An integrated high-resolution ratio-metric wavelength monitor (RMWM) is demonstrated on SOI platform. The device consists of a reconfigurable demultiplexing filter based on cascaded thermally tunable microring resonators (MRRs) and Ge-Si photodetectors integrated with each drop port of the MRRs. The MRRs are supposed to achieve specific resonant wavelength spacing to form the “X-type” spectral response between adjacent channels. The ratio of the two drop power between adjacent channels varies linearly with the wavelength in the “X-type” spectral range, thus the wavelength can be monitored by investigating the drop power ratio between two pre-configured resonant channels. The functional wavelength range and monitor resolution can be adjusted flexibly by thermally tuning the resonant wavelength spacing between adjacent rings, and an ultra-high resolution of 5 pm or higher is achieved while the resonant spacing is tuned to 1.2nm. By tuning the resonant wavelength of the two MRRs synchronously, the monitor can cover the whole 9.6nm free spectral range (FSR) with only two ring channels. The power consumption is as small as 8 mW/nm. We also demonstrate the multi-channel monitor that can measure multi-wavelength-channel simultaneously and cover the whole FSR by presetting the resonant wavelengths of every MRR without any additional power consumption. The improvements to increase the resolution are also discussed.
Classification of images in many categorized datasets has rapidly improved in recent years. However, methods that perform well on particular datasets typically have one or more limitations, such as insufficient image-transformation invariance or significant performance degradation as the number of classes is increased. We attempt to overcome these challenges by extracting and matching visual features only at the focuses of visual saliency instead of the entire scene. First, we propose a visual-saliency detection method that combines the respective merits of color-saliency boosting and global-region-based contrast schemes to achieve more accurate saliency maps. Using a single feature type, we then obtain good performance on three public datasets when compared to other state-of-the-art approaches. Overall, our results prove that robust and efficient fixation-based classification, in terms of reducing the complexity of feature extraction, is possible.
A model based on the charge-density flux (CDF) is proposed for the electric-field-assisted (EFA) ion-exchange, which is suitable for various EFA ion exchanging processes. Theoretical analysis shows that the CDF model is equivalent to the voltage model when the local temperature change around the glass wafer is negligible when a constant voltage is applied to the ion exchanging process. However, our experiments show that the CDF model is more applicable than the voltage model because the constant-voltage scheme shows a positive feedback process and the local temperature rising is unavoidable in the ion exchanging process. Our further experiments also show that the EFA ion exchanging process can be conveniently characterized by the proposed CDF model with the monitored electrical current, no matter the EFA ion exchanging process is with a constant-voltage/current scheme, a mixed scheme, or even a scheme with random voltage change, while additional complicated measures will be required to characterize the EFA ion exchanging process with the traditional voltage model.