With the global deployment of 5G networks, the demand for high bandwidth and low latency has driven innovations in fronthaul architectures. However, traditional solutions face challenges related to security and signal interference. This paper proposes an innovative quantum-enhanced converged wireless-fiber 5G fronthaul system that integrates quantum key distribution (QKD) technology with a passive wavelength division multiplexing (WDM) architecture to achieve physical-layer secure communication. The system deploys the quantum transmitter (Alice) at the active antenna unit (AAU) side and the receiver (Bob) at the distribution unit (DU) side, leveraging the Heisenberg Uncertainty Principle, quantum measurement collapse theory, and the no-cloning theorem to detect eavesdropping attempts. Simulation results demonstrate the significant impact of noise detection probability, fiber loss, and transmission distance on both the secure key rate and quantum bit error rate (QBER). Key findings highlight the need to minimize fiber loss, reduce transmission distance, lower filter loss, decrease noise detection probability, and improve detection efficiency. These optimizations collectively enhance the secure key rate while reducing the QBER, thereby improving overall system reliability. Remarkably, this solution achieves these security enhancements without requiring large-scale modifications to existing infrastructure, offering a practical and cost-effective upgrade path for current 5G deployments while providing a future-proof foundation for emerging 6G networks. The architecture’s inherent scalability and compatibility make it particularly suitable for high-security applications, including government communications, financial transactions, and critical infrastructure protection—bridging the gap between theoretical quantum security and practical wireless network implementation.

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Object detection is a cornerstone task in computer vision, impacting diverse domains from autonomous systems to medical imaging. This study presents a systematic evaluation of three leading detection architectures—Faster Region-based Convolutional Neural Networks (Faster R-CNN), You Only Look Once version 8 (YOLOv8), and Detection Transformer (DETR), with the objective of evaluating their performance characteristics and practical deployment potential. Through comparative experiments, the research evaluates Faster R-CNN’s precision (Delivers 76.4% mean Average Precision with a processing speed of 5-7 Frames Per Second (FPS)), YOLOv8’s real-time efficiency (53.9% mean Average Precision (mAP) at 80+ FPS), and DETR attention-based innovation (42% AP). Results highlight the strengths and weaknesses of each model: Faster R-CNN excels in accuracy-demanding applications like medical diagnosis, YOLOv8 dominates real-time tasks such as autonomous driving, and DETR offers promising temporal analysis capabilities despite higher computational costs. The study proposes innovative solutions, including cross-scale attention mechanisms and dynamic inference techniques, to address limitations such as small-object detection and edge deployment. The findings provide valuable insights for architecture selection, offering actionable guidelines for industrial implementation and laying the groundwork for future advancements in multimodal fusion and self-supervised learning paradigms. This framework accelerates the development of next-generation detection systems tailored for emerging Artificial Intelligence (AI) applications.

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The demand for efficient and precise diagnosis in the field of medical diagnosis is currently increasing exponentially. Traditional medical diagnostic methods face significant limitations in processing vast amounts of medical data and strained medical resources. However, with the development of computer technology, deep learning techniques have been increasingly applied in various areas of medical diagnosis, significantly accelerating the processing of large volumes of medical data and effectively enhancing the efficiency and accuracy of medical diagnosis. By compiling multiple literature sources on the application of deep learning techniques in medical diagnosis, this article summarizes two different application forms of deep learning in medical diagnosis: the integration of transfer learning and deep learning, and the combination of deep learning and the Internet of Things (IoT). It also discusses the application of these techniques in medical imaging and the auxiliary diagnosis of clinical diseases, as well as their primary impacts on medical development. The article emphasizes the advantages brought by the application of deep learning in medical diagnosis.

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Algorithmic composition is the use of automated processes in music composition as a means of removing human intervention from the compositional process. Many composers throughout history have experimented with diverse approaches, with or without the computer. This STUDY is an exploration of the potentials of random walk in music composition using the Nyquist IDE. While random walks possess inherent limitations, such as their unbounded range, they can generate sequences of pitches that resemble melodies with constraints applied. According to the analysis, linear functions were particular effective in shaping melodic contours. Pitches deviating from the established chord progression were quantized to maintain consonance. A worked example inspired by Canon in D illustrates how this approach can generate distinctive harmonic textures, highlighting its creative potential. This model holds relevance across diverse musical contexts, from real-time improvisation for multimedia to collaborative composition and performance, offering artists a tool for inspiration and an accessible means of composition.

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