Inertial attitude estimation is a crucial component of many modern systems and applications. Attitude estimation from commercial-grade inertial sensors has been the subject of an abundance of research in recent years due to the proliferation of Inertial Measurement Units (IMUs) in mobile devices, such as the smartphone. Traditional methodologies involve probabilistic, iterative-state estimation; however, these approaches do not generalise well over changing motion dynamics and environmental conditions, as they require context-specific parameter tuning. In this work, we explore novel methods for attitude estimation from low-cost inertial sensors using a self-attention-based neural network, the Attformer. This paper proposes to part ways from the traditional cycle of continuous integration algorithms, and formulate it as an optimisation problem. This approach separates itself by leveraging attention operations to learn the complex patterns and dynamics associated with inertial data, allowing for the linear complexity in the dimension of the feature vector to account for these patterns. Additionally, we look at combining traditional state-of-the-art approaches with our self-attention method. These models were evaluated on entirely unseen sequences, over a range of different activities, users and devices, and compared with a recent alternate deep learning approach, the unscented Kalman filter and the iOS CoreMotion API. The inbuilt iOS had a mean angular distance from the true attitude of 117.31∘, the GRU 21.90∘, the UKF 16.38∘, the Attformer 16.28∘ and, finally, the UKF–Attformer had mean angular distance of 10.86∘. We show that this plug-and-play solution outperforms previous approaches and generalises well across different users, devices and activities.
The newborn EEG seizure is a nonstationary signal. The time-varying nature of the newborn EEG seizure can be characterized by time-frequency representations (TFRs) such as quadratic time-frequency distributions. The underlying time-frequency signatures of newborn EEG seizure, however, can be severely masked by short-time and high amplitude (STHA), or impulsive, artefacts. This type of artefact can be modelled as heavy-tailed noise. Robust time-frequency distributions (RTFDs) have been proposed as methods for TFRs which are robust to heavy-tailed noise. In this paper, we investigate the use of RTFDs for representing the underlying time-frequency characteristics of newborn EEG seizure in the presence of STHA artefacts.
Inertial localisation is an important technique as it enables ego-motion estimation in conditions where external observers are unavailable. However, low-cost inertial sensors are inherently corrupted by bias and noise, which lead to unbound errors, making straight integration for position intractable. Traditional mathematical approaches are reliant on prior system knowledge, geometric theories and are constrained by predefined dynamics. Recent advances in deep learning, which benefit from ever-increasing volumes of data and computational power, allow for data-driven solutions that offer more comprehensive understanding. Existing deep inertial odometry solutions rely on estimating the latent states, such as velocity, or are dependent on fixed-sensor positions and periodic motion patterns. In this work, we propose taking the traditional state estimation recursive methodology and applying it in the deep learning domain. Our approach, which incorporates the true position priors in the training process, is trained on inertial measurements and ground truth displacement data, allowing recursion and learning both motion characteristics and systemic error bias and drift. We present two end-to-end frameworks for pose invariant deep inertial odometry that utilises self-attention to capture both spatial features and long-range dependencies in inertial data. We evaluate our approaches against a custom 2-layer Gated Recurrent Unit, trained in the same manner on the same data, and tested each approach on a number of different users, devices and activities. Each network had a sequence length weighted relative trajectory error mean ≤0.4594 m, highlighting the effectiveness of our learning process used in the development of the models.
Australian directors who incur debts while their companies are insolvent can be pursued by the corporate regulator for compensation when their companies fail. Under the Australian insolvent trading laws, directors no longer experience ‘true’ limited liability, and as expected, they adjust their behaviour as a result. Identifying director's rational behaviour in an insolvent trading world is difficult as there are no formal economic models of director decision‐making under Australian current corporate law. In this paper, we develop such a model primarily for private companies. We incorporate the threat of insolvent trading as well as director's tactical use of voluntary administration to avoid insolvent trading litigation. We show that neither a combination of insolvent trading or voluntary administration can simultaneously ensure creditors‐best outcomes, eliminate insolvent trading and reduce director underinvestment.
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