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.

A method is proposed to maximize the minimum user rate for full-duplex (FD) rate splitting multiple access (RSMA) systems. Firstly, the optimization problem of maximizing the minimum user rate in the system is constructed by taking the base station precoding matrix, power allocation, and common rate as optimization parameters, and the user power and rate requirements as constraint conditions. Then, the optimization objective function and the constraints are simplified by introducing slack variables respectively, and the first-order Taylor expansion is used to linearize constraints, so as to transform the non-convex optimization problem into a convex one. Finally, the optimization problem is solved based on iteration and convex (CVX) method. The simulation results show that the minimum user rate increases with the increase of the transmit power of the base station, and the minimum user rate of the proposed scheme is higher than that the existing scheme for the same scenario.

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.

Deep convolutional neural networks are widely used in structural magnetic resonance imaging (sMRI) analysis for the early diagnosis of Alzheimer's disease. To address the challenge of efficient representation learning in sMRI, this study proposes a two-pathway convolutional network that improves the computational efficiency of sMRI feature extraction by representation decoupling, and further strengthens the representation discriminability through adaptive feature fusion. The network consists of three parts：1) A high-channel-capacity slice path, which processes sparse slices to encode semantic information of slice images；2) A low-channel-capacity context path, which processes dense slices to capture inter-slice contextual information；3) An adaptive feature fusion module, which integrates the decoupled information from both paths to generate more effective sMRI representations. The proposed method was evaluated on two tasks—Alzheimer's disease classification and mild cognitive impairment conversion prediction—using the Alzheimer's disease neuroimaging initiative (ADNI) dataset. The results demonstrate that the proposed approach surpasses the baseline models in both computational efficiency and diagnostic performance, while achieving results comparable to those of current state-of-the-art methods.

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.

The 5th generation of mobile communications system (5G) virtual private network carrying power business not only increase system capacity, but also bring new security challenges due to the openness. Conducting security analysis on wireless power systems can effectively identify security risks and guide the design of subsequent security protection strategies. However, existing methods lack targeted framework construction and fail to provide intuitive quantitative evaluations. In this paper, a security risk assessment method for power 5G virtual private network is proposed based on the characteristics of power business. On the basis of building the assessment indicators framework, the analysis of security risks is conducted from subjective and objective aspects through analytical hierarchy process and entropy value method. Finally, the effectiveness and applicability of the proposed safety risk assessment method is proved through example analysis.

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.

In the sixth generation (6G) mobile communication system, intelligent reflecting surfaces (IRS) enhance wireless transmission efficiency by dynamically adjusting the wireless propagation environment. In distributed IRS-assisted multiple-input multiple-output (MIMO) systems, when the number of users significantly exceeds the number of antennas at the base station (BS) , the joint optimization of user selection and beamforming is crucial for reducing BS transmission power and promoting green communication. To address this, a model is established with the objective of minimizing BS transmission power by jointly optimizing user selection, BS beamforming vectors, and phase shift matrices of distributed IRSs while ensuring user quality of service (QoS). To simplify the model, it is decoupled into two subproblems that are iteratively optimized to approximate the optimal solution of the original problem. First，the artificial bee colony（ABC）algorithm and second-order cone programming（SOCP）are employed to determine the optimal user selection strategy and BS beamforming vectors. Then，the phase shift matrices of the IRSs are optimized using the semidefinite relaxation（SDR）method. Simulation results demonstrate that the proposed algorithm not only achieves good convergence but also effectively reduces the transmission power of the BS.

Aiming at the problems of inadequate utilization of structural semantics and poor repair results of detailed features in the existing deep learning methods for repairing ancient murals, a structure-guided diffusion generative algorithm was proposed. Firstly, a mural structure reconstruction module composed of gated convolution and fast Fourier residual block is constructed, and the edge structure after reconstruction is used to guide the repair of damaged murals, so as to overcome the problem of insufficient utilization of structural semantic repair. Then, a generative diffusion module based on stochastic differential equation is proposed, which performs forward diffusion processing on the mural image to be repaired by stochastic differential equation. Next, a mask-enhanced backward iterative reconstruction module is designed to enhance the semantic consistency between the damaged area and the intact area of the mural, and improve the repair ability of the detailed features of the mural. Finally, the digital inpainting experiments and analysis are carried out on the Dunhuang mural data set. The experimental results show that the proposed algorithm can effectively complete the mural restoration, and the objective evaluation indicators are better than the comparison algorithms.

Brain tumor segmentation is a key task in medical image analysis due to the heterogeneous and irregular nature of tumor regions. To address the limitations of existing methods in modeling long-range dependencies and reducing resource consumption, we propose a lightweight segmentation model based on a hybrid convolutional neural network (CNN) and Transformer encoder. Depthwise separable convolutions are employed in shallow layers to reduce computation, while the proposed shuffle former block (SFB) integrates Transformer and ShuffleNet v2 to effectively capture both global and local context. Furthermore, lightweightattention modules are introduced to model long-range dependencies and enhance local perception. Experimental results on the BraTS 2019 dataset demonstrate that our model achieves Dice scores of 93.1% in whole tumor (WT) , 92.2% in tumor core (TC) , and 91.2% in enhancing tumor (ET) , with only 0.98M parameters and 54.60G floating point operations per second, achieving a superior balance between segmentation accuracy and computational efficiency for deployment in resource-constrained clinical settings.