Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward ManageableCost-Effective 5G C-RAN: Semi-Active Front-Haul by Multi-Carrier Pilot-Tone Oam and Mwdm

Front-haul is an important sub-system in 5G centralized, collaborative, cloud, and clean radio access network (C-RAN), which connects base stations and distributed units. However, it is still a challenge to achieve manageable cost-effective front-haul supporting 12-channel transmission over 10 km G.652. D fibre. Currently, a novel front-haul approach based on so-called medium wavelength-division multiplexing (MWDM) is in the process of standardization in various international organizations, such as ITU-T and O-RAN. In this article, we provide a tutorial that systematically overviews the semi-active WDM front-haul based on multi-carrier pilot tone operation, administration and maintenance (OAM), and MWDM. Firstly, the semi-active WDM architecture based on multi-carrier pilot-tone is introduced. Afterwards, the principle of 12-channel bidirectional MWDM system is discussed. The MWDM scheme is performed by shifting design parameters of the optical grate of the coarse WDM (CWDM) direct modulation laser (DML) at the blue side and the red side, respectively, which can reuse the low-cost and mature industry chain of 25-Gb/s 6-channel CWDM. Next, the architecture of centralized and software defined network (SDN)-based management and control system for semi-active WDM is also presented. Finally, real-time transmission of 300 Gb/s 12-channel MWDM-based semi-active system over 10-km are achieved in lab test and field trial, demonstrating the feasibility and performance of the MWDM-based semi-active solution. We believe that this work might greatly promote the practical applications of 5G C-RAN front-haul.
Characterization of trap-related transient-current effects in AlGaN/GaN nanochannels

This work explores transient-current effects associated with the capture and emission mechanisms of surface traps in AlGaN/GaN nanochannels at room temperature. The nanochannels have been fabricated by etching two isolating trenches and show a current-voltage characteristic influenced by the presence of surface charges. The conductance of these devices increases or decreases depending on the history of applied voltage, since it changes the occupation of the surface states and therefore the depletion region created by the sidewalls. Moreover, the lateral field effect plays an important role in these devices, because, in addition to promoting trap charging or discharging, modifies the depletion region around the trenches, both processes determining the conductance of the channel.
An Envelope Tracking oriented, 17-20GHz, Average 64QAM Modulated 2W, PAE>37%, OBO 8dB GaN MMIC Power Amplifier

This article presents the design and the realization of a 4W reactively-matched Power Amplifier (PA), in GaN MMIC technology operating in the 17-20 GHz band. The design has been oriented to be used with Envelope Tracking (ET) systems. The designed PA for Envelope Tracking (PA-ET) is a two-stage reactively-matched PA and it is based on the use of three power transistors from the UMS GaN GH15 technology. Large signal characterizations with 64 QAM modulation show that PA-ET provides an average dynamic output power $\left( {{{\bar P}_{outdyn}}} \right)$ greater than 2 W (33 dBm) associated to an average dynamic Power Added Efficiency $\left( {\overline {PA{E_{dyn}}} } \right)$ greater than 37 % and an insertion gain (GIdyn) greater than 11 dB in the 17-20 GHz band. The ET improves efficiency performance by 21 points compared to a PA using the same technology with fixed bias.
Force Estimation-Based Adaptive Impedance Control for Lower-Limb Exoskeleton Robots With Disturbance

In this paper, considering that many robotic systems in rehabilitation applications that are not equipped with force/torque sensors at their physical interaction points, an adaptive impedance control strategy for exoskeleton robots is proposed based on the interaction force estimation. The external disturbance is considered, and a novel controller consisting of a disturbance observer is given. The radial basis function neural network (RBFNN) is used to estimate the interaction forces, an update law for online adjustment of the neural network weights is proposed, which allows the estimation accuracy to be guaranteed even when the interaction forces change. The system's stability is examined through the application of Lyapunov methods, followed by the execution of simulations for validation.
Access Control for Collaboration in Cloud Environment: A Comparative Analysis

Collaborative systems enable users to collaborate on shared tasks using distributed platforms. In such collaborations, cloud-based solutions are commonly used to store data and capitalize on cloud computing advantages. However, participants may need access to resources controlled by other collaborators, including sensitive data. Ensuring access control for collaboration and cross-tenant access in multi-cloud environment is a crucial challenge to address. This paper provides an in-depth exploration of access control models in multi-tenant environments, specifically for collaboration scenarios involving tenants from the same or different clouds. Each model is comprehensively explained, emphasizing its advantages and limitations. Additionally, a comparative analysis of the discussed access control models is included. Based on this study, we propose guidelines for implementing effective access control, carefully considering the specific authorization requirements for cross-tenant access in a multi-cloud environment.
An Iterative Learning Approach for Novice Engineering Students to Grasp Important Concepts in Autonomous Systems

This work describes the design of an iterative learning approach to teach basic concepts in autonomous and control systems to novice engineering students. The Learning Continuum approach builds on three theoretical principles: Zone of Proximal Development, Selective Exposure, and Concrete before Abstract. The developed curriculum unit splits the lesson into two iterations. The first focuses more on the high-level concepts using tools that avoid low-level complexities of typical microcontrollers. The second iteration then uses a more traditional microcontroller setup to explore the learned ideas. We tested this framework with 217 novice engineering students against a control group consisting of 111 students. Results from a range of data collected show that this approach helps students during the learning process. Instructors in the study have adopted our learning continuum for regular use in their course.
Hybrid intrusion detection system based on Random forest, decision tree and Multilayer Perceptron (MLP) algorithms

With the rapid increase in network intrusions, applying an active network intrusion defense is more important than ever before. Different learning algorithms have been combined to achieve better performance. To improve the accuracy and efficiency of the network intrusion detection system, a new hybrid algorithm is designed, which combines the Random forest, Decision tree and Multilayer Perceptron (MLP) algorithms. The experimental results show that the hyprid model has a better true Positive Rate (TPR) for attack activities, rapidly data preprocessing speed, and shorter training time. In particular, the accuracy of multi-class classification can reach as high as 99.7% in the NSL-KDD dataset, 77.99% in the UNSW-NB15 dataset and 84.89% in the CIC-IDS-2017.
Formation Control with Obstacle Avoidance for Multi-Agent Systems Using Barrier Functions

This paper focuses on the problem of formation control for multi-agent systems, where a barrier Lyapunov function (BLF) is introduced to meet the requirement of communication maintenance, collision and obstacle avoidance. Then, combining the BLF and consensus theory, the formation control protocols are developed respectively for the leader and followers. The stability of multi-agent systems is analyzed by Lyapunov theory. Finally, simulation results are provided to illustrate the effectiveness of the proposed method.
A Review of Data-Driven Methods for Power Flow Analysis

This paper presents a comprehensive review of the existing methodologies of data-driven power flow analysis. It begins by discussing the fundamental concepts of power flow analysis and highlighting the challenges faced by conventional methods in solving power flow. Subsequently, it presents the key principles and techniques underlying data-driven approaches. Then, it overviews and classifies different machine learning models that have been employed in the literature to solve the power flow problem. Further, the challenges faced by these approaches in solving the power flow are explored. Finally, the paper concludes by discussing future research directions and potential advancements in data-driven approaches in power flow solutions.
Early glaucoma screening Decision Tree using in-depth clinical analysis of the optic nerve head

To assess the performance of an end-to-end computer vision algorithm for the early screening and diagnosis of glaucoma from fundus photographs. Development and evaluation of a computer vision approach for the extraction of physiologic measurements from the optic nerve head (ONH) and the detection of glaucoma. 101 fundus photographs were included for the development and validation of the screening algorithm. We automatically detect and extract the ONH within the retina, and compute two methods for segmenting of the optic cup (OC) and the optic disc (OD). Referable ophthalmic features including the cup-to-disc ratio (CDR), the Inferior- Superior-Nasal-Temporal (ISNT) rule and the neuroretinal rim (NRR) area are exploited, to quantify the structural changes within the ONH. Then, we elaborate a clinical protocol to provide trustworthy, easily interpretable glaucoma screening process, and indications about the stages of disease spreading.Proposed approach allows to accurately estimate the clinical measurements with 0.11 ± 0.08 to 0.15 ± 0.12 mean-std error rates on CDRs, ISNT sectors and NRR area. Also, outstanding glaucoma screening performance was achieved with 93.07% (95% confidence interval (CI), 86.24% to 97.17%) accuracy, 98.57% (95% CI, 92.30% to 99.96%) sensitivity and 80.65% (95% CI, 62.53% to 92.55%) specificity rates, competing with the state-of-the-art. We formulated a clinical protocol aiming at evaluating the structural changes occurring within the ONH, for assessing the different spreading stages of the neuropathy. Excellent performance has been obtained on glaucoma screening process, allowing to integrate such algorithm into computer-aided diagnosis systems and support ophthalmologists.
A 10 Gb/s Broadband Transimpedance Amplifier in 0.18 μm CMOS Technology for Optical Communications

This study presents the design of a 10 Gb/s high-speed optical receiver (RX), incorporating various techniques for bandwidth extension. The RX architecture consists of a pseudo-differential transimpedance amplifier (TIA), a continuous-time linear equalizer (CTLE), and an output buffer stage based on fT-doubler. Additionally, inductive peaking technique is employed to further extend the bandwidth. The RX design is implemented using 0.18 μm CMOS technology, achieving a transimpedance gain of 55 dBΩ and a bandwidth of 7.5 GHz. A comparison with the conventional RGC-TIA reveals a bandwidth extension ratio (BWER) of 1.8 for the proposed design. The RX overall consumes a power of 14 mW from 1.8-V voltage supply, while maintaining an average input-referred noise current below 18.2pA/√Hz
Registration for Urban Modeling Based on Linear and Planar Features

The production of a Building Information Model (BIM) from an existing asset is currently expensive and needs automation of the registration of the different acquisition data, including the registration of indoor and outdoor data. This kind of registration is considered a challenging problem, especially when both data sets are acquired separately and use different types of sensors. Besides, comparing a BIM to as-built data is an important factor to perform building progress monitoring and quality control. To carry out this comparison, the data sets must be registered. In order to solve both registration problems, we introduce two efficient algorithms. The first offers a potential solution for indoor/outdoor registration based on heterogeneous features (openings and planes). The second is based on linear features and proposes a potential solution for LiDAR data/BIM model registration. The common point between the approaches consists in the definition of a global robust distance between two segment sets and the minimization of this distance based on the RANSAC paradigm, finding the rigid geometric transformation that is the most consistent with all the information in the data sets.
Formation of Transparent Contacts to p-NiO by DC Magnetron Sputtering ITO

This paper investigates the optical and electrical properties of ITO films for their application as a transparent contact electrode to NiO films. The films were fabricated by magnetron sputtering at various parameters of the discharge power and oxygen content in the working gas mixture. Structural analysis showed an increase in the crystallinity of the films during their subsequent annealing. The films demonstrate high conductivity and transparency with an absorption edge higher than that of NiO films. The results of the study show a high potential for using ITO films to create a transparent electrode to NiO layers.
A Two-way Automatic Alignment Visible Light Communication System Based on Red Laser

Visible light communication(VLC) has shown in explosive growth recent years, and LEDs based visible light wireless communication has the potential to change lives in the future with applications such as LiFi, smart lighting and indoor positioning. Due to the low bandwidth of LED devices, the VLC systems using LDs(laser diodes) as light sources can achieve faster transmission speed. In this paper, we propose a high-speed visible light communication system with automatic alignment function using red laser. Each tranceiver in the system can be moved in the angle of view of another, and be aligned in real time using cameras, computers and two-dimensional turntable. TCP data transmission rate more than 700 Mbps with a distance of 6 m free-space link, $\pm 10^{\circ}\text{angle}$ of view was achieved.
Dual-Input Mixed-Mode Doherty-Outphasing Power Amplifier

This work proposes a new output combiner for the mixed-mode Doherty-Outphasing power amplifier (DOPA) architecture, which is able to guarantee a real load-modulation during the Doherty operation. A comprehensive explanation of the proposed DOPA combiner and its optimum driving signals is presented. For validation, an ideal DOPA was implemented in a simulator using ideal piece-wise current sources for representing the nonlinear transistors and an exhaustive search for the optimum driving signals was conducted, demonstrating the real load-modulation in the Doherty region. To demonstrate the feasibility of the proposed combiner, a 2 GHz 60 W Mixed-Mode Doherty-Outphasing PA based on GaN HEMTs was designed, presenting an efficiency higher than 50% over 11 dB of output power back-off.
A Visual Odometry System for Autonomous Vehicles Based on Squared Planar Markers Map

In the context of autonomous vehicles operating in small-scale environments such as industrial parks, residential areas, and university campuses, we propose a visual odometry system with a stereo camera based on a map of squared planar markers (SPM). Our contributions are primarily reflected in two aspects. Firstly, we have proposed a method for creating SPM maps. Compared to high-definition (HD) maps, our approach utilizes squared planar markers that are easier to create than road markings, traffic lights, and buildings, resulting in significant cost savings. Secondly, building on the foundation of stereo ORB-SLAM2, our proposed system accurately estimates the camera's position within the pre-existing SPM map by leveraging the pose information obtained from the planar markers. It also provides real-time outputs of latitude and longitude information. Furthermore, our system incorporates loop closure detection and correction capabilities, enabling the correction of camera pose by utilizing the relative poses between multiple planar markers. Extensive experimental evaluations demonstrate the promising performance of our system in outdoor scenarios, showcasing localization accuracy improvements of up to 30% compared to stereo ORB-SLAM2.
Cardiff Behavioral Model from Passive Load-Pull while Enforcing S-Parameters

This paper presents a new extraction methodology for the Cardiff Behavioral Model formulation using passive load-pull data where the model is forced to accurately predict the S-parameters. The work starts by presenting the additional considerations that are required when the extraction of the Cardiff Model's coefficients are moved from active load-pull to a passive one. It is also explained which Cardiff model coefficients must be fixed to ensure the S-parameters. The model was implemented in a commercial CAD simulator and it was used to simulate a PA.
FullRepair: Towards Optimal Repair Pipelining in Erasure-Coded Clustered Storage Systems

Clustered storage systems often deploy erasure coding that encodes data into coded chunks and distributes them across nodes to tolerate node failures. It is a storage-efficient redundancy scheme but incurs high repair penalty; thus some state-of-the-arts aim to pipeline the above repair process to improve the repair performance. However, we observe that all existing repair pipelining methods only use a single pipeline, making network bandwidth resources of storage nodes underutilized.In this paper, we propose FullRepair, a new repair pipelining mechanism based on multiple pipelines with the aim of fully exploiting all available bandwidth resources during repair. We construct four constraints to model the repair pipelining problem such that we can obtain the optimal pipelined repair throughput under full bandwidth utilization. We design a multi-pipeline scheduling scheme for FullRepair so as to achieve the above optimality. Experiments on the Amazon EC2 show that compared with the state-of-the-art repair pipelining methods RP and PivotRepair, FullRepair reduces the repair time of single chunk by up to 45.40% and 33.19%, respectively.
Control Strategy for Three-Phase Grid-Connected PV Inverters Enabling Current Limitation Under Unbalanced Faults

—Power quality and voltage control are among the most important aspects of the grid-connected power converter operation under faults. Non-sinusoidal current may be injected during unbalanced voltage sag and active or/and reactive power may include double frequency content. This paper introduces a novel control strategy to mitigate the double grid frequency oscillations in the active power and dc-link voltage of the two-stage three-phase grid-connected Photovoltaic (PV) inverters during unbalanced faults. With the proposed control method, PV inverter injects sinusoidal currents under unbalanced grid faults. In addition, an efficient and easy-to-implement current limitation method is introduced, which can effectively limit the injected currents to the rated value during faults. In this case, the fault-ride-through operation is ensured and it will not trigger the overcurrent protection. A Non-MPPT operation mode is proposed for the dc-dc converter. The mode is enabled under severe faults, when the converter cannot handle the maximum PV power. Finally experimental validation is provided by implementing method in an experimental setup including a 2kW PV inverter. considering the characteristics of a renewable energy source, PV or wind. The proposed method [23] has improved the Dual Vector Current Control (DVCC) method to control the high peak currents and minimize the power ripple. In [24], a three-phase system has been investigated, which offers six current control freedoms with a zero-sequence current path to mitigate both active and reactive power oscillations and also inject sinusoidal currents. However,
Wind Power Forecasting Based on Adaptive Spatio-Temporal Graph Neural Network

The rapid increase in wind power consumption presents new challenges for power system operation. Accurate forecasting of wind power is recognized as a major contribution for promoting its consumption and improving the stability of grid operation. This study focuses on forecasting wind power for an onshore wind farm in the United States by incorporating spatiotemporal relationships among 200 wind turbines. We proposed a novel encoder-decoder structure model. In the encoder module, a graph neural network structure is used to capture the intricate geographical distance features and sequence similarity features of the wind turbines as spatial features. The extracted spatial features are then integrated with temporal features into the model by serving as inputs to the gated recurrent unit (GRU) module. In the decoder module, the last state of the top layer of the encoder serves as the initial hidden parameters of the GRU blocks. Finally, the predicted wind powers are given by the decoder. Our model exhibits superior accuracy and enhanced generalization capabilities.
Random Forest Applied to Mass Imbalance Classification in Wind Turbines

Current predictive maintenance techniques are able to estimate when an equipment may fail and indicate the time to perform corrective actions can be taken before the failure. The objective is to carry out the maintenance at the best time in terms of convenience and cost-effectiveness. This work is based on implementing the random forest algorithm to classify a common fault that occurs in wind turbines, which is mass imbalance. Statistical analysis and data analysis techniques were used to better classify five different failure conditions and one condition without mass imbalance using Random Forest algorithm.
Characterization of A 3D-Printed PLA-Based Slotted SIW Antenna

This paper presents the characterization of a slotted substrate integrated waveguide (SIW) antenna made of a 3D printing technology in the fabrication process. The integration of rectangular slots into an SIW structure offers an opportunity to explore new design methods of antenna and optimize its performances for various applications. Meanwhile, the 3D printing technology based on polylactid acid (PLA) as the main material is utilized as it demonstrates a potential and an efficient solution for manufacturing radio frequency (RF) devices. The combination of slotted SIW antenna and 3D printing technology opens up exciting possibilities for the development of other RF devices. The proposed slotted SIW antenna is designed and simulated to operate in the frequency range from 3 GHz to 6 GHz suitable for several wireless communication systems. The measurement result indicates that the fabricated 3D-printed PLA-based slotted SIW antenna have a good agreement with the simulation result, whereby it works within the frequency range of 2.92 GHz to 5.6 GHz.
A study on the characteristics of CNT for the development of a long-term neural recording

Neuroplasticity is pivotal in understanding the intricacies of the brain. To study neuroplasticity, prolonged recording of neuronal cells is necessary. However, existing equipment has limits in terms of its suitability for use in living organisms and its electrical attributes, leading to a maximum recording period of 12 months. Overcoming this hurdle requires a material that has high biocompatibility and excellent electrical characteristics. Carbon nanotubes (CNTs) have emerged as a promising candidate to meet both requirements. This study validates the attributes of CNTs when polydopamine/polyethyleneimine is used to improve CNT adhesion to enhance electrode electrical properties.