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A novel kernel algorithm is proposed for nonlinear prediction whereby the signal is modelled as a state of a hidden Markov model (HMM). The transition function of the HMM is approximated using kernels, whose weights are also part of the state of the system and are learnt in an unsupervised fashion by a sample importance resampling (SIR) particle filter. The SIR proposal density is designed so as to...
In this paper we consider the identification of a class of linear-in-the parameters nonlinear filters that has been recently introduced, the so-called even mirror Fourier nonlinear filters. We show that perfect periodic sequences can be derived for these filters. A periodic sequence is perfect for a nonlinear filter if all cross-correlations between two different basis functions, estimated over a...
Multi-modal densities appear frequently in time series and practical applications. However, they are not well represented by common state estimators, such as the Extended Kalman Filter and the Unscented Kalman Filter, which additionally suffer from the fact that uncertainty is often not captured sufficiently well. This can result in incoherent and divergent tracking performance. In this paper, we...
This paper deals with the linearization of RF power amplifiers (PAs) using digital predistortion (DPD) technique. One of the most important constraint on DPD implementation is digitization of PA output signal needed for identification of predistorter model. The bandwidth of this signal may be 3 to 7 times wider than the bandwidth of the input signal. The sampling rate required for accurate compensation...
This work proposes a novel probabilistic multi-attribute item ranking framework to estimate the probability of an item being a user's best choice and rank items accordingly. It uses indifference curve from microeconomics to model users' personal preference, and addresses the inter-attribute tradeoff and inter-item competition issues at the same time with little information loss. The proposed framework...
Adaptive Rejection Metropolis Sampling (ARMS) is a well-known MCMC scheme for generating samples from one-dimensional target distributions. ARMS is widely used within Gibbs sampling, where automatic and fast samplers are often needed to draw from univariate full-conditional densities. In this work, we propose an alternative adaptive algorithm (IA2RMS) that overcomes the main drawback of ARMS (an uncomplete...
It is hard to obtain a general error model for range-based wireless indoor target tracking system due to the complicated hybrid LOS/NLOS environment. In this paper, we employ a dynamic Gaussian model (DGM) to describe the indoor ranging error. A general Gaussian distribution is constructed firstly. The instantaneous LOS or NLOS error at a typical time is considered as the drift from this general distribution...
We propose a robustification of the mean-shift algorithm. We understand robustness in the statistical sense as the deviation from the nominal, distributional assumption. The derivation of the robust mean-shift vector is based on a robust version of the kernel density estimator (KDE), where the KDE is interpreted as an inner product in a higher dimensional feature space. The mean in this formulation...
Incorporating spatial information into hyperspectral unmixing procedures has been shown to have positive effects, due to the inherent spatial-spectral duality in hyperspectral scenes. Current research works that consider spatial information are mainly focused on the linear mixing model. In this paper, we investigate a variational approach to incorporating spatial correlation into a nonlinear unmixing...
In current color image super-resolution methods, superresolution based on sparse representation achieves state-of-the-art performance. However, the exploited sparse representation models deal with the color images as independent channel planes. Consequently, these approaches process the color pixels as scalar quantity, lacking of accuracy in describing inter-relationship among color channels. In this...
Existing support vector regression (SVR) based image superresolution (SR) methods always utilize single layer SVR model to reconstruct source image, which are incapable of restoring the details and reduce the reconstruction quality. In this paper, we present a novel image SR approach, where a multi-layer SVR model is adopted to describe the relationship between the low resolution (LR) image patches...
This paper investigates the filter bank (FB) based selection diversity combining as well as linear equalization for single carrier (SC)transmissions over frequency selective channels. In contrast to the multicarrier carrier (MC) transmissions, e.g., OFDM, the FB based approach avoids the use of cyclic prefix (CP) or guard band and offers a number of superior properties such as synchronization, and...
In this paper, we present a sparse coding (SC) inspired method to reconstruct a high-resolution (HR) image from one single low-resolution (LR) image. Instead of restricting the coding coefficients of LR and HR image patches to be equal or linearly mapped, we introduce kernel regression to nonlinearly relate the coding coefficients of LR patches and those of corresponding HR ones in an implicit fashion...
This paper deals with the synthesis of a new reception scheme for the uplink of a single-carrier interleaved frequency-division multiple-access (SC-IFDMA) wireless network. In such networks, the in-phase/quadrature-phase (I/Q) imbalance, introduced at each user transmitter and at the base station receiver, and the carrier frequency offsets (CFOs) between the transmitters and the receiver are sources...
The decision feedback equalizer (DFE) is an efficient scheme to suppress intersymbol interference (ISI) in various communication and magnetic recording systems. However, most DFE implementations suffer from the phenomenon of error propagation, which degrades its bit error rate (BER) performance. In this paper, We use sphere detector (SD) to achieve maximum likelihood (ML) detection and significantly...
Herein, we consider the problem of detecting primary users' signals in the presence of noise correlation, which may arise due to imperfections in fltering and oversampling operations in a Cognitive Radio (CR) receiver. In this context, we study a Maximum Eigenvalue (ME) detection technique using recent results from Random Matrix Theory (RMT) for characterizing the distribution of the maximum eigenvalue...
Deep architectures have recently been explored in hybrid hidden Markov model/artificial neural network (HMM/ANN) framework where the ANN outputs are usually the clustered states of context-dependent phones derived from the best performing HMM/Gaussian mixture model (GMM) system. We can view a hybrid HMM/ANN system as a special case of recently proposed Kullback-Leibler divergence based hidden Markov...
We propose a new method, robust binary fused compressive sensing (RoBFCS), to recover sparse piece-wise smooth signals from 1-bit compressive measurements. The proposed method is a modification of our previous binary fused compressive sensing (BFCS) algorithm, which is based on the binary iterative hard thresholding (BIHT) algorithm. As in BIHT, the data term of the objective function is a one-sided...
State-of-the-art calibration and fusion approaches for spoken term detection (STD) systems currently rely on a multi-pass approach where the scores are calibrated, then fused, and finally re-calibrated to obtain a single decision threshold across keywords. While the above techniques are theoretically correct, they rely on metaparameter tuning and are prone to over-fitting. This study presents an efficient...
Data augmentation using label preserving transformations has been shown to be effective for neural network training to make invariant predictions. In this paper we focus on data augmentation approaches to acoustic modeling using deep neural networks (DNNs) for automatic speech recognition (ASR). We first investigate a modified version of a previously studied approach using vocal tract length perturbation...
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