Sequence Generation via Subsequence Similarity: Theory and Application to UAV Identification

The ability to generate synthetic sequences is crucial for a wide range of applications, and recent advances in deep learning architectures and generative frameworks have greatly facilitated this process. Particularly, unconditional one-shot generative models constitute an attractive line of research that focuses on capturing the internal information of a single image, video, etc. to generate samples with similar contents. Since many of those one-shot models are shifting toward efficient non-deep and non-adversarial approaches, we examine the versatility of a one-shot generative model for augmenting whole datasets. In this work, we focus on how similarity at the subsequence level affects similarity at the sequence level, and derive bounds on the optimal transport of real and generated sequences based on that of corresponding subsequences. We use a one-shot generative model to sample from the vicinity of individual sequences and generate subsequence-similar ones and demonstrate the improvement of this approach by applying it to the problem of Unmanned Aerial Vehicle (UAV) identification using limited radio-frequency (RF) signals. In the context of UAV identification, RF fingerprinting is an effective method for distinguishing legitimate devices from malicious ones, but heterogenous environments and channel impairments can impose data scarcity and affect the performance of classification models. By using subsequence similarity to augment sequences of RF data with a low ratio (5\%-20\%) of training dataset, we achieve significant improvements in performance metrics such as accuracy, precision, recall, and F1 score.Comment: 12 pages, 5 figures, 2 table

Robust Control Frequency Analysis of a Moving Walking Bipedal Robot

Abstract-Dynamic equations of the biped robots are easily obtained by the Lagrangian method. Because of terrible nonlinearity of these dynamic equations, the actuator's dynamic is employed to linearize these equations. By this way, the heavy nonlinear equations are replaced with a second order linear system which their coefficients are achievable by the various identification methods. Here Least Square method is utilized to determine these second order transfer function's coefficients. Employing a set of linear transfer function for each joint and also considering unstructured uncertainty, an H∞ controller is designed and the Robust Stability and Robust Performance criteria are to be handeled with the µ analysis theory