be efficiently provisioned without the need of over-provisioning the resources. Furthermore, a customized network slicing over legacy equipment guarantees the service requirements.
Built In-Stack Localisation Services with ALO

Localisation is a major topic in the Wireless Sensor Networks and the Internet Of Things. This Work-in-Progress paper introduces ALO, an Ad-hoc Localisation Object proposed to help in designing in-stack localisation services, i.e. a service that enables the computation of mobile node positions, relying only on information from the protocol stack such as signal properties, RSSI, message timestamps, neighbour tables and routing tables. The paper presents ALO and then focuses on the implementation of a Time-Of-Flight ranging process using ALO. A suitable ranging protocol, minimizing overhead, is presented and evaluated on the LocURa4IoT testbed, using Ultra-Wide Band (UWB)-based wireless sensor nodes. The results show that the ranging accuracy is under 20cm in indoor Line of Sight conditions, using this ranging protocol included in regular traffic messages, instead of using dedicated ranging messages.
NCC Wireless Networks: Reed-Solomon Channel Coding and Outage Probability

In this paper, an accurate closed-form expression of the outage probability (OP) is derived for one relay network coded and cooperative (NCC) wireless networks. Objective proves of error correction coding benefits are also provided using Reed-Solomon codes RS(n,k). All links are subject to Nakagami-m fading effect and transmitted signals are modulated using different orders of M-ary phase shift keying (M-PSK) constellations. Network coding operation is performed by application of bit-wise exclusive OR (XOR) at the relay. Simulation of the scenario where the relay is equipped with channel coding/decoding shows significant symbol error rate (SER) improvement. Simulation results show that channel coding gain in terms of SER increases with PSK modulation order M. Monte-Carlo simulations were conducted to validate the derived OP analytic expression.
A Novel Diagnostic Scheme for Gear Pitting Fault Using Fiber Bragg Grating Based Strain Sensors

The performance of gearbox vibration monitoring techniques may degrade due to the signal distortion and attenuation which is usually caused by the complicated transmission path. The fiber Bragg grating sensing technique has attracting characteristics such as the small size, multiple measurement points on one fiber and resistance to oil corrosion. Therefore, this paper proposes a new approach for online monitoring of gearboxes based on fiber Bragg grating based strain sensors, in which the fiber Bragg grating sensors can be mounted closely to the meshing position of gear teeth. In this study, the working principle of the fiber Bragg grating and the configuration of the test rig is firstly presented. Then, the tooth root dynamic strain signals measured by fiber Bragg gratings under the healthy and gear pitting conditions were presented and compared with the finite element simulations. The comparison between the measured and simulated signals agrees well, which validates the feasibility of the employment of fiber Bragg gratings for the online monitoring of gearboxes. Furthermore, a novel diagnostic algorithm based on dynamic time warping is proposed to process the measured signals using fiber Bragg grating based strain sensors. The results show the proposed algorithm is able to diagnose the healthy and gear pitting conditions without the need of the pre-measured baselines.
Vulnerability Analysis through Interface-based Checker Design

Checkers design is a main step for static analysis of different vulnerabilities. This paper focuses on static analysis on code property graph, which combines abstract syntax tree, control flow graph, and program dependence graph. Developing checkers on code property graph directly is usually complex and difficult. In this paper, we have analyzed a large number of checkers of different vulnerabilities, and extracted those most commonly used operations as a set of interfaces. We have implemented these interfaces and developed a set of vulnerability checkers based on them. The practical efficacy of these checkers are evaluated on the Linux kernel source code. Experimental results show that our proposed interfaces are strong enough to express most vulnerabilities and our implementation is effective for vulnerabilities detection.
Testbed-based Performance Evaluation of the Information-Centric Network

Proliferation of the Internet usage is rapidly increasing, and it is necessary to support the performance requirements for multimedia applications, including lower latency, improved security, faster content retrieval, and adjustability to the traffic load. Nevertheless, because the current Internet architecture is a host-oriented one, it often fails to support the necessary demands such as fast content delivery. A promising networking paradigm called Information-Centric Networking (ICN) focuses on the name of the content itself rather than the location of that content. A distinguished alternative to this ICN concept is Content-Centric Networking (CCN) that exploits more of the performance requirements by using in-network caching and outperforms the current Internet in terms of content transfer time, traffic load control, mobility support, and efficient network management. In this paper, instead of using the saturated method of validating a theory by simulation, we present a testbed-based performance evaluation of the ICN network. We used several new functions of the proposed testbed to improve the performance of the basic CCN. In this paper, we also show that the proposed testbed architecture performs better in terms of content delivery time compared to the basic CCN architecture through graphical results.
Forcible Search with Mixed Gibbs Sampling Massive MIMO Detection

In this paper, mixed Gibbs sampling multiple- input multiple-output (MIMO) detection with forcible search is proposed. In conventional Gibbs sampling MIMO detection, the problem of stalling occurs under high signal-to-noise ratios (SNRs) which degrades the detection performance. Mixed GS (MGS) is one solution to this problem. In the MGS, random sampling is carried out with a constant probability without judging if a current search is at a local minimum. In the proposed scheme, combined with MGS, multiple candidate symbols are forcibly changed when the search is captured by a local minimum. The search restarts away from the local minimum which effectively enlarges the search area in a solution space. Numerical results obtained through computer simulation show that the proposed scheme achieves better performance in a large scale MIMO system with QPSK signals.
Minimized Junction Leakage Current for Nanoscale Mosfet Applications

Ultra-low leakage current development for extreme high threshold voltage (EHVT) devices had become a challenge for the low power consumption application in advanced CMOS technology. This paper experimentally shows the reduction of leakage current by engineering Halo and LDD implant to achieve the EHVT device via 28nm high-k metal-gate (HKMG). The drain (Ioff) and bulk (Ioffb) leakage current exhibit 75% and 88% reduction comparing with regular sample at the same on-state current (Ion). The leakage current mechanisms as well as the correlation with experimental conditions have been discussed in detailed in this paper. The ultra-low leakage device has great potential for low power application.
Attacking Trivium at the Bitstream Level

In this paper, we present a bitstream modification attack on the Trivium stream cipher, an international standard under ISO/IEC 29192–3. By changing the content of three LUTs in the bitstream, we reduce the non-linear state updating function of Trivium to a linear one. This makes it possible to recover the key from 288 keystream bits using at most 219.41 operations. We also propose a countermeasure against bitstream modification attacks which obfuscates the bitstream using dummy and camouflaged LUTs which look legitimate to the attacker. We present an algorithm for injecting dummy LUTs directly into the bitstream without causing any performance or power penalty.
AC filter inductor design for three-phase PWM inverter

This paper presents the design method for a three-phase pulse width modulation (PWM) inverter with an AC filter inductor. In general, increasing the switching frequency is known to increase the switching device loss and decrease the volume of the passive components. An inverter design should consider the balance between conversion efficiency and inverter volume. However, as per our knowledge, no study has focused on the optimal design of a PWM inverter that includes an AC filter inductor. This is because the iron loss of the AC filter inductor changes in complicated ways depending on the switching ripple current, DC-bias excitation, etc. The authors clarified this iron loss mechanism and realized a highly accurate iron loss calculation method. Therefore, in this study, we designed a three-phase PWM inverter with an AC filter based on the switching frequency. The design method is confirmed by comparing the simulation and experimental results.
IoT-Based HVAC Monitoring System for Smart Factory

This paper presents an Internet of Things (IoT) Based HVAC monitoring platform for smart factory. A system model of energy-efficient Heating, Ventilation and Air Conditioning (HVAC) management in smart factory, which is useful for monitoring, controlling and saving energy. The hardware implementation was done using prototype built with Raspberry pi model B, temperature sensor, humidity sensor, current sensor and Micro-controller to gather and monitor data related to the HVAC for a typical smart factory. The system can collect local/raw data needed for the analysis using an off-the-shelf Big data analytics software. MATLAB pattern recognition toolbox was used to test, train and vaildate the behaviour of data collected and processed using R programming software. The paper presents a promising insights into the combined usage of IoT technologies, Big data analytics and neural network to solve future generation smart factory problems of HVAC management
Human Comfortability: Integrating Ergonomics and Muscular-Informed Metrics for Manipulability Analysis During Human-Robot Collaboration

The ability to compute a quality index for manipulation tasks, in different configurations, has been widely used in robotics. However, it is poorly explored in human manipulation and physical human-robot collaboration (pHRC). Existing works that evaluate efficiency of human manipulation often focus only on heurisitic-based, biomechanics or ergonomics methods/tasks. Complementarity between these performance features allows for a better evaluation and more general criteria, applicable across tasks. This letter addressess this gap by generating a new metric that combines offline pre-computation of biomechanics, ergonomics, muscle assessment and joint constraints, and reducing the online time complexity, enhancing the response query time. The proposed solution allow us to build a quality distribution in the human's workspace which can be quickly tailored to specific tasks and filtered for design purposes. This method simplifies human manipulability assessment for both general and task-specific applications and, in contrast to existing works, is suitable for real-time and/or resource-limited applications. Numerical evidence shows the proposed analysis greatly outperforms previous results in terms of computing time without compromising performance.
GPU-specific Task Offloading in the Mobile Edge Computing Network

Graphics processing unit (GPU)-specific tasks can be done by mobile edge computing in 5G networks because user equipments (UEs) offload the tasks near to Edge Server such as smart phones, access points, and so on. The data produced by Internet of Things devices can not be managed by traditional cloud computing system because of limited resource. Edge Computing is promising solution to this problem. The edge computing server is placed at the edge of network near the UEs. As a result, edge computing system guarantees low latency and energy-efficient task processing of the UEs. This paper introduces the system model for GPU-specific Task Offloading in the Mobile Edge Computing Networks and discusses the solutions for this problem.
Hybrid Precoding Design for Two-Way Relay-Assisted Terahertz Massive MIMO Systems

Hybrid precoding has emerged as a promising technique to reduce the hardware cost and complexity in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, due to the drastic increase in the number of user devices and higher data rate demand, new frequency bands (i.e., terahertz (THz) communications) are needed to explore. At THz frequencies, the precoding technique can provide a large beamforming gain, but still, the blockage and high path-loss remain a significant problem. To overcome the blockage problem, in this paper, we introduce the two-way relay design where the well-known two-way amplify-and-forward (AF) relay in THz-MIMO orthogonal frequency-division multiplexing (OFDM) systems is employed. The optimal two-way relay hybrid precoding problem is non-convex due to the practical hardware constraints (i.e., unit-modulus and block-diagonal constraints). To solve this problem, we first propose to use mathematical manipulations to remove the block-diagonal constraints from its analog part and then reformulate the original two-way relay hybrid precoding problem into a quadratic-convex problem with only power constraint. Finally, we obtain the closed-form solution for the two-way relay hybrid precoding problem. Simulation results reveal that our proposed solution can achieve better sum-rate and energy efficiency than existing hybrid schemes.
Early Detection At-Risk Students using Machine Learning

Machine Learning is one of the most popular technologies using in many industries, especially to analyze the data and find key insight or new knowledge. In education industry, many studies have applied machine learning techniques for various purposes. One important area is to early detect at-risk students by using data from various sources such as log data from learning management systems (LMSs), class attendances, and actual score from both formative and summative assessments. We present a comparative study aiming to find the most important features and the best classification algorithms to classify at-risk students based on they behaviors. The data are collected from Moodle system [1], printing services system, and students grad system at one of the faculty in the university. The experiment results are evaluated in terms of overall accuracy, precision, and recall. The random forest with oversampling on minority class shows the best result. The performances of the models is better when we have more data in each week of the semester. During week 5, the model can detect about 74 percent of at-risk students.
MFCW Radar’s Range and Velocity Estimation using Local Polynomial Approximation Based Function

Multi-frequency continuous waveform (MFCW) radar transmits a set of CW waveforms with different frequencies, which increase in a stepwise manner. This type of radar usually employs the spectrogram analysis of the baseband received echo signal, and therefore is capable of detecting and resolving two targets with different velocities but at the same range. However, this spectrogram approach renders difficulties in resolving two targets with the same velocity but at different ranges. We propose a new method that solves this problem by enabling the MFCW radar to estimate the velocity and range simultaneously using the local polynomial approximation (LPA). Namely, the range of a moving target is expanded using the Taylor series, attaining up to the second order terms, and the two Taylor coefficients, i.e., the range and velocities are estimated by the grid search. Simulation results show that the proposed LPA-based algorithm is able to resolve multiple targets with the same velocity satisfactorily.
Magnifier Network for Ceramic Z-directional Height Estimation with a Single Image

Due to the AI development, productivity of manufactures has been increased. This paper introduces a method that detects three dimensional size defects in ceramic products using a monocular depth estimation method. Conventional depth estimation is weak to deal with two-sided biased data. This paper addresses a brand new magnifier network in order to magnify important parts among an input with various ranges. Deep neural networks have considered that they are weak to learn partial area. However, the proposed method overcomes this weak point by applying to a proposed magnifier method. In this paper, we present that the proposed method increases the performance in terms of qualitative and quantitative aspects.
A Comprehensive Review on Optimal Location and Sizing of Reactive Power Compensation Using Hybrid-Based Approaches for Power Loss Reduction, Voltage Stability Improvement, Voltage Profile Enhancement and Loadability Enhancement

With the modernization of power grids, the network optimal utilization is essential to ensure that voltage profile at each bus is maintained within an acceptable range, voltage stability of the system is enhanced, power losses in lines are minimized, reliability and security of system are improved and etc. These can be achieved by introducing reactive power compensation devices such as Flexible Alternating Current Transmission System (FACTS) devices, Custom Power (CP) devices, synchronous condenser, capacitor bank and etc in distribution or transmission networks. Optimal location and sizing of the reactive power compensation devices are significantly important to ensure sufficient investment onto this device. Recently, most of conducted studies had focused on the techniques for determining the optimal location and sizing of various reactive power compensation devices in the power system using various indices proposed in the literature to access the power loss, voltage stability, voltage profile and line loadability. However, no review paper had discussed on the application of the existing indices adopted in the available techniques for solving the optimal location and sizing problems for all types of reactive power compensation devices. In this paper, current literature survey on optimal location and sizing of reactive power compensations had been discussed which includes analytical, conventional, metaheuristic and hybrid based approaches. The main objectives are to reduce power losses, to mitigate voltage deviations, to increase voltage stability and to improve reliability and security of the system.
GraphSDH: A General Graph Sampling Framework with Distribution and Hierarchy

Large-scale graphs play a vital role in various applications, but it is limited by the long processing time. Graph sampling is an effective way to reduce the amount of graph data and accelerate the algorithm. However, previous work usually lacks theoretical analysis related to graph algorithm models. In this study, GraphSDH (Graph Sampling with Distribution and Hierarchy), a general large-scale graph sampling framework is established based on the vertex-centric graph model. According to four common sampling techniques, we derive the sampling probability to minimize the variance, and optimize the design according to whether there is a pre-estimation process for the intermediate value. In order to further improve the accuracy of the graph algorithm, we propose a stratified sampling method based on vertex degree and a hierarchical optimization scheme based on sampling position analysis. Extensive experiments on large graphs show that GraphSDH can achieve over 95% accuracy for PageRank by sampling only 10% edges of the original graph, and speed up PageRank by several times than that of the non-sampling case. Compared with random neighbor sampling, GraphSDH can reduce the mean relative error of PageRank by about 17% at a sampling neighbor ratio (sampling fraction) of 20%. Furthermore, GraphSDH can be applied to various graph algorithms, such as Breadth-First Search (BFS), Alternating Least Squares (ALS) and Label Propagation Algorithm (LPA).