Frequency filtering characteristics by cavities of three different lengths situated in X-shaped photonic crystal waveguide with triangular lattice was simulated by constrained interpolated profile (CIP) method. From analysisi by fast Fourier transform of output signal, the resonant peak frequencies of filtered spectrum by microwave experiment and those by the simulation coincided each other in difference of order of 1%.

The ubiquitous QR codes and some similar barcodes are becoming a convenient and popular approach to impromptu communication between mobile devices and their surrounding cyber-physical world. However, such codes suffer from two common drawbacks: poor viewing experience and inability to be identified through itself. In this work, we propose ART-code-- Adaptive Robust doT matrix barcode, which aims to preserve ART and CODE features in one visual pattern. It works on any surface (paper or electronic displays) and is able to convert any image or any form of human-readable contents (e.g., a picture, a logo, a slogan) into an ARTcode. It looks like an image which retains human-readable and aesthetically pleasant contents, and in the meanwhile, it acts as a QR code which conveys data bits over the visual channel. The core enablers in ARTcode are (1) the design of the colored dot matrix for data embedding with little distortion from the original image and (2) a comprehensive error correction scheme which enhances decoding robustness against noises and interferences from the original image in ARTcode. We implement ARTcode with the receiver on Android phones and the sender from a PC or a phone (it can be printed in paper). We conduct extensive user survey and experiments for evaluation. It validates the effectiveness and wide applicability of ARTcode: It works well with all of 197 images randomly downloaded, covering representative categories of the gray-scale images, logos, colored ones with low/medium/strong contrasts. The image quality is quite acceptable in a subjective user-perception survey with 50 participants and data communication accuracy achieves as high as 99% in almost all the cases (> 96% raw accuracy in ARTcode without error detection and other schemes).

How to integrate labels from multiple labelers in order to obtain an accurate estimate of the ground truth is a major topic of crowdsourcing. One challenging issue is that, the labelers' abilities may vary significantly and the tasks distinguish each other in difficulties. Moreover, for a crowdsourcing system, task distributors have no idea in advance how many labels will be enough for each task. Consequently, an online task assignment mechanism based on the labeler expertise and question heterogeneousness becomes necessary. In this paper, we present such an online task assignment algorithm based on a probabilistic model consisting of both labeler abilities and question difficulties. We apply the online EM (Expectation Maximization) algorithm to make online estimations of system parameters, based on which we assign tasks adaptively. A series of simulation results have been demonstrated to show that our proposed scheme outperforms the conventional EM algorithm in efficiency and accuracy.