Miracle-dz/UniFAR-FTUs huggingface dataset:
Miniaturized Antenna Based on Vertical Split-Ring Resonator

In this work, a vertical split-ring resonator (VSRR) based metamaterial antenna for WIFI Internet of Thing (IOT) application is proposed. The antenna has two features: a direct inductive feeding and an all-metal folded miniaturized metamaterial structure. It could be equivalent to a magnetic dipole antenna placed on a ground. The resonance frequency of the antenna is determined by the VSRR meta-resonator, which could be guided and adjusted by the VSRR equivalent circuit parameters. The all-metal structure could eliminate dielectric loss therefore enhance the radiation performance. The antenna shows an electrical size of $\mathbf{0.129\lambda_0 \times 0.064 \lambda_0 \times 0.056\lambda_0}$ only and the measured peak radiation efficiency of −0.46 dB. The return loss is better than −8 dB inside the 2.4-2.48 GHz. Good agreement has been observed between simulation and measurement.
Cut-Off-Less Coplanar Waveguide Connector with Mode-Selectable Filter

Herein, we study a novel coplanar waveguide and its connector structure that allows all three waveguide propagation modes: transverse-electric (TE), transverse-magnetic (TM), and transverse electromagnetic (TEM). With a split structure and appropriate connectors, the waveguide can be used both over and below the cutoff frequency. This study presents a design for such a capped coplanar waveguide that is easily assembled. The cap is insulated from the waveguide body to allow TEM-mode trans-mission. The TEM mode allows transmission below the wave-guide‘s cutoff frequency, and waves can propagate in the TE and TM modes similar to a typical inner waveguide with low loss. Furthermore, by adding a low-pass filter to the waveguide-coaxial conversion, the transmission mode can be selected for each frequency, which weakens the coupling between the modes. Transmission both under and over the cutoff frequencies was confirmed in simulations of the proposed waveguide. We also discuss the waveguide structure and the connectors required for practical use.
Personalized Travel Spot Recommendation based on Unsupervised Learning Approach

Tourism in Sri Lanka is an evolving field which is significantly influencing on the development of the country. With the rapid advancement of Affective computing and its’ diverse paths where applications are being implemented by facilitating user needs and emotions, tourism has become one of the prominent fields to provide a comprehensive analysis of useful inclination in specific travel spots based on the user interests and emotions. Traditional tourism methodologies where a travel guide guides on a tourists’ journey has nowadays become an old fashion where the tourist himself has innovative applications which provide a guide in almost all the areas in his journey beginning to end. This study proposes a solution where tourist gets a personalized recommendation on travel spots to visit based on his behavioral patterns observed in social media mainly in Facebook platform. This approach makes ease traveler’s life providing personalized recommendations based on collaborative based and content based filtering approaches.
GroupIt: Lightweight Group Key Management for Dynamic IoT Environments

With the proliferation of Internet of Things (IoT) devices that collect sensitive data, access control is more crucial than ever to safeguard IoT data from unauthorized use. To enforce access control policies without trusted online entity, one promising approach is to maintain a group key shared between a device and its current subscribers, such that the device can encrypt its data and only the subscribers can decrypt it. However, prior group key management (GKM) schemes fail to efficiently address new challenges introduced by the massive scale of IoT devices, dynamic memberships of users, and changes in the number of devices. This paper explores efficient GKM to accommodate multiple devices (in addition to multiple users) and to handle frequent membership and device number changes. Inspired by the observation that devices with similar functionalities often have similar access permissions, we propose a two-tier GKM architecture called GroupIt, in which each device is assigned to one of many predefined groups, and key management is performed within each group as well as between groups to improve efficiency. Despite being conceptually simple, GroupIt addresses technical challenges including: 1) preventing a malicious device from obtaining extra information about other devices in the same group and 2) ensuring forward/backward secrecy and preventing collusion attacks when gluing two existing GKMs together. The probability of a successful collusion attack quickly drops to 0.3% after five membership changes even in a small device group (e.g., 8). This paper provides both theoretical analysis and a proof-of-concept implementation based on Alljoyn, an opensource IoT communication framework to demonstrate the feasibility of GroupIt.
Data-Driven Estimation of Backward Reachable and Invariant Sets for Unmodeled Systems via Active Learning

Ensuring control performance with state and input constraints is facilitated by the understanding of reachable and invariant sets. While exploiting dynamical models have provided many set-based algorithms for constructing these sets, set-based methods typically do not scale well, or rely heavily on model accuracy or structure. In contrast, it is relatively simple to generate state trajectories in a data-driven manner by numerically simulating complex systems from initial conditions sampled from within an admissible state space, even if the underlying dynamics are completely unknown. These samples can then be leveraged for reachable/invariant set estimation via machine learning, although the learning performance is strongly linked to the sampling pattern. In this paper, active learning is employed to intelligently select batches of samples that are most informative and least redundant to previously labeled samples via submodular maximization. Selective sampling reduces the number of numerical simulations required for constructing the invariant set estimator, thereby enhancing scalability to higher-dimensional state spaces. The potential of the proposed framework is illustrated via a numerical example.
Effects of EEG Electrode Positional Deviations for Classification Accuracy on Different Days

EEG electrode positional deviations inevitably exist when EEG data are recorded from the same subject on different days. Although the existence of such deviations is not a secret, it remains unclear what factors cause positional deviations and how they affect the classification accuracy of EEG-based brain-machine interfaces. This paper measured the EEG electrode locations on different days of identical subjects that were placed by the same experimenter on different days and investigated the tendency of the positional deviations. We also measured the electrode locations from identical subjects placed by different experimenters on different days and evaluated the effects of classification performances using realistic EEG data. Our results indicated that the classification accuracies largely depended on the EEG electrode deviations especially when the cortical activation areas were small.
How to Measure the Performance of Automated Program Repair

As with the advance of automated program repair, many novel repair approaches have been proposed in recent. There also exist empirical work focusing on the performance comparison among those approaches. In this paper we survey recent repair work in five main venues, and plan to answer the question of how to measure the performance of automated program repair in term of evaluation metrics. We summary the evaluation metrics in literature, and conduct the discussion on how to construct the common metrics for further research in the area of automated program repair.
Exact Convergence of Gradient-Free Distributed Optimization Method in a Multi-Agent System

In this paper, a gradient-free algorithm is proposed for a set constrained distributed optimization problem in a multi-agent system under a directed communication network. For each agent, a pseudo-gradient is designed locally and utilized instead of the true gradient information to guide the decision variables update. Compared with most gradient-free optimization methods where a doubly-stochastic weighting matrix is usually employed, this algorithm uses a row-stochastic matrix plus a column-stochastic matrix, and is able to achieve exact asymptotic convergence to the optimal solution.
Broad Learning System for Control of Nonlinear Dynamic Systems

We apply the Broad Learning System (BLS) to control of nonlinear dynamic systems by proposing an iterative learning algorithm using the gradient descent method. The parameters in the enhancement layer and the top layer consisting of weights connecting the feature nodes to the enhancement nodes and the output nodes, the weights connecting the enhancement nodes to the output nodes, as well as the corresponding bias terms, are adjusted to reduce the error between the plant output and desired signal, while the remain weights and bias terms are randomly generated. The convergence of this learning algorithm is guaranteed by proving a Lyapunov stability theorem. The control performance of BLS with the proposed iterative learning algorithm on two dynamic plants indicates its effectiveness and practicability. Moreover, the BLS based controller produces correct control inputs much faster with fewer parameters, and behaves more accurate than some neuro and fuzzy-neuro models.
Collaborative Additional Variational Autoencoder for Top-N Recommender Systems

Collaborative filtering (CF) has been generally used in recommender systems when faced some practical problems. Due to the sparsity of the rating matrix, the traditional CF-based approach has a significant decline in recommendation performance. Gradually, a hybrid method, using side information and rating information, has been widely employed and achieves great performance. Together with side information and rating information, the hybrid method can overcome the data sparsity and cold-start problems. However, they seem to fail to take into consideration the fact that the sparsity of single side information. To solve this problem, we take full advantage of the characteristics of deep learning that can learn effective representation and propose a novel deep learning model named additional variational autoencoder that considers both content and tag information of the item. The model learns effective latent representations from additional side information, including content information and tag information in an unsupervised manner. With the help of graphical models, it can extract the implicit relationships between users and items effectively. A large number of experimental results on two actual datasets show that our proposed model is superior to other methods, and the performance improvement is achieved.
Low Loss 28 GHz Digital Phase Shifter for 5G Phased Array Transceivers

Integrated 28-GHz reflection-type phase shifter (RTPS) with near-constant insertion-loss for next generation 5G applications is demonstrated in this paper. This differential RTPS design employs a diagonal configuration for the coupler and a 6-bit binary weighted switched varactors array for PVT-robust phase tuning. The IC occupies area of $\pmb{840\ \text{x}\ 800\mu \text{m}^{2}}$ and was fabricated in $0.12\ \mu\text{m}$ SiGe BiCMOS process, using CMOS features only. The measured results show state of the art insertion loss of $4\pm 1\text{dB}$ and phase tuning range larger than 180 degrees at 24–31 GHz. At 28 GHz, the phase shifter exhibits insertion loss of $4\pm 0.35\ \text{dB}$, and achieves tuning range of 210 degrees with zero power consumption.
Observation of Static Domain Structures of Thin-Film Magnetoimpedance Elements With DC Bias Current

We investigate changes in domain structures of thin-film magnetoimpedance elements with the easy-axis angles in the range of 0°–30° upon the application of dc bias current directly to the elements as well as the dc external magnetic field. All of the elements 180° domains at zero field when the bias current is zero. The angle of 180° domains depends on the direction of the easy axis. The changes in a domain configuration due to the bias dc current do not have a systematic behavior; instead, complex variations are observed. However, the field range at which multi-domain structures appear tends to be narrower with the increase in the absolute dc bias current value. The complex changes depend on the complex field distribution based on the applied dc external field and bias field generated by the dc current. The impedance does not exhibit significant changes around fields where changes in domain configuration occur, which implies that the magnetization rotation is the dominant factor for the impedance changes at high frequencies.
Alpha Model Domination in Multiple Choice Learning

Multiple Choice Learning (MCL) algorithms involve an 'oracle' user with unmodeled biases that selects a preferred output among several possible hypotheses. We identify a shortcoming and training obstacle of the recent Stochastic Multiple Choice Learning (sMCL) algorithm. When an ensemble of neural networks trains under sMCL, the best performing model receives the majority of parameter updates at the expense of other ensemble members. We refer to this sMCL training issue as Alpha Model Domination (AMD) and empirically demonstrate that AMD does not resolve itself with longer training time. We introduce several novel MCL loss functions that both avoid AMD and yield statistically significant improvements in oracle accuracy (OA) compared to sMCL. Using the MNIST, CIFAR-10, and ImageNet classification datasets, we empirically demonstrate the superior performance of our proposed loss functions.
A DC to 40 GHZ 4-VPP Output High-Efficiency Linear Driver for Optical Communication

This paper presents a low-power high-efficiency linear driver for optical modulators in a $\mathbf{0.25-\mu \mathbf{m}}$ SiGe: C BiCMOS technology. The driver features a small-signal gain of 18 dB and a 3-dB bandwidth of 40 GHz and delivers a maximum output amplitude of 4 Vpp to a $\mathbf{100 \Omega}$ differential load. With the inductor-peaking method, this driver achieves $\mathbf{34 \ ps \pm 3 \ ps}$ group delay from DC to 50 GHz. For time-domain measurement, this driver achieves a symbol rate of 56 Gb/s NRZ and 28.05 Gbaud PAM4 limited by measurement platform limitations while it has potential to achieve 50 Gbaud PAM4 from design analysis and S-parameter measurement. The bandwidth/fr ratio is 22.2%, the power efficiency is 4%, and the power consumption is 499 mW which is 40% lower than the state-of-the-art.
Passivity Analysis for Memristor-Based Inertial Neural Networks With Discrete and Distributed Delays

The existing results of passivity for neural networks mainly concentrated on first-order derivative of the states, whereas it is also significant to study the passivity with high-order derivative. In this paper, a class of memristor-based inertial neural networks (MINNs) with external inputs and outputs are concerned. First, by choosing appropriate variable transformation, the original networks are rewritten as first-order differential equations. Then a criterion of passivity for the MINNs is presented by nonsmooth analysis and linear matrix inequality (LMI) techniques, which could also result in NP-hard problem since it needs at least exponential-time to solve the passivity condition. Meanwhile, based on the obtained passivity criterion, asymptotic stability criterion is accordingly derived for MINNs. In order to avoid the NP-hard problem, we employ matrix-analysis-techniques with the property that the difference-matrix between the given matrix and the proposed matrix is seminegative definite. Then the time complexity for solving the proposed passivity criterion is reduced to constant-time with respect to the number of LMIs. Robust passivity for MINNs is also studied for bounded uncertain parameters. The MINN widens the application ranges for designing neural networks. Finally, relevant simulation examples are given to show the effectiveness of the obtained results.
Speech Enhancement Using Compressed Sensing-based method

A procedure based on compressed sensing (CS) is proposed for speech enhancement. The latter is Prior-noise-estimation free. This approach is motivated by the fact that CS allows the recovery of only the sparse signal in the presence of non-sparse signal (noise). An application to an Arabic speech signal corrupted with white Gaussian noise is studied. Comparison of CS-based enhancement with three state-of-the-art methods is performed in terms of segmental SNR, root mean square error, and perceptual evaluation of speech quality (PESQ); and proved promising performances.
Design of a MRAC-Based Fractional order PIλDμ Regulator for DC Motor Speed Control

This paper deals with the design of a fractional-order model reference adaptive control (MRAC)-based PI<sup>λ</sup>D<sup>μ</sup> controller using MIT rule and the ideal Bode transfer function for the speed control of a DC motor. The ideal Bode’s model is based on a frequency domain fractional order integrator and the so-called singularity function approximation method is used to convert it into rational function. The designed PI<sup>λ</sup>D<sup>μ</sup> controller parameters are obtained such that the feedback control system has minimum performance index. Simulation examples illustrate the effectiveness of the proposed PID control adjustment algorithm showing the improvement of the closed loop output behavior.
A Low-Profile and High-Gain Circularly Polarized Antenna Based on Holographic Principle

This paper presents a low-profile and high-gain right circularly polarized antenna based on holographic artificial impedance surface. The holographic antenna (HA) is comprised of around 2000 various elements printed on grounded dielectric substrate, excited by a horizontally omnidirectional ring-slot antenna. By using anisotropic rectangle patch as unit cell, the cross polarization is suppressed and the cross-polar level is of −37.8 dB in broadside. The −10 dB impedance bandwith covers 9 GHz to 11 GHz and the 3-dB axial ratio bandwith covers 9.12 GHz to 11 GHz. The maximum right circularly polarized gain is 20.8 dB and the 3-dB beamwidth is 6.08° at 20 GHz. The simulated results show the proposed antenna possesses low-profile, high-gain, and narrow pencil beam in the operating band.
Mean Field Optimal Energy Management of Plug-In Hybrid Electric Vehicles

This paper addresses the problem of energy consumption management of a large population of plug-in hybrid electric vehicles (PHEVs). A PHEV can be derived either on its gasoline or the battery modes while traveling, and can be either charged from or discharged to the grid while parking at home, respectively. This feature is the main novel consideration in our system model, besides taking into account the constraints like traveling time and distance, and the bounds on the charging power and state of charge of the battery. On the basis of the proposed model of PHEV, the optimal battery-gasoline energy management strategy is obtained in order to minimize the cost of energy and battery degradation for each user. Such a strategy can also compensate the peak load. Since the strategy of each PHEV affects other users’ cost through the electricity price, we model such an interaction as a game problem and we prove that there exist a unique Nash equilibrium point of the game. Then, due to the fact that the effect of a single PHEV of a large population on the other users’ costs is negligible, the theory of mean filed (MF) game is utilized to develop a decentralized control strategy for PHEVs. The lightweight information exchange of the proposed MF algorithm ensures a low computational cost and communication overhead. The proof of convergence for the proposed algorithm is given and it is shown that as the number of PHEVs goes to infinity the MF optimal strategy of the players converges to the unique Nash equilibrium point of the game. In simulation results, the effectiveness of the proposed method in terms of demand side management, peak shaving, convergence rate, and robustness is studied.
Optimizing Optical Parameters to Facilitate Correlation of Laser- and Heavy-Ion-Induced Single-Event Transients in SiGe HBTs

An approach for determining the optimal laser parameters (i.e., pulse energy, focused spot size, wavelength, and pulse duration) for correlating single-event transients induced via two-photon absorption (TPA) and heavy ions is presented. The approach focuses on identification and extraction of waveform characteristics, or “features,” and minimizing the error between features produced by TPA and ions. Modifying optical parameters can directly impact the waveform features of the TPA-induced transients. Consequently, optimal laser parameters that minimize the error between features extracted for laser- and ion-induced waveforms can be determined. In this paper, the laser pulse energy and spot size were varied, while maintaining a fixed pulse duration and wavelength, and the resulting transients were compared to ion-induced transients. When the optimized optical parameters are used, excellent agreement was achieved between laser- and ion-induced transient waveforms in a silicon-germanium heterojunction bipolar transistor (SiGe HBT). In addition, a one-to-one correlation between heavy-ion linear energy transfer (LET) and laser pulse energy was obtained for a particular spot size ( $\omega _{0} = 1.89\,\,\mu \text{m}$ (HW $1/e^{2}$ )). These results show that a correlation between LET and pulse energy is achievable when the remaining optical parameters are selected appropriately. Although the results presented are for SiGe HBTs, the generality of the approach should allow it to be extended to other semiconductor platforms.
Online Detection of Real-World Faces in ECoG Signals

Previous neuroimaging studies have reported that the ventral temporal cortex (VTC) processes visual stimuli and thereby establish visual categories, which can be detected in electrophysiological signals such as electrocorticography (ECoG). However, most of the studies are based on visual stimulation through a computer. Thus, the degree to which those categories can be generalized is unclear under real-world conditions. This study extends the findings of a previous experiment, which aimed in real-time detection of visual perception, and investigated whether neural face and kanji categories obtained by computer stimuli can be confirmed in a real-world scenario. The real-time decoder accuracy and latency of two patients with epilepsy revealed that real-world faces and kanji can be detected with 79.9% and 28.4% accuracy, respectively, showing an average online detection latency of 447 ms with respect to presentation time. Hence, the VTC cortex elicits robust and similar responses to computer stimuli and real-world face, leading to a powerful brain-computer interface to track a person's attention in a real-world scenario.