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Abstract. In this paper, a novel rasorber with both

A geometrically symmetrical fractal structure is presented in this paper to provide an alternative approach for the miniaturization design of microstrip bandpass filters (BPFs). The generation process of the geometric geometry is described in detail, and a new fractal resonator called Greek-cross fractal resonator (GCFR) is produced by etching the proposed fractal configuration on the surface of the conventional dual-mode meandered loop resonator. Four microstrip BPFs based on the first four iterations GCFR are modeled and simulated. The simulation results show that with the increase of the number of iterations, the central frequency of the BPF is gradually moving towards the low frequency, which indicates that the proposed fractal resonator has the characteristic of miniaturization. In addition, the parameter optimization and surface current density distribution are also analyzed in order to better understand the performance of the BPF. Finally, a compact dual-mode BPF based on the third iteration GCFR is designed, fabricated and measured. The measurement results are in good agreement with the simulation ones.

Abstract:The paper presents an automatic region detection based method to reconstruct street scenes from driving recorder images. The driving recorder in this paper is a dashboard camera that collects images while the motor vehicle is moving. An enormous number of moving vehicles are included in the collected data because the typical recorders are often mounted in the front of moving vehicles and face the forward direction, which can make matching points on vehicles and guardrails unreliable. Believing that utilizing these image data can reduce street scene reconstruction and updating costs because of their low price, wide use, and extensive shooting coverage, we therefore proposed a new method, which is called the Mask automatic detecting method, to improve the structure results from the motion reconstruction. Note that we define vehicle and guardrail regions as "mask" in this paper since the features on them should be masked out to avoid poor matches. After removing the feature points in our new method, the camera poses and sparse 3D points that are reconstructed with the remaining matches. Our contrast experiments with the typical pipeline of structure from motion (SfM) reconstruction methods, such as Photosynth and VisualSFM, OPEN ACCESSRemote Sens. 2015, 7 9092 demonstrated that the Mask decreased the root-mean-square error (RMSE) of the pairwise matching results, which led to more accurate recovering results from the camera-relative poses. Removing features from the Mask also increased the accuracy of point clouds by nearly 30%-40% and corrected the problems of the typical methods on repeatedly reconstructing several buildings when there was only one target building.

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