Ultrasonic wind velocity measurement based on DSP

Ultrasonic transmitting, receiving and amplifying circuits are designed. Thereceived signals are sampled with the high speed ADC (analog-to-digital converter), and dealt withthe DSP (digital signal processing). A forward-backward IIR (infinitive impulse response) filterwith no delay is designed to filter the sampled data, and series A and B are achieved by narrow andwide band filtering, respectively. In series A, the start point of the cycle first exceeding thethreshold is calculated accuratelyby interpolation, and the start cycle is detected by fittingcycles in series A and its inversion A' to cycles in B with variance analysis. Therefore, the startpoint of the start cycle is calculated precisely. By deriving the relationships between the traveltime in the opposite directions of three axes and the airflow velocities, the wind velocity anddirection are calculated. Experiments show that the reliability and the precision are improved, andthe circuits are simplified.

Signal extraction for GPS deformation monitoring in mining survey

The basic signal model of deformation monitoring with GPS was introduced and the main problems of GPS deformation monitoring in mining area were discussed. For the problem of noise signal extraction in GPS deformation monitoring, the Kalman-EMD method was proposed to obtain the effective deformation signal. The reliability and effectiveness of the methodology were tested and verified by analog signal. The results of experiment in Mongolia show that the accuracy of the proposed GPS deformation monitoring model is equivalent to that of level method.

An adaptive noise attenuation method for edge and amplitude preservation

Noise intensity distributed in seismic data varies with different frequencies or frequency bands; thus, noise attenuation on the full-frequency band affects the dynamic properties of the seismic reflection signal and the subsequent seismic data interpretation, reservoir description, hydrocarbon detection, etc. Hence, we propose an adaptive noise attenuation method for edge and amplitude preservation, wherein the wavelet packet transform is used to decompose the full-band seismic signal into multiband data and then process these data using nonlinear anisotropic dip-oriented edge-preserving fi ltering. In the fi ltering, the calculated diffusion tensor from the structure tensor can be exploited to establish the direction of smoothing. In addition, the fault confidence measure and discontinuity operator can be used to preserve the structural and stratigraphic discontinuities and edges, and the decorrelation criteria can be used to establish the number of iterations. These parameters can minimize the intervention and subjectivity of the interpreter, and simplify the application of the proposed method. We applied the proposed method to synthetic and real 3D marine seismic data. We found that the proposed method could be used to attenuate noise in seismic data while preserving the effective discontinuity information and amplitude characteristics in seismic refl ection waves, providing high-quality data for interpretation and analysis such as high-resolution processing, attribute analysis, and inversion.

Edge enhancement of gravity anomalies and gravity gradient tensors using an improved small sub-domain filtering method

In order to enhance geological body boundary visual effects in images and improve interpretation accuracy using gravity and magnetic field data, we propose an improved small sub-domain filtering method to enhance gravity anomalies and gravity gradient tensors. We discuss the effect of Gaussian white noise on the improved small sub-domain filtering method, as well as analyze the effect of window size on geological body edge recognition at different extension directions. Model experiments show that the improved small sub-domain filtering method is less affected by noise, filter window size, and geological body edge direction so it can more accurately depict geological body edges than the conventional small sub-domain filtering method. It also shows that deeply buried body edges can be well delineated through increasing the filter window size. In application, the enhanced gravity anomalies and calculated gravity gradient tensors of the Hulin basin show that the improved small sub-domain filtering can recognize more horizontal fault locations than the conventional method.

Random noise attenuation by f–x spatial projection-based complex empirical mode decomposition predictive filtering

The frequency–space（f–x） empirical mode decomposition（EMD） denoising method has two limitations when applied to nonstationary seismic data. First, subtracting the first intrinsic mode function（IMF） results in signal damage and limited denoising. Second, decomposing the real and imaginary parts of complex data may lead to inconsistent decomposition numbers. Thus, we propose a new method named f–x spatial projection-based complex empirical mode decomposition（CEMD） prediction filtering. The proposed approach directly decomposes complex seismic data into a series of complex IMFs（CIMFs） using the spatial projection-based CEMD algorithm and then applies f–x predictive filtering to the stationary CIMFs to improve the signal-to-noise ratio. Synthetic and real data examples were used to demonstrate the performance of the new method in random noise attenuation and seismic signal preservation.

An interacting multiple model-based two-stage Kalman filter for vehicle positioning

To address the problem that a general augmented state Kalman filter or a two-stage Kalman filter cannot achieve satisfactory positioning performance when facing uncertain noise of the micro-electro-mechanical system（MEMS） inertial sensors, a novel interacting multiple model-based two-stage Kalman filter（IMM-TSKF） is proposed to adapt to the uncertain inertial sensor noise. Three bias filters are developed based on different noise characteristics to cover a wide range of noise levels. Then, an accurate estimation of biases is calculated by the interacting multiple model algorithm to correct the bias-free filter. Thus, the vehicle positioning system can achieve good performance when suffering from uncertain inertial sensor noise. The experimental results indicate that the average position error of the proposed IMMTSKF is 25% lower than that of the general TSKF.

A 4.8GHz CMOS Fully Integrated LC Balanced Oscillator with Symmetrical Noise Filter Technique and Large Tuning Range

This paper presents a fully integrated 4 8GHz VCO with an invention——symmetrical noise filter technique.This VCO,with relatively low phase noise and large tuning range of 716MHz,is fabricated with the 0 25μm SMIC CMOS process.The oscillator consumes 6mA from 2 5V supply.Another conventional VCO is also designed and simulated without symmetrical noise filter on the same process,which also consumes 6mA current and is with the same tuning.Simulation result describes that the first VCO’ phase noise is 6dBc/Hz better than the latter’s at the same offset frequency from 4 8GHz.Measured phase noise at 1MHz away from the carrier in this 4 8GHz VCO with symmetrical noise filter is -123 66dBc/Hz.This design is suitable for the usage in a phase locked loop and other consumer electronics.It is amenable for future technologies and allows easy porting to different CMOS manufacturing process.

DIGITAL FILTER FOR SINGLE FREQUENCY SINUSOID SERIES

The sinusoid curve fit is widely applied in the evaluation of digitized measurement equipment, such as data acquisition system, digital storage oscilloscope, waveform recorder and A/D converter,etc. Because of the distortion and noise of sinusoid signal generator, the digitizing and the non linearity errors in measurement, it is impossible to avoid the distortion and the noise in sinusoid sampling series. The distortion and the noise limit the accuracy of curve fit results. Therefore, it is desirable to find a filter that can filter out both distortion and noise of the sinusoid sampling series, and in the meantime, the filter doesn′t influence the amplitude, the frequency, the phase and DC bias of fitting curve of the sine wave. And then, the uncertainty of fitting parameter can be reduced. This filter is designed and realized. Its realization in time domain is described and its transfer function in frequency domain is presented.

The Algorithms of Adaptive Active Noise Control Systems in a Duct

On the basis of the theory of adaptive active noise control(AANC) in a duct, this article discusses the algorithms of the adaptive control, compares the algorithm characteristics using LMS, RLS and LSL algorithms in the adaptive filter in the AANC system, derives the recursive formulas of LMS algorithm. and obtains the LMS algorithm in computer simulation using FIR and IIR filters in AANC system. By means of simulation, we compare the attenuation levels with various input signals in AANC system and discuss the effects of step factor, order of filters and sound delay on the algorithm's convergence rate and attenuation level.We also discuss the attenuation levels with sound feedback using are and IIR filters in AANC system.

SPEECH ENHANCEMENT METHOD FOR LPC AUTOREGRESSIVE MODEL AND SYSTEM IMPLEMENTATION OF COMMAND WORD RECOGNITION USED IN NOISY ENVIRONMENT

At present, almost all the systems and products for speech recognition are working in quiet environment and their performances are degraded or even can′t work when they are operated in high noisy environment. In this paper, after analyzing the features of speech and noise, a speech enhancement method for LPC autoregressive model for command words recognition used in noisy environment is proposed, and an experimental system is realized. In different background noisy environments, we conduct experiments about SNR, basic accuracy, noise resistant ability and system environment adaptability with different microphones. The experimental results show that the system has good recognition performance in high noisy environments. The system can resist many kinds of noises and meet the needs of application areas on the whole such as military, traffic, marketplace and factory etc.

Effect of the Ultrasound Generated by Flat Plate Transducer Cleaning on Polluted Polyvinylidenefluoride Hollow Fiber Ultrafiltration Membrane

Polyvinylidenefluoride （PVDF） hollow fiber ultrafiltration membrane is frequently employed in water treatment. However, the fouling of ultrafiltration membranes affects the economic effectiveness of such process significantly. The ultrasound generated by flat plate transducer （UFPT） was used to clean the polluted PVDF ultrafiltration membrane with 2 g·L^-1 of citric acid aqueous solution in our study. The effects of UFPT intensity on the membrane surface were studied. The new membrane was easy to be polluted by the saturated CaCl2 solution. A synergistic effect of UFPT and 2 g·L^-1 citric acid aqueous solution could remove the foul of the membrane, and its flux could be recovered about 81%. The flux recovery of old membrane polluted was increased to 73.2% after 7 h soaking in citric acid aqueous solution, but its flux recovery without soaking was only increased to 56.2%.

Filter algorithm based on cochlear mechanics and neuron filter mechanism and application on enhancement of audio signals

A filter algorithm based on cochlear mechanics and neuron filter mechanism is proposed from the view point of vibration.It helps to solve the problem that the non-linear amplification is rarely considered in studying the auditory filters.A cochlear mechanical transduction model is built to illustrate the audio signals processing procedure in cochlea,and then the neuron filter mechanism is modeled to indirectly obtain the outputs with the cochlear properties of frequency tuning and non-linear amplification.The mathematic description of the proposed algorithm is derived by the two models.The parameter space,the parameter selection rules and the error correction of the proposed algorithm are discussed.The unit impulse responses in the time domain and the frequency domain are simulated and compared to probe into the characteristics of the proposed algorithm.Then a 24-channel filter bank is built based on the proposed algorithm and applied to the enhancements of the audio signals.The experiments and comparisons verify that,the proposed algorithm can effectively divide the audio signals into different frequencies,significantly enhance the high frequency parts,and provide positive impacts on the performance of speech enhancement in different noise environments,especially for the babble noise and the volvo noise.

Enhancing the quality metric of protein microarray image

The novel method of improving the quality metric of protein microarray image presented in this paper reduces impulse noise by using an adaptive median filter that employs the switching scheme based on local statistics characters; and achieves the impulse detection by using the difference between the standard deviation of the pixels within the filter window and the current pixel of concern. It also uses a top-hat filter to correct the background variation. In order to decrease time consumption, the top-hat filter core is cross structure. The experimental results showed that, for a protein microarray image contaminated by impulse noise and with slow background variation, the new method can significantly increase the signal-to-noise ratio, correct the trends in the background, and enhance the flatness of the background and the consistency of the signal intensity.

A 16bit 96kHz Chopper-Stabilized Sigma-Delta ADC

A 16bit sigma-delta audio analog-to-digital converter is developed.It consists of an analog modulator and a digital decimator.A standard 2-order single-loop architecture is employed in the modulator.Chopper stabilization is applied to the first integrator to eliminate the 1/f noise.A low-power,area-efficient decimator is used,which includes a poly-phase comb-filter and a wave-digital-filter.The converter achieves a 92dB dynamic range over the 96kHz audio band.This single chip occupies 2.68mm2 in a 0.18μm six-metal CMOS process and dissipates only 15.5mW power.

REDUCING PERIODIC NOISE USING SOFT MORPHOLOGY FILTER

A novel spatial domain method--soft morphology filter is presented for reducing the periodic noise in image processing. The simulation results are presented to demonstrate the effectiveness of the method in comparison with a frequency domain method and other spatial domain filters.

Self-Tuning Asynchronous Filter for Linear Gaussian System and Applications

In this paper, optimal filtering problem for a class of linear Gaussian systems is studied. The system states are updated at a fast uniform sampling rate and the measurements are sampled at a slow uniform sampling rate. The updating rate of system states is several times the sampling rate of measurements and the multiple is constant. To solve the problem,we will propose a self-tuning asynchronous filter whose contributions are twofold. First, the optimal filter at the sampling times when the measurements are available is derived in the linear minimum variance sense. Furthermore, considering the variation of noise statistics, a regulator is introduced to adjust the filtering coefficients adaptively. The case studies of wheeled robot navigation system and air quality evaluation system will show the effectiveness and practicability in engineering.

EFFICIENT IMAGE SEGMENTATION FOR SEMANTIC OBJECT GENERATION

This letter presents an efficient and simple image segmentation method for semantic object spatial segmentation. First, the image is filtered using contour-preserving filters. Then it is quasi-flat labeled. The small regions near the contour are classified as uncertain regions and are eliminated by region growing and merging. Further region merging is used to reduce the region number. The simulation results show its efficiency and simplicity. It can preserve the semantic object shape while emphasize on the perceptual complex part of the object. So it conforms to the human visual perception very well.

螺杆式空压机吸气过程技术改造

通过设计应用新的吸气消声滤气器、优化吸气管长,对螺杆式空压机的吸气过程进行改造。

Crack detection of reinforced concrete bridge using video image

With the digital image technology,a crack detection method of reinforced concrete bridge was studied for the performance assessment.The effects including the image gray level,pixel rate,noise filter,and edge detection were analyzed considering cracks qualities.A computer program was developed by visual C++6.0 programming language to detect the cracks,which was tested by 15cases of bridge video images.The results indicate that the relative error is within 6%for cracks larger than 0.3 mm cracks and it is less than 10%for crack width between 0.2 mm and 0.3 mm.In addition,for the crack below 0.1 mm,the relative error is more than30%because the bridge is in safe stage and it is very difficult to detect the actual width of crack.

A Particle Filter Based Compressive Sensing Method for Tracking Moving Wideband Sound Sources

Tracking moving wideband sound sources is one of the most challenging issues in the acoustic array signal processing which is based on the direction of arrival(DOA) estimation. Compressive sensing(CS) is a recent theory exploring the signal sparsity representation, which has been proved to be superior for the DOA estimation. However, the spatial aliasing and the offset at endfire are the main obstacles for CS applied in the wideband DOA estimation. We propose a particle filter based compressive sensing method for tracking moving wideband sound sources. First, the initial DOA estimates are obtained by wideband CS algorithms. Then, the real sources are approximated by a set of particles with different weights assigned. The kernel density estimator is used as the likelihood function of particle filter. We present the results for both uniform and random linear array. Simulation results show that the spatial aliasing is disappeared and the offset at endfire is reduced. We show that the proposed method can achieve satisfactory tracking performance regardless of using uniform or random linear array.