Zobrazeno 1 - 10
of 1 618
pro vyhledávání: '"Geethu, A."'
Autor:
He, Yanbin, Joseph, Geethu
This work studies the problem of jointly estimating unknown parameters from Kronecker-structured multidimensional signals, which arises in applications like intelligent reflecting surface (IRS)-aided channel estimation. Exploiting the Kronecker struc
Externí odkaz:
http://arxiv.org/abs/2412.00310
We develop a framework for communication-control co-design in a wireless networked control system with multiple geographically separated controllers and controlled systems, modeled via a Poisson point process. Each controlled system consists of an ac
Externí odkaz:
http://arxiv.org/abs/2411.19598
Autor:
He, Yanbin, Joseph, Geethu
In this work, we study the restricted isometry property (RIP) of Kronecker-structured matrices, formed by the Kronecker product of two factor matrices. Previously, only upper and lower bounds on the restricted isometry constant (RIC) in terms of the
Externí odkaz:
http://arxiv.org/abs/2409.08699
This paper considers the design of sparse actuator schedules for linear time-invariant systems. An actuator schedule selects, for each time instant, which control inputs act on the system in that instant. We address the optimal scheduling of control
Externí odkaz:
http://arxiv.org/abs/2407.12125
Almost all optimization algorithms have algorithm-dependent parameters, and the setting of such parameter values can significantly influence the behavior of the algorithm under consideration. Thus, proper parameter tuning should be carried out to ens
Externí odkaz:
http://arxiv.org/abs/2407.02537
We consider the control of discrete-time linear dynamical systems using sparse inputs where we limit the number of active actuators at every time step. We develop an algorithm for determining a sparse actuator schedule that ensures the existence of a
Externí odkaz:
http://arxiv.org/abs/2407.00385
Autor:
Joseph, Geethu
The convergence of expectation-maximization (EM)-based algorithms typically requires continuity of the likelihood function with respect to all the unknown parameters (optimization variables). The requirement is not met when parameters comprise both d
Externí odkaz:
http://arxiv.org/abs/2401.17763
Sparsity constraints on the control inputs of a linear dynamical system naturally arise in several practical applications such as networked control, computer vision, seismic signal processing, and cyber-physical systems. In this work, we consider the
Externí odkaz:
http://arxiv.org/abs/2312.02082
In this paper, we address the problem of detecting anomalies among a given set of binary processes via learning-based controlled sensing. Each process is parameterized by a binary random variable indicating whether the process is anomalous. To identi
Externí odkaz:
http://arxiv.org/abs/2312.00088
This paper studies the problem of modifying the input matrix of a structured system to make the system strongly structurally controllable. We focus on the generalized structured systems that rely on zero/nonzero/arbitrary structure, i.e., some entrie
Externí odkaz:
http://arxiv.org/abs/2311.05653