Strong Noise Immunity Research Articles

Separation algorithm of vital sign signal in complex environments based on time-frequency filtering

Life detection radar that combines radar technology with biomedical engineering detects human physiological signals (respiration, heartbeat, body movement, etc.) from a long distance with non-contact method. In this field, vital sign detection and parameter extraction are hot issues in current researches, and the acquisition of vital sign signal of human target with radar may very helpful. In this paper, a separation method for vital sign signals based on time-frequency filtering (TFF) is proposed, which mainly predicts the instantaneous frequency (IF) by combining the Viterbi algorithm (VA) with strong noise immunity and taking advantage of the high-resolution time-frequency transformation method with good cross-term inhibitory effect in B distribution (BD), so as to extract the weak vital sign signals in the radar echo effectively. According to the simulation result, this algorithm has good resolution precision and anti-noise performance, and it is applicable for the extraction of vital sign signals in low signal-to-noise ratio, which may provide basis for the further launch of parameter extraction of the vital sign signals.

SAR Image Change Detection Based on Iterative Label-Information Composite Kernel Supervised by Anisotropic Texture

Kernel methods with specifically designed kernel function are suitable for dealing with practical nonlinear problems. However, kernel methods have found limited applications to synthetic aperture radar (SAR) image change detection in that their performances are affected by the inherent multiplicative speckle noise of SAR images. It is known that the spatial-contextual information is helpful in suppressing the degrading effects of the noise. Therefore, a label-information composite kernel ( LIC kernel ) constructed on the basis of the spatial-contextual information is proposed in this paper for SAR image change detection. A typical spatial information, the output-space label-neighborhood information that is extracted using all labels in the neighborhood of each pixel, may enhance noise immunity, but with inaccurate edge locations simultaneously. Consequently, the anisotropic Gaussian kernel model is utilized for analyzing anisotropic textures of the bitemporal images, and then, a comparison scheme acting on the input-space textures of the bi-temporal images is proposed to supervise the extraction of the output-space label-neighborhood information in the construction of the LIC kernel . The constructed LIC kernel is of good preservation of edge locations of changed areas as well as strong noise immunity. The LIC kernel is updated iteratively with the newest change map outputted from the support vector machine, until the change map converges. Experiments on real SAR images demonstrate the effectiveness of the LIC kernel method and illustrate that it has both strong noise immunity and good preservation of edge locations of changed areas for SAR image change detection.

Quantitative morphological analysis of curvilinear network for microscopic image based on individual fibre segmentation (IFS).

Microscopic images of curvilinear fibre network structure like cytoskeleton are traditionally analysed by qualitative observation, which can hardly provide quantitative information of their morphological properties. However, such information is crucially contributive to the understanding of important biological events, even helps to learn about the inner relations hard to perceive. Individual fibre segmentation-based curvilinear structure detector proposed in this study can identify each individual fibre in the network, as well as connections between different fibres. Quantitative information of each individual fibre, including length, orientation and position, can be extracted; so are the connecting modes in the fibre network, such as bifurcation, intersection and overlap. Distribution of fibres with different morphological properties is also presented. No manual intervening or subjective judging is required in the analysing process. Both synthesized and experimental microscopic images have verified that the detector is capable to segment curvilinear network at the subcellular level with strong noise immunity. The proposed detector is finally applied to the morphological study on cytoskeleton. It is believed that the individual fibre segmentation-based curvilinear structure detector can greatly enhance our understanding of those biological images generated from tons of biological experiments.

Harmonic Detection in Power Signals Based on Fast Modified S-Transform

In this paper, a new approach for harmonic detection in power signals based on fast modified S-transform (FMST) is proposed, which combines the advantages of the incomplete S-transform and the modified S-transform. Initially, the Fast Fourier Transform was performed and dynamic measurement values were obtained from the envelope of power spectrum, then the valid harmonic frequency points were detected by selecting the dynamic measurement values which were larger than the set thresholds. Further, the FMST was specifically performed on these major frequency points and a complex matrix was generated. The row vectors of the matrix reflected the phase and the time location information of harmonic disturbances, while the column vectors reflected the amplitude-frequency characteristics. And the feature vectors extracted from the complex matrix were used to detect the harmonic amplitude, phase and transient information. Simulation results validate the high accuracy, strong noise immunity and rapidity of the proposed approach.

Harmonic Detection in Micro-Grid Based on Modified S-Transform

The excellent time–frequency resolution of the modified S-transform (MST) makes it an attractive candidate for analysis and detection of harmonic in micro-grid. This paper presents a new approach for micro-grid harmonic detection based on the MST. Firstly, the MST was performed for the harmonic signal, and then the feature vectors were extracted from the resulting time-frequency matrix. Finally, the frequency, amplitude and phase of the harmonic were obtained by analyzing and processing these feature vectors. Simulation results show that the proposed approach can detect the harmonic in micro-grid with high accuracy and strong noise immunity.

An adaptive ultrasonic backscattered signal processing technique for instantaneous characteristic frequency detection.

Ultrasonic diagnosis that is convenient and nondestructive to the human body is widely used in medicine. In clinical, ultrasonic backscattered signals characteristics are utilized to acquire information of the human body tissues to perform diagnosis. In this paper, an adaptive ultrasonic backscattered signal processing technique for instantaneous characteristic frequency detection based on the marginal spectrum is presented. In the beginning, the ultrasonic backscattered signal is decomposed into a series of intrinsic mode functions (IMFs) by the Ensemble Empirical Mode Decomposition (EEMD) algorithm. Then the Hilbert spectrum is gained by the Hilbert transform on the IMFs decomposed and screened. Finally, the time-frequency information in the Hilbert spectrum is utilized to extract the instantaneous characteristic frequency based on the marginal spectrum features to detect the objective. With this technique, the spacing between tissues can be estimated for tissue characterization by processing multiple echoes even in the complicated environment. In the simulation study, comparing with the FFT, the technique presented shows its strong noise immunity and indicates its validity in instantaneous characteristic frequency detection.

Inversion of photon correlation spectroscopy based on truncated singular value decomposition and cascadic multigrid technology

For the low accuracy of single-scale inversion method in photon correlation spectroscopy technology, a cascadic multigrid (CMG)-truncated singular value decomposition (TSVD) inversion method that combines the TSVD regularization with CMG technology is proposed. This method decomposes the original problem into several subproblems in different scale grid space. According to the particle sizes inverted from the coarsest scale to the finest scale, the solution of an original inversion problem can be obtained. For the inversion of each subproblem, TSVD method is used. The simulation and experimental data were respectively inverted by TSVD and CMG-TSVD methods. The inversion results demonstrate that the CMG-TSVD method has higher accuracy, more strong noise immunity and better smoothness than the TSVD method.

A Study and Improvements on Canny Algorithm

This paper studies principle of the traditional Canny edge detection algorithm and the existing problems. Some solutions toward the problems are also proposed. The improvement methods consisted of two parts. On the one hand, as Canny edge algorithm itself does not have strong noise immunity while ensuring edge positioning accuracy, an adaptive median filter denoising program based on the Canny Gaussian filter is proposed to enhance the Canny algorithm filter performance. On the other hand, given the Canny algorithm high and low threshold parameter is not determined by the feature information of the image edge, but rather set manually which does not have the adaptive ability, this paper proposes an adaptive threshold setting scheme for high and low thresholds to improve the adaptive ability of Canny algorithm to different images.

A Practicable Method for Ferromagnetic Object Moving Direction Identification

The direction of a moving target is an important piece of information in many wireless sensor network (WSN) applications, such as in boundary security, traffic flow control, etc. Due to its robustness, the magnetic sensor can be used to detect a passing ferromagnetic object. By using the orthogonality of two perpendicularly placed sensing units in a monomer magnetic sensor, a linear algorithm based on a magnetic dipole model to identify the ferromagnetic object's moving direction is introduced in this paper. It has been successfully applied in real WSN applications to reduce the numbers of nodes, and prolong the lifetime of the network. Both simulation and field experiments show it has strong noise immunity and more than 95% correction rate in direction detecting.

Application of hydrologic forecast model

In order to overcome the shortcoming of the solution may be trapped into the local minimization in the traditional TSK (Takagi-Sugeno-Kang) fuzzy inference training, this paper attempts to consider the TSK fuzzy system modeling approach based on the visual system principle and the Weber law. This approach not only utilizes the strong capability of identifying objects of human eyes, but also considers the distribution structure of the training data set in parameter regulation. In order to overcome the shortcoming of it adopting the gradient learning algorithm with slow convergence rate, a novel visual TSK fuzzy system model based on evolutional learning is proposed by introducing the particle swarm optimization algorithm. The main advantage of this method lies in its very good optimization, very strong noise immunity and very good interpretability. The new method is applied to long-term hydrological forecasting examples. The simulation results show that the method is feasible and effective, the new method not only inherits the advantages of traditional visual TSK fuzzy models but also has the better global convergence and accuracy than the traditional model.

Axial nanodisplacement measurement based on astigmatism effect of crossed cylindrical lenses

Spot distortion caused by astigmatism effect in orthogonal cylindrical lenses is utilized to measure axial displacement with 30 nm resolution, strong noise immunity, and compact experiment setup. Axial displacement of the sample surface is determined by four-quadrant difference processing of distorted laser spots' energy distribution images received by CCD. Four-quadrant difference processing results indicate an applicable measuring range of 5.6 μm (cubic fitting r=0.9988) with a highly linear range of 1.2 μm (linear fitting r=0.9996). Factors affecting measuring range and sensitivity are analyzed by theoretical deduction and numerical simulation. This technique has potential applications in drifting sample tracking and measurement in advanced microscopy.

Measurements of Particle Size Distribution Based on Mie Scattering Theory and Markov Chain Inversion Algorithm

Measuring particle size distribution through calculating light scattering intensity is a typical inverse problem. This paper builds an inverse mathematical model based on Mie scattering, deduces the inversion formulas for particle size, and calculates the relative coefficients through programming with built-in functions in MATLAB. In order to improve the accuracy and noise immunity of particle size distribution measurement, the development of stochastic inversion algorithm: an inverse problem model based on Markov chain algorithm is proposed. Results of numerical simulation are added acceptable noise indicate that the algorithm of Markov chain has strong noise immunity and can meet the requirements of on-line measurement.

Application Research of GMRES Algorithm in dynamic light scattering Particles Size Distribution Inversion

Abstract For ill-posed inversion problem of dynamic light scattering (DLS), this paper is discusses DLS inversion of GMRES algorithm. Combining Tikhonov regularization inversion strategy with GMRES algorithm, a GMRES-TIK inversion algorithm is designed in this paper. The simulation datum was respectively inversed by two methods. The inversion results demonstrate that, correct inversion results can’t obtained by GMRES algorithm, however inversion results of GMRES-TIK algorithm is reasonable under a certain noise level. Therefore, we can draw conclusions that GMRES algorithm can’t directly used for DLS inversion, after combining Tikhonov regularization method, it can used for DLS inversion and has strong noise immunity.

Wavelets-regularization method for particle size inversion in photon correlation spectroscopy

For ill-posed inversion problem of photon correlation spectroscopy (PCS), a wavelet-regularization inversion method (WRIM) which combines wavelet multiscale inversion strategy with classical regularization inversion method (CRIM) was proposed. By using this method, the original inversion problem is decomposed into several subproblems on different multiscale spaces. As a result, we can successively obtain solution of original inversion problem according to the particle sizes inverted from the coarsest scale to the finest scale. The simulation and experimental data was respectively inverted by two methods. The inversion results demonstrate that WRIM has better global convergence, higher accuracy and more strong noise immunity than CRIM.

Extraction of Feature Photoacoustic Spectroscopy Probed by a Sensor against a Large Background Noise

An algorithm of independent component analysis from blind source separation domain is used and a Δλ-model strong-noise immunity is proposed in this work. The test results showed that maximal extraction error is respectively 0.28% and 3.7% under a SNR of 1/886 and an excellent agreementbetween the numerical simulation and the actual detection value is found, and the detection limit of test sample is improved from 16 ppb with the model based on common linear equations to 2 ppb using the proposed model.

Extraction of Feature Photoacoustic Spectroscopy Probed by a Sensor against a Large Background Noise

An algorithm of independent component analysis from blind source separation domain is used and a Δλ-model strong-noise immunity is proposed in this work. The test results showed that maximal extraction error is respectively 0.28% and 3.7% under a SNR of 1/886 and an excellent agreementbetween the numerical simulation and the actual detection value is found, and the detection limit of test sample is improved from 16 ppb with the model based on common linear equations to 2 ppb using the proposed model.

基于自适应尺度的遥感影像渐进配准

Remote sensing image registration is a fundamental problem in image processing. In this paper, we develop a progressive image registration method based on adaptive scale for multimodal and multitemporal remote sensing images. In an iterative process proceeding from coarse to fine registration, geometric positioning errors computed from the results of the last registration can be used for scale determination. This reduces the number of matches and time cost compared with pyramid methods. In addition, we employ a feature-extraction and matching process using an algorithm based on the Harris-Laplace and phase correlation algorithms, which is invariant to sub-pixel offset, rotation, and scale change, una?ected by changes in contrast and gray scale, and displaying strong noise immunity. Finally, we applied pumping dilute corner optimization to a limited search area to improve the performance of the algorithm. By applying this method in experiments and in the automatic batch processing of CBERS-02B satellite L3 data, we show that the algorithm is accurate, efficient and resistant to attacks.

The Smith-PID Control of Three-Tank-System Based on Fuzzy Theory

According to the character of the volume-lag of the controlled process of three-tank-system, Smith predictor was adopted to compensate three-tank-system fuzzy adaptive control system. Fuzzy adaptive Smith predictive control system is composed with Smith predictor and fuzzy adaptive controller. The PID parameters were setting on line. This algorithm uses fuzzy adaptive PID control to improve the resistance ability to random disturbance and Smith predictive control to overcome the time-delay character of controlled object. Simulation results showed that this control algorithm has the advantages of strong adaptive ability and noise immunity.