Kan Li

selected publications

1. Biologically-Inspired Pulse Signal Processing for Intelligence at the Edge

   Kan Li and José C. Príncipe,

   Frontiers in Artificial Intelligence 2021

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   There is an ever-growing mismatch between the proliferation of data-intensive, power-hungry deep learning solutions in the machine learning (ML) community and the need for agile, portable solutions in resource-constrained devices, particularly for intelligence at the edge. In this paper, we present a fundamentally novel approach that leverages data-driven intelligence with biologically-inspired efficiency. The proposed Sparse Embodiment Neural-Statistical Architecture (SENSA) decomposes the learning task into two distinct phases: a training phase and a hardware embedment phase where prototypes are extracted from the trained network and used to construct fast, sparse embodiment for hardware deployment at the edge. Specifically, we propose the Sparse Pulse Automata via Reproducing Kernel (SPARK) method, which first constructs a learning machine in the form of a dynamical system using energy-efficient spike or pulse trains, commonly used in neuroscience and neuromorphic engineering, then extracts a rule-based solution in the form of automata or lookup tables for rapid deployment in edge computing platforms. We propose to use the theoretically-grounded unifying framework of the Reproducing Kernel Hilbert Space (RKHS) to provide interpretable, nonlinear, and nonparametric solutions, compared to the typical neural network approach. In kernel methods, the explicit representation of the data is of secondary nature, allowing the same algorithm to be used for different data types without altering the learning rules. To showcase SPARK’s capabilities, we carried out the first proof-of-concept demonstration on the task of isolated-word automatic speech recognition (ASR) or keyword spotting, benchmarked on the TI-46 digit corpus. Together, these energy-efficient and resource-conscious techniques will bring advanced machine learning solutions closer to the edge.

2. Functional Bayesian Filter

   Kan Li and José C. Príncipe,

   IEEE Transactions on Signal Processing 2021

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   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. Using a positive definite kernel function satisfying Mercer’s conditions to compute and evolve information quantities, the FBF exploits both the statistical and time-domain information about the signal, extracts higher-order moments, and preserves the properties of covariances without the ill effects due to conventional arithmetic operations. We apply this novel kernel adaptive filtering (KAF) to recurrent network training, chaotic time-series estimation and cooperative filtering using Gaussian and non-Gaussian noises, and inverse kinematics modeling. Simulation results show FBF outperforms existing Kalman-based algorithms.

3. Non-invasive Assessment of Elevated LVEDP Using a Small Portable Device: Design for Telemonitoring

   Kan Li, Diego Pava, Arash Andalib, and Kaustubh Kale,

   In Heart Failure Society of America (HFSA) Annual Scientific Meeting Sep 2021

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   Left ventricular end-diastolic pressure (LVEDP) provides crucial information on the LV operating compliance and is a key marker for congestive heart failure and pulmonary hypertension. The diagnosis and monitoring of the left-sided filling pressure often requiring invasive procedures. HEMOTAG, is a small portable device that uses micro-sensors to capture cardiac vibrations and electrocardiogram (ECG), transduced via thoracic electrodes. It is a viable option to measure cardiac time intervals (CTIs), surrogate markers of hemodynamics and intracardiac pressure. In this study, diastolic-to-systolic time ratio (DSR) and systolic time ratio (STR) along with the patient’s age, height, and weight were assessed as valid markers for elevated LVEDP, compared against left heart catheterization LVEDP measurement.

4. A Novel Non-Invasive Device for Screening and Optimized Management to Improve Heart Failure Outcomes in Patients with Diabetes Mellitus

   Diego Pava, Arash Andalib, Kan Li, and Kaustubh Kale,

   In Heart Failure Society of America (HFSA) Annual Scientific Meeting Sep 2021

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   Evidence shows diabetes mellitus (DM) is major independent risk factor for several cardiovascular disorders including heart failure (HF). Large, randomized clinical trials for screening and revascularization of stable macrovascular disease in diabetics, have failed to demonstrate a significant reduction in cardiac events and HF episodes. Studies have demonstrated that early detection of left ventricular dysfunction and prevention of microvascular complications through glycemic control in diabetes patients is a critical mechanism for reducing the incidence and severity of left ventricular dysfunction and HF. There is a need for a non-invasive, affordable, accurate, absolute and actionable method to facilitate optimized management of diabetes patients across the continuity of care. HEMOTAG, is a small portable device that uses micro-sensors to capture cardiac vibrations and electrocardiogram, transduced via thoracic electrodes. It is a viable option to measure cardiac time intervals (CTIs), surrogate markers for measurements of left ventricular dysfunction.