对称性是本质上的无所话话，并且由许多物种的视觉系统感知，因为它有助于检测我们环境中的生态重要的物体类。对称感知需要抽象图像区域之间的非局部空间依赖性，并且其底层的神经机制仍然难以捉摸。在本文中，我们评估了深度神经网络（DNN）架构关于从示例学习对称感知的任务。我们证明了在对象识别任务上建模人类性能的前馈DNN，不能获取对称的一般概念。即使当DNN被重建以捕获非局部空间依赖项，例如通过`扩张的“卷曲和最近引入的”变压器“设计，也是如此。相比之下，我们发现经常性架构能够通过将非局部空间依赖性分解成一系列本地操作来学习对称性，这对于新颖的图像来说是可重复使用的。这些结果表明，经常性联系可能在人工系统中对称性感知中发挥重要作用，也可能是生物学的。

Deep learning models operating in the complex domain are used due to their rich representation capacity. However, most of these models are either restricted to the first quadrant of the complex plane or project the complex-valued data into the real domain, causing a loss of information. This paper proposes that operating entirely in the complex domain increases the overall performance of complex-valued models. A novel, fully complex-valued learning scheme is proposed to train a Fully Complex-valued Convolutional Neural Network (FC-CNN) using a newly proposed complex-valued loss function and training strategy. Benchmarked on CIFAR-10, SVHN, and CIFAR-100, FC-CNN has a 4-10% gain compared to its real-valued counterpart, maintaining the model complexity. With fewer parameters, it achieves comparable performance to state-of-the-art complex-valued models on CIFAR-10 and SVHN. For the CIFAR-100 dataset, it achieves state-of-the-art performance with 25% fewer parameters. FC-CNN shows better training efficiency and much faster convergence than all the other models.

Building segmentation in high-resolution InSAR images is a challenging task that can be useful for large-scale surveillance. Although complex-valued deep learning networks perform better than their real-valued counterparts for complex-valued SAR data, phase information is not retained throughout the network, which causes a loss of information. This paper proposes a Fully Complex-valued, Fully Convolutional Multi-feature Fusion Network(FC2MFN) for building semantic segmentation on InSAR images using a novel, fully complex-valued learning scheme. The network learns multi-scale features, performs multi-feature fusion, and has a complex-valued output. For the particularity of complex-valued InSAR data, a new complex-valued pooling layer is proposed that compares complex numbers considering their magnitude and phase. This helps the network retain the phase information even through the pooling layer. Experimental results on the simulated InSAR dataset show that FC2MFN achieves better results compared to other state-of-the-art methods in terms of segmentation performance and model complexity.

Object detection and classification using aerial images is a challenging task as the information regarding targets are not abundant. Synthetic Aperture Radar(SAR) images can be used for Automatic Target Recognition(ATR) systems as it can operate in all-weather conditions and in low light settings. But, SAR images contain salt and pepper noise(speckle noise) that cause hindrance for the deep learning models to extract meaningful features. Using just aerial view Electro-optical(EO) images for ATR systems may also not result in high accuracy as these images are of low resolution and also do not provide ample information in extreme weather conditions. Therefore, information from multiple sensors can be used to enhance the performance of Automatic Target Recognition(ATR) systems. In this paper, we explore a methodology to use both EO and SAR sensor information to effectively improve the performance of the ATR systems by handling the shortcomings of each of the sensors. A novel Multi-Modal Domain Fusion(MDF) network is proposed to learn the domain invariant features from multi-modal data and use it to accurately classify the aerial view objects. The proposed MDF network achieves top-10 performance in the Track-1 with an accuracy of 25.3 % and top-5 performance in Track-2 with an accuracy of 34.26 % in the test phase on the PBVS MAVOC Challenge dataset [18].

This paper addresses the problem of position estimation in UAVs operating in a cluttered environment where GPS information is unavailable. A model learning-based approach is proposed that takes in the rotor RPMs and past state as input and predicts the one-step-ahead position of the UAV using a novel spectral-normalized memory neural network (SN-MNN). The spectral normalization guarantees stable and reliable prediction performance. The predicted position is transformed to global coordinate frame which is then fused along with the odometry of other peripheral sensors like IMU, barometer, compass etc., using the onboard extended Kalman filter to estimate the states of the UAV. The experimental flight data collected from a motion capture facility using a micro-UAV is used to train the SN-MNN. The PX4-ECL library is used to replay the flight data using the proposed algorithm, and the estimated position is compared with actual ground truth data. The proposed algorithm doesn't require any additional onboard sensors, and is computationally light. The performance of the proposed approach is compared with the current state-of-art GPS-denied algorithms, and it can be seen that the proposed algorithm has the least RMSE for position estimates.

In this paper, the Multi-Swarm Cooperative Information-driven search and Divide and Conquer mitigation control (MSCIDC) approach is proposed for faster detection and mitigation of forest fire by reducing the loss of biodiversity, nutrients, soil moisture, and other intangible benefits. A swarm is a cooperative group of Unmanned Aerial Vehicles (UAVs) that fly together to search and quench the fire effectively. The multi-swarm cooperative information-driven search uses a multi-level search comprising cooperative information-driven exploration and exploitation for quick/accurate detection of fire location. The search level is selected based on the thermal sensor information about the potential fire area. The dynamicity of swarms, aided by global regulative repulsion and merging between swarms, reduces the detection and mitigation time compared to the existing methods. The local attraction among the members of the swarm helps the non-detector members to reach the fire location faster, and divide-and-conquer mitigation control ensures a non-overlapping fire sector allocation for all members quenching the fire. The performance of MSCIDC has been compared with different multi-UAV methods using a simulated environment of pine forest. The performance clearly shows that MSCIDC mitigates fire much faster than the multi-UAV methods. The Monte-Carlo simulation results indicate that the proposed method reduces the average forest area burnt by $65\%$ and mission time by $60\%$ compared to the best result case of the multi-UAV approaches, guaranteeing a faster and successful mission.

在本文中，我们提出了针对无人接地车辆（UGV）的新的控制屏障功能（CBF），该功能有助于避免与运动学（非零速度）障碍物发生冲突。尽管当前的CBF形式已经成功地保证了与静态障碍物的安全/碰撞避免安全性，但动态案例的扩展已获得有限的成功。此外，借助UGV模型，例如Unicycle或自行车，现有CBF的应用在控制方面是保守的，即在某些情况下不可能进行转向/推力控制。从经典的碰撞锥中汲取灵感来避免轨迹规划，我们介绍了其新颖的CBF配方，并具有对独轮车和自行车模型的安全性保证。主要思想是确保障碍物的速度W.R.T.车辆总是指向车辆。因此，我们构建了一个约束，该约束确保速度向量始终避开指向车辆的向量锥。这种新控制方法的功效在哥白尼移动机器人上进行了实验验证。我们将其进一步扩展到以自行车模型的形式扩展到自动驾驶汽车，并在Carla模拟器中的各种情况下证明了避免碰撞。

本文解决了使用异质多机器人系统进行合作目标跟踪的问题，该系统在该系统上通过动态通信网络进行通信，而异质性则是在机器人中安装的不同类型的传感器和预测算法方面。该问题被投入到分布式学习框架中，在该框架中，机器人被认为是通过动态通信网络连接的“代理”。他们的预测算法被认为是“专家”，对目标轨迹的看法预测。在本文中，提出了一种新颖的分散分布式专家辅助学习（D2EAL）算法，提出了通过使每个机器人通过其信息共享来改善目标轨迹的外观预测，并运行加权信息，从而改善了整体跟踪性能。融合过程结合了基于预测损失度量的在线学习权重。对D2EAL进行了理论分析，该分析涉及对累积预测损失的最坏情况界限的分析以及权重分析。仿真研究表明，在涉及专家预测中涉及大动态偏见或漂移的不利场景中，D2EAL优于众所周知的基于协方差的估计/预测融合方法，无论是在预测性能和可伸缩性方面。

在存在对抗数据攻击的情况下，我们研究在线和分布式方案中的强大平均估计。在每个时间步骤中，网络中的每个代理都会收到一个潜在损坏的数据点，其中数据点最初是独立的，并且是随机变量的相同分布的样本。我们建议所有代理商在线和分发算法，以渐近地估计平均值。我们将估计值的错误结合和收敛属性提供给我们算法下的真实均值。基于网络拓扑，我们进一步评估了每个代理商在合并邻居的数据和仅在本地观察中学习之间的融合率的权衡。

我们研究以分布式和在线方式估算未知参数的问题。现有在分布式在线学习的工作通常专注于渐近分析，或者为后悔提供界限。但是，这些结果可能不会直接转化为有限的时间段数后学习模型的误差的界限。在本文中，我们提出了一种分布式的在线估计算法，该算法使网络中的每个代理都可以通过与邻居进行通信来提高其估计精度。我们在估计误差上提供了非反应界限，利用了基础模型的统计特性。我们的分析表明，估计错误和通信成本之间的权衡。此外，我们的分析使我们能够确定可以停止通信的时间（由于与通信相关的成本），同时达到所需的估计准确性。我们还提供了一个数值示例来验证我们的结果。