Ultra-Wideband (UWB) technology has recently emerged as a transformative enabler of high-precision positioning systems. Despite its growing adoption across diverse applications, prior studies have claimed several successful distance deception attacks against UWB. To address heightened security concerns, companies like Apple introduce the ranging-awareness defense mechanism into their new version of the UWB interaction frameworks, which is proven to be effective against most known attacks. In this paper, we critically focus on the design flaws of state-of-the-art UWB interaction frameworks and propose Urey-ML, a novel machine learning-based UWB distance deception attack targeting UWB systems. To the best of our knowledge, this is the first attack capable of circumventing the defense mechanisms implemented in Apple’s UWB Nearby Interaction Framework (ANIF). Specifically, Urey-ML is built upon two critical breakthroughs. First, through network packet analysis, we discover that ANIF leaves a crucial message for key negotiation in an unprotected state. This vulnerability enables Urey-ML to bypass the encryption protection implemented by standard UWB systems. Second, to break the ranging-awareness defense, Urey-ML involves a reinforcement learning-based algorithm to optimize attack parameters. By leveraging this approach, Urey-ML can automatically and craftily generate attack signals that mimic the variations typically caused by normal human movement. Our experiments on commercial-off-the-shelf UWB products show that Urey-ML achieves centimeter-level UWB distance deception, with more than 25.79% signals circumventing the defense check of the victim device, which is only 0.56% (or failed) in prior works.

Overlapped speech processing has attracted more and more attention in recent years, and it is a key problem when processing multi-talker mixed speech under the cocktail party scenario. It is commonly observed that the performance of overlapped speech processing can be significantly improved if the number of speakers is given in advance. However, such prior knowledge is often unavailable in real-world conditions, so a robust overlapped speech detection and speaker counting system is demanded. Most existing works focus on combining different handcrafted features to tackle this task, which can be sub-optimal since there are no direct connections between the features and the task. In this work, we try to solve these two problems with an end-to-end manner. First, an end-to-end framework for overlapped speech detection and speaker counting is proposed, which extracts features from the raw waveform directly. Then a curriculum learning strategy is applied to make better use of the training data. The proposed methods are evaluated on multi-talker mixed speech generated from the LibriSpeech corpus. Experimental results show that our proposed methods outperform the model with handcrafted features on both tasks, achieving more than 2% and 4% absolute accuracy improvement on overlapped speech detection and speaker counting respectively.

We derive a delay-dependent H∞ performance criterion with a decoupled structure for systems with neutral time delay. We then extend it to an H∞ controller synthesis for systems with polytopic uncertainty. All conditions are given in terms of linear matrix inequalities (LMIs). In some previous descriptor system methods, the products of the controller and Lyapunov matrices are completely separated for the performance analysis, but not for controller synthesis - the method developed in the current paper eliminates this weakness. We present a numerical example to illustrate the effectiveness of the solution.