Showing papers on "Noise measurement published in 2008"

Bayesian Compressive Sensing

Abstract: The data of interest are assumed to be represented as N-dimensional real vectors, and these vectors are compressible in some linear basis B, implying that the signal can be reconstructed accurately using only a small number M Lt N of basis-function coefficients associated with B. Compressive sensing is a framework whereby one does not measure one of the aforementioned N-dimensional signals directly, but rather a set of related measurements, with the new measurements a linear combination of the original underlying N-dimensional signal. The number of required compressive-sensing measurements is typically much smaller than N, offering the potential to simplify the sensing system. Let f denote the unknown underlying N-dimensional signal, and g a vector of compressive-sensing measurements, then one may approximate f accurately by utilizing knowledge of the (under-determined) linear relationship between f and g, in addition to knowledge of the fact that f is compressible in B. In this paper we employ a Bayesian formalism for estimating the underlying signal f based on compressive-sensing measurements g. The proposed framework has the following properties: i) in addition to estimating the underlying signal f, "error bars" are also estimated, these giving a measure of confidence in the inverted signal; ii) using knowledge of the error bars, a principled means is provided for determining when a sufficient number of compressive-sensing measurements have been performed; iii) this setting lends itself naturally to a framework whereby the compressive sensing measurements are optimized adaptively and hence not determined randomly; and iv) the framework accounts for additive noise in the compressive-sensing measurements and provides an estimate of the noise variance. In this paper we present the underlying theory, an associated algorithm, example results, and provide comparisons to other compressive-sensing inversion algorithms in the literature.

Evaluation of Objective Quality Measures for Speech Enhancement

Abstract: In this paper, we evaluate the performance of several objective measures in terms of predicting the quality of noisy speech enhanced by noise suppression algorithms. The objective measures considered a wide range of distortions introduced by four types of real-world noise at two signal-to-noise ratio levels by four classes of speech enhancement algorithms: spectral subtractive, subspace, statistical-model based, and Wiener algorithms. The subjective quality ratings were obtained using the ITU-T P.835 methodology designed to evaluate the quality of enhanced speech along three dimensions: signal distortion, noise distortion, and overall quality. This paper reports on the evaluation of correlations of several objective measures with these three subjective rating scales. Several new composite objective measures are also proposed by combining the individual objective measures using nonparametric and parametric regression analysis techniques.

1-Bit compressive sensing

Abstract: Compressive sensing is a new signal acquisition technology with the potential to reduce the number of measurements required to acquire signals that are sparse or compressible in some basis. Rather than uniformly sampling the signal, compressive sensing computes inner products with a randomized dictionary of test functions. The signal is then recovered by a convex optimization that ensures the recovered signal is both consistent with the measurements and sparse. Compressive sensing reconstruction has been shown to be robust to multi-level quantization of the measurements, in which the reconstruction algorithm is modified to recover a sparse signal consistent to the quantization measurements. In this paper we consider the limiting case of 1-bit measurements, which preserve only the sign information of the random measurements. Although it is possible to reconstruct using the classical compressive sensing approach by treating the 1-bit measurements as plusmn 1 measurement values, in this paper we reformulate the problem by treating the 1-bit measurements as sign constraints and further constraining the optimization to recover a signal on the unit sphere. Thus the sparse signal is recovered within a scaling factor. We demonstrate that this approach performs significantly better compared to the classical compressive sensing reconstruction methods, even as the signal becomes less sparse and as the number of measurements increases.

Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data

Abstract: We present a simple and usable noise model for the raw-data of digital imaging sensors This signal-dependent noise model, which gives the pointwise standard-deviation of the noise as a function of the expectation of the pixel raw-data output, is composed of a Poissonian part, modeling the photon sensing, and Gaussian part, for the remaining stationary disturbances in the output data We further explicitly take into account the clipping of the data (over- and under-exposure), faithfully reproducing the nonlinear response of the sensor We propose an algorithm for the fully automatic estimation of the model parameters given a single noisy image Experiments with synthetic images and with real raw-data from various sensors prove the practical applicability of the method and the accuracy of the proposed model

A low-noise self-calibrating dynamic comparator for high-speed ADCs

Abstract: This paper presents a low offset voltage, low noise dynamic latched comparator using a self-calibrating technique. The new calibration technique does not require any amplifiers for the offset voltage cancellation and quiescent current. It achieves low offset voltage of 1.69 mV at 1 sigma in low power consumption, while 13.7 mV is measured without calibration. Furthermore the proposed comparator requires only one phase clock while conventionally two phase clocks were required leading to relaxed clock. Moreover, a low input noise of 0.6 mV at 1 sigma, three times lower than the conventional one, is obtained. Prototype comparators are realized in 90 nm 10M1P CMOS technology. Experimental and simulated results show that the comparator achieves 1.69 mV offset at 250 MHz operating, while dissipating 40 muW/GHz ( 20 fJ/conv. ) from a 1.0 V supply.

Monte-Carlo Sure: A Black-Box Optimization of Regularization Parameters for General Denoising Algorithms

Abstract: We consider the problem of optimizing the parameters of a given denoising algorithm for restoration of a signal corrupted by white Gaussian noise. To achieve this, we propose to minimize Stein's unbiased risk estimate (SURE) which provides a means of assessing the true mean-squared error (MSE) purely from the measured data without need for any knowledge about the noise-free signal. Specifically, we present a novel Monte-Carlo technique which enables the user to calculate SURE for an arbitrary denoising algorithm characterized by some specific parameter setting. Our method is a black-box approach which solely uses the response of the denoising operator to additional input noise and does not ask for any information about its functional form. This, therefore, permits the use of SURE for optimization of a wide variety of denoising algorithms. We justify our claims by presenting experimental results for SURE-based optimization of a series of popular image-denoising algorithms such as total-variation denoising, wavelet soft-thresholding, and Wiener filtering/smoothing splines. In the process, we also compare the performance of these methods. We demonstrate numerically that SURE computed using the new approach accurately predicts the true MSE for all the considered algorithms. We also show that SURE uncovers the optimal values of the parameters in all cases.

Noise and Signal Estimation in Magnitude MRI and Rician Distributed Images: A LMMSE Approach

Abstract: A new method for noise filtering in images that follow a Rician model-with particular attention to magnetic resonance imaging-is proposed. To that end, we have derived a (novel) closed-form solution of the linear minimum mean square error (LMMSE) estimator for this distribution. Additionally, a set of methods that automatically estimate the noise power are developed. These methods use information of the sample distribution of local statistics of the image, such as the local variance, the local mean, and the local mean square value. Accordingly, the dynamic estimation of noise leads to a recursive version of the LMMSE, which shows a good performance in both noise cleaning and feature preservation. This paper also includes the derivation of the probability density function of several local sample statistics for the Rayleigh and Rician model, upon which the estimators are built.

Extension of a two-step calibration methodology to include nonorthogonal sensor axes

Abstract: We present an extension of the nonlinear two-step estimation algorithm originally developed for the calibration of solid-state strapdown magnetometers. We expand the algorithm to include nonorthogonality within a sensor set for both two- and three-axis sensors. Nonorthogonality can result from manufacturing issues, installation geometry, and in the case of magnetometers, from soft iron bias errors. Simulation studies for both two- and three-axis sensors show convergence of the improved algorithm to the true values, even in the presence of realistic measurement noise. Finally the algorithm is experimentally validated on a low-cost solid-state three-axis magnetometer set, which shows definite improvement postcalibration. We note that the algorithm is general and can be applied to any two- or three-axis sensor set (such as accelerometers) with an error model consisting of scale, offset, and nonorthogonality errors.

Coherent and random noise attenuation using the curvelet transform

Abstract: This paper discusses an effective approach to attenuate random and coherent linear noise in a 3D data set from a carbonate environment. Figure 1 illustrates a seismic inline section from a noisy 3D seismic cube. Clearly, the section in Figure 1 is corrupted by undesirable random noise and coherent noise that are linear and vertically dipping in nature

Characterizing application sensitivity to OS interference using kernel-level noise injection

Abstract: Operating system noise has been shown to be a key limiter of application scalability in high-end systems. While several studies have attempted to quantify the sources and effects of system interference using user-level mechanisms, there are few published studies on the effect of different kinds of kernel-generated noise on application performance at scale. In this paper, we examine the sensitivity of real-world, large-scale applications to a range of OS noise patterns using a kernel-based noise injection mechanism implemented in the Catamount lightweight kernel. Our results demonstrate the importance of how noise is generated, in terms of frequency and duration, and how this impact changes with application scale. For example, our results show that 2.5% net processor noise at 10,000 nodes can have no impact or can result in over a factor of 20 slowdown for the same application, depending solely on how the noise is generated. We also discuss how the characteristics of the applications we studied, for example computation/communication ratios, collective communication sizes, and other characteristics, related to their tendency to amplify or absorb noise. Finally, we discuss the implications of our findings on the design of new operating systems, middleware, and other system services for high-end parallel systems.

Low-Area Active-Feedback Low-Noise Amplifier Design in Scaled Digital CMOS

Abstract: The emerging concept of multistandard radios calls for low-noise amplifier (LNA) solutions able to comply with their needs. Meanwhile, the increasing cost of scaled CMOS pushes towards low-area solutions in standard, digital CMOS. Feedback LNAs are able to meet both demands. This paper is devoted to the design of low-area active-feedback LNAs. We discuss the design of wideband, narrowband and multiband implementations. We demonstrate that competitive RF performance is achievable thanks to CMOS downscaling, pleasing many applications because of their low cost (digital CMOS) and low area (bondpad size).

A fast method for image noise estimation using Laplacian operator and adaptive edge detection

Abstract: We present a simple and fast algorithm for image noise estimation. The input image is assumed to be corrupted by additive zero mean Gaussian noise. To exclude structures or details from contributing to the noise variance estimation, a simple edge detection algorithm using first-order gradients is applied first. Then a Laplacian operator followed by an averaging over the whole image will provide very accurate noise variance estimation. There is only one parameter which is self-determined and adaptive to the image contents. Simulation results show that the proposed algorithm performs well for different types of images over a large range of noise variances. Performance comparisons against other approaches are also provided.

Active noise control system

Abstract: The present disclosure relates to an active noise control (ANC) system. In accordance with one aspect of the invention, the ANC system includes a plurality of microphones and a plurality of loudspeakers. Each microphone is configured to provide an error signal that represents a residual noise signal. Each loudspeaker is configured to receive a loudspeaker signal and to radiate a respective acoustic signal. The ANC system further includes an adaptive filter bank, which is supplied with a reference signal and configured to filter the reference signal to provide the loudspeaker signals as filtered signals. The filter characteristics of the adaptive filter bank are adapted such that a cost function is minimized. The cost function thereby represents the weighted sum of the squared error signals.

Salt-and-pepper noise detection and reduction using fuzzy switching median filter

Abstract: This paper presents a new fuzzy switching median (FSM) filter employing fuzzy techniques in image processing. The proposed filter is able to remove salt-and-pepper noise in digital images while preserving image details and textures very well. By incorporating fuzzy reasoning in correcting the detected noisy pixel, the low complexity FSM filter is able to outperform some well known existing salt-and-pepper noise fuzzy and classical filters.

System and method for providing single microphone noise suppression fallback

Abstract: Systems and methods for providing single microphone noise suppression fallback are provided. In exemplary embodiments, primary and secondary acoustic signals are received. A single microphone noise estimate may be generated based on the primary acoustic signal, while a dual microphone noise estimate may be generated based on the primary and secondary acoustic signals. A combined noise estimate based on the single and dual microphone noise estimates is then determined. Using the combined noise estimate, a gain mask may be generated and applied to the primary acoustic signal to generate a noise suppressed signal. Subsequently, the noise suppressed signal may be output.

Generating nonstationary multisensor signals under a spatial coherence constraint.

Abstract: Noise fields encountered in real-life scenarios can often be approximated as spherical or cylindrical noise fields. The characteristics of the noise field can be described by a spatial coherence function. For simulation purposes, researchers in the signal processing community often require sensor signals that exhibit a specific spatial coherence function. In addition, they often require a specific type of noise such as temporally correlated noise, babble speech that comprises a mixture of mutually independent speech fragments, or factory noise. Existing algorithms are unable to generate sensor signals such as babble speech and factory noise observed in an arbitrary noise field. In this paper an efficient algorithm is developed that generates multisensor signals under a predefined spatial coherence constraint. The benefit of the developed algorithm is twofold. Firstly, there are no restrictions on the spatial coherence function. Secondly, to generate M sensor signals the algorithm requires only M mutually independent noise signals. The performance evaluation shows that the developed algorithm is able to generate a more accurate spatial coherence between the generated sensor signals compared to the so-called image method that is frequently used in the signal processing community.

Tracking of Nonstationary Noise Based on Data-Driven Recursive Noise Power Estimation

Abstract: This paper considers estimation of the noise spectral variance from speech signals contaminated by highly nonstationary noise sources. The method can accurately track fast changes in noise power level (up to about 10 dB/s). In each time frame, for each frequency bin, the noise variance estimate is updated recursively with the minimum mean-square error (mmse) estimate of the current noise power. A time- and frequency-dependent smoothing parameter is used, which is varied according to an estimate of speech presence probability. In this way, the amount of speech power leaking into the noise estimates is kept low. For the estimation of the noise power, a spectral gain function is used, which is found by an iterative data-driven training method. The proposed noise tracking method is tested on various stationary and nonstationary noise sources, for a wide range of signal-to-noise ratios, and compared with two state-of-the-art methods. When used in a speech enhancement system, improvements in segmental signal-to-noise ratio of more than 1 dB can be obtained for the most nonstationary noise sources at high noise levels.

Transmitter Noise Effect on the Performance of a MIMO-OFDM Hardware Implementation Achieving Improved Coverage

Abstract: This paper presents analysis of performance measurements from a MIMO-OFDM IEEE 802.11n hardware implementation at 5.2 GHz using four transmitters and four receivers. Two spatial multiplexing systems are compared; one which uses a zero-forcing (ZF) detector and the other a list sphere detector (LSD). We show that the measured results do not align with standard prediction based on simulation assuming uncorrelated receiver noise. We show that the discrepancy can be explained by the inclusion of transmitter noise into the channel model. This effect is not included in existing MIMO-OFDM channel models. The measured results from our hardware implementation show successful packet transmission at 600 Mb/s with 15 bits/s/Hz spectral efficiency at 73% coverage for ZF and 84% coverage for LSD with an average receiver signal to noise ratio (SNR) of 26 dB.

Sampling bounds for sparse support recovery in the presence of noise

Abstract: It is well known that the support of a sparse signal can be recovered from a small number of random projections. However, in the presence of noise all known sufficient conditions require that the per-sample signal-to-noise ratio (SNR) grows without bound with the dimension of the signal. If the noise is due to quantization of the samples, this means that an unbounded rate per sample is needed. In this paper, it is shown that an unbounded SNR is also a necessary condition for perfect recovery, but any fraction (less than one) of the support can be recovered with bounded SNP. This means that a finite rate per sample is sufficient for partial support recovery. Necessary and sufficient conditions are given for both stochastic and non-stochastic signal models. This problem arises in settings such as compressive sensing, model selection, and signal denoising.

Content-Dependent Scan Rate Converter with Adaptive Noise Reduction

Abstract: A content-dependent scan rate converter with adaptive noise reduction that provides a highly integrated, implementation efficient de-interlacer. By identifying and using redundant information from the image (motion values and edge directions), this scan rate converter is able to perform the tasks of film-mode detection, motion-adaptive scan rate conversion, and content-dependent video noise reduction. Adaptive video noise reduction is incorporated in the process where temporal noise reduction is performed on the still parts of the image, thus preserving high detail spatial information, and data-adaptive spatial noise reduction is performed on the moving parts of the image. A low-pass filter is used in flat fields to smooth out Gaussian noise and a direction-dependent median filter is used in the presence of impulsive noise or an edge. Therefore, the selected spatial filter is optimized for the particular pixel that is being processed to maintain crisp edges.

Identification of short transmission-line parameters from synchrophasor measurements

Abstract: Accurate knowledge of transmission line impedance parameters helps to improve accuracy in relay settings, post-event fault location and transmission power flow modeling. Four methods are presented in this paper to identify transmission line impedance parameters from synchronized measurements for short transmission lines. Estimates of parameters for short transmission lines is more challenging than for long transmission lines since measurement noise often causes large errors in the estimates. The effectiveness of these methods is verified through simulations. These simulations incorporate two types of measurement errors: biased and non-biased noise. The different effects of bias errors and random noise on the accuracy of the calculated impedance parameters are quantified. Last, some complicating factors and challenges inherent in real world measurements are discussed.

Optimal Filtering for Systems With Multiple Packet Dropouts

Abstract: This paper is concerned with the optimal filtering problem for discrete-time stochastic linear systems with multiple packet dropouts, where the number of consecutive packet dropouts is limited by a known upper bound. Without resorting to state augmentation, the system is converted to one with measurement delays and a moving average (MV) colored measurement noise. An unbiased optimal filter is developed in the linear least-mean-square sense. Its solution depends on the recursion of a Riccati equation and a Lyapunov equation. A numerical example shows the effectiveness of the proposed filter.

Universal Impulse Noise Filter Based on Genetic Programming

Abstract: In this paper, we present a novel method for impulse noise filter construction, based on the switching scheme with two cascaded detectors and two corresponding estimators. Genetic programming as a supervised learning algorithm is employed for building two detectors with complementary characteristics. The first detector identifies the majority of noisy pixels. The second detector searches for the remaining noise missed by the first detector, usually hidden in image details or with amplitudes close to its local neighborhood. Both detectors are based on the robust estimators of location and scale-median and MAD. The filter made by the proposed method is capable of effectively suppressing all kinds of impulse noise, in contrast to many existing filters which are specialized only for a particular noise model. In addition, we propose the usage of a new impulse noise model-the mixed impulse noise, which is more realistic and harder to treat than existing impulse noise models. The proposed model is the combination of commonly used noise models: salt-and-pepper and uniform impulse noise models. Simulation results show that the proposed two-stage GP filter produces excellent results and outperforms existing state-of-the-art filters.

Linearized Analysis of a Digital Bang-Bang PLL and Its Validity Limits Applied to Jitter Transfer and Jitter Generation

Abstract: In the last few years, several digital implementations of phase-locked loops (PLLs) have emerged, in some cases outperforming analog ones. Some of these PLLs use a bang-bang phase detector to convert the phase error into a digital value. Unfortunately, that introduces a hard nonlinearity in the loop which prevents the use of the traditional linear analysis. Nevertheless, authors resort to linearized models for the noise analysis of this kind of loops, but to the author's knowledge, no attempt has been made to evaluate the limits of this approach. In this paper, we address the problem of investigating the limits of the linearized approach, and we apply it to the computation of the jitter transfer and the jitter generation depending on the level of noise at the binary phase detector input. The results will be compared to phase noise measurements obtained from a digital bang-bang PLL implemented in 130-nm CMOS technology.

Kalman filter based synchronisation methods

Abstract: Single- and three-phase synchronisation methods based on optimum filtering theory are proposed. These methods are based mainly on the Kalman filter and are therefore termed Kalman filter-phase locked loop. They explicitly include in the problem formulation the presence of harmonics, voltage unbalance, measurement noise, transients and frequency deviation. Such perturbations degrade the performance of many synchronisation structures presented in literature. The formulation presented here makes the synchronisation signals less sensitive to these perturbations. It is also shown that the proposed methods can be helpful by also providing the amplitude, instantaneous phase and frequency of grid voltages that can be useful for the analysis of power quality. Furthermore, the Kalman filter provides a way of obtaining the best compromise between transient response and measurement noise rejection for the synchronisation signals. The paper sets out the development of the proposed methods together with the choice of tuning parameters and their physical meaning. Simulations and experimental results using a DSP TMS320F2812 are presented to show the effectiveness of the proposed schemes.

Microphone Array Shape Calibration in Diffuse Noise Fields

Abstract: This correspondence presents a microphone array shape calibration procedure for diffuse noise environments. The procedure estimates intermicrophone distances by fitting the measured noise coherence with its theoretical model and then estimates the array geometry using classical multidimensional scaling. The technique is validated on noise recordings from two office environments.

Through wall radar imaging using UWB noise waveforms

Abstract: This paper examines the results of our research on the use of ultra-wideband noise waveforms for imaging objects behind walls. The advantages of using thermally generated noise as a probing signal are introduced. The technique of heterodyne correlation, used to inject coherence in the random noise probing signal and to collapse the wideband reflected signal into a single frequency, is presented. We address issues related to locating, detection, and tracking humans behind walls using the Hilbert-Huang transform approach for human activity characterization. The results indicate that noise radar technology combined with modern signal processing approaches is indeed a viable technique for covert high-resolution imaging of obscured stationary and moving targets.