A 1-Mbit MRAM based on 1T1MTJ bit cell integrated with copper interconnects

A low-power 1-Mb magnetoresistive random access memory (MRAM) based on a one-transistor and one-magnetic tunnel junction (1T1MTJ) bit cell is demonstrated. This is the largest MRAM memory demonstration to date. In this circuit, the magnetic tunnel junction (MTJ) elements are integrated with CMOS using copper interconnect technology. The copper interconnects are cladded with a high-permeability layer which is used to focus magnetic flux generated by current flowing through the lines toward the MTJ devices and reduce the power needed for programming. The 25-mm/sup 2/ 1-Mb MRAM circuit operates with address access times of less than 50 ns, consuming 24 mW at 3.0 V and 20 MHz. The 1-Mb MRAM circuit is fabricated in a 0.6-/spl mu/m CMOS process utilizing five layers of metal and two layers of poly.     

Tracking biological cells in time-lapse microscopy: an adaptive technique combining motion and topological features

This paper presents a vision-based method for automatic tracking of biological cells in time-lapse microscopy by combining the motion features with the topological features of the cells. The automation of tracking frequently faces problems of segmentation error and of finding correct cell correspondence in consecutive frames, since the cells are of varying size and shape, and may have uneven movement; these problems become more acute when the cell population is very high. To reduce the segmentation error, we introduce a cell-detection method based on h-maxima transformation, followed by the fitting of an ellipse for the nucleus shape. To find the correct correspondence between the detected cells, the topological features, namely, color compatibility, area overlap and deformation are combined with the motion features of skewness and displacement. This reduces the ambiguity of matching and constructs accurately the trajectories of the cell proliferation. Finally, a template-matching-based backward tracking procedure is employed to recover any break in a cell trajectory that may occur due to the segmentation errors or the presence of a mitosis. The tracking procedure is tested using a number of different cell sequences with nonuniform illumination, or uneven cell motion, and is shown to provide high accuracy both in the detection and the tracking of the cells.     

Partial-Update L∞ -Norm Based Algorithms

The computational complexity of an adaptive filtering algorithm increases with increasing the filter tap length and therefore, the use of such a filter can become prohibitive for certain applications, especially for real-time implementation. In this paper, we develop low-complexity adaptive filtering algorithms by incorporating the concept of partial updating of the filter coefficients into the technique of finding the gradient vector in the hyperplane based on the L_infin-norm criterion. Two specific partial update algorithms based on the sequential and M-Max coefficient updating are proposed. The statistical analyses of the two algorithms are carried out, and evolution equations for the mean and mean-square of the filter coefficient misalignment as well as the stability bounds on the step size are obtained. It is shown that the proposed partial update algorithm employing the M-Max coefficient updating can achieve a convergence rate that is closest to that of the full update algorithm. Finally, simulations are carried out to validate the theoretical results and study the convergence rate of the proposed algorithms     

Transfer learning in deep neural network-based receiver coil sensitivity map estimation

Magnetic Resonance Materials in Physics, Biology and Medicine - The success of parallel Magnetic Resonance Imaging algorithms like SENSitivity Encoding (SENSE) depends on an accurate estimation of...     

A joint correlation noise estimation and decoding algorithm for distributed video coding

Multimedia Tools and Applications - Distributed video coding is relatively a novel video coding paradigm that enables a lower complex video encoding compared to conventional video coding schemes,...     

Conceptual Hybrid Model for Wearable Cardiac Monitoring System

Wireless Personal Communications - The electrocardiogram is the most convenient and widely used method of cardiac monitoring. The information provided by an ECG, has the potential to be used as a...     

Bayesian wavelet-based image denoising using the gauss-hermite expansion

The probability density functions (PDFs) of the wavelet coefficients play a key role in many wavelet-based image processing algorithms, such as denoising. The conventional PDFs usually have a limited number of parameters that are calculated from the first few moments only. Consequently, such PDFs cannot be made to fit very well with the empirical PDF of the wavelet coefficients of an image. As a result, the shrinkage function utilizing any of these density functions provides a substandard denoising performance. In order for the probabilistic model of the image wavelet coefficients to be able to incorporate an appropriate number of parameters that are dependent on the higher order moments, a PDF using a series expansion in terms of the Hermite polynomials that are orthogonal with respect to the standard Gaussian weight function, is introduced. A modification in the series function is introduced so that only a finite number of terms can be used to model the image wavelet coefficients, ensuring at the same time the resulting PDF to be non-negative. It is shown that the proposed PDF matches the empirical one better than some of the standard ones, such as the generalized Gaussian or Bessel K-form PDF. A Bayesian image denoising technique is then proposed, wherein the new PDF is exploited to statistically model the subband as well as the local neighboring image wavelet coefficients. Experimental results on several test images demonstrate that the proposed denoising method, both in the subband-adaptive and locally adaptive conditions, provides a performance better than that of most of the methods that use PDFs with limited number of parameters.     

A High-Performance MIMO-OFDM System with Walsh Block Coding

In this paper we present a multiple-input, multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) system that uses orthogonal Walsh sequences for block coding. Two configurations, space-time block coding (STBC) and the Vertical Bell Laboratories Layered Space-Time (V-BLAST) architecture, are considered for the proposed system. A least-squares channel estimation with the fast Fourier transform method is utilized in the system to replicate real-life scenarios. The idea of employing block coding based on orthogonal Walsh sequences is inspired by the IS-95 standard and is attempted for the first time in MIMO-OFDM systems. A simulation study is carried out by considering different antenna configurations, code sizes, and channel delays. Computer simulations show that the proposed system achieves a significant performance improvement compared to MIMO-OFDM systems that do not use the proposed block coding scheme. It is also shown that the new system is superior to some previous systems in computational complexity.     

An efficient method of cast shadow removal using multiple features

Features of images are often used for cast shadow removal. A technique based on using only a single feature cannot universally distinguish an object pixel from a shadow pixel of a video frame. On the other hand, the use of multiple features increases the computational cost of a shadow removal technique considerably. In this paper, an efficient yet simple method for cast shadow removal from video sequences with static background using multiple features is developed. The basic idea of the proposed technique is that a simultaneous use of a small number of multiple features, if chosen judiciously, can reduce the similarity between object and shadow pixels without an excessive increase in the computational cost. Using the features of gray levels, color composition, and gradients of foreground and background pixels, a method is devised to create a complete object mask. First, based on each of the three features, three individual shadow masks are constructed, from which three corresponding object masks are obtained through a simple subtraction operation. The object masks are then merged together to generate a single object mask. Each of the three shadow masks is created so as to cover as many shadow pixels as possible, even if it results in falsely including in them some of the object pixels. As a result, the subsequent object masks may lose some of these pixels. However, the object pixels missed by one of the object masks should be able to be recovered by at least one of the other two, since they are generated based on features complementary to the one used to construct the first one. The final object mask obtained through a logical OR operation of the three individual masks can, therefore, be expected to include most of the object pixels. The proposed method is applied to a number of video sequences. The simulation results demonstrate that the proposed method provides a mechanism for shadow removal that is superior to some of the recently proposed techniques without imparting an excessive computational cost.     

A vehicle detection scheme based on two-dimensional HOG features in the DFT and DCT domains

Multidimensional Systems and Signal Processing - Histogram of oriented gradients (HOG) are often used as features for object detection in images, since they are robust to changes in illumination...