Microarchitectures for Managing Chip Revenues under Process Variations

As transistor feature sizes continue to shrink intothe sub-90nm range and beyond, the effects of process variationson critical path delay and chip yields have amplified. A commonconcept to remedy the effects of variation is speed-binning, bywhich chips from a single batch are rated by a discrete range offrequencies and sold at different prices. In this paper, we discussstrategies to modify the number of chips in different bins andhence enhance the profits obtained from them. Particularly, wepropose a scheme that introduces a small Substitute Cacheassociated with each cache way to replicate the data elementsthat will be stored in the high latency lines. Assuming a fixedpricing model, this method increases the revenue by as much as13.8% without any impact on the performance of the chips.     

The effect of 9-beta-D-arabinofuranosyl-2-fluoroadenine and 1-beta-D-arabinofuranosylcytosine on the cell cycle phase distribution, topoisomerase II level, mitoxantrone cytotoxicity, and DNA strand break production in K562 human leukemia cells

The UvrA and UvrB proteins form part of the UvrABc endonuclease, which is responsible for nucleotide excision repair in Escherichia coli. Using a mobility shift gel assay we have studied the binding of UvrA dimer, UvrB monomer and UvA(2)B trimer complexes with 40, 50 and 136 bp (32)P-end-labelled DNA fragments adducted with aflatoxin B(1). UvrA was shown to re-associate with adduct specific UvrB: DNA complexes, a phenomenon which could be reversed by the addition of 500 mM potassium chloride or anti-UvrA anti-sera. Re-association was shown to be UvrA concentration dependent. Re-association of UvrA(2)B to the UvrB:DNA complex was not seen. We have also shown that the UvrB:DNA complex, in the case of aflatoxin B(1), is extremely stable with a half-life excess of 400 min and that fragment termini are not a specific substrate for UvrA binding.     

Neural networks for signal detection in non-Gaussian noise

We employ neural networks to detect known signals in additive non-Gaussian noise. Training of the neural network for signal detection and its operation at some specified probability of false alarm are discussed. Performance of neural detectors are presented under several non-Gaussian noise environments and are compared with those of matched filter and locally optimum detectors     

Robust pose invariant face recognition using coupled latent space discriminant analysis

We propose a novel pose-invariant face recognition approach which we call Discriminant Multiple Coupled Latent Subspace framework. It finds the sets of projection directions for different poses such that the projected images of the same subject in different poses are maximally correlated in the latent space. Discriminant analysis with artificially simulated pose errors in the latent space makes it robust to small pose errors caused due to a subject’s incorrect pose estimation. We do a comparative analysis of three popular latent space learning approaches: Partial Least Squares (PLSs), Bilinear Model (BLM) and Canonical Correlational Analysis (CCA) in the proposed coupled latent subspace framework. We experimentally demonstrate that using more than two poses simultaneously with CCA results in better performance. We report state-of-the-art results for pose-invariant face recognition on CMU PIE and FERET and comparable results on MultiPIE when using only four fiducial points for alignment and intensity features.     

Improved corrosion protection of aluminum alloys by electrodeposited silanes

Organofunctional silanes recently have emerged as outstanding, environmentally friendly corrosion protectors for metal substrates, compared with conventional chromate treatments. A simple immersion technique is typically used to coat the metal surface with silane films. However, the thickness and uniformity of the films are uncontrolled in this process. This paper proposes a new deposition technique for the silane films on the metal surface, i.e., by electrodeposition. Hydrolyzed silanes are water-soluble, ionized molecules, so they can be deposited on metals by electrodeposition. Various combinations of silane mixtures were tested at different voltages, pH values, bath concentrations, and exposure times on panels of alloy aluminum and mirror-polished ferro-plate. The surface structure was characterized by scanning electron microscopy (SEM) and ellipsometry. The resistance of the film to corrosion was investigated by direct current (DC) polarization and electrochemical impedance spectroscopy (EIS) techniques. Electrodeposition results in a more organized and uniform film with fewer pores, compared with immersed or dipped films. This paper was presented at the 2nd International Surface Engineering Congress sponsored by ASM International, on September 15–17, 2003, in Indianapolis, Indiana, and appears on pp. 320–26 of the Proceedings.     

The influence of sulfur dioxide on propane oxidation activity over supported platinum and palladium

Earlier studies have shown that sulfur dioxide and metal-support interaction can strongly influence propane oxidation over platinum. In particular, oxidation activity is enhanced when platinum is supported on sulfated -alumina or zirconia compared to -alumina. Therefore, it is of interest to compare the performance of palladium under the same experimental conditions. Four model catalysts were examined: Pt/-alumina, Pt/zirconia, Pd/-alumina and Pd/zirconia. The metal loading was kept at or below 0.05 wt% to emphasize changes in activity attributable to metal-support interaction. Reaction rates were measured with and without sulfur dioxide. Surface sulfation was analyzed by measuring acid strength and evaluating spectra obtained by Fourier-transform infrared spectroscopy. In contrast to platinum, sulfation does not promote propane oxidation on Pd/-alumina, and Pd/zirconia is less active than Pd/-alumina.     

The C-loss function for pattern classification

This paper presents a new loss function for neural network classification, inspired by the recently proposed similarity measure called Correntropy. We show that this function essentially behaves like the conventional square loss for samples that are well within the decision boundary and have small errors, and L₀ or counting norm for samples that are outliers or are difficult to classify. Depending on the value of the kernel size parameter, the proposed loss function moves smoothly from convex to non-convex and becomes a close approximation to the misclassification loss (ideal 0–1 loss). We show that the discriminant function obtained by optimizing the proposed loss function in the neighborhood of the ideal 0–1 loss function to train a neural network is immune to overfitting, more robust to outliers, and has consistent and better generalization performance as compared to other commonly used loss functions, even after prolonged training. The results also show that it is a close competitor to the SVM. Since the proposed method is compatible with simple gradient based online learning, it is a practical way of improving the performance of neural network classifiers.     

A method for automatic classification of gender based on text- independent handwriting

Handwriting recognition is used for the prediction of various demographic traits such as age, gender, nationality, etc. Out of all the applications gender prediction is mainly admired topic among researchers. The relation between gender and handwriting can be seen from the physical appearance of the handwriting. This research work predicts gender from handwriting using the landmarks of differences between the two genders. We use the shape or visual appearance of the handwriting for extracting features of the handwriting such as slanteness (direction), area (no of pixels occupied by text), perimeter (length of edges), etc. Classification is carried out using the Support Vector Machine (SVM) as a classifier which transforms the nonlinear problem into linear using its kernel trick, logistic regression, KNN and at the end to enhance the classification rates we use Majority Voting. The experimental results obtained on a dataset of 282 writers with 2 samples per writer shows that the proposed method attains appealing performance on writer detection and text-independent environment.

     

Estimation of impact forces during multi-point collisions involving small deformations

Multibody System Dynamics - Collision between hard objects causes abrupt changes in the velocities of the system, which are characterized by very large contact forces over very small time...     

Ferroelectric liquid crystals: Excellent tool for modern displays and photonics

The ferroelectric liquid crystals, because of their fast electro‐optical response, are one of the most important classes of liquid crystals. Here, in this review, we have summarized the different electro‐optical modes for ferroelectric liquid crystals. Clark–Lagerwall effect (surface stabilized ferroelectric liquid crystal), deformed helix ferroelectric (DHF) effect, electrically suppressed helix (ESH) mode, DHF orientational Kerr effect, and ESH diffraction modes have been discussed. All of the crucial features, that is, optics, electro‐optics, dynamics, and their dependence on material parameters, operational regime, and applications, have been reviewed.