Soham Shanbhag

This paper addresses the dual challenge of improving anomaly detection and signal integrity in high-speed dynamic random access memory signals. To achieve this, we propose a joint training framework that integrates an autoencoder with a classifier to learn more distinctive latent representations by focusing on valid data features. Our approach is evaluated across three anomaly detection algorithms and consistently outperforms two baseline methods. Detailed ablation studies further support these findings. Furthermore, we introduce a signal integrity enhancement algorithm that improves signal integrity by an average of 11.3%. The source code and data used in this study are available at https://github.com/Usama1002/learning-latent-representations.

In this paper, a machine learning based observer for systems evolving on manifolds is designed such that the state of the observer is restricted to the Lie group on which the system evolves. Conventional techniques involving machine learning based observers on systems evolving on Lie groups involve designing charts for the Lie group, training a machine learning based observer for each chart, and switching between the trained models based on the state of the system. We propose a novel deep learning based technique whose predictions are restricted to a measure 0 subset of Euclidean space without using charts. Using this network, we design an observer ensuring that the state of the observer is restricted to the Lie group, and predicting the state using only one trained algorithm. The deep learning network predicts an ``error term'' on the Lie algebra of the Lie group, uses the map from the Lie algebra to the group, and uses the group action and the present state to estimate the state at the next epoch. This model being purely data driven does not require the model of the system. The proposed algorithm provides a novel framework for constraining the output of machine learning networks to a measure 0 subset of a Euclidean space without chart specific training and without requiring switching. We show the validity of this method using Monte Carlo simulations performed of the rigid body rotation and translation system.

Rigid body systems usually consider measurements of the pose of the body using onboard cameras/LiDAR systems, that of linear acceleration using an accelerometer and of angular velocity using an IMU. However, the measurements of the linear acceleration and angular velocity are usually biased with an unknown constant or slowly varying bias. We propose a measurement bias estimator for such systems under assumption of boundedness of angular velocity. We also provide continuous estimates to the state of the system, i.e. the pose, linear velocity, and position of the body. These estimates are globally exponentially convergent to the state of the rigid body system. We propose two bias estimators designed with the estimate of the pose in the ambient Euclidean space of the Special Euclidean group and show global exponential convergence of the proposed observers to the state of the system. The first observer assumes knowledge of bounds of the angular velocity, while the second observer uses a Riccati observer to overcome this limitation. We show the convergence with an example of a rigid body rotation and translation system on the special Euclidean group. We show that the observer is able to estimate the bias using data collected from an Intel Realsense camera.

We propose a globally exponentially convergent observer for the dynamical system evolving on matrix Lie groups with bounded velocity with unknown bound. We design the observer in the ambient Euclidean space and show exponential convergence of the observer to the state of the system. We show the convergence with an example of a rigid body rotation and translation system on the special Euclidean group. We compare the proposed observer with an observer present in the literature.

We propose a globally asymptotically convergent hybrid observer for the rigid body rotation and translation system evolving on the special Euclidean group SE(3) in the presence of intermittent measurements of the pose and continuous measurements of the velocities. We embed the system into an ambient Euclidean space and design an observer on the Euclidean space for global convergence. We perform numerical simulation of the proposed observer to show convergence. We also perform simulation using data collected from an Intel Realsense T265 camera.