Variation Of Noise Power Research Articles

Noise Reduction Using Sparsity Constrained and Regularized Iterative Thresholding Algorithm and Dictionary

Dictionary-based methods are recognized for their ability to estimate clear speech from speech contaminated with noise. However, these techniques face a limitation in distinguishing between speech and noise components. The presented algorithm addresses this challenge by introducing an innovative solution through an iterative thresholding method. In this method, the thresholding is based on noise characteristics and remains independent of variations in noise power. To determine the stopping criteria for thresholding, the algorithm leverages the structure of the speech signal in time–frequency domains using the Gini Index. Remarkably, this technique adeptly extracts reliable signal components from a noisy time–frequency magnitude spectrum, performing well under non-varying and varying Signal-to-Noise Ratio (SNR) conditions. Importantly, it achieves this without the need for mask functions, voice activity detection techniques, noise or mixture dictionaries, or SNR information, which are essential components in other dictionary-based methods. Following the thresholding process, the clean speech is assessed using a dictionary-based approach to restore perceptual loss. In evaluating its performance, the algorithm is compared with traditional enhancement techniques concerning perceptual evaluation of speech quality and short-time objective intelligibility. The assessment is conducted under various background noise conditions, including babble, white, factory, and Volvo noises. The proposed algorithm exhibits superior performance, particularly in low and varying SNR scenarios.

Channel-Adaptive Wireless Image Transmission With OFDM

We present a learning-based channel-adaptive joint source and channel coding (CA-JSCC) scheme for wireless image transmission over multipath fading channels. The proposed method is an end-to-end autoencoder architecture with a dual-attention mechanism employing orthogonal frequency division multiplexing (OFDM) transmission. Unlike the previous works, our approach is adaptive to channel-gain and noise-power variations by exploiting the estimated channel state information (CSI). Specifically, with the proposed dual-attention mechanism, our model can learn to map the features and allocate transmission-power resources judiciously based on the estimated CSI. Extensive numerical experiments verify that CA-JSCC achieves state-of-the-art performance among existing JSCC schemes. In addition, CA-JSCC is robust to varying channel conditions and can better exploit the limited channel resources by transmitting critical features over better subchannels.

Hybrid CPFSK/OQPSK modulation transmission techniques’ performance efficiency with RZ line coding–based fiber systems in passive optical networks

<p><span>This study shows hybrid continuous-phase frequency shift keying (CPFSK)/optical quadrature-phase shift keying (OQPSK) modulation transmission techniques’ performance efficiency with return-to-zero (RZ) line coding scheme–based fiber systems in passive optical networks. Max. Q factor/min. bit error rate variations versus modulation frequency and fiber length are studied in detail for various bits/symbol, based on hybrid proposed modulation transmission techniques. Also, optical power and received electrical power variations are simulated with fiber-length variations at a specified modulation frequency of 300 GHz. Max. Q Factor, min. BER, max. signal power, and min. noise power variations are based on hybrid modulation techniques for CPFSK/OQPSK of 32 bits/symbol and a modulation frequency of 500 GHz through a fiber length of 30 km.</span></p>

Theoretical and numerical analyses for PDM-IM signals using Stokes vector receivers

Different from current coherent-detection-based long-haul transmission systems, inter- and intradatacenter transmissions require a simpler transmitter and receiver. A promising way to significantly meet the demands of datacenter transmission is polarization division multiplexing intensity modulation with direct detection (PDM-IM-DD) using a Stokes vector receiver (SVR). However, for different SVR architectures, the corresponding demultiplexing matrix is required to recover the Stokes vectors from the detected signals, which are combined with an arbitrary state of polarization (SOP), will change the effect of noise dynamically and significantly influence the system performance. In this study, PDM-IM signals using four SVRs, i.e., a 90° optical hybrid with 2 balanced photodetectors (BPDs) and 2 photodetectors (PDs), a 90° optical hybrid with 4 PDs, a Stokes analyzer and a 3 × 3 coupler with 4 PDs, are studied theoretically and numerically. Theoretical system models using the four SVRs are developed, and the noise power variations are analyzed quantitatively based on these models. Moreover, the performance of the systems is also investigated for 224 Gbit/s polarization division multiplexing pulse amplitude modulation 4 level with direct detection (PDM-PAM4-DD) transmission in a simulation. The simulation results show that the bit error rate (BER) performance of the systems is consistent with the theoretical noise power variation curves. The theoretical analysis scheme is helpful for the practical design of SVR-based systems.

Seasonal and Diurnal Variations in Long‐Period Noise at SPREE Stations: The Influence of Soil Characteristics on Shallow Stations’ Performance

Abstract The Superior Province Rifting Earthscope Experiment (SPREE) recorded continuous seismic data over the Midcontinent Rift from April 2011 through October 2013. Analysis of power spectral density (PSD) estimates shows that horizontal noise levels at periods >20 s vary seasonally and diurnally. During winter, horizontal noise power at many SPREE stations is within 5 dB of nearby Transportable Array (TA) stations. As the ground thaws, SPREE stations in fine‐grained material such as silt or clay become noisier due to changes in the mechanical properties of the soil. During summer, the daily mean PSD value of stations in fine‐grained material is approximately 10–20 dB higher than in the winter, and daytime noise levels are 20–30 dB higher than nights. Stations in sandy material also show diurnal variations of 20–30 dB during summer, but the daily mean PSD value varies no more than 5–10 dB during the year. Most neighboring TA stations have relatively constant daily mean PSDs, and their horizontal components show summer diurnal variations of 10–15 dB. Some very quiet TA stations, such as SPMN, show a 5–10 dB increase in horizontal noise power during winter. The timing and amplitude of horizontal noise power variations between 20 and 800 s correlate with variations in atmospheric pressure PSDs. We propose that the grain size and pore water content of the material surrounding a shallow seismic station influences the local response to atmospheric pressure. Stations that must be placed in soft sediments should be installed in sandy, well‐drained material to minimize long‐period noise generated by atmospheric pressure variations. Online Material: Figures with examples of signals, power spectra, and their differences.

Gradient-Based Real-Time Spectrum Sensing at Low SNR

One of the main steps of enabling dynamic spectrum allocation for cognitive radios is spectrum sensing. Two critical problems in sensing a wide bandwidth are variations in noise power and degraded performance at a low SNR. The common FCME approach may lead to missed detection in sensing a wideband. The improved gradient-based energy detection method proposed here satisfactorily addresses the problem of choosing an appropriate threshold in such a scenario. The sensing bandwidth is divided into smaller subbands, and the gradient of the mean of subbands (GMSB) is computed and summed over past data samples in each subband. Applying a histogram-based threshold on the GMSB enables the accurate distinction of signal and noise without perfect knowledge of the noise floor. The theory of the proposed algorithm has been validated with numerical simulations over a wide range of low SNR values at different signal bandwidths. The detection performance of the approach is more effective than the FCME. The technique is tested over real-time wideband sensing in the GSM and CDMA bands.

A new approach to robust, weighted signal averaging

In this paper, a new approach for robust, weighted averaging of time-aligned signals is proposed. Suppression of noise in such case can be achieved with the use of the averaging technique. The signals are time-aligned and then the average template is determined. To this end, the arithmetic mean operator is often applied to the synchronized signal samples or its various modifications. However, the disadvantage of the mean operator is its sensitivity to outliers. The weighted averaging operation can be regarded as special case of clustering. For that reason in this work the averaging process is formulated as the problem of certain criterion function minimization and a few different cost functions are employed. The maximum likelihood estimator of location based on the generalized Cauchy distribution is used as the cost function. Such approach allows to suppress various types of impulsive noise. The proposed methods performance is experimentally evaluated and compared to the reference methods using electrocardiographic signal in the presence of the impulsive noise and the real muscle noise as well as the case of noise power variations.

Optimum array design to maximize Fisher information for bearing estimation

Source bearing estimation is a common application of linear sensor arrays. The Cramer-Rao bound (CRB) sets a lower bound on the achievable mean square error (MSE) of any unbiased bearing estimate. In the spatially white noise case, the CRB is minimized by placing half of the sensors at each end of the array. However, many realistic ocean environments have a mixture of both white noise and spatially correlated noise. In shallow water environments, the correlated ambient noise can be modeled as cylindrically isotropic. This research designs a fixed aperture linear array to maximize the bearing Fisher information (FI) under these noise conditions. The FI is the inverse of the CRB, so maximizing the FI minimizes the CRB. The elements of the optimum array are located closer to the array ends than uniform spacing, but are not as extreme as in the white noise case. The optimum array results from a trade off between maximizing the array bearing sensitivity and minimizing output noise power variation over the bearing. Depending on the source bearing, the resulting improvement in MSE performance of the optimized array over a uniform array is equivalent to a gain of 2-5 dB in input signal-to-noise ratio.

Performance Analysis of a Robust Matched Subspace Detector

The area of robust detection in the presence of partly unknown useful signal or interference is a widespread task in many signal processing applications. In this paper, we consider the robustness of a matched subspace detector in additive white Gaussian noise, under the condition that the noise power is known under null hypothesis, and unknown under alternative hypothesis when the useful signal triggers an variation of noise power, and we also consider the mismatch between the signal subspace and receiver matched filter. The test statistic of this detection problem is derived based on generalized likelihood ratio test, and the distribution of the test statistic is analysis. The computer simulation is used to validate the performance analysis and the robustness of this algorithm at low SNR, compared with other matched subspace detectors.

SUBBAND VARIANCE COMPUTATION OF HOMOSCEDASTIC ADDITIVE NOISE IN DISCRETE DYADIC WAVELET TRANSFORM

The paper deals with noise power variation that occurs when Discrete Dyadic Wavelet Transform (DDWT) is applied to signals affected by Wide Sense Stationary (WSS) additive white noise owing to the use of a non orthonormal expansion. An exact relationship between the noise variance in the original signal and the noise variance in the wavelet coefficients at a generic level is derived. This relationship is crucial in the application of wavelet thresholding for signal denoising to properly select the threshold in each subband.

TU‐D‐L100J‐02: Noise Stationarity in Spiral CT

Purpose: The properties of noise in CT images are important in system design, algorithm development, and assessment of diagnostic observer performance, as well as for developing accurate dose reduction simulators. Conventional analysis assumes noise stationarity, even though it is well known that elements in a CT scanner, such as bowtie filters or tube‐current modulation, as well as filtered backprojection reconstruction, introduce variation in measurement statistics. In this presentation, two additional contributors to nonstationarity, hardware component variation and data interpolation, are analyzed, and the properties of noise in CT images are characterized. Method and Materials: Raw sinogram data was collected from open gantry scans. Offline reconstruction software, with access to data at individual processing steps, such as linear interpolation, was used for image reconstruction. White noise data was injected at various stages of reconstruction and changes in statistical properties at subsequent processing steps measured. Multiple simulations were used to create data ensembles for analysis. Covariance measures were calculated in the sinogram and image domains. Results: Raw CT data is nonstationary during the acquisition cycle, with covariances as high as 20% in unattenuated signals. Image reconstruction steps, such as linear interpolation and filtered backprojection, introduce variations in noise power on the order of +/− 50% in sinogram data. The variation of noise power can be up to 600% across a uniform image. The off‐diagonal covariance coefficients are on the order of 10% with nearest neighbor pixels. Conclusion: The conventional assumption of noise stationarity in CT images needs to be extended to include additional processes that introduce local variations in image statistics.

1/f noise near the “metal‐to‐insulator transition” in the 2DEG in a Si‐MOSFET

AbstractContrary to some recent observations of a gradual increase of 1/f ‐noise with decreasing carrier density in a 2DEG in Si and a 2DHG in GaAs, we have observed a non‐monotonic variation of noise power in a 2DEG in Si. We discuss the role of the following effects in the noise near the carrier density where ∂R /∂T changes its sign: i) formation of an electron glass; ii) percolation transition; iii) existence of a narrow impurity band at the Si‐SiO2 interface. We also analyse the effect of temperature fluctuations in the observed non‐monotonic behaviour of the noise power. (© 2006 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Narrowband, low data rate communications on the low-voltage mains in the CENELEC frequencies. II. Multiplicative signal fading and efficient modulation schemes

This is the second of a two-part series on communications over the European 240 V domestic powerline, in the frequencies identified by the CENELEC European mains signalling standard, suitable for low-cost equipment in applications requiring data rates of no more than hundreds of bits per second (b/s). To minimize costs, narrow-band modulation techniques are considered. The series of two papers identifies the key impairments introduced by the channel, proposes a novel model structure to simulate them, and describes modulation techniques. The first paper describes the principal linear impairments; cyclic noise power variation and linear signal attenuation. The present paper describes nonlinear multiplicative signal fading and shows how orthogonal frequency division modulation (OFDM) can be used to overcome these impairments.

Narrow-Band, Low Data Rate Communications on the Low-Voltage Mains in the CENELEC Frequencies, Part 2: Multiplicative Signal Fading and Efficient Modulation Schemes

This is the second of a two-part series on communications over the European 240 V domestic powerline, in the frequencies identified by the CENELEC European mains signalling standard, suitable for low-cost equipment in applications requiring data rates of no more than hundreds of bits per second (b/s). To minimize costs, narrow-band modulation techniques are considered. The series of two papers identifies the key impairments introduced by the channel, proposes a novel model structure to simulate them, and describes modulation techniques. The first paper describes the principal linear impairments; cyclic noise power variation and linear signal attenuation. The present paper describes nonlinear multiplicative signal fading and shows how orthogonal frequency division modulation (OFDM) can be used to overcome these impairments.

CFAR processor for ESM systems applications

The authors consider the design of a CFAR processor for the adaptive thresholding system of an ESM receiver. For this purpose, the important specifications of ESM systems are the reception of pulses with various widths and rates, and vulnerability to noise jammers. The major difficulty in determining the statistics of the background noise is the problem of interfering signals. In situations of tolerable variations in background noise power, an MEx-CFAR processor is proposed, which exhibits good robustness against interfering pulses. When large variations in noise power are possible, an AMEx-CFAR processor is proposed. The feedback loop used in the structure of the AMEx processor permits it to adapt itself quickly to new background conditions. Performance of the proposed CFAR processors is investigated in various cases by means of Monte Carlo simulation and methods for the determination of their design parameters are discussed.

Studies of track-edge noise using a 3-D micro-track model

The characteristics of track-edge noise are studied using an extension of a 2-D micro-track model. Various experimentally observed characteristics of the noise power variation with cross-track position can be explained using the 3-D micro-track model. These include the noise peaks at the track-edge and the variation of the noise power with linear density. The 3-D micro-track model is much faster than micromagnetic simulation for calculations of noise power versus off-track position.

DC track edge interactions at close spacing

We investigate dc track edge interactions by placing edges at close spacings and observing the output through head readback and magnetic force microscopy. We find a correlation between the onset of edge interaction and the recording medium coercivity. Readback experiments display a region of edge interaction that results in linear noise power variation; microscopy imaging supports this conclusion.

Weighted averaging using adaptive estimation of the weights

Signal averaging is often used in biomedical signal processing. Unfortunately the classical hypothesis of perfect alignment of equal shape signals embedded in stationary and uncorrelated noise is rarely exactly verified. So the optimum improvement may be not achieved, and the estimated mean signal may be quite different from an individual realization. In this work we are interested in the noise power variation. In this case, it is more appropriate to use a weighted averaging instead of a uniform one. The main difficulty is the estimation of the signal to noise ratio for each realization in order to find the optimum weights. We show here that the optimum weights can be estimated adaptively using a constrained minimization algorithm. The constraint ensures that the estimated amplitude of the signal is not altered by the averaging procedure. Three different algorithms to estimate the optimal weights are presented and compared using simulations: (1) a direct method based on Lagrange multiplier; (2) an indirect method based on the penalty method; (3) a method based on the resolution of Kuhn and Tucker equations by means of Newton method. From the simulations, the Kuhn and Tucker method has the best performances and is used in the analysis of real electrocardiograms.

Modeling thin-film storage channels

A straightforward method for modeling nonlinearity and data-dependent (transition-dependent) media noise in a thin-film storage channel is presented. The least-squares approach for measuring the channel linear pulse response is described, and this FIR (finite impulse response) channel model is contrasted with a nonlinear moving average modeling technique described in terms of a random-access memory used as a finite state machine (FSM). It is shown that the channel nonlinearity is largely trailing in time, and performance figures of modeling accuracy for the channel FSM are presented. The nonlinear distortion increased dramatically at higher data rates for the thin-film disk examined. In addition, the variation of the channel noise power according to the flux transition density is described. A jitter noise effect is identified. A FSM modeling technique for identifying this noise power variation as a function of the channel input data pattern is given. Finally, it is shown how to combine these two modeling techniques to create arbitrarily long, representative read signals for the thin-film disk. By using these compact channel models, one can easily test new coding or equalization techniques. >

Measurement and statistical analysis of wideband MF atmospheric radio noise: 1. Structure and distribution and time variation of noise power

Wideband measurements (100 kHz) of medium frequency atmospheric noise have been made over the past several years in the southwestern United States. In part 1 of a two‐part paper the measurement and data transcription system and the statistical analysis software used to analyze the data are presented. In part 2, representative first‐ and higher‐order statistics and the impact of the data on bandwidth and system performance are described. Measurement results presented in part 1 include the temporal structure of atmospheric noise, the distribution and time variation of the measured average noise power, and comparisons with predictions by the International Radio Consultative Committee (CCIR).