2,130 research outputs found
A model for programming characteristics of Sonos type flash with high-kappa dielectrics
Silicon Oxide Nitride Oxide Silicon (SONOS) FLASH memories have recently gained a lot of attention due to better retention and scaling opportunities over the conventional Floating Gate FLASH memories. The constant demand for device scaling, to attain higher density, higher performance, and low cost per bit, has posed charge leakage problems. SONOS type devices with high-kappa storage layers and/or high-kappa blocking oxide have been proposed to alleviate the demand for constant tunnel oxide scaling. In comparison to conventional FLASH, these devices operate at lower voltages, exhibit higher programming speeds, comparable retention times, less over-erase problem and better compatibility with low power CMOS logic; The objective of this thesis is to develop a comprehensive model which can be used to obtain the programming characteristics, i.e., shift in threshold voltage vs. program time, for trap-based FLASH memories with high-kappa dielectrics. The proposed model is used to obtain the programming characteristics for SONOS type devices. The results from this model are compared with the experimental results and in general the agreement is good. For SONOS type devices with high-kappa blocking oxides, the density of available nitride traps for charge storage is shown to have a linear dependence with the potential energy difference between the silicon substrate and the nitride storage for different gate biases. The model is also used to get an estimate of available trap energy levels in the nitride layer as a function of applied voltage
Head Pose Estimation Using Multi-scale Gaussian Derivatives
International audienceIn this paper we approach the problem of head pose estimation by combining Multi-scale Gaussian Derivatives with Support Vector Machines. We evaluate the approach on the Pointing04 and CMU-PIE data sets and to estimate the pan and tilt of the head from facial images. We achieved a mean absolute error of 6.9 degrees for pan and 8.0 degrees for tilt on the Pointing04 data set
Low Degree Metabolites Explain Essential Reactions and Enhance Modularity in Biological Networks
Recently there has been a lot of interest in identifying modules at the level
of genetic and metabolic networks of organisms, as well as in identifying
single genes and reactions that are essential for the organism. A goal of
computational and systems biology is to go beyond identification towards an
explanation of specific modules and essential genes and reactions in terms of
specific structural or evolutionary constraints. In the metabolic networks of
E. coli, S. cerevisiae and S. aureus, we identified metabolites with a low
degree of connectivity, particularly those that are produced and/or consumed in
just a single reaction. Using FBA we also determined reactions essential for
growth in these metabolic networks. We find that most reactions identified as
essential in these networks turn out to be those involving the production or
consumption of low degree metabolites. Applying graph theoretic methods to
these metabolic networks, we identified connected clusters of these low degree
metabolites. The genes involved in several operons in E. coli are correctly
predicted as those of enzymes catalyzing the reactions of these clusters. We
independently identified clusters of reactions whose fluxes are perfectly
correlated. We find that the composition of the latter `functional clusters' is
also largely explained in terms of clusters of low degree metabolites in each
of these organisms. Our findings mean that most metabolic reactions that are
essential can be tagged by one or more low degree metabolites. Those reactions
are essential because they are the only ways of producing or consuming their
respective tagged metabolites. Furthermore, reactions whose fluxes are strongly
correlated can be thought of as `glued together' by these low degree
metabolites.Comment: 12 pages main text with 2 figures and 2 tables. 16 pages of
Supplementary material. Revised version has title changed and contains study
of 3 organisms instead of 1 earlie
Flux-based classification of reactions reveals a functional bow-tie organization of complex metabolic networks
Unraveling the structure of complex biological networks and relating it to
their functional role is an important task in systems biology. Here we attempt
to characterize the functional organization of the large-scale metabolic
networks of three microorganisms. We apply flux balance analysis to study the
optimal growth states of these organisms in different environments. By
investigating the differential usage of reactions across flux patterns for
different environments, we observe a striking bimodal distribution in the
activity of reactions. Motivated by this, we propose a simple algorithm to
decompose the metabolic network into three sub-networks. It turns out that our
reaction classifier which is blind to the biochemical role of pathways leads to
three functionally relevant sub-networks that correspond to input, output and
intermediate parts of the metabolic network with distinct structural
characteristics. Our decomposition method unveils a functional bow-tie
organization of metabolic networks that is different from the bow-tie structure
determined by graph-theoretic methods that do not incorporate functionality.Comment: 11 pages, 6 figures, 1 tabl
Algorithm-Human-Algorithm: A New Classification Approach to Integrating Judgemental Adjustments
Modern-day firms face the predicament of blending the comparative advantages of their two core resources: machines and humans. When forecasting demand (e.g., for a product), extant literature documents that always permitting (or prohibiting) human revision of a machine forecast is beneficial if the humans\u27 private information role is larger (or smaller) than that of machine-accessible public information. We propose and design a complementary framework that shifts the focus to the regulation of each human revision; and, in doing so, adjusts for human vulnerability to systematic biases. To test our framework, we collaborate with a European retailer to compile a large dataset (~1.1 mn transactions) on machine-led demand forecasts and human revisions. In an out-of-sample analysis, our revision-level regulation approach picks the best of available forecasts in 14% more instances, compared to an always-permit or prohibit strategy at the product-store level. Our approach is theory-driven and easy to implement for practitioners
A Study of Locator ID Separation Protocol
Thesis advisor: Dr. Deep MedhiTitle from PDF of title page, viewed on November 9, 2010.Includes bibliographic references (pages 74-75).Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2010.Vita.Locator ID/Separation Protocol (LISP) aims at solving the issues in the current Internet Routing Architecture. The growth of the BGP routing table and Forwarding Information bases on core routers is very high. In addition, the number of BGP messages that are currently being processed by the BGP routers is a worrisome issue. Locator/ID Separation Protocol (LISP) is a recently proposed approach that provides a solution to these problems. By employing LISP, it is anticipated that significant scaling benefits can be achieved among which are the reduction of routing table sizes, traffic engineering capabilities, mobility without address changing. We present an analysis of how much these improvements are. Furthermore, a detailed study of this protocol is carried out and is compared against other solutions that are proposed along with an analysis of LISP as well.Abstract -- Illustrations -- List of Tables -- Glossary -- Acknowledgments -- Introduction -- Assumptions in Routing Architecture -- Locator ID/Separation Protocol -- Overview and Tunneling Details -- Messages -- Interworking LISP with IPv4 and IPv6 -- NERD -- LISP and Mobility -- Impact on Routing Table and Edge Network Routers -- Competitive Comparison of LISP -- Advantages and Disadvantages of LISP -- Conclusion -- References -- Vita
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