Abstract:Brain computer interface can realize the information interaction between human and outer devices and change “thinking” in the mind into real actions without the contact of the limb or neuromuscular system. Braincomputer interface is a novel product combining brain neuroscience and engineering technology, and also a new technique for clinical neural function rehabilitation and assistive motor control. It hopefully provides a brand new enhancing treatment and rehabilitation means for the patients losing the ability of language communication and body movement control partly or completely (such as stroke, spinal cord injury (SCI), amyotrophic lateral sclerosis (ALS) and etc.). However, current practical applications are still facing the technical bottlenecks of low information processing efficiency, long rehabilitation training time and poor control model generality. In this paper, these technical difficulties are reviewed. Then, taking the motor imagery BCI and BCI speller as examples, the possible model optimization strategies and solution schemes are introduced. The future development direction of BCI is discussed in the end of the paper.

Abstract:The work presents a review on the technological advancements of functional electrical stimulation (FES) neuroprostheses to restore gait walking over the last decades. The aim of an FES intervention is to functionally restore and rehabilitate individuals with motor disorders, such as stroke, spinal cord injury, multiple sclerosis, and others. The technique has been applied for widespread practical use for several years due to the rapid development of micro and nanotechnology. This technical review covers neuroprostheses developed within academia and currently available on the market. These systems are thoroughly analyzed and discussed with particular emphasis on the sensing techniques and control strategies. In the last part, a combination of FES technology and exoskeletons is presented as an emerging solution to overcome the drawbacks of current FESbased neuroprostheses, and recommendations on future research direction are suggested.

Abstract:Mental workload estimation has been under extensive investigation over the years, because the capability of monitoring the cognitive workload enables the prevention of cognitive overloading and improvement of workplace safety. Electroencephalogram (EEG) signals has been found to be an objective and nonintrusive measure of mental workload. However, the evaluation of cognitive workload based on singletrial EEG data, which is an essential step towards realtime workload monitoring and braincomputer interface, has been a major challenge. Recently, a number of advanced feature extraction methods and machine learning algorithms have been employed in EEGbased mental workload assessment. In this study, we performed singletrial workload classification using the EEG data recorded during the performance of nback tasks with 2 levels of difficulty (corresponding to low and high levels of workload respectively), examined the effectiveness of 3 types of feature extraction (spectral power, discrete wavelet transform and common spatial filtering), and evaluated the performance of 4 classification algorithms (support vector machine, Knearest neighbors, random forest and gradient boosting classifiers). Our findings indicate that common spatial filtering was the bestperforming individual feature extraction method for singletrialbased workload classification, and the optimal performance was achieved by combining the features from either spectral power or discrete wavelet transform with those from common spatial filtering, and adopting the random forest classifier. This study might provide some helpful guidance on the selection of feature extraction methods as well as machine learning algorithms in mental workload evaluation based on singletrial EEG data.

Abstract:Mental fatigue can reduce work performance and cause safety accidents in humanmachine systems. Therefore, it is important to detect fatigue in real time. A great deal of work has focused on this problem, but there are still no standards for the physiological index. Multiphysiological measurement becomes a trend, at the same time, the increasing complexity of instruments for multiphysiological measurement brings challenges due to the complicacy of mental fatigue. Functional NearInfrared Spectroscopy (fNIRS) can measure cerebral hemoglobin and reflect cognitive function indirectly. However, cardiac and respiratory signals in the fNIRS signal are sensitive to physiological activity, which have always been removed as interference in previous studies. To increase the information capacity and establish a multiphysiological fatigue detection model using fNIRS, this paper extracts the cardiac and respiratory features from the fNIRS signal as new sensitive feature. A fatigue detection model is proposed based on the support vector machine (SVM) by combining cardiac and respiratory features with common features, such as the mean value and slope. We use a verbal 2back task for a total of 60 minutes to induce mental fatigue. The fNIRS signals from 10 channels in the prefrontal cortex (PFC) are measured from 15 healthy subjects. The results show that the new cardiac and respiratory features are significantly sensitive to the fatigue state and increase the classification accuracy compared with a common fatigue model based on fNIRS (84%→90%). Our findings can detect mental fatigue effectively and reduce the complexity of equipment significantly for multiphysiological fatigue detection.

Abstract:P300 evoked by visual or auditory stimulation is a commonly used paradigm in Braincomputer interface (BCI). However, accurate P300 signal by visual or auditory stimulus can not be obtained due to lots of patients whose vision or hearing are impaired. This brings several limitations in visual or auditory P300 BCIs due to disables. In this study, we apply spatial somatosensory electrical stimuli as a new paradigm for P300 BCI system. Fifteen subjects are required to focus on the electrical stimulation of different fingers, while a singlechannel P300 signal is used to generate BCI for evaluation of classification accuracy and information transmission rate. Test results show that all participants are able to elicit a P300 wave with stable latencies around 300ms. Mean performance of classification accuracy reaches 77.96%±5.04%, that is much higher than chance lever (25%). The maximal information transfer rate (ITR) is 15.97 bit/min. Experiments prove that spatial somatosensory electrical stimuli can elicit a stable P300 as a new paradigm for BCI. In addition, the singlechannel data analysis is capable of obtaining a satisfied performance in classification accuracy and information transfer rate, making it convenient for users.

Abstract:In order to investigate the longrange temporal correlations (LRTC) of broadband EEG oscillation in poststroke depression (PSD) patients with unilateral hemispheric injury, Broadband EEG (0.6~46 Hz) of 18 poststroke depression patients (8 left hemispheric lesion and 10 right hemispheric lesion) and 22 poststroke nondepression patients (12 left hemispheric lesion and 10 right hemispheric lesion) in the time range of 0.2~3 s. The results show that, although the depressive symptoms is more severe in depressed patients of left lesion than right lesion, significantly decreased LRTC in depressive related brain regions only found in PSD patients with right hemispheric lesion. The results that the pathophysiology of PSD in different hemispheric damage different and support the notion that the association between poststroke depression and lesion location is complex.

Abstract:Aiming at the problems of single classification mode and poor universality of existing epilepsy EEG recognition algorithms, a novel EEG signal automatic recognition method is proposed based on DoubleDensity Discrete Wavelet Transform (DDDWT) and LogLogistics parameter regression (LLPR). This method not only utilizes the decomposition capacity of DDDWT algorithm, but also constructs the LLPR model for EEG signal, integrates the two algorithms organically, and fully exploits the advantages of the two algorithms. In this study, the filtered EEG signals are decomposed into six levels with DDDWT, and the wavelet coefficients of various subbands are transformed to the energy waveforms in wavelet domain to acquire the feature parameters using the LLPR model. The scale parameter α and shape parameter β are calculated to characterize the EEG signal. The feature parameters extracted from all the subbands are composed as the eigenvalues, which are fed to support vector machine (SVM) optimized with genetic algorithm (GA) to obtain the final classification results, thus the EEG signal automatic recognition is achieved. When the proposed method was used to deal with two multimode EEG classification problems of A＼D＼E and AB＼CD＼E, the satisfied accuracies of 98.9% and 97.75% were obtained respectively. Experiment results indicate that the proposed method can meet the actual application requirement, is more appropriate for solving the recognition problems of multiclass EEG signals, has good universality and classification performance, and has great value in practical applications dealing with epileptics.

Abstract:Iris annular line detection in the gray image directly with the edge detection operator will lost a lot of useful information, and false rate and missing rate is high, due to the signal intensity of iris annular line is weak compared with the background and the background image texture is very rich and the gray value of the annular line is not completely continuous. Thus, an iris annular line detection method is presented in this paper on the basis of analyzing the features of the iris annular lines. Firstly, the two part of the annular areas are selected as the region of interest due to the annular line position is fixed in the iris image; Secondly, the vector weighted line detection operator is designed to transform the random nature of the dominant signal into the adaptive weighted value, and the vector image is transformed into the single channel image with the most prominent edge information. Afterwards, the line detection matrix is designed to detect ROI according to the line characteristic distribution of annular line. Finally, nonannular lines are eliminated by shape factors of the binary images and the annular lines are detected. The method is used to detect 1921 lines that were artificially marked in our gallery, and the detection accuracy is up to 91.78%．The experimental results show the proposed method is suitable for detecting the iris annular lines from the visible iris images compared classic edge operators.

Abstract:Cochlear implant is a surgically implanted electronic device that can provide a sense of sound to a person who is severely hard of hearing. The effect of a cochlear implant on residual, low frequency, hearing is complex and not well understood. This research focuses on changes of the cochlear mechanics due to a cochlear implant by comparing the basilar membrane, BM, response before and after the implantation using a computational model of the cochlea. In the model, cochlea implants were introduced into the lower cochlear fluid chamber and the active amplification process of the cochlear is not considered, since a passive cochlear model whose response does not depend on stimulus level can reasonably well represent the cochlea for subjects with hearing impairment. The results for the basilar membrane velocity show that the volume change in the fluid chambers due to the implant has a little effect, less than 3 dB at low frequencies, on the basilar membrane velocity. A more extreme condition, in which the cochlear implant is assumed to touch the basilar membrane at some or the whole positions and thus impeded its motion, was also studied. Although there is no travelling wave propagating in the basal region in the latter case, the remainder of the cochlea is still coupled to the stapes by the incompressible fluid. The basilar membrane velocity at low frequencies is relatively unaffected by the blocking of the basilar membrane motion in the basal region, although the effect is more dramatic for excitation frequency whose characteristic place is close to the end of the implant. Although this work does not model every aspect of the hearing loss after cochlear implantation as measured clinically, it does provide a way of predicting the possible mechanical effects of the implantation on the cochlear passive mechanics and residual hearing.

Abstract:The soft tissue model with high modeling speed and accuracy can improve the realism in a virtual surgery simulation system. In this paper, we propose a new virtual soft tissue torsion model based on membrane analogy and Kriging interpolation. The model adopts the membrane analogy method to compute the deformation of all the key sample points on the ridges, and uses the Kriging interpolation method to compute the deformation of any points among the ridges. An experiment simulation system was built on the haptic interaction device of PHANTOM OMNI, and the deformation simulation of virtual small intestine under the action of the applied torsion was realized. The experiment results show that the proposed model has such advantages as fast operation speed, high calculation accuracy, realistic deformation and accurate, reliable haptic feedback.

Abstract:In the needle intervenes in the prostate, there are displacement and deformation of prostate, needle tip deflection and other issues. In order to improve the positioning accuracy of robot manipulation of the needle, the vibration and rotation puncture mechanism are focused in this work. The needletissue interaction mechanical model is also established. The evaluation experiments of vibration and rotating puncture are respectively conducted by using the constructed experimental platform. By analyzing the experimental results, a strategy of high accuracy puncture is proposed based on vibration and rotation puncture, and the corresponding control software of puncture strategy is designed. Finally, the puncture force evaluation test is implemented using high puncture strategy, and the results verify the effectiveness of the proposed method.

Abstract:The magnetic memory method can effectively determine the stress concentration areas of ferromagnetic metal components. However, at present, the magnetic memory signals in the elastic stage and plastic stage of the components are hard to be distinguished, and the stress concentration degree and service life of the components cannot be evaluated effectively. In this paper, the boundary slip model of the magnetic memory effect is built based on the theory of solidstate electronics, and the linear muffintin orbital (LMTO) algorithm is used to calculate the variations of the system energy of the solid and the spin density of states of the electrons at different orbits in the elastic stage and plastic stage. Then, the changing rules of the magnetic memory signals of the components after yielding are quantitatively analyzed. The research results show that the stress concentration degree is in direct linear proportional relationship with the system boundary slip energy and in the inverse linear proportional relationship with the peak to peak value of the electron spin density of states and the magnetic memory signals. After the plastic deformation of the components, the system energy and electronic spin are changed irreversibly, and a turning point appears in the magnetic memory signal curve. The initial value of the magnetic memory signal is getting less and the slope of the curve is getting lower after every plastic deformation of the component.

Abstract:The operation condition of rolling bearing performance is describled using time series of the friction torque current signal, which are segmented to establish intrinsic sequences. Based on grey relation, each section of the friction torque signal is sorted to match the intrinsic sequence, as a result, the grey confidence level is acquired. The grey confidence level is used to determine the extent of stability on bearing performance. Via bootstrap resampling for the segmented data, the probability density function is calculated by using the maximum entropy method, and estimated interval is obtained according to the corresponding grey confidence level. Relying on the counting process, the raw information of variation intensity is simulated. The reliability function is constructed with the Poisson process to realtime monitor the reliability evolution of rolling bearing. Simulation cases and experimental test show that the proposed model can truly monitor the stability and reliability of bearing running performance, and effectively deal with the time series with strong fluctuation and varied trend.

Abstract:An impact feature extraction method, based on twodimensional kernel density estimation for S transform spectrum, is proposed to analyze the vibration signal for gear fault diagnosis. In this approach, S transform is used to process the vibration signal, firstly. Secondly, the obtained Stransform spectrum is multiplied by a factor and then rounded to obtain an integer matrix. Finally, the time and the frequency of the Stransform spectrum are used to construct a twodimensional random variable, and the elements in the integer matrix are taken as the corresponding sample number of the twodimensional random variable. The kernel density of the twodimensional random variable is consequently estimated and a twodimensional kernel density function is obtained. Specifically, the kernel density function is acquired by the smoothing and denoising procedure of the S transform spectrum, in which the noise is effectively suppressed while the impulse signature is enhanced. By means of the processing of the simulated vibration signal and the gearbox fault vibration signals, results show that the proposed method can extract the periodic impact characteristics from the vibration signal effectively, which means the proposed method can be used for gearbox fault diagnosis.

Abstract:Aiming at the problem that projectile body geomagnetic survey is susceptible to various errors, which leads to the decreasing of the geomagnetic attitude measurement accuracy, the geomagnetic measurement error of the ellipsoid model is established on the basis of analyzing its own errors and environment errors, the maximum likelihood estimation algorithm is used to solve the static error compensation coefficient; taking the static error coefficient as initial value, recursive least squares method is adopted to obtain the real time update algorithm of the error coefficient. From above studies, the online combination correction algorithm for geomagnetic measurement error compensation is formed. The simulation and experimental results show that the maximum attitude angle error is less than 5° in near blind zone direction, the online combination correction algorithm can ensure the accuracy of the attitude detection system under different shooting conditions.

Abstract:Aeromagnetic exploration is an essential issue in the geophysics. The data measured by optically pumped magnetometers (OPM) is seriously affected by the interference magnetic fields generated by the aircraft. Therefore, aeromagnetic compensation is very essential, and a nonlinearity aeromagnetic compensation method is proposed. Firstly, the weighted signal sequences are used to predict the output of the sensors. Then, LevenbergMarquardt (LM) algorithm is utilized to realize aeromagnetic compensation. The simulation results show that the nonlinearity aeromagnetic compensation method is effective to the interference magnetic fields caused by the delay between the sensors. The standard deviation of the compensated signal is decreased to 10－4nT/m. The residual error of the compensation signal is the same order as the noise floor of the OPMs. The proposed method can be applied to obtain highquality aeromagnetic survey data. A field experiment is carried out to prove the validities of the theoretical analysis and the simulation results.

Abstract:Inaccurate receiver position information directly influences the timing results for users. The impact of receiver position error on timing is studied in the terms of both theory and experiment. Theoretically, the biggest impact on receiver position error is obtained by differentiating the pseudorange equation in the first order. To avoid the influence of satellite position error, the precise satellite positions provided by IGS are used to correct the theoretical results. In experiments, the impact of position error on timing result is also studied by analyzing GPS/GLONASS receiver observation data under the errors in different directions and orders of magnitude. The different situations in the practical scenarios are also analyzed. Experimental results show that the timing accuracy of GPS and GLONASS is less than 5and 15 ns by every increase of 1 arc second latitude error, and timing stability is less than 10 and 15 ns respectively. The timing accuracy of GPS and GLONASS is less than 1 ns for each additional arc second error of longitude, and both less than 10 ns of timing stability. Every increase one meter of elevation error can cause about 3 ns timing error and the influence on timing stability is about 0.3 ns/m. In the practical applications, users can refer to this conclusion for considering input coordinates of timing receivers according to the demand of timing precision.

Abstract:Lot of micronano force measurement systems are set up and very high measurement accuracy is obtained. However, transferring of micronano force value is rarely studied. Using the passive cantilever as a transferred standard of micronano force value, 'analogous reference cantilever method' is presented on the basis of the principle of electrostatic force. The cantilever stiffness can be accurately and conveniently measured and be traced to the international unit (SI) such as displacement, voltage and capacitance. Utilized 'analogous reference cantilever method', the cantilever with normal stiffness 1.45 ~ 91 N/m is test, the relative variance is less than 0.6%, the results show that the analogous reference cantilever method has good stability. The factors affected the measured results are analyzed experimentally or theoretically. Combined measurement uncertainty of 'analogous reference cantilever method' to measure the stiffness of cantilever is less than 5%, which shows the feasibility of the proposed method. 'Analogous reference cantilever method' effectively improves the uncertainty of cantilever stiffness test, and indicates significance in the highprecision micronano force value measurement of AFM.

Abstract:The method of measurement and accurate assessment of structural fatigue damage is a challenging issue. In the process of structural fatigue damage, material magnetic properties changes, and the magnetic measurement is simple and convenient. Hence, it is significant to detect structure fatigue damage using the change of material magnetic properties. Using Q235 steel as test sample, this work focuses the magnetic measurement method of structure fatigue damage based on hysteresis loop under cyclic tensile stress, in which the test platform is set up. The results show that the hysteresis loop changes when the fatigue damage is different. The coercive force Hc and remanence Br of the hysteresis loop are extracted as feature parameters. The analysis results indicate that the whole fatigue process can be roughly divided into two stages before the specimen failure. Hc and Br rise rapidly in the first stage, whose sensitivity of the fatigue damage are higher. Comparatively, Hc and Br change slowly in the second stage, whose sensitivity of the fatigue damage are lower. Finally, the quantitative relationships of Hc, Br and fatigue accumulation damage D are analyzed. The proposed method can provide quantitative evaluation of the structural fatigue damage and online monitoring technology.

Abstract:Traditional linear ultrasonic testing method is unable to detect the closed microcrack. Thus, the vibroacoustic modulation(VAM) detection system is established and used to perform VAM experiments on the first order sideband and whole nonlinear signals are extracted from detection signals on aluminum plate contained micro cracks, and reversed in the time domain. Then, the preprocessed signals are loaded into a finite element model built by ABAQUS. The cloud image and particle displacement are obtained and can provide the location information of micro cracks. The simulated results show that there is a stronger energy focusing around the original crack location at focusing time of the reversal signals. The whole nonlinear signals has better focusing performance than the signal only contain first order sideband. The combination of VAM and time reversal (TR) method can realize the detection and localization of micro crack.

Abstract:A new nondestructive testing method is proposed to fast scan and detect the macroscopic defects of materials using the variation characteristics of the differential permeability of ferromagnetic materials. Firstly, the ferromagnetic material, such as steel plate, is placed under the bias magnetic field, and the defects in the material will inevitably lead to the distortion of its internal magnetic flux. Secondly, the magnetic flux distortion is further reflected in the difference of the differential permeability distribution on the surface of the tested material. Thirdly, using a specially designed probe, the distribution of differential permeability on the surface of tested material is rapidly scanned. Finally, from the difference of the differential permeability distribution, the discontinuity information in the material can be obtained, so as to realize the nondestructive testing of macroscopic defects. Series of experiments were carried out, and the results show that, compared with traditional magnetic flux leakage method, the new method has the advantages of low magnetic intensity, less flux leakage and stable detection signals. Moreover, the detection signals for the frontside and backside defects have obviously different distribution characteristics, and the method also has obvious advantages in the field of defect depth identification.

Abstract:Aiming at the complex signals of pulsed eddy current sensor for ferromagnetic materials, a detection model of pulsed eddy current rectangular sensor based on finite element is established. This paper proposes that there are pulsed eddy current detection area and pulsed magnetic flux leakage detection area in pulsed eddy current rectangular sensor. The simulation analysis of pulsed electromagnetic detection is carried out. The best detection position of the angles between the defect and the axis of the rectangular probe is studied. Both simulation and experiment results show that the effective detection area of the pulsed eddy current of the rectangular probe is the border area under the probe, and the effective detection area of the pulsed magnetic flux leakage detection is the area under the center of the rectangular coil. The best detecting point of pulsed eddy current is the position at the angle of 10° between the defect and the axis of the rectangular probe, and the best detecting point of pulse magnetic flux leakage is the position at the angle of 70° between the defect and the axis of the rectangular probe.

Abstract:In order to improve the efficiency of crude oil production, the water injection production and horizontal well production are widely used in the oil fields. This brings higher requirements for online water holdup measurement of oilwater twophase in downhole. There are two main challenges: the one is to guarantee the high detection accuracy of sensor under the condition of high water holdup and low water holdup, and the other is the small structure of single sensor to arrange multiple sensors conveniently to form detection array on the cross section of the oil well, so as to improve the spatial resolution of the measurement by providing the distribution information about water hold up on the crosssection of horizontal wells or highly deviated wells. Hence, this work presents the water holdup detection method of oilwater twophase flow based on coplanar microstrip transmission line. Conformal transformation method is utilized to analyze the relationship between the structure parameters and material parameters of coplanar microstrip transmission line sensor and electromagnetic wave propagation characteristics. The doublesided and Sshaped wiring sheet structure is used not only to miniaturize the sensor but also improve the dynamic range of detection Numerical simulation and experimental results show that the phase shifts of the detected signal are approximately linear with the water holdup of the measured fluid. The resolution is less than 3% in the whole range of water holdup from 0% to 100%. This method overcomes the disadvantages of capacitance method and conductance method, and is suitable for low water holdup and high water holdup, respectively.

Abstract:Metal magnetic memory (MMM) technology is a nondestructive testing method, which can conduct effective diagnosis of early microscopic damage of ferromagnetic material. In order to eliminate the uncertain influence factors of magnetic memory signal and improve the accuracy of damage state recognition, the magnetic gradient tensor and magnetic field signal vertical distribution feature analysis methods are introduced. Firstly, the magnetic gradient tensors of the MMM signals on the crack fracture zone and stress concentration zone are measured using a triaxis magnetometer. From the measured results, both the plane and vertical characteristics of the MMM signal distributions are obtained. To remove the influence of the measuring direction selection on experiment results, a new magnetic field invariant characteristic parameter  the magnetic total gradient modulus is introduced to determine the location and boundary of the damage and damage zone. Then, the vertical distribution features of the magnetic total gradient modulus are acquired by measuring the plane distribution of the magnetic total gradient modulus under different lift offs. Finally, the difference of the vertical distribution features of the magnetic total gradient modulus at the boundaries of different types of defects is analyzed. Theoretical analysis and experiment result show that as the lift off increases gradually, the attenuation velocity and amplitude of the magnetic total gradient modulus on the boundary of the crack are far greater than the ones caused by stress concentration, and the vertical distribution features of the magnetic total gradient modulus can be used to identify the defect state effectively.

Abstract:When users interact with touch screen, effective and realtime haptic interaction technology is very important to enhance the sense of reality and immersion. In this work, a fingerskeleton wearable haptic interactive device is designed to interact with images on a touch screen. The device can provide continuously controllable force feedback for the finger by planar fourbar linkage and DC motor. Two types of vibrotactile feedbacks by a linear resonant actuator and piezoceramic actuator can also be provided. The device uses Bluetooth technique and rechargeable battery to achieve miniaturization and portability, which is small size and light weight. In order to display space haptic information of virtual objects in an image, realtime haptic modeling algorithm is introduced. When operator uses the haptic device to slide on an image, feature information of the image can be transmitted to the device via Bluetooth, including the height of each pixel, the edges of an image and the shape of virtual objects, so that operator can get multimode haptic sensation. Finally, haptic interaction experiments are carried out to evaluate the performance of the haptic device in displaying the height, contour and edge of an image.

Abstract:In synthetic aperture radar (SAR) imaging system, vibrating target imaging possesses the Doppler characteristics of paired echoes, which is not conducive to the detection and identification of vibrating targets. A vibrating target imaging method based on phase retrieval principle is proposed in this paper. Firstly, according to the stripmap SAR imaging mode, the geometric model of vibrating target is established, and the possibility of applying the phase retrieval principle to the SAR vibrating target imaging is deduced theoretically. Then, taking the echo data of SAR vibrating target and support domain information as the prior information of the oversampling smoothing (OSS) iterative algorithm. through changing the parameters of the smoothing filter to adjust the filter bandwidth, the interference of the external support domain information to the vibrating target is reduced, At the same time, through reducing the size of the support domain, improve the convergence of the iterative algorithm and eliminate paired echoes of vibrating target, finally the focused high resolution image of the vibrating target is obtained. The simulation experiment results prove the effectiveness of the proposed method.

Abstract:In sound field visualization systems, imprecise spatial position and angle relationship between the microphone array and camera could lead to the error of the sound source localization. However, few of the previous studies have considered the spatial relationship between the camera and microphone array in sound field visualization systems. So there lacks a simple and effective spatial relationship calibration method for the spatial relationship between the camera and microphone array. In this paper, the calibration method for calculating any spatial orientation and position relationship between microphone array and camera is proposed, which can effectively eliminate the sound source localization error caused by the mismatched spatial relationship between the microphone array and camera, and then improve the precision of the sound source localization for sound field visualization system. Through minimizing the sound source localization errors of the known sound source in multiple positions, the spatial position and orientation between the camera and microphone array could be calculated respectively with stepped iteration calculation. Simulation and experiment results both show that the proposed method can accurately calculate any spatial orientation and position relationship between the camera and microphone array in a sound field visualization system, and could effectively improve the sound source localization accuracy of sound field visualization systems. The proposed spatial relationship calibration method could be used in various sound field visualization systems, and has an active significance for improving the sound source localization accuracy of sound field visualization systems.

Abstract:This work demonstrates a standing wave focusing method to complete twodimension and sheathfree focusing of particles for flow cytometer. Cells or beads in a volume of less than 82 μL are forced to cross the detection area one by one under a flow rate higher than 0.5 mL/min, which can improve the analysis accuracy and realize undiluted sample recovery. The principle of standing wave formation, acoustic resistance matching and the particle force equilibrium are discussed, and the motion trajectory of particle under different parameters is simulated. Referring to this model, an acoustic focusing experimental platform driving in a frequency of 1.462 MHz is built to verify the feasibility and performance by polystyrene beads in diameters of 10 and 20 μm. The results showed that beads in random distribution can be focused into a plane, and more tightly focusing can be achieved at low flow rate and high driving voltage. It is proved that the bigger beads are focused more easily than the smaller ones. All the experimental results coincides well with the simulation.

Abstract:To overcome the poor timeliness and the low automaticity of existing aquaculture pathogen detection, an integrated microfluidic system for rapid detection is proposed. The microelectrode is set on the bottom of the microfluidic channel and connected to a digital impedance measurement circuit in this system. The magnetic beads is encased by the aquatic pathogens antibody on its surface. The aquatic pathogens are captured by magnetic beads because of the induction and control of the external Gauss magnetic field. The combination of aquatic pathogens and beads is induced to the microelectrode array. The content of aquatic pathogens can be detected effectually by the counting circuit based on impedance measurement fixed at the electrode terminals. The research results show that the amount of aquatic pathogens can be detected effectively by this system. Moreover, the proposed system can reduce the detection time to onesixtieth compared with traditional laboratory test.