Imaging spectroscopy with the atomic force microscope

Force curve imaging spectroscopy involves acquiring a force-distance curve at each pixel of an atomic force microscope image. Processing of the resulting data yields images of sample hardness and tip-sample adhesion. These images resemble Z modulation images and the sum of forward and reverse friction images, respectively, and like them exhibit a number of potentially misleading contrast mechanisms. In particular, XY tip motion has a pronounced effect on hardness images and the meniscus force on adhesion images

MEDICAL IMAGE PROCESSING USING MATLAB

MATLAB and the Image Processing Toolbox provide a wide range of advanced image processing functions and interactive tools for enhancing and analyzing digital images. The interactive tools allowed us to perform spatial image transformations, morphological operations such as edge detection and noise removal, region-of-interest processing, filtering, basic statistics, curve fitting, FFT, DCT and Radon Transform. Making graphics objects semitransparent is a useful technique in 3-D visualization which furnishes more information about spatial relationships of different structures. The toolbox functions implemented in the open MATLAB language has also been used to develop the customized algorithms.Histogram, 3-D Surface Plot, Round-off Noise Power Spectrum

ESTIMATED IMPACT OF NON-PRICE COORDINATION OF FED CATTLE PURCHASES ON MEAT PACKER PROCESSING COSTS

Stochastic simulation of daily slaughter level was used in conjunction with an estimated packing plant cost curve to assess potential reductions in processing costs due to improved vertical coordination between feedlots and packing plants. Results indicate that processing cost reductions of $1 to$5 per head may be possible. Savings result from ensuring a more stable processing volume that is near the plant's cost-minimizing level of production.cattle, cost curve, meat packing, vertical coordination, Industrial Organization, Livestock Production/Industries,

Real-time Exponential Curve Fits Using Discrete Calculus

This paper presents an improved solution for curve fitting data to an exponential equation (Y = AeBt + C). This improvement is in four areas ? speed, stability, determinant processing time, and the removal of limits. The solution presented in this paper avoids iterative techniques and their stability errors by using three mathematical ideas ? discrete calculus, a special relationship (between exponential curves and the Mean Value Theorem for Derivatives), and a simple linear curve fit algorithm. This method can also be applied to fitting data to the general power law equation Y = AxB + C and the general geometric growth equation Y = AkBt + C

A Study of Energy and Locality Effects using Space-filling Curves

The cost of energy is becoming an increasingly important driver for the operating cost of HPC systems, adding yet another facet to the challenge of producing efficient code. In this paper, we investigate the energy implications of trading computation for locality using Hilbert and Morton space-filling curves with dense matrix-matrix multiplication. The advantage of these curves is that they exhibit an inherent tiling effect without requiring specific architecture tuning. By accessing the matrices in the order determined by the space-filling curves, we can trade computation for locality. The index computation overhead of the Morton curve is found to be balanced against its locality and energy efficiency, while the overhead of the Hilbert curve outweighs its improvements on our test system.Comment: Proceedings of the 2014 IEEE International Parallel & Distributed Processing Symposium Workshops (IPDPSW