With the development of millimeter-wave technology, the miniaturization and integration of antennas have become key requirements. This paper presents a millimeter-wave second-order fractal antenna, including its structural design, fabrication method, and application in a silicon-based three-dimensional integrated structure. This antenna realizes a miniaturized structure with a Peano fractal curve on the substrate, featuring a novel structure, small size, and compatibility with other devices and chip processes. Meanwhile, a large-thickness BCB dielectric layer is adopted, which contributes to the miniaturization and integrated integration of the system. By elaborating on the design principle, fabrication steps, and performance of the antenna in the three-dimensional integrated structure, this paper provides a valuable reference for the development of millimeter-wave three-dimensional integration technology.

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In today’s data-driven world, big data environments are becoming increasingly complex, characterized by high volume, variety, and velocity. Traditional data processing methods are no longer sufficient to handle such challenges. Artificial Intelligence (AI) provides powerful solutions for extracting value from diverse and dynamic data sources. This paper reviews key AI techniques—including machine learning, deep learning, natural language processing, graph-based models, and federated learning—and discusses their applications in complex scenarios such as healthcare, finance, smart cities, and Industry 4.0. It also highlights major challenges, including data quality, model interpretability, computational cost, and privacy concerns. Finally, the paper explores future directions in AI development, such as multimodal learning and real-time decision-making. These advancements will play a vital role in enabling intelligent, efficient, and ethical data analytics in the years to come.

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With the rapid development of Internet of Vehicles (IoV) technologies, the automotive industry is undergoing a profound transformation from traditional mechanical systems to intelligent and connected systems. By integrating technologies such as 5G communication, V2X, edge computing, and artificial intelligence, the IoV has established an intelligent transportation system characterized by vehicle-road-cloud-terminal collaboration. Focusing on the two core topics of automotive safety and user experience in the IoV era, this paper systematically reviews the security challenges and pathways for optimizing user experience, while also exploring future trends in their coordinated development. The study finds that IoV technologies offer significant advantages in enhancing traffic safety, improving traffic flow, and reducing energy consumption. However, they also face risks such as cybersecurity threats, data privacy breaches, and system reliability issues. Measures such as optimizing smart cockpit interaction and expanding full-scenario service ecosystems can effectively enhance both automotive safety and user experience. In the future, as technology continues to advance and supportive policies are further implemented, IoV technologies will drive the automotive industry toward a safer, smarter, and more efficient direction.

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Text and speech processing technologies encounter bottlenecks in understanding complex emotions within Human-Computer Interaction (HCI), with significant limitations particularly in the semantic parsing of intonation and tone. This paper employs a literature review approach to systematically synthesize core advancements in applied psychology and speech recognition technology within HCI, focusing on the intersection of emotion recognition technology and psychological intervention scenarios. The study indicates that the deep integration of these fields can significantly enhance intervention precision, especially in scenarios such as psychological diagnosis in children (e.g., emotion recognition in autism) and personalized Cognitive Behavioral Therapy (CBT) guidance. The research further points out that Artificial Intelligence (AI) has not yet fully grasped complex human emotions, necessitating the deeper embedding of psychological theories into speech emotion analysis frameworks to improve the ability to interpret semantically ambiguous expressions. This review provides a theoretical framework and a practical pathway for the application of AI technology and applied psychology in intelligent HCI.

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