Matrix of Polynomials Model based Polynomial Dictionary Learning Method for Acoustic Impulse Response Modeling

We study the problem of dictionary learning for signals that can be represented as polynomials or polynomial matrices, such as convolutive signals with time delays or acoustic impulse responses. Recently, we developed a method for polynomial dictionary learning based on the fact that a polynomial matrix can be expressed as a polynomial with matrix coefficients, where the coefficient of the polynomial at each time lag is a scalar matrix. However, a polynomial matrix can be also equally represented as a matrix with polynomial elements. In this paper, we develop an alternative method for learning a polynomial dictionary and a sparse representation method for polynomial signal reconstruction based on this model. The proposed methods can be used directly to operate on the polynomial matrix without having to access its coefficients matrices. We demonstrate the performance of the proposed method for acoustic impulse response modeling.Comment: 5 pages, 2 figure

Measuring semantic similarity between concepts in visual domain

Author name used in this publication: Dagan FengRefereed conference paper2008-2009 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

USCID fourth international conference

Presented at the Role of irrigation and drainage in a sustainable future: USCID fourth international conference on irrigation and drainage on October 3-6, 2007 in Sacramento, California.Includes bibliographical references.Water level in open canal fluctuates with the change of the discharge. The water flow from one steady state to another will take a period of time. For a certain canal, the process of the dynamic response is affected by using different automatic control methods. In order to shorten the response time and limit the fluctuating range of water level, maintain the performance of automatic control canal system stability, proper automatic canal control methods should be adopted. In this study, taking the middle route of China's South-to-North Water Diversion Project as an example, the relationship of operation stability and automatic control methods of open canal is studied by means of numerical simulation of unsteady flow in open canal with different control methods, and some useful results on the automatic canal control system of the middle route of Chinese South-to-North Water Diversion Project are obtained

Low-dimensional Denoising Embedding Transformer for ECG Classification

The transformer based model (e.g., FusingTF) has been employed recently for Electrocardiogram (ECG) signal classification. However, the high-dimensional embedding obtained via 1-D convolution and positional encoding can lead to the loss of the signal's own temporal information and a large amount of training parameters. In this paper, we propose a new method for ECG classification, called low-dimensional denoising embedding transformer (LDTF), which contains two components, i.e., low-dimensional denoising embedding (LDE) and transformer learning. In the LDE component, a low-dimensional representation of the signal is obtained in the time-frequency domain while preserving its own temporal information. And with the low dimensional embedding, the transformer learning is then used to obtain a deeper and narrower structure with fewer training parameters than that of the FusingTF. Experiments conducted on the MIT-BIH dataset demonstrates the effectiveness and the superior performance of our proposed method, as compared with state-of-the-art methods.Comment: To appear at ICASSP 202

An innovative high accuracy autonomous navigation method for the Mars rovers

Autonomous navigation is an important function for a Mars rover to fulfill missions successfully. It is a critical technique to overcome the limitations of ground tracking and control traditionally used. This paper proposes an innovative method based on SINS (Strapdown Inertial Navigation System) with the aid of star sensors to accurately determine the rovers position and attitude. This method consists of two parts: the initial alignment and navigation. The alignment consists of a coarse position and attitude initial alignment approach and fine initial alignment approach. The coarse one is used to determine approximate position and attitude for the rover. This is followed by fine alignment to tune the approximate solution to accurate one. Upon the completion of initial alignment, the system can be used to provide real-time navigation solutions for the rover. An autonomous navigation algorithm is proposed to estimate and compensate the accumulated errors of SINS in real time. High accuracy attitude information from star sensor is used to correct errors in SINS. Simulation results demonstrate that the proposed methods can achieve a high precision autonomous navigation for Mars rovers. © 2014 IAA