Modeling and experiments of a nano-positioning and high frequency scanning piezoelectric platform based on function module actuator

A piezoelectric platform using function module actuator is presented to achieve nano-positioning and high frequency scanning in large working range. A function module actuator is designed to produce a pair of orthogonal bending deformations and a longitudinal deformation through partition exciting. The bending deformations are used to actuate the planar motion,while the longitudinal deformation is utilized to dynamically adjust the driving force and broaden the scanning frequency. The dynamic model of the platform system is developed. The open-loop performances of a prototype are first tested: a scan frequency of 308 Hz in a scanning range of 3.368 μm×3.396 μm is measured in direct actuation mode,and the displacement resolution is measured to be 16 nm;maximum speed is measured to be 3.38 mm s^-1 in the inertial actuation mode. Furthermore,the closedloop experiments are carried out and a switching strategy is proposed to obtain the switching of the inertial and direct actuation modes automatically;the platform achieves the scanning with frequency of 300 Hz at the set position.

Discovering the cellular-localized functional modules and modular interactions in response to liver cancer

In this paper, we firstly identify the functional modules enriched with differentially expressed genes （DEGs） and characterized by biological processes in specific cellular locations, based on gene ontology （GO） and microarray data. Then, we further define and filter disease relevant signature modules according to the ranking of the disease discriminating abilities of the pre-selected functional modules. At last, we analyze the potential way by which they cooperate towards human disease. Application of the proposed method to the analysis of a liver cancer dataset shows that, using the same false discovery rate （ FDR ） threshold, we can find more biologically meaningful and detailed processes by using the cellular localization information. Some biological evidences support the relevancy of our biological modules to the disease mechanism.

Analysis of the optical quality by determining the modulation transfer function for anterior corneal surface in myopes

AIM: To describe the characteristics of modulation transfer function (MTF) of anterior corneal surface, and obtain the the normal reference range of MTF at different spatial frequencies and optical zones of the anterior corneal surface in myopes. METHODS: Four hundred eyes from 200 patients were examined under SIRIUS corneal topography system. Phoenis analysis software was applied to simulate the MTF curves of anterior corneal surface at vertical and horizontal meridians at the 3, 4, 5, 6, 7mm optical zones of cornea. The MTF values at spatial frequencies of 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55 and 60 cycles/degree (c/d) were selected. RESULTS: The MTF curve of anterior corneal surface decreased rapidly from low to intermediate frequency (0-15cpd) at various optical zones of cornea, the value decreased to 0 slowly at higher frequency (>15cpd). With the increase of the optical zones of cornea, MTF curve decreased gradually. 3) In the range of 3 mm- 6 mm optical zones of the cornea, the MTF values measured at horizontal meridian were greater than the corresponding values at horizontal meridian of each spatial frequency, the difference was statistically significant (P<0.05). At 7 mm optical zones of cornea, the MTF values measured at horizontal meridian were less than the corresponding values at vertical meridian at 10-60 spatial frequencies (cpd), and the difference was statistically significant in 25, 30, 35, 40, 45, 50 cpd(P<0.05). CONCLUSION: MTF can be used to describe the imaging quality of optical systems at anterior corneal surface objectively in detail.

Modulation transfer function characteristic of uniform-doping transmission-mode GaAs/GaAlAs photocathode

The resolution characteristic can be obtained by the modulation transfer function （MTF） of a GaAs/GaA1As photocathode. After establishing the theoretical model of GaAs（100）-oriented atomic configuration and the formula for the ionized impurity scattering of the non-equilibrium carriers, this paper calculates the trajectories of photoelectrons in a photocathode. Thus the distribution of photoelectron spots on the emit-face is obtained, which is namely the point spread function. The MTF is obtained by Fourier transfer of the line spread function obtained from the point spread function. The MTF obtained from these calculations is shown to depend heavily on the electron diffusion length, and enhanced considerably by decreasing the electron diffusion length and increasing the doping concentration. Furthermore, the resolution is enhanced considerably by increasing the active-layer thickness, especially at high spatial frequencies. The best spatial resolution is 860 lp/mm, for the GaAs photocathode of doping concentration 1 ×10^19 cm 3 electron diffusion length 3.6 μm and the active-layer thickness 2 μm, under the 633-nm light irradiated. This research will contribute to the future improvement of the cathode＇s resolution for preparing a high performance GaAs photocathode, and improve the resolution of a low light level image intensifier.

Unbiased parameter estimation of continuous-time system based on modulating functions with input and output white noises

An efficient unbiased estimation method is proposed for the direct identification of linear continuous-time system with noisy input and output measurements.Using the Gaussian modulating filters,by numerical integration,an equivalent discrete identification model which is parameterized with continuous-time model parameters is developed,and the parameters can be estimated by the least-squares (LS) algorithm.Even with white noises in input and output measurement data,the LS estimate is biased,and the bias is determined by the variances of noises.According to the asymptotic analysis,the relationship between bias and noise variances is derived.One equation relating to the measurement noise variances is derived through the analysis of the LS errors.Increasing the degree of denominator of the system transfer function by one,an extended model is constructed.By comparing the true value and LS estimates of the parameters between original and extended model,another equation with input and output noise variances is formulated.So,the noise variances are resolved by the set of equations,the LS bias is eliminated and the unbiased estimates of system parameters are obtained.A simulation example by comparing the standard LS with bias eliminating LS algorithm indicates that the proposed algorithm is an efficient method with noisy input and output measurements.

A new modulation transfer function for ocean wave spectra retrieval from X-band marine radar imagery

When imaging ocean surface waves by X-band marine radar, the radar backscatter from the sea surface is modulated by the long surface gravity waves. The modulation transfer function （MTF） comprises tilt, hydrodynamic, and shadowing modulations. A conventional linear MTF was derived using HH-polarized radar observations under conditions of deep water. In this study, we propose a new quadratic polynomial MTF based on W-polarized radar measurements taken from heterogeneous nearshore wave fields. This new MTF is obtained using a radar-observed image spectrum and in situ buoy-measured wave frequency spectrum. We validate the MTF by comparing peak and mean wave periods retrieved from X-band marine radar image sequences with those measured by the buoy. It is shown that the retrieval accuracies of peak and mean wave periods of the new MTF are better than the conventional MTF. The results also show that the bias and root mean square errors of the peak and mean wave periods of the new MTF are 0.05 and 0.88 s, and 0.32 and 0.53 s, respectively, while those of the conventional MTF are 0.61 and 0.98 s, and 1.39 and 1.48 s, respectively. Moreover, it is also shown that the retrieval results are insensitive to the coefficients in the proposed MTF.

Comparison of the retinal modulation transfer function between amblyopes successfully corrected and normal subjects

AIM:To analyze the retinal modulation transfer function between amblyopes whose visual acuity was corrected to 5.0 and normal subjects at the same age. METHODS: RM-800 used to detect contrast sensitivity was adopted to measure MTF of 96 amblyopes (96 eyes) whose visual acuity was corrected to 5.0 and 80 normal controls (80 eyes) at the same age under six interference fringes (IVA=0.06, 0.1, 0.2, 0.4, 0.6, 0.8). RESULTS: The functional values of amblyopes were significantly lower than those of normal subjects in every fringe (P<0.01), especially in medium and high frequency. CONCLUSION: For amblyopes, MTF was still abnormal after stopping the treatments.

Solving the atmospheric scattering optical transfer function using the multi-coupled single scattering method

The atmospheric scattering optical transfer function （OTF） is solved by applying the multi-coupled single scattering （MCSS） method to the three-dimensional radiative transfer equation （RTE） under the periodic ground condition. This approach is a direct hit to the atmospheric scattering OTF using the same original context of modulation transfer function （MTF） measurement, i.e., images of sinusoidal grating at different spatial frequencies. Both the amplitude and phase shift of the OTF at various zenith and azimuth angles can be obtained at an arbitrary spatial frequency.

Fuzzy image restoration using modulation transfer function

The key difficulty of restoring a fuzzy image is to estimate its point spread function( PSF). In the paper,PSF is modelled based on modulation transfer function( MTF). The first step is calculating the image MTF. In the traditional slanted-edge method,a sub-block is always manually extracted from original image and its MTF will be viewed as the result of the whole image. However,handcraft extraction is inefficient and will lead to inaccurate results. Given this,an automatic MTF computation algorithm is proposed,which extracts and screens out all the effective sub-blocks and calculates their average MTF as the final result. Then,a two-dimensional MTF restoration model is constructed by multiplying the horizontal and vertical MTF,and it is combined with conventional image restoration methods to restore fuzzy image. Experimental results indicate the proposed method implementes a fast and accurate MTF computation and the MTF model improves the performance of conventional restoration methods significantly.

Linux-based Platform for Open Architecture Controller and Its Modular Developing Method

Linux-based Platform for Open Architecture Controller ( POAC ), a new open architecture controller and its modular developing method are discussed. POAC divides the application software of controller into the developing system and the application system. In the developing system, PAOC abstracts a series of function modules with unified data interface and function interface. In the application system, POAC defines the model of the architecture module, realizing the interoperability and interchangeability between the architecture modules. The modular developing method entities the users to make up an application system with some architecture modules, which consist of a set of function modules. The modular developing method decreases the developing time from the standard of controller architecture to the product.

Multi-objective Particle Swarm Optimization Algorithm Based on Performance and Reliability of Discrete System Resources Configuration

Considering research on multi-objective optimization for reliability and performance suffering cost constraints in digital circuits,an improved multi-objective optimization algorithm based on performance and reliability was proposed to solve the problem of discrete system resources configuration in this paper. This algorithm used the particle-swarm optimization( PSO) to evaluate the tradeoffs configuration of the system resources between reliability and performance and proved the feasibility through the simulation.Finally, the information of resources configuration from optimization algorithm was used to effectively guide the system design so as to mitigate soft errors caused by single event effect( SEE).

Identification of Risk Pathways and Functional Modules for Coronary Artery Disease Based on Genome-wide SNP Data

Coronary artery disease （CAD） is a complex human disease, involving multiple genes and their nonlinear interactions, which often act in a modular fashion. Genome-wide single nucleotide polymorphism （SNP） profiling provides an effective technique to unravel these underlying genetic interplays or their functional involvements for CAD. This study aimed to identify the susceptible pathways and modules for CAD based on SNP omics. First, the Wellcome Trust Case Control Consortium （WTCCC） SNP datasets of CAD and control samples were used to assess the joint effect of multiple genetic variants at the pathway level, using logistic kernel machine regression model. Then, an expanded genetic network was constructed by integrating statistical gene-gene interactions involved in these susceptible pathways with their protein protein interaction （PPI） knowledge. Finally, risk functional modules were identified by decomposition of the network. Of 276 KEGG pathways analyzed, 6 pathways were found to have a significant effect on CAD. Other than glycerolipid metabolism, glycosaminoglycan biosynthesis, and cardiac muscle contraction pathways, three pathways related to other diseases were also revealed, including Alzheimer＇s disease, non-alcoholic fatty liver disease, and Huntington＇s disease. A genetic epistatic network of 95 genes was further constructed using the abovementioned integrative approach. Of 10 functional modules derived from the network, 6 have been annotated to phospholipase C activity and cell adhesion molecule binding, which also have known functional involvement in Alzheimer＇s disease. These findings indicate an overlap of the underlying molecular mechanisms between CAD and Alzheimer＇s disease, thus providing new insights into the molecular basis for CAD and its molecular relationships with other diseases.

Functional modules with disease discrimination abilities for various cancers

Selecting differentially expressed genes(DEGs) is one of the most important tasks in microarray applications for studying multi-factor diseases including cancers.However,the small samples typically used in current microarray studies may only partially reflect the widely altered gene expressions in complex diseases,which would introduce low reproducibility of gene lists selected by statistical methods.Here,by analyzing seven cancer datasets,we showed that,in each cancer,a wide range of functional modules have altered gene expressions and thus have high disease classification abilities.The results also showed that seven modules are shared across diverse cancers,suggesting hints about the common mechanisms of cancers.Therefore,instead of relying on a few individual genes whose selection is hardly reproducible in current microarray experiments,we may use functional modules as functional signatures to study core mechanisms of cancers and build robust diagnostic classifiers.

Identification of linear continuous-time system using wavelet modulating filters

An approach to identification of linear continuous-time system is studied with modulating functions. Based on wavelet analysis theory, the multi-resolution modulating functions are designed, and the corresponding filters have been analyzed. Using linear modulating filters, we can obtain an identification model that is parameterized directly in continuous-time model parameters. By applying the results from discrete-time model identification to the obtained identification model, a continuous-time estimation method is developed. Considering the accuracy of parameter estimates, an instrumental variable (Ⅳ) method is proposed, and the design of modulating integral filter is discussed. The relationship between the accuracy of identification and the parameter of modulating filter is investigated, and some points about designing Gaussian wavelet modulating function are outlined. Finally, a simulation study is also included to verify the theoretical results.

Urban niche dynamics of Kaifeng city

Niche theory is one of the most important ecological theories. It is widely applied to analyzing such phenomena as competition among, and evolution of, urban ecosystem functional modules. This paper describes a study concerning＇different functional modules of Kaifeng city urban ecosystem. Niche theory and techniques were used to analyze the changes of these functional modules in the period 1994-2003. The results showed that, in the period 1994-2003： （1） Niche value of the atmospheric environment and urban virescence modules increased, while niche value of the water environment and sound environment modules decreased; （2） niche value of the tertiary industry module increased, niche value of the secondary industry module decreased, while niche value of the primary industry module showed little change; and （3） niche value of the infrastructure, resource distribution, and production ＆ social security modules increased, while niche value of the population module decreased. This study may contribute to macroscopic planning of urban functional modules, economic development, and environmental protection.

Temporal Characteristics of GaAs NEA and Alkali Metal Photocathodes

The temporal characteristics of GaAs NEA and alkali metal photocathodes are studied using Monte Carlo simulation method. The electron transit time and its distribution functions in the photocathodes are calculated. Based on the results, the time modulation transfer functions and temporal resolutions of the photocathodes are obtained. The results show that the response time and temporal resolution of alkali metal photocathode is in femitosecond order and those of GaAs NEA photocathode are in picosecond order.

An Improved Coupling of Numerical and Physical Models for Simulating Wave Propagation

An improved coupling of numerical and physical models for simulating 2D wave propagation is developed in this paper. In the proposed model, an unstructured finite element model （FEM） based Boussinesq equations is applied for the numerical wave simulation, and a 2D piston-type wavemaker is used for the physical wave generation. An innovative scheme combining fourth-order Lagrange interpolation and Runge-Kutta scheme is described for solving the coupling equation. A Transfer function modulation method is presented to minimize the errors induced from the hydrodynamic invalidity of the coupling model and/or the mechanical capability of the wavemaker in area where nonlinearities or dispersion predominate. The overall performance and applicability of the coupling model has been experimentally validated by accounting for both regular and irregular waves and varying bathymetry. Experimental results show that the proposed numerical scheme and transfer function modulation method are efficient for the data transfer from the numerical model to the physical model up to a deterministic level.

Characterization of a Multimodal and Multispectral Led Imager: Application to Organic Polymer’s Microspheres with Diameter Φ = 10.2 μm

Multispectral microscopy enables information enhancement in the study of specimens because of the large spectral band used in this technique. A low cost multimode multispectral microscope using a camera and a set of quasi-monochromatic Light Emitting Diodes (LEDs) ranging from ultraviolet to near-infrared wavelengths as illumination sources was constructed. But the use of a large spectral band provided by non-monochromatic sources induces variation of focal plan of the imager due to chromatic aberration which rises up the diffraction effects and blurs the images causing shadow around them. It results in discrepancies between standard spectra and extracted spectra with microscope. So we need to calibrate that instrument to be a standard one. We proceed with two types of images comparison to choose the reference wavelength for image acquisition where diffraction effect is more reduced. At each wavelength chosen as a reference, one image is well contrasted. First, we compare the thirteen well contrasted images to identify that presenting more reduced shadow. In second time, we determine the mean of the shadow size over the images from each set. The correction of the discrepancies required measurements on filters using a standard spectrometer and the microscope in transmission mode and reflection mode. To evaluate the capacity of our device to transmit information in frequency domain, its modulation transfer function is evaluated. Multivariate analysis is used to test its capacity to recognize properties of well-known sample. The wavelength 700 nm was chosen to be the reference for the image acquisition, because at this wavelength the images are well contrasted. The measurement made on the filters suggested correction coefficients in transmission mode and reflection mode. The experimental instrument recognized the microsphere’s properties and led to the extraction of the standard transmittance and reflectance spectra. Therefore, this microscope is used as a conventional instrument.

A Network Partition Algorithm for Mining Gene Functional Modules of Colon Cancer from DNA Microarray Data

Computational analysis is essential for transforming the masses of microarray datainto a mechanistic understanding of cancer. Here we present a method for findinggene functional modules of cancer from microarray data and have applied it tocolon cancer. First, a colon cancer gene network and a normal colon tissue genenetwork were constructed using correlations between the genes. Then the modulesthat tended to have a homogeneous functional composition were identified by split-ting up the network. Analysis of both networks revealed that they are scale-free.Comparison of the gene functional modules for colon cancer and normal tissuesshowed that the modules’ functions changed with their structures.

MDA-TOEPGA:A novel method to identify miRNA-disease association based on two-objective evolutionary programming genetic algorithm

The association between miRNA and disease has attracted more and more attention.Until now,existing methods for identifying miRNA related disease mainly rely on top-ranked association model,which may not provide a full landscape of association between miRNA and disease.Hence there is strong need of new computational method to identify the associations from miRNA group view.In this paper,we proposed a framework,MDA-TOEPGA,to identify miRNAdisease association based on two-objective evolutionary programming genetic algorithm,which identifies latent miRNAdisease associations from the view of functional module.To understand the miRNA functional module in diseases,the case study is presented.We have been compared MDA-TOEPGA with several state-of-the-art functional module algorithm.Experimental results showed that our method cannot only outperform classical algorithms,such as K-means,IK-means,MCODE,HC-PIN,and ClusterONE,but can also achieve an ideal overall performance in terms of a composite score consisting of f1,Sensitivity,and Accuracy.Altogether,our study showed that MDA-TOEPGA is a promising method to investigate miRNA-disease association from the landscapes of functional module.