Vertical-cavity surface-emitting lasers (VCSELs) are the ideal optical sources for data communication and sensing. In data communication, large data rates combined with excellent energy efficiency and temperature stability have been achieved based on advanced device design and modulation formats. VCSELs are also promising sources for photonic integrated circuits due to their small footprint and low power consumption. Also, VCSELs are commonly used for a wide variety of applications in the consumer electronics market. These applications range from laser mice to three-dimensional (3D) sensing and imaging, including various 3D movement detections, such as gesture recognition or face recognition. Novel VCSEL types will include metastructures, exhibiting additional unique properties, of largest importance for next-generation data communication, sensing, and photonic integrated circuits.
In a non-stationary environment, newly received data may have different knowledge patterns to the data used to train learning models. As time passes, the performance of learning models becomes increasingly unreliable. This problem is known as concept drift and is a common issue in real-world domains. Concept drift detection has attracted increasing attention in recent years, however, hardly any existing methods pay attention to small regional drifts, and their drift detection accuracy may vary due to different statistical significance test. To address these problems, this paper presents a novel concept drift detection method that is based on regional density estimation, named nearest neighbor-based density variation identification (NN-DVI). It consists of three components. The first one is a k-nearest neighborbased space partitioning schema (NNPS) which transforms unmeasurable discrete data instances into a set of shared subspaces for density estimation. The second one is a distance function that accumulates the density discrepancies in these subspaces and quantifies the overall discrepancies. The last component is a tailored statistical significant test by which the confidence interval of a concept drift can be accurately determined. The distance applied in NN-DVI is sensitive to regional drift, and has been proven to follow a normal distribution. As a result, both the accuracy and false alarm rate of NN-DVI are statistically guaranteed. In addition, several benchmarks have been used to evaluate the method, including both synthetic and real-world datasets. The overall results show that NN-DVI has better performance in terms of addressing concept-drift-detection-related problems.
2-dimensional simulations of high-contrast gratings (HCGs) of finite size are carried out, targeting at their applications in vertical-cavity surface-emitting lasers (VCSELs). Finite HCGs show a very different behavior from infinite grating ones. The reflectivity of a finite HCG strongly depends on the HCG size and the source size. Our simulation results predict finite reflectivity and transmission values, well consistent with reported experimental results. The band of high reflectivity (>99.5%) of finite HCGs is less broad as compared to the infinite case. Losses into a guided mode excited in the HCG plane are identified as being at the root. This guided mode is excited due to the nonzero angular components in the finite source size, and greatly enhances the transmission and the light leakage from the slab. In addition, the simulation results show that the details of the finite HCG can shape the output beam, whilst a Gaussian-like reflected wave is typically achieved. Our simulations can explain the current discrepancies between numerical predictions of reflectivities approaching 100% and working HCG-VCSELs showing finite reflectivities and nearly Gaussian-like output. Consequently, our analysis of finite HCGs is indispensable for HCG-VCSEL design.
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