Miracle-dz/UniFAR-FTUs huggingface dataset:
Balancing three-level neutral point clamped inverter DC bus using closed-loop space vector modulation: real-time implementation and investigation

In this study, a real-time controller implementing closed-loop space vector modulation (CLSVM) technique combined with DC bus voltage control is applied on a three-phase four-wire three-level neutral point clamped (NPC) inverter to be used in sustainable energy conversion systems with higher efficiency and lower switching frequency than conventional two-level inverters. The redundant switching states of CLSVM is dictated based on the voltage feedback obtained from DC link in order to constantly balance the DC bus capacitors voltages under various load conditions. The main feature of this technique is the use of only voltage sensors for balancing the DC capacitors voltages in four-wire system which makes the algorithm act fast, while permitting reduction of DC bus capacitors voltage ripple significantly and therefore smaller capacitors are used. The NPC inverter has been modelled and effects of redundant switching states have been analysed mathematically on DC capacitors voltages. Comprehensive experimental tests are conducted on a four-wire three-leg NPC operating as uninterruptible power supply application for sustainable energy conversion to validate the designed approach and the dynamic response under various load conditions including linear, non-linear, balanced, unbalanced and motor loads.
VAPNIC: A VersAtile shortest path-free VNF Placement using a divide-and-coNquer tactIC

Orchestration mechanisms play a pivotal role in assisting service providers in deploying their increasingly complex virtual network services seamlessly thanks to Network Function Virtualization (NFV) and Software-defined networking (SDN) technology enablers. Unfortunately, existing state-of-the-art orchestration techniques suffer from non-scalability and time-efficiency aptitude when the services require VNFs to be distributed across cloud and edge environments with complex dimensions. Furthermore, they provide competitive solutions in good execution time only for small-scale scenarios (e.g., in seconds for 50 nodes) but generally require an exorbitant amount of time to converge towards feasible solutions for medium-scale or even large-scale schemes. This paper proposes VAPNIC: an innovative approach that solves the VNF placement and chaining problem with lower algorithmic complexity using a disjoint-set data structure aided divide-and-conquer strategy. Our method's unique design is the non-use of any existing shortest path search algorithms to chain the virtual network functions. To the best of our knowledge, this is the first work that strives to tackle the placement and chaining of the VNFs from a distinctive perspective in the case of medium-and-large scale scenarios with a fast and scalable heuristic that exploits the divide-and-conquer design paradigm based on multi-branched recursion. Experimental results indicate that VAPNIC outperforms existing approaches in acceptance rate, resource utilization, scalability, and time efficiency.
Robust Linear Parameter-Varying Control for Multi-Megawatt Wind Turbine Testing

Wind turbine testing is of great relevance for the wind industry to reduce the levelized cost of energy. For this purpose, hardware-in-the-loop simulators have been developed within the last years and the concept has been validated using multi-megawatt state-of-the-art nacelles. From the experimental experience of several years, different physical phenomena have been identified, which are insufficiently and unsystematically handled by state-of-the-art control algorithms. These effects include multi-physical couplings, nonlinear friction, and periodic disturbances due to unbalanced masses. We analyze the test bench control problem by means of linear parameter-varying (LPV) control theory and arrive at a formulation with the scheduling parameters rotation speed and torque. Based on experimental data, we derive a quasi-LPV plant model for which controller synthesis is conducted. In particular, the superior performance of different self-scheduled dynamic output feedback LPV controllers compared to a standard robust controller is shown. By using parameter-dependent Lyapunov functions, we guarantee both stability and performance with respect to scheduling parameters with a bounded rate of variation. Furthermore, we propose an approach for systematically handling highly dynamic fault cases on a test bench by reconfiguring controller properties through the introduction of fault specific dynamic weighting functions. A converter failure test case is analyzed in simulation to show the benefits of the here proposed controller design.
When QUIC's Connection Migration Meets Middleboxes A case study on mobile Wi-Fi hotspot

QUIC's connection migration provides an opportunity for network middleboxes to control paths of network traffic in a fine time granularity without worrying about breaking down the transport layer connections. This paper takes the first step to investigate its feasibility and effectiveness. We apply connection migration to a multi-carrier Wi-Fi relay to counter the latency caused by cellular network suspension. We identify a deadlock in such a situation, which eliminates the gain of passive migration. We analyze the root cause and propose a simple but effective approach to resolve the deadlock. Our findings may also motivate similar applications in other middlebox scenarios.
PRFL: Predicate Rank based Fault Localization

Fault localization (FL) is one of the most tedious, time consuming and effort-intensive task during the complete process of software debugging. Any improvement in this task leads to a reduction in total software development cost. For effective fault localization, we require a sufficient test suite comprised of the balanced pass and fail test cases. However, many times we don’t have access to these types of test suites. To solve this problem, we introduce a verification-based fault-localization technique, which prioritizes the program statements at predicate-level granularity. We use CBMC a Bounded Model Checker tool for the verification of buggy C-programs. We also consider the comprehensibility of the conditions presents a predicate to decide their suspiciousness scores. The input to our proposed approach is a buggy C-program and final outcome is a prioritized list of predicates and the executable statements using suspiciousness scores for a fault.
Optimal boundary feedback control of high intensity focused ultrasound processes

Optimal control problem governed by an acoustic equation with thermal relaxation is considered. Questions of boundary stabilizability and feedback on line control are investigated. On line control is implemented by Gain Feedback based on nonstandard Riccati equation with unbounded coefficients. While optimal control problems with smooth controls have been considered in the recent literature, [17], our new challenge is to consider "rough controls" with L2 forcing which then lead to non-smooth optimization problem and non-standard Riccati equations.
Design of Interval Observers for Uncertain Linear Impulsive Systems

This paper presents a novel approach to design interval estimators for uncertain linear impulsive systems. We consider situations where model disturbances and measurement noises are unknown but lie in given intervals. We propose a new architecture providing more degrees of freedom than standard interval observer structures for linear impulsive systems. We test the efficiency of the proposed methodology through numerical simulations.
Transmit Beamforming Optimization for Integrated Sensing and Communication

This paper studies the transmit beamforming in a downlink integrated sensing and communication (ISAC) system, where a base station (BS) equipped with a uniform linear array (ULA) sends combined information-bearing and dedicated radar signals to simultaneously perform downlink multiuser communication and radar target sensing. Under this setup, we minimize the radar sensing beampattern matching errors, subject to the communication users' minimum signal-to-interference-plus-noise ratio (SINR) requirements and the BS's transmit power constraints. In particular, we consider two types of communication receivers, namely Type-I and Type-II receivers, which do not have and do have the capability of cancelling the interference from the a-priori known dedicated radar signals, respectively. Under both Type-I and Type-II receivers, the nonconvex beampattern matching problems are globally optimally solved via applying the semidefinite relaxation (SDR) technique. It is shown that at the optimality, dedicated radar signals are not required with Type-I receivers under some specific conditions, while dedicated radar signals are always needed to enhance the performance with Type-II receivers. Numerical results show that by exploiting the capability of canceling the interference caused by the radar signals, the case with Type-II receivers results in better sensing performance in terms of beampattern matching error than that with Type-I receivers and other conventional designs.
Collaborative D2D Pairing in Cache-Enabled Underlay Cellular Networks

In this paper, we propose a collaborative smart solution for online traffic offloading among device-to-device (D2D) users underlying a cellular network. Specifically, we investigate the distributed pairing problem between requesting users and caching devices in their vicinity. Given that this problem is NP-hard, we propose a novel multi-agent reinforcement learning approach based on QMIX algorithm, where each requesting user is an agent capable of deciding to which cache device to pair, while respecting the quality-of-service of cellular users. Through simulations, we show the efficiency of the proposed algorithm in achieving D2D pairing. Finally, the impact of several parameters, such as the size of the network, size of files library, and communication requirements, is investigated.
Traffic-Light Control at Urban Intersections Using Expected Waiting-Time Information

We consider an optimal traffic-light control framework for urban traffic intersections to alleviate congestion phenomena. We analyze a scenario in which we provide drivers with information about the waiting time at the intersections. We model the drivers’ lane-changing information-based behavior as the solution of a convex optimization problem. We compute the optimal traffic-light control mechanism as the solution to a bi-level optimization problem. We provide a complete analysis in terms of (i) the existence of a solution; (ii) an iterative algorithm to compute it; (iii) sufficient conditions for the solution’s uniqueness and the algorithm’s convergence. Early simulation results show the proposed control scheme’s effectiveness compared with an optimal control algorithm in the absence of waiting-time information.
Paper Title (use style: paper title)

When traditional deep learning model is used for multiple object recognition on the road, it has some problems such as large amount of calculation and poor timeliness. In order to solve these problems, this paper proposes a lightweight improvement strategy based on the YOLOv4 algorithm. First, in basic network architecture, the lightweight network MobileNetv2 is used to replace CSPDarknet53, which effectively reduces the amount of parameter extraction; then, in the deep network structure, the ordinary convolution layer is replaced with the deepthwise separable convolution, which further reduces the network parameters. Finally, we realized the lightweight YOLOv4 network. The multi-object recognition data set is used to train and test YOLOv4 and lightweight YOLOv4 respectively. The experimental results show that the mAP of the improved YOLOv4 network is 85.12%, which can effectively achieve the multi-object recognition task on the road. At the same time, compared with the YOLOv4, the detection speed of the improved network is increased by 75%, and the parameters are significantly reduced, thus saving about 83% of the storage space. The improved YOLOv4 has better application prospects in multiobject recognition task on the road. Keywords—multi-object recognition, YOLOv4, MobileNetV2, lightweight network
Privacy-Preserving Federated Reinforcement Learning for Popularity-Assisted Edge Caching

In this paper, we investigate the problem of edge caching (EC) optimization in a multi-user privacy-preserving mobile edge computing (MEC) system. The time-varying content popularity is considered and the primary objective is to maximize the EC hit rate on each caching entity in the distributed network. To this end, we introduce the concept of local and global popularities and cast the time-varying local popularities as model-free Markov chains. Next, an unsupervised recurrent federated learning (URFL) algorithm is proposed to predict the popularities while achieving privacy-preserving goal. The underlying distributed optimization problem is then reformulated as a distributed Markov decision process and solved by the privacy-preserving distributed deep deterministic policy gradient algorithm incorporating the URFL algorithm. Simulation results demonstrate the superiority of the proposed scheme in terms of prediction error and hit rate over the baseline methods.
Energy Efficient Approximate MACs

Approximate computing or sacrificing computation efficiency for computational effort, has recently emerged as a promising design strategy. Several research studies have investigated approximate computation at both the software and hardware levels of abstraction over the last decade, with promising results. The multiply-add unit, the most basic and commonly used arithmetic block in digital systems has gotten a lot of attention for approximation at the hardware level of abstraction. We propose six Approximate MAC units (AMACU-1 to AMACU-6) for high-performance and energy-efficient approximate computing in this paper. The proposed AMACUs’ main design goal is to reduce the number of partial product stages to a minimum error rate. In terms of error efficiency and overhead analysis, the proposed AMACUs along with the conventional MAC and existing approximate MAC units are compared. It is noticed that, there is a considerable reduction in area and power by 64. 45AMACUs along with the existing approximate MAC units in an image smoothing application. Good PSNR and SSIM values were obtained.
Model Learning Predictive Control in Nonlinear Dynamical Systems

We study the problem of online learning and control in partially observable nonlinear dynamical systems, where the model dynamics are unknown and the controlling agent has only access to the system outputs. We propose Model Learning Predictive Control (MLPC), an efficient online control framework that learns to control the unknown system and minimizes the overall control cost. MLPC employs Random Fourier Features (RFF) to represent the nonlinear system dynamics and learns the underlying system up to a confidence interval. Once a reliable estimate of the dynamics is obtained, MLPC deploys an MPC oracle with the estimated system dynamics for planning. MLPC occasionally updates the underlying model estimates and improves the accuracy and the effectiveness of the MPC policies. We derive a novel finite-time approximation error bound under RFF learning and provide stability guarantees for single trajectory online control. We show that MLPC attains $\tilde {\mathcal{O}}\left( {{T^{2/3}}} \right)$ regret after T time steps in online control of stable partially observable nonlinear systems against the controller that uses the same MPC oracle with the true system dynamics. We empirically demonstrate the performance of MLPC on the inverted pendulum task and show the flexibility of the proposed general framework via deploying different planning strategies for the controller design to achieve low-cost control policies.
Evolutionary Multitasking for Cross-domain Task Optimization via Vehicular Edge Computing

Efficient optimization is a key enabler for emerging intelligent applications in Internet of Vehicles (IoV). However, existing studies in IoV only focus on solving a single domain-specific optimization problem at a time, which undermines their efficiency on tackling various cross-domain optimization tasks in IoV. In this paper, we make the first effort on investigating a novel optimization framework in IoV for cross-domain tasks via vehicular edge computing. Specifically, two typical cross-domain tasks in IoV are presented, namely, the data dissemination (DD) task and the computing offloading (CO) task. Then, a cross-domain problem called DD-CO is formulated to facilitate the sharing of task features and knowledge during the solution searching. On this basis, we propose an evolutionary multitasking approach named EMA, which consists of an integer based unified representation scheme for encoding both the DD and CO tasks in a single solution, a corresponding decoding operator for task-specific solution evaluation, and a new population evolution mechanism for better adaptation to the cross-domain problem optimization. Finally, we build the simulation model and give a comprehensive performance evaluation, which demonstrate the advancement of the new optimization framework via vehicular edge computing and the effectiveness of the proposed EMA method.
Servo Configuration and Evaluation of Numerical Printing and Die-cutting Machinery of Corrugated Board

The evaluation system structure of servo configuration of the machinery was studied considering its characteristic in production,running and service.The analysis and calculation of the evaluation index of the servo configuration of the machinery was realized using fuzzy set,analytic hierarchy process,and range analysis method.A servo configuration design system based on iSIGHT was developed,and the alternatives were evaluated by using technique for order preference by similarity to ideal solution(TOPSIS). An example of servo configuration was used verify the proposed approach.The result showed that the proposed approach with FAHP and TOPSIS can perform the configuration evaluation more effectively and objectively for the machinery.
Passive Unsupervised Localization and Tracking using a Multi-Static UWB Radar Network

The indoor localization and tracking of objects and humans with high accuracy is becoming increasingly important in a number of applications including healthcare, ambient assistant-living, surveillance, among others. Since Ultra-wideband (UWB) systems have a large operating bandwidth, they can provide centimeter (cm) level localization accuracy in Line-of-Sight (LoS) scenarios. However, current commercial UWB systems require the target to carry an active device (tag) so that it can be precisely located and this may be impractical in applications such as security and surveillance. In this work, we process experimental data obtained from a multi-static UWB radar network for the passive indoor localization of a moving target. Current fingerprinting-based passive localization techniques require a substantial radio-map survey in the offline training phase and labor-intensive fingerprint updates when there are changes in the environment. In this work, on the other hand, we propose to use the fine-grained physical layer information, known as Channel Impulse response (CIR), that is exchanged between UWB modules and capitalize on the fact that a moving person induces variations in the CIR that stand out against the background signal. Our results show that a walking target can be passively located with a median distance error as low as 0.55m in an indoor environment.
Improving Intrusion Detection Confidence Through a Moving Target Defense Strategy

Despite the promising results reported in the literature, the intrusion detection schemes cannot deal with new network traffic behaviors making such proposals unfeasible to be deployed in production environments. This paper presents an intrusion detection model that relies on a moving target defense strategy to face new network traffic behavior in a two stage process. First, the system select the most suitable classifiers set to assign a class (normal or attack) according to the current event behavior. Second, we evaluate if the performed classification is reliable by validating its confidence values. The goal is to ensure that only the higher confident classifications from the most suitable classifiers are used to trigger intrusion detection alerts, keeping the system reliable over time. Experiments performed on a dataset that spans over 97GB of data with seven categories of network traffic shows that current machine learning techniques cannot cope with novel traffic behavior, failing to detect up to four new traffic categories. In contrast, the proposed model can select the most confident classifiers, reducing the average false-negative rates by up to 39%, regardless of the current network traffic category.
Channel Estimation for mmWave Satellite Communications with Reconfigurable Intelligent Surface

We consider an mmWave satellite communication system with a reconfigurable intelligent surface (RIS) to enhance the signal coverage, where both the satellite and the served users are equipped with phased arrays. Different from the existing methods that separately estimate the uplink channel from the user to the RIS and that from the RIS to the satellite, we directly estimate the cascaded channel by proposing two schemes. In the first scheme with two stages, we power off the last antennas of the satellite, user and the RIS in the first stage and then transceive some pilot symbols, while in the second stage we power off the first antennas of the satellite, user and the RIS and then transceive the same pilot symbols. Then we perform the channel estimation based on the estimating-signal-parameter-via-rotational-invariance-techniques (ESPRIT) method. In the second scheme that does not power off any antenna and needs only one stage, we propose a null space projection (NSP) algorithm, where the equivalent channel matrix is estimated through projecting the dictionary steering vectors to the null space of the received signal covariance matrices. Simulation results show that the NSP scheme needs much fewer pilots and much lower hardware complexity than the ESPRIT scheme but with some sacrifice in channel estimation performance.
Refined Density Evolution Analysis of LDPC Codes for Successive Interference Cancellation

Successive interference cancellation (SIC) is a fundamental decoding technique for Gaussian multiple access channels (GMAC). In SIC, transmit signals of each user are separately decoded. In this paper, we analyze an asymptotic decoding threshold of SIC decoding for practical low-density parity-check (LDPC) codes over $N$-user GMAC. Conventionally, the decoding thresholds are evaluated based on the so-called channel approximation (CA) in which the channel model of each decoding stage of a SIC decoder is approximated by simple Gaussian noise channels resulting in errors of decoding thresholds. To avoid this, we propose a refined density evolution (DE) analysis called DE-SIC which uses mixed-Gaussian noise channels corresponding to each decoding stage. We demonstrate DE-SIC by solving a received power optimization problem in GMAC and comparing it to the conventional DE analysis with CA. The results show that DE-SIC accurately evaluates the decoding thresholds whereas the conventional analysis underestimates the thresholds.