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Date: September 26-28, 2022
Location: Shanghai, China
Submission Deadline: May 30, 2022
Call for Proposals Document
Date: October 26-November 11, 2022
Location: Yokohama, Japan
CFP Announcement
Submission Deadline: April 10, 2022
Call for Proposals Document
Manuscript Due: 10 December 2022
Publication Date: September 2023
CFP Document
A brief introduction to state estimation in multi-object system that arises from applications where the number of objects and their states are unknown and vary randomly with time. State space model (SSM) is a fundamental concept in system theory that permeated through many fields of study.
We are seeking a highly motivated and skilled PhD student to work with us on the development of a digital phenotyping strategy for children with autism. The PhD student will use machine learning approaches to provide automated measures of body movement and social scenes for children with autism, with the goal to support automated autism diagnosis and/or fine-grained characterization of autistic symptoms.
The large antenna arrays with hybrid analog and digital (HAD) architectures can provide a large aperture with low cost and hardware complexity, resulting in enhanced direction-of-arrival (DOA) estimation and reduced power consumption. This paper investigates the trade-off between DOA estimation and power consumption in large antenna arrays with HAD architectures.
We present a general nonlinear Bayesian filter for high-dimensional state estimation using the theory of reproducing kernel Hilbert space (RKHS). By applying the kernel method and the representer theorem to perform linear quadratic estimation in a functional space, we derive a Bayesian recursive state estimator for a general nonlinear dynamical system in the original input space. Unlike existing nonlinear extensions of the Kalman filter where the system dynamics are assumed known, the state-space representation for the Functional Bayesian Filter (FBF) is completely learned online from measurement data in the form of an infinite impulse response (IIR) filter or recurrent network in the RKHS, with universal approximation property.