Miracle-dz/UniFAR-FTUs huggingface dataset:
Lumped-Element Wilkinson Power Combiners Using Reactively Compensated Star/Delta Coupled Coils in 28-nm Bulk CMOS

This paper presents four compact lumped-element Wilkinson power combiners (WPC) operating at 5 GHz in 28-nm bulk CMOS. To minimize the chip area, the inductances in the designs are implemented using mutually coupled coils. Two designs use star inverting coupled coils (SICC), while the other two employ delta noninverting coupled coils (DNICC). One of the two SICC-based designs (and similarly for the DNICC-based designs) incorporates a second harmonic (2f0) trap to lower the total harmonic distortion and reduce the required inductances by 25%, further reducing the circuits footprint. A design methodology for effectively exploiting the mutual coupling and nullifying the coupling parasitics is presented. The coupling parasitic in the DNICC-based design is exploited to provide the WPC isolation resistance, resulting in a lumped-element WPC requiring only three components: a coupled coil and two capacitors. The measurement and simulation results are presented to confirm the theory validity. The SICC-based WPC with an area of 0.13 mm2 achieved input return losses (RL) >17.5 dB, output RL > 11.8 dB, isolation >11.2 dB, and insertion losses (IL) <1.5 dB across 3.5–5.4-GHz frequency range. Its variant with a 2f0 trap achieved, from 3.5–5.35 GHz, input RL >18.2 dB, output RL >11.6 dB, isolation >11 dB, IL <1.4 dB, and peak 2f0 rejection of 18 dB at 10.2 GHz in an area of 0.13 mm2.The DNICC-based WPC with an area of 0.11 mm2 achieved input RL and isolation >10 dB, output RL >15.3 dB, and IL <1.2 dB across 4.1–5.45-GHz frequency range. Its variant with a 2f0 trap achieved, from 4.3–5.3 GHz, input RL and isolation >10.1 dB, output RL >14.6, IL <1.2 dB, and peak 2f0 rejection of 23 dB at 10.4 GHz in an area of 0.09 mm2.
Parametric Amplification and Wavelength Conversion in Dual-Core Highly Nonlinear Fibers

In this paper we experimentally show parametric amplification and wavelength conversion in a custom manufactured dual-core highly nonlinear fiber. On-off gain <inline-formula><tex-math notation="LaTeX">$>$</tex-math></inline-formula>10 dB and conversion efficiencies between <inline-formula><tex-math notation="LaTeX">$-1$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$-8.5$</tex-math></inline-formula> dB were measured for both cores. The estimated effective nonlinear parameter for the cores of the fiber are 6.6 W<inline-formula><tex-math notation="LaTeX">$^{-1}\mathrm{km}^{-1}$</tex-math></inline-formula> and 6.3 W<inline-formula><tex-math notation="LaTeX">$^{-1}\mathrm{km}^{-1}$</tex-math></inline-formula>, while the zero-dispersion wavelength for the individual cores is shown to be relatively close from each other. Furthermore, complementary analytical and numerical results show that coupled cores fiber optical parametric amplifier offer the potential of wide-band gain even when they have significantly distinct zero-dispersion wavelengths.
A 12-GHz Reconfigurable Multicore CMOS DCO, With a Time-Variant Analysis of the Impact of Reconfiguration Switches on Phase Noise

This article introduces a 28-nm CMOS digitally controlled oscillator (DCO) based on eight oscillator cores, where the number of active cores can be reconfigured to be either 2, 4, 6, or 8, trading power consumption for phase noise without incurring an additional phase noise penalty. The impact of the reconfiguration pMOS switches on the phase noise performance is determined through a simple yet rigorous time-variant circuit analysis. The octa-core DCO covers a 27% frequency tuning range between 10.7 and 14.1 GHz with an average resolution of 6 MHz. A very low phase noise level of −126 dBc/Hz is measured at 1-MHz offset from 10.7 GHz with a power consumption of 173 mW, corresponding to a phase-noise figure-of-merit of −184 dBc/Hz that remains almost constant across the tuning range.
Dark Current Suppression in MOS2/h-BN/Graphene Photodetector for Self-Powering Applications

A photodetector with MoS<inf>2</inf>/h-BN/graphene had shown performance improvement at zero biasing, indicating the potential for self-powering applications. However, the influence of device dimensions towards performances was lacking in the literature. So, this work had extended to characterize and evaluate the performances, in terms of dark current and ratio of photocurrent versus dark current (I<inf>photo</inf>/I<inf>dark</inf>) of MoS<inf>2</inf>/h-BN/graphene photodetector experimentally, to support previous numerical modeling studies. The presence of MoS<sup>2</sup>, h-BN and graphene layers throughout the device development were checked and further confirmed through optical imaging under magnification of <tex>$200\ \mu\mathrm{m}$</tex>. The photodetector with thin MoS<sup>2</sup> and two layers of h-BN had shown significant dark current suppression up to <tex>$6.54\times 10^{-12}$</tex> A with I<inf>photo</inf>/I<inf>dark</inf> at <tex>$1.9\times 10^{1}$</tex> at zero biasing.
IntelligentChain: Blockchain and Machine Learning based Intelligent Security Application for Internet of Vehicles (IoV)

An IoV enables secure transmission and storage of real-time traffic accident information among IoV devices like Vehicles, Road Side Unit (RSU), and Edge Servers to reduce unintended loss. Regardless of the advantages, accurate traffic accident prediction and decentralized data storage remain challenges for a reliable IoV system. We propose IntelligentChain, a blockchain and machine learning-based edge server for IoV that enables low-latency IoV device registration and secure traffic accident information communication between them. IntelligentChain then implements novel decentralized trust, consensus procedures for blockchain-based traffic accident information storage and predicts localized area traffic accidents using LSTM recurrent neural networks. IntelligentChain has been implemented on the Raspberry Pi 4. The experiment results illustrate that IntelligentChain achieves 90% prediction accuracy on the Kaggle dataset while decreasing transaction approval delay.
Dynamic stability improvement of a micro grid system by optimized PSS controller

This work proposes an optimal fractional power system stabilizer control action to improve dynamic stability of grid integrated micro grid system. A fractional PID controller-based PSS has been implemented here whose gains are optimized by sailfish algorithm. The solar and wind generations in the micro grid are varied in step and random manner creating disturbances which is variation in angular frequency of power system. By proposed sailfish algorithm tuned PSS action this variation in angular frequency is heavily damped that has been compared with PSO & DE algorithms. System Eigen analysis has been performed to validate proposed optimal control action. The system eigen distributions and results analysis predict that proposed action is more efficient and is simple to implement for a micro grid system.
Design of a Soft Rat Robot Based on Pneumatic Actuator

Currently, progress in soft robotics is contributing to advancements in the field of robotics. A soft robot has several advantages over a traditional robot including portability, durability, and increased flexibility. A suitable design of the actuators in a robot is crucial for increasing the robot's flexibility. A suitable design of the actuators present in a robot is crucial for increasing the robot's flexibility. This study aims to develop a soft rat robot based on pneumatic actuators that can interact with real rats. The pneumatic actuator design developed by Suzumori et al. in 2017, the flexible micro actuator (FMA), is chosen as the main actuator to drive the robot. This actuator is rodlike, made using silicone fiber, and driven by filling high-pressure air into a specially designed container. An actuator based on the FMA design with a diameter of 25 mm is fabricated to form a soft rat robot that can simulate the motions of a real rat— rearing and rotating left and right. Finally, the properties of the pneumatic actuator are tested and analyzed to optimize the structure of the robot.
Annealing Effect On Ultraviolet Sensor Performance With Porous Silicon Based

Nowadays, most semiconductor industries apply silicon-based material in developing advanced electronic device. Concerns with heat management of electronic devices which getting worst day by day, the nanostructures technologies were introduced in order to overcome the problem. The nanostructures development on bulk crystalline silicon known as porous silicon which could be produced via anodization method. In this study, the anodized porous silicon was annealed at temperatures of 200°C up to 800°C for the research purpose of annealing effect on ultraviolet sensor performance. The structures of porous silicon were analyzed via FESEM and XRD characterization in order to identify the morphology and crystallinity of porous silicon. Then, the ultraviolet sensors were produced and the sensors performance were analyzed. After post-annealing treatment, the anodized porous silicon has the strongest crystalline peak at 600°C and 800°C, while below 400°C, the porous silicon samples have a broadened XRD peak around $2\theta=33^{\circ}$. However, our experimental results show that an ultraviolet sensor which anneal at temperature 200°C have the best sensing performance.
Flipbook in Learning: Bibliometric Analysis using Scopus Data and Google Scholar

This study aims to map articles that examine flipbooks as the variables. Scopus and Google Scholar are used to gather the data for this study. The selected articles are those published in international journals and proceedings indexed by Scopus and/or Google Scholar. The data collection applied is a documentation technique. The procedure for data documentation begins with Scopus data containing the article keyword "flipbook in learning". The data from Google Scholar is then cataloged in the Mendeley software. Furthermore, each data is mapped using the VOSviewer software. The data is limited according to the needs of the discussion of the results of the study, namely articles that discuss flipbooks in learning. The data analysis process uses a bibliometric approach. The results show that research on flipbooks in learning published in international journals and proceedings is increasingly in demand, particularly in 2020 and 2021. However, in terms of quantity, flipbook articles still need to be improved. This is following the need for teaching materials, especially teaching materials that can be used during the distance learning system during the COVID-19 pandemic.
The Spacing Effect in Remote Information-Integration Category Learning

ion because irrelevant information is forgotten sooner than relevant information and cause the retrieval of learnt experiences becomes more effortful, creating a desirable difficulty in the learning process (Vlach, 2014). This hypothesis builds upon study-phase retrieval theory, holding that memory is ameliorated by the recall of past learning events (Thios & D’Agostino, 1976). The encoding variability hypothesis also builds upon study-phase retrieval theory, supposing that learning is more effective when more distinct retrieval cues are
Throughput Based Adaptive Beamforming in 5G Millimeter Wave Massive MIMO Cellular Networks via Machine Learning

In this paper the performance of an adaptive beamforming framework is evaluated, when deployed in fifth-generation massive multiple-input multiple-output millimeter wave cellular networks. To this end, active beams are formed dynamically according to traffic demands, in order to maximize spectral and energy efficiency (SE, EE) with reduced hardware and algorithmic complexity. In the same context, a machine learning (ML) approach is considered as well, where the configuration of the active beams per cell is directly related to the requested throughput in the cell’s angular space. According to the presented results, the ML-assisted beamforming framework can improve EE with reduced algorithmic complexity compared to the non-ML case, depending on the tolerable amount of blocking probability.
Ensemble Regression Modelling for Genetic Network Inference

An accurate reconstruction of Gene Regulatory Networks (GRNs) from time series gene expression data is crucial for discovering complex biological interactions. Among many different approaches for inferring GRNs, there are several methods which produce high false positive interactions, and are unstable, requiring fine tuning for many of their parameters. In this paper, we consider the GRN inference problem as a regression problem, and propose a simple ensemble regression-based feature selection model which is a combination of cross-validated Lasso and cross-validated Ridge algorithms for reconstructing GRNs. Due to the novelty of the proposed ensemble model, it is able to eliminate overfitting, multi co-linearity issues, and irrelevant genes within one computational approach. While observing the type of gene-gene regulatory interactions the regression model also identifies the direction of these interactions. A new coefficient of determination (R2)-based approach identifies the best model to fit the data among LassoCV and RidgeCV, and evaluates the model importance in term of gene-wise maximum in-degree which decides the maximum number of regulatory genes including self-regulations that can be selected from a given method. Then, an evaluated gene score-based majority voting technique aggregates the selected gene lists from each method. In our experiments, the performance of the proposed ensemble approach was evaluated using gene expression datasets from three small-scale real gene networks. Our proposed model outperformed other state-of-the-art methods, producing high true positives, reducing false positives, and obtaining high Structural Accuracy, while maintaining model stability and efficiency.
Towards Sensorless Configuration Estimation in Multi-DoF Soft Robotic Structures Driven by Rolled Dielectric Elastomer Actuators

Soft robots have the potential to overcome some of the limitations of conventional, rigid manipulators, especially in those applications where safe interaction and high flexibility are required. One critical issue concerns the need for estimating the position, the configuration, or even the shape of the soft robot without introducing bulky external sensors. Soft robots based on dielectric elastomers (DEs) offer a potential solution to this problem, due to their self-sensing feature. Thanks to self-sensing, the information on DE displacement can be estimated online via electrical measurements, performed simultaneously with high-voltage actuation, and used to potentially reconstruct the full configuration of the robot. This work presents a first investigation towards this system-level self-sensing concept, by choosing a bendable soft robotic module driven by rolled DE actuators as case study. An extended Kalman filter is implemented based on a physical model, and used to estimate the full module configuration via DE-level information only, i.e., the actuators lengths available through simple electrical measurements. The effectiveness of the architecture is evaluated by means of camera-based experiments, showing that an accurate and robust estimation of the system configuration variables can be effectively achieved.
Automatic Machine Learning Classification Algorithms for Stability Detection of Smart Grid

The smart grid is a kind of leap in the power grid that emerged to improve electrical service and reduce losses. This work tackles the stability detection problem in smart grids using machine learning algorithms. Four different preprocessing scenarios are used along with five different classifiers (Naive Bayes (NB), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM)) to find the best classifier and suitable preprocessing steps to achieve the best accuracy. Automated machine learning (AutoML) is used to develop a proposed model to answer two questions. First, is it feasible to use AutoML to solve problems using a limited dataset size? Do complex algorithms (such as deep learning) constantly improve system accuracy? In this study, feature selection (as a preprocessing step) was sufficient to obtain 100% accuracy with three classifiers (LG, DT, SVM). Compared to other studies that use the same dataset, it was found that it is not always beneficial to choose a complex algorithm to get the best results. Moreover, for researchers with limited professionalism in data analysis, AutoML helps study the dataset and select the appropriate machine learning algorithms before turning to use complex algorithms.
Comparative Study of Sample Based Model Predictive Control With Application to Autonomous Racing

In this paper we outline some of the numerical heuristics used in existing sample-based MPC techniques and present a generic sample-based MPC algorithm for nonlinear optimal control. Compared to most of the existing techniques our generic algorithm does not place any restrictions on the form of the cost functions and dynamics used in the control problem formulation. We apply the numerical heuristics to the presented algorithm and compare their effectiveness individually by evaluating the control on an autonomous racing environment.
Cross-Attention-Based Feature Extraction Network for 3D Point Cloud Registration

In recent years, the feature-based point cloud registration methods have attracted more attention. However, most existing methods focus on extracting features with strong antiinterference ability from a single point cloud while neglecting the differences within point cloud pairs. In this paper, unlike these methods treating each point cloud independently, we instead consider the information between point cloud pairs when extracting features. Specifically, we propose a cross-attention-based network for modeling the correlation between a pair of point clouds, where a 3D cross-attention mechanism is proposed and combined with 3D convolution elegantly for feature extraction. The extracted features achieve better robustness under various conditions, such as rotation and translation changes. Then accurate point cloud registration is achieved by matching these features. Experimental results on 3DMatch dataset show that the proposed method achieves state-of-the-art performance on feature matching and point cloud registration tasks compared with the previous feature-based methods.
Cascade Flight Control of Quadrotors Based on Deep Reinforcement Learning

Numerous algorithms have been proposed for quadrotor flight control. Conventional methods require massive labor of parameter adjustment. Deep reinforcement learning (DRL) methods also need enormous computation and complicated hyperparameter tuning, since most of them regard the quadrotor dynamics as a black box. To overcome the drawbacks of both conventional and learning-based methods, this letter proposes a DRL-based cascade quadrotor flight controller. Under the small-angle restriction, the quadrotor dynamics are decomposed into six subsystems, each containing only one degree of freedom (DOF) for the agent to control. Six agents are sequentially trained to fully control the corresponding DOF without any prior knowledge of quadrotor dynamic parameters. Experiments show that, with a total training time of 17 min, the proposed controller could accomplish the fixed point tracking task with an error lower than 5 mm, rise time lower than 1.5 s, and peak value lower than 1%. The controller could also track time-variant trajectories.
On Epidemic-aware Socio Spatial POI Recommendation

Epidemics such as COVID-19, SARS, H1N1 have highly transmissible viruses and spread wildly through the population with negative consequences. Multiple studies have shown the correlation between the contact networks between individuals and the transmission of infections due to contact between colocated individuals. To mitigate the transmission of the virus, intervention measures have been applied without decisive success. Therefore, reducing transmissions through suitable epidemicaware POI recommendations to users is necessary to cope with user mobility. Current POI recommendation approaches do not take into consideration the transmission of infections between co-located users. In this paper, we formulate a new query named Epidemic-aware POI Recommendation Query (EPQ), to timely recommend a set of POIs to users at different time steps, while considering the spread of infection between co-located users, their social friendships, and their preference. We prove that EPQ is NP-hard and propose an effective and efficient algorithm, Epidemic-aware POI Recommendation (EpRec) to tackle EPQ. We evaluate EpRec on existing location-based social networks and pandemic datasets against state-of-the-art algorithms. The experimental results show that EpRec outperforms the baselines in effectiveness and efficiency.
Trajectory Planning for Data Collection in Multi-UAV Assisted WSNs

The deployment of unmanned aerial vehicles (UAVs) for data collection in wireless sensor networks (WSNs) is gaining recently more research interest, thanks to the numerous advantages of UAVs. In fact, UAVs can be deployed quickly almost anywhere and are able to access difficult terrains. However, both WSNs and UAV suffer from serious energy limitation challenges. Fortunately, when they are available, using multiple UAVs along with appropriate trajectory planning schemes can highly reduce the negative impact of this problem. In this paper, we deal with the problem of data collection in a WSN assisted by multiple UAVs. Our main goal is to minimize the mission total time by jointly optimizing the trajectories of all the UAVs while serving the sensor nodes (SNs). The mission total time is defined as the time required by the UAVs to transfer energy to all using wireless power transfer (WPT) and collect data from these SNs. In addition to the highly complex optimal solution, we propose two heuristics to solve the formulated problem, namely the nearest insertion algorithm (NIA) and the greedy algorithm (GA).
Preventing Catastrophic Forgetting using Prior Transfer in Physics Informed Bayesian Neural Networks

Predictive models can be integrated in the sensing and monitoring methodologies of mechatronic systems in operation. When systems change or are subject to varying operating conditions, adaptivity of the models is needed. The goal of this paper is to enable this adaptivity by presenting a framework for continual learning. The framework aims to transfer and remember information from previously learned systems when a model is updated to new operating conditions. We achieve this by means of the following three key mechanisms. We first include physical information about the system, heavily regularizing the model output. Secondly, the usage of epistemic uncertainty, used as an indicator of the changing system, shows to what extend a transfer is desired. Last but not least the usage of a prior within a Bayesian framework allows to regularize models further according to previously obtained information. The last two principles are enabled thanks to the use of Bayesian neural networks. The methodology will be applied to a cam-follower system in a simulation environment, where results show that previously trained systems are better remembered with an increase of 72% compared to normal training procedures.