Noisy Output Signal Research Articles

Self-Regulating Dynamic Measurement Method

A dynamic measuring system is considered. A new model of the measuring system, a method for processing of measurement results and a method of restoring the input signal of the system by the noisy output signal are proposed. The estimation of the accuracy of the method and a computational experiment demonstrating the efficiency of the signal recovery method are presented.

Test Methods for Signal Reconstruction of Linear Stationary Systems

The test methods designed for mathematical reconstruction of input signals of linear stationary information processing systems from distorted and noisy output signals are proposed and analyzed. It is shown that the use of test data allows one to carry out a non-blind reconstruction without determining the hardware functions of processing systems, which, in general, can belong to the class of generalized functions. The features of the use of the regularization technique in test methods are considered when solving ill-posed and ill-conditioned problems of reconstruction of real non-deterministic signals. The criteria for selecting test signals are analyzed. The results of numerical experiments on the restoration of one-dimensional signals and two-dimensional images at different noise levels are presented.

Low-temperature supercritical activation enables high-performance detection of cell-free DNA by all-carbon-nanotube transistor

Biosensors based on carbon nanotube (CNT) thin-film transistors (TFTs) have drawn intense interests for their exhibiting great potential in ultrasensitive and label-free DNA detection. However, the inevitable defects in the dielectric and channel of CNT-based TFTs may induce noisy or unreliable output signals. Here, we propose a low-temperature supercritical carbon dioxide (SCCO2) fluid activation method to enhance the performance of transistor-based biosensors. An activation model of SCCO2 treatment is proposed to elaborate that the SCCO2 treatment can repair the dangling bonds of SiO2 dielectric facilely and passivate the hydroxyl groups effectively. Furthermore, a cell-free DNA detection platform is built up based on all-carbon-nanotube TFTs (AC-TFTs) modified by the CW-Peptide probe. By adopting the SCCO2 treatment, the response of AC-TFT biosensors to AKT2 gene related to triple-metastatic negative breast cancer can be enhanced by more than 114% with high uniformity, and a broad detection range of six orders of magnitude is yielded with a theoretical limit of detection of 42 fM. Additionally, the response has a linear correlation with the length of DNA targets. The nondestructive and eco-friendly SCCO2 activation method provides a promising and universal strategy for achieving highly accurate and sensitive transistor-based biosensors in future clinical applications.

Численный метод обработки результатов динамических измерений

The problem of processing data obtained during dynamic measurements is one of the central problems in measuring technology. Purpose of the study. The article is devoted to the study of the stability of the method for solving the problem of processing the results of dynamic measurements with respect to the error in the initial data. Therefore, an urgent task is the development of algorithms for processing the results of dynamic measurements. Materials and methods. This article proposes an algorithm for processing the data obtained during dynamic measurements based on the finite-difference approach. The main prerequisites of the mathematical model of the problem of dynamic measurements associated with the processes of restoration of the input signal in conditions of incomplete and noisy initial data are as follows. Initially, the function of the noisy output signal is known. The restoration of the input signal is carried out using the transfer function of the sensor. The transfer function of the sensor is presented in the form of a differential equation. This equation describes the state of a dynamic system in real time. The proposed computational scheme of the method is based on finite-difference analogs of partial derivatives and the Tikhonov regularization method was used to construct a numerical model of the sensor. The problem of stability of the method for solving high-order differential equations is also one of the central problems of data processing in automatic control systems. Based on the approach of the generalized quasi-optimal choice of the regularization parameter in the Lavrent'ev method, the dependence of the regularization parameter, the parameters of the dynamic measuring system, the noise index and the required level of accuracy was found. Results. The main goal of the computational experiment was to construct a numerical solution to the problem under consideration. Standard test functions were considered as input signals. Test signals simulating various physical processes were used as an input signal. The function of the output signal was found using the proposed numerical method, the found function was noisy with an additive noise of 5 %. Conclusion. The input signal was restored from the noisy signal. The deviation of the reconstructed signal from the initial one in all experiments was no more than 0.05, which indicates the stability of this method with respect to noisy data.

Probabilistic Modeling and Bayesian Filtering for Improved State Estimation for Soft Robots

State estimation is one of the key requirements in robot control, which has been achieved by kinematic and dynamic models combined with motion sensors in traditional robotics. However, it is challenging to acquire accurate proprioceptive information in soft robots due to relatively high noise levels and hysteretic responses of soft actuators and sensors. In this article, we propose a method of estimating real-time states of soft robots by filtering noisy output signals and including hysteresis in the models using a Bayesian network. This approach is useful in constructing a state observer for soft robot control when both the kinematic model of the actuator and the model of the sensor are used. In our method, we regard a hysteresis function as a conditional random process model. We then introduce a dynamic Bayesian network composed of the actuator and the sensor models of the target system using distribution hysteresis mapping. Finally, we show that solving a Bayesian filtering problem is equivalent to suboptimal state estimation of the soft system. This article describes two ways for defining modeling and filtering; one is by Gaussian process regression combined with an extended Kalman filter, and the other is based on variational inference with a particle filter. While the first approach relaxes the uncertainty level in modeling to Gaussian, the second approach illustrates a general probability distribution. We experimentally validate the proposed methods through real-time state estimation of a sensor-integrated soft robotic gripper. The result shows significant improvement in state estimation compared to conventional estimation methods.

Efficient flexibility identification method using structured target rank approximation and extended Prony's method

An accurate estimation of the structural flexibility is crucial in applications such as deflection prediction, damage detection, and finite element model updating. By introducing a unit impulsive response function (UIRF) matrix estimated from noisy input and output signals collected through impact testing, this paper proposes a new method for estimating the structural flexibility matrix from a noisy UIRF. The advantages lie in the way the noise and decoupling modes of the impulse response signal are handled in the time domain. The proposed method consists of three steps: (1) Constructing an enhanced unit impulse response function (EUIRF) from the UIRF by utilizing the orthogonality of the displacement mode shapes for mode decoupling, (2) Noise removal from the constructed EUIRF to obtain a filtered one by implementing the improved Cadzow's algorithm for the structured target rank approximation (STRA) of the Hankel matrix constructed from the EUIRF, and (3) Structural flexibility identification from the EUIRF using the extended Prony's method to identify the basic modal parameters and modal scaling factors for a single-mode impulse response function with one degree of freedom. To validate the applicability of the proposed method, numerical and laboratory case studies were performed. A noisy impact test was simulated for a three-span continuous beam bridge, and the identified modal parameters and structural flexibility matrix were found to be accurate. Moreover, an impact test was conducted on a simply supported beam in a laboratory, and the results showed that the deformation predicted from the identified flexibility matrix and the one measured from the static load test are in good agreement.

GLSDC Based Parameter Estimation Algorithm for a PMSM Model

In this study, a GLSDC (Gaussian Least Squares Differential Correction) based parameter estimation algorithm is used to identify a PMSM (Permanent Magnet Synchronous Motor) model. In this method, a nonlinear model is assumed to be the correct representation of the underlying state dynamics and the output signals are assumed to be measured in a noisy environment. Using noisy input and output signals, parameters that constitute the coefficients of the nonlinear state and input signal terms are to be estimated using the state transition matrix which is computed by the numerical means that are detailed. Since a GLSDC algorithm requires correct initial state value, this term is also estimated in addition to the unknown coefficients whose bounds are assumed to be known, which is mostly the case in the industrial applications. The batch input and output signals are used to iteratively estimate the parameter set before and after the convergence, and to recover the filtered state trajectories. A couple of different scenarios are tested by means of numerical simulations and the results are addressed. Different methods are discussed to compute better initial estimate values, to shorten the convergence time.

Identification of the parameters of the cable production process

The object of research is the process of producing electric cables with polymer insulation for ultrahigh voltages. One of the most problematic places is the presence in the contours of the regulation of the thickness of insulation layers, the noise of measuring the speeds of worms of extruders and the diameters of the insulation layers, and also the time delay. The noisiness of useful signals and the time delay adversely affect the accuracy of layer thickness control and the speed of regulation and can lead to loss of system stability. To solve this problem, a parametric identification method is proposed, which under real conditions of noisy measurements of control object variables gives an estimate close to the exact values of the parameters.A modified least squares method is used, which in the situation of noisy input and output signals provides an unbiased estimate parameters, and a smaller spread rating than conventional least squares method. This is due to the fact that the proposed method makes it possible to reduce the spread of the quadratic functional by additional averaging on the set of quasi-static independent functionals.In comparison with the analogous well-known least squares method, it provides an increase in the accuracy of identifying parameters in conditions of noiselessness of not only output but also input signals. The introduction of research results in systems of adaptive control of the thickness of polymer insulation layers in the production of cables for ultra-high voltage will improve product quality and improve production efficiency.

Impact identification and localization using a sample-force-dictionary - General Theory and its applications to beam structures

Monitoring of impact loads is a very important technique in the field of structural health monitoring (SHM). However, in most cases it is not possible to measure impact events directly, so they need to be reconstructed. Impact load reconstruction refers to the problem of estimating an input to a dynamic system when the system output and the impulse response function are usually known. Generally this leads to a so called ill-posed inverse problem. It is reasonable to use prior knowledge of the force in order to develop more suitable reconstruction strategies and to increase accuracy. An impact event is characterized by a short time duration and a spatial concentration. Moreover the force time history of an impact has a specific shape, which also can be taken into account. In this contribution these properties of the external force are employed to create a sample-force-dictionary and thus to transform the ill-posed problem into a sparse recovery task. The sparse solution is acquired by solving a minimization problem known as basis pursuit denoising (BPDN). The reconstruction approach shown here is capable to estimate simultaneously the magnitude of the impact and the impact location, with a minimum number of accelerometers. The possibility of reconstructing the impact based on a noisy output signal is first demonstrated with simulated measurements of a simple beam structure. Then an experimental investigation of a real beam is performed.

LPV system identification under noise corrupted scheduling and output signal observations

Most of the approaches available in the literature for the identification of Linear Parameter-Varying (LPV) systems rely on the assumption that only the measurements of the output signal are corrupted by the noise, while the observations of the scheduling variable are considered to be noise free. However, in practice, this turns out to be an unrealistic assumption in most of the cases, as the scheduling variable is often related to a measured signal and, thus, it is inherently affected by a measurement noise. In this paper, it is shown that neglecting the noise on the scheduling signal, which corresponds to an error-in-variables problem, can lead to a significant bias on the estimated parameters. Consequently, in order to overcome this corruptive phenomenon affecting practical use of data-driven LPV modeling, we present an identification scheme to compute a consistent estimate of LPV Input/Output (IO) models from noisy output and scheduling signal observations. A simulation example is provided to prove the effectiveness of the proposed methodology.

Stochastic Optimal Control as a Theory of Brain-Machine Interface Operation

The closed-loop operation of brain-machine interfaces (BMI) provides a framework to study the mechanisms behind neural control through a restricted output channel, with emerging clinical applications to stroke, degenerative disease, and trauma. Despite significant empirically driven improvements in closed-loop BMI systems, a fundamental, experimentally validated theory of closed-loop BMI operation is lacking. Here we propose a compact model based on stochastic optimal control to describe the brain in skillfully operating canonical decoding algorithms. The model produces goal-directed BMI movements with sensory feedback and intrinsically noisy neural output signals. Various experimentally validated phenomena emerge naturally from this model, including performance deterioration with bin width, compensation of biased decoders, and shifts in tuning curves between arm control and BMI control. Analysis of the model provides insight into possible mechanisms underlying these behaviors, with testable predictions. Spike binning may erode performance in part from intrinsic control-dependent constraints, regardless of decoding accuracy. In compensating decoder bias, the brain may incur an energetic cost associated with action potential production. Tuning curve shifts, seen after the mastery of a BMI-based skill, may reflect the brain's implementation of a new closed-loop control policy. The direction and magnitude of tuning curve shifts may be altered by decoder structure, ensemble size, and the costs of closed-loop control. Looking forward, the model provides a framework for the design and simulated testing of an emerging class of BMI algorithms that seek to directly exploit the presence of a human in the loop.

Optimized Wiener filter

We consider using a Wiener filter for denoising a set of input signals that is degraded by additive white Gaussian noise. The Wiener filter is designed to minimize the mean square error, and it requires the knowledge of covariance of input signals and variance of the noise. The mean square error is defined as a distance between input signals and input signals estimated by the Wiener filter from noisy output signals. Although we have to infer input signals from only noisy output signals, the input signals are required for the evaluation of the mean square error. We reformulate the mean square error using noisy output signals and the covariance of the input signals. The covariance of the input signals is estimated by minimizing the mean square error. We apply our method to the case when an input signal is not generated by the assumed prior probability. In particular, we apply our method to image restoration and obtain good estimation results.

GPI observer-based non-linear integral sliding mode control and synchronisation of uncertain chaotic system

An output feedback control scheme for a class of triangular chaotic systems in the presence of system uncertainties, unknown disturbances, parameter perturbations and measurement noises is proposed in this paper. A high gain general proportional integral (GPI) observer is used for the accurate linear estimation of the error states, non-linear endogenous and exogenous disturbances from the noisy output signals of the system, and then a non-linear integral sliding mode controller with active disturbance cancellation is proposed for the tracking control and synchronisation of the chaotic systems. The uncertain Gesino chaotic system is used to validate the proposed control method and the convincing numerical results illustrate the proposed method is effective to deal with various system uncertainties only based on the utilisation of the input-output data.

Deconvolution of pulse trains with the L0 penalty

The output of many instruments can be modeled as a convolution of an impulse response and a series of sharp spikes. Deconvolution considers the inverse problem: estimate the input spike train from an observed (noisy) output signal. We approach this task as a linear inverse problem, solved using penalized regression. We propose the use of an L 0 penalty and compare it with the more common L 2 and L 1 penalties. In all cases a simple and iterative weighted regression procedure can be used. The model is extended with a smooth component to handle drifting baselines. Application to three different data sets shows excellent results.

Estimation robuste par synthèse H2de micro- et nanoforce à l'aide de ressorts magnétiques actifs

This article deals with the problematic of micro- and nanoforce measurement. The force is the unknown excitatory signal of a transducer whose only the output is measurable. This general problematic of an unknown input signal reconstruction from a noisy output signal is developed inside the H2 synthesis framework. The resulting methodology is implemented in a micro- and nanoforce sensor that uses a macroscopic seismic mass controlled by active magnetic springs. This methodology uses only a very basic knowledge on the force dynamic and thanks to H2 synthesis, ensures that the force estimation remains correct despite the transducer dynamic, the noise measurement, the uncertainty on the force dynamic and the sensor modeling errors.

Chaotic parameters and their role in quantifying noise in the output signals from UV, TGA and DSC apparatus

Two fractal dimensions and the Liapunov exponent (LE) have been applied to detect noisy output signals from UV spectrophotometer (UV), thermogravimetric analyzer (TGA) and differential scanning calorimeter (DSC) apparatus of 1-ethyl-3-methylimidazolium ethylsulfate ionic liquid ([emim][EtSO4]). The data collected from these three pieces of equipment were classified before calculating LE, regularization (RD) and box dimensions (BD). The RD and LE are able individually to detect and quantify noisy output signals with a mean error value less than 5% in all cases tested. Given that the LE can be calculated using a really simple method, this chaotic parameter has been selected as the most suitable to detect noise of signals from these apparatus.

Continuous monitoring can improve indistinguishability of a single-photon source

An engineering technique using continuous quantum measurement in conjunction with feed forward is proposed to improve indistinguishability of a single-photon source. The technique involves continuous monitoring of the state of the emitter, processing the noisy output signal with a simple linear estimation algorithm, and feed forward to control a variable delay at the output. In the weak-coupling regime, the information gained by monitoring the state of the emitter is used to reduce the time uncertainty inherent in photon emission from the source, which improves the indistinguishability of the emitted photons.

Design and optimisation of a filter based on neural networks. Application to reduce noise in experimental measurement by TGA of thermal degradation of 1-ethyl-3-methylimidazolium ethylsulfate ionic liquid

In this paper a new computerised approach to the accurate determination of thermal degradation of 1-ethyl-3-methylimidazolium ethylsulfate ionic liquid is considered. In this approach, an integrated neural network (NN)/thermogravimetric analyser (TGA) is designed to filter a noisy signal from TGA equipment. Data collected (sample weight loss as a function of sample temperature) from the TGA equipment were transferred into a NN trained computer for modeling and filtering of output. Such an integrated NN/TGA filter is capable to filter the noisy TGA output signal based on the created models and patterns, without any previous phenomenological knowledge. The NN/TGA equipment is able to filter the output of the experimental process with mean error less than 0.3%.

A MEMS Complete Blood Count Sensor with Vanes for Reduction in Influence of Electrolysis Gas

To develop a miniature complete blood count (CBC) analyzer for point-of-care testing (POCT), a MEMS CBC sensor based on the impedance method is discussed. A novel MEMS CBC sensor that is fabricated through a simple photolithography process using SU-8 is realized. However, the fabricated sensor exhibits a noisy output signal due to electrolysis gas. The relationship between the noise and the gas is revealed through microscopic observation and finite element method (FEM) simulation. To solve the problem of electrolysis gas, an improved MEMS CBC sensor with vanes is developed. The improved sensor is unaffected by electrolysis gas. Moreover, the signal stability of the sensor and the signals detected for latex particles are successfully evaluated.

Non-destructive evaluation of magnetic metallic materials using Hall sensors

In this paper we present a laboratory developed Hall sensor for non-destructive testing of ferromagnetic surfaces, based on magnetic anomaly detection phenomena. The principle of operation is based on the detection of the magnetic flux leakage in the dimensional boundaries of a gap, mainly of the vertical component Bz referring to the surface plane. Hall sensor is set parallel and very close to the magnetized surface, so as to increase the sensitivity, since the present fields are relatively weak. Small changes in its direction and lift-off movements result in decreasing and noisy output signal. Sensor properties in combination with an appropriate signal processing unit and an effective and fully controlled inspection technique can be used to applications where a rapid and qualitative inspection is needed.