no code implementations • 3 Dec 2024 • Juncheng Dong, Zihao Wu, Hamid Jafarkhani, Ali Pezeshki, Vahid Tarokh
Many challenges in science and engineering, such as drug discovery and communication network design, involve optimizing complex and expensive black-box functions across vast search spaces.
no code implementations • 16 Sep 2024 • Shyam Venkatasubramanian, Ali Pezeshki, Vahid Tarokh
Moreover, we present the Analytic Neural Network, which incorporates a consistency penalty that encourages analytic signal representations in the latent space of the Steinmetz neural network.
no code implementations • 8 Sep 2024 • Brandon Van Over, Bowen Li, Edwin K. P. Chong, Ali Pezeshki
We first generalize both of the $\alpha_G$ and $\alpha_G''$ bounds to string optimization problems in a manner that includes maximizing submodular set functions over matroids as a special case.
no code implementations • 14 Jun 2024 • Shyam Venkatasubramanian, Bosung Kang, Ali Pezeshki, Muralidhar Rangaswamy, Vahid Tarokh
We present a large-scale dataset for radar adaptive signal processing (RASP) applications to support the development of data-driven models within the adaptive radar community.
no code implementations • 10 Apr 2024 • Bowen Li, Brandon Van Over, Edwin K. P. Chong, Ali Pezeshki
We prove that our bound is superior to the greedy curvature bound of Conforti and Cornu\'ejols.
no code implementations • 14 Mar 2023 • Shyam Venkatasubramanian, Sandeep Gogineni, Bosung Kang, Ali Pezeshki, Muralidhar Rangaswamy, Vahid Tarokh
Via the use of space-time adaptive processing (STAP) techniques and convolutional neural networks, these data-driven approaches to target localization have helped benchmark the performance of neural networks for matched scenarios.
no code implementations • 7 Nov 2022 • Bowen Li, Suya Wu, Erin E. Tripp, Ali Pezeshki, Vahid Tarokh
We develop a recursive least square (RLS) type algorithm with a minimax concave penalty (MCP) for adaptive identification of a sparse tap-weight vector that represents a communication channel.
no code implementations • 7 Sep 2022 • Shyam Venkatasubramanian, Sandeep Gogineni, Bosung Kang, Ali Pezeshki, Muralidhar Rangaswamy, Vahid Tarokh
Leveraging the advanced functionalities of modern radio frequency (RF) modeling and simulation tools, specifically designed for adaptive radar processing applications, this paper presents a data-driven approach to improve accuracy in radar target localization post adaptive radar detection.
no code implementations • 26 Jan 2022 • Shyam Venkatasubramanian, Chayut Wongkamthong, Mohammadreza Soltani, Bosung Kang, Sandeep Gogineni, Ali Pezeshki, Muralidhar Rangaswamy, Vahid Tarokh
In this regard, we will generate a large, representative adaptive radar signal processing database for training and testing, analogous in spirit to the COCO dataset for natural images.
no code implementations • 18 Nov 2019 • Christopher Robbiano, Edwin K. P. Chong, Mahmood R. Azimi-Sadjadi, Louis L. Scharf, Ali Pezeshki
Occupancy grids encode for hot spots on a map that is represented by a two dimensional grid of disjoint cells.
no code implementations • 19 Nov 2012 • Zhenliang Zhang, Edwin K. P. Chong, Ali Pezeshki, William Moran
In the case where the flipping probabilities converge to 1/2, we derive a necessary condition on the convergence rate of the flipping probabilities such that the decisions still converge to the underlying truth.