Showing papers on "Noise (signal processing) published in 2006"

Just relax: convex programming methods for identifying sparse signals in noise

Abstract: This paper studies a difficult and fundamental problem that arises throughout electrical engineering, applied mathematics, and statistics. Suppose that one forms a short linear combination of elementary signals drawn from a large, fixed collection. Given an observation of the linear combination that has been contaminated with additive noise, the goal is to identify which elementary signals participated and to approximate their coefficients. Although many algorithms have been proposed, there is little theory which guarantees that these algorithms can accurately and efficiently solve the problem. This paper studies a method called convex relaxation, which attempts to recover the ideal sparse signal by solving a convex program. This approach is powerful because the optimization can be completed in polynomial time with standard scientific software. The paper provides general conditions which ensure that convex relaxation succeeds. As evidence of the broad impact of these results, the paper describes how convex relaxation can be used for several concrete signal recovery problems. It also describes applications to channel coding, linear regression, and numerical analysis

Photonic signal processing of microwave signals

Abstract: Photonic signal processing offers the prospect of realizing extremely high multigigahertz sampling frequencies, overcoming inherent electronic limitations. This stems from the intrinsic excellent delay properties of optical delay lines. These processors provide new capabilities for realizing high time-bandwidth operation and high-resolution performance. In-fiber signal processors are inherently compatible with fiber-optic microwave systems and can provide connectivity with built-in signal conditioning. Fundamental principles of photonic signal processing, including sampling, tuning, and noise, are discussed. Structures that can extend the performance of photonic signal processors are presented, including methods for improving the filter shape characteristics of interference mitigation filters, techniques to increase the stopband attenuation of bandpass filters, and methods to achieve large free spectral range. Several photonic signal processors, including high-resolution microwave filters, widely tunable filters, arbitrary waveform generators, and fast signal correlators, are discussed. Techniques to solve the fundamental noise problem in photonic signal processors are described, and coherence-free structures for few-tap notch filters are discussed. Finally, a new concept for realizing multiple-tap coherence-free processor filters, based on a new frequency-shifting technique, is presented. The structure not only eliminates the phase-induced intensity noise limitation, but can also generate a large number of taps to enable the achievement of processors with high performance and high resolution.

Experimental study of spectrum sensing based on energy detection and network cooperation

Abstract: Spectrum sensing has been identified as a key enabling functionality to ensure that cognitive radios would not interfere with primary users, by reliably detecting primary user signals. Recent research studied spectrum sensing using energy detection and network cooperation via modeling and simulations. However, there is a lack of experimental study that shows the feasibility and practical performance limits of this approach under real noise and interference sources in wireless channels. In this work, we implemented energy detector on a wireless testbed and measured the required sensing time needed to achieve the desired probability of detection and false alarm for modulated and sinewave-pilot signals in low SNR regime. We measured the minimum detectable signal levels set by the receiver noise uncertainties. Our experimental study also measured the sensing improvements achieved via network cooperation, identified the robust threshold rule for hard decision combining and quantified the effects of spatial separation between radios in indoor environments.

Gaussian Processes for Signal Strength-Based Location Estimation

Abstract: Estimating the location of a mobile device or a robot from wireless signal strength has become an area of highly active research. The key problem in this context stems from the complexity of how signals propagate through space, especially in the presence of obstacles such as buildings, walls or people. In this paper we show how Gaussian processes can be used to generate a likelihood model for signal strength measurements. We also show how parameters of the model, such as signal noise and spatial correlation between measurements, can be learned from data via hyperparameter estimation. Experiments using WiFi indoor data and GSM cellphone connectivity demonstrate the superior performance of our approach.

Seismic interferometry-turning noise into signal

Abstract: Turning noise into useful data—every geophysicist's dream? And now it seems possible. The field of seismic interferometry has at its foundation a shift in the way we think about the parts of the signal that are currently filtered out of most analyses—complicated seismic codas (the multiply scattered parts of seismic waveforms) and background noise (whatever is recorded when no identifiable active source is emitting, and which is superimposed on all recorded data). Those parts of seismograms consist of waves that reflect and refract around exactly the same subsurface heterogeneities as waves excited by active sources. The key to the rapid emergence of this field of research is our new understanding of how to unravel that subsurface information from these relatively complex-looking waveforms. And the answer turned out to be rather simple. This article explains the operation of seismic interferometry and provides a few examples of its application.

Kickback noise reduction techniques for CMOS latched comparators

Abstract: The latched comparator is a building block of virtually all analog-to-digital converter architectures. It uses a positive feedback mechanism to regenerate the analog input signal into a full-scale digital level. The large voltage variations in the internal nodes are coupled to the input, disturbing the input voltage-this is usually called kickback noise. This brief reviews existing solutions to minimize the kickback noise and proposes two new ones. HSPICE simulations of comparators implemented in a 0.18-/spl mu/m technology demonstrate their effectiveness.

Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage

Abstract: In this paper, a new noise reduction algorithm is introduced and applied to the problem of denoising hyperspectral imagery. This algorithm resorts to the spectral derivative domain, where the noise level is elevated, and benefits from the dissimilarity of the signal regularity in the spatial and the spectral dimensions of hyperspectral images. The performance of the new algorithm is tested on two different hyperspectral datacubes: an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) datacube that is acquired in a vegetation-dominated site and a simulated AVIRIS datacube that simulates a geological site. The new algorithm provides signal-to-noise-ratio improvement up to 84.44% and 98.35% in the first and the second datacubes, respectively.

120-GHz-band millimeter-wave photonic wireless link for 10-Gb/s data transmission

Abstract: A 120-GHz-band wireless link that uses millimeter-wave (MMW) photonic techniques was developed. The output power and noise characteristics of 120-GHz-band MMWs generated by converting a 125-GHz optical subcarrier signal were evaluated. It was then shown that the noise characteristics of the 125-GHz signal generated with these photonic technologies is sufficient for 10-Gb/s data transmission. We constructed a compact 120-GHz-band wireless link system, and evaluated its data transmission characteristics. This system achieved error-free transmission of OC-192 and 10-GbE signals over a distance of more than 200 m with a received power of below -30 dBm.

Random Sampling for Analog-to-Information Conversion of Wideband Signals

Abstract: We develop a framework for analog-to-information conversion that enables sub-Nyquist acquisition and processing of wideband signals that are sparse in a local Fourier representation The first component of the framework is a random sampling system that can be implemented in practical hardware The second is an efficient information recovery algorithm to compute the spectrogram of the signal, which we dub the sparsogram A simulated acquisition of a frequency hopping signal operates at 33times sub-Nyquist average sampling rate with little degradation in signal quality

Initial results in power system identification from injected probing signals using a subspace method

Abstract: In this paper, the authors use the Numerical algorithm for Subspace State Space System IDentification (N4SID) to extract dynamic parameters from phasor measurements collected on the western North American Power Grid. The data were obtained during tests on June 7, 2000, and they represent wide area response to several kinds of probing signals, including low-level pseudo-random noise (LLPRN) and single-mode square wave (SMSW) injected at the Celilo terminal of the Pacific HVDC Intertie (PDCI). An identified model is validated using a cross validation method. Also, the obtained electromechanical modes are compared with the results from Prony analysis of a ringdown and with signal analysis of ambient data measured under similar operating conditions. The consistent results show that methods in this class can be highly effective, even when the probing signal is small

Comparison of centroid computation algorithms in a Shack–Hartmann sensor

Abstract: Analytical theory is combined with extensive numerical simulations to compare different flavours of centroiding algorithms: thresholding, weighted centroid, correlation, quad cell (QC). For each method, optimal parameters are defined in function of photon flux, readout noise and turbulence level. We find that at very low flux the noise of QC and weighted centroid leads the best result, but the latter method can provide linear and optimal response if the weight follows spot displacements. Both methods can work with average flux as low as 10 photons per subaperture under a readout noise of three electrons. At high-flux levels, the dominant errors come from non-linearity of response, from spot truncations and distortions and from detector pixel sampling. It is shown that at high flux, centre of gravity approaches and correlation methods are equivalent (and provide better results than QC estimator) as soon as their parameters are optimized. Finally, examples of applications are given to illustrate the results obtained in the paper.

Robust separation of speech signals in a noisy environment

Abstract: A method for improving the quality of a speech signal extracted from a noisy acoustic environment is provided. In one approach, a signal separation process (180) is associated with a voice activity detector (185). The voice activity detector (185) is a two-channel (178,182) detector, which enables a particularly robust and accurate detection of voice activity. When a speech is detected, the voice activity detector generates a control signal (411). The control signal (411) is used to activate, adjust, or control signal separation processes or post -processing operations (195) to improve the quality of the resulting speech signal. In another approach, a signal separation process (180) is provided as a learning stage (752) and an output stage (756). The learning stage (752) aggressively adjus to current acoustic conditions and passes coefficients to the output stage (756). The output stage (756) adapts more slowly and generates a speech-content signal (181,770) and a noise dominant signal (407,773). When the learning stage (752) becomes unstable only the learning stage (752) is reset, allowing the output stage (756) to continue outputting a high quality speech signal.

Phase-rectified signal averaging detects quasi-periodicities in non-stationary data

Abstract: We present an efficient technique for the study of quasi-periodic oscillations in noisy, non-stationary signals, which allows the assessment of system dynamics despite phase resetting and noise. It is based on the definition of anchor points in the signal (in the simplest case increases or decreases of the signal) which are used to align (i.e., phase-rectify) the oscillatory fluctuations followed by an averaging of the surroundings of the anchor points. We give theoretical arguments for the advantage of the technique, termed phase-rectified signal averaging (PRSA), over conventional spectral analysis and show in a numerical test using surrogate heartbeat data that the threshold intensity for the detection of additional quasi-periodic components is approximately 75% lower with PRSA. With the use of different anchor point criteria PRSA is capable of separately analysing quasi-periodicities that occur during increasing or decreasing parts of the signal. We point to a variety of applications in the analysis of medical, biological, and geophysical data containing quasi-periodicities besides non-stationarities and 1 / f noise.

Automatic underwater image pre-processing

Abstract: A novel pre-processing filter is proposed for underwater image restoration. Because of specific transmission properties of light in the water, underwater image suffers from limited range, non uniform lighting, low contrast, color diminished, important blur. . .Today preprocessing methods typically only concentrates on non uniform lighting or color correction and often require additional knowledge of the environment. The algorithm proposed in this paper is an automatic algorithm to pre-process underwater images. It reduces underwater perturbations, and improves image quality. It is composed of several successive independent processing steps which correct non uniform illumination, suppress noise, enhance contrast and adjust colors. Performances of filtering will be assessed using an edge detection robustness criterion.

Physiological monitoring wearable having three electrodes

Abstract: A wearable system or garment comprises at least three conductive electrodes that may, for example, be made of stretch-recovery electrically conductive yarns integrated with non-conductive stretch-recovery yarns that make up the remaining portion of the wearable system or garment. The wearable or garment further comprises means for using three electrodes to monitor at least one physiological or biophysical event or characteristic of the wearer. One electrode is specifically used to feed back an inverted noise signal to the wearer to destructively interfere with the wearer generated noise. Specifically, the wearer's heart rate, ECG and associated electrical characteristics may be monitored in high resolution under dry electrode conditions.

Three-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Beam Profiles, Data Processing, Radiometer Characterization and Systematic Error Limits

Abstract: The WMAP satellite has completed 3 years of observations of the cosmic microwave background radiation. The 3-year data products include several sets of full sky maps of the Stokes I, Q and U parameters in 5 frequency bands, spanning 23 to 94 GHz, and supporting items, such as beam window functions and noise covariance matrices. The processing used to produce the current sky maps and supporting products represents a significant advancement over the first year analysis, and is described herein. Improvements to the pointing reconstruction, radiometer gain modeling, window function determination and radiometer spectral noise parametrization are presented. A detailed description of the updated data processing that produces maximum likelihood sky map estimates is presented, along with the methods used to produce reduced resolution maps and corresponding noise covariance matrices. Finally two methods used to evaluate the noise of the full resolution sky maps are presented along with several representative year-to-year null tests, demonstrating that sky maps produced from data from different observational epochs are consistent.

Signal Detection, Modularity, and the Correlation between Extrinsic and Intrinsic Noise in Biochemical Networks

Abstract: We present an expression for the power spectrum of the output signal of a biochemical network, which reveals that the reactions that allow a network to detect biochemical signals, induce correlations between the extrinsic noise of the input signals and the intrinsic noise of the reactions that form the network. We show that anticorrelations between the extrinsic and intrinsic noise enhance the robustness of zero-order ultrasensitive networks to biochemical noise. We discuss the consequences for a modular description of noise transmission using the mitogen-activated protein kinase cascade.

Optimal Noise Filtering in the Chemotactic Response of Escherichia coli

Abstract: Information-carrying signals in the real world are often obscured by noise. A challenge for any system is to filter the signal from the corrupting noise. This task is particularly acute for the signal transduction network that mediates bacterial chemotaxis, because the signals are subtle, the noise arising from stochastic fluctuations is substantial, and the system is effectively acting as a differentiator which amplifies noise. Here, we investigated the filtering properties of this biological system. Through simulation, we first show that the cutoff frequency has a dramatic effect on the chemotactic efficiency of the cell. Then, using a mathematical model to describe the signal, noise, and system, we formulated and solved an optimal filtering problem to determine the cutoff frequency that bests separates the low-frequency signal from the high-frequency noise. There was good agreement between the theory, simulations, and published experimental data. Finally, we propose that an elegant implementation of the optimal filter in combination with a differentiator can be achieved via an integral control system. This paper furnishes a simple quantitative framework for interpreting many of the key notions about bacterial chemotaxis, and, more generally, it highlights the constraints on biological systems imposed by noise.

A review of wavelet denoising in MRI and ultrasound brain imaging

Abstract: There is a growing interest in using multiresolution noise filters in a variety of medical imaging applications. We review recent wavelet denoising techniques for medical ultrasound and for magnetic resonance images and discuss some of their potential applications in the clinical investigations of the brain. Our goal is to present and evaluate noise suppression methods based on both image processing and clinical expertise. We analyze two types of filters for magnetic resonance images (MRI): noise Suppression in magnitude MRI images and denoising blood oxygen level-dependent (BOLD) response in functional MRI images (fMRI). The noise distribution in magnitude MRI images is Rician, while the noise distribution in BOLD images has been recently shown to follow a Gaussian model well. We evaluate different methods based on signal to noise ratio improvement and based on the preservation of the shape of the activated regions in fMRI. A critical view on the problem of speckle filtering in ultrasound images is given where we discuss some of the issues that are overlooked in many speckle filters like the relevance of the "speckled texture", expert-defined features of interest and the reliability of the common speckle models. We analyze the use of multiresolution speckle filters to improve the automatic processing steps in the clinical research of non-cystic periventricular leukomalacia. In particular we apply speckle filters to ultrasound neonatal brain images and we evaluate the influence of the filtering on the effectiveness of the subsequent classification and segmentation of flares of affected tissue in comparison with the manual delineation of clinicians.

ECG denoising based on the empirical mode decomposition.

Abstract: The electrocardiogram (ECG) has been widely used for diagnosis purposes of heart diseases. Good quality ECG are utilized by the physicians for interpretation and identification of physiological and pathological phenomena. However, in real situations, ECG recordings are often corrupted by artifacts. One prominent artifact is the high frequency noise caused by electromyogram induced noise, power line interferences, or mechanical forces acting on the electrodes. Noise severely limits the utility of the recorded ECG and thus need to be removed for better clinical evaluation. Several methods have been developed for ECG denoising. In this paper, we proposed a new ECG denoising method based on the recently developed Empirical Mode Decomposition (EMD). The proposed EMD-based method is able to remove high frequency noise with minimum signal distortion. The method is validated through experiments on the MIT-BIH database. Both quantitative and qualitative results are given. The results show that the proposed method provides very good results for denoising.

Measurements vs. Bits: Compressed Sensing meets Information Theory

Abstract: Compressed sensing is a new framework for acquiring sparse signals based on the revelation that a small number of linear projections (measurements) of the signal contain enough information for its reconstruction. The foundation of Compressed sensing is built on the availability of noise-free measurements. However, measurement noise is unavoidable in analog systems and must be accounted for. We demonstrate that measurement noise is the crucial factor that dictates the number of measurements needed for reconstruction. To establish this result, we evaluate the information contained in the measurements by viewing the measurement system as an information theoretic channel. Combining the capacity of this channel with the ratedistortion function of the sparse signal, we lower bound the rate-distortion performance of a compressed sensing system. Our approach concisely captures the effect of measurement noise on the performance limits of signal reconstruction, thus enabling to benchmark the performance of specific reconstruction algorithms.

Electrocardiogram (ECG) Signal Processing

Abstract: Signal processing of electrocardiographic signals has a long and rich history and has greatly helped to enhance the diagnostic capability, especially when signals are recorded in noisy environments. This article gives an introductory presentation of the basic set of algorithms used for conditioning the ECG with respect to different types of noise and artifacts, detecting heartbeats, extracting basic ECG measurements, and performing data compression. Signal processing in clinical applications is exemplified by the high-resolution ECG and T wave alternans.

Signal Waveform's Optimal-under-Restriction Design for Active Sensing

Abstract: We consider Signal Waveform's Optimal-under-Restriction Design (SWORD) for active sensing. In the presence of colored interference and noise with known statistical properties, waveform optimization for active sensors such as radar can significantly increase the signal-to-interference-plus-noise ratio needed for much improved target detection. However, the so-obtained optimal waveforms can result in significant modulus variation, poor range resolution, and/or high peak sidelobe levels. To mitigate these problems, we can constrain the waveform optimization problem by restricting the sought-after waveform to be similar to a desired waveform, which is known to have, for example, constant modulus as well as reasonable range resolution and peak sidelobe level. One example of the desired waveform is the widely used linear frequency modulated waveform or chirp. We will provide a detailed solution to the constrained optimization problem and explain how it is related with the existing waveform optimization methods

Denoising via empirical mode decomposition

Abstract: In this paper a signal denoising scheme based a multiresolution approach referred to as Empirical mode decomposition (EMD) [1] is presented. The denoising method is a fully data driven approach. Noisy signal is decomposed adaptively into intrinsic oscillatory components called Intrinsic mode functions (IMFs) using a decomposition algorithm algorithm called sifting process. The basic principle of the method is to reconstruct the signal with IMFs previously filtered or thresholded. The denoising method is applied to one real signal et to four simulated signals with different noise levels and the results compared to Wavelets, Averaging and Median methods. The effect of level noise value on the performances of the proposed denoising is analyzed. The study is limited to signals corrupted by additive white Gaussian random noise.

Digital PLL control for single-phase photovoltaic system

Abstract: Utility-voltage information, such as the frequency, phase angle and amplitude, is very important in many industrial systems. In a three-phase system, the utility-voltage information can easily be obtained using a utility-voltage vector, as the magnitude and angle of the voltage vector indicate the amplitude and angle of the utility voltage, respectively. However, for a single-phase system, the utility-voltage information is much more difficult to acquire. Conventionally, the frequency and phase angle of a single-phase voltage are obtained by detecting the zero-cross point. Yet, this method cannot provide the utility-voltage information instantaneously and is very sensitive to noise. Accordingly, the paper presents a novel digital phase-locked loop (PLL) algorithm for single-phase photovoltaic systems. The algorithm uses two virtual phases, and its performance is demonstrated under various utility conditions to show the effectiveness of the proposed algorithm.

Random Telegraph Signal in CMOS Image Sensor Pixels

Abstract: In this work, the 1/f noise of the Source Follower (SF) in pinned-photodiode CMOS pixels is characterized. It is found that the 1/f noise in these pixels is actually due to a very limited number of traps and results in a Random Telegraph Signal (RTS). It is pointed out how the correlated-double sampling (CDS) reacts on this RTS. The temperature dependency of the imager read noise revealed two mechanisms of RTS during CDS.