CPW structure is became common structure for UWB and multi band antenna design and SRR structure is well-known kind of metamaterial that has been used in antenna and filter design for multi band application. In this paper, a SRR dual band monopole antenna with CPW-fed for WLAN and WiMAX is presented. The prototype antenna is designed for wireless communication such as WLAN and WIMAX respectively at 2.4 GHz and 5 GHz. The HFSS and CST microwave studio are used to simulate the prototype antenna for two different FEM and time domain method and they have also been compared with the experimental results. The total size of the antenna is 60mm×55mm×1.6mm and it is fabricated on FR-4 low cost substrate. The antenna is connected to a 50 Ω CPW feed line. Its bandwidth is around 3% for 2.45 GHz (2.4-2.5 GHz) and 33% for 5.15GHz (4.3-6 GHz).Its limited bandwidth in 2.4 GHz frequency is benefit for power saving at indoor application. The antenna has 2-7 dBi gain in the mentioned bands with an Omni-directional pattern. The antenna experimental result shows good similarity to simulation kind for return loss and pattern. Here, the effect of parasitic SRR on current distribution has been studied in presence and absence of parasitic element. The simulation of polarization is confirmed that the antenna has linear polarization. Here comparison between antenna return losses in absence of each parasitic element is presented. تفاصيل المقالة

The emerging field of compressive sensing (CS) enables the reconstruction of the signal from a small set of linear projections. Traditional CS deals with a single signal; while one can jointly reconstruct multiple signals via distributed CS (DCS) algorithm. DCS inversion method exploits both the inter- and intra-signal correlations via joint sparsity models (JSM). Since the wavelet coefficients of many signals is sparse, in this paper, the wavelet transform is used as sparsifying transform, and a new wavelet-based Bayesian DCS algorithm (WB-DCS) is proposed, which takes into account the inter-scale dependencies among the wavelet coefficients via hidden Markov tree model (HMT), as well as the inter-signal correlations. This paper uses the Bayesian procedure to statistically model this correlations via the prior distributions. Also, in this work, a type-1 JSM (JSM-1) signal model is used for jointly sparse signals, in which every sparse coefficient vector is considered as the sum of a common component and an innovation component. In order to jointly reconstruct multiple sparse signals, the centralized approach is used in DCS, in which all the data is processed in the fusion center (FC). Also, variational Bayes (VB) procedure is used to infer the posterior distributions of unknown variables. Simulation results demonstrate that the structure exploited within the wavelet coefficients provides superior performance in terms of average reconstruction error and structural similarity index. تفاصيل المقالة