Abstract-In this paper, we design and implement a low profile frequency reconfigurable Planar Inverted-F Antenna (PIFA) for WLAN, m-WiMAX and UMTS applications. Different from several conventional designs, the air layer in our antenna is removed, while the radiator patches and the ground plane are printed on two sides of the same substrate. This makes the antenna structure thin and lightweight. The defected ground structure (DGS) and coplanar sorting-trips are also designed for adjusting lower operating frequencies without increasing the antenna's size. Three PIN-diodes are used in appropriate positions for accurate switches between frequency bands. Moreover, the three radiator patches' parameters are optimally selected on all configurations using Genetic Algorithm (GA). Simulation results show that depending on the ON/OFF states of the PIN-diodes, the antenna can operate in three applicable frequency bands, i.e., 2.1 GHz, 2.4 GHz, and 3.5 GHz with the corresponding peak gains of 0.48 dBi, 3.55 dBi, and 4.33 dBi. The antenna occupies an overall size of 63.5×33.5×1.6 mm 3 , which can be easily fabricated and integrated into small wireless devices. Simulated and measured results are also compared to validate the correctness of the antenna design.
Synthetic Aperture Radar (SAR) imagery is capable of monitoring Earth surface on a massif scale with high resolution. New generation of SAR satellites such as RADARSAT-2, TerraSAR-X, Cosmo-SKymed, etc. with metric resolution and high frequency bands (C-band, Xband) provide the possibility to better characterize surface objects. Moreover, the short revisit time of these satellites allows us to capture time series of images on the same region, which enables the development of change detection techniques and their applications. The Spherically Invariant Random Vector (SIRV) model was designed specifically for the analysis of heterogeneous clutters in high resolution radar images. In this paper, we study four algorithms of change detection based on different criteria including: Gaussian (sample covariance matrix estimator), Gaussian (fixed point estimator), Fisher texture-based and KummerU-based (Fisher distributed texture along with SIRV model). These algorithms are evaluated through simulated dataset and radar images from TerraSAR-X satellite.
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