No abstract

Abstract-Microwave induced thermo-acoustic tomography (MI-TAT) has great potential in early breast cancer detection because it utilizes the advantages of both microwave imaging and ultrasound imaging. In this paper, a fast and efficient simulation approach based on a hybrid method which combines finite integration time domain (FITD) method and pseudo-spectral time domain (PSTD) method is developed. By using this approach, energy deposition of biology tissue illuminated by electromagnetic fields can be accurately simulated. Meanwhile, acoustic properties of the tissue can be efficiently simulated as well. Based on this approach, a MITAT model is created and some simulated results are analyzed. Furthermore, some real breast tissues are adopted to perform the thermo-acoustic imaging experiment. Comparisons between experimental and simulated results are made. The feasibility and effectiveness of the proposed approach are demonstrated by both numerical simulations and experimental results.

A compact planar inverted-L diversity antenna for handheld terminals is presented. Three diversity antennas operating at 2.15 GHz are designed. The isolation is found to be higher than 13 dB and the usable bandwidth is about 13% in measurement. The measured radiation patterns of the proposed diversity antenna attain 2 dBi gain. Furthermore, the diversity performance with mean effective gain (MEG) is evaluated by taking both azimuth and elevation spectrum into account. A closed-form expression for MEG is presented. The effect of some parameters on MEG is analyzed numerically in indoor propagation environments.

No abstract

EFIE (electric field integral equation) suffers from internal resonance, and the remedy is to use MFIE (magnetic field integral equation) to come up with a CFIE (combined field integral equation) to remove the internal resonance problem. However, MFIE is fundamentally a very different integral equation from EFIE. Many questions have been raised about the differences.First, it has often been observed that EFIE has better accuracy than MFIE. On the other hand, MFIE has better convergence rate when solved with an iterative solver [1,2]. Also, EFIE has low-frequency breakdown, but MFIE does not have an apparent low-frequency problem [3].We will perform error analysis to explain why EFIE has better accuracy compared to MFIE [4][5][6][7]. Mathematical analysis shows that EFIE has a smoothing operator, while MFIE has a non-smoothing operator [8][9][10]. This difference often gives rise to better accuracy for EFIE compared to MFIE.MFIE is a second kind integral equation while EFIE is a first kind integral equation [10]. Hence, the eigenvalues of the EFIE operator tends to cluster around the origin, while the eigenvalues of the MFIE operator are shifted away from the origin. Consequently, when solved with an iterative solver, the convergence behavior of MFIE is superior to that of EFIE.It is well-known that EFIE suffers from the low-frequency breakdown problem. MFIE does not suffer from apparent low-frequency breakdown, but it suffers from low-frequency inaccuracy [3]. All these problems can be taken care of by performing the loop-tree decomposition.The EFIE operator is often known as the L operator and the MFIE operator is often known as the K operator in the literature. The L operator is a symmetric operator while the K operator is an asymmetric operator. In some integral equations such as those involving dielectric interfaces, these two operators appear simultaneously. They also appear concurrently in the invocation of the equivalence principle. Their discretization often gives rise 978-1-4244-2042-1/08/$25.00 ©2008 IEEE