A Procedure for the 3D Reconstruction of Biological Organs from 2D Image Sequences

In this work, a procedure is presented for the reconstruction of biological organs from image sequences obtained through CT-scan. Although commercial software, which can accomplish this task, are readily available, the procedure presented here needs only free software. The procedure has been applied to reconstruct a liver from the scan data available in literature. 3D biological organs obtained this way can be used for the finite element analysis of biological organs and this has been demonstrated by carrying out an FE analysis on the reconstructed liver.

Magneto-structural correlation in (mu-alkoxo/hydroxo)(mu-carboxylato)dicopper(II) systems: Synthesis, X-ray structure and magnetic properties of aquo(mu-hydroxo) (mu-arylcarboxylato)bis(N,N,N ',N '-tetramethylethane-1,2-diamine)dicopper(II) diperchlorate

Asymmetrically dibridged dicopper(II) complexes, [Cu-2(OH)(O2CC6H4-p-Me)(tmen)(2)(H2O)](ClO4)(2) (1) and [Cu-2(OH)(O2CC6H4-p-OMe)(tmen)(2)(H2O)](ClO4)(2) (2) (tmen = N,N,N',N'-tetramethylethane-1,2-diamine), were prepared and structurally characterized. Complex 1 crystallizes in the monoclinic space group P2(1)/a with a = 17.718(2), b = 9.869(1), c = 19.677(2) Angstrom, beta = 115.16(1)degrees, V = 3114.3(6) Angstrom(3) and Z = 4. The structure was refined to R(wR(2)) = 0.067(0.178). Complex 2 crystallizes in the monoclinic space group P2(1)/a with a = 17.695(3), b = 9.574(4), c = 20.104(2) Angstrom, beta = 114.18(1)degrees, V = 3107(1) Angstrom(3) and Z = 4. The final residuals are R(wR(2)) = 0.067(0.182). The complexes have a [Cu-2(mu-OH)(mu-OH)(mu-O2CAr)](2+) core with tmen Ligands occupying the terminal sites of the core. In addition, one copper is axially bound to a water molecule. The Cu ... Cu distances and the Cu-OH Cu angles in the core are 3.394(1) Angstrom, 124.4(2)degrees for 1 and 3.374(1) Angstrom, 123.3(3)degrees for 2. The complexes show axial X-band EPR spectral features in methanol glass at 77 K giving g(perpendicular to) = 2.02, g(parallel to) = 2.3 (A(parallel to) = 165 x 10(-4) cm(-1)) and a visible band near similar to 630 nm in methanol. The complexes are weakly antiferromagnetic. A theoretical fit of the magnetic susceptibility data in the temperature range 40-295 K gives -J = 10 cm(-1), g = 2.05 for 1 and -J = 10 cm(-1), g = 2.0 for 2. Plots of -2J versus the Cu-OH-Cu angle (phi) in this class of asymmetrically dibridged dicopper(II) complexes having d(x2-y2)-d(x2-y2) magnetic orbitals show a linear magneto-structural correlation: -2J(cm(-1)) = 11.48 phi(deg) - 1373.

The effect of mischmetal addition on the structure and mechanical properties of a cast Al-7Si-0.3Mg alloy containing excess iron (up to 0.6 Pct)

The effect of Fe content (0.2 to 0.6 pct) on the microstructure and mechanical properties of a cast Al-7Si-0.3Mg (LM 25/356) alloy has been investigated. Further, 1 pct mischmetal (MM) additions (a mixture of rare-earth (RE) elements) were made to these alloys, and their mechanical properties at room and at elevated temperatures (up to 200 degreesC) were evaluated. A structure-property correlation on this alloy was attempted using optical microstructure analysis, fractographs, X-ray diffraction, energy-dispersive analysis of X-rays (EDX), and quantitative metallography by image analysis. An increase in Fe content increased the volume percentage of Fe-bearing intermetallic compounds (beta and pi phases), contributing to the lower yield strength (YS), ultimate tensile strength (UTS), percentage elongation, and higher hardness. An addition of 1 pct MM to the alloys containing 0.2 and 0.6 pct Fe was found to refine the microstructure; modify the eutectic silicon and La, Ce, and Nd present in the MM; form different intermetallic compounds with Al, Si, Fe, and Mg; and improve the mechanical properties of the alloys both at room and elevated temperatures.

Axially homogeneous, zero mean flow buoyancy-driven turbulence in a vertical pipe

We report an experimental study of a new type of turbulent flow that is driven purely by buoyancy. The flow is due to an unstable density difference, created using brine and water, across the ends of a long (length/diameter=9) vertical pipe. The Schmidt number Sc is 670, and the Rayleigh number (Ra) based on the density gradient and diameter is about 108. Under these conditions the convection is turbulent, and the time-averaged velocity at any point is ‘zero’. The Reynolds number based on the Taylor microscale, Reλ, is about 65. The pipe is long enough for there to be an axially homogeneous region, with a linear density gradient, about 6–7 diameters long in the midlength of the pipe. In the absence of a mean flow and, therefore, mean shear, turbulence is sustained just by buoyancy. The flow can be thus considered to be an axially homogeneous turbulent natural convection driven by a constant (unstable) density gradient. We characterize the flow using flow visualization and particle image velocimetry (PIV). Measurements show that the mean velocities and the Reynolds shear stresses are zero across the cross-section; the root mean squared (r.m.s.) of the vertical velocity is larger than those of the lateral velocities (by about one and half times at the pipe axis). We identify some features of the turbulent flow using velocity correlation maps and the probability density functions of velocities and velocity differences. The flow away from the wall, affected mainly by buoyancy, consists of vertically moving fluid masses continually colliding and interacting, while the flow near the wall appears similar to that in wall-bound shear-free turbulence. The turbulence is anisotropic, with the anisotropy increasing to large values as the wall is approached. A mixing length model with the diameter of the pipe as the length scale predicts well the scalings for velocity fluctuations and the flux. This model implies that the Nusselt number would scale as Ra1/2Sc1/2, and the Reynolds number would scale as Ra1/2Sc−1/2. The velocity and the flux measurements appear to be consistent with the Ra1/2 scaling, although it must be pointed out that the Rayleigh number range was less than 10. The Schmidt number was not varied to check the Sc scaling. The fluxes and the Reynolds numbers obtained in the present configuration are much higher compared to what would be obtained in Rayleigh–Bénard (R–B) convection for similar density differences.

Believe it or Not! Multicore CPUs can Match GPUs for FLOP-intensive Applications

In this work, we evaluate performance of a real-world image processing application that uses a cross-correlation algorithm to compare a given image with a reference one. The algorithm processes individual images represented as 2-dimensional matrices of single-precision floating-point values using O(n4) operations involving dot-products and additions. We implement this algorithm on a nVidia GTX 285 GPU using CUDA, and also parallelize it for the Intel Xeon (Nehalem) and IBM Power7 processors, using both manual and automatic techniques. Pthreads and OpenMP with SSE and VSX vector intrinsics are used for the manually parallelized version, while a state-of-the-art optimization framework based on the polyhedral model is used for automatic compiler parallelization and optimization. The performance of this algorithm on the nVidia GPU suffers from: (1) a smaller shared memory, (2) unaligned device memory access patterns, (3) expensive atomic operations, and (4) weaker single-thread performance. On commodity multi-core processors, the application dataset is small enough to fit in caches, and when parallelized using a combination of task and short-vector data parallelism (via SSE/VSX) or through fully automatic optimization from the compiler, the application matches or beats the performance of the GPU version. The primary reasons for better multi-core performance include larger and faster caches, higher clock frequency, higher on-chip memory bandwidth, and better compiler optimization and support for parallelization. The best performing versions on the Power7, Nehalem, and GTX 285 run in 1.02s, 1.82s, and 1.75s, respectively. These results conclusively demonstrate that, under certain conditions, it is possible for a FLOP-intensive structured application running on a multi-core processor to match or even beat the performance of an equivalent GPU version.

Two new families of low-correlation interleaved QAM sequence

Two families of low correlation QAM sequences are presented here. In a CDMA setting, these sequences have the ability to transport a large amount of data as well as enable variable-rate signaling on the reverse link. The first family Á2SQ - B2− is constructed by interleaving 2 selected QAM sequences. This family is defined over M 2-QAM, where M = 2 m , m ≥ 2. Over 16-QAM, the normalized maximum correlation [`(q)]maxmax is bounded above by <~1.17 ÖNUnknown control sequence '\lesssim' , where N is the period of the sequences in the family. This upper bound on [`(q)]maxmax is the lowest among all known sequence families over 16-QAM.The second family Á4SQ4 is constructed by interleaving 4 selected QAM sequences. This family is defined over M 2-QAM, where M = 2 m , m ≥ 3, i.e., 64-QAM and beyond. The [`(q)]maxmax for sequences in this family over 64-QAM is upper bounded by <~1.60 ÖNUnknown control sequence '\lesssim' . For large M, [`(q)]max <~1.64 ÖNUnknown control sequence '\lesssim' . These upper bounds on [`(q)]maxmax are the lowest among all known sequence families over M 2-QAM, M = 2 m , m ≥ 3.

Low correlation interleaved QAM sequences

A low correlation interleaved QAM sequence family is presented here. In a CDMA setting, these sequences have the ability to transport a large amount of data as well as enable variable-rate signaling on the reverse link. The new interleaved selected family INQ has period N, normalized maximum correlation parameter thetasmacrmax bounded above by lsim a radicN, where a ranges from 1.17 in the 16-QAM case to 1.99 for large M2-QAM, where M = 2m, m ges 2. Each user is enabled to transfer m + 1 bits of data per period of the spreading sequence. These constructions have the lowest known value of maximum correlation of any sequence family with the same alphabet.