Showing papers on "Noise (signal processing) published in 2009"

Ensemble empirical mode decomposition: a noise-assisted data analysis method

Abstract: A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a time–space analysis method, the added white noise is averaged out with sufficient number of trials; the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF. With this ensemble mean, one can separate scales naturall...

Eigenvalue-based spectrum sensing algorithms for cognitive radio

Abstract: Spectrum sensing is a fundamental component in a cognitive radio. In this paper, we propose new sensing methods based on the eigenvalues of the covariance matrix of signals received at the secondary users. In particular, two sensing algorithms are suggested, one is based on the ratio of the maximum eigenvalue to minimum eigenvalue; the other is based on the ratio of the average eigenvalue to minimum eigenvalue. Using some latest random matrix theories (RMT), we quantify the distributions of these ratios and derive the probabilities of false alarm and probabilities of detection for the proposed algorithms. We also find the thresholds of the methods for a given probability of false alarm. The proposed methods overcome the noise uncertainty problem, and can even perform better than the ideal energy detection when the signals to be detected are highly correlated. The methods can be used for various signal detection applications without requiring the knowledge of signal, channel and noise power. Simulations based on randomly generated signals, wireless microphone signals and captured ATSC DTV signals are presented to verify the effectiveness of the proposed methods.

Non-Parametric Detection of the Number of Signals: Hypothesis Testing and Random Matrix Theory

Abstract: Detection of the number of signals embedded in noise is a fundamental problem in signal and array processing. This paper focuses on the non-parametric setting where no knowledge of the array manifold is assumed. First, we present a detailed statistical analysis of this problem, including an analysis of the signal strength required for detection with high probability, and the form of the optimal detection test under certain conditions where such a test exists. Second, combining this analysis with recent results from random matrix theory, we present a new algorithm for detection of the number of sources via a sequence of hypothesis tests. We theoretically analyze the consistency and detection performance of the proposed algorithm, showing its superiority compared to the standard minimum description length (MDL)-based estimator. A series of simulations confirm our theoretical analysis.

Multiple microphone switching and configuration

Abstract: A mobile communications device contains at least two microphones. One microphone is designated by a selector to provide a voice dominant signal and another microphone is designated to provide a noise or echo dominant signal, for a call or a recording. The selector communicates the designations to a switch that routes the selected microphone signals to the inputs of a processor for voice signal enhancement. The selected voice dominant signal is then enhanced by suppressing ambient noise or canceling echo therein, based on the selected noise or echo dominant signal. The designation of microphones may change at any instant during the call or recording depending on various factors, e.g. based on the quality of the microphone signals. Other embodiments are also described.

Characterization and limits of a cold-atom Sagnac interferometer

Abstract: We present the full evaluation of a cold-atom gyroscope based on atom interferometry. We have performed extensive studies to determine the systematic errors, scale factor and sensitivity. We demonstrate that the acceleration noise can be efficiently removed from the rotation signal, allowing us to reach the fundamental limit of the quantum projection noise for short term measurements. The technical limits to the long term sensitivity and accuracy have been identified, clearing the way for the next generation of ultrasensitive atom gyroscopes.

Environmental acoustics and the evolution of bird song

Abstract: Any signal must get from a sender to a receiver if information is to be transmitted. In the case of bird song, the acoustic properties of the habitat may hinder this being achieved. However, birds as senders and receivers have evolved numerous adaptations to overcome the problem of getting the message across. In this chapter, we explore habitat-dependent patterns of sound transmission, the effects of noise, signal perception, and signal interpretation such as auditory distance assessment with a specific focus on the solutions that selection has generated. We argue that along with other possible selective forces, such as sexual selection, the combination of environmental constraints on signal transmission, noise levels, and the use of signal degradation as a distance cue need mutual consideration to gain a more thorough understanding of the astounding variety of avian song and the many different ways in which birds use it.

Innovated higher criticism for detecting sparse signals in correlated noise

Abstract: Higher criticism is a method for detecting signals that are both sparse and weak. Although first proposed in cases where the noise variables are independent, higher criticism also has reasonable performance in settings where those variables are correlated. In this paper we show that, by exploiting the nature of the correlation, performance can be improved by using a modified approach which exploits the potential advantages that correlation has to offer. Indeed, it turns out that the case of independent noise is the most difficult of all, from a statistical viewpoint, and that more accurate signal detection (for a given level of signal sparsity and strength) can be obtained when correlation is present. We characterize the advantages of correlation by showing how to incorporate them into the definition of an optimal detection boundary. The boundary has particularly attractive properties when correlation decays at a polynomial rate or the correlation matrix is Toeplitz.

Optimised data reduction for the AMBER/VLTI instrument

Abstract: Context. The signal processing of multi-aperture monomode interferometers using multiaxial recombination, such as AMBER/VLTI, makes use of the modeling of the fringes in the image space called the “P2VM method”. This method was only validated on simulated data.Aims. We aim to validate the P2VM method on-sky, and to use the knowledge acquired during more than three years of use of the instrument to provide improved data processing algorithms.Methods. We compare the on-sky results of the P2VM algorithm with those provided by the standard, well known, and robust Fourier method.Results. We first prove that the current implementation of the P2VM method used in the AMBER data reduction is biased for intermediate and low flux measurements. We determine the physical origin of these biases, then modify the data model accordingly, and introduce an improved noise model. We demonstrate that the P2VM method, together with the more realistic data and noise models, give results that are now in accordance with those provided by the Fourier method.

Adaptive noise control system

Abstract: An active noise cancellation system that reduces, at a listening position, power of a noise signal radiated from a noise source to the listening position. The system includes an adaptive filter, at least one acoustic actuator and a signal processing device. The adaptive filter receives a reference signal representing the noise signal, and provides a compensation signal. The at least one acoustic actuator radiates the compensation signal to the listening position. The signal processing device evaluates and assesses the stability of the adaptive filter.

Persistence Diagrams of Cortical Surface Data

Abstract: We present a novel framework for characterizing signals in images using techniques from computational algebraic topology. This technique is general enough for dealing with noisy multivariate data including geometric noise. The main tool is persistent homology which can be encoded in persistence diagrams. These diagrams visually show how the number of connected components of the sublevel sets of the signal changes. The use of local critical values of a function differs from the usual statistical parametric mapping framework, which mainly uses the mean signal in quantifying imaging data. Our proposed method uses all the local critical values in characterizing the signal and by doing so offers a completely new data reduction and analysis framework for quantifying the signal. As an illustration, we apply this method to a 1D simulated signal and 2D cortical thickness data. In case of the latter, extra homological structures are evident in an control group over the autistic group.

Waveform Design for Multistatic Radar Detection

Abstract: We derive the optimal Neyman-Pearson (NP) detector and its performance, and then present a methodology for the design of the transmit signal for a multistatic radar receiver The detector assumes a Swerling I extended target model as well as signal-dependent noise, ie, clutter It is shown that the NP detection performance does not immediately lead to an obvious signal design criterion so that as an alternative, a divergence criterion is proposed for signal design A simple method for maximizing the divergence, termed the maximum marginal allocation algorithm, is presented and is guaranteed to find the global maximum The overall approach is a generalization of previous work that determined the optimal detector and transmit signal for a monostatic radar

Procedural noise using sparse Gabor convolution

Abstract: Noise is an essential tool for texturing and modeling. Designing interesting textures with noise calls for accurate spectral control, since noise is best described in terms of spectral content. Texturing requires that noise can be easily mapped to a surface, while high-quality rendering requires anisotropic filtering. A noise function that is procedural and fast to evaluate offers several additional advantages. Unfortunately, no existing noise combines all of these properties.In this paper we introduce a noise based on sparse convolution and the Gabor kernel that enables all of these properties. Our noise offers accurate spectral control with intuitive parameters such as orientation, principal frequency and bandwidth. Our noise supports two-dimensional and solid noise, but we also introduce setup-free surface noise. This is a method for mapping noise onto a surface, complementary to solid noise, that maintains the appearance of the noise pattern along the object and does not require a texture parameterization. Our approach requires only a few bytes of storage, does not use discretely sampled data, and is nonperiodic. It supports anisotropy and anisotropic filtering. We demonstrate our noise using an interactive tool for noise design.

Optimal Noise Benefits in Neyman–Pearson and Inequality-Constrained Statistical Signal Detection

Abstract: We present theorems and an algorithm to find optimal or near-optimal ldquostochastic resonancerdquo (SR) noise benefits for Neyman-Pearson hypothesis testing and for more general inequality-constrained signal detection problems. The optimal SR noise distribution is just the randomization of two noise realizations when the optimal noise exists for a single inequality constraint on the average cost. The theorems give necessary and sufficient conditions for the existence of such optimal SR noise in inequality-constrained signal detectors. There exists a sequence of noise variables whose detection performance limit is optimal when such noise does not exist. Another theorem gives sufficient conditions for SR noise benefits in Neyman-Pearson and other signal detection problems with inequality cost constraints. An upper bound limits the number of iterations that the algorithm requires to find near-optimal noise. The appendix presents the proofs of the main results.

Source camera identification using enahnced sensor pattern noise

Abstract: Sensor pattern noises (SPN), extracted from digital images as device fingerprints, have been proved as an effective way for digital device identification. However, the limitation of the current method of extracting the sensor pattern noise is that the SPNs extracted from images are highly contaminated by the details from the scene and as a result the misclassification rate is high unless images of large size are used. In this work we propose a novel approach for enhancing sensor pattern noises so as to improve the performance of the identifier. The hypothesis underlying our fingerprint enhancer is that the stronger a signal component is, the less trustworthy the component should be and thus should be attenuated. An enhanced fingerprint can be obtained by assigning weighting factors inversely proportional to the magnitude of the signal components.

The signal-cognition interface: interactions between degraded auditory signals and cognitive processes.

Abstract: A hearing loss leads to problems with speech perception; this is exacerbated when competing noise is present. The speech signal is recognized by the cognitive system of the listener; noise and distortion tax the cognitive system when interpreting it. The auditory system must interact with the cognitive system for optimal signal decoding. This article discusses this interaction between the signal and cognitive system based on two models: an auditory model describing signal transmission and degeneration due to a hearing loss and a cognitive model for Ease of Language Understanding. The signal distortion depends on the specifics of the hearing impairment and thus differently distorted signals can affect the cognitive system in different ways. Consequently, the severity of a hearing loss may not only depend on the lesion itself but also on the cognitive recourses required to interpret the signal.

Network analysis of biochemical logic for noise reduction and stability: a system of three coupled enzymatic and gates.

Abstract: We develop an approach aimed at optimizing the parameters of a network of biochemical logic gates for reduction of the "analog" noise buildup. Experiments for three coupled enzymatic AND gates are reported, illustrating our procedure. Specifically, starch, one of the controlled network inputs, is converted to maltose by beta-amylase. With the use of phosphate (another controlled input), maltose phosphorylase then produces glucose. Finally, nicotinamide adenine dinucleotide (NAD(+)), the third controlled input, is reduced under the action of glucose dehydrogenase to yield the optically detected signal. Network functioning is analyzed by varying selective inputs and fitting standardized few-parameters "response-surface" functions assumed for each gate. This allows a certain probe of the individual gate quality, but primarily yields information on the relative contribution of the gates to noise amplification. The derived information is then used to modify our experimental system to put it in a regime of a less noisy operation.

Feature extraction for robust speech recognition using a power-law nonlinearity and power-bias subtraction.

Abstract: This paper presents a new feature extraction algorithm called Power-Normalized Cepstral Coefficients (PNCC) that is based on auditory processing. Major new features of PNCC processing include the use of a power-law nonlinearity that replaces the traditional log nonlinearity used for MFCC coefficients, and a novel algorithm that suppresses background excitation by estimating SNR based on the ratio of the arithmetic to geometric mean power, and subtracts the inferred background power. Experimental results demonstrate that the PNCC processing provides substantial improvements in recognition accuracy compared to MFCC and PLP processing for various types of additive noise. The computational cost of PNCC is only slightly greater than that of conventional MFCC processing.

Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction

Abstract: Electromyography gives an electrical representation of neuromuscular activation associated with a contracting muscle. The electromyography signal acquires noise while travelling though different media. The wavelet transform is employed for removing noise from surface electromyography (SEMG) and higher order statistics are applied for analysing the signal. With the appropriate choice of wavelet, it is possible to remove interference noise (denoise) effectively in order to analyse the SEMG. Daubechies wavelets (db2, db4, db5, db6, db8), symmlet (sym4, sym5) and the orthogonal Meyer (dmey) wavelet can efficiently remove noise from the recorded SEMG signals. However, the most effective wavelet for SEMG denoising is chosen by calculating the root mean square difference and signal-to-noise ratio values. Results for both root mean square difference and signal-to-noise ratio show that wavelet db2 performs denoising best out of the wavelets. Furthermore, the higher order statistics method is applied for SEMG signal analysis because of its unique properties when applied to random time series, such as parameter estimation, testing of Gaussianity and linearity, deterministic and non-deterministic signal detection etc. Gaussianity and linearity tests as part of higher order statistics are conducted to understand changes in muscle contraction and to quantify the effectiveness of the noise removal process. According to the results, the SEMG signal becomes less Gaussian and more linear with increased force.

Distance Estimation From Received Signal Strength Under Log-Normal Shadowing: Bias and Variance

Abstract: In source localization, one estimates the location of a source using a variety of relative position information. Many algorithms use certain powers of distances to effect localization. In practice, exact distance measurement is not directly available and must be estimated from information such as received signal strength (RSS), time of arrival, or time difference of arrival. This letter considers bias and variance issues in estimating powers of distances from RSS affected by practical log-normal shadowing. We show that the underlying estimation problem is inefficient and that the maximum likelihood estimate yields a bias and a mean-square error (MSE) that both increase exponentially with the noise power. We then characterize the class of unbiased estimates and show that there is only one estimator in this class, but that its MSE also grows exponentially with the noise power. Finally, we provide the linear minimum mean-square error (MMSE) estimate and show that its bias and MSE are both bounded in the noise power.

Long GPS coordinate time series: multipath and geometry effects

Abstract: Within analyses of Global Positioning System (GPS) observations, unmodelled sub-daily signals are known to propagate into long-period signals via a number of different mechanisms. We report on the effects of time-variable satellite geometry and the propagation of an unmodelled multipath signal. Multipath reflectors at H=3D 0.1 m, 0.2 m and 1.5 m below the antenna are modelled and their effects on GPS coordinate time series are examined. Simulated time series at 20 global IGS sites for 2000-2008 were derived using the satellite geometry as defined by daily broadcast orbits, in addition to that defined using a perfectly repeating synthetic orbit. For the simulations generated using the broadcast orbits with a perfectly clear horizon, we observe the introduction of a time variable bias in the time series of up to several centimetres. Considerable site to site variability of the frequency and magnitude of the signal is observed, in addition to variation as a function of multipath source. When adopting realistic GPS observation geometries obtained from real data (e.g., those that include the effects of tracking outages, local obstructions, etc.), we observe concerning levels of temporal coordinate variation in the presence of the multipath signals. In these cases, we observe spurious signals across the frequency domain, in addition to what appears as offsets and secular trends. Velocity biases of more than 1mm/yr are evident at some few sites. The propagated signal in the vertical component is consistent with a noise model with a spectral index marginally above flicker noise (mean index -1.4), with some sites exhibiting power law magnitudes at comparable levels to actual height time series generated in GIPSY. The propagated signal also shows clear spectral peaks across all coordinate components at harmonics of the draconitic year for a GPS satellite (351.2 days). When a perfectly repeating synthetic GPS constellation is used, the simulations show near-negligible power law variability highlighting that subtle variations in the GPS constellation can propagate multipath signals differently over time, producing significant temporal variations in time series. We conclude that the time variable nature of GPS observation geometry and satellite orbits combined with a multipath signal that is manifested as an elevation dependent bias can introduce a spurious signal that is a potential significant contributor to time-correlated noise present in GPS time series. Further, the spurious signal also makes a potential significant contribution to the energy present at frequencies related to the draconitic year and harmonic thereof observed in GPS analyses.

DOA estimation of quasi-stationary signals via Khatri-Rao subspace

Abstract: This paper addresses the problem of direction-of-arrival (DOA) estimation of quasi-stationary signals, which finds applications in array processing of speech and audio. By studying the subspace structures of the local second-order statistics (SOSs) of quasi-stationary signals, we develop a Khatri-Rao (KR) subspace approach that has two notable advantages. First, the approach can operate in underdetermined cases. It is proven that if N is the number of sensors in the array, then the proposed approach can identify up to 2N − 2 source DOAs in an unambiguous fashion. Second, the approach can handle the problem of unknown noise covariance. Essentially, the KR subspace formulation is found to provide a simple and effective way of annihilating the (unknown) noise covariance from the observed signal SOSs. Simulation results, with an emphasis on underdetermined and colored-noise cases, illustrate that the KR subspace approach provides promising mean square estimation error performance.