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  1. The Energy Scaling Advantages of RRAM Crossbars.

    Abstract not provided.
  2. Social-Media Network Collection Problems.

    Abstract not provided.
  3. Extracting hidden messages in steganographic images

    The eventual goal of steganalytic forensic is to extract the hidden messages embedded in steganographic images. A promising technique that addresses this problem partially is steganographic payload location, an approach to reveal the message bits, but not their logical order. It works by finding modified pixels, or residuals, as an artifact of the embedding process. This technique is successful against simple least-significant bit steganography and group-parity steganography. The actual messages, however, remain hidden as no logical order can be inferred from the located payload. This paper establishes an important result addressing this shortcoming: we show that the expected mean residualsmore » contain enough information to logically order the located payload provided that the size of the payload in each stego image is not fixed. The located payload can be ordered as prescribed by the mean residuals to obtain the hidden messages without knowledge of the embedding key, exposing the vulnerability of these embedding algorithms. We provide experimental results to support our analysis.« less
  4. Acceleration of Neural Algorithms using Nanoelectronic Resistive Memory Crossbars.

    Abstract not provided.
  5. Acceleration of Neural Algorithms using Nanoelectronic Resistive Memory Crossbars.

    Abstract not provided.
  6. Acceleration of Neural Algorithms using Nanoelectronic Resistive Memory Crossbars.

    Abstract not provided.
  7. Energy scaling advantages of resistive memory crossbar based computation and its application to sparse coding

    In this study, the exponential increase in data over the last decade presents a significant challenge to analytics efforts that seek to process and interpret such data for various applications. Neural-inspired computing approaches are being developed in order to leverage the computational properties of the analog, low-power data processing observed in biological systems. Analog resistive memory crossbars can perform a parallel read or a vector-matrix multiplication as well as a parallel write or a rank-1 update with high computational efficiency. For an N × N crossbar, these two kernels can be O(N) more energy efficient than a conventional digital memory-basedmore » architecture. If the read operation is noise limited, the energy to read a column can be independent of the crossbar size (O(1)). These two kernels form the basis of many neuromorphic algorithms such as image, text, and speech recognition. For instance, these kernels can be applied to a neural sparse coding algorithm to give an O(N) reduction in energy for the entire algorithm when run with finite precision. Sparse coding is a rich problem with a host of applications including computer vision, object tracking, and more generally unsupervised learning.« less
  8. A Cortical-Hippocampal Neural Architecture for Episodic Memory with Information Theoretic Model Analysis.

    Abstract not provided.
  9. A general model of resource production and exchange in systems of interdependent specialists.

    Infrastructures are networks of dynamically interacting systems designed for the flow of information, energy, and materials. Under certain circumstances, disturbances from a targeted attack or natural disasters can cause cascading failures within and between infrastructures that result in significant service losses and long recovery times. Reliable interdependency models that can capture such multi-network cascading do not exist. The research reported here has extended Sandia's infrastructure modeling capabilities by: (1) addressing interdependencies among networks, (2) incorporating adaptive behavioral models into the network models, and (3) providing mechanisms for evaluating vulnerability to targeted attack and unforeseen disruptions. We have applied these capabilitiesmore » to evaluate the robustness of various systems, and to identify factors that control the scale and duration of disruption. This capability lays the foundation for developing advanced system security solutions that encompass both external shocks and internal dynamics.« less
  10. Phoenix : Complex Adaptive System of Systems (CASoS) engineering version 1.0.

    Complex Adaptive Systems of Systems, or CASoS, are vastly complex ecological, sociological, economic and/or technical systems which we must understand to design a secure future for the nation and the world. Perturbations/disruptions in CASoS have the potential for far-reaching effects due to pervasive interdependencies and attendant vulnerabilities to cascades in associated systems. Phoenix was initiated to address this high-impact problem space as engineers. Our overarching goals are maximizing security, maximizing health, and minimizing risk. We design interventions, or problem solutions, that influence CASoS to achieve specific aspirations. Through application to real-world problems, Phoenix is evolving the principles and discipline ofmore » CASoS Engineering while growing a community of practice and the CASoS engineers to populate it. Both grounded in reality and working to extend our understanding and control of that reality, Phoenix is at the same time a solution within a CASoS and a CASoS itself.« less

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"Quach, Tu-Thach"

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