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Microfluidic channels were studied, in which the surface is modified from a solid/liquid interface into solid/liquid and air/liquid alternating interface, creating the equivalent of a superhydrophobic surface on the interior of the channel. The composite microchannel can be easily fabricated using embossing or cast molding of PDMS. The channels are stable under typical microfluidic conditions. For the most part, fluid flow behavior is not significantly changed; however, interesting mass transport effects can be observed in such channels under appropriate conditions. An application example of a microvalve based on the mass transport effect is demonstrated, showing advantages of simple design, fabrication, no moving part and zero dead volume.

This paper reports the design, fabrication and simulation of a novel array of micromachined electrodes with different lengths for use in auditory nerve prostheses. A 10 x 10 array is created in a 1 mm2 on silicon with bulk micromachining technology. The electrode lengths vary from 200 microm to 400 microm. This design could provide access to most fascicles within the auditory nerve and thus allow stimulation of a broad tonotopic range within the nerve fiber. A multichannel ultra-low power circuit is also designed, fabricated, and tested for neural signal recording. The chip is fabricated in 0.5 microm AMI CMOS technology with a die size of 1.5 mm x 1.5 mm. The total power consumption is less than 100 microW.

This paper reports the design, fabrication and simulation of a novel micromachined electrode array with a form-fitting profile for use in auditory nerve prostheses. A 10times10 electrode array is created in a 1mm 2 area using bulk micromachining technology. The space between the individual electrodes within the array is filled with a layer of SU-8 molded to conform to the curved surface of the auditory nerve. This layer enables the implant to be secured to the auditory nerve and to have a good sealing between the array and the nerve tissue after insertion. An electrical model for a single electrode is built. Both mechanical and electrochemical finite element analyses (FEA) of the array are also performed.

The purpose of this study was to investigate the ability of a robotic device, "the rat stepper", to assess intrinsic locomotor recovery following spinal cord contusion injury in adult rats. The device consists of a motorized body weight support mechanism that precisely controls the load to the hindlimbs during stepping, and two small robotic arms that measure and manipulate hindlimb movement. Sixteen rats received a contusion injury to the mid thoracic spinal cord with different severity levels (mild, moderate, severe, and sham). The animals were then evaluated weekly using the rat stepper, beginning one week after injury and continuing for a period of twelve weeks, across a range of body weight support levels. The contused animals demonstrated recovery in a standard locomotor assessment score (the BBB score), with most of the recovery occurring by four weeks post injury. We analyzed fourteen robotic measures of stepping and found that the measures that were most sensitive to intrinsic recovery were step velocity and inter limb coordination. These measures were also significantly correlated with the BBB score. The number of steps taken during testing was not sensitive to intrinsic recovery, nor correlated to the BBB score. These results suggest that step quality, rather than quantity, best reflects recovery after contusion injury in adult, untrained rats. Thus, robotic motion capture of only a few steps can provide a sensitive, valid measure of locomotor recovery after contusion.

We present flow induced admittance spectra for electrolytes, cell culture media, different sizes of DNA solutions and neural cells using flow induced admittance spectra in a microfluidics device. The device comprises of a PDMS channel aligned with a pair of channel electrodes fabricated on glass. The peak of the flow induced admittance spectra and frequency at which the peak occurs are the key parameters used for the characterization of sensing. The response of this sensor is a function of the conductivity and dielectrivity of the effective solution. The flow induced admittances of the particles studied are corrected with their media. This sensing will be a primary component of an electrical based cytometer.

A clamp-and-ratchet microstructure based on poly crystalline silicon (polysilicon) microelectromechanical systems (MEMS) technology has been designed to exert mechanical tension along radial glial processes between groups of neural stem cells to study the effect of tension on cerebral cortex neurogenesis. FEA analysis shows that the design should not fail under expected loading conditions. Preliminary studies show that embryonic brain tissue survives under tension for at least six days. Neurospheres have been successfully cultured on Poly(dimethylsiloxane) (PDMS) for eight days and exhibit radial extensions which appear to support neuronal migration. Stretching the PDMS using the clamp and ratchet will produce tension in these radial extensions which may modulate neuronal migration, a key process in cerebral cortex development.

A novel screening platform for the screening and optimization of protein crystallization is reported. Here we present two key experiments to generate protein crystallization phase diagrams. One is to precisely generate spatial gradient droplet arrays; the other is to trap and identify each droplet in place in order to carry out temporal and spatial analysis. The generated concentration and volume gradient ranges from 0% to 100% and from 155 to 310 pico liters, respectively. Furthermore, the active trapping of a droplet array in a microfluidic chip for hours to days to study the dynamic phase changes in protein crystallization is demonstrated. This microfluidic platform can be used for rapidly generating the solubility diagram for protein in various salt solutions.

Dynamic medical imaging problems are typically structured as "partial" inverse problems: the desired time series of images (a spatiotemporal matrix) is subject to a purely spatial transformation (its product with a transfer matrix) - so that the forward operator does not address all of the variables of the function to be transformed (analogous to partial differentiation, which is also a partial inverse problem). Given that the transfer matrix is ill-conditioned, the problem of providing the image sequence requires regularization. Under rather general conditions applicable to the setting of partial inverse problems, the regularization parameter of Tikhonov regularization generalizes to a regularization parameter operator, which in proper combination with a standard (spatial) Tikhonov regularizing operator describes the minimum-mean-square-error estimate analogously to that of the Bayesian interpretation of Tikhonov regularization as applied to a "complete" inverse problem. This is in distinction to usual methodology employed for dynamic imaging problems, including usual applications of Tikhonov regularization in this realm, which cannot similarly supply the minimum-mean-square-estimate (under the stated general conditions). The potential power of the implied methodology is illustrated with a numerical example - indicating that substantial improvements in solution estimation are possible.

Color textures contain a large amount of spectral and spatial structure that can be exploited for recognition. Recent work has demonstrated that spatial filters offer a convenient means of extracting illumination-invariant spatial information from a color image. In this paper, we address the problem of deriving optimal filters for illumination-invariant color texture discrimination. Color textures are represented by a set of illumination-invariant features that characterize the color distribution of a filtered image region. Similar features have been used in previous studies. Given a pair of color textures, we derive a spatial filter that maximizes the distance between these textures in feature space. We provide a method for using the pairwise result to obtain a filter that maximizes discriminability among multiple classes. A set of experiments on a database of deterministic and random color textures obtained under different illumination conditions demonstrates the improved discriminatory power achieved by using an optimized filter.

A new neural classifier allows visualization of the training set and decision regions, providing benefits for both the designer and the user. We demonstrate the visualization capabilities of this visual neural classifier using synthetic data, and compare the visualization performance to Kohunen's self-organizing map. We show in applications to image segmentation and medical diagnosis that visualization enables a designer to refine the classifier to achieve low error rates and enhances a user's ability to make classifier-assisted decisions.

Spatial filters provide a useful and efficient means of analyzing an input color image into components that capture different spatial properties. Representations based on spatial filtering have restricted usefulness for recognition, however, because the output of a spatial filter across an image depends on the scene illumination conditions. We use a physically accurate linear model for spectral reflectance to derive invariants of distributions in spatially filtered color images that do not depend on the scene illumination. These invariants can be used for the illumination-invariant recognition of regions following an arbitrary linear filtering operation. We describe a method for illumination correction based on color distributions and introduce an illumination change consistency constraint that is useful for verifying matches obtained using the invariants. We show, using a set of classification experiments, that the filtered distribution invariants can significantly improve the capability of a recognition system in environments where illumination cannot be controlled.

In this paper, we attack the problem of distinguishing textured images of real surfaces using small samples. We first analyze experimental data that results from applying ordinary conditional Markov fields. In the face of the disappointing performance of these models, we introduce a random field with spatial interaction that is itself a random variable (usually referred to as a random field in a random environment). For this class of models, we establish the power spectrum and the autocorrelation function as well-defined quantities, and we devise a scheme for the estimation of related parameters. The new set of features that resulted from this approach was applied to real images. Accurate discrimination was observed even for boxes of size 10x16.

We investigate to what extent textures can be distinguished using conditional Markov fields and small samples. We establish that the least square (LS) estimator is the only reasonable choice for this task, and we prove its asymptotic consistency and normality for a general class of random fields that includes Gaussian Markov fields as a special case. The performance of this estimator when applied to textured images of real surfaces is poor if small boxes are used (20x20 or less). We investigate the nature of this problem by comparing the behavior predicted by the rigorous theory to the one that has been experimentally observed. Our analysis reveals that 20x20 samples contain enough information to distinguish between the textures in our experiments and that the poor performance mentioned above should be attributed to the fact that conditional Markov fields do not provide accurate models for textured images of many real surfaces. A more general model that exploits more efficiently the information contained in small samples is also suggested.

A motion immune dual-energy subtraction technique in which X-ray tube voltage and beam filtration were switched at 30 Hz between 60 kVp (2.0 mm Al filter) and 120 kVp (2.00 mm Al+2.5 mm Cu filter) was previously reported. In this study the effects of camera lag on the dual-energy iodine signal is investigated. The temporal lag of the lead oxide vidicon tested reduced the dual-energy iodine signal by a factor of 2.3, as compared to a mode that included 4 scrub frames between low- and high-energy images, for an iodine phantom with thicknesses of 0-86.0 mg/cm(2), imaged over a 15 cm thick Lucite phantom. On the other hand, the Charge-Coupled Device (CCD) camera has inherently no temporal lag and its versatile scanning characteristics make it near ideal for dual-energy DSA. The CCD camera eliminates the reduction of dual-energy iodine signal, since it does not mix low- and high-energy image data. Another benefit of the CCD camera is that the separation time between low and high-energy images is not limited to the frame period, as is the lead oxide vidicon; and as small as a 5-msec time difference is possible. The short time interval between low and high-energy images minimizes motion misregistration artifacts. Due to these advantages, the CCD camera significantly improves the utility of dual-energy DSA.

The authors present a new algorithmic procedure for the analysis of brain images. This procedure is specifically designed to image the activity and functional organization of the brain. The authors' results are tested on data collected and previously analyzed with the technique known as in vivo optical imaging of intrinsic signals. The authors' procedure enhances the applicability of this technique and facilitates the extension of the underlying ideas to other imaging problems (e.g., functional MRI). The authors' thrust is two fold. First, they give a systematic method to control the blood vessel artifacts which typically reduce the dynamic range of the image. They propose a mathematical model for the vibrations in time of the veins and arteries and they design a new method for cleaning the images of the vessels with the highest time variations. This procedure is based on the analysis of the singularities of the images. The use of wavelet transform is of crucial importance in characterizing the singularities and reconstructing appropriate versions of the original images. The second important component of the authors' work is the analysis of the time evolution of the fine structure of the images. They show that, once the images have been cleaned of the blood vessel vibrations/variations, the principal component of the time evolutions of the signals is due to the functional activity following the stimuli. The part of the brain where this function takes place can be localized and delineated with precision.

In order to quantitate anatomical and physiological parameters such as vessel dimensions and volumetric blood flow, it is necessary to make corrections for scatter and veiling glare, which are the major sources of nonlinearities in videodensitometric digital subtraction angiography (DSA). A convolution filtering technique has been investigated to estimate scatter-glare distribution in DSA images without the need to sample the scatter-glare intensity for each patient. This technique utilizes exposure parameters and image gray levels to assign equivalent Lucite thickness for every pixel in the image. The thickness information is then used to estimate scatter-glare intensity on a pixel-by-pixel basis. To test its ability to estimate scatter-glare intensity, the correction technique was applied to images of a Lucite step phantom, anthropomorphic chest phantom, head phantom, and animal models at different thicknesses, projections, and beam energies. The root-mean-square (rms) percentage error of these estimates was obtained by comparison with direct scatter-glare measurements made behind a lead strip. The average rms percentage errors in the scatter-glare estimate for the 25 phantom studies and the 17 animal studies were 6.44% and 7.96%, respectively. These results indicate that the scatter-glare intensity can be estimated with adequate accuracy for a wide range of thicknesses, projections, and beam energies using exposure parameters and gray level information.

A new analytical three-dimensional cone beam reconstruction algorithm is presented for truncated spherical detection geometry. The basic idea of the proposed algorithm is the formation of spatially invariant 3D blurred back-projected volumetric image by the use of the weighted backprojection of cone beam projection data and subsequent 3D filtering using an acceptance angle dependent rho filter. The backprojection weighting function is calculated on the basis of each given geometrical condition, i.e. detection geometry or degree of truncation, position of cone beam apex, and backprojection point. The proposed algorithm is derived analytically and is computationally efficient. Performance of the algorithm is evaluated by the reconstruction of 3D volumetric images using simulated data from arbitrarily truncated spherical detector geometries.

An analysis of guided-wave acousto-optic tunable filters (AOTFs) that employ simple acousto-optic (AO) weighted coupling techniques for sidelobe reduction and the calculated and experimental results from a specific example that involves only variation of the width of a surface acoustic wave (SAW) slot waveguide are presented. The calculations on single- and multi-stage AOTFs consisting of an optical channel waveguide and a SAW slot waveguide in LiNbO(3) substrate show that waveguide width weighting using generalized Hamming functions would provide significant improvement in sidelobe suppression. Calculated results together with the design, fabrication, and measured performance characteristics of a single-stage AOTF that utilizes a weighted-aperture SAW slot waveguide in YX-LiNbO(3) substrate at the optical wavelength of 1.55 mum and the acoustic center frequency of 175 MHz are reported. The measured sidelobe level is -13.6 dB and the measured FWHM bandwidth is 26 A, as compared to the theoretical values of -15.0 dB and 15 A, respectively. The RF drive power was measured to be 1.0 W at a mode-conversion efficiency of 100%

The authors provide a rigorous treatment of the convergence of the topology preserving neural networks proposed by Kohonen for the one-dimensional case. The approach extends the original work by Kohonen on the convergence properties of such networks in several respects. First, the authors investigate the convergence of the neuron weights directly as compared to Kohonen's treatment of the dynamic behavior of the expectation values of the weights. Second, the problem is formulated for a more general case of selecting the neighborhood amplitude of interaction rather than the uniform amplitude. Third, the proof of convergence is based on the well-known Gladyshev theorem which uses Lyapunov's function method. The authors provide a step-by-step constructive proof which establishes the asymptotic convergence to a unique solution. This proof also provides the relation between the boundary neurons' weight vectors and the number of neurons in the network. The approach is then extended to the two-dimensional case and the result is stated in a theorem.

A baseband integrated acoustooptic (AO) frequency shifter/modulator module that consists of a pair of titanium-indiffused proton-exchanged (TIPE) waveguide lenses and a pair of cascaded guided-wave AO Bragg cells has been realized in a Y-cut LiNbO(3) waveguide substrate 0.1 cmx1.0 cmx2.0 cm in size. A device module operating at the optical wavelength of 1.3 mum has provided a -3-dB tunable bandwidth of 120 MHz at baseband. The frequency-shifted or -modulated light propagates in a fixed direction, irrespective of the magnitude of frequency shift or modulation, and is focused into a spot (FWHM) of 6.2-mum size on the output edge of the waveguide. Accordingly, this optical frequency shifter/module can be directly interfaced with single-mode optical fibers to facilitate applications in fiber optic systems.

The recent development of titanium-indiffusion proton-exchange (TIPE) microlenses and lens arrays has made possible the construction of a variety of single- and multichannel integrated acoustooptic (AO) and acoustooptic-electrooptic (EO) circuits in LiNbO(3) channel-planar waveguides 0.1x1.0x2.0 cm(3) in size. These hybrid AO and AO-EO circuits can be fabricated through compatible and well-established technologies. The most recent realization of ion-milled microlenses and lens arrays together with the recent development of gigahertz AO Bragg modulators and EO Bragg modulator arrays have also paved the way for construction of similar but monolithic AO and AO-EO GaAs/GaAlAs waveguides of comparable size. Both types of integrated AO and AO-EO circuits suggest versatile applications in communications signal processing, and computing. Efficient and simultaneous excitation of the channel waveguide array using an ion-milled planar microlens array has facilitated the demonstration of some of these applications.

An evolution-oriented learning algorithm is presented for the optimal interpolative (OI) artificial neural net proposed by R. J. P. deFigueiredo (1990). The algorithm is based on a recursive least squares training procedure. One of its key attributes is that it incorporates in the structure of the net the smallest number of prototypes from the training set T necessary to correctly classify all the members of T. Thus, the net grows only to the degree of complexity that it needs in order to solve a given classification problem. It is shown how this approach avoids some of the difficulties posed by the backpropagation algorithm because of the latter's inflexible network architecture. The performance of this new algorithm is demonstrated by experiments with real data, and comparisons with other methods are also presented.

A flexible-mask algorithm that effectively provides correction for motion artifacts and gray-level variations in digital subtraction angiography (DSA) images is presented. The algorithm makes use of a flexible local registration of the mask with the live image to minimize the main distortions caused by the complex motion of the heart, namely spatial translation, rotation, and nonisotropic scaling. It also reduces the gray-level variations caused by the diffusion of the contrast medium into parts of the heart other than the arteries. It is concluded from experiments on X-ray coronary angiograms that when the background near the arteries is rugged or provides significant interference, flexible mask subtraction offers important improvements in quantitative estimates of the sizes of the arteries. In all the X-ray images used in the experiments, the precision of the method was at the 0.1-pixel level. The computation is extensive. However, some improvements can be achieved by doing the subtraction only at the coronary arteries and the immediate neighborhood instead of the whole image. The algorithm lends itself to implementation by parallel-structured hardware.

Resolution degradation due to gradient coil deformation and vibration in NMR microscopy is investigated using finite element analysis. From the analysis, deformations due to the Lorentz force can be as large as 1-10 mum depending on the gradient strength and coil frame material. Thus, these deformations can be one of the major resolution limiting factors in NMR microscopy. Coil vibration, which depends on the input current waveform and resolution degradation due to time-variant deformation and time-invariant deformation are investigated by numerical simulations.

An algorithm that reconstructs the cross sections of the lumens of coronary arteries from two mutually orthogonal X-ray projections is described. The algorithm accommodates the possibility of elliptical, crescent, or star shapes. It represents each biplane projection of a transverse slice of the arterial lumen as a binary-valued image. The single-coordinate moments of these two projection images are equal to those of the slice. Since the cross-coordinate moments of the slice are not available from the projections, an algorithm to estimate these moments based on assumptions of smoothness and connectivity is developed. Once all the missing moments are estimated, the image of the slice can be estimated by inverting these moments, using the uniqueness theorem governing the relation between an image and its moments. Preliminary tests of the algorithm on synthetic data, on hardware phantoms and on a segment of a barium-enhanced in vitro coronary artery are reported.

Several major deconvolution techniques commonly used for seismic applications are studied and adapted for ultrasonic NDE (nondestructive evaluation) applications. Comparisons of the relative merits of these techniques are presented based on a complete set of simulations on some real ultrasonic pulse echoes. Methods that rely largely on a reflection seismic model, such as one-at-a-time L(1) spike extraction and MVD (minimum variance deconvolution), are not suitable for the NDE applications discussed here because they are limited by their underlying model. L(2) and Wiener filtering, on the other hand, do not assume such a model and are, therefore, more flexible and suitable for these applications. The L(2) solutions, however, are often noisy due to numerical ill conditions. This problem is partially solved in Wiener filtering, simply by adding a constant desensitizing factor q. The computational complexities of these Wiener filtering-based techniques are relatively moderate and are, therefore, more suitable for potential real-time implementations.

Reduction of eddy currents by a temporal compensation of the input current waveform to the gradient coil is studied with an analytic solution. The technique is the inverse filtering of the eddy-current affected field response, which is calculated from the diffusion equation. The limitation of the temporal compensation due to the spatially variant eddy currents is also investigated for whole-body diagnostic imaging systems and small-bore nuclear magnetic resonance (NMR) microscopy systems. Within a limited imaging volume of less than 60% of the gradient coil diameter, most of eddy-current problems can be solved by the technique.

Necessary and sufficient conditions are derived for the weights of a generalized correlation matrix of a bidirectional associative memory (BAM) which guarantee the recall of all training pairs. A linear programming/multiple training (LP/MT) method that determines weights which satisfy the conditions when a solution is feasible is presented. The sequential multiple training (SMT) method is shown to yield integers for the weights, which are multiplicities of the training pairs. Computer simulation results, including capacity comparisons of BAM, LP/MT BAM, and SMT BAM, are presented.

A fast backprojection scheme for parallel beam geometries is proposed. Known as the incremental algorithm, it performs backprojection on a ray-by-ray (beam-by-beam) basis rather than the pixel-by-pixel backprojection in the conventional algorithm. By restructuring a conventional backprojection algorithm, the interdependency of pixel computations (position and value) is transformed to a set of incremental relations for a beam, where a beam is a set of pixels enclosed by two adjacent rays in 2-D computed tomography (CT), and a set of voxels enclosed by four adjacent rays in 3-D CT. To minimize the overhead of searching for the next pixels, a searching flow technique has been developed to implement the first-order and second-order incremental relations for 2-D and 3-D CTs, respectively. The values of all the pixels in each beam (except the first pixel) are computed with additions only, the key idea of the proposed backprojection scheme. The incremental algorithm has been implemented on two different machines and compared to B.F. Shepp and L.A. Logan's (1974) algorithm. The present implementation results show the superiority of this approach over the conventional algorithm.

Enhancements of the encoding strategy of a discrete bidirectional associative memory (BAM) reported by B. Kosko (1987) are presented. There are two major concepts in this work: multiple training, which can be guaranteed to achieve recall of a single trained pair under suitable initial conditions of data, and dummy augmentation, which can be guaranteed to achieve recall of all trained pairs if attaching dummy data to the training pairs is allowable. In representative computer simulations, multiple training has been shown to lead to an improvement over the original Kosko strategy for recall of multiple pairs as well. A sufficient condition for a correlation matrix to make the energies of the training pairs be local minima is discussed. The use of multiple training and dummy augmentation concepts are illustrated, and theorems underlying the results are presented.

A spectral technique for effectively and accurately measuring acoustic attenuation over a wide frequency range is reported. The spectral technique for phase measurement developed by W. Sachse and Y.H. Pao (1978) was used to determine the acoustic dispersion. For acoustic waves, a very simple and useful Kramers-Kronig relationship was previously derived by M. O'Donnell, E.T. Jaynes, and J.G. Miller (1981). The attenuation was calculated, using this relationship, from the measured dispersion and then compared with the attenuation that was measured independently. Dispersion was deduced from the measured attenuation and compared with the measured dispersion. The results of two highly attenuative specimens are presented. The agreement between the calculated attenuation and measured attenuation is excellent. The deduced dispersion also agrees well with the measured one. This agreement verifies the simple Kramers-Kronig relationship used. It further shows the accuracy of the spectral techniques for attenuation and dispersion measurements over a wide frequency range.

The minimal number of times for using a pair for training to guarantee recall of that pair among a set of training pairs is derived for a bidirectional associative memory.

If a flow field exists within an object that is being imaged, there could be a velocity spectrum in each voxel due to the finite voxel size. Consequently, the mean value and the variance of velocity in each voxel can be defined and used to describe the velocity spectrum. An NMR (nuclear magnetic resonance) imaging technique for performing the spatially resolved measurement of the mean value and the variance of velocity as well as the conventional spin density is proposed. A theoretical formalism and experimental results are presented and show good agreement. This technique is expected to be useful in the spatially resolved measurement of random directional flow, such as the fluid flow in living tissue and through porous materials.

A method for estimating the three-dimensional (3D) skeletons and transverse areas of the lumens of coronary arteries from digital X-ray angiograms is described. The method is based on the use of a 3D generalized cylinder (GC) consisting of a series of 3D elliptical disks transverse to and centered on a 3D skeleton (medial axis) of the coronary arteries. The estimates of the transverse areas are based on a nonlinear least-squares-error estimation technique described by D.W. Marquardt (1963). This method exploits densitometric profiles, boundary estimates, and the orientation of the arterial skeleton in 3-space and includes an automatic artery tracking procedure. It applies an adaptive window to the densitometric profile data that are used in the parameter estimation. Preliminary experimental tests of the procedure on angiograms of in vivo human coronaries and on synthetic images yield encouraging results.