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Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
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.
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
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.
Section: Publications & Resources
Publication: IEEE Journal of Selected Topics in Signal Processing
Network slicing to support multi-tenancy plays a key role in improving the performance of 5G and beyond networks. In this paper, we study dynamically slicing network resources in the backhaul and Radio Access Network (RAN) prior to user demand observations across multiple tenants, where each tenant owns and operates several slices to provide different services to users.
Section: Publications & Resources
Publication: IEEE Open Journal of Signal Processing
Beside the minimizationof the prediction error, two of the most desirable properties of a regression scheme are stability and interpretability . Driven by these principles, we propose continuous-domain formulations for one-dimensional regression problems. In our first approach, we use the Lipschitz constant as a regularizer, which results in an implicit tuning of the overall robustness of the learned mapping.
Section: Publications & Resources
Publication: IEEE Signal Processing Letters
Recent years have witnessed remarkable success of Graph Fourier Transform (GFT) in point cloud attribute compression. Existing researches mainly utilize geometry distance to define graph structure for coding attribute (e.g., color), which may distribute high weights to the edges connecting points across texture boundaries.
Section: Publications & Resources
In many real-world settings, machine learning models need to identify user inputs that are out-of-domain (OOD) so as to avoid performing wrong actions. This work focuses on a challenging case of OOD detection, where no labels for in-domain data are accessible (e.g., no intent labels for the intent classification task).
Section: Publications & Resources
Deep neural networks (DNNs) represent the mainstream methodology for supervised speech enhancement, primarily due to their capability to model complex functions using hierarchical representations. However, a recent study revealed that DNNs trained on a single corpus fail to generalize to untrained corpora, especially in low signal-to-noise ratio (SNR) conditions.
Section: Publications & Resources
Publication: IEEE Transactions on Computational Imaging
Hyperspectral imaging (HSI) has become an invaluable imaging tool for many applications in astrophysics or Earth observation. Unfortunately, direct observation of hyperspectral images is impossible since the actual measurements are 2-D and suffer from strong spatial and spectral degradations, especially in the infrared.
Section: Publications & Resources
With the wide use of smartphones, more private data are collected and saved in the smartphones. This raises higher requirements for secure and effective user authentication scheme. Continuous authentication leverages behavioral biometrics as identity information and shows promising characteristics for user verification in a continuous and passive means.
Section: Publications & Resources
Near-InfraRed and VISual (NIR-VIS) face matching, as one of the most representative tasks in Heterogeneous Face Recognition (HFR), aims at retrieving a face image across different domains. With the development of deep learning and the growing demand for intelligent surveillance, it has aroused more and more research attention in the computer vision community.
