A multi-layer dictionary learning algorithm that joints global constraints and Fisher discrimination (JGCFD-MDL) for image classification tasks was proposed. The algorithm reveals the manifold structure of the data by learning the global constraint dictionary and introduces the Fisher discriminative constraint dictionary to minimize the intra-class dispersion of samples and increase the inter-class dispersion. To further quantify the abstract features that characterize the data, a multi-layer dictionary learning framework is constructed to obtain high-level complex semantic structures and improve image classification performance. Finally, the algorithm is verified on the multi-label dataset of court costumes in the Ming Dynasty and Qing Dynasty, and better performance is obtained. Experiments show that compared with the local similarity algorithm, the average precision is improved by 3.34% . Compared with the single-layer dictionary learning algorithm, the one-error is improved by 1.00% , and the average precision is improved by 0.54% . Experiments also show that it has better performance on general datasets.

In this paper, a novel superjunction 4H-silicon carbide (4H-SiC) trench-gate insulated-gate bipolar transistor (IGBT) featuring an integrated clamping PN diode between the P-shield and emitter (TSD-IGBT) is designed and theoretically studied. The heavily doping superjunction layer contributes to a low specific on-resistance, excellent electric field distribution, and quasi-unipolar drift current. The anode of the clamping diode is in floating contact with the P-shield. In the on-state, the potential of the P-shield is raised to the turn-on voltage of the clamping diode, which prevents the hole extraction below the N-type carrier storage layer (NCSL). Additionally, during the turn-off transient, once the clamping diode is turned on, it also promotes an additional hole extraction path. Furthermore, the potential dropped at the semiconductor near the trench-gate oxide is effectively reduced in the off-state.

   Few-shot named entity recognition (NER) aims to identify named entities in new domains using a limited amount of annotated data. Previous methods divided this task into entity span detection and entity classification, achieving good results. However these methods are limited by the imbalance between the entity and non-entity categories due to the use of sequence labeling for entity span detection. To this end, a point-proto network ( PPN) combining pointer and prototypical networks was proposed. Specifically, the pointer network generates the position of entities in sentences in the entity span detection stage. The prototypical network builds semantic prototypes of entity types and classifies entities based on their distance from these prototypes in the entity classification stage. Moreover, the low-rank adaptation ( LoRA) fine-tuning method, which involves freezing the pre-trained weights and injecting a trainable decomposition matrix, reduces the parameters that need to be trained and saved. Extensive experiments on the few-shot NER Dataset (Few-NERD) and Cross-Dataset demonstrate the superiority of PPN in this domain.

   How to protect cultural retics is of great significance to the transmission and dissemination of history and culture. Digital 3-dimensional (3D) modeling of cultural relics is an effective way to preserve them. The efficiency and complexity of cultural relic model reconstruction algorithms are significant challenges due to redundant data. To tackle the above issue, a 3D reconstruction algorithm, named COLMAP + LSH, was proposed for movable cultural relics based on salient region optimization. COLMAP + LSH algorithm introduces saliency region detection and locality-sensetive Hashing (LSH) to achieve efficient, accurate, and robust digital 3D modeling of cultural relics. Specifically, 400 cultural model data were collected through offline and online collection. COLMAP + LSH algorithm detects the salient region interactively and reduces the number of images in the salient region by feature diffusion. Additionally, COLMAP + LSH algorithm utilizes LSH to calculate the image selection scores and employs the image selection scores to reduce the redundant image. The experiments on the self-constructed cultural relics dataset show that COLMAP + LSH algorithm can efficiently achieve image feature diffusion and ensure the quality of artifact reconstruction while selecting most of the redundant image data.

In this work, β-Ga₂O₃ thin films were grown on SiO₂ substrate by atomic layer deposition (ALD) and annealed in N₂ atmosphere to enhance the crystallization quality of the thin films, which were verified from X-rays diffraction (XRD). Based on the grown β-Ga₂O₃ thin films, vertical metal-semiconductor-metal (MSM) interdigital photodetectors (PDs) were fabricated and investigated. The PDs have an ultralow dark current of 1.92 pA, ultra-high photo-to-dark current ratio (PDCR) of 1.7×10⁶, and ultra-high detectivity of 4.25×10¹⁴ Jones at a bias voltage of 10 V under 254 nm deep ultraviolet (DUV). Compared with the horizontal MSM PDs under the same process, the PDCR and detectivity of the fabricated vertical PDs are increased by 1 000 times and 100 times, respectively. In addition, the vertical PDs possess a high responsivity of 34.24 A/W and an external quantμm efficiency of 1.67×10⁴%, and also exhibit robustness and repeatability, which indicate excellent performance. Then the effects of electrode size and external irradiation conditions on the performance of the vertical PDs continued to be investigated.