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A new modification of sigma filter is proposed and tested in this paper. This modification is suitable for processing images corrupted by Gaussian multiplicative and impulsive noises and it avoids some typical disadvantages of the standard sigma filter while possessing robust properties. The test images include both simulated and real radar images. It is seen that the proposed modification provides...
Composite signals are nonstationary processes consisting of trend, noise and cyclic components. A cyclic component consists of periodic or almost periodic data. In this paper we present a method based on nonlinear order statistics that evaluates the fundamental period of a cyclic component. This information can be used for decomposition of composite signals.
In this paper, we present a novel technique for embedding digital "watermarks" into digital audio signals. Watermarking is a technique used to label digital media by hiding copyright or other information into the underlying data. The watermark must be imperceptible and should be robust to attacks and other types of distortion. In addition, the watermark also should be undetectable by all...
The paper describes the use of associative models for integrating different sensors. Integrated associative structures are outlined and related to previous approaches; the enhanced robustness resulting from the integration of Associative Memories (AMs) and Neural Networks (NNs) is shown. Discussion then focuses on how different information sources can cooperate on associative visual recognition. Experimental...
In this contribution a new robust technique for adjusting the step size of the Least Mean Squares (LMS) adaptive algorithm is introduced. The proposed method exhibits faster convergence, enhanced tracking ability and lower steady state excess error compared to the fixed step size LMS and other previously developed variable step size algorithms, while retaining much of the LMS computational simplicity...
Target motion analysis (TMA) for a rectilinear source movement (RSM) has been intensively studied in the last ten years. But difficulties still exist, especially when source heading or speed changes are within the same time as the conventional TMA convergence time. This paper is concerned with a new method of batch TMA for maneuvering sources using a non-linear least-squares fit between the whole...
In many applications, very fast methods are required for estimating and measurement of parameters of harmonic signals distorted by noise. This follows from the fact that signals have often time varying amplitudes. Most of the known digital algorithms are not fully parallel, so that the speed of processing is quite limited. In this paper we propose new parallel algorithms, which can be implemented...
Using periodic auditory stimuli, it is possible to evoke so-called auditory steady-state responses (ASSRs) in the brain, which can be measured using electroencephalography (EEG). They can be used to objectively estimate frequency-specific hearing thresholds, which is especially useful for early hearing assessment in newborns. The main problem is the extremely low signal-to-noise ratio (SNR), necessitating...
In this paper, a method of adaptive noise suppression combining spatially robust fixed beamforming and the TRINICON blind source separation algorithm is presented. A multichannel sensor array is first processed using complementary fixed beamformers into maximum and minimum SINR channels. The channels form the inputs to a single 2×2 second-order statistics TRINICON-BSS system which adaptively compensates...
This paper lies in the lineage of recent works studying the asymptotic behaviour of robust-scatter estimators in the case where the number of observations and the dimension of the population covariance matrix grow at infinity with the same pace. In particular, we analyze the fluctuations of bilinear forms of the robust shrinkage estimator of covariance matrix. We show that this result can be leveraged...
Traditional sound event recognition methods based on informative front end features such as MFCC, with back end sequencing methods such as HMM, tend to perform poorly in the presence of interfering acoustic noise. Since noise corruption may be unavoidable in practical situations, it is important to develop more robust features and classifiers. Recent advances in this field use powerful machine learning...
Arterial spin labeling MRI (ASL-MRI) can provide quantitative signals correlated to the cerebral blood flow and neural activity. However, the low signal-to-noise ratio in ASL requires repeated acquisitions to improve the signal reliability, leading to prolonged scanning time. At fewer repetitions, noise and corruptions arise due to motion and physiological artifacts, introducing errors into the cerebral...
This paper presents a Bayesian algorithm for linear spectral unmixing that accounts for outliers present in the data. The proposed model assumes that the pixel reflectances are linear mixtures of unknown endmembers, corrupted by an additional term modelling outliers and additive Gaussian noise. A Markov random field is considered for outlier detection based on the spatial and spectral structures of...
This paper presents a hybrid method for single-source localization in wireless sensor networks, fusing noisy range measurements with angular information extracted from video. Although recent works found in the literature explore hybrid schemes, these include several cumbersome assumptions. We develop and test, both numerically and experimentally, a hybrid localization algorithm which surpasses the...
This paper addresses the problem of impulse denoising from hyper-spectral images. Impulse noise is sparse; removing impulse noise requires minimizing an l1-norm data fidelity term. Prior studies have exploited the intra-band spatial correlation (leading to sparsity in transform domain) and inter-band spectral-correlation (joint-sparsity) of hyper-spectral images for Gaussian denoising. In this work,...
The applicability and performance of motion detection methods dramatically degrade with the increasing noise. In this paper, we propose a robust dictionary-based background subtraction approach, which formulates background modeling as a linear and sparse combination of atoms in a pre-learned dictionary. Motion detection is then implemented to compare the difference between sparse representations of...
We study a distributed node-specific signal estimation problem where the node-specific desired signals and/or the sensor observations can have partially-overlapping latent signal subspaces. First, we provide the minimum number of linear combinations of observed sensor signals that each node can broadcast to still let all other nodes achieve the network-wide Linear Minimum Mean-Square Error (LMMSE)...
In hypothesis testing, the phenomenon of label noise, in which hypothesis labels are switched at random, contaminates the likelihood functions. In this paper, we develop a new method to determine the decision rule when we do not have knowledge of the uncontaminated likelihoods and contamination probabilities, but only have knowledge of the contaminated likelihoods. In particular we pose a minimax...
This paper proposes a novel biologically inspired method for sound event classification which combines spike coding with a spiking neural network (SNN). Our spike coding extracts keypoints that represent the local maxima components of the sound spectrogram, and are encoded based on their local time-frequency information; hence both location and spectral information are being extracted. We then design...
Both consumer market and manufacturing industry makes heavy use of 1D (linear) barcodes. From helping the visually impaired to identifying the products to industrial automated industry management, barcodes are the prevalent source of item tracing technology. Because of this ubiquitous use, in recent years, many algorithms have been proposed targeting barcode decoding from high-accessibility devices...
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