Laser-assisted machining of compacted graphite iron

Compacted graphite iron (CGI) is a material currently under study for the new generation of engines, including blocks, cylinder liners, and cylinder heads. Its unique graphite structure yields desirable high strength, but makes it difficult to machine, thus resulting in a machining cost. Laser-assisted machining (LAM) is adopted to improve its machinability and hence machining economics. The machinability of CGI is studied by varying depth of cut, feed, and material removal temperature and then evaluating resultant cutting forces, specific cutting energy, surface roughness, and tool wear. At a material removal temperature of 400 °C and a feed of 0.150 mm/rev at a cutting speed of 1.7 m/s, it is shown that tool life is 60% greater than conventional conditions at a feed of 0.100 mm/rev. Surface roughness is improved 5% as compared to conventional machining at a feed of 0.150 mm/rev. CGI microstructure evaluated post machining by sectioning and polishing shows no change. An economic analysis shows that LAM can offer an approximately 20% cost savings for the machining of an engine cylinder liner.     

A timing controller embedded driver IC with 3.24‐Gbps eDP interface for chip‐on‐glass TFT‐LCD applications

This paper presents a timing controller embedded driver (TED) IC with 3.24‐Gbps embedded display port (eDP), which is implemented using a 45‐nm high‐voltage CMOS process for the chip‐on‐glass (COG) TFT‐LCD applications. The proposed TED‐IC employs the input offset calibration scheme, the zero‐adjustable equalizer, and the phase locked loop‐based bang‐bang clock and data recovery to enhance the maximum data rate. Also, the proposed TED‐IC provides efficient power management by supporting advanced link power management feature of eDP standard v1.4. Additionally, the smart charge sharing is proposed to reduce the dynamic power consumption of output buffers. Measured result demonstrates the maximum data rate of 3.24 Gbps from a 1.1 V supply voltage with a 7.9‐inch QXGA 60‐Hz COG‐LCD prototype panel and 44% power saving from the display system.     

Rendering of HDR images to improve brightness discrimination

The impression of quality of images can be enhanced on a high dynamic range (HDR) displays. Generally, a conventional 8‐bit image can be processed to an HDR image by inverse tone mapping operators. Among the operators, brightness discrimination mapping by applying brightness adaptation model attempted to mimic the human visual system. In this paper, we use a brightness adaptation model to derive a brightness discrimination mapping algorithm for HDR displays. The proposed algorithm maximizes a function, which represents the local and global brightness discrimination range by exploiting characteristics of the human visual system. Enhancement of details is verified by visualizing HDR images from dark to bright regions. Improvement of dynamic range is quantified by measuring increased discrimination ratio.     

A noninvasive method to predict fluid viscosity and nanoparticle size using total internal reflection fluorescence microscopy (TIRFM) imaging

This study examines the development of an automated particle tracking algorithm to predict the hindered Brownian movement of fluorescent nanoparticles within an evanescent wave field created using total internal reflection fluorescent microscopy. The two-dimensional motion of the fluorescent nanoparticles was tracked, with sub-pixel resolution, by fitting the intensity distribution of the particles to a known Gaussian distribution, thus providing the particle center within a single pixel. Spherical yellow-green polystyrene nanoparticles (200, 500, and 1000 nm in diameter) were suspended in deionized water (control), 10 wt% d-glucose, and 10 wt% glycerol solutions, with 1 mM of NaCl added to each. The motion of tracked nanoparticles was compared with the theoretical tangential hindered Brownian motion to estimate particle diameters and fluid viscosity using a nonlinear regression technique. The automatic tracking algorithm was initially validated by comparing the automated results with manually tracked particles, 1 µm in size. Our results showed that both particle size and solution viscosity were accurately predicted from the experimental mean square displacement. Specifically, the results show that the error of particle size prediction is below 10 % and the error of solution viscosity prediction is less than 1 %. The proposed automatic analysis tool could prove to be useful in bio-application fields for examination of single protein tracking, drug delivery, and cytotoxicity. Furthermore, the proposed tool could be useful in microfluidic areas such as particle tracking velocimetry and noninvasive viscosimetry.     

Mbius-invariant curve and surface energies and their applications

Curvature-based surface energies are frequently used in mathematics, physics, thin plate and shell engineering, and membrane chemistry and biology studies. Invariance under rotations and shifts makes curvature-based energies very attractive for modeling various phenomena. In computer-aided geometric design, the Willmore surfaces and the so-called minimum variation surfaces （MVS） are widely used for shape modeling purposes. The Willmore surfaces are invariant w.r.t conformal transformations （Mbius or conformal invariance）, and studied thoroughly in differential geometry and related disciplines. In contrast, the minimum variation surfaces are not conformal invariant. In this paper, we suggest a simple modification of the minimum variation energy and demonstrate that the resulting modified MVS enjoy Mbius invariance （so we call them conformal-invariant MVS or, shortly, CI-MVS）. We also study connections of CI-MVS with the cyclides of Dupin. In addition, we consider several other conformal-invariant curve and surface energies involving curvatures and curvature derivatives. In particular, we show how filtering with a conformal-invariant curve energy can be used for detecting salient subsets of the principal curvature extremum curves used by Hosaka and co-workers for shape quality inspection purposes.     

Ultrastructural investigation of intact orbital implant surfaces using atomic force microscopy

This study examined the surface nanostructures of three orbital implants: nonporous poly(methyl methacrylate) (PMMA), porous aluminum oxide and porous polyethylene. The morphological characteristics of the orbital implants surfaces were observed by atomic force microscopy (AFM). The AFM topography, phase shift and deflection images of the intact implant samples were obtained. The surface of the nonporous PMMA implant showed severe scratches and debris. The surface of the aluminum oxide implant showed a porous structure with varying densities and sizes. The PMMA implant showed nodule nanostructures, 215.56 ± 52.34 nm in size, and the aluminum oxide implant showed crystal structures, 730.22 ± 341.02 nm in size. The nonporous PMMA implant showed the lowest roughness compared with other implant biomaterials, followed by the porous aluminum oxide implant. The porous polyethylene implant showed the highest roughness and severe surface irregularities. Overall, the surface roughness of orbital implants might be associated with the rate of complications and cell adhesion. SCANNING 33: 211–221, 2011. © 2011 Wiley Periodicals, Inc.     

Can 3DTV Create Immersive Environments?

This article has been retracted.     

Can traditional fault prediction models be used for vulnerability prediction?

Finding security vulnerabilities requires a different mindset than finding general faults in software—thinking like an attacker. Therefore, security engineers looking to prioritize security inspection and testing efforts may be better served by a prediction model that indicates security vulnerabilities rather than faults. At the same time, faults and vulnerabilities have commonalities that may allow development teams to use traditional fault prediction models and metrics for vulnerability prediction. The goal of our study is to determine whether fault prediction models can be used for vulnerability prediction or if specialized vulnerability prediction models should be developed when both models are built with traditional metrics of complexity, code churn, and fault history. We have performed an empirical study on a widely-used, large open source project, the Mozilla Firefox web browser, where 21% of the source code files have faults and only 3% of the files have vulnerabilities. Both the fault prediction model and the vulnerability prediction model provide similar ability in vulnerability prediction across a wide range of classification thresholds. For example, the fault prediction model provided recall of 83% and precision of 11% at classification threshold 0.6 and the vulnerability prediction model provided recall of 83% and precision of 12% at classification threshold 0.5. Our results suggest that fault prediction models based upon traditional metrics can substitute for specialized vulnerability prediction models. However, both fault prediction and vulnerability prediction models require significant improvement to reduce false positives while providing high recall.