Noise-shaping sense amplifier for MRAM cross-point arrays

A sensing technique using a voltage-mode architecture, noise-shaping modulator, and digital filter (a counter) is presented for use with cross-point MRAM arrays and magnetic tunnel junction memory cells. The presented technique eliminates the need for precision components, the use of calibrations, and reduces the effects of power supply noise. To obviate the effects of cell-to-cell variations in the array, a digital self-referencing scheme using the counter is presented. Measured experimental results in a 180-nm CMOS process indicate an RMS sensing noise of 20 /spl mu/V for a 5-/spl mu/s sense time. Further increases in sense time are shown to increase the signal-to-noise ratio. The current used by the sense amplifier and counter was measured as 10 /spl mu/A when running at 100 MHz or 10 mA when 1000 sense amplifiers are used with a memory subarray having 1000 bitlines.     

Parametric Feature Detection

Most visual features are parametric in nature, including, edges, lines, corners, and junctions. We propose an algorithm to automatically construct detectors for arbitrary parametric features. To maximize robustness we use realistic multi-parameter feature models and incorporate optical and sensing effects. Each feature is represented as a densely sampled parametric manifold in a low dimensional subspace of a Hilbert space. During detection, the vector of intensity values in a window about each pixel in the image is projected into the subspace. If the projection lies sufficiently close to the feature manifold, the feature is detected and the location of the closest manifold point yields the feature parameters. The concepts of parameter reduction by normalization, dimension reduction, pattern rejection, and heuristic search are all employed to achieve the required efficiency. Detectors have been constructed for five features, namely, step edge (five parameters), roof edge (five parameters), line (six parameters), corner (five parameters), and circular disc (six parameters). The results of detailed experiments are presented which demonstrate the robustness of feature detection and the accuracy of parameter estimation.     

Kreinovich,V., Lakeyev,A., Rohn,J., and Kahl,P.: Computational Complexity and Feasibility of Data Processing and Interval Computations

Reliable Computing -     

Nonlinear Mohr Envelopes Based on Triaxial Data

A substantial amount of experimental evidence suggests that Mohr envelopes of many soils are not linear, particularly in the range of small normal stresses. This departure from linearity is significant for slope stability calculations since for a wide range of practical stability problems, critical slip surfaces are shallow and normal stresses acting on such surfaces are small. The present work presents a procedure for estimating parameters of the Mohr form of Hoek and Brown empirical failure criterion, based on triaxial data. This nonlinear Mohr envelope provides significantly better representation of experimental information than the Mohr–Coulomb criterion in cases when the data set includes tests at small enough normal stress. Application of the estimation procedure to published data sets shows that effective cohesive strength of most soils is close to zero, and Mohr envelopes are continuous at the tensile strength limit.     

Dislocation identification and in situ straining in the spinodal Fe30Ni20Mn25Al25 alloy

Dislocations in the spinodal alloy Fe(30)Ni(20)Mn(25)Al(25), which is composed of alternating BCC and B2 (ordered BCC) phases, have been investigated using weak-beam transmission electron microscopy (TEM). The alloy was compressed at room temperature in an as-hot-extruded state to strains of approximately 3% for post-mortem dislocation analysis. Dislocations with a/2<111> Burgers vectors were found to glide in pairs on both {110} and {112} slip planes. TEM in situ straining experiments were also performed on both the as-extruded alloy and an arc-melted alloy. The in situ straining observations confirmed that dislocations were able to pass between both spinodal phases. Partial dislocation separations were relatively wide in the BCC phase and narrow in the B2 phase. Dislocation glide, as opposed to twinning (both of which have been observed in other BCC-based spinodals), was also found to be the only room temperature deformation mechanism.     

Avalanches on a conical bead pile: scaling with tuning parameters

Uniform spherical beads were used to explore the scaling behavior of a granular system near its critical angle of repose on a conical 3D bead pile. We found two tuning parameters that could take the system to a critical point. The existence of those tuning parameters violates the fundamental assumption of self-organized criticality, which proposed that complex dynamical systems self-organize to a critical point without need for tuning. Our avalanche size distributions were well described by a simple power-law, as is characteristic of a critical point, with the power τ = 1.5 when dropping beads slowly onto the apex of a bead pile from a small height. However, we could also move the system from the critical point using either of two tuning parameters: the height from which the beads fell onto the top of the pile or the region over which the beads struck the pile. As the drop height increased, the system did not reach the critical point yet the resulting distributions were independent of the bead mass, coefficient of friction, or coefficient of restitution. All our apex-dropping distributions for any type of bead (glass, stainless steel, zirconium) showed universality by scaling onto a common curve with τ = 1.5 and σ =?1.0, where 1/σ is the power of the tuning parameter. From independent calculations using the moments of the distribution, we find values for τ = 1.6 ± 0.1 and σ =?0.91 ± 0.15. When beads were dropped across the surface of the pile instead of solely on the apex, then the system also moved from the critical point and again the avalanche size distributions fell on a common curve when scaled similarly using the same values of τ and σ. We also observed that an hcp structure on the base of the pile caused an emergent structure in the pile that had six faces with some fcc or hcp structure; this structure did not affect the distribution of avalanche sizes.     

Modelling and analysis of bacterial tracks suggest an active reorientation mechanism in Rhodobacter sphaeroides

Most free-swimming bacteria move in approximately straight lines, interspersed with random reorientation phases. A key open question concerns varying mechanisms by which reorientation occurs. We combine mathematical modelling with analysis of a large tracking dataset to study the poorly understood reorientation mechanism in the monoflagellate species Rhodobacter sphaeroides. The flagellum on this species rotates counterclockwise to propel the bacterium, periodically ceasing rotation to enable reorientation. When rotation restarts the cell body usually points in a new direction. It has been assumed that the new direction is simply the result of Brownian rotation. We consider three variants of a self-propelled particle model of bacterial motility. The first considers rotational diffusion only, corresponding to a non-chemotactic mutant strain. Two further models incorporate stochastic reorientations, describing ‘run-and-tumble’ motility. We derive expressions for key summary statistics and simulate each model using a stochastic computational algorithm. We also discuss the effect of cell geometry on rotational diffusion. Working with a previously published tracking dataset, we compare predictions of the models with data on individual stopping events in R. sphaeroides. This provides strong evidence that this species undergoes some form of active reorientation rather than simple reorientation by Brownian rotation.     

Managing information and supplies inventory operations in a manufacturing environment. Part 2: An order-timing and sizing algorithm

We develop a new, flexible independent demand forecasting-optimisation algorithm, and apply it to nine difficult-to-manage maintenance and repair products at the AREVA nuclear fuel rod manufacturing facility. The algorithm results in a 27% reduction in inventory holding and ordering costs relative to AREVA's baseline ERP method. This is in addition to improving the line item fill rates from 96 to 98%. This new algorithm is more flexible than the baseline method in that (1) our forecast error distribution is not assumed to be normal—we automatically find the best-fitting distribution from a large family of distributions, (2) we jointly optimise the order quantity and reorder point by using an optimisation routine that is embedded in a simulation methodology. Our algorithm can therefore handle a non-stationary demand process during the planning horizon, and (3) we dynamically select the best time series forecaster for demand based on the most recent history. This flexibility drove the performance improvements. Our algorithm can be easily adapted to any independent demand situation across any industry's supply chain.     

Exploratory analysis of excitation-emission matrix fluorescence spectra with self-organizing maps—A tutorial

Large datasets are common in chemical and environmental engineering applications and tools for their analysis are in great demand. Here, the outputs of a series of fluorescence spectroscopy analyses are utilised to demonstrate the application of the self-organising map (SOM) technique for data analysis. Fluorescence spectroscopy is a well-established technique of organic matter fingerprinting in water. The technique can provide detailed information on the physico-chemical properties of water. However, analysis of fluorescence spectra requires the application of robust statistical and computational data pre-processing and analysis tools.This paper presents a tutorial for training engineering postgraduate researchers in the use of SOM techniques using MATLAB^®. Via a tutorial, the application of SOM to fluorescence spectra and, in particular, the characterisation of organic matter removal in water treatment, is presented. The tutorial presents a step-by-step example of the application of SOM to fluorescence data analysis and includes the source code for MATLAB^®, together with presentation and discussion of the results. With this tutorial we hope to popularise this robust pattern recognition technique for fluorescence data analysis and large data sets in general, and also to provide educational practitioners with a novel tool with which to train engineering students in SOM.