Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review on Metaheuristic Algorithms: Recent Trends, Benchmarking and Applications

Metaheuristic algorithms can be summarized as a form of stochastic optimization algorithm which does not depend on the surface gradient for optimization. These algorithms draw inspiration from various sources to improve fitness but most of these algorithms are nature inspired. They have been actively researched area due to their vast applications in engineering and artificial Intelligence application. This research area and their applications has been tremendously growing as shown through the last 2 decades research. This review paper is aimed to give a brief overview of evolutionary algorithms, their benchmarking and their recent successful applications. This paper will focus on 3 fundamental issues: state of the art algorithms, benchmarking issues and applications.
Compact Dual-Band Dual-Polarized Base-Station Antenna Array With a Small Frequency Ratio Using Filtering Elements

This paper presents a compact dual-band ±45° dual-polarized base-station array based on embedded filtering elements. The proposed antenna array consists of 9 dual-band antenna units, which operate at the lower band (LB) of 790–862 MHz and the higher band (HB) of 880–960 MHz with a small frequency spacing. In each unit, one antenna element is embedded into the other one, featuring a very compact size. To alleviate the serious cross-band mutual coupling between the two elements, the LB patch antenna generates a boresight radiation null at its higher band edge, and the HB element is designed with a band-stop response at its lower band edge. In this way, high cross-band isolation between the two embedded elements can be obtained. For demonstration, a compact dual-band ±45° dual-polarized filtering antenna array with 9 units is designed and fabricated to fit the specification of LB and HB bands ( $\vert S_{11}\vert < -15$ dB). The overall width of the array is only 280 mm, which is much narrower than that of typical industrial products using two side-by-side sub-arrays (~576 mm). The measured performance of the antenna array can meet the requirement of base stations.
A Broadband Tunable Coaxial Attenuator Based on Graphene

A dynamically tunable coaxial attenuator based on graphene is presented in this paper. The attenuator is achieved by inserting a graphene sandwich structure (GSS) inside the dielectric of coaxial line along the propagation direction of electromagnetic waves to dissipate the electromagnetic field. The mechanism of attenuator is theoretically analyzed, and an approximate formula to calculate attenuation with different parameters is proposed. We experimentally demonstrate that attenuation of the attenuator can be tuned from 3 dB to 14.5 dB with good flatness in broad operation frequency from 2 GHz to 9 GHz when graphene impedance is changed by different bias voltage. The theoretical calculation and simulation results have a good agreement with experimental results, while the $\vert S_{11}\vert $ can always remain low level under −15 dB.
A DOA Tracking Algorithm for Coprime Planar Array via PAST

Compared to traditional planar arrays, coprime planar arrays have more precise direction finding precision and higher degrees of freedom. As there is little research on DOA tracking of arbitrary dynamic targets in coprime arrays, a DOA tracking algorithm based on Projection Approximation Subspace Tracking (PAST) for coprime planar array is proposed. It can achieve the real-time tracking of arbitrary dynamic target's two-dimensional arrival angle. The computational complexity of the proposed algorithm is much lower than conventional Esprit algorithm because it does not need to construct the autocorrelation matrix and perform eigenvalue decomposition, as well as the online tracking feature of the proposed algorithm ensures the real-time tracking performance. The simulation results verify the effectiveness of the algorithm on DOA tracking and show that the proposed algorithm has better performance on DOA estimation than traditional uniform planar array.
Surface Texturing with Multi-objective Particle Swarm Optimization for Absorption Enhancement in Silicon Photovoltaics

Increasing light absorption in ultrathin-film silicon solar cells is important to improve efficiency and reduce costs. In this study, multi-objective particle swarm optimization (MOPSO) is employed to find proper solar cell parameters for minimum reflection and maximum light trapping which give rise to enhanced absorption. Numerical investigations for two different surface patterns (rectangular and pyramid) are conducted. The results show that, for the ultrathin-film silicon solar cell having an equivalent thickness of 2 μm, random surfaced pyramid structure provides an ideal photocurrent of 36.96 mA/cm2 and random surfaced rectangular structure provides an ideal photocurrent of 34.67 mA/cm2. From this point of view, proposed approach can be applied to various semiconductor film thicknesses by providing robustness against metallic loss at the back plate of the solar cells.
Reduction of Power Consumption of Optical Links Based on Novel VCSEL Optimization Strategies

Thirst for rapidly increasing data rates to satisfy the demands of our internet community leads to an explosion of the energy consumption of data centers. Joint optimization of minimum energy-to-data-ratio EDR and maximum bit rate x distance, damping this trend, seemed to be impossible until now. VCSELs are at the heart of optical links up to distances of ~ 1 km. Systematic tuning of their cavity photon lifetime was recently discovered by us to increase the maximum data rate in concert with a reduction of the EDR. Our VCSELs are emitting at 850 nm, 880 nm, 910 nm, and 940 nm, the next IEEE 802.3 wavelength-multiplexing standard for 200+ Gbit/s single fiber data transmission across multimode fiber. Our results for optimized VCSELs across OM5 fiber show, that 50 Gbit/s as bit rate goal in the presently ongoing standardization discussions can be achieved for an EDR = 400 fJ/bit for simple NRZ modulation or alternatively for an EDR much below 100 fJ/bit at 25 Gbit/s. 4 wavelength multiplexing is thus competing with 8 wavelength multiplexing, the latter achieving the same data rates, but for less than 25% of the input power.
Complex Dynamics of Long Cavity Lasers

In this paper, we will discuss the properties of long cavity frequency sweeping lasers and demonstrate various scenarios of coherence deterioration in such lasers. The long cavity lasers are known to demonstrate rich variety of dynamical regimes including formation of localised structures and transition to turbulence. The interest to frequency sweeping long cavity lasers has also recently increased due to their application for imaging and sensing. For these applications, the stability of the laser is an important parameter as it directly influences its coherence and therefore, for example, the quality of the obtained images. We investigated static, quasi static and synchronisation regimes of such lasers and analysed possible instabilities in such system. Experimentally, we considered different laser configurations which has allowed us to study the influence of the cavity length, frequency sweeping speed and the detuning. Numerically, we used a model based on a system of delayed differential equations. The numerical simulation of our model showed excellent agreement with the experimental data. In particularly, we studied the formation of dark pulses, both periodic and non-periodic, and showed that they are closely connected to Nozaki-Bekki holes previously predicted in the complex Ginzburg-Landau equation.
A Data-Driven and Load-Aware Interference Management Approach for Ultra-Dense Networks

The ultra-dense network (UDN) has been widely accepted as a promising technology to improve the network performance. However, the severe co-channel interference (CCI) generated due to the densely deployed femtocells greatly limits the network throughput. Different from most conventional methods that model the inter-user interference intensities based on the accurate geographical distance information, which is usually hard to obtain in reality, a more practical machine learning based relative interference intensity modeling method is proposed. The proposed method models the relative interference intensities by mining the resource block (RB) allocation data, the new data indicator (NDI) data, the acknowledgement (ACK) and negative acknowledgement (NACK) data collected from the network, which could achieve an extremely high accuracy that is validated by the simulation results. In addition, we propose a load-aware resource allocation approach which calculates each user’s boundary of reusing the common RBs and allocating the orthogonal RBs with its interfering sources based on the relative interference intensities modeled above and the network load in each transmission time interval (TTI). The orthogonal interfering source set of each user is generated based on its time-varying boundary. Simulation results show that the proposed load-aware resource allocation approach outperforms all the benchmark algorithms under most network densities and network loads especially when the network load is heavy and the network is ultra-dense.
Local Higher-Order Community Detection Based on Fuzzy Membership Functions

Local community detection, only considering the regional information of the large network, can be used to identify a densely connected community containing the seed node in a network, aiming to address the efficiency problem faced by global community detection. However, most existing studies in local community detection did not account for the higher-order structures crucial to the network, but rather have simply focused single nodes or edges. Moreover, existing higher-order solutions are not purely local methods, as they still use global search to find the best local community, which leads to a global search problem. Furthermore, the quality of the detected community depends on the location of the seed node, which leads to a seed-dependent problem. Thus, in this paper, we proposed a fuzzy agglomerative algorithm (FuzLhocd) for local higher-order community detection based on different fuzzy membership functions. To solve the global search problem, we introduce a novel, purely localized metric called local motif modularity. Based on this local metric, FuzLhocd only needs to visit a limited number of neighborhoods around the seed node. To solve the seed-dependent problem, we systematically studied the formation of the local community, divided the process of local community detection into three stages and employed various fuzzy membership functions at different stages. Our extensive experiments based on both real-world and synthetic networks demonstrated that FuzLhocd not only runs efficiently locally but also effectively solves the seed-dependent problem and achieves a high accuracy as well. We concluded that our local motif modularity metric and FuzLhocd algorithm is highly effective for local higher-order community detection.
Corporate Social Responsibility in the Human Recources Management Practice

The significance of the Corporate Social Responsibility (CSR) for obtaining organizational sustainability is highly emphasized in the contemporary business practices. The socially responsible activities are being implemented in the organizational strategies in order to evaluate the impact on the environment, the society, the employees, the vendors and the customers. In the context of the human resources management, the organizations aim to realize how the corporate social responsibility concerns the functions, the roles and the activities related to the human resources, like: recruitment and access to employment, training and career development, as well as well-being and motivation at the work place.
Two-Time-Scale Braking Controller Design With Sliding Mode for Electric Vehicles Over CAN

This paper proposed a braking torque controller via two-time-scale design with a sliding mode for electric vehicles with four in-wheel motors. According to the different changing rates between the vehicle and wheel motion during the braking process, the design of the braking controller is carried out in two steps. In the first step, a nominal braking controller is developed over the slow-time scale without considering the tire-road friction. Then, a tire-road friction observer is adopted in the fast-time scale to recover the performance of the nominal braking controller. Owning to the high nonlinearity and complexity of the braking system, a sliding mode surface is further added in the nominal braking controller to ensure the stability and robustness of the proposed braking controller. A braking supervisor is adopted to enable the proposed braking controller, which is based on the wheel slip as well as vehicle speed condition. And a torque allocation scheme is presented for the coordination between the regenerative braking system and the friction braking system in each wheel. Co-simulation is conducted using MATLAB/Simulink and CarSim. The effectiveness of proposed controller under different braking conditions is fully validated. A delicate controller area network (CAN) bus model is developed via SimEvent, by which the robust performance of proposed braking controller against CAN-induced time-varying delays is also investigated.
Accuracy of CMOS-Based Piezoresistive Stress Sensor for Engineering Applications of Thermal Loading Condition: Theoretical Review and Experimental Validation

Measurement uncertainties of a CMOS-based piezoresistive stress sensor are studied for low cycle thermal loading applications. After the fundamentals of the sensor are reviewed briefly, the random uncertainties associated with the data acquisition unit are evaluated first using raw current signals obtained from uniquely fabricated free-standing stress sensor chips. The free-standing sensor chips are tested further for systematic uncertainties associated with the manufacturing-induced residual stresses by subjecting them to a thermal cycle. Finally, the stress measurement accuracy of the sensor chip under an in-situ thermal loading is quantified by a numerical model verified by a sub-micron sensitivity optical technique while incorporating the quantified uncertainties.
Dynamic ARMA-Based Background Subtraction for Moving Objects Detection

Background subtraction is a prevailing method for moving object detection in videos with stationary backgrounds. However, accurate and real-time moving object detection is challenging in the presence of complex dynamic scenes. This paper presents a novel technique for background subtraction based on the dynamic autoregressive moving average (ARMA) model. Specifically, we utilize the temporal and spatial correlation of images in a video sequence to model each pixel to accurately model the background image’s dynamic characteristics. In addition, we apply an adaptive least mean square (LMS) scheme to update the parameters of the background model to offset the dramatically dynamic characteristic of the background. The proposed algorithm is evaluated on two publicly available benchmark datasets with complex dynamic backgrounds. The experimental results show that this technique is robust and effective for background subtraction in complex dynamic backgrounds and is a promising moving object detection scheme for real-time visual surveillance.
Motor Imagery EEG Signals Classification Based on Mode Amplitude and Frequency Components Using Empirical Wavelet Transform

As one of the key techniques determining the overall system performances, efficient and reliable algorithms for improving the classification accuracy of motor imagery (MI) based electroencephalography (EEG) signals are highly desired for the development of brain-computer interface (BCI) systems. In this study, we propose, for the first time to the best of our knowledge, a novel data adaptive empirical wavelet transform (EWT) based signal decomposition method for improving the classification accuracy of MI based EEG signals. Specifically, to reduce the system complexity and execution time, the proposed method selects 18 electrodes out of 118 to analyze the non-stationary and nonlinear EEG signal behaviors. Meanwhile, the method adopts the Welch power spectral density (PSD) analysis method for single mode selection out of the total 10 for each channel, and the Hilbert transform (HT) method for both instantaneous amplitude (IA) and instantaneous frequency (IF) signal components extraction for each selected mode. With seven commonly used machine-learning classifiers adopted, extensive experiments were conducted with the benchmark dataset IVa from BCI competition III to evaluate the performance of the proposed method. Results show that with the IA and IF component features being tested using the least-square support vector machine (LS-SVM) classifier, the EWT method achieves an average classification accuracy of 95.2% and 94.6% respectively, which is higher as compared with the existing methods. While for every participant, a classification accuracy of at least 80% could be achieved by employing a single feature only. Results also show that a combination of EWT and higher order statistics features, which contain both kurtosis and skewness of the extracted instantaneous components, help achieve a higher success rate. The better performances of EWT over those of the existing methods demonstrate the effectiveness and great potential of EWT for BCI system applications.
Semantic Weighted Multi-View Clustering for Web Content

Clustering is a long-standing important research problem. However, it remains challenging when handling large-scale web data from different types of information resources such as user profile, comments, user preferences and so on. All these aspects can be seen as different views and often admit the same underlying clustering of the data. In this paper, we present a novel Semantic Weighted Non-negative Matrix Factorization (<inline-formula> <tex-math notation="LaTeX">$SWNMF$ </tex-math></inline-formula>) multi-view clustering framework, which can provide an efficient weighted matrix factorization framework, dexterously manipulate multi-view web content, and easily explore the sparseness problem in semantic space of data. Specifically, each view of dataset forming a huge sparse matrix, which results in the non-robust characteristic during the matrix decomposition process, and further influences the accuracy of clustering results. To address above problem, we attempt to use some preference information (e.g. rating values) given by the users as latent semantic information to handle those features that are unobserved in each data point so as to resolve the sparseness problem in all views matrices. To combine multiple views in our large corpus, the overall objective of our proposed <inline-formula> <tex-math notation="LaTeX">$SWNMF$ </tex-math></inline-formula> is to minimize the loss function of weighted <italic>non-negative matrix factorization</italic> (NMF) under the <inline-formula> <tex-math notation="LaTeX">$l_{2,1}$ </tex-math></inline-formula>-norm and the co-regularized constraint under the <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula>-norm. Extensive experiments on our large-scale multi-view web datasets demonstrate the competitive performance of our solution.
A Unified Error Analysis of Uncoded Space-Time Labeling Diversity with Three Transmit Antennas in Rician Fading Channels

This paper exploits an exponential-type approximation to express bit error rate (BER) for MQAM uncoded space-time labeling diversity with three transmit antennas in Rician fading channel. The average BER for diversity combining is obtained by using probability density function and moment generating functions. Arbitrarily tight approximations for the Gaussian probability integral are obtained from the numerical analysis technique; the trapezoidal rule. Simulation results show excellent agreement with the derived approximation expression in the high SNR. The solutions are general enough to include Rayleigh fading (K =3D 0) as a special case with varied K-factor for frequency non-selective slow Rician fading channels. The dependence of BER performance on diversity reception and the impact of channel correlation are also demonstrated.
After GDPR, Still Tracking or Not? Understanding Opt-Out States for Online Behavioral Advertising

A recent trend in Internet advertising has been online behavioral advertising (OBA). For users concerned about their privacy, ad agencies provide an OBA opt-out opportunity. However, previous work has shown that many users misunderstand what it means to have opted out on an opt-out website. In fact, ad agencies still track the browsers of users who have opted out of OBA. In this study, we clarified what it means to be in the OBA opt-out state by crawling numerous websites and collecting browser cookies. Moreover, we analyzed the difference between the attitudes of agencies regarding OBA between before and after the EU General Data Protection Regulation (GDPR) was implemented. We found that around half of agencies stop web tracking after opt-out. However, some agencies start tracking again when users begin browsing, so the number of agencies stopping web tracking eventually declines. Furthermore, we found no evidence of a difference in agency attitudes between before and after GDPR implementation.
Steady‐state imaging with inhomogeneous magnetization transfer contrast using multiband radiofrequency pulses

Inhomogeneous magnetization transfer (ihMT) is an emerging form of MRI contrast that may offer high specificity for myelinated tissue. Existing ihMT and pulsed MT sequences often use separate radiofrequency pulses for saturation and signal excitation. This study investigates the use of nonselective multiband radiofrequency pulses for simultaneous off‐resonance saturation and on‐resonance excitation specifically for generation of ihMT contrast within rapid steady‐state pulse sequences.
Genetic Characterization and Zoonotic Potential of Highly Pathogenic Avian Influenza Virus A(H5N6/H5N5), Germany, 2017–2018

We genetically characterized highly pathogenic avian influenza virus A(H5N6) clade 2.3. 4.4b isolates found in Germany in 2017–2018 and assessed pathogenicity of representative H5N5 and H5N6 viruses in ferrets. These viruses had low pathogenicity; however, continued characterization of related isolates is warranted because of their high potential for reassortment.
A molecular recognition feature mediates ribosome-induced SRP-receptor assembly during protein targeting

Hwang Fu et al. show that a molecular recognition feature (MoRF) in the mammalian signal recognition particle (SRP) receptor accelerates SRP-receptor assembly in response to the ribosome. The MoRF functionally replaces the bacterial SRP RNA to sense cargo loading and activate cotranslational protein targeting.