One of central topics of kernel machines in the field of machine learning is a model selection, especially a selection of a kernel or its parameters. In our previous work, we dis...
We investigate secure communications for a four-node relayeavesdropper channel with multiple data stream transmission, assuming that the eavesdropper’s channel state information...
Bearings-only localization with light-of-sight (LOS) propagation is well understood. This paper concentrates on bearing-only localization with non-line-of-sight (NLOS) measurement...
A key problem in using the output of an auditory model as the input to a machine-learning system in a machine-hearing application is to find a good feature-extraction layer. For ...
Compressed sensing is a novel technique where one can recover sparse signals from the undersampled measurements. In this paper, a K × N measurement matrix for compressed sensing ...
This paper proposes a simple yet new and effective framework by combining generative model and discriminative model for natural scene categorization. A state-of-the-art approach f...
We present learning and inference algorithms for a versatile class of partially observed vector autoregressive (VAR) models for multivariate time-series data. VAR models can captu...
Distributed microphone systems in cars usually provide dedicated microphones for several speakers where each microphone captures the desired speech signal at the best. The signal ...
In this paper we describe a method for detection and measurement of elliptical particles in atomic force microscopy (AFM) images. AFM imaging is used in physics to scan surfaces; ...
In this work we propose the use of Functional Data Analysis (FDA) as a powerful methodology to tackle problems where multiple continuous speech parameters have to be analyzed join...