Noise Compensation Research Articles

Local model dynamics for two qubits

The density matrix model described in an earlier paper “Model Dynamics for Quantum Computing” is applied to the case of two qubits. The earlier paper provided a density matrix dynamics with unitary time-dependent gates including qubit energy splitting, entropy constraints, external bath and noise effects. The Lindblad formulation as extended by Beretta was adopted in designing this model. Visualization was provided by examining the time-evolution of the polarization vector. In this paper, the same ideas are applied to two-qubit systems, where there are now 15 time-dependent observables; namely, six polarization vectors (P→A,P→B), and nine spin-correlations (T↔). These time-dependent observables provide ready visualization of two qubit dynamics. Special emphasis is placed on the CNOT gate, which is implemented following recent outstanding developments in using silicon-based dots. By invoking a different splitting for each qubit, plus a spin–spin interaction and a carefully designed Rabi oscillation, a CNOT gate is generated. Careful analysis of the time-dependence of these aspects provides insight into CNOT dynamics. The model can be used to ascertain the sensitivity and efficacy of such a CNOT gate when subject to external environmental effects such as noise disturbances, and noise compensation, using the single-system formulation of steepest-entropy-ascent (SEA) non-equilibrium quantum thermodynamics, suitable to model controlled-gate implementations as long as the two qubits remain localized and effectively behave as a single physical four-level system. The role of careful timing in constructing an efficient CNOT gate is illustrated. The formation of Bell states and the evolution of entanglement including noise, bath and entropy considerations are examined. Extensions to the two-qubit swap, and to the three-qubit Toffoli gate dynamics are outlined. It is also shown that a simple Lindblad form can be used to introduce weak and distinct qubit noise pulses. A simple scheme for noise compensation is designed to increase purity and decrease entropy, without invocation of quantum correction methodology. It is based on using the preexisting entanglement of the environment with the quantum system and carefully designed non-Hermitian Lindblad pulses.

Adaptive Neural-Network-Based Unscented Kalman Filter for Robust Pose Tracking of Noncooperative Spacecraft

This paper presents a neural-network-based unscented Kalman filter (UKF) to estimate and track the pose (i.e., position and orientation) of a known, noncooperative, tumbling target spacecraft in a close-proximity rendezvous scenario. The UKF estimates the target’s orbit and attitude relative to the servicer based on the pose information provided by a multitask convolutional neural network (CNN) from incoming monocular images of the target. To enable reliable tracking, the process noise covariance matrix of the UKF is tuned online using adaptive state noise compensation, which leverages a newly developed closed-form process noise model for relative attitude dynamics. This paper also introduces the Satellite Hardware-In-the-loop Rendezvous Trajectories (SHIRT) dataset to enable comprehensive analyses of the performance and robustness of the proposed pipeline. SHIRT comprises the labeled images of two representative rendezvous trajectories in a low Earth orbit created using both a graphics renderer and a robotic testbed. Specifically, the CNN is solely trained on synthetic data, whereas functionality and performance of the complete navigation pipeline are evaluated on real images from the robotic testbed. The proposed UKF is evaluated on SHIRT and is shown to have subdecimeter-level position and degree-level orientation errors at steady state.

Noise Compensation Methods for Optical Fiber Frequency Sweeping Interferometry: A Review

Noise induced by the fluctuation of laser phase, frequency, intensity, etc., can deteriorate the point spread function (PSF) in distributed optical fiber sensing and testing systems, which is the core concern for measurement performance, such as measurement spatial resolution, length range, accuracy and dynamic range. In this paper, we review the development of noise compensation methods for improving the PSF in optical fiber frequency sweeping interferometry (OFFSI) systems and provide a reference to select appropriate compensation methods in different applications (e.g., fiber optics component characterization, optical fiber network diagnosis). A discussion of noise impacts on PSF is made, including laser source phase noise, laser source intensity noise and ambient noise. The most serious noise is the laser source phase noise involved in frequency tunable lasers, including deterministic frequency tuning nonlinearity and stochastic laser phase noise. Also, a comprehensive and detailed classification of the compensation strategies for different noise is provided according to the dominant noise in the laser source. Recent progress on each kind of compensation method is analyzed based on the principles, performance, advantages and limitations, and a comparison of compensation methods is presented. Finally, an overview of trends and future developments is given.

Low-cost, high-resolution and no-manning distributed sensing system for the continuous monitoring of fruit growth in precision farming

Accurate, continuous and reliable data gathering and recording about crop growth and state of health, by means of a network of autonomous sensor nodes that require minimal management by the farmer will be essential in future Precision Agriculture. In this paper, a low-cost multi-channel sensor-node architecture is proposed for the distributed monitoring of fruit growth throughout the entire ripening season. The prototype presented is equipped with five independent sensing elements that can be attached each to a sample fruit at the beginning of the season and are capable of estimating the fruit diameter from the first formation up to the harvest. The sensor-node is provided with a LoRa transceiver for wireless communication with the decision making central, is energetically autonomous thanks to a dedicated energy harvester and an accurate design of power consumption, and each measuring channel provides sub-mm 9.0-ENOB effective resolution with a full-scale range of 12 cm. The accurate calibration procedure of the sensor-node and its elements is described in the paper, which allows for the compensation of temperature dispersion, noise and non-linearities. The prototype was tested on field in real application, in the framework of the research activity for next-generation Precision Farming performed at the experimental farm of the Department of Agricultural and Food Science of the University of Bologna, Cadriano, Italy.

Model-less prediction filter for adaptive adjustment process noise.

In this study, a filtering scheme suitable for high-precision sensors was proposed to extract high-precision sensor information. According to the principle of Kalman gain based on data fusion, a model-less prediction filter with minimum gain measurement noise compensation and process noise posteriori constraint adjustment was developed. In comparison to various Kalman filter methods, the proposed algorithm demonstrated better accuracy in the steady state. The high precision performance and effectiveness of the model-less prediction filter were verified under a digitally controlled linear power supply.

Sequential Fusion Filter for State Estimation of Nonlinear Multi-Sensor Systems with Cross-Correlated Noise and Packet Dropout Compensation

This paper is concerned with the problem of state estimation for nonlinear multi-sensor systems with cross-correlated noise and packet loss compensation. In this case, the cross-correlated noise is modeled by the synchronous correlation of the observation noise of each sensor, and the observation noise of each sensor is correlated with the process noise at the previous moment. Meanwhile, in the process of state estimation, since the measurement data may be transmitted in an unreliable network, data packet dropout will inevitably occur, leading to a reduction in estimation accuracy. To address this undesirable situation, this paper proposes a state estimation method for nonlinear multi-sensor systems with cross-correlated noise and packet dropout compensation based on a sequential fusion framework. Firstly, a prediction compensation mechanism and a strategy based on observation noise estimation are used to update the measurement data while avoiding the noise decorrelation step. Secondly, a design step for a sequential fusion state estimation filter is derived based on an innovation analysis method. Then, a numerical implementation of the sequential fusion state estimator is given based on the third-degree spherical-radial cubature rule. Finally, the univariate nonstationary growth model (UNGM) is combined with simulation to verify the effectiveness and feasibility of the proposed algorithm.

Compensation of Phase Noise Induced by Higher–Order Codirectional Raman Amplifiers

Codirectional Raman amplification with higher–order pumping is a promising solution for improving performance of commercial unrepeatered submarine links and thus to allow for an upgrade to advanced modulation formats offering larger capacity. However, power fluctuations of the involved high–power pump induce phase shifts in phase modulated signals via the nonlinear Kerr effect that destroy the theoretically possible performance improvement. A technique for reducing the impact of this effect on system performance by jointly processing signals of neighboring channels and evaluating the phase of both channels at a time for symbol detection is presented and evaluated.

Measurement performance evaluation and noise compensation for a Fourier transform infrared–based pyrometer

The Fourier transform infrared (FTIR) spectrometer is widely used for spectrum analysis and radiation intensity measurement. In this work, a pyrometer with the temperature measurement range of 800°C to 1200°C is developed based on the FTIR spectrometer, and the calibration of the instrument coefficients using a standard radiation source is performed. On this basis, the readout noise and shot noise in the spectral signal are analyzed, and a detailed quantitative evaluation of the influences of two kinds of noise on the temperature measurement accuracy is also carried out in this work. Experimental results show that the temperature deviation caused by the two kinds of noise is less than 1°C when the wavelength is within the range of 2.0 to 5.4 μm. However, when the wavelength is greater than 5.4 μm, the maximum temperature deviation caused by the readout noise and shot noise can reach about 12°C and 18°C, respectively. In view of this, a Kalman filter method is introduced in this work to reduce temperature fluctuations caused by these noises, where the maximum fluctuation can be reduced from 83.9°C to 6.1°C with this filter method. These experimental results can provide a valuable guidance for the general-purpose calibration and assessment of the FTIR-based pyrometer.

The inverse Q filtering method based on a novel variable stability factor

The inverse Q filtering method with a stabilization factor solves the noise amplification problem caused by the exponential growth of the amplitude compensation operator in traditional inverse Q filtering to a certain extent, but it is not ideal for reducing noise and amplitude compensation of deep seismic records. The inverse Q filtering method with a variable stabilization factor is superior to the inverse Q filtering method with a constant stable factor in reducing noise, but the amplitude compensation for the deep seismic record is still not ideal, especially when the quality factor Q value is small. In addition, because its compensation function changing with time and frequency is fixed, its amplitude compensation cannot be flexibly carried out according to the characteristic of seismic records. We have developed a novel inverse Q filtering method with a new variable stabilization factor under the constraint of signal-to-noise ratio (S/N). The S/N of seismic data is taken as prior information, and the stabilization factor is a function of the S/N and the quality factor. The amplitude compensation operator changes dynamically with time, frequency, S/N, and the quality factor, which can accurately compensate for the effective amplitude while avoiding enhancing the noise energy. Theoretical synthetic seismic data and real seismic data using the new method find significant improvements in amplitude compensation and suppression of noise energy. The new method has the strongest comprehensive ability of amplitude compensation and noise suppression. The resolution and S/N of seismic data processed by the new method have been improved.

Evolving Error Feedback Fuzzy Model for Improved Robustness Under Measurement Noise

In this article, we propose a new variant of evolving fuzzy model for regression problems, which is based on error feedback integration in order to compensate measurement noise and to achieve more robust predictions; thus, it is termed as evolving error feedback fuzzy model. Thereby, we define an autoregressive-based noise model, localized per rule for being able to model different possible noise behaviors in different parts of the input space. Its predictions are added to the real fuzzy model predictions for noise correction purposes. This additive aspect leads to an extended version of the recursive fuzzily weighted least squares estimator (RFWLS-N = RFWLS with noise compensation), inheriting all its favorable convergence properties. For rule antecedent learning, we perform an evolving clustering process with the integration of a new forgetting strategy to be able to achieve more flexible updates in order to account for possible drifts. Therefore, a specific sample weighting concept is designed, with which different forgetting types can be achieved, ranging from (slow) logarithmic forgetting to (uniformly) linear forgetting and to (fast) exponential forgetting. The integration of the weights into the updates of rule centers and inverse covariance matrices is underlined with a theoretical analysis of their sample-wise contributions in the updates. Results on several noise-affected real-world datasets and system identification problems showed significantly improved model performances, compared to related evolving fuzzy methods, to the conventional RFWLS estimator and to the recursive correntropy approach. Antecedent forgetting helped us to improve the error trends in the case of abrupt drifts, while it never worsened the trends in the case of no drift.

Power Normalized Gammachirp Cepstral (PNGC) coefficients-based approach for robust speaker recognition

Speaker identification or recognition task aims to identify persons from their voices. This paper introduces a new feature extraction approach for robust speaker recognition named Power Normalized Gammachirp Cepstral (PNGC). Key aspect of our method is the use of a biologically motivated auditory perceptual model. For speaker modeling, we use the Gaussian Mixture Model-Universal Background Model (GMM-UBM). The proposed approach includes the following main processing steps: (1) an efficient auditory filter model based on the normalized Gammachirp auditory Filterbank to estimate the cochlea spectral behavior, (2) an environmental noise compensation bloc that employs: a medium-time power analysis, an asymmetric noise-suppression module with a temporal masking module, and a frequency smoothing module to compensate the environmental noise effects, and (3) a power-law nonlinearity. Conducted experimentations on TIMIT and Aurora datasets proved that our proposed PNGC approach achieves an improved recognition accuracy compared to the MFCC and recent PNCC methods for noisy speaker recognition.

A new approach for fault diagnosis in TCSC compensated transmission lines using FWHT and machine learning techniques

In this research a new approach is introduced for detecting and classifying faults in a Thyristor-Controlled Series Capacitor (TCSC) compensated power system. Our proposed scheme utilizes both the Fast Walsh Hadamard Transform (FWHT) and machine learning algorithms. The FWHT is employed to extract fault features from current data obtained from the TCSC compensated transmission line, while machine learning algorithms such as K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) are used to classify the extracted features for the purpose of fault detection and identification. To evaluate the performance of our proposed scheme, simulation studies were conducted on a test system under various fault conditions. The simulation results demonstrate that our approach is highly effective in accurately and quickly detecting and classifying faults, even when noise and TCSC compensation are present. This scheme has the potential to enhance the reliability and efficiency of power transmission systems.

Claim for Compensation due to Environmental Infringement

Claim for Compensation due to Environmental Infringement

The effect of signal-to-noise ratio value on the error in measuring acoustic emission parameters: statistical assessment

Modern acoustic emission diagnostic systems and complexes are a sensitive tool for detecting developing defects at an early stage when monitoring the technical condition of objects under operational loads. A significant limitation of the application acoustic emission method is the difficulty in isolating signals against the background of acoustic and electromagnetic interference. The effect of interference during acoustic emission recording significantly complicates the interpretation of parameters that characterize the technical condition of the test object. To increase the value signal-to-noise ratio and increase the reliability of the results of acoustic emission testing in the quantitative assessment of parameters, filtering methods are used. The subject of this study is the study of the effect of signal-to-noise ratio value on the measurement error acoustic emission parameters formatted during noise compensation using the polynomial filtering method. The basis of the statistical model characterizing the effect of signal-to-noise ratio value on the measurement error acoustic emission parameters is based on the machine learning method — linear regression. The dependence of the measurement error on the signal-to-noise ratio value was approximated by the least-squares method and visualized using a scattergram. It was found that when using the Butterworth filter, the relative measurement error acoustic emission parameters do not exceed 3 %, which are orders of magnitude lower than the values obtained for the Bessel filter and Daubechies mother functions 8 based on wavelet filter. A high inverse non-random correlation was established (r > 0.9), due to a decrease in the values of the relative measurement error emission parameters and an increase in the signal-to-noise ratio value. The developed statistical model describes the effect of the signal-to-noise ratio value on the value relative error in estimating the acoustic emission parameters. The adequacy of the developed model was confirmed by calculating the coefficient of determination and checking its statistical significance. It is shown that the use of Butterworth filter to compensate for interference significantly increases the information content of the results of measurements of acoustic emission parameters. The developed statistical model can be used in the development of new or improvement of existing complexes and systems for processing acoustic emission data to improve the reliability of the results of acoustic testing.

MEMS Rogowski coil current sensor with spiral return coil

AbstractWe have developed a MEMS Rogowski coil current sensor with spiral return coil. The Rogowski coil is formed on a silicon substrate clockwise and counterclockwise from the edges, for current detection and external magnetic noise compensation (device size 10 × 10 × 0.3 mm3, spiral coil 135 turns, via diameter 100 μm). When the slew rate (di/dt) was kept constant at 10 A/μs, and the current was changed by 5 A up to 40 A, MEMS Rogowski coil could detect current linearly. When the slew rate was changed by 10 A/μs up to 100 A/μs, it was found that the MEMS Rogowski coil could detect current almost linearly following the reference sensor. Hence, external noise compensation by the return coil has been confirmed.

An online hybrid estimation method for core temperature of Lithium-ion battery with model noise compensation

Temperature monitoring plays an important role in developing advanced battery management systems, ensuring safety, and improving cell performance. Core temperature provides more accurate indications of battery natures than surface temperature, but it cannot be measured directly. In this paper, a novel hybrid method by fusing a model-based method and a data-driven method is proposed to estimate the battery core temperature with model noise compensation. In the model-based method, an extended Kalman filter (EKF) is developed to estimate the core temperature based on an electro-thermal coupling model. The model parameters are updated with the feedback of the estimated core temperature and state of charge. In the data-driven method, a neural network is trained to characterize the battery model noises. For model noise compensation, the noise covariances of the EKF are dynamically adjusted by minimizing the estimation errors between the EKF and the neural network with particle swarm optimization. Experiments for implementing and validating the proposed method are conducted in a wide range of ambient temperatures. Compared with three existing methods, the proposed method can improve the estimation accuracy by at least 56.8% at −15 °C and 60.9% at 5 °C.

A solution for co-frequency and low SNR problems in heart rate estimation based on photoplethysmography signals.

In order to realize high-accuracy heart rate (HR) estimation based on photoplethysmography (PPG) under the scenes of low signal-to-noise ratio (SNR) and co-frequency caused by motion artifacts (MAs), this paper presents a novel framework integrating two-stage variational mode decomposition (VMD) denoising method, noise compensation technology, and hidden Markov model (HMM)-based tracking algorithm. The two-stage VMD denoising method is designed to separate the HR signal from MA under low SNR scene. The noise compensation technology is applied to solve the problem of co-frequency. HMM-based HR tracking method is adopted to obtain the global optimization performance of HR estimation. The effectiveness and superiority of the proposed framework in solving problems of low SNR and co-frequency associated with motion artifacts have been verified by the HR estimation experiments carried out on three public high-SNR PPG databases (ISPC, BAMI I, BAMI II) and a self-built low-SNR database (WeData). Compared with the two classical frameworks namely joint sparse spectrum reconstruction (JOSS) and convolutional neural network-long short-term memory network (CNN-LSTM), the proposed framework obtains the lowest HR estimation errors (0.94 beats per minute (BPM) and 1.81 BPM respectively) on both BAMI 2 with the highest SNR (0.40dB) and WeData with the lowest SNR (- 9.07dB). For the low-SNR database Wedata, the average absolute error (AAE) decreases by more than 21 BPM. The research result of this study provides a solution for the realization of high-accuracy PPG-based HR estimation in exercise scenarios.

Improved Phase Noise Compensation in OFDM Systems

Improved Phase Noise Compensation in OFDM Systems

A Low-Power Indirect Time-of-Flight CMOS Image Sensor With Fixed Depth Noise Compensation and Dual-Mode Imaging for Depth Dynamic Range Enhancement

We present a low-power indirect time-of-flight (iTOF) image sensor with fixed depth noise compensation and dual-mode imaging for depth dynamic range (DDR) enhancement. To reduce the power consumption from high-frequency pixel modulation, a TX driver with a single-sided clock chain is employed in the sensor. The inherent phase delay of the clock chain and the delay of the row bus are measured using row-parallel and column-parallel time-to-digital converters (TDCs) to compensate for the column and row fixed depth noise (FDN). To achieve a wide depth dynamic range (WDDR), the reconfigurable pixels and column circuits support dual-mode: short-range (SR) and long-range (LR) modes. A WDDR image is generated in a single frame through the mixed reconfiguration of the pixel array and interpolation. In addition, the temporal noise is suppressed without a significant time budget through a fast multiple sampling (FMS) scheme with 10b successive approximation register (SAR) analog-to-digital (ADCs). A prototype iTOF image sensor was fabricated using a 110 nm frontside illumination (FSI) CMOS image sensor (CIS) process and fully characterized. The sensor achieved a DDR of 4 m (0.7 to 4.7 m) with less than 1.7% nonlinearity and 0.9% depth noise. The FDN was suppressed to less than 2.1 cm at a low power consumption below 70 mW through the proposed compensation scheme using row and column TDCs. The temporal noise was only 0.48 mV <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{\mathbf {rms}}$ </tex-math></inline-formula> owing to the FMS.

Optimal Compensation of MEMS Gyroscope Noise Kalman Filter Based on Conv-DAE and MultiTCN-Attention Model in Static Base Environment.

Errors in microelectromechanical systems (MEMS) inertial measurement units (IMUs) are large, complex, nonlinear, and time varying. The traditional noise reduction and compensation methods based on traditional models are not applicable. This paper proposes a noise reduction method based on multi-layer combined deep learning for the MEMS gyroscope in the static base state. In this method, the combined model of MEMS gyroscope is constructed by Convolutional Denoising Auto-Encoder (Conv-DAE) and Multi-layer Temporal Convolutional Neural with the Attention Mechanism (MultiTCN-Attention) model. Based on the robust data processing capability of deep learning, the noise features are obtained from the past gyroscope data, and the parameter optimization of the Kalman filter (KF) by the Particle Swarm Optimization algorithm (PSO) significantly improves the filtering and noise reduction accuracy. The experimental results show that, compared with the original data, the noise standard deviation of the filtering effect of the combined model proposed in this paper decreases by 77.81% and 76.44% on the x and y axes, respectively; compared with the existing MEMS gyroscope noise compensation method based on the Autoregressive Moving Average with Kalman filter (ARMA-KF) model, the noise standard deviation of the filtering effect of the combined model proposed in this paper decreases by 44.00% and 46.66% on the x and y axes, respectively, reducing the noise impact by nearly three times.