In this study, a dual−core D-shaped photonic crystal fiber (PCF) surface plasmon resonance sensor coated with gold grating is designed and analyzed using the finite-element method (FEM). The surface plasmon resonance (SPR) effect between the fiber core modes and surface plasmon polariton (Spp) modes is used to measure the analyte refractive index. The effects of the PCF structure parameters (polishing depths, large holes, and small holes) and grating parameters (grating heights, grating periods, and grating duty) are discussed, and a two-feature interrogation method that combines wavelength and intensity interrogations is introduced to enhance the resolution. The results show that the grating and dual-core play important roles in enhancing the sensor properties. The proposed sensor achieves an average wavelength sensitivity of 994.5 nm/RIU when the analyte refractive index increases from 1.33 to 1.37. Furthermore, a maximum amplitude sensitivity of 181.049 RIU⁻¹ is obtained. The two-feature interrogation is determined to have a resolution of 2.03 × 10⁻⁶ RIU, which is better than the wavelength and amplitude interrogations. The proposed sensor has a good sensing performance and is highly suitable for practical applications.

With the continuous advancement of optical fiber micromachining technology, C-type optical fibers have demonstrated significant potential in the field of optical fiber sensing. By partially or completely removing specific regions of the cladding, a “leakage window” is created, enabling interaction between the optical field and external substances. This structure has facilitated the development of various sensors. This paper reviews recent progress in the research and applications of C-type optical fibers in optical sensing. Based on sensing principles and application scenarios, C-type optical fiber sensors can be categorized into two main types: interferometric and photonic crystal types. This article discusses the fundamental operating principles and structural characteristics of each type, and provides a detailed comparison of their respective advantages and disadvantages. Studies have shown that sensors based on C-type fiber structures offer notable benefits such as simple fabrication, excellent mechanical performance, strong anti-interference capability, and high sensitivity. Therefore, they hold great promise for applications in intelligent monitoring, environmental detection, and healthcare. Finally, this review outlines future research directions for C-type fiber sensors. As technology continues to evolve, future studies are expected to focus on improving sensor stability, expanding application scenarios, and addressing challenges in current fabrication techniques.

Building indoor dangerous behavior recognition is a specific application in the field of abnormal human recognition. A human dangerous behavior recognition method based on LSTM-GCN with attention mechanism (GLA) model was proposed aiming at the problem that the existing human skeleton-based action recognition methods cannot fully extract the temporal and spatial features. The network connects GCN and LSTM network in series, and inputs the skeleton sequence extracted by GCN that contains spatial information into the LSTM layer for time sequence feature extraction, which fully excavates the temporal and spatial features of the skeleton sequence. Finally, an attention layer is designed to enhance the features of key bone points, and Softmax is used to classify and identify dangerous behaviors. The dangerous behavior datasets are derived from NTU-RGB+D and Kinetics data sets. Experimental results show that the proposed method can effectively identify some dangerous behaviors in the building, and its accuracy is higher than those of other similar methods.

Given that the equipment for the semiconductor packaging line adopts the fixed grouping production method, thus failing to dynamically match the processing task demand capacity, in the present study, we proposed a semiconductor bonding equipment-grouping method based on processing task matching. This method sets the device group closed position constraint and the matching constraint between the device type and the processing type and uses the graph theory method to establish the device grouping model. The dynamic grouping of equipment under the capacity demand of different processing tasks was achieved by changing the relationship matrix between devices. The drawback of this grouping method is rather large grouping deviation, which we tried to solve with the clustering by fast search and find of density peaks (CFSFDP) that was added to cluster the sets of attribute information of the devices so as to obtain the maximum number of grouping groups obtained to reduce the grouping deviation. Simulation comparison experiments were carried out under different circumstances considering the size of the formation, the distribution of demand capacity, and the coefficient of difference in demand capacity. Compared with the standard device grouping method, the grouping method based on semiconductor bonding equipment and CFSFDP algorithm for dynamic matching according to processing tasks had better performance in solving the dynamic grouping problem.

Aiming at the problem that the exiting human skeleton-based action recognition methods cannot fully extract the relevant information before and after the action, resulting in low utilization efficiency of skeleton points, we propose a two-layer LSTM (long short term memory) network with attention mechanism. The network has two layers, the first LSTM network is used for skeleton coding and initialization of system storage units and the second LSTM network integrates attention mechanism to further process the data of the first layer network. An algorithm is designed to assign different weights to skeleton points according to the importance of human body, which greatly increases the recognition accuracy. Action classification is accomplished by multiple support vector machines. Through training and testing, the average recognition rate of 98.5% is achieved on KTH dataset. The experimental result shows that the proposed method is effective in human behavior recognition.

Temperature-humidity (TH) induced failure mechanism (FM) of metal contacting interfaces in integrated circuit (IC) systems has played a significant role in system reliability issues. This paper focuses on central processing unit (CPU)/motherboard interfaces and studies several factors that are believed to have a great impact on TH performance. They include: Enabling load, surface finish quality, and contacting area. Test vehicles (TVs) of Clarkdale package and of Ibex peak motherboard were designed to measure low level contact resistance (LLCR) for catching any failure. Several sets of design of experiments (DOE) were conducted on 85°C/85% relative humidity and test results were analyzed. A proposal that correlates asperity spots and contact tip design with contact resistance was proposed and thus a cost-effective solution for improving electrical performance under TH was deduced. The proposal has proven to be reasonably effective in practice.