According to the rapid development of drone technologies, drones are widely used in many applications including military domains. In this paper, a novel situation-aware DRL- based autonomous nonlinear drone mobility control algorithm in cyber-physical loitering munition applications. On the battlefield, the design of DRL-based autonomous control algorithm is not straightforward because real-world data gathering is generally not available. Therefore, the approach in this paper is that cyber-physical virtual environment is constructed with Unity environment. Based on the virtual cyber-physical battlefield scenarios, a DRL-based automated nonlinear drone mobility control algorithm can be designed, evaluated, and visualized. Moreover, many obstacles exist which is harmful for linear trajectory control in real-world battlefield scenarios. Thus, our proposed autonomous nonlinear drone mobility control algorithm utilizes situation-aware components those are implemented with a Raycast function in Unity virtual scenarios. Based on the gathered situation-aware information, the drone can autonomously and nonlinearly adjust its trajectory during flight. Therefore, this approach is obviously beneficial for avoiding obstacles in obstacle-deployed battlefields. Our visualization-based performance evaluation shows that the proposed algorithm is superior from the other linear mobility control algorithms.

While witnessing the noisy intermediate-scale quantum (NISQ) era and beyond, quantum federated learning (QFL) has recently become an emerging field of study. In QFL, each quantum computer or device locally trains its quantum neural network (QNN) with trainable gates, and communicates only these gate parameters over classical channels, without costly quantum communications. Towards enabling QFL under various channel conditions, in this article we develop a depth-controllable architecture of entangled slimmable quantum neural networks (eSQNNs), and propose an entangled slimmable QFL (eSQFL) that communicates the superposition-coded parameters of eS-QNNs. Compared to the existing depth-fixed QNNs, training the depth-controllable eSQNN architecture is more challenging due to high entanglement entropy and inter-depth interference, which are mitigated by introducing entanglement controlled universal (CU) gates and an inplace fidelity distillation (IPFD) regularizer penalizing inter-depth quantum state differences, respectively. Furthermore, we optimize the superposition coding power allocation by deriving and minimizing the convergence bound of eSQFL. In an image classification task, extensive simulations corroborate the effectiveness of eSQFL in terms of prediction accuracy, fidelity, and entropy compared to Vanilla QFL as well as under different channel conditions and various data distributions.

In modern on-driving computing environments, many sensors are used for context-aware applications. This paper utilizes two deep learning models, U-Net and EfficientNet, which consist of a convolutional neural network (CNN), to detect hand gestures and remove noise in the Range Doppler Map image that was measured through a millimeter-wave (mmWave) radar. To improve the performance of classification, accurate pre-processing algorithms are essential. Therefore, a novel pre-processing approach to denoise images before entering the first deep learning model stage increases the accuracy of classification. Thus, this paper proposes a deep neural network based high-performance nonlinear pre-processing method.

网络安全研究中的关键主题之一是自动COA（行动）攻击搜索方法。被动搜索攻击的传统COA攻击方法可能很困难，尤其是随着网络变大。为了解决这些问题，正在开发新的自动COA技术，其中，本文设计了一种智能的空间算法，以在可扩展网络中有效运行。除空间搜索外，还考虑了基于蒙特卡洛（MC）的时间方法来照顾时间变化的网络行为。因此，我们为可扩展和时变网络的时空攻击COA搜索算法提出了一个时空攻击。

量子联合学习（QFL）最近受到了越来越多的关注，其中量子神经网络（QNN）集成到联邦学习（FL）中。与现有的静态QFL方法相反，我们在本文中提出了可靠的QFL（SLIMQFL），这是一个动态QFL框架，可以应对时变的通信通道和计算能量限制。通过利用QNN的独特性质，可以分别训练并动态利用其角度参数，从而使其可行。模拟结果证实了SLIMQFL比香草QFL更高的分类精度，尤其是在较差的通道条件下。

Federated learning (FL) is a key enabler for efficient communication and computing, leveraging devices' distributed computing capabilities. However, applying FL in practice is challenging due to the local devices' heterogeneous energy, wireless channel conditions, and non-independently and identically distributed (non-IID) data distributions. To cope with these issues, this paper proposes a novel learning framework by integrating FL and width-adjustable slimmable neural networks (SNN). Integrating FL with SNNs is challenging due to time-varying channel conditions and data distributions. In addition, existing multi-width SNN training algorithms are sensitive to the data distributions across devices, which makes SNN ill-suited for FL. Motivated by this, we propose a communication and energy-efficient SNN-based FL (named SlimFL) that jointly utilizes superposition coding (SC) for global model aggregation and superposition training (ST) for updating local models. By applying SC, SlimFL exchanges the superposition of multiple-width configurations decoded as many times as possible for a given communication throughput. Leveraging ST, SlimFL aligns the forward propagation of different width configurations while avoiding inter-width interference during backpropagation. We formally prove the convergence of SlimFL. The result reveals that SlimFL is not only communication-efficient but also deals with non-IID data distributions and poor channel conditions, which is also corroborated by data-intensive simulations.

在许多深层神经网络（DNN）应用中，在行业领域收集高质量数据的困难阻碍了DNN的实际使用。因此，转移学习的概念已经出现，该概念利用了在大规模数据集中训练的DNN的验证知识。因此，本文提出了受神经体系结构搜索（NAS）的启发的两阶段建筑微调。主要思想之一是突变，它使用给定的架构信息降低了搜索成本。此外，还考虑了早期停滞，这通过事先终止搜索过程来降低NAS成本。实验结果验证我们提出的方法可降低32.4％的计算和22.3％的搜索成本。

自动驾驶汽车和自主驾驶研究一直受到现代人工智能应用中主要有希望的前景。根据先进的驾驶员辅助系统（ADAS）的演变，自动驾驶车辆和自主驱动系统的设计变得复杂和安全至关重要。通常，智能系统同时和有效地激活ADAS功能。因此，必须考虑可靠的ADAS功能协调，安全地控制驱动系统。为了处理这个问题，本文提出了一种随机的对抗性模仿学习（RAIL）算法。铁路是一种新的无衍生仿制学习方法，用于具有各种ADAS功能协调的自主驾驶;因此，它模仿决策者的运作，可以使用各种ADAS功能控制自动驾驶。该方法能够培训涉及激光雷达数据的决策者，并控制多车道复合道环境中的自主驾驶。基于仿真的评估验证了所提出的方法实现了所需的性能。

移动设备是大数据的不可或缺的来源。联合学习（FL）通过交换本地培训的模型而不是其原始数据来利用这些私人数据具有很大的潜力。然而，移动设备通常是能量有限且无线连接的，并且FL不能灵活地应对它们的异构和时变的能量容量和通信吞吐量，限制采用。通过这些问题，我们提出了一种新颖的能源和通信有效的流动框架，被创造的Slimfl。为了解决异构能量容量问题，SLIMFL中的每个设备都运行宽度可调可泥瓦神经网络（SNN）。为了解决异构通信吞吐量问题，每个全宽（1.0倍）SNN模型及其半宽度（0.5美元$ x）模型在传输之前是叠加编码的，并且在接收后连续解码为0.5x或1.0美元$ 1.0 $ x模型取决于频道质量。仿真结果表明，SLIMFL可以通过合理的精度和收敛速度同时培养0.5美元和1.0美元的X模型，而是使用2美元的通信资源分别培训这两种型号。令人惊讶的是，SLIMFL甚至具有比Vanilla FL的较低的能量占地面积更高的精度，对于较差的通道和非IID数据分布，Vanilla Fl会缓慢收敛。

本文旨在整合两个协同技术，联合学习（FL）和宽度可调的可泥质网络（SNN）架构。通过交换当地培训的移动设备模型来保留数据隐私。通过采用SNNS作为本地模型，FL可以灵活地应对移动设备的时变能容量。然而，结合FL和SNN是非琐碎的，特别是在与时变通道条件的无线连接下。此外，现有的多宽SNN训练算法对跨设备的数据分布敏感，因此不适用于FL。由此激励，我们提出了一种通信和节能SNN的FL（命名SLIMFL），共同利用叠加编码（SC）进行全局模型聚合和叠加训练（ST），以更新本地模型。通过施加SC，SLIMFL交换多个宽度配置的叠加，这对于给定的通信吞吐量尽可能多地解码。利用ST，SLIMFL对准不同宽度配置的前向传播，同时避免在背部衰退期间的横宽干扰。我们正式证明了Slimfl的融合。结果表明，SLIMFL不仅是通信的，而且可以抵消非IID数据分布和差的信道条件，这也被模拟证实。