Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve performance. Metamaterial antennas can overcome the bandwidth constraint associated with tiny antennas. Machine learning is receiving a lot of interest in optimizing solutions in a variety of areas. Machine learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today's technology. The accuracy of the forecast is mostly determined by the model used. The purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial Antenna. Support Vector Machines (SVM), Random Forest, K-Neighbors Regressor, and Decision Tree Regressor were utilized as the basic models. The Adaptive Dynamic Polar Rose Guided Whale Optimization method, named AD-PRS-Guided WOA, was used to pick the optimal features from the datasets. The suggested model is compared to models based on five variables and to the average ensemble model. The findings indicate that the presented model using Random Forest results in a Root Mean Squared Error (RMSE) of (0.0102) for bandwidth and RMSE of (0.0891) for gain. This is superior to other models and can accurately predict antenna bandwidth and gain.     

Robust Prediction of the Bandwidth of Metamaterial Antenna Using Deep Learning

The design of microstrip antennas is a complex and time-consuming process, especially the step of searching for the best design parameters. Meanwhile, the performance of microstrip antennas can be improved using metamaterial, which results in a new class of antennas called metamaterial antenna. Several parameters affect the radiation loss and quality factor of this class of antennas, such as the antenna size. Recently, the optimal values of the design parameters of metamaterial antennas can be predicted using machine learning, which presents a better alternative to simulation tools and trial-and-error processes. However, the prediction accuracy depends heavily on the quality of the machine learning model. In this paper, and benefiting from the current advances in deep learning, we propose a deep network architecture to predict the bandwidth of metamaterial antenna. Experimental results show that the proposed deep network could accurately predict the optimal values of the antenna bandwidth with a tiny value of mean-square error (MSE). In addition, the proposed model is compared with current competing approaches that are based on support vector machines, multi-layer perceptron, K-nearest neighbors, and ensemble models. The results show that the proposed model is better than the other approaches and can predict antenna bandwidth more accurately.     

Multipatches multilayered UWB microstrip antennas

The authors present multipatches multilayered ultra-wideband (UWB) microstrip antennas. The antenna comprises a driven patch radiator with five parasitic patch radiators. Two antennas with different dielectric substrate combinations are studied. The antenna with low-high-low dielectric constant substrate combination (Antenna no. 1) has an improved performance in terms of impedance bandwidth, gain, overall antenna size and beam-squinting over the antenna with low-low-low dielectric constant substrate combination (Antenna no. 2). The low-high-low dielectric constant combination consisting of three dielectric substrates, namely low dielectric constant (ϵr = 3.38) for both bottom and upper substrate but, high dielectric constant (ϵr = 6.15) for middle substrate. Five parasitic patches and multi-dielectric layers are used for wide impedance bandwidth and less boresight gain variation with frequency. A measured 10 dB return loss bandwidth of 48% with boresight gain .5.0 dBi is achieved. Antenna no. 1 can have 8% wider impedance bandwidth, 40% overall area reduction and less beam-squinting compared with Antenna no. 2.     

Hybrid Sine Cosine and Stochastic Fractal Search for Hemoglobin Estimation

The sample's hemoglobin and glucose levels can be determined by obtaining a blood sample from the human body using a needle and analyzing it. Hemoglobin (HGB) is a critical component of the human body because it transports oxygen from the lungs to the body's tissues and returns carbon dioxide from the tissues to the lungs. Calculating the HGB level is a critical step in any blood analysis job. The HGB levels often indicate whether a person is anemic or polycythemia vera. Constructing ensemble models by combining two or more base machine learning (ML) models can help create a more improved model. The purpose of this work is to present a weighted average ensemble model for predicting hemoglobin levels. An optimization method is utilized to get the ensemble's optimum weights. The optimum weight for this work is determined using a sine cosine algorithm based on stochastic fractal search (SCSFS). The proposed SCSFS ensemble is compared to Decision Tree, Multilayer perceptron (MLP), Support Vector Regression (SVR) and Random Forest Regressors as model-based approaches and the average ensemble model. The SCSFS results indicate that the proposed model outperforms existing models and provides an almost accurate hemoglobin estimate.     

Transmit sector selection with link correlation for SDM/OFDM

More spectrum-efficient techniques are required for wireless communications with a limited amount of bandwidth. Space division multiplexing (SDM) is one of the most promising techniques for achieving more efficient bandwidth utilisation, since it enables the transmission rate over multiple-input multiple-output chennels to be increased by using multiple antennas on both the transmitter and receiver sides. Recently, since the cost of RF transmitters is much higher than that of antennas, there is a growing interest in techniques that use a larger number of antennas than RF transmitters. These methods rely on selecting the optimal transmitter antennas and connecting them to the respective RF. In this case, feedback information (FBI) is required to select the optimal transmitter antenna elements. However, the transmission of FBI through a feedback channel is limited. Moreover, a multiple antenna system requires an antenna separation of five to ten wavelengths to keep the correlation coefficient below 0.7 to achieve diversity gain. In this case, the base station requires a large space to set up multiple antennas. To reduce these problems, a transmit sector antenna selection while considering the link correlation for SDM/OFDM without FBI is proposed and analysed.     

Performance Evaluation of Deep Dense Layer Neural Network for Diabetes Prediction

Diabetes is one of the fastest-growing human diseases worldwide and poses a significant threat to the population’s longer lives. Early prediction of diabetes is crucial to taking precautionary steps to avoid or delay its onset. In this study, we proposed a Deep Dense Layer Neural Network (DDLNN) for diabetes prediction using a dataset with 768 instances and nine variables. We also applied a combination of classical machine learning (ML) algorithms and ensemble learning algorithms for the effective prediction of the disease. The classical ML algorithms used were Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN), and Naïve Bayes (NB). We also constructed ensemble models such as bagging (Random Forest) and boosting like AdaBoost and Extreme Gradient Boosting (XGBoost) to evaluate the performance of prediction models. The proposed DDLNN model and ensemble learning models were trained and tested using hyperparameter tuning and K-Fold cross-validation to determine the best parameters for predicting the disease. The combined ML models used majority voting to select the best outcomes among the models. The efficacy of the proposed and other models was evaluated for effective diabetes prediction. The investigation concluded that the proposed model, after hyperparameter tuning, outperformed other learning models with an accuracy of 84.42%, a precision of 85.12%, a recall rate of 65.40%, and a specificity of 94.11%.     

New CPW-fed monopole antennas with both linear and circular polarisations

A low-profile, planar, circularly polarised monopole antenna with a shorting sleeve strip fed using a coplanar-waveguide transmission line for wireless communication in the digital communication system and the global positioning system bands is studied. By utilising the coupling effect between the monopole antenna and sleeve, two excited resonant modes, including the monopole and travelling-wave modes, cover the 1.57- and 1.8-GHz bands. Through modification with antennas of various geometrical parameters, the proposed antenna exhibits the wide bandwidth in the desired frequency bands, which has a bandwidth of 45% at 1.6%GHz for an input reflection coefficient of less than %10%dB. Meanwhile, the antenna has a 3-dB axial ratio bandwidth of 5%. Details of the design considerations for the proposed antennas are described, and the results of the antenna performances obtained are presented and discussed.     

Broadband low-profile antennas for wireless applications

Two novel broadband low-profile antennas are developed for the mobile terminals of wireless applications. The first one is a quasiplanar antenna which has a height of 0.06lambda₀, where lambda₀ is the free-space wavelength at the centre frequency. The second one is a planar antenna which has a height of 0.056lambda₀. It is demonstrated that the quasiplanar antenna can achieve a bandwidth for VSWR<2 of more than 45%, while the planar antenna realises a bandwidth of more than 40%. More importantly, over these bandwidths the broadband low-profile antennas have a quite constant omnidirectional radiation pattern with a peak gain of around 1 dBi. The antennas are designed on a commonly used RT/Duroid substrate; hence it is easy to integrate with RF front-end circuits. The antenna structures are described and the simulation and experimental results are presented     

Antenna subset selection in measured indoor channels

Antenna subset selection can greatly reduce the implementation complexity of multiple input multiple output (MIMO) systems while retaining most of their benefits. This paper investigates the diversity gain and capacity of such systems in wireless personal area networks. Considered scenarios include both the communication between access point to a laptop, and between two handheld devices. We analyse the performance of different antenna selection algorithms and signal combining methods in measured dual-polarised narrowband and wideband propagation channels. We find that line-of-sight and non-line-of-sight situations have fairly similar behaviour. Different polarisations result in similar signal-to-noise ratio gains when the multiple antennas are used for diversity, but result in noticeably different capacities in spatial-multiplexing systems. We also find that radiofrequency (RF) preprocessing of the signals is less effective for handheld handsets with non-uniform antenna arrangements than for uniform linear arrays. For communications between handheld devices, simple selection (of one out of four antennas) shows extremely high performance gains compared to no-selection. Finally, we compare bulk selection (same antenna subset is used for all frequency sub-channels) to per-tone selection (different antenna subsets can be used for each frequency sub-channel) for wideband channels. Bulk selection together with RF preprocessing performs almost as well as per-tone selection for some scenarios.     

High-temperature superconductor antennas: Utilization of low rf losses and of nonlinear effects

The low radio frequency (r.f.) losses in epitaxial HTS thin films allow the realization of novel antenna structures which have to be excluded in conventional antenna techniques with normal conductors because of the highly reduced radiation efficiency. Thus, the design of miniaturized but nevertheless highly efficient antennas down to a lower limit determined by both the required order of radiation pattern and the frequency bandwidth becomes possible. For a bandwidth of more than about 1%, a considerable margin for a size reduction below the critical size is restricted to the case of electrically small antennas and of superdirective antennas with a relatively low order of the radiation pattern, e.g. antennas with a beam of less than 15 dB maximum gain. If the size approaches the lower limit, the antennas show a sharp bandpass frequency response. This is demonstrated by means of experimental results for a novel HTS meander antenna. These bandpass characteristics can be utilized in compact multiport antenna systems in order to decouple subantennas for adjacent frequency bands. Besides the low losses in HTS's, their nonlinear properties can be used in order to realize current-controlled HTS switches for antenna systems.     

Compact ultrahigh-frequency antenna designs for low-cost passive radio frequency identification transponders

Compact planar antennas for low-cost radio frequency identification (RFID) passive transponders are disclosed. The proposed ultrahigh-frequency antenna takes advantage of its unique topology to assure conjugate matching with essentially complex impedance of the electronic chip directly embedded into the radiator. Rectenna design issues are also emphasised. An original method to characterise IC chips and antennas as taken in its entirety of transponders is presented. The characterisation of the chip takes into account the impact of connecting antennas to the rectifier by flip-chip bonding process. The proposed experimental method allows finding chip impedance exactly as it seen by antennas. Refined rectifier circuitry effectively overcomes dependence of transponder performances on the type deviation of the connected antennas. Very good antenna performance is predicted theoretically and validated experimentally over an operating bandwidth of actual RFID systems.     

Multilevel spatial modulation

In this paper, a new Multilevel Spatial Modulation technique is proposed. It combines computationally efficient multilevel oding and spatial modulation based on trellis codes to increase coding gain, diversity gain, and bandwidth efficiency. The trellis complexity of the single-stage system increases exponentially, whereas in the proposed multilevel system the complexity increases linearly. The proposed system is analyzed with optimal Viterbi and suboptimal sequential decoding algorithms. The results show that sequential decoding saves 75% of the computational power with a loss of 2 dB SNR approximately, when compared with optimal Viterbi decoding, over both fast- and slow-fading channel conditions. Since the antenna index is used as a source of information in spatial modulation, the number of antennae required increases with the throughput and packing a large number of antennas make cross-correlation unavoidable. In this paper, a low complexity modified decoding technique is also proposed for the multilevel spatial modulation system, in which the correlated received signals are equally combined and decoded by the multistage decoder using the Viterbi algorithm. This technique exploits the receiver antenna correlation and makes the decoding complexity independent of number of antennas. The simulation results indicate that the proposed low complexity algorithm gives approximately 8–10 dB gain when compared with optimal Viterbi decoder with equivalent computational complexity when the eight highly correlated signals are equally combined. This may be a suitable solution for mobile handsets where size and computational complexity are the major issues.     

Analysis and design of broadband U-slot cut rectangular microstrip antennas

Broadband microstrip antenna using variations of U-slot has been widely reported. However, in most of the reported work, an in-depth explanation about the mode introduced by U-slot and procedure to design U-slot cut antennas at any given frequency is not explained. In this paper, first an extensive analysis to study the broadband response in symmetrical and a new configuration of asymmetrical U-slot cut rectangular microstrip antennas is presented. The U-slot tunes higher-order orthogonal mode resonance frequency of the patch with respect to fundamental mode to realise wider bandwidth. Further formulation in resonant length at modified patch modes in symmetrical U-slot cut antenna is proposed. Frequencies calculated using these formulations show closer agreement with simulated and measured results. Using proposed formulations, a procedure to design U-slot cut antenna at different frequencies over 800–4000 MHz range which shows broadband response is explained. Thus, the proposed work gives an insight into the functioning of widely used U-slot cut antennas and the formulations will be helpful for designing at any given frequency.     

两种天线方案抖振位移响应的比较研究

本对置于某一超高层建筑顶部的钢质天线抖振响应进行了研究。对两种天线单体方案的抖振性能进行了气动弹性模型试验和理论计算和比较，最后分析了天线的鞭稍效应。计算和试验结果为天线结构的分析设计提供了依据。     

Effects of intrinsic radiation ϕ on mismatch factor of three types of small antennas: single-resonance, gradual-transition and cascaded-resonance types

Resonators are traditionally characterised by their quality factor Q, which is inversely proportional to the relative bandwidth. Small antennas are often resonant, so they can be characterised by a Q, except for the fact that the correct quality measure of an antenna is the inverse Q, that is the relative bandwidth, rather than Q. Still, it has become common to study fundamental size limitations of small antennas in terms of a so-called radiation Q (or antenna Q). This study explains how this intrinsic radiation Q relates to: (i) the bandwidth-efficiency product of small single-resonance-type antennas, (ii) the gradual cut-off of spherical waves for wideband gradual-transition-type small antennas and (iii) the number of resonances needed to cover a certain frequency band for wideband cascaded-resonance-type small antennas. The study also introduces one intrinsic radiation Q for basic single TE and TM spherical mode sources, and another for combined TE and TM sources.     

Gain-Enhanced Metamaterial Based Antenna for 5G Communication Standards

Metamaterial surfaces play a vital role to achieve the surface waves suppression and in-phase reflection, in order to improve the antenna performance. In this paper, the performance comparison of a fifth generation (5G) antenna design is analyzed and compared with a metamaterial-based antenna for 5G communication system applications. Metamaterial surface is utilized as a reflector due to its in-phase reflection characteristic and high-impedance nature to improve the gain of an antenna. As conventional conducting ground plane does not give enough surface waves suppression which affects the antenna performance in terms of efficiency and gain etc. These factors are well considered in this work and improved by using the metamaterial surface. The radiating element of the proposed metamaterial based antenna is made up of copper material which is backed by the substrate, i.e., Rogers-4003 with a standard thickness, loss tangent and a relative permittivity of 1.524 mm, 0.0027 and 3.55, correspondingly. The proposed antenna with and without metamaterial surface operates at the central frequency of 3.32 GHz and 3.60 GHz, correspondingly. The traditional antenna yields a boresight gain of 2.76 dB which is further improved to 6.26 dB, using the metamaterial surface. The radiation efficiency of the proposed metamaterial-based 5G antenna is above 85% at the desired central frequency.     

Application of dual-mode filter techniques to the broadband matching of microstrip patch antennas

Structures such as square or circular microstrip patch antennas may support two orthogonal resonant modes. The paper presents a new method of utilising the dual-mode property to increase the bandwidth of microstrip antennas. The input impedance of such a dual-mode antenna may be represented as a second-order ladder network of coupled resonators, where each resonator is coupled to a load resistor. A theoretical method for evaluating the coupling values in the network is presented, enabling the bandwidth of a dual-mode antenna to be maximised. A theoretical bandwidth improvement of up to 3:1 is achieved when compared to a single-mode antenna. This is confirmed with an experimental dual-mode circular microstrip patch antenna     

Advance Artificial Intelligence Technique for Designing Double T-Shaped Monopole Antenna

Machine learning (ML) has taken the world by a tornado with its prevalent applications in automating ordinary tasks and using turbulent insights throughout scientific research and design strolls. ML is a massive area within artificial intelligence (AI) that focuses on obtaining valuable information out of data, explaining why ML has often been related to stats and data science. An advanced meta-heuristic optimization algorithm is proposed in this work for the optimization problem of antenna architecture design. The algorithm is designed, depending on the hybrid between the Sine Cosine Algorithm (SCA) and the Grey Wolf Optimizer (GWO), to train neural network-based Multilayer Perceptron (MLP). The proposed optimization algorithm is a practical, versatile, and trustworthy platform to recognize the design parameters in an optimal way for an endorsement double T-shaped monopole antenna. The proposed algorithm likewise shows a comparative and statistical analysis by different curves in addition to the ANOVA and T-Test. It offers the superiority and validation stability evaluation of the predicted results to verify the procedures’ accuracy.     

Broadband flexible comb-shaped monopole antenna

A broadband comb-shaped monopole antenna is proposed. The antenna has dimensions of 19 mm x 12 mm. The measured results show good agreement with the numerical prediction, and broadband operation with 10 dB impedance bandwidth of 44.75% (1.7-2.68 GHz). The antenna is built on one side of a flexible-printed circuit board (PCB) dielectric substrate. Folded and rolled antenna structures, which are transformed by the proposed planar antenna structure, are presented. Each antenna has a broadband impedance bandwidth that covers the PCS, UMTS, WiBro, WLAN and SDMB bands. Also, omni-directional radiation patterns over the operating bands have been obtained. The proposed antennas are suitable for mobile communication applications requiring a small antenna.     

Lambda/4 resonance of an optical monopole antenna probed by single molecule fluorescence

We present a resonant optical nanoantenna positioned at the end of a metal-coated glass fiber near-field probe. Antenna resonances, excitation conditions, and field localization are directly probed in the near field by single fluorescent molecules and compared to finite integration technique simulations. It is shown that the antenna is equivalent to its radio frequency analogue, the monopole antenna. For the right antenna length and local excitation conditions, antenna resonances occur that lead to an enhanced localized field near the antenna apex. Direct mapping of this field with single fluorescent molecules reveals a spatial localization of 25 nm, demonstrating the importance of such antennas for nanometer resolution optical microscopy.