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In the field of multimaterial optical fibers, the demand for high‐performance single‐crystal core glass‐clad multimaterial optical fibers is increasing. However, the applications of single‐crystal fibers are restricted by the complex fabrication processes and the slow growth of single‐crystal materials. Here, a two‐step method is demonstrated to achieve single‐crystal tellurium (Te) core fibers with high crystal quality over the length of the fiber. This method starts with the thermal drawing of a fiber preform into polycrystalline Te core multimaterial fibers (precursor fibers) that are long and mechanically stable. A 532‐nm continuous laser is then employed to recrystallize the Te core in the precursor fiber into a single crystal state along the entire length of the fiber. Experimental studies of these single‐crystal fibers demonstrate that the single‐crystal Te core fibers possess high transmittance (>90% at 2–10 μm) and high thermoelectric performance (ZT values from 0.03 to 0.13 at 300 K temperature). They are superior to previous reports and our previous work. This method works for fabricating various single‐crystal fibers and has important applications in the field of optical fibers, functional fibers, and their integrated devices. [ABSTRACT FROM AUTHOR]

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How to represent and classify a testing sample for the representation-based classification (RBC) plays an important role in the filed of pattern recognition. As a typical kind of the representation-based classification with promising performance, collaborative representation-based classification (CRC) adopts all the training samples to collaboratively represent and then classify each testing sample with the reconstructive residuals among all the classes. However, most of the CRC methods fail to make full use of the localities and discrimination information of data in collaborative representation. To address this issue to further improve the classification performance, we design a novel supervised CRC method entitled locality-constrained weighted collaborative-competitive representation-based classification (LWCCRC). In the proposed method, the localities of data are taken into account by using the positive and negative nearest samples of each testing sample with their corresponding weighted constraints. Such devised locality-constrained weighted term can model the similarity and natural discrimination information contained in the neighborhood region for each testing sample to obtain the favorable representation. Moreover, a competitive constraint is introduced to enhance pattern discrimination among the categorical collaborative representations. To explore the effectiveness of our proposed LWCCRC, the extensive experiments are carried out on three different types of data sets. The experimental results demonstrate that the proposed LWCCRC significantly outperforms the recent state-of-the-art CRC methods. [ABSTRACT FROM AUTHOR]