Optimal planning for autonomous vehicles over long time horizons, where sensor information and multiple objectives form part of the decision-making process, is still in its infancy. The challenges of uncertainty, inherent in sensor information, and the computational complexity of their implementation typically render direct approaches infeasible. We present a practical, close to optimal, approach to the problem of long horizon path planning and goal-seeking for an autonomous mobile platform with sensing capability. Optimal autonomous vehicle planning is notoriously difficult, presenting several significant challenges: handling the uncertainty inherent in the sensor data and in a potentially unknown environment; coping with the ostensibly large data storage requirements; and the computational complexity of looking many epochs ahead. To overcome these problems, our model-based solution leverages a stochastic search methodology to obtain long-term, continuous, trajectories. We demonstrate its capability in handling both uncertainty in sensor measurements as well as multiple, possibly conflicting, objectives.
The integrated sensing and communication (ISAC) paradigm focuses on the interaction between communication parameters and their optimization to improve the sensing performance in sixth-generation cellular systems. Beamsteering is particularly beneficial in communication systems, where highly directional beams provide fine-grained spatial information which are essential for accurate localization and tracking in ISAC systems. This paper investigates passive object localization based on beam pattern searching and selection in 60 GHz frequency band. We design a passive localization algorithm that uses beam information as the main feature, along with machine learning tools for localization. The approach is tested through experimental activities conducted in the considered frequency band, forming multiple extremely narrow beams. These beams can be selectively oriented towards moving subjects or objects in the area or directed to illuminate different regions of the same environment. Initial results presented in the paper are very promising, confirming that high accuracy (up to 99 %) is achievable under controlled monitoring conditions.
Phase retrieval is a fundamental problem in optics, particularly in ptychography, where reconstructing an unknown wavefield from intensity-only measurements poses significant computational challenges. In this work, we introduce a novel phase retrieval method using the bilinear Hessian and Hessian operator to improve optimization efficiency. By formulating the problem in a differential calculus framework over linear spaces, we derive gradient and Hessian expressions without explicitly differentiating, enabling efficient second-order based solvers. This enables to implement and analyze conjugate gradient, quasi-Newton, and gradient descent methods with Newton step size, demonstrating a one-order-of-magnitude improvement in convergence speed compared to traditional approaches. Numerical experiments on near-field ptychography data validate the method’s robustness and computational efficiency. Our approach significantly reduces reconstruction time, making it highly relevant for real-time imaging applications, such as in-situ experiments.
A hybrid filterbanks is a convolutional neural network (convnet) whose learnable filters operate over the subbands of a non-learnable filterbank, which is designed from domain knowledge. While hybrid filterbanks have found successful applications in speech enhancement, our paper shows that they remain susceptible to large deviations of the energy response due to randomness of convnet weights at initialization. Against this issue, we propose a variant of hybrid filterbanks, by inspiration from residual neural networks (ResNets). The key idea is to introduce a shortcut connection at the output of each non-learnable filter, bypassing the convnet. We prove that the shortcut connection in a residual hybrid filterbank lowers the relative standard deviation of the energy response while the pairwise cosine distances between non-learnable filters contributes to preventing duplicate features.
In this paper, we present a bespoke Variational Autoencoder (VAE) architecture focused on identifying critical parameters of hazardous material releases, including source location, release start time, and wind speeds affecting dispersion, from a temporal series of color images. Our VAE architecture is unique in its incorporation of dual latent spaces. The primary latent space is dedicated to the probabilistic inference of source term attributes, along with their associated uncertainties. Concurrently, a secondary latent space is designed to capture salient features of the release that are complementary to the source parameters inferred in the primary space. We benchmark our methodology against a recent deterministic deep learning model using simulated data, demonstrating superior performance across different temporal resolutions. Real-world validation on the Jack Rabbit II Trial 7 experiment confirms the VAE’s ability to estimate key release parameters with reliable uncertainty quantification.
