Resonance conditions for a substrate-superstrate printed antenna geometry which allow for large antenna gain are presented. Asymptotic formulas for gain, beamwidth, and bandwidth are given, and the bandwidth limitation of the method is discussed. The method is extended to produce narrow patterns about the horizon, and directive patterns at two different angles.

Gain enhancement methods for printed circuit antennas

A novel multifrequency printed monopole antenna applied to both WLAN and WiMAX systems is proposed in this letter. The antenna structure is simple, and the size is 34 $\times$ 18 mm $^{2}$ . The proposed microstrip-fed antenna mainly comprises a fork-shaped strip that is etched on a modified rectangular ring and a rectangular-defected ground plane. To experimentally analyze the characteristics of this antenna, the design, fabrication, and measurement have been completed. Good antenna performances have been obtained.

A Compact Triple-Band Printed Monopole Antenna for WLAN/WiMAX Applications

Efforts to understand the influence of historical climate change, at global and regional levels, have been increasing over the past decade. In particular, the estimates of air temperatures have been considered as a key factor in climate impact studies on agricultural, ecological, environmental, and industrial sectors. Accurate temperature prediction helps to safeguard life and property, playing an important role in planning activities for the government, industry, and the public. The primary aim of this study is to review the different machine learning strategies for temperature forecasting, available in the literature, presenting their advantages and disadvantages and identifying research gaps. This survey shows that Machine Learning techniques can help to accurately predict temperatures based on a set of input features, which can include the previous values of temperature, relative humidity, solar radiation, rain and wind speed measurements, among others. The review reveals that Deep Learning strategies report smaller errors (Mean Square Error = 0.0017 °K) compared with traditional Artificial Neural Networks architectures, for 1 step-ahead at regional scale. At the global scale, Support Vector Machines are preferred based on their good compromise between simplicity and accuracy. In addition, the accuracy of the methods described in this work is found to be dependent on inputs combination, architecture, and learning algorithms. Finally, further research areas in temperature forecasting are outlined.

Air Temperature Forecasting Using Machine Learning Techniques: A Review

This article is a tutorial on using genetic algorithms to optimize antenna and scattering patterns. Genetic algorithms are "global" numerical-optimization methods, patterned after the natural processes of genetic recombination and evolution. The algorithms encode each parameter into binary sequences, called a gene, and a set of genes is a chromosome. These chromosomes undergo natural selection, mating, and mutation, to arrive at the final optimal solution. After providing a detailed explanation of how a genetic algorithm works, and a listing of a MATLAB code, the article presents three examples. These examples demonstrate how to optimize antenna patterns and backscattering radar-cross-section patterns. Finally, additional details about algorithm design are given. >

Introduction to Genetic Algorithms in Electromagnetics

A multi-layered electromagnetic band gap (EBG) structure is proposed and incorporated into a MIMO antenna to reduce unexpected mutual coupling between antenna elements. The proposed multi-layered EBG (ML-EBG) structure is comprised of an improved EBG and three loading patches with a same distance. The proposed ML-EBG structure is designed at 2.55 GHz and it is utilized in a MIMO antenna array with an edge-to-edge distance of 0.13 lambda to reduce the mutual couplings. The simulated and measured results have been put forward to prove that the mutual coupling has been reduced by 30 dB between the antenna elements compared to the MIMO antenna without the ML-EBG structure.

A Low Mutual Coupling MIMO Antenna Using Periodic Multi-Layered Electromagnetic Band Gap Structures

Solar systems are influenced by their functioning temperature, which is mainly a result of the ambient air temperature as well as the level of sunlight. For this reason, we have proposed an intelligent autoregressive model based on the multilayer neural networks with delayed exogenous input sequence representing the Global Solar Radiation (GSR) data to forecast the Air Temperature (AT) values on a half hour scale in Marrakesh, Morocco, which is recorded during 2014. The developed model will helps us to monitor and to overcome such a problem related to the intermittence or the absence of AT measurements in this region. For building this model, the database is divided into two subsets containing the same size of data. The first one used to train the model using Levenberg-Marquardt algorithm, and the second is used to test the performance and the robustness of the proposed model. The obtained results shown that the developed model has two hidden layers with a few hidden neurons number can predict accurately the target that is demonstrated by the high value of the correlation coefficient, which reaches 99.80.

Air temperature forecasting using artificial neural networks with delayed exogenous input

The ground plane plays an important role on the performance of a planar inverted-F antenna (PIFA). In this regard, the ground plane modification using slots is proposed in this research to obtain a dual band antenna for WiMAX application. Â Â Â  Popular optimization techniques, as genetic algorithm (GA) and particle swarm optimization (PSO), were used to design the shape of the ground plane in order to improve the bandwidth and to achieve dual band antenna instead of single band antenna. Â Â  The proposed method divides the overall ground plane into different cells. Through this technique it is possible to obtain a dual band antenna reduced by more than 50%, thereby reducing the overall ground plane. Â Â  Both GA and PSO designs are provided. This new method shows many advantages over PIFA antenna in terms of performance and weight.

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The study of the ground plane effect on a Multiband PIFA Antenna by using Genetic Algorithm and Particle Swarm Optimization

Miniaturized broadband‐multiband planar antenna with a symmetric quarter‐circular ground plane for WLAN/WiMAX standards

A coplanar waveguide (CPW)-fed patch antenna with triple wideband is presented for simultaneously satisfying Wireless Local Area Network (WLAN) and Worldwide Interoperability for Microwave Access (WiMAX) applications. The proposed antenna mainly consists by three radiating elements: inverted L-shaped Stub1, inverted L-shaped Stub2, and a rectangle Stub3 with a defected ground structure for band broadening. By adjusting the lengths of the three Stubs, three resonant frequencies can be achieved and adjusted separately. The proposed antenna with overall size of only 20x37mm 2 explores good triple wideband operation with −10 dB impedance bandwidths of 25.70%, 45.63%, and 50.10% at 2.47, 3.20, and 4.92 GHz, respectively, covering the 2.4/5.2/5.8 GHz WLAN and 2.5/3.5/5.5 GHz WiMAX operation bandwidths. This design gives suitable results with a reduction in size and weight and allows integration in handheld devices. Furthermore, nearly omnidirectional radiation patterns over the operating bands have been obtained.

/pdf/miniaturized-triple-wideband-cpw-fed-patch-antenna-with-a-4oirxhx9q0.pdf

Miniaturized Triple Wideband CPW-Fed Patch Antenna With a Defected Ground Structure for WLAN/WiMAX Applications

Recently, the demand for wireless devices that support multiband frequency has increased. The integration of such technology in mobile communication system has led to a great demand in developing small size antenna with multiband operation, which is able to operate in the required system. In this paper, a novel type planar inverted F antenna (PIFA) with gridded ground plane structure and overlapping cells is presented. By controlling the overlapping size, we improve the characteristics of the proposed antenna. This antenna is developed to achieve multiband operation with small size and good performance. The particle swarm optimization (PSO) is employed to a PIFA antenna to get rid of the limitations of single band operation by searching the optimal localization and length of linear slots on the ground plane to give triband operation. This PIFA antenna can be integrated to operate for several mobile applications as Bluetooth/WLAN, WIMAX, and 4G (UMTS2100, LTE). The optimized antenna is simulated by both Ansoft HFSS and computer simulation technology microwave studio (CSTMWS) in terms of -parameters. A good agreement between simulated performances by both software types is achieved. A parametric study is made to analyze the effect of different PIFA parameters on the operating frequency and the reflection coefficient in order to enhance the antenna performances. In these frequency bands, the antenna has nearly omnidirectional radiation pattern.

/pdf/multiband-operation-and-performance-enhancement-of-the-pifa-2xm73b6js5.pdf

Saida Ibnyaich

Papers

Air temperature forecasting using artificial neural networks with delayed exogenous input

The study of the ground plane effect on a Multiband PIFA Antenna by using Genetic Algorithm and Particle Swarm Optimization

Miniaturized broadband‐multiband planar antenna with a symmetric quarter‐circular ground plane for WLAN/WiMAX standards

Miniaturized Triple Wideband CPW-Fed Patch Antenna With a Defected Ground Structure for WLAN/WiMAX Applications

Multiband Operation and Performance Enhancement of the PIFA Antenna by Using Particle Swarm Optimization and Overlapping Method