Search

The cascading of logic gates is one of the primary challenges for spintronic computing, as there is a need to dynamically create magnetic fields. Spin-diode logic provides this essential cascading, as the current through each spin-diode is modulated by a magnetic field created by the current through other spin-diodes. This logic family can potentially be applied to any device exhibiting strong positive or negative magnetoresistance, and allows for the creation of circuits with exceptionally high performance. These novel circuit structures provide an opportunity for spintronics to replace CMOS in general-purpose computing.

Two recently proposed spintronic logic families, spin-diode logic and emitter-coupled spin-transistor logic, provide an opportunity for high-performance cascaded logic circuits. These logic families make use of magnetoresistive semiconductor heterojunctions, in which the presence of a magnetic field causes a large increase in resistance. Spin-diode logic and emitter-coupled spin-transistor logic produce highly compact circuits with superior speed and power characteristics. In particular, circuits realized in these logic families use three to ten times fewer devices than conventional CMOS. Additionally, there is minimal dynamic power dissipation, presenting a pathway for low power high performance computing beyond 10 GHz.

Using a 94-GHz homodyne interferometer employing a highly-directional quasi-optical lens antenna aimed at a human subject's chest, we can measure chest wall displacement from up to 10m away and through common clothing. Within the chest displacement signal are motions due to cardiac activity, respiration, and gross body movement. Our goal is to find the heart rate of the subject being monitored, which implies isolation of the minute movements due to cardiac activity from the much larger movements due to respiration and body movement. To accomplish this, we first find a subset of the true heartbeat temporal locations (called confident" heartbeats) in the displacement signal using a multi-resolution wavelet approach, utilizing Symlet wavelets. Although the chest displacement due to cardiac activity is orders of magnitude smaller than that due to respiration and body movement, wavelets find those heartbeat locations due to several useful properties, such as shape matching, high-pass filtering, and vanishing moments. Using the assumption that the confident" heartbeats are randomly selected from the set of all heartbeats, we are able to find the maximum a posteriori statistics of an inverse Gaussian probability distribution modeling the inter-heartbeat times. We then analyze the confident" heartbeats and decide which heartbeats are probabilistically correct and which are not, based on the inverse Gaussian distribution we calculated earlier. The union of the confident" set, after pruning, and the interpolated set forms a very close approximation to the true heartbeat temporal location set, and thus allows us to accurately calculate a heart rate.

A continuous wave (CW) 94-GHz millimeter wave (mmW) standoff biosensor has been developed for remote biometric sensing applications. The sensor measures the demodulated in-phase (I) and quadrature-phase (Q) components of the received reflected mmW signal from a subject. Both amplitude and phase of the reflected signal are obtained from downconverted I and Q channels from the quadrature mixer. The mmW sensor can faithfully monitor human vital signs (heartbeat and respiration) at relatively long standoff distances. Principle Component Analysis (PCA) is used to extract the heartbeat, the respiration and the body motion signals. The approach allows one to deduce information about amplitude and beat-to-beat rate of the respiration and the heartbeat. Experimental results collected from a subject were analyzed and compared to the signal obtained with a three-electrode ECG monitoring instrument.

A modified polarized Monte Carlo code is developed that allows heterogeneous structure to be modeled. The code is validated with existent polarized Monte Carlo code. Heterogeneous structure simulating colon tissue is simulated to understand the difference between simulations of homogeneous vs heterogeneous tissue structure. Reflectance measurements from simulations containing increased blood vessel size and increased blood volume fraction, both markers for potential cancerous tissue, are studied in order to better interpret reflectance measurement from diagnostic probes.

This paper examines a super-exponential method for blind deconvolution. Possibly non-minimal phase point spread functions (PSFs) are identified. The PSF is assumed to he low pass in nature. No other prior knowledgeof the PSF or the original image is necessary to assure convergence of the algorithm. Results are shown usingsynthetically degraded satellite images in order to demonstrate the accuracy of the PSF estimates. In addition, radiographic images are restored with no knowledge of the PSF of the x-ray imaging system. These experimentssuggest a promising application of this algorithm to a variety of blur identification probleins.Keywords: blur identification, cumulants. higher order statistics. higher order spectra, blind deconvolution 1. INTRODUCTION Blur identification is an important and necessary step in the restoration of images having linear, space invariantdegradations. Since the 1960s. research in blind deconvolution, where there is no assumed knowledge of the degra-dation system's point spread function (PSE), has centered around communication signals. Initial research dealt withsystems whose PSF could he modeled parametrically using zero crossing information preserved in the spectrum of theblurred signal. Only certain classes of blurs. such as cylindrical or i-D motion blurs, could be successfully extractedusing these methods. Stochastic methods were soon introduced. Autoregressive Moving-Average (ARMA) modelsalong with maximum likelihood based approaches became successful due to their ability to deconvolve signals withlow to moderate noise [1, 2].Recently, the use of higher order statistics/spectra. (HOS) has become popular in PSF identification in commu-nication channels [3, 4, 5, 6. 7]. HOS preserve important phase information which is lost when using second orderspectra. Another attractive feature is that given enough realizations of a signal containing additive Gaussian noise,the use of HOS allows us to asi1y filter out the noise. These properties permit us to identify non-minimum phasesystems, such as non-causal blurs.In this work we extend and apply a blur identification algorithm based on HOS, which was previously used forthe 1-D communication signal problem [8], to 2-D radiographic images. Our primary motivation in this work is toprovide fast and accurate restorations of blurred radiographs of fatigued aircraft structures. To do this, we use ablind deconvolution algorithm that converges at a super-exponential rate independent of initialization, asproven in[8]. The algorithm can be applied to the blind deconvolutioii of any type of image, such as those obtained by theHubble Space Telescope, satellites, etc.The general blind deconvolution 1)1ohle1i is illustrated in Figure 1. In this model

In this paper, we develop an algorithm for obtaining the maximum a posteriori (MAP) estimate of the displacement vector field (DVF) from two consecutive image frames of an image sequence acquired under quantum-limited conditions. The estimation of the DVF has applications in temporal filtering, object tracking, and frame registration in low-light level image sequences as well as low-dose clinical x-ray image sequences. The quantum-limited effect is modeled as an undesirable, Poisson-distributed, signal-dependent noise artifact. The specification of priors for the DVF allows a smoothness constraint for the vector field. In addition, discontinuities of the field are taken into account through the introduction of a line process for neighboring vectors. A Bayesian formulation is used in this paper to estimate the DVF and a block component algorithm is employed in obtaining a solution. Several experiments involving a phantom sequence show the effectiveness of this estimator in obtaining the DVF under severe quantum noise conditions.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Clinical x-ray image sequences acquired through fluoroscopy systems may be corrupted by quantum mottle--a Poisson-distributed, signal-dependent noise that arises with a controlled x- ray dosage reduction in an attempt to lower the exposure to the patient and the medical staff. In this paper, an approach to temporally filter this sequence is presented. It relies on a joint estimation of the signal and the displacement field through a maximum likelihood approach. Implementation is done via a modified EM algorithm to facilitate a more tractable solution.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A method is described for the spatio-temporal filtering of digital angiographic image sequences corrupted by simulated quantum mottle. An x-ray dosage reduction in coronary imaging studies inevitably leads to the introduction of quantum mottle --a Poisson distributed, signal dependent noise that occurs as a result of statistical fluctuations in the arrival of photons at the image intensifier tube. Although spatial filtering of individual frames in the sequence is often performed to improve image quality, this technique does not utilize valuable information from temporal correlations between images. The spatio-temporal filter here estimates motion trajectories for individual pixels and then filters along the direction of motion. This method is different from temporal filtering techniques that do not use motion compensation as the latter always blur the edges of the coronary arteries. Although the method is derived for the estimation of a single frame from two degraded frames of a sequence, it is easily generalized to multi-frame estimates. The performance of the above filter is examined using real image sequences corrupted by quantum mottle.

This paper deals with the estimation of the motion field in digital angiographic sequences. An approach is developed according to which each frame is first segmented into a moving object of interest and the background. The original images are converted into binary images by using a Gaussian smoothing filter and thresholding. In the binary images pixels in moving objects have the value of " 1" and pixels in the background have the value of " 0" . The moving objects in the binary images are thinned to skeletons with unit width by using the Safe-Point Thinning Algorithm (SPTA) with restriction windows we introduce. Then a block-matching algorithm is used in estimating the motion for the pixels which belong to the skeleton. This approach to motion estimation results in reduced computations since only binary multiplications need to be performed for determining the match between two blocks. Therefore an effective searching method is proposed for finding the direction of displacement in successive skeleton frames. Very satisfactory results are obtained by applying the algorithm to 64 x 64. pixel digital angiographic image sequences. 1© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A method is described for the computer simulation of quantum mottle in digital angiographic images obtained through an image intensifier (II) based system. The model corrupts a "perfect" image-one taken at high exposure levels-with Poisson distributed noise to simulate an image obtained through a lower x-ray dose. A mapping scheme is employed which effectively correlates gray level intensities at the image display to photon fluence at the front end of the II. The utility of the noise model is demonstrated by using it to simulate the effect of variable x-ray exposure conditions on an angiographic sequence. Such a sequence is valuable in the development of temporal filtering techniques for digital angiography.