Showing papers on "Signal-to-noise ratio published in 1999"

Adaptive reconstruction of phased array NMR imagery

Abstract: A method to model the NMR signal and/or noise functions as stochastic processes. Locally relevant statistics for the signal and/or noise processes are derived directly from the set of individual coil images, in the form of array correlation matrices, by averaging individual coil image cross-products over two or more pixel locations. An optimal complex weight vector is computed on the basis of the estimated signal and noise correlation statistics. The weight vector is applied to coherently combine the individual coil images at a single pixel location, at multiple pixel locations, or over the entire image field of view (FOV).

On the performance of adaptive modulation in cellular systems

Abstract: Adaptive modulation techniques have the potential to substantially increase the spectrum efficiency and to provide different levels of service to users, both of which are considered important for third-generation cellular systems. In this work, we propose a general framework to quantify the potential gains of such techniques. Specifically, we study the throughput performance gain that may be achieved by combining adaptive modulation and power control. Our results show that: (1) using adaptive modulation even without any power control provides a significant throughput advantage over using signal-to-interference-plus-noise ratio (SINR) balancing power control and (2) combining adaptive modulation and a suitable power control scheme leads to a significantly higher throughput as compared to no power control or using SINR-balancing power control. The first observation is especially important from an implementation point of view. Adjusting the modulation level without changing the transmission power requires far fewer measurements and feedback as compared to the SINR-balancing power control or the optimal power control. Hence, it is significantly easier to implement. Although presented in the context of adaptive modulation, the results also apply to other variable rate transmission techniques, e.g., rate adaptive coding schemes, coded modulation schemes, etc. This work provides valuable insight into the performance of variable rate transmission techniques in multi-user environments.

Single channel speech enhancement based on masking properties of the human auditory system

Abstract: This paper addresses the problem of single channel speech enhancement at very low signal-to-noise ratios (SNRs) (<10 dB). The proposed approach is based on the introduction of an auditory model in a subtractive-type enhancement process. Single channel subtractive-type algorithms are characterized by a tradeoff between the amount of noise reduction, the speech distortion, and the level of musical residual noise, which can be modified by varying the subtraction parameters. Classical algorithms are usually limited to the use of fixed optimized parameters, which are difficult to choose for all speech and noise conditions. A new computationally efficient algorithm is developed based on masking properties of the human auditory system. It allows for an automatic adaptation in time and frequency of the parametric enhancement system, and finds the best tradeoff based on a criterion correlated with perception. This leads to a significant reduction of the unnatural structure of the residual noise. Objective and subjective evaluation of the proposed system is performed with several noise types form the Noisex-92 database, having different time-frequency distributions. The application of objective measures, the study of the speech spectrograms, as well as subjective listening tests, confirm that the enhanced speech is more pleasant to a human listener. Finally, the proposed enhancement algorithm is tested as a front-end processor for speech recognition in noise, resulting in improved results over classical subtractive-type algorithms.

Investigations of power analysis attacks on smartcards

Abstract: This paper presents actual results from monitoring smartcard power signals and introduces techniques that help maximize such side-channel information. Adversaries will obviously choose attacks that maximize side-channel information, so it is very important that the strongest attacks be considered when designing defensive strategies. In this paper, power analysis techniques used to attack DES are reviewed and analyzed. The noise characteristics of the power signals are examined and an approach to model the signal to noise ratio is proposed. Test results from monitoring power signals are provided. Next, approaches to maximize the information content of the power signals are developed and tested. These results provide guidance for designing smartcard solutions that are secure against power analysis attacks.

Frequency estimation of distorted power system signals using extended complex Kalman filter

Abstract: The paper proposes an extended complex Kalman filter and employs it for the estimation of power system frequency in the presence of random noise and distortions. From the discrete values of the 3-phase voltage signals of a power system, a complex voltage vector is formed using the well known /spl alpha//spl beta/-transform. A nonlinear state space formulation is then obtained for this complex signal and an extended Kalman filtering approach is used to compute the true state of the model iteratively with significant noise and harmonic distortions. As the frequency is modeled as a state, the estimation of the state vector yields the unknown power system frequency. Several computer simulations test results are presented in the paper to highlight the usefulness of this approach in estimating near nominal and off-nominal power system frequencies.

Effects of Colored Noise on Stochastic Resonance in Sensory Neurons

Abstract: Noise can assist neurons in the detection of weak signals via a mechanism known as stochastic resonance (SR). We demonstrate experimentally that SR-type effects can be obtained in rat sensory neurons with white noise, 1/f noise, or 1/f{sup 2} noise. For low-frequency input noise, we show that the optimal noise intensity is the lowest and the output signal-to-noise ratio the highest for conventional white noise. We also show that under certain circumstances, 1/f noise can be better than white noise for enhancing the response of a neuron to a weak signal. We present a theory to account for these results and discuss the biological implications of 1/f noise. {copyright} {ital 1999} {ital The American Physical Society}

A comparison of two methods for measuring the signal to noise ratio on MR images

Abstract: The signal to noise ratio (SNR) is one of the important measures of the performance of a magnetic resonance imaging (MRI) system. The object of this study was to compare a single acquisition method, which estimates the noise from background pixels, with a dual acquisition method which estimates the noise from the subtraction of two sequentially acquired images. The dual acquisition method is more exact, but is slower to perform and requires image manipulation. A comparison between the two methods gave a good correlation, and a regression equation of SNRsingle = 1.1 + 0.94 SNRdual. The single acquisition method is therefore appropriate for use in a quality assurance programme, since it is quicker and simpler to perform and is a good indicator of the more exact measure.

Redundant filterbank precoders and equalizers. II. Blind channel estimation, synchronization, and direct equalization

Abstract: For pt.I see ibid., vol.47, no.7, p.1988-2006 (1999). Transmitter redundancy introduced using finite impulse response (FIR) filterbank precoders offers a unifying framework for single- and multiuser transmissions. With minimal rate reduction, FIR filterbank transmitters with trailing zeros allow for perfect (in the absence of noise) equalization of FIR channels with FIR zero-forcing equalizer filterbanks, irrespective of the input color and the channel zero locations. Exploiting this simple form of redundancy, blind channel estimators, block synchronizers, and direct self-recovering equalizing filterbanks are derived in this paper. The resulting algorithms are computationally simple, require small data sizes, can be implemented online, and remain consistent (after appropriate modifications), even at low SNR colored noise. Simulations illustrate applications to blind equalization of downlink CDMA transmissions, multicarrier modulations through channels with deep fades, and superior performance relative to CMA and existing output diversity techniques relying on multiple antennas and fractional sampling.

Clipping noise mitigation for OFDM by decision-aided reconstruction

Abstract: Clipping is often used to reduce the large peak-to-mean envelope power ratio of orthogonal frequency-division multiplexing (OFDM) signals. However, it introduces additional noise that degrades the system performance. A technique called decision-aided reconstruction (DAR) is proposed for mitigating the clipping noise. The performance of the proposed technique is evaluated for additive white Gaussian noise and static intersymbol interference channels. The effect of using a realistic channel estimate is also examined. Results show that the DAR technique can mitigate the clipping noise significantly for OFDM systems that have large block sizes.

CMOS imager with storage capacitor

Abstract: A CMOS imager having an improved signal to noise ratio and improved dynamic range is disclosed. The CMOS imager provides improved charge storage by fabricating a storage capacitor in parallel with the photocollection area of the imager. The storage capacitor may be a flat plate capacitor formed over the pixel, a stacked capacitor or a trench imager formed in the photosensor. The CMOS imager thus exhibits a better signal-to-noise ratio and improved dynamic range. Also disclosed are processes for forming the CMOS imager.

Noise in microelectromechanical system resonators

Abstract: Microelectromechanical system (MEMS) and nanoelectromechanical system (NEMS) based resonators and filters, ranging in frequencies from kHz to GHz, have been proposed. The question of how the stabilities of such resonators scale with dimensions is examined in this paper, with emphasis on the noise characteristics. When the dimensions of a resonator become small, instabilities that are negligible in macro-scale devices become prominent. The effects of fluctuations in temperature, adsorbing/desorbing molecules, outgassing, Brownian motion, Johnson noise, drive power and self-heating, and random vibration are explored. When the device is small, the effects of fluctuations in the numbers of photons, phonons, electrons and adsorbed molecules can all affect the noise characteristics. For all but the random vibration-induced noise, reducing the dimensions increases the noise. At submicron dimensions, especially, the frequency noise due to temperature fluctuations, Johnson noise, and adsorption/desorption are likely to limit the applications of ultra-small resonators.

Oximeter sensor with encoded temperature characteristic

Abstract: A method for operating an oximeter sensor, and corresponding apparatus, which includes an encoded temperature characteristic of a light emitter (52) in the sensor (50). The encoded temperature characteristic is read, and is used to modify a drive of the light emitter (52) in the sensor (50). This enables a light emitter (52) to be operated at its maximum allowable intensity to maximize a signal to noise ratio, without burning a patient, in accordance with the particular characteristics of that light emitter (52).

Using unpredictable information to minimize leakage from smartcards and other cryptosystems

Abstract: Methods and apparatuses are disclosed for securing cryptosystems against external monitoring attacks by reducing the amount (and signal to noise ratio) of useful information leaked during processing. This is generally accomplished by incorporating unpredictable information (101) into the cryptographic processing. Various embodiments of the invention use techniques such as reduction of signal to noise ratios, random noise generation (101, 105), clock skipping (240), and introducing entropy into the order of processing operations or the execution path. The techniques may be implemented in hardware or software, may use a combination of digital and analog techniques, and may be deployed in a variety of cryptographic devices.

Estimation of the noisy component of anatomical backgrounds.

Abstract: The knowledge of the relationship that links radiationdose and image quality is a prerequisite to any optimization of medicaldiagnostic radiology. Image quality depends, on the one hand, on the physical parameters such as contrast, resolution, and noise, and on the other hand, on characteristics of the observer that assesses the image. While the role of contrast and resolution is precisely defined and recognized, the influence of image noise is not yet fully understood. Its measurement is often based on imaging uniform test objects, even though real images contain anatomical backgrounds whose statistical nature is much different from test objects used to assess system noise. The goal of this study was to demonstrate the importance of variations in background anatomy by quantifying its effect on a series of detection tasks. Several types of mammographic backgrounds and signals were examined by psychophysical experiments in a two-alternative forced-choice detection task. According to hypotheses concerning the strategy used by the human observers, their signal to noise ratio was determined. This variable was also computed for a mathematical model based on the statistical decision theory. By comparing theoretical model and experimental results, the way that anatomical structure is perceived has been analyzed. Experiments showed that the observer’s behavior was highly dependent upon both system noise and the anatomical background. The anatomy partly acts as a signal recognizable as such and partly as a pure noise that disturbs the detection process. This dual nature of the anatomy is quantified. It is shown that its effect varies according to its amplitude and the profile of the object being detected. The importance of the noisy part of the anatomy is, in some situations, much greater than the system noise. Hence, reducing the system noise by increasing the dose will not improve task performance. This observation indicates that the tradeoff between dose and image quality might be optimized by accepting a higher system noise. This could lead to a better resolution, more contrast, or less dose.

Maximal-ratio combining over Nakagami fading channels with an arbitrary branch covariance matrix

Abstract: The error probability of maximal ratio combiners (MRCs) in a correlated Nakagami environment with an arbitrary branch covariance matrix is not available in the literature although some work has been done for two special cases with constant and exponential correlations. Correlation structures of this type, though of theoretical interest, may not match to practical situations, even for an antenna array of a totally symmetrical configuration. In this paper, we tackle the general problem by virtue of characteristic functions, avoiding the difficulty of explicitly obtaining the probability density function (PDF) for the signal-to-noise ratio (SNR) at the MRC output. We derive a simple closed-form solution for arbitrarily correlated channels with an integral fading parameter and a solution in the form of a one-fold integral for a fading parameter of nonintegral values. Simple algorithms have also been developed for their efficient implementation. The formulas are then used to analyze two possible antenna configurations for a base station, ending up with some findings of interest to system design.

Phase-Domain Processing of Optical Coherence Tomography Images

Abstract: In optical coherence tomography (OCT), images are usually formed from the envelope of the measured interference signal. Computation of the absolute magnitude of the signal for measurement of the envelope is a nonlinear process that destroys phase information. This study explores the idea of recording and processing the phase of the OCT interference signal before calculation of the magnitudes for display. Processing the partially coherent OCT signals in the complex domain provides the opportunity to correct phase aberrations responsible for speckle noise in OCT images. We describe an OCT system that incorporates a quadraturedemodulation scheme for accurate recording of the phase and amplitude of OCT signals from single or multiple detectors. A speckle-reduction technique that works in the complex domain, called the zeroadjustment procedure (ZAP), is investigated as an example of complex-domain processing. After demonstrating its speckle-correction properties mathematically and in numerical simulations, we apply ZAP to OCT images of living skin. The results show that ZAP reduces speckle contrast in regions where scatterer density is high and expands the range of gray values in the image. However, as presently implemented, ZAP tends to blur sharp boundaries between image features. © 1999 Society of Photo-Optical Instrumentation Engineers. [S1083-3668(99)01501-4]

Wavelet packet denoising of magnetic resonance images: importance of Rician noise at low SNR.

Abstract: Wavelet packet analysis is a mathematical transformation that can be used to post-process images, for example, to remove image noise (“denoising”). At a very low signal-to-noise ratio (SNR <5), standard magnitude magnetic resonance images have skewed Rician noise statistics that degrade denoising performance. Since the quadrature images have approximately Gaussian noise, it was postulated that denoising would produce better contrast and sharper edges if performed before magnitude image formation. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge blurring effects of these two approaches were examined in synthetic, phantom, and human MR images. While magnitude and complex denoising both significantly improved SNR and CNR, complex denoising yielded sharper edges and better low-intensity feature contrast. Magn Reson Med 1999;41:631–635, 1999. © 1999 Wiley-Liss, Inc.

Adaptive time-varying cancellation of wideband interferences in spread-spectrum communications based on time-frequency distributions

Abstract: The paper proposes an adaptive method for suppressing wideband interferences in spread-spectrum (SS) communications. The proposed method is based on the time-frequency representation of the received signal from which the parameters of an adaptive time-varying interference excision filter are estimated. The approach is based on the generalized Wigner-Hough transform as an effective way to estimate the instantaneous frequency of parametric signals embedded in noise. The performance of the proposed approach is evaluated in the presence of linear and sinusoidal FM interferences plus white Gaussian noise in terms of the SNR improvement factor and bit error rate (BER).

Instantaneous frequency estimation of polynomial FM signals using the peak of the PWVD: statistical performance in the presence of additive gaussian noise

Abstract: The peak of the polynomial Wigner-Ville distribution (PWVD) has been previously proposed as an estimator of the instantaneous frequency (IF) for a monocomponent polynomial frequency modulated (FM) signal. In this paper, we evaluate the statistical performance of this estimator in the case of additive white Gaussian noise and provide an analytical expression for the variance. We show that for a given PWVD order, the estimator performance can be improved by a proper choice of the kernel coefficients in the PWVD. A performance comparison between the PWVD based IF estimator and another previously proposed one based on the high-order ambiguity function (HAF) is also provided, Simulation results show that for a signal-to-noise ratio larger than 3 dB, the proposed sixth-order PWVD outperforms the HAF in estimating the IF of a third- or fourth-order polynomial phase signal, evaluated at the central point of the observation interval.

Signaling constellations for fading channels

Abstract: The performance of various coherent 8-ary and 16-ary modulations in additive white Gaussian noise (AWGN) and slowly fading channels are analyzed. New expressions for the exact symbol error rates (SER) in fading with diversity combining are derived for any two-dimensional signaling format having polygonal decision boundaries. Maximal ratio combining, equal gain combining, and selection combining are considered. The SER formulas obtained make it possible for the first time to optimize parameters of various constellations precisely and to determine,which constellation has the lowest probability of error. For example, a star constellation such as that specified in the CCITT V.29 standard can be improved by adjusting the amplitude ratios of the points in the constellation to save about 0.63 dB power in AWGN without sacrificing the phase error tolerance, while maintaining the same error rate. The sensitivity of each constellation to phase error is presented and comparisons are made. Six 8-ary signal sets and 11 16-ary signal sets are examined using the new symbol error probability formulas to determine best signal sets for fading channels.

Signal-to-noise ratio and signal-to-noise efficiency in SMASH imaging.

Abstract: A general theory of signal-to-noise ratio (SNR) in simultaneous acquisition of spatial harmonics (SMASH) imaging is presented, and the predictions of the theory are verified in imaging experiments and in numerical simulations. In a SMASH image, multiple lines of k-space are generated simultaneously through combinations of magnetic resonance signals in a radiofrequency coil array. Here, effects of noise correlations between array elements as well as new correlations introduced by the SMASH reconstruction procedure are assessed. SNR and SNR efficiency in SMASH images are compared with results using traditional array combination strategies. Under optimized conditions, SMASH achieves the same average SNR efficiency as ideal pixel-by-pixel array combinations, while allowing imaging to proceed at otherwise unattainable speeds. The k-space nature of SMASH reconstructions can lead to oscillatory spatial variations in noise standard deviation, which can produce local enhancements of SNR in particular regions.

Modeling of CMOS digital-to-analog converters for telecommunication

Abstract: This paper gives an overview of some of the effects caused by circuit mismatch and parasitics in binary weighted digital-to-analog converters (DACs), and, as a special case, a current-steering CMOS converter. Matlab is used as a behavior-level simulator. In telecommunications applications, the frequency-domain parameters are of the greatest importance. Therefore, the characterization of the device and its performance is determined by frequency parameters such as the signal-to-noise ratio, spurious-free dynamic range, multitone power ratio, etc. In this paper, we show how these frequency-domain parameters are affected when mismatch errors and finite output impedance are applied to a current-steering CMOS DAC. We discuss how static performance is affected when changing the size of the errors and fundamental circuit parameters. The impact of dynamic errors such as glitches, slewing, and bit skew is discussed. Measurement results from 14-bit DACs are also shown to illustrate the correlation with the modeling.

A general efficient method for chaotic signal estimation

Abstract: This article presents a general, computationally efficient method for chaotic signal estimation based on the connection between the symbolic sequence and the initial condition of a chaotic system. The performance of the method in white Gaussian noise is evaluated. The new method is asymptotically unbiased and attains the Cramer-Rao lower bound at high signal-to-noise ratios.

A dynamic system approach to speech enhancement using the H/sub /spl infin// filtering algorithm

Abstract: This paper presents a new approach to speech enhancement based on the H/sub /spl infin// filtering. This approach differs from the traditional modified Wiener/Kalman filtering approach in the following two aspects: (1) no a priori knowledge of the noise source statistics is required, the only assumption made is that noise signals have a finite energy; (2) the estimation criterion for the filter design is to minimize the worst possible amplification of the estimation error signals in terms of the modeling errors and additive noise. Since most additive noise in speech are nonGaussian, this estimation approach is highly robust and more appropriate in practical speech enhancement. The proposed approach is straightforward to implement, as detailed in this paper. Experimental results show consistently superior enhancement performance of the H/sub /spl infin// filtering algorithm over the Kalman filtering counterpart, measured by the global signal-to-noise ratio (SNR). Examination of the spectrogram displays for the enhanced speech shows that the H/sub /spl infin// filtering approach tends to be more effective where the assumptions on the noise statistics are less valid.

Comparative analysis of SNR for image sensors with enhanced dynamic range

Abstract: Dynamic range is a critical figure of merit for image sensors. Often a sensor with higher dynamic range is regarded as higher quality than one with lower dynamic range. For CCD and CMOS sensors operating in the integration mode the sensor SNR monotonically increases with the signal. Therefore, a sensor with higher dynamic range, generally, produces higher quality images than one with lower dynamic range. This, however, is not necessarily the case when dynamic range enhancement schemes are used. For example, suing the well capacity adjusting scheme dynamic range is enhanced but at the expense of substantial degradation in SNR. On the other hand, using multiple sampling dynamic range can be enhanced without degrading SNR. Therefore, even if both schemes achieve the same dynamic range the latter can produce higher image quality than the former. The paper provides a quantitative framework for comparing SNR for image sensors with enhanced dynamic range. We introduce a simple model to describe the sensor output response as a function of the photogenerate signal, dark signal, and noise for sensors operation in integration mode with and without dynamic range enhancement schemes. We use the model to quantify and compare dynamic range and SNR for three sensor operation modes, integration with shuttering, using the well capacity adjusting scheme, and using multiple sampling.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Optimal linear data analysis for surface plasmon resonance biosensors

Abstract: Surface plasmon resonance biosensors measure the thickness or molecular concentration of a biolayer by analyzing small changes in measured reflection spectra. In this paper, we describe linear spectral analysis techniques designed to produce measurements with the maximum possible signal-to-noise ratio. We show how, under appropriate assumptions, an optimal analysis method may be derived for measuring any system parameter, and how measurements of multiple parameters may be made independent in exchange for an decrease in signal to noise ratio. Compared to two conventional data analysis techniques (quadratic fit and centroid methods) using simulated data, the linear techniques show a 30% increase in signal to noise ratio. In application to actual thiol binding data, the linear method yields a signal to noise ratio 46% greater than that of the centroid method and 65% greater than that of the quadratic fit method. This level of noise reduction was achieved by using the ability of the linear methods to reject noise caused by light source brightness variations.

An adaptive noise canceller with low signal distortion for speech codecs

Abstract: This paper proposes an adaptive noise canceller (ANC) with low signal distortion for speech codecs. The proposed ANC has two adaptive filters: a main filter (MF) and a subfilter (SF). The signal-to-noise ratio (SNR) of input signals is estimated using the SF. To reduce signal distortion in the output signal of the ANC, a step size for coefficient update in the MF is controlled according to the estimated SNR. Computer simulation results using speech and diesel engine noise recorded in a special-purpose vehicle show that the proposed ANC reduces signal distortion in the output signal by up to 15 dB compared with a conventional ANC. Results of subjective listening tests show that the mean opinion scores (MOSs) for the proposed ANC with and without a speech codec are one point higher than the scores for the conventional ANC.

Localization of wideband signals using least-squares and total least-squares approaches

Abstract: In this paper, we introduce a new focusing technique for localization of wideband signals. Relaxing the unitary assumption for the focusing matrices, we formulate the least-square (LS) and the total least-square (TLS) coherent signal-subspace methods. The TLS is an alternative to the conventional LS and uses the fact that errors can exist both in the focusing location matrix as well as in the estimated location matrix at a given frequency bin. To prevent the focusing loss, we use a class of focusing matrices that are constant under multiplication by their Hermitian transpose. The class of unitary matrices comports with this property. We then develop a new focusing technique based on a modification to the TLS (MTLS). It is shown that the computational complexity of the new technique is significantly lower than that for the rotational signal subspace method (RSS). The focusing gain of the new technique is also larger than the focusing gain of the RSS algorithm. The simulation study shows that, compared with the RSS, the new algorithm has a smaller resolution signal to-noise ratio (SNR).

Exact image deconvolution from multiple FIR blurs

Abstract: We address the problem of restoring an image from its noisy convolutions with two or more blur functions (channels). Deconvolution from multiple blurs is, in general, better conditioned than from a single blur, and can be performed without regularization for moderate noise levels. We characterize the problem of missing data at the image boundaries, and show that perfect reconstruction is impossible (even in the no-noise case) almost surely unless there are at least three channels. Conversely, when there are at least three channels, we show that perfect reconstruction is not only possible almost surely in the absence of noise, but also that it can be accomplished by finite impulse response (FIR) filtering. Such FIR reconstruction is vastly more efficient computationally than the least-squares solution, and is suitable for low noise levels. Even in the high-noise case, the estimates obtained by FIR filtering provide useful starting points for iterative least-squares algorithms. We present results on the minimum possible sizes of such deconvolver filters. We derive expressions for the mean-square errors in the FIR reconstructions, and show that performance comparable to that of the least-squares reconstruction may be obtained with relatively small deconvolver filters. Finally, we demonstrate the FIR reconstruction on synthetic and real data.