The luminescent properties of gallium oxide (Ga₂ O₃) doped with rare-earth erbium (Er³⁺) have gained significantattention due to their potential in optoelectronic and semiconductor applications. In this study, five Ga₂ O₃ bulk materials with varying erbium doping concentrations were prepared using solid-phase sintering. The crystal structure, micro-morphology, fluorescence characteristics, and the effect of doping concentration were systematically examined. The results show that Er³⁺ substitutes Ga³⁺and also forms a new erbium-gallium garnet (Er₃ Ga₅ O₁₂) phase. As the doping concentration increases, the content of the garnet phase rises, leading to a linear increase in luminescence intensity. However, the sample with 1.00at% erbium doping shows lower red-green luminescence intensity compared to the 0.75at% sample dominated by the garnet phase, indicating that the erbium-gallium garnet phase offers superior fluorescence performance in these regions compared to the substitutional doping phase.

To overcome the influence of beam split in terahertz communication, for the problems of a large number of large-range delay devices, high hardware complexity and high power consumption in traditional fully connected antenna structure schemes based on single-layer and double-layer delay devices, a multi-user hybrid analog/digital precoding scheme based on multi-layer delayers was considered. In this scheme, the time delays were divided into multi-layer arrangement at the base station, and hybrid precoding design was carried out based on the multi-layer delayers. Specifically, firstly the bit number of time delays and the set of discrete time delay based on the number of time delays in each layer and antennas were deduced. Secondly according to the array response vector at the center frequency, the phase shift of phase shifter was designed. Then the propagation delay of antenna array aperture corresponding to the subarray where the delayer is located was used as the delay of time delays, and the delayer delay was discretized based on the discrete delay set. Afterwards, analog precoding was designed based on the phase shift of phase shifter and the delay of time delays. Finally, low complexity zero forcing precoding technology was used to design digital precoding. Simulation and analysis results show that compared to traditional scheme, the proposed scheme can greatly reduce the number of large range delay devices and hardware complexity while sacrificing a small amount of rate performance.

To address the challenges of excessive resource demands and difficulty in meeting low-power requirements in the embedded domain when field programmable gate array (FPGA) are utilized to accelerate convolution operations, a resource-efficient FPGA-based convolution acceleration method for binary neural networks (BNN) is proposed. First, the computational characteristics and resource consumption patterns of various parallelization schemes during the forward inference process of the convolution layer are systematically analyzed. Leveraging the lowbit-width feature of BNN, a high dimensional data splicing and dimensionality reduction storage scheme is introduced. Subsequently, a channel dimension reduction rotation cache (CDR) structure tailored for BNN is proposed, aiming to achieve the combined benefits of intra-convolution kernel parallelism and inter-feature map parallelism with moderate cache bandwidth expansion. Furthermore, to fully exploit the performance advantages of the CDR structure, a specialized CDR processing unit is designed, and the adder tree structure is optimized. The processing unit supports flexible adjustment of different pipeline levels and can achieve higher-level parallel computing capabilities through a repeated invocation mechanism, adapting to diverse system requirements. Experimental results demonstrate that the BNN accelerator based on the CDR architecture achieves significantly superior computational density and storage density compared to existing state-of-the-art solutions when deployed on the Xilinx XC7Z020 chip. It also exhibits low-power characteristics and enables faster inference speed，making it well-suited for resource-and power constrained embedded platforms.

To effectively mitigate Sinkhole attacks in wireless sensor networks (WSN) , this paper proposes a novel Sinkhole attack detection and defense strategy (IF-GBO) that integrates isolation forest (IF) and gradient-based optimizer (GBO) . First, a detection threshold is established to trigger the IF-GBO intrusion detection mechanism, thereby reducing network overhead and improving detection efficiency. Second, considering the characteristics of Sinkhole attacks and the dynamic real-time nature of WSN data, a multidimensional feature dataset is designed, incorporating node hop count, energy consumption, packet reception/forwarding rate, and time delay. The model is trained using a sliding window sampling approach, which not only enhances the algorithm's operational efficiency but also improves the accuracy of malicious node identification. Finally, a multi-objective path selection function is developed，leveraging the GBO algorithm to assist nodes in rapidly identifying alternative transmission paths to counter Sinkhole attacks. This approach effectively ensures reliable data transmission，extends network lifetime，and resolves the issue of delayed delivery of anomaly detection results to the legitimate sink node. Experimental results demonstrate that compared to conventional anomaly detection models such as support vector machine（SVM），k-nearest neighbors（KNN）and local outlier factor（LOF），IF-GBO achieves higher accuracy in identifying malicious nodes with lower false positive rates and superior generalization capability. Furthermore，when compared to dedicated Sinkhole attack detection algorithms like hop count-based detection scheme for Sinkhole attack（HCODESSA）and a Sinkhole detection algorithm based on the random routes selected by minimum hop（RMHSD），the GBO-based defense strategy significantly mitigates the disruptive effects of Sinkhole attacks on the network，ensuring routing security and reliability.

The in-depth analysis of the semantic information contained in traditional Chinese medicine (TCM) prescriptions is of great significance for both clinical applications and the discovery of new formulas. Existing TCM prescription generation algorithms define the interactions between all symptom herb pairs solely based on co-occurrence, without considering the categorization of herbal properties. To address this issue, this paper proposes a prescription recommendation algorithm based on herbal property driven compatibility mechanism semantic modeling (HPDCM). First, the analysis of prescriptions takes into account the herbal property categories, which are defined as entities when constructing the knowledge graph (KG). Second, the algorithm integrates compatibility rules to model the interactions between symptoms and herbs with weighted connections. This is followed by aggregating higher-order heterogeneous path information of nodes through a graph convolutional network (GCN) model. Finally, an attention mechanism is employed to fuse information from symptom interaction graphs, symptom-herb interaction graphs, and herb interaction graphs, distinguishing the influence of different dimensions of TCM semantic information. Experimental results, compared with existing formula generation algorithms, demonstrate that HPDCM achieves higher accuracy and is more in line with the TCM diagnostic and therapeutic principles of syndrome differentiation and treatment.

A low complexity cyclic redundancy check (CRC)-headbiting convolutional code (HBCC)-Miller cascaded encoding scheme has been proposed to combat interference in long-distance wireless transmission environments and greatly improve the communication quality of cellular passive Internet of things systems. This scheme can utilize the combination of CRC and HBCC for encoding and decoding. Compared with convolutional CC encoding, theminimum number of registers can be reduced to 1/3 of the number of long term evolution (LTE) tail-biting CC (TBCC) registers, significantly reducing the computational cost of encoding and further reducing label encoding power consumption. The simulation results show that the proposed low complexity cascaded encoding scheme has a performance improvement of 4.2dB compared to Miller8 encoding, further enhancing anti-interference performance, expanding single station communication distance, and retaining rich clock information between codewords, which can better adapt to the label encoding requirements of ultra-low power cellular IoT systems.

To address the issue that the single red-green-blue (RGB) modality contains limited semantic information, is susceptible to noise interference, and exhibits suboptimal segmentation performance, this paper proposes an RGB-depth (RGB-D) semantic segmentation algorithm based on an asymmetric feature interaction method. First, a two-stream network is employed to extract features from the RGB and depth modalities separately. By incorporating an asymmetric feature correction module, features from one modality are used to correct those of the other, thereby suppressing intra-modal noise. Then, an asymmetric fusion module is applied to further enhance information interaction between the modalities. Additionally, multi-scale feature fusion is introduced in the decoder, and adversarial training is adopted as an auxiliary strategy during the training process to effectively leverage contextual information and improve overall accuracy. Experimental results demonstrate that the proposed algorithm effectively suppresses intra-modal noise and enhances the interaction of valid semantic information across modalities，achieving mean intersection over union（mIoU）scores of 57.4% and 52.1% on the New York University depth dataset v2（NYUDepthv2）and the Stanford University RGB-D dataset（SUN-RGBD），respectively.

High-performance electromagnetic shielding materials can effectively protect against the increasingly prominent electromagnetic interference and damage, in order to overcome the shortcomings of traditional rigid electromagnetic shielding materials such as poor flexibility and controllability in practical applications, a flexible, mouldable and conductive silver nanosheets composite hydrogel shielding material was developed, and its three-dimensional structure and effective component composition were determined by scanning electron microscope (SEM) , X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) . A vector network analyser was further used to confirm the excellent electromagnetic shielding performance of the hydrogel in the frequency band of 8.0～12.4GHz (consistently stabilized above 35dB). Based on the controllable water content of the hydrogel, a dehydration-induced spatial interconnection of silver nanolayers to form a macroscopic conductive pathway is designed and proposed, which greatly improves the electrical conductivity of the composite hydrogel and thus achieves a high level of electromagnetic shielding, and to a certain extent, provides a practical and effective solution for the controllable design and preparation of high-performance electromagnetic shielding materials.

Addressing challenges in complex structural health monitoring arising from heterogeneous sensor node sampling, feature drift, and limited model generalization ability in distributed vibration signals, this study constructs a distributed vibration signal with augmented generation (DVSAG) dataset. It utilizes cross-diffusion for adaptive sampling while preserving the spatiotemporal correlation of the original signal, combines the frequency domain to unify input dimensions, and enhances inputs by calculating residuals using fault-free reference signals. A fault diagnosis network with a convolutional block attention module (CBAM) is designed to extract multi-scale features from distributed vibration signals. These features are converted into word embeddings, combined with user questions, and input into a distributed vibration signal large language model (DVSLLM). Finally, a feature alignment and semantic mapping framework is used to achieve fine-grained interaction from vibration signals to natural language. Experiments show that the proposed method effectively improves fault diagnosis accuracy and model generalization ability under multiple operating conditions, providing reliable support for multi-task decision-making in complex structural health monitoring.

With the growing demand for understanding complex three dimensions (3D) environments in fields like Internet of things (IoT)-driven autonomous navigation and related applications, the automatic reconstruction of structured, editable computer-aided design (CAD) models from point cloud data has become a critical task. However, current research mainly focuses on reconstructing CAD models from CAD command sequences, sketches, and extrusion operations, and commonly faces challenges such as excessive reconstruction steps and strong platform dependence. To this end, a high-precision geometric and topological CAD reconstruction method is proposed, specifically for IoT 3D perception. First, the boundary representation of CAD is decomposed into parametric information of geometric primitives and their topological structure. Then, a primitive variational autoencoder (PVAE) and a topological variational autoencoder (TVAE) are designed to model their geometric features and topological relationships, respectively. Furthermore, PointNet++ is used to extract multi-scale local features from point cloud data and fuse them into global features. A topological decoder and primitive decoders are then used to predict topological tree sequences and primitive parameters, achieving high-precision CAD model reconstruction with clearer boundary details. To validate the method's effectiveness, metrics such as chamfer distance, edge chamfer distance, and structural consistency are used to evaluate its performance on two datasets. The experimental results show that the proposed model outperforms existing methods in terms of topological integrity and geometric reconstruction accuracy，enabling higher-precision CAD model reconstruction.