In this paper, an efficient time domain simulation algorithm is proposed to analyze the electromagnetic scattering and radiation problems. The algorithm is based on discontinuous Galerkin time domain (DGTD) method and parallelization acceleration technique using the graphics processing units (GPU), which offers the capability for accelerating the computational electromagnetics analyses. The bottlenecks using the GPU DGTD acceleration for electromagnetic analyses are investigated, and potential strategies to alleviate the bottlenecks are proposed. We first discuss the efficient parallelization strategies handling the local-element differentiation, surface integrals, RK time-integration assembly on the GPU platforms, and then, we explore how to implement the DGTD method on the Compute Unified Device Architecture (CUDA). The accuracy and performance of the DGTD method are analyzed through illustrated benchmarks. We demonstrate that the DGTD method is better suitable for GPUs to achieve significant speedup improvement over modern multi-core CPUs.
Compared with the backscattering configuration, the bistatic scattering echoes can provide multidimensional information on land surface. Based on the Michigan Microwave Canopy Scattering (MIMICS) model, a first-order microwave bistatic scattering model for vegetations is developed in this paper. The dominant scattering mechanism for wheat and soybean in the L and C bands is analyzed by simulating the bistatic scattering echoes in multiple viewpoints, which can help us understand the interaction between incident wave and vegetation parameters. The influence of crop height, leaf size and moisture of vegetations and down layer soil on the scattering echoes is fully investigated. The simulations show that the bistatic scattering echoes are more sensitive to the vegetation parameters than that in backscattering configuration. There exist optimal scattering angles, in specular direction and in direction perpendicular to the incident plane, to improve the retrieval accuracy of vegetation parameters and moisture of soil surface. Moreover, the simulations demonstrate that bistatic scattering echoes in high frequency (C band) are a good choice to retrieve the vegetation parameters, and the echoes in low frequency (L band) are preferred to retrieve the soil parameters. This research can be used to provide reference for crop monitoring and future bistatic system design.
The principal purpose of this work is the simulation of the ship wake in Synthetic Aperture Radar (SAR) imaging based on a facet scattering model. The hydrodynamic model of the surface waves mainly considers the Kelvin wake waves and the wind driven waves. For the prediction of radar returns from the composite surface, the semi-deterministic facet scattering model (SDFSM) is proposed, which is verified to have good performance through a comparison with the experiment by SASS-II. Then, the distributions of backscattering normalized radar cross section (NRCS) of facets are investigated for both VV and HH polarizations and characteristics of the wake pattern are shown with good visibility. On the basis of these, an application of velocity bunching (VB) imaging model is presented in detail for the simulation of SAR imaging of sea surface waves with Kelvin wake. Finally, several numerical results provide states of the effects of ship speed, wind speed and the ship sailing direction on the characteristics of Kelvin wake in SAR images. Thus, this simulation may enable us to provide a theoretical basis to the detection of ship wakes.
Recently, researchers were interested in neural algorithms for optimization problems for several communication systems. This paper shows a novel algorithm based on neural technique presented to enhance the performance analysis of beam-forming in smart antenna technology using N elements for Uniform Circular Array (UCA) and Concentric Circular Array (CCA) geometries. To demonstrate the effectiveness and reliability of the proposed approach, simulation results are carried out in MATLAB. The radiators are considered isotropic, and hence mutual coupling effects are ignored. The proposed array shows a considerable improvement against the existing structures in terms of 3-D scanning, size, directivity, HPBW and SLL reduction. The results show that multilayer feed-forward neural networks are robust and can solve complex antenna problems. However, artificial neural network (ANN) is able to generate very fast the results of synthesis by using generalization with early stopping method. Important gain in the running time and memory used is obtained using this latter method for improving generalization (called early stopping). To validate this work, several examples are shown.
The topology of grounding grid is important for diagnosing its status, which plays a critical role in the safety of personnel and stable operation of power system. The electromagnetic field method and derivative of surface magnetic flux density on the line has been used to measure the branch position in case the grid is parallel to the plane of earth surface that in practice is unknown while the node points and connections were not discussed. This paper introduces a method that uses derivative of surface flux density on circles and lines in a systematic order to find the position of the grid in the plane of the earth surface and connecting the nodes to measure the full topology. This method even identifies any angled branch present in the mesh of a grid. Software simulations and experimental tests verify that the method is feasible and can be applied to identify the topology of a grounding grid.
Multiple-input-multiple-output antennas are investigated for terrestrial point-to-point wireless link. The lack of rich scatters in a terrestrial wireless channel results in an ill-conditioned channel matrix for a long range link with compact arrays, which can cause a high bit error rate. This paper demonstrates that the channel matrix can be improved by carefully selecting the antenna spacing. Unfortunately, an optimum antenna spacing that guarantees a good channel matrix is too large to implement for most long range terrestrial wireless links. On the other hand, a channel capacity study of an 8×8 link reveals that multiple antennas do provide more capacity even with small antenna spacing. Constellation multiplexing is then applied to the compact array configuration to solve the unreliable communication problem. In addition, a multilevel maximum ratio combining technique is introduced to improve detection efficiency.
In this paper, the application of Artificial Neural Network (ANN) with back-propagation algorithm and weighted Fourier method are used for the synthesis of antenna arrays. The neural networks facilitate the modelling of antenna arrays by estimating the phases. The most important synthesis problem is to find the weights of the linear antenna array elements that are optimum to provide the radiation pattern with maximum reduction in the side lobe level. This technique is used to prove its effectiveness in improving the performance of the antenna array. To achieve this goal, antenna array for Wi-Fi IEEE 802.11a with frequency at 2.4 GHz to 2.5 GHz is implemented using Hybrid Fourier-Neural Networks method. To verify the validity of the technique, several illustrative examples of uniform excited array patterns with the main beam are placed in the direction of the useful signal. The neural network synthesis method not only allows to establish important analytical equations for the synthesis of antenna array, but also provides a great flexibility between the system parameters in input and output which makes the synthesis possible due to the explicit relation given by them.
This paper presents the design and fabrication of a broadband UHF RFID tag for bio-monitoring applications. The proposed tag is realized with a thin FR4 substrate of 200 μm, which can be considered as flexible. It shows good performances both in free space and placed on human body. For instance, in free space, the tag can be read at 14 m in the UHF RFID band and at an average distance of 4.6 m when it is placed on the human body. The overall tag size is only 80 mm × 50mm × 200 μm.
In the present paper we focus on the study of polarization properties of biaxial metamaterial, which consist of alternate ferrite and semiconductor layers, located on an ideally conducted metal substrate. The system is placed into an external magnetic field along the boundaries of the layers. The effective medium theory is applied. Effective linear-to-elliptic polarization conversion has been shown, by means of physical and geometrical parameters of the system under consideration.
A categorized radio channel modeling for wireless ultra-wideband on-body body area networks is discussed. Measurements in an anechoic chamber at fourteen antenna locations are conducted in a 2-8 GHz band. The dipole and double loop antenna types are used. Six link classes are formed based on the antenna spots on the torso, head or limb. The limb-limb and the head-limb links have the lowest and highest path losses, respectively. The head-limb links have the shortest channel impulse responses (CIRs) and limb-limb links the longest ones. The CIR amplitudes follow the inverse Gaussian distribution. The tap indexes and the total excess delays are modeled with the negative binomial distribution. In most cases, the CIRs decay faster for the dipole. Otherwise no major differences exist between the antennas.