标题： AI混合TRNG：基于内核的深度学习方法从物理噪声中获取近似均匀的熵源 显示英文标题

标题： AI-Hybrid TRNG: Kernel-Based Deep Learning for Near-Uniform Entropy Harvesting from Physical Noise

摘要： AI-Hybrid TRNG 是一种深度学习框架，它直接从物理噪声中提取近似均匀的熵，消除了对大型量子设备或昂贵的实验室级射频接收器的需求。 相反，它依赖于低成本、拇指大小的射频前端以及 CPU 定时抖动进行训练，然后生成无需任何量化步骤的 32 位高熵流。 与确定性或训练的人工智能随机数生成器（RNG）不同，我们的动态内外网络结合了自适应自然源和重新种子，产生真正不可预测且自主的序列。 生成的数字通过 NIST SP 800-22 测试集，效果优于基于 CPU 的方法。 它还通过了十九种专门设计的统计测试，用于位级和整数级分析。 所有结果都符合密码学标准，而正向和反向预测实验显示没有可利用的偏差。 该模型的占用空间低于 0.5 MB，使其可在微控制器单元（MCU）和 FPGA 软核上部署，并适用于其他资源受限的平台。 通过将随机性质量与专用硬件分离，AI-Hybrid TRNG 扩展了高质量随机数生成器在安全系统、密码协议、嵌入式和边缘设备、随机模拟以及需要随机性的服务器应用中的应用范围。 显示英文摘要

摘要： AI-Hybrid TRNG is a deep-learning framework that extracts near-uniform entropy directly from physical noise, eliminating the need for bulky quantum devices or expensive laboratory-grade RF receivers. Instead, it relies on a low-cost, thumb-sized RF front end, plus CPU-timing jitter, for training, and then emits 32-bit high-entropy streams without any quantization step. Unlike deterministic or trained artificial intelligence random number generators (RNGs), our dynamic inner-outer network couples adaptive natural sources and reseeding, yielding truly unpredictable and autonomous sequences. Generated numbers pass the NIST SP 800-22 battery better than a CPU-based method. It also passes nineteen bespoke statistical tests for both bit- and integer-level analysis. All results satisfy cryptographic standards, while forward and backward prediction experiments reveal no exploitable biases. The model's footprint is below 0.5 MB, making it deployable on MCUs and FPGA soft cores, as well as suitable for other resource-constrained platforms. By detaching randomness quality from dedicated hardware, AI-Hybrid TRNG broadens the reach of high-integrity random number generators across secure systems, cryptographic protocols, embedded and edge devices, stochastic simulations, and server applications that need randomness.