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Showing papers on "Noise measurement published in 2015"


Journal ArticleDOI
TL;DR: The proposed DNN approach can well suppress highly nonstationary noise, which is tough to handle in general, and is effective in dealing with noisy speech data recorded in real-world scenarios without the generation of the annoying musical artifact commonly observed in conventional enhancement methods.
Abstract: In contrast to the conventional minimum mean square error (MMSE)-based noise reduction techniques, we propose a supervised method to enhance speech by means of finding a mapping function between noisy and clean speech signals based on deep neural networks (DNNs). In order to be able to handle a wide range of additive noises in real-world situations, a large training set that encompasses many possible combinations of speech and noise types, is first designed. A DNN architecture is then employed as a nonlinear regression function to ensure a powerful modeling capability. Several techniques have also been proposed to improve the DNN-based speech enhancement system, including global variance equalization to alleviate the over-smoothing problem of the regression model, and the dropout and noise-aware training strategies to further improve the generalization capability of DNNs to unseen noise conditions. Experimental results demonstrate that the proposed framework can achieve significant improvements in both objective and subjective measures over the conventional MMSE based technique. It is also interesting to observe that the proposed DNN approach can well suppress highly nonstationary noise, which is tough to handle in general. Furthermore, the resulting DNN model, trained with artificial synthesized data, is also effective in dealing with noisy speech data recorded in real-world scenarios without the generation of the annoying musical artifact commonly observed in conventional enhancement methods.

1,250 citations


Journal ArticleDOI
TL;DR: An approximate analytical expression is derived for the uplink achievable rate of a massive multiinput multioutput (MIMO) antenna system when finite precision analog-digital converters (ADCs) and the common maximal-ratio combining technique are used at the receivers.
Abstract: In this letter, we derive an approximate analytical expression for the uplink achievable rate of a massive multiinput multioutput (MIMO) antenna system when finite precision analog-digital converters (ADCs) and the common maximal-ratio combining technique are used at the receivers. To obtain this expression, we treat quantization noise as an additive quantization noise model. Considering the obtained expression, we show that low-resolution ADCs lead to a decrease in the achievable rate but the performance loss can be compensated by increasing the number of receiving antennas. In addition, we investigate the relation between the number of antennas and the ADC resolution, as well as the power-scaling law. These discussions support the feasibility of equipping highly economical ADCs with low resolution in practical massive MIMO systems.

353 citations


Journal ArticleDOI
Wenchao Xue, Wenyan Bai, Sheng Yang1, Kang Song1, Yi Huang, Hui Xie1 
TL;DR: The experimental results demonstrate that the proposed controller can ensure high deviation precision of AFR despite both uncertain dynamics and measurement noise, and validates the effectiveness of the AESO's gain by which the performance of ADRC on mitigating uncertainties can be improved.
Abstract: This paper proposes the adaptive extended state observer (AESO)-based active disturbance rejection control (ADRC) to deal with the uncertainties, both in the plant and in the sensors. The gain of ESO is automatically timely tuned to reduce the estimation errors of both states and “total disturbance” against the measurement noise. Furthermore, the satisfactory performance of the closed-loop system is achieved by compensation for uncertainties. This novel controller is applied to the air–fuel ratio (AFR) control of gasoline engine, which has large nonlinear uncertainties due to the unknown speed change, fuel film dynamics, etc. In addition, the measurement of AFR is polluted by sensor noise. The experimental results demonstrate that the proposed controller can ensure high deviation precision of AFR despite both uncertain dynamics and measurement noise. Moreover, the experimental comparison validates the effectiveness of the AESO's gain by which the performance of ADRC on mitigating uncertainties can be improved.

285 citations


Posted Content
TL;DR: This paper presents a complete and quantitative analysis of noise models available in digital images and expresses a brief overview of various noise models.
Abstract: Noise is always presents in digital images during image acquisition, coding, transmission, and processing steps. Noise is very difficult to remove it from the digital images without the prior knowledge of noise model. That is why, review of noise models are essential in the study of image denoising techniques. In this paper, we express a brief overview of various noise models. These noise models can be selected by analysis of their origin. In this way, we present a complete and quantitative analysis of noise models available in digital images.

256 citations


Proceedings ArticleDOI
01 Jul 2015
TL;DR: A new notion of observability is introduced - termed “observability under attacks” - that addresses the question of whether or not it is possible to uniquely reconstruct the state of the system by observing its inputs and outputs over a period of time, with the understanding that some of the available system's outputs may have been corrupted by the opponent.
Abstract: We address the problem of state estimation for multi-output continuous-time linear systems, for which an attacker may have control over some of the sensors and inject (potentially unbounded) additive noise into some of the measured outputs. To characterize the resilience of a system against such sensor attacks, we introduce a new notion of observability — termed “observability under attacks” — that addresses the question of whether or not it is possible to uniquely reconstruct the state of the system by observing its inputs and outputs over a period of time, with the understanding that some of the available system's outputs may have been corrupted by the opponent. We provide computationally efficient tests for observability under attacks that amount to testing the (standard) observability for an appropriate finite set of systems. In addition, we propose two state estimation algorithms that permit the state reconstruction in spite of the attacks. One of these algorithms uses observability Gramians and a finite window of measurements to reconstruct the initial state. The second algorithm takes the form of a switched observer that asymptotically converges to the correct state estimate in the absence of additive noise and disturbances, or to a neighborhood of the correct state estimate in the presence of bounded noise and disturbances.

229 citations


Journal ArticleDOI
TL;DR: This work focuses on single-channel speech enhancement algorithms which rely on spectrotemporal properties, and can be employed when the miniaturization of devices only allows for using a single microphone.
Abstract: With the advancement of technology, both assisted listening devices and speech communication devices are becoming more portable and also more frequently used. As a consequence, users of devices such as hearing aids, cochlear implants, and mobile telephones, expect their devices to work robustly anywhere and at any time. This holds in particular for challenging noisy environments like a cafeteria, a restaurant, a subway, a factory, or in traffic. One way to making assisted listening devices robust to noise is to apply speech enhancement algorithms. To improve the corrupted speech, spatial diversity can be exploited by a constructive combination of microphone signals (so-called beamforming), and by exploiting the different spectro?temporal properties of speech and noise. Here, we focus on single-channel speech enhancement algorithms which rely on spectrotemporal properties. On the one hand, these algorithms can be employed when the miniaturization of devices only allows for using a single microphone. On the other hand, when multiple microphones are available, single-channel algorithms can be employed as a postprocessor at the output of a beamformer. To exploit the short-term stationary properties of natural sounds, many of these approaches process the signal in a time-frequency representation, most frequently the short-time discrete Fourier transform (STFT) domain. In this domain, the coefficients of the signal are complex-valued, and can therefore be represented by their absolute value (referred to in the literature both as STFT magnitude and STFT amplitude) and their phase. While the modeling and processing of the STFT magnitude has been the center of interest in the past three decades, phase has been largely ignored.

210 citations


Journal ArticleDOI
TL;DR: This paper formulates the distance-dependent noise model, and proves that using the extra information about the source location in the functional variance improves the estimation accuracy of TDOA-based source localization, but contributes little under a sufficiently small noise level.
Abstract: This paper focuses on the problem of source localization using time-difference-of-arrival (TDOA) measurements in both 2-D and 3-D spaces. Different from existing studies where the variance of TDOA measurement noises is assumed to be independent of the associated source-to-sensor distances, we consider the more realistic model where the variance is a function of the source-to-sensor distances, which dramatically complicates TDOA-based source localization. After formulating the distance-dependent noise model, we prove that using the extra information about the source location in the functional variance improves the estimation accuracy of TDOA-based source localization, but contributes little under a sufficiently small noise level. Further, we theoretically analyze the problem of optimal sensor placement, and derive the necessary and sufficient conditions for optimizing localization performance under different circumstances. Then, a localization scheme based on the iteratively reweighted generalized least squares (IRGLS) method is proposed to efficiently exploit the extra source location information. Finally, a simulation analysis confirms our theoretical studies, and shows that the performance of the proposed localization scheme is comparable to the Cramer-Rao lower bound (CRLB) given moderate TDOA measurement noises.

147 citations


Journal ArticleDOI
TL;DR: In this paper, the Dutta-Horn model has been used to predict low-frequency excess noise in thin metal films, MOS transistors, and GaN/AlGaN high-electron mobility transistors.
Abstract: This paper reviews and compares predictions of the Dutta-Horn model of low-frequency excess ( ${\bf 1}/\mbi{f}$ ) noise with experimental results for thin metal films, MOS transistors, and GaN/AlGaN high-electron mobility transistors (HEMTs). For metal films, mobility fluctuations associated with carrier-defect scattering lead to ${\bf 1}/\mbi{f}$ noise. In contrast, for most semiconductor devices, the noise usually results from fluctuations in the number of carriers due to charge exchange between the channel and defects, usually at or near a critical semiconductor/insulator interface. The Dutta-Horn model describes the noise with high precision in most cases. Insight into the physical mechanisms that lead to noise in microelectronic materials and devices has been obtained via total-ionizing-dose irradiation and/or thermal annealing, as illustrated with several examples. With the assistance of the Dutta-Horn model, measurements of the noise magnitude and temperature and/or voltage dependence of the noise enable estimates of the energy distributions of defects that lead to ${\bf 1}/\mbi{f}$ noise. The microstructure of several defects and/or impurities that cause noise in MOS devices (primarily O vacancies) and GaN/AlGaN HEMTs (e.g., hydrogenated impurity centers, N vacancies, and/or Fe centers) have been identified via experiments and density functional theory calculations.

139 citations


Journal ArticleDOI
TL;DR: A novel discrete-time estimator is proposed, which is employed for simultaneous estimation of system states, and actuator/sensor faults in a discrete-Time dynamic system, and the integrated discrete- time fault estimation and fault-tolerant control technique is applied to the vehicle lateral dynamics, which demonstrates the effectiveness of the developed techniques.
Abstract: In this paper, a novel discrete-time estimator is proposed, which is employed for simultaneous estimation of system states, and actuator/sensor faults in a discrete-time dynamic system. The existence of the discrete-time simultaneous estimator is proven mathematically. The systematic design procedure for the derivative and proportional observer gains is addressed, enabling the estimation error dynamics to be internally proper and stable, and robust against the effects from the process disturbances, measurement noise, and faults. Based on the estimated fault signals and system states, a discrete-time fault-tolerant design approach is addressed, by which the system may recover the system performance when actuator/sensor faults occur. Finally, the proposed integrated discrete-time fault estimation and fault-tolerant control technique is applied to the vehicle lateral dynamics, which demonstrates the effectiveness of the developed techniques.

138 citations


Journal ArticleDOI
TL;DR: Preliminary results on a new filter for noise identification are shown, which is based on two of the complexity measures which were more sensitive to the presence of label noise.

115 citations


Journal ArticleDOI
TL;DR: A weighted couple sparse representation model is presented to remove IN, where the dictionary is directly trained on the noisy raw data by addressing a weighted rank-one minimization problem, which can capture more features of the original data.
Abstract: Many impulse noise (IN) reduction methods suffer from two obstacles, the improper noise detectors and imperfect filters they used. To address such issue, in this paper, a weighted couple sparse representation model is presented to remove IN. In the proposed model, the complicated relationships between the reconstructed and the noisy images are exploited to make the coding coefficients more appropriate to recover the noise-free image. Moreover, the image pixels are classified into clear, slightly corrupted, and heavily corrupted ones. Different data-fidelity regularizations are then accordingly applied to different pixels to further improve the denoising performance. In our proposed method, the dictionary is directly trained on the noisy raw data by addressing a weighted rank-one minimization problem, which can capture more features of the original data. Experimental results demonstrate that the proposed method is superior to several state-of-the-art denoising methods.

Journal ArticleDOI
TL;DR: In the simulations the proposed methods achieve better accuracy than the alternative methods, the computational complexity of the filter being roughly 5 to 10 times that of the Kalman filter.
Abstract: Filtering and smoothing algorithms for linear discrete- time state-space models with skewed and heavy-tailed measurement noise are presented. The algorithms use a variational Bayes approximation of the posterior distribution of models that have normal prior and skew- $t$ -distributed measurement noise. The proposed filter and smoother are compared with conventional low- complexity alternatives in a simulated pseudorange positioning scenario. In the simulations the proposed methods achieve better accuracy than the alternative methods, the computational complexity of the filter being roughly 5 to 10 times that of the Kalman filter.

Journal ArticleDOI
TL;DR: An effective temporal signal separation and determination method that can effectively extract human intrusion signals, and separate the influences of slow change of the system and other environmental interferences is proposed.
Abstract: Phase-sensitive optical time domain reflectometry (Φ-OTDR) is easy to be interfered by ambient noises, and the nonlinear coherent addition of different interferences always makes it difficult to detect real human intrusions and causes high nuisance alarm rates (NARs) in practical applications. In this paper, an effective temporal signal separation and determination method is proposed to improve its detection performance in complicated noisy environments. Unlike the conventional analysis of transverse spatial signals, the time-evolving sensing signal of Φ-OTDR system is at first obtained for each spatial point by accumulating the changing OTDR traces at different moments. Then, its longitudinal temporal signal is decomposed and analyzed by a multi-scale wavelet decomposition method. By selectively recombining the corresponding scale components, it can effectively extract human intrusion signals, and separate the influences of slow change of the system and other environmental interferences. Compared with the conventional differentiation way and fast Fourier transformation denoising method, the SNRs of the detecting signals for the proposed method is always the best, which can be raised by up to ∼35 dB for the best case. Moreover, from the decomposed components, different event signals can be effectively determined by their energy distribution features, and the NAR can be controlled to be less than 2% in the test.

Proceedings ArticleDOI
19 Apr 2015
TL;DR: It is found that system performance can be improved significantly, with relative improvements up to 75% in far-field conditions, by employing a combination of multi-style training and a proposed novel formulation of automatic gain control that estimates the levels of both speech and background noise.
Abstract: We explore techniques to improve the robustness of small-footprint keyword spotting models based on deep neural networks (DNNs) in the presence of background noise and in far-field conditions. We find that system performance can be improved significantly, with relative improvements up to 75% in far-field conditions, by employing a combination of multi-style training and a proposed novel formulation of automatic gain control (AGC) that estimates the levels of both speech and background noise. Further, we find that these techniques allow us to achieve competitive performance, even when applied to DNNs with an order of magnitude fewer parameters than our base-line.

Journal ArticleDOI
TL;DR: This work proposes here a multiscale denoising algorithm adapted to this broad noise model, which is demonstrated on real JPEG images and on scans of old photographs for which the formation model is unknown, and is compared with the unique state of the art previous blind Denoising method.
Abstract: Arguably several thousands papers are dedicated to image denoising. Most papers assume a fixed noise model, mainly white Gaussian or Poissonian. This assumption is only valid for raw images. Yet, in most images handled by the public and even by scientists, the noise model is imperfectly known or unknown. End users only dispose the result of a complex image processing chain effectuated by uncontrolled hardware and software (and sometimes by chemical means). For such images, recent progress in noise estimation permits to estimate from a single image a noise model, which is simultaneously signal and frequency dependent. We propose here a multiscale denoising algorithm adapted to this broad noise model. This leads to a blind denoising algorithm which we demonstrate on real JPEG images and on scans of old photographs for which the formation model is unknown. The consistency of this algorithm is also verified on simulated distorted images. This algorithm is finally compared with the unique state of the art previous blind denoising method.

Proceedings ArticleDOI
18 Oct 2015
TL;DR: The Acoustic Characterization of Environments (ACE) Challenge is a competition to identify the most promising non-intrusive DRR and T60 estimation methods using real noisy reverberant speech.
Abstract: Knowledge of the Direct-to-Reverberant Ratio (DRR) and Reverberation Time (T 60 ) can be used to better perform speech and audio processing such as dereverberation. Established methods compute these parameters from measured Acoustic Impulse Responses (AIRs). However, in many practical situations the AIR is not available and the parameters must be estimated non-intrusively directly from noisy speech or audio signals. The Acoustic Characterization of Environments (ACE) Challenge is a competition to identify the most promising non-intrusive DRR and T 60 estimation methods using real noisy reverberant speech. We describe the ACE corpus comprising multi-channel AIRs, and multi-channel noise including ambient, fan and babble noise recorded in the same environment as the measured AIRs, along with the corresponding DRR and T 60 measurements. The evaluation methodology is discussed and comparative results are shown.

Journal ArticleDOI
TL;DR: The simulations and experiments prove that the proposed self-adaption Kalman observer (SAKO) can observe speed and load torque precisely and timely and that the feedforward and feedback control system of PMSM can improve the speed tracking ability.
Abstract: This paper proposes a self-adaption Kalman observer (SAKO) used in a permanent-magnet synchronous motor (PMSM) servo system. The proposed SAKO can make up measurement noise of the absolute encoder with limited resolution ratio and avoid differentiating process and filter delay of the traditional speed measuring methods. To be different from the traditional Kalman observer, the proposed observer updates the gain matrix by calculating the measurement noise at the current time. The variable gain matrix is used to estimate and correct the observed position, speed, and load torque to solve the problem that the motor speed calculated by the traditional methods is prone to large speed error and time delay when PMSM runs at low speeds. The state variables observed by the proposed observer are used as the speed feedback signals and compensation signal of the load torque disturbance in PMSM servo system. The simulations and experiments prove that the SAKO can observe speed and load torque precisely and timely and that the feedforward and feedback control system of PMSM can improve the speed tracking ability.

Journal ArticleDOI
TL;DR: In this paper, an efficient method for the numerical simulation of near and far-field propagation of stochastic electromagnetic (EM) fields is presented based on the transformation of field correlation dyadics using Green's functions or the field transfer functions computed for deterministic fields.
Abstract: In this paper, an efficient method for the numerical simulation of near- and far-field propagation of stochastic electromagnetic (EM) fields is presented. The method is based on the transformation of field correlation dyadics using Green's functions or the field transfer functions computed for deterministic fields. The method accounts for arbitrary correlations between the noise radiation sources and allows to compute the spatial distribution of the spectral energy density of noisy electromagnetic sources. The introduced methodology can be combined with available electromagnetic modeling tools. It is shown that the method of moments can be applied to solve noisy electromagnetic field problems by network methods applying correlation matrix techniques. Examples demonstrating the strong influence of the correlation between the sources on the spatial distribution of the radiated noise field are presented.

Journal ArticleDOI
TL;DR: The fuzzy-logic-based adaptive strong tracking Kalman filter method is implemented in a CTP system to mitigate the effect of measurement noise and provide a smooth tracking trajectory at different speeds and effectively measures and quantifies the “smoothness” of the touched trajectory.
Abstract: This paper presents a novel 7-in capacitive touch panel (CTP) system with a smooth tracking algorithm that accurately estimates the position where the panel is touched and tracks the trajectory of touch. The proposed CTP system consists of a microcontroller unit, a sensor IC, and an interface board. When a user draws at different speeds, the measurement noise caused by the sensor IC induces an error in the touched position and zigzag trajectory, especially when the motion is slow. The fuzzy-logic-based adaptive strong tracking Kalman filter method is implemented in a CTP system to mitigate the effect of measurement noise and provide a smooth tracking trajectory at different speeds. Moreover, the approach effectively measures and quantifies the “smoothness” of the touched trajectory. Experimental results indicate that the proposed method reduces the measurement noise and decreases the mean tracking error by 85.4% over that achieved using the moving average filter.

Journal ArticleDOI
TL;DR: Numerical simulations with the Bernoulli-Gaussian, k-dense and Student's-t signals demonstrate that the parametric SURE-AMP does not only achieve the state-of-the-art recovery but also runs more than 20 times faster than the EM-GM-GAMP algorithm.
Abstract: Both theoretical analysis and empirical evidence confirm that the approximate message passing (AMP) algorithm can be interpreted as recursively solving a signal denoising problem: at each AMP iteration, one observes a Gaussian noise perturbed original signal. Retrieving the signal amounts to a successive noise cancellation until the noise variance decreases to a satisfactory level. In this paper, we incorporate the Stein's unbiased risk estimate (SURE) based parametric denoiser with the AMP framework and propose the novel parametric SURE-AMP algorithm. At each parametric SURE-AMP iteration, the denoiser is adaptively optimized within the parametric class by minimizing SURE, which depends purely on the noisy observation. In this manner, the parametric SURE-AMP is guaranteed with the best-in-class recovery and convergence rate. If the parametric family includes the families of the mimimum mean squared error (MMSE) estimators, we are able to achieve the Bayesian optimal AMP performance without knowing the signal prior. In the paper, we resort to the linear parameterization of the SURE based denoiser and propose three different kernel families as the base functions. Numerical simulations with the Bernoulli–Gaussian, $k$ -dense and Student's-t signals demonstrate that the parametric SURE-AMP does not only achieve the state-of-the-art recovery but also runs more than 20 times faster than the EM-GM-GAMP algorithm. Natural image simulations confirm the advantages of the parametric SURE-AMP for signals without prior information.

Journal ArticleDOI
TL;DR: Wavelet-based denoising with a new histogram-based threshold function and selection rule is proposed, and two signal-to-noise ratio (SNR) estimation techniques are derived to fit with actual PD signals corrupted with real noise.
Abstract: Online condition assessment of the power system devices and apparatus is considered vital for robust operation, where partial discharge (PD) detection is employed as a diagnosis tool. PD measurements, however, are corrupted with different types of noises such as white noise, random noise, and discrete spectral interferences. Hence, the denoising of such corrupted PD signals remains a challenging problem in PD signal detection and classification. The challenge lies in removing these noises from the online PD signal measurements effectively, while retaining its discriminant features and characteristics. In this paper, wavelet-based denoising with a new histogram-based threshold function and selection rule is proposed. The proposed threshold estimation technique obtains two different threshold values for each wavelet sub-band and uses a prodigious thresholding function that conserves the original signal energy. Moreover, two signal-to-noise ratio (SNR) estimation techniques are derived to fit with actual PD signals corrupted with real noise. The proposed technique is applied on different acoustic and current measured PD signals to examine its performance under different noisy environments. The simulation results confirm the merits of the proposed denoising technique compared with other existing wavelet-based techniques by measuring four evaluation metrics: 1) SNR; 2) cross-correlation coefficient; 3) mean square error; and 4) reduction in noise level.

Proceedings ArticleDOI
29 Dec 2015
TL;DR: This work focuses on the most general model for sensor attacks where any signal can be injected via the compromised sensors, and presents an l0-based state estimator that can be formulated as a mixed-integer linear program and its convex relaxation based on the l1 norm.
Abstract: We consider the problem of attack-resilient state estimation in the presence of noise. We focus on the most general model for sensor attacks where any signal can be injected via the compromised sensors. An l0-based state estimator that can be formulated as a mixed-integer linear program and its convex relaxation based on the l1 norm are presented. For both l0 and l1-based state estimators, we derive rigorous analytic bounds on the state-estimation errors. We show that the worst-case error is linear with the size of the noise, meaning that the attacker cannot exploit noise and modeling errors to introduce unbounded state-estimation errors. Finally, we show how the presented attack-resilient state estimators can be used for sound attack detection and identification, and provide conditions on the size of attack vectors that will ensure correct identification of compromised sensors.

Journal ArticleDOI
TL;DR: This work proposes a new method that can accurately estimate the non-stationary parameters of noise from just a single magnitude image and shows the better performance and the lowest error variance of the proposed methodology when compared to the state-of-the-art methods.

Journal ArticleDOI
TL;DR: In this paper, the first quanta image sensor with photon counting capability is demonstrated, and the lowvoltage device demonstrates less than 0.3e-r.m.s. read noise on a single read out without the use of avalanche gain and single-electron signal quantization.
Abstract: The first quanta image sensor jot with photon counting capability is demonstrated. The low-voltage device demonstrates less than 0.3e- r.m.s. read noise on a single read out without the use of avalanche gain and single-electron signal quantization is observed. A new method for determining read noise and conversion gain is also introduced.

Journal ArticleDOI
TL;DR: The analysis presented in this letter closely predicts the behavior of the PLC system under the combined effect of background and impulsive noises.
Abstract: Power line communication (PLC) is the use of power lines for the purpose of electronic data transmission. The presence of additive noise, namely, background noise and impulsive noise, significantly affects the performance of a PLC system. While the background noise is modeled by Nakagami- $m$ distribution, the impulsive noise is modeled using Middleton class A distribution. In this letter, we study the performance of a PLC system under the combined effect of Nakagami- $m$ background noise and Middleton class A impulsive noise assuming binary phase shift keying signaling. The probability density function of decision variable under the influence of additive noise (sum of background noise and impulsive noise) is derived. We also derive an analytical expression for the average bit error rate of the considered PLC system. The analytical expressions are validated by close matching to the simulation results. The analysis presented in this letter closely predicts the behavior of the PLC system under the combined effect of background and impulsive noises.

Proceedings ArticleDOI
05 Mar 2015
TL;DR: A brief analysis of different techniques used for speckle noise reduction, along with their advantages and disadvantages, in a comparative manner are presented.
Abstract: Noise refers to the random variation of intensity of a pixel, which modifies the actual information of the image. As a result, pixels which appear in the image are not the actual pixels. Addition of extraneous values to the image causes the occurrence of noise. Noise is categorized into impulse (salt-and-pepper) noise, uniform noise, Gaussian noise, exponential noise, Erlang (gamma) noise, photon noise, speckle noise, etc. Speckle noise is the noise that arises due to the effect of environmental conditions on the imaging sensor during image acquisition. Speckle noise is mostly detected in case of medical images, active Radar images and Synthetic Aperture Radar (SAR) images. Various researchers have performed experiments to overcome this kind of noise using different filtering techniques based on soft computing approaches, such as Fuzzy Filter, Genetic Algorithm, Particle Swarm Optimization, Artificial Bee Colony Optimization, Neural Networks, etc. In this paper, we present a brief analysis of different techniques used for speckle noise reduction, along with their advantages and disadvantages, in a comparative manner.

Journal ArticleDOI
TL;DR: The state estimation problem for discrete-time linear systems influenced by multiplicative and time-correlated additive measurement noises is considered, and an optimal linear estimator is proposed, which does not require computing the inverse of state transition matrix.
Abstract: In this paper, the state estimation problem for discrete-time linear systems influenced by multiplicative and time-correlated additive measurement noises is considered where the multiplicative noises are zero-mean white noise sequences, and the time-correlated additive noise is described by a linear system model with white noise An optimal linear estimator for the system under consideration is proposed, which does not require computing the inverse of state transition matrix The proposed estimator has a recursive structure, and has time-independent computation and storage load Computer simulations are carried out to demonstrate the performance of the proposed estimator The simulation results show the superiority of the proposed estimator

Journal ArticleDOI
TL;DR: The performance of the PL system with AF relays is found to be superior compared to that of a direct transmission PLC system for fixed transmission power.
Abstract: The use of repeaters (relays) has recently been introduced in power line communication (PLC) systems for long-distance data transfer and thus, it becomes important to analyze the performance of relay based PLC systems. A simplified system model with distance dependent signal attenuation and additive white Gaussian noise may not cover all the factors affecting the data transfer, such as variation in amplitude (fading) and occurrence of impulsive noise. Hence, a more realistic system model with log-normal fading, distance dependent signal attenuation, and a Bernoulli-Gaussian impulsive noise is considered in this paper to study the end-to-end average bit error rate (BER) and the end-to-end average channel capacity of a PLC system equipped with amplify-and-forward (AF) relays. Approximate closed-form expressions of the end-to-end average BER for binary phase-shift keying and the average channel capacity for high signal-to-noise ratio are obtained. The performance of the PLC system with AF relays is found to be superior compared to that of a direct transmission PLC system for fixed transmission power.

Journal ArticleDOI
TL;DR: A new instrument was developed, the ultrastable low-noise current amplifier (ULCA), which allows the measurement or generation of 100-pA direct current with an uncertainty of one part in 107 and was successfully used to investigate the uncertainty of the established capacitor charging method.
Abstract: We present the latest improvements in the traceable measurement and generation of small electric currents A central tool in our traceability chain for small direct currents is a new binary cryogenic current comparator (CCC) with a total of 18 276 turns This 14-bit CCC is well suited for the calibration of high-value resistors and current amplifiers, but also for the direct amplification of small currents A noise level of 5 fA/ $\surd $ Hz at 005 Hz is routinely achieved The systematic uncertainty due to noise rectification was exemplarily investigated in a ratio-error test configuration, showing that a total uncertainty of about one part in $10^{6}$ can be achieved at 100 pA For further improvement, a new instrument was developed, the ultrastable low-noise current amplifier (ULCA) Its transfer coefficient is highly stable versus time, temperature, and current amplitude within a full dynamic range of ±5 nA The ULCA is calibrated with the 14-bit CCC at high current amplitude, and allows the measurement or generation of 100-pA direct current with an uncertainty of one part in $10^{7}$ The novel setup was successfully used to investigate the uncertainty of the established capacitor charging method A quantum metrology triangle experiment based on the presented instruments is proposed

Journal ArticleDOI
TL;DR: In this article, the authors investigate the properties of transmission links amplified by phase-sensitive amplifiers (PSAs) and show that in a link with standard single mode fiber (SSMF) the optimum dispersion map for efficient nonlinearity mitigation corresponds to precompensation of an amount equal to the effective loss length.
Abstract: In this paper, we investigate the properties of transmission links amplified by phase-sensitive amplifiers (PSAs). Using an analytic description, we explain the principles enabling improved sensitivity compared to conventional links amplified by phase-insensitive amplifiers (PIAs) and mitigation of nonlinear transmission distortions. We demonstrate these features using numerical simulations, and in particular, we show the possibility of efficiently mitigating both self-phase modulation (SPM)-induced distortions and nonlinear phase noise (NLPN) if the link dispersion map is optimized. The properties of the noise on signal and idler are important and to enable NLPN mitigation, the noise must be correlated at the link input. We investigate the role of the dispersion map in detail and show that in a link with standard single mode fiber (SSMF) the optimum dispersion map for efficient nonlinearity mitigation corresponds to precompensation of an amount equal to the effective loss length. Furthermore, we experimentally demonstrate both improved sensitivity and mitigation of nonlinearities in a 105 km PSA-amplified link transmitting 10 GBd 16-ary quadrature amplitude modulation (16QAM) data. We measure a combined effect allowing for more than 12 dB larger span loss in a PSA-amplified link compared to a conventional PIA-amplified link to reach the same bit error ratio (BER) of $1\times 10^{-3}$ .