Electronics (Basel)

High-power laser diodes are extensively utilized in advanced optoelectronic systems. These devices typically operate under high-current injection conditions, under which intrinsic parasitic parameters become non-negligible and exert a substantial influence on their electro-optical response characteristics. Furthermore, when multiple single emitters are monolithically integrated into a linear array along the epitaxial-layer direction on a single substrate, additional parasitic elements are inevitably introduced. These parameters are critical for characterizing the output performance of high-power laser diodes. This paper presents the implementation of an equivalent circuit model for large-signal laser-diode operation within the Advanced Design System (ADS) computer-aided environment. The proposed model enables accurate simulation of the device’s operating-voltage waveform and optical-output-power response under both DC steady-state and pulsed-transient driving conditions, thereby achieving a coupled representation of electrical behavior and optical emission. Sensitivity analysis of various parasitic elements is performed to systematically evaluate their influence on output characteristics and device reliability. The results provide theoretical guidance for structural optimization and packaging design, offering new insights into future modeling and reliability assessment of high-power laser diodes.

Electromagnetic spectrum planning is a critical challenge in modern wireless communication systems, characterized by multiple conflicting objectives including spectrum utilization efficiency, interference minimization, and fairness among users. This paper proposes an Enhanced Discrete Multi-Objective Particle Swarm Optimization (EDMOPSO) algorithm specifically designed for spectrum assignment problems. The proposed method introduces a novel probabilistic discrete velocity update mechanism with adaptive dynamic bounds, an adaptive inertia weight strategy based on normalized population diversity, and an improved archiving technique with enhanced diversity preservation. To handle the discrete nature of spectrum allocation, we develop a binary encoding scheme combined with a problem-specific repair mechanism for constraint satisfaction. The algorithm is evaluated on both synthetic benchmark problems and real-world spectrum planning scenarios. Experimental results demonstrate that EDMOPSO achieves competitive performance advantages over seven established multi-objective evolutionary algorithms, with Hypervolume improvements of 18.7% and Inverted Generational Distance reductions of 23.4% compared to the second-best-performing algorithm. A comprehensive ablation study with 15 configurations validates the synergistic interaction between components. The proposed method provides an effective solution for macro-level periodic spectrum management in complex electromagnetic environments.

This paper presents a current-reused vertically stacked (CRVS) topology for a high-dynamic-range amplifier (HDRA) implemented in a 0.1 μm GaAs pHEMT process, targeting wideband millimeter-wave (mm-wave) receiver front-ends. The proposed design breaks the inherent trade-off between noise figure (NF), linearity, and bandwidth, achieving simultaneous enhancement of transconductance efficiency, Miller effect suppression, and wideband matching. The fabricated prototype operates over a continuous 20–43 GHz bandwidth (covering K- and Ka-bands), demonstrating state-of-the-art performance: a flat gain of 24 ± 0.6 dB, a minimum NF of 2.2 dB, a maximum output 1 dB compression point (OP1dB) of 15.8 dBm and a low power consumption of 5 V/65 mA, with both input and output return losses better than −10 dB across the entire band. The results validate the effectiveness of the CRVS topology and highlight the competitiveness of GaAs pHEMT technology for high-performance wideband mm-wave front-ends, making the design suitable for applications including 5G/6G communication, satellite systems, and mm-wave test equipment.

Texture anomaly segmentation aims to localize irregularities on textured surfaces, a task critical for industrial quality control. Supervised methods require extensive labeled data, while unsupervised approaches often struggle to generalize to unseen target domains. Recent zero-shot methods based on vision-language models such as Contrastive Language-Image Pretraining (CLIP) enable anomaly detection through text prompts without target-domain training data. However, existing approaches typically rely on generic prompts and show limited sensitivity to fine-grained texture variations. To address these limitations, we propose TextureCLIP, a cross-dataset zero-shot framework with auxiliary training for texture anomaly segmentation. The framework is trained on source texture data from the MVTec AD texture subset using annotated source-domain samples and directly evaluated on six unseen target datasets without access to target-domain training images, annotations, or fine-tuning. The proposed Triadic Descriptive Prompting (TriDP) integrates normal prompts, generic anomaly prompts, and descriptive anomaly prompts to provide complementary semantic cues for improved cross-domain generalization. To enhance spatial sensitivity, Dual Attention Modules (DAMs) are incorporated into the CLIP image encoder to refine local feature representations. In addition, Softmax-Weighted Averaging (SMWA) aggregates multiple anomaly cues by emphasizing the prompt responses with higher similarity scores. Experimental results demonstrate that TextureCLIP achieves strong and consistent performance across diverse texture datasets, attaining 67.06% AP and 65.69% F1-max, with improvements of 5.17 and 2.66 percentage points over the competitive baselines, respectively.

Most artistic expressions are conveyed through images (e.g., painting) and audio (e.g., music), and deep learning has been successfully applied to neural style transfer within each of these modalities. However, there is still a lack of deep models that explicitly learn to transfer style between images and audio. Motivated by synesthesia, which reflects intrinsic connections between vision and hearing in the human brain, we propose a deep hybrid synesthesia model for audio–image style transfer. Our framework consists of two main components: (1) a component conversion module that learns cross-modal mappings between audio rhythm/spectrum and image color/shape in a continuous valence–arousal (VA) emotion space; and (2) a style conversion module that transfers high-level artistic styles between Eastern (ink-wash, shui-mo) and Western painting and their corresponding musical counterparts. We first learn emotion-aware feature networks that align low-level audio and visual components based on shared affective representations, and then model long-term stylistic structures for cross-modal style transfer. Experiments include “seeing the sound” (audio-to-image generation with controllable components) and full audio–image style transformations. Both objective analyses and subjective evaluations suggest that our model can produce cross-modal artworks whose perceived style and emotional content are consistent with human synesthetic impressions.