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In this paper, we propose a novel feature compensation approach based on the interacting multiple model (IMM) algorithm specially designed for joint processing of background noise and acoustic reverberation. Our approach to cope with the time-varying environmental parameters is to establish a switching linear dynamic model for the additive and convo-lutive distortions in the log-spectral domain. The...
Wireless capsule endoscopy (WCE) is a new innovative solution for gastrointestinal disease detection. The image quality of WCE is not satisfactory for medical applications since some of them are dark and low-contrast. The WCE image enhancement is a challenge task, mainly because the diversity of the WCE images of different people and the need to preserve the local fine details of WCE images. Hence,...
In this paper, we propose a novel image quality assessment (IQA) metric based on nonnegative matrix factorization (NM-F). With nonnegativity and parts-based properties, NMF well demonstrates how human brain learns the parts of objects. This makes NMF distinguished from other feature extraction methods like singular value decomposition (SVD), principal components analysis (PCA), etc. Inspired by this,...
In this contribution, a generic framework for linearly-constrained multichannel noise and interference suppression algorithms is presented. It is derived from a linearly-constrained minimum mutual information (LCMMI) criterion between mutually statistically independent desired and undesired components, which also accounts for three fundamental signal properties characteristic, e.g., for speech and...
Cardiovascular system study using ECG signals have evolved tremendously in the domain of electronics and signal processing. However, there are certain floating challenges unresolved in the analysis and detection of abnormal performances of cardiovascular system. As the medical field is moving towards more automated and intelligent systems, wrong detection or wrong interpretations of ECG waveform of...
The paper focuses on 3D structure and motion factorization from uncalibrated image sequences. An alternative weighted factorization algorithm is proposed to handle noisy image measurement. The novelty and main contributions of the paper are as follows: (i) A simple while accurate quasi-perspective projection model is adopted for structure and motion factorization; (ii) feature uncertainties are estimated...
The altitude information is very important for the safety of aircraft. Among many types of modulation, the Frequency Modulated Continuous Wave (FMCW) was the most common used in radar altimeter. Although classical Fast Fourier Transform (FFT) method has been successfully used in short range measurement for Radar Altimeter [1], the problem of altitude estimation in long distance for Radar Altimeter...
Subpixel detection is an important but difficult problem in hy-perspectral image. Due to the small size of the target, only spectral information can be used for detection. Many algorithms have been proposed to reduce this problem, and most of them assume that the distribution of hyperspectral image is multinormal. However, this assumption may not be an appropriate description of the distribution in...
For a three-node amplify-and-forward (AF) two-way relay network (TWRN), we investigate the power allocation problem during the channel training process for both terminals to estimate the global channel state information (CSI) of the whole network. This includes the power allocations between different training steps and among different nodes. Two power allocation schemes are proposed: a mean-square-error...
This paper proposes a new audio authenticity detection algorithm based on the max offset for cross correlation (MOCC) between the extracted ENF (Electric Network Frequency) signal and the reference signal. We first extract the ENF signal from a query audio signal. And then we partition it into overlapping blocks for forgery detection. The MOCC between the extracted ENF and the reference signal is...
In this paper, we study the quantization errors of modulo sigma-delta modulated finite, asymptotically-infinite, infinite causal stable ARMA processes. We show that the normalized quantization error can be taken as a uniformly distributed white noise for all the cases. Moreover, we find that this nice property is guaranteed by two different mechanisms: the high-enough quantization resolution and the...
Orthogonal matching pursuit (OMP) algorithm for the multiple measurement vectors (MMV) is a greedy method to find the sparse matrix with few nonzero rows that represents the measurement vectors under the sensing matrix. This paper analyzes the recovery performance of OMP for MMV (OMPMMV) in the bounded noise scenarios, and provides the sufficient conditions that are related to the sensing matrix and...
In this paper, we give an overview of the background for, the ideas behind, and the challenges to be addressed in the project "Spatio-Temporal Filtering Methods for Enhancement and Separation of Speech Signals," which is funded by the Villum Foundation. The project aims at addressing the problem of enhancing and separating speech signals from noisy mixtures, a problem also known as the cocktail...
Cognitive communications has attracted a large interest during the last decade due to spectrum scarcity. In combination with multiantenna techniques, cognitive communications have the ability to increase spectral efficiency by enabling the coexistence of a primary and secondary systems. In this paper, we focus in two specific cognitive approaches: a) Multiantenna Interference Alignment (IA) and b)...
The ability to automatically recognize sound events in real-life conditions is an important part of applications such as acoustic surveillance and smart home automation. The main challenge of these applications is that the sound sources often come from unknown distances under different acoustic environments, which are also noisy and reverberant. Among the noises in the home, the most difficult to...
The ISAR (inverse synthetic aperture radar) imaging technology is an important tool for the ballistic missile midcourse target recognitions. Considering the rotationally symmetric targets, the sparse representation model of the ballistic midcourse targets with micro-motion is established. The sparse recovery algorithm named SBL (Sparse Bayesian Learning) is analyzed, which can provide a much sparser...
In this paper we present a set of theoretical results regarding inference algorithms for hierarchical Bayesian networks. More specifically we focus on a specific type of networks which result in highly sparse models for the input. Bayesian inference in these networks usually is based on optimising a non-convex cost function of the model parameters. We extend previous work done in this field by providing...
In this paper, we investigate eight objective speech intelligibility prediction measures for noisy signals before and after noise-reduction processing in Japanese. The Japanese speech signals were first corrupted by three types of noises at two signal-to-noise ratios and processed by four classes of noise-reduction algorithms, whose intelligibility was subsequently predicted by objective measures...
Hyperspectral imagery provides more powerful information than multispectral remote sensing data. However, when hyperspectral data is used for classification task, the highdimension features often lead to ill-conditioned problems, such as the Hughes phenomenon. To tackle this problem, various supervised dimensional reduction methods are proposed. However, these methods only exploit the labeled training...
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