Showing papers on "Noise (signal processing) published in 1996"

Active Noise Control Systems: Algorithms and DSP Implementations

Abstract: From the Publisher: Active noise control (ANC) is rapidly becoming the most effective way to reduce noises that can otherwise be very difficult and expensive to control ANC is achieved by introducing a canceling "anti-noise" wave through an appropriate array of secondary sources When applied accurately, ANC can provide effective solutions to noise-related problems in a broad range of areas, including manufacturing and industrial operations as well as consumer products Consequently, ANC research and development has become an important focus of both industrial applications and engineering research Active Noise Control Systems: Algorithms and DSP Implementations introduces the basic concepts of ANC with an emphasis on digital signal processing (DSP) hardware and adaptive signal processing algorithms, both of which have come into prominence within the last decade The authors emphasize the practical aspects of ANC systems by combining the principles of adaptive signal processing with both experimental results and practical implementation Applications are cited in many fields and encompass all types of noise media, including air-acoustic, hydroacoustic, vibrations, and others The specific implementation stressed is based on the TMS320 family of signal processors from Texas Instruments, which are the most widely used worldwide Coverage of theory includes concise derivations and analyses of commonly used adaptive structures and algorithms for active noise control applications, which are enhanced by the inclusion of a floppy disk featuring C and assembly programs for implementing many ANC systems Mathematical representations are employed and the source code included on the disk is in a form that is easily accessible to anyone using the book For practicing engineers, researchers, and advanced students in signal processing, Active Noise Control Systems: Algorithms and DSP Implementations will serve as a comprehensive, state-of-the-art text/reference on this important and rapidly de

Principal Component Neural Networks: Theory and Applications

Abstract: A Review of Linear Algebra. Principal Component Analysis. PCA Neural Networks. Channel Noise and Hidden Units. Heteroassociative Models. Signal Enhancement Against Noise. VLSI Implementation. Appendices. Bibliography. Index.

Subspace-based channel estimation for code division multiple access communication systems

Abstract: We consider the estimation of channel parameters for code-division multiple access (CDMA) communication systems operating over channels with either single or multiple propagation paths. The multiuser channel estimation problem is decomposed into a series of single user problems through a subspace-based approach. By exploiting the eigenstructure of the received signal's sample correlation matrix, the observation space can be partitioned into a signal subspace and a noise subspace without prior knowledge of the unknown parameters. The channel estimate is formed by projecting a given user's spreading waveform into the estimated noise subspace and then either minimizing the likelihood or minimizing the Euclidean norm of this projection. Both of these approaches yield algorithms which are near-far resistant and do not require a preamble.

A course in digital signal processing

Abstract: From the Publisher: This book provides readers with a precise, comprehensive, practical, and up-to-date exposition on digital signal processing. Both mathematical and useful, it presents a rigorous course of study to help readers learn the theory and practice of DSP. Porat includes physical and engineering application, coupled with mathematical derivations to the extent necessary for understanding DSP concepts and methods. The book contains detailed discussion of practical spectral analysis, including the use of windows for spectral analysis, sinusoidal signal analysis, and the effect of noise. There is also comprehensive treatment of both FIR and IIR filters, including detailed design procedures and MATLAB tools.

Singular Spectrum Analysis: A New Tool in Time Series Analysis

Abstract: Mathematical Notes: Review of Linear Algebra. Eigenvalues and Eigenvectors. Multivariate Statistics. Theory and Methods: Foundations of SSA. Details. Noise. Applications: Signal Detection. Filtering. Prediction. Phase Space Reconstruction. Index.

Advanced Signal Processing and Digital Noise Reduction

Abstract: Introduction to Signal Processing and Noise Reduction Stochastic Processes and Statistical Characterization of Signals Signal Transforms Bayesian Probabilistic Estimation Theory Wiener Filters and Kalman Filters Linear Prediction Models Sample-Adaptive Least Squared Error Filters Power Spectrum Estimation Finite-State Statistical Models for Non-stationary Stochastic Processes Interpolation of a Sequence of Samples Modelling, Detection and Removal of Impulsive Noise Spectral Subtraction Removal of Transient Noise Pulses Echo Cancellation and Multi-Input Adaptive Noise Reduction Adaptive Notch Filters Channel Equalization Noise Compensation for Speech Recognition in Adverse Environments.

Watermarking method and apparatus for compressed digital video

Abstract: A digital watermarking method and apparatus allows for the watermarking of a digital video signal in a compressed form, thereby allowing watermarking of a pre-compressed video sequence without requiring the decoding and re-coding of the signal. The watermark signal is a sequence of information bits which has been modulated by a pseudo-random noise sequence to spread it in the frequency domain. The video signal is transform coded, preferably with a discrete cosine transform, and a watermark signal, which has been transform coded using the same type of transform, is added to the coded video signal. The system also includes bitstream control to prevent an increase in the bit rate of the video signal. This allows the system to be used with transmission channels having strict bit rate constraints. For each transform coefficient of the video signal, the number of bits necessary to encode the watermarked coefficient is compared to the number of bits necessary to encode the unwatermarked coefficient. If more bits are required to transmit a watermarked coefficient than to transmit the corresponding unwatermarked coefficient, the watermarked coefficient is not output, and the unwatermarked coefficient is output in its place. When watermarking interframe coded data, a drift compensation signal may be used to compensate for the accumulating variations in the decoded video signal stored at the receiver. The system may also include an encryption/decryption capability, with the watermarking apparatus located at either the transmitting or receiving end of the transmission channel.

Differential chaos shift keying : A robust coding for chaotic communication

Abstract: The statistical attributes of the received chaotic signal are evaluated in the noncoherent chaos shift keying (CSK) modulation scheme in order to perform the demodulation. This approach offers a very simple circuit configuration, but like the other solutions proposed before, it is sensitive to the channel noise and imperfections. In the differential chaos shift keying (DCSK) approach every incoming information bit is encoded into two bits. The first signal serves as a reference, while the second one carries the information. If the binary information to be transmitted is ”1” then first the reference signal is transmitted and after it that signal is repeated. For a ”0”, the inverted version of the reference signal is transmitted. The receiver has a storage capability and the demodulation is performed by evaluating the cross-correlation of the two signals. The effect of noise is reduced by averaging and proper design of the channel filter. The effect of channel imperfections is also reduced, because both the signal and its reference are sent via the same channel. The DCSK approach offers a very robust solution for the chaos communication and it can be implemented with very simple circuitry even in the microwave frequency region.

Stochastic Resonance in a Neuronal Network from Mammalian Brain

Abstract: Stochastic resonance, a nonlinear phenomenon in which random noise optimizes a system’s response to a signal, has been postulated to provide a role for noise in information processing in the brain. In these experiments, a time varying electric field was used to deliver both signal and noise directly to a network of neurons from mammalian brain. As the magnitude of the stochastic component of the field was increased, resonance was observed in the response of the neuronal network to a weak periodic signal. This is the first demonstration of stochastic resonance in neuronal networks from the brain. [S0031-9007(96)01583-9]

Aperiodic stochastic resonance.

Abstract: Stochastic resonance (SR) is a phenomenon wherein the response of a nonlinear system to a weak periodic input signal is optimized by the presence of a particular level of noise. Recently, we presented a method and theory for characterizing SR-type behavior in excitable systems with aperiodic (i.e., broadband) input signals [Phys. Rev. E 52, R3321(1995)]. We coined the term aperiodic stochastic resonance (ASR) to describe this general type of behavior. In that earlier study, we demonstrated ASR in the FitzHugh-Nagumo neuronal model. Here we demonstrate ASR in three additional systems: a bistable-well system, an integrate-and-fire neuronal model, and the Hodgkin-Huxley (HH) neuronal model. We present computational and theoretical results for each system. In the context of the HH model, we develop a general theory for ASR in excitable membranes. This work clearly shows that SR-type behavior is not limited to systems with periodic inputs. Thus, in general, noise can serve to enhance the response of a nonlinear system to a weak input signal, regardless of whether the signal is periodic or aperiodic. \textcopyright{} 1996 The American Physical Society.

Reduction of physiological fluctuations in fMRI using digital filters

Abstract: Data obtained from functional magnetic resonance imaging are often limited by a low signal-to-noise ratio. The time-course data obtained from activated regions contain both system noise and physiological noise, primarily linked to the heart and respiratory rates, that are superimposed on task induced signals. Time averaging of a practical number of data sets is not very effective in improving the signal-to-noise ratio because neither system nor physiological noise is truly random. In this paper, a method is presented for filtering unwanted physiological fluctuations, including aliased signals that are formed as a result of long repetition time (TR) values. A pulse oximeter was used to obtain cardiac and respiratory information during the scanning period. Finite impulse response band-reject digital filters were designed to remove the physiological fluctuations. For comparison, cross-correlation analyses were performed at the same level of statistical significance on both filtered and unfiltered data. We demonstrate that this method can improve the detection of weak signals without increasing the probability of false positives.

Paper 11 – Circuit implementation of synchronized chaos with applications to communications

Abstract: Publisher Summary An analog circuit implementation of the chaotic Lorenz system is described and used to demonstrate two possible approaches to private communications based on synchronized chaotic systems. Furthermore, the potential approach to communications applications is based on signal masking and recovery. In signal masking, a noise like masking signal is added at the transmitter to the information-bearing signal m(t) and at the receiver the masking is removed. In our system, the basic idea is to use the received signal to regenerate the masking signal at the receiver and subtract it from the received signal to recover m(t).

Nonlinear unsharp masking methods for image contrast enhancement

Abstract: In the unsharp masking approach for image enhancement, a fraction of the highpass filtered version of the image is added to the original image to form the enhanced version The method is simple, but it suffers from two serious drawbacks First, it enhances the contrast in the darker areas perceptually much more strongly than that in the lighter areas Second, it enhances the noise and/or digitization effects, particularly in the darker regions, resulting in visually less pleasing enhanced images In general, noise can be suppressed with lowpass filters, which are associated with the blurring of the edges On the other hand, contrast can be enhanced with highpass filters, which are associated with noise amplification A reasonable solution, therefore, is to use suitable nonlinear filters which combine the features of both highpass and lowpass filters This paper outlines several new methods of unsharp masking based on the use of such nonlinear filters Computer simulations have verified the superior results obtained using these filters In addition, a new measure of contrast enhancement is introduced which quantitatively supports the improvement obtained using the proposed methods

Estimation of the directions of arrival of spatially dispersed signals in array processing

Abstract: The problem of estimating the DOA of spatially distributed signals is examined. A mathematical model is first established by making some reasonable assumptions. The correlation matrix of a distributed signal is then derived. The important properties of the correlation matrix are studied, revealing that even if the matrix is of full rank (being equal to the number of sensors), which renders conventional high resolution array processing methods inapplicable, the dimensionality of the signal subspace can be approximated to a number usually much smaller than the number of sensors. From the observation, the quasi-signal and noise subspaces are identified, and, utilising the orthogonality of the signal and noise subspaces, an algorithm (DISPARE) has been developed. Analytic studies and numerical evaluations are carried out to examine the performance of the algorithm under various environments and show that it is indeed effective.

Speech signal transmitting and receiving apparatus with noise sensitive volume control

Abstract: A speech signal transmitting receiving apparatus, such as a portable telephone set, includes a speech signal transmitting encoding circuit, a noise domain detection unit, a noise level detection unit and a controller. The speech signal transmitting encoding circuit compresses input speech signals by digital signal processing at a high efficiency. The noise domain detection unit detects the noise domain using an analytic pattern produced by the speech signal transmitting encoding circuit. The noise level detection unit detects the noise level of the noise domain detected by the noise domain detection unit. The controller controls the received sound volume responsive to the noise level detected by the noise level detection unit.

Threshold detection of wideband signals : a noise-induced maximum in the mutual information

Abstract: In some nonlinear dynamic systems, the addition of noise to a weak periodic signal can increase the detectability of the signal, a phenomenon belonging to a class of noise-induced cooperative behavior known as stochastic resonance (SR). There has been much recent speculation on the possible role of SR in signal processing by sensory neurons. However, most results have focused exclusively on increasing the output signal-to-noise ratio (SNR) of time-periodic signals, even though many real-world signals (e.g., those encountered in some neurophysiological and communications applications) are not of this form. Here we consider a generalization of SR, based on the Shannon mutual information between the transmitted and received signal. This generalization can be applied to cases (e.g., the information transmitted by the output spike train of an integrate-fire model neuron which we consider here), involving aperiodic input signals for which the output SNR might be ill-defined, uninformative, or irrelevant. Since the SR-like effect in the transmitted information disappears with the optimal choice of model parameters, we suggest that such an effect is likely to be particularly relevant to systems, e.g., neuronal populations, in which natural circuit constraints may render parameter optimization impractical.

The constant modulus array for cochannel signal copy and direction finding

Abstract: The constant modulus (CM) array is a blind adaptive beamformer capable of recovering a narrowband signal among several cochannel sources without using a pilot or training signal. It is a conventional weight-and-sum adaptive beamformer whose weights are updated by the constant modulus algorithm. An adaptive signal canceller follows the beamformer to remove the captured signal from the array input and to provide an estimate of its direction vector. Based on a Wiener model, we investigate the steady-state properties of the CM array and the signal canceller. For mutually uncorrelated sources and noise, it is shown that the signal canceller exactly removes the source captured by the array. Thus, identical stages of the CM array and signal canceller may be used in a multistage system to recover several cochannel sources. Computer simulations are presented to verify the analytical results and to illustrate the transient behavior of the system.

Automatic power control system for a code division multiple access (cdma) communications system

Abstract: A receiver receives signals and noise over a frequency spectrum of a desired received signal. The desired received signal is spread using code division multiple access. The received signals and noise are demodulated to produce a demodulated signal. The demodulated signal is despread using a code uncorrelated with a code associated with the desired received signal. A power level of the despread demodulated signal is measured as an estimate of the noise level of the frequency spectrum.

A fractal-based algorithm for detecting first arrivals on seismic traces

Abstract: A new algorithm is proposed for the automatic picking of seismic first arrivals that detects the presence of a signal by analyzing the variation in fractal dimension along the trace. The “divider-method” is found to be the most suitable method for calculating the fractal dimension. A change in dimension is found to occur close to the transition from noise to signal plus noise, that is the first arrival. The nature of this change varies from trace to trace, but a detectable change is always found to occur. The algorithm has been tested on real data sets with varying S/N ratios and the results compared to those obtained using previously published algorithms. With an appropriate tuning of its parameters, the fractal-based algorithm proved more accurate than all these other algorithms, especially in the presence of significant noise. The fractal method proved able to tolerate noise up to 80% of the average signal amplitude. However, the fractal-based algorithm is considerably slower than the other methods and hence is intended for use only on data sets with low S/N ratios.

Digital signal processing for linearization of small input signals to a tri-state power switch

Abstract: An all digital switching amplifier includes an input over-sampling filter (20) for receiving a pulse code modulated (PCM) digital input signal. Oversampled PCM data are supplied to a multibit noise shaper (22), which frequency shapes quantization error. The oversampled, noise-shaped, PCM data is applied to an amplitude-to-time converter (24), which produces variable-width command pulses. The command pulses from converter (24) are applied to switch drive logic circuit (28) to enable switches (26) to connect a filter (30) and load (32) to power supply (34).

Self-interference cancellation for two-party relayed communication

Abstract: A source transmitted signal is cancelled at the receiver (110) associated with the transmitter (104), so that the desired received signal can be extracted from a composite received signal consisting of the source signal relayed from the relay station (Relay Device) along with the desired received signal from the other user in the pair, plus additive noise. The invention takes advantage of the fact that each of the users (User 1, User 2) knows a priori the exact structure of its source transmitted signal and can estimate the channel characteristics between the relay station and itself.

Signal correlation technique for a receiver of a spread spectrum signal including a pseudo-random noise code that reduces errors when a multipath signal is present

Abstract: A receiver of a radio frequency signal having a pseudo-random noise (PRN) code modulated on a carrier, and techniques of processing such a signal that are especially adapted for ranging applications. Such an application is in a global positioning system (GPS or GLONASS) receiver. Both of the receiver DLL code and PLL carrier loops include a loop component that senses an error in its main loop caused by the presence of a multipath signal. The main loop is continuously adjusted by this sensed error, thereby causing the loop to track more precisely and minimize the effect of the multipath signal. The result is a more accurate range measurement.

Compact image capture device

Abstract: The image capture device incorporates an array of photodetectors (100), utilizing an integral current mirror formed at each photodetector location to increase photodetector current output. A correlated double sampling circuit is also formed at each photodetector location to sum the current generated by the current mirror over each exposure period, so as to produce a voltage proportional to the radiation intensity incident at each photodetector location. The correlated double sampling circuit is used to reduce noise in the photodetected signal and to eliminate the effect of dark current. Combining the image capture device with a unique lenslet array (210) forms an extremely compact optical array camera. An embodiment with a mechanical shutter is also disclosed.

Verification techniques for substrate coupling and their application to mixed-signal IC design

Abstract: This paper presents techniques for the analysis of substrate-coupled noise in mixed-signal integrated circuits. Advantages and limitations of some commonly employed verification techniques for substrate coupling are outlined. A preprocessed boundary element method introduced in this paper utilizes precomputed z parameters to generate an analytical model for substrate impedance in a preprocessing stage. Truncated series expansions of the analytical impedance model are used to accelerate solution of the resulting boundary element equations. A methodology that applies these fast techniques to the verification of large mixed-signal circuits and results that confirm its efficiency are described. This complete methodology has been applied to the design and verification of an industrial mixed-signal video analog-to-digital converter IC for substrate noise problems.

Maximum likelihood array processing for stochastic coherent sources

Abstract: Maximum likelihood (ML) estimation in array signal processing for the stochastic noncoherent signal case is well documented in the literature We focus on the equally relevant case of stochastic coherent signals Explicit large-sample realizations are derived for the ML estimates of the noise power and the (singular) signal covariance matrix The asymptotic properties of the estimates are examined, and some numerical examples are provided In addition, we show the surprising fact that the ML estimates of the signal parameters obtained by ignoring the information that the sources are coherent coincide in large samples with the ML estimates obtained by exploiting the coherent source information Thus, the ML signal parameter estimator derived for the noncoherent case (or its large-sample realizations) asymptotically achieves the lowest possible estimation error variance (corresponding to the coherent Cramer-Rao bound)

Noise-reducing stethoscope

Abstract: A noise-reducing stethoscope comprising a body sound sensor (24) for placement on a body to detect internal body sounds and output in response an electrical signal, an ambient noise sensor (26), proximate the body sound sensor (24), to detect ambient noise and output in response an electrical signal, and a difference comparator (46) for accepting the electrical signals from both sensors and providing in response a difference signal, in which the difference signal contribution from ambient noise is less than the contribution from ambient noise to the body sound sensor electrical signal to cancel noise artifacts in the body sound sensor electrical signal.

Noise cancellation system

Abstract: A noise controlled type handset processes the output of non-directional noise detector installed outside of the handset by a first adaptive filter, delivers a control sound from a control speaker, controls the first adaptive filter so that the output of an error detector near the ear may be small, simultaneously processes the noise detection signal in a second adaptive filter, subtracts from the noise signal mixing into a bi-directional talking microphone, and controls the second adaptive filter so that the subtraction signal may be small, thereby taking back the signal of voice only by reducing noise by delivering through the speaker, and sending a transmission output at the same time.

Feed-forward correction loop with adaptive predistortion injection for linearization of RF power amplifier

Abstract: A linearization scheme for an RF power amplifier combines an adaptive predistortion modulator with a feedforward error correction loop, which cancels noise imparted by predistortion modulation to the amplified signal, and minimizes distortion in the RF amplifier's output to a level that allows the use of a low cost auxiliary RF error amplifier in the feed-forward loop. The predistortion correction mechanism produces a predistortion signal based upon the input signal and is adaptively adjusted by an error signal extracted from the output of the a main RF power amplifier. The input signal is supplied to a work function generator unit and to a subtraction unit, which is also coupled to receive a fractional portion of the amplifier output signal and outputs the RF error component. The RF error component is coupled to a predistortion function generator, which is driven by the work function generator unit. The predistortion modulator uses the output of the predistortion function generator to predistort the input signal by a compensation characteristic equal and opposite to the distortion expected at the output of the main RF amplifier. When subjected to the transfer function of the RF amplifier, the predistortion signal injected into the input signal path will effectively cancel the amplifier's anticipated distortion behavior. The predistortion is made adaptive by tracking the error signal and coupling this error signal to the predistortion function generator.

Analysis of nonlinear FM signals by pattern recognition of their time-frequency representation

Abstract: The aim is to propose a method for detection and parameter estimation of nonlinear FM signals, mono- or multicomponent, embedded in white Gaussian noise. The proposed approach consists in mapping the signal into the time-frequency plane by a time-frequency distribution with reassignment, and then in applying a pattern recognition technique, like the Hough transform, to the time-frequency representation to recognize specific shapes. The advantages of this method over the conventional maximum likelihood estimator are (1) a simpler implementation, because it reduces the dimension of the search space and (2) a consistent attenuation of the interference terms between different components of a signal or between signal and noise.